United States Department of Agriculture
Central Appalachians Forest Ecosystem
Vulnerability Assessment and Synthesis:
A Report from the Central Appalachians
Climate Change Response Framework Project
Forest
Service
Northern
Research Station
General Technical
Report NRS-146
February 2015
ABSTRACT
Forest ecosystems across the Central Appalachians will be afected directly and indirectly by
a changing climate over the 21st century. This assessment evaluates the vulnerability of nine
forest ecosystems in the Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow
and Eastern Broadleaf Forest Provinces of Ohio, West Virginia, and Maryland for a range of
future climates. We synthesized and summarized informaion on the contemporary landscape,
provided informaion on past climate trends, and described a range of projected future climates.
This informaion was used to parameterize and run muliple vegetaion impact models, which
provided a range of potenial tree responses to climate. Finally, we brought these results before
a mulidisciplinary panel of scienists, land managers, and academics familiar with the forests of
this region to assess ecosystem vulnerability through a formal consensus-based expert elicitaion
process.
The summary of the contemporary landscape ideniies major forest trends and stressors
currently threatening forests in the region. Observed trends in climate over the past century
reveal that average minimum temperatures have increased in the area, paricularly in summer
and fall. Precipitaion has also increased in the area, paricularly in fall. Projected climate trends
for the next 100 years using downscaled global climate model data indicate a potenial increase
in mean annual temperature of 2 to 8 °F for the assessment area. Projecions for precipitaion
indicate increases in winter and spring precipitaion, and summer and fall precipitaion
projecions vary by scenario. We ideniied potenial impacts on forests by incorporaing these
future climate projecions into three forest impact models (DISTRIB, LINKAGES, and LANDIS PRO).
Model projecions suggest that many mesic species, including American beech, eastern hemlock,
eastern white pine, red spruce, and sugar maple may fare worse under future condiions, but
other species such as eastern redcedar may beneit from projected changes in climate. Published
literature on climate impacts related to wildire, invasive species, and forest pests and diseases
also contributed to the overall determinaion of climate change vulnerability.
We assessed vulnerability for nine forest ecosystems in the assessment area. The assessment
was conducted through a formal elicitaion process of 19 science and management experts
from across the area, who considered vulnerability in terms of the potenial impacts on a
forest ecosystem and the adapive capacity of the ecosystem. Appalachian (hemlock)/northern
hardwood forests, large stream loodplain and riparian forests, small stream riparian forests,
and spruce/ir forests were determined to be the most vulnerable ecosystems. Dry/mesic oak
forests and dry oak and oak/pine forests and woodlands were perceived as less vulnerable to
projected changes in climate. These projected changes in climate and the associated impacts and
vulnerabiliies will have important implicaions for economically valuable imber species, forestdependent wildlife and plants, recreaion, and long-term natural resource planning.
Cover Photo
Small stream riparian forest with a red spruce forest in the background. Photo by Patricia Butler,
Northern Insitute of Applied Climate Science and Michigan Tech, used with permission.
Manuscript received for publication June 2014
Published by:
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February 2015
Visit our homepage at: http://www.nrs.fs.fed.us/
Central Appalachians Forest Ecosystem
Vulnerability Assessment and Synthesis:
A Report from the Central Appalachians Climate
Change Response Framework Project
Patricia R. Butler, Louis R. Iverson, Frank R. Thompson III, Leslie A. Brandt,
Stephen D. Handler, Maria K. Janowiak, P. Danielle Shannon, Chris Swanston,
Kent Karriker, Jarel Barig, Stephanie Connolly, William D. Dijak, Scot Bearer,
Steve L. Blat, Andrea Brandon, Elizabeth Byers, Cheryl Coon, Tim Culbreth,
Jad Daly, Wade Dorsey, David Ede, Chris Euler, Neil Gillies, David M. Hix,
Catherine Johnson, Latasha Lyte, Stephen Mathews, Dawn McCarthy,
Dave Minney, Daniel Murphy, Claire O’Dea, Rachel Orwan, Mathew Peters,
Anantha Prasad, Coton Randall, Jason Reed, Cynthia Sandeno, Tom Schuler,
Lesley Sneddon, Bill Stanley, Al Steele, Susan Stout, Randy Swaty, Jason Teets,
Tim Tomon, Jim Vanderhorst, John Whatley, and Nicholas Zegre
AuThoRS
PATRICIA R. BUTLER is a climate change outreach specialist with
the Northern Insitute of Applied Climate Science, Michigan
Technological University, U.J. Noblet Forestry Building,
1400 Townsend Drive, Houghton, MI 49931, prbutler@mtu.edu.
LOUIS R. IVERSON is a landscape ecologist with the U.S. Forest
Service, Northern Research Staion, 359 Main Road, Delaware,
OH 43015, liverson@fs.fed.us.
FRANK R. THOMPSON III is a research wildlife biologist with the
U.S. Forest Service, Northern Research Staion, 202 AnheuserBusch Natural Resources Building, University of Missouri –
Columbia, Columbia, MO 65211, frthompson@fs.fed.us.
LESLIE A. BRANDT is a climate change specialist with the
Northern Insitute of Applied Climate Science, U.S. Forest
Service, Northern Research Staion, 1992 Folwell Avenue,
St. Paul, MN 55108, lbrandt@fs.fed.us.
STEPHEN D. HANDLER is a climate change specialist with the
Northern Insitute of Applied Climate Science, U.S. Forest
Service, 410 MacInnes Drive, Houghton, MI 49931,
sdhandler@fs.fed.us.
MARIA K. JANOWIAK is a climate change adaptaion and carbon
management scienist with the Northern Insitute of Applied
Climate Science, U.S. Forest Service, 410 MacInnes Drive,
Houghton, MI 49931, mjanowiak02@fs.fed.us.
P. DANIELLE SHANNON is a climate change outreach specialist
with the Northern Insitute of Applied Climate Science,
Michigan Technological University, U.J. Noblet Forestry Building,
1400 Townsend Drive, Houghton, MI 49931,
dshannon@mtu.edu.
CHRIS SWANSTON is a research ecologist with the U.S. Forest
Service, Northern Research Staion, and director of the
Northern Insitute of Applied Climate Science, 410 MacInnes
Drive, Houghton, MI 49931, cswanston@fs.fed.us.
KENT KARRIKER is the ecosystems group leader with the
Monongahela Naional Forest, 200 Sycamore Street, Elkins, WV
26241, kkarriker@fs.fed.us.
JAREL BARTIG is an ecologist with the Wayne Naional Forest,
13700 U.S. Hwy 33, Nelsonville, OH 45764, jbarig@fs.fed.us.
STEPHANIE CONNOLLY is a forest soil scienist with the
Monongahela Naional Forest, 200 Sycamore Street, Elkins, WV
26241, sconnolly@fs.fed.us.
WILLIAM D. DIJAK is a wildlife biologist and geographic
informaion systems specialist with the U.S. Forest Service,
Northern Research Staion, 202 Anheuser-Busch Natural
Resources Building, University of Missouri – Columbia,
Columbia, MO 65211, wdijak@fs.fed.us.
SCOTT BEARER is a senior conservaion scienist with The
Nature Conservancy, 220 West Fourth Street, 3rd Floor,
Williamsport, PA 17701, sbearer@tnc.org.
STEVE L. BLATT is a forest biologist with the Wayne Naional
Forest, 13700 U.S. Hwy 33, Nelsonville, OH 45764,
sblat@fs.fed.us.
ANDREA BRANDON is the Central Appalachians program
coordinator with The Nature Conservancy, 194 Airport Road,
Elkins, WV 26241, abrandon@tnc.org.
ELIZABETH BYERS is a vegetaion ecologist with the West
Virginia Division of Natural Resources, Natural Heritage
Program, P.O. Box 67, Elkins, WV 26241,
elizabeth.a.byers@wv.gov.
CHERYL COON is a forest botanist with the Hoosier Naional
Forest, 811 Consituion Avenue, Bedford, IN 47421,
ccoon@fs.fed.us.
TIM CULBRETH is the Chesapeake Watershed Forester for the
Maryland Forest Service, 580 Taylor Avenue, E-1, Annapolis, MD
21401, tculbreth@dnr.state.md.us.
JAD DALEY is a program director at the Trust for Public Land,
660 Pennsylvania Avenue SE, Suite 401, Washington, DC 20003,
jad.daley@tpl.org.
WADE DORSEY is the forest manager of the Savage River
State Forest, 127 Headquarters Lane, Grantsville, MD 21536,
wdorsey@dnr.state.md.us.
DAVID EDE (reired) was the forest planner and Naional
Environmental Policy Act (NEPA) coordinator with the
Monongahela Naional Forest, 200 Sycamore Street, Elkins, WV
26241. He can be reached at ededm86@gmail.com.
CHRIS EULER is an archaeologist with the Wayne Naional
Forest, 13700 U.S. Hwy 33, Nelsonville, OH 45764,
ceuler@fs.fed.us.
NEIL GILLIES is the director of science and educaion at the
Cacapon Insitute, P.O. Box 68, High View, WV, 26808,
ngillies@cacaponinsitute.org.
DAVID M. HIX is an associate professor with the School of
Environment and Natural Resources at Ohio State University,
365A Kotman Hall, 2021 Cofey Road, Columbus, OH 43210,
hix.6@osu.edu.
CATHERINE JOHNSON is a wildlife biologist with the
Monongahela Naional Forest, 200 Sycamore Street, Elkins, WV
26241, catherinejohnson@fs.fed.us.
LATASHA LYTE is a forest soil scienist with the Wayne Naional
Forest, 13700 U.S. Hwy 33, Nelsonville, OH 45764,
ljlyte@fs.fed.us.
STEPHEN MATTHEWS is a research assistant professor with
the School of Environment and Natural Resources at Ohio
State University, and an ecologist with the U.S. Forest Service,
Northern Research Staion, 359 Main Road, Delaware, OH
43015, snmathews@fs.fed.us.
DAWN McCARTHY is the assistant district ranger for operaions
with the Wayne Naional Forest, 13700 U.S. Hwy 33,
Nelsonville, OH 45764, dmccarthy02@fs.fed.us.
DAVE MINNEY is a ire ecologist with Allegheny Ecological
Services, 2315 Wickline Road, Beaver, OH 45613,
dminney57@gmail.com.
DANIEL MURPHY is an assistant professor of anthropology at
the University of Cincinnai, 462 Braunstein Hall, Cincinnai, OH
45221, murphdl@ucmail.uc.edu.
CLAIRE O’DEA is an air quality specialist with the U.S. Forest
Service, Eastern Region, 400 Independence Avenue, SW.,
Mailstop: 1121, Washington, DC 20250, cbodea@fs.fed.us.
RACHEL ORWAN is the NEPA, objecions and liigaion
coordinator with the Wayne Naional Forest,
13700 U.S. Hwy 33, Nelsonville, OH 45764, rorwan@fs.fed.us.
MATTHEW PETERS is a geographic informaion systems
technician with the U.S. Forest Service, Northern Research
Staion, 359 Main Road, Delaware, OH 43015,
mathewpeters@fs.fed.us.
ANANTHA PRASAD is an ecologist with the U.S. Forest Service,
Northern Research Staion, 359 Main Road, Delaware, OH
43015, aprasad@fs.fed.us.
COTTON RANDALL is the special projects administrator with the
Ohio Division of Forestry, 2045 Morse Road, H-1, Columbus, OH
43229, coton.randall@dnr.state.oh.us.
JASON REED is the engineering and natural resources group
leader with the Monongahela Naional Forest,
200 Sycamore Street, Elkins, WV 26241, jareed02@fs.fed.us.
CYNTHIA SANDENO is an ecologist with the Monongahela
Naional Forest, 200 Sycamore Street, Elkins, WV 26241,
cmsandeno@fs.fed.us.
TOM SCHULER is a project leader and research forester with the
Fernow Experimental Forest, P.O. Box 404, Parsons, WV 26287,
tschuler@fs.fed.us.
LESLEY SNEDDON is a naional ecologist with NatureServe,
c/o University of Massachusets at Boston, Biology Department,
100 Morissey Boulevard, Boston, MA 02125,
lesley_sneddon@natureserve.org.
BILL STANLEY is the director of conservaion with The Nature
Conservancy in Ohio, 6375 Riverside Drive, Suite 100, Dublin,
OH 43017, bstanley@tnc.org.
AL STEELE is a physical scienist with the U.S. Forest Service,
Northeastern Area State & Private Forestry, 180 Canield Street,
Morgantown, WV 26505, asteele@fs.fed.us.
SUSAN STOUT is a project leader with the U.S. Forest Service,
Northern Research Staion, 359 Main Road, Delaware, OH
43015, sstout@fs.fed.us.
RANDY SWATY is an ecologist with The Nature Conservancy,
101 South Front Street, Suite 105, Marquete, MI 49855,
rswaty@tnc.org.
JASON TEETS is an ecological site descripion specialist with
the Natural Resources Conservaion Service, 201 Scot Avenue,
Morgantown, WV 26508, jason.teets@wv.usda.gov.
TIM TOMON is a forest entomologist with the West Virginia
Department of Agriculture, Plant Industries Division,
1900 Kanawha Boulevard, E., Charleston, WV 25305,
tomon@wvda.us.
JIM VANDERHORST is a vegetaion ecologist with the West
Virginia Division of Natural Resources, Natural Heritage
Program, P.O. Box 67, Elkins, WV 26241,
james.p.vanderhorst@wv.gov.
JOHN WHATLEY is a zone ire management oicer with the
Wayne Naional Forest, 13700 U.S. Hwy 33, Nelsonville, OH
45764, jwhatley@fs.fed.us.
NICHOLAS ZEGRE is an assistant professor of hydrology at
West Virginia University, Division of Forestry and Natural
Resources, P.O. Box 6125, Morgantown, WV 26506,
nicolas.zegre@mail.wvu.edu.
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PREFACE
CoNTEXT AND SCoPE
This assessment is a fundamental component
of the Central Appalachians Climate Change
Response Framework project. The Framework is
a collaborative, cross-boundary approach among
scientists, managers, and landowners to incorporate
climate change considerations into natural
resource management. Six Framework projects
are currently underway, covering approximately
250 million acres in the northeastern and midwestern
United States: Northwoods, Central Appalachians,
Central Hardwoods, Mid-Atlantic, New England,
and Urban. Each project interweaves four
components: science and management partnerships,
vulnerability assessments, adaptation resources, and
demonstration projects.
We designed this assessment to be a synthesis of
the best available scientific information on climate
change and forest ecosystems. Its primary goal is to
inform forest managers in the Central Appalachians
region, in addition to people who study, recreate,
and live in these forests. As new scientific
information arises, our understanding of climate
change and forest ecosystems will be strengthened.
Most importantly, this assessment does not make
recommendations about how this information should
be used.
The scope of the assessment is terrestrial forest
ecosystems, with a particular focus on tree
species. Climate change will also have impacts on
aquatic systems, wildlife, and human systems, but
addressing these issues in depth is beyond the scope
of this assessment.
The large list of authors reflects the highly
collaborative nature of this assessment. The overall
document structure and much of the language
was a coordinated effort among Leslie Brandt,
Patricia Butler, Maria Janowiak, Stephen Handler,
and Chris Swanston. Danielle Shannon conducted
much of the data analysis and developed maps
for Chapters 1, 3, and 4. Louis Iverson, Stephen
Matthews, Matthew Peters, and Anantha Prasad
provided and interpreted Tree Atlas information for
Chapter 5, and assisted with the data processing
for the climate data presented in Chapter 4. Frank
Thompson and William Dijak provided results and
interpretation of the LINKAGES and LANDIS
PRO models. All modeling teams coordinated their
efforts impressively. Kent Karriker, Jarel Bartig,
and Stephanie Connolly provided substantial input
throughout the document.
Among the many others who made valuable
contributions to the assessment, Scott Pugh (U.S.
Department of Agriculture, Forest Service, Forest
Inventory and Analysis [FIA] Program) provided
technical and analytical support for querying FIA
databases. We also thank Kevin Potter (North
Carolina State University), James Rentch (West
Virginia University), and an additional reviewer,
who provided formal technical reviews of the
assessment. Their thorough reviews greatly
improved the quality of this assessment.
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CoNTENTS
Execuive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
Introducion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8
Chapter 1: The Contemporary Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Chapter 2: Climate Change Science and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Chapter 3: Observed Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes . . . . . . . . . . . . . . . . . 89
Chapter 5: Future Climate Change Impacts on Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Chapter 6: Forest Ecosystem Vulnerabiliies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Chapter 7: Management Implicaions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Appendix 1: Species Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
Appendix 2: Trend Analysis and Historical Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
Appendix 3: Addiional Future Climate Projecions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
Appendix 4: Addiional Impact Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
Appendix 5: Vulnerability and Conidence Determinaion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
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EXECuTiVE SuMMARY
This assessment evaluates key vulnerabilities
for forest ecosystems in the Central Appalachian
Broadleaf Forest-Coniferous Forest-Meadow and
Eastern Broadleaf Forest Provinces of Ohio, West
Virginia, and Maryland across a range of future
climate scenarios. This assessment was completed
as part of the Central Appalachians Climate Change
Response Framework project, a collaborative
approach among researchers, managers, and
landowners to incorporate climate change
considerations into forest management.
ecosystems, and how climate may interact with other
stressors on the landscape.
The assessment summarizes current conditions and
key stressors and identifies past and projected trends
in climate. This information is then incorporated
into model projections of future forest change. These
projections, along with published research and local
knowledge and expertise, are used to identify the
factors that contribute to the vulnerability of nine
major forest ecosystems within the assessment
area through the end of this century. A final chapter
summarizes the implications of these impacts
and vulnerabilities on a variety of forest-related
ecological, social, and economic topics across the
region.
• Fragmentation and land-use change
• Shifts in natural disturbance regimes (e.g.,
shifts in drought or flood frequencies)
• Forest diseases and insect pests
• Nonnative plant species invasion
• Shifts in fire regime
• Mineral, gas, and wind energy development
• Erosion and sedimentation
Main Points
●
The assessment area of the Central Appalachians
region contains about 29 million acres, of
which 18.9 million acres are forest land. Private
individuals and organizations own more than 85
percent of forest land.
●
Current major stressors and threats to forest
ecosystems in the region are:
●
Repeated periods of warming and cooling over
the last 15,000 years have resulted in multiple
waves of species retracting and expanding from
the south and from climatic refuges along the
Atlantic coast.
●
Historical land use and past management
practices (17th century onward) have resulted in
second-growth forests that have been rebounding
from large-scale deforestation and wildfire.
Secondary forests are largely even-aged with
poor structure and reduced species diversity.
●
The forest products and forest-related recreation
industries are major contributors to the region’s
economy, and an increasing amount of the forest
land in the assessment area is managed according
to at least one sustainability certification standard.
ChAPTER 1: ThE CoNTEMPoRARY
LANDSCAPE
This chapter describes the forests and related
ecosystems across the Central Appalachian
Broadleaf Forest-Coniferous Forest-Meadow and
Eastern Broadleaf Forest Provinces of Ohio, West
Virginia, and Maryland and summarizes current
threats and management trends. This information
lays the foundation for understanding how shifts
in climate may contribute to changes in forest
1
EXECuTiVE SuMMARY
ChAPTER 2: CLiMATE ChANGE
SCiENCE AND MoDELiNG
ChAPTER 3: oBSERVED CLiMATE
ChANGE
This chapter provides a brief background on climate
change science, models that simulate future climate
change, and models that project the effects of
climate change on tree species and ecosystems. This
chapter also describes the climate data used in this
assessment.
Many of the climatic changes that have been
observed across the world over the past century are
also evident in the assessment area. This chapter
summarizes our current understanding of observed
changes and current climate trends in the assessment
area and across the Central Appalachians region,
with a focus on the last 100 years.
Main Points
●
Temperatures have been increasing at a global
scale and across the United States over the past
century.
●
Climate scientists attribute this increase in
temperature to human activities.
●
Major contributors to warming are greenhouse
gases from fossil fuel burning, agriculture, and
changes in land use.
Main Points
●
Annual minimum temperatures increased over
the past century, with summer and fall minimum
temperatures warming the most rapidly. April,
June, July, August, and November had the
greatest increases in minimum temperature.
Maximum temperatures decreased during July,
September, and October. Hot days are occurring
more frequently.
Hemlock on the Monongahela Naional Forest, West Virginia. Photo by Patricia Butler, Northern Insitute of Applied Climate
Science (NIACS) and Michigan Tech, used with permission.
2
EXECuTiVE SuMMARY
●
●
●
Precipitation patterns have changed across the
region, with the most change occurring in fall
(increase of 2.3 inches). The number of intense
precipitation events has increased.
Snowfall decreased across the assessment area,
and lake ice duration has declined.
Climate change is also indicated by positive
trends in growing season length, shifts in
flowering phenology, and changes in wildlife
emergence and migration.
ChAPTER 4: PRoJECTED ChANGES
iN CLiMATE, EXTREMES, AND
PhYSiCAL PRoCESSES
This chapter describes climate projections for the
assessment area over the 21st century, including
projections related to patterns of extreme weather
events and other climate-related processes.
Temperature and precipitation projections are
derived from downscaled simulations of climate
models. Published scientific literature provides the
basis for describing possible trends in a range of
climate-driven processes, such as extreme weather
events and snowfall.
●
The number of hot days is expected to increase
and the number of cold days is projected to
decrease.
●
Intense precipitation events are expected to
become more frequent.
●
Streamflow and flooding potential are expected to
increase in the winter and spring, and decrease in
the summer and fall.
ChAPTER 5: FuTuRE CLiMATE
ChANGE iMPACTS oN FoRESTS
This chapter summarizes the potential impacts of
climate change on forests in the assessment area,
drawing on information from a coordinated series of
model simulations and published research.
Main Points
●
Many temperate tree species present within the
assessment area are expected to tolerate a mild
degree of warming, but are expected to decline
under higher rates of warming.
●
Many mesic species, including American beech,
eastern hemlock, eastern white pine, red spruce,
and sugar maple are among those projected
to have reductions in suitable habitat, growth
potential, and biomass under a high degree of
warming over the next century.
●
Many species are expected to lose establishment
and regeneration potential over the next century,
but in the absence of other mortality factors, may
persist as mature individuals that continue to
grow for much longer.
●
Species with ranges that extend largely to the
south such as eastern redcedar, post oak, and
shortleaf pine may have increases in suitable
habitat and biomass. Loblolly pine, currently
only in plantations in the assessment area, is also
expected to fare well under the future climate.
Main Points
●
●
Temperatures are expected to increase over the
next century, under a range of climate scenarios
and in all seasons.
Precipitation is projected to increase in winter
and spring across a range of climate scenarios.
Projections of summer and fall precipitation
are more variable; depending on the scenario,
precipitation is projected to decrease during either
summer or fall.
●
Late season droughts or localized soil moisture
deficits are expected to become more frequent.
●
The growing season length is expected to increase
by up to a month.
3
EXECuTiVE SuMMARY
●
The model projections used in this assessment
do not account for many other factors that may
change under a changing climate. Scientific
literature was used to provide additional
information on these factors, including:
• Drought stress
• Wildfire frequency and severity
• Acid deposition and carbon dioxide
fertilization
• Altered nutrient cycling
• Changes in invasive species, insect pests, and
forest diseases
• Effects of herbivory on young regeneration
• Interactions among these factors
ChAPTER 6: FoREST ECoSYSTEM
VuLNERABiLiTiES
change, with an emphasis on shifts in dominant
species, system drivers, and stressors. The adaptive
capacity of forest systems was also examined as a
key component to overall vulnerability. Synthesis
statements are provided to capture general trends.
Detailed vulnerability determinations are also
provided for nine forest ecosystems (Table 1). We
consider a system to be vulnerable if it is at risk
of a composition change leading to a new identity,
or if the system is anticipated to suffer substantial
declines in acreage, health, or productivity.
Main Points
Potenial Impacts of Climate Change on
Drivers and Stressors
●
This chapter focuses on the vulnerability of major
forest ecosystems in the assessment area to climate
Temperatures will increase (robust evidence,
high agreement). All downscaled climate models
project that average temperatures will increase
across much of the assessment area.
Table 1.—Vulnerability determinaion summaries for forest ecosystems considered in this assessment
Potenial impacts
Adapive capacity
Vulnerability
Evidence
Agreement
Negaive
Low-Moderate
High
Medium
Medium
Dry calcareous forest,
woodland, and glade
Neutral-Negaive
Low-Moderate
Moderate-High
Limited-Medium
Medium
Dry oak and oak/pine
forest and woodland
Posiive
Moderate-High
Low
Medium
Medium-High
Dry/mesic oak forest
Posiive-Neutral
High
Low- Moderate
Medium
Medium-High
Large stream loodplain
and riparian forest
Negaive
Low
High
Medium
Medium
Mixed mesophyic and
cove forest
Neutral-Negaive
Moderate-High
Moderate
Limited-Medium
Medium
North-central interior
beech/maple forest
Neutral
Moderate
Moderate
Limited-Medium
Medium
Small stream riparian
forest
Negaive
Moderate
Moderate-High
Medium
Medium
Spruce/ir forest
Negaive
Moderate
High
Limited-Medium
Medium
Forest ecosystem
Appalachian (hemlock)/
northern hardwood
forest
4
EXECuTiVE SuMMARY
●
●
●
Growing seasons will get longer (robust
evidence, high agreement). There is high
agreement among evidence that projected
temperature increases will continue the current
trend of longer growing seasons in the assessment
area.
The amount and timing of precipitation will
change (medium evidence, high agreement).
All downscaled climate models agree that there
will be changes in precipitation patterns across
the assessment area.
Intense precipitation events will continue to
become more frequent (medium evidence,
medium agreement). There is some agreement
that the number of heavy precipitation events will
continue to increase in the assessment area. If so,
impacts from flooding and soil erosion may also
become more damaging.
will lead to increases in certain pest and pathogen
outbreaks, but research to date has examined few
species in the assessment area.
●
Many invasive plants will increase in extent
or abundance (medium evidence, high
agreement). Evidence indicates that an increase
in temperature and more frequent disturbances
will lead to increases in many invasive plant
species.
Potenial Impacts of Climate Change
on Forests
●
Suitable habitat for northern species will
decline (medium evidence, high agreement).
All three impact models project a decrease in
suitability for northern species such as eastern
hemlock, red spruce, and sugar maple, compared
to current climate conditions.
●
Severe storms will increase in frequency
and severity (medium evidence, medium
agreement). There is some agreement that future
climate change will destabilize atmospheric
circulation patterns and processes, leading to
increased risk of severe weather.
●
Habitat is projected to become more suitable
for southern species (medium evidence, high
agreement). All three impact models project an
increase in suitability for southern species such as
eastern redcedar and loblolly pine, compared to
current climate conditions.
●
Soil moisture patterns will change (medium
evidence, high agreement), with drier soil
conditions later in the growing season
(medium evidence, medium agreement).
Studies show that climate change will have
impacts on soil moisture, but there is some
disagreement among climate and impact models
on how soil moisture will change during the
growing season.
●
Species composition will change across the
landscape (limited evidence, high agreement).
Although few models have specifically examined
how species composition may change, model
results from individual species, paleoecological
data, and ecological principles suggest that
recognized communities may dissolve to form
new mixes of species.
●
●
Climate conditions will increase wildfire risk
by the end of the century (medium evidence,
medium agreement). Some national and global
studies suggest that wildfire risk will increase
in the region, but few studies have specifically
looked at wildfire potential in the assessment
area.
●
Certain insect pests and pathogens will
increase in occurrence or become more
damaging (medium evidence, high agreement).
Evidence indicates that an increase in temperature
A major transition in forest composition is not
expected until after the middle of the century
(2040 to 2069) (medium evidence, medium
agreement). Although some models indicate
major changes in habitat suitability, results
from spatially dynamic forest landscape models
indicate that a major shift in forest composition
across the landscape may take 100 years or more
in the absence of major disturbances.
5
EXECuTiVE SuMMARY
●
Climate change is expected to affect early
growth and regeneration conditions (medium
evidence, medium agreement). Seedlings are
more vulnerable than mature trees to changes in
temperature, moisture, and other seedbed and
early growth requirements.
●
Increased fire frequency and harvesting will
accelerate shifts in forest composition across
the landscape (medium evidence, medium
agreement). Studies from other regions show
that increased fire frequency can accelerate
the decline of species negatively affected by
climate change and can accelerate the northward
migration of southern tree species.
●
Net change in forest productivity is expected
to be minimal (medium evidence, low
agreement). A few studies have examined the
impact of climate change on forest productivity,
but they disagree on how multiple factors may
interact to influence it.
Adapive Capacity Factors
●
Low-diversity ecosystems are at greater risk
(medium evidence, high agreement). Studies
have consistently shown that diverse ecosystems
are more resilient to disturbance, and lowdiversity ecosystems are more vulnerable to
change.
●
Species in fragmented landscapes will have
less opportunity to migrate long distances in
response to climate change (limited evidence,
high agreement). Evidence suggests that species
may not be able to disperse overthe distances
required to keep up with climate change, but little
research has been done in the region on this topic.
●
Ecosystems that are highly limited by
hydrologic regime or geological features
may be topographically constrained (limited
evidence, medium agreement). Our current
understanding of the ecology of Central
Appalachians ecosystems suggests that some
species will be unable to migrate to new areas
due to topographic constraints.
●
Ecosystems that are tolerant of disturbance
or are disturbance-adapted have less risk of
declining on the landscape (medium evidence,
high agreement). Basic ecological theory and
other evidence support the idea that systems that
are adapted to more frequent disturbance will be
at lower risk.
●
Fire-adapted ecosystems will be more resilient
to climate change (high evidence, medium
agreement). Studies have shown that fireadapted ecosystems are better able to recover
after disturbances and can promote many of
the species that are expected to do well under a
changing climate.
●
Ecosystems occupying habitat in areas of high
landscape complexity have more opportunities
for persistence in pockets of refugia (medium
evidence, medium agreement). The diversity
of landscape positions occupied by forest may
provide opportunities for natural refugia, for
example where cool air and moisture accumulate
in valley bottoms.
ChAPTER 7: MANAGEMENT
iMPLiCATioNS
This chapter summarizes the implications of
potential climate change impacts on important facets
of forest management and planning in the Central
Appalachians region, such as impacts on wildlife or
cultural resources. The process we used to assess the
vulnerability of forest ecosystems was not used to
consider these topics. Rather, we point out important
implications, ongoing research, and sources for more
information on how climate change is expected
to affect these topics. This chapter does not make
recommendations as to how management should be
adjusted to cope with these impacts, because impacts
and responses will vary by ecosystem, ownership,
and management objective.
EXECuTiVE SuMMARY
Main Points
●
Management of endemic plants and animals that
depend on forests may face additional challenges
as the climate shifts.
●
Prevention and eradication of nonnative invasive
plant species are expected to become more
difficult and require more resources.
●
The timing of activities, including prescribed
fire, recreation, or timber removal may need to be
shifted as temperatures and precipitation patterns
change.
●
Responses to increased risk of wildfire or
large-scale wind and storm events may require
reassessing emergency response plans, water
resource infrastructure, and available resources.
●
Climate change may present opportunities for
the forest products industry, recreation, and other
sectors if resource managers are able to anticipate
and respond to changing conditions.
A riparian hemlock community in West Virginia. Photo by Patricia Butler, NIACS and Michigan Tech, used with permission.
7
iNTRoDuCTioN
CoNTEXT
This assessment is part of a regional effort called
the Central Appalachians Climate Change Response
Framework (Framework; www.forestadaptation.
org). The Framework project was initiated in 2009
in northern Wisconsin with the overarching goal
of helping managers incorporate climate change
considerations into forest management. To meet
the challenges brought about by climate change, a
team of federal and state land management agencies,
private forest owners, conservation organizations,
and others have come together to accomplish three
objectives:
1. Provide a forum for people working across
the Central Appalachians to effectively and
efficiently share experiences and lessons
learned.
2. Develop new user-friendly information and
tools to help land managers factor climate
change considerations into decisionmaking.
3. Support efforts to implement actions for
addressing climate change impacts in the
Central Appalachians.
The Framework process is designed to work
at multiple scales. The Central Appalachians
Framework is coordinated across the region, but
activities are generally conducted at the state or local
level to allow for greater specificity. Additionally,
regional Framework projects are underway in
several other regions: Central Hardwoods, MidAtlantic, New England, Northwoods, and an Urban
pilot project in Chicago.
8
The Central Appalachians Framework is an
expansion of the original northern Wisconsin effort,
and has been supported in large part by the U.S.
Forest Service. Across the Central Appalachians
region, the project is being guided by an array of
partners with an interest in forest management,
including:
• Northern Institute of Applied Climate Science
• U.S. Forest Service, Eastern Region
• U.S. Forest Service, Northern Research Station
• U.S. Forest Service, Northeastern Area State &
Private Forestry
• Trust for Public Land
• The Nature Conservancy
• NatureServe
• Natural Resources Conservation Service
• Ohio Department of Natural Resources
• West Virginia Division of Natural Resources
• Maryland Department of Natural Resources
This assessment is designed to provide detailed
information for forest ecosystems across the Central
Appalachians region. Several independent efforts
related to climate change, natural ecosystems,
and human well-being are also occurring at
the state level. This assessment complements
other assessments that have been created for
the assessment area and for the broader Central
Appalachians region. The Framework project will
also work to integrate the results and outcomes from
other projects related to climate change and natural
resource management.
iNTRoDuCTioN
This assessment bears some similarity to other
synthesis documents about climate change science,
such as the National Climate Assessment (Melillo
et al. 2014) and the Intergovernmental Panel on
Climate Change (IPCC) reports (working group
contributions to the Fifth Assessment at http://www.
ipcc.ch/report/ar5/). Where appropriate, we refer
to these larger-scale documents when discussing
national and global changes. However, this
assessment differs from these reports in many ways.
This assessment was not commissioned by any
federal government agency nor does it give advice
or recommendations to any federal government
agency. It also does not evaluate policy options or
provide input into federal priorities. Instead, this
report was developed by the authors to fulill a
joint need of understanding local impacts of climate
change on forests and assessing which tree species
and forest ecosystems may be the most vulnerable
in the Central Appalachians region. Although it was
written to be a resource for forest managers, it is irst
and foremost a scientiic document that represents
the views of the authors.
SCoPE AND GoALS
The primary goal of this assessment is to summarize
potential changes to the forest ecosystems of
the Central Appalachians region under a range
of possible future climates, and determine the
vulnerability of forest ecosystems to these changes
during the next century. Included is a synthesis of
information about the current landscape as well as
projections of climate and vegetation changes used
to assess these vulnerabilities. Uncertainties and
gaps in understanding are discussed throughout the
document.
This assessment covers about 18.9 million acres of
forest land in Ohio, West Virginia, and Maryland
(Fig. 1). The assessment area boundaries are deined
by the Eastern Broadleaf Forest (Ecological Province
221) and the Central Appalachian Broadleaf ForestConiferous Forest-Meadow (Ecological Province
M221) (McNab and Avers 1994, McNab et al. 2007).
In addition to these state and ecological boundaries,
we used county-level information that most closely
Figure 1.—The assessment area overlaps two secions of the Eastern Broadleaf Forest Province (green) and three secions of
the Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow Province (blue) within Ohio, West Virginia, and Maryland
(Cleland et al. 2007a).
9
iNTRoDuCTioN
represented the assessment area when ecoregional
data were not available, limiting our selections to the
counties that are most analogous to the assessment
area (21 Ohio counties, all West Virginia counties,
and 3 Maryland counties).
Chapter 3: Observed Climate Change provides
information on the past and current climate of the
assessment area, summarized from the interactive
ClimateWizard database and published literature.
This chapter also summarizes some relevant
ecological indicators of observed climate change.
Land ownership is fairly similar across the three
states. Overall, more than 85 percent of forest
land in the assessment area is owned by private
individuals and organizations. Approximately
8 percent of land is federally owned, with the Wayne
and Monongahela National Forests administering
the bulk of federal lands. State agencies own
5 percent of forest land; and county, municipal, and
local governments own 1.4 percent. This assessment
synthesizes information covering all forest lands
in the assessment area in recognition of the area’s
dispersed patterns of forest composition and land
ownership.
Chapter 4: Projected Changes in Climate,
Extremes, and Physical Processes presents
downscaled climate change projections for the
assessment area, including future temperature and
precipitation data. It also includes summaries of
other climate-related trends that have been projected
within the assessment area and the broader Midwest
and Northeast.
ASSESSMENT ChAPTERS
This assessment contains the following chapters:
Chapter 1: The Contemporary Landscape
describes existing conditions, providing background
on the physical environment, ecological
character, and broad socioeconomic dimensions
of the assessment area. It defines the nine forest
ecosystems we refer to in later chapters.
Chapter 2: Climate Change Science and
Modeling contains background on climate change
science, projection models, and impact models. It
also describes the techniques used in developing
climate projections to provide context for the model
results presented in later chapters.
10
Chapter 5: Future Climate Change Impacts on
Forests summarizes ecosystem model results that
were prepared for this assessment. Three modeling
approaches were used to simulate climate change
impacts on forests: a species distribution model
(DISTRIB of the Climate Change Tree Atlas),
and two forest simulation models (LINKAGES
and LANDIS PRO). This chapter also includes a
literature review of other climate-related impacts on
forests that the models did not consider.
Chapter 6: Forest Ecosystem Vulnerabilities
synthesizes the potential effects of climate change
on the forest ecosystems of the assessment area and
provides detailed vulnerability determinations for
nine major forest ecosystems.
Chapter 7: Management Implications draws
connections from the forest ecosystem vulnerability
determinations to a wider network of related
concerns shared by forest managers, including forest
management, recreation, cultural resources, and
forest-dependent wildlife.
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
The Central Appalachians region is home to
some of the most biologically diverse forests in
North America. The diverse forests of the Central
Appalachians provide many environmental, cultural,
and economic benefits. This forested landscape
sustains the people of the region by providing
economically important forest products, outdoor
recreation opportunities, and other services. This
chapter includes a brief introduction to the complex
factors that shape the forests in the region and
provides context for the modeling results and
interpretations provided in later chapters.
LANDSCAPE SETTiNG
The assessment area covers nearly 29 million acres,
and is defined by a combination of ecological
and political boundaries. The area is bounded by
Ecological Provinces M221 (Central Appalachian
Broadleaf Forest-Coniferous Forest-Meadow) and
221 (Eastern Broadleaf Forest) of the National
Hierarchical Framework of Ecological Units, and
by the state boundaries of Ohio, West Virginia, and
Maryland (McNab et al. 2007). Provinces are broad
geographic areas that share similar coarse features,
such as climate, glacial history, and vegetation types.
Provinces are divided by sections that are based on
similarities in geologic parent material, elevation,
plant distribution, and regional climate (McNab et al.
2007). To gain a better understanding of differences
in forest ecosystems across the landscape, we
focused on the five sections within these two
provinces (Fig. 1). The major physical and biological
features of the sections are summarized below.
Physical Environment
Climate
The existing climates within the Central
Appalachians are strongly influenced by atmospheric
circulation patterns, latitude, topography, and
abrupt changes in elevation. The primary factors
influencing the climate are latitude and proximity
to Lake Erie in the glaciated and gently dissected
northern and western sections, and elevation and
complex topography in the mountainous eastern
sections. Three major air masses move through the
assessment area. Hot, dry air from the southwest
and cold, dry air from the north affect much of Ohio
and West Virginia. Warm, moist air from the Gulf
of Mexico sweeps east of the Allegheny Mountains
to affect the eastern panhandle of West Virginia and
Maryland (U.S. Geological Survey [USGS] 1989).
Occasional easterlies can also sweep moist air from
the Atlantic Ocean across Maryland to the Allegheny
Mountains.
The Allegheny Mountains (M221B) and Western
Glaciated Allegheny Plateau (221F) have cooler and
wetter climates than the rest of the assessment area,
although microclimates within these sections are
highly variable due to the effects of topography and
relief. Average annual temperatures are 49 °F, with
winters averaging 28 to 30 °F and winter minimum
temperatures averaging 20 °F. Summers are also
cooler in these sections, averaging 7 to 9 °F, with
average maximum temperatures of 78 to 81 °F. In
the highest elevations of M221B, daily minimum
temperatures are the most extreme in the region,
11
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
and can reach -15 to -20 °F (U.S. Department of
Agriculture 2012). The freeze-free growing season
is less than 150 days, and can be shorter than 100
days in valleys that are subject to frost pocket effects
(Koss et al. 1988). Annual precipitation may reach
70 inches in the high-elevation and lake-effect
areas, whereas low-elevation and inland areas may
get as little as 30 inches (National Oceanic and
Atmospheric Administration [NOAA] 2014c).
Annual snowfall is included in these averages and
follows a similar pattern, with the high-elevation and
lake-effect areas averaging more than 72 inches and
lower elevation and inland areas receiving as little as
24 inches (NOAA 2014c).
The warmest parts of the assessment area are
Sections 221E, M221A, and M221C, where
average annual temperatures range from 51 to 52 °F
(Appendix 2). Winter average temperatures range
from 32 to 34 °F, and minimum temperatures range
from 22 to 24 °F. Summers are relatively hot, with
average temperatures ranging from 70 to 71 °F, and
maximum summer temperatures ranging from 82 to
83 °F.
Precipitation is even more variable across the
assessment area. In general, precipitation ranges
from 35 inches per year in the lower elevation
areas to 5 inches per year in the highest elevations
(Chapter 3). The northernmost corner of Ohio’s
Western Glaciated Allegheny Plateau receives
slightly more precipitation because of its proximity
to Lake Erie. Precipitation is also slightly higher
on the western slopes of the Allegheny Mountains,
where it can reach 70 inches per year in the highest
elevations. Prevailing winds moving across Ohio
and West Virginia pick up moisture, and release it as
the air is forced to rise rapidly over the mountains.
Moisture-laden air is obstructed by the mountain
ridge, which allows precipitation and runoff to
enter the western watershed, but not the eastern
watershed, resulting in a rainshadow effect.
Lake-effect snow from Lake Erie can also
12
generate winter storms, which are more frequent in
northeastern Ohio, producing up to three snowstorms
per decade with more than inches of snow, and up
to five ice storms per decade (Kunkel et al. 2013a).
The frequency of these winter storms decreases
to the south, with southeastern Ohio receiving an
average of one -inch snowstorm and three ice
storms per decade. Lake effect may combine with
local weather processes to generate up to 30 percent
of annual snowfall in the northern mountains of West
Virginia (Kunkel et al. 2009a, 2009b).
Extreme weather events in the area include highintensity rains associated with occasional hurricanes,
short and infrequent drought periods, heat waves,
windstorms and tornadoes, and ice storms (McNab
et al. 2007). A more detailed description of past and
contemporary climate of the region can be found in
Chapter 3.
Geology, Landform, and Soils
The assessment area comprises both glaciated lands
in northern Ohio and unglaciated, elevated lands in
Ohio, West Virginia, and Maryland. There is much
variation in elevation, from 17 feet in the Maryland
portion of the assessment area to 4,81 feet at
Spruce Knob in West Virginia (Fig. 2).
Six groups of parent material formed the soils that
dominate this assessment area. One group, residuum,
developed in place by the weathering of underlying
bedrock. Another group, colluvium, weathered
from bedrock and was deposited at the base of steep
slopes. Alluvium, lacustrine sediments, and outwash
materials (e.g., silt, sand, gravel, and clay) were
deposited by water. Fine-grained material (loess)
was deposited by wind, and glacial till was deposited
by ice. The last group of parent material is mine
spoil, found in areas that have been strip-mined for
coal (McNab and Avers 1994). Soil characteristics
for each section are described in general terms
below, and information on specific soil types is
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Figure 2.—Elevaion zones within Ohio, West Virginia, and Maryland (Danielson and Gesch 2011).
available in the U.S. Department of Agriculture,
Natural Resources Conservation Service (NRCS)
Web soil survey portal. Although anthropogenic
activities (e.g., agriculture, mining, urban
development) have influenced soil characteristics to
some degree in many areas, mountaintop removal
mining has been primarily responsible for removal
of the soil and bedrock from large areas in southern
West Virginia (U.S. Environmental Protection
Agency [EPA] 2005, 2011; Wickham et al. 2013).
Secion 221F—Western Glaciated Allegheny
Plateau
Unlike the rest of the assessment area, the geologic
history of this section includes major alteration of
the land surface caused by the movement of massive
glaciers of the Pleistocene Epoch (Ice Age) (Ohio
Department of Natural Resources [ODNR] 2005).
The land was scoured and depressed under the
weight of glaciers, and subsequent melting released
the Earth’s crust, deposited boulders and other
materials, and carved river beds and other landscape
features, such as the rounded hills, ridges, and broad
valleys that characterize the area. Glacial features
include valley scour, moraines, kames, eskers, and
kettle outwash plains. The bedrock consists of shale,
siltstone, sandstone, minor conglomerate, and coal
(McNab and Avers 1994). The subsurface geology
has a generally uniform resistance to erosion, and
drainage from smaller streams regularly joins
larger streams within a watershed in a regular
dendritic pattern. Erosion is caused by two primary
geomorphic processes; the first occurs when gravity
interacts with moisture on steep slopes to cause
slipping of the soil (mass wasting). The second
occurs as rivers and streams erode the surrounding
earth, transport the soil, and deposit it in a new
location (fluvial transport and deposition). Elevation
in this section ranges from 50 to 1,500 feet (ODNR
1998).
Ridges, flat uplands, hills, and hummocks in this
section are dissected by steep valleys covered by
thin glacial till and stratified drift. Lower slopes
13
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
and valley floors are covered by sediment and
unconsolidated glacial materials. Soils are very
deep to bedrock, reaching hundreds of feet in some
places (ODNR 2013). The glacial deposits range
from coarse-textured to fine-textured, with coarser
and better drained soils in the south of this section
(ODNR 2013). Lowland surfaces in this section are
characterized by gently rolling terrain covered by
thin to thick glacial drift with frequent areas of poor
drainage and extensive wetlands (ODNR 1998).
Secion 221E—Southern Unglaciated Allegheny
Plateau
This section of the Allegheny Plateau was not
covered by glaciers, but was influenced by
proximate glaciers as they melted. Fluvial erosion
severely dissected the plateau, now characterized by
high hills, sharp ridges, and narrow valleys (McNab
and Avers 1994). The bedrock is frequently exposed
and consists of limestone, siltstone, sandstone, shale,
and numerous coal seams. Three major preglacial
streams drained the area until many tributaries
were blocked by advancing ice sheets, and the
accumulation of water formed lakes and deposited
sediment in the valleys. This section now has a high
density of streams ranging from high gradient, steep
headwater streams to low gradient rivers that flow
into the Ohio River. Some streams in the preglacial
valleys are underlain by relatively shallow silt,
sand, or gravel alluvium, and others are filled with
deep glacial deposits. Small springs are numerous,
but most are ephemeral. Natural streamflow and
topography have been greatly modified by oil,
gas, and coal extraction activities in this section.
Elevation ranges from 490 to 1,400 feet (ODNR
1998).
Soils in this section are characterized by a relatively
high percentage of clay in the subsoil, associated
with remnants of an ancient stream system, where
economically important sources of clay and coal are
located (ODNR 1998). Clay is found extensively in
the lowlands, and red or yellowish-brown silt-loams,
14
silt-clay loam colluvium, and lacustrine silt cover the
upland areas (ODNR 1998).
Secion M221A—Northern Ridge and Valley
This section is characterized by a series of mountain
ranges and narrow valleys created by differential
erosion of tightly folded, intensely faulted bedrock.
The ridges and valleys run parallel from southwest
to northeast. From the base of the Blue Ridge
Mountains in the east, this section sweeps west
across the Great Valley to the Allegheny Mountains
and ends at the Allegheny Front, a steep, high ridge
marking the eastern boundary of the Allegheny
Plateau (McNab and Avers 1994). The bedrock of
the ridges consists primarily of resistant sandstone
and limestone, and the valleys consist of less
resistant shale and siltstone. Erosion and transport
of the water-soluble limestone have resulted in
sinkholes, caves, and other karst features common
in this landscape, and is responsible for the flat
topography of the Great Valley (Box 1) (McNab and
Avers 1994). Drainage in this section is constricted
by the regular folding of bedrock, which forces
tributaries to join the main river at right angles in
a trellis-shaped pattern. As a result, streams flow
in narrow, steep-sided channels (Bruce and Smith
1921). Mass wasting events (landslides), fluvial
erosion, and karst solution are common in this
section. Elevation ranges from 300 to 4,000 feet.
The alluvial soils in this section developed from
the weathering of underlying bedrock and the
subsequent deposition of sediments laid down in
floodplains during stream overflow (Bruce and
Smith 1921). Soils are relatively shallow over side
slopes, back slopes, and ridges, showing frequent
outcropping and escarpments of bedrock (Bruce
and Smith 1921, McNab and Avers 1994). The
shallowness of the soil is due to the erosion of soil
as it forms, the slow formation of soils from highly
resistant bedrock, and drier conditions resulting
from its position in the rainshadow of the Allegheny
Mountains (Bruce and Smith 1921).
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Box 1: Karst Topography
Karst landscapes occur where the topography and
its disincive features are formed by the dissoluion
of soluble rock, especially dolomite and limestone
(Fig. 3). The resuling surface features include
subterranean drainages, caves, sinkholes, springs,
disappearing streams, dry valleys and hollows,
natural bridges, arches, and other related features.
Sinkholes are karst features that develop as a result
of a collapse of surface material into nearby caviies
(usually caves). Cold-water springs are characterized
by a coninuous low of mineralized groundwater
when surface precipitaion percolates through
Figure 3.—Potenial karst formaions in the Central
Appalachians. Drat map from Weary (2008).
Secion M221B—Allegheny Mountains
This section is characterized by a series of high,
sharp ridges, broad plateaus, low mountains, and
narrow valleys created by folded and eroded bedrock
(McNab and Avers 1994). The parallel ridges and
valleys run southwest to northeast. Bedrock consists
of shale, siltstone, limestone, sandstone, and coal.
The Greenbrier Limestone generally forms a ring
midslope in some mountains, and can be seen as
an outcrop in the Canaan Valley in West Virginia.
fractures in bedrock including sinkholes, losing
streams, caves, and bedrock aquifers.
The Northern Ridge and Valley (M221A) and
Allegheny Mountains (M221B) secions contain the
assessment area’s largest concentraion of soluble
rock, therefore the largest karst (Weary 2008). The
karst caves are simple or complex subterranean
networks that trend northeast along the mountains.
Cave and karst systems play an integral role in the
area’s biological producivity and provide habitat
to rare and endangered species. In West Virginia,
3,754 caves are known and 5 percent of those are
reported to support cave-obligate species (Schneider
and Culver 2004). Approximately 76 known aquaic
and terrestrial species that are dependent on caves
are recorded in West Virginia. Several species of
state or global viability concern also reside in cave
and karst habitats in the assessment area (Byers and
Norris 2011). The Indiana bat and Virginia big-eared
bat are globally threatened, and populaions of
other bat species also depend on these caves. The
Carter Cave spider, eastern cave-loving funnel web
spider, and Dry Fork Valley cave pseudoscorpion
are some of the invertebrate species occurring only
in caves, and in some cases, only in a paricular
cave in the assessment area (Kovarik 2013). Litle is
known about the ecology and life history of many
of the cave-dwelling species in the assessment area,
making it diicult to determine whether they may be
afected by a changing climate.
Erosion and transport of the water-soluble limestone
has resulted in sinkholes, caves, and other karst
features common in this landscape. Drainage is
primarily dendritic, but trellis drainage occurs
where the topography controls the direction of
streamflow. Mass wasting, karst solution, and fluvial
erosion are the dominant geomorphic processes.
Elevation generally ranges from 1,000 to 4,500 feet,
but reaches up to 4,81 feet at Spruce Knob, West
Virginia.
15
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
The alluvial soils in this section developed from
the weathering of underlying bedrock and the
subsequent deposition of sediments laid down in
floodplains during stream overflow (Bruce and
Smith 1921). Residuum developed mainly from
sandstone, shale, and siltstone (Soil Conservation
Service [SCS] 1974). Soils that developed in steeply
sloping areas are moderately deep, and have a high
rock fragment content (35 percent or greater) with
moderately well-drained soils in coves (SCS 1974).
Gently sloping soils are moderately well-drained and
level soils are generally very poorly drained (SCS
1974). Soils also tend to be nutrient-poor and acidic.
Soils at higher elevations tend to be shallow with
severe soil erosion and lower forest productivity than
lower elevation sites. Most of the soils have a severe
erosion potential related often to slope but also to
other physical properties. Massive soil loss from
erosion and wildfires occurred during the logging era
(circa 1930s), when thick organic mats were burned
to bedrock in places, and sediment filled stream
bottoms. Bituminous coal mining has also disturbed
large areas of soil, and erosion is associated with
stream siltation and acidification. High-elevation
soils (above 3,000 feet) receive greater amounts of
atmospheric pollutants, especially sulfate (SO4-2) and
nitrate (NO3– ), which has led to the loss of important
nutrients (e.g., calcium) and the mobilization of
others (e.g., aluminum) (Elliott et al. 2013).
Secion M221C—Northern Cumberland
Mountains
This section is characterized by highly dissected
uplands and low mountains where less than
20 percent of the area is gently sloping (McNab and
Avers 1994). The bedrock consists of shale, coal,
sandstone, and limestone. The subsections are named
“Eastern Coal Fields” and “Western Coal Fields,”
reflecting their realized potential for coal extraction.
Drainage is primarily dendritic. Primary geomorphic
processes include mass wasting, fluvial erosion, and
transport and deposition. Elevation ranges from 00
to 3,900 feet.
1
The soils in this section have formed from residuum
on the ridges and mountaintops, colluvium on
the slopes, and colluvial and alluvial materials in
the valleys. Soils are sandy textured on uplands
and loamy on the valley bottoms, where soils are
moderately deep to deep and well-drained. Soils can
also be shallow and excessively drained in places
(NRCS 200). Adequate soil moisture helps to
overcome the limitations of these otherwise nutrientpoor soils.
hydrology
The hydrologic characteristics of the Central
Appalachians are influenced, depending on location,
by past glaciation, topographic complexity, and
proximity to Lake Erie or the Atlantic Ocean.
Natural lakes are uncommon in this region; many
lakes were created by flooding valleys and building
reservoirs (Moore et al. 1997). Many streams have
been classified as perennial runoff streams with low
baseflow and small variations in year-to-year low
flow (Poff 1992).
Secion 221F—Western Glaciated Allegheny
Plateau
This section covers northeastern Ohio, where
dominant drainage basins are Lake Erie and the Ohio
River. Temporal variations in streamflow primarily
follow precipitation and season; snowmelt in early
spring increases streamflow, and evapotranspiration
during the growing season reduces streamflow.
The hydrology of this section is influenced by the
remnants of ancient rivers, buried by glacial till.
The landscape is characterized by large rivers and
floodplains and relatively low topographic relief,
with glacial features including kames, kettles,
moraines, flat-bottom valleys, bogs, and deranged
stream networks. Agriculture and developed lands
make up more of the land base in this section than
in any other section, placing greater demand on the
area’s water resources.
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
A shallow slow-moving stream, one of many found throughout the assessment area. Photo by Patricia Butler, Northern
Insitute of Applied Climate Science (NIACS) and Michigan Tech, used with permission.
Secion 221E—Southern Unglaciated Allegheny
Plateau
This section covers southeastern Ohio and western
West Virginia and contains more than 55,400
miles of streams; 511 lakes, reservoirs, and ponds
totaling 27,825 acres; and 12,595 acres of wetlands
(Pitchford et al. 2012, West Virginia Department
of Environmental Protection [WVDEP] 2013b).
Only one lake in West Virginia is natural; the rest
were constructed in order to store water. Dominant
drainage basins are the Kanawha River and Ohio
River basins, which drain westerly to the Gulf of
Mexico, and the Potomac River basin, which drains
east to the Chesapeake Bay. Streamflow follows the
general pattern of growing season and precipitation;
intermittent streams are typically dry in late August
or early September through early November,
when precipitation is low and evapotranspiration
is high. Urban and industrial activity is common
in valleys along the major rivers. Bituminous coal
mining is widespread and has diminished water
quality and reduced fish diversity; recent stream
quality improvements have occurred in some
rivers including the Allegheny, Monongahela,
Youghiogheny, and Ohio (Woods et al. 1999).
17
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Secion M221A—Northern Ridge and Valley
Dominant drainage basins in this section are the
Youghiogheny River draining west to the Ohio
River; the Shenandoah River, which joins with the
Potomac River; and the Potomac River, which drains
east to the Chesapeake Bay. Temporal variations
in streamflow primarily follow precipitation and
season; snowmelt in early spring increases flow and
evapotranspiration during the growing season (June
through September) reduces streamflow.
Secion M221B—Allegheny Mountains
Hydrology varies widely with relief, from flat
mountain bogs to steep water gaps. Small ephemeral
channels run down ridges to join perennial streams
and larger rivers, including the Cheat River,
Greenbrier River, and Tygart Valley River. The
eastern side of this section is drained by the James
River. Steep topography and complex landforms
restrict the flow of water so that the swift, actively
down-cutting streams run off steep ridges to join
the valleys perpendicularly (Woods et al. 1999).
Other large rivers such as the Susquehanna River
cross ridges, cutting deep gorges in the process
(Woods et al. 1999). High-gradient cold-water
streams and waterfalls are common in water gaps
and on ridge slopes, whereas low-gradient and
warmer, meandering streams are common in flatter
areas. Because resistant sandstone and shale are
not as permeable, surface streams are larger and
drainage density is higher than in adjacent limestone
areas. Soil erosion is common, and as a result,
the stream turbidity can be relatively high and the
stream habitat impaired. Bituminous coal mines
are common and associated stream siltation and
acidification have occurred. Streams do not have
much buffering capacity; many reaches, including
some not affected by mine drainage, are too acidic to
support fish.
18
Secion M221C—Northern Cumberland
Mountains
Hydrology varies widely with relief, from flat
mountain bogs to steep water gaps. Small ephemeral
channels run down ridges to join perennial streams
and larger rivers, including the Greenbrier River,
Guyandotte River, New River, and Tug Fork of
the Big Sandy River. This section contains rolling,
agricultural lowlands in southeast West Virginia,
where limestone bedrock results in karst formation.
Stream density is low due to the abundance of
saucer-shaped sinkholes. Underground solution
channels occur, and subsurface drainage feeds the
Greenbrier River.
Land Cover
The Central Appalachians region is dominated by
extensive forests, but also contains other natural
ecosystems, rich agricultural lands, urban population
centers, and industrial mining lands. Satellite
imagery from the National Land Cover Dataset
estimates forest cover at 7 percent (Fig. 4) (USGS
2011). The remaining land cover is classified as
agricultural land (19 percent), developed land
(10 percent), grassland (2 percent), water
(1 percent), and wetland (1 percent). Shrublands and
barren land (containing no vegetation) make up less
than 1 percent of the assessment area. Most of the
developed land is located in Ohio, which also has
a higher concentration of agricultural lands (crops
and hay). A similar concentration of agriculture and
development is located in the Great Valley region
of Maryland’s panhandle. The relatively small
percentage of agricultural land in West Virginia is
primarily limited to mountain valleys and gently
rolling terrain. Wetlands are scattered throughout
the assessment area, occurring over clay soils at the
lowest elevations and in geologic depressions at the
highest elevations.
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Figure 4.—Land cover classes in the assessment area (USGS 2011).
DEMoGRAPhiC AND ECoNoMiC
CoNDiTioNS
There are some important socioeconomic differences
among the states within the assessment area. The
highest populations are found near Ohio’s lakes and
rivers, and in Maryland’s Great Valley. West Virginia
remains largely forest, with relatively small urban
centers spread throughout the state. Approximately
7.5 million people reside within the assessment
area (Headwaters Economics 2011). Seventy-two
percent of the population is located in the Ohio
portion of the assessment area, with 25 and 3 percent
residing in the West Virginia and Maryland portions,
respectively. The higher population in Ohio reflects
the abundance of lands suitable for agriculture, port
access to Lake Erie, and a major shipping industry.
The population in the whole assessment area has
increased 1 percent since 1970. The population in
the Maryland portion of the assessment area has
increased the most since 1970 (21 percent). The
population in the West Virginia portion has increased
by percent since 1970, and the population in
Ohio has decreased by 2 percent. These trends for
larger population growth in Maryland are primarily
due to the development of Garrett and Washington
Counties. The amount of residential acreage
increased by 45 percent in Garrett County from 2000
to 2010, largely due to the development of second
homes (an increase of 27 percent from 2000 to
2010).
The economic well-being of people residing in
the assessment area varies across the three states.
Unemployment has been highest in the Maryland
portion over the past 20 years, but only slightly
19
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
lower in Ohio and West Virginia (Headwaters
Economics 2011). In Maryland, growth in
employment (57 percent) and personal income
(119 percent) over the last 40 years has been greater
than in the other two states. Unemployment in the
entire assessment area has increased 4 percent since
2007, similar to trends across the United States
(Headwaters Economics 2011).
Economic Sectors
Forest Products industry
The forest products industry is measured by
grouping census codes from the North American
Industry Classification System, and can include
logging, forest nurseries, forest products,
timber, agriculture and forestry support services,
manufacturing of paper and furniture, and other
related activities. The forest products industry
is important within the assessment area, but the
grouping of these codes can vary by state report, and
may not be comparable to other states. The forest
industry in Maryland is the fifth largest industry in
the state; forestry and wood derivatives generated
$4.7 billion in 2005 (Maryland Department of
Natural Resources [MDNR] 2010). In Ohio, the
gross domestic product (GDP) for the manufacturing
of wood products and furniture and related products
was $2. billion, which represented 0. percent
of Ohio’s total GDP in 2007 (ODNR 2010b). The
forest products industry in West Virginia contributes
approximately $4 billion to the state economy
annually (West Virginia Division of Forestry 2010).
Forests support jobs and revenue in forest
management and logging, sawmills and paper
mills, and wood products manufacturing. Within
the assessment area, more than 25,000 people are
employed in the forest sector, accounting for almost
1 percent of total jobs (Headwaters Economics
2011). The proportion of forestry jobs to total jobs
is highest in the Maryland portion (1.9 percent),
20
followed by West Virginia (1.3 percent), and the
Ohio portion (0.8 percent). Forestry employment has
decreased across the assessment area from 1998 to
2010, which is similar to trends for the United States
as a whole (Headwaters Economics 2011).
Agriculture
The agricultural lands of the assessment area are
primarily located where the growing season can
last up to 175 days: in the Lake Erie floodplains,
Maryland’s Great Valley, and the long, narrow
valleys of West Virginia (Woods et al. 199) (Fig. 4).
The main agricultural activities are corn, soybean,
and beef and dairy livestock farming; Christmas
tree plantations; beekeeping; aquaculture; and
oilseed and grain farming. Farm employment in
the assessment area makes up 1.5 percent of all
employment, with a slightly higher percentage of
farm employment in West Virginia (2.4 percent)
than in other portions of the assessment area. Farm
employment has decreased consistently across the
assessment area in recent decades, resulting in a net
loss of 21 percent of farm jobs from 1970 to 2011
(Headwaters Economics 2011).
Muliple land uses in West Virginia. Agriculture and
development dominate the lat valleys. Photo by Patricia
Butler, NIACS and Michigan Tech, used with permission.
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Recreaion
The forested lands within the assessment area are
a primary destination for recreation opportunities,
which are also economically important to the region.
The travel and tourism sector generates employment
in retail trade, passenger transportation, arts and
entertainment, recreation and food, and lodging.
In Maryland, increasing demand for forest-based
recreation is resulting in increased conflict between
motorized and nonmotorized users, and the amount
of forest land open to the public is decreasing.
Recreation in West Virginia, measured mainly by
hunting, fishing, and wildlife viewing receipts,
generated $803 million. Tourism in West Virginia
generated approximately $4.8 billion, 72 percent
of which can be attributed to activities using the
state’s forests (West Virginia Division of Forestry
2010). National forests are also a major part of the
recreation and tourism industry. Common activities
in national forests are viewing natural features,
hiking, relaxing, viewing wildlife, driving for
pleasure, fishing, motorized trail activity, picnicking,
nature study, nature center activities, hunting,
gathering forest products, camping, and downhill
skiing, among many others (U.S. Forest Service
[USFS] 2011b). Travel and tourism provide more
than 14 percent of all jobs within the assessment
area: 18 percent of jobs within the Maryland portion
of the assessment area, followed by West Virginia
(1 percent) and Ohio (12 percent) (Headwaters
Economics 2011). Travel spending in West Virginia
totaled $4.25 billion in 2010, and contributed
$988 million in job earnings (Headwaters
Economics 2011). Travel-related spending, earnings,
and employment have all been increasing in West
Virginia since 2000 (Runyan 2011). Total spending
on local and nonlocal visits to the two national
forests within the assessment area are approximately
$50 million per year (USFS 2013).
A hiking trail over the Blackwater Falls in the Canaan Valley,
West Virginia. Recreaion opportuniies centered around
natural resources may also be afected by climate change.
Photo by Patricia Butler, NIACS and Michigan Tech, used with
permission.
Mining
Mining jobs make up 1.4 percent of all jobs within
the assessment area, creating employment in oil
and gas extraction, coal mining, metal ore mining,
mineral mining, and other related mining work
(Headwaters Economics 2011). Coal mining
supports the most jobs (27,237 jobs), followed by
oil and gas extraction (7,300 jobs), and other related
work, such as pipeline construction (3,732 jobs).
From 1998 to 2010 mining jobs have decreased in
Ohio (-8.2 percent) and Maryland (-19 percent), but
increased in West Virginia by 41 percent. Mining
is the leading export-oriented industry in West
Virginia, generating $.1 billion in GDP in 2009,
an increase from its contribution of $5. billion in
2008 (Runyan 2011). Most mining jobs (82 percent)
are located in the West Virginia portion of the
assessment area (Headwaters Economics 2011).
21
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
ECoSYSTEM CoMPoSiTioN
Forest Ecosystems
The assessment area is a remarkable landscape
of high biodiversity and extensive forests. Many
combinations of tree and plant species exist in the
variety of habitat conditions that are represented in
the area’s 18.9 million acres of forest land (USFS
2013). An ecosystem is defined as a spatially
explicit, relatively homogeneous unit of the
Earth that includes all interacting organisms and
components of the abiotic environment within its
boundaries (Society of American Foresters 2014).
For the purposes of this assessment, the term “forest
ecosystem” refers to a specific classification system
and is never used to describe ecosystems in general
(Table 2). For example, the Appalachian (hemlock)/
northern hardwood forest is an ecosystem defined by
its species assemblage, its spatial distribution, and
other distinct characteristics. Common and scientific
names of trees and other species mentioned in this
assessment are found in Tables 24 through 2 in
Appendix 1.
Table 2.—Forest classiicaion systems used in this assessmenta
Forest ecosystem used
in this assessment
NatureServe ecological systems
represented by the forest
ecosystems used in this assessment
FiA forest-type
groups
Common tree species
in forest ecosystem
Appalachian (hemlock)/
northern hardwood
forest
Appalachian (hemlock)/northern
hardwood forest
maple/beech/
birch, aspen/
birch
sugar maple, American basswood,
American beech, white ash, black
cherry, yellow birch, sweet birch, red
maple, eastern hemlock, red spruce,
tulip tree
Dry calcareous forest,
woodland, and glade
Southern Ridge and Valley/
Cumberland dry calcareous
oak/hickory
eastern redcedar, chinkapin
oak, eastern redbud, eastern
hophornbeam, white oak, post oak,
shagbark hickory
Central Appalachian alkaline glade
and woodland
North-central Appalachian
circumneutral clif and talus
Dry oak and oak/pine
forest and woodland
Allegheny/Cumberland dry oak
forest and woodland
Central Appalachian dry oak/pine
forest
Central Appalachian pine/oak rocky
woodland
oak/hickory, oak/ white oak, black oak, chestnut oak,
mockernut hickory, pignut hickory,
pine, loblolly/
shortleaf pine
scarlet oak, shortleaf pine, pitch
pine, Virginia pine, eastern white
pine, Table Mountain pine, scrub oak
Appalachian shale barrens
North-central Appalachian acidic clif
and talus
(coninued on next page)
22
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Table 2 (coninued).
Forest ecosystem used
in this assessment
Dry/mesic oak forest
NatureServe ecological systems
represented by the forest
ecosystems used in this assessment
Northeastern interior dry/mesic oak
forest
Central and southern Appalachian
montane oak forest
FiA forest-type
groups
Common tree species
in forest ecosystem
oak/hickory,
oak/pine, white/
red/jack pine,
aspen/birch
white oak, black oak, northern red
oak, scarlet oak, red maple, pignut
hickory, mockernut hickory, shagbark
hickory, sugar maple, chestnut
oak, sweet birch, American beech,
blackgum, tulip tree, white ash
oak/gum/
cypress, elm/
ash/cotonwood
silver maple, eastern cotonwood,
pin oak, red maple, black willow,
sycamore, sweetgum, green ash,
bur oak, American hornbeam, black
walnut, American elm, boxelder,
black oak
maple/beech/
birch
sugar maple, white ash, American
basswood, yellow buckeye, tulip
tree, red maple, eastern hemlock,
cucumbertree, American beech,
sweet birch, northern red oak, black
cherry, mountain magnolia, black oak
maple/beech/
birch
sugar maple, American beech,
northern red oak, American
basswood, eastern hemlock, black
cherry, tulip tree, red maple, white
ash, eastern hophornbeam
elm/ash/
cotonwood
sycamore, red maple, silver maple,
river birch, boxelder, eastern
hemlock, black walnut, pawpaw,
American hornbeam, American elm
spruce/ir
red spruce, yellow birch, eastern
hemlock, red maple, sweet birch,
cucumbertree, American mountain
ash, black cherry, American beech,
mountain magnolia, balsam ir, black
ash, sugar maple
Southern Appalachian oak forest
Large stream loodplain
and riparian forest
South-central interior large
loodplain
Central Appalachian river loodplain
Cumberland riverscour
North-central interior lood plain
Mixed mesophyic and
cove forest
South-central interior mesophyic
forest
Southern and central Appalachian
cove forest
North-central interior
beech/maple forest
North-central interior beech/maple
forest
North-central interior wet latwoods
Small stream riparian
forest
South-central interior small stream
and riparian
Central Appalachian stream and
riparian
Cumberland seepage forest
Spruce/ir forest
Central and southern Appalachian
spruce/ir forest
Southern Appalachian grass and
shrub bald
High Allegheny wetland
Forest-type groups are used to present broad-scale informaion on forest trends from U.S. Forest Service, Forest Inventory
and Analysis (FIA) data. In this assessment, forest ecosystems are used to describe speciic forest communiies and associated
environments as commonly grouped by local forest management organizaions. NatureServe (2013) ecological systems were used
to describe forest ecosystems, and in many cases, muliple NatureServe systems were combined to describe forest ecosystems
within the assessment area. Forest-type groups are classiied diferently from forest ecosystems, and the comparison above is a
rough cross-walk between the two systems.
a
23
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Forest Classiicaion Systems Used
in this Assessment
Different organizations describe forests using
different classification systems. In this assessment,
we describe forests by using two classification
systems: (1) USFS Forest Inventory and Analysis
(FIA) program (Miles et al. 200) and (2) forest
ecosystems, based on NatureServe ecological
systems (NatureServe 2013). These classification
systems are used for different reasons and convey
different types of information. Although there are
some general relationships between the two systems,
they are organized differently enough that one
cannot be substituted for the other. Both types of
information are relevant to this assessment; thus,
both classification systems are used. FIA data are
used to present trends in forest cover, growth, and
mortality for forest-type groups, which are defined
by tree species composition. Forest ecosystems
are also defined by tree species composition,
but include associated understory and wildlife
species, hydrologic regime, landscape position, and
geographic range. FIA forest-type groups are thereby
more broadly defined, and can represent several
forest ecosystems (Table 2).
The FIA program was created by the USFS to
characterize forests across the nation. In this
assessment, we describe acres, ownership categories,
and volume of timber by using “forest-type groups”
based on FIA data. FIA classifications describe
existing vegetation, and only for vegetated areas
dominated by trees (i.e., forests). Forest types are a
classification of forest land based upon and named
for the dominant tree species. Forest-type groups
are a combination of forest types that share closely
associated species or site requirements. The FIA
system measures tree species composition on a
set of systematic plots across the country and uses
that information to provide area estimates for each
forest-type group. However, it does not make any
inferences about what vegetation was historically
on the landscape and does not distinguish between
24
naturally occurring and modified conditions.
Something that is classified as “forest land” by FIA
may have been historically a glade or woodland.
Likewise, areas dominated by tree species that are
not native to the area would still be assigned to a
forest-type group based on dominant species.
Throughout this assessment, we also use a
classification of “forest ecosystems” as the primary
classification system whenever possible because
these better describe the forest ecosystems present
in the assessment area (Table 2). These forest
ecosystems are based upon ecological systems as
described by the NatureServe Explorer, a system
which is familiar to the Wayne and Monongahela
National Forests, state agencies, and other forest
management organizations in the assessment area.
NatureServe ecological systems describe vegetation
as it currently exists on the landscape (NatureServe
2011). An advantage to using the ecological systems
is that landforms, soils, and other site features
are used whenever possible to help inform the
classification. A disadvantage is that the ecological
systems have not yet been spatially verified and
only rough estimates of abundance are available. In
this assessment, we defined our forest ecosystems
by combining 24 NatureServe ecological systems
into nine forest ecosystems, and then modified the
list of dominant species to better reflect the existing
vegetation found within the borders of the Central
Appalachians assessment area. We used these forest
ecosystems to assess vulnerability to climate change
(Chapter ). Common and scientific names of trees
and other species mentioned in this assessment are
found in Tables 24 through 2 in Appendix 1.
Forest Ecosystems
of the Central Appalachians
The following descriptions of forest ecosystems are
based on the ecological systems described by the
NatureServe Explorer database, which characterizes
terrestrial ecosystems at a broad scale across multistate regions (Comer et al. 2003, NatureServe
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
2013). These systems are classified based on
vegetation associations, land cover class, spatial
pattern, soil type, and geographic distribution. The
assessment area is a region of high biodiversity, and
therefore contains more than 40 ecological systems
represented within the assessment area boundaries.
Of these, we identified the 24 most common forest
ecosystems (based on area) and merged systems
that were similar or commonly occurred together
in order to create nine forest ecosystems that could
be assessed by a large panel of experts (Table 2,
Appendix 5). Descriptions of each forest ecosystem
were further modified to better describe the extent
and dominant species of the forest ecosystems as
they occur within the boundaries of the assessment
area. The resulting forest ecosystems were assessed
for their vulnerability to climate change (Chapter ).
For original descriptions of NatureServe ecological
systems, visit the online database at http://www.
natureserve.org/explorer/. Please note that Web
addresses are current as of the publication date of
this assessment but are subject to change.
Appalachian (Hemlock)/Northern Hardwood
Forest
This forest ecosystem includes only one NatureServe
system: the “Appalachian (Hemlock)/Northern
Hardwood Forest” system, which extends from
southeastern Ohio to the higher-elevation mountains
of Maryland and West Virginia. These largely
deciduous forests are sometimes mixed with
hemlock in the assessment area, distinguishing
them from the more montane Southern Appalachian
northern hardwood forest. These ecosystems occur
on gentle to steep slopes on soils that range from
slightly acidic to very acidic with various amounts
of nutrients, depending on landscape position and
parent material. On colluvial soils, which tend to be
on the less acidic end of the spectrum, the canopy
is typically dominated by sugar maple, American
basswood, American beech, and white ash, with
lesser amounts of black cherry, yellow birch, sweet
birch, red maple, and tulip tree. Sites on the more
acidic end of the spectrum are usually dominated
by combinations of yellow birch, American beech,
black cherry, red maple, and eastern hemlock,
although most sites will not have all five species
present as canopy dominants. Minor components
on the more acidic sites include sweet birch,
red spruce, and tulip tree. On both colluvial and
slightly less acidic soils, sweet birch and tulip
tree may become dominant in response to heavy
disturbance. Wind-driven gap disturbances are the
most influential natural disturbance. Historically,
this forest ecosystem was probably subject to
only extremely rare natural fires. Logging and
subsequent slash fires around the turn of the 20th
century probably promoted deciduous species at the
expense of hemlock and red spruce. Consequently,
many examples of this forest ecosystem on more
acidic sites are likely successional to the spruce/fir
forest ecosystem. The eastern hemlock component
is currently being decimated in large parts of its
range by the hemlock woolly adelgid, which will
likely result in replacement of hemlocks by other
canopy trees (Hessl and Pederson 2013). Likewise,
the American beech component is being decimated
by beech bark disease, which typically results in
conversion to broken-canopied stands with a dense
understory of beech sucker sprouts.
An Appalachian (hemlock)/northern hardwood forest.
Photo by Patricia Butler, NIACS and Michigan Tech,
used with permission.
25
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Dry Calcareous Forest, Woodland, and Glade
This forest ecosystem is mostly formed by small
patches of three NatureServe (2013) systems that
occur on thinner circumneutral and calcareous
soils over limestone or dolostone. The “Central
Appalachian Alkaline Glade and Woodland” system
occurs on ridges, summits, and upper to lower slopes
and usually grades into closed-canopy forests at
low to moderate elevations. Common tree species
include eastern redcedar, chinkapin oak, eastern
redbud, and eastern hophornbeam. Prairie grasses
and forbs dominate the herbaceous layer, with a
number of rare forbs. Most existing open patches
appear to be maintained by drought and landslides.
Fire frequency and intensity influence the relative
ratio of deciduous to evergreen trees, with eastern
redcedar increasing in the absence of fire (Smith
and Johnson 2004). Whether the woodland portion
occupied larger areas under a historic regime of
frequent fire is debatable. Soils are excessively welldrained, with low water holding capacity. The open
canopies and relatively high pH soils make these
communities more susceptible to invasive species.
On deeper soils in extreme southeast West Virginia,
the “Southern Ridge and Valley/Cumberland Dry
Calcareous Forest” system may be dominated by
white oak and shagbark hickory, and sometimes
contains eastern redcedar as a significant component.
The “North-Central Appalachian Circumneutral
Cliff and Talus” system occurs in small patches on
vertical or near-vertical cliffs and steep talus slopes
at low to moderate elevations with alkaline soils.
Lichens are generally the most abundant vegetation,
although stunted northern white cedar may occur on
north-facing cliffs. The historic natural disturbance
regime included frequent low-intensity fires, but
contemporary disturbances include exposure and
landslide events.
Dry Oak and Oak/Pine Forest and Woodland
This forest ecosystem includes major patchforming forests and woodlands. NatureServe (2013)
describes this forest as the “Allegheny/Cumberland
2
Dry Oak Forest and Woodland” on predominantly
acidic substrates on southwest-facing slopes in the
Allegheny Plateau, where it is dominated by white
oak, black oak, chestnut oak, mockernut hickory,
pignut hickory, and scarlet oak with small inclusions
of shortleaf pine and Virginia pine. The “Central
Appalachian Dry Oak/Pine Forest” (NatureServe
2013) occurs in the rainshadow areas of the Ridge
and Valley section at low to high elevations, and
is dominated by a variable mixture of dry-site oak
and pine species including chestnut oak, white oak,
scarlet oak, pitch pine, Virginia pine, Table Mountain
pine, and eastern white pine. Ericaceous shrubs
are common in the understory of both systems. On
wooded hilltops, outcrops, cliff faces, and rocky
slopes, NatureServe (2013) classifies this forest as
“Central Appalachian Pine/Oak Rocky Woodland”
and “North-Central Appalachian Acidic Cliff and
Talus” systems. These smaller patches are dominated
by lichens and stunted trees and may form a
woodland with pitch, Virginia, and Table Mountain
pines mixed with xerophytic oak species and sprouts
of American chestnut. Another patch system,
“Appalachian Shale Barrens” (NatureServe 2013),
occurs where exposed shale creates extreme growing
conditions. Many of these patches occur as open
land or as woodland dominated by stunted chestnut
oak, Virginia pine, pignut hickory, and scrub oak. As
many as 15 endemic herbaceous species may occur
on these shale systems. Soils are generally xeric and
sandy, and have low water holding capacity. Fire
was historically frequent in this forest ecosystem,
but contemporary fire suppression has led to shifts in
species composition and stand structure.
Dry/Mesic Oak Forest
This forest ecosystem includes two matrix-forming
oak-dominated systems that are only weakly
differentiated and occupies more area than any other
forest ecosystem in the assessment area. NatureServe
(2013) describes this forest as “Northeastern Interior
Dry/Mesic Oak Forest” in the north, “Southern
Appalachian Oak Forest” in the south, and “Central
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
and Southern Appalachian Montane Oak Forest”
in the Allegheny Mountains. The dry/mesic oak
forests are drier than the mixed mesophytic and
cove forest and more mesic compared to the dry
oak and oak/pine forest and woodland. This system
is often stunted and wind-flagged on exposed
southwest slopes and ridge crests. Common species
include white oak, northern red oak, black oak, and
scarlet oak. Associated canopy trees also include
red maple, pignut hickory, mockernut hickory,
shagbark hickory, sugar maple, chestnut oak, tulip
tree, sweet birch, white ash, American beech, and
blackgum. American chestnut and eastern white pine
were historically dominant or codominant species
in some areas. Fire is an important driver in this
forest ecosystem, but contemporary fire suppression
has favored maple species over oaks. Wind and ice
storms continue to be important disturbances.
Large Stream Floodplain and Riparian Forest
This forest ecosystem is found across the assessment
area as a complex of wetland and upland vegetation
associated with medium to large rivers or streams
where topography and alluvial processes have
resulted in a well-developed floodplain. NatureServe
(2013) describes these forests as “Central
Appalachian River Floodplain,” “Cumberland
Riverscour,” and “North-Central Interior
Floodplain.” There is typically a gradient from moist
or periodically dry, somewhat nutrient-enriched
conditions upslope to moist and highly enriched
conditions downslope. Most areas are inundated with
seasonal flooding, most commonly in the spring;
microtopography determines how long the various
habitats are inundated. Seasonal flooding and floodscouring contribute to sediment deposition and
can be abrasive forces along the riverbanks. Some
Yellow birch and hemlock, common riparian species in the Allegheny Mountains. Photo by Patricia Butler, NIACS and Michigan
Tech, used with permission.
27
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
areas are also prone to severe drought periods that
may stress or kill vegetation. A variety of alluvial
and loess soil types, in combination with various
flooding regimes, create a diversity of vegetation
communities such as floodplain forests, herbaceous
sloughs, shrub wetlands, riverside prairies, and
woodlands. The wettest areas are dominated by
silver maple, eastern cottonwood, pin oak, red
maple, and black willow. Better-drained areas are
dominated by sycamore, sweetgum, green ash, bur
oak, American hornbeam, black walnut, American
elm, boxelder, and black oak. Some common shrubs
are hazel alder, common buttonbush, silky dogwood,
coastal plain willow, pawpaw, spicebush, and
eastern redcedar. Anthropogenic land conversion
and invasive plant species are major stressors in this
forest ecosystem.
Mixed Mesophyic and Cove Forest
This forest ecosystem is located entirely south of
the glacial boundary, and it is predominantly found
in West Virginia within the assessment area. The
Allegheny Front separates two similar systems:
NatureServe (2013) describes this system as “SouthCentral Interior Mesophytic Forest” in the west,
and “Southern and Central Appalachian Cove
Forest” in the east. This forest ecosystem consists
of mesophytic hardwood or hemlock-hardwood
forests in sheltered topographic positions, often on
concave slopes or in areas with high precipitation.
Common species include sugar maple, white ash,
American basswood, yellow buckeye, tulip tree,
red maple, eastern hemlock, American beech,
cucumbertree, sweet birch, northern red oak, black
cherry, and mountain magnolia. Black oak and black
walnut can also occur as minor canopy species.
Soils are predominantly colluvial, and range from
slightly basic to very acidic, with various amounts
of nutrients. Rich coves collect moisture and
nutrients from higher positions, and support higher
28
diversity and density in the herbaceous layer and
tree layer. Acidic coves often have a dense shrub
layer dominated by great laurel and mountain laurel.
This system is naturally dominated by unevenaged forests, with gap-phase regeneration, although
current conditions resemble more even-aged
second-growth forests. Occasional extreme wind
or ice events may disturb larger patches. Natural
fires are probably extremely rare and have occurred
only in years that were extremely dry. Most of
the component species are among the least firetolerant in the region. Trees may grow very large in
undisturbed areas, but repeated harvesting can result
in smaller age-class distributions and favor tulip tree
and red maple.
North-Central Interior Beech/Maple Forest
This forest ecosystem includes the NatureServe
(2013) “North-Central Interior Beech/Maple Forest”
system on gently rolling uplands to moderate slopes,
and the “North-Central Interior Wet Flatwoods”
on poorly drained uplands or in clay-lined glacial
depressions. This forest ecosystem is primarily
found in the glaciated portion of Ohio, where
various microtopography and moisture regimes
create mixed communities of upland and lowland
species. These forests can be composed of deciduous
or mixed evergreen-deciduous species including
sugar maple, American beech, northern red oak,
American basswood, eastern hemlock, black
cherry, tulip tree, red maple, white ash, and eastern
hophornbeam. On upland sites, soils are loamy over
glacial till, limestone, or calcareous shales, and have
adequate or abundant levels of nutrients. In wetter
locations, soils typically have an impermeable clay
layer resulting in ponding and complete saturation
during spring and possible drought during summer.
The disturbance interval is long, with wind as the
primary disturbance, and this forest ecosystem is
generally intolerant of fire.
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Small Stream Riparian Forest
This forest ecosystem is a matrix of uplands and
wetlands found along creeks, small streams, and
medium rivers (e.g., Shaver’s Fork) with low to
moderately high gradients and oxbows. NatureServe
(2013) systems include “South-Central Interior
Small Stream and Riparian,” “Central Appalachian
Stream and Riparian,” and “Cumberland Seepage
Forest.” Flooding and scouring both influence
this system, but the nature of the landscape (i.e.,
steeper side slopes and higher gradients) prevents
the kind of floodplain development found along
larger rivers. Soils are colluvial and alluvial deposits
with moderate inherent fertility, ranging from
moist to periodically dry (i.e., poorly to excessively
well-drained). The vegetation is a mosaic of
forests, woodlands, shrublands, and herbaceous
communities. Typical tree species may include
sycamore, red maple, silver maple, river birch,
boxelder, eastern hemlock, black walnut, pawpaw,
American hornbeam, and American elm, as well
as many of the tree species that occur in adjacent
upland forests. The eastern hemlock component is
threatened by the hemlock woolly adelgid, which
will likely result in its replacement by other canopy
trees. Some characteristic shrubs may include bushy
St. Johnswort, coastal plain willow, and hazel alder.
Spruce/Fir Forest
This forest ecosystem consists of forests, woody
wetlands, shrublands, and grasslands on a variety
of landforms in the highest elevation zone of the
Allegheny Mountains, ranging from 2,400 to
4,00 feet. NatureServe (2013) describes these
forests as “Central and Southern Appalachian
Spruce/Fir Forest,” “Southern Appalachian Grass
and Shrub Bald,” and “High Allegheny Wetland.”
Elevation and topography make the climate cool
and wet, with heavy moisture input from fog as
well as high amounts of rain and snow. Soils are
moist year-round, usually acidic, and often very
rocky, originating from weathered parent material
or from organic deposits over boulders. The forest
canopy is typically dominated or codominated by
A red spruce and mixed hardwood forest in West Virginia.
Photo by David Ede (reired), Monongahela Naional Forest.
red spruce, with associates including yellow birch,
red maple, and eastern hemlock. In some places,
sweet birch, cucumbertree, American mountain ash,
black cherry, American beech, sugar maple, and
mountain magnolia may also appear. The eastern
hemlock component is currently being decimated
in large parts of its range by the hemlock woolly
adelgid, which will likely result in replacement
of hemlocks by other canopy trees. Likewise, the
American beech component is being decimated
by beech bark disease, which typically results in a
dense understory of beech sucker sprouts. Balsam
fir and black ash can also dominate in wet areas
on limestone or calcareous shale. On upland sites,
the shrub layer can range from sparse to dense and
may include great laurel and southern mountain
cranberry. Around the edges of some wetlands,
the shrub layer may be dense and may contain a
variety of species, including wild-raisin, velvetleaf
huckleberry, speckled alder, bushy St. Johnswort,
common winterberry, and black chokeberry. The
herbaceous layer is generally sparse in upland areas
and dense in wetlands. Fine-scale disturbances (e.g.,
debris avalanches, wind, and ice) are generally
the most influential in this forest ecosystem. Red
spruce and eastern hemlock are both expanding into
portions of their historic niches, recovering from
large anthropogenic disturbances at the beginning of
the last century.
29
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Forest Composiion and Abundance
Analysis of satellite imagery from the National
Land Cover Database estimates forest coverage at
7 percent of the land base. The FIA program, using
a network of permanent field plots, estimates that
percent of land is forested (Table 3) (USFS
2013). Northern Ohio (221F) is the least forested
section (31 percent), and southern West Virginia
(M221C) is the most heavily forested section
(89 percent). Timberland is forest land that is
currently producing or capable of producing more
than 20 cubic feet of wood per acre per year.
Approximately 97 percent of the forest land in the
assessment area is classified as timberland (USFS
2013).
Based on FIA data, the oak-hickory forest-type
group is the most common in the assessment area,
covering 70 percent of the total forested area
(Table 4). The other common forest-type groups
across the assessment area are the maple/beech/birch
group (19 percent), elm/ash/cottonwood group
Table 3.—Acreage (total and forest land) for each
ecological secion within the assessment area, as
determined by FIA (USFS 2013)
Ecological
secion
Total land
(acres)
Forest land Forest land
(acres)
(%)
221E
221F
M221A
M221B
M221C
13,485,413
4,961,053
2,512,194
4,374,448
3,504,533
9,056,910
1,561,695
1,778,653
3,366,526
3,130,745
67
31
71
77
89
Assessment area
28,837,641
18,894,529
66
(4 percent), oak/pine group (2 percent), and loblolly/
shortleaf pine group (1 percent). The remaining
forest-type groups each equal less than 1 percent
of the forest land. There are also more than 8,000
acres of nonnative blue spruce plantation that was
classified as the fir/spruce/mountain hemlock foresttype group. Differences among forest types can
influence the amount of carbon stored aboveground
and belowground (Box 2).
Table 4.—Forest land by FIA forest-type group (USFS 2013)
FIA forest-type group
Total
assessment
area (acres)
Total
assessment
area (%)
Oak/hickory
Maple/beech/birch
Elm/ash/cotonwood
Oak/pine
Loblolly/shortleaf pine
White/red/jack pine
Other hardwoods
Nonstocked
Aspen/birch
Exoic hardwoods
Spruce/ir group
Exoic sotwoods group
Other eastern sotwoods
Oak/gum/cypress
Fir/spruce/mountain hemlock
13,311,652
3,601,227
667,362
430,319
241,679
162,162
141,683
98,697
85,235
43,649
40,368
22,031
20,945
19,376
8,144
70.5
19.1
3.5
2.3
1.3
0.9
0.7
0.5
0.5
0.2
0.2
0.1
0.1
0.1
0.0
Total
18,894,529
30
100
Ecological secion within the assessment area (acres)
221E
221F
M221A
M221B
M221C
6,602,077
1,461,080
392,774
232,885
135,343
62,503
23,396
62,566
54,007
30,279
–
–
–
–
–
828,364
424,142
196,052
–
–
12,050
12,611
21,662
21,024
6,292
–
18,920
–
12,434
8,144
1,328,991 1,926,512 2,625,709
149,336 1,154,314
412,356
33,296
5,470
39,770
111,333
54,373
31,729
83,496
16,179
6,660
27,842
57,692
2,075
25,320
74,987
5,370
6,397
8,072
–
–
10,204
–
–
–
7,078
–
40,368
–
–
3,111
–
12,642
8,302
–
–
6,942
–
–
–
–
9,056,910 1,561,695
1,778,653 3,366,526 3,130,747
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Box 2: Forest Carbon in the Assessment Area
Forests play a valuable role as carbon sinks.
The accumulated terrestrial carbon pool within
forest soils, belowground biomass, dead wood,
aboveground live biomass, and liter represents
an enormous store of carbon (Birdsey et al. 2006).
Terrestrial carbon stocks in the region have generally
been increasing for the past few decades, and
the potenial for managing forests to maximize
and maintain this carbon is gaining atenion
(Malmsheimer et al. 2011). Carbon sequestraion
and storage in forest ecosystems depend on the
health and funcion of those ecosystems in addiion
to human management, episodic disturbances, and
forest stressors.
Forest lands within the assessment area are
esimated to hold approximately 1.3 billion metric
tons of carbon, or roughly 69.1 metric tons per acre
(USFS 2013). Depending on the forest-type group,
carbon density ranges from 42.7 metric tons
per acre (other eastern sotwoods group) to
98.6 metric tons per acre (oak/gum/cypress group)
(Fig. 5). The spruce/ir and maple/beech/birch
groups store greater amounts of carbon per acre
than the oak/hickory and oak/pine groups. However,
because the vast majority of forest land is classiied
as oak/hickory and maple/beech/birch, most of the
total carbon in the assessment area is found in these
two types (67 percent in oak/hickory and 22 percent
in maple/beech/birch).
Carbon density also varies by ownership. The highest
density of carbon is on federal lands administered
by the USFS, the Naional Park Service, the U.S. Fish
and Wildlife Service (USFWS), and the Department
of Defense, ranging from 76.5 to 87.7 metric tons
per acre. Private lands store only 67.7 metric tons
per acre. However, most of the forest land in the
assessment is private land. Therefore, 1 billion metric
tons of carbon is stored on private land, versus 209
million metric tons stored on public lands.
100
C arb o n d en sity (m etric to n s p er acre)
90
80
70
60
50
40
30
20
S o il o rg anic
Litter
D ead w o o d
Liv e b elo w g ro und
Liv e ab o v eg ro und
10
0
Figure 5.—Forest carbon density by forest-type group. Forest-type groups are arranged from let to right by area
of forest land (USFS 2013).
31
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
DRiVERS oF ChANGE
iN FoREST ECoSYSTEMS
The forest ecosystems of the assessment area have
undergone significant changes over the past several
thousand years. These changes were largely driven
by periodic climate change and anthropogenic
pressures on the natural resource base, which in
turn have had major implications for fire occurrence
and behavior, invasive species establishment, soil
stability and structure, hydrology, and other drivers
of species composition and structure.
Past Ecosystem Change
Paleoecological records from pollen and
macrofossils have been collected from lakes and
bogs throughout the eastern United States as a
means to determine long-term vegetation dynamics
(Davis 1983). During the last glacial maximum
18,000 to 20,000 years before present (YBP), only
a portion of the assessment area (Section 221F
in Ohio) was covered in ice. In the rest of the
assessment area, a belt of tundra extended from
Pennsylvania southward along the mountains. Forest
species formed novel assemblages of conifers such
as spruce and pine, and deciduous species were
conspicuously absent (Davis 1983). After the last
glaciers began to retreat from the northern latitudes,
at the start of the Holocene epoch approximately
11,700 YBP, tree species started to respond to the
warming climate and the melting glaciers. Many
tree species were able to migrate northward at a rate
of 700 to 1,000 feet per year, but expansion over
the Appalachian Mountains was generally slower
(around 300 feet per year) (Davis 1983). Spruce and
fir moved northward at different rates, depending
on the suitability of climate, seed dispersal, and
establishment success (Davis 1983). The large-scale
replacement of eastern white pine and other northern
species by deciduous or mixed deciduous forest
occurred between 10,000 and 12,000 YBP (Jacobson
et al. 1987). Elms and maples arrived from the west
around 10,000 to 12,000 YBP. Oaks arrived 10,000
32
YBP and dominated the landscape until 500 YBP.
Some species arrived relatively recently in the
assessment area, largely due to the migration barrier
presented by the Appalachian Mountains. Hickories
arrived 10,000 YBP in Ohio, but expanded slowly
over the Appalachian Mountains, reaching Maryland
only 5,000 YBP. Chestnut arrived in Tennessee
10,000 YBP, and reached Maine after a slow
northeast migration only 2,000 YBP. Eastern white
pine and eastern hemlock expanded from ancient
forest refugia near the Atlantic coast and arrived
10,000 YBP (Davis 1983).
Repeated periods of warming and cooling over the
last 15,000 years have resulted in multiple waves of
species expanding from the south and from climatic
refuges along the Atlantic coast (Shuman et al.
2002). These waves of species migrations resulted
in very different species assemblages from those
typical today, partially because not all species were
able to migrate at the same rate. The last major shift
in climate occurred approximately 3,000 YBP. This
wetter and cooler climate is similar to our present
climate, which favors tree growth and reforestation.
At the same time, the effects of Native Americans
on the vegetation of the region became evident.
Charcoal scars throughout the region have
confirmed a link between oak dominance and fire
(Abrams 1992, Nowacki and Abrams 2008). Native
Americans are thought to be responsible for the
numerous low-intensity fires that promoted oak
regeneration (Abrams 1992). Native American
cultures centered on maize agriculture were in
place by 1,000 YBP. The development of smallscale agriculture and other activities also resulted in
extensive trail and trade networks and subsistencebased manipulation of the vegetation. By the early
17th century, the use of fire by Native Americans
began to diminish as native populations crashed
from disease and as European settlers laid claim to
land. Witness trees, as recorded in surveyors’ notes,
are often the only indication of what composed
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
presettlement forests and their disturbance regimes
(Black and Abrams 2001). When grouped by fire
relations, witness trees can be converted to show
spatial differences in presettlement fire regimes
(Thomas-Van Gundy and Nowacki 2013). Witness
tree data show that white oak was dominant over
large areas of the Central Appalachians (Table 5)
(Abrams 2003). Exceptions occurred in Ohio’s
glaciated plateau, where white oak was codominant
in maple-beech forests, and in the many landforms
of the Appalachian Mountains. Analysis of witness
trees within the Monongahela National Forest
correlates white oak with low elevation and high
moisture, whereas higher elevations supported sugar
maple, American beech, birch, red spruce, eastern
hemlock, and black cherry, among others (ThomasVan Gundy and Strager 2012). Industrialization
and settlement during the 18th and 19th centuries
also created heavy demands on forests within the
Central Appalachians region. The logging boom of
1880 to 1930 is often considered the most important
driver of forest ecosystems in the assessment area,
although the forests of Ohio had already declined
from 95 percent of land cover to 40 percent by 1880,
largely due to agriculture (Birch and Wharton 1982,
Widmann et al. 2007). Large-scale clearcutting was
conducted for the purposes of wood harvesting and
agricultural land clearing. The effects of repeated
logging that removed most old-growth forests, and
the subsequent wildfires, are still being observed
today. Secondary forests are largely even-aged
with poor structure and reduced species diversity.
Other impacts include the loss of soil that will take
thousands of years to replace, degraded stream
channels, and old railroad grades and logging roads
that impair watershed hydrology and create edge
effects. In the early 1900s, frequent and intense
fires favored oaks, hickories, and yellow pines at
the expense of hemlock, red spruce, white pine, and
mesophytic hardwoods.
Table 5.—Witness tree observaions from various locaions in the Central Appalachians
Region, locaion
Presetlement forest composiion
Reference
Southwestern Pennsylvania
White oak (40%), black oak (9%), hickory (9%),
dogwood (8%)
(Abrams and Downs 1990)
Eastern West Virginia Ridges
White oak (35%), chestnut (15%),
chestnut oak (13%), black oak (12%)
(Abrams and McCay 1996)
Eastern West Virginia Valleys
White oak (23%), maple (22%), pine (15%),
basswood (10%)
(Abrams and McCay 1996)
Southern West Virginia
White oak (24%), chestnut (12%), hickory (9%), (Abrams et al. 1995)
chestnut oak (6%)
Monongahela Naional Forest
White oak (19%), sugar maple (10%),
American beech (8%)
(Thomas Van-Gundy and Strager 2012)
Southeastern Ohio
White oak (40%), hickory (14%),
black oak (12%), American beech (8%)
(Dyer 2001)
Northeastern Ohio Fine ill
American beech (36%), sugar maple (17%),
white oak (14%)
(Whitney 1994)
Northeastern Ohio Coarse ill
White oak (37%), hickory (13%), black oak (6%) (Whitney 1994)
33
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Primary Agents of Change
Agents of change within the assessment area include
both natural and anthropogenic pressures. Fire
suppression, wind events, severe weather, pests
and diseases, invasive species, large-scale surface
mining, acid deposition, fragmentation, and land
use change are the primary agents of change in the
Central Appalachians region. Each of the forest
ecosystems addressed in this assessment faces a
particular suite of threats and stressors (Table ).
We define threats and stressors as agents that tend to
disrupt the natural functioning of forest ecosystems
or impair their health and productivity. This
information is collected from published literature
as well as local forest managers. The impacts of
particular threats and stressors are very dependent on
local conditions and are not consistent across an area
as large and diverse as the Central Appalachians.
These particular threats should be considered in
addition to landscape-level threats such as acid
deposition, forest fragmentation, the legacy of past
management practices, and altered disturbance
regimes. It is often difficult to examine the
effects of just one of these landscape-level threats
in isolation, because they have all interacted
across the assessment area over the past century.
Fragmentation caused by mining, agricultural and
urban development, forest management, and other
factors has tended to reduce the ratio of interior
to edge conditions in forests (Drohan et al. 2012a,
Irwin and Bockstael 2007). The disruption of natural
disturbance regimes has included fire suppression in
upland systems as well as hydrologic disruption in
riparian and lowland forests. Natural regeneration
and succession of forest ecosystems is strongly tied
to disturbance regimes, so in many cases alteration
of disturbance regimes has resulted in regeneration
failure for those disturbance-adapted species and
reduced landscape diversity (Abrams and Nowacki
1992, Nowacki and Abrams 2008, Patterson
200). Conversely, other species may benefit from
the altered disturbance regime, particularly firesensitive, shade-tolerant trees.
