Forest

Service

New England and Northern New York 

Forest Ecosystem Vulnerability 

Assessment and Synthesis:

A Report from the New England Climate Change 

Response Framework Project

United States Department of Agriculture

General Technical

Report NRS-173

Northern

Research Station January 2018
Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies, contact:

USDA FOREST SERVICE USDA Forest Service

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NEWTOWN SQUARE, PA 19073 359 Main Road

Delaware, OH 43015

January 2018 Fax: 740-368-0152

Manuscript received for publication March 2017

Forest ecosystems will face direct and indirect impacts from a changing climate over the 21st 

century. This assessment evaluates the vulnerability of forest ecosystems across the New England 

region (Connecticut, Maine, Massachusetts, New Hampshire, northern New York, Rhode Island, 

and Vermont) 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 

vegetation impact models, which provided a range of potential vegetative responses to climate. 

Finally, we brought these results before a multidisciplinary panel of scientists and natural 

resource professionals familiar with the forests of this region to assess ecosystem vulnerability 

through a formal consensus-based expert elicitation process. 

Observed trends in climate over the historical record from 1901 through 2011 show that the 

mean annual temperature has increased across the region by 2.4 °F, with even greater warming 

during winter. Precipitation patterns also changed during this time, with a slight trend toward 

greater annual precipitation and a substantial increase in extreme precipitation events. Projected 

climate trends using downscaled global climate model data indicate a potential increase in mean 

annual temperature of 3 to 8 °F for the assessment area by 2100. Projections for precipitation 

indicate an increase in fall and winter precipitation, and spring and summer precipitation 

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

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

PRO). Model projections suggest that many northern and boreal species, including balsam fir, red 

spruce, and black spruce, may fare worse under future conditions, but other species may benefit 

from projected changes in climate. Published literature on climate impacts related to wildfire, 

invasive species, and forest pests and diseases also contributed to the overall determination of 

climate change vulnerability. 

We assessed vulnerability for eight forest communities in the assessment area. The assessment 

was conducted through a formal elicitation process with 20 scientists and resource managers 

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

adaptive capacity for an individual community. Montane spruce-fir, low-elevation spruce-fir, and 

lowland mixed conifer forests were determined to be the most vulnerable communities. Central 

hardwoods, transition hardwoods, and pitch pine-scrub oak forests were perceived as having 

lower vulnerability 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.

ABSTRACT

Cover Photo

Montane ecosystems, as seen from Mount Jefferson on the White Mountain National Forest. 

These ecosystems are particularly vulnerable to climate change. Photo by Toni Lyn Morelli, 

U.S. Geological Survey.
New England and Northern New York 

Forest Ecosystem Vulnerability 

Assessment and Synthesis: A Report 

from the New England Climate Change 

Response Framework Project

Maria K. Janowiak, Anthony W. D’Amato, Christopher W. Swanston, 

Louis Iverson, Frank R. Thompson III, William D. Dijak, 

Stephen Matthews, Matthew P. Peters, Anantha Prasad, 

Jacob S. Fraser, Leslie A. Brandt, Patricia Butler-Leopold, 

Stephen D. Handler, P. Danielle Shannon, Diane Burbank, 

John Campbell, Charles Cogbill, Matthew J. Duveneck, 

Marla R. Emery, Nicholas Fisichelli, Jane Foster, Jennifer Hushaw, 

Laura Kenefic, Amanda Mahaffey, Toni Lyn Morelli, Nicholas J. Reo, 

Paul G. Schaberg, K. Rogers Simmons, Aaron Weiskittel, 

Sandy Wilmot, David Hollinger, Erin Lane, Lindsey Rustad, 

and Pamela H. Templer
AUTHORS

MARIA K. JANOWIAK is the deputy director of the Northern 

Institute of Applied Climate Science, U.S. Forest Service, 

410 MacInnes Drive, Houghton, MI 49931, 

mjanowiak02@fs.fed.us.

ANTHONY W. D’AMATO is an associate professor of silviculture 

and applied forest ecology with the University of Vermont, 

Rubenstein School of Environment and Natural Resources, 

81 Carrigan Drive, Burlington, VT 05405, awdamato@uvm.edu.

CHRISTOPHER W. SWANSTON is a research ecologist with the 

U.S. Forest Service, Northern Research Station, and the director 

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

Service, 410 MacInnes Drive, Houghton, MI 49931, 

cswanston@fs.fed.us.

LOUIS IVERSON is a landscape ecologist with the U.S. Forest 

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

OH 43015, liverson@fs.fed.us. 

FRANK R. THOMPSON III is a research wildlife biologist with the 

U.S. Forest Service, Northern Research Station, 202 AnheuserBusch Natural Resources Building, University of Missouri –

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

WILLIAM D. DIJAK is a wildlife biologist and geographic 

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

Northern Research Station, 202 Anheuser-Busch Natural 

Resources Building, University of Missouri – Columbia, 

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

STEPHEN MATTHEWS is an assistant professor of wildlife 

landscape ecology at Ohio State University, 2021 Coffey Road, 

Columbus, OH 43210, matthews.204@osu.edu.

MATTHEW P. PETERS is an ecologist with the U.S. Forest Service, 

Northern Research Station, 359 Main Road, Delaware OH 

43015, matthewpeters@fs.fed.us.

ANANTHA PRASAD is a research ecologist with the U.S. Forest 

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

OH 43015, aprasad@fs.fed.us.

JACOB S. FRASER is a research specialist and doctoral student, 

University of Missouri – Columbia, Department of Forestry, 

203 Anheuser-Busch Natural Resources Building, Columbia, MO 

65211, fraserjs@missouri.edu.

LESLIE A. BRANDT is a climate change specialist with the 

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

Service, 1992 Folwell Avenue, St. Paul, MN 55108, 

lbrandt@fs.fed.us.

PATRICIA BUTLER-LEOPOLD is a climate change outreach 

specialist with the Northern Institute of Applied Climate 

Science, Michigan Technological University, School of Forest 

Resources and Environmental Science, 1400 Townsend Drive, 

Houghton, MI 49931, pleopold@mtu.edu. 

STEPHEN D. HANDLER is a climate change specialist with the 

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

Service, 410 MacInnes Drive, Houghton, MI 49931, 

sdhandler@fs.fed.us.

P. DANIELLE SHANNON is the coordinator of the USDA Northern 

Forests Climate Hub, Northern Institute of Applied Climate 

Science, Michigan Technological University, School of Forest 

Resources and Environmental Science, 1400 Townsend Drive, 

Houghton, MI 49931, dshannon@mtu.edu.

DIANE BURBANK is an ecologist and geographic information 

systems coordinator with the U.S. Forest Service, Green 

Mountain National Forest, 231 N. Main Street, Rutland, VT 

05701, dburbank@fs.fed.us.

JOHN CAMPBELL is a research ecologist with the U.S. Forest 

Service, Northern Research Station, 271 Mast Road, Durham, 

NH 03824, jlcampbell@fs.fed.us.

CHARLES COGBILL is an independent scientist in Plainfield, VT 

05667.

MATTHEW J. DUVENECK is a research associate with the 

Harvard Forest, 324 N. Main Street, Petersham, MA 01366, 

mduveneck@gmail.com.

MARLA R. EMERY is a research geographer with the U.S. 

Forest Service, Northern Research Station, 81 Carrigan Drive, 

Burlington, VT 05405, memery@fs.fed.us.

NICHOLAS FISICHELLI is the forest ecology program director 

with Schoodic Institute at Acadia National Park, 

9 Atterbury Circle, Winter Harbor, ME 04063, 

NFisichelli@SchoodicInstitute.org. 

JANE FOSTER is a senior research associate with the University 

of Vermont, Rubenstein School of Environment and Natural 

Resources, 81 Carrigan Drive, Burlington, VT 05405, 

Jane.R.Foster@uvm.edu. 

JENNIFER HUSHAW is an applied forest scientist at Manomet, 

P.O. Box 1770, Manomet, MA 02345, jhushaw@manomet.org. 

LAURA KENEFIC is a research forester with the U.S. Forest 

Service, Northern Research Station, 686 Government Road, 

Bradley, ME 04411, lkenefic@fs.fed.us. 

AMANDA MAHAFFEY is the Northeast region director of the 

Forest Stewards Guild, 612 W. Main Street, Suite 200, Madison, 

WI 53703, amanda@forestguild.org. 

TONI LYN MORELLI is a research ecologist with the U.S. 

Geological Survey at the Department of the Interior Northeast 

Climate Science Center, Amherst, MA 01035, tmorelli@usgs.gov. 

NICHOLAS J. REO is an assistant professor of environmental 

studies and Native American studies at Dartmouth College, 

6182 Steele Hall, Hanover, NH 03755, 

Nicholas.J.Reo@dartmouth.edu. 
PAUL G. SCHABERG is a research plant physiologist with the U.S. 

Forest Service, Northern Research Station, 81 Carrigan Drive, 

Burlington, VT 05405, pschaberg@fs.fed.us.

K. ROGERS SIMMONS is retired and was the natural resources 

staff officer with the U.S. Forest Service, White Mountain 

National Forest, 71 White Mountain Drive, Campton, NH 03253. 

AARON WEISKITTEL is an associate professor of forest modeling 

and biometrics at the University of Maine, School of Forest 

Resources, 5755 Nutting Hall, Orono, ME 04469, 

aaron.weiskittel@maine.edu. 

SANDY WILMOT is a forest health specialist with the Vermont 

Agency of Natural Resources, Department of Forests, Parks and 

Recreation, 111 West Street, Essex Junction, VT 05452. 

DAVID HOLLINGER is a plant physiologist and project leader 

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

271 Mast Road, Durham, NH 03824, dhollinger@fs.fed.us. 

ERIN LANE is a natural resource specialist with the U.S. Forest 

Service, Northern Research Station, 271 Mast Road, Durham, 

NH 03824, edlane@fs.fed.us. 

LINDSEY RUSTAD is a research ecologist with the U.S. Forest 

Service, Northern Research Station, 271 Mast Road, Durham, 

NH 03824, lrustad@fs.fed.us. 

PAMELA H. TEMPLER is a professor at Boston University, 

Department of Biology, 5 Cummington Mall, Boston, MA 02215, 

ptempler@bu.edu. 
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PREFACE

This assessment is a fundamental component of the 

New England Climate Change Response Framework 

project. The Framework is a collaborative, crossboundary 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, Mid-Atlantic, 

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 and natural resource 

professionals in New England and northern New 

York, in addition to other people who study, 

recreate, and live in these forests. As new scientific 

information arises, we may 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 forested 

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 was 

a coordinated effort among Leslie Brandt, Patricia 

Butler-Leopold, Maria Janowiak, Stephen Handler, 

and Chris Swanston. Danielle Shannon conducted 

much of the data analysis and developed maps for 

Chapters 1, 2, and 3. Louis Iverson, Steve Matthews, 

Matthew Peters, and Anantha Prasad provided and 

interpreted Tree Atlas information for Chapter 4, 

and assisted with the data processing for the climate 

data presented in Chapter 3. Frank Thompson, 

Bill Dijak, and Jacob Fraser provided results and 

interpretation of the LINKAGES and LANDIS PRO 

models. All modeling teams coordinated their efforts 

impressively. 

In addition to the authors listed, a number of people 

made valuable contributions to the assessment. 

We especially thank Colin Beier and Erika 

Rowland, 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Chapter 1: Current Forest Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Chapter 2: Observed Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Chapter 3: Projected Changes in Climate and Physical Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Chapter 4: Future Climate Change Impacts on Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Chapter 5: Forest Ecosystem Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Chapter 6: Management Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Appendix 1: Common and Scientific Names of Species Mentioned in this Report . . . . . . . . . . . . 172

Appendix 2: Historical Climate Data and Trend Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

Appendix 3: Additional Climate Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

Appendix 4: Supplementary Climate Change Tree Atlas Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

Appendix 5: Supplementary LINKAGES Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

Appendix 6: Supplementary LANDIS PRO Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

Appendix 7: Vulnerability and Confidence Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

Appendix 8: Future Climate Change Impacts on Biodiversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
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1

EXECUTIVE SUMMARY

This assessment evaluates key ecosystem 

vulnerabilities for forest ecosystems in New 

England and northern New York (Fig. 1) across a 

range of future climate scenarios. This assessment 

was completed as part of the New England 

Climate Change Response Framework project, 

a collaborative approach among researchers, 

managers, and landowners to incorporate climate 

change considerations into forest management. 

The assessment summarizes current conditions and 

key stressors and identifies past and projected trends 

in climate. This information is then incorporated 

into model projections of future forest change. These 

projections, along with published research and 

local knowledge and expertise, are used to identify 

the factors that contribute to the vulnerability of 

major forest systems within the assessment area 

through this century. A final chapter summarizes the 

implications of these impacts and vulnerabilities for 

forest management across the region. 

CHAPTER 1: CURRENT FOREST 

CONDITIONS

This chapter describes the forests and related 

ecosystems of New England and northern New York 

and summarizes current threats and management 

trends. The 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 53 million acres of land in the 

assessment area of New England and northern 

New York, about 40 million acres are forest. 

Maple/beech/birch and spruce/fir are the most 

abundant forest-type groups across the area. 

Private individuals and organizations own about 

80 percent of the forest land.

• Historical land use and past management 

practices have tended to favor younger forests 

across the landscape and have often reduced 

species diversity and structural complexity. 

• Current major stressors and threats to forest 

ecosystems in the assessment area include:

▪ Fragmentation and land-use change

▪ Fire regime shifts 

▪ Invasion by nonnative species

▪ Forest diseases and insect pests

▪ Overbrowsing 

▪ Extreme weather events. 

• The forest products industry is a major 

contributor to the regional economy. Ninety 

percent of the forest land in the assessment area 

is classified as timberland, meaning that it is 

Figure 1.—The assessment area (green shaded area): the six 

New England states and northern New York.
EXECUTIVE SUMMARY

2

considered suitable for wood production because 

forest harvesting is not prohibited. Across the 

assessment area, the amount of wood harvested 

each year is less than the amount of forest 

growth.

CHAPTER 2: 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 in the assessment 

area and across the Northeast region, with a focus on 

the last 50 to 100 years.

Main Points

• Across the assessment area, the mean annual 

temperature increased by 2.4 °F (1.3 °C) between 

1901 and 2011. Mean, minimum, and maximum 

temperatures increased across all seasons over the 

past century, with winter temperatures warming 

the most rapidly. 

• Precipitation patterns have changed across 

the region, with a trend toward greater annual 

precipitation in the assessment area. The number 

of extreme precipitation events has increased. 

Snowfall has generally decreased across the 

assessment area.

• Climate change has caused substantial sea-level 

rise, with an increase of 12 inches across the 

northeastern United States and an even greater 

increase along the New England coastline 

observed since 1900.

• Climate change has also been indicated by trends 

showing reductions in lake ice, increased growing 

season length, and shifts in plant and animal 

phenology.

CHAPTER 3: 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 continue to 

increase over the next century. A range of climate 

scenarios project warming 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. Intense precipitation events are 

expected to continue to become more frequent. 

• Winters will continue to become shorter and 

milder. Snowfall is projected to continue to 

decline across the assessment area, with more 

winter precipitation falling as rain. Soils are 

projected to be frozen for shorter periods during 

winter. 

CHAPTER 4: 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 

assessment area. 

Main Points

• Boreal species such as balsam fir, red spruce, 

and black spruce are projected to have reductions 

in suitable habitat and biomass over the next 

century.
EXECUTIVE SUMMARY

3

• Species with ranges that extend to the south such 

as red maple, northern red oak, black cherry, 

and American basswood may have increases in 

suitable habitat and biomass.

• Many currently common species are projected 

to decline under a hotter, drier future climate 

scenario, particularly in the southern portion of 

the assessment area.

• Forest productivity will be influenced by a 

combination of factors such as carbon dioxide 

fertilization, water and nutrient availability, 

succession, disturbance, and species migration. 

• The model projections used in this assessment 

do not account for many other factors that may 

change under a changing climate, such as forest 

disturbance. Scientific literature was used to 

provide additional information on these factors, 

including:

▪ Altered precipitation and hydrology

▪ Drought stress

▪ Wildfire frequency and severity

▪ Altered nutrient cycling

▪ Changes in invasive species, insect pests, and 

forest diseases 

▪ Interactions among these factors.

CHAPTER 5: FOREST ECOSYSTEM 

VULNERABILITIES

Forest ecosystems across New England and northern 

New York will face direct and indirect impacts 

from a changing climate over the 21st century. We 

assessed the vulnerability of major forest systems 

in the assessment area to climate change through 

the year 2100, focusing on shifts in dominant 

species, system drivers, and stressors. The adaptive 

capacity of forest systems was also examined as a 

key component of overall vulnerability. Synthesis 

statements are provided to capture general trends, 

and detailed vulnerability determinations are 

provided for eight major forest systems (Table 1).

Main Points

Potential Impacts on 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 

assessment area.

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

through the end of the 21st century

Forest system Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Central hardwood-pine Neutral-Positive Moderate-High Low Medium Medium-High

Low-elevation spruce-fir Neutral-Negative Moderate Moderate-High Medium Medium

Lowland and riparian hardwood Positive and Negative Moderate-High Moderate Limited Limited

Lowland mixed conifer Neutral-Negative Low-Moderate Moderate-High Limited-Medium Medium

Montane spruce-fir Neutral-Negative Moderate Moderate-High Medium Medium

Northern hardwood Positive and Negative Moderate-High Low-Moderate Medium Medium

Pitch pine-scrub oak Neutral-Positive Moderate Low Medium Medium

Transition hardwood Positive and Negative Moderate-High Low-Moderate Medium Medium-High
EXECUTIVE SUMMARY

4

• Winter processes will change (robust evidence, 

high agreement). There is strong evidence that 

temperatures will increase more in winter than in 

other seasons across the assessment area, leading 

to changes in snowfall, soil frost, and other winter 

processes.

