Mid-Atlantic Forest Ecosystem 

Vulnerability Assessment and Synthesis:

A Report from the Mid-Atlantic Climate Change 

Response Framework Project

United States Department of Agriculture

Forest

Service

Northern

Research Station

General Technical

Report NRS-181 October 2018
ABSTRACT

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Manuscript received for publication February 2018

Forest ecosystems will be affected directly and indirectly by a changing climate over the 21st 

century. This assessment evaluates the vulnerability of 11 forest ecosystems in the Mid-Atlantic 

region (Pennsylvania, New Jersey, Delaware, eastern Maryland, and southern New York) under 

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 forest impact 

models, which provided a range of potential tree responses to climate. Finally, we brought these 

results before two multidisciplinary panels of scientists and land managers familiar with the 

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

elicitation process. 

Each chapter of this assessment builds on the previous chapter. The description of the 

contemporary landscape presents major forest trends and stressors currently threatening forests 

in the Mid-Atlantic region and defines the forest communities being assessed. The background 

information in Chapter 2 summarizes climate data analysis and climate models. Analysis of 

climate records in Chapter 3 indicates that average temperatures and total precipitation in the 

region have increased. Downscaled climate models in Chapter 4 project potential increases in 

temperature in every season, but projections for precipitation indicate slight increases in winter 

and spring, and high variability in summer and fall projections, depending on the scenario. 

Potential impacts on forests in Chapter 5 were identified by incorporating the future climate 

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

project declines in growth and suitable habitat for many mesic species, including American beech, 

eastern hemlock, eastern white pine, red spruce, and sugar maple. Species that tolerate hotter, 

drier conditions are projected to persist or increase, including black oak, northern red oak, pignut 

hickory, sweetgum, and white oak. Climate impacts related to topics such as wildfire, invasive 

species, and forest pests were not included in the forest impact models, but were summarized 

from published literature. 

In Chapter 6, we assessed vulnerability for 11 forest communities in the Mid-Atlantic region. 

Twenty-six science and management experts from across the region considered vulnerability in 

terms of the potential impacts on a forest ecosystem and the adaptive capacity of the ecosystem. 

The montane spruce-fir and lowland conifer forest communities were determined to be the 

most vulnerable ecosystems in the interior portion of the Mid-Atlantic region. Maritime and tidal 

swamp forest communities were determined to be the most vulnerable ecosystems in the coastal 

plain portion of the region. The woodland, glade, and barrens forest community was perceived 

as less vulnerable to projected changes in climate. Forest ecosystem vulnerabilities are expected 

to affect other forest-dependent topics such as wildlife management, timber production, and 

recreation. Information on these and other topics is summarized in Chapter 7.

Cover Photo

Allegheny Reservoir. This 25-mile-long lake touches nearly 100 miles of forested shoreline within 

the Allegheny National Forest boundaries, and provides both recreation opportunities and 

municipal water. Photo by USDA Forest Service, Eastern Region, via flickr.com.
Mid-Atlantic Forest Ecosystem 

Vulnerability Assessment and Synthesis: 

A Report from the Mid-Atlantic Climate Change 

Response Framework Project

Patricia R. Butler-Leopold, Louis R. Iverson, Frank R. Thompson III, 

Leslie A. Brandt, Stephen D. Handler, Maria K. Janowiak, 

P. Danielle Shannon, Christopher W. Swanston, Scott Bearer, 

Alexander M. Bryan, Kenneth L. Clark, Greg Czarnecki, 

Philip DeSenze, William D. Dijak, Jacob S. Fraser, Paul F. Gugger, 

Andrea Hille, Justin Hynicka, Claire A. Jantz, Matthew C. Kelly, 

Katrina M. Krause, Inga Parker La Puma, Deborah Landau, 

Richard G. Lathrop, Laura P. Leites, Evan Madlinger, 

Stephen N. Matthews, Gulnihal Ozbay, Matthew P. Peters, 

Anantha Prasad, David A. Schmit, Collin Shephard, 

Rebecca Shirer, Nicholas S. Skowronski, Al Steele, Susan Stout, 

Melissa Thomas-Van Gundy, John Thompson, Richard M. Turcotte, 

David A. Weinstein, and Alfonso Yáñez 
AUTHORS

PATRICIA R. BUTLER-LEOPOLD 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, 

pleopold@mtu.edu.

LOUIS R. IVERSON is a landscape ecologist with the USDA 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 

USDA 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, USDA 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, USDA Forest 

Service, 410 MacInnes Drive, Houghton, MI 49931, 

sdhandler@fs.fed.us.

MARIA K. JANOWIAK is the deputy director of the Northern 

Institute of Applied Climate Science, USDA 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.

CHRISTOPHER W. SWANSTON is a research ecologist with the 

USDA 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.

SCOTT BEARER is the president and conservation ecologist of 

Appalachian Conservation, LLC, 1677 Gap Road, Allenwood, PA 

17810. He is also a fire landscape ecologist with the U.S. Fish 

and Wildlife Service in Albuquerque, NM, 

scott_bearer@fws.gov.

ALEXANDER M. BRYAN is the climate assessments and scenario 

planning postdoctoral fellow with the Northeast Climate 

Science Center, 233 Morrill Science Center, 611 North Pleasant 

Street, University of Massachusetts, Amherst, MA 01003-9297, 

abryan@usgs.gov.

KENNETH L. CLARK is a research forester with the USDA Forest 

Service, Northern Research Station, Silas Little Experimental 

Forest, 501 Four Mile Road, New Lisbon, NJ 08064, 

kennethclark@fs.fed.us. 

GREG CZARNECKI is the climate change and research 

coordinator with the Pennsylvania Department of Conservation 

and Natural Resources, 400 Market Street, Harrisburg, PA 

17105, gczarnecki@pa.gov.

PHILIP A. DeSENZE is the public affairs officer with the USDA 

Forest Service, Angeles National Forest, San Gabriel Mountains 

National Monument, 110 N. Wabash Avenue, Glendora, CA 

91741, padesenze@fs.fed.us.

WILLIAM D. DIJAK is a wildlife biologist and geographic 

information systems specialist with the USDA Forest Service, 

Northern Research Station, 202 Anheuser-Busch Natural 

Resources Building, University of Missouri – Columbia, 

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

JACOB S. FRASER is a resource specialist with the Department of 

Natural Resources, University of Missouri – Columbia, 

203 Anheuser-Busch Natural Resources Building, Columbia, MO 

65211, fraserjs@missouri.edu.

PAUL F. GUGGER is an assistant professor with the University 

of Maryland Center for Environmental Science, Appalachian 

Laboratory, 301 Braddock Road, Frostburg, MD 21532, 

pgugger@umces.edu.

ANDREA HILLE is the forest silviculturist with the Allegheny 

National Forest, 4 Farm Colony Drive, Warren, PA 16365, 

ahille@fs.fed.us. 

JUSTIN HYNICKA is the manager of forest conservation with 

American Forests, 1220 L Street, Suite 750, Washington, DC 

20005, jhynicka@gmail.com.

CLAIRE A. JANTZ is a professor with the Department of 

Geography and Earth Science and the director of the Center 

for Land Use and Sustainability at Shippensburg University, 

1871 Old Main Drive, Shippensburg, PA 17257, 

cajant@ship.edu.

MATTHEW C. KELLY is an assistant professor of natural resource 

management, School of Forest Resources and Environmental 

Science at Michigan Technological University, U.J. Noblet 

Forestry Building, 1400 Townsend Drive, Houghton, MI 49931, 

mackelly@mtu.edu. 

KATRINA M. KRAUSE is a social science analyst with the USDA 

Forest Service, Northern Research Station, Baltimore Field 

Station, 5523 Research Park Drive, Suite 350, Baltimore, MD 

21228-4783, kkrause@fs.fed.us. 

INGA PARKER LA PUMA is the science communication 

director with the North Atlantic Fire Science Exchange and a 

visiting scientist with the Grant F. Walton Center for Remote 

Sensing and Spatial Analysis at Rutgers University, School of 

Environmental and Biological Sciences, 14 College Farm Road, 

New Brunswick, NJ 08901-8551, inga.lapuma@rutgers.edu.
DEBORAH LANDAU is a conservation ecologist with The Nature 

Conservancy, 425 Barlow Place, Suite 100, Bethesda, MD 20814, 

dlandau@tnc.org. 

RICHARD G. LATHROP is a professor of environmental 

monitoring with the Department of Ecology, Evolution & Natural 

Resources at Cook College, Rutgers University, New Brunswick, 

NJ 08901-8551, lathrop@crssa.rutgers.edu. 

LAURA P. LEITES is an assistant professor of quantitative 

forest ecology with the Department of Ecosystem Science and 

Management at Pennsylvania State University, 

312 Forest Resources Building, University Park, PA 16802, 

lpl3@psu.edu.

EVAN MADLINGER is a biologist with the Natural Resources 

Conservation Service, Frenchtown Service Center, 

687 Pittstown Road, Suite 2, Frenchtown, NJ 08825, 

evan.madlinger@nj.usda.gov.

STEPHEN N. MATTHEWS is an ecologist with the USDA Forest 

Service, Northern Research Station, 359 Main Road, Delaware, 

OH 43015, snmatthews@fs.fed.us.

GULNIHAL OZBAY is a professor and extension specialist with 

the Department of Agriculture and Natural Resources at 

Delaware State University, 3 James W.W. Baker Building, 

1200 N. Dupont Highway, Dover, DE 19901, gozbay@desu.edu.

MATTHEW P. PETERS is an ecologist with the USDA Forest 

Service, Northern Research Station, 359 Main Road, Delaware, 

OH 43015, matthewpeters@fs.fed.us.

ANANTHA PRASAD is an ecologist with the USDA Forest Service, 

Northern Research Station, 359 Main Road, Delaware, OH 

43015, aprasad@fs.fed.us.

DAVID A. SCHMIT is a forest health specialist with the 

Pennsylvania Department of Conservation and Natural 

Resources, 400 Market Street, Harrisburg, PA 17105, 

dschmit@pa.gov.

COLLIN SHEPHARD is the forest ecologist with the Allegheny 

National Forest, 4 Farm Colony Drive, Warren, PA 16365, 

collinmshephard@fs.fed.us.

REBECCA SHIRER is a senior conservation scientist with 

The Nature Conservancy New York, 195 New Karner Road, 

Suite 201, Albany, NY 12205, rshirer@tnc.org. 

NICHOLAS S. SKOWRONSKI is a research forester with the USDA 

Forest Service, Northern Research Station, 180 Canfield Street, 

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

AL STEELE is a physical scientist with the USDA Forest Service, 

Northeastern Area State and Private Forestry, 

180 Canfield Street, Morgantown, WV 26505, 

asteele@fs.fed.us.

SUSAN STOUT is a project leader with the USDA Forest Service, 

Northern Research Station, 359 Main Road, Delaware, OH 

43015, sstout@fs.fed.us.

MELISSA THOMAS-VAN GUNDY is a research forester with the 

USDA Forest Service, Northern Research Station, P.O. Box 404, 

Parsons, WV 26287, mthomasvangundy@fs.fed.us.

JOHN THOMPSON is the Catskill Regional Invasive Species 

Partnership coordinator at Catskill Center, 43355 Route 28, 

Arkville, NY 12406, jthompson@catskillcenter.org.

RICHARD M. TURCOTTE is the forest health group leader 

with the USDA Forest Service, Northeastern Area State and 

Private Forestry, 180 Canfield Street, Morgantown, WV 26505, 

rturcotte@fs.fed.us.

DAVID A. WEINSTEIN is a senior research associate with the 

Department of Natural Resources at Cornell University, 

119 Fernow Hall, Ithaca, NY 14853-5905, daw5@cornell.edu.

ALFONSO YÁÑEZ is a research analyst with the Department of 

Geography and Earth Science at Shippensburg University, 

21 Dauphin Drive, Shippensburg, PA 17257, 

ayanezmorillo@ship.edu. 
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PREFACE

CONTEXT AND SCOPE

This assessment is a fundamental component 

of the Mid-Atlantic Climate Change Response 

Framework project led by the Northern Institute 

of Applied Climate Science. 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 about 250 million acres in 

the U.S. Midwest and Northeast: Northwoods, 

Central Appalachians, Central Hardwoods, MidAtlantic, New England, and Urban. Each regional 

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 Mid-Atlantic region, 

in addition to other people who study, recreate, and 

live in these forests. As new scientific information 

arises, we will develop future versions to reflect 

that accumulated knowledge and understanding. 

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. 

We acknowledge that 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 

were coordinated by Leslie Brandt, Patricia ButlerLeopold, 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 Climate Change Tree 

Atlas information for Chapter 5, and assisted with 

the data processing for the climate data presented 

in Chapter 4. Frank Thompson, William Dijak, and 

Jacob Fraser provided results and interpretation 

of the LINKAGES and LANDIS PRO models. 

All modeling teams coordinated their efforts 

impressively. 

Among the many others who made valuable 

contributions to the assessment, Scott Pugh (USDA 

Forest Service, Forest Inventory and Analysis 

[FIA] program) provided technical and analytical 

support for querying FIA databases. We also thank 

Kathleen Walz (New Jersey Natural Heritage 

Program) for valuable contributions throughout the 

writing of this assessment. We also thank Margot 

Kaye (Penn State), Stephen Shifley (USDA Forest 

Service, Northern Research Station), and John 

Drake (State University of New York College of 

Environmental Science and Forestry), who provided 

formal technical reviews of the assessment. Their 

thorough reviews greatly improved the quality of 

this assessment. 
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CONTENTS

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Chapter 1: The Contemporary Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 2: Climate Change Science and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Chapter 3: Observed Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Chapter 4: Projected Changes in Climate and Physical Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Chapter 5: Future Climate Change Impacts on Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Chapter 6: Forest Ecosystem Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Chapter 7: Management Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

Appendix 1: Common and Scientific Names of Species Mentioned in this Report . . . . . . . . . . . . 251

Appendix 2: Trend Analysis and Historical Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

Appendix 3: Additional Future Climate Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Appendix 4: Additional Impact Model Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

Appendix 5: Vulnerability and Confidence Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
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1

EXECUTIVE SUMMARY

This assessment evaluates key vulnerabilities 

for forest ecosystems in the Mid-Atlantic region 

across a range of future climate scenarios. This 

assessment was completed as part of the MidAtlantic 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 forest impact models that project 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 major forest ecosystems within the 

assessment area through the end of this century. A 

final chapter summarizes the implications of these 

impacts and vulnerabilities for a variety of forestrelated ecological, social, and economic topics 

across the region. 

CHAPTER 1: THE CONTEMPORARY 

LANDSCAPE

Forests are a prominent feature of the landscape 

across the Mid-Atlantic region. Stretching from 

the Atlantic coast to the peaks of the Appalachian 

Mountains, our assessment area covers about 60 

million acres and is approximately 50 percent 

forested (Fig. 1). This chapter describes the 

assessment area and purpose of this document. It 

also describes the forest communities assessed in 

later chapters and summarizes current forest 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 

• Of the nearly 60 million acres of land in the 

assessment area of the Mid-Atlantic region, about 

32 million acres is forested. Private individuals, 

corporations, and conservation organizations own 

74 percent of the forest land, and the remaining 

forest is owned by Federal, State, and municipal 

entities. Oak/hickory and maple/beech/birch are 

the most abundant forest-type groups across the 

area.

Figure 1.—The assessment area (shaded in green): eastern 

Maryland, southern New York, and the whole states of 

Pennsylvania, Delaware, and New Jersey.
EXECUTIVE SUMMARY

2

• Historical land use and past management 

practices have resulted in second-growth forests 

that are young compared to pre-European 

settlement conditions.

• Current major stressors and threats to forest 

ecosystems in the Mid-Atlantic region include:

▪ Fragmentation and land-use change 

(e.g., energy, agriculture, or residential 

development)

▪ Shifts in natural disturbance regimes (e.g., 

shifts in fire regimes, drought frequency, or 

flood frequency) 

▪ Forest diseases and insect pests

▪ Establishment of nonnative invasive plant 

species 

▪ Sea-level rise

▪ Extreme weather events

▪ Herbivory

• The forest products and forest-related recreation 

industries are major contributors to the regional 

economy, and an increasing amount of forest 

land is managed according to a sustainability 

certification standard. 

• Net forest growth (gross growth minus mortality) 

is nearly three times as great as removals. Private 

forest lands, which include both industrial and 

nonindustrial ownerships, accumulate the most 

growing stock. 

• Eleven forest communities are used to describe 

the forests in the Mid-Atlantic region. The 

descriptions of forest communities were based on 

macrogroups defined in the Northeast Terrestrial 

Habitat Classification System, but were revised 

as part of the expert elicitation process.

CHAPTER 2: CLIMATE CHANGE 

SCIENCE AND MODELING

This chapter provides a brief background on climate 

change science, models that simulate future climate 

change, and forest impact 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 increased at a global scale 

and across the United States over the past 

century. Climate scientists attribute this increase 

in temperature to increases in greenhouse gases 

resulting from human activities. 

• Scientists use models, which are simplified 

representations of reality, to simulate future 

climates. In this assessment, general circulation 

models are used to project future climate 

and as inputs to forest impact models. The 

GFDL model developed by the National 

Oceanic and Atmospheric Administration is 

considered moderately sensitive to changes in 

greenhouse gas concentrations, and the PCM 

model developed by the National Center for 

Atmospheric Research is considered to have low 

sensitivity to greenhouse gas concentrations. 

• General circulation models require estimates 

of future greenhouse gas concentrations. This 

assessment pairs the GFDL model with the most 

fossil-fuel intensive scenario developed by the 

Intergovernmental Panel on Climate Change 

[IPCC] Special Report on Emission Scenarios 

(A1FI) and pairs the PCM model with the least 

fossil-fuel intensive scenario (B1). These two 

model-scenario combinations represent the ends 

of a range of possible climate futures which are 

logical trajectories from the current climate. 

• Climate projections for this assessment 

were statistically downscaled by using an 

asynchronous regional regression model. Daily 

mean, minimum, and maximum temperature and 

total daily precipitation were downscaled to an 

approximately 7.5-mile resolution grid across the 

United States. 
EXECUTIVE SUMMARY

3

• Downscaled climate projections from general 

circulation models provide important information 

about future climate, but forest impact models 

are required to explore how climate change 

may affect soil moisture, hydrology, forest 

composition, productivity, or interactions 

between these factors. In this assessment, we 

used one species distribution model, the Climate 

Change Tree Atlas, and two process models, 

LINKAGES and LANDIS PRO. These forest 

impact models operate at different spatial scales 

and provide different kinds of information.

CHAPTER 3: OBSERVED CLIMATE 

CHANGE

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 across the MidAtlantic region, with a focus on the last 100 years.

Main Points 

• Temperature minimums (lows) and maximums 

(highs) have increased. Minimum temperatures 

have increased more than maximum temperatures 

in every season except spring, with the greatest 

increase in temperature during the winter. 

• Precipitation patterns have changed across the 

region, with the most change occurring in fall 

(increase of 3.2 inches). The number of intense 

precipitation events has increased.

• Sea levels have risen in the Mid-Atlantic faster 

than global sea levels, about 12 inches since 1900 

along the Atlantic coastline.

• Climate change is also indicated by observed 

changes in biological processes, such as growing 

season length, shifts in flowering phenology, and 

changes in wildlife emergence and migration.

CHAPTER 4: PROJECTED CHANGES 

IN CLIMATE AND PHYSICAL 

PROCESSES

This chapter describes climate projections for the 

assessment area over the 21st century. 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 climatedriven 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.

• Localized soil moisture deficits are expected to 

become more frequent.

• The growing season length is expected to increase 

by up to 1 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. 

• Sea level in the Mid-Atlantic is projected to 

rise by up to 7 feet by 2100, resulting in more 

flooding and storm surge.
EXECUTIVE SUMMARY

4

CHAPTER 5: FUTURE CLIMATE 

CHANGE IMPACTS ON FORESTS

This chapter, drawing on information from a 

coordinated series of model simulations and 

published research, summarizes the potential 

impacts of climate change on forests in the MidAtlantic region. 

Main Points

• Many mesic forest species, including American 

beech, eastern hemlock, eastern white pine, 

red spruce, and yellow birch, 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 regeneration 

potential over the next century, but mature 

individuals could continue to grow for much 

longer in the absence of other mortality factors.

• Many southern species—species with ranges 

extending largely south of the Mid-Atlantic 

region, including post oak, scarlet oak, and 

southern red oak—are projected to increase in 

suitable habitat and biomass within the MidAtlantic region. 

• The forest impact models used in this assessment 

isolate the effects of climate change on tree 

species’ growth and habitat, and do not account 

for many other factors that influence forests. 

Scientific literature was used to provide 

additional information on the effects of climate 

change on other factors such as:

▪ Moisture stress

▪ Acid deposition and carbon dioxide 

fertilization

▪ Altered nutrient cycling

▪ Invasive species, insect pests, and forest 

diseases 

▪ Herbivory on young regeneration

▪ Interactions among these factors

CHAPTER 6: FOREST ECOSYSTEM 

VULNERABILITIES

This chapter focuses on the vulnerability of major 

forest ecosystems in the Mid-Atlantic region to 

climate change (Table 1). Detailed vulnerability 

determinations are provided for 11 forest ecosystems 

with an emphasis on dominant species, features 

that define a system (drivers), and features that 

disturb a system (stressors). The adaptive capacity 

of each forest ecosystem was also examined as a 

key component to overall vulnerability. 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, 

IPCC 2007). We further rated the evidence used 

in assessing vulnerability as well as the level of 

agreement between sources of evidence. We consider 

a system to be vulnerable if it is at risk of a species 

composition change leading to a substantially 

different character for the forest system, or if the 

system is anticipated to suffer substantial declines 

in acreage, health, or productivity. General trends in 

climate change impacts and adaptive capacity factors 

for the Mid-Atlantic region are also captured in 

overarching synthesis statements. 

Main Points

Potential Impacts of Climate Change on 

Ecosystem Drivers and Stressors

• Temperatures will increase (robust evidence, 

high agreement). All global climate models 

agree that temperatures will increase with 

continued increases in atmospheric greenhouse 

gas concentrations.

• Growing seasons will lengthen (robust 

evidence, high agreement). There is strong 

agreement that projected temperature increases 

will lead to longer growing seasons in the MidAtlantic region.
EXECUTIVE SUMMARY

5

Forest community Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Coastal Plain

Maritime forest Negative Moderate-Low High Medium-Robust Medium-High

Oak-pine-hardwood Moderate-Positive High Moderate-Low Medium Medium-High

Pine-oak barrens Moderate Moderate Moderate-Low Medium-Robust Medium-High

Swamp Moderate Moderate-High Moderate-Low Medium Medium

Tidal swamp Moderate-Negative Moderate-Low Moderate-High Medium Medium-High

Interior

Central oak-pine Moderate-Positive Moderate-High Moderate-Low Medium Medium-High

Lowland conifer Negative Moderate-Low High Medium Medium

Lowland and riparian hardwood Moderate Moderate Moderate Medium-Limited Medium

Montane spruce-fir Negative Low High Medium-Robust High

Northern hardwood Moderate-Negative Moderate Moderate-High Medium-Robust Medium-High

Woodland, glade, and barrens Positive Moderate-High Low Medium Medium-High

Table 1.—Summary of vulnerability determination for the forest systems considered in this assessment evaluated 

through the end of the 21st century

• The amount and timing of precipitation will 

change (robust evidence, high agreement).

There is strong agreement that precipitation 

patterns will change across the Mid-Atlantic 

region. 

• Intense precipitation events will continue 

to become more frequent (robust evidence, 

high agreement). There is strong agreement 

among climate models that the number of heavy 

precipitation events will continue to increase 

in the Mid-Atlantic region. If they do increase, 

impacts from flooding and soil erosion may 

become more damaging. 

• Sea levels will continue to rise (robust 

evidence, high agreement). There is substantial 

evidence that ongoing sea-level rise will continue 

to affect low-lying coastal areas and increase 

potential impacts from flooding, saltwater 

intrusion, and storm surge.

• Soil moisture patterns will change in 

response to temperature and precipitation 

(medium evidence, high agreement). Warmer 

temperatures and altered precipitation are 

expected to change soil moisture patterns 

throughout the year, but there is uncertainty about 

the direction and magnitude of the changes at 

specific locations.

• Forest vegetation may face increased risk of 

physiological drought during the growing 

season (medium evidence, medium agreement).

Warmer temperatures can lead to decreased soil 

moisture even without an associated decrease in 

precipitation, resulting in a temporary inability 

for a tree to meet water demand. 

• Climate conditions will increase wildfire risk 

by the end of the century (medium evidence, 

medium agreement). Some national and global 

studies suggest that conditions favorable for 

wildfire will increase, but few studies have 

specifically looked at wildfire risk in the MidAtlantic region. Wildfire risk will also depend 

on ignition, fire weather, ecosystem type, 

topography, fragmentation, and other regional 

characteristics. 
EXECUTIVE SUMMARY

6

• Certain insect pests and pathogens will 

increase in occurrence or become more 

damaging (medium evidence, high agreement).

Evidence indicates that an increase in 

temperature, longer growing seasons, and more 

frequent disturbances will lead to increased 

threats from insect pests and pathogens, but 

research to date has examined relatively few 

species.

• Many invasive plants will increase in extent 

or abundance (medium evidence, high 

agreement). Evidence indicates that increases in 

temperature, longer growing seasons, and more 

frequent disturbances will lead to increases in 

many invasive plant species. 

Potential Impacts of Climate Change 

on Forest Communities 

• Northern and remnant boreal tree species 

will face increasing stress from climate 

change (medium evidence, high agreement). 

Ecosystem models agree that these species may 

have reduced suitable habitat and biomass across 

the Mid-Atlantic region. These species may be 

less able than temperate forest species to take 

advantage of longer growing seasons and warmer 

temperatures. 

• Habitat will become more suitable for 

southern species (medium evidence, high 

agreement). All three forest impact models 

project an increase in suitability and growth for 

southern species such as post oak, scarlet oak, 

and southern red oak compared to current climate 

conditions.

• Forest composition will change across the 

landscape (medium evidence, high agreement). 

Forest impact model results predict that habitat 

and biomass of individual tree species will 

change, and that tree species will respond 

uniquely. However, few studies have specifically 

examined how assemblages of species may 

change.

• Tree regeneration and recruitment will change 

(medium evidence, high agreement). Seedlings 

are more vulnerable than mature trees to changes 

in temperature, moisture, and other seedbed and 

early growth requirements; they are also expected 

to be more responsive to favorable conditions.

• Forest productivity will increase during 

the next several decades in the absence of 

significant stressors (medium evidence, 

medium agreement). Some studies have 

examined the impact of climate change on forest 

productivity within the Mid-Atlantic region, 

but they disagree on how other factors such as 

species composition, stand age, disturbance, or 

pollution may interact to influence productivity. 

Changes are not expected to be consistent 

within a species, and the diversity of forest site 

conditions across the landscape suggests that 

changes will be spatially variable. 

Adaptive Capacity Factors

• Low-diversity forest communities are 

at greater risk (medium evidence, high 

agreement). Studies have consistently shown that 

diverse systems are more resilient to disturbance, 

and low-diversity ecosystems are more vulnerable 

to change.

• Most tree species in isolated or fragmented 

landscapes will have reduced ability to migrate 

to new areas in response to climate change 

(limited evidence, high agreement). The 

dispersal ability of most individual tree species is 

reduced in fragmented landscapes, but the degree 

of landscape fragmentation in the future is an area 

of uncertainty. 

• Species or systems that are limited to 

particular environments will have less 

opportunity to migrate in response to climate 

change (limited evidence, high agreement). Our 

current ecological understanding indicates that 

migration to new areas may be impossible for 

tree species and forest communities with narrow 

habitat requirements. 
EXECUTIVE SUMMARY

7

• Forest communities that have high tolerance 

to disturbance will be at lower risk of 

decline from shifting climate extremes 

(medium evidence, high agreement). Basic 

ecological theory and other evidence suggest 

that communities adapted to disturbance will 

be at lower risk of declining on the landscape. 

However, some communities may tolerate 

only a narrow range of conditions related to a 

disturbance and may be susceptible to different, 

or more frequent and severe, disturbances. 

CHAPTER 7: MANAGEMENT 

IMPLICATIONS

This chapter summarizes the implications of 

potential climate change impacts on important 

facets of forest management and planning in the 

Mid-Atlantic region, such as impacts on timber 

output, wildlife, or cultural resources. We point out 

key 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 differ by ecosystem, ownership, 

and management objective. 

Main Points

• Climate change will present risks to forest 

management such as more disturbance, as well as 

opportunities such as longer growing seasons. 

• Over the next century, climate change is expected 

to have profound effects on forest ecosystems, 

which will in turn lead to habitat changes for a 

variety of plant and animal species; management 

of forest-dependent plants and animals may face 

additional challenges as the climate shifts. 

• Land conservation planning is expected to 

include more emphasis on climate adaptation 

strategies related to carbon mitigation, refugia 

for at-risk species and habitats, landscape 

connectivity for migration corridors, and water 

supply protection.

• Changes in climate and extreme weather events 

are expected to affect infrastructure such as roads, 

bridges, and culverts on forest lands throughout 

the region. 

• The timing of activities, including timber 

removal, prescribed fire, and recreation, 

may need to be shifted as temperatures and 

precipitation patterns change. 

• Responses to increased risk of wildfire may 

require more resources to reduce fuel loads, 

suppress fires after ignition, and manage 

ecosystems affected by wildfire.

• Climate change is expected to increase 

respiratory allergies and diseases, gastrointestinal 

illnesses, heat stress, and vector-borne diseases.
8

INTRODUCTION

CONTEXT

This assessment is part of a regional effort called 

the Mid-Atlantic Climate Change Response 

Framework (www.forestadaptation.org). The first 

Framework project was begun in 2009 in northern 

Wisconsin, and each regional project is conducted 

with the overarching goal of helping managers 

incorporate climate change considerations into forest 

management. To meet the challenges brought about 

by climate change in the Mid-Atlantic region, 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 region 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 region.

The Framework process is designed to work at 

multiple scales. The Mid-Atlantic Framework is 

coordinated across the region, but activities are 

generally conducted at the state or local level 

to allow for greater specificity. Other regional 

Framework projects are underway in the Central 

Appalachians, Central Hardwoods, New England, 

Northwoods, and Urban forests.

The Mid-Atlantic Framework is an expansion of 

the original northern Wisconsin effort, and has been 

supported in large part by the USDA Forest Service. 

Across the Mid-Atlantic region, the project is being 

guided by an array of partners with an interest in 

forest management, including:

• Northern Institute of Applied Climate Science

• USDA Forest Service, Eastern Region

• USDA Forest Service, Northern Research Station

• USDA Forest Service, Northeastern Area 

State & Private Forestry

• American Forests

• Center for Land Use and Sustainability

• Trust for Public Land

• The Nature Conservancy

• Natural Resources Conservation Service

• Northeast Climate Adaptation Science Center

• Pennsylvania Department of Conservation and 

Natural Resources

• Maryland Department of Natural Resources

This assessment is designed to provide detailed 

information for forest ecosystems across the MidAtlantic 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 Mid-Atlantic region. The 

Framework project will also work to integrate the 

results and outcomes from other projects related to 

climate change and natural resource management.

This assessment bears some similarity to other 

synthesis documents about climate change 

science, such as the National Climate Assessment 

(https://nca2014.globalchange.gov/) and the 

Intergovernmental Panel on Climate Change 
INTRODUCTION

9

reports (Working Group contributions to the Fifth 

Assessment at https://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 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 MidAtlantic 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 

Mid-Atlantic region under a range of possible 

future climates, and determine the vulnerability 

of forest ecosystems to these changes throughout 

the 21st century. Included is a synthesis of 

information about the current landscape as well 

as projections of climate and vegetation changes 

used to assess vulnerability. Uncertainties and 

gaps in understanding are discussed throughout the 

document.

This assessment covers about 60 million acres 

in eastern Maryland, Delaware, New Jersey, 

Pennsylvania, and much of New York (Fig. 1). The 

assessment area boundaries within these states are 

defined by six ecological provinces, according to 

the National Hierarchical Framework of Ecological 

Units: Northeastern Mixed Forest (211), Eastern 

Broadleaf Forest (221), Midwest Broadleaf Forest 

(222), Outer Coastal Plain Mixed Forest (232), 

Adirondack-New England Mixed Forest-Coniferous 

Forest-Alpine Meadow (M211), and Central 

Appalachian Broadleaf Forest-Coniferous ForestMeadow (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 

represented the assessment area when ecoregional 

data were not available. We limited our selections 

to the counties that are most analogous to the 

assessment area.

Land ownership is fairly similar across the states 

or portions of states in the Mid-Atlantic region. 

Overall, more than 73 percent of forest land in the 

assessment area is owned by private individuals and 

organizations. State, county, and municipal lands 

compose the largest percentages of public forest 

land, followed by federal lands in National Forests, 

National Park Service land, and U.S. Department 

of Defense military installations. 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 CHAPTERS

This assessment contains the following chapters: 

Chapter 1: The Contemporary Landscape

describes existing conditions, providing background 

on the physical environment, ecological character, 

and broad socioeconomic dimensions of the 

assessment area. It defines the 11 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.
INTRODUCTION

10

Chapter 3: Observed Climate Change provides 

information on the past and current climate of the 

assessment area, summarized from the interactive 

Climate Wizard database and published literature. 

This chapter also summarizes some relevant 

ecological indicators of observed climate change.

Chapter 4: Projected Changes in Climate 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 

11 major forest ecosystems.

Chapter 7: Management Implications draws 

connections from the forest ecosystem vulnerability 

determinations to a wider range of related concerns 

shared by forest managers, including forest 

management, forest-dependent wildlife, recreation, 

and cultural resources.

Red fox kits in Presque Isle State Park, Erie County, Pennsylvania. Photo by Greg Czarnecki, Pennsylvania Department of 

Conservation and Natural Resources, used with permission.
11

CHAPTER 1: THE CONTEMPORARY LANDSCAPE

The Mid-Atlantic region contains some of the most 

biologically diverse forests in North America. It 

is also home to almost 49 million people, most 

of whom reside in urban centers. Forests in the 

Mid-Atlantic region are primarily family-owned; 

state, federal, and industrial forest lands account 

for a relatively minor proportion of the forest land 

base. This chapter describes the current condition 

and major stressors of forests across the region to 

provide context for how these forests may change 

in the future. 

REGIONAL SETTING

The assessment area is the Mid-Atlantic region, 

which is defined here by a combination of ecological 

and political boundaries and covers about 60 million 

acres in eastern Maryland, Delaware, New Jersey, 

Pennsylvania, and much of New York (Fig. 2).

We hereafter use “assessment area” and “MidAtlantic region” interchangeably in this assessment. 

The Mid-Atlantic region overlaps six ecological 

provinces, according to the National Hierarchical 

Framework of Ecological Units: Northeastern 

Mixed Forest (211), Eastern Broadleaf Forest (221), 

Midwest Broadleaf Forest (222), Outer Coastal 

Plain Mixed Forest (232), Adirondack-New England 

Mixed Forest-Coniferous Forest-Alpine Meadow 

(M211), and Central Appalachian Broadleaf ForestConiferous Forest-Meadow (M221) (McNab and 

Avers 1994, McNab et al. 2007). New York north 

of the Catskill Mountains is included with the New 

England region in a separate assessment (Janowiak 

et al. 2018) because of the extensive northern forest 

communities that stretch from the Adirondack 

Mountains to Maine.

Figure 2.—Ecological provinces of the northeastern United States. The Mid-Atlantic region assessment area partly covers six 

ecological provinces and includes all or part of five states (Pennsylvania, Delaware, New Jersey, Maryland, and New York) 

(Cleland et al. 2007).

Mid-Atlantic region
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

12

Box 1: The Climate of the Mid-Atlantic Region

The current climate of the Mid-Atlantic region is 

strongly influenced by atmospheric circulation 

patterns, latitude, topography, and elevation 

(Fig. 3). In general, temperatures are warmer in the 

southern Mid-Atlantic, but they also increase as 

elevation drops from western mountainous terrain 

to the eastern coastal plain (Polsky et al. 2000). The 

Appalachian Mountains, which run from southwest 

to northeast, form a barrier to surface winds and 

contribute to different climatic conditions for the 

coastal versus inland areas (Kunkel et al. 2013b). The 

climate of the coastal areas is influenced by warm 

and humid easterly winds and by the Atlantic Ocean 

itself. In contrast, the climate of the inland area is 

influenced by a range of elevations, Lakes Ontario 

and Erie, and relatively dry westerly winds. Many 

climate extremes are observed in the Mid-Atlantic, 

including: extreme precipitation, flooding, winter 

storms (e.g., nor’easters), ice storms, drought, heat 

waves, tornadoes, tropical cyclones, and hurricanes 

(Kunkel et al. 2013a, McNab et al. 2007). 

Based on the 1971 to 2000 climate average, the 

average annual temperature of the Mid-Atlantic 

region is 49 °F (10 °F) (Table 2). Mean winter 

temperature drops to 28 °F (-2 °C), and the coldest 

month is January, when the mean minimum 

temperature is 17 °F (-8 °C). Summer temperature 

averages 69 °F (21 °C), and the hottest month 

is July, when the mean maximum temperature 

reaches 82 °F (28 °C). Because of both geographic 

variation and daily variation, locations within the 

Mid-Atlantic region have experienced minimum and 

maximum temperatures that exceed these long-term 

regional averages. The freeze-free growing season 

is more than 200 days along the Atlantic coast, 

and becomes slightly shorter moving inland and 

upward in elevation. Annual precipitation averages 

43.4 inches for the entire assessment area, but 

differs greatly from location to location (Appendix 

3). Precipitation is most abundant in the higher 

elevations of the Catskill Mountains, where it can 

reach 70 inches per year. Precipitation is lowest over 

western New York, from Buffalo to Syracuse and 

south into Pennsylvania, where it can total 30 to 40 

inches per year. Snowfall equivalents are included 

in these averages and follow a similar geographic 

pattern, with the high-elevation and lake-effect areas 

receiving a greater share of precipitation in the form 

of snow.

Ecological provinces are broad geographic areas that 

share similar coarse features, such as climate 

(Box 1), glacial history and soils, and vegetation 

types. The major physical and biological features of 

each ecological province in the Mid-Atlantic region 

are summarized next. 

The Midwest Broadleaf Forest Province has a 

continental climate with warm to hot summers and 

frequent water deficits during the growing season. 

Lakes Ontario and Erie moderate temperatures 

throughout the year, and lake-effect precipitation 

is important in the fall and early winter. The 

topography is flat to hilly, with the lowest elevation 

close to Lake Erie, where characteristics of former 

glaciations are evident (Fig. 3).

Mean temperature (°F)

Mean minimum 

temperature (°F)

Mean maximum 

temperature (°F) Mean total precipitation (inches)

Annual 49.2 38.8 59.5 43.4

Winter 28.5 19.8 37.3 9.1

Spring 47.6 36.4 58.7 11.1

Summer 69.0 57.8 80.2 12.1

Fall 51.6 41.3 61.9 11.1

a

Additional data and maps are available in Appendix 2.

Table 2.—Average climate information for the assessment area, 1971 through 2000 (data source: Climate 

Wizard [2014])a
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

13

Figure 3.—Elevation zones within the Mid-Atlantic region. Data source: U.S. Geological Survey (1996).

The Northeastern Mixed Forest Province has 

a climate that is moderated by its proximity to 

the Atlantic Ocean and Great Lakes. Winters 

are generally long with continuous snow cover. 

Vegetation in this area generally reflects a transition 

between boreal conifer forests in colder, northern 

locations and the deciduous hardwood forests 

present to the south. 

The Eastern Broadleaf Forest Province has a warmer 

climate and longer growing season relative to 

the other provinces. The topography and bedrock 

geology vary greatly in this area, from broad, hilly 

plateaus in western Pennsylvania to the Atlantic 

coast in eastern New York. In western Pennsylvania, 

landscape features reflect a past glacial influence. 

This province generally has a warmer climate 

and longer growing season relative to the other 

provinces. 

The Central Appalachian Broadleaf ForestConiferous Forest-Meadow Province has a temperate 

climate with cool summers and short, mild winters. 

Annual precipitation is abundant and is distributed 

relatively evenly throughout the year. This area 

is mountainous with a high degree of diversity in 

topography, geology, and soils.

The Outer Coastal Plain Mixed Forest Province 

has a distinct maritime climate with high humidity, 

mild winters, and warm summers. Precipitation 

is abundant and periods of drought are rare. The 

topography slopes down to the Atlantic Ocean, 

where elevation is near, at, or below sea level 

(Box 2). 

The Adirondack-New England Mixed ForestConiferous Forest-Alpine Meadow Province is a tiny 

portion of the assessment area and is more similar 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

14

in many ways to the Northeastern Mixed Forest 

Province extending up through the Adirondacks in 

New England. Forests in this province are assessed 

in Janowiak et al. (2018). 

Land and Forest Cover

The Mid-Atlantic region is dominated by extensive 

forests, but also contains other natural ecosystems, 

rich agricultural lands, major urban population 

centers, and industrial mining lands (Fig. 4). About 

half of the region is forested. Based on satellite 

imagery from the National Land Cover Dataset 

(NLCD), forests cover 49 percent of the land 

Box 2: The Coastal Plain

Starting east of the hills of the Piedmont, the 

coastal plain gently slopes toward the waters of the 

Atlantic Ocean (Fig. 3). The region is characterized 

by deep, sandy soils, with a high infiltration 

capacity (Markewich et al. 1990). Fed by abundant 

groundwater supplies, the plains are dissected 

by slow-moving rivers and streams bordered by 

extensive lowland forest swamps. The region’s 

landscape and community types can be further 

subdivided based on soil characteristics (Brush et 

al. 1980, Collins and Anderson 1994). In addition to 

being low in nutrients and acidic, the coarsest sandy 

soils on the outer coastal plain have the highest 

percolation rates, leaving little moisture behind in 

the surface layer. These soils were not conducive 

to agriculture. The forests growing on them were 

harvested repeatedly for timber and charcoal before 

being left to regrow as large contiguous patches of 

scrubby pine-dominated forest; these forests are 

locally known as the “Pine Barrens” of New Jersey 

and Long Island (Forman 1979, Kurczewski and 

Boyle 2000). In contrast, the inner coastal plain has 

finer textured soils that are much more suitable to 

agriculture, resulting in a mosaic of farmland and 

forest land. 

Sprawl and suburban development cover extensive 

areas of the coastal plain. Farmland was often the 

first land developed as major metropolitan areas 

grew. More recent trends point to an increase in the 

conversion of forest land to urban land uses in some 

locations (Hasse and Lathrop 2010). Development 

has threatened to destroy the character of the Pine 

Barrens of New Jersey and Long Island. These forests 

have been subject to special land-use planning 

regulations and open space protection, such as the 

Pine Barrens Protection Act (Central Pine Barrens 

Joint Planning and Policy Commission 2004, State of 

New Jersey 1980). Although these measures have 

prevented the loss of these unique ecosystems, the 

complex nature of the wildland-urban interface still 

creates challenges for wildfire management in these 

fire-prone systems (Jordan et al. 2003, La Puma et al. 

2013). 

The coastal plain shoreline is preceded by a long 

series of barrier islands, extensive tidal salt marshes, 

and shallow lagoons punctuated by several major 

larger riverine estuaries (Chesapeake, Delaware, 

Hudson). Major stretches of this barrier island coast 

are heavily developed with resort and vacation 

homes and serve as the summer playground for the 

entire Mid-Atlantic region and beyond. Sea-level 

rise associated with climate change has resulted 

in increased coastal flood risk from episodic storm 

surges, which affect both human and natural 

communities adjacent to the ocean and estuaries, 

especially those located on barrier islands and 

coastal bays (Chapter 4).

(Table 3) (Fry et al. 2011, Maryland Department 

of Natural Resources 2010). The remaining land is 

classified as agricultural (25 percent), developed 

(15 percent), wetland (6 percent), grassland/

shrubland (3 percent), and inland water bodies 

(2 percent). Barren land (containing no vegetation) 

makes up less than one-half percent of the land. The 

most developed areas are located to take advantage 

of shipping ports on Lakes Erie and Ontario, 

the Atlantic Ocean, and numerous commercial 

waterways such as the Hudson and Delaware Rivers, 

in support of local industries including iron, glass, 

steel, shale oil, natural gas, and coal. Forests cover 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

15

Figure 4.—Land cover classes in the Mid-Atlantic region based on the 2006 National Land Cover Dataset. Data source: 

Fry et al. (2011).

Table 3.—Land cover in the Mid-Atlantic region based 

on the 2006 National Land Cover Dataset (data source: 

Fry et al. [2011])

Land cover class Acres Percent

Forest 29,705,648 49.1

Agriculture 14,921,605 24.7

Developed 8,876,834 14.7

Wetland 3,802,152 6.3

Shrubland 1,665,798 2.8

Water 1,325,972 2.2

Barren land 229,377 0.4

Total 60,527,386 100

wide expanses in the interior, especially at higher 

elevations and on slopes. Agricultural lands occupy 

flat valley bottoms at lower elevations. Wetlands 

are scattered throughout the Mid-Atlantic region, 

occurring in geologic depressions, over clay soils, or 

in low-lying coastal plains subject to tidal flooding.

The USDA Forest Service Forest Inventory and 

Analysis (FIA) program provides another estimate of 

forest cover, which is based on inventories of forest 

plots. The FIA program estimates that forest land 

covers approximately 32 million acres, or 53 percent 

of the Mid-Atlantic region. The amount of forest 

land varies by state, reflecting regional land use 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

16

patterns (Table 4) (USDA Forest Service 2018). The 

FIA estimate of forest land is somewhat higher than 

the NLCD estimate because FIA definitions of forest 

land include forested wetlands, plantations, and 

other land uses that NLCD would classify as woody 

wetlands or developed lands.

The oak/hickory forest-type group is the most 

common in the Mid-Atlantic region, covering 

44 percent of the total forested area (Table 5). 

Most of the oak/hickory forest is concentrated in 

Pennsylvania and New York. Loblolly/shortleaf pine 

make up a larger proportion of forest in the coastal 

states than in Pennsylvania and New York 

(Fig. 5). Other common forest-type groups across 

the Mid-Atlantic region include maple/beech/birch 

(34 percent), elm/ash/cottonwood (5 percent), and 

oak/pine (3 percent). Differences among forest 

types can influence the amount of carbon stored 

aboveground and belowground (Box 3). Please refer 

to Appendix 1 for common and scientific names of 

species mentioned in this report. 

Table 4.—Forest cover for the Mid-Atlantic region by state (data source: USDA Forest Service [2018])

Table 5.—Forest land, by area and percentage of total forest land, in the assessment area by Forest Service Forest 

Inventory and Analysis (FIA) forest-type group (data source: USDA Forest Service [2018])

State or portion of state 

within assessment area Forest land Nonforest Total area Percent forest cover

Area (acres)

Delaware 362,115 948,864 1,310,979 28

Eastern Maryland 1,845,666 3,472,053 5,317,719 35

New Jersey 2,001,608 2,861,626 4,863,234 41

Southern New York 10,719,923 9,340,737 20,060,660 53

Pennsylvania 16,999,249 12,015,025 29,014,274 59

Mid-Atlantic region 31,928,560 28,638,305 60,566,866 53

Forest cover

FIA forest-type group Acres Percent

Oak/hickory 14,177,242 44

Maple/beech/birch 10,749,040 34

Othera 1,548,688 5

Elm/ash/cottonwood 1,459,399 5

Oak/pine 1,084,204 3

Loblolly/shortleaf pine 960,043 3

White/red/jack pine 931,722 3

Aspen/birch 602,516 2

Nonstocked 337,329 1

Spruce/fir group 78,377 0

Total forest land 31,928,560 100 

a

 “Other” includes Douglas-fir, exotic hardwoods, exotic softwoods, fir/spruce/hemlock, oak/gum/cypress, other eastern softwoods, and other 

hardwoods forest-type groups.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

17

A s p en/b irc h

E lm/as h/c o tto nwo o d

Lo b lo lly /s ho rtleaf p ine

Map le/b eec h/b irc h

No ns to c k ed

Oak /hic k o ry

Oak /p ine

S p ruc e/f ir

White/red /jac k p ine

Other 1

Southern New York

Delaware Eastern Maryland New Jersey

Pennsylvania Mid-Atlantic Region

Figure 5.—Proportion of USDA Forest Service Forest Inventory and Analysis forest-type groups in the Mid-Atlantic region and 

for eastern Maryland, southern New York, and the whole states of Pennsylvania, Delaware, and New Jersey. Data source: 

USDA Forest Service (2018). 

1 “Other” includes Douglas-fir, exotic hardwoods, exotic softwoods, fir/spruce/hemlock, oak/gum/cypress, other eastern softwoods, and other 

hardwoods forest-type groups.

Fall colors in a northern hardwood forest, Pennsylvania. Photo by Greg Czarnecki, Pennsylvania Department of Conservation 

and Natural Resources, used with permission.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

18

Box 3: Forest Carbon

Forests play a valuable role as carbon sinks. The 

accumulated terrestrial carbon pools within 

forest soils, belowground biomass, dead wood, 

aboveground live biomass, and litter represent an 

enormous store of carbon (Birdsey et al. 2006). 

Forest land in the Mid-Atlantic region is estimated 

to store more than 2.3 billion metric tons of carbon, 

an average of 73.4 metric tons of carbon per acre 

(USDA Forest Service 2018). Carbon density is lowest 

in the nonstocked forest-type group (46 metric tons 

per acre) and highest in the maple/beech/birch 

group (87 metric tons per acre) (Fig. 6). The most 

visible—and often most disturbed—carbon is in 

live aboveground vegetation (e.g., trees, stems, 

branches, leaves). 

Carbon density on forest land also varies by 

ownership. The highest mean density of carbon on 

forest land is found on federal lands administered 

by the Forest Service (87.3 metric tons per acre), 

followed by the National Park Service and Fish and 

Wildlife Service (78.9 and 78.3 metric tons per 

acre, respectively), state lands (75.4 metric tons per 

acre) and county/municipal lands (73.1 metric tons 

per acre). Private lands store 72.6 metric tons of 

carbon per acre, a relatively low value that reflects 

less formal management compared to public lands 

(Mazza and Ralph 2010). However, most forest land 

in the Mid-Atlantic region is private land (Table 6), 

which stores a total of 1.7 billion metric tons of 

carbon compared to 1.5 billion metric tons of carbon 

on public lands. 

-

20

40

60

80

100

CarbonDensity (metric tons/acre)

Soil organic Live belowground Deadwood Litter Live aboveground

Figure 6.—Forest carbon density by forest-type group. Data source: USDA Forest Service (2018). 

1 “Other” includes Douglas-fir, exotic hardwoods, exotic softwoods, fir/spruce/hemlock, oak/gum/cypress, other eastern softwoods, and 

other hardwoods forest-type groups. 

1
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

19

FOREST OWNERSHIP 

AND MANAGEMENT

There are numerous types of forest landowners 

within the Mid-Atlantic region (Fig. 7) that manage 

forest land for a variety of reasons (Box 4). About 

73.5 percent of forest land is owned by three 

types of private landowners (Table 6) that reflect 

a diversity of landowner types: families, industrial 

and corporate organizations, and conservation 

organizations. A majority (60 percent) of private 

land in the Mid-Atlantic region is owned by 

private families, with corporate (10 percent) and 

nongovernmental/conservation organizations 

(4 percent) owning the rest. The remaining 

26.5 percent of all forest land (8.4 million acres) 

in the Mid-Atlantic region is held in trust for the 

American public. State, county, and municipal lands 

compose the largest percentages of public forest 

land, followed by public lands in National Forest, 

National Park Service land, and U.S. Department 

of Defense military installations. The Allegheny 

National Forest in northwestern Pennsylvania 

encompasses 513,175 acres, and the Finger Lakes 

National Forest in New York encompasses 16,260 

acres. 

Forest cover - entire area Forest cover by state (thousand acres)

Ownership Thousand acres Percent Delaware Eastern Maryland New Jersey Southern New York Pennsylvania

Private 23,482 73.5 279 1,386 971 8,885 11,961

State 6,093 19.1 67 259 626 1,263 3,879

County, 

municipal, 

and local

1,464 4.6 7 133 286 502 536

National Forest 516 1.6 - - - 14 502

Other federal 374 1.2 9 68 119 57 121

Total 31,929 362 1,846 2,002 10,720 16,999

Table 6.—Forest land ownership for the Mid-Atlantic region and by state

Figure 7.—Forest land ownership across the Mid-Atlantic region. “NGO” indicates nongovernmental organization. Data source: 

Hewes et al. (2017).

Corporate (private)

Family (private)

Federal (public)

Local (public)

NGO/Conservation (private)

State (public)

Forest Ownership
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

20

Box 4: Forest Management and its Many Forms

Millions of nonindustrial private family forest owners 

hold 60 percent of the forest land in the Mid-Atlantic 

region. These families own woodlands for different 

reasons including privacy, scenery, protection of 

nature, a legacy for the next generation, and hunting 

and fishing (Butler 2008). Regardless of the primary 

objective, timber harvesting is also common and 

was reported by 46 percent of the family owners, 

who own 69 percent of family forest land (Butler 

2008). Family owners invest various amounts of 

time in managing their woods. Family owners 

can enroll their lands in voluntary conservation 

easements, which permanently limit uses of the 

land in order to protect a conservation value, or 

enroll in certification programs such as American 

Tree Farm System, which promote forest products 

that originate from sustainably managed forests. 

American Tree Farm System currently certifies 

more than 1 million acres in Maryland, New Jersey, 

New York, and Pennsylvania (American Tree Farm 

System 2017). Family owners are provided technical 

assistance and other resources for managing forests 

by extension agents, conservation districts, and 

private consultants. Corporate and industrial forest 

owners hold 10 percent of the land and manage 

primarily for timber products or land value. Many 

corporate owners voluntarily participate in thirdparty certification such as the Forest Stewardship 

Council (Forest Stewardship Council n.d.) and the 

Sustainable Forestry Initiative (Sustainable Forestry 

Initiative 2017) (Table 7). 

Public (federal, state, and county) agencies own 

26.5 percent of forest land in the Mid-Atlantic 

region. These lands are often managed to provide a 

number of environmental benefits and ecosystem 

services, including wildlife habitat, water protection, 

soil conservation, nature preservation, timber 

production, recreation, cultural resources, and a 

variety of other uses (Maryland Department of 

Natural Resources 2010, Pennsylvania Department 

of Conservation and Natural Resources 2016, USDA 

Forest Service 2007). Federal land is managed by 

completing a process required by the National 

Environmental Policy Act of 1969 (NEPA). This 

process entails scoping, involving the public, 

identifying issues, using an interdisciplinary 

approach, gathering data, developing alternatives, 

estimating the effects of the alternatives, and 

documentation (Brandt and Schultz 2016).

Forest land enrolled in certification program (thousand acres)

State

American Tree 

Farm System 

(ATFS)2,5,6

Forest 

Stewardship 

Council (FSC)3

Sustainable 

Forestry Initiative 

(SFI)4

Total area 

enrolled, 

by state

Percentage of forest land 

enrolled in a 

certification program

Delaware 17 1 10 29 8

Maryland 151 247 209 607 33

New Jersey 52 1 - 53 3

New York 564 1,560 1,472 3,596 34

Pennsylvania 331 2,416 105 2,852 17

Five-state total 1,114 4,225 1,797 7,136 22

1

 Data sources: 2ATFS (2017); 3FSC (n.d.); 4SFI (2017); 5G. Daly, New Jersey Tree Farm Program, pers. email, Jan. 6, 2016; 6L.K. Yowell, 

Sussex County, Delaware, pers. email, Dec. 14, 2017.

Table 7.—Forest land enrolled in certification programs1
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

21

Ownership

Annual net growth 

(million cubic feet)

Annual removals 

(million cubic feet) Annual net growth:removals

Private 1,094 335 3.3 

State 169 95 1.8 

County, municipal, and local 58 22 2.6 

National Forest 16 11 1.4 

Other federal 12 4 2.8 

Othera 2 2 0.8 

Total 1,351 470 2.9

a “Other removals” refers to growth and losses associated with changes in land use, such as conversion from forest to nonforest uses.

Table 8.—Annual net growth and removals of growing stock on forest land in the Mid-Atlantic region (data source: 

USDA Forest Service [2018])

Timber Harvest and Forest Products

Forests contribute to the Mid-Atlantic region’s 

economically important wood products industry. 

More than 30.2 million acres, or 95 percent of the 

forest land in the Mid-Atlantic region, are classified 

as timberland by the FIA program and considered 

suitable for wood production (USDA Forest Service 

2015). For example, more than 470.4 million cubic 

feet of industrial roundwood was produced across 

the Mid-Atlantic region per year on average from 

2009 to 2013 (USDA Forest Service 2018). This 

material includes saw logs, veneer logs, pulpwood, 

fuelwood, and other wood products used by wood 

processing mills and other facilities within the 

Mid-Atlantic region. Across the five Mid-Atlantic 

states, saw logs and pulpwood make up nearly 

77 percent of wood use, although some states—

notably New York—also have substantial use of 

fuelwood (Shifley et al. 2012). 

Across the Mid-Atlantic region, the amount of wood 

harvested each year is less than the amount of forest 

growth (Table 8). Comparison of net annual forest 

growth to removals provides a relative indicator of 

utilization pressure on the timber resource (Butler 

et al. 2015, Shifley et al. 2012). The growth-toremovals ratio is based on FIA data and provides one 

measure of sustainability by comparing 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. Across forest-type 

groups in the Mid-Atlantic region, the growth-toremovals ratio was 2.9, meaning that forest growth 

was nearly three times as great as removals. Private 

forest lands, which include both industrial and 

nonindustrial ownerships, are accumulating the 

most growing stock, and national forests the least; 

removals from Department of Defense forest land 

exceeded growth. Differences among ownerships in 

annual growth-to-removals ratios probably reflect 

different management goals and objectives and 

different intensities of active management (Box 4).

DEMOGRAPHIC CONDITIONS 

AND ECONOMIC SECTORS

Due to various methods of reporting, it is not always 

possible to summarize demographic and economic 

conditions for the assessment area as a whole. Much 

of this information is described next and summarized 

for entire states within the Mid-Atlantic region: New 

York, Pennsylvania, Delaware, New Jersey, and 

Maryland.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

22

Population Growth and Distribution 

The Mid-Atlantic region is home to more than 48.5 

million people, which accounts for 15 percent of 

the total U.S. population, and includes metropolitan 

areas with some of the highest population densities 

in the country (Headwaters Economics n.d.). Within 

the Mid-Atlantic region, most people (87 percent) 

reside in major urban areas such as New York City, 

Philadelphia, Washington, DC, Baltimore, and 

Pittsburgh. Included are some of the most populated 

cities in the Nation; New York ranks first and 

Philadelphia ranks fifth. With a combined population 

of 10 million residents, New York and Philadelphia 

alone account for one-fifth of the total population 

in the Mid-Atlantic region (Headwaters Economics 

n.d.). 

Urban areas are highly developed centers of highdensity infrastructure interspersed with natural areas 

and residential neighborhoods. These intensively 

developed areas are subject to conditions that are 

unique to the urban environments. For example, 

the urban heat island effect is responsible for 

hotter temperatures within the urban core, and 

impermeable surfaces create problems for storm 

sewers, roadways, and municipal water supplies 

(Kunkel et al. 2013b). Urban forests include all 

the street and yard trees, parks, woodlots, and 

undeveloped green spaces; by definition they are 

located close to infrastructure and people (Nowak 

et al. 2001). As urban areas continue to expand, 

increased resource use and development may cause 

further stress on these valuable resources.

Population growth within the Mid-Atlantic region 

has been modest since 1970 (16 percent) compared 

to the 58-percent increase in population at the 

national level during the same period. However, 

population growth rates differ appreciably among 

states within the Mid-Atlantic region, with the 

smallest states registering much higher densities. 

Population growth rates since 1970 were highest 

in Delaware (72 percent), Maryland (53 percent), 

and New Jersey (25 percent), whereas the rate 

of increase has been lowest in Pennsylvania and 

New York (8 percent each). Since 2000, Delaware 

continued to see a higher relative increase in 

population (18 percent) even as it represented the 

smallest absolute change of any of the five MidAtlantic States. Between 2000 and 2010, the rate 

of increase in residential land development was 

also greatest in Delaware (20 percent); increases in 

Maryland, New Jersey, New York, and Pennsylvania 

were below the 12-percent rate seen nationally 

(Headwaters Economics n.d.). 

There are also many rural areas within the MidAtlantic region; the lowest population densities are 

located within the Allegheny Plateau of northern 

Pennsylvania, the Catskill Mountains of New York, 

and the Pine Barrens of New Jersey. These regions 

are heavily forested and contain large portions of 

public land. 

Employment and Income 

More than 29 million people, or 60 percent of the 

population in the five Mid-Atlantic states, work in 

full- or part-time jobs (Headwaters Economics n.d.). 

Unemployment rates in 2015 averaged 5.3 percent. 

Per capita income in 2015 was $56,000. This is 

nearly $8,000 greater than the national per capita 

income, but the cost of living is generally higher 

(Headwaters Economics n.d.). 

Economic Sectors 

in the Mid-Atlantic Region

The five states of the Mid-Atlantic region together 

generated $3.2 trillion in gross domestic product in 

2016 (Bureau of Economic Analysis 2017). Some of 

the economic sectors most important to the regional 

economy are manufacturing, construction, wholesale 

trade, retail trade, finance and insurance, real estate, 

and private services. Though representing a smaller 

contribution to the regional economy (less than 

1 percent), several economic sectors directly 

influence land use and natural resources in the 

Mid-Atlantic region, including the forest products 

industry, agriculture, recreation, and resource 

extraction. These industries are especially important 

to the human communities located near forests and 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

23

farms, and have the most potential to influence 

forests in the Mid-Atlantic region. 

Forest Products Industry

The U.S. share of global wood products output has 

fallen steadily since the late 1990s, from 28 percent 

to 17 percent (Prestemon et al. 2015). A number 

of factors contributed to this decline, including 

reduced demand for writing paper and newsprint, 

a shrinking manufacturing sector, a slowdown in 

housing construction starts, and the rise in foreign 

production, particularly in China and Russia. More 

recently, signs of a recovering housing market and 

an emerging wood bioenergy market have resulted 

in modest gains since 2014 (Prestemon et al. 2015). 

The five states in the Mid-Atlantic region produced 

more than $22 billion of wood and paper products 

in 2013, representing 8.3 percent of total value for 

all wood and paper products shipped in the United 

States that year (Headwaters Economics n.d.). 

More than 82,000 people were employed in the 

forest products sector (i.e., forestry, logging, mills, 

paper and wood products) in the five Mid-Atlantic 

states in 2014, which is 45,000 fewer jobs than in 

1998 (Headwaters Economics n.d.). Pennsylvania 

maintained the highest percentage of total private 

employment in the forest products sector during this 

time, starting at 1.2 percent in 1998 and falling to 

0.8 percent by 2014.

Agriculture

Agricultural enterprises in the five Mid-Atlantic 

states generated $18.6 billion in gross income and 

spent $16.4 billion in production costs in 2015, 

resulting in a total realized net income of about 

$2 billion (Headwaters Economics n.d.). Across the 

Mid-Atlantic states, dairy products accounted for 

the greatest portion of farm income (29 percent), 

followed by chickens (12 percent) and corn 

(10 percent) (U.S. Department of Agriculture 2015). 

Dairy was the highest grossing agricultural product 

in both Pennsylvania and New York, while chickens 

generated the greatest revenue for Maryland and 

Delaware. The greenhouse and nursery industry 

earned the greatest revenues in New Jersey, although 

that state contributed the least to total agricultural 

earnings among the five states (6.8 percent). 

In 2015, there were 118,624 farms occupying 

18.1 million acres in the five Mid-Atlantic States (or 

about 26 percent of the five-state area) (Headwaters 

Economics n.d.). Farm businesses employed a total 

of 169,479 workers, most of whom were located 

in Pennsylvania and New York. From 1970 to 

2015, farm employment decreased by about 25 

percent. The number of farms decreased, but farms 

became larger with fewer owners. Total farmland 

has declined slightly, but agricultural production 

has increased since mid-century, largely due to 

increased mechanization. Forest land is often 

converted to agricultural uses, such as cropland and 

pasture; pasture land continually reverts naturally 

to forest. Recently this shifting mosaic of forest and 

agricultural land has resulted in a net gain of forest 

land (Alig et al. 2010). 

Recreation

The Mid-Atlantic region includes a variety of federal 

and state lands that provide ample opportunities for 

hunting, fishing, hiking, camping, nature viewing, 

biking, and skiing. The Allegheny National Forest, 

Delaware Water Gap National Recreation Area, New 

Jersey Pinelands National Reserve, Catskills Forest 

Preserve, Appalachian Trail (a 431-mile stretch), and 

hundreds of state and county parks and forests draw 

visitors and generate income for communities in the 

Mid-Atlantic region. 

In fact, outdoor and forest recreation is a large driver 

of economic activity in the Mid-Atlantic region. 

Across the five Mid-Atlantic states, consumers 

spent $106.9 billion on equipment and travel-related 

expenditures for activities such as hunting, fishing, 

skiing, running, biking, and hiking (Table 9). 

Spending on outdoor recreation also supported 

845,000 direct jobs in 2017 and generated 

$7.8 billion in local and state tax revenues (Outdoor 

Industry Association 2018).
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

24

Consumer spending 

($ billion)

Wages and salaries 

($ billion)

State and local tax 

revenue ($ million) Direct jobs

Delaware 3.1 1.0 145 29,000

Maryland 14.0 4.4 951 109,000

New Jersey 18.9 5.9 1,200 143,000

New York 41.8 14.0 3,600 313,000

Pennsylvania 29.1 8.6 1,900 251,000

Total 106.9 33.9 7,796 845,000

Table 9.—Economic impact of outdoor recreation, by state (data source: Outdoor Industry Association [2018])

Resource Extraction

Mining in the five Mid-Atlantic states generated 

$24.8 billion dollars in 2012 and included oil 

and gas extraction, coal mining, and metal and 

nonmetallic ore mining (Headwaters Economics 

n.d.). The mining industry is particularly strong in 

Pennsylvania, which employed five times as many 

workers as the other four states combined in 2014. 

Between 2002 and 2013, mining employment in 

Pennsylvania more than doubled, going from 16,037 

to 33,228 employees. This dramatic increase was 

largely spurred by the Marcellus shale gas boom, 

which caused oil and gas extraction jobs to grow 

by more than 250 percent between 2007 and 2012, 

and placed Pennsylvania sixth highest in gas and 

oil extraction employment in the Nation (Cruz 

et al. 2014). In 2010, employment in gas and oil 

extraction surpassed that in coal mining, which has 

been a historically important and stable industry 

in Pennsylvania (Cruz et al. 2014). Outside of 

Pennsylvania, the greatest number of workers were 

employed in mining for nonmetallic minerals (e.g., 

gravel, granite, and other stone) among the various 

mining industries. These states have also restricted 

or prohibited the use of fracking technologies to 

extract gas and oil. Resource extraction is a major 

cause of forest land conversion in the Mid-Atlantic 

region. For example, shale-gas development 

continues to increase in forested areas, and is 

causing fragmentation and loss of core forest 

(Drohan et al. 2012).

FOREST COMMUNITIES 

OF THE MID-ATLANTIC REGION 

Although the FIA-derived forest-type groups are 

useful for quantifying data about regional forests, 

forest communities are often described differently 

by regional and local conservation and management 

organizations (Box 5). In the rest of this document, 

a set of “forest communities” are typically used to 

describe the forests currently common across the 

region. These forest communities are generally 

based on macrogroups described by the Northeast 

Habitat Classification System (NETHCS), which 

provides a consistent classification system for 

aquatic and terrestrial ecosystems at a coarse scale 

across the Northeast (Anderson et al. 2013b). 

The following descriptions of forest communities 

were based on NETHCS macrogroups (Table 10) 

and include information on ecological drivers 

and the dominant species as they actually occur 

within the boundaries of the Mid-Atlantic region. 

Even so, the actual species composition in any 

given location may differ depending on local site 

factors, such as landscape position, microclimate, 

hydrology, and disturbance regime. Thus Figure 8 is 

a coarse representation of the distribution of forest 

communities in the Mid-Atlantic region and cannot 

be quantified with accuracy. Five of the forest types 

are found only within the coastal plain. Appendix 5 

explains the expert elicitation process for defining 

the forest communities in the Mid-Atlantic region.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

25

Box 5: Forest Types Used in this Report

Different organizations describe forests using a 

variety of classification systems. This assessment 

uses two classification systems to 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. 

One system was created by the Forest Service Forest 

Inventory and Analysis (FIA) program to characterize 

forests across the Nation. FIA data are used to 

present trends in forest cover, growth, and mortality 

for forest-type groups, which are defined by tree 

species composition (Woudenberg et al. 2010). 

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.

The second system is forest communities, which 

are groupings of the Northeast Terrestrial Habitat 

Classification System (NETHCS), a product of 

collaboration among The Nature Conservancy, 

Association of Northeast Fish and Wildlife Agencies, 

NatureServe, the Natural Heritage Programs, and the 

U.S. Fish and Wildlife Service. NETHCS was created to 

describe wildlife habitat throughout the Mid-Atlantic 

and Northeast. Habitats are based on ecological 

cover types and incorporate other characteristics 

such as biogeographic region, dominant cover 

type, and disturbance regime. Although the final 

classification system describes 143 habitat systems, 

grouped into 35 “macrogroups,” most of the forested 

communities in the assessment area are described 

by only 40 habitats representing 8 macrogroups 

(for a crosswalk between NETHCS and forest 

communities, see Table 10). We used these forested 

habitats and macrogroups to describe 11 forest 

communities that are assessed for vulnerability 

to climate change in Chapter 6. These forest 

communities are similar in scale to the macrogroups. 

However, in this assessment we considered forest 

communities in the coastal plain separately from 

those communities found outside the coastal plain, 

with little overlap between the two at a coarse scale.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

26

Forest community used 

in this assessment Related NETHCS habitats Common species by forest community

Maritime forest 

(coastal plain)

North Atlantic Coastal Plain Maritime Forest pitch pine, Virginia pine, loblolly pine, shortleaf 

pine, scarlet oak, black oak, scrub oak, post oak, 

eastern redcedar, black cherry, American holly, 

sassafras, red maple

Oak-pine-hardwood 

(coastal plain)

North Atlantic Coastal Plain Hardwood Forest 

Southern Atlantic Coastal Plain Mesic Hardwood Forest

white oak, southern red oak, chestnut oak, black 

oak, scarlet oak, red maple, sassafras, gray birch, 

bigtooth and quaking aspen, hazelnut, pitch pine, 

Virginia pine, loblolly pine, shortleaf pine, sugar 

maple, American beech

Pine-oak barrens 

(coastal plain) 

North Atlantic Coastal Plain Pitch Pine Barrens pitch pine, scrub oak, scarlet oak, blackjack oak, 

chestnut oak, black oak, white oak, post oak

Swamp (coastal plain) North Atlantic Coastal Plain Basin Peat Swamp 

North Atlantic Coastal Plain Basin Swamp 

 and Wet Hardwood Forest 

North Atlantic Coastal Plain Pitch Pine Lowland 

Piedmont-Coastal Plain Large River Floodplain

red maple, sweetgum, blackgum, willow oak, green 

ash, pitch pine, Atlantic white-cedar, baldcypress, 

shagbark hickory, pin oak, swamp white oak, 

overcup oak 

In the south: loblolly pine

Tidal swamp 

(coastal plain)

North Atlantic Coastal Plain Tidal Swamp baldcypress, pumpkin ash, red maple, green ash, 

blackgum, water tupelo, American elm, black 

willow, loblolly pine

Central oak-pine 

(interior)

Allegheny-Cumberland Dry Oak Forest and Woodland 

Central Appalachian Dry Oak-Pine Forest 

Central Appalachian Pine-Oak Rocky Woodland 

Northeastern Interior Dry-Mesic Oak Forest 

Northeastern Interior Pine Barrens 

Piedmont Hardpan Woodland and Forest 

Southern Appalachian Montane Pine Forest and Woodland

northern red oak, white oak, black oak, chestnut 

oak, scarlet oak, red maple, sassafras, pignut 

hickory, mockernut hickory 

On exposed ridges and outcrops: pitch pine, 

eastern white pine, Virginia pine

Lowland conifer 

(interior)

Laurentian-Acadian Alkaline Conifer-Hardwood Swamp 

High Allegheny Headwater Wetland 

North-Central Interior and Appalachian Rich Swamp 

North-Central Appalachian Acidic Swamp 

Northern Appalachian-Acadian Conifer-Hardwood Acidic Swamp

black spruce, tamarack, eastern hemlock, black 

ash, yellow birch, red maple 

At high elevations: red spruce, balsam fir

Lowland and riparian 

hardwood (interior)

Glacial Marine & Lake Wet Clayplain Forest 

North-Central Appalachian Large River Floodplain 

North-Central Interior Large River Floodplain 

North-Central Interior Wet Flatwoods 

Piedmont Upland Depression Swamp 

Piedmont-Coastal Plain Large River Floodplain

pin oak, swamp white oak, shagbark hickory, 

sweetgum, silver maple, sycamore, boxelder, 

American hornbeam (musclewood), blackgum, 

red maple, black ash, river birch, green ash, 

cottonwood (rare), bur oak (rare)

Montane spruce-fir 

(interior)

Acadian-Appalachian Montane Spruce-Fir-Hardwood Forest eastern red spruce, balsam fir, yellow birch, 

paper birch, mountain maple, striped maple, 

mountain-ash 

Northern hardwood 

(interior)

Appalachian (Hemlock)-Northern Hardwood Forest

Laurentian-Acadian Northern Hardwood Forest

Laurentian-Acadian Northern Pine-(Oak) Forest

Laurentian-Acadian Pine-Hemlock-Hardwood Forest 

North-Central Interior Beech-Maple Forest

South-Central Interior Mesophytic Forest

Southern and Central Appalachian Cove Forest

Southern Piedmont Mesic Forest

sugar maple, yellow birch, American beech, tulip 

tree, basswood, northern red oak, black walnut, 

black cherry, white pine, eastern hemlock 

On dry sites: white pine, red pine, northern red 

oak, bigtooth aspen, quaking aspen, paper birch

Woodland, glade, and 

barrens (interior)

Appalachian Shale Barrens 

Central Appalachian Alkaline Glade and Woodland 

Eastern Serpentine Woodland 

Great Lakes Alvar

eastern redcedar, sugar maple, northern red 

oak, white oak, pignut hickory, eastern redbud, 

hackberry 

On dry sites: pitch pine, Virginia pine, white oak

Table 10.—Forest communities and Northeast Habitat Classification System (NETHCS) macrogroups
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

27

Figure 8.—Forest communities of the Mid-Atlantic region. These communities are aggregates of systems mapped by the 

Northeast Habitat Classification System (Anderson et al. 2013b).

Maritime Forest (Coastal Plain)

The maritime forest (coastal plain) community is 

a relatively uncommon mosaic of forest-shrubland 

that exists only near the Atlantic Ocean on barrier 

islands, bluffs, and sand dunes. In these locations, 

it is subject to unique maritime stresses including 

salt spray, high winds, dune shifting, sandblasting, 

and tidal overwash. Soils are coarse to fine sand 

with some organic material mixing into top layers. 

Forests have relatively few understory or canopy 

species. Stunted trees occur in various combinations 

of a few pine and oak species. Pine species can 

include pitch, Virginia, loblolly, and shortleaf. Oak 

species can include scarlet, black, scrub, and post. 

Other species may also find suitable microhabitat 

in upland or lowland depressions; these include 

eastern redcedar, black cherry, American holly, 

sassafras, and red maple. Among the primary natural 

disturbances are hurricanes and storm surge, which 

can result in shifting sand and uprooting of trees. 

This forest community is based on the North Atlantic 

Coastal Plain Maritime Forest NETHCS habitat.

Oak-Pine-Hardwood (Coastal Plain)

The coastal plain oak-pine-hardwood community 

occurs as small patches on flat to rolling hills 

and dunes. Soils are sandy outwash that is deep, 

generally coarse-textured, and variable in moisture 

and pH (ranging from dry to mesic and acidic 

to neutral). Relatively high rates of fine litter 

production and accumulation coupled with dry 

conditions foster periodic fire, which promotes 

several oak species, including white, southern 

red, chestnut, black, and scarlet. Dry sites in the 

southerly locations support pine species, including 

pitch, Virginia, loblolly, and shortleaf. Numerous 

hardwood species may be present as codominants 

in early successional sites, including red maple, 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

28

sassafras, gray birch, bigtooth aspen, quaking aspen, 

and hazelnut. In more mesic areas, dominant species 

can be a mix of American beech and sugar maple 

within a mix of oaks and loblolly pine. Fire is an 

important natural disturbance in xeric pine- and oakdominated sites and can be less frequent in mesic 

sites. Periodic outbreaks of southern pine beetle 

can temporarily influence the pine component. 

This forest community is based on the following 

NETHCS habitats: 

• North Atlantic Coastal Plain Hardwood Forest

• Southern Atlantic Coastal Plain Mesic Hardwood 

Forest 

Pine-Oak Barrens (Coastal Plain)

Coastal plain pine-oak barrens occur on dry, flat sites 

within the New Jersey portion of the Outer Coastal 

Plain Mixed Forest Province and the Long Island 

portion of the Eastern Broadleaf Forest (Fig. 2). 

Soils are low in nutrients, deep, and sandy. This 

forest community is adapted to dry conditions with 

frequent to occasional fire, and forest composition 

and structure vary with fire frequency and severity. 

Pitch pines dominate the canopy and occasionally 

mix with oaks (including scarlet, chestnut, black, 

white, and post) in stands with a longer fire return 

interval. Severe fire may eliminate associates which 

produce seed at a later age than pitch pine. Scrub 

oak stands may also occur without pine cover in 

low-lying areas where cold air drainage limits pine. 

The occurrence of oak generally decreases as fire 

frequency increases. In stands with very frequent 

fire—where fire returns in 10 years or less—dwarf 

pitch pine is the dominant cover. Although almost 

genetically identical to pitch pine, dwarf pitch pine 

result from frequent fire by developing serotinous 

cones and low stature, ranging from prostrate shrubs 

to a height of only 3 to 10 feet tall. This forest 

community is based on the North Atlantic Coastal 

Plain Pitch Pine Barrens NETHCS habitat.

Pitch pine-oak-heath woodland in the Central Long Island Pine Barrens, New York. Photo by Gregory J. Edinger, New York 

Natural Heritage Program, used with permission.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

29

Swamp (Coastal Plain)

The coastal plain swamp type occurs mainly within 

the Outer Coastal Plain Mixed Forest Province 

(Fig. 2) in low-lying areas such as depressions 

and basins, as well as near streams and rivers. 

These forests are heavily influenced by local 

groundwater hydrology, with plant communities 

that reflect the presence of standing water for half 

the growing season or longer. The soils are mineral 

and acidic and can be covered by peat, sphagnum, 

or other organic material. Alluvial soils in large 

river floodplains are often redeposited by seasonal 

flooding. Common basin species include red maple, 

sweetgum, blackgum, willow oak, and green ash; 

loblolly pine may occur in locations south of the 

Delaware Bay. Baldcypress, shagbark hickory, and 

wet oaks (pin, swamp white, willow, and overcup) 

are often found on better drained soils within active 

floodplains. Atlantic white-cedar stands are found 

in acidic muck and peat-accumulating basins. Pitch 

pine lowlands are included here to reflect their 

occupation of saturated deep peats, but typically 

form a mosaic with upland pitch pine barrens. Fire 

is limited mainly to the pitch pine lowlands, where it 

helps to maintain a more open structure. This forest 

community is based on the following NETHCS 

habitats: 

• North Atlantic Coastal Plain Basin Peat Swamp 

• North Atlantic Coastal Plain Basin Swamp and 

Wet Hardwood Forest 

• North Atlantic Coastal Plain Pitch Pine Lowland 

• Piedmont-Coastal Plain Large River Floodplain

Coastal plain swamp in New Jersey. Photo by Rick Lathrop, Rutgers University, used with permission.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

30

Tidal Swamp (Coastal Plain)

The coastal plain tidal swamp occurs as small 

patches mainly within the Outer Coastal Plain Mixed 

Forest Province (232). This hummock-and-hollow 

topography is situated in the uppermost portions 

of tidal rivers, which are predominantly freshwater 

with regular short-term flooding of saline water. 

Soils are poorly drained slightly acidic tidal muck 

consisting of variable amounts of silt, clay, and fine 

sands mixed with root-rich peats. Species richness 

is high and many communities are dominated by 

baldcypress, or by a mix of red maple and pumpkin 

ash or green ash. Other associates can include 

blackgum, water tupelo, American elm, and black 

willow. In higher salinity areas that are flooded 

irregularly (i.e., less than daily), loblolly pine can 

dominate and often transitions to tidal marshlands 

as salinity increases. Primary natural disturbances 

include hurricanes, drought, and fire. This forest 

community is based on the North Atlantic Coastal 

Plain Tidal Swamp NETHCS habitat. 

Central Oak-Pine (Interior)

Oak and oak-pine forests cover more area than any 

other forest community in the Mid-Atlantic region. 

They occur as a variety of dry to mesic habitats 

forming large patch and matrix forests at various 

elevations. Soils are often acidic, and range from 

dry and nutrient-poor to mesic and enriched. Oaks 

are dominant, especially northern red, white, black, 

chestnut, or scarlet. Depending on site conditions, 

eastern white pine and numerous hardwood species 

may be present as codominants, including red maple, 

sassafras, pignut hickory, and mockernut hickory. 

American chestnut was once a common canopy 

tree before chestnut blight devastated the species 

a century ago. Pines (pitch, shortleaf, red, eastern 

white, Virginia) can outnumber oaks on exposed 

ridgetops and outcrops. Most oak-pine forests were 

extensively altered during harvesting that occurred 

between the mid-to-late 1800s and 1930s and 

thus are second growth (Whitney 1996). There is 

evidence that fire was a recurring natural disturbance 

prior to the early 19th century, but fire has been 

suppressed or excluded as part of wildfire prevention 

efforts since the 1930s (Lafon et al. 2017, Nowacki 

and Abrams 2008). Fire can have a large influence 

on species composition; appropriate fire regimes can 

promote oaks and pines on sites which would have 

a greater proportion of mesic species in the absence 

of fire. This forest community includes the following 

NETHCS habitats: 

• Allegheny-Cumberland Dry Oak Forest and 

Woodland

• Central Appalachian Dry Oak-Pine Forest 

• Central Appalachian Pine-Oak Rocky Woodland 

• Northeastern Interior Dry-Mesic Oak Forest

• Northeastern Interior Pine Barrens 

• Piedmont Hardpan Woodland and Forest 

• Southern Appalachian Montane Pine Forest and 

Woodland 

Lowland Conifer (Interior)

Lowland conifer forests occur over a range of 

low-lying areas that include glacial depressions, 

basin wetlands, and seepage areas. These forested 

wetlands typically have saturated soils throughout 

the year and may also be flooded seasonally. Many 

lowland sites are nutrient-rich alkaline wetlands 

associated with limestone; other sites are acidic 

and nutrient-poor. In both cases, soils are primarily 

mineral, though there may be peat development or 

organic muck accumulation in headwater wetlands 

and depressions. Depending on local site conditions, 

a variety of conifer species may be present, such 

as black spruce, tamarack, and eastern hemlock. 

Black ash, yellow birch, and red maple are common 

associates in many sites. Basins above 1,200 

feet (e.g., northern Pennsylvania) are sometimes 

cool enough to support red spruce and balsam fir. 

Partially due to wet conditions and shallow rooting, 

the primary natural disturbance is gap formation 

from wind events. This forest community is based on 

the following NETHCS habitats: 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

31

• Laurentian-Acadian Alkaline Conifer-Hardwood 

Swamp

• High Allegheny Headwater Wetland 

• North-Central Interior and Appalachian Rich 

Swamp

• North-Central Appalachian Acidic Swamp 

• Northern Appalachian-Acadian ConiferHardwood Acidic Swamp

Lowland and Riparian Hardwood 

(Interior)

Lowland and riparian hardwood forests encompass 

a range of forested wetlands found in depressions 

and low-lying areas, along waterways, and in 

floodplains. These forests are heavily influenced 

by local hydrology, with plant communities 

that reflect the occurrence of seasonal flooding, 

ponding, erosion, groundwater seepage, or other 

local dynamics. Poorly drained or saturated soils 

can support silver maple, sycamore, boxelder, 

American hornbeam, blackgum, sweetgum, red 

maple, black ash, eastern hemlock, river birch, and 

green ash. Better drained soils may support a variety 

of hardwood species often dominated by pin oak, 

swamp white oak, shagbark hickory, and sweetgum. 

Microtopography and fluctuating moisture levels 

can create complexes of forest upland and wetland. 

Partially due to wet conditions and shallow rooting, 

the primary natural disturbance is gap formation 

from wind events. This forest community is based on 

the following NETHCS habitats: 

• Glacial Marine & Lake Wet Clayplain Forest

• North-Central Appalachian Large River 

Floodplain

• North-Central Interior Large River Floodplain

• North-Central Interior Wet Flatwoods

• Piedmont Upland Depression Swamp

• Piedmont-Coastal Plain Large River Floodplain

Floodplain forest along the Hudson River near Bemis Heights in Saratoga County, New York. Photo by Gregory J. Edinger, New 

York Natural Heritage Program, used with permission.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

32

Montane Spruce-Fir (Interior)

Within the Mid-Atlantic region, montane spruce-fir 

forests occur only in the Catskills of New York at 

the highest elevations (above 3,350 feet) where the 

growing season is shorter and summer temperatures 

are cooler than lower altitudes. The presence of 

cloud cover provides much of the forest’s water 

supply, and positive feedbacks create a microclimate 

with plenty of water and cool temperatures (Cogbill 

and White 1991). These forests are dominated 

by populations of red spruce and balsam fir that 

are isolated from expansive spruce-fir forests to 

the north and remnant populations located farther 

south. Although spruce-fir forests are dominated by 

conifers, they may contain a number of associated 

northern hardwood species, such as yellow birch, 

paper birch, mountain maple, striped maple, and 

mountain ash. Soils are low to moderate fertility, 

acidic, and glaciated (Comer et al. 2003). The 

primary natural disturbance regime is gap formation 

from landslides or wind, snow, or ice damage. 

This forest community is based on the AcadianAppalachian Montane Spruce-Fir-Hardwood Forest 

NETHCS habitat. 

Northern Hardwood (Interior)

Northern hardwood forests are diverse and widely 

distributed across much of the Mid-Atlantic region. 

They occur as a variety of habitats forming a large 

and complex matrix on a range of sites from about 

800 to 3,500 feet in elevation. Soils can vary greatly 

and can include conditions ranging from glaciated 

to unglaciated, shallow to deep, dry-mesic to wetmesic, and nutrient-poor to nutrient-enriched. The 

highest elevations support sugar maple, yellow 

birch, and American beech, sometimes mixed 

with or dominated by eastern hemlock. Tulip tree, 

basswood, northern red oak, black walnut, black 

cherry, and white pine are often found on moist, 

well-drained sites where beech, sugar maple, red 

maple, white ash, gray birch, and sweet birch 

occur less frequently. Red pine, white pine, and 

northern red oak can dominate relatively dry 

sites at lower elevations, with associates of sugar 

maple, red maple, beech, aspen, sweet birch, and 

paper birch. Most northern hardwood forests are 

extensively altered and second growth, a result of 

the intensive harvesting that occurred between the 

mid-to-late 1800s and 1930s (Whitney 1996). The 

primary natural disturbance regime is gap formation 

from wind, tornadoes, snow, and ice damage, but 

occasional large-scale blowdown events can also 

affect large areas (Ruffner and Abrams 2003). 