Table 6.—Major disturbances and threats to forest ecosystems in the Central Appalachians
Forest Ecosystem
References
All forest ecosystems (Central Appalachians)
Atmospheric deposiion of nitrates, sulfates, ozone, and other anthropogenic emissions
negaively afects forest producivity and resilience.
(Poter et al. 2010)
Deer herbivory is considered a keystone driver through impacts on plant regeneraion,
structure, and species diversity, especially where deer density is high.
(Collins and Carson 2003,
MDNR 2010, ODNR 2010b)
Drought can lead to increased ire hazard, decreased plant growth, regeneraion failure,
and increased suscepibility to insects and diseases.
(ODNR 2010b)
Energy development for wind energy and shale-gas installaions alter ecosystem structure
through vegetaion clearing, soil disturbance, increased erosion potenial, fragmentaion,
and direct impacts on forest wildlife species.
(Drohan et al. 2012b, Naional
Research Council 2007)
Fragmentaion from industrial and urban development has resulted in dispersal barriers
that impede migraion of species and exchange of geneic material, reduced forest patch
size, and increased forest edge.
(Irwin and Bockstael 2007,
Poter et al. 2010, Riiters
2011)
Geographic dispersal barriers slow the dispersal and migraion of species in muliple
direcions across the Appalachian Mountains.
(Davis 1983)
insect pests and diseases increase the risk of individual tree mortality and species
exincion or exirpaion.
(DeSanis et al. 2013, Lovet et
al. 2006, MDNR 2010, ODNR
2010b, Poter et al. 2010)
(coninued on next page)
34
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Table 6 (coninued).
Forest Ecosystem
References
Appalachian (Hemlock)/Northern Hardwood Forest
Acid deposiion negaively afects forest producivity and resilience.
(ODNR 2010b, Poter et al.
2010)
Deer herbivory results in reduced growth and mortality of seedlings and saplings of target
browse species.
(Collins and Carson 2003,
MDNR 2010)
insect pests and diseases such as emerald ash borer, hemlock woolly adelgid, hypoxylon
canker, and beech bark disease can cause reduced growth and mortality of target species.
(Anderson 1995, Burns and
Honkala 1990, DeSanis et
al. 2013, Hessl and Pederson
2013, MDNR 2010)
Invasive plants such as garlic mustard, ailanthus, Japanese siltgrass, basket grass, and
paper mulberry reduce natural regeneraion, facilitate other exoic species, and alter
understory plant communiies.
(Graton 2013, Kurtz 2013)
Dry Calcareous Forest, Woodland, and Glade
insect pests and diseases such as red oak borer, gypsy moth, sudden oak death, oak
decline, and armillaria root disease can cause reduced growth and mortality of target
species.
(MDNR 2010, ODNR 2010b)
Invasive plants such as ailanthus, Asiaic bitersweet, garlic mustard, mulilora rose,
Japanese honeysuckle, bush honeysuckle, autumn olive, spoted knapweed, viper’s
bugloss, Japanese siltgrass, and Canada bluegrass can reduce suitable condiions
for natural regeneraion, facilitate other exoic species, and alter understory plant
communiies.
(Graton 2013, Hutchinson and
Vankat 1998, Kurtz 2013)
Suppression of natural ire regimes has contributed to woody encroachment of eastern
redcedar and mountain laurel; overabundance of these shrubs can reduce diversity and
afect species regeneraion.
(Abrams 1992, Nowacki and
Abrams 2008, Smith and
Johnson 2004)
Dry Oak and Oak/Pine Forest and Woodland
insect pests and diseases such as gypsy moth, sirex woodwasp, southern pine beetle,
oak decline, and armillaria root disease can cause reduced growth and mortality of target
species.
(MDNR 2010, ODNR 2010b)
Invasive plants such as ailanthus, Japanese siltgrass, mulilora rose, Japanese
honeysuckle, bush honeysuckle, autumn olive, Japanese barberry, sericea lespideza,
yellow sweetclover, and crown vetch reduce suitable condiions for natural regeneraion,
facilitate other exoic species, and alter understory plant communiies.
(Graton 2013, Kurtz 2013)
Past management aciviies which created microsite condiions conducive to pine
regeneraion are diicult to reconstruct without intense ire, resuling in a gradual
conversion from pine to oak species.
(Vose et al. 1993)
Suppression of natural ire regimes has reduced structural and species diversity, allowed
mesic hardwood encroachment on many sites, and limited suitable condiions for natural
regeneraion.
(Abrams 2003, Nowacki and
Abrams 2008, Paterson 2006,
Sharitz et al. 1992)
(coninued on next page)
35
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Table 6 (coninued).
Forest Ecosystem
References
Dry/Mesic Oak Forest
insect pests and diseases such as ambrosia beetle, red oak borer, gypsy moth, oak decline, (Lovet et al. 2006, ODNR
armillaria root disease, hypoxylon canker, and phytophtora root rot can cause reduced
2010b, Rafa et al. 2008)
growth and mortality of target species.
Invasive plants such as ailanthus, Japanese siltgrass, and garlic mustard reduce suitable
condiions for natural regeneraion, facilitate other exoic species, and alter understory
plant communiies.
(Graton 2013, Kurtz 2013,
MDNR 2010)
Suppression of natural ire regimes has reduced structural and species diversity, allowed
mesic hardwood encroachment on many sites, and limited suitable condiions for natural
regeneraion.
(Abrams 2003, Nowacki and
Abrams 2008, Paterson 2006,
Sharitz et al. 1992)
Large Stream Floodplain and Riparian Forest
Energy development for wind energy and shale-gas installaions alter ecosystem structure
through vegetaion clearing, soil disturbance, increased erosion potenial, polluion,
fragmentaion, mine land abandonment, and direct impacts on forest wildlife species.
(Drohan et al. 2012b, Naional
Research Council 2007)
Erosion from improperly designed or poorly maintained roads, trails, or log landings can
(Pennsylvania Department
increase the amount of siltaion and sedimentaion transported and deposited by streams. of Environmental Protecion
2012)
Industrial and urban development has resulted in hydrologic infrastructure that afects
the lood regime, such as impoundments, channelizaion, dams and reservoirs, and
drainage and clearing for agriculture.
(Irwin and Bockstael 2007,
Poter et al. 2010, Riiters
2011)
insect pests and diseases such as emerald ash borer, thousand cankers disease, and elm
yellows can cause reduced growth and mortality of target species.
(DeSanis et al. 2013, Graton
2013, Kurtz 2013)
Invasive plants are transported by water and establish more rapidly here than in other
systems. Species such as Japanese siltgrass, Japanese hops, and bush honeysuckle can
reduce suitable condiions for natural regeneraion, facilitate other exoic species, and
alter understory plant communiies.
(Graton 2013, Kurtz 2013)
Mixed Mesophyic and Cove Forest
Forest arson and debris burning are the major causes of wildire.
(MDNR 2010, ODNR 2010b)
insect pests and diseases such as emerald ash borer, hemlock woolly adelgid, and beech
bark disease can cause reduced growth and mortality of target species.
(Hessl and Pederson 2013,
ODNR 2010b)
Invasive plants such as Japanese siltgrass, garlic mustard, ailanthus, and bush
honeysuckle can reduce suitable condiions for natural regeneraion, facilitate other exoic
species, and alter understory plant communiies.
(Graton 2013, Kurtz 2013)
Mountaintop removal mining and valley ill changes topography, soil water capacity, and
runof; and buries headwater streams where mining waste is dumped.
(U.S. EPA 2005, 2009)
(coninued on next page)
3
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Table 6 (coninued).
Forest Ecosystem
References
North-Central Interior Beech/Maple Forest
Energy development for wind energy and shale-gas installaions alter ecosystem structure
through vegetaion clearing, soil disturbance, increased erosion potenial, fragmentaion,
and direct impacts on forest wildlife species.
(Drohan et al. 2012b, Naional
Research Council 2007)
Fragmentaion and urban development has resulted in dispersal barriers that impede
migraion of species and exchange of geneic material, reduced forest patch size, and
increased forest edge.
(Irwin and Bockstael 2007,
Poter et al. 2010, Riiters
2011)
Invasive plants such as princess tree, silk tree, garlic mustard, creeping charlie, Japanese
siltgrass, ailanthus, and glossy buckthorn reduce suitable condiions for natural
regeneraion, facilitate other exoic species, and alter understory plant communiies.
(Graton 2013, Kurtz 2013)
insect pests and diseases such as hemlock woolly adelgid, gypsy moth, emerald ash borer,
sirex woodwasp, anthracnose disease, sudden oak death, and beech bark disease can
cause reduced growth and mortality of target species.
(DeSanis et al. 2013, Hessl
and Pederson 2013, ODNR
2010b)
Small Stream Riparian Forest
Energy development for wind energy and shale-gas installaions alter ecosystem structure
through vegetaion clearing, soil disturbance, increased erosion potenial, polluion,
fragmentaion, mine land abandonment, and direct impacts on forest wildlife species.
(Drohan et al. 2012b, Naional
Research Council 2007)
Erosion from improperly designed or poorly maintained roads, trails, or log landings can
(Pennsylvania Department
increase the amount of siltaion and sedimentaion transported and deposited by streams. of Environmental Protecion
2012)
Industrial and urban development has resulted in hydrologic infrastructure that afects
the lood regime, such as impoundments, channelizaion, dams and reservoirs, and
drainage and clearing for agriculture.
(Irwin and Bockstael 2007,
Poter et al. 2010, Riiters
2011)
Invasive plants are transported by water and establish more rapidly here than in other
systems. Species such as Japanese siltgrass, Japanese hops, and bush honeysuckle can
reduce suitable condiions for natural regeneraion, facilitate other exoic species, and
alter understory plant communiies.
(Graton 2013, Kurtz 2013)
insect pests and diseases such as emerald ash borer, hemlock woolly adelgid, thousand
cankers disease, and elm yellows cause reduced growth or mortality of target species.
(DeSanis et al. 2013, Graton
2013, Kurtz 2013)
Spruce/Fir Forest
Acid deposiion at high-elevaion sites adversely afects the growth and physiology of red (Friedland et al. 1984,
spruce. Acid deposiion has also been linked to increased predisposiion to frost damage in McLaughlin et al. 1990,
red spruce.
Schuler and Collins 2002)
Anthropogenic impacts from surface mining and wind energy development, roads,
(Schuler and Collins 2002)
recreaion, and residenial development have resulted in fragmentaion, altered hydrology,
and forest conversion.
Deer browse results in reduced growth and mortality of seedlings and saplings of target
browse species (e.g., eastern hemlock).
(Michael 1992, Schuler and
Collins 2002)
Frost damage is a major cause of foliar loss in red spruce.
(Friedland et al. 1984)
insect pests and diseases such as hemlock and balsam woolly adelgids, emerald ash borer, (DeSanis et al. 2013, Hessl
and beech bark disease can cause reduced growth and mortality of target species.
and Pederson 2013, Schuler
and Collins 2002)
37
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Fragmentaion and Land-use Change
Residential and urban development has led to the
fragmentation of forests across the assessment
area, resulting in a patchwork of public and private
parcels of natural, agricultural, and developed lands.
As mentioned earlier, 40 percent of the assessment
area is now agricultural or developed land (USFS
2013). Northern Ohio and western Maryland have
a particularly large proportion of these developed
and agricultural lands, and the percentage of interior
forest is lowest in the area (0 to 27 percent) (USFS
2011). The most affected lands are those on the
fringes of major towns and cities, and in rural
areas where second homes contribute to sprawling
development (Irwin and Bockstael 2007, USFS
2011). Forest lands across the assessment area are
often heavily dissected by roads, private property,
trails, and utility lines. In Ohio, only 25 percent
of the forest land is more than 0.25 mile from a
road (Widmann et al. 2009). Parcelization is also
a concern as the number of forest land owners is
increasing and the size of parcels is decreasing
(Widmann et al. 2009).
Fragmentation of natural landscapes creates isolated
plant and animal populations that are unable to
migrate easily and exchange genetic information,
leading to reduction in biological and genetic
diversity (Riitters 2011). It also causes increased
incidence of edges along forest boundaries (Sisk
et al. 1997). Fragmentation has also resulted in the
degradation of watersheds, loss of wildlife habitat,
increased disturbances, and the spread of invasive
species (Widmann et al. 2007).
Natural Disturbances
Natural disturbance has historically been a regular
influence on forest ecosystems in the assessment
area. Forest systems have distinct disturbance
regimes, characterized in part by the soils,
landforms, and vegetation (McNab et al. 2007).
Small-scale canopy disturbances are often caused
by drought, wind, ice, snow, flooding, landslides,
insect outbreaks, intermediate-intensity fires,
38
and pathogens (NatureServe 2011). Larger scale
canopy disturbances, potentially affecting entire
stands and swaths of forest across the landscape,
include tornadoes, hurricanes, high-intensity fires,
periodic flooding along major river floodplains, and
catastrophic insect and pathogen outbreaks. Annual
spring floods along rivers and streams are also
typical disturbance events, but hydrology has been
modified by channelization, drainage tiles, dams,
roads, and other anthropogenic activities that change
soil or runoff characteristics (NatureServe 2011).
Beaver historically affected floodplains along small
streams by building dams that sometimes killed
relatively large stands of trees and created temporary
ponds and wetlands; beaver remain a small
disturbance agent in the contemporary landscape.
insect Pests and Diseases
Insect and disease outbreaks have long influenced
the structure of forest ecosystems in the Central
Appalachians. Before European settlement and the
introduction of nonnative species, outbreaks were
caused by native insect species, including the spring
hemlock looper and forest tent caterpillar. Recent
outbreaks of another native species, the southern
pine beetle, have occurred in the New Jersey Pine
Barrens, and increasing populations in southeast
Ohio warrant monitoring of this pest (NRCS 2014a).
International trade and the inadvertent movement of
nonnative invasive species from countries around
the world have amplified the amount of exposure
to, and impacts on, tree species of the Central
Appalachians region. Gypsy moth is a serious pest
and has caused huge losses of basal area in valuable
red and white oak (MDNR 2010, ODNR 2010b).
Beech bark disease has resulted in mortality of
beech trees across millions of acres in the eastern
United States, and has yet to invade the majority of
the beech range (Morin et al. 2007). The hemlock
woolly adelgid has threatened hundreds of thousands
of eastern hemlocks with needle loss, followed by
branch dieback, and eventually death (Jonas et al.
2012). The emerald ash borer has caused mortality
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
in all ash species, including white ash, black ash,
and green ash, resulting in the loss of more than
50 million trees between 2002 and 2009 (Kovacs
et al. 2010). The Asian longhorned beetle is not
confirmed in the assessment area, but its arrival from
adjacent areas would result in damage and mortality
to many species including maples, buckeyes,
birches, willows, and elms (Townsend Peterson and
Scachetti-Pereira 2004). Diseases, such as chestnut
blight fungus, have virtually eliminated American
chestnut as a canopy tree although chestnut stump
sprouts and saplings persist in the understory
(Merkle et al. 2007). One or more species of fungus
in the genus Hypoxylon can injure or kill trees
weakened by other factors, such as drought, logging,
and root disease (Anderson 1995).
Nonnaive and Invasive Plants
Nonnative plant species are a risk to forest
ecosystems when they become invasive. These
species affect forest ecosystems through direct
competition for resources, alteration of fire or
hydrologic conditions, disruption of natural
succession and pollination, and other cascading
influences (Frelich et al. 2012, Tu et al. 2001).
Invasive plant species can be introduced into native
ecosystems by the transportation of seed on vehicles
or equipment, on the soles of shoes, in manure from
domestic or wild animals, or by wind and water.
The FIA program has monitored 25 invasive species
in the eastern United States since 2007 (Fig. ). In
West Virginia, it is estimated that 28 percent of plant
species occurring in the wild are nonnative invasive
species (Kurtz 2013). Kudzu, glossy buckthorn, bush
honeysuckle, autumn olive, crown vetch, Japanese
knotweed, Japanese stiltgrass, garlic mustard,
ailanthus, mile-a-minute, and multiflora rose are
among the area’s most problematic invasives
(Grafton 2013).
Figure 6.—Number of invasive plant species observed per FIA plot (2005 to 2010) (Kurtz 2013).
39
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Shits in Fire Regime
Fire regimes have shifted in the assessment area
over the past several hundred years, and these shifts
inluence the composition of forest ecosystems. Both
natural and human-caused ire has been a component
of the eastern United States for thousands of years,
although the return interval, intensity, and extent
are largely dependent on landscape position in
the Central Appalachians region (Abrams 1992,
Nowacki and Abrams 2008, Thomas-Van Gundy and
Nowacki 2013). Studies in the eastern United States
and Canada that have dated ire scars in oak forests
have shown that ire-return intervals either increased
or decreased immediately after European settlement,
depending on the stand and location. A 400-year
reconstruction of ire history in western Maryland
used ire scars as evidence that pre-European ires
were as important as post-European ires, and that
ire suppression in the 20th century has coincided
with the increase of shade-tolerant species, to the
detriment of oaks, hickories, and other ire-tolerant
species (Shumway et al. 2001). The historic role
of ire in the development and maintenance of oak
forests has been well-established in the literature
(Abrams 1992, Brose and Van Lear 1999). By the
1950s, ire exclusion began to favor red maple, sugar
maple, American beech, and black cherry (Brose and
Van Lear 1999, Nowacki and Abrams 2008, Schuler
and Gillespie 2000, Wright and Bailey 1982). Oaks
continue to be replaced by other hardwood species,
especially red maple (Brose et al. 2008).
Mineral, Gas,
and Wind Energy Development
Coal mining, natural gas fracturing, and wind
power development are today’s most inluential
natural resource extraction activities in the Central
Appalachians. Coal mining is the dominant driver
of land-use change in West Virginia, primarily
changing forested conditions to nonforest (Liu et al.
2006). The mountaintop removal form of surface
mining is a process that removes tons of bedrock
from the sides and tops of mountains to reach the
40
underlying coal seams. The dramatic alteration
of the landform is permanent, and waste disposal
into valleys has resulted in the burial of headwater
streams. Although the Surface Mining Control &
Reclamation Act provides federal regulations on
coal mining and reclamation operations, mined and
reclaimed areas generally have lower iniltration
capacity and higher runoff than pre-mine conditions
(Townsend et al. 2009). Grading of the topsoil
and subsoil, followed by seeding with grasses and
herbs, has generally resulted in nearly impervious
reclamation lands with compacted soils and
herbaceous cover, rather than native forest (Bussler
et al. 1984, Chong and Cowsert 1997, Holl 2002,
Negley and Eshleman 2006, Simmons et al. 2008).
Alterations to the water table, transition to overland
low as the dominant runoff process, and increases in
peak streamlow also are common consequences of
mine land reclamation (Negley and Eshleman 2006).
Natural gas wells irst appeared in the mid-1800s,
and new wells continue to be drilled even as old
wells are capped. In West Virginia and Ohio,
there are more than 87,400 active gas-producing
wells, with 487 horizontally drilled Marcellus
wells (Kasey 2012, Resources for the Future 2012,
WVDEP 2013a). Maryland had only seven gas wells
operating in 2010, and although the Marcellus shale
formation extends into western Maryland, horizontal
drilling permits have been denied, pending research
reports on the safety of hydraulic fracturing
(O’Malley 2011). Electricity produced from natural
gas creates approximately half the carbon dioxide
emissions of electricity produced from coal;
however, hydraulic fracturing requires extensive
road and pipeline networks and millions of gallons
of water. It is estimated that approximately 30 acres
of land are disturbed for each shale gas drilling pad
(Drohan et al. 2012b). Many well pads are located
on soils with high to very high runoff potential,
making actions to minimize long-term ecosystem
degradation critical (Drohan et al. 2012a).
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Wind turbines in the Allegheny Mountains, West Virginia. Photo by Patricia Butler, NIACS and Michigan Tech, used with
permission.
Although wind energy has the potential to reduce
greenhouse gas emissions and other adverse
emissions, wind turbine placement has had notable
ecological impacts, such as land surface disturbance
(1 to 7 acres per turbine), road construction,
vegetation clearing, soil removal and compaction,
and fragmentation of forests (National Research
Council [NRC] 2007). These disturbances can
have subsequent impacts on forest composition
and structure, as well as forest species sensitive to
edge effects. Collision with wind turbines can cause
significant mortality of birds and bats. Turbine
design, site characteristics, location, and temporal
patterns of use can all influence the rates of bird and
bat mortality, but changes in how the turbines are
operated can reduce mortality (NRC 2007).
FOREST-DEPENDENT WILDLIFE
The Central Appalachians region is one of the
most ecologically diverse regions of the eastern
United States (The Nature Conservancy 2003). The
variations in topography, geology, and temperature
and precipitation regimes in the region have resulted
in the development of an exceptional variety of
habitats supporting an abundance of wildlife,
including more than 540 species identified as species
of conservation concern in West Virginia alone (West
Virginia Division of Natural Resources [WVDNR]
2014).
41
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
One of the most prevalent and well-known wildlife
species is the white-tailed deer. It was almost
eliminated from the region in the early 1900s as
a result of deforestation and unregulated hunting,
but now exists at higher densities than in the past
several hundred years. Since the extirpation of the
eastern cougar and eastern timber wolf from the
region, deer have few natural predators to control
population numbers, although black bear, bobcat,
and coyotes do prey on fawns opportunistically.
White-tailed deer, which can double in population
size annually under optimum conditions, have
exceeded their environmental carrying capacity in
some areas (Côté 2004, Waller and Alverson 1997).
At high densities, deer can have a keystone effect on
the forest ecosystem. As deer browse plant species
preferentially, they change the relative abundance
and diversity of native species and promote the
establishment of invasive species (Abrams and
Johnson 2012, Collins and Carson 2003, Horsley et
al. 2003, Knight et al. 2009). High deer densities can
alter the availability of food to other wildlife species
such as wild turkey and eastern gray squirrels that
also rely on hard mast crops.
Although deer occur throughout a variety of forest
types in the assessment area, the distributions
of some wildlife species are limited to specific
White-tailed deer, which can have a signiicant impact on vegetaion near the forest loor. Photo by Patricia Butler, NIACS and
Michigan Tech, used with permission.
42
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
environmental conditions for all or a portion of
their life cycle. Vernal pool obligate species, such
as the spotted salamander, tiger salamander, and
red-spotted newt breed only in isolated wetlands that
are dry for part of the year. Because these ephemeral
wetlands can occur where other water resources
are scarce, they also provide important habitat
for migratory and resident birds, large and small
mammals (e.g., bats), and other species. Forestry
practices also often result in destruction of these
habitats because they are difficult to distinguish from
the surrounding forest when pools have dried up in
the summer.
The spruce/fir forest ecosystem of the Allegheny
Mountains section provides other unique habitats
supporting endemic and obligate species. Many
sensitive wildlife species residing there are
competitive only in the microclimates provided
by high-elevation and complex topography. The
Cheat Mountain salamander is listed as a federally
threatened species and the West Virginia northern
flying squirrel was removed from the endangered
species list in 2013, but remains a USFS Regional
Forester’s Sensitive Species and a State Species
of Greatest Conservation Need in West Virginia
(WVDNR 2014). The dense shading and moist
microclimate associated with spruce and spruce/
northern hardwood forests, along with highly
organic and often acidic and rocky soils beneath
the conifers, provide a habitat where the Cheat
Mountain salamander may have a competitive
advantage over other salamanders, such as the
red-backed salamander, which is dominant at
lower elevations. The northern flying squirrel also
has an intricate relationship with these habitats.
Like the Cheat Mountain salamander, the northern
flying squirrel competes with a similar species,
the southern flying squirrel, which is dominant
at lower elevations. Many boreal bird species are
characteristic of these high-elevation habitats as
well, ranging from predatory birds such as the
northern goshawk and saw-whet owl to Neotropical
Ephemeral pool. Seasonally wet areas like this one are
important for amphibian reproducion. Photo by Patricia
Butler, NIACS and Michigan Tech, used with permission.
migrants such as the blackburnian warbler and red
crossbill, all of which reach the southeastern extent
of their breeding ranges in the Central Appalachians.
Deciduous forests in the region also provide
critical breeding bird habitat. Four of the birds of
highest conservation priority in the Appalachian
Mountains region are hardwood interior forest
species: the cerulean warbler, Kentucky warbler,
wood thrush, and worm-eating warbler (Appalachian
Mountains Joint Venture Board 2008). Within
the assessment area, these species require mature
deciduous or mixed forest habitat for breeding, and
each is associated with particular herbaceous and
understory structure. These Neotropical migrants are
vulnerable to a variety of threats, including tropical
deforestation on wintering grounds and forest habitat
loss, fragmentation, and modification of breeding
grounds. Another bird common in mature oak and
oak-pine forests in the region is the wild turkey, an
important game species (USFWS 2010).
Bats are also associated with forested habitats and
are primary predators of nocturnal insects, including
many forest and agricultural pests. Populations of
many bat species in the eastern United States are
43
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
in a state of rapid decline as a result of white-nose
syndrome, first detected in New York in 2007.
This disease is caused by the fungus Geomyces
destructans, which has spread through hibernacula
throughout eastern North America. Bat mortality
can reach 100 percent at infected sites. As of
2012, approximately million bats had died from
the disease in the United States and Canada. The
syndrome was confirmed in West Virginia in the
winter of 2008-2009 and in Maryland and Ohio
in the following two winters. It has already had a
major impact on many bat species, including the
endangered Indiana bat, northern bat, little brown
bat, small-footed bat, and tri-colored bat. The
last four “forest bats” are on the USFS Regional
Forester’s Sensitive Species list as well as state
sensitive species listings, and may be soon proposed
for federal listing.
Riparian habitats can be critical for many other
wildlife species, providing breeding and foraging
habitat for a wide variety of waterfowl, amphibians,
reptiles, and mammals, as well as diverse
invertebrate fauna. The beaver, another species
that was nearly extirpated at the start of the 20th
century, requires riparian systems for habitat and
plays a keystone role in creating and maintaining
open water wetland habitats. The brook trout is the
only trout species native to the assessment area and
much of the eastern United States, and is often used
as an indicator of the health of a watershed. Primary
threats to brook trout include poor land management,
high water temperatures, urbanization, acid
deposition and runoff, sedimentation, surface and
ground water withdrawals and impoundments, and
introduction of nonnative fish species. As a result of
these and other factors, brook trout populations have
been greatly reduced in Ohio and West Virginia,
and only three intact subwatersheds remain in the
western panhandle of Maryland (Trout Unlimited
200).
44
CuRRENT LAND MANAGEMENT
TRENDS
Forest ownership
There are numerous types of forest landowners
within the assessment area (Table 7, Fig. 7). About
14 percent of forest land in the region is publicly
owned. National forests and state land compose the
largest percentages of public forest land, followed by
land owned by county and municipal governments,
the National Park Service, and the U.S. Department
of Defense. The Monongahela National Forest
administers approximately 920,000 acres in West
Virginia, and the Wayne National Forest administers
approximately 250,000 acres in Ohio. Most of
the forests in the assessment area, however, are
privately owned. This category reflects a diversity of
landowner types, including industrial and corporate
organizations, conservation organizations, families,
and individuals. As a result, private ownership
patterns are complex and change over time.
Trends in Forest use and Management
Most private forest land is held by hundreds of
thousands of nonindustrial family forest owners
Table 7.—ownership categories of forest land in the
assessment area (USFS 2013)
Ownership
Forest land (acres)
%
Private
Naional forest
State
County and municipal
Naional Park Service
Department of Defense
Other federal
Other local government
U.S. Fish and Wildlife Service
16,194,332
1,267,057
946,630
251,036
85,911
80,562
34,622
22,553
11,826
85.7
6.7
5.0
1.3
0.5
0.4
0.2
0.1
0.1
Total
18,894,530
100
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Figure 7.—Public and private forest ownership within the assessment area (Hewes et al. 2014).
(Butler 2008). The primary reasons for forest
ownership are privacy, scenery, part of home or
cabin, nature protection, to pass land on to heirs,
and for access to hunting and fishing (Butler 2008).
A survey of 5-year management plans identified
a variety of landowner goals and management
styles: minimal or no activity, harvesting firewood,
harvesting pulp and sawlogs, transferring to heirs,
and buying more forest land. Family owners can
enroll their lands in conservation easements or
forest certification programs such as the American
Tree Farm System (ATFS), which require forests
to have written management plans (Box 3). About
1.5 million acres in Ohio, West Virginia, and
Maryland are currently certified by the ATFS (ATFS
2014). Engaged family forest owners often look to
extension agents, Conservation Districts, and private
consultants to provide technical assistance and other
resources for managing forests.
Industrial forest landowners manage forest lands
for timber products, and have a vested interest in
long-term forest management. Millions of acres
of corporate land have been transferred in the last
decade to REITs and TIMOs, which are considered
private nonindustrial forest landowners, largely due
to unfavorable taxation on industry-owned forests
(Froese et al. 2007, Zhang et al. 2012). REITs
own and operate income-producing real estate
and timberland holdings, sometimes made public
through trading of shares on a stock exchange, and
thus are able to take advantage of more favorable
tax policies. TIMOs act as investment managers
for institutional clients who own the timberlands
as investments or partnership shares (Fernholz et
al. 2007). The goal in both cases is to maximize
the growth of the timberland asset over the short
term. Thus, the purchase of timberland by REITs
and TIMOs raises concerns about parcelization,
45
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
development, and high-yield management practices
(Fernholz et al. 2007). TIMOs in particular have
managed forested lands similar to high-intensity
industrial forests, with a high percentage in pine
plantations (Fernholz et al. 2007).
Public (federal, state, and county) agencies and tribal
organizations own extensive tracts of forest in the
assessment area. These lands are often managed
to provide many environmental benefits, often
including wildlife habitat, water protection, soil
conservation, nature preservation, timber production,
recreation, cultural resources, and a variety of other
uses (MDNR 2010; ODNR 2010b; USFS 200a,
200b).
Box 3: Programs for Private Landowners
All three states ofer incenives to private forest
landowners, with the intent of maintaining larger
parcels of privately owned forest and promoing
sustainable producion of forest products. About
85 percent of the forested land in the area is
privately owned, however, and most of these
lands lack a management plan (USFS 2008).
ohio
More than 65,000 acres of private forest lands
in Ohio are enrolled in the Ohio Forest Tax Law
program under the “new law” rules implemented
in 1993 with the overarching goal to protect land
from urban sprawl. This program requires at least
10 acres and a commitment to manage for soil and
water conservaion and producive forest land.
Together with protected lands, about 870,000
acres, or roughly 10 percent of Ohio’s forests, have
commitments to soil and water conservaion (ODNR
2010b). The Current Agricultural Use Value program
is designed to promote imber producion, and
property assessment values are reduced to $100
per acre. To qualify, a landowner must devote land
exclusively to agricultural use, which includes the
growth of imber for a noncommercial purpose.
This program does not require a management plan
(ODNR 2014).
Maryland
Maryland also administers programs to help ease
property taxes and maintain healthy forests. The
Forest Conservaion Management Agreement
4
(FCMA) is a conservaion easement program that
lowers assessed values to $125 per acre on a
minimum of 5 acres and a minimum expiraion
date of 15 years (MDNR 2014). As of January 2014,
1,300 landowners had 84,000 acres enrolled. In
the Western Region, which overlaps the Maryland
porion of the assessment area, 234 landowners had
17,670 acres enrolled (Tim Culbreth, MDNR, pers.
commun.). The Woodland Assessment Program
is a county program; similar to FCMA, there are
no enrollment fees or imeframe, but property
assessment values are reduced to $187.50 per acre
of forest. The Maryland Income Tax Modiicaion
program allows woodland owners to deduct
double the cost for reforestaion and imber stand
improvement pracices on 3 to 1,000 acres from the
federal adjusted gross income on the Maryland tax
return.
West Virginia
In West Virginia, the Managed Timberland Program
provides tax incenives for forest landowners who
pracice sustainable forestry on their nonindustrial,
privately owned forestland comprising 10 acres
or more (West Virginia Division of Forestry 2012).
Paricipaion in this program has been growing
steadily since 1997, and there are now nearly
2.4 million acres enrolled. Many paricipaing
landowners have also paricipated in the USFS Forest
Stewardship Program in order to receive assistance
with wriing a forest management plan at a reduced
cost (Dye 2013).
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
Forest Ceriicaion
Forest certification is a process designed to ensure
that forest products originate from forests that are
sustainably managed. Forest lands are certified
through several systems, including the Forest
Stewardship Council (FSC), the Sustainable
Forestry Initiative (SFI), and the ATFS (Table 8).
The Ohio Department of Natural Resources has dual
certification for sustainable forest management of
its state forests through FSC and SFI, with a total
of 202,927 certified acres. Nearly all of Maryland’s
211,000 acres of state forests are dual certified under
FSC and SFI.
Timber Harvest and Forest Products
As mentioned above, the forestry sector is a notable
economic contributor in the assessment area. Within
the Central Appalachians region, forest removals
(not including mortality) averaged 277 million
cubic feet in 2011 (USFS 2013). Over half the total
harvested roundwood was used as pulpwood and
around 30 percent was used as saw logs, with the
remainder being diverted to a variety of uses or
left behind as logging slash. Pulpwood production
peaked in 1994 and has been gradually declining
over recent years. More specific harvest data is
available at the state level (Table 9). Across the
assessment area, hardwoods account for most
commercial species, including tulip tree, red
and white oaks, soft and hard maples, and black
cherry (Piva and Cook 2011, Walters et al. 2008,
Wiedenbeck and Sabula 2008). In West Virginia,
97 percent of industrial roundwood processed in
2007 consisted of hardwood species, 37 percent of
which was tulip tree (Piva and Cook 2011).
The FIA data also provide more information
about the amount of wood removed from forests
in the assessment area through timber harvest or
conversion of forest to nonforest, with the vast
Table 8.—Forest land enrolled in ceriicaion programs (acres)a
State
Forest Stewardship Council
Council (FSC)
Maryland
Ohio
West Virginia
a
–
203,957
39,039
Forest land enrolled in ceriicaion program (acres)
Sustainable Forestry
American Tree Farm
Iniiaive (SFI)
System (ATFS)
–
202,927
257,044
139,021
293,585
1,013,352
Dual-ceriied
(FSC and SFI)
211,000
202,927
–
Data compiled from muliple sources (ATFS 2014, Forest2Market 2013, SFI 2013).
Table 9.—Statewide average annual roundwood removals in million cubic feet (Piva and Cook 2011, Walters et al.
2008, Wiedenbeck and Sabula 2008)
Average annual roundwood removals (milllion cubic feet)
Ohio (2003 to 2006)
Maryland (2008)
West Virginia (2007)
Total industrial roundwood
Domesic logs
Pulpwood
91.2
67.7
23.5
29.1
16
12.5
189
104
66.7
47
ChAPTER 1: ThE CoNTEMPoRARY LANDSCAPE
majority of removals in this region being due to
timber harvest. The amount of wood harvested
annually in the Central Appalachians region is less
than the amount that is grown each year, suggesting
that the harvest of timber products is biologically
sustainable (Lister and Perdue 2013, Widmann and
Morin 2012, Widmann et al. 2007). The net annual
growth-to-removal ratio is based upon FIA data and
provides a primary measure of sustainability. This
ratio compares net growth (i.e., gross growth minus
mortality) to removals from forest management for
forested lands; values greater than 1.0 indicate that
net annual growth is greater than annual removals
and that the removal rate is sustainable. Across
all ownership classes in the assessment area, the
growth-to-removal ratio was 2.3 for the most recent
inventory period (2008 through 2012), meaning that
growth was more than double removals (Table 10).
Among ownership classes in the assessment area,
national forests and national parks have the highest
growth-to-removal ratio, indicating low levels of
harvest compared to other owners (Table 10).
ChAPTER SuMMARY
The climate, geology, and soils of the Central
Appalachians region of Ohio, West Virginia, and
Maryland support a mosaic of forest ecosystems.
These communities supply important benefits to the
people of the area, including forest products and
recreation opportunities. Past changes in climate,
fire regime, and land use have shaped the landscape
into its current condition. Shifts in fire regime,
habitat fragmentation, species invasions, insect pests
and diseases, and other alterations to the landscape
threaten the integrity and diversity of the ecosystems
and the benefits they provide. Management on
public lands in recent decades has focused on
reducing these stressors and improving ecosystem
function. About 85 percent of the forested land in the
area is privately owned, however, and the majority
of these lands lack a management plan. New
opportunities and incentives have arisen in recent
years to help private and public land managers to
restore and conserve the ecosystems of the Central
Appalachians for future generations.
Table 10.—Growth, mortality, and removals of growing stock on forest land in the assessment area (USFS 2013)
Ownership
Annual net growth (cubic feet)
Annual removals (cubic feet) Annual net growth:removals
Naional Forest
Naional Park Service
U.S. Fish and Wildlife Service
Department of Defense
Other federal
State
County and municipal
Other local government
Private
Othera
42,954,113
5,706,942
508,390
1,967,529
757,053
28,140,927
10,964,142
1,403,549
768,866,740
2,234,386
324,147
66,516
–
–
–
10,821,876
202,251
258,295
310,059,634
52,090,765
132.5
85.8
–
–
–
2.6
54.2
5.4
2.5
0.0
Total
863,503,771
373,823,484
2.3
a
Represents esimated net growth and removals for lands that were diverted from forest and nonforest.
48
ChAPTER 2: CLiMATE ChANGE SCiENCE
AND MoDELiNG
This chapter provides a brief background on climate
change science, climate simulation models, and
models that project the impacts of changes in
climate on tree species and ecosystems. Throughout
the chapter, boxes indicate resources to find more
information on each topic. The resources listed are
up-to-date, nontechnical reports based on the best
available science. A more detailed scientific review
of climate change science, trends, and modeling can
be found in the Intergovernmental Panel on Climate
Change (IPCC) Fourth Assessment Report (IPCC
2007), and the third National Climate Assessment
(Melillo et al. 2014).
CLiMATE ChANGE
Climate is not the same thing as weather. Weather
is a set of the meteorological conditions for a
given point in time in one particular place (such
as the temperature at 3:00 p.m. on June 22 in
Athens, OH). Climate, in contrast, is the long-term
average of meteorological conditions and patterns
for a geographic area. This climate average is
calculated from individual measurements taken at
multiple locations across a geographic area, and
at different points through time. The IPCC (2007:
30) defines climate change as “a change in the
state of the climate that can be identified (e.g.,
by using statistical tests) by changes in the mean
and/or the variability of its properties, and that
persists for an extended period, typically decades
or longer.” A key finding of the IPCC in its Fourth
Assessment Report (2007) was that “warming of the
climate system is unequivocal.” This was the first
assessment report in which the IPCC considered the
evidence strong enough to make such a statement.
Current observations of higher global surface, air,
and ocean temperatures and thousands of long-term
(more than 20 years) data sets from all continents
and oceans contributed to this conclusion. These
data sets showed significant changes in snow, ice,
and frozen ground; hydrology; coastal processes;
and terrestrial, marine, and biological systems.
The IPCC’s Fifth Assessment Report contains the
most recent and comprehensive evidence of global
changes synthesized to date (see Box 4 for a link to
the draft). Selected global and national assessments
are listed in Box 4.
The Warming Trend
The Earth is warming, and the rate of warming
is increasing (IPCC 2007). Measurements from
weather stations across the globe indicate that the
global mean temperature has risen steadily over
the past 50 years, and that the year 2011 was 0.9 °F
(0.5 °C) warmer than the 1951 to 1980 mean (IPCC
2007) (Fig. 8). The first 13 years of the 21st century
rank among the warmest 14 years in the 134-year
period of record of global temperature (National
Oceanic and Atmospheric Administration [NOAA]
2014b). Temperatures in the United States have
risen by 2 °F (1.1 °C) in the last 50 years (Karl et
al. 2009). The 2012 continental U.S. average annual
temperature of 55.3 °F was 3.1 °F above the
20th-century average, and was the warmest year
in the 1895 to 2013 period of record for the nation
(NOAA 2014b).
49
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Box 4: Global and Naional Assessments
Intergovernmental Panel on Climate Change
u.S. Global Change Research Program
The Intergovernmental Panel on Climate Change
(IPCC; htp://www.ipcc.ch/) is the leading
internaional body for the assessment of climate
change. It was established by the United Naions
Environment Programme (UNEP) and the World
Meteorological Organizaion (WMO) in 1988 to
provide the world with a clear scieniic view on the
current state of knowledge in climate change and its
potenial environmental and socioeconomic impacts.
Its Fith Assessment Report consists of the Climate
Change 2014 Synthesis Report and reports by
Working Groups I, II, and III. Drats of these reports
are available for download at the Web address
below. Please note that Web addresses are current
as of the publicaion date of this assessment but are
subject to change.
The U.S. Global Change Research Program (USGCRP;
htp://www.globalchange.gov/) is a federal program
that coordinates and integrates global change
research across 13 government agencies to ensure
that it efecively and eiciently serves the naion
and the world. Mandated by Congress in the Global
Change Research Act of 1990, the USGCRP has since
made the world’s largest scieniic investment in the
areas of climate science and global change research.
It has released several naional synthesis reports
on climate change in the United States, which are
available for download at the Web addresses below.
Climate Change 2014: Synthesis Report and
Working Group contribuions to the Fith
Assessment Report
www.ipcc.ch/report/ar5/
Climate Change 2007: Synthesis Report
www.ipcc.ch/publicaions_and_data/ar4/syr/en/
contents.html
Synthesis and Assessment Products
htp://library.globalchange.gov/products/
assessments/
Naional Climate Assessment
htp://nca2014.globalchange.gov/
Efects of Climaic Variability and Change on Forest
Ecosystems: a Comprehensive Science Synthesis for
the u.S.
www.treesearch.fs.fed.us/pubs/42610
Figure 8.—Trends in global temperature compared to the 1951 to 1980 mean. Data source: NASA Goddard Insitute for Space
Studies. Image courtesy of NASA Earth Observatory, Robert Simmon; www.giss.nasa.gov/research/news/20120119/.
50
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Average annual global temperature increases of the
last 50 years are just one aspect of a more complex
and wide-ranging set of climatic changes. For
example, the frequency of cold days, cold nights,
and frosts has decreased over many regions of the
world while the frequency of hot days and nights
has increased (IPCC 2007). The frequency of heat
waves and heavy precipitation events has increased
over this period, with new records for both heat
and precipitation in portions of the United States in
July 2011 and March 2012 (NOAA 2012). Global
rises in sea level, decreasing extent of snow and ice,
and shrinking of mountain glaciers have all been
observed over the past 50 years, and are consistent
with a warming climate (IPCC 2007).
Average temperature increases of a few degrees may
seem small, but even small increases can result in
substantial changes in the severity of storms, the
nature and timing of precipitation, droughts and heat
waves, ocean temperature and volume, and snow
and ice—all of which affect humans and ecosystems.
Temperature increases above 3. °F (2 °C) are
likely to cause major societal and environmental
disruptions through the rest of the century and
beyond (Richardson et al. 2009). The synthesis
report of the International Scientific Congress on
Climate Change concluded that “recent observations
show that societies and ecosystems are highly
vulnerable to even modest levels of climate change,
with poor nations and communities, ecosystem
services and biodiversity particularly at risk”
(Richardson et al. 2009: 12).
Based on available evidence, 97 percent of the
climate science community attributes this increase in
temperature and associated changes in precipitation
and other weather events to human activities
(Anderegg et al. 2010, Cook et al. 2013, Doran and
Zimmerman 2009, Stott et al. 2010). Scientists have
been able to attribute these changes to human causes
by using climate model simulations of the past, both
with and without human-induced changes in the
atmosphere, and then comparing those simulations
to observational data. Overall, these studies have
shown a clear human “fingerprint” on recent
changes in temperature, precipitation, and other
climate variables due to changes in greenhouse gases
and particulate matter in the air (Stott et al. 2010).
Chapter 3 provides specific information about recent
climate trends for the assessment area.
The Greenhouse Efect
The greenhouse effect is the process by which
certain gases in the atmosphere absorb and re-emit
energy that would otherwise be lost into space
(Fig. 9). The greenhouse effect is necessary for
human survival: without it, Earth would have an
average temperature of about 0 °F (-18 °C) and be
covered in ice, rather than a comfortable 59 °F
(15 °C). Several naturally occurring greenhouse
gases in the atmosphere, including carbon dioxide
(CO2 ), methane, nitrous oxide, and water vapor,
contribute to the greenhouse effect. Water vapor is
the most abundant greenhouse gas; its residence time
in the atmosphere, however, is on the order of days
as it responds to changes in temperature and other
factors. Carbon dioxide, methane, nitrous oxide, and
other greenhouse gases reside in the atmosphere for
decades to centuries. Thus, these other long-lived
gases are of primary concern with respect to longterm warming.
Human Inluences on Greenhouse Gases
Humans have increased the concentrations of carbon
dioxide, methane, nitrous oxide, and halocarbons
in the atmosphere since the beginning of the
industrial era (Fig. 10). More carbon dioxide has
been released by humans into the atmosphere than
any other greenhouse gas. Carbon dioxide levels
increased at a rate of 1.4 parts per million (ppm) per
year from 190 to 2005 (IPCC 2007), and reached
an average of 395 ppm in January 2013 (Tans and
Keeling 2013). In recent decades, fossil fuel burning
has accounted for an estimated 83 to 94 percent
51
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Figure 9.—Idealized model of the natural greenhouse efect. Figure courtesy of IPCC (2007).
Figure 10.—Concentraions of greenhouse
gases showing increases in concentraions
since 1750 atributable to human aciviies
in the industrial era. Concentraion units
are parts per million (ppm) or parts per
billion (ppb), indicaing the number of
molecules of the greenhouse gas per
million or billion molecules of air. Figure
courtesy of IPCC (2007).
52
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
of the human-induced increase in carbon dioxide.
The remaining to 17 percent of human-induced
emissions comes primarily from deforestation of
land for conversion to agriculture, which releases
carbon dioxide when forests burn or decompose
(van der Werf et al. 2009). However, increases in
fossil fuel emissions over the past decade mean that
the contribution from land-use changes has become
a smaller proportion of the total (Le Quéré et al.
2009).
Methane is responsible for roughly 14 percent
of greenhouse gas emissions in terms of CO2equivalent (CO2-eq) (IPCC 2007). Concentrations
of this gas have also been increasing as a result of
human activities, including agricultural production
of livestock and increases in rice production.
Livestock production contributes to methane
emissions primarily from fermentation in the guts of
cattle and other ruminants. Rice production requires
wet conditions that are also ideal for microbial
methane production. Other sources of methane
include biomass burning, microbial-induced methane
emissions from landfills, fossil fuel combustion,
and leakage of natural gas during extraction and
distribution.
Nitrous oxide accounts for about 8 percent of global
greenhouse gas emissions in terms of CO2-eq (IPCC
2007). The primary human source of nitrous oxide
is agriculture. The use of fertilizer causes emissions
from soil as microbes break down nitrogencontaining products. This is especially dramatic
in areas where tropical forests are converted to
agricultural lands. Other human-caused sources
of nitrous oxide include nylon production and
combustion of fossil fuels.
Humans have also reduced ozone, which protects
us from ultraviolet radiation, in the atmosphere
through the use of chlorofluorocarbons (CFCs)
once used widely in refrigeration, air conditioning,
and other uses. Restrictions against the use of
CFCs under the Montreal Protocol led to a decline
in CFC emissions, and reductions in ozone have
subsequently slowed. After CFCs were banned,
another class of halocarbons, hydrofluorocarbons
(HFCs, also known as F-gases), largely replaced
CFCs in refrigeration and air conditioning. HFCs
do not deplete stratospheric ozone, but many are
powerful greenhouse gases. Currently HFCs account
for about 1 percent of greenhouse gas emissions in
terms of CO2-eq (IPCC 2007).
CLiMATE MoDELS
Scientists use models, which are simplified
representations of reality, to simulate future
climates. Models can be theoretical, mathematical,
conceptual, or physical. General circulation models
(GCMs) combine complex mathematical formulas
representing physical processes in the ocean,
atmosphere, and land surface within large computer
simulations. In this assessment, GCMs are used
to project future climate and as inputs to impact
models.
General Circulaion Models
General circulation models simulate physical
processes in the earth, oceans, and atmosphere
through time using mathematical equations in threedimensional space. They can work in time steps as
small as minutes or hours in simulations covering
decades to centuries. Because of their high level
of complexity, GCMs require intensive computing
power, and must be run on supercomputers.
Although climate models use highly sophisticated
computers, limits on computing power mean that
projections are limited to relatively coarse spatial
scales. Instead of simulating climate for every single
53
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
point on Earth, modelers divide the land surface,
ocean, and atmosphere into a three-dimensional grid
(Fig. 11). Each cell within the grid is treated as an
individual unit, and is able to interact with adjacent
cells. Although each model is slightly different, the
size of each cell in the grid is usually between
2 and 3° latitude and longitude, or for the middle
latitudes, about the size of West Virginia. These
horizontal grids are stacked in interconnected
vertical layers that simulate ocean depth or
atmospheric thickness at increments usually ranging
from 50 to 3,280 feet.
Figure 11.—Schemaic describing climate models, which are systems of diferenial equaions based on the basic laws of
physics, luid moion, and chemistry. The planet is divided into a three-dimensional grid that is used to apply basic equaions;
atmospheric models calculate winds, heat transfer, radiaion, relaive humidity, and surface hydrology within each grid and
evaluate interacions with neighboring points. Figure courtesy of NOAA (2008).
54
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Several research groups from the United States
and abroad have developed GCMs that have been
used in climate projections for the IPCC reports
and elsewhere (Box 5). These models have been
developed by internationally renowned climate
research centers such as NOAA’s Geophysical Fluid
Dynamics Laboratory (GFDL CM2) (Delworth
et al. 200), the United Kingdom’s Hadley
Centre (HadCM3) (Pope et al. 2000), and the
National Center for Atmospheric Research (PCM)
(Washington et al. 2000). These models use slightly
different grid sizes and ways of quantitatively
representing physical processes. They also differ
in sensitivity to changes in greenhouse gas
concentrations, which means that some models will
tend to project higher increases in temperature than
others under the same greenhouse gas concentrations
(Winkler et al. 2012).
Like all models, GCMs have strengths and
weaknesses (Box ). In general, they are useful
and reliable tools because they are based on wellunderstood physical processes and have been judged
in part by their ability to accurately simulate past
climate. Simulations with GCMs can be run for past
climate, and output from these simulations generally
correspond well with proxy-based estimates of
ancient climates and actual historical measurements
of recent climates. Projections by GCMs are not
perfect, however. Sources of error in model output
include incomplete scientific understanding of some
climate processes and the fact that some influential
climate processes occur at spatial scales that are too
small to be modeled with current computing power.
Technological advances in the computing industry
along with scientific advances in our understanding
of Earth’s physical processes will lead to continued
improvements in GCM projections.
Emissions Scenarios
General circulation models require significant
amounts of information to project future climates.
Some of this information, like future greenhouse
gas concentrations, is not known and must be
estimated. Although human populations, economies,
and technological developments will certainly
affect future greenhouse gas concentrations, these
developments cannot be completely foreseen. One
common approach for dealing with uncertainty about
future greenhouse gas concentrations is to develop
storylines (narratives) about how the future may
unfold and calculate the potential greenhouse gas
concentrations for each storyline. The IPCC’s set of
standard emissions scenarios is a widely accepted set
of such storylines (IPCC 2007). In GCMs, the use
of different emissions scenarios results in different
climate projections.
Box 5: More Resources on Climate Models and Emissions Scenarios
U.S. Forest Service
Intergovernmental Panel on Climate Change
Climate Projecions FAQ
www.treesearch.fs.fed.us/pubs/40614
Chapter 8: Climate Models and Their Evaluaion
www.ipcc.ch/publicaions_and_data/ar4/wg1/en/
ch8.html
u.S. Global Change Research Program
Special Report on Emissions Scenarios:
Summary for Policymakers
htp://www.ipcc.ch/ipccreports/sres/emission/index.
php?idp=0
Climate Models: an Assessment of Strengths
and Limitaions
library.globalchange.gov/sap-3-1-climate-modelsan-assessment-of-strengths-and-limitaions
55
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Box 6: Model Limitaions and Uncertainty
“All models are wrong, some are useful.”
–George Box (Box and Draper 1987)
Models are conceptual representaions of reality,
and any model output must be evaluated for its
accuracy to simulate a biological or physical response
or process. The overall intenion is to provide the
best informaion possible for land managers given
the uncertainty and limitaions inherent in models.
Model results are not considered standalone
components of this vulnerability assessment because
there are many assumpions made about the
processes simulated by GCMs and impact models,
uncertainty in future greenhouse gas concentraions,
and limitaions on the grid scale and numbers of
inputs that a model can reliably handle. Precipitaion
projecions usually have much more variability
among models than temperature. Regions with
complex topography contain much more diversity
in microclimates than many models can capture.
Many nonclimate stressors, such as insect pests or
pathogens, can overshadow the impact of climate on
a species or community, especially in the short term.
Therefore, model results used in this assessment
were evaluated by local experts to idenify
regional caveats and limitaions of each model,
and are considered with addiional knowledge and
experience in the forest ecosystems being assessed.
Emissions scenarios quantify the effects of
alternative demographic, technological, or
environmental developments on atmospheric
greenhouse gas concentrations. None of the current
scenarios includes any changes in national or
international policies, such as the Kyoto Protocol,
directed specifically at climate change. However,
some of the scenarios that include a reduction in
greenhouse gases through other means suggest what
we could expect if these policies were implemented.
Six different emissions scenarios are commonly used
in model projections for reports such as the IPCC
Fourth Assessment Report (Fig. 12).
5
We integrated fundamentally diferent types
of impact models into our assessment of forest
vulnerability to climate change. These models
operate at diferent spaial scales and provide
diferent kinds of informaion. The DISTRIB model
projects the amount of available suitable habitat
for a species. The LINKAGES model projects species
establishment and growth. The LANDIS PRO model
projects changes in basal area and abundance.
There are similariies between some inputs into
these models—downscaled climate models and
scenarios, simulaion ime periods, and many of
the same species—but because of the fundamental
diferences in their architecture, their results are not
directly comparable. Their value lies in their ability to
provide insights into how various interrelated forest
components may respond to climate change under a
range of possible future climates.
Models can be useful, but they are inherently
incomplete. For that reason, an integrated approach
using muliple models and expert judgment is
needed. The basic inputs, outputs, and architecture
of each model are summarized in this chapter with
clear descripions of the limitaions and caveats
of each model. Limitaions of these models with
speciic applicability to the Central Appalachians
forest ecosystems are discussed in more detail in
Chapter 5.
The A1FI scenario is the most fossil-fuel intensive,
and thus projects the highest future greenhouse gas
concentrations; GCM simulations using the A1FI
scenario project the highest future warming. On the
other end of the spectrum, the B1 scenario represents
a future where alternative energies decrease
our reliance on fossil fuels and greenhouse gas
concentrations increase the least. GCM simulations
using the B1 scenario project the lowest increase in
global temperature. Although these scenarios were
designed to describe a range of future emissions
over the coming decades, it is important to note that
the future will likely be different from any of the
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Figure 12.—Projected global greenhouse gas (GHG)
emissions (in gigatons [Gt] of CO2-eq per year) assuming
no change in climate policies under six scenarios (B1, A1T,
B2, A1B, A2, and A1FI) originally published in the Special
Report on Emissions Scenarios (SRES) (IPCC 2000), and the
80th-percenile range (gray shaded area) of recent scenarios
published since SRES. Dashed lines show the full range of
post-SRES scenarios. Figure courtesy of IPCC (2007).
developed scenarios. It is highly unlikely that future
greenhouse gas emissions will be less than described
by the B1 scenario even if national or international
policies were implemented immediately. In fact,
current emissions more closely track the greenhouse
gas emissions of the A1FI scenario, and global
emissions since the year 2000 have even exceeded
those values in some years (Raupach et al. 2007).
Downscaling
As mentioned previously, GCMs simulate climate
conditions only for relatively large areas on a
relatively coarse scale. To better examine the future
climate of areas within the Central Appalachians
region, a smaller grid scale is needed. One method
of improving the resolution uses statistical
downscaling, a technique by which statistical
relationships between GCM model outputs and
on-the-ground measurements are derived for the
past (Hayhoe et al. 2007, Stoner et al. 2013). First,
a statistical relationship is developed between GCM
output for a past “training period,” and observed
climate variables of interest (e.g., temperature and
precipitation). The historical relationship between
GCM output and monthly or daily climate variables
at the regional scale can then be tested by using
an independent historical “evaluation period” to
confirm the relationship is robust. Finally, the
historical relationship between GCM output and
observed climate variables is used to downscale
both historical and future GCM simulations to the
same scale as the initial observations. The statistical
relationships are then used to adjust large-scale
GCM simulations of the future to much smaller
spatial scales. The grid resolution for downscaled
climate projections is typically around .2 miles
(i.e., a cell represents 38.4 square miles).
Statistical downscaling has several advantages and
disadvantages (Daniels et al. 2012). It is a relatively
simple and inexpensive way to produce smallerscale projections using GCMs. One limitation is
that downscaling assumes that past relationships
between large-scale weather systems and local
climate will remain consistent under future change.
Evaluation of this assumption was performed by
applying the asynchronous regional regression
model (ARRM) to a high-resolution (15.5 miles)
GCM data set under the new RCP 8.5 scenario,
and comparing the high-resolution output directly
to the projections using SRES scenarios (Hayhoe
et al. 2013). The RCP 8.5 scenario is one of the
newest suite of scenarios developed by the IPCC,
and is comparable to the SRES A1FI scenario used
in this assessment. Hayhoe and others (2013) found
that the assumption holds true for small projections
of change, but larger projections of change may
result in small biases. Maximum daily temperatures
showed bias within the assessment area only for
hot days, whereas minimum daily temperatures
showed more widespread bias, indicating potential
overestimation of increases in warm nights.
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ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Precipitation projections appear to have widespread
bias within the assessment area only on the wettest
days. Another limitation is that downscaling depends
on local climatological data. If there are too few
weather stations in the area of interest, it will be
difficult to obtain a good downscaled estimate of
future climate for that area. Finally, local influences
on climate that occur at finer scales (such as land
cover type or topography) cannot be addressed by
statistical downscaling, adding to uncertainty when
downscaling climate projections.
Another approach, dynamical downscaling, uses a
regional climate model (RCM) embedded within
a GCM (Daniels et al. 2012). Like GCMs, RCMs
simulate physical processes through mathematical
representations on a grid. However, RCMs operate
on a finer resolution than GCMs, typically ranging
from 15.5 to 31 miles, but can be finer than
.2 miles. Thus, they can more realistically simulate
the effects of topography, land cover, lakes, and
regional circulation patterns that operate on smaller
scales. However, dynamical downscaling requires
even more computational power than statistical
downscaling. This means that dynamically
downscaled data are usually available for only
one or two GCMs or scenarios, and for limited
geographic areas. Because dynamically downscaled
data are currently limited for the assessment area, we
use statistically downscaled data in this report.
Downscaled GCMs
used in this Assessment
In this assessment, we report statistically
downscaled climate projections for two modelemissions scenario combinations: GFDL A1FI and
PCM B1 (unless otherwise noted). Both models and
both scenarios were included in the IPCC Fourth
Assessment Report (IPCC 2007). The latest version
of the National Climate Assessment (NCA) (Melillo
et al. 2014) also draws on statistically downscaled
data based on IPCC models and scenarios but uses
the A2 scenario as an upper bound, which projects
58
lower emissions compared to A1FI. The IPCC
Assessment includes several other models, which
are represented as a multi-model average in its
reports. The NCA takes a similar approach in using
a multi-model average. For this assessment, we
instead selected two models that simulated climate
in the eastern United States with low error and that
bracketed a range of temperature and precipitation
futures (Hayhoe 2010a). This approach gives readers
a better understanding of the level of agreement
among models and provides a range of alternative
scenarios that can be used by managers in planning
and decisionmaking. Working with a range of
plausible futures helps managers avoid placing false
confidence in a single scenario given uncertainty in
projecting future climate.
The GFDL model developed by NOAA is considered
moderately sensitive to changes in greenhouse gas
concentrations (Delworth et al. 200). In other
words, any change in greenhouse gas concentration
would lead to a change in temperature that is higher
in some models and lower than others. The A1FI
scenario is the highest greenhouse gas emissions
scenario used in the 2007 IPCC assessment, and is
most similar to current trends in global greenhouse
gas emissions. Therefore the GFDL A1FI scenario
represents a higher-end projection for future
temperature increases.
The PCM, in contrast, is considered to have low
sensitivity to greenhouse gas concentrations. The
B1 scenario is the lowest greenhouse gas emissions
scenario used in the 2007 IPCC assessment, and
is much lower than the most likely trajectory for
greenhouse gas emissions for the coming decades.
Therefore, the PCM B1 combination represents a
lower-end projection of future climate change.
Together, the GFDL A1FI and PCM B1 scenarios
span a large range of possible future climate
scenarios. Although both projections are possible,
the GFDL A1FI scenario represents a more realistic
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
A woods road in the Scioto Trail State Park, Ohio. Photo by the Ohio Department of Natural Resources, used with permission.
projection of future greenhouse gas emissions and
temperature increases (Raupach et al. 2007). No
likelihood has been attached to any of the emissions
scenarios, however, and it is possible that actual
emissions and temperature increases could be lower
or higher than these projections (IPCC 2007).
This assessment uses a statistically downscaled
climate data set (Hayhoe 2010a). Daily mean,
minimum, and maximum temperature and total daily
precipitation were downscaled to an approximately
7.5-mile resolution grid across the United States.
This data set uses a modified statistical ARRM to
downscale daily GCM output and historical climate
data (Stoner et al. 2013).
Asynchronous quantile regression used historical
gridded meteorological data from 190 through 1999
at 1/8-degree resolution (.2 to 9.3 miles, depending
on the latitude) (Maurer et al. 2002). In addition to
the gridded data set, weather station data from the
Global Historical Climatology Network were used to
train the downscaling model. Weather stations were
required to have at least two decades of continuous
daily observations in order to robustly sample from
the range of natural climate variability and to avoid
overfitting model results (Hayhoe 2010b).
This data set was chosen for several reasons. First,
it covered the entire United States, and thus allowed
a consistent data set to be used in this and other
regional vulnerability assessments being conducted
simultaneously. Second, it included downscaled
projections for the A1FI emissions scenario, which
is the scenario that most closely matches current
trends in global greenhouse gas emissions (Raupach
et al. 2007). Third, the availability of data at daily
time steps was advantageous because it was needed
59
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
for some impact models used in this report. Fourth,
the quantile regression method is more accurate at
reproducing extreme values at daily time steps than
simpler statistical downscaling methods (Hayhoe
2010b). Finally, the 7.5-mile grid scale resolution
was fine enough to be useful for informing land
management decisions. A disadvantage is that some
cells within the assessment area represent highly
complex landforms with steep elevation gradients
from valleys to ridges, but cannot account for the
local microclimates or changes in microclimates on
a smaller scale.
Summarized projected climate data are shown in
Chapter 4. To show projected changes in temperature
and precipitation, we calculated the average daily
mean, minimum, and maximum temperatures for
each month for three 30-year time periods (2010
through 2039, 2040 through 209, 2070 through
2099). The monthly averages were used to calculate
seasonal and annual values. Mean sums of average
daily precipitation were also calculated for each
season and annually for the same time periods. We
then subtracted these values from the corresponding
baseline climate average (1971 through 2000)
to determine the departure from current climate
conditions. Historical climate data used for the
departure analysis was taken from ClimateWizard
(Girvetz et al. 2009). Chapter 3 includes more
information about the observed climate data from
ClimateWizard.
The downscaled future climate projections were also
used in each of the forest impact models described
below. This consistency in future climate data allows
for more effective comparison across different model
results. These models generally require monthly
precipitation and temperature values as inputs. They
also operate on grid scales that may be larger or
smaller than the grid scale of the downscaled data
set, and grid scales were adjusted accordingly.
iMPACT MoDELS
Downscaled climate projections from GCMs provide
important information about future climate, but they
tell us nothing about how climate change might
affect soil moisture, hydrology, forest composition,
productivity, or interactions between these factors.
Other models, commonly called impact models, are
needed to project impacts on physical and biological
processes (Fig. 13). Impact models use downscaled
GCM projections as inputs, as well as information
about tree species, life history traits of individual
Figure 13.—Steps in the development of climate impact models using projecions from general circulaion models (GCMs) and
speciic steps taken in this assessment.
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ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
species, and soil types. Several different models
are used to simulate impacts on species and forest
ecosystems. These models generally fall in one of
two main categories: species distribution models
(SDMs) and process models. In this assessment, we
used one species distribution model, the Climate
Change Tree Atlas (Landscape Change Research
Group 2014), and two process models, LINKAGES
(version 2.2; Wullschleger et al. 2003) and LANDIS
PRO (Wang et al. 2013). These models operate at
different spatial scales and provide different kinds of
information. We chose them because they have been
used to assess climate change impacts on ecosystems
in our geographic area of interest, and have stood up
to rigorous peer review in scientific literature.
MoDELS FoR ASSESSiNG
FoREST ChANGE
Species distribution models establish a statistical
relationship between the current distribution of a
tree species and key attributes of its habitat. This
relationship is used to predict how the range of the
species will shift as climate change affects those
attributes. Species distribution models, such as the
Tree Atlas, are much less computationally expensive
than process models, so they can typically provide
projections for the suitable habitat of many species
over a larger area. There are some caveats that users
should be aware of when using them, however
(Wiens et al. 2009). These models use a species’
realized niche instead of its fundamental niche. The
realized niche is the actual habitat a species occupies
given predation, disease, and competition with other
species. A species’ fundamental niche, in contrast, is
the habitat it could potentially occupy in the absence
of competitors, diseases, or herbivores. Given
that a species’ fundamental niche may be greater
than its realized niche, SDMs may underestimate
current niche size and future suitable habitat. In
addition, species distributions in the future might be
constrained by competition, disease, and predation
in ways that do not currently occur. If so, SDMs
could overestimate the amount of suitable habitat
in the future. Furthermore, fragmentation or other
physical barriers to migration may create obstacles
for species otherwise poised to occupy new habitat.
Therefore, a given species might not actually be able
to enter the assessment area in the future, even if an
SDM like the Tree Atlas projects it will gain suitable
habitat. Additionally, the Tree Atlas does not suggest
that existing trees will die if suitable habitat moves
out of an area. Rather, this is an indication that they
will be living farther outside their ideal range and
will be exposed to more climate-related stress.
In contrast to SDMs, process models, such as
LANDIS PRO and LINKAGES, simulate ecosystem
and tree species dynamics based on mathematical
representations of physical and biological processes.
Process models can simulate future change in tree
species dispersal, succession, biomass, and nutrient
dynamics over space and time. Because these
models simulate spatial and temporal dynamics of a
variety of complex processes and operate at a finer
pixel size, they typically require more computational
power than SDMs. Therefore, fewer species can be
modeled compared to SDMs. Process models also
have several assumptions and uncertainties that
should be taken into consideration when applying
results to management decisions. Process models
rely on empirical and theoretical relationships that
are specified by the modeler. Any uncertainties in
these relationships can be compounded over time
and space, leading to potential biases.
Although useful for projecting future changes, both
process models and SDMs share some important
limitations. They assume that species will not
adapt evolutionarily to changes in climate. This
assumption may be true for species with long
generation times (such as trees), but some short-lived
species may be able to adapt even while climate is
rapidly changing. Both types of models may also
magnify the uncertainty inherent in their input
data. Data on the current distribution of trees, site
1
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
characteristics, and downscaled GCM projections
are estimates that add to uncertainty. No single
model can include all possible variables, and there
are “unknown unknowns”; thus there are important
inputs that will be excluded from individual models.
In this assessment, competition from understory
vegetation, herbivory, and pest outbreaks are a few
of the processes excluded from the impact models.
Given these limitations, it is important for all model
results to pass through a filter of local expertise
to ensure that results match with reality on the
ground. Chapter and Appendix 5 explain the expert
elicitation process for determining the vulnerability
of forest ecosystems based on local expertise and
model synthesis.
Climate Change Tree Atlas
The Climate Change Tree Atlas incorporates a
diverse set of information about potential shifts in
the distribution of tree species habitat in the eastern
United States over the next century (Landscape
Change Research Group 2014). The species
distribution model DISTRIB measures relative
abundance, referred to as importance value, for 134
eastern tree species. The model then projects future
importance values and suitable habitat for individual
tree species by using downscaled GCM data
readjusted to a 12.4-mile grid of the eastern United
States (east of the 100th meridian) (Landscape
Change Research Group 2014).
The DISTRIB model uses inputs of tree abundance,
climate, and the environment to simulate species
habitat. Tree abundance is estimated from the U.S.
Forest Service’s Forest Inventory and Analysis
(FIA) data plots (Miles et al. 200). Current and
future climates are simulated from the most recent
downscaled climate data created by Hayhoe and
colleagues (Hayhoe 2010a) for two GCMs (GFDL
and PCM) and two emissions scenarios (A1FI and
B1) (see Chapter 4 for maps of downscaled climate
data for the assessment area). Inputs characterizing
land use, fragmentation, climate, elevation, soil
class, and soil properties were obtained from various
agencies and data clearinghouses to provide the 38
predictor variables (Table 11) (Iverson et al. 2008b,
Riitters et al. 2002). The reliability of individual
habitat models is evaluated through the calculation
of a model reliability score, which is based on
statistically quantified measures of fitness (methods
are fully described in Matthews et al. [2011a]).
Steep slopes, prone to soil erosion and slippage. Photo by
Patricia Butler, Northern Insitute of Applied Climate Science
(NIACS) and Michigan Tech, used with permission.
2
Each tree species is further evaluated for additional
factors not accounted for in the statistical models
(Matthews et al. 2011b). These modifying factors
(Appendix 4) are supplementary information on life
history characteristics such as dispersal ability or
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Table 11.—Parameters used to predict current and future tree species habitat (Iverson et al. 2008b)
Land use and fragmentaion (%)
Cropland
Nonforest land
Forest land
Water
Fragmentaion index (Riiters et al. 2002)
Climate (°C, mm)
Mean annual temperature
Mean January temperature
Mean July temperature
Mean May through September temperature
Annual precipitaion
Mean May through September precipitaion
Mean diference between July and January temperature
Elevaion (m)
Elevaion coeicient of variaion
Minimum elevaion
Maximum elevaion
Average elevaion
Range of elevaion
fire tolerance as well as information on sensitivity to
disturbances such as pests and diseases that have had
negative effects on the species. This supplementary
information allows us to identify when an individual
species may do better or worse than model
projections suggest.
There are important strengths and limitations of
the Tree Atlas that should be considered when
interpreting results. Importantly, DISTRIB projects
where the habitat suitability may potentially change
for a particular species, but does not project where
the species may actually occur by a certain time. The
actual rate of migration into the new suitable habitat
will be influenced by large time lags, dispersal and
establishment limitations, and availability of refugia.
The FIA data plots are nonbiased and extensive
across the assessment area, but are spatially sparse at
Soil class (%)
Alisol
Aridisol
Enisol
Histosol
Incepisol
Mollisol
Spodosol
Ulisol
Verisol
Soil property
Soil bulk density (g/cm3)
Potenial soil producivity (m3/ha imber)
Percent clay (<0.002 mm size)
Soil erodibility factor
Soil permeability rate (cm/h)
Percent soil passing sieve no. 10 (coarse)
Soil pH
Depth to bedrock (cm)
Percent soil passing sieve no. 200 (ine)
Soil slope (%) of a soil component
Organic mater content (% by weight)
Total available water capacity (cm)
a 12.4-mile resolution. Species that are currently rare
on the landscape are often undersampled in the FIA
data, and consequently have lower model reliability.
Likewise, species that are currently abundant on
the landscape usually have higher model reliability.
The methods assume the species are in equilibrium
with the environment, and do not account for species
that rapidly change distributions (e.g., invasive
species). The models do not account for biological
or disturbance factors (e.g., competition or fire)
that affect species’ abundance. Thus, the modifying
factors are provided as a supplement to the model
output to help address these deficiencies.
For this assessment, DISTRIB uses the GFDL A1FI
and PCM B1 climate scenarios. The results provided
in Chapter 5 are now available from the online
Climate Change Tree Atlas (www.nrs.fs.fed.us/atlas)
under “Regional Assessments.”
3
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
LiNKAGES
The LINKAGES model (version 2.2; Wullschleger
et al. 2003) is a forest succession and ecosystem
dynamics process model modified from an earlier
version of LINKAGES (Pastor and Post 1985).
The LINKAGES model integrates establishment
and growth of individual tree species over
30 years on a plot from bare ground, incorporating
ecosystem functions such as soil-water balance, litter
decomposition, nitrogen cycling, soil hydrology,
and evapotranspiration. Inputs to the model
include climate variables (e.g., daily temperature,
precipitation, wind speed, and solar radiation), soil
characteristics (e.g., soil moisture capacity and
percent rock, sand, and clay for multiple soil layers),
and biological traits for each tree species (e.g.,
growth rate and tolerance to cold and shade).
A full list of model inputs is presented in
Table 12. Outputs to the model include tree species
composition, number of stems, biomass, leaf litter,
available nitrogen, humus, and organic matter, as
well as hydrologic dynamics such as runoff. Unlike
the LANDIS PRO model (below), LINKAGES is
not spatially dynamic, and does not simulate tree
dispersal or any other spatial interaction among grid
cells. Simulations are done at yearly time steps on
multiple 0.2-acre circular plots, which correspond
to the average gap size when a tree dies and falls
over. Typically, the model is run for a specified
number of plots in an area of interest, and results are
averaged to determine relative species biomass and
composition across the landscape over time.
For this assessment, LINKAGES simulates changes
in tree species establishment probability over the
next century for 23 common tree species within the
Table 12.—Parameters used in the LiNKAGES model
Locaion
Laitude, longitude
Climate (daily)
Total daily precipitaion
Daily minimum temperature
Daily maximum temperature
Daily total solar radiaion
Mean monthly wind speed
Soil
Field capacity for 12 soil layers
Wiling point for 12 soil layers
Hydrological coeicients for 12 soil layers (based on
percent sand and clay)
Organic mater (Mg/ha)
Nitrogen (Mg/ha)
Percent rock for 12 soil layers
Tree speciesa
Total annual degree day maximum and minimum
(Moscow Forestry Sciences Laboratory 2014)
Height and diameter growth equaion coeicients
(Miles et al. 2006)
Typical maximum mortality age (Loehle 1988)
Frost tolerance (Moscow Forestry Sciences Laboratory
2014)
Shade tolerance
Drought tolerance
Nitrogen equaion coeicients (Natural Resources
Conservaion Service 2014b, Post and Pastor 1996)
Sprout stump number and minimum
and maximum diameter
Mineral or organic seed bed
Maximum seeding rate
Crown area coeicients
Root:shoot raio by species
Leaf liter quality class
Foliage retenion ime
Leaf weight per unit crown area
a
4
From Post and Pastor (1996) unless noted otherwise.
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Central Appalachians region. The model projects
changes in forest composition by using downscaled
daily mean temperature and precipitation under
GFDL A1FI and PCM B1, and compares these
projections with those under a current climate
scenario (i.e., the climate during 1990 through
2009) at a future time period. One hundred and
fifty-six 0.2-acre virtual plots were parameterized in
LINKAGES; this number represents 1 plot for each
of landforms in 2 ecological subsections.
There are important strengths and limitations
of LINKAGES that should be considered when
interpreting results. Section-level estimates were
derived from the weighted average of landforms
in a subsection and the weighted average of
subsections in a section. Most of the 15 plots were
located at the geographic center of a subsection,
which provided climate variables that represented
average conditions for that subsection. However,
plot locations were modified for subsections with
large elevation gradients, such as the Northern
High Allegheny Mountain subsection within the
Allegheny Mountains section (elevation 1,7 to
4,7 feet). For these subsections, the geographic
center was often located at either the highest
elevation or the lowest elevation, which skewed
the temperature values to appear colder (at high
elevation) or warmer (at low elevation) than the
average of the subsection. Therefore, plots were
located at a representative point at a mid-elevation.
This approach better reflects the average climate
conditions for a subsection, but nevertheless
fails to fully address the elevation gradient and
associated climate conditions for tree species within
mountainous sections. Therefore some species that
occur only at the upper or lower end of an elevation
gradient may appear to have lower growth potential
than expected, because the results represent the
average of the entire subsection.
LANDiS PRo
The LANDIS PRO model (Wang et al. 2014) is
a spatially dynamic process model that simulates
species-, stand-, and landscape-level processes. It is
derived from the LANDIS model (Mladenoff 2004),
but has been modified extensively from its original
version. The LANDIS PRO model can simulate very
large landscapes (millions of acres) at relatively
fine spatial and temporal resolutions (typically 200
to 300 feet and 1- to 10-year time steps). One new
feature of the model compared to previous versions
is that inputs and outputs of tree species data
include tree density and volume and are compatible
with FIA data. Thus, the model can be directly
initialized, calibrated, and validated with FIA data.
This compatibility ensures the starting simulation
conditions reflect what is best known on the ground
and allows the modelers to quantify the uncertainties
embedded in the initial data.
Species-level processes include seedling germination
and establishment, growth, vegetative reproduction,
and tree mortality. Species-level processes are
simulated from known life history characteristics
and empirical equations. Stand-level processes
include competition and succession. Landscapelevel processes include fire, wind, insect outbreaks,
disease, invasive species, harvesting, silviculture,
and fuels treatments. The LANDIS PRO model
stratifies the landscape into land types based on
topography, climate, soil, and other environmental
characteristics. Within a land type, species
establishment and resource availability are assumed
to be similar. Combined with anthropogenic and
natural disturbances, these land type-specific
processes are capable of simulating landscape
heterogeneity, time, and space.
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ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Basic inputs to the LANDIS PRO model include
maps of species composition, land types, stands,
management areas, and disturbance areas. In
addition, species characteristics such as longevity,
maturity, shade tolerance, average seed production,
and maximum diameter at breast height are given
as inputs into the model. A software program,
Landscape Builder, is used to generate the species
composition map (Dijak 2013). Landscape Builder
uses the FIA unit map, national forest type map,
national size class map, the National Land Cover
Dataset (U.S. Geological Survey 2011), and
landform maps to assign the number of trees by age
cohort and species to each grid cell. Landform maps
specify the slope, aspect, and landscape position to
replicate the complex topography of the assessment
area (Fig. 14). Initialized landscapes are compared to
FIA data at both the landscape and land type scale.
Species models are calibrated by adjusting the model
input parameters until simulation results match
FIA data. In this assessment, the initial landscape
was simulated from 1978 through 2003 data, and
the number of trees and basal area by species was
compared to 2003 FIA data. Results of the model
predictions are validated by comparing simulations
from 1978 through 2008 to the FIA data of 2008.
The Landscape Builder model was also validated to
verify that the theories and assumptions in LANDIS
PRO are valid. The calibrated and validated model
was further validated by comparing long-term
simulations (150 years) to Gingrich stocking charts
and Reineke density diagrams to verify that stand
development processes and relationships were
adequately simulated (Wang et al. 2013).
Figure 14.—Example landform map (e.g., subsecion 890) used in landscape iniializaion in the LANDIS PRO model (Dijak
2013).
ChAPTER 2: CLiMATE ChANGE SCiENCE AND MoDELiNG
Basic outputs in LANDIS PRO for a species or
species cohort include biomass, age, and carbon.
Disturbance and harvest history can also be
simulated across space and time. The spatially
dynamic nature of the model and its fine spatial
resolution are unique advantages of LANDIS
PRO compared to LINKAGES (described above)
and statistically based models such as DISTRIB.
Disadvantages of LANDIS PRO are that it is too
computationally intensive to be run for a large
number of species (in contrast to DISTRIB) and
does not account for ecosystem processes such as
nitrogen cycling or decomposition (in contrast to
LINKAGES).
For this assessment, LANDIS PRO simulates
changes in basal area and trees per acre on 8-foot
grid cells over the next century for 1 dominant
tree species across the Central Appalachians region.
The model projects changes in forest composition
by using downscaled daily mean temperature and
precipitation from the GFDL A1FI and PCM B1
climate scenarios, and compares these projections
with those under a current climate scenario.
ChAPTER SuMMARY
Temperatures have been increasing in recent decades
at global and national scales, and the overwhelming
majority of climate scientists attribute this change
to increases in greenhouse gases from human
activities. Even if dramatic changes are made to help
curtail greenhouse gas emissions, these greenhouse
gases will persist in our atmosphere for decades to
come. Scientists can model how these increases in
greenhouse gases may affect global temperature
and precipitation patterns by using GCMs. These
large-scale climate models can be downscaled and
incorporated into other types of models that project
changes in forest composition and ecosystem
processes. Although there are inherent uncertainties
in what the future holds, all of these types of models
can help us frame a range of possible futures. This
information can then be used in combination with
the local expertise of researchers and managers to
provide important insights about the potential effects
of climate change on forest ecosystems.
There are important strengths and limitations of
LANDIS PRO that should be considered when
interpreting results. This model assumes that
historical successional dynamics are held constant
into the future. It is also assumed that the resource
availability by land type was able to capture the
effects of landscape heterogeneity at the 8-foot
resolution. Species that are currently rare on the
landscape are often undersampled in the FIA data,
and consequently have lower model reliability.
Species that are currently abundant on the landscape
have higher model reliability.
7
ChAPTER 3: oBSERVED CLiMATE ChANGE
As discussed in Chapter 1, climate is one of the
principal factors that has determined the composition
and extent of forest ecosystems in the Central
Appalachians over the past several thousand years.
This chapter describes the climate trends in the
assessment area that have been observed over the
past century, including documented patterns of
climate-related processes and extreme weather
events. It also presents evidence that ecosystems
in the Central Appalachians are already exhibiting
signals that they are responding to shifts in
temperature and precipitation.
CuRRENT CLiMATE
The existing climates within the Central
Appalachians are strongly influenced by atmospheric
weather, latitude, topography, and changes in
elevation (Chapter 1). Lake-effect precipitation is
a factor in the north and west, whereas dramatic
changes in elevation are responsible for orographic
effects on rain and snow in the mountainous regions.
This heterogeneity of climates across the region can
be seen at finer scales, but is often lost in broadscale averages.
Temperature and precipitation at weather stations in
the Central Appalachians region have been recorded
for more than 100 years. The average temperature
and precipitation across the assessment area was
examined by using the ClimateWizard Custom
Analysis tool (ClimateWizard 2013, Girvetz et al.
2009). Data for the tool are derived from PRISM
(Parameter-elevation Regressions on Independent
Slopes Model) (Gibson et al. 2002), which models
historical measured point data onto a continuous
2.5-mile grid over the entire United States.
Temperature and precipitation data were used to
derive annual, seasonal, and monthly values for the
30-year average (also referred to as the “climate
normal”) for 1971 through 2000 (Table 13, Figs. 15
and 1) and for each section (Appendix 2).
Table 13.—Annual and seasonal mean, minimum, and maximum temperature and total precipitaion for 1971
through 2000 (ClimateWizard 2013)
Season
Annual
Winter (Dec-Feb)
Spring (Mar-May)
Summer (Jun-Aug)
Fall (Sep-Nov)
8
Mean
temperature (°F)
Minimum
temperature (°F)
Maximum
temperature (°F)
Mean
precipitaion (inches)
51.1
31.2
50.2
70.1
53.0
40.0
21.7
38.0
58.5
41.6
62.3
40.7
62.4
81.6
64.4
43.1
9.2
11.5
12.7
9.7
ChAPTER 3: oBSERVED CLiMATE ChANGE
Figure 15.—Thirty-year annual and seasonal averages of mean, minimum, and maximum temperatures across the assessment
area from 1971 through 2000. Data source: ClimateWizard (2013).
9
ChAPTER 3: oBSERVED CLiMATE ChANGE
Figure 16.—Thirty-year averages of mean annual and seasonal precipitaion across the assessment area from 1971 through
2000. Data source: ClimateWizard (2013).
70
ChAPTER 3: oBSERVED CLiMATE ChANGE
hiSToRiCAL TRENDS
iN TEMPERATuRE
AND PRECiPiTATioN
The Central Appalachians region has experienced
changes in temperature and precipitation over the
past 111 years, and the rate of change appears to be
increasing. Long-term trends from 1901 through
2011 were examined by using the ClimateWizard
Custom Analysis tool to gain a better understanding
of how climate has been changing (Appendix 2).
Trends in annual, seasonal, and monthly temperature
(mean, minimum, and maximum) and total
precipitation were examined both for the entire
assessment area, and separately for each ecological
section within the assessment area (Tables 14
and 15). Long-term trends show that some aspects
of the climate have been changing. Accompanying
tables and figures present the change over the
111-year period estimated from the slope of the
linear trend. In the following text we highlight
increasing or decreasing trends for which we have
moderate to high confidence that they did not
occur by chance. For more information regarding
confidence in trends and the PRISM data, refer to
Appendix 2. Observed changes in other ecological
indicators are often described on a statewide basis
because finer resolution data were not available,
unless otherwise indicated.
Temperature
Between 1901 and 2011, annual mean temperatures
fluctuated from year to year by several degrees. The
coolest year on record was 1917, and the warmest
year on record was 1921 (Fig 17). Many of the
highest temperatures on record were between 1921
and the mid-1950s, and there was a cool period in
the 190s and 1970s. Temperatures appear to be
increasing in the past few decades, but they are not
as high as were experienced in the mid-20th century.
Although annual mean temperatures increased both
globally and across the United States over the same
time period, the increase in the assessment area
was very small (0.5 °F) (Fig. 17). Seasonal mean
temperatures did not change overall (Table 14),
but there were several trends when monthly mean
temperatures were examined (Fig. 18). April mean
temperatures increased by 2.4 °F. August mean
temperatures increased by 1.2 °F, and November
mean temperatures increased by 2.3 °F. Although
the direction of change appears negative for the
maximum temperatures in all seasons except spring,
trends in maximum temperatures were small enough
that they may have occurred by chance. Maximum
temperatures increased the most in April (3.2 °F),
and decreased the most in September (-2.1 °F),
October (-2 °F), and July (-1.2 °F). Annual minimum
temperature increased by 1.1 °F. Minimum
temperatures also increased in summer (1. °F)
and fall (1.4 °F) (Fig. 18). Minimum temperatures
increased the most in April (1. °F), June (1.4 °F),
July (1.3 °F), August (2.1 °F), and November
(2.8 °F). April and November are notable because
both minimum and maximum temperatures
increased in those months.
71
ChAPTER 3: oBSERVED CLiMATE ChANGE
Table 14.—Change in annual and seasonal mean temperatures and precipitaion from 1901 to 2011 in the
assessment area
Season
Annual
Winter (Dec-Feb)
Spring (Mar-May)
Summer (Jun-Aug)
Fall (Sep-Nov)
a
Mean
temperature (°F)
Minimum
temperature (°F)a
Maximum
temperature (°F)
Precipitaion
(inches)
0.5
0.3
0.8
0.6
0.3
1.1
0.8
0.6
1.6
1.4
-0.1
-0.1
0.9
-0.4
-0.7
1.7
-1.0
0.7
-0.3
2.3
Values in boldface indicate less than 10-percent probability that the trend could have occurred by chance alone.
Table 15.—Change in annual and seasonal mean temperatures and precipitaion from 1901 to 2011 by ecological
secion within the assessment area (ClimateWizard 2013)
Secion
Season
Mean
temperature (°F)a
Minimum
temperature (°F)
Maximum
temperature (°F)
Precipitaion
(inches)
221E
Annual
Winter
Spring
Summer
Fall
0.3
-0.1
0.4
0.4
0.3
0.7
0.4
0.1
1.2
1.0
-0.1
-0.5
0.8
-0.4
-0.5
1.3
-1.2
0.4
-0.1
2.3
221F
Annual
Winter
Spring
Summer
Fall
0.5
0.9
1.1
0.2
-0.1
1.0
1.3
0.9
1.1
0.7
0.1
0.4
1.3
-0.7
-0.8
4.2
0.1
0.8
1.0
2.4
M221A
Annual
Winter
Spring
Summer
Fall
1.4
1.7
1.8
1.6
0.6
2.1
2.2
1.6
2.6
2.1
0.7
1.1
1.9
0.6
-0.9
2.0
-0.4
1.7
-2.2
2.8
M221B
Annual
Winter
Spring
Summer
Fall
0.8
0.4
1.1
1.0
0.6
1.9
0.8
1.6
2.6
2.4
-0.3
0.0
0.6
-0.7
-1.2
0.0
-1.8
0.9
-1.3
2.2
M221C
Annual
Winter
Spring
Summer
Fall
0.3
-0.4
0.3
0.7
0.6
1.1
-0.1
0.4
1.8
2.1
-0.5
-0.6
0.1
-0.5
-0.9
0.8
-1.6
0.7
-0.4
2.1
a
Values in boldface indicate less than 10-percent probability that the trend could have occurred by chance alone.
72
ChAPTER 3: oBSERVED CLiMATE ChANGE
Figure 17.—Annual mean temperature across the assessment area from 1901 through 2011. The blue line represents the
rolling 5-year mean. The red regression line shows the trend across the enire ime period (0.005 °F per year; p = 0.28).
Data source: ClimateWizard (2013).
73
ChAPTER 3: oBSERVED CLiMATE ChANGE
4.0
*
3.0
*
*
*
*
2.0
*
*
T em p eratu re c h ang e (°F )
*
*
*
1.0
M ean
0.0
M inim um
M ax im um
-1.0
*
-2.0
*
*
-3.0
-4.0
Ja n u a ry
Fe b ru a ry
M a rch
A p ril
May
Ju n e
Ju ly
A u g u st S e p te m b e r O cto b e r
N o ve m b e r D e ce m b e r
M o nth
Figure 18.—Change in monthly mean, minimum, and maximum temperatures across the assessment area from 1901 through
2011. Asterisks indicate there is less than 10-percent probability that the trend could have occurred by chance alone. Data
source: ClimateWizard (2013).
74
ChAPTER 3: oBSERVED CLiMATE ChANGE
Temperature trends also differed geographically
across the assessment area, with some areas warming
or cooling more than others (Table 14). In general,
the easternmost sections (M221A and M221B) have
changed more than other sections (see Chapter 1
for a map showing ecological sections). Annual
mean temperatures increased by 1.4 °F in M221A
and by 0.8 °F in M221B. There were no trends in
mean winter and fall temperatures in any section
of the assessment area. Mean spring temperatures
increased in 221F, M221A, and M221B. Mean
summer temperatures increased in M221A, M221B,
and M221C. Minimum temperatures increased the
most in M221A and M221B. In M221A, minimum
temperatures increased annually and in all seasons.
In M221B, minimum temperatures increased
annually and in all seasons except winter. In both
M221A and M221B, minimum temperatures
increased the least in spring (1. °F) and the most in
summer (2. °F). Maximum temperatures increased
in two sections, both in spring (221F and M221A).
Maximum temperature cooled significantly only in
fall and only in Section M221B.
Spatially interpolated trends in temperature are
available through 2011 and are presented in Fig. 19.
Stippling on the maps indicates trends which have
moderate to high probability that they did not occur
by chance. Spatial analysis showed that increases
in annual temperatures ranged from 1 to 4 °F over
large portions of the assessment area, whereas
decreases were observed in only a few isolated
locations, indicated by the stippling. The greatest
increases are observed in minimum temperatures,
with increases of up to °F consistently appearing
in Sections M221A and M221B and increases of up
to 3 °F appearing in widespread areas throughout the
assessment area. The greatest decreases are observed
in maximum temperatures, with widespread
areas cooling by as much as 5 °F. These observed
decreases in summer maximums may be evidence of
a regional “warming hole” (see section on “Regional
Patterns Contributing to Local Trends”).
Precipitaion
From 1901 through 2011, mean annual precipitation
within the assessment area fluctuated by as much
as 20 inches from year to year (ClimateWizard
2013) (Fig. 20). The driest year on record for the
assessment area as a whole was 1929. Precipitation
was lower than the long-term average during distinct
periods over the last century, including a few years
during the “Dust Bowl” era of the 1930s, a dry
spell from 190 through 199, and 1987. The four
wettest years on record occurred during the past 20
years. The time series of annual precipitation for
the assessment area displays high variability from
year to year and the surge in precipitation at the
beginning of the 21st century may be driving an
upward trend, although there are not enough data
years from the 21st century to determine whether the
trend is real or due to chance (Box 7).
Because of the large interannual variability of
precipitation averaged across the assessment area,
any positive or negative trend observed in mean
annual or seasonal precipitation in the assessment
area was small enough that it could have occurred by
chance, except for fall (Table 14). Fall precipitation
increased by 2.3 inches from 1901 to 2011.
Several trends were observed in monthly mean
precipitation (Fig. 22). When averaged across the
entire assessment area, precipitation increased in
May (0.9 inches), September (0.9 inches), and
November (1.2 inches).
75
ChAPTER 3: oBSERVED CLiMATE ChANGE
Figure 19.—Annual and seasonal change in mean, minimum, and maximum temperatures across the assessment area from
1901 through 2011. Sippling indicates there is less than 10-percent probability that the trend could have occurred by chance
alone. Data source: ClimateWizard (2013).
7
ChAPTER 3: oBSERVED CLiMATE ChANGE
Figure 20.—Annual mean precipitaion across the assessment area from 1901 through 2011 (ClimateWizard 2013). The blue
line represents the rolling 5-year mean. The red regression line shows the trend across the enire period (0.015 inches per
year; p = 0.26). Data source: ClimateWizard (2013).
77
ChAPTER 3: oBSERVED CLiMATE ChANGE
Box 7: Climate Changes over the 21st Century
In this chapter, we present changes in climate
over the enire historical record for which spaially
interpolated data trends are available for the
assessment area. Looking across the enire record
is helpful in detecing long-term changes, but it can
also obscure short-term trends. In fact, the long-term
trend is made up of shorter periods of warming and
cooling, depending on the ime period analyzed.
The period from 2001 to 2012 was the warmest
on record for the world, North America, and the
United States (Blunden and Arndt 2012, World
Meteorological Organizaion 2011). Statewide
averages for the early 21st century can be explored
within the enire climate record (1895 to 2012)
through the Naional Climaic Data Center’s Climate
at a Glance maps (Naional Oceanic and Atmospheric
Administraion [NOAA] 2014b). Temperatures across
Ohio were above the long-term average for 7 years
during this period, and below average for 4 years
(NOAA 2014d). Ohio experienced its record warmest
temperatures since the 1920s in 1998 and again in
2012 (NOAA 2014d) (Fig. 21). Maryland experienced
its second warmest year in 2012 (its warmest year
was in 1998). Since 2000, only 1 year was below
the long-term average in Maryland, and the rest
were above average. West Virginia experienced its
record low for average annual temperature in 1917,
followed by its record high in 1921. West Virginia
displays an enormous amount of variaion from year
to year, with the third warmest temperature in 2012.
Precipitaion has also changed dramaically during
2001 through 2012. Ohio experienced its wetest
year since 1895 in 2011. Since 2000, 7 years have
been above average or much above average,
whereas only 4 years were slightly below average
(NOAA 2014d). Maryland and West Virginia also
follow this patern; both experienced their wetest
year in 2003, and both have had more above-average
years than below average.
And what about the “warming hole” paterns of
low summer temperatures and high spring and
summer precipitaion? Across the assessment area,
summer temperatures during the 21st century
were much higher than the long-term average for
the area, with record warming in Maryland (NOAA
2014d). Although it is too early to determine a
trend, the recent warming temperatures suggest a
possible reversal of the “warming hole.” Summer
precipitaion trends have not changed markedly
in the assessment area over the 21st century, but
spring precipitaion has been higher than average
(NOAA 2014d). Overall, the climate informaion
from the 21st century seems to be consistent with
the trends over the past century in some ways but
not others. The area is geing generally weter, and
the 1930s coninues to be the warmest decade on
record.
Figure 21.—Annual mean temperatures for Ohio from 1895
through 2011. Image courtesy of NOAA (2014d).
78
ChAPTER 3: oBSERVED CLiMATE ChANGE
1.5
*
C h an ge in p recipitation (inches)
1.0
*
*
0.5
0.0
-0.5
-1.0
J an uary
F ebruary
M arc h
Ap ril
M ay
J un e
J uly
Aug us t
Sep tem ber
O c to ber
N o v em ber
D ec em ber
M o n th
Figure 22.—Change in monthly mean precipitaion within the assessment area from 1901 through 2011. Asterisks indicate
there is less than 10-percent probability that the trend could have occurred by chance alone. Data source: ClimateWizard
(2013).
When we examined changes at the ecological section
level, trends emerged in some areas (Table 14). In
general, the greatest increase in precipitation was
observed in Ohio (Section 221F), where an increase
of 2.4 inches was observed in fall, contributing to a
total increase of 4.2 inches in annual precipitation.
Precipitation increased in fall in every section,
with the greatest increase in M221A (2.8 inches).
In the easternmost section (M221A), summer
precipitation decreased by 2.2 inches from 1901 to
2011. In southern West Virginia (M221C), winter
precipitation decreased by 1. inches.
Spatially interpolated trends in precipitation are
available through 2011 and are presented in
Figure 23. Spatial analysis showed that increases
in annual precipitation ranged from 1 to 4 inches
over large portions of the assessment area, whereas
decreases were observed in only a few isolated
locations. Precipitation has increased the most
during fall, and has decreased the most during
winter. Precipitation has increased over large areas
in spring, but has decreased during the summer in
the easternmost sections.
79
ChAPTER 3: oBSERVED CLiMATE ChANGE
Figure 23.—Annual and seasonal changes in mean precipitaion from 1901 through 2011 in the assessment area. Sippling
indicates there is less than 10-percent probability that the trend has occurred by chance. Data source: ClimateWizard (2013).
80
ChAPTER 3: oBSERVED CLiMATE ChANGE
Regional Paterns
Contribuing to Local Trends
Some studies have observed a decrease in
temperature, especially summer highs, in the
southeastern and central United States since the
1950s, a phenomenon that has been referred to as
a “warming hole” (Kunkel et al. 2013b, Meehl et
al. 2012, Pan et al. 2004, Portmann et al. 2009). A
recent study examined mean temperatures across the
United States from 1950 through 1999 and found
that decadal variations in sea-surface temperature
were the most important contributor to the observed
warming hole (Meehl et al. 2012). These findings
are consistent with other studies that found that
decreases in summer high temperature are correlated
with increases in sea-surface temperatures (Kunkel
et al. 200), precipitation (Pan et al. 2004, Portmann
et al. 2009), aerosols (Leibensperger et al. 2012),
and increased soil moisture availability (Pan et al.
2004). Further research is needed to understand the
“warming hole” and its implications for the region
as global air and sea surface temperatures continue
to rise. An analysis of recent climate trends in
the United States suggests that the warming hole
may have already disappeared as the mean annual
temperature has increased significantly in all states
since 1970 (Tebaldi et al. 2012).
Observed temperature and precipitation trends in the
assessment area are consistent with the “warming
hole” pattern in the regional climate. When averaged
across the assessment area, maximum temperatures
decreased and precipitation increased in July
(Fig. 18) (ClimateWizard 2013).
hiSToRiCAL TRENDS iN EXTREMES
Although it can be very instructive to examine longterm trends in mean temperature and precipitation,
in many circumstances extreme events can have a
greater impact on forest ecosystems and the human
communities that depend on them. Weather or
climate extremes are defined as individual weather
events or long-term patterns that are unusual in their
occurrence or have destructive potential (Bader
et al. 2008). These events can trigger catastrophic
disturbances in forest ecosystems, along with
significant socioeconomic disasters. In addition,
the distribution of individual species or forest types
is often controlled by particular climatic extremes.
Scientists agree that climate change has increased
the probability of several kinds of extreme weather
events, although it is difficult to directly attribute
one particular event to climate change (Coumou
and Rahmstorf 2012). As mean summer and winter
temperatures have increased at a national scale, the
chance of experiencing unusually warm or cool
seasons has become higher over the last 30 years
(Hansen et al. 2012). Extreme events are difficult
to analyze with standard statistical methods, so
long-term studies of weather and climate trends
are necessary.
Extreme Temperatures
Extreme temperatures can influence forest
ecosystems in a variety of ways: some tree species
are limited by hot growing-season temperatures,
and others are limited by cold winter temperatures.
Extreme temperatures may also be associated with
disturbance events like drought, wildfire, ice storms,
and flooding. Warmer mean temperatures are often
correlated with higher extreme temperatures (Kling
et al. 2003, Kunkel et al. 2008). Long-term records
indicate that the number of hot days (exceeding the
95th percentile of warm temperatures) has increased
across most of the contiguous United States since
the 190s, a time series which excludes the hot,
droughty years of the 1930s and 1950s (DeGaetano
and Allen 2002; Kunkel et al. 2013b, 2013c; Perera
et al. 2012; Peterson et al. 2008). The number of
extreme cold days (not exceeding the 5th percentile
of cold maximum and minimum temperatures) has
decreased across the United States since the 190s
(DeGaetano and Allen 2002). Winter maximum
81
ChAPTER 3: oBSERVED CLiMATE ChANGE
and minimum temperatures across the country have
increased by an average 3.5 °F over the second half
of the 20th century (Peterson et al. 2008). These
trends correspond to global patterns of increasing
occurrence of extreme hot weather and decreasing
occurrence of extreme cool weather (Hansen et
al. 2012). The frequency of extreme temperatures
within the assessment area is a function of latitude
and elevation, with northerly and high-elevation
areas likely to experience fewer hot days over 90 °F
and more cold days below 0 °F than southern and
low-elevation areas (Polsky et al. 2000).
Intense Precipitaion
Precipitation has increased over the United States
by an average of 5 percent during the second half of
the 20th century (Karl et al. 2009, NOAA 2014b).