• 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.

• The amount and timing of precipitation will 

change (robust evidence, high agreement).

There is strong agreement that precipitation 

patterns will change across the assessment area. 

Total precipitation is generally expected to 

increase during winter and spring, but summer 

and fall projections are more uncertain.

• Intense precipitation events will continue to 

become more frequent (robust evidence, high 

agreement). Climate models generally project 

that the number of heavy precipitation events will 

continue to increase in the assessment area. If 

they do increase, damage from flooding and soil 

erosion may also become more severe.

• Soil moisture patterns will change in 

response to temperature and precipitation 

(medium evidence, high agreement). Warmer 

temperatures and altered precipitation will 

interact to change soil moisture patterns 

throughout the year, but there is uncertainty about 

the direction and magnitude of the changes.

• Forest vegetation may face increased risk 

of moisture deficit and drought during the 

growing season (medium evidence, medium 

agreement). Studies show that climate change 

will affect soil moisture, but there is some 

disagreement among climate and impact models 

on how soil moisture and drought will change 

during the growing season.

• Certain insect pests and pathogens will 

increase in occurrence or become more 

damaging (medium evidence, high agreement).

Evidence indicates that increases in temperature 

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 on Forests

• Many northern and boreal tree species will 

face increasing stress from climate change 

(medium evidence, high agreement). Ecosystem 

models agree that northern and boreal tree species 

will have reduced suitable habitat and biomass 

across the assessment area, and that they may 

be less able to take advantage of longer growing 

seasons and warmer temperatures than warmadapted, temperate forest species.

• Habitat will become more suitable for 

southern species (medium evidence, high 

agreement). Ecosystem models agree that longer 

growing seasons and warmer temperatures will 

increase suitable habitat and biomass for many 

temperate species across the assessment area.

• Forest composition will change across the 

landscape (medium evidence, high agreement).

Although few models have specifically examined 

how forest communities may change, model 

results from individual species and ecological 

principles suggest that recognized forest 

community assemblages will change.

• Shifts in forest composition will take at least 

several decades to occur in the absence of 

major disturbance (medium evidence, medium 

agreement). Although some models indicate 

major changes in habitat suitability, results 

from spatially dynamic forest landscape models 

indicate that a major shift in forest composition 

across the landscape may take 100 years or more 

in the absence of major disturbances.
EXECUTIVE SUMMARY

5

• Conditions affecting 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.

• 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 assessment area, but they 

disagree on how multiple factors may interact 

to influence productivity. The diversity of forest 

conditions across the assessment area suggests 

that changes will be spatially variable.

Adaptive Capacity Factors

• Low-diversity systems are at greater risk 

(medium evidence, high agreement). Studies 

have consistently shown that high-diversity 

systems are more resilient to disturbance. Lowdiversity systems are expected to be more 

vulnerable to climate change.

• 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 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 will be particularly 

difficult for tree species and forest communities 

with narrow habitat requirements.

• Ecosystems that have greater tolerance to 

disturbance have less risk of declining on the 

landscape (medium evidence, high agreement).

Basic ecological theory and other evidence 

support the idea that systems adapted to more 

frequent disturbance will be at lower risk.

CHAPTER 6: MANAGEMENT 

IMPLICATIONS

This chapter summarizes the implications of 

potential climate change to forest management and 

planning in the assessment area. 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

• Plants, animals, and people that depend on forests 

may face additional challenges as the climate 

shifts. 

• Greater financial investments may be required to 

manage forests and infrastructure and to prepare 

for severe weather events. 

• Management activities such as wildfire 

suppression or recreational activities such as 

snowmobiling and skiing may need to be altered 

as temperatures and precipitation patterns change. 

• Climate change may present opportunities for 

the forest products industry, recreation, and other 

sectors if changing conditions are anticipated.
6

INTRODUCTION

Forests are a prominent feature of the landscape 

across the six New England states (Connecticut, 

Maine, Massachusetts, New Hampshire, Rhode 

Island, and Vermont) and northern New York. 

Stretching from the coast of the Atlantic Ocean 

to the peaks of the Appalachian Mountains, this 

assessment area contains more than 50 million acres 

of land (Fig. 2), of which 40 million acres are forest. 

Increasingly, the effects of a changing climate are 

altering the forests of the Northeast, and many of 

these impacts are projected to continue and intensify 

in the future. Many of the important factors that 

influence forest composition and distribution are 

expected to change, including seasonal temperatures, 

precipitation timing and type, soil moisture patterns, 

natural disturbance severity and frequency, and pest 

and disease abundance. Future climatic changes 

and their associated impacts and vulnerabilities 

are anticipated to affect forests in expected and 

unexpected ways, which will unfold over the next 

several decades. Ecosystems are expected to respond 

to these changes in a variety of ways, often in 

reaction to increased stress.

Ongoing and future research will continue to provide 

more detailed information about specific impacts, 

but enough information is currently available 

to understand the anticipated vulnerabilities of 

regional forest ecosystems given climate variability 

and change. Numerous assessments describe the 

potential impacts of climate change on the forests 

of the Northeast (see the next three pages). In this 

document, we synthesize the available information 

to provide a more complete understanding of how 

forests in the assessment area are expected to 

respond to changing conditions in order to better 

inform management and conservation activities 

taking place in the region.

CONTEXT AND SCOPE

This assessment is a fundamental component of the 

New England Climate Change Response Framework 

project. The Framework is a collaborative, crossboundary approach among scientists, managers, 

and landowners to incorporate climate change 

considerations into natural resource management 

(www.forestadaptation.org). Six Framework projects 

are currently underway, covering approximately 250 

million acres in the northeastern and midwestern 

United States: Northwoods, Central Appalachians, 

Central Hardwoods, Mid-Atlantic, New England, 

and Urban. Each regional project interweaves four 

components: science and management partnerships, 

vulnerability assessments, adaptation resources, and 

demonstration projects (Swanston et al. 2016). 

We designed this assessment to highlight the best 

available scientific information on the topic of 

climate change and forest ecosystems. Its primary 

purpose is to inform natural resource managers 

in New England and northern New York, as well 

as other people who study, recreate, and live in 

Figure 2.—The assessment area (green shaded area): the six 

New England states and northern New York.
INTRODUCTION

7

these forests. As new scientific information arises, 

we may develop future assessments 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 forested 

ecosystems, with a particular focus on tree species. 

We acknowledge that climate change will also have 

impacts on aquatic systems, animals, and human 

systems, but addressing these issues in depth is 

beyond the scope of this assessment. 

ASSESSMENT CHAPTERS

This assessment contains the following chapters: 

Chapter 1: Current Forest Conditions describes 

existing conditions and provides background on 

the physical environment, ecological character, and 

broad socioeconomic dimensions of the assessment 

area.

Chapter 2: Observed Climate Change provides 

information on the past and current climate of the 

assessment area, as summarized from the interactive 

Climate Wizard database and published literature. 

This chapter also summarizes some relevant 

ecological indicators of observed climate change. 

Chapter 3: 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 for the assessment area and 

the Northeast region. 

Chapter 4: Future Climate Change Impacts 

on Forests summarizes ecosystem model results 

for the region and the assessment area. Three 

modeling approaches were used to model climate 

change impacts on forests: a species distribution 

model (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. 

Chapter 5: Forest Ecosystem Vulnerabilities

synthesizes the potential effects of climate change on 

the forested ecosystems of the assessment area and 

provides detailed vulnerability determinations for 

each of the eight major forest systems.

Chapter 6: Management Implications draws 

connections from the forest ecosystem vulnerability 

determinations to a wider network of related 

concerns shared by forest managers, including 

habitat for forest-dependent animals, forest 

management, recreation, and cultural resources. 

RELEVANT CLIMATE CHANGE 

ASSESSMENTS

Global and National Assessments

Climate Science Special Report: Fourth National 

Climate Assessment, Vol. 1 (Wuebbles et al. 2017)

Forests (Joyce et al. 2014)

Climate Change Impacts in the United States: the 

Third National Climate Assessment (Melillo et al. 

2014)

Climate Change 2013: the Physical Science Basis

(Intergovernmental Panel on Climate Change 2013) 

Climate Change in the Northeast—a Sourcebook: 

Draft Technical Input Report Prepared for the U.S. 

National Climate Assessment (Horton et al. 2012)

Effects of Climatic Variability and Change on Forest 

Ecosystems: a Comprehensive Science Synthesis for 

the U.S. Forest Sector (Vose et al. 2012)

Regional Assessments

Maine’s Climate Future: 2015 Update (Fernandez et 

al. 2015)

Considering Vermont’s Future in a Changing 

Climate: the First Vermont Climate Assessment

(Galford et al. 2014)

Climate Change in Northern New Hampshire: Past, 

Present, and Future (Wake et al. 2014a) 

Climate Change in Southern New Hampshire: Past, 

Present, and Future (Wake et al. 2014b)
INTRODUCTION

8

Climate Change and Biodiversity in Maine: 

Vulnerability of Habitats and Priority Species

(Whitman et al. 2014) 

Regional Climate Trends and Scenarios for the U.S. 

National Climate Assessment. Part 1. Climate of the 

Northeast U.S. (Kunkel et al. 2013)

Implementing Climate Smart Conservation in 

Northeastern Upland Forests: a Report to the 

Wildlife Conservation Society and Northeastern 

Association of Fish and Wildlife Agencies (Manomet 

Center for Conservation Sciences and National 

Wildlife Federation 2013a) 

The Vulnerabilities of Fish and Wildlife Habitats 

in the Northeast to Climate Change: a Report to 

the Northeastern Association of Fish and Wildlife 

Agencies and to the North Atlantic Landscape 

Conservation Cooperative (Manomet Center for 

Conservation Sciences and National Wildlife 

Federation 2013b)

The Vulnerabilities of Northeastern Fish and 

Wildlife Habitats to Sea Level Rise: a Report to 

the Northeastern Association of Fish and Wildlife 

Agencies and to the North Atlantic Landscape 

Conservation Cooperative (Manomet Center for 

Conservation Sciences and National Wildlife 

Federation 2013c)

Ecosystems and Wildlife Climate Change Adaptation 

Plan: Amendment to the New Hampshire Wildlife 

Action Plan (New Hampshire Fish and Game 

Department 2013) 

Climate Change Adaptation Framework: Full 

Report (Tetra Tech 2013)

Climate Change and Biodiversity in Maine: a 

Climate Change Exposure Summary for Species and 

Key Habitats (revised) (Whitman et al. 2013) 

A high-elevation ecosystem in northern New Hampshire. Photo by Todd Ontl, U.S. Forest Service.
INTRODUCTION

9

Climate Change and Cold Water Fish Habitat in the 

Northeast—A Vulnerability Assessment: a Report to 

the Northeastern Association of Fish and Wildlife 

Agencies and to the North Atlantic Landscape 

Conservation Cooperative (Manomet Center for 

Conservation Sciences and National Wildlife 

Federation 2012)

Changing Climate, Changing Forests: the Impacts 

of Climate Change on Forests of the Northeastern 

United States and Eastern Canada (Rustad et al. 

2012)

NEAFWA Regional Vulnerability Assessment 

Project—Report 2: the Habitat Vulnerability Model

(Galbraith 2011a)

Report to the NEAFWA Vulnerability Assessment 

Expert Panel: Exposure Information (Galbraith 

2011b)

Responding to Climate Change in New York State 

(New York State Energy Research and Development 

Authority 2011)

The Impacts of Climate Change on Connecticut 

Agriculture, Infrastructure, Natural Resources, and 

Public Health (Adaptation Subcommittee to the 

Governor’s Steering Committee on Climate Change 

2010)

NEAFWA Regional Vulnerability Assessment 

Project—Report 1: Forming the Expert Panel

(Galbraith 2010)

Climate Change and Biodiversity in Maine: a 

Climate Change Exposure Summary for Participants 

of the Maine Climate Change Species Vulnerability 

Assessment (Manomet Center for Conservation 

Sciences 2010a)

Climate Change and Massachusetts Fish and 

Wildlife: Habitat Management (Volume 3) (Manomet 

Center for Conservation Sciences and Massachusetts 

Division of Fisheries and Wildlife (2010b)

Climate Change and Massachusetts Fish and 

Wildlife: Habitat and Species Vulnerability 

(Volume 2) (Manomet Center for Conservation 

Sciences and Massachusetts Division of Fisheries 

and Wildlife 2010a)

Climate Change and Massachusetts Fish and 

Wildlife: Introduction and Background (Volume 1)

(Manomet Center for Conservation Sciences and 

Massachusetts Division of Fisheries and Wildlife 

2010c) 

Climate Change in the Champlain Basin: What 

Natural Resource Managers can Expect and Do

(Stager and Thill 2010)

Maine’s Climate Future: an Initial Assessment

(Jacobson et al. 2009)

New Hampshire Climate Action Plan: a Plan for 

New Hampshire’s Energy, Environmental and 

Economic Development Future (New Hampshire 

Department of Environmental Services 2009)

Confronting Climate Change in the U.S. Northeast: 

Science, Impacts, and Solutions (Frumhoff et al. 

2007)
10

CHAPTER 1: CURRENT FOREST CONDITIONS

The current landscape of New England and 

northern New York is diverse, shaped by an array 

of environmental and social influences. Forest 

ecosystems are prominent across the area, providing 

benefits that include extensive vibrant fall colors, 

recreational opportunities, water quality protection, 

wildlife habitat, and forest-based products and 

livelihoods. This chapter describes the current 

condition of forests across the Northeast to provide 

context for how these forests may change in the 

future.

REGIONAL SETTING 

This report focuses on forest ecosystems found 

across New England and northern New York within 

the northeastern United States. These forests are 

part of three ecological provinces, as delineated 

by the U.S. Forest Service (Cleland et al. 2007): 

Northeastern Mixed Forest (211), Adirondack–

New England Mixed Forest (M211), and Eastern 

Broadleaf Forest (M221) (Fig. 3). Despite the 

diversity of the overall region, these three provinces 

each have similar traits such as climate, geology, 

soils, and vegetation that transcend political 

boundaries and distinguish them from other parts of 

the Northeast (McNab and Avers 1994, McNab et al. 

2007). 

• The Northeastern Mixed Forest Province has a 

climate that is moderated by proximity to the 

Atlantic Ocean and Great Lakes. Winters are 

generally long with continuous snow cover. 

Vegetation in this region generally reflects a 

transition between boreal conifer forests in colder 

and more northerly locations and the deciduous 

hardwood forests present to the south. 

• The Adirondack–New England Mixed Forest 

Province is similar in many ways to the 

Northeastern Mixed Forest Province, although 

the region has a more continental climate that 

results in long, cold winters. Additionally, this 

area is mountainous (Fig. 4) and has topography, 

geology, and soils that reflect a combination of 

local bedrock and glacial features.

• The Eastern Broadleaf Forest Province is the 

most southerly area covered in this report. The 

topography and bedrock geology vary greatly in 

this area, from broad, hilly plateaus to the coastal 

zone along the Atlantic Ocean. This area has a 

warmer climate and longer growing season than 

the other ecological provinces.

Figure 3.—Distribution of the three ecological provinces in 

the assessment area.
CHAPTER 1: CURRENT FOREST CONDITIONS

11

The assessment area includes the six states of New 

England (Connecticut, Maine, Massachusetts, New 

Hampshire, Rhode Island, and Vermont), as well 

as northern New York. In many ways, the climate 

and forests of northern New York are more similar 

to the northern forests of New England than to 

areas of New York farther south. Therefore, the 

northern portion of New York—the Northeastern 

Mixed Forest (ecological province 211) and the 

Adirondack–New England Mixed Forest (ecological 

province M211)—is included in this report. This 

report summarizes broad-scale trends and conditions 

across the assessment area and the surrounding 

region, such as climate (Box 1), but it is important 

to note that the specific forest conditions at any 

particular place are a reflection of numerous 

environmental and social factors, both past and 

current.

Figure 4.—Elevation (above mean sea level) of the 

Northeast.

Box 1: Climate in the Region

The climate in the assessment area is diverse, 

with large seasonal and spatial variation based on 

latitude, elevation, and proximity to the Atlantic 

Ocean or large inland water bodies (Kunkel et al. 

2013). Winter is generally cold, with the jetstream 

bringing westerly, continental air and frigid 

temperatures to the region. Also affecting the area 

in important ways are several climatic phenomena 

such as extreme precipitation that leads to flooding, 

winter storms and nor’easters, ice storms, drought, 

and tropical cyclones (Kunkel et al. 2013).

Climate data from 1971 through 2000 provide a 

general picture of the contemporary climate 

(Table 2). Mean air temperature fluctuates by about 

45 °F (25 °C) between winter and summer across the 

assessment area. July is the warmest month with a 

mean temperature of 67.2 °F (19.6 °C), and January 

is the coldest month with a mean temperature 

of 17.2 °F (–8.2 °C; Table 15 in Appendix 2). 

Temperatures tend to be higher in the southern and 

coastal parts of New England throughout the year, 

although differences are smaller during the warm 

season due to the moderating effect of the Atlantic 

Ocean. Precipitation is relatively constant throughout 

the year, with each season receiving 10 to 12 inches 

on average and precipitation tending to be greatest 

near the coast and at higher elevations (Table 15).