This forest community is based on the following 

NETHCS habitats: 

• Appalachian (Hemlock)-Northern Hardwood 

Forest 

• Laurentian-Acadian Northern Hardwood Forest

• Laurentian-Acadian Northern Pine-(Oak) Forest

• Laurentian-Acadian Pine-Hemlock-Hardwood 

Forest 

• North-Central Interior Beech-Maple Forest

• South-Central Interior Mesophytic Forest

• Southern and Central Appalachian Cove Forest

• Southern Piedmont Mesic Forest

Montane spruce-fir forest on Hunter Mountain in the Catskill 

Mountains, New York. Photo by Troy W. Weldy, New York 

Natural Heritage Program, used with permission.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

33

Woodland, Glade, and Barrens 

(Interior)

“Woodland, glade, and barrens ” describes several 

extreme habitats that occur on upper slopes and 

ridgetops and are associated with specific rock 

substrates. Soils are thin and xeric when they cover 

limestone, dolomite, serpentinite, basalt, or other 

calcareous or ultramafic rock, although sometimes 

vegetation grows on bare shale or limestone. These 

forests occupy the driest and most exposed sites in 

the Mid-Atlantic region. Some sites are maintained 

by fire, and others are protected from fire by lack 

of ground fuel or by a landscape barrier. Sugar 

maple, northern red and white oak, pignut hickory, 

eastern redbud, and hackberry can form an open, 

often stunted canopy on shale slopes, glades, and 

woodlands. Eastern redcedar can be common in 

the absence of fire. Pitch pine, Virginia pine, scrub 

oak, and white oak can occupy the driest areas. 

In addition to fire, drought is a primary natural 

disturbance that helps maintain the openness of this 

forest community. This forest community is based 

on the following NETHCS habitats: 

• Appalachian Shale Barrens 

• Central Appalachian Alkaline Glade and 

Woodland

• Eastern Serpentine Woodland

• Great Lakes Alvar (very rare) 

DRIVERS OF CHANGE IN FOREST 

ECOSYSTEMS

The forest ecosystems of the Mid-Atlantic region 

have undergone significant changes during 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 of pollen and macrofossils 

have been collected from lakes and bogs throughout 

the eastern United States to determine long-term 

vegetation dynamics (Davis 1983, Williams et 

al. 2004). About 21,000 years ago glaciers in the 

Mid-Atlantic region carved out the Great Lakes 

and the Finger Lakes in New York and extended 

as far south as northern New Jersey and Long 

Island. Where glaciers were absent, tundra extended 

southward along the Appalachian Mountains. As 

the last glaciers retreated about 14,000 years ago, 

tree species migrated northward toward favorable 

habitat (Davis 1983, Williams et al. 2004). These 

dates are approximate to reflect some uncertainty 

in determining range limits and arrival dates from 

paleoecological data, especially when small or lowdensity populations may have existed (McLachlan et 

al. 2005). 

In general, species moved at different rates into 

the Mid-Atlantic region from multiple locations 

including the Midwest, Deep South, or Atlantic 

Coast, depending on the suitability of climate, seed 

dispersal, and establishment success (Davis 1983). 

Oaks arrived relatively early, from 15,000 to 12,000 

years ago. Eastern white pine and eastern hemlock 

arrived around 12,000 years ago, and elms and 

maples arrived 12,000 to 10,000 years ago (Williams 

et al. 2004). Some species arrived relatively recently 

in the Mid-Atlantic region, possibly delayed by 

the migration barrier presented by the Appalachian 

Mountains. For example, hickories and chestnut 

arrived only 8,000 to 5,000 years ago. This 

migration spanning thousands of years resulted in 

unique assemblages of species that are not common 

today, such as the spruce-pine forests that initially 

established in the Mid-Atlantic region (Jackson and 

Williams 2004, Williams et al. 2001).

Several broad periods of natural climate change 

occurred since the glaciers retreated, including Early 

Holocene Warming from 11,700 to 8,200 years ago, 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

34

the Holocene Thermal Maximum from about 9,000 

to 5,000 years ago, and the more recent Neoglacial 

Cooling (Nowacki and Abrams 2015). Within these 

broad periods, tree ring analysis provides some of 

the evidence for smaller periods of climate change 

that occurred over tens and hundreds of years, such 

as the wetter decades that occurred 700 to 800 

years ago and the drier decades that occurred 450 to 

550 years ago (Maxwell et al. 2011, 2012). These 

climatic cycles were major drivers of ecosystem 

change in the Mid-Atlantic region. 

Other potential drivers, depending on various local 

factors, are coupled with Native American migration 

to the area around 11,500 years ago. A review 

of evidence from archaeology, ethnobotany, and 

palynology identified important influences of Native 

American land uses on eastern North American 

forests, indicating that human land use became an 

increasingly important driver of ecosystem change 

after the arrival of Native Americans in the region 

(Abrams and Nowacki 2008). Native American 

activities in some locations may have included fire, 

land clearing, and possible management of fruit and 

mast tree species. Fire and land clearing promoted 

the development of oak and pine forests, while 

unmodified areas promoted northern hardwoods and 

beech-maple forests. 

There are few written records of forest conditions 

reflecting Native American land use prior to 

European settlement. But forest characteristics can 

be inferred from early land surveys conducted at the 

time of European settlement that recorded witness 

trees to delineate boundaries. These data can be used 

to reconstruct forest composition and structure, and 

infer fire history before European settlement 

(Table 11). Witness tree data can also provide key 

evidence of where fire was an important natural 

disturbance within the Northeast and Mid-Atlantic 

region. One recent study characterized witness 

tree species by fire tolerance. A predominantly 

fire-tolerant species was rated as a high pyrophilic 

percentage, whereas a predominantly fire-intolerant 

species was rated as a low pyrophilic percentage 

(Thomas-Van Gundy et al. 2015). Displaying 

past forest composition in these terms showed the 

transition from fire-tolerant oak-hickory forests in 

the southern portion of the region to fire-intolerant 

northern hardwoods to the north, demarcated by 

the tension zone line (Fig. 9) (Cogbill 2000). Firetolerant witness trees followed major river systems 

and the Atlantic coast, offering further evidence 

of the role of Native Americans and fire on the 

landscape (Thomas-Van Gundy et al. 2015). 

European settlement of the Mid-Atlantic region 

began 400 years ago, and immediately became a 

new important driver of ecosystem change. The 

widespread logging and clearing for timber and 

agriculture resulted in dramatic changes to forests 

across the eastern United States. Before European 

settlement, forests in the northern part of the MidAtlantic region were dominated by American 

beech, sugar maple, and hemlock with small, 

localized pockets of fire-dependent oak, hickory, 

and American chestnut. Farther south, there is 

evidence of widespread fire-dependent oak, hickory, 

and American chestnut with smaller areas of pine. 

With the arrival of European settlers, fire increased 

across the region, largely due to slash burning and 

subsequent wildfires. Then, the implementation of 

fire suppression policies drastically reduced fire 

on the landscape to the longest fire return intervals 

in recent history (Brose et al. 2014). The current 

increase in mesic forests that has been observed 

in the absence of landscape burning (Abrams and 

Downs 1990, Abrams and Nowacki 1992, McEwan 

et al. 2011) is strong evidence for the role of fire in 

creating and maintaining the eastern oak ecosystems.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

35

Location Pre-European settlement forest composition Reference

Southeastern New York White oak (36%), black oak (15%), hickory (10%); more red maple 

in current forest

(Glitzenstein et al. 1990)

Eastern New Jersey, 

southeast New York

White oak, oak spp., American chestnut; pine (7%); Atlantic 

white-cedar/red maple swamps

(Loeb 1987)

Central and western New York Central New York dominated by American beech (46%), maple 

(20%), hemlock (5%), black ash swamps, oak forests; western New 

York dominated by American beech (22%), sugar maple (20%), 

hemlock (19%)

(Marks et al. 1992)

Western New York Allegheny Plateau – American beech (19%), sugar maple (12%), 

hemlock (11%); Till Plains – American beech (32%), sugar maple 

(18%), basswood (12%)

(Seischab 1990)

Western New York American beech (37%), sugar maple (21%), hemlock (8%) (Wang 2007)

Northern New Jersey White oak (34%), black oak (18%), hickory (15%); more birch and 

maple in current forests

(Russell 1981)

Southwestern Pennsylvania White oak (40%), hickory (9%), black oak (9%) (Abrams and Downs 1990)

Southeastern Pennsylvania Black oak (33%), white oak (17%), American chestnut (15%) (Mikan et al. 1994)

Southeastern Pennsylvania Uplands dominated by oaks, hickory, American chestnut; 

lowlands dominated by white oak, black oak, hickory

(Black and Abrams 2001a)

Southeastern Pennsylvania Lowlands dominated by black oak (33%), white oak (29%), 

hickory (15-28%); uplands dominated by white oak (26%), 

black oak (24%), and American chestnut (18%)

(Black and Abrams 2001b)

Northwestern Pennsylvania American beech (20%), hemlock (15%), white oak (14%) in 

areas of high Native American influence; American beech (49%), 

hemlock (20%), maple (9%) in areas of low Native American 

influence

(Black et al. 2006)

Northwestern Pennsylvania White oak (21%), American beech (13%), sugar maple (9%) 

with oak and hickory more common on south-facing slopes, 

hemlock on north-facing slopes; more black cherry and red maple 

in current forests

(Whitney and DeCant 2003)

North-central Pennsylvania American beech, hemlock, American chestnut on plateau tops; 

Allegheny Mountains dominated by white pine, maple, white oak, 

with differences found between landforms

(Abrams and Ruffner 1995)

Central Pennsylvania Ridges dominated by oaks (42%), pines (27%), American 

chestnut (13%); coves dominated by oaks (33%), pines (27%), 

hemlock (17%); valley floors dominated by oaks (56%), pines 

(13%), hickory (12%); valley ravines dominated by oaks (25%), 

hickory (13%), walnut (10%); more maple, black cherry, 

and birch in current forests

(Nowacki and Abrams 1992)

Central Pennsylvania Mountains dominated by pine (32%), chestnut oak (14%), 

white oak (12%); valleys dominated by white oak (43%), 

pine (11%), black oak (11%) 

(Gonsalves 2011)

Northeastern United States American beech (22%), oaks (17%), maples (11.3%), hemlock 

(10.9%), spruces (7.6%), birches (6.9%), pines (6.8%), chestnut 

(3.3%); more red maple, black cherry, aspen spp. in current 

forests

(Thompson et al. 2013)

Table 11.—Forest composition estimates from witness trees in the Mid-Atlantic region
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

36

Figure 9.—Pyrophilic character of pre-European settlement 

forests in Pennsylvania, New York, and New Jersey based 

on witness tree data from land surveys during 1670 to 1890 

(Thomas-Van Gundy et al. 2015).

Primary Agents of Change

Agents of change within the Mid-Atlantic region 

include both natural and anthropogenic disturbances 

(Table 12). Natural disturbances, such as wind, 

wildfire, storms and severe weather, and native 

pests and diseases, have shaped contemporary 

forests, and forests have adapted in response to 

patterns of disturbance (Gutschick and BassiriRad 

2003). Anthropogenic disturbances have altered 

forests more recently and include deforestation, 

fragmentation, large-scale surface mining, acid 

deposition, and the introduction of exotic plants and 

pests. Disturbances can disrupt ecosystem services, 

carbon storage, timber production, and primary 

productivity (Thom and Seidl 2016). On the other 

hand, disturbances can improve species richness, 

habitat quality, and overall diversity.

Natural Disturbances 

and Disturbance Regimes

Natural disturbances have regularly influenced 

the structure, composition, function, and spatialtemporal dynamics of forest ecosystems (Seidl et 

al. 2011). Small-scale disturbances are often caused 

by wind, drought, ice, snow, flooding, landslides, 

insect outbreaks, intermediate-intensity fires, and 

pathogens (Anderson et al. 2013b, NatureServe 

2017). Large-scale disturbances, which can affect 

entire stands and swaths of forest across the 

landscape, include tornadoes, hurricanes, wildfire, 

flooding along major rivers, and catastrophic insect 

and pathogen outbreaks. 

Forest systems have distinct disturbance regimes, 

characterized in part by the soils, landforms, and 

vegetation (McNab et al. 2007). The disruption of 

natural disturbance regimes has included harvesting 

and fire suppression as well as hydrologic disruption 

in riparian and lowland forests. Natural regeneration 

and succession of forest ecosystems are 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 

2005). Conversely, other species may benefit from 

the altered disturbance regime, particularly firesensitive, shade-tolerant trees, especially red maple.

Fragmentation and Land-use Change

Residential and urban development has led to the 

fragmentation of forests across the Mid-Atlantic 

region, resulting in a patchwork of public and private 

parcels of natural, agricultural, and developed 

lands (Riitters 2011). As mentioned earlier, about 

40 percent of the Mid-Atlantic region is now 

agricultural or developed land (Jin et al. 2013). 

The Mid-Atlantic region encompasses the major 

metropolitan areas of New York City, Baltimore, 

and Washington, DC, which compose the core of the 

urbanized region stretching along the Mid-Atlantic 

coast (Short 2007). The most vulnerable lands are 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

37

Driver of forest change References

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

negatively affects forest productivity and resilience.

(Driscoll et al. 2016, Pan et al. 2004, 

Thomas et al. 2010)

Herbivory, particularly from white-tailed deer, is considered a keystone driver through 

impacts on plant regeneration, structure, and species diversity, especially where deer 

density is high.

(Comisky et al. 2005, Horsley et al. 

2003, Knight et al. 2009, Rawinski 

2016, Redding 1995)

Drought reduces plant growth, causes regeneration failure, and increases susceptibility to 

insect pests, diseases, and other environmental stressors. The potential for wildfire increases 

where drought causes plant mortality and thus fuels for fire from dried plant materials.

(Brose et al. 2013, Clark et al. 2016, 

Luce et al. 2016, Vose et al. 2016) 

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.

(Cruz et al. 2014, Drohan et al. 

2012, Johnson et al. 2010)

Soil 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 

and result in reduced water quality.

(Eisenbies et al. 2007, Ezer et al. 

2014, Pennsylvania Department of 

Environmental Protection 2012)

Fragmentation associated with 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.

(Drohan et al. 2012, Irwin and 

Bockstael 2007, Jantz et al. 2005, 

Riitters 2011)

Invasive plants compete for resources and alter natural forest dynamics. A large number 

of invasive plant species are present, including garlic mustard, ailanthus, stiltgrass, and 

nonnative honeysuckles and buckthorns. 

(Hoffberg and Mauricio 2016, 

Kurtz 2013)

Insect pests can cause reduced growth or mortality of target species. Pests of concern vary 

widely based on susceptibility of trees at a particular site, depending on tree age, density, 

health, and other factors. Problematic insect species include budworm, hemlock woolly 

adelgid, emerald ash borer, Asian longhorned beetle, and forest tent caterpillar. 

(Krist et al. 2007, 

USDA Forest Service n.d.b)

Forest pathogens increase the risk of tree mortality and species extinction or extirpation. 

Pathogens of concern vary widely based on susceptibility of trees at a particular site, 

depending on tree age, density, health, and other factors. Diseases include beech bark 

disease, Dutch elm disease, elm yellows, and chestnut blight.

(Krist et al. 2007, 

USDA Forest Service n.d.b)

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.

(Clark et al. 2016, La Puma et al. 

2013, Nowacki and Abrams 2008, 

Patterson 2006)

Table 12.—Major drivers of change to forest ecosystems in the Mid-Atlantic region
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

38

those on the fringes of these cities and major towns, 

and in rural areas where second homes contribute 

to sprawling development (Irwin and Bockstael 

2007, Jantz et al. 2005, USDA Forest Service 2011). 

In other areas, industrial land-use change drives 

fragmentation. For example, installation of natural 

gas well pads in Pennsylvania disturbs 3 to 5 acres 

of land per well pad over Marcellus shale (Drohan 

et al. 2012, Johnson et al. 2010). Forest lands 

across the Mid-Atlantic region are often heavily 

dissected by roads, trails, and utility lines that serve 

and connect residential, business, and industrial 

complexes.

Parcelization is also a concern as the number of 

forest landowners is increasing and the size of 

parcels is decreasing (Widmann et al. 2015). For 

example, more than 70 percent of forest land in 

Pennsylvania is in private ownership, with 70 

percent of family forest owners owning 9 acres or 

less (Albright et al. 2017). Large natural features, 

such as the Chesapeake Bay and its tributaries, 

exert a strong attractive force on new development, 

resulting in continued forest fragmentation (Boesch 

and Greer 2003, Irwin and Bockstael 2007). 

Fragmentation of natural landscapes increases edges 

along forest boundaries and reduces the amount 

of interior forest (Drohan et al. 2012, Irwin and 

Bockstael 2007). Reduced connectivity creates 

isolated populations that are unable to exchange 

genetic information, leading to reduced biological 

and genetic diversity (Riitters 2011). Fragmentation 

has also led to the physical loss of wetlands and 

wildlife habitat, increased exposure to disturbances, 

and the spread of invasive species. 

Insect Pests and Forest Diseases

Insect and disease outbreaks regularly influence 

the structure and health of forest ecosystems in the 

Mid-Atlantic region. Native insects and pathogens 

are responsible for natural cycles of mortality and 

reduced productivity in healthy ecosystems (Stolte 

et al. 2012). Under certain conditions, a native pest 

population may increase dramatically, overwhelming 

host trees and causing widespread mortality. Largescale population dynamics are captured in annual 

surveys that observe damage from common insect 

pests, and the level of damage varies from year 

to year (Man 2012). For example, the eastern tent 

caterpillar regularly defoliates black cherry in the 

Mid-Atlantic region, and fall cankerworm defoliates 

hardwoods. Defoliation by the forest tent caterpillar 

is known for interannual fluctuations and hotspots, 

such as a severe outbreak in New York in 2005, the 

same year defoliation in Pennsylvania was reduced 

from 2004. Recent outbreaks of another native 

species, the southern pine beetle, have occurred in 

the New Jersey Pine Barrens and in Long Island, 

New York, and warrant monitoring and management 

of this pest (Natural Resources Conservation Service 

2013). 

International trade and the inadvertent transport 

of nonnative species from countries around the 

world have amplified exposure to new diseases and 

impacts on tree species of the Mid-Atlantic region. 

Dutch elm disease, chestnut blight, and beech bark 

disease are particularly devastating and have reduced 

or eliminated keystone species from their native 

habitats (Stolte et al. 2012). Gypsy moth is another 

serious pest and has caused substantial losses 

of white oak, red oak, basswood, and sweetgum 

Gypsy moth caterpillars. During the larval stage, gypsy moths 

eat for 7 weeks, stripping plants and trees of their leaves. 

Photo by Greg Czarnecki, Pennsylvania Department of 

Conservation and Natural Resources, used with permission.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

39

(Stolte et al. 2012). The hemlock woolly adelgid 

continues to threaten eastern hemlock with needle 

loss, followed by branch dieback and eventual 

death (Jonas et al. 2012). Beech bark disease has 

resulted in mortality of beech trees across millions of 

acres in New England and the Mid-Atlantic region, 

and has yet to invade the bulk of the American 

beech range in the Midwest (Morin et al. 2007). 

Furthermore, where heavy mortality has resulted 

in newly opened gaps, beech sprouts and seedlings 

have often become dense to the detriment of advance 

regeneration of beech or other species (Giencke et 

al. 2014, Houston 1994). The emerald ash borer has 

caused mortality 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, 

and mortality is expected to increase as the beetle 

spreads (Kovacs et al. 2010, Morin et al. 2016). The 

Asian longhorned beetle was confirmed in central 

New Jersey and Long Island, and its potential to 

spread to adjacent states could result in mortality to 

many species including maples, buckeyes, birches, 

willows, and elms (State of New Jersey 2015, 

Townsend Peterson and Scachetti-Pereira 2004). 

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 transport of seed on vehicles or 

equipment, on the soles of shoes, in manure from 

domestic or wild animals, or via dissemination by 

wind and water. Major shipments from international 

origins are often the source of new exotic species, 

such as Amur honeysuckle and reed canarygrass. 

The Forest Service’s FIA program has monitored 

25 invasive species in the eastern United States since 

2007 (Fig. 10) (Kurtz 2013). The density of invasive 

species was found to be highest in the Piedmont 

stretching from western Maryland to eastern New 

York, where five to seven invasive species were 

found in most of the plots. Another study using 

FIA data found more species and abundance in 

Figure 10.—Abundance of 25 invasive plant species monitored by the USDA Forest Service Forest Inventory and Analysis 

program from 2005 through 2010 (Kurtz 2013). Plot locations are approximate. 
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

40

fragmented forest landscapes, including areas of the 

Mid-Atlantic region (Iannone et al. 2015). Glossy 

buckthorn, bush honeysuckles, autumn olive, crown 

vetch, Japanese knotweed, Japanese stiltgrass, garlic 

mustard, ailanthus, mile-a-minute, and multiflora 

rose are among the most problematic invasive 

species in the Mid-Atlantic region (Grafton 2003). 

In some cases, exotic species may be present, but 

limited by current climate conditions in the MidAtlantic region. For example, kudzu and other 

southerly invasive species are currently being 

limited by fall and winter minimum temperatures 

and have the potential to become more problematic 

as the climate warms (Bradley et al. 2010, Hoffberg 

and Mauricio 2016). Another study found that 

nonnative climbing vines on trees are increasing in 

abundance and number of species, and that vines 

are greatest in fragmented areas near forest edges 

(Matthews et al. 2016).

Fire on the Landscape

Fire regimes have shifted in the Mid-Atlantic 

region during the past several hundred years, and 

these shifts influence the composition of forest 

communities found here. Both natural and humancaused fire has been a driver of eastern forests for 

thousands of years, although the return interval, 

intensity, and extent are largely dependent on human 

activity, landscape position, and environmental 

factors (Abrams 1992, Nowacki and Abrams 2008, 

Thomas-Van Gundy and Nowacki 2013). For many 

fire-dependent communities in the eastern United 

States, there are few quantitative data describing 

historical fire regime attributes such as frequency, 

severity, and seasonality, or how these varied 

through time (Marschall et al. 2016). Studies in the 

Mid-Atlantic region are especially sparse compared 

to the eastern United States, but soil charcoal 

and fire scars in oak and pine forests indicate 

that fire return increased due to land clearing 

during European settlement, with the exact timing 

depending on the stand and location (Brose et al. 

2014). After the peak logging stopped in the early 

1900s, fire suppression efforts nearly eliminated the 

occurrence of fire, even in drought years (Brose et 

al. 2013). By the 1950s, fire exclusion across the 

Mid-Atlantic region 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, in the absence of fire (Brose et 

al. 2008). For example, forests dominated by mesic 

hardwoods on the Allegheny Plateau in Pennsylvania 

and New York are largely characterized by a 

low incidence of wildfires, and infrequent fuels 

management, compared to pine- and oak-dominated 

forests.

The historical role of fire in the development and 

maintenance of oak forests has been well established 

in the literature (Brose et al. 2014). Efforts to restore 

oaks by using prescribed fire have successfully 

promoted advance regeneration, but require other 

methods (e.g., thinning or herbicide) to promote 

growth into larger oaks and hickories, especially 

on mesic sites (Brose et al. 2012, Hutchinson et al. 

2012). 

Prescribed fire at the Buckaloons Recreation Area, Allegheny 

National Forest, Pennsylvania. Controlled fires are used to 

promote warm season grassland. Photo by Kathleen Creek, 

Allegheny National Forest.
CHAPTER 1: THE CONTEMPORARY LANDSCAPE

41

In comparison, pine-dominated and oak-pine forests 

throughout the Mid-Atlantic tend to be the most 

wildfire prone, and are typically the focus of fire 

management activities on the coastal plain. There 

is evidence that shifting fire regimes in the coastal 

plain were subject to similar drivers, but with overall 

shorter fire return intervals (Clark et al. 2013, 

2015). Fire-tolerant pines or oaks dominate the 

overstory and ericaceous shrubs are common in the 

understory. The dominant tree in the coastal plain is 

pitch pine. This species has fire-adapted traits such 

as serotinous cones, which require fire to open and 

release seeds. It also has the ability to produce new 

shoots from the trunk and branches after fire (Ledig 

and Little 1979). 

Forest-dependent Wildlife

The Mid-Atlantic region is remarkably diverse in 

both habitats and species. In the western part of 

the region are large, unbroken tracts of deciduous 

forests bisected by cold-water streams that are home 

to native brook trout. To the east are coastal forests 

such as the New Jersey Pine Barrens, home to the 

colorful Pine Barrens tree frog. Due to its central 

location on the Atlantic flyway, the entire MidAtlantic region is critically important for migratory 

songbirds, waterfowl, shorebirds, and raptors. Thus, 

the region is the focus of many connectivity and 

conservation projects. 

Perhaps no wildlife species has had a greater impact 

on the Mid-Atlantic forest than white-tailed deer 

(Rawinski 2008). Populations of this keystone 

species are relatively high today compared to the 

near-extirpation of deer during the early Europeansettlement period (Horsley et al. 2003). In some 

parts of the Mid-Atlantic region, native vegetation 

dynamics have been dramatically altered by deer, 

leading to a less diverse forest with limited tree 

regeneration and an understory dominated by 

deer-resistant species, including invasive species 

such as Japanese stiltgrass and garlic mustard 

(Rawinski 2008). Although the ecological impacts 

of deer are complex, deer overabundance has been 

most detrimental to forest health and sustainability 

(Rawinski 2008). 

Other species are suffering habitat loss in the 

Mid-Atlantic region and are listed among the 

many species of high conservation concern. For 

example, the golden-winged warbler is a Neotropical 

migrant that prefers early successional forests 

created by fire, timber harvesting, or reversion of 

abandoned farmland to forest. But the species has 

been declining due to habitat replacement by aging 

forests, hybridization, and other causes. From 1966 

through 2012, annual declines in golden-winged 

warbler populations ranged from 5.3 percent in New 

York to nearly 10 percent in New Jersey (Sauer et 

al. 2014). Although this species is often featured in 

efforts to publicize the need for early successional 

habitat, there are many other species that also 

depend on early successional habitat and face 

similar challenges as forests continue to age in large, 

continuous tracts.

CHAPTER SUMMARY

The Mid-Atlantic region of Pennsylvania, New 

York, New Jersey, Delaware, and Maryland 

supports a mosaic of forest ecosystems. These 

forests supply important benefits to the people of 

the area, including wood products and recreation 

opportunities. Changes in climate, weather extremes, 

and fire regime; habitat fragmentation; species 

invasions; insect pests and diseases; and other 

alterations to the landscape continually shape forest 

ecosystems. For this assessment, forest communities 

are broadly based on the Northeast Terrestrial 

Habitat Classification System but modified 

according to the ecological drivers and dominant 

species present within the Mid-Atlantic region. 

Ecosystem vulnerability for each of the 11 major 

forest communities that we defined is discussed in 

Chapter 6.
42

CHAPTER 2: CLIMATE CHANGE SCIENCE 

AND MODELING

This chapter provides a brief background on climate 

change science, climate simulation models, and 

models that project the impacts of changes in climate 

on tree species and ecosystems. Throughout the 

chapter, boxes list resources for more information 

on each topic. A more detailed scientific review of 

climate change science, trends, and modeling can 

be found in the Intergovernmental Panel on Climate 

Change (IPCC) Fifth Assessment Report (IPCC 

2014), and the Fourth National Climate Assessment 

(U.S. Global Climate Research Program [USGCRP] 

2017).

CLIMATE CHANGE

Climate is not the same thing as weather. Weather 

is a set of the meteorological conditions for a given 

point in time in one particular place (such as the 

temperature at 3:00 p.m. on May 1 in Annville, PA). 

Climate, in contrast, is the long-term average 

(30 years or more) 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. 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 (IPCC 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, 

and the Fifth Assessment Report repeated it. Current 

observations of higher global surface, air, and ocean 

temperatures and thousands of long-term (since 1950 

or earlier) datasets from all continents and oceans 

contributed to these conclusions. These datasets 

show 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. Selected 

global and national assessments are listed in Box 6. 

The Warming Trend

The Earth is warming, and the rate of warming 

is increasing (IPCC 2014). Measurements from 

weather stations across the globe indicate that 

warming of the global mean temperature is 

unprecedented since the 1950s, and that the period 

from 1983 through 2012 was the warmest 30-year 

period in 800 years for the Northern Hemisphere 

(IPCC 2014). In the contiguous United States, 2012 

ranked as the warmest year on record during the 

1985 to 2015 base period, and 2016 ranked second 

(National Oceanic and Atmospheric Administration 

[NOAA] 2017c). Temperatures in the United States 

have risen by an average of 0.5 °F (0.3 °C) per 

decade since the 1970s, a total of 2.3 °F (1.3 °C) 

between 1970 and 2016 (NOAA 2017c).

Average annual global temperature increases since 

1970 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 

since the 1950s has decreased over many regions 

of the world while the frequency of hot days, hot 

nights, heat waves, and heavy precipitation has 

increased (IPCC 2014). Global rises in sea level, 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

43

Box 6: Global and National Assessments

Intergovernmental Panel on Climate Change

The Intergovernmental Panel on Climate Change 

(IPCC; 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. 

These reports are available for download at the 

Web addresses that follow. 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 Program

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

https://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. 

Synthesis and Assessment Products

https://www.globalchange.gov/browse/reports 

decreasing extent of snow and ice, and shrinking of 

arctic ice sheets have all been observed during the 

past 50 years, and are consistent with a warming 

climate (IPCC 2014).

Average temperature increases of 2.3 °F may 

seem small, but even small increases can result in 

substantial changes in the severity of storms, the 

nature and timing of precipitation, droughts, heat 

waves, ocean temperature and volume, and snow 

and ice—all of which affect humans and ecosystems. 

Temperature increases above 3.6 °F (2.0 °C) are 

likely to cause major societal and environmental 

disruptions through the rest of the century and 

beyond (Richardson et al. 2009). 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: 6).

Based on available evidence, 97 percent of the 

climate science community attributes increases 

in temperature and changes in precipitation and 

extreme 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). 

The Paris Agreement was ratified in 2016 by parties 

of the United Nations Framework Convention on 

Climate Change with the goal of limiting global 

temperature rise in this century to 3.6 °F above 

preindustrial levels, and of striving further to limit 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

44

global temperature rise to 2.7 °F (1.5 °C) from 

preindustrial levels (United Nations 2016). 

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. 

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 greenhouse gases occurring naturally in the 

atmosphere contribute to the greenhouse effect; these 

include carbon dioxide, methane, nitrous oxide, 

and water vapor. Water vapor is the most abundant 

greenhouse gas; it resides in the atmosphere 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 long-term 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. More carbon dioxide has been 

released by humans into the atmosphere than any 

other greenhouse gas, and it is currently the largest 

contributor to the greenhouse gas effect (NOAA 

n.d.a). Global carbon dioxide levels increased 

at a rate of 3.3 parts per million (ppm) in 2016, 

surpassing 400 ppm in December 2015, the highest 

recorded values to date (Dlugokencky and Tans 

2016) (Fig. 11). By comparison, preindustrial carbon 

Figure 11.—Atmospheric carbon dioxide (CO2) at Mauna Loa Observatory in Hawaii (NOAA 2017b). These concentrations are 

similar to the global average. The full record of CO2 data (red curve), measured as the mole fraction in dry air, on Mauna Loa 

constitute the longest record of direct measurements of CO2 in the atmosphere. The black curve represents the seasonally 

corrected data.
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

45

dioxide levels were around 280 ppm (IPCC 2007). 

Fossil fuel combustion and industrial processes 

have accounted for an estimated 78 percent of the 

anthropogenic increase in carbon dioxide from 1970 

to 2010 (IPCC 2014). The remaining 22 percent of 

human-induced emissions comes primarily from 

deforestation of land for conversion to agriculture, 

which releases carbon dioxide when forests burn or 

decompose (IPCC 2014, van der Werf et al. 2009). 

However, increases in fossil fuel emissions during 

the past decade have reduced the contribution from 

land-use changes to the total carbon emissions 

(IPCC 2014, Le Quéré et al. 2009).

Methane accounted for roughly 11 percent of 

global greenhouse gas emissions in 2010 (IPCC 

2014). Concentrations of this gas have also been 

increasing as a result of human activities, including 

agricultural production of livestock and increases in 

rice production (IPCC 2013). 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 production, and leakage of natural gas 

during extraction and distribution. 

Nitrous oxide accounts for about 6 percent of global 

greenhouse gas emissions (IPCC 2014). The primary 

human source of nitrous oxide is agriculture. The 

use of fertilizer causes emissions from soil as 

microbes break down nitrogen-containing products. 

This is especially dramatic in areas where tropical 

forests were converted to agricultural lands, because 

tropical areas have high rates of biological turnover 

and decomposition due to warmer, wetter conditions 

(Meurer et al. 2016). Other human-caused sources 

of nitrous oxide include nylon production and 

combustion of fossil fuels.

Humans have also reduced stratospheric 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 applications. 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 2 percent of greenhouse 

gas emissions (IPCC 2014).

CLIMATE MODELS

Scientists use models, which are simplified 

representations of reality, to simulate future climates. 

Models can be theoretical, mathematical, conceptual, 

or physical. General circulation models (GCMs, 

also called global climate models) 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 

conditions, which are in turn used as inputs to forest 

impact models.

General Circulation Models

General circulation models simulate physical 

processes through time at the Earth’s surface, 

in the oceans, and in the atmosphere by using 

mathematical equations in three-dimensional space. 

They can work in timesteps 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, requirements on computing power mean 

that projections are limited to relatively coarse 

spatial scales. Instead of simulating climate for 

every single point on Earth, modelers divide the 

land surface, ocean, and atmosphere into a three-
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

46

dimensional grid (Fig. 12). Each cell within the grid 

is treated as an individual unit, and is able to interact 

with adjacent cells. Although each GCM is slightly 

different, the size of each cell in the grid is usually 

between 2 and 3° latitude and longitude, which is 

about the size of Pennsylvania. 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. 

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 7). 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 

Figure 12.—Schematic describing climate models, which are systems of differential equations based on the fundamental laws 

of physics, fluid motion, and chemistry (NOAA 2017b). 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.

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 

may 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 8). In general, they are useful 

and reliable tools because they are based on wellunderstood physical processes and have been 

selected in part for 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 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

47

Box 7: More Resources on Climate Models and Emissions Scenarios

USDA Forest Service

Climate Projections FAQ

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

Climate Data

https://www.fs.usda.gov/ccrc/library/climate-data 

U.S. Global Change Research Program

Climate Models: an Assessment of Strengths and 

Limitations

https://www.globalchange.gov/browse/reports/

sap-31-climate-models-assessment-strengths-andlimitations 

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

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

php?idp=0

Box 8: Models Limitations and Uncertainty

“Essentially, 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 intent is to provide the best 

available scientific information to 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 general circulation models 

(GCMs) and forest impact models, future greenhouse 

gas concentrations, and the grid scale and number of 

inputs that a model can reliably handle. At the global 

scale, precipitation projections usually have much 

more variability among GCMs than temperature, 

and this variability is present in downscaled 

projections. Complex topography and elevational 

gradients can support a diversity in microclimates 

that many models cannot capture. Therefore, model 

results are interpreted 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. 

Models can be useful, but they are inherently 

incomplete. 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 

component of the Climate Change Tree Atlas 

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 by 

species. 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. 

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 Mid-Atlantic forest ecosystems are 

discussed in more detail in Chapter 5.
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

48

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 may 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, such as 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 Special 

Report on Emission Scenarios (SRES) created six 

standard emissions scenarios that have served as a 

widely accepted set of such storylines for the third 

and fourth IPCC global climate assessments (Fig. 13) 

(Burkett et al. 2014, IPCC 2000). The IPCC’s fifth 

assessment uses a new and different set of 

Figure 13.—(a) Projected radiative forcing (RF,W m–2) and 

(b) global mean surface temperature change (°C) over 

the 21st century using the Special Report on Emissions 

Scenarios (SRES) and Representative Concentration 

Pathway (RCP) scenarios. RF for the RCPs are taken from 

their published CO2-equivalent (Meinshausen et al., 2011), 

and RF for SRES are from the Third Assessment Report 

Appendix II (Table II.3.11). For RF derived from the Coupled 

Model Intercomparison Project Phase 5 (CMIP5) models, 

see WGI (Section 12.3; Tables AII.6.9, 6.10). The ensemble 

total effective RF at 2100 for CMIP5 concentration-driven 

projections are 2.2, 3.8, 4.8, and 7.6 W m–2 for RCP2.6, 

RCP4.5, RCP6.0, and RCP8.5, respectively. The SRES RF are 

shifted upward by 0.12 W m–2 to match the RCPs at year 

2000 because the climate change over the 21st century 

is driven primarily by the changes in RF and the offset is 

due primarily to improvements in model physics including 

the aerosol RF. For more details and comparison with 

pre-SRES scenarios, see WGI AR5 Chapter 1 (Figure 1-15). 

Temperature changes are decadal averages (e.g., 2020s = 

2016–2025) based on the model ensemble mean CMIP5 

data for the RCPs (colored lines). The same analysis is 

applied to CMIP3 SRES A1B (yellow circles). See WGI AR5 

Chapters 11, 12; Table AII.7.5. The colored squares show 

the temperature change for all six SRES scenarios based on 

a simple climate model tuned to the CMIP3 models (WGI 

AR4 Figure 10.26). The difference between the yellow circles 

and yellow squares reflects differences between the simple 

model and analysis of the CMIP3 model ensemble in parallel 

with the CMIP5 data. Figure courtesy of Burkett et al. 

(2014: 179); caption reused intact as requested (Burkett 

et al. 2014).
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

49

storylines called Representative Concentration 

Pathways (RCPs), which are largely consistent with 

these and other scenarios in the literature, such as 

the Shared Socio-Economic Pathways (Fig. 13) 

(IPCC 2014, Knutti and Sedláček 2013, Kriegler 

et al. 2017, van Vuuren et al. 2011). Notably, they 

differ from the SRES scenarios in that they are not 

emissions scenarios; rather they are radiative forcing 

scenarios. The Fourth National Climate Assessment 

also uses RCPs (Fig. 13) (USGCRP 2017). The 

A1FI scenario is roughly comparable to the RCP 8.5 

emissions storyline from the fifth IPCC assessment, 

which represents the upper range of potential 

emissions; the B1 scenario is roughly comparable 

to the RCP 4.5 storyline, which represents a 

commitment to sustainability and conservation 

(Kriegler et al. 2017, Meinshausen et al. 2011, 

USGCRP 2017).

The use of different emissions scenarios in GCMs 

results in different projections of climate, depending 

on the model and scenario combination. The A1FI 

scenario, which is used in this assessment, is the 

most fossil-fuel intensive, and thus projects the 

highest future greenhouse gas concentrations. On the 

opposite end of the spectrum is the B1 scenario, the 

other scenario used in this assessment. It represents 

a future where the use of alternative energies 

decreases our reliance on fossil fuels and greenhouse 

gas concentrations increase the least. 

Although these scenarios were designed to describe 

a range of future emissions during the coming 

decades, it is important to note that global emissions 

in the future are likely to differ from the developed 

scenarios, whether SRESs or RCPs. Emissions 

scenarios quantify the effects of alternative 

demographic, technological, or environmental 

developments on atmospheric greenhouse gas 

concentrations. None of the current scenarios in 

this assessment includes any changes in national or 

international policies, such as the Kyoto Protocol or 

the Paris Agreement, directed specifically at climate 

change. However, some of the scenarios that include 

a reduction in greenhouse gases through other 

means, such as advances in technology, demonstrate 

the possible effects of reduced emissions. 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, global emissions are currently 

near the top end of the original SRES scenarios, 

more closely tracking the greenhouse gas emissions 

of the A1FI scenario (Le Quéré et al. 2009, Raupach 

et al. 2007).

Downscaling

As mentioned previously, GCMs simulate climate 

conditions for relatively large areas on a relatively 

coarse scale. To better examine the future climate 

of areas within the Mid-Atlantic region, a smaller 

grid scale is needed. One method of improving 

the resolution uses statistical downscaling, a 

technique that establishes statistical relationships 

between GCM model outputs and on-the-ground 

measurements (Hayhoe et al. 2007, Stoner et al. 

2012a). First, a statistical relationship is developed 

between GCM output for a past “training period,” 

and observed climate variables of interest (e.g., 

temperature, 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 grid resolution for the downscaled 

climate projections can range anywhere from 50 km 

(i.e, each cell represents 31 square miles) to 800 m 

(a cell represents 0.5 square mile). 

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 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

50

climate will remain consistent under future change. 

Another limitation is that downscaling depends 

on local climatological data. If there are too few 

weather stations in the area of interest, estimates 

of future climate may reflect some weather station 

bias 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 to simulate physical processes through 

mathematical representations on a grid (Daniels et 

al. 2012; Jones et al. 1995, 1997). RCMs operate 

on a finer resolution than GCMs, typically ranging 

from 15.5 to 31.0 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. Another approach, probabilistic 

downscaling, uses a high-resolution grid to predict 

the time-varying probability density function for 

each point in the grid (Nelson Institute Center for 

Climatic Research 2018). Thus, the probability 

method can provide more realistic projections of 

climate extremes. Because of limitations with these 

other approaches at the start of this assessment, we 

use statistically downscaled data in this report. 

Downscaled General Circulation Models 

Used in this Assessment 

We report statistically downscaled climate 

projections for two model-emissions 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 Third National Climate 

Assessment (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 (Fig. 13). The IPCC Assessment includes 

several other models, which are represented as 

a multi-model average in its reports. 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 2014). This 

approach attempts to give readers 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. We note, 

however, that the two models selected here represent 

the range of possible futures in terms of average 

annual and seasonal temperature and precipitation 

trends. These models do not necessarily represent 

the bracketed range in terms of other metrics such 

as daily maximums and minimums, or extremes. 

Therefore, readers should exercise caution when 

interpreting trends.

The GFDL general circulation model developed by 

NOAA is considered moderately sensitive to changes 

in greenhouse gas concentrations (Delworth et al. 

2006). In other words, an increase in greenhouse gas 

concentrations in GFDL would lead to a projected 

change in temperature that is greater compared 

to less-sensitive models and smaller than moresensitive models. The A1FI scenario is the highest 

greenhouse gas emissions scenario used in the 2007 

IPCC assessment (IPCC 2000). Therefore, the GFDL 

A1FI scenario represents a higher-end projection 

for future temperature increases. The PCM model, 

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 lower than the 

likely trajectory for greenhouse gas emissions for the 

coming decades (Washington et al. 2000). 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 futures. Although both projections 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

51

are possible, carbon emissions over the past 15 to 

20 years have been more consistent with the A1FI 

scenario (Raupach et al. 2007, USGCRP 2017). 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 2013). 

Climate projections for this assessment were 

statistically downscaled by using an asynchronous 

regional regression model (Hayhoe 2014, Stoner et 

al. 2012a). Daily mean, minimum, and maximum 

temperature and total daily precipitation were 

downscaled to an approximately 7.5-mile resolution 

grid across the United States. Asynchronous quantile 

regression used historical gridded meteorological 

data from 1960 through 1999 at 1/8o resolution 

(6.2 to 9.3 miles, depending on 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 2010).

These climate projections (GFDL A1FI and 

PCM B1) were chosen for several reasons. First, 

they cover the entire United States, and thus a 

consistent data set can be used in this and other 

regional vulnerability assessments being conducted 

simultaneously. Second, they included downscaled 

projections for the A1FI emissions scenario, which 

tracks more closely with recent trends (last two 

decades) in global greenhouse gas emissions (Peters 

et al. 2012, Raupach et al. 2007, USGCRP 2017). 

Third, the availability of data at daily timesteps 

was advantageous because it was needed for some 

impact models used in this report. Fourth, the 

quantile regression method is more accurate at 

reproducing extreme values at daily timesteps than 

simpler statistical downscaling methods (Hayhoe 

2010). Finally, the 7.5-mile grid scale resolution was 

considered useful for informing land management 

decisions. 

To show projected changes in temperature and 

precipitation, we calculated the average daily mean, 

minimum, and maximum temperature 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 were taken from Climate Wizard 

(Girvetz et al. 2009). Chapter 3 includes more 

information about the observed climate data from 

Climate Wizard. Summarized projected climate data 

are shown in Chapter 4.

The downscaled future climate projections were also 

used in each of the forest impact models described 

next. 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. 

FOREST IMPACT MODELS 

Downscaled climate projections from GCMs 

provide important information about future climate. 

Although some downscaled climate models 

attempt to simulate soil moisture, hydrology, forest 

composition, productivity, or interactions between 

these factors, they generally do not perform as 

well as impact models that have been designed 

specifically to simulate these processes (Fig. 14). 

Impact models use downscaled GCM projections 

as inputs, as well as information about tree species 

and soil types. Several different models are used to 

simulate impacts on species and forest ecosystems. 

These models generally fall into one of two main 

categories: species distribution models (SDMs) and 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

52

Figure 14.—Steps in the development of forest impact models using projections from general circulation models (GCMs) and 

specific steps taken in this assessment.

process models. In this assessment, we used one 

species distribution model, the Climate Change Tree 

Atlas (USDA Forest Service n.d.a), and two process 

models, LINKAGES, version 3.0 (Dijak et al. 

2016) and LANDIS PRO (Wang et al. 2013, 2015). 

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 (Brandt et al. 2017, 

Dijak et al. 2016, Iverson et al. 2016).

Models for Assessing Forest Change

Species distribution models establish a statistical 

relationship between the current distribution of 

a tree species and key attributes of its habitat. 

This relationship is used to predict changes in the 

spatial distribution of suitable habitat as climate 

change affects those attributes. Species distribution 

models, such as the DISTRIB component of 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 to 

identify the current suitable habitat. 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 may 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 may not actually be able to take 

advantage of new suitable habitat in the future, even 

if an SDM like the Tree Atlas projects it may gain 

suitable habitat. Additionally, the Tree Atlas does 

not suggest that existing trees will die if suitable 

habitat is reduced in a particular area. Rather, this is 

an indication that they may be living farther outside 

their ideal habitat and may be exposed to more 

climate-related stress. 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

53

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. 

LANDIS PRO can simulate change in tree species 

dispersal, succession, and biomass 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 by these 

two models compared to SDMs. Process models 

also have several assumptions and uncertainties 

that should be considered when results are applied 

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 

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 may be overlooked for individual models. 

In this assessment, competition from understory 

vegetation, herbivory, and pest outbreaks are a few 

of the processes not included in the impact models. 

View of a northern hardwoods forest at Hawk Mountain in southeastern Pennyslvania. Photo by Greg Czarnecki, Pennsylvania 

Department of Conservation and Natural Resources, used with permission.
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

54

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 6 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 during the 21st century (USDA Forest 

Service n.d.a). The species distribution model 

DISTRIB measures relative abundance, referred 

to as the “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 (USDA 

Forest Service n.d.a). 

The DISTRIB model uses inputs of tree abundance, 

climate, and environmental attributes to simulate 

current and future species habitat. Current tree 

abundance is estimated from the Forest Service’s 

Forest Inventory and Analysis (FIA) data plots 

(Miles et al. 2006). Future climates are simulated 

from downscaled climate data created by the 

Climate Science Center at Texas Tech University 

(Hayhoe 2014) 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). Thirty-eight predictor variables 

describe 4 land uses, percent fragmentation, 

7 climate variables, 5 elevation variables, 9 soil 

classes, and 12 soil properties obtained from various 

agencies and data clearinghouses (Table 13) (Iverson 

et al. 2008a, Riitters et al. 2002). The reliability 

of individual species models is evaluated through 

the calculation of a model reliability score, which 

is based on statistically quantified measures of 

fitness (Matthews et al. 2011). The strengths and 

limitations of the Tree Atlas should be considered 

when results are being interpreted. Importantly, 

DISTRIB projects where the habitat suitability may 

change for a particular species, but does not project 

where the species may actually occur at a given 

future time. The actual rate of migration into the 

new suitable habitat may be influenced by large time 

lags, dispersal and establishment limitations, and 

availability of refugia. Each tree species is further 

evaluated for additional factors not accounted for in 

the statistical models (Matthews et al. 2011). These 

modifying factors (Appendix 4) are supplementary 

information on life-history characteristics such 

as dispersal ability or 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 situations where an individual 

species may do better or worse than DISTRIB model 

projections suggest.

The FIA data plots are nonbiased and extensive 

across the assessment area, but are spatially sparse 

at the standard 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. 

Results from the DISTRIB model are provided in 

Chapter 5. They are also available from the online 

Climate Change Tree Atlas (https://www.fs.fed.

us/nrs/atlas/products).
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

55

Climate (°C, mm)

• Mean annual temperature • Annual precipitation

• Mean January temperature • Mean May through September 

 precipitation 

• Mean July temperature • Mean difference between July 

 and January temperature

• Mean May through September temperature

Elevation (m)

• Elevation coefficient of variation • Average elevation 

• Minimum elevation • Range of elevation

• Maximum elevation

Soil class (%)

• Alfisol • Mollisol 

• Aridisol • Spodosol

• Entisol • Ultisol

• Histosol • Vertisol

• Inceptisol

Soil property

• Soil bulk density (g/cm3) • Soil pH

• Potential soil productivity (m3/ha timber) • Depth to bedrock (cm)

• Percent clay (<0.002 mm size) • Percent soil passing sieve no. 200 (fine)

• Soil erodibility factor • Soil slope (%) of a soil component

• Soil permeability rate (cm/h) • Organic matter content (% by weight)

• Percent soil passing sieve no. 10 (coarse) • Total available water capacity (cm)

Land use and fragmentation (%)

• Cropland • Water

• Nonforest land • Fragmentation index (Riitters et al. 2002)

• Forest land 

Table 13.—Parameters used to predict current and future tree species habitat with the DISTRIB model (Iverson et al. 

2008a)

LINKAGES 

The LINKAGES model, version 3.0 (Dijak et al. 

2016) is an ecosystem dynamics process model 

modified from earlier versions of LINKAGES 

(Pastor and Post 1985, Wullschleger et al. 2003). 

LINKAGES can model forest succession when 

initialized with tree plot data. But as used here, it 

is initialized from bare ground so that it models 

tree establishment and growth of individual 

tree species from 0 to 30 years. It also models 

ecosystem functions such as soil-water balance, litter 

decomposition, nitrogen cycling, soil hydrology, and 

evapotranspiration. Inputs to the model are climate 

variables (e.g., daily temperature, precipitation, 

wind speed, and solar radiation), soil characteristics 

(e.g., soil moisture capacity and rock, sand, and 

clay percentages 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 14. Outputs 

from the model include number of stems, biomass, 

leaf litter, available nitrogen, humus, and organic 

matter, as well as hydrologic variables such as 

runoff. LINKAGES projections, like Tree Atlas 

projections, estimate the unconstrained response in 

potential fundamental niche, or the habitat a species 

could occupy, to climate change. LANDIS PRO 

utilizes this fundamental niche information provided 

by LINKAGES and constrains each species’ 

distribution through tree competition into the 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

56

realized niche (the habitat that a species may actually 

occupy). Unlike the LANDIS PRO model (described 

next), LINKAGES is not spatially dynamic, and 

does not simulate tree dispersal or any other spatial 

interaction among grid cells. Simulations are done at 

yearly timesteps 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 across the landscape over time. 

For this assessment, LINKAGES simulates changes 

in tree species establishment probability during the 

21st century for 24 common tree species within the 

Mid-Atlantic region. The model projects changes 

in tree species distributions by using downscaled 

daily mean temperature and precipitation under 

GFDL A1FI and PCM B1 for the end of the century 

Location • Latitude, longitude

Climate (daily)

• Total daily precipitation • Daily total solar radiation

• Daily minimum temperature • Mean monthly wind speed

• Daily maximum temperature

Soil 

• Field capacity for 12 soil layers • Organic matter (Mg/ha)

• Wilting point for 12 soil layers • Nitrogen (Mg/ha)

• Hydrological coefficients for 12 soil layers (based on percent sand

and clay)

• Percent rock for 12 soil layers

Tree species

• Total annual degree day maximum and minimum (Virginia Tech

and USDA Forest Service 2018)

• Mineral or organic seedbed

• Height and diameter growth equation coefficients (Miles et al. 2006) • Maximum seeding rate

• Typical maximum mortality age (Loehle 1988) • Crown area coefficients

• Frost tolerance (Virginia Tech and USDA Forest Service 2018) • Root:shoot ratio by species

• Shade tolerance • Leaf litter quality class

• Drought tolerance • Foliage retention time

• Nitrogen equation coefficients (Natural Resources Conservation Service

2014, Post and Pastor 1996)

• Leaf weight per unit crown area

• Sprout stump number and minimum and maximum diameter

a From Post and Pastor (1996) unless noted otherwise.

Table 14.—Parameters used in the LINKAGES modela

(2070 to 2099), and compares these projections with 

those under a current climate scenario (i.e., the 

climate during 1980 through 2009) at the end of the 

century. One hundred and fifty-six 0.2-acre virtual 

plots were located at the geographic center of a 

subsection and parameterized in LINKAGES; this 

number represents 1 plot for each of 6 landforms in 

26 ecological subsections. Ecological processes are 

modeled stochastically, so each of the 156 plots was 

replicated 30 times and results were averaged. 

Section-level estimates were derived from the 

area-weighted average of landforms in a subsection 

and the weighted average of subsections in a 

section. Therefore, some heterogeneity in species 

establishment and growth can be masked by this 

averaging because the results represent the average 

of the entire subsection.
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

57

LANDIS PRO

The LANDIS PRO model (Wang et al. 2014) is 

a spatially dynamic process model that simulates 

species-, stand-, and landscape-level processes. It is 

derived from the LANDIS model (Mladenoff 2004), 

but has been modified extensively from its original 

version. The LANDIS PRO model can simulate very 

large landscapes (millions of acres) at relatively 

fine spatial and temporal resolutions (typically 200 

to 300 feet at 1- to 10-year timesteps). One new 

feature of the LANDIS PRO 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 inherent in the initial data (Wang et al. 

2014). 

Basic inputs to the LANDIS PRO model include 

maps of species composition, land types, stands, 

management areas, and disturbance areas (Table 15). 

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 forest size class map, the National Land 

Cover Dataset (MRLC 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. 15).

The model simulates processes at three levels: the 

species, stand, and landscape. At the species level, 

LANDIS PRO simulates seedling germination and 

establishment, growth, vegetative reproduction, and 

tree mortality. At the stand level, the model simulates 

competition and succession. At the landscape level, 

the model is capable of simulating disturbances (e.g., 

fire, wind, and disease), harvesting, and silviculture 

treatments. However, only the harvest levels were 

a component of simulations in this assessment. The 

LANDIS PRO model stratifies the landscape into 

land types based on topography, climate, soil, and 

other environmental characteristics. Within a land 

Initial forest conditions

• Land type map

• Species map (imputed from FIA)

Species biological traits

• Reproductive age • Maximum stand density index (SDI)

• Longevity age • Maximum d.b.h.

• Maximum seed dispersal distance • Maximum number of germinating seeds

• Seed dispersal shape • Species growth rate

• Shade tolerance • Species stump sprout age (if applicable)

Species physiological response 

to climate changea • Species establishment probability by land type • Maximum growing space capacity by land type

Harvest

• Harvest method • Desired postharvest basal area

• Percentage of management unit to harvest • Rule for ranking harvest priority

• Minimum basal area to initiate harvest • Species priority ranking for harvest

• Management unit map • Stand map

a From the LINKAGES model

Table 15.—Parameters used in the LANDIS PRO model
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

58

Figure 15.—Example landform map used in landscape initialization in the LANDIS PRO model (Dijak et al. 2016).

type, species establishment and resource availability 

are assumed to be similar. Combined with 

anthropogenic and natural disturbances, these land 

type-specific processes allow the model to simulate 

landscape heterogeneity over time and space.

Basic outputs in LANDIS PRO for a species or 

species cohort include aboveground biomass, age, 

and distribution. Disturbance and harvest history 

can also be summarized. The spatially dynamic 

nature of the model and its fine spatial resolution 

are unique advantages of LANDIS PRO compared 

to LINKAGES 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-foot 

grid cells during the 21st century for 20 dominant 

tree species across the Mid-Atlantic region. 

Species establishment probabilities generated by 

LINKAGES are then used by LANDIS PRO to 

incorporate climate change effects into the LANDIS 

PRO forest landscape simulation. 

There are important strengths and limitations to 

LANDIS PRO that should be considered when 

results are being interpreted. This model assumes 
CHAPTER 2: CLIMATE CHANGE SCIENCE AND MODELING

59

that historical successional dynamics are held 

constant into the future. It is also assumed that the 

resource availability by land type accurately reflects 

the effects of landscape heterogeneity at 866-foot 

resolution. Additionally, only harvest was included 

in simulations for this assessment; fire, wind, insect 

outbreaks, disease, invasive species, and fuels 

treatments were not included but are important 

landscape processes.

CHAPTER SUMMARY

Temperatures have been increasing across the 

world in recent decades largely due to increases 

in greenhouse gases from human activities. Even 

if dramatic changes are made to help curtail 

greenhouse gas emissions, the existing greenhouse 

gases are expected to 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 to inform local decisions. 

Although there are inherent uncertainties in what the 

future holds, all of these types of models can help 

frame a range of possible futures. This information 

can be most useful in combination with the local 

expertise of researchers and managers to provide 

important insights about the potential effects of 

climate change on forest ecosystems.

Red maple-hardwood swamp at West Mountain near the Appalachian Trail. Photo by Aissa L. Feldmann, New York Natural 

Heritage Program, used with permission.
60

CHAPTER 3: OBSERVED CLIMATE CHANGE

As discussed in Chapter 1, climate is one of 

the principal factors that have determined the 

composition and extent of forest ecosystems in the 

Mid-Atlantic region during the past several thousand 

years. This chapter describes the climate trends in 

the assessment area that have been observed during 

the past century, including documented patterns 

of climate-related processes and extreme weather 

events. It also presents evidence that ecosystems 

in the Mid-Atlantic region are already exhibiting 

signals that they are responding to shifts in 

temperature and precipitation. 

OBSERVED TRENDS 

IN TEMPERATURE 

AND PRECIPITATION 

The Mid-Atlantic region has experienced changes 

in temperature and precipitation patterns from 1901 

through 2011, and the rate of change appears to be 

increasing. Long-term trends in annual, seasonal, 

and monthly temperature (mean, minimum, and 

maximum) and total precipitation over these 111 

years were examined for the assessment area by 

using the Climate Wizard Custom tool (Box 9 and 

Appendix 2). Observed changes in other ecological 

indicators (e.g., streamflow and flooding) are 

often described on a statewide basis because finer 

resolution data were not available, unless otherwise 

indicated.

Temperature 

Substantial changes in temperature have occurred 

throughout the northeastern United States during 

the past 100 years (Kunkel et al. 2013b). Although 

the annual mean (average) temperature varies from 

year to year, there is a long-term warming trend 

(Fig. 16) that is consistent with changes at the state, 

continental, and global scales (Intergovernmental 

Panel on Climate Change [IPCC] 2014, U.S. Global 

Change Research Program [USGCRP] 2017). The 

trend for the 111-year period shows the Mid-Atlantic 

region has warmed at a rate of 0.016 °F (0.009 °C) 

per year, or 1.8 °F (1.0 °C) during the entire record. 

The coolest average temperatures on record were 

observed in the early part of the century, warmer 

temperatures followed in the 1940s and 1950s, and 

a cold period characterized the 1960s and 1970s. 

The transition into the 21st century is punctuated 

by the warmest year on record (2012) and a series 

of warmer-than-average temperatures (National 

Oceanic and Atmospheric Administration [NOAA] 

2018).

Wetland in Lebanon County, Pennsylvania. Photo by 

Greg Czarnecki, Pennsylvania Department of Conservation 

and Natural Resources, used with permission.
CHAPTER 3: OBSERVED CLIMATE CHANGE

61

Box 9: Where Are these Data from?

Weather stations in the region have recorded 

measurements of temperature and precipitation 

for more than 100 years, providing a rich set of 

information to evaluate changes in climate over time. 

The Climate Wizard Custom Analysis Application was 

used to estimate the changes in temperature and 

precipitation across the assessment area (Climate 

Wizard 2014). This tool uses high-quality data 

from PRISM (Parameter-elevation Regressions on 

Independent Slopes Model), which converts monthly 

measured point data from about 8,000 weather 

stations onto a continuous 2.5-mile grid over the 

entire United States (Gibson et al. 2002, Karl et al. 

1996). Temperature and precipitation data for the 

assessment area were used to derive long-term 

trends in annual, seasonal, and monthly values for 

the period 1901 to 2011. Additional details about 

the data presented in this chapter are available in 

Appendix 2.

Accompanying tables and figures present the change 

over the 111-year period estimated from the slope of 

the linear trend. In the following text, trends which 

have moderate to high probability (p < 0.10) that 

they did not occur by chance are highlighted over 

less certain trends (for p-values, see Appendix 2). We 

also evaluated trends beginning in 1970, but did not 

find changes in the sign (i.e., positive or negative) 

of these trends (data not shown). However, the rate 

of warming has increased dramatically since the 

1970s (Tebaldi et al. 2012), roughly three times faster 

than the 20th-century trend (Climate Wizard 2014, 

Ellwood et al. 2013).

Gridded historical climate products like the PRISMbased data used in this assessment can be helpful 

for understanding recent climatic changes at regional 

scales to support decisionmaking, but there are also 

some caveats that limit the ways that they should 

be used (Beier et al. 2012, Bishop and Beier 2013). 

One major challenge is that data are interpolated 

(spatially estimated) in the areas between existing 

weather stations, which increases the uncertainty of 

the values in areas that have few weather stations. 

Additionally, the statistical methods used to develop 

these products are less robust at high elevations 

and in coastal areas and potentially overestimate or 

underestimate the change occurring in a particular 

location (Beier et al. 2012, Bishop and Beier 2013). 

These limitations suggest that maps are best 

used to understand the overall trends that have 

been observed across the region (and which are 

supported by multiple lines of evidence) and are less 

appropriate for evaluating the amount of change in a 

specific location. 

Figure 16.—Annual mean temperature in the assessment area, 1901 through 2011. The blue line represents the rolling 5-year 

mean. The red regression line shows the trend across the entire time period (a rate of increase of 0.016 °F or 0.01 °C per 

year). Data source: Climate Wizard (2014).
CHAPTER 3: OBSERVED CLIMATE CHANGE

62

Both temperature minimums (lows) and maximums 

(highs) have increased. Minimum temperatures 

have increased more than maximum temperatures in 

every season except spring (Fig. 17, Table 16). The 

Month or season

Mean 

temperature 

(°F)a

Mean 

temperature 

change (°F)

Mean minimum 

(°F)

Mean minimum 

change (°F)

Mean maximum 

(°F)

Mean maximum 

change (°F)

January 26.5 -0.1 17.8 0.5 35.1 -0.6

February 27.4 4.1 18.0 4.2 36.8 4.0

March 36.4 1.3 26.3 1.1 46.4 1.6

April 47.4 2.8 36.1 2.3 58.7 3.4

May 57.9 1.1 46.0 1.3 69.8 0.8

June 66.6 1.8 55.0 2.5 78.1 1.2

July 71.0 0.9 59.6 1.6 82.4 0.3

August 69.3 2.3 58.1 2.9 80.6 1.7

September 62.7 0.5 51.4 1.3 73.9 -0.2

October 51.7 0.0 40.6 0.6 62.8 -0.6

November 40.9 3.2 31.8 3.3 50.0 3.1

December 30.2 3.1 22.1 3.3 38.3 2.9

Winter 28.0 2.4 19.3 2.6 36.7 2.2

Spring 47.2 1.8 36.1 1.6 58.3 1.9

Summer 69.0 1.7 57.6 2.3 80.4 1.0

Fall 51.8 1.3 41.3 1.7 62.2 0.8

Annual 49.0 1.8 38.6 2.1 59.4 1.5

a

 Values in boldface indicate less than 10-percent probability that the trend was due to chance alone.

Table 16.— Mean annual, seasonal, and monthly temperatures, and changea, in the assessment area, 1901 through 

2011 (data source: Climate Wizard [2014])

Figure 17.—Change in monthly mean, minimum, and maximum temperatures in the assessment area, 1901 through 2011. 

Asterisks indicate there is less than 10-percent probability that the trend could have occurred by chance alone. Data source: 

Climate Wizard (2014).

-0.6

0.6

1.8

3

4.2

Change in Temperature (°F)

Mean

Minimum

Maximum

*

*

* *

*

*

*

*

*

*

*

* * * *

*

*

* * * *
CHAPTER 3: OBSERVED CLIMATE CHANGE

63

greatest change in temperature has occurred during 

the winter, with an increase in minimum temperature 

of 2.6 °F (1.5 °C) and in maximum temperature 

of 2.2 °F (1.2 °C) (Table 16). Mean, minimum, 

and maximum temperatures have all increased the 

most in February, each by more than 4 °F (2.2 °C). 

November and December minimum and maximum 

temperatures also warmed considerably, whereas 

January temperatures changed very little. February, 

April, August, November, and December are notable 

months because mean, minimum, and maximum 

temperatures all increased significantly in those 

months.

Within the assessment area, there are local 

differences in the magnitude and direction of 

observed temperature changes (Fig. 18). Mean 

temperatures have warmed more along the 

Figure 18.—Change in annual and seasonal mean, minimum, and maximum temperature (°F) in the assessment area, 1901 

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

Data source: Climate Wizard (2014).
CHAPTER 3: OBSERVED CLIMATE CHANGE

64

Atlantic coast than in areas inland to the north 

and west. For example, New Jersey and Long 

Island winter temperatures have warmed by up 

to 6.0 °F (3.3 °C) while relatively little change 

has occurred in southwestern Pennsylvania. 

Summer and fall temperatures indicate warmer 

maximum temperatures for the coastal section, 

and significant cooling near Lake Erie. Notable 

increases or decreases in mapped data should be 

regarded with caution because of the potential for 

localized anomalies or errors inherent in a particular 

observational data product, such as the PRISM data 

presented here (Box 9) (Beier et al. 2012).

The general trend toward increasing temperatures 

in the Mid-Atlantic region is similar to observations 

that have been reported elsewhere. The mean surface 

air temperature across the globe increased 1.5 °F 

(0.85 °C) during the last century (IPCC 2013). 

Average temperatures across the United States 

warmed by 1.8 °F (1 °C) since 1895, with rapid 

warming occurring since 1979 (USGCRP 2017). The 

rate of warming has increased and temperatures in 

the United States have risen by an average of 0.5 °F 

(0.3 °C) per decade since the 1970s, a total of 2.3 °F 

(1.3 °C) above the 20th-century average during the 

1970-2016 period (NOAA 2018). 

Precipitation

From 1901 to 2011, mean annual precipitation 

increased by 4.5 inches, or about 10 percent across 

the assessment area (Table 17) (Climate Wizard 

2014). The time series of annual precipitation for the 

assessment area displays a consistent upward trend 

despite a high degree of year-to-year variability 

(Fig. 19). During the entire record, there are six 

years with greater than 50 inches of precipitation, 

and five of them occurred since 1971. The three 

wettest years on record occurred during the past 

10 years. 

Figure 19.—Annual precipitation in the assessment area, 

1901 through 2011. The blue line represents the rolling 

5-year mean. The red regression line shows the trend across 

the entire time period (a rate of increase of 0.04 inch per 

year). Data source: Climate Wizard (2014).

Month or season

Mean 

precipitation 

(inches)

Mean 

precipitation 

change (inches)

January 3.0 -0.1

February 2.6 -0.1

March 3.4 0.3

April 3.6 0.2

May 3.8 0.6

June 4.0 0.2

July 4.1 -0.1

August 4.0 -0.1

September 3.7 1.3

October 3.3 0.6

November 3.2 1.3

December 3.2 0.3

Winter 8.8 0.1

Spring 10.7 1.1

Summer 12.1 0.0

Fall 10.3 3.2

Annual 41.9 4.5

a Values in boldface indicate there is less than 10-percent probability 

that the trend was due to chance alone.

Table 17.—Mean annual, seasonal, and monthly 

precipitation, and changea, in the assessment area, 1901 

through 2011 (data source: Climate Wizard [2014])
CHAPTER 3: OBSERVED CLIMATE CHANGE

65

The apparent trend in the assessment area is that 

fall has been getting wetter, whereas winter, spring, 

and summer are changing too little to establish a 

significant trend (Table 17). The largest absolute 

increase in measured precipitation from 1901 

through 2011 occurred in fall (3.2 inches). Trends 

in individual months from 1901 through 2011 

indicate that September and November precipitation 

increased by 1.3 inches each (Fig. 20). It is 

important to note that monthly (Fig. 20) and seasonal 

averages combine data from across the assessment 

area, and that changes are geographically variable 

across the landscape (Fig. 21). For example, summer 

precipitation has increased in the northwestern and 

northeastern parts of the region, but has decreased in 

some southern areas, suggesting increased potential 

for summer moisture deficits.

Observed increases in precipitation are consistent 

with observations reported elsewhere. Across the 

Northeast, annual precipitation increased 5 inches 

between 1901 and 2011 (NOAA 2018). Similarly, 

another study of the Northeast (including most of 

the Mid-Atlantic region) points to an increase of 

nearly 0.75 inch of precipitation per decade, or about 

4.3 inches total, during 1948 through 2007 (Spierre 

and Wake 2010). This study also found larger 

increases in summer and fall than in spring and 

winter. The trend in increased fall precipitation was 

also observed in the adjacent Central Appalachians 

region (Butler et al. 2015). The northeastern United 

States has generally had some of the greatest 

precipitation increases of any region in the country, 

and the past four decades have been wetter than 

during the period from 1901 to 1960 (Melillo et al. 

2014, Pederson et al. 2012) (Box 10).

-0.2

0.2

0.6

1

1.4 * *

Figure 20.—Change in annual monthly precipitation within 

the assessment area, 1901 through 2011. Asterisks indicate 

there is less than 10-percent probability that the trend could 

have occurred by chance alone. Data source: Climate Wizard 

(2014). Change in Precipitation (inches)

Figure 21.—Change in annual and seasonal precipitation in 

the assessment area, 1901 through 2011. Stippling indicates 

there is less than 10-percent probability that the trend could 

have occurred by chance alone. Data source: Climate Wizard 

(2014).
CHAPTER 3: OBSERVED CLIMATE CHANGE

66

Box 10: Climate Changes in 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 (1901 through 2011). Looking 

across the entire record is helpful in detecting longterm changes, but it can also obscure shorter trends. 

In fact, the long-term trend is made up of shorter 

periods of warming and cooling, depending on the 

time period analyzed. 

Annual average temperature and precipitation can 

be explored within the entire climate record (1895 

through 2016) through the Climate at a Glance 

tools from the National Centers for Environmental 

Information (NOAA 2018). Analysis of historical data 

shows temperatures above and below the longterm average for the Northeast, which includes the 

entire Mid-Atlantic region and New England (NOAA 

2018) (Fig. 22). Eight of the last 16 years have ranked 

among the highest recorded temperatures in history: 

2012 (the hottest year on record), 2006, 2016, 

2010, 2011, 2001, 2002, and 2005. The other seven 

hottest years were, in descending order, 1998, 1990, 

1999, 1953, 1949, 1991, 1931, and 1921. The record 

coldest years all occurred before the 21st century, 

with the 15 coldest occurring before 1978. 

Precipitation is much more variable, but has broken 

some records during the 21st century. The wettest 

year on record occurred in 2011 for the Northeast 

on average, as well as for the individual states of 

Pennsylvania, New York, and New Jersey. Four 

other years in this century have ranked among the 

10 wettest: 2003 (Maryland’s record wettest year), 

2008, 2005, and 2006. Only 1 year, 2001, ranked 

among the 10 driest years (NOAA 2018).

Figure 22.—Annual mean temperature in the northeastern United States, which includes the Mid-Atlantic region 

and New England. Data source: NOAA (2018).
CHAPTER 3: OBSERVED CLIMATE CHANGE

67

HISTORICAL TRENDS IN EXTREMES

Although it can be very instructive to examine longterm trends in mean temperature and precipitation, 

climate extremes can have a greater impact on 

forest ecosystems and the human communities that 

depend on them. Extreme weather events include 

droughts, floods, heavy precipitation events, heat 

waves, cold spells, tropical and extratropical 

storms, and coastal sea-level storm surges (Box 

11). Weather or climate extremes are defined as 

individual weather events or patterns that are 

unusual in their occurrence or have destructive 

potential (Climate Change Science Program 2008). 

These events can trigger catastrophic disturbances 

in forest ecosystems (USGCRP 2016). In addition, 

the distribution of individual species or forest types 

is often controlled by particular climatic extremes, 

for example, winter minimum temperatures. The 

effects of extreme events may differ depending 

on existing conditions, timing, and the ecology of 

individual organisms and processes. For example, a 

100 °F (38 °C) day in the New Jersey Pine Barrens 

may have different consequences from a 100 °F day 

in the Finger Lakes region. Similarly, record hot 

temperatures in spring may have different effects on 

ecosystem processes than record hot temperatures 

in summer. Scientists agree that climate change has 

increased the probability of several kinds of extreme 

weather events, although it is not possible to predict 

the timing of future extreme events or to directly 

attribute one particular event to climate change 

(Coumou and Rahmstorf 2012). 

Box 11: Sea-level Rise

Climate change has caused sea-level rise both 

globally and regionally. Sea-level rise is the result of 

numerous interacting dynamics within the oceans. 

As water temperatures increase, water expands and 

increases the volume of the ocean. Melting glaciers 

and ice sheets further increase the amount of water 

going into the oceans (DeConto and Pollard 2016). 

These changes cause additional changes to the 

circulation of the oceans as gradients in temperature 

and ocean salinity are altered. 

Sea levels are not constant across the world 

due to differences in water temperature and 

salinity, the shape of the Earth, and the Earth’s 

rotation (Sallenger et al. 2012). Sea levels along 

the northeastern coast of North America are 

generally higher than in other places, partly due to 

local influences of ocean circulation (Boon 2012, 

Sallenger et al. 2012). Global sea levels have risen 

about 8 inches over the past century, and the 

rate of rise has been increasing in recent decades 

(Melillo et al. 2014). The amount of increase has 

been greater in the northeastern United States, 

with an overall increase of about 12 inches since 

1900 along the Atlantic coast (Horton et al. 2014, 

Melillo et al. 2014). The accelerated sea-level rise 

in the Northeast is a result of many complex factors 

including development, land subsidence from 

groundwater withdrawal, and changes in oceanic 

currents (Horton et al. 2014). The Atlantic coast is 

considered a hotspot of accelerated sea level rise, 

and has experienced three to four times the global 

rate during the second half of the 20th century 

(IPCC 2007, Kunkel et al. 2013b, McCabe et al. 2001, 

Sallenger et al. 2012, USDA Forest Service 2007). In 

the Northeast, sea-level rise has increased the risk 

of erosion, damage from storm surges, flooding, and 

damage to infrastructure and coastal ecosystems 

(Boesch et al. 2013, USDA Forest Service 2007). 

Increased salinity of surface and groundwater 

threatens natural habitat and human systems in the 

coastal plain. 
CHAPTER 3: OBSERVED CLIMATE CHANGE

68

Extreme Temperatures

Extreme temperatures can influence forest 

ecosystems in a variety of ways. Some tree species 

are limited by hot temperatures during the growing 

season, and others are limited by cold winter 

temperatures. Extreme temperatures may also be 

associated with disturbance events such as drought, 

wildfire, ice storms, and flooding. Globally and 

across the Northeast, the number of warm days and 

nights has increased and the number of cold days 

and nights has decreased since the 1960s (Alexander 

2016, Brown et al. 2010, Griffiths and Bradley 

2007, IPCC 2013). Furthermore, both the hottest 

and coldest temperatures have been increasing, so 

that the coldest temperatures are not as cold and the 

warmest temperatures are warmer than historical 

averages (NOAA n.d.c). Minimum temperature 

extremes have been much above normal in recent 

decades, more so than maximum temperature 

extremes, which have also been increasing. Extreme 

maximum temperatures, defined as temperatures 

much above normal or extreme conditions that fall 

in the upper 10th percentile, were calculated for the 

Northeast for the period 1910 through 2016 (NOAA 

n.d.c) (Fig. 23). Within a single year, the difference 

between the highest maximum temperature for 

Figure 23.—Extreme temperatures (expressed as the percentage above or below normal) for the northeastern United States. 

Data source: NOAA (n.d.c).
CHAPTER 3: OBSERVED CLIMATE CHANGE

69

summer and the lowest minimum temperature for 

winter has been decreasing (Griffiths and Bradley 

2007), resulting in decreased range of extreme 

temperatures (Alexander et al. 2006). Since the 

1960s, the number of warm nights (where minimum 

temperatures exceeded the 90th percentile) increased 

along the Mid-Atlantic coast, and decreased 

farther inland (Griffiths and Bradley 2007). The 

largest increases in daily maximum and minimum 

temperatures in the United States have occurred 

in the coldest months. These trends correspond to 

global patterns of increasing occurrence of extreme 

hot weather and decreasing occurrence of extreme 

cool weather (Hansen et al. 2012, Robeson et al. 

2014). 

Intense Precipitation

The Mid-Atlantic region is located in one of the 

wetter regions of the country, and many areas have 

been experiencing increases in total precipitation 

during the last century (Fig. 21). Despite high 

variability in the number of extreme precipitation 

events that occur in any single year or decade, there 

is clear evidence that large precipitation events 

have become more frequent in the region during the 

past century (Kunkel et al. 1999, 2013b; Melillo 

et al. 2014). One study found that most weather 

stations in the Northeast had increases of 1- and 

2-inch precipitation events during 1948 through 

2007 (Spierre and Wake 2010). Another study 

found increases in the number of extreme 2-day 

precipitation events over the United States from 

1900 through 2014 (Melillo et al. 2014).

A common way to estimate the change in extreme 

precipitation events is to look at the 99th percentile 

of rainfall during a 24-hour period, which averaged 

1.04 inches in the Northeast during 1948 through 

2007 (Spierre and Wake 2010). Analysis of this 

50-year time series shows that the top 1 percent of 

24-hour events is delivering more precipitation. 

Another study found that the heaviest 1 percent of 

daily precipitation events increased by 71 percent in 

the Northeast between 1958 and 2012, the most of 

any region in the United States (Melillo et al. 2014). 

Similarly, recurrence intervals are becoming shorter 

across the Northeast (i.e., a 50-year rain event may 

occur every 40 years) (DeGaetano 2009). A study 

of the eastern United States found that the heavy 

precipitation events (relative to a local weather 

station’s largest recorded storm) are occurring 

55 percent more frequently in the Mid-Atlantic 

region; heavy precipitation events that used to occur 

every 12 months are now occurring every 

7.7 months (Madsen and Willcox 2012). 

Severe Thunderstorms and Tornadoes

Numerous types of storms frequently occur within 

the region as a result of its diverse climate, including 

thunderstorms, ice storms, tropical storms, and 

hurricanes (Box 12), and nor’easters originating 

from mid-latitude westerly winds (Dolan et al. 

1988, Kunkel et al. 2013a). Strong thunderstorms 

occur most frequently from June to August within 

the assessment area (Changnon 2003b), and there 

is a general expansion northward and eastward 

as the season progresses (Robinson et al. 2013). 

Thunderstorm frequency is generally greatest in the 

southern parts of the assessment area, averaging 

30 to 35 days per year in the Chesapeake Bay region 

and southeastern Pennsylvania, compared to only 

20 to 25 days in southern New York (Changnon 

2003b, Changnon 2011). A study of severe 

thunderstorm observations over the eastern United 

States identified an increase in thunderstorm 

frequency during the last 60 years; however, it is 

uncertain if those increases are biased by increased 

accuracy in storm reporting (Robinson et al. 

2013). There is no evidence that the frequency of 

nor’easters, which occur often from October through 

April, has changed during the last century (Brooks 

2013, Brooks et al. 2014). 

Tornadoes also affect the assessment area. They 

occur less frequently than thunderstorms, but 
CHAPTER 3: OBSERVED CLIMATE CHANGE

70

Box 12: Coastal Storms and Hurricanes

Hurricanes and other warm-water tropical storms 

are a major cause of damage in coastal areas, 

whereas smaller “extratropical” storms often 

produce waves that are responsible for coastal 

erosion on a regular basis (Dolan et al. 1988). 

Hurricane activity in the Atlantic Ocean typically 

occurs June through November (National Oceanic 

and Atmospheric Administration n.d.b). Although 

not every hurricane or storm formed in the Atlantic 

makes landfall, associated winds or storm surge can 

damage coastal areas. In fact, hurricanes that do 

not make landfall can be more damaging to coastal 

ecosystems because of the lack of rain to help 

dilute intruded saltwater. Technology for observing 

hurricanes has improved over recent decades with 

the increased use of satellites, and observations are 

probably biased over the long-term climate record, 

as they are for tornadoes. 

There is also some debate about whether increases 

in hurricane frequency are attributable to climate 

change or to natural variability. However, evidence 

suggests that Atlantic hurricane frequency has 

increased over the period since high-quality satellite 

data became available (1981 to 2010) (Bell et al. 

2012, Melillo et al. 2014). One study divided the 

hurricane record into three distinct periods and 

found that each period contained 50 percent more 

hurricanes than the previous period (Webster et al. 

2005). Other evidence indicates 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). Additionally, there is some evidence 

that storm tracks have shifted poleward, suggesting 

that tropical cyclones are maintaining their strength 

farther north (Kossin et al. 2014). Long-term records 

from tide gauges may also describe the storm surge 

resulting from tropical cyclones without the need 

to direct observe cyclones themselves, thereby 

reducing potential bias in observation. One study 

used tide gauges to develop a surge index and found 

an increase in large surge events, with twice as many 

large events occurring in warm years than in cold 

years (Grinsted et al. 2012). Another study linked 

increased storm surge with increased flood risk in 

New York City, and found that the return interval for 

a 7.4-foot flood has decreased from 500 years to less 

than 25 years (Reed et al. 2015).

locations where they occur often suffer severe 

localized damage. Based on a 30-year average from 

1985 to 2014, Pennsylvania experienced the most 

tornadoes per year (15), followed by New York (10), 

Maryland (8), New Jersey (3), and Delaware (1) 

(National Weather Service Storm Prediction Center 

2016). Tornado outbreaks occur when six or more 

tornados occur in quick succession; most tornadorelated damage occurs during these outbreaks 

(Tippett et al. 2016). One recent study found that 

the number of outbreak tornadoes increased by 

about 15 percent from 1973 to 2010, while the 

number of non-outbreak tornadoes decreased by 

20 percent (Fuhrmann et al. 2014). The same study 

found that the number of outbreaks is increasing. 

This shift in tornado behavior was found to be 

statistically significant, but could also be biased due 

to increased technology and reporting success, such 

as the introduction of Doppler Radar technology 

in the 1980s and an enhanced Fujita scale that 

includes more damage indicators (Fuhrmann et al. 

2014, Widen et al. 2015). Other studies also report 

no change in overall tornado frequency and fewer 

tornado days, but a higher number of tornadoes on 

days that they do occur (Brooks et al. 2014, Elsner et 

al. 2015). 
CHAPTER 3: OBSERVED CLIMATE CHANGE

71

PHYSICAL PROCESSES

Many physical processes important for forest 

ecosystems are also driven by climate and weather 

patterns. These processes are influenced by climatedriven processes such as snowpack and flooding, 

which can regulate annual phenology, nutrient 

cycling, and other ecosystem dynamics. Changes 

to these physical processes can result in impacts 

and stress that might not be anticipated from mean 

climate values alone. This section presents a few key 

trends that have been observed in the Mid-Atlantic 

or throughout the broader region.

Streamflow and Flooding

Several studies have identified close relationships 

between climate and streamflow in the Mid-Atlantic 

region (Neff et al. 2000, Schulte et al. 2016). A 

nationwide study of streamflow during 1944 to 1993 

demonstrated that baseflow and median streamflow 

have increased at many streams in the Midwest 

and Mid-Atlantic region (Lins and Slack 1999). 

More recent studies have confirmed increased 

annual streamflow from 1961 through 1990, at 

least partially due to increased storm frequency 

(Groisman et al. 2004, Schulte et al. 2016). Changes 

in streamflow are driven by increased precipitation, 

as well as changes in land cover and land use 

(DeWalle et al. 2000, Groisman et al. 2004). After 

accounting for these factors, however, streams in 

the eastern United States still exhibited increased 

discharge during the past several decades, which is 

attributed to climate change (Patterson et al. 2013, 

Wang and Hejazi 2011). 

Flood occurrence is driven partially by weatherrelated factors, such as the timing of spring 

snowmelt, heavy rainfall, and storm surge resulting 

from hurricanes and tropical storms (Fig. 24). 

Flood occurrence also depends on soil saturation, 

soil temperature, and drainage capabilities. Floods 

can develop slowly as the water table rises, or 

quickly if large amounts of rainfall rapidly exceed 

moisture thresholds. Although snowpack in the 

Mid-Atlantic region 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). Although many small floods 

originate from small, unmonitored watersheds 

and go unreported, major regional floods are 

typically recorded by stream gauge measurements 

(Mohamoud and Parmar 2006). In Maryland, 

57 major floods were recorded from 1860 to 2004, 

with at least 13 of them associated with hurricanes 

(Joyce and Scott 2005). The Delaware River Basin, 

which stretches between Delaware, New Jersey, 

Pennsylvania, and New York, has experienced 

10 major floods from 1903 to 2011, about 1 every 

15 years (Delaware River Basin Commission 2015). 

The City of Pittsburgh, at the confluence that forms 

the Ohio River, reports 20 major floods since 1861. 

Damage from floods has been increasing in recent 

decades due to larger flood events (Villarini et al. 

2011, 2014). Hurricanes contribute to flooding 

across the eastern United States (Box 12), causing 

severe flooding hundreds of miles inland, and 

moderate flooding farther inland (Fig. 24). 

Freeze-free and 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, which represents the biological growing 

season (Linderholm 2006). A large body of research 

using observations from the last 50 to 110 years 

concurs that the frost-free 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; Griffiths 

and Bradley 2007; Linderholm 2006; Schwartz et al. 

2006, 2013). Regional studies of weather stations in 

the northeastern United States provide evidence of 

similar trends in the freeze-free season: an increase 
CHAPTER 3: OBSERVED CLIMATE CHANGE

72

Figure 24.—Composite map of floods associated with hurricanes that made landfall from 1981 through 2011, based on stream 

gauge data. The flood ratio (Q) indicates the magnitude of departure from the 10-year flood peak; values larger (smaller) than 

1 indicate floods were larger (smaller) than the 10-year flood peak. Figure adapted from data compiled by Villarini et al. (2014) 

and presented here from U.S. Global Change Research Program (2016).

of 0.7 days per decade during 1915 to 2003, and 

2.5 days per decade from 1970 to 2000 (Brown et al. 