The assessment area is located in one of the wetter
regions of the country, and some areas of the
assessment area have been experiencing increases
in precipitation (e.g., the Ohio portion). Similar
studies corroborate precipitation increases up to
25 percent in the same area (Karl et al. 2009). From
1948 through 2011, the amount of precipitation
falling during a state’s largest annual storm increased
by 15 percent in Ohio, 14 percent in Maryland, and
percent in West Virginia (Madsen and Willcox
2012). The timing of precipitation events has
shifted, however, and intense precipitation events
have become more frequent while light rain events
have not changed (Groisman et al. 2012, Kunkel
et al. 2008). Throughout the Midwest during the
last 40 years (including Ohio and West Virginia),
there was a 50-percent increase in the frequency
of days with more than 4 inches of rainfall and a
40-percent increase in the frequency of days with
more than inches of rainfall (Groisman et al. 2004,
2005, 2012). A study of the eastern United States
found that heavy precipitation events are occurring
more frequently in Ohio and West Virginia; heavy
precipitation events that used to occur every
82
12 months are now occurring every 8.9 months
(Madsen and Willcox 2012). A study of the Ohio
River Basin (which includes all of the assessment
area) also observed an increase in heavy rainfall
from 1908 to 2007; the greatest increase was found
for 1-year events (25 percent), with smaller increases
for events having longer average recurrence intervals
(3 percent) (Bonnin et al. 2011). A study of trends
in return intervals from 1950 to 2007 also found
that threshold precipitation events are occurring
more frequently across the Midwest and Northeast,
suggesting that extreme rainfall events are becoming
more frequent, even where there have been no
observed increases in total precipitation (DeGaetano
and Allen 2002).
Severe Thunderstorms,
Tornadoes, and hurricanes
Storm movement across the Central Appalachians
region is generally from west to east, but strong
storms from the eastern seaboard can also influence
weather within the assessment area. The higher
Allegheny Mountains buffer the West Virginia and
Ohio portions of the assessment area, but Maryland
can be heavily influenced by these east coast storms.
Strong thunderstorms occur most frequently from
May to August within the assessment area, and there
is a general increase in frequency and expansion
northward and eastward as the season progresses
(Robinson et al. 2013). Thunderstorm frequency is
higher west of the Appalachian Mountains than in
the rest of the assessment area in April and May.
Based on long-term data from 189 to 1995, the
assessment area averaged 35 to 45 thunderstorm
days per year (Changnon 2003). A study of severe
thunderstorm observations over the eastern United
States identified an increase in thunderstorm
frequency over the last 0 years, but it is difficult
to determine whether those increases are biased by
increased accuracy in storm reporting (Robinson et
al. 2013).
ChAPTER 3: oBSERVED CLiMATE ChANGE
Tornadoes also affect the assessment area, although
less frequently than thunderstorms. Most of these
tornadoes occur within Ohio (17 tornadoes per year
on average), with occasional occurrences in West
Virginia (2) and Maryland (7) (National Weather
Service 2012). Although the number of tornadoes
observed in a year appears to be increasing, the
slightly positive trend is biased due to increased
technology and reporting success, such as the
introduction of Doppler radar technology in the
1990s (NOAA 2013a). The increase in tornado
occurrence is observed in only the weakest
tornadoes, and there is no evidence of increasing
frequency of stronger tornadoes (Kunkel et al.
2013a). Hail is often produced during tornado
weather and is more prevalent in the mountainous
panhandle of Maryland due to orographic lifting (as
moist air is forced into high elevations) and cooler
ground temperatures, which allow for less melt on
descent (Mogil and Seaman 2009).
Hurricanes tracking up the Atlantic seaboard also
affect the assessment area. From 1985 to 2009, four
major hurricanes and more than a dozen tropical
storms tracked up the eastern seaboard. As a result,
much of the assessment area has been subjected
to intense rain, hail, wind, and flooding, although
the Allegheny Mountains buffer the Ohio portion
from much of the impact (Kunkel et al. 2013c).
Not every hurricane formed in the Atlantic makes
landfall or affects the assessment area, but there
is some evidence that the strength and frequency
of hurricanes have been increasing since 1970,
and that this increase is associated with warming
sea surface temperatures (Holland and Webster
2007, Kunkel et al. 2008). Based on the average
number of hurricanes from 1981 to 2010, the 2011
hurricane season was above average, and was the
12th above-average season since 1995 (Blunden
and Arndt 2012). There is no evidence of change
in the frequency of hurricanes that make landfall
(Holland and Webster 2007, Kunkel et al. 2008).
Hurricane Isabel made landfall in 2003, followed
by Irene in 2008, and Sandy in 2013. Trends in
severe weather frequency are difficult to attribute
to changes in climate only; recent advances in
technology, population density, and social media
have contributed to increases in storm and tornado
reporting (Robinson et al. 2013). Losses from
catastrophic storms, defined as a storm producing
more than $1 million in damages, have been used
to explore trends in storm frequency and severity
across the central and northeastern United States
(Changnon 2011a, 2011b).
Windstorms
In warm months of the year, the assessment area
occasionally experiences very powerful straightline windstorms, otherwise known as derechos.
These events can result in substantial wind-throw
disturbances in forest ecosystems. A recent example
was the April 2011 storm that passed through Ohio
and the northeastern border of West Virginia on its
southwest-to-northeast track through the central
United States. This single storm produced wind
gusts of 58 to 74 miles per hour, hail, and tornadoes
(NOAA 2013c). A much larger storm caused
22 deaths and widespread damage across the eastern
United States, including the assessment area, on
June 29, 2012 (NOAA 2013b). The average annual
frequency of derechos within the assessment area
decreases from Ohio (11) to Maryland (9) (Coniglio
and Stensrud 2004). There is not enough evidence
currently available to examine trends in derecho
frequency and distribution due to limited data in the
first half of the 20th century (Peterson 2000).
83
ChAPTER 3: oBSERVED CLiMATE ChANGE
PhYSiCAL PRoCESSES
Climate and weather patterns also drive many
physical processes important for forest ecosystems.
Climate-driven factors such as snowpack and
soil frost can regulate annual phenology, nutrient
cycling, and other ecosystem dynamics. Changes
to climate-driven physical processes can result in
impacts and stress on forest ecosystems that might
not be anticipated from mean climate values alone.
This section presents a few key trends that have been
observed in the Central Appalachians and throughout
the broader region.
Flooding and Streamlow
Although floods also depend on soil saturation,
soil temperature, and drainage capabilities, floods
are primarily attributed to spring snowmelt, heavy
rainfall, tropical storms, and hurricanes. Floods can
develop slowly as the water table rises, or quickly
if large amounts of rainfall rapidly exceed moisture
thresholds. Although snowpack in the Central
Appalachians is generally short-lived, melting can
contribute substantial volume to winter and spring
peak flow and flooding (Eisenbies et al. 2007,
Kochenderfer et al. 2007). Areas with steep and
narrow terrain are more prone to flash flooding of
the smaller rivers, streams, and tributaries (Eisenbies
et al. 2007). Long-term data on flooding are difficult
to interpret because of the variety of measures
used to describe floods. Many floods originate
from small, unmonitored watersheds, and thus go
unreported (Wiley and Atkins 2010). Major regional
floods can be observed through stream gauge
measurements and are reported for the three states
within the assessment area. Sixteen major floods
have been recorded in West Virginia since 1844
(Wiley and Atkins 2010). In Maryland, 57 floods
were recorded from 180 to 2004, with at least 13
of them attributed to hurricanes (Joyce and Scott
A small stream meandering through hemlock forest. Photo by Patricia Butler, NIACS and Michigan Tech, used with permission.
84
ChAPTER 3: oBSERVED CLiMATE ChANGE
2005). In Ohio, 38 major floods were recorded from
181 to 1990, 315 minor flood events from 2000 to
2007, and a major flood in 2011 (Ohio Emergency
Management Agency 2011, Robertson et al. 2011).
Damage from floods has been increasing in the
Midwest in recent decades (Villarini et al. 2011).
A nationwide study of streamflow between 1944
and 1993 demonstrated that baseflow and median
(average) streamflow have increased at many
streams in the Midwest and Mid-Atlantic (Lins and
Slack 1999). More recent studies have confirmed
increased annual and low streamflow from 191
to 1990, at least partially due to increased storm
frequency (Groisman et al. 2004). At the same time,
maximum flow (including floods) did not change
(Lins and Slack 1999).
Several factors complicate the explanation of trends
in flood frequency. Changes in flooding frequency
are driven not only by increased precipitation
but also by changes in land cover and land use
(Groisman et al. 2004, Jones et al. 2012, Wang and
Cai 2010). In particular, human-caused land-use
change over the past century has had a considerable
influence on flooding frequency (Villarini et al.
2011). After these factors have been taken into
account, however, studied watersheds in the
Midwest still exhibited increased discharge over
the past several decades, which may be attributed to
climate change (Tomer and Schilling 2009).
Snow and Winter Storms
Cold and snowy winters are characteristic of the
Central Appalachians region, which lies between
two major storm tracks of the eastern United States
(Hartley 1999). The assessment area experiences
more snowstorms than nearby southern states,
but fewer than nearby northern and eastern states
(Changnon and Changnon 2007). Snowfall in the
Central Appalachians is influenced by many factors
including winter temperature, lake-effect weather,
and elevation. Although precipitation has been
increasing, the proportion that falls as snow has been
decreasing (Kunkel et al. 2009a, 2009b). The ratio
of snow to precipitation is strongly correlated with
mean daily temperature across the United States
(Feng and Hu 2007). As daily temperature increased
from 1949 to 2005, the proportion of precipitation
falling as snow decreased over non-lake effect
areas of Ohio and most of West Virginia (Feng and
Hu 2007). Decreasing trends in seasonal snowfall
were also observed in the central and southern
Appalachians from 193 to 1993 (Hartley 1999).
Regional trends indicate that although snowfall is
quite variable from year to year, the most recent
30 years have had fewer heavy snowfalls, but moreintense snowfalls when they do occur (Feng and Hu
2007). Snowfall has increased over the same period
in the lake-effect area of Ohio, and in the Northern
Ridge and Valley section of West Virginia and
Maryland (Feng and Hu 2007). Long-term records
from across the Great Lakes indicate that lake-effect
snow increased gradually during the 20th century,
likely due to the warming of these water bodies and
the decreasing trend in lake-ice cover.
Across the Midwest and Northeast, long-term
records have shown that ice on inland lakes is
breaking up earlier in the spring and forming later
in the fall (Benson et al. 2012). Annual ice cover on
Lake Erie has declined by half from 1973 to 2010
(Wang et al. 2012).The combined effect of these
trends is a longer ice-free period for lakes across
the region and the assessment area, including Lake
Erie, which influences climate and weather in the
assessment area.
Drought
Droughts are among the greatest stressors on
forest ecosystems, and can often lead to secondary
effects of insect and disease outbreaks on stressed
trees and increased fire risk (Maherali et al. 200).
Because droughts often affect large regions, data
are available at regional and statewide scales,
but not at finer scales. There is no evidence for
increased drought severity, frequency, or extent on
85
ChAPTER 3: oBSERVED CLiMATE ChANGE
Fall colors on the Hocking State Forest, Ohio. Photo by the Ohio Department of Natural Resources, used with permission.
average across the assessment area. The Palmer
Drought Severity Index (PDSI) is a soil moisture
index which measures meteorological drought by
calculating the cumulative departure (from the
long-term mean) in moisture supply and demand
(Dai et al. 2004). The Palmer Hydrological Drought
Index (PHDI) measures hydrological drought based
on precipitation and evaporation. Both indicators
can be important in understanding the effects on
groundwater supply. In North America and the
United States, there has been a trend toward wetter
conditions since 1950, and there is no detectable
trend for increased drought based on the PDSI
(Dai et al. 2004, Karl et al. 199). Another study
of hydrologic trends in the United States over the
last century (1915 through 2003) also observed
reduced duration and severity of droughts across the
Central Appalachians region as a result of increased
precipitation (Andreadis and Lettenmaier 200).
Statewide data (NOAA 2014d) were also explored to
examine changes in the yearly and seasonal Palmer
drought indices. A positive (wetter) trend from 1895
to 2013 was observed in West Virginia and Ohio
annually and for each season according to both
8
indices. In Maryland, the PDSI shows no increasing
or decreasing trend in annual or spring droughts,
but shows that fall and winter have been getting
wetter and summer has been getting drier (NOAA
2014d). The PHDI shows that winter and spring
have been getting wetter, whereas annual, summer,
and fall conditions have been getting slightly drier in
Maryland (NOAA 2014d).
Growing Season Length
Growing season length is often estimated as the
period between the last spring freeze and first
autumn freeze (climatological growing season), but
can also be estimated through the study of plant
phenology (biological growing season) (Linderholm
200). A large body of research indicates that the
growing season has lengthened by 10 to 20 days at
global, hemispheric, and national scales, primarily
due to an earlier onset of spring (Christidis et al.
2007, Easterling 2002, Linderholm 200, Parmesan
2007, Parmesan and Yohe 2003, Root et al. 2003,
Schwartz et al. 200b, Zhang et al. 2007). There is
evidence, however, of both positive and negative
ChAPTER 3: oBSERVED CLiMATE ChANGE
regional trends being dissolved into these broadscale averages. Several studies suggest that the
growing season is lengthening within the assessment
area, but primarily due to a later onset of fall. In fact,
a recent study exploring past trends in spring onset
dates in the Southeast, including the assessment area,
showed that spring has been occurring later by 4 to
8 days since the 1950s (Schwartz et al. 2013). This
phenomenon has been linked to the warming hole,
and specifically, to processes that promote cooling
during the winter (Meehl et al. 2012). Another study
of the Southeast and New England also found an
anomalous trend toward delayed onset of spring in
nearby Virginia (Fitzjarrald et al. 2001).
The onset of fall is also highly influenced by local
temperature changes rather than global mean
temperatures (Badeck et al. 2004). Remote sensing
of vegetation patterns is one method commonly used
to estimate the start, end, and length of the growing
season. Studies using remote sensing have found
no significant trend in the start of season, but did
find that the end of season occurred later, and the
total growing season lengthened by approximately
9 days from 1981 through 2008 (Jeong et al. 2011,
Julien and Sobrino 2009). Another study using colddegree days and satellite imagery found a correlation
between increasing midsummer temperatures and
later fall senescence, which causes autumn colors
(Dragoni and Rahman 2012). The authors also
found that end-of-season dates varied by latitude
and elevation, with earlier senescence occurring in
forests at higher latitudes and elevations (Dragoni
and Rahman 2012). For example, despite regional
trends toward later senescence from 1989 through
2008, the end of season occurs earlier in the
Appalachian range than surrounding areas (Dragoni
and Rahman 2012). Increases in the growing season
length are causing some noticeable changes in the
timing of biological activities, such as bird migration
(Box 8).
Box 8. Phenological indicators of Change
Changes in growing season length can be observed
through studies of phenology. Phenology is the
iming of recurring plant and animal life-cycle
stages, such as leaf-out and senescence, lowering,
maturaion of agricultural plants, insect emergence,
and bird migraion. A few studies examining
phenology in the Central Appalachians indicate
recent changes:
• In a survey of 270 lowering plants in
southwestern Ohio, 60 percent showed earlier
spring lowering over the period from 1976 to
2003 of about 10 to 32 days (McEwan et al.
2011). The diferences among species may be
atributed to diferences in sensiivity to climate
as a cue to begin lowering as opposed to other
indicators such as day length.
• Ten species of naive bees in the Northeast
(including the enire assessment area) have been
emerging an average of 10 days earlier over the
last 130 years, with much of the change linked to
warming trends since 1970. Bee-pollinated plants
are also blooming earlier, suggesing that these
generalist species are keeping pace with changes
in plant phenological shits (Bartomeus et al.
2011).
• The purple marin, a long-distance migratory
songbird that overwinters in the assessment
area, has been declining across North America
and Canada (Nebel et al. 2010). Populaion
declines are linked to an increasing mismatch
between spring arrival date and iming of food
availability (Fraser et al. 2013). A recent study
tracking spring migraion from the Amazon basin
to two breeding sites in Pennsylvania and Virginia
found that purple marins were unable to depart
earlier, migrate faster, or claim breeding sites
earlier in response to earlier green-up and insect
emergence.
87
ChAPTER 3: oBSERVED CLiMATE ChANGE
ChAPTER SuMMARY
Notable shifts have been observed in climate,
extreme weather events, and phenology within
the assessment area. Broad regional trends have
translated into high spatial variability across the
region. Mean and minimum annual, spring, and
summer temperatures have increased more in
the mountainous parts than in other parts of the
assessment area. Minimum temperatures have
generally increased, and maximum temperatures
have generally decreased in parts of the assessment
area. Precipitation increases were detected in the
fall season in every part of the assessment area, and
changes during other seasons differed with location.
Summer precipitation decreased in the far eastern
part of the assessment area, but remained relatively
stable elsewhere. Drought indices indicate that
the frequency and severity of droughts have not
changed. Heavy precipitation events have become
more frequent and intense. Characteristic winter
conditions such as snowfall and lake ice have been
diminishing with warmer temperatures. In addition,
the growing season has lengthened due to later onset
of fall. These trends are generally consistent with
regional, national, and global observations related to
anthropogenic climate change, but with subtle local
differences. Ecological indicators are beginning
to reflect these changes as well, as evidenced by
changing arrival of migratory birds and changing
phenology. Sources of information on historical
climate trends and ecological indicators are listed in
Box 9.
Box 9. More Historical Climate Informaion
Much more informaion on historical climate
trends and ecological indicators for the Central
Appalachians region exists than was possible to
present in this chapter. Interested readers will be
able to ind more informaion from the following
resources:
New Jersey, New York, Pennsylvania, Rhode Island,
Vermont, and West Virginia. It provides highquality climate data, derived informaion, and data
summaries for the Midwest.
Naional Informaion
State-level Informaion
The Naional Climaic Data Center (NCDC) is the
world’s largest acive archive of weather data.
The NCDC’s Climate Data Online provides free,
downloadable data from the Global Historical
Climatology Network. Please note that Web
addresses are current as of the publicaion date of
this assessment but are subject to change.
www.ncdc.noaa.gov/oa/ncdc.html
Regional Informaion
The Northeast Regional Climate Center is a
cooperaive program between the Naional Climaic
Data Center (above) and the state climate oices
serving the 12-state region of Connecicut, Delaware,
Massachusets, Maryland, Maine, New Hampshire,
88
www.stateclimate.org/regional.php?region=
northeast
State climatologists provide informaion about
current and historical trends in climate throughout
their states. Visit your state climatologist’s Web
site for more informaion about trends and climate
paterns in your paricular state:
Oice of the State Climatologist for Ohio
htp://www.geography.osu.edu/faculty/rogers/
statclim.html
West Virginia State Climate Oice & Meteorology
htp://www.marshall.edu/met/
Maryland State Climatologist Oice
htp://metosrv2.umd.edu/~climate/
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE,
EXTREMES, AND PhYSiCAL PRoCESSES
In Chapter 3, we examined how climate has changed
in the Central Appalachians region over the past
111 years, based on measurements. This chapter
examines how climate may change through the
end of this century, including changes in extreme
weather events and other climate-related processes.
General circulation models (GCMs) are used to
project future change at coarse spatial scales and
are then downscaled in order to be relevant at scales
where land management decisions are made. In some
cases, these downscaled data are then incorporated
into forest species distribution models and process
models (see Chapters 2 and 5). Chapter 2 more fully
describes the models, data sources, and methods
used to generate these downscaled projections, as
well as the inherent uncertainty in making long-term
projections. In this chapter, we focus on two climate
scenarios for the assessment area, chosen to bracket
a range of plausible climate futures. Information
related to future weather extremes and other impacts
is drawn from published research.
mean, minimum, and maximum temperatures and
total daily precipitation were downscaled to an
approximately 7.5-mile grid across the United States.
For all climate projections, two climate scenarios are
reported: GFDL A1FI and PCM B1 (see Chapter 2).
The GFDL A1FI climate scenario projects greater
changes in future temperature and precipitation than
the PCM B1 climate scenario (hereafter referred to
simply as PCM B1 and GFDL A1FI). Although both
climate scenarios are possible, GFDL A1FI matches
current trends in emissions and temperature more
closely than PCM B1 (Raupach et al. 2007). It is
possible that the future will be different from any of
the developed scenarios, and therefore it is important
to consider the range of possible climate conditions
over the coming decades rather than one particular
scenario. The 1971 through 2000 climate averages
from ClimateWizard (Girvetz et al. 2009) were
used as the baseline from which future departure
from current climate conditions was calculated (see
Chapter 3 and Appendix 2).
PRoJECTED TRENDS
iN TEMPERATuRE
AND PRECiPiTATioN
Climate projections are presented in two ways in this
chapter. In general assessment area-wide trends are
described first, followed by maps that show spatial
variation in these trends. When the assessment
area is averaged as a whole, the projections of
temperature are positive, whereas projections of
precipitation are positive and negative, depending
on the season and model. When climate data were
averaged for each grid cell within the assessment
area, groups of pixels on a map begin to show
subregional climate trends, such as warming in one
area and cooling in another (mainly the Allegheny
Mountains section; see also Box 10).
The assessment area has experienced changes in
temperature and precipitation over the past 100
years, and those changes are projected to increase
in intensity over the next 100 years. Projected
changes in temperature and precipitation within
the assessment area were examined by using a
statistically downscaled climate data set for three
30-year time periods through the end of this
century (2010 through 2039, 2040 through 209,
and 2070 through 2099) (Stoner et al. 2012). Daily
89
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Box 10. Climate Modeling in Areas of Complex Topography
Areas of complex topography, such as the Allegheny
Mountains and Northern Ridge and Valley secions
of West Virginia and Maryland, contain some
of the highest biological diversity in the world
(Hoekstra et al. 2010). Paterns of ridges, valleys,
slope, rainshadow efects, cold air pooling, and
other ine-scale processes create a complex suite
of ecological niches with various temperature
and moisture regimes which may actually provide
the assessment area with addiional resilience
to changes (Anderson and Ferree 2010). Terrain
creates various levels of decoupling between the
climate experienced at a site and the broad climate
trends for any given region (Dobrowski 2011,
Fridley 2009). Precipitaion paterns in mountainous
areas are paricularly diicult to model, owing to
the complexity of atmospheric circulaion, wind
speed, rainshadow efects, and orographic liting of
moisture to higher elevaions. Although we can use
the downscaled climate data at the regional level
to gain an understanding of broad-scale trends,
staisical downscaling oten does not capture
landscape heterogeneity seen in some porions of
the assessment area.
Although few studies have invesigated iner scale
modeling of mountain ranges in the United States,
there have been some studies that may shed
light on how downscaled climate models may be
overesimaing or underesimaing temperature
and precipitaion trends at various elevaions and
landscape posiions. A study in the Oregon Cascades,
which is prone to cold-air pooling similar to the
Allegheny Mountains, found that temperatures
Temperature
The assessment area is projected to experience
substantial warming over the 21st century, especially
for GFDL A1FI (Fig. 24). Early-century (2010
through 2039) temperature increases are projected
to be relatively small when averaged across the
90
in sheltered valley botoms are decoupled from
the free atmosphere, and consequently are
somewhat bufered from changes projected for
the whole study area (Daly et al. 2010). Modeled
warming of 4.5 °F at closely spaced sites simulated
temperature diferences of up to 10.8 °F between
low-elevaion valleys and high-elevaion ridge tops.
In a study of mountainous terrain at the Hubbard
Brook Experimental Forest in New Hampshire,
three climate models overesimated observed
precipitaion by 20 percent for the period 1979
through 2008 (Campbell et al. 2010), so that future
projected values were corrected downward by 20
percent. A study in the southern Appalachians found
that the winter northwest low-level air low is nearly
perpendicular to the southwest-northeast mountain
range, producing orographic liting and subsequent
snowfall on northwest slopes and higher elevaions,
despite warmer temperatures at lower elevaions
(Perry and Konrad 2006).
These studies suggest that there are diiculies in
accurately modeling areas with complex topography
and rapid elevaion change. Regional climate models
have not performed as well as in areas of relaive
homogeneity, and some correcion may be necessary
to account for elevaion, slope, aspect, and relaive
exposure or isolaion from the elements. Finerresoluion modeling would help idenify biases in the
data based on these factors. Unil such ine modeling
eforts can be executed, the coarse-resoluion data
sets used in this assessment can provide a broad
foundaion of plausible future climates from which
to consider the caveats above.
assessment area, with little change projected for
PCM B1 (0.8 °F) and a modest increase of 2 °F
for GFDL A1FI (Fig. 24, Table 1). Projections of
temperature do not diverge substantially for the two
future scenarios until mid-century (2040 through
209), when much larger temperature increases are
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
projected for GFDL A1FI than PCM B1 through
the end of the century. Compared to the 1971
through 2000 baseline period, the average annual
temperature at the end of the century is projected
to increase by 1.9 °F for PCM B1 and by 7.8 °F for
GFDL A1FI (Table 1). Seasonal changes follow
this pattern, with modest changes projected during
the early century, and the highest temperature
increases projected for GFDL A1FI at the end of the
century (see Appendix 3 for projected changes in
mean, minimum, and maximum temperatures during
the early, mid, and late century for all four seasons).
Table 16.—Projected changes in annual mean, minimum, and maximum temperatures and precipitaion in the
assessment area averaged over 30-year periods
Baseline
(1971-2000)a
Mean temperature (°F)
Annual
51.1
Winter (Dec-Feb)
31.2
Spring (Mar-May)
50.2
Summer (Jun-Aug)
70.1
Fall (Sep-Nov)
53.0
Minimum temperature (°F)
Annual
40.0
Winter (Dec-Feb)
21.7
Spring (Mar-May)
38.1
Summer (Jun-Aug)
58.5
Fall (Sep-Nov)
41.6
Maximum temperature (°F)
Annual
a
62.3
Winter (Dec-Feb)
40.7
Spring (Mar-May)
62.4
Summer (Jun-Aug)
81.6
Fall (Sep-Nov)
64.4
Scenario
Departure from baseline
2010-2039
2040-2069
2070-2099
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
0.8
2.0
0.7
1.6
0.3
0.8
0.9
3.2
1.4
2.5
1.5
5.3
2.1
4.1
1.3
4.4
1.4
6.9
1.5
5.5
1.9
7.8
2.1
5.5
1.8
7.1
1.8
9.4
1.7
9.0
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
0.7
1.9
0.6
1.5
0.5
1.0
0.7
2.8
1.1
2.3
1.4
5.2
2.1
4.4
1.3
4.6
1.4
6.5
0.9
5.2
1.9
7.7
2.3
5.9
1.9
7.1
1.7
9.0
1.5
8.7
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
PCM B1
GFDL A1FI
0.9
2.1
0.8
1.6
0.0
0.6
1.1
3.6
1.7
2.7
1.7
5.3
2.0
3.9
1.2
4.2
1.3
7.3
2.1
5.8
1.9
7.8
1.9
5.2
1.9
7.2
1.9
9.8
1.9
9.2
The 1971 through 2000 value is based on observed data from weather staions.
91
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
An n u a l
75
70
T e m p e ra tu re (0 F )
65
60
55
50
45
40
35
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
GFD L A 1FI Mean
G F D L A 1 F I M in im u m
G F D L A 1 F I M a xim u m
P C M B 1 Mean
P C M B 1 M in im u m
P C M B 1 M a xim u m
Figure 24.—Projected changes in annual mean, minimum, and maximum temperatures across the assessment area averaged
over 30-year periods. The 1971 through 2000 value is based on observed data from weather staions. See Appendix 3 for
projected changes by season.
Changes in mean temperature are projected to
vary greatly by season. Under PCM B1, winter is
projected to warm the most by the end of the century
(2.1 °F), followed by spring and summer (1.8 °F),
and fall (1.7 °F). For GFDL A1FI, greater increases
are projected for summer (9.4 °F) and fall (9.0 °F)
than spring (7.1 °F) and winter (5.5 °F). Maximum
temperatures are projected to increase more than
minimum temperatures for both scenarios across
nearly all seasons. Winter is the exception to this
trend, with minimum temperature projected to
increase by 2.3 °F for PCM B1 and by 5.9 °F for
GFDL A1FI by the end of the century (Table 1).
Maximum annual temperatures are projected to
change by 1.9 °F for PCM B1 and 7.8 °F for GFDL
A1FI by the end of the century.
92
These changes in temperature are projected
to differ across the assessment area (Figs. 25
through 27). For example, the Ohio portion is
projected to experience larger mean and minimum
temperature increases during winter at the end of
the century for both scenarios than other locations
in the assessment area. The Ohio portion is also
projected to experience larger end-of-century
increases in maximum temperature during summer.
This pattern holds true for early- and mid-century
projections in the Ohio portion, with the addition
that fall maximum temperature during these periods
is also projected to increase (Appendix 3). There
are also noticeable areas within the higher-elevation
Allegheny Mountains in West Virginia and Maryland
(Section M221B) that are projected to cool slightly
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Figure 25.—Projected diference in daily mean temperature at the end of the century (2070 through 2099) compared to
baseline (1971 through 2000) for two climate scenarios.
93
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Figure 26.—Projected diference in daily minimum temperature at the end of the century (2070 through 2099) compared to
baseline (1971 through 2000) for two climate scenarios.
94
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Figure 27.—Projected diference in daily maximum temperature at the end of the century (2070 through 2099) compared to
baseline (1971 through 2000) for two climate scenarios.
95
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
under PCM B1, and warm by several degrees less
than lower-elevation areas under GFDL A1FI.
The baseline climate (1971 through 2000) of this
section is consistently several degrees cooler than
surrounding areas (see Chapter 3: Fig. 15), and
temperatures at the end of the century are projected
to be several degrees cooler. A narrow strip running
parallel to the southwest to northeast ridges in
this section is projected to be 1 to 3 °F cooler for
PCM B1 at the end of the century compared to the
baseline climate. This trend is visible for mean,
minimum, and maximum temperatures through all
seasons. The pattern is also visible for GFDL A1FI,
which projects warming in this strip, but several
degrees less than surrounding areas.
Although the two climate scenarios project different
amounts of warming, they are largely in agreement
that mean, maximum, and minimum temperatures
will increase throughout much of the assessment
area both annually and in all seasons. The two
models are less in agreement about projections of
seasonal change, with PCM B1 projecting winter
temperature to increase the most (1.8 °F increase
in mean temperature) and GFDL A1FI projecting
summer and fall to increase the most (8.5 °F and
8.1 °F, respectively). See also Box 11.
Box 11. Revisiing the “Warming Hole”
In Chapter 3, we discussed the “warming hole” that
has been observed across the central United States.
Although the core of the warming hole is centered
on Midwestern states, the efect extends into the
assessment area to a lesser degree, characterized
by a reducion in summer high temperatures over
the past several decades. Will this patern coninue
into the future? If we examine only the staisically
downscaled GCM data presented in this chapter, we
might conclude that the warming hole will be gone in
the next century.
However, at least one study suggests that the large
grid-scale of GCMs fails to account for regional-scale
processes that are important contributors to the
warming hole (Liang et al. 2006). Using a dynamical
downscaling approach to compare a ine-scale
(18.6 miles) regional climate model, CMM5, with
the PCM model as an input, this study found a large
discrepancy between the downscaled projecions
and the coarse-scale PCM projecions in the central
9
United States. Although both projected an increase
in summer temperature, the downscaled CMM5
projected an increase of less than 0.5 °F, whereas
the coarse-scale PCM projected a mid-century
increase of 5.4 °F or more averaged over 10 years
(2041 through 2050). The staisically downscaled
projecions for PCM presented in this chapter
also suggest a more modest mid-century (2040
through 2069) increase of 0.5 °F in mean summer
temperature.
So what does this mean for the “warming hole”?
These results suggest that, as with past observaions,
there may coninue to be regional climate processes,
such as cumulus cloud formaion, that reduce the
amount of warming experienced during the summer
months, at least over the short term. However,
dynamical downscaling studies such as this one
remain limited, further jusifying the consideraion
of a range of potenial future climate scenarios when
preparing for future climate change.
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Precipitaion
Due to the highly variable nature of precipitation
and difficulty in modeling it, projections of
precipitation differ considerably from model to
model, and generally carry with them a higher level
of uncertainty than projections of temperature
(Kunkel et al. 2013b, 2013c; Winkler et al. 2012).
The two climate model-scenario combinations used
in this assessment describe a wide range of possible
future precipitation for the assessment area
(Figs. 28 and 29). However, other GCM and
emissions scenario combinations could project
values outside of this range. Within the assessment
area, annual precipitation is projected to increase by
2 inches for PCM B1 and only slightly (0.2 inch) for
GFDL A1FI at the end of the century (Table 17)
(see Appendix 3 for maps of projected changes in
early- and mid-century precipitation). It is more
important, however, to consider changes by season,
as the timing of increases or decreases have the most
implications for forest ecosystems. Under PCM B1,
precipitation is projected to increase in winter
(0.7 inch), spring (0.7 inch), and summer
(1.8 inches) and decrease in fall (-1.2 inches). Under
GFDL A1FI, precipitation is projected to increase
in fall (0.4 inch), winter (2.1 inches), and spring
(1.7 inches) and decrease in summer (-4.1 inches).
Notably, for GFDL A1FI, an increase of 1.7 inches
in spring precipitation is followed by a decrease of
4.1 inches in summer precipitation at the end of the
century. That represents a 13-percent increase from
baseline precipitation (Chapter 3) in spring, followed
by a 48-percent decrease in summer. These projected
summer and fall decreases in precipitation, and
their timing during the growing season, could have
important consequences for tree growth, seedling
establishment, and other forest processes that are
dependent on adequate soil moisture.
An n u a l
46
P re c ip ita tio n (in c h e s )
45
44
43
42
1971 - 2000
2010 - 2039
GFD L A 1FI
2040 - 2069
2070 - 2099
PCM B1
Figure 28.—Projected changes in annual mean precipitaion across the assessment area averaged over 30-year periods. The
1971 through 2000 value is based on observed data from weather staions. See Appendix 3 for projected changes by season.
97
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Figure 29.—Projected diference in mean precipitaion at the end of the century (2070 through 2099) compared to baseline
(1971 through 2000) for two climate scenarios.
98
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Table 17.—Projected changes in mean precipitaion in the assessment area averaged over 30-year periods
Baseline
(1971-2000)a
Departure from baseline
2040-2069
2070-2099
Scenario
2010-2039
43.1
PCM B1
GFDL A1FI
0.2
-0.3
1.1
-1.1
2.0
0.2
Winter (Dec-Feb)
9.2
PCM B1
GFDL A1FI
0.0
0.9
0.6
1.2
0.7
2.1
Spring (Mar-May)
11.5
PCM B1
GFDL A1FI
0.8
0.7
1.0
0.5
0.7
1.7
Summer (Jun-Aug)
12.7
PCM B1
GFDL A1FI
0.7
-1.1
1.4
-2.6
1.8
-4.1
9.7
PCM B1
GFDL A1FI
-1.3
-0.7
-2.0
-0.2
-1.2
0.4
Precipitaion (inches)
Annual
Fall (Sep-Nov)
a
The 1971 through 2000 value is based on observed data from weather staions.
Annual precipitation across the assessment area is
projected to increase throughout the 21st century
for PCM B1, with the rate of change increasing
after the early century time period. Under GFDL
A1FI, precipitation is projected to decrease through
mid-century before ultimately increasing slightly
at the end of the century (Fig. 28). The seasonal
precipitation trends for summer and fall exhibit
even more departure from the baseline between the
two scenarios (Appendix 3). For example, PCM B1
projects summer precipitation to increase steadily
through the end of the 21st century, but GFDL A1FI
projects summer precipitation to steadily decrease.
Projections for fall follow a similar pattern, but the
magnitude of change is less.
These changes in precipitation are projected to
vary across the assessment area (Fig. 29). Similar
to differences in past and future temperature, there
is a noticeable trend of decreased precipitation that
corresponds with the higher-elevation Allegheny
Mountains in West Virginia and Maryland (Section
M221B). The baseline climate (1971 through
2000) of this section is consistently much wetter
than surrounding areas (see Chapter 3: Fig. 15),
especially in spring and summer. Precipitation at the
end of the century, however, is projected to decrease
more than surrounding areas, by as much as 4 to
5 inches for both PCM B1 and GFDL A1FI. This
trend is visible through all seasons. Precipitation is
also projected to vary spatially by season, notably
with a projected summer increase followed by the
fall decrease for PCM B1. Under GFDL A1FI,
this sign change occurs earlier in the season, with
a projected spring increase followed by summer
decrease.
99
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
PRoJECTED TRENDS iN EXTREMES
Mean temperature and precipitation are not the only
climatic factors that can have important effects on
forest ecosystems. As outliers from the average
climate, extreme weather events are difficult to
forecast and model reliably. In general, there is
less confidence in model projections of extreme
events over the next century compared with general
temperature and precipitation changes, but recent
research is beginning to provide more evidence
for the magnitude and direction of change in many
extreme weather events across the eastern United
States (Kunkel et al. 2013a).
Extreme Temperatures
In addition to projecting mean temperatures,
downscaled daily climate data can be used to
estimate the frequency of extreme high and low
temperatures in the future. Studies of extreme
temperatures often define hot days as days hotter
than 95 °F and cold days as days colder than 32 °F.
A study of the United States projects an increase in
hot days in the next three decades (Diffenbaugh and
Ashfaq 2010). However, heat waves are difficult to
analyze regionally because a heat wave in one area
may be considered within the normal temperature
range in another area. To account for anomalies
across a broad landscape, temperature extremes are
often analyzed using the distribution of temperatures
(e.g., 95th percentile of maximum daily temperature)
or a specific threshold temperature (e.g., 95 °F).
Studies from across the Midwest and Northeast
consistently project 20 to 30 more hot days per
year by the end of the century (Diffenbaugh et al.
2005, Ebi and Meehl 2007, Gutowski et al. 2008,
Intergovernmental Panel on Climate Change [IPCC]
2012, Meehl and Tebaldi 2004, Winkler et al. 2012).
Under the A2 emissions scenario (see Chapter 2),
the West Virginia and Maryland portions of the
assessment area are projected to double their number
of hot days by 2050 (Horton et al. 2013). The
number of days above 90 °F is projected to increase
100
by 19 days in the Midwest and 2 days in the
Northeast by mid-century, and days over 100 °F are
projected to increase by 11 and 8 days, respectively
(Kunkel et al. 2013b, 2013c). Furthermore, the
hottest days that occur every 20 years are projected
to occur every other year by the end of the century
(Gutowski et al. 2008). The frequency of multi-day
heat waves is also projected to increase by 3 to
days in southeastern Ohio and northwestern West
Virginia (Diffenbaugh et al. 2005).
The frequency of cold days and cold nights in the
assessment area is projected to decrease by 12 to
15 days by the end of the century (Diffenbaugh et al.
2005). These trends are consistent with assessments
covering the entire Midwest and Northeast regions,
which projected that the assessment area could
experience 22 to 2 fewer days below 32 °F and
9 to 10 fewer days below 0 °F by the middle of the
21st century (Kunkel et al. 2013b, 2013c).
Intense Precipitaion
As described in Chapter 3, there is a clear trend
toward more heavy precipitation events in the
Midwest and Northeast (Gutowski et al. 2008,
Kunkel et al. 2008, Saunders et al. 2012). Rainfall
from these high-intensity events represents a larger
proportion of the total annual and seasonal rainfall,
meaning that the precipitation regime is becoming
more episodic. Climate models project an overall
increase in the number of heavy precipitation
events globally by the end of the century (IPCC
2007, 2012). Global model projections indicate a
potential increase in these events in the central and
northeastern United States, especially during winter
months (IPCC 2012). Future climate projections
for the contiguous United States indicate that the
Central Appalachians may experience 2 to 4 more
days of heavy (greater than 3 inches) precipitation
annually by the end of the century (2070 through
2095) (Diffenbaugh et al. 2005). The same study
projected that the frequency of dry days will increase
by 8 to 10 days annually by the end of the century.
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Multiple models originating from the Climate Model
Intercomparison Project (15 models), statistically
downscaled models (8 models), and dynamically
downscaled models (11 models) were run under a
high emissions scenario (A2) and a low scenario
to create a range of simulations for comparison of
projections of precipitation and extremes. Multiple
simulations for the Midwest (including the Ohio
portion of the assessment area) generally agree
that mid-century heavy precipitation days (greater
than 1 inch) could increase by 10 to 20 percent,
although models differ widely (Kunkel et al. 2013b).
Downscaled projections for the Northeast (including
the West Virginia and Maryland portions of the
assessment area) indicate increases of up to 30
percent in heavy precipitation events (Kunkel et al.
2013c). Within some areas in West Virginia, more
than 50 percent of climate models show increases.
Although simulations consistently project an upward
trend in extreme events, the magnitude of change
is more uncertain, reflecting the high spatial and
temporal variability in extreme precipitation data.
It is important to consider this trend in combination
with the projected changes in mean precipitation
over the 21st century. A given increase or decrease
in precipitation is unlikely to be distributed evenly
across a season or even a month. Additionally,
large-scale modeling efforts have also suggested
that climate change will increase the year-to-year
variability of precipitation across the Midwest and
Northeast (Boer 2009). Further, ecological systems
are not all equally capable of holding moisture
that comes in the form of extreme events. Areas
dominated by very coarse- or very fine-textured
or shallow soils may not have the water holding
capacity to retain moisture received during intense
rainstorms. More episodic rainfall could result in
increased risk of drought stress between rainfall
events or higher rates of runoff during rainfall
events. Landscape position will also influence the
ability of a particular location to retain moisture
from extreme events; steep slopes shed runoff faster
than flatter surfaces.
Severe Weather: Thunderstorms,
hurricanes, and Tornadoes
The frequency of strong convective storms has
increased in recent decades over the entire Midwest
region (Changnon 2011a, 2011b; Diffenbaugh
et al. 2008). Projected changes in temperature,
precipitation, and convective available potential
energy are expected to result in more frequent
days over the next century with conditions that are
favorable for severe storms (Trapp et al. 2007, 2009,
2011). Several model simulations project increases
in thunderstorm frequency within the assessment
area for both mid-range (A1B) and higher (A2)
emissions scenarios (Trapp et al. 2007, 2009). These
changes in storm-forming factors are also expected
to influence the formation of tornadoes, although a
recent synthesis report on extreme weather events
stated that “there is low confidence in projections
of small spatial-scale phenomena such as tornadoes
and hail because competing physical processes may
affect future trends and because current climate
models do not simulate such phenomena” (IPCC
2012). As the sophistication of global and regional
climate models increases, our understanding of how
patterns in hail and tornadoes may change in the
future will as well. A recent study using five model
simulations projected that the frequency of days
favorable for tornadoes rated F2 and greater will
increase, and that the peak of tornado season may
shift earlier in the season, from May to April (Lee
2012).
Projections of hurricane frequency have been
associated with too much uncertainty for identifying
a clear trend, but it is likely that the spatial
distribution of hurricanes will change (Gutowski et
al. 2008). For every 1.8 °F increase in sea surface
temperature, North Atlantic hurricanes are expected
to develop increased wind speeds (1 to 8 percent)
and core rainfall rates ( to 18 percent) (Gutowski
et al. 2008). Orographic effects of tropical storms
and hurricanes in the mountainous sections of the
assessment area also have the potential to increase
precipitation and subsequent flooding of river
channels (Sturdevant-Rees et al. 2001).
101
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
The Allegheny Mountains, home to a diverse array of high-elevaion wetlands. Photo by Patricia Butler, NIACS and Michigan
Tech, used with permission.
PhYSiCAL PRoCESSES
Information regarding how temperature and
precipitation patterns may change across the
assessment area can further be used to examine
how these changes may affect the cycling of water
in forest ecosystems. Across the globe, increases in
temperature are projected to intensify the hydrologic
cycle, leading to greater evaporative losses and
more heavy precipitation events (IPCC 2007).
By examining soil moisture, evapotranspiration,
and various drought indices, we can gain an
understanding of how these changes may affect
water availability for trees, understory plants,
102
wetlands, and rivers. In addition, examining changes
in runoff and streamflow can help us assess potential
flood risks and changes in watershed dynamics.
Flooding and Streamlow
Floods occur from a combination of hydrologic,
climatological, and biogeographical conditions.
High-intensity rainfall events are linked to both
localized flash flooding and widespread regional
floods, and their effects depend on soil saturation
and stream levels at the time of the event. Earlier
in this chapter, we discussed projected increases in
annual precipitation, and more importantly, a shift
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
towards more episodic and extreme precipitation
events. The amount of precipitation that exceeds
soil water-holding capacity is available as runoff,
which ultimately determines streamflow. Therefore,
streamflow can be used as an indicator of the
potential for increased flooding, in the absence of
more direct indicators. A study in the Mid-Atlantic
region projected that increases in temperature at
the end of the century would lead to increased
evapotranspiration and an increase in summer and
fall water deficit (Moore et al. 1997). Consequently,
mean annual streamflow was projected to decrease
across the assessment area by 21 percent, with
the most dramatic decreases occurring in the fall
and winter (Moore et al. 1997). Another study in
the Mid-Atlantic region projected that increases
in precipitation in winter and spring will result in
increased streamflow early in the year, and that
decreases in precipitation in summer will result in
decreased streamflow late in the year (Neff et al.
2000).
Snow and Winter Storms
Recent studies across much of the Midwest and
Northeast have shown that the ratio of snow to
rain is strongly correlated with daily mean air
temperature in winter (Feng and Hu 2007, Kunkel et
al. 2002). Within the assessment area, it is projected
that winter mean temperatures will increase by
2.1 °F for PCM B1 and by 5.5 °F for GFDL A1FI by
the end of the century, so that winter precipitation in
the form of rain is likely to increase.
Global models have projected decreases in snow
cover across the mid-latitudes with exceptions at
high elevation, such as the Sierra Nevada mountain
range in the western United States (Hosaka et al.
2005, Kapnick and Delworth 2013). The highest
elevations within the assessment area do not produce
similar exceptions in these broad-scale models,
which project shorter snow duration and decreased
snow-water equivalent (IPCC 2007, Lemke et al.
2007). According to two GFDL models, snowfall in
the assessment area is projected to decrease by
20 to 50 percent over the next 70 years
(Fig. 30) (Kapnick and Delworth 2013). Regional
snow cover is projected to decrease by 1.2 to
4 inches by the end of the century for a mid-range
emissions scenario (A1B; see Chapter 2) (Hosaka
et al. 2005). These are consistent with projections
of decreased snow events, snowpack, and snow
duration in the Northeast and Midwest (Campbell et
al. 2010, Hayhoe et al. 2007, Kunkel et al. 2013a).
In general, warming temperatures may lead to a
decrease in the overall frequency of ice storms
and snowstorms due to a reduction in the number
of days that are cold enough for those events to
occur. However, there is research to suggest that
snowfall in lake-effect areas may increase over the
short term if the necessary conditions are present:
reduced ice cover on the Great Lakes must result
in increased evaporation from the open water, and
winter temperatures must remain cold enough
for the movement of increased moisture over the
land surface to generate snow (Burnett et al. 2003,
Wright et al. 2013). Ice cover has declined in recent
years on both Lakes Erie and Michigan (Burns et
al. 2005, Wang et al. 2012). Projected increases in
air temperatures are expected to drive decreases in
ice cover duration and extent on the Great Lakes,
potentially allowing more winter evaporation and
lake-effect snow (Kling et al. 2003, Wright et al.
2013).
Shifts in winter precipitation and temperature are
expected to advance the timing of snowmelt runoff
earlier into the year (Hodgkins and Dudley 200).
The ability of soils to absorb this moisture will
depend on land cover, infiltration rates, and the
soil frost regime (Eisenbies et al. 2007). If soils are
able to absorb and retain more of this moisture, soil
moisture could be higher at the outset of the growing
season. If this moisture is instead lost to runoff,
103
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Figure 30.—Projected change in annual mean snowfall across the United States over the next 70 years (Kapnick and Delworth
2013).
forests in the assessment area could be more likely to
enter the growing season without sufficient moisture
to sustain them throughout the growing season.
Snow Cover and Soil Frost
The dynamics of snow and frozen soil can have
important implications for water availability at
the beginning of the growing season. Winter
temperatures are projected to increase across the
assessment area for both PCM B1 and GFDL
A1FI, especially minimum winter temperatures
(see Figures 25 through 27). Snow cover typically
insulates forest soils, so reduced snowpack
could leave the soil surface more exposed to
fluctuations in air temperature (Campbell et al.
2010). The degree of warming, and its effects on
snowpack, therefore, is likely to determine the
impacts on soil temperature, water infiltration,
and spring photosynthesis. There are currently no
published studies available that have examined this
104
relationship in the assessment area, but studies from
adjacent areas can help us understand potential
changes. A study that attempted to integrate these
complex trends at the Hubbard Brook Experimental
Forest used three climate models (Hadley, GFDL,
and PCM) for two scenarios (A1FI and B1) through
the year 2100 (Campbell et al. 2010). Four of the
six scenarios projected increases in total annual
and winter precipitation. Although there are no
projected changes in soil frost depth, and only
a slight increase in freeze-thaw events, the total
number of days of soil frost is projected to decline as
a direct result of declining snowpack (Campbell et
al. 2010). Therefore, it is likely that warmer winter
air temperatures will more than counteract the loss
of snow insulation and soil frost will generally
be reduced across the assessment area. These
projections are generally consistent with studies of
snowpack and soil frost in the Midwest (Sinha and
Cherkauer 2010).
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Drought and Soil Moisture
Changes in soil moisture are largely driven by the
balance of precipitation and evapotranspiration, and
there is some uncertainty about future precipitation
changes, especially in areas of complex topography.
Further, projections differ widely among models,
and an increase in precipitation (and also soil
moisture) is expected during the winter and spring.
Conversely, decreases are expected in summer or
fall, and late-season droughts may become more
frequent and more severe, especially when higher air
temperatures increase potential evapotranspiration
(Gutowski et al. 2008). Many model simulations
have projected an increase in summer drying in the
mid-latitudes, indicating increased risk of drought
(Gutowski et al. 2008). In a study of the northeastern
United States, the frequency of short- (1 to 3 months),
medium- (3 to months), and long-term ( months
or longer) drought was projected to increase by 3,
0.4, and 0.04 droughts, respectively, per 30-year
time interval (Hayhoe et al. 2007).
The Variable Infiltration Capacity model, used to
explore seasonal soil saturation across the United
States during 2071 through 2100, also projected
summer and fall decreases in soil moisture, with the
greatest decrease (10 percent) in the West Virginia
portion of the assessment area (Ashfaq et al. 2010).
These broad-scale trends can be useful for estimating
a range of potential changes; however, local soil
moisture responses to changes in temperature and
precipitation are likely to be highly variable within
the Central Appalachians, depending on landscape
position, normal variability in weather events, and
degree of climate change.
Evapotranspiraion
Evapotranspiration is an important indicator of
moisture availability in an ecosystem and the
amount of water available to be lost as runoff.
Increased precipitation can provide more water
available to be evaporated from the soil or transpired
by plants. Increased temperature can also drive
increases in evapotranspiration, but only as long as
there is enough water available. Projected changes in
evapotranspiration differ considerably by hydrologic
model and climate models used, and whether
changes in vegetation are also considered. A study
using a regional climate model to examine changes
across the continental United States projected an
increase in evapotranspiration across the assessment
area in summer, which was closely associated
with increased precipitation and soil moisture
(Diffenbaugh et al. 2005). Another study examining
changes averaged over 2071 through 2100 projected
increases in evapotranspiration across the assessment
area in spring (Ashfaq et al. 2010). In the summer,
the largest increases in evapotranspiration were
projected in the Allegheny Mountains. Moderate
increases during fall were projected mostly east of
the Allegheny Mountains, and there was little to no
change in evapotranspiration during winter (Ashfaq
et al. 2010).
Projections of evapotranspiration were modeled at
a finer scale by Pitchford et al. (2012) within the
mountainous Mid-Atlantic Highlands region of the
assessment area (covering all but the Ohio portion).
This study area is topographically complex, with
microclimates that are cooler and warmer than
regional averages. As temperatures increased by
1.8 and 9 °F, evapotranspiration increased by
0.2 and 1.3 inches per month, with much of the
change occurring in the summer months. These
results suggest that increasing temperatures could
reduce soil water availability.
As we will discuss in Chapters 5 and , climate
change is further projected to affect the distribution
of trees and other plant species, which could also
affect evapotranspiration on the landscape. Increases
in carbon dioxide are expected to lead to changes in
the water use efficiency of vegetation (Drake et al.
1997), but these changes are not currently accounted
for in model projections of evapotranspiration across
the region.
105
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
Growing Season Length
The assessment area has experienced shifts in the
growing season over the past century, as noted in
Chapter 3. Growing seasons are dictated by a variety
of factors, including day length, air temperatures,
soil temperatures, and dates of first and last frost
(Linderholm 200). Therefore, there are a variety
of metrics to describe how growing seasons may
continue to change for a range of climate scenarios.
A study covering the entire Midwest region
(including the Ohio portion of the assessment area)
examined the changes in dates for the last spring
frost and first fall frost by using two models (PCM
and HadCM3) for four climate scenarios (Wuebbles
and Hayhoe 2004). This study projected that the
growing season will be extended by 30 days for the
B1 emissions scenario and 70 days for the A1FI
scenarios as the last spring frost dates are projected
to shift earlier into the year at approximately the
same rate that first fall frost dates will retreat
later into the year. A study covering the Northeast
(including the West Virginia and Ohio portions of
the assessment area) examined changes in the last
spring frost and first fall frost by using multiple
models with the A2 scenario (which projects lower
greenhouse gas emissions than A1FI at mid-century)
and predicted that the freeze-free season will
increase by 19 days by 2055 (Fig. 31) (Kunkel et
al. 2013c). A similar study of the freeze-free season
in the Midwest region (including the Ohio portion
of the assessment area) projected an increase of
22 to 25 frost-free days (Fig. 31) (Kunkel et al.
2013b). How this translates into the actual length
of the growing season, as determined by leaf-out
and senescence, has not yet been examined for the
region.
Figure 31.—Projected changes in length of frost-free season across the Midwest (Kunkel et al. 2013b) and Northeast (Kunkel
et al. 2013c). Projecions from 2041 through 2070 are shown relaive to the 1980 through 2000 baseline. Projecions are the
mean of eight simulaions for the A2 scenario. Modiied from Kunkel et al. (2013b, 2013c).
10
ChAPTER 4: PRoJECTED ChANGES iN CLiMATE, EXTREMES, AND PhYSiCAL PRoCESSES
ChAPTER SuMMARY
Projected trends in annual, seasonal, and monthly
temperature (mean, minimum, and maximum)
and total precipitation indicate that the climate
will continue to change through the end of this
century. Temperatures are projected to increase
across all seasons, with extreme warming for the
high emissions scenario over the 21st century.
The “worst-case scenario” (A1FI) projects annual
temperatures that reach 8 to 10 °F higher than the
last 30 years of the 20th century. The PCM B1
scenario, despite projecting only slight increases for
other seasons, projects winter minimum temperature
to increase by 2 to 4 °F over most of the assessment
area. Precipitation is projected to increase in winter
and spring by 2 to 5 inches (depending on scenario),
leading to potential spring increases in runoff and
streamflow. Projections of precipitation differ among
climate models in summer and fall; however, higher
temperatures during those seasons mean that much
of that precipitation will contribute to increased
evapotranspiration. Changes in temperature and
precipitation are projected to lead to changes in
extreme weather events and local hydrology. There
is fairly high certainty that heavy precipitation
events will increase, snow cover will decrease, and
eventually soil frost will decrease as well. However,
more uncertainty remains with respect to changes
in tornadoes and thunderstorms, seasonal soil
moisture patterns, and flooding. In the next chapter,
we examine the ecological implications of these
anticipated changes on forest ecosystems.
107
ChAPTER 5: FuTuRE CLiMATE ChANGE
iMPACTS oN FoRESTS
Changes in climate have the potential to profoundly
affect forests of the Central Appalachians region.
Many tree species that are currently present may
fare worse with warmer temperatures and altered
precipitation patterns. Other species may do better
under these conditions, and some species not
currently present may have the potential to do well if
conditions allow them to disperse to newly suitable
areas. In addition, climate change can have indirect
effects on forests in the region by changing the
populations and dynamics of insect pests, pathogens,
invasive species, nutrient cycling, and wildfire
regimes. In this chapter, we summarize the potential
impacts of climate change on forests in the Central
Appalachians region over the next century, with an
emphasis on changes in tree species distribution and
abundance.
MoDELED PRoJECTioNS
oF FoREST ChANGE
Forest ecosystems in the assessment area may
respond to climate change in a variety of ways.
Potential changes include shifts in the spatial
distribution, abundance, and productivity of
tree species. For this assessment, we rely on a
combination of three forest impact models to
describe these potential changes: the Climate Change
Tree Atlas (DISTRIB), LINKAGES, and LANDIS
PRO (Table 18). The Tree Atlas uses statistical
techniques to model changes in suitable habitat for
individual species over broad geographic areas.
LINKAGES predicts establishment and growth of
trees based on soils and other site information and
climate. LANDIS PRO simulates changes in basal
area and trees per acre to project the abundance and
108
distribution of individual tree species. No single
model offers a comprehensive projection of future
impacts on forest ecosystems, but each tool is
valuable for a particular purpose or set of questions.
Similarities in patterns across models suggest less
uncertainty in projections than when patterns differ,
and differences in patterns provide opportunities
to better understand the nuances of ecological
responses given the strengths and limitations of
the models. For a more thorough description of the
different models, and specifically how they were
applied for this assessment, see Chapter 2.
All three research teams used the same downscaled
climate projections from two combinations of
general circulation models (GCMs) and emissions
scenarios described in detail in Chapter 4:
GFDL A1FI and PCM B1. Projected changes in
temperature and precipitation for GFDL A1FI
represent the higher end of the range of changes, and
projections for PCM B1 represent the lower end.
This consistency in the climate data used in each
modeling approach means that the forest impact
models describe potential forest changes over the
same range of plausible future climates.
These model results are most useful to describe
trends across large areas and over long time scales.
These models are not designed to deliver precise
results for individual forest stands or a particular
year in the future, despite the temptation to examine
particular data points or locations on a map. Maps
are spatially representative but not spatially exact. In
this chapter, we present model results for the end of
the 21st century. Data for intermediate time periods
are provided in Appendix 4.
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Table 18.—Overview of the three models used in this assessment
Feature
Tree Atlas
LiNKAGES
LANDiS PRo
Summary
Suitable habitat distribuion model
(DISTRIB) + supplementary informaion
(modifying factors)
Patch-level forest succession
and ecosystem dynamics
process model
Spaially dynamic
forest landscape
process model
Primary outputs for
this assessment
Area-weighted importance values and
modifying factors by species
Species establishment and
growth maps (% change)
Basal area, and trees
per acre by species
Model-scenario
combinaions
GFDL A1FI and PCM B1
Assessment area
Central Appalachians assessment area within Ohio, West Virginia, Maryland: Secions 221E and
221F in Province 221; Secions M221A, M221B, and M221C in Province M221
Resoluion
12-mile grid
0.2-acre (1/12-ha) plots
represening landforms in
subsecions
886-foot grid
Number of species
evaluated
94
23
17
Control/baseline
climate
1971 through 2000
1990 through 2009
n/a
Climate periods
evaluated
2010 through 2039, 2040 through 2069,
2070 through 2099
1990 through 2009, 2080
through 2099
2009 through 2099
Simulaion period
n/a
30 years
2009 through 2099
Compeiion, survival,
and reproducion
No
Yes
Yes
Disturbances
No (but addressed through modifying
factors)
No
Timber harvest
Tree physiology
feedbacks
No
Yes
No
Succession or
ecosystem shits
No
No
Yes
Biogeochemical
feedbacks
No
Yes
No
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Tree Atlas
Importance values of 134 eastern tree species were
modeled for potential habitat suitability in the
assessment area by using the DISTRIB model, a
component of the Tree Atlas toolset (Landscape
Change Research Group 2014). From U.S. Forest
Service Forest Inventory and Analysis (FIA) data,
the number of stems and the basal area were used
to calculate importance values for each 12.4-mile
grid cell for each tree species. For an individual grid
cell, the importance value for a species can range
from 0 (not present) to 100 (completely covering the
area). Importance values for each pixel were then
summed across the assessment area to calculate the
area-weighted importance value for a species; thus
area-weighted importance values can be greater than
100. This analysis was conducted for the assessment
area and for individual ecological sections within the
assessment area. Results for the entire assessment
area are presented in the text below. Appendix 4
contains the full set of results summarized by
ecological section. More information on Tree Atlas
methods can be found in Chapter 2.
Of the 134 species modeled, 93 currently have or
are projected to have suitable habitat in the area.
The projected changes in suitable habitat for the
93 species were calculated for the years 2070
through 2099 for the GFDL A1FI and PCM B1
scenarios and compared to present values (Table 19).
Species were categorized based upon whether the
results from the two climate scenarios projected an
increase, decrease, or no change in suitable habitat
compared to current conditions, or if the model
results were mixed. Further, some tree species that
are currently not present in the assessment area were
identified as having potential suitable habitat in the
future for one or both scenarios. See Appendix 4
for complete results from the DISTRIB model for
early (2010 through 2039), middle (2040 through
209), and end (2070 through 2099) of century time
periods. Roughly half of the tree species modeled
110
are found in every section of the assessment area,
whereas half are missing from at least one section.
Section M221B (Allegheny Mountains) contains the
highest number of species (74), and Section M221C
(Northern Cumberland Mountains) has the lowest
number of species (2). This is not an accurate
reflection of species diversity, however, because
only the most common species were modeled.
Modifying factors have also been incorporated into
the Tree Atlas to provide additional information on
potential forest change. Modifying factors include
life history traits and environmental factors that
make a species more or less able to persist in the
eastern United States (Matthews et al. 2011b).
These factors are not explicitly included in the
DISTRIB outputs, and are based on a review of
a species’ life-history traits, known stressors, and
other factors. Examples of modifying factors include
drought tolerance, dispersal ability, shade tolerance,
site specificity, and susceptibility to insect pests
and diseases. Factors are identified for a species
throughout its range and do not account for sitespecific conditions which may also influence a
species’ potential for change. For each modifying
factor, a species was scored on a scale from -3
(very negative response) through +3 (very positive
response), and further weighted by confidence and
relevance to future projected climate change. The
means of these scores were plotted to determine an
overall score of adaptability (see Appendix 4 for
detailed methods). Information on adaptability is
included in the summary of projected changes in
habitat (Table 19), where a plus (+) or minus (-) sign
after a species name indicates that certain modifying
factors could lead the species to do better or worse,
respectively, than DISTRIB model results indicate.
As an example, the species with the five highest and
five lowest adaptability scores are displayed in
Table 20. Appendix 4 contains more information
on the specific modifying factors and overall
adaptability scores for each species.
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Table 19.—Potenial changes in suitable habitat for 93 tree speciesa in the Central Appalachians region
Common name
PCM B1
GFDL A1Fi
Small Decrease
Large Decrease
Large Decrease
Small Decrease
Small Decrease
Small Decrease
Large Decrease
Large Decrease
Small Decrease
Small Decrease
Large Decrease
Exirpated
Small Decrease
Large Decrease
Exirpated
Large Decrease
Large Decrease
Large Decrease
Large Decrease
Large Decrease
No Change under Both Scenarios
American chestnut
American holly
American hornbeam
Bear oak (scrub oak)
Blackgum (+)
Cucumbertree
Mountain maple (+)
Northern pin oak (+)
Pignut hickory
Pitch pine
Serviceberry
Southern magnolia
Tamarack (naive) (-)
Yellow buckeye (-)
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
No change
increases under Both Scenarios
Biternut hickory (+)
Blackjack oak (+)
Chinkapin oak
Common persimmon (+)
Eastern redcedar
Eastern redbud
Green ash
Hackberry (+)
Honeylocust
Osage-orange (+)
Post oak (+)
Shagbark hickory
Shingle oak
Shortleaf pine
Southern red oak
Sugarberry
Sweetgum
Winged elm
Large Increase
Small Increase
Large Increase
Large Increase
Large Increase
Small Increase
Small Increase
Small Increase
Small Increase
Small Increase
Large Increase
Small Increase
Small Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
PCM B1
GFDL A1Fi
Mixed Results
Declines under Both Scenarios
Balsam ir (-)
Bigtooth aspen
Black ash (-)
Black cherry (-)
Chokecherry
Pin cherry
Quaking aspen
Red pine
Striped maple
Yellow birch
Common name
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Small Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
American basswood
No Change
American beech
No Change
American elm
No Change
Black locust
No Change
Black maple
No Change
Black oak
No Change
Black walnut
Small Increase
Black willow (-)
Small Decrease
Blue ash (-)
No Change
Boxelder (+)
No Change
Bur oak (+)
No Change
Buternut (-)
No Change
Chestnut oak (+)
No Change
Eastern cotonwood
Small Decrease
Eastern hemlock (-)
No Change
Eastern hophornbeam (+) No Change
Eastern white pine
No Change
Flowering dogwood
No Change
Loblolly pine (-)
No Change
Mockernut hickory
No Change
Northern catalpa
No Change
Northern red oak
No Change
Ohio buckeye (-)
No Change
Pawpaw
No Change
Pin oak (-)
No Change
Red maple (+)
No Change
Red mulberry
No Change
Red spruce (-)
No Change
River birch
No Change
Rock elm (-)
No Change
Sassafras
No Change
Scarlet oak
No Change
Shumard oak (+)
NA
Silver maple (+)
Small Decrease
Slippery elm
No Change
Sourwood (+)
Small Increase
Sugar maple (+)
No Change
Swamp white oak
No Change
Sweet birch
No Change
Sycamore
No Change
Table Mountain pine (+) No Change
Tulip tree
No Change
Virginia pine
Small Increase
White ash (-)
No Change
White oak (+)
No Change
Willow oak
NA
Large Decrease
Large Decrease
Small Decrease
Small Decrease
Large Decrease
Large Increase
No Change
Large Increase
Small Decrease
Small Increase
Large Increase
Exirpated
Small Decrease
Large Increase
Small Decrease
Small Increase
Large Decrease
Small Decrease
Large Increase
Small Increase
Small Increase
Small Decrease
Large Decrease
Large Decrease
Small Increase
Large Decrease
Large Increase
Large Decrease
Small Increase
Large Increase
Small Decrease
Small Decrease
Large Increase
Large Increase
Small Decrease
Small Decrease
Large Decrease
Large Decrease
Large Decrease
Small Increase
Small Increase
Large Decrease
No Change
Large Decrease
Small Increase
Large Increase
(coninued on next page)
111
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Table 19 (coninued).
Common name
PCM B1
GFDL A1F
New Habitat
NA
NA
NA
NA
New Habitat
New Habitat
New Habitat
New Habitat
New Habitat
New Suitable habitat
Black hickory
Cedar elm
Northern white-cedar
Water locust
Water oak
Species are grouped according to change classes (e.g., increase,
no change) based on the percentage change in the areaweighted importance value projected for the end of century
(2070 through 2099) for two climate-emissions scenarios.
Species with the 20 highest or 20 lowest modifying factor
scores are marked with plus (+) and minus (-) signs, respecively.
Appendix 4 contains descripions of change classes and
complete results for all 93 species for the assessment area
and for each ecological secion.
a
When examining these results, it is important to
keep in mind that model reliability is generally
higher for common species than for rare species.
FIA data also tend to undersample riparian areas
as they are usually narrow strips within an upland
matrix (Iverson et al. 2008). When model reliability
is low, less certainty exists for the model results. See
Appendix 4 for specific rankings of model reliability
for each species.
Table 20.—Species with the ive highest and ive lowest values for adapive capacity based on Tree Atlas modifying
factors
Species
Factors that afect raing
Highest adapive capacity
1. Red maple
high probability of seedling establishment, wide range of habitats, shade tolerant,
high dispersal ability
2. Boxelder
high probability of seedling establishment, shade tolerant, high dispersal ability,
wide range of temperature tolerances, drought tolerant
3. Sourwood
good light compeitor, wide range of habitats
4. Bur oak
drought tolerant, ire tolerant
5. Eastern hophornbeam
shade tolerant, wide range of temperature tolerances, wide range of habitats
Lowest adapive capacity
1. Black ash
emerald ash borer suscepibility, poor light compeitor, limited dispersal ability,
poor seedling establishment, ire intolerant, dependent on speciic hydrologic regime
2. Buternut
buternut canker, drought intolerant, ire intolerant, poor light compeitor
3. Balsam ir
spruce budworm and other insect pests, ire intolerant, drought intolerant
4. White ash
emerald ash borer, drought intolerant, suscepible to ire topkill
5. Blue ash
emerald ash borer suscepibility, limited dispersal ability, ire intolerant,
poor light compeitor, dependent on speciic hydrologic regime
112
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Decreases in Suitable habitat
For the Central Appalachians region, 10 of the 93
modeled species are projected to undergo large or
small declines in suitable habitat for the full range
of projected climate futures. Projected declines in
habitat (as measured by a ratio of potential future
importance value to current importance value) are
more severe for GFDL A1FI than PCM B1 for
most of these species. These reductions in suitable
habitat do not imply that all or most mature trees
will die or the species will be extirpated; rather,
these results indicate that these species will be
living outside their ideal climatic envelope. As a
result, trees living in less suitable habitats may have
greater susceptibility to stressors, and may also be
at greater risk of regeneration failure. Generally,
the changing climate tends to intensify or add to the
stresses that may already exist for the species and
increases susceptibility to drought, pests, diseases, or
competition from other species including invasives.
Black cherry is currently abundant within the
assessment area, but is projected to decline for both
scenarios, more so for GFDL A1FI. The nine other
species in this category are much less common
on the landscape, and are projected to lose a large
portion of suitable habitat for GFDL A1FI. Many
of the species are currently near the southern limit
of their range in the assessment area or exist as
disjunct populations. Balsam fir and red pine are
glacial relicts that are currently limited to higher
elevations in West Virginia, and the majority of these
species’ ranges are much farther north (Hessl et al.
2011, Potter et al. 2010). Black ash distribution in
the assessment area is closely tied to the Greenbrier
Limestone. Decreases in suitable habitat may be
catastrophic for these highly localized populations.
Other species are not geographically limited, and
are therefore more widespread throughout the
assessment area. Bigtooth aspen and chokecherry
are relatively widespread throughout the assessment
area, and are projected to lose all suitable habitat for
GFDL A1FI. Black ash, pin cherry, quaking aspen,
striped maple, and yellow birch are currently rare on
the landscape, and their suitable habitat is projected
to decrease substantially.
Balsam fir and black ash also have highly negative
modifying factors, suggesting that there are lifehistory traits or disturbance stressors that may cause
these species to lose even more suitable habitat
than the model results indicate. For example, the
expanding presence of emerald ash borer in the
assessment area is expected to greatly reduce the
importance of black ash in the area; its impact will
be much more than the loss that is projected to
occur from changing climatic conditions. Modifying
factors for balsam fir include susceptibility to balsam
woolly adelgid and drought.
No Change in Suitable habitat
Fourteen species are projected to undergo less than
a 20-percent change in suitable habitat for both
scenarios. American hornbeam, blackgum, and
pignut hickory are currently abundant across the
region and their habitat is not projected to decrease
or increase substantially. Blackgum has one of the
highest adaptive capacity scores, partially because of
its fire tolerance and shade tolerance, and it is likely
to do better than projected. Serviceberry, pitch pine,
cucumbertree, and yellow buckeye are less common
on the landscape, and American chestnut, tamarack,
mountain maple, scrub oak, and others are extremely
rare (Appendix 4). Tamarack and yellow buckeye
have several negative modifying factors, including
habitat specificity and susceptibility to fire, insect
pests, and drought, suggesting these species may
fare worse than projected.
Mixed Results in Suitable habitat
The model results projected different outcomes
for PCM B1 and GFDL A1FI for almost half of
the species (44 of 93). For 23 of these species,
DISTRIB projected that suitable habitat will not
change or increase for PCM B1 but will decrease
for GFDL A1FI, and one species (butternut) was
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
projected to lose all suitable habitat. Many of these
species are currently common in the assessment
area, including American beech, American elm,
black locust, black oak, chestnut oak, flowering
dogwood, northern red oak, red maple, sassafras,
sugar maple, white ash, white oak, and tulip tree.
Chestnut oak, red maple, sugar maple, and white
oak all have positive modifying factors that indicate
that the species may fare better than the models
suggest. This is particularly notable for red maple,
which has the greatest strength of positive modifying
factors among the 134 species that were assessed
across the eastern United States. White ash has
negative modifying factors due to emerald ash borer
and drought mortality and may have even greater
decreases than the model predicts.
For 17 species, DISTRIB projected that suitable
habitat will increase for GFDL A1FI while not
changing substantially for PCM B1. Black oak,
eastern hophornbeam, mockernut hickory, and white
oak are currently common in the assessment area.
The remaining species are relatively infrequent
or rare, including boxelder, bur oak, pin oak, and
sycamore. These species are more frequently
found southwest of the assessment area, and
DISTRIB results suggest that suitable habitat will
move northeast for future conditions. Boxelder,
bur oak, and white oak have positive modifying
factors, suggesting these species may do better than
projected.
The remaining six species are projected to change
for PCM B1 but have the opposite direction of
change or no change for GFDL A1FI. Eastern
cottonwood, black willow, and silver maple are
projected to lose suitable habitat for PCM B1, but
gain suitable habitat for GFDL A1FI. For sourwood,
the trend is reversed. Virginia pine and black walnut
gain suitable habitat for PCM B1, but maintain
their current relative amounts for GFDL A1FI.
These species may take advantage of increased
temperatures by colonizing habitat at higher
114
elevations that were previously too cool. Sourwood
is currently a common species in the assessment
area, frequently associated with pine and oak forests.
The positive modifying factors associated with this
species, including shade tolerance and an ability to
occupy a wide range of sites, suggest that it may fare
better than what the model projects.
increases in Suitable habitat
Suitable habitat for 18 species is projected to
increase for both models by the end of the century.
All of these species are considered rare in the
assessment area (see Appendix 4 for Tree Atlas
rules regarding rare species). Many of these species,
such as blackjack oak, chinkapin oak, common
persimmon, eastern redcedar, eastern redbud,
hackberry, honeylocust, post oak, shingle oak,
shortleaf pine, southern red oak, sweetgum, and
winged elm are close to the northern or eastern
extent of their range.
Several species common to the south of the
assessment area may become more widespread
throughout the landscape, assuming higher
regeneration success for future forest conditions.
Because many of the species projected to lose
suitable habitat are still expected to be major
components of forest ecosystems by the end of the
century, forests in the assessment area may have the
potential to contain a higher diversity of species in
the future, with a blend of southern and temperate
species.
A few species within the increase category, such
as shortleaf pine and winged elm, have negative
modifying factors, which suggest that they may be
less able to take advantage of increases in suitable
habitat. At the same time, several species have
positive modifying factors, such as bitternut hickory,
blackjack oak, common persimmon, and post
oak, and may be better able to cope with potential
changes in climate, beyond what the models suggest.
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
New Suitable habitat
The DISTRIB model projects gains in suitable
habitat for five species (black hickory, cedar elm,
northern white-cedar, water locust, and water oak)
that are currently not present in the assessment area
for GFDL A1FI. This result does not necessarily
mean that a given species will be able to migrate to
newly available habitat and colonize successfully;
it indicates instead that conditions may be suitable
for a species to occupy the site if it can establish.
Habitat fragmentation and the limited dispersal
ability of seeds could hinder the northward
movement of southern species, despite the increase
in habitat suitability (Ibáñez et al. 2008), and most
species can be expected to migrate more slowly than
their habitats will shift (Iverson et al. 2004a, 2004b).
Geographic Trends
Projected changes are not uniform across the
assessment area, and areas of suitable tree habitat
are governed by soils, moisture gradients, and other
factors in addition to climate. The geographic and
biological complexity of the Central Appalachians
region warranted a closer look at the five ecological
sections within the broader assessment area (see
Chapter 1 for a map). Furthermore, Section 221E
was split along the Ohio and West Virginia state
lines and Tree Atlas results are available for those
two smaller areas. Slightly more than half of the
species modeled currently have or are projected to
have suitable habitat in all six sections, and onequarter of the species are modeled in four or five
sections. The remaining one-quarter of the species
are modeled in three or fewer sections. Among
the species projected to have suitable habitat
across four or more sections of the assessment
area, distinct differences in climate, landform,
and other characteristics often result in a variety
of projected change classes between sections
for a single species. Species showing significant
geographic trends include: American elm, American
hornbeam, blackgum, blackjack oak, black walnut,
black willow, boxelder, butternut, chestnut oak,
cucumbertree, eastern hophornbeam, eastern white
pine, flowering dogwood, green ash, Ohio buckeye,
pignut hickory, pitch pine, sassafras, scarlet oak,
silver maple, and slippery elm. Appendix 4 shows a
comparison of model results by section.
Outputs from DISTRIB can also be visualized as
maps, such as those available online through the
Climate Change Tree Atlas Web site (www.nrs.
fs.fed.us/atlas), and these maps can provide greater
context for interpreting the projected changes in
suitable habitat. It is important to note that these
maps detect relative change on a more detailed pixel
by pixel basis rather than averaged by section within
the assessment area, as presented above. For this
assessment, the section boundaries were added to
regional Tree Atlas maps in order to help orient the
reader.
Maps for four species (chestnut oak, sugar maple,
eastern white pine, and red spruce) are shown below.
Chestnut oak is projected to retain a large amount
of suitable habitat in the assessment area for PCM
B1, whereas suitable habitat decreases more for
GFDL A1FI, with the greatest loss of suitable habitat
projected in Section 221F (Fig. 32). Under PCM
B1, chestnut oak is projected to gain new suitable
habitat in the western portion of the assessment area
(221F and 221E Ohio), with no change in the eastern
portion (Fig. 32). Sugar maple suitable habitat is
projected to decrease across the assessment area,
especially around the center of the assessment area
(221E Ohio and West Virginia, M221C) for PCM
B1, with a much greater loss of habitat projected
for GFDL A1FI (Fig. 33). Eastern white pine is
currently largely absent or of low importance value
across most of the assessment area, with declines
projected over most of the current habitat for PCM
B1, and complete loss of habitat projected across
Province 221 (221F and 221E Ohio and West
Virginia). The only suitable habitat remaining for
white pine for GFDL A1FI is in Province M221,
where suitable habitat is projected to stay the same
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Figure 32.—Modeled importance values for chestnut oak
across the assessment area and the larger geographic region
for current climate condiions (top) and projected for the
end of the century (2070 through 2099) for the PCM B1
(middle) and GFDL A1FI (botom) climate scenarios, from
the Tree Atlas model. Importance values can range from 0 to
100. An importance value of zero (light yellow) indicates that
the species is not present currently, or will not have suitable
habitat at the end of the century.
11
Figure 33.—Modeled importance values for sugar maple
across the assessment area and the larger geographic region
for current climate condiions (top) and projected for the
end of the century (2070 through 2099) for the PCM B1
(middle) and GFDL A1FI (botom) climate scenarios, from
the Tree Atlas model. Importance values can range from 0 to
100. An importance value of zero (light yellow) indicates that
the species is not present currently, or will not have suitable
habitat at the end of the century.
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
in M221B and M221C, and decrease in M221A
(Fig. 34). Red spruce is even more geographically
constrained for current climate, largely limited
to Section M221B (eastern West Virginia), and
habitat suitability is projected to decrease for GFDL
A1FI (Fig. 35). Red spruce is currently recovering
from past harvesting and fire disturbance, and
is expanding on the landscape to refill its niche
(Nowacki et al. 2009, Seidel et al. 2009). Earlycentury increases in red spruce due to succession and
planting efforts may help the species do better than
models project in the short term.
These maps should be interpreted carefully. As
mentioned above, DISTRIB results indicate only
a change in suitable habitat, not necessarily that
a given species will be able to migrate to newly
available habitat. Additionally, these results do
not incorporate the influence of modifying factors
(positive for sugar maple and chestnut oak, negative
for eastern white pine and red spruce). Suitable
habitat maps assessing the whole eastern United
States are available online through the Climate
Change Tree Atlas Web site (www.nrs.fs.fed.us/atlas)
for all the species in this assessment (see Appendix
4). As is the case for interpreting any spatial model
outputs, local knowledge of soils, landforms, and
other factors is necessary to determine if particular
sites may indeed be suitable habitat for a given
species in the future. These maps serve only as an
illustration of broad trends.
The Allegheny Mountains, home to a diverse array of high-elevaion wetlands. Photo by Patricia Butler, NIACS and Michigan
Tech, used with permission.
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Figure 34.—Modeled importance values for eastern white
pine across the assessment area and the larger geographic
region for current climate condiions (top) and projected for
the end of the century (2070 through 2099) for the PCM B1
(middle) and GFDL A1FI (botom) climate scenarios, from
the Tree Atlas model. Importance values can range from 0 to
100. An importance value of zero (light yellow) indicates that
the species is not present currently, or will not have suitable
habitat at the end of the century.
118
Figure 35.—Modeled importance values for red spruce
across the assessment area and the larger geographic region
for current climate condiions (top) and projected for the
end of the century (2070 through 2099) for the PCM B1
(middle) and GFDL A1FI (botom) climate scenarios, from
the Tree Atlas model. Importance values can range from 0 to
100. An importance value of zero (light yellow) indicates that
the species is not present currently, or will not have suitable
habitat at the end of the century.
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
LiNKAGES
The LINKAGES model was used to predict
tree growth (biomass) for 23 species within the
assessment area after 30 years of establishment
and growth on a plot from bare ground (Chapter 2)
(Wullschleger et al. 2003). Section-level estimates
were derived from the weighted average of 0.2acre plots within landforms in 2 subsections. We
report projected tree growth (biomass) for current
climate (1990 through 2009) and projected climate
(2080 through 2099) using the PCM B1 and GFDL
A1FI climate scenarios. Changes in biomass for
PCM B1 and GFDL A1FI are calculated as the
difference from projected biomass for a current
climate scenario at the end of the century (2080
through 2099). The potential change in biomass for
GFDL A1FI and PCM B1 is presented as classes of
change in maps for each species (Figs. 3 and 37;
Appendix 4).
Figure 36.—Projected changes in establishment and growth for sugar maple at the end of the century (2080 through 2099) for
two climate scenarios.
Figure 37.—Projected changes in establishment and growth for loblolly pine at the end of the century (2080 through 2099) for
two climate scenarios.
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Species establishment probabilities (SEPs) are an
important input into the LANDIS PRO model, and
were calculated by standardizing the LINKAGES
biomass projections across species on a scale from 0
to 1. Absolute and percentage changes in SEP were
calculated for the entire assessment area and are
presented in Table 21. Results for each of the five
sections within the assessment area are provided
in Appendix 4. Species establishment probabilities
reflect the ability of a species to establish and grow
on a site and can be thought of as a measure of
habitat suitability, but they do not account for the
effects of interspecific competition and disturbance.
Because SEPs are derived from the LINKAGES
estimates of biomass, the percentage change in SEPs
is largely congruent with the mapped changes in
biomass; minor differences exist for some species
due to the rescaling and rounding involved in
calculating the SEPs.
Table 21.—Projected changes in mean species establishment probability (SEP) valuesa from current climate at year
2100 across the assessment area
Species
American beech
American elm
Balsam irb
Black cherry
Blackgum
Black oak
Chestnut oak
Eastern redcedar
Eastern hemlock
Eastern white pine
Flowering dogwood
Loblolly pine
Northern red oak
Pignut hickory
Post oak
Red maple
Red spruceb
Scarlet oak
Shortleaf pine
Sugar maple
Tulip tree
White ash
White oak
a
Current climate
SEP
SEP
0.22
0.14
0.00
0.28
0.16
0.19
0.20
0.20
0.13
0.34
0.05
0.13
0.29
0.35
0.05
0.31
0.00
0.17
0.08
0.51
0.76
0.43
0.32
0.22
0.16
0.00
0.30
0.19
0.20
0.20
0.24
0.13
0.35
0.08
0.29
0.31
0.37
0.10
0.34
0.00
0.17
0.22
0.50
0.83
0.53
0.35
Future climate
PCM B1
GFDL A1Fi
% change
SEP
% change
0.0
14.3
0.0
7.1
18.8
5.3
0.0
20.0
0.0
2.9
60.0
123.1
6.9
5.7
100.0
9.7
0.0
0.0
175.0
-2.0
9.2
23.3
9.4
SEP absolute values were rounded to two decimal places.
Absolute values are small enough that percentage changes are more important.
b
120
0.02
0.18
0.00
0.30
0.21
0.13
0.12
0.26
0.01
0.04
0.10
0.51
0.18
0.36
0.17
0.37
0.00
0.04
0.35
0.05
0.86
0.41
0.35
-90.9
28.6
0.0
7.1
31.3
-31.6
-40.0
30.0
-92.3
-88.2
100.0
292.3
-37.9
2.9
240.0
19.4
0.0
-76.5
337.5
-90.2
13.2
-4.7
9.4
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Projected changes in both biomass and SEPs
represent a species’ potential growth based on
site and climate factors (Chapter 2). Both positive
and negative changes in potential growth were
consistently greater for GFDL A1FI than PCM
B1 (Table 21). Suitable habitat was projected to
decrease or be extirpated for PCM B1 for only
two species: balsam fir and red spruce. American
beech, balsam fir, eastern hemlock, eastern white
pine, red spruce, and sugar maple suitable habitat
was potentially extirpated from all or portions of
the assessment region for GFDL A1FI. Scarlet oak
potential growth was projected to decrease across the
region for GFDL A1FI. Modest to large increases in
potential growth are projected for GFDL A1FI, and
to a lesser extent for PCM B1, for American elm,
blackgum, eastern redcedar, flowering dogwood,
loblolly pine, post oak, shortleaf pine, and tulip tree.
Results for other species varied between sections,
increasing or decreasing for black cherry, black
oak, chestnut oak, northern red oak, pignut hickory,
and white ash (Appendix 5). Loblolly and shortleaf
pine are projected to have the largest increases in
potential growth, partly because their biomass and
SEP were very low for current climate and even
a small increase could double the biomass. As
mentioned above, LINKAGES results indicate only
potential growth, not necessarily that a given species
will be able to colonize newly available habitat.
Some species showing large increases in potential
growth are currently absent from the region or have
very limited distributions. It would take a long time
for them to respond (especially without planting) to
this increase in potential growth and establishment.
For example, loblolly pine is currently rare and
exists mostly in plantations within the assessment
area. Future potential growth is projected to increase
for both climate scenarios and more so for GFDL
A1FI, suggesting that habitat will become more
favorable for this southern species.
Projected changes in both biomass and SEPs do
not represent actual current or future distributions.
Furthermore, LINKAGES is not spatially dynamic
and does not simulate tree dispersal or any other
spatial interaction, such as competition, among grid
cells. Rather, this spatial interaction is examined by
using LINKAGES results as input in the LANDIS
PRO model.
LANDiS PRo
The LANDIS PRO model was used to simulate
changes in basal area (BA) and trees per acre
(TPA) for 17 species over 90 years (2009 through
2100). Basal area and number of trees per acre
were simulated for each 88-foot grid cell and
then summarized for ecological sections and the
entire assessment area. The LANDIS PRO model
used the SEPs from LINKAGES (see above) to
link tree establishment and growth to climate
and additional parameters that reflect species life
histories and landscape processes such as succession
and competition, seed dispersal, and timber harvest.
Parameters were initially based on current known
silvics for each species, and then adjusted so that
simulations using current climate produced values
for species abundance that are consistent with
FIA data and earlier growth studies in the region.
Forest management was simulated as tree harvest
on 8 to 13 percent of the forested area per decade,
with the older stands harvested first. The model
did not include wind or fire disturbance; that is,
simulations represent forests with succession and
management but without mortality from fire, wind,
insects, disease, or other disturbances. The LANDIS
PRO model differs substantially from the Climate
Change Tree Atlas and LINKAGES because it
simulates tree, stand, and landscape dynamics over
time; therefore, the composition and structure of a
pixel can be examined for any point in time in the
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
simulation. Furthermore, LANDIS PRO accounts for
natural stand dynamics in addition to climate effects
on establishment and growth and is a prediction of
actual forest composition and structure for a future
year.
Future forest composition and structure were
reported as BA and TPA for each tree species. Basal
area is the area of tree stems at breast height per
acre. High BA can be driven by many large-diameter
trees, an even greater number of small-diameter
trees, or a combination of the two. Therefore, TPA is
also included as another measure of abundance and
density, regardless of tree size (see Appendix 4 for
area graphs of BA and TPA for PCM B1 and GFDL
A1FI). A low BA with a high TPA indicates many
small trees. A high BA with a low TPA indicates a
higher proportion of large trees.
Projected change in BA and TPA is presented in
area charts for current climate (Figs. 38 and 39) and
future climate for PCM B1 and GFDL A1FI through
2100 (Appendix 4). Estimates of percentage change
in BA and TPA for current climate were calculated as
the change from observed 2009 values and represent
change due to succession and management over the
time period. Percentage change in BA and TPA for
PCM B1 and GFDL A1FI at 2040, 2070, and 2100
was calculated as the change from current climate
in the same model year (2040, 2070, or 2100) and
represents the change due to the alternative climate,
which is in addition to change due to succession and
management. Percentage change in BA and TPA at
year 2100 is presented in Table 22.
Figure 38.—Projected changes in basal area for 17 species across the assessment area under the current climate scenario.
Assessment area values were derived from the weighted average of secions. The width of the colored line represents the
relaive basal area for each species through the year 2110. For example, red maple had the highest basal area in 2010, and
basal area is projected to increase due to natural succession and management for current climate.
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Table 22.—Absolute and percentage change in basal area (BA) (square feet per acre) and trees per acre (TPA)
predicted by the LANDiS PRo model for 17 species for current and future climate scenarios for the assessment area
in year 2100
Tree species
BA
in
2009
BA by year and climate scenarioa
Current
PCM B1
BA
Change
BA
Change
in
from year
year
from current
2100
2009
2100
climateb
GFDL A1Fi
BA
Change
year
from current
2100
climateb
American beech
Black cherry
Black oak
Chestnut oak
Eastern hemlock
Eastern redcedar
Eastern white pine
Loblolly pine
Northern red oak
Pignut hickory
Red maple
Red spruce
Scarlet oak
Sugar maple
Tulip tree
White ash
White oak
5.1
7.3
6.8
5.6
1.7
0.1
1.2
0.1
6.8
2.6
16.0
1.0
4.8
6.1
6.5
2.5
5.7
4.6
4.6
1.5
4.9
1.1
0.0
1.0
0.2
2.8
2.7
30.0
0.3
0.7
8.0
10.5
2.9
8.4
4.2
4.5
1.5
4.6
1.0
0.0
0.9
0.2
3.1
2.7
30.3
0.3
0.7
7.3
10.8
2.9
8.3
Tree species
TPA
year
2009
TPA by year and climate scenarioa
Current
PCM B1
TPA
Change
TPA
Change
year
from year
year
from current
2100
2009
2100
climateb
American beech
Black cherry
Black oak
Chestnut oak
Eastern hemlock
Eastern redcedar
Eastern white pine
Loblolly pine
Northern red oak
Pignut hickory
Red maple
Red spruce
Scarlet oak
Sugar maple
Tulip tree
White ash
White oak
26.8
18.9
8.4
14.9
6.1
0.8
7.3
0.5
11.9
7.3
77.4
5.1
4.0
54.1
22.9
11.9
12.8
a
14.2
7.2
2.0
30.9
3.8
0.1
2.5
0.2
3.6
5.4
66.9
0.4
1.1
53.6
104.3
7.3
57.7
-9%
-38%
-78%
-12%
-35%
-89%
-13%
127%
-58%
5%
88%
-67%
-86%
30%
62%
18%
46%
-47%
-62%
-77%
108%
-38%
-91%
-66%
-68%
-70%
-27%
-13%
-92%
-72%
-1%
356%
-39%
349%
4.6
4.6
1.5
5.1
1.1
0.0
1.0
0.2
3.3
2.8
30.5
0.3
0.7
8.0
10.8
3.0
8.6
13.9
7.3
2.0
32.6
3.6
0.0
2.4
0.2
6.0
5.6
70.3
0.4
1.2
50.8
112.1
7.9
60.9
0%
1%
1%
3%
1%
0%
0%
0%
15%
3%
2%
0%
8%
0%
3%
4%
2%
-2%
2%
1%
5%
-6%
0%
-2%
43%
68%
4%
5%
0%
3%
-5%
8%
8%
6%
-10%
-1%
-2%
-5%
-9%
0%
-7%
0%
9%
1%
1%
0%
-2%
-9%
3%
0%
-1%
GFDL A1Fi
TPA
Change
year
from current
2100
climateb
8.1
7.3
1.8
25.0
2.0
0.1
1.3
0.3
5.2
5.6
74.5
0.2
0.9
23.4
114.8
6.8
60.8
-43%
2%
-10%
-19%
-47%
87%
-46%
81%
45%
4%
11%
0%
-17%
-56%
10%
-7%
5%
Assessment area values were derived from the weighted average of secions.
b
Change represents the diference from current climate in 2100 and represents potenial change due to climate change.
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Several notable changes are predicted for current
climate by the end of the century due to succession
and management. Changes for current climate
generally represent decreases in BA or TPA of
short-lived and relatively shade intolerant species
and increases in longer-lived, more shade tolerant,
or faster growing species. For example, eastern
redcedar, black oak, scarlet oak, black cherry, pignut
hickory, and eastern white pine are projected to
decline in BA and TPA whereas the more shadetolerant or longer lived white oak, sugar maple, and
red maple are projected to increase (Figs. 38 and 39).
The fast-growing and competitive species, such as
tulip tree, increased more in TPA than BA, indicative
of regeneration and growth after mortality.
With a few exceptions, there were small to moderate
differences in BA and TPA predicted for PCM B1
and GFDL A1FI compared to current climate by the
end of the century (Table 22). Differences tended
to be greater for GFDL A1FI than PCM B1, which
is consistent with the Tree Atlas and LINKAGES
results, and is attributed to the projections of higher
average temperatures at the end of the century. The
modest size of differences due to climate by the
year 2100, especially given the potential for change
indicated by the Tree Atlas and LINKAGES, is
partly because trees are long-lived and turnover in
species composition takes time. Species that showed
declines across the region in BA (between 2 and
10 percent) and in TPA (between 17 and 5 percent)
for GFDL A1FI by 2100 were American beech,
Figure 39.—Projected changes in trees per acre for 17 species across the assessment area for the current climate scenario.
Assessment area values were derived from the weighted average of secions. The width of the colored line represents trees
per acre for each species at various points through ime. For example, red maple had the most trees per acre in 2010, but
American beech is projected to become most abundant by the end of this century.
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
eastern hemlock, eastern white pine, scarlet oak, and
sugar maple. The LINKAGES model predicted large
decreases in potential growth for these species, for
some to zero (extirpation). Species that generally
increased in BA (up to 9 percent) and in TPA (up to
87 percent) for GFDL A1FI by the end of the century
were loblolly pine, northern red oak, red maple, and
tulip tree. The LINKAGES model also predicted
large increases in potential growth for loblolly pine
and tulip tree, and decreases or increases in northern
red oak depending on section. Simulations by
LANDIS PRO for 300 years into the future are not
presented here because they are outside the scope of
this assessment, but they show additional changes
in species abundances in the directions suggested
by the Tree Atlas and LINKAGES. Care should be
used when interpreting values of percentage change
because a large percentage change can occur for a
small absolute change in BA or TPA if the initial
values of BA or TPA were very small (Appendix 4).
Geographic Trends
The geographic and biological complexity of the
assessment area warranted a closer look at the five
ecological sections within the broader assessment
area (see Appendix 4 for complete model results).
For some species, BA or TPA are projected to
increase in some sections while decreasing in others.
For example, although northern red oak is projected
to increase within the assessment area, these
increases are largely concentrated in sections in
northern Ohio (221F), and the easternmost sections
(M221A and M221B) (Appendix 4). Although
eastern hemlock is projected to decrease across
the assessment area, most of the decrease in BA is
projected in section 221E. Likewise, much of the
decrease in basal area for chestnut oak is projected
for GFDL A1FI in 221E (decrease of 14 percent),
with no change projected in 221F and a 9-percent
increase in M221A.
Outputs from LANDIS PRO can be visualized
spatially, and can provide greater context for
interpreting the projected changes in tree volume.
Figure 40 illustrates projected changes in basal area
for northern red oak. It is important to note that
these maps detect relative change on a pixel by pixel
basis rather than averaged by section within the
assessment area, as presented above.
Figure 40.—Projected changes in basal area of northern red oak in 2100 compared to basal area in 2009. Basal area in 2100
under current climate represents the change in basal area atributable to succession and management. Basal area in 2100 for
GFDL A1FI represents the total change in basal area atributable to succession and management plus high greenhouse gas
emissions.
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
DiSCuSSioN oF MoDEL RESuLTS
The three different models used in this assessment
represent different facets of potential forest change
as a result of a changing climate. Therefore, the
ability to make comparisons between the different
models facilitates a deeper understanding of
which parts of a forest ecosystem may be most
responsive or vulnerable to change. However,
the differences between the models, in terms of
design, outputs, strengths, and weaknesses, also
make direct comparisons among model results
difficult. This section describes areas of agreement
and disagreement between the results and provides
context for how the results from multiple models can
be integrated to better understand forest change.
Agreements
The DISTRIB model used by the Tree Atlas allowed
the most species to be modeled, and the LANDIS
PRO model allowed the fewest. Therefore, only 17
species can be compared across all three models.
The LANDIS PRO model demonstrates that forests
are changing due to succession and management
even without climate change. Succession and
management will likely continue to be the most
significant drivers of change over the next century,
but actions that accelerate succession (e.g.,
management or natural disturbances) can facilitate
climate-related changes. The LANDIS PRO model
shows the beginnings of change in the directions
suggested by LINKAGES and the Tree Atlas,
but climate-related changes are too small to be
conclusive for many of the species modeled. Where
the Tree Atlas and LINKAGES are consistent with
each other, results have the most certainty.
All three models suggest that conditions for some
species (e.g., American beech, eastern hemlock,
eastern white pine, red spruce, and sugar maple)
will become unfavorable by the end of the century
for the higher climate scenario (GFDL A1FI). At the
same time, all three models suggest that conditions
12
for other species (e.g., eastern redcedar and loblolly
pine) will become more favorable by the end of the
century, especially for GFDL A1FI. Additionally,
both the Tree Atlas and LINKAGES tend to agree
that many species will remain stable or increase
for PCM B1 conditions and decrease for GFDL
A1FI. These results support the idea that GFDL
A1FI represents a future climate that is beyond
the tolerance of many species. Additionally, these
results suggest that many temperate species currently
present in the assessment area could tolerate a mild
degree of warming with corresponding increase in
growing season precipitation, as represented by the
PCM B1 scenario.
Disagreements
There do not appear to be any major discrepancies
between individual species when LANDIS PRO
and Tree Atlas results are compared, although there
are some differences. The LANDIS PRO model
projects changes (increase or decrease) in basal area
of less than 20 percent for each species, but larger
changes in trees per acre, particularly for GFDL
A1FI, suggesting that young trees on the landscape
will increase for several species. Whereas LANDIS
PRO projects northern red oak to increase for GFDL
A1FI, the DISTRIB and LINKAGES models project
small decreases in suitable habitat and potential
growth. This may be explained by LANDIS
PRO’s ability to simulate changes in tree growth
and biomass, whereas DISTRIB and LINKAGES
describe potential suitable habitat that is available to
a species. Although the amount of suitable habitat
may decline, the remaining habitat may continue
to be favorable for northern red oak, including the
regeneration of northern red oak (in the absence of
herbivory, competition, or other stressors).
For many of the species above, LINKAGES and
DISTRIB suggest great potential for landscape
change in terms of basal area and trees per acre.
However, LANDIS PRO results suggest that much
of the change in forests in the next 100 years will
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
be due to succession, management, and disturbance;
climate-related changes may take longer to manifest
because trees are long-lived and disperse slowly.
Changes in species establishment probability, and
the consequences of losing habitat suitability, may
not become evident by the end of the century,
although changes in climate may already be setting
the stage for substantial long-term changes in species
composition that may include extirpation of some
species and large expansion for others.
Limitaions
All models are simplified representations of reality,
and no model can fully consider the entire range
of ecosystem processes, stressors, interactions, and
future changes to forest ecosystems. Each model
omits processes or drivers that may critically
influence ecosystem change in the future. Future
uncertainty is not limited to climate scenarios; there
is also uncertainty associated with future human
interactions with forests. Examples of factors that
are not considered in these models are:
• Land management and policy responses to
climate change or impacts to forests
• Land-use change or forest fragmentation
• Future changes in forest industry, including
products and markets
• Changes in phenology and potential timing
mismatches for key ecosystem processes
• Responses of understory vegetation, soil
microorganisms, or soil mycorrhizal associations
• Extreme weather events, which are not captured
well in climate data or forest impact models
• Future wildfire behavior, fire suppression, and
ability to apply prescribed fire
• Novel successional pathways for current forest
ecosystems
• Major insect pests or disease agents
• Future herbivory pressure, particularly from
white-tailed deer
• Interactions among all these factors
Most of these factors could drive large changes in
forest ecosystems throughout the assessment area,
depending on how much change occurs in the future.
The potential for interactions among these factors
adds layers of complexity and uncertainty. Despite
these limitations, impact models are still the best
tools available and can simulate a range of possible
future outcomes. It is important to keep the above
limitations in mind when weighing the results from
different models and use them to inform an overall
assessment. In the following section, we draw upon
published literature to address other factors that may
influence how forest ecosystems in the assessment
area respond to climate change.
SuMMARY oF CuRRENT
SCIENTIFIC KNOWLEDGE
oN FoREST iMPACTS
The results presented above provide us with
important projections of tree species distributions
across a range of future climates, but these models
do not account for all factors that may influence
species and communities for a changing climate.