Mean 

temperature (°F)

Mean minimum 

temperature (°F)

Mean maximum 

temperature (°F)

Mean 

precipitation (in)

Annual 43.1 32.5 53.7 45.2

Winter 20.1 10.0 30.2 10.1

Spring 41.3 30.3 52.4 11.1

Summer 64.9 53.5 76.3 11.9

Fall 46.1 36.2 56.1 12.0

*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])*
CHAPTER 1: CURRENT FOREST CONDITIONS

12

PAST CHANGES 

IN FOREST ECOSYSTEMS

Climatic changes occurring since the retreat of the 

glaciers about 10,000 years ago have influenced the 

migration and composition of northeastern forests. 

During the early Holocene (about 10,000 years ago), 

regional forests underwent dynamic transformations, 

including species migration and forest succession, 

in response to climatic changes. Most of the plant 

species present in New England today had migrated 

into the region by 8,000 years ago; American 

chestnut is among the most recent arrivals, about 

3,000 years ago (Davis 1983, 1986). Throughout 

this time, species distributions continually changed 

across the landscape in response to climate, insect 

and disease outbreaks, and competition among 

species. Pollen records point to substantial changes 

in the abundance of different tree species even into 

the current millennium (Grimm and Jacobson 1992, 

Lindbladh et al. 2003). Please refer to Appendix 1 

for common and scientific names of species 

mentioned in this report.

A variety of drivers continued to change forests 

until Euro-American settlement. In addition to 

climate, primary natural drivers of change in the 

regional forests included wind and ice storms, 

insects and diseases, beaver activity, and fires 

(Lorimer and White 2003). Intense wind events 

from tropical storms, hurricanes, and thunderstorms 

were the dominant natural disturbance events in 

many forest communities (Lorimer and White 

2003). For example, the estimated return interval 

for severe hurricanes ranged from 85 to 400 years, 

with the highest frequency in the southeastern part 

of the region (Boose et al. 2001). Fire was locally 

important and most prevalent in pitch pine-scrub oak 

ecosystems, with average return intervals estimated 

at 20 years (Lorimer and White 2003, Parshall and 

Foster 2002). Presettlement land survey records and 

other historical sources suggest that fire rotation 

periods for northern hardwoods and other forest 

types were much less frequent, with return intervals 

that often exceeded 1,000 years (Lorimer and White 

2003, Parshall and Foster 2002). 

Native Americans also influenced the landscape 

across New England and northern New York in 

many ways, although uncertainty remains about 

the degree to which indigenous people affected 

plant communities at a regional scale (Fuller et 

al. 1998, Parshall and Foster 2002). Evidence for 

human-caused vegetation change is largely focused 

on southern New England, where many Native 

American people practiced subsistence agriculture 

(Fuller et al. 1998, Patterson and Sassaman 1988). 

Fire was used in river valleys and other lowelevation ecosystems largely dominated by oak 

and pine species to enhance conditions suitable for 

small-scale agriculture, increase mast production, 

and facilitate hunting (Abrams and Nowacki 2008, 

Fuller et al. 1998, Patterson and Sassaman 1988). 

Indigenous populations were also present in northern 

New England but did not practice agriculture, and 

burning was less advantageous to these people and 

not used as often as in the southern portion of the 

region (Patterson and Sassaman 1988). 

Human activity over the past several hundred 

years has profoundly changed the regional forests 

(Foster et al. 1998). Euro-American settlement, land 

clearing for agriculture, and industrial logging began 

to greatly affect forests starting in the early 1700s 

and continuing through the mid-1800s (Foster et al. 

1998, Fuller et al. 1998). In southern New England, 

which was more than 90 percent forested during 

presettlement periods, small woodlands covered 

less than 30 percent of the area in 1830 following 

agricultural conversion (Foster and O’Keefe 2000). 

Farmland was largely abandoned during the mid-tolate 1800s as agriculture expanded to more-fertile 

regions in the Midwest and populations shifted to 

cities in response to industrialization (Foster et al. 

1998). Although northern New England experienced 

similar dynamics in more-fertile and settled areas, 

such as the Champlain Valley of Vermont and coastal 

Maine, much of its more remote and rugged portions 

remained forested and served as the center for 

important timber-producing operations beginning in 

the early 1800s through the 1900s (Whitney 1994). 

Today, forest is once again the dominant land cover 

type across the entire assessment area; however, the 

structure and composition of these areas have been 

strongly influenced and altered by this complex 

land-use legacy (Foster 1992, 2006).
CHAPTER 1: CURRENT FOREST CONDITIONS

13

LAND AND FOREST COVER

The northeastern United States is one of the most 

forested regions of the country. This report focuses 

on an assessment area spanning more than 

52.8 million acres. Nearly two-thirds (34.4 million 

acres) of this land area is forest (Fig. 5, Table 3), 

according to the National Land Cover Dataset 

(NLCD) (Fry et al. 2011). Although dominated by 

forest (65 percent of land cover), the assessment area 

also contains substantial components of wetlands 

(10 percent) and developed land (8 percent). 

Additionally, agricultural lands (7 percent), water 

bodies (5 percent), and other shrubland, herbaceous 

cover, or barren lands (6 percent) complete the 

landscape (Fry et al. 2011). 

The U.S. Forest Service Forest Inventory and 

Analysis (FIA) program provides similar estimates 

of land cover. Based on the FIA data, forest land 

covers 40.4 million acres (76 percent) of the total 

area (Table 4). The FIA estimate of forest land is 

higher than the NLCD estimate of forest because 

FIA captures many forested wetlands and other 

forest habitats that the NLCD may have classified as 

other land uses, such as wetlands, planted lands, or 

low-intensity developed lands.

Forest cover varies by state, largely reflecting the 

patterns of human settlement and development. 

Connecticut, Massachusetts, and Rhode Island have 

the lowest percentage of forest cover; however, 

these states are still heavily forested, with more 

than half of the land area being classified as forest 

(Table 4). Maine has the greatest proportion of 

forest land (85 percent) and the greatest acreage of 

forest land. As discussed earlier, the area covered by 

forest is substantially greater than during the 1700s 

and 1800s because diminishing farming activity 

allowed many areas to return to forest. During the 

past century, forest cover has increased substantially 

in New York and Maine, while forest cover has 

remained stable or has had smaller increases in 

other states (Shifley et al. 2012). In recent decades, 

land-use change for residential and commercial 

development and other uses has increased, placing 

new pressures on forest lands. One study found 

that deforestation in New England increased from 

Figure 5.—Land cover in the Northeast based on the 2006 

National Land Cover Dataset. Data source: Fry et al. (2011).

Land cover class Acres Percent

Forest 34,416,515 65.2

Wetland 5,095,632 9.6

Developed 4,120,218 7.8

Agriculture 3,718,225 7.0

Water 2,397,736 4.5

Shrubland 2,344,330 4.4

Grassland 498,181 0.9

Barren land 222,541 0.4

Total 52,813,379 100

Table 3.—Land cover classifications within the 

assessment area based on the 2006 National Land Cover 

Dataset (data source: Fry et al. [2011])
CHAPTER 1: CURRENT FOREST CONDITIONS

14

*Only the northern portion of New York is included in this report.

Table 4.—Forest cover by state for the assessment area (data source: U.S. Forest Service [2015])

1985 to 2011, primarily as a result of residential 

development, and that there was not an increase in 

forest expansion elsewhere to counteract this effect 

(Olofsson et al. 2016). Rates of land-use change 

have slowed and there has been relatively little 

change in total forest acreage since 2007 (Butler et 

al. 2015, Morin et al. 2015), but the loss of forest 

cover remains an important issue in parts of the 

Northeast (Ducey et al. 2016, Foster et al. 2010). 

FOREST COMPOSITION 

AND ABUNDANCE

Several different types of forest are found across the 

40.4 million acres of forest land in the assessment 

area (Fig. 6, Table 5). The FIA program provides 

data to describe the forest-type groups present across 

the assessment area (Box 2) (U.S. Forest Service 

2015). Maple/beech/birch (19.1 million acres) and 

spruce/fir (7.4 million acres) are the most abundant 

forest-type groups across the area (Table 5). Foresttype groups vary greatly across the region, with 

Forest land Nonforest Total

Area (acres) Percent forest cover

Connecticut 1,799,342 1,392,234 3,191,577 56

Maine 17,636,080 3,140,719 20,776,800 85

Massachusetts 3,035,792 2,175,378 5,211,169 58

New Hampshire 4,783,477 1,144,583 5,928,060 81

New York* 8,228,567 2,778,457 11,007,024 75

Rhode Island 367,372 334,198 701,570 52

Vermont 4,514,171 1,659,368 6,173,539 73

Entire area 40,364,801 12,624,937 52,989,738 76

spruce/fir most abundant in Maine (Figs. 6, 7). 

Maple/beech/birch forests are most common in 

northern New York, Vermont, New Hampshire, and 

portions of Maine and Massachusetts. Oak/hickory, 

oak/pine, and white/red/jack pine forests are most 

common in the southern portion of the region 

(Figs. 6, 7). These forest-type groups store various 

amounts of carbon (Box 3).

FOREST SYSTEMS 

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 2). In the rest of this document, a 

set of “forest systems” is generally used to describe 

the forests currently common across the region 

(Table 6). These systems can also be associated with 

terrestrial habitats and natural communities that have 

been described regionally (Anderson et al. 2013, 

Comer et al. 2003).
CHAPTER 1: CURRENT FOREST CONDITIONS

15

Figure 6.—Distribution of forest-type groups in the 

Northeast. Data source: Ruefenacht et al. (2013).

Table 5.—Forest land, by area and percentage of total 

forest land area, in the assessment area by U.S. Forest 

Service Forest Inventory and Analysis (FIA) forest-type 

group (data source: U.S. Forest Service [2015])

Forest cover

FIA forest-type group Acres Percent

Aspen/birch 3,052,102 7.6

Elm/ash/cottonwood 1,310,809 3.2

Maple/beech/birch 19,099,102 47.3

Oak/hickory 3,904,921 9.7

Oak/pine 1,473,585 3.7

Spruce/fir 7,406,580 18.3

White/red/jack pine 3,163,344 7.8

Other* 793,075 2.0

Nonstocked 161,283 0.4

Total forest land 40,364,801 100.0

*Other includes the exotic hardwoods, exotic softwoods, loblolly/

shortleaf pine, oak/gum/cypress, other eastern softwoods, and other 

hardwoods forest-type groups.

Box 2: Forest Types Used in this Report

Different organizations describe forests in different 

ways based on their local forests and unique 

management needs. For example, each state 

natural resources agency defines the natural 

ecosystems, habitats, or communities in that state, 

and classifications will vary from state to state. This 

assessment uses two classification systems, which 

are used 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 U.S. Forest Service 

Forest Inventory and Analysis (FIA) program to 

characterize forests across the Nation. In this 

assessment, we describe acres, ownership category, 

and volume of timber using “forest-type groups” 

based on the FIA classification system (Woudenberg 

et al. 2010). The FIA system measures tree species 

composition on a set of systematic plots across the 

United States and uses that information to provide 

area estimates for each forest type, making it a good 

way to estimate what is currently on the landscape 

and the relative abundance of different forest types. 

In this report, FIA forest-type groups are used to 

provide quantitative data about regional forest 

conditions (Table 5). Additionally, forest-type groups 

have been mapped regionally and nationally (Fig. 6).

FIA forest-type groups are intentionally broad in 

order to characterize diverse forests across the 

Nation, but for this reason, they are less useful 

for describing local differences among similar 

forest ecosystems. For example, the spruce/fir 

FIA forest-type group combines several unique 

forest communities that correspond to different 

site conditions and are subject to different types 

(continued on next page)
CHAPTER 1: CURRENT FOREST CONDITIONS

16

Box 2 (continued)

of natural disturbances and forest management 

activities. Therefore, spruce/fir is too broad for 

use at a regional or local level. Throughout this 

report, we also use a set of “forest systems” as the 

primary classification system whenever possible to 

better describe the forest ecosystems present in the 

region (Table 6 on pp. 18-19). We developed these 

categories based on the classification systems used 

by numerous forest management organizations in 

the assessment area, as well as regional descriptions 

of terrestrial habitats and natural communities 

(Anderson et al. 2013, Comer et al. 2003). We also 

used these forest systems to summarize forest 

vulnerability to climate change (Chapter 5). 

Although these are different ways of categorizing 

forests, there are many similarities between the two 

systems. For example, the FIA maple/beech/birch 

forest-type group is largely synonymous with the 

northern hardwood forest system, and the FIA elm/

ash/cottonwood forest-type group includes many 

forests similar to the lowland and riparian hardwood 

forest system. In this region, the FIA loblolly/

shortleaf pine forest-type group is predominantly 

pitch pine forest.

Massachusetts Connecticut Rhode Island Assessment Area

Aspen/birch

Elm/ash/cottonwood

Maple/beech/birch

Oak/hickory

Oak/pine

Spruce/fir

White/red/jack pine

Other1

Nonstocked

NorthernNew York Vermont New Hampshire Maine

Figure 7.—Proportion of U.S. Forest Service Forest Inventory and Analysis forest-type groups by state and entire assessment 

area. Data source: U.S. Forest Service (2015). 

1

“Other” includes the exotic hardwoods, exotic softwoods, loblolly/shortleaf pine, oak/gum/cypress, other eastern softwoods, and 

other hardwoods forest-type groups.
CHAPTER 1: CURRENT FOREST CONDITIONS

17

Box 3: Forest Carbon

Forest ecosystems around the world play a valuable 

role as carbon sinks. The accumulated terrestrial 

carbon pool within forest soils, belowground 

biomass, dead wood, aboveground live biomass, 

and litter represents an enormous store of carbon 

(Birdsey 1996). Terrestrial carbon stocks in the 

region have generally been increasing for the 

past few decades (Shifley et al. 2012), and there 

is increased attention on the potential to manage 

forests to maximize and maintain this carbon pool 

(Malmsheimer et al. 2011). Carbon sequestration 

and storage in forest ecosystems depends on the 

health and function of those ecosystems resulting 

from human management, episodic disturbances, 

climate variability, and forest stressors. 

Forest lands within the assessment area are 

estimated to hold about 2.7 billion metric tons 

of carbon. This is about one-fifth of the 13 billion 

metric tons of carbon stored in forests nationally, 

even though the region makes up a much smaller 

proportion of the Nation’s forests (Shifley et al. 

2012). Across the region, forests store about 66.6 

metric tons per acre, although that number will vary 

widely depending on location, forest composition, 

and management history. The oak/pine and white/

red/jack pine forest-type groups generally have the 

highest carbon stocks, storing nearly 80 metric tons 

per acre (Fig. 8). Aspen/birch forests generally have 

the lowest carbon stocks, with a regional average 

of 63.1 metric tons per acre. Spruce/fir and elm/

ash/cottonwood forests have a higher proportion of 

carbon stored in soil organic matter.

0

20

40

60

80

100

Carbon (metric tons/acre)

Soil organic Liv e belowground Litter Dead wood Liv e abov eground

Figure 8.—Average carbon stocks per acre by forest-type group and carbon pool. Data source: U.S. Forest Service 

(2015).
CHAPTER 1: CURRENT FOREST CONDITIONS

18

Forest system Description

Related terrestrial habitats 

(Anderson et al. 2013)

Central 

 hardwood-pine

Central hardwood forests are found in dry to mesic conditions 

across a variety of sites in the southern portion of the 

assessment area. Dominant species in these forests include 

several oak species, especially red, white, black, or scarlet oak. 

Numerous hardwood species may be present as codominants 

based on the site conditions, including red maple, sassafras, 

or birch or hickory species. Pine species are more likely to be 

present In the warmer and drier conditions more common in 

the southernmost and coastal portions of the assessment area.

North Atlantic Coastal Plain 

 Hardwood Forest 

Northeastern Interior 

 Dry-Mesic Oak Forest 

Central Appalachian Dry Oak-Pine Forest 

Central Appalachian 

 Pine-Oak Rocky Woodland 

Laurentian-Acadian 

 Northern Pine-(Oak) Forest 

Northeastern Coastal 

 and Interior Pine-Oak Forest

Low-elevation 

 spruce-fir 

Spruce-fir forests are dominated by boreal species that include 

red spruce, white spruce, and balsam fir. These systems are 

typically found at lower elevations in cold pockets, depressions, 

or valley bottoms. Sites typically have shallow acidic or 

nutrient-poor soils, and may be seasonally wet. Hardwood 

species, such as yellow birch and red maple, may also be 

present.

Acadian Low-Elevation 

 Spruce-Fir Hardwood Forest 

Acadian Sub-boreal Spruce Flat

Lowland and 

 riparian hardwood

This system encompasses a broad 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 flooding, erosion, groundwater seepage, or other dynamics. 

These forests are typically dominated by one or more of 

various hardwood species such as white ash, green ash, red 

maple, silver maple, swamp white oak, sycamore, American 

elm, or river birch.

Central Appalachian Stream and Riparian 

Glacial Marine 

 and Lake Wet Clayplain Forest 

North Atlantic Coastal Plain 

 Stream and River 

Northern Appalachian-Acadian 

 Large River Floodplain 

North-Central Appalachian 

 Large River Floodplain 

North-Central Interior 

 and Appalachian Rich Swamp 

North-Central Interior Wet Flatwoods

Lowland 

 mixed conifer

This system encompasses a broad range of forested wetlands 

with a conifer or mixed conifer-hardwood overstory. Forested 

wetlands typically have saturated soils, which may also be 

seasonally flooded. Many of these forests have acidic and 

nutrient-poor soils, which may be either organic or mineral. A 

greater variety of species may be present depending on local 

conditions, such as black spruce, red spruce, tamarack, balsam 

fir, eastern hemlock, and red maple. Northern or Atlantic 

white-cedar and black ash may also be present. Forested 

swamps occurring in areas with limestone or other calcareous 

substrate have less acidic soils. A greater variety of species 

is typically found in these forests compared to low-elevation 

spruce-fir forests.