2010, Frumhoff et al. 2007). 

There is also phenological evidence from remote 

sensing and satellite imagery that deciduous forests 

in the eastern United States are retaining leaves later 

in the fall, especially for forests at lower elevations 

(Elmore et al. 2012). This delay is associated with a 

decrease in the number of cold days occurring after 

the summer maximum temperature and subsequent 

delay in leaf senescence (Dragoni and Rahman 

2012). Another study using remote sensing across 

the northern hemisphere 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 about 9 days from 1981 through 2008 

(Jeong et al. 2011). Increases in the growing season 

length are causing some noticeable changes in the 

timing of biological activities, such as bird migration 

(Box 13).

Snow and Winter Storms

Warmer temperatures have caused precipitation 

to increasingly fall as rain in winter (Feng and Hu 

2007). Although precipitation has increased across 

the eastern United States, warmer average winter 

temperatures have caused a smaller proportion 

to fall as snow (Kunkel et al. 2009). In the MidAtlantic region, several studies indicate a strong 

downward trend in snowfall. A study using long-
CHAPTER 3: OBSERVED CLIMATE CHANGE

73

Box 13: Ecological Indicators of Change

The timing of biological events (phenology), such 

as bird migration, wildlife breeding, and plant 

flowering and fruiting is determined by many 

variables, including seasonal temperature, food 

availability, and pollination mechanisms (Bradley 

et al. 1999). Increases in the growing season length 

and other climatic changes have caused noticeable 

changes in the timing of biological activities across 

the world (Walther et al. 2002). Likewise, numerous 

phenological changes have been observed across the 

region:

• The snowshoe hare, whose coat turns white 

during the winter to camouflage it in the snow, 

has been declining throughout the Appalachian 

Mountains (Maryland Department of Natural 

Resources 2016). Increased predation during 

the winter is a result of a mismatch between the 

animal’s white winter fur and its surroundings 

due to a longer snow-free season (Mills et al. 

2013; Zimova et al. 2014, 2016). The range of 

snowshoe hare has contracted to the coldest 

regions of Pennsylvania that still have persistent 

snowpack (Diefenbach et al. 2016, Gigliotti 2016). 

This range contraction northward is primarily 

attributed to a reduction in snow cover duration 

in the southern historical range (Pauli 2016).

• Ten species of native bees in the Northeast 

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 bee species are 

keeping pace with changes in plant phenology 

(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.

• One hundred native plant species were 

monitored in a 100-mile radius near Washington, 

DC, and 86 percent showed earlier flowering 

times (Abu-Asab et al. 2001). Two species of 

cherry, Prunus serrulata and P. yedoensis, are 

blooming 6 and 7 days earlier, respectively, than 

they did in 1970. This plant is featured during 

Washington, DC’s Cherry Blossom Festival, which 

relies on predicting peak-flowering season to 

meet tourist expectations.

Snowshoe hare in Elk County, Pennsylvania. Photo by 

Hal Korber, Pennsylvania Game Commission, used with 

permission.
CHAPTER 3: OBSERVED CLIMATE CHANGE

74

term snowfall totals from 1930 through 2007 found a 

trend of decreasing snow in the Mid-Atlantic region, 

especially along the coast (Kunkel et al. 2009). 

Another study observed a decrease of 1.5 snow days 

in the Northeast between 1970 and 2000 (Hayhoe 

et al. 2007). Regional trends indicate that although 

snowfall is highly variable from year to year, the 

most recent 30 years have had fewer heavy snowfall 

years, but with more intense snowfalls when they 

do occur (Feng and Hu 2007). One exception to 

the decreasing trends is in the lee of Lakes Erie and 

Ontario, where warmer lake surface temperatures 

have fueled increases in lake-effect snowfall during 

the past 50 years (Burnett et al. 2003).

Lake and River Ice

Warmer water temperatures and reduced ice cover 

often interact in a positive feedback cycle where 

warmer winter air and water temperatures reduce 

ice cover and increase the duration of open water 

conditions. The ensuing open water conditions allow 

the water to absorb more heat, further increasing 

water temperatures (Austin and Colman 2007). With 

increases in air temperatures, water temperatures 

also increase. The timing and extent of lake ice 

formation have been recorded for more than a 

century across the region. Ice-out, which is the date 

in spring when ice cover leaves a lake, is strongly 

related to air temperatures in the month or two 

preceding ice-out and serves as a useful indicator 

of climate change in winter and spring (Hodgkins 

and Dudley 2006, Magnuson et al. 2000). A study of 

selected U.S. lakes analyzed ice-out dates for three 

inland lakes in New York and found earlier ice-out 

dates for all of them (U.S. Environmental Protection 

Agency 2015). Lake Otsego in central New York has 

records of lake ice dating back to 1894 and analysis 

shows that the lake has thawed earlier by 11 days 

during the 165-year period. Across the Midwest 

and Northeast, records beginning in the 1850s have 

shown that ice on inland lakes is also thawing earlier 

in the spring and forming later in the fall (Benson et 

al. 2012). Annual ice cover on Lake Ontario declined 

by 88 percent between 1973 and 2010 while ice 

cover on Lake Erie declined by half (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.

CHAPTER SUMMARY

Temperatures have been warming faster since 

the 1970s, with 15 of the 16 warmest years on 

record occurring during the 21st century. There 

are significant geographic patterns that show 

less warming in some parts of the region (e.g., 

southwestern Pennsylvania) than warming observed 

along the Atlantic coast. Most of the change in 

precipitation totals is attributed to large increases 

in precipitation in the fall. Precipitation changes 

during other seasons were smaller and varied 

geographically. The hottest days are getting hotter 

and the number of hot days is increasing. The 

coldest days are also getting warmer and the number 

of cold days is decreasing. 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. Other ecological indicators are 

beginning to reflect these changes as well, as shown 

by novel mismatches between animals and their food 

and habitat. These trends are generally consistent 

with regional, national, and global observations 

related to anthropogenic climate change.
75

CHAPTER 4: PROJECTED CHANGES IN CLIMATE 

AND PHYSICAL PROCESSES

In Chapter 3, we examined how climate has changed 

in the Mid-Atlantic region during the past century. 

This chapter examines how climate is expected to 

change during the 21st century, including changes 

in extreme weather events and other climaterelated processes. General circulation models, also 

called global climate models (GCMs), are used to 

project future change at coarse spatial scales and 

then downscaled in order to be relevant at scales 

where land management decisions are made. 

These downscaled data can then be incorporated 

into forest species distribution models and process 

models (results are presented in Chapter 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 Chapter 4, we 

focus on two climate scenarios for the assessment 

area, chosen to bracket a range of plausible changes 

in average annual and seasonal temperatures and 

precipitation totals. We note, however, that the two 

models selected here do not necessarily represent 

the bracketed range in terms of other metrics such 

as daily maximums and minimums, or extremes. 

Therefore, readers should exercise caution when 

interpreting future trends. Information related to 

future weather extremes and physical processes is 

drawn from published research. 

PROJECTED TRENDS 

IN TEMPERATURE 

AND PRECIPITATION

Projected changes in temperature and precipitation 

within the Mid-Atlantic region were examined by 

using a statistically downscaled climate dataset 

(Chapter 2). Daily mean, minimum, and maximum 

temperature and total daily precipitation were 

downscaled to an approximately 7.5-mile grid 

across the United States. To show projected changes 

in temperature and precipitation, we calculated 

the average mean, minimum, and maximum 

temperatures and precipitation for each month 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. 2012b). The use 

of 30-year periods reduces the influence of natural 

year-to-year variation that may bias calculations of 

change and allows for more direct comparison with 

the 1971 through 2000 baseline (see Chapter 2 and 

Appendix 2) from which changes are calculated 

(Girvetz et al. 2009). For all climate projections, two 

GCM-emissions scenario combinations are reported: 

GFDL A1FI and PCM B1. The A1FI scenario used 

in this assessment 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 used in 

this assessment represents a future where alternative 

energy sources 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. The GFDL 

A1FI model-scenario combination consistently 

projects greater changes in future temperature and 

precipitation than the PCM B1 model-scenario 

combination (hereafter referred to simply as GFDL 

A1FI and PCM B1). Because the future may be 

different from any of the developed scenarios, it is 

important to consider the range of possible climate 

conditions during the coming decades rather than 

one particular scenario. 
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

76

The PCM B1 and GFDL A1FI scenarios used in this 

assessment are just two of many climate scenarios 

that are available. The projections from alternative 

scenarios can vary widely, but these two scenarios 

serve as “bookends” for a broad range of potential 

future climate conditions (Chapter 2). Projected 

changes in temperature and precipitation for GFDL 

A1FI represent a greater level of greenhouse gas 

emissions and projected climate warming than the 

PCM B1 scenario. When possible, the results from 

these two scenarios are compared with other datasets 

that are available for the region.

Temperature

The Mid-Atlantic region is projected to experience 

substantial climate warming during the 21st century, 

especially under the GFDL A1FI scenario (Fig. 25). 

Early-century (2010 through 2039) annual 

average temperature is projected to increase by 

0.9 °F (0.5 °C) for PCM B1 and 1.9 °F (1.1 °C) 

for GFDL A1FI (Fig. 25, Table 18). Projections 

of temperature diverge substantially over time, 

resulting in increasingly larger differences between 

the two scenarios. By the end of the century, these 

differences result in a projected temperature increase 

that is 5.4 °F (3.0 °C) larger for GFDL A1FI than 

for PCM B1. Compared to the 1971 through 2000 

baseline climate, the average annual temperature at 

the end of the century is projected to increase by 

2.2 °F (1.2 °C) for PCM B1 and by 7.6 °F (4.3 °C) 

for GFDL A1FI (Table 18). Seasonal changes follow 

this pattern, with less change projected during the 

early century period, and more change projected at 

the end of the century, especially for GFDL A1FI. 

See Appendix 3 for projected changes in mean, 

minimum, and maximum temperatures during the 

early, mid-, and late century for all four seasons. 

Figure 25.—Projected annual mean, minimum, and maximum temperature (°F) in the assessment area averaged over 30-year 

periods for two climate-model emissions scenario combinations (Climate Wizard 2014). 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

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

T

e

m

p

erature (0F)

GFDL A1FI Mean GFDL A1FI Minimum GFDL A1FI Maximum

PCM B1 Mean PCM B1 Minimum PCM B1 Maximum
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

77

Baseline Departure from baseline

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

Mean temperature (°F)

Annual 48.9 PCM B1 0.9 1.8 2.2

GFDL A1FI 1.9 5.2 7.6

Winter (Dec-Feb) 28.2 PCM B1 1.0 2.3 2.4

GFDL A1FI 1.7 4.4 5.9

Spring (Mar-May) 47.3 PCM B1 0.1 1.3 1.9

GFDL A1FI 0.7 4.0 6.6

Summer (Jun-Aug) 68.8 PCM B1 1.1 1.8 2.3

GFDL A1FI 3.0 6.6 9.2

Fall (Sep-Nov) 51.3 PCM B1 1.6 1.8 2.2

GFDL A1FI 2.2 5.5 8.6

Minimum temperature (°F)

Annual 38.5 PCM B1 1.0 1.8 2.2

GFDL A1FI 1.9 5.1 7.5

Winter (Dec-Feb) 19.4 PCM B1 1.1 2.6 2.9

GFDL A1FI 1.9 5.0 6.7

Spring (Mar-May) 36.2 PCM B1 0.6 1.7 2.1

GFDL A1FI 1.0 4.3 6.6

Summer (Jun-Aug) 57.5 PCM B1 0.9 1.8 2.0

GFDL A1FI 2.6 6.1 8.6

Fall (Sep-Nov) 41.0 PCM B1 1.3 1.3 2.0

GFDL A1FI 2.0 4.9 8.1

Maximum temperature (°F)

Annual 59.3 PCM B1 0.9 1.8 2.1

GFDL A1FI 2.0 5.2 7.6

Winter (Dec-Feb) 36.9 PCM B1 0.9 2.0 1.9

GFDL A1FI 1.5 3.8 5.1

Spring (Mar-May) 58.5 PCM B1 -0.5 1.0 1.8

GFDL A1FI 0.5 3.8 6.5

Summer (Jun-Aug) 80.1 PCM B1 1.3 1.9 2.6

GFDL A1FI 3.4 7.2 9.8

Fall (Sep-Nov) 61.6 PCM B1 1.9 2.2 2.3

GFDL A1FI 2.4 6.1 9.2

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

Table 18.—Projected change in mean daily mean, minimum, and maximum temperature in the assessment area 

averaged over 30-year periods compared to baseline temperature
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

78

Although the two climate scenarios project different 

amounts of warming, they are largely in agreement 

that mean, minimum, and maximum temperatures 

will increase throughout the assessment area both 

annually and in all seasons. During the past century, 

minimum temperatures have warmed more than 

maximum temperatures in winter, summer, and 

fall (Chapter 3). For both scenarios, this trend is 

expected to shift slightly during the 21st century, so 

that minimum temperatures are projected to warm 

more than maximum temperatures in both winter 

and spring, but maximum temperatures are projected 

to increase more than minimum temperatures in 

summer and fall. The amount of change varies 

considerably among scenarios. Under PCM B1, 

minimum and maximum temperatures are projected 

to increase by 1.8 to 2.9 °F (1.0 to 1.6 °C) for winter, 

spring, summer, and fall between 2070 and 2099. 

Under GFDL A1FI, however, changes in minimum 

and maximum temperatures during this period are 

projected to increase by 5.1 to 9.8 °F (2.9 to 5.6 °C), 

with the highest temperature increases projected for 

summer and fall.

Projected changes in temperature are expected to 

vary geographically across the assessment area 

(Figs. 26 through 28). As described in Chapter 3, 

climate of the Mid-Atlantic region is influenced by 

latitude, elevation, and proximity to large bodies 

of water. However, uncertainties introduced in 

historical data can be compounded in downscaled 

future climate models, which are not very sensitive 

to regional landscape features (Box 14) (Horton et 

al. 2011, Polsky et al. 2000). Thus, mapped data 

should be at best considered representative of largescale trends because of the potential for localized 

anomalies or errors associated with landscape 

topography and water bodies (Beier et al. 2012). 

These data are consistent with several other 

modeling efforts in the region and globally. 

Although the temperature increases projected for 

individual climate models do differ, a vast array of 

models developed across the globe project a warmer 

future climate and a greater magnitude of warming 

than historical trends (IPCC 2014, Nelson Institute 

Center for Climatic Research 2018, U.S. Global 

Change Research Program [USGCRP] 2017). The 

Intergovernmental Panel on Climate Change (IPCC) 

created another set of climate scenarios for use in its 

Fifth Assessment Report (IPCC 2013). The newer 

datasets use Representative Concentration Pathways 

(RCPs) (Knutti and Sedláček 2013, Meinshausen 

et al. 2011, Taylor et al. 2012). The greenhouse gas 

concentration and global temperature projections 

are roughly comparable between the A1FI emissions 

scenario and RCP 8.5 and between the B1 emissions 

scenario and RCP 4.5 (Sun et al. 2015). Global mean 

temperatures for 2081 to 2100, relative to a 1985 

to 2005 climate normal, were projected to warm by 

2.0 to 4.7 °F (1.1 to 2.6 °C) under RCP 4.5 and by 

4.7 to 8.6 °F (2.6 to 4.8 °C) under RCP 8.5 (IPCC 

2013). However, differences in the periods chosen 

to represent climate normals, as well as differences 

between the scenarios and RCPs, prevent direct 

comparison (Chapter 2).

Entrance to a 120-acre tract of old-growth hemlock in 

Pennsylvania. Hemlock is a species vulnerable to climate 

change. Photo by Greg Czarnecki, Pennsylvania Department 

of Conservation and Natural Resources, used with 

permission.
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

79

Figure 26.—Projected difference in mean daily mean temperature (°F) at the end of the century (2070 through 2099) 

compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations. See Appendix 3 for maps 

of projected changes in early- and mid-century mean daily mean temperature.
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

80

Figure 27.—Projected difference in mean daily minimum temperature (°F) at the end of the century (2070 through 2099) 

compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations. See Appendix 3 for maps 

of projected changes in early- and mid-century mean daily minimum temperature.
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

81

Figure 28.—Projected difference in mean daily maximum temperature (°F) at the end of the century (2070 through 2099) 

compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations. See Appendix 3 for maps 

of projected changes in early- and mid-century mean daily maximum temperature.
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

82

Box 14: Climate Modeling in Complex Topography

Areas of complex topography contain some of the 

highest biological diversity in the world (Hoekstra 

et al. 2010). Landscape patterns of ridges, valleys, 

plains, and water features can influence precipitation 

through rainshadow effects, temperature through 

cold air pooling, and other fine-scale processes 

that create a complex suite of ecological niches 

with various temperature and moisture regimes 

(Anderson and Ferree 2010). Terrain amplifies 

disparities between the climate at a site and the 

broad climate trends for any given region (Daly et 

al. 2010), yet there is often a paucity of weather 

stations in high elevations (Daly et al. 2002). 

Modeling precipitation patterns in coastal areas is 

particularly complicated owing to the complexity of 

evaporation, atmospheric circulation, rainshadow 

effects, and orographic lifting of moisture to higher 

elevations (Daly et al. 2002). Large water bodies 

are also associated with temperature gradients that 

extend from coastal to inland areas (Daly et al. 2008). 

Although few studies have investigated finer 

scale modeling of coastal plains and mountain 

ranges in the eastern 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 examining climate data and trend maps 

in the Northeast detected strong bias in both 

montane and coastal areas (Beier et al. 2012). For 

example, data from a single weather station showed 

a warming trend in the Adirondack Mountains, 

whereas statistical interpolation of those data 

onto a gridded historical climate map produced 

cooling trends. This discrepancy was attributed to a 

processing error associated with that single station, 

resulting in a bias in the mapped data. An example 

of bias in Long Island, NY, is attributed to different 

methods of dealing with the boundary between land 

and water (Beier et al. 2012). A study in the Oregon 

Cascades, which is prone to cold-air pooling similar 

to the Catskill Mountains, found that temperatures 

in sheltered valley bottoms are somewhat buffered 

from changes projected for the whole study area 

(Daly et al. 2010). 

These studies suggest inaccurate modeling in areas 

with complex topography and rapid elevation 

change. Regional climate models for the Mid-Atlantic 

region have not performed as well as in areas of 

relative homogeneity (e.g., upper Midwest), and 

some correction may be necessary to account for 

elevation, slope, aspect, and relative exposure 

or isolation from the elements. Finer resolution 

modeling would help identify biases in the data 

based on these factors, but resolution will vary 

with the complexity of topography, and is still likely 

to produce some bias. Though relatively coarse, 

the resolution of data used in this assessment can 

provide a broad foundation of plausible future 

climates from which to consider the caveats 

mentioned.
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

83

Precipitation

Due to the highly variable nature of precipitation 

and complexity in modeling it, projections of 

precipitation are more variable between models 

and generally carry with them a higher level of 

uncertainty than projections of temperature (Bryan 

et al. 2015, Kunkel et al. 2013b, Winkler et al. 

2012). The two climate scenarios we chose for 

this assessment bracket the potential change in 

temperature across the assessment area. They also 

describe two markedly different scenarios of future 

precipitation for the assessment area (Figs. 29, 30). 

Other future projections of precipitation across the 

Northeast also differ substantially (Fan et al. 2014, 

Lynch et al. 2016). For this reason, it is important 

to keep in mind that other scenarios may project 

precipitation values outside of the range presented in 

this assessment. 

Within the assessment area, annual precipitation 

is projected to increase by 2.1 inches for PCM B1 

and 2.6 inches for GFDL A1FI at the end of the 

century (Table 19). Although the projections for 

the end of the century are discussed throughout this 

assessment, the precipitation regime is dynamic and 

these patterns may be slightly different in the early 

and middle periods of the 21st century. For example, 

although precipitation is expected to increase for 

GFDL A1FI in winter and spring at the end of the 

century, the amount of increase is reduced during the 

middle part of the century (Table 19). 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, while GFDL 

A1FI projects summer precipitation to steadily 

decrease. 

Figure 29.—Projected trends in mean annual precipitation in the assessment area averaged over 30-year periods for two 

climate model-emissions scenario combinations. The 1971 through 2000 value is based on observed data from weather 

stations. See Appendix 3 for projected changes by season.

41

42

42

43

43

44

44

45

45

46

46

47

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

Pre

cipitatio

n (in

c

h

e

s)

PCM B1 GFDL A1FI
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

84

Figure 30.—Projected difference in mean precipitation (inches) at the end of the century (2070 through 2099) compared to 

baseline (1971 through 2000) for two climate model-emissions scenario combinations. See Appendix 3 for maps of projected 

changes in early- and mid-century mean precipitation.
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

85

Baseline precipitation 

(inches),

1971-2000a

Departure from baseline (inches)

Scenario 2010-2039 2040-2069 2070-2099

Annual 43.3 PCM B1 -0.5 0.2 2.1

GFDL A1FI 1.3 0.7 2.6

Winter (Dec-Feb) 9.0 PCM B1 0.0 0.7 0.6

GFDL A1FI 1.1 1.1 2.1

Spring (Mar-May) 11.1 PCM B1 0.3 0.4 0.3

GFDL A1FI 0.8 0.4 1.5

Summer (Jun-Aug) 12.1 PCM B1 0.3 1.1 1.6

GFDL A1FI 0.1 -0.4 -2.3

Fall (Sep-Nov) 11.1 PCM B1 -1.2 -1.9 -0.5

GFDL A1FI -0.6 -0.4 1.3

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

Table 19.—Projected change in annual precipitation in the assessment area averaged over 30-year periods compared 

to baseline precipitation

It is more important, however, to consider changes 

by season than by mean annual total, as the timing 

of increases or decreases has the most implications 

for forest ecosystems. During the end of the 

century (2070 through 2099), winter precipitation 

is projected to be 0.6 to 2.1 inches more than the 

baseline climate (1971 through 2000). Summer 

precipitation projections are more variable and are 

expected to change by -2.3 to 1.6 inches during the 

end of the century. Relative to the baseline climate, 

winter, spring, and fall precipitation is projected to 

remain about the same under PCM B1, but increase 

under GFDL A1FI by 23 percent in winter, 

14 percent in spring, and 12 percent in fall. In 

summer, precipitation is projected to increase by 

13 percent under PCM B1 and decrease by 

19 percent under GFDL A1FI (Table 19). 

Changes in precipitation are also projected to vary 

across the assessment area (Fig. 30). In winter and 

spring, changes for PCM B1 are slight (< 2 inches) 

and vary in direction (increasing or decreasing) 

across the landscape. Summer projections show 

many areas with increasing precipitation under PCM 

B1 and many areas with decreasing precipitation 

under GFDL A1FI (Fig. 30). Under GFDL A1FI, 

precipitation is projected to increase most in winter, 

followed by spring and fall across large portions of 

the assessment area, with annual totals increasing by 

1 to 5 inches. In terms of growing season (spring, 

summer, and fall), it is notable that a summer 

increase is followed by a fall decrease for PCM B1. 

Under GFDL A1FI, this sign change occurs earlier 

in the season, with a spring increase followed by a 

significant summer decrease. Within the bracket of 

least to greatest amount of projected change, these 

patterns suggest a moisture deficit sometime during 

the growing season, with low confidence to predict 

the timing of precipitation decreases in summer or 

fall.

These data are consistent with a number of recent 

modeling efforts for the Northeast that consistently 

present greater variability in projected precipitation 

than in temperature (Fan et al. 2014, Hayhoe et 

al. 2007, Kunkel et al. 2013b, Lynch et al. 2016, 

Thibeault and Seth 2014b). Models generally 

detected precipitation increases in all seasons except 
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

86

for summer, despite an overall increase in total 

annual precipitation. Models generally disagreed on 

whether future summer precipitation may increase 

or decrease. A recent comparison of multiple climate 

models found projections for summer precipitation 

that ranged from a 25-percent or greater decrease to 

an equivalent increase for the 2070 to 2099 period 

(Kunkel et al. 2013b). Another recent study found 

a high degree of variation among multiple climate 

models and an overall modest increase in summer 

precipitation when the results were averaged (Lynch 

et al. 2016). The newest set of climate scenarios in 

the IPCC Fifth Assessment Report project similar 

precipitation changes except for summer; summer 

precipitation in the Mid-Atlantic region is projected 

to increase by 10 percent under RCP 8.5 (Walsh 

et al. 2014). It is also an important consideration 

that climate change may increase the year-to-year 

variation of precipitation across the Northeast (Boer 

2009, Thibeault and Seth 2014a).

PROJECTED TRENDS IN EXTREMES

Although it is instructive to examine long-term 

means of climate and weather data, in many 

circumstances extreme events can have a greater 

impact on forest ecosystems. Weather or climate 

extremes are defined as individual weather events 

or short-term patterns that are unusual in their 

occurrence or have destructive potential (Climate 

Change Science Program 2008). Extreme events 

are stochastic by nature, and usually occur at fine 

spatial scales (i.e., a particular place). Thus, extreme 

events are difficult to predict and they are obscured 

in long-term or large-scale averages. Moreover, it is 

not possible to directly attribute the occurrence of 

a single extreme event to climate change (Coumou 

and Rahmstorf 2012, Stott et al. 2010).

Despite these limitations, many lines of evidence 

indicate that some extreme events have become 

more frequent and severe across the United States 

and globally, in part due to global climate change 

(Buckley and Huey 2016, Coumou and Rahmstorf 

2012, IPCC 2012). Several studies have projected 

increases in some weather and climate extremes in 

the Mid-Atlantic region and the Northeast (Brown 

et al. 2010, Bryan et al. 2015, Griffiths and Bradley 

2007, Kunkel et al. 2013b, Ning et al. 2015). Sealevel rise is expected to exacerbate flooding and 

storm-related damage in coastal areas (Box 15). 

Extreme events such as floods, droughts, heat waves, 

cold waves, and windstorms can trigger catastrophic 

disturbances in forest ecosystems and entail 

significant socioeconomic impacts.

Extreme Temperatures

In addition to projecting mean temperatures, 

downscaled daily climate data can be used to 

estimate the frequency of extreme high and low 

temperatures in the future (Ning et al. 2015). Studies 

of extreme temperatures often define hot days as 

days hotter than 90 °F (32 °C) and cold days as days 

colder than 32 °F (0 °C). Climate 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, IPCC 2014, Meehl and Tebaldi 

2004, Ning et al. 2015, Winkler et al. 2012). Another 

climate study projected hot days through 2070 under 

the A2 scenario to increase by 30 to 40 days across 

much of the Mid-Atlantic region, and by more than 

60 days per year in the coastal areas of New Jersey, 

Maryland, and Delaware (Horton et al. 2014). By 

2090, models predict increases of 75 to 90 days per 

year in those southern coastal areas (Nelson Institute 

Center for Climatic Research 2018). Days above 

100 °F (38 °C) are projected to increase mainly 

in southeastern Pennsylvania (by 7 to 21 days per 

year), and New Jersey, Maryland, and Delaware (by 

21 to 28 days per year) (Nelson Institute Center for 

Climatic Research 2018). The frequency of multiday 

heat waves is also projected to increase by 4 to 

6 days throughout the region (Ning et al. 2012).
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

87

Box 15: Projected Sea-level Rise

The Mid-Atlantic region is home to large populations 

of people in high-density cities located along 

the Atlantic coast. The accelerated sea-level rise 

observed in the Northeast is a result of many 

complex factors including development, land 

subsidence from groundwater withdrawal, and 

changes in oceanic currents (Chapter 3; Box 11) 

(Horton et al. 2014). The dynamic processes that 

drive sea-level rise— fresh-water inputs from 

melting ice, warming air and water temperatures, 

increasing water volume, changing salinity, and 

altered circulation patterns—introduce uncertainty 

in projecting the magnitude of sea-level rise over the 

next century (Landerer et al. 2007, Sallenger et al. 

2012, Yin et al. 2009). Including the addition of water 

from the Greenland and Antarctica ice sheets, global 

sea level is projected to rise an additional 0.8 to 2.6 

feet by 2100 under a low emissions scenario and 

by 1.6 to 4.3 feet under a higher emissions scenario 

(Girvetz et al. 2009, USGCRP 2017). Results from 

several regional studies in the Northeast estimate 

additional sea-level rise due to changes in the Gulf 

Stream and in ocean circulation, with no consensus 

on the timing or magnitude (Rahmstorf et al. 2007; 

Sallenger et al. 2012; Schwartz et al. 2013; Walsh 

et al. 2014; Yin et al. 2009; L. Yowell, email, Dec. 

14, 2017). One study in the Mid-Atlantic region 

discussed the influence of bedrock geology on the 

rate of sea-level rise; sea level on the sandy coastal 

plain is expected to rise 3.6 inches more than on 

adjacent areas underlain by bedrock (Miller et al. 

2013). 

Higher emissions scenarios generally project 

greater sea-level rise (Pennsylvania Department of 

Conservation and Natural Resources 2016, Walsh et 

al. 2014). At the same time, even the best models 

cannot simulate the effects of rapid changes in 

ice sheet dynamics, which makes it likely that 

estimates of future sea-level rise are underestimated 

(Maryland Department of Natural Resources 2010, 

Walsh et al. 2014). While methods to forecast sealevel rise continue to improve, one set of studies 

has focused on using flooding statistics to detect 

acceleration in both sea-level rise and flooding 

extent (Ezer and Atkinson 2014, Ezer and Corlett 

2012). The area of land exposed to inundation is 

also projected to increase: 14.5 percent more land 

in Washington, DC, 11.4 percent in Delaware, and 

10.2 percent in Pennsylvania. One study of the 

Atlantic coast found that a sea-level rise of 2.6 feet 

would result in 7 to 20 percent more storm surge 

inundation (Maloney and Preston 2014). Increases 

in sea-level rise directly and immediately influence 

storm surge and erosion potential of low-lying areas. 

Natural habitats and developed areas will continue 

to be increasingly exposed to sea-level rise and 

storms (Chapter 7). 

Studies from across the region also project the 

annual frequency of cold days and cold nights to 

decrease by 12 to 15 days by the end of the century 

(Diffenbaugh et al. 2005, Gutowski et al. 2008, 

IPCC 2012). One study observed that the ratio 

of extreme record highs to record lows has been 

increasing since the 1970s, and extreme record high 

temperatures may outpace record low temperatures 

by 50 to 1 by the end of the century (Meehl et al. 

2009). These trends are consistent with studies 

covering the entire Midwest and Northeast regions, 

which project that the assessment area could 

experience 22 to 26 fewer days below 32 °F and 

9 to 10 fewer days below 0 °F (-18 °C) by the 

middle of the 21st century (Kunkel et al. 2013b, 

Ning et al. 2012, Peterson et al. 2013b). It is 

important to note, however, that the enhanced 

warming occurring in polar regions greatly 

influences weather patterns in the mid-latitudes and 

can lead to periods of extreme cold, even as the 

overall climate becomes warmer (Francis and Vavrus 

2012, Vavrus et al. 2006). During the growing 

season, these cold air outbreaks can be damaging to 

vegetation that has already been stimulated by warm 

temperatures to develop buds, leaves, or fruit (Ault 

et al. 2013). 
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

88

Intense Precipitation

There is a clear trend toward more heavy 

precipitation events in the assessment area, and 

this is expected to continue (Gutowski et al. 2008, 

Kunkel et al. 2008, Ning et al. 2012, Thibeault and 

Seth 2014a). 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. 

Downscaled projections for the Northeast estimate 

up to 30-percent increases in heavy precipitation 

events (i.e., days exceeding 1 inch) (Kunkel et al. 

2013b). One recent study projected that the MidAtlantic region may receive 1 to 4 more days per 

year of precipitation events exceeding 0.4 inch with 

greater intensities during the 2050 through 2099 

period relative to the 1950 to 1999 period (Ning 

et al. 2015). Another modeling effort classified 

precipitation totals, and projected the number of 

days with 1 inch or more precipitation to increase 

by 21 days per decade across much of the MidAtlantic region (Nelson Institute Center for Climatic 

Research 2018). Although simulations consistently 

project a continued increase in extreme events, the 

magnitude of change is more uncertain, reflecting 

the high spatial and temporal variability in these 

events. 

It is important to consider this trend in combination 

with the projected changes in total precipitation 

during the 21st century. A given increase or decrease 

in precipitation is unlikely to be distributed evenly 

across a season or even a month. Large-scale 

modeling efforts have also suggested that climate 

change may increase the year-to-year variability 

of precipitation across the Midwest and Northeast 

(Boer 2009, Thibeault and Seth 2014a). Further, 

Dam on Laurel Lake, Pine Grove Furnace State Park, Pennsylvania. Photo by Greg Czarnecki, Pennsylvania Department of 

Conservation and Natural Resources, used with permission.
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

89

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 moisture stress 

between rainfall events or higher rates of runoff 

during rainfall events. Landscape position may also 

influence the ability of a particular location to retain 

moisture from extreme events; for example, steep 

slopes shed runoff faster than flatter surfaces.

Severe Weather: Thunderstorms, 

Hurricanes, and Tornadoes

Several studies concluded that projected changes 

in temperature and precipitation may lead to more 

frequent days with conditions that are favorable 

for severe storms and tornadoes, increasing the 

probability that a storm may occur (Brooks 2013, 

Diffenbaugh et al. 2013, Lee 2012, Trapp et al. 

2007). These studies suggest that climate change 

may influence storm characteristics, although the 

nature of change is uncertain. A 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: 13). As the 

sophistication of global and regional climate models 

increases, our understanding of how patterns in hail 

and tornadoes may change in the future may increase 

as well. 

Increases in thunderstorm frequency were projected 

within the assessment area for both mid-range 

(A1B) and higher (A2) emissions scenarios (Trapp 

et al. 2007, 2009). Models suggest that the nature 

of hurricanes may also change (Gutowski et al. 

2008). One study estimated that for every 1.8 °F 

(1 °C) increase in tropical sea surface temperature, 

hurricane wind speeds may increase up to 8 percent, 

and core rainfall rates may increase by 6 to 

18 percent (Gutowski et al. 2008). Another study 

found that although tropical storm frequency is 

projected to decrease under three downscaled 

model ensembles, both tropical storm intensity 

and hurricane intensity are projected to increase 

(Knutson et al. 2013). 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). 

PHYSICAL PROCESSES

Across the globe, increases in temperature are 

projected to intensify the hydrologic cycle, leading 

to greater evaporative losses and more heavy 

precipitation events (IPCC 2014). Changes in 

runoff and streamflow can contribute to changing 

watershed dynamics and risk associated with 

flooding and erosion. At the same time, increases in 

temperature are projected to lengthen the growing 

season, a time when vegetation requires adequate 

moisture for growth and regeneration. 

Growing Season Length

The assessment area has experienced an expansion 

of the growing (i.e., freeze-free) season during 

the past century, and these changes are expected 

to continue as a result of warmer temperatures. 

Although the change in growing season length 

was not modeled using the PCM B1 and GFDL 

A1FI scenarios, other models project an increase 

in the growing season length at the end of the 

century (2081 to 2100) of 21 to 35 days under a low 

emissions scenario (B1) and by 42 to 50 or more 

days under a high emissions scenario (A2) (Nelson 

Institute Center for Climatic Research 2018). The 

projected expansion of the growing season is a 

result of nearly equal shifts toward earlier spring 

freeze-free dates and later fall freezes. Other studies 

across the Northeast also project similar increases in 

growing season length throughout the 21st century 

(Hayhoe et al. 2007, Kunkel et al. 2013b). 
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

90

In addition to a longer growing season from spring 

through fall, winters are projected to become 

milder, with increased risk of warm spells. Winter 

temperature variability is expected to alter plant 

phenology, which is another measure of growing 

season length. Earlier bud break and leaf onset can 

increase the risk of frost damage during subsequent 

spring frost events, which are expected to remain 

the same or increase due to increasing variability 

in daily temperature (Augspurger 2013, Pagter and 

Arora 2013, Rigby and Porporato 2008). However, 

the occurrence and timing of future frost events may 

depend on tree physiology and interactions with 

atmospheric moisture, temperature, wind speed, 

cloud cover, and air pollution (Hufkens et al. 2012; 

Inouye 2000, 2008).

Snow and Freezing Rain

Warmer temperatures are expected to continue to 

have dramatic impacts on the winter season. Total 

snowfall and the proportion of precipitation falling 

as snow decreased across the region during the 20th 

century (Chapter 3), and these trends are expected 

to continue (Hayhoe et al. 2007, Ning et al. 2015). 

Although the change in snowfall was not modeled 

using the PCM B1 and GFDL A1FI scenarios, other 

models project a decrease in total snowfall by the 

end of the 21st century (2081 to 2100) of 30 to 50 

percent under a low (B1) emissions scenario and by 

more than 50 percent under a high (A2) emissions 

scenario (Nelson Institute Center for Climatic 

Research 2018). In the coastal region of southern 

New Jersey, Delaware, and Maryland, total snowfall 

is projected to decline even more (50 to 70 percent 

under B1 and more than 70 percent under A2). The 

most substantial decrease in snow is expected to 

occur at the beginning of winter, in December and 

January (Notaro et al. 2014).

Similarly, the number of days with snowpack is 

projected to decrease across the Mid-Atlantic region, 

largely due to earlier melting of snow accumulations 

(Brown and Mote 2009, Hay et al. 2011). Changes 

in snow cover and duration may be observed sooner 

in mountainous regions and maritime environments 

(Brown and Mote 2009). Days with measurable 

snowpack are expected to be fewer by 30 to 50 

percent across the region under the B1 scenario, 

with 50 to 70 percent fewer days along the coastal 

areas (Nelson Institute Center for Climatic Research 

2018). Under the A2 scenario, days with snowpack 

are projected to decrease by 50 to 70 percent, except 

for the coastal plain, where they are projected to 

decrease by more than 70 percent. Some studies 

suggest that the frequency and severity of freezing 

rain may increase as the boundary between snowfall 

and rainfall moves northward with warming 

temperatures (Cheng et al. 2007, 2011).

Streamflow

Projected changes in temperature and precipitation 

are expected to alter streamflow in the Mid-Atlantic 

region (Hayhoe et al. 2007, Neff et al. 2000). One 

study in the Mid-Atlantic region projected increased 

streamflow under a range of climate models due to 

reduced snowpack (Neff et al. 2000). A more recent 

study in the Northeast projected an advance in peak 

flows of 10 to more than 15 days by the end of the 

century, with greater shifts in the north due to the 

influence of snowmelt on streamflow (Hayhoe et al. 

2007). Summer streamflows are generally projected 

to decrease as more water evaporates due to higher 

temperatures (Neff et al. 2000). Fall streamflow 

projections are variable based on the degree to 

which scenarios warm the climate and interactions 

with vegetation (Campbell et al. 2011, Hayhoe et al. 

2007). There is also expected to be greater annual 

variation, with increases in both low- and high-flow 

events throughout the year (Campbell et al. 2011, 

Demaria et al. 2015). Researchers projected heavy 

peak streamflows to increase by 19 days during the 

period 2028 to 2082 under two climate scenarios 

(Demaria et al. 2015). Similarly, low streamflows 

are generally projected to be lower, particularly 

during the fall and under scenarios projecting greater 

warming (Demaria et al. 2015, Hayhoe et al. 2007). 
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

91

Box 16: What is Drought?

Most simply, drought is a lack of water. A drought 

does not simply imply dry conditions, as certain 

ecosystems and forest communities are well adapted 

to dry conditions. Thus, a drought occurs when 

conditions are dry relative to long-term averages in 

a particular place, often causing moisture stress on 

plants adapted to that place. Drought is described in 

several ways within the scientific literature, often as 

meteorological, hydrologic, or agricultural drought. 

Meteorological drought is a function of precipitation 

frequency, and hydrologic drought is a measure 

of how much water is available in a watershed. 

Agricultural drought takes into account changes in 

the amount of water that evaporates from the soil 

and is transpired by plants, as well as information 

about soil moisture and groundwater supply. All 

three indicators can be important in understanding 

the effects of climate change on water within forest 

ecosystems and determining whether systems are 

lacking sufficient water.

In the United States as well as throughout North 

America, there has been a trend toward wetter 

conditions since 1950, and there is no detectable 

trend for increased drought based on the Palmer 

Drought Severity Index (Dai et al. 2004). Other 

studies of hydrologic trends over the last century 

generally observed little change or slight reductions 

in the duration and severity of droughts across the 

region as a result of increased precipitation in the 

eastern United States (Andreadis and Lettenmaier 

2006, Peterson et al. 2013b). Regional data from 

the Northeast support this general pattern (Peters 

et al. 2014). Between 1895 and 2014, there have 

been periods of drought; the mid-1960s represent 

the most extreme droughts during the period of 

record. Over the entire period, however, there 

has been no trend toward increasing drought 

incidence during the growing season (June through 

September) (Dobrowski 2011, Kunkel et al. 2013b, 

National Oceanic and Atmospheric Administration 

[NOAA] 2018). State-level data show increasingly wet 

conditions in New York and Pennsylvania over the 

last century, and decreasing moisture in Maryland, 

New Jersey, and Delaware (NOAA 2018). However, 

in 2016 and early 2017, many of these states along 

with the rest of the Northeast experienced the worst 

drought conditions since the 1960s (NOAA 2017a). 

The effects of drought on vegetation vary with 

timing, length, and severity of drought, the water 

holding capacity of soil, a species’ tolerance to 

drought, and whether other stressors are present. 

The effects of drought and moisture deficit on forests 

are discussed in Chapter 5.

Soil Moisture and Drought

In forest ecosystems, drought is a deficit of soil 

moisture available to plants and other organisms. 

Soil moisture is necessary for maintaining stomatal 

conductance and plant function; it also mediates 

microbial activity, decomposition, and nutrient 

turnover (Luce et al. 2016). Changes in soil moisture 

are largely driven by the balance of temperature, 

precipitation, runoff, and evapotranspiration; this 

balance equals the total amount of water added to or 

lost from the system (Box 16). Moisture stress can 

occur when increases in temperature and evaporation 

are not offset by a corresponding increase in 

precipitation (Clark et al. 2016, Hayhoe et al. 2007). 

Within the climate scenarios used in this assessment, 

the potential for more frequent droughts and 

moisture stress during the growing season appears 

to be highest under the GFDL A1FI scenario. Even 

under the milder PCM B1 scenario, however, 

warmer temperatures may also lead to greater 

evaporative demand of the atmosphere and 

physiological stress if increases in precipitation do 

not correspond to temperature increases. Although 

precipitation projections have greater uncertainty 

than temperature projections, a number of modeling 

studies point to substantially higher temperatures 

with no more than relatively modest increases in 

growing season precipitation (Hayhoe et al. 2007, 
CHAPTER 4: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

92

Kunkel et al. 2013b, Lynch et al. 2016, Nelson 

Institute Center for Climatic Research 2018). 

Model projections may differ because different 

model-scenario combinations project opposite 

trends. Many models generally agree on an 

increase in precipitation (and also soil moisture) 

in the Mid-Atlantic region during the winter and 

spring. However, models disagree on the direction 

and magnitude of change in the summer and fall, 

depending on the model scenario (Lynch et al. 

2016). 

Modeling soil moisture, especially in areas of 

complex topography, is complicated by the 

spatial and temporal variability in precipitation 

during the growing season. Many climate models 

cannot simulate the fine-scale local climate 

processes involving interactions of temperature 

and precipitation which result in changes in the 

hydrologic properties of soil (Ashfaq et al. 2010). 

The Variable Infiltration Capacity model simulated 

seasonal soil saturation across the United States 

during 2071 through 2100 and projected summer 

and fall decreases in soil moisture; within the MidAtlantic region, it projected the greatest decrease 

(10 to 15 percent) in Pennsylvania (Ashfaq et al. 

2010). A more recent study mapped the Hadley 

Centre Climate Model (A2 scenario) and the KeetchByram drought index on a grid for the 2041 to 2070 

period in the Mid-Atlantic region and projected 

large increases in drought potential during summer 

and fall (Liu et al. 2013). Even without substantial 

decreases in precipitation, higher temperatures are 

expected to drive increases in evapotranspiration and 

overall moisture loss from the soil and vegetation 

(Naz et al. 2016). These results suggest that the MidAtlantic region may experience more short-duration 

(1 to 3 months) warm-season droughts, but that 

the number of longer duration or severe droughts 

may not change significantly (Huntington et al. 

2009). Changes in temperature, precipitation, and 

soil moisture are likely to be highly variable within 

the Mid-Atlantic region, depending on landscape 

position, site characteristics, variability in weather 

events, and degree of climate change (Singh et al. 

2014). 

CHAPTER SUMMARY 

Projected trends in annual, seasonal, and monthly 

temperature and total precipitation indicate that the 

climate will continue to change through the end of 

this century. Temperatures are projected to increase 

in all seasons, and extreme warming is projected 

under the GFDL A1FI scenario at the end of the 

century. Future average temperature increases range 

from 1 to 8 °F with even higher potential increases 

in summer and fall. Precipitation patterns will also 

change and, combined with warmer temperatures, 

suggest a potential moisture deficit during a 

longer growing season. Changes in temperature 

and precipitation are also expected to destabilize 

long-term atmospheric patterns and result in more 

intense storms and subsequently more frequent 

flooding. The heightened uncertainty in projected 

summer and fall precipitation totals, and the ratio 

of precipitation events to dry spells, could have 

important consequences for tree growth, seedling 

establishment, and other forest processes that depend 

on adequate soil moisture. In the next chapter, 

we examine the ecological implications of these 

anticipated changes on forest ecosystems.
93

CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS 

ON FORESTS

Climate change is expected to have wide-ranging 

effects on forests in the Mid-Atlantic region. Some 

of these effects will be the direct effects of an altered 

climate, such as warmer temperatures and extreme 

precipitation. Climate change may also lead to many 

indirect effects, including interactions with other 

disturbances, which have the potential to severely 

change forest ecosystems. This chapter describes 

potential changes in forest ecosystems from the 

direct and indirect effects of climate change. The 

chapter is organized into two sections. First we 

present the results of three forest impact models to 

gather perspective on how individual tree species 

are generally expected to change through the end 

of the century. In the second section, we provide a 

synthesis of existing literature on climate change and 

regional forest ecosystems to put the model results 

into context and present additional complexity that 

is not included in the models. This information 

provides a foundation to assess the potential 

vulnerability of forest ecosystems in the assessment 

area (Chapter 6).

MODELED PROJECTIONS 

OF FOREST CHANGE 

Forest ecosystems in the assessment area may 

respond to climate change in a variety of ways. 

Potential changes include shifts in the spatial 

distribution, abundance, and productivity of 

tree species. For this assessment, we relied on 

a combination of three forest impact models to 

describe these potential changes: the Climate 

Change Tree Atlas (DISTRIB), LINKAGES, and 

LANDIS PRO (Table 20). The Tree Atlas’ DISTRIB 

model uses statistical techniques to model changes 

in suitable habitat for individual species over broad 

geographic areas. The LINKAGES model predicts 

establishment and growth of trees based on climate, 

soils, and other site information. The LANDIS 

PRO model simulates changes in the abundance, 

density, 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 (Iverson et al. 2016). Although each model 

has different inputs and produces different outputs 

(e.g., potential suitable habitat or realized landscape 

change), similarities in patterns across models 

suggest less uncertainty in projections than when 

patterns differ. Differences provide opportunities 

to better understand the nuances of ecological 

responses given the strengths and limitations of each 

model (Iverson et al. 2016). 

All three models used the same downscaled climate 

projections from two combinations of general 

circulation models (GCMs) and emissions scenarios: 

GFDL A1FI and PCM B1 (Chapters 2 and 4). 

Projected changes in temperature and precipitation 

for GFDL A1FI represent a greater degree of 

projected climate warming and change compared 

to PCM B1, so comparisons can be made across a 

range of potential future change. This consistency 

in the climate data used in each modeling approach 

allows the forest impact models to describe potential 

forest changes over the same range of future 

climates. A single simulation for each climate-model 

scenario was completed for each forest impact 

model.
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

94

Feature Tree Atlas LINKAGES LANDIS PRO

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 (percentage 

change)

Basal area and trees per 

acre by species

Model-scenario combinations GFDL A1FI and PCM B1 (see Chapter 2)

Assessment area Mid-Atlantic assessment area and six subregions 

Resolution 20-km (12-mile) grid

0.8-ha (0.2-acre) plots 

representing landforms in 

subsections

270-m (886-foot) grid

Number of species evaluated 112 24 24

Control/baseline climate 1971 through 2000 1980 through 2009 1980 through 2009

Climate periods evaluated

2010 through 2039, 

2040 through 2069, 

2070 through 2099

1980 through 2009, 

2070 through 2099 2009 through 2099

Simulation period n/a 30 years 2009 through 2099

Competition, survival, and 

reproduction 

No (but addressed through 

modifying factors) 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 20.—Overview of the three forest impact models used in this assessment (see Chapter 2 for detailed 

descriptions of each model)

The forest impact model results are most useful for 

describing trends across large areas and over long 

timescales. 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 moments or locations on a map. 

In this chapter, we present simulations for the end 

of the 21st century across the entire Mid-Atlantic 

region. Model results are presented for each model 

separately, and areas of agreement and disagreement 

between models are discussed. Model data for six 

subregions (Fig. 31) are also provided and describe 

some geographic differences across the assessment 

area; these subregions are based on the ecological 

provinces described in Chapter 1. For a few species, 

maps are provided to illustrate changes in the 

relative abundances and distributions of tree species 

in the Mid-Atlantic region. Data for intermediate 

time periods and geographic subregions are provided 

in Appendix 4. 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

95

Figure 31.—Assessment area subregions based on ecological provinces and sections (Fig. 2) mapped by Cleland et al. (2007) 

and described by McNab et al. (2007).

In general, there are only minor differences in model 

projections between the Mid-Atlantic region as a 

whole, and the individual subregions. However, the 

coastal plain subregion is notable for projections that 

differ considerably from those presented here for 

the overall region; these differences are attributed to 

unique climate in the coastal plain (see Chapters 3 

and 4). Therefore, the model results presented here 

for the region best reflect the interior subregions 

(subregions 1-5). For the coastal plain expert panel 

to assess vulnerability, we used the model results 

only for the coastal subregion. For the interior expert 

panel, we used the results for the whole Mid-Atlantic 

region.

Climate Change Tree Atlas

The Climate Change Tree Atlas (USDA Forest 

Service n.d.a) was used to evaluate potential 

changes in suitable habitat for tree species within 

the assessment area. The Tree Atlas does not model 

where species may occur in the future, but rather 

projects where suitable habitat for individual 

tree species may be present. As such, Tree Atlas 

projections should be interpreted not as expected 

species migration patterns, but as shifts in the 

distribution of favorable habitat conditions for a 

given species. A species distribution model called 

DISTRIB, which is a component of the Tree Atlas, 

was used to examine the features that contribute 

to the current habitat of a tree species and then to 

project where similar habitat conditions are likely 

to occur in the future (USDA Forest Service n.d.a). 

Habitat suitability (measured in terms of a species’ 

importance value) was modeled for 134 eastern tree 

species, 112 of which are currently present in the 

Mid-Atlantic region or are projected to have suitable 

habitat in the region during the 21st century under 

one or both climate scenarios. 
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96

The projected changes in potential suitable habitat 

were calculated for the years 2070 through 2099 

for the GFDL A1FI and PCM B1 scenarios and 

compared to suitable habitat under the present 

climate (Table 21). Species were categorized 

based on whether the results from the two climate 

scenarios projected an increase, decrease, or no 

change in suitable habitat compared to current 

climate conditions. Model results were considered 

mixed if an increase was projected under one 

scenario while a decrease was projected under the 

other scenario. Several tree species that are currently 

not present in the assessment area were identified 

as having potential new suitable habitat in the 

future under one or both scenarios. The DISTRIB 

model projects future habitat for each species 

individually, and the model reliability varies for 

each species. Model reliability is generally higher 

for common species than for rare species because 

forest inventories tend to undersample rare species 

(Iverson et al. 2008a). Table 30 (Appendix 4) 

contains the full set of results from the DISTRIB 

model, including model reliability and projections 

for three time periods (2010 through 2039, 2040 

through 2069, and 2070 through 2099) and six 

subregions within the Mid-Atlantic region. Table 31 

(Appendix 4) summarizes model results at the end 

of the century for the region and the six subregions. 

Results for each subregion are available in Tables 

32-37 (Appendix 4). 

Common name PCM B1 GFDL A1FI

Declines under Both Scenarios

American beech Small decrease Large decrease

American mountain-ash (-) Large decrease Large decrease

Balsam fir (-) Large decrease Large decrease

Balsam poplar Small decrease Small decrease

Black ash (-) Large decrease Large decrease

Black maple Small decrease Large decrease

Black spruce Small decrease Large decrease

Chokecherry Small decrease Large decrease

Eastern hemlock (-) Small decrease Large decrease

Eastern white pine (-) Small decrease Large decrease

Gray birch Small decrease Small decrease

Jack pine Large decrease Large decrease

Mountain maple (+) Small decrease Large decrease

Northern white-cedar Large decrease Large decrease

Paper birch Large decrease Large decrease

Pin cherry Small decrease Large decrease

Quaking aspen Small decrease Large decrease

Red pine Small decrease Large decrease

Red spruce (-) Small decrease Small decrease

Tamarack (native) (-) Small decrease Small decrease

White spruce Small decrease Small decrease

Common name PCM B1 GFDL A1FI

Increases under Both Scenarios

Black walnut Small increase Small increase

Blackgum (+) Small increase Small increase

Chinkapin oak Small increase Large increase

Eastern redcedar Small increase Large increase

Flowering dogwood Small increase Large increase

Hackberry (+) Small increase Large increase

Loblolly pine Small increase Large increase

Persimmon (+) Large increase Large increase

Pin oak (-) Small increase Large increase

Pond pine (-) Large increase Large increase

Post oak (+) Large increase Large increase

Sassafras Small increase Small increase

Scarlet oak Small increase Small increase

Scrub oak (bear oak) Small increase Small increase

Shagbark hickory Small increase Large increase

Southern red oak Small increase Large increase

Swamp tupelo (-) Large increase Small increase

Sweetgum Small increase Large increase

Sycamore Small increase Large increase

Winged elm Large increase Large increase

(continued on next page)

Table 21.—Potential changea in suitable habitat projected by the DISTRIB model for tree species in the Mid-Atlantic 

region
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

97

Common name PCM B1 GFDL A1FI

Increases under High Emissions

American elm No change Small increase

Baldcypress No change Small increase

Bitternut hickory (+) No change Large increase

Black oak No change Large increase

Black willow (-) No change Large increase

Blackjack oak (+) No change Large increase

Boxelder (+) No change Small increase

Bur oak (+) No change Large increase

Cherrybark oak No change Large increase

Eastern cottonwood No change Large increase

Eastern redbud No change Large increase

Green ash No change Large increase

Honeylocust No change Large increase

Mockernut hickory No change Large increase

Northern catalpa No change Small increase

Osage-orange (+) No change Small increase

Pignut hickory No change Small increase

Red mulberry No change Large increase

Rock elm (-) No change Large increase

Shellbark hickory No change Large increase

Shingle oak No change Large increase

Shortleaf pine No change Large increase

Slippery elm No change Small increase

Water oak (+) No change Large increase

White oak No change Small increase

Willow oak No change Small increase

New Suitable Habitat

Black hickory New habitat New habitat

Cedar elm (-) NA New habitat

Laurel oak** New habitat New habitat

Longleaf pine** New habitat New habitat

Ohio buckeye** New habitat New habitat

Overcup oak** New habitat New habitat

Redbay** (+) New habitat NA

Shumard oak** (+) NA New habitat

Slash pine New habitat New habitat

Sugarberry** New habitat New habitat

Turkey oak (+) New habitat New habitat

Water hickory NA New habitat

Common name PCM B1 GFDL A1FI

Declines under High Emissions

American basswood No change Small decrease

American holly No change Small decrease

Atlantic white-cedar (-) No change Small decrease

Bigtooth aspen No change Large decrease

Black cherry (-) No change Large decrease

Butternut (-) No change Large decrease

Red maple (+) No change Large decrease

Serviceberry No change Small decrease

Striped maple No change Large decrease

Sugar maple (+) No change Small decrease

Sweet birch (-) No change Large decrease

White ash (-) No change Small decrease

Yellow birch No change Large decrease

No Change under Both Scenarios

American chestnut No change No change

American hornbeam No change No change

Black locust No change No change

Chestnut oak (+) No change No change

Eastern hophornbeam (+) No change No change

Northern red oak No change No change

Pawpaw No change No change

Pitch pine No change No change

River birch No change No change

Swamp chestnut oak No change No change

Swamp white oak No change No change

Sweetbay No change No change

Virginia pine No change No change

Water tupelo (-) No change No change

Yellow buckeye (-) No change No change

Mixed Results

Cucumber tree Small increase Small decrease

Silver maple (+) Small decrease Large increase

Sourwood (+) Small increase No change

Table Mountain pine (+) Small decrease No change

Tulip tree Small increase Small decrease

a

 Species are grouped according to change classes (e.g., increase, 

no change) based on the proportional change in the area-weighted 

importance value 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 species. 

**Not observed in the Forest Inventory and Analysis data, but other 

data suggest species is present, but rare.

Table 21 (continued).—Potential changea in suitable habitat projected by the DISTRIB model for tree species in the 

Mid-Atlantic region
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

98

The DISTRIB results indicate that climate change 

is likely to lead to changes in the suitable habitat of 

many common tree species. At the same time, the 

ways in which tree species may actually respond to 

climate change are also influenced by life-history 

traits (e.g., dispersal mechanism, fire tolerance) 

not included in the DISTRIB model. Thus, a set 

of “modifying factors” supplements the DISTRIB 

results and provides additional information about 

whether species may be expected to do better or 

worse than the future suitable habitat values would 

suggest (Table 22) (Matthews et al. 2011). For 

example, although suitable habitat for red maple 

is projected to remain roughly the same under the 

milder climate scenario (PCM B1) and decrease 

under the harsher climate scenario (Table 21), red 

maple can take advantage of a wide range of habitat 

conditions and can disperse easily, suggesting that 

it may be able to compensate for potential loss of 

suitable habitat. Modifying factors are based on a 

literature review of the life-history traits, known 

stressors, and other factors unique to individual 

Species Factors that affect rating

Highest adaptive capacity

1. Red maple high probability of seedling establishment, wide range of habitats and soils, shade tolerant, high 

dispersal ability

2. Boxelder high probability of seedling establishment, high dispersal ability, drought tolerant, shade tolerant, 

wide range of temperature tolerances

3. Sourwood good light competitor, wide range of habitats

4. Bur oak drought tolerant, fire tolerant

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

Lowest adaptive capacity

1. Black ash emerald ash borer susceptibility, shade intolerant, low dispersal ability, drought intolerant, poor 

seedling establishment, fire intolerant, narrow range of soils

2. Pecan fire intolerant, susceptible to insect pests, shade intolerant

3. Water tupelo drought intolerant, fire intolerant, shade intolerant, narrow range of suitable habitats

4. Butternut fire intolerant, shade intolerant, drought intolerant, susceptible to butternut canker

5. Pond pine drought intolerant, shade intolerant, susceptible to southern pine beetle and other insect pests, low 

dispersal ability

Table 22.—Tree species with the five highest and five lowest values for adaptive capacity based on Climate Change 

Tree Atlas modifying factors

species. Other examples of modifying factors are 

drought tolerance, dispersal ability, shade tolerance, 

site specificity, and susceptibility to insect pests 

and diseases, all of which are highly related to the 

adaptive capacity of a species (Matthews et al. 

2011). See Appendix 4 for a detailed description of 

modifying factors and adaptability scores for each 

tree species. 

Decreases in Suitable Habitat

For the Mid-Atlantic region, 21 species are projected 

to undergo large or small declines in suitable habitat 

for both climate scenarios at the end of the 21st 

century (2070 through 2099), and declines are 

generally projected to be more severe under GFDL 

A1FI than PCM B1 (Table 21). These reductions 

in suitable habitat do not imply that mature trees 

will die within this century or that the species will 

be extirpated; rather, these results indicate that 

these species may be living under declining habitat 

conditions. As a result, trees living on already 

marginal sites may have greater susceptibility 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

99

to stressors (e.g., drought, pests, diseases, or 

competition from other species including invasives), 

or be at greater risk of regeneration failure.

American beech, eastern hemlock, eastern white 

pine, and quaking aspen are currently abundant 

within the assessment area, but suitable habitats 

for these species are projected to decline for both 

scenarios, especially under GFDL A1FI. Many of the 

species projected to decline under both scenarios are 

currently near the southern limit of their range in the 

Mid-Atlantic region or exist as disjunct populations 

in areas of glacial refugia. Red spruce, northern 

white-cedar, and balsam fir are glacial relicts that 

are currently limited to cool environments found at 

higher elevations, and the majority of these species’ 

ranges is much farther north (Hessl et al. 2011, 

Potter et al. 2010). Red spruce and balsam fir also 

have highly negative modifying factors (Table 39 in 

Appendix 4), suggesting that there are life-history 

traits or disturbance stressors that may cause these 

species to lose even more suitable habitat than the 

model results indicate. Eastern hemlock and red 

spruce are currently suffering attacks by hemlock 

woolly adelgid and spruce budworm. Balsam fir and 

red spruce are rated very low in adaptability (Table 

39) to climate change due to their susceptibility to 

fire topkill and a number of other disturbances, but 

fir can regenerate successfully in a wider range of 

site conditions (Day et al. 2014).

Other species are not as geographically limited or 

climate restricted, and therefore occupy a wider 

range of sites and conditions throughout the 

assessment area. Red pine, pin cherry, gray birch, 

paper birch, and black ash are projected to decrease 

substantially, but their current distributions may 

allow species movement into suitable refugia. Black 

ash also has highly negative modifying factors; 

black ash is shade intolerant and susceptible to 

drought, and the emerald ash borer is expected to 

cause high rates of mortality for all ash species in the 

region. Gray birch, paper birch, and pin cherry have 

some positive and some negative modifying factors 

(Table 39).

For 13 species, DISTRIB projected no change in 

suitable habitat under PCM B1 and a decrease under 

GFDL A1FI. Red maple, black cherry, white ash, 

and sugar maple are currently the most abundant 

species in the Mid-Atlantic, but all are projected 

to experience decreases in suitable habitat for 

GFDL A1FI. Red maple and sugar maple have high 

adaptability scores, suggesting they may do better 

than the models suggest, whereas white ash and 

black cherry have low adaptability scores. Other 

common species projected to lose suitable habitat 

under the high emissions scenario include sweet 

birch, striped maple, serviceberry, yellow birch, 

bigtooth aspen, American basswood, American 

holly, and butternut. 

Atlantic white-cedar is limited to a narrow coastal 

band (within 100 miles) along the Atlantic Ocean. 

Projected decreases in suitable habitat may be 

catastrophic for these highly localized populations, 

as they are unlikely to find alternate refugia within 

the assessment area. A negative modifying factor 

for Atlantic white-cedar is its narrow range of 

soil requirements, often limited to acidic muck 

bordering tidal marsh lands; as sea-level rise 

continues to encroach beyond current tidal habitats, 

salt intolerance is likely to have a more immediate 

impact on this species.

No Change in Suitable Habitat

The DISTRIB model projected “no change” (i.e., 

less than 20 percent change) in suitable habitat for 

15 species under both scenarios (Table 21). Northern 

red oak and chestnut oak are currently abundant and 

widespread across the region and their habitats are 

not projected to decrease or increase substantially. 

Chestnut oak has one of the highest adaptability 

scores and it is expected to do better than projected, 

partly because of its successful seed dispersal and 

establishment potential, ability to resprout, and 

resistance to fire topkill. Eastern hophornbeam, 

American hornbeam, pitch pine, and Virginia pine 

are somewhat less common on the landscape. 

American chestnut, swamp white oak, pawpaw, and 

yellow buckeye are considered relatively rare on 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

100

the landscape. Eastern hophornbeam has positive 

modifying factors, including drought tolerance, 

shade tolerance, and regeneration success. Yellow 

buckeye has several negative modifying factors, 

including specific habitat requirements and 

susceptibility to fire and drought, suggesting this 

species may fare worse than projected. 

Increases in Suitable Habitat

Suitable habitats for 20 species are projected to 

increase under both models by the end of the 

century (Table 21). Some of these species are 

already common in the assessment area: sassafras, 

blackgum, sweetgum, flowering dogwood, scarlet 

oak, black walnut, eastern redcedar, loblolly pine, 

scrub oak, shagbark hickory, sycamore, pin oak, 

southern red oak, and hackberry. Other species are 

considered rare but are projected to gain suitable 

habitat: persimmon, post oak, chinkapin oak, pond 

pine, swamp tupelo, and winged elm. 

Some of the species projected to increase have 

positive modifying factors that could help them 

occupy newly available habitat; these are blackgum, 

southern red oak, hackberry, persimmon, and post 

oak. Pin oak, pond pine, and swamp tupelo have 

negative modifying factors, which suggest that they 

may face additional stresses that could reduce their 

ability to take advantage of new suitable habitat.

The assessment area is currently at the northern 

range limit for some species, including loblolly pine, 

post oak, southern red oak, and sweetgum. These 

species’ ranges can potentially shift northward as 

the distribution of suitable habitat potentially moves 

northward. Because many of the species projected to 

lose suitable habitat through the end of the century 

are already established, forests in the assessment 

area may undergo changes, even increases, in 

species richness as species respond differently on the 

landscape. 

Red maple-sweetgum swamp at Magnolia Swamp, New York. Photo by David M. Hunt, New York Natural Heritage Program, 

used with permission.
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101

For 26 species, DISTRIB projected that suitable 

habitat will not change under PCM B1 but will 

increase under GFDL A1FI. White oak, black oak, 

and American elm are relatively common across 

the assessment area and have positive modifying 

factors such as drought tolerance or fire tolerance. 

Pignut hickory, and mockernut hickory are also 

relatively abundant in the assessment area, but these 

species are close to the northern extent of their range 

and DISTRIB results suggest that suitable habitat 

may move northeast in the future. The remaining 

species are relatively infrequent and in some cases 

limited to specific habitats. For example, slippery 

elm, boxelder, black willow, green ash, eastern 

cottonwood, and water oak are typically associated 

with moist, rich soils of lower slopes, floodplains, 

and bottomlands or occasionally grow on limestone 

formations.

Mixed Results in Suitable Habitat

For five species, the Tree Atlas model projected 

different responses for the two scenarios (Table 21). 

Mixed results for tulip tree and cucumber tree reflect 

projected habitat increases under a slightly warmer 

climate (PCM B1) but projected habitat decreases 

under a much warmer and drier climate (GFDL 

A1FI). The positive modifying factors associated 

with tulip tree, including its ability to disperse 

and regenerate successfully on a wide range of 

sites, suggest that it may do better than the model 

projected. Suitable habitat for sourwood is expected 

to increase under PCM B1, but persist under GFDL 

A1FI. Suitable habitat for silver maple is projected 

to decrease under PCM B1, but increase under 

GFDL A1FI. A positive modifying factor associated 

with sourwood and silver maple is shade tolerance. 

Table Mountain pine is projected to decrease under 

PCM B1 and not change under GFDL A1FI but has 

a positive modifying factor associated with drought 

tolerance. 

New Suitable Habitat

The DISTRIB model projected gains in newly 

suitable habitat for 12 species (Table 21) that are 

currently not present at detectable levels (i.e., in FIA 

inventory) in the assessment area. Black hickory, 

laurel oak, longleaf pine, Ohio buckeye, overcup 

oak, slash pine, sugarberry, and turkey oak are 

projected to gain new suitable habitat within the 

Mid-Atlantic region under both climate scenarios. 

Cedar elm, Shumard oak, and water hickory are 

projected to gain new habitat only under GFDL 

A1FI. Redbay is projected to gain new suitable 

habitat only under PCM B1. Many of these species 

have ranges that extend close to the assessment area, 

and some, such as laurel oak, overcup oak, redbay, 

slash pine, sugarberry, and turkey oak, are actually 

present in the southern edge of the assessment area. 

But these species are relatively rare and therefore 

not recognized by the Tree Atlas as currently on the 

landscape. 

Other species that are not currently present in the 

Mid-Atlantic region would require long-distance 

migration, whether natural or assisted, to establish 

and occupy suitable habitat in the assessment area. 

Habitat fragmentation and the limited dispersal 

ability of seeds could hinder the movement of 

species, despite the increase in habitat suitability 

(Ibáñez et al. 2008). Most species are expected to 

migrate more slowly than their habitats can shift 

(Iverson et al. 2004a, 2004b). 

Geographic Trends

Projected changes are not uniform across the 

assessment area, and areas of suitable tree habitat 

are governed by soils, salinity, moisture gradients, 

elevation, and other factors in addition to climate. 

The geographic and biological complexity of the 

Mid-Atlantic region warranted a closer look at 

the six subregions within the broader assessment 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

102

area (see Figure 31 for a map). Appendix 4 shows 

complete model results by subregion. About half 

of the species modeled were detected in all six 

subregions. Among the species projected to have 

suitable habitat across four or more subregions, 

distinct differences in climate, landform, and other 

characteristics often result in a variety of projected 

change classes between sections for a single species. 

The Piedmont (subregion 5) contains the most 

species (107), and the Western Allegheny Plateau 

(subregion 1) has the fewest species (83). This is not 

a complete reflection of species richness or diversity, 

however, because some additional rare species may 

be present but not at levels abundant enough to 

be detected by FIA inventories and subsequently 

modeled. 

Nine species were currently present or modeled 

only in the Piedmont or Coastal Plain subregion, 

or both subregions: American holly, bluejack oak, 

laurel oak, longleaf pine, redbay, slash pine, turkey 

oak, water hickory, and water locust. Of 31 species 

showing significant geographic trends (Appendix 4), 

13 species exhibit noticeable differences in modeled 

species response in the Coastal Plain compared to 

inland areas. For example, habitat for scarlet oak 

and blackgum is projected to increase in every 

subregion except the Coastal Plain, where habitat is 

projected to decrease. Conversely, eastern hemlock 

is projected to decline in every subregion except the 

Coastal Plain, where habitat is projected to increase 

but remain rare. 

Outputs from DISTRIB can also be visualized as 

maps, such as those available online through the 

Climate Change Tree Atlas Web site (https://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 (20 km × 20 km [12.5 miles × 12.5 miles]) 

basis rather than averaged by subregion within the 

assessment area (Table 20). For this assessment, 

maps for six species—black cherry, chestnut oak, 

northern red oak, pitch pine, red spruce, and sugar 

maple—were clipped to the shape of the MidAtlantic region (Fig. 32). These species were chosen 

to represent several species that are important for 

their abundance, economic value, or keystone 

species status. The maps highlight geographic trends 

in suitable habitat under two climate scenarios to 

show that projected changes are not uniform across 

the region, and that areas of suitable habitat are 

related to both climate change and local conditions. 

Suitable habitat for chestnut oak and northern red 

oak is currently widespread and was not projected 

to change considerably overall under both scenarios, 

although some areas show potential habitat loss 

under GFDL A1FI (Fig. 32). Suitable habitat for 

black cherry and sugar maple was not projected 

to change considerably under PCM B1 but was 

projected to decrease under GFDL A1FI; for black 

cherry the loss of habitat is evident across much 

of the assessment area and the remaining suitable 

habitat is largely concentrated in the New York 

portion (Fig. 32). Pitch pine is currently important 

only in the coastal plain and its habitat is projected 

to remain steady under both climate scenarios, with 

suitable habitat potentially increasing in central 

Pennsylvania and New York, although it is not 

likely to migrate on its own (Fig. 32). Red spruce 

is a keystone species currently limited to the cooler 

temperatures and moister conditions that occur 

above 3,000 feet in the Catskill Mountains of New 

York. The DISTRIB model projected, with high 

reliability, suitable habitat for red spruce to decrease 

under PCM B1 and to be extirpated from the MidAtlantic region under the high emissions scenario 

(Fig. 32). As temperatures continue to warm, local 

populations may be unable to migrate northward 

because these populations are already positioned at 

the highest elevations. 

These maps should be interpreted carefully. As 

mentioned earlier, DISTRIB results indicate only a 

change in the amount and geographic distribution of 

suitable habitat, not necessarily that a given species 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

103

Figure 32.—Modeled importance values for six tree species. Maps show current importance values modeled by DISTRIB, using 

USDA Forest Service Forest Inventory and Analysis data (top) and projected for the end of the century (2070 through 2099) 

under PCM B1 (middle) and GFDL A1FI (bottom) climate model-emissions scenarios. Importance values can range from 0 to 

100, with 0 indicating that the species is not present.
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

104

will be able to migrate to newly available habitat. 

Additionally, these results do not incorporate the 

influence of modifying factors (positive for sugar 

maple, northern red oak, and chestnut oak; negative 

for pitch pine, black cherry, and red spruce). As is 

the case for interpreting any spatial model outputs, 

local knowledge of soils, landforms, microclimate, 

and other factors is necessary to determine whether 

particular sites may indeed be suitable habitat for 

a given species in the future. These maps serve 

only as an illustration of broad patterns. Suitable 

habitat maps for all the species considered in this 

assessment are available online through the Climate 

Change Tree Atlas Web site (https://www.nrs.fs.fed.

us/atlas/tree; see also Appendix 4). 

LINKAGES 

The LINKAGES model integrates soil, climate, and 

species attributes to simulate changes in tree species 

growth potential and total biomass production at the 

landscape scale under future climate scenarios 

(Table 20). This information was used to 

parameterize the LANDIS model described in the 

following section. Growth potential represents a 

species’ ability to establish from seed and grow 

from bare ground at a particular site, assuming the 

presence of an adequate seed source and the absence 

of disturbance and competition from other species. 

Species growth is measured by the maximum 

biomass reached by a species at year 30. This 

30-year timespan is used because young regeneration 

is most susceptible to climate warming. Forest stand 

dynamics are more realistically addressed during 

longer periods by using the LANDIS PRO model. 

For this assessment, the LINKAGES model was 

used to predict tree growth potential for 24 species 

within the Mid-Atlantic region and for 6 subregions 

(Fig. 31). Estimates were derived from the weighted 

average of 0.2-acre plots within 6 to 8 landforms 

in 47 subsections. Absolute and percent changes in 

biomass were calculated by comparing the period 

2070 through 2099 under the PCM B1 and GFDL 

A1FI climate scenarios to the current climate during 

1980 through 2009 (Table 23). Change classes were 

calculated by dividing the modeled future biomass 

by the current climate biomass (see Appendix 4 for 

more change class methods). 

The LINKAGES model projected growth potential 

to decrease under both climate scenarios for seven 

species: yellow birch, quaking aspen, pitch pine, 

balsam fir, northern white-cedar, red spruce, and 

black spruce. With the exception of pitch pine, 

growth potential for these species was projected 

to reach zero (extirpation of species) under GFDL 

A1FI, suggesting that the higher emissions scenario 

may prevent tree regeneration and advanced growth 

in the Mid-Atlantic region. LINKAGES projected 

a large decrease in growth potential for pitch pine 

under GFDL A1FI. 

Eleven species exhibited no change under PCM B1, 

and decreases under GFDL A1FI. These decreases 

under GFDL A1FI were moderate for white ash, 

northern red oak, red maple, black cherry, scarlet 

oak, black oak, and pignut hickory. Sugar maple, 

eastern white pine, and eastern hemlock were 

projected to decline to a larger degree. These results 

are indicative of seedling sensitivity to soil moisture 

in the LINKAGES model, and suggest additional 

challenges to tree regeneration under GFDL A1FI.

Loblolly pine was the only species modeled to 

increase under both scenarios, with great increases 

under GFDL A1FI. LINKAGES projected no change 

(i.e., less than 20 percent change) in growth potential 

for chestnut oak and white oak. Growth potential for 

shagbark hickory and Virginia pine is projected to 

increase under PCM B1, but decrease under GFDL 

A1FI, suggesting that a small degree of climate 

change may benefit these species but that too much 

change may be detrimental. Tulip tree was projected 

to increase slightly (21 percent) under PCM B1, but 

not change from current levels under GFDL A1FI 

(14 percent).
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

105

Current 

climate

Future climate

PCM B1 GFDL A1FI

Species

Biomass 

(metric 

tons/acre)

Biomass 

(metric 

tons/acre)

Change 

(%) Change class

Biomass 

(metric 

tons/acre)

Change 

(%) Change class

Decreases under Both Scenarios

Balsam fir 2.74 0.89 -67 Large decrease 0.00 -100 Extirpated

Black sprucea 0.27 0.04 -86 Large decrease 0.00 -100 Extirpated

Northern white-cedar 2.38 0.49 -79 Large decrease 0.00 -100 Extirpated

Pitch pine 35.88 27.42 -24 Decrease 7.86 -78 Large decrease

Quaking aspen 86.96 55.20 -37 Decrease 0.00 -100 Extirpated

Red spruce 1.89 0.78 -59 Large decrease 0.00 -100 Extirpated

Yellow birch 96.74 61.54 -36 Decrease 0.03 -100 Extirpated

Decreases under High Emissions

American beech 96.30 87.36 -9 No change 14.99 -84 Large decrease

Black cherry 106.58 114.52 7 No change 71.47 -33 Decrease

Black oak 88.74 101.42 14 No change 60.13 -32 Decrease

Eastern hemlock 19.05 17.34 -9 No change 0.93 -95 Extirpated

Eastern white pine 50.47 53.26 6 No change 3.69 -93 Large decrease

Northern red oak 147.06 136.18 -7 No change 83.25 -43 Decrease

Pignut hickory 85.62 98.38 15 No change 60.09 -30 Decrease

Red maple 131.36 135.82 3 No change 89.96 -32 Decrease

Scarlet oak 91.38 99.62 9 No change 58.49 -36 Decrease

Sugar maple 123.80 107.84 -13 No change 28.89 -77 Large decrease

White ash 159.45 166.85 5 No change 93.01 -42 Decrease

Increases under Both Scenarios

Loblolly pine 42.84 55.62 30 Increase 87.13 103 Large increase

No Change under Both Scenarios

Chestnut oak 84.95 101.08 19 No change 71.39 -16 No change

White oak 130.63 124.12 -5 No change 116.08 -11 No change

Mixed Results

Shagbark hickory 60.82 83.28 37 Increase 18.96 -69 Large decrease

Tulip tree 182.12 220.43 21 Increase 207.85 14 No change

Virginia pine 12.34 29.42 138 Large increase 6.01 -51 Decrease

a A species with a biomass value ≥1.0 could exist at very low levels or not at all.

Table 23.—Change in tree species growth potential measured in maximum biomass reached in 30 years starting from 

bare ground as projected by the LINKAGES model in the Mid-Atlantic region under a current climate scenario 

(1980-2009) and two climate model-emissions scenarios at the end of the century (2070-2099)
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

106

Large increases in biomass are projected for some 

species that are currently absent from the region 

or have very limited distributions. For example, 

loblolly pine is currently uncommon east of the 

Ridge and Valley subregion (Fig. 31). LINKAGES 

projected a large increase in biomass for this species 

under GFDL A1FI partly because the modest 

increase in biomass more than doubled its presence 

on the landscape, increasing its biomass to levels 

comparable to black cherry and scarlet oak under 

current climate. Conversely, LINKAGES projected 

large decreases in biomass under both scenarios for 

balsam fir, northern white-cedar, red spruce, and 

black spruce partly because these species currently 

have low biomass in the region and even a small 

reduction in biomass resulted in large declines on the 

landscape. To account for these relationships, change 

percentages and change classes should always be 

compared to the absolute biomass values for the 

current climate at year 2100 and for each climate 

scenario. 

Geographic Trends

In addition to LINKAGES model results for the 

entire Mid-Atlantic region, model results are 

provided for six subregions to explore how trends 

may differ from one subregion to another (Appendix 

4). Sixteen species exhibit important subregional 

responses under PCM B1: black cherry, black oak, 

chestnut oak, eastern hemlock, eastern white pine, 

northern red oak, pignut hickory, pitch pine, red 

maple, scarlet oak, sugar maple, tulip tree, Virginia 

pine, white ash, white oak, and yellow birch. Many 

species also exhibit important subregional responses 

under GFDL A1FI. Projected species establishment 

values are substantially different in the Coastal Plain 

compared to other subregions in the assessment area. 

Model results for the entire region show no 

detectable change for black cherry under PCM B1, 

and a decrease in growth potential under GFDL 

A1FI (Table 23); however, when results are explored 

at the subregional level, we see some geographic 

variation (Fig. 33). Under PCM B1, black cherry 

is projected to increase in three subregions and 

decrease in two subregions. However, the subregions 

projected to increase under PCM B1 are projected 

to decrease under GFDL A1FI, especially in the 

Coastal Plain. This discrepancy suggests that black 

cherry may benefit from a small amount of warming 

projected by PCM B1 but that the large amount 

of warming projected by GFDL A1FI may exceed 

this species’ ecological limits. Chestnut oak is not 

projected to change under either scenario across the 

entire region, yet it also shows a mixture of increases 

and decreases when viewed on a subregional level 

(Fig. 33). 

LINKAGES results indicate only potential growth. 

Projected changes in biomass do not represent actual 

current or future distributions and do not predict 

that a given species will be able to colonize newly 

available habitat. We focused on establishment 

and growth of young trees, but mature trees could 

persist on a site for hundreds of years. Furthermore, 

LINKAGES is not spatially dynamic and does 

not simulate tree dispersal or any other spatial 

interaction, such as competition. This spatial 

interaction is examined by using LINKAGES results 

as input in the LANDIS PRO model. As is the case 

for interpreting any spatial model outputs, local 

knowledge of soils, landforms, and other factors is 

necessary to determine whether particular sites may 

indeed be suitable habitat for a given species in the 

future. These maps serve only as an illustration of 

broad trends.
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

107

Figure 33.—Change in growth potential projected by the LINKAGES model for six tree species under two climate modelemissions scenario combinations at the end of the century (2070 through 2099) relative to a current climate scenario (1980 

through 2009). Appendix 4 contains maps of all modeled species.
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

108

LANDIS PRO

Forest landscape change was simulated by using 

the LANDIS PRO model to project changes in tree 

abundance (basal area per acre) and density (trees 

per acre) for 24 tree species through the year 2100 

and beyond (Wang et al. 2017). The LANDIS PRO 

model differs substantially from the Climate Change 

Tree Atlas and LINKAGES because it simulates 

tree, stand, and landscape dynamics over time and 

can provide a prediction about the composition and 

structure of an individual pixel or larger area for any 

point in time during the simulation. To incorporate 

the effects of climate on species establishment and 

early growth, we based the species establishment 

parameter in LANDIS PRO on the biomass 

values projected by LINKAGES (Wang et al. 

2017). LANDIS PRO accounts for natural stand 

dynamics, including growth, mortality, competition, 

and succession, in addition to climate effects on 

establishment and growth. Because trees are longlived, near-term projections of forest change are 

more heavily influenced by the current forest 

conditions and management; the effects of climate 

change become increasingly pronounced over the 

long term (Duveneck et al. 2016, Iverson et al. 2016, 

Wang et al. 2017). 

Although the current climate is expected to change 

during the 21st century (Chapter 4), a current climate 

scenario—which holds current climate steady 

through 2100—is useful for understanding changes 

in tree species abundance and forest composition 

that occur as a result of natural succession and 

management, as opposed to changes driven by 

climate. Natural succession is important in the 

forests for this region as areas continue to recover 

from historical land clearing and timber harvest 

(Chapter 1). Because many forests in the region are 

still recovering from past disturbances, the basal area 

of most tree species is generally expected to increase 

throughout the century under all climate scenarios 

as forests undergo succession. All LANDIS PRO 

model results reflect current levels of forest harvest 

(based on FIA data), but do not include natural 

disturbances such as wind, fire, or insects. Forest 

harvest was simulated within management units 

(private industrial, nonindustrial, and public forest 

lands) in order to capture harvest variation across the 

region. Further details and methods were published 

by Wang et al. (2017).

The remainder of this section describes the LANDIS 

PRO model projections of basal area and trees per 

acre by species for the year 2100 (Table 24). The 

number of trees and basal area per acre are most 

informative in combination. For a given number 

of trees per acre, basal area increases with larger 

tree diameters. Thus, a high basal area with a 

relatively low number of trees can indicate a forest 

composed of trees that are relatively older and 

large in diameter. Conversely, a low basal area 

with a high number of trees per acre can indicate a 

forest composed of trees that are relatively younger 

and smaller in diameter. Change classes were 

calculated by dividing the modeled future basal 

area by the current climate basal area, and modeled 

trees per acre by the current climate trees per acre 

(Appendix 4). When model results are interpreted, it 

is important to compare the absolute values (which 

represent abundance) to the change percentages and 

change classes. Additional projections in 2040 and 

2070, as well as 2200, were also used to understand 

the long-term response of forests to climate change 

(Appendix 4). 