Climate change has the potential to alter the
distribution, abundance, and productivity of forests
and their associated species in a variety of ways.
These can broadly be divided into the direct effects
of temperature and precipitation on forests and the
indirect effects on forests through the alteration of
current stressors or the development of additional
stressors. For the most part, models such as the
ones described above consider only direct effects
such as average temperature and precipitation.
Changes to forest management methods and their
interaction with climate change may yield different
outcomes. It is also important to note that some of
the impacts may in fact be positive or beneficial
to native forest ecosystems. The remainder of this
chapter summarizes the current state of scientific
knowledge on additional direct and indirect effects
of climate change on forests in the assessment area
and throughout the eastern United States.
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Drought Stress and Mortality
There is evidence for an increased risk of future
drought stress in the assessment area (see
Chapter 4). Temperatures are expected to rise
over the next century, and evapotranspiration in
ecosystems is expected to increase as a result.
Moisture stress may occur when increases in
evapotranspiration are not offset by a corresponding
increase in precipitation and soil moisture. Within
the assessment area, the potential for more frequent
droughts and moisture stress during the growing
season appears to be much greater for the GFDL
A1FI scenario (Chapter 4). However, for the
milder PCM B1 scenario, warmer temperatures
may also lead to increased evapotranspiration and
physiological stress if increases in precipitation
do not correspond to temperature increases.
Additionally, there is evidence that precipitation
is more likely to occur during larger precipitation
events, which may increase the interval between
rainfall events (Diffenbaugh et al. 2005).
Drought can affect forests in many ways, including
altering ecosystem processes, reducing forest
productivity, increasing susceptibility to other
stressors, and increasing tree mortality (Dale et al.
2001). Nearly all forests are susceptible to drought.
For example, a recent study found that forests in
both wet and dry environments around the world
typically operate within a relatively narrow range of
tolerance for drought conditions (Choat et al. 2012).
Drought stress causes air bubbles to form in the
xylem of growing trees (cavitation), which reduces
the ability of trees to move water and causes reduced
productivity or mortality. Forest species from rain
forests, temperate forests, and dry woodlands all
show a similarly low threshold for resisting droughtinduced cavitation (Choat et al. 2012).
The potential effects of drought on forests will
depend upon a number of factors, including
drought duration and severity, as well as site-level
characteristics of the forest. High stand density may
128
compound susceptibility to moisture stress because
high-density stands face increased competition for
available moisture (Keyser and Brown 2014, Olano
and Palmer 2003). Additionally, drought-stressed
trees are typically more vulnerable to insect pests
and diseases (Dukes et al. 2009).
Blowdowns
Together with fire and ice, wind is a primary natural
disturbance within the assessment area (Franklin
et al. 2007, Ohio Department of Natural Resources
[ODNR] 2010b). Blowdowns from large and small
windstorms can have an important influence on
the structure and species composition of forests
(Abrams et al. 1995, Peterson 2000). Although
tornadoes are relatively infrequent, intense winds
generated from hurricanes, micro-bursts, and
other storms can cause small patches of trees to
uproot, especially on steep slopes (ODNR 2010b;
Ulbrich et al. 2008, 2009). Hurricanes affecting
the east coast can cause significant wind damage
and blowdowns as far inland as western Maryland
and West Virginia, where wind speeds can reach 50
miles per hour (Boucher et al. 2005). To date, the
amount of evidence of changes in extreme storms
in this region is rather limited (Dale et al. 2001,
Intergovernmental Panel on Climate Change [IPCC]
2012). Some model projections suggest there may
be an overall increase in the average wind speed
in the area, but models disagree on whether trends
in extreme cyclone frequency and intensity are
increasing or decreasing (IPCC 2012, Ulbrich et al.
2009). If wind speeds do increase, the species that
are most susceptible to blowdowns will likely differ
by location across the assessment area. Blowdowns
appear to disproportionately affect larger trees,
shallow-rooted species, and thinned stands (Boucher
et al. 2005, Dale et al. 2001). Sugar maple, sweet
birch, and yellow birch are generally more wind
resistant than black cherry, red maple, and tulip tree
(Peterson et al. 2013). More frequent or widespread
blowdown events can be expected to release the
understory and accelerate the transition to
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
shade-tolerant species (Abrams and Scott 1989).
This is especially the case in fire-dependent
communities where shade-tolerant understories
have developed in the absence of fire (Abrams
and Nowacki 1992, Holzmueller et al. 2012).
Gap-creating events may open up opportunities
for regeneration of intermediate shade-tolerant
species such as white oak, flowering dogwood,
and various hickory species, especially at higher
elevations (Abrams et al. 1998, Campbell et al.
2005). Blowdowns will continue to be an important
ecological process in many Central Appalachians
ecosystems, but existing scientific literature provides
no clear indication of how blowdowns will be
affected by the changing climate.
create gaps, allowing regeneration of species such as
red maple. If these events decrease or are eliminated
from the area, new recruitment opportunities from
this disturbance type may be limited.
Winter Storm Damage
Hydrology is tightly linked to the health and
function of forest ecosystems, whether through
maintenance of soil moisture during the growing
season, seasonal flooding, creating necessary
decomposition conditions, or other processes. Many
forest systems in the assessment area have particular
soil moisture requirements for the seasonality and
extent of saturation. Additionally, certain species
such as eastern cottonwood, eastern hemlock, and
red spruce have particular seedbed requirements that
are tightly linked to hydrologic conditions (Burns
and Honkala 1990, Cornett et al. 2000).
Snow and ice damage occurs occasionally across
the area, and varies substantially with topography,
elevation, exposure, and extent (ODNR 2010b).
The most common cause of ice formation is when a
winter warm front passes over much colder air. As
rain falls from the warm layer through the layer at
or below 32 °F, it becomes supercooled and able to
freeze onto any surface it encounters (Turcotte et al.
2012). Although the number of days cold enough for
snow and ice is projected to decrease, the intensity
of these events when they do occur is projected to
increase (Chapter 4). The decurrent growth habit (a
wide crown with secondary trunks emerging from
a main trunk) of many northern hardwoods makes
them more vulnerable to ice damage than trees with
a central leader (Turcotte et al. 2012). Species such
as oaks, hickories, maples, and ashes appear to be
particularly susceptible to branch and stem breakage,
whereas conical species such as spruce are less
susceptible. A study of a 2003 ice storm in Ohio
found that oaks were more likely to show dieback
than maples, and red maples were more likely to
show dieback than sugar maples (Turcotte et al.
2012). Within species, damage appears to be greater
in older, taller individuals, with higher mortality in
sawtimber size classes than in pole or sapling size
classes (Turcotte et al. 2012). These events also
Although snow and ice will likely decrease across
the area, some evidence suggests that storm events
will actually increase during the winter months
(Wang and Zhang 2008). With the projected increase
in winter temperature, these events will more likely
result in flooding and wind damage than in snow and
ice damage, suggesting winter storms will function
more like summer storms across the region.
hydrologic impacts on Forests
Climate change is likely to alter hydrologic regimes
throughout the assessment area. As discussed in
Chapters 3 and 4, heavy precipitation events have
been increasing across the assessment area over the
past century and this trend is expected to continue.
In addition to more episodic precipitation events,
future climate scenarios also project a wide possible
range of seasonal precipitation and soil moisture
(Chapter 4). Such variability may expose forest
ecosystems to greater risk of hydrologic extremes:
water-logging and flooding on one hand, and
moisture stress and drought on the other. Forests that
are accustomed to seasonal or annual variations in
water availability may be better able to tolerate this
variability.
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
A stream meandering through a small stream riparian forest. Photo by Patricia Butler, NIACS and Michigan Tech, used with
permission.
In a review of the consequences of precipitation
variability on forests, Knapp et al. (2008) proposed
that extreme precipitation events with longer
intervals between events could have positive or
negative impacts on a system, depending on its
typical state in regards to soil moisture thresholds
(Knapp et al. 2008). For example, xeric systems
(adapted to dry conditions) would generally be less
affected by dry periods because they are already
limited by moisture stress, and larger precipitation
events could recharge soil water levels, allowing for
slightly longer periods of moisture. On the other end
of the spectrum, hydric (i.e., wetland) systems are
limited by anoxia rather than soil moisture, so longer
dry periods between precipitation events would
lower the water table, allowing oxygen to reach the
roots of aquatic plants and ultimately increasing
biomass productivity. Mesic systems (adapted to
moderately moist conditions) would be the most
affected by the increasing duration and severity of
soil water stress because they are not well adapted to
prolonged dry periods. This conceptual framework
does not incorporate modiiers like soil texture and
root depth, but the general principles are useful.
130
Flooding can affect forest systems differently,
depending on the frequency and duration of loods,
and the soil, vegetation, and topographic complexity
of the landscape. In mountainous areas, loods
are generally brief and intense, with loodwaters
funneling rapidly down steep slopes and into valley
streams (Eisenbies et al. 2007, Swanson et al. 1998).
These swift, ierce loods often damage trees by
breaking stems and limbs, and scouring vegetation.
In lowland areas, loods are generally more
gradual and last longer, with longer periods of soil
saturation and less tree breakage. Extreme or very
heavy precipitation events can also have important
consequences on riparian and lowland systems
when they result in looding, which can increase
erosion and transport of nutrients, contaminants,
and pathogens (Groffman et al. 2014). Disturbances
caused by loods, drought, scouring by ice, and
river channeling often drive tree species and forest
diversity, especially in lowland and riparian forests
(Vadas and Sanger 1997).
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
Soil Erosion
Wildire
As climate change continues to intensify the
hydrologic cycle, the increase in heavy rainfall
events is projected to continue across the assessment
area. One of the potential impacts of this trend is that
soil erosion rates will increase (Nearing et al. 2004).
Soil erosion is considered one of the major threats to
the Central Appalachians region, but many studies
examining the effects of climate change on soil
erosion have focused on agricultural settings, rather
than forest ecosystems (Pennsylvania Department of
Environmental Protection [PDEP] 2012). Although
additional vegetative cover and root stabilization in
forest systems may make forests less prone to soil
erosion, not all forest soils will be equally protected.
Reductions in vegetative cover due to a variety of
climate-related impacts, such as prolonged drought,
could lead to an increase in susceptibility to erosion.
Additionally, the intensification of precipitation
changes combined with orographic effects may
increase risk or severity of erosion in mountainous
areas (Beniston 2003, Sturdevant-Rees et al. 2001).
Wildfire was historically an important driver for
some forest ecosystems in the assessment area,
although it has been largely suppressed since
the 1930s. In contrast to the large wildfires that
occur periodically in the western United States,
contemporary wildfire events in the eastern
United States consist of numerous small fires in
the wildland-urban interface (Peters et al. 2013).
Development and fragmentation in the form of high
housing density are the biggest source of ignition,
but access to the surrounding infrastructure allows
wildfires to be extinguished relatively quickly (Bar
Massada et al. 2009). Ignitions are caused primarily
by humans and less frequently by lightning (National
Interagency Fire Center 2013). The conditions
responsible for wildfire behavior are the result of
weather, topography, and fuels (Moritz et al. 2012).
Climate can directly affect the frequency, size, and
severity of fires, and indirectly affect fire regimes
through effects on vegetation vigor, structure, and
composition (Moritz et al. 2012, Sommers et al.
2011).
Soil erosion is also closely correlated with
precipitation. One study estimates that for every
1-percent increase in rainfall, runoff could increase
by 2 percent, and erosion could increase by 1.7
percent (Nearing et al. 2004). Another study
examined changes in erosivity across the United
States at a very large grid scale and found that
erosion may increase or decrease in the assessment
area depending on the climate model used (Nearing
2001). This study looked only at broad-scale
changes in precipitation, and does not account for
other factors that may affect the vulnerability of soil
to erosion such as vegetation cover, slope, or soil
type. Reductions in biomass and vegetative cover
due to climate change impacts or land-use changes
(e.g., forest roads) could also lead to an increase in
erosion susceptibility (Nearing 2001).
Invasive species may also interact with climate to
increase the frequency, intensity, or length of the fire
season (Brooks and Lusk 2008). Invasive shrubs and
herbs may increase the density of the understory,
thereby increasing fuel. On the other hand, many
invasive shrubs and herbs begin growing earlier in
spring than native plants. This early green-up may
reduce the flammability of fire-adapted communities
during the spring fire season (Brooks and Lusk
2008). Invasive pests can also interact with climate
and wildfire by altering forest fuels and forest
structure (Ehrenfeld 2010, Krist et al. 2007, Lovett
et al. 200, Szlavecz et al. 2010).
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
An analysis of fire probability across the globe using
1 downscaled climate models found low agreement
among projections of climate change effects on fire
probability in the central United States in the near
term (2010 to 2039), but most models projected
an increase in wildfire probability by the end of
the century (2070 to 2099) (Moritz et al. 2012).
This agreement is particularly high for temperate
coniferous forests and temperate broadleaf and
mixed forests, where fire probability models were
most sensitive to mean temperature of the warmest
month. If temperature and evapotranspiration
increase drying of the forest floor in spring, amplify
the effects of declining precipitation, or overwhelm
modest precipitation increases, the annual area
burned and length of the fire season will likely
increase. Projected increases in lightning-producing
convective storms may also increase ignition
frequency (Sommers et al. 2011). Another global
study using a sensitive model and a high emissions
scenario projected increased fire potential across the
United States, including the assessment area (Liu
et al. 2010). Duration of the fire season is projected
to lengthen by several months by the end of the
century, primarily due to warming (Liu et al. 2010).
How a change in fire risk across the region translates
to effects at local scales in forests of the assessment
area also depends on land use and management
decisions. Fire suppression has already been
linked with mesophication in eastern forests, and
fire management is expected to continue to drive
vegetation and succession in the future (Nowacki
and Abrams 2008). To understand how climate
change may interact with wildfire in the United
States, model simulations of vegetation cover
types were conducted for high and low emissions
scenarios (A2 and B2; see Chapter 2) with wildfire
suppressed and unsuppressed for the period 2070
through 2099 (Lenihan et al. 2008). Under both
suppressed and unsuppressed wildfire, the range of
temperate deciduous forest across the eastern United
States was projected to shift northward, with a
132
large loss of cool mixed forest. Under unsuppressed
wildfire, some forest just outside the assessment area
in Ohio was projected to transition to a woodland
or savanna type, and there is potential that existing
woodlands and savannas may expand where they
do occur (Lenihan et al. 2008). Fire suppression
does not allow this expansion; cool mixed forest
is projected to be largely replaced by temperate
deciduous forest as both biomes shift northward.
Many aspects of the fire regime within the
assessment area will likely be affected by changes
in climate, with response to climate change varying
over time and space. Dry-mesic oak, dry oak and
pine-oak, and dry calcareous forests are often tied
to wildfire dynamics, but fire could also become
an increasing source of disturbance in other forest
types if climatic shifts over the 21st century result in
different fire behavior. Forest ecosystems adapted to
dry habitat conditions (e.g., oak, pine, and hickory)
may be the most likely areas to burn. Forest systems
adapted to habitats with abundant moisture (e.g.,
northern hardwoods), and those reliant on ground
seepage at higher elevations may be able to better
compensate for increased evapotranspiration and
higher temperatures. However, even these systems
may be more likely to burn if projected temperature
increases result in drier habitat conditions. Fire
effects on nutrient availability depend not only
on species composition but also on the intensity
and duration of the fires (Certini 2005). Lowintensity fire can release nutrients, but higher fire
temperatures may result in mineralization and
volatilization, especially on acidic soils, which
dominate most of the higher elevation portion of
the assessment area (Gray and Dighton 200).
A watershed-scale study of prescribed fire in
southeastern Ohio found that periodic low-intensity
prescribed fire can return nutrient cycling and
microbial activity to presettlement levels, which
can restore ecosystem functions of mixed oak
forests (Boerner 200). Authors of a review paper
on climate and wildfire conclude that fire-related
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
impacts may be more important to some ecosystems
than the direct effects of climate change on species
fitness and migration (Sommers et al. 2011). Fire
could have a greater influence because it can be a
catalyst for change in vegetation, perhaps prompting
more rapid change than would be expected based
only on the changes in temperature and moisture
availability. As with wind disturbances, the potential
exists for novel successional pathways after wildfire
if climatic conditions, seed sources, or management
decisions favor different forest types.
increases in Carbon Dioxide
In addition to effects on climate, carbon dioxide
itself can affect plant productivity and species
composition. Elevated carbon dioxide may enhance
growth and water use efficiency of some species
(Ainsworth and Rogers 2007, Norby et al. 2005),
potentially offsetting the effects of drier growing
seasons. This is commonly called “carbon dioxide
fertilization.” There is already some evidence for
increased forest growth in the eastern United States
(Cole et al. 2010, McMahon et al. 2010, Pan et al.
2009), but it remains unclear if long-term enhanced
growth can be sustained (Bonan 2008, Foster et
al. 2010). Nutrient and water availability, ozone
pollution, and tree age and size all play major roles
in the ability of trees to capitalize on carbon dioxide
fertilization (Ainsworth and Long 2005). Ecosystem
community shifts may take place as some species are
genetically better able to take advantage of carbon
dioxide fertilization than others (Souza et al. 2010).
Some models account for changes in carbon dioxide,
but these models tend to focus on nutrient cycling
and general vegetation types, and not specific
species (Lenihan et al. 2008, Ollinger et al. 2008).
Therefore, this assessment is not able to combine
the effects of carbon dioxide fertilization with the
effects of temperature and precipitation on particular
species.
Changes in Nutrient Cycling
and Acid Deposiion
As air temperatures warm and precipitation patterns
change, the way nutrients are cycled between plants,
soils, and the atmosphere may also change. The
long-term effects of acid deposition have an added
effect that makes this cycle more complex and hard
to predict in the future. Increases in droughts and
floods, changes in phenology, and the interaction
among these factors can also impair nutrient cycling
and the availability of nitrogen to trees and other
vegetation (Rennenberg et al. 2009).
Decomposition of vegetation is carried out primarily
by enzymes released from bacteria and fungi. These
enzymes are sensitive to changes in temperature,
and thus there is generally a positive effect of
temperature on the rate of enzymatic activity as long
as moisture is also sufficient (Brzostek et al. 2012,
Finzi et al. 200, Rustad et al. 2001). A number of
studies have examined the effects of extended dry
periods followed by moisture pulses on nutrient
cycling (Borken and Matzner 2009). Although
these moisture pulses do lead to a flush of mineral
nitrogen, it is not sufficient to compensate for the
lack of microbial activity during dry periods. Thus,
an increase in wet-dry cycles appears to lead to a
reduction in nutrient availability for trees.
Although warmer temperatures have the potential
to increase enzymatic activity and nutrient cycling,
acidification will remain an important consideration.
Anthropogenic emissions of nitrogen and sulfur have
increased over the last century, peaking in the 1970s.
These emissions undergo chemical transformations
that produce nitrates and sulfates, which are
eventually deposited on the ground (Elliott et al.
2013). These sulfur and nitrogen compounds are
also deposited at high concentrations through rain
and snow in the eastern United States, particularly
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
in high-elevation sites (Pardo et al. 2011). In forest
ecosystems, hydrogen ions associated with nitrogen
and sulfur deposition replace nutrient base cations of
calcium, magnesium, and potassium, depleting these
nutrients and allowing them to leach into drainage
waters. At the same time, toxic cations of aluminum
are mobilized, and the combined effects of nutrient
depletion and increased toxicity have been proven
to reduce the health and productivity of forests and
streams through acidification (Elliott et al. 2013,
Long et al. 2013, Schaberg et al. 200). Nitrogen
saturation has also been shown to reduce carbon
allocation to plant roots and mycorrhizae (Pardo
et al. 2011). Acid deposition has likely contributed
to the increased susceptibility of forests to drought
and insect attack, and is expected to contribute
to reduced ability to withstand climate changes
(Friedland et al. 1984, McNulty and Boggs 2010,
Pardo et al. 2011).
Researchers simulating the effects of nitrogen and
sulfur deposition on wilderness areas in North
Carolina found that even with dramatic reductions
in acid deposition, ecosystems will take decades
to recover from the effects of acidification (Elliott
et al. 2013). Future levels of nitrogen and sulfur
deposition can be controlled through efforts to
significantly reduce air pollution by 204 (e.g.,
through the Clean Air Act amendments of 1977). In
the interim, projected increases in winter and spring
precipitation could facilitate the deposition of air
pollutants. The effects of climate change on nutrient
cycling will likely be overshadowed by the impacts
of nitrogen and sulfur deposition in the assessment
area over the next 50 years or longer.
space, water, nutrients, and light (Brown and Peet
2003, Dukes et al. 2009). Wetland and riparian
areas are particularly susceptible to nonnative plant
invasion, partially due to passive seed dispersal via
surface waters (Nilsson et al. 2010). Invasive species
in riparian areas are likely better competitors for
nutrient pulses supplied by runoff (PDEP 2004).
Some of the most prolific riparian invasives are the
mile-a-minute vine, purple loosestrife, Japanese
knotweed, common reed, Japanese stiltgrass, and
reed canarygrass.
Many invasive species that currently threaten forests
in the Central Appalachians region may benefit
directly from projected climate change or benefit
from the slow response of native species (Rebbeck
2012). Increases in carbon dioxide have been shown
to have positive effects on growth for many plant
species, including some of the most invasive weeds
in the United States (Ziska 2003). Experiments with
carbon dioxide fertilization on kudzu seedlings have
indicated increased growth, increased competition
with native species, and range expansion (Sasek and
Strain 1988, 1989). Models have also projected that
increased carbon dioxide emissions and subsequent
warmer winter temperatures will likely expand the
Invasive Plant Species
As described in Chapter 1, nonnative invasive
species are a major threat to all forest ecosystems
across the eastern United States. Many invasives are
able to establish rapidly following a disturbance, and
are able to outcompete native vegetation for growing
134
Naive grasses and forbs, dominant plants in this lat, wet
area. Photo by Patricia Butler, NIACS and Michigan Tech,
used with permission.
ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
northern ranges of bush honeysuckle (Sasek and
Strain 1990), privet, kudzu, and cogongrass (Bradley
et al. 2010). Cogongrass in the southeastern United
States has contributed to altered fire regimes and
is expected to advance northward with warmer
temperatures (Lippincott 2000). Some invasive
species are tolerant of drought and fire, and may
be at an even greater advantage for future climate
conditions. Ailanthus and bush (amur) honeysuckle
are woody invasives that currently have negative
impacts on forests across the assessment area
(Hutchinson and Vankat 1998). In addition to
directly outcompeting native oak-hickory trees
by rapid growth, ailanthus and bush honeysuckle
have allelopathic effects on soils, exuding a toxin
that discourages the growth of other plants (ODNR
2011b, Williams 2005). Other species, such as garlic
mustard and Japanese stiltgrass, are not particularly
drought-tolerant, but their persistent seed banks
enable them to recover in wetter years (Fryer 2011,
Munger 2001).
Milder winters may allow some invasive plant
species to survive farther north than they had
previously (Dukes et al. 2009). For example,
kudzu is a drought-tolerant invasive vine that has
invaded forests in the southeastern United States
and has already appeared within the assessment
area (Munger 2002). The northern distribution of
kudzu is limited by cold winter temperatures, and
models have projected that warmer temperatures will
result in expansion northward (Bradley et al. 2010).
Chinese and European privet are invasive flowering
shrubs that crowd out native species and form dense
thickets. Habitat models project increased risk
for privet invasion into Ohio, West Virginia, and
Maryland by the end of the century (Bradley et al.
2010).
insect Pests and Pathogens
Warmer temperatures, moisture deficit, and
compounding stressors may increase the
susceptibility of trees to insect pests and pathogens
(Weed et al. 2013). Warmer winter temperatures
may also result in increased abundance of pests
and pathogens that are currently present in the
assessment area. For example, hemlock woolly
adelgid populations are currently limited by low
winter temperatures and freeze-thaw cycles, and
populations of hemlock woolly adelgid have
increased or expanded northward during mild
winters (Pennsylvania Department of Conservation
and Natural Resources 2013). The emerald ash borer,
currently devastating populations of ash species, has
been observed to produce more generations under
warmer conditions (DeSantis et al. 2013, Venette
and Abrahamson 2010, Wei et al. 2007). Other pest
outbreaks, including those of native species (e.g.,
forest tent caterpillar and spruce budworm), are
more common when trees are stressed by factors
such as drought (Babin-Fenske and Anand 2011,
Gray 2008). The interacting effects of drought
and increased pests and pathogens may result in
increased risk of oak decline, which is largely
driven by insect pests and pathogens predisposed
to invasion in drought conditions (Clatterbuck and
Kauffman 200, McConnell and Balci 2014).
There is evidence that other species may be
disadvantaged by climate change; for example, the
hatching of gypsy moth eggs is dependent on the
budburst of host trees. Changes in phenology could
result in starvation if the eggs hatch before budburst
(Ward and Masters 2007). Tree pathogens, such as
the fungus Armillaria mellea, can also potentially
increase in abundance and range, and may result in
increased disease that stresses or kills forest trees.
Armillaria populations will likely increase with
increasing temperatures, and become a more severe
threat during drought periods, when host trees are
more susceptible to root diseases (Kliejunas 2011).
Warmer temperatures will also increase the
susceptibility of tree species to pests and diseases
that are not currently a problem in the assessment
area (Logan et al. 2003). Projections of gypsy
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ChAPTER 5: FuTuRE CLiMATE ChANGE iMPACTS oN FoRESTS
moth population dynamics for a changing climate
suggest substantial increases in the probability of
establishment in the coming decades (Logan et
al. 2003). Oak species that would otherwise do
well in a changing climate could consequently
be at risk. In addition, future northward range
expansion attributed to warming temperatures has
been projected for southern pine beetle (Ungerer
et al. 1999). A recent outbreak of southern pine
beetle in New Jersey has already been attributed to
warmer temperatures (Weed et al. 2013). Southern
pine beetle could become a threat if shortleaf pine
expands in the region.
Efects of Vertebrate Species
Herbivory, seed predation, and disturbance by
vertebrates can be important stressors in the Central
Appalachians region. Currently, little is known
about how these factors could be affected by climate
change. Deer overbrowsing and seed predation
may reduce the overall success of species that are
otherwise projected to do well under future climate
change (Ibáñez et al. 2008). For example, white
oak is projected to increase in habitat suitability
and basal area, but the models mentioned earlier
in this chapter do not account for the herbivory of
young oak regeneration by deer. Currently, there
is little evidence to indicate how deer and other
vertebrate species will respond to climate change in
the assessment area. An analysis of climate change
impacts on white-tailed deer in Wisconsin suggests
that deer in that area will likely be subject to a
mixture of positive impacts from milder winters
coupled with negative impacts from increased
disease outbreaks (Wisconsin Initiative on Climate
Change Impacts 2011). How these two factors may
influence deer populations in Ohio, West Virginia,
and Maryland remains unknown.
13
ChAPTER SuMMARY
Although models are useful for exploring
potential future changes, all models are simplified
representations of reality, and no model can fully
consider the entire range of ecosystem processes,
stressors, interactions, and future changes to
forest ecosystems. The DISTRIB (Tree Atlas),
LINKAGES, and LANDIS PRO models suggest
that conditions for some species (e.g., American
beech, eastern hemlock, eastern white pine, red
spruce, and sugar maple) will become unfavorable
by the end of the century for GFDL A1FI. At the
same time, all three models suggest that conditions
for other species (e.g., eastern redcedar and loblolly
pine) will become more favorable by the end of the
century, especially for GFDL A1FI. Additionally,
the Tree Atlas and LINKAGES tend to agree that
many species will remain stable or increase for PCM
B1 and decrease for GFDL A1FI. These results
support the idea that GFDL A1FI future climate is
beyond the tolerance of many species, but that many
species could tolerate a mild degree of warming
with a corresponding increase in growing season
precipitation, as represented by PCM B1.
Generally, the changing climate tends to intensify
the stressors that may already exist for many species
and increases susceptibility to drought, pests,
diseases, or competition from other species. It is the
interaction among all these factors that will drive
the response of forests to climate change. All of
these factors need to be taken into account when
evaluating the vulnerability of Central Appalachians
forests to climate change. The vulnerability of nine
forest ecosystems is described in Chapter .
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Changes in species distribution and abundance due
to climate change can have important implications
for the habitats in which those species live, leading
to shifts in community composition and changes
in ecosystem processes (Janetos et al. 2008, Vose
et al. 2012). In addition, climate change itself can
alter ecosystem drivers and exacerbate or ameliorate
current stressors (Janetos et al. 2008, Vose et al.
2012). This chapter describes the climate change
vulnerability of nine major forest ecosystems in
the Central Appalachians assessment area (see
Chapter 1 for a description of the nine forest
ecosystems). Vulnerability is the susceptibility
of an ecosystem to the adverse effects of climate
change (Intergovernmental Panel on Climate Change
[IPCC] 2007). It is a function of the potential
impacts (a combination of exposure and sensitivity)
to an ecosystem and the adaptive capacity of the
ecosystem to tolerate those impacts (Fig. 41). We
consider a forest ecosystem to be vulnerable if it
is at risk for no longer being recognizable as that
ecosystem, or if the ecosystem is anticipated to
suffer substantial declines in health or productivity.
We considered the vulnerability of an ecosystem
to climate change independent of the economic or
social values associated with the ecosystem, even
though forest management, land-use changes, and
human population pressures can have dramatic
and immediate effects on ecosystems. The ultimate
decision of whether to use resources to try to
conserve a vulnerable ecosystem or allow it to shift
to an alternate state will depend on the individual
objectives and resources of land management
organizations.
This chapter is organized into two sections. First,
we present an overall synthesis of potential climate
impacts on forest ecosystems, organized according
to drivers and stressors, ecosystem impacts, and
factors that influence adaptive capacity. This
synthesis is based on the current scientific consensus
of published literature (Chapters 4 and 5). In the
second section, we present individual vulnerability
determinations for the nine forest ecosystems
considered in this assessment.
Figure 41.—Key components of vulnerability, illustraing
the relaionship among exposure, sensiivity, and adapive
capacity. Adapted from Glick et al. (2011).
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
A SYNThESiS oF CLiMATE ChANGE
iMPACTS oN FoREST ECoSYSTEMS
Potential impacts are the direct and indirect
consequences of climate change on individual
ecosystems. Impacts are a function of an ecosystem’s
exposure to climate change and its sensitivity to
those changes. Impacts could be beneficial to an
ecosystem if the changes result in improved health
or productivity, occupation of an expanded area,
or a tendency to maintain the current identity of
the ecosystem. Negative potential impacts would
include declining health and productivity, reduced
area occupied, or a composition shift that leads to a
substantially different identity of the ecosystem.
Throughout this chapter, statements about potential
impacts and adaptive capacity factors are qualified
with a confidence statement. These confidence
statements are formatted according to a confidence
determination diagram from the IPCC’s recent
guidance for authors (Mastrandrea et al. 2010)
(Fig. 42). Confidence was determined by gauging
both the amount of available evidence and the
level of agreement among that evidence. Evidence
was robust when multiple lines of evidence were
available in addition to an established theoretical
understanding to support the vulnerability
determination. Agreement refers to the agreement
among the available sources of evidence, not among
authors of this assessment. If theories, observations,
and models tended to suggest similar outcomes,
the sum of the evidence resulted in a high level of
agreement.
Potenial Impacts of Climate Change
on Drivers and Stressors
Many physical and biological factors contribute to
the current state of forest ecosystems in the Central
138
Figure 42.—Conidence determinaion diagram used in the
assessment. Adapted from Mastrandrea et al. (2010).
Appalachians region. Some of these factors serve
as drivers, or defining features that determine the
identity of an ecosystem. Other factors can serve
as stressors, reducing the health, productivity, and
integrity of specific ecosystems. Many factors, such
as flooding or fire, may be drivers in one ecosystem
and stressors in another.
Temperatures will increase (robust evidence, high
agreement). All downscaled climate models project
that average temperatures will increase across much
of the assessment area.
A large amount of evidence from across the
globe shows that average temperatures have
been increasing and will continue to increase due
to human activities (Chapter 2). Temperatures
across the assessment area have already been
changing over the last century (Chapter 3), and
temperature increases are projected even under the
most conservative climate scenario, with dramatic
increases projected under the high climate scenario
(Chapter 4).
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Growing seasons will get longer (robust evidence,
high agreement). There is high agreement among
evidence that projected temperature increases will
continue the current trend of longer growing seasons
in the assessment area.
Evidence at both global and regional scales indicates
that growing seasons have been getting longer, and
this trend is likely to become even more pronounced
over the next century (Chapters 3 and 4). Longer
growing seasons have the potential to affect the
timing and duration of ecosystem and physiological
processes across the region (Dragoni and Rahman
2012). As seasons shift so that spring arrives earlier
and fall extends later into the year, plant species
may respond to changes in temperature regimes
with changes in the timing of leaf-out, reproductive
maturation, and other developmental processes
(Schwartz et al. 200a, Walther et al. 2002), and
some of these changes have already been observed
(McEwan et al. 2011). Longer growing seasons,
especially those that are extended in the fall, could
also result in greater growth and productivity of
trees and other vegetation, but only if balanced by
available water and nutrients (Chapter 5). Longer
growing seasons could also lead to changes in the
distributions of plant and animal species (Iverson
et al. 2008b).
The amount and timing of precipitation will
change (medium evidence, high agreement).
All downscaled climate models agree that there
will be changes in precipitation patterns across
the assessment area.
For the climate projections used in this assessment
(Chapter 4) and other publications, projected
changes in precipitation are highly variable (Kunkel
et al. 2013a, 2013b). The PCM B1 scenario projects
annual precipitation to increase over most of
the assessment area, but decrease sharply in the
Allegheny Mountains section. The GFDL A1FI
scenario projects increases over much of the Ohio
and Maryland portions of the assessment area,
and widespread decreases over larger areas of
West Virginia, including the Allegheny Mountains
(Chapter 4). Models also project changes in
precipitation patterns between seasons (Kunkel
et al. 2013b). Precipitation increases are expected
under both scenarios in winter and spring, with
larger increases under GFDL. Summer and fall
precipitation projections suggest a wide range of
potential responses, from decreases to increases,
depending on the climate scenario (Chapter 4).
Intense precipitation events will continue to
become more frequent (medium evidence,
medium agreement). There is some agreement
that the number of heavy precipitation events will
continue to increase in the assessment area. If so,
impacts from flooding and soil erosion may also
become more damaging.
Total precipitation in the assessment area has
increased the most in fall, by 2.3 inches over the last
century. The timing and magnitude of precipitation
events have shifted, however, so that more rain is
falling during larger events, and light rainfall events
are becoming less common (Chapter 3). Rainfall
from these high-intensity events represents a larger
proportion of the total rainfall, meaning that the
precipitation regime is becoming more episodic with
potentially longer dry spells between events. Climate
models project this trend to continue through the
end of the century, with an additional increase in the
number of heavy precipitation events throughout
the central and northeastern United States (IPCC
2012). Ecosystems are not all equally capable of
holding moisture that comes in the form of extreme
events. Increases in runoff after heavy precipitation
events could also lead to an increase in soil erosion,
specifically channel erosion (Nearing et al. 2004).
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Severe storms will increase in frequency and
severity (medium evidence, medium agreement).
There is some agreement that future climate change
will destabilize atmospheric circulation patterns
and processes, leading to increased risk of severe
weather.
Although the positive trend in historic storm
frequency is muddled with greater public awareness,
reporting, and recent advances in technology,
future trends can be predicted by using atmospheric
models. Projected increases in temperature,
precipitation, and convective available potential
energy over the next century are expected to
result in more frequent days when conditions are
favorable for severe storms (Trapp et al. 2007, 2009,
2011). Many storms affecting the assessment area
are generated in the southwestern United States
and from the Atlantic Ocean; therefore changes
in conditions in these regions may contribute to
increased frequency and severity of storms within
the assessment area (Trapp et al. 2007).
Soil moisture patterns will change (medium
evidence, high agreement), with drier soil
conditions later in the growing season (medium
evidence, medium agreement). Studies show that
climate change will have impacts on soil moisture,
but there is some disagreement among climate and
impact models on how soil moisture will change
during the growing season.
As discussed above, seasonal changes in
precipitation are expected across the assessment
area. Due to potential decreases in summer and
fall precipitation and increases in winter and
spring precipitation, it is likely that soil moisture
regimes will also shift. Longer growing seasons
and warmer temperatures may also result in greater
evapotranspiration losses and lower soil-water
availability later in the growing season (Chapter 4).
The Variable Infiltration Capacity model projected
140
summer and fall decreases in soil moisture, with
the greatest decrease (10 percent) in the West
Virginia portion of the assessment area (Ashfaq
et al. 2010). How these broad trends affect the
Central Appalachians region will depend greatly on
landscape and topographic position and therefore
exposure to climate changes. South-facing slopes
may be particularly vulnerable to soil drying in late
summer or fall. Seedlings will be more vulnerable
to these effects than mature individuals; just one
severely dry growing season per decade may
greatly reduce regeneration success of most species
(Gómez-Aparicio et al. 2008).
Climate conditions will increase wildfire risk
by the end of the century (medium evidence,
medium agreement). Some national and global
studies suggest that wildfire risk will increase in the
region, but few studies have specifically looked at
wildfire potential in the assessment area.
Although there is greater uncertainty around future
fire behavior for the near term, model simulations
tend to agree that there will be global increases in
fire activity by the end of the 21st century (Moritz
et al. 2012). The duration of the fire season in the
Central Appalachians is closely linked with increases
in average temperature during the summer (Liu et
al. 2010). Interactions between complex patterns of
land use and ownership, forest fragmentation, and
both human and natural ignition sources, make it
difficult to determine how an increase in fire weather
conditions might be manifested. In addition to the
direct effects of temperature and precipitation,
increases in fuel loads from pest-induced mortality,
exotic species invasion, or blowdown events could
also increase fire risk (Lovett et al. 200, Weed et
al. 2013). Forest fragmentation and unknown future
wildfire management decisions may limit individual
fires even as fire risk increases.
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Certain insect pests and pathogens will increase
in occurrence or become more damaging
(medium evidence, high agreement). Evidence
indicates that an increase in temperature will lead
to increases in pest and pathogen outbreaks, but
research to date has examined few species in the
assessment area.
Changes in climate may allow some pests and
pathogens to expand their ranges, or to become a
larger threat. Changes in climate may also increase
tree species’ susceptibility to the entire suite of
native and nonnative pests and pathogens, including
hemlock woolly adelgid, southern pine beetle,
and forest tent caterpillar. Pests and pathogens
are generally more damaging in drought-stressed
ecosystems, so there is high potential for these
agents to interact with other climate-mediated
stressors. For example, susceptibility of trees to
sudden oak death is linked to periods of drought
stress. The fungus Phytophthora ramorum, a known
contributor to sudden oak death in California and
Europe, is currently spread through nurseries and
has appeared in nursery samples in Connecticut. The
climate of the assessment area is favorable to this
fungus, which is likely to increase in abundance and
extent in association with wetter springs (Kliejunas
2011). Furthermore, the abundance of potential host
species in the assessment area increases the threat
from introduction of this new disease (Ockels et al.
2004). Unfortunately, we lack basic information on
the climatic thresholds that apply to many forest
pests, and our ability to predict the mechanisms of
infection, dispersal, and transmission for disease
agents remains low. It is also not possible to predict
all future nonnative species, pests, or pathogens
that may enter the assessment area during the 21st
century.
Many invasive plants will increase in extent or
abundance (medium evidence, high agreement).
Evidence indicates that an increase in temperature
and more frequent disturbances will lead to
increases in many invasive plant species.
Many invasive species that currently threaten forests
in the Central Appalachians region may benefit
directly from projected climate change or benefit
from the slow response of native species (Rebbeck
2012). Increases in carbon dioxide have been
shown to have positive effects on growth for many
plant species, including some of the most invasive
weeds in the United States (Ziska 2003). Changes
in climate may allow some invasive plant species
to expand their ranges northward, such as bush
honeysuckle, privet, kudzu, and cogongrass. Milder
winters may allow some invasive plant species
to survive farther north than they had previously
(Dukes et al. 2009). Some invasive species are
tolerant of drought and fire, and may be at an even
greater advantage for future climate conditions.
Future increases in fire or flooding are likely to
benefit the many invasive plants that are able to
establish quickly and outcompete native vegetation
on disturbed sites (Brown and Peet 2003, Dukes et
al. 2009). Increases in riparian flooding is expected
to contribute to more frequent disturbance, and
therefore higher impacts from invasive species.
Potenial Impacts of Climate Change
on Forests
Shifts in drivers and stressors mentioned above
will naturally lead to changes in forest ecosystems
throughout the assessment area. Indirect impacts of
climate change may be indicated by shifts in suitable
habitat, species composition, or function of forest
ecosystems.
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Suitable habitat for northern species will decline
(medium evidence, high agreement). All three
impact models project a decrease in suitability
for northern species such as eastern hemlock, red
spruce, and sugar maple, compared to current
climate conditions.
Across northern latitudes, past periods of warmer
temperatures have resulted in changes in species
distribution northward and to higher elevations
(Chen et al. 2011, Parmesan and Yohe 2003). The
ranges of eastern hemlock and red spruce are largely
to the north of the assessment area, but these species
currently persist in microhabitats that remain cool
and moist enough to support them. Red spruce is
more limited within the assessment area, occurring
at high elevations in the Allegheny Mountains
section of West Virginia. Hemlock is more
widespread, occupying cool and wet sites at lower
elevations. As these species’ ranges continue to shift
northward, they may become rare or extirpated from
the area. In the absence of other mortality agents,
long-lived individuals already established in cool,
wet microhabitats may persist for many years, even
when habitat becomes unsuitable for regeneration
or growth (Iverson and Prasad 1998). Due to the
geographic limitations of these species’ current
habitat, however, they are unlikely to migrate even if
newly suitable habitat becomes available elsewhere
in the assessment area. Results from climate impact
models also suggest declines in suitable habitat
Eastern hemlock. This species is threatened by the hemlock woolly adelgid. Photo by Patricia Butler, Northern Insitute of
Applied Climate Science (NIACS) and Michigan Tech, used with permission.
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
for northern species that are not as geographically
limited, such as sugar maple (Chapter 5). These
species near the southern edge of their range may
also be able to persist in southern refugia if potential
new competitors from farther south are unable to
colonize these areas, although they are expected
to have reduced vigor and be under greater stress
(Iverson et al. 2008b).
Habitat is projected to become more suitable
for southern species (medium evidence, high
agreement). All three impact models project an
increase in suitability for southern species such as
eastern redcedar and loblolly pine, compared to
current climate conditions.
Model results project that tree species currently at
their northern range limits south of the assessment
area will become more abundant and more
widespread under a range of climate futures
(Chapter 5). The range of eastern redcedar is
widespread throughout the eastern United States, but
currently occupies a small portion of its range within
the assessment area. The range of loblolly pine
lies entirely south of the assessment area, although
disjunct populations have been planted in some
locations within Ohio and Maryland. Models agree
that loblolly pine will fare well in terms of habitat
and basal area, especially under GFDL A1FI. Post
oak and shortleaf pine are also projected to fare well
under both scenarios. The ranges of both species are
largely south and west of the assessment area, with
populations most abundant to the west. Blackjack
oak, common persimmon, osage-orange, southern
red oak, sugarberry, sweetgum, and winged elm are
also projected to increase, but were modeled only by
the Tree Atlas. Several species that do not currently
exist within the assessment area are projected to
have new suitable habitat: water oak, water locust,
and cedar elm. However, habitat fragmentation and
the limited dispersal ability of seeds are expected to
hinder movement of these southern species despite
the increase in habitat suitability (Ibáñez et al. 2008).
Most tree species can be expected to migrate more
slowly than their habitats will shift (Davis and Shaw
2001). Indeed, in a simulation for five tree species,
a maximum of 15 percent of newly suitable habitat
would have a chance of getting colonized over
100 years (Iverson et al. 2004a, 2004b). Pests and
diseases such as fusiform rust, annosus root rot, and
southern pine beetle may also limit the expansion
of loblolly pine. As suitable habitat increases for
some tree species and decreases for others, there
will be new opportunities for species to become new
components of novel forest types or commercial
plantations (Iverson et al. 2008a).
Species composition will change across the
landscape (limited evidence, high agreement).
Although few models have specifically examined
how species composition may change, model results
from individual species, paleoecological data,
and ecological principles suggest that recognized
communities may dissolve to form new mixes of
species.
Decoupling of the drivers, stressors, and dominant
tree species that define forest ecosystems is expected
to lead to a rearrangement of suitable conditions
for tree species within the assessment area. This
rearrangement may result in the dissolution of
current plant community relationships, which
paleoecological evidence shows occurred in the
past (Davis et al. 2005, Root et al. 2003, Webb and
Bartlein 1992). Canopy and understory species
composition is closely tied to soil moisture,
aspect, slope position, and other environmental
variables (Hix and Pearcy 1997). Shifts in overstory
and understory structure may follow somewhat
predictable pathways based on shifts in soil
moisture, fire frequency, and disturbance regime,
but will still be strongly correlated to landscape
position (Iverson et al. 1997). For example, on the
Wayne National Forest, dominant tree species are
expected to be oaks on dry ridge tops, and tulip tree
and black cherry on mesic sites (Iverson et al. 1997).
The model results presented in Chapter 5 raise
the possibility for potentially large differences in
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
species’ responses across the Central Appalachians.
Generally, the models indicate that climate trends
may favor oaks and pines, although ecological
lag times and management decisions may slow
conversions of forest types. Repeated harvesting,
grazing, and other large-scale disturbances have
already created atypical relationships among the
canopy and understory species in many areas,
lending more uncertainty to future community
composition (Root et al. 2003). If associated species
such as pollinators and mycorrhizae do not migrate
into newly suitable areas, further constraints could
be placed on native species colonization (Clark et al.
1998). Furthermore, nonnative invasive plants may
be better able to fill newly created niches (Hellmann
et al. 2008).
A major transition in forest composition is not
expected until after the middle of the century
(2040 to 2069) (medium evidence, medium
agreement). Although some models indicate
major changes in habitat suitability, results from
spatially dynamic forest landscape models indicate
that a major shift in forest composition across
the landscape may take 100 years or more in the
absence of major disturbances.
Model results from the Tree Atlas and LINKAGES
(Chapter 5) indicate substantial changes in habitat
suitability or establishment probability for many
species on the landscape, but do not account for
migration constraints or differences among age
classes. Forest landscape models such as LANDIS
PRO can incorporate spatial configurations of
current forest ecosystems, seed dispersal, and
potential interactions between native species and
the invasion and establishment of nonnative plant
species (He et al. 1999, 2005). In addition, forest
landscape models can account for differences among
age classes, and have generally found mature trees
to be more tolerant of warming (He and Mladenoff
1999). Because mature trees are expected to remain
on the landscape, and recruitment of new species is
expected to be limited, it is not expected that major
144
shifts in species composition will be observed by the
middle of the century, except in areas that undergo
more intensive harvesting or major stand-replacing
disturbance events (Ryan et al. 2008). Climate
change is projected to increase the intensity, scope,
or frequency of some stand-replacing events such as
wildfire, ice storms, and insect outbreaks, promoting
major shifts in species composition where these
events occur.
Climate change is expected to affect early growth
and regeneration conditions (medium evidence,
medium agreement). Seedlings are more vulnerable
than mature trees to changes in temperature,
moisture, and other seedbed and early growth
requirements.
Evidence of climate change impacts on forest
ecosystems is more likely to be seen in seedlings
and early growth than in mature individuals.
Temperature and moisture requirements for seed
dormancy and germination are often much more
critical than habitat requirements of an adult tree
(Kitajima and Fenner 2000). Predicted changes
in temperature, precipitation, growing season
onset, and soil moisture may alter the duration
or manifestation of germination conditions.
For example, regeneration failure in balsam fir
populations has been attributed, at least partially, to
climate change (Abrams et al. 2001). For species
with seeds that disperse successfully, these changes
may result in redistribution on the landscape as seeds
germinate only where conditions are met (Walck
et al. 2011). Others species may fail to regenerate
under altered future conditions, or may germinate
without having sufficient conditions to develop.
Warmer winters may promote the establishment
of eastern redcedar and other southern species,
although warmer temperatures alone are unlikely
to drive their establishment (Abrams 2003). After
establishment, advance regeneration (i.e., saplings)
are still more sensitive than mature trees to drought,
heat stress, frost, and other disturbances, such as fire,
flooding, and herbivory (Kitajima and Fenner 2000).
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Increased fire frequency and harvesting will
accelerate shifts in forest composition across
the landscape (medium evidence, medium
agreement). Studies from other regions show that
increased fire frequency can accelerate the decline
of species negatively affected by climate change and
can accelerate the northward migration of southern
tree species.
Days with conditions that are suitable for wildfire
ignition are expected to become more frequent,
although the occurrence of wildfire will depend on
both ignition and human response (Chapter 5).
Frequent, low-intensity fires in certain forest
ecosystems (e.g., beech-maple) can reduce or inhibit
seedling establishment of tree species projected to
decline under climate change (e.g., sugar maple,
American beech). In other forest ecosystems
(e.g., dry-mesic oak), fire can be beneficial for
restoration and to promote regeneration. In some
ecosystems (e.g., dry oak-pine), infrequent, highintensity fires can promote regeneration and release
growing space for tree species that may be better
adapted to future conditions. Fire (including lowintensity prescribed fire) is expected to accelerate
changes in forest composition, promoting faster
changes than those caused by increased temperature
or moisture availability (He et al. 2002, Shang et al.
2004).
Net change in forest productivity is expected to
be minimal (medium evidence, low agreement).
A few studies have examined the impact of climate
change on forest productivity, but they disagree on
how multiple factors may interact to influence it.
Changes in productivity will likely be mixed and
localized. Increases in drought, invasive plants,
insects, disease, and wildfire are expected to
negatively affect forest productivity in some parts
of the region (Hanson and Weltzin 2000). Longer
growing seasons, with adequate moisture, may lead
to greater annual productivity. Future increases
in carbon dioxide may enhance growth rates and
water use efficiency of some species through
carbon dioxide fertilization (Ainsworth and Rogers
2007, Norby et al. 2005), potentially offsetting the
effects of drier growing seasons. Sulfur dioxide,
a component of acid deposition, has been shown
to reduce carbon dioxide fertilization effects in
eastern redcedar in West Virginia (Thomas et al.
2013). Decreases in sulfur dioxide after the Clean
Air Act of 1970 are allowing a slow recovery,
which is expected to result in increased carbon
uptake by trees. There is already some evidence
for increased forest growth in the eastern United
States (Cole et al. 2010, McMahon et al. 2010, Pan
et al. 2009), but it remains unclear if long-term
enhanced growth can be sustained (Bonan 2008,
Foster et al. 2010). Nutrient and water availability,
ozone pollution, and tree age and size all play major
roles in the ability of trees to capitalize on carbon
dioxide fertilization (Ainsworth and Long 2005).
Productivity in the Central Appalachians is already
affected by acid deposition, especially in those
forests at the highest elevations (Elliott et al. 2013).
Modeling results from LANDIS PRO, which do
not include the possible effects of carbon dioxide
fertilization or reductions in acid deposition, project
minimal changes in basal area across the assessment
area, even for GFDL A1FI, but large changes for
some species in certain locations (Appendix 4).
Elevation and aspect, and their influence on soil
water availability, will also be a major driver of local
ecosystem response (Vanderhorst et al. 2008).
Adapive Capacity Factors
Adaptive capacity is the ability of a species or
ecosystem to accommodate or cope with potential
climate change impacts with minimal disruption
(Glick et al. 2011). Below, we summarize factors
that could reduce or increase the adaptive capacity
of forest ecosystems within the assessment area.
Greater adaptive capacity tends to reduce climate
change vulnerability, and lower adaptive capacity
tends to increase vulnerability (Appendix 5).
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Low-diversity ecosystems are at greater risk
(medium evidence, high agreement). Studies
have consistently shown that diverse ecosystems
are more resilient to disturbance, and low-diversity
ecosystems are more vulnerable to change.
In general, species-rich ecosystems have exhibited
greater resilience to extreme environmental
conditions and greater potential to recover from
disturbance than less diverse ecosystems (Tilman
199, 1999). Consequently, less diverse ecosystems
are inherently more susceptible to future changes
and stressors (Swanston et al. 2011). Conversely,
ecosystems that have low species diversity or low
functional diversity (where multiple species occupy
the same niche) may be less resilient to climate
change or its associated stressors (Peterson et al.
1998, Walker 1992, Walker et al. 1999). Forest
stands with low diversity of species, age classes,
and genotypes have been more vulnerable to insect
and disease outbreaks than diverse stands (Raffa
et al. 2008). Genetic diversity within species is also
critical for the ability of populations to adapt to
climate change, because species with high genetic
variation are more apt to have individuals that can
withstand a wide range of environmental stressors
(Reusch et al. 2005).
Species in fragmented landscapes will have
less opportunity to migrate long distances in
response to climate change (limited evidence, high
agreement). Evidence suggests that species may not
be able to disperse over the distances required to
keep up with climate change, but little research has
been done in the region on this topic.
Habitat fragmentation can hinder the ability of
tree species to migrate to more suitable habitat on
the landscape, especially if the surrounding area is
nonforested (Ibáñez et al. 200, Iverson et al. 2004).
It is estimated that a plant would need to migrate
90 miles north or 550 feet in altitude in order to
escape a 1.8 °F increase in temperature (Jump and
Peñuelas 2005). Modeling results indicate that
14
suitable habitat for tree species will migrate between
0 and 350 miles by the year 2100 under a high
emissions scenario and between 30 and 250 miles
under milder climate change scenarios (Iverson
et al. 2004). Based on gathered data of seedling
distributions, it has been estimated that tree species
could possibly migrate northward at a rate of up to
100 miles per century (Woodall et al. 2009), and
other evidence indicates that natural migration rates
could be far slower for some species (Iverson et al.
2004a, McLachlan et al. 2005, Murphy et al. 2010).
This research also suggests that range migration has
already begun; centers of seedling densities were
often more than 12 miles north of species’ centers
of biomass (Woodall et al. 2009). Fragmentation
makes migration even more challenging, because the
landscape is essentially less permeable to migration
(Jump and Peñuelas 2005, Scheller and Mladenoff
2008).
Ecosystems that are highly limited by
hydrologic regime or geological features may be
topographically constrained (limited evidence,
medium agreement). Our current understanding
of the ecology of Central Appalachians ecosystems
suggests that some species will be unable to migrate
to new areas due to topographic constraints.
Communities that require specific hydrologic
regimes, unique soils or geology, or narrow
elevation ranges may not be able to shift across
the landscape, even if conditions are favorable.
For example, high-elevation spruce/fir ecosystems
are found exclusively in the highest elevations of
the Allegheny Mountains, as remnant populations
surviving in the coolest and wettest habitats in
the region (Byers et al. 2007). These ecosystems,
which range from wetlands to uplands, are already
restricted to the highest elevations, and if habitat
becomes unsuitable, it is doubtful that there will be
alternate sites or that they would be able to migrate
over unsuitable habitat to reach potential northern
sites (Nowacki et al. 2009).
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Ecosystems that are tolerant of disturbance
or are disturbance-adapted have less risk of
declining on the landscape (medium evidence,
high agreement). Basic ecological theory and
other evidence support the idea that systems that
are adapted to more frequent disturbance will be at
lower risk.
Disturbances such as drought, flooding, wildfire,
and insect outbreaks have the potential to increase
in the assessment area (Chapters 4 and 5). Several
ecosystems (e.g., Appalachian [hemlock]-northern
hardwood and north-central interior beechmaple forests) are adapted to frequent gap-phase
disturbances, but undergo stand-replacing events
on the scale of hundreds or thousands of years.
Therefore, these systems may be less tolerant
of more frequent stand-level disturbances, such
as drought or fire. Mesic ecosystems can create
conditions that could buffer against fire and drought
to some extent (Nowacki and Abrams 2008).
However, even species in mesic ecosystems could
decline if soil moisture declines significantly.
Forest ecosystems that are more tolerant of drought,
flooding, or fire (e.g., dry oak and oak/pine forest
and woodland) will likely be better able to withstand
climate-driven disturbances (Wagner et al. 2012).
This principle holds true only up to a point,
because it is also possible for disturbance-adapted
ecosystems to undergo too much disruption. For
example, oak and pine ecosystems might cover a
greater extent under drier conditions with more
frequent fire, but these systems might also convert
to barrens or open grasslands if fire becomes too
frequent or drought becomes too severe.
Fire-adapted ecosystems will be more resilient
to climate change (high evidence, medium
agreement). Studies have shown that fire-adapted
ecosystems are better able to recover after
disturbances and can promote many of the species
that are expected to do well under a changing
climate.
In general, fire-adapted ecosystems that have a
more open structure and composition are less
prone to high-severity wildfire (Shang et al. 2004).
Frequent low-severity fire has also been shown to
promote many species projected to do well under
future climate projections, such as shortleaf pine,
pitch pine, and a number of oak species (Aldrich
et al. 2010, Brose and Waldrop 200, Brose et
al. 2013, Patterson 200). In these ecosystems,
fire suppression has resulted in sometimes heavy
encroachment of woody species in the understory
that compete with oak and pine regeneration
(Nowacki and Abrams 2008, Patterson 200,
Sharitz et al. 1992). In addition, fire suppression
in fire-adapted ecosystems can lead to increased
susceptibility to damaging insect infestations
(McCullough et al. 1998). Since the mid-1900s,
suppression of fire has led to an increase in red
maple and sugar maple across the eastern forests
(Abrams 1998, Nowacki and Abrams 2008). These
species are not projected to fare well under climate
change, largely due to regeneration failure (Chapter
5), and the opportunity may arise to restore firesuppressed ecosystems. However, the effects of fire
on seedling establishment, tree growth, and nutrient
cycling can vary by site conditions, species, and
burn regime (Brose et al. 2013, McCullough et al.
1998).
Ecosystems occupying habitat in areas of high
landscape complexity have more opportunities
for persistence in pockets of refugia (medium
evidence, medium agreement). The diversity of
landscape positions occupied by forest may provide
opportunities for natural refugia, for example where
cool air and moisture accumulate in valley bottoms.
Species diversity in the Central Appalachians has
been linked to geophysical diversity of the area
(Anderson and Ferree 2010). With increasing
topographic and landform complexity come a
greater number of landscape characteristics and
microhabitats that buffer against climate changes
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
(Anderson et al. 2012, Fridley 2009). Many areas
across West Virginia and Maryland, such as the
Allegheny Mountains, have a high diversity of
landscape characteristics, such as geophysical
setting, landscape complexity, and connectivity, that
contribute to the high species diversity (Anderson
et al. 2012). This diversity of landscape features
supports a variety of rare, endemic, and localized
plant and animal species, some of which are
restricted to a single geology (Anderson and Ferree
2010). The Tree Atlas modeled the most common
tree species, but did not model many of the rare
species (Chapter 5). Even the relatively flat areas of
the assessment area contain complex ridge systems
and associated soil moisture regimes that support
a high diversity of species. Although climate will
largely determine a species’ potential range, it
is the influence of geology that creates areas of
microhabitat offering refugia against the effects
of climate change (Anderson and Ferree 2010).
VuLNERABiLiTY DETERMiNATioNS
FoR iNDiViDuAL
FoREST ECoSYSTEMS
Climate-induced shifts in drivers, stressors, and
dominant tree species will result in different impacts
to forest ecosystems within the assessment area.
Some ecosystems may have a greater capacity to
adapt to these changes than others, whereas some
may be susceptible to relatively minor impacts.
Therefore, it is helpful to consider these factors
for individual forest ecosystems in addition to
describing general principles related to vulnerability
and adaptive capacity. Table 23 presents a summary
of current major drivers and stressors for each forest
ecosystem covered in this assessment.
The following vulnerability determinations draw on
the information presented in previous chapters, as
Table 23.—Vulnerability determinaion summaries for forest ecosystems considered in this assessment
Potenial impacts
Adapive capacity
Vulnerability
Evidence
Agreement
Negaive
Low-Moderate
High
Medium
Medium
Dry calcareous forest,
woodland, and glade
Neutral-Negaive
Low-Moderate
Moderate-High
Limited-Medium
Medium
Dry oak and oak/pine
forest and woodland
Posiive
Moderate-High
Low
Medium
Medium-High
Dry/mesic oak forest
Posiive-Neutral
High
Low- Moderate
Medium
Medium-High
Large stream loodplain
and riparian forest
Negaive
Low
High
Medium
Medium
Mixed mesophyic and
cove forest
Neutral-Negaive
Moderate-High
Moderate
Limited-Medium
Medium
North-central interior
beech/maple forest
Neutral
Moderate
Moderate
Limited-Medium
Medium
Small stream riparian
forest
Negaive
Moderate
Moderate-High
Medium
Medium
Spruce/ir forest
Negaive
Moderate
High
Limited-Medium
Medium
Forest ecosystem
Appalachian (hemlock)/
northern hardwood
forest
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
well as an expert panel assembled from a variety of
organizations and disciplines across the assessment
area. The 19 panelists evaluated anticipated climate
trends for the assessment area and ecosystem model
projections (Chapter 5), in combination with their
own expertise. For each forest ecosystem, panelists
considered the potential impacts and adaptive
capacity to assign a vulnerability determination
(Fig. 43) and a level of confidence in that
determination using the same confidence scale
described above. For a complete description of
the methods used to determine vulnerability, see
Appendix 5.
Overall vulnerability determinations were rated
lowest for dry oak and oak/pine forest and woodland
and highest for Appalachian (hemlock)/northern
hardwood, spruce/fir, and large stream floodplain
and riparian forests (Table 23). Impacts were rated
as being most negative for Appalachian (hemlock)/
northern hardwood, large stream floodplain and
riparian, and spruce/fir forests. Impacts were
rated most positive for dry oak and oak/pine
forest. Several negative and positive impacts were
identified for north-central interior beech/maple
forest, which was given an overall rating of neutral
impacts. Adaptive capacity was rated lowest for
large stream floodplain and riparian forest, and
highest for dry/mesic oak forest.
Panelists tended to rate the amount of evidence as
limited to medium (between limited and robust) for
most forest ecosystems. Incomplete knowledge of
future wildfire regimes, interactions among stressors,
and precipitation regimes were common factors
limiting this component of overall confidence.
The ratings of agreement among evidence also
tended to be in the medium range. Contrasting
information related to precipitation regimes under
Figure 43.—Vulnerability diagram used in the assessment.
the high and low climate change scenarios was one
factor that limited the level of agreement among
evidence. The way that forest ecosystems were
organized and described for this assessment also
limited the agreement in some instances. In general,
ratings were slightly higher for agreement than for
evidence. Evidence appears not to be as robust as
the experts would prefer, but the various information
sources that are available tend to support similar
conclusions.
In the sections that follow, we summarize the
climate-related impacts on drivers, stressors, and
dominant tree species that were major contributors
to the vulnerability determination for each forest
ecosystem. In addition, we summarize the main
factors contributing to the adaptive capacity of each
ecosystem. For a list of common tree species in each
forest ecosystem, see Chapter 1.
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Appalachian (Hemlock)/Northern Hardwood Forest
High Vulnerability (medium evidence, medium agreement)
Climate change may intensify several interacing stressors, such as drought, forest pests, and invasive
species. Any increased wildire acivity would be detrimental to the health of this forest type. Reduced
species diversity may decrease resilience to the future climate. Valley botoms and other microsites in
areas of complex topography may be bufered from the efects of climate change, providing refugia.
Negaive Potenial Impacts
Drivers: Decreased precipitation and increased
temperatures may interact to ultimately decrease
soil moisture during summer and fall. Increased
heat and moisture stress, exposure to insect pests
and pathogens, and more frequent disturbances are
expected to interact and place increased stress on
this ecosystem. Climate change may also alter the
gap-phase dynamics that enable the regeneration of
many shade-tolerant species if damaging storms,
pest outbreaks, or wildfires become more frequent or
widespread.
Dominant Species: Model results indicate that
American beech, eastern hemlock (considered a
keystone species where it occurs), and sugar maple
will remain relatively stable for PCM B1, but will
lose suitable habitat, growth potential, and volume
in the assessment area for GFDL A1FI (Chapter 5).
These species are vulnerable to the direct changes
in temperature and precipitation, and are susceptible
to moisture stress, beech bark disease, hemlock
woolly adelgid, and other climate and nonclimate
stresses. Results are mixed for red maple, tulip tree,
black cherry, and white ash, which are projected to
lose suitable habitat but maintain potential growth
and volume. Although the amount of suitable
habitat may contract, models agree that remaining
suitable habitat may allow regeneration of these
species in the absence of other stressors. Red spruce,
considered a minor component in the eastern part
150
of the assessment area, is projected to experience
a dramatic decline in growth potential and suitable
habitat, especially for GFDL A1FI, although
established adults are likely to persist even after they
no longer regenerate successfully.
Stressors: Climate change may amplify several
major stressors to northern hardwoods, particularly
for stands occurring on southwest slopes or
marginally suitable soils. Hemlock woolly adelgid,
beech bark disease, emerald ash borer, and other
pests currently attack many species in this ecosystem
and may cause more frequent and severe damage
in climate-stressed forests. Pests such as Asian
longhorned beetle and gypsy moth may present new
risks if they are able to expand from established
locations adjacent to the assessment area (Chapter
5). With the exception of spruce/fir forests, acid
deposition afflicts this ecosystem more than any
other due to its distribution across acid-sensitive
geologies (USDA 200). Acid deposition damages
ecosystem health, and it is unclear how climate
change may affect the ability of ecosystems to
cope with acid deposition in the future. Increases
in wildfire risk would be a severe impact for this
ecosystem because many tree species within this
ecosystem do not tolerate fire. Interactions between
stressors, such as drought, pests, acid deposition,
invasive species, and wildfire are likely to have
greater impacts than temperature or precipitation
alone.
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Low-Moderate Adapive Capacity
This ecosystem typically supports a variety of
plant species on gentle to steep slopes in diverse
and complex terrain, but is limited to the highest
elevations in the Allegheny Mountains. This
ecosystem is often found on soils with high waterholding capacity in areas that normally receive
abundant moisture from precipitation and ground
seepage. However, the combined effects of acid
deposition, drought, and defoliation have already
resulted in lower species diversity and reduced
adaptive capacity (Chapter 5). Eastern hemlock
is currently susceptible to widespread mortality
A hemlock stand in Wooster Memorial Park, Ohio. Photo by
David M. Hix, Ohio State University, used with permission.
from hemlock woolly adelgid, which is expected to
dramatically reduce eastern hemlock populations
over the next few decades. Red spruce is currently
expanding on the landscape, and may persist where
cool, wet conditions provide refugia. Sites on moist
soils that continue to receive abundant moisture may
be buffered from seasonal moisture stress, whereas
sites on exposed slopes may be more sensitive
to moisture stress. The diversity of landscape
positions occupied by this forest may also provide
opportunities for natural refugia, for example, where
cool air and moisture pool in north-facing pockets
and valley bottoms.
An Appalachian (hemlock)/northern hardwood forest.
Photo by Jim Vanderhorst, West Virginia Division of Natural
Resources, Natural Heritage Program, used with permission.
An Appalachian (hemlock)/northern hardwood forest. Photo
by Brian Streets, West Virginia Division of Natural Resources,
Natural Heritage Program, used with permission.
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Dry Calcareous Forest, Woodland, and Glade
Moderate-high Vulnerability (limited-medium evidence, medium agreement)
Many of the species in this ecosystem are projected to do well under a range of plausible climate
futures. However, this ecosystem’s occupaion of extreme habitat on unique soil types implies that it is
geographically limited, and unable to shit freely on the landscape.
Neutral-Negaive Potenial Impacts
Drivers: Drought and ire have been important
factors in maintaining the open form of this
ecosystem, and increased drought is not expected to
harm many species, unless it becomes too severe.
Wildire potential could increase under drier
conditions, although ire intensity will determine
whether it is a positive or negative impact. Lowintensity ire could beneit this ecosystem by
reducing the eastern redcedar component in the
woodland portions of this ecosystem that are
becoming overgrown. However, high-intensity
ire that results in widespread mortality of eastern
redcedar will dramatically change this ecosystem.
Dominant Species: Projected changes in climate
are expected to beneit many of the common tree
species in this ecosystem. Models project that
eastern redcedar, white oak, and post oak will
remain relatively stable or increase in suitable
habitat, potential growth, and volume under both
climate scenarios (Chapter 5). White oak is longlived and able to persist in the shaded understory
until openings are naturally created in the canopy.
Eastern redcedar, and to a lesser extent white
oak, is dependent on disturbance and expected to
beneit from soil moisture deicits, ire, and other
disturbances. Chinkapin oak, eastern redbud, eastern
hophornbeam, and shagbark hickory were modeled
only by the Tree Atlas, and were similarly projected
to increase in suitable habitat.
152
Stressors: Increased drought duration and extent
may increase susceptibility to oak decline, or may
combine with insect and disease factors to increase
stress or mortality. Invasive species are also common
in this forest ecosystem, and climate change is
expected to promote establishment and growth
of invasives, resulting in increased competition
with the many rare native plants in this ecosystem.
Increases in invasive species such as cheat grass,
stilt grass, and bush honeysuckles could increase ire
fuels in this type, leading to potentially more-intense
ire when it does occur.
Low-Moderate Adapive Capacity
This ecosystem is characterized by high species
diversity, but has been affected by limestone
quarrying, agriculture, and fragmentation. The cooccurrence of the dominant species is tightly linked
to the unique soils derived from limestone, and
movement on the landscape is limited to landscape
positions where those soils form. Many of these
species tolerate temperature and moisture extremes,
especially on exposed landscape features, allowing
them to outcompete other species. Severe drought,
projected temperature increases, and increased ire
may allow expansion of the woodland and glade
elements at the expense of the forested areas.
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
A north-central Appalachian circumneutral clif and talus ecosystem. Photo by Jim
Vanderhorst, West Virginia Division of Natural Resources, Natural Heritage Program, used
with permission.
A dry calcareous outcrop at Castle Rock, West Virginia.
Photo by Jim Vanderhorst, West Virginia Division of Natural
Resources, Natural Heritage Program, used with permission.
A dry calcareous woodland. Photo by Jim Vanderhorst, West
Virginia Division of Natural Resources, Natural Heritage
Program, used with permission.
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Dry Oak and Oak/Pine Forest and Woodland
Low Vulnerability (medium evidence, medium-high agreement)
This ecosystem is the most resilient to heat and drought, with many of the species currently doing well,
and projected to do well under future climate. Periodic condiions that limit regeneraion may be bufered
by oak’s ability to resprout. Increased drought and ire are likely to beneit this ecosystem, discourage
invasive species, and maintain an open structure that promotes oak and pine regeneraion.
Posiive Potenial Impacts
Drivers: This dry ecosystem is characterized by
thin, droughty, and nutrient-poor soils. Soils can
become hydrophobic for short periods of time,
which can be made worse by fire. Fire frequency
was historically higher than it is currently, largely
due to fire suppression over the last 50 years. Drier
soil conditions in summer and fall, especially
on south-facing slopes, may increase the risk of
wildfire (Chapter 5). Shale barrens and ridge tops
are especially exposed to extreme temperatures.
Increased frequency of extreme weather events
(e.g., windstorms and ice storms) may lead to more
frequent disturbances.
Dominant Species: Many of the common species
in this ecosystem are projected to remain relatively
stable in total volume, but volume is expected to
shift from many smaller trees in younger age classes
to fewer larger trees in older age classes. Many of
the species in this ecosystem will require active
management, such as prescribed fire, to stimulate
regeneration. Models project that suitable habitat,
potential growth, and trees per acre for chestnut oak
and scarlet oak will remain stable for PCM B1 and
decrease for GFDL A1FI. Black oak is projected
to remain stable for PCM B1, but for GFDL A1FI
154
suitable habitat is expected to increase while growth
potential and trees per acre decrease. Models project
increases in suitable habitat and potential growth for
only one species, loblolly pine, which is expected
to benefit from increased temperatures under both
scenarios. Pitch pine, Table Mountain pine, and
Virginia pine were modeled only by the Tree Atlas,
which projected suitable habitat to remain stable or
increase for both scenarios.
Stressors: Increased drought conditions, especially
during the growing season, may increase
susceptibility to red oak borer, gypsy moth,
armillaria root disease, and other insect pests and
diseases. Southern pine beetle outbreaks have
been observed in New Jersey and Pennsylvania
systems recently, and may increase due to warmer
temperatures. Ailanthus, Japanese stiltgrass,
multiflora rose, bush honeysuckle, autumn olive,
and Japanese barberry often outcompete native herbs
and shrubs in this ecosystem, and are also likely to
benefit from warmer temperatures and increased
disturbance. Invasive shrubs in the understory may
provide additional ladder fuels and increase fire
intensity where wildfire occurs; impacts will depend
on severity of fire.
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Moderate-High Adapive Capacity
This ecosystem is the most resilient to extreme heat
and moisture deficits of the ecosystems examined in
the assessment area. Many pine and oak species are
fire adapted and drought-tolerant, some requiring
high-intensity fire to regenerate (Vose et al. 1993).
A history of fire suppression and succession has
contributed to a reduced pine component in favor of
oak species. Increased wildfire frequency could help
regenerate and promote both oak and pine species.
Low-severity late-season drought generally favors
oak species, although severe drought may hinder
regeneration, or combine with other stressors to
make individuals more susceptible to mortality or
reduced productivity. This ecosystem benefits from
disturbance regimes, such as fire and windthrow,
which promote conditions for regeneration. This
ecosystem’s wide distribution on a range of habitat
conditions makes it well-poised to take advantage
of new habitat that may become too dry for other
species.
A dry oak forest ecosystem with rhododendron in the
understory. Photo by Jim Vanderhorst, West Virginia Division
of Natural Resources, Natural Heritage Program, used with
permission.
A dry oak forest ecosystem with an open canopy. Photo
by Jim Vanderhorst, West Virginia Division of Natural
Resources, Natural Heritage Program, used with permission.
A dry oak forest ecosystem. Photo by Jim Vanderhorst, West
Virginia Division of Natural Resources, Natural Heritage
Program, used with permission.
An oak/pine woodland. The understory of this dry site is
dominated by sedge and grasses. Photo by Brian Streets,
West Virginia Division of Natural Resources, Natural Heritage
Program, used with permission.
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Dry/Mesic Oak Forest
Low-Moderate Vulnerability (medium evidence, medium-high agreement)
This ecosystem supports a high number of tree species and occurs over a wide range of habitats. Many
species are tolerant of dry soil condiions and ire, although young regeneraion may be sensiive to
severe drought and ire. Southern oak and hickory species are likely to beneit from projected changes
in climate.
Posiive-Neutral Potenial Impacts
Drivers: Fire frequency was historically higher than
it is currently, largely due to fire suppression over
the last 50 years. Drier soil conditions in summer
and fall, especially on south-facing slopes, may
increase the risk of wildfire. Increased frequency
of extreme weather events (e.g., windstorms and
ice storms) may lead to more frequent large-gap
disturbances. Increases in extreme precipitation
events may increase the potential for erosion and
channeling.
Dominant Species: Of the many species modeled,
suitable habitat was generally projected to increase
for the southern oaks and hickories, whereas
other common species are projected to persist
over a smaller extent. Models project that habitat
suitability, basal area and trees per acre, and
potential growth for pignut hickory and white oak
will remain relatively stable or increase slightly
under both scenarios. Results for northern red oak
are highly variable across the assessment area,
but suggest positive effects on regeneration where
suitable habitat remains. Other common species
are not expected to do as well, especially for GFDL
A1FI: models project that suitable habitat, potential
growth, and trees per acre will decrease for chestnut
oak and scarlet oak. Black oak is projected to remain
stable for PCM B1, but for GFDL A1FI suitable
habitat is expected to increase while growth potential
and trees per acre decrease. Mockernut hickory and
shagbark hickory were modeled only by the Tree
Atlas, and both are projected to increase in suitable
habitat.
15
Stressors: Increased drought risk, especially during
the growing season, may increase susceptibility
to red oak borer, ambrosia beetle, gypsy moth,
armillaria root disease, and other insect pests and
diseases. Ailanthus, Japanese stiltgrass, and garlic
mustard, which often outcompete native herbs and
shrubs in this ecosystem, are expected to do well
in warmer temperatures. Low-severity late-season
drought generally favors oak species, although
severe drought may hinder regeneration, or combine
with other stressors to make individuals more
susceptible to mortality or reduced productivity.
High Adapive Capacity
A history of fire suppression and timber harvesting
has facilitated a shift to more mesic soils and
associated hardwood species (e.g., sugar maple,
American beech, tulip tree). Increased fire frequency
could help regenerate oak species and restore the
understory composition. However, very frequent
fires have the potential to kill young seedlings of
any species, even those species that have relatively
fire-resistant, thick bark as adults. This ecosystem
is widely distributed, representative of a range
of habitat conditions, and likely to expand on the
landscape. American chestnut was historically a
dominant canopy tree but now cannot grow past
sapling size due to chestnut blight. Blight-resistant
American chestnut variants are currently under
development and experimental planting is already
occurring, resulting in increased species diversity in
select areas (Jacobs et al. 2013).
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
A mesic oak forest with maple regeneraing in the understory. Photo by Brian Streets, West
Virginia Division of Natural Resources, Natural Heritage Program, used with permission.
A dry oak forest with grasses dominaing the open
understory. Photo by Jim Vanderhorst, West Virginia Division
of Natural Resources, Natural Heritage Program, used with
permission.
A dry/mesic oak forest. Photo by Jim Vanderhorst, West
Virginia Division of Natural Resources, Natural Heritage
Program, used with permission.
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ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Large Stream Floodplain and Riparian Forest
High Vulnerability (medium evidence, medium agreement)
Climate change is expected to alter the water regimes in riparian systems, and may amplify the efects
of insect pests, invasive species, and polluion. Dependence on periodic inundaion, combined with
compeiion from invasive species, may result in a reduced ability of naive tree species to tolerate
increased disturbances.
Negaive Potenial Impacts
Drivers: Potential changes to the precipitation
regime could intensify peak streamflow and
shift the timing to earlier in the spring. Reduced
precipitation in the summer and fall would result in
drier conditions, increasing the potential for latesummer drought. An increase in intense precipitation
events is likely to result in more frequent flooding.
Wildfire, currently episodic and human-caused,
could increase under drier conditions, although the
extent would be limited by the fragmented nature of
riparian and floodplain ecosystems.
Dominant Species: Many riverine species in this
forest type were modeled only by the Tree Atlas;
thus evidence is somewhat limited regarding
dominant species. Black willow, green ash,
sweetgum, and sycamore are projected to increase
in suitable habitat over much of the assessment area.
Silver maple had mixed results, but is projected to
generally decrease in suitable habitat for PCM B1
and increase for GFDL A1FI. Eastern cottonwood
and bur oak occurred at sufficient densities to be
modeled only in the Ohio portion of the assessment
158
area, and are projected to decrease slightly for
PCM B1 and increase for GFDL A1FI. Pin oak, also
adequately abundant only in Ohio, is projected to
increase and expand into West Virginia, where pin
oak swamps currently exist in isolated locations.
These species are all tightly linked to moisture
availability, and are especially threatened by
potentially drier soil conditions.
Stressors: Climate change is expected to intensify
several key stressors for large stream riparian and
floodplain forests. Many invasive plant species
currently threaten this ecosystem and are expected
to benefit from climate change and outcompete
native species. Drought-stressed trees may become
more susceptible to insect pests such as emerald ash
borer and diseases such as thousand cankers and
elm yellows. Interactions among multiple stressors
may also lead to more severe climate change
impacts. Increases in storm intensity and flooding
events have the potential to increase soil erosion
and sedimentation, and compound anthropogenic
stressors such as agricultural runoff and industrial
pollution.
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Low Adapive Capacity
This ecosystem exists in many variations within a
relatively small proportion of the assessment area,
but is extremely altered by habitat destruction,
fragmentation, and disconnection of floodplain
forests from rivers and streams (e.g., by roads or
other infrastructure that impedes the flow of water).
The high number of invasive species outcompeting
natives has already reduced the adaptive capacity of
this ecosystem. Although this ecosystem is highly
dependent on disturbance and a regular influx of
A large stream loodplain forest on the Buckhanon River,
West Virginia. Photo by Brian Streets, West Virginia Division
of Natural Resources, Natural Heritage Program, used with
permission.
seeds, nutrients, and water during periodic flooding,
increases in flood intensity or more frequent drought
may not be tolerated by many species, especially
in the early growth stages. Mortality of ash species
from emerald ash borer is likely to eliminate this
species by mid-century, reducing overall native
species diversity. Forests located along river
corridors may be buffered from water deficit better
than those located farther away on the flood plain,
but will be more exposed to flooding effects.
A large stream loodplain forest on the Meadow River, West
Virginia. Photo by Jim Vanderhorst, West Virginia Division
of Natural Resources, Natural Heritage Program, used with
permission.
A large stream loodplain forest on the Greenbrier River, West Virginia. Photo
by Brian Streets, West Virginia Division of Natural Resources, Natural Heritage
Program, used with permission.
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Mixed Mesophyic and Cove Forest
Moderate Vulnerability (limited-medium evidence, medium agreement)
This relaively sheltered ecosystem may face a suite of ampliied disturbances, including wildire,
drought, and invasion by invasive species. Suitable habitat for many species is projected to decline,
although there is great potenial for the complex topography to provide refugia where disjunct
populaions may persist.
Neutral-Negaive Potenial Impacts
Drivers: This ecosystem is adapted to generally
wet or mesic sites in the Allegheny Plateau and
Allegheny Mountains sections, and is characterized
by a high number of tree species. If drought becomes
more frequent or widespread in late summer or fall,
seedlings and saplings may be at risk of desiccation.
Drought would lead to increased risk of wildfire,
which this ecosystem would not tolerate well.
Increased frequency of extreme weather events
(e.g., windstorms and ice storms) may lead to more
frequent large-gap disturbances.
cherry, and white ash, which are projected to lose
suitable habitat but maintain potential growth and
volume. Although the amount of suitable habitat
may contract, models agree that remaining suitable
habitat may allow regeneration of these species in
the absence of other stressors. Results for northern
red oak are highly variable across the assessment
area, but suggest positive effects on regeneration
where suitable habitat remains. Black oak is
projected to remain stable for PCM B1, but for
GFDL A1FI suitable habitat is expected to increase
while growth potential and trees per acre decrease.
Dominant Species: Many species are commonly
associated with this ecosystem, and individual
species responses are expected to differ with
ecological sections and expected degree of climaterelated changes. Models project that American
beech, eastern hemlock (considered a keystone
species where it occurs), and sugar maple will
remain relatively stable for PCM B1, but will lose
suitable habitat, growth potential, and volume in
the assessment area for GFDL A1FI (Chapter 5).
These species are vulnerable to the direct changes
in temperature and precipitation, and are susceptible
to moisture stress, beech bark disease, mortality
from hemlock woolly adelgid, and other stresses
resulting from indirect impacts of climate change.
Results are mixed for red maple, tulip tree, black
Stressors: Increased drought conditions may
increase susceptibility of trees in this system to
hemlock woolly adelgid, forest tent caterpillar,
beech bark disease, and other insect pests and
diseases. Eastern hemlock is currently susceptible to
widespread mortality from hemlock woolly adelgid,
which is expected to dramatically reduce eastern
hemlock populations over the next few decades.
Japanese stiltgrass, garlic mustard, ailanthus, and
bush honeysuckle have already shifted understory
species composition, and are expected to increase
in response to warmer temperatures. Increases in
invasive species could increase fire fuels in this type,
leading to potentially more-intense fire when it does
occur. Most species are fire-intolerant, although oak
species would benefit from an increase in fire.
10
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Moderate-High Adapive Capacity
This ecosystem currently has high species diversity,
and its sheltered position on concave slopes in
complex topography may buffer against climate
changes. The ability of coves to collect water and
nutrients from higher areas may benefit species
by creating refugia from temperature increases,
precipitation changes, and wind. Ecosystem response
to climate change impacts will vary across the
landscape depending on current landscape position,
individual species response, and connectivity. In the
mountains, species may be able to migrate upwards
more easily than northwards to escape warming
temperatures. Emerald ash borer infestations
have already damaged and killed many ash trees.
This forest ecosystem has been diminished by
fragmentation and conversion to agriculture, coal
mining, and logging. Especially in southeastern
Ohio, remaining forest blocks occur in a highly
fragmented mosaic of second-growth forests and
have reduced biodiversity.
A cove forest in the Allegheny Mountains of West Virginia.
Photo by Jim Vanderhorst, West Virginia Division of Natural
Resources, Natural Heritage Program, used with permission.
A Southern and Central Appalachian cove forest. Photo by Brian
Streets, West Virginia Division of Natural Resources, Natural Heritage
Program, used with permission.
11
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
North-Central Interior Beech/Maple Forest
Moderate Vulnerability (limited-medium evidence, medium agreement)
Although sugar maple and American beech are projected to decline to some degree, many associated
species in this ecosystem are projected to do well under a range of future climates. This forest’s posiion
on glacial ill loodplains, moraines, and plateaus promotes and preserves moist soil condiions, a criical
feature which may help bufer the impacts of changing temperature and hydrologic regimes.
Neutral Potenial Impacts
Drivers: This forest occurs largely within the lakeeffect zone of Lake Erie, where heavy-textured soils
and glacial landforms help retain soil moisture.
Other instances are found on lowland positions
supplied by wetland hydrology. Projected decreases
in precipitation in late summer and fall may
increase the frequency or extent of drought. This
system is intolerant of fire, and is characterized by
long disturbance intervals. Increased frequency of
extreme weather events is likely to promote canopy
gap disturbances of larger size and extent than at
present.
Dominant Species: Models project that American
beech, sugar maple, and eastern hemlock (occurring
locally in the glaciated Ohio and eastern portions
of the assessment area) will remain relatively
stable for PCM B1, but will lose suitable habitat,
growth potential, and volume in the assessment
area for GFDL A1FI (Chapter 5). These species
are vulnerable to the direct changes in temperature
and precipitation, and are susceptible to increased
moisture stress and other indirect impacts of climate
change. Results are mixed for red maple, tulip tree,
black cherry, and white ash, which are projected to
lose suitable habitat but maintain potential growth
and volume. Although the amount of suitable habitat
may contract, models agree that remaining suitable
habitat may allow regeneration of these species in
the absence of other stressors. Results for northern
red oak are highly variable across the assessment
area, but suggest positive effects on regeneration
where suitable habitat remains.
12
Stressors: Beech bark disease, emerald ash borer,
hemlock woolly adelgid, anthracnose disease, and a
variety of other pests and pathogens currently affect
this ecosystem. Certain insects, such as hemlock
woolly adelgid, may benefit from warmer winter
temperatures, creating additional stress for these
forests. The emerald ash borer has already reduced
the white ash component in parts of the assessment
area. Invasive plants such as princesstree, silktree,
ailanthus, and glossy buckthorn compete directly
with understory plants and native tree regeneration
and these invasives are likely to take advantage of
increased temperatures and disturbance.
Moderate Adapive Capacity
This ecosystem supports relatively high species
diversity. Its position on moist soils in glacial
topography, and its proximity to lake-effect
precipitation, helps to maintain soil moisture,
which may buffer against drought and discourage
conditions that promote wildfire. However, these
benefits decrease with increasing distance from
Lake Erie. Many of the dominant tree species are
not tolerant of drought or fire. Drought-stressed
trees may be more susceptible to invasives
or disease complexes, resulting in decreased
productivity or mortality. An increase in wildfire
could promote transition to primarily fire-adapted
species (e.g., oaks), changing the identity of this
ecosystem. Heavy deer browsing is also limiting
seedling establishment and growth, and protection
from herbivory will be critical in establishing
regeneration, now and under future climate
conditions.
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Sugar maple and beech canopy in an Ohio beech/maple forest. Photo by David M. Hix, Ohio
State University, used with permission.
A north-central interior beech/maple forest at Crall Woods,
Ohio. Photo by David M. Hix, Ohio State University, used
with permission.
13
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Small Stream Riparian Forest
Moderate-High Vulnerability (medium evidence, medium agreement)
This ecosystem is adapted to natural disturbance, but is threatened by ampliicaion of the disturbance
regime, and by invasive plants, insects, and pathogens. Many species are projected to remain stable or
increase under a range of future climate condiions, but a keystone species, hemlock, is likely to disappear
in many areas.
Negaive Potenial Impacts
Drivers: Changes to the timing and intensity of
precipitation events may lead to increased flashiness
and more frequent high water events in spring.
Spring flooding and inundation have the potential
for increased erosion, silt loads, and sedimentation.
Summer and fall moisture deficits have the potential
to create dry vegetation conditions, stressing
hydrophilic seedlings and supporting wildfire
conditions. Mortality and damage from drought or
storms may result in increased coarse woody debris,
contributing to wildfire fuels.
Dominant Species: Many riverine species in this
forest type were modeled only by the Tree Atlas, so
evidence is somewhat limited regarding dominant
species (Chapter 5). Additionally, some of these
species are not common on the landscape, and are
therefore difficult to model. Suitable habitat is
projected to remain stable or increase for sycamore,
river birch, black walnut, and boxelder. Silver maple
and cottonwood are projected to decrease for PCM
B1 and increase for GFDL A1FI. Hemlock and red
maple were modeled by all three models. Eastern
hemlock is projected to remain stable or decrease
in suitable habitat and potential growth; basal area
and trees per acre are projected to decrease due to
succession, and to a lesser extent due to climate.
Red maple had mixed results for suitable habitat
and potential growth, and basal area and trees per
acre are projected to increase due to succession and
climate change. Many of these species, except red
maple, are tightly linked to moisture availability.
14
Stressors: Invasive plants are very problematic
in this ecosystem, with greater impacts generally
occurring downstream. Increased flashiness
followed by dry periods could cause amplification of
the current hydrologic cycle, potentially increasing
the spread and establishment of current and newly
introduced invasive species. Drought-stressed
trees may be more susceptible to diseases such
as thousand cankers and elm yellows, and insect
pests such as hemlock woolly adelgid. Increases
in storm frequency and flood intensity have the
potential to increase soil erosion and sedimentation,
and compound anthropogenic stressors such as
agricultural runoff and industrial pollution.
Moderate Adapive Capacity
This ecosystem exists in many variations or settings
across the landscape with various assemblages of
a fairly diverse set of species, many of which are
projected to remain stable or even increase under
climate change. Further, this ecosystem type is
adapted to cope with a high level of variability and
natural disturbance, and may be able to handle many
impacts of temperature and precipitation changes
except for extreme drought or severe flooding.
Cold air pooling in valleys and shelters may also
provide refugia that are buffered from temperature
increases. In the cooler and moister sites, hemlock
is a keystone species that has been declining and
is projected to decline further. For these forests,
the loss of hemlock is likely to change the species
assemblage dramatically, with fast-growing
generalists like red maple or a variety of invasive
species likely to overtake the newly vacated niche.
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
A small stream riparian forest with a large herbaceous
component. Photo by Jim Vanderhorst, West Virginia
Division of Natural Resources, Natural Heritage Program,
used with permission.
A small stream riparian forest. Photo by Jim Vanderhorst,
West Virginia Division of Natural Resources, Natural Heritage
Program, used with permission.
Cotonwood and other hardwoods along a stream at Alum Creek, Ohio. Photo by David M.
Hix, Ohio State University, used with permission.
15
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Spruce/Fir Forest
High Vulnerability (limited-medium evidence, medium agreement)
This ecosystem is dependent on very moist condiions, and persists only in the coolest, wetest, and
highest elevaion sites in mountainous secions. Projected increases in temperature and decreases in
summer and fall precipitaion may exceed the ecological tolerances of this ecosystem’s deining species.
Complex topography may provide cool pockets of habitat where these species would be likely to persist.
Negaive Potenial Impacts
Drivers: This ecosystem type is adapted to cool
temperatures and abundant moisture in the form
of rain, snow, and fog drip. Projected increases in
temperature and potential decreases in precipitation
later in the growing season may decrease the amount
of atmospheric moisture that could develop into
fog in summer or snow in winter. If soils in this
ecosystem dry out, the entire ecosystem would be
affected. Drier conditions could also increase the
risk of duff fire, previously not a threat except after
extreme anthropogenic disturbances. Changes in
winter processes could affect this high-elevation
ecosystem more than others; interacting effects
of reduced snow cover (warm temperatures) or
increased snow cover (lake effect) may alter soil
freezing conditions.
Dominant Species: Red spruce and balsam fir (the
two keystone species in this ecosystem) are limited
to the Allegheny Mountains and the Northern Ridge
and Valley sections, and models project suitable
habitat and growth potential to decline dramatically
for both species under both climate scenarios
(Chapter 5). Models also project suitable habitat,
growth potential, and trees per acre to decline for
1
eastern hemlock and eastern white pine, but only for
GFDL A1FI. Results are mixed for red maple, tulip
tree, black cherry, and white ash, which are projected
to lose suitable habitat but maintain potential growth
and volume. Although the amount of suitable habitat
may contract, models agree that remaining suitable
habitat may allow regeneration of these species
in the absence of other stressors. Other common
species were modeled only by the Tree Atlas:
cucumbertree, yellow birch, and sweet birch are
also projected to lose suitable habitat in the sections
occupied by this ecosystem.
Stressors: Insect pests such as the hemlock and
balsam woolly adelgids currently affect this
ecosystem and have the potential to increase when
winter temperatures no longer limit populations.
There is also potential for new invasive plants,
although they may be limited by acidic soils. If
deer populations benefit from warmer temperatures,
herbivory on hemlock and balsam fir could increase,
but red spruce would benefit because it is not a
preferred browse species. Acid deposition damages
ecosystem health, and it is unclear how climate
change may affect the ability of ecosystems to cope
with acid deposition in the future.
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
Moderate Adapive Capacity
This forest ecosystem is currently stable and
expanding on the landscape to reoccupy available
suitable habitat. Red spruce, projected to decline
for GFDL A1FI at the end of the century, influences
the soil to create positive edaphic conditions that
are favorable to its own regeneration. Red spruce
has been negatively affected by acid deposition,
which may decrease its natural resistance to changes
(McLaughlin and Kohut 1992, McLaughlin et al.
1990, Schuler and Collins 2002). Balsam fir has
the lowest adaptive capacity of all the species in
this ecosystem, largely due to its fire- and droughtintolerance and susceptibility to balsam woolly
adelgid and other insect pests. Eastern hemlock
is currently susceptible to widespread mortality
from hemlock woolly adelgid, which is expected to
dramatically reduce eastern hemlock populations
over the next few decades. The potential for
drought may be buffered by high rainfall and fog
generated at higher elevations. Suitable habitat
for this ecosystem is already limited to the highest
elevations in the Central Appalachians and the range
of this ecosystem may contract as climate change
forces species upward. Cold air pooling in valleys
and shelters may provide areas of refugia buffered
from temperature increases. Red spruce is currently
expanding on the landscape, and may persist where
cool, wet conditions provide refugia.
A high-elevaion spruce/ir forest in West Virginia. Photo by Elizabeth Byers, West Virginia Division of
Natural Resources, Natural Heritage Program, used with permission.
17
ChAPTER 6: FoREST ECoSYSTEM VuLNERABiLiTiES
ChAPTER SuMMARY
Forest ecosystems across the assessment area
will be affected by climate change, although
these ecosystems and individual tree species will
respond to these changes differently. The synthesis
statements in the first half of this chapter can
be applied as general principles when specific
information about expected climate change impacts
is lacking. Overall, we expect that forest ecosystems
will be most severely affected by projected decreases
in late season precipitation; decreases are projected
for summer for GFDL A1FI and for fall for PCM
B1. Forest ecosystems that are adapted to dry
conditions and frequent disturbances are expected
to be less vulnerable to the range of future climates.
Forest ecosystems that are adapted to tolerate a
wide range of conditions and disturbances, and have
higher mobility on the landscape, are also expected
to be better able to persist under a range of plausible
climates.
The vulnerability determinations for individual
forest ecosystems are best interpreted as broad
trends and expectations across the assessment area.
For some species, climate-related changes over
the next century may be a continuation of current
trends. For other species, it may take more than 100
years before such changes become apparent. For
long-lived species especially, substantial changes on
the landscape within this century will likely be the
result of succession, management, and disturbance.
Vulnerability to anthropogenic stressors such as
fragmentation, urban development, and arson
impinges on an ecosystem’s adaptive capacity, and
may be much more influential on ecosystems than
climate change, especially over the first half
of this century. This assessment uses the most
up-to-date information from the scientific literature,
a coordinated set of ecosystem modeling results and
climate projections, and the input of a large team
18
of local experts. Even so, there are limitations and
unknowns that make these determinations imperfect.
As new information continues to be generated on the
potential impacts of climate change on forests in this
region, this assessment should be supplemented with
additional resources and stand-level information.
The high diversity in landforms, microclimates,
hydrology, and species assemblages across the
assessment area greatly complicates model
projections and interpretation. In this assessment,
forest ecosystems were combined and generalized
based on NatureServe’s ecological systems, which
are themselves made up of hundreds of unique
“associations” (Chapter 1). Forest ecosystems have
the potential to manifest themselves in very different
ways across the assessment area (e.g., varying in
species associations and landscape position), and it
is important to have a good working knowledge of
forest ecosystems at the local level in each section.
It is essential to consider local characteristics such
as past management history, soils, topographic
features, species composition, forest health issues,
and recent disturbances when interpreting these
general vulnerabilities at local scales. Some
site-level factors may amplify these expected
vulnerabilities, yet others may buffer the effects of
climate change. Developing a clear understanding of
potential vulnerabilities across relevant scales will
then enable forest managers, landowners, planners,
and other resource specialists to consider appropriate
adaptation responses. This is true whether the task
is to manage a single stand over a few years, or to
design a long-term management plan for a large tract
of land.
In the following chapter, we extend the discussion
to consider the implications of climate trends and
forest ecosystem vulnerabilities for other ecosystem
services and resource areas that are often important
to forest managers.
ChAPTER 7: MANAGEMENT iMPLiCATioNS
The previous chapters of this assessment have
described observed and anticipated climate
trends, potential impacts to forest ecosystems,
and the climate-related vulnerability of nine forest
ecosystems in the assessment area. This chapter
takes one additional step and summarizes some
implications of these climate change impacts and
vulnerabilities for a variety of topics important to
forest managers. Changes in climate, impacts on
forest ecosystems, and ecosystem vulnerability will
combine to create both challenges and opportunities
in forest management.
Topics were selected to encompass major resource
areas that are priorities for public and private land
managers. These topics, and the descriptions of
climate change implications, are not comprehensive.
Some topics have received less scientific attention
or contain greater uncertainty. For some topics
we relied on input from subject-area experts to
discuss climate change implications. Our goal
is to provide a springboard for thinking about
management implications of climate change and to
connect managers to other relevant resources. When
available, the “more information” sections provide
links to key resources for managers to find more
information about the impacts of climate change
on that particular topic. The topics addressed are:
wildlife, threatened and endangered plant species,
nonnative invasive plant species, fire and fuels,
infrastructure, air and water quality, forest products,
nontimber forest products, forest carbon, recreation,
wilderness, cultural resources, urban forests,
forest-associated towns and cities, and planning for
conservation and natural resource management.
This chapter does not make recommendations as to
how management should be adjusted to cope with
climate impacts. We recognize that the implications
of climate change will vary by ecosystem,
ownership, and management objective. Therefore,
we provide broad summaries rather than focusing on
particular management issues. A separate document,
Forest Adaptation Resources, has been developed to
assist land managers in a decisionmaking process to
adapt their land management to projected impacts
(Swanston and Janowiak 2012).
WILDLIFE
Climate change is likely to have both shortand long-term effects on individual organisms,
populations, species, and wildlife communities in
the Central Appalachians region. These effects may
range from direct habitat loss to complex indirect
impacts on wildlife populations and their habitats.
Changes to habitats discussed in Chapter will
likely result in range expansion for some species and
the reduction or complete loss of available suitable
habitat for others. Wildlife populations may respond
by adapting to new conditions or migrating to follow
shifts in suitable habitat; species that are unable to
adapt or have limited dispersal ability, particularly
those that are already rare, may face substantial
challenges in a changing climate. Managing wildlife
species may require adjustments to accommodate
shifting ranges or to provide supplemental food
sources during critical periods. Climate change
vulnerability assessments have been conducted
for many individual species within West Virginia
(Byers and Norris 2011), and the broader Central
19
ChAPTER 7: MANAGEMENT iMPLiCATioNS
Appalachians region (Furedi et al. 2011, Schlesinger
et al. 2011). These assessments are generally focused
on state-listed sensitive species for which climate
change is only one of a multitude of stressors which
have already affected population ranges or viability.
The Climate Change Bird Atlas uses forest inventory
data and species-specific habitat requirements to
examine the potential for climate change to alter the
distribution of 147 bird species across the eastern
United States (Landscape Change Research Group
2014).
Birds appear to be less vulnerable to climate change
impacts than other taxonomic groups because they
tend to have less habitat specificity, are able to
disperse long distances, and are not as hindered
by natural and anthropogenic obstacles on the
landscape. However, bird species that are dependent
on specific habitat types (e.g., high-elevation
conifer forest) may be unable to meet their habitat
requirements in a new location, or habitat shifts may
introduce new competitors and predators (Matthews
et al. 2011a). Other potential climate change impacts
include changes in the timing of migration for some
birds, or the resources (e.g., flowers, seeds, larvae)
upon which they depend. Many short-distance
migrants have been observed to respond to local
changes by adjusting their arrival or departure dates,
but long-distance (e.g., transcontinental) migrants
respond to cues at their origin, and are unable to
predict conditions at their summer grounds (Hurlbert
and Liang 2012). Birds arriving either too early
or too late could face suboptimal conditions (e.g.,
limited food resources or difficulty finding mates),
resulting in adverse impacts to fitness and survival
(Fraser et al. 2013).