North Atlantic 

 Coastal Plain Basin Peat Swamp 

Laurentian-Acadian Alkaline 

 Conifer-Hardwood Swamp 

North-Central Appalachian Acidic Swamp

Northern Appalachian-Acadian 

 Conifer-Hardwood Acidic Swamp

(continued on next page)

Table 6.—Descriptions of the forest systems considered in this assessment*
CHAPTER 1: CURRENT FOREST CONDITIONS

19

*The list of characteristic species is not exhaustive, and composition may differ substantially from site to site across the assessment area.

Forest system Description

Related terrestrial habitats 

(Anderson et al. 2013)

Montane spruce-fir Montane spruce-fir forests occur at higher elevations (generally 

above 1,500 feet) in the Appalachian Mountains. These forests 

are dominated by boreal species, particularly red spruce 

or balsam fir. Although spruce-fir forests are dominated by 

conifers, they may contain a number of associated northern 

hardwood species, such as yellow birch or sugar maple.

Acadian Low-Elevation 

 Spruce-Fir Hardwood Forest 

Acadian Sub-boreal Spruce Flat 

Northern 

 hardwood

Northern hardwood forests are widely distributed over a 

variety of sites with dry-mesic to wet-mesic conditions and 

nutrient-poor to -rich soils. This forest type is generally found 

at low to moderate elevations. Species that are commonly 

dominant include sugar maple, yellow birch, American beech, 

eastern hemlock, and red spruce.

Laurentian-Acadian 

 Northern Hardwood Forest 

Laurentian-Acadian 

 Red Oak-Northern Hardwood Forest 

Pitch pine-

 scrub oak

Pitch pine-scrub oak forests and barrens are found on xeric 

sites with deep, sandy soils. Pitch pine and scrub oak are 

the primary species. This is a fire-dependent system, and 

the species composition and structure of forests are heavily 

influenced by fire frequency. More frequent fires will promote 

pines. Oaks will have greater presence in areas with longer 

intervals between fires, or in areas where cold-air drainage 

creates conditions unsuitable for pine.

Northeastern Interior Pine Barrens 

North Atlantic Coastal Plain 

 Pitch Pine Barrens

Transition 

 hardwood 

Transition hardwood forests reflect the transition between 

central hardwood forests in warmer and more southerly 

portions of New England and northern hardwood forests 

in northern New England. Northern species such as sugar 

maple, yellow birch, and American beech may be dominant 

or common in this forest type. In addition, numerous more 

southerly species can also be present or dominant, such as red 

maple, yellow-poplar, black cherry, sweet birch, and northern 

red oak. Eastern hemlock and eastern white pine may also be a 

component of this forest type.

Appalachian 

 (Hemlock)-Northern Hardwood Forest 

Glacial Marine 

 and Lake Mesic Clayplain Forest 

Laurentian-Acadian 

 Pine-Hemlock-Hardwood Forest

Table 6 (continued).—Descriptions of the forest systems considered in this assessment
CHAPTER 1: CURRENT FOREST CONDITIONS

20

Driver of forest change References

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

impairs forest health and productivity. Acid deposition has caused red spruce to decline 

in high-elevation forests, particularly during the mid-1900s.

(Dietze and Moorcroft 2011, Groffman 

et al. 2012, Johnson and Siccama 1983)

Herbivory, particularly from white-tailed deer and moose, is considered a keystone 

driver through impacts on plant regeneration, structure, and species diversity, especially 

where deer density is high.

(Dobson and Blossey 2015, Rawinski 

2016, Shipley and Spalinger, Waller and 

Alverson 1997)

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 tree dieback and mortality.

(Clark et al. 2016, Hayhoe et al. 2007, 

Millar and Stephenson 2015)

Soil erosion and reduced water quality from improperly designed or poorly maintained 

roads, trails, or log landings can increase the amount of silt and sediment transported 

and deposited by streams.

(Anderson and Lockaby 2010, Aust and 

Blinn 2004, Hornbeck et al. 1986)

Exotic earthworms reduce forest litter, alter nutrient and water cycling, alter soil 

conditions, facilitate exotic plant species, decrease regeneration suitability for many 

forest species, and increase drought susceptibility for sugar maple.

(Bohlen et al. 2004a, 2004b; Burtelow 

et al. 1998; Frelich et al. 2006)

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.

(Burchfield et al. 2006, Clark et al. 2009, 

Collinge 1996)

Invasive plants compete for resources and alter natural forest dynamics. Many invasive 

plant species are present. Problematic species include garlic mustard, buckthorn, 

nonnative honeysuckles, Asiatic bittersweet, Japanese barberry, and Japanese 

knotweed.

(Center for Invasive Species and 

Ecosystem Health 2016, Kurtz 2013)

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

concern vary widely depending on the agents present in a geographic location and 

the susceptibility of the forest community based on species composition, age, health, 

and other factors. Problematic species include spruce budworm, balsam and hemlock 

woolly adelgids, emerald ash borer, Asian longhorned beetle, forest tent caterpillar, 

gypsy moth, and winter moth.

(Shifley et al. 2012, U.S. Forest Service 

2016)

Forest pathogens increase the risk of individual tree mortality and species extinction 

or extirpation. Pathogens of concern vary widely depending on the agents present in 

a geographic location and the susceptibility of the forest community based on species 

composition, age, health, and other factors. Problematic pathogens and disease 

complexes include beech bark disease, Dutch elm disease, and chestnut blight.

(Shifley et al. 2012, U.S. Forest Service 

2016)

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.

(Nowacki and Abrams 2008, Patterson 

2006)

Table 7.—Major drivers of change to forest ecosystems in the assessment area

MAJOR DRIVERS OF FOREST 

CHANGE

Agents of change within the assessment area 

include both natural and anthropogenic pressures. 

Past and current drivers of change are diverse and 

include fire suppression, severe weather events, 

pests and diseases, invasive species, acid deposition, 

fragmentation, and land-use change (Table 7). The 

impacts of particular threats and stressors are very 

dependent on local conditions and are not consistent 

across an area as large and diverse as New England 

and northern New York.
CHAPTER 1: CURRENT FOREST CONDITIONS

21

Disturbance Regimes

Wind, fire, insects, and diseases are still the primary 

natural disturbances influencing vegetation in much 

of the assessment area, although anthropogenic 

disturbances are also major drivers of forest 

composition (Schulte and Mladenoff 2005, White 

and Mladenoff 1994). Wind events can affect forest 

structure and composition on a broad spatial scale 

over time, although stand-replacing events are 

relatively rare (Lorimer and White 2003). The return 

intervals of stand-replacing wind events are about 

1,000 to 7,500 years for northern hardwood forests 

and 575 to 2,800 years for spruce-fir dominated 

systems. Across most of the assessment area, smallscale blowdown events have been the primary 

natural disturbance agent. Windthrow events are 

highly variable in large part because the exposure 

of an individual site to extreme wind is strongly 

influenced by its geographic location and landscape 

position. Return intervals for windthrow events vary 

geographically and have not been consistent over 

the past 40 years (Coniglio and Stensrud 2004). 

Additionally, some tree species and age classes are 

more susceptible to windthrow, which affects both 

the spatial extent of windthrow and the degree of 

tree mortality from a single event (Rich et al. 2007). 

Fire is an important disturbance regime across 

the region, although the role of fire varies among 

different forest systems. Fire occurrence has varied 

widely over time in response to climate, dominant 

vegetation, and human activities (Fuller et al. 1998, 

Parshall and Foster 2002). Before the arrival of the 

Europeans, fires were uncommon in the northern 

hardwood and hemlock forests that are more 

common across northern New England (Lorimer 

and White 2003, Parshall and Foster 2002). The 

mesic and wet forests of New England have often 

been referred to as “asbestos” forests because of 

the high levels of fire resistance (Bormann and 

Likens 1979, Lorimer and White 2003). In contrast, 

pollen records indicate that fire has long been more 

important in the pine- and oak-dominated forests 

that are more common in southern New England 

(Parshall and Foster 2002). These fire-dependent 

systems inherently benefit from the presence of 

fire (Cogbill et al. 2002, Lorimer and White 2003). 

Native Americans also used fire to influence at 

least some southern New England forests (Abrams 

and Nowacki 2008, Patterson and Sassaman 1988). 

Human activities have dramatically changed 

both forest composition and fire regimes in the 

Northeast during the past 300 years. Large-scale 

wildfires associated with logging and land-clearing 

dramatically affected some forests in the 1800s and 

early 1900s (Lorimer 1977, Ziegler 2000), leading 

to suppression efforts that have widely limited fire 

during the last century (Fuller et al. 1998, Nowacki 

and Abrams 2008). A relative lack of fire and 

disturbance has increased the abundance of mesic 

species such as red maple and beech (Cogbill et al. 

2002, Fuller et al. 1998), and many of the forest 

communities present today are not fire-associated 

ecosystems (Nowacki and Abrams 2008, Pederson et 

al. 2014).

Pests and Diseases

Insect and disease outbreaks have also influenced 

the vegetation of the assessment area. Before 

European settlement, outbreaks were caused by 

native species, such as spruce budworm and forest 

tent caterpillar. Spruce budworm, a native defoliator 

of balsam fir and red, white, and black spruce has 

caused extensive damage to spruce-fir with an 

outbreak every 30 to 60 years (Fraver et al. 2007). 

More recently, insect and disease outbreaks have 

occurred at an increasing frequency because of 

introduction and establishment of nonnative insects 

and disease agents. For example, outbreaks of the 

nonnative gypsy moth have caused mortality in 

oak forests across the Northeast since the early 

1900s, with severe regional impacts observed 

during outbreaks in the early 1980s (Elkinton and 

Liebhold 1990). Similarly, the introduced hemlock 

woolly adelgid, a scale-like insect, has caused 

complete mortality of eastern hemlock in portions of 

Connecticut and Massachusetts (Orwig et al. 2002). 

The exotic emerald ash borer and Asian longhorned 

beetle were introduced in parts of the assessment 

area in the mid-2000s (Shifley et al. 2012, U.S. 

Forest Service 2016). Beyond insect pests and forest 

diseases, earthworm introduction into previously 
CHAPTER 1: CURRENT FOREST CONDITIONS

22

glaciated regions of the Northeast has dramatically 

altered soil composition and structure, and organic 

matter decay rates and processes, making seedbed 

and germination conditions less favorable for some 

native plants (Bohlen et al. 2004, Hale et al. 2006).

Nonnative Plant Species

Nonnative plant species have become an increasing 

concern across the assessment area because of their 

potential to outcompete native species and affect 

species interactions that are important to ecosystem 

function. Some nonnative species can establish 

more rapidly than native species, in part because 

native diseases or pests are not adapted to compete 

against them (Ricciardi et al. 2013, Tu et al. 2001). 

This can lead to a decline of native species and, 

in combination with other stressors, can reduce 

biological diversity (Didham et al. 2005). Past and 

current changes in land use within the assessment 

area have made conditions more favorable for the 

introduction and spread of many nonnative invasive 

plant species. The presence of several common 

nonnative plant species, such as Japanese barberry, 

multiflora rose, and Asiatic bittersweet, was greatest 

along roads and in smaller, more fragmented parcels 

in southern New England; past land use had an even 

stronger influence than current land use, such that 

lands that were formerly residential lands or fields 

were more likely to have nonnative species currently 

(Lundgren et al. 2004). Similarly, another study 

looking across the six New England states found 

that housing patterns influenced the abundance 

and composition of different nonnative invasive 

species, largely by creating conditions favorable to 

the introduction and growth of these plants (GavierPizarro et al. 2010). These issues are being addressed 

by the establishment of numerous cooperative weed 

management areas to control invasive plant species 

across political boundaries. Land management 

organizations in the assessment area have been 

actively combating the spread of nonnative invasive 

plants with integrated pest management tools that 

include prescribed fire, mechanical treatments, and 

herbicide application. 

FOREST OWNERSHIP AND USE

Ownership

There are numerous types of forest landowners 

within the assessment area (Table 8), each with 

different approaches to forest management (Box 4). 

About one-fifth of the forest land in the assessment 

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

Ownership Acres Percent Connecticut Maine Massachusetts

New 

Hampshire New York1 

Rhode 

Island Vermont

National 

Forest

1,297,962 3.2 – 58,221 – 793,384 – – 446,357 

Other 

federal2

410,199 1.0 14,320 127,579 81,133 62,366 80,584 – 44,217 

State 5,275,821 13.1 303,690 883,817 592,223 209,368 2,858,097 60,661 367,964 

County, 

municipal, 

and local

1,241,971 3.1 173,751 211,021 408,376 244,330 88,772 42,951 72,771 

Private 32,138,848 79.8 1,307,581 16,355,442 1,954,060 3,474,029 5,201,114 263,760 3,582,863 

Total 40,364,801 100 1,799,342 17,636,080 3,035,792 4,783,477 8,228,567 367,372 4,514,171 

1

Only the northern portion of New York (ecological provinces 211–Northeastern Mixed Forest and M211–Adirondack-New England Mixed Forest) is 

included in this report.

2

Includes other federal agencies such as the National Park Service and Fish and Wildlife Service in the U.S. Department of the Interior, and the 

Department of Defense.

Table 8.—Forest land ownership in the assessment area, by state (data source: U.S. Forest Service [2015])
CHAPTER 1: CURRENT FOREST CONDITIONS

23

Box 4: Owner Motivations for Land Management

Family forest owners were asked about their reasons 

for owning forest land as part of the National 

Woodland Owner Survey (Butler et al. 2016). The 

leading reasons that most families acquire or retain 

forest lands were related to beauty, protection of 

nature, privacy, family legacy, and hunting or other 

recreational activities (Butler et al. 2016). Fewer than 

25 percent of families owned forest for the primary 

purposes of firewood production, timber production, 

or the collection of nontimber forest products. 

Family owners can enroll their lands in conservation 

easements or forest certification programs, such 

as the American Tree Farm System, which require 

forests to have written management plans. Engaged 

family forest owners often look to extension agents, 

conservation districts, and private consultants to 

provide technical assistance and other resources for 

managing forests. 

Industrial forest landowners manage for timber 

products. Many industrial forest landowners 

voluntarily participate in third-party certification, 

but millions of acres of corporate land have 

been transferred to real estate investment trusts 

(REITs) and timberland investment management 

organizations (TIMOs) in recent decades (Bliss et 

al. 2009, Jin and Sader 2006). REITs and TIMOs are 

considered private (nonindustrial) forest landowners. 

They have been acquiring large areas of land and 

receiving much attention in the last 20 years. REITs 

own and operate income-producing real estate 

and timberland holdings, sometimes made public 

through trading of shares on a stock exchange. REITs 

are required to distribute at least 90 percent of 

taxable income to shareholders annually, which is an 

allowable deduction from corporate taxable income. 

TIMOs act as investment managers for institutional 

clients who therefore own the timberlands as 

investments or partnership shares (Fernholz et 

al. 2007). The goal is to maximize the growth of 

the timberland asset, and the TIMOs advise on 

management of the timberland. In contrast to 

corporate holdings, the risk of large investment 

losses is spread out among investors, as are the 

frequency and rate at which capital gains are taxed. 

The purchase of timberland by REITs and TIMOs 

raises concerns about parcelization, development, 

and high-yield management practices due to 

landholding objectives and timeframes (Bliss et al. 

2009, Fernholz et al. 2007, Jin and Sader 2006). 

Public (federal, state, and local) agencies and tribal 

organizations own extensive tracts of forest in the 

assessment area. These lands are often managed to 

provide a complex array of environmental benefits 

based on public or community needs, which may 

include wildlife habitat, water protection, nature 

preservation, timber production, recreation, cultural 

resources, and a variety of other uses (Shifley et al. 

2012).

area is publicly owned, which includes federal, 

state, and local ownership. The U.S. Forest Service 

manages approximately 1.3 million acres, about 3 

percent of the forest area, in the Green Mountain 

National Forest in Vermont and the White Mountain 

National Forest in New Hampshire and western 

Maine. About 1 percent of the forest area is owned 

by other federal agencies and includes such 

places as the Silvio O. Conte National Fish and 

Wildlife Refuge located in parts of Connecticut, 

Massachusetts, New Hampshire, and Vermont; the 

Appalachian National Scenic Trail, which starts 

in Maine and traverses all states in the assessment 

area except Rhode Island; and Marsh-BillingsRockefeller National Historical Park in Vermont. 

Notable among state-owned lands is the Adirondack 

Forest Preserve in northern New York, which 

contains 2.6 million acres of forest interspersed 

within a patchwork of private lands (New York 

Department of Environmental Conservation 2016). 

Lands in the Adirondack Forest Preserve were 

afforded protection through the state constitution 

in the late 1800s to be “forever kept as wild forest 

lands.” The broader 6-million-acre Adirondack 

Park encompasses the Forest Preserve along with 

other public and private lands. About 3 percent of 

forest land in the assessment area is owned by local 

governments, and this ownership is most common 

in southern New England; about 10 percent of forest 
CHAPTER 1: CURRENT FOREST CONDITIONS

24

lands in Connecticut, Massachusetts, and Rhode 

Island are owned by local entities.