Results from the PCM B1 and GFDL A1FI scenarios 

were compared to a current climate scenario, which 

maintained the climate observed during 1960 to 

2010 through the end of the century. Under current 

climate, changes from 2000 to 2100 are attributed 

to succession and management (Fig. 34). Decreases 

represented tree removal due to harvest or natural 

mortality without recruitment. Under the current 

climate scenario, LANDIS PRO projected both 

basal area and trees per acre to decrease for 

black spruce, northern white-cedar, and balsam 

fir (Table 24). Increases under the current climate 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

109

Current climate PCM B1 GFDL A1FI

Tree species

BA in 

2000 

(ft2/

acre)

BA in 

2100 

(ft2/

acre)

Change 

from 

2000

Current 

change class

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climate 

PCM 

change 

class

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climate 

GFDL 

change 

class

American beech 3.49 6.49 +86% Increase 6.01 -7% No change 7.46 +15% No change

Balsam fir 0.06 0.05 -25% Decrease 0.04 -7% No change 0.05 +9% No change

Black cherry 7.90 9.14 +16% No change 9.02 -1% No change 9.63 +5% No change

Black oak 2.16 2.37 +10% No change 2.38 +1% No change 2.52 +6% No change

Black spruce* 0.00 0.00 -46% Large decrease 0.00 -30% Decrease 0.00 +5% No change

Chestnut oak 4.67 5.73 +23% Increase 5.94 +4% No change 7.25 +26% Increase

Eastern hemlock 5.30 4.72 -11% No change 4.48 -5% No change 5.09 +8% No change

Eastern white pine 3.38 2.86 -15% No change 2.63 -8% No change 2.84 -1% No change

Loblolly pine 1.32 1.17 -11% No change 1.18 +1% No change 1.45 +24% Increase

Northern red oak 6.54 5.33 -18% No change 5.18 -3% No change 5.81 +9% No change

Northern white-cedar 0.04 0.03 -39% Decrease 0.02 -24% Decrease 0.03 +2% No change

Pignut hickory 0.81 1.30 +60% Increase 1.24 -4% No change 1.42 +9% No change

Pitch pine 1.84 2.83 +54% Increase 2.90 +2% No change 3.47 +23% Increase

Quaking aspen 0.88 4.31 +391% Large increase 3.77 -13% No change 3.52 -18% No change

Red maple 16.50 23.61 +43% Increase 23.61 +0% No change 24.94 +6% No change

Red spruce 0.24 0.23 -5% No change 0.21 -9% No change 0.25 +8% No change

Scarlet oak 1.41 2.39 +70% Increase 2.40 +0% No change 2.50 +5% No change

Shagbark hickory 0.35 0.92 +163% Large increase 0.88 -4% No change 1.07 +17% No change

Sugar maple 7.11 8.67 +22% Increase 8.08 -7% No change 9.24 +7% No change

Tulip tree 3.38 2.92 -14% No change 2.94 +1% No change 3.47 +19% No change

Virginia pine 0.49 0.71 +45% Increase 0.72 +1% No change 0.81 +14% No change

White ash 4.16 7.60 +83% Increase 7.11 -6% No change 8.93 +18% No change

White oak 3.33 5.89 +77% Increase 5.52 -6% No change 7.10 +21% Increase

Yellow birch 0.84 1.90 +125% Large increase 1.69 -11% No change 1.93 +2% No change

*Species is present but rare; the zero value is the result of rounding to two decimal places. (continued on next page)

Table 24.—Change in basal area (BA) and trees per acre (TPA) projected by the LANDIS PRO model under a current 

climate scenario and two future climate model-emissions scenarios in the year 2100 for 24 species in the MidAtlantic region (see Appendix 4 for explanations for the change classes)
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

110

Current climate PCM B1 GFDL A1FI

Tree species

TPA 

in 

2000

TPA 

in 

2100

Change 

from 

2000 

Current 

change class

TPA 

in 

2100

Change 

from 

current 

climate 

PCM 

change 

class

TPA 

in 

2100

Change 

from 

current 

climate 

GFDL 

change 

class

American beech 25.9 18.3 -29% Decrease 16.1 -12% No change 14.1 -23% Decrease

Balsam fir 0.5 0.1 -87% Large 

decrease 0.0 -54% Large decrease 0.0 -47% Large decrease

Black cherry 24.0 18.8 -21% Decrease 18.6 -1% No change 21.4 +14% No change

Black oak 3.0 3.5 +18% No change 3.8 +9% No change 3.4 -2% No change

Black spruce* 0.0 0.0 -67% Large 

decrease 0.0 -62% Large decrease 0.0 -41% Large decrease

Chestnut oak 9.3 24.5 +165% Large 

increase 32.2 +32% Increase 41.2 +68% Increase

Eastern hemlock 17.1 5.1 -70% Large 

decrease 5.2 +2% No change 2.8 -46% Large decrease

Eastern white pine 9.0 8.8 -2% No change 7.7 -12% No change 3.6 -59% Large 

decrease

Loblolly pine 3.8 3.3 -14% No change 3.5 +9% No change 3.2 -1% No change

Northern red oak 10.3 8.9 -14% No change 8.3 -7% No change 12.3 +38% Increase

Northern white-cedar 0.8 0.2 -77% Large 

decrease 0.1 -68% Large decrease 0.1 -68% Large decrease

Pignut hickory 2.8 2.7 -4% No change 2.7 +2% No change 2.6 -2% No change

Pitch pine 7.3 4.4 -40% Decrease 4.0 -8% No change 4.1 -7% No change

Quaking aspen 2.4 35.3 +1,354% Large 

increase 27.0 -23% Decrease 9.9 -72% Large decrease

Red maple 74.5 42.6 -43% Large 

decrease 44.1 +3% No change 52.9 +24% Increase

Red spruce 1.7 0.4 -73% Large 

decrease 0.2 -48% Large decrease 0.2 -50% Large decrease

Scarlet oak 3.0 3.7 +22% Increase 4.1 +11% No change 3.0 -20% No change

Shagbark hickory 1.3 2.9 +113% Large 

increase 3.0 +6% No change 3.4 +20% No change

Sugar maple 29.6 26.6 -10% No change 24.1 -10% No change 18.1 -32% Decrease

Tulip tree 4.2 13.8 +230% Large 

increase 14.8 +7% No change 20.4 +48% Increase

Virginia pine 1.1 1.1 +1% No change 1.1 +0% No change 0.9 -21% Decrease

White ash 16.4 25.9 +58% Increase 24.1 -7% No change 32.2 +24% Increase

White oak 7.2 32.5 +353% Large 

increase 29.7 -9% No change 42.7 +31% Increase

Yellow birch 3.7 6.5 +74% Increase 5.2 -19% No change 2.6 -60% Large 

decrease

*Species is present but rare; the zero value is the result of rounding to one decimal place. 

Table 24 (continued).—Change in basal area (BA) and trees per acre (TPA) projected by the LANDIS PRO model under 

a current climate scenario and two future climate model-emissions scenarios in the year 2100 for 24 species in the 

Mid-Atlantic region (see Appendix 4 for explanations for the change classes)
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

111

Figure 34.—Change in basal area projected by the LANDIS PRO model for 24 tree species in the assessment area.

-
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

112

scenario represented pioneer species such as quaking 

aspen capturing new growing space, or longlived shade-tolerant species such as sugar maple 

regenerating and growing under existing canopies. 

Increases were projected in both trees per acre and 

basal area for chestnut oak, quaking aspen, scarlet 

oak, shagbark hickory, white ash, white oak, and 

yellow birch. Pitch pine is projected to increase 

in basal area, but decrease in the number of trees 

per acre. Black cherry is projected to change little 

in basal area, but decrease in the number of trees 

per acre. Trees per acre was projected to increase 

for tulip tree whereas basal area changed little, 

suggesting that this species may be able to take 

advantage of reduced competition as other species 

decline. Eastern white pine, loblolly pine, northern 

red oak, pignut hickory, sugar maple, and Virginia 

pine are projected to change little in the number of 

trees per acre, and change little or increase slightly 

in overall basal area.

Because of the strong influence of forest growth and 

succession during the 21st century, climate change 

is expected to have a relatively subtle influence on 

forest composition between now and 2100. There 

was no change in basal area projected under both 

scenarios for most of the species modeled, and the 

remaining six species were projected to increase or 

decrease moderately under only one scenario 

(Table 24). Most of those (chestnut oak, loblolly 

pine, pitch pine, and white oak) were projected 

to increase under GFDL A1FI. Black spruce and 

northern white-cedar were projected to decrease 

under PCM B1 but not change under GFDL A1FI.

The number of trees per acre is also an important 

measure of abundance and was projected to stay 

the same under both climate scenarios for black 

cherry, black oak, loblolly pine, pignut hickory, 

pitch pine, scarlet oak, and shagbark hickory (Table 

24). Combined with the information on basal area, 

black cherry, black oak, and pignut hickory were 

not expected to change much in the number of trees 

per acre and basal area under both scenarios. The 

number of trees per acre was also projected to stay 

the same under both climate scenarios for loblolly 

pine and pitch pine, but those species increased 

in basal area under GFDL A1FI, suggesting that 

conditions under the higher emissions scenario 

may not impede regeneration while the remaining 

trees continue to accrue biomass. Five species were 

projected to decrease substantially (see Appendix 4 

for classification methods) in trees per acre under 

both scenarios: balsam fir, black spruce, northern 

white-cedar, quaking aspen, and red spruce. 

Balsam fir, black spruce, northern white-cedar, 

and red spruce were also projected to decrease due 

to succession or management under the current 

climate scenario, suggesting that climate change will 

exacerbate the decline of these species. Chestnut oak 

was projected to increase in trees per acre under both 

GFDL A1FI and PCM B1, and due to succession 

under the current climate scenario. Chestnut oak 

basal area was also projected to increase under the 

current climate scenario, with no change projected 

under PCM B1 and an increase under GFDL A1FI, 

suggesting that climate change may further promote 

an increasing trend on the landscape. 

Geographic Trends

LANDIS PRO results point to notable differences in 

how species and forests respond to climate change 

across the Mid-Atlantic region. LANDIS PRO can 

provide information about the projected composition 

and structure of an individual pixel for any point 

in time during the simulation (Fig. 35). For some 

species, basal area is projected to increase in some 

areas while decreasing in others. For example, 

although chestnut oak is projected to increase on 

average for the Mid-Atlantic region, these increases 

are largely concentrated in the Northern Allegheny 

Plateau and Catskill Mountains (subregion 3), while 

the Hudson Valley and Piedmont (subregion 5) and 

the Coastal Plain (subregion 6) showed much more 

mixed responses for both climate scenarios. For 

many species, the Northern Allegheny Plateau (the 

large subregion in the middle of the Mid-Atlantic 

region) is a hotspot of activity, showing a fine-scale 

mixture of increases, decreases, and no change 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

113

Table 25.—Comparison of change classes for the end of century (2070 through 2099) period under two climate 

model-emissions scenarios for the 24 tree species in the Mid-Atlantic region modeled by all three forest impact 

models

so that the whole region looks gray. Additionally, 

maps for some species, such as chestnut oak, 

have a noticeable cutoff in data in the Northern 

Allegheny Plateau; this cutoff is the result of using 

statewide FIA data. Chestnut oak does not actually 

stop at the state line, but the higher abundance in 

Pennsylvania was reflected in the FIA data up to 

the state line. The scarcity of chestnut oak in New 

York reflects a much lower abundance in the New 

York FIA dataset. Additionally, the maps indicate 

abrupt transitions between change classes that are 

evident at the subregional lines, which are based on 

ecological provinces. The abrupt transitions are the 

result of using a particular soil for each subsection. 

In the hierarchy of ecological units, subsections 

are aggregated by section, which are aggregated 

by provinces. Provinces are areas of distinct soils, 

climate, and geological features, and these features 

seem to set the stage for how tree species respond to 

changes in climate.

LINKAGES growth 

potential

Tree Atlas (DISTRIB) 

suitable habitat

LANDIS PRO 

trees per acre

LANDIS PRO 

basal area

PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL A1FI PCM B1 GFDL A1FI

American beech No change Large decrease Decrease Large decrease No change Decrease No change No change

Balsam fir Large decrease Extirpated Large decrease Large decrease Large Decrease Large Decrease No change No change

Black cherry No change Decrease No change Large decrease No change No change No change No change

Black oak No change Decrease No change Large increase No change No change No change No change

Black spruce Large decrease Extirpated Decrease Large decrease Large Decrease Large Decrease Decrease No change

Chestnut oak No change No change No change No change Increase Increase No change Increase

Eastern hemlock No change Large decrease Decrease Large decrease No change Large Decrease No change No change

Eastern white pine No change Large decrease Decrease Large decrease No change Large Decrease No change No change

Loblolly pine Increase Large increase Increase Large increase No change No change No change Increase

Northern red oak No change Decrease No change No change No change Increase No change No change

Northern white-cedar Large decrease Extirpated Large decrease Large decrease Large Decrease Large Decrease Decrease No change

Pignut hickory No change Decrease No change Increase No change No change No change No change

Pitch pine Decrease Large decrease No change No change No change No change No change Increase

Quaking aspen Decrease Extirpated Decrease Large decrease Decrease Large Decrease No change No change

Red maple No change Decrease No change Large decrease No change Increase No change No change

Red spruce Large decrease Extirpated Decrease Decrease Large Decrease Large Decrease No change No change

Scarlet oak No change Decrease Increase Increase No change No change No change No change

Shagbark hickory Increase Large decrease Increase Large increase No change No change No change No change

Sugar maple No change Large decrease No change Decrease No change Decrease No change No change

Tulip tree Increase No change Increase Decrease No change Increase No change No change

Virginia pine Large increase Decrease No change No change No change Decrease No change No change

White ash No change Decrease No change Decrease No change Increase No change No change

White oak No change No change No change Increase No change Increase No change Increase

Yellow birch Decrease Extirpated No change Large decrease No change Large Decrease No change No change
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

114

Figure 35.—Change in basal area projected by the LANDIS PRO model for six tree species under two climate model-emissions 

scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009). Appendix 4 contains values for 

all modeled species.
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

115

DISCUSSION OF MODEL RESULTS

The three different models used in this assessment 

represent different facets of potential forest change 

in response to a changing climate. Therefore, 

the ability to make comparisons between the 

different models gives us a deeper understanding 

of which parts of a forest ecosystem may be most 

responsive or vulnerable to change (i.e., habitat 

and tree establishment, growth, and density) 

(Iverson et al. 2016). At the same time, however, the 

differences between the models, in terms of design, 

outputs, strengths, and weaknesses, prevent direct 

comparisons among model results (Iverson et al. 

2016). 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. A 

comparison using a suite of metrics among the three 

models for this and other regions has recently been 

published (Iverson et al. 2016).

Areas of Agreement

The DISTRIB model used by the Tree Atlas was able 

to characterize habitat for 112 species in the MidAtlantic region, and the LINKAGES and LANDIS 

PRO models simulated 24 species. Therefore, only 

24 species can be compared across all three models 

(Table 25 and Appendix 4). The changes in trees 

per acre from the LANDIS PRO model generally 

agree with LINKAGES and the Tree Atlas DISTRIB 

model. But climate-related changes in basal 

area were not evident by the end of the century, 

suggesting that mature trees may persist in even 

unsuitable habitat, in the absence of nonclimatic 

mortality factors. Where DISTRIB and LINKAGES 

results agree with LANDIS PRO estimates of trees 

per acre, there is higher confidence that results can 

suggest changes in tree establishment and growth.

All three models suggest that conditions at the 

end of the century will become less favorable for 

balsam fir, black spruce, eastern hemlock, northern 

white-cedar, quaking aspen, red spruce, and yellow 

birch, especially under the scenario of greater 

climate change (GFDL A1FI). At the same time, all 

three models suggest that conditions will remain 

favorable or become more favorable for black oak, 

chestnut oak, loblolly pine, and white oak, especially 

under GFDL A1FI. Additionally, the models tend 

to agree that many species that remain stable for 

PCM B1 are projected to increase or decrease under 

GFDL A1FI, and many species that are projected 

to decrease under PCM B1 are projected to decline 

further under GFDL A1FI. These results support 

the idea that the GFDL A1FI scenario represents 

a future climate that is beyond the tolerance of 

many species. These results also 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. 

The LANDIS PRO model simulates the pace at 

which forests are changing due to succession and 

management, with and without the benefits and 

drawbacks of climate change. These forests are often 

still responding to the significant human intervention 

during the past 300 years, and have substantial 

inertia in their species assemblages and growth 

patterns. However, model results generally do not 

incorporate large-scale disturbance events, including 

temperature and precipitation extremes, wind and 

ice storms, pests and diseases, and fire. Individual 

disturbances, and especially interactions between 

them, may result in rapid changes to existing forests 

and their long-term trajectories and response to 

climate change. 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

116

Disagreements

There do not appear to be any major discrepancies 

between results for individual species when the 

three models are compared, although there are some 

differences that can be explained by the differences 

between the model outputs. DISTRIB and 

LINKAGES both project suitable habitat, but key 

differences in LANDIS PRO are due to the added 

components of succession and dispersal. Although 

DISTRIB and LINKAGES projected small to large 

decreases for black cherry habitat in the MidAtlantic region, LANDIS PRO projected no change 

in basal area because black cherry trees are expected 

to persist where individuals are already established. 

In other words, although the amount of suitable 

habitat may decline, affecting a species’ ability to 

establish and grow past sapling stage, mature trees 

are likely to persist or even thrive in the absence of 

herbivory, competition, or other stressors. LANDIS 

PRO results emphasize that changes in habitat and 

species establishment largely complement changes 

in the number of trees per acre within the next 

century, but that changes in overstory composition 

and basal area are likely to take much longer in the 

absence of severe disturbance. 

The LANDIS PRO model projected no change in 

basal area for most species, but predicted more 

changes in trees per acre, particularly under GFDL 

A1FI. This result suggests that climate changes 

during the next century may have a more obvious 

impact on tree regeneration and recruitment, and 

that without other large-scale disturbances, changes 

in overall biomass may take longer. There were 

also several cases where model results disagreed on 

the direction of change. For example, under GFDL 

A1FI, LINKAGES projected a small decrease for 

scarlet oak, whereas Tree Atlas projected an increase 

for scarlet oak, suggesting that despite retaining 

suitable habitat, this species may face future 

challenges with regeneration and early growth. 

Limitations

The three different models used in this assessment 

were selected because each model represents a 

different mechanism of potential forest change as 

a result of a changing climate (Iverson et al. 2016). 

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. 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

• Genetic adaptation or phenotypic plasticity 

leading to diverse responses within a population

• Responses of understory vegetation, soil microorganisms, or soil mycorrhizal associations

• Changes in nutrient cycling due to changes in 

nitrogen deposition

• 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
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

117

Most of these factors could drive large changes in 

forest ecosystems throughout the assessment area, 

and 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. To inform an overall assessment, 

it is important to keep the preceding limitations in 

mind when the results from different models are 

weighed. 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. 

Aftermath of a rain storm that damaged a section of the Appalachian Trail in Pennsylvania. Such extreme events are not well 

modeled in climate projections. Photo by Patricia Leopold, Northern Institute of Applied Climate Science and Michigan Tech, 

used with permission.
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118

SUMMARY OF CURRENT 

SCIENTIFIC KNOWLEDGE 

ON POTENTIAL FOREST IMPACTS 

ASSOCIATED WITH CLIMATE 

CHANGE

The results presented earlier provide us with 

important projections of tree species distributions 

and forest response across a range of future climates, 

but these models do not account for all factors that 

may influence tree species and forest communities 

in 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 (Joyce et al. 2014, Vose et al. 

2012). These impacts can be coarsely divided into 

the direct effects of changing climate variables 

(e.g., temperature, precipitation, and carbon dioxide 

levels) on forests and the indirect effects of altered, 

new, and interacting stressors. For the most part, 

models such as the ones just described consider 

direct effects from changes in climate variables, but 

we recognize that the indirect effects of stressors can 

have important effects that models may not capture. 

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 

about additional direct and indirect effects of 

climate change on forests in the assessment area. 

The following information focuses on biological 

and atmospheric drivers of change rather than 

anthropogenic drivers of change (e.g., forest 

management), which can also have a major influence 

on forest change. Chapter 7 highlights climate 

change effects on topics such as forest management, 

human communities, and development.

Changes in Forest Productivity

One of the major implications of climate change 

is the potential for changes in forest productivity. 

Forest productivity describes the net growth rate 

of forests, which can be thought of as the total 

amount of biomass produced in a forest annually 

after losses from respiration and other causes are 

taken into account. It is an important way to assess 

the condition of a forest because it is related to 

the rate at which forests can sequester carbon and 

produce timber. This section describes the potential 

effects of altered temperature and precipitation 

and nonclimatic factors such as carbon dioxide 

enrichment and ozone on forest productivity and 

carbon gain. Other complex factors that may 

also influence forest growth—such as enhanced 

disturbance and intensified stressors—are discussed 

in subsequent sections.

Growing Season Length and Temperature

Warmer temperatures have increased the length of 

the growing season across the region (Chapter 3), 

and this trend is expected to continue (Chapter 4). 

There is evidence both worldwide and regionally 

that longer growing seasons during the past 

century have increased the time available to plants 

for photosynthesis and are partly responsible for 

observed increases in forest growth and carbon 

sequestration (Keenan et al. 2013, 2014; White et al. 

1999). One study of increased growing season length 

in the eastern United States found that carbon uptake 

advanced in the spring and extended later in the fall 

(Keenan et al. 2014). Projections of forest growth 

at four sites in the Northeast generally showed 

increases of up to 25 percent in productivity under 

scenarios of mild and moderate climate warming 

(Ollinger et al. 2008). Another study found that a 

1-percent increase in growing season length 

resulted in a 1.6-percent increase in net ecosystem 

productivity (White et al. 1999).

Temperature influences forest growth through 

effects on both photosynthesis and respiration. Plant 

respiration increases with increasing temperature, 

although plants are able to become acclimated to 

different temperature regimes (Aber and Melillo 

1991, Aber et al. 1995, Sendall et al. 2015). Some 

studies suggest that the increased respiration 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

119

under warmer temperatures is offset by increases 

in growth, resulting in a net gain in productivity 

(Loehle et al. 2016, Richardson et al. 2010). One 

study of forests across the Mid-Atlantic attributed a 

modest average increase in productivity of 4 percent 

due to the positive influence of warmer temperatures 

on photosynthesis (Pan et al. 2009). Another study 

focused on the New Jersey Pine Barrens projected 

increases in biomass for black oak, chestnut oak, 

pitch pine, and white oak (Scheller et al. 2012), all 

of which were projected to increase in biomass by 

the LANDIS PRO model. However, most plants 

have specific ecological thresholds for survival 

and reproduction within a specific range of 

minimum and maximum temperatures. Variability 

of temperatures within a single year is likely to 

continue to limit individuals as temperatures 

exceed those thresholds (Jackson et al. 2009). For 

example, the New Jersey study projected decreases 

in species establishment probability, suggesting that 

established individuals may benefit from climate 

change, but that regeneration may begin to fail. 

The New Jersey study also projected decreases in 

biomass for Atlantic white-cedar and swamp tupelo 

(Scheller et al. 2012). Another simulation of forests 

in the Northeast predicted slower growth rates when 

temperatures under A1FI exceeded the optima for 

photosynthesis, especially in spruce-dominated 

forests, which have a lower optimum temperature 

for photosynthesis (Ollinger et al. 2008). 

As temperatures rise during the next century, 

midsummer drought stress is projected to increase in 

regional forests (Campbell et al. 2009, Hayhoe et al. 

2007). The warmer temperatures that cause growing 

seasons to lengthen also accelerate hydrologic 

cycles (Chapter 4). As peak streamflows shift earlier 

toward spring, there is an increased potential for soil 

moisture deficits late in summer and fall (Chapter 

4), which is further compounded by increases in 

extreme precipitation events (Anandhi et al. 2013, 

Campbell et al. 2009, Hayhoe et al. 2007, Moore et 

al. 1997). Increased evapotranspiration may have 

greater influence than decreasing precipitation on 

summer soil moisture (Campbell et al. 2009). The 

effects of soil moisture and drought on forests are 

discussed later in this chapter.

Shorter winters and longer growing seasons may 

also affect other ecosystem processes, such as 

evapotranspiration, soil moisture, and streamflows, 

leading to positive or negative impacts on 

productivity (Anandhi et al. 2013, Campbell et 

al. 2009, Richardson et al. 2010). Shifts in the 

phenology of leaf emergence in response to warmer 

spring temperatures have the potential to increase 

the vulnerability of leaves and buds to late spring 

frosts and freezes (Ault et al. 2013, Rollinson and 

Kaye 2012, Zohner et al. 2017). Likewise, reduced 

snowpack can lead to frozen soils, affecting complex 

water, nutrient, and biotic dynamics. Where soils 

are exposed to extreme cold air temperatures, frozen 

soils may impede the infiltration of water into the 

soil and increase runoff (Hardy et al. 2001, Iwata 

et al. 2010). Deeper, more consistent frost has also 

been associated with increased export of nutrients, 

especially nitrogen and potassium, in stream water 

the following season (Fitzhugh et al. 2003, Mitchell 

et al. 1996). In winters when below-freezing air 

temperatures correspond to a lack of sufficient 

snow cover, increased depth and duration of soil 

freezing can lead to reductions in root biomass and 

rates of stem respiration (Reinmann and Templer 

2016). Northern hardwood species are generally 

shallow-rooted and more vulnerable to freezing, and 

frost-related mortality in this forest type has been 

observed elsewhere in the northern United States 

(Auclair et al. 2010). A smaller winter snowpack and 

greater depth and duration of soil freezing are also 

associated with declines in soil arthropod abundance 

and diversity in northern hardwood forests (Templer 

et al. 2012). Furthermore, in a complex landscape 

such as the Mid-Atlantic region, leaf phenology 

also depends on microclimate as characterized 

by proximity to urban areas, tidal streams, and 

elevational gradients (Elmore et al. 2012). For 

example, mountain valleys can be prone to overnight 

cooling, a phenomenon that results in the pooling of 
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120

cold air and increases the potential for frost even as 

snowfall decreases (Anandhi et al. 2013).

Although climate change is expected to increase 

forest growth in many ways, it may not be possible 

to separate climate-driven changes from other 

changes that are occurring in forests. For example, 

past land use in the region has resulted in secondgrowth forests that are young compared to preEuropean settlement conditions (Willard et al. 

2015). Several modeling studies demonstrate that 

forests across the region are generally expected to 

accumulate carbon during the next several decades 

simply due to succession and forest maturation 

(McGarvey et al. 2015; Pan et al. 2004, 2009). In 

the absence of severe disturbance, projected changes 

in forest productivity and biomass are generally 

driven by this successional change through midcentury, after which the effects of climate change 

on forest growth, whether positive or negative, 

become more apparent (Pan et al. 2009, Wang et al. 

2017). Additionally, changes in land use that result 

in forest conversion to nonforest have the potential 

to decrease any carbon gained through either 

forest succession or growth from climate change 

(Thompson et al. 2011). 

Carbon Dioxide Fertilization

One of the biggest uncertainties about the effects 

of climate change on forests may be the influence 

of carbon dioxide on plant productivity. Elevated 

carbon dioxide has a direct, positive effect on 

photosynthesis and increases the efficiency of water 

use in trees (Ainsworth and Rogers 2007, Norby 

and Zak 2011, Ollinger et al. 2008, Pan et al. 2009). 

There is evidence that elevated carbon dioxide has 

contributed to enhanced tree growth during the past 

two centuries (Cole et al. 2010, Franks et al. 2013, 

Norby and Zak 2011) and potentially offset some of 

the effects of drier growing seasons (Franks et al. 

2013, Wang et al. 2006).

Modeling studies examining productivity in forests 

across the Mid-Atlantic and Northeast consistently 

simulate greater increases when elevated carbon 

dioxide is included in modeling (Aber et al. 1995, 

Ollinger et al. 2008, Pan et al. 2009). For example, 

projections of forest growth at four sites in the 

Northeast generally showed increases of 9 to 25 

percent in productivity under scenarios of mild 

and moderate climate warming without carbon 

dioxide fertilization; under elevated carbon dioxide, 

increases in productivity were much higher, ranging 

from 25 to 75 percent (Ollinger et al. 2008). This 

effect is particularly strong for deciduous forests; 

the benefit of carbon dioxide was projected to be 

less in spruce forests because of the sensitivity to 

temperature increases (Aber et al. 1995, Ollinger 

et al. 2008). Often, the models suggest that carbon 

dioxide fertilization has a greater effect on forest 

productivity than does climate (Aber et al. 1995, 

Hickler et al. 2015, Ollinger et al. 2008, Pan et al. 

2009). As discussed earlier, warmer temperatures 

and longer growing seasons can lead to increased 

evapotranspiration, water loss, and potential for 

moisture stress. Elevated carbon dioxide can 

partially offset this effect by improving water use 

efficiency (Dangal et al. 2014, Ollinger et al. 2008). 

Although carbon dioxide enrichment experiments 

and models suggest net primary productivity will 

increase under elevated carbon dioxide, the carbon 

dioxide fertilization effect is moderated by other 

environmental change factors, including nutrient 

and water availability, ozone pollution, and tree 

species, age, and size (Ainsworth and Long 2005, 

Norby and Zak 2011, Norby et al. 2005, Pan et al. 

2009). Productivity increases under elevated carbon 

dioxide could be partially offset by reductions in 

productivity from warming-induced moisture stress 

or the effects of future disturbances (Dieleman et al. 

2012, Franks et al. 2013). In fact, climate changerelated disturbances such as fire, insects, disease, 

and management could reduce forest productivity 

independent of carbon dioxide fertilization. 

Numerous models simulate the effects of moderately 

elevated carbon dioxide on tree growth, but few 

experiments have examined the effects of carbon 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

121

dioxide on the distribution of carbon to wood 

production (Hickler et al. 2015). Furthermore, few 

studies in the Northeast have examined the effects of 

elevated carbon dioxide above 600 parts per million 

(ppm) (Ollinger et al. 2008). Thus, we know little 

about how regional forests may respond under even 

higher levels of atmospheric carbon dioxide, such 

as the 900 ppm levels projected under the A1FI 

emissions scenario for 2100 (Chapter 2). 

Ozone

Forests are exposed to ozone deposition and 

individual plants vary in their response to ozone, 

independent of species or proximity to another 

individual (Smith et al. 2012). Some species, 

including black cherry, tulip tree, and white ash 

are injured by ozone (Smith et al. 2012). Ozone 

affects stomatal control, causing reduced water use 

efficiency, and has also been linked to needle blights 

in white pine (Mohan et al. 2009). Studies suggest 

that ozone exposure can offset carbon dioxideinduced gains in productivity and increase water 

stress (Karnosky et al. 2003, McLaughlin et al. 2007, 

Mohan et al. 2009). Ozone can also cause changes in 

leaf chemical composition and emission of volatile 

compounds, which have the potential to affect plant 

defense mechanisms or attractiveness to herbivores 

(Mohan et al. 2009). Recent pollution control 

policies have reduced emissions of ozone precursors 

such as nitrogen oxides and carbon monoxide, but 

ozone is still chronically present at moderate levels. 

Despite a decreasing trend in ozone-related tree 

Black cherry in successional maritime forest in Gateway National Recreation Area, New Jersey. Photo by Gregory J. Edinger, 

New York Natural Heritage Program, used with permission.
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122

injuries from 1994 to 2010, ozone levels in the MidAtlantic states have caused injury to trees in every 

year or two of that period (Smith et al. 2012). Ozone 

injury has been shown to increase with soil moisture 

and decrease during times of moisture deficit (Davis 

and Orendovici 2006, McLaughlin et al. 2007, 

Orendovici-Best et al. 2010, Smith et al. 2012). 

Ozone injury may increase with climate change 

as wet conditions support the reaction of volatile 

organic compounds and nitrogen oxides to produce 

ozone (Rustad et al. 2012).

Nutrient Cycling

As air temperatures warm and precipitation 

patterns change, the cycling of nutrients between 

plants, soils, and the atmosphere may also change. 

Many factors, including changes in temperature, 

precipitation, soil moisture, and acid deposition, 

and the interactions among these factors, can 

impair nutrient cycling and the availability of 

nitrogen to trees and other vegetation (Campbell 

et al. 2009, Rennenberg et al. 2009). For example, 

increased nutrient leaching may occur where snow 

melt, soil warming, and soil biological activity 

begin earlier in the spring while the onset of leafout, photosynthesis, and overstory plant nutrient 

uptake still happens later (Campbell et al. 2010, 

Groffman et al. 2012). Likewise, extremes in 

light environment, temperature, precipitation, 

pathogen attack, and herbivory can induce or 

amplify nutrient imbalances in sugar maple forests 

(St. Clair et al. 2008). The results of soil warming 

experiments indicate that warmer temperatures 

are likely to increase the amount of carbon lost 

from forests through soil respiration (Campbell 

et al. 2009, McDaniel et al. 2014, Rustad et al. 

2001), although the degree of soil respiration is 

related to the availability of soil moisture and 

nutrients. Alterations in nutrient cycling have 

important implications for the productivity of forest 

ecosystems, which can be limited by nutrients such 

as phosphorus, nitrogen, calcium, magnesium, and 

potassium (Campbell et al. 2009, Templer et al. 

2012).

Decomposition of vegetation is a major component 

of most nutrient cycles and 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). In 

studies that examined the effects of extended dry 

periods followed by moisture pulses on nutrient 

cycling, moisture pulses led to a flush of mineral 

nitrogen, but was not sufficient to compensate for 

the lack of microbial activity during dry periods 

(Borken and Matzner 2009, McDaniel et al. 2014). 

Thus, an increase in wet-dry cycles appears to lead 

to a reduction in nutrient availability for trees. 

These results suggest that the increasingly episodic 

precipitation regime in the assessment area may add 

further stress to forest ecosystems in the future.

The long-term effects of past and ongoing acid 

deposition increase the complexity of connected 

nutrient cycles and their interactions. Although 

warmer temperatures have the potential to increase 

enzymatic activity and nutrient cycling, acid 

deposition will remain an important consideration. 

Anthropogenic emissions of nitrogen and sulfur 

increased during 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 deposited at high concentrations 

through rain and snow in the eastern United States, 

particularly in high-elevation sites (Pardo et al. 

2011, Smith et al. 2016). In forest ecosystems, 

hydrogen ions associated with nitrogen and sulfur 

deposition displace 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 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

123

streams through acidification (Aber et al. 1989, 

1998; Elliott et al. 2013; Fernandez et al. 2003; 

Long et al. 2013; Schaberg et al. 2006). Nitrogen 

saturation has also been shown to reduce carbon 

allocation to plant roots and mycorrhizae and 

suppress organic matter decomposition (Frey et al. 

2014, Pardo et al. 2011). Available evidence suggests 

that nitrogen and sulfur deposition has contributed to 

the increased susceptibility of forests to drought and 

insect attack, and that continued acid deposition is 

expected to contribute to reduced ability to withstand 

climatic changes (Friedland et al. 1984, McNulty 

and Boggs 2010, Pardo et al. 2011). 

Other stressors notwithstanding, some research 

suggests that nitrogen deposition could be beneficial 

and help fuel forest growth under elevated carbon 

dioxide (Devaraju et al. 2016, Rustad et al. 2012, 

Thornton et al. 2007). In a study of North American 

trees, earlier spring phenology has caused increased 

demand for nitrogen by plants (Elmore et al. 2016). 

Some species have been observed to respond 

positively to nitrogen deposition, including tulip 

tree, black cherry, red maple, sugar maple, and 

red oak, while other species responded negatively, 

including red pine and red spruce (Thomas et al. 

2010). A study focusing on the New Jersey Pine 

Barrens simulated effects of fire and climate change 

in a nutrient-poor and nitrogen-limited landscape 

and predicted declining nitrogen as a result of 

increased leaching under higher precipitation 

(Lucash et al. 2014). Future rates of deposition are 

unknown, but have the potential to decrease in the 

future as an indirect effect of potential reductions in 

greenhouse gas emissions (Driscoll et al. 2014). The 

potential impacts of elevated nitrogen deposition in a 

changing climate remain unclear.

Sea-level Rise and Saltwater Intrusion

Forest ecosystems along coasts will be affected by 

climate change. Mid-Atlantic coastal ecosystems—

including wetlands, salt marshes, estuaries, and 

forests—provide a number of benefits, such as 

water filtration, habitat for fish, carbon storage, and 

recreation for some of the most populated areas in 

the country (Moser et al. 2014, Scavia et al. 2002, 

Willard et al. 2015). Additionally, coastal ecosystems 

help to buffer storm surges and waves and reduce 

impacts from flooding. As sea levels rise, sea water 

is expected to inundate land, and water features that 

are currently inland may be subjected to increased 

water salinity, higher acidity, and other changes in 

ecosystem dynamics (Moser et al. 2014). Coastal 

ecosystems play an important role in buffering the 

extreme conditions along the coasts, and impacts 

on these systems can reduce their ability to protect 

against effects such as storm surges and flooding 

(Groffman et al. 2014). 

Coastal habitats are threatened by a range of 

climatic and environmental stressors that reflect a 

complexity of natural and anthropogenic influences. 

Forested wetlands, swamps, and adjacent marshes 

are sensitive to changes in sea level. Several 

studies note that salinity increases of 2 parts per 

thousand can cause a freshwater swamp forest to 

transition to marsh (Anderson et al. 2013a, Krauss 

et al. 2009). Freshwater tree species can tolerate 

low chronic levels or acute episodes of moderately 

increased salinity, but may suffer during periods 

of higher exposure (Doyle et al. 2007a). However, 

low amounts of salinity can have severe effects on 

freshwater systems that are not usually reached by 

saltwater (Middleton 2016, Stanturf et al. 2007). 

Salinity levels can remain high for months or 

longer, especially in situations where soil salinity 

is increased, and may result in suppression of 

regeneration (Middleton 2016). Increased salinity 

can also affect nitrogen inputs, ultimately impeding 

forest growth (Krauss et al. 2009). 

Drought can also influence saltwater intrusion; as 

streamflow decreases during a drought, saltwater 

is able to move farther upriver (Doyle et al. 2007a, 

Rheinhardt 1992). Drought-induced salinity stress 

has caused widespread mortality and long-term 

negative effects on tree growth in forests along the 

Atlantic coast (Anderson et al. 2013a; Doyle et al. 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

124

2007a, 2007b). The sea level along the Atlantic coast 

continues to rise, resulting in not only increased 

salinity and intrusion, but land subsidence and 

flooding (Climate Change Science Program 2009, 

Kearney and Stevenson 1991). Evidence suggests 

that coastal forests in the Mid-Atlantic region are 

already in decline from the effects of sea-level rise 

(Glick et al. 2008). 

Disturbance Frequency and Intensity

Climate change may increase the frequency 

and severity of disturbances, such as drought, 

catastrophic winds, ice storms, rainstorms, wildfires, 

and floods (Dale et al. 2001, Hanson and Weltzin 

2000, Itter et al. 2017, Vose et al. 2016, Weed et al. 

2013), and indeed, evidence continues to mount that 

some disturbance events are already increasing in 

frequency and intensity (Dale et al. 2016). Changes 

Great blue heron at Beaver Meadows Recreation Area, Allegheny National Forest, Pennsylvania. Photo by Kathleen Creek, 

Allegheny National Forest. 

in these various disturbance regimes, with their 

ability to fundamentally alter ecosystems, may have 

the most obvious and even drastic effects of climate 

change on Mid-Atlantic forests. Some of these 

disturbances may also interact to increase system 

susceptibility to other disturbances; for example, 

tree mortality and increased downed wood caused by 

extreme wind events may increase wildfire risk.

Extreme Precipitation and Floods

One of the most striking effects of climate change 

is that the hydrologic cycle is intensified as a 

consequence of more energy in the atmosphere, 

resulting in a greater amount of precipitation falling 

in large events (Chapter 4). Extreme precipitation 

can have substantial effects on ecosystems, 

particularly when rainfall occurs as part of an 

extreme storm event. As one example, wind- and 
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125

pressure-driven storm surges during hurricanes can 

result in flooding, particularly when these events 

occur in conjunction with high tides (Frumhoff et 

al. 2007). This type of interaction occurred during 

the Great Hurricane of 1938 and Hurricane Sandy 

in 2012, both of which made landfall on the MidAtlantic coast. 

Increased extreme precipitation is expected to 

exacerbate runoff and soil erosion rates (Nearing 

et al. 2004), although most studies examining the 

effects of climate change on soil erosion have 

focused on agricultural settings, rather than forest 

ecosystems. Additional vegetative cover and root 

stabilization typically found in forest systems may 

make forests less prone to soil erosion, but not all 

forest soils will be equally protected. Reductions 

in vegetative cover from climate-related impacts 

or disturbance events such as prolonged drought, 

wildfire, or increased tree mortality, could lead 

to greater susceptibility to erosion. Additionally, 

reduced snow cover and a shift of winter 

precipitation from snow to rain may make forest 

soils and streams particularly vulnerable to erosion 

during the late fall and early spring. The high density 

of roads and impervious surfaces in the Mid-Atlantic 

region is likely to intensify flooding and erosion 

potential.

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 

and soils. In lowland areas, floods are generally 

more gradual and last longer, with longer periods 

of soil saturation and less tree breakage. Flooding 

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 strongly 

influence tree species and forest diversity, especially 

in lowland and riparian forests (Vadas and Sanger 

1997).

Wind Disturbance

Wind disturbances, including hurricanes, tornadoes, 

downbursts, gales, and intense windstorms, are 

a primary ecological driver of many regional 

forests; both small-scale and stand-replacing wind 

events influence the tree species composition, 

forest structure, and landscape complexity (Xi 

and Peet 2011). These types of disturbance events 

have historically been an important component 

of the disturbance regime for forests along the 

Mid-Atlantic coast, with large-tree disturbances 

increasing from north to south (Vanderwel et al. 

2013). The effects of wind disturbances can vary 

greatly, occurring at different spatial scales and 

causing different types of damage to individual trees 

or landscapes, including abrasion, leaf stripping, 

breakage of limbs and stems, and uprooting (Stanturf 

et al. 2007). The physical effects of a given wind 

event on forests may be influenced by many factors, 

such as storm severity, forest composition, stand age, 

soils, and topography (Peterson 2000, Xi and Peet 

2011). Impacts that are common across most wind 

disturbances include tree mortality, altered forest 

structure, and altered tree species composition and 

diversity (Xi and Peet 2011). Although tornadoes 

are relatively infrequent, intense winds generated 

from hurricanes (>74 miles per hour [mph]), 

microbursts (>170 mph), and other storms can 

cause trees to uproot or break (Ulbrich et al. 2008, 

2009). Hurricanes affecting the Atlantic coast can 

cause significant wind damage and blowdowns as 

far inland as western Maryland and West Virginia, 

where wind speeds can reach 50 mph (Boucher et al. 

2005).

Some evidence indicates that severe convective 

storms (e.g., thunderstorms, hailstorms) or extreme 

wind events have increased in recent decades in 

the region (Bryan et al. 2015, Kunkel et al. 2013b). 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

126

There is also some evidence that wind events may 

increase in frequency or severity as the atmospheric 

conditions leading to high winds become more 

common (Del Genio et al. 2007; Peterson 2000; 

Trapp et al. 2007, 2011) and return intervals for 

severe wind events shorten (Frelich and Reich 

2010). Although there is little information on 

localized wind events, there is greater evidence 

that the conditions leading to tropical storms and 

hurricanes may increase as a result of climate change 

(Chapter 4).

If wind disturbances do increase as a result of 

climate change, forest ecosystem dynamics may 

also change. Catastrophic wind disturbances can 

alter successional pathways and have long-lasting 

effects on species composition and diversity 

(Xi and Peet 2011). Wind damage from less 

severe events can shift a system into smaller 

tree size-class distributions as larger trees suffer 

more bole breakage, leaving smaller trees as 

survivors (Peterson 2000). 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. 2013a). Succession may be set back if sprouts 

of damaged trees reclaim the canopy, or altered 

altogether if understory species shift the composition 

toward late-seral species (Peterson 2000), as was 

observed in many forests after the 1938 hurricane 

(Spurr 1956). 

More frequent or widespread blowdown events may 

release the understory and accelerate the transition 

to shade-tolerant species (Abrams and Scott 1989). 

Events that create large openings may provide 

opportunities for regeneration of intermediate 

shade-tolerant species such as white oak, flowering 

dogwood, and various hickory species, especially 

in higher elevations (Abrams et al. 1998, Campbell 

et al. 2005). As with more severe events, local site 

conditions including forest composition, stand age, 

soils, and topography have a substantial influence on 

the specific effects of a particular disturbance event.

Under climate change, stand-replacing wind 

events could potentially act as a catalyst for more 

rapid ecosystem change than would occur through 

migration and competition alone. This may be 

especially true where regeneration consists of novel 

species mixes or where other stressors, such as 

invasive species or overabundant herbivores, have 

greatly altered forest understory and regeneration 

conditions. Moreover, tree mortality as a result of 

future wind events may increase the risk of wildfire. 

Finally, postdisturbance management actions, 

such as salvage logging, may also compound the 

severity of these events, creating novel regeneration 

environments.

Ice Storms

Ice storms are particularly prevalent in the eastern 

United States, and these storms can cause substantial 

damage to ecosystems and infrastructure (Changnon 

2003a, Irland 2000, Rustad and Campbell 2012). 

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 (Changnon 

2003a). 

In forests, the accumulation of ice on trees can 

cause effects ranging from minor twig breakage to 

extensive crown damage. 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 and hemlock 

are less susceptible (Irland 2000, Turcotte et al. 

2012). Within species, damage appears to be greater 

in older, taller individuals, with higher mortality in 

sawtimber size classes (>10 inches diameter) than 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

127

in pole or sapling size classes (<10 inches diameter) 

(Turcotte et al. 2012). Residual trees can have 

reduced photosynthesis due to the loss of crown 

or decreased productivity as resources are used to 

close wounds or protect against pathogens. They 

are also more susceptible to infection by pests and 

pathogens. Gap formation from branch and tree loss 

can alter light regimes, soil climate, and seedling 

establishment (Rustad and Campbell 2012). 

Wildfire

Climate change has the potential to affect patterns of 

wildfire disturbance in a number of ways, although 

the specific effects on eastern forests are complex, 

hard to predict, and likely to differ geographically, 

by forest community, and over time. Climate can 

directly affect the frequency, size, and severity 

of fires, as well as indirectly affect fire regimes 

through influence on vegetation structure and 

composition (Sommers et al. 2011). Fire can be a 

catalyst for change in vegetation in many ways, such 

as by prompting more rapid change than would be 

expected based only on the changes in temperature 

and moisture availability (Gillett et al. 2004). As 

with wind disturbances, the potential exists for novel 

successional pathways following wildfire if climatic 

conditions, seed sources, or management decisions 

favor different forest types.

The conditions responsible for wildfire behavior are 

the result of weather, topography, and fuels (Moritz 

et al. 2012). Climate change is expected to alter 

temperatures, precipitation, and evapotranspiration, 

thereby influencing future wildfire risk. If warmer 

temperature and greater evapotranspiration exceed 

modest precipitation increases, conditions supporting 

wildfire may become more frequent (Drever et al. 

2009, Guyette et al. 2014). This may be particularly 

important during the early spring and late fall, 

when vegetation holds less moisture and the drier 

conditions are more favorable for wildfire (Heilman 

et al. 2015). In addition to the direct effects of 

temperature and precipitation, increases in fuel loads 

from pest-induced mortality or blowdown events 

could increase fire risk, but the relationship between 

these factors can be complex (Hicke et al. 2012, 

Sommers et al. 2011). For example, in the MidAtlantic coastal plain, drought and insect damage 

from gypsy moth and southern pine beetle have the 

potential to increase standing dead fuels (La Puma 

et al. 2013). Fire can also promote invasive species, 

which may increase the flammability of an area and 

thus the frequency, intensity, or length of the fire 

season (Brooks and Lusk 2008).

Many fire-dependent communities in the 

northeastern United States have few quantitative 

data describing historical fire regime attributes 

such as frequency, severity, and seasonality, or how 

these varied through time (Marschall et al. 2016). 

Furthermore, relatively few studies have modeled 

how climate change may affect wildfire in regional 

forests. At global and national scales, models 

generally project an increase in wildfire probability, 

particularly for boreal forests, temperate coniferous 

forests, and temperate broadleaf forests (Bachelet 

et al. 2001, Moritz et al. 2012). Several recent 

modeling efforts suggest that wildfire risk may 

increase moderately (10 to 20 percent) in the MidAtlantic region, with the largest increases projected 

in August (Guyette et al. 2014, Heilman et al. 2015, 

Tang et al. 2015). A study of wildfire activity in the 

Mid-Atlantic region suggests that many models may 

not capture the additional atmospheric moisture 

from coastal humidity and tropical storm activity, 

resulting in overestimation of wildfire probability 

(Clark et al. 2013). Forest composition changes, gap 

disturbances, understory fuels, and fire suppression 

are expected to limit wildfire occurrence and 

severity throughout the region (Clark et al. 2013). 

For example, fire suppression has contributed to a 

shift toward northern hardwood forests and more 

mesic conditions in eastern forests, and fire in these 

systems is relatively rare (Mohan et al. 2009). 

In the fire-prone Mid-Atlantic coastal plain pine 

barrens, urban development and land-use change 

have necessitated increased fire suppression, which 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

128

has contributed to a shift in forest composition 

away from pitch pine to a mixture of oak species, 

especially in the wildland-urban interface (La Puma 

et al. 2013). Landscape modeling of climate, fire, 

and land-use change in the southern New Jersey 

pinelands suggests that pitch pine-dominated forests 

may continue to shift toward oak dominance or a 

mixture of pine and oak in the absence of fire (La 

Puma et al. 2013). Fire management is expected 

to continue to influence vegetation and succession 

(Nowacki and Abrams 2008).

Intensified Stressors

Moisture Stress and Drought

There is evidence for an increased risk of future 

moisture stress and drought in the assessment area 

(see Chapter 4). Temperatures are expected to rise 

during the next century, and evapotranspiration 

in ecosystems is expected to increase as a result 

(Kunkel et al. 2013b, Nelson Institute Center for 

Climatic Research 2018, U.S. Global Change 

Research Project 2017). Moisture stress and drought 

can occur when increases in evapotranspiration 

are not offset by a corresponding increase in 

precipitation and soil moisture. Within the 

assessment area, the potential for moisture stress and 

more frequent droughts during the growing season 

appears to be much greater under the GFDL A1FI 

scenario and driven by much warmer temperatures 

(Chapter 4). However, under the milder PCM B1 

scenario, warmer temperatures may also lead to 

increased evapotranspiration and physiological 

stress if increases in precipitation do not accompany 

temperature increases. Additionally, because 

precipitation is more likely to occur during larger 

precipitation events, the number of consecutive days 

without precipitation is also expected to increase 

(Diffenbaugh et al. 2005, Peters et al. 2015). These 

increasingly episodic events may result in higher 

cumulative stress on tree species and may have the 

potential to initiate changes in forest composition 

(Peters et al. 2015). 

The initial soil moisture regime and drought 

tolerance of any system determine the positive or 

negative outcomes of extreme precipitation events 

with longer intervals between events (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 often 

limited by anoxia rather than soil moisture, so longer 

intervals between precipitation events may lower the 

water table, allowing oxygen to reach the roots of 

aquatic plants and increasing biomass productivity. 

However, a study that subjected Atlantic white-cedar 

seedlings to drought found decreases in biomass and 

stem diameter, suggesting that these hydric species 

are vulnerable to extended drought (Steven and 

Gaddis 2017). 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. 

Moisture availability is a critical determinant for 

forests worldwide and within the Mid-Atlantic 

region, where drought has been linked to decline 

of oak and ash trees (Choat et al. 2012, Clark et 

al. 2016, Millers et al. 1989, Mohan et al. 2009, 

Pederson et al. 2014). Early-season moisture is 

critical for seed germination and establishment. 

Although mature trees are better able to resist 

increases in temperature and reductions in available 

moisture, severe or sustained drought can increase 

tree mortality, open the forest canopy, alter forest 

growth and composition, and increase susceptibility 

to other stressors (Clark et al. 2016, Dale et al. 

2001, Pederson et al. 2014). Furthermore, droughtstressed trees are typically more vulnerable to insect 

pests and diseases (Dale et al. 2001, Millar and 

Stephenson 2015, Ryan and Vose 2012, Shifley et al. 

2012).
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129

Invasive Plant Species

Nonnative invasive species are a major threat to 

many forest communities across the eastern United 

States (Chapter 1). Many invasive species are able 

to establish rapidly after a disturbance, and are 

able to outcompete native vegetation for growing 

space, water, nutrients, and light (Brown and Peet 

2003, Dukes et al. 2009). Climatic factors that could 

influence the ability of a species to invade include 

warmer temperatures, earlier springs, and reduced 

snowpack (Hellmann et al. 2008, Ryan and Vose 

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 on 

kudzu seedlings have indicated increased growth, 

increased competition with native species, and range 

expansion with carbon dioxide fertilization (Sasek 

and Strain 1990). Models have also projected that 

increased carbon dioxide emissions and subsequent 

warmer winter temperatures are likely to expand the 

northern ranges of ailanthus, bush honeysuckles, 

privet, and kudzu (Bradley et al. 2010, Clark et al. 

2014). 

Further, as discussed throughout this chapter, many 

potential effects of climate change are expected 

to increase stress and disturbance within forest 

ecosystems, which certainly raises the potential for 

invasive species to exploit altered environments 

(Hellmann et al. 2008). Disturbances such as 

flooding, ice storms, and wildfire can open forest 

canopies, expose mineral soil, and reduce tree cover, 

providing greater opportunities for invasion (Ryan 

and Vose 2012). 

Some invasive species are tolerant of drought and 

fire, and may be at an even greater advantage under 

future climate conditions. Other species, such as 

garlic mustard and Japanese stiltgrass, are not 

particularly drought tolerant, but their persistent 

seedbanks enable them to recover in wetter years 

(Fryer 2011, Munger 2001). Other invasive species 

may contribute to increased disturbance regimes; 

for example, cogongrass has contributed to altered 

fire regimes in the southeastern United States and 

is expected to advance northward with warmer 

temperatures (Lippincott 2000). Once established, 

invasive plant species can also limit regeneration of 

native tree species through increased competition 

or allelopathic defenses (Gorchov and Trisel 

2003). Invasive species such as ailanthus and bush 

honeysuckles may exude a toxin that discourages 

the growth of other plants and have been shown to 

impair forest productivity (Hartman and McCarthy 

2007, Knapp and Canham 2000).

Kudzu. This nonnative vine kills other plants by smothering 

them, girdling woody stems, and toppling trees with their 

weight. Photo by Greg Czarnecki, Pennsylvania Department 

of Conservation and Natural Resources, used with 

permission.
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

130

Insect Pests and Pathogens

The response of forest insect pests and pathogens 

to a warmer future will vary widely by modes 

of infection, transmission, winter effects on pest 

lifecycles, and tree response (Dukes et al. 2009, 

Régnière et al. 2012). Pests and pathogens are 

generally expected to become more damaging in 

forest ecosystems as the climate changes, because 

they may be able to adapt more quickly to new 

climatic conditions, migrate more quickly to suitable 

habitat, and reproduce at faster rates than host tree 

species (Ryan and Vose 2012, Weed et al. 2013). 

Reviews examining forest pests and diseases in light 

of potential climate change impacts highlight the 

potential for interactions involving other stressors 

that increase susceptibility to these agents (Sturrock 

et al. 2011, Trotter 2013, Weed et al. 2013). 

Although few studies have examined the effects 

of climate change on specific forest insects, 

information from a few studies suggests an 

intensification of insect activity. Research on the 

hemlock woolly adelgid suggests that its range is 

generally limited by cold winter temperatures with 

mortality occurring at temperatures below -20 °F 

and that warmer winters may allow it to expand 

(Dukes et al. 2009, Paradis et al. 2008, Skinner et al. 

2003). Similarly, warmer winters have contributed 

to a southern pine beetle epidemic in the New 

Jersey Pine Barrens that is expanding northward 

(Ungerer et al. 1999, Weed et al. 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). 

Damage from other pest outbreaks, including those 

of native species (e.g., forest tent caterpillar and 

spruce budworm), can be more severe when trees 

are stressed by factors such as drought (BabinFenske and Anand 2011, Gray 2008, Manion 1981). 

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 2013). The fungal pathogen 

Armillaria is already widespread, but could expand 

or become more abundant under warmer and drier 

conditions, and particularly in response to drought 

(Kliejunas 2011). There is also evidence that climate 

change may be detrimental to some pest species. For 

example, the early survival of gypsy moth larvae 

depends on the availability of leaves; thus, changes 

in phenology could result in starvation if the eggs 

hatch before budburst (Ward and Masters 2007). 

Effects of Vertebrate Species

Herbivory, seed predation, and disturbance by 

vertebrates can be important stressors in the MidAtlantic 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 the future, but the models 

mentioned earlier in this chapter do not account 

for the herbivory of young oak regeneration by 

deer. Deer herbivory may also favor species which 

are not preferred browse species, such as eastern 

hophornbeam and black cherry, or invasive species 

such as buckthorns or Japanese barberry. Currently, 

Hemlock woolly adelgid on hemlock. Photo by Greg 

Czarnecki, Pennsylvania Department of Conservation and 

Natural Resources, used with permission.
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

131

there is little evidence to indicate how deer and other 

vertebrate species may 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 are likely to 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 the Mid-Atlantic 

region remains unknown. 

Changes in Forest Composition

Trees and other plant species have responded to 

past climate change in a number of ways. The 

ranges of tree species in eastern North America 

shifted in response to climate since the last ice 

age (Davis 1983), and tree species are expected to 

White-tailed deer fawn after a thunderstorm. Photo by Greg Czarnecki, Pennsylvania Department of Conservation and Natural 

Resources, used with permission.

shift in response to climate change (Iverson et al. 

2004a, Vose et al. 2012). Across the Midwest and 

Northeast, there is some evidence that tree species 

and other organisms may be moving northward 

(Fisichelli et al. 2014b, Parmesan and Yohe 2003, 

Woodall et al. 2009) and upward in elevation (Lee 

et al. 2005). High rates of migration have even been 

observed for American basswood, bigtooth aspen, 

and northern red oak (Woodall et al. 2009). Evidence 

also suggests that ranges may be contracting as 

species retreat at the southern edge of their range 

in response to changed climatic conditions, without 

a corresponding expansion at the northern edge of 

the range (Murphy et al. 2010, Zhu et al. 2012). 

However, forest composition changes slowly due to 

the long-lived nature of trees, and climate change 

may not be the only factor influencing species 

migration (Fei et al. 2017).
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

132

The modeling results presented earlier in this chapter 

describe projected changes, negative and positive, 

in future tree species distribution. In general, trees 

that are near the species range boundary are more 

likely to be influenced by climate change. Warmer 

temperatures are expected to be less favorable to 

species located at the southern extent of their range 

(Parmesan and Yohe 2003), and many species 

with more northerly distributions are projected to 

undergo the greatest declines. Declines could occur 

in different life stages, depending on the species. 

For example, some species may have a decline in 

seed set or declines in successful germination or 

establishment, whereas others may suffer reduced 

growth or inability to reach maturity (Ibáñez et al. 

2008). Mature trees may initially fare better than 

young trees due to greater access to resources and 

a greater ability to resist heat and drought stress, 

but this may be a relatively short-term effect if the 

species as a whole is unable to reproduce (Ibáñez et 

al. 2008).

Ecosystem models indicate that trees currently near 

the northern limits of the tree species range may 

become more abundant and more widespread under 

a variety of climate futures. As discussed earlier 

in this chapter, it is possible that some species that 

are not currently common or even present in the 

Mid-Atlantic may migrate into the region, such 

as black hickory and longleaf pine. However, it is 

expected that species may not be able to migrate 

northward without substantially lagging behind 

changes in climate (Dobrowski et al. 2013, Iverson 

and McKenzie 2013, Iverson et al. 2004a, Renwick 

and Rocca 2015). The migration of new species is 

constrained by a number of factors, including seed 

dispersal dynamics and landscape fragmentation 

(Ibáñez et al. 2008, Scheller and Mladenoff 2008). 

Catastrophic natural disturbances, such as wildfire, 

could help colonizing species from the south 

establish if environmental conditions promote 

germination and vigor of establishing seedlings, 

but also have the potential to reduce the ability to 

maintain forest cover (Camill and Clark 2000). 

Assisted migration, the intentional movement of 

species to areas expected to provide suitable habitat, 

could also provide new sources for spread, thereby 

accelerating the rate of migration (Duveneck and 

Scheller 2015, Iverson and McKenzie 2013, Pedlar 

et al. 2012). Management of forest ecosystems, 

including planting and harvesting, is also expected 

to influence changes in forest composition but is 

outside the scope of this assessment (Chapter 7).

Interactions

Although this chapter focused on the potential 

effects of climate change on forests, substantial 

interactions between climate change and other 

changes are also occurring within the Mid-Atlantic 

region. Climate change has the potential to alter 

an array of complex ecosystem processes, and the 

interactions among these impacts may be critically 

important in determining the resulting changes 

to forest ecosystems across the assessment area. 

Although many of these potential interactions are 

described in this chapter, many others are not. 

Examples of additional community interactions 

that could alter forest ecosystems are changes in 

mycorrhizal associations, changes in synchrony 

among plants and pollinators, and changes in the 

relationships among hosts, predators, and parasites 

(Bartomeus et al. 2011, Trotter 2013). In the MidAtlantic region, factors related to land use and 

management heavily influence how climate change 

may affect natural systems but are beyond the scope 

of this assessment (Larsen et al. 2012, Ordonez et al. 

2014).

Recognizing the potential for these interactions 

will be necessary to accurately assess the risks that 

climate change poses to forest ecosystems. Scientific 

research is beginning to clarify how biotic and 

abiotic stressors can operate in concert, but these 

types of studies are still relatively rare (Gellesch et 

al. 2013, Trotter 2013). As one example, it has long 

been known that stressed trees are more susceptible 

to certain insect pests and diseases. Earthworm 

invasion tends to create warmer, drier soil surface 
CHAPTER 5: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

133

conditions with more bare soil in forest systems, 

which may favor species that can germinate in these 

conditions (Eisenhauer et al. 2012). Earthworm 

invasion may also make northern hardwood forests 

more vulnerable to the effects of drought (Larson 

et al. 2010), leading to greater risk of disease and 

pest outbreak. This example is simply one chain of 

interactions, and many more connections could be 

drawn to phenological changes, fire seasons, and 

other climate-mediated impacts.

Likewise, there is increasing evidence for 

interactions among drought and insect pests or 

pathogens leading first to tree decline and mortality, 

and then sometimes to increased wildfire risk (Allen 

et al. 2010, Anderegg et al. 2015). Ultimately, 

ecosystems facing multiple interacting stressors 

may reach thresholds that fundamentally change 

ecosystem character and function (Manion 1981, 

Millar and Stephenson 2015). Much of the literature 

to date on this subject focuses on global and national 

analyses, resulting in greater uncertainty at the 

regional scale (Allen et al. 2015, Anderegg et al. 

2015, Millar and Stephenson 2015).

CHAPTER SUMMARY 

Although models are useful for exploring 

potential future changes, all models are simplified 

representations of reality, and no model can fully 

consider the entire complexity 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., balsam fir, 

black spruce, northern white-cedar, quaking aspen, 

and yellow birch) will become unfavorable by the 

end of the century under both climate scenarios. 

At the same time, all three models suggest that 

conditions for other species (e.g., loblolly pine and 

shagbark hickory) will become more favorable by 

the end of the century, especially under GFDL A1FI. 

Additionally, the Tree Atlas and LINKAGES tend 

to agree that many species will remain stable or 

increase under the relatively mild PCM B1 climate 

scenario and decrease under GFDL A1FI. These 

results support the idea that a future climate like 

GFDL A1FI is beyond the tolerance of some species 

in the Mid-Atlantic region, but also that many 

other species could tolerate the milder warming 

represented by PCM B1. 

Several interacting factors that are not simulated 

by these three models could drive forest changes, 

especially in the short term. 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. All of these factors 

must be taken into account with the model results 

in evaluations of the vulnerability of Mid-Atlantic 

forests to climate change. The vulnerability of forest 

ecosystems is described in Chapter 6.
134

CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

Climate change is expected to drive significant 

changes in species composition and ecosystem 

processes (Ryan and Vose 2012). In addition, climate 

change can alter fundamental ecosystem drivers and 

exacerbate or ameliorate current stressors, such as 

insect populations or wildfire risk (Joyce et al. 2014, 

Rustad et al. 2012, Ryan and Vose 2012, Vose et al. 

2016). This chapter is organized into two sections. 

In the first section, we present an overall synthesis 

of climate change vulnerability of the Mid-Atlantic 

region, 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) and regional expertise. In the 

second section, we present individual vulnerability 

determinations for 11 forest communities considered 

in this assessment; these determinations were 

developed through an expert elicitation process 

(Brandt et al. 2017) (described in Appendix 5). 

Vulnerability is the susceptibility of a forest 

ecosystem to the adverse effects of climate change 

(Glick et al. 2011, Intergovernmental Panel on 

Climate Change [IPCC] 2014). It is a function of the 

potential climate change impacts and the adaptive 

capacity of the ecosystem (Fig. 36). 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, 

IPCC 2007). It is strongly related to the concept 

of ecological resilience, which refers to the ability 

to return to prior conditions after a disturbance 

(Holling 1973, Stein et al. 2014). In this assessment, 

we consider a forest ecosystem to be vulnerable if 

it is at risk of a shift in composition that leads to a 

substantially different character for the forest, or if 

the forest is anticipated to suffer substantial declines 

in extent, health, or productivity. Although economic 

and social values can affect the way a forest 

ecosystem is managed and therefore have some 

influence on the adaptive capacity of the system, 

the assessment of vulnerability presented in this 

chapter is based on the ability of forest communities 

to persist given projected changes in climate 

without additional management interventions for 

adaptation. The ultimate decision of how to use 

this information—whether to conserve vulnerable 

communities, allow them to shift toward an alternate 

state, direct their transition, or do nothing—will 

depend on the individual objectives and actions of 

private landowners, land management agencies, and 

their stakeholders.

Figure 36.—Key components of vulnerability, illustrating 

the relationship among exposure, sensitivity, and adaptive 

capacity. Adapted from Glick et al. (2011).
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

135

Throughout this chapter, statements about potential 

impacts and adaptive capacity factors are qualified 

with a confidence statement, phrased according 

to definitions from the IPCC (Mastrandrea et al. 

2010). Confidence was determined by gauging 

both the level of evidence and level of agreement 

among information (Fig. 37). “Evidence” refers to 

the body of information available based on theory, 

data, models, expert judgment, and other sources. It 

was considered robust when multiple observations 

or models, as well as an established theoretical 

understanding to support a statement, were available. 

“Agreement” refers to the degree of consistent 

independent lines of high-quality evidence. If 

theories, observations, and models tended to suggest 

similar outcomes, then agreement was considered 

to be high. Agreement does not refer to the level 

of agreement among the authors of this assessment 

(more information on the process for determining 

confidence is found in Appendix 5).

Synthesis of Climate Change Impacts 

on Forest Ecosystems

Climate change is expected to cause wide-ranging 

direct and indirect impacts on forest ecosystems 

as a function of the degree to which a system is 

exposed to climatic changes and its sensitivity to 

these changes. Impacts could be beneficial to a 

forest ecosystem if the changes result in improved 

health or productivity, a greater area occupied by 

the system, or a tendency to maintain the current 

characteristics of the forest. They could be negative 

if they disrupt the ecosystem by decreasing health 

and productivity, reducing the area occupied by the 

system, or causing a shift in species composition 

that leads to a substantially different character for 

the system. The following summary includes the 

potential positive and negative impacts of climate 

change on the Mid-Atlantic region through the end 

of this century. This synthesis is based on the current 

scientific knowledge in published literature and 

described in more detail in the preceding chapters.

Figure 37.—Confidence determination matrix used in the 

assessment. Adapted from Mastrandrea et al. (2010).

Potential Impacts on Drivers and Stressors

Many physical, chemical, and biological factors 

contribute to the current state of forest ecosystems 

in the Mid-Atlantic region. These factors include 

drivers (the most fundamental forces that shape 

a particular ecosystem) and stressors (agents that 

can reduce forest health or productivity or impair 

ecosystem functions). Some factors, such as forest 

insects, may be drivers in one situation and stressors 

in another; for example, the effect of southern pine 

beetle on pitch pine or shortleaf pine may start out 

as a stressor but may eventually be a driver after 

it becomes a long-term agent of forest change. 

Other examples include the effects of chestnut 

blight, beech bark disease, and Dutch elm disease 

on unique forest communities. Similarly, some 

disturbances such as flooding or fire act as a driver 

in certain communities, but can also increase stress 

on communities if the timing or intensity of the 

disturbance changes. 

Temperatures will increase (robust evidence, high 

agreement). All global climate models agree that 

temperatures will increase with continued increases 

in atmospheric greenhouse gas concentrations.

A large amount of evidence from across the globe 

shows that temperatures have been increasing and 

will continue to increase due to human activities 
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

136

(Chapters 3 and 4). Temperatures across the MidAtlantic region have already exhibited substantial 

increases (Chapter 3). Continued temperature 

increases are projected for the Mid-Atlantic region 

even under the most conservative future climate 

scenarios (Chapter 4).

Growing seasons will lengthen (robust evidence, 

high agreement). There is strong agreement that 

projected temperature increases will lead to longer 

growing seasons in the Mid-Atlantic region.

Evidence at both global and local scales indicates 

that growing seasons have been getting longer, 

and this trend is expected to become even more 

pronounced during the 21st 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, Elmore et al. 2012, Rustad et al. 

2012). Earlier springs and longer growing seasons 

are expected to cause shifts in phenology for plant 

species that rely on temperature as a cue for the 

timing of leaf-out, reproductive maturation, and 

other developmental processes (Schwartz et al. 

2006, Walther et al. 2002), and some of these effects 

have already been observed (Dragoni and Rahman 

2012, Richardson et al. 2006, Rollinson and Kaye 

2012). Longer growing seasons may also result in 

greater growth and productivity of trees and other 

vegetation, but only if balanced by available water 

and nutrients (Chapter 5) (Keenan et al. 2014). 

Unfortunately, some nonnative invasive species can 

also be more adept at responding to temperature 

variation than many native plants and may become 

more competitive (Willis et al. 2010). 

The amount and timing of precipitation will 

change (robust evidence, high agreement). There 

is strong agreement that precipitation patterns will 

change across the Mid-Atlantic region. 

Among the climate projections used in this 

assessment (Chapter 4) and other publications, 

projected changes in precipitation are highly variable 

in magnitude and spatial distribution, more so than 

for temperatures (Kunkel et al. 2013a, Lynch et al. 

2016, Nelson Institute Center for Climatic Research 

2018). Although individual model projections for 

the Mid-Atlantic region differ seasonally, there is 

general agreement that total annual precipitation 

will increase during the 21st century, largely due to 

more intense precipitation events (Ning et al. 2015). 

Seasonally, total precipitation is projected to increase 

for the winter and spring seasons, whereas summer 

and fall precipitation projections range from slight 

increases to substantial decreases, depending on the 

climate scenario (Chapter 4).

Intense precipitation events will continue 

to become more frequent (robust evidence, 

high agreement). There is strong agreement 

among climate models that the number of heavy 

precipitation events will continue to increase in the 

Mid-Atlantic region. If they do increase, impacts 

from flooding and soil erosion may become more 

damaging.

Since the middle of the 20th century, heavy 

precipitation events have increased in number and 

severity in the Northeast, more so than in other 

regions of the United States (Horton et al. 2014, 

Walsh et al. 2014), and many models agree that this 

trend will continue during the next century (Nelson 

Institute Center for Climatic Research 2018, Walsh 

et al. 2014). Most heavy precipitation events in 

the Mid-Atlantic region currently occur during 

the warm season from May through September, 

although increases in intense rainfall are projected 

for all seasons (Bryan et al. 2015, Ning et al. 

2015). Increases in extreme precipitation events are 

generally expected to be greatest under scenarios 

that project greater amounts of warming, because 

of greater water vapor retention in the atmosphere 

(Ning et al. 2015). Extreme precipitation events 

could lead to more frequent or severe flooding 

and an increase in soil erosion (Horton et al. 2014, 

Nearing et al. 2004). The risk from floods, erosion, 

and other related impacts may ultimately depend on 

local site conditions, current infrastructure, and land 

use, as well as future decisions about infrastructure 

and land use.
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Sea levels will continue to rise (robust evidence, 

high agreement). There is substantial evidence that 

ongoing sea-level rise will continue to affect lowlying coastal areas and increase potential impacts 

from flooding, saltwater intrusion, and storm surge.

There is strong evidence that global sea levels have 

risen during the past century, and that they will 

continue to rise at an increased rate through the 

21st century (Bindoff et al. 2007, Kopp et al. 2014). 

Evidence attributes sea-level rise to the thermal 

expansion of ocean waters as water warms, and the 

melting of land ice flowing into the ocean (Bindoff 

et al. 2007, Church et al. 2008). Observations from 

tidal gauges have shown that sea levels have risen 

faster along the coastline of the Mid-Atlantic region 

(New York to Virginia) than the global average, 

about 1 foot during the 20th century (Buonaiuto 

et al. 2010, Williams et al. 2009). Sea levels in 

the Mid-Atlantic region may rise another 3 feet 

during the 21st century, with the higher estimate 

expected on the coastal plain (Chapter 4) (Miller 

et al. 2013). Coastal forests and ecosystems will 

be further threatened by inundation, more frequent 

coastal erosion, flooding, and saltwater intrusion 

(Anderson et al. 2013a, Conner and Askew 1992, 

Kane et al. 2015). Additionally, severe storms are 

more destructive under higher sea levels, causing 

increased damage from storm surges and flooding 

(Buonaiuto et al. 2010, Sallenger et al. 2012).

Soil moisture patterns will change in response to 

temperature and precipitation (medium evidence, 

high agreement). Warmer temperatures and altered 

precipitation are expected to change soil moisture 

patterns throughout the year, but there is uncertainty 

about the direction and magnitude of the changes at 

specific locations.

Soil moisture is expected to change in response 

to warmer temperatures and seasonal changes in 

precipitation, although uncertainty remains regarding 

the amount and timing of precipitation (Hay et 

al. 2011, Lynch et al. 2016). More intense and 

prolonged precipitation events would be expected 

to create wetter soil conditions, whereas increased 

temperatures and less frequent rainfall events would 

lead to drier soils (Dai et al. 2004, Liu et al. 2013, 

Peters et al. 2015). Wetter conditions may become 

more frequent during winter and spring; however, 

soils may dry during the growing season as warmer 

temperatures drive increases in evaporation and 

transpiration that are not offset by corresponding 

increases in precipitation (Clark et al. 2016). 

Locations where soils and landforms cannot retain 

the water from intense precipitation events may be 

more prone to drier conditions during the growing 

season. 

Forest vegetation may face increased risk of 

physiological drought during the growing season 

(medium evidence, medium agreement). Warmer 

temperatures can lead to decreased soil moisture 

even without an associated decrease in precipitation, 

resulting in a temporary inability for a tree to meet 

water demand. 

Meteorological droughts (relatively prolonged 

moisture deficits) are not expected to change much 

during the 21st century, although predictions of 

drought are complicated by uncertainty in the 

timing, duration, and extent of future precipitation 

patterns (Allen et al. 2015, Trenberth 2011). Shortterm moisture deficits are more likely and are 

expected to result in physiological drought and 

moisture stress for plants (Vose et al. 2016). Warmer 

temperatures can result in decreased soil moisture 

even without an associated decrease in precipitation, 

resulting in a temporary inability for a tree to meet 

water demand. Forests that are affected by moisture 

deficits and drought are more likely to have reduced 

tree vigor and increased mortality, both of which 

can affect forest composition and structure (Peters 

et al. 2015, Vose et al. 2016). Further, extremely hot 

days can drive or enhance drought-induced mortality 

by disrupting plant physiology (Allen et al. 2015, 

McDowell et al. 2008). This “hotter drought” can 

also interact with other forest stressors to cause tree 

death and forest die-off (Allen et al. 2010, 2015; 

Millar and Stephenson 2015).
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Climate conditions will increase wildfire risk 

by the end of the century (medium evidence, 

medium agreement). Some national and global 

studies suggest that conditions favorable for wildfire 

will increase, but few studies have specifically 

looked at wildfire risk in the Mid-Atlantic region. 

Wildfire risk will also depend on ignition, fire 

weather, ecosystem type, topography, fragmentation, 

and other regional characteristics. 

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 (Guyette 

et al. 2014, Moritz et al. 2012). The duration of the 

fire season in the Mid-Atlantic region 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, may ultimately determine how an increase 

in fire weather conditions might be manifested 

(Clark et al. 2013). 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). Forest fragmentation 

and future wildfire management decisions may limit 

the number, extent, or severity of individual fires 

even as fire risk increases.

Certain insect pests and pathogens will increase 

in occurrence or become more damaging 

(medium evidence, high agreement). Evidence 

indicates that an increase in temperature, longer 

growing seasons, and more frequent disturbances 

will lead to increased threats from insect pests 

and pathogens, but research to date has examined 

relatively few species.

A warming climate is expected to allow some pests 

and pathogens to become a greater threat 

(Chapter 5). Evidence is mounting that the warming 

climate can increase the susceptibility of trees to 

native and nonnative pests and pathogens (Paradis 

et al. 2008, Trân et al. 2007). Forest pests and 

pathogens are generally able to respond rapidly to 

changes in climate, and species may use different 

strategies to cope with change, including increasing 

the number of generations per year, shifting 

distributions, and expanding into new ecosystem 

types (Weed et al. 2013). The loss of a consistently 

cold climate and short growing season is already 

allowing some insect pests and pathogens, such as 

hemlock woolly adelgid and southern pine beetle, 

to expand their ranges northward (Chapter 5). 

Forest impacts from insect pests and pathogens 

are generally more severe in communities that are 

Symptoms of emerald ash borer at Memorial Lake, 

Pennsylvania. Photo by Greg Czarnecki, Pennsylvania 

Department of Conservation and Natural Resources, used 

with permission.
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139

stressed by drought and other stressors (Bentz et 

al. 2010, Sturrock et al. 2011). Basic information is 

often lacking on the climatic thresholds that trigger 

increased populations of many forest pests, and 

our ability to predict the mechanisms of infection, 

dispersal, and transmission for disease agents 

remains low (Weed et al. 2013). Further, due to the 

numerous anthropogenic and natural mechanisms of 

transport, we can expect the arrival of new pests and 

pathogens during the 21st century (Liebhold et al. 

2013).

Many invasive plants will increase in extent or 

abundance (medium evidence, high agreement). 

Evidence indicates that increases in temperature, 

longer growing seasons, and more frequent 

disturbances will lead to increases in many invasive 

plant species.

Many invasive species that currently threaten 

regional forests may benefit directly from projected 

climate change or benefit from the relatively slower 

adaptation response of native species (Sorte et al. 

2013). Native forest communities in the eastern 

United States compete with many species of 

invasive trees, shrubs, vines, herbs, and grasses, 

in part due to deer herbivory and human land use 

(Oswalt et al. 2015). Increases in carbon dioxide can 

enhance growth for many plant species, including 

some of the most invasive weeds in the United 

States (Ziska 2003). Milder winters may have 

allowed some invasive plant species, including 

bush honeysuckles, privet, and kudzu, to expand 

their ranges northward (Bradley et al. 2010). Other 

invasive plants have shown phenological shifts, 

such as earlier flowering in response to warmer 

temperatures and longer growing seasons (Ziska et 

al. 2011). Some invasive species, such as ailanthus 

and princess tree, are tolerant of fire; these and 

other drought- or fire-tolerant species may be 

very competitive under future climate conditions 

(Rebbeck 2012). Future increases in fire or flooding 

are likely to benefit many invasive plants that are 

able to establish quickly and outcompete native 

vegetation on disturbed sites (Dukes et al. 2009). 

A lack of information about the climatic thresholds 

that apply to many invasive plants limits the 

ability to predict the mechanisms of introduction, 

dispersal rates and directions, and spread for specific 

agents. Additionally, it is not possible to predict all 

future nonnative plant species that may enter the 

assessment area during the 21st century.

Potential Impacts of Climate Change 

on Forest Communities

Shifts in drivers and stressors just mentioned are 

expected to lead to shifts in suitable habitat for 

some dominant species and changes in species 

composition or function of forest communities in the 

Mid-Atlantic region.

Northern and remnant boreal tree species will 

face increasing stress from climate change 

(medium evidence, high agreement). Ecosystem 

models agree that these species may have reduced 

suitable habitat and declines in biomass across the 

Mid-Atlantic region. These species may be less able 

than temperate forest species to take advantage of 

longer growing seasons and warmer temperatures.

Across northern latitudes, past periods of warmer 

temperatures have driven species migration 

northward and to higher elevations (Chen et al. 

2011, Parmesan and Hanley 2015), resulting in 

now disjunct populations of red spruce and balsam 

fir at high elevations (Abrams et al. 2001). Across 

the eastern United States, increasingly warmer 

temperatures are expected to become less favorable 

to trees near the southern (warmer) extent of the 

species’ range (Iverson et al. 2008a, Mohan et al. 

2009, Reich et al. 2015, Rustad et al. 2012). Results 

from climate impact models projected a decline in 

suitable habitat and landscape-level biomass for 

remnant boreal species such as black spruce, red 

spruce, and northern white-cedar (Iverson et al. 

2016). In the absence of other mortality agents, 

long-lived individuals already established in cool, 

wet microhabitats may persist through a typical 

lifespan, even when habitat becomes unsuitable 

for regeneration (Iverson and Prasad 1998). Near 
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140

the southern edge of their range, other northern 

species such as sugar maple and northern red oak 

may also be able to persist, but these trees are 

expected to have greater competition from southern 

species, suffer greater stress than individuals in 

cooler northern locations, and display reduced vigor 

(Iverson et al. 2008b).

Habitat will become more suitable for southern 

species (medium evidence, high agreement). All 

three forest impact models project an increase in 

suitability and growth for southern species such as 

loblolly pine and shagbark hickory compared to 

current climate conditions.

Model results suggest that tree species currently near 

their northern range limits in the Mid-Atlantic region 

may become more abundant and more widespread 

under a range of climate futures (Chapter 5). Species 

that are currently present in the Mid-Atlantic region 

and projected to gain suitable habitat include 

loblolly pine and shagbark hickory (Chapter 5). 

Some species, however, may be limited in their 

ability to move into new habitats by their need for 

specific soil or site conditions (Ibáñez et al. 2006). 

Habitat fragmentation or natural barriers may also 

hinder the northward movement of southern tree 

species, despite increases in habitat suitability (Clark 

et al. 2016, Ibáñez et al. 2008). Although tree species 

are expected to differ in response to climate change, 

most species can be expected to migrate more slowly 

than suitable habitats can shift on the landscape 

(Iverson et al. 2004a, 2004b, 2016; Woodall et al. 

2009). Pests and diseases such as emerald ash borer, 

Asian longhorned beetle, and oak decline are also 

expected to limit attainment of modeled increases in 

habitat or biomass for some species (Iverson et al. 

2016).

Forest composition will change across the 

landscape (medium evidence, high agreement).

Forest impact model results predict that habitat and 

biomass of individual tree species will change, and 

that tree species will respond uniquely. However, few 

studies have specifically examined how assemblages 

of species may change. 

Paleoecological studies have provided evidence of 

how species have responded individually to climate 

change over periods spanning thousands of years 

(Davis et al. 2005, Root et al. 2003, Webb and 

Bartlein 1992). Future climate change is likewise 

expected to affect tree species differently and drive 

the rearrangement of habitat for some tree species in 

the Mid-Atlantic region. The model results presented 

in Chapter 5 raise the possibility of changes in tree 

species distribution, particularly as climate trends 

generally favor southern species across the MidAtlantic region by the end of the century (Iverson et 

al. 2008a, Lenihan et al. 2008). However, some tree 

species may be tied to particular soils or landscape 

positions or be less able to expand ranges northward 

into new areas at a pace commensurate with changes 

in climate (Ibáñez et al. 2006, Woodall et al. 2009). 

Because mature trees are more tolerant of warming 

and recruitment of new species is expected to be 

limited, major climate-driven shifts in species 

composition are not expected before the mid-21st 

century (Wang et al. 2017). However, increases 

in the intensity, scope, or frequency of standreplacing events such as windstorms, ice storms, 

and insect outbreaks may promote rapid shifts 

in species composition where these events occur 

(Duveneck et al. 2014, Millar and Stephenson 2015, 

Thompson et al. 2013). Invasive plant species may 

become a larger component of forest ecosystems 

as populations expand on the landscape, especially 

where native species are relatively limited in 

mobility following disturbances (Hellmann et al. 

2008).

Tree regeneration and recruitment will change 

(medium evidence, high agreement). Seedlings 

are more vulnerable than mature trees to changes in 

temperature, moisture, and other seedbed and early 

growth requirements; they are also expected to be 

more responsive to favorable conditions.

Temperature and moisture requirements for seed 

dormancy and germination at the forest floor are 

often much more critical than habitat requirements 

of an adult tree (Fisichelli et al. 2013, Kitajima and 
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141

Fenner 2000). Projected changes in temperature, 

precipitation, growing season onset, and soil 

moisture may alter the duration or manifestation of 

germination conditions, with severity of impacts 

varying among individuals and species (Fisichelli 

et al. 2014a). For example, regeneration failure in 

balsam fir populations has been attributed, at least 

partly, to climate change (Abrams et al. 2001). 

Warmer winters may promote the establishment 

of more southerly species, although warmer 

temperatures alone are unlikely to drive their 

establishment (Abrams 2003). For species with high 

dispersal capabilities, climate change may result in 

a redistribution of species on the landscape when 

seeds germinate on sites where suitable conditions 

are met (Walck et al. 2011). Other species may fail to 

regenerate under altered future climate conditions, or 

may germinate under suboptimal conditions and then 

fail to survive. Climate affects species establishment 

following disturbance due to the sensitivity of 

regeneration to climate variability (Jackson et al. 

2009). After establishment, saplings are still more 

sensitive than mature trees to disturbances such as 

drought, heat stress, fire, flooding, and herbivory 

(Fisichelli et al. 2012, Kitajima and Fenner 2000). 

Changes in tree regeneration and recruitment 

are expected to have long-term effects on forest 

composition and structure.

Forest productivity will increase during the next 

several decades in the absence of significant 

stressors (medium evidence, medium agreement). 

Some studies have examined the impact of climate 

change on forest productivity within the Mid-Atlantic 

region, but they disagree on how other factors such 

as species composition, stand age, disturbance, or 

pollution may interact to influence productivity. 

Changes are not expected to be consistent within a 

species, and the diversity of forest conditions across 

the landscape suggests that changes will be spatially 

variable.

Northern forests are currently a carbon sink 

(Williams et al. 2012), and growth of secondary 

forests, most of which are between 40 and 100 years 

old, is generally expected to continue or increase 

during the next several decades in the absence of 

major disturbances (Chapter 5) (Shifley et al. 2012). 

LANDIS PRO model results indicate increased 

growth for many species during the next few decades 

even under current climate conditions (Chapter 5). 

Oak-hickory forests are expected to benefit more 

from warmer temperatures than pines, northern 

hardwoods, and spruce-fir forests (Pan et al. 2009). 

Projections of forest growth and carbon balance 

point to increased tree growth and ecosystem 

carbon sequestration under warmer temperatures 

and longer growing seasons where soil moisture is 

not limiting (Ollinger et al. 2008, Pan et al. 2009). 

Many studies also point to the beneficial effects of 

carbon dioxide fertilization on forest productivity, 

although this effect can be dampened by nutrient 

and water limitations, ozone exposure, and tree age 

(Ainsworth and Long 2005, Dieleman et al. 2012, 

Franks et al. 2013). Changes in forest productivity 

are likely to be spatially variable due to the spatial 

heterogeneity of site conditions (Loehle et al. 2016). 

Increasing stressors, such as increased salinity from 

sea-level rise, fires, windstorms, and pest outbreaks, 

and changes in land use could substantially reduce 

forest productivity. Such disturbances have only 

recently been incorporated into simulation models 

and together constitute a significant caveat to 

expectations of continued productivity (Loehle et al. 

2016, Scheller et al. 2012). 

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, IPCC 2007). The focus of 

adaptive capacity is on the ability to adapt to 

climate-related stimuli (IPCC 2007) without 

transitioning to a different state. We next summarize 

factors that could reduce or increase the adaptive 

capacity of Mid-Atlantic forest communities.
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Low-diversity forest communities are at greater 

risk (medium evidence, high agreement). Studies 

have consistently shown that diverse systems are 

more resilient to disturbance, and low-diversity 

ecosystems are more vulnerable to change.

In general, forest communities that support diversity 

exhibit greater resilience to extreme environmental 

conditions and have greater potential to recover 

from disturbance than less diverse communities 

(Forrester and Bauhus 2016, Isbell et al. 2015). This 

suggests that communities with few species or low 

diversity are inherently more susceptible to future 

changes and stressors than those with high diversity 

(Forrester and Bauhus 2016). Within a community, 

the range of potential responses of a system to 

environmental change is a critical component of 

resilience (Elmqvist et al. 2003, Hooper et al. 2005). 

For example, mixed hardwood forests generally 

support a large number of tree species with many 

different traits and therefore have many possible 

future trajectories, whereas pitch pine-dominated 

forests have fewer potential options. 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 extreme events 

and adapt to changes over time (Reusch et al. 2005). 

Most tree species in isolated or fragmented 

landscapes will have reduced ability to migrate 

to new areas in response to climate change 

(limited evidence, high agreement). The dispersal 

ability of most individual tree species is reduced in 

fragmented landscapes, but the degree of landscape 

fragmentation in the future is an area of uncertainty.

Habitat fragmentation can hinder the ability of tree 

species to migrate to more suitable habitat on the 

landscape. The degree of dispersal limitation may be 

influenced by the level of fragmentation (relatively 

high in the Mid-Atlantic region), land cover and use, 

and the dispersal characteristics of individual species 

(Ibáñez et al. 2008, Iverson et al. 2004a). Modeling 

results indicate that average centers of suitable 

habitat for various tree species may shift 60 to 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. 2004a). 

Based on gathered data of seedling distributions, it 

has been estimated that many northern tree species 

could possibly migrate northward at a rate of 60 

miles per century (Woodall et al. 2009), but other 

evidence indicates that natural migration rates could 

be far slower for other species (McLachlan et al. 

2005, Murphy et al. 2010). Fragmentation creates 

additional challenges by making the landscape 

less permeable to migration (Jump and Peñuelas 

2005, Jump et al. 2009, McGuire et al. 2016). The 

potential for humans to remove migration barriers or 

facilitate the migration of species to newly suitable 

areas (Pedlar et al. 2012) reflects adaptation actions 

that are beyond the scope of this vulnerability 

assessment.

Species or systems that are limited to particular 

environments will have less opportunity to 

migrate in response to climate change (limited 

evidence, high agreement). Our current ecological 

understanding indicates that migration to new 

areas may be impossible for tree species and forest 

communities with narrow habitat requirements.

Several species and forest types in the Mid-Atlantic 

region are confined to certain habitats on the 

landscape, whether through particular requirements 

for temperature, hydrologic regimes, or soil types, 

or other reasons (Abrams et al. 2001, Manomet 

Center for Conservation Sciences and National 

Wildlife Federation 2014). Like species occurring 

only in fragmented landscapes, isolated species and 

ecosystems face additional barriers to migration 

(McGuire et al. 2016). For example, species 

restricted to riparian forests are not expected to 

migrate to upland areas because they depend on 

seasonal flood dynamics for regeneration and a 

competitive advantage. Similarly, Atlantic whitecedar swamps rely on a humid, maritime climate 

in a narrow coastal belt (Burns and Honkala 1990). 

These systems face greater challenges in migration 

than more widespread species with broad ecological 
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

143

tolerances. Conversely, some species that are 

widespread and have broad habitat requirements 

are expected to more easily find new habitat on the 

landscape. 

Forest communities that have high tolerance to 

disturbance will be at lower risk of decline from 

shifting climate extremes (medium evidence, 

high agreement). Basic ecological theory and 

other evidence suggest that communities adapted 

to disturbance will be at lower risk of declining on 

the landscape. However, some communities may 

tolerate only a narrow range of conditions related to 

a disturbance and may be susceptible to different, or 

more frequent and severe, disturbances.

Disturbances such as extreme heat, drought, 

wildfire, flooding, and pest outbreaks are expected 

to increase in the Mid-Atlantic region (Chapters 4 

and 5). Each disturbance affects a community in a 

different way, and some communities have become 

composed of disturbance-tolerant species (Côté 

and Darling 2010). Forest systems that are more 

tolerant of drought, flooding, or fire may be better 

able to withstand future changes in climate-driven 

disturbances (Thompson et al. 2009). For example, 

species in floodplain and wetland habitats have 

developed the capacity to exist in a wet phase or a 

dry phase in response to fluctuating water levels, 

temperature, and oxygen content; these systems 

repeatedly develop characteristic vegetation and 

return to a fully functioning system following both 

floods and droughts (Colloff and Baldwin 2010). 

Glades and barrens have become resilient to extreme 

weather conditions, fire, drought, and defoliation. 

This principle is limited, however, because it is 

also possible for disturbance-adapted systems 

to experience novel disturbances, or interacting 

disturbances that result in too much disruption (Dale 

et al. 2001). For example, pitch pine systems could 

cover a greater extent under drier conditions with 

more frequent fire, but these systems also could 

convert to shrubland or savanna if fire becomes too 

frequent or drought becomes too severe.

Vulnerability Determinations 

for Individual Forest Communities

Climate-induced shifts in drivers, stressors, and 

dominant tree species are expected to have different 

impacts on forest communities within the assessment 

area. Some forest communities may have greater 

resilience than others; some may be susceptible to 

relatively minor impacts. Therefore, it is helpful 

to consider these factors for individual forest 

communities.

We assessed the vulnerability of 11 forest 

communities (described in Chapter 1) to climate 

change impacts. We assembled two expert panels 

to assess forest types in the Mid-Atlantic coastal 

plain and interior (Fig. 38), drawing upon scientists 

and managers from a variety of organizations 

and disciplines across the Mid-Atlantic region 

(Appendix 5). The 26 panelists considered the 

information from the previous chapters, evaluated 

the projected changes in climate and tree responses 

(Chapters 3 through 5), and used their expertise 

to interpret the information. For each forest 

community, panelists considered the potential 

impacts and adaptive capacity in order to assign a 

vulnerability determination and a level of confidence 

in that determination using the confidence scale 

described earlier in this chapter (Brandt et al. 2017). 

A complete description of the methods used to 

determine vulnerability can be found in Appendix 5.

The forest communities were assessed as having 

different levels of vulnerability, which ranged 

from low to high based on the interaction between 

potential impacts and adaptive capacity (Table 26). 

Ratings of evidence for the vulnerability 

determinations were medium or medium-robust 

partly because important interactions expected 

among dominant tree species and potential stressors 

were generally unknown. The ratings of agreement 

among information sources also tended to be 

medium or medium-high. The level of agreement 

was limited primarily because of uncertainty about 

future precipitation patterns.
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144

In the following sections, we summarize the 

climate-related impacts on drivers, stressors, and 

dominant tree species that were major contributors 

to the vulnerability determination for each forest 

system across the Mid-Atlantic region. In addition, 

we summarize the main factors contributing to 

the adaptive capacity of each system. Importantly, 

these determinations were developed for forest 

communities for the entire Mid-Atlantic region. 

At a local scale, forest communities vary due to 

differences in elevation, climate, landform, soils, 

disturbance, past management, and numerous other 

factors; thus, the vulnerability in a particular location 

is likely to be different—even markedly so—from 

the broad-scale information highlighted in this 

chapter. For this reason, the following summaries 

are best used as starting points for considering forest 

ecosystem vulnerability at finer spatial scales.

Figure 38.—Subregions of the Mid-Atlantic region. Forest types were assessed by two separate teams in order to consider 

differences in climate and responses to climate change in the coastal plain and interior regions. Source: McNab et al. (2007).

Forest community Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Coastal Plain

Maritime forest Negative Moderate-Low High Medium-Robust Medium-High

Oak-pine-hardwood Moderate-Positive High Moderate-Low Medium Medium-High

Pine-oak barrens Moderate Moderate Moderate-Low Medium-Robust Medium-High

Swamp Moderate Moderate-High Moderate-Low Medium Medium

Tidal swamp Moderate-Negative Moderate-Low Moderate-High Medium Medium-High

Interior

Central oak-pine Moderate-Positive Moderate-High Moderate-Low Medium Medium-High

Lowland conifer Negative Moderate-Low High Medium Medium

Lowland and riparian hardwood Moderate Moderate Moderate Medium-Limited Medium

Montane spruce-fir Negative Low High Medium-Robust High

Northern hardwood Moderate-Negative Moderate Moderate-High Medium-Robust Medium-High

Woodland, glade, and barrens Positive Moderate-High Low Medium Medium-High

Table 26.—Summary of vulnerability determination for the forest communities considered in this assessment 

evaluated through the end of the 21st century 
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

145

Rimrock Overlook above Kinzua Bay in the Allegheny Reservoir, Allegheny National Forest, Pennsylvania. Photo by 

Kathleen Creek, Allegheny National Forest.
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146

Maritime Forest (Coastal Plain)

High Vulnerability (medium-robust evidence, medium-high agreement) 

The proximity of this forest community to ocean coasts means that changes in coastal dynamics, such as 

sea level and storm surges, are greater drivers than changes in temperature and precipitation. Sea level 

and exposure to saltwater and disturbance are expected to drive changes in species composition. 

Negative Potential Impacts

Drivers: This forest community represents a small 

percentage of forest cover in the Mid-Atlantic 

region, and exists only on barrier islands or in 

narrow bands close to the bays, estuaries, islands, 

and coastal zones of the Atlantic Ocean. Maritime 

forests are typically subjected to various impacts, 

depending on landscape position and exposure to 

salt spray, sea-level rise, and erosion. Prolonged 

inundation with saltwater may cause stress or 

mortality of trees, depending on the tolerance of 

individual species to salt and inundation. Shifting 

sands may alter soil characteristics, destabilize root 

systems, and cause erosion. Rising sea levels are 

increasing storm surge and flooding, both of which 

may be even more problematic if storms become 

more frequent or severe. 

Dominant Species: Forest impact model results 

for the Coastal Plain subregion were used for this 

forest community. Forest impact models projected 

increases under both climate scenarios for shortleaf 

pine, loblolly pine, post oak, red maple, and pitch 

pine. Models projected decreases under both 

scenarios for black cherry, scarlet oak, and Virginia 

pine and under the high emissions scenario only for 

American holly, black oak, red maple, and sassafras. 

Stressors: Maritime forest habitat and species 

are threatened by many stressors, including 

development, damage from off-road vehicles, 

nutrient and contaminant runoff and sedimentation, 

and continued sea-level rise and increasing coastal 

surge. These communities occur in dynamic coastal 

environments and are often converted to other 

community types through natural disturbances. 

Increases in extreme weather events, including 

convective and tropical storms and hurricanes, could 

disrupt soil structure, remove soil layers, increase 

exposure to contaminants, or increase salinity in the 

system even without added precipitation. 

Moderate-Low Adaptive Capacity

This forest community is restricted to the fringes of 

the Atlantic coast and is already highly fragmented 

due to land development. Maritime forest is 

presumed to be the sparser, more extreme version 

of the oak-pine-hardwood forest existing inland, 

which could serve as seed sources for replenishing 

maritime forest after disturbance (Bellis 1995). Fire 

suppression is also leading to successional changes 

in many of these sites. Salt tolerance is expected 

to influence how species respond to the changing 

environment, but this factor was not included in 

modeled scenarios. Salt-tolerant species include 

pitch pine, red oak, white oak, black cherry, and 

eastern redcedar, and forests containing these 

species may be better able to tolerate future changes. 

Hickories, sweetgum, and maples are less tolerant 

and generally increase in relative abundance with 

increasing distance from the beach.
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147

Maritime dunes at Fort Tilden in Gateway National Recreation Area, New Jersey. Photo by 

Gregory J. Edinger, New York Natural Heritage Program, used with permission.

Maritime redcedar forest at Sandy Hook, New Jersey, in 

Gateway National Recreation Area. Photo by Gregory J. 

Edinger, New York Natural Heritage Program, used with 

permission.

Successional maritime forest on Fishers Island, New York. 

Photo by Gregory J. Edinger, New York Natural Heritage 

Program, used with permission.
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148

Oak-Pine-Hardwood (Coastal Plain)

Moderate-Low Vulnerability (medium evidence, medium-high agreement)

This forest community is expected to benefit from changes in climate, though severe drought or fire may 

kill trees or change community structure. With increased frequency of drought and wildfire, pine species 

may become dominant. 

Moderate-Positive Potential Impacts

Drivers: This community type often occupies 

dry sandy areas conducive to periodic fire and 

dominated by oak. It can also occupy moist sites 

on lower slopes and along rivers and streams; these 

sites afford natural protection from fire and favor 

mesic hardwood species. Moisture stress, especially 

during hot periods, may reduce regeneration 

potential and seedling establishment. Drought may 

also stress mature trees, leading to mortality of 

mesic species and shifting the species composition 

to oaks and pines. Increased frequency of drought 

and wildfire, particularly on hotter or drier sites, may 

favor pine species. 

Dominant Species: Forest impact model results for 

the Coastal Plain subregion were used for this forest 

community. This forest community contains many 

species projected to increase under both climate 

scenarios, including shortleaf pine, southern red 

oak, water oak, shagbark hickory, bitternut hickory, 

willow oak, post oak, loblolly pine, and pitch pine. 

New habitat is projected under both scenarios for 

chinkapin oak and under the high emissions scenario 

for Shumard oak. Habitat decreases are projected 

under both scenarios for chestnut oak, Virginia pine, 

and bigtooth aspen. Under GFDL A1FI, bigtooth 

aspen is projected to lose all suitable habitat in the 

coastal plain by the end of the 21st century. Sugar 

maple is projected to gain some new suitable habitat 

under the low emissions scenario, and lose suitable 

habitat under the high emissions scenario.

Stressors: Historical logging and land development 

have led to habitat fragmentation in this forest 

community. Herbivory, particularly from deer, is 

currently suppressing oak regeneration and seedling 

establishment, and deer pressure is not expected to 

change substantially. Forest pests and pathogens 

including gypsy moth, southern pine beetle, chestnut 

blight, and pine looper are expected to benefit 

from warmer and drier conditions. Invasive shrubs 

may find opportunities to dominate if canopy trees 

become stressed; ailanthus, princess tree, autumn 

olive, bush honeysuckles, and multiflora rose can 

benefit from disturbance. Invasive vines, such as 

kudzu, wisteria, Japanese honeysuckle, and winter 

creeper, are expected to spread under a range of 

future climates, though increased fire frequency may 

help control invasive species.

High Adaptive Capacity

This forest community is relatively diverse in terms 

of species and ecosystem functions. It thrives across 

a variety of soil moisture conditions and is expected 

to find microhabitats and refugia in order to persist 

in some form on the landscape. Drought- and heattolerant species may become more dominant in 

warmer, drier conditions. The occurrence of fire is 

expected to strongly influence whether oak or pine 

species are dominant in the future, with fire likely to 

favor pine species, particularly in drier sites. At the 

same time, land development, fragmentation, and 

fire suppression may reduce the potential for wildfire 

over a large scale, which could reduce the ability of 

pine to increase.
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149

Coastal oak-beech forest on Fishers Island, New York. Photo 

by Gregory J. Edinger, New York Natural Heritage Program, 

used with permission.

Coastal oak-white pine forest near Northwest Creek on Long 

Island, New York. Photo by David M. Hunt, New York Natural 

Heritage Program, used with permission.

American holly, a characteristic tree of coastal oak-holly forests and maritime holly forests 

in New York. Photo by Julie A. Lundgren, New York Natural Heritage Program, used with 

permission.
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150

Pine-Oak Barrens (Coastal Plain)

Moderate-Low Vulnerability (medium-robust evidence, medium-high agreement)

This forest community is tolerant of fire, drought, and disturbance, and the future fire regime is a primary 

factor that will determine species composition. Moisture deficits are becoming more common and can kill 

young regeneration and mature trees. This system has low species diversity, and the loss of pitch pine to 

any stressor or combination of stressors would jeopardize the identity of this forest community. 

Moderate Potential Impacts

Drivers: Fire is a major driver of species 

composition and dominance in pine barrens; short 

fire return intervals tend to favor pitch pine, while 

longer fire return intervals favor oak species. Very 

frequent fire (return interval of 8 to 10 years) favors 

dwarf pitch pine, which reaches a maximum height 

of 3 to 10 feet. Windstorms resulting in blowdowns 

can accelerate succession to oak forests in the 

absence of fire. Heavy precipitation drains quickly 

due to sandy soils, and longer dry periods between 

events could increase the risk of moisture stress, 

which can be lethal to young regeneration of pitch 

pine and oaks. 

Dominant Species: Forest impact model results 

for the Coastal Plain subregion were used for this 

forest community. Pitch pine, blackjack oak, and 

post oak are the dominant species and are projected 

to increase under both climate scenarios. Common 

associates including black oak and white oak are 

projected to decrease only under the high emissions 

scenario. Chestnut oak and scarlet oak are projected 

to decrease under both scenarios. 

Stressors: Warmer winter temperatures contribute 

to the northward expansion of southern pine 

beetles, which can result in greater than 90 percent 

mortality of overstory pines in infested stands (New 

Jersey Department of Environmental Protection 

n.d., Weed et al. 2013). Invasive species in this 

community include barren bromegrass, cheat grass, 

Japanese bromegrass, spotted knapweed, Japanese 

honeysuckle, and garlic-mustard. 

Moderate Adaptive Capacity

This forest community currently occupies one-tenth 

of its original extent, largely due to development 

(Clark et al. 2015). Fire suppression has contributed 

to the decline of pitch pine and the increase of 

oak in some areas. In fire-prone areas, trees have 

characteristics adapted to frequent fire, including 

thick bark and serotinous cones, which need fire 

to release the seeds. Pitch pine and scrub oak can 

resprout, increasing their ability to survive when 

fires are too frequent to permit seed regeneration. 

Deep roots are considered to contribute to drought 

tolerance and fire tolerance, although very hot 

droughts or very hot fires can damage roots and 

prevent resprouting. Adjacent oak-pine-hardwood 

forests serve as potential seed sources for pine-oak 

barrens, while the sandy, droughty soils discourage 

encroachment of mesic hardwood trees and 

associated understory communities. Although pitch 

pine is expected to overcome many future stressors, 

it is a keystone species and the loss of pitch pine 

to any stressor or combination of stressors would 

jeopardize the identity of this low-diversity forest 

community. 
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

151

Pitch pine cones. Photo by Stephen M. Young, New York 

Natural Heritage Program, used with permission.

Dwarf pine plains in the Central Long Island Pine Barrens, 

New York. Photo by Gregory J. Edinger, New York Natural 

Heritage Program, used with permission.

Pitch pine-oak forest near Manorville Hills in the 

Central Long Island Pine Barrens, New York. Photo by 

Gregory J. Edinger, New York Natural Heritage Program, 

used with permission.
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152

Swamp (Coastal Plain)

Moderate-Low Vulnerability (medium evidence, medium agreement)

As temperatures continue to rise, more locations may experience moisture deficits, reducing tree growth 

and increasing the risk of tree mortality. The hydrology of these areas may allow some changes in the 

position or size of swamps without complete loss of the system, though existing infrastructure and 

development may restrict movement.

Moderate Potential Impacts

Drivers: Although surface water within the swamps 

is largely derived from groundwater, precipitation 

can lower or raise standing water levels. Warmer 

temperatures may lead to greater evapotranspiration 

and increased risk of moisture deficits between 

precipitation events. Hot droughts, even of short 

duration, can result in mortality of swamp trees 

(Allen et al. 2015). Continuing sea-level rise is 

projected to permanently flood areas where elevation 

is close to sea level, compounding the effects of 

storm surge, flooding, and salt spray (Climate 

Change Science Program 2009). Saltwater intrusion 

can kill Atlantic white-cedar forests and may 

damage other species, depending on the intensity 

and duration of the disturbance. 

Dominant Species: Forest impact model results 

for the Coastal Plain subregion were used for this 

forest community. Many species are projected to 

persist under a range of future climates including 

baldcypress, green ash, pin oak, pitch pine, 

sweetgum, loblolly pine, and willow oak, but ash 

species are highly susceptible to damage by emerald 

ash borer. Suitable habitat for Atlantic white-cedar, 

blackgum, and swamp white oak is projected to 

decline under both climate scenarios. Red maple is 

expected to become more competitive. 

Stressors: Historical logging and development 

have reduced the extent of this forest community, 

and much of the alluvial soil has been converted 

for agricultural use. Increased flooding can 

increase runoff and discharge from farmland and 

concentrated animal feedlots, thus increasing 

nutrient loads. Groundwater withdrawals in the 

coastal plain are increasing due to inadequate 

recharge, lowering the water table and thus water 

supply for swamps (Shedlock and Bolton 2006). 

Disturbance may create opportunities for invasive 

species, such as phragmites, burning bush, multiflora 

rose, wineberry, and Oriental bittersweet. Deer use 

cedar swamps to avoid severe winter weather, and 

even low deer populations can be damaging. 

Moderate-High Adaptive Capacity

Impacts to this forest community are expected to 

vary with proximity to saltwater, and reliance on 

groundwater. In areas disconnected from saltwater 

intrusion, hardwood species are likely to persist, and 

red maple may be more competitive on sites with 

reduced soil moisture. Rising sea levels, combined 

with storm surge and erosion, may drive the coastal 

zone inland, leading to rapid changes in tree species’ 

habitat. Tree and other plant species have different 

tolerances to saltwater, which may allow some to 

persist. Atlantic white-cedar is a keystone species 

restricted to the coast, and rapid changes in salinity 

and water depth may result in the total loss of cedar 

swamps. For example, mortality of Atlantic whitecedar stands was highest in areas where water was 

impounded after Hurricane Sandy (New Jersey 

Department of Environmental Protection 2015).
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153

Red maple-blackgum swamp along the Carman’s River, New York. Photo by Adele M. Olivero, 

New York Natural Heritage Program, used with permission.

Mature coastal plain Atlantic white-cedar swamp in the New 

Jersey Pine Barrens. Photo by Gregory J. Edinger, New York 

Natural Heritage Program, used with permission.

Red maple-sweetgum swamp on Staten Island, New York. 

Photo by Aissa L. Feldmann, New York Natural Heritage 

Program, used with permission.
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154

Tidal Swamp (Coastal Plain)

Moderate-High Vulnerability (medium evidence, medium-high agreement)

The combined effects of sea-level rise and saltwater intrusion due to spray and storm surge are expected 

to cause irreversible habitat loss and tree mortality in this forest community. As salinity increases, the salt 

tolerance of tree species may determine which ones persist. 

Moderate-Negative Potential Impacts

Drivers: Hydrology determines variations of this 

community type. Precipitation can cause changes 

in salinity as freshwater inputs constantly dilute 

saltwater inputs. Drought can reduce freshwater 

inputs and drive saltwater farther upstream (Moser 

et al. 2014). As sea level rises, increasing salinity 

levels may interact with other stressors, and the salt 

tolerance of individual trees may factor into tree 

response. Sea-level rise is expected to absorb current 

habitat for this community type. Tidal forest that 

undergoes salinization exceeding its tolerance may 

be replaced by tidal marsh (Rheinhardt 2007).

Dominant Species: Forest impact model results for 

the Coastal Plain subregion were used for this forest 

community. Many species are projected to increase 

or remain steady under both climate scenarios, 

including American elm, baldcypress, water tupelo, 

loblolly pine, and green ash. However, both green 

and pumpkin ash (not modeled) are susceptible to 

emerald ash borer and may face high mortality rates 

in the next few decades. Red maple is expected 

to become more competitive under both climate 

scenarios. Blackgum is the only species projected to 

decline under both scenarios. 

Stressors: Historical logging and land development 

have reduced the extent of this forest, and much of 

the alluvial soil has been converted for agricultural 

use. Increased flooding can increase runoff and 

discharge from farmland and concentrated animal 

feedlots. Groundwater withdrawals in the coastal 

plain are increasing due to population pressures and 

inadequate recharge (Shedlock and Bolton 2006), 

lowering the water table and thus available water for 

swamps. Disturbance may create opportunities for 

invasive species such as phragmites, burning bush, 

multiflora rose, wineberry, and Oriental bittersweet. 

Moderate-Low Adaptive Capacity

This forest community is expected to respond to 

sea-level change by changing the shape and position 

of wetlands and coastal habitat, depending on how 

changes in hydrology conform to local topography. 

River flow and hydrology in the coastal plain have 

been altered by channelization, road networks, 

development, and a variety of land-use changes, 

and these changes may inhibit the expansion of 

wetlands in response to climate changes. As the 

sea and coastal zone move inland, the habitat for 

current coastal species may change rapidly. Only 

baldcypress and green ash are resistant to salt spray. 

As tidal forests lose habitat, they could potentially 

replace nontidal riverine and lowland forests in 

areas where soils become saturated or seasonally 

inundated (Rheinhardt 2007). 
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155

Freshwater tidal swamp at Mill Creek, New York. Photo by 

Carly Voight, New York Natural Heritage Program, used with 

permission.

Freshwater tidal swamp at Mill Creek, New York. Photo by 

Carly Voight, New York Natural Heritage Program, used with 

permission.
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156

Central Oak-Pine (Interior)

Moderate-Low Vulnerability (medium evidence, medium-high agreement)

This diverse forest community occurs over a wide range of habitats. Many species tolerate or are adapted 

to dry soil conditions and fire, although young trees may be sensitive to severe drought and high-intensity 

fire. Many oak and hickory species are likely to benefit from projected changes in climate. 

Moderate-Positive Potential Impacts

Drivers: This community is widespread and 

common throughout the interior portion of the 

Mid-Atlantic region. Warmer, drier summers may 

increase the occurrence and severity of drought, 

particularly on xeric sites, which could result in 

seedling mortality for some species. Higher moisture 

availability in spring and early summer may reduce 

fire risk while increasing vegetation growth. Late 

summer and fall moisture deficits and prolonged 

higher temperatures may increase fire risk, 

especially in places where vegetation dries or coarse 

woody debris accumulates from natural mortality or 

storm damage. Low to moderate fire intensity may 

benefit oak and pine species, but high-intensity fire 

can be fatal to trees. 

Dominant Species: Most of the dominant species, 

including black oak, chestnut oak, mockernut 

hickory, northern red oak, pignut hickory, pitch 

pine, scarlet oak, shortleaf pine, Virginia pine, and 

white oak, are projected to remain stable or increase 

under both climate scenarios. Although no longer a 

dominant species, American chestnut is projected to 

remain stable under both climate scenarios. Several 

associated species are projected to lose habitat 

including eastern white pine, red pine, and sassafras. 

Red maple is generally expected to become more 

competitive. 

Stressors: Some insect pests such as gypsy moth 

and southern pine beetle are already posing a serious 

threat to oak and pine species. Increased moisture 

stress combined with pests such as the two-lined 

chestnut borer may increase the risk of oak decline 

or sudden oak death (Venette and Cohen 2006). 

Deer herbivory is currently limiting to seedling 

establishment and growth, and deer populations 

are not expected to change due to climate change 

alone. Invasive species such as glossy buckthorn, 

honeysuckles, and garlic mustard can also impair 

regeneration, and may become more competitive 

with native species. 

Moderate-High Adaptive Capacity

Many species of oak, hickory, and pine are tolerant 

of drought and fire and therefore expected to fare 

well under moderate climate changes. The relatively 

high species richness may increase the number of 

ways in which the ecosystem can adjust to changing 

conditions while maintaining important ecosystem 

functions. This community also occupies a range of 

site conditions over a large geographic area, which 

increases the potential of persistence on various 

sites. A history of fire suppression and increasing 

shade under the forest canopy have facilitated 

shifts to more mesic conditions in some places. 

Where mesic conditions have developed, northern 

hardwoods such as red maple, American beech, 

and tulip tree have established, and regeneration of 

oak and pine species has become a notorious forest 

management challenge that may affect the future 

composition and distribution of this community. 
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157

Appalachian oak-hickory forest in the Taconic Mountains, New York. Photo by Timothy G. 

Howard, New York Natural Heritage Program, used with permission.

Appalachian oak-hickory forest at Saratoga National Historical Park, New York. Photo by 

Gregory J. Edinger, New York Natural Heritage Program, used with permission.
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

158

Lowland Conifer (Interior)

High Vulnerability (medium evidence, medium agreement)

This forested wetland community is limited to areas that remain wetter and cooler than adjacent uplands. 

This community has relatively few species compared to other forest communities and many of them 

are threatened by insect pests. As the current dominant species decline, the functional identity of this 

ecosystem will be greatly challenged. 

Negative Potential Impacts

Drivers: Increases in temperature and altered 

precipitation patterns could significantly change 

the hydrology of this community. Peak streamflow 

is expected to shift to earlier in the spring and 

increased precipitation is expected to intensify spring 

peak flows. An increase in intense precipitation is 

likely to result in more frequent flooding. Reduced 

precipitation in the summer and fall may result in 

drier conditions and a lower water table, which 

would negatively affect rain-fed ecosystems. The 

increasing risk of wildfire is a serious threat to drier 

peatlands, which contain tree species that are not fire 

tolerant. 

Dominant Species: Fewer than a dozen species 

make up the lowland conifer community, and 

most are projected to decline under both climate 

scenarios, including balsam fir, black ash, black 

spruce, eastern hemlock, eastern white pine, red 

spruce, tamarack, and northern white-cedar. Yellow 

birch and red maple are projected to decline under 

the high emissions scenario only. Although no 

species are projected to increase under climate 

change, red maple may take advantage of openings 

and disturbance to become a dominant species in 

these areas. 

Stressors: Warmer temperatures may dampen the 

effects of the eastern spruce budworm, but allow 

balsam woolly adelgid and hemlock woolly adelgid 

to increase and spread more easily (Chapter 5). 

Tree susceptibility to insect infestations is expected 

to increase as trees become moisture stressed. 

Historical land use has already resulted in altered 

hydrology in some locations; this legacy is likely 

to continue to stress the system as the precipitation 

regime changes. Deer use conifer-rich lowlands 

to avoid severe winter weather, and even low deer 

populations can be damaging. Browsing pressure on 

hardwood species may increase as northern whitecedar and other conifers decline.

Moderate-Low Adaptive Capacity

Impacts on lowland conifer forests are expected 

to vary with site conditions, and the response of 

these forests to climate change may be greatly 

influenced by surface geology, hydrology, soils, 

dominant tree species, and local changes in climate. 

Although prolonged flooding may exceed the 

saturation tolerance of some species, an increased 

risk of drought is also a serious threat that many 

species are not likely to withstand. Fens may 

not be as susceptible to water deficits due to the 

reliance on groundwater. The physical structure 

and function of conifer communities create the 

shady, cool microclimates where they thrive, and 

there are relatively few native conifers to fill this 

functional role. As the keystone conifers decline, the 

identity of this forest community may be severely 

compromised.
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

159

Spruce-fir swamp at Johnnycake Lake Swamp, New York. 

Photo by Gregory J. Edinger, New York Natural Heritage 

Program, used with permission.

Hemlock-hardwood swamp at Johnnycake Lake Swamp, New 

York. Photo by Gregory J. Edinger, New York Natural Heritage 

Program, used with permission.

Red maple-tamarack peat swamp at Lake Superior State 

Park, New York. Photo by D.J. Evans, New York Natural 

Heritage Program, used with permission.
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

160

Lowland and Riparian Hardwood (Interior)

Moderate Vulnerability (medium-limited evidence, medium agreement)

This community type is threatened by changes in the hydrologic cycle that increase variability in water 

availability. Invasive plants and insect pests are major stressors for species that are expected to decline 

under a range of climate scenarios. Many common species are expected to shift in distribution across the 

broader landscape, but persist in these moist lowlands. 

Moderate Potential Impacts

Drivers: Changes to the timing and intensity 

of precipitation events are expected to result in 

increased flooding, erosion, and sedimentation 

during precipitation events, as well as potentially 

increased risk of drought between precipitation 

events. Hotter and drier conditions could reduce 

water table levels and water availability to trees. 

The effects of hotter and drier conditions during 

the growing season are likely to vary widely based 

on both site and weather conditions, and trees that 

are shallow rooted, on droughty soils, or already 

stressed may be most at risk. Prolonged flooding 

during the growing season may kill tree species that 

cannot withstand long periods of inundation. 

Dominant Species: Many dominant species are 

expected to remain stable or increase under both 

climate scenarios, including American hornbeam, 

blackgum, boxelder, bur oak, eastern cottonwood, 

green ash, pin oak, shagbark hickory, swamp white 

oak, sweetgum, and sycamore. Only black ash and 

eastern hemlock are expected to lose a large amount 

of suitable habitat under both climate scenarios. 

Some of these species are tightly linked to moisture 

availability. Model projections for red maple 

are mixed, but the species is generally expected 

to become more competitive under changing 

conditions. Future projections for species in this 

community may have greater uncertainty because 

many of these species are less common and there are 

challenges to modeling wetland habitats.

Stressors: Invasive plants are very problematic in 

this community type, with greater impacts generally 

occurring downstream. Increases in flooding are 

likely to benefit many invasive plants that are able to 

establish quickly and outcompete native vegetation 

on disturbed sites (Dukes et al. 2009). Increases in 

extreme precipitation events and flooding have the 

potential to increase soil erosion and sedimentation, 

compounding existing stressors from agricultural 

and industrial runoff. 

Moderate Adaptive Capacity

This forest community exists in many variations 

across the landscape, and many species are 

projected to remain stable or even increase under 

climate change. The community can cope with a 

high level of natural variability and disturbance 

and is expected to tolerate some additional change 

with the exception of extreme drought, extreme 

erosion, or prolonged flooding. However, interacting 

disturbances that result in too much disruption may 

exceed the tolerance thresholds of this disturbanceadapted system.
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

161

Floodplain forest along the Neversink River, New York. Photo 

by Timothy G. Howard, New York Natural Heritage Program, 

used with permission.

Red maple-hardwood swamp at Grand Pond Swamp, New 

York. Photo by Elizabeth A. Spencer, New York Natural 

Heritage Program, used with permission.

Mixture of riparian hardwood species in Pennsylvania. Photo by Greg Czarnecki, Pennsylvania 

Department of Conservation and Natural Resources, used with permission.
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

162

Montane Spruce-Fir (Interior)

High Vulnerability (medium-robust evidence, high agreement)

This forest community is restricted to cool, moist environments at the highest elevations in the MidAtlantic region. Northern and boreal conifer species are expected to decrease where they currently 

persist. Protected valleys or coves may continue to provide cool microhabitats for spruce and fir. 

Negative Potential Impacts

Drivers: This community is adapted to cold 

temperatures and abundant moisture. Projected 

increases in temperature could lead to moisture 

stress, even without a decrease in precipitation. 

Red spruce seeds may not germinate if moisture 

is insufficient and temperatures exceed 92 °F (34 

°C) for a prolonged time (Burns and Honkala 

1990). Balsam fir and red spruce seedlings are at 

risk of mortality during periods of drought or if 

soil surface temperatures exceed 115 °F (47 °C). 

Reduced snowfall and snowpack, which lead to 

earlier spring melt, may also play a large role in soil 

moisture availability. Lack of snowpack can result 

in increased risk of shallow roots freezing. Fires are 

rare in this forest community, but extreme drought or 

tree mortality could increase fire risk.

Dominant Species: All dominant species in this 

forest community are projected to lose habitat and 

productivity under both climate scenarios, with more 

substantial impacts projected under greater warming. 

Balsam fir, red spruce, American mountain-ash, 

and paper birch are expected to decrease under both 

scenarios. Striped maple and yellow birch habitats 

are projected to remain relatively stable under the 

low emissions scenario and suffer large decreases 

under the high emissions scenario. 

Stressors: This forest community is currently 

recovering from historical acid deposition and 

logging, which significantly reduced the extent of 

this forest. Heavy rainfall could increase runoff and 

soil erosion, as well as lead to increased risk from 

windthrow on saturated or destabilized soils. Spruce 

budworm outbreaks occur in periodic natural cycles 

in mature spruce-fir, causing individual mortality 

after one or more years of heavy defoliation. Warmer 

winter temperatures could result in higher insect 

mortality, and outbreaks of spruce budworm could 

become less prevalent in the long term. 

Low Adaptive Capacity

Several factors contribute to low capacity to adapt to 

climate change. There is relatively low species and 

genetic diversity in these forests, which are isolated 

at the highest elevations in the region. This forest 

community is projected to lose physical habitat as 

the climate warms and the species are limited in their 

upward migration. At the same time, this community 

may benefit somewhat from isolation and its 

competitiveness in cold and nutrient-poor sites. For 

example, both balsam fir and red spruce can respond 

to release after many years of suppression. This 

community is currently expanding on the landscape 

as it recovers from past logging and salvage 

operations that had greatly reduced its extent. 

This current rebound of montane spruce-fir on the 

landscape may mask climate-induced migration or 

decline of the system in coming decades (Foster and 

D’Amato 2015). However, the typically slow rate of 

recovery in response to disturbance is a factor in the 

low adaptive capacity. 
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163

Montane fir forest on Westkill Mountain in the Catskill 

Mountains, New York. Photo by Timothy G. Howard, 

New York Natural Heritage Program, used with permission.

Montane spruce-fir forest on Kaaterskill High Peak in the 

Catskill Mountains, New York. Photo by Kelly A. Perkins, 

New York Natural Heritage Program, used with permission.

Montane fir forest on Blackhead Mountain in the Catskill 

Mountains, New York. Photo by Timothy G. Howard, 

New York Natural Heritage Program, used with permission.
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164

Northern Hardwood (Interior)

Moderate-High Vulnerability (medium-robust evidence, medium-high agreement)

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

species. Anticipated future reductions in tree species diversity in this community may decrease resilience 

to a variety of climate-related stressors. 

Moderate-Negative Potential Impacts

Drivers: This forest community is sensitive to 

reduced soil moisture and possible drought that 

could occur on some sites under warmer and drier 

conditions. Changes in soil temperature and moisture 

and associated changes in nutrient availability or soil 

processes could have substantial effects on sugar 

maple and other dominant species (Groffman et al. 

2012). A combination of severe warming and drier 

conditions could increase wildfire risk in the MidAtlantic region, but topography, fragmentation, and 

fire suppression are likely to limit wildfire (Guyette 

et al. 2014). Disturbance dynamics may also 

change, and increases in extreme weather events are 

expected to result in accelerated gap formation and 

regeneration. 

Dominant Species: This forest community is 

relatively diverse in tree species. American beech, 

eastern hemlock (which can form homogenous 

pockets), and eastern white pine are generally 

expected to decline, especially under the warmer 

climate scenario. American basswood, black cherry, 

sugar maple, sweet birch, and tulip tree are generally 

projected to decline under the warmer scenario only. 

Northern red oak is projected to remain stable under 

both scenarios. 

Stressors: Deer herbivory is currently limiting 

to seedling establishment and growth, and deer 

populations are not expected to change dramatically 

due to climate alone. Invasive species such as garlic 

mustard and Japanese stiltgrass are expected to 

expand in newly formed gaps and compete with 

native species. Eastern hemlock, American beech, 

and several ash species have already declined on 

the landscape due to insect pests such as hemlock 

woolly adelgid, beech bark disease, and emerald ash 

borer. Insect pests, pathogens, and interactions with 

drought and other disturbances may result in decline 

of other species in the near term, with the Asian 

longhorned beetle posing a serious threat to northern 

hardwood species.

Moderate Adaptive Capacity

Current regional strongholds for this community 

are fragmented on the landscape due to agriculture, 

development, and natural resource extraction. These 

factors, along with forest management, strongly 

influence the diversity of the forest community. 

Positive characteristics include a relatively high 

number of species with broad geographic ranges, 

large populations, and high genetic diversity. Even 

as some species decline, others are well established 

to fill in the new gaps on a variety of sites. Valley 

bottoms and other microsites in areas of complex 

topography may be buffered from some of the 

effects of climate change.
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165

Beech-maple mesic forest at The Pinnacle in Washington County, New York. Photo by 

Gregory J. Edinger, New York Natural Heritage Program, used with permission.

Maple-basswood rich mesic forest at Jerden Falls, New York. 

Photo by Gregory J. Edinger, New York Natural Heritage 

Program, used with permission.

Beech-maple mesic forest at Wilcox Mountain 

in the Adirondack Mountains, New York. Photo by 

Gregory J. Edinger, New York Natural Heritage Program, 

used with permission.
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166

Woodland, Glade, and Barrens (Interior)

Low Vulnerability (medium evidence, medium-high agreement)

Many of the species in this ecosystem are projected to do well under a range of future climate scenarios. 

Further, encroachment of novel species may be reduced because this community is geographically 

constrained due to extreme site conditions. 

Positive Potential Impacts

Drivers: This forest community thrives in the 

hottest, driest, and most exposed slopes underlain 

by shale and limestone. Warmer, drier summers 

are likely to increase the risk of drought and fire in 

these locations, which would help maintain the open 

conditions that favor this community type (Tyndall 

2015). Although this community is generally tolerant 

of short periods of severe drought, longer or more 

extreme drought can delay germination or kill 

seedlings and even long-established trees. Because 

the bedrock sheds water easily, increases in extreme 

precipitation events may increase erosion or result in 

the disintegration of shale downslope.

Dominant Species: This community is characterized 

by fewer than a dozen species, which vary based on 

the presence of shale or limestone bedrock. Most 

dominant species are projected to increase or remain 

stable under both climate scenarios, including 

eastern redcedar, eastern redbud, hackberry, 

northern red oak, pignut hickory, pitch pine, scrub 

oak, Virginia pine, and white oak. Sugar maple is 

projected to decline under a substantially warmer 

and drier climate, and would be the species most 

likely to disappear from this community type due to 

moisture deficit. 

Stressors: Some invasive species, including some 

nonnative grasses, spotted knapweed, Japanese 

honeysuckle, Chinese bush clover, and ailanthus, 

are very competitive in this forest community. These 

invasive species may pose a greater threat if they 

can outcompete native species. Forest health is 

not greatly challenged by pests and pathogens, but 

could become degraded if the system becomes very 

drought stressed. 

Moderate-High Adaptive Capacity

This community is adapted to extreme weather and 

natural disturbance, and already occupies some of 

the driest and hottest habitat in the region, all of 

which suggests that it can adapt to various climaterelated stressors. The presence of fire, either natural 

or managed, is an important disturbance process that 

maintains open conditions in the barrens, glades, 

and woodlands. This community type can change 

very quickly in the absence of fire, which may 

allow eastern redcedar, red maple, and nonnative 

buckthorn to establish or increase. Both drought 

and fire can benefit this community by keeping an 

open state where it is currently present, and even 

potentially creating new habitat where adjacent 

oak-pine forest declines. Shale bedrock restricts 

the number of species that could compete with this 

community type, although invasive trees and shrubs 

are an increasing problem.
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167

Wet alvar grassland (foreground) and alvar woodland 

(background) at Three Mile Creek Road Barrens, Jefferson 

County, New York. Photo by Kimberly J. Smith, New York 

Natural Heritage Program, used with permission.

Fissures or grikes (or grykes) in limestone in alvar pavement 

grassland at The Nature Conservancy’s Chaumont Barrens, 

Jefferson County, New York. Photo by Gregory J. Edinger, 

New York Natural Heritage Program, used with permission.

Redcedar rocky summit on Mount Tom in Washington 

County, New York. Photo by Gregory J. Edinger, New York 

Natural Heritage Program, used with permission.

Serpentine barrens in Soldier’s Delight, Maryland. Photo by 

Jennifer Dean, New York Natural Heritage Program, used 

with permission.
CHAPTER 6: FOREST ECOSYSTEM VULNERABILITIES

168

CHAPTER SUMMARY

Forest ecosystems across the Mid-Atlantic region 

will be affected by climate change, although 

ecosystems and individual tree species are expected 

to respond 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 severely 

affected by changes in water availability. On the 

coastal plain, vegetation will also be vulnerable 

to sea-level rise and increasing salinity. 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 variety 

of conditions and disturbances are also expected to 

persist under a range of plausible climates. 

The vulnerability determinations for individual 

forest communities 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 are likely to 

be influenced by succession, management, and 

natural disturbances. Vulnerability to anthropogenic 

stressors such as fragmentation and urban 

development are also expected to influence the 

adaptive capacity of an ecosystem, but are beyond 

the scope of this assessment. This assessment makes 

use of 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 

updated tools, scientific publications, and stand-level 

information such as can be obtained through stand 

and stock surveys. The high diversity in landforms, 

microclimates, hydrology, and species assemblages 

across the assessment area greatly complicates 

model projections and interpretation. In this 

assessment, forest communities were roughly based 

on NETHCS systems (Chapter 1). Forest ecosystems 

have the potential to manifest themselves in very 

different ways across the assessment area (e.g., 

by varying in species associations and landscape 

position), and it is important to have a good working 

knowledge of forest communities at the local level.

It is essential to consider local characteristics such 

as management history, soils, topographic features, 

species composition, forest health issues, and recent 

disturbances when these general vulnerabilities are 

being interpreted 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 

important to forest managers.
169

CHAPTER 7: MANAGEMENT IMPLICATIONS

The previous chapters of this assessment have 

described observed and anticipated climate trends, 

potential impacts to forest ecosystems, and the 

climate-related vulnerability of major 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 natural resource managers working in forest 

ecosystems. Changes in climate, impacts on forests, 

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.

This chapter does not make recommendations as to 

how management should be adjusted to cope with 

climate impacts. We recognize that climate change 

will have varying implications for different forest 

systems, ownerships, and management objectives. 

Additionally, climate change is only one of many 

factors considered in making land management 

decisions. 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 

natural resource management to projected impacts 

(Swanston et al. 2016).

NATURAL RESOURCE 

MANAGEMENT PLANNING

Until recently, climate change has not played a large 

role in natural resource planning. Many federal and 

state-level land management agencies have initiated 

efforts to address the issue, however. For example, 

the recently updated Forest Service regulations for 

National Forest System Land Management Planning 

(also known as the 2012 Planning Rule) directly 

address the impacts and ramifications of climate 

change (USDA Forest Service 2012). In fact, climate 

change was among the stated purposes for revising 

the rule (USDA Forest Service 2012: 21163-21164); 

the Allegheny National Forest is required to address 

climate change under the 2012 rule during future 

revisions of management plans. Similarly, recent 

state-level forest strategies identify climate change 

as a potential threat to the long-term sustainability 

of forests. Although most state forest management 

plans have not addressed climate change, climate 

change-related concerns are considered in some 

forest plans. For example, the Pennsylvania State 

Forest Resource Management Plan outlines climaterelated impacts on forests, management strategies, 

and agency-wide climate change initiatives 

(Pennsylvania Department of Conservation and 

Natural Resources 2016). Another example is found 
CHAPTER 7: MANAGEMENT IMPLICATIONS

170

in the Savage River State Forest Annual Work 

Plan, which describes expanding the use of native 

and nonnative conifers as a wildlife management 

component and for adaptation to climate change 

and invasive pests and pathogens (Maryland Forest 

Service 2017).

Incorporating climate change considerations 

into natural resource planning will always be a 

complicated endeavor. The uncertainties associated 

with planning over long time horizons are only 

compounded with climate change. Management 

plans for federal, state, and local agencies, as well 

as private lands, are typically written to guide 

management for a 10- to 25-year period, and it 

may not be feasible to address the potential longterm effects of climate change within a relatively 

short planning horizon. Further, major storms or 

disturbance events are inherently unpredictable, 

and often force managers to deviate from 

planned analysis or treatment cycles. If climate 

change results in more frequent disturbances or 

unanticipated interactions among major stressors, 

it may become more challenging to adhere to the 

stated goals, objectives, and priorities in current 

plans. Future land management plans may have 

to incorporate adaptive management principles, 

include greater flexibility, or coordinate across 

land ownerships to address shifting conditions and 

priorities.

Corporate, industrial, and family woodland owners, 

who own about 73 percent of the forest land in the 

assessment area, are also beginning to consider the 

implications of climate change for their planning 

and management. Those who have considered 

climate change may be motivated by material 

risk posed to their forest land. For corporate or 

industrial owners, the interest may also be inspired 

by questions from outside funding, investment, 

and certification agencies regarding their “climate 

preparedness.” For forest management, in particular, 

climate change will present risks such as more 

severe drought, increased pest pressure, and heavier 

precipitation events, as well as opportunities, such 

as longer growing seasons, potential for carbon 

fertilization, and habitat to support novel species. 

In the near term, the biggest climate-driven impacts 

are likely to come from changing pest and disease 

dynamics and increased risk from extreme events, 

such as heavy rainfall, storms, and more frequent 

drought conditions (Chapter 5). In the long term, 

managers may need to adjust for suboptimal 

growing conditions induced by shifts in habitat for 

commercially important tree species. Managers are 

increasingly thinking of climate change as a new 

lens through which to view management activities.

More Information

• More information on the Forest Service’s 2012 

Planning Rule can be found here: 

www.fs.usda.gov/planningrule 

• The Climate Change Resource Center is a Webbased resource that connects land managers and 

decisionmakers nationwide with usable science 

to address climate change in planning and 

application. www.fs.usda.gov/ccrc 

• State forest action plans have been prepared for 

all states in the assessment area. These statewide 

assessment and strategy documents include 

discussions of climate change. 

www.forestactionplans.org/regional-state 

• The Forest Stewardship Program, which 

encourages private landowners to actively 

manage their forest and related resources, 

provides guidance on including carbon 

sequestration and climate change resilience 

in Forest Stewardship Plans. www.fs.fed.us/

cooperativeforestry/programs/loa/fsp.shtml 

• The Climate Change Response Framework, led 

by the Northern Institute of Applied Climate 

Science, is a collaborative, cross-boundary 

effort working to incorporate climate change 

considerations into natural resource management. 

It provides an integrated set of tools, partnerships, 

and actions to support climate-informed 

conservation and forest management.
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171

The Climate Change Response Framework Web 

site provides access to presentations, briefings, 

and other products that help integrate climate 

change into management planning and activities. 

The Web site highlights real-world adaptation 

demonstrations across public, tribal, and private 

lands. 

www.forestadaptation.org 

• Forest Adaptation Resources: Climate change 

tools and approaches for land managers, 

2nd edition provides concepts and tools for 

integrating climate change considerations into 

natural resource planning and management. 

https://doi.org/10.2737/NRS-GTR-87-2. 

• An online adaptation workbook and associated 

Web site, workshops, and training sessions 

have given managers sound science and the 

tools to better and more proactively manage 

forests while taking climate vulnerability into 

consideration. 

www.adaptationworkbook.org 

• The Climate Smart Land Network, led by 

Manomet (a private nonprofit organization), 

provides forest landowners and managers with 

direct access to experts on forests and climate, 

and the opportunity to learn from other forest 

landowners in the network. The Web site has 

publicly available bulletins synthesizing a 

wide variety of topics, as well as additional 

information about its services. 

www.climatesmartnetwork.org/ 

• The National Wildlife Federation (NWF) has 

developed a guide to provide conservation 

practitioners and natural resource managers 

guidance for conservation in a changing climate. 

www.nwf.org/What-We-Do/Energy-and-Climate/

Climate-Smart-Conservation.aspx 

HABITAT MANAGEMENT

Climate change is expected to have profound effects 

on forest ecosystems (Chapter 5), which may in turn 

lead to habitat changes for a variety of plant and 

animal species (Manomet Center for Conservation 

Sciences and NWF 2013, NWF and Manomet 

Center for Conservation Sciences 2014, Staudinger 

et al. 2013). These changes mean that managers will 

increasingly need to consider the effects of climate 

change when managing wildlife habitats or working 

to conserve biodiversity (Mawdsley et al. 2009). 

Climate change vulnerability assessments have 

been conducted for many species within the MidAtlantic region, especially those of conservation 

concern (Furedi et al. 2011, Schlesinger et al. 

2011). The assessments take into account many 

factors including current threats, habitat and dietary 

specificity, genetic variation, mobility, and natural 

and anthropogenic barriers to movement. Although 

the factors for each species are unique, some 

generalizations can be made.

Aquatic species and those that inhabit seasonally wet 

habitats are nearly all rated as highly or extremely 

vulnerable to climate change because of warmer 

water temperatures, habitat specificity, natural and 

humanmade barriers to dispersal, and drying of highelevation streams and isolated wetlands. Most birds, 

on the other hand, are rated as stable or likely to 

increase because of their ability to disperse over long 

distances, move around anthropogenic obstacles, and 

tolerate a wider range of temperature and hydrologic 

regimes. Climate change has the potential to 

negatively affect even common species. The brook 

trout, for example, is considered highly vulnerable 

due to warming water temperatures and habitat 

isolation. On the other hand, climate vulnerability 

analyses show that the golden-winged warbler, 

which is currently considered threatened in the MidAtlantic region, may expand due to an increase in 

early successional habitat (Audubon n.d.). 
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172

Golden-winged warbler in Bald Eagle State Park, Center County, Pennsylvania. Photo by Darin McNeil, Indiana University of 

Pennsylvania, used with permission.

Many species are expected to respond to changes in 

climate by moving northward, upslope, or upstream, 

whereas others may adapt in place or be unable to 

cope with changes (Staudinger et al. 2013). Climate 

change is expected to affect species differently, such 

that some species may decline while others expand 

under future conditions. Species that are relatively 

free to move around on the landscape are expected 

to seek favorable habitat even as the distribution of 

habitat changes. Because many species, such as the 

Appalachian cottontail, eastern spotted skunk, and 

eastern fence lizard, are at either the northern or 

southern edge of their range within the Mid-Atlantic 

region, range shifts are likely as habitats change. 

Black-capped chickadees, for example, are already 

retreating northward while their southern cousin, 

the Carolina chickadee, is moving in behind them 

(Taylor et al. 2014). A zone of hybridization, which 

is sliding northward at about 0.6 mile per year, has 

formed where the two species overlap in southern 

Pennsylvania.

Relocating in response to climate change is not an 

option for some species due to limited mobility, 

narrow habitat requirements, or codependence 

on other species (Trani Griep and Manley 2012). 

Freshwater mussels, which include some of the 

region’s most endangered species, such as the 

eastern pearlshell and dwarf wedgemussel, embody 

all of these characteristics and are considered highly 

vulnerable to climate change. Another at-risk group 

are amphibians, such as the Jefferson salamander, 

that breed in vernal pools. Not only are they habitat 

specialists with limited mobility, but the wetlands 

they inhabit are at risk due to higher temperatures 

and extended dry periods. Some evidence suggests 

that aquatic systems and water-dependent habitats 

such as ephemeral ponds may be at higher risk 

because of changing hydrologic regimes, rising 

water temperatures, reduced oxygen levels, and 

altered nutrient cycling (Groffman et al. 2014, 

Staudinger et al. 2013, Trani Griep and Manley 

2012). Coastal ecosystems are especially vulnerable 

to rising sea levels (Climate Change Science 

Program 2009, NWF and Manomet Center for 

Conservation Sciences 2014). 
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173

Threatened and endangered species often face 

population declines due to a variety of nonclimatic 

stressors, such as habitat loss, competition from 

invasive species, and disease, all of which can be 

exacerbated by climate change. Many organizations 

are taking a deeper look into the effects of climate 

change on the habitats that they manage. For 

example, state agencies are working to incorporate 

climate change information into their statelevel wildlife action plans. These plans identify 

wildlife species and associated habitats that are 

of greatest conservation need, many of which 

may be particularly vulnerable to climate change. 

There is also an increasing interest in strategies to 

support climate change adaptation (Mawdsley et al. 

2009, Stein et al. 2014). Available strategies vary 

widely and include reducing nonclimatic stressors, 

maintaining fundamental ecosystem processes 

and features, enhancing connectivity, protecting 

refugia, and relocating organisms (Mawdsley et 

al. 2009, Stein et al. 2014, Swanston et al. 2016). 

The selection of specific strategies and actions may 

depend on the needs and scope of a particular project 

and location (Stein et al. 2014, Swanston et al. 

2016). 

More Information

• Many states have incorporated climate change

information into their state wildlife action plans. 

The Northeast Climate Science Center developed 

a regional synthesis document to support the 

revision of these plans. The synthesis includes a 

summary of the current scientific knowledge of 

biological responses for wildlife species with a 

focus on Regional Species of Greatest 

Conservation Need. https://necsc.umass. edu/

projects/integrating-climate-change-statewildlife-action-plans

• The Climate Change Bird Atlas, developed by the 

USDA Forest Service, is a companion to the

Climate Change Tree Atlas, and uses information

about climate change and effects on forest habitat 

to project changes in bird species distributions. 

www.nrs.fs.fed.us/atlas/bird 

• The Forest Service Climate Change Resource

Center provides topic pages that summarize how

climate change may affect wildlife species and

aquatic ecosystems. www.fs.usda.gov/ccrc/topics

• NatureServe and Heritage Program collaborators

have developed a Climate Change Vulnerability

Index (CCVI) to provide a rapid, scientifically

defensible assessment of species vulnerability to

climate change for 60 species found in the North

Atlantic Coastal Zone. http://northatlanticlcc.org/

projects/CCVI-northeast-spp/CCVI-northeast-spp

• The National Wildlife Federation has developed

a guide to provide conservation practitioners

and natural resource managers guidance for

conservation in a changing climate. www.nwf.

org/What-We-Do/Energy-and-Climate/ClimateSmart-Conservation.aspx

Clutch of hybrid chickadees. In Pennsylvania, chickadees 

are hybridizing as the ranges for black-capped chickadees 

and Carolina chickadees shift northward. Photo by Greg 

Czarnecki, Pennsylvania Department of Conservation and 

Natural Resources, used with permission.
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174

WILDERNESS MANAGEMENT

The federal 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. Five Wilderness areas are contained 

within the Mid-Atlantic region, and are managed 

by several federal agencies. The Hickory Creek 

and Allegheny Island Wilderness areas are located 

on the Allegheny National Forest and are managed 

by the Forest Service. The Great Swamp National 

Wildlife Refuge Wilderness and the Brigantine 

National Wildlife Refuge are located in New Jersey 

and are managed by the Fish and Wildlife Service. 

The Otis Pike Fire Island High Dune Wilderness is 

located near Long Island, NY, and is managed by 

the National Park Service. These areas play a special 

role in the regional landscape because of their 

remote and unmanaged character and their scenic, 

recreational, and ecological value. Wilderness areas 

are designed to “secure for the American people of 

present and future generations the benefits of an 

enduring resource of wilderness” (USDA Forest 

Service 2007).

Climate change is poised to affect wilderness areas 

in a number of ways. Weather and climate could 

influence recreational use; a shorter winter season 

may increase participation in some activities and 

areas. Although natural hazards and obstacles 

are inherently part of the wilderness experience, 

increased tree mortality from storms, drought, or 

insect or disease attack may pose increased risk 

to visitors, and extreme precipitation events may 

damage infrastructure. Increased disturbances may 

also reduce food supply or available habitat for 

wildlife species within wilderness areas. 

The potential for extensive ecosystem change 

resulting from climate change raises questions about 

the future management of these and other wilderness 

areas. In some cases, it is uncertain how climaterelated impacts may influence management in 

wilderness areas because of differences in wilderness 

restrictions among different land management 

agencies and organizations. For example, federally 

designated Wilderness areas are legally required to 

be natural and untrammeled, and any changes to the 

management of these areas would require a thorough 

planning process to consider potential benefits 

and drawbacks. However, Special Provisions for 

the Otis Pike Fire Island Dune Wilderness declare 

that “Wilderness designation shall not preclude the 

repair of breaches that occur in the wilderness area, 

in order to prevent loss of life, flooding, and others 

severe economic and physical damage to the Great 

South Bay and surrounding areas” (Williams and 

Foley 2007: 8). A report from the National Park 

Rock outcrop overlooking forest scenery in southeastern 

Pennsylvania. Photo by Greg Czarnecki, Pennsylvania 

Department of Conservation and Natural Resources, used 

with permission.
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175

Service provides insights into the new paradigm of 

wilderness stewardship, which includes responses 

to climate change (Nelson 2015), and this topic is 

likely to become more important across all agencies. 

More Information

• The Wilderness.net Climate Change Toolbox 

provides information about climate change and 

wilderness, including management guidelines and 

strategies. www.wilderness.net/climate 

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect Wilderness area management. 

www.fs.fed.us/ccrc/topics/wilderness/ 

LAND CONSERVATION

Climate change has many important implications 

for land conservation planning in the assessment 

area, and climate change science can be used to 

help prioritize land conservation investments and 

help guide project design. For example, it may be 

important to identify parcels that have a large carbon 

mitigation potential and prioritize these for land 

acquisition and conservation. This is particularly 

important in the Northeast, where human population 

densities and levels of forest fragmentation are 

relatively high and projected to increase further 

(Shifley et al. 2012). Climate change trends and 

ecosystems models can also be used to identify lands 

that have long-term potential to provide refugia 

for at-risk species and habitats, enhance landscape 

connectivity, or protect water supplies. Planning 

for conservation of terrestrial habitat “strongholds” 

from climate change requires a close look at the 

landscape to identify those corridors and habitats 

that may be most resilient in the face of projected 

shifts (Anderson and Ferree 2010, 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.

In the design of land conservation projects, there 

are important decisions to make about long-term 

ownership and management prescriptions attached to 

the conservation agreement (Rissman et al. 2015). In 

some cases, the best 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, perhaps where complex restoration or speciesspecific management is needed, an appropriate 

conservation strategy may be to seek a public 

agency 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. Private not-for-profit organizations, 

government agencies, landowners, and potential 

funders will increasingly need research-based 

results on anticipated climate trends and impacts, 

including spatially explicit information on how these 

shifts may play out over the land. This science can 

enable effective use of funding, staff time, and other 

resources that are essential to advancing “climateinformed” conservation of forests in the region 

and shaping conservation efforts to deliver a more 

resilient landscape.

More Information

• The Open Space Institute developed the Resilient 

Landscapes Initiative to protect habitats that may 

serve as strongholds for plants and animals to 

adapt even as the climate changes. www.osiny.

org/site/PageServer?pagename=Issues_Habitat 

• The Nature Conservancy’s Northeast 

Resilience Project identified places that 

may be more resilient to climate change and 

serve as natural strongholds for diversity into 

the future. www.conservationgateway.org/

ConservationByGeography/NorthAmerica/

UnitedStates/edc/reportsdata/terrestrial/resilience/

ne/Pages/default.aspx
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FOREST PRODUCTS

The forest products industry is important to the 

economies of the assessment area (Chapter 1). 

Tree species and forest composition are projected 

to change during the 21st century (Chapters 5 

and 6). Changes in forest composition across the 

landscape may be influenced by forest management, 

and may in turn influence forest management and 

the forest products industry (Moser et al. 2016). 

Several commercially important species, such as 

beech, aspen, and eastern white pine, are projected 

to undergo significant declines under a range of 

climate scenarios during the next century. Black 

cherry, another important commercial species in 

the Mid-Atlantic region, is projected to decline 

only under the high emissions scenario. Black 

walnut is projected to gain suitable habitat under 

both emissions scenarios, whereas white oak is 

projected to gain suitable habitat only under the 

high emissions scenario. 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 will be able to take 

advantage of awareness of anticipated climate trends 

and shifts in forest species. 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 undoubtedly continue to change during 

the coming century (Moser et al. 2016). Major 

socioeconomic factors include national and regional 

economic policies, demand for wood products, and 

competing values for forests (Irland et al. 2001). 

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 

(Stein et al. 2014, Swanston et al. 2016). There 

may be local differences in forest responses, as well 

as potential opportunities for new merchantable 

species to gain suitable habitat in the Mid-Atlantic 

region. The forest products industry has adjusted to 

substantial changes during the past 100 years, and 

continued responsiveness can help the sector remain 

viable.

More Information

• The Forest Service 2010 Resources Planning 

Act Assessment includes future projections for 

forest products and other resources through the 

year 2060 and examines social, economic, land 

use, and climate change influences. www.fs.fed.

us/research/rpa/ 

• The Northern Forests Futures Project uses the 

latest inventory data and scientific projections 

to understand how forests in the Midwest and 

Northeast may change as climate and other 

stressors change. www.nrs.fs.fed.us/futures/ 

• The Climate Change Tree Atlas provides 

information on the projected suitable habitat for 

tree species under climate change. www.nrs.

fs.fed.us/atlas/ 

FOREST HARVEST OPERATIONS

Climate variability and change present many 

challenges for forest managers who seek to maintain 

the diverse goods and services that forests provide. 

In particular, changes in winter conditions in the 

assessment area may shorten the time available for 

conventional forest management operations. Harvest 

operations in lowland areas and on soils prone 

to compaction or erosion are often accomplished 

during winter months, but changes in winter climate 

(e.g., shorter seasons of frozen ground, more 

midwinter thaws, less snowpack, and more rain) 

may reduce the ability to harvest in those locations 

without damaging soil (Chapter 4). Although special 

equipment is available to increase flotation on 

shallow snowpack or in the absence of snowpack, 

this equipment is costly. Additionally, a lack of 

frozen ground may increase the need to build roads 

to facilitate winter harvest, which would be more 
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177

Marking crew staff identifying a tree to be cut during a timber sale. The objective of the silvicultural prescription was to 

improve the health and vigor of an oak stand on the Allegheny National Forest, Pennsylvania. Photo by Kevin Wiltsie, 

Allegheny National Forest.

expensive than conventional practices. Analysis 

of timber harvest records in northern Wisconsin 

have identified some consequences of the changes 

in frozen ground conditions (Geisler et al. 2016, 

Rittenhouse and Rissman 2015). Warmer winters 

can limit operability in forests with wet soils and 

shift harvest to upland forest types. Growing-season 

restrictions on harvest designed to limit the spread of 

forest diseases can further shorten the annual harvest 

window.

Projected changes in precipitation during the 

growing season could also have important 

implications for forest management operations. 

Intense precipitation events could delay harvest 

operations in areas of poor drainage, but these 

events may be less disruptive in areas with coarse, 

sandy soils. Alternatively, summer dry periods and 

droughts could possibly extend operating windows 

in low-lying areas or clay soils. 

Projected changes in severe weather patterns could 

increase the number and extent of salvage harvests. 

Harvesting green timber allows resource managers 

to strategically achieve desired objectives and 

outcomes. Salvage harvesting following a wind 

event or pest or disease outbreak, by contrast, 

generally arises from a more immediate need to 

remove hazardous fuels or clear affected forest 

areas. A salvage sale also does not garner as high a 
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178

financial return as a green timber sale. Additionally, 

ongoing stressors of overcapitalization, loan and 

insurance payments, and high fuel prices may 

increase pressure on loggers to harvest yearround. Thus, climate change impacts on forestry 

operations have complex implications for timber 

production, loggers’ livelihoods, water quality, and 

transportation systems.

INFRASTRUCTURE 

ON FOREST LAND

Changes in climate and extreme weather events 

are expected to affect infrastructure, such as roads, 

bridges, and culverts on forest lands, throughout 

the region. Many landowners and agencies are 

also responsible for managing water-related 

infrastructure such as dams, drainage ditches, and 

culverts. The current specifications for infrastructure 

are generally based on past climate patterns and 

are often considered inadequate. The current trend 

of intensifying precipitation has placed additional 

strains on old and fragile infrastructure. 

Heavy precipitation events, which are already 

increasing and projected to increase more in the 

future (Chapters 3 and 4), may overload existing 

infrastructure that has not been built to that capacity. 

For example, older road systems may be susceptible 

to increased rainfall events due to improper location 

or outdated building standards. Many of these aging 

structures are being replaced, with the expectation 

that new culverts may need to last up to 100 years 

and be able to sustain heavier precipitation events. 

Replacing infrastructure often results in greater costs 

in order to upgrade to higher standards and capacity. 

Extreme events may also require more frequent 

maintenance of roads and other infrastructure, 

even if the structures are designed to appropriate 

specifications. Furthermore, forest managers may 

find it necessary to take additional precautions to 

prevent erosion when designing road networks or 

other infrastructure.

NONTIMBER FOREST PRODUCTS

Hundreds of nontimber forest products are used for 

food, medicine, craft materials, and other purposes 

across the assessment area, providing important 

cultural and economic benefits and contributing to 

food security for some human populations (Vaughan 

et al. 2013). Many of these products may be affected 

by changes in climate; each product may be uniquely 

affected based on the impacts of climate change 

on individual species of wild plants, fungi, and 

animals. For example, foraging for morels and other 

mushrooms is a passion for many people throughout 

the assessment area for their commercial value, 

medicinal properties, and culinary applications. 

Some evidence suggests that the relationship 

between the onset of the growing season and fungal 

phenology may lead to earlier or longer fruiting 

periods of morels and other edible fungi (Emery 

and Barron 2010, Gange et al. 2007, Kauserud et al. 

2008). 

Mushrooms in Fowler’s Hollow State Park, Perry County, 

Pennsylvania. Photo by Greg Czarnecki, Pennsylvania 

Department of Conservation and Natural Resources, used 

with permission.
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Maple syrup is a nontimber forest product that is 

important in some areas of the Mid-Atlantic region. 

Fur trader records show that maple sugar was an 

important exchange good from the early days of 

settlement (Emery 2002). Today, gathering and 

boiling sugar maple sap remains culturally and 

economically important in the region. Commercial 

production of maple syrup and related products 

provides millions of dollars of revenue in the MidAtlantic region, and this does not include maple 

syrup production that never enters the market, such 

as the product of small family operations. Sap flow 

necessary for maple syrup production requires a 

combination of warm days and freezing nights that is 

highly seasonal. These conditions occur earlier than 

in the past and this trend is projected to continue. 

Sugar maple habitat is also expected to decline, 

especially in more southerly locations where the sap 

flow season is also likely to be shorter (Matthews 

and Iverson 2017). Maple syrup producers report 

that their ability to adapt to changing climate 

conditions is largely related to the health of the 

forest and the ability of producers to adopt new 

technologies (Kuehn et al. 2016). A study concluded 

that additional taps would be needed to make up 

for projected losses of sugar maple habitat, but the 

required warm days and freezing nights may limit 

syrup production, especially in the southerly states 

(Matthews and Iverson 2017).

FIRE AND FUELS

Weather and climate are major drivers of fire 

behavior. Across the Mid-Atlantic region, the fire 

season is controlled by a combination of day length, 

weather, and fuel conditions. After snowmelt, 

organic material on the forest floor is exposed to 

sunlight and wind, which dry the material, often 

enough to burn. After leaf-out occurs, humidity 

increases on the forest floor and the litter is less 

flammable. Typically, short day lengths, cool 

temperatures, and wet fuels delay the onset of fire 

season until April or May. Although the summer 

months have the longest days and warmest 

temperatures, living vegetation requires extended 

dry periods of 2 weeks or more to increase fire 

ignition and spread potential. Live trees drop leaves 

and go dormant in the fall, and most forests become 

increasingly fire prone around the same time that 

short days and cool temperatures return.

Projected changes in climate could affect fire and 

fuels management in the assessment area. Climate 

change is generally expected to increase total 

annual precipitation, but there is potential for drier 

conditions late in the growing season (Chapter 4). 

Understory and herbaceous vegetation is expected 

to initially become more lush during wet springs 

before drying out later in the growing season. This 

increase in forest fuels may heighten the potential 

for more intense fires. High-intensity wildfire can 

result in tree mortality, increases in invasive species, 

changes in soil dynamics (e.g., compaction, altered 

nutrient cycling, sterilization), or altered hydrology 

(e.g., increased runoff or erosion). Compared to 

the western United States, where fire frequency 

and severity are expected to increase significantly, 

wildfire frequency in the Mid-Atlantic region has 

generally been lower and only moderate increases 

in fire are expected (Clark et al. 2013). However, 

in fire-adapted forests such as the New Jersey Pine 

Barrens, prescribed fire is the primary management 

practice used to reduce hazardous fuels. Other 

wildfire-prone forests requiring fire management 

in the Mid-Atlantic region include pine-dominated, 

oak-pine, and, sometimes, oak-hickory forests 

(Clark et al. 2013). 

A combination of warmer temperatures and greater 

evapotranspiration may at times (e.g., spring or 

fall) exceed modest precipitation increases, creating 

conditions that support wildfire (Guyette et al. 

2014, Heilman et al. 2015). Under intense fire 

weather conditions, wildfires could also become 

a hazard and safety risk to the public, firefighters, 

and infrastructure near or within forest land. More 

resources may be needed to reduce fuel loads to 
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180

prevent these catastrophic wildfires, fight them 

when they do occur, and restore ecosystems after a 

catastrophic event. 

Although some ecosystems may be negatively 

affected by wildfire, any increases in wildfire could 

also be beneficial in some areas. Increased fire 

potential could increase opportunities for restoring 

pitch pine or oak forests, for example (Brose et 

al. 2012, Clark et al. 2015). Projected changes in 

climate could also affect the ability of public, tribal, 

and private land managers to apply prescribed fire 

on the landscape. Wetter springs could make it 

more challenging to conduct prescribed burns in 

spring, shifting opportunities for dormant-season 

burning to the fall. On the other hand, if summer or 

fall becomes drier, burning under those conditions 

could involve greater risk and managers may be less 

inclined to implement this practice.

More Information

• The North Atlantic Fire Science Exchange 

provides fire science information to resource 

managers, landowners, and the public about the 

use, application, and effects of fire. 

www.nrs.fs.fed.us/disturbance/fire/nafse/ 

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect wildland fire in forest 

ecosystems. www.fs.fed.us/ccrc/topics/wildfire/ 

CARBON SEQUESTRATION

Forests in the assessment area store a tremendous 

amount of carbon in live trees, dead trees and wood, 

the forest floor, and soils (Chapter 1). Climate 

change and associated impacts to forest ecosystems 

may change the ability of forests to store carbon. 

A longer growing season and carbon dioxide 

fertilization may lead to increased productivity and 

carbon storage in forests in the Mid-Atlantic region 

(Chapter 4). Several modeling studies suggest that 

forests are likely to continue to sequester additional 

carbon during the next several decades as relatively 

young forests continue to mature and forests benefit 

from slightly warmer conditions (Keenan et al. 2014, 

Pan et al. 2009, Scheller et al. 2012, Wang et al. 

2017). Over time, this increase could be offset by 

climate-related physical and biological disturbances 

(Gough et al. 2008, Hicke et al. 2012, Loehle et al. 

2016), leading to increases in carbon storage in some 

areas and decreases in others. 

As forests change in response to climate change, 

patterns of carbon storage are likely to change on 

the landscape as well. Different forest types in the 

assessment area store different amounts of carbon 

(Chapter 1). On average, oak/pine and white/red/

jack pine forests store the most carbon. Spruce/fir 

forests store slightly less carbon overall, but a much 

greater proportion of the carbon in this forest type 

is in soils. There is also mounting evidence that 

tree growth responses vary by geographic location 

and inherent temperature tolerance; for example, 

local populations of a southern species near the 

cold range limit have more potential for increased 

growth than local populations of the same species 

near the warm range limit (Reich et al. 2015). 

Carbon storage may also be affected by soil water 

holding capacity; reduced soil moisture in areas 

with typically high soil water holding capacity is 

expected to reduce total carbon. In areas with low 

water holding capacity, reduced soil moisture may 

not result in total carbon changes during the 21st 

century (Scheller et al. 2012). As long as forests 

are maintained as forests in the assessment area, a 

large-scale decline in carbon stocks is not expected. 

Additionally, forest management can be used to 

increase forest carbon stores and reduce carbon 

emissions (McKinley et al. 2011, Ryan et al. 2010).

More Information

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect the ability of forests to store 

carbon, including a video short course for land 

managers. More information can be found here: 

www.fs.usda.gov/ccrc/topics/forests-and-carbonstorage 
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• A review article, A Synthesis of the Science on 

Forests and Carbon for U.S. Forests, summarizes 

the key issues related to forest management and 

carbon. www.treesearch.fs.fed.us/pubs/35006 

RECREATION

Forests are the centerpieces of outdoor recreation 

in the Mid-Atlantic region (Chapter 1). People 

throughout this region enjoy a variety of recreational 

activities, including hunting, fishing, camping, 

wildlife watching, skiing, and snowboarding. People 

also explore trails on foot, bicycles, skis, snowshoes, 

and horseback, and in off-highway vehicles, among 

many other recreational pursuits. The vulnerabilities 

associated with climate change in forests may result 

in shifted timing or participation opportunities for 

forest-based recreation (Bowker and Askew 2013, 

Sign post at a portion of the Appalachian Trail in Pennsylvania. Photo by Greg Czarnecki, Pennsylvania Department of 

Conservation and Natural Resources, used with permission.

Fisichelli et al. 2015). Forest-based recreation and 

tourism are strongly seasonal, and most visits to 

public lands are planned during times when the 

weather is most conducive to particular activities.

Projections indicate that seasonal shifts may 

continue toward shorter, milder winters and longer, 

hotter summers, which could reduce opportunities 

for popular winter-based recreational activities in 

the long term. Climate change has already caused 

reductions in the duration of lake ice and snow in 

the Mid-Atlantic region (Chapter 3), and activities 

such as ice fishing and pond hockey have the 

potential to be harmed as conditions continue to 

change (Fairley et al. 2015). Much of the region 

will have substantially less snow by the end of the 

century, which will create challenges to popular 

and economically important activities, such as 

snowmobiling and skiing in undeveloped areas, 
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182

and downhill skiing (Bowker and Askew 2013, 

Burakowski and Magnusson 2012, Scott et al. 2008). 

Because impacts on winter recreation activities are 

closely tied to winter temperatures, southern parts 

of the Mid-Atlantic region are at even greater risk in 

coming decades than northern parts of the region and 

may lose recreation opportunities such as downhill 

skiing (Scott et al. 2008). Recreationists may change 

the ways in which they participate in these activities, 

perhaps by changing the time or location of their 

participation, or switch to different activities that do 

not require snow (Dawson and Scott 2013).

It is also expected that recreational activities during 

the spring, summer, and fall will shift in response to 

warmer and more variable climate conditions. Some 

warm-weather forms of nature-based recreation 

such as mountain biking, motorized vehicle use, and 

fishing may benefit from extended seasons (Bowker 

and Askew 2013, Nicholls 2012). Conditions that 

are warmer, but not overly hot, could increase park 

use and participation in warm-weather activities 

(Bowker and Askew 2013, Fisichelli et al. 2015). 

Warmer spring and fall weather may increase 

the length of the recreation season, which could 

have implications for staffing (Nicholls 2012). 

Increasingly warm fall days may also extend the 

hiking season and leaf peeping season, bringing 

them into conflict with deer hunting season. 

Regional increases in average temperatures and heat 

waves in the summer could shift visitor behavior, 

depending on the magnitude of changes (Fisichelli 

et al. 2015, Nicholls 2012). Extreme weather events 

could also negatively affect recreation and tourism. 

For example, increased precipitation, severe storms, 

and associated flooding could damage infrastructure 

such as visitor centers, campsites, and trails.

Climate can also have important influences on 

hunting and fishing. The timing of certain hunts or 

fishing seasons correspond to seasonal events, which 

are in part driven by climate. Waterfowl hunting 

seasons, for example, are designed to correspond 

to the times when birds are migrating south in the 

fall, an event that is expected to shift to later in 

the year as temperatures warm (NWF 2013c). As 

mentioned earlier, climate change may also result in 

substantial changes in habitat availability and quality 

for wildlife and fish species (Glick et al. 2007). 

Big game species such as deer and elk are expected 

to undergo greater stress as a result of climate 

change (NWF 2013a). Projected changes in water 

temperatures and fish species habitat may reduce 

opportunities for ice fishing and cold-water stream 

fishing but increase opportunities for warm-water 

lake fishing (NWF 2013b). 

More Information

• The Northern Forests Futures Project uses the 

latest inventory data and scientific projections to 

understand how recreation opportunities in the 

Midwest and Northeast may change as climate 

and other stressors change. www.nrs.fs.fed.us/

futures/ 

ARCHAEOLOGICAL AND 

HISTORIC RESOURCES

The remnants of past human activity, such as 

paintings, sculptures, historic sites and buildings, 

and objects from everyday life, are present within 

the Mid-Atlantic region. 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 character and condition 

of these cultural resources (Holtz et al. 2014). For 

example, increases in extreme rain events and a 

more episodic precipitation regime may 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 slopes and on 
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183

Iron furnace stack at Pine Grove Furnace State Park, Pennsylvania. The Pine Grove Iron Works was a smelting facility during 

the Industrial Revolution and is now a historic site. Photo by Greg Czarnecki, Pennsylvania Department of Conservation and 

Natural Resources, used with permission.

flood terraces. The Sea Level Rise Vulnerability 

Assessment for the State of Delaware identified 244 

known historic sites that could be inundated by a 

sea level rise of 1.6 feet; the number of sites nearly 

doubles (441 sites) for a rise in sea level of 3.2 feet 

(Delaware Coastal Programs 2012). Floodwaters can 

further threaten the integrity of historic structures in 

low-lying areas by eroding the foundation, or adding 

moisture. The increased moisture can generate 

more mold and fungus growth, thereby hastening 

deterioration of wooden and other constructed 

features (Schiffer 1996). Flooding and storm damage 

can also render structures unsafe or inaccessible, as 

was the case of Ellis Island after Hurricane Sandy; 

this icon of American immigration was closed to 

visitors for more than a year during remediation 

(Holtz et al. 2014). Managing cultural resources will 

become more challenging because of the direct and 

indirect impacts of climate change, and is likely to 

require increased protections against extreme events.

More Information

• The National Park Service has published a report 

on coastal assets, Adapting to Climate Change 

in Coastal Parks: Estimating the Exposure of 

Park Assets to 1 m of Sea-level Rise. The full 

report and results for individual assets describe 

exposure, economic values, and case studies. 

https://www.nature.nps.gov/geology/coastal/

coastal_assets_report.cfm
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FOREST-ASSOCIATED 

TOWNS AND CITIES

Many towns and cities in the Mid-Atlantic region 

are particularly dependent on the health and 

functioning of surrounding forests, whether for 

economic, cultural, recreational, or subsistence 

reasons (Morzillo et al. 2015). They include cities 

near or containing forest, smaller towns and remote 

townships surrounded by forest, and communities 

in between. They are responsible for balancing 

activities related to timber production, land sales, 

wildfire suppression, water supply and wastewater 

treatment, watershed health, and federal laws 

(e.g., the Endangered Species Act of 1973), and 

a relatively high number of jobs are tied to the 

forestry sector. State and municipal agencies as 

well as private companies are also responsible 

for maintaining infrastructure in the Mid-Atlantic 

region, including roads, power lines, sewer lines, 

dams, drainage ditches, and culverts. Warmer 

temperatures are expected to drive changes in forest 

tree species; some species may decline while others 

become more important, resulting in changes to 

timber supplies for specific markets, or to forest 

products for cultural use. 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. Climate-related 

changes in the frequency and severity of droughts 

and floods may place greater stress on water supplies 

and water treatment infrastructure; consequences 

Kinzua Bridge in McKean County, Pennsylvania. This landmark was heavily damaged by a tornado in 2003. Photo by Greg 

Czarnecki, Pennsylvania Department of Conservation and Natural Resources, used with permission.
CHAPTER 7: MANAGEMENT IMPLICATIONS

185

could include increased costs associated with 

construction of flood barriers or green infrastructure 

to protect existing facilities (e.g., low-lying 

water or wastewater treatment plants), enhanced 

infrastructure for groundwater recharge and storage, 

increased reservoir storage capacity, and relocation 

of existing infrastructure to higher ground (Olmstead 

2014). 

Every forest-associated community has particular 

conditions, capacities, and constraints that may make 

it more vulnerable or resilient to climate change. 

Moreover, the effects of climate change and forest 

impacts are not evenly distributed geographically 

or socially. The ability of human communities to 

respond to environmental changes is directly related 

to their adaptive capacity—resources that can be 

leveraged by the human community to monitor, 

anticipate, and proactively manage stressors and 

disturbances. Although models exist that predict 

ecological community responses to climate change, 

considerably less is known about the social and 

cultural impacts of climate or forest change and 

how human communities might best respond. 

If resource professionals, community leaders, 

and local organizations are to help communities 

adapt to changes, they must identify community 

vulnerabilities and sensitivities and also build 

capacity to organize and engage community 

members and other resources (Moser et al. 2008). 

In the Northeast, much of the work done to date to 

assess the vulnerability of human communities and 

develop adaptation plans has focused on coastal 

communities and infrastructure (Holtz et al. 2014, 

Woodruff and Stults 2016, Zimmerman and Faris 

2010). 

More Information

• The Resilience Alliance has created Assessing 

Resilience in Social-Ecological Systems: 

Workbook for Practitioners 2.0 to assess 

resilience of social-ecological systems. 

www.resalliance.org/resilience-assessment 

• The U.S. Department of Energy examined current 

and potential future impacts of climate trends on 

the U.S. energy sector. www.energy.gov/articles/

climate-change-effects-our-energy 

• The National Climate Assessment provides 

summaries of how climate change may affect 

different regions and sectors of the United States.

• Urban systems and infrastructure: 

https://nca2014.globalchange.gov/report/

sectors/urban

• Rural communities: 

https://nca2014.globalchange.gov/highlights/

regions/rural-communities 

• Indigenous peoples, lands, and resources: 

https://nca2014.globalchange.gov/report/

sectors/indigenous-peoples 

• The Environmental Protection Agency’s Green 

Streets, Green Towns, Green Jobs (G3) Initiative 

provides tools and resources for communities 

to create green infrastructure that helps manage 

stormwater runoff, protect water quality, address 

climate change, and create green jobs. 

https://www.epa.gov/G3/green-streets-green-jobsgreen-towns-g3-initiative-and-approach 

URBAN FORESTS

Climate change also affects urban forests in the 

assessment area, which include nature preserves, 

river corridors, wetlands, urban parks, street 

trees, gardens, buffers, and greenways. Urban 

environments can pose unique stresses to urban 

trees compared to rural trees, including vehicle 

exhaust, confined root environments, and road salts. 

Climate change is expected to exacerbate the effects 

of common stressors, although there is uncertainty 

about how stressors may interact with each other 

under changing conditions.

Storms, extreme temperatures, longer growing 

seasons, and warmer winters can pose particular 

challenges for infrastructure. Impervious surfaces 
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186

can make urban environments more susceptible to 

flash floods, placing flood-intolerant species at risk. 

Extreme cold and freeze-thaw cycles can accelerate 

deterioration of concrete and other common 

infrastructure surrounding or containing trees. 

Extreme heat and longer growing seasons can result 

in rising costs associated with roadside and power 

line vegetation management.

Responses of trees to disturbances such as drought 

may vary by land use within an urban area, 

complicating predictions of how the forest may 

respond as a whole (Fahey et al. 2013). Native 

species that are projected to decline due to climate 

change are likely to be unable to tolerate the even 

more extreme conditions presented by urban 

settings. Conversely, urban environments may favor 

Waymart wind farm in Wayne, Pennsylvania. Photo by Greg Czarnecki, Pennsylvania Department of Conservation and Natural 

Resources, used with permission.

heat-tolerant or drought-tolerant native species or 

new migrants that are projected to benefit from 

climate change. 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. For example, 

many community forests already address the urban 

heat island effect by planting species that are from 

hardiness 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, 
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187

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.

Projected changes in climate can pose both 

challenges and opportunities for the management 

of urban forests, and some cities have started 

assessing their vulnerability (Brandt et al. 2016, 

City of Baltimore 2013, City of New York 2013, 

Philadelphia Water Department 2011). Shifts in 

temperature and changes in extreme events may 

have effects on species selection for planting (Yang 

2009). 

More Information

• The Forest Service Climate Change Resource 

Center provides a summary of how climate 

change may affect urban forests. www.fs.fed.

us/ccrc/topics/urban-forests 

• The Georgetown Climate Center highlights 

state and local adaptation plans. http://www.

georgetownclimate.org/adaptation/plans.html 

• Several urban areas have developed adaptation 

guides to help communities learn how to use 

urban forests to reduce climate change impacts 

and adapt urban forests to future conditions.

• British Columbia: www.toolkit.bc.ca/

Resource/Urban-Forests-Climate-AdaptationGuide 

• Toronto, Ontario’s urban forest: www.

cleanairpartnership.org/pdf/climate_change_

adaptation.pdf 

• The Climate Change Response Framework is 

working with urban communities in the Midwest 

and Northeast to assess the vulnerability of urban 

forests to climate change and to identify and 

develop tools to aid adaptation of urban forests to 

climate change. www.forestadaptation.org/urban 

HUMAN HEALTH

Climate change can affect in many ways the health 

of the people who live, work, or recreate in the 

forests and communities of the Mid-Atlantic region. 

Climate change can influence a wide array of 

human health issues through complex interactions 

in the environment and the human body (Portier et 

al. 2013, U.S. Global Change Research Program 

[USGCRP] 2016). Respiratory allergies and diseases 

may increase as longer growing seasons and changes 

in plant abundance lead to more pollen, or if warmer, 

moister conditions increase mold production (Ziska 

et al. 2011). Gastrointestinal illness could increase 

due to contaminated water caused by flooding 

or failed water infrastructure (USGCRP 2016). 

Extremely high temperatures can lead to heat stress, 

which can exacerbate cardiovascular disease or lead 

to heat-related illness and death.

Vector-borne diseases, such as Lyme disease and 

West Nile virus, pose an important risk to natural 

resource managers, local residents, and tourists 

alike, and this issue may become increasingly 

important during the 21st century. Vector-borne 

diseases are transmitted by arthropods such as 

ticks or mosquitoes and cycle back and forth 

between arthropod vectors and animal hosts. 

Humans are typically infected incidentally when 

they are bitten instead of animal hosts. Changes in 

climate can influence vector-borne disease risk by 

altering the abundance and distribution of ticks or 

mosquitoes, the percentage of infected vectors, and 

the abundance, distribution, and available habitat 

of animal hosts. For example, blacklegged ticks 

(i.e., “deer ticks”), the vector for Lyme disease 

and several other diseases, are most abundant in 

wooded or brushy habitats with abundant numbers 

of small mammals and deer. Projected expansion of 

tick ranges combined with earlier seasonal activity 

may increase the incidence of tick-borne diseases 

if humans frequently visit those habitats (USGCRP 

2016).
CHAPTER 7: MANAGEMENT IMPLICATIONS

188

More Information

• The U.S. Global Change Research Program 

provides a scientific assessment of the impacts 

of climate change on human health in the United 

States, with summaries for various sectors and 

regions. This report was based on the 2014 

National Climate Assessment. 

https://health2016.globalchange.gov/downloads 

• The National Climate Assessment provides a 

summary of how climate change may affect 

human health. 

https://nca2014.globalchange.gov/report/sectors/

human-health 

• The Natural Resources Defense Council hosts 

an online Web viewer that provides state-level 

information about various threats to human health 

associated with climate change. 

www.nrdc.org/health/climate/ 

• The Centers for Disease Control and Prevention 

Climate and Health Program includes information 

on a variety of subjects. 

www.cdc.gov/climateandhealth/ 

CHAPTER SUMMARY

The breadth of these topics highlights the wide range 

of effects that climate change may have on forest 

management in the Mid-Atlantic region. It is not 

the role of this assessment to identify adaptation 

actions that should be taken to address these climaterelated 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 in the region may be 

influenced by economic, political, ecological, and 

societal factors. These factors are specific to each 

landowner and agency, and are unpredictable.

Addressing the challenge of climate change also 

presents opportunities for managers and other 

decisionmakers to plan ahead, manage for 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 (Stein et al. 2014, 

Swanston et al. 2016), and more information on this 

subject is available at www.forestadaptation.org. 

This assessment is intended as a useful foundation 

for land managers in that process, to be further 

enriched by local knowledge and site-specific 

information.
189

GLOSSARY

acid deposition

a complex chemical and atmospheric phenomenon 

that occurs when emissions of sulfur and nitrogen 

compounds are transformed in the atmosphere and 

deposited on land in either wet or dry form.

adaptive capacity

the ability of a species or ecosystem to accommodate 

or cope with potential climate change impacts 

with minimal disruption. It is strongly related to 

the concept of ecological resilience, which refers 

to the ability to return to prior conditions after a 

disturbance.

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 both 

scattering and absorbing radiation; by acting as 

condensation nuclei for cloud formation; or by 

modifying the properties and lifetime of clouds. 

archaeology

the study of human history and prehistory through 

the excavation of sites and the analysis of artifacts 

and other physical remains.

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.

barrens

plant communities that occur on sandy soils and are 

dominated by grasses, low shrubs, small trees, and 

scattered large trees.

baseflow

the condition of only groundwater providing 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 forest 

a forest that is found only between 50-55o and 65-

70o N latitude and that is adapted to cool northern 

temperatures and low rainfall (less than 20 inches). 

carbon dioxide (CO2) fertilization 

increased plant uptake of CO2 through photosynthesis in response to higher concentrations of 

atmospheric CO2.

carbon sequestration

a natural or artificial process by which carbon 

dioxide is removed from the atmosphere and held in 

solid or liquid form. Forest carbon is often stored in 

wood, roots, leaves, and soil.

climate extreme

the occurrence of a value of a weather or climate 

variable above (or below) a threshold value near 

the upper (or lower) ends of the range of observed 

values of the variable. For simplicity, both extreme 

weather events and extreme climate events are 

referred to collectively as “climate extremes” (IPCC 

2007).
GLOSSARY

190

CO2-equivalent

the concentration of CO2 that would cause the same 

amount of radiative forcing as a given mixture of 

CO2 and other forcing components.

coastal plain

flat, low-lying land adjacent to the Atlantic Ocean.

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. 

disturbance

stresses and destructive agents such as invasive 

species, diseases, and fire; changes in climate and 

severe 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. 

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.

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.

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 (IPCC 2007).

ethnobotany

the scientific study of the traditional knowledge 

and customs of a people concerning plants and their 

medical, religious, and other uses.

evapotranspiration

the sum of evaporation from the soil and 

transpiration from plants.

extratropical cyclone

a cyclone in the middle or high latitudes often 

containing a cold front that extends toward the 

equator for hundreds of miles.

fen

a wetland fed by surface water or groundwater, or 

both; characterized by its water chemistry, which is 

neutral or alkaline. 

fire-return interval

the number of years between two successive fire 

events at a specific location.

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.
GLOSSARY

191

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.

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.

gale

an area of sustained surface winds of 39-54 miles 

per hour (34-47 knots).

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.

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 and crops 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 and larger. 

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 (for example, red maple, paper birch, 

quaking aspen, and American elm) and hard 

hardwoods (for example, 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, 100 = most 

abundant).

industrial ownership

forest products companies that hold land and harvest 

and market timber.

intensity

amount of precipitation falling per unit of time.

interpolation

estimation of a value within two known values in a 

sequence of values.

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).

lake-effect

the phenomena created in the surrounding area by 

weather passing over a large lake, especially any of 

the Great Lakes of the United States.

maritime

living near or at the ocean’s edge.
GLOSSARY

192

mesic

pertaining to sites or habitats characterized by 

intermediate (moist, but neither wet nor dry) soil 

moisture conditions.

microclimate

the climate of a very small or restricted area, 

especially when this differs from the climate of the 

surrounding area.

model reliability score

for the Climate Change Tree Atlas: a “tri-model” 

approach to assess reliability of model predictions 

for each species, classified as high, medium, or low.

modifying factor

an environmental variable (for example, site 

conditions, interspecies competition, disturbance, 

dispersal ability) that influences the way a tree may 

respond to climate change.

nonindustrial ownership

an ownership class of private lands where the owner 

does not operate wood-using plants.

nonstocked

land that currently has less than 10 percent stocking 

but formerly met the definition of forest land. Forest 

conditions meeting this definition have few, if any, 

trees sampled. In these instances, the algorithm 

cannot assign a specific forest type and the resulting 

forest type code is 999, meaning nonstocked.

nor’easter

a storm along the East Coast of North America, so 

called because the winds over the coastal area are 

typically from the northeast. These storms may 

occur at any time of year but are most frequent and 

most violent between September and April.

northern hardwoods

forest type with wet-mesic to dry-mesic soils, 

medium to high soil nutrient level, and supporting 

trees species such as sugar maple (dominant), 

American basswood, hemlock, yellow birch, red 

maple, and white ash.

orographic lift

as an air mass is forced from a low elevation to a 

higher elevation, adiabatic cooling can raise the 

relative humidity to 100 percent, resulting in clouds 

and precipitation.

paleoecology

the study of fossil animals and plants in order 

to deduce their ecology and the environmental 

conditions in which they lived.

palynology

the study of pollen grains and other spores, 

especially as found in archaeological or geological 

deposits.

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 study of 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.

pioneer species 

a plant capable of invading bare sites (for example, 

newly exposed soil), and persisting there until 

supplanted by successional species; or any new 

arrival in the early stages of succession.

plasticity

the ability of an organism to change its 

characteristics (gene expression or behavior) in 

response to changes in the environment.

process model

a model that relies on computer simulations based 

on mathematical representations of physical and 

biological processes that interact over space and 

time.
GLOSSARY

193

projection

a model-derived estimate of future climate, and the 

pathway leading to it.

proxy

a 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 (for 

example, 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. 

pyrophilic (pyrophobic)

a measure of fire tolerance (intolerance) related 

to tree attributes, such as bark thickness or leaf 

flammability.

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.

realized niche

the portion of potential habitat 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 (that is, aquifers).

refugia

locations and habitats that support populations of 

organisms that are limited to small fragments of their 

previous geographic range.

regression analysis

a statistical process for estimating the relationships 

among variables. Linear regression models the past 

relationship among variables to predict their future 

behavior. It includes many techniques for modeling 

and analyzing several variables.

resilience

the ability to return to prior or near-prior conditions 

after a disturbance, albeit with sometimes fluctuating 

populations or shifts in condition. Resilience is 

effective until the degree of disturbance exceeds the 

ability of the system to cope, resulting in transition 

to another state. 

respiration

the process by which plants absorb free molecules of 

oxygen and use them to create water, carbon dioxide, 

and energy, which help the plant grow. Water and 

carbon dioxide can be released into the air. 

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.

saltwater intrusion

the movement of saline water into freshwater 

aquifers through several pathways, including by 

lateral intrusion from the ocean; by upward intrusion 

from deeper, more saline zones of a groundwater 

system; and by downward intrusion from coastal 

waters.
GLOSSARY

194

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 6 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, 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).

sea level

the level of the ocean’s surface, which can change 

regularly with the tides, wind, and currents. Other 

factors that contribute to the sea level include water 

temperature and salinity, air pressure, seasonal 

changes, the amount of stream runoff, and the 

amount of water that is stored as ice or snow. The 

standard for terrestrial and atmospheric elevation 

or ocean depths is called the mean sea level and is 

calculated as the average of hourly tide levels.

senescence

the final stage of leaf development, which results in 

autumn colors in forests of deciduous trees, and is 

part of the process by which nutrients are recycled to 

other parts of the plant.

serotinous cone

resin-covered cones that require heat, such as from 

wildfire, to melt the resin so that the cone can open 

and release seeds.

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.

solar radiation

energy radiated from the sun in the form of 

electromagnetic waves, including visible and 

ultraviolet light and infrared radiation.

species distribution model

a model that uses statistical relationships to project 

future change.

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.

stochastic

referring to patterns resulting from random effects.

storm surge

water that is pushed onto shore during a rising of the 

sea as a result of atmospheric pressure changes and 

wind.
GLOSSARY

195

stratosphere

the layer of the Earth’s atmosphere which lies 

between 6 and 30 miles above the Earth.

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.

tidal flooding

the temporary inundation of low-lying areas, 

especially streets, during exceptionally high tide 

events, such as at full and new moons.

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.

urban heat island effect

a term describing the condition of built-up areas that 

are hotter than nearby rural areas. For example, the 

annual mean air temperature of a city with 1 million 

people or more can be 1.8 to 5.4 °F (1 to 3 °C) 

warmer than its surroundings.

veneer

a roundwood product from which veneer is sliced 

or sawn and that usually meets certain standards of 

minimum diameter and length, and maximum defect. 

vulnerability

susceptibility to a threat.

witness trees

trees that rested at the imaginary corners and angles 

of the parcels to mark their boundaries; these trees 

were documented by early land surveyors. 
196

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APPENDIXES

These five appendixes are an expanded version 

of Appendixes 1 through 5 in the print edition of 

Butler-Leopold and others’ (2018) Mid-Atlantic 

Forest Ecosystem Vulnerability Assessment and 

Synthesis: a Report from the Mid-Atlantic Climate 

Change Response Framework Project. In the 

following pages, you’ll find:

Appendix 1: Common and Scientific Names of 

Species Mentioned in this Report 

Appendix 2: Trend Analysis and Historical Climate 

Data 

Appendix 3: Additional Future Climate Projections 

Appendix 4: Additional Impact Model Results and 

Discussion 

Appendix 5: Vulnerability and Confidence 

Determination 
251

APPENDIX 1: COMMON AND SCIENTIFIC NAMES OF 

SPECIES MENTIONED IN THIS REPORT

Common name Scientific name

American basswood Tilia americana

American beech Fagus grandifolia

American chestnut Castanea dentata

American elm Ulmus americana

American hazelnut Corylus americana

American holly Ilex opaca

American hornbeam (musclewood) Carpinus caroliniana

American mountain-ash Sorbus americana

Atlantic white-cedar Chamaecyparis thyoides

baldcypress Taxodium distichum

balsam fir Abies balsamea

balsam poplar Populus balsamifera

bigtooth aspen Populus grandidentata

bitternut hickory Carya cordiformis

black ash Fraxinus nigra

black cherry Prunus serotina

black hickory Carya texana

black locust Robinia pseudoacacia

black maple Acer nigrum 

black oak Quercus velutina

black spruce Picea mariana

black walnut Juglans nigra

black willow Salix nigra

blackgum Nyssa sylvatica

blackjack oak Quercus marilandica

bluejack oak Quercus incana

boxelder Acer negundo

bur oak Quercus macrocarpa

butternut Juglans cinerea

cedar elm Ulmus crassifolia

cherrybark oak Quercus pagoda

chestnut oak Quercus prinus

chinkapin oak Quercus muehlenbergii

Common name Scientific name

chokecherry Prunus virginiana

common hackberry Celtis occidentalis

cucumber tree Magnolia acuminata

eastern cottonwood Populus deltoides

eastern hemlock Tsuga canadensis

eastern hophornbeam Ostrya virginiana

eastern redbud Cercis canadensis

eastern redcedar Juniperus virginiana

eastern white pine Pinus strobus

flowering dogwood Cornus florida

gray birch Betula populifolia

green ash Fraxinus pennsylvanica

honeylocust Gleditsia triacanthos

jack pine Pinus banksiana

laurel oak Quercus laurifolia

loblolly pine Pinus taeda

longleaf pine Pinus palustris

mockernut hickory Carya alba

mountain maple Acer spicatum

northern catalpa Catalpa speciosa

northern red oak Quercus rubra

northern white-cedar Thuja occidentalis

Ohio buckeye Aesculus glabra

Osage-orange Maclura pomifera

overcup oak Quercus lyrata

paper birch Betula papyrifera

pawpaw Asimina triloba

pecan Carya illinoinensis

persimmon Diospyros virginiana

pignut hickory Carya glabra

pin cherry Prunus pensylvanica

pin oak Quercus palustris

pitch pine Pinus rigida

(continued on next page)

Table 27.—Common and scientific names of native plant species mentioned in this assessment
APPENDIX 1

252

Common name Scientific name

pond pine Pinus serotina

post oak Quercus stellata

pumpkin ash Fraxinus profunda

quaking aspen Populus tremuloides

red maple Acer rubrum

red mulberry Morus rubra

red pine Pinus resinosa

red spruce Picea rubens

redbay Persea borbonia

river birch Betula nigra

rock elm Ulmus thomasii

sassafras Sassafras albidum

scarlet oak Quercus coccinea

scrub oak (bear oak) Quercus ilicifolia 

serviceberry Amelanchier Medik.

shagbark hickory Carya ovata

shellbark hickory Carya laciniosa

shingle oak Quercus imbricaria

shortleaf pine Pinus echinata

Shumard oak Quercus shumardii

silver maple Acer saccharinum

slash pine Pinus elliottii

slippery elm Ulmus rubra

sourwood Oxydendrum arboreum

southern red oak Quercus falcata

striped maple Acer pensylvanicum

sugar maple Acer saccharum

Common name Scientific name

sugarberry Celtis laevigata

swamp chestnut oak Quercus michauxii

swamp tupelo Nyssa biflora

swamp white oak Quercus bicolor

sweet birch Betula lenta

sweetbay Magnolia virginiana

sweetgum Liquidambar styraciflua

sycamore Platanus occidentalis

Table Mountain pine Pinus pungens

tamarack Larix laricina

tulip tree (yellow-poplar) Liriodendron tulipifera

turkey oak Quercus cerris

Virginia pine Pinus virginiana

water elm Planera aquatica

water hickory Carya aquatica

water locust Gleditsia aquatica

water oak Quercus nigra

water tupelo Nyssa aquatica

white ash Fraxinus americana

white oak Quercus alba

white spruce Picea glauca

white trillium Trillium grandiflorum

willow oak Quercus phellos

winged elm Ulmus alata

yellow birch Betula alleghaniensis

yellow buckeye Aesculus flava

Table 27 (continued).
APPENDIX 1

253

Nonnative invasive plants

Common name Scientific name

ailanthus Ailanthus altissima

garlic mustard Alliaria petiolata

Japanese barberry Berberis thunbergii

Japanese bromegrass Bromus japonicus

barren bromegrass Bromus sterilis

cheatgrass Bromus tectorum

Oriental bittersweet Celastrus orbiculatus

spotted knapweed Centaurea maculosa

autumn olive Elaeagnus umbellata

winter creeper Euonymus fortunei

burning bush Euonymus spp.

Japanese knotweed Reynoutria japonica Houtt.

common buckthorn Frangula alnus

Chinese bushclover Lespedeza cuneata

privet Ligustrum vulgare

Japanese honeysuckle Lonicera japonica

Amur honeysuckle Lonicera maackii

bush honeysuckles Lonicera mackii and others

Japanese stiltgrass Microstegium vimineum

princess tree Paulownia tomentusa

mile-a-minute vine Persicaria perfoliata

reed canarygrass Phalaris arundinacea

common reed (phragmites) Phragmites australis

kudzu Pueraria lobata

glossy buckthorn Rhamnus spp.

multiflora rose Rosa multiflora

wineberry Rubus phoenicolasius

crown vetch Securigera varia

wisteria Wisteria frutescens

Fauna, fungi, and pathogens

Common name Scientific name

balsam woolly adelgid Adelges piceae

hemlock woolly adelgid Adelges tsugae

two-lined chestnut borer Agrilus bilineatus (Weber)

emerald ash borer Agrilus planipennis

dwarf wedgemussel Alasmidonta heterodon

fall cankerworm Alsophila pometaria

Jefferson salamander Ambystoma jeffersonianum

Asian longhorned beetle Anoplophora glabripennis

armillaria Armillaria mellea

Lyme disease Borrelia burgdorferi

pine looper Bupalus piniaria

elm yellows Candidatus phytoplasma ulmi

spruce budworm Choristoneura fumiferana

chestnut blight Cryphonectria parasitica

beech bark disease a complex of the scale insect 

Cryptococcus fagisuga and the 

fungus Nectria spp.

southern pine beetle Dendroctonus frontalis 

Zimmermann

West Nile virus Flavivirus spp.

pine barrens tree frog Hyla andersonii

black-legged tick Ixodes scapularis

snowshoe hare Lepus americanus

earthworm Lumbricina spp.

gypsy moth Lymantria dispar dispar

eastern tent caterpillar Malacosoma americanum

forest tent caterpillar Malacosoma disstria

eastern pearlshell Margaritifera margaritifera

morel mushroom Morchella esculenta

white-tailed deer Odocoileus virginianus

Dutch elm disease Ophiostoma ulmi 

sudden oak death Phytophthora ramorum

black-capped chickadee Poecile atricapillus

Carolina chickadee Poecile carolinensis

purple martin Progne subis

brook trout Salvelinus fontinalis

eastern fence lizard Sceloporus undulatus

eastern spotted skunk Spilogale putorius

Appalachian cottontail Sylvilagus obscurus

golden-winged warbler Vermivora chrysoptera

red fox Vulpes vulpes

Table 28.—Common and scientific names of nonnative invasive plants, and fauna, fungus, and pathogen species 

mentioned in this assessment
254

APPENDIX 2: TREND ANALYSIS 

AND HISTORICAL CLIMATE DATA 

We used the Climate Wizard Custom Analysis 

Tool to examine historical averages and trends in 

mean temperature and mean precipitation within 

the assessment area (Climate Wizard 2014). Data 

for Climate Wizard are derived from PRISM 

(Parameter-elevation Regressions on Independent 

Slopes Model) (Gibson et al. 2002, Girvetz et al. 

2009). 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 observations will 

have on the final, predicted value for that cell. More 

information on PRISM can be found at: www.prism.

oregonstate.edu. 

A 30-year climate “normal” for the assessment area 

was calculated from the mean for the period 1971 

through 2000 (Figs. 39-40). Linear trend analysis 

was performed for the period of 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 Intergovernmental 

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 timestep 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 by year, 

season, and month 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 29).

The developers of the Climate Wizard Tool advise 

users to interpret the linear trend maps in relation 

to the respective map of statistical confidence 

(Figs. 41-42). 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 greater caution.

In addition, because maps are developed from 

weather station observations that have been spatially 

interpolated, developers of the Climate Wizard tool 

and PRISM dataset 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, 
APPENDIX 2

255

Figure 39.—Annual and seasonal mean, minimum, and maximum temperatures (°F) during the 30-year period from 1971 

through 2000. Data from Climate Wizard (2014).
APPENDIX 2

256

Season Mean temperature (°F) Minimum temperature (°F) Maximum temperature (°F) Mean precipitation (inches)

Annual 49.2 38.8 59.5 43.4

Winter (Dec-Feb) 28.5 19.8 37.3 9.1

Spring (Mar-May) 47.6 36.4 58.7 11.1

Summer (Jun-Aug) 69.0 57.8 80.2 12.1

Fall (Sep-Nov) 51.6 41.3 61.9 11.1

Table 29.—Annual and seasonal mean, minimum, and maximum temperature and annual total precipitation in the 

Mid-Atlantic region for the 30-year period from 1971 through 2000 (data source: Climate Wizard [2014]) 

Figure 40.—Mean annual and mean seasonal precipitation during the 30-year period from 1971 through 2000. Data from 

Climate Wizard (2014).
APPENDIX 2

257

Figure 41.—Statistical confidence (p-values for the linear regression) for trends in temperature, 1901 through 2011. 

Gray values represent areas of low statistical confidence.
APPENDIX 2

258

Figure 42.—Statistical confidence (p-values for the linear regression) for trends in precipitation, 1901 through 2011. 

Gray values represent areas of low statistical confidence.
APPENDIX 2

259

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 Centers for 

Environmental Information (https://www.ncdc.noaa.

gov). 

We selected the time period 1901 through 2011 

because it was long enough to capture interdecadal 

and intradecadal variation in climate for the MidAtlantic 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.

LITERATURE CITED

Climate Wizard. 2014. Climate Wizard Custom 

Analysis application. The Nature Conservancy, 

University of Washington, University of 

Southern Mississippi, Climate Central, 

and Santa Clara University. Available at 

www.climatewizardcustom.org (accessed 2014).

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. 

https://doi.org/10.1371/journal.pone.0008320. 

Trenberth, K.E.; Jones, P.D.; Ambenje, P.; Bojariu, 

R.; Easterling, D.; Klein Tank, A.; Parker, D.; 

Rahimzadeh, 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. 

https://www.ipcc.ch/publications_and_data/ar4/

wg1/en/ch3sappendix-3-b.html (accessed 

August 3, 2017).
260

APPENDIX 3: ADDITIONAL FUTURE 

CLIMATE PROJECTIONS

This appendix provides maps of projected change 

in temperature and precipitation in the Mid-Atlantic 

region for the early and mid-21st century 

(Figs. 43-50) as supplementary information to 

Chapter 4. Also presented are graphs of historical 

(baseline) temperature and precipitation by season in 

the assessment area, and projected trends through the 

end of the century (Figs. 51-55).

White trillium along the Longhouse Scenic Byway, Allegheny National Forest, Pennsylvania. This byway is home to many other 

spring wildflowers. Photo by Kathleen Creek, Allegheny National Forest.
APPENDIX 3

261

Figure 43.—Projected difference in mean daily mean temperature (°F) at the beginning of the century (2010 through 2039) 

compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

262

Figure 44.—Projected difference in mean daily minimum temperature (°F) at the beginning of the century (2010 through 

2039) compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

263

Figure 45.—Projected difference in mean daily maximum temperature (°F) at the beginning of the century (2010 through 

2039) compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

264

Figure 46.—Projected difference in precipitation (inches) at the beginning of the century (2010 through 2039) compared to 

baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

265

Figure 47.—Projected difference in mean daily mean temperature (°F) for the middle of the century (2040 through 2069) 

compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

266

Figure 48.—Projected difference in mean daily minimum temperature (°F) for the middle of the century (2040 through 2069) 

compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

267

Figure 49.—Projected difference in mean daily maximum temperature (°F) for the middle of the century (2040 through 2069) 

compared to baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

268

Figure 50.—Projected difference in precipitation (inches) for the middle of the century (2040 through 2069) compared to 

baseline (1971 through 2000) for two climate model-emissions scenario combinations.
APPENDIX 3

269

Figure 51.—Projected change in mean winter mean, minimum, and maximum temperatures in the assessment area averaged 

over 30-year periods for two climate model-emissions scenarios. The 1971 through 2000 value is based on observed data 

from weather stations.

Figure 52.—Projected change in mean spring mean, minimum, and maximum temperatures in the assessment area averaged 

over 30-year periods for two climate model-emissions scenario combinations. The 1971 through 2000 value is based on 

observed data from weather stations.

15

20

25

30

35

40

45

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

T

e

m

p

erature (qF)

Winter (Dec - Feb)

GFDL A1FI Mean GFDL A1FI Minimum GFDL A1FI Maximum

PCM B1 Mean PCM B1 Minimum PCM B1 Maximum

35

40

45

50

55

60

65

70

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

T

e

m

p

erature (qF)

Spring (Mar - May)

GFDL A1FI Mean GFDL A1FI Minimum GFDL A1FI Maximum

PCM B1 Mean PCM B1 Minimum PCM B1 Maximum

(°F) (°F)
APPENDIX 3

270

55

60

65

70

75

80

85

90

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

T

e

m

p

erature (qF)

Summer(June - Aug)

GFDL A1FI Mean GFDL A1FI Minimum GFDL A1FI Maximum

PCM B1 Mean PCM B1 Minimum PCM B1 Maximum

Figure 53.—Projected change in mean summer mean, minimum, and maximum temperatures in the assessment area 

averaged over 30-year periods for two climate model-emissions scenario combinations. The 1971 through 2000 value is based 

on observed data from weather stations.

Figure 54.—Projected change in mean fall mean, minimum, and maximum temperatures in the assessment area averaged 

over 30-year periods for two climate model-emissions scenario combinations. The 1971 through 2000 value is based on 

observed data from weather stations.

35

40

45

50

55

60

65

70

75

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

T

e

m

p

erature (qF)

Fall (Sep - Nov)

GFDL A1FI Mean GFDL A1FI Minimum GFDL A1FI Maximum

PCM B1 Mean PCM B1 Minimum PCM B1 Maximum

(°F) (°F)
APPENDIX 3

271

8

9

10

11

12

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

Precipitation (inches)

Winter (Dec - Feb)

PCM B1 GFDL A1FI

10

11

12

13

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

Precipitation (inches)

Spring (Mar - May)

PCM B1 GFDL A1FI

9

10

11

12

13

14

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

Precipitation (inches)

Summer(June - Aug)

PCM B1 GFDL A1FI

9

10

11

12

13

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

Precipitation (inches)

Fall (Sep - Nov)

PCM B1 GFDL A1FI

Figure 55.—Projected change in winter, spring, summer, and fall precipitation in the assessment area averaged over 30-year 

periods for two climate model-emissions scenario combinations. The 1971 through 2000 value is based on observed data 

from weather stations. Note that the precipitation axes are different depending on the season.
272

APPENDIX 4: ADDITIONAL IMPACT MODEL 

RESULTS AND DISCUSSION

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. We discuss each of the three 

forest impact models further and explain how change 

classes were determined for each model. Scientific 

names for all species are provided in Appendix 1. 

See Chapter 2 for a description of the models 

and Chapter 5 for a discussion of model results, 

uncertainty, and limitations. 

CLIMATE CHANGE TREE ATLAS 

MODEL RESULTS

Tables 30 through 37, beginning on page 275, 

show results of the DISTRIB model used in the 

Tree Atlas averaged over the whole assessment 

area, and for each subregion within the assessment 

area. Measured area-weighted importance values 

(IVs) from Forest Service Forest Inventory and 

Analysis (FIA) as well as modeled current (1971 

through 2000) and future IVs (2010 through 2039, 

2040 through 2069, 2070 through 2099) from the 

DISTRIB models were calculated for each time 

period. Across the eastern United States, 134 tree 

species were initially modeled. If a species never 

had an area-weighted IV greater than 3 (FIA, current 

modeled, or future) in the Mid-Atlantic region, it 

was deleted from the list because the species either 

does not have or is not projected to have sufficient 

habitat in the region, or there were not enough data. 

Therefore, only a subset of 112 of the 134 possible 

species is shown in Table 30, and a subset of 116 

species is shown in Table 31. Black maple was rare 

within individual subregions and was modeled only 

at the regional level. Bluejack oak, pecan, water 

elm, and water locust were modeled only at the 

subregional level. Species establishment, growth, 

and habitat suitability are assumed to be 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 botanical evidence that 

documents their existence.

A set of rules was established to determine change 

classes for the years 2070 through 2099, which was 

used to create Tables 21, 23, and 24 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 0 and its FIA value was greater

than 3.
APPENDIX 4

273

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 × 12.5-mile cell). The change 

classes are calculated differently for these species 

because their current infrequency tends to inflate 

the projected percent change. The cutoffs for each 

portion of the assessment area were as follows: 

	 Cutoff IV for

Pixels rare species

Mid-Atlantic region 789 78.9

Subregion 1: 

Western Allegheny Plateau 121 12.1

Subregion 2: 

Erie and Ontario Lake Plain 76 7.6

Subregion 3: 

Northern Allegheny Plateau 217 21.7

Subregion 4: 

Ridge and Valley 138 13.8

Subregion 5: 

Piedmont 158 15.8

Subregion 6: 

Coastal Plain 79 7.9

When a species was below the cutoff, it was 

considered rare, and the following rules applied:

Future:Current modeled IV Class

<0.2 large decrease

0.2 through <0.6 small decrease

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 IV = 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 38 and 39, beginning on page 309, 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 

the DISTRIB model (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 such as competition 

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 is used to determine whether a 

species is likely to be more tolerant of (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 to climate change and 1 

indicated not highly relevant. 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.
APPENDIX 4

274

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 prone to flooding. Or a species may be 

subjected to local impacts of insects and disease that 

reduce its adaptability 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 Table 39 for each species.

Model results are arranged alphabetically, but it 

may also be practical to arrange results by relative 

abundance, forest community, genus, or other 

category; thus, editable model results are available 

online at https://www.fs.fed.us/nrs/atlas/products, or 

can be provided by the authors of this assessment.

American chestnut seed hulls, found near Rimrock Overlook, Allegheny National Forest, Pennsylvania. These hulls came from 

a native population of American chestnuts. Photo by Kathleen Creek, Allegheny National Forest.
APPENDIX 4

275

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 30.—Complete DISTRIB model results for 112 tree species in the Mid-Atlantic region

American basswood 502 569 M 484 531 494 440 491 362 0.85 0.93 0.87 0.77 0.86 0.64 No decAmerican beech 3308 3309 H 3085 2117 2741 1346 2612 1041 0.93 0.64 0.83 0.41 0.79 0.32 dec DECAmerican chestnut 108 40 M 79 66 78 51 80 40 1.98 1.65 1.95 1.28 2.00 1.00 No NoAmerican elm 943 1221 M 1007 1839 1007 2265 1095 2325 0.83 1.51 0.83 1.86 0.90 1.90 No incAmerican holly 235 238 H 262 248 292 187 277 161 1.10 1.04 1.23 0.79 1.16 0.68 No decAmerican hornbeam 791 839 M 754 807 757 831 781 806 0.90 0.96 0.90 0.99 0.93 0.96 No NoAmerican mountain-ash 9 0 M 3 1 2 1 1 1 0.33 0.11 0.22 0.11 0.11 0.11 DEC DECAtlantic white-cedar 82 71 L 60 48 52 44 49 41 0.85 0.68 0.73 0.62 0.69 0.58 No decBaldcypress 1 22 M 29 78 53 105 82 168 1.32 3.55 2.41 4.77 3.73 7.64 No incBalsam fir 21 91 H 28 12 12 4 12 6 0.31 0.13 0.13 0.04 0.13 0.07 DEC DECBalsam poplar 13 6 H 2 2 2 1 2 2 0.33 0.33 0.33 0.17 0.33 0.33 dec decBigtooth aspen 553 610 H 532 446 510 190 490 54 0.87 0.73 0.84 0.31 0.80 0.09 No DECBitternut hickory 91 66 L 66 260 81 502 116 654 1.00 3.94 1.23 7.61 1.76 9.91 No INCBlack ash 131 133 H 76 41 57 12 44 9 0.57 0.31 0.43 0.09 0.33 0.07 DEC DECBlack cherry 5163 4927 H 4702 3762 4369 2124 4299 1483 0.95 0.76 0.89 0.43 0.87 0.30 No DECBlack hickory 0 15 H 40 333 62 1159 106 1926 2.67 22.20 4.13 77.27 7.07 128.40 New NewBlack locust 637 732 L 774 845 862 989 853 989 1.06 1.15 1.18 1.35 1.17 1.35 No NoBlack maple 5 12 L 4 2 5 0 4 0 0.33 0.17 0.42 0.00 0.33 0.00 dec DECBlack oak 1099 1379 H 1389 1972 1531 2974 1599 3501 1.01 1.43 1.11 2.16 1.16 2.54 No INCBlack spruce 13 8 H 6 2 4 0 2 0 0.75 0.25 0.50 0.00 0.25 0.00 dec DECBlack walnut 363 440 M 438 740 534 914 623 868 1.00 1.68 1.21 2.08 1.42 1.97 inc incBlack willow 352 345 L 261 531 251 616 288 737 0.76 1.54 0.73 1.79 0.84 2.14 No INCBlackgum 1053 1060 H 1135 1195 1222 1329 1273 1465 1.07 1.13 1.15 1.25 1.20 1.38 inc incBlackjack oak 10 30 M 49 240 76 876 112 1653 1.63 8.00 2.53 29.20 3.73 55.10 No INCBoxelder 371 338 M 321 393 334 414 355 483 0.95 1.16 0.99 1.23 1.05 1.43 No incBur oak 29 17 M 13 41 11 76 13 159 0.77 2.41 0.65 4.47 0.77 9.35 No INCButternut 113 64 L 70 44 57 15 42 10 1.09 0.69 0.89 0.23 0.66 0.16 No DEC

(continued on next page)
APPENDIX 4

276

Cedar elm 0 0 L 0 23 0 117 1 352 NA New NA New NA New NA NewCherrybark oak 6 9 M 19 39 25 94 33 142 2.11 4.33 2.78 10.44 3.67 15.78 No INCChestnut oak 2048 1985 H 2189 2068 2379 1978 2344 1654 1.10 1.04 1.20 1.00 1.18 0.83 No NoChinkapin oak 8 14 M 23 152 37 422 58 556 1.64 10.86 2.64 30.14 4.14 39.71 inc INCChokecherry 342 275 L 240 123 225 13 207 3 0.87 0.45 0.82 0.05 0.75 0.01 dec DECCucumber tree 129 87 H 115 78 117 54 121 52 1.32 0.90 1.35 0.62 1.39 0.60 inc decEastern cottonwood 166 169 L 141 363 131 729 153 1196 0.83 2.15 0.78 4.31 0.91 7.08 No INCEastern hemlock 1770 1794 H 1589 1396 1444 889 1424 704 0.89 0.78 0.81 0.50 0.79 0.39 dec DECEastern hophornbeam 964 1031 M 926 990 897 1062 919 1201 0.90 0.96 0.87 1.03 0.89 1.17 No NoEastern redbud 38 63 M 80 318 140 804 193 947 1.27 5.05 2.22 12.76 3.06 15.03 No INCEastern redcedar 362 449 M 485 1267 604 2341 751 2800 1.08 2.82 1.35 5.21 1.67 6.24 inc INCEastern white pine 1274 1500 H 1305 1320 1234 964 1200 701 0.87 0.88 0.82 0.64 0.80 0.47 dec DECFlowering dogwood 791 992 H 1096 1633 1369 2233 1476 2202 1.11 1.65 1.38 2.25 1.49 2.22 inc INCGray birch 185 167 M 132 144 120 103 129 91 0.79 0.86 0.72 0.62 0.77 0.55 dec decGreen ash 331 346 M 293 427 288 601 310 815 0.85 1.23 0.83 1.74 0.90 2.36 No INCHackberry 65 112 M 89 484 125 973 169 1199 0.80 4.32 1.12 8.69 1.51 10.71 inc INCHoneylocust 7 24 L 17 188 26 556 45 1030 0.71 7.83 1.08 23.17 1.88 42.92 No INCJack pine 7 16 H 4 4 1 0 1 0 0.25 0.25 0.06 0.00 0.06 0.00 DEC DECLaurel oak** 0 0 H 0 1 5 12 10 30 NA NA New New New New New NewLoblolly pine 343 395 H 498 619 653 843 661 1388 1.26 1.57 1.65 2.13 1.67 3.51 inc INCLongleaf pine** 0 3 H 0 4 10 11 66 16 0.00 1.33 3.33 3.67 22.00 5.33 New NewMockernut hickory 495 702 H 643 842 770 1197 782 1424 0.92 1.20 1.10 1.71 1.11 2.03 No INCMountain maple 27 4 H 7 4 3 0 2 0 1.75 1.00 0.75 0.00 0.50 0.00 dec DECNorthern catalpa 16 5 L 6 6 6 5 6 26 1.20 1.20 1.20 1.00 1.20 5.20 No incNorthern red oak 2643 2708 H 2675 2656 2625 2479 2587 2277 0.99 0.98 0.97 0.92 0.96 0.84 No NoNorthern white-cedar 28 71 H 18 8 11 9 10 12 0.25 0.11 0.16 0.13 0.14 0.17 DEC DECOhio buckeye** 0 0 L 4 43 6 47 18 22 New New New New New New New New

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 30 (continued).—Complete DISTRIB model results for 112 tree species in the Mid-Atlantic region

(continued on next page)
APPENDIX 4

277

Osage-orange 19 48 M 33 117 42 209 53 303 0.69 2.44 0.88 4.35 1.10 6.31 No incOvercup oak** 0 9 M 0 10 1 57 3 93 0.00 1.11 0.11 6.33 0.33 10.33 NA NewPaper birch 138 175 H 124 73 75 3 63 1 0.71 0.42 0.43 0.02 0.36 0.01 DEC DECPawpaw 65 47 L 46 84 54 115 80 116 0.98 1.79 1.15 2.45 1.70 2.47 No NoPersimmon 47 79 M 116 403 150 1126 185 1821 1.47 5.10 1.90 14.25 2.34 23.05 INC INCPignut hickory 581 835 H 765 1024 914 1294 983 1276 0.92 1.23 1.10 1.55 1.18 1.53 No incPin cherry 194 141 M 126 39 94 11 82 9 0.89 0.28 0.67 0.08 0.58 0.06 dec DECPin oak 144 157 M 152 286 171 343 243 329 0.97 1.82 1.09 2.19 1.55 2.10 inc INCPitch pine 691 567 H 564 521 493 545 478 584 1.00 0.92 0.87 0.96 0.84 1.03 No NoPond pine 6 1 H 5 33 5 59 23 61 5.00 33.00 5.00 59.00 23.00 61.00 inc incPost oak 23 100 H 203 984 300 3378 379 5705 2.03 9.84 3.00 33.78 3.79 57.05 INC INCQuaking aspen 771 851 H 678 380 519 34 445 8 0.80 0.45 0.61 0.04 0.52 0.01 dec DECRed maple 8807 8789 H 8664 7374 8477 5012 8400 3755 0.99 0.84 0.97 0.57 0.96 0.43 No DECRed mulberry 21 21 L 19 253 26 713 50 1188 0.91 12.05 1.24 33.95 2.38 56.57 No INCRed pine 209 252 M 172 169 151 31 145 15 0.68 0.67 0.60 0.12 0.58 0.06 dec DECRed spruce 59 75 H 36 39 30 20 32 16 0.48 0.52 0.40 0.27 0.43 0.21 dec decRedbay** 0 0 H 0 0 0 0 11 0 NA NA NA NA New NA New NARiver birch 37 21 L 20 27 21 28 29 53 0.95 1.29 1.00 1.33 1.38 2.52 No NoRock elm 8 7 L 8 33 7 36 24 63 1.14 4.71 1.00 5.14 3.43 9.00 No INCSassafras 1128 1191 H 1258 1403 1387 1480 1512 1484 1.06 1.18 1.17 1.24 1.27 1.25 inc incScarlet oak 621 655 H 705 762 824 936 818 987 1.08 1.16 1.26 1.43 1.25 1.51 inc incScrub oak (bear oak) 192 110 L 140 178 146 195 162 183 1.27 1.62 1.33 1.77 1.47 1.66 inc incServiceberry 665 608 M 618 578 610 491 608 444 1.02 0.95 1.00 0.81 1.00 0.73 No decShagbark hickory 187 254 M 227 505 272 774 330 840 0.89 1.99 1.07 3.05 1.30 3.31 inc INCShellbark hickory 4 1 L 0 47 0 106 2 131 0.00 47.00 0.00 106.00 2.00 131.00 No INCShingle oak 26 14 M 18 127 23 309 44 367 1.29 9.07 1.64 22.07 3.14 26.21 No INCShortleaf pine 35 74 H 99 268 109 954 131 1911 1.34 3.62 1.47 12.89 1.77 25.82 No INC

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 30 (continued).—Complete DISTRIB model results for 112 tree species in the Mid-Atlantic region

(continued on next page)
APPENDIX 4

278

Shumard oak** 0 0 L 0 6 0 80 0 234 NA New NA New NA New NA NewSilver maple 227 321 M 183 674 187 976 244 1152 0.57 2.10 0.58 3.04 0.76 3.59 dec INCSlash pine 0 0 H 0 0 0 17 21 98 NA NA NA New New New New NewSlippery elm 373 452 M 419 654 480 770 528 792 0.93 1.45 1.06 1.70 1.17 1.75 No incSourwood 2 28 H 77 22 169 14 146 42 2.75 0.79 6.04 0.50 5.21 1.50 inc NoSouthern red oak 138 148 H 181 256 203 506 217 973 1.22 1.73 1.37 3.42 1.47 6.57 inc INCStriped maple 677 651 H 626 509 591 348 549 292 0.96 0.78 0.91 0.54 0.84 0.45 No DECSugar maple 4411 4707 H 4323 3983 4073 3224 4148 2373 0.92 0.85 0.87 0.69 0.88 0.50 No decSugarberry** 0 8 M 9 79 16 315 27 767 1.13 9.88 2.00 39.38 3.38 95.88 New NewSwamp chestnut oak 15 8 M 11 18 15 19 16 17 1.38 2.25 1.88 2.38 2.00 2.13 No NoSwamp tupelo 3 18 H 20 57 54 72 146 77 1.11 3.17 3.00 4.00 8.11 4.28 INC incSwamp white oak 87 69 L 53 80 59 85 67 69 0.77 1.16 0.86 1.23 0.97 1.00 No NoSweet birch 1817 1667 H 1787 1460 1785 1056 1736 810 1.07 0.88 1.07 0.63 1.04 0.49 No DECSweetbay 58 68 H 53 60 61 56 92 62 0.78 0.88 0.90 0.82 1.35 0.91 No NoSweetgum 723 740 H 810 1033 931 1269 1054 1749 1.10 1.40 1.26 1.72 1.42 2.36 inc INCSycamore 155 187 M 210 371 247 614 288 664 1.12 1.98 1.32 3.28 1.54 3.55 inc INCTable Mountain pine 7 2 M 1 2 1 4 1 6 0.50 1.00 0.50 2.00 0.50 3.00 dec NoTamarack 43 21 H 15 13 10 6 8 7 0.71 0.62 0.48 0.29 0.38 0.33 dec decTulip tree 1308 1348 H 1536 1230 1744 1139 1737 984 1.14 0.91 1.29 0.85 1.29 0.73 inc decTurkey oak 0 0 H 0 1 2 0 29 8 NA NA NA NA New New New NewVirginia pine 458 514 H 503 353 571 501 474 603 0.98 0.69 1.11 0.98 0.92 1.17 No NoWater hickory 0 0 M 0 0 0 19 0 24 NA NA NA New NA New NA NewWater oak 44 48 H 75 115 95 282 96 532 1.56 2.40 1.98 5.88 2.00 11.08 No INCWater tupelo 20 25 M 18 43 43 59 61 82 0.72 1.72 1.72 2.36 2.44 3.28 No NoWhite ash 5067 4834 H 4530 4272 4285 3295 4326 2556 0.94 0.88 0.89 0.68 0.90 0.53 No decWhite oak 1809 2197 H 2239 2829 2394 3667 2437 3909 1.02 1.29 1.09 1.67 1.11 1.78 No incWhite spruce 87 56 M 35 23 26 14 20 12 0.63 0.41 0.46 0.25 0.36 0.21 dec dec

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 30 (continued).—Complete DISTRIB model results for 112 tree species in the Mid-Atlantic region

(continued on next page)
APPENDIX 4

279

Willow oak 54 62 M 65 113 77 204 84 280 1.05 1.82 1.24 3.29 1.36 4.52 No inc

Winged elm 1 15 H 79 335 129 1157 190 2365 5.27 22.33 8.60 77.13 12.67 157.67 INC INC

Yellow birch 601 635 H 572 395 527 190 526 163 0.90 0.62 0.83 0.30 0.83 0.26 No DEC

Yellow buckeye 12 10 M 8 6 8 8 11 8 0.80 0.60 0.80 0.80 1.10 0.80 No No

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 30 (continued).—Complete DISTRIB model results for 112 tree species in the Mid-Atlantic region

a FIA IV is the measured area-weighted importance values (IVs) as reported from Forest Inventory and Analysis. b Current Modeled IV (1971 through 2000) and Modeled IV for future time periods (2010 through 2039, 2040 through 2069, and 2070 through 2099) are simulated from the DISTRIB model; importance 

values are the sum of the average IV for each pixel in the assessment area. 

c Model reliability for DISTRIB scores, which is based on statistically quantified measures of fitness (Matthews et al. 2011), is abbreviated L (low), M (medium), and H (high). d Future:Current suitable habitat is a ratio of projected importance value to current importance value. This is a measure of habitat change (not where a species will be), where a ratio of ~1 = no change; 

ratio<1 = decrease; ratio>1 = increase.

e Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), 

and NA (not detected). 

**Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.
APPENDIX 4

280

Mid-Atlantic 

region Subregion 1 Subregion 2 Subregion 3 Subregion 4 Subregion 5 Subregion 6

Common name

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

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PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

American basswood No dec No No dec DEC No dec dec dec No No - -

American beech dec DEC dec DEC dec DEC dec DEC No DEC No DEC No DEC

American chestnut No No No dec - - No No inc No No No dec dec

American elm No inc dec DEC No No No dec No INC No inc inc INC

American holly No dec - - - - - - - - NA NA No dec

American hornbeam No No No dec No No No No dec inc No No No No

American mountain-ash DEC DEC - - - - DEC DEC - - - - - -

Atlantic white-cedar No dec - - dec dec - - - - NA NA dec dec

Baldcypress No inc - - - - - - - - NA New INC INC

Balsam fir DEC DEC - - DEC DEC DEC X - - DEC DEC - -

Balsam poplar dec dec - - - - No DEC - - - - - -

Bigtooth aspen No DEC dec DEC No No No No dec DEC dec DEC DEC X

Bitternut hickory No INC No INC No INC No INC inc INC inc INC No INC

Black ash DEC DEC dec dec DEC DEC dec DEC - - dec DEC - -

Black cherry No DEC No DEC No INC No No No DEC dec DEC dec DEC

Black hickory New New NA New NA New NA New New New New New New New

Black locust No No No inc inc INC INC INC No No No No No DEC

Black maple dec DEC - - - - - - - - - - - -

Black oak No INC inc INC No INC inc INC No INC No inc No dec

Black spruce dec DEC - - - - dec DEC DEC DEC - - - -

Black walnut inc inc inc INC inc INC INC INC inc inc inc No No DEC

Black willow No INC dec DEC No dec dec DEC DEC INC No INC dec INC

Blackgum inc inc inc INC INC INC inc INC inc inc No No dec dec

Blackjack oak No INC New New NA New NA New New New New New INC INC

Bluejack oak - - - - - - - - - - - - NA New

Boxelder No inc No dec No INC inc INC No inc No No inc No

Bur oak No INC dec No dec DEC DEC inc DEC inc No inc - -

Butternut No DEC No DEC dec DEC No DEC dec DEC dec DEC - -

Cedar elm NA New NA New NA New NA New NA New NA New NA New

Cherrybark oak No INC - - - - - - NA New NA New inc INC

Chestnut oak No No No inc inc INC inc INC No dec No dec dec DEC

Chinkapin oak inc INC inc inc No inc DEC inc No INC No inc New New

Chokecherry dec DEC dec DEC dec DEC No dec dec X DEC DEC - -

Cucumber tree inc dec inc inc No DEC inc DEC No dec - - - -

Eastern cottonwood No INC No INC No inc No INC DEC INC No INC dec INC

Eastern hemlock dec DEC dec DEC dec DEC No dec dec DEC dec DEC inc inc

Eastern hophornbeam No No No DEC No dec No DEC No inc No inc inc INC

(continued on next page)

Table 31.—Change classesa for the end of century (2070 through 2099) period projected by the DISTRIB model for 116 

tree species in the Mid-Atlantic region and six subregionsb
APPENDIX 4

281

Mid-Atlantic 

region Subregion 1 Subregion 2 Subregion 3 Subregion 4 Subregion 5 Subregion 6

Common name

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Eastern redbud No INC inc INC New New inc inc INC INC INC INC No inc

Eastern redcedar inc INC New New INC INC INC INC inc INC inc INC No inc

Eastern white pine dec DEC dec DEC dec DEC No DEC No DEC dec DEC DEC DEC

Flowering dogwood inc INC inc INC INC INC INC INC inc inc No inc No No

Gray birch dec dec dec DEC DEC DEC No No dec dec dec DEC No No

Green ash No INC inc INC No No No INC No INC dec inc No INC

Hackberry inc INC inc INC NA New No INC No INC inc INC No INC

Honeylocust No INC No INC NA New No inc No INC No INC NA New

Jack pine DEC DEC - - NA NA DEC DEC - - NA NA - -

Laurel oak** New New - - - - - - - - New NA New New

Loblolly pine inc INC NA New NA New New New NA New INC INC inc INC

Longleaf pine** New New - - - - - - - - New NA New New

Mockernut hickory No INC inc INC No inc No INC No INC No inc dec inc

Mountain maple dec DEC - - - - dec DEC - - No DEC - -

Northern catalpa No inc - - NA NA DEC No NA New inc INC inc inc

Northern red oak No No No DEC No inc No inc No dec No dec dec DEC

Northern white-cedar DEC DEC - - DEC DEC DEC X - - dec dec - -

Ohio buckeye** New New New New New New NA New - - NA New - -

Osage-orange No inc No No inc INC No inc No inc No inc NA New

Overcup oak** NA New - - - - - - - - NA New NA New

Paper birch DEC DEC dec DEC DEC DEC DEC DEC DEC X DEC DEC - -

Pawpaw No No inc inc New New NA New No No inc INC DEC DEC

Pecan - - NA NA NA NA NA NA NA NA NA NA NA NA

Persimmon INC INC inc inc NA New NA New inc INC INC INC inc INC

Pignut hickory No inc inc INC No INC inc INC No inc No No dec No

Pin cherry dec DEC DEC X dec DEC dec DEC DEC X dec DEC No dec

Pin oak inc INC No inc NA New No inc No inc inc inc INC INC

Pitch pine No No No inc No No inc INC No No dec dec No No

Pond pine inc inc - - - - - - - - inc inc inc inc

Post oak INC INC New New NA New NA New New New inc INC INC INC

Quaking aspen dec DEC DEC X DEC DEC dec DEC DEC X DEC DEC DEC DEC

Red maple No DEC No dec No inc No inc No DEC No DEC No DEC

Red mulberry** No INC NA New New New NA New No INC No INC No INC

Red pine dec DEC dec DEC dec DEC dec DEC dec X dec DEC - -

Red spruce dec dec DEC DEC DEC DEC DEC DEC dec DEC DEC DEC - -

Redbay** New NA - - - - - - - - New NA New NA

River birch No No - - - - No No No inc No inc inc inc

(continued on next page)

Table 31 (continued).—Change classesa for the end of century (2070 through 2099) period projected by the DISTRIB 

model for 116 tree species in the Mid-Atlantic region and six subregionsb
APPENDIX 4

282

Mid-Atlantic 

region Subregion 1 Subregion 2 Subregion 3 Subregion 4 Subregion 5 Subregion 6

Common name

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Rock elm No INC dec No dec No No inc NA New inc inc New NA

Sassafras inc inc inc INC INC INC inc INC inc No No No No DEC

Scarlet oak inc inc inc INC INC INC INC INC No inc inc No dec DEC

Scrub oak (bear oak) inc inc No No - - INC INC inc inc INC INC dec No

Serviceberry No dec No No inc INC No No No dec No dec dec DEC

Shagbark hickory inc INC inc INC No inc inc INC No INC inc inc INC INC

Shellbark hickory** No INC - - NA New DEC inc DEC inc NA inc NA New

Shingle oak No INC inc inc NA New No inc No INC No INC No inc

Shortleaf pine No INC New New NA New NA New inc INC No INC inc INC

Shumard oak** NA New NA New NA New NA New NA New NA New NA New

Silver maple dec INC dec DEC No dec dec DEC dec INC No INC dec INC

Slash pine New New - - - - - - - - New New New New

Slippery elm No inc No inc inc INC inc INC No inc No inc No INC

Sourwood inc No INC DEC - - inc inc New NA New New INC dec

Southern red oak inc INC NA New NA New NA New No inc No INC inc inc

Striped maple No DEC dec DEC dec dec No DEC No dec dec DEC - -

Sugar maple No dec No dec No dec dec DEC No dec No DEC inc DEC

Sugarberry** New New NA New NA New NA New New New New New New New

Swamp chestnut oak No No - - - - - - No DEC NA inc inc No

Swamp tupelo INC inc - - - - - - - - New NA INC INC

Swamp white oak No No No No dec DEC inc inc - - inc inc DEC dec

Sweet birch No DEC No No No inc inc INC No DEC No DEC DEC DEC

Sweetbay No No - - - - - - - - No dec inc No

Sweetgum** inc INC NA New NA New inc inc INC INC inc INC inc inc

Sycamore inc INC inc INC INC INC No INC inc INC inc inc No INC

Table Mountain pine dec No - - - - - - No No - - - -

Tamarack (native) dec dec - - DEC dec dec DEC No dec No No - -

Tulip tree inc dec inc INC INC INC INC INC inc dec No DEC dec DEC

Turkey oak New New - - - - - - - - New NA New New

Virginia pine No No INC INC New New No dec No inc No dec dec DEC

Water elm - - - - - - - - - - NA NA - -

Water hickory NA New - - - - - - - - NA NA NA NA

Water locust - - - - - - - - - - - - NA New

Water oak No INC NA New - - - - NA New NA New inc INC

Water tupelo No No - - - - - - - - No No INC INC

White ash No dec No DEC No No No dec No dec No DEC No dec

White oak No inc No inc No inc inc INC No inc No inc No dec

(continued on next page)

Table 31 (continued).—Change classesa for the end of century (2070 through 2099) period projected by the DISTRIB 

model for 116 tree species in the Mid-Atlantic region and six subregionsb
APPENDIX 4

283

Mid-Atlantic 

region Subregion 1 Subregion 2 Subregion 3 Subregion 4 Subregion 5 Subregion 6

Common name

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

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PCM 

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PCM 

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PCM 

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PCM 

B1

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A1FI

White spruce dec dec No DEC DEC DEC DEC X dec dec dec dec - -

Willow oak No inc - - NA New - - NA New NA New inc INC

Winged elm INC INC NA New NA New DEC inc New New New New New New

Yellow birch No DEC dec DEC dec DEC No dec dec DEC dec DEC - -

Yellow buckeye No No No No - - - - - - - - - -

a

 Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC 

(large decrease), New (new habitat), X (extirpated), and NA (not detected). Dash (-) indicates not present in modeled or future habitat.

b

 See Figure 38 (Chapter 6, p. 144) for location of subregions.

** Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.

Table 31 (continued).—Change classesa for the end of century (2070 through 2099) period projected by the DISTRIB 

model for 116 tree species in the Mid-Atlantic region and six subregionsb
APPENDIX 4

284

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 32.—Complete DISTRIB model results for 83 tree species in subregion 1 (West Allegheny Plateau)

American basswood 100 109 M 99 100 106 69 105 47 0.91 0.92 0.97 0.63 0.96 0.43 No NoAmerican beech 558 592 H 518 326 455 222 426 150 0.88 0.55 0.77 0.38 0.72 0.25 dec DECAmerican chestnut 8 3 M 5 4 5 2 5 1 1.67 1.33 1.67 0.67 1.67 0.33 No decAmerican elm 229 292 M 240 370 232 449 234 409 0.82 1.27 0.80 1.54 0.80 1.40 dec DECAmerican hornbeam 162 167 M 148 152 152 133 159 130 0.89 0.91 0.91 0.80 0.95 0.78 No decBigtooth aspen 201 177 H 175 117 151 24 138 0 0.99 0.66 0.85 0.14 0.78 0.00 dec DECBitternut hickory 23 8 L 11 47 18 107 21 122 1.38 5.88 2.25 13.38 2.63 15.25 No INCBlack ash 7 7 H 2 2 0 1 2 2 0.29 0.29 0.00 0.14 0.29 0.29 dec decBlack cherry 2117 1767 H 1761 1157 1525 382 1501 250 1.00 0.66 0.86 0.22 0.85 0.14 No DECBlack hickory 0 0 H 0 29 1 230 3 441 NA New NA New NA New NA NewBlack locust 168 198 L 210 206 226 203 213 195 1.06 1.04 1.14 1.03 1.08 0.99 No incBlack oak 160 215 H 222 397 266 764 289 911 1.03 1.85 1.24 3.55 1.34 4.24 inc INCBlack walnut 72 99 M 102 175 131 201 153 179 1.03 1.77 1.32 2.03 1.55 1.81 inc INCBlack willow 61 48 L 30 71 29 100 37 108 0.63 1.48 0.60 2.08 0.77 2.25 dec DECBlackgum 123 162 H 186 235 232 267 252 288 1.15 1.45 1.43 1.65 1.56 1.78 inc INCBlackjack oak 0 0 M 1 35 3 241 6 486 NA New NA New New New New NewBoxelder 39 46 M 38 46 41 57 43 62 0.83 1.00 0.89 1.24 0.94 1.35 No decBur oak 7 0 M 2 3 2 6 2 19 0.29 0.43 0.29 0.86 0.29 2.71 dec NoButternut 11 2 L 3 2 3 0 2 0 1.50 1.00 1.50 0.00 1.00 0.00 No DECCedar elm 0 0 L 0 0 0 3 0 87 NA NA NA New NA New NA NewChestnut oak 167 321 H 310 353 384 337 377 238 0.97 1.10 1.20 1.05 1.17 0.74 No incChinkapin oak 2 1 M 6 50 12 121 21 127 6.00 50.00 12.00 121.00 21.00 127.00 inc incChokecherry 131 76 L 77 16 75 0 59 0 1.01 0.21 0.99 0.00 0.78 0.00 dec DECCucumber tree 74 53 H 70 46 70 27 71 24 1.32 0.87 1.32 0.51 1.34 0.45 inc incEastern cottonwood 23 8 L 10 27 10 58 10 166 1.25 3.38 1.25 7.25 1.25 20.75 No INCEastern hemlock 333 341 H 280 172 233 91 213 73 0.82 0.50 0.68 0.27 0.63 0.21 dec DECEastern hophornbeam 130 175 M 142 151 146 173 149 215 0.81 0.86 0.83 0.99 0.85 1.23 No DEC

(continued on next page)
APPENDIX 4

285

Eastern redbud 2 10 M 24 82 47 195 57 179 2.40 8.20 4.70 19.50 5.70 17.90 inc INCEastern redcedar 0 8 M 26 271 46 536 98 599 3.25 33.88 5.75 67.00 12.25 74.88 New NewEastern white pine 116 194 H 151 222 153 121 156 69 0.78 1.14 0.79 0.62 0.80 0.36 dec DECFlowering dogwood 185 241 H 291 436 387 536 419 487 1.21 1.81 1.61 2.22 1.74 2.02 inc INCGray birch 2 7 M 0 2 2 0 2 1 0.00 0.29 0.29 0.00 0.29 0.14 dec DECGreen ash 50 32 M 43 50 49 97 49 136 1.34 1.56 1.53 3.03 1.53 4.25 inc INCHackberry 9 16 M 11 75 16 177 22 215 0.69 4.69 1.00 11.06 1.38 13.44 inc INCHoneylocust 1 4 L 4 27 6 64 11 155 1.00 6.75 1.50 16.00 2.75 38.75 No INCLoblolly pine 0 0 H 0 0 0 1 0 52 NA NA NA New NA New NA NewMockernut hickory 70 136 H 137 196 180 300 183 352 1.01 1.44 1.32 2.21 1.35 2.59 inc INCNorthern red oak 553 550 H 527 509 513 464 500 447 0.96 0.93 0.93 0.84 0.91 0.81 No DECOhio buckeye** 0 0 L 1 17 1 14 5 4 NA New NA New New New New NewOsage-orange 9 24 M 14 44 18 54 22 83 0.58 1.83 0.75 2.25 0.92 3.46 No NoPaper birch 5 5 H 2 1 2 0 2 0 0.40 0.20 0.40 0.00 0.40 0.00 dec DECPawpaw 2 1 L 6 12 5 10 8 5 6.00 12.00 5.00 10.00 8.00 5.00 inc incPecan 0 45 L 0 0 0 3 0 22 0.00 0.00 0.00 0.07 0.00 0.49 NA NAPersimmon 2 0 M 6 49 13 228 9 373 3.00 24.50 6.50 114.00 4.50 186.50 inc incPignut hickory 82 154 H 150 215 198 278 210 274 0.97 1.40 1.29 1.81 1.36 1.78 inc INCPin cherry 59 29 M 35 1 22 1 12 1 1.21 0.03 0.76 0.03 0.41 0.03 DEC XPin oak 9 24 M 22 35 27 50 28 41 0.92 1.46 1.13 2.08 1.17 1.71 No incPitch pine 15 14 H 12 24 12 30 16 33 0.86 1.71 0.86 2.14 1.14 2.36 No incPost oak 0 3 H 5 201 15 911 25 1486 1.67 67.00 5.00 303.67 8.33 495.33 New NewQuaking aspen 197 147 H 140 33 97 0 72 0 0.95 0.22 0.66 0.00 0.49 0.00 DEC XRed maple 1840 1738 H 1725 1368 1655 758 1643 527 0.99 0.79 0.95 0.44 0.95 0.30 No decRed mulberry** 0 0 L 1 28 2 116 3 221 NA New NA New NA New NA NewRed pine 27 29 M 24 30 23 2 21 2 0.83 1.03 0.79 0.07 0.72 0.07 dec DECRed spruce 5 1 H 0 0 0 0 0 0 0.00 0.00 0.00 0.00 0.00 0.00 DEC DEC

(continued on next page)

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 32 (continued).—Complete DISTRIB model results for 83 tree species in subregion 1 (West Allegheny Plateau)
APPENDIX 4

286

Rock elm 5 0 L 3 3 2 7 2 9 0.60 0.60 0.40 1.40 0.40 1.80 dec NoSassafras 357 307 H 357 386 403 388 418 350 1.16 1.26 1.31 1.26 1.36 1.14 inc INCScarlet oak 73 109 H 122 156 157 218 160 221 1.12 1.43 1.44 2.00 1.47 2.03 inc INCScrub oak (bear oak) 2 8 L 3 9 3 12 5 14 0.38 1.13 0.38 1.50 0.63 1.75 No NoServiceberry 138 120 M 120 104 117 83 118 79 1.00 0.87 0.98 0.69 0.98 0.66 No NoShagbark hickory 34 41 M 40 97 48 154 67 152 0.98 2.37 1.17 3.76 1.63 3.71 inc INCShingle oak 25 1 M 10 26 9 65 10 59 10.00 26.00 9.00 65.00 10.00 59.00 inc incShortleaf pine 0 2 H 1 34 4 241 17 551 0.50 17.00 2.00 120.50 8.50 275.50 New NewShumard oak** 0 0 L 0 0 0 8 0 54 NA NA NA New NA New NA NewSilver maple 25 46 M 24 58 19 107 25 121 0.52 1.26 0.41 2.33 0.54 2.63 dec DECSlippery elm 186 177 M 179 213 188 201 188 166 1.01 1.20 1.06 1.14 1.06 0.94 No incSourwood 1 6 H 26 1 80 0 58 0 4.33 0.17 13.33 0.00 9.67 0.00 INC DECSouthern red oak 0 0 H 0 1 0 48 0 196 NA NA NA New NA New NA NewStriped maple 64 103 H 71 52 70 31 66 29 0.69 0.51 0.68 0.30 0.64 0.28 dec DECSugar maple 907 941 H 915 807 885 575 882 373 0.97 0.86 0.94 0.61 0.94 0.40 No decSugarberry 0 0 M 0 0 0 13 0 139 NA NA NA New NA New NA NewSwamp white oak 18 16 L 16 17 17 16 17 6 1.00 1.06 1.06 1.00 1.06 0.38 No NoSweet birch 329 319 H 328 260 326 144 329 98 1.03 0.82 1.02 0.45 1.03 0.31 No NoSweetgum** 0 0 H 0 8 1 47 3 125 NA New NA New NA New NA NewSycamore 23 40 M 45 84 57 119 61 126 1.13 2.10 1.43 2.98 1.53 3.15 inc INCTulip tree 268 267 H 367 327 463 279 483 187 1.38 1.23 1.73 1.05 1.81 0.70 inc INCVirginia pine 10 25 H 53 51 98 88 88 102 2.12 2.04 3.92 3.52 3.52 4.08 INC INCWater oak 0 0 H 0 0 0 0 0 24 0.00 NA NA NA NA New NA NewWhite ash 687 723 H 655 635 627 429 655 330 0.91 0.88 0.87 0.59 0.91 0.46 No DECWhite oak 306 385 H 383 619 430 921 451 882 1.00 1.61 1.12 2.39 1.17 2.29 No incWhite spruce 13 1 M 5 1 5 0 3 0 5.00 1.00 5.00 0.00 3.00 0.00 No DECWinged elm 0 0 H 0 10 0 184 1 579 0.00 New 0.00 New NA New NA New

(continued on next page)

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 32 (continued).—Complete DISTRIB model results for 83 tree species in subregion 1 (West Allegheny Plateau)
APPENDIX 4

287

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 32 (continued).—Complete DISTRIB model results for 83 tree species in subregion 1 (West Allegheny Plateau)

Yellow birch 130 131 H 107 33 87 3 79 2 0.82 0.25 0.66 0.02 0.60 0.02 dec DECYellow buckeye 11 6 M 8 6 8 5 11 5 1.33 1.00 1.33 0.83 1.83 0.83 No No

a FIA IV is the measured area-weighted importance values (IVs) as reported from Forest Inventory and Analysis. b Current Modeled IV (1971 through 2000) and Modeled IV for future time periods (2010 through 2039, 2040 through 2069, and 2070 through 2099) are simulated from the DISTRIB model; importance 

values are the sum of the average IV for each pixel in the subregion. 

c Model reliability for DISTRIB scores, which is based on statistically quantified measures of fitness (Matthews et al. 2011), is abbreviated L (low), M (medium), and H (high). d Future:Current suitable habitat is a ratio of projected importance value to current importance value. This is a measure of habitat change (not where a species will be), where a ratio of ~1 = no change; 

ratio<1 = decrease; ratio>1 = increase.

e Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), 

X (extirpated), and NA (not detected). 

** Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.
APPENDIX 4

288

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 33.—Complete DISTRIB model results for 86 tree species in subregion 2 (Erie and Ontario Lake Plain)

American basswood 156 157 M 134 138 124 93 118 64 0.85 0.88 0.79 0.59 0.75 0.41 dec DECAmerican beech 257 334 H 284 239 269 179 261 125 0.85 0.72 0.81 0.54 0.78 0.37 dec DECAmerican elm 286 327 M 298 379 300 427 311 420 0.91 1.16 0.92 1.31 0.95 1.28 No NoAmerican hornbeam 153 135 M 130 138 127 135 126 136 0.96 1.02 0.94 1.00 0.93 1.01 No NoAtlantic white-cedar 8 0 L 4 4 4 4 4 4 0.50 0.50 0.50 0.50 0.50 0.50 dec decBalsam fir 4 20 H 6 3 5 3 5 3 0.30 0.15 0.25 0.15 0.25 0.15 DEC DECBigtooth aspen 47 85 H 76 77 82 27 79 5 0.89 0.91 0.97 0.32 0.93 0.06 No NoBitternut hickory 19 17 L 19 51 20 78 19 86 1.12 3.00 1.18 4.59 1.12 5.06 No INCBlack ash 110 82 H 56 22 43 7 32 5 0.68 0.27 0.52 0.09 0.39 0.06 DEC DECBlack cherry 553 525 H 545 564 576 315 583 187 1.04 1.07 1.10 0.60 1.11 0.36 No INCBlack hickory 0 0 H 0 0 0 74 0 181 NA NA NA New NA New NA NewBlack locust 48 62 L 83 110 107 152 107 173 1.34 1.77 1.73 2.45 1.73 2.79 inc INCBlack oak 14 98 H 77 118 105 227 114 333 0.79 1.20 1.07 2.32 1.16 3.40 No INCBlack walnut 39 54 M 60 132 88 180 100 191 1.11 2.44 1.63 3.33 1.85 3.54 inc INCBlack willow 185 152 L 136 156 127 155 137 176 0.90 1.03 0.84 1.02 0.90 1.16 No decBlackgum 18 15 H 27 33 42 65 47 71 1.80 2.20 2.80 4.33 3.13 4.73 INC INCBlackjack oak 0 0 M 0 0 0 72 0 201 NA NA NA New NA New NA NewBoxelder 99 97 M 95 123 99 130 107 146 0.98 1.27 1.02 1.34 1.10 1.51 No INCBur oak 16 9 M 7 21 6 34 5 58 0.78 2.33 0.67 3.78 0.56 6.44 dec DECButternut 20 20 L 18 14 16 4 15 1 0.90 0.70 0.80 0.20 0.75 0.05 dec DECCedar elm 0 0 L 0 0 0 0 0 17 NA NA NA NA NA New NA NewChestnut oak 9 76 H 82 121 132 168 135 130 1.08 1.59 1.74 2.21 1.78 1.71 inc INCChinkapin oak 1 0 M 1 21 1 84 1 96 1.00 21.00 1.00 84.00 1.00 96.00 No incChokecherry 83 87 L 80 38 65 4 64 2 0.92 0.44 0.75 0.05 0.74 0.02 dec DECCucumber tree 16 0 H 12 3 11 2 12 2 0.75 0.19 0.69 0.13 0.75 0.13 No DECEastern cottonwood 98 72 L 81 128 74 216 76 249 1.13 1.78 1.03 3.00 1.06 3.46 No incEastern hemlock 209 241 H 206 159 188 97 183 80 0.86 0.66 0.78 0.40 0.76 0.33 dec DEC

(continued on next page)
APPENDIX 4

289

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 33 (continued).—Complete DISTRIB model results for 86 tree species in subregion 2 (Erie and Ontario Lake Plain)

Eastern hophornbeam 133 173 M 158 170 152 159 154 162 0.91 0.98 0.88 0.92 0.89 0.94 No decEastern redbud 0 1 M 1 86 11 195 27 226 1.00 86.00 11.00 195.00 27.00 226.00 New NewEastern redcedar 14 23 M 34 183 67 374 82 436 1.48 7.96 2.91 16.26 3.57 18.96 INC INCEastern white pine 101 221 H 163 201 171 143 173 84 0.74 0.91 0.77 0.65 0.78 0.38 dec DECFlowering dogwood 31 48 H 84 145 122 216 129 229 1.75 3.02 2.54 4.50 2.69 4.77 INC INCGray birch 8 31 M 12 13 9 5 12 7 0.39 0.42 0.29 0.16 0.39 0.23 DEC DECGreen ash 190 133 M 136 170 125 213 123 258 1.02 1.28 0.94 1.60 0.93 1.94 No NoHackberry 0 13 M 11 99 28 196 38 233 0.85 7.62 2.15 15.08 2.92 17.92 NA NewHoneylocust 0 8 L 6 77 10 154 17 211 0.75 9.63 1.25 19.25 2.13 26.38 NA NewJack pine 1 5 H 1 2 0 0 0 0 0.20 0.40 0.00 0.00 0.00 0.00 NA NALoblolly pine 0 0 H 0 1 0 4 0 10 NA NA NA New NA New NA NewMockernut hickory 68 94 H 76 112 90 150 92 164 0.81 1.19 0.96 1.60 0.98 1.75 No incNorthern catalpa 0 2 L 0 0 0 0 0 5 0.00 0.00 0.00 0.00 0.00 2.50 NA NANorthern red oak 110 254 H 225 345 245 384 255 315 0.89 1.36 0.97 1.51 1.00 1.24 No incNorthern white-cedar 18 36 H 9 1 5 3 4 6 0.25 0.03 0.14 0.08 0.11 0.17 DEC DECOhio buckeye 0 0 L 3 25 5 29 13 14 NA New New New New New New NewOsage-orange 3 12 M 8 38 13 56 16 61 0.67 3.17 1.08 4.67 1.33 5.08 inc INCPaper birch 2 30 H 16 8 8 0 6 0 0.53 0.27 0.27 0.00 0.20 0.00 DEC DECPawpaw** 0 0 L 0 6 3 23 7 30 NA New NA New New New New NewPecan 0 52 L 0 0 0 14 0 32 0.00 0.00 0.00 0.27 0.00 0.62 NA NAPersimmon 0 0 M 0 0 0 85 0 160 NA NA NA New NA New NA NewPignut hickory 68 102 H 89 129 108 175 113 175 0.87 1.27 1.06 1.72 1.11 1.72 No INCPin cherry 26 22 M 23 8 15 4 14 3 1.05 0.36 0.68 0.18 0.64 0.14 dec DECPin oak 0 20 M 36 58 47 85 47 93 1.80 2.90 2.35 4.25 2.35 4.65 NA NewPitch pine 2 5 H 2 4 4 4 4 4 0.40 0.80 0.80 0.80 0.80 0.80 No NoPost oak 0 0 H 0 2 1 300 1 668 NA NA NA New NA New NA NewQuaking aspen 178 216 H 173 69 129 5 102 4 0.80 0.32 0.60 0.02 0.47 0.02 DEC DEC

(continued on next page)
APPENDIX 4

290

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 33 (continued).—Complete DISTRIB model results for 86 tree species in subregion 2 (Erie and Ontario Lake Plain)

Red maple 711 805 H 795 723 796 549 792 439 0.99 0.90 0.99 0.68 0.98 0.55 No incRed mulberry 0 0 L 0 67 2 127 6 163 NA New NA New New New New NewRed pine 41 72 M 45 34 42 5 41 4 0.63 0.47 0.58 0.07 0.57 0.06 dec DECRed spruce 7 11 H 7 4 5 3 4 3 0.64 0.36 0.46 0.27 0.36 0.27 DEC DECRock elm 1 6 L 1 4 1 6 2 22 0.17 0.67 0.17 1.00 0.33 3.67 dec NoSassafras 10 41 H 54 84 82 125 93 144 1.32 2.05 2.00 3.05 2.27 3.51 INC INCScarlet oak 5 16 H 22 37 43 76 45 87 1.38 2.31 2.69 4.75 2.81 5.44 INC INCServiceberry 66 55 M 68 65 67 54 66 48 1.24 1.18 1.22 0.98 1.20 0.87 inc INCShagbark hickory 20 46 M 33 79 42 157 46 148 0.72 1.72 0.91 3.41 1.00 3.22 No incShellbark hickory 0 0 L 0 2 0 33 0 37 NA NA NA New NA New NA NewShingle oak 0 3 M 1 14 2 90 2 92 0.33 4.67 0.67 30.00 0.67 30.67 NA NewShortleaf pine 0 1 H 0 1 0 15 1 91 0.00 1.00 0.00 15.00 1.00 91.00 NA NewShumard oak** 0 0 L 0 0 0 0 0 6 NA NA NA NA NA New NA NewSilver maple 96 94 M 67 200 73 257 76 268 0.71 2.13 0.78 2.73 0.81 2.85 No decSlippery elm 35 66 M 61 122 86 134 97 142 0.92 1.85 1.30 2.03 1.47 2.15 inc INCSouthern red oak 0 0 H 0 0 0 0 0 29 NA NA NA NA NA New NA NewStriped maple 27 50 H 40 42 40 36 40 34 0.80 0.84 0.80 0.72 0.80 0.68 dec decSugar maple 869 790 H 777 704 729 599 728 424 0.98 0.89 0.92 0.76 0.92 0.54 No decSugarberry 0 0 M 0 0 0 17 0 76 NA NA NA New NA New NA NewSwamp white oak 5 22 L 11 15 14 30 13 24 0.50 0.68 0.64 1.36 0.59 1.09 dec DECSweet birch 15 84 H 65 66 84 64 84 48 0.77 0.79 1.00 0.76 1.00 0.57 No incSweetgum 0 3 H 1 4 2 26 7 59 0.33 1.33 0.67 8.67 2.33 19.67 NA NewSycamore 7 17 M 26 62 40 83 49 90 1.53 3.65 2.35 4.88 2.88 5.29 INC INCTamarack 6 6 H 2 2 0 1 0 2 0.33 0.33 0.00 0.17 0.00 0.33 DEC decTulip tree 26 22 H 48 63 74 82 82 81 2.18 2.86 3.36 3.73 3.73 3.68 INC INCVirginia pine 0 1 H 6 1 12 35 6 40 6.00 1.00 12.00 35.00 6.00 40.00 New NewWhite ash 1379 1152 H 1169 1148 1098 781 1080 526 1.02 1.00 0.95 0.68 0.94 0.46 No No

(continued on next page)
APPENDIX 4

291

Table 33 (continued).—Complete DISTRIB model results for 86 tree species in subregion 2 (Erie and Ontario Lake Plain)

White oak 39 155 H 118 184 148 375 154 404 0.76 1.19 0.96 2.42 0.99 2.61 No incWhite spruce 14 11 M 5 7 4 4 5 3 0.46 0.64 0.36 0.36 0.46 0.27 DEC DECWillow oak 0 0 M 0 0 0 0 0 5 NA NA NA NA NA New NA NewWinged elm 0 0 H 0 0 0 21 0 152 NA NA NA New NA New NA NewYellow birch 90 92 H 83 44 79 24 72 16 0.90 0.48 0.86 0.26 0.78 0.17 dec DEC

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

a FIA IV is the measured area-weighted importance values (IVs) as reported from Forest Inventory and Analysis. b Current Modeled IV (1971 through 2000) and Modeled IV for future time periods (2010 through 2039, 2040 through 2069, and 2070 through 2099) are simulated from the DISTRIB model; 

importance values are the sum of the average IV for each pixel in the subregion. 

c Model reliability for DISTRIB scores, which is based on statistically quantified measures of fitness (Matthews et al. 2011), is abbreviated L (low), M (medium), and H (high). d Future:Current suitable habitat is a ratio of projected importance value to current importance value. This is a measure of habitat change (not where a species will be), where a ratio of ~1 = no change; 

ratio<1 = decrease; ratio>1 = increase.

e Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), 

and NA (not detected). 

** Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.
APPENDIX 4

292

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 34.—Complete DISTRIB model results for 92 tree species in subregion 3 (North Allegheny Plateau)

American basswood 235 256 M 235 250 245 247 240 213 0.92 0.98 0.96 0.97 0.94 0.83 No decAmerican beech 2196 2115 H 1990 1214 1684 700 1539 570 0.94 0.57 0.80 0.33 0.73 0.27 dec DECAmerican chestnut 45 9 M 29 29 33 27 33 18 3.22 3.22 3.67 3.00 3.67 2.00 No NoAmerican elm 213 284 M 227 593 239 824 257 889 0.80 2.09 0.84 2.90 0.91 3.13 No decAmerican hornbeam 340 357 M 327 349 321 339 315 298 0.92 0.98 0.90 0.95 0.88 0.84 No NoAmerican mountain-ash 6 0 M 2 1 1 1 1 1 0.33 0.17 0.17 0.17 0.17 0.17 DEC DECBalsam fir 9 56 H 8 4 1 0 1 0 0.14 0.07 0.02 0.00 0.02 0.00 DEC XBalsam poplar 8 1 H 1 1 1 0 1 0 1.00 1.00 1.00 0.00 1.00 0.00 No DECBigtooth aspen 244 250 H 242 262 254 156 253 47 0.97 1.05 1.02 0.62 1.01 0.19 No NoBitternut hickory 20 7 L 8 32 9 98 10 204 1.14 4.57 1.29 14.00 1.43 29.14 No INCBlack ash 16 21 H 9 8 6 0 6 0 0.43 0.38 0.29 0.00 0.29 0.00 dec DECBlack cherry 1501 1666 H 1612 1635 1691 1114 1730 596 0.97 0.98 1.02 0.67 1.04 0.36 No NoBlack hickory 0 0 H 0 1 0 112 0 558 NA NA NA New NA New NA NewBlack locust 67 110 L 154 230 232 416 221 441 1.40 2.09 2.11 3.78 2.01 4.01 INC INCBlack oak 156 192 H 222 395 306 777 336 1245 1.16 2.06 1.59 4.05 1.75 6.48 inc INCBlack spruce 7 7 H 6 2 4 0 2 0 0.86 0.29 0.57 0.00 0.29 0.00 dec DECBlack walnut 41 35 M 46 169 72 338 96 391 1.31 4.83 2.06 9.66 2.74 11.17 INC INCBlack willow 109 146 L 98 217 95 241 112 290 0.67 1.49 0.65 1.65 0.77 1.99 dec DECBlackgum 82 115 H 141 234 189 368 211 435 1.23 2.04 1.64 3.20 1.84 3.78 inc INCBlackjack oak 0 0 M 0 0 0 66 0 428 NA NA NA New NA New NA NewBoxelder 54 37 M 38 68 42 89 48 126 1.03 1.84 1.14 2.41 1.30 3.41 inc INCBur oak 4 2 M 1 4 2 11 0 50 0.50 2.00 1.00 5.50 0.00 25.00 DEC incButternut 38 18 L 24 17 21 4 14 2 1.33 0.94 1.17 0.22 0.78 0.11 No DECCedar elm 0 0 L 0 0 0 0 0 8 NA NA NA NA NA New NA NewChestnut oak 479 513 H 608 707 754 950 761 800 1.19 1.38 1.47 1.85 1.48 1.56 inc INCChinkapin oak 3 0 M 0 8 0 99 0 225 0.00 2.67 0.00 33.00 0.00 75.00 DEC incChokecherry 97 102 L 91 72 99 10 100 1 0.89 0.71 0.97 0.10 0.98 0.01 No dec

(continued on next page)
APPENDIX 4

293

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 34 (continued).—Complete DISTRIB model results for 92 tree species in subregion 3 (North Allegheny Plateau)

Cucumber tree 42 38 H 43 29 47 22 52 23 1.13 0.76 1.24 0.58 1.37 0.61 inc DECEastern cottonwood 17 20 L 12 85 14 253 19 525 0.60 4.25 0.70 12.65 0.95 26.25 No INCEastern hemlock 1002 977 H 922 917 871 593 883 440 0.94 0.94 0.89 0.61 0.90 0.45 No decEastern hophornbeam 518 515 M 482 462 448 475 426 478 0.94 0.90 0.87 0.92 0.83 0.93 No DECEastern redbud 1 0 M 2 25 6 291 11 414 2.00 25.00 6.00 291.00 11.00 414.00 inc incEastern redcedar 55 39 M 56 157 66 705 83 1018 1.44 4.03 1.69 18.08 2.13 26.10 INC INCEastern white pine 688 645 H 613 668 575 625 568 452 0.95 1.04 0.89 0.97 0.88 0.70 No DECFlowering dogwood 94 111 H 152 452 269 927 311 1016 1.37 4.07 2.42 8.35 2.80 9.15 INC INCGray birch 96 76 M 71 88 67 58 77 45 0.93 1.16 0.88 0.76 1.01 0.59 No NoGreen ash 53 40 M 39 47 42 73 46 149 0.98 1.18 1.05 1.83 1.15 3.73 No INCHackberry 2 6 M 3 95 7 356 11 503 0.50 15.83 1.17 59.33 1.83 83.83 No INCHoneylocust 3 3 L 2 40 4 252 5 542 0.67 13.33 1.33 84.00 1.67 180.67 No incJack pine 2 8 H 1 1 0 0 0 0 0.13 0.13 0.00 0.00 0.00 0.00 DEC DECLoblolly pine 0 1 H 1 1 1 4 4 56 1.00 1.00 1.00 4.00 4.00 56.00 New NewMockernut hickory 92 143 H 114 190 164 348 165 464 0.80 1.33 1.15 2.43 1.15 3.25 No INCMountain maple 17 4 H 3 2 2 0 1 0 0.75 0.50 0.50 0.00 0.25 0.00 dec DECNorthern catalpa 2 3 L 0 0 0 0 0 5 0.00 0.00 0.00 0.00 0.00 1.67 DEC NoNorthern red oak 900 917 H 975 1004 965 1083 970 1055 1.06 1.10 1.05 1.18 1.06 1.15 No incNorthern white-cedar 7 25 H 5 0 3 0 2 0 0.20 0.00 0.12 0.00 0.08 0.00 DEC XOhio buckeye** 0 0 L 0 4 0 12 1 8 NA New NA New New New NA NewOsage-orange 1 2 M 3 24 4 93 5 118 1.50 12.00 2.00 46.50 2.50 59.00 No incPaper birch 84 89 H 68 38 37 1 32 0 0.76 0.43 0.42 0.01 0.36 0.00 DEC DECPawpaw** 0 0 L 1 6 2 25 3 29 NA New NA New NA New NA NewPecan 0 151 L 0 0 1 14 1 75 0.00 0.00 0.01 0.09 0.01 0.50 NA NAPersimmon 0 0 M 0 6 0 153 0 545 NA New NA New NA New NA NewPignut hickory 100 156 H 138 245 203 418 229 478 0.89 1.57 1.30 2.68 1.47 3.06 inc INCPin cherry 75 74 M 64 17 55 1 48 1 0.87 0.23 0.74 0.01 0.65 0.01 dec DEC

(continued on next page)
APPENDIX 4

294

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 34 (continued).—Complete DISTRIB model results for 92 tree species in subregion 3 (North Allegheny Plateau)

Pin oak 25 10 M 17 28 21 47 22 51 1.70 2.80 2.10 4.70 2.20 5.10 No incPitch pine 59 29 H 44 57 46 72 47 81 1.52 1.97 1.59 2.48 1.62 2.79 inc INCPost oak** 0 0 H 0 7 1 384 2 1644 NA New NA New NA New NA NewQuaking aspen 462 528 H 435 310 363 22 331 0 0.82 0.59 0.69 0.04 0.63 0.00 dec DECRed maple 3340 3247 H 3395 3184 3401 2390 3405 1581 1.05 0.98 1.05 0.74 1.05 0.49 No incRed mulberry 0 1 L 0 39 1 261 3 493 0.00 39.00 1.00 261.00 3.00 493.00 NA NewRed pine 122 130 M 95 95 80 15 74 1 0.73 0.73 0.62 0.12 0.57 0.01 dec DECRed spruce 33 46 H 16 26 18 11 20 8 0.35 0.57 0.39 0.24 0.44 0.17 DEC DECRiver birch 4 2 L 1 2 1 2 2 3 0.50 1.00 0.50 1.00 1.00 1.50 No NoRock elm 1 0 L 1 1 0 1 1 11 1.00 1.00 0.00 1.00 1.00 11.00 No incSassafras 148 166 H 204 285 287 465 322 526 1.23 1.72 1.73 2.80 1.94 3.17 inc INCScarlet oak 101 89 H 134 195 198 332 198 452 1.51 2.19 2.23 3.73 2.23 5.08 INC INCScrub oak (bear oak) 67 33 L 52 74 62 86 71 72 1.58 2.24 1.88 2.61 2.15 2.18 INC INCServiceberry 380 356 M 364 351 364 301 362 257 1.02 0.99 1.02 0.85 1.02 0.72 No NoShagbark hickory 44 30 M 41 87 48 215 52 296 1.37 2.90 1.60 7.17 1.73 9.87 inc INCShellbark hickory 1 0 H 0 1 0 7 0 15 0.00 1.00 0.00 7.00 0.00 15.00 DEC incShingle oak 3 0 M 0 6 2 54 2 121 0.00 2.00 0.67 18.00 0.67 40.33 No incShortleaf pine 0 4 H 3 8 3 94 5 436 0.75 2.00 0.75 23.50 1.25 109.00 NA NewShumard oak** 0 0 L 0 0 0 0 0 8 NA NA NA NA NA New NA NewSilver maple 34 47 M 23 97 26 191 30 237 0.49 2.06 0.55 4.06 0.64 5.04 dec DECSlippery elm 57 66 M 63 164 89 261 115 314 0.96 2.49 1.35 3.96 1.74 4.76 inc INCSourwood 1 1 H 8 3 24 3 8 13 8.00 3.00 24.00 3.00 8.00 13.00 inc incSouthern red oak 0 0 H 0 0 0 13 0 100 NA NA NA New NA New NA NewStriped maple 403 420 H 400 330 386 230 365 184 0.95 0.79 0.92 0.55 0.87 0.44 No DECSugar maple 2409 2513 H 2252 1796 2010 1628 1950 1362 0.90 0.72 0.80 0.65 0.78 0.54 dec DECSugarberry 0 1 M 0 0 0 12 0 105 0.00 0.00 0.00 12.00 0.00 105.00 NA NewSwamp white oak 22 4 L 11 14 16 18 17 17 2.75 3.50 4.00 4.50 4.25 4.25 inc inc

(continued on next page)
APPENDIX 4

295

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 34 (continued).—Complete DISTRIB model results for 92 tree species in subregion 3 (North Allegheny Plateau)

Sweet birch 689 675 H 771 724 824 597 811 426 1.14 1.07 1.22 0.88 1.20 0.63 inc INCSweetgum 2 1 H 4 13 5 59 14 209 4.00 13.00 5.00 59.00 14.00 209.00 inc incSycamore 28 9 M 17 72 21 206 31 220 1.89 8.00 2.33 22.89 3.44 24.44 No INCTamarack 26 12 H 6 8 4 0 3 0 0.50 0.67 0.33 0.00 0.25 0.00 dec DECTulip tree 60 104 H 152 193 260 407 279 399 1.46 1.86 2.50 3.91 2.68 3.84 INC INCVirginia pine 12 26 H 24 19 37 154 25 228 0.92 0.73 1.42 5.92 0.96 8.77 No decWhite ash 2113 2078 H 1942 1943 1859 1747 1847 1348 0.94 0.94 0.90 0.84 0.89 0.65 No decWhite oak 545 473 H 617 769 690 1179 711 1648 1.30 1.63 1.46 2.49 1.50 3.48 inc INCWhite spruce 34 33 M 17 7 10 2 8 1 0.52 0.21 0.30 0.06 0.24 0.03 DEC XWinged elm 1 0 H 0 1 0 40 0 349 0.00 1.00 0.00 40.00 0.00 349.00 DEC incYellow birch 338 377 H 358 279 358 128 370 111 0.95 0.74 0.95 0.34 0.98 0.29 No dec

a FIA IV is the measured area-weighted importance values (IVs) as reported from Forest Inventory and Analysis. b Current Modeled IV (1971 through 2000) and Modeled IV for future time periods (2010 through 2039, 2040 through 2069, and 2070 through 2099) are simulated from the DISTRIB model; 

importance values are the sum of the average IV for each pixel in the subregion. 

c Model reliability for DISTRIB scores, which is based on statistically quantified measures of fitness (Matthews et al. 2011), is abbreviated L (low), M (medium), and H (high). d Future:Current suitable habitat is a ratio of projected importance value to current importance value. This is a measure of habitat change (not where a species will be), where a ratio of ~1 = no change; 

ratio<1 = decrease; ratio>1 = increase.

e Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), 

X (extirpated), and NA (not detected). 

**Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.
APPENDIX 4

296

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 35.—Complete DISTRIB model results for 88 tree species in subregion 4 (Ridge and Valley)

American basswood 98 115 M 89 112 90 84 91 68 0.77 0.97 0.78 0.73 0.79 0.59 dec decAmerican beech 308 356 H 295 264 275 239 296 169 0.83 0.74 0.77 0.67 0.83 0.48 No DECAmerican chestnut 55 26 M 45 36 42 28 44 27 1.73 1.39 1.62 1.08 1.69 1.04 inc NoAmerican elm 90 158 M 107 331 115 430 130 415 0.68 2.10 0.73 2.72 0.82 2.63 No INCAmerican hornbeam 56 104 M 60 88 61 127 69 135 0.58 0.85 0.59 1.22 0.66 1.30 dec incBigtooth aspen 125 162 H 126 72 103 11 96 2 0.78 0.44 0.64 0.07 0.59 0.01 dec DECBitternut hickory 30 14 L 17 67 18 134 25 142 1.21 4.79 1.29 9.57 1.79 10.14 inc INCBlack cherry 1183 1120 H 1099 816 981 357 957 329 0.98 0.73 0.88 0.32 0.85 0.29 No DECBlack hickory 0 1 H 1 64 5 366 5 511 1.00 64.00 5.00 366.00 5.00 511.00 New NewBlack locust 229 255 L 266 245 258 249 238 244 1.04 0.96 1.01 0.98 0.93 0.96 No NoBlack oak 372 392 H 409 562 432 891 441 993 1.04 1.43 1.10 2.27 1.13 2.53 No INCBlack spruce 4 0 H 0 0 0 0 0 0 0.00 0.00 0.00 0.00 0.00 0.00 DEC DECBlack walnut 94 122 M 109 168 124 177 152 165 0.89 1.38 1.02 1.45 1.25 1.35 inc incBlack willow 4 21 L 6 54 7 88 8 112 0.29 2.57 0.33 4.19 0.38 5.33 DEC INCBlackgum 366 356 H 394 423 428 414 450 441 1.11 1.19 1.20 1.16 1.26 1.24 inc incBlackjack oak 0 0 M 1 47 5 317 6 502 NA New New New New New New NewBoxelder 79 51 M 56 62 55 62 60 81 1.10 1.22 1.08 1.22 1.18 1.59 No incBur oak 2 0 M 0 0 0 15 0 39 0.00 0.00 0.00 7.50 0.00 19.50 DEC incButternut 16 9 L 8 2 4 0 3 0 0.89 0.22 0.44 0.00 0.33 0.00 dec DECCedar elm 0 0 L 0 0 0 16 0 101 NA NA NA New NA New NA NewCherrybark oak 0 0 M 0 2 0 3 2 9 NA NA NA NA NA New NA NewChestnut oak 1232 1047 H 1152 1061 1142 757 1118 620 1.10 1.01 1.09 0.72 1.07 0.59 No decChinkapin oak 2 10 M 6 44 8 130 15 138 0.60 4.40 0.80 13.00 1.50 13.80 No INCChokecherry 57 39 L 31 13 28 1 20 0 0.80 0.33 0.72 0.03 0.51 0.00 dec XCucumber tree 42 31 H 33 26 33 22 32 19 1.07 0.84 1.07 0.71 1.03 0.61 No decEastern cottonwood 13 15 L 4 33 6 131 6 227 0.27 2.20 0.40 8.73 0.40 15.13 DEC INC

(continued on next page)
APPENDIX 4

297

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 35 (continued).—Complete DISTRIB model results for 88 tree species in subregion 4 (Ridge and Valley)

Eastern hemlock 367 357 H 312 259 274 154 269 132 0.87 0.73 0.77 0.43 0.75 0.37 dec DECEastern hophornbeam 137 159 M 135 160 134 190 148 254 0.85 1.01 0.84 1.20 0.93 1.60 No incEastern redbud 27 32 M 36 107 58 165 70 168 1.13 3.34 1.81 5.16 2.19 5.25 INC INCEastern redcedar 63 96 M 95 322 117 566 155 660 0.99 3.35 1.22 5.90 1.62 6.88 inc INCEastern white pine 283 302 H 288 283 268 174 256 117 0.95 0.94 0.89 0.58 0.85 0.39 No DECFlowering dogwood 215 319 H 315 446 387 537 412 471 0.99 1.40 1.21 1.68 1.29 1.48 inc incGray birch 60 26 M 28 22 21 19 20 20 1.08 0.85 0.81 0.73 0.77 0.77 dec decGreen ash 51 55 M 43 66 45 96 48 144 0.78 1.20 0.82 1.75 0.87 2.62 No INCHackberry 31 35 M 33 128 33 235 38 271 0.94 3.66 0.94 6.71 1.09 7.74 No INCHoneylocust 2 6 L 3 12 4 113 7 189 0.50 2.00 0.67 18.83 1.17 31.50 No INCLoblolly pine 0 12 H 7 11 13 34 12 190 0.58 0.92 1.08 2.83 1.00 15.83 NA NewMockernut hickory 125 190 H 171 227 217 338 218 390 0.90 1.20 1.14 1.78 1.15 2.05 No INCNorthern catalpa 0 1 L 0 0 0 0 0 13 0.00 0.00 0.00 0.00 0.00 13.00 NA NewNorthern red oak 912 823 H 847 750 809 592 775 529 1.03 0.91 0.98 0.72 0.94 0.64 No decOsage-orange 5 6 M 5 11 6 24 7 48 0.83 1.83 1.00 4.00 1.17 8.00 No incPaper birch 11 15 H 5 1 4 0 3 0 0.33 0.07 0.27 0.00 0.20 0.00 DEC XPawpaw 5 10 L 4 16 8 27 11 23 0.40 1.60 0.80 2.70 1.10 2.30 No NoPecan 0 71 L 0 4 0 29 0 41 0.00 0.06 0.00 0.41 0.00 0.58 NA NAPersimmon 2 6 M 7 103 15 371 24 502 1.17 17.17 2.50 61.83 4.00 83.67 inc INCPignut hickory 136 218 H 199 255 237 319 252 288 0.91 1.17 1.09 1.46 1.16 1.32 No incPin cherry 43 21 M 21 3 11 0 10 0 1.00 0.14 0.52 0.00 0.48 0.00 DEC XPin oak 18 24 M 13 28 12 47 22 38 0.54 1.17 0.50 1.96 0.92 1.58 No incPitch pine 96 101 H 96 119 94 113 93 119 0.95 1.18 0.93 1.12 0.92 1.18 No NoPost oak 0 8 H 8 245 39 1157 53 1651 1.00 30.63 4.88 144.63 6.63 206.38 New NewQuaking aspen 54 72 H 36 12 18 1 15 0 0.50 0.17 0.25 0.01 0.21 0.00 DEC XRed maple 2063 2021 H 1983 1611 1879 905 1905 688 0.98 0.80 0.93 0.45 0.94 0.34 No DECRed mulberry 6 7 L 5 40 5 192 8 293 0.71 5.71 0.71 27.43 1.14 41.86 No INC

(continued on next page)
APPENDIX 4

298

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 35 (continued).—Complete DISTRIB model results for 88 tree species in subregion 4 (Ridge and Valley)

Red pine 40 31 M 29 24 18 0 18 0 0.94 0.77 0.58 0.00 0.58 0.00 dec XRed spruce 9 4 H 0 0 1 0 1 0 0.00 0.00 0.25 0.00 0.25 0.00 dec DECRiver birch 7 3 L 4 6 4 6 5 14 1.33 2.00 1.33 2.00 1.67 4.67 No incRock elm** 0 0 L 0 3 0 7 2 18 NA NA NA New NA New NA NewSassafras 463 436 H 466 510 486 478 533 439 1.07 1.17 1.12 1.10 1.22 1.01 inc NoScarlet oak 191 205 H 209 235 243 296 241 266 1.02 1.15 1.19 1.44 1.18 1.30 No incScrub oak (bear oak) 111 63 L 77 100 76 95 89 92 1.22 1.59 1.21 1.51 1.41 1.46 inc incServiceberry 167 161 M 155 145 149 119 152 115 0.96 0.90 0.93 0.74 0.94 0.71 No decShagbark hickory 51 66 M 54 134 60 180 67 182 0.82 2.03 0.91 2.73 1.02 2.76 No INCShellbark hickory 1 0 L 0 5 0 11 0 11 0.00 5.00 0.00 11.00 0.00 11.00 DEC incShingle oak 4 5 M 1 31 2 61 5 55 0.20 6.20 0.40 12.20 1.00 11.00 No INCShortleaf pine 2 4 H 8 70 14 373 17 677 2.00 17.50 3.50 93.25 4.25 169.25 inc INCShumard oak** 0 0 L 0 1 0 10 0 71 NA NA NA New NA New NA NewSilver maple 41 50 M 27 140 24 172 32 191 0.54 2.80 0.48 3.44 0.64 3.82 dec INCSlippery elm 98 127 M 105 146 112 176 117 175 0.83 1.15 0.88 1.39 0.92 1.38 No incSourwood 0 12 H 22 8 40 7 41 18 1.83 0.67 3.33 0.58 3.42 1.50 New NASouthern red oak 1 2 H 2 19 5 105 6 281 1.00 9.50 2.50 52.50 3.00 140.50 No incStriped maple 220 173 H 186 148 172 102 155 90 1.08 0.86 0.99 0.59 0.90 0.52 No decSugar maple 526 680 H 559 622 536 586 575 419 0.82 0.92 0.79 0.86 0.85 0.62 No decSugarberry** 0 1 M 0 13 2 79 4 223 0.00 13.00 2.00 79.00 4.00 223.00 New NewSwamp chestnut oak 13 5 M 4 9 3 5 5 0 0.80 1.80 0.60 1.00 1.00 0.00 No DECSweet birch 849 672 H 736 554 691 332 658 278 1.10 0.82 1.03 0.49 0.98 0.41 No DECSweetgum 1 15 H 10 61 25 133 45 269 0.67 4.07 1.67 8.87 3.00 17.93 INC INCSycamore 39 39 M 38 75 44 120 56 141 0.97 1.92 1.13 3.08 1.44 3.62 inc INCTable Mountain pine 7 1 M 1 1 1 2 1 3 1.00 1.00 1.00 2.00 1.00 3.00 No NoTamarack 16 2 H 3 1 2 1 2 1 1.50 0.50 1.00 0.50 1.00 0.50 No decTulip tree 253 318 H 366 323 447 250 480 177 1.15 1.02 1.41 0.79 1.51 0.56 inc decVirginia pine 119 136 H 133 121 174 149 150 185 0.98 0.89 1.28 1.10 1.10 1.36 No inc

(continued on next page)
APPENDIX 4

299

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 35 (continued).—Complete DISTRIB model results for 88 tree species in subregion 4 (Ridge and Valley)

Water oak 0 0 H 0 0 0 6 0 44 NA NA NA New NA New NA NewWhite ash 873 730 H 757 658 701 477 709 396 1.04 0.90 0.96 0.65 0.97 0.54 No decWhite oak 516 603 H 585 774 613 1000 622 934 0.97 1.28 1.02 1.66 1.03 1.55 No incWhite spruce 17 5 M 2 2 3 3 2 3 0.40 0.40 0.60 0.60 0.40 0.60 dec decWillow oak 0 1 M 1 1 1 5 1 10 1.00 1.00 1.00 5.00 1.00 10.00 NA NewWinged elm 0 0 H 3 43 6 338 8 727 New New New New New New New NewYellow birch 81 90 H 64 40 56 11 58 7 0.71 0.44 0.62 0.12 0.64 0.08 dec DEC

a FIA IV is the measured area-weighted importance values (IVs) as reported from Forest Inventory and Analysis. b Current Modeled IV (1971 through 2000) and Modeled IV for future time periods (2010 through 2039, 2040 through 2069, and 2070 through 2099) are simulated from the DISTRIB model; 

importance values are the sum of the average IV for each pixel in the subregion. 

c Model reliability for DISTRIB scores, which is based on statistically quantified measures of fitness (Matthews et al. 2011), is abbreviated L (low), M (medium), and H (high). d Future:Current suitable habitat is a ratio of projected importance value to current importance value. This is a measure of habitat change (not where a species will be), where a ratio of ~1 = no change; 

ratio<1 = decrease; ratio>1 = increase.

e Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), 

X (extirpated), and NA (not detected). 

** Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.
APPENDIX 4

300

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 36.—Complete DISTRIB model results for 107 tree species in subregion 5 (Piedmont)

American basswood 45 86 M 57 81 63 75 74 74 0.66 0.94 0.73 0.87 0.86 0.86 No NoAmerican beech 566 548 H 546 439 526 289 539 224 1.00 0.80 0.96 0.53 0.98 0.41 No DECAmerican chestnut 30 8 M 23 16 20 12 21 10 2.88 2.00 2.50 1.50 2.63 1.25 No NoAmerican elm 276 371 M 291 533 298 549 345 536 0.78 1.44 0.80 1.48 0.93 1.45 No incAmerican holly 0 19 H 20 37 31 46 34 45 1.05 1.95 1.63 2.42 1.79 2.37 NA NAAmerican hornbeam (musclewood) 197 204 M 195 201 197 200 211 206 0.96 0.99 0.97 0.98 1.03 1.01 No NoAtlantic white-cedar 0 19 L 7 7 7 5 7 5 0.37 0.37 0.37 0.26 0.37 0.26 NA NABaldcypress 0 16 M 13 33 19 41 33 68 0.81 2.06 1.19 2.56 2.06 4.25 NA NewBalsam fir 12 33 H 15 5 6 1 6 3 0.46 0.15 0.18 0.03 0.18 0.09 DEC DECBigtooth aspen 78 103 H 67 60 72 20 66 7 0.65 0.58 0.70 0.19 0.64 0.07 dec DECBitternut hickory 25 35 L 25 116 33 162 60 188 0.71 3.31 0.94 4.63 1.71 5.37 inc INCBlack ash 13 42 H 18 16 14 4 11 2 0.43 0.38 0.33 0.10 0.26 0.05 dec DECBlack cherry 954 920 H 792 567 687 406 627 379 0.86 0.62 0.75 0.44 0.68 0.41 dec DECBlack hickory 0 7 H 21 179 38 421 52 544 3.00 25.57 5.43 60.14 7.43 77.71 New NewBlack locust 182 192 L 197 200 207 220 219 189 1.03 1.04 1.08 1.15 1.14 0.98 No NoBlack oak 413 520 H 524 679 543 820 556 806 1.01 1.31 1.04 1.58 1.07 1.55 No incBlack walnut 165 182 M 184 241 211 224 240 170 1.01 1.32 1.16 1.23 1.32 0.93 inc NoBlack willow 48 70 L 43 145 48 158 61 188 0.61 2.07 0.69 2.26 0.87 2.69 No INCBlackgum 306 322 H 356 327 360 339 382 380 1.11 1.02 1.12 1.05 1.19 1.18 No NoBlackjack oak 0 17 M 28 140 47 282 70 383 1.65 8.24 2.77 16.59 4.12 22.53 New NewBoxelder 185 159 M 154 178 162 162 171 169 0.97 1.12 1.02 1.02 1.08 1.06 No NoBur oak 1 8 M 3 18 3 25 6 35 0.38 2.25 0.38 3.13 0.75 4.38 No incButternut 51 34 L 36 23 31 10 22 8 1.06 0.68 0.91 0.29 0.65 0.24 dec DECCedar elm 0 0 L 0 3 0 49 1 114 NA NA NA New NA New NA NewCherrybark oak 0 7 M 6 17 7 39 14 78 0.86 2.43 1.00 5.57 2.00 11.14 NA NewChestnut oak 730 532 H 649 466 636 383 624 344 1.22 0.88 1.20 0.72 1.17 0.65 No dec

(continued on next page)
APPENDIX 4

301

Table 36 (continued).—Complete DISTRIB model results for 107 tree species in subregion 5 (Piedmont)

Chinkapin oak 3 10 M 14 53 21 106 30 123 1.40 5.30 2.10 10.60 3.00 12.30 No incChokecherry 43 48 L 33 21 30 1 23 0 0.69 0.44 0.63 0.02 0.48 0.00 DEC DECEastern cottonwood 40 70 L 49 143 45 194 63 274 0.70 2.04 0.64 2.77 0.90 3.91 No INCEastern hemlock 320 398 H 319 272 283 179 280 159 0.80 0.68 0.71 0.45 0.70 0.40 dec DECEastern hophornbeam 217 241 M 201 258 205 272 237 326 0.83 1.07 0.85 1.13 0.98 1.35 No incEastern redbud 17 31 M 34 78 50 149 69 181 1.10 2.52 1.61 4.81 2.23 5.84 INC INCEastern redcedar 275 316 M 334 497 382 654 428 717 1.06 1.57 1.21 2.07 1.35 2.27 inc INCEastern white pine 406 474 H 386 301 354 171 348 156 0.81 0.64 0.75 0.36 0.73 0.33 dec DECFlowering dogwood 316 392 H 405 478 443 494 468 473 1.03 1.22 1.13 1.26 1.19 1.21 No incGray birch 60 71 M 50 47 45 39 40 34 0.70 0.66 0.63 0.55 0.56 0.48 dec DECGreen ash 39 131 M 75 145 77 179 97 225 0.57 1.11 0.59 1.37 0.74 1.72 dec incHackberry 42 68 M 51 203 67 256 91 277 0.75 2.99 0.99 3.77 1.34 4.07 inc INCHoneylocust 5 13 L 9 71 13 125 17 206 0.69 5.46 1.00 9.62 1.31 15.85 No INCJack pine 0 4 H 1 2 0 0 0 0 0.25 0.50 0.00 0.00 0.00 0.00 NA NALaurel oak** 0 0 H 0 0 0 0 5 0 NA NA NA NA New NA New NALoblolly pine 17 51 H 50 120 91 255 131 565 0.98 2.35 1.78 5.00 2.57 11.08 INC INCLongleaf pine** 0 3 H 0 2 7 1 39 1 0.00 0.67 2.33 0.33 13.00 0.33 New NAMockernut hickory 205 250 H 255 277 269 329 277 355 1.02 1.11 1.08 1.32 1.11 1.42 No incMountain maple 12 2 H 6 2 2 0 2 0 3.00 1.00 1.00 0.00 1.00 0.00 No DECNorthern catalpa 13 1 L 5 5 5 4 5 9 5.00 5.00 5.00 4.00 5.00 9.00 inc INCNorthern red oak 820 757 H 741 673 712 546 701 501 0.98 0.89 0.94 0.72 0.93 0.66 No decNorthern white-cedar 8 24 H 8 7 6 5 6 5 0.33 0.29 0.25 0.21 0.25 0.21 dec decOhio buckeye** 0 0 L 0 3 0 4 2 4 NA NA NA New NA New NA NewOsage-orange 6 12 M 12 31 12 36 16 68 1.00 2.58 1.00 3.00 1.33 5.67 No incOvercup oak** 0 7 M 0 3 1 20 2 38 0.00 0.43 0.14 2.86 0.29 5.43 NA NewPaper birch 52 73 H 54 33 36 2 30 1 0.74 0.45 0.49 0.03 0.41 0.01 DEC DEC

(continued on next page)

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI
APPENDIX 4

302Table 36 (continued).—Complete DISTRIB model results for 107 tree species in subregion 5 (Piedmont)

Pawpaw 49 29 L 32 53 39 60 57 63 1.10 1.83 1.35 2.07 1.97 2.17 inc INCPecan 0 137 L 1 18 3 36 6 69 0.01 0.13 0.02 0.26 0.04 0.50 NA NAPersimmon 19 33 M 54 206 82 377 108 491 1.64 6.24 2.49 11.42 3.27 14.88 INC INCPignut hickory 256 317 H 306 341 326 342 347 299 0.97 1.08 1.03 1.08 1.10 0.94 No NoPin cherry 32 27 M 18 13 13 5 15 4 0.67 0.48 0.48 0.19 0.56 0.15 dec DECPin oak 113 96 M 85 157 94 150 147 133 0.89 1.64 0.98 1.56 1.53 1.39 inc incPitch pine 66 106 H 84 72 74 70 71 74 0.79 0.68 0.70 0.66 0.67 0.70 dec decPond pine 2 0 H 1 5 1 10 12 11 0.50 2.50 0.50 5.00 6.00 5.50 inc incPost oak 3 55 H 117 538 200 1121 248 1483 2.13 9.78 3.64 20.38 4.51 26.96 inc INCQuaking aspen 107 116 H 88 51 62 8 53 4 0.76 0.44 0.53 0.07 0.46 0.03 DEC DECRed maple 2039 2112 H 2001 1591 1905 1097 1856 920 0.95 0.75 0.90 0.52 0.88 0.44 No DECRed mulberry 8 16 L 9 116 14 189 31 324 0.56 7.25 0.88 11.81 1.94 20.25 No INCRed pine 29 55 M 28 35 31 11 31 9 0.51 0.64 0.56 0.20 0.56 0.16 dec DECRed spruce 29 28 H 19 14 12 10 13 9 0.68 0.50 0.43 0.36 0.46 0.32 DEC DECRedbay 0 0 H 0 0 0 0 6 0 NA NA NA NA New NA New NARiver birch 9 7 L 4 10 5 10 9 29 0.57 1.43 0.71 1.43 1.29 4.14 No incRock elm 1 1 L 3 25 3 22 19 25 3.00 25.00 3.00 22.00 19.00 25.00 inc incSassafras 300 354 H 351 382 380 365 412 347 0.99 1.08 1.07 1.03 1.16 0.98 No NoScarlet oak 236 214 H 244 245 260 233 261 203 1.14 1.15 1.22 1.09 1.22 0.95 inc NoScrub oak (bear oak) 68 20 L 44 46 46 51 47 47 2.20 2.30 2.30 2.55 2.35 2.35 INC INCServiceberry 125 102 M 105 96 105 83 103 79 1.03 0.94 1.03 0.81 1.01 0.78 No decShagbark hickory 72 122 M 102 216 127 221 155 219 0.84 1.77 1.04 1.81 1.27 1.80 inc incShellbark hickory 1 0 L 0 44 0 67 2 77 0.00 44.00 0.00 67.00 2.00 77.00 NA incShingle oak 1 9 M 6 70 11 104 31 121 0.67 7.78 1.22 11.56 3.44 13.44 No INCShortleaf pine 1 21 H 37 134 45 329 55 533 1.76 6.38 2.14 15.67 2.62 25.38 No INCShumard oak** 0 0 L 0 2 0 17 0 74 NA NA NA New NA New NA New

(continued on next page)

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI
APPENDIX 4

303

Table 36 (continued).—Complete DISTRIB model results for 107 tree species in subregion 5 (Piedmont)

Silver maple 65 131 M 68 279 78 342 122 373 0.52 2.13 0.60 2.61 0.93 2.85 No INCSlash pine 0 0 H 0 0 0 0 16 4 NA NA NA NA New New New NewSlippery elm 54 112 M 87 148 102 171 119 179 0.78 1.32 0.91 1.53 1.06 1.60 No incSourwood 0 8 H 14 12 28 8 27 26 1.75 1.50 3.50 1.00 3.38 3.25 New NewSouthern red oak 12 24 H 43 112 68 246 81 379 1.79 4.67 2.83 10.25 3.38 15.79 No INCStriped maple 101 92 H 88 71 78 46 72 38 0.96 0.77 0.85 0.50 0.78 0.41 dec DECSugar maple 846 940 H 877 971 871 686 953 454 0.93 1.03 0.93 0.73 1.01 0.48 No DECSugarberry** 0 7 M 7 42 14 173 24 287 1.00 6.00 2.00 24.71 3.43 41.00 New NewSwamp chestnut oak 1 0 M 0 3 0 7 0 10 0.00 3.00 0.00 7.00 0.00 10.00 NA incSwamp tupelo 0 8 H 1 8 9 12 46 15 0.13 1.00 1.13 1.50 5.75 1.88 New NASwamp white oak 46 25 L 23 48 28 36 38 33 0.92 1.92 1.12 1.44 1.52 1.32 inc incSweet birch 479 425 H 441 322 412 227 391 184 1.04 0.76 0.97 0.53 0.92 0.43 No DECSweetbay 8 13 H 5 8 9 7 29 7 0.39 0.62 0.69 0.54 2.23 0.54 No decSweetgum 127 196 H 221 372 291 493 392 705 1.13 1.90 1.49 2.52 2.00 3.60 inc INCSycamore 78 90 M 103 135 110 171 123 179 1.14 1.50 1.22 1.90 1.37 1.99 inc incTamarack 15 5 H 8 5 6 3 5 3 1.60 1.00 1.20 0.60 1.00 0.60 No NoTulip tree 652 593 H 639 416 641 284 605 254 1.08 0.70 1.08 0.48 1.02 0.43 No DECTurkey oak 0 0 H 0 0 1 0 9 0 NA NA NA NA New NA New NAVirginia pine 106 159 H 168 109 201 107 157 122 1.06 0.69 1.26 0.67 0.99 0.77 No decWater elm 0 5 L 2 2 2 2 2 6 0.40 0.40 0.40 0.40 0.40 1.20 NA NAWater hickory 0 5 M 1 2 0 3 1 7 0.20 0.40 0.00 0.60 0.20 1.40 NA NAWater oak 0 3 H 3 13 7 77 9 218 1.00 4.33 2.33 25.67 3.00 72.67 NA NewWater tupelo 11 9 M 5 8 8 14 14 21 0.56 0.89 0.89 1.56 1.56 2.33 No NoWhite ash 1372 1252 H 1150 923 1047 634 1072 519 0.92 0.74 0.84 0.51 0.86 0.42 No DECWhite oak 463 612 H 642 792 700 869 716 886 1.05 1.29 1.14 1.42 1.17 1.45 No incWhite spruce 34 18 M 12 14 12 9 8 8 0.67 0.78 0.67 0.50 0.44 0.44 dec dec

(continued on next page)

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

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APPENDIX 4

304

Willow oak 0 11 M 11 22 14 77 20 128 1.00 2.00 1.27 7.00 1.82 11.64 NA NewWinged elm 0 6 H 36 186 67 517 105 828 6.00 31.00 11.17 86.17 17.50 138.00 New NewYellow birch 134 137 H 122 86 98 58 99 51 0.89 0.63 0.72 0.42 0.72 0.37 dec DEC

Table 36 (continued).—Complete DISTRIB model results for 107 tree species in subregion 5 (Piedmont)

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

a FIA IV is the measured area-weighted importance values (IVs) as reported from Forest Inventory and Analysis. b Current Modeled IV (1971 through 2000) and Modeled IV for future time periods (2010 through 2039, 2040 through 2069, and 2070 through 2099) are simulated from the DISTRIB model; 

importance values are the sum of the average IV for each pixel in the subregion. 

c Model reliability for DISTRIB scores, which is based on statistically quantified measures of fitness (Matthews et al. 2011), is abbreviated L (low), M (medium), and H (high). d Future:Current suitable habitat is a ratio of projected importance value to current importance value. This is a measure of habitat change (not where a species will be), where a ratio of ~1 = no change; 

ratio<1 = decrease; ratio>1 = increase.

e Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), 

and NA (not detected). 

** Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.
APPENDIX 4

305

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 37.—Complete DISTRIB model results for 91 tree species in subregion 6 (Coastal Plain)

American beech 247 228 H 228 146 218 88 205 76 1.00 0.64 0.96 0.39 0.90 0.33 No DECAmerican chestnut 4 3 M 1 1 1 1 1 1 0.33 0.33 0.33 0.33 0.33 0.33 dec decAmerican elm 33 60 M 47 122 47 205 73 285 0.78 2.03 0.78 3.42 1.22 4.75 inc INCAmerican holly 235 235 H 258 235 283 162 266 134 1.10 1.00 1.20 0.69 1.13 0.57 No decAmerican hornbeam 61 94 M 74 80 79 91 84 94 0.79 0.85 0.84 0.97 0.89 1.00 No NoAtlantic white-cedar 73 58 L 50 38 42 35 39 32 0.86 0.66 0.72 0.60 0.67 0.55 dec decBaldcypress 1 11 M 20 58 41 82 61 129 1.82 5.27 3.73 7.46 5.55 11.73 INC INCBigtooth aspen 20 9 H 3 0 2 0 1 0 0.33 0.00 0.22 0.00 0.11 0.00 DEC XBitternut hickory 1 5 L 1 15 5 64 17 88 0.20 3.00 1.00 12.80 3.40 17.60 No INCBlack cherry 311 302 H 212 146 176 134 152 138 0.70 0.48 0.58 0.44 0.50 0.46 dec DECBlack hickory 0 12 H 30 136 37 249 72 244 2.50 11.33 3.08 20.75 6.00 20.33 New NewBlack locust 103 88 L 75 74 71 23 87 5 0.85 0.84 0.81 0.26 0.99 0.06 No DECBlack oak 272 313 H 296 339 281 276 289 187 0.95 1.08 0.90 0.88 0.92 0.60 No decBlack walnut 26 52 M 33 51 36 27 49 6 0.64 0.98 0.69 0.52 0.94 0.12 No DECBlack willow 9 14 L 9 33 8 41 10 72 0.64 2.36 0.57 2.93 0.71 5.14 dec INCBlackgum 368 363 H 324 276 290 269 281 285 0.89 0.76 0.80 0.74 0.77 0.79 dec decBlackjack oak 10 21 M 30 77 41 104 60 112 1.43 3.67 1.95 4.95 2.86 5.33 INC INCBluejack oak 0 0 M 0 1 0 1 0 8 NA NA NA NA NA New NA NewBoxelder 72 40 M 51 52 52 42 52 39 1.28 1.30 1.30 1.05 1.30 0.98 inc NoCedar elm 0 0 L 0 23 0 73 1 84 NA New NA New NA New NA NewCherrybark oak 6 4 M 15 27 22 70 24 84 3.75 6.75 5.50 17.50 6.00 21.00 inc INCChestnut oak 122 129 H 111 57 108 37 94 34 0.86 0.44 0.84 0.29 0.73 0.26 dec DECChinkapin oak** 0 1 M 1 6 4 9 4 7 1.00 6.00 4.00 9.00 4.00 7.00 New NewEastern cottonwood 6 20 L 8 31 7 60 11 97 0.40 1.55 0.35 3.00 0.55 4.85 dec INCEastern hemlock 30 9 H 15 14 15 13 16 13 1.67 1.56 1.67 1.44 1.78 1.44 inc incEastern hophornbeam 16 24 M 25 37 27 51 38 81 1.04 1.54 1.13 2.13 1.58 3.38 inc INCEastern redbud 1 3 M 1 5 1 15 2 28 0.33 1.67 0.33 5.00 0.67 9.33 No incEastern redcedar 50 91 M 70 114 80 145 98 135 0.77 1.25 0.88 1.59 1.08 1.48 No inc

(continued on next page)
APPENDIX 4

306

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 37 (continued).—Complete DISTRIB model results for 91 tree species in subregion 6 (Coastal Plain)

Eastern white pine 15 30 H 19 4 13 0 11 1 0.63 0.13 0.43 0.00 0.37 0.03 DEC DECFlowering dogwood 132 136 H 129 133 129 140 132 148 0.95 0.98 0.95 1.03 0.97 1.09 No NoGray birch 10 4 M 4 4 4 4 4 6 1.00 1.00 1.00 1.00 1.00 1.50 No NoGreen ash 16 35 M 26 46 22 81 29 96 0.74 1.31 0.63 2.31 0.83 2.74 No INCHackberry 2 13 M 4 17 5 29 13 45 0.31 1.31 0.39 2.23 1.00 3.46 No INCHoneylocust** 0 1 L 0 2 0 2 0 17 0.00 2.00 0.00 2.00 0.00 17.00 NA NewLaurel oak 0 0 H 0 1 5 12 10 30 NA NA New New New New New NewLoblolly pine 329 355 H 464 545 591 669 574 810 1.31 1.54 1.67 1.89 1.62 2.28 inc INCLongleaf pine 0 2 H 0 2 4 11 42 16 0.00 1.00 2.00 5.50 21.00 8.00 New NewMockernut hickory 48 78 H 53 55 50 65 52 94 0.68 0.71 0.64 0.83 0.67 1.21 dec incNorthern catalpa 12 1 L 5 5 5 4 5 5 5.00 5.00 5.00 4.00 5.00 5.00 inc incNorthern red oak 132 180 H 140 115 134 114 130 69 0.78 0.64 0.74 0.63 0.72 0.38 dec DECOsage-orange** 0 1 M 1 2 1 1 1 6 1.00 2.00 1.00 1.00 1.00 6.00 NA NewOvercup oak** 0 1 M 0 9 1 48 3 66 0.00 9.00 1.00 48.00 3.00 66.00 NA NewPawpaw 9 23 L 9 7 6 1 11 1 0.39 0.30 0.26 0.04 0.48 0.04 DEC DECPecan** 0 49 L 0 5 2 6 6 13 0.00 0.10 0.04 0.12 0.12 0.27 NA NAPersimmon 38 59 M 75 127 75 219 89 263 1.27 2.15 1.27 3.71 1.51 4.46 inc INCPignut hickory 70 100 H 72 90 75 98 79 101 0.72 0.90 0.75 0.98 0.79 1.01 dec NoPin cherry 13 5 M 4 2 4 1 6 1 0.80 0.40 0.80 0.20 1.20 0.20 No decPin oak 19 20 M 15 44 14 40 41 50 0.75 2.20 0.70 2.00 2.05 2.50 INC INCPitch pine 533 402 H 408 331 337 341 324 362 1.02 0.82 0.84 0.85 0.81 0.90 No NoPond pine 6 1 H 5 32 5 55 21 57 5.00 32.00 5.00 55.00 21.00 57.00 inc incPost oak 21 64 H 125 211 127 351 148 358 1.95 3.30 1.98 5.48 2.31 5.59 INC INCQuaking aspen 9 8 H 2 2 2 2 2 2 0.25 0.25 0.25 0.25 0.25 0.25 DEC DECRed maple 1317 1253 H 1156 943 1154 641 1096 567 0.92 0.75 0.92 0.51 0.88 0.45 No DECRed mulberry* 9 6 L 8 11 7 12 10 56 1.33 1.83 1.17 2.00 1.67 9.33 No INC

(continued on next page)
APPENDIX 4

307

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 37 (continued).—Complete DISTRIB model results for 91 tree species in subregion 6 (Coastal Plain)

Redbay** 0 0 H 0 0 0 0 10 0 NA NA NA NA New NA New NARiver birch 28 13 L 16 17 16 18 22 21 1.23 1.31 1.23 1.39 1.69 1.62 inc incRock elm** 0 0 L 1 9 2 1 7 0 NA New NA NA New NA New NASassafras 218 239 H 200 176 173 103 194 95 0.84 0.74 0.72 0.43 0.81 0.40 No DECScarlet oak 198 191 H 172 106 151 48 139 51 0.90 0.56 0.79 0.25 0.73 0.27 dec DECScrub oak (bear oak) 23 17 L 15 13 13 14 13 16 0.88 0.77 0.77 0.82 0.77 0.94 dec NoServiceberry 4 0 M 3 1 1 1 2 0 0.75 0.25 0.25 0.25 0.50 0.00 dec DECShagbark hickory 3 13 M 10 29 18 44 30 68 0.77 2.23 1.39 3.39 2.31 5.23 INC INCShellbark hickory** 0 1 L 0 5 0 9 0 18 0.00 5.00 0.00 9.00 0.00 18.00 NA NewShingle oak* 1 3 M 3 5 2 9 5 12 1.00 1.67 0.67 3.00 1.67 4.00 No incShortleaf pine 33 60 H 75 96 71 175 72 212 1.25 1.60 1.18 2.92 1.20 3.53 inc INCShumard oak 0 0 L 0 5 0 54 0 70 NA New NA New NA New NA NewSilver maple 23 34 M 22 62 21 142 26 231 0.65 1.82 0.62 4.18 0.77 6.79 dec INCSlash pine 0 0 H 0 0 0 17 13 95 NA NA NA New New New New NewSlippery elm 7 15 M 8 20 11 20 17 44 0.53 1.33 0.73 1.33 1.13 2.93 No INCSourwood 1 5 H 23 5 38 0 44 2 4.60 1.00 7.60 0.00 8.80 0.40 INC decSouthern red oak 135 139 H 165 184 165 220 171 250 1.19 1.32 1.19 1.58 1.23 1.80 inc incSugar maple 3 119 H 91 127 102 45 153 8 0.77 1.07 0.86 0.38 1.29 0.07 inc DECSugarberry** 0 2 M 5 46 7 100 13 115 2.50 23.00 3.50 50.00 6.50 57.50 New NewSwamp chestnut oak 14 8 M 11 17 15 15 16 7 1.38 2.13 1.88 1.88 2.00 0.88 inc NoSwamp tupelo 3 16 H 20 57 53 70 132 72 1.25 3.56 3.31 4.38 8.25 4.50 INC INCSwamp white oak 14 10 L 4 5 2 4 4 6 0.40 0.50 0.20 0.40 0.40 0.60 DEC decSweet birch 9 23 H 5 5 5 6 6 2 0.22 0.22 0.22 0.26 0.26 0.09 DEC DECSweetbay 58 62 H 53 59 60 56 82 62 0.86 0.95 0.97 0.90 1.32 1.00 inc NoSweetgum 701 658 H 714 779 773 786 798 809 1.09 1.18 1.18 1.20 1.21 1.23 inc incSycamore 19 37 M 31 40 31 73 34 82 0.84 1.08 0.84 1.97 0.92 2.22 No INCTulip tree 495 441 H 427 259 392 167 340 145 0.97 0.59 0.89 0.38 0.77 0.33 dec DEC

(continued on next page)
APPENDIX 4

308

a FIA IV is the measured area-weighted importance values (IVs) as reported from Forest Inventory and Analysis. b Current Modeled IV (1971 through 2000) and Modeled IV for future time periods (2010 through 2039, 2040 through 2069, and 2070 through 2099) are simulated from the DISTRIB model; 

importance values are the sum of the average IV for each pixel in the subregion. 

c Model reliability for DISTRIB scores, which is based on statistically quantified measures of fitness (Matthews et al. 2011), is abbreviated L (low), M (medium), and H (high). d Future:Current suitable habitat is a ratio of projected importance value to current importance value. This is a measure of habitat change (not where a species will be), where a ratio of ~1 = no change; 

ratio<1 = decrease; ratio>1 = increase.

e Change classes are based on rules in Appendix 4 and abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), 

X (extirpated), and NA (not detected). 

* Species is present but rare.** Not observed in the Forest Inventory and Analysis data, but other data suggest species is present, but rare.

Modeled IV Future:Current suitable habitatd Change classe

2010 - 2039 2040 - 2069 2070 - 2099 2010 - 2039 2040 - 2069 2070 - 2099 2070-2099

Common name FIA IVa

Current 

IVb

Model 

reliabilityc

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

PCM 

B1

GFDL 

A1FI

Table 37 (continued).—Complete DISTRIB model results for 91 tree species in subregion 6 (Coastal Plain)

Turkey oak 0 0 H 0 1 1 0 26 8 NA NA NA NA New New New NewVirginia pine 286 271 H 225 132 181 105 155 97 0.83 0.49 0.67 0.39 0.57 0.36 dec DECWater hickory** 0 5 M 1 3 0 5 1 7 0.20 0.60 0.00 1.00 0.20 1.40 NA NAWater locust 0 0 L 0 0 0 18 0 19 NA NA NA New NA New NA NewWater oak 44 46 H 74 111 93 248 92 357 1.61 2.41 2.02 5.39 2.00 7.76 inc INCWater tupelo 9 21 M 14 37 38 53 54 72 0.67 1.76 1.81 2.52 2.57 3.43 INC INCWhite ash 164 159 H 116 140 145 114 156 122 0.73 0.88 0.91 0.72 0.98 0.77 No decWhite oak 496 542 H 509 421 463 333 437 299 0.94 0.78 0.85 0.61 0.81 0.55 No decWillow oak 54 57 M 60 105 70 160 75 189 1.05 1.84 1.23 2.81 1.32 3.32 inc INCWinged elm 0 14 H 60 183 90 330 124 352 4.29 13.07 6.43 23.57 8.86 25.14 New New
APPENDIX 4

309

Table 38.—Key to modifying factor codesa

Code Title Type Description (if positive) Description (if negative)

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

EHS Environmental habitat 

specificity

Biological Wide range of suitable habitat 

conditions

Narrow range of suitable habitat 

conditions

ESP Edaphic specificity Biological Wide range of soil tolerance Narrow range of soil requirements

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

TGR Temperature gradient Disturbance Has a high tolerance for a large 

variation in temperature

Has a low tolerance for a large variation 

in temperature

VRE Vegetative reproduction Biological Capable of vegetative reproduction 

through stump sprouts or cloning

N/A

a

 These codes are used to describe positive or negative modifying factors. A species was given a code if information from the literature suggested that 

it had these characteristics. See Matthews et al. (2011) for a more thorough description of these factors and how they were assessed.
APPENDIX 4

310

Table 39.—Modifying factora and adaptabilityb information for the 116 tree species in the assessment area that were 

modeled using DISTRIB

Modifying factors Adaptability scores

Common name

DISTRIB 

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 EHS DISE INS -0.8 0.3 4.0 ○ 

American holly High COL EHS FTK -0.1 0.5 4.5 ○ 

American hornbeam 

(musclewood)

Medium COL SES FTK DRO 0.6 0.6 5.1 ○ 

American mountain-ash Medium FTK COL EHS -0.2 -1.6 3.1 -

Atlantic white-cedar Low DISP FTK DRO EHS -0.6 -1.2 3.0 -

Baldcypress Medium DISP FTK 0.4 -1.0 3.9 ○ 

Balsam fir High COL INS FTK DRO -3.0 -0.4 2.7 -

Balsam poplar High FRG VRE COL DRO 0.1 -0.6 4.0 ○ 

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 EHS INS FTK COL -1.6 -0.3 3.0 -

Black hickory High EHS COL 1.0 -2.3 4.1 ○ 

Black locust Low COL INS 0.0 -0.6 3.8 ○ 

Black maple Low COL EHS FTK 0.5 0.9 5.2 ○ 

Black oak High DRO EHS INS DISE 0.5 0.4 4.9 ○ 

Black spruce High COL EHS DISP FTK INS DRO -2.1 1.2 4.3 ○ 

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 +

Bluejack oak Medium 0.7 0.0 4.8 ○ 

Boxelder Medium SES DISP DRO COL 

TGR

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 ○ 

Cherrybark oak Medium INS FTK -0.5 0.1 3.9 ○ 

Chestnut oak High SES VRE EHS FTK INS DISE 1.4 1.3 6.1 +

Chinkapin oak Medium TGR 1.2 -0.7 4.8 ○ 

Chokecherry Low COL 0.2 -0.9 3.8 ○ 

(continued on next page)
APPENDIX 4

311

Modifying factors Adaptability scores

Common name

DISTRIB 

model 

reliability Positive traits Negative traits DistFact BioFact Adapt Adapt Class

Cucumber tree High FTK 0.0 -1.1 3.6 ○ 

Eastern cottonwood Low TGR 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 EHS TGR 1.7 1.3 6.4 +

Eastern redbud Medium -- -- 0.9 0.0 4.9 ○ 

Eastern redcedar Medium DRO FTK COL INS 0.6 -1.5 3.9 ○ 

Eastern white pine High DISP DRO FTK INS -2.0 0.1 3.3 ○ 

Flowering dogwood High COL 0.1 1.0 5.0 ○ 

Gray birch Medium DISP EHS FTK COL INS DISE -1.1 0.0 3.6 ○ 

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 +

Jack pine High DRO COL INS 1.9 -1.2 5.2 ○ 

Laurel oak High COL TGR FTK 0.2 0.1 4.5 ○ 

Loblolly pine High EHS INS INP DRO COL -0.5 -0.7 3.4 ○ 

Longleaf pine High FTK COL 1.0 -1.7 4.2 ○ 

Mockernut hickory High FTK 1.7 -0.3 5.4 +

Mountain maple High COL VRE EHS DRO FTK 0.8 1.5 5.9 +

Northern catalpa Low COL EHS 0.9 -1.6 4.2 ○ 

Northern red oak High INS 1.4 0.1 5.4 +

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 EHS ESP 2.3 0.3 6.3 +

Overcup oak Medium FTK INS DRO -0.5 -1.0 3.2 -

Paper birch High FRG DISP EHS FTK COL INS DRO -1.7 0.2 3.4 ○ 

Pawpaw Low COL DRO -0.5 -0.3 3.7 ○ 

Pecan Low FTK INS COL -1.2 -1.7 2.2 -

Persimmon Medium COL EHS 1.2 1.0 5.8 +

Pignut hickory High EHS 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 ○ 

Pond pine High DRO COL INS DISP -1.1 -1.5 2.4 -

Post oak High DRO TGR FTK COL INS DISE 2.2 -0.6 5.7 +

Quaking aspen High TGR FRG EHS COL DRO FTK 0.6 0.0 4.7 ○ 

Red maple High SES EHS ESP COL 

DISP

3.0 3.0 8.5 +

(continued on next page)

Table 39 (continued).—Modifying factora and adaptabilityb information for the 116 tree species in the assessment 

area that were modeled using DISTRIB
APPENDIX 4

312

Modifying factors Adaptability scores

Common name

DISTRIB 

model 

reliability Positive traits Negative traits DistFact BioFact Adapt Adapt Class

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 EHS COL FTK SES -1.3 -0.6 2.9 -

Redbay High INS DISP COL 2.6 -0.1 6.3 +

River birch Low DISP FTK COL DRO -0.5 -0.3 3.7 ○ 

Rock elm Low EHS ESP SES -0.2 -2.6 2.8 -

Sassafras High COL FTK 0.5 -0.6 4.2 ○ 

Scarlet oak High VRE EHS ESP INS DISE FTK -0.4 0.7 4.6 ○ 

Scrub oak (bear oak) Low FRG VRE COL FTK 1.0 -0.8 4.6 ○ 

Serviceberry Medium COL SES DRO -0.4 1.0 4.8 ○ 

Shagbark hickory Medium INS FTK -0.2 0.4 4.4 ○ 

Shellbark hickory Low COL FTK EHS -0.5 -0.3 3.7 ○ 

Shingle oak Medium EHS COL 1.3 -0.7 4.9 ○ 

Shortleaf pine High EHS COL INS DRO 0.0 -1.0 3.6 ○ 

Shumard oak Low DRO TGR COL 2.5 -1.0 5.8 +

Silver maple Medium DISP SES COL DRO FTK 0.1 1.6 5.6 +

Slash pine High DISP FTK COL INS 1.1 -1.7 4.3 ○ 

Slippery elm Medium COL FTK DISE 0.0 0.7 4.8 ○ 

Sourwood High COL EHS 2.6 1.0 6.9 +

Southern red oak High TGR 1.2 0.2 5.3 +

Striped maple High COL SES DRO 1.0 0.3 5.1 ○ 

Sugar maple High COL EHS 0.9 1.3 5.8 +

Sugarberry Medium COL SES FTK -0.2 0.6 4.6 ○ 

Swamp chestnut oak Medium TGR COL INS 1.1 -0.8 4.6 ○ 

Swamp tupelo High DRO FTK COL EHS -0.7 -1.7 2.7 -

Swamp white oak Low -- -- 1.0 -0.3 4.9 ○ 

Sweet birch High DISP FTK COL INS DISE -1.3 -0.3 3.2 -

Sweetbay High FTK INS 1.4 -0.5 5.1 ○ 

Sweetgum High VRE EHS 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 High FTK COL INS -0.5 -1.2 3.1 -

Tulip tree High SES DISP EHS INP 0.1 1.3 5.3 +

Turkey oak High SES DRO TGR COL 2.6 -0.9 6.0 +

Virginia pine High COL POL 0.1 -0.8 3.8 ○ 

Water elm Low COL FTK EHS 0.1 -0.8 3.8 ○

Water hickory Medium FTK EHS 0.9 -2.0 4.0 ○ 

Water locust Low -- -- 0.0 -0.6 3.8 ○ 

(continued on next page)

Table 39 (continued).—Modifying factora and adaptabilityb information for the 116 tree species in the assessment 

area that were modeled using DISTRIB
APPENDIX 4

313

Modifying factors Adaptability scores

Common name

DISTRIB 

model 

reliability Positive traits Negative traits DistFact BioFact Adapt Adapt Class

Water oak High TGR FTK COL -0.2 -0.6 3.7 ○ 

Water tupelo Medium DRO FTK COL EHS -0.9 -2.1 2.3 -

White ash High INS FTK COL -2.0 -0.5 2.7 -

White oak High EHS ESP TGR FTK INS DISE 1.7 1.0 6.1 +

White spruce Medium INS 0.1 -0.6 3.9 ○ 

Willow oak Medium SES TGR 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 EHS 

DISP

0.0 -2.1 3.1 -

Table 39 (continued).—Modifying factora and adaptabilityb information for the 116 tree species in the assessment 

area that were modeled using DISTRIB

a

 Modifying factor codes are described in Table 38.

b

 Adaptability scores are described in the Appendix 4 text.
APPENDIX 4

314

LINKAGES MODEL RESULTS 

LINKAGES 3.0 was used to evaluate tree species 

growth potential and total biomass production under 

alternative climate scenarios. This information is 

utilized here to understand species potential under 

future climate. This information was also used to 

parameterize the forest landscape model LANDIS 

PRO, which is also used in this assessment to 

evaluate forest succession under climate change. 

Change in early growth is based on biomass 

predicted by the LINKAGES model after 30 years 

of establishment and growth from bare ground 

and calculated as predicted biomass for the future 

climate scenario divided by predicted biomass 

under current climate. Change values were put into 

categories. Break points for the change classes 

were calculated by first dividing the modeled future 

biomass by the current climate biomass. Change was 

classified according to the following divisions:

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

Future biomass projections for 24 tree species are 

presented for the assessment area as a whole and 

by subregion for the end-of-century period (2070 

through 2099) (Table 40). Early growth potential 

(first 30 years) was also mapped for each species 

modeled by LINKAGES (Fig. 56).
APPENDIX 4

315

Table 40.—Change in tree species growth potential measured in maximum biomass reached in 30 years starting 

from bare ground as projected by the LINKAGES model in the Mid-Atlantic region (MAR) and six subregions under a 

current climate scenario (1980-2009) and two climate model-emissions scenarios at the end of the century 

(2070-2099)

American 

beech

MAR 96.30 87.36 -8.93 -9 0.91 No 14.99 -81.30 -84 0.16 DEC

1 100.96 42.01 -58.95 -58 0.42 DEC 0.00 -100.96 -100 0.00 X

2 113.85 128.57 14.72 13 1.13 No 43.16 -70.69 -62 0.38 DEC

3 113.76 111.89 -1.87 -2 0.98 No 26.47 -87.29 -77 0.23 DEC

4 121.70 80.09 -41.61 -34 0.66 dec 8.23 -113.47 -93 0.07 DEC

5 91.34 100.68 9.33 10 1.10 No 10.50 -80.85 -89 0.11 DEC

6 14.08 41.14 27.06 192 2.92 INC 0.01 -14.07 -100 0.00 X

Balsam fir

MAR 2.74 0.89 -1.85 -67 0.33 DEC 0.00 -2.74 -100 0.00 X

1 1.33 1.51 0.18 13 1.13 No 0.00 -1.33 -100 0.00 X

2 5.59 0.08 -5.51 -99 0.01 DEC 0.00 -5.59 -100 0.00 X

3 4.06 1.58 -2.48 -61 0.39 DEC 0.00 -4.06 -100 0.00 X

4 4.71 1.35 -3.36 -71 0.29 DEC 0.00 -4.71 -100 0.00 X

5 0.49 0.10 -0.39 -80 0.20 DEC 0.00 -0.49 -100 0.00 X

6 - - - - - - - - - - -

Black cherry

MAR 106.58 114.52 7.94 7 1.07 No 71.47 -35.12 -33 0.67 dec

1 135.09 82.78 -52.31 -39 0.61 dec 133.74 -1.35 -1 0.99 No

2 142.16 172.40 30.24 21 1.21 inc 74.40 -67.76 -48 0.52 dec

3 94.62 120.29 25.67 27 1.27 inc 51.48 -43.14 -46 0.54 dec

4 130.43 91.40 -39.03 -30 0.70 dec 130.51 0.07 0 1.00 No

5 109.41 125.04 15.63 14 1.14 No 45.35 -64.05 -59 0.41 dec

6 37.09 105.16 68.07 184 2.84 INC 5.68 -31.41 -85 0.15 DEC

Black oak

MAR 88.74 101.42 12.68 14 1.14 No 60.13 -28.61 -32 0.68 dec

1 127.07 77.00 -50.08 -39 0.61 dec 41.10 -85.98 -68 0.32 DEC

2 97.05 132.90 35.85 37 1.37 inc 78.67 -18.37 -19 0.81 dec

3 41.92 88.48 46.56 111 2.11 INC 64.87 22.95 55 1.55 inc

4 135.25 91.06 -44.19 -33 0.67 dec 130.35 -4.90 -4 0.96 No

5 106.90 117.33 10.42 10 1.10 No 35.92 -70.99 -66 0.34 DEC

6 51.83 120.19 68.36 132 2.32 INC 0.19 -51.64 -100 0.00 X

(continued on next page)

Current 

climate PCM B1 GFDL A1FI

Tree species

Region or 

subregiona

Biomass 

in 2100 

(metric 

tons/

acre)

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%) F:Cb

Change 

classc

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%)b F:Cb

Change 

classc
APPENDIX 4

316

Black spruce

MAR 0.27 0.04 -0.24 -86 0.14 DEC 0.00 -0.27 -100 0.00 X

1 - - - - - - - - - - -

2 0.38 0.00 -0.38 -100 0.00 X 0.00 -0.38 -100 0.00 X

3 0.66 0.11 -0.55 -83 0.17 DEC 0.00 -0.66 -100 0.00 X

4 - - - - - - - - - - -

5 0.09 0.01 -0.08 -91 0.09 DEC 0.00 -0.09 -100 0.00 X

6 - - - - - - - - - - -

Chestnut 

oak

MAR 84.95 101.08 16.13 19 1.19 No 71.39 -13.56 -16 0.84 No

1 124.85 78.94 -45.91 -37 0.63 dec 40.57 -84.28 -68 0.32 DEC

2 87.88 145.02 57.14 65 1.65 inc 110.30 22.42 26 1.26 inc

3 19.71 64.43 44.73 227 3.27 INC 98.04 78.33 397 4.97 INC

4 121.39 85.43 -35.96 -30 0.70 dec 131.06 9.66 8 1.08 No

5 117.27 133.08 15.81 13 1.13 No 30.65 -86.62 -74 0.26 DEC

6 81.80 140.71 58.90 72 1.72 inc 0.04 -81.76 -100 0.00 X

Eastern 

hemlock

MAR 19.05 17.34 -1.71 -9 0.91 No 0.93 -18.12 -95 0.05 DEC

1 39.94 19.47 -20.47 -51 0.49 DEC 0.00 -39.94 -100 0.00 X

2 18.86 23.78 4.92 26 1.26 inc 0.05 -18.81 -100 0.00 X

3 6.86 13.05 6.19 90 1.90 inc 0.12 -6.74 -98 0.02 DEC

4 52.81 48.16 -4.64 -9 0.91 No 5.27 -47.53 -90 0.10 DEC

5 4.39 3.07 -1.32 -30 0.70 dec 0.04 -4.35 -99 0.01 DEC

6 0.20 0.03 -0.17 -86 0.14 DEC 0.00 -0.20 -100 0.00 X

Eastern 

white pine

MAR 50.47 53.26 2.79 6 1.06 No 3.69 -46.78 -93 0.07 DEC

1 63.90 56.04 -7.86 -12 0.88 No 0 -63.90 -100 0.00 X

2 54.02 81.45 27.42 51 1.51 inc 1.93 -52.09 -96 0.04 DEC

3 43.20 74.66 31.46 73 1.73 inc 2.34 -40.87 -95 0.05 DEC

4 85.17 76.80 -8.36 -10 0.90 No 16.01 -69.16 -81 0.19 DEC

5 47.39 20.65 -26.73 -56 0.44 dec 0.89 -46.49 -98 0.02 DEC

6 7.59 0.27 -7.31 -96 0.04 DEC 0 -7.59 -100 0.00 X

(continued on next page)

Current 

climate PCM B1 GFDL A1FI

Tree species

Region or 

subregiona

Biomass 

in 2100 

(metric 

tons/

acre)

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%) F:Cb

Change 

classc

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%)b F:Cb

Change 

classc

Table 40 (continued).—Change in tree species growth potential measured in maximum biomass reached in 30 years 

starting from bare ground as projected by the LINKAGES model in the Mid-Atlantic region (MAR) and six subregions 

under a current climate scenario (1980-2009) and two climate model-emissions scenarios at the end of the century 

(2070-2099)
APPENDIX 4

317

Loblolly pine

MAR 42.84 55.62 12.78 30 1.30 inc 87.13 44.29 103 2.03 INC

1 93.72 92.15 -1.56 -2 0.98 No 231.70 137.98 147 2.47 INC

2 0 0.01 0.01 0 - - 25.68 25.68 New - New

3 0 0 0 0 - - 4.69 4.69 New - New

4 74.30 87.01 12.71 17 1.17 No 242.73 168.43 227 3.27 INC

5 14.92 76.05 61.14 410 5.10 INC 51.58 36.67 246 3.46 INC

6 113.64 106.86 -6.78 -6 0.94 No 3.65 -109.98 -97 0.03 DEC

Northern 

red oak

MAR 147.06 136.18 -10.87 -7 0.93 No 83.25 -63.81 -43 0.57 dec

1 141.79 89.83 -51.95 -37 0.63 dec 41.95 -99.84 -70 0.30 DEC

2 157.00 160.05 3.05 2 1.02 No 139.49 -17.51 -11 0.89 No

3 153.29 156.07 2.78 2 1.02 No 125.66 -27.63 -18 0.82 No

4 144.13 99.49 -44.64 -31 0.69 dec 131.49 -12.64 -9 0.91 No

5 151.86 150.51 -1.36 -1 0.99 No 36.43 -115.43 -76 0.24 DEC

6 128.18 151.85 23.67 18 1.18 No 0.02 -128.16 -100 0.00 X

Northern 

white-cedar

MAR 2.38 0.49 -1.89 -79 0.21 DEC 0 -2.38 -100 0.00 X

1 - - - - - - - - - - -

2 4.35 0.33 -4.01 -92 0.08 DEC 0 -4.35 -100 0.00 X

3 4.47 1.36 -3.11 -69 0.31 DEC 0 -4.47 -100 0.00 X

4 1.60 0 -1.60 -100 0.00 X 0 -1.60 -100 0.00 X

5 0.61 0.08 -0.53 -87 0.13 DEC 0 -0.61 -100 0.00 X

6 - - - - - - - - - - -

Pignut 

hickory

MAR 85.62 98.38 12.76 15 1.15 No 60.09 -25.53 -30 0.70 dec

1 116.29 76.13 -40.16 -35 0.65 dec 128.05 11.77 10 1.10 No

2 102.65 132.38 29.73 29 1.29 inc 57.89 -44.77 -44 0.56 dec

3 63.22 99.06 35.84 57 1.57 inc 40.58 -22.64 -36 0.64 dec

4 117.59 85.25 -32.34 -28 0.72 dec 116.85 -0.74 -1 0.99 No

5 99.09 110.56 11.47 12 1.12 No 29.42 -69.67 -70 0.30 DEC

6 24.14 92.96 68.82 285 3.85 INC 1.02 -23.11 -96 0.04 DEC

(continued on next page)

Current 

climate PCM B1 GFDL A1FI

Tree species

Region or 

subregiona

Biomass 

in 2100 

(metric 

tons/

acre)

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%) F:Cb

Change 

classc

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%)b F:Cb

Change 

classc

Table 40 (continued).—Change in tree species growth potential measured in maximum biomass reached in 30 years 

starting from bare ground as projected by the LINKAGES model in the Mid-Atlantic region (MAR) and six subregions 

under a current climate scenario (1980-2009) and two climate model-emissions scenarios at the end of the century 

(2070-2099)
APPENDIX 4

318

Pitch pine

MAR 35.88 27.42 -8.46 -24 0.76 dec 7.86 -28.01 -78 0.22 DEC

1 - - - - - - - - - - -

2 46.71 53.94 7.24 15 1.15 No 7.75 -38.96 -83 0.17 DEC

3 29.49 43.97 14.48 49 1.49 inc 20.33 -9.16 -31 0.69 dec

4 48.89 2.01 -46.89 -96 0.04 DEC 0.00 -48.89 -100 0.00 X

5 39.86 29.31 -10.55 -26 0.74 dec 2.34 -37.52 -94 0.06 DEC

6 17.78 1.48 -16.29 -92 0.08 DEC 0 -17.78 -100 0.00 X

Quaking 

aspen

MAR 86.96 55.20 -31.77 -37 0.63 DEC 0 -86.96 -100 0.00 X

1 - - - - - - - - - - -

2 129.86 101.24 -28.62 -22 0.78 dec 0 -129.86 -100 0.00 X

3 118.84 122.63 3.79 3 1.03 No 0 -118.84 -100 0.00 X

4 131.16 0 -131.16 -100 0.00 X 0 -131.16 -100 0.00 X

5 38.27 20.07 -18.20 -48 0.52 dec 0 -38.27 -100 0.00 X

6 - - - - - - - - - - -

Red maple

MAR 131.36 135.82 4.46 3 1.03 No 89.96 -41.40 -32 0.68 dec

1 144.40 117.09 -27.32 -19 0.81 No 155.40 11.00 8 1.08 No

2 148.23 158.32 10.10 7 1.07 No 92.72 -55.51 -37 0.63 dec

3 137.74 146.17 8.43 6 1.06 No 71.34 -66.40 -48 0.52 dec

4 145.37 119.93 -25.44 -18 0.82 No 149.64 4.27 3 1.03 No

5 138.36 140.04 1.68 1 1.01 No 68.86 -69.50 -50 0.50 dec

6 61.20 130.71 69.51 114 2.14 INC 9.59 -51.61 -84 0.16 DEC

Red spruce

MAR 1.89 0.78 -1.11 -59 0.41 dec 0 -1.89 -100 0.00 X

1 0 1.65 1.65 New - New - - - - -

2 3.86 0.24 -3.62 -94 0.06 DEC 0 -3.86 -100 0.00 X

3 2.79 1.15 -1.64 -59 0.41 dec 0 -2.79 -100 0.00 X

4 3.86 1.19 -2.67 -69 0.31 DEC 0 -3.86 -100 0.00 X

5 0.48 0.07 -0.40 -84 0.16 DEC 0 -0.48 -100 0.00 X

6 - - - - - - - - - - -

(continued on next page)

Current 

climate PCM B1 GFDL A1FI

Tree species

Region or 

subregiona

Biomass 

in 2100 

(metric 

tons/

acre)

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%) F:Cb

Change 

classc

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%)b F:Cb

Change 

classc

Table 40 (continued).—Change in tree species growth potential measured in maximum biomass reached in 30 years 

starting from bare ground as projected by the LINKAGES model in the Mid-Atlantic region (MAR) and six subregions 

under a current climate scenario (1980-2009) and two climate model-emissions scenarios at the end of the century 

(2070-2099)
APPENDIX 4

319

Scarlet oak

MAR 91.38 99.62 8.24 9 1.09 No 58.49 -32.89 -36 0.64 dec

1 129.67 79.14 -50.54 -39 0.61 dec 45.58 -84.09 -65 0.35 DEC

2 99.39 125.76 26.37 27 1.27 inc 74.63 -24.76 -25 0.75 dec

3 54.38 94.31 39.93 73 1.73 inc 63.72 9.34 17 1.17 No

4 136.99 90.99 -46.00 -34 0.66 dec 135.33 -1.66 -1 0.99 No

5 100.31 109.93 9.63 10 1.10 No 23.62 -76.69 -76 0.24 DEC

6 48.89 108.24 59.35 121 2.21 INC 0.09 -48.80 -100 0.00 X

Shagbark 

hickory

MAR 60.82 83.28 22.46 37 1.37 inc 18.96 -41.86 -69 0.31 DEC

1 - - - - - - - - - - -

2 75.38 111.75 36.37 48 1.48 inc 45.25 -30.13 -40 0.60 dec

3 33.29 67.81 34.51 104 2.04 INC 33.82 0.52 2 1.02 No

4 104.74 97.60 -7.14 -7 0.93 No 0.00 -104.74 -100 0.00 X

5 78.85 90.47 11.62 15 1.15 No 13.1 -65.78 -83 0.17 DEC

6 23.10 63.91 40.81 177 2.77 INC 0.0 -23.08 -100 0.00 X

Sugar maple

MAR 123.80 107.84 -15.97 -13 0.87 No 28.89 -94.91 -77 0.23 DEC

1 128.77 91.26 -37.51 -29 0.71 dec 0.00 -128.77 -100 0.00 X

2 134.29 137.50 3.21 2 1.02 No 54.36 -79.94 -60 0.40 dec

3 132.99 134.41 1.42 1 1.01 No 69.41 -63.58 -48 0.52 dec

4 132.53 114.62 -17.91 -14 0.86 No 13.13 -119.41 -90 0.10 DEC

5 127.40 113.21 -14.19 -11 0.89 No 11.85 -115.55 -91 0.09 DEC

6 73.58 28.58 -44.99 -61 0.39 DEC 0 -73.58 -100 0.00 X

Tulip tree

MAR 182.12 220.43 38.31 21 1.21 inc 207.85 25.73 14 1.14 No

1 229.25 178.62 -50.63 -22 0.78 dec 298.35 69.10 30 1.30 inc

2 201.99 272.97 70.98 35 1.35 inc 243.66 41.66 21 1.21 inc

3 78.41 183.74 105.33 134 2.34 INC 205.59 127.18 162 2.62 INC

4 225.95 190.65 -35.29 -16 0.84 No 285.86 59.92 27 1.27 inc

5 238.38 264.97 26.59 11 1.11 No 186.33 -52.05 -22 0.78 dec

6 193.89 274.41 80.52 42 1.42 inc 15.47 -178.42 -92 0.08 DEC

(continued on next page)

Current 

climate PCM B1 GFDL A1FI

Tree species

Region or 

subregiona

Biomass 

in 2100 

(metric 

tons/

acre)

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%) F:Cb

Change 

classc

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%)b F:Cb

Change 

classc

Table 40 (continued).—Change in tree species growth potential measured in maximum biomass reached in 30 years 

starting from bare ground as projected by the LINKAGES model in the Mid-Atlantic region (MAR) and six subregions 

under a current climate scenario (1980-2009) and two climate model-emissions scenarios at the end of the century 

(2070-2099)
APPENDIX 4

320

Virginia pine

MAR 12.34 29.42 17.08 138 2.38 INC 6.01 -6.33 -51 0.49 dec

1 - - - - - - - - - - -

2 1.58 32.55 30.97 1965 20.65 INC 19.20 17.63 1118 12.18 INC

3 0.07 2.22 2.15 2903 30.03 New 8.74 8.66 11707 118.07 New

4 9.23 68.75 59.53 645 7.45 INC 0 -9.23 -100 0.00 X

5 32.46 45.68 13.22 41 1.41 inc 4.39 -28.07 -86 0.14 DEC

6 20.89 8.83 -12.06 -58 0.42 dec 0 -20.88 -100 0.00 X

White ash

MAR 159.45 166.85 7.40 5 1.05 No 93.01 -66.44 -42 0.58 dec

1 162.33 129.41 -32.92 -20 0.80 dec 55.27 -107.06 -66 0.34 DEC

2 193.09 199.36 6.27 3 1.03 No 147.96 -45.13 -23 0.77 dec

3 174.88 182.88 8.01 5 1.05 No 109.53 -65.35 -37 0.63 dec

4 153.81 131.62 -22.19 -14 0.86 No 159.97 6.16 4 1.04 No

5 171.36 175.50 4.15 2 1.02 No 70.74 -100.61 -59 0.41 DEC

6 86.10 180.76 94.66 110 2.10 INC 0.68 -85.42 -99 0.01 DEC

White oak

MAR 130.63 124.12 -6.51 -5 0.95 No 116.08 -14.56 -11 0.89 No

1 126.84 77.13 -49.71 -39 0.61 dec 135.14 8.30 7 1.07 No

2 141.69 148.95 7.26 5 1.05 No 134.83 -6.85 -5 0.95 No

3 122.37 139.96 17.59 14 1.14 No 125.22 2.85 2 1.02 No

4 132.59 86.90 -45.69 -34 0.66 dec 131.29 -1.31 -1 0.99 No

5 141.25 142.20 0.95 1 1.01 No 116.15 -25.10 -18 0.82 No

6 125.03 143.44 18.40 15 1.15 No 40.81 -84.22 -67 0.33 DEC

Yellow birch

MAR 96.74 61.54 -35.19 -36 0.64 dec 0.03 -96.71 -100 0.00 X

1 - - - - - - - - - - -

2 131.00 128.23 -2.77 -2 0.98 No 0 -131.00 -100 0.00 X

3 128.94 124.67 -4.26 -3 0.97 No 0 -128.94 -100 0.00 X

4 133.59 0 -133.59 -100 0.00 X 0 -133.59 -100 0.00 X

5 65.06 31.33 -33.72 -52 0.48 dec 0.14 -64.92 -100 0.00 X

6 - - - - - - - - - - -

Current 

climate PCM B1 GFDL A1FI

Tree species

Region or 

subregiona

Biomass 

in 2100 

(metric 

tons/

acre)

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%) F:Cb

Change 

classc

Biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(metric 

tons/

acre)

Change 

from 

current 

climate 

biomass 

in 2100 

(%)b F:Cb

Change 

classc

Table 40 (continued).—Change in tree species growth potential measured in maximum biomass reached in 30 years 

starting from bare ground as projected by the LINKAGES model in the Mid-Atlantic region (MAR) and six subregions 

under a current climate scenario (1980-2009) and two climate model-emissions scenarios at the end of the century 

(2070-2099)

a

 Subregions: 1—Western Allegheny Plateau, 2—Erie and Ontario Lake Plain, 3—Northern Allegheny Plateau, 4—Ridge and Valley, 5—Piedmont, 

6—Coastal Plain. See Figure 38 (Chapter 6, p. 144) for locations.

b

 F:C is the ratio of biomass projected under the climate model-emissions scenario to biomass projected under a current climate scenario for the 

period 2070 through 2100.

c

 Change classes are abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new 

habitat), and X (extirpated). Dash (-) indicates not present.
APPENDIX 4

321

Figure 56.—Change in growth potential projected by the LINKAGES model for 30 tree species under two climate modelemissions scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009).
APPENDIX 4

322

Figure 56 (continued).—Change in growth potential projected by the LINKAGES model for 30 tree species under two climate 

model-emissions scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009).
APPENDIX 4

323

Figure 56 (continued).—Change in growth potential projected by the LINKAGES model for 30 tree species under two climate 

model-emissions scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009).
APPENDIX 4

324

Figure 56 (continued).—Change in growth potential projected by the LINKAGES model for 30 tree species under two climate 

model-emissions scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009).
APPENDIX 4

325

LANDIS PRO MODEL RESULTS

In contrast to predictions by LINKAGES, LANDIS 

PRO simulates stand- and landscape-level processes 

such as competition, management, seed dispersal, 

and disturbance. In the following scenarios, 

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.

“Change from 2000 under current climate” 

represents the difference in basal area and trees 

per acre between a future climate period and at the 

year 2000 due to succession and management, but 

not climate. “Change from current climate” under 

PCM B1 and GFDL A1FI represents the difference 

in basal area and trees per acre at a particular future 

time period and represents the potential change 

due to climate change alone. In both cases, it is 

important for the reader to consider both the absolute 

and the percent changes, especially if considering 

multiple species. Percent changes are relative only to 

a particular species and may exaggerate a projected 

change, especially if the species is currently low in 

abundance or density. Furthermore, the effects of 

climate change are calculated from the effects of 

succession and management during a 30-year period. 

Therefore, it may be useful for the reader to examine 

the change in succession and management while 

interpreting the change under the two future climate 

change scenarios. 

Change classes are also presented to assist the reader 

in interpreting the percent change from current 

climate under each scenario for each time period and 

are based on percent change, as follows: 

Percent change in basal 

area or trees per acre Class

Abbreviation 

for change class

-100% extirpated X

-41% to -99% large decrease DEC

-21% to -40% small decrease Dec

-20% to +20% no change No

+21% to +100% small increase Inc

+101% or greater large increase INC

Future tree abundance (basal area) and density 

(trees per acre) were projected for 24 common tree 

species within the assessment area by subregion 

for 4 years: 2040 (Table 41), 2070 (Table 42), 2100 

(Table 43), and 2200 (Table 44). Estimated and 

projected abundance are graphed in Figure 57 on 

pages 350 and 351. Relative amount and direction of 

change in projected tree abundance at year 2100 was 

also mapped for each species modeled by LANDIS 

PRO, except for loblolly pine and Virginia pine 

(Fig. 58, beginning on page 352).

LITERATURE CITED

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): 1460-1472.

https://doi.org/10.1016/j.foreco.2011.06.047.
APPENDIX 4

326Table 41.—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate model-emissions scenario combinations 

in the year 2040

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2040 

(ft2/

acre)

Change 

from 

2000b

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2040

Change 

from 

2000b

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

American beech

1 2.5 2.1 -16% 2.1 0% No 2.1 -2% No 10.9 3.6 -67% 3.6 0% No 3.3 -9% No

2 3.3 4.5 35% 4.5 0% No 4.5 0% No 21.4 13.9 -35% 13.9 0% No 14.1 1% No

3 7.0 10.2 47% 10.2 0% No 10.2 0% No 58.6 34.3 -41% 32.0 -7% No 37.5 9% No

4 1.7 1.2 -29% 1.2 0% No 1.1 -4% No 8.4 2.0 -76% 2.0 1% No 1.8 -10% No

5 2.0 3.0 47% 3.0 0% No 3.0 0% No 18.4 8.1 -56% 7.8 -3% No 9.4 16% No6 1.9 2.0 7% 2.0 0% No 2.0 0% No 10.2 3.8 -63% 3.8 1% No 4.0 4% No

Balsam fir

1 - - - - - - - - - - - - - - - - - -

2 0.3 0.4 8% 0.4 0% No 0.4 0% No 3.6 0.9 -74% 0.9 -4% No 1.0 3% No

3 0.0 0.0 16% 0.0 0% No 0.0 0% No 0.1 0.0 -74% 0.0 0% No 0.0 1% No4 - - - - - - - - - - - - - - - - - -

5 0.1 0.1 -4% 0.1 0% No 0.1 0% No 0.8 0.2 -79% 0.2 -2% No 0.2 2% No

6 - - - - - - - - - - - - - - - - - -

Black cherry

1 18.8 34.3 82% 34.3 0% No 34.2 0% No 58.0 42.3 -27% 42.2 0% No 41.7 -1% No

2 7.2 6.0 -17% 6.0 0% No 6.0 0% No 17.5 39.4 125% 38.8 -2% No 40.7 3% No

3 8.2 5.5 -33% 5.5 0% No 5.5 0% No 21.7 23.7 9% 22.6 -4% No 25.4 7% No

4 10.2 18.0 77% 18.0 0% No 17.9 0% No 34.5 17.4 -50% 17.6 1% No 17.5 0% No

5 2.5 5.4 115% 5.4 0% No 5.4 0% No 8.7 22.6 159% 22.4 -1% No 24.2 7% No6 1.0 2.6 173% 2.6 0% No 2.6 0% No 6.4 9.7 52% 10.2 5% No 10.6 9% No

Black oak

1 2.3 3.6 58% 3.6 0% No 3.6 0% No 4.5 3.5 -22% 3.5 0% No 3.4 -3% No2 0.2 0.1 -23% 0.1 0% No 0.1 0% No 0.2 0.6 284% 0.6 0% No 0.6 3% No

3 1.2 0.7 -38% 0.7 0% No 0.7 0% No 1.6 1.7 5% 1.7 2% No 1.8 9% No

4 4.0 6.1 50% 6.1 0% No 6.1 0% No 5.9 3.4 -42% 3.5 1% No 3.4 1% No5 3.2 2.8 -14% 2.8 0% No 2.8 0% No 2.7 9.7 254% 9.7 -1% No 9.9 1% No

6 1.6 2.2 38% 2.2 0% No 2.2 0% No 3.0 6.4 113% 6.6 3% No 6.6 4% No

(continued on next page)
APPENDIX 4

327

Table 41 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate model-emissions scenario combinations in the year 2040

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2040 

(ft2/

acre)

Change 

from 

2000b

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2040

Change 

from 

2000b

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

Black spruce

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -13% 0.0 0% No 0.0 0% No 0.2 0.1 -13% 0.1 -15% No 0.1 -1% No3 - - - - - - - - - - - - - - - - - -

4 - - - - - - - - - - - - - - - - - -

5 0.0 0.0 -22% 0.0 0% No 0.0 0% No 0.0 0.0 -61% 0.0 -29% dec 0.0 -29% dec

6 - - - - - - - - - - - - - - - - - -

Chestnut oak

1 2.8 2.6 -10% 2.6 0% No 2.5 -3% No 5.7 10.7 89% 10.7 0% No 9.6 -11% No

2 0.3 0.4 55% 0.4 0% No 0.4 0% No 0.3 2.5 722% 2.7 6% No 3.1 22% inc

3 3.4 4.4 29% 4.4 0% No 4.4 0% No 5.1 14.2 178% 16.8 19% No 21.3 50% inc4 13.4 11.0 -18% 11.0 0% No 10.5 -5% No 31.9 24.6 -23% 26.1 6% No 25.5 4% No

5 4.6 8.5 84% 8.5 0% No 8.5 0% No 6.4 51.9 705% 50.8 -2% No 57.4 11% No6 1.5 2.7 76% 2.7 0% No 2.7 0% No 3.6 17.0 369% 17.9 6% No 18.6 10% No

Eastern hemlock

1 6.4 4.3 -32% 4.3 0% No 4.2 -2% No 18.8 5.9 -68% 5.9 -1% No 5.4 -9% No2 4.8 5.7 19% 5.7 0% No 5.7 0% No 22.9 10.9 -53% 11.3 4% No 10.5 -4% No

3 9.1 9.4 3% 9.4 0% No 9.4 0% No 24.6 9.8 -60% 9.5 -3% No 10.3 5% No

4 5.2 3.4 -33% 3.4 -1% No 3.3 -4% No 18.0 4.2 -77% 4.3 3% No 3.8 -9% No

5 2.9 2.9 -1% 2.9 0% No 2.9 0% No 12.9 3.3 -75% 3.1 -5% No 3.3 2% No

6 0.1 0.1 -20% 0.1 0% No 0.1 0% No 0.2 0.0 -80% 0.0 7% No 0.0 35% inc

Eastern white pine

1 1.6 1.8 15% 1.8 0% No 1.7 -5% No 5.9 11.5 93% 11.5 1% No 9.3 -19% No2 2.2 3.1 40% 3.1 0% No 3.1 0% No 3.1 18.8 499% 19.3 3% No 18.0 -4% No3 6.5 7.2 11% 7.2 0% No 7.2 0% No 14.1 27.9 98% 27.0 -3% No 28.1 1% No

4 2.6 2.6 1% 2.6 0% No 2.4 -6% No 13.1 12.6 -4% 12.7 1% No 10.2 -19% No

5 3.6 4.6 27% 4.6 0% No 4.6 0% No 9.0 16.4 82% 15.4 -6% No 16.0 -2% No

6 0.3 0.3 10% 0.3 0% No 0.3 0% No 0.5 0.5 4% 0.5 -4% No 0.6 4% No

(continued on next page)
APPENDIX 4

328Table 41 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate model-emissions scenario 

combinations in the year 2040

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2040 

(ft2/

acre)

Change 

from 

2000b

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2040

Change 

from 

2000b

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

Loblolly pine

1 0.0 0.0 398% 0.0 2% No 0.0 3% No 0.1 0.0 -97% 0.0 10% No 0.0 15% No

2 - - - - - - - - - - - - - - - - - -

3 - - - - - - - - - - - - - - - - - -

4 0.0 0.0 117% 0.0 2% No 0.0 9% No 0.1 0.0 -94% 0.0 9% No 0.0 42% inc

5 0.1 0.1 110% 0.1 0% No 0.1 0% No 0.2 0.3 71% 0.3 -3% No 0.4 15% No

6 10.2 12.1 18% 12.1 0% No 12.1 0% No 29.0 45.5 57% 45.4 0% No 50.1 10% No

Northern red oak

1 7.3 6.6 -10% 6.6 0% No 6.5 -1% No 14.9 6.1 -59% 6.6 8% No 6.9 13% No

2 1.9 1.9 -3% 1.9 0% No 1.9 0% No 3.1 5.7 84% 5.4 -4% No 6.1 8% No3 7.6 6.5 -15% 6.5 0% No 6.5 0% No 9.7 15.1 56% 14.3 -6% No 16.2 7% No

4 11.7 12.4 6% 12.4 0% No 12.2 -2% No 22.3 9.2 -59% 9.5 3% No 9.9 8% No

5 6.0 4.9 -17% 4.9 0% No 4.9 0% No 6.8 7.9 15% 7.6 -3% No 8.3 5% No6 1.1 0.8 -23% 0.8 0% No 0.8 0% No 2.1 1.9 -9% 1.9 1% No 2.1 9% No

Northern whitecedar

1 - - - - - - - - - - - - - - - - - -

2 0.4 0.3 -22% 0.3 0% No 0.3 0% No 7.2 2.3 -67% 2.4 2% No 2.6 13% No

3 0.0 0.0 -18% 0.0 0% No 0.0 0% No 0.1 0.1 -53% 0.0 -45% DEC 0.1 3% No

4 - - - - - - - - - - - - - - - - - -

5 0.0 0.0 18% 0.0 0% No 0.0 0% No 0.0 0.0 52% 0.0 -7% No 0.0 3% No

6 - - - - - - - - - - - - - - - - - -

Pignut hickory

1 0.7 0.7 5% 0.7 0% No 0.7 -1% No 1.8 1.5 -16% 1.5 0% No 1.5 -1% No

2 0.4 0.5 37% 0.5 0% No 0.5 0% No 1.2 1.7 45% 1.7 -2% No 1.9 9% No

3 0.6 0.9 49% 0.9 0% No 0.9 0% No 1.8 2.2 23% 2.2 0% No 2.3 4% No

4 1.1 1.3 12% 1.3 0% No 1.2 -1% No 4.6 2.0 -57% 2.1 5% No 2.1 4% No5 1.4 2.2 55% 2.2 0% No 2.2 0% No 5.2 6.6 28% 6.4 -3% No 7.2 10% No

6 0.4 0.4 21% 0.4 0% No 0.4 0% No 1.3 1.0 -21% 1.1 5% No 1.1 5% No

(continued on next page)
APPENDIX 4

329

Table 41 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate model-emissions scenario combinations in the year 2040

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2040 

(ft2/

acre)

Change 

from 

2000b

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2040

Change 

from 

2000b

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

Pitch pine

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 23% 0.0 0% No 0.0 0% No 0.0 0.1 455% 0.0 -21% dec 0.1 -11% No3 0.4 0.6 51% 0.6 0% No 0.6 0% No 1.0 1.9 85% 1.7 -9% No 2.1 9% No

4 - - - - - - - - - - - - - - - - - -

5 2.7 7.6 179% 7.6 0% No 7.6 0% No 11.8 62.8 432% 62.8 0% No 66.8 6% No6 9.5 16.0 68% 16.0 0% No 16.0 0% No 36.9 82.2 123% 84.4 3% No 93.9 14% No

Quaking aspen

1 - - - - - - - - - - - - - - - - - -

2 2.4 7.6 209% 7.6 0% No 7.6 0% No 5.9 79.6 1243% 76.1 -4% No 78.0 -2% No3 1.7 4.2 150% 4.2 0% No 4.2 0% No 5.5 36.3 557% 33.5 -8% No 35.2 -3% No4 - - - - - - - - - - - - - - - - - -

5 0.9 1.4 56% 1.4 0% No 1.4 0% No 1.7 9.8 490% 9.5 -3% No 9.6 -2% No

6 0.0 0.0 417% 0.0 0% No 0.0 0% No 0.0 0.0 -38% 0.0 0% No 0.0 2% No

Red maple

1 20.6 38.4 86% 38.4 0% No 38.4 0% No 95.2 71.4 -25% 71.6 0% No 71.5 0% No

2 12.3 14.5 18% 14.5 0% No 14.5 0% No 59.9 142.6 138% 141.4 -1% No 146.9 3% No

3 22.5 22.1 -2% 22.1 0% No 22.1 0% No 81.3 72.0 -11% 69.2 -4% No 75.2 4% No

4 18.1 31.6 75% 31.6 0% No 31.4 -1% No 105.7 56.9 -46% 58.0 2% No 57.5 1% No

5 8.7 13.4 55% 13.4 0% No 13.4 0% No 39.3 88.2 124% 88.2 0% No 90.6 3% No6 11.9 15.0 26% 15.0 0% No 15.0 0% No 61.4 102.7 67% 108.8 6% No 106.0 3% No

Red spruce

1 0.3 0.1 -66% 0.1 0% No 0.1 0% No 1.0 0.1 -89% 0.1 0% No 0.1 0% No

2 0.4 0.8 93% 0.8 0% No 0.8 0% No 4.6 5.4 18% 4.6 -14% No 5.0 -7% No

3 0.2 0.2 13% 0.2 0% No 0.2 0% No 2.2 0.8 -63% 0.7 -14% No 0.8 2% No

4 0.2 0.1 -56% 0.1 -1% No 0.1 -2% No 0.6 0.2 -62% 0.2 -14% No 0.2 -17% No5 0.4 0.5 22% 0.5 0% No 0.5 0% No 1.9 1.1 -45% 1.0 -10% No 1.1 -2% No6 - - - - - - - - - - - - - - - - - -

(continued on next page)
APPENDIX 4

330

Scarlet oak

1 1.2 1.9 58% 1.9 0% No 1.9 0% No 2.0 1.3 -33% 1.3 0% No 1.3 -4% No

2 0.0 0.0 74% 0.0 0% No 0.0 0% No 0.0 0.0 267% 0.0 -5% No 0.0 -4% No

3 0.6 0.8 36% 0.8 0% No 0.8 0% No 1.3 1.8 41% 1.8 -1% No 1.8 4% No

4 3.0 4.8 63% 4.8 0% No 4.8 0% No 6.4 3.4 -47% 3.4 1% No 3.3 -1% No5 1.6 3.5 120% 3.5 0% No 3.5 0% No 3.9 11.5 193% 11.5 0% No 11.7 2% No6 2.1 3.3 55% 3.3 0% No 3.3 0% No 4.7 8.8 87% 9.0 2% No 9.1 3% No

Shagbark hickory

1 - - - - - - - - - - - - - - - - - -

2 0.8 1.7 115% 1.7 0% No 1.7 0% No 5.6 8.2 46% 8.2 0% No 8.6 6% No

3 0.6 0.7 26% 0.7 0% No 0.7 0% No 1.5 1.7 12% 1.7 -3% No 1.9 11% No

4 - - - - - - - - - - - - - - - - - -

5 0.5 0.9 71% 0.9 0% No 0.9 0% No 1.8 2.7 52% 2.6 -3% No 2.9 7% No

6 0.0 0.0 -24% 0.0 0% No 0.0 0% No 0.0 0.0 7% 0.0 23% inc 0.0 53% inc

Sugar maple

1 5.9 5.3 -10% 5.3 0% No 5.2 -2% No 28.7 12.9 -55% 12.8 -1% No 11.3 -13% No

2 10.6 11.8 11% 11.8 0% No 11.8 0% No 53.7 25.9 -52% 25.3 -2% No 26.6 3% No

3 14.0 16.1 15% 16.1 0% No 16.1 0% No 49.0 28.5 -42% 27.0 -5% No 30.7 8% No

4 3.5 3.3 -6% 3.3 0% No 3.1 -4% No 21.5 9.9 -54% 10.0 1% No 8.4 -15% No

5 4.1 5.1 25% 5.1 0% No 5.1 0% No 16.0 8.2 -48% 7.9 -4% No 8.2 0% No6 0.1 0.1 -32% 0.1 0% No 0.1 0% No 0.8 0.2 -80% 0.2 12% No 0.2 11% No

Tulip tree

1 2.6 3.1 19% 3.1 0% No 3.0 -1% No 5.5 13.3 140% 13.5 2% No 13.4 1% No2 0.1 0.2 215% 0.2 0% No 0.2 0% No 0.2 4.2 2084% 4.2 0% No 4.8 14% No

3 0.7 0.3 -53% 0.3 0% No 0.3 0% No 0.4 1.4 248% 1.6 14% No 1.9 37% inc

4 2.4 2.8 17% 2.8 0% No 2.7 -2% No 6.6 6.4 -3% 6.7 4% No 6.7 5% No5 5.6 6.0 7% 6.0 0% No 6.0 0% No 6.4 74.8 1075% 72.9 -2% No 93.0 24% inc

6 5.6 4.5 -20% 4.5 0% No 4.5 0% No 7.4 59.7 704% 60.6 1% No 67.1 12% No

(continued on next page)

Table 41 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate model-emissions scenario combinations in the year 2040

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2040 

(ft2/

acre)

Change 

from 

2000b

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2040

Change 

from 

2000b

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd
APPENDIX 4

331

Virginia pine

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -7% 0.0 0% No 0.0 0% No 0.0 0.0 -62% 0.0 2% No 0.0 6% No

3 0.0 0.1 3% 0.1 0% No 0.1 0% No 0.1 0.0 -56% 0.0 4% No 0.0 9% No

4 - - - - - - - - - - - - - - - - - -

5 0.7 1.3 79% 1.3 0% No 1.3 0% No 1.4 6.9 402% 7.0 1% No 7.3 6% No

6 2.6 3.0 15% 3.0 0% No 3.0 0% No 6.2 16.1 161% 16.3 1% No 16.4 2% No

White ash

1 3.9 5.0 29% 5.0 0% No 5.0 -1% No 19.2 20.2 5% 20.9 3% No 20.5 1% No

2 5.5 13.5 147% 13.5 0% No 13.5 0% No 23.9 93.0 290% 92.3 -1% No 99.6 7% No

3 7.3 11.2 53% 11.2 0% No 11.2 0% No 23.7 53.9 127% 50.9 -6% No 58.2 8% No4 2.7 3.3 18% 3.2 0% No 3.2 -2% No 14.5 5.7 -61% 5.8 2% No 5.7 1% No5 3.9 10.5 170% 10.5 0% No 10.5 0% No 11.2 48.8 337% 48.1 -1% No 51.9 6% No

6 0.9 1.3 44% 1.3 0% No 1.3 0% No 2.0 7.0 254% 7.9 12% No 7.8 11% No

White oak

1 3.1 3.0 -5% 3.0 0% No 2.9 -2% No 5.7 11.6 102% 11.7 1% No 11.5 0% No

2 0.5 0.6 30% 0.6 0% No 0.6 0% No 0.3 3.4 933% 3.3 -3% No 3.6 7% No

3 3.4 5.3 53% 5.3 0% No 5.3 0% No 4.9 24.5 402% 23.0 -6% No 27.4 12% No4 5.7 5.2 -10% 5.1 0% No 5.0 -4% No 11.9 16.0 35% 16.3 1% No 15.9 -1% No

5 3.2 5.1 61% 5.1 0% No 5.1 0% No 7.2 15.9 121% 15.4 -3% No 18.8 18% No6 5.7 8.9 56% 8.9 0% No 8.9 0% No 13.0 42.4 227% 43.1 2% No 48.0 13% No

Yellow birch

1 - - - - - - - - - - - - - - - - - -

2 1.7 3.4 102% 3.4 0% No 3.4 0% No 9.3 20.3 118% 19.8 -3% No 20.2 -1% No3 1.9 3.1 63% 3.1 0% No 3.1 0% No 6.8 12.4 82% 11.7 -6% No 12.3 -1% No

4 - - - - - - - - - - - - - - - - - -

5 0.8 1.3 69% 1.3 0% No 1.3 0% No 4.6 5.0 10% 4.8 -4% No 4.9 -2% No

6 0.1 0.0 -28% 0.0 0% No 0.0 0% No 0.2 0.0 -84% 0.0 5% No 0.0 8% No

Table 41 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate model-emissions scenario combinations in the year 2040

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2040 

(ft2/

acre)

Change 

from 

2000b

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2040 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2040

Change 

from 

2000b

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

TPA in 

2040

Change 

from 

current 

climatec

Change 

classd

a Subregions: 1 – Western Allegheny Plateau, 2 – Erie and Ontario Lake Plain, 3 – Northern Allegheny Plateau, 4 – Ridge and Valley, 5 – Piedmont, 6 – Coastal Plain. See Figure 38 (Chapter 6, p. 144).b Change under current climate represents the difference from year 2009 through year 2040 due to succession and management, but not climate.c Change is the difference between this scenario and current climate in 2040 and represents the potential change due to climate change. d Change classes are abbreviated No (no change), inc (small increase), and dec (small decrease). Dash (-) indicates not present. 
APPENDIX 4

332Table 42.—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in 

the year 2070

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2070 

(ft2/

acre)

Change 

from 

2000b

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2070

Change 

from 

2000b

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

American beech

1 2.5 2.2 -14% 2.2 0% No 2.1 -4% No 10.9 3.5 -68% 3.6 2% No 2.3 -36% dec

2 3.3 5.8 73% 5.6 -3% No 6.3 9% No 21.4 16.0 -25% 15.1 -5% No 14.5 -9% No

3 7.0 12.8 84% 11.8 -8% No 14.8 15% No 58.6 31.0 -47% 26.5 -14% No 32.6 5% No

4 1.7 1.2 -25% 1.2 -1% No 1.2 -7% No 8.4 1.8 -79% 1.8 2% No 1.3 -26% dec5 2.0 3.7 82% 3.5 -5% No 4.2 13% No 18.4 6.6 -64% 6.0 -8% No 7.3 11% No

6 1.9 2.3 25% 2.4 2% No 2.6 10% No 10.2 4.8 -53% 5.4 14% No 4.1 -13% No

Balsam fir

1 - - - - - - - - - - - - - - - - - -

2 0.4 0.3 -6% 0.3 -3% No 0.4 13% No 3.6 0.6 -84% 0.4 -27% dec 0.5 -7% No

3 0.0 0.0 18% 0.0 0% No 0.0 6% No 0.1 0.0 -83% 0.0 0% No 0.0 -2% No

4 - - - - - - - - - - - - - - - - - -

5 0.1 0.1 -18% 0.1 -2% No 0.1 7% No 0.8 0.1 -90% 0.1 -2% No 0.1 6% No

6 - - - - - - - - - - - - - - - - - -

Black cherry

1 18.9 28.7 52% 28.7 0% No 28.5 -1% No 58.0 36.3 -37% 35.9 -1% No 35.3 -3% No

2 7.2 5.9 -18% 5.9 -1% No 6.1 4% No 17.5 23.8 36% 23.7 -1% No 25.6 7% No

3 8.2 5.5 -33% 5.3 -5% No 5.9 8% No 21.7 19.1 -12% 17.3 -9% No 21.6 13% No

4 10.2 16.0 57% 16.0 0% No 15.9 -1% No 34.5 13.0 -62% 13.7 6% No 13.8 6% No

5 2.5 6.4 150% 6.2 -2% No 7.1 12% No 8.7 12.3 41% 11.9 -3% No 14.4 17% No

6 1.0 4.4 350% 4.5 4% No 5.0 15% No 6.4 7.9 24% 9.2 17% No 8.8 11% No

Black oak

1 2.3 2.6 16% 2.6 0% No 2.6 -1% No 4.5 3.0 -34% 3.0 0% No 2.6 -11% No

2 0.2 0.2 21% 0.2 -1% No 0.2 2% No 0.2 0.5 230% 0.6 5% No 0.6 17% No

3 1.2 0.7 -43% 0.7 0% No 0.7 6% No 1.6 1.6 -1% 1.7 7% No 1.9 22% inc

4 4.1 2.9 -27% 3.0 0% No 2.9 0% No 5.9 1.9 -67% 2.1 9% No 2.2 12% No

5 3.2 4.3 36% 4.3 -1% No 4.6 5% No 2.7 7.5 175% 7.5 0% No 7.7 2% No

6 1.6 3.1 90% 3.2 3% No 3.4 8% No 3.0 4.9 63% 5.5 13% No 5.1 5% No

(continued on next page)
APPENDIX 4

333

Table 42 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2070

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2070 

(ft2/

acre)

Change 

from 

2000b

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2070

Change 

from 

2000b

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

Black spruce

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -23% 0.0 -25% dec 0.0 -5% No 0.2 0.1 -45% 0.1 -33% dec 0.1 -21% dec

3 - - - - - - - - - - - - - - - - - -

4 - - - - - - - - - - - - - - - - - -

5 0.0 0.0 -67% 0.0 30% inc 0.0 54% inc 0.0 0.0 -92% 0.0 33% inc 0.0 67% inc

6 - - - - - - - - - - - - - - - - - -

Chestnut oak

1 2.9 2.5 -13% 2.5 -1% No 2.2 -10% No 5.7 11.6 104% 11.4 -1% No 8.2 -29% dec

2 0.3 0.5 78% 0.5 0% No 0.6 27% inc 0.3 2.6 756% 3.3 24% inc 4.4 68% inc

3 3.4 4.8 41% 4.9 2% No 6.4 33% inc 5.1 16.5 223% 24.7 50% inc 41.6 153% INC

4 13.4 9.9 -26% 10.0 1% No 9.3 -6% No 31.9 20.8 -35% 25.1 21% inc 23.9 15% No5 4.6 8.5 85% 8.2 -4% No 10.0 17% No 6.4 37.8 487% 36.6 -3% No 41.9 11% No

6 1.5 2.8 80% 3.0 8% No 3.5 27% inc 3.6 12.0 231% 14.0 17% No 11.1 -7% No

Eastern hemlock

1 6.4 3.9 -39% 3.9 0% No 3.8 -4% No 18.8 5.8 -69% 5.7 -1% No 3.6 -37% dec2 4.8 6.0 23% 5.8 -2% No 6.4 7% No 22.9 7.6 -67% 8.1 6% No 6.6 -14% No

3 9.1 9.7 6% 9.1 -6% No 10.9 12% No 24.6 6.6 -73% 6.2 -7% No 6.5 -1% No

4 5.2 3.1 -40% 3.1 -1% No 2.9 -7% No 18.0 2.9 -84% 3.1 9% No 2.2 -24% dec5 2.9 3.1 6% 2.9 -5% No 3.3 8% No 12.9 1.9 -85% 1.7 -9% No 1.9 -1% No

6 0.1 0.1 -11% 0.1 14% No 0.1 39% inc 0.2 0.0 -74% 0.0 5% No 0.1 38% inc

Eastern white pine

1 1.6 1.8 12% 1.8 0% No 1.5 -15% No 5.9 9.1 54% 9.2 1% No 4.4 -52% DEC

2 2.2 2.5 14% 2.4 -3% No 2.6 4% No 3.1 9.1 189% 8.7 -4% No 6.9 -23% dec

3 6.5 6.2 -4% 5.8 -8% No 6.8 9% No 14.1 17.5 24% 15.6 -11% No 14.2 -19% No

4 2.6 2.5 -4% 2.4 -2% No 2.1 -16% No 13.1 8.3 -36% 8.4 1% No 4.3 -48% DEC

5 3.6 3.9 6% 3.7 -4% No 4.1 6% No 9.0 5.9 -35% 5.0 -14% No 5.1 -13% No

6 0.3 0.3 -1% 0.3 0% No 0.3 3% No 0.5 0.2 -61% 0.2 -2% No 0.2 -3% No

(continued on next page)
APPENDIX 4

334Table 42 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario 

combinations in the year 2070

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2070 

(ft2/

acre)

Change 

from 

2000b

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2070

Change 

from 

2000b

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

Loblolly pine

1 0.0 0.0 774% 0.0 3% No 0.0 2% No 0.1 0.0 -98% 0.0 46% inc 0.0 78% inc

2 - - - - - - - - - - - - - - - - - -

3 - - - - - - - - - - - - - - - - - -

4 0.0 0.0 237% 0.0 4% No 0.0 15% No 0.1 0.0 -95% 0.0 18% No 0.0 61% inc

5 0.1 0.1 99% 0.1 -6% No 0.1 19% No 0.2 0.1 -36% 0.1 6% No 0.2 35% inc

6 10.2 10.5 3% 10.6 1% No 12.0 15% No 29.0 28.7 -1% 29.0 1% No 30.8 7% No

Northern red oak

1 7.4 6.5 -12% 6.6 1% No 6.5 1% No 14.9 5.1 -66% 7.1 39% inc 8.5 67% inc

2 1.9 2.2 13% 2.1 -3% No 2.3 7% No 3.1 5.1 67% 4.9 -4% No 6.1 19% No

3 7.6 6.1 -20% 5.7 -6% No 6.5 7% No 9.7 16.8 73% 14.4 -15% No 18.2 8% No

4 11.7 12.4 6% 12.4 0% No 12.1 -2% No 22.3 7.1 -68% 8.2 15% No 10.8 51% inc

5 6.0 4.4 -27% 4.3 -2% No 4.6 5% No 6.8 5.0 -26% 4.8 -4% No 5.3 5% No

6 1.1 0.8 -26% 0.9 5% No 1.0 18% No 2.1 1.4 -31% 1.6 9% No 1.4 -3% No

Northern 

white-cedar

1 - - - - - - - - - - - - - - - - - -

2 0.4 0.2 -35% 0.2 -13% No 0.3 7% No 7.2 1.7 -76% 1.0 -41% DEC 1.3 -27% dec

3 0.0 0.0 -20% 0.0 -41% DEC 0.0 -9% No 0.1 0.0 -61% 0.0 -71% DEC 0.0 -52% DEC

4 - - - - - - - - - - - - - - - - - -

5 0.0 0.0 -9% 0.0 0% No 0.0 25% inc 0.0 0.0 -27% 0.0 -42% DEC 0.0 9% No

6 - - - - - - - - - - - - - - - - - -

Pignut hickory

1 0.7 0.7 9% 0.7 0% No 0.7 -1% No 1.8 1.5 -17% 1.5 0% No 1.5 -1% No

2 0.4 0.6 65% 0.6 -1% No 0.6 7% No 1.2 1.8 54% 1.9 2% No 2.0 9% No

3 0.6 1.1 79% 1.1 -4% No 1.2 7% No 1.8 2.4 36% 2.4 -1% No 2.4 1% No

4 1.1 1.3 20% 1.3 0% No 1.3 -3% No 4.6 1.4 -70% 1.6 14% No 1.6 16% No

5 1.4 2.7 92% 2.6 -4% No 3.1 11% No 5.2 5.1 -1% 5.0 -2% No 5.5 6% No

6 0.4 0.5 31% 0.5 5% No 0.5 11% No 1.3 0.8 -36% 1.0 17% No 0.8 -4% No

(continued on next page)
APPENDIX 4

335

Table 42 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2070

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2070 

(ft2/

acre)

Change 

from 

2000b

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2070

Change 

from 

2000b

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

Pitch pine

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -23% 0.0 -14% No 0.0 -12% No 0.0 0.0 43% 0.0 -1% No 0.0 0% No

3 0.4 0.5 27% 0.4 -8% No 0.5 6% No 1.0 0.8 -27% 0.7 -9% No 0.8 12% No

4 - - - - - - - - - - - - - - - - - -

5 2.7 7.5 175% 7.4 -1% No 8.5 14% No 11.8 25.6 116% 24.8 -3% No 26.2 2% No

6 9.5 14.8 55% 15.5 4% No 17.5 18% No 36.9 27.9 -24% 28.0 0% No 32.0 15% No

Quaking aspen

1 - - - - - - - - - - - - - - - - - -

2 2.4 9.0 270% 8.7 -4% No 9.0 -1% No 5.9 69.3 1069% 61.2 -12% No 51.0 -26% dec

3 1.7 5.9 251% 5.3 -9% No 5.7 -2% No 5.5 49.9 805% 41.8 -16% No 33.7 -32% dec

4 - - - - - - - - - - - - - - - - - -

5 0.9 1.3 43% 1.3 -3% No 1.3 0% No 1.7 6.8 310% 6.1 -10% No 5.2 -23% dec

6 0.0 0.0 720% 0.0 9% No 0.0 19% No 0.0 0.0 -64% 0.0 15% No 0.0 24% inc

Red maple

1 20.7 44.1 113% 44.1 0% No 44.0 0% No 95.2 57.1 -40% 57.9 1% No 59.0 3% No2 12.3 16.5 34% 16.3 -1% No 17.3 5% No 59.9 73.7 23% 72.5 -2% No 81.2 10% No

3 22.6 20.7 -8% 20.2 -2% No 21.4 3% No 81.3 39.9 -51% 36.9 -8% No 42.9 7% No

4 18.1 39.1 116% 39.2 0% No 38.7 -1% No 105.7 46.5 -56% 49.1 6% No 49.1 6% No

5 8.7 16.3 88% 16.2 -1% No 17.4 7% No 39.3 50.1 28% 50.5 1% No 55.7 11% No6 11.9 17.8 49% 18.4 4% No 19.6 10% No 61.4 63.5 3% 70.0 10% No 72.8 15% No

Red spruce

1 0.3 0.0 -83% 0.0 0% No 0.0 0% No 1.0 0.0 -96% 0.0 0% No 0.0 -1% No

2 0.4 0.8 82% 0.7 -6% No 0.8 7% No 4.6 3.1 -32% 2.3 -25% dec 2.4 -21% dec

3 0.2 0.2 -3% 0.2 -13% No 0.2 13% No 2.2 0.4 -82% 0.2 -38% dec 0.3 -12% No

4 0.2 0.0 -71% 0.0 -4% No 0.0 -9% No 0.6 0.1 -85% 0.1 -34% dec 0.1 -39% dec

5 0.4 0.5 20% 0.4 -4% No 0.5 8% No 1.9 0.5 -76% 0.4 -14% No 0.4 -4% No

6 0.0 0.0 0% 0.0 0% No 0.0 0% No 0.0 0.0 0% 0.0 0% No 0.0 0% No

(continued on next page)
APPENDIX 4

336Table 42 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario 

combinations in the year 2070

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2070 

(ft2/

acre)

Change 

from 

2000b

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2070

Change 

from 

2000b

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

Scarlet oak

1 1.2 1.2 0% 1.2 0% No 1.2 -2% No 2.0 0.9 -52% 0.9 0% No 0.8 -19% No

2 0.0 0.0 88% 0.0 1% No 0.0 -7% No 0.0 0.0 391% 0.0 -38% dec 0.0 10% No

3 0.6 0.9 45% 0.9 -3% No 0.9 3% No 1.3 1.7 35% 1.7 1% No 1.8 6% No

4 3.0 3.8 28% 3.8 0% No 3.8 -1% No 6.4 2.3 -64% 2.4 6% No 2.2 -5% No

5 1.6 4.6 187% 4.5 -1% No 4.8 5% No 3.9 7.5 92% 7.5 -1% No 7.5 0% No

6 2.1 4.0 85% 4.1 3% No 4.3 9% No 4.7 6.2 33% 6.9 11% No 6.2 -1% No

Shagbark hickory

1 - - - - - - - - - - - - - - - - - -

2 0.8 2.6 216% 2.5 -1% No 2.8 10% No 5.6 9.4 68% 10.2 9% No 10.9 16% No

3 0.6 0.9 57% 0.9 -7% No 1.0 12% No 1.5 2.3 47% 2.2 -2% No 2.7 20% No

4 - - - - - - - - - - - - - - - - - -

5 0.5 1.2 115% 1.2 -2% No 1.3 9% No 1.8 2.3 29% 2.4 3% No 2.4 3% No

6 0.0 0.0 -30% 0.0 11% No 0.0 29% inc 0.0 0.0 -61% 0.0 122% INC 0.0 49% inc

Sugar maple

1 5.9 5.3 -11% 5.3 0% No 5.0 -5% No 28.7 14.7 -49% 14.9 1% No 8.6 -41% DEC

2 10.6 12.5 18% 12.1 -3% No 13.6 9% No 53.7 22.1 -59% 20.4 -8% No 22.5 2% No

3 14.0 17.2 23% 16.0 -7% No 19.0 10% No 49.0 23.6 -52% 20.6 -13% No 26.1 10% No

4 3.5 3.3 -5% 3.3 -1% No 3.0 -9% No 21.5 11.3 -47% 11.5 2% No 6.7 -41% DEC

5 4.1 5.6 38% 5.4 -4% No 6.0 6% No 16.0 7.2 -55% 6.4 -11% No 6.9 -4% No

6 0.1 0.1 -20% 0.1 11% No 0.1 25% inc 0.8 0.4 -43% 0.6 29% inc 0.4 -1% No

Tulip tree

1 3.2 3.1 0% 3.2 0% No 3.0 -3% No 5.5 16.0 191% 16.5 3% No 16.2 1% No

2 0.1 0.2 185% 0.2 -1% No 0.3 28% inc 0.2 2.2 1019% 2.2 1% No 3.3 52% inc

3 0.7 0.2 -68% 0.2 6% No 0.3 32% inc 0.4 1.0 157% 1.5 40% inc 2.4 132% INC

4 5.7 5.4 -5% 5.4 0% No 5.1 -6% No 6.6 18.6 182% 19.6 5% No 18.8 1% No

5 5.6 5.4 -4% 5.3 -2% No 7.1 31% inc 6.4 33.1 420% 31.8 -4% No 51.4 55% inc6 5.6 3.7 -34% 3.9 3% No 4.6 22% inc 7.4 29.8 302% 32.1 8% No 33.6 13% No

(continued on next page)
APPENDIX 4

337

Table 42 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2070

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2070 

(ft2/

acre)

Change 

from 

2000b

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2070 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2070

Change 

from 

2000b

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

TPA in 

2070

Change 

from 

current 

climatec

Change 

classd

Virginia pine

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -67% 0.0 28% inc 0.0 -2% No 0.0 0.0 -91% 0.0 106% INC 0.0 36% inc

3 0.0 0.0 -39% 0.0 -2% No 0.0 3% No 0.1 0.0 -83% 0.0 4% No 0.0 66% inc

4 - - - - - - - - - - - - - - - - - -

5 0.7 1.3 83% 1.3 0% No 1.5 10% No 1.4 3.4 149% 3.6 4% No 3.2 -5% No

6 2.6 2.9 12% 3.0 1% No 3.2 9% No 6.2 7.9 28% 8.2 3% No 7.9 -1% No

White ash

1 2.6 3.4 31% 3.4 0% No 3.3 -1% No 19.2 16.3 -15% 17.2 6% No 17.2 6% No

2 5.5 11.3 106% 10.9 -3% No 12.5 11% No 23.9 37.3 56% 35.1 -6% No 44.4 19% No

3 7.3 11.1 51% 10.2 -7% No 12.4 12% No 23.7 30.4 28% 26.5 -13% No 35.2 16% No4 2.4 3.1 29% 3.1 1% No 3.0 -2% No 14.5 8.7 -40% 9.5 9% No 9.8 13% No

5 3.9 8.7 123% 8.4 -3% No 9.7 12% No 11.2 18.1 62% 17.4 -4% No 20.7 14% No

6 0.9 1.1 23% 1.2 9% No 1.3 23% inc 2.0 3.2 61% 4.0 25% inc 3.2 1% No

White oak

1 3.9 5.6 43% 5.6 0% No 5.5 -2% No 5.7 19.2 235% 21.0 10% No 20.1 5% No

2 0.5 0.7 40% 0.7 -3% No 0.8 16% No 0.3 4.9 1395% 4.5 -7% No 6.7 38% inc

3 3.4 6.0 73% 5.3 -11% No 6.9 15% No 4.9 37.0 659% 30.9 -17% No 44.1 19% No

4 2.8 3.6 32% 3.6 0% No 3.5 -4% No 11.9 4.0 -67% 4.3 8% No 4.3 9% No

5 3.2 4.7 47% 4.5 -4% No 5.4 15% No 7.2 13.8 91% 12.6 -9% No 18.2 32% inc6 5.7 9.4 65% 9.7 3% No 11.7 24% inc 13.0 58.6 352% 62.4 6% No 73.0 24% inc

Yellow birch

1 - - - - - - - - - - - - - - - - - -

2 1.7 3.7 117% 3.6 -3% No 3.9 7% No 9.3 15.4 65% 14.8 -4% No 12.1 -21% dec

3 1.9 4.3 129% 4.0 -7% No 4.6 6% No 6.8 14.8 117% 13.1 -12% No 11.1 -25% dec

4 - - - - - - - - - - - - - - - - - -

5 0.8 1.6 114% 1.6 -4% No 1.7 5% No 4.6 3.6 -21% 3.3 -8% No 3.0 -18% No

6 0.1 0.0 -24% 0.0 5% No 0.0 8% No 0.2 0.0 -89% 0.0 7% No 0.0 11% No

a Subregions: 1 – Western Allegheny Plateau, 2 – Erie and Ontario Lake Plain, 3 – Northern Allegheny Plateau, 4 – Ridge and Valley, 5 – Piedmont, 6 – Coastal Plain. See Figure 38 (Chapter 6, p. 144).b Change under current climate represents the difference from year 2009 through year 2070 due to succession and management, but not climate.c Change is the difference between this scenario and current climate in 2070 and represents the potential change due to climate change. d Change classes are abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), and DEC (large decrease). Dash (-) indicates not present. 
APPENDIX 4

338Table 43.—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in 

the year 2100

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2100 

(ft2/

acre)

Change 

from 

2000b

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2100

Change 

from 

2000b

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

American beech

1 2.5 2.3 -7% 2.3 0% No 2.1 -11% No 10.9 5.7 -48% 5.9 4% No 1.8 -68% DEC

2 3.3 7.0 109% 6.7 -4% No 7.7 11% No 21.4 21.4 0% 20.4 -5% No 18.0 -16% No

3 7.0 14.8 113% 13.3 -10% No 17.7 19% No 58.6 41.0 -30% 32.9 -20% No 34.5 -16% No

4 1.7 1.3 -20% 1.3 -1% No 1.2 -11% No 8.4 2.4 -72% 2.5 6% No 1.5 -37% dec

5 2.0 4.4 117% 4.1 -7% No 5.1 16% No 18.4 13.9 -24% 13.0 -7% No 9.8 -29% dec

6 1.9 2.8 51% 3.0 6% No 3.0 8% No 10.2 8.7 -15% 10.6 22% inc 3.7 -58% DEC

Balsam fir

1 - - - - - - - - - - - - - - - - - -

2 0.4 0.3 -22% 0.3 -7% No 0.3 11% No 3.6 0.5 -86% 0.2 -61% inc 0.2 -52% DEC3 0.0 0.0 -3% 0.0 -4% No 0.0 4% No 0.1 0.0 -88% 0.0 -18% No 0.0 -29% dec

4 - - - - - - - - - - - - - - - - - -

5 0.1 0.1 -36% 0.1 -7% No 0.1 6% No 0.8 0.1 -90% 0.0 -39% dec 0.0 -35% dec

6 - - - - - - - - - - - - - - - - - -

Black cherry

1 18.9 20.9 11% 20.7 -1% No 20.4 -2% No 58.0 42.4 -27% 41.3 -3% No 46.2 9% No

2 7.2 7.1 0% 7.0 -1% No 7.6 6% No 17.5 23.4 34% 23.5 0% No 25.4 8% No

3 8.2 6.1 -25% 5.7 -7% No 6.9 12% No 21.7 19.2 -12% 16.4 -15% No 22.6 18% No

4 10.2 10.7 5% 10.8 1% No 10.6 -1% No 34.5 12.1 -65% 13.9 15% No 14.8 22% inc

5 2.5 7.2 184% 7.0 -3% No 8.3 15% No 8.7 11.5 32% 11.2 -3% No 14.2 23% inc

6 1.0 5.3 443% 5.7 8% No 6.1 17% No 6.4 8.7 37% 12.0 38% inc 8.5 -2% No

Black oak

1 2.3 2.1 -9% 2.1 0% No 1.9 -6% No 4.5 3.5 -21% 3.5 -1% No 2.7 -24% dec

2 0.2 0.3 60% 0.3 0% No 0.3 7% No 0.2 0.6 277% 0.7 14% No 0.7 20% No

3 1.2 0.7 -37% 0.8 1% No 0.9 15% No 1.6 1.8 13% 2.1 15% No 2.5 38% inc

4 4.1 1.4 -65% 1.5 3% No 1.4 2% No 5.9 1.6 -73% 2.0 24% inc 2.1 35% inc

5 3.2 5.8 83% 5.7 -2% No 6.3 7% No 2.7 7.8 186% 7.7 -2% No 7.1 -9% No

6 1.6 3.8 134% 4.0 6% No 4.2 11% No 3.0 5.4 79% 7.0 31% inc 4.6 -14% No

(continued on next page)
APPENDIX 4

339

Table 43 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2100

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2100 

(ft2/

acre)

Change 

from 

2000b

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2100

Change 

from 

2000b

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

Black spruce

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -46% 0.0 -30% dec 0.0 5% No 0.2 0.1 -67% 0.0 -62% DEC 0.0 -41% DEC

3 - - - - - - - - - - - - - - - - - -

4 - - - - - - - - - - - - - - - - - -

5 - - - - - - - - - - - - - - - - - -

6 - - - - - - - - - - - - - - - - - -

Chestnut oak

1 2.9 2.8 -3% 2.7 -2% No 2.2 -21% dec 5.7 14.5 156% 14.1 -3% No 7.3 -49% DEC

2 0.3 0.6 129% 0.6 2% No 0.9 56% inc 0.3 3.7 1110% 5.6 50% inc 8.7 132% INC

3 3.4 5.2 53% 5.9 14% No 9.3 80% inc 5.1 21.6 325% 42.3 96% inc 92.1 326% INC

4 13.4 9.8 -26% 10.2 4% No 9.2 -7% No 31.9 21.8 -32% 29.9 37% inc 28.9 32% inc

5 4.6 9.2 100% 8.7 -6% No 11.4 24% inc 6.4 51.0 691% 49.7 -2% No 42.7 -16% No6 1.5 3.6 135% 4.0 13% No 4.5 26% inc 3.6 21.5 495% 28.1 31% inc 9.6 -55% DEC

Eastern hemlock

1 6.4 3.9 -38% 3.9 -1% No 3.6 -9% No 18.8 9.7 -49% 9.4 -3% No 2.8 -71% DEC2 4.8 5.8 20% 5.6 -3% No 6.3 9% No 22.9 7.5 -67% 8.7 16% No 4.1 -46% DEC

3 9.1 9.1 -1% 8.4 -8% No 10.4 15% No 24.6 6.4 -74% 6.3 -1% No 4.9 -23% dec

4 5.2 3.0 -42% 3.0 0% No 2.7 -11% No 18.0 4.0 -78% 5.5 37% inc 2.0 -51% DEC5 2.9 3.1 8% 2.9 -6% No 3.4 8% No 12.9 3.2 -75% 2.1 -35% dec 1.5 -53% DEC

6 0.1 0.1 5% 0.1 7% No 0.1 33% inc 0.2 0.1 -18% 0.1 -50% DEC 0.1 -54% DEC

Eastern white pine

1 1.6 1.8 15% 1.8 -1% No 1.3 -28% dec 5.9 9.2 55% 9.2 1% No 1.5 -84% DEC2 2.2 2.0 -7% 2.0 -4% No 2.1 2% No 3.1 7.8 148% 8.4 9% No 3.6 -54% DEC

3 6.5 5.1 -22% 4.5 -12% No 5.5 8% No 14.1 16.4 16% 13.2 -19% No 8.2 -50% DEC

4 2.6 2.4 -6% 2.3 -4% No 1.8 -26% dec 13.1 8.0 -39% 7.9 -2% No 2.0 -75% DEC

5 3.6 3.1 -16% 2.9 -7% No 3.3 7% No 9.0 4.6 -49% 3.1 -31% dec 2.4 -46% DEC

6 0.3 0.2 -25% 0.2 -1% No 0.2 4% No 0.5 0.2 -70% 0.1 -29% dec 0.1 -40% dec

(continued on next page)
APPENDIX 4

340Table 43 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario 

combinations in the year 2100

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2100 

(ft2/

acre)

Change 

from 

2000b

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2100

Change 

from 

2000b

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

Loblolly pine

1 0.0 0.0 1107% 0.0 13% No 0.0 23% inc 0.1 0.0 -98% 0.0 100% inc 0.0 228% INC

2 - - - - - - - - - - - - - - - - - -

3 - - - - - - - - - - - - - - - - - -

4 0.0 0.0 249% 0.0 6% No 0.0 24% inc 0.1 0.0 -96% 0.0 17% No 0.0 172% INC

5 0.1 0.1 91% 0.1 -13% No 0.1 17% No 0.2 0.2 -18% 0.2 13% No 0.3 106% INC

6 10.2 9.0 -12% 9.1 1% No 11.1 24% inc 29.0 25.3 -13% 27.5 9% No 24.7 -2% No

Northern red oak

1 7.4 6.1 -17% 6.6 7% No 6.8 11% No 14.9 5.1 -65% 9.8 92% inc 15.6 204% INC

2 1.9 2.4 23% 2.2 -6% No 2.7 15% No 3.1 5.7 87% 5.5 -4% No 7.6 33% inc

3 7.6 5.8 -23% 5.0 -14% No 6.5 11% No 9.7 20.3 109% 14.6 -28% dec 20.5 1% No

4 11.7 11.5 -2% 11.7 2% No 12.0 4% No 22.3 6.4 -71% 9.0 40% inc 17.1 166% INC

5 6.0 3.2 -46% 3.1 -5% No 3.6 10% No 6.8 4.3 -37% 3.8 -12% No 4.1 -4% No

6 1.1 0.8 -25% 0.9 7% No 1.0 25% inc 2.1 1.6 -22% 1.8 10% No 0.9 -42% DEC

Northern 

white-cedar

1 - - - - - - - - - - - - - - - - - -

2 0.4 0.2 -41% 0.2 -22% dec 0.2 4% No 7.2 1.6 -78% 0.5 -66% DEC 0.5 -66% DEC

3 0.0 0.0 -16% 0.0 -55% DEC 0.0 -28% dec 0.1 0.1 -54% 0.0 -91% DEC 0.0 -86% DEC4 - - - - - - - - - - - - - - - - - -

5 0.0 0.0 -17% 0.0 -17% No 0.0 12% No 0.0 0.0 -65% 0.0 -59% DEC 0.0 -21% dec

6 - - - - - - - - - - - - - - - - - -

Pignut hickory

1 0.7 0.6 -12% 0.6 -1% No 0.6 -2% No 1.8 2.2 22% 2.2 -1% No 2.5 13% No

2 0.4 0.7 97% 0.7 -3% No 0.8 14% No 1.2 2.4 101% 2.5 3% No 2.5 2% No3 0.6 1.2 97% 1.2 -6% No 1.3 9% No 1.8 3.2 79% 3.2 1% No 3.0 -7% No

4 1.1 1.1 1% 1.1 0% No 1.1 -4% No 4.6 1.2 -75% 1.5 25% inc 1.6 40% inc5 1.4 2.9 102% 2.7 -6% No 3.3 15% No 5.2 4.9 -4% 4.7 -4% No 4.5 -10% No

6 0.4 0.5 28% 0.5 9% No 0.5 15% No 1.3 0.8 -37% 1.2 46% inc 0.6 -22% dec

(continued on next page)
APPENDIX 4

341

Table 43 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2100

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2100 

(ft2/

acre)

Change 

from 

2000b

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2100

Change 

from 

2000b

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

Pitch pine

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -62% 0.0 -25% dec 0.0 -13% No 0.0 0.0 -32% 0.0 -24% dec 0.0 -8% No

3 0.4 0.4 4% 0.3 -14% No 0.4 8% No 1.0 0.5 -49% 0.5 -12% No 0.6 20% No

4 - - - - - - - - - - - - - - - - - -

5 2.7 6.4 137% 6.4 -1% No 7.8 21% inc 11.8 11.5 -3% 10.9 -5% No 11.3 -2% No

6 9.5 11.7 22% 12.4 6% No 14.6 25% inc 36.9 15.9 -57% 14.2 -11% No 13.4 -15% No

Quaking aspen

1 - - - - - - - - - - - - - - - - - -

2 2.4 13.5 453% 12.5 -7% No 11.5 -15% No 5.9 101.1 1605% 84.5 -16% No 30.8 -70% DEC

3 1.7 9.7 480% 8.1 -16% No 7.6 -21% dec 5.5 86.1 1460% 63.3 -26% dec 23.0 -73% DEC

4 - - - - - - - - - - - - - - - - - -

5 0.9 1.6 73% 1.5 -6% No 1.5 -8% No 1.7 8.1 387% 6.2 -23% dec 2.9 -64% DEC

6 0.0 0.0 915% 0.0 11% No 0.0 6% No 0.0 0.0 -78% 0.0 8% No 0.0 14% No

Red maple

1 20.7 38.6 87% 38.7 0% No 39.1 1% No 95.2 75.4 -21% 78.0 3% No 102.2 36% inc

2 12.3 16.8 36% 16.5 -2% No 18.2 9% No 59.9 47.4 -21% 46.0 -3% No 54.0 14% No

3 22.6 15.9 -29% 15.2 -5% No 16.9 6% No 81.3 25.8 -68% 22.6 -12% No 27.5 7% No

4 18.1 37.5 107% 38.0 1% No 37.3 -1% No 105.7 53.4 -50% 61.6 15% No 68.0 28% inc

5 8.7 17.8 104% 17.7 -1% No 19.9 12% No 39.3 33.4 -15% 33.7 1% No 39.5 18% No

6 11.9 19.7 65% 20.8 6% No 23.4 19% No 61.4 38.6 -37% 44.0 14% No 52.7 36% inc

Red spruce

1 0.3 0.0 -88% 0.0 0% No 0.0 -1% No 1.0 0.0 -98% 0.0 0% No 0.0 -1% No

2 0.4 0.8 80% 0.7 -9% No 0.8 7% No 4.6 2.9 -36% 1.4 -50% DEC 1.2 -58% DEC

3 0.2 0.2 -7% 0.2 -18% No 0.2 13% No 2.2 0.2 -89% 0.1 -53% DEC 0.2 -33% dec

4 0.2 0.0 -77% 0.0 -10% No 0.0 -18% No 0.6 0.1 -85% 0.0 -70% DEC 0.0 -74% DEC

5 0.4 0.5 20% 0.4 -5% No 0.5 9% No 1.9 0.3 -84% 0.2 -30% dec 0.2 -23% dec

6 - - - - - - - - - - - - - - - - - -

(continued on next page)
APPENDIX 4

342Table 43 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario 

combinations in the year 2100

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2100 

(ft2/

acre)

Change 

from 

2000b

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2100

Change 

from 

2000b

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

Scarlet oak

1 1.2 0.7 -45% 0.7 -1% No 0.6 -12% No 2.0 1.1 -46% 1.0 -1% No 0.5 -54% DEC

2 0.0 0.0 188% 0.0 -5% No 0.0 3% No 0.0 0.0 394% 0.0 -14% No 0.0 48% inc

3 0.6 1.0 61% 0.9 -4% No 1.0 5% No 1.3 2.0 62% 2.2 7% No 2.2 7% No

4 3.0 2.0 -34% 2.0 2% No 1.9 -4% No 6.4 1.7 -73% 2.1 22% inc 1.5 -16% No

5 1.6 5.8 265% 5.7 -2% No 6.2 6% No 3.9 8.2 110% 8.4 2% No 6.8 -18% No

6 2.1 4.6 116% 4.9 6% No 5.0 8% No 4.7 8.8 87% 10.9 24% inc 5.8 -33% dec

Shagbark hickory

1 - - - - - - - - - - - - - - - - - -

2 0.8 3.5 329% 3.5 0% No 4.1 17% No 5.6 14.1 152% 15.2 8% No 17.1 21% inc3 0.6 1.2 100% 1.1 -8% No 1.4 21% inc 1.5 3.3 117% 3.5 5% No 4.4 33% inc4 - - - - - - - - - - - - - - - - - -

5 0.5 1.3 132% 1.2 -3% No 1.4 10% No 1.8 2.6 46% 2.7 4% No 2.5 -6% No

6 0.0 0.0 -37% 0.0 20% No 0.0 40% inc 0.0 0.0 -48% 0.0 191% INC 0.0 -5% No

Sugar maple

1 5.9 6.4 8% 6.4 0% No 5.3 -18% No 28.7 39.6 38% 38.9 -2% No 7.6 -81% DEC

2 10.6 12.6 18% 12.0 -4% No 14.0 12% No 53.7 30.6 -43% 28.9 -5% No 29.0 -5% No

3 14.0 16.9 20% 15.2 -10% No 19.3 14% No 49.0 34.0 -31% 27.8 -18% No 37.5 10% No

4 3.5 4.1 17% 4.0 -1% No 3.3 -18% No 21.5 26.4 23% 26.8 1% No 11.4 -57% DEC5 4.1 6.2 52% 5.8 -7% No 6.5 3% No 16.0 21.7 36% 18.0 -17% No 9.8 -55% DEC

6 0.1 0.2 57% 0.2 13% No 0.2 13% No 0.8 1.5 102% 1.7 12% No 0.6 -61% DEC

Tulip tree

1 3.2 4.0 26% 4.0 0% No 3.8 -4% No 5.5 21.5 290% 22.6 5% No 23.9 11% No

2 0.1 0.2 194% 0.2 0% No 0.4 71% inc 0.2 2.7 1283% 2.8 7% No 6.4 141% INC3 0.7 0.2 -76% 0.2 30% inc 0.4 124% INC 0.4 1.0 145% 2.2 118% INC 5.3 432% INC

4 5.7 6.4 12% 6.5 1% No 5.9 -8% No 6.6 24.0 264% 26.4 10% No 25.4 6% No

5 5.6 4.4 -22% 4.2 -4% No 6.7 54% inc 6.4 18.4 189% 16.7 -9% No 43.0 134% INC

6 5.6 3.0 -46% 3.2 5% No 4.1 35% inc 7.4 21.0 183% 24.5 17% No 19.3 -8% No

(continued on next page)
APPENDIX 4

343

Table 43 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2100

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2100 

(ft2/

acre)

Change 

from 

2000b

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2100 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2100

Change 

from 

2000b

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

TPA in 

2100

Change 

from 

current 

climatec

Change 

classd

Virginia pine

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -100% - - - - - - 0.0 0.0 -100% - - - - - -

3 0.0 0.0 -96% 0.0 10% No 0.0 121% INC 0.1 0.0 -98% 0.0 56% inc 0.0 1060% INC

4 - - - - - - - - - - - - - - - - - -

5 0.7 1.6 122% 1.6 1% No 1.8 15% No 1.4 2.4 75% 2.5 5% No 2.0 -19% No

6 2.6 3.1 19% 3.2 2% No 3.5 13% No 6.2 4.9 -21% 4.7 -3% No 3.7 -23% dec

White ash

1 2.6 3.3 29% 3.4 1% No 3.5 4% No 19.2 26.9 40% 29.1 8% No 38.2 42% inc

2 5.5 12.8 133% 12.2 -5% No 15.2 19% No 23.9 43.4 82% 40.4 -7% No 57.8 33% inc3 7.3 11.9 63% 10.4 -13% No 14.3 20% No 23.7 38.9 64% 31.0 -20% No 47.3 22% inc

4 2.4 3.0 28% 3.1 3% No 3.0 -1% No 14.5 15.6 8% 17.7 13% No 18.7 20% No

5 3.9 10.1 159% 9.7 -4% No 12.1 20% No 11.2 20.6 85% 19.6 -5% No 24.1 17% No

6 0.9 1.2 35% 1.4 18% No 1.6 31% inc 2.0 4.3 114% 5.9 37% inc 2.6 -38% dec

White oak

1 3.9 5.6 44% 5.7 1% No 5.6 0% No 5.7 21.7 279% 25.3 16% No 30.4 40% inc2 0.5 0.9 85% 0.9 -7% No 1.2 31% inc 0.3 7.7 2269% 7.1 -8% No 11.6 50% inc

3 3.4 7.2 109% 5.9 -18% No 8.6 19% No 4.9 48.7 899% 34.7 -29% dec 57.6 18% No4 2.8 3.6 31% 3.6 0% No 3.4 -5% No 11.9 3.3 -72% 3.8 16% No 4.0 23% inc5 3.2 5.0 57% 4.7 -6% No 6.2 24% inc 7.2 19.9 177% 18.2 -8% No 32.2 62% inc

6 5.7 11.7 105% 12.1 3% No 16.5 41% inc 13.0 86.9 569% 93.5 8% No 115.4 33% inc

Yellow birch

1 - - - - - - - - - - - - - - - - - -

2 1.7 3.2 88% 3.0 -4% No 3.4 6% No 9.3 12.1 30% 11.1 -8% No 5.1 -58% DEC

3 1.9 4.8 151% 4.1 -13% No 4.8 0% No 6.8 17.2 151% 13.4 -22% dec 6.6 -61% DEC

4 - - - - - - - - - - - - - - - - - -

5 0.8 1.4 89% 1.3 -9% No 1.5 3% No 4.6 2.9 -36% 2.3 -21% dec 1.4 -53% DEC

6 0.1 0.0 -42% 0.0 8% No 0.0 11% No 0.2 0.0 -94% 0.0 9% No 0.0 13% No

a Subregions: 1 – Western Allegheny Plateau, 2 – Erie and Ontario Lake Plain, 3 – Northern Allegheny Plateau, 4 – Ridge and Valley, 5 – Piedmont, 6 – Coastal Plain. See Figure 38 (Chapter 6, p. 144).b Change under current climate represents the difference from year 2009 through year 2100 due to succession and management, but not climate.c Change is the difference between this scenario and current climate in 2100 and represents the potential change due to climate change. d Change classes are abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), and DEC (large decrease). Dash (-) indicates not present. 
APPENDIX 4

344Table 44.—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in 

the year 2200

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2200 

(ft2/

acre)

Change 

from 

2000b

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2200

Change 

from 

2000b

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

American beech

1 2.5 4.0 58% 4.0 2% No 1.6 -60% DEC 10.9 5.5 -50% 5.6 3% No 0.5 -90% DEC

2 3.3 15.6 368% 14.8 -5% No 14.3 -8% No 21.4 53.1 148% 51.0 -4% No 36.5 -31% dec

3 7.0 20.2 189% 18.1 -10% No 18.4 -9% No 58.6 62.7 7% 51.7 -18% No 35.6 -43% DEC

4 1.7 2.4 45% 2.4 0% No 1.4 -41% DEC 8.4 3.3 -60% 3.5 5% No 1.2 -63% DEC

5 2.0 6.1 198% 5.9 -4% No 7.2 18% No 18.4 19.9 8% 18.7 -6% No 17.3 -13% No6 1.9 7.1 285% 8.1 13% No 2.5 -64% DEC 10.2 12.2 20% 14.8 22% inc 0.9 -93% DEC

Balsam fir

1 - - - - - - - - - - - - - - - - - -

2 0.4 0.1 -74% 0.0 -95% DEC 0.0 -92% DEC 3.6 0.3 -92% 0.0 -99% DEC 0.0 -99% DEC

3 0.0 0.0 -97% 0.0 148% INC 0.0 -84% DEC 0.1 0.0 -99% 0.0 173% INC 0.0 -96% DEC

4 - - - - - - - - - - - - - - - - - -

5 0.1 0.0 -94% 0.0 -63% DEC 0.0 -82% DEC 0.8 0.0 -98% 0.0 -78% DEC 0.0 -94% DEC

6 - - - - - - - - - - - - - - - - - -

Black cherry

1 18.9 27.8 47% 26.0 -6% No 27.6 -1% No 58.0 44.8 -23% 41.8 -7% No 44.3 -1% No

2 7.2 6.8 -6% 7.1 5% No 6.7 -1% No 17.5 37.2 112% 40.6 9% No 36.4 -2% No

3 8.2 5.6 -31% 5.2 -8% No 6.2 11% No 21.7 18.2 -16% 15.4 -15% No 18.7 3% No

4 10.2 7.4 -27% 9.1 23% inc 9.4 26% inc 34.5 8.6 -75% 11.5 33% inc 12.1 41% inc

5 2.5 4.1 62% 4.1 1% No 4.5 10% No 8.7 12.8 47% 13.1 2% No 15.2 19% No

6 1.0 5.3 444% 6.8 30% inc 4.0 -24% dec 6.4 12.7 99% 20.9 64% inc 8.6 -33% dec

Black oak

1 2.3 4.2 86% 4.1 -2% No 3.1 -27% dec 4.5 6.7 49% 6.6 -1% No 4.6 -32% dec

2 0.2 0.6 211% 0.7 16% No 0.7 19% No 0.2 2.0 1098% 2.2 14% No 2.3 18% No

3 1.2 1.0 -19% 1.3 33% inc 1.4 49% inc 1.6 2.6 64% 3.4 32% inc 4.3 64% inc

4 4.1 1.7 -58% 2.4 39% inc 2.9 69% inc 5.9 1.8 -70% 2.7 54% inc 3.4 96% inc

5 3.2 7.0 117% 6.8 -2% No 4.9 -30% dec 2.7 20.4 644% 19.8 -3% No 13.2 -35% dec

6 1.6 5.2 218% 6.5 24% inc 2.9 -45% DEC 3.0 12.2 309% 16.4 34% inc 5.4 -56% DEC

(continued on next page)
APPENDIX 4

345

Table 44 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2200

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2200 

(ft2/

acre)

Change 

from 

2000b

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2200

Change 

from 

2000b

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

Black spruce

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -86% 0.0 -86% DEC 0.0 -55% DEC 0.2 0.0 -94% 0.0 -98% DEC 0.0 -91% DEC

3 - - - - - - - - - - - - - - - - - -

4 - - - - - - - - - - - - - - - - - -

5 - - - - - - - - - - - - - - - - - -

6 - - - - - - - - - - - - - - - - - -

Chestnut oak

1 2.9 3.9 38% 3.6 -8% No 1.6 -59% DEC 5.7 17.6 210% 15.7 -11% No 2.9 -83% DEC

2 0.3 1.6 536% 2.1 29% inc 3.8 132% INC 0.3 8.1 2525% 13.6 68% inc 23.8 194% INC

3 3.4 6.3 86% 13.5 115% INC 27.6 340% INC 5.1 27.8 445% 76.4 175% INC 198.8 616% INC

4 13.4 10.0 -26% 11.4 14% No 9.9 -1% No 31.9 17.0 -47% 28.0 65% inc 25.1 48% inc

5 4.6 16.0 246% 15.1 -5% No 19.3 21% inc 6.4 78.0 1110% 74.8 -4% No 66.0 -15% No

6 1.5 10.5 585% 11.2 7% No 6.2 -40% dec 3.6 42.5 1077% 50.2 18% No 5.4 -87% DEC

Eastern hemlock

1 6.4 3.0 -54% 2.9 -3% No 1.7 -42% DEC 18.8 4.4 -77% 4.2 -5% No 0.5 -89% DEC

2 4.8 2.4 -50% 2.6 7% No 1.9 -20% No 22.9 6.3 -72% 8.2 29% inc 0.8 -87% DEC

3 9.1 2.4 -74% 2.2 -8% No 2.6 11% No 24.6 2.3 -91% 2.3 2% No 1.1 -52% DEC4 5.2 2.2 -58% 2.5 13% No 1.4 -36% dec 18.0 2.3 -87% 3.3 43% inc 0.4 -82% DEC5 2.9 1.3 -55% 1.0 -25% dec 0.9 -28% dec 12.9 2.1 -84% 1.0 -52% DEC 0.4 -82% DEC

6 0.1 0.0 -47% 0.0 -31% dec 0.0 3% No 0.2 0.0 -79% 0.0 -73% DEC 0.0 -75% DEC

Eastern white pine

1 1.6 1.5 -7% 1.4 -6% No 0.4 -70% DEC 5.9 7.1 19% 6.6 -6% No 0.1 -98% DEC2 2.2 0.8 -61% 0.9 12% No 0.5 -45% DEC 3.1 4.9 56% 5.8 17% No 0.5 -89% DEC

3 6.5 1.7 -74% 1.3 -22% dec 1.1 -35% dec 14.1 8.6 -39% 5.7 -34% dec 1.2 -86% DEC

4 2.6 1.5 -40% 1.2 -23% dec 0.5 -65% DEC 13.1 5.3 -59% 4.0 -24% dec 0.3 -94% DEC

5 3.6 0.6 -83% 0.5 -19% No 0.5 -15% No 9.0 1.5 -83% 0.9 -42% DEC 0.2 -83% DEC6 0.3 0.0 -96% 0.0 7% No 0.0 24% inc 0.5 0.0 -95% 0.0 -35% dec 0.0 -87% DEC

(continued on next page)
APPENDIX 4

346Table 44 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario 

combinations in the year 2200

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2200 

(ft2/

acre)

Change 

from 

2000b

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2200

Change 

from 

2000b

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

Loblolly pine

1 0.0 0.0 1337% 0.0 236% INC 0.0 465% INC 0.1 0.0 -97% 0.0 467% INC 0.0 1348% INC

2 - - - - - - - - - - - - - - - - - -

3 - - - - - - - - - - - - - - - - - -

4 0.0 0.0 -35% 0.0 149% INC 0.0 1772% INC 0.1 0.0 -99% 0.0 145% INC 0.0 3215% INC

5 0.1 0.0 -20% 0.1 27% inc 0.1 75% inc 0.2 0.1 -59% 0.2 115% INC 0.2 189% INC

6 10.2 4.8 -53% 4.4 -9% No 4.7 -4% No 29.0 15.8 -46% 14.3 -9% No 11.2 -29% dec

Northern red oak

1 7.4 2.5 -66% 5.8 131% INC 10.9 335% INC 14.9 2.9 -80% 12.3 320% INC 34.8 1090% INC

2 1.9 2.7 40% 2.5 -9% No 4.0 48% inc 3.1 6.3 105% 5.4 -14% No 10.6 69% inc

3 7.6 7.4 -3% 4.8 -35% dec 6.9 -7% No 9.7 20.8 114% 9.1 -56% DEC 15.2 -27% dec

4 11.7 4.2 -64% 6.9 64% inc 15.5 270% INC 22.3 4.5 -80% 10.6 132% INC 39.2 764% INC

5 6.0 1.3 -78% 1.2 -13% No 1.5 10% No 6.8 2.2 -67% 1.7 -22% dec 2.0 -10% No

6 1.1 0.8 -27% 0.7 -8% No 0.3 -57% DEC 2.1 2.4 17% 2.3 -5% No 0.2 -93% DEC

Northern 

white-cedar

1 - - - - - - - - - - - - - - - - - -

2 0.4 0.1 -64% 0.1 -54% DEC 0.1 -38% dec 7.2 0.9 -87% 0.1 -88% DEC 0.0 -96% DEC

3 0.0 0.0 -62% 0.0 -59% DEC 0.0 -56% DEC 0.1 0.0 -83% 0.0 -96% DEC 0.0 -98% DEC

4 - - - - - - - - - - - - - - - - - -

5 0.0 0.0 -82% 0.0 -36% dec 0.0 -37% dec 0.0 0.0 -96% 0.0 -81% DEC 0.0 -81% DEC

6 - - - - - - - - - - - - - - - - - -

Pignut hickory

1 0.7 0.1 -81% 0.1 -6% No 0.1 -26% dec 1.8 0.5 -71% 0.5 -4% No 0.4 -21% dec

2 0.4 1.3 276% 1.4 7% No 1.4 6% No 1.2 4.7 291% 5.0 7% No 4.3 -9% No

3 0.6 2.5 301% 2.6 2% No 2.0 -21% dec 1.8 6.5 268% 6.5 -1% No 4.2 -36% dec

4 1.1 0.1 -92% 0.1 19% No 0.1 2% No 4.6 0.3 -95% 0.4 52% inc 0.4 47% inc

5 1.4 2.8 96% 2.7 -4% No 2.2 -20% No 5.2 5.0 -3% 4.9 -2% No 2.9 -42% DEC6 0.4 0.4 5% 0.6 51% inc 0.2 -35% dec 1.3 0.9 -33% 1.8 110% INC 0.3 -70% DEC

(continued on next page)
APPENDIX 4

347

Table 44 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2200

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2200 

(ft2/

acre)

Change 

from 

2000b

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2200

Change 

from 

2000b

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

Pitch pine

1 - - - - - - - - - - - - - - - - - -

2 0.0 0.0 -96% 0.0 -14% No 0.0 -8% No 0.0 0.0 -84% 0.0 23% inc 0.0 -13% No

3 0.4 0.0 -89% 0.0 -34% dec 0.0 19% No 1.0 0.2 -83% 0.1 -54% DEC 0.2 1% No

4 - - - - - - - - - - - - - - - - - -

5 2.7 2.9 8% 2.8 -3% No 3.2 10% No 11.8 9.5 -20% 8.7 -9% No 1.3 -86% DEC

6 9.5 1.9 -80% 1.9 1% No 2.3 24% inc 36.9 7.0 -81% 4.1 -42% DEC 0.8 -88% DEC

Quaking aspen

1 - - - - - - - - - - - - - - - - - -

2 2.4 40.9 1575% 32.7 -20% No 2.5 -94% DEC 5.9 332.2 5503% 247.5 -25% dec 1.8 -99% DEC

3 1.7 34.6 1976% 25.5 -26% dec 2.5 -93% DEC 5.5 234.9 4157% 149.4 -36% dec 1.7 -99% DEC

4 - - - - - - - - - - - - - - - - - -

5 0.9 3.6 292% 2.4 -34% dec 0.2 -95% DEC 1.7 25.0 1405% 14.2 -43% DEC 0.1 -100% X

6 0.0 0.0 -97% 0.0 -60% DEC 0.0 224% INC 0.0 0.0 -99% 0.0 -95% DEC 0.0 -57% DEC

Red maple

1 20.7 41.8 102% 42.9 2% No 57.6 38% inc 95.2 63.5 -33% 66.1 4% No 103.6 63% inc

2 12.3 5.9 -52% 5.8 -3% No 7.0 17% No 59.9 56.5 -6% 57.5 2% No 73.5 30% inc

3 22.6 3.8 -83% 3.3 -13% No 3.7 -3% No 81.3 14.9 -82% 12.7 -14% No 17.2 16% No

4 18.1 39.8 120% 47.6 20% No 52.7 32% inc 105.7 55.5 -48% 74.1 33% inc 88.1 59% inc

5 8.7 6.0 -31% 6.1 2% No 7.3 22% inc 39.3 39.5 1% 41.6 5% No 44.8 13% No

6 11.9 9.2 -23% 10.4 13% No 11.5 26% inc 61.4 56.1 -9% 67.9 21% inc 52.7 -6% No

Red spruce

1 0.3 0.0 -92% 0.0 0% No 0.0 -2% No 1.0 0.0 -99% 0.0 -6% No 0.0 -8% No

2 0.4 0.5 11% 0.3 -40% dec 0.3 -37% dec 4.6 1.6 -65% 0.3 -83% DEC 0.1 -94% DEC

3 0.2 0.1 -69% 0.1 -22% dec 0.1 4% No 2.2 0.1 -98% 0.0 -72% DEC 0.0 -69% DEC

4 0.2 0.0 -84% 0.0 -21% dec 0.0 -29% dec 0.6 0.1 -91% 0.0 -89% DEC 0.0 -90% DEC

5 0.4 0.1 -68% 0.1 -2% No 0.1 9% No 1.9 0.1 -97% 0.0 -52% DEC 0.0 -50% DEC

6 - - - - - - - - - - - - - - - - - -

(continued on next page)
APPENDIX 4

348Table 44 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario 

combinations in the year 2200

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2200 

(ft2/

acre)

Change 

from 

2000b

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2200

Change 

from 

2000b

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

Scarlet oak

1 1.2 1.4 20% 1.4 -2% No 0.3 -76% DEC 2.0 2.0 0% 1.9 -1% No 0.2 -89% DEC

2 0.0 0.0 486% 0.0 -9% No 0.0 6% No 0.0 0.0 1413% 0.0 7% No 0.0 12% No

3 0.6 1.4 131% 1.6 12% No 1.3 -10% No 1.3 3.3 163% 3.6 9% No 2.8 -15% No

4 3.0 1.6 -47% 2.2 38% inc 1.1 -27% dec 6.4 1.5 -76% 2.3 49% inc 1.0 -34% dec5 1.6 6.0 277% 6.2 4% No 3.9 -35% dec 3.9 16.7 325% 17.4 4% No 8.7 -48% DEC

6 2.1 5.7 165% 7.1 26% inc 2.1 -63% DEC 4.7 13.9 194% 20.5 48% inc 3.3 -76% DEC

Shagbark hickory

1 - - - - - - - - - - - - - - - - - -

2 0.8 8.4 932% 8.9 7% No 10.3 23% inc 5.6 29.1 419% 32.9 13% No 36.7 26% inc

3 0.6 2.6 339% 2.7 5% No 3.5 36% inc 1.5 7.2 367% 7.5 5% No 10.3 44% inc

4 - - - - - - - - - - - - - - - - - -

5 0.5 1.4 152% 1.5 5% No 1.3 -5% No 1.8 2.4 36% 2.8 13% No 1.9 -21% dec

6 0.0 0.0 -80% 0.0 98% inc 0.0 -14% No 0.0 0.0 -70% 0.0 72% inc 0.0 -87% DEC

Sugar maple

1 5.9 10.5 78% 10.2 -2% No 4.0 -62% DEC 28.7 26.2 -9% 24.9 -5% No 1.5 -94% DEC2 10.6 11.5 9% 10.6 -8% No 14.1 22% inc 53.7 44.5 -17% 39.2 -12% No 48.2 8% No

3 14.0 18.1 29% 16.7 -8% No 24.8 37% inc 49.0 40.1 -18% 35.4 -12% No 54.8 37% inc

4 3.5 9.3 166% 8.4 -9% No 4.1 -56% DEC 21.5 29.8 39% 26.4 -11% No 5.5 -82% DEC

5 4.1 17.5 326% 15.8 -10% No 10.7 -39% dec 16.0 53.4 234% 46.9 -12% No 23.3 -56% DEC

6 0.1 1.3 1148% 1.3 -3% No 0.3 -76% DEC 0.8 3.9 415% 3.9 -1% No 0.1 -97% DEC

Tulip tree

1 3.2 7.1 126% 7.2 1% No 6.3 -11% No 5.5 29.9 442% 31.8 6% No 32.7 9% No

2 0.1 0.6 656% 0.7 26% inc 1.6 177% INC 0.2 6.9 3496% 9.8 41% inc 23.4 238% INC

3 0.7 0.1 -80% 0.4 174% INC 1.4 970% INC 0.4 1.2 203% 3.2 163% INC 17.6 1337% INC

4 5.7 10.8 89% 11.2 3% No 9.5 -12% No 6.6 32.9 400% 37.5 14% No 34.4 5% No5 5.6 3.1 -44% 3.0 -4% No 5.4 73% inc 6.4 15.4 142% 14.8 -4% No 36.8 138% INC

6 5.6 2.6 -54% 2.7 7% No 3.2 27% inc 7.4 19.6 164% 22.1 13% No 14.3 -27% dec

(continued on next page)
APPENDIX 4

349

Table 44 (continued).—Absolute and percentage change in basal area (BA) and trees per acre (TPA) in the six subregions of the Mid-Atlantic region, as projected by the LANDIS PRO model for 24 species under a current climate scenario (1980 through 2009) and two climate-model emissions scenario combinations in the year 2200

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

Subregiona

BA in 

2000 

(ft2/

acre)

BA in 

2200 

(ft2/

acre)

Change 

from 

2000b

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

BA in 

2200 

(ft2/

acre)

Change 

from 

current 

climatec

Change 

classd

TPA in 

2000

TPA in 

2200

Change 

from 

2000b

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

TPA in 

2200

Change 

from 

current 

climatec

Change 

classd

Virginia pine

1 - - - - - - - - - - - - - - - - - -

2 - - - - - - - - - - - - - - - - - -

3 - - - - - - - - - - - - - - - - - -

4 - - - - - - - - - - - - - - - - - -

5 0.7 0.6 -17% 0.8 24% inc 0.2 -63% DEC 1.4 3.3 144% 4.3 28% inc 0.4 -89% DEC

6 2.6 1.1 -57% 1.0 -12% No 0.1 -87% DEC 6.2 9.5 53% 9.4 -1% No 0.0 -100% X

White ash

1 2.6 7.6 196% 7.5 -2% No 8.9 17% No 19.2 51.6 169% 55.4 7% No 77.0 49% inc

2 5.5 14.9 172% 14.3 -4% No 22.4 50% inc 23.9 50.8 113% 48.1 -5% No 91.6 80% inc

3 7.3 14.8 102% 12.2 -18% No 20.0 35% inc 23.7 43.0 81% 30.0 -30% dec 60.4 40% inc

4 2.4 6.8 186% 7.5 10% No 5.6 -18% No 14.5 34.3 137% 38.9 13% No 32.9 -4% No

5 3.9 12.3 217% 11.8 -4% No 14.4 17% No 11.2 30.4 172% 28.8 -5% No 35.1 15% No

6 0.9 1.5 71% 1.8 22% inc 0.8 -50% DEC 2.0 6.7 235% 8.8 32% inc 0.7 -89% DEC

White oak

1 3.9 1.7 -58% 1.8 10% No 1.1 -34% dec 5.7 11.7 105% 13.7 17% No 8.0 -32% dec

2 0.5 2.0 298% 1.7 -11% No 3.7 87% inc 0.3 10.4 3103% 9.6 -8% No 23.3 124% INC

3 3.4 11.9 244% 6.8 -43% DEC 11.2 -6% No 4.9 58.4 1097% 22.5 -62% DEC 39.7 -32% dec

4 2.8 0.2 -93% 0.2 19% No 0.2 10% No 11.9 0.6 -95% 0.8 39% inc 0.8 37% inc

5 3.2 4.2 33% 4.0 -4% No 9.4 123% INC 7.2 12.1 68% 11.0 -10% No 44.8 270% INC

6 5.7 19.1 235% 18.1 -5% No 36.8 93% inc 13.0 104.9 708% 104.1 -1% No 182.8 74% inc

Yellow birch

1 - - - - - - - - - - - - - - - - - -

2 1.7 1.6 -4% 1.5 -5% - 0.7 -58% DEC 9.3 7.5 -19% 7.1 -6% No 0.2 -97% DEC

3 1.9 6.3 232% 4.8 -24% dec 1.9 -70% DEC 6.8 18.9 176% 12.3 -35% dec 0.7 -96% DEC

4 - - - - - - - - - - - - - - - - - -

5 0.8 0.7 -11% 0.5 -22% dec 0.4 -43% DEC 4.6 1.7 -62% 1.2 -32% dec 0.1 -92% DEC

6 0.1 0.0 -100% 0.0 51% inc 0.0 94% inc 0.2 0.0 -100% - - - - - -

a Subregions: 1 – Western Allegheny Plateau, 2 – Erie and Ontario Lake Plain, 3 – Northern Allegheny Plateau, 4 – Ridge and Valley, 5 – Piedmont, 6 – Coastal Plain. See Figure 38 (Chapter 6, p. 144).b Change under current climate represents the difference from year 2009 through year 2200 due to succession and management, but not climate.c Change is the difference between this scenario and current climate in 2200 and represents the potential change due to climate change. d Change classes are abbreviated No (no change), inc (small increase), INC (large increase), dec (small decrease), DEC (large decrease), New (new habitat), and X (extirpated). Dash (-) indicates 

not present. These projections illustrate how changes that we begin to see in 2100 may continue through 2200, but are not used in this vulnerability assessment for forest ecosystems.
APPENDIX 4

350

Figure 57.—Change in basal area projected by the LANDIS PRO model for 24 species within the assessment area. Assessment 

area values were derived from the weighted average of sections. The black line indicates projected change due to succession 

and management. The green and red lines indicate projected change due to a low (green) and high (red) climate modelemissions scenario.
APPENDIX 4

351

Figure 57 (continued).—Change in basal area projected by the LANDIS PRO model for 24 species within the assessment area. 

Assessment area values were derived from the weighted average of sections. The black line indicates projected change due to 

succession and management. The green and red lines indicate projected change due to a low (green) and high (red) climate 

model-emissions scenario.
APPENDIX 4

352

Figure 58.—Change in basal area projected by the LANDIS PRO model for 22 species under two climate model-emissions 

scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009). Assessment area values were 

derived from the weighted average of sections.
APPENDIX 4

353

Figure 58 (continued).—Change in basal area projected by the LANDIS PRO model for 22 species under two climate 

model-emissions scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009). 

Assessment area values were derived from the weighted average of sections. 
APPENDIX 4

354

Figure 58 (continued).—Change in basal area projected by the LANDIS PRO model for 22 species under two climate 

model-emissions scenario combinations at year 2100 relative to a current climate scenario (1980 through 2009). 

Assessment area values were derived from the weighted average of sections. 
355

APPENDIX 5: VULNERABILITY 

AND CONFIDENCE DETERMINATION

To assess vulnerabilities to climate change for each 

forest community, we elicited input from 26 experts 

from a variety of land management and research 

organizations across the Mid-Atlantic region 

(Table 45). We sought two teams of panelists who 

Name Affiliation at time of workshop

Scott Bearer The Nature Conservancy

Alex Bryan Department of the Interior Northeast Climate Science Center

Ken Clark USDA Forest Service, Northern Research Station 

Greg Czarnecki Pennsylvania Department of Conservation and Natural Resources

Phil DeSenze USDA Forest Service, Northeastern Area State & Private Forestry

Paul Gugger University of Maryland Center for Environmental Science Appalachian Laboratory

Andrea Hille Allegheny National Forest

Justin Hynicka Maryland Forest Service

Louis Iverson USDA Forest Service, Northern Research Station 

Katrina Krause USDA Northeast Climate Hub

Deborah Landau The Nature Conservancy: Maryland/DC Chapter

Laura Leites Pennsylvania State University

Patricia Leopold1 Northern Institute of Applied Climate Science and Michigan Technological University

Evan Madlinger Natural Resources Conservation Service

Gulnihal (Rose) Ozbay Delaware State University

David Schmit Pennsylvania Department of Conservation and Natural Resources, Bureau of Forestry

Danielle Shannon1 Northern Institute of Applied Climate Science and Michigan Technological University

Collin Shephard Allegheny National Forest

Rebecca Shirer The Nature Conservancy: New York

Nick Skrowronski USDA Forest Service, Northern Research Station

Al Steele USDA Forest Service, Northeastern Area State & Private Forestry

Susan Stout USDA Forest Service, Northern Research Station

Frank Thompson USDA Forest Service, Northern Research Station

John Thompson Mohonk Preserve

David Weinstein Cornell University

Alfonso Yáñez Delaware Basin Project

1

Workshop facilitator

Table 45.—Participants in the November 2015 expert panel workshop

would be able to contribute a diversity of subject 

area expertise, knowledge of management history, 

and organizational perspectives on the interior and 

coastal forest communities of the Mid-Atlantic 

region. Most panelists had extensive knowledge 
APPENDIX 5

356

about the ecology, management, and climate change 

impacts on forests in the assessment area. These 

panels were assembled in Germantown, PA, in 

November 2015. Here we describe the structured 

discussion process that the panels used. 

FOREST ECOSYSTEMS ASSESSED

The authors of this assessment decided to use 

forest communities based on the Northeast Habitat 

Classification System (NETHCS) for classifying and 

describing forest ecosystems within the assessment 

area (see Chapter 1 and Table 10). For each forest 

ecosystem, we characterized the dominant species, 

major ecosystem drivers, and stressors from 

the relevant ecological literature. The panelists 

were asked to suggest modifications to the forest 

ecosystem descriptions, based on their experience 

and expertise in the assessment area, and those 

suggestions were incorporated into the descriptions. 

POTENTIAL IMPACTS 

To examine potential impacts, the panels were 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 panels were directed to focus on impacts to 

each forest type from the present through the end of 

the century, but more weight was given to the endof-century period. The panels 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 area. 

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 

ecosystem 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 on the forest ecosystem 

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 

during the 21st century. To assess potential impacts 

on dominant tree species, the panelists examined 

Tree Atlas, LINKAGES, and LANDIS PRO model 

results, and were asked to consider those results 

in addition to their knowledge of life-history 

traits and ecology of those species. The panels 

evaluated how much agreement existed among 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 ecosystem models.

ADAPTIVE CAPACITY

Panelists discussed the adaptive capacity of 

each forest ecosystem based on their ecological 

knowledge and management experience with the 

forest composition in the assessment area. Panelists 

were told to focus on ecosystem characteristics that 

would increase or decrease the adaptive capacity 

of that system. Factors that the panels considered 

included characteristics of dominant species 

within each ecosystem (for example, dispersal 

ability, genetic diversity, range limits) as well 

as comprehensive ecosystem characteristics (for 

example, 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 ecosystem given past and current management 

regimes, with no consideration of potential 

adaptation actions that could take place in the future. 
APPENDIX 5

357

VULNERABILITY

After extensive group discussion, each panelist 

evaluated the potential impacts and adaptive 

capacity of each forest ecosystem to arrive at a 

vulnerability rating. Each participant was provided 

with a worksheet (Fig. 59) and asked to list which 

impacts they felt were most important to a forest 

ecosystem in addition to the major factors that would 

contribute to the adaptive capacity of that ecosystem. 

Panelists were directed to mark their rating in twodimensional space on the individual worksheet and 

on a large group poster (Fig. 60A). This vulnerability 

figure required the participants to evaluate the 

degree of potential impacts related to climate 

change as well as the adaptive capacity of the forest 

ecosystem to tolerate those impacts (Brandt et al. 

2017). Individual ratings were compared, 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. 

CONFIDENCE 

Panelists were also directed to give a confidence 

rating to each of their individual vulnerability 

determinations (Fig. 60B). Panelists were asked 

to evaluate the amount of evidence they felt was 

available to support their vulnerability determination 

and the level of agreement among the available 

evidence (Mastrandrea et al. 2010). Panelists 

evaluated confidence individually and as a group, in 

a similar fashion to the vulnerability determination. 

Vulnerability and Confidence Figures

For reference, figures of individual and group 

determinations for all 11 forest ecosystems 

considered in this assessment are displayed in 

Figures 61 through 71. 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. 

Vulnerability Statements

Recurring themes and patterns that transcended 

individual forest ecosystems were identified 

and developed into the vulnerability statements 

(boldface text) and supporting text in Chapter 6. The 

coordinating 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 coordinating 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 panels for review. Panelists were asked 

to review each statement for accuracy, whether 

the confidence determination should be raised or 

lowered, whether there was additional literature 

that was overlooked, and whether any additional 

statements needed to be made. Any changes that 

were suggested by a single panelist were brought 

forth for discussion. Changes to vulnerability 

statements required approval by the entire panel.
APPENDIX 5

358

Figure 59.—Worksheet used for vulnerability and confidence determination by expert panelists, based on Swanston and 

Janowiak (2016).
APPENDIX 5

359

LITERATURE CITED

Brandt, L.A.; Butler, P.R.; Handler, S.D.; Janowiak, 

M.K.; Shannon, P.D.; Swanston, C.W. 2017. 

Integrating science and management to assess 

forest ecosystem vulnerability to climate 

change. Journal of Forestry. 115(3): 212-221. 

https://doi.org/10.5849/jof.15-147.

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. Available at https://www.ipcc.ch/pdf/

supporting-material/uncertainty-guidance-note.

pdf (accessed July 11, 2017).

Swanston, C.W.; Janowiak, M.K.; Brandt, L.A.; 

Butler, P.R.; Handler, S.D.; Shannon, P.D.; 

Derby Lewis, A.; Hall, K.; Fahey, R.T.; Scott, L.; 

Kerber, A.; Miesbauer, J.W.; Darling, L. 2016. 

Forest Adaptation Resources: climate change 

tools and approaches for land managers, 2nd 

ed. Gen. Tech. Rep. NRS-87-2. Newtown Square, 

PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 161 p. 

https://doi.org/10.2737/NRS-GTR-87-2.

Figure 60.—Figure used for (A) vulnerability determination by expert panelists, based on Swanston and Janowiak (2016) and 

described by Brandt et al. (2017); and (B) confidence rating among expert panelists, adapted from Mastrandrea et al. (2010).

A B
APPENDIX 5

360

Figure 61.—Vulnerability and confidence determinations for the maritime forest (coastal plain) community. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 62.—Vulnerability and confidence determinations for the oak-pine-hardwood (coastal plain) forest community. 

Circles indicate individual determinations by each panelist and squares indicate the group determination after consensus 

was reached.
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361

Figure 63.—Vulnerability and confidence determinations for the pine-oak barrens (coastal plain) forest community. 

Circles indicate individual determinations by each panelist and squares indicate the group determination after consensus 

was reached.

Figure 64.—Vulnerability and confidence determinations for the swamp (coastal plain) forest community. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.
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362

Figure 65.—Vulnerability and confidence determinations for the tidal swamp (coastal plain) forest community. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 66.—Vulnerability and confidence determinations for the central oak-pine (interior) forest community. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.
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363

Figure 67.—Vulnerability and confidence determinations for the lowland conifer (interior) forest community. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 68.—Vulnerability and confidence determinations for the lowland and riparian hardwood (interior) forest community. 

Circles indicate individual determinations by each panelist and squares indicate the group determination after consensus was 

reached.
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364

Figure 69.—Vulnerability and confidence determinations for the montane spruce-fir (interior) forest community. 

Circles indicate individual determinations by each panelist and squares indicate the group determination after consensus 

was reached.

Figure 70.—Vulnerability and confidence determinations for the northern hardwood (interior) forest community. 

Circles indicate individual determinations by each panelist and squares indicate the group determination after consensus 

was reached.
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365

Figure 71.—Vulnerability and confidence determinations for the woodland, glade, and barrens (interior) forest community. 

Circles indicate individual determinations by each panelist and squares indicate the group determination after consensus 

was reached.

Printed on recycled paper

Butler-Leopold, Patricia R.; Iverson, Louis R.; Thompson, Frank R., III; Brandt, Leslie 

A.; Handler, Stephen D.; Janowiak, Maria K.; Shannon, P. Danielle; Swanston, 

Christopher W.; Bearer, Scott; Bryan, Alexander M.; Clark, Kenneth L.; Czarnecki, 

Greg; DeSenze, Philip; Dijak, William D.; Fraser, Jacob S.; Gugger, Paul F.; Hille, 

Andrea; Hynicka, Justin; Jantz, Claire A.; Kelly, Matthew C.; Krause, Katrina M.; 

La Puma, Inga Parker; Landau, Deborah; Lathrop, Richard G.; Leites, Laura P.; 

Madlinger, Evan; Matthews, Stephen N.; Ozbay, Gulnihal; Peters, Matthew P.; 

Prasad, Anantha; Schmit, David A.; Shephard, Collin; Shirer, Rebecca; Skowronski, 

Nicholas S.; Steele, Al; Stout, Susan; Thomas-Van Gundy, Melissa; Thompson, 

John; Turcotte, Richard M.; Weinstein, David A.; Yáñez, Alfonso. 2018. Mid-Atlantic 

forest ecosystem vulnerability assessment and synthesis: a report from the 

Mid-Atlantic Climate Change Response Framework project. Gen. Tech. Rep. 

NRS-181. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 294 p. https://doi.org/10.2737/NRS-GTR-181.

Forest ecosystems will be affected directly and indirectly by a changing climate over 

the 21st century. This assessment evaluates the vulnerability of 11 forest ecosystems 

in the Mid-Atlantic region (Pennsylvania, New Jersey, Delaware, eastern Maryland, and 

southern New York) under 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 forest impact models, which provided a range of potential 

tree responses to climate. Finally, we brought these results before two multidisciplinary 

panels of scientists and land managers familiar with the forests of this region to assess 

ecosystem vulnerability through a formal consensus-based expert elicitation process.

Analysis of climate records indicates that average temperatures and total precipitation 

in the region have increased. Downscaled climate models project potential increases 

in temperature in every season, but vary in projections for precipitation. The forest 

impact models project declines in growth and suitable habitat for many mesic species, 

including American beech, eastern hemlock, eastern white pine, red spruce, and sugar 

maple. Species that tolerate hotter, drier conditions are projected to persist or increase, 

including black oak, northern red oak, pignut hickory, sweetgum, and white oak. The 

montane spruce-fir and lowland conifer forest communities were determined to be the 

most vulnerable ecosystems in the interior portion of the Mid-Atlantic region. Maritime 

and tidal swamp forest communities were determined to be the most vulnerable 

ecosystems in the coastal plain portion of the region. The woodland, glade, and barrens 

forest community was 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, forest-dependent 

animals and plants, recreation, and long-term natural resource planning.

KEY WORDS: Climate Change Tree Atlas, LINKAGES, LANDIS PRO, 

 adaptive capacity, expert elicitation, climate projection, Delaware, 

 Maryland, New Jersey, New York, Pennsylvania

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