A young bird amid rhododendron and hemlock. Photo by Patricia Butler, Northern Insitute of Applied Climate Science (NIACS)
and Michigan Tech, used with permission.
170
ChAPTER 7: MANAGEMENT iMPLiCATioNS
Bat species that rely on insects for food after
emerging from hibernation may face similar
challenges; shifts in insect populations can
influence bats’ ability to regain weight lost during
hibernation and to reproduce successfully. Bats may
be particularly sensitive to climate change because
many aspects of their ecology and life history are
closely tied to temperature and precipitation, and
many species in the assessment area have already
suffered catastrophic declines as a result of whitenose syndrome. Modeling of Indiana bat habitat used
maternity habitat requirements of less than 82 °F and
projected the summer range to contract to climatic
refugia in the northeastern United States and
Appalachian Mountains (Loeb and Winters 2013).
Other mobile mammal species found in the
assessment area may face similar range reductions,
particularly species that are adapted to cool, moist
habitats. However, species that are dependent on a
narrow range of conditions or have limited mobility
may not be able to shift to alternate locations as
climate and habitat conditions change. For example,
the West Virginia northern flying squirrel is closely
tied to high-elevation spruce/northern hardwood
forests and is restricted in its ability to exploit
alternative habitats because of competition with the
southern flying squirrel. In addition, as the habitat
and range of the southern flying squirrel expands
in response to climate change, the potential for
hybridization (and loss of genetic integrity) with
northern flying squirrels increases (Garroway et
al. 2010). Squirrels and other wildlife species that
depend on mast trees may benefit from increases in
those tree species projected to do well, such as post
and white oaks and pignut hickory.
Most regional amphibians and fish are poor
dispersers and less able to shift to alternate locations
in response to adverse changes in local habitat
conditions. In addition, many of these species are
aquatic or closely associated with specific aquatic
and wetland habitats. As a result, these taxonomic
groups make up the majority of species considered
to be extremely or highly vulnerable in the state
vulnerability assessments noted above. The
exceptions are cave-obligate species, because caves
and associated groundwater-fed aquatic systems
appear to be largely buffered from climatic changes.
Vulnerable amphibians include the Cheat Mountain
and green salamanders, which are constrained by
narrow habitat niches; the Jefferson salamander,
which is dependent on ephemeral wetlands; and
the eastern hellbender and eastern spadefoot toad,
which require specific aquatic and riparian habitat
features. Mollusk and fish species are threatened by
natural and anthropogenic barriers to movement,
and physical habitat specificity contributes to their
vulnerability to changes in water temperature and
precipitation patterns (Byers and Norris 2011). As a
group, mollusks are especially vulnerable to negative
impacts associated with climate change because of
their limited dispersal ability and dependence on a
few fish species to serve as larval hosts. Cold- and
cool-water fish species, such as brook trout, sculpin
species, and redside dace are highly vulnerable to
climate change impacts, particularly populations
inhabiting small, high-elevation streams that may
experience drying of stream beds or elevated water
temperatures.
Some reptiles and invertebrates are also likely to
be affected by climate change. Reptiles rely on
ambient environmental temperature to maintain
their physiological processes and are uniquely
sensitive to changes in temperature. The sex of
offspring of many turtle species is determined by
ambient temperature; thus, concerns for already
sensitive species such as the spotted turtle and bog
turtle include physiological impacts that may affect
long-term fitness of a population regardless of
vegetative habitat changes. Although some research
has been conducted on how climate change might
affect insects, most of that work is focused on
European butterflies and insects of economic and
environmental concern in forestry and agriculture
171
ChAPTER 7: MANAGEMENT iMPLiCATioNS
Eastern garter snake. Photo by Patricia Butler, NIACS and Michigan Tech, used with permission.
(Andrew et al. 2013). Within the assessment area,
climate-related changes in hydrology and declines
in stream quality are expected to adversely affect
several dragonflies, such as the crimson-ringed
whiteface, rapids clubtail, and green-faced clubtail
(Furedi et al. 2011).
The topic of climate change impacts to fish
and wildlife is an area of very active research,
with new insights into species’ adaptations and
management ideas to help populations meet these
challenges emerging constantly. In addition to
research publications, several tools are available
online to assist land managers in evaluating species
vulnerabilities and potential changes to fish and
wildlife resources. A few of these resources follow:
172
More Informaion
• The U.S. Forest Service’s Climate Change
Resource Center provides information related to
climate change impacts to wildlife and species’
responses: www.fs.fed.us/ccrc/topics/wildlife/.
Please note that Web addresses are current as of
the publication date of this assessment but are
subject to change.
• NatureServe’s Climate Change Vulnerability
Index (CCVI) tool uses readily available
information about a species’ natural history and
distribution and about the landscape to predict
whether it will likely suffer a range contraction
and population reductions due to climate change:
https://connect.natureserve.org/science/climatechange/ccvi
ChAPTER 7: MANAGEMENT iMPLiCATioNS
• The Climate Change Bird Atlas is a companion
to the Climate Change Tree Atlas and uses
information about the direct climate effects as
well as changes in habitat to project changes
in bird species distributions:
www.nrs.fs.fed.us/atlas/bird/
• The Appalachian Landscape Conservation
Cooperative (LCC) Web site provides links to
resources, documents, papers, webinar series
announcements, and other information about
drivers and impacts of climate change (including
those affecting wildlife and fish), particularly
in relation to the Appalachian landscape:
http://applcc.org/resources/climate-change
• Many states are working to incorporate climate
change information into their state wildlife action
plans. Voluntary guidance has been provided by
the Association of Fish and Wildlife Agencies:
www.fishwildlife.org/files/AFWA-VoluntaryGuidance-Incorporating-Climate-Change_
SWAP.pdf
• West Virginia’s Climate Change Vulnerability
Assessment for Species of Concern provides
evaluations of climate change impacts for
many plants and animals in the assessment
area based on NatureServe’s CCVI:
http://wvdnr.gov/publications/PDFFiles/
ClimateChangeVulnerability.pdf
• The Ohio Department of Natural Resources
(ODNR) provides a Web page with a
variety of links to vulnerability assessment
resources: http://www.dnr.state.oh.us/
Home/ExperienceWildlifeSubHomePage/
where_to_viewwildlifelandingpage/
OldWomanCreekDefault/ClimateandWildlife/
climate_wlvulnerability/tabid/2372/Default.aspx
ThREATENED AND ENDANGERED
PLANT SPECiES
The Central Appalachians region contains a great
diversity of threatened, endangered, and rare plants.
Within the assessment area, the U.S. Fish and
Wildlife Service (USFWS) lists eight plant species
as threatened or endangered (T&E): running buffalo
clover, northern wild monkshood, eastern prairie
fringed orchid, Virginia spiraea, small whorled
pogonia, northeastern bulrush, harperella, and shale
barren rockcress (USFWS 2014). These species
occur in habitats that include wetlands, riparian
areas, deciduous forests, grasslands, and small patch
habitats such as shale barrens. In addition, the U.S.
Forest Service lists 81 plant species as Regional
Forester’s Sensitive Species due to their rarity on
the Monongahela and Wayne National Forests. State
Natural Heritage Programs track many more rare
plant species, including well over 400 species in
West Virginia alone, with additional species tracked
in the Appalachian portions of Ohio and Maryland.
Thus, rare plants can be found in all of the forest
ecosystems that are included in this assessment.
Given the numerous habitats in which rare plants
are found, the effects of climate change on rare
plants are likely to vary widely. In general,
species with limited distributions are believed to
be disproportionately vulnerable to the negative
impacts of climate change because suitable habitat
may not be available, or because they have no way
of migrating to suitable habitat that may become
available (Schwartz et al. 200b). However,
predicting impacts on individual species can be
difficult because many rare species may be limited
by narrow ecological tolerances that are not related
to climate sensitivity (Schwartz et al. 200b).
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
The West Virginia Natural Heritage Program applied
NatureServe’s Climate Change Vulnerability Index
to 18 rare plant species and predicted that 7 of them
would be highly vulnerable or extremely vulnerable
to negative impacts, including 4 T&E species
(northeastern bulrush, harperella, small whorled
pogonia, and shale barren rockcress). Eight rare
species were predicted to be moderately vulnerable,
including two T&E species (Virginia spiraea and
running buffalo clover). Only four (non-T&E) rare
plant species were predicted to remain stable under
a changing climate (Bentley’s coralroot, Torrey’s
mountainmint, Tennessee pondweed, and lillydale
onion). Increased fire may benefit some threatened
and endangered plants by maintaining habitat or
promoting flowering, as evidence suggests for
running buffalo clover and eastern prairie fringed
orchid (Hessl and Spackman 1995).
More Informaion
• U.S. Fish and Wildlife Service Endangered
Species Database:
http://www.fws.gov/endangered/
• NatureServe’s Climate Change Vulnerability
Index: https://connect.natureserve.org/science/
climate-change/ccvi
• Ohio Natural Heritage Database and Ohio
Rare Plant List: http://www.dnr.state.
oh.us/Home/wild_resourcessubhomepage/
ResearchandSurveys/OhioBiodiversityDatabase/
tabid/2352/Default.aspx
• Maryland Natural Heritage Program Rare,
Threatened, and Endangered Plants:
http://dnr.maryland.gov/wildlife/Plants_Wildlife/
rte/rteplants.asp
• West Virginia Natural Heritage Program Rare,
Threatened, and Endangered Species:
http://www.wvdnr.gov/Wildlife/Endangered.shtm
• Climate Change Vulnerability Assessment of
Species of Concern in West Virginia (Byers and
Norris 2011): http://wvdnr.gov/publications/
PDFFiles/ClimateChangeVulnerability.pdf
174
Wildlowers of West Virginia. Photo by Patricia Butler, NIACS
and Michigan Tech, used with permission.
NoNNATiVE iNVASiVE
PLANT SPECiES
Various researchers and predictive models suggest
that climate change will likely increase the ability of
many invasive plants to invade and spread (Alpert et
al. 2000, Dukes et al. 2009, Hellmann et al. 2008).
However, the overall impact of invasive plants will
vary based on individual species responses, and in
some cases the distributions of invasive plants may
decrease (Bradley et al. 2009). In general, increased
invasions of warm climate species and decreased
invasions of cold climate species might be expected
in the assessment area. Projected increases in fire
activity and disturbances related to extreme weather
may favor the expansion of disturbance-adapted
invasive species, especially southern climate species
like cogongrass and kudzu. Cogongrass in the
southeastern United States has contributed to altered
fire regimes and is expected to advance northward
with warmer temperatures (Lippincott 2000).
In addition, a changing climate has the potential
to affect the life cycle of invasive species that are
already established in the assessment area. The
phenology of temperate plants, such as flowering
and leaf-out dates, has been well documented and
ChAPTER 7: MANAGEMENT iMPLiCATioNS
is known to be especially sensitive to temperature
(Cleland et al. 2007b, Parmesan and Yohe 2003).
Species that are most responsive to temperature
in terms of their flowering date—that is, species
that flower earlier in warm years and later in cold
years—are the ones that will likely increase in
abundance in the face of climate change. Research
has shown that many nonnative invasive plants have
more flexible flowering dates and have shifted these
dates to earlier in the spring than native plants or
even nonnative plants that are not invasive (Primack
and Miller-Rushing 2012, Willis et al. 2010). For
example, the invasive plant purple loosestrife was
found to bloom several weeks earlier than it did a
century ago, whereas the flowering dates of many
other species, such as most native lilies and orchids,
did not shift (Primack and Miller-Rushing 2012).
As invasive plant invasions become more
widespread, forest managers may need to invest
more resources to control invasive plant populations
and minimize impacts to forests (e.g., prescribed
burns and timber harvest). For example, ailanthus
is a particularly problematic invasive species that
has already been increasing in the assessment
area and may benefit from climate change. Data
from the Wayne National Forest show ailanthus
trees >5 inches in diameter have increased from
0.7 percent of cover to 1. percent in a little over
a decade; without action this species is likely to
increase exponentially. The Wayne National Forest
is working with ODNR, the Appalachian Ohio Weed
Control Partnership, and the U.S. Forest Service’s
Northern Research Station and Northeastern
Area State & Private Forestry to aerially map
and strategically treat 500,000 acres of ailanthus
across all ownerships in southeastern Ohio. These
collaborators are able to identify pockets of heavy
infestations that can be treated with standard
herbicide treatments and future experimental
control with a biological agent.
More Informaion
• Appalachian Ohio Weed Control Partnership:
http://appalachianohioweeds.org/
• Huebner and partners at the USFS Northern
Research Station, West Virginia University, and
Ohio State University are currently finishing a
4-year study that looks at the impacts of timber
harvesting and prescribed fire on three invasive
species (garlic mustard, Japanese stiltgrass,
and ailanthus) in comparison to red oak. This
study may shed more light on the likelihood of
increased invasions due to climate change-related
increases in disturbance: Huebner, C.D.; McGill,
D.; Matlock, G.; Minocha, R.; Dickinson, M.;
Miller, G. (unpublished work). Defining an
effective forest management strategy that deters
invasion by exotic plants: invasive plant response
to five forest management regimes. For more
information, visit http://nrs.fs.fed.us/people/
chuebner.
FiRE AND FuELS
Potential climate change impacts include an increase
in wildfire risk, especially during summer and fall.
As mentioned in Chapter 5, invasive shrub and
herbaceous cover can increase fuel abundance, as
can mortality of native plants. Increased levels of
downed woody debris resulting from winter storm
and wind events can also contribute to dry fuel
loads.
There are three fire seasons in the assessment area:
spring, late summer, and fall. The spring season
generally lasts from March through late April before
leaf-out, and provides the longest burn window
when fuels are dry. By the end of May or early
June, green-up of understory vegetation raises
fuel moisture and tree leaf-out prevents adequate
daytime drying of fuels. The late summer season
generally lasts from late August through September,
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
when leaves and ground fuels begin to dry out.
Droughty weather or at least 7 to 10 rain-free days
are necessary for fuel moisture to be low enough to
burn. The fall season typically begins in mid- to late
October after the first hard frost (and the start of leaf
fall) and runs through November. This burn window
is extremely variable and fire behavior can be more
extreme in fall due to the presence of dry leaf litter,
especially oak, that has not yet been compressed by
rain or snow.
Projected changes in climate could affect the ability
to apply prescribed fire in the assessment area. In
spring, increased rainfall could make it difficult to
conduct prescribed burns. Throughout the spring
and summer, changing precipitation patterns,
such as intense rain events followed by longer dry
periods, could result in longer periods of drier burn
conditions. Burning under drier conditions may
result in more intense and hotter fires, including
fires that use ladder fuels to move into the forest
canopy. As the growing season is extended later into
the fall, there is even more potential for increased
fuels accumulation. On an interannual level, drought
increases wildfire risk during all fire seasons (Lafon
et al. 2005) and is likely to play a critical role in
future shifts in fire windows and behavior.
Shifts in climate that result in a longer fire season
or extension of critical fire weather days would,
in turn, increase the potential risk of wildland fire.
Change in fire risk across the assessment area and its
impacts at local scales will depend on both land use
and management decisions. Potential management
responses might include rescheduling prescribed
burns as optimal burn windows shift toward summer
and fall. Fuel models may also need to adjust to
climate-related vegetation changes such as increased
density of invasive plants, or shifts in species
composition that affect fuels on the forest floor (e.g.,
from maple to oak). Policy and funding decisions
and public attitude will ultimately define the
17
response that makes the most sense, and responses
may differ between landowners, land managers, and
organizations.
More Informaion
• The U.S. Forest Service’s Climate Change
Resource Center: Wildfire and Climate Change:
www.fs.fed.us\ccrc\topics\wildfire\
• The Consortium of Appalachian Fire Managers
and Scientists (CAFMS): www.cafms.org
iNFRASTRuCTuRE
Many landowners and agencies are responsible for
managing infrastructure on the forested landscape,
such as roads, power lines, sewer lines, dams,
drainage ditches, and culverts. Specifications for
water infrastructure are based on past climate
patterns, and the current trend of intensifying
precipitation has placed additional strain on outdated
infrastructure. Storms, extreme temperatures, longer
growing seasons, and warmer winters can pose
particular challenges for infrastructure. Extreme heat
and longer growing seasons can result in rising costs
associated with roadside and power line vegetation
management. Extreme cold and freeze-thaw cycles
can accelerate road deterioration. Intense rainfall
could increase the potential for erosion on dirt and
gravel roads common in forest landscapes, logging
projects, gas development, and rural areas. Water
resource infrastructure such as bridges, sewers,
major culverts, low-water crossings, and dams may
have to be redesigned and rebuilt to accommodate
flows of increased duration and intensity.
Improved stream bank stabilization may have to be
incorporated to prevent scouring. Costs associated
with debris removal in waterways could also rise.
Projected increases in average temperature, summer
heat waves, and summer storms are expected to
place additional strain on electrical infrastructure.
ChAPTER 7: MANAGEMENT iMPLiCATioNS
An old culvert. Land managers are beginning to replace culverts like this one with larger culverts designed to accommodate
larger peak lows and allow the passage of aquaic organisms. Photo by Patricia Butler, NIACS and Michigan Tech, used with
permission.
Although not directly attributed to climate change,
an extreme weather event can serve to illustrate
the impacts of such events on electrical systems.
On June 29, 2012, a derecho with sustained winds
of 0 miles per hour gusting to 100 miles per hour
ravaged a 00-mile swath across 11 states including
Ohio, West Virginia, and Maryland. Across the
region, 4.2 million electrical customers lost service.
West Virginia, a rural state with sparse populations
and mountainous topography, was particularly
devastated; more than 00,000 customers lost
power for 10 days or more (U.S. Department of
Energy 2012). According to an analysis by the U.S.
Department of Energy, extensive debris, downed-tree
removal operations, additional storms, and unusually
high heat hindered the restoration of power (U.S.
Department of Energy 2012). The derecho made the
National Oceanic and Atmospheric Administration’s
(NOAA’s) list of billion-dollar weather events
($2.8 billion) and resulted in the death of 28 people
(NOAA 2014a). Dominion Power reported the
derecho to be the most severe weather event in the
company’s 100-year history after Hurricanes Irene
and Isabel (Knight 2012). Following the derecho,
the region experienced record high temperatures,
which complicated efforts to restore power.
Although millions of residents had to go without air
conditioning during this particular storm, heat waves
are expected to increase in frequency and duration,
and are likely to put great demand on electricity
supply.
More Informaion
• American Society of Civil Engineers 2013 Report
Card for America’s Infrastructure: http://www.
infrastructurereportcard.org/a/#p/home
• The U.S. Energy Sector Vulnerabilities to Climate
Change and Extreme Weather: http://energy.gov/
sites/prod/files/2013/07/f2/20130710-EnergySector-Vulnerabilities-Report.pdf
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
AIR QUALITY
The direct and indirect effects of a changing climate
have important implications for air quality and its
management. Although future changes in pollutant
emissions can be estimated, air quality impacts will
continue to be strongly influenced by climate and
weather variables, such as temperature, humidity,
and air flow (Mickley et al. 2004). Because mercury,
nitrogen, and sulfur are deposited onto the landscape
through rain and snow, projected increases in
precipitation may increase atmospheric deposition,
thus increasing mercury contamination and the
acidification of soils and surface waters (Driscoll
et al. 2007). Tropospheric ozone in the Central
Appalachians is projected to increase as a result
of higher temperatures and decreased ventilation
resulting from changes in air flow (Wu et al. 2008).
Because heat waves and air stagnation retain ozone
levels for extended periods, these climate changes
affect ozone pollution episodes more than mean
ozone levels, and are projected to offset and surpass
decreases in ozone brought about by regulation
(Wu et al. 2008). There is evidence that warmer
temperatures and the burning of vegetation can
result in increased volatilization of mercury soil
reservoirs, potentially releasing mercury into the
atmosphere and transferring it between ecosystems,
with deposition occurring in a more mobile and toxic
form (Jacob and Winner 2009). Particulate matter
may also be affected by changes in climate, although
changes are less predictable than for ozone. Because
particulate matter is cleaned from the air by rainfall,
increases in precipitation frequency due to climate
change could have a beneficial effect. However,
other climate-related changes in stagnant air
episodes, wind patterns, emissions from vegetation,
wildfire, and the chemistry of atmospheric pollutants
will also influence particulate matter levels in
different ways. Air quality regulations are important
in controlling emissions, but when climate change
impacts are taken into consideration, the current
thresholds may not be adequate to meet air quality
targets.
178
More Informaion
• Integrating Knowledge to Inform Mercury
Policy: www.mercurynetwork.org.uk/policylinks/
mercury-and-climate-change/
• The Monongahela National Forest monitors
wet deposition, dry deposition, ozone, and
particulate matter using the National Atmospheric
Deposition Program National Trends Network
(NADP/NTN), Clean Air Status and Trends
Network (CASTNET), and Interagency
Monitoring of Protected Visual Environments
(IMPROVE): www.fs.usda.gov/Internet/FSE_
DOCUMENTS/fsm9_01135.pdf
• Researchers at Penn State are investigating
the effects of soil acidification on sugar maple
decline on the Allegheny National Forest: http://
ecosystems.psu.edu/directory/wes
WATER QUALITY
It is widely accepted that streamflow is primarily
governed by climate, watershed morphology, and
land cover, and that hydrology largely controls
sediment and nutrient export. Any change in state
variables that alters watershed hydrology also
influences water quality dynamics (Likens and
Bormann 1995). Climate change is already creating
challenges in water management by affecting water
availability (Georgakakos et al. 2014). Projected
increases in total precipitation in spring, intense
precipitation events, and storm frequency are
expected to lead to more runoff at that time of year,
and a subsequent reduction in water quality arising
from increased erosion and sedimentation (Liu et
al. 2008, U.S. Environmental Protection Agency
[EPA] 1998). Increased runoff also promotes
flushing of nutrients (e.g., nitrogen and phosphorus)
that build up in natural and disturbed ecosystems,
thereby increasing the potential for downstream
eutrophication and hypoxia (Peterjohn et al. 199,
Vitousek et al. 2010). Additional factors such as fire
and insect defoliation exacerbated by climate change
are also expected to increase runoff, erosion, and
ChAPTER 7: MANAGEMENT iMPLiCATioNS
The steep ridges and valleys in the Allegheny Mountains. These landforms are at greater risk of high-velocity runof and
erosion. Photo by Patricia Butler, NIACS and Michigan Tech, used with permission.
sedimentation. Late summer soil moisture deficits
combined with a longer growing season have the
potential to decrease runoff in the latter half of the
year, thereby decreasing the capacity of a stream
system to dilute larger loads of nutrients (Delpla et
al. 2009).
Anthropogenic activities have already damaged
aquatic ecosystems by increasing soil erosion and
stream sedimentation rates, fragmenting aquatic
habitats, reducing channel and floodplain functions,
degrading habitats, acidifying and burying streams,
and otherwise altering watershed hydrology. Under
the range of projected climate changes, aquatic
ecosystems would tend to have more varied and
more extreme environmental conditions. Changes
of this nature tend to place additional hardship
on these systems and can further compromise
various aquatic resource conditions such as habitat
suitability. Aquatic ecosystems that were once
intact and naturally functioning can be repaired
to various degrees under accelerated timeframes
through restoration actions. Accelerating the rate
of recovery back toward their inherent state can
increase the resiliency of these systems to stressors
and disturbances. Water resource managers may
minimize risks and impacts by accommodating
expanding floodplains, redesigning stormwater and
sewer systems, restoring and managing wetlands for
stormwater management, and developing novel ways
to buffer intense runoff, such as through green roofs
and other infrastructure (U.S. EPA 2008).
More Informaion
• National Climate Assessment: Water Resources:
http://nca2014.globalchange.gov/report/sectors/
water
• National Water Program 2008 Strategy: Response
to Climate Change: http://water.epa.gov/scitech/
climatechange/upload/2008-National-WaterProgram-Strategy-Response-to-Climate-Change.
pdf
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
FoREST PRoDuCTS
The forest products industry is important to the
economies of the assessment area (Chapter 1). Tree
species and forest composition are projected to
change over the 21st century (Chapters 5 and ).
Changes in forest composition across the landscape
will be influenced by forest management, and will
in turn influence forest management and the forest
products industry. Several commercially important
species, such as black cherry and sugar maple, are
projected to decline significantly under a range of
possible climate futures during the next century.
Conversely, post oak, white oak, and shortleaf
pine are projected to increase in the assessment
area. Large potential shifts in commercial species
availability may pose risks for the forest products
sector if the shifts are rapid and the industry is
unprepared. The forest products industry may
benefit from awareness of anticipated climate
trends and shifts in forest species. In many cases,
forest managers can take actions to reduce potential
risks associated with climate change or proactively
encourage species and forest types anticipated to
fare better under future conditions (Swanston and
Janowiak 2012). There may be regional differences
in forest responses, as well as potential opportunities
for new merchantable species to gain suitable habitat
in the assessment area.
Overall, the effects of climate change on the forest
products industry depend not only on ecological
responses to the changing climate, but also on
socioeconomic factors that will continue to change
over the coming century. Major socioeconomic
factors include national and regional economic
policies, demand for wood products, and competing
values for forests (Irland et al. 2001). Large
uncertainties are associated with each of these
factors. The forest products industry has adjusted
to substantial changes over the past 100 years, and
continued responsiveness can help the sector remain
viable.
180
More Informaion
• The U.S. Forest Service 2010 Resources Planning
Act Assessment includes future projections for
forest products and other resources through the
year 200 and examines social, economic, landuse, and climate change influences: www.fs.fed.
us/research/rpa/
• The Climate Change Tree Atlas provides
information on the projected suitable habitat for
tree species under climate change: www.nrs.
fs.fed.us/atlas/bird/
NoNTiMBER FoREST PRoDuCTS
Hundreds of nontimber forest products are used for
food, medicine, craft materials, and other purposes
in the assessment area (Chamberlain et al. 2009).
Changes in climate will have implications for these
products in the assessment area and throughout
the broader region. Many of these products will
be affected by changes in temperature, hydrology,
and species assemblages. As illustrations, effects of
climate change on two nontimber forest products
with broad cultural and economic importance are
discussed briefly here: American ginseng and
mushrooms.
American ginseng is a perennial herbaceous plant
indigenous to the eastern United States that has been
traded internationally since the 1700s (Taylor 200).
Concerns over the sustainability of wild American
ginseng under heavy harvest pressure in Canada,
China, and the United States resulted in international
protection under the Convention on International
Trade in Endangered Species of Wild Fauna and
Flora (CITES). The USFWS monitors exports in
order to examine trends in wild ginseng harvest and
set harvest guidelines and restrictions. Individual
states also monitor the harvest and export of ginseng
and regulate harvesting.
ChAPTER 7: MANAGEMENT iMPLiCATioNS
Recent research has examined the potential response
of wild ginseng to temperature variations in the
assessment area. Findings suggest that genetics
of populations at individual locations play a
large role in population growth rate responses to
temperature. Thus, models that predict responses
based on northern and southern boundaries of this
species likely underestimate the negative impacts
of temperature increases at specific locations
(Souther and McGraw 2011). Precipitation may
also constrain the overall distribution of ginseng
(Souther and McGraw 2011). Neither factor showed
a positive population growth response to predicted
changes. The combination of harvest pressure and
climate change raises concerns about the long-term
stability of the American ginseng population in the
assessment area.
Hunting morels and other mushrooms is a passion
for many people throughout the assessment area for
their commercial value, medicinal properties, and
culinary applications (Emery and Barron 2010).
An analysis of fungal fruiting patterns from southern
England over a 5-year-period showed a lengthened
fruiting period from 33.2 days in the 1950s to
74.8 days in the current decade (Gange et al. 2007).
This change corresponded to increased temperatures
in August through October. Another study of
83 species in Norway found an average delay in
fruiting of nearly 13 days since 1980, coinciding
with warming temperatures and a longer growing
season (Kauserud et al. 2008). Although longer
growing seasons have lengthened the fruiting season
of some fungal species, and shifted the timing of
fruiting later in the spring and fall, future responses
to changes in temperature and precipitation may
be tightly linked to local conditions rather than
broad geographic trends. Management of nontimber
forest products may require increased monitoring of
habitats to ensure viable populations under changing
conditions.
More Informaion
• Forest Farming: www.extension.org/forest_
farming
• Connecting Non-timber Forest Products
Stakeholders to Information and Knowledge: A
Case Study of an Intranet Web Site: www.srs.
fs.usda.gov/pubs/gtr/gtr_srs11/gtr_srs11-04.pdf
• Using Local Ecological Knowledge to Assess
Morel Decline in the U.S. Mid-Atlantic Region:
www.nrs.fs.fed.us/pubs/jrnl/2010/nrs_2010_
emery_001.pdf
FoREST CARBoN
Forest carbon sequestration can mitigate greenhouse
gas emissions in the atmosphere. However, climate
change and indirect impacts to forest ecosystems
may change the ability of forests in the Central
Appalachians to store carbon. In this assessment,
carbon dioxide fertilization effects on forest
ecosystems were not directly modeled or assessed,
but are considered an important implication for
forest management. Within the assessment area,
climate change is projected to lead to longer growing
seasons and warmer temperatures, which potentially
could support increased forest productivity and
carbon storage, as long as water and nutrients are
available for photosynthesis. This increase could be
offset by climate-related disturbances, such as more
insect pests or disease, leading to increases in carbon
storage in some areas and decreases in others (Hicke
et al. 2012, Knicker 2007). Increases in ozone would
reduce photosynthesis and carbon sequestration
(Felzer et al. 2003).
The greatest impacts on forest carbon storage will
likely occur through changes in species composition.
Habitat suitability models forecast shifts in tree
species’ geographic ranges in response to climate
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
changes (Chapter 5). Within the assessment area,
the oak/gum/cypress and spruce/fir forests store the
most carbon per acre, followed by maple/beech/birch
and elm/ash/cottonwood forests (Chapter 1).
Oak/hickory and oak/pine forests contain
considerably less carbon per acre, but are projected
to be more resilient to or even benefit from climate
change. Not all forests store carbon in the same
pools; for example, oak/hickory forests store more
carbon aboveground than in the soil and the spruce/
fir group stores more carbon in the soil. Thus,
shifts in species or assemblages of species on the
landscape may result in shifts in carbon storage.
Invasive plant species also have the potential to alter
species composition and ecosystem functioning. The
invasive tree ailanthus can increase carbon cycling
rates and alter soil chemistry to favor rapid growth
and subsequent forest colonization by new ailanthus
seedlings (Gómez-Aparicio et al. 2008).
Carbon management and conservation of carbon
stocks will require managing species composition
and maintaining forest cover on the landscape. The
biggest loss of forest carbon in the assessment area
has already occurred as a result of historic logging,
loss of soil from erosion and volatilization from
fire, and decades of land conversion from forests to
agriculture and urbanization. The ability of existing
ecosystems to sequester carbon may be further
hindered by increased disturbances and stresses
brought on by climate change. Carbon management
can benefit future landscape-scale restoration
projects. Riparian restoration and wetland restoration
have the potential to help landscapes slow the export
of nutrients or even capture and store soil carbon that
would otherwise leave the watershed. Replanting
riparian areas and encouraging the regrowth of
these areas can help to address the historic forest
carbon loss for several of the ecosystems analyzed
in the assessment. Opportunities to focus restoration
management on stabilizing soils, planting trees, and
addressing historic land degradation are numerous.
More Informaion
Soil from federal lands. These public lands contain the
highest density of carbon in the Central Appalachians. Photo
by Patricia Butler, NIACS and Michigan Tech, used with
permission.
182
• The U.S. Forest Service’s Climate Change
Resource Center: Forests and Carbon Storage:
www.fs.fed.us/ccrc/topics/forests-carbon/
• A Synthesis of the Science on Forests and Carbon
for U.S. Forests: www.fs.fed.us/rm/pubs_other/
rmrs_2010_ryan_m002.pdf
ChAPTER 7: MANAGEMENT iMPLiCATioNS
The Blackwater Falls in the Canaan Valley, West Virginia, a popular recreaion area. Photo by Patricia Butler, NIACS and
Michigan Tech, used with permission.
RECREATioN
Opportunities for outdoor recreation depend on the
natural resource (e.g., spelunking in caves versus
hiking mountain trails) and the weather on any
given day. Projected increases in temperature and
precipitation, especially heat waves and intense
precipitation events (Chapter 4), are expected to
change recreation patterns. Warmer spring and fall
weather may increase the length of the recreation
season, which could require a shift in the open
season for recreation areas, requiring more staff
hours and potentially more infrastructure. Regional
increases in average temperatures and heat waves
during summer months could shift visitor behavior,
depending on the magnitude of changes. Many
visitors to the Monongahela National Forest arrive
during the summer to escape the heat at lower
elevations or in urban areas, and temperature
increases could result in higher visitation rates
(Loomis and Crespi 1999, Mendelsohn and
Neumann 2004, Richardson and Loomis 2004). If
temperatures become too hot for outdoor recreation,
however, visitation and outdoor recreation and
tourism could decrease (Nicholls 2012). Specific
activities such as fishing or skiing may also be
limited by warmer temperatures (Morris and Walls
2009).
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
Projected increases in intense precipitation and
strong storm events could lead to more frequent
closings of public recreation areas. The same
derecho of 2012 that knocked out power to several
states also blew down thousands of trees across the
region, and caused many public places to close,
such as Lake Sherwood Recreation Area in West
Virginia. In 2013, effects of Hurricane Sandy closed
a large part of the Monongahela National Forest for
several months because of such hazards as broken,
hanging, and down trees, which damaged facilities,
and closed roads and trails. Many recreation areas
are located near rivers and streams, which are
regularly subject to flood events. To properly protect
recreation visitors, short- and long-term closings
may be needed to repair damage caused by intense
precipitation and strong storm events.
Warmer winter temperatures could also affect winter
recreation. Warmer temperatures that prevent Lake
Erie from freezing may allow more moisture to
evaporate from the lake and fall as snow on land.
However, warmer average temperatures may also
increase the probability that precipitation will fall
as rain rather than snow. A particular economic
concern is the decreased viability of downhill skiing
during the holiday season, which can generate
as much as one-third of a ski resort’s annual
revenue (Dunnington 2011). Although downhill
ski areas can generate artificial snow, few options
exist for adapting cross-country skiing, sledding,
snowshoeing, and other snow-dependent winter
sports to warmer temperatures (Morris and Walls
2009). These winter activities may be replaced
by hiking and other activities not dependent on
snow, requiring adjustments in how recreation
areas are managed. The degree of climate change
will ultimately influence the severity of impacts
on recreation activities, but there are many
opportunities for visitors and managers to adapt their
activities by changing the timing or location (Morris
and Walls 2009).
184
More Informaion
• National Climate Assessment Midwest Technical
Input Report: Recreation and Tourism Sector:
glisa.msu.edu/docs/NCA/MTIT_RecTourism.pdf
• Climate Change and Outdoor Recreation
Resources: www.rff.org/RFF/Documents/RFFBCK-ORRG_ClimateChange.pdf
WILDERNESS
The Wilderness Act of 194 was established
to protect areas in their natural condition and
to assure that an increasing human population,
accompanied by expanding settlement and growing
mechanization, does not modify all areas within
the United States (Wilderness Act of 194). U.S.
Forest Service policy directs the agency to “manage
the wilderness resource to ensure its character and
values are dominant and enduring” (U.S. Forest
Service 2007). According to the Monongahela
National Forest Land and Management Plan,
management emphasis for its eight wilderness areas
on the Forest is to “preserve wilderness attributes
and the natural environment for future generations”
(U.S. Forest Service 200a).
It has been argued that climate change would have
the greatest impacts on species that are confined to
protected areas, largely because populations would
not be able to migrate with changing range limits
for species (Peters and Darling 1985). Additionally,
species within protected areas would potentially
face new competitors, predators, or diseases as
many native and nonnative species move around on
the landscape. Models of climate change impacts
on ecosystems project that more than 40 percent
of Canada’s protected areas will undergo a major
change in vegetation (Lemieux and Scott 2005).
Management of wilderness areas may need to
address difficult questions about whether to protect
current species assemblages, or to allow new species
assemblages to form, and if the latter, to what extent.
ChAPTER 7: MANAGEMENT iMPLiCATioNS
An increase in intense precipitation and strong
storm events would cause muddy conditions on
trails, erosion of trail tread, and down trees across
trails. Mechanized equipment is not allowed in
wilderness areas; the additional physical labor to
complete trail maintenance is expensive and time
consuming. For example, after Hurricane Sandy
in 2013, trails within Otter Creek Wilderness and
Cranberry Wilderness areas were closed for several
months until specialized crews were funded to clear
the trails with crosscut saws and axes. Responding
to increased disturbances may require additional
resources to manage wilderness areas.
More Informaion
• Climate Change Toolbox: Effect of Climate
Change on Wilderness and Protected Areas:
www.wilderness.net/climate
• The U.S. Forest Service’s Climate Change
Resource Center: Wilderness and Climate
Change: www.fs.fed.us/ccrc/topics/wilderness/
Private lands juxtaposed with the Shawnee State Forest, Ohio. Photo by the Ohio Department of Natural Resources, used with
permission.
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
CuLTuRAL RESouRCES
The remnants of past human activity, such as
paintings, sculptures, and objects for everyday
life, are present within the assessment area. These
resources date to both prehistoric and historic time
periods, and exist both above and below the ground
surface. Climate change impacts on the physical
environment have the potential to affect the nature,
character, and condition of these cultural resources.
Increases in extreme precipitation events, in
combination with a more episodic regime, are
expected to intensify erosion and weathering of
cultural resources. Consequently, the physical
integrity of historic structures could be undermined
and subsurface resources threatened if the soil
covering them is washed away. As precipitation
increases, the risk of flooding also escalates;
flooding would hasten the erosion process of sites
on ridge tops and on flood terraces. Floodwaters can
further threaten the integrity of historic structures
in low-lying areas by eroding the foundation,
or adding moisture. The increased moisture can
promote mold and fungus growth, thereby hastening
deterioration of wooden and other constructed
features (Schiffer 199). Erosion of rock shelters
has already been witnessed within the assessment
area on sites composed largely of erodible sandstone
that are more frequently being inundated with water.
Artifacts and other cultural materials located in these
shelters have been transported by water to nearby
creeks. Increased moisture levels and damage from
freeze/thaw cycles and subsequent erosion have
resulted in roof collapse within these rock shelters as
well. Projected increases in freeze/thaw events and
deep soil frost would exacerbate these effects.
Longer growing seasons and range shifts in native
and invasive plants expand the potential for these
taxa to damage historic structures as these plants
tend to cling to structures at points of weakness,
18
accelerating structural degradation (Schiffer 199).
An altered fire regime could become an increasing
source of disturbance if climate shifts encourage
more frequent or intense fire behavior. Fire and
firefighting activities can destroy historic structures
and threaten all types of cultural resources (Buenger
2003). Managing cultural resources will become
more challenging as a result of the direct and
indirect impacts of climate change. Identifying and
documenting existing cultural resources now will be
critical in conserving these important artifacts and
historical information.
More Informaion
• Climate Change and World Heritage: whc.unesco.
org/documents/publi_wh_papers_22_en.pdf
• National Park Service Climate Change Response
Strategy: http://www.nps.gov/orgs/ccrp/upload/
NPS_CCRS.pdf
uRBAN FoRESTS
Climate change will likely affect urban forests in
the assessment area as well. Urban environments
can pose additional stresses to trees not encountered
in natural environments, such as pollution from
vehicle exhaust, confined root environments,
and road salts. Urban environments also cause a
“heat island effect,” and thus warming in cities
will likely be even greater than that experienced
in natural communities. Impervious surfaces can
make urban environments more susceptible to flash
floods, placing flood-intolerant species at risk. All
of these abiotic stressors can make urban forests
more susceptible to nonnative species invasion, and
insect and pathogen attack, especially because only
a limited range of species and genotypes is typically
planted in urban areas. Urban settings are also
the most likely places for exotic insect pests to be
introduced.
ChAPTER 7: MANAGEMENT iMPLiCATioNS
Projected changes in climate can pose both
challenges and opportunities for the management
of urban forests. Shifts in temperature and changes
in extreme events may have effects on species
selection for planting. Native species projected
to decline under climate change will likely not
tolerate even more extreme conditions presented
by urban settings. Conversely, urban environments
may favor heat-tolerant or drought-tolerant native
species or new migrants (Chapter 5). Determining
appropriate species for planting may be a challenge,
but community foresters are already familiar
with the practice of planting species novel to an
area. Because of urban effects on climate, many
community forests already contain species that are
from planting zones south of the area or cultivars
that tolerate a wide range of climate conditions.
Large disturbance events may also become more
frequent or intense in the future, necessitating
informed decisions in response. For example, wind
events or pest outbreaks may be more damaging
to already stressed trees. If leaf-out dates advance
earlier in the spring due to climate change,
community forests may be increasingly susceptible
to early-season frosts or snowstorms. More people
and larger budgets may be required to handle an
increase in the frequency or intensity of these events,
which may become more difficult in the face of
reduced municipal budgets and staffing.
More Informaion
• The U.S. Forest Service’s Climate Change
Resource Center: Urban Forests and Climate
Change: www.fs.fed.us/ccrc/topics/urban-forests/
• Urban Forests: Climate Adaptation Guide: www.
toolkit.bc.ca/Resource/Urban-Forests-ClimateAdaptation-Guide
• Climate Change Adaptation Options for Toronto’s
Urban Forest: www.cleanairpartnership.org/pdf/
climate_change_adaptation.pdf
FoREST-ASSoCiATED
TOWNS AND CITIES
The forests of the Central Appalachians are deeply
and intimately linked to human communities.
Conversely, these communities are tied to the health
and functioning of surrounding forests, whether for
economic, cultural, recreational, or other reasons.
Climate change impacts on forest ecosystems are
likely to affect the human communities that use these
resources and to change or challenge how those
communities use and relate to these forests. These
complex feedbacks could very well pose a challenge
to current forest management goals and activities.
Consequently, it is important to address potential
climate change impacts on forest-associated towns,
cities, and other communities, and the implications
for managing healthy ecosystems.
Although impact models can predict species
or community responses to climate change,
considerably less is known about the potential
social and cultural impacts of climate or forest
change and how human communities might best
respond. Community vulnerability to climate
change is a function of the community’s exposure to
change, such as being situated within a flood plain
projected to receive increased precipitation, and its
relative sensitivity to such changes, such as being
constrained by reduced funding from the Federal
Emergency Management Act (FEMA) due to budget
cuts or other national priorities. Community adaptive
capacity is a function of the community’s ability to
act in an adaptive way and includes both material
(i.e., capital) and nonmaterial (i.e., leadership)
resources that can be leveraged by the community to
monitor, anticipate, and proactively manage hazards,
stressors, and disturbances.
These concepts help frame the issue of climate
change from a community perspective, but it
is important to keep in mind that every forest-
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
Muliple land uses in West Virginia. Agriculture and development dominate the lat valleys. Photo by Patricia Butler, NIACS
and Michigan Tech, used with permission.
associated community has particular conditions,
capacities, and constraints that might make it more
vulnerable or resilient to climate change than other
communities. For example, forest users from a
city like Huntington, WV, face different sources of
vulnerability than forest users from a small town
like Glouster, OH. Moreover, the effects of climate
change and forest impacts are not evenly distributed
geographically or socially. For example, a tourismdependent community may be more or less exposed
to climate change than certain social groups
188
within communities (e.g., individuals working in
forest products industries), or they may be equally
exposed, but more or less able to adapt.
If resource professionals, community leaders, and
local organizations are to help communities mitigate
the impacts of climate change and adapt, they must
be able to assess community vulnerabilities and
capacities to organize and engage various resources
(Fischer et al. 2013).
ChAPTER 7: MANAGEMENT iMPLiCATioNS
More Informaion
• Assessing Resilience in Social-Ecological
Systems: Workbook for Practitioners: www.
resalliance.org/index.php/resilience_assessment
• Assessing Social Vulnerability to Climate Change
in Human Communities near Public Forests and
Grasslands: A Framework for Resource Managers
and Planners: http://people.oregonstate.edu/
~hammerr/SVI/Fischer_etal_JoF_2013.pdf
• Community Vulnerability and Adaptive Capacity
Project: http://www.cfc.umt.edu/VAC/default.php
• A study is underway to explore the perceived
vulnerability and adaptive capacity of forestassociated human communities in southeastern
Ohio. For more information, contact Dr. Daniel
Murphy at the University of Cincinnati: http://
asweb.artsci.uc.edu/collegedepts/anthro/fac_staff/
profile_details.aspx?ePID=MzA0ODcx
CoNSERVATioN PLANNiNG
Climate change has many important implications
for land conservation planning in the Central
Appalachians. Climate change science can be used
to help prioritize land conservation investments and
help guide project design. Some of the most useful
decision-support tools for conservation planning are
site-specific technical assistance through scientific
experts to geographic information systems (GIS)
mapping tools that allow the user to assess how
individual parcels of land relate to variables such as
forest carbon and projected “climate-safe” habitat
areas.
Conservation in the complex landscapes of
the Central Appalachians also requires careful
analysis to evaluate the potential contribution of
conservation projects to climate adaptation. The
region’s forests provide vital ecosystem services to
human and natural communities. These services,
such as drinking water supplies and cold-water
habitats, could be affected by greater extremes
of precipitation and other manifestations of
climate change. Given the steep slopes in the
region, watersheds are naturally prone to flooding
and at particular risk from increases in extreme
precipitation events. Conservation linked with
adaptive management can be directed to the most
vulnerable watersheds to help them withstand these
impacts.
Further, climate change analysis is nuanced
by the region’s globally significant mixture of
microhabitats and connecting habitat corridors
stretched across rugged landscapes. Planning for
conservation of terrestrial habitat “strongholds” from
climate change requires a close look at the landscape
to identify those corridors and habitats that will
be most resilient in the face of projected shifts. As
evidence of the unique opportunities in the upper
Potomac watershed, the Open Space Institute and
Doris Duke Charitable Foundation have targeted a
special conservation funding source to this region
for conservation of important sites for climate
adaptation. The Nature Conservancy’s resilience
analysis project identifies sites across the Northeast
that have high or low resilience to climate changes
based on geophysical characteristics (Anderson et
al. 2012). Integrating this kind of information into
conservation planning and prioritization can help
identify and protect areas that have unique potential
for conservation.
Carbon dioxide emissions have directly contributed
to ongoing climate change, and it is unclear how
emissions levels may change over the course of
the century. Identifying forest tracts that have
high carbon stocks or potential for high carbon
levels through conservation- and carbon-oriented
management can help maintain and even increase
this important source of carbon mitigation.
U.S. forests currently sequester 10 to 20 percent of
the nation’s carbon emissions each year (Ryan et al.
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ChAPTER 7: MANAGEMENT iMPLiCATioNS
2010). Carbon-oriented prioritization is particularly
important in the Central Appalachians region, where
the region’s forests have substantial carbon stores.
For example, oak/hickory forests in the region can
hold as much as 132 tons of carbon per acre in soils
and aboveground biomass.
Land managers can prioritize protection on sites
that are strong carbon sinks, or that have potential
for resilience under climate change. Designing
land conservation projects for climate objectives
may require specific long-term ownership and
management prescriptions to be attached to a
conservation agreement. In some cases, a good
conservation strategy may be to leave lands in
private ownership, and to develop conservation
easement terms that support adaptive management
by the landowner to address climate shifts. In other
cases, where complex restoration or species-specific
management is needed, an appropriate conservation
strategy might be to seek a public agency owner that
can provide the necessary financial and technical
resources. In either instance, the key principle is to
use available climate information to assess projected
stressors on the property in the future, and then to
integrate those considerations into project design.
All of the efforts described above will be advanced
by new science and data products to guide project
selection and design. Private nonprofit organizations,
government agencies, landowners, and potential
funders will increasingly need spatially explicit
information on how climate shifts will play out
over the land. This science can enable effective
use of funding, staff time, and other resources that
are essential to advancing “climate-informed”
conservation of forests in the Central Appalachians,
and shaping conservation efforts to deliver a more
resilient landscape.
More Informaion
• The Open Space Institute and Doris Duke
Charitable Foundation: www.osiny.org/site/PageS
erver?pagename=Issues_Habitat
190
• The Nature Conservancy Northeast Resilience
Analysis: www.conservationgateway.org/
ConservationByGeography/NorthAmerica/
UnitedStates/edc/reportsdata/terrestrial/resilience/
ne/Pages/default.aspx
NATuRAL RESouRCE
MANAGEMENT PLANNiNG
Until recently, climate change has not played a large
role in natural resource planning. However, many
federal and state-level land management agencies
are beginning to address the issue. For example, the
U.S. Forest Service’s 2012 Planning Rule directly
addresses the impacts and ramifications of climate
change. In fact, climate change was among the
stated purposes for revising the rule. Similarly, the
state forestry agencies of Ohio, West Virginia, and
Maryland began to officially address climate change
in the 2010 State Forest Resource Assessment and
Strategy.
Private lands make up about 85 percent of forest
lands in the Central Appalachians region
(Chapter 1). Northeastern Area State & Private
Forestry oversees the Forest Stewardship Program to
assist private landowners with conservation planning
and to provide forest management plans at low
cost. This unit is currently funding two examples
of forest adaptation to climate change, using the
tools in Forest Adaptation Resources (Swanston
and Janowiak 2012) to identify adaptation actions
in Forest Stewardship Plans. Because the goals for
private landowners are diverse and can include goals
for soil and water conservation, timber production,
wildlife, and many more values, each example of
adaptation will differ based on landowner needs.
The Northeastern Area unit is also working with
the Northern Institute of Applied Climate Science
to develop an online version of the adaptation
workbook presented in Forest Adaptation Resources
that will be more accessible to natural resource
managers.
ChAPTER 7: MANAGEMENT iMPLiCATioNS
Management plans for national forests or state
agencies are typically written to guide management
for a 10- to 15-year period, and it may be difficult
to foresee projected shifts in climate within this
short planning horizon. If climate change results
in more frequent disturbances or unanticipated
interactions among major stressors, managers may
find it more difficult to adhere to the stated goals,
objectives, and priorities in current Forest Plans.
Incorporating adaptive management principles
and including flexibility to address shifting
conditions and priorities may be a strategy to
handle the uncertainties of climate change. But
building that flexibility into forest plans may pose
a challenge both in completing the analysis (with
specialists who may be unaccustomed to analyzing
adaptive management strategies) and in educating
the public about the need for proposed actions.
Project-level planning on national forests will face
challenges with interdisciplinary teams grappling
to understand both the impacts that projects may
have on greenhouse gas emissions and carbon
sequestration levels and the impacts that climate
change may have on projects. Input from the
public is expected to increasingly question these
relationships and interdisciplinary teams must be
able to respond. Draft guidance is available from the
Council on Environmental Quality (CEQ) and the
U.S. Forest Service on project-level climate change
considerations.
More Informaion
• Forest Steward Program for private landowners:
http://www.na.fs.fed.us/stewardship/index.shtm
• Region 9 Climate Change Guidance: www.fs.fed.
us/emc/nepa/climate_change/includes/cc_nepa_
guidance.pdf
• Statewide Forest Action Plans: http://www.
forestactionplans.org/regions/northeastern-region
• Forest Adaptation Resources: Climate Change
Tools and Approaches for Land Managers
provides concepts and tools for integrating
climate change considerations into natural
resource planning and management:
www.nrs.fs.fed.us/pubs/40543
ChAPTER SuMMARY
The breadth of the topics above highlights the wide
range of effects that climate change may have on
forest management in the Central Appalachians
region. It is not the role of this assessment to identify
adaptation actions that should be taken to address
these climate-related risks and vulnerabilities, nor
would it be feasible to prescribe suitable responses
for all future circumstances. Decisions to address
climate-related risks for forest ecosystems will
be affected by economic, political, ecological,
and societal factors. These factors will be specific
to each land owner and agency, and are highly
unpredictable.
Confronting the challenge of climate change
presents opportunities for managers and other
decision-makers to plan ahead, build resilient
landscapes, and ensure that the benefits that forests
provide are sustained into the future. Resources
are available to help forest managers and planners
incorporate climate change considerations into
existing decisionmaking processes (Swanston and
Janowiak 2012) (www.forestadaptation.org). This
assessment will be a useful foundation for land
managers in that process, to be further enriched by
local knowledge and site-specific information.
• Draft National Environmental Policy Act
Guidance on Consideration of the Effects of
Climate Change and Greenhouse Gas Emissions:
http://energy.gov/sites/prod/files/CEQ_Draft_
Guidance-ClimateChangeandGHGemissions2.18.10.pdf
191
GLoSSARY
aerosol
biomass
a suspension of fine solid particles or liquid droplets
in a gas, such as smoke, oceanic haze, air pollution,
and smog. Aerosols may influence climate by either
scattering and absorbing radiation, or by acting
as condensation nuclei for cloud formation or
modifying the properties and lifetime of clouds.
the mass of living organic matter (plant and animal)
in an ecosystem; biomass also refers to organic
matter (living and dead) available on a renewable
basis for use as a fuel; biomass includes trees and
plants (both terrestrial and aquatic), agricultural
crops and wastes, wood and wood wastes, forest and
mill residues, animal wastes, livestock operation
residues, and some municipal and industrial wastes.
asynchronous quanile regression
a type of regression used in statistical downscaling.
Quantile regression models the relation between a
set of predictor variables and specific percentiles (or
quantiles) of the response variable.
boreal
a zone between 50 and 55° and 5 and 70° latitude
in the Northern Hemisphere characterized by cool
northern temperatures and low rainfall (<20 inches).
bagging trees
This statistical technique begins with a “regression
tree” approach, but recognizes that part of the
output error in using a single regression tree comes
from the specific selection of an original data set.
The bagging trees method uses another statistical
technique called “bootstrapping” to create several
similar data sets. Regression trees are then produced
from these new data sets and results are averaged.
carbon dioxide (Co2) ferilizaion
barrens
Co2-equivalent (CO2-eq)
plant communities that occur on sandy soils and that
are dominated by grasses, low shrubs, small trees,
and scattered large trees.
the concentration of carbon dioxide (CO2 ) that
would cause the same amount of radiative forcing
as a given mixture of CO2 and other forcing
components.
baselow
the condition in which groundwater provides the
entire flow of a stream. (During most of the year,
streamflow is composed of both groundwater
discharge and land surface runoff.)
192
increased plant uptake of CO2 through
photosynthesis in response to higher concentrations
of atmospheric CO2 .
climate normal
the arithmetic mean of a climatological element
computed over three consecutive decades.
convecive storm
Convection is a process whereby heat is transported
vertically within the atmosphere. Convective storms
result from a combination of convection, moisture,
and instability. Convective storms can produce
thunderstorms, tornadoes, hail, heavy rains, and
straight-line winds.
GLoSSARY
dendriic drainage
eastern deciduous forest
a stream drainage pattern that resembles the
branching pattern of a tree, with tributaries joining
larger streams at angles <90°. This type of drainage
occurs where the subsurface geology has a uniform
resistance to erosion, and therefore little influence on
the direction that tributaries take.
a forest dominated by trees such as oaks, maples,
beech, hickories, and birches that drop their leaves.
Evergreen conifers do live in this forest, but are
rarely dominant. This forest develops under cold
winters (but not as cold as the boreal region to the
north), and annual rainfall is higher in this forest
than anywhere else in North America except for the
subtropical and tropical areas to the south.
derecho
widespread and long-lived convective windstorm
that is associated with a band of rapidly moving
showers or thunderstorms characterized by wind
gusts that are greater than 57 miles per hour and that
may exceed 100 miles per hour (National Oceanic
and Atmospheric Administration 2012).
ecological processes
processes fundamental to the functioning of a
healthy and sustainable ecosystem, usually involving
the transfer of energy and substances from one
medium or trophic level to another.
disturbance
ecoregion
stresses and destructive agents such as invasive
species, diseases, and fire; changes in climate and
serious weather events such as hurricanes and ice
storms; pollution of the air, water, and soil; real
estate development of forest lands; and timber
harvest. Some of these are caused by humans, in part
or entirely; others are not.
repetitive pattern of ecosystems associated with
commonalities in soil and landform that characterize
that larger region.
edaphic
of or pertaining to soil characteristics.
emissions scenario
downscaling
a method for obtaining high-resolution climate or
climate change information from relatively coarseresolution general circulation models (GCMs);
involves examining the statistical relationship
between past climate data and on-the-ground
measurements.
a plausible representation of the future development
of emissions of greenhouse gases and aerosols that
are potentially radiatively active, based on certain
demographic, technological, or environmental
developments (Intergovernmental Panel on Climate
Change [IPCC] 2007).
esker
driver
any natural or human-induced factor that directly or
indirectly causes a change in an ecosystem.
a serpentine ridge of glacial drift, originally
deposited by a meltwater stream running beneath a
glacier.
dynamical downscaling
evapotranspiraion
a method for obtaining high-resolution climate or
climate change information from relatively coarseresolution general circulation models (GCMs) using
a limited-area, high-resolution model (a regional
climate model, or RCM) driven by boundary
conditions from a GCM to derive smaller-scale
information.
the sum of evaporation from the soil and
transpiration from plants.
luvial
of, relating to, produced by, or inhabiting a stream or
river.
193
GLoSSARY
forest type
greenhouse efect
a classification of forest land based on the dominant
species present, as well as associate species
commonly occurring with the dominant species.
the rise in temperature that the Earth experiences
because certain gases in the atmosphere (water
vapor, carbon dioxide, nitrous oxide, and methane,
for example) absorb and emit energy from the sun.
forest-type group
based on FIA definitions, a combination of forest
types that share closely associated species or site
requirements and are generally combined for brevity
of reporting.
growing season
the period in each year when the weather and
temperature are right for plants to grow.
growing stock
fragmentaion
a disruption of ecosystem or habitat connectivity,
caused by human or natural disturbance, creating a
mosaic of successional and developmental stages
within or between forested tracts of varying patch
size, isolation (distance between patches), and edge
length.
funcional diversity
the value, range, and relative abundance of
functional traits in a given ecosystem.
a classification of timber inventory that includes
live trees of commercial species meeting specified
standards of quality or vigor. When associated with
volume, this includes only trees ≥5.0 inches in
diameter at breast height.
habitat
those parts of the environment (aquatic, terrestrial,
and atmospheric) often typified by a dominant
plant form or physical characteristic, on which an
organism depends, directly or indirectly, in order to
carry out its life processes.
fundamental niche
the total habitat available to a species based on
climate, soils, and land cover type in the absence of
competitors, diseases, or predators.
general circulaion model (GCM)
a mathematical model of the general circulation of
a planetary atmosphere or ocean and based on the
Navier–Stokes equations on a rotating sphere with
thermodynamic terms for various energy sources.
glacial drit (ill)
unsorted and unstratified drift (typically a
heterogeneous mix of sand, silt, clay, gravel, and
stones) deposited directly by and underneath a
glacier without subsequent reworking by meltwater.
hardwood
a dicotyledonous tree, usually broad-leaved and
deciduous. Hardwoods can be split into soft
hardwoods (red maple, paper birch, quaking aspen,
and American elm) and hard hardwoods (sugar
maple, yellow birch, black walnut, and oaks).
impact model
simulations of impacts on trees, animals, and
ecosystems; these models use GCM projections
as inputs, and include additional inputs such as
tree species, soil types, and life history traits of
individual species.
importance value
an index of the relative abundance of a species in
a given community (0 = least abundant, 50 = most
abundant).
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GLoSSARY
industrially owned forest
mesophicaion
land owned by forest product companies that harvest
and market timber.
a process “whereby microenvironmental conditions
(cool, damp, and shaded conditions; less flammable
fuel beds) continually improve for shade-tolerant
mesophytic species and deteriorate for shadeintolerant, fire-adapted species” (Nowacki and
Abrams 2008: 123).
intensity
amount of precipitation falling per unit of time.
kame
a short ridge or mound of stratified drift deposited
from a retreating glacier.
karst
an area of irregular limestone (calcium carbonate)
in which erosion has produced fissures, sinkholes,
underground streams, and caverns. Most caves are
formed below the water table, resulting in stalactites
and stalagmites.
ketle
a depression left in a mass of glacial drift, formed by
the melting of an isolated block of glacial ice.
model error
uncertainty caused by a lack of complete
understanding of some climate processes, or by
the inability of models to pick up small-scale but
influential climate processes.
model reliability score
for the Tree Atlas: a “tri-model” approach to assess
reliability of model predictions for each species,
classified as high, medium, or low, depending on the
assessment of the stability of the bagged trees and
the R2 in RandomForest (Iverson et al. 2008b: 392).
modifying factor
Kyoto Protocol
Adopted at the 1997 Third Session of the Conference
of Parties to the UN Framework Convention on
Climate Change in Kyoto, Japan, it contains legally
binding commitments to reduce anthropogenic
greenhouse gas emissions by at least 5 percent below
1990 levels in the period 2008-2012 (IPCC 2007).
environmental variables (e.g., site conditions,
interspecies competition, disturbance, dispersal
ability) that influence the way a tree may respond to
climate change.
moraine
an accumulation of boulders, stones, or other debris
carried and deposited by a glacier.
lacustrine
pertaining to or formed in a lake.
nonindustrial private landowners
mass wasing
an ownership class of private lands where the owner
does not operate wood-using plants.
movement of water and other materials as controlled
by gravity; occurs on slopes under influence of
gravitational stress. Gravity pulls on a mass until
a critical shear-failure point is reached; thus, the
greater the slope, the more mass wasting.
mesic
northern hardwoods
forest type with wet-mesic to dry-mesic soils,
medium to high soil nutrient level, and supporting
tree species such as sugar maple (dominant),
basswood, hemlock, yellow birch, ironwood, red
maple, and white ash.
pertaining to sites or habitats characterized by
intermediate (moist, but not wet or dry) soil moisture
conditions.
195
GLoSSARY
orographic liting
pulpwood
the process in which an air mass is forced from a low
elevation to a higher elevation. Adiabatic cooling
can subsequently raise the relative humidity to 100
percent, resulting in clouds and precipitation.
roundwood, whole-tree chips, or wood residues used
for the production of wood pulp for making paper
and paperboard products.
parcelizaion
radiaive forcing
peak low
the change in net irradiance between different
layers of the atmosphere. A positive forcing (more
incoming energy) tends to warm the system; a
negative forcing (more outgoing energy) tends to
cool it. Causes include changes in solar radiation
or concentrations of radiatively active gases and
aerosols.
the maximum instantaneous discharge of a stream or
river at a given location.
RandomForests
the subdivision of a single forest ownership into
two or more ownerships. Parcelization may result
in fragmentation if habitat is altered under new
ownership.
phenology
the timing of natural events such as the date that
migrating birds return, the first flower dates for
plants, and the date on which a lake freezes in the
autumn or opens in the spring. Also refers to the
study of this subject.
RandomForests is a statistical technique similar to
bagging trees in that it also uses bootstrapping to
construct multiple regression trees. The difference is
that each tree is produced with a random subset of
predictors. Typically, 500 to 2,000 trees are produced
and the results are aggregated by averaging. This
technique eliminates the possibility of overfitting
data.
process model
a model that relies on computer simulations based
on mathematical representations of physical and
biological processes that interact over space and
time.
Real Estate Investment Trust (REIT)
projecion
realized niche
a model-derived estimate of future climate, and the
pathway leading to it.
the portion of potential habitat that a species
occupies; usually it is less than what is available
because of predation, disease, and competition with
other species.
proxy
a figure or data source that is used as a substitute
for another value in a calculation. Ice and sediment
cores, tree rings, and pollen fossils are all examples
of things that can be analyzed to infer past climate.
The size of rings and the isotopic ratios of elements
(e.g., oxygen, hydrogen, and carbon) in rings and
other substrates allow scientists to infer climate and
timing.
19
Considered private, nonindustrial landowners,
REITS own and operate large acreages of
timberland.
recharge
the natural process of movement of rainwater from
land areas or streams through permeable soils into
water-holding rocks that provide underground
storage (i.e., aquifers).
GLoSSARY
refugia
scenario
locations and habitats that support populations of
organisms that are limited to small fragments of their
previous geographic range.
a coherent, internally consistent, and plausible
description of a possible future state of the world.
It is not a forecast; rather, each scenario is one
alternative image of how the future can unfold.
A projection may serve as the raw material for a
scenario, but scenarios often require additional
information (IPCC 2007).
resampling
a method to resize or change the resolution of a data
grid in geographic information systems. Resampling
should not be confused with downscaling.
Resampling is performed only on grids that are
larger than the original cell size.
senescence
roundwood
the process of aging in plants. Leaf senescence
causes leaves of deciduous trees to change color in
autumn.
logs, bolts, and other round timber generated from
harvesting trees for industrial or consumer use.
signiicant trend
runof
that part of the precipitation that appears in surface
streams. It is the same as streamflow unaffected by
artificial diversions or storage.
saw log
a log meeting minimum standards of diameter,
length, and defect, including logs at least 8 feet long,
sound and straight, and with a minimum diameter
inside bark of inches for softwoods and 8 inches
for hardwoods, or meeting other combinations of
size and defect specified by regional standards.
sawimber
a live tree of commercial species containing at least
a 12-foot saw log or two noncontiguous 8-foot or
longer saw logs, and meeting specifications for
form; softwoods must be at least 9 inches, and
hardwoods must be at least 11 inches, respectively,
in diameter outside the bark.
significant trends are least-squares regression pvalues of observed climate trends. In this report,
significant trends (p < 0.10) are shown by stippling
on maps of observed climate trends. Where no
stippling appears (p > 0.10), observed trends have
a higher probability of being due to chance alone
(Girvetz et al. 2009).
snowpack
layers of accumulated snow that usually melts during
warmer months.
sotwood
a coniferous tree, usually evergreen, having needles
or scale-like leaves.
species distribuion model
a model that uses statistical relationships to project
future change.
197
GLoSSARY
staisical downscaling
a method for obtaining high-resolution climate or
climate change information from relatively coarseresolution general circulation models (GCMs) by
deriving statistical relationships between observed
small-scale (often station level) variables and larger(GCM-) scale variables. Future values of the largescale variables obtained from GCM projections of
future climate are then used to drive the statistical
relationships and so estimate the smaller-scale
details of future climate.
Timber Investment Management Organizaion
(TIMO)
Considered private, nonindustrial landowners,
TIMOs act as investment managers for clients who
own timberlands as partnership shares.
topkill
death of aboveground tree stem and branches.
transpiraion
liquid water phase change occurring inside plants
with the vapor diffusing to the atmosphere.
stormlow
runoff that occurs due to a heavy precipitation event.
uncertainty
streamlow
a term used to describe the range of possible values
around a best estimate, sometimes expressed in
terms of probability or likelihood.
discharge that occurs in a natural surface stream
course whether or not it is diverted or regulated.
threat
a source of danger or harm.
198
vulnerability
susceptibility to a threat.
LiTERATuRE CiTED
Abrams, M.D. 1992. Fire and the development of
oak forests. BioScience. 42(5): 34-353.
Abrams, M.D. 1998. The red maple paradox.
BioScience. 48(5): 355.
Abrams, M.D. 2003. Where has all the white oak
gone? BioScience. 53(10): 927-939.
Abrams, M.D.; Copenheaver, C.A.; Black, B.A.; van
de Gevel, S. 2001. Dendroecology and climatic
impacts for a relict, old-growth, bog forest
in the Ridge and Valley Province of central
Pennsylvania, USA. Canadian Journal of Botany.
79(1): 58-9.
Abrams, M.D.; Johnson, S.E. 2012. Long-term
impacts of deer exclosures on mixed-oak forest
composition at the Valley Forge National
Historical Park, Pennsylvania, USA. The
Journal of the Torrey Botanical Society. 139(2):
17-180.
Abrams, M.D.; Nowacki, G.J. 1992. Historical
variation in fire, oak recruitment, and postlogging accelerated succession in central
Pennsylvania. The Journal of the Torrey
Botanical Society. 119(1): 19-28.
Abrams, M.D.; Orwig, D.A.; Demeo, T.E. 1995.
Dendroecological analysis of successional
dynamics for a presettlement-origin whitepine-mixed-oak forest in the southern
Appalachians, USA. Journal of Ecology.
83: 123-133.
Abrams, M.D.; Ruffner, C.M.; DeMeo, T.E. 1998.
Dendroecology and species co-existence in
an old-growth Quercus-Acer-Tilia talus slope
forest in the central Appalachians, USA. Forest
Ecology and Management. 10(1): 9-18.
Abrams, M.D.; Scott, M.L. 1989. Disturbancemediated accelerated succession in two
Michigan forest types. Forest Science.
35(1): 42-49.
Ainsworth, E.A.; Long, S.P. 2005. What have
we learned from 15 years of free-air CO2
enrichment (FACE)? A meta-analytic review
of the responses of photosynthesis, canopy
properties and plant production to rising CO2 .
New Phytologist. 15(2): 351-372.
Ainsworth, E.A.; Rogers, A. 2007. The response of
photosynthesis and stomatal conductance to
rising [CO2 ]: mechanisms and environmental
interactions. Plant, Cell & Environment. 30(3):
258-270.
Aldrich, S.R.; Lafon, C.W.; Grissino-Mayer,
H.D.; DeWeese, G.G.; Hoss, J.A. 2010. Three
centuries of fire in montane pine-oak stands
on a temperate forest landscape. Applied
Vegetation Science. 13(1): 3-4.
Alpert, P.; Bone, E.; Holzapfel, C. 2000.
Invasiveness, invasibility and the role of
environmental stress in the spread of nonnative plants. Perspectives in Plant Ecology,
Evolution and Systematics. 3(1): 52-.
American Tree Farm System [ATFS]. 2014.
State forestry fact sheet (Ohio, West Virginia,
and Maryland). Available at https://www.
treefarmsystem.org/about-maryland-tree-farmprogram. (Accessed January 14, 2014).
Anderegg, W.R.; Prall, J.W.; Harold, J.; Schneider,
S.H. 2010. Expert credibility in climate
change. Proceedings of the National Academy of
Sciences. 107(27): 12107-12109.
199
LiTERATuRE CiTED
Anderson, M.; Ferree, C. 2010. Conserving the
stage: climate change and the geophysical
underpinnings of species diversity. PLoS ONE.
5(7): e11554.
Anderson, M.G.; Clark, M.; Sheldon, A.O. 2012.
Resilient sites for terrestrial conservation
in the Northeast and Mid-Atlantic Region.
Boston, MA: The Nature Conservancy, Eastern
Conservation Science. 18 p.
Anderson, R.L. 1995. How to identify and
control Hypoxylon cankers of oaks and other
hardwoods. Protection Report R8-PR 29.
Atlanta, GA: U.S. Department of Agriculture,
Forest Service, Southern Region. 9 p. Available at
http://www.fs.fed.us/r8/foresthealth/publications/
r8_pr_29.pdf. (Accessed August 8, 2014).
Andreadis, K.M.; Lettenmaier, D.P. 200. Trends
in 20th century drought over the continental
United States. Geophysical Research Letters.
33(10): L10403.
Andrew, N.R.; Hill, S.J.; Binns, M.; Bahar, M.H.;
Ridley, E.V.; Jung, M.-P.; Fyfe, C.; Yates, M.;
Khusro, M. 2013. Assessing insect responses
to climate change: What are we testing for?
Where should we be heading? PeerJ. 1: e11.
Appalachian Mountains Joint Venture Management
Board. 2008. Implementation plan for the
Appalachian Mountains Joint Venture: a
foundation for all-bird conservation in the
region. Smith, B., ed. Frankfort, KY: Appalachian
Mountains Joint Venture. 92 p. Available at http://
amjv.org/documents/implementation_plan.pdf.
(Accessed August 21, 2014).
Ashfaq, M.; Bowling, L.C.; Cherkauer, K.; Pal, J.S.;
Diffenbaugh, N.S. 2010. Influence of climate
model biases and daily-scale temperature and
precipitation events on hydrological impacts
assessment: a case study of the United States.
Journal of Geophysical Research. 115(D14):
D1411.
200
Babin-Fenske, J.; Anand, M. 2011. Agent-based
simulation of effects of stress on forest tent
caterpillar (Malacosoma disstria Hübner)
population dynamics. Ecological Modelling.
222(14): 251-259.
Badeck, F.W.; Bondeau, A.; Böttcher, K.; Doktor, D.;
Lucht, W.; Schaber, J.; Sitch, S. 2004. Responses
of spring phenology to climate change. New
Phytologist. 12(2): 295-309.
Bader, D.C.; Covey, C.; Gutkowski, W.J., Jr.; Held,
I.M.; Kunkel, K.E.; Miller, R.L.; Tokmakian,
R.T.; Zhang, M.H. 2008. Climate models: an
assessment of strengths and limitations. U.S.
Climate Change Science Program Synthesis
and Assessment Product 3.1. Washington, DC:
U.S. Department of Energy, Office of Biological
and Environmental Research. 124 p. Available
at http://pubs.giss.nasa.gov/abs/ba09200l.html.
(Accessed August 20, 2014).
Bar Massada, A.; Radeloff, V.C.; Stewart, S.I.;
Hawbaker, T.J. 2009. Wildfire risk in the
wildland-urban interface: a simulation study
in northwestern Wisconsin. Forest Ecology and
Management. 258(9): 1990-1999.
Bartomeus, I.; Ascher, J.S.; Wagner, D.; Danforth,
B.N.; Colla, S.; Kornbluth, S.; Winfree, R. 2011.
Climate-associated phenological advances
in bee pollinators and bee-pollinated plants.
Proceedings of the National Academy of
Sciences. 108(51): 2045-2049.
Beniston, M. 2003. Climatic change in mountain
regions: a review of possible impacts. Climatic
Change. 59: 5-31.
Benson, B.J.; Magnuson, J.J.; Jensen, O.P.; Card,
V.M.; Hodgkins, G.; Korhonen, J.; Livingstone,
D.M.; Stewart, K.M.; Weyhenmeyer, G.A.;
Granin, N.G. 2012. Extreme events, trends,
and variability in Northern Hemisphere lakeice phenology (1855-2005). Climatic Change.
112(2): 299-323.
LiTERATuRE CiTED
Birch, T.W.; Wharton, E.H. 1982. Land use change
in Ohio, 1952 to 1979. Resour. Bull. NE-70.
Newtown Square, PA: U.S. Department of
Agriculture, Forest Service, Northeastern Forest
Experiment Station. Available at http://www.
fs.fed.us/ne/newtown_square/publications/
resource_bulletins/pdfs/scanned/OCR/ne_rb70.
pdf. (Accessed August 21, 2014).
Birdsey, R.; Pregitzer, K.; Lucier, A. 200. Forest
carbon management in the United States:
1600-2100. American Society of Agronomy:
35(4): 141-149.
Black, B.A.; Abrams, M.D. 2001. Influences of
Native Americans and surveyor biases on
metes and bounds witness-tree distribution.
Ecology. 82(9): 2574-258.
Blunden, J.; Arndt, D.S., eds. 2012. State of the
climate in 2011. Bulletin of the American
Meteorological Society. 93(7): S1-S24.
Boer, G. 2009. Changes in interannual variability
and decadal potential predictability under
global warming. Journal of Climate. 22(11):
3098-3109.
Boerner, R.E.J. 200. Unraveling the Gordian
Knot: interactions among vegetation,
topography, and soil properties in the central
and southern Appalachians. The Journal of the
Torrey Botanical Society. 133(2): 321-31.
Bonan, G.B. 2008. Forests and climate change:
forcings, feedbacks, and the climate benefits of
forests. Science. 320(5882): 1444-1449.
Bonnin, G.M.; Maitaria, K.; Yekta, M. 2011. Trends
in rainfall exceedances in the observed record
in selected areas of the United States. Journal
of the American Water Resources Association.
47(): 1173-1182.
Boucher, D.; Rodick, C.; Bailey, J.; Snitzer, J.;
Kyde, K.; Prudden, B. 2005. Hurricane Isabel
and the forests of the mid-Atlantic Piedmont
and Blue Ridge: short-term impacts and
long-term implications. In: Sellner, K. G., ed.
Hurricane Isabel in perspective. Edgewater,
MD: Chesapeake Research Consortium. 05-10.
Available at http://www.chesapeake.org/pubs/
Isabel/Boucher%20et%20al.pdf. (Accessed
August 20, 2014).
Box, G.E.P.; Draper, N.R. 1987. Empirical modelbuilding and response surfaces. Wiley Series
in probability and mathematical statistics. New
York, NY: John Wiley & Sons. 9 p.
Bradley, B.A.; Oppenheimer, M.; Wilcove, D.S.
2009. Climate change and plant invasions:
restoration opportunities ahead? Global
Change Biology. 15(): 1511-1521.
Bradley, B.A.; Wilcove, D.S.; Oppenheimer,
M. 2010. Climate change increases risk of
plant invasion in the eastern United States.
Biological Invasions. 12(): 1855-1872.
Brooks, M.; Lusk, M. 2008. Fire management and
invasive plants: a handbook. Arlington, VA:
US Fish and Wildlife Service. 27 p. Available
at http://www.fws.gov/invasives/pdfs/usfws_
firemgtandinvasivesplants_a_handbook.pdf.
(Accessed August 20, 2014).
Brose, P.; Van Lear, D. 1999. Effects of seasonal
prescribed fires on residual overstory trees in
oak-dominated shelterwood stands. Southern
Journal of Applied Forestry. 23(2): 88-93.
Brose, P.H.; Dey, D.C.; Phillips, R.J.; Waldrop,
T.A. 2013. A meta-analysis of the fire-oak
hypothesis: Does prescribed burning promote
oak reproduction in eastern North America?
Forest Science. 59(3): 322-334.
Borken, W.; Matzner, E. 2009. Reappraisal
of drying and wetting effects on C and N
mineralization and fluxes in soils. Global
Change Biology. 15(4): 808-824.
201
LiTERATuRE CiTED
Brose, P.H.; Gottschalk, K.W.; Horsley, S.B.; Knopp,
P.D.; Kochenderfer, J.N.; McGuinness, B.J.;
Miller, G.W.; Ristau, T.E.; Stoleson, S.H.; Stout,
S.L. 2008. Prescribing regeneration treatments
for mixed-oak forests in the Mid-Atlantic
region. Gen. Tech. Rep. NRS-33. Newtown
Square, PA: U.S. Department of Agriculture,
Forest Service, Northern Research Station. 108 p.
Brose, P.H.; Waldrop, T.A. 200. Fire and the
origin of Table Mountain pine pitch pine
communities in the southern Appalachian
mountains, USA. Canadian Journal of Forest
Research. 3(3): 710-718.
Brown, R.L.; Peet, R.K. 2003. Diversity and
invasibility of southern Appalachian plant
communities. Ecology. 84(1): 32-39.
Bruce, O.C.; Smith, A.M. 1921. Soil survey of
Allegany County, Maryland. Field Operations
of the Bureau of Soils. 103-1090. Available
at http://soils.usda.gov/survey/online_surveys/
maryland/alleganyMD1921/alleganyMD1921.
pdf. (Accessed August 21, 2014).
Brzostek, E.R.; Blair, J.M.; Dukes, J.S.; Frey, S.D.;
Hobbie, S.E.; Melillo, J.M.; Mitchell, R.J.;
Pendall, E.; Reich, P.B.; Shaver, G.R. 2012.
The effect of experimental warming and
precipitation change on proteolytic enzyme
activity: positive feedbacks to nitrogen
availability are not universal. Global Change
Biology. 18(8): 217-225.
Buenger, B.A. 2003. The impact of wildland and
prescribed fire on archaeological resources.
Lawrence, KS: University of Kansas.
351 p. Ph.D. dissertation. Available at http://
www.blm.gov/pgdata/etc/medialib/blm/wo/
Planning_and_Renewable_Resources/coop_
agencies/cr_publications.Par.30817.File.dat/
DissertationBuenger_for_merge.pdf. (Accessed
August 21, 2014).
202
Burnett, A.W.; Kirby, M.E.; Mullins, H.T.; Patterson,
W.P. 2003. Increasing great lake-effect snowfall
during the twentieth century: A regional
response to global warming? Journal of
Climate. 1(21): 3535-3542.
Burns, N.M.; Rockwell, D.C.; Bertram, P.E.;
Dolan, D.M.; Ciborowski, J.J. 2005. Trends
in temperature, secchi depth, and dissolved
oxygen depletion rates in the central basin of
Lake Erie, 1983-2002. Journal of Great Lakes
Research. 31: 35-49.
Burns, R.M.; Honkala, B.H. 1990. Silvics of
North America: 1. Conifers; 2. Hardwoods.
Washington, DC: U.S. Department of Agriculture,
Forest Service: 877 p.
Bussler, B.H.; Byrnes, W.R.; Pope, P.E.; Chaney,
W.R. 1984. Properties of minesoil reclaimed for
forest land use. Soil Science Society of America
Journal. 48(1): 178-184.
Butler, B.J. 2008. Family forest owners of the
United States, 2006. Gen. Tech. Rep. NRS-27.
Newtown Square, PA: U.S. Department of
Agriculture, Forest Service, Northern Research
Station. 73 p.
Byers, E.; Norris, S. 2011. Climate change
vulnerability assessment of species of concern
in West Virginia. Project Report. Elkins, WV:
West Virginia Division of Natural Resources.
9 p. Available at http://wvdnr.gov/publications/
PDFFiles/ClimateChangeVulnerability.pdf.
(Accessed August 20, 2014).
Byers, E.A.; Vanderhorst, J.P.; Streets, B.P. 2007.
Classification and conservation assessment
of high elevation wetland communities in
the Allegheny mountains of West Virginia.
Elkins, WV: West Virginia Division of Natural
Resources, Wildlife Resources Section. 92 p.
LiTERATuRE CiTED
Campbell, J.L.; Mitchell, M.J.; Groffman, P.M.;
Christenson, L.M.; Hardy, J.P. 2005. Winter in
northeastern North America: a critical period
for ecological processes. Frontiers in Ecology
and the Environment. 3(): 314-322.
Campbell, J.L.; Ollinger, S.V.; Flerchinger, G.N.;
Wicklein, H.; Hayhoe, K.; Bailey, A.S. 2010. Past
and projected future changes in snowpack and
soil frost at the Hubbard Brook Experimental
Forest, New Hampshire, USA. Hydrological
Processes. 24(17): 245-2480.
Certini, G. 2005. Effects of fire on properties of
forest soils: a review. Oecologia. 143(1): 1-10.
Chamberlain, J.; Winn, M.; Hammett, A.L. 2009.
Connecting non-timber forest products
stakeholders to information and knowledge: a
case study of an intranet Web site. In: Ashton,
S.F.; Hubbard, W.G.; Rauscher, H.M., eds. A
Southern Region conference on technology
transfer and extension. Gen. Tech. Rep. SRS-11.
Asheville, NC: U.S. Department of Agriculture,
Forest Service, Southern Research Station: 87-94.
Available at http://www.srs.fs.usda.gov/pubs/gtr/
gtr_srs11/gtr_srs11-04.pdf. (Accessed
August 20, 2014).
Changnon, D.; Changnon, S.A. 2007. Snowstorm
dimensions across the central and eastern
United States. Physical Geography. 28(3):
218-232.
Changnon, S. 2011a. Temporal distribution of
weather catastrophes in the USA. Climatic
Change. 10(2): 129-140.
Changnon, S.A. 2003. Geographical and temporal
variations in thunderstorms in the contiguous
United States during the 20th century. Physical
Geography. 30(2): 138-152.
Changnon, S.A. 2011b. Major damaging
convective storms in the United States. Physical
Geography. 32(3): 28-294.
Chen, I.-C.; Hill, J.K.; Ohlemüller, R.; Roy, D.B.;
Thomas, C.D. 2011. Rapid range shifts of
species associated with high levels of climate
warming. Science. 333(045): 1024-102.
Choat, B.; Jansen, S.; Brodribb, T.J.; Cochard,
H.; Delzon, S.; Bhaskar, R.; Bucci, S.J.; Feild,
T.S.; Gleason, S.M.; Hacke, U.G. 2012. Global
convergence in the vulnerability of forests to
drought. Nature. 491: 752-75.
Chong, S.-K.; Cowsert, P. 1997. Infiltration in
reclaimed mined land ameliorated with deep
tillage treatments. Soil and Tillage Research.
44(3): 255-24.
Christidis, N.; Stott, P.A.; Brown, S.; Karoly, D.J.;
Caesar, J. 2007. Human contribution to the
lengthening of the growing season during
1950-99. Journal of Climate. 20(21): 5441-5454.
Clark, J.S.; Fastie, C.; Hurtt, G.; Jackson, S.T.;
Johnson, C.; King, G.A.; Lewis, M.; Jason, L.;
Pacala, S.; Prentice, C.; Schupp, E.W.; Webb, T.,
III; Wyckoff, P. 1998. Reid’s paradox of rapid
plant migration. BioScience. 48(1): 13-24.
Clatterbuck, W.K.; Kauffman, B.W. 200.
Managing oak decline. SP75. Knoxville, TN:
University of Tennessee, UT Extension. p.
Available at http://www.uky.edu/Ag/Forestry/
extension/pub/pdf/for99.pdf. (Accessed
August 21, 2014).
Cleland, D.T.; Freeouf, J.; Keys, J., Jr.; Nowacki,
G.J.; Carpenter, C.; McNab, W.H. 2007a.
Ecological subregions: sections and
subsections of the conterminous United
States [1:3,500,000] [CD-ROM]. Online
database. Sloan, A.M., cartog. Gen. Tech. Rep.
WO-7. Washington, DC: U.S. Department
of Agriculture, Forest Service. Available at
http://data.fs.usda.gov/geodata/edw/datasets.
php?dsetCategory=biota. (Accessed
October 22, 2013).
203
LiTERATuRE CiTED
Cleland, E.E.; Chuine, I.; Menzel, A.; Mooney,
H.A.; Schwartz, M.D. 2007b. Shifting plant
phenology in response to global change. Trends
in Ecology & Evolution. 22(7): 357-35.
ClimateWizard. 2013. Climate Wizard Custom
Analysis application. The Nature Conservancy,
University of Washington, University of
Southern Mississippi, Climate Central,
Santa Clara University. Available at http://
climatewizardcustom.org/. (Accessed May 2013).
Cole, C.T.; Anderson, J.; Lindroth, R.L.; Waller,
D.M. 2010. Rising concentrations of
atmospheric CO2 have increased growth in
natural stands of quaking aspen (Populus
tremuloides). Global Change Biology. 1(8):
218-2197.
Collins, R.J.; Carson, W.P. 2003. The fire and oak
hypothesis: incorporating the influence of deer
browsing and canopy gaps. In: Van Sambeek,
J.W.; Dawson, J.O.; Ponder, F., Jr.; Loewenstein,
E.F.; Fralish, J.S., eds. Proceedings, 13th Central
Hardwoods Forest conference. Gen. Tech.
Rep. NC-234. St. Paul, MN: U.S. Department
of Agriculture, Forest Service, North Central
Research Station: 44-3.
Comer, P.; Faber-Langendoen, D.; Evans, R.;
Gawler, S.; Josse, C.; Kittel, G.; Menard, S.;
Pyne, M.; Reid, M.; Schultz, K.; Snow, K.;
Teague, J. 2003. Ecological systems of the
United States: a working classification of U.S.
terrestrial systems. Arlington, VA: NatureServe.
83 p.
Coniglio, M.C.; Stensrud, D.J. 2004. Interpreting
the climatology of derechos. Weather and
Forecasting. 19(3): 595-05.
Cook, J.; Nuccitelli, D.; Green, S.A.; Richardson,
M.; Winkler, B.; Painting, R.; Way, R.; Jacobs, P.;
Skuce, A. 2013. Quantifying the consensus on
anthropogenic global warming in the scientific
literature. Environmental Research Letters.
8(024024): 7.
204
Cornett, M.W.; Reich, P.B.; Puettmann, K.J.;
Frelich, L.E. 2000. Seedbed and moisture
availability determine safe sites for early Thuja
occidentalis (Cupressaceae) regeneration.
American Journal of Botany. 87(12): 1807-1814.
Côté, S.D.; Rooney, T.P.; Tremblay, J.P.; Dussault,
C.; Waller, D.M. 2004. Ecological impacts of
deer overabundance. Annual Review of Ecology
Evolution and Systematics. 35: 113-147.
Coumou, D.; Rahmstorf, S. 2012. A decade of
weather extremes. Nature Climate Change.
2: 491-49.
Dai, A.; Trenberth, K.E.; Qian, T. 2004. A global
dataset of Palmer drought severity index for
1870-2002: relationship with soil moisture
and effects of surface warming. Journal of
Hydrometeorology. 5: 1117-1129.
Dale, V.H.; Joyce, L.A.; McNulty, S.; Neilson,
R.P.; Ayres, M.P.; Flannigan, M.D.; Hanson,
P.J.; Irland, L.C.; Lugo, A.E.; Peterson, C.J.;
Simberloff, D.; Swanson, F.J.; Stocks, B.J.;
Wotton, B.M. 2001. Climate change and forest
disturbances. BioScience. 51(9): 723-734.
Daly, C.; Conklin, D.R.; Unsworth, M.H. 2010.
Local atmospheric decoupling in complex
topography alters climate change impacts.
International Journal of Climatology. 30(12):
1857-184.
Daniels, A.E.; Morrison, J.F.; Joyce, L.A.;
Crookston, N.L.; Chen, S.C.; McNulty, S.G.
2012. Climate projections FAQ. Gen. Tech.
Rep. RMRS-GTR-277WWW. Fort Collins, CO:
U.S. Department of Agriculture, Forest Service,
Rocky Mountain Research Station. 32 p.
Davis, M.B. 1983. Quaternary history of
deciduous forests of eastern North-America
and Europe. Annals of the Missouri Botanical
Garden. 70(3): 550-53.
LiTERATuRE CiTED
Davis, M.B.; Shaw, R.G. 2001. Range shifts and
adaptive responses to quaternary climate
change. Science. 292(5517): 73-79.
Davis, M.B.; Shaw, R.G.; Etterson, J.R. 2005.
Evolutionary responses to changing climate.
Ecology. 8(7): 1704-1714.
DeGaetano, A.T.; Allen, R.J. 2002. Trends in
twentieth-century temperature extremes
across the United States. Journal of Climate.
15(22): 3188-3205.
Delpla, I.; Jung, A.-V.; Baures, E.; Clement, M.;
Thomas, O. 2009. Impacts of climate change on
surface water quality in relation to drinking
water production. Environment International.
35(8): 1225-1233.
Delworth, T.L.; Broccoli, A.J.; Rosati, A.; Stouffer,
R.J.; Balaji, V.; Beesley, J.A.; Cooke, W.F.;
Dixon, K.W.; Dunne, J.; Dunne, K.A.; Durachta,
J.W.; Findell, K.L.; Ginoux, P.; Gnanadesikan,
A.; Gordon, C.T.; Griffies, S.M.; Gudgel,
R.; Harrison, M.J.; Held, I.M.; Hemler, R.S.;
Horowitz, L.W.; Klein, S.A.; Knutson, T.R.;
Kushner, P.J.; Langenhorst, A.R.; Lee, H.C.;
Lin, S.J.; Lu, J.; Malyshev, S.L.; Milly, P.C.D.;
Ramaswamy, V.; Russell, J.; Schwarzkopf,
M.D.; Shevliakova, E.; Sirutis, J.J.; Spelman,
M.J.; Stern, W.F.; Winton, M.; Wittenberg, A.T.;
Wyman, B.; Zeng, F.; Zhang, R. 200. GFDL’s
CM2 global coupled climate models. Part I:
formulation and simulation characteristics.
Journal of Climate. 19(5): 43-74.
DeSantis, R.D.; Moser, W.K.; Huggett, R.J.; Li,
R.; Wear, D.N.; Miles, P.D. 2013. Modeling
the effects of emerald ash borer on forest
composition in the Midwest and Northeast
United States. Gen. Tech. Rep. NRS-112.
Newtown Square, PA: U.S. Department of
Agriculture, Northern Research Station. 23 p.
Diffenbaugh, N.S.; Pal, J.S.; Trapp, R.J.; Giorgi,
F. 2005. Fine-scale processes regulate the
response of extreme events to global climate
change. Proceedings of the National Academy
of Sciences of the United States of America.
102(44): 15774-15778.
Diffenbaugh, N.S.; Trapp, R.J.; Brooks, H. 2008.
Does global warming influence tornado
activity? Eos, Transactions American
Geophysical Union. 89(53): 553-554.
Dijak, W. 2013. Landscape Builder: software for
the creation of initial landscapes for LANDIS
from FIA data. Computational Ecology and
Software. 3(2): 17-25.
Dobrowski, S.Z. 2011. A climatic basis for
microrefugia: the influence of terrain on
climate. Global Change Biology. 17(2):
1022-1035.
Doran, P.T.; Zimmerman, M.K. 2009. Examining
the scientific consensus on climate change.
Eos, Transactions American Geophysical Union.
90(3): 22.
Dragoni, D.; Rahman, A.F. 2012. Trends in fall
phenology across the deciduous forests of
the Eastern USA. Agricultural and Forest
Meteorology. 157(15): 9-105.
Drake, B.G.; Gonzalez-Meler, M.A.; Long, S.P.
1997. More efficient plants: A consequence of
rising atmospheric CO2 ? Annual Review of
Plant Physiology and Plant Molecular Biology.
48(1): 09-39.
Driscoll, C.T.; Han, Y.-J.; Chen, C.Y.; Evers, D.C.;
Lambert, K.F.; Holsen, T.M.; Kamman, N.C.;
Munson, R.K. 2007. Mercury contamination
in forest and freshwater ecosystems in the
northeastern United States. BioScience.
57(1): 17-28.
Diffenbaugh, N.S.; Ashfaq, M. 2010. Intensification
of hot extremes in the United States.
Geophysical Research Letters. 37(15): L15701.
205
LiTERATuRE CiTED
Drohan, P.; Brittingham, M.; Bishop, J.; Yoder,
K. 2012a. Early trends in landcover change
and forest fragmentation due to shale-gas
development in Pennsylvania: a potential
outcome for the north central Appalachians.
Environmental Management. 49: 101-1075.
Drohan, P.J.; Finley, J.C.; Roth, P.; Schuler, T.M.;
Stout, S.L.; Brittingham, M.C.; Johnson,
N.C. 2012b. Oil and gas impacts on forest
ecosystems: findings gleaned from the 2012
Goddard Forum at Penn State University.
Environmental Practice.
doi:10.10170S140412000300.
Dukes, J.S.; Pontius, J.; Orwig, D.; Garnas,
J.R.; Rodgers, V.L.; Brazee, N.; Cooke, B.;
Theoharides, K.A.; Stange, E.E.; Harrington, R.;
Ehrenfeld, J.; Gurevitch, J.; Lerdau, M.; Stinson,
K.; Wick, R.; Ayres, M. 2009. Responses of
insect pests, pathogens, and invasive plant
species to climate change in the forests of
northeastern North America: What can we
predict? Canadian Journal of Forest Research.
39(2): 231-248.
Dunnington, G. 2011. The potential impacts of
climate change on recreation in Vermont.
Climate Change Adaptation White Paper
Series. Montpelier, VT: Vermont Agency of
Natural Resources. 5 p. Available at http://
www.anr.state.vt.us/anr/climatechange/Pubs/
VTCCAdaptRecreation.pdf. (Accessed
August 20, 2014).
Dye, C.R. 2013. Report on managed timberland
program. 4 p. Charleston, WV: West Virginia
Legislature. Available at http://www.legis.state.
wv.us/legisdocs/reports/agency/F03_FY_2013_
2259.pdf. (Accessed August 21, 2014).
Dyer, J.M. 2001. Using witness trees to assess
forest change in southeastern Ohio. Canadian
Journal of Forest Research. 31(10): 1708-1718.
20
Easterling, D.R. 2002. Recent changes in frost days
and the frost-free season in the United States.
Bulletin of the American Meteorological Society.
83(9): 1327-1332.
Ebi, K.L.; Meehl, G.A. 2007. The heat is on:
climate change & heatwaves in the Midwest.
In: Ebi, K.L.; Meel, G.A., eds. Regional impacts
of climate change: four case studies in the United
States. Arlington, VA: Pew Center on Global
Climate Change: 8-21. Available at http://www.
c2es.org/docUploads/Regional-Impacts-Midwest.
pdf. (Accessed August 21, 2014).
Ehrenfeld, J.G. 2010. Ecosystem consequences of
biological invasions. Annual Review of Ecology,
Evolution, and Systematics. 41: 59-80.
Eisenbies, M.; Aust, W.; Burger, J.; Adams, M.B.
2007. Forest operations, extreme flooding
events, and considerations for hydrologic
modeling in the Appalachians - a review.
Forest Ecology and Management. 242(2): 77-98.
Elliott, K.J.; Vose, J.M.; Jackson, W.A. 2013.
Effects of future sulfate and nitrate deposition
scenarios on Linville Gorge and Shining Rock
Wildernesses. Asheville, NC: U.S. Department
of Agriculture, Forest Service, Southern Research
Station. 30 p. Available at http://www.srs.fs.fed.
us/pubs/gtr/gtr_srs181.pdf. (Accessed August 21,
2014).
Emery, M.R.; Barron, E.S. 2010. Using local
ecological knowledge to assess morel decline in
the U.S. Mid-Atlantic region. Economic Botany.
4(3): 205-21.
Felzer, B.S.; Reilly, J.M.; Melillo, J.M.; Kicklighter,
D.W.; Wang, C.; Prinn, R.G.; Sarofim, M.C.;
Zhuang, Q. 2003. Past and future effects of
ozone on net primary production and carbon
sequestration using a global biogeochemical
model. Report No. 103. Cambridge, MA: MIT
Joint Program on the Science and Policy of
Global Change. Available at http://dspace.mit.
edu/bitstream/handle/1721.1/4053/MITJPSPGC_
Rpt_103.pdf?sequence=2. (Accessed August 21,
2014).
LiTERATuRE CiTED
Feng, S.; Hu, Q. 2007. Changes in winter snowfall/
precipitation ratio in the contiguous United
States. Journal of Geophysical Research.
112(D15): D15109.
Fernholz, K.; Bowyer, J.; Howe, J. 2007. TIMOs
and REITs: what, why, and how they might
impact sustainable forestry. Minneapolis, MN:
Dovetail Partners, Inc. 14 p. Available at http://
www.iatp.org/files/181_2_98730.pdf. (Accessed
August 21, 2014).
Finzi, A.C.; Sinsabaugh, R.L.; Long, T.M.; Osgood,
M.P. 200. Microbial community responses to
atmospheric carbon dioxide enrichment in a
warm-temperate forest. Ecosystems.
9: 215-22.
Fischer, A.P.; Paveglio, T.; Carroll, M.; Murphy,
D.; Brenkert-Smith, H. 2013. Assessing
social vulnerability to climate change in
human communities near public forests
and grasslands: a framework for resource
managers and planners. Journal of Forestry.
111(5): 357-35.
Fitzjarrald, D.R.; Acevedo, O.C.; Moore, K.E. 2001.
Climatic consequences of leaf presence in the
eastern United States. Journal of Climate.
14(4): 598-14.
Forest2Market. 2013. SFI certification by region
and state. Available at http://www.forest2market.
com/uploads/Legacy/Certification_by_Region_
and_State.pdf. (Accessed August 21, 2014).
Foster, J.R.; Burton, J.I.; Forrester, J.A.; Liu, F.;
Muss, J.D.; Sabatini, F.M.; Scheller, R.M.;
Mladenoff, D.J. 2010. Evidence for a recent
increase in forest growth is questionable.
Proceedings of the National Academy of
Sciences. 107(21): E8-87.
Franklin, J.F.; Mitchell, R.J.; Palik, B. 2007. Natural
disturbance and stand development principles
for ecological forestry. Gen. Tech. Rep. NRS19. Newtown Square, PA: U.S. Department of
Agriculture, Forest Service, Northern Research
Station. 48 p.
Fraser, K.C.; Silverio, C.; Kramer, P.; Mickle,
N.; Aeppli, R.; Stutchbury, B.J.M. 2013. A
trans-hemispheric migratory songbird does
not advance spring schedules or increase
migration rate in response to record-setting
temperatures at breeding sites. PLoS ONE.
8(5): e4587.
Frelich, L.E.; Peterson, R.O.; Dovciak, M.; Reich,
P.B.; Vucetich, J.A.; Eisenhauer, N. 2012.
Trophic cascades, invasive species and bodysize hierarchies interactively modulate climate
change responses of ecotonal temperate-boreal
forest. Philosophical Transactions of the Royal
Society B: Biological Sciences. 37(105):
2955-291.
Fridley, J.D. 2009. Downscaling climate over
complex terrain: high finescale (< 1000 m)
spatial variation of near-ground temperatures
in a Montane forested landscape (Great Smoky
Mountains). Journal of Applied Meteorology and
Climatology. 48(5): 1033-1049.
Friedland, A.J.; Gregory, R.A.; Karenlampi, L.;
Johnson, A.H. 1984. Winter damage to foliage
as a factor in red spruce decline. Canadian
Journal of Forest Research. 14(): 93-95.
Froese, R.; Hyslop, M.; Miller, C.; Garmon, B.;
McDiarmid, H., Jr.; Shaw, A.; Leefers, L.A.;
Lorenzo, M.; Brown, S.; Shy, M. 2007. Largetract forestland ownership change: land use,
conservation, and prosperity in Michigan’s
Upper Peninsula. Ann Arbor, MI: National
Wildlife Federation. 54 p. Available at http://
forestlands.mtu.edu/products/UP_Corporate_
Forest_Land_Report.pdf. (Accessed August 21,
2014).
Fryer, J.L. 2011. Microstegium vimineum. Fire
effects information system. [Online]. Missoula,
MT: U.S. Department of Agriculture, Forest
Service, Rocky Mountain Research Station, Fire
Sciences Laboratory. Available at http://www.
fs.fed.us/database/feis/plants/graminoid/micvim/
all.html. (Accessed August 21, 2014).