The majority of forest land, more than 30 million 

acres and about 80 percent of all forest, is 

privately held. This category reflects a diversity 

of landowner types, including industrial and 

corporate organizations in northern New England, 

conservation organizations, families, individuals, 

and tribes (Oswalt et al. 2014). Private ownership 

patterns are complex and change over time. For 

example, in recent decades a substantial amount 

of forest previously owned by the forest products 

industry has been sold to real estate investment trusts 

and timber investment management organizations 

(Box 4). Regionally, about one-quarter of privately 

owned land is currently held by corporate entities 

(Shifley et al. 2012).

Changes are also occurring among smaller private 

landowners, and these changes affect how forests are 

managed across the landscape (Box 4). Many parts 

of the Northeast are experiencing population growth, 

with development expanding into forested areas 

to support new primary and secondary homes and 

associated amenities (Ducey et al. 2016, Olofsson 

et al. 2016). Although the total area of forest land 

has been relatively stable in recent decades, the 

average size of forest parcels has decreased (Shifley 

et al. 2012). The size of forest ownerships affects 

many behaviors among family forest owners (Butler 

2008, Butler et al. 2016). Family forest owners 

with larger parcels are more likely to engage in 

forest management, conservation easements, costshare programs, and forest certification programs 

(Butler 2008). Smaller parcels are managed with 

less efficiency in terms of writing and executing 

management plans, and as parcel size decreases, 

public access and wildlife habitat can be threatened, 

for example by residential development of forest 

land (Shifley et al. 2012). Concern about the loss of 

forests and associated environmental benefits in the 

region has spurred greater action to protect lands 

through conservation easements and other activities 

(Foster et al. 2010, Meyer et al. 2014). 

Forest Harvest and Products

Forests contribute to the Northeast’s economically 

important wood products industry. For example, 

more than 750 million cubic feet of wood was 

harvested for commercial use across the region 

in 2006, which is nearly 5 percent of national 

production (Shifley et al. 2012). This material 

includes saw logs, veneer logs, pulpwood, fuelwood, 

and other wood products used by wood processing 

mills and other facilities in the region. Saw logs 

and pulpwood make up the majority of wood 

use, although some states—notably Vermont and 

New York—also produce a substantial amount of 

fuelwood (Shifley et al. 2012).

More than 36 million acres, or 90 percent, of the 

forest land in the region is classified as timberland, 

meaning that it is considered suitable for wood 

production because forest harvesting is not 

prohibited. Across the assessment area, the amount 

of wood harvested each year is less than the amount 

of forest growth (Fig. 9). This comparison of net 

annual forest growth to removals provides a relative 

indicator of pressure to use the timber resource 

(Butler et al. 2011, Shifley et al. 2012). The growthto-removals ratio is based on FIA data and compares 

net growth (i.e., gross growth minus mortality) to 

removals from forest management for forested lands. 

Values greater than 1.0 indicate that net annual 

growth is greater than annual removals and that the 

timber resource is increasing across the landscape 

(Butler et al. 2011). The growth-to-removals ratio for 

the assessment area was 1.8 during the most recent 

inventory period. This means that forest growth was 

about 80 percent greater than removals, although this 

value varies by location and forest-type group 

(Fig. 9).

The FIA data also provide more information 

about the amount of wood removed from forests 

in the assessment area through timber harvest 

or conversion of forest to nonforest, with most 

removals in the Northeast being due to timber 

harvest. Among the major forest-type groups, 
CHAPTER 1: CURRENT FOREST CONDITIONS

25

0 400 800 1200 1600

Vermont

Rhode Island

Northern New York

New Hampshire

Massachusetts

Maine

Connecticut

Assessment area

Million cubic feet

Net growth Removals

0 400 800

Other removals1

White/red/jack pine

Spruce/fir

Oak/pine

Oak/hickory

Maple/beech/birch

Elm/ash/cottonwood

Aspen/birch

Million cubic feet

Net growth Removals

Figure 9.—Annual net growth and removals for individual states and the entire assessment area (left) and major forest-type 

groups (right) in the assessment area. 

1

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

uses. Data source: U.S. Forest Service (2015).

oak/hickory had the highest growth-to-removals 

ratio (7.0), and most forest-type groups had ratios 

greater than the regional average. Removals 

from forest management and timber harvest in 

the assessment area were greatest in the most 

commercially important forest-type groups: maple/

beech/birch (53 percent of total removals), spruce/fir 

(14 percent), and white/red/jack pine (9 percent). 

Other removals—a category that includes loss of 

forest growth to conversion of land to other uses or 

to a protected (nontimberland) status—represented 

8 percent of removals. Accounting for state size, 

Connecticut, Massachusetts, and Rhode Island had 

a higher volume of other removals (i.e., growing 

stock converted to nontimber or nonforest land 

uses) than the areas farther north. At the same 

time, these three states also had higher growth-toremovals ratios, which points to a lower level of 

forest utilization for wood products. Combined, the 

data provide evidence of both lower levels of forest 

harvest in southern New England and a higher rate 

of conversion to nontimber or nonforest land uses.

SUMMARY

Forests are a defining feature across the 

Northeast and have been shaped by a multitude 

of factors, including climate, geology, glaciation, 

land conversion and development, and human 

management. In addition to being the dominant 

land cover, forests are important for wildlife habitat, 

carbon storage, economic and cultural resources, and 

other values. The context presented in this chapter 

will be helpful for interpreting information in the 

chapters that follow. It may be particularly important 

to refer to this information when considering 

information on climate change impacts (Chapter 4), 

forest ecosystem vulnerability (Chapter 5), and 

connections with other aspects of natural resource 

management and planning (Chapter 6).
26

CHAPTER 2: OBSERVED CLIMATE CHANGE

As discussed in Chapter 1, climate is one of 

the principal factors that have determined the 

composition and extent of regional forest ecosystems 

over the past several thousand years. This chapter 

describes the climate trends in the assessment area 

that have been observed over the past century, 

including documented patterns of climate-related 

processes and extreme weather events. It also 

presents evidence that regional ecosystems are 

already exhibiting signals that they are responding to 

shifts in temperature and precipitation.

OBSERVED TRENDS 

IN TEMPERATURE 

AND PRECIPITATION

Temperature

The entire northeastern United States has 

experienced substantial changes in temperature and 

precipitation over the past 100 years (Kunkel et al. 

2013). Although there is variation in annual mean 

(average) temperature from year to year, there is a 

long-term warming trend (Fig. 10) that is consistent 

with changes observed at state, continental, and 

global scales (Intergovernmental Panel on Climate 

Change [IPCC] 2013). Across the assessment area, 

the mean annual temperature increased by about 

2.4 °F (1.3 °C) between 1901 and 2011 (Fig. 10) 

based on data from the Climate Wizard Custom tool 

(Box 5, Appendix 2).

Among individual seasons, the greatest temperature 

change during the 20th century was observed 

in winter, with an estimated increase in mean 

temperature of 3.5 °F (1.9 °C) and an increase in 

minimum (low) temperature of 4.2 °F (2.3 °C) 

across the assessment area. Warming has been 

pronounced throughout the winter; the greatest 

increases in mean, minimum, and maximum 

Figure 10.—Mean annual temperature across the 

assessment area, 1901 through 2011. Open circles represent 

the mean for each year. 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 0.022 °F/year). 

Data source: Climate Wizard (2014).

temperatures were observed in February (Fig. 11). 

Mean and minimum temperatures have increased 

over all seasons and months, whereas changes in 

maximum temperature have been more variable 

(Figs. 11, 12; Appendix 2). Observed temperature 

increases are spatially heterogeneous across the 

region. For example, temperatures at five weather 

stations across New Hampshire showed increases in 

minimum temperatures ranging from 0.4 °F (0.2 °C) 

to 2.9 °F (1.6 °C) during 1895 through 2012 (Wake 

et al. 2014a, 2014b). There are no clear trends in 

geographic variability across the region (Fig. 12), 

and notable increases or decreases in mapped data 

should be regarded with some skepticism because of 

the potential for localized anomalies or errors (Beier 

et al. 2012a).
CHAPTER 2: OBSERVED CLIMATE CHANGE

27

Box 5: Where Are these Data from?

Weather stations in the Northeast 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, Girvetz et al. 2009, 

Karl et al. 1996). This tool uses data from PRISM 

(Parameter-elevation Regressions on Independent 

Slopes Model) (Gibson et al. 2002, Karl et al. 1996), 

which converts measured point data from weather 

stations onto a continuous 2.5-mile grid over the 

entire United States. 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 through 2011. Additional 

details about the data presented in this chapter are 

available in Appendix 2.

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. 2012a, 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 areas with complex topography 

and potentially overestimate or underestimate the 

change occurring in a particular location (Beier et 

al. 2012a). 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. 

-1

0

1

2

3

4

5

6

Chang e in Tem per atu re (?F)

Mean temp erature

Mean minimum temp erature

Mean max imum temp erature

Figure 11.—Change in mean monthly temperature across the assessment area, 1901 through 2011. Data source: Climate 

Wizard (2014). Values are provided in Appendix 2. Change in Temperature (oF)
CHAPTER 2: OBSERVED CLIMATE CHANGE

28

Figure 12.—Estimated change in annual and seasonal mean, minimum, and maximum temperature 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). Additional information is available in Appendix 2.
CHAPTER 2: OBSERVED CLIMATE CHANGE

29

The general trend toward increasing temperatures in 

the assessment area is similar to observations that 

have been reported elsewhere. The mean surface air 

temperature across the globe increased 1.5 °F (0.85 

°C) over the last century (IPCC 2013). Average 

temperature across the United States warmed by 1.3 

to 1.9 °F (0.7 to 1.1 °C) since 1895, with most of the 

increase occurring since 1970 (Melillo et al. 2014). 

Across the Northeast, temperatures rose an average 

of 0.16 °F (0.06 °C) per decade during 1885 through 

2011, for a total increase of about 1.8 °F (1.0 °C) 

during that period (Kunkel et al. 2013). 

Growing Season Length

Warmer temperatures have resulted in a longer 

freeze-free season and longer growing season across 

the region (Frumhoff et al. 2007, Kunkel et al. 

2013). The freeze-free season, which is the period 

between the last occurrence of 32 °F in the spring 

and the first occurrence in the fall, was about 10 

days longer in the Northeast during 1991 to 2010 

compared to 1961 to 1990 (Kunkel et al. 2013). An 

examination of the end of season for forests (based 

on leaf senescence) from 1989 to 2008 found that the 

end of season is occurring later in the fall and that 

for much of the region, the delay in end of season is 

related to the number of cold-degree days (Dragoni 

and Rahman 2012). Increases in the growing season 

length and other climatic factors have caused 

noticeable changes in the timing of biological 

activities in the Northeast as well as across the world 

(Ellwood et al. 2013, Schwartz et al. 2006, Walther 

et al. 2002) (Box 6).

Box 6: Ecological Indicators of Climate Change

The timing of biological events, 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). 

Numerous phenological changes have been observed 

across the region, such as the following:

Water

• Winter ice cover on lakes has declined by 2 or 

more weeks across much of the region (Beier et 

al. 2012b, Hodgkins et al. 2002).

• On rivers across New England, the timing of 

spring flows advanced by 1 to 2 weeks during the 

last three decades of the 20th century (Hodgkins 

et al. 2003). 

Plants

• The green canopy duration of trees at Hubbard 

Brook Experimental Forest in New Hampshire 

increased about 10 days during a 14-year period 

(Richardson et al. 2006).

• The date of first flowering is 1 week earlier on 

average compared to Henry David Thoreau’s 

records from the mid-1800s. Highbush 

blueberries and slender yellow woodsorrel are 

flowering several weeks earlier (Miller-Rushing 

and Primack 2009). Invasive and nonnative 

species have been better able to respond than 

native species (Willis et al. 2010).

• Comparison of the same clone of lilac across 

72 sites in the Northeast showed a 4-day 

advancement since the 1960s (Wolfe et al. 2005).

Animals

• Migratory birds are arriving earlier and breeding 

earlier, with the ranges of some species shifting 

or contracting (Rahbeck et al. 2007, Waite and 

Strickland 2006, Zuckerberg et al. 2009) (Fig. 13).

Figure 13.—Nashville warbler, one of several northern 

bird species that have been observed as having 

a northward shift in southern range boundaries 

(Zuckerberg et al. 2009). Photo by Bill Thompson, used 

with permission.
CHAPTER 2: OBSERVED CLIMATE CHANGE

30

Precipitation

Regional precipitation patterns have also changed 

over the past century (Kunkel et al. 2013, 

Spierre and Wake 2010). Although annual mean 

precipitation varies widely from year to year, there 

is a trend toward greater annual precipitation in 

the assessment area. Mean annual precipitation 

increased by 6.9 inches, or about 16 percent, across 

the assessment area from 1901 through 2011 (Figs. 

14, 15). Precipitation patterns have also changed 

across seasons. Seasonally, the greatest increase 

was observed during the fall (3.0 inches), and the 

smallest increase during winter (0.6 inch) (Figs. 15, 

16). It is important to note that monthly (Fig. 15) 

and seasonal averages combine data from across the 

assessment area, and that changes are geographically 

variable across the landscape (Fig. 16). 

These observed changes in precipitation are 

consistent with observations reported elsewhere. 

Across the Northeast, annual precipitation increased 

0.43 inch per decade between 1895 and 2011, which 

translates to a total increase of about 5 inches during 

that period (National Oceanic and Atmospheric 

Administration [NOAA] National Climatic Data 

Center 2014, Rahbek et al. 2007). Precipitation 

across the six New England states increased 0.56 

inch per decade, or 6.6 inches total, from 1895 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Change in P recipitation (inches)

Figure 14.—Mean annual precipitation across the 

assessment area, 1901 to 2011. Open circles represent the 

mean for each year. 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 0.062 inches/

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

Figure 15.—Change in mean monthly precipitation across the assessment area, 1901 through 2011. Data source: Climate 

Wizard (2014).

through 2011 (NOAA National Climatic Data 

Center 2014). Another study points to a precipitation 

increase of nearly 0.75 inch per decade across the 

Northeast, or about 4.3 inches total, from 1948 

through 2007 (Spierre and Wake 2010). 
CHAPTER 2: OBSERVED CLIMATE CHANGE

31

Figure 16.—Estimated 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). Additional information is available in Appendix 2.
CHAPTER 2: OBSERVED CLIMATE CHANGE

32

Precipitation has generally increased across the 

assessment area, with high levels of variability from 

year to year (Hayhoe et al. 2007, Kunkel et al. 2013, 

NOAA National Climatic Data Center 2014, Waite 

and Strickland 2006) (Box 7). Additionally, the 

frequency of consecutive wet days has increased, 

while the number of consecutive dry days has 

decreased (Brown et al. 2010). The Northeast has 

generally had some of the greatest precipitation 

increases of any region in the United States, and the 

past four decades have been wetter than during the 

period from 1901 through 1960 (Melillo et al. 2014).

OBSERVED TRENDS IN EXTREME 

WEATHER EVENTS

Weather or climate extremes are defined as 

individual weather events or long-term patterns that 

are unusual in their occurrence or have destructive 

potential (Karl et al. 2008). These events can trigger 

catastrophic disturbances in forest ecosystems 

and can have significant socioeconomic impacts. 

There is evidence that extreme events are becoming 

more frequent and severe across the United States 

and globally, in part due to global climate change 

(Coumou and Rahmstorf 2012, IPCC 2012, Kunkel 

et al. 2008, Thibeault and Seth 2014). It is difficult 

to directly attribute the occurrence of a single 

event to climate change, although researchers are 

increasingly identifying changes in the underlying 

thermodynamic and biophysical relationships 

contributing to these events (Coumou and Rahmstorf 

2012, Stott et al. 2010, Trenberth et al. 2015).

Temperature Extremes

Warmer mean temperatures are often correlated 

with higher extreme temperatures (Kling et al. 

2003, Kunkel et al. 2008). The number of hot days 

and heat waves has increased across much of the 

United States (Kunkel et al. 2008, Melillo et al. 

2014). In the Northeast, however, the occurrence of 

heat waves did not change during the 20th century 

(Kunkel et al. 2013). There were fewer intense heat 

waves from the late 1950s into the early 1980s than 

during periods earlier or later in the century. Some 

increases in the number of hot days and heat waves 

have been observed since the 1980s with moderately 

high heat wave index values occurring in 2001, 

2002, 2010, and 2011 (Frumhoff et al. 2007, Kunkel 

et al. 2013).

Box 7: Drought

State-level data show particularly wet growingseason conditions since about 2005 in Maine, New 

Hampshire, and Vermont (NOAA National Climatic 

Data Center 2014), but in 2016, these states and the 

rest of the Northeast experienced the worst drought 

conditions since the 1960s. Droughts are among the 

greatest stressors on forest ecosystems, and can 

often lead to secondary effects of insect and disease 

outbreaks on stressed trees and increased fire risk. 

In North America and the United States, there has 

been a trend toward wetter conditions since 1950, 

and there is no detectable trend for increased 

drought based on the 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 Northeast as 

a result of increased precipitation (Andreadis and 

Lettenmaier 2006, Peterson et al. 2013b). Regional 

data from the Northeast support this general 

pattern (NOAA National Centers for Environmental 

Information 2017, Peters et al. 2014). Between 1895 

and 2014, there has been no change in drought 

incidence during the growing season (June through 

September), with the mid-1960s representing the 

most extreme droughts during the period of record 

(Kunkel et al. 2013, NOAA National Climatic Data 

Center 2014). 
CHAPTER 2: OBSERVED CLIMATE CHANGE

33

The number of extremely cold weather events 

appears to be decreasing. Minimum temperatures 

have increased more than maximum temperatures 

in many parts of the United States, including the 

Northeast (DeGaetano and Allen 2002, Peterson 

et al. 2008). These trends correspond to global 

and regional patterns of increasing occurrence of 

extreme hot weather and decreasing occurrence of 

extreme cold weather (Brown et al. 2010, Hansen et 

al. 2012). 

Precipitation Extremes

Despite the high amount of 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 over the past century (Kunkel 

et al. 1999, 2013; Melillo et al. 2014). Extreme 

precipitation events totaling more than 2 inches 

in 48 hours increased during the 1980s and 1990s 

(Frumhoff et al. 2007). Another study found that 

most weather stations in the Northeast had increases 

of 1- and 2-inch precipitation events from 1948 

through 2007, and nearly 75 percent of stations also 

showed increases in 4-inch precipitation events 

during that period (Spierre and Wake 2010) 

(Fig. 17).

Another way to estimate the change in extreme 

precipitation events is to look at the 99th percentile 

of rainfall during a 24-hour period. Between 

1948 and 2007, 86 percent of weather stations in 

the Northeast showed an increasing trend in the 

magnitude of the 99th percentile value. Across 

the Northeast, the heaviest 1 percent of daily 

precipitation events increased 71 percent between 

1958 and 2012, the most of any region in the United 

States (Melillo et al. 2014). Similarly, recurrence 

intervals (such as a 50-year event) are also shorter 

than in the past (DeGaetano 2009). 

Severe Storms

Numerous types of storms occur in the region as a 

result of its diverse climate, including thunderstorms, 

ice storms, tropical cyclones and hurricanes, and 

nor’easters (Kunkel et al. 2013). There is evidence 

of poleward movement among storm tracks in 

Figure 17.—Change in extreme precipitation events in the 

Northeast and Pennsylvania between 1948 and 2007: mean 

decadal change in (A) 1-inch events, (B) 2-inch events, and 

(C) 4-inch events (adapted from Spierre and Wake [2010], 

used with permission).

B

A

C
CHAPTER 2: OBSERVED CLIMATE CHANGE

34

the North Atlantic since the middle of the 20th 

century, which may influence the pattern of storms 

(Huntington et al. 2009). The relative infrequency 

of extreme events and lack of consistent, long-term 

datasets create substantial challenges for detecting 

changes over time (Easterling et al. 2000, Kunkel 

et al. 2012) (Box 8). For example, a study of severe 

thunderstorm observations over the eastern United 

States identified an increase in thunderstorm 

frequency over the last 60 years, but it is difficult 

to assess whether those increases are biased by 

increased accuracy in storm reporting (Robinson et 

al. 2013). As a result, there has been a shift away 

from using observational data on thunderstorms to 

examining the environmental conditions associated 

with these events (Kunkel et al. 2012). Similarly, 

research on the frequency and intensity of tropical 

storms and hurricanes has turned up a variety of 

results (Box 9). 

OBSERVED TRENDS IN SEA LEVEL

Climate change has caused substantial sea-level 

rise both globally and regionally. Sea-level rise is 

the result of numerous interacting dynamic forces 

within the oceans. As water temperatures increase, 

water expands and increases the volume of the 

Box 8: Climate Variability

“... [O]ne of the brightest gems in the New 

England weather is the dazzling uncertainty of it. 

There is only one thing certain about it, you are 

certain there is going to be plenty of weather.”

~Mark Twain

Although there is increasing and unequivocal 

evidence that the planet is warming and that the 

global climate is changing (IPCC 2007a, 2013), 

one of the challenges is distinguishing between 

the long-term trends that are attributable to 

climate change and those changes that are a 

result of natural climate variability. This can 

be particularly challenging in the northeastern 

United States because the regional climate 

generally has high levels of variability (Kunkel et 

al. 2013). Extreme events―including hurricanes, 

extreme wind, ice storms, and droughts―are 

influenced by changes in the Earth’s climate, and 

many of these extreme events have increased 

in frequency or severity in recent decades (IPCC 

2012). Although it is not possible to attribute 

a single event to climate change (Coumou and 

Rahmstorf 2012, Jacobson et al. 2009, Kunkel et 

al. 2012, Peterson et al. 2013b), new research 

is able to discern the relative contribution of 

climate change to a single event by looking at 

how an event is affected by known changes in 

the Earth’s thermodynamic state (Herring et al. 

2014, Trenberth et al. 2015).

Box 9: Tropical Storms and Hurricanes

Tropical cyclones and hurricanes moving up the 

Atlantic seaboard also affect the assessment 

area. From 1900 to 2010, coastal areas in 

New England were affected by as many as 

nine hurricanes and numerous tropical storms 

(Kunkel et al. 2013, Stoner et al. 2012). The 

assessment area has sustained intense rain, hail, 

wind, and flooding as a result of hurricanes, with 

the 1938 hurricane and recent hurricanes Irene 

and Sandy being particularly notable (Kunkel 

et al. 2013, Peterson et al. 2013b). Although 

not every hurricane formed in the Atlantic 

makes landfall or affects the assessment area, 

there is some evidence that the strength and 

frequency of hurricanes have been increasing 

since 1970, and that this increase is associated 

with warming sea surface temperatures (Holland 

and Webster 2007). Based on the average 

number of hurricanes from 1981 to 2010, the 

2011 hurricane season was above average, and 

was the 12th such above-average season since 

1995 (Blunden and Arndt 2012, Melillo et al. 

2014). But there is no evidence of change in 

the frequency of hurricanes that make landfall 

(Holland and Webster 2007). Trends in severe 

weather frequency are difficult to attribute 

to changes in the climate (Kunkel et al. 2012, 

Peterson et al. 2013b) as recent advances in 

technology, population density, and social media 

have contributed to increases in storm reporting 

(Hayhoe et al. 2007, Miller-Rushing and Primack 

2009, Peterson et al. 2013b, Robinson et al. 

2013, Willis et al. 2010).
CHAPTER 2: OBSERVED CLIMATE CHANGE

35

ocean. Melting glaciers and ice sheets further 

increase the amount of water going into the oceans. 

These changes cause additional changes to the 

circulation of the oceans as gradients (differences) in 

temperature and ocean salinity are altered. 

Observations of sea-level rise show greater increases 

regionally compared to the global trend. Since 1900, 

the global average sea level rose 8 inches, whereas 

the increase was 12 inches across the northeastern 

United States and even more along the New England 

coastline (Horton et al. 2014). Between 1950 and 

2009, observed increases in sea levels along the 

northeastern coast of North America were 3 to 4 

times greater than the global average increase, with 

accelerating rates of increase in recent decades 

(Boon 2012, Holland and Webster 2007, Sallenger et 

al. 2012, Zuckerberg et al. 2009). The larger increase 

in the Northeast is a result of many complex factors 

including land subsidence and changes in oceanic 

currents (Horton et al. 2014). 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. Sea levels along the northeastern 

coast of North America were historically lower than 

elsewhere as a result of pressure gradients associated 

with the Atlantic Meridional Overturning Circulation 

(Boon 2012, Sallenger et al. 2012). In the Northeast, 

sea-level rise over the last century has increased the 

risk of erosion, damage from storm surges, flooding, 

and damage to infrastructure and coastal ecosystems.

OBSERVED HYDROLOGIC CHANGE

Snowfall and Snow Cover

Cold and snowy winters are characteristic of much 

of the region, particularly in the north. Cold-season 

precipitation has increased across much of the 

northeastern United States in recent decades, but 

warmer temperatures have resulted in a greater 

proportion of precipitation falling as rain rather 

than snow (Burakowski et al. 2008, Huntington et 

al. 2004). Total snowfall decreased in the region 

between 1965 and 2005. The largest decreases 

in snowfall occurred in December, with smaller 

decreases in February (Burakowski et al. 2008). 

Although snowfall amounts are quite variable 

from year to year, these results are similar to other 

observations of fewer heavy snowfall years across 

the Northeast during the last 30 years (Kunkel et 

al. 2013). The change in cold-season precipitation 

patterns also influences the timing and amount of 

streamflow (Box 10).

Snow cover is also changing. From 1948 through 

1988, the average number of days with snow cover 

greater than 1.0 inch ranged from fewer than 50 days 

in coastal Connecticut and Rhode Island to more 

than 100 days in the Adirondack Mountains of New 

York and in the northern Appalachian Mountains, 

although there was large year-to-year variation 

(Leathers and Luff 1997). The number of snowBox 10: Streamflow

In New England, peak river flows typically occur in 

the spring when rain falls on snow or saturated soils 

(Hodgkins et al. 2003). Warmer temperatures, earlier 

snowmelt, and increases in winter and early spring 

rainfall have resulted in peak streamflows occurring 

earlier in the year (Huntington et al. 2009, Karl et al. 

2008, Kunkel et al. 2013). Similarly, the winter/spring 

center of volume, which is when half of the spring 

volume of river flow has occurred, advanced across 

27 streams in New England where long-term records 

were available (Hodgkins et al. 2003). The greatest 

changes were in northern and mountainous areas 

of New England, where the date has occurred 1 to 2 

weeks earlier since the late 1960s. Average annual 

streamflow also increased on 22 of the 27 streams 

(Daly et al. 2008, Hodgkins and Dudley 2005, Karl et 

al. 2008, Walsh et al. 2014), which is similar to other 

observations of increased flows (Campbell et al. 

2011). Higher peak flows can increase flood risk, and 

there is evidence that flood magnitude has increased 

across New England since the mid-1900s and 

particularly since 1970 (Collins 2009, Lins and Slack 

1999, Peterson et al. 2013b, Richardson et al. 2006).
CHAPTER 2: OBSERVED CLIMATE CHANGE

36

covered days decreased across the region between 

1965 and 2005, particularly during December, 

January, and February (Burakowski et al. 2008). 

Reductions in snow cover appear to be related to 

increases in winter temperatures as well as the 

amount of precipitation falling as rain instead of 

snow (Burakowski et al. 2008, Hamburg et al. 2013, 

Huntington et al. 2004). The proportion of total 

precipitation falling as snow decreased at 50 percent 

of the observation locations in New England during 

the second half of the 20th century; these changes 

were predominantly due to decreasing snowfall, 

although some of the change was due to increasing 

rainfall at some locations (Huntington et al. 2004). 

None of the sites examined had a significant increase 

in the proportion of precipitation falling as snow 

(Huntington et al. 2004).

Lake and River Ice Cover

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 

100 years across the region, where records of fall 

ice-in and spring ice-out are used to determine the 

duration of ice cover (Hodgkins 2013). Ice-out 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 2013, Hodgkins et al. 2002, Magnuson 

et al. 2000). Ice-out dates advanced substantially 

between 1850 and 2000, with spring ice-out 

occurring 9 days earlier in more northerly and 

mountainous regions and 16 days earlier in southern 

New England (Hayhoe et al. 2007, Hodgkins et al. 

2002). Similarly, earlier ice-out dates and reduced 

ice thickness were observed on the Piscataquis River 

in Maine between 1912 and 2001 (Huntington et al. 

2003). Long-term records in the Adirondacks show 

that ice cover is forming later and leaving sooner, 

resulting in about 40 fewer days of lake ice cover in 

the 2000s than in the late 1800s (Beier et al. 2012b). 

These patterns correspond with trends that have 

been observed across the Great Lakes region, as well 

as the entire Northern Hemisphere (Jensen et al. 

2007, Johnson and Stefan 2006, Kling et al. 2003, 

Magnuson et al. 2000). 

Extreme flooding at Hubbard Brook in central New Hampshire. Photo by Scott Bailey, U.S. Forest Service.
37

CHAPTER 3: PROJECTED CHANGES 

IN CLIMATE AND PHYSICAL PROCESSES

Climate across the Northeast has changed over the 

past century, and it will continue to change in the 

future. This chapter describes climate projections for 

the assessment area over the 21st century, including 

projections related to patterns of extreme weather 

events and other climatic processes. Temperature 

and precipitation projections are derived from 

downscaled simulations of climate models. 

Information related to future weather extremes and 

other impacts is drawn from published research.

PROJECTED CHANGES 

IN TEMPERATURE 

AND PRECIPITATION

Projections of future climate show the potential for 

dramatic changes over this century. Temperature 

and precipitation are projected to change, with 

important seasonal variations and associated changes 

in snow and ice cover, growing season length, 

soil moisture, lake levels, and streamflow. In this 

chapter, we present climate projections using two 

climate scenarios, PCM B1 and GFDL A1FI (unless 

otherwise noted). These data are from a climate 

dataset where global data have been “downscaled” 

to provide regional data and a finer spatial scale 

(Hayhoe 2011). In this chapter, projected climate 

data for three 30-year periods in the 21st century 

(2010 through 2039, 2040 through 2069, and 2070 

through 2099) are compared with the baseline 

historical data from 1971 through 2000 (presented 

in Chapter 1) to determine projected future change 

from current climatic conditions.

The PCM B1 and GFDL A1FI scenarios used in 

this assessment are just two of the many climate 

scenarios that are available for the Northeast (e.g., 

Karmalker and Bradley 2017, Lynch et al. 2016). 

The projections from individual models can vary 

widely, and these two scenarios can generally serve 

as “bookends” describing a broad range of potential 

future climatic conditions (Box 11, Appendix 3). 

Projected changes in temperature and precipitation 

for GFDL A1FI represent a greater degree 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

Scientists agree with greater than 90-percent 

certainty that the global climate will get warmer 

during the 21st century (IPCC 2007a, 2013). This 

warming will translate into a wide-ranging set of 

changes to the climate system, globally as well 

as locally. The assessment area is projected to 

experience substantial warming during this century 

(Figs. 18-21, Box 11, Appendix 3). Although 

temperatures are projected to increase by 1 to 2 oF 

(0.6 to 1.1 oC) under both scenarios during the 2010 

to 2039 period, the projections under the two future 

scenarios diverge mid-century, with the GFDL 

A1FI scenario projecting much larger temperature 

increases. By the end of the century, mean annual 

temperature (compared to the 1971 through 2000 

baseline period) is projected to increase 2.6 oF 

(1.4 oC) under PCM B1 and 7.6 oF (4.2 oC) under 

GFDL A1FI (Fig. 18). There are few trends 

that describe geographic variations in projected 

temperature across the region (Figs. 19-21), and 

decreases or substantial increases in mapped data 

(i.e., differences in one or a small number of pixels) 

should be regarded with some skepticism because of 

the potential for localized anomalies or errors.
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

38

Box 11: Where Are these Data from?

In this chapter, we report downscaled climate 

projections for two scenarios of potential change. 

These scenarios combine general circulation models 

(GCMs; also called global climate models) that 

simulate physical processes on the Earth’s surface 

and in the oceans and atmosphere with greenhouse 

gas emissions scenarios, which represent potential 

“storylines” of future climate forcing. The 

Geophysical Fluid Dynamics Laboratory (GFDL) 

climate model is considered moderately sensitive 

to changes in greenhouse gas concentrations 

(Delworth et al. 2006). In other words, any change 

in greenhouse gas concentration would lead to a 

change in temperature that is higher than many 

models and lower than others. In contrast, the 

Parallel Climate Model (PCM) has lower sensitivity 

to greenhouse gas concentrations (Kopp et al. 2014, 

Washington et al. 2000). 

These models are paired with different greenhouse 

gas emissions scenarios that were developed 

through the Intergovernmental Panel on Climate 

Change (IPCC) Special Report on Emission Scenarios 

(IPCC 2000, Kunkel et al. 2013). The A1FI scenario 

has the greatest increases in greenhouse gases 

by the end of the century and is closer to current 

trends in greenhouse gas emissions (Raupach et 

al. 2007), and the B1 scenario has the lowest level 

of greenhouse gas emissions. The IPCC created 

newer sets of climate scenarios for use in its Fifth 

Assessment Report (IPCC 2013). The newer datasets 

use Representative Concentration Pathways (RCPs) 

(Karmalkar and Bradley 2017, Knutti and Sedláček 

2013, Meinshausen et al. 2011, Taylor et al. 2012). 

Although it is hard to compare the two sets of 

scenarios directly, there are many consistencies 

between the datasets (Knutti and Sedláček 2012). 

Projections for the greenhouse gas concentrations 

and global temperature are comparable between the 

A1FI emissions scenario and RCP 8.5, and between 

the B1 emissions scenario and RCP 4.5 (Sun et al. 

2015). 

This report uses two scenarios, GFDL A1FI and PCM 

B1 (unless otherwise noted), from a statistically 

downscaled climate dataset (Hayhoe 2011), which 

span a large range of possible futures. Both models 

and both scenarios were included in the IPCC Fourth 

Assessment Report (IPCC 2007b). Although the IPCC 

(2007b, 2013) and National Climate Assessment 

(Cook et al. 2016, Hayhoe et al. 2007, Kunkel et al. 

2013, Melillo et al. 2014, Thibeault and Seth 2014a) 

reports present averages of multiple models, we 

instead selected two models that had relatively 

good skill at simulating climate in the eastern United 

States and that bracket a range of temperature and 

precipitation futures. This approach gives readers 

a better understanding of the range of projected 

changes in climate and provides a set of alternative 

scenarios that managers can use in planning and 

decisionmaking. It is important to note that actual 

emissions and temperature increases could be lower 

or higher than these projections. The actual future 

climate will be different from any of the developed 

scenarios, and therefore we encourage readers to 

consider the range of possible climate conditions 

over the coming decades rather than one particular 

scenario. 

Using data from the GFDL A1FI and PCM B1 

scenarios, we calculated the values for mean, 

minimum, and maximum temperature and mean 

precipitation for each season and the entire year 

for three 30-year periods (2010 through 2039, 

2040 through 2069, and 2070 through 2099). We 

compared these projections for future periods with 

the baseline historical data from 1971 through 2000, 

presented in Chapter 1, to determine the projected 

future change from current climate conditions. 

Appendix 3 contains additional climate information.
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

39

20

30

40

50

60

70

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

Temperature (0F)

Annual

GFDL A1FI Mean GFDL A1FI Maximum GFDL A1FI Minimum

PCM B1 Mean PCM B1 Maximum PCM B1 Minimum

0

10

20

30

40

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

Temperature (0F)

Winter (Dec–Feb)

50

60

70

80

90

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

Temperature (0F)

Summer(Jun–Aug)

20

30

40

50

60

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

Temperature (0F)

Spring (Mar–May)

30

40

50

60

70

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

Temperature (0F)

Fall (Sep–Nov)

Figure 18.—Projected mean, minimum, and maximum temperature (°F) in the assessment area averaged over 30-year periods 

for the entire year and by season for two climate model-emissions scenario combinations. The 1971 to 2000 value is based on 

observed data from weather stations. Note that the panels have different Y-axis values. Data source: Climate Wizard (2014).
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

40

Figure 19.—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.
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

41

Figure 20.—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.
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

42

Figure 21.—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.
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

43

Although the two climate scenarios project different 

amounts of warming, they are in agreement that 

mean, minimum, and maximum temperature will 

increase in the assessment area during all seasons. 

The PCM B1 scenario projects much less warming 

than the GFDL A1FI scenario for the end of the 

century. The projected increase in mean temperature 

by the end of the 21st century is not equal across all 

seasons. The PCM B1 scenario projects relatively 

similar amounts of warming throughout the year, 

with increases of 1.9 to 3.2 °F (1.0 to 1.8 °C) during 

each season. In contrast, the GFDL A1FI scenario 

projects larger temperature increases than PCM 

B1, with all seasons projected to have temperature 

increases greater than 6.0 °F (3.4 °C). Under GFDL 

A1FI, the projected temperature increases vary 

across the seasons, with summer projected to have 

the most substantial warming with an increase of 

8.3 °F (4.6 °C). 

By the end of the century, the average annual 

minimum temperature is projected to increase across 

the assessment area by 2.7 oF (1.5 °C) under PCM 

B1 and 8.0 °F (4.4 °C) under GFDL A1FI (Fig. 

20). Winter minimum temperature is projected to 

increase the most under the GFDL A1FI scenario, 

warming 9.8 °F (5.5 °C) by the end of the century. 

Maximum annual temperature is projected to 

increase by 2.5 to 7.1 °F (1.4 to 3.9 °C) across the 

two scenarios by the end of the century. Summer 

maximum temperatures are projected to increase 

by 3.2 °F (1.8 °C) under PCM B1 and 8.7 °F (4.8 

°C) under GFDL A1FI by the end of the century. 

Extreme temperatures are discussed in more detail 

later in this chapter.

These data are consistent with several other 

modeling efforts in the region. Although the 

temperature increases projected by individual 

climate models do differ, all models project that 

the future climate will be warmer and that the 

magnitude of future warming will be large compared 

to historical trends (Bryan et al. 2015, Center for 

Climatic Research 2017, Hayhoe et al. 2007, Kunkel 

et al. 2013, Lynch et al. 2016). Additionally, climate 

models from a variety of studies also demonstrate 

that higher greenhouse gas levels result in a greater 

degree of warming (Bryan et al. 2015, Center for 

Climatic Research 2017, Fan et al. 2014, Hayhoe et 

al. 2007, Karmalkar and Bradley 2017, Kunkel et al. 

2013, Lynch et al. 2016). 

Growing Season Length

The growing (i.e., freeze-free) season has expanded 

in the assessment area over the past century, and is 

expected to continue to expand into the future 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 modeling 

efforts project that the growing season length in the 

assessment area will increase by 20 to more than 50 

days under a range of emissions scenarios (Center 

for Climatic Research 2017, Ning et al. 2015). The 

projected expansion of the growing season is a result 

of both earlier spring freeze-free dates and later fall 

freezes (Center for Climatic Research 2017). Other 

studies across the Northeast also project similar 

increases in growing season length throughout the 

21st century (e.g., Hayhoe et al. 2007, Kunkel et al. 

2013, Ning et al. 2015).

Precipitation

Climate change is expected to alter precipitation 

regimes and hydrologic conditions throughout the 

region. 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. 22, 23). 

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. 
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

44

42

43

44

45

46

47

48

49

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

Precipitation (inches)

Annual

GFDL A1FI PCM B1

8

9

10

11

12

13

14

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

Precipitation (inches)

Winter (Dec–Feb)

8

9

10

11

12

13

14

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

Precipitation (inches)

Summer(Jun–Aug)

8

9

10

11

12

13

14

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

Precipitation (inches)

Spring (Mar–May)

8

9

10

11

12

13

14

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

Precipitation (inches)

Fall (Sep–Nov)

Figure 22.—Projected trends in mean precipitation in the assessment area averaged over 30-year periods for the entire year 

and by season for two climate model-emissions scenario combinations. The 1971 to 2000 value is based on observed data 

from weather stations.
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

45

Figure 23.—Projected difference in mean precipitation at the end of the century (2070 through 2099) compared to baseline 

(1971 through 2000) for two climate model-emissions scenario combinations.
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

46

For the assessment area, mean annual precipitation is 

projected to increase by 0.9 inch (2.0 percent) under 

the PCM B1 scenario by the end of the 21st century 

(Fig. 23, Appendix 3) compared to the 1971 through 

2000 baseline. In contrast, annual precipitation is 

projected to increase more substantially under the 

GFDL A1FI scenario, by an average of 3.0 inches 

(7 percent). Seasonal precipitation for the two 

scenarios generally follows the same trends, with 

small increases or decreases in precipitation by 

season over the 21st century. Winter precipitation is 

projected to increase the most of any season under 

both scenarios, increasing by 0.8 inch (8 percent) 

under PCM B1 and 2.3 inches (23 percent) under 

GFDL A1FI. Under the PCM B1 scenario, relatively 

little change is projected during the spring and fall at 

the end of the century. Summer precipitation under 

this scenario is projected to decrease slightly during 

mid-century, but then have an overall increase of 

0.1 inch (1 percent) by the end of the century 

(compared to the 1971 to 2000 baseline). The GFDL 

A1FI scenario projects a much sharper pattern of 

altered seasonality in precipitation, with spring 

precipitation increasing 1.0 inch (9 percent) and 

summer precipitation declining by 1.7 inches 

(14 percent) by the end of the century. 

One of the striking consistencies in the climate 

data presented here and in many other studies is 

that variability in projected precipitation is much 

greater than for temperature across virtually all 

climate modeling efforts (Fan et al. 2014, Hayhoe 

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

Thibeault and Seth 2014b). The data just presented 

are generally consistent with other projections for 

the region, although there are some differences. 

Similar to these results, several other modeling 

efforts indicate that annual precipitation is expected 

to increase in the assessment area and that the 

greatest precipitation increases will occur during 

the winter (Bryan et al. 2015, Hayhoe et al. 2007, 

Kunkel et al. 2013, Lynch et al. 2016). There is 

greater variability among projections of precipitation 

during other seasons, particularly summer. For 

example, one recent comparison of multiple climate 

models found that individual model projections for 

summer precipitation ranged from a decrease of 

25 percent or more to an equivalent increase for the 

2070 to 2099 period (Kunkel et al. 2013). Another 

recent study modeled future precipitation using 

multiple climate models and one high emissions 

scenario, and found a high degree of variation 

in projections of summer precipitation; summer 

precipitation showed a modest increase when the 

results from several models were averaged (Lynch 

et al. 2016). Although the total amount of summer 

precipitation may not change greatly, projected 

increases in extreme precipitation events suggest 

that the number of days without precipitation will 

correspondingly increase (Bryan et al. 2015).

PROJECTED CHANGES IN EXTREME 

WEATHER EVENTS

Extreme events such as heat waves, cold waves, 

windstorms, floods, and droughts are projected 

to become more frequent or severe as a result of 

climate change (IPCC 2012, Melillo et al. 2014). In 

general, there is less confidence in model projections 

of the magnitude and direction of change in extreme 

events over the next century compared with general 

temperature and precipitation changes (Bryan et 

al. 2015, Kunkel et al. 2012, Peterson et al. 2013b, 

Wuebbles et al. 2014). The stochastic nature of 

extreme events increases the difficulty of detecting 

changes in the frequency or intensity of events over 

time, but it is likely that the frequency of extreme 

weather events will increase across the Northeast 

(Brown et al. 2010, Bryan et al. 2015, Guilbert et al. 

2015, Kunkel et al. 2013, Ning et al. 2015, Spierre 

and Wake 2010).

Temperature Extremes

In addition to projecting mean temperatures, 

downscaled daily climate data can be used to 

estimate the frequency of extreme high and low 

temperatures in the future. Studies from across 

the region point to an increasing frequency of hot 

days across the assessment area, particularly in the 

southern portions of the assessment area (Horton 

et al. 2014, Kunkel et al. 2013, Ning et al. 2015). 

Under a low emissions scenario, southern parts of 

New England are projected to have at least 
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

47

15 more days per year with a maximum temperature 

exceeding 90 °F. Under a high emissions scenario, 

extremely hot days are projected to increase by 16 to 

60 days per year over most of the assessment area, 

with the greatest increases in the southern coastal 

areas (Center for Climatic Research 2017, Ning et al. 

2015). This increase in hot days is also expected to 

increase the frequency of heat waves lasting 3 days 

or more (Ning et al. 2015).

Extremely cold temperatures are projected to 

become less frequent as the climate warms, although 

they will not disappear completely. The frequency, 

intensity, and duration of cold-air outbreaks are 

expected to decrease (Horton et al. 2014). The 

number of days with a minimum temperature 

less than 10 °F is projected to decrease by 9 to 24 

days per year (Kunkel et al. 2013). Another study 

indicates that number of days with a minimum 

temperature below 0 °F is also projected to decrease 

across the assessment area (Center for Climatic 

Research 2017). 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).

Intense Precipitation

There is a clear trend toward more frequent and 

more extreme precipitation events in the assessment 

area, and this is expected to continue (Horton et 

al. 2014, Kunkel et al. 2013, Thibeault and Seth 

2014a). One recent assessment projected that the 

entire Northeast will receive 21 percent more rainfall 

events greater than 1 inch by 2100, with larger 

events increasing by progressively larger amounts 

(Kunkel et al. 2013). Projections based on other 

climate data also indicate an increased frequency of 

large precipitation events. The occurrence of events 

producing more than 1 inch of rain is projected 

to increase by 12 or more days per decade by the 

end of the century, according to the Center for 

Climatic Research (2017). Coastal areas in southern 

and central New England are projected to have 

the greatest increases in heavy rainfall (Center 

for Climatic Research 2017). It is important to 

consider this trend in combination with the projected 

increases or decreases in mean precipitation over the 

21st century, because a given increase or decrease in 

precipitation may not be distributed uniformly across 

a season or even a month. Additionally, climate 

change may increase the year-to-year variation of 

precipitation across the northern United States (Boer 

2009, Thibeault and Seth 2014a). Therefore, the 

assessment area may experience more extremely wet 

and dry years in the future.

Extreme Storms and Wind

Beyond intense precipitation, extreme storm events 

(e.g., wind, hurricanes, ice storms) are expected 

to change as a result of a changing climate. The 

stochastic nature of extreme events increases the 

difficulty of both detecting changes in the frequency 

or intensity of events over time and attributing 

these alterations to climate change (Coumou and 

Rahmstorf 2012, Stott et al. 2010, Wuebbles et 

al. 2014). However, there is increasingly greater 

evidence for projected increases in many extreme 

weather events across the Northeast (Horton et al. 

2014, Kunkel et al. 2013).

Ice storms are of particular concern in many parts 

of the region because of the potential impacts on 

infrastructure and ecosystems. Although future 

climate projections are generally available at 

relatively coarse spatial scales, some modeling work 

has investigated potential changes in ice storm risk 

in the northeastern United States and adjacent areas 

of Canada (Cheng et al. 2007, 2011). For example, 

one study projected an 8- to 40-percent increase in 

freezing rain events by the 2050s as compared to 

the past 40 years in south-central Canada, with the 

greatest projected increases in the northern portion 

of the area (Cheng et al. 2007). A more recent 

study by the same authors suggested that all of 

eastern Canada could experience more freezing rain 

events late this century during the coldest months 

(December through February), with greater increases 

in the north (Cheng et al. 2011).
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

48

PROJECTED CHANGES IN SEA LEVEL

Sea level is expected to continue to rise as 

temperatures warm across the globe. The complex 

dynamics that cause sea-level rise—including air 

and water temperature increases, freshwater inputs 

from melting ice, changes in salinity, and altered 

circulation patterns—make it difficult to project the 

magnitude of sea-level rise over the next century 

(Landerer et al. 2007, Sallenger et al. 2012, Yin et 

al. 2009). Results from a number of studies suggest 

that global sea level will rise between 1 and 4 feet in 

this century (Sallenger et al. 2012, Walsh et al. 2014, 

Yin et al. 2009). There is high variability among 

these estimates, however, and higher emissions 

scenarios lead to greater estimates of future sea-level 

rise (Kopp et al. 2014, Walsh et al. 2014). At the 

same time, even the best models cannot simulate the 

effects of rapid changes in ice sheet dynamics, so it 

is possible that the magnitude of future sea-level rise 

is being underestimated (Walsh et al. 2014). 

PROJECTED CHANGES AFFECTING 

WATER AND SNOW COVER

Snowfall and Snow Cover

Warmer temperatures are expected to continue 

to have dramatic impacts on the winter season. 

The total amount of snowfall and the proportion 

of precipitation falling as snow decreased across 

the Northeast during the 20th century (Chapter 2), 

and these trends are expected to continue (Danco 

et al. 2016, Hayhoe et al. 2007, Ning and Bradley 

2015). By the end of the 21st century, total snowfall 

is projected to decrease by 10 to 50 percent under 

a low emissions scenario and by 30 to 70 percent 

under a high emissions scenario (Center for Climatic 

Research 2017, Ning and Bradley 2015, Notaro et al. 

2014). The most substantial loss of snow is expected 

to occur at the beginning of the winter season, which 

is December for most of the region and January for 

southern coastal New England (Notaro et al. 2014).

Similarly, the number of days with snow cover is 

projected to decrease and more southern parts of the 

region may lose a substantial portion of days with 

snow. One study projected decreases in the number 

of snow-covered days across the region and found 

decreases of as much as 30 days per year in northern 

New York, Vermont, New Hampshire, and Maine by 

the year 2100 (Hayhoe et al. 2007). A more recent 

study projected declines in snow depth of 40 percent 

or more across the region, with snow depth declining 

more than 80 percent in localized areas (Center for 

Climatic Research 2017, Notaro et al. 2014) 

(Fig. 24). 

Soil Temperature and Frost

As increasing winter temperatures lead to a 

reduction in the depth and duration of snowpack, it 

is expected that the frequency of soil freeze-thaw 

events will increase. Across much of the Northeast, 

snow cover insulates the soil surface from changes 

in air temperature, thereby helping reduce both the 

number of freeze-thaw cycles and the depth to which 

frost penetrates the soil (Brown and DeGaetano 

2011, Hardy et al. 2001). One study that manipulated 

snow depth at Hubbard Brook Experimental Forest 

in New Hampshire found that areas covered in deep 

snow were protected from extensive freezing while 

areas that were shoveled to reduce snow depth 

had substantial freezing (Hardy et al. 2001). Even 

when snow depth was not manipulated, soils froze 

early in the season before a late-arriving snowpack 

and remained frozen into April despite snowpack 

of more than 2 feet (Hardy et al. 2001). These 

results are consistent with projections of future soil 

temperature and frost, suggesting increased soil frost 

where snowpack is reduced (Brown and DeGaetano 

2011). Conversely, areas of southern New England 

currently receive less snow and do not typically have 

the same dense snowpack that protects soils from 

frost; warming air temperatures may be more likely 

to warm soils in these areas (Brown and DeGaetano 

2011). 

Streamflow

The shifts in winter precipitation and temperature 

described earlier are expected to alter several 

hydrologic variables. Winter snow cover is 

an important factor for regulating streamflow 

throughout much of the year (Hodgkins et al. 2012). 
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

49

Figure 24.—Projected change (percent) in snow depth, based on nine global climate models from the Coupled Model 

Intercomparison Project Phase 3 (CMIP3), computed as the difference between 2081 to 2100 and 1981 to 2000. Results are 

shown for the B1 (top) and A2 (bottom) emissions scenarios from a daily, statistically downscaled climate product. The A2 

emissions scenario projects greenhouse gas emissions similar to the A1FI scenario. Data source: Center for Climatic Research 

(2017).

Percent Percent
CHAPTER 3: PROJECTED CHANGES IN CLIMATE AND PHYSICAL PROCESSES

50

Warmer temperatures can accelerate the hydrologic 

cycle by increasing cold-season rainfall and runoff 

(Cherkauer and Sinha 2010, Hayhoe et al. 2007). 

Changes in temperature and precipitation are 

expected to shift peak (winter/spring) streamflow 

earlier, with higher winter flows and lower spring 

flows (Campbell et al. 2011, Demaria et al. 2016, 

Hayhoe et al. 2007, Tu 2009). One 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; one study in eastern Massachusetts 

projected that streamflow during the summer months 

may be reduced by more than 50 percent for many 

watersheds (Tu 2009). Fall streamflow projections 

are variable and depend on the degree to which 

scenarios warm the climate and on interactions with 

vegetation (Campbell et al. 2011, Demaria et al. 

2016, 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, Hayhoe et al. 2007). 

Soil Moisture and Drought

Drought is a lack of water, and in forest ecosystems, 

drought is closely tied to the availability of soil 

water for maintaining stomatal conductance and 

plant function (Luce et al. 2016a, 2016b). It also 

mediates microbial activity, decomposition, and 

nutrient turnover. Changes in soil moisture are 

largely driven by the balance of temperature, 

precipitation, runoff, and evapotranspiration—that 

is, the combined amount of water lost through 

evaporation from plant surfaces, litter, and soils 

as well as through transpiration from plants. As 

temperatures increase, the atmosphere is able to hold 

larger quantities of water, which causes evaporation 

to increase. Plants also transpire more. Moisture 

stress may occur when increases in evaporation 

and transpiration are not offset by a corresponding 

increase in precipitation and soil moisture (Hayhoe 

et al. 2007, Vose et al. 2016).

There is an increased potential for reduced soil 

moisture and increased drought stress in the future 

(Luce et al. 2016b). Of the climate scenarios used 

in this assessment, the potential for more frequent 

droughts and moisture stress in the assessment 

area during the growing season appears to be 

higher under the GFDL A1FI scenario. Even under 

the milder PCM B1 scenario, however, warmer 

temperatures may lead to greater evaporative 

demand from the atmosphere and physiological 

stress if increases in precipitation do not correspond 

to temperature increases. Although precipitation 

projections have greater uncertainty than 

temperature projections, several modeling studies 

point to substantially higher temperatures with no 

more than relatively modest increases in growing 

season precipitation (Center for Climatic Research 

2017, Hayhoe et al. 2007, Kunkel et al. 2013, Lynch 

et al. 2016). 

Relatively few studies have projected future 

changes in soil moisture, making it difficult to 

make inferences about the effects of changes in 

future drought frequency or severity. One study 

using scenarios of future climate similar to those 

presented in this chapter projected that potential 

evapotranspiration, the evaporative demand from 

the atmosphere, would increase most during 

the spring and summer, which could reduce soil 

moisture toward the latter part of the growing 

season and increase the frequency of droughts 

lasting short durations of 1 to 3 months (Hayhoe 

et al. 2007). More-recent studies of future drought 

potential across North America and globally 

suggest that increased temperatures may increase 

evapotranspiration, reduce soil moisture, or increase 

drought risk in the northeastern United States 

through the next century (Berg et al. 2017, Jeong et 

al. 2014, Zhao and Dai 2016). Droughts are expected 

to occur more frequently in the future in areas where 

precipitation is not sufficient to compensate for 

increased temperatures during the growing season 

(Hayhoe et al. 2007, Luce et al. 2016a).
51

CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS 

ON FORESTS

Climate change is expected to have wide-ranging 

effects on forests in the Northeast. Some of these 

effects will be the direct effects of an altered climate, 

such as warmer temperatures and extreme storm 

events. Climate change will also lead to many 

indirect effects, including interactions with other 

disturbances that have the potential to severely 

change forest ecosystems across the region and 

worldwide. This chapter describes potential effects 

of climate change on forest ecosystems across New 

England and northern New York. It is organized into 

two sections. First, we present the results from three 

forest impact models to gather perspective on how 

forests 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 5).

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 9). The Tree Atlas uses 

statistical techniques to model changes in suitable 

habitat for individual species over broad geographic 

areas. The LINKAGES model projects establishment 

and growth of trees based on climate, soils, and 

other site information. LANDIS PRO 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. 2017). Although each model 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 in patterns provide 

opportunities to better understand the nuances 

of ecological responses given the strengths and 

limitations of the models (Iverson et al. 2017). 

All three models used the same downscaled climate 

projections from two combinations of general 

circulation models (GCMs) and emissions scenarios 

described in detail in Chapter 3: GFDL A1FI and 

PCM B1. Projected changes in temperature and 

precipitation for GFDL A1FI represent a greater 

degree of projected climate warming and change 

compared to PCM B1, allowing for comparisons 

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. 

These model results are most useful for describing 

trends across large areas and over long time scales. 

These models are not designed to deliver spatially 

precise results for individual forest stands or a 

particular year in the future, despite the temptation to 

examine particular datapoints or locations on a map. 

Although the projections are not spatially exact, they 

indicate the relative abundances and spatial patterns 

across the assessment area. In this chapter, we 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

52

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

Potential suitable habitat 

(area-weighted importance 

values) and modifying factors 

by species

Species establishment and 

growth by species (percentage 

change)

Basal area, and trees per acre 

by species

Model-scenario combinations GFDL A1FI and PCM B1

Assessment area New England and northern New York assessment area delineated by three subregions (Fig. 25)

Resolution 20-km (12-mile) grid

1/12 ha (0.2 acre) plots 

representing landforms in 

subsections

270-meter (886-foot) grid 

Number of tree species 

 evaluated 102 24 24

Control/baseline climate 1971 through 2000 1980 through 2009 n/a

Climate periods evaluated

2010 through 2039, 

2040 through 2069, 

2070 through 2099

1980 through 2009, 

2080 through 2099 2009 through 2099

Simulation period n/a 30 years 2009 through 2099

Competition, survival, and 

reproduction No Yes Yes

Disturbances No (but addressed through 

modifying factors) No Timber harvest only

Tree physiology feedbacks No Yes No

Succession or ecosystem shifts No No Yes

Biogeochemical feedbacks No Yes No

n/a: not applicable

Table 9.—Overview of the three forest impact models used in this assessment
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

53

present simulations for the end of the 21st century 

across the entire assessment area. Model data for 

three subregions within the assessment area (Fig. 25) 

are used to describe geographic differences across 

the assessment area. 

Climate Change Tree Atlas

The Climate Change Tree Atlas (U.S. Forest 

Service n.d.) was used to evaluate potential changes 

in suitable habitat for tree species within the 

assessment area. The Tree Atlas does not model 

where species will occur in the future (i.e., occupied 

habitat), but rather projects where future habitat for 

individual tree species may be suitable in the future. 

As such, Tree Atlas projections should be interpreted 

not as expected species migration patterns, but 

instead as shifts in the distribution of favorable 

conditions for a given species to persist under future 

climatic conditions. The DISTRIB component of 

the Tree Atlas, a species distribution model, 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. Habitat suitability (measured in terms 

of importance value) was modeled for 134 eastern 

tree species (Iverson et al. 2008, 2010), 102 of which 

are currently present in the assessment area or are 

projected to have suitable habitat in the area by the 

end of this century. Most of these tree species are 

currently present in some part of the assessment 

area, and the remaining species may have newly 

suitable habitat in the future under one or both 

climate scenarios.

The projected change in potential suitable habitat 

for the 102 species was calculated for the years 

2070 through 2099 using the PCM B1 and GFDL 

A1FI scenarios and compared to the present climate 

(Table 10). Species were categorized based on 

whether the results from the two climate-emissions 

scenarios projected an increase, decrease, or no 

change in suitable habitat compared to current 

climatic conditions. Further, some tree species 

that are currently not present in the assessment 

Figure 25.—Assessment area subregions based on ecological 

provinces and sections (Fig. 3) mapped by Cleland et al. 

(2007) and described by McNab et al. (2007).

area are identified as having potential suitable 

habitat under future climate scenarios. Appendix 4 

contains complete results from the DISTRIB model, 

including projections for three different periods 

(2010 through 2039, 2040 through 2069, and 2070 

through 2099) and for each of the subregions. 

The DISTRIB results indicate that climate change 

is likely to lead to changes in the suitable habitat 

of many common tree species; however, the ways 

in which tree species will actually respond to 

climate change are also influenced by life-history 

traits not included in the DISTRIB model. 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 (Matthews et al. 2011) (Table 11). These 

factors are based on a literature review of the lifehistory traits, known stressors, and other factors 

unique to individual species. 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).
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

54

Common Name PCM B1 GFDL A1FI

Declines under Both Scenarios

Balsam fir (–) Small decrease Large decrease

Balsam poplar Small decrease Large decrease

Black ash (–) Small decrease Small decrease

Black spruce Large decrease Large decrease

Mountain maple (+) Small decrease Large decrease

Northern white-cedar Small decrease Large decrease

Paper birch Small decrease Large decrease

Red spruce (–) Small decrease Large decrease

Tamarack (native) (–) Small decrease Small decrease

White spruce Small decrease Large decrease

Declines under High Emissions

American beech No change Small decrease

American mountain-ash (–) No change Large decrease

Bigtooth aspen No change Small decrease

Black maple No change Small decrease

Butternut (–) No change Small decrease

Chokecherry No change Large decrease

Eastern hemlock (–) No change Small decrease

Eastern white pine No change Small decrease

Gray birch No change Small decrease

Pin cherry No change Small decrease

Quaking aspen No change Large decrease

Red maple (+) No change Small decrease

Striped maple No change Large decrease

Sugar maple (+) No change Small decrease

Yellow birch No change Large decrease

No Change under Both Scenarios

American chestnut No change No change

Atlantic white-cedar (–) No change No change

Scrub oak No change No change

River birch NA No change

White ash (–) Small increase No change

Common Name PCM B1 GFDL A1FI

Increases under Both Scenarios

American elm Small increase Large increase

Black locust Large increase Large increase

Black oak Small increase Large increase

Black walnut Small increase Large increase

Black willow (–) Small increase Large increase

Chestnut oak (+) Small increase Large increase

Eastern cottonwood Small increase Large increase

Eastern redbud Small increase Small increase

Eastern redcedar Large increase Large increase

Flowering dogwood Large increase Large increase

Mockernut hickory (+) Small increase Large increase

Northern red oak (+) Small increase Small increase

Ohio buckeye Small increase Small increase

Pignut hickory Small increase Large increase

Pin oak (–) Small increase Large increase

Post oak (+) Small increase Small increase

Red mulberry Large increase Large increase

Sassafras Large increase Large increase

Scarlet oak Small increase Large increase

Shagbark hickory Small increase Large increase

Silver maple (+) Small increase Large increase

Slippery elm Small increase Large increase

Sourwood (+) Small increase Small increase

Sweet birch (–) Small increase Small increase

White oak (+) Small increase Large increase

Yellow-poplar (+) Large increase Large increase

Table 10.—Potential change in suitable habitat for tree species in the assessment area*
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

55

Common Name PCM B1 GFDL A1FI

Increases under High Emissions

American basswood No change Large increase

American holly No change Large increase

American hornbeam No change Large increase

Bitternut hickory (+) No change Large increase

Black cherry (–) No change Small increase

Blackgum (+) No change Large increase

Boxelder (+) No change Large increase

Bur oak (+) No change Large increase

Eastern hophornbeam (+) No change Small increase

Green ash No change Large increase

Hackberry (+) No change Large increase

Honeylocust (+) No change Large increase

Jack pine Small decrease Small increase

Northern pin oak (+) No change Small increase

Pitch pine No change Small increase

Red pine No change Large increase

Serviceberry No change Small increase

Shellbark hickory No change Small increase

Swamp chestnut oak No change Small increase

Swamp white oak No change Small increase

Sycamore No change Large increase

Winged elm No change Small increase

Common Name PCM B1 GFDL A1FI

New Suitable Habitat

Bald cypress New habitat New habitat

Black hickory NA New habitat

Blackjack oak** (+) NA New habitat

Cedar elm NA New habitat

Cherrybark oak New habitat New habitat

Chinkapin oak** NA New habitat

Common persimmon (+) New habitat New habitat

Loblolly pine New habitat New habitat

Longleaf pine New habitat NA

Osage-orange (+) NA New habitat

Pawpaw New habitat New habitat

Pond pine (–) New habitat New habitat

Rock elm (–) NA New habitat

Sand pine (–) New habitat New habitat

Shingle oak NA New habitat

Shortleaf pine** New habitat New habitat

Shumard oak (+) NA New habitat

Southern red oak (+) New habitat New habitat

Sugarberry NA New habitat

Sweetbay** New habitat NA

Sweetgum** New habitat New habitat

Virginia pine New habitat New habitat

Water oak NA New habitat

Willow oak NA New habitat

Table 10 (continued).—Potential change in suitable habitat for tree species in the assessment area

*Species are grouped into change classes based on the proportional change between the year 2100 biomass under the PCM B1 and GFDL A1FI 

scenarios and the biomass under the current climate scenario in the year 2100. Large increases or decreases indicate that the proportional change 

under one or both climate change scenarios is greater than 50 percent. 

“No change” indicates that biomass is within 20 percent of what is projected under the current climate scenario. Species with the highest and lowest 

modifying factor scores are marked with plus (+) and minus (–) signs, respectively.

See Appendix 4 for complete results for all species. 

NA  = No suitable habitat.

**Not observed in the Forest Inventory and Analysis data, but other data (e.g., State surveys) suggest species is present though rare.
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

56

Species Modifying factors that affect rating

Highest adaptive capacity

1. Red maple high seedling establishment rate, wide range of habitats, shade-tolerant, high dispersal ability

2. Boxelder high seedling establishment rate, shade-tolerant, high dispersal ability, wide range of 

temperature tolerances, drought-tolerant

3. Bur oak drought-tolerant, fire-tolerant

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

5. Osage-orange wide range of habitats

Lowest adaptive capacity

1. Black ash susceptible to emerald ash borer, poor light competitor, limited dispersal ability, poor seedling 

establishment, fire-intolerant, dependent on specific hydrologic regime

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

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

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

5. Eastern hemlock susceptible to hemlock woolly adelgid, drought-intolerant

Table 11.—Eastern tree species with the five highest and five lowest values for adaptive capacity based on Climate 

Change Tree Atlas modifying factors

Decreases in Suitable Habitat

Ten species are projected to undergo large or 

small declines in suitable habitat under the range 

of climate scenarios, and declines are generally 

projected to be more severe under the GFDL A1FI 

scenario than under PCM B1 (Table 10). These 

reductions in suitable habitat do not imply that these 

trees will die or the species will be extirpated by 

the end of the century; rather, these results indicate 

that these species will be living outside of their ideal 

suitable habitat by the end of the century. As a result, 

trees living on marginal sites may have greater 

susceptibility to new or existing stressors such as 

drought, pests, diseases, or competition from other 

species including invasive species. Climate-related 

stress at these sites may also increase the risk of 

regeneration failure. 

Many of the species projected to decline are 

common in the more northerly portions of the 

assessment area. These boreal or northern species 

are currently near the southern limit of their range 

in the assessment area and include balsam fir, 

red spruce, paper birch, white spruce, and black 

spruce. These species are currently common across 

the landscape and play an important role in many 

forests. The reduction of suitable habitat for these 

species is expected to affect ecologically important 

northern and high-elevation forests in the region. 

Highly negative modifying factors are associated 

with three of the species projected to decline—

balsam fir, red spruce, and black ash—suggesting 

the presence of life-history traits or disturbance 

stressors that may cause these species to lose even 

more suitable habitat than the DISTRIB model 

results indicate. For example, the expanding 

presence of emerald ash borer in the assessment area 

is expected to substantially reduce the abundance of 

black ash in the area; the impact of this nonnative 

insect on black ash and other ash species is expected 

to be far greater than the impacts from changing 

climatic conditions over the next few decades. 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

57

There are also 15 species that are projected to 

decrease in suitable habitat under GFDL A1FI and 

undergo little change under PCM B1 (less than 20-

percent increase or decrease from current abundance 

under PCM B1) (Table 10). This group also contains 

boreal species (quaking and bigtooth aspen) and 

species with negative modifying factors (eastern 

hemlock and butternut). In the case of eastern 

hemlock, the expansion of hemlock woolly adelgid 

is expected to have substantial near-term impacts 

on the species that are greater than the anticipated 

future effects from climate change. Similarly, 

butternut canker has already significantly reduced 

the abundance of butternut in the assessment area 

and will continue to be the dominant factor affecting 

habitat suitability in the near term. Overall habitat 

of both red maple and sugar maple is projected 

to decrease under the GFDL A1FI scenario and 

have very little change (less than 5 percent) under 

PCM B1. Both of these species have positive 

modifying factors that indicate the species may 

fare better than the model suggests. Red maple in 

particular has the most positive modifying factors 

among all 134 species that were assessed across 

the eastern United States. A formidable resilience 

to disturbances and a wide tolerance of soil types, 

moisture conditions, and pH levels give red maple 

an apparent advantage over other species; it is likely 

to do much better than modeled, and perhaps even 

flourish as some competitors decline (Table 11). 

The outlook for sugar maple is less clear. Sugar 

maple has some characteristics, such as high shade 

tolerance and fewer disease and insect pests relative 

to other species, that may allow it to fare better 

than the models suggest. At the same time, sugar 

maple appears to be especially sensitive to nutrient 

deficiencies caused by historical soil acidification in 

the region (Hallett et al. 2006, Sullivan et al. 2013).

No Change in Suitable Habitat

Five species, including white ash, scrub oak, and 

American chestnut, are projected to have less than a 

20-percent change in suitable habitat under either of 

the scenarios (Table 10). These species are relatively 