Forest

Service

Central Appalachians Forest Ecosystem 

Vulnerability Assessment and Synthesis:

A Report from the Central Appalachians 

Climate Change Response Framework Project

United States Department of Agriculture

Northern

Research Station February 2015

General Technical

Report NRS-146
Visit our homepage at: http://www.nrs.fs.fed.us/

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USDA FOREST SERVICE USDA Forest Service

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NEWTOWN SQUARE, PA 19073-3294 359 Main Road

Delaware, OH 43015-8640

February 2015 Fax: 740-368-0152

Manuscript received for publication June 2014

Forest ecosystems across the Central Appalachians will be affected 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 information on the contemporary landscape, 

provided information on past climate trends, and described a range of projected future climates. 

This information was used to parameterize and run multiple vegetation impact models, which 

provided a range of potential tree responses to climate. Finally, we brought these results before 

a multidisciplinary panel of scientists, land managers, and academics familiar with the forests of 

this region to assess ecosystem vulnerability through a formal consensus-based expert elicitation 

process. 

The summary of the contemporary landscape identifies 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, particularly in summer 

and fall. Precipitation has also increased in the area, particularly in fall. Projected climate trends 

for the next 100 years using downscaled global climate model data indicate a potential increase 

in mean annual temperature of 2 to 8 °F for the assessment area. Projections for precipitation 

indicate increases in winter and spring precipitation, and summer and fall precipitation 

projections vary by scenario. We identified potential impacts on forests by incorporating these 

future climate projections into three forest impact models (DISTRIB, LINKAGES, and LANDIS PRO). 

Model projections suggest that many mesic species, including American beech, eastern hemlock, 

eastern white pine, red spruce, and sugar maple may fare worse under future conditions, but 

other species such as eastern redcedar may benefit from projected changes in climate. Published 

literature on climate impacts related to wildfire, invasive species, and forest pests and diseases 

also contributed to the overall determination of climate change vulnerability. 

We assessed vulnerability for nine forest ecosystems in the assessment area. The assessment 

was conducted through a formal elicitation process of 19 science and management experts 

from across the area, who considered vulnerability in terms of the potential impacts on a 

forest ecosystem and the adaptive capacity of the ecosystem. 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 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 

vulnerabilities will have important implications for economically valuable timber species, forestdependent wildlife and plants, recreation, and long-term natural resource planning.

ABSTRACT

Cover Photo

Small stream riparian forest with a red spruce forest in the background. Photo by Patricia Butler, 

Northern Institute of Applied Climate Science and Michigan Tech, used with permission.
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 Bartig, Stephanie Connolly, William D. Dijak, Scott Bearer, 

Steve L. Blatt, 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 Matthews, Dawn McCarthy, 

Dave Minney, Daniel Murphy, Claire O’Dea, Rachel Orwan, Matthew Peters, 

Anantha Prasad, Cotton 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 
Autho

PATRICIA R. BUTLER is a climate change outreach specialist with 

the Northern Institute 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 Station, 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 Station, 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 Institute of Applied Climate Science, U.S. Forest 

Service, Northern Research Station, 1992 Folwell Avenue, 

St. Paul, MN 55108, lbrandt@fs.fed.us.

STEPHEN D. HANDLER is a climate change specialist with the 

Northern Institute 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 adaptation and carbon 

management scientist with the Northern Institute 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 Institute 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 Station, and director of the 

Northern Institute of Applied Climate Science, 410 MacInnes 

Drive, Houghton, MI 49931, cswanston@fs.fed.us.

KENT KARRIKER is the ecosystems group leader with the 

Monongahela National Forest, 200 Sycamore Street, Elkins, WV 

26241, kkarriker@fs.fed.us.

JAREL BARTIG is an ecologist with the Wayne National Forest, 

13700 U.S. Hwy 33, Nelsonville, OH 45764, jbartig@fs.fed.us.

STEPHANIE CONNOLLY is a forest soil scientist with the 

Monongahela National Forest, 200 Sycamore Street, Elkins, WV 

26241, sconnolly@fs.fed.us. 

WILLIAM D. DIJAK is a wildlife biologist and geographic 

information systems specialist with the U.S. Forest Service, 

Northern Research Station, 202 Anheuser-Busch Natural 

Resources Building, University of Missouri – Columbia, 

Columbia, MO 65211, wdijak@fs.fed.us.

SCOTT BEARER is a senior conservation scientist 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 National 

Forest, 13700 U.S. Hwy 33, Nelsonville, OH 45764, 

sblatt@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 vegetation 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 National 

Forest, 811 Constitution 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 (retired) was the forest planner and National 

Environmental Policy Act (NEPA) coordinator with the 

Monongahela National Forest, 200 Sycamore Street, Elkins, WV 

26241. He can be reached at ededm86@gmail.com. 

CHRIS EULER is an archaeologist with the Wayne National 

Forest, 13700 U.S. Hwy 33, Nelsonville, OH 45764, 

ceuler@fs.fed.us.

NEIL GILLIES is the director of science and education at the 

Cacapon Institute, P.O. Box 68, High View, WV, 26808, 

ngillies@cacaponinstitute.org.

DAVID M. HIX is an associate professor with the School of 

Environment and Natural Resources at Ohio State University, 

365A Kottman Hall, 2021 Coffey Road, Columbus, OH 43210, 

hix.6@osu.edu.

CATHERINE JOHNSON is a wildlife biologist with the 

Monongahela National Forest, 200 Sycamore Street, Elkins, WV 

26241, catherinejohnson@fs.fed.us.

LATASHA LYTE is a forest soil scientist with the Wayne National 

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 Station, 359 Main Road, Delaware, OH 

43015, snmatthews@fs.fed.us.
DAWN McCARTHY is the assistant district ranger for operations 

with the Wayne National Forest, 13700 U.S. Hwy 33, 

Nelsonville, OH 45764, dmccarthy02@fs.fed.us.

DAVE MINNEY is a fire 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 Cincinnati, 462 Braunstein Hall, Cincinnati, 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, objections and litigation 

coordinator with the Wayne National Forest, 

13700 U.S. Hwy 33, Nelsonville, OH 45764, rorwan@fs.fed.us.

MATTHEW PETERS is a geographic information systems 

technician with the U.S. Forest Service, Northern Research 

Station, 359 Main Road, Delaware, OH 43015, 

matthewpeters@fs.fed.us.

ANANTHA PRASAD is an ecologist with the U.S. Forest Service, 

Northern Research Station, 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, cotton.randall@dnr.state.oh.us.

JASON REED is the engineering and natural resources group 

leader with the Monongahela National Forest, 

200 Sycamore Street, Elkins, WV 26241, jareed02@fs.fed.us.

CYNTHIA SANDENO is an ecologist with the Monongahela 

National 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 national ecologist with NatureServe, 

c/o University of Massachusetts at Boston, Biology Department, 

100 Morissey Boulevard, Boston, MA 02125, 

lesley_sneddon@natureserve.org.

BILL STANLEY is the director of conservation with The Nature 

Conservancy in Ohio, 6375 Riverside Drive, Suite 100, Dublin, 

OH 43017, bstanley@tnc.org.

AL STEELE is a physical scientist with the U.S. Forest Service, 

Northeastern Area State & Private Forestry, 180 Canfield Street, 

Morgantown, WV 26505, asteele@fs.fed.us.

SUSAN STOUT is a project leader with the U.S. Forest Service, 

Northern Research Station, 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, Marquette, MI 49855, 

rswaty@tnc.org.

JASON TEETS is an ecological site description specialist with 

the Natural Resources Conservation Service, 201 Scott 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, 

ttomon@wvda.us.

JIM VANDERHORST is a vegetation 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 fire management officer with the 

Wayne National 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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CONTEXT AND S

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. 

P
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Executive Summary 

Introduction 

Chapter 1: The Contemporary Landscape . 

Chapter 2: Climate Change Science and Modeling . 

Chapter 3: Observed Climate Change . 

Chapter 4: Projected Changes in Climate, Extremes, and Physical Processes . 

Chapter 5: Future Climate Change Impacts on Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Chapter 6: Forest Ecosystem Vulnerabilities . .

Chapter 7: Management Implications . .

Glossary . .

Literature Cited . .

Appendix 1: Species Lists . .

Appendix 2: Trend Analysis and Historical Climate Data . .

Appendix 3: Additional Future Climate Projections . .

Appendix 4: Additional Impact Model Results . .

Appendix 5: Vulnerability and Confidence Determination . .

Cont
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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. 

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. 

CHAPTER 1: THE CONTEM

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 

ecosystems, and how climate may interact with other 

stressors on the landscape.

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:

• 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 

● 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. 

Executi
Executi

CHAPTER 2: CLIMATE CH

SCIENCE AND MODEL

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.

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.

CHAPTER 3: OBSERVED CLI

CHANG

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 

● 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 National Forest, West Virginia. Photo by Patricia Butler, Northern Institute of Applied Climate 

Science (NIACS) and Michigan Tech, used with permission.
Executi

● 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 CHA

IN CLIMATE, EXTREMES, 

PHYSICAL PROCES

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. 

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.

● 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 C

CHANGE IMPACTS ON F

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.
Executi

Table 1.—Vulnerability determination summaries for forest ecosystems considered in this assessment

Forest ecosystem Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Appalachian (hemlock)/

northern hardwood 

forest

Negative Low-Moderate High Medium Medium

Dry calcareous forest, 

woodland, and glade Neutral-Negative Low-Moderate Moderate-High Limited-Medium Medium 

Dry oak and oak/pine 

forest and woodland Positive Moderate-High Low Medium Medium-High

Dry/mesic oak forest Positive-Neutral High Low- Moderate Medium Medium-High

Large stream floodplain 

and riparian forest Negative Low High Medium Medium

Mixed mesophytic and 

cove forest Neutral-Negative Moderate-High Moderate Limited-Medium Medium

North-central interior 

beech/maple forest Neutral Moderate Moderate Limited-Medium Medium

Small stream riparian 

forest Negative Moderate Moderate-High Medium Medium

Spruce/fir forest Negative Moderate High Limited-Medium Medium

● 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 ECOS

VULNERABIL

This chapter focuses on the vulnerability of major 

forest ecosystems in the assessment area to climate 

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

Potential Impacts of Climate Change on 

Drivers and Stressors

● Temperatures will increase (robust evidence, 

high agreement). All downscaled climate models 

project that average temperatures will increase 

across much of the assessment area.
Executi

● 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.

● 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.

● 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. 

● 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 

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. 

Potential 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.

● 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.

● 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.

● 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.
Executi



● 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.

Adaptive 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: MANAGEM

IMPLICA

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. 
Executi

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.
CONT

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.

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. 

Introd
INTRODUCTION

9

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 fulfill 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 first 

and foremost a scientific 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 defined 

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 sections of the Eastern Broadleaf Forest Province (green) and three sections of 

the Central Appalachian Broadleaf Forest-Coniferous Forest-Meadow Province (blue) within Ohio, West Virginia, and Maryland 

(Cleland et al. 2007a).
Introd

10

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). 

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.

ASSESSMENT CHAPT

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.

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. 

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. 

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. 
11

Chapter 1: The 

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 SETT

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 67 to 69 °F, wit

average maximum temperatures of 78 to 81 °F. In 

the highest elevations of M221B, daily minimum 

temperatures are the most extreme in the region, 
Chapter 1: The 

12

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 65 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 

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 6-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 167 feet in the Maryland

portion of the assessment area to 4,681 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 

13

Figure 2.—Elevation 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).

Section 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 650 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 
Chapter 1: The 

14

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).

Section 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, 

silt-clay loam colluvium, and lacustrine silt cover the 

upland areas (ODNR 1998).

Section 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 

15

Box 1: Karst Topography

Karst landscapes occur where the topography and 

its distinctive features are formed by the dissolution 

of soluble rock, especially dolomite and limestone 

(Fig. 3). The resulting 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 cavities 

(usually caves). Cold-water springs are characterized 

by a continuous flow of mineralized groundwater 

when surface precipitation percolates through 

fractures in bedrock including sinkholes, losing 

streams, caves, and bedrock aquifers. 

The Northern Ridge and Valley (M221A) and 

Allegheny Mountains (M221B) sections contain the 

assessment area’s largest concentration 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 productivity 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 aquatic 

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 populations 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 particular 

cave in the assessment area (Kovarik 2013). Little is 

known about the ecology and life history of many 

of the cave-dwelling species in the assessment area, 

making it difficult to determine whether they may be 

affected by a changing climate. 

Figure 3.—Potential karst formations in the Central 

Appalachians. Draft map from Weary (2008).

Section 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. 

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 4,861 feet at Spruce Knob, West

Virginia. 
Chapter 1: The 

1

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).

Section 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 600

to 3,900 feet. 

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 2006). Adequate soil moisture helps to

overcome the limitations of these otherwise nutrientpoor soils. 

Hydrolog

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). 

Section 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 

17

Section 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 162,595 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).

A shallow slow-moving stream, one of many found throughout the assessment area. Photo by Patricia Butler, Northern 

Institute of Applied Climate Science (NIACS) and Michigan Tech, used with permission.
Chapter 1: The 

18

Section 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. 

Section 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. 

Section 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 67 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 

19

Figure 4.—Land cover classes in the assessment area (USGS 2011).

DEMOGRAPHIC AND ECO

CONDI

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 
Chapter 1: The 

20

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 Industr

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 $2.6 billion, which represented 0.6 percen

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), 

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. 1996) (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).

Multiple land uses in West Virginia. Agriculture and 

development dominate the flat valleys. Photo by Patricia 

Butler, NIACS and Michigan Tech, used with permission.
Chapter 1: The 

21

Recreation

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 $4.86 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 

(16 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). 

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 $6.1 billion in GDP in 2009,

an increase from its contribution of $5.6 billion in

2008 (Runyan 2011). Most mining jobs (82 percent) 

are located in the West Virginia portion of the 

assessment area (Headwaters Economics 2011). 

A hiking trail over the Blackwater Falls in the Canaan Valley, 

West Virginia. Recreation opportunities centered around 

natural resources may also be affected by climate change. 

Photo by Patricia Butler, NIACS and Michigan Tech, used with 

permission.
Chapter 1: The 

22

ECOSYSTEM COM

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 26 in

Appendix 1.

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

Central Appalachian alkaline glade 

and woodland

North-central Appalachian 

circumneutral cliff and talus

oak/hickory eastern redcedar, chinkapin 

oak, eastern redbud, eastern 

hophornbeam, white oak, post oak, 

shagbark hickory

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

Appalachian shale barrens

North-central Appalachian acidic cliff 

and talus 

oak/hickory, oak/

pine, loblolly/

shortleaf pine

white oak, black oak, chestnut oak, 

mockernut hickory, pignut hickory, 

scarlet oak, shortleaf pine, pitch 

pine, Virginia pine, eastern white 

pine, Table Mountain pine, scrub oak

Table 2.—Forest classification systems used in this assessmenta

(continued on next page)
Chapter 1: The 

23

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

Dry/mesic oak forest Northeastern interior dry/mesic oak 

forest

Central and southern Appalachian 

montane oak forest

Southern Appalachian oak forest

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

Large stream floodplain 

and riparian forest

South-central interior large 

floodplain

Central Appalachian river floodplain

Cumberland riverscour

North-central interior flood plain

oak/gum/

cypress, elm/

ash/cottonwood 

silver maple, eastern cottonwood, 

pin oak, red maple, black willow, 

sycamore, sweetgum, green ash, 

bur oak, American hornbeam, black 

walnut, American elm, boxelder, 

black oak

Mixed mesophytic and 

cove forest

South-central interior mesophytic 

forest 

Southern and central Appalachian 

cove forest

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

North-central interior 

beech/maple forest

North-central interior beech/maple 

forest

North-central interior wet flatwoods

maple/beech/

birch

sugar maple, American beech, 

northern red oak, American 

basswood, eastern hemlock, black 

cherry, tulip tree, red maple, white 

ash, eastern hophornbeam

Small stream riparian 

forest

South-central interior small stream 

and riparian

Central Appalachian stream and 

riparian

Cumberland seepage forest

elm/ash/

cottonwood

sycamore, red maple, silver maple, 

river birch, boxelder, eastern 

hemlock, black walnut, pawpaw, 

American hornbeam, American elm

Spruce/fir forest Central and southern Appalachian 

spruce/fir forest

Southern Appalachian grass and 

shrub bald

High Allegheny wetland

spruce/fir red spruce, yellow birch, eastern 

hemlock, red maple, sweet birch, 

cucumbertree, American mountain 

ash, black cherry, American beech, 

mountain magnolia, balsam fir, black 

ash, sugar maple

a

Forest-type groups are used to present broad-scale information on forest trends from U.S. Forest Service, Forest Inventory 

and Analysis (FIA) data. In this assessment, forest ecosystems are used to describe specific forest communities and associated 

environments as commonly grouped by local forest management organizations. NatureServe (2013) ecological systems were used 

to describe forest ecosystems, and in many cases, multiple NatureServe systems were combined to describe forest ecosystems 

within the assessment area. Forest-type groups are classified differently from forest ecosystems, and the comparison above is a 

rough cross-walk between the two systems.

Table 2 (continued).
Chapter 1: The 

24

Forest Classification 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. 2006) 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 

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 6). Common and scientific names of trees

and other species mentioned in this assessment are 

found in Tables 24 through 26 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 

25

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 6).

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.
Chapter 1: The 

2

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 

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 

27

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.
Chapter 1: The 

28

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 Mesophytic 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 

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 

29

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 

4,600 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 

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.

A red spruce and mixed hardwood forest in West Virginia. 

Photo by David Ede (retired), Monongahela National Forest.
Chapter 1: The 

30

Forest Composition and Abundance 

Analysis of satellite imagery from the National 

Land Cover Database estimates forest coverage at 

67 percent of the land base. The FIA program, using

a network of permanent field plots, estimates that 

66 percent of land is forested (Table 3) (USF

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 

(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). 

Ecological Total land Forest land Forest la

section (acres) (acres) (%)

221E 13,485,413 9,056,910 67

221F 4,961,053 1,561,695 31

M221A 2,512,194 1,778,653 71

M221B 4,374,448 3,366,526 77

M221C 3,504,533 3,130,745 89

Assessment area 28,837,641 18,894,529 66

Table 3.—Acreage (total and forest land) for each 

ecological section within the assessment area, as 

determined by FIA (USFS 2013)

 Total T

 assessment assessment Ecological section within the assessment area (acres)

FIA forest-type group area (acres) area (%) 221E 221F M221A M221B M221C

Oak/hickory 13,311,652 70.5 6,602,077 828,364 1,328,991 1,926,512 2,625,709 

Maple/beech/birch 3,601,227 19.1 1,461,080 424,142 149,336 1,154,314 412,356 

Elm/ash/cottonwood 667,362 3.5 392,774 196,052 33,296 5,470 39,770 

Oak/pine 430,319 2.3 232,885 – 111,333 54,373 31,729 

Loblolly/shortleaf pine 241,679 1.3 135,343 – 83,496 16,179 6,660 

White/red/jack pine 162,162 0.9 62,503 12,050 27,842 57,692 2,075 

Other hardwoods 141,683 0.7 23,396 12,611 25,320 74,987 5,370 

Nonstocked 98,697 0.5 62,566 21,662 6,397 8,072 –

Aspen/birch 85,235 0.5 54,007 21,024 – 10,204 –

Exotic hardwoods 43,649 0.2 30,279 6,292 – – 7,078 

Spruce/fir group 40,368 0.2 – – – 40,368 –

Exotic softwoods group 22,031 0.1 – 18,920 – 3,111 –

Other eastern softwoods 20,945 0.1 – – 12,642 8,302 –

Oak/gum/cypress 19,376 0.1 – 12,434 – 6,942 –

Fir/spruce/mountain hemlock 8,144 0.0 – 8,144 – – –

Total 18,894,529 100 9,056,910 1,561,695 1,778,653 3,366,526 3,130,747 

Table 4.—Forest land by FIA forest-type group (USFS 2013)
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

31

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 litter 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 potential for managing forests to maximize 

and maintain this carbon is gaining attention 

(Malmsheimer et al. 2011). Carbon sequestration 

and storage in forest ecosystems depend on the 

health and function of those ecosystems in addition 

to human management, episodic disturbances, and 

forest stressors. 

Forest lands within the assessment area are 

estimated 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 softwoods group) to 

98.6 metric tons per acre (oak/gum/cypress group) 

(Fig. 5). The spruce/fir 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 classified 

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 National 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. 

Figure 5.—Forest carbon density by forest-type group. Forest-type groups are arranged from left to right by area 

of forest land (USFS 2013).

0

10

20

30

40

50

60

70

80

90

100

Carb o n d en sity (m etric to n s p er acre)

S o il o rg anic

Litter

D ead wo o d

Liv e b elo wg ro und

Liv e ab o v eg ro und
Chapter 1: The 

32

DRIVERS OF CHAN

IN FOREST ECOSY

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 

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 

33

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. 

Region, location Presettlement forest composition 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%), 

chestnut oak (6%)

(Abrams et al. 1995)

Monongahela National 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 till American beech (36%), sugar maple (17%), 

white oak (14%)

(Whitney 1994)

Northeastern Ohio Coarse till White oak (37%), hickory (13%), black oak (6%) (Whitney 1994)

Table 5.—Witness tree observations from various locations in the Central Appalachians
Chapter 1: The 

34

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 6).

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 

2006). 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 Appalachian

Forest Ecosystem References

All forest ecosystems (Central Appalachians)

Atmospheric deposition of nitrates, sulfates, ozone, and other anthropogenic emissions 

negatively affects forest productivity and resilience.

(Potter et al. 2010)

Deer herbivory is considered a keystone driver through impacts on plant regeneration, 

structure, and species diversity, especially where deer density is high.

(Collins and Carson 2003, 

MDNR 2010, ODNR 2010b)

Drought can lead to increased fire hazard, decreased plant growth, regeneration failure, 

and increased susceptibility to insects and diseases.

(ODNR 2010b)

Energy development for wind energy and shale-gas installations alter ecosystem structure 

through vegetation clearing, soil disturbance, increased erosion potential, fragmentation, 

and direct impacts on forest wildlife species.

(Drohan et al. 2012b, National 

Research Council 2007)

Fragmentation from industrial and urban development has resulted in dispersal barriers 

that impede migration of species and exchange of genetic material, reduced forest patch 

size, and increased forest edge.

(Irwin and Bockstael 2007, 

Potter et al. 2010, Riitters 

2011)

Geographic dispersal barriers slow the dispersal and migration of species in multiple 

directions across the Appalachian Mountains.

(Davis 1983)

Insect pests and diseases increase the risk of individual tree mortality and species

extinction or extirpation.

(DeSantis et al. 2013, Lovett et 

al. 2006, MDNR 2010, ODNR 

2010b, Potter et al. 2010)

(continued on next page)
Chapter 1: The 

35

Table 6 (continued).

Forest Ecosystem References

Appalachian (Hemlock)/Northern Hardwood Forest

Acid deposition negatively affects forest productivity and resilience. (ODNR 2010b, Potter 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, DeSantis et 

al. 2013, Hessl and Pederson 

2013, MDNR 2010)

Invasive plants such as garlic mustard, ailanthus, Japanese stiltgrass, basket grass, and 

paper mulberry reduce natural regeneration, facilitate other exotic species, and alter 

understory plant communities.

(Grafton 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, Asiatic bittersweet, garlic mustard, multiflora rose, 

Japanese honeysuckle, bush honeysuckle, autumn olive, spotted knapweed, viper’s 

bugloss, Japanese stiltgrass, and Canada bluegrass can reduce suitable conditions 

for natural regeneration, facilitate other exotic species, and alter understory plant 

communities.

(Grafton 2013, Hutchinson and 

Vankat 1998, Kurtz 2013)

Suppression of natural fire regimes has contributed to woody encroachment of eastern 

redcedar and mountain laurel; overabundance of these shrubs can reduce diversity and 

affect species regeneration.

(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 stiltgrass, multiflora rose, Japanese 

honeysuckle, bush honeysuckle, autumn olive, Japanese barberry, sericea lespideza, 

yellow sweetclover, and crown vetch reduce suitable conditions for natural regeneration, 

facilitate other exotic species, and alter understory plant communities.

(Grafton 2013, Kurtz 2013)

Past management activities which created microsite conditions conducive to pine 

regeneration are difficult to reconstruct without intense fire, resulting in a gradual 

conversion from pine to oak species.

(Vose et al. 1993)

Suppression of natural fire regimes has reduced structural and species diversity, allowed 

mesic hardwood encroachment on many sites, and limited suitable conditions for natural 

regeneration.

(Abrams 2003, Nowacki and 

Abrams 2008, Patterson 2006, 

Sharitz et al. 1992)

(continued on next page)
Chapter 1: The 

3

Table 6 (continued).

Forest Ecosystem References

Dry/Mesic Oak Forest

Insect pests and diseases such as ambrosia beetle, red oak borer, gypsy moth, oak decline,

armillaria root disease, hypoxylon canker, and phytophtora root rot can cause reduced 

growth and mortality of target species. 

(Lovett et al. 2006, ODNR 

2010b, Raffa et al. 2008)

Invasive plants such as ailanthus, Japanese stiltgrass, and garlic mustard reduce suitable 

conditions for natural regeneration, facilitate other exotic species, and alter understory 

plant communities.

(Grafton 2013, Kurtz 2013, 

MDNR 2010)

Suppression of natural fire regimes has reduced structural and species diversity, allowed 

mesic hardwood encroachment on many sites, and limited suitable conditions for natural 

regeneration.

(Abrams 2003, Nowacki and 

Abrams 2008, Patterson 2006, 

Sharitz et al. 1992)

Large Stream Floodplain and Riparian Forest

Energy development for wind energy and shale-gas installations alter ecosystem structure 

through vegetation clearing, soil disturbance, increased erosion potential, pollution, 

fragmentation, mine land abandonment, and direct impacts on forest wildlife species.

(Drohan et al. 2012b, National 

Research Council 2007)

Erosion from improperly designed or poorly maintained roads, trails, or log landings can 

increase the amount of siltation and sedimentation transported and deposited by streams.

(Pennsylvania Department 

of Environmental Protection 

2012)

Industrial and urban development has resulted in hydrologic infrastructure that affects 

the flood regime, such as impoundments, channelization, dams and reservoirs, and 

drainage and clearing for agriculture.

(Irwin and Bockstael 2007, 

Potter et al. 2010, Riitters 

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.

(DeSantis et al. 2013, Grafton 

2013, Kurtz 2013)

Invasive plants are transported by water and establish more rapidly here than in other 

systems. Species such as Japanese stiltgrass, Japanese hops, and bush honeysuckle can 

reduce suitable conditions for natural regeneration, facilitate other exotic species, and 

alter understory plant communities.

(Grafton 2013, Kurtz 2013)

Mixed Mesophytic and Cove Forest

Forest arson and debris burning are the major causes of wildfire. (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 stiltgrass, garlic mustard, ailanthus, and bush 

honeysuckle can reduce suitable conditions for natural regeneration, facilitate other exotic 

species, and alter understory plant communities.

(Grafton 2013, Kurtz 2013)

Mountaintop removal mining and valley fill changes topography, soil water capacity, and 

runoff; and buries headwater streams where mining waste is dumped.

(U.S. EPA 2005, 2009)

(continued on next page)
Chapter 1: The 

37

Table 6 (continued).

Forest Ecosystem References

North-Central Interior Beech/Maple Forest

Energy development for wind energy and shale-gas installations alter ecosystem structure 

through vegetation clearing, soil disturbance, increased erosion potential, fragmentation, 

and direct impacts on forest wildlife species.

(Drohan et al. 2012b, National 

Research Council 2007)

Fragmentation and urban development has resulted in dispersal barriers that impede 

migration of species and exchange of genetic material, reduced forest patch size, and 

increased forest edge.

(Irwin and Bockstael 2007, 

Potter et al. 2010, Riitters 

2011)

Invasive plants such as princess tree, silk tree, garlic mustard, creeping charlie, Japanese 

stiltgrass, ailanthus, and glossy buckthorn reduce suitable conditions for natural 

regeneration, facilitate other exotic species, and alter understory plant communities.

(Grafton 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.

(DeSantis et al. 2013, Hessl 

and Pederson 2013, ODNR 

2010b)

Small Stream Riparian Forest

Energy development for wind energy and shale-gas installations alter ecosystem structure 

through vegetation clearing, soil disturbance, increased erosion potential, pollution, 

fragmentation, mine land abandonment, and direct impacts on forest wildlife species.

(Drohan et al. 2012b, National 

Research Council 2007)

Erosion from improperly designed or poorly maintained roads, trails, or log landings can 

increase the amount of siltation and sedimentation transported and deposited by streams.

(Pennsylvania Department 

of Environmental Protection 

2012)

Industrial and urban development has resulted in hydrologic infrastructure that affects 

the flood regime, such as impoundments, channelization, dams and reservoirs, and 

drainage and clearing for agriculture.

(Irwin and Bockstael 2007, 

Potter et al. 2010, Riitters 

2011)

Invasive plants are transported by water and establish more rapidly here than in other 

systems. Species such as Japanese stiltgrass, Japanese hops, and bush honeysuckle can 

reduce suitable conditions for natural regeneration, facilitate other exotic species, and 

alter understory plant communities.

(Grafton 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.

(DeSantis et al. 2013, Grafton 

2013, Kurtz 2013)

Spruce/Fir Forest

Acid deposition at high-elevation sites adversely affects the growth and physiology of red 

spruce. Acid deposition has also been linked to increased predisposition to frost damage in 

red spruce.

(Friedland et al. 1984, 

McLaughlin et al. 1990, 

Schuler and Collins 2002)

Anthropogenic impacts from surface mining and wind energy development, roads, 

recreation, and residential development have resulted in fragmentation, altered hydrology, 

and forest conversion.

(Schuler and Collins 2002)

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,

and beech bark disease can cause reduced growth and mortality of target species.

(DeSantis et al. 2013, Hessl 

and Pederson 2013, Schuler 

and Collins 2002)
Chapter 1: The 

38

Fragmentation 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, 

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 Disease

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 

39

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).

Nonnative 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. 6). 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).
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

40

Shifts in Fire Regime

Fire regimes have shifted in the assessment area 

over the past several hundred years, and these shifts 

influence the composition of forest ecosystems. Both 

natural and human-caused fire 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 fire scars in oak forests 

have shown that fire-return intervals either increased 

or decreased immediately after European settlement, 

depending on the stand and location. A 400-year 

reconstruction of fire history in western Maryland 

used fire scars as evidence that pre-European fires 

were as important as post-European fires, and that 

fire suppression in the 20th century has coincided 

with the increase of shade-tolerant species, to the 

detriment of oaks, hickories, and other fire-tolerant 

species (Shumway et al. 2001). The historic role 

of fire 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, fire 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 influential 

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 

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 infiltration 

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 

flow as the dominant runoff process, and increases in 

peak streamflow also are common consequences of 

mine land reclamation (Negley and Eshleman 2006). 

Natural gas wells first 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 

41

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). 

Wind turbines in the Allegheny Mountains, West Virginia. Photo by Patricia Butler, NIACS and Michigan Tech, used with 

permission.

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). 
Chapter 1: The 

42

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 significant impact on vegetation near the forest floor. Photo by Patricia Butler, NIACS and 

Michigan Tech, used with permission.
Chapter 1: The 

43

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 

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 

Ephemeral pool. Seasonally wet areas like this one are 

important for amphibian reproduction. Photo by Patricia 

Butler, NIACS and Michigan Tech, used with permission.
Chapter 1: The 

44

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 

2006)

CURRENT LAND MANAGEM

TREND

Forest Ownershi

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 Manageme

Most private forest land is held by hundreds of 

thousands of nonindustrial family forest owners 

Ownership Forest land (acres) %

Private 16,194,332 85.7

National forest 1,267,057 6.7

State 946,630 5.0

County and municipal 251,036 1.3

National Park Service 85,911 0.5

Department of Defense 80,562 0.4

Other federal 34,622 0.2

Other local government 22,553 0.1

U.S. Fish and Wildlife Service 11,826 0.1

Total 18,894,530 100

Table 7.—Ownership categories of forest land in th

assessment area (USFS 2013)
Chapter 1: The 

45

(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.

Figure 7.—Public and private forest ownership within the assessment area (Hewes et al. 2014).

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, 
Chapter 1: The 

4

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 2006a,

2006b)

Box 3: Programs for Private Landowners

All three states offer incentives to private forest 

landowners, with the intent of maintaining larger 

parcels of privately owned forest and promoting 

sustainable production 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).

Ohi

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 conservation and productive forest land. 

Together with protected lands, about 870,000 

acres, or roughly 10 percent of Ohio’s forests, have 

commitments to soil and water conservation (ODNR 

2010b). The Current Agricultural Use Value program 

is designed to promote timber production, 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 timber 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 Conservation Management Agreement 

(FCMA) is a conservation easement program that 

lowers assessed values to $125 per acre on a 

minimum of 5 acres and a minimum expiration 

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 

portion 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 timeframe, but property 

assessment values are reduced to $187.50 per acre 

of forest. The Maryland Income Tax Modification 

program allows woodland owners to deduct 

double the cost for reforestation and timber stand 

improvement practices 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 incentives for forest landowners who 

practice sustainable forestry on their nonindustrial, 

privately owned forestland comprising 10 acres 

or more (West Virginia Division of Forestry 2012). 

Participation in this program has been growing 

steadily since 1997, and there are now nearly 

2.4 million acres enrolled. Many participating 

landowners have also participated in the USFS Forest 

Stewardship Program in order to receive assistance 

with writing a forest management plan at a reduced 

cost (Dye 2013).
Chapter 1: The 

47

Forest Certification

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 

Forest land enrolled in certification program (acres)

Forest Stewardship Council Sustainable Forestry American Tree Farm Dual-certified

State Council (FSC) Initiative (SFI) System (ATFS) (FSC and SFI)

Maryland – – 139,021 211,000

Ohio 203,957 202,927 293,585 202,927

West Virginia 39,039 257,044 1,013,352 –

a

Data compiled from multiple sources (ATFS 2014, Forest2Market 2013, SFI 2013).

Table 8.—Forest land enrolled in certification programs (acres)a

Average annual roundwood removals (milllion cubic feet)

Ohio (2003 to 2006) Maryland (2008) West Virginia (2007)

Total industrial roundwood 91.2 29.1 189

Domestic logs 67.7 16 104

Pulpwood 23.5 12.5 66.7

Table 9.—Statewide average annual roundwood removals in million cubic feet (Piva and Cook 2011, Walters et al. 

2008, Wiedenbeck and Sabula 2008)
Chapter 1: The 

48

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 SUMM

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.

Ownership Annual net growth (cubic feet) Annual removals (cubic feet) Annual net growth:removals

National Forest 42,954,113 324,147 132.5 

National Park Service 5,706,942 66,516 85.8 

U.S. Fish and Wildlife Service 508,390 – –

Department of Defense 1,967,529 – –

Other federal 757,053 – –

State 28,140,927 10,821,876 2.6 

County and municipal 10,964,142 202,251 54.2 

Other local government 1,403,549 258,295 5.4 

Private 768,866,740 310,059,634 2.5 

Othera 2,234,386 52,090,765 0.0 

Total 863,503,771 373,823,484 2.3 

a

Represents estimated net growth and removals for lands that were diverted from forest and nonforest.

Table 10.—Growth, mortality, and removals of growing stock on forest land in the assessment area (USFS 2013)
49

Chapter 2: Clima

and 

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 CHA

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). 
Chapter 2: Climat Ch

50

Box 4: Global and National Assessments

Intergovernmental Panel on Climate Change

The Intergovernmental Panel on Climate Change 

(IPCC; http://www.ipcc.ch/) is the leading 

international body for the assessment of climate 

change. It was established by the United Nations 

Environment Programme (UNEP) and the World 

Meteorological Organization (WMO) in 1988 to 

provide the world with a clear scientific view on the 

current state of knowledge in climate change and its 

potential environmental and socioeconomic impacts. 

Its Fifth Assessment Report consists of the Climate 

Change 2014 Synthesis Report and reports by 

Working Groups I, II, and III. Drafts of these reports 

are available for download at the Web address 

below. Please note that Web addresses are current 

as of the publication date of this assessment but are 

subject to change. 

Climate Change 2014: Synthesis Report and 

Working Group contributions to the Fifth 

Assessment Report

www.ipcc.ch/report/ar5/ 

Climate Change 2007: Synthesis Report 

www.ipcc.ch/publications_and_data/ar4/syr/en/

contents.html 

U.S. Global Change Research Progra

The U.S. Global Change Research Program (USGCRP; 

http://www.globalchange.gov/) is a federal program 

that coordinates and integrates global change 

research across 13 government agencies to ensure 

that it effectively and efficiently serves the nation 

and the world. Mandated by Congress in the Global 

Change Research Act of 1990, the USGCRP has since 

made the world’s largest scientific investment in the 

areas of climate science and global change research. 

It has released several national synthesis reports 

on climate change in the United States, which are 

available for download at the Web addresses below. 

Synthesis and Assessment Products

http://library.globalchange.gov/products/

assessments/ 

National Climate Assessment

http://nca2014.globalchange.gov/ 

Effects of Climatic 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 Institute for Space 

Studies. Image courtesy of NASA Earth Observatory, Robert Simmon; www.giss.nasa.gov/research/news/20120119/. 
Chapter 2: Climat Ch

51

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 3.6 °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 Effect

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 Influences 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 1960 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 
Chapter 2: Climat Ch

52

Figure 9.—Idealized model of the natural greenhouse effect. Figure courtesy of IPCC (2007).

Figure 10.—Concentrations of greenhouse 

gases showing increases in concentrations 

since 1750 attributable to human activities 

in the industrial era. Concentration units 

are parts per million (ppm) or parts per 

billion (ppb), indicating the number of 

molecules of the greenhouse gas per 

million or billion molecules of air. Figure 

courtesy of IPCC (2007).
Chapter 2: Climat Ch

53

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 CO2-

equivalent (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 MOD

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 Circulation 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 
Chapter 2: Climat Ch

54

Figure 11.—Schematic describing climate models, which are systems of differential equations based on the basic laws of 

physics, fluid motion, and chemistry. The planet is divided into a three-dimensional grid that is used to apply basic equations; 

atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and 

evaluate interactions with neighboring points. Figure courtesy of NOAA (2008).

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 650 to 3,280 feet.
Chapter 2: Climat Ch

55

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. 2006), 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 6). 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

Climate Projections FAQ

www.treesearch.fs.fed.us/pubs/40614

U.S. Global Change Research Progra

Climate Models: an Assessment of Strengths 

and Limitations

library.globalchange.gov/sap-3-1-climate-modelsan-assessment-of-strengths-and-limitations 

Intergovernmental Panel on Climate Change

Chapter 8: Climate Models and Their Evaluation

www.ipcc.ch/publications_and_data/ar4/wg1/en/

ch8.html

Special Report on Emissions Scenarios: 

Summary for Policymakers

http://www.ipcc.ch/ipccreports/sres/emission/index.

php?idp=0
Chapter 2: Climat Ch

5

Box 6: Model Limitations and Uncertainty

“All models are wrong, some are useful.” 

–George Box (Box and Draper 1987) 

Models are conceptual representations of reality, 

and any model output must be evaluated for its 

accuracy to simulate a biological or physical response 

or process. The overall intention is to provide the 

best information possible for land managers given 

the uncertainty and limitations inherent in models.

Model results are not considered standalone 

components of this vulnerability assessment because 

there are many assumptions made about the 

processes simulated by GCMs and impact models, 

uncertainty in future greenhouse gas concentrations, 

and limitations on the grid scale and numbers of 

inputs that a model can reliably handle. Precipitation 

projections 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 identify 

regional caveats and limitations of each model, 

and are considered with additional knowledge and 

experience in the forest ecosystems being assessed. 

We integrated fundamentally different types 

of impact models into our assessment of forest 

vulnerability to climate change. These models 

operate at different spatial scales and provide 

different kinds of information. 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 similarities between some inputs into 

these models—downscaled climate models and 

scenarios, simulation time periods, and many of 

the same species—but because of the fundamental 

differences 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 multiple models and expert judgment is 

needed. The basic inputs, outputs, and architecture 

of each model are summarized in this chapter with 

clear descriptions of the limitations and caveats 

of each model. Limitations of these models with 

specific applicability to the Central Appalachians 

forest ecosystems are discussed in more detail in 

Chapter 5. 

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).

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: Climat Ch

57

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 6.2 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. 

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-percentile 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).
Chapter 2: Climat Ch

58

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 

6.2 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 

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. 2006). 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: Climat Ch

59

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 1960 through 1999

at 1/8-degree resolution (6.2 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 

A woods road in the Scioto Trail State Park, Ohio. Photo by the Ohio Department of Natural Resources, used with permission.
Chapter 2: Climat Ch

6

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 2069, 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 MODE

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 projections from general circulation models (GCMs) and 

specific steps taken in this assessment.
Chapter 2: Climat Ch

6

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 ASSESSI

FOREST CHA

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 
Chapter 2: Climat Ch

6

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. 2006). 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]).

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 Steep slopes, prone to soil erosion and slippage. Photo by 

Patricia Butler, Northern Institute of Applied Climate Science 

(NIACS) and Michigan Tech, used with permission.
Chapter 2: Climat Ch

6

Land use and fragmentation (%)

Cropland 

Nonforest land 

Forest land 

Water

Fragmentation index (Riitters et al. 2002)

Climate (°C, mm)

Mean annual temperature 

Mean January temperature

Mean July temperature

Mean May through September temperature

Annual precipitation

Mean May through September precipitation 

Mean difference between July and January temperature

Elevation (m)

Elevation coefficient of variation 

Minimum elevation

Maximum elevation

Average elevation 

Range of elevation

Soil class (%)

Alfisol 

Aridisol 

Entisol 

Histosol 

Inceptisol

Mollisol 

Spodosol

Ultisol

Vertisol

Soil property

Soil bulk density (g/cm3

)

Potential soil productivity (m3/ha timber)

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 (fine)

Soil slope (%) of a soil component

Organic matter content (% by weight)

Total available water capacity (cm)

Table 11.—Parameters used to predict current and future tree species habitat (Iverson et al. 2008b)

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 

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.” 
Chapter 2: Climat Ch

6

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 

Location

Latitude, longitude

Climate (daily)

Total daily precipitation

Daily minimum temperature

Daily maximum temperature

Daily total solar radiation

Mean monthly wind speed

Soil

Field capacity for 12 soil layers

Wilting point for 12 soil layers

Hydrological coefficients for 12 soil layers (based on 

 percent sand and clay)

Organic matter (Mg/ha)

Nitrogen (Mg/ha)

Percent rock for 12 soil layers

Tree species

Total annual degree day maximum and minimum 

 (Moscow Forestry Sciences Laboratory 2014)

Height and diameter growth equation coefficients 

 (Miles et al. 2006)

Typical maximum mortality age (Loehle 1988)

Frost tolerance (Moscow Forestry Sciences Laboratory 

 2014)

Shade tolerance 

Drought tolerance

Nitrogen equation coefficients (Natural Resources 

 Conservation Service 2014b, Post and Pastor 1996)

Sprout stump number and minimum 

 and maximum diameter

Mineral or organic seed bed 

Maximum seeding rate

Crown area coefficients

Root:shoot ratio by species

Leaf litter quality class

Foliage retention time

Leaf weight per unit crown area

a

From Post and Pastor (1996) unless noted otherwise.

Table 12.—Parameters used in the LINKAGES mod
Chapter 2: Climat Ch

6

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 26 ecological subsection

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 156 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 1,676 t

4,766 feet). For these subsections, the geographi

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 P

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. 
Chapter 2: Climat Ch

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., subsection 890) used in landscape initialization in the LANDIS PRO model (Dijak 

2013).
Chapter 2: Climat Ch

6

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 866-foo

grid cells over the next century for 16 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. 

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 866-foo

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.

CHAPTER SUMM

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.
6

Chapter3: Obs

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 CLI

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 16) and for each section (Appendix 2).

Mean Minimum Maximum Mean

Season temperature (°F) temperature (°F) temperature (°F) precipitation (inches)

Annual 51.1 40.0 62.3 43.1

Winter (Dec-Feb) 31.2 21.7 40.7 9.2

Spring (Mar-May) 50.2 38.0 62.4 11.5

Summer (Jun-Aug) 70.1 58.5 81.6 12.7

Fall (Sep-Nov) 53.0 41.6 64.4 9.7

Table 13.—Annual and seasonal mean, minimum, and maximum temperature and total precipitation for 1971 

through 2000 (ClimateWizard 2013)
Chapter3: Obs

6

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).
Chapter3: Obs

70

Figure 16.—Thirty-year averages of mean annual and seasonal precipitation across the assessment area from 1971 through 

2000. Data source: ClimateWizard (2013).
Chapter3: Obs

71

HISTORICAL T

IN TEMPERAT

AND PRECIPIT

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 1960s 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 (1.6 °F)

and fall (1.4 °F) (Fig. 18). Minimum temperatures 

increased the most in April (1.6 °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.
CHAPTER 3: OBSERVED CLIMATE CHANGE

72

Mean	 Minimum	 Maximum	 Precipitation

Season	 temperature (°F)	 temperature (°F)a	 temperature (°F)	 (inches)

Annual 0.5 1.1 -0.1 1.7

Winter (Dec-Feb) 0.3 0.8 -0.1 -1.0

Spring (Mar-May) 0.8 0.6 0.9 0.7

Summer (Jun-Aug) 0.6 1.6 -0.4 -0.3

Fall (Sep-Nov) 0.3 1.4 -0.7 2.3

a

Values in boldface indicate less than 10-percent probability that the trend could have occurred by chance alone.

Table 14.—Change in annual and seasonal mean temperatures and precipitation from 1901 to 2011 in the

assessment area

Mean	 Minimum	 Maximum	 Precipitation

Section	 Season	 temperature (°F)a	 temperature (°F)	 temperature (°F)	 (inches)

221E Annual 0.3 0.7 -0.1 1.3

Winter -0.1 0.4 -0.5 -1.2

Spring 0.4 0.1 0.8 0.4

Summer 0.4 1.2 -0.4 -0.1

Fall 0.3 1.0 -0.5 2.3

221F Annual 0.5 1.0 0.1 4.2

Winter 0.9 1.3 0.4 0.1

Spring 1.1 0.9 1.3 0.8

Summer 0.2 1.1 -0.7 1.0

Fall -0.1 0.7 -0.8 2.4

M221A Annual 1.4 2.1 0.7 2.0

Winter 1.7 2.2 1.1 -0.4

Spring 1.8 1.6 1.9 1.7

Summer 1.6 2.6 0.6 -2.2

Fall 0.6 2.1 -0.9 2.8

M221B Annual 0.8 1.9 -0.3 0.0

Winter 0.4 0.8 0.0 -1.8

Spring 1.1 1.6 0.6 0.9

Summer 1.0 2.6 -0.7 -1.3

Fall 0.6 2.4 -1.2 2.2

M221C Annual 0.3 1.1 -0.5 0.8

Winter -0.4 -0.1 -0.6 -1.6

Spring 0.3 0.4 0.1 0.7

Summer 0.7 1.8 -0.5 -0.4

Fall 0.6 2.1 -0.9 2.1

a

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 precipitation from 1901 to 2011 by ecological

section within the assessment area (ClimateWizard 2013)
Chapter3: Obs

73

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 entire time period (0.005 °F per year; p = 0.28). 

Data source: ClimateWizard (2013).
Chapter3: Obs

74

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

Ja n u a ry Fe b ru a ry Ma rch Ap ril Ma y Ju n e Ju ly Au g u st Se p tem b e r Octo b e r N o vem b e r D e cem b e r

Temp eratu re c h ang e (°F)

Mo nth

Mean

Minimum

Max imum

*

*

*

*

*

* *

*

* *

*

*

*

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).
Chapter3: Obs

75

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 (1.6 °F) and the most in

summer (2.6 °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”). 

Precipitation

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 1960 through 1969, and 1987. The fou

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). 
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Figure 19.—Annual and seasonal change in mean, minimum, and maximum temperatures across the assessment area from 

1901 through 2011. Stippling indicates there is less than 10-percent probability that the trend could have occurred by chance 

alone. Data source: ClimateWizard (2013).
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Figure 20.—Annual mean precipitation 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 entire period (0.015 inches per 

year; p = 0.26). Data source: ClimateWizard (2013).
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Box 7: Climate Changes over the 21st Century

In this chapter, we present changes in climate 

over the entire historical record for which spatially 

interpolated data trends are available for the 

assessment area. Looking across the entire record 

is helpful in detecting 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 time 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 Organization 2011). Statewide 

averages for the early 21st century can be explored 

within the entire climate record (1895 to 2012) 

through the National Climatic Data Center’s Climate 

at a Glance maps (National Oceanic and Atmospheric 

Administration [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 variation from year 

to year, with the third warmest temperature in 2012.

Precipitation has also changed dramatically during 

2001 through 2012. Ohio experienced its wettest 

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 pattern; both experienced their wettest 

year in 2003, and both have had more above-average 

years than below average. 

And what about the “warming hole” patterns of 

low summer temperatures and high spring and 

summer precipitation? 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 

precipitation trends have not changed markedly 

in the assessment area over the 21st century, but 

spring precipitation has been higher than average 

(NOAA 2014d). Overall, the climate information 

from the 21st century seems to be consistent with 

the trends over the past century in some ways but 

not others. The area is getting generally wetter, and 

the 1930s continues to be the warmest decade on 

record. 

Figure 21.—Annual mean temperatures for Ohio from 1895 

through 2011. Image courtesy of NOAA (2014d).
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-1.0

-0.5

0.0

0.5

1.0

1.5

J an uary F ebruary Marc h Ap ril May J un e J uly Aug us t Sep tember O c to ber No v ember Dec ember

Ch an ge in p recipitation (inches)

Mo n th

* *

*

Figure 22.—Change in monthly mean precipitation 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 1.6 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. 
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Figure 23.—Annual and seasonal changes in mean precipitation from 1901 through 2011 in the assessment area. Stippling 

indicates there is less than 10-percent probability that the trend has occurred by chance. Data source: ClimateWizard (2013).
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Regional Patterns 

Contributing 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. 2006), 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 

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 Temperature

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 1960s, 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 1960s

(DeGaetano and Allen 2002). Winter maximum 
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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 Precipitation

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 

6 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 

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 Hurrican

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 1896 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 60 years, but it is difficult

to determine whether those increases are biased by 

increased accuracy in storm reporting (Robinson et 

al. 2013). 
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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). 
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PHYSICAL PROCES

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 Streamflow

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 1860 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.
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2005). In Ohio, 38 major floods were recorded from 

1861 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 1961

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 1963 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. 2006).

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 
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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. 1996). 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 2006).

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 

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 

2006). 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 2006, Parmesan

2007, Parmesan and Yohe 2003, Root et al. 2003, 

Schwartz et al. 2006b, Zhang et al. 2007). There is

evidence, however, of both positive and negative 

Fall colors on the Hocking State Forest, Ohio. Photo by the Ohio Department of Natural Resources, used with permission.
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Box 8. Phenological Indicators of Chang

Changes in growing season length can be observed 

through studies of phenology. Phenology is the 

timing of recurring plant and animal life-cycle 

stages, such as leaf-out and senescence, flowering, 

maturation of agricultural plants, insect emergence, 

and bird migration. A few studies examining 

phenology in the Central Appalachians indicate 

recent changes:

• In a survey of 270 flowering plants in 

southwestern Ohio, 60 percent showed earlier 

spring flowering over the period from 1976 to 

2003 of about 10 to 32 days (McEwan et al. 

2011). The differences among species may be 

attributed to differences in sensitivity to climate 

as a cue to begin flowering as opposed to other 

indicators such as day length. 

• Ten species of native bees in the Northeast 

(including the entire 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, suggesting that these 

generalist species are keeping pace with changes 

in plant phenological shifts (Bartomeus et al. 

2011).

• The purple martin, a long-distance migratory 

songbird that overwinters in the assessment 

area, has been declining across North America 

and Canada (Nebel et al. 2010). Population 

declines are linked to an increasing mismatch 

between spring arrival date and timing of food 

availability (Fraser et al. 2013). A recent study 

tracking spring migration from the Amazon basin 

to two breeding sites in Pennsylvania and Virginia 

found that purple martins were unable to depart 

earlier, migrate faster, or claim breeding sites 

earlier in response to earlier green-up and insect 

emergence.

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). 
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CHAPTER SUMM

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 Information

Much more information 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 find more information from the following 

resources: 

National Information

The National Climatic Data Center (NCDC) is the 

world’s largest active 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 publication date of 

this assessment but are subject to change.

www.ncdc.noaa.gov/oa/ncdc.html

Regional Information

The Northeast Regional Climate Center is a 

cooperative program between the National Climatic 

Data Center (above) and the state climate offices 

serving the 12-state region of Connecticut, Delaware, 

Massachusetts, Maryland, Maine, New Hampshire, 

New Jersey, New York, Pennsylvania, Rhode Island, 

Vermont, and West Virginia. It provides highquality climate data, derived information, and data 

summaries for the Midwest.

www.stateclimate.org/regional.php?region= 

northeast 

State-level Information

State climatologists provide information about 

current and historical trends in climate throughout 

their states. Visit your state climatologist’s Web 

site for more information about trends and climate 

patterns in your particular state:

Office of the State Climatologist for Ohio

http://www.geography.osu.edu/faculty/rogers/

statclim.html 

West Virginia State Climate Office & Meteorology

http://www.marshall.edu/met/

Maryland State Climatologist Office

http://metosrv2.umd.edu/~climate/ 
89

Chapter 4: Projected 

Extrmes, a

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. 

PROJECTED TREN

IN TEMPERAT

AND PRECIPI

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 2069,

and 2070 through 2099) (Stoner et al. 2012). Daily 

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). 

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).
Chapter 4: Projected Chanes in 

90

Box 10. Climate Modeling in Areas of Complex Topograph

Areas of complex topography, such as the Allegheny 

Mountains and Northern Ridge and Valley sections 

of West Virginia and Maryland, contain some 

of the highest biological diversity in the world 

(Hoekstra et al. 2010). Patterns of ridges, valleys, 

slope, rainshadow effects, cold air pooling, and 

other fine-scale processes create a complex suite 

of ecological niches with various temperature 

and moisture regimes which may actually provide 

the assessment area with additional 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). Precipitation patterns in mountainous 

areas are particularly difficult to model, owing to 

the complexity of atmospheric circulation, wind 

speed, rainshadow effects, and orographic lifting of 

moisture to higher elevations. Although we can use 

the downscaled climate data at the regional level 

to gain an understanding of broad-scale trends, 

statistical downscaling often does not capture 

landscape heterogeneity seen in some portions of 

the assessment area. 

Although few studies have investigated finer 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 

overestimating or underestimating temperature 

and precipitation trends at various elevations and 

landscape positions. A study in the Oregon Cascades, 

which is prone to cold-air pooling similar to the 

Allegheny Mountains, found that temperatures 

in sheltered valley bottoms are decoupled from 

the free atmosphere, and consequently are 

somewhat buffered from changes projected for 

the whole study area (Daly et al. 2010). Modeled 

warming of 4.5 °F at closely spaced sites simulated 

temperature differences of up to 10.8 °F between 

low-elevation valleys and high-elevation ridge tops. 

In a study of mountainous terrain at the Hubbard 

Brook Experimental Forest in New Hampshire, 

three climate models overestimated observed 

precipitation 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 flow is nearly 

perpendicular to the southwest-northeast mountain 

range, producing orographic lifting and subsequent 

snowfall on northwest slopes and higher elevations, 

despite warmer temperatures at lower elevations 

(Perry and Konrad 2006). 

These studies suggest that there are difficulties in 

accurately modeling areas with complex topography 

and rapid elevation change. Regional climate models 

have not performed as well as in areas of relative 

homogeneity, and some correction may be necessary 

to account for elevation, slope, aspect, and relative 

exposure or isolation from the elements. Finerresolution modeling would help identify biases in the 

data based on these factors. Until such fine modeling 

efforts can be executed, the coarse-resolution data 

sets used in this assessment can provide a broad 

foundation of plausible future climates from which 

to consider the caveats above. 

Temperatur

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 

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 16). Projections of

temperature do not diverge substantially for the two 

future scenarios until mid-century (2040 through 

2069), when much larger temperature increases are
Chapter 4: Projected Chanes in 

91

Baseline Departure from baseline

(1971-2000)a Scenario 2010-2039 2040-2069 2070-2099

Mean temperature (°F)

Annual 51.1 PCM B1 0.8 1.5 1.9

GFDL A1FI 2.0 5.3 7.8

Winter (Dec-Feb) 31.2 PCM B1 0.7 2.1 2.1

GFDL A1FI 1.6 4.1 5.5

Spring (Mar-May) 50.2 PCM B1 0.3 1.3 1.8

GFDL A1FI 0.8 4.4 7.1

Summer (Jun-Aug) 70.1 PCM B1 0.9 1.4 1.8

GFDL A1FI 3.2 6.9 9.4

Fall (Sep-Nov) 53.0 PCM B1 1.4 1.5 1.7

GFDL A1FI 2.5 5.5 9.0

Minimum temperature (°F)

Annual 40.0 PCM B1 0.7 1.4 1.9

GFDL A1FI 1.9 5.2 7.7

Winter (Dec-Feb) 21.7 PCM B1 0.6 2.1 2.3

GFDL A1FI 1.5 4.4 5.9

Spring (Mar-May) 38.1 PCM B1 0.5 1.3 1.9

GFDL A1FI 1.0 4.6 7.1

Summer (Jun-Aug) 58.5 PCM B1 0.7 1.4 1.7

GFDL A1FI 2.8 6.5 9.0

Fall (Sep-Nov) 41.6 PCM B1 1.1 0.9 1.5

GFDL A1FI 2.3 5.2 8.7

Maximum temperature (°F)

Annual 62.3 PCM B1 0.9 1.7 1.9

GFDL A1FI 2.1 5.3 7.8

Winter (Dec-Feb) 40.7 PCM B1 0.8 2.0 1.9

GFDL A1FI 1.6 3.9 5.2

Spring (Mar-May) 62.4 PCM B1 0.0 1.2 1.9

GFDL A1FI 0.6 4.2 7.2

Summer (Jun-Aug) 81.6 PCM B1 1.1 1.3 1.9

GFDL A1FI 3.6 7.3 9.8

Fall (Sep-Nov) 64.4 PCM B1 1.7 2.1 1.9

GFDL A1FI 2.7 5.8 9.2

a

The 1971 through 2000 value is based on observed data from weather stations.

Table 16.—Projected changes in annual mean, minimum, and maximum temperatures and precipitation in the 

assessment area averaged over 30-year periods

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 16). 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). 
Chapter 4: Projected Chanes in 

92

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 16).

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.

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 

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 stations. See Appendix 3 for 

projected changes by season.

35

40

45

50

55

60

65

70

75

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

Temperature (0 F)

Annual

GFD L A 1FI Mean GFD L A 1FI Min imum GFD L A 1FI Maximum

PCM B 1 Mean PCM B 1 Min imum PCM B 1 Maximum
Chapter 4: Projected Chanes in 

93

Figure 25.—Projected difference in daily mean temperature at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate scenarios.
Chapter 4: Projected Chanes in 

94

Figure 26.—Projected difference in daily minimum temperature at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate scenarios.
Chapter 4: Projected Chanes in 

95

Figure 27.—Projected difference in daily maximum temperature at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate scenarios.
Chapter 4: Projected Chanes in 

9

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. Revisiting 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 effect extends into the 

assessment area to a lesser degree, characterized 

by a reduction in summer high temperatures over 

the past several decades. Will this pattern continue 

into the future? If we examine only the statistically 

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 fine-scale 

(18.6 miles) regional climate model, CMM5, with 

the PCM model as an input, this study found a large 

discrepancy between the downscaled projections 

and the coarse-scale PCM projections in the central 

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 statistically downscaled 

projections 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 observations, 

there may continue to be regional climate processes, 

such as cumulus cloud formation, 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 justifying the consideration 

of a range of potential future climate scenarios when 

preparing for future climate change.
Chapter 4: Projected Chanes in 

97

Precipitation

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. 

Figure 28.—Projected changes in annual mean precipitation across the assessment area averaged over 30-year periods. The 

1971 through 2000 value is based on observed data from weather stations. See Appendix 3 for projected changes by season.

42

43

44

45

46

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches)

Annual

GFD L A 1FI PCM B 1
Chapter 4: Projected Chanes in 

98

Figure 29.—Projected difference in mean precipitation at the end of the century (2070 through 2099) compared to baseline 

(1971 through 2000) for two climate scenarios.
Chapter 4: Projected Chanes in 

99

Baseline Departure from baseline

(1971-2000)a Scenario 2010-2039 2040-2069 2070-2099

Precipitation (inches)

Annual 43.1 PCM B1 0.2 1.1 2.0

GFDL A1FI -0.3 -1.1 0.2

Winter (Dec-Feb) 9.2 PCM B1 0.0 0.6 0.7

GFDL A1FI 0.9 1.2 2.1

Spring (Mar-May) 11.5 PCM B1 0.8 1.0 0.7

GFDL A1FI 0.7 0.5 1.7

Summer (Jun-Aug) 12.7 PCM B1 0.7 1.4 1.8

GFDL A1FI -1.1 -2.6 -4.1

Fall (Sep-Nov) 9.7 PCM B1 -1.3 -2.0 -1.2

GFDL A1FI -0.7 -0.2 0.4

a

The 1971 through 2000 value is based on observed data from weather stations. 

Table 17.—Projected changes in mean precipitation in the assessment area averaged over 30-year periods

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. 
Chapter 4: Projected Chanes in 

100

PROJECTED TRENDS IN EXT

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 Temperature

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 

by 19 days in the Midwest and 26 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 26 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 Precipitation

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 Chanes in 

101

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 Tornado

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 (6 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). 
Chapter 4: Projected Chanes in 

102

PHYSICAL PROCES

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, 

The Allegheny Mountains, home to a diverse array of high-elevation wetlands. Photo by Patricia Butler, NIACS and Michigan 

Tech, used with permission.

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 Streamflow

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 Chanes in 

103

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 2006).

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, 
Chapter 4: Projected Chanes in 

104

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 

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 Chanes in 

105

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 (6 month

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. 

Evapotranspiration

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 6, 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.
Chapter 4: Projected Chanes in 

10

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 2006). 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). Projections from 2041 through 2070 are shown relative to the 1980 through 2000 baseline. Projections are the 

mean of eight simulations for the A2 scenario. Modified from Kunkel et al. (2013b, 2013c).
Chapter 4: Projected Chanes in 

107

CHAPTER SUMMA

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.
108

Chapter 5: Future 

Impacts

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 PROJECT

OF FOREST CHA

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 

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 

109

Feature Tree Atlas LINKAGES LANDIS

Summary Suitable habitat distribution model 

(DISTRIB) + supplementary information 

(modifying factors)

Patch-level forest succession 

and ecosystem dynamics 

process model

Spatially 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 

combinations

GFDL A1FI and PCM B1

Assessment area Central Appalachians assessment area within Ohio, West Virginia, Maryland: Sections 221E and 

221F in Province 221; Sections M221A, M221B, and M221C in Province M221 

Resolution 12-mile grid 0.2-acre (1/12-ha) plots 

representing landforms in 

subsections

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

Simulation period n/a 30 years 2009 through 2099

Competition, survival, 

and reproduction 

No Yes Yes

Disturbances No (but addressed through modifying 

factors)

No Timber harvest

Tree physiology 

feedbacks

No Yes No

Succession or 

ecosystem shifts

No No Yes

Biogeochemical 

feedbacks

No Yes No

Table 18.—Overview of the three models used in this assessment
Chapter 5: Future Climate 

110

Tree Atla

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 

2069), and end (2070 through 2099) of century time

periods. Roughly half of the tree species modeled 

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 (62). 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 

111

Table 19.—Potential changes in suitable habitat for 93 tree speciesa in the Central Appalachians region 

Common name PCM B1 GFDL A1F

Mixed Results

American basswood No Change Large Decrease

American beech No Change Large Decrease

American elm No Change Small Decrease

Black locust No Change Small Decrease

Black maple No Change Large Decrease

Black oak No Change Large Increase

Black walnut Small Increase No Change

Black willow (-) Small Decrease Large Increase

Blue ash (-) No Change Small Decrease

Boxelder (+) No Change Small Increase

Bur oak (+) No Change Large Increase

Butternut (-) No Change Extirpated

Chestnut oak (+) No Change Small Decrease

Eastern cottonwood Small Decrease Large Increase

Eastern hemlock (-) No Change Small Decrease

Eastern hophornbeam (+) No Change Small Increase

Eastern white pine No Change Large Decrease

Flowering dogwood No Change Small Decrease

Loblolly pine (-) No Change Large Increase

Mockernut hickory No Change Small Increase

Northern catalpa No Change Small Increase

Northern red oak No Change Small Decrease

Ohio buckeye (-) No Change Large Decrease

Pawpaw No Change Large Decrease

Pin oak (-) No Change Small Increase

Red maple (+) No Change Large Decrease

Red mulberry No Change Large Increase

Red spruce (-) No Change Large Decrease

River birch No Change Small Increase

Rock elm (-) No Change Large Increase

Sassafras No Change Small Decrease

Scarlet oak No Change Small Decrease

Shumard oak (+) NA Large Increase

Silver maple (+) Small Decrease Large Increase

Slippery elm No Change Small Decrease

Sourwood (+) Small Increase Small Decrease

Sugar maple (+) No Change Large Decrease

Swamp white oak No Change Large Decrease

Sweet birch No Change Large Decrease

Sycamore No Change Small Increase

Table Mountain pine (+) No Change Small Increase

Tulip tree No Change Large Decrease

Virginia pine Small Increase No Change

White ash (-) No Change Large Decrease

White oak (+) No Change Small Increase

Willow oak NA Large Increase

Common name PCM B1 GFDL A1F

Declines under Both Scenarios

Balsam fir (-) Small Decrease Large Decrease

Bigtooth aspen Large Decrease Extirpated

Black ash (-) Large Decrease Small Decrease

Black cherry (-) Small Decrease Large Decrease

Chokecherry Small Decrease Extirpated

Pin cherry Small Decrease Large Decrease

Quaking aspen Large Decrease Large Decrease

Red pine Large Decrease Large Decrease

Striped maple Small Decrease Large Decrease

Yellow birch Small Decrease Large Decrease

No Change under Both Scenarios

American chestnut No change No change

American holly No change No change

American hornbeam No change No change

Bear oak (scrub oak) No change No change

Blackgum (+) No change No change

Cucumbertree No change No change

Mountain maple (+) No change No change

Northern pin oak (+) No change No change

Pignut hickory No change No change

Pitch pine No change No change

Serviceberry No change No change

Southern magnolia No change No change

Tamarack (native) (-) No change No change

Yellow buckeye (-) No change No change

Increases under Both Scenario

Bitternut hickory (+) Large Increase Large Increase

Blackjack oak (+) Small Increase Large Increase

Chinkapin oak Large Increase Large Increase

Common persimmon (+) Large Increase Large Increase

Eastern redcedar Large Increase Large Increase

Eastern redbud Small Increase Small Increase

Green ash Small Increase Large Increase

Hackberry (+) Small Increase Large Increase

Honeylocust Small Increase Large Increase

Osage-orange (+) Small Increase Large Increase

Post oak (+) Large Increase Large Increase

Shagbark hickory Small Increase Large Increase

Shingle oak Small Increase Large Increase

Shortleaf pine Large Increase Large Increase

Southern red oak Large Increase Large Increase

Sugarberry Large Increase Large Increase

Sweetgum Large Increase Large Increase

Winged elm Large Increase Large Increase

(continued on next page)
Chapter 5: Future Climate 

112

a

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, respectively. 

Appendix 4 contains descriptions of change classes and 

complete results for all 93 species for the assessment area 

and for each ecological section. 

Common name PCM B1 GFDL A1F

New Suitable Habita

Black hickory New Habitat New Habitat

Cedar elm NA New Habitat

Northern white-cedar NA New Habitat

Water locust NA New Habitat

Water oak NA New Habitat

Table 19 (continued). 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.

Species Factors that affect rating

Highest adaptive 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 competitor, wide range of habitats

4. Bur oak drought tolerant, fire tolerant

5. Eastern hophornbeam shade tolerant, wide range of temperature tolerances, wide range of habitats 

Lowest adaptive capacity

1. Black ash emerald ash borer susceptibility, poor light competitor, limited dispersal ability, 

poor seedling establishment, fire intolerant, dependent on specific hydrologic regime

2. Butternut butternut canker, drought intolerant, fire intolerant, poor light competitor

3. Balsam fir spruce budworm and other insect pests, fire intolerant, drought intolerant

4. White ash emerald ash borer, drought intolerant, susceptible to fire topkill 

5. Blue ash emerald ash borer susceptibility, limited dispersal ability, fire intolerant, 

poor light competitor, dependent on specific hydrologic regime

Table 20.—Species with the five highest and five lowest values for adaptive capacity based on Tree Atlas modifying 

factors
Chapter 5: Future Climate 

113

Decreases in Suitable Habita

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 Habita

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 Habita

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 
Chapter 5: Future Climate 

114

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 

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 Habit

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 

115

New Suitable Habita

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 Trend

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 
Chapter 5: Future Climate 

11

Figure 32.—Modeled importance values for chestnut oak 

across the assessment area and the larger geographic region 

for current climate conditions (top) and projected for the 

end of the century (2070 through 2099) for the PCM B1 

(middle) and GFDL A1FI (bottom) 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.

Figure 33.—Modeled importance values for sugar maple 

across the assessment area and the larger geographic region 

for current climate conditions (top) and projected for the 

end of the century (2070 through 2099) for the PCM B1 

(middle) and GFDL A1FI (bottom) 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 

117

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-elevation wetlands. Photo by Patricia Butler, NIACS and Michigan 

Tech, used with permission.
Chapter 5: Future Climate 

118

Figure 34.—Modeled importance values for eastern white 

pine across the assessment area and the larger geographic 

region for current climate conditions (top) and projected for 

the end of the century (2070 through 2099) for the PCM B1 

(middle) and GFDL A1FI (bottom) 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.

Figure 35.—Modeled importance values for red spruce 

across the assessment area and the larger geographic region 

for current climate conditions (top) and projected for the 

end of the century (2070 through 2099) for the PCM B1 

(middle) and GFDL A1FI (bottom) 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 

119

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 0.2-

acre plots within  landforms in 26 subsections. W

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. 36 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.
Chapter 5: Future Climate 

120

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.

Future climate

Current climate PCM B1 GFDL A1F

Species SEP SEP % change SEP % change

American beech 0.22 0.22 0.0 0.02 -90.9

American elm 0.14 0.16 14.3 0.18 28.6

Balsam firb 0.00 0.00 0.0 0.00 0.0

Black cherry 0.28 0.30 7.1 0.30 7.1

Blackgum 0.16 0.19 18.8 0.21 31.3

Black oak 0.19 0.20 5.3 0.13 -31.6

Chestnut oak 0.20 0.20 0.0 0.12 -40.0

Eastern redcedar 0.20 0.24 20.0 0.26 30.0

Eastern hemlock 0.13 0.13 0.0 0.01 -92.3

Eastern white pine 0.34 0.35 2.9 0.04 -88.2

Flowering dogwood 0.05 0.08 60.0 0.10 100.0

Loblolly pine 0.13 0.29 123.1 0.51 292.3

Northern red oak 0.29 0.31 6.9 0.18 -37.9

Pignut hickory 0.35 0.37 5.7 0.36 2.9

Post oak 0.05 0.10 100.0 0.17 240.0

Red maple 0.31 0.34 9.7 0.37 19.4

Red spruceb 0.00 0.00 0.0 0.00 0.0

Scarlet oak 0.17 0.17 0.0 0.04 -76.5

Shortleaf pine 0.08 0.22 175.0 0.35 337.5

Sugar maple 0.51 0.50 -2.0 0.05 -90.2

Tulip tree 0.76 0.83 9.2 0.86 13.2

White ash 0.43 0.53 23.3 0.41 -4.7

White oak 0.32 0.35 9.4 0.35 9.4

a

SEP absolute values were rounded to two decimal places.

b

Absolute values are small enough that percentage changes are more important.

Table 21.—Projected changes in mean species establishment probability (SEP) valuesa from current climate at year 

2100 across the assessment area
Chapter 5: Future Climate 

121

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 P

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 886-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 
Chapter 5: Future Climate 

122

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 sections. The width of the colored line represents the 

relative 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.
Chapter 5: Future Climate 

123

 TPA by year and climate scenari

Current PCM B1 GFDL A1F

 TPA TPA Change TPA Change TP

year year from year year from current year from current

Tree species 2009 2100 2009 2100 climateb 2100 climate

American beech 26.8 14.2 -47% 13.9 -2% 8.1 -43%

Black cherry 18.9 7.2 -62% 7.3 2% 7.3 2%

Black oak 8.4 2.0 -77% 2.0 1% 1.8 -10%

Chestnut oak 14.9 30.9 108% 32.6 5% 25.0 -19%

Eastern hemlock 6.1 3.8 -38% 3.6 -6% 2.0 -47%

Eastern redcedar 0.8 0.1 -91% 0.0 0% 0.1 87%

Eastern white pine 7.3 2.5 -66% 2.4 -2% 1.3 -46%

Loblolly pine 0.5 0.2 -68% 0.2 43% 0.3 81%

Northern red oak 11.9 3.6 -70% 6.0 68% 5.2 45%

Pignut hickory 7.3 5.4 -27% 5.6 4% 5.6 4%

Red maple 77.4 66.9 -13% 70.3 5% 74.5 11%

Red spruce 5.1 0.4 -92% 0.4 0% 0.2 0%

Scarlet oak 4.0 1.1 -72% 1.2 3% 0.9 -17%

Sugar maple 54.1 53.6 -1% 50.8 -5% 23.4 -56%

Tulip tree 22.9 104.3 356% 112.1 8% 114.8 10%

White ash 11.9 7.3 -39% 7.9 8% 6.8 -7%

White oak 12.8 57.7 349% 60.9 6% 60.8 5%

a

Assessment area values were derived from the weighted average of sections.

b

Change represents the difference from current climate in 2100 and represents potential change due to climate change. 

BA by year and climate scenarioa

Current PCM B1 GFDL A1F

BA BA Change BA Change BA Change

in in from year year from current year from current

Tree species 2009 2100 2009 2100 climateb 2100 climate

American beech 5.1 4.6 -9% 4.6 0% 4.2 -10%

Black cherry 7.3 4.6 -38% 4.6 1% 4.5 -1%

Black oak 6.8 1.5 -78% 1.5 1% 1.5 -2%

Chestnut oak 5.6 4.9 -12% 5.1 3% 4.6 -5%

Eastern hemlock 1.7 1.1 -35% 1.1 1% 1.0 -9%

Eastern redcedar 0.1 0.0 -89% 0.0 0% 0.0 0%

Eastern white pine 1.2 1.0 -13% 1.0 0% 0.9 -7%

Loblolly pine 0.1 0.2 127% 0.2 0% 0.2 0%

Northern red oak 6.8 2.8 -58% 3.3 15% 3.1 9%

Pignut hickory 2.6 2.7 5% 2.8 3% 2.7 1%

Red maple 16.0 30.0 88% 30.5 2% 30.3 1%

Red spruce 1.0 0.3 -67% 0.3 0% 0.3 0%

Scarlet oak 4.8 0.7 -86% 0.7 8% 0.7 -2%

Sugar maple 6.1 8.0 30% 8.0 0% 7.3 -9%

Tulip tree 6.5 10.5 62% 10.8 3% 10.8 3%

White ash 2.5 2.9 18% 3.0 4% 2.9 0%

White oak 5.7 8.4 46% 8.6 2% 8.3 -1%

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 are

in year 2100
Chapter 5: Future Climate 

124

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 56 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 sections. The width of the colored line represents trees 

per acre for each species at various points through time. 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.
Chapter 5: Future Climate 

125

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 Trend

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 attributable to succession and management. Basal area in 2100 for 

GFDL A1FI represents the total change in basal area attributable to succession and management plus high greenhouse gas 

emissions.
Chapter 5: Future Climate 

12

DISCUSSION OF MODEL

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 

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 

127

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. 

Limitations

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 CUR

SCIENTIFIC KNOWLEDGE 

ON FOREST IM

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. 
Chapter 5: Future Climate 

128

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 

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 

129

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. 

Winter Storm Damage

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 

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. 

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 Fores

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). 

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. 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS 

130

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 modifiers like soil texture and 

root depth, but the general principles are useful.

Flooding can affect forest systems differently, 

depending on the frequency and duration of floods, 

and the soil, vegetation, and topographic complexity 

of the landscape. In mountainous areas, floods 

are generally brief and intense, with floodwaters 

funneling rapidly down steep slopes and into valley 

streams (Eisenbies et al. 2007, Swanson et al. 1998). 

These swift, fierce floods often damage trees by 

breaking stems and limbs, and scouring vegetation. 

In lowland areas, floods 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 flooding, which can increase 

erosion and transport of nutrients, contaminants, 

and pathogens (Groffman et al. 2014). Disturbances 

caused by floods, drought, scouring by ice, and 

river channeling often drive tree species and forest 

diversity, especially in lowland and riparian forests 

(Vadas and Sanger 1997). 

A stream meandering through a small stream riparian forest. Photo by Patricia Butler, NIACS and Michigan Tech, used with 

permission.
Chapter 5: Future Climate 

131

Soil Erosion

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).

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). 

Wildfire

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). 

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. 2006, Szlavecz et al. 2010)
Chapter 5: Future Climate 

132

An analysis of fire probability across the globe using 

16 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 

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 2006).

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 2006). Authors of a review paper

on climate and wildfire conclude that fire-related 
Chapter 5: Future Climate 

133

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 Dioxid

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 Deposition

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. 2006, 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 
Chapter 5: Future Climate 

134

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. 2006). 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 2064 (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.

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 

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 

Native grasses and forbs, dominant plants in this flat, wet 

area. Photo by Patricia Butler, NIACS and Michigan Tech, 

used with permission.
Chapter 5: Future Climate 

135

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 Pathogen

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 2006, 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 
Chapter 5: Future Climate 

13

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. 

Effects 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. 

CHAPTER SUMMA

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 6
137

Chapter 6: Forest Eco

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, illustrating 

the relationship among exposure, sensitivity, and adaptive 

capacity. Adapted from Glick et al. (2011).
Chapter 6: Forest Eco

138

A SYNTHESIS OF CLIMATE 

IMPACTS ON FOREST ECOS

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. 

Potential Impacts of Climate Change 

on Drivers and Stressors 

Many physical and biological factors contribute to 

the current state of forest ecosystems in the Central 

Figure 42.—Confidence determination 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 Eco

139

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. 2006a, 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). 
Chapter 6: Forest Eco

140

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 

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. 2006, Weed et

al. 2013). Forest fragmentation and unknown future 

wildfire management decisions may limit individual 

fires even as fire risk increases. 
Chapter 6: Forest Eco

141

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. 

Potential 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. 
Chapter 6: Forest Eco

142

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 Institute of 

Applied Climate Science (NIACS) and Michigan Tech, used with permission.
Chapter 6: Forest Eco

143

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 
Chapter 6: Forest Eco

144

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 

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 Eco

145

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). 

Adaptive 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). 
Chapter 6: Forest Eco

14

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 

1996, 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. 2006, 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 

suitable habitat for tree species will migrate between 

60 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 Eco

147

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 2006, Brose et

al. 2013, Patterson 2006). 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 2006,

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 
Chapter 6: Forest Eco

148

(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 DETERM

FOR INDIVI

FOREST ECOSYS

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 determination summaries for forest ecosystems considered in this assessment

Forest ecosystem Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Appalachian (hemlock)/

northern hardwood 

forest

Negative Low-Moderate High Medium Medium

Dry calcareous forest, 

woodland, and glade Neutral-Negative Low-Moderate Moderate-High Limited-Medium Medium 

Dry oak and oak/pine 

forest and woodland Positive Moderate-High Low Medium Medium-High

Dry/mesic oak forest Positive-Neutral High Low- Moderate Medium Medium-High

Large stream floodplain 

and riparian forest Negative Low High Medium Medium

Mixed mesophytic and 

cove forest Neutral-Negative Moderate-High Moderate Limited-Medium Medium

North-central interior 

beech/maple forest Neutral Moderate Moderate Limited-Medium Medium

Small stream riparian 

forest Negative Moderate Moderate-High Medium Medium

Spruce/fir forest Negative Moderate High Limited-Medium Medium
Chapter 6: Forest Eco

149

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.

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.
Chapter 6: Forest Eco

150

Appalachian (Hemlock)/Northern Hardwood Forest 

High Vulnerability (medium evidence, medium agreement) 

Climate change may intensify several interacting stressors, such as drought, forest pests, and invasive 

species. Any increased wildfire activity would be detrimental to the health of this forest type. Reduced 

species diversity may decrease resilience to the future climate. Valley bottoms and other microsites in 

areas of complex topography may be buffered from the effects of climate change, providing refugia. 

Negative Potential 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 

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 2006). 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. 
Chapter 6: Forest Eco

151

Low-Moderate Adaptive 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 

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. 

A hemlock stand in Wooster Memorial Park, Ohio. Photo by 

David M. Hix, Ohio State University, used with permission.

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.
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

152

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 occupation of extreme habitat on unique soil types implies that it is 

geographically limited, and unable to shift freely on the landscape. 

Neutral-Negative Potential Impacts

Drivers: Drought and fire 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. 

Wildfire potential could increase under drier 

conditions, although fire intensity will determine 

whether it is a positive or negative impact. Lowintensity fire could benefit this ecosystem by 

reducing the eastern redcedar component in the 

woodland portions of this ecosystem that are 

becoming overgrown. However, high-intensity 

fire that results in widespread mortality of eastern 

redcedar will dramatically change this ecosystem.

Dominant Species: Projected changes in climate 

are expected to benefit 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 

benefit from soil moisture deficits, fire, 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. 

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 fire 

fuels in this type, leading to potentially more-intense 

fire when it does occur. 

Low-Moderate Adaptive 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 fire 

may allow expansion of the woodland and glade 

elements at the expense of the forested areas. 
Chapter 6: Forest Eco

153

A north-central Appalachian circumneutral cliff 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.
Chapter 6: Forest Eco

154

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 conditions that limit regeneration may be buffered 

by oak’s ability to resprout. Increased drought and fire are likely to benefit this ecosystem, discourage 

invasive species, and maintain an open structure that promotes oak and pine regeneration. 

Positive Potential 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 

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 Eco

155

Moderate-High Adaptive 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.
Chapter 6: Forest Eco

15

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 conditions and fire, although young regeneration may be sensitive to 

severe drought and fire. Southern oak and hickory species are likely to benefit from projected changes 

in climate. 

Positive-Neutral Potential 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. 

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 Adaptive 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 Eco

157

A mesic oak forest with maple regenerating 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 dominating 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.
Chapter 6: Forest Eco

158

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 effects 

of insect pests, invasive species, and pollution. Dependence on periodic inundation, combined with 

competition from invasive species, may result in a reduced ability of native tree species to tolerate 

increased disturbances. 

Negative Potential 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 

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 Eco

159

Low Adaptive 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 

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 floodplain forest on the Buckhanon River, 

West Virginia. Photo by Brian Streets, West Virginia Division 

of Natural Resources, Natural Heritage Program, used with 

permission.

A large stream floodplain 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 floodplain forest on the Greenbrier River, West Virginia. Photo 

by Brian Streets, West Virginia Division of Natural Resources, Natural Heritage 

Program, used with permission.
Chapter 6: Forest Eco

16

Mixed Mesophytic and Cove Forest 

Moderate Vulnerability (limited-medium evidence, medium agreement) 

This relatively sheltered ecosystem may face a suite of amplified disturbances, including wildfire, 

drought, and invasion by invasive species. Suitable habitat for many species is projected to decline, 

although there is great potential for the complex topography to provide refugia where disjunct 

populations may persist. 

Neutral-Negative Potential 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. 

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 

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.

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. 
Chapter 6: Forest Eco

16

Moderate-High Adaptive 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.
Chapter 6: Forest Eco

16

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 position 

on glacial till floodplains, moraines, and plateaus promotes and preserves moist soil conditions, a critical 

feature which may help buffer the impacts of changing temperature and hydrologic regimes. 

Neutral Potential 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. 

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 Adaptive 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 Eco

16

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.
Chapter 6: Forest Eco

16

Small Stream Riparian Forest

Moderate-High Vulnerability (medium evidence, medium agreement) 

This ecosystem is adapted to natural disturbance, but is threatened by amplification 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 conditions, but a keystone species, hemlock, is likely to disappear 

in many areas. 

Negative Potential 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.

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 Adaptive 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 Eco

16

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.

Cottonwood and other hardwoods along a stream at Alum Creek, Ohio. Photo by David M. 

Hix, Ohio State University, used with permission.
Chapter 6: Forest Eco

1

Spruce/Fir Forest 

High Vulnerability (limited-medium evidence, medium agreement) 

This ecosystem is dependent on very moist conditions, and persists only in the coolest, wettest, and 

highest elevation sites in mountainous sections. Projected increases in temperature and decreases in 

summer and fall precipitation may exceed the ecological tolerances of this ecosystem’s defining species. 

Complex topography may provide cool pockets of habitat where these species would be likely to persist. 

Negative Potential 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 

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 Eco

16

Moderate Adaptive 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-elevation spruce/fir forest in West Virginia. Photo by Elizabeth Byers, West Virginia Division of 

Natural Resources, Natural Heritage Program, used with permission.
Chapter 6: Forest Eco

16

CHAPTER SUMM

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 

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.
16

Chapter 7: Man

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 
Chapter 7: Man

170

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 Institute of Applied Climate Science (NIACS) 

and Michigan Tech, used with permission.
Chapter 7: Man

171

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 
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(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: 

More Information

• 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 

Eastern garter snake. Photo by Patricia Butler, NIACS and Michigan Tech, used with permission.
Chapter 7: Man

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• 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/23672/Default.asp

THREATENED AND ENDANGER

PLANT SPECIE

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. 2006b). 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. 2006b).
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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 Information

• 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/23652/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 

Wildflowers of West Virginia. Photo by Patricia Butler, NIACS 

and Michigan Tech, used with permission.

NONNATIVE INVA

PLANT SPECI

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: Man

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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 1.6 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 Information

• 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 FUEL

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, 
Chapter 7: Man

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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 

response that makes the most sense, and responses 

may differ between landowners, land managers, and 

organizations.

More Information 

• 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

INFRASTRU

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: Man

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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 60 miles per hour gusting to 100 miles per hour

ravaged a 600-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 600,000 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 Information 

• 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 

An old culvert. Land managers are beginning to replace culverts like this one with larger culverts designed to accommodate 

larger peak flows and allow the passage of aquatic organisms. Photo by Patricia Butler, NIACS and Michigan Tech, used with 

permission.
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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. 

More Information 

• 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_011356.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. 1996,

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: Man

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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 Information 

• 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 

The steep ridges and valleys in the Allegheny Mountains. These landforms are at greater risk of high-velocity runoff and 

erosion. Photo by Patricia Butler, NIACS and Michigan Tech, used with permission.
Chapter 7: Man

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FOREST PRO

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 6).

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.

More Information

• The U.S. Forest Service 2010 Resources Planning 

Act Assessment includes future projections for 

forest products and other resources through the 

year 2060 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 

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 2006).

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: Man

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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 65-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 Information 

• 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_srs116/gtr_srs116-04.pd

• 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 CARB

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 
Chapter 7: Man

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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 Information

• 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 

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.
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RECREATI

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).

The Blackwater Falls in the Canaan Valley, West Virginia, a popular recreation area. Photo by Patricia Butler, NIACS and 

Michigan Tech, used with permission.
Chapter 7: Man

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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). 

More Information

• 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 1964 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 1964). 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 2006a)

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. 
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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. 

Private lands juxtaposed with the Shawnee State Forest, Ohio. Photo by the Ohio Department of Natural Resources, used with 

permission.

More Information

• 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/ 
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18

CULTURAL RESO

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 1996). 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, 

accelerating structural degradation (Schiffer 1996).

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 Information

• 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 FORE

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. 
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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 Information

• 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-ASSOCI

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-
Chapter 7: Man

188

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 

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). 

Multiple land uses in West Virginia. Agriculture and development dominate the flat valleys. Photo by Patricia Butler, NIACS 

and Michigan Tech, used with permission.
Chapter 7: Man

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More Information

• 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 PLAN

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. 
Chapter 7: Man

190

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 Information

• The Open Space Institute and Doris Duke 

Charitable Foundation: www.osiny.org/site/PageS

erver?pagename=Issues_Habitat 

• The Nature Conservancy Northeast Resilience 

Analysis: www.conservationgateway.org/

ConservationByGeography/NorthAmerica/

UnitedStates/edc/reportsdata/terrestrial/resilience/

ne/Pages/default.aspx 

NATURAL RESOU

MANAGEMENT PLANNI

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: Man

191

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 Information

• 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 

• 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

• 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 SUMM

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. 
192

Gl

aerosol

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. 

asynchronous quantile 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.

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.

barrens

plant communities that occur on sandy soils and that 

are dominated by grasses, low shrubs, small trees, 

and scattered large trees.

baseflow

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.)

biomass

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.

boreal 

a zone between 50 and 55° and 65 and 70° latitude

in the Northern Hemisphere characterized by cool 

northern temperatures and low rainfall (<20 inches). 

carbon dioxide (CO) fertilization 

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.

CO-equivalent (CO2-eq)

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.

convective 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. 
Gl

193

dendritic drainage

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.

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). 

disturbance

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. 

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. 

driver

any natural or human-induced factor that directly or 

indirectly causes a change in an ecosystem.

dynamical downscaling

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.

eastern deciduous forest

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.

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.

ecoregion

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

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

a serpentine ridge of glacial drift, originally 

deposited by a meltwater stream running beneath a 

glacier.

evapotranspiration

the sum of evaporation from the soil and 

transpiration from plants.

fluvial

of, relating to, produced by, or inhabiting a stream or 

river.
Gl

194

forest type

a classification of forest land based on the dominant 

species present, as well as associate species 

commonly occurring with the dominant species.

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.

fragmentation

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.

functional diversity

the value, range, and relative abundance of 

functional traits in a given ecosystem.

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 circulation 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 drift (till)

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. 

greenhouse effect

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.

growing season

the period in each year when the weather and 

temperature are right for plants to grow.

growing stock

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.

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).
Gl

195

industrially owned forest

land owned by forest product companies that harvest 

and market timber.

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.

kettle

a depression left in a mass of glacial drift, formed by 

the melting of an isolated block of glacial ice.

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).

lacustrine 

pertaining to or formed in a lake.

mass wasting

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

pertaining to sites or habitats characterized by 

intermediate (moist, but not wet or dry) soil moisture 

conditions.

mesophication 

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).

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

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.

nonindustrial private landowners

an ownership class of private lands where the owner 

does not operate wood-using plants.

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.
Gl

19

orographic lifting

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.

parcelization

the subdivision of a single forest ownership into 

two or more ownerships. Parcelization may result 

in fragmentation if habitat is altered under new 

ownership. 

peak flow

the maximum instantaneous discharge of a stream or 

river at a given location. 

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.

process model

a model that relies on computer simulations based 

on mathematical representations of physical and 

biological processes that interact over space and 

time.

projection

a model-derived estimate of future climate, and the 

pathway leading to it.

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.

pulpwood

roundwood, whole-tree chips, or wood residues used 

for the production of wood pulp for making paper 

and paperboard products. 

radiative forcing

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.

RandomForests

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.

Real Estate Investment Trust (REIT)

Considered private, nonindustrial landowners, 

REITS own and operate large acreages of 

timberland.

realized niche

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.

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).
Gl

197

refugia

locations and habitats that support populations of 

organisms that are limited to small fragments of their 

previous geographic range.

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. 

roundwood

logs, bolts, and other round timber generated from 

harvesting trees for industrial or consumer use. 

runoff

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. 

sawtimber 

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. 

scenario

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).

senescence

the process of aging in plants. Leaf senescence 

causes leaves of deciduous trees to change color in 

autumn.

significant trend

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.

softwood

a coniferous tree, usually evergreen, having needles 

or scale-like leaves.

species distribution model

a model that uses statistical relationships to project 

future change.
Gl

198

statistical 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.

stormflow

runoff that occurs due to a heavy precipitation event.

streamflow 

discharge that occurs in a natural surface stream 

course whether or not it is diverted or regulated.

threat

a source of danger or harm.

Timber Investment Management Organization 

(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.

transpiration

liquid water phase change occurring inside plants 

with the vapor diffusing to the atmosphere.

uncertainty

a term used to describe the range of possible values 

around a best estimate, sometimes expressed in 

terms of probability or likelihood.

vulnerability

susceptibility to a threat.
199

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234

Appendix 1. 

Table 24.—Common and scientific names of native plants mentioned in this assessment

Common Name Scientific Name

balsam fir Abies balsamea

boxelder Acer negundo

black maple Acer nigrum 

striped maple Acer pensylvanicum

red maple Acer rubrum

silver maple Acer saccharinum

sugar maple Acer saccharum

mountain maple Acer spicatum

northern wild monkshood Aconitum noveboracense

yellow buckeye Aesculus flava

Ohio buckeye Aesculus glabra

lillydale onion Allium oxyphilum

speckled alder Alnus incana

hazel alder Alnus serrulata

serviceberry Amelanchier Medik.

shale barren rockcress Arabis serotina

pawpaw Asimina triloba

yellow birch Betula alleghaniensis

sweet birch Betula lenta

river birch Betula nigra

American hornbeam Carpinus caroliniana

mockernut hickory Carya alba

bitternut hickory Carya cordiformis

pignut hickory Carya glabra

shagbark hickory Carya ovata

black hickory Carya texana

American chestnut Castanea dentata

northern catalpa Catalpa speciosa

sugarberry Celtis laevigata

common hackberry Celtis occidentalis

common buttonbush Cephalanthus occidentalis

eastern redbud Cercis canadensis

Bentley’s coralroot Corallorhiza bentleyi

Common Name Scientific Name

silky dogwood Cornus amomum

roughleaf dogwood Cornus drummondii

flowering dogwood Cornus florida

ram’s-head lady’s-slipper Cypripedium arietinum

common persimmon Diospyros virginiana

American beech Fagus grandifolia

white ash Fraxinus americana

black ash Fraxinus nigra

green ash Fraxinus pennsylvanica

blue ash Fraxinus quadrangulata

water locust Gleditsia aquatica

honeylocust Gleditsia triacanthos

bushy St. Johnswort Hypericum densiflorum

American holly Ilex opaca

common winterberry Ilex verticillata

small whorled pogonia Isotria medeoloides

butternut Juglans cinerea

black walnut Juglans nigra

eastern redcedar Juniperus virginiana

mountain laurel Kalmia latifolia

tamarack Larix laricina

northern spicebush Lindera benzoin

sweetgum Liquidambar styraciflua

tulip tree Liriodendron tulipifera

osage orange Maclura pomifera

cucumbertree Magnolia acuminata

mountain magnolia Magnolia fraseri

southern magnolia Magnolia grandiflora

red mulberry Morus rubra

blackgum Nyssa sylvatica

eastern hophornbeam Ostrya virginiana

sourwood Oxydendrum arboreum

American ginseng Panax quinquefolius
Appe

235

Common Name Scientific Name

black chokeberry Photinia melanocarpa

red spruce Picea rubens

shortleaf pine Pinus echinata

Table Mountain pine Pinus pungens

red pine Pinus resinosa

pitch pine Pinus rigida

eastern white pine Pinus strobus

loblolly pine Pinus taeda

Virginia pine Pinus virginiana

eastern prairie fringed 

orchid

Platanthera leucophaea

sycamore Platanus occidentalis

eastern cottonwood Populus deltoides

bigtooth aspen Populus grandidentata

quaking aspen Populus tremuloides

Tennessee pondweed Potamogeton tennesseensis

pin cherry Prunus pensylvanica

black cherry Prunus serotina

chokecherry Prunus virginiana

harperella Ptilimnium nodosum

Torrey’s mountainmint Pycnanthemum torrei

white oak Quercus alba

swamp white oak Quercus bicolor

scarlet oak Quercus coccinea

northern pin oak Quercus ellipsoidalis

southern red oak Quercus falcata

bear oak/scrub oak Quercus ilicifolia 

shingle oak Quercus imbricaria

bur oak Quercus macrocarpa

blackjack oak Quercus marilandica

Common Name Scientific Name

chinkapin oak Quercus muehlenbergii

water oak Quercus nigra

pin oak Quercus palustris

willow oak Quercus phellos

chestnut oak Quercus prinus

northern red oak Quercus rubra

Shumard’s oak Quercus shumardii

post oak Quercus stellata

black oak Quercus velutina

great laurel Rhododendron maximum

black locust Robinia pseudoacacia

coastal plain willow Salix caroliniana

black willow Salix nigra

sassafras Sassafras albidum

northeastern bulrush Scirpus ancistrochaetus

American mountain ash Sorbus americana

Virginia spiraea Spiraea virginiana

northern white-cedar Thuja occidentalis

American basswood Tilia americana

running buffalo clover Trifolium stoloniferum

eastern hemlock Tsuga canadensis

winged elm Ulmus alata

American elm Ulmus americana

cedar elm Ulmus crassifolia

slippery elm Ulmus rubra

rock elm Ulmus thomasii

southern mountain 

cranberry

Vaccinium erythrocarpum

velvetleaf huckleberry Vaccinium myrtilloides

wild raisin (withe-rod) Viburnum nudum

Table 24 (continued).
Appe

23

Table 25.—Common and scientific names of pathogens and nonnative plants mentioned in this assessment

Common Name Scientific Name

Pathogens

armillaria Armillaria mellea

Lyme disease Borrelia burgdorferi

elm yellows Candidatus phytoplasma ulmi

white pine blister rust Cronartium ribicola

chestnut blight Cryphonectria parasitica

diplodia Diplodia pinea 

and D. scrobiculata

West Nile virus Flavivirus spp.

Common Name Scientific Name

Pathogens

scleroderris canker Gremmeniella abietina

annosum root disease Heterobasidion irregulare

hypoxylon canker Hypoxylon mammatum

sudden oak death Phytophthora ramorum

phytophthora root rot Phytophthora spp.

sirococcus shoot blight Sirococcus conigenus

sphaeropsis shoot blight Sphaeropsis sapinea

Common Name Scientific Name

Nonnative invasive plants

Norway maple Acer platanoides 

ailanthus Ailanthus altissima

silk tree Albizia julibrissin

garlic mustard Alliaria petiolata

porcelain berry Ampelopsis 

brevipedunculata

dwarf mistletoe Arceuthobium pusillum

Japanese barberry Berberis thunbergii

paper mulberry Broussonetia papyrifera

Asiatic bittersweet Celastrus orbiculatus

spotted knapweed Centaurea stoebe

hayscented fern Dennstaedtia punctilobula

viper’s bugloss Echium vulgare

autumn olive Elaeagnus umbellata

burning bush Euonymus spp.

Japanese knotweed Fallopia japonica

buckthorn Frangula alnus

creeping charlie Glechoma hederacea

English ivy Hedera helix 

Common Name Scientific Name

Nonnative invasive plants

cogongrass Imperata cylindrica

sericea lespedeza Lespedeza cuneata

privet Ligustrum vulgare

Japanese honeysuckle Lonicera japonica

bush honeysuckle Lonicera mackii 

purple loosestrife Lythrum salicaria

yellow sweetclover Melilotus officinalis

Japanese stiltgrass Microstegium vimineum

basket grass Oplismenus hirtellus

princess tree Paulownia tomentusa

mile-a-minute vine Persicaria perfoliata

reed canarygrass Phalaris arundinacea

common reed (phragmites) Phragmites australis

Canada bluegrass Poa compressa

kudzu Pueraria lobata

glossy buckthorn Rhamnus spp.

multiflora rose Rosa multiflora

crown vetch Securigera varia

Japanese spiraea Spiraea japonica
Appe

237

Table 26.—Common and scientific names of fauna mentioned in this assessment

Common Name Scientific Name

hemlock woolly adelgid Adelges tsugae

saw-whet owl Aegolius acadicus

emerald ash borer Agrilus planipennis

Jefferson salamander Ambystoma jeffersonianum

spotted salamander Ambystoma maculata

tiger salamander Ambystoma tigrinum

green salamander Aneides aeneus

Asian longhorned beetle Anoplophora glabripennis

Dry Fork Valley cave 

pseudoscorpion

Apochthonius pauscisinosus

eastern cave-loving funnel 

web spider

Calymmaria cavicola

coyote Canis latrans

eastern timber wolf Canis lupus lycaon

beaver Castor canadensis

spruce budworm Choristoneura fumiferana

spotted turtle Clemmys guttata

redside dace Clinostomus elongatus

Virginia big-eared bat Corynorhinus townsendii 

virginianus

sculpin Cottus spp.

eastern hellbender Cryptobranchus 

alleganiensis

beech scale Cryptococcus fagisuga

earthworms (nonnative) Dendrobaena octaedra, 

Lumbricus rubellus, 

and L. terrestris

southern pine beetle Dendroctonus frontalis

blackburnian warbler Dendroica fusca

birch leaf miner Fenusa pusilla

southern flying squirrel Glaucomys volans

bog turtle Glyptemys muhlenbergii

rapids clubtail Gomphus quadricolor

green-faced clubtail Gomphus viridifrons

worm-eating warbler Helmitheros vermivorus

wood thrush Hylocichla mustelina

Common Name Scientific Name

bark beetle Ips spp. and

Dendroctonus spp.

spring hemlock looper Lambdina fiscellaria 

fiscellaria

silver-haired bat Lasionycteris noctivagans

eastern red bat Lasiurus borealis

hoary bat Lasiurus cinereus

crimson-ringed whiteface Leucorrhinia glacialis

red crossbill Loxia curvirostra

gypsy moth Lymantria dispar dispar

bobcat Lynx rufus

forest tent caterpillar Malacosoma disstria

wild turkey Meleagris gallopavo

small-footed bat Myotis leibii

little brown bat Myotis lucifugus

northern bat Myotis septentrionalis

Indiana bat Myotis sodalis

Carter cave spider Nesticus carteri

jumping oak gall wasp Neuroterus sp.

red-spotted newt Notophthalmus viridescens

white-tailed deer Odocoileus virginianus

Kentucky warbler Oporornis formosus

tri-colored bat Perimyotis subflavus

red-backed salamander Plethodon cinereus

Cheat Mountain 

salamander

Plethodon nettingi

eastern cougar Puma concolor couguar

northern flying squirrel Sabrinus glaucomys fuscus

brook trout Salvelinus fontinalis

eastern spadefoot toad Scaphiopus holbrookii

eastern gray squirrel Sciurus carolinensis

cerulean warbler Setophaga cerulea

black bear Ursus americanus

ambrosia beetle Xyloterinus politus
238

Appendx 

and Historica

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 

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.
Appe

239

Ecological Precipitation Mean Minimum Maximum

section Season (inches) temperature (°F) temperature (°F) temperature (°F)

221E Annual 42.3 52.1 40.7 63.5

Fall 9.3 53.9 42.2 65.6

Spring 11.3 51.3 38.8 63.8

Summer 12.7 71.3 59.6 83.0

Winter 9.1 32.0 22.3 41.6

221F Annual 39.6 49.3 39.2 59.5

Fall 9.6 51.8 41.6 62.0

Spring 10.3 48.1 37.1 59.1

Summer 12.0 69.4 58.2 80.7

Winter 7.7 28.0 19.8 36.2

M221A Annual 39.1 51.3 39.8 62.9

Fall 9.6 53.0 41.1 64.9

Spring 10.5 50.3 38.0 62.6

Summer 11.2 70.1 58.0 82.2

Winter 7.8 31.9 21.9 41.9

M221B Annual 48.5 49.0 37.8 60.2

Fall 10.7 50.7 39.3 62.2

Spring 13.1 48.1 36.0 60.2

Summer 13.7 66.8 55.5 78.1

Winter 10.9 30.3 20.4 40.1

M221C Annual 47.0 52.4 41.0 63.9

Fall 10.0 53.9 42.3 65.5

Spring 12.7 51.9 39.1 64.6

Summer 13.7 70.2 58.9 81.6

Winter 10.5 33.8 23.8 43.8

Table 27.—Annual and seasonal mean values for selected climate variables from 1971 through 2000 for ecological 

sections within the assessment area

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.
Appe

240

*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.

Mean Precip. Mean TMea  Mean Tin  Mea

Month precip. change Precip. TMean change TMean TMin change TMin TMax

or season (inches) (inches) p-valuea (°F) (°F) p-valuea (°F) (°F) p-valuea (°F) (°F) p-valuea

January 3.3 -0.7 0.14 29.7 -2.4 0.24 20.4 -1.6 0.41 39.1 -3.1 0.14

February 2.8 -0.1 0.70 31.4 1.7 0.38 21.2 2.0 0.33 41.5 1.4 0.48

March 3.8 -0.4 0.40 40.3 -0.1 0.94 29.1 -0.3 0.79 51.5 0.1 0.95

April 3.7 0.2 0.51 50.5 2.4 0.00 38.0 1.6 0.02 62.9 3.2 0.00

May 4.1 0.9 0.05 60.0 0.0 0.96 47.4 0.6 0.42 72.7 -0.7 0.48

June 4.2 -0.4 0.38 68.2 0.5 0.50 56.0 1.4 0.07 80.3 -0.4 0.68

July 4.5 0.3 0.33 71.9 0.0 0.97 60.1 1.3 0.06 83.7 -1.2 0.10

Aug 3.9 -0.2 0.47 70.5 1.2 0.03 58.8 2.1 0.00 82.3 0.3 0.64

Sept 3.2 0.9 0.04 64.3 -0.6 0.51 52.0 0.9 0.40 76.6 -2.1 0.03

Oct 2.8 0.3 0.48 53.2 -0.8 0.42 40.7 0.4 0.73 65.7 -2.0 0.07

Nov 3.0 1.2 0.01 42.2 2.3 0.01 31.6 2.8 0.00 52.8 1.8 0.08

Dec 3.2 -0.1 0.70 32.6 1.5 0.25 23.6 1.7 0.19 41.6 1.2 0.37

Winter 3.1 -1.0 0.14 31.2 0.3 0.81 21.7 0.7 0.58 40.7 -0.1 0.91

Spring 3.9 0.7 0.29 50.3 0.8 0.19 38.2 0.6 0.24 62.4 0.9 0.20

Summer 4.2 -0.3 0.64 70.2 0.6 0.22 58.3 1.6 0.00 82.1 -0.4 0.46

Fall 3.0 2.3 0.00 53.2 0.3 0.57 41.4 1.4 0.04 65.0 -0.7 0.27

Annual 42.4 1.7 0.26 51.2 0.5 0.29 39.9 1.1 0.03 62.6 -0.1 0.87

Table 28.—Annual, seasonal, and monthly mean values and linear trend analysis for selected climate variables from 

1901 through 2011 for the assessment area. 

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.

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: 

234-336.
Appe

241

Figure 44.—Statistical confidence (p-values for the linear regression) for trends in temperature from 1901 through 2011. Gray 

values represent areas of low statistical confidence. Data source: ClimateWizard (2013).
Appe

242

Figure 45.—Statistical confidence (p-values for the linear regression) for trends in precipitation from 1901 through 2011. Gray 

values represent areas of low statistical confidence. Data source: ClimateWizard (2013).
243

Appendix 3:

Future Climate

This appendix provides supplementary information 

to Chapter 4, presented as maps of projected change 

for early- and mid-century (Figs. 46 through 53) and

graphs of early-, mid-, and late-century departures 

from baseline climate (Figs. 54 through 58).

Figure 46.—Projected difference in daily mean temperature at the beginning of the century (2010 through 2039) compared to 

baseline (1971 through 2000) for two climate scenarios.
Appe

244

Figure 47.—Projected difference in daily minimum temperature at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000) for two climate scenarios.
Appe

245

Figure 48.—Projected difference in daily maximum temperature at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000) for two climate scenarios.
Appe

24

Figure 49.—Projected difference in precipitation at the beginning of the century (2010 through 2039) compared to baseline 

(1971 through 2000) for two climate scenarios.
Appe

247

Figure 50.—Projected difference in daily mean temperature for the middle of the century (2040 through 2069) compared to 

baseline (1971 through 2000) for two climate scenarios.
Appe

248

Figure 51.—Projected difference in daily minimum temperature for the middle of the century (2040 through 2069) compared 

to baseline (1971 through 2000) for two climate scenarios.
Appe

249

Figure 52.—Projected difference in daily maximum temperature for the middle of the century (2040 through 2069) compared 

to baseline (1971 through 2000) for two climate scenarios.
Appe

250

Figure 53.—Projected difference in precipitation for the middle of the century (2040 through 2069) compared to baseline 

(1971 through 2000) for two climate scenarios.
Appe

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20

25

30

35

40

45

50

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

Temperature (0 F)

W inter (D ec - Feb)

GFD L A 1FI Mean GFD L A 1FI Maximum GFD L A 1FI Min imum

PCM B 1 Mean PCM B 1 Maximum PCM B 1 Min imum

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 stations.

35

40

45

50

55

60

65

70

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

Temperature (0 F)

S pring (Mar - May)

GFD L A 1FI Mean GFD L A 1FI Maximum GFD L A 1FI Min imum

PCM B 1 Mean PCM B 1 Maximum PCM B 1 Min imum

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 stations.
Appe

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55

60

65

70

75

80

85

90

95

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

Temperature (0 F)

S ummer (June - Aug)

GFD L A 1FI Mean GFD L A 1FI Maximum GFD L A 1FI Min imum

PCM B 1 Mean PCM B 1 Maximum PCM B 1 Min imum

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 stations.

40

45

50

55

60

65

70

75

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

Temperature (0 F)

Fall (S ep - N ov)

GFD L A 1FI Mean GFD L A 1FI Maximum GFD L A 1FI Min imum

PCM B 1 Mean PCM B 1 Maximum PCM B 1 Min imum

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 stations.
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8

9

10

11

12

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches)

Winter (Dec - F eb)

GFD L A 1FI PCM B 1

11

12

13

14

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches)

S pring (Mar - May)

GFD L A 1FI PCM B 1

8

9

10

11

12

13

14

15

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches)

S ummer (J une - Aug)

GFD L A 1FI PCM B 1

7

8

9

10

11

1971 - 2000 2010 - 2039 2040 - 2069 2070 - 2099

P recipitation (inches)

F all (S ep - Nov)

GFD L A 1FI PCM B 1

Figure 58.—Projected changes in seasonal precipitation across the assessment area averaged over 30-year periods. The 1971 

through 2000 value is based on observed data from weather stations. Note the precipitation axes are different depending on 

the season.
254

Appendix 4: Addition

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 A

MODEL RESU

Tables 29 through 36 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 (1961 through 1990) and future

(2010 through 2039, 2040 through 2069, 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) 

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 large decrease

0.5 through 0.8 small decrease

>0.8 through <1.2 no change 

1.2 through 2.0 small increase

>2 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 
Appe

255

current infrequency tends to inflate the percentage 

change that is projected. The cutoffs for each portion 

of the assessment area were as follows:

Cutoff IV

 Section Pixels for rare species

 Assessment area 332 33

 221F 54 5

 OH 221E 85 9

 WV 221E 65

 M221A 37 4

 M221B 53 5

 M221C 38 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 <0.6 small decrea

0.6 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 6) 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.
Appe

25

Mixed hardwoods on the Shawnee State Forest, Ohio. Photo by the Ohio Department of Natural Resources, used with 

permission.
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American basswood 357 335 Medium 317 243 296 114 294 97 0.95 0.73 0.88 0.34 0.88 0.29 No Change Large Decrease

American beech 1319 1275 High 1280 948 1275 647 1272 547 1.00 0.74 1.00 0.51 1.00 0.43 No Change Large Decrease

American chestnut 78 59 Medium 66 63 61 56 60 55 1.12 1.07 1.03 0.95 1.02 0.93 No Change No ChangeAmerican elm 1170 1102 Medium 1012 1040 892 967 895 751 0.92 0.94 0.81 0.88 0.81 0.68 No Change Small Decrease

American holly 13 2 High 5 3 5 2 5 2 2.50 1.50 2.50 1.00 2.50 1.00 No Change No ChangeAmerican hornbeam 407 403 Medium 372 365 367 408 368 421 0.92 0.91 0.91 1.01 0.91 1.05 No Change No ChangeBalsam fir 3 7 High 7 1 7 1 3 1 1.00 0.14 1.00 0.14 0.43 0.14 Small Decrease Large Decrease

Bear oak (scrub oak) 17 18 Low 12 13 10 14 12 14 0.67 0.72 0.56 0.78 0.67 0.78 No Change No ChangeBigtooth aspen 243 251 High 173 51 121 4 100 0 0.69 0.20 0.48 0.02 0.40 0.00 Large Decrease Extirpated

Bitternut hickory 56 44 Low 63 219 82 329 110 367 1.43 4.98 1.86 7.48 2.50 8.34 Large Increase Large IncreaseBlack ash 23 20 High 6 3 3 4 2 5 0.30 0.15 0.15 0.20 0.10 0.25 Large Decrease Small Decrease

Black cherry 2163 2337 High 1864 1094 1577 660 1481 536 0.80 0.47 0.68 0.28 0.63 0.23 Small Decrease Large DecreaseBlack hickory 0 7 High 5 293 11 859 24 1171 0.71 41.86 1.57 122.71 3.43 167.29 No Change Large Increase

Black locust 904 900 Low 855 789 819 660 781 590 0.95 0.88 0.91 0.73 0.87 0.66 No Change Small Decrease

Black maple 87 47 Low 57 24 54 14 49 14 1.21 0.51 1.15 0.30 1.04 0.30 No Change Large Decrease

Black oak 916 985 High 978 1559 1018 2203 1037 2118 0.99 1.58 1.03 2.24 1.05 2.15 No Change Large Increase

Black walnut 434 485 Medium 526 559 576 577 609 463 1.09 1.15 1.19 1.19 1.26 0.96 Small Increase No ChangeBlack willow 63 56 Low 40 124 36 218 42 223 0.71 2.21 0.64 3.89 0.75 3.98 Small Decrease Large IncreaseBlackgum 703 799 High 843 867 899 854 910 854 1.06 1.09 1.13 1.07 1.14 1.07 No Change No ChangeBlackjack oak 11 8 Medium 17 333 24 1088 42 1473 2.13 41.63 3.00 136.00 5.25 184.13 Small Increase Large Increase

Blue ash 8 0 Low 6 5 5 5 5 3 NA NA NA NA NA NA No Change Small DecreaseBoxelder 214 222 Medium 194 234 211 293 209 284 0.87 1.05 0.95 1.32 0.94 1.28 No Change Small Increase

Bur oak 16 5 Medium 7 15 9 54 8 70 1.40 3.00 1.80 10.80 1.60 14.00 No Change Large Increase

Butternut 71 10 Low 37 10 25 4 19 0 3.70 1.00 2.50 0.40 1.90 0.00 No Change ExtirpatedCedar elm 0 0 Low 0 0 0 239 0 584 NA NA NA Migrant NA Migrant NA New Habitat

Chestnut oak 1621 1577 High 1658 1503 1703 1082 1622 979 1.05 0.95 1.08 0.69 1.03 0.62 No Change Small Decrease

Chinkapin oak 42 42 Medium 73 270 109 327 156 324 1.74 6.43 2.60 7.79 3.71 7.71 Large Increase Large IncreaseChokecherry 19 32 Low 11 4 8 0 8 0 0.34 0.13 0.25 0.00 0.25 0.00 Small Decrease Extirpated

Common persimmon 70 59 Medium 97 343 113 571 128 810 1.64 5.81 1.92 9.68 2.17 13.73 Large Increase Large IncreaseCucumbertree 213 173 High 187 175 186 156 189 151 1.08 1.01 1.08 0.90 1.09 0.87 No Change No ChangeEastern cottonwood 58 56 Low 31 89 26 142 31 171 0.55 1.59 0.46 2.54 0.55 3.05 Small Decrease Large IncreaseEastern hemlock 259 325 High 281 255 278 210 276 196 0.87 0.79 0.86 0.65 0.85 0.60 No Change Small Decrease

Eastern hophornbeam 420 485 Medium 424 459 408 549 411 637 0.87 0.95 0.84 1.13 0.85 1.31 No Change Small Increase

Eastern redbud 294 295 Medium 357 517 406 661 413 556 1.21 1.75 1.38 2.24 1.40 1.89 Small Increase Small IncreaseEastern redcedar 98 193 Medium 337 1048 464 1456 590 1648 1.75 5.43 2.40 7.54 3.06 8.54 Large Increase Large Increase

Eastern white pine 354 446 High 386 353 378 217 370 196 0.87 0.79 0.85 0.49 0.83 0.44 No Change Large Decrease

Flowering dogwood 1625 1571 High 1686 1724 1723 1440 1730 1141 1.07 1.10 1.10 0.92 1.10 0.73 No Change Small Decrease

Green ash 88 103 Medium 97 206 126 448 145 535 0.94 2.00 1.22 4.35 1.41 5.19 Small Increase Large IncreaseHackberry 91 102 Medium 122 308 149 418 167 423 1.20 3.02 1.46 4.10 1.64 4.15 Small Increase Large IncreaseHoneylocust 86 72 Low 108 179 118 282 124 350 1.50 2.49 1.64 3.92 1.72 4.86 Small Increase Large IncreaseLoblolly pine 3 15 High 9 47 39 233 30 563 0.60 3.13 2.60 15.53 2.00 37.53 No Change Large Increase

Mockernut hickory 751 838 High 814 939 846 1048 847 1099 0.97 1.12 1.01 1.25 1.01 1.31 No Change Small Increase

Mountain maple 23 4 High 9 10 9 10 10 10 2.25 2.50 2.25 2.50 2.50 2.50 No Change No Change

DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 29.—Complete DISTRIB model resultsa for the 93 tree species in the assessment a

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 29 (continued). Northern catalpa 12 4 Low 3 3 3 7 2 20 0.75 0.75 0.75 1.75 0.50 5.00 No Change Small IncreaseNorthern pin oak 6 2 Medium 4 3 3 1 3 1 2.00 1.50 1.50 0.50 1.50 0.50 No Change No ChangeNorthern red oak 1397 1400 High 1313 1322 1260 1206 1244 1099 0.94 0.94 0.90 0.86 0.89 0.79 No Change Small Decrease

Northern white-cedar 0 4 High 1 1 1 5 1 6 0.25 0.25 0.25 1.25 0.25 1.50 Small Decrease No Change

Ohio buckeye 51 32 Low 37 76 41 21 49 5 1.16 2.38 1.28 0.66 1.53 0.16 No Change Large Decrease

Osage-orange 63 67 Medium 67 113 86 159 92 254 1.00 1.69 1.28 2.37 1.37 3.79 Small Increase Large IncreasePawpaw 172 132 Low 133 132 146 69 144 51 1.01 1.00 1.11 0.52 1.09 0.39 No Change Large Decrease

Pignut hickory 759 864 High 848 940 874 810 887 766 0.98 1.09 1.01 0.94 1.03 0.89 No Change No ChangePin cherry 52 18 Medium 15 3 10 2 7 2 0.83 0.17 0.56 0.11 0.39 0.11 Small Decrease Large Decrease

Pin oak 128 87 Medium 79 131 80 197 79 158 0.91 1.51 0.92 2.26 0.91 1.82 No Change Small Increase

Pitch pine 134 138 High 131 129 131 121 124 129 0.95 0.94 0.95 0.88 0.90 0.94 No Change No ChangePost oak 47 57 High 120 1513 176 3340 257 4767 2.11 26.54 3.09 58.60 4.51 83.63 Large Increase Large Increase

Quaking aspen 54 82 High 36 5 22 4 14 3 0.44 0.06 0.27 0.05 0.17 0.04 Large Decrease Large Decrease

Red maple 3395 3501 High 3240 2390 3179 1664 3114 1299 0.93 0.68 0.91 0.48 0.89 0.37 No Change Large Decrease

Red mulberry 13 13 Low 13 129 15 295 23 366 1.00 9.92 1.15 22.69 1.77 28.15 No Change Large Increase

Red pine 74 51 Medium 42 20 30 10 24 10 0.82 0.39 0.59 0.20 0.47 0.20 Large Decrease Large Decrease

Red spruce 67 39 High 40 20 33 15 34 14 1.03 0.51 0.85 0.39 0.87 0.36 No Change Large Decrease

River birch 20 4 Low 11 14 12 16 10 32 2.75 3.50 3.00 4.00 2.50 8.00 No Change Small IncreaseRock elm 5 1 Low 4 11 3 44 4 20 4.00 11.00 3.00 44.00 4.00 20.00 No Change Large Increase

Sassafras 1111 1143 High 1179 1154 1191 880 1229 820 1.03 1.01 1.04 0.77 1.08 0.72 No Change Small Decrease

Scarlet oak 514 582 High 612 705 657 522 646 345 1.05 1.21 1.13 0.90 1.11 0.59 No Change Small DecreaseServiceberry 287 259 Medium 269 239 251 227 243 230 1.04 0.92 0.97 0.88 0.94 0.89 No Change No ChangeShagbark hickory 144 207 Medium 204 408 249 558 277 559 0.99 1.97 1.20 2.70 1.34 2.70 Small Increase Large IncreaseShingle oak 31 8 Medium 22 125 19 123 35 106 2.75 15.63 2.38 15.38 4.38 13.25 Small Increase Large IncreaseShortleaf pine 40 53 High 79 467 115 1312 152 1972 1.49 8.81 2.17 24.76 2.87 37.21 Large Increase Large Increase

Shumard oak 1 0 Low 0 7 0 132 0 282 NA NA NA NA NA NA NA Large Increase

Silver maple 183 135 Medium 96 222 87 407 98 425 0.71 1.64 0.64 3.02 0.73 3.15 Small Decrease Large IncreaseSlippery elm 610 596 Medium 590 617 558 529 560 402 0.99 1.04 0.94 0.89 0.94 0.67 No Change Small DecreaseSourwood 312 362 High 400 336 505 275 480 223 1.11 0.93 1.40 0.76 1.33 0.62 Small Increase Small Decrease

Southern magnolia 2 4 Medium 4 1 8 2 4 2 1.00 0.25 2.00 0.50 1.00 0.50 No Change No ChangeSouthern red oak 11 5 High 14 136 43 374 54 609 2.80 27.20 8.60 74.80 10.80 121.80 Large Increase Large Increase

Striped maple 261 226 High 206 142 179 103 170 95 0.91 0.63 0.79 0.46 0.75 0.42 Small Decrease Large Decrease

Sugar maple 2817 2601 High 2700 2226 2681 1068 2700 587 1.04 0.86 1.03 0.41 1.04 0.23 No Change Large Decrease

Sugarberry 1 1 Medium 0 57 5 328 9 648 0.00 57.00 5.00 328.00 9.00 648.00 Large Increase Large IncreaseSwamp white oak 59 53 Low 42 51 41 26 43 6 0.79 0.96 0.77 0.49 0.81 0.11 No Change Large Decrease

Sweet birch 440 474 High 453 353 453 248 451 208 0.96 0.75 0.96 0.52 0.95 0.44 No Change Large Decrease

Sweetgum 15 8 High 26 85 51 222 78 357 3.25 10.63 6.38 27.75 9.75 44.63 Large Increase Large Increase

Sycamore 277 277 Medium 305 346 310 352 317 368 1.10 1.25 1.12 1.27 1.14 1.33 No Change Small Increase

Table Mountain pine 44 41 Medium 39 46 39 53 36 63 0.95 1.12 0.95 1.29 0.88 1.54 No Change Small IncreaseTamarack (native) 3 2 High 1 1 1 6 1 6 0.50 0.50 0.50 3.00 0.50 3.00 No Change No ChangeTulip tree 1685 1652 High 1783 1564 1895 939 1939 687 1.08 0.95 1.15 0.57 1.17 0.42 No Change Large Decrease

Virginia pine 489 526 High 581 579 716 500 700 491 1.11 1.10 1.36 0.95 1.33 0.93 Small Increase No ChangeWater locust 0 0 Low 0 0 0 6 0 56 NA NA NA Migrant NA Migrant NA New Habitat

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 29 (continued). aCurrent 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 Water oakprovided for the end of century (2070 through 2099) period. Explanations for the change classes are described in the text. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value.

0 0 High 0 0 0 89 0 323 NA NA NA Migrant NA Migrant NA New Habitat

White ash 1483 1657 High 1477 1250 1394 827 1422 747 0.89 0.75 0.84 0.50 0.86 0.45 No Change Large Decrease

White oak 1572 1643 High 1720 2487 1791 2519 1853 2023 1.05 1.51 1.09 1.53 1.13 1.23 No Change Small Increase

Willow oak 2 0 Medium 0 0 0 1 0 20 NA NA NA NA NA NA NA Large Increase

Winged elm 3 2 High 11 388 43 1229 68 2272 5.50 194.00 21.50 614.50 34.00 1136.00 Large Increase Large IncreaseYellow birch 151 135 High 113 53 106 33 103 29 0.84 0.39 0.79 0.24 0.76 0.22 Small Decrease Large Decrease

Yellow buckeye 202 161 Medium 176 175 175 177 176 174 1.09 1.09 1.09 1.10 1.09 1.08 No Change No Change
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American basswood 73 73 Medium 56 55 56 11 57 5 0.77 0.75 0.77 0.15 0.78 0.07 Decrease Large Decrease

American beech 228 178 High 182 151 180 62 183 51 1.02 0.85 1.01 0.35 1.03 0.29 No Change Large Decrease

American elm 495 454 Medium 468 434 426 403 422 224 1.03 0.96 0.94 0.89 0.93 0.49 No Change DecreaseAmerican hornbeam 79 73 Medium 68 62 68 69 68 67 0.93 0.85 0.93 0.95 0.93 0.92 No Change No ChangeBigtooth aspen 34 57 High 53 18 55 0 44 0 0.93 0.32 0.97 0.00 0.77 0.00 Decrease Large Decrease

Bitternut hickory 8 22 Low 20 59 37 64 47 58 0.91 2.68 1.68 2.91 2.14 2.64 Large Increase Large IncreaseBlack ash 7 20 High 5 3 2 4 1 5 0.25 0.15 0.10 0.20 0.05 0.25 Large Decrease Large Decrease

Black cherry 705 691 High 655 289 606 111 546 41 0.95 0.42 0.88 0.16 0.79 0.06 Decrease Large DecreaseBlack hickory 0 0 High 0 7 0 56 0 105 NA New NA New NA New NA New Habitat

Black locust 74 101 Low 115 114 123 72 121 99 1.14 1.13 1.22 0.71 1.20 0.98 Increase No Change

Black maple 30 17 Low 18 10 18 5 18 5 1.06 0.59 1.06 0.29 1.06 0.29 No Change Large Decrease

Black oak 40 79 High 69 132 91 237 96 223 0.87 1.67 1.15 3.00 1.22 2.82 Increase Large Increase

Black walnut 55 80 Medium 108 137 152 143 162 103 1.35 1.71 1.90 1.79 2.03 1.29 Increase Increase

Black willow 30 36 Low 28 49 26 61 32 57 0.78 1.36 0.72 1.69 0.89 1.58 No Change IncreaseBlackgum 35 34 High 50 33 59 32 59 38 1.47 0.97 1.74 0.94 1.74 1.12 Increase No Change

Blackjack oak 0 0 Medium 0 8 2 120 3 149 NA New New New New New New Habitat New Habitat

Boxelder 31 51 Medium 44 52 49 64 49 59 0.86 1.02 0.96 1.26 0.96 1.16 No Change IncreaseBur oak 12 5 Medium 6 13 8 42 7 43 1.20 2.60 1.60 8.40 1.40 8.60 Increase Large Increase

Cedar elm 0 0 Low 0 0 0 2 0 61 NA NA NA New NA New NA New HabitatChestnut oak 0 12 High 29 34 46 6 42 3 2.42 2.83 3.83 0.50 3.50 0.25 New Habitat Large Decrease

Chinkapin oak 0 1 Medium 4 56 13 64 30 54 4.00 56.00 13.00 64.00 30.00 54.00 New Habitat New HabitatCommon persimmon 0 0 Medium 0 27 3 45 1 110 NA New New New New New New Habitat New Habitat

Cucumbertree 18 5 High 11 7 11 5 11 5 2.20 1.40 2.20 1.00 2.20 1.00 Large Increase No Change

Eastern cottonwood 26 34 Low 20 53 16 79 20 76 0.59 1.56 0.47 2.32 0.59 2.24 Decrease Large Increase

Eastern hemlock 9 18 High 9 3 8 1 5 1 0.50 0.17 0.44 0.06 0.28 0.06 Large Decrease Large Decrease

Eastern hophornbeam 91 123 Medium 82 98 75 64 73 83 0.67 0.80 0.61 0.52 0.59 0.68 Decrease DecreaseEastern redcedar 0 6 Medium 29 173 79 218 111 220 4.83 28.83 13.17 36.33 18.50 36.67 New Habitat New HabitatEastern redbud 0 10 Medium 30 77 50 159 57 114 3.00 7.70 5.00 15.90 5.70 11.40 New Habitat New HabitatEastern white pine 48 44 High 50 40 51 0 41 0 1.14 0.91 1.16 0.00 0.93 0.00 No Change Large Decrease

Flowering dogwood 115 113 High 146 143 155 116 158 115 1.29 1.27 1.37 1.03 1.40 1.02 Increase No Change

Green ash 16 29 Medium 21 42 38 135 40 146 0.72 1.45 1.31 4.66 1.38 5.03 Increase Large Increase

Hackberry 4 21 Medium 34 125 51 152 57 133 1.62 5.95 2.43 7.24 2.71 6.33 Large Increase Large IncreaseHoneylocust 28 19 Low 47 76 62 97 66 104 2.47 4.00 3.26 5.11 3.47 5.47 Large Increase Large IncreaseMockernut hickory 84 101 High 93 140 106 170 104 153 0.92 1.39 1.05 1.68 1.03 1.52 No Change IncreaseNorthern pin oak 4 2 Medium 4 3 3 1 3 1 2.00 1.50 1.50 0.50 1.50 0.50 No Change DecreaseNorthern red oak 191 192 High 168 191 169 178 166 116 0.88 1.00 0.88 0.93 0.87 0.60 No Change DecreaseNorthern white-cedar 0 3 High 0 0 0 5 0 6 0.00 0.00 0.00 1.67 0.00 2.00 NA No ChangeOhio buckeye 4 7 Low 12 39 17 16 24 4 1.71 5.57 2.43 2.29 3.43 0.57 Large Increase Decrease

Osage-orange 45 26 Medium 39 60 52 66 52 79 1.50 2.31 2.00 2.54 2.00 3.04 Increase Large Increase

Pignut hickory 86 107 High 105 152 115 159 114 130 0.98 1.42 1.08 1.49 1.07 1.22 No Change IncreasePin cherry 15 6 Medium 5 2 5 1 4 1 0.83 0.33 0.83 0.17 0.67 0.17 Decrease Large Decrease

Pin oak 106 74 Medium 68 89 65 107 63 84 0.92 1.20 0.88 1.45 0.85 1.14 No Change No ChangePost oak 0 0 High 0 112 9 401 15 655 NA New New New New New New Habitat New Habitat

DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 30.—Complete DISTRIB model resultsa for the 68 tree species in Section 

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 30 (continued).Quaking aspen 33 49 High 23 0 13 3 7 3 0.47 0.00 0.27 0.06 0.14 0.06 Large Decrease Large Decrease

Red maple 553 537 High 418 288 390 237 373 201 0.78 0.54 0.73 0.44 0.70 0.37 Decrease Large DecreaseRed mulberry 0 1 Low 1 49 4 61 7 78 1.00 49.00 4.00 61.00 7.00 78.00 New Habitat New HabitatRed pine 29 28 Medium 26 11 17 9 16 9 0.93 0.39 0.61 0.32 0.57 0.32 Decrease Large DecreaseRock elm 0 0 Low 0 0 0 9 0 11 NA NA NA New New New NA New HabitatSassafras 32 72 High 91 102 112 80 119 97 1.26 1.42 1.56 1.11 1.65 1.35 Increase Increase

Scarlet oak 7 6 High 8 21 29 38 28 30 1.33 3.50 4.83 6.33 4.67 5.00 Large Increase Large IncreaseServiceberry 3 2 Medium 2 1 2 1 2 1 1.00 0.50 1.00 0.50 1.00 0.50 No Change DecreaseShagbark hickory 25 44 Medium 36 109 61 131 69 79 0.82 2.48 1.39 2.98 1.57 1.80 Increase Increase

Shingle oak 7 0 Medium 5 69 5 67 7 49 New New New New New New No Change IncreaseShortleaf pine 0 0 High 0 0 0 8 0 40 NA NA NA New New New NA New HabitatShumard oak 0 0 Low 0 0 0 0 0 27 NA NA NA NA New New NA New HabitatSilver maple 90 56 Medium 45 95 43 141 49 104 0.80 1.70 0.77 2.52 0.88 1.86 No Change IncreaseSlippery elm 101 121 Medium 139 149 146 141 145 77 1.15 1.23 1.21 1.17 1.20 0.64 Increase Decrease

Southern red oak 0 0 High 0 0 0 1 0 14 NA NA NA New New New NA New HabitatSugar maple 538 490 High 509 480 544 191 562 109 1.04 0.98 1.11 0.39 1.15 0.22 No Change Large Decrease

Sugarberry 0 0 Medium 0 0 0 14 0 121 NA NA NA New New New NA New Habitat

Swamp white oak 52 48 Low 41 46 39 25 41 6 0.85 0.96 0.81 0.52 0.85 0.13 No Change Large Decrease

Sweet birch 1 3 High 4 2 9 0 11 0 1.33 0.67 3.00 0.00 3.67 0.00 No Change NASweetgum 0 0 High 0 0 2 1 3 6 NA NA New New New New New Habitat New HabitatSycamore 22 41 Medium 60 63 63 56 64 61 1.46 1.54 1.54 1.37 1.56 1.49 Increase Increase

Tulip tree 66 75 High 85 80 118 59 124 52 1.13 1.07 1.57 0.79 1.65 0.69 Increase Decrease

White oak 53 118 High 103 230 125 260 134 167 0.87 1.95 1.06 2.20 1.14 1.42 No Change IncreaseWinged elm 0 0 High 0 0 0 30 0 217 NA NA NA New New New NA New Habitat

Yellow birch 7 9 High 6 0 3 0 1 0 0.67 0.00 0.33 0.00 0.11 0.00 Large Decrease Large Decrease

aCurrent 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. Explanations for the change classes are described in the text. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value.
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American basswood 43 55 Medium 45 38 49 6 49 3 0.82 0.69 0.89 0.11 0.89 0.06 No Change Large Decrease

American beech 194 202 High 212 149 217 78 228 62 1.05 0.74 1.07 0.39 1.13 0.31 No Change Large Decrease

American elm 395 379 Medium 305 316 254 249 258 177 0.81 0.83 0.67 0.66 0.68 0.47 Decrease DecreaseAmerican hornbeam 133 108 Medium 114 112 111 122 113 127 1.06 1.04 1.03 1.13 1.05 1.18 No Change IncreaseBigtooth aspen 149 116 High 72 11 38 0 35 0 0.62 0.10 0.33 0.00 0.30 0.00 Large Decrease Extirpated

Bitternut hickory 15 12 Low 17 76 20 87 22 95 1.42 6.33 1.67 7.25 1.83 7.92 Increase Large Increase

Black cherry 582 650 High 459 223 348 155 335 137 0.71 0.34 0.54 0.24 0.52 0.21 Decrease Large DecreaseBlack hickory 0 0 High 0 115 2 224 6 308 NA New New New New New New Habitat New Habitat

Black locust 259 254 Low 203 179 180 158 165 128 0.80 0.71 0.71 0.62 0.65 0.50 Decrease DecreaseBlack maple 41 23 Low 27 10 25 7 25 7 1.17 0.44 1.09 0.30 1.09 0.30 No Change Large Decrease

Black oak 248 261 High 288 508 301 640 303 566 1.10 1.95 1.15 2.45 1.16 2.17 Increase Large Increase

Black walnut 165 171 Medium 169 166 172 151 175 93 0.99 0.97 1.01 0.88 1.02 0.54 No Change DecreaseBlack willow 15 13 Low 5 46 4 73 4 63 0.39 3.54 0.31 5.62 0.31 4.85 Large Decrease Large IncreaseBlackgum 133 144 High 174 179 193 151 188 153 1.21 1.24 1.34 1.05 1.31 1.06 Increase No Change

Blackjack oak 2 1 Medium 4 130 7 339 11 441 4.00 130.00 7.00 339.00 11.00 441.00 Increase Increase

Boxelder 47 62 Medium 45 65 52 104 49 98 0.73 1.05 0.84 1.68 0.79 1.58 Decrease IncreaseBur oak 2 0 Medium 0 0 0 8 0 21 NA NA NA New New New Large Decrease Large Increase

Cedar elm 0 0 Low 0 0 0 107 0 194 NA NA NA New New New NA New Habitat

Chestnut oak 220 219 High 248 242 278 174 256 167 1.13 1.11 1.27 0.80 1.17 0.76 Increase Decrease

Chinkapin oak 13 16 Medium 32 90 50 87 62 76 2.00 5.63 3.13 5.44 3.88 4.75 Large Increase Large IncreaseCommon persimmon 32 31 Medium 46 134 52 142 53 188 1.48 4.32 1.68 4.58 1.71 6.07 Increase Large Increase

Cucumbertree 12 2 High 7 5 7 4 8 4 3.50 2.50 3.50 2.00 4.00 2.00 Large Increase Increase

Eastern cottonwood 27 9 Low 9 28 6 46 5 67 1.00 3.11 0.67 5.11 0.56 7.44 Decrease Large Increase

Eastern hemlock 9 13 High 3 3 3 2 4 2 0.23 0.23 0.23 0.15 0.31 0.15 Large Decrease Large Decrease

Eastern hophornbeam 106 120 Medium 103 110 100 156 103 169 0.86 0.92 0.83 1.30 0.86 1.41 No Change IncreaseEastern redcedar 47 73 Medium 141 358 182 390 219 436 1.93 4.90 2.49 5.34 3.00 5.97 Large Increase Large IncreaseEastern redbud 108 94 Medium 119 164 131 189 128 136 1.27 1.75 1.39 2.01 1.36 1.45 Increase Increase

Eastern white pine 98 98 High 75 49 58 1 55 1 0.77 0.50 0.59 0.01 0.56 0.01 Decrease Extirpated

Flowering dogwood 595 476 High 552 517 546 355 548 265 1.16 1.09 1.15 0.75 1.15 0.56 Increase Decrease

Green ash 24 25 Medium 26 73 31 163 37 180 1.04 2.92 1.24 6.52 1.48 7.20 Increase Large Increase

Hackberry 30 39 Medium 39 101 47 118 55 106 1.00 2.59 1.21 3.03 1.41 2.72 Increase Large Increase

Honeylocust 40 37 Low 41 67 37 94 39 126 1.11 1.81 1.00 2.54 1.05 3.41 No Change Large Increase

Loblolly pine 0 1 High 0 4 2 37 1 109 0.00 4.00 2.00 37.00 1.00 109.00 NA New Habitat

Mockernut hickory 198 206 High 220 245 231 256 230 258 1.07 1.19 1.12 1.24 1.12 1.25 No Change IncreaseNorthern catalpa 9 1 Low 2 2 2 1 1 1 2.00 2.00 2.00 1.00 1.00 1.00 No Change No ChangeNorthern red oak 233 271 High 245 294 249 229 253 220 0.90 1.09 0.92 0.85 0.93 0.81 No Change DecreaseOhio buckeye 29 15 Low 13 27 11 2 13 1 0.87 1.80 0.73 0.13 0.87 0.07 No Change Large Decrease

Osage-orange 12 26 Medium 18 30 21 48 25 93 0.69 1.15 0.81 1.85 0.96 3.58 No Change Large Increase

Pawpaw 51 48 Low 41 52 48 30 48 18 0.85 1.08 1.00 0.63 1.00 0.38 No Change Large Decrease

Pignut hickory 204 218 High 239 265 251 234 259 231 1.10 1.22 1.15 1.07 1.19 1.06 Increase No Change

Pin oak 16 8 Medium 7 29 9 68 9 51 0.88 3.63 1.13 8.50 1.13 6.38 No Change Large Increase

Pitch pine 24 16 High 20 15 23 15 21 14 1.25 0.94 1.44 0.94 1.31 0.88 Increase No Change

Post oak 16 17 High 40 658 60 1052 86 1388 2.35 38.71 3.53 61.88 5.06 81.65 Large Increase Large Increase

DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 31.—Complete DISTRIB model resultsa for the 72 tree species in Section 221E (O

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 31 (continued).Quaking aspen 12 10 High 3 0 1 0 0 0 0.30 0.00 0.10 0.00 0.00 0.00 Extirpated Extirpated

Red maple 816 769 High 734 397 718 265 690 227 0.95 0.52 0.93 0.35 0.90 0.30 No Change Large Decrease

Red mulberry 0 2 Low 2 41 2 87 2 93 1.00 20.50 1.00 43.50 1.00 46.50 NA New HabitatRed pine 18 6 Medium 3 1 2 1 0 1 0.50 0.17 0.33 0.17 0.00 0.17 Large Decrease Large Decrease

River birch 10 3 Low 5 10 7 13 7 22 1.67 3.33 2.33 4.33 2.33 7.33 No Change IncreaseSassafras 323 323 High 341 328 355 239 365 218 1.06 1.02 1.10 0.74 1.13 0.68 No Change DecreaseScarlet oak 88 108 High 127 165 135 106 132 74 1.18 1.53 1.25 0.98 1.22 0.69 Increase Decrease

Serviceberry 33 25 Medium 30 29 28 29 26 27 1.20 1.16 1.12 1.16 1.04 1.08 No Change No ChangeShagbark hickory 49 76 Medium 75 143 86 146 94 128 0.99 1.88 1.13 1.92 1.24 1.68 Increase Increase

Shingle oak 19 8 Medium 17 48 13 46 22 45 2.13 6.00 1.63 5.75 2.75 5.63 No Change IncreaseShortleaf pine 15 15 High 26 136 35 299 44 445 1.73 9.07 2.33 19.93 2.93 29.67 Large Increase Large IncreaseShumard oak 0 0 Low 0 3 0 41 0 89 NA New NA New New New NA NASilver maple 57 52 Medium 29 81 23 185 27 187 0.56 1.56 0.44 3.56 0.52 3.60 Decrease Decrease

Slippery elm 182 195 Medium 175 198 167 143 168 95 0.90 1.02 0.86 0.73 0.86 0.49 No Change No ChangeSourwood 69 49 High 81 43 109 34 89 36 1.65 0.88 2.22 0.69 1.82 0.74 Increase Increase

Southern red oak 0 1 High 3 31 8 83 10 137 3.00 31.00 8.00 83.00 10.00 137.00 New Habitat New HabitatSugar maple 665 634 High 652 524 651 163 659 76 1.03 0.83 1.03 0.26 1.04 0.12 No Change No ChangeSugarberry 0 1 Medium 0 24 1 123 2 226 0.00 24.00 1.00 123.00 2.00 226.00 New Habitat New HabitatSweet birch 8 26 High 15 6 23 2 22 2 0.58 0.23 0.89 0.08 0.85 0.08 Decrease Decrease

Sweetgum 10 1 High 6 11 9 25 14 47 6.00 11.00 9.00 25.00 14.00 47.00 Increase Increase

Sycamore 111 109 Medium 115 119 115 103 113 95 1.06 1.09 1.06 0.95 1.04 0.87 No Change No ChangeTulip tree 473 429 High 484 342 499 195 501 159 1.13 0.80 1.16 0.46 1.17 0.37 Increase Increase

Virginia pine 117 126 High 173 130 222 97 210 106 1.37 1.03 1.76 0.77 1.67 0.84 Increase Increase

Water locust 0 0 Low 0 0 0 1 0 16 NA NA NA New New New NA NAWater oak 0 0 High 0 0 0 23 0 87 NA NA NA New New New NA NAWhite ash 409 444 High 391 323 364 202 373 174 0.88 0.73 0.82 0.46 0.84 0.39 Decrease Decrease

White oak 411 429 High 482 784 518 655 532 472 1.12 1.83 1.21 1.53 1.24 1.10 Increase Increase

Winged elm 0 1 High 1 159 7 342 13 682 1.00 159.00 7.00 342.00 13.00 682.00 New Habitat New HabitatYellow buckeye 57 47 Medium 49 46 49 50 48 48 1.04 0.98 1.04 1.06 1.02 1.02 No Change No Change

aCurrent 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. Explanations for the change classes are described in the text. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value.
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American basswood 58 53 Medium 59 24 50 4 43 3 1.11 0.45 0.94 0.08 0.81 0.06 Decrease Large Decrease

American beech 215 211 High 233 151 232 83 237 76 1.10 0.72 1.10 0.39 1.12 0.36 Decrease Large DecreaseAmerican chestnut 7 1 Medium 2 2 2 0 2 0 2.00 2.00 2.00 0.00 2.00 0.00 No Change ExtirpatedAmerican elm 143 141 Medium 116 129 100 115 101 120 0.82 0.92 0.71 0.82 0.72 0.85 Decrease No Change

American hornbeam 110 97 Medium 100 89 99 84 97 87 1.03 0.92 1.02 0.87 1.00 0.90 Decrease No ChangeBigtooth aspen 32 38 High 22 2 13 0 6 0 0.58 0.05 0.34 0.00 0.16 0.00 Large Decrease Extirpated

Bitternut hickory 11 5 Low 10 45 8 62 21 74 2.00 9.00 1.60 12.40 4.20 14.80 Increase Large Increase

Black cherry 302 333 High 220 121 161 101 154 100 0.66 0.36 0.48 0.30 0.46 0.30 Large Decrease Large Decrease

Black hickory 0 4 High 2 89 3 268 11 302 0.50 22.25 0.75 67.00 2.75 75.50 New Habitat New HabitatBlack locust 170 142 Low 129 111 118 96 111 82 0.91 0.78 0.83 0.68 0.78 0.58 Decrease DecreaseBlack maple 7 4 Low 4 1 4 1 2 1 1.00 0.25 1.00 0.25 0.50 0.25 Decrease DecreaseBlack oak 208 224 High 209 337 209 495 213 441 0.93 1.50 0.93 2.21 0.95 1.97 Decrease IncreaseBlack walnut 95 103 Medium 117 99 110 102 124 83 1.14 0.96 1.07 0.99 1.20 0.81 Increase Decrease

Black willow 4 0 Low 0 12 0 35 0 42 NA New New New New New NA IncreaseBlackgum 117 137 High 141 148 152 151 158 149 1.03 1.08 1.11 1.10 1.15 1.09 Decrease No Change

Blackjack oak 2 3 Medium 4 113 4 300 16 356 1.33 37.67 1.33 100.00 5.33 118.67 Increase Increase

Boxelder 51 57 Medium 54 60 58 67 60 63 0.95 1.05 1.02 1.18 1.05 1.11 Decrease No Change

Butternut 12 1 Low 6 0 5 0 3 0 6.00 0.00 5.00 0.00 3.00 0.00 No Change ExtirpatedCedar elm 0 0 Low 0 0 0 82 0 150 NA NA NA New New New NA New Habitat

Chestnut oak 206 235 High 238 170 258 114 243 115 1.01 0.72 1.10 0.49 1.03 0.49 Decrease Decrease

Chinkapin oak 5 11 Medium 18 62 24 64 39 64 1.64 5.64 2.18 5.82 3.55 5.82 No Change IncreaseCommon persimmon 28 23 Medium 32 76 30 124 33 137 1.39 3.30 1.30 5.39 1.44 5.96 Increase Large Increase

Cucumbertree 40 30 High 39 34 39 29 39 29 1.30 1.13 1.30 0.97 1.30 0.97 Increase No Change

Eastern hemlock 12 15 High 13 8 15 3 15 4 0.87 0.53 1.00 0.20 1.00 0.27 Decrease Large Decrease

Eastern hophornbeam 85 84 Medium 91 92 92 115 92 129 1.08 1.10 1.10 1.37 1.10 1.54 Decrease Increase

Eastern redcedar 7 45 Medium 85 221 101 273 130 298 1.89 4.91 2.24 6.07 2.89 6.62 Large Increase Large IncreaseEastern redbud 93 94 Medium 105 119 114 114 112 100 1.12 1.27 1.21 1.21 1.19 1.06 Decrease No ChangeEastern white pine 14 28 High 24 23 29 3 40 1 0.86 0.82 1.04 0.11 1.43 0.04 Increase Extirpated

Flowering dogwood 379 407 High 402 391 401 274 403 175 0.99 0.96 0.99 0.67 0.99 0.43 Decrease Large DecreaseGreen ash 23 18 Medium 24 47 28 75 38 88 1.33 2.61 1.56 4.17 2.11 4.89 Large Increase Large IncreaseHackberry 23 14 Medium 19 36 21 60 22 56 1.36 2.57 1.50 4.29 1.57 4.00 Increase Large Increase

Honeylocust 4 4 Low 3 14 3 45 5 54 0.75 3.50 0.75 11.25 1.25 13.50 No change Large Increase

Loblolly pine 2 1 High 3 21 17 80 11 169 3.00 21.00 17.00 80.00 11.00 169.00 Increase Increase

Mockernut hickory 192 211 High 203 218 206 229 211 245 0.96 1.03 0.98 1.09 1.00 1.16 Decrease IncreaseNorthern red oak 218 211 High 198 196 194 195 194 207 0.94 0.93 0.92 0.92 0.92 0.98 Decrease No ChangeOhio buckeye 3 2 Low 1 0 1 0 1 0 0.50 0.00 0.50 0.00 0.50 0.00 Decrease ExtirpatedOsage-orange 4 8 Medium 4 13 5 28 6 54 0.50 1.63 0.63 3.50 0.75 6.75 No Change IncreasePawpaw 60 49 Low 53 48 53 23 53 18 1.08 0.98 1.08 0.47 1.08 0.37 Decrease Large Decrease

Pignut hickory 191 208 High 208 203 207 131 208 119 1.00 0.98 1.00 0.63 1.00 0.57 Decrease Decrease

Pin cherry 10 2 Medium 1 0 0 0 0 0 0.50 0.00 0.00 0.00 0.00 0.00 Extirpated ExtirpatedPin oak 0 2 Medium 1 3 1 10 3 15 0.50 1.50 0.50 5.00 1.50 7.50 New Habitat New HabitatPitch pine 21 20 High 20 11 19 8 16 8 1.00 0.55 0.95 0.40 0.80 0.40 Decrease Large Decrease

Post oak 20 27 High 43 390 42 858 73 1089 1.59 14.44 1.56 31.78 2.70 40.33 Large Increase Large Increase

DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 32.—Complete DISTRIB model results for the 71 tree speciesa in Section 221E (West Virgi

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 32 (continued).Red maple 588 599 High 569 360 564 199 546 173 0.95 0.60 0.94 0.33 0.91 0.29 Decrease Large Decrease

Red mulberry 5 4 Low 4 15 4 58 4 63 1.00 3.75 1.00 14.50 1.00 15.75 No Change Large Increase

Rock elm 1 1 Low 3 3 2 10 3 3 3.00 3.00 2.00 10.00 3.00 3.00 Large Increase Large IncreaseSassafras 317 305 High 315 267 298 175 303 157 1.03 0.88 0.98 0.57 0.99 0.52 Decrease DecreaseScarlet oak 105 121 High 127 153 132 60 135 28 1.05 1.26 1.09 0.50 1.12 0.23 Decrease Large Decrease

Serviceberry 32 31 Medium 32 26 31 28 33 32 1.03 0.84 1.00 0.90 1.07 1.03 Decrease No Change

Shagbark hickory 30 46 Medium 48 65 51 98 57 113 1.04 1.41 1.11 2.13 1.24 2.46 Increase Large Increase

Shortleaf pine 6 13 High 21 184 40 459 64 625 1.62 14.15 3.08 35.31 4.92 48.08 Large Increase Large IncreaseShumard oak 0 0 Low 0 3 0 50 0 66 NA New NA New New New NA New HabitatSilver maple 9 6 Medium 3 8 3 12 2 43 0.50 1.33 0.50 2.00 0.33 7.17 Large Decrease Large IncreaseSlippery elm 191 158 Medium 148 124 128 82 127 67 0.94 0.79 0.81 0.52 0.80 0.42 Decrease Large DecreaseSourwood 75 90 High 97 58 121 31 116 13 1.08 0.64 1.34 0.34 1.29 0.14 Increase Large Decrease

Southern red oak 4 2 High 4 57 19 144 28 204 2.00 28.50 9.50 72.00 14.00 102.00 Increase Increase

Sugar maple 563 487 High 534 389 516 115 516 46 1.10 0.80 1.06 0.24 1.06 0.09 Decrease Large Decrease

Sugarberry 0 0 Medium 0 19 1 84 3 120 NA New New New New New New Habitat New Habitat

Sweet birch 36 38 High 41 15 42 5 44 5 1.08 0.40 1.11 0.13 1.16 0.13 Decrease Large Decrease

Sweetgum 2 1 High 7 29 19 66 31 91 7.00 29.00 19.00 66.00 31.00 91.00 Increase Increase

Sycamore 68 66 Medium 65 66 65 67 66 66 0.99 1.00 0.99 1.02 1.00 1.00 Decrease No ChangeTable Mountain pine 7 2 Medium 3 4 2 4 2 4 1.50 2.00 1.00 2.00 1.00 2.00 No Change No ChangeTulip tree 454 428 High 457 367 462 126 463 100 1.07 0.86 1.08 0.29 1.08 0.23 Decrease Large Decrease

Virginia pine 174 168 High 159 149 201 74 202 71 0.95 0.89 1.20 0.44 1.20 0.42 Increase Large Decrease

Water locust 0 0 Low 0 0 0 2 0 16 NA NA NA New New New NA New HabitatWater oak 0 0 High 0 0 0 31 0 102 NA NA NA New New New NA New Habitat

White ash 341 331 High 292 210 270 135 280 129 0.88 0.63 0.82 0.41 0.85 0.39 Decrease Large Decrease

White oak 423 411 High 422 596 432 566 452 427 1.03 1.45 1.05 1.38 1.10 1.04 Decrease No Change

Willow oak 0 0 Medium 0 0 0 0 0 9 NA NA NA NA New New NA New HabitatWinged elm 2 0 High 5 127 21 416 33 598 New New New New New New Increase Increase

Yellow buckeye 77 56 Medium 64 61 61 57 63 57 1.14 1.09 1.09 1.02 1.13 1.02 Decrease No Change

aCurrent 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. Explanations for the change classes are described in the text. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value.
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American basswood 30 31 Medium 27 24 21 20 25 21 0.87 0.77 0.68 0.65 0.81 0.68 Decrease DecreaseAmerican beech 52 63 High 61 54 58 63 67 54 0.97 0.86 0.92 1.00 1.06 0.86 No Change No ChangeAmerican chestnut 9 12 Medium 8 8 8 8 8 8 0.67 0.67 0.67 0.67 0.67 0.67 Decrease DecreaseAmerican elm 15 43 Medium 26 52 26 76 28 77 0.61 1.21 0.61 1.77 0.65 1.79 Decrease IncreaseAmerican hornbeam 10 29 Medium 14 18 12 31 12 35 0.48 0.62 0.41 1.07 0.41 1.21 Large Decrease Increase

Bear oak (scrub oak) 14 7 Low 8 8 7 7 7 8 1.14 1.14 1.00 1.00 1.00 1.14 No Change No ChangeBigtooth aspen 8 13 High 8 2 2 0 1 0 0.62 0.15 0.15 0.00 0.08 0.00 Large Decrease Extirpated

Bitternut hickory 3 0 Low 2 13 2 35 4 37 New New New New 4.00 37.00 Increase Increase

Black cherry 168 175 High 136 107 101 72 94 73 0.78 0.61 0.58 0.41 0.54 0.42 Decrease Large Decrease

Black hickory 0 1 High 2 38 5 102 6 124 2.00 38.00 5.00 102.00 6.00 124.00 New Habitat New HabitatBlack locust 129 114 Low 120 105 111 88 104 67 1.05 0.92 0.97 0.77 0.91 0.59 No Change DecreaseBlack oak 167 148 High 156 211 156 289 155 278 1.05 1.43 1.05 1.95 1.05 1.88 No Change IncreaseBlack walnut 42 51 Medium 48 47 49 48 48 40 0.94 0.92 0.96 0.94 0.94 0.78 No Change DecreaseBlack willow 1 1 Low 0 10 0 30 0 30 0.00 10.00 0.00 30.00 0.00 30.00 NA IncreaseBlackgum 148 160 High 153 150 158 133 159 126 0.96 0.94 0.99 0.83 0.99 0.79 No Change DecreaseBlackjack oak 6 2 Medium 6 42 10 121 10 169 3.00 21.00 5.00 60.50 5.00 84.50 Increase Increase

Boxelder 20 26 Medium 20 22 17 25 18 28 0.77 0.85 0.65 0.96 0.69 1.08 Decrease No ChangeButternut 16 4 Low 11 3 4 1 3 0 2.75 0.75 1.00 0.25 0.75 0.00 Decrease ExtirpatedCedar elm 0 0 Low 0 0 0 18 0 68 NA NA NA New 0.00 68.00 NA New HabitatChestnut oak 490 388 High 423 348 406 238 377 218 1.09 0.90 1.05 0.61 0.97 0.56 No change DecreaseChinkapin oak 14 8 Medium 9 27 11 36 13 35 1.13 3.38 1.38 4.50 1.63 4.38 Increase Large Increase

Chokecherry 4 1 Low 3 0 2 0 1 0 3.00 0.00 2.00 0.00 1.00 0.00 No Change ExtirpatedCommon persimmon 3 3 Medium 9 50 15 79 20 103 3.00 16.67 5.00 26.33 6.67 34.33 Increase Increase

Cucumbertree 11 23 High 14 18 16 18 14 17 0.61 0.78 0.70 0.78 0.61 0.74 Decrease DecreaseEastern cottonwood 1 0 Low 0 1 0 3 0 11 NA New NA New 0.00 11.00 NA IncreaseEastern hemlock 27 56 High 41 43 38 40 38 39 0.73 0.77 0.68 0.71 0.68 0.70 Decrease DecreaseEastern hophornbeam 33 44 Medium 37 41 34 62 36 67 0.84 0.93 0.77 1.41 0.82 1.52 Decrease IncreaseEastern redcedar 31 40 Medium 50 127 61 181 70 203 1.25 3.18 1.53 4.53 1.75 5.08 Increase Large Increase

Eastern redbud 38 39 Medium 41 55 41 63 41 56 1.05 1.41 1.05 1.62 1.05 1.44 No Change IncreaseEastern white pine 69 104 High 82 66 72 47 67 47 0.79 0.64 0.69 0.45 0.64 0.45 Decrease Decrease

Flowering dogwood 155 162 High 160 174 171 164 172 127 0.99 1.07 1.06 1.01 1.06 0.78 No Change DecreaseGreen ash 2 8 Medium 7 13 6 26 6 40 0.88 1.63 0.75 3.25 0.75 5.00 Decrease Large Increase

Hackberry 24 18 Medium 19 26 16 42 17 52 1.06 1.44 0.89 2.33 0.94 2.89 No Change Large Increase

Honeylocust 0 1 Low 1 2 1 14 0 23 1.00 2.00 1.00 14.00 0.00 23.00 NA New HabitatLoblolly pine 1 7 High 4 5 8 20 4 52 0.57 0.71 1.14 2.86 0.57 7.43 Decrease Large Increase

Mockernut hickory 80 92 High 89 95 87 100 86 107 0.97 1.03 0.95 1.09 0.94 1.16 No Change IncreaseNorthern red oak 249 222 High 213 187 186 148 177 131 0.96 0.84 0.84 0.67 0.80 0.59 Decrease DecreaseOsage-orange 2 1 Medium 2 4 2 8 2 15 2.00 4.00 2.00 8.00 2.00 15.00 No Change IncreasePawpaw 4 8 Low 4 5 3 4 3 2 0.50 0.63 0.38 0.50 0.38 0.25 Large Decrease Large Decrease

Pignut hickory 80 98 High 91 90 90 78 94 83 0.93 0.92 0.92 0.80 0.96 0.85 No Change DecreasePin cherry 7 1 Medium 2 1 1 1 1 1 2.00 1.00 1.00 1.00 1.00 1.00 No Change No ChangePitch pine 43 39 High 40 37 38 34 36 33 1.03 0.95 0.97 0.87 0.92 0.85 No Change DecreasePost oak 0 3 High 22 176 39 378 46 505 7.33 58.67 13.00 126.00 15.33 168.33 New Habitat New Habitat

DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 33.—Complete DISTRIB model results for the 73 tree speciesa in Section M

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 33 (continued).Quaking aspen 6 4 High 3 1 3 0 3 0 0.75 0.25 0.75 0.00 0.75 0.00 Decrease ExtirpatedRed maple 293 357 High 333 272 328 179 324 136 0.93 0.76 0.92 0.50 0.91 0.38 No Change Large Decrease

Red mulberry 2 3 Low 2 9 1 32 1 40 0.67 3.00 0.33 10.67 0.33 13.33 Decrease Increase

Red spruce 3 3 High 2 0 1 0 1 0 0.67 0.00 0.33 0.00 0.33 0.00 Decrease ExtirpatedSassafras 76 99 High 91 109 97 97 107 90 0.92 1.10 0.98 0.98 1.08 0.91 No Change No ChangeScarlet oak 94 114 High 110 107 110 83 108 42 0.97 0.94 0.97 0.73 0.95 0.37 No Change Large Decrease

Serviceberry 77 63 Medium 69 55 58 52 52 53 1.10 0.87 0.92 0.83 0.83 0.84 Decrease Decrease

Shagbark hickory 8 14 Medium 12 27 15 50 17 60 0.86 1.93 1.07 3.57 1.21 4.29 Increase Large Increase

Shortleaf pine 7 6 High 7 42 10 126 10 197 1.17 7.00 1.67 21.00 1.67 32.83 Increase Large Increase

Shumard oak 0 0 Low 0 1 0 19 0 34 NA New NA New 0.00 34.00 NA New HabitatSilver maple 24 13 Medium 14 23 12 46 14 58 1.08 1.77 0.92 3.54 1.08 4.46 No Change Large Increase

Slippery elm 55 51 Medium 48 51 38 45 37 41 0.94 1.00 0.75 0.88 0.73 0.80 Decrease Decrease

Sourwood 5 22 High 24 15 35 22 32 23 1.09 0.68 1.59 1.00 1.46 1.05 Increase No Change

Southern red oak 2 1 High 3 15 6 35 5 62 3.00 15.00 6.00 35.00 5.00 62.00 Increase Increase

Striped maple 44 45 High 36 27 26 20 25 21 0.80 0.60 0.58 0.44 0.56 0.47 Decrease DecreaseSugar maple 146 173 High 151 166 147 121 154 59 0.87 0.96 0.85 0.70 0.89 0.34 No Change Large Decrease

Sugarberry 0 0 Medium 0 12 3 52 4 78 NA New New New 4.00 78.00 New Habitat New HabitatSweet birch 90 98 High 83 62 74 47 75 46 0.85 0.63 0.76 0.48 0.77 0.47 Decrease Decrease

Sweetgum 0 4 High 3 10 6 13 9 37 0.75 2.50 1.50 3.25 2.25 9.25 New Habitat New HabitatSycamore 24 23 Medium 22 30 20 39 24 40 0.96 1.30 0.87 1.70 1.04 1.74 No Change IncreaseTable Mountain pine 21 20 Medium 20 25 20 27 18 30 1.00 1.25 1.00 1.35 0.90 1.50 No Change IncreaseTulip tree 69 119 High 97 102 120 79 134 64 0.82 0.86 1.01 0.66 1.13 0.54 No Change DecreaseVirginia pine 133 109 High 129 102 129 86 116 85 1.18 0.94 1.18 0.79 1.06 0.78 No Change DecreaseWater locust 0 0 Low 0 0 0 2 0 9 NA NA NA New 0.00 9.00 NA New HabitatWater oak 0 0 High 0 0 0 7 0 46 NA NA NA New 0.00 46.00 NA New HabitatWhite ash 102 146 High 108 114 94 88 97 80 0.74 0.78 0.64 0.60 0.66 0.55 Decrease DecreaseWhite oak 254 254 High 265 304 260 300 254 244 1.04 1.20 1.02 1.18 1.00 0.96 No Change No ChangeWinged elm 0 0 High 4 44 9 146 14 242 New New New New 14.00 242.00 New Habitat New Habitat

Yellow birch 8 7 High 7 4 6 4 6 4 1.00 0.57 0.86 0.57 0.86 0.57 No Change DecreaseYellow buckeye 1 5 Medium 1 3 2 4 1 4 0.20 0.60 0.40 0.80 0.20 0.80 Large Decrease Decrease

aCurrent 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. Explanations for the change classes are described in the text. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value.
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American basswood 69 60 Medium 58 53 53 41 55 30 0.97 0.88 0.88 0.68 0.92 0.50 No Change DecreaseAmerican beech 383 362 High 342 231 340 202 306 168 0.95 0.64 0.94 0.56 0.85 0.46 Decrease DecreaseAmerican chestnut 39 32 Medium 37 37 34 37 35 36 1.16 1.16 1.06 1.16 1.09 1.13 No Change No ChangeAmerican elm 14 27 Medium 24 29 24 56 22 78 0.89 1.07 0.89 2.07 0.82 2.89 Decrease Large Increase

American holly 5 1 High 3 2 2 1 2 1 3.00 2.00 2.00 1.00 2.00 1.00 No Change No ChangeAmerican hornbeam 35 45 Medium 33 38 34 50 34 53 0.73 0.84 0.76 1.11 0.76 1.18 Decrease IncreaseBalsam fir 3 7 High 7 1 7 1 3 1 1.00 0.14 1.00 0.14 0.43 0.14 Large Decrease Large Decrease

Bear oak (scrub oak) 2 8 Low 2 3 1 5 3 4 0.25 0.38 0.13 0.63 0.38 0.50 Large Decrease Decrease

Bigtooth aspen 17 22 High 16 17 10 4 13 0 0.73 0.77 0.46 0.18 0.59 0.00 Decrease Extirpated

Bitternut hickory 5 2 Low 2 6 3 33 3 53 1.00 3.00 1.50 16.50 1.50 26.50 No Change IncreaseBlack cherry 323 339 High 288 275 268 159 269 110 0.85 0.81 0.79 0.47 0.79 0.32 Decrease Large Decrease

Black hickory 0 2 High 1 2 1 63 1 143 0.50 1.00 0.50 31.50 0.50 71.50 Decrease Increase

Black locust 161 162 Low 165 161 161 145 154 121 1.02 0.99 0.99 0.90 0.95 0.75 No Change DecreaseBlack oak 100 115 High 115 161 121 243 124 305 1.00 1.40 1.05 2.11 1.08 2.65 No Change Large Increase

Black walnut 28 31 Medium 30 50 35 57 39 63 0.97 1.61 1.13 1.84 1.26 2.03 Increase Increase

Black willow 2 1 Low 1 1 1 4 1 12 1.00 1.00 1.00 4.00 1.00 12.00 No Change IncreaseBlackgum 108 160 High 153 180 164 211 171 221 0.96 1.13 1.03 1.32 1.07 1.38 No Change IncreaseBlackjack oak 0 0 Medium 0 3 0 60 0 133 NA New NA New NA New NA New Habitat

Boxelder 10 14 Medium 5 9 8 10 7 16 0.36 0.64 0.57 0.71 0.50 1.14 Decrease No Change

Butternut 12 3 Low 10 6 9 3 7 0 3.33 2.00 3.00 1.00 2.33 0.00 No Change ExtirpatedCedar elm 0 0 Low 0 0 0 0 0 17 NA NA NA NA NA New NA New HabitatChestnut oak 339 384 High 378 405 375 340 378 274 0.98 1.06 0.98 0.89 0.98 0.71 No Change DecreaseChinkapin oak 2 0 Medium 2 9 2 31 1 46 New New New New New New No Change IncreaseChokecherry 6 10 Low 5 4 5 0 7 0 0.50 0.40 0.50 0.00 0.70 0.00 Decrease ExtirpatedCommon persimmon 0 0 Medium 3 15 5 85 8 147 New New New New New New New Habitat New Habitat

Cucumbertree 60 54 High 54 55 54 50 57 49 1.00 1.02 1.00 0.93 1.06 0.91 No Change No ChangeEastern hemlock 110 152 High 129 129 131 106 133 93 0.85 0.85 0.86 0.70 0.88 0.61 No Change DecreaseEastern hophornbeam 58 62 Medium 62 61 58 74 58 92 1.00 0.98 0.94 1.19 0.94 1.48 No Change IncreaseEastern redcedar 8 11 Medium 10 82 13 239 18 290 0.91 7.46 1.18 21.73 1.64 26.36 Increase Large Increase

Eastern redbud 10 11 Medium 12 31 16 59 19 63 1.09 2.82 1.46 5.36 1.73 5.73 Increase Large Increase

Eastern white pine 86 121 High 102 120 107 128 103 110 0.84 0.99 0.88 1.06 0.85 0.91 No Change No ChangeFlowering dogwood 132 166 High 172 227 190 278 197 271 1.04 1.37 1.15 1.68 1.19 1.63 Increase Increase

Green ash 13 15 Medium 8 14 8 20 8 32 0.53 0.93 0.53 1.33 0.53 2.13 Decrease Large Increase

Hackberry 3 1 Medium 1 1 1 13 1 30 1.00 1.00 1.00 13.00 1.00 30.00 No Change IncreaseHoneylocust 9 1 Low 4 6 3 14 3 23 4.00 6.00 3.00 14.00 3.00 23.00 No Change IncreaseLoblolly pine 0 4 High 0 1 4 23 5 80 0.00 0.25 1.00 5.75 1.25 20.00 No Change IncreaseMockernut hickory 83 92 High 87 101 91 137 92 157 0.95 1.10 0.99 1.49 1.00 1.71 No Change IncreaseNorthern red oak 312 303 High 297 267 283 249 274 234 0.98 0.88 0.93 0.82 0.90 0.77 No Change DecreaseOhio buckeye 6 4 Low 4 4 4 2 5 0 1.00 1.00 1.00 0.50 1.25 0.00 No Change ExtirpatedPawpaw 5 2 Low 3 3 3 0 3 0 1.50 1.50 1.50 0.00 1.50 0.00 No Change ExtirpatedPignut hickory 83 94 High 83 99 88 109 90 104 0.88 1.05 0.94 1.16 0.96 1.11 No Change No ChangePin cherry 11 8 Medium 6 0 3 0 2 0 0.75 0.00 0.38 0.00 0.25 0.00 Large Decrease Extirpated

Pitch pine 24 36 High 29 42 31 44 32 47 0.81 1.17 0.86 1.22 0.89 1.31 No Change Increase

DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 34.—Complete DISTRIB model results for the 74 tree speciesa in Section M

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 34 (continued).Post oak 3 0 High 4 27 6 211 8 437 New New New New New New Increase Increase

Quaking aspen 2 17 High 7 4 5 1 4 0 0.41 0.24 0.29 0.06 0.24 0.00 Large Decrease Extirpated

Red maple 645 686 High 663 640 657 497 660 348 0.97 0.93 0.96 0.72 0.96 0.51 No Change DecreaseRed mulberry 0 0 Low 0 3 0 29 1 44 NA New NA New New New New Habitat New HabitatRed pine 15 9 Medium 10 7 10 0 8 0 1.11 0.78 1.11 0.00 0.89 0.00 No Change ExtirpatedRed spruce 64 35 High 38 20 32 15 33 14 1.09 0.57 0.91 0.43 0.94 0.40 No Change Large Decrease

Sassafras 121 137 High 128 156 131 154 132 140 0.93 1.14 0.96 1.12 0.96 1.02 No Change No ChangeScarlet oak 103 126 High 132 140 138 155 137 126 1.05 1.11 1.10 1.23 1.09 1.00 No Change No ChangeServiceberry 101 88 Medium 91 82 89 71 86 71 1.03 0.93 1.01 0.81 0.98 0.81 No Change DecreaseShagbark hickory 17 12 Medium 13 20 13 51 15 79 1.08 1.67 1.08 4.25 1.25 6.58 Increase Large Increase

Shortleaf pine 4 6 High 8 21 9 120 10 264 1.33 3.50 1.50 20.00 1.67 44.00 Increase Large Increase

Shumard oak 0 0 Low 0 0 0 2 0 19 NA NA NA New NA New NA New HabitatSilver maple 1 1 Medium 0 1 1 7 0 14 0.00 1.00 1.00 7.00 0.00 14.00 Extirpated Increase

Slippery elm 25 26 Medium 25 42 26 65 29 72 0.96 1.62 1.00 2.50 1.12 2.77 No Change Large Increase

Sourwood 64 87 High 84 96 102 81 107 72 0.97 1.10 1.17 0.93 1.23 0.83 Increase Decrease

Southern red oak 2 0 High 0 4 1 32 0 79 NA New New New NA New Extirpated IncreaseStriped maple 154 138 High 129 92 119 60 114 50 0.94 0.67 0.86 0.44 0.83 0.36 Decrease Large DecreaseSugar maple 483 452 High 447 356 433 318 415 230 0.99 0.79 0.96 0.70 0.92 0.51 No Change DecreaseSugarberry 1 0 Medium 0 0 0 15 0 34 NA NA NA New NA New Extirpated IncreaseSweet birch 193 196 High 200 183 196 139 196 104 1.02 0.93 1.00 0.71 1.00 0.53 No Change DecreaseSweetgum 0 0 High 0 3 0 42 1 85 NA New NA New New New New Habitat New HabitatSycamore 15 12 Medium 16 28 18 45 18 51 1.33 2.33 1.50 3.75 1.50 4.25 Increase Large Increase

Table Mountain pine 5 15 Medium 10 11 12 16 11 21 0.67 0.73 0.80 1.07 0.73 1.40 Decrease IncreaseTulip tree 204 232 High 246 278 275 270 297 195 1.06 1.20 1.19 1.16 1.28 0.84 Increase Decrease

Virginia pine 38 63 High 60 95 68 128 71 126 0.95 1.51 1.08 2.03 1.13 2.00 No Change IncreaseWater oak 0 0 High 0 0 0 3 0 17 NA NA NA New NA New NA New HabitatWhite ash 125 141 High 123 143 109 129 109 113 0.87 1.01 0.77 0.92 0.77 0.80 Decrease Decrease

White oak 221 214 High 226 280 228 398 239 424 1.06 1.31 1.07 1.86 1.12 1.98 No Change IncreaseWinged elm 0 0 High 0 7 0 93 0 207 NA New NA New NA New NA New Habitat

Yellow birch 102 99 High 80 41 80 23 79 20 0.81 0.41 0.81 0.23 0.80 0.20 Decrease Large DecreaseYellow buckeye 23 21 Medium 23 25 25 28 25 27 1.10 1.19 1.19 1.33 1.19 1.29 Increase Increase

aCurrent 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. Explanations for the change classes are described in the text. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value.
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American basswood 81 52 Medium 66 43 60 28 59 31 1.27 0.83 1.15 0.54 1.14 0.60 No Change DecreaseAmerican beech 227 222 High 215 188 213 137 214 118 0.97 0.85 0.96 0.62 0.96 0.53 No Change DecreaseAmerican chestnut 19 11 Medium 17 15 16 10 14 10 1.55 1.36 1.46 0.91 1.27 0.91 Increase No Change

American elm 20 16 Medium 19 22 18 32 18 46 1.19 1.38 1.13 2.00 1.13 2.88 No Change Large Increase

American hornbeam 39 40 Medium 37 41 37 42 38 41 0.93 1.03 0.93 1.05 0.95 1.03 No Change No ChangeBitternut hickory 5 0 Low 5 11 4 35 5 37 New New New New New New No Change Large Increase

Black cherry 56 91 High 63 48 54 43 52 59 0.69 0.53 0.59 0.47 0.57 0.65 Decrease Decrease

Black hickory 0 0 High 0 29 0 116 0 151 NA New NA New New New NA New Habitat

Black locust 83 94 Low 90 93 93 75 96 70 0.96 0.99 0.99 0.80 1.02 0.75 No Change DecreaseBlack oak 118 118 High 107 157 106 219 110 235 0.91 1.33 0.90 1.86 0.93 1.99 No Change IncreaseBlack walnut 22 27 Medium 27 35 27 50 28 67 1.00 1.30 1.00 1.85 1.04 2.48 No Change Large Increase

Blackgum 103 126 High 125 135 124 140 128 134 0.99 1.07 0.98 1.11 1.02 1.06 No Change No ChangeBlackjack oak 0 1 Medium 1 21 0 109 0 175 1.00 21.00 0.00 109.00 0.00 175.00 NA New Habitat

Butternut 11 1 Low 7 0 5 0 5 0 7.00 0.00 5.00 0.00 5.00 0.00 Increase Extirpated

Cedar elm 0 0 Low 0 0 0 21 0 72 NA NA NA New New New NA New HabitatChestnut oak 238 261 High 243 231 248 161 241 156 0.93 0.89 0.95 0.62 0.92 0.60 No Change DecreaseChinkapin oak 5 2 Medium 2 14 2 32 3 37 1.00 7.00 1.00 16.00 1.50 18.50 No Change IncreaseCommon persimmon 6 2 Medium 6 26 3 73 5 95 3.00 13.00 1.50 36.50 2.50 47.50 No Change IncreaseCucumbertree 68 55 High 58 53 55 47 56 44 1.06 0.96 1.00 0.86 1.02 0.80 No Change DecreaseEastern hemlock 72 62 High 72 60 71 49 70 48 1.16 0.97 1.15 0.79 1.13 0.77 No Change DecreaseEastern hophornbeam 40 37 Medium 38 41 37 60 38 75 1.03 1.11 1.00 1.62 1.03 2.03 No Change IncreaseEastern redcedar 2 4 Medium 6 45 8 101 12 141 1.50 11.25 2.00 25.25 3.00 35.25 Large Increase Large IncreaseEastern redbud 29 32 Medium 34 47 38 52 38 69 1.06 1.47 1.19 1.63 1.19 2.16 Increase Large Increase

Eastern white pine 18 35 High 34 42 43 30 47 30 0.97 1.20 1.23 0.86 1.34 0.86 Increase No Change

Flowering dogwood 207 193 High 204 220 209 209 202 154 1.06 1.14 1.08 1.08 1.05 0.80 No Change DecreaseGreen ash 7 6 Medium 6 7 6 12 7 25 1.00 1.17 1.00 2.00 1.17 4.17 Increase Large Increase

Hackberry 0 0 Medium 0 2 0 11 1 28 NA New NA New New New NA New HabitatLoblolly pine 0 2 High 2 14 7 66 8 139 1.00 7.00 3.50 33.00 4.00 69.50 New Habitat New HabitatMockernut hickory 84 103 High 91 101 93 111 94 137 0.88 0.98 0.90 1.08 0.91 1.33 No Change IncreaseMountain maple 12 3 High 8 10 8 10 8 10 2.67 3.33 2.67 3.33 2.67 3.33 No Change No ChangeNorthern red oak 154 156 High 149 139 139 166 139 155 0.96 0.89 0.89 1.06 0.89 0.99 No Change No ChangePawpaw 42 23 Low 26 20 26 9 21 8 1.13 0.87 1.13 0.39 0.91 0.35 No Change Large Decrease

Pignut hickory 85 104 High 89 91 88 69 87 71 0.86 0.88 0.85 0.66 0.84 0.68 Decrease DecreasePitch pine 13 18 High 12 17 11 13 10 20 0.67 0.94 0.61 0.72 0.56 1.11 Decrease No ChangePost oak 2 6 High 6 78 9 305 14 507 1.00 13.00 1.50 50.83 2.33 84.50 No Change IncreaseRed maple 417 448 High 427 360 425 236 427 168 0.95 0.80 0.95 0.53 0.95 0.38 No Change Large Decrease

Red mulberry 5 2 Low 3 6 3 17 4 35 1.50 3.00 1.50 8.50 2.00 17.50 No Change IncreaseRiver birch 5 1 Low 3 2 2 2 2 3 3.00 2.00 2.00 2.00 2.00 3.00 Increase Large Increase

Sassafras 210 177 High 180 159 166 106 169 93 1.02 0.90 0.94 0.60 0.96 0.53 No Change DecreaseScarlet oak 88 83 High 82 96 85 68 82 38 0.99 1.16 1.02 0.82 0.99 0.46 No Change DecreaseServiceberry 30 42 Medium 35 38 35 38 36 38 0.83 0.91 0.83 0.91 0.86 0.91 No Change No ChangeShagbark hickory 10 5 Medium 9 24 9 62 10 82 1.80 4.80 1.80 12.40 2.00 16.40 Increase Large Increase

Shortleaf pine 7 11 High 15 67 19 250 21 342 1.36 6.09 1.73 22.73 1.91 31.09 Increase Large Increase

DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 35.—Complete DISTRIB model results for the 62 tree speciesa in Section M

(continued on next page)
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DISTRIB resu

Modeled IV Future : Current Suitable Habitat Change Cla

DISTRIB 2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2

FIA Current Model PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFDL PCM GFD

Common Name IV IV Reliability B1 A1FI B1 A1FI B1 A1FI B1 A1FI B1 A1F

Table 35 (continued).aCurrent 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 Shumard oakprovided for the end of century (2070 through 2099) period. Explanations for the change classes are described in the text. Future:Current Suitable Habitat is a ratio of projected importance value to current importance value.

1 0 Low 0 0 0 14 0 35 NA NA NA New New New NA Increase

Slippery elm 35 24 Medium 34 31 31 35 34 37 1.42 1.29 1.29 1.46 1.42 1.54 Increase Increase

Sourwood 94 100 High 102 113 119 98 119 71 1.02 1.13 1.19 0.98 1.19 0.71 Increase Decrease

Southern red oak 2 1 High 2 22 6 65 9 93 2.00 22.00 6.00 65.00 9.00 93.00 Increase Increase

Striped maple 38 32 High 31 18 27 18 24 19 0.97 0.56 0.84 0.56 0.75 0.59 Decrease Decrease

Sugar maple 355 288 High 331 249 313 129 316 53 1.15 0.87 1.09 0.45 1.10 0.18 No Change Large Decrease

Sugarberry 0 0 Medium 0 1 0 27 0 43 NA New NA New New New NA New HabitatSweet birch 101 100 High 99 75 95 48 89 44 0.99 0.75 0.95 0.48 0.89 0.44 No Change Large Decrease

Sweetgum 3 2 High 8 28 11 69 16 82 4.00 14.00 5.50 34.50 8.00 41.00 Increase Increase

Sycamore 17 15 Medium 16 24 16 30 18 39 1.07 1.60 1.07 2.00 1.20 2.60 Increase Large Increase

Tulip tree 361 323 High 356 340 357 186 357 95 1.10 1.05 1.11 0.58 1.11 0.29 No Change Large Decrease

Virginia pine 14 42 High 40 79 60 93 68 87 0.95 1.88 1.43 2.21 1.62 2.07 Increase Large Increase

Water locust 0 0 Low 0 0 0 1 0 9 NA NA NA New New New NA New HabitatWater oak 0 0 High 0 0 0 21 0 57 NA NA NA New New New NA New HabitatWhite ash 70 68 High 64 66 61 66 61 75 0.94 0.97 0.90 0.97 0.90 1.10 No Change No ChangeWhite oak 145 160 High 158 205 163 262 176 230 0.99 1.28 1.02 1.64 1.10 1.44 No Change IncreaseWinged elm 0 1 High 1 35 5 153 7 235 1.00 35.00 5.00 153.00 7.00 235.00 New Habitat New HabitatYellow birch 26 17 High 19 7 16 6 16 5 1.12 0.41 0.94 0.35 0.94 0.29 No Change Large Decrease

Yellow buckeye 31 29 Medium 33 34 33 33 34 33 1.14 1.17 1.14 1.14 1.17 1.14 Increase No Change
Appe

272

American basswood Decrease Large Decrease No Change Large Decrease Decrease Large Decrease

American beech No Change Large Decrease No Change Large Decrease Decrease Large Decrease

American chestnut - - - - No Change Extirpated

American elm No Change Decrease Decrease Decrease Decrease No Change

American holly - - - - - -

American hornbeam No Change No Change No Change Increase Decrease No Change

Balsam fir - - - - - -

Bear oak (scrub oak) - - - - - -

Bigtooth aspen Decrease Extirpated Large Decrease Extirpated Large Decrease Extirpated

Bitternut hickory Large Increase Large Increase Increase Large Increase Increase Large Increase

Black ash 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

Blackgum Increase No Change Increase No Change Decrease No Change

Blackjack oak New New Increase Increase Increase Increase

Boxelder No Change Increase Decrease Increase Decrease No Change

Bur oak Increase Large Increase Large Decrease Large Increase - -

Butternut - - - - No Change Extirpated

Cedar elm 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

Chokecherry - - - - - -

Common persimmon New New Increase Large Increase Increase Large Increase

Cucumbertree Large Increase No Change Large Increase Increase Increase No Change

Eastern cottonwood Decrease Large Increase Decrease Large Increase - -

Eastern hemlock Large Decrease Large Decrease Large Decrease Large Decrease Decrease Large Decrease

Eastern hophornbeam Decrease Decrease No Change Increase Decrease Increase

Eastern redbud New New Increase Increase Decrease No Change

Eastern redcedar New New Large Increase Large Increase Large Increase Large Increase

Eastern white pine No Change Extirpated Decrease Extirpated Increase Extirpated

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

Ecological section within the assessment area boundaries

221F 221E OH 221E WV

Common Name PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL

Table 36.—Comparison of change classes for two climate scenarios from the DISTRIB model results for all tree spec

in six ecological sections of the assessment areaa

(continued on next page)
Appe

273

Loblolly pine - - NA New Increase Increase

Mockernut hickory No Change Increase No Change Increase Decrease Increase

Mountain maple - - - - - -

Northern catalpa - - No Change No Change - -

Northern pin oak No Change Decrease - - - -

Northern red oak No Change Decrease No Change Decrease Decrease No Change

Northern white-cedar Decrease No Change - - - -

Ohio buckeye Large Increase Decrease No Change Large Decrease Decrease Extirpated

Osage-orange Increase Large Increase No Change Large Increase No Change Increase

Pawpaw - - No Change Large Decrease Decrease Large Decrease

Pignut hickory No Change Increase Increase No Change Decrease Decrease

Pin cherry Decrease Large Decrease - - Extirpated Extirpated

Pin oak No Change No Change No Change Large Increase New New

Pitch pine - - Increase No Change Decrease Large Decrease

Post oak New New Large Increase Large Increase Large Increase Large Increase

Quaking aspen Large Decrease Large Decrease Extirpated Extirpated - -

Red maple Decrease Large Decrease No Change Large Decrease Decrease Large Decrease

Red mulberry New New NA New No Change Large Increase

Red pine Decrease Large Decrease Large Decrease Large Decrease - -

Red spruce - - - - - -

River birch - - No Change Increase - -

Rock elm NA New - - Large Increase Large Increase

Sassafras Increase Increase No Change Decrease Decrease Decrease

Scarlet oak Large Increase Large Increase Increase Decrease Decrease Large Decrease

Serviceberry No Change Decrease No Change No Change Decrease No Change

Shagbark hickory Increase Increase Increase Increase Increase Large Increase

Shingle oak No Change Increase No Change Increase - -

Shortleaf pine 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

Sourwood - - Increase Increase Increase Large Decrease

Southern red oak NA New New New Increase Increase

Striped maple - - - - - -

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 Extirpated Decrease Decrease Decrease Large Decrease

Sweetgum New New Increase Increase Increase Increase

Sycamore Increase Increase No Change No Change Decrease No Change

Ecological section within the assessment area boundaries

221F 221E OH 221E WV

Common Name PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL

Table 36 (continued).

(continued on next page)
Appe

274

Table Mountain pine - - - - No Change No Change

Tulip tree 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 Extirpated - - - -

Yellow buckeye - - No Change No Change Decrease No Change

a

Change classes are provided for the end-of-century (2070 through 2099) period. Explanations 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.

Ecological section within the assessment area boundaries

221F 221E OH 221E WV

Common Name PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL

Table 36 (continued).

(continued on next page)
Appe

275

American basswood No Change Decrease No Change Decrease Decrease Decrease

American beech No Change Decrease Decrease Decrease No Change No Change

American chestnut 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 hornbeam No Change No Change Decrease Increase Large Decrease Increase

Balsam fir - - Large Decrease Large Decrease - -

Bear oak (scrub oak) - - Large Decrease Decrease No Change No Change

Bigtooth aspen - - Decrease Extirpated Large Decrease Extirpated

Bitternut hickory No Change Large Increase No Change Increase Increase Increase

Black ash - - - - - -

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

Blackgum No Change No Change No Change Increase No Change Decrease

Blackjack oak NA New NA New Increase Increase

Boxelder - - Decrease No Change Decrease No Change

Bur oak - - - - - -

Butternut Increase Extirpated No Change Extirpated Decrease Extirpated

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 Extirpated No Change Extirpated

Common persimmon No Change Increase New New Increase Increase

Cucumbertree No Change Decrease No Change No Change Decrease Decrease

Eastern cottonwood - - - - NA Increase

Eastern hemlock No Change Decrease No Change Decrease Decrease Decrease

Eastern hophornbeam No Change Increase No Change Increase Decrease Increase

Eastern redbud Increase Large Increase Increase Large Increase No Change Increase

Eastern redcedar Large Increase Large Increase Increase Large Increase Increase Large Increase

Eastern white pine Increase No Change No Change No Change Decrease Decrease

Flowering dogwood 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

Honeylocust - - No Change Increase NA New

Ecological section within the assessment area boundaries

M221C M221B M221A

Common Name PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL

Table 36 (continued).—Comparison of change classes for two climate scenarios from the DISTRIB model results for all 

tree species in six ecological sections of the assessment areaa

(continued on next page)
Appe

27

Loblolly pine 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 Extirpated - -

Osage-orange - - - - No Change Increase

Pawpaw No Change Large Decrease No Change Extirpated Large Decrease Large Decrease

Pignut hickory Decrease Decrease No Change No Change No Change Decrease

Pin cherry - - Large Decrease Extirpated No Change No Change

Pin oak - - - - - -

Pitch pine Decrease No Change No Change Increase No Change Decrease

Post oak No Change Increase Increase Increase New New

Quaking aspen - - Large Decrease Extirpated Decrease Extirpated

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 Extirpated - -

Red spruce - - No Change Large Decrease Decrease Extirpated

River birch Increase Large Increase - - - -

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

Shagbark hickory Increase Large Increase Increase Large Increase Increase Large Increase

Shingle oak - - - - - -

Shortleaf pine Increase Large Increase Increase Large Increase Increase Large Increase

Shumard oak NA Increase NA New NA New

Silver maple - - Extirpated 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 Extirpated 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 Extirpated Increase New New

Swamp white oak - - - - - -

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

Ecological section within the assessment area boundaries

M221C M221B M221A

Common Name PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL

Table 36 (continued).

(continued on next page)
Appe

277

Table Mountain pine - - Decrease Increase No Change Increase

Tulip tree No Change Large Decrease Increase Decrease No Change Decrease

Virginia pine 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

Yellow buckeye Increase No Change Increase Increase Large Decrease Decrease

a

Change classes are provided for the end-of-century (2070 through 2099) period. Explanations 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.

Ecological section within the assessment area boundaries

M221C M221B M221A

Common Name PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL

Table 36 (continued).
Appe

278

Table 37.—Modifying factor and adaptability information for the 93 tree species in the assessment area that were 

modeled by using DISTR

DISTRIB Modifying Factorsa Adaptability Sco

Common Name Model Reliability Positive Traits Negative Traits DistFact BioFact Adapt Adapt Class

American basswood Medium COL FTK 0.3 0.2 4.6 ○ 

American beech High COL INS FTK -1.1 0.0 3.6 ○ 

American chestnut Medium COL DISE FTK 0.1 0.3 4.5 ○ 

American elm Medium ESP DISE INS -0.8 0.3 4.0 ○ 

American holly High COL ESP FTK -0.1 0.5 4.5 ○ 

American hornbeam Medium COL SES FTK DRO 0.6 0.6 5.1 ○ 

Balsam fir High COL INS FTK DRO -3.0 -0.4 2.7 -

Bear oak (scrub oak) Low FRG VRE COL FTK 1.0 -0.8 4.6 ○ 

Bigtooth aspen High FRG DISP COL DRO FTK 1.0 0.2 5.1 ○ 

Bitternut hickory Low DRO COL 2.2 -0.8 5.6 +

Black ash High INS COL DISP DRO SES FTK ESP -1.3 -3.0 1.7 -

Black cherry High DRO ESP INS FTK COL -1.6 -0.3 3.0 -

Black hickory High ESP COL 1.0 -2.3 4.1 ○ 

Black locust Low COL INS 0.0 -0.6 3.8 ○ 

Black maple Low COL ESP FTK 0.5 0.9 5.2 ○ 

Black oak High DRO ESP INS DISE 0.5 0.4 4.9 ○ 

Black walnut Medium SES COL DRO 0.4 -0.8 4.0 ○ 

Black willow Low COL FTK DRO -0.3 -2.1 2.8 -

Blackgum High COL FTK 1.5 0.8 5.9 +

Blackjack oak Medium DRO SES FRG VRE COL FTK 1.6 0.2 5.6 +

Blue ash Low INS DISP FTK COL ESP -0.4 -2.4 2.7 -

Boxelder Medium SES DISP DRO COL SES FTK 2.4 2.1 7.4 +

Bur oak Medium DRO FTK 2.8 -0.2 6.4 +

Butternut Low FTK COL DRO DISE -1.4 -1.3 2.3 -

Cedar elm Low DISE -0.3 -1.2 3.3 ○ 

Chestnut oak High SES VRE ESP FTK INS DISE 1.4 1.3 6.1 +

Chinkapin oak Medium SES 1.2 -0.7 4.8 ○ 

Chokecherry Low COL 0.2 -0.9 3.8 ○ 

Common persimmon Medium COL ESP 1.2 1.0 5.8 +

Cucumbertree High FTK 0.0 -1.1 3.6 ○ 

Eastern cottonwood Low SES INS COL DISE FTK 0.2 -0.8 3.9 ○ 

Eastern hemlock High COL INS DRO -1.3 -0.9 2.7 -

Eastern hophornbeam Medium COL ESP SES 1.7 1.3 6.4 +

Eastern redcedar Medium DRO FTK COL INS 0.6 -1.5 3.9 ○ 

Eastern redbud Medium 0.9 0.0 4.9 ○ 

Eastern white pine High DISP DRO FTK INS -2.0 0.1 3.3 ○ 

Flowering dogwood High COL 0.1 1.0 5.0 ○ 

Green ash Medium INS FTK COL -0.1 -0.3 4.0 ○ 

Hackberry Medium DRO FTK 1.7 0.3 5.7 +

Honeylocust Low COL 1.9 -0.5 5.5 +

Loblolly pine High ESP INS INP DRO COL -0.5 -0.7 3.4 ○ 

Mockernut hickory High FTK 1.7 -0.3 5.4 +

Mountain maple High COL VRE ESP DRO FTK 0.8 1.5 5.9 +

Northern catalpa Low COL ESP 0.9 -1.6 4.2 ○ 

Northern pin oak Medium DRO FTK COL 2.5 -0.6 6.0 +

Northern red oak High INS 1.4 0.1 5.4 +

(continued on next page)
Appe

279

DISTRIB Modifying Factorsa Adaptability Sco

Common Name Model Reliability Positive Traits Negative Traits DistFact BioFact Adapt Adapt Class

Table 37 (continued).

Northern white-cedar High COL FTK -0.7 0.5 4.2 ○ 

Ohio buckeye Low COL SES FTK 0.4 -1.9 3.5 ○ 

Osage-orange Medium ESP ESP 2.3 0.3 6.3 +

Pawpaw Low COL DRO -0.5 -0.3 3.7 ○ 

Pignut hickory High ESP INS DRO 0.2 0.4 4.7 ○ 

Pin cherry Medium SES FRG FTK COL 0.5 -0.7 4.2 ○ 

Pin oak Medium FTK COL INS DISE -0.7 -1.4 2.8 -

Pitch pine High COL INS 0.6 -1.8 3.8 ○ 

Post oak High DRO SES FTK COL INS DISE 2.2 -0.6 5.7 +

Quaking aspen High SES FRG ESP COL DRO FTK 0.6 0.0 4.7 ○ 

Red maple High SES ESP ESP COL DISP 3.0 3.0 8.5 +

Red mulberry Low COL DISP FTK 0.1 0.6 4.7 ○ 

Red pine Medium INS COL DISP 0.9 -2.4 3.9 ○ 

Red spruce High ESP COL FTK SES -1.3 -0.6 2.9 -

River birch Low DISP FTK COL DRO -0.5 -0.3 3.7 ○ 

Rock elm Low ESP ESP SES -0.2 -2.6 2.8 -

Sassafras High COL FTK 0.5 -0.6 4.2 ○ 

Scarlet oak High VRE ESP ESP INS DISE FTK -0.4 0.7 4.6 ○ 

Serviceberry Medium COL SES DRO -0.4 1.0 4.8 ○ 

Shagbark hickory Medium INS FTK -0.2 0.4 4.4 ○ 

Shingle oak Medium ESP COL 1.3 -0.7 4.9 ○ 

Shortleaf pine High ESP COL INS DRO 0.0 -1.0 3.6 ○ 

Shumard oak Low DRO SES COL 2.5 -1.0 5.8 +

Silver maple Medium DISP SES COL DRO FTK 0.1 1.6 5.6 +

Slippery elm Medium COL FTK DISE 0.0 0.7 4.8 ○ 

Sourwood High COL ESP 2.6 1.0 6.9 +

Southern magnolia Medium SES COL FTK DRO EHS 0.6 0.4 4.9 ○ 

Southern red oak High SES 1.2 0.2 5.3 +

Striped maple High COL SES DRO 1.0 0.3 5.1 ○ 

Sugar maple High COL ESP 0.9 1.3 5.8 +

Sugarberry Medium COL SES FTK -0.2 0.6 4.6 ○ 

Swamp white oak Low 1.0 -0.3 4.9 ○ 

Sweet birch High DISP FTK COL INS DISE -1.3 -0.3 3.2 -

Sweetgum High VRE ESP FTK COL DRO -0.4 0.2 4.1 ○ 

Sycamore Medium 1.3 -0.9 4.8 ○ 

Table Mountain pine Medium DRO COL 2.6 -1.1 5.9 +

Tamarack (native) High FTK COL INS -0.5 -1.2 3.1 -

Tulip tree High SES DISP ESP INP 0.1 1.3 5.3 +

Virginia pine High COL POL 0.1 -0.8 3.8 ○ 

Water locust Low 0.0 -0.6 3.8 ○ 

Water oak High SES FTK COL -0.2 -0.6 3.7 ○ 

White ash High INS FTK COL -2.0 -0.5 2.7 -

White oak High ESP ESP SES FTK INS DISE 1.7 1.0 6.1 +

Willow oak Medium SES SES COL 0.6 0.0 4.7 ○ 

Winged elm High INS DISE -0.6 -0.3 3.6 ○ 

Yellow birch High DISP FTK INS DISE -1.4 0.0 3.4 ○ 

Yellow buckeye Medium COL DRO SES FTK ESP DISP 0.0 -2.1 3.1 -

a

Modifying factor codes are described in Table 38. Adaptability scores are described in the appendix text.
Appe

280

Code Title Type Description (if positive) Description (if negativ

COL Competition-light Biological Tolerant of shade or limited 

light conditions

Intolerant of shade or limited light 

conditions

DISE Disease Disturbance N/A Has a high number and/or severity of 

known pathogens that attack the species

DISP Dispersal Biological High ability to effectively 

produce and distribute seeds

N/A

DRO Drought Biological Drought-tolerant Susceptible to drought

ESP Edaphic specificity Biological Wide range of soil 

requirements

Narrow range of soil requirements

EHS Environmental 

habitat specificity

Biological Wide range of suitable habitat 

conditions

Narrow range of suitable habitat conditions

FRG Fire regeneration Disturbance Regenerates well after fire N/A

FTK Fire topkill Disturbance Resistant to fire topkill Susceptible to fire topkill

INP Invasive plants Disturbance N/A Strong negative effects of invasive plants on 

the species, either through competition for 

nutrients or as a pathogen

INS Insect pests Disturbance N/A Has a high number and/or severity of 

known insects that attack the species

POL Pollution Disturbance N/A Strong negative effects of pollution on the 

species

SES Seedling 

establishment

Biological High ability to regenerate 

with seeds to maintain future 

populations

Low ability to regenerate with seeds to 

maintain future populations

VRE Vegetative 

reproduction

Biological Capable of vegetative 

reproduction through stump 

sprouts or cloning

N/A

Table 38.—Key to modifying factor codes

a

These codes are used to describe positive or negative modifying factors in Table 37. A species was given that code if information 

from the literature suggested that it had these characteristics (Matthews et al. 2011). See Matthews et al. (2011) for a more thorough 

description of these factors and how they were assessed.
Appe

281

biolo

gic

alsc

ore

Highest

possible

adaptability

score: 8.5

-3 -2 -1 0 1 2 3

-3

-2

-1

0

1

2

3

disturbance score

Lowest

possible

adaptability

score: 0

Mid-range

adaptability

score: 4.25

High biological,low

disturbance scores

High disturbance,

low biological

scores

0 Length 6

Le

n

gth

0

6

Figure 59.—Schematic showing how adaptability was determined for information for tree species modeled using the Climate 

Change Tree Atlas. 
Appe

282

Modeled:Current biomass Class

<0.4 large decrease

0.4 through <0.8 small decrease

0.8 through <1.2 no change

1.2 through <2.0 small increase

>2.0 large increase

current climate = 0 and 

future climate model = 0

not present

current climate > 0 and 

future climate model = 0

extirpated

LINKAGES MODEL RES

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. 60).

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.

American beech Assessment area 0.22 0.22 0.0 0.02 -90.9

221E 0.22 0.21 -5.5 0.00 Extirpated

221F 0.26 0.26 0.7 0.00 Extirpated

M221A 0.13 0.15 14.9 0.00 Extirpated

M221B 0.23 0.24 3.5 0.14 -38.1

M221C 0.24 0.23 -5.8 0.00 Extirpated

American elm Assessment area 0.14 0.16 14.3 0.18 28.6

221E 0.16 0.19 14.4 0.19 14.4

221F 0.13 0.16 23.0 0.18 40.8

M221A 0.07 0.12 76.3 0.14 111.2

M221B 0.10 0.13 22.3 0.18 77.7

M221C 0.16 0.18 12.7 0.19 17.1

Balsam fir Assessment area 0.00 0.00 0.0 0.00 0.0

221E 0.00 0.00 0.0 0.00 0.0

221F 0.00 0.00 0.0 0.00 0.0

M221A 0.00 0.00 0.0 0.00 0.0

M221B 0.02 0.01 -36.8 0.00 Extirpated

M221C 0.00 0.00 0.0 0.00 0.0

Black cherry Assessment area 0.28 0.30 7.1 0.30 7.1

221E 0.31 0.32 1.5 0.31 -2.1

221F 0.30 0.32 6.4 0.32 8.3

M221A 0.11 0.18 65.0 0.21 93.9

M221B 0.26 0.28 8.5 0.31 19.9

M221C 0.32 0.32 1.8 0.31 -1.3

Blackgum Assessment area 0.16 0.19 18.8 0.21 31.3

221E 0.18 0.21 14.7 0.21 16.8

221F 0.15 0.18 20.2 0.22 44.7

M221A 0.08 0.13 75.0 0.16 109.3

M221B 0.12 0.14 22.6 0.21 78.1

M221C 0.18 0.21 14.3 0.22 19.3

Current Climate PCM B1 GFDL A1F

Species Sectiona SEPb SEPb % change SEPb % change

Table 39.— Changes in early growth of tree species predicted by the LINKAGES model for two climate scenarios a

the end of the century (2080 through 2099) compared to current climate (1990 through 2009) for 23 species in the 

assessment area

(continued on next page)
Appe

283

Black oak Assessment area 0.19 0.20 5.3 0.13 -31.6

221E 0.20 0.21 0.5 0.08 -63.1

221F 0.20 0.21 2.1 0.20 -0.4

M221A 0.11 0.16 47.6 0.15 40.2

M221B 0.18 0.19 8.0 0.20 10.7

M221C 0.20 0.21 0.8 0.13 -37.5

Chestnut oak Assessment area 0.20 0.20 0.0 0.12 -40.0

221E 0.21 0.21 0.1 0.06 -72.0

221F 0.21 0.21 0.0 0.20 -6.1

M221A 0.10 0.15 45.2 0.14 32.0

M221B 0.19 0.20 5.2 0.20 3.3

M221C 0.21 0.21 -0.2 0.13 -37.9

Eastern red cedar Assessment area 0.20 0.24 20.0 0.26 30.0

221E 0.23 0.26 14.7 0.26 15.3

221F 0.21 0.25 21.9 0.28 36.5

M221A 0.09 0.13 43.1 0.16 67.3

M221B 0.15 0.19 23.6 0.26 71.4

M221C 0.24 0.27 14.1 0.28 17.3

Eastern hemlock Assessment area 0.13 0.13 0.0 0.01 -92.3

221E 0.14 0.12 -15.2 0.00 Extirpated

221F 0.15 0.15 0.5 0.00 -99.8

M221A 0.06 0.09 43.6 0.00 Extirpated

M221B 0.14 0.14 1.4 0.07 -46.7

M221C 0.15 0.13 -13.6 0.00 Extirpated

Eastern white pine Assessment area 0.34 0.35 2.9 0.04 -88.2

221E 0.35 0.35 1.8 0.00 Extirpated

221F 0.38 0.38 0.0 0.01 -98.3

M221A 0.21 0.25 18.2 0.00 Extirpated

M221B 0.35 0.36 2.5 0.24 -31.0

M221C 0.37 0.38 2.1 0.00 Extirpated

Flowering dogwood Assessment area 0.05 0.08 60.0 0.10 100.0

221E 0.07 0.10 41.9 0.10 49.5

221F 0.04 0.06 66.0 0.10 162.6

M221A 0.03 0.05 118.1 0.07 187.9

M221B 0.02 0.04 80.9 0.10 318.3

M221C 0.06 0.09 45.7 0.10 61.1

Loblolly pine Assessment area 0.13 0.29 123.1 0.51 292.3

221E 0.20 0.39 94.3 0.54 167.9

221F 0.03 0.17 464.8 0.50 1535.1

M221A 0.08 0.27 260.2 0.51 577.5

M221B 0.02 0.10 455.6 0.45 2496.5

M221C 0.17 0.35 106.1 0.53 212.1

Northern red oak Assessment area 0.29 0.31 6.9 0.18 -37.9

221E 0.29 0.31 6.2 0.09 -70.4

221F 0.33 0.33 0.9 0.31 -5.1

M221A 0.17 0.20 19.5 0.18 5.5

M221B 0.30 0.31 3.5 0.30 1.0

M221C 0.32 0.33 3.9 0.20 -36.4

Current Climate PCM B1 GFDL A1F

Species Sectiona SEPb SEPb % change SEPb % change

Table 39 (continued).

(continued on next page)
Appe

284

Pignut hickory Assessment area 0.35 0.37 5.7 0.36 2.9

221E 0.38 0.39 2.1 0.36 -6.5

221F 0.37 0.38 5.2 0.39 5.8

M221A 0.15 0.25 62.0 0.28 81.6

M221B 0.31 0.34 10.8 0.38 23.2

M221C 0.38 0.39 1.1 0.38 -2.1

Post oak Assessment area 0.05 0.10 100.0 0.17 240.0

221E 0.07 0.13 81.7 0.18 149.8

221F 0.03 0.06 103.5 0.18 458.5

M221A 0.03 0.08 182.8 0.14 401.6

M221B 0.02 0.04 147.2 0.15 795.6

M221C 0.06 0.12 86.7 0.18 184.1

Red maple Assessment area 0.31 0.34 9.7 0.37 19.4

221E 0.34 0.36 7.8 0.37 10.7

221F 0.31 0.33 9.2 0.37 22.5

M221A 0.24 0.32 33.4 0.35 45.3

M221B 0.27 0.30 10.8 0.37 34.1

M221C 0.33 0.36 7.9 0.37 12.7

Red spruce Assessment area 0.00 0.00 0.0 0.00 0.0

221E 0.00 0.00 0.0 0.00 0.0

221F 0.00 0.00 0.0 0.00 0.0

M221A 0.00 0.00 0.0 0.00 0.0

M221B 0.02 0.01 -49.9 0.00 Extirpated

M221C 0.00 0.00 0.0 0.00 0.0

Scarlet oak Assessment area 0.17 0.17 0.0 0.04 -76.5

221E 0.18 0.18 0.5 0.00 -99.7

221F 0.18 0.18 1.1 0.07 -60.7

M221A 0.09 0.13 44.4 0.02 -74.8

M221B 0.17 0.17 3.2 0.14 -19.6

M221C 0.18 0.18 -0.2 0.01 -96.5

Shortleaf pine Assessment area 0.08 0.22 175.0 0.35 337.5

221E 0.14 0.31 125.3 0.36 159.2

221F 0.02 0.12 536.6 0.38 2017.0

M221A 0.03 0.14 305.5 0.22 529.6

M221B 0.01 0.07 830.0 0.34 4515.9

M221C 0.11 0.29 153.4 0.38 232.5

Sugar maple Assessment area 0.51 0.50 -2.0 0.05 -90.2

221E 0.52 0.49 -4.5 0.00 Extirpated

221F 0.52 0.52 -0.1 0.00 -99.2

M221A 0.44 0.49 11.0 0.00 -100.0

M221B 0.51 0.52 0.8 0.33 -34.5

M221C 0.52 0.51 -2.0 0.00 Extirpated

Tulip tree Assessment area 0.76 0.83 9.2 0.86 13.2

221E 0.77 0.85 10.9 0.85 11.0

221F 0.92 0.97 4.6 0.98 6.1

M221A 0.22 0.38 69.6 0.48 112.6

M221B 0.78 0.84 7.1 0.91 15.8

M221C 0.88 0.93 5.9 0.94 7.0

Current Climate PCM B1 GFDL A1F

Species Sectiona SEPb SEPb % change SEPb % change

Table 39 (continued).

(continued on next page)
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Current Climate PCM B1 GFDL A1F

Species Sectiona SEPb SEPb % change SEPb % change

Table 39 (continued).

White ash Assessment area 0.43 0.53 23.3 0.41 -4.7

221E 0.37 0.49 34.2 0.25 -32.1

221F 0.76 0.79 5.0 0.79 4.6

M221A 0.03 0.06 88.5 0.06 113.1

M221B 0.50 0.56 12.8 0.60 20.6

M221C 0.49 0.62 28.3 0.51 4.5

White oak Assessment area 0.32 0.35 9.4 0.35 9.4

221E 0.33 0.35 7.3 0.35 5.7

221F 0.36 0.38 4.9 0.38 5.5

M221A 0.19 0.23 23.6 0.25 34.4

M221B 0.31 0.33 8.1 0.36 17.6

M221C 0.36 0.37 4.5 0.37 4.9

a

Assessment area values were derived from the weighted average of sections.

b

Species establishment probabilities (SEP) are derived from LINKAGES model results. SEP is a value reflecting the ability of a species 

to establish and grow on a site, is scaled 0 through 1, and is relative 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 forest road on the Monongahela National Forest, West Virginia. Photo by Patricia Butler, NIACS and Michigan Tech, used 

with permission.
Appe

28

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 after 

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.
Appe

287

Figure 60 (continued).
Appe

288

Figure 60 (continued).
Appe

289

LANDIS PRO MODEL RE

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 61 through 64 present these change

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. 262(8): 1460-147

Figure 60 (continued).
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American beech Assessment area 5.1 4.4 -15% 4.4 1% 4.3 -2% 26.8 15.5 -42% 15.7 1% 12.8 -18%

221E 3.9 3.8 -3% 3.8 0% 3.6 -5% 19.7 13.3 -32% 13.6 2% 10.5 -21%

221F 4.5 4.0 -11% 4.1 2% 3.9 -3% 16.7 15.3 -8% 15.2 -1% 12.8 -16%

M221A 7.2 5.4 -25% 5.7 6% 5.6 4% 39.8 18.1 -55% 18.8 4% 15.0 -17%

M221B 6.6 4.9 -26% 5.0 2% 5.0 2% 36.0 17.9 -50% 17.9 0% 15.5 -13%

M221C 6.1 5.0 -18% 5.0 0% 4.8 -4% 36.2 18.2 -50% 18.1 -1% 15.2 -16%

Black cherry Assessment area 7.3 9.0 23% 9.0 0% 9.4 4% 18.9 12.8 -33% 12.9 1% 11.5 -10%

221E 7.2 9.1 26% 9.1 0% 9.4 3% 19.3 13.5 -30% 13.7 1% 12.1 -10%

221F 13.8 16.7 21% 16.8 1% 17.6 5% 42.3 34.2 -19% 34.4 1% 30.9 -10%

M221A 8.0 8.8 10% 8.9 1% 9.1 3% 15.8 8.4 -47% 8.7 4% 7.6 -10%

M221B 7.4 9.1 23% 9.1 0% 9.4 3% 15.4 8.9 -42% 9.0 1% 8.2 -8%

M221C 3.6 4.3 19% 4.3 0% 4.6 7% 10.8 5.6 -48% 5.6 0% 4.9 -13%

Black oak Assessment area 6.8 4.8 -30% 4.8 0% 3.4 -30% 8.4 3.9 -54% 4.0 2% 3.1 -21%

221E 7.3 5.2 -29% 5.2 0% 3.6 -31% 9.5 4.4 -54% 4.5 2% 3.5 -20%

221F 2.1 1.4 -33% 1.4 0% 0.8 -43% 1.5 1.1 -27% 1.1 0% 0.8 -27%

M221A 6.4 4.2 -34% 4.2 0% 3.0 -29% 8.9 3.7 -58% 3.9 5% 3.1 -16%

M221B 7.0 5.1 -27% 5.1 0% 3.7 -27% 9.0 4.0 -56% 4.0 0% 3.2 -20%

M221C 8.0 5.4 -33% 5.4 0% 3.8 -30% 7.7 3.9 -49% 4.0 3% 3.0 -23%

Chestnut oak Assessment area 5.6 4.5 -20% 4.6 2% 4.4 -2% 14.9 25.8 73% 27.0 5% 24.4 -5%

221E 4.2 3.6 -14% 3.6 0% 3.4 -6% 9.6 20.1 109% 21.2 5% 16.5 -18%

221F 0.2 0.1 -50% 0.1 0% 0.1 0% 0.9 1.8 100% 1.6 -11% 1.7 -6%

M221A 8.3 6.5 -22% 6.9 6% 6.8 5% 21.7 31.6 46% 36.2 15% 36.2 15%M221B 8.3 6.5 -22% 6.6 2% 6.7 3% 24.2 41.0 69% 41.2 0% 42.9 5%M221C 8.2 6.1 -26% 6.2 2% 5.8 -5% 24.1 35.6 48% 36.8 3% 32.8 -8%

Eastern hemlock Assessment area 1.7 1.2 -29% 1.2 1% 1.1 -5% 6.1 3.2 -47% 3.3 3% 2.5 -21%

221E 0.5 0.4 -20% 0.4 0% 0.4 0% 1.7 1.0 -41% 1.1 10% 0.8 -20%

221F 0.2 0.2 0% 0.2 0% 0.2 0% 0.7 1.1 57% 1.0 -9% 0.7 -36%

M221A 3.2 2.1 -34% 2.2 5% 2.0 -5% 9.4 5.2 -45% 5.5 6% 3.9 -25%M221B 3.4 2.3 -32% 2.3 0% 2.2 -4% 11.0 6.2 -44% 6.3 2% 5.1 -18%

M221C 3.1 2.3 -26% 2.3 0% 2.1 -9% 15.3 6.7 -56% 6.8 1% 5.2 -22%

Eastern redcedar Assessment area 0.1 0.0 -56% 0.0 0% 0.0 0% 0.8 0.8 2% 0.8 0% 0.5 -41%

221E 0.1 0.0 -100% 0.0 0% 0.0 0% 0.7 0.4 -43% 0.4 0% 0.2 -50%

221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 0.2 0.2 0% 0.2 0% 0.2 0% 1.6 2.9 81% 3.2 10% 1.8 -38%

M221B 0.1 0.1 0% 0.1 0% 0.1 0% 1.2 1.4 17% 1.3 -7% 0.9 -36%

M221C 0.0 0.0 0% 0.0 0% 0.0 0% 0.4 0.4 0% 0.4 0% 0.2 -50%

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

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2040 2009b 2040 climatec 2040 climatec 2009 2040 2009b 2040 climatec 2040 climatec

(continued on next page)
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Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2040 2009b 2040 climatec 2040 climatec 2009 2040 2009b 2040 climatec 2040 climatec

Table 40 (continued).Eastern whitepine Assessment area 1.2 1.2 7% 1.2 0% 1.2 -7% 7.3 6.6 -10% 6.8 3% 4.6 -30%

221E 0.9 1.0 11% 1.0 0% 0.9 -10% 4.7 4.8 2% 5.0 4% 3.2 -33%

221F 0.4 0.4 0% 0.4 0% 0.4 0% 1.3 4.1 215% 4.1 0% 2.5 -39%

M221A 1.4 1.8 29% 1.8 0% 1.7 -6% 12.8 11.1 -13% 12.0 8% 7.8 -30%

M221B 1.9 2.2 16% 2.2 0% 2.1 -5% 14.7 12.8 -13% 12.7 -1% 9.6 -25%

M221C 1.4 1.0 -29% 1.0 0% 0.9 -10% 6.8 3.2 -53% 3.4 6% 2.1 -34%

Loblolly pine Assessment area 0.1 0.1 39% 0.1 0% 0.1 17% 0.5 0.3 -34% 0.4 15% 0.3 0%

221E 0.1 0.2 100% 0.2 0% 0.2 0% 0.8 0.6 -25% 0.7 17% 0.6 0%

221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 0.1 0.1 0% 0.1 0% 0.1 0% 0.4 0.1 -75% 0.1 0% 0.1 0%M221B 0.1 0.0 -100% 0.0 0% 0.1 NA 0.3 0.1 -67% 0.1 0% 0.1 0%M221C 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%

Northern red oak Assessment area 6.8 7.7 14% 7.8 1% 8.0 3% 11.9 6.8 -43% 7.5 11% 6.9 2%

221E 5.2 6.1 17% 6.1 0% 6.3 3% 10.2 6.1 -40% 6.5 7% 5.9 -3%

221F 5.9 6.1 3% 6.3 3% 6.2 2% 10.9 6.2 -43% 8.3 34% 8.2 32%

M221A 8.7 9.4 8% 9.5 1% 9.8 4% 13.6 7.6 -44% 8.5 12% 7.7 1%M221B 9.3 10.8 16% 10.9 1% 11.2 4% 16.6 8.2 -51% 9.2 12% 8.6 5%

M221C 7.9 9.1 15% 9.2 1% 9.2 1% 11.3 6.9 -39% 7.4 7% 6.7 -3%

Pignut hickory Assessment area 2.6 2.5 -2% 2.6 1% 2.6 1% 7.3 6.3 -14% 6.5 3% 5.9 -7%

221E 2.6 2.6 0% 2.6 0% 2.6 0% 6.9 6.4 -7% 6.6 3% 5.7 -11%

221F 0.5 0.4 -20% 0.4 0% 0.4 0% 0.7 1.0 43% 1.0 0% 1.0 0%M221A 2.9 2.9 0% 3.0 3% 3.0 3% 10.3 7.2 -30% 7.9 10% 7.1 -1%

M221B 2.9 2.9 0% 3.0 3% 3.1 7% 9.8 8.3 -15% 8.3 0% 8.0 -4%

M221C 3.0 2.8 -7% 2.8 0% 2.7 -4% 7.6 6.1 -20% 6.3 3% 5.7 -7%

Red maple Assessment area 16.0 22.7 42% 22.9 1% 24.6 8% 77.4 61.4 -21% 62.8 2% 59.0 -4%

221E 13.8 21.0 52% 21.1 0% 22.9 9% 72.7 60.5 -17% 62.0 2% 57.2 -5%221F 23.6 32.3 37% 32.6 1% 34.2 6% 81.6 110.8 36% 111.7 1% 109.1 -2%M221A 18.0 22.4 24% 22.8 2% 24.0 7% 66.8 50.9 -24% 55.5 9% 53.8 6%M221B 17.5 23.2 33% 23.5 1% 25.0 8% 77.0 49.0 -36% 49.4 1% 47.9 -2%M221C 15.5 22.3 44% 22.4 0% 24.4 9% 97.2 57.7 -41% 58.6 2% 52.8 -8%

Red spruce Assessment area 1.0 0.5 -50% 0.5 2% 0.5 -6% 5.1 1.3 -74% 1.3 0% 1.0 0%

221E 0.3 0.1 -67% 0.1 0% 0.1 0% 1.7 0.2 -88% 0.2 0% 0.1 -50%

221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 3.4 1.7 -50% 1.8 6% 1.6 -6% 17.0 3.2 -81% 3.3 3% 2.5 -22%

M221B 2.5 1.4 -44% 1.4 0% 1.3 -7% 13.0 4.8 -63% 4.6 -4% 3.6 -25%

M221C 0.3 0.1 -67% 0.1 0% 0.1 0% 1.4 0.2 -86% 0.2 0% 0.1 -50%

(continued on next page)
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Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2040 2009b 2040 climatec 2040 climatec 2009 2040 2009b 2040 climatec 2040 climatec

Table 40 (continued).Scarlet oak Assessment area 4.8 3.5 -27% 3.5 0% 2.4 -33% 4.0 2.3 -43% 2.3 2% 1.8 -23%

221E 3.4 2.0 -41% 2.0 0% 1.2 -40% 2.5 1.4 -44% 1.4 0% 1.0 -29%

221F 0.3 0.3 0% 0.3 0% 0.3 0% 0.5 0.4 -20% 0.4 0% 0.4 0%M221A 6.7 5.5 -18% 5.4 -2% 4.1 -25% 6.4 3.5 -45% 3.6 3% 2.8 -20%M221B 7.4 6.3 -15% 6.3 0% 4.5 -29% 6.7 3.9 -42% 4.0 3% 3.2 -18%

M221C 7.8 5.6 -28% 5.6 0% 3.5 -38% 6.2 3.5 -44% 3.6 3% 2.6 -26%

Sugar maple Assessment area 6.1 7.2 17% 7.3 2% 7.1 -1% 54.1 54.4 0% 55.7 2% 39.6 -27%

221E 5.9 7.3 24% 7.4 1% 7.1 -3% 53.4 55.9 5% 57.7 3% 38.1 -32%

221F 7.0 8.4 20% 8.5 1% 8.3 -1% 73.8 73.4 -1% 72.3 -1% 51.3 -30%

M221A 6.8 7.4 9% 7.9 7% 7.7 4% 47.2 62.1 32% 67.8 9% 49.4 -20%

M221B 6.4 6.9 8% 7.0 1% 7.1 3% 49.9 48.0 -4% 47.7 -1% 39.3 -18%

M221C 5.5 6.1 11% 6.2 2% 6.0 -2% 54.7 41.4 -24% 41.9 1% 31.8 -23%

Tulip tree Assessment area 6.5 8.4 31% 8.6 1% 8.7 3% 22.9 67.3 194% 69.6 3% 68.5 2%

221E 6.6 9.0 36% 9.1 1% 9.1 1% 25.0 76.0 204% 79.5 5% 75.7 0%221F 2.1 3.5 67% 3.5 0% 3.8 9% 9.5 62.1 554% 61.1 -2% 59.0 -5%

M221A 5.8 6.1 5% 6.3 3% 6.3 3% 14.1 21.3 51% 24.1 13% 22.4 5%M221B 5.7 7.2 26% 7.3 1% 7.7 7% 16.9 53.3 215% 52.4 -2% 57.8 8%M221C 9.9 12.5 26% 12.7 2% 13.0 4% 36.6 88.7 142% 92.4 4% 93.1 5%

 White ash Assessment area 2.5 2.8 14% 2.8 0% 2.8 -2% 11.9 10.9 -8% 11.3 4% 9.6 -12%

221E 2.7 3.1 15% 3.1 0% 3.0 -3% 14.0 10.4 -26% 11.0 6% 8.6 -17%

221F 4.8 5.5 15% 5.6 2% 5.3 -4% 19.5 35.9 84% 36.3 1% 32.9 -8%M221A 1.9 2.1 11% 2.1 0% 2.1 0% 8.1 3.8 -53% 4.1 8% 3.4 -11%

M221B 1.7 2.0 18% 2.0 0% 2.1 5% 8.0 8.5 6% 8.6 1% 8.1 -5%

M221C 1.7 1.8 6% 1.8 0% 1.8 0% 8.0 5.9 -26% 6.1 3% 5.1 -14%

White oak Assessment area 5.7 5.9 3% 6.0 2% 6.0 2% 12.8 36.6 185% 38.1 4% 35.2 -4%

221E 6.6 7.1 8% 7.2 1% 7.0 -1% 15.0 43.2 188% 45.2 5% 40.0 -7%221F 1.1 1.4 27% 1.4 0% 1.4 0% 2.9 13.8 376% 14.1 2% 12.8 -7%

M221A 5.6 5.2 -7% 5.5 6% 5.7 10% 12.8 29.8 133% 33.0 11% 30.2 1%

M221B 5.6 5.4 -4% 5.5 2% 5.8 7% 12.3 34.5 180% 34.9 1% 35.0 1%

M221C 5.7 5.6 -2% 5.7 2% 5.8 4% 12.2 35.3 189% 36.2 3% 35.9 2%

aAssessment area values were derived from the weighted average of sections.bChange under current climate represents the difference from 2009 through 2100 due to succession and management, but not climate.cChange from current climate for PCM B1 and GFDL A1FI represents the difference between these scenarios and current climate in 2100 and represents the potential change due to climate change. 
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AmerAmerican beech Assessment area 5.1 4.4 -14% 4.4 1% 4.3 -3% 26.8 12.9 -52% 12.7 -1% 10.3 -20%

221E 3.9 3.8 -3% 3.8 0% 3.6 -5% 19.7 11.0 -44% 10.7 -3% 8.1 -26%

221F 4.5 3.8 -16% 3.9 3% 3.7 -3% 16.7 11.8 -29% 11.9 1% 9.1 -23%

M221A 7.2 5.6 -22% 5.9 5% 5.6 0% 39.8 14.7 -63% 15.7 7% 12.1 -18%

M221B 6.6 5.1 -23% 5.1 0% 5.1 0% 36.0 15.8 -56% 15.7 -1% 14.5 -8%

M221C 6.1 5.1 -16% 5.1 0% 4.9 -4% 36.2 14.9 -59% 14.2 -5% 12.1 -19%

Black cherry Assessment area 7.3 7.6 4% 7.8 1% 7.7 1% 18.9 9.4 -51% 9.5 1% 9.2 -1%

221E 7.2 7.8 8% 8.0 3% 7.9 1% 19.3 10.0 -48% 10.0 0% 9.7 -3%

221F 13.8 15.6 13% 15.5 -1% 15.6 0% 42.3 26.5 -37% 26.6 0% 26.0 -2%M221A 8.0 6.4 -20% 6.4 0% 6.5 2% 15.8 5.3 -66% 5.6 6% 5.6 6%

M221B 7.4 7.1 -4% 7.1 0% 7.0 -1% 15.4 6.2 -60% 6.4 3% 6.4 3%

M221C 3.6 4.1 14% 4.2 2% 4.1 0% 10.8 4.0 -63% 4.1 2% 4.0 0%

Black oak Assessment area 6.8 2.1 -69% 2.2 3% 2.1 1% 8.4 2.4 -72% 2.4 2% 2.3 -4%

221E 7.3 2.4 -67% 2.5 4% 2.4 0% 9.5 2.8 -71% 2.8 0% 2.6 -7%

221F 2.1 0.4 -81% 0.4 0% 0.4 0% 1.5 0.6 -60% 0.6 0% 0.6 0%

M221A 6.4 1.9 -70% 2.0 5% 2.0 5% 8.9 2.2 -75% 2.4 9% 2.3 5%M221B 7.0 2.3 -67% 2.3 0% 2.4 4% 9.0 2.3 -74% 2.4 4% 2.4 4%M221C 8.0 2.1 -74% 2.1 0% 2.1 0% 7.7 2.2 -71% 2.2 0% 2.1 -5%

Chestnut oak Assessment area 5.6 4.7 -17% 4.8 3% 4.5 -3% 14.9 29.4 97% 31.2 6% 25.5 -13%

221E 4.2 3.6 -14% 3.7 3% 3.3 -8% 9.6 22.4 133% 22.6 1% 14.6 -35%

221F 0.2 0.2 0% 0.2 0% 0.2 0% 0.9 1.4 56% 1.4 0% 1.3 -7%

M221A 8.3 6.8 -18% 7.5 10% 7.3 7% 21.7 36.5 68% 46.8 28% 44.2 21%

M221B 8.3 7.1 -14% 7.2 1% 7.2 1% 24.2 48.4 100% 52.0 7% 50.2 4%M221C 8.2 6.4 -22% 6.5 2% 6.0 -6% 24.1 40.0 66% 40.8 2% 32.4 -19%

Eastern hemlock Assessment area 1.7 1.1 -34% 1.1 1% 1.1 -1% 6.1 2.9 -52% 2.8 -3% 2.1 -27%

221E 0.5 0.4 -20% 0.4 0% 0.4 0% 1.7 1.1 -35% 1.1 0% 0.7 -36%

221F 0.2 0.2 0% 0.2 0% 0.2 0% 0.7 1.2 71% 1.3 8% 0.6 -50%

M221A 3.2 1.9 -41% 2.0 5% 1.9 0% 9.4 4.0 -57% 4.4 10% 2.9 -28%

M221B 3.4 2.1 -38% 2.1 0% 2.1 0% 11.0 5.5 -50% 5.4 -2% 4.7 -15%

M221C 3.1 2.1 -32% 2.1 0% 2.0 -5% 15.3 5.8 -62% 5.1 -12% 3.9 -33%

Eastern redcedar Assessment area 0.1 0.0 -56% 0.0 0% 0.0 0% 0.8 0.2 -75% 0.2 0% 0.2 0%

221E 0.1 0.0 -100% 0.0 0% 0.0 0% 0.7 0.1 -86% 0.1 0% 0.1 0%221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 0.2 0.2 0% 0.2 0% 0.2 0% 1.6 0.8 -50% 0.9 13% 0.8 0%M221B 0.1 0.1 0% 0.1 0% 0.1 0% 1.2 0.3 -75% 0.3 0% 0.3 0%M221C 0.0 0.0 0% 0.0 0% 0.0 0% 0.4 0.1 0% 0.1 0% 0.1 0%

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

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2070 2009b 2070 climatec 2070 climatec 2009 2070 2009b 2070 climatec 2070 climatec

(continued on next page)
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Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2070 2009b 2070 climatec 2070 climatec 2009 2070 2009b 2070 climatec 2070 climatec

Table 41 (continued).Eastern white pine Assessment area 1.2 1.1 -2% 1.1 1% 1.1 -6% 7.3 3.7 -49% 3.7 1% 2.7 -26%

221E 0.9 0.9 0% 0.9 0% 0.8 -11% 4.7 2.9 -38% 2.8 -3% 1.8 -38%

221F 0.4 0.4 0% 0.4 0% 0.4 0% 1.3 2.0 54% 2.1 5% 1.2 -40%

M221A 1.4 1.7 21% 1.8 6% 1.6 -6% 12.8 6.7 -48% 7.2 7% 4.8 -28%

M221B 1.9 2.0 5% 2.0 0% 2.0 0% 14.7 6.8 -54% 6.9 1% 6.2 -9%

M221C 1.4 0.9 -36% 0.9 0% 0.8 -11% 6.8 1.7 -75% 1.7 0% 1.1 -35%

Loblolly pine Assessment area 0.1 0.2 127% 0.2 0% 0.2 0% 0.5 0.2 -54% 0.3 22% 0.3 22%

221E 0.1 0.3 200% 0.3 0% 0.3 0% 0.8 0.4 -50% 0.5 25% 0.5 25%

221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 0.1 0.1 0% 0.1 0% 0.1 0% 0.4 0.1 -75% 0.1 0% 0.1 0%M221B 0.1 0.1 0% 0.1 0% 0.1 0% 0.3 0.1 -67% 0.1 0% 0.1 0%M221C 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%

Northern red oak Assessment area 6.8 5.8 -15% 5.9 3% 5.9 2% 11.9 4.9 -59% 6.4 32% 5.9 22%

221E 5.2 4.7 -10% 4.9 4% 4.8 2% 10.2 4.9 -52% 5.7 16% 4.9 0%221F 5.9 4.5 -24% 4.8 7% 4.9 9% 10.9 4.0 -63% 8.4 110% 8.1 103%M221A 8.7 6.6 -24% 6.7 2% 6.7 2% 13.6 5.2 -62% 6.8 31% 6.5 25%

M221B 9.3 7.8 -16% 8.0 3% 8.1 4% 16.6 4.9 -70% 7.6 55% 7.6 55%

M221C 7.9 6.9 -13% 6.9 0% 6.8 -1% 11.3 4.9 -57% 5.9 20% 5.4 10%

Pignut hickory Assessment area 2.6 2.7 4% 2.7 2% 2.7 0% 7.3 5.6 -24% 5.8 4% 5.6 0%

221E 2.6 2.7 4% 2.8 4% 2.7 0% 6.9 5.8 -16% 5.8 0% 5.4 -7%

221F 0.5 0.4 -20% 0.4 0% 0.4 0% 0.7 1.0 43% 1.0 0% 1.0 0%

M221A 2.9 3.0 3% 3.1 3% 3.1 3% 10.3 5.5 -47% 6.6 20% 6.6 20%

M221B 2.9 3.2 10% 3.2 0% 3.2 0% 9.8 7.2 -27% 7.7 7% 7.7 7%M221C 3.0 3.0 0% 3.0 0% 2.9 -3% 7.6 5.6 -26% 5.9 5% 5.4 -4%

Red maple Assessment area 16.0 27.6 73% 27.9 1% 27.6 0% 77.4 58.2 -25% 60.5 4% 59.6 2%

221E 13.8 26.6 93% 27.0 2% 26.4 -1% 72.7 58.7 -19% 59.3 1% 57.6 -2%221F 23.6 35.9 52% 35.9 0% 35.8 0% 81.6 108.7 33% 114.5 5% 113.0 4%M221A 18.0 24.8 38% 25.7 4% 25.8 4% 66.8 51.7 -23% 61.1 18% 59.8 16%M221B 17.5 26.7 53% 26.6 0% 27.1 1% 77.0 44.8 -42% 46.1 3% 48.2 8%M221C 15.5 28.8 86% 29.1 1% 28.4 -1% 97.2 48.6 -50% 50.9 5% 49.4 2%

Red spruce Assessment area 1.0 0.4 -57% 0.4 0% 0.4 0% 5.1 0.7 -86% 0.7 0% 0.6 0%

221E 0.3 0.1 -67% 0.1 0% 0.1 0% 1.7 0.1 -94% 0.1 0% 0.1 0%221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 3.4 1.4 -59% 1.4 0% 1.4 0% 17.0 1.5 -91% 1.5 0% 1.4 -7%

M221B 2.5 1.2 -52% 1.2 0% 1.2 0% 13.0 2.8 -78% 2.7 -4% 2.2 -21%

M221C 0.3 0.1 -67% 0.1 0% 0.1 0% 1.4 0.1 -93% 0.1 0% 0.1 0%

(continued on next page)
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Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2070 2009b 2070 climatec 2070 climatec 2009 2070 2009b 2070 climatec 2070 climatec

Table 41 (continued).Scarlet oak Assessment area 4.8 0.9 -81% 0.9 0% 0.9 -2% 4.0 1.3 -68% 1.3 3% 1.2 -10%

221E 3.4 0.5 -85% 0.5 0% 0.5 0% 2.5 0.8 -68% 0.8 0% 0.7 -13%

221F 0.3 0.2 -33% 0.2 0% 0.2 0% 0.5 0.4 -20% 0.4 0% 0.3 -25%

M221A 6.7 1.5 -78% 1.5 0% 1.5 0% 6.4 1.8 -72% 2.0 11% 1.7 -6%

M221B 7.4 1.6 -78% 1.6 0% 1.6 0% 6.7 2.2 -67% 2.2 0% 2.1 -5%

M221C 7.8 1.5 -81% 1.5 0% 1.4 -7% 6.2 2.0 -68% 2.1 5% 1.7 -15%

Sugar maple Assessment area 6.1 7.5 23% 7.6 1% 7.2 -4% 54.1 43.2 -20% 42.0 -3% 30.3 -30%

221E 5.9 7.6 29% 7.7 1% 7.2 -5% 53.4 42.0 -21% 40.5 -4% 27.5 -35%

221F 7.0 8.5 21% 8.6 1% 8.3 -2% 73.8 44.7 -39% 44.4 -1% 30.2 -32%

M221A 6.8 8.1 19% 8.5 5% 8.0 -1% 47.2 63.3 34% 64.3 2% 41.6 -34%

M221B 6.4 7.3 14% 7.3 0% 7.3 0% 49.9 42.9 -14% 41.3 -4% 37.6 -12%

M221C 5.5 6.4 16% 6.4 0% 6.1 -5% 54.7 33.6 -39% 31.9 -5% 23.1 -31%

Tulip tree Assessment area 6.5 9.9 53% 10.0 2% 9.8 -1% 22.9 80.2 251% 84.6 5% 81.2 1%

221E 6.6 10.4 58% 10.6 2% 10.2 -2% 25.0 91.0 264% 94.4 4% 88.6 -3%

221F 2.1 4.9 133% 4.9 0% 4.9 0% 9.5 83.1 775% 84.6 2% 83.1 0%

M221A 5.8 6.0 3% 6.3 5% 6.5 8% 14.1 19.9 41% 24.8 25% 27.2 37%M221B 5.7 8.7 53% 8.8 1% 8.9 2% 16.9 65.4 287% 70.5 8% 72.8 11%M221C 9.9 14.9 51% 15.0 1% 14.6 -2% 36.6 100.6 175% 108.7 8% 101.1 0%

 White ash Assessment area 2.5 3.0 20% 3.0 1% 2.9 -1% 11.9 8.7 -27% 9.2 6% 8.2 -6%

221E 2.7 3.3 22% 3.3 0% 3.2 -3% 14.0 8.0 -43% 8.7 9% 6.9 -14%

221F 4.8 5.3 10% 5.2 -2% 5.2 -2% 19.5 30.9 58% 30.5 -1% 29.8 -4%

M221A 1.9 2.2 16% 2.3 5% 2.2 0% 8.1 2.3 -72% 2.6 13% 2.6 13%

M221B 1.7 2.2 29% 2.3 5% 2.3 5% 8.0 7.2 -10% 7.8 8% 7.8 8%M221C 1.7 2.0 18% 2.1 5% 2.0 0% 8.0 4.4 -45% 4.8 9% 4.3 -2%

White oak Assessment area 5.7 6.9 21% 7.1 3% 6.8 -2% 12.8 48.5 278% 51.1 5% 48.1 -1%

221E 6.6 8.1 23% 8.3 2% 7.8 -4% 15.0 56.8 279% 58.4 3% 53.8 -5%221F 1.1 1.5 36% 1.6 7% 1.5 0% 2.9 18.4 534% 18.9 3% 17.8 -3%

M221A 5.6 6.3 13% 6.7 6% 6.5 3% 12.8 38.2 198% 46.1 21% 43.9 15%M221B 5.6 6.7 20% 6.8 1% 6.8 1% 12.3 47.5 286% 51.2 8% 51.0 7%M221C 5.7 6.9 21% 7.0 1% 6.7 -3% 12.2 46.8 284% 49.2 5% 46.2 -1%

aAssessment area values were derived from the weighted average of sections.bChange under current climate represents the difference from 2009 through 2100 due to succession and management, but not climate. cChange from current climate for PCM B1 and GFDL A1FI represents the difference between these scenarios and current climate in 2100 and represents the potential change due to climate change. 
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American beech Assessment area 5.1 4.6 -9% 4.6 0% 4.2 -10% 26.8 14.2 -47% 13.9 -2% 8.1 -43%

221E 3.9 4.0 3% 4.0 0% 3.5 -13% 19.7 11.4 -42% 11.0 -4% 5.9 -48%

221F 4.5 3.6 -20% 3.7 3% 3.2 -11% 16.7 12.7 -24% 13.2 4% 5.4 -57%

M221A 7.2 5.7 -21% 6.0 5% 5.3 -7% 39.8 14.7 -63% 15.3 4% 8.2 -44%

M221B 6.6 5.5 -17% 5.5 0% 5.3 -4% 36.0 17.9 -50% 18.1 1% 14.0 -22%

M221C 6.1 5.4 -11% 5.3 -2% 4.8 -11% 36.2 19.0 -48% 17.4 -8% 9.2 -52%

Black cherry Assessment area 7.3 4.6 -38% 4.6 1% 4.5 -1% 18.9 7.2 -62% 7.3 2% 7.3 2%

221E 7.2 4.8 -33% 4.9 2% 4.7 -2% 19.3 7.6 -61% 7.7 1% 7.6 0%221F 13.8 10.6 -23% 10.6 0% 10.5 -1% 42.3 21.8 -48% 21.7 0% 21.3 -2%

M221A 8.0 3.0 -63% 3.0 0% 3.0 0% 15.8 3.2 -80% 3.8 19% 4.0 25%

M221B 7.4 3.5 -53% 3.5 0% 3.5 0% 15.4 4.6 -70% 4.9 7% 5.1 11%

M221C 3.6 2.7 -25% 2.8 4% 2.7 0% 10.8 3.2 -70% 3.2 0% 3.2 0%

Black oak Assessment area 6.8 1.5 -78% 1.5 1% 1.5 -2% 8.4 2.0 -77% 2.0 1% 1.8 -10%

221E 7.3 1.7 -77% 1.7 0% 1.6 -6% 9.5 2.3 -76% 2.3 0% 1.9 -17%221F 2.1 0.3 -86% 0.3 0% 0.3 0% 1.5 0.5 -67% 0.5 0% 0.5 0%M221A 6.4 1.5 -77% 1.6 7% 1.6 7% 8.9 1.7 -81% 1.9 12% 1.9 12%

M221B 7.0 1.6 -77% 1.6 0% 1.7 6% 9.0 2.0 -78% 2.0 0% 2.1 5%M221C 8.0 1.6 -80% 1.6 0% 1.5 -6% 7.7 1.8 -77% 1.8 0% 1.6 -11%

Chestnut oak Assessment area 5.6 4.9 -12% 5.1 3% 4.6 -5% 14.9 30.9 108% 32.6 5% 25.0 -19%

221E 4.2 3.6 -14% 3.7 3% 3.1 -14% 9.6 21.7 126% 21.8 0% 10.0 -54%

221F 0.2 0.2 0% 0.2 0% 0.2 0% 0.9 1.5 67% 1.6 7% 1.5 0%

M221A 8.3 7.4 -11% 8.2 11% 8.1 9% 21.7 43.6 101% 58.3 34% 60.9 40%

M221B 8.3 7.7 -7% 7.8 1% 7.8 1% 24.2 53.5 121% 55.1 3% 53.7 0%

M221C 8.2 6.8 -17% 6.8 0% 6.1 -10% 24.1 41.6 73% 41.1 -1% 28.6 -31%

Eastern hemlock Assessment area 1.7 1.1 -35% 1.1 1% 1.0 -9% 6.1 3.8 -38% 3.6 -6% 2.0 -47%

221E 0.5 0.4 -20% 0.4 0% 0.3 -25% 1.7 1.4 -18% 1.3 -7% 0.6 -57%

221F 0.2 0.2 0% 0.2 0% 0.2 0% 0.7 1.5 114% 1.5 0% 0.5 -67%

M221A 3.2 1.8 -44% 1.9 6% 1.8 0% 9.4 4.2 -55% 4.8 14% 2.2 -48%

M221B 3.4 2.1 -38% 2.1 0% 2.0 -5% 11.0 8.1 -26% 7.8 -4% 5.7 -30%

M221C 3.1 2.0 -35% 2.0 0% 1.8 -10% 15.3 7.3 -52% 6.1 -16% 2.9 -60%

Eastern redcedar Assessment area 0.1 0.0 -89% 0.0 0% 0.0 0% 0.8 0.1 -91% 0.0 0% 0.1 87%

221E 0.1 0.0 -100% 0.0 0% 0.0 0% 0.7 0.0 -100% 0.0 0% 0.1 NA

221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 0.2 0.1 -50% 0.1 0% 0.1 0% 1.6 0.5 -69% 0.3 -40% 0.6 20%

M221B 0.1 0.0 -100% 0.0 0% 0.0 0% 1.2 0.1 -92% 0.1 0% 0.1 0%M221C 0.0 0.0 0% 0.0 0% 0.0 0% 0.4 0.0 -100% 0.0 0% 0.0 0%

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

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2100 2009b 2100 climatec 2100 climatec 2009 2100 2009b 2100 climatec 2100 climatec

(continued on next page)
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Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2100 2009b 2100 climatec 2100 climatec 2009 2100 2009b 2100 climatec 2100 climatec

Table 42 (continued).Eastern white pine Assessment area 1.2 1.0 -13% 1.0 0% 0.9 -7% 7.3 2.5 -66% 2.4 -2% 1.3 -46%

221E 0.9 0.8 -11% 0.8 0% 0.7 -13% 4.7 1.8 -62% 1.8 0% 0.7 -61%

221F 0.4 0.3 -25% 0.3 0% 0.3 0% 1.3 1.6 23% 1.5 -6% 0.4 -75%

M221A 1.4 1.6 14% 1.6 0% 1.4 -13% 12.8 4.7 -63% 5.1 9% 1.9 -60%

M221B 1.9 1.8 -5% 1.8 0% 1.8 0% 14.7 4.8 -67% 4.4 -8% 3.9 -19%

M221C 1.4 0.8 -43% 0.8 0% 0.8 0% 6.8 1.1 -84% 1.0 -9% 0.5 -55%

Loblolly pine Assessment area 0.1 0.2 127% 0.2 0% 0.2 0% 0.5 0.2 -68% 0.2 43% 0.3 81%

221E 0.1 0.3 200% 0.3 0% 0.3 0% 0.8 0.3 -63% 0.4 33% 0.5 67%

221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 0.1 0.1 0% 0.1 0% 0.1 0% 0.4 0.1 -75% 0.1 0% 0.2 100%

M221B 0.1 0.1 0% 0.1 0% 0.1 0% 0.3 0.0 -100% 0.1 NA 0.1 NA

M221C 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%

Northern red oak Assessment area 6.8 2.8 -58% 3.3 15% 3.1 9% 11.9 3.6 -70% 6.0 68% 5.2 45%

221E 5.2 2.8 -46% 3.1 11% 2.8 0% 10.2 4.0 -61% 5.2 30% 3.8 -5%

221F 5.9 2.2 -63% 3.1 41% 3.1 41% 10.9 2.7 -75% 9.4 248% 9.0 233%

M221A 8.7 2.8 -68% 3.3 18% 3.2 14% 13.6 3.6 -74% 5.6 56% 5.6 56%

M221B 9.3 3.2 -66% 3.9 22% 3.9 22% 16.6 2.8 -83% 7.2 157% 7.3 161%M221C 7.9 2.8 -65% 3.0 7% 2.8 0% 11.3 3.6 -68% 5.3 47% 4.5 25%

Pignut hickory Assessment area 2.6 2.7 5% 2.8 3% 2.7 1% 7.3 5.4 -27% 5.6 4% 5.6 4%

221E 2.6 2.7 4% 2.8 4% 2.7 0% 6.9 5.4 -22% 5.4 0% 5.1 -6%

221F 0.5 0.3 -40% 0.3 0% 0.3 0% 0.7 1.1 57% 1.1 0% 1.0 -9%

M221A 2.9 2.8 -3% 3.0 7% 3.0 7% 10.3 4.8 -53% 6.1 27% 6.9 44%

M221B 2.9 3.3 14% 3.4 3% 3.4 3% 9.8 7.1 -28% 7.4 4% 7.9 11%

M221C 3.0 3.2 7% 3.2 0% 3.1 -3% 7.6 5.9 -22% 6.1 3% 6.0 2%

Red maple Assessment area 16.0 30.0 88% 30.5 2% 30.3 1% 77.4 66.9 -13% 70.3 5% 74.5 11%

221E 13.8 30.1 118% 30.4 1% 29.9 -1% 72.7 66.2 -9% 68.7 4% 70.6 7%

221F 23.6 40.0 69% 40.3 1% 40.6 2% 81.6 143.5 76% 145.9 2% 160.7 12%

M221A 18.0 25.2 40% 27.5 9% 27.1 8% 66.8 63.4 -5% 75.9 20% 81.8 29%

M221B 17.5 26.8 53% 27.0 1% 27.7 3% 77.0 48.5 -37% 50.6 4% 56.6 17%M221C 15.5 30.9 99% 31.3 1% 30.6 -1% 97.2 50.4 -48% 52.8 5% 55.1 9%

Red spruce Assessment area 1.0 0.3 -67% 0.3 0% 0.3 0% 5.1 0.4 -92% 0.4 0% 0.2 0%

221E 0.3 0.1 -67% 0.1 0% 0.1 0% 1.7 0.0 -100% 0.0 0% 0.0 0%221F 0.0 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%M221A 3.4 1.0 -71% 1.0 0% 1.0 0% 17.0 0.7 -96% 0.7 0% 0.7 0%M221B 2.5 0.9 -64% 0.9 0% 0.9 0% 13.0 1.7 -87% 1.6 -6% 0.9 -47%

M221C 0.3 0.1 -67% 0.1 0% 0.1 0% 1.4 0.1 -93% 0.0 -100% 0.0 -100%

(continued on next page)
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Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A

Change Change from Change from Change Change from Change from

BA in BA in from BA in current BA in current TPA in TPA in from TPA in current TPA in

Tree species Sectiona 2009 2100 2009b 2100 climatec 2100 climatec 2009 2100 2009b 2100 climatec 2100 climatec

Table 42 (continued).Scarlet oak Assessment area 4.8 0.7 -86% 0.7 8% 0.7 -2% 4.0 1.1 -72% 1.2 3% 0.9 -17%

221E 3.4 0.4 -88% 0.5 25% 0.4 0% 2.5 0.7 -72% 0.7 0% 0.5 -29%221F 0.3 0.1 -67% 0.1 0% 0.1 0% 0.5 0.3 -40% 0.3 0% 0.2 -33%

M221A 6.7 1.0 -85% 1.1 10% 1.0 0% 6.4 1.5 -77% 1.8 20% 1.3 -13%M221B 7.4 1.2 -84% 1.2 0% 1.2 0% 6.7 2.0 -70% 2.0 0% 2.0 0%M221C 7.8 1.1 -86% 1.1 0% 1.0 -9% 6.2 1.8 -71% 1.8 0% 1.3 -28%

Sugar maple Assessment area 6.1 8.0 30% 8.0 0% 7.3 -9% 54.1 53.6 -1% 50.8 -5% 23.4 -56%

221E 5.9 8.0 36% 8.0 0% 7.2 -10% 53.4 47.7 -11% 44.9 -6% 18.3 -62%

221F 7.0 8.3 19% 8.3 0% 7.7 -7% 73.8 43.4 -41% 42.6 -2% 16.7 -62%

M221A 6.8 9.2 35% 9.5 3% 8.0 -13% 47.2 91.4 94% 88.0 -4% 27.4 -70%

M221B 6.4 8.0 25% 8.0 0% 7.8 -3% 49.9 59.9 20% 57.3 -4% 43.5 -27%

M221C 5.5 6.8 24% 6.7 -1% 6.1 -10% 54.7 46.3 -15% 43.0 -7% 16.8 -64%

Tulip tree Assessment area 6.5 10.5 62% 10.8 3% 10.8 3% 22.9 104.3 356% 112.1 8% 114.8 10%

221E 6.6 11.1 68% 11.5 4% 11.3 2% 25.0 113.0 352% 123.8 10% 123.3 9%

221F 2.1 7.0 233% 6.9 -1% 7.1 1% 9.5 123.3 1198% 127.2 3% 128.1 4%

M221A 5.8 4.6 -21% 5.1 11% 5.6 22% 14.1 19.3 37% 27.7 44% 39.6 105%

M221B 5.7 9.6 68% 9.7 1% 9.9 3% 16.9 95.2 463% 97.6 3% 105.3 11%

M221C 9.9 15.5 57% 15.8 2% 15.6 1% 36.6 130.7 257% 137.4 5% 139.3 7%

 White ash Assessment area 2.5 2.9 18% 3.0 4% 2.9 0% 11.9 7.3 -39% 7.9 8% 6.8 -7%

221E 2.7 3.2 19% 3.4 6% 3.2 0% 14.0 6.4 -54% 7.5 17% 5.3 -17%

221F 4.8 4.6 -4% 4.7 2% 4.5 -2% 19.5 25.1 29% 24.4 -3% 24.3 -3%

M221A 1.9 2.1 11% 2.1 0% 2.1 0% 8.1 1.4 -83% 1.6 14% 1.7 21%

M221B 1.7 2.4 41% 2.4 0% 2.5 4% 8.0 7.2 -10% 7.3 1% 7.7 7%M221C 1.7 2.2 29% 2.2 0% 2.2 0% 8.0 4.0 -50% 4.4 10% 4.0 0%

White oak Assessment area 5.7 8.4 46% 8.6 2% 8.3 -1% 12.8 57.7 349% 60.9 6% 60.8 5%

221E 6.6 9.5 44% 9.7 2% 9.2 -3% 15.0 64.3 329% 68.1 6% 65.8 2%221F 1.1 1.9 73% 1.9 0% 1.9 0% 2.9 20.8 617% 22.2 7% 21.5 3%

M221A 5.6 7.8 39% 8.4 8% 8.4 8% 12.8 50.0 291% 56.1 12% 61.0 22%M221B 5.6 8.5 52% 8.6 1% 8.8 4% 12.3 60.6 393% 61.7 2% 65.1 7%M221C 5.7 8.5 49% 8.7 2% 8.4 -1% 12.2 58.7 381% 61.8 5% 61.7 5%

aAssessment area values were derived from the weighted average of sections.bChange under current climate represents the difference from 2009 through 2100 due to succession and management, but not climate. cChange from current climate for PCM B1 and GFDL A1FI represents the difference between these scenarios and current climate in 2100 and represents the potential change due to climate change. 
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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 sections. The width of the colored line represents the basal area for each species 

at various points through time. 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 addition 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 sections. The width of the colored line represents trees per acre for each species 

at various points through time. 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, partially offsetting the projected 13-percent decrease due 

to natural succession and management (see also Table 42).
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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 sections. The width of the colored line represents trees per acre for each 

species at various points through time. 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 offsetting the projected 13-percent 

decrease due to natural succession and management (see also Table 42).

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 sections. The width of the colored line represents trees per acre for each species 

at various points through time. 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 addition to the projected 88-percent increase due to natural succession 

and management (see also Table 42).
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Appendix 5: 

Confidence

EXPERT PANEL PROCE

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 Organization

Jarel Bartig Wayne National Forest

Scott Bearer The Nature Conservancy - Pennsylvania

Steve Blatt Wayne National Forest

Andrea Brandon The Nature Conservancy - West Virginia

Patricia Butler* Michigan Technological University & Northern Institute of Applied Climate Science

Stephanie Connolly Monongahela National Forest

Tim Culbreth Maryland Department of Natural Resources, Forest Service

William Dijak U.S. Forest Service, Northern Research Station

Wade Dorsey Savage River State Forest

Neil Gillies Cacapon Institute

Louis Iverson U.S. Forest Service, Northern Research Station

Maria Janowiak* U.S. Forest Service, Northern Research Station & Northern Institute of Applied Climate Science

Kent Karriker Monongahela National Forest

Dave Minney Independent consultant

Cotton 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 Station

Jason Teets Natural Resources Conservation Service

Frank Thompson U.S. Forest Service, Northern Research Station

*Workshop facilitator
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FOREST SYSTEMS ASSES

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 IM

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 

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 CAPA

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. 
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VULNERABI

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. 65).

Panelists were directed to mark their rating in 

two-dimensional space on the individual worksheet 

and on a large group poster (Fig. 66a). Thi

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. 

CONFIDENC

Panelists were also directed to give a confidence 

rating to each of their individual vulnerability 

determinations (Fig. 66b). Panelists were aske

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. 

Vulnerability and Confidence Figures

For reference, figures of individual and group 

determinations for all nine forest ecosystems 

considered in this assessment are displayed in 

Figures 67 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. 

Box 12: A Note on Forest Impact Models Used in this Assessmen

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 section. This methodology 

was found to be less effective in areas of complex 

topography, where steep elevational gradients 

result in climatic gradients as well. The inclusion of 

climate data from the center of the section projected 

unrealistic responses for several tree species in the 

Allegheny Mountain and Northern Ridge and Valley 

sections of the assessment area. The climate value at 

the center of the section happened to fall on a highelevation area, which represented the climate for the 

whole region as being much colder than the average 

climate. This was identified as an issue with the 

preliminary LANDIS PRO model results at the expert 

panel workshop, and many participants 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 better represent the 

average climate. All results summarized in Chapters 

5 and 6 were vetted with the expert panelists 

to ensure their vulnerability rankings were still 

consistent with the final LANDIS PRO model results.
Appe

304

Example Vulnerability Determination Worksheet

Name: Ecosystem/Forest Type: 

How familiar are you with this ecosystem? (circle one)

What do you think are the greatest potential impacts to the ecosystem?

What factors do you think contribute most to the adaptive capacity of the ecosystem?

Medium

I do some management 

or research in this 

system, or have read 

a lot about it.

Low

I have some basic 

knowledge about this 

system and how it 

operates

Hig

I regularly do 

management or 

research in this system

Vulnerability Determination

Use the handout for the vulnerability determination 

process and the notes that you have taken to plot 

your assessment of vulnerability on the figure below.

Confidence Rating

Use the handout for the confidence rating process 

and the notes that you have taken to rate confidence 

using the figure below.

The ratings above are for the entire analysis area. Please note where you think potential impacts 

or adaptive capacity may vary substantially within the analysis area (e.g., forests in the eastern 

portion may be more prone to impact X).

Figure 65.—Worksheet used for vulnerability and confidence determination by expert panelists, based on Swanston and 

Janowiak (2012).
Appe

305

(a) (b)

Figure 66.—Figure used for (a) vulnerability determination by expert panelists, based on Swanston and Janowiak (2012), and 

(b) confidence rating among expert panelists, adapted from Mastrandrea et al. (2010).

Figure 67.—Vulnerability and confidence determinations for Appalachian (hemlock)/northern hardwood forest. Circles 

indicate individual determinations by each panelist and squares indicate the group determination after consensus was 

reached.
Appe

30

Figure 69.—Vulnerability and confidence determinations for dry oak and oak/pine forest and woodland. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 68.—Vulnerability and confidence determinations for dry calcareous forest, woodland, and glade. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.
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307

Figure 71.—Vulnerability and confidence determinations for large stream floodplain and riparian forest. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 70.—Vulnerability and confidence determinations for dry/mesic oak forest. Circles indicate individual determinations 

by each panelist and squares indicate the group determination after consensus was reached.
Appe

308

Figure 73.—Vulnerability and confidence determinations for north-central interior maple/beech forest. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 72.—Vulnerability and confidence determinations for mixed mesophytic and cove forest. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.
Appe

309

Figure 74.—Vulnerability and confidence determinations for small stream riparian forest. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 75.—Vulnerability and confidence determinations for spruce/fir forest. Circles indicate individual determinations by 

each panelist and squares indicate the group determination after consensus was reached.
Appe

310

VULNERABILITY STA

Recurring themes and patterns that transcended 

individual forest ecosystems were identified and 

developed into the vulnerability statements in 

boldface and supporting text in Chapter 6. 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. 

REFERENCES

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).

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).
Printed on recycled paper

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, for

 Climate Change Tree Atlas, DISTRIB, LANDIS PRO, LINKAGES, 

 expert elicitation, climate projections, impacts

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