207
LiTERATuRE CiTED
Furedi, M.; Leppo, B.; Kowalski, M.; Davis, T.;
Eichelberger, B. 2011. Identifying species
in Pennsylvania potentially vulnerable to
climate change. Pittsburgh, PA: Pennsylvania
Natural Heritage Program, Western Pennsylvania
Conservancy. Available at http://www.
naturalheritage.state.pa.us/ccvi/ccvi_final_report.
pdf. (Accessed August 21, 2014).
Gómez-Aparicio, L.; Pérez Ramos, I.M.; Mendoza,
I.; Matías, L.; Quero, J.; Castro, J.; Zamora,
R.; Marañón, T. 2008. Oak seedling survival
and growth along resource gradients
in Mediterranean forests: implications
for regeneration in current and future
environmental scenarios. Oikos. 117(11):
183-199.
Gange, A.; Gange, E.; Sparks, T.; Boddy, L. 2007.
Rapid and recent changes in fungal fruiting
patterns. Science. 31(5821): 71-71.
Grafton, E. 2013. Dirty dozen. West Virginia
Division of Natural Resources, Wildlife
Resources Section. Available at http://www.
wvdnr.gov/Wildlife/DirtyDozen.shtm.
(Accessed August 21, 2014).
Garroway, C.J.; Bowman, J.; Holloway, G.L.;
Malcolm, J.R.; Wilson, P.J. 2011. The genetic
signature of rapid range expansion by flying
squirrels in response to contemporary climate
warming. Global Change Biology. 17(5):
170-179.
Georgakakos, A.; Fleming, P.; Dettinger, M.;
Peters-Lidard, C.; Richmond, T.; Reckhow, K.;
White, K.; Yates, D. 2014. Chapter 3: water
resources. In: Melillo, J.M.; Richmond, T.;
Yohe, G., eds. Climate change impacts in
the United States: the third National Climate
Assessment. Washington, DC: U.S. Global
Change Research Program: 9-112.
Gibson, W.P.; Daly, C.; Kittel, T.; Nychka, D.;
Johns, C.; Rosenbloom, N.; McNab, A.;
Taylor, G. 2002. Development of a 103-year
high-resolution climate data set for the
conterminous United States. Proceedings
of the 13th American Meteorological Society
Conference on Applied Climatology: 181-183.
Girvetz, E.H.; Zganjar, C.; Raber, G.T.; Maurer, E.P.;
Kareiva, P.; Lawler, J.J. 2009. Applied climatechange analysis: the Climate Wizard tool.
PLoS ONE. 4(12): e8320.
Glick, P.; Stein, B.A.; Edelson, N.A. 2011. Scanning
the conservation horizon: a guide to climate
change vulnerability assessment. Washington,
DC: National Wildlife Federation. 17 p.
208
Gray, D.M.; Dighton, J. 200. Mineralization of
forest litter nutrients by heat and combustion.
Soil Biology and Biochemistry. 38():
149-1477.
Gray, D.R. 2008. The relationship between climate
and outbreak characteristics of the spruce
budworm in eastern Canada. Climatic Change.
87(3-4): 31-383.
Groffman, P.M.; Kareiva, P.; Carter, S.; Grimm,
N.B.; Lawler, J.; Mack, M.; Matzek, V.; Tallis, H.
2014. Chapter 8: ecosystems, biodiversity, and
ecosystem services. In: Melillo, J.M.; Richmond,
T.; Yohe, G., eds. Climate change impacts in
the United States: the third National Climate
Assessment. Washington, DC: U.S. Global
Change Research Program: 195-219.
Groisman, P.Y.; Knight, R.W.; Easterling, D.R.;
Karl, T.R.; Hegerl, G.C.; Razuvaev, V.N. 2005.
Trends in intense precipitation in the climate
record. Journal of Climate. 18(9): 132-1350.
Groisman, P.Y.; Knight, R.W.; Karl, T.R.
2012. Changes in intense precipitation
over the central United States. Journal of
Hydrometeorology. 13(1): 47-.
LiTERATuRE CiTED
Groisman, P.Y.; Knight, R.W.; Karl, T.R.;
Easterling, D.R.; Sun, B.; Lawrimore, J.H. 2004.
Contemporary changes of the hydrological
cycle over the contiguous United States: trends
derived from in situ observations. Journal of
Hydrometeorology. 5(1): 4-85.
Gutowski, W.J.; Hegerl, G.C.; Holland, G.J.;
Knutson, T.R.; Mearns, L.O.; Stouffer, R.J.;
Webster, P.J.; Wehner, M.F.; Zwiers, F.W. 2008.
Causes of observed changes in extremes and
projections of future changes. In: Karl, T.R.;
Meehl, G.A.; Miller, C.D.; Hassol, S.J.; Waple,
A.M.; and Murray, W.L., eds. Weather and
climate extremes in a changing climate. Regions
of focus: North America, Hawaii, Caribbean,
and U.S. Pacific Islands. Washington, DC:
U.S. Climate Change Science Program and the
Subcommittee on Global Change Research:
81-11.
Hansen, J.; Sato, M.; Ruedy, R. 2012. Perception
of climate change. Proceedings of the National
Academy of Sciences. 109(37): E2415-E2423.
Hanson, P.J.; Weltzin, J.F. 2000. Drought
disturbance from climate change: response of
United States forests. The Science of the Total
Environment. 22(3): 205-220.
Hartley, S. 1999. Winter Atlantic climate
and snowfall in the south and central
Appalachians. Physical Geography. 20(1): 1-13.
Hayhoe, K. 2010a. 1/8 degree CONUS daily
downscaled climate projections
*PROVISIONAL*. USGS Geodata Portal
[Online]: Texas Tech University, Center for
Integrated Data Analytics. Available at http://cida.
usgs.gov/thredds/catalog.html?dataset=cida.usgs.
gov/thredds/dcp/conus. (Accessed July 11, 2011).
Hayhoe, K.; Stoner, A.; Yang, X.; Crow, C.;
Swaminathan, R.; Scott-Fleming, I.;
Ryu, J-H.; Gelca, R.; Swain, S. 2013.
Development and dissemination of a highresolution national climate change dataset.
Texas Tech University High Performance
Computing Center. 497 p. Available at http://cida.
usgs.gov/thredds/fileServer/dcp/files/Hayhoe_
USGS_downscaled_database_final_report.pdf.
(Accessed August 21, 2014).
Hayhoe, K.; Wake, C.P.; Huntington, T.G.; Luo,
L.F.; Schwartz, M.D.; Sheffield, J.; Wood, E.;
Anderson, B.; Bradbury, J.; DeGaetano, A.;
Troy, T.J.; Wolfe, D. 2007. Past and future
changes in climate and hydrological indicators
in the US Northeast. Climate Dynamics. 28(4):
381-407.
Hayhoe, K.A. 2010b. A standardized framework
for evaluating the skill of regional climate
downscaling techniques. Urbana, IL: University
of Illinois at Urbana-Champaign. 153 p. Ph.D.
dissertation. Available at http://www.snap.uaf.
edu/attachments/1_Hayhoe_Katharine.pdf.
(Accessed August 21, 2014).
He, H.S.; Hao, Z.; Mladenoff, D.J.; Shao, G.; Hu,
Y.; Chang, Y. 2005. Simulating forest ecosystem
response to climate warming incorporating
spatial effects in northeastern China. Journal of
Biogeography. 32(12): 2043-205.
He, H.S.; Mladenoff, D.J. 1999. The effects of seed
dispersal on the simulation of long-term forest
landscape change. Ecosystems. 2(4): 308-319.
He, H.S.; Mladenoff, D.J.; Crow, T.R. 1999. Linking
an ecosystem model and a landscape model
to study forest species response to climate
warming. Ecological Modelling. 114(2):
213-233.
209
LiTERATuRE CiTED
He, H.S.; Mladenoff, D.J.; Gustafson, E.J. 2002.
Study of landscape change under forest
harvesting and climate warming-induced fire
disturbance. Forest Ecology and Management.
155(1): 257-270.
Headwaters Economics. 2011. Economic profile
system-human dimensions toolkit. Available
at http://headwaterseconomics.org/tools/eps-hdt.
(Accessed July 11, 2011).
Hellmann, J.J.; Byers, J.E.; Bierwagen, B.G.;
Dukes, J.S. 2008. Five potential consequences
of climate change for invasive species.
Conservation Biology. 22(3): 534-543.
Hessl, A.; Pederson, N. 2013. Hemlock legacy
project (HeLP): a paleoecological requiem
for eastern hemlock. Progress in Physical
Geography. 37(1): 114-129.
Hessl, A.; Spackman, S. 1995. Effects of fire
on threatened and endangered plants:
an annotated bibliography. Information
and Technology Report. Boulder, CO: U.S.
Department of the Interior, National Biological
Service. 55 p. Available at http://www.fws.
gov/fire/ifcc/esr/library/teplants.pdf. (Accessed
September 24, 2014).
Hessl, A.E.; Saladyga, T.; Schuler, T.; Clark, P.;
Wixom, J. 2011. Fire history from three species
on a central Appalachian ridgetop. Canadian
Journal of Forest Research. 41(10): 2031-2039.
Hewes, J.H.; Butler, B.J.; Liknes, G.C.; Nelson,
M.D.; Snyder, S.A. 2014. Public and private
forest ownership in the conterminous United
States: distribution of six ownership types
- geospatial dataset. Fort Collins, CO: Forest
Service Research Data Archive. Available at
http://dx.doi.org/10.2737/RDS-2014-0002.
(Accessed December 1, 2014).
210
Hicke, J.A.; Allen, C.D.; Desai, A.R.; Dietze, M.C.;
Hall, R.J.; Kashian, D.M.; Moore, D.; Raffa, K.F.;
Sturrock, R.N.; Vogelmann, J. 2012. Effects of
biotic disturbances on forest carbon cycling in
the United States and Canada. Global Change
Biology. 18(1): 7-34.
Hix, D.M.; Pearcy, J.N. 1997. Forest ecosystems
of the Marietta Unit, Wayne National Forest,
southeastern Ohio: multifactor classification
and analysis. Canadian Journal of Forest
Research. 27(7): 1117-1131.
Hodgkins, G.A.; Dudley, R.W. 200. Changes
in the timing of winter-spring streamflows
in eastern North America, 1913-2002.
Geophysical Research Letters. 33(): L0402.
Hoekstra, J.M.; Molnar, J.L.; Jennings, M.; Revenga,
C.; Spalding, M.D. 2010. The atlas of global
conservation. Berkeley, CA: University of
California Press: 272 p.
Holl, K.D. 2002. Long-term vegetation recovery
on reclaimed coal surface mines in the eastern
USA. Journal of Applied Ecology. 39():
90-970.
Holland, G.J.; Webster, P.J. 2007. Heightened
tropical cyclone activity in the North Atlantic:
Natural variability or climate trend?
Philosophical Transactions of the Royal Society
A: Mathematical, Physical and Engineering
Sciences. 35(180): 295-271.
Holzmueller, E.J.; Gibson, D.J.; Suchecki, P.F. 2012.
Accelerated succession following an intense
wind storm in an oak-dominated forest. Forest
Ecology and Management. 279: 141-14.
Horsley, S.B.; Stout, S.L.; deCalesta, D.S. 2003.
White-tailed deer impact on the vegetation
dynamics of a northern hardwood forest.
Ecological Applications. 13(1): 98-118.
LiTERATuRE CiTED
Horton, R.; Yohe, G.; Easterling, W.; Kates, D.R.W.;
Ruth, M.; Sussman, E.; Whelchel, A.; Wolfe, D.
2013. National Climate Assessment, chapter
16: northeast. Draft for Public Comment
(v.11 Jan 2013). Available at http://ncadac.
globalchange.gov/download/NCAJan112013-publicreviewdraft-chap1-northeast.pdf.
(Accessed August 22, 2014).
Hosaka, M.; Nohara, D.; Kitoh, A. 2005. Changes
in snow cover and snow water equivalent due
to global warming simulated by a 20km-mesh
global atmospheric model. Sola. 1(0): 93-9.
Hurlbert, A.H.; Liang, Z. 2012. Spatiotemporal
variation in avian migration phenology: citizen
science reveals effects of climate change. PLoS
ONE. 7(2): e312.
Hutchinson, T.F.; Vankat, J.L. 1998. Landscape
structure and spread of the exotic shrub
Lonicera maackii (Amur honeysuckle) in
Southwestern Ohio forests. The American
Midland Naturalist. 139(2): 383-390.
Ibáñez, I.; Clark, J.S.; Dietz, M.C. 2008. Evaluating
the sources of potential migrant species:
implications under climate change. Ecological
Applications. 18(7): 14-178.
Ibáñez, I.; Clark, J.S.; Dietze, M.C.; Feeley,
K.; Hersh, M.; LaDeau, S.; McBride, A.;
Welch, N.E.; Wolosin, M.S. 200. Predicting
biodiversity change: outside the climate
envelope, beyond the species-area curve.
Ecology. 87(8): 189-190.
Intergovermental Panel on Climate Change [IPCC].
2000. Special report on emission scenarios.
In: Nakicenovic, N.; Swart, R., eds. Cambridge,
UK: Cambridge University Press. 570 p.
Available at http://www.grida.no/publications/
other/ipcc_sr/?src=/climate/ipcc/emission/.
(Accessed August 21, 2014).
Intergovermental Panel on Climate Change.
[IPCC]. 2007. Climate change 2007: synthesis
report. Contribution of Working Groups I,
II, and III to the Fourth Assessment Report
of the Intergovernmental Panel on Climate
Change. [Core Writing Team, Pachauri, R.K. and
Reisinger, A. (eds.)]. Geneva, Switzerland. 104
p. Available at http://www.ipcc.ch/publications_
and_data/publications_ipcc_fourth_assessment_
report_synthesis_report.htm. (Accessed
August 21, 2014).
Intergovermental Panel on Climate Change.
[IPCC]. 2012. Managing the risks of extreme
events and disasters to advance climate
change adaptation. In: Field, C.B.; Barros, V.;
Stocker, T.F.; Qin, D.; Dokken, D.J.; Ebi, K.L.;
Mastrandrea, M.D.; Mach, K.J.; Plattner, G.-K.;
Allen, S.K.; Tignor, M.; and Midgley, P. M., eds.
A special report of Working Groups I and II of
the Intergovernmental Panel on Climate Change.
Cambridge, UK and New York, NY: Cambridge
University Press. 582 p.
Irwin, E.G.; Bockstael, N.E. 2007. The
evolution of urban sprawl: evidence of
spatial heterogeneity and increasing land
fragmentation. Proceedings of the National
Academy of Sciences. 104(52): 2072-2077.
Iverson, L.R.; Dale, M.E.; Scott, C.T.; Prasad, A.
1997. A GIS-derived integrated moisture index
to predict forest composition and productivity
of Ohio forests (USA). Landscape Ecology.
12(5): 331-348.
Iverson, L.R.; Prasad, A.M. 1998. Predicting
abundance of 80 tree species following climate
change in the eastern United States. Ecological
Monographs. 8(4): 45-485.
Iverson, L.R.; Prasad, A.M.; Matthews, S. 2008a.
Modeling potential climate change impacts
on the trees of the northeastern United States.
Mitigation and Adaptation Strategies for Global
Change. 13(5): 487-51.
211
LiTERATuRE CiTED
Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters,
M. 2008b. Estimating potential habitat for
134 eastern US tree species under six climate
scenarios. Forest Ecology and Management.
254(3): 390-40.
Jeong, S.-J.; Ho, C.-H.; Gim, H.-J.; Brown, M.E.
2011. Phenology shifts at start vs. end of
growing season in temperate vegetation over
the Northern Hemisphere for the period 19822008. Global Change Biology. 17(7): 2385-2399.
Iverson, L.R.; Schwartz, M.W.; Prasad, A.M. 2004a.
How fast and far might tree species migrate
in the eastern United States due to climate
change? Global Ecology and Biogeography.
13(3): 209-219.
Jonas, S.Z.; Xi, W.; Waldron, J.D.; Coulson, R.N.
2012. The impacts of hemlock decline and the
ecological considerations for forest restoration
following Hemlock Woolly Adelgid outbreaks.
Tree and Forestry Science and Biotechnology.
(1): 27-33.
Iverson, L.R.; Schwartz, M.W.; Prasad, A.M. 2004b.
Potential colonization of new available tree
species habitat under climate change: an
analysis for five eastern US species. Landscape
Ecology. 19: 787-799.
Jacob, D.J.; Winner, D.A. 2009. Effect of
climate change on air quality. Atmospheric
Environment. 43(1): 51-3.
Jacobs, D.F.; Dalgleish, H.J.; Nelson, C.D. 2013.
A conceptual framework for restoration
of threatened plants: the effective model
of American chestnut (Castanea dentata)
reintroduction. New Phytologist. 197(2):
378-393.
Jacobson, G.L., Jr.; Webb, T., III; Grimm, E.C. 1987.
Patterns and rates of vegetation change during
the deglaciation of eastern North America.
In: The geology of North America. Vol. K-3:
North America and adjacent oceans during the
last deglaciation. Boulder, CO: The Geological
Society of America: 277-288.
Janetos, A.; Hansen, L.; Inouye, D.W.; Kelly, B.P.;
Meyerson, L.; Peterson, B.; Shaw, R. 2008.
Biodiversity. In: The effects of climate change on
agriculture, land resources, water resources, and
biodiversity in the United States. Washington,
DC: U.S. Climate Change Science Program and
the Subcommittee on Global Change Research.
151-181.
212
Jones, J.A.; Creed, I.F.; Hatcher, K.L.; Warren,
R.J.; Adams, M.B.; Benson, M.H.; Boose, E.;
Brown, W.A.; Campbell, J.L.; Covich, A. 2012.
Ecosystem processes and human influences
regulate streamflow response to climate
change at long-term ecological research sites.
BioScience. 2(4): 390-404.
Joyce, J.M.; Scott, M.S. 2005. An assessment of
Maryland’s vulnerability to flood damage.
Baltimore, MD: Maryland Department of the
Environment. 87 p. Available at http://www.esrgc.
org/pdf/hazus/An%20Assessment%20of%20Mar
yland’s%20Vulnerability%20to%20Flooding.pdf.
(Accessed September 11, 2014).
Julien, Y.; Sobrino, J.A. 2009. Global land surface
phenology trends from GIMMS database.
International Journal of Remote Sensing. 30(13):
3495-3513.
Jump, A.S.; Peñuelas, J. 2005. Running to stand
still: adaptation and the response of plants to
rapid climate change. Ecology Letters. 8(9):
1010-1020.
Kapnick, S.B.; Delworth, T.L. 2013. Controls of
global snow under a changed climate. Journal
of Climate. 2: 5537-552.
Karl, T.R.; Knight, R.W.; Easterling, D.R.; Quayle,
R.G. 199. Indices of climate change for
the United States. Bulletin of the American
Meteorological Society. 77(2): 279-292.
LiTERATuRE CiTED
Karl, T.R.; Melillo, J.M.; Peterson, T.C., eds. 2009.
Global climate change impacts in the United
States: a state of knowledge report from the
U.S. Global Change Research Program. New
York, NY: Cambridge University Press. 189 p.
Kasey, P. 2012. Which county leads WV’s
Marcellus production? The State Journal.
Charleston, WV: WorldNow and WVSTATE.
Available at http://www.statejournal.com/
story/19853093/which-county-leads-wvsmarcellus-production. (Accessed September 11,
2014).
Kauserud, H.; Stige, L.C.; Vik, J.O.; Økland, R.H.;
Høiland, K.; Stenseth, N.C. 2008. Mushroom
fruiting and climate change. Proceedings of the
National Academy of Sciences. 105(10):
3811-3814.
Keyser, T.L.; Brown, P.M. 2014. Long-term
response of yellow-poplar to thinning in the
southern Appalachian Mountains. Forest
Ecology and Management. 312(0): 148-153.
Kitajima, K.; Fenner, M. 2000. Ecology of seedling
regeneration. In: Fenner, M., ed. Seeds: the
ecology of regeneration in plant communities.
Wallingford, UK: CABI: 331-359.
Kliejunas, J.T. 2011. A risk assessment of climate
change and the impact of forest diseases on
forest ecosystems in the Western United States
and Canada. Gen. Tech. Rep. PSW-GTR-23.
Albany, CA: U.S. Department of Agriculture,
Forest Service, Pacific Southwest Research
Station. 70 p. Available at http://www.fs.fed.
us/psw/publications/documents/psw_gtr23/psw_
gtr23.pdf. (Accessed August 21, 2014).
Kling, G.W.; Hayhoe, K.; Johnson, L.B.; Magnuson,
J.J.; Polasky, S.; Robinson, S.K.; Shuter, B.J.;
Wander, M.M.; Wuebbles, D.J.; Zak, D.R.;
Lindroth, R.L.; Moser, S.C.; M.L.Wilson. 2003.
Confronting climate change in the Great
Lakes region: impacts on our communities
and ecosystems. Cambridge, MA: Union of
Concerned Scientists; Ecological Society of
America. Available at http://ucsusa.org/assets/
documents/global_warming/greatlakes_final.pdf.
(Accessed November 23, 2013).
Knapp, A.K.; Beier, C.; Briske, D.D.; Classen, A.T.;
Luo, Y.; Reichstein, M.; Smith, M.D.; Smith,
S.D.; Bell, J.E.; Fay, P.A. 2008. Consequences
of more extreme precipitation regimes for
terrestrial ecosystems. BioScience. 58(9):
811-821.
Knicker, H. 2007. How does fire affect the nature
and stability of soil organic nitrogen and
carbon? A review. Biogeochemistry. 85(1):
91-118.
Knight, M. 2012. Dominion power outages
third-largest after hurricanes Isabel and
Irene. Available at http://wtkr.com/2012/07/01/
dominion-power-outages-third-largest-afterhurricanes-isabel-and-irene/. (Accessed
January 28, 2014).
Knight, T.M.; Dunn, J.L.; Smith, L.A.; Davis, J.;
Kalisz, S. 2009. Deer facilitate invasive plant
success in a Pennsylvania forest understory.
Natural Areas Journal. 29(2): 110-11.
Kochenderfer, J.N.; Adams, M.B.; Miller, G.W.;
Helvey, J.D. 2007. Factors affecting large
peakflows on Appalachian watersheds:
lessons from the Fernow Experimental Forest.
Res. Pap. NRS-3. Newtown Square, PA: U.S.
Department of Agriculture, Forest Service,
Northern Research Station. 24 p.
213
LiTERATuRE CiTED
Koss, W.J.; Owensby, J.R.; Steurer, P.M.; Ezell,
D.S. 1988. Freeze/frost data. Climatography of
the U.S. Asheville, NC: National Climatic Data
Center. 20 p.
Kovacs, K.F.; Haight, R.G.; McCullough, D.G.;
Mercader, R.J.; Siegert, N.W.; Liebhold, A.M.
2010. Cost of potential emerald ash borer
damage in U.S. communities, 2009-2019.
Ecological Economics. 9(3): 59-578.
Kovarik, J.L. 2013. Geologic management of cave
and karst resources on national forest system
lands. Proceedings from the National Cave
and Karst Management Symposium; Carlsbad,
NM; November 4-7, 2013. Available at http://
scholarcommons.usf.edu/cgi/viewcontent.cgi?a
rticle=1013&context=nckms_2013. (Accessed
January 30, 2015).
Krist, F.J.; Sapio, F.J.; Tkacz, B.M. 2007. Mapping
risk from forest insects and diseases. Available
at http://www.fs.fed.us/foresthealth/technology/
pdfs/FHTET2007-0_RiskMap.pdf. (Accessed
September 11, 2014).
Kunkel, K.; Bromirski, P.; Brooks, H.; Cavazos,
T.; Douglas, A.; Easterling, D.; Emanuel, K.;
Groisman, P.Y.; Holland, G.; Knutson, T. 2008.
Observed changes in weather and climate
extremes. In: Weather and climate extremes
in a changing climate. Regions of focus: North
America, Hawaii, Caribbean, and U.S. Pacific
Islands. Washington, DC: U.S. Climate Change
Science Program and the Subcommittee on
Global Change Research: 35-80.
214
Kunkel, K.E.; Karl, T.R.; Brooks, H.; Kossin,
J.; Lawrimore, J.H.; Arndt, D.; Bosart, L.;
Changnon, D.; Cutter, S.L.; Doesken, N.;
Emanuel, K.; Groisman, P.Y.; Katz, R.W.;
Knutson, T.; O’Brien, J.; Paciorek, C.J.;
Peterson, T.C.; Redmond, K.; Robinson, D.;
Trapp, J.; Vose, R.; Weaver, S.; Wehner, M.;
Wolter, K.; Wuebbles, D. 2013a. Monitoring
and understanding trends in extreme storms:
state of knowledge. Bulletin of the American
Meteorological Society. 94(4): 499-514.
Kunkel, K.E.; Liang, X.-Z.; Zhu, J.; Lin, Y. 200.
Can CGCMs simulate the twentieth-century
“warming hole” in the central United States?
Journal of Climate. 19(17): 4137-4153.
Kunkel, K.E.; Palecki, M.A.; Ensor, L.; Easterling,
D.; Hubbard, K.G.; Robinson, D.; Redmond,
K. 2009a. Trends in twentieth-century U.S.
extreme snowfall seasons. Journal of Climate.
22(23): 204-21.
Kunkel, K.E.; Palecki, M.; Ensor, L.; Hubbard,
K.G.; Robinson, D.; Redmond, K.; Easterling,
D. 2009b. Trends in twentieth-century U.S.
snowfall using a quality-controlled dataset.
Journal of Atmospheric and Oceanic Technology.
2(1): 33-44.
Kunkel, K.E.; Stevens, L.E.; Stevens, S.E.; Sun, L.;
Janssen, E.; Wuebbles, D.; Hilberg, S.D.; Timlin,
M.S.; Stoecker, L.; Westcott, N.E.; Dobson, J.G.
2013b. Regional climate trends and scenarios
for the U.S. National Climate Assessment. Part
3 - climate of the Midwest U.S. Washington,
DC: U.S. Department of Commerce, National
Oceanic and Atmospheric Administration.
102 p. Available at http://scenarios.globalchange.
gov/sites/default/files/NOAA_NESDIS_Tech_
Report_142-3-Climate_of_the_Midwest_U.S_
0.pdf. (Accessed August 21, 2014).
LiTERATuRE CiTED
Kunkel, K.E.; Stevens, L.E.; Stevens, S.E.; Sun,
L.; Janssen, E.; Wuebbles, D.; Rennells, J.;
DeGaetano, A.; Dobson, J.G. 2013c. Regional
climate trends and scenarios for the U.S.
National Climate Assessment. Part 1 - climate
of the Northeast U.S. 79 p. Available at http://
scenarios.globalchange.gov/sites/default/files/
NOAA_NESDIS_Tech_Report_142-1-Climate_
of_the_Northeast_U.S_1.pdf. (Accessed
August 21, 2014).
Kunkel, K.E.; Westcott, N.E.; Kristovich, D.A.R.
2002. Assessment of potential effects of climate
change on heavy lake-effect snowstorms near
Lake Erie. Journal of Great Lakes Research.
28(4): 521-53.
Kurtz, C.M. 2013. An assessment of invasive
plant species monitored by the Northern
Research Station Forest Inventory and
Analysis program, 2005 through 2010. Gen.
Tech. Rep. NRS-109. Newtown Square, PA:
U.S. Department of Agriculture, Forest Service,
Northern Research Station. 70 p.
Landscape Change Research Group. 2014. Climate
change atlas. Newtown Square, PA: U.S.
Department of Agriculture, Forest Service,
Northern Research Station. Available at www.nrs.
fs.fed.us/atlas. (Accessed September 8, 2014).
Le Quéré, C.; Raupach, M.R.; Canadell, J.G.;
Marland, G. 2009. Trends in the sources and
sinks of carbon dioxide. Nature Geoscience.
2: 831-83.
Lee, C.C. 2012. Utilizing synoptic climatological
methods to assess the impacts of climate
change on future tornado-favorable
environments. Natural Hazards. 2(2): 325-343.
Leibensperger, E.; Mickley, L.; Jacob, D.; Chen,
W.; Seinfeld, J.; Nenes, A.; Adams, P.; Streets,
D.; Kumar, N.; Rind, D. 2012. Climatic effects
of 1950-2050 changes in US anthropogenic
aerosols - Part 2: climate response.
Atmospheric Chemistry and Physics.
12(7): 3349-332.
Lemieux, C.J.; Scott, D.J. 2005. Climate change,
biodiversity conservation and protected area
planning in Canada. The Canadian Geographer/
Le Géographe canadien. 49(4): 384-397.
Lemke, P.; Ren, J.; Alley, R.B.; Allison, I.;
Carrasco, J.; Flato, G.; Fujii, Y.; Kaser, G.;
Mote, P.; Thomas, R.H.; Zhang, T. 2007.
Observations: changes in snow, ice and frozen
ground. In: Solomon, S.; Qin, D.; Manning,
M.; Chen, Z.; Marquis, M.; Averyt, K.B.;
Tignor, M.; Miller, H.L., eds. Climate change
2007: the physical science basis; summary for
policymakers, technical summary and frequently
asked questions. Part of the Working Group I
contribution to the Fourth Assessment Report of
the Intergovernmental Panel on Climate Change.
Cambridge, UK and New York, NY: Cambridge
University Press: 337-383. Available at http://
www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4wg1-chapter4.pdf. (Accessed September 24,
2014).
Lenihan, J.M.; Bachelet, D.; Neilson, R.P.; Drapek,
R. 2008. Simulated response of conterminous
United States ecosystems to climate change
at different levels of fire suppression, CO2
emission rate, and growth response to CO2 .
Global and Planetary Change. 4(1): 1-25.
Likens, G.E.; Bormann, F.H. 1995.
Biogeochemistry of a forested ecosystem. New
York, NY: Springer-Verlag New York Inc.
Linderholm, H.W. 200. Growing season changes
in the last century. Agricultural and Forest
Meteorology. 137(1-2): 1-14.
Lins, H.F.; Slack, J.R. 1999. Streamflow trends in
the United States. Geophysical Research Letters.
2(2): 227-230.
Lippincott, C.L. 2000. Effects of Imperata
cylindrica (L.) Beauv. (Cogongrass) invasion
on fire regime in Florida sandhill. Natural
Areas Journal. 20(2): 140-149.
215
LiTERATuRE CiTED
Lister, T.W.; Perdue, J. 2013. Maryland’s forest
resources, 2012. Res. Note NRS-191. Newtown
Square, PA: U.S. Department of Agriculture,
Forest Service, Northern Research Station. 4 p.
Available at http://www.fs.fed.us/nrs/pubs/rn/
rn_nrs191.pdf.
Liu, J.; Liu, S.; Loveland, T.R. 200. Temporal
evolution of carbon budgets of the
Appalachian forests in the U.S. from 1972 to
2000. Forest Ecology and Management. 222:
191-201.
Liu, X.; Zhang, X.; Zhang, M. 2008. Major factors
influencing the efficacy of vegetated buffers
on sediment trapping: a review and analysis.
Journal of Environmental Quality. 37(5):
17-174.
Liu, Y.; Stanturf, J.; Goodrick, S. 2010. Trends in
global wildfire potential in a changing climate.
Forest Ecology and Management. 259(4):
85-97.
Loeb, S.C.; Winters, E.A. 2013. Indiana bat
summer maternity distribution: effects of
current and future climates. Ecology and
Evolution. 3(1): 103-114.
Loehle, C. 1988. Tree life history strategies: the
role of defenses. Canadian Journal of Forest
Research. 18(2): 209-222.
Logan, J.A.; Regniere, J.; Powell, J.A. 2003.
Assessing the impacts of global warming on
forest pest dynamics. Frontiers in Ecology and
the Environment. 1(3): 130-137.
Long, R.P.; Horsley, S.B.; Hall, T.J. 2013. Longterm impact of liming on growth and vigor
of northern hardwoods. Canadian Journal of
Forest Research. 41(): 1295-1307.
Loomis, J.B.; Crespi, J. 1999. Estimated effects of
climate change on selected outdoor recreation
activities in the United States. In: Mendelsohn,
R.; Neumann, J.E. The impact of climate change
on the United States economy. Cambridge, UK:
Cambridge University Press: 289-314.
21
Lovett, G.M.; Canham, C.D.; Arthur, M.A.;
Weathers, K.C.; Fitzhugh, R.D. 200. Forest
ecosystem responses to exotic pests and
pathogens in eastern North America.
BioScience. 5(5): 395-405.
Madsen, T.; Willcox, N. 2012. When it rains, it
pours: global warming and the increase in
extreme precipitation from 1948 to 2011.
Trenton, NJ: Environment America Research &
Policy Center. 47 p.
Maherali, H.; Moura, C.F.; Caldeira, M.C.;
Willson, C.J.; Jackson, R.B. 200. Functional
coordination between leaf gas exchange and
vulnerability to xylem cavitation in temperate
forest trees. Plant, Cell & Environment. 29(4):
571-583.
Malmsheimer, R.W.; Bowyer, J.L.; Fried, J.S.;
Gee, E.; Izlar, R.L.; Miner, R.A.; Munn, I.A.;
Oneil, E.; Stewart, W.C. 2011. Managing forests
because carbon matters: integrating energy,
products, and land management policy. Journal
of Forestry. 109(Supplement 1): S7-S51.
Mastrandrea, M.D.; Field, C.B.; Stocker, T.F.;
Edenhofer, O.; Ebi, K.L.; Frame, D.J.; Held,
H.; Kriegler, E.; Mach, K.J.; Matschoss, P.R.;
Plattner, G.-K.; Yohe, G.W.; Zwiers, F.W.
2010. Guidance note for lead authors of the
IPCC Fifth Assessment Report on consistent
treatment of uncertainties. Intergovernmental
Panel on Climate Change (IPCC). Available at
http://www.ipcc.ch/activities/activities.shtml.
(Accessed Feb. 28, 2011).
Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters,
M.P. 2011a. Changes in potential habitat of
147 North American breeding bird species in
response to redistribution of trees and climate
following predicted climate change. Ecography.
34(): 933-945.
LiTERATuRE CiTED
Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters,
M.P.; Rodewald, P.G. 2011b. Modifying climate
change habitat models using tree speciesspecific assessments of model uncertainty
and life history-factors. Forest Ecology and
Management. 22(8): 140-1472.
Maurer, E.; Wood, A.; Adam, J.; Lettenmaier, D.;
Nijssen, B. 2002. A long-term hydrologically
based dataset of land surface fluxes and states
for the conterminous United States. Journal of
Climate. 15(22): 3237-3251.
McConnell, M.E.; Balci, Y. 2014. Phytophthora
cinnamomi as a contributor to white oak
decline in Mid-Atlantic United States forests.
Plant Disease 98(3): 319-327.
McCullough, D.G.; Werner, R.A.; Neumann, D.
1998. Fire and insects in northern and boreal
forest ecosystems of North America. Annual
Review of Entomology. 43(1): 107-127.
McEwan, R.W.; Brecha, R.J.; Geiger, D.R.; John,
G.P. 2011. Flowering phenology change and
climate warming in southwestern Ohio. Plant
Ecology. 212(1): 55-1.
McLachlan, J.S.; Clark, J.S.; Manos, P.S. 2005.
Molecular indicators of tree migration
capacity under rapid climate change. Ecology.
8(8): 2088-2098.
McLaughlin, S.; Andersen, C.; Edwards, N.; Roy,
W.; Layton, P. 1990. Seasonal patterns of
photosynthesis and respiration of red spruce
saplings from two elevations in declining
southern Appalachian stands. Canadian Journal
of Forest Research. 20(5): 485-495.
McLaughlin, S.B.; Kohut, R.J. 1992. The effects
of atmospheric deposition and ozone on
carbon allocation and associated physiological
processes in red spruce. In: Eagar, C.; Adams,
M.B., eds. Ecology and decline of red spruce
in the eastern United States. New York, NY:
Springer-Verlag: 338-382.
McMahon, S.M.; Parker, G.G.; Miller, D.R. 2010.
Evidence for a recent increase in forest
growth. Proceedings of the National Academy
of Sciences. 107(8): 311-315.
McNab, W.; Cleland, D.; Freeouf, J.; Keys, J., Jr.;
Nowacki, G.; Carpenter, C. 2007. Description
of ecological subregions: sections of the
conterminous United States. Gen. Tech. Rep.
WO-7B. Washington, DC: U.S. Department of
Agriculture, Forest Service. Available at http://
www.edc.uri.edu/aTmT-dSS/ReportForecast/
SectionDescriptions.pdf. (Accessed December ,
2011).
McNab, W.H.; Avers, P.E. 1994. Ecological
subregions of the United States: section
descriptions. Administrative Publication
WO-WSA-5. Washington, DC: U.S. Department
of Agriculture, Forest Service, Ecosystem
Management. 27 p. Available at
http://www.fs.fed.us/land/pubs/ecoregions/.
(Accessed September 22, 2014).
McNulty, S.G.; Boggs, J.L. 2010. A conceptual
framework: redefining forest soil’s critical acid
loads under a changing climate. Environmental
Pollution. 158(): 2053-2058.
Maryland Department of Natural Resources
[MDNR]. 2010. Maryland forest resource
assessment. Annapolis, MD: Maryland
Department of Natural Resources, Forest Service.
90 p.
Maryland Department of Natural Resources
[MDNR]. 2014. Forest conservation and
management program. Annapolis, MD:
Maryland Department of Natural Resources,
Forest Service. Available at http://www.dnr.state.
md.us/forests/programapps/fcmp.html. (Accessed
January 14, 2014).
Meehl, G.A.; Arblaster, J.M.; Branstator, G. 2012.
Mechanisms contributing to the warming hole
and the consequent US east-west differential
of heat extremes. Journal of Climate. 25(18):
394-408.
217
LiTERATuRE CiTED
Meehl, G.A.; Tebaldi, C. 2004. More intense, more
frequent, and longer lasting heat waves in the
21st century. Science. 305(58): 994-997.
Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds.
2014. Climate change impacts in the United
States: the third National Climate Assessment.
Washington, DC: U.S. Global Change Research
Program. 841 p. Available at
http://nca2014.globalchange.gov/downloads.
(Accessed August 22, 2014).
Mendelsohn, R.; Neumann, J.E. 2004. The impact
of climate change on the United States
economy. Cambridge, UK: Cambridge University
Press. 344 p.
Merkle, S.; Andrade, G.; Nairn, C.; Powell, W.;
Maynard, C. 2007. Restoration of threatened
species: a noble cause for transgenic trees.
Tree Genetics & Genomes. 3(2): 111-118.
Mickley, L.; Jacob, D.; Field, B.; Rind, D. 2004.
Effects of future climate change on regional
air pollution episodes in the United States.
Geophysical Research Letters. 31: L24103.
Miles, P.D.; Brand, G.J.; Alerich, C.L.; Bednar, L.F.;
Woudenberg, S.W.; Glover, J.F.; Ezell, E.N. 200.
The Forest Inventory and Analysis database
description and users manual version 1.0.
Gen. Tech. Rep. NC-218. St. Paul, MN: U.S.
Department of Agriculture, Forest Service,
North Central Research Station. 130 p.
Mladenoff, D.J. 2004. LANDIS and forest
landscape models. Ecological Modelling.
180(1): 7-19.
Mogil, H.M.; Seaman, K.L. 2009. The climate
and weather of Delaware, Maryland, and
Washington, D.C. Philadelphia, PA: Taylor
& Francis Group. Available at http://www.
weatherwise.org/Archives/Back%20Issues/2009/
July-August%202009/full-mogil.html. (Accessed
September 9, 2014).
218
Moore, M.V.; Pace, M.L.; Mather, J.R.; Murdoch,
P.S.; Howarth, R.W.; Folt, C.L.; Chen, C.Y.;
Hemond, H.F.; Flebbe, P.A.; Driscoll, C.T.
1997. Potential effects of climate change on
freshwater ecosystems of the New England/
Mid-Atlantic region. Hydrological Processes.
11(8): 925-947.
Morin, R.S.; Liebhold, A.M.; Tobin, P.C.;
Gottschalk, K.W.; Luzader, E. 2007. Spread
of beech bark disease in the eastern United
States and its relationship to regional forest
composition. Canadian Journal of Forest
Research. 37(4): 72-73.
Moritz, M.A.; Parisien, M.A.; Batllori, E.;
Krawchuk, M.A.; Van Dorn, J.; Ganz, D.J.;
Hayhoe, K. 2012. Climate change and
disruptions to global fire activity. Ecosphere.
3: 49.
Morris, D.; Walls, M.A. 2009. Climate change and
outdoor recreation resources. Washington, DC:
Resources for the Future.
Moscow Forest Sciences Laboratory. 2014. Climate
estimates and plant-climate relationships:
current climate data. Moscow, ID: U.S.
Department of Agriculture, Forest Service, Rocky
Mountain Research Station. Available at
http://forest.moscowfsl.wsu.edu/. (Accessed
September 19, 2014).
Munger, G.T. 2001. Alliaria petiolata. Fire effects
information system. [Online]. Missoula, MT:
U.S. Department of Agriculture, Forest Service,
Rocky Mountain Research Station, Fire Sciences
Laboratory. Available at http://www.fs.fed.us/
database/feis/plants/forb/allpet/introductory.html.
(Accessed September 22, 2014).
Munger, G.T. 2002. Pueraria montana var. lobata.
Fire effects information system. [Online].
Missoula, MT: U.S. Department of Agriculture,
Forest Service, Rocky Mountain Research
Station, Fire Sciences Laboratory. Available at
http://www.fs.fed.us/database/feis/plants/vine/
puemonl/introductory.html. (Accessed
September 22, 2014).
LiTERATuRE CiTED
Murphy, H.T.; VanDerWal, J.; Lovett-Doust, J. 2010.
Signatures of range expansion and erosion in
eastern North American trees. Ecology Letters.
13(10): 1233-1244.
National Interagency Fire Center Statistics. 2013.
Lightning vs. human caused fires and acres
(stats reported from 2001). [Online]. Boise,
ID. Available at http://www.nifc.gov/fireInfo/
fireInfo_stats_lightng.html. (Accessed
September 8, 2014).
National Oceanic and Atmospheric Administration
[NOAA]. 2008. Top ten breakthroughs:
the first climate model. Washington, DC.
Available at http://celebrating200years.noaa.gov/
breakthroughs/climate_model/welcome.html.
National Oceanic and Atmospheric Administration
[NOAA]. 2012. State of the climate: national
overview for annual 2012. National Climatic
Data Center. Available at http://www.ncdc.noaa.
gov/sotc/national/2012/13. (Accessed May 8,
2013).
National Oceanic and Atmospheric Administration
[NOAA]. 2013a. Enhanced Fujita scale (EF
scale). Norman, OK: National Weather Service.
Available at http://www.spc.noaa.gov/efscale/.
(Accessed June 19, 2013).
National Oceanic and Atmospheric Administration
[NOAA]. 2013b. Historic “derecho” severe
wind event storms across the Midwest and
Mid-Atlantic on June 29, 2012. Blacksburg,
VA: National Weather Service, Weather Forecast
Office. Available at http://www.erh.noaa.gov/
rnk/events/2012/Jun29_derecho/summary.php.
(Accessed October 3, 2013).
National Oceanic and Atmospheric Administration
[NOAA]. 2013c. The Ozarks / Ohio Valley
derecho of April 2011. Storm Prediction Center.
Available at http://www.spc.noaa.gov/misc/
AbtDerechos/casepages/apr202011page.htm.
(Accessed August 21, 2014).
National Oceanic and Atmospheric Administration
[NOAA]. 2014a. Billion-dollar weather/climate
disasters. National Climatic Data Center.
Available at http://www.ncdc.noaa.gov/billions/
events. (Accessed September 10, 2014).
National Oceanic and Atmospheric Administration
[NOAA]. 2014b. Climate at a glance. National
Climatic Data Center. Available at
http://www.ncdc.noaa.gov/cag/time-series/global.
(Accessed January 27, 2014).
National Oceanic and Atmospheric Administration
[NOAA]. 2014c. Climate maps of the United
States (1961-1990). National Climatic Data
Center. Available at http://cdo.ncdc.noaa.gov/cgibin/climaps/climaps.pl?directive=quick_results&
subrnum=&pop=YES. (Accessed September 8,
2014).
National Oceanic and Atmospheric Administration
[NOAA]. 2014d. National temperature and
precipitation maps. National Climatic Data
Center. Available at http://www.ncdc.noaa.gov/
temp-and-precip/maps.php. (Accessed August 21,
2014).
National Research Council [NRC]. 2007.
Environmental impacts of wind-energy
projects. National Research Council, Committee
on Environmental Impacts of Wind-Energy
Projects. 394 p. Available at
http://www.nap.edu/catalog/11935.html.
(Accessed September 22, 2014).
National Weather Service. 2012. Storm Prediction
Center. Available at http://www.spc.noaa.gov/
wcm/. (Accessed April 1, 2012).
Natural Resources Conservation Service [NRCS].
200. Land resource regions and major
land resource areas of the United States, the
Carribbean, and the Pacific Basin. Agric.
Handb. 29. 71 p. Available at http://www.
nrcs.usda.gov/Internet/FSE_DOCUMENTS/
nrcs142p2_051845.pdf. (Accessed September 9,
2014).
219
LiTERATuRE CiTED
Natural Resources Conservation Service [NRCS].
2014a. NJ biology technical note: southern
pine beetle and forest management.
Available at http://www.nrcs.usda.gov/Internet/
FSE_DOCUMENTS/nrcs141p2_018399.pdf.
(Accessed January 14, 2014).
Natural Resources Conservation Service [NRCS].
2014b. PLANTS database. [Online]. National
Plant Data Team. Available at http://plants.usda.
gov/java/. (Accessed September 9, 2014).
The Nature Conservancy. 2003. The Cumberlands
and Southern Ridge & Valley Ecoregion:
a plan for biodiversity conservation.
Arlington, VA. 232 p. Available at http://www.
conservationgateway.org/ConservationPlanning/
SettingPriorities/EcoregionalReports/Documents/
CSRVPlan.pdf. (Accessed August 21, 2014).
NatureServe. 2011. NatureServe Explorer.
Arlington, VA. Available at http://www.
natureserve.org/explorer/classeco.htm.
(Accessed September 11, 2014).
NatureServe. 2013. NatureServe Explorer: an
online encyclopedia of life. Arlington, VA.
Available at http://www.natureserve.org/explorer.
(Accessed September 1, 2013).
Nearing, M. 2001. Potential changes in rainfall
erosivity in the US with climate change during
the 21st century. Journal of Soil and Water
Conservation. 5(3): 229-232.
Nearing, M.; Pruski, F.; O’Neal, M. 2004. Expected
climate change impacts on soil erosion rates: a
review. Journal of Soil and Water Conservation.
59(1): 43-50.
Nebel, S.; Mills, A.; McCracken, J.D.; Taylor, P.D.
2010. Declines of aerial insectivores in North
America follow a geographic gradient. Avian
Conservation and Ecology. 5(2): 1.
220
Neff, R.; Chang, H.; Knight, C.G.; Najjar, R.G.;
Yarnal, B.; Walker, H.A. 2000. Impact of
climate variation and change on Mid-Atlantic
region hydrology and water resources. Climate
Research. 14(3): 207-218.
Negley, T.L.; Eshleman, K.N. 200. Comparison
of stormflow responses of surface-mined
and forested watersheds in the Appalachian
mountains, USA. Hydrological Processes.
20(1): 347-3483.
Nicholls, S. 2012. Outdoor recreation and
tourism. In: Winkler, J.; Hatfield, J.; Bidwell,
D.; Brown, D., eds. U.S. National Climate
Assessment Midwest Technical Input Report
Great Lakes Integrated Sciences and Assessments
(GLISA) Center. Available at http://www.glisa.
umich.edu/docs/NCA/MTIT_RecTourism.pdf.
(Accessed September 10, 2014).
Nilsson, C.; Brown, R.L.; Jansson, R.; Merritt, D.M.
2010. The role of hydrochory in structuring
riparian and wetland vegetation. Biological
Reviews. 85(4): 837-858.
Norby, R.; DeLucia, E.; Gielen, B.; Calfapietra, C.;
Giardina, C.; King, J.; Ledford, J.; McCarthy,
H.; Moore, D.; Ceulemans, R. 2005. Forest
response to elevated CO2 is conserved across a
broad range of productivity. Proceedings of the
National Academy of Sciences. 102(50): 18052.
Nowacki, G.; Carr, R.; Van Dyck, M. 2009. The
current status of red spruce in the eastern
United States: distribution, population
trends, and environmental drivers. In: Rentch,
J.S.; Schuler, T.M., eds. Proceedings from the
conference on ecology and management of
high-elevation forests in the central and southern
Appalachian Mountains. Gen. Tech. Rep.
NRS-P-4. Newtown Square, PA: U.S.
Department of Agriculture, Forest Service,
Northern Research Station: 140-12.
LiTERATuRE CiTED
Nowacki, G.J.; Abrams, M.D. 2008. The demise
of fire and “mesophication” of forests in the
eastern United States. Bioscience. 58(2):
123-138.
O’Malley, M. 2011. Executive order 01.01.2011.11:
the Marcellus shale safe drilling initiative.
Annapolis, MD: State of Maryland, Executive
Department. Available at http://www.governor.
maryland.gov/executiveorders/01.01.2011.11.pdf.
(Accessed September 8, 2014).
Ockels, F.S.; Mielke, M.; Bonello, P. 2014. Sudden
oak death: monitoring Phytophthora ramorum
in the North-central United States. Columbus,
OH: Ohio State University. Available at
http://ohioline.osu.edu/sc195/020.html.
(Accessed May 14, 2014).
Ohio Department of Natural Resources [ODNR].
1998. Physiographic regions of Ohio. [Pagesize map with text, scale 1:2,100,00]. Division of
Geological Survey. 2 p. Available at http://www.
epa.state.oh.us/portals/27/SIP/Nonattain/F2physiographic_regions_of_Ohio.pdf. (Accessed
September 9, 2014).
Ohio Department of Natural Resources [ODNR].
2010a. Invasive plants of Ohio: tree-of-heaven.
Fact sheet 17. Columbus, OH. Available at
http://ohiodnr.gov/portals/0/pdfs/invasives/
17FactsheetTreeofHeaven.pdf. (Accessed
September 18, 2014).
Ohio Department of Natural Resources [ODNR].
2005. Glacial map of Ohio. Division of
Geological Survey. Available at http://geosurvey.
ohiodnr.gov/portals/geosurvey/PDFs/Glacial/
glacial.pdf. (Accessed September 12, 2014).
Ohio Department of Natural Resources [ODNR].
2010b. Ohio’s statewide forest resource
assessment. Columbus, OH: Division of Forestry.
188 p.
Ohio Department of Natural Resources [ODNR].
2013. Soil regions of Ohio. Division of Soil and
Water Conservation. Available at http://www2.
ohiodnr.com/soilwater/soil-conservation/ohiosoil-regions. (Accessed August 21, 2014).
Ohio Department of Natural Resources [ODNR].
2014. Ohio forest tax law (OFTL). Division
of Forestry. Available at http://forestry.ohiodnr.
gov/oftl. (Accessed August 21, 2014).
Ohio Emergency Management Agency. 2011.
State of Ohio hazard mitigation plan.
Available at http://ema.ohio.gov/Documents/
OhioMitigationPlan/2011/Section%202_
HIRA%20Part%201.pdf. (Accessed August 21,
2014).
Olano, J.M.; Palmer, M.W. 2003. Stand dynamics
of an Appalachian old-growth forest during
a severe drought episode. Forest Ecology and
Management. 174(1-3): 139-148.
Ollinger, S.; Goodale, C.; Hayhoe, K.; Jenkins, J.
2008. Potential effects of climate change
and rising CO2 on ecosystem processes in
northeastern U.S. forests. Mitigation and
Adaptation Strategies for Global Change.
13(5): 47-485.
Pan, Y.; Birdsey, R.; Hom, J.; McCullough, K. 2009.
Separating effects of changes in atmospheric
composition, climate and land-use on carbon
sequestration of US Mid-Atlantic temperate
forests. Forest Ecology and Management.
259(2): 151-14.
Pan, Z.; Arritt, R.W.; Takle, E.S.; Gutowski,
W.J.; Anderson, C.J.; Segal, M. 2004. Altered
hydrologic feedback in a warming climate
introduces a warming hole. Geophysical
Research Letters. 31(17).
221
LiTERATuRE CiTED
Pardo, L.H.; Robin-Abbott, M.J.; Driscoll, C.T.
2011. Assessment of nitrogen deposition effects
and empirical critical loads of nitrogen for
ecoregions of the United States. Gen.Tech. Rep.
NRS-80. Newtown Square, PA: U.S. Department
of Agriculture, Forest Service, Northern Research
Station. 291 p.
Parmesan, C. 2007. Influences of species,
latitudes and methodologies on estimates of
phenological response to global warming.
Global Change Biology. 13(9): 180-1872.
Parmesan, C.; Yohe, G. 2003. A globally coherent
fingerprint of climate change impacts across
natural systems. Nature. 421(918): 37-42.
Pastor, J.; Post, W. 1985. Development of a linked
forest productivity-soil process model. Oak
Ridge National Laboratory, Environmental
Sciences Division Publication No. 2455.
Springfield, VA: U.S. Department of Commerce.
17 p.
Patterson, W. 200. The paleoecology of fire and
oaks in eastern forests. In: Dickinson, M.B.,
ed. Fire in eastern oak forests: delivering science
to land managers, proceedings of a conference;
2005 November 15-17; Columbus, OH. Gen.
Tech. Rep. NRS-P-1. Newtown Square, PA:
U.S. Department of Agriculture, Forest Service,
Northern Research Station: 2-19.
Pennsylvania Department of Conservation and
Natural Resources. 2013. Forest health fact
sheet: hemlock woolly adelgid. Available at
http://www.dcnr.state.pa.us/cs/groups/public/
documents/document/dcnr_007179.pdf.
(Accessed August 21, 2014).
Pennsylvania Department of Environmental
Protection [PDEP]. 2004. Pennsylvania field
guide: common invasive plants in riparian
areas. 29 p. Available at http://www.dep.state.
pa.us/dep/deputate/watermgt/wc/subjects/
streamreleaf/Docs/Invasive%20Plants.pdf.
(Accessed August 21, 2014).
222
Pennsylvania Department of Environmental
Protection [PDEP]. 2012. Erosion and sediment
pollution control program manual. Technical
Guidance No. 33-2124-008. 53 p. Available at
http://www.elibrary.dep.state.pa.us/dsweb/Get/
Document-88925/33-2134-008.pdf. (Accessed
August 21, 2014).
Perera, E.M.; Sanford, T.; White-Newsome, J.L.;
Kalkstein, L.S.; Vanos, J.K.; Weir, K. 2012.
Heat in the Heartland: 60 years of warming
in the Midwest. Union of Concerned Scientists.
40 p. Available at http://www.ucsusa.org/
assets/documents/global_warming/Heat-in-theHeartland-Full-Report.pdf. (Accessed August 21,
2014).
Perry, L.B.; Konrad, C.E. 200. Relationships
between NW flow snowfall and topography
in the southern Appalachians, USA. Climate
Research. 32(1): 35.
Peterjohn, W.T.; Adams, M.B.; Gilliam, F.S.
199. Symptoms of nitrogen saturation in
two central Appalachian hardwood forest
ecosystems. Biogeochemistry. 35(3): 507-522.
Peters, R.L.; Darling, J.D. 1985. The greenhouse
effect and nature reserves. BioScience. 35(11):
707-717.
Peters, M.P.; Iverson, L.R.; Matthews, S.N.;
Prasad, A.M. 2013. Wildfire hazard mapping:
exploring site conditions in eastern US
wildland-urban interfaces. International Journal
of Wildland Fire. 22(5): 57-578.
Peterson, C.J. 2000. Catastrophic wind damage
to North American forests and the potential
impact of climate change. The Science of the
Total Environment. 22: 287-311.
Peterson, C.J.; Krueger, L.M.; Royo, A.A.; Stark,
S.; Carson, W.P. 2013. Disturbance size and
severity covary in small and mid-size wind
disturbances in Pennsylvania northern
hardwoods forests. Forest Ecology and
Management. 302: 273-279.
LiTERATuRE CiTED
Peterson, G.C.; Allen, R.; C.S.Holling. 1998.
Ecological resilience, biodiversity, and scale.
Ecosystems. 1(1): -18.
Peterson, T.C.; Zhang, X.; Brunet-India, M.;
Vázquez-Aguirre, J.L. 2008. Changes in North
American extremes derived from daily
weather data. Journal of Geophysical Research.
113(D7): D07113.
Pitchford, J.L.; Wu, C.; Lin, L.S.; Petty, J.T.;
Thomas, R.; Veselka, W.E.; Welsch, D.; Zegre,
N.; Anderson, J.T. 2012. Climate change effects
on hydrology and ecology of wetlands in the
Mid-Atlantic highlands. Wetlands. 32: 21-33.
Piva, R.J.; Cook, G.W. 2011. West Virginia timber
industry: an assessment of timber product
output and use, 2007. Gen. Tech. Rep.
NRS-4. Newtown Square, PA: U.S. Department
of Agriculture, Forest Service, Northern Research
Station. 4 p.
Poff, N.L. 1992. Regional hydrologic response to
climate change: an ecological perspective. In:
Firth, P.; Fisher, S., eds. Global climate change
and freshwater ecosystems. New York, NY:
Springer: 88-115.
Polsky, C.; Allard, J.; Currit, N.; Crane, R.; Yarnal,
B. 2000. The Mid-Atlantic region and its
climate: past, present, and future. Climate
Research. 14(3): 11-173.
Potter, K.M.; Hargrove, W.W.; Koch, F.H. 2010.
Predicting climate change extirpation risk
for central and southern Appalachian forest
tree species. In: Rentch, J.S.; Schuler, T.M., eds.
Proceedings from the conference on ecology
and management of high-elevation forests of the
central and southern Appalachian Mountains.
Gen. Tech. Rep. NRS-P-4. Newtown Square,
PA: U.S. Department of Agriculture, Forest
Service, Northern Research Station: 179-189.
Primack, R.B.; Miller-Rushing, A.J. 2012.
Uncovering, collecting, and analyzing records
to investigate the ecological impacts of climate
change: a template from Thoreau’s Concord.
BioScience. 2(2): 170-181.
Raffa, K.F.; Aukema, B.H.; Bentz, B.J.; Carroll,
A.L.; Hicke, J.A.; Turner, M.G.; Romme,
W.H. 2008. Cross-scale drivers of natural
disturbances prone to anthropogenic
amplification: the dynamics of bark beetle
eruptions. BioScience. 58(): 501-517.
Raupach, M.R.; Marland, G.; Ciais, P.; Le Quere,
C.; Canadell, J.G.; Klepper, G.; Field, C.B. 2007.
Global and regional drivers of accelerating
CO2 emissions. Proceedings of the National
Academy of Sciences. 104(24): 10288-10293.
Pope, V.; Gallani, M.L.; Rowntree, P.R.; Stratton,
R.A. 2000. The impact of new physical
parameterizations in the Hadley Centre
climate model: HadAM3. Climate Dynamics.
1: 123-14.
Rebbeck, J. 2012. Fire management and woody
invasive plants in oak ecosystems. In: Dey,
D.C.; Stambaugh, M.C.; Clark, S.L.; Schweitzer,
C. J., eds. Proceedings of the 4th fire in eastern
forests conference. Gen. Tech. Rep. NRS-P-102.
Newtown Square, PA: U.S. Department of
Agriculture, Forest Service, Northern Research
Station: 142-155.
Portmann, R.W.; Solomon, S.; Hegerl, G.C. 2009.
Spatial and seasonal patterns in climate
change, temperatures, and precipitation across
the United States. Proceedings of the National
Academy of Sciences. 10(18): 7324-7329.
Rennenberg, H.; Dannenmann, M.; Gessler, A.;
Kreuzwieser, J.; Simon, J.; Papen, H. 2009.
Nitrogen balance in forest soils: nutritional
limitation of plants under climate change
stresses. Plant Biology. 11(s1): 4-23.
Post, W.M.; Pastor, J. 199. Linkages—an
individual-based forest ecosystem model.
Climatic Change. 34(2): 253-21.
223
LiTERATuRE CiTED
Resources for the Future. 2012. A review of shale
gas regulations by state. Washington, DC:
Resources For the Future (RFF), Center for
Energy Economics and Policy. Available at
http://www.rff.org/centers/energy_economics_
and_policy/Pages/Shale_Maps.aspx. (Accessed
August 21, 2014).
Reusch, T.B.H.; Ehlers, A.; Hammerli, A.; Worm, B.
2005. Ecosystem recovery after climatic
extremes enhanced by genotypic diversity.
Proceedings of the National Academy of
Sciences. 102(8): 282-2831.
Richardson, K.; Steffen, W.; Schellnhuber, H.J.;
Alcamo, J.; Barker, T.; Kammen, D.; Leemans,
R.; Liverman, D.; Monasinghe, M.; OsmanElasha, B.; Stern, N.; Waever, O. 2009.
Synthesis report from Climate change: global
risks challenges and decisions. Copenhagen,
Denmark: University of Copenhagen. Available at
http://edepot.wur.nl/10197. (Accessed August 21,
2014).
Richardson, R.B.; Loomis, J.B. 2004. Adaptive
recreation planning and climate change: a
contingent visitation approach. Ecological
Economics. 50(1): 83-99.
Riitters, K.H. 2011. Spatial patterns of land cover
in the United States. A technical document
supporting the Forest Service 2010 RPA
assessment. Gen. Tech. Rep. SRS-13. Asheville,
NC: U.S. Department of Agriculture, Forest
Service, Southern Research Station. 4 p.
Riitters, K.H.; Wickham, J.D.; O’Neill, R.V.; Jones,
K.B.; Smith, E.R.; Coulston, J.W.; Wade, T.G.;
Smith, J.H. 2002. Fragmentation of continental
United States forests. Ecosystems. 5(8):
0815-0822.
224
Robertson, A.W.; Kushnir, Y.; Lall, U.; Nakamura,
J. 2011. On the connection between lowfrequency modulation of large-scale weather
regimes and springtime extreme flooding over
the Midwest of the United States. 3th Annual
climate diagnostics and prediction workshop.
National Oceanographic and Atmospheric
Administration: Science and Technology Infusion
Climate Bulletin: 150-152. Available at http://
www.nws.noaa.gov/ost/climate/STIP/3CDPW/
3cdpw-arobertson.pdf. (Accessed August 21,
2014).
Robinson, E.D.; Trapp, R.J.; Baldwin, M.E. 2013.
The geospatial and temporal distributions of
severe thunderstorms from high-resolution
dynamical downscaling. Journal of Applied
Meteorology and Climatology. 52: 2147-211.
Root, T.L.; Price, J.T.; Hall, K.R.; Schneider, S.H.;
Rosenzweig, C.; Pounds, J.A. 2003. Fingerprints
of global warming on wild animals and plants.
Nature. 421(918): 57-0.
Runyan. 2011. Economic impact of travel
on West Virginia 2000-2010: detailed
state and county estimates. Portland, OR:
Dean Runyan and Associates. Available at
http://www.wvcommerce.org/App_Media/
assets/doc/travelandrec/industry/marketing/
2010%20Economic%20Impact.pdf. (Accessed
August 21, 2014).
Rustad, L.; Campbell, J.; Marion, G.; Norby,
R.; Mitchell, M.; Hartley, A.; Cornelissen, J.;
Gurevitch, J. 2001. A meta-analysis of the
response of soil respiration, net nitrogen
mineralization, and aboveground plant
growth to experimental ecosystem warming.
Oecologia. 12(4): 543-52.
LiTERATuRE CiTED
Ryan, M.G.; Archer, S.R.; Birdsey, R.; Dahm, C.;
Heath, L.; Hicke, J.; Hollinger, D.; Huxman, T.;
Okin, G.; Oren, R.; Randerson, J.; Schlesinger,
W. 2008. Land resources. In: The effects of
climate change on agriculture, land resources,
water resources, and biodiversity in the United
States. Washington, DC: U.S. Climate Change
Science Program and the Subcommittee on
Global Change Research: 75-120.
Ryan, M.G.; Harmon, M.E.; Birdsey, R.A.; Giardina,
C.P.; Heath, L.S.; Houghton, R.A.; Jackson,
R.B.; McKinley, D.C.; Morrison, J.F.; Murray,
B.C. 2010. A synthesis of the science on forests
and carbon for US forests. Issues In Ecology:
Ecological Society of America. 13: 1-1.
Sasek, T.W.; Strain, B.R. 1988. Effects of carbon
dioxide enrichment on the growth and
morphology of kudzu (Pueraria lobata). Weed
Science. 3: 28-3.
Sasek, T.W.; Strain, B.R. 1989. Effects of carbon
dioxide enrichment on the expansion and
size of kudzu (Pueraria lobata) leaves. Weed
Science. 37: 23-28.
Sasek, T.W.; Strain, B.R. 1990. Implications of
atmospheric CO2 enrichment and climatic
change for the geographical distribution of two
introduced vines in the USA. Climatic Change.
1(1): 31-51.
Saunders, S.; Findlay, D.; Easley, T.; Spencer, T.
2012. Doubled trouble: more Midwestern
extreme storms. Louisville, CO: Rocky
Mountain Climate Organization; New York,
NY: Natural Resources Defense Council. 42 p.
Available at http://www.rockymountainclimate.
org/images/Doubled%20Trouble.pdf. (Accessed
August 21, 2014).
Schaberg, P.G.; Tilley, J.W.; Hawley, G.J.; DeHayes,
D.H.; Bailey, S.W. 200. Associations of
calcium and aluminum with the growth and
health of sugar maple trees in Vermont. Forest
Ecology and Management. 223(1): 159-19.
Scheller, R.M.; Mladenoff, D.J. 2008. Simulated
effects of climate change, fragmentation,
and inter-specific competition on tree species
migration in northern Wisconsin, USA.
Climate Research. 3(3): 191-202.
Schiffer, M.B. 199. Formation processes of the
archaeological record. Salt Lake City, UT:
University of Utah Press. 448 p.
Schlesinger, M.D.; Corser, J.D.; Perkins, K.A.;
White, E.L. 2011. Vulnerability of at risk
species to climate change in New York. Albany,
NY: New York Natural Heritage Program. 7 p.
Available at https://connect.natureserve.org/
sites/default/files/documents/ccvi_report_ny.pdf.
(Accessed August 21, 2014).
Schneider, K.; Culver, D.C. 2004. Estimating
subterranean species richness using intensive
sampling and rarefaction curves in a high
density cave region in West Virginia. Journal of
Cave and Karst Studies. (2): 39-45.
Schuler, T.M.; Collins, R.J. 2002. Successional
dynamics and restoration implications of
a montane coniferous forest in the central
Appalachians, USA. Natural Areas Journal.
22(2): 88-98.
Schuler, T.M.; Gillespie, A.R. 2000. Temporal
patterns of woody species diversity in a central
Appalachian forest from 1856 to 1997. Journal
of the Torrey Botanical Society. 127: 149-11.
Schwartz, M.D.; Ault, T.R.; Betancourt, J.L. 2013.
Spring onset variations and trends in the
continental United States: past and regional
assessment using temperature-based indices.
International Journal of Climatology. 33(13):
2917-2922.
Schwartz, M.D.; R.Ahas; Aasa, A. 200a. Onset
of spring starting earlier across the Northern
Hemisphere. Global Change Biology. 12(2):
343-351.
225
LiTERATuRE CiTED
Schwartz, M.W.; Iverson, L.R.; Prasad, A.M.;
Matthews, S.N.; O’Connor, R.J. 200b.
Predicting extinctions as a result of climate
change. Ecology. 87(7): 111-115.
Sisk, T.D.; Haddad, N.M.; Ehrlich, P.R. 1997. Bird
assemblages in patchy woodlands: modeling
the effects of edge and matrix habitats.
Ecological Applications. 7(4):1170-1180.
Seidel, T.M.; Weihrauch, D.M.; Kimball, K.D.;
Pszenny, A.A.; Soboleski, R.; Crete, E.; Murray,
G. 2009. Evidence of climate change declines
with elevation based on temperature and
snow records from 1930s to 2006 on Mount
Washington, New Hampshire, USA. Arctic,
Antarctic, and Alpine Research. 41(3): 32-372.
Smith, D.L.; Johnson, L. 2004. Vegetationmediated changes in microclimate reduce
soil respiration as woodlands expand into
grasslands. Ecology. 85(12): 3348-331.
Shang, B.Z.; He, H.S.; Crow, T.R.; Shifley, S.R.
2004. Fuel load reductions and fire risk
in central hardwood forests of the United
States: a spatial simulation study. Ecological
Modelling. 180(1): 89-102.
Sharitz, R.R.; Boring, L.R.; Lear, D.H.V.; Pinder,
J.E., III. 1992. Integrating ecological concepts
with natural resource management of southern
forests. Ecological Applications. 2(3): 22-237.
Shuman, B.; Bartlein, P.; Logar, N.; Newby, P.;
Webb, T. 2002. Parallel climate and vegetation
responses to the early Holocene collapse of
the Laurentide ice sheet. Quaternary Science
Reviews. 21(1): 1793-1805.
Shumway, D.L.; Abrams, M.D.; Ruffner, C.M. 2001.
A 400-year history of fire and oak recruitment
in an old-growth oak forest in western
Maryland, U.S.A. Canadian Journal of Forest
Research. 31(8): 1437-1443.
Simmons, J.A.; Currie, W.S.; Eshleman, K.N.;
Kuers, K.; Monteleone, S.; Negley, T.L.; Pohlad,
B.R.; Thomas, C.L. 2008. Forest to reclaimed
mine land use change leads to altered
ecosystem structure and function. Ecological
Applications. 18(1): 104-118.
Sinha, T.; Cherkauer, K.A. 2010. Impacts of future
climate change on soil frost in the midwestern
United States. Journal of Geophysical Research:
Atmospheres (1984-2012). 115(D8).
22
Society of American Foresters. 2014. The
dictionary of forestry. Bethesda, MD: Society
of American Foresters. Available at http://
dictionaryofforestry.org/dict/term/ecosystem.
(Accessed January 10, 2014).
Soil Conservation Service [SCS]. 1974. Soil survey
of Garrett County, Maryland. Washington,
DC: U.S. Department of Agriculture, Soil
Conservation Service and Maryland Agriculture
Experiment Station. 87 p.
Sommers, W.T.; Coloff, S.G.; Conard, S.G. 2011.
Fire history and climate change. Report
submitted to the Joint Fire Science Program
for Project 09-02-1-09. 190 p. Available at
https://www.firescience.gov/projects/09-2-019/project/09-2-01-9_09_2_01_9_Deliverable_
01.pdf. (Accessed August 21, 2014).
Souther, S.; McGraw, J.B. 2011. Evidence of local
adaptation in the demographic response of
American ginseng to interannual temperature
variation. Conservation Biology. 25(5): 922-931.
Souza, L.; Belote, R.; Kardol, P.; Weltzin, J.;
Norby, R. 2010. CO2 enrichment accelerates
successional development of an understory
plant community. Journal of Plant Ecology.
3(1): 33-39.
Spence, B.; Kunkel, C.; Schewel, E.; Boettner, T.;
Martin, L. 2013. Who owns West Virginia?
Charleston, WV: West Virginia Center on Budget
and Policy and the American Friends Service
Committee. 57 p. Available at http://www.
wvpolicy.org/wp-content/uploads/2013/12/landstudy-paper-final3.pdf. (Accessed August 21,
2014).
LiTERATuRE CiTED
Stoner, A.M.K.; Hayhoe, K.; Yang, X.; Wuebbles,
D.J. 2013. An asynchronous regional
regression model for statistical downscaling of
daily climate variables. International Journal of
Climatology. 33(11): 2473-2494.
Szlavecz, K.; McCormick, M.; Xia, L.; Saunders, J.;
Morcol, T.; Whigham, D.; Filley, T.; Csuzdi, C.
2010. Ecosystem effects of non-native
earthworms in Mid-Atlantic deciduous forests.
Biological Invasions. 13(5): 115-1182.
Stott, P.A.; Gillett, N.P.; Hegerl, G.C.; Karoly,
D.J.; Stone, D.A.; Zhang, X.; Zwiers, F. 2010.
Detection and attribution of climate change:
a regional perspective. Wiley Interdisciplinary
Reviews: Climate Change. 1(2): 192-211.
Tans, P.; Keeling, R. 2013. Trends in atmospheric
carbon dioxide. National Oceanic and
Atmospheric Administration, Earth System
Research Laboratory. Available at
http://www.esrl.noaa.gov/gmd/ccgg/trends/.
(Accessed August 21, 2014).
Sturdevant-Rees, P.; Smith, J.A.; Morrison, J.;
Baeck, M.L. 2001. Tropical storms and the
flood hydrology of the Central Appalachians.
Water Resources Research. 37(8): 2143-218.
Sustainable Forestry Initiative [SFI]. 2013. SFI
in Brief: April 2013 newsletter. Available at
http://www.sfiprogram.org/media-resources/
newsletter1/april-2013/. (Accessed August 21,
2014).
Swanson, F.J.; Johnson, S.L.; Gregory, S.V.; Acker,
S.A. 1998. Flood disturbance in a forested
mountain landscape. BioScience. 48(9):
81-89.
Swanston, C.; Janowiak, M.; Iverson, L.; Parker,
L.; Mladenoff, D.; Brandt, L.; Butler, P.; Pierre,
M.S.; Prasad, A.; Matthews, S.; Peters, M.;
Higgins, D.; Dorland, A. 2011. Ecosystem
vulnerability assessment and synthesis: a
report from the Climate Change Response
Framework project in northern Wisconsin.
Gen. Tech. Rep. NRS-82. Newtown Square, PA:
U.S. Department of Agriculture, Forest Service,
Northern Research Station. 142 p.
Swanston, C.W.; Janowiak, M.K. 2012. Forest
adaptation resources: climate change tools and
approaches for land managers. Gen. Tech. Rep.
NRS-87. Newtown Square, PA: U.S. Department
of Agriculture, Forest Service, Northern Research
Station. 121 p. Available at http://www.nrs.fs.fed.
us/pubs/40543. (Accessed August 21, 2014).
Taylor, D.A. 200. Ginseng, the divine root: the
curious history of the plant that captivated the
world. Chapel Hill, NC: Algonquin Books.
304 p.
Tebaldi, C.; Adams-Smith, D.; Heller, N. 2012.
The heat is on: US temperature trends.
Princeton, NJ: Climate Central. 22 p. Available
at http://www.climatecentral.org/wgts/heat-ison/HeatIsOnReport.pdf. (Accessed August 21,
2014).
Thomas, R.B.; Spal, S.E.; Smith, K.R.; Nippert,
J.B. 2013. Evidence of recovery of Juniperus
virginiana trees from sulfur pollution after
the Clean Air act. Proceedings of the National
Academy of Sciences. 110(38): 15319-15324.
Thomas-Van Gundy, M.A.; Nowacki, G.J. 2013.
The use of witness trees as pyro-indicators for
mapping past fire conditions. Forest Ecology
and Management. 304(15): 333-344.
Thomas-Van Gundy, M.A.; Strager, M.P. 2012.
European settlement-era vegetation of the
Monongahela National Forest, West Virginia.
Gen. Tech. Rep. NRS-101. Newtown Square, PA:
U.S. Department of Agriculture, Forest Service,
Northern Research Station. 39 p.
Tilman, D. 199. Biodiversity: population versus
ecosystem stability. Ecology. 77(2): 350-33.
227
LiTERATuRE CiTED
Tilman, D. 1999. The ecological consequences of
changes in biodiversity: a search for general
principles. Ecology. 80(5): 1455-1474.
Tomer, M.D.; Schilling, K.E. 2009. A simple
approach to distinguish land-use and climatechange effects on watershed hydrology. Journal
of Hydrology. 37(1): 24-33.
Townsend, P.A.; Helmers, D.P.; Kingdon, C.C.;
McNeil, B.E.; de Beurs, K.M.; Eshleman, K.N.
2009. Changes in the extent of surface mining
and reclamation in the Central Appalachians
detected using a 1976-2006 Landsat time
series. Remote Sensing of Environment. 113(1):
2-72.
Townsend Peterson, A.; Scachetti-Pereira, R.
2004. Potential geographic distribution
of Anoplophora glabripennis (Coleoptera:
Cerambycidae) in North America. The
American Midland Naturalist. 151(1): 170-178.
Trapp, R.J.; Diffenbaugh, N.S.; Brooks, H.E.;
Baldwin, M.E.; Robinson, E.D.; Pal, J.S. 2007.
Changes in severe thunderstorm environment
frequency during the 21st century caused by
anthropogenically enhanced global radiative
forcing. Proceedings of the National Academy
of Sciences of the United States of America.
104(50): 19719-19723.
Trapp, R.J.; Diffenbaugh, N.S.; Gluhovsky, A. 2009.
Transient response of severe thunderstorm
forcing to elevated greenhouse gas
concentrations. Geophysical Research Letters.
3(1): L01703.
Trapp, R.J.; Robinson, E.D.; Baldwin, M.E.;
Diffenbaugh, N.S.; Schwedler, B.R. 2011.
Regional climate of hazardous convective
weather through high-resolution dynamical
downscaling. Climate Dynamics. 37(3-4):
77-88.
228
Trout Unlimited. 200. Eastern Brook trout: status
and threats. Arlington, VA. 40 p. Available at
http://easternbrooktrout.org/reports/easternbrook-trout-status-and-threats/at_download/file.
(Accessed August 21, 2014).
Tu, M.; Callie, H.; M., R.J. 2001. Weed control
methods handbook: tools and techniques for
use in natural areas. Davis, CA: The Nature
Conservancy. Available at http://www.invasive.
org/gist/handbook.html. (Accessed August 21,
2014).
Turcotte, R.M.; Elliott, T.R.; Fajvan, M.A.; Park,
Y.-L.; Snider, D.A.; Tobin, P.C. 2012. Effects of
ice storm damage on hardwood survival and
growth in Ohio. Northern Journal of Applied
Forestry. 29(2): 53-59.
Ulbrich, U.; Leckebusch, G.; Pinto, J. 2009. Extratropical cyclones in the present and future
climate: a review. Theoretical and Applied
Climatology. 9(1-2): 117-131.
Ulbrich, U.; Pinto, J.; Kupfer, H.; Leckebusch,
G.; Spangehl, T.; Reyers, M. 2008. Changing
Northern Hemisphere storm tracks in an
ensemble of IPCC climate change simulations.
Journal of Climate. 21(8): 19-179.
Ungerer, M.J.; Ayres, M.P.; Lombardero, M.J.
1999. Climate and the northern distribution
limits of Dendroctonus frontalis Zimmermann
(Coleoptera: Scolytidae). Journal of
Biogeography. 2(): 1133-1145.
U.S. Department of Agriculture. 2012. USDA plant
hardiness zone map. Agricultural Research
Service. Available at http://planthardiness.ars.
usda.gov/PHZMWeb/. (Accessed September 9,
2014).
U.S. Department of Energy. 2012. A review of
power outages and restoration following the
June 2012 derecho. Infrastructure Security and
Energy Restoration, Office of Electricity Delivery
and Energy Reliability. Available at http://
energy.gov/sites/prod/files/Derecho%202012_
%20Review_0.pdf. (Accessed August 21, 2014).
LiTERATuRE CiTED
U.S. Environmental Protection Agency [EPA]. 1998.
National water quality inventory: 1998 report
to Congress 305b. Available at http://water.epa.
gov/lawsregs/guidance/cwa/305b/98report_index.
cfm. (Accessed 15 January 2014).
U.S. Environmental Protection Agency [EPA]. 2005.
Mountaintop mining/valley fills in Appalachia.
Final Programmatic Environmental Impact
Statement. Philadelphia, PA: EPA Region 3.
507 p. Available at http://nepis.epa.gov/Exe/
ZyPDF.cgi/20005XA.PDF?Dockey=20005XA.
PDF. (Accessed August 21, 2014).
U.S. Environmental Protection Agency [EPA]. 2008.
National water program strategy: response to
climate change. U.S. Environmental Protection
Agency, Office of Water. Available at http://water.
epa.gov/scitech/climatechange/upload/2008National-Water-Program-Strategy-Responseto-Climate-Change.pdf. (Accessed August 21,
2014).
U.S. Environmental Protection Agency [EPA].
2011. The effects of mountaintop mines
and valley fills on aquatic ecosystems of the
central Appalachian coalfields. EPA/00/R09/138F. Washington, DC: National Center for
Environmental Assessment. Available at file:///
C:/Users/prbutler/Downloads/MOUNTAINTOP_
MINING_&_VALLEY_FILL_-_FINAL.PDF.
(Accessed August 21, 2014).
U.S. Forest Service [USFS]. 200a. Monongahela
National Forest final environmental impact
statement for plan revision. Elkins, WV: U.S.
Department of Agriculture, Forest Service.
259 p. Available at http://www.fs.usda.gov/
Internet/FSE_DOCUMENTS/fsm9_01135.pdf.
(Accessed August 21, 2014).
U.S. Forest Service. 200b. Wayne National
Forest 2006 forest plan. Land and Resource
Management Plan (200 Forest Plan).
Milwaukee, WI: U.S. Department of Agriculture,
Forest Service, Eastern Region. 30 p. Available
at http://www.fs.usda.gov/Internet/FSE_
DOCUMENTS/fsm9_00523.pdf.
(Accessed August 21, 2014).
U.S. Forest Service. 2007. USDA Forest Service
manual 2300: recreation, wilderness, and
related resource management. Chapter 2320:
Wilderness Management. Washington, DC:
U.S. Department of Agriculture, Forest Service,
National Headquarters (WO). 5 p. Available
at http://www.fs.usda.gov/Internet/FSE_
DOCUMENTS/fsbdev3_053277.pdf.
(Accessed August 21, 2014).
U.S. Forest Service. 2008. Who owns America’s
forests? 8 p. Available at http://www.nrs.fs.fed.
us/pubs/inf/NRS-INF-0-08.pdf.
(Accessed August 21, 2014).
U.S. Fish and Wildlife Service [USFWS]. 2010.
Turkey hunting in 2006: an analysis of hunter
demographics, trends, and economic impacts.
Arlington, VA. 2 p.
U.S. Forest Service. 2011a. National report on
sustainable forests - 2010. FS 979. Available
at http://www.fs.fed.us/research/sustain/docs/
national-reports/2010/2010-sustainability-report.
pdf. (Accessed August 21, 2014).
U.S. Fish and Wildlife Service [USFWS]. 2014.
Engangered species database. Available at
http://www.fws.gov/endangered/.
(Accessed May 14, 2014).
U.S. Forest Service. 2011b. National visitor
use monitoring results. Available at
http://apps.fs.fed.us/nrm/nvum/results/.
(Accessed August 22, 2014).
229
LiTERATuRE CiTED
U.S. Forest Service. 2013. FIA DataMart: FIADB
version 5.1. Microsoft Access 2007. Online
database. Washington, DC: U.S. Department of
Agriculture, Forest Service, Forest Inventory and
Analysis Program. Available at http://apps.fs.fed.
us/fiadb-downloads/datamart.html. (Accessed
April 11, 2014).
U.S. Geological Survey [USGS]. 1989. West
Virginia floods and droughts. Available at
http://md.water.usgs.gov/publications/wsp-2375/
wv/. (Accessed September 9, 2014).
U.S. Geological Survey [USGS]. 2011. National
Land Cover Dataset 2006 land cover. Available
at http://www.mrlc.gov/index.php. (Accessed
December 8, 2011).
Vadas, R.L., Jr.; Sanger, J.E. 1997. Lateral zonation
of trees along a small Ohio stream. Ohio
Journal of Science. 97(5): 107-112.
van der Werf, G.R.; Morton, D.C.; DeFries, R.S.;
Olivier, J.G.; Kasibhatla, P.S.; Jackson, R.B.;
Collatz, G.J.; Randerson, J. 2009. CO2 emissions
from forest loss. Nature Geoscience. 2(11):
737-738.
Vanderhorst, J.; Streets, B.; Jeuck, J.; Gawler, S.
2008. Vegetation classification and mapping
of Bluestone National Scenic River, West
Virginia. Technical Report NPS/NER/NRTR2008/10. Philadelphia, PA: U.S. Department
of the Interior, National Park Service. Available
at http://irmafiles.nps.gov/reference/holding/
150158?accessType=DOWNLOAD. (Accessed
August 21, 2014).
Venette, R.C.; Abrahamson, M. 2010. Cold
hardiness of emerald ash borer, Agrilus
planipennis: a new perspective. Black ash
symposium: proceedings of the meeting;
May 25-27, 2010; Bemidji, MN. Cass Lake,
MN: U.S. Department of Agriculture, Forest
Service, Chippewa National Forest. 5 p. Available
at http://www.treesearch.fs.fed.us/pubs/45307.
(Accessed September 18, 2014).
230
Villarini, G.; Smith, J.A.; Baeck, M.L.; Krajewski,
W.F. 2011. Examining flood frequency
distributions in the Midwest U.S. Journal of the
American Water Resources Association. 47(3):
447-43.
Vitousek, P.M.; Porder, S.; Houlton, B.Z.;
Chadwick, O.A. 2010. Terrestrial phosphorus
limitation: mechanisms, implications, and
nitrogen-phosphorus interactions. Ecological
Applications. 20(1): 5-15.
Vose, J.; Clinton, B.; Swank, W. 1993. Fire,
drought, and forest management influences
on pine/hardwood ecosystems in the southern
Appalachians. A paper presented at the 12th
conference on fire and forest meteorology;
1993 October 2-28; Jekyll Island, GA. 232-238.
Available at http://www.treesearch.fs.fed.us/
pubs/472. (Accessed August 21, 2014).
Vose, J.M.; Peterson, D.L.; Patel-Weynand, T. 2012.
Effects of climatic variability and change on
forest ecosystems: a comprehensive science
synthesis for the US forest sector. Gen. Tech.
Rep. PNW-GTR-870. U.S. Department of
Agriculture, Forest Service, Pacific Northwest
Research Station. 282 p.
Wagner, R.J.; Kaye, M.W.; Abrams, M.D.; Hanson,
P.J.; Martin, M. 2012. Tree-ring growth and
wood chemistry response to manipulated
precipitation variation for two temperate
Quercus species. Tree-Ring Research.
8(1): 17-29.
Walck, J.L.; Hidayati, S.N.; Dixon, K.W.;
Thompson, K.E.N.; Poschlod, P. 2011. Climate
change and plant regeneration from seed.
Global Change Biology. 17(): 2145-211.
Walker, B.; Kinzig, A.; Langridge, J. 1999. Original
articles: plant attribute diversity, resilience,
and ecosystem function: the nature and
significance of dominant and minor species.
Ecosystems. 2(2): 95-113.
LiTERATuRE CiTED
Walker, B.H. 1992. Biodiversity and ecological
redundancy. Conservation Biology. (1): 18-23.
Waller, D.M.; Alverson, W.S. 1997. The whitetailed deer: a keystone herbivore. Wildlife
Society Bulletin. 25(2): 217-22.
Walters, B.F.; Rider, D.R.; Piva, R.J. 2008.
Maryland timber industry: an assessment
of timber product output and use, 2008.
Resour. Bull. NRS-4. Newtown Square, PA:
U.S. Department of Agriculture, Forest Service,
Northern Research Station. 52 p.
Ward, N.L.; Masters, G.J. 2007. Linking climate
change and species invasion: an illustration
using insect herbivores. Global Change Biology.
13(8): 105-115.
Washington, W.M.; Weatherly, J.W.; Meehl,
G.A.; Semtner, A.J., Jr.; Bettge, T.W.; Craig,
A.P.; Strand, W.G., Jr.; Arblaster, J.; Wayland,
V.B.; James, R.; Zhang, Y. 2000. Parallel
climate model (PCM) control and transient
simulations. Climate Dynamics. 1(10):
755-774.
Walther, G.R.; Post, E.; Convey, P.; Menzel, A.;
Parmesan, C.; Beebee, T.J.C.; Fromentin,
J.M.; Hoegh-Guldberg, O.; Bairlein, F. 2002.
Ecological responses to recent climate change.
Nature. 41(879): 389-395.
Weary, D.J. 2008. Preliminary map of potentially
karstic carbonate rocks in the Central and
Southern Appalachian states [Online]. Open
File Report 2008-1154. U.S. Geological Survey.
Available at http://pubs.usgs.gov/of/2008/1154/.
(Accessed August 21, 2014).
Wang, D.; Cai, X. 2010. Comparative study of
climate and human impacts on seasonal
baseflow in urban and agricultural
watersheds. Geophysical Research Letters.
37().
Webb, T.P.; Bartlein, J. 1992. Global changes
during the last 3 million years: climatic
controls and biotic responses. Annual Review
of Ecology and Systematics. 23(1): 141.
Wang, J.; Bai, X.; Hu, H.; Clites, A.; Colton,
M.; Lofgren, B. 2012. Temporal and spatial
variability of Great Lakes ice cover, 1973-2010.
Journal of Climate. 25(4): 1318-1329.
Weed, A.S.; Ayres, M.P.; Hicke, J. 2013.
Consequences of climate change for biotic
disturbances in North American forests.
Ecological Monographs. 83: 441-470.
Wang, J.; Zhang, X. 2008. Downscaling
and projection of winter extreme daily
precipitation over North America. Journal of
Climate. 21(5): 923-937.
Wei, X.; Wu, Y.; Reardon, R.; Sun, T.H.; Lu, M.;
Sun, J.H. 2007. Biology and damage traits
of emerald ash borer (Agrilus planipennis
Fairmaire) in China. Insect Science. 14(5):
37-373.
Wang, W.; He, H.S.; Spetich, M.A.; Shifley, S.R.;
Thompson, F.R.; Larsen, D.R.; Fraser, J.S.;
Yang, J. 2013. A large-scale forest landscape
model incorporating multi-scale processes and
utilizing forest inventory data. Ecosphere.
4(9): art10.
West Virginia Department of Environmental
Protection [WVDEP]. 2013a. Office of Oil and
Gas [Web site]. Available at http://www.dep.
wv.gov/oil-and-gas/Pages/default.aspx.
(Accessed March 7, 2013).
Wang, W.J.; He, H.S.; Fraser, J.S.; Thompson, F.R.;
Shifley, S.R.; Spetich, M.A. 2014. LANDIS
PRO: a landscape model that predicts forest
composition and structure changes at regional
scales. Ecography. 37: 225-229.
West Virginia Department of Environmental
Protection [WVDEP]. 2013b. West Virginia
water resources management plan. Charleston,
WV. Available at http://www.dep.wv.gov/WWE/
wateruse/WVWaterPlan/Documents/WV_
WRMP.pdf. (Accessed August 21, 2014).
231
LiTERATuRE CiTED
West Virginia Division of Forestry. 2010. West
Virginia statewide forest resource assessment.
Charleston, WV. 57 p.
West Virginia Division of Forestry. 2012. Managed
timberland fact sheet. 2 p. Available at http://
www.wvforestry.com/MT%20Fact%20Sheet.pdf.
(Accessed August 21, 2014).
West Virginia Division of Natural Resources
[WVDNR]. 2014. Rare, threatened, and
endangered species in West Virginia. Wildlife
Resources Section. Available at http://www.
wvdnr.gov/Wildlife/PDFFiles/RTE_Animals_
2012.pdf. (Accessed January 14, 2014).
Wickham, J.; Wood, P.B.; Nicholson, M.C.; Jenkins,
W.; Druckenbrod, D.; Suter, G.W.; Strager,
M.P.; Mazzarella, C.; Galloway, W.; Amos, J.
2013. The overlooked terrestrial impacts of
mountaintop mining. BioScience. 3(5):
335-348.
Wiedenbeck, J.K.; Sabula, A. 2008. Ohio
roundwood utilization by the timber industry
in 2006. Newtown Square, PA: U.S. Department
of Agriculture, Forest Service, Northern Research
Station. 18 p.
Wiens, J.A.; Stralberg, D.; Jongsomjit, D.; Howell,
C.A.; Snyder, M.A. 2009. Niches, models, and
climate change: assessing the assumptions
and uncertainties. Proceedings of the National
Academy of Sciences. 10(Supplement 2):
19729-1973.
Wilderness Act of 1964. 1 U.S.C. 1121 (note),
1131-113. Available at http://www.fs.usda.gov/
Internet/FSE_DOCUMENTS/stelprdb5253940.
pdf. (Accessed August 21, 2014).
Wiley, J.B.; Atkins, J.T. 2010. Estimation of floodfrequency discharges for rural, unregulated
streams in West Virginia. Scientific
Investigations Report 2010-5033. Reston, VA:
U.S. Department of the Interior, U.S. Geological
Survey. 88 p.
Widmann, R.H.; Balser, D.; Barnett, C.J.; Butler,
B.J.; Griffith, D.M.; Lister, T.W.; Moser, W.K.;
Perry, C.H.; Riemann, R.; Woodall, C. 2009.
Ohio forests: 2006. Resour. Bull. NRS-3.
Newtown Square, PA: U.S. Department of
Agriculture, Forest Service, Northern Research
Station. 119 p.
Williams, C.E. 2005. Fact sheet: exotic bush
honeysuckles. Clarion, PA: Plant Conservation
Alliance’s Alien Plant Working Group. Available
at http://www.nps.gov/plants/alien/fact/pdf/loni1.
pdf. (Accessed August 21, 2014).
Widmann, R.H.; Dye, C.R.; Cook, G.W. 2007.
Forests of the Mountain State. Resour. Bull.
NRS-17. Newtown Square, PA: U.S. Department
of Agriculture, Forest Service, Northern Research
Station. 32 p.
Willis, C.G.; Ruhfel, B.R.; Primack, R.B.; MillerRushing, A.J.; Losos, J.B.; Davis, C.C. 2010.
Favorable climate change response explains
non-native species’ success in Thoreau’s
woods. PLoS ONE. 5(1): e8878.
Widmann, R.H.; Morin, R.S. 2012. Ohio’s forest
resources, 2012. Res. Note NRS-187. Newtown
Square, PA: U.S. Department of Agriculture,
Forest Service, Northern Research Station. 4 p.
Available at http://www.fs.fed.us/nrs/pubs/rn/rn_
nrs187.pdf. (Accessed August 21, 2014).
Winkler, J.A.; Arritt, R.; Pryor, S. 2012. Climate
projections for the Midwest: availability,
interpretation and synthesis. In: Winkler, J.;
Andresen, J.; Hatfield, J.; Bidwell, D.; Brown, D.,
eds. U.S. National Climate Assessment Midwest
technical input report. 24 p. Available at http://
glisa.umich.edu/docs/NCA/MTIT_Future.pdf.
(Accessed August 21, 2014).
232
LiTERATuRE CiTED
Wisconsin Initiative on Climate Change Impacts
[WICCI]. 2011. Wisconsin’s changing climate:
impacts and adaptation. Madison, WI. 224 p.
Wright, H.A.; Bailey, A.W. 1982. Fire ecology:
United States and southern Canada. New York,
NY: John Wiley & Sons. 528 p.
Woodall, C.W.; Oswalt, C.M.; Westfall, J.A.;
Perry, C.H.; Nelson, M.D.; Finley, A.O. 2009.
An indicator of tree migration in forests of
the eastern United States. Forest Ecology and
Management. 257(5): 1434-1444.
Wu, S.; Mickley, L.J.; Leibensperger, E.M.; Jacob,
D.J.; Rind, D.; Streets, D.G. 2008. Effects of
2000-2050 global change on ozone air quality
in the United States. Journal of Geophysical
Research. 113(D0302): 1-12.
Woods, A.J.; Omernik, J.M.; Brown, D.D. 1999.
Level III and IV ecoregions of Delaware,
Maryland, Pennsylvania, Virginia, and West
Virginia. Corvallis, OR: U.S. Environmental
Protection Agency, National Health and
Environmental Effects Research Laboratory.
Available at http://www.epa.gov/wed/pages/
ecoregions/reg3_eco.htm. (Accessed August 21,
2014).
Wuebbles, D.J.; Hayhoe, K. 2004. Climate change
projections for the United States Midwest.
Mitigation and Adaptation Strategies for Global
Change. 9: 335-33.
Woods, A.J.; Omernik, J.M.; Brown, D.D.;
Kiilsgaard, C.W. 199. Level III and IV
ecoregions of Pennsylvania and the Blue Ridge
Mountains, the Ridge and Valley, and Central
Appalachians of Virginia, West Virginia, and
Maryland. EPA/00/R-0/077. Washington, DC:
U.S. Environmental Protection Agency, National
Health and Environmental Effects Research
Laboratory. 50 p.
World Meteorological Organization. 2011. WMO
statement on the status of the global climate
in 2011. Geneva, Switzerland. Available
at www.wmo.int/pages/prog/wcp/wcdmp/
documents/1085_en.pdf. (Accessed May 10,
2013).
Wright, D.M.; Posselt, D.J.; Steiner, A.L. 2013.
Sensitivity of lake-effect snowfall to lake ice
cover and temperature in the Great Lakes
region. Monthly Weather Review. 141(2):
70-89.
Wullschleger, S.D.; Gunderson, C.A.; Tharp,
M.L.; West, D.C.; Post, W.M. 2003. Simulated
patterns of forest succession and productivity
as a consequence of altered precipitation. In:
Hanson, P.J.; Wullschleger, S.D., eds. North
American temperate deciduous forest responses
to changing precipitation regimes. New York,
NY: Springer: 433-44.
Whitney, G.G. 1994. From coastal wilderness
to fruited plain: a history of environmental
change in temperate North America 1500
to the present. New York, NY: Cambridge
University Press. 451 p.
Zhang, D.; Butler, B.J.; Nagubadi, R.V. 2012.
Institutional timberland ownership in the US
South: magnitude, location, dynamics, and
management. Journal of Forestry. 110(7):
355-31.
Zhang, X.; Tarpley, D.; Sullivan, J.T. 2007. Diverse
responses of vegetation phenology to a
warming climate. Geophysical Research Letters.
34(19): L19405.
Ziska, L.H. 2003. Evaluation of the growth
response of six invasive species to past, present
and future atmospheric carbon dioxide.
Journal of Experimental Botany. 54(381):
395-404.
233
APPENDiX 1. SPECiES LiSTS
Table 24.—Common and scieniic names of naive plants menioned in this assessment
Common Name
Scieniic Name
Common Name
Scieniic Name
balsam ir
Abies balsamea
silky dogwood
Cornus amomum
boxelder
Acer negundo
roughleaf dogwood
Cornus drummondii
black maple
Acer nigrum
lowering dogwood
Cornus lorida
striped maple
Acer pensylvanicum
ram’s-head lady’s-slipper
Cypripedium arieinum
red maple
Acer rubrum
common persimmon
Diospyros virginiana
silver maple
Acer saccharinum
American beech
Fagus grandifolia
sugar maple
Acer saccharum
white ash
Fraxinus americana
mountain maple
Acer spicatum
black ash
Fraxinus nigra
northern wild monkshood
Aconitum noveboracense
green ash
Fraxinus pennsylvanica
yellow buckeye
Aesculus lava
blue ash
Fraxinus quadrangulata
Ohio buckeye
Aesculus glabra
water locust
Gleditsia aquaica
lillydale onion
Allium oxyphilum
honeylocust
Gleditsia triacanthos
speckled alder
Alnus incana
bushy St. Johnswort
Hypericum densilorum
hazel alder
Alnus serrulata
American holly
Ilex opaca
serviceberry
Amelanchier Medik.
common winterberry
Ilex vericillata
shale barren rockcress
Arabis seroina
small whorled pogonia
Isotria medeoloides
pawpaw
Asimina triloba
buternut
Juglans cinerea
yellow birch
Betula alleghaniensis
black walnut
Juglans nigra
sweet birch
Betula lenta
eastern redcedar
Juniperus virginiana
river birch
Betula nigra
mountain laurel
Kalmia laifolia
American hornbeam
Carpinus caroliniana
tamarack
Larix laricina
mockernut hickory
Carya alba
northern spicebush
Lindera benzoin
biternut hickory
Carya cordiformis
sweetgum
Liquidambar styracilua
pignut hickory
Carya glabra
tulip tree
Liriodendron tulipifera
shagbark hickory
Carya ovata
osage orange
Maclura pomifera
black hickory
Carya texana
cucumbertree
Magnolia acuminata
American chestnut
Castanea dentata
mountain magnolia
Magnolia fraseri
northern catalpa
Catalpa speciosa
southern magnolia
Magnolia grandilora
sugarberry
Celis laevigata
red mulberry
Morus rubra
common hackberry
Celis occidentalis
blackgum
Nyssa sylvaica
common butonbush
Cephalanthus occidentalis
eastern hophornbeam
Ostrya virginiana
eastern redbud
Cercis canadensis
sourwood
Oxydendrum arboreum
Bentley’s coralroot
Corallorhiza bentleyi
American ginseng
Panax quinquefolius
234
APPENDiX 1
Table 24 (coninued).
Common Name
Scieniic Name
Common Name
Scieniic Name
black chokeberry
Phoinia melanocarpa
chinkapin oak
Quercus muehlenbergii
red spruce
Picea rubens
water oak
Quercus nigra
shortleaf pine
Pinus echinata
pin oak
Quercus palustris
Table Mountain pine
Pinus pungens
willow oak
Quercus phellos
red pine
Pinus resinosa
chestnut oak
Quercus prinus
pitch pine
Pinus rigida
northern red oak
Quercus rubra
eastern white pine
Pinus strobus
Shumard’s oak
Quercus shumardii
loblolly pine
Pinus taeda
post oak
Quercus stellata
Virginia pine
Pinus virginiana
black oak
Quercus veluina
eastern prairie fringed
orchid
Platanthera leucophaea
great laurel
Rhododendron maximum
black locust
Robinia pseudoacacia
sycamore
Platanus occidentalis
coastal plain willow
Salix caroliniana
eastern cotonwood
Populus deltoides
black willow
Salix nigra
bigtooth aspen
Populus grandidentata
Sassafras albidum
quaking aspen
sassafras
Populus tremuloides
northeastern bulrush
Scirpus ancistrochaetus
Tennessee pondweed
Potamogeton tennesseensis
American mountain ash
Sorbus americana
pin cherry
Prunus pensylvanica
Prunus seroina
Virginia spiraea
Spiraea virginiana
black cherry
Thuja occidentalis
chokecherry
northern white-cedar
Prunus virginiana
Tilia americana
Pilimnium nodosum
American basswood
harperella
running bufalo clover
Trifolium stoloniferum
Torrey’s mountainmint
Pycnanthemum torrei
eastern hemlock
Tsuga canadensis
white oak
Quercus alba
winged elm
Ulmus alata
swamp white oak
Quercus bicolor
American elm
Ulmus americana
scarlet oak
Quercus coccinea
cedar elm
Ulmus crassifolia
northern pin oak
Quercus ellipsoidalis
slippery elm
Ulmus rubra
southern red oak
Quercus falcata
rock elm
Ulmus thomasii
bear oak/scrub oak
Quercus ilicifolia
Vaccinium erythrocarpum
shingle oak
Quercus imbricaria
southern mountain
cranberry
bur oak
Quercus macrocarpa
velvetleaf huckleberry
Vaccinium myrilloides
blackjack oak
Quercus marilandica
wild raisin (withe-rod)
Viburnum nudum
235
APPENDiX 1
Table 25.—Common and scieniic names of pathogens and nonnaive plants menioned in this assessment
Common Name
Scieniic Name
Pathogens
Common Name
Scieniic Name
Pathogens
armillaria
Armillaria mellea
scleroderris canker
Gremmeniella abieina
Lyme disease
Borrelia burgdorferi
annosum root disease
Heterobasidion irregulare
elm yellows
Candidatus phytoplasma ulmi
hypoxylon canker
Hypoxylon mammatum
white pine blister rust
Cronarium ribicola
sudden oak death
Phytophthora ramorum
chestnut blight
Cryphonectria parasiica
phytophthora root rot
Phytophthora spp.
diplodia
Diplodia pinea
and D. scrobiculata
sirococcus shoot blight
Sirococcus conigenus
sphaeropsis shoot blight
Sphaeropsis sapinea
West Nile virus
Common Name
Flavivirus spp.
Scieniic Name
Common Name
Scieniic Name
Nonnaive invasive plants
Nonnaive invasive plants
Norway maple
Acer platanoides
cogongrass
Imperata cylindrica
ailanthus
Ailanthus alissima
sericea lespedeza
Lespedeza cuneata
silk tree
Albizia julibrissin
privet
Ligustrum vulgare
garlic mustard
Alliaria peiolata
Japanese honeysuckle
Lonicera japonica
porcelain berry
Ampelopsis
brevipedunculata
bush honeysuckle
Lonicera mackii
purple loosestrife
Lythrum salicaria
dwarf mistletoe
Arceuthobium pusillum
yellow sweetclover
Melilotus oicinalis
Japanese barberry
Berberis thunbergii
Broussoneia papyrifera
Japanese siltgrass
Microstegium vimineum
paper mulberry
basket grass
Oplismenus hirtellus
Asiaic bitersweet
Celastrus orbiculatus
princess tree
Paulownia tomentusa
spoted knapweed
Centaurea stoebe
Dennstaedia puncilobula
mile-a-minute vine
Persicaria perfoliata
hayscented fern
reed canarygrass
Phalaris arundinacea
viper’s bugloss
Echium vulgare
common reed (phragmites)
Phragmites australis
autumn olive
Elaeagnus umbellata
Canada bluegrass
Poa compressa
burning bush
Euonymus spp.
kudzu
Pueraria lobata
Japanese knotweed
Fallopia japonica
glossy buckthorn
Rhamnus spp.
buckthorn
Frangula alnus
mulilora rose
Rosa mulilora
creeping charlie
Glechoma hederacea
crown vetch
Securigera varia
English ivy
Hedera helix
Japanese spiraea
Spiraea japonica
23
APPENDiX 1
Table 26.—Common and scieniic names of fauna menioned in this assessment
Common Name
Scieniic Name
Common Name
Scieniic Name
hemlock woolly adelgid
Adelges tsugae
bark beetle
saw-whet owl
Aegolius acadicus
Ips spp. and
Dendroctonus spp.
emerald ash borer
Agrilus planipennis
spring hemlock looper
Jeferson salamander
Ambystoma jefersonianum
Lambdina iscellaria
iscellaria
spoted salamander
Ambystoma maculata
silver-haired bat
Lasionycteris nocivagans
iger salamander
Ambystoma igrinum
eastern red bat
Lasiurus borealis
green salamander
Aneides aeneus
hoary bat
Lasiurus cinereus
Asian longhorned beetle
Anoplophora glabripennis
crimson-ringed whiteface
Leucorrhinia glacialis
Dry Fork Valley cave
pseudoscorpion
Apochthonius pauscisinosus
red crossbill
Loxia curvirostra
gypsy moth
Lymantria dispar dispar
eastern cave-loving funnel
web spider
Calymmaria cavicola
bobcat
Lynx rufus
forest tent caterpillar
Malacosoma disstria
coyote
Canis latrans
wild turkey
Meleagris gallopavo
eastern imber wolf
Canis lupus lycaon
small-footed bat
Myois leibii
beaver
Castor canadensis
litle brown bat
Myois lucifugus
spruce budworm
Choristoneura fumiferana
northern bat
Myois septentrionalis
spoted turtle
Clemmys gutata
Indiana bat
Myois sodalis
redside dace
Clinostomus elongatus
Carter cave spider
Nesicus carteri
Virginia big-eared bat
Corynorhinus townsendii
virginianus
jumping oak gall wasp
Neuroterus sp.
Notophthalmus viridescens
Cotus spp.
red-spoted newt
sculpin
white-tailed deer
Odocoileus virginianus
eastern hellbender
Cryptobranchus
alleganiensis
Kentucky warbler
Oporornis formosus
beech scale
Cryptococcus fagisuga
tri-colored bat
Perimyois sublavus
earthworms (nonnaive)
Dendrobaena octaedra,
Lumbricus rubellus,
and L. terrestris
red-backed salamander
Plethodon cinereus
Cheat Mountain
salamander
Plethodon neingi
southern pine beetle
Dendroctonus frontalis
eastern cougar
Puma concolor couguar
blackburnian warbler
Dendroica fusca
northern lying squirrel
Sabrinus glaucomys fuscus
birch leaf miner
Fenusa pusilla
brook trout
Salvelinus foninalis
southern lying squirrel
Glaucomys volans
eastern spadefoot toad
Scaphiopus holbrookii
bog turtle
Glyptemys muhlenbergii
eastern gray squirrel
Sciurus carolinensis
rapids clubtail
Gomphus quadricolor
cerulean warbler
Setophaga cerulea
green-faced clubtail
Gomphus viridifrons
black bear
Ursus americanus
worm-eaing warbler
Helmitheros vermivorus
ambrosia beetle
Xyloterinus politus
wood thrush
Hylocichla mustelina
237
APPENDiX 2: TREND ANALYSiS
AND hiSToRiCAL CLiMATE DATA
We used the ClimateWizard Custom Analysis
tool to examine historical averages and trends in
precipitation and temperature within the assessment
area (Gibson et al. 2002, Girvetz et al. 2009).
Data for ClimateWizard are derived from PRISM
(Parameter-elevation Regressions on Independent
Slopes Model) (Gibson et al. 2002). The PRISM
model interpolates historical data from the National
Weather Service cooperative stations, the Midwest
Climate Data Center, and the Historical Climate
Network, among others. Data undergo strict quality
control procedures to check for errors in station
measurements. The PRISM model finds linear
relationships between these station measurements
and local elevation by using a digital elevation
model (digital gridded version of a topographic
map). Temperature and precipitation are then derived
for each pixel on a continuous 2.5-mile grid across
the conterminous United States. The closer a station
is to a grid cell of interest in distance and elevation,
and the more similar it is in its proximity to coasts
or topographic features, the higher the weight the
station will have on the final, predicted value for that
cell. More information on PRISM can be found at:
www.prism.oregonstate.edu/. Please note that Web
addresses are current as of the publication date of
this assessment but are subject to change.
A 30-year climate “normal” for the assessment area
and each ecological section within the assessment
area was calculated from the mean for 1971
through 2000 (Table 27). Linear trend analysis was
238
performed for 1901 through 2011 by using restricted
maximum likelihood (REML) estimation (Girvetz et
al. 2009). Restricted maximum likelihood methods
were used for trend analysis of past climate for the
International Panel on Climate Change Working
Group 1 Report and are considered an effective
way to determine trends in climate data over time
(Trenberth et al. 2007). A first-order autoregression
was assumed for the residuals, meaning that values
one time step away from each other are assumed
to be correlated. This method was used to examine
trends for every 2.5-mile grid cell. The slope and
p-values for the linear trend over time were
calculated annually, seasonally, and monthly for
each climate variable, and then mapped. An overall
trend for an area is based on the trend analysis of the
average value for all grid cells within the area over
time (Table 28).
The developers of the ClimateWizard tool advise
users to interpret the linear trend maps in relation to
the respective map of statistical confidence
(Figs. 44 and 45). In this case, statistical confidence
is described by using p-values from a t-test applied
to the linear regression. A p-value can be interpreted
as the probability of the slope being different from
zero by chance. For this assessment, p-values of less
than 0.1 were considered to have sufficient statistical
confidence. Areas with low statistical confidence in
the rate of change (gray areas on the map) should be
interpreted with caution.
APPENDiX 2
Table 27.—Annual and seasonal mean values for selected climate variables from 1971 through 2000 for ecological
secions within the assessment area
Ecological
secion
Season
Precipitaion
(inches)
Mean
temperature (°F)
Minimum
temperature (°F)
Maximum
temperature (°F)
221E
Annual
Fall
Spring
Summer
Winter
42.3
9.3
11.3
12.7
9.1
52.1
53.9
51.3
71.3
32.0
40.7
42.2
38.8
59.6
22.3
63.5
65.6
63.8
83.0
41.6
221F
Annual
Fall
Spring
Summer
Winter
39.6
9.6
10.3
12.0
7.7
49.3
51.8
48.1
69.4
28.0
39.2
41.6
37.1
58.2
19.8
59.5
62.0
59.1
80.7
36.2
M221A
Annual
Fall
Spring
Summer
Winter
39.1
9.6
10.5
11.2
7.8
51.3
53.0
50.3
70.1
31.9
39.8
41.1
38.0
58.0
21.9
62.9
64.9
62.6
82.2
41.9
M221B
Annual
Fall
Spring
Summer
Winter
48.5
10.7
13.1
13.7
10.9
49.0
50.7
48.1
66.8
30.3
37.8
39.3
36.0
55.5
20.4
60.2
62.2
60.2
78.1
40.1
M221C
Annual
Fall
Spring
Summer
Winter
47.0
10.0
12.7
13.7
10.5
52.4
53.9
51.9
70.2
33.8
41.0
42.3
39.1
58.9
23.8
63.9
65.5
64.6
81.6
43.8
In addition, because maps are developed from
weather station observations that have been spatially
interpolated, developers of the ClimateWizard tool
and PRISM data set recommend that inferences
about trends should not be made for single grid cells
or even small clusters of grid cells. The number of
weather stations has also changed over time, and
station data are particularly limited before 1948,
meaning grid cells from earlier in the century are
based on an interpolation of fewer points than later
in the century (Gibson et al. 2002). Therefore,
interpretations should be based on many grid cells
showing regional patterns of climate change with
high statistical confidence. For those interested
in understanding trends in climate at a particular
location, it is best to refer to weather station data
for the closest station in the Global Historical
Climatology Network from the National Climatic
Data Center (http://www.ncdc.noaa.gov/).
We selected the time period 1901 through 2011
because it was sufficiently long to capture
interdecadal and intradecadal variation in climate
for the region. We acknowledge that different trends
can be inferred by selecting different beginning and
end points in the analysis. Therefore, trends should
be interpreted based on their relative magnitude and
direction, and the slope of any single trend should be
interpreted with caution.
239
APPENDiX 2
Table 28.—Annual, seasonal, and monthly mean values and linear trend analysis for selected climate variables from
1901 through 2011 for the assessment area.
Month
or season
Mean Precip.
Mean
precip. change Precip. TMean
(inches) (inches) p-valuea (°F)
TMean
Mean
change TMean TMin
(°F)
p-valuea (°F)
TMin
change TMin
(°F) p-valuea
Mean
TMax
(°F)
TMax
change TMax
(°F) p-valuea
January
February
March
April
May
June
July
Aug
Sept
Oct
Nov
Dec
3.3
2.8
3.8
3.7
4.1
4.2
4.5
3.9
3.2
2.8
3.0
3.2
-0.7
-0.1
-0.4
0.2
0.9
-0.4
0.3
-0.2
0.9
0.3
1.2
-0.1
0.14
0.70
0.40
0.51
0.05
0.38
0.33
0.47
0.04
0.48
0.01
0.70
29.7
31.4
40.3
50.5
60.0
68.2
71.9
70.5
64.3
53.2
42.2
32.6
-2.4
1.7
-0.1
2.4
0.0
0.5
0.0
1.2
-0.6
-0.8
2.3
1.5
0.24
0.38
0.94
0.00
0.96
0.50
0.97
0.03
0.51
0.42
0.01
0.25
20.4
21.2
29.1
38.0
47.4
56.0
60.1
58.8
52.0
40.7
31.6
23.6
-1.6
2.0
-0.3
1.6
0.6
1.4
1.3
2.1
0.9
0.4
2.8
1.7
0.41
0.33
0.79
0.02
0.42
0.07
0.06
0.00
0.40
0.73
0.00
0.19
39.1
41.5
51.5
62.9
72.7
80.3
83.7
82.3
76.6
65.7
52.8
41.6
-3.1
1.4
0.1
3.2
-0.7
-0.4
-1.2
0.3
-2.1
-2.0
1.8
1.2
0.14
0.48
0.95
0.00
0.48
0.68
0.10
0.64
0.03
0.07
0.08
0.37
Winter
Spring
Summer
Fall
Annual
3.1
3.9
4.2
3.0
42.4
-1.0
0.7
-0.3
2.3
1.7
0.14
0.29
0.64
0.00
0.26
31.2
50.3
70.2
53.2
51.2
0.3
0.8
0.6
0.3
0.5
0.81
0.19
0.22
0.57
0.29
21.7
38.2
58.3
41.4
39.9
0.7
0.6
1.6
1.4
1.1
0.58
0.24
0.00
0.04
0.03
40.7
62.4
82.1
65.0
62.6
-0.1
0.9
-0.4
-0.7
-0.1
0.91
0.20
0.46
0.27
0.87
*P-values represent the probability of observing that trend by chance. P-values in boldface indicate a less than 10-percent probability that the trend
was due to chance. TMean = mean temperature, TMin = minimum temperature, TMax = maximum temperature.
REFERENCES
ClimateWizard. 2013. Climate Wizard Custom
Analysis application. The Nature Conservancy,
University of Washington, University of
Southern Mississippi, Climate Central,
Santa Clara University. Available at http://
climatewizardcustom.org/. (Accessed May 2013).
Gibson, W.P.; Daly, C.; Kittel, T.; Nychka, D.; Johns,
C.; Rosenbloom, N.; McNab, A.; Taylor, G. 2002.
Development of a 103-year high-resolution
climate data set for the conterminous United
States. Proceedings of the 13th American
Meteorological Society Conference on Applied
Climatology: 181-183.
240
Girvetz, E.H.; Zganjar, C.; Raber, G.T.; Maurer, E.P.;
Kareiva, P.; Lawler, J.J. 2009. Applied climatechange analysis: the Climate Wizard tool.
PLoS ONE. 4(12): e8320.
Trenberth, K.E.; Jones, P.D.; Ambenje, P.; Bojariu,
R.; Easterling, D.; Tank, A.K.; Parker, D.;
Rahmizadeh, F.; Renwick, J.A.; Rusticucci, M.;
Soden, B.; Zhai, P. 2007. Observations: surface
and atmospheric climate change. In: Solomon,
S.; Qin, D.; Manning, M.; Chen, Z.; Marquis,
M.; Averyt, K.B.; Tignor, M.; Miller, H.L., eds.
Climate change 2007: the physical science basis.
Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge, UK and
New York, NY: Cambridge University Press:
234-33.
APPENDiX 2
Figure 44.—Staisical conidence (p-values for the linear regression) for trends in temperature from 1901 through 2011. Gray
values represent areas of low staisical conidence. Data source: ClimateWizard (2013).
241
APPENDiX 2
Figure 45.—Staisical conidence (p-values for the linear regression) for trends in precipitaion from 1901 through 2011. Gray
values represent areas of low staisical conidence. Data source: ClimateWizard (2013).
242
APPENDiX 3: ADDiTioNAL
FuTuRE CLiMATE PRoJECTioNS
This appendix provides supplementary information
to Chapter 4, presented as maps of projected change
for early- and mid-century (Figs. 4 through 53) and
graphs of early-, mid-, and late-century departures
from baseline climate (Figs. 54 through 58).
Figure 46.—Projected diference in daily mean temperature at the beginning of the century (2010 through 2039) compared to
baseline (1971 through 2000) for two climate scenarios.
243
APPENDiX 3
Figure 47.—Projected diference in daily minimum temperature at the beginning of the century (2010 through 2039)
compared to baseline (1971 through 2000) for two climate scenarios.
244
APPENDiX 3
Figure 48.—Projected diference in daily maximum temperature at the beginning of the century (2010 through 2039)
compared to baseline (1971 through 2000) for two climate scenarios.
245
APPENDiX 3
Figure 49.—Projected diference in precipitaion at the beginning of the century (2010 through 2039) compared to baseline
(1971 through 2000) for two climate scenarios.
24
APPENDiX 3
Figure 50.—Projected diference in daily mean temperature for the middle of the century (2040 through 2069) compared to
baseline (1971 through 2000) for two climate scenarios.
247
APPENDiX 3
Figure 51.—Projected diference in daily minimum temperature for the middle of the century (2040 through 2069) compared
to baseline (1971 through 2000) for two climate scenarios.
248
APPENDiX 3
Figure 52.—Projected diference in daily maximum temperature for the middle of the century (2040 through 2069) compared
to baseline (1971 through 2000) for two climate scenarios.
249
APPENDiX 3
Figure 53.—Projected diference in precipitaion for the middle of the century (2040 through 2069) compared to baseline
(1971 through 2000) for two climate scenarios.
250
APPENDiX 3
W in te r (D e c - F e b )
50
T e m p e ra tu re (0 F )
45
40
35
30
25
20
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
GFD L A 1FI Mean
G F D L A 1 F I M a xim u m
G F D L A 1 F I M in im u m
P C M B 1 Mean
P C M B 1 M a xim u m
P C M B 1 M in im u m
Figure 54.—Projected changes in winter mean, minimum, and maximum temperatures across the assessment area averaged
over 30-year periods. The 1971 through 2000 value is based on observed data from weather staions.
S p rin g (M a r - M a y )
70
T e m p e ra tu re (0 F )
65
60
55
50
45
40
35
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
GFD L A 1FI Mean
G F D L A 1 F I M a xim u m
G F D L A 1 F I M in im u m
P C M B 1 Mean
P C M B 1 M a xim u m
P C M B 1 M in im u m
Figure 55.—Projected changes in spring mean, minimum, and maximum temperatures across the assessment area averaged
over 30-year periods. The 1971 through 2000 value is based on observed data from weather staions.
251
APPENDiX 3
S u m m e r (J u n e - Au g )
95
90
T e m p e ra tu re (0 F )
85
80
75
70
65
60
55
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
GFD L A 1FI Mean
G F D L A 1 F I M a xim u m
G F D L A 1 F I M in im u m
P C M B 1 Mean
P C M B 1 M a xim u m
P C M B 1 M in im u m
Figure 56.—Projected changes in summer mean, minimum, and maximum temperatures across the assessment area averaged
over 30-year periods. The 1971 through 2000 value is based on observed data from weather staions.
F a ll (S e p - N o v )
75
70
T e m p e ra tu re (0 F )
65
60
55
50
45
40
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
GFD L A 1FI Mean
G F D L A 1 F I M a xim u m
G F D L A 1 F I M in im u m
P C M B 1 Mean
P C M B 1 M a xim u m
P C M B 1 M in im u m
Figure 57.—Projected changes in fall mean, minimum, and maximum temperatures across the assessment area averaged over
30-year periods. The 1971 through 2000 value is based on observed data from weather staions.
252
APPENDiX 3
S pring (Mar - May)
Winter (Dec - F eb)
14
11
P re c ip ita tio n (in c h e s )
P re c ip ita tio n (in c h e s )
12
10
13
12
9
11
8
1971 - 2000
2010 - 2039
2040 - 2069
GFD L A 1FI
1971 - 2000
2070 - 2099
2010 - 2039
GFD L A 1FI
PCM B1
S ummer (J une - Aug)
2040 - 2069
2070 - 2099
PCM B1
F all (S ep - Nov)
15
11
14
P re c ip ita tio n (in c h e s )
P re c ip ita tio n (in c h e s )
10
13
12
11
10
9
8
9
7
8
1971 - 2000
2010 - 2039
GFD L A 1FI
2040 - 2069
PCM B1
2070 - 2099
1971 - 2000
2010 - 2039
GFD L A 1FI
2040 - 2069
2070 - 2099
PCM B1
Figure 58.—Projected changes in seasonal precipitaion across the assessment area averaged over 30-year periods. The 1971
through 2000 value is based on observed data from weather staions. Note the precipitaion axes are diferent depending on
the season.
253
APPENDiX 4: ADDiTioNAL iMPACT MoDEL RESuLTS
This appendix provides supplementary information
to Chapter 5. The following pages contain additional
model results and modifying factors from the
Climate Change Tree Atlas, LINKAGES, and
LANDIS PRO models. Scientific names for all
species are provided in Appendix 1. See Chapter
2 for a description of the models and Chapter 5
for a discussion of model results, uncertainty, and
limitations.
CLiMATE ChANGE TREE ATLAS
MoDEL RESuLTS
Tables 29 through 3 show results of the DISTRIB
model used in the Tree Atlas averaged over the
whole assessment area, and for each section within
the assessment area. Section 221E was further
divided based on state boundaries into West Virginia
221E and Ohio 221E. Measured area-weighted
importance values (IVs) from the U.S. Forest
Service, Forest Inventory and Analysis (FIA) as well
as modeled current (191 through 1990) and future
(2010 through 2039, 2040 through 209, and 2070
through 2099) IVs from DISTRIB were calculated
for each time period. One hundred thirty-four tree
species were initially modeled. If a species never
had an area-weighted IV greater than 3 (FIA, current
modeled, or future) across the region, it was deleted
from the list because the species either has not had
or is not projected to have habitat in the region or
there were not enough data. Therefore, only a subset
of 93 of the 134 possible species is shown. Three
species (blue ash, southern magnolia, and tamarack)
were rare within sections and were modeled only at
the regional level. Species establishment, growth,
and habitat suitability are a function of current (FIA)
254
values. Therefore, it is possible for model results
to show species occupying areas where they do not
naturally occur (e.g., pine plantations). Conversely,
rare species are especially difficult to model at a
large regional scale, and may not appear in the FIA
data, despite finer inventory data that document their
existence.
A set of rules was established to determine change
classes for the years 2070 through 2099, which was
used to create tables in Chapter 5. For most species,
the following rules applied, based on the ratio of
future IVs to current modeled IVs:
Future:Current modeled IV
Class
<0.5
0.5 through 0.8
>0.8 through <1.2
1.2 through 2.0
>2
large decrease
small decrease
no change
small increase
large increase
A few exceptions applied to these general rules.
When there was a zero in the numerator or
denominator, a ratio could not be calculated.
Instead, a species was classified as gaining new
habitat if its FIA value was 0 and the future IV was
greater than 3. A species’ habitat was considered
to be extirpated if the future IV was zero and FIA
values were greater than 3.
Special rules were created for rare species. A species
was considered rare if it had a current modeled
area-weighted IV that equaled less than 10 percent
of the number of pixels in the assessment area (each
pixel is a 12.5-mile cell). The change classes are
calculated differently for these species because their
APPENDiX 4
current infrequency tends to inflate the percentage
change that is projected. The cutoffs for each portion
of the assessment area were as follows:
Section
Assessment area
221F
OH 221E
WV 221E
M221A
M221B
M221C
Pixels
Cutoff IV
for rare species
332
54
85
5
37
53
38
33
5
9
7
4
5
4
When a species was below the cutoff above, it was
considered rare, and the following rules applied:
Future:Current modeled IV
Class
<0.2
large decrease
0.2 through <0.
small decrease
0. through <4
no change
4 through 8
small increase
>8
large increase (not used
when current modeled
IV ≤3)
“Extirpated” was not used in this case because of
low confidence.
Special rules also applied to species that were known
to be present (current FIA IV >0) but not modeled
as present (current modeled = 0). In these cases, the
FIA IV was used in place of the current modeled IV
to calculate ratios. Then, change class rules were
applied based on the FIA IV.
Tables 37 and 38 describe the modifying factors and
adaptability scores used in the Tree Atlas. These
factors were developed by using a literature-based
scoring system to capture the potential adaptability
of species to changes in climate that cannot be
adequately captured by DISTRIB (Matthews et
al. 2011). This approach was used to assess the
capacity for each species to adapt and considered
nine biological traits reflecting innate characteristics
including competition ability for light and edaphic
specificity. Twelve disturbance characteristics
addressed the general response of a species to
events such as drought, insect pests, and fire. This
information distinguishes between species likely
to be more tolerant (or sensitive) to environmental
changes than the habitat models alone suggest.
For each biological and disturbance factor, a species
was scored on a scale from -3 through +3. A score of
-3 indicated a very negative response of that species
to that factor. A score of +3 indicated a very positive
response to that factor. To account for confidence
in the literature about these factors, each of these
scores was then multiplied by 0.5, 0.75, or 1, with
0.5 indicating low confidence and 1 indicating high
confidence. Finally the score was further weighted
by its relevance to future projected climate change
by multiplying it by a relevance factor. A score of 4
indicated highly relevant and 1 indicated not highly
relevant to climate change. Means for individual
biological scores and disturbance scores were then
calculated to arrive at an overall biological and
disturbance score for the species.
To arrive at an overall adaptability score for the
species that could be compared across all modeled
tree species, the mean, rescaled (0 through ) values
for biological and disturbance characteristics were
plotted to form two sides of a right triangle; the
hypotenuse was then a combination (disturbance and
biological characteristics) metric, ranging from 0
through 8.5 (Fig. 59).
Note that modifying factors and adaptability scores
are calculated for a species across its entire range.
Many species may have higher or lower adaptability
in certain areas. For example, a species with a low
flooding tolerance may have higher adaptability in
areas not subject to flooding. Likewise, local impacts
of insects and disease may reduce the adaptability
of a species in that area. Only the traits that elicited
a combination of a strong positive or negative
response, high certainty, and high future relevance
for a combined score of 4.5 or greater are listed in
the following tables for each species.
255
APPENDiX 4
Mixed hardwoods on the Shawnee State Forest, Ohio. Photo by the Ohio Department of Natural Resources, used with
permission.
25
Table 29.—Complete DiSTRiB model resultsa for the 93 tree species in the assessment area
Common Name
357
1319
78
1170
13
407
3
17
243
56
23
2163
0
904
87
916
434
63
703
11
8
214
16
71
0
1621
42
19
70
213
58
259
420
294
98
354
1625
88
91
86
3
751
23
2010-2039
PCM
GFDL
B1
A1Fi
335
1275
59
1102
2
403
7
18
251
44
20
2337
7
900
47
985
485
56
799
8
0
222
5
10
0
1577
42
32
59
173
56
325
485
295
193
446
1571
103
102
72
15
838
4
317
1280
66
1012
5
372
7
12
173
63
6
1864
5
855
57
978
526
40
843
17
6
194
7
37
0
1658
73
11
97
187
31
281
424
357
337
386
1686
97
122
108
9
814
9
Medium
High
Medium
Medium
High
Medium
High
Low
High
Low
High
High
High
Low
Low
High
Medium
Low
High
Medium
Low
Medium
Medium
Low
Low
High
Medium
Low
Medium
High
Low
High
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
High
High
243
948
63
1040
3
365
1
13
51
219
3
1094
293
789
24
1559
559
124
867
333
5
234
15
10
0
1503
270
4
343
175
89
255
459
517
1048
353
1724
206
308
179
47
939
10
296
1275
61
892
5
367
7
10
121
82
3
1577
11
819
54
1018
576
36
899
24
5
211
9
25
0
1703
109
8
113
186
26
278
408
406
464
378
1723
126
149
118
39
846
9
114
647
56
967
2
408
1
14
4
329
4
660
859
660
14
2203
577
218
854
1088
5
293
54
4
239
1082
327
0
571
156
142
210
549
661
1456
217
1440
448
418
282
233
1048
10
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
294
1272
60
895
5
368
3
12
100
110
2
1481
24
781
49
1037
609
42
910
42
5
209
8
19
0
1622
156
8
128
189
31
276
411
413
590
370
1730
145
167
124
30
847
10
97
547
55
751
2
421
1
14
0
367
5
536
1171
590
14
2118
463
223
854
1473
3
284
70
0
584
979
324
0
810
151
171
196
637
556
1648
196
1141
535
423
350
563
1099
10
0.95
1.00
1.12
0.92
2.50
0.92
1.00
0.67
0.69
1.43
0.30
0.80
0.71
0.95
1.21
0.99
1.09
0.71
1.06
2.13
NA
0.87
1.40
3.70
NA
1.05
1.74
0.34
1.64
1.08
0.55
0.87
0.87
1.21
1.75
0.87
1.07
0.94
1.20
1.50
0.60
0.97
2.25
0.73
0.74
1.07
0.94
1.50
0.91
0.14
0.72
0.20
4.98
0.15
0.47
41.86
0.88
0.51
1.58
1.15
2.21
1.09
41.63
NA
1.05
3.00
1.00
NA
0.95
6.43
0.13
5.81
1.01
1.59
0.79
0.95
1.75
5.43
0.79
1.10
2.00
3.02
2.49
3.13
1.12
2.50
0.88
1.00
1.03
0.81
2.50
0.91
1.00
0.56
0.48
1.86
0.15
0.68
1.57
0.91
1.15
1.03
1.19
0.64
1.13
3.00
NA
0.95
1.80
2.50
NA
1.08
2.60
0.25
1.92
1.08
0.46
0.86
0.84
1.38
2.40
0.85
1.10
1.22
1.46
1.64
2.60
1.01
2.25
0.34
0.51
0.95
0.88
1.00
1.01
0.14
0.78
0.02
7.48
0.20
0.28
122.71
0.73
0.30
2.24
1.19
3.89
1.07
136.00
NA
1.32
10.80
0.40
Migrant
0.69
7.79
0.00
9.68
0.90
2.54
0.65
1.13
2.24
7.54
0.49
0.92
4.35
4.10
3.92
15.53
1.25
2.50
0.88
1.00
1.02
0.81
2.50
0.91
0.43
0.67
0.40
2.50
0.10
0.63
3.43
0.87
1.04
1.05
1.26
0.75
1.14
5.25
NA
0.94
1.60
1.90
NA
1.03
3.71
0.25
2.17
1.09
0.55
0.85
0.85
1.40
3.06
0.83
1.10
1.41
1.64
1.72
2.00
1.01
2.50
0.29
0.43
0.93
0.68
1.00
1.05
0.14
0.78
0.00
8.34
0.25
0.23
167.29
0.66
0.30
2.15
0.96
3.98
1.07
184.13
NA
1.28
14.00
0.00
Migrant
0.62
7.71
0.00
13.73
0.87
3.05
0.60
1.31
1.89
8.54
0.44
0.73
5.19
4.15
4.86
37.53
1.31
2.50
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
No Change
No Change
No Change
No Change
No Change
No Change
Small Decrease
No Change
Large Decrease
Large Increase
Large Decrease
Small Decrease
No Change
No Change
No Change
No Change
Small Increase
Small Decrease
No Change
Small Increase
No Change
No Change
No Change
No Change
NA
No Change
Large Increase
Small Decrease
Large Increase
No Change
Small Decrease
No Change
No Change
Small Increase
Large Increase
No Change
No Change
Small Increase
Small Increase
Small Increase
No Change
No Change
No Change
Large Decrease
Large Decrease
No Change
Small Decrease
No Change
No Change
Large Decrease
No Change
Exirpated
Large Increase
Small Decrease
Large Decrease
Large Increase
Small Decrease
Large Decrease
Large Increase
No Change
Large Increase
No Change
Large Increase
Small Decrease
Small Increase
Large Increase
Exirpated
New Habitat
Small Decrease
Large Increase
Exirpated
Large Increase
No Change
Large Increase
Small Decrease
Small Increase
Small Increase
Large Increase
Large Decrease
Small Decrease
Large Increase
Large Increase
Large Increase
Large Increase
Small Increase
No Change
257
(coninued on next page)
APPENDiX 4
American basswood
American beech
American chestnut
American elm
American holly
American hornbeam
Balsam ir
Bear oak (scrub oak)
Bigtooth aspen
Biternut hickory
Black ash
Black cherry
Black hickory
Black locust
Black maple
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Blue ash
Boxelder
Bur oak
Buternut
Cedar elm
Chestnut oak
Chinkapin oak
Chokecherry
Common persimmon
Cucumbertree
Eastern cotonwood
Eastern hemlock
Eastern hophornbeam
Eastern redbud
Eastern redcedar
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Loblolly pine
Mockernut hickory
Mountain maple
FiA
iV
DiSTRiB
Current Model
iV
Reliability
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
Common Name
Northern catalpa
Northern pin oak
Northern red oak
Northern white-cedar
Ohio buckeye
Osage-orange
Pawpaw
Pignut hickory
Pin cherry
Pin oak
Pitch pine
Post oak
Quaking aspen
Red maple
Red mulberry
Red pine
Red spruce
River birch
Rock elm
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slippery elm
Sourwood
Southern magnolia
Southern red oak
Striped maple
Sugar maple
Sugarberry
Swamp white oak
Sweet birch
Sweetgum
Sycamore
Table Mountain pine
Tamarack (naive)
Tulip tree
Virginia pine
Water locust
FiA
iV
12
6
1397
0
51
63
172
759
52
128
134
47
54
3395
13
74
67
20
5
1111
514
287
144
31
40
1
183
610
312
2
11
261
2817
1
59
440
15
277
44
3
1685
489
0
DiSTRiB
Current Model
iV
Reliability
2010-2039
PCM
GFDL
B1
A1Fi
4
2
1400
4
32
67
132
864
18
87
138
57
82
3501
13
51
39
4
1
1143
582
259
207
8
53
0
135
596
362
4
5
226
2601
1
53
474
8
277
41
2
1652
526
0
3
4
1313
1
37
67
133
848
15
79
131
120
36
3240
13
42
40
11
4
1179
612
269
204
22
79
0
96
590
400
4
14
206
2700
0
42
453
26
305
39
1
1783
581
0
Low
Medium
High
High
Low
Medium
Low
High
Medium
Medium
High
High
High
High
Low
Medium
High
Low
Low
High
High
Medium
Medium
Medium
High
Low
Medium
Medium
High
Medium
High
High
High
Medium
Low
High
High
Medium
Medium
High
High
High
Low
3
3
1322
1
76
113
132
940
3
131
129
1513
5
2390
129
20
20
14
11
1154
705
239
408
125
467
7
222
617
336
1
136
142
2226
57
51
353
85
346
46
1
1564
579
0
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
3
3
1260
1
41
86
146
874
10
80
131
176
22
3179
15
30
33
12
3
1191
657
251
249
19
115
0
87
558
505
8
43
179
2681
5
41
453
51
310
39
1
1895
716
0
7
1
1206
5
21
159
69
810
2
197
121
3340
4
1664
295
10
15
16
44
880
522
227
558
123
1312
132
407
529
275
2
374
103
1068
328
26
248
222
352
53
6
939
500
6
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
2
3
1244
1
49
92
144
887
7
79
124
257
14
3114
23
24
34
10
4
1229
646
243
277
35
152
0
98
560
480
4
54
170
2700
9
43
451
78
317
36
1
1939
700
0
20
1
1099
6
5
254
51
766
2
158
129
4767
3
1299
366
10
14
32
20
820
345
230
559
106
1972
282
425
402
223
2
609
95
587
648
6
208
357
368
63
6
687
491
56
0.75
2.00
0.94
0.25
1.16
1.00
1.01
0.98
0.83
0.91
0.95
2.11
0.44
0.93
1.00
0.82
1.03
2.75
4.00
1.03
1.05
1.04
0.99
2.75
1.49
NA
0.71
0.99
1.11
1.00
2.80
0.91
1.04
0.00
0.79
0.96
3.25
1.10
0.95
0.50
1.08
1.11
NA
0.75
1.50
0.94
0.25
2.38
1.69
1.00
1.09
0.17
1.51
0.94
26.54
0.06
0.68
9.92
0.39
0.51
3.50
11.00
1.01
1.21
0.92
1.97
15.63
8.81
NA
1.64
1.04
0.93
0.25
27.20
0.63
0.86
57.00
0.96
0.75
10.63
1.25
1.12
0.50
0.95
1.10
NA
0.75
1.50
0.90
0.25
1.28
1.28
1.11
1.01
0.56
0.92
0.95
3.09
0.27
0.91
1.15
0.59
0.85
3.00
3.00
1.04
1.13
0.97
1.20
2.38
2.17
NA
0.64
0.94
1.40
2.00
8.60
0.79
1.03
5.00
0.77
0.96
6.38
1.12
0.95
0.50
1.15
1.36
NA
1.75
0.50 5.00
0.50
1.50 0.50
0.86
0.89 0.79
1.25
0.25 1.50
0.66
1.53 0.16
2.37
1.37 3.79
0.52
1.09 0.39
0.94
1.03 0.89
0.11
0.39 0.11
2.26
0.91 1.82
0.88
0.90 0.94
58.60
4.51 83.63
0.05
0.17 0.04
0.48
0.89 0.37
22.69
1.77 28.15
0.20
0.47 0.20
0.39
0.87 0.36
4.00
2.50 8.00
44.00
4.00 20.00
0.77
1.08 0.72
0.90
1.11 0.59
0.88
0.94 0.89
2.70
1.34 2.70
15.38
4.38 13.25
24.76
2.87 37.21
NA
NA
NA
3.02
0.73 3.15
0.89
0.94 0.67
0.76
1.33 0.62
0.50
1.00 0.50
74.80 10.80 121.80
0.46
0.75 0.42
0.41
1.04 0.23
328.00
9.00 648.00
0.49
0.81 0.11
0.52
0.95 0.44
27.75
9.75 44.63
1.27
1.14 1.33
1.29
0.88 1.54
3.00
0.50 3.00
0.57
1.17 0.42
0.95
1.33 0.93
Migrant NA Migrant
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
No Change
No Change
No Change
Small Decrease
No Change
Small Increase
No Change
No Change
Small Decrease
No Change
No Change
Large Increase
Large Decrease
No Change
No Change
Large Decrease
No Change
No Change
No Change
No Change
No Change
No Change
Small Increase
Small Increase
Large Increase
NA
Small Decrease
No Change
Small Increase
No Change
Large Increase
Small Decrease
No Change
Large Increase
No Change
No Change
Large Increase
No Change
No Change
No Change
No Change
Small Increase
NA
Small Increase
No Change
Small Decrease
No Change
Large Decrease
Large Increase
Large Decrease
No Change
Large Decrease
Small Increase
No Change
Large Increase
Large Decrease
Large Decrease
Large Increase
Large Decrease
Large Decrease
Small Increase
Large Increase
Small Decrease
Small Decrease
No Change
Large Increase
Large Increase
Large Increase
Large Increase
Large Increase
Small Decrease
Small Decrease
No Change
Large Increase
Large Decrease
Large Decrease
Large Increase
Large Decrease
Large Decrease
Large Increase
Small Increase
Small Increase
No Change
Large Decrease
No Change
New Habitat
(coninued on next page)
APPENDiX 4
258
Table 29 (coninued).
Table 29 (coninued).
Common Name
FiA
iV
Water oak
White ash
White oak
Willow oak
Winged elm
Yellow birch
Yellow buckeye
0
1483
1572
2
3
151
202
DiSTRiB
Current Model
iV
Reliability
2010-2039
PCM
GFDL
B1
A1Fi
0
1657
1643
0
2
135
161
0
1477
1720
0
11
113
176
High
High
High
Medium
High
High
Medium
0
1250
2487
0
388
53
175
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
0
1394
1791
0
43
106
175
89
827
2519
1
1229
33
177
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
0
1422
1853
0
68
103
176
323
747
2023
20
2272
29
174
NA
0.89
1.05
NA
5.50
0.84
1.09
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
NA
NA Migrant
NA Migrant
NA
0.75
0.84 0.50
0.86 0.45
No Change
1.51
1.09 1.53
1.13 1.23
No Change
NA
NA
NA
NA
NA
NA
194.00 21.50 614.50 34.00 1136.00 Large Increase
0.39
0.79 0.24
0.76 0.22 Small Decrease
1.09
1.09 1.10
1.09 1.08
No Change
New Habitat
Large Decrease
Small Increase
Large Increase
Large Increase
Large Decrease
No Change
Current importance values (Current IV) are based on results from the DISTRIB model. Early-, mid-, and late-century importance values are average values for the indicated years. Change classes are
provided for the end of century (2070 through 2099) period. Explanaions for the change classes are described in the text. Future:Current Suitable Habitat is a raio of projected importance value to
current importance value.
a
APPENDiX 4
259
Common Name
FiA
iV
American basswood
American beech
American elm
American hornbeam
Bigtooth aspen
Biternut hickory
Black ash
Black cherry
Black hickory
Black locust
Black maple
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Boxelder
Bur oak
Cedar elm
Chestnut oak
Chinkapin oak
Common persimmon
Cucumbertree
Eastern cotonwood
Eastern hemlock
Eastern hophornbeam
Eastern redcedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Mockernut hickory
Northern pin oak
Northern red oak
Northern white-cedar
Ohio buckeye
Osage-orange
Pignut hickory
Pin cherry
Pin oak
Post oak
73
228
495
79
34
8
7
705
0
74
30
40
55
30
35
0
31
12
0
0
0
0
18
26
9
91
0
0
48
115
16
4
28
84
4
191
0
4
45
86
15
106
0
DiSTRiB
Current Model
iV
Reliability
73
178
454
73
57
22
20
691
0
101
17
79
80
36
34
0
51
5
0
12
1
0
5
34
18
123
6
10
44
113
29
21
19
101
2
192
3
7
26
107
6
74
0
Medium
High
Medium
Medium
High
Low
High
High
High
Low
Low
High
Medium
Low
High
Medium
Medium
Medium
Low
High
Medium
Medium
High
Low
High
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
Medium
High
High
Low
Medium
High
Medium
Medium
High
2010-2039
PCM
GFDL
B1
A1Fi
56
182
468
68
53
20
5
655
0
115
18
69
108
28
50
0
44
6
0
29
4
0
11
20
9
82
29
30
50
146
21
34
47
93
4
168
0
12
39
105
5
68
0
55
151
434
62
18
59
3
289
7
114
10
132
137
49
33
8
52
13
0
34
56
27
7
53
3
98
173
77
40
143
42
125
76
140
3
191
0
39
60
152
2
89
112
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
56
180
426
68
55
37
2
606
0
123
18
91
152
26
59
2
49
8
0
46
13
3
11
16
8
75
79
50
51
155
38
51
62
106
3
169
0
17
52
115
5
65
9
57
183
422
68
44
47
1
546
0
121
18
96
162
32
59
3
49
7
0
42
30
1
11
20
5
73
111
57
41
158
40
57
66
104
3
166
0
24
52
114
4
63
15
11
62
403
69
0
64
4
111
56
72
5
237
143
61
32
120
64
42
2
6
64
45
5
79
1
64
218
159
0
116
135
152
97
170
1
178
5
16
66
159
1
107
401
5
51
224
67
0
58
5
41
105
99
5
223
103
57
38
149
59
43
61
3
54
110
5
76
1
83
220
114
0
115
146
133
104
153
1
116
6
4
79
130
1
84
655
0.77
1.02
1.03
0.93
0.93
0.91
0.25
0.95
NA
1.14
1.06
0.87
1.35
0.78
1.47
NA
0.86
1.20
NA
2.42
4.00
NA
2.20
0.59
0.50
0.67
4.83
3.00
1.14
1.29
0.72
1.62
2.47
0.92
2.00
0.88
0.00
1.71
1.50
0.98
0.83
0.92
NA
0.75
0.85
0.96
0.85
0.32
2.68
0.15
0.42
New
1.13
0.59
1.67
1.71
1.36
0.97
New
1.02
2.60
NA
2.83
56.00
New
1.40
1.56
0.17
0.80
28.83
7.70
0.91
1.27
1.45
5.95
4.00
1.39
1.50
1.00
0.00
5.57
2.31
1.42
0.33
1.20
New
0.77 0.15
1.01 0.35
0.94 0.89
0.93 0.95
0.97 0.00
1.68 2.91
0.10 0.20
0.88 0.16
NA
New
1.22 0.71
1.06 0.29
1.15 3.00
1.90 1.79
0.72 1.69
1.74 0.94
New New
0.96 1.26
1.60 8.40
NA
New
3.83 0.50
13.00 64.00
New New
2.20 1.00
0.47 2.32
0.44 0.06
0.61 0.52
13.17 36.33
5.00 15.90
1.16 0.00
1.37 1.03
1.31 4.66
2.43 7.24
3.26 5.11
1.05 1.68
1.50 0.50
0.88 0.93
0.00 1.67
2.43 2.29
2.00 2.54
1.08 1.49
0.83 0.17
0.88 1.45
New New
0.78 0.07
1.03 0.29
0.93 0.49
0.93 0.92
0.77 0.00
2.14 2.64
0.05 0.25
0.79 0.06
NA
New
1.20 0.98
1.06 0.29
1.22 2.82
2.03 1.29
0.89 1.58
1.74 1.12
New
New
0.96 1.16
1.40 8.60
NA
New
3.50 0.25
30.00 54.00
New
New
2.20 1.00
0.59 2.24
0.28 0.06
0.59 0.68
18.50 36.67
5.70 11.40
0.93 0.00
1.40 1.02
1.38 5.03
2.71 6.33
3.47 5.47
1.03 1.52
1.50 0.50
0.87 0.60
0.00 2.00
3.43 0.57
2.00 3.04
1.07 1.22
0.67 0.17
0.85 1.14
New New
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Decrease
No Change
No Change
No Change
Decrease
Large Increase
Large Decrease
Decrease
NA
Increase
No Change
Increase
Increase
No Change
Increase
New Habitat
No Change
Increase
NA
New Habitat
New Habitat
New Habitat
Large Increase
Decrease
Large Decrease
Decrease
New Habitat
New Habitat
No Change
Increase
Increase
Large Increase
Large Increase
No Change
No Change
No Change
NA
Large Increase
Increase
No Change
Decrease
No Change
New Habitat
Large Decrease
Large Decrease
Decrease
No Change
Large Decrease
Large Increase
Large Decrease
Large Decrease
New Habitat
No Change
Large Decrease
Large Increase
Increase
Increase
No Change
New Habitat
Increase
Large Increase
New Habitat
Large Decrease
New Habitat
New Habitat
No Change
Large Increase
Large Decrease
Decrease
New Habitat
New Habitat
Large Decrease
No Change
Large Increase
Large Increase
Large Increase
Increase
Decrease
Decrease
No Change
Decrease
Large Increase
Increase
Large Decrease
No Change
New Habitat
(coninued on next page)
APPENDiX 4
20
Table 30.—Complete DiSTRiB model resultsa for the 68 tree species in Secion 221F
Table 30 (coninued).
Common Name
FiA
iV
Quaking aspen
Red maple
Red mulberry
Red pine
Rock elm
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slippery elm
Southern red oak
Sugar maple
Sugarberry
Swamp white oak
Sweet birch
Sweetgum
Sycamore
Tulip tree
White oak
Winged elm
Yellow birch
33
553
0
29
0
32
7
3
25
7
0
0
90
101
0
538
0
52
1
0
22
66
53
0
7
DiSTRiB
Current Model
iV
Reliability
49
537
1
28
0
72
6
2
44
0
0
0
56
121
0
490
0
48
3
0
41
75
118
0
9
High
High
Low
Medium
Low
High
High
Medium
Medium
Medium
High
Low
Medium
Medium
High
High
Medium
Low
High
High
Medium
High
High
High
High
2010-2039
PCM
GFDL
B1
A1Fi
23
418
1
26
0
91
8
2
36
5
0
0
45
139
0
509
0
41
4
0
60
85
103
0
6
0
288
49
11
0
102
21
1
109
69
0
0
95
149
0
480
0
46
2
0
63
80
230
0
0
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
13
390
4
17
0
112
29
2
61
5
0
0
43
146
0
544
0
39
9
2
63
118
125
0
3
7
373
7
16
0
119
28
2
69
7
0
0
49
145
0
562
0
41
11
3
64
124
134
0
1
3
237
61
9
9
80
38
1
131
67
8
0
141
141
1
191
14
25
0
1
56
59
260
30
0
3
201
78
9
11
97
30
1
79
49
40
27
104
77
14
109
121
6
0
6
61
52
167
217
0
0.47
0.78
1.00
0.93
NA
1.26
1.33
1.00
0.82
New
NA
NA
0.80
1.15
NA
1.04
NA
0.85
1.33
NA
1.46
1.13
0.87
NA
0.67
0.00
0.54
49.00
0.39
NA
1.42
3.50
0.50
2.48
New
NA
NA
1.70
1.23
NA
0.98
NA
0.96
0.67
NA
1.54
1.07
1.95
NA
0.00
0.27 0.06
0.73 0.44
4.00 61.00
0.61 0.32
NA New
1.56 1.11
4.83 6.33
1.00 0.50
1.39 2.98
New New
NA
New
NA
NA
0.77 2.52
1.21 1.17
NA
New
1.11 0.39
NA
New
0.81 0.52
3.00 0.00
New New
1.54 1.37
1.57 0.79
1.06 2.20
NA
New
0.33 0.00
0.14 0.06
0.70 0.37
7.00 78.00
0.57 0.32
New New
1.65 1.35
4.67 5.00
1.00 0.50
1.57 1.80
New New
New New
New New
0.88 1.86
1.20 0.64
New New
1.15 0.22
New New
0.85 0.13
3.67 0.00
New New
1.56 1.49
1.65 0.69
1.14 1.42
New New
0.11 0.00
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Large Decrease
Decrease
New Habitat
Decrease
NA
Increase
Large Increase
No Change
Increase
No Change
NA
NA
No Change
Increase
NA
No Change
NA
No Change
No Change
New Habitat
Increase
Increase
No Change
NA
Large Decrease
Large Decrease
Large Decrease
New Habitat
Large Decrease
New Habitat
Increase
Large Increase
Decrease
Increase
Increase
New Habitat
New Habitat
Increase
Decrease
New Habitat
Large Decrease
New Habitat
Large Decrease
NA
New Habitat
Increase
Decrease
Increase
New Habitat
Large Decrease
Current importance values (Current IV) are based on results from the DISTRIB model. Early-, mid-, and late-century importance values are average values for the indicated years. Change classes are
provided for the end of century (2070 through 2099) period. Explanaions for the change classes are described in the text. Future:Current Suitable Habitat is a raio of projected importance value to
current importance value.
a
APPENDiX 4
21
Common Name
FiA
iV
American basswood
American beech
American elm
American hornbeam
Bigtooth aspen
Biternut hickory
Black cherry
Black hickory
Black locust
Black maple
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Boxelder
Bur oak
Cedar elm
Chestnut oak
Chinkapin oak
Common persimmon
Cucumbertree
Eastern cotonwood
Eastern hemlock
Eastern hophornbeam
Eastern redcedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Loblolly pine
Mockernut hickory
Northern catalpa
Northern red oak
Ohio buckeye
Osage-orange
Pawpaw
Pignut hickory
Pin oak
Pitch pine
Post oak
43
194
395
133
149
15
582
0
259
41
248
165
15
133
2
47
2
0
220
13
32
12
27
9
106
47
108
98
595
24
30
40
0
198
9
233
29
12
51
204
16
24
16
DiSTRiB
Current Model
iV
Reliability
55
202
379
108
116
12
650
0
254
23
261
171
13
144
1
62
0
0
219
16
31
2
9
13
120
73
94
98
476
25
39
37
1
206
1
271
15
26
48
218
8
16
17
Medium
High
Medium
Medium
High
Low
High
High
Low
Low
High
Medium
Low
High
Medium
Medium
Medium
Low
High
Medium
Medium
High
Low
High
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
High
Low
High
Low
Medium
Low
High
Medium
High
High
2010-2039
PCM
GFDL
B1
A1Fi
45
212
305
114
72
17
459
0
203
27
288
169
5
174
4
45
0
0
248
32
46
7
9
3
103
141
119
75
552
26
39
41
0
220
2
245
13
18
41
239
7
20
40
38
149
316
112
11
76
223
115
179
10
508
166
46
179
130
65
0
0
242
90
134
5
28
3
110
358
164
49
517
73
101
67
4
245
2
294
27
30
52
265
29
15
658
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
49
217
254
111
38
20
348
2
180
25
301
172
4
193
7
52
0
0
278
50
52
7
6
3
100
182
131
58
546
31
47
37
2
231
2
249
11
21
48
251
9
23
60
49
228
258
113
35
22
335
6
165
25
303
175
4
188
11
49
0
0
256
62
53
8
5
4
103
219
128
55
548
37
55
39
1
230
1
253
13
25
48
259
9
21
86
6
78
249
122
0
87
155
224
158
7
640
151
73
151
339
104
8
107
174
87
142
4
46
2
156
390
189
1
355
163
118
94
37
256
1
229
2
48
30
234
68
15
1052
3
62
177
127
0
95
137
308
128
7
566
93
63
153
441
98
21
194
167
76
188
4
67
2
169
436
136
1
265
180
106
126
109
258
1
220
1
93
18
231
51
14
1388
0.82
1.05
0.81
1.06
0.62
1.42
0.71
NA
0.80
1.17
1.10
0.99
0.39
1.21
4.00
0.73
NA
NA
1.13
2.00
1.48
3.50
1.00
0.23
0.86
1.93
1.27
0.77
1.16
1.04
1.00
1.11
0.00
1.07
2.00
0.90
0.87
0.69
0.85
1.10
0.88
1.25
2.35
0.69
0.74
0.83
1.04
0.10
6.33
0.34
New
0.71
0.44
1.95
0.97
3.54
1.24
130.00
1.05
NA
NA
1.11
5.63
4.32
2.50
3.11
0.23
0.92
4.90
1.75
0.50
1.09
2.92
2.59
1.81
4.00
1.19
2.00
1.09
1.80
1.15
1.08
1.22
3.63
0.94
38.71
0.89 0.11
1.07 0.39
0.67 0.66
1.03 1.13
0.33 0.00
1.67 7.25
0.54 0.24
New New
0.71 0.62
1.09 0.30
1.15 2.45
1.01 0.88
0.31 5.62
1.34 1.05
7.00 339.00
0.84 1.68
NA
New
NA
New
1.27 0.80
3.13 5.44
1.68 4.58
3.50 2.00
0.67 5.11
0.23 0.15
0.83 1.30
2.49 5.34
1.39 2.01
0.59 0.01
1.15 0.75
1.24 6.52
1.21 3.03
1.00 2.54
2.00 37.00
1.12 1.24
2.00 1.00
0.92 0.85
0.73 0.13
0.81 1.85
1.00 0.63
1.15 1.07
1.13 8.50
1.44 0.94
3.53 61.88
0.89 0.06
1.13 0.31
0.68 0.47
1.05 1.18
0.30 0.00
1.83 7.92
0.52 0.21
New New
0.65 0.50
1.09 0.30
1.16 2.17
1.02 0.54
0.31 4.85
1.31 1.06
11.00 441.00
0.79 1.58
New New
New New
1.17 0.76
3.88 4.75
1.71 6.07
4.00 2.00
0.56 7.44
0.31 0.15
0.86 1.41
3.00 5.97
1.36 1.45
0.56 0.01
1.15 0.56
1.48 7.20
1.41 2.72
1.05 3.41
1.00 109.00
1.12 1.25
1.00 1.00
0.93 0.81
0.87 0.07
0.96 3.58
1.00 0.38
1.19 1.06
1.13 6.38
1.31 0.88
5.06 81.65
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
No Change
No Change
Decrease
No Change
Large Decrease
Increase
Decrease
New Habitat
Decrease
No Change
Increase
No Change
Large Decrease
Increase
Increase
Decrease
Large Decrease
NA
Increase
Large Increase
Increase
Large Increase
Decrease
Large Decrease
No Change
Large Increase
Increase
Decrease
Increase
Increase
Increase
No Change
NA
No Change
No Change
No Change
No Change
No Change
No Change
Increase
No Change
Increase
Large Increase
Large Decrease
Large Decrease
Decrease
Increase
Exirpated
Large Increase
Large Decrease
New Habitat
Decrease
Large Decrease
Large Increase
Decrease
Large Increase
No Change
Increase
Increase
Large Increase
New Habitat
Decrease
Large Increase
Large Increase
Increase
Large Increase
Large Decrease
Increase
Large Increase
Increase
Exirpated
Decrease
Large Increase
Large Increase
Large Increase
New Habitat
Increase
No Change
Decrease
Large Decrease
Large Increase
Large Decrease
No Change
Large Increase
No Change
Large Increase
(coninued on next page)
APPENDiX 4
22
Table 31.—Complete DiSTRiB model resultsa for the 72 tree species in Secion 221E (Ohio)
Table 31 (coninued).
Common Name
FiA
iV
Quaking aspen
Red maple
Red mulberry
Red pine
River birch
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slippery elm
Sourwood
Southern red oak
Sugar maple
Sugarberry
Sweet birch
Sweetgum
Sycamore
Tulip tree
Virginia pine
Water locust
Water oak
White ash
White oak
Winged elm
Yellow buckeye
12
816
0
18
10
323
88
33
49
19
15
0
57
182
69
0
665
0
8
10
111
473
117
0
0
409
411
0
57
DiSTRiB
Current Model
iV
Reliability
10
769
2
6
3
323
108
25
76
8
15
0
52
195
49
1
634
1
26
1
109
429
126
0
0
444
429
1
47
High
High
Low
Medium
Low
High
High
Medium
Medium
Medium
High
Low
Medium
Medium
High
High
High
Medium
High
High
Medium
High
High
Low
High
High
High
High
Medium
2010-2039
PCM
GFDL
B1
A1Fi
3
734
2
3
5
341
127
30
75
17
26
0
29
175
81
3
652
0
15
6
115
484
173
0
0
391
482
1
49
0
397
41
1
10
328
165
29
143
48
136
3
81
198
43
31
524
24
6
11
119
342
130
0
0
323
784
159
46
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
1
718
2
2
7
355
135
28
86
13
35
0
23
167
109
8
651
1
23
9
115
499
222
0
0
364
518
7
49
0
690
2
0
7
365
132
26
94
22
44
0
27
168
89
10
659
2
22
14
113
501
210
0
0
373
532
13
48
0
265
87
1
13
239
106
29
146
46
299
41
185
143
34
83
163
123
2
25
103
195
97
1
23
202
655
342
50
0
227
93
1
22
218
74
27
128
45
445
89
187
95
36
137
76
226
2
47
95
159
106
16
87
174
472
682
48
0.30
0.95
1.00
0.50
1.67
1.06
1.18
1.20
0.99
2.13
1.73
NA
0.56
0.90
1.65
3.00
1.03
0.00
0.58
6.00
1.06
1.13
1.37
NA
NA
0.88
1.12
1.00
1.04
0.00
0.52
20.50
0.17
3.33
1.02
1.53
1.16
1.88
6.00
9.07
New
1.56
1.02
0.88
31.00
0.83
24.00
0.23
11.00
1.09
0.80
1.03
NA
NA
0.73
1.83
159.00
0.98
0.10
0.93
1.00
0.33
2.33
1.10
1.25
1.12
1.13
1.63
2.33
NA
0.44
0.86
2.22
8.00
1.03
1.00
0.89
9.00
1.06
1.16
1.76
NA
NA
0.82
1.21
7.00
1.04
0.00
0.35
43.50
0.17
4.33
0.74
0.98
1.16
1.92
5.75
19.93
New
3.56
0.73
0.69
83.00
0.26
123.00
0.08
25.00
0.95
0.46
0.77
New
New
0.46
1.53
342.00
1.06
0.00
0.90
1.00
0.00
2.33
1.13
1.22
1.04
1.24
2.75
2.93
New
0.52
0.86
1.82
10.00
1.04
2.00
0.85
14.00
1.04
1.17
1.67
New
New
0.84
1.24
13.00
1.02
0.00
0.30
46.50
0.17
7.33
0.68
0.69
1.08
1.68
5.63
29.67
New
3.60
0.49
0.74
137.00
0.12
226.00
0.08
47.00
0.87
0.37
0.84
New
New
0.39
1.10
682.00
1.02
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Exirpated
No Change
NA
Large Decrease
No Change
No Change
Increase
No Change
Increase
No Change
Large Increase
NA
Decrease
No Change
Increase
New Habitat
No Change
New Habitat
Decrease
Increase
No Change
Increase
Increase
NA
NA
Decrease
Increase
New Habitat
No Change
Exirpated
Large Decrease
New Habitat
Large Decrease
Increase
Decrease
Decrease
No Change
Increase
Increase
Large Increase
NA
Decrease
No Change
Increase
New Habitat
No Change
New Habitat
Decrease
Increase
No Change
Increase
Increase
NA
NA
Decrease
Increase
New Habitat
No Change
Current importance values (Current IV) are based on results from the DISTRIB model. Early-, mid-, and late-century importance values are average values for the indicated years. Change classes are
provided for the end of century (2070 through 2099) period. Explanaions for the change classes are described in the text. Future:Current Suitable Habitat is a raio of projected importance value to
current importance value.
a
APPENDiX 4
23
Common Name
FiA
iV
American basswood
American beech
American chestnut
American elm
American hornbeam
Bigtooth aspen
Biternut hickory
Black cherry
Black hickory
Black locust
Black maple
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Boxelder
Buternut
Cedar elm
Chestnut oak
Chinkapin oak
Common persimmon
Cucumbertree
Eastern hemlock
Eastern hophornbeam
Eastern redcedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Loblolly pine
Mockernut hickory
Northern red oak
Ohio buckeye
Osage-orange
Pawpaw
Pignut hickory
Pin cherry
Pin oak
Pitch pine
Post oak
58
215
7
143
110
32
11
302
0
170
7
208
95
4
117
2
51
12
0
206
5
28
40
12
85
7
93
14
379
23
23
4
2
192
218
3
4
60
191
10
0
21
20
DiSTRiB
Current Model
iV
Reliability
53
211
1
141
97
38
5
333
4
142
4
224
103
0
137
3
57
1
0
235
11
23
30
15
84
45
94
28
407
18
14
4
1
211
211
2
8
49
208
2
2
20
27
Medium
High
Medium
Medium
Medium
High
Low
High
High
Low
Low
High
Medium
Low
High
Medium
Medium
Low
Low
High
Medium
Medium
High
High
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
High
High
Low
Medium
Low
High
Medium
Medium
High
High
2010-2039
PCM
GFDL
B1
A1Fi
59
233
2
116
100
22
10
220
2
129
4
209
117
0
141
4
54
6
0
238
18
32
39
13
91
85
105
24
402
24
19
3
3
203
198
1
4
53
208
1
1
20
43
24
151
2
129
89
2
45
121
89
111
1
337
99
12
148
113
60
0
0
170
62
76
34
8
92
221
119
23
391
47
36
14
21
218
196
0
13
48
203
0
3
11
390
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
50
232
2
100
99
13
8
161
3
118
4
209
110
0
152
4
58
5
0
258
24
30
39
15
92
101
114
29
401
28
21
3
17
206
194
1
5
53
207
0
1
19
42
43
237
2
101
97
6
21
154
11
111
2
213
124
0
158
16
60
3
0
243
39
33
39
15
92
130
112
40
403
38
22
5
11
211
194
1
6
53
208
0
3
16
73
4
83
0
115
84
0
62
101
268
96
1
495
102
35
151
300
67
0
82
114
64
124
29
3
115
273
114
3
274
75
60
45
80
229
195
0
28
23
131
0
10
8
858
3
76
0
120
87
0
74
100
302
82
1
441
83
42
149
356
63
0
150
115
64
137
29
4
129
298
100
1
175
88
56
54
169
245
207
0
54
18
119
0
15
8
1089
1.11
1.10
2.00
0.82
1.03
0.58
2.00
0.66
0.50
0.91
1.00
0.93
1.14
NA
1.03
1.33
0.95
6.00
NA
1.01
1.64
1.39
1.30
0.87
1.08
1.89
1.12
0.86
0.99
1.33
1.36
0.75
3.00
0.96
0.94
0.50
0.50
1.08
1.00
0.50
0.50
1.00
1.59
0.45
0.72
2.00
0.92
0.92
0.05
9.00
0.36
22.25
0.78
0.25
1.50
0.96
New
1.08
37.67
1.05
0.00
NA
0.72
5.64
3.30
1.13
0.53
1.10
4.91
1.27
0.82
0.96
2.61
2.57
3.50
21.00
1.03
0.93
0.00
1.63
0.98
0.98
0.00
1.50
0.55
14.44
0.94
1.10
2.00
0.71
1.02
0.34
1.60
0.48
0.75
0.83
1.00
0.93
1.07
New
1.11
1.33
1.02
5.00
NA
1.10
2.18
1.30
1.30
1.00
1.10
2.24
1.21
1.04
0.99
1.56
1.50
0.75
17.00
0.98
0.92
0.50
0.63
1.08
1.00
0.00
0.50
0.95
1.56
0.08
0.39
0.00
0.82
0.87
0.00
12.40
0.30
67.00
0.68
0.25
2.21
0.99
New
1.10
100.00
1.18
0.00
New
0.49
5.82
5.39
0.97
0.20
1.37
6.07
1.21
0.11
0.67
4.17
4.29
11.25
80.00
1.09
0.92
0.00
3.50
0.47
0.63
0.00
5.00
0.40
31.78
0.81
1.12
2.00
0.72
1.00
0.16
4.20
0.46
2.75
0.78
0.50
0.95
1.20
New
1.15
5.33
1.05
3.00
New
1.03
3.55
1.44
1.30
1.00
1.10
2.89
1.19
1.43
0.99
2.11
1.57
1.25
11.00
1.00
0.92
0.50
0.75
1.08
1.00
0.00
1.50
0.80
2.70
0.06
0.36
0.00
0.85
0.90
0.00
14.80
0.30
75.50
0.58
0.25
1.97
0.81
New
1.09
118.67
1.11
0.00
New
0.49
5.82
5.96
0.97
0.27
1.54
6.62
1.06
0.04
0.43
4.89
4.00
13.50
169.00
1.16
0.98
0.00
6.75
0.37
0.57
0.00
7.50
0.40
40.33
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Decrease
Decrease
No Change
Decrease
Decrease
Large Decrease
Increase
Large Decrease
New Habitat
Decrease
Decrease
Decrease
Increase
NA
Decrease
Increase
Decrease
No Change
NA
Decrease
No Change
Increase
Increase
Decrease
Decrease
Large Increase
Decrease
Increase
Decrease
Large Increase
Increase
No change
Increase
Decrease
Decrease
Decrease
No Change
Decrease
Decrease
Exirpated
New Habitat
Decrease
Large Increase
Large Decrease
Large Decrease
Exirpated
No Change
No Change
Exirpated
Large Increase
Large Decrease
New Habitat
Decrease
Decrease
Increase
Decrease
Increase
No Change
Increase
No Change
Exirpated
New Habitat
Decrease
Increase
Large Increase
No Change
Large Decrease
Increase
Large Increase
No Change
Exirpated
Large Decrease
Large Increase
Large Increase
Large Increase
Increase
Increase
No Change
Exirpated
Increase
Large Decrease
Decrease
Exirpated
New Habitat
Large Decrease
Large Increase
(coninued on next page)
APPENDiX 4
24
Table 32.—Complete DiSTRiB model results for the 71 tree speciesa in Secion 221E (West Virginia)
Table 32 (coninued).
Common Name
FiA
iV
Red maple
Red mulberry
Rock elm
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shortleaf pine
Shumard oak
Silver maple
Slippery elm
Sourwood
Southern red oak
Sugar maple
Sugarberry
Sweet birch
Sweetgum
Sycamore
Table Mountain pine
Tulip tree
Virginia pine
Water locust
Water oak
White ash
White oak
Willow oak
Winged elm
Yellow buckeye
588
5
1
317
105
32
30
6
0
9
191
75
4
563
0
36
2
68
7
454
174
0
0
341
423
0
2
77
DiSTRiB
Current Model
iV
Reliability
599
4
1
305
121
31
46
13
0
6
158
90
2
487
0
38
1
66
2
428
168
0
0
331
411
0
0
56
High
Low
Low
High
High
Medium
Medium
High
Low
Medium
Medium
High
High
High
Medium
High
High
Medium
Medium
High
High
Low
High
High
High
Medium
High
Medium
2010-2039
PCM
GFDL
B1
A1Fi
569
4
3
315
127
32
48
21
0
3
148
97
4
534
0
41
7
65
3
457
159
0
0
292
422
0
5
64
360
15
3
267
153
26
65
184
3
8
124
58
57
389
19
15
29
66
4
367
149
0
0
210
596
0
127
61
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
564
4
2
298
132
31
51
40
0
3
128
121
19
516
1
42
19
65
2
462
201
0
0
270
432
0
21
61
546
4
3
303
135
33
57
64
0
2
127
116
28
516
3
44
31
66
2
463
202
0
0
280
452
0
33
63
199
58
10
175
60
28
98
459
50
12
82
31
144
115
84
5
66
67
4
126
74
2
31
135
566
0
416
57
173
63
3
157
28
32
113
625
66
43
67
13
204
46
120
5
91
66
4
100
71
16
102
129
427
9
598
57
0.95
1.00
3.00
1.03
1.05
1.03
1.04
1.62
NA
0.50
0.94
1.08
2.00
1.10
NA
1.08
7.00
0.99
1.50
1.07
0.95
NA
NA
0.88
1.03
NA
New
1.14
0.60
3.75
3.00
0.88
1.26
0.84
1.41
14.15
New
1.33
0.79
0.64
28.50
0.80
New
0.40
29.00
1.00
2.00
0.86
0.89
NA
NA
0.63
1.45
NA
New
1.09
0.94
1.00
2.00
0.98
1.09
1.00
1.11
3.08
NA
0.50
0.81
1.34
9.50
1.06
New
1.11
19.00
0.99
1.00
1.08
1.20
NA
NA
0.82
1.05
NA
New
1.09
0.33
14.50
10.00
0.57
0.50
0.90
2.13
35.31
New
2.00
0.52
0.34
72.00
0.24
New
0.13
66.00
1.02
2.00
0.29
0.44
New
New
0.41
1.38
NA
New
1.02
0.91
1.00
3.00
0.99
1.12
1.07
1.24
4.92
New
0.33
0.80
1.29
14.00
1.06
New
1.16
31.00
1.00
1.00
1.08
1.20
New
New
0.85
1.10
New
New
1.13
0.29
15.75
3.00
0.52
0.23
1.03
2.46
48.08
New
7.17
0.42
0.14
102.00
0.09
New
0.13
91.00
1.00
2.00
0.23
0.42
New
New
0.39
1.04
New
New
1.02
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Decrease
No Change
Large Increase
Decrease
Decrease
Decrease
Increase
Large Increase
NA
Large Decrease
Decrease
Increase
Increase
Decrease
New Habitat
Decrease
Increase
Decrease
No Change
Decrease
Increase
NA
NA
Decrease
Decrease
NA
Increase
Decrease
Large Decrease
Large Increase
Large Increase
Decrease
Large Decrease
No Change
Large Increase
Large Increase
New Habitat
Large Increase
Large Decrease
Large Decrease
Increase
Large Decrease
New Habitat
Large Decrease
Increase
No Change
No Change
Large Decrease
Large Decrease
New Habitat
New Habitat
Large Decrease
No Change
New Habitat
Increase
No Change
Current importance values (Current IV) are based on results from the DISTRIB model. Early-, mid-, and late-century importance values are average values for the indicated years. Change classes are
provided for the end of century (2070 through 2099) period. Explanaions for the change classes are described in the text. Future:Current Suitable Habitat is a raio of projected importance value to
current importance value.
a
APPENDiX 4
25
Common Name
FiA
iV
American basswood
American beech
American chestnut
American elm
American hornbeam
Bear oak (scrub oak)
Bigtooth aspen
Biternut hickory
Black cherry
Black hickory
Black locust
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Boxelder
Buternut
Cedar elm
Chestnut oak
Chinkapin oak
Chokecherry
Common persimmon
Cucumbertree
Eastern cotonwood
Eastern hemlock
Eastern hophornbeam
Eastern redcedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Loblolly pine
Mockernut hickory
Northern red oak
Osage-orange
Pawpaw
Pignut hickory
Pin cherry
Pitch pine
Post oak
30
52
9
15
10
14
8
3
168
0
129
167
42
1
148
6
20
16
0
490
14
4
3
11
1
27
33
31
38
69
155
2
24
0
1
80
249
2
4
80
7
43
0
DiSTRiB
Current Model
iV
Reliability
31
63
12
43
29
7
13
0
175
1
114
148
51
1
160
2
26
4
0
388
8
1
3
23
0
56
44
40
39
104
162
8
18
1
7
92
222
1
8
98
1
39
3
Medium
High
Medium
Medium
Medium
Low
High
Low
High
High
Low
High
Medium
Low
High
Medium
Medium
Low
Low
High
Medium
Low
Medium
High
Low
High
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
High
High
Medium
Low
High
Medium
High
High
2010-2039
PCM
GFDL
B1
A1Fi
27
61
8
26
14
8
8
2
136
2
120
156
48
0
153
6
20
11
0
423
9
3
9
14
0
41
37
50
41
82
160
7
19
1
4
89
213
2
4
91
2
40
22
24
54
8
52
18
8
2
13
107
38
105
211
47
10
150
42
22
3
0
348
27
0
50
18
1
43
41
127
55
66
174
13
26
2
5
95
187
4
5
90
1
37
176
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
21
58
8
26
12
7
2
2
101
5
111
156
49
0
158
10
17
4
0
406
11
2
15
16
0
38
34
61
41
72
171
6
16
1
8
87
186
2
3
90
1
38
39
25
67
8
28
12
7
1
4
94
6
104
155
48
0
159
10
18
3
0
377
13
1
20
14
0
38
36
70
41
67
172
6
17
0
4
86
177
2
3
94
1
36
46
20
63
8
76
31
7
0
35
72
102
88
289
48
30
133
121
25
1
18
238
36
0
79
18
3
40
62
181
63
47
164
26
42
14
20
100
148
8
4
78
1
34
378
21
54
8
77
35
8
0
37
73
124
67
278
40
30
126
169
28
0
68
218
35
0
103
17
11
39
67
203
56
47
127
40
52
23
52
107
131
15
2
83
1
33
505
0.87
0.97
0.67
0.61
0.48
1.14
0.62
New
0.78
2.00
1.05
1.05
0.94
0.00
0.96
3.00
0.77
2.75
NA
1.09
1.13
3.00
3.00
0.61
NA
0.73
0.84
1.25
1.05
0.79
0.99
0.88
1.06
1.00
0.57
0.97
0.96
2.00
0.50
0.93
2.00
1.03
7.33
0.77
0.86
0.67
1.21
0.62
1.14
0.15
New
0.61
38.00
0.92
1.43
0.92
10.00
0.94
21.00
0.85
0.75
NA
0.90
3.38
0.00
16.67
0.78
New
0.77
0.93
3.18
1.41
0.64
1.07
1.63
1.44
2.00
0.71
1.03
0.84
4.00
0.63
0.92
1.00
0.95
58.67
0.68
0.92
0.67
0.61
0.41
1.00
0.15
New
0.58
5.00
0.97
1.05
0.96
0.00
0.99
5.00
0.65
1.00
NA
1.05
1.38
2.00
5.00
0.70
NA
0.68
0.77
1.53
1.05
0.69
1.06
0.75
0.89
1.00
1.14
0.95
0.84
2.00
0.38
0.92
1.00
0.97
13.00
0.65
1.00
0.67
1.77
1.07
1.00
0.00
New
0.41
102.00
0.77
1.95
0.94
30.00
0.83
60.50
0.96
0.25
New
0.61
4.50
0.00
26.33
0.78
New
0.71
1.41
4.53
1.62
0.45
1.01
3.25
2.33
14.00
2.86
1.09
0.67
8.00
0.50
0.80
1.00
0.87
126.00
0.81
1.06
0.67
0.65
0.41
1.00
0.08
4.00
0.54
6.00
0.91
1.05
0.94
0.00
0.99
5.00
0.69
0.75
0.00
0.97
1.63
1.00
6.67
0.61
0.00
0.68
0.82
1.75
1.05
0.64
1.06
0.75
0.94
0.00
0.57
0.94
0.80
2.00
0.38
0.96
1.00
0.92
15.33
0.68
0.86
0.67
1.79
1.21
1.14
0.00
37.00
0.42
124.00
0.59
1.88
0.78
30.00
0.79
84.50
1.08
0.00
68.00
0.56
4.38
0.00
34.33
0.74
11.00
0.70
1.52
5.08
1.44
0.45
0.78
5.00
2.89
23.00
7.43
1.16
0.59
15.00
0.25
0.85
1.00
0.85
168.33
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Decrease
No Change
Decrease
Decrease
Large Decrease
No Change
Large Decrease
Increase
Decrease
New Habitat
No Change
No Change
No Change
NA
No Change
Increase
Decrease
Decrease
NA
No change
Increase
No Change
Increase
Decrease
NA
Decrease
Decrease
Increase
No Change
Decrease
No Change
Decrease
No Change
NA
Decrease
No Change
Decrease
No Change
Large Decrease
No Change
No Change
No Change
New Habitat
Decrease
No Change
Decrease
Increase
Increase
No Change
Exirpated
Increase
Large Decrease
New Habitat
Decrease
Increase
Decrease
Increase
Decrease
Increase
No Change
Exirpated
New Habitat
Decrease
Large Increase
Exirpated
Increase
Decrease
Increase
Decrease
Increase
Large Increase
Increase
Decrease
Decrease
Large Increase
Large Increase
New Habitat
Large Increase
Increase
Decrease
Increase
Large Decrease
Decrease
No Change
Decrease
New Habitat
(coninued on next page)
APPENDiX 4
2
Table 33.—Complete DiSTRiB model results for the 73 tree speciesa in Secion M221A
Table 33 (coninued).
Common Name
FiA
iV
Quaking aspen
Red maple
Red mulberry
Red spruce
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shortleaf pine
Shumard oak
Silver maple
Slippery elm
Sourwood
Southern red oak
Striped maple
Sugar maple
Sugarberry
Sweet birch
Sweetgum
Sycamore
Table Mountain pine
Tulip tree
Virginia pine
Water locust
Water oak
White ash
White oak
Winged elm
Yellow birch
Yellow buckeye
6
293
2
3
76
94
77
8
7
0
24
55
5
2
44
146
0
90
0
24
21
69
133
0
0
102
254
0
8
1
DiSTRiB
Current Model
iV
Reliability
4
357
3
3
99
114
63
14
6
0
13
51
22
1
45
173
0
98
4
23
20
119
109
0
0
146
254
0
7
5
High
High
Low
High
High
High
Medium
Medium
High
Low
Medium
Medium
High
High
High
High
Medium
High
High
Medium
Medium
High
High
Low
High
High
High
High
High
Medium
2010-2039
PCM
GFDL
B1
A1Fi
3
333
2
2
91
110
69
12
7
0
14
48
24
3
36
151
0
83
3
22
20
97
129
0
0
108
265
4
7
1
1
272
9
0
109
107
55
27
42
1
23
51
15
15
27
166
12
62
10
30
25
102
102
0
0
114
304
44
4
3
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
3
328
1
1
97
110
58
15
10
0
12
38
35
6
26
147
3
74
6
20
20
120
129
0
0
94
260
9
6
2
3
324
1
1
107
108
52
17
10
0
14
37
32
5
25
154
4
75
9
24
18
134
116
0
0
97
254
14
6
1
0
179
32
0
97
83
52
50
126
19
46
45
22
35
20
121
52
47
13
39
27
79
86
2
7
88
300
146
4
4
0
136
40
0
90
42
53
60
197
34
58
41
23
62
21
59
78
46
37
40
30
64
85
9
46
80
244
242
4
4
0.75
0.93
0.67
0.67
0.92
0.97
1.10
0.86
1.17
NA
1.08
0.94
1.09
3.00
0.80
0.87
NA
0.85
0.75
0.96
1.00
0.82
1.18
NA
NA
0.74
1.04
New
1.00
0.20
0.25
0.76
3.00
0.00
1.10
0.94
0.87
1.93
7.00
New
1.77
1.00
0.68
15.00
0.60
0.96
New
0.63
2.50
1.30
1.25
0.86
0.94
NA
NA
0.78
1.20
New
0.57
0.60
0.75 0.00
0.92 0.50
0.33 10.67
0.33 0.00
0.98 0.98
0.97 0.73
0.92 0.83
1.07 3.57
1.67 21.00
NA
New
0.92 3.54
0.75 0.88
1.59 1.00
6.00 35.00
0.58 0.44
0.85 0.70
New New
0.76 0.48
1.50 3.25
0.87 1.70
1.00 1.35
1.01 0.66
1.18 0.79
NA
New
NA
New
0.64 0.60
1.02 1.18
New New
0.86 0.57
0.40 0.80
0.75
0.91
0.33
0.33
1.08
0.95
0.83
1.21
1.67
0.00
1.08
0.73
1.46
5.00
0.56
0.89
4.00
0.77
2.25
1.04
0.90
1.13
1.06
0.00
0.00
0.66
1.00
14.00
0.86
0.20
0.00
0.38
13.33
0.00
0.91
0.37
0.84
4.29
32.83
34.00
4.46
0.80
1.05
62.00
0.47
0.34
78.00
0.47
9.25
1.74
1.50
0.54
0.78
9.00
46.00
0.55
0.96
242.00
0.57
0.80
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Decrease
No Change
Decrease
Decrease
No Change
No Change
Decrease
Increase
Increase
NA
No Change
Decrease
Increase
Increase
Decrease
No Change
New Habitat
Decrease
New Habitat
No Change
No Change
No Change
No Change
NA
NA
Decrease
No Change
New Habitat
No Change
Large Decrease
Exirpated
Large Decrease
Increase
Exirpated
No Change
Large Decrease
Decrease
Large Increase
Large Increase
New Habitat
Large Increase
Decrease
No Change
Increase
Decrease
Large Decrease
New Habitat
Decrease
New Habitat
Increase
Increase
Decrease
Decrease
New Habitat
New Habitat
Decrease
No Change
New Habitat
Decrease
Decrease
Current importance values (Current IV) are based on results from the DISTRIB model. Early-, mid-, and late-century importance values are average values for the indicated years. Change classes are
provided for the end of century (2070 through 2099) period. Explanaions for the change classes are described in the text. Future:Current Suitable Habitat is a raio of projected importance value to
current importance value.
a
APPENDiX 4
27
Common Name
FiA
iV
American basswood
American beech
American chestnut
American elm
American holly
American hornbeam
Balsam ir
Bear oak (scrub oak)
Bigtooth aspen
Biternut hickory
Black cherry
Black hickory
Black locust
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Boxelder
Buternut
Cedar elm
Chestnut oak
Chinkapin oak
Chokecherry
Common persimmon
Cucumbertree
Eastern hemlock
Eastern hophornbeam
Eastern redcedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Loblolly pine
Mockernut hickory
Northern red oak
Ohio buckeye
Pawpaw
Pignut hickory
Pin cherry
Pitch pine
69
383
39
14
5
35
3
2
17
5
323
0
161
100
28
2
108
0
10
12
0
339
2
6
0
60
110
58
8
10
86
132
13
3
9
0
83
312
6
5
83
11
24
DiSTRiB
Current Model
iV
Reliability
60
362
32
27
1
45
7
8
22
2
339
2
162
115
31
1
160
0
14
3
0
384
0
10
0
54
152
62
11
11
121
166
15
1
1
4
92
303
4
2
94
8
36
Medium
High
Medium
Medium
High
Medium
High
Low
High
Low
High
High
Low
High
Medium
Low
High
Medium
Medium
Low
Low
High
Medium
Low
Medium
High
High
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
High
High
Low
Low
High
Medium
High
2010-2039
PCM
GFDL
B1
A1Fi
58
342
37
24
3
33
7
2
16
2
288
1
165
115
30
1
153
0
5
10
0
378
2
5
3
54
129
62
10
12
102
172
8
1
4
0
87
297
4
3
83
6
29
53
231
37
29
2
38
1
3
17
6
275
2
161
161
50
1
180
3
9
6
0
405
9
4
15
55
129
61
82
31
120
227
14
1
6
1
101
267
4
3
99
0
42
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
53
340
34
24
2
34
7
1
10
3
268
1
161
121
35
1
164
0
8
9
0
375
2
5
5
54
131
58
13
16
107
190
8
1
3
4
91
283
4
3
88
3
31
55
306
35
22
2
34
3
3
13
3
269
1
154
124
39
1
171
0
7
7
0
378
1
7
8
57
133
58
18
19
103
197
8
1
3
5
92
274
5
3
90
2
32
41
202
37
56
1
50
1
5
4
33
159
63
145
243
57
4
211
60
10
3
0
340
31
0
85
50
106
74
239
59
128
278
20
13
14
23
137
249
2
0
109
0
44
30
168
36
78
1
53
1
4
0
53
110
143
121
305
63
12
221
133
16
0
17
274
46
0
147
49
93
92
290
63
110
271
32
30
23
80
157
234
0
0
104
0
47
0.97
0.95
1.16
0.89
3.00
0.73
1.00
0.25
0.73
1.00
0.85
0.50
1.02
1.00
0.97
1.00
0.96
NA
0.36
3.33
NA
0.98
New
0.50
New
1.00
0.85
1.00
0.91
1.09
0.84
1.04
0.53
1.00
4.00
0.00
0.95
0.98
1.00
1.50
0.88
0.75
0.81
0.88
0.64
1.16
1.07
2.00
0.84
0.14
0.38
0.77
3.00
0.81
1.00
0.99
1.40
1.61
1.00
1.13
New
0.64
2.00
NA
1.06
New
0.40
New
1.02
0.85
0.98
7.46
2.82
0.99
1.37
0.93
1.00
6.00
0.25
1.10
0.88
1.00
1.50
1.05
0.00
1.17
0.88
0.94
1.06
0.89
2.00
0.76
1.00
0.13
0.46
1.50
0.79
0.50
0.99
1.05
1.13
1.00
1.03
NA
0.57
3.00
NA
0.98
New
0.50
New
1.00
0.86
0.94
1.18
1.46
0.88
1.15
0.53
1.00
3.00
1.00
0.99
0.93
1.00
1.50
0.94
0.38
0.86
0.68
0.56
1.16
2.07
1.00
1.11
0.14
0.63
0.18
16.50
0.47
31.50
0.90
2.11
1.84
4.00
1.32
New
0.71
1.00
NA
0.89
New
0.00
New
0.93
0.70
1.19
21.73
5.36
1.06
1.68
1.33
13.00
14.00
5.75
1.49
0.82
0.50
0.00
1.16
0.00
1.22
0.92
0.85
1.09
0.82
2.00
0.76
0.43
0.38
0.59
1.50
0.79
0.50
0.95
1.08
1.26
1.00
1.07
NA
0.50
2.33
NA
0.98
New
0.70
New
1.06
0.88
0.94
1.64
1.73
0.85
1.19
0.53
1.00
3.00
1.25
1.00
0.90
1.25
1.50
0.96
0.25
0.89
0.50
0.46
1.13
2.89
1.00
1.18
0.14
0.50
0.00
26.50
0.32
71.50
0.75
2.65
2.03
12.00
1.38
New
1.14
0.00
New
0.71
New
0.00
New
0.91
0.61
1.48
26.36
5.73
0.91
1.63
2.13
30.00
23.00
20.00
1.71
0.77
0.00
0.00
1.11
0.00
1.31
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
No Change
Decrease
No Change
Decrease
No Change
Decrease
Large Decrease
Large Decrease
Decrease
No Change
Decrease
Decrease
No Change
No Change
Increase
No Change
No Change
NA
Decrease
No Change
NA
No Change
No Change
Decrease
New Habitat
No Change
No Change
No Change
Increase
Increase
No Change
Increase
Decrease
No Change
No Change
No Change
No Change
No Change
No Change
No Change
No Change
Large Decrease
No Change
Decrease
Decrease
No Change
Large Increase
No Change
Increase
Large Decrease
Decrease
Exirpated
Increase
Large Decrease
Increase
Decrease
Large Increase
Increase
Increase
Increase
New Habitat
No Change
Exirpated
New Habitat
Decrease
Increase
Exirpated
New Habitat
No Change
Decrease
Increase
Large Increase
Large Increase
No Change
Increase
Large Increase
Increase
Increase
Increase
Increase
Decrease
Exirpated
Exirpated
No Change
Exirpated
Increase
(coninued on next page)
APPENDiX 4
28
Table 34.—Complete DiSTRiB model results for the 74 tree speciesa in Secion M221B
Table 34 (coninued).
Common Name
FiA
iV
Post oak
Quaking aspen
Red maple
Red mulberry
Red pine
Red spruce
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shortleaf pine
Shumard oak
Silver maple
Slippery elm
Sourwood
Southern red oak
Striped maple
Sugar maple
Sugarberry
Sweet birch
Sweetgum
Sycamore
Table Mountain pine
Tulip tree
Virginia pine
Water oak
White ash
White oak
Winged elm
Yellow birch
Yellow buckeye
3
2
645
0
15
64
121
103
101
17
4
0
1
25
64
2
154
483
1
193
0
15
5
204
38
0
125
221
0
102
23
DiSTRiB
Current Model
iV
Reliability
0
17
686
0
9
35
137
126
88
12
6
0
1
26
87
0
138
452
0
196
0
12
15
232
63
0
141
214
0
99
21
High
High
High
Low
Medium
High
High
High
Medium
Medium
High
Low
Medium
Medium
High
High
High
High
Medium
High
High
Medium
Medium
High
High
High
High
High
High
High
Medium
2010-2039
PCM
GFDL
B1
A1Fi
4
7
663
0
10
38
128
132
91
13
8
0
0
25
84
0
129
447
0
200
0
16
10
246
60
0
123
226
0
80
23
27
4
640
3
7
20
156
140
82
20
21
0
1
42
96
4
92
356
0
183
3
28
11
278
95
0
143
280
7
41
25
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
6
5
657
0
10
32
131
138
89
13
9
0
1
26
102
1
119
433
0
196
0
18
12
275
68
0
109
228
0
80
25
8
4
660
1
8
33
132
137
86
15
10
0
0
29
107
0
114
415
0
196
1
18
11
297
71
0
109
239
0
79
25
211
1
497
29
0
15
154
155
71
51
120
2
7
65
81
32
60
318
15
139
42
45
16
270
128
3
129
398
93
23
28
437
0
348
44
0
14
140
126
71
79
264
19
14
72
72
79
50
230
34
104
85
51
21
195
126
17
113
424
207
20
27
New
0.41
0.97
NA
1.11
1.09
0.93
1.05
1.03
1.08
1.33
NA
0.00
0.96
0.97
NA
0.94
0.99
NA
1.02
NA
1.33
0.67
1.06
0.95
NA
0.87
1.06
NA
0.81
1.10
New
0.24
0.93
New
0.78
0.57
1.14
1.11
0.93
1.67
3.50
NA
1.00
1.62
1.10
New
0.67
0.79
NA
0.93
New
2.33
0.73
1.20
1.51
NA
1.01
1.31
New
0.41
1.19
New New
0.29 0.06
0.96 0.72
NA
New
1.11 0.00
0.91 0.43
0.96 1.12
1.10 1.23
1.01 0.81
1.08 4.25
1.50 20.00
NA
New
1.00 7.00
1.00 2.50
1.17 0.93
New New
0.86 0.44
0.96 0.70
NA
New
1.00 0.71
NA
New
1.50 3.75
0.80 1.07
1.19 1.16
1.08 2.03
NA
New
0.77 0.92
1.07 1.86
NA
New
0.81 0.23
1.19 1.33
New New
0.24 0.00
0.96 0.51
New New
0.89 0.00
0.94 0.40
0.96 1.02
1.09 1.00
0.98 0.81
1.25 6.58
1.67 44.00
NA
New
0.00 14.00
1.12 2.77
1.23 0.83
NA
New
0.83 0.36
0.92 0.51
NA
New
1.00 0.53
New New
1.50 4.25
0.73 1.40
1.28 0.84
1.13 2.00
NA
New
0.77 0.80
1.12 1.98
NA
New
0.80 0.20
1.19 1.29
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
Increase
Large Decrease
No Change
New Habitat
No Change
No Change
No Change
No Change
No Change
Increase
Increase
NA
Exirpated
No Change
Increase
Exirpated
Decrease
No Change
Exirpated
No Change
New Habitat
Increase
Decrease
Increase
No Change
NA
Decrease
No Change
NA
Decrease
Increase
Increase
Exirpated
Decrease
New Habitat
Exirpated
Large Decrease
No Change
No Change
Decrease
Large Increase
Large Increase
New Habitat
Increase
Large Increase
Decrease
Increase
Large Decrease
Decrease
Increase
Decrease
New Habitat
Large Increase
Increase
Decrease
Increase
New Habitat
Decrease
Increase
New Habitat
Large Decrease
Increase
Current importance values (Current IV) are based on results from the DISTRIB model. Early-, mid-, and late-century importance values are average values for the indicated years. Change classes are
provided for the end of century (2070 through 2099) period. Explanaions for the change classes are described in the text. Future:Current Suitable Habitat is a raio of projected importance value to
current importance value.
a
APPENDiX 4
29
Common Name
FiA
iV
American basswood
American beech
American chestnut
American elm
American hornbeam
Biternut hickory
Black cherry
Black hickory
Black locust
Black oak
Black walnut
Blackgum
Blackjack oak
Buternut
Cedar elm
Chestnut oak
Chinkapin oak
Common persimmon
Cucumbertree
Eastern hemlock
Eastern hophornbeam
Eastern redcedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Loblolly pine
Mockernut hickory
Mountain maple
Northern red oak
Pawpaw
Pignut hickory
Pitch pine
Post oak
Red maple
Red mulberry
River birch
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shortleaf pine
81
227
19
20
39
5
56
0
83
118
22
103
0
11
0
238
5
6
68
72
40
2
29
18
207
7
0
0
84
12
154
42
85
13
2
417
5
5
210
88
30
10
7
DiSTRiB
Current Model
iV
Reliability
52
222
11
16
40
0
91
0
94
118
27
126
1
1
0
261
2
2
55
62
37
4
32
35
193
6
0
2
103
3
156
23
104
18
6
448
2
1
177
83
42
5
11
Medium
High
Medium
Medium
Medium
Low
High
High
Low
High
Medium
High
Medium
Low
Low
High
Medium
Medium
High
High
Medium
Medium
Medium
High
High
Medium
Medium
High
High
High
High
Low
High
High
High
High
Low
Low
High
High
Medium
Medium
High
2010-2039
PCM
GFDL
B1
A1Fi
66
215
17
19
37
5
63
0
90
107
27
125
1
7
0
243
2
6
58
72
38
6
34
34
204
6
0
2
91
8
149
26
89
12
6
427
3
3
180
82
35
9
15
43
188
15
22
41
11
48
29
93
157
35
135
21
0
0
231
14
26
53
60
41
45
47
42
220
7
2
14
101
10
139
20
91
17
78
360
6
2
159
96
38
24
67
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
60
213
16
18
37
4
54
0
93
106
27
124
0
5
0
248
2
3
55
71
37
8
38
43
209
6
0
7
93
8
139
26
88
11
9
425
3
2
166
85
35
9
19
59
214
14
18
38
5
52
0
96
110
28
128
0
5
0
241
3
5
56
70
38
12
38
47
202
7
1
8
94
8
139
21
87
10
14
427
4
2
169
82
36
10
21
28
137
10
32
42
35
43
116
75
219
50
140
109
0
21
161
32
73
47
49
60
101
52
30
209
12
11
66
111
10
166
9
69
13
305
236
17
2
106
68
38
62
250
31
118
10
46
41
37
59
151
70
235
67
134
175
0
72
156
37
95
44
48
75
141
69
30
154
25
28
139
137
10
155
8
71
20
507
168
35
3
93
38
38
82
342
1.27
0.97
1.55
1.19
0.93
New
0.69
NA
0.96
0.91
1.00
0.99
1.00
7.00
NA
0.93
1.00
3.00
1.06
1.16
1.03
1.50
1.06
0.97
1.06
1.00
NA
1.00
0.88
2.67
0.96
1.13
0.86
0.67
1.00
0.95
1.50
3.00
1.02
0.99
0.83
1.80
1.36
0.83
0.85
1.36
1.38
1.03
New
0.53
New
0.99
1.33
1.30
1.07
21.00
0.00
NA
0.89
7.00
13.00
0.96
0.97
1.11
11.25
1.47
1.20
1.14
1.17
New
7.00
0.98
3.33
0.89
0.87
0.88
0.94
13.00
0.80
3.00
2.00
0.90
1.16
0.91
4.80
6.09
1.15
0.96
1.46
1.13
0.93
New
0.59
NA
0.99
0.90
1.00
0.98
0.00
5.00
NA
0.95
1.00
1.50
1.00
1.15
1.00
2.00
1.19
1.23
1.08
1.00
NA
3.50
0.90
2.67
0.89
1.13
0.85
0.61
1.50
0.95
1.50
2.00
0.94
1.02
0.83
1.80
1.73
0.54
0.62
0.91
2.00
1.05
New
0.47
New
0.80
1.86
1.85
1.11
109.00
0.00
New
0.62
16.00
36.50
0.86
0.79
1.62
25.25
1.63
0.86
1.08
2.00
New
33.00
1.08
3.33
1.06
0.39
0.66
0.72
50.83
0.53
8.50
2.00
0.60
0.82
0.91
12.40
22.73
1.14
0.96
1.27
1.13
0.95
New
0.57
New
1.02
0.93
1.04
1.02
0.00
5.00
New
0.92
1.50
2.50
1.02
1.13
1.03
3.00
1.19
1.34
1.05
1.17
New
4.00
0.91
2.67
0.89
0.91
0.84
0.56
2.33
0.95
2.00
2.00
0.96
0.99
0.86
2.00
1.91
0.60
0.53
0.91
2.88
1.03
New
0.65
New
0.75
1.99
2.48
1.06
175.00
0.00
New
0.60
18.50
47.50
0.80
0.77
2.03
35.25
2.16
0.86
0.80
4.17
New
69.50
1.33
3.33
0.99
0.35
0.68
1.11
84.50
0.38
17.50
3.00
0.53
0.46
0.91
16.40
31.09
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
No Change
No Change
Increase
No Change
No Change
No Change
Decrease
NA
No Change
No Change
No Change
No Change
NA
Increase
NA
No Change
No Change
No Change
No Change
No Change
No Change
Large Increase
Increase
Increase
No Change
Increase
NA
New Habitat
No Change
No Change
No Change
No Change
Decrease
Decrease
No Change
No Change
No Change
Increase
No Change
No Change
No Change
Increase
Increase
Decrease
Decrease
No Change
Large Increase
No Change
Large Increase
Decrease
New Habitat
Decrease
Increase
Large Increase
No Change
New Habitat
Exirpated
New Habitat
Decrease
Increase
Increase
Decrease
Decrease
Increase
Large Increase
Large Increase
No Change
Decrease
Large Increase
New Habitat
New Habitat
Increase
No Change
No Change
Large Decrease
Decrease
No Change
Increase
Large Decrease
Increase
Large Increase
Decrease
Decrease
No Change
Large Increase
Large Increase
(coninued on next page)
APPENDiX 4
270
Table 35.—Complete DiSTRiB model results for the 62 tree speciesa in Secion M221C
Table 35 (coninued).
Common Name
FiA
iV
Shumard oak
Slippery elm
Sourwood
Southern red oak
Striped maple
Sugar maple
Sugarberry
Sweet birch
Sweetgum
Sycamore
Tulip tree
Virginia pine
Water locust
Water oak
White ash
White oak
Winged elm
Yellow birch
Yellow buckeye
1
35
94
2
38
355
0
101
3
17
361
14
0
0
70
145
0
26
31
DiSTRiB
Current Model
iV
Reliability
0
24
100
1
32
288
0
100
2
15
323
42
0
0
68
160
1
17
29
Low
Medium
High
High
High
High
Medium
High
High
Medium
High
High
Low
High
High
High
High
High
Medium
2010-2039
PCM
GFDL
B1
A1Fi
0
34
102
2
31
331
0
99
8
16
356
40
0
0
64
158
1
19
33
0
31
113
22
18
249
1
75
28
24
340
79
0
0
66
205
35
7
34
Modeled iV
2040-2069
PCM GFDL
B1
A1Fi
DiSTRiB results
Future : Current Suitable habitat
2070-2099
2010-2039
2040-2069
2070-2099
PCM GFDL
PCM GFDL
PCM GFDL PCM GFDL
B1
A1Fi
B1 A1Fi
B1
A1Fi
B1
A1Fi
0
31
119
6
27
313
0
95
11
16
357
60
0
0
61
163
5
16
33
0
34
119
9
24
316
0
89
16
18
357
68
0
0
61
176
7
16
34
14
35
98
65
18
129
27
48
69
30
186
93
1
21
66
262
153
6
33
35
37
71
93
19
53
43
44
82
39
95
87
9
57
75
230
235
5
33
NA
1.42
1.02
2.00
0.97
1.15
NA
0.99
4.00
1.07
1.10
0.95
NA
NA
0.94
0.99
1.00
1.12
1.14
NA
1.29
1.13
22.00
0.56
0.87
New
0.75
14.00
1.60
1.05
1.88
NA
NA
0.97
1.28
35.00
0.41
1.17
NA
New
1.29 1.46
1.19 0.98
6.00 65.00
0.84 0.56
1.09 0.45
NA New
0.95 0.48
5.50 34.50
1.07 2.00
1.11 0.58
1.43 2.21
NA
New
NA
New
0.90 0.97
1.02 1.64
5.00 153.00
0.94 0.35
1.14 1.14
New New
1.42 1.54
1.19 0.71
9.00 93.00
0.75 0.59
1.10 0.18
New New
0.89 0.44
8.00 41.00
1.20 2.60
1.11 0.29
1.62 2.07
New
New
New
New
0.90 1.10
1.10 1.44
7.00 235.00
0.94 0.29
1.17 1.14
Change Class
2070-2099
PCM
GFDL
B1
A1Fi
NA
Increase
Increase
Increase
Decrease
No Change
NA
No Change
Increase
Increase
No Change
Increase
NA
NA
No Change
No Change
New Habitat
No Change
Increase
Increase
Increase
Decrease
Increase
Decrease
Large Decrease
New Habitat
Large Decrease
Increase
Large Increase
Large Decrease
Large Increase
New Habitat
New Habitat
No Change
Increase
New Habitat
Large Decrease
No Change
Current importance values (Current IV) are based on results from the DISTRIB model. Early-, mid-, and late-century importance values are average values for the indicated years. Change classes are
provided for the end of century (2070 through 2099) period. Explanaions for the change classes are described in the text. Future:Current Suitable Habitat is a raio of projected importance value to
current importance value.
a
APPENDiX 4
271
APPENDiX 4
Table 36.—Comparison of change classes for two climate scenarios from the DiSTRiB model results for all tree species
in six ecological secions of the assessment areaa
221F
Common Name
American basswood
American beech
American chestnut
American elm
American holly
American hornbeam
Balsam ir
Bear oak (scrub oak)
PCM B1
Ecological secion within the assessment area boundaries
221E OH
221E WV
GFDL A1Fi
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
Decrease
Large Decrease
No Change
Large Decrease
Decrease
Large Decrease
No Change
Large Decrease
No Change
-
-
-
Large Decrease
Decrease
Large Decrease
-
No Change
Exirpated
No Change
Decrease
Decrease
Decrease
Decrease
No Change
-
-
-
-
-
-
No Change
No Change
No Change
Increase
Decrease
No Change
-
-
-
-
-
-
-
-
-
-
-
-
Decrease
Exirpated
Large Decrease
Exirpated
Large Decrease
Exirpated
Biternut hickory
Large Increase
Large Increase
Increase
Large Increase
Increase
Large Increase
Black ash
Bigtooth aspen
Large Decrease
Large Decrease
-
-
-
-
Black cherry
Decrease
Large Decrease
Decrease
Large Decrease
Large Decrease
Large Decrease
Black hickory
NA
New
New
New
New
New
Black locust
Increase
No Change
Decrease
Decrease
Decrease
Decrease
Black maple
No Change
Large Decrease
No Change
Large Decrease
Decrease
Decrease
Black oak
Increase
Large Increase
Increase
Large Increase
Decrease
Increase
Black walnut
Increase
Increase
No Change
Decrease
Increase
Decrease
Black willow
No Change
Increase
Large Decrease
Large Increase
NA
Increase
Increase
No Change
Increase
No Change
Decrease
No Change
New
New
Increase
Increase
Increase
Increase
Blackgum
Blackjack oak
Boxelder
No Change
Increase
Decrease
Increase
Decrease
No Change
Bur oak
Increase
Large Increase
Large Decrease
Large Increase
-
-
-
-
-
-
No Change
Exirpated
Buternut
NA
New
NA
New
NA
New
Chestnut oak
New
Large Decrease
Increase
Decrease
Decrease
Decrease
Chinkapin oak
New
New
Large Increase
Large Increase
No Change
Increase
Cedar elm
Chokecherry
Common persimmon
Cucumbertree
Eastern cotonwood
Eastern hemlock
Eastern hophornbeam
Eastern redbud
Eastern redcedar
-
-
-
-
-
-
New
New
Increase
Large Increase
Increase
Large Increase
Large Increase
No Change
Large Increase
Increase
Increase
No Change
Decrease
Large Increase
Decrease
Large Increase
-
-
Large Decrease
Large Decrease
Large Decrease
Large Decrease
Decrease
Large Decrease
Decrease
Decrease
No Change
Increase
Decrease
Increase
New
New
Increase
Increase
Decrease
No Change
New
New
Large Increase
Large Increase
Large Increase
Large Increase
Eastern white pine
No Change
Exirpated
Decrease
Exirpated
Increase
Exirpated
Flowering dogwood
Increase
No Change
Increase
Decrease
Decrease
Large Decrease
Green ash
Increase
Large Increase
Increase
Large Increase
Large Increase
Large Increase
Hackberry
Large Increase
Large Increase
Increase
Large Increase
Increase
Large Increase
Honeylocust
Large Increase
Large Increase
No Change
Large Increase
No Change
Large Increase
(coninued on next page)
272
APPENDiX 4
Table 36 (coninued).
221F
Common Name
Loblolly pine
Mockernut hickory
Mountain maple
PCM B1
Ecological secion within the assessment area boundaries
221E OH
221E WV
GFDL A1Fi
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
-
-
NA
New
Increase
Increase
No Change
Increase
No Change
Increase
Decrease
Increase
-
-
-
-
-
-
Northern catalpa
-
-
No Change
No Change
-
-
Northern pin oak
No Change
Decrease
-
-
-
-
Northern red oak
No Change
Decrease
No Change
Decrease
Decrease
No Change
Decrease
No Change
-
-
-
-
Northern white-cedar
Ohio buckeye
Large Increase
Decrease
No Change
Large Decrease
Decrease
Exirpated
Osage-orange
Increase
Large Increase
No Change
Large Increase
No Change
Increase
-
-
No Change
Large Decrease
Decrease
Large Decrease
No Change
Increase
Increase
No Change
Decrease
Decrease
Decrease
Large Decrease
-
-
Exirpated
Exirpated
No Change
No Change
No Change
Large Increase
New
New
Pawpaw
Pignut hickory
Pin cherry
Pin oak
Pitch pine
-
-
Increase
No Change
Decrease
Large Decrease
Post oak
New
New
Large Increase
Large Increase
Large Increase
Large Increase
Large Decrease
Large Decrease
Exirpated
Exirpated
-
-
Decrease
Large Decrease
No Change
Large Decrease
Decrease
Large Decrease
New
New
NA
New
No Change
Large Increase
Decrease
Large Decrease
Large Decrease
Large Decrease
-
-
Quaking aspen
Red maple
Red mulberry
Red pine
Red spruce
-
-
-
-
-
-
River birch
-
-
No Change
Increase
-
-
Rock elm
NA
New
-
-
Large Increase
Large Increase
Sassafras
Increase
Increase
No Change
Decrease
Decrease
Decrease
Large Increase
Large Increase
Increase
Decrease
Decrease
Large Decrease
No Change
Decrease
No Change
No Change
Decrease
No Change
Increase
Increase
Increase
Increase
Increase
Large Increase
Scarlet oak
Serviceberry
Shagbark hickory
No Change
Increase
No Change
Increase
-
-
Shortleaf pine
Shingle oak
NA
New
Large Increase
Large Increase
Large Increase
Large Increase
Shumard oak
NA
New
NA
NA
NA
New
Silver maple
No Change
Increase
Decrease
Decrease
Large Decrease
Large Increase
Slippery elm
Increase
Decrease
No Change
No Change
Decrease
Large Decrease
-
-
Increase
Increase
Increase
Large Decrease
NA
New
New
New
Increase
Increase
-
-
-
-
-
-
Sugar maple
No Change
Large Decrease
No Change
No Change
Decrease
Large Decrease
Sugarberry
NA
New
New
New
New
New
Swamp white oak
No Change
Large Decrease
-
-
-
-
Sweet birch
No Change
Exirpated
Decrease
Decrease
Decrease
Large Decrease
Sweetgum
New
New
Increase
Increase
Increase
Increase
Sycamore
Increase
Increase
No Change
No Change
Decrease
Sourwood
Southern red oak
Striped maple
No Change
(coninued on next page)
273
APPENDiX 4
Table 36 (coninued).
221F
Common Name
Table Mountain pine
PCM B1
Ecological secion within the assessment area boundaries
221E OH
221E WV
GFDL A1Fi
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
-
-
-
-
No Change
No Change
Increase
Decrease
Increase
Increase
Decrease
Large Decrease
Virginia pine
-
-
Increase
Increase
Increase
Large Decrease
Water oak
-
-
NA
NA
NA
New
Water locust
-
-
NA
NA
NA
New
White ash
No Change
Large Decrease
Decrease
Decrease
Decrease
Large Decrease
White oak
No Change
Increase
Increase
Increase
Decrease
No Change
Willow oak
-
-
-
-
NA
New
Winged elm
NA
New
New
New
Increase
Increase
Yellow birch
Large Decrease
Exirpated
-
-
-
-
-
-
No Change
No Change
Decrease
Tulip tree
Yellow buckeye
Change classes are provided for the end-of-century (2070 through 2099) period. Explanaions for the change
classes are described in the text. Blue ash, southern magnolia, and tamarack were present only at the regional
level and do not appear here.
a
274
No Change
(coninued on next page)
APPENDiX 4
Table 36 (coninued).—Comparison of change classes for two climate scenarios from the DISTRIB model results for all
tree species in six ecological secions of the assessment areaa
Common Name
Ecological secion within the assessment area boundaries
M221C
M221B
M221A
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
American basswood
No Change
Decrease
No Change
Decrease
Decrease
Decrease
American beech
No Change
Decrease
Decrease
Decrease
No Change
No Change
Increase
No Change
No Change
No Change
Decrease
Decrease
American elm
No Change
Large Increase
Decrease
Large Increase
Decrease
Increase
American holly
-
-
No Change
No Change
-
-
American chestnut
No Change
No Change
Decrease
Increase
Large Decrease
Increase
Balsam ir
American hornbeam
-
-
Large Decrease
Large Decrease
-
-
Bear oak (scrub oak)
-
-
Large Decrease
Decrease
No Change
No Change
Bigtooth aspen
Biternut hickory
Black ash
-
-
Decrease
Exirpated
Large Decrease
Exirpated
No Change
Large Increase
No Change
Increase
Increase
Increase
-
-
-
-
-
-
Black cherry
Decrease
Decrease
Decrease
Large Decrease
Decrease
Large Decrease
Black hickory
NA
New
Decrease
Increase
New
New
Black locust
No Change
Decrease
No Change
Decrease
No Change
Decrease
Black maple
-
-
-
-
-
-
Black oak
No Change
Increase
No Change
Large Increase
No Change
Increase
Black walnut
No Change
Large Increase
Increase
Increase
No Change
Decrease
Black willow
-
-
No Change
Increase
NA
Increase
No Change
No Change
No Change
Increase
No Change
Decrease
Blackgum
Blackjack oak
NA
New
NA
New
Increase
Increase
Boxelder
-
-
Decrease
No Change
Decrease
No Change
Bur oak
-
-
-
-
-
-
Buternut
Increase
Exirpated
No Change
Exirpated
Decrease
Exirpated
Cedar elm
NA
New
NA
New
NA
New
Chestnut oak
No Change
Decrease
No Change
Decrease
No Change
Decrease
Chinkapin oak
No Change
Increase
No Change
Increase
Increase
Large Increase
Chokecherry
-
-
Decrease
Exirpated
No Change
Exirpated
Common persimmon
No Change
Increase
New
New
Increase
Increase
Cucumbertree
No Change
Decrease
No Change
No Change
Decrease
Decrease
-
-
-
-
NA
Increase
Eastern hemlock
No Change
Decrease
No Change
Decrease
Decrease
Decrease
Eastern hophornbeam
No Change
Increase
No Change
Increase
Decrease
Increase
Increase
Large Increase
Increase
Large Increase
No Change
Increase
Large Increase
Large Increase
Increase
Large Increase
Increase
Large Increase
Eastern white pine
Increase
No Change
No Change
No Change
Decrease
Decrease
Flowering dogwood
Eastern cotonwood
Eastern redbud
Eastern redcedar
No Change
Decrease
Increase
Increase
No Change
Decrease
Green ash
Increase
Large Increase
Decrease
Large Increase
Decrease
Large Increase
Hackberry
NA
New
No Change
Increase
No Change
Large Increase
-
-
No Change
Increase
NA
New
Honeylocust
(coninued on next page)
275
APPENDiX 4
Table 36 (coninued).
Common Name
Loblolly pine
Ecological secion within the assessment area boundaries
M221C
M221B
M221A
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
New
New
No Change
Increase
Decrease
Large Increase
Mockernut hickory
No Change
Increase
No Change
Increase
No Change
Increase
Mountain maple
No Change
No Change
-
-
-
-
Northern catalpa
-
-
-
-
-
-
Northern pin oak
-
-
-
-
-
-
Northern red oak
No Change
No Change
No Change
Decrease
Decrease
Decrease
Northern white-cedar
-
-
-
-
-
-
Ohio buckeye
-
-
No Change
Exirpated
-
-
Osage-orange
Pawpaw
Pignut hickory
Pin cherry
-
-
-
-
No Change
Increase
No Change
Large Decrease
No Change
Exirpated
Large Decrease
Large Decrease
Decrease
Decrease
No Change
No Change
No Change
Decrease
-
-
Large Decrease
Exirpated
No Change
No Change
-
-
-
-
-
-
Pitch pine
Decrease
No Change
No Change
Increase
No Change
Decrease
Post oak
No Change
Increase
Increase
Increase
New
New
Pin oak
-
-
Large Decrease
Exirpated
Decrease
Exirpated
Red maple
No Change
Large Decrease
No Change
Decrease
No Change
Large Decrease
Red mulberry
No Change
Increase
New
New
Decrease
Increase
Red pine
-
-
No Change
Exirpated
-
-
Red spruce
-
-
No Change
Large Decrease
Decrease
Exirpated
River birch
Increase
Large Increase
-
-
-
-
Quaking aspen
Rock elm
-
-
-
-
-
-
Sassafras
No Change
Decrease
No Change
No Change
No Change
No Change
Scarlet oak
No Change
Decrease
No Change
No Change
No Change
Large Decrease
Serviceberry
No Change
No Change
No Change
Decrease
Decrease
Decrease
Increase
Large Increase
Increase
Large Increase
Increase
Large Increase
-
-
-
-
-
-
Shortleaf pine
Increase
Large Increase
Increase
Large Increase
Increase
Large Increase
Shumard oak
NA
Increase
NA
New
NA
New
Silver maple
-
-
Exirpated
Increase
No Change
Large Increase
Slippery elm
Increase
Increase
No Change
Large Increase
Decrease
Decrease
Sourwood
Increase
Decrease
Increase
Decrease
Increase
No Change
Southern red oak
Increase
Increase
Exirpated
Increase
Increase
Increase
Striped maple
Decrease
Decrease
Decrease
Large Decrease
Decrease
Decrease
Sugar maple
No Change
Large Decrease
No Change
Decrease
No Change
Large Decrease
Sugarberry
NA
New
Exirpated
Increase
New
New
-
-
-
-
-
-
Sweet birch
No Change
Large Decrease
No Change
Decrease
Decrease
Decrease
Sweetgum
Increase
Increase
New
New
New
New
Sycamore
Increase
Large Increase
Increase
Large Increase
No Change
Increase
Shagbark hickory
Shingle oak
Swamp white oak
(coninued on next page)
27
APPENDiX 4
Table 36 (coninued).
Common Name
Table Mountain pine
Tulip tree
Virginia pine
Ecological secion within the assessment area boundaries
M221C
M221B
M221A
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
PCM B1
GFDL A1Fi
-
-
Decrease
Increase
No Change
Increase
No Change
Large Decrease
Increase
Decrease
No Change
Decrease
Increase
Large Increase
No Change
Increase
No Change
Decrease
Water oak
NA
New
NA
New
NA
New
Water locust
NA
New
-
-
NA
New
White ash
No Change
No Change
Decrease
Decrease
Decrease
Decrease
White oak
No Change
Increase
No Change
Increase
No Change
No Change
Willow oak
-
-
-
-
-
-
Winged elm
New
New
NA
New
New
New
Yellow birch
No Change
Large Decrease
Decrease
Large Decrease
No Change
Decrease
Increase
No Change
Increase
Increase
Large Decrease
Decrease
Yellow buckeye
Change classes are provided for the end-of-century (2070 through 2099) period. Explanaions for the change classes are described in the text. Blue
ash, southern magnolia, and tamarack were present only at the regional level and do not appear here.
a
277
APPENDiX 4
Table 37.—Modifying factor and adaptability informaion for the 93 tree species in the assessment area that were
modeled by using DiSTRiB
Common Name
American basswood
American beech
American chestnut
American elm
American holly
American hornbeam
Balsam ir
Bear oak (scrub oak)
Bigtooth aspen
Biternut hickory
Black ash
Black cherry
Black hickory
Black locust
Black maple
Black oak
Black walnut
Black willow
Blackgum
Blackjack oak
Blue ash
Boxelder
Bur oak
Buternut
Cedar elm
Chestnut oak
Chinkapin oak
Chokecherry
Common persimmon
Cucumbertree
Eastern cotonwood
Eastern hemlock
Eastern hophornbeam
Eastern redcedar
Eastern redbud
Eastern white pine
Flowering dogwood
Green ash
Hackberry
Honeylocust
Loblolly pine
Mockernut hickory
Mountain maple
Northern catalpa
Northern pin oak
Northern red oak
DiSTRiB
Modifying Factorsa
Model Reliability Posiive Traits
Negaive Traits
Medium
High
Medium
Medium
High
Medium
High
Low
High
Low
High
High
High
Low
Low
High
Medium
Low
High
Medium
Low
Medium
Medium
Low
Low
High
Medium
Low
Medium
High
Low
High
Medium
Medium
Medium
High
High
Medium
Medium
Low
High
High
High
Low
Medium
High
COL
COL
COL
ESP
COL ESP
COL SES
COL
FRG VRE
FRG DISP
DRO
DRO ESP
COL ESP
DRO ESP
SES
FTK
INS FTK
DISE FTK
DISE INS
FTK
FTK DRO
INS FTK DRO
COL FTK
COL DRO FTK
COL
INS COL DISP DRO SES FTK ESP
INS FTK COL
ESP COL
COL INS
FTK
INS DISE
COL DRO
COL FTK DRO
COL FTK
DRO SES FRG VRE
COL FTK
INS DISP FTK COL ESP
SES DISP DRO COL SES
FTK
DRO FTK
FTK COL DRO DISE
DISE
SES VRE ESP FTK
INS DISE
SES
COL
COL ESP
FTK
SES
INS COL DISE FTK
COL
INS DRO
COL ESP SES
DRO
FTK COL INS
DISP
COL
DRO
ESP
COL VRE ESP
DRO FTK
DRO FTK INS
INS FTK COL
FTK
COL
INS INP DRO COL
FTK
DRO FTK
COL ESP
COL
INS
Adaptability Scores
DistFact BioFact Adapt Adapt Class
0.3
-1.1
0.1
-0.8
-0.1
0.6
-3.0
1.0
1.0
2.2
-1.3
-1.6
1.0
0.0
0.5
0.5
0.4
-0.3
1.5
1.6
-0.4
2.4
2.8
-1.4
-0.3
1.4
1.2
0.2
1.2
0.0
0.2
-1.3
1.7
0.6
0.9
-2.0
0.1
-0.1
1.7
1.9
-0.5
1.7
0.8
0.9
2.5
1.4
0.2
0.0
0.3
0.3
0.5
0.6
-0.4
-0.8
0.2
-0.8
-3.0
-0.3
-2.3
-0.6
0.9
0.4
-0.8
-2.1
0.8
0.2
-2.4
2.1
-0.2
-1.3
-1.2
1.3
-0.7
-0.9
1.0
-1.1
-0.8
-0.9
1.3
-1.5
0.0
0.1
1.0
-0.3
0.3
-0.5
-0.7
-0.3
1.5
-1.6
-0.6
0.1
4.6
3.6
4.5
4.0
4.5
5.1
2.7
4.6
5.1
5.6
1.7
3.0
4.1
3.8
5.2
4.9
4.0
2.8
5.9
5.6
2.7
7.4
6.4
2.3
3.3
6.1
4.8
3.8
5.8
3.6
3.9
2.7
6.4
3.9
4.9
3.3
5.0
4.0
5.7
5.5
3.4
5.4
5.9
4.2
6.0
5.4
○
○
○
○
○
○
○
○
+
○
○
○
○
○
+
+
+
+
○
+
○
○
+
○
○
+
○
○
○
○
○
+
+
○
+
+
○
+
+
(coninued on next page)
278
APPENDiX 4
Table 37 (coninued).
Common Name
Northern white-cedar
Ohio buckeye
Osage-orange
Pawpaw
Pignut hickory
Pin cherry
Pin oak
Pitch pine
Post oak
Quaking aspen
Red maple
Red mulberry
Red pine
Red spruce
River birch
Rock elm
Sassafras
Scarlet oak
Serviceberry
Shagbark hickory
Shingle oak
Shortleaf pine
Shumard oak
Silver maple
Slippery elm
Sourwood
Southern magnolia
Southern red oak
Striped maple
Sugar maple
Sugarberry
Swamp white oak
Sweet birch
Sweetgum
Sycamore
Table Mountain pine
Tamarack (naive)
Tulip tree
Virginia pine
Water locust
Water oak
White ash
White oak
Willow oak
Winged elm
Yellow birch
Yellow buckeye
a
DiSTRiB
Modifying Factorsa
Model Reliability Posiive Traits
Negaive Traits
High
Low
Medium
Low
High
Medium
Medium
High
High
High
High
Low
Medium
High
Low
Low
High
High
Medium
Medium
Medium
High
Low
Medium
Medium
High
Medium
High
High
High
Medium
Low
High
High
Medium
Medium
High
High
High
Low
High
High
High
Medium
High
High
Medium
COL
COL
ESP ESP
COL
ESP
SES FRG FTK
DRO SES FTK
SES FRG ESP
SES ESP ESP COL DISP
COL DISP
ESP COL
DISP
VRE ESP ESP
COL SES
ESP
ESP
DRO SES
DISP SES COL
COL
COL ESP
SES COL FTK
SES
COL SES
COL ESP
COL SES
FTK
SES FTK
DRO
INS DRO
COL
FTK COL INS DISE
COL INS
COL INS DISE
COL DRO FTK
FTK
INS COL DISP
FTK SES
FTK COL DRO
ESP ESP SES
COL FTK
INS DISE FTK
DRO
INS FTK
COL
COL INS DRO
COL
DRO FTK
FTK DISE
DRO EHS
DRO
FTK
DISP
VRE ESP
FTK COL INS DISE
FTK COL DRO
DRO
COL
FTK COL INS
INP
COL POL
SES DISP ESP
SES
ESP ESP SES FTK
SES SES
DISP
COL
FTK COL
INS FTK COL
INS DISE
COL
INS DISE
FTK INS DISE
DRO SES FTK ESP DISP
Adaptability Scores
DistFact BioFact Adapt Adapt Class
-0.7
0.4
2.3
-0.5
0.2
0.5
-0.7
0.6
2.2
0.6
3.0
0.1
0.9
-1.3
-0.5
-0.2
0.5
-0.4
-0.4
-0.2
1.3
0.0
2.5
0.1
0.0
2.6
0.6
1.2
1.0
0.9
-0.2
1.0
-1.3
-0.4
1.3
2.6
-0.5
0.1
0.1
0.0
-0.2
-2.0
1.7
0.6
-0.6
-1.4
0.0
0.5
-1.9
0.3
-0.3
0.4
-0.7
-1.4
-1.8
-0.6
0.0
3.0
0.6
-2.4
-0.6
-0.3
-2.6
-0.6
0.7
1.0
0.4
-0.7
-1.0
-1.0
1.6
0.7
1.0
0.4
0.2
0.3
1.3
0.6
-0.3
-0.3
0.2
-0.9
-1.1
-1.2
1.3
-0.8
-0.6
-0.6
-0.5
1.0
0.0
-0.3
0.0
-2.1
4.2
3.5
6.3
3.7
4.7
4.2
2.8
3.8
5.7
4.7
8.5
4.7
3.9
2.9
3.7
2.8
4.2
4.6
4.8
4.4
4.9
3.6
5.8
5.6
4.8
6.9
4.9
5.3
5.1
5.8
4.6
4.9
3.2
4.1
4.8
5.9
3.1
5.3
3.8
3.8
3.7
2.7
6.1
4.7
3.6
3.4
3.1
○
○
+
○
○
○
○
+
○
+
○
○
○
○
○
○
○
○
○
+
+
○
+
○
+
○
+
○
○
○
○
+
+
○
○
○
+
○
○
○
-
Modifying factor codes are described in Table 38. Adaptability scores are described in the appendix text.
279
APPENDiX 4
Table 38.—Key to modifying factor codesa
Code
Title
Type
Descripion (if posiive)
Descripion (if negaive)
COL
Compeiion-light
Biological
Tolerant of shade or limited
light condiions
Intolerant of shade or limited light
condiions
DISE
Disease
Disturbance
N/A
Has a high number and/or severity of
known pathogens that atack the species
DISP
Dispersal
Biological
High ability to efecively
produce and distribute seeds
N/A
DRO
Drought
Biological
Drought-tolerant
Suscepible to drought
ESP
Edaphic speciicity
Biological
Wide range of soil
requirements
Narrow range of soil requirements
EHS
Environmental
habitat speciicity
Biological
Wide range of suitable habitat
condiions
Narrow range of suitable habitat condiions
FRG
Fire regeneraion
Disturbance
Regenerates well ater ire
N/A
FTK
Fire topkill
Disturbance
Resistant to ire topkill
Suscepible to ire topkill
INP
Invasive plants
Disturbance
N/A
Strong negaive efects of invasive plants on
the species, either through compeiion for
nutrients or as a pathogen
INS
Insect pests
Disturbance
N/A
Has a high number and/or severity of
known insects that atack the species
POL
Polluion
Disturbance
N/A
Strong negaive efects of polluion on the
species
SES
Seedling
establishment
Biological
High ability to regenerate
with seeds to maintain future
populaions
Low ability to regenerate with seeds to
maintain future populaions
VRE
Vegetaive
reproducion
Biological
Capable of vegetaive
reproducion through stump
sprouts or cloning
N/A
These codes are used to describe posiive or negaive modifying factors in Table 37. A species was given that code if informaion
from the literature suggested that it had these characterisics (Mathews et al. 2011). See Mathews et al. (2011) for a more thorough
descripion of these factors and how they were assessed.
a
280
APPENDiX 4
6
3
biological score
Length
2
High biological, low
disturbance scores
1
Mid-range
adaptability
score: 4.25
0
-1
Lowest
possible
adaptability
score: 0
-2
0
Highest
possible
adaptability
score: 8.5
-3
-3
0
-2
High disturbance,
low biological
scores
-1
0
1
disturbance score
Length
2
3
6
Figure 59.—Schemaic showing how adaptability was determined for informaion for tree species modeled using the Climate
Change Tree Atlas.
281
APPENDiX 4
LiNKAGES MoDEL RESuLTS
Species establishment probabilities for 23 tree
species predicted by the LINKAGES model are
presented for the assessment area as a whole, and
for each section within the assessment area (Table
39). Early growth potential was also mapped for
each species modeled by LINKAGES (Fig. 0).
Change in early growth was calculated by dividing
the modeled future biomass by the current climate
biomass. Change was classified according to the
ratios tabulated at right.
Modeled:Current biomass
<0.4
0.4 through <0.8
0.8 through <1.2
1.2 through <2.0
>2.0
current climate = 0 and
future climate model = 0
current climate > 0 and
future climate model = 0
Class
large decrease
small decrease
no change
small increase
large increase
not present
extirpated
Table 39.— Changes in early growth of tree species predicted by the LiNKAGES model for two climate scenarios at
the end of the century (2080 through 2099) compared to current climate (1990 through 2009) for 23 species in the
assessment area
PCM B1
SEPb
% change
GFDL A1Fi
SEPb
% change
0.22
0.22
0.26
0.13
0.23
0.24
0.22
0.21
0.26
0.15
0.24
0.23
0.0
-5.5
0.7
14.9
3.5
-5.8
0.02
0.00
0.00
0.00
0.14
0.00
Assessment area
221E
221F
M221A
M221B
M221C
0.14
0.16
0.13
0.07
0.10
0.16
0.16
0.19
0.16
0.12
0.13
0.18
14.3
14.4
23.0
76.3
22.3
12.7
0.18
0.19
0.18
0.14
0.18
0.19
Balsam ir
Assessment area
221E
221F
M221A
M221B
M221C
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.0
0.0
0.0
0.0
-36.8
0.0
0.00
0.00
0.00
0.00
0.00
0.00
0.0
0.0
0.0
0.0
Exirpated
0.0
Black cherry
Assessment area
221E
221F
M221A
M221B
M221C
0.28
0.31
0.30
0.11
0.26
0.32
0.30
0.32
0.32
0.18
0.28
0.32
7.1
1.5
6.4
65.0
8.5
1.8
0.30
0.31
0.32
0.21
0.31
0.31
7.1
-2.1
8.3
93.9
19.9
-1.3
Blackgum
Assessment area
221E
221F
M221A
M221B
M221C
0.16
0.18
0.15
0.08
0.12
0.18
0.19
0.21
0.18
0.13
0.14
0.21
18.8
14.7
20.2
75.0
22.6
14.3
0.21
0.21
0.22
0.16
0.21
0.22
31.3
16.8
44.7
109.3
78.1
19.3
Species
Seciona
American beech
Assessment area
221E
221F
M221A
M221B
M221C
American elm
Current Climate
SEPb
-90.9
Exirpated
Exirpated
Exirpated
-38.1
Exirpated
28.6
14.4
40.8
111.2
77.7
17.1
(coninued on next page)
282
APPENDiX 4
Table 39 (coninued).
PCM B1
SEPb
% change
GFDL A1Fi
SEPb
% change
0.19
0.20
0.20
0.11
0.18
0.20
0.20
0.21
0.21
0.16
0.19
0.21
5.3
0.5
2.1
47.6
8.0
0.8
0.13
0.08
0.20
0.15
0.20
0.13
-31.6
-63.1
-0.4
40.2
10.7
-37.5
Assessment area
221E
221F
M221A
M221B
M221C
0.20
0.21
0.21
0.10
0.19
0.21
0.20
0.21
0.21
0.15
0.20
0.21
0.0
0.1
0.0
45.2
5.2
-0.2
0.12
0.06
0.20
0.14
0.20
0.13
-40.0
-72.0
-6.1
32.0
3.3
-37.9
Eastern red cedar
Assessment area
221E
221F
M221A
M221B
M221C
0.20
0.23
0.21
0.09
0.15
0.24
0.24
0.26
0.25
0.13
0.19
0.27
20.0
14.7
21.9
43.1
23.6
14.1
0.26
0.26
0.28
0.16
0.26
0.28
30.0
15.3
36.5
67.3
71.4
17.3
Eastern hemlock
Assessment area
221E
221F
M221A
M221B
M221C
0.13
0.14
0.15
0.06
0.14
0.15
0.13
0.12
0.15
0.09
0.14
0.13
0.0
-15.2
0.5
43.6
1.4
-13.6
0.01
0.00
0.00
0.00
0.07
0.00
-92.3
Exirpated
-99.8
Exirpated
-46.7
Exirpated
Eastern white pine
Assessment area
221E
221F
M221A
M221B
M221C
0.34
0.35
0.38
0.21
0.35
0.37
0.35
0.35
0.38
0.25
0.36
0.38
2.9
1.8
0.0
18.2
2.5
2.1
0.04
0.00
0.01
0.00
0.24
0.00
-88.2
Exirpated
-98.3
Exirpated
-31.0
Exirpated
Flowering dogwood
Assessment area
221E
221F
M221A
M221B
M221C
0.05
0.07
0.04
0.03
0.02
0.06
0.08
0.10
0.06
0.05
0.04
0.09
60.0
41.9
66.0
118.1
80.9
45.7
0.10
0.10
0.10
0.07
0.10
0.10
100.0
49.5
162.6
187.9
318.3
61.1
Loblolly pine
Assessment area
221E
221F
M221A
M221B
M221C
0.13
0.20
0.03
0.08
0.02
0.17
0.29
0.39
0.17
0.27
0.10
0.35
123.1
94.3
464.8
260.2
455.6
106.1
0.51
0.54
0.50
0.51
0.45
0.53
292.3
167.9
1535.1
577.5
2496.5
212.1
Northern red oak
Assessment area
221E
221F
M221A
M221B
M221C
0.29
0.29
0.33
0.17
0.30
0.32
0.31
0.31
0.33
0.20
0.31
0.33
6.9
6.2
0.9
19.5
3.5
3.9
0.18
0.09
0.31
0.18
0.30
0.20
-37.9
-70.4
-5.1
5.5
1.0
-36.4
Species
Seciona
Black oak
Assessment area
221E
221F
M221A
M221B
M221C
Chestnut oak
Current Climate
SEPb
(coninued on next page)
283
APPENDiX 4
Table 39 (coninued).
PCM B1
SEPb
% change
GFDL A1Fi
SEPb
% change
0.35
0.38
0.37
0.15
0.31
0.38
0.37
0.39
0.38
0.25
0.34
0.39
5.7
2.1
5.2
62.0
10.8
1.1
0.36
0.36
0.39
0.28
0.38
0.38
2.9
-6.5
5.8
81.6
23.2
-2.1
Assessment area
221E
221F
M221A
M221B
M221C
0.05
0.07
0.03
0.03
0.02
0.06
0.10
0.13
0.06
0.08
0.04
0.12
100.0
81.7
103.5
182.8
147.2
86.7
0.17
0.18
0.18
0.14
0.15
0.18
240.0
149.8
458.5
401.6
795.6
184.1
Red maple
Assessment area
221E
221F
M221A
M221B
M221C
0.31
0.34
0.31
0.24
0.27
0.33
0.34
0.36
0.33
0.32
0.30
0.36
9.7
7.8
9.2
33.4
10.8
7.9
0.37
0.37
0.37
0.35
0.37
0.37
19.4
10.7
22.5
45.3
34.1
12.7
Red spruce
Assessment area
221E
221F
M221A
M221B
M221C
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.0
0.0
0.0
0.0
-49.9
0.0
0.00
0.00
0.00
0.00
0.00
0.00
0.0
0.0
0.0
0.0
Exirpated
0.0
Scarlet oak
Assessment area
221E
221F
M221A
M221B
M221C
0.17
0.18
0.18
0.09
0.17
0.18
0.17
0.18
0.18
0.13
0.17
0.18
0.0
0.5
1.1
44.4
3.2
-0.2
0.04
0.00
0.07
0.02
0.14
0.01
-76.5
-99.7
-60.7
-74.8
-19.6
-96.5
Shortleaf pine
Assessment area
221E
221F
M221A
M221B
M221C
0.08
0.14
0.02
0.03
0.01
0.11
0.22
0.31
0.12
0.14
0.07
0.29
175.0
125.3
536.6
305.5
830.0
153.4
0.35
0.36
0.38
0.22
0.34
0.38
337.5
159.2
2017.0
529.6
4515.9
232.5
Sugar maple
Assessment area
221E
221F
M221A
M221B
M221C
0.51
0.52
0.52
0.44
0.51
0.52
0.50
0.49
0.52
0.49
0.52
0.51
-2.0
-4.5
-0.1
11.0
0.8
-2.0
0.05
0.00
0.00
0.00
0.33
0.00
-90.2
Exirpated
-99.2
-100.0
-34.5
Exirpated
Tulip tree
Assessment area
221E
221F
M221A
M221B
M221C
0.76
0.77
0.92
0.22
0.78
0.88
0.83
0.85
0.97
0.38
0.84
0.93
9.2
10.9
4.6
69.6
7.1
5.9
0.86
0.85
0.98
0.48
0.91
0.94
Species
Seciona
Pignut hickory
Assessment area
221E
221F
M221A
M221B
M221C
Post oak
Current Climate
SEPb
13.2
11.0
6.1
112.6
15.8
7.0
(coninued on next page)
284
APPENDiX 4
Table 39 (coninued).
Current Climate
SEPb
SEPb
Assessment area
221E
221F
M221A
M221B
M221C
0.43
0.37
0.76
0.03
0.50
0.49
0.53
0.49
0.79
0.06
0.56
0.62
23.3
34.2
5.0
88.5
12.8
28.3
0.41
0.25
0.79
0.06
0.60
0.51
-4.7
-32.1
4.6
113.1
20.6
4.5
Assessment area
221E
221F
M221A
M221B
M221C
0.32
0.33
0.36
0.19
0.31
0.36
0.35
0.35
0.38
0.23
0.33
0.37
9.4
7.3
4.9
23.6
8.1
4.5
0.35
0.35
0.38
0.25
0.36
0.37
9.4
5.7
5.5
34.4
17.6
4.9
Species
Seciona
White ash
White oak
PCM B1
% change
GFDL A1Fi
SEPb
% change
Assessment area values were derived from the weighted average of secions.
Species establishment probabiliies (SEP) are derived from LINKAGES model results. SEP is a value relecing the ability of a species
to establish and grow on a site, is scaled 0 through 1, and is relaive to the other species considered. It is important to look at
absolute and percentage changes together; in some cases a small absolute change can result in a large percentage change.
a
b
A forest road on the Monongahela Naional Forest, West Virginia. Photo by Patricia Butler, NIACS and Michigan Tech, used
with permission.
285
APPENDiX 4
Figure 60.—Changes in early growth of tree species under two climate scenarios for the end of the century (2080 through
2099) compared to current climate (1990 through 2009). Change is based on predicted biomass by the LINKAGES model ater
30 years of establishment and growth from bare ground and calculated as predicted biomass for each future climate scenario
divided by predicted biomass under current climate, and then put into categories.
28
APPENDiX 4
Figure 60 (coninued).
287
APPENDiX 4
Figure 60 (coninued).
288
APPENDiX 4
Figure 60 (coninued).
LANDiS PRo MoDEL RESuLTS
In contrast to predictions by LINKAGES, LANDIS
PRO simulates stand- and landscape-level processes
such as competition, management, seed dispersal,
and disturbance. In the scenarios below, however,
these factors were held constant among model
simulations, so that differences among current
climate and future climate scenarios are the result
of the effects of precipitation and temperature on
species basal area (square feet per acre) and trees
per acre.
Percentage change over time within a climate
scenario (columns) shows how current species are
predicted to change under that scenario (Tables 40
through 42). The relative differences in the values
for percentage change across scenarios indicate
the differences between climate scenarios. It is
important to consider both the absolute and the
percentage change, especially if considering multiple
species. Percentage changes are relative only to a
particular species and may exaggerate a projected
change. Figures 1 through 4 present these changes
in basal area and trees per acre for PCM B1 and
GFDL A1FI. The width of a line represents the
species’ relative abundance; over time the width of
the line increases or decreases in response to climate
variables.
REFERENCES
Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters,
M.P.; Rodewald, P.G. 2011. Modifying climate
change habitat models using tree speciesspecific assessments of model uncertainty
and life history-factors. Forest Ecology and
Management. 22(8): 140-1472.
289
Current climate
Change
BA in BA in from
2009 2040 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2040
climatec 2040 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2040 2009b 2040 climatec 2040
climatec
Tree species
Seciona
American beech
Assessment area
221E
221F
M221A
M221B
M221C
5.1
3.9
4.5
7.2
6.6
6.1
4.4
3.8
4.0
5.4
4.9
5.0
-15%
-3%
-11%
-25%
-26%
-18%
4.4
3.8
4.1
5.7
5.0
5.0
1%
0%
2%
6%
2%
0%
4.3
3.6
3.9
5.6
5.0
4.8
-2%
-5%
-3%
4%
2%
-4%
26.8
19.7
16.7
39.8
36.0
36.2
15.5
13.3
15.3
18.1
17.9
18.2
-42%
-32%
-8%
-55%
-50%
-50%
15.7
13.6
15.2
18.8
17.9
18.1
1%
2%
-1%
4%
0%
-1%
12.8
10.5
12.8
15.0
15.5
15.2
-18%
-21%
-16%
-17%
-13%
-16%
Black cherry
Assessment area
221E
221F
M221A
M221B
M221C
7.3
7.2
13.8
8.0
7.4
3.6
9.0
9.1
16.7
8.8
9.1
4.3
23%
26%
21%
10%
23%
19%
9.0
9.1
16.8
8.9
9.1
4.3
0%
0%
1%
1%
0%
0%
9.4
9.4
17.6
9.1
9.4
4.6
4%
3%
5%
3%
3%
7%
18.9
19.3
42.3
15.8
15.4
10.8
12.8
13.5
34.2
8.4
8.9
5.6
-33%
-30%
-19%
-47%
-42%
-48%
12.9
13.7
34.4
8.7
9.0
5.6
1%
1%
1%
4%
1%
0%
11.5
12.1
30.9
7.6
8.2
4.9
-10%
-10%
-10%
-10%
-8%
-13%
Black oak
Assessment area
221E
221F
M221A
M221B
M221C
6.8
7.3
2.1
6.4
7.0
8.0
4.8
5.2
1.4
4.2
5.1
5.4
-30%
-29%
-33%
-34%
-27%
-33%
4.8
5.2
1.4
4.2
5.1
5.4
0%
0%
0%
0%
0%
0%
3.4
3.6
0.8
3.0
3.7
3.8
-30%
-31%
-43%
-29%
-27%
-30%
8.4
9.5
1.5
8.9
9.0
7.7
3.9
4.4
1.1
3.7
4.0
3.9
-54%
-54%
-27%
-58%
-56%
-49%
4.0
4.5
1.1
3.9
4.0
4.0
2%
2%
0%
5%
0%
3%
3.1
3.5
0.8
3.1
3.2
3.0
-21%
-20%
-27%
-16%
-20%
-23%
Chestnut oak
Assessment area
221E
221F
M221A
M221B
M221C
5.6
4.2
0.2
8.3
8.3
8.2
4.5
3.6
0.1
6.5
6.5
6.1
-20%
-14%
-50%
-22%
-22%
-26%
4.6
3.6
0.1
6.9
6.6
6.2
2%
0%
0%
6%
2%
2%
4.4
3.4
0.1
6.8
6.7
5.8
-2%
-6%
0%
5%
3%
-5%
14.9
9.6
0.9
21.7
24.2
24.1
25.8
20.1
1.8
31.6
41.0
35.6
73%
109%
100%
46%
69%
48%
27.0
21.2
1.6
36.2
41.2
36.8
5%
5%
-11%
15%
0%
3%
24.4
16.5
1.7
36.2
42.9
32.8
-5%
-18%
-6%
15%
5%
-8%
Eastern hemlock
Assessment area
221E
221F
M221A
M221B
M221C
1.7
0.5
0.2
3.2
3.4
3.1
1.2
0.4
0.2
2.1
2.3
2.3
-29%
-20%
0%
-34%
-32%
-26%
1.2
0.4
0.2
2.2
2.3
2.3
1%
0%
0%
5%
0%
0%
1.1
0.4
0.2
2.0
2.2
2.1
-5%
0%
0%
-5%
-4%
-9%
6.1
1.7
0.7
9.4
11.0
15.3
3.2
1.0
1.1
5.2
6.2
6.7
-47%
-41%
57%
-45%
-44%
-56%
3.3
1.1
1.0
5.5
6.3
6.8
3%
10%
-9%
6%
2%
1%
2.5
0.8
0.7
3.9
5.1
5.2
-21%
-20%
-36%
-25%
-18%
-22%
Eastern redcedar
Assessment area
221E
221F
M221A
M221B
M221C
0.1
0.1
0.0
0.2
0.1
0.0
0.0 -56%
0.0 -100%
0.0
0%
0.2
0%
0.1
0%
0.0
0%
0.0
0.0
0.0
0.2
0.1
0.0
0%
0%
0%
0%
0%
0%
0.0
0.0
0.0
0.2
0.1
0.0
0%
0%
0%
0%
0%
0%
0.8
0.7
0.0
1.6
1.2
0.4
0.8
0.4
0.0
2.9
1.4
0.4
2%
-43%
0%
81%
17%
0%
0.8
0.4
0.0
3.2
1.3
0.4
0%
0%
0%
10%
-7%
0%
0.5
0.2
0.0
1.8
0.9
0.2
-41%
-50%
0%
-38%
-36%
-50%
(coninued on next page)
APPENDiX 4
290
Table 40.—Absolute and percentage change in basal area (BA) and trees per acre (TPA) predicted by the LANDIS PRO model for 17 species for current climate
and two climate scenarios for the assessment area in year 2040
Table 40 (coninued).
Current climate
BA in
2009
Change
BA in from
2040 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2040
climatec 2040 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2040 2009b 2040 climatec 2040
climatec
Seciona
Eastern whitepine
Assessment area
221E
221F
M221A
M221B
M221C
1.2
0.9
0.4
1.4
1.9
1.4
1.2
1.0
0.4
1.8
2.2
1.0
7%
11%
0%
29%
16%
-29%
1.2
1.0
0.4
1.8
2.2
1.0
0%
0%
0%
0%
0%
0%
1.2
0.9
0.4
1.7
2.1
0.9
-7%
-10%
0%
-6%
-5%
-10%
7.3
4.7
1.3
12.8
14.7
6.8
6.6
4.8
4.1
11.1
12.8
3.2
-10%
2%
215%
-13%
-13%
-53%
6.8
5.0
4.1
12.0
12.7
3.4
3%
4%
0%
8%
-1%
6%
4.6
3.2
2.5
7.8
9.6
2.1
-30%
-33%
-39%
-30%
-25%
-34%
Loblolly pine
Assessment area
221E
221F
M221A
M221B
M221C
0.1
0.1
0.0
0.1
0.1
0.0
0.1
39%
0.2 100%
0.0
0%
0.1
0%
0.0 -100%
0.0
0%
0.1
0.2
0.0
0.1
0.0
0.0
0%
0%
0%
0%
0%
0%
0.1
0.2
0.0
0.1
0.1
0.0
17%
0%
0%
0%
NA
0%
0.5
0.8
0.0
0.4
0.3
0.0
0.3
0.6
0.0
0.1
0.1
0.0
-34%
-25%
0%
-75%
-67%
0%
0.4
0.7
0.0
0.1
0.1
0.0
15%
17%
0%
0%
0%
0%
0.3
0.6
0.0
0.1
0.1
0.0
0%
0%
0%
0%
0%
0%
Northern red oak
Assessment area
221E
221F
M221A
M221B
M221C
6.8
5.2
5.9
8.7
9.3
7.9
7.7
6.1
6.1
9.4
10.8
9.1
14%
17%
3%
8%
16%
15%
7.8
6.1
6.3
9.5
10.9
9.2
1%
0%
3%
1%
1%
1%
8.0
6.3
6.2
9.8
11.2
9.2
3%
3%
2%
4%
4%
1%
11.9
10.2
10.9
13.6
16.6
11.3
6.8
6.1
6.2
7.6
8.2
6.9
-43%
-40%
-43%
-44%
-51%
-39%
7.5
6.5
8.3
8.5
9.2
7.4
11%
7%
34%
12%
12%
7%
6.9
5.9
8.2
7.7
8.6
6.7
2%
-3%
32%
1%
5%
-3%
Pignut hickory
Assessment area
221E
221F
M221A
M221B
M221C
2.6
2.6
0.5
2.9
2.9
3.0
2.5
2.6
0.4
2.9
2.9
2.8
-2%
0%
-20%
0%
0%
-7%
2.6
2.6
0.4
3.0
3.0
2.8
1%
0%
0%
3%
3%
0%
2.6
2.6
0.4
3.0
3.1
2.7
1%
0%
0%
3%
7%
-4%
7.3
6.9
0.7
10.3
9.8
7.6
6.3
6.4
1.0
7.2
8.3
6.1
-14%
-7%
43%
-30%
-15%
-20%
6.5
6.6
1.0
7.9
8.3
6.3
3%
3%
0%
10%
0%
3%
5.9
5.7
1.0
7.1
8.0
5.7
-7%
-11%
0%
-1%
-4%
-7%
Red maple
Assessment area
221E
221F
M221A
M221B
M221C
16.0
13.8
23.6
18.0
17.5
15.5
22.7
21.0
32.3
22.4
23.2
22.3
42%
52%
37%
24%
33%
44%
22.9
21.1
32.6
22.8
23.5
22.4
1%
0%
1%
2%
1%
0%
24.6
22.9
34.2
24.0
25.0
24.4
8%
9%
6%
7%
8%
9%
77.4
72.7
81.6
66.8
77.0
97.2
61.4
60.5
110.8
50.9
49.0
57.7
-21%
-17%
36%
-24%
-36%
-41%
62.8
62.0
111.7
55.5
49.4
58.6
2%
2%
1%
9%
1%
2%
59.0
57.2
109.1
53.8
47.9
52.8
-4%
-5%
-2%
6%
-2%
-8%
Red spruce
Assessment area
221E
221F
M221A
M221B
M221C
1.0
0.3
0.0
3.4
2.5
0.3
0.5
0.1
0.0
1.7
1.4
0.1
-50%
-67%
0%
-50%
-44%
-67%
0.5
0.1
0.0
1.8
1.4
0.1
2%
0%
0%
6%
0%
0%
0.5
0.1
0.0
1.6
1.3
0.1
-6%
0%
0%
-6%
-7%
0%
5.1
1.7
0.0
17.0
13.0
1.4
1.3
0.2
0.0
3.2
4.8
0.2
-74%
-88%
0%
-81%
-63%
-86%
1.3
0.2
0.0
3.3
4.6
0.2
0%
0%
0%
3%
-4%
0%
1.0
0.1
0.0
2.5
3.6
0.1
0%
-50%
0%
-22%
-25%
-50%
(coninued on next page)
APPENDiX 4
291
Tree species
Current climate
BA in
2009
Change
BA in from
2040 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2040
climatec 2040 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2040 2009b 2040 climatec 2040
climatec
Tree species
Seciona
Scarlet oak
Assessment area
221E
221F
M221A
M221B
M221C
4.8
3.4
0.3
6.7
7.4
7.8
3.5
2.0
0.3
5.5
6.3
5.6
-27%
-41%
0%
-18%
-15%
-28%
3.5
2.0
0.3
5.4
6.3
5.6
0%
0%
0%
-2%
0%
0%
2.4
1.2
0.3
4.1
4.5
3.5
-33%
-40%
0%
-25%
-29%
-38%
4.0
2.5
0.5
6.4
6.7
6.2
2.3
1.4
0.4
3.5
3.9
3.5
-43%
-44%
-20%
-45%
-42%
-44%
2.3
1.4
0.4
3.6
4.0
3.6
2%
0%
0%
3%
3%
3%
1.8
1.0
0.4
2.8
3.2
2.6
-23%
-29%
0%
-20%
-18%
-26%
Sugar maple
Assessment area
221E
221F
M221A
M221B
M221C
6.1
5.9
7.0
6.8
6.4
5.5
7.2
7.3
8.4
7.4
6.9
6.1
17%
24%
20%
9%
8%
11%
7.3
7.4
8.5
7.9
7.0
6.2
2%
1%
1%
7%
1%
2%
7.1
7.1
8.3
7.7
7.1
6.0
-1%
-3%
-1%
4%
3%
-2%
54.1
53.4
73.8
47.2
49.9
54.7
54.4
55.9
73.4
62.1
48.0
41.4
0%
5%
-1%
32%
-4%
-24%
55.7
57.7
72.3
67.8
47.7
41.9
2%
3%
-1%
9%
-1%
1%
39.6
38.1
51.3
49.4
39.3
31.8
-27%
-32%
-30%
-20%
-18%
-23%
Tulip tree
Assessment area
221E
221F
M221A
M221B
M221C
6.5
6.6
2.1
5.8
5.7
9.9
8.4
9.0
3.5
6.1
7.2
12.5
31%
36%
67%
5%
26%
26%
8.6
9.1
3.5
6.3
7.3
12.7
1%
1%
0%
3%
1%
2%
8.7
9.1
3.8
6.3
7.7
13.0
3%
1%
9%
3%
7%
4%
22.9
25.0
9.5
14.1
16.9
36.6
67.3
76.0
62.1
21.3
53.3
88.7
194%
204%
554%
51%
215%
142%
69.6
79.5
61.1
24.1
52.4
92.4
3%
5%
-2%
13%
-2%
4%
68.5
75.7
59.0
22.4
57.8
93.1
2%
0%
-5%
5%
8%
5%
White ash
Assessment area
221E
221F
M221A
M221B
M221C
2.5
2.7
4.8
1.9
1.7
1.7
2.8
3.1
5.5
2.1
2.0
1.8
14%
15%
15%
11%
18%
6%
2.8
3.1
5.6
2.1
2.0
1.8
0%
0%
2%
0%
0%
0%
2.8
3.0
5.3
2.1
2.1
1.8
-2%
-3%
-4%
0%
5%
0%
11.9
14.0
19.5
8.1
8.0
8.0
10.9
10.4
35.9
3.8
8.5
5.9
-8%
-26%
84%
-53%
6%
-26%
11.3
11.0
36.3
4.1
8.6
6.1
4%
6%
1%
8%
1%
3%
9.6
8.6
32.9
3.4
8.1
5.1
-12%
-17%
-8%
-11%
-5%
-14%
White oak
Assessment area
221E
221F
M221A
M221B
M221C
5.7
6.6
1.1
5.6
5.6
5.7
5.9
7.1
1.4
5.2
5.4
5.6
3%
8%
27%
-7%
-4%
-2%
6.0
7.2
1.4
5.5
5.5
5.7
2%
1%
0%
6%
2%
2%
6.0
7.0
1.4
5.7
5.8
5.8
2%
-1%
0%
10%
7%
4%
12.8
15.0
2.9
12.8
12.3
12.2
36.6
43.2
13.8
29.8
34.5
35.3
185%
188%
376%
133%
180%
189%
38.1
45.2
14.1
33.0
34.9
36.2
4%
5%
2%
11%
1%
3%
35.2
40.0
12.8
30.2
35.0
35.9
-4%
-7%
-7%
1%
1%
2%
Assessment area values were derived from the weighted average of secions.
Change under current climate represents the diference from 2009 through 2100 due to succession and management, but not climate.
c
Change from current climate for PCM B1 and GFDL A1FI represents the diference between these scenarios and current climate in 2100 and represents the potenial change due to climate change.
a
b
APPENDiX 4
292
Table 40 (coninued).
Table 41.—Absolute and percentage change in basal area (BA) and trees per acre (TPA) predicted by the LANDIS PRO model for 17 species under current
climate and two climate scenarios for the assessment area in year 2070
Tree species
Seciona
AmerAmerican beech Assessment area
221E
221F
M221A
M221B
M221C
Current climate
Change
BA in BA in from
2009 2070 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2070
climatec 2070 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2070 2009b 2070 climatec 2070
climatec
5.1
3.9
4.5
7.2
6.6
6.1
4.4
3.8
3.8
5.6
5.1
5.1
-14%
-3%
-16%
-22%
-23%
-16%
4.4
3.8
3.9
5.9
5.1
5.1
1%
0%
3%
5%
0%
0%
4.3
3.6
3.7
5.6
5.1
4.9
-3%
-5%
-3%
0%
0%
-4%
26.8
19.7
16.7
39.8
36.0
36.2
12.9
11.0
11.8
14.7
15.8
14.9
-52%
-44%
-29%
-63%
-56%
-59%
12.7
10.7
11.9
15.7
15.7
14.2
-1%
-3%
1%
7%
-1%
-5%
10.3
8.1
9.1
12.1
14.5
12.1
-20%
-26%
-23%
-18%
-8%
-19%
Assessment area
221E
221F
M221A
M221B
M221C
7.3
7.2
13.8
8.0
7.4
3.6
7.6
7.8
15.6
6.4
7.1
4.1
4%
8%
13%
-20%
-4%
14%
7.8
8.0
15.5
6.4
7.1
4.2
1%
3%
-1%
0%
0%
2%
7.7
7.9
15.6
6.5
7.0
4.1
1%
1%
0%
2%
-1%
0%
18.9
19.3
42.3
15.8
15.4
10.8
9.4
10.0
26.5
5.3
6.2
4.0
-51%
-48%
-37%
-66%
-60%
-63%
9.5
10.0
26.6
5.6
6.4
4.1
1%
0%
0%
6%
3%
2%
9.2
9.7
26.0
5.6
6.4
4.0
-1%
-3%
-2%
6%
3%
0%
Black oak
Assessment area
221E
221F
M221A
M221B
M221C
6.8
7.3
2.1
6.4
7.0
8.0
2.1
2.4
0.4
1.9
2.3
2.1
-69%
-67%
-81%
-70%
-67%
-74%
2.2
2.5
0.4
2.0
2.3
2.1
3%
4%
0%
5%
0%
0%
2.1
2.4
0.4
2.0
2.4
2.1
1%
0%
0%
5%
4%
0%
8.4
9.5
1.5
8.9
9.0
7.7
2.4
2.8
0.6
2.2
2.3
2.2
-72%
-71%
-60%
-75%
-74%
-71%
2.4
2.8
0.6
2.4
2.4
2.2
2%
0%
0%
9%
4%
0%
2.3
2.6
0.6
2.3
2.4
2.1
-4%
-7%
0%
5%
4%
-5%
Chestnut oak
Assessment area
221E
221F
M221A
M221B
M221C
5.6
4.2
0.2
8.3
8.3
8.2
4.7
3.6
0.2
6.8
7.1
6.4
-17%
-14%
0%
-18%
-14%
-22%
4.8
3.7
0.2
7.5
7.2
6.5
3%
3%
0%
10%
1%
2%
4.5
3.3
0.2
7.3
7.2
6.0
-3%
-8%
0%
7%
1%
-6%
14.9
9.6
0.9
21.7
24.2
24.1
29.4
22.4
1.4
36.5
48.4
40.0
97%
133%
56%
68%
100%
66%
31.2
22.6
1.4
46.8
52.0
40.8
6%
1%
0%
28%
7%
2%
25.5
14.6
1.3
44.2
50.2
32.4
-13%
-35%
-7%
21%
4%
-19%
Eastern hemlock
Assessment area
221E
221F
M221A
M221B
M221C
1.7
0.5
0.2
3.2
3.4
3.1
1.1
0.4
0.2
1.9
2.1
2.1
-34%
-20%
0%
-41%
-38%
-32%
1.1
0.4
0.2
2.0
2.1
2.1
1%
0%
0%
5%
0%
0%
1.1
0.4
0.2
1.9
2.1
2.0
-1%
0%
0%
0%
0%
-5%
6.1
1.7
0.7
9.4
11.0
15.3
2.9
1.1
1.2
4.0
5.5
5.8
-52%
-35%
71%
-57%
-50%
-62%
2.8
1.1
1.3
4.4
5.4
5.1
-3%
0%
8%
10%
-2%
-12%
2.1
0.7
0.6
2.9
4.7
3.9
-27%
-36%
-50%
-28%
-15%
-33%
Eastern redcedar
Assessment area
221E
221F
M221A
M221B
M221C
0.1
0.1
0.0
0.2
0.1
0.0
0.0 -56%
0.0 -100%
0.0
0%
0.2
0%
0.1
0%
0.0
0%
0.0
0.0
0.0
0.2
0.1
0.0
0%
0%
0%
0%
0%
0%
0.0
0.0
0.0
0.2
0.1
0.0
0%
0%
0%
0%
0%
0%
0.8
0.7
0.0
1.6
1.2
0.4
0.2
0.1
0.0
0.8
0.3
0.1
-75%
-86%
0%
-50%
-75%
0%
0.2
0.1
0.0
0.9
0.3
0.1
0%
0%
0%
13%
0%
0%
0.2
0.1
0.0
0.8
0.3
0.1
0%
0%
0%
0%
0%
0%
(coninued on next page)
APPENDiX 4
293
Black cherry
Current climate
BA in
2009
Change
BA in from
2070 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2070
climatec 2070 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2070 2009b 2070 climatec 2070
climatec
Tree species
Seciona
Eastern white pine
Assessment area
221E
221F
M221A
M221B
M221C
1.2
0.9
0.4
1.4
1.9
1.4
1.1
0.9
0.4
1.7
2.0
0.9
-2%
0%
0%
21%
5%
-36%
1.1
0.9
0.4
1.8
2.0
0.9
1%
0%
0%
6%
0%
0%
1.1
0.8
0.4
1.6
2.0
0.8
-6%
-11%
0%
-6%
0%
-11%
7.3
4.7
1.3
12.8
14.7
6.8
3.7
2.9
2.0
6.7
6.8
1.7
-49%
-38%
54%
-48%
-54%
-75%
3.7
2.8
2.1
7.2
6.9
1.7
1%
-3%
5%
7%
1%
0%
2.7
1.8
1.2
4.8
6.2
1.1
-26%
-38%
-40%
-28%
-9%
-35%
Loblolly pine
Assessment area
221E
221F
M221A
M221B
M221C
0.1
0.1
0.0
0.1
0.1
0.0
0.2
0.3
0.0
0.1
0.1
0.0
127%
200%
0%
0%
0%
0%
0.2
0.3
0.0
0.1
0.1
0.0
0%
0%
0%
0%
0%
0%
0.2
0.3
0.0
0.1
0.1
0.0
0%
0%
0%
0%
0%
0%
0.5
0.8
0.0
0.4
0.3
0.0
0.2
0.4
0.0
0.1
0.1
0.0
-54%
-50%
0%
-75%
-67%
0%
0.3
0.5
0.0
0.1
0.1
0.0
22%
25%
0%
0%
0%
0%
0.3
0.5
0.0
0.1
0.1
0.0
22%
25%
0%
0%
0%
0%
Northern red oak
Assessment area
221E
221F
M221A
M221B
M221C
6.8
5.2
5.9
8.7
9.3
7.9
5.8
4.7
4.5
6.6
7.8
6.9
-15%
-10%
-24%
-24%
-16%
-13%
5.9
4.9
4.8
6.7
8.0
6.9
3%
4%
7%
2%
3%
0%
5.9
4.8
4.9
6.7
8.1
6.8
2%
2%
9%
2%
4%
-1%
11.9
10.2
10.9
13.6
16.6
11.3
4.9
4.9
4.0
5.2
4.9
4.9
-59%
-52%
-63%
-62%
-70%
-57%
6.4
5.7
8.4
6.8
7.6
5.9
32%
16%
110%
31%
55%
20%
5.9
4.9
8.1
6.5
7.6
5.4
22%
0%
103%
25%
55%
10%
Pignut hickory
Assessment area
221E
221F
M221A
M221B
M221C
2.6
2.6
0.5
2.9
2.9
3.0
2.7
2.7
0.4
3.0
3.2
3.0
4%
4%
-20%
3%
10%
0%
2.7
2.8
0.4
3.1
3.2
3.0
2%
4%
0%
3%
0%
0%
2.7
2.7
0.4
3.1
3.2
2.9
0%
0%
0%
3%
0%
-3%
7.3
6.9
0.7
10.3
9.8
7.6
5.6
5.8
1.0
5.5
7.2
5.6
-24%
-16%
43%
-47%
-27%
-26%
5.8
5.8
1.0
6.6
7.7
5.9
4%
0%
0%
20%
7%
5%
5.6
5.4
1.0
6.6
7.7
5.4
0%
-7%
0%
20%
7%
-4%
Red maple
Assessment area
221E
221F
M221A
M221B
M221C
16.0
13.8
23.6
18.0
17.5
15.5
27.6
26.6
35.9
24.8
26.7
28.8
73%
93%
52%
38%
53%
86%
27.9
27.0
35.9
25.7
26.6
29.1
1%
2%
0%
4%
0%
1%
27.6
26.4
35.8
25.8
27.1
28.4
0%
-1%
0%
4%
1%
-1%
77.4
72.7
81.6
66.8
77.0
97.2
58.2
58.7
108.7
51.7
44.8
48.6
-25%
-19%
33%
-23%
-42%
-50%
60.5
59.3
114.5
61.1
46.1
50.9
4%
1%
5%
18%
3%
5%
59.6
57.6
113.0
59.8
48.2
49.4
2%
-2%
4%
16%
8%
2%
Red spruce
Assessment area
221E
221F
M221A
M221B
M221C
1.0
0.3
0.0
3.4
2.5
0.3
0.4
0.1
0.0
1.4
1.2
0.1
-57%
-67%
0%
-59%
-52%
-67%
0.4
0.1
0.0
1.4
1.2
0.1
0%
0%
0%
0%
0%
0%
0.4
0.1
0.0
1.4
1.2
0.1
0%
0%
0%
0%
0%
0%
5.1
1.7
0.0
17.0
13.0
1.4
0.7
0.1
0.0
1.5
2.8
0.1
-86%
-94%
0%
-91%
-78%
-93%
0.7
0.1
0.0
1.5
2.7
0.1
0%
0%
0%
0%
-4%
0%
0.6
0.1
0.0
1.4
2.2
0.1
0%
0%
0%
-7%
-21%
0%
(coninued on next page)
APPENDiX 4
294
Table 41 (coninued).
Table 41 (coninued).
Current climate
BA in
2009
Change
BA in from
2070 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2070
climatec 2070 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2070 2009b 2070 climatec 2070
climatec
Tree species
Seciona
Scarlet oak
Assessment area
221E
221F
M221A
M221B
M221C
4.8
3.4
0.3
6.7
7.4
7.8
0.9
0.5
0.2
1.5
1.6
1.5
-81%
-85%
-33%
-78%
-78%
-81%
0.9
0.5
0.2
1.5
1.6
1.5
0%
0%
0%
0%
0%
0%
0.9
0.5
0.2
1.5
1.6
1.4
-2%
0%
0%
0%
0%
-7%
4.0
2.5
0.5
6.4
6.7
6.2
1.3
0.8
0.4
1.8
2.2
2.0
-68%
-68%
-20%
-72%
-67%
-68%
1.3
0.8
0.4
2.0
2.2
2.1
3%
0%
0%
11%
0%
5%
1.2
0.7
0.3
1.7
2.1
1.7
-10%
-13%
-25%
-6%
-5%
-15%
Sugar maple
Assessment area
221E
221F
M221A
M221B
M221C
6.1
5.9
7.0
6.8
6.4
5.5
7.5
7.6
8.5
8.1
7.3
6.4
23%
29%
21%
19%
14%
16%
7.6
7.7
8.6
8.5
7.3
6.4
1%
1%
1%
5%
0%
0%
7.2
7.2
8.3
8.0
7.3
6.1
-4%
-5%
-2%
-1%
0%
-5%
54.1
53.4
73.8
47.2
49.9
54.7
43.2
42.0
44.7
63.3
42.9
33.6
-20%
-21%
-39%
34%
-14%
-39%
42.0
40.5
44.4
64.3
41.3
31.9
-3%
-4%
-1%
2%
-4%
-5%
30.3
27.5
30.2
41.6
37.6
23.1
-30%
-35%
-32%
-34%
-12%
-31%
Tulip tree
Assessment area
221E
221F
M221A
M221B
M221C
6.5
6.6
2.1
5.8
5.7
9.9
9.9
10.4
4.9
6.0
8.7
14.9
53%
58%
133%
3%
53%
51%
10.0
10.6
4.9
6.3
8.8
15.0
2%
2%
0%
5%
1%
1%
9.8
10.2
4.9
6.5
8.9
14.6
-1%
-2%
0%
8%
2%
-2%
22.9
25.0
9.5
14.1
16.9
36.6
80.2
91.0
83.1
19.9
65.4
100.6
251%
264%
775%
41%
287%
175%
84.6
94.4
84.6
24.8
70.5
108.7
5%
4%
2%
25%
8%
8%
81.2
88.6
83.1
27.2
72.8
101.1
1%
-3%
0%
37%
11%
0%
White ash
Assessment area
221E
221F
M221A
M221B
M221C
2.5
2.7
4.8
1.9
1.7
1.7
3.0
3.3
5.3
2.2
2.2
2.0
20%
22%
10%
16%
29%
18%
3.0
3.3
5.2
2.3
2.3
2.1
1%
0%
-2%
5%
5%
5%
2.9
3.2
5.2
2.2
2.3
2.0
-1%
-3%
-2%
0%
5%
0%
11.9
14.0
19.5
8.1
8.0
8.0
8.7
8.0
30.9
2.3
7.2
4.4
-27%
-43%
58%
-72%
-10%
-45%
9.2
8.7
30.5
2.6
7.8
4.8
6%
9%
-1%
13%
8%
9%
8.2
6.9
29.8
2.6
7.8
4.3
-6%
-14%
-4%
13%
8%
-2%
White oak
Assessment area
221E
221F
M221A
M221B
M221C
5.7
6.6
1.1
5.6
5.6
5.7
6.9
8.1
1.5
6.3
6.7
6.9
21%
23%
36%
13%
20%
21%
7.1
8.3
1.6
6.7
6.8
7.0
3%
2%
7%
6%
1%
1%
6.8
7.8
1.5
6.5
6.8
6.7
-2%
-4%
0%
3%
1%
-3%
12.8
15.0
2.9
12.8
12.3
12.2
48.5
56.8
18.4
38.2
47.5
46.8
278%
279%
534%
198%
286%
284%
51.1
58.4
18.9
46.1
51.2
49.2
5%
3%
3%
21%
8%
5%
48.1
53.8
17.8
43.9
51.0
46.2
-1%
-5%
-3%
15%
7%
-1%
Assessment area values were derived from the weighted average of secions.
Change under current climate represents the diference from 2009 through 2100 due to succession and management, but not climate.
c
Change from current climate for PCM B1 and GFDL A1FI represents the diference between these scenarios and current climate in 2100 and represents the potenial change due to climate change.
a
295
APPENDiX 4
b
Current climate
Change
BA in BA in from
2009 2100 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2100
climatec 2100 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2100 2009b 2100 climatec 2100
climatec
Tree species
Seciona
American beech
Assessment area
221E
221F
M221A
M221B
M221C
5.1
3.9
4.5
7.2
6.6
6.1
4.6
4.0
3.6
5.7
5.5
5.4
-9%
3%
-20%
-21%
-17%
-11%
4.6
4.0
3.7
6.0
5.5
5.3
0%
0%
3%
5%
0%
-2%
4.2
3.5
3.2
5.3
5.3
4.8
-10%
-13%
-11%
-7%
-4%
-11%
26.8
19.7
16.7
39.8
36.0
36.2
14.2
11.4
12.7
14.7
17.9
19.0
-47%
-42%
-24%
-63%
-50%
-48%
13.9
11.0
13.2
15.3
18.1
17.4
-2%
-4%
4%
4%
1%
-8%
8.1
5.9
5.4
8.2
14.0
9.2
-43%
-48%
-57%
-44%
-22%
-52%
Black cherry
Assessment area
221E
221F
M221A
M221B
M221C
7.3
7.2
13.8
8.0
7.4
3.6
4.6
4.8
10.6
3.0
3.5
2.7
-38%
-33%
-23%
-63%
-53%
-25%
4.6
4.9
10.6
3.0
3.5
2.8
1%
2%
0%
0%
0%
4%
4.5
4.7
10.5
3.0
3.5
2.7
-1%
-2%
-1%
0%
0%
0%
18.9
19.3
42.3
15.8
15.4
10.8
7.2
7.6
21.8
3.2
4.6
3.2
-62%
-61%
-48%
-80%
-70%
-70%
7.3
7.7
21.7
3.8
4.9
3.2
2%
1%
0%
19%
7%
0%
7.3
7.6
21.3
4.0
5.1
3.2
2%
0%
-2%
25%
11%
0%
Black oak
Assessment area
221E
221F
M221A
M221B
M221C
6.8
7.3
2.1
6.4
7.0
8.0
1.5
1.7
0.3
1.5
1.6
1.6
-78%
-77%
-86%
-77%
-77%
-80%
1.5
1.7
0.3
1.6
1.6
1.6
1%
0%
0%
7%
0%
0%
1.5
1.6
0.3
1.6
1.7
1.5
-2%
-6%
0%
7%
6%
-6%
8.4
9.5
1.5
8.9
9.0
7.7
2.0
2.3
0.5
1.7
2.0
1.8
-77%
-76%
-67%
-81%
-78%
-77%
2.0
2.3
0.5
1.9
2.0
1.8
1%
0%
0%
12%
0%
0%
1.8
1.9
0.5
1.9
2.1
1.6
-10%
-17%
0%
12%
5%
-11%
Chestnut oak
Assessment area
221E
221F
M221A
M221B
M221C
5.6
4.2
0.2
8.3
8.3
8.2
4.9
3.6
0.2
7.4
7.7
6.8
-12%
-14%
0%
-11%
-7%
-17%
5.1
3.7
0.2
8.2
7.8
6.8
3%
3%
0%
11%
1%
0%
4.6
3.1
0.2
8.1
7.8
6.1
-5%
-14%
0%
9%
1%
-10%
14.9
9.6
0.9
21.7
24.2
24.1
30.9
21.7
1.5
43.6
53.5
41.6
108%
126%
67%
101%
121%
73%
32.6
21.8
1.6
58.3
55.1
41.1
5%
0%
7%
34%
3%
-1%
25.0
10.0
1.5
60.9
53.7
28.6
-19%
-54%
0%
40%
0%
-31%
Eastern hemlock
Assessment area
221E
221F
M221A
M221B
M221C
1.7
0.5
0.2
3.2
3.4
3.1
1.1
0.4
0.2
1.8
2.1
2.0
-35%
-20%
0%
-44%
-38%
-35%
1.1
0.4
0.2
1.9
2.1
2.0
1%
0%
0%
6%
0%
0%
1.0
0.3
0.2
1.8
2.0
1.8
-9%
-25%
0%
0%
-5%
-10%
6.1
1.7
0.7
9.4
11.0
15.3
3.8
1.4
1.5
4.2
8.1
7.3
-38%
-18%
114%
-55%
-26%
-52%
3.6
1.3
1.5
4.8
7.8
6.1
-6%
-7%
0%
14%
-4%
-16%
2.0
0.6
0.5
2.2
5.7
2.9
-47%
-57%
-67%
-48%
-30%
-60%
Eastern redcedar
Assessment area
221E
221F
M221A
M221B
M221C
0.1
0.1
0.0
0.2
0.1
0.0
0.0 -89%
0.0 -100%
0.0
0%
0.1 -50%
0.0 -100%
0.0
0%
0.0
0.0
0.0
0.1
0.0
0.0
0%
0%
0%
0%
0%
0%
0.0
0.0
0.0
0.1
0.0
0.0
0%
0%
0%
0%
0%
0%
0.8
0.7
0.0
1.6
1.2
0.4
0.1 -91%
0.0 -100%
0.0
0%
0.5 -69%
0.1 -92%
0.0 -100%
0.0
0.0
0.0
0.3
0.1
0.0
0%
0%
0%
-40%
0%
0%
0.1
0.1
0.0
0.6
0.1
0.0
87%
NA
0%
20%
0%
0%
(coninued on next page)
APPENDiX 4
29
Table 42.—Absolute and percent change in basal area (BA) and trees per acre (TPA) predicted by the LANDIS PRO model for 17 species for current climate
and two climate scenarios for the assessment area in year 2100
Table 42 (coninued).
Current climate
BA in
2009
Change
BA in from
2100 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2100
climatec 2100 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2100 2009b 2100 climatec 2100
climatec
Seciona
Eastern white pine
Assessment area
221E
221F
M221A
M221B
M221C
1.2
0.9
0.4
1.4
1.9
1.4
1.0
0.8
0.3
1.6
1.8
0.8
-13%
-11%
-25%
14%
-5%
-43%
1.0
0.8
0.3
1.6
1.8
0.8
0%
0%
0%
0%
0%
0%
0.9
0.7
0.3
1.4
1.8
0.8
-7%
-13%
0%
-13%
0%
0%
7.3
4.7
1.3
12.8
14.7
6.8
Loblolly pine
Assessment area
221E
221F
M221A
M221B
M221C
0.1
0.1
0.0
0.1
0.1
0.0
0.2
0.3
0.0
0.1
0.1
0.0
127%
200%
0%
0%
0%
0%
0.2
0.3
0.0
0.1
0.1
0.0
0%
0%
0%
0%
0%
0%
0.2
0.3
0.0
0.1
0.1
0.0
0%
0%
0%
0%
0%
0%
0.5
0.8
0.0
0.4
0.3
0.0
Northern red oak
Assessment area
221E
221F
M221A
M221B
M221C
6.8
5.2
5.9
8.7
9.3
7.9
2.8
2.8
2.2
2.8
3.2
2.8
-58%
-46%
-63%
-68%
-66%
-65%
3.3
3.1
3.1
3.3
3.9
3.0
15%
11%
41%
18%
22%
7%
3.1
2.8
3.1
3.2
3.9
2.8
9%
0%
41%
14%
22%
0%
Pignut hickory
Assessment area
221E
221F
M221A
M221B
M221C
2.6
2.6
0.5
2.9
2.9
3.0
2.7
2.7
0.3
2.8
3.3
3.2
5%
4%
-40%
-3%
14%
7%
2.8
2.8
0.3
3.0
3.4
3.2
3%
4%
0%
7%
3%
0%
2.7
2.7
0.3
3.0
3.4
3.1
Red maple
Assessment area
221E
221F
M221A
M221B
M221C
16.0
13.8
23.6
18.0
17.5
15.5
30.0
30.1
40.0
25.2
26.8
30.9
88%
118%
69%
40%
53%
99%
30.5
30.4
40.3
27.5
27.0
31.3
2%
1%
1%
9%
1%
1%
Red spruce
Assessment area
221E
221F
M221A
M221B
M221C
1.0
0.3
0.0
3.4
2.5
0.3
0.3
0.1
0.0
1.0
0.9
0.1
-67%
-67%
0%
-71%
-64%
-67%
0.3
0.1
0.0
1.0
0.9
0.1
0%
0%
0%
0%
0%
0%
2.5
1.8
1.6
4.7
4.8
1.1
-66%
-62%
23%
-63%
-67%
-84%
2.4
1.8
1.5
5.1
4.4
1.0
-2%
0%
-6%
9%
-8%
-9%
1.3
0.7
0.4
1.9
3.9
0.5
-46%
-61%
-75%
-60%
-19%
-55%
0.2 -68%
0.3 -63%
0.0
0%
0.1 -75%
0.0 -100%
0.0
0%
0.2
0.4
0.0
0.1
0.1
0.0
43%
33%
0%
0%
NA
0%
0.3
0.5
0.0
0.2
0.1
0.0
81%
67%
0%
100%
NA
0%
11.9
10.2
10.9
13.6
16.6
11.3
3.6
4.0
2.7
3.6
2.8
3.6
-70%
-61%
-75%
-74%
-83%
-68%
6.0
5.2
9.4
5.6
7.2
5.3
68%
30%
248%
56%
157%
47%
5.2
3.8
9.0
5.6
7.3
4.5
45%
-5%
233%
56%
161%
25%
1%
0%
0%
7%
3%
-3%
7.3
6.9
0.7
10.3
9.8
7.6
5.4
5.4
1.1
4.8
7.1
5.9
-27%
-22%
57%
-53%
-28%
-22%
5.6
5.4
1.1
6.1
7.4
6.1
4%
0%
0%
27%
4%
3%
5.6
5.1
1.0
6.9
7.9
6.0
4%
-6%
-9%
44%
11%
2%
30.3
29.9
40.6
27.1
27.7
30.6
1%
-1%
2%
8%
3%
-1%
77.4
72.7
81.6
66.8
77.0
97.2
66.9
66.2
143.5
63.4
48.5
50.4
-13%
-9%
76%
-5%
-37%
-48%
70.3
68.7
145.9
75.9
50.6
52.8
5%
4%
2%
20%
4%
5%
74.5
70.6
160.7
81.8
56.6
55.1
11%
7%
12%
29%
17%
9%
0.3
0.1
0.0
1.0
0.9
0.1
0%
0%
0%
0%
0%
0%
5.1
1.7
0.0
17.0
13.0
1.4
0.4 -92%
0.0 -100%
0.0
0%
0.7 -96%
1.7 -87%
0.1 -93%
0.4
0.0
0.0
0.7
1.6
0.0
0%
0%
0%
0%
-6%
-100%
0.2
0.0
0.0
0.7
0.9
0.0
0%
0%
0%
0%
-47%
-100%
(coninued on next page)
APPENDiX 4
297
Tree species
Current climate
BA in
2009
Change
BA in from
2100 2009b
PCM B1
GFDL A1Fi
Change from
Change from
BA in
current BA in current
2100
climatec 2100 climatec
Current climate
PCM B1
GFDL A1Fi
Change
Change from
Change from
TPA in TPA in from TPA in current TPA in current
2009
2100 2009b 2100 climatec 2100
climatec
Tree species
Seciona
Scarlet oak
Assessment area
221E
221F
M221A
M221B
M221C
4.8
3.4
0.3
6.7
7.4
7.8
0.7
0.4
0.1
1.0
1.2
1.1
-86%
-88%
-67%
-85%
-84%
-86%
0.7
0.5
0.1
1.1
1.2
1.1
8%
25%
0%
10%
0%
0%
0.7
0.4
0.1
1.0
1.2
1.0
-2%
0%
0%
0%
0%
-9%
4.0
2.5
0.5
6.4
6.7
6.2
1.1
0.7
0.3
1.5
2.0
1.8
-72%
-72%
-40%
-77%
-70%
-71%
1.2
0.7
0.3
1.8
2.0
1.8
3%
0%
0%
20%
0%
0%
0.9
0.5
0.2
1.3
2.0
1.3
-17%
-29%
-33%
-13%
0%
-28%
Sugar maple
Assessment area
221E
221F
M221A
M221B
M221C
6.1
5.9
7.0
6.8
6.4
5.5
8.0
8.0
8.3
9.2
8.0
6.8
30%
36%
19%
35%
25%
24%
8.0
8.0
8.3
9.5
8.0
6.7
0%
0%
0%
3%
0%
-1%
7.3
7.2
7.7
8.0
7.8
6.1
-9%
-10%
-7%
-13%
-3%
-10%
54.1
53.4
73.8
47.2
49.9
54.7
53.6
47.7
43.4
91.4
59.9
46.3
-1%
-11%
-41%
94%
20%
-15%
50.8
44.9
42.6
88.0
57.3
43.0
-5%
-6%
-2%
-4%
-4%
-7%
23.4
18.3
16.7
27.4
43.5
16.8
-56%
-62%
-62%
-70%
-27%
-64%
Tulip tree
Assessment area
221E
221F
M221A
M221B
M221C
6.5
6.6
2.1
5.8
5.7
9.9
10.5
11.1
7.0
4.6
9.6
15.5
62%
68%
233%
-21%
68%
57%
10.8
11.5
6.9
5.1
9.7
15.8
3%
4%
-1%
11%
1%
2%
10.8
11.3
7.1
5.6
9.9
15.6
3%
2%
1%
22%
3%
1%
22.9
25.0
9.5
14.1
16.9
36.6
104.3 356%
113.0 352%
123.3 1198%
19.3
37%
95.2 463%
130.7 257%
112.1
123.8
127.2
27.7
97.6
137.4
8%
10%
3%
44%
3%
5%
114.8
123.3
128.1
39.6
105.3
139.3
10%
9%
4%
105%
11%
7%
White ash
Assessment area
221E
221F
M221A
M221B
M221C
2.5
2.7
4.8
1.9
1.7
1.7
2.9
3.2
4.6
2.1
2.4
2.2
18%
19%
-4%
11%
41%
29%
3.0
3.4
4.7
2.1
2.4
2.2
4%
6%
2%
0%
0%
0%
2.9
3.2
4.5
2.1
2.5
2.2
0%
0%
-2%
0%
4%
0%
11.9
14.0
19.5
8.1
8.0
8.0
7.3
6.4
25.1
1.4
7.2
4.0
-39%
-54%
29%
-83%
-10%
-50%
7.9
7.5
24.4
1.6
7.3
4.4
8%
17%
-3%
14%
1%
10%
6.8
5.3
24.3
1.7
7.7
4.0
-7%
-17%
-3%
21%
7%
0%
White oak
Assessment area
221E
221F
M221A
M221B
M221C
5.7
6.6
1.1
5.6
5.6
5.7
8.4
9.5
1.9
7.8
8.5
8.5
46%
44%
73%
39%
52%
49%
8.6
9.7
1.9
8.4
8.6
8.7
2%
2%
0%
8%
1%
2%
8.3
9.2
1.9
8.4
8.8
8.4
-1%
-3%
0%
8%
4%
-1%
12.8
15.0
2.9
12.8
12.3
12.2
57.7
64.3
20.8
50.0
60.6
58.7
349%
329%
617%
291%
393%
381%
60.9
68.1
22.2
56.1
61.7
61.8
6%
6%
7%
12%
2%
5%
60.8
65.8
21.5
61.0
65.1
61.7
5%
2%
3%
22%
7%
5%
Assessment area values were derived from the weighted average of secions.
Change under current climate represents the diference from 2009 through 2100 due to succession and management, but not climate.
c
Change from current climate for PCM B1 and GFDL A1FI represents the diference between these scenarios and current climate in 2100 and represents the potenial change due to climate change.
a
b
APPENDiX 4
298
Table 42 (coninued).
APPENDiX 4
Figure 61.—Projected changes in basal area for 17 species across the assessment area for PCM B1. Assessment area values
were derived from the weighted average of secions. The width of the colored line represents the basal area for each species
at various points through ime. For example, red maple had the highest basal area in 2010, and basal area is projected to
increase by 2 percent for the PCM B1 scenario, in addiion to the projected 88-percent increase due to natural succession and
management (see also Table 42).
Figure 62.—Projected changes in trees per acre for 17 species across the assessment area for PCM B1. Assessment area values
were derived from the weighted average of secions. The width of the colored line represents trees per acre for each species
at various points through ime. For example, red maple had the highest trees per acre in 2010, and the number of trees per
acre is projected to increase by 5 percent for the PCM B1 scenario, parially ofseing the projected 13-percent decrease due
to natural succession and management (see also Table 42).
299
APPENDiX 4
Figure 63.—Projected changes in basal area for 17 species across the assessment area for GFDL A1FI. Assessment area values
were derived from the weighted average of secions. The width of the colored line represents trees per acre for each species
at various points through ime. For example, red maple had the highest basal area in 2010, and basal area is projected to
increase by 1 percent for the GFDL A1FI scenario, in addiion to the projected 88-percent increase due to natural succession
and management (see also Table 42).
Figure 64.—Projected changes in trees per acre for 17 species across the assessment area for GFDL A1FI. Assessment area
values were derived from the weighted average of secions. The width of the colored line represents trees per acre for each
species at various points through ime. For example, red maple had the highest trees per acre in 2010, and the number of
trees per acre is projected to increase by 11 percent for the GFDL A1FI scenario, almost ofseing the projected 13-percent
decrease due to natural succession and management (see also Table 42).
300
APPENDiX 5: VuLNERABiLiTY AND
CoNFiDENCE DETERMiNATioN
EXPERT PANEL PRoCESS
To assess vulnerabilities to climate change for
each natural community type, we elicited input
from a panel of 19 experts from a variety of land
management and research organizations across the
assessment area. We sought a team of panelists
who would be able to contribute a diversity of
subject area expertise, management history,
and organizational perspectives. Most panelists
had extensive knowledge about the ecology,
management, and climate change impacts on forests
in the assessment area. This panel was assembled at
an in-person workshop in Morgantown, WV, in April
2013. Here we describe the structured discussion
process that the panel used.
Name
Organizaion
Jarel Barig
Wayne Naional Forest
Scot Bearer
The Nature Conservancy - Pennsylvania
Steve Blat
Wayne Naional Forest
Andrea Brandon
The Nature Conservancy - West Virginia
Patricia Butler*
Michigan Technological University & Northern Insitute of Applied Climate Science
Stephanie Connolly
Monongahela Naional Forest
Tim Culbreth
Maryland Department of Natural Resources, Forest Service
William Dijak
U.S. Forest Service, Northern Research Staion
Wade Dorsey
Savage River State Forest
Neil Gillies
Cacapon Insitute
Louis Iverson
U.S. Forest Service, Northern Research Staion
Maria Janowiak*
U.S. Forest Service, Northern Research Staion & Northern Insitute of Applied Climate Science
Kent Karriker
Monongahela Naional Forest
Dave Minney
Independent consultant
Coton Randall
Ohio Department of Natural Resources, Division of Forestry
Tom Schuler
U.S. Forest Service, Fernow Experimental Forest
Bill Stanley
The Nature Conservancy - Ohio
Al Steele
U.S. Forest Service, Northeastern Area, State & Private Forestry
Susan Stout
U.S. Forest Service, Northern Research Staion
Jason Teets
Natural Resources Conservaion Service
Frank Thompson
U.S. Forest Service, Northern Research Staion
*Workshop facilitator
301
APPENDiX 5
FoREST SYSTEMS ASSESSED
The authors of this assessment modified and
combined NatureServe (2011) ecological systems
in order to describe specific forest ecosystems
within the assessment area (see Chapter 1). For each
forest ecosystem, we collected information related
to the major system drivers, dominant species, and
stressors that characterize that ecosystem from the
relevant ecological literature. The panel was asked to
comment on the forest ecosystem descriptions, and
those comments were used to revise the descriptions
in Chapter 1.
PoTENTiAL iMPACTS
To examine potential impacts, the panel was given
several sources of background information on past
and future climate change in the region (summarized
in Chapters 3 and 4) and projected impacts on
dominant tree species (summarized in Chapter 5).
The panel was directed to focus on impacts to each
forest ecosystem from the present through the end of
the century, but more weight was given to the endof-century period. The panel assessed impacts by
considering a range of climate futures bracketed
by two scenarios: GFDL A1FI and PCM B1.
Panelists were then led through a structured
discussion process to consider this information for
each forest ecosystem in the assessment.
Potential impacts on ecosystem drivers and
stressors were summarized based on climate
model projections, the published literature, and
insights from the panelists. Impacts on drivers
were considered positive or negative if they would
alter system drivers in a way that would be more
or less favorable for that forest ecosystem. Impacts
on stressors were considered negative if they
increased the influence of that stressor or positive
if they decreased the influence of that stressor on
the forest ecosystem. Panelists were also asked to
302
consider the potential for climate change to facilitate
new stressors in the assessment area over the next
century.
To assess potential impacts on dominant tree
species, the panelists examined results from three
forest impact models (Tree Atlas, LINKAGES,
and LANDIS-PRO), and were asked to consider
those results in addition to their knowledge of life
history traits and ecology of those species. The panel
evaluated how much agreement existed within the
available information, between climate scenarios,
and across space and time. Finally, panelists were
asked to consider the potential for interactions
among anticipated climate trends, species impacts,
and stressors. Input on these future ecosystem
interactions relied primarily on the panelists’
expertise and judgment because there are not
many examples of published literature on complex
interactions, nor are future interactions accurately
represented by forest impact models (Box 12).
ADAPTiVE CAPACiTY
Panelists discussed the adaptive capacity of
each forest ecosystem based on their ecological
knowledge and management experience. Panelists
were told to focus on characteristics that would
increase or decrease the adaptive capacity of that
system. Factors that the panel considered included
characteristics of dominant species within each
forest ecosystem (e.g., dispersal ability, genetic
diversity, range limits) as well as comprehensive
ecosystem characteristics (e.g., functional
and species diversity, tolerance to a variety of
disturbances, distribution across the landscape). The
panelists were directed to base their considerations
on the current condition of the system given past and
current management regimes, with no consideration
of potential adaptation actions that could take place
in the future.
APPENDiX 5
Box 12: A Note on Forest impact Models used in this Assessment
During the expert panel workshop, preliminary
LANDIS PRO results were used that included the
climate parameters based on the average climate at
the center of an ecological secion. This methodology
was found to be less efecive in areas of complex
topography, where steep elevaional gradients
result in climaic gradients as well. The inclusion of
climate data from the center of the secion projected
unrealisic responses for several tree species in the
Allegheny Mountain and Northern Ridge and Valley
secions of the assessment area. The climate value at
the center of the secion happened to fall on a highelevaion area, which represented the climate for the
whole region as being much colder than the average
climate. This was ideniied as an issue with the
preliminary LANDIS PRO model results at the expert
panel workshop, and many paricipants expressed
their tendency to “discount” the results for black
cherry and tulip tree. Following the expert panel
workshop, the LANDIS PRO model was recalibrated
with alternate climate data that beter represent the
average climate. All results summarized in Chapters
5 and 6 were veted with the expert panelists
to ensure their vulnerability rankings were sill
consistent with the inal LANDIS PRO model results.
VuLNERABiLiTY
CoNFiDENCE
After extensive group discussion, each panelist
evaluated the potential impacts to and adaptive
capacity of each forest ecosystem to arrive at a
vulnerability rating. Participants were provided
with individual worksheets and asked to list which
impacts they felt were most important to that system
in addition to the major factors that would contribute
to the adaptive capacity of that ecosystem (Fig. 5).
Panelists were also directed to give a confidence
rating to each of their individual vulnerability
determinations (Fig. b). Panelists were asked
to evaluate the amount of evidence they felt was
available to support their vulnerability determination
and the level of agreement among the available
evidence (Mastrandrea et al. 2010). Panelists
evaluated confidence individually and as a group, in
a similar fashion to the vulnerability determination.
Panelists were directed to mark their rating in
two-dimensional space on the individual worksheet
and on a large group poster (Fig. a). This
vulnerability figure required the participants to
evaluate the degree of potential impacts related to
climate change as well as the adaptive capacity of
the ecosystem to tolerate those impacts (Swanston
and Janowiak 2012). Individual ratings were
compared and discussed and used to arrive at a
group determination. In many cases, the group
determination was at or near the centroid of all
individual determinations. Sometimes the group
determination deviated from the centroid because
further discussion convinced some group members
to alter their original response.
Vulnerability and Conidence Figures
For reference, figures of individual and group
determinations for all nine forest ecosystems
considered in this assessment are displayed in
Figures 7 through 75. In each figure, individual
panelist votes are indicated with a small circle and
the group determination is indicated with a large
square. We do not intend for direct comparison
between these figures because the axes represent
subjective, qualitative scales.
303
APPENDiX 5
Example Vulnerability Determinaion Worksheet
Name:
Ecosystem/Forest Type:
How familiar are you with this ecosystem? (circle one)
Low
Medium
high
I have some basic
knowledge about this
system and how it
operates
I do some management
or research in this
system, or have read
a lot about it.
I regularly do
management or
research in this system
What do you think are the greatest potenial impacts to the ecosystem?
What factors do you think contribute most to the adapive capacity of the ecosystem?
Vulnerability Determinaion
Conidence Raing
Use the handout for the vulnerability determinaion
process and the notes that you have taken to plot
your assessment of vulnerability on the igure below.
Use the handout for the conidence raing process
and the notes that you have taken to rate conidence
using the igure below.
The raings above are for the enire analysis area. Please note where you think potenial impacts
or adapive capacity may vary substanially within the analysis area (e.g., forests in the eastern
porion may be more prone to impact X).
Figure 65.—Worksheet used for vulnerability and conidence determinaion by expert panelists, based on Swanston and
Janowiak (2012).
304
APPENDiX 5
(a)
(b)
Figure 66.—Figure used for (a) vulnerability determinaion by expert panelists, based on Swanston and Janowiak (2012), and
(b) conidence raing among expert panelists, adapted from Mastrandrea et al. (2010).
Figure 67.—Vulnerability and conidence determinaions for Appalachian (hemlock)/northern hardwood forest. Circles
indicate individual determinaions by each panelist and squares indicate the group determinaion ater consensus was
reached.
305
APPENDiX 5
Figure 68.—Vulnerability and conidence determinaions for dry calcareous forest, woodland, and glade. Circles indicate
individual determinaions by each panelist and squares indicate the group determinaion ater consensus was reached.
Figure 69.—Vulnerability and conidence determinaions for dry oak and oak/pine forest and woodland. Circles indicate
individual determinaions by each panelist and squares indicate the group determinaion ater consensus was reached.
30
APPENDiX 5
Figure 70.—Vulnerability and conidence determinaions for dry/mesic oak forest. Circles indicate individual determinaions
by each panelist and squares indicate the group determinaion ater consensus was reached.
Figure 71.—Vulnerability and conidence determinaions for large stream loodplain and riparian forest. Circles indicate
individual determinaions by each panelist and squares indicate the group determinaion ater consensus was reached.
307
APPENDiX 5
Figure 72.—Vulnerability and conidence determinaions for mixed mesophyic and cove forest. Circles indicate individual
determinaions by each panelist and squares indicate the group determinaion ater consensus was reached.
Figure 73.—Vulnerability and conidence determinaions for north-central interior maple/beech forest. Circles indicate
individual determinaions by each panelist and squares indicate the group determinaion ater consensus was reached.
308
APPENDiX 5
Figure 74.—Vulnerability and conidence determinaions for small stream riparian forest. Circles indicate individual
determinaions by each panelist and squares indicate the group determinaion ater consensus was reached.
Figure 75.—Vulnerability and conidence determinaions for spruce/ir forest. Circles indicate individual determinaions by
each panelist and squares indicate the group determinaion ater consensus was reached.
309
APPENDiX 5
VuLNERABiLiTY STATEMENTS
REFERENCES
Recurring themes and patterns that transcended
individual forest ecosystems were identified and
developed into the vulnerability statements in
boldface and supporting text in Chapter . The lead
author developed the statements and supporting text
based on workshop notes and literature pertinent to
each statement. An initial confidence determination
(evidence and agreement) was assigned based on
the lead author’s interpretation of the amount of
information available to support each statement and
the extent to which the information agreed. Each
statement and its supporting literature discussion
were sent to the expert panel for review. Panelists
were asked to review each statement for accuracy,
whether the confidence determination should be
raised or lowered, if there was additional literature
that was overlooked, and if there were any additional
statements that needed to be made. Any changes that
were suggested by a single panelist were brought
forth for discussion and approved by the entire
panel.
Mastrandrea, M.D.; Field, C.B.; Stocker, T.F.;
Edenhofer, O.; Ebi, K.L.; Frame, D.J.; Held,
H.; Kriegler, E.; Mach, K.J.; Matschoss,
P.R.; Plattner, G.-K.; Yohe, G.W.; Zwiers,
F.W. 2010. Guidance note for lead authors
of the IPCC Fifth Assessment Report on
consistent treatment of uncertainties. Geneva,
Switzerland: Intergovernmental Panel on Climate
Change (IPCC). Available at http://www.ipcc.
ch/activities/activities.shtml. (Accessed Feb. 28,
2011).
310
NatureServe. 2011. NatureServe Explorer.
Arlington, VA. Available at http://www.
natureserve.org/explorer/classeco.htm.
(Accessed September 11, 2014).
Swanston, C.W.; Janowiak, M.K. 2012. Forest
adaptation resources: climate change tools and
approaches for land managers. Gen. Tech. Rep.
NRS-87. Newtown Square, PA: U.S. Department
of Agriculture, Forest Service, Northern Research
Station. 121 p. Available at http://www.nrs.fs.fed.
us/pubs/40543. (Accessed August 21, 2014).
Butler, Patricia R.; Iverson, Louis; Thompson, Frank R., III; Brandt, Leslie;
Handler, Stephen; Janowiak, Maria; Shannon, P. Danielle; Swanston, Chris;
Karriker, Kent; Bartig, Jarel; Connolly, Stephanie; Dijak, William; Bearer, Scott;
Blatt, Steve; Brandon, Andrea; Byers, Elizabeth; Coon, Cheryl; Culbreth, Tim;
Daly, Jad; Dorsey, Wade; Ede, David; Euler, Chris; Gillies, Neil; Hix, David M.;
Johnson, Catherine; Lyte, Latasha; Matthews, Stephen; McCarthy, Dawn;
Minney, Dave; Murphy, Daniel; O’Dea, Claire; Orwan, Rachel; Peters, Matthew;
Prasad, Anantha; Randall, Cotton; Reed, Jason; Sandeno, Cynthia;
Schuler, Tom; Sneddon, Lesley; Stanley, Bill; Steele, Al; Stout, Susan;
Swaty, Randy; Teets, Jason; Tomon, Tim; Vanderhorst, Jim; Whatley, John;
Zegre, Nicholas. 2015. Central Appalachians forest ecosystem vulnerability
assessment and synthesis: a report from the Central Appalachians Climate
Change Response Framework project. Gen. Tech. Rep. NRS-146. Newtown
Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research
Station. 310 p.
Forest ecosystems in the Central Appalachians will be affected directly and
indirectly by a changing climate over the 21st century. This assessment evaluates
the vulnerability of forest ecosystems in the Central Appalachian Broadleaf ForestConiferous Forest-Meadow and Eastern Broadleaf Forest Provinces of Ohio, West
Virginia, and Maryland for a range of future climates. Information on current forest
conditions, observed climate trends, projected climate changes, and impacts on
forest ecosystems was considered by a multidisciplinary panel of scientists, land
managers, and academics in order to assess ecosystem vulnerability to climate
change. Appalachian (hemlock)/northern hardwood forests, large stream floodplain
and riparian forests, small stream riparian forests, and spruce/fir forests were
determined to be the most vulnerable. Dry/mesic oak forests and dry oak and
oak/pine forests and woodlands were determined to be least vulnerable. Projected
changes in climate and the associated impacts and vulnerabilities will have
important implications for economically valuable timber species, forest-dependent
wildlife and plants, recreation, and long-term natural resource planning.
KEy WORDS: climate change, vulnerability, adaptive capacity, forests,
Climate Change Tree Atlas, DISTRIB, LANDIS PRO, LINKAGES,
expert elicitation, climate projections, impacts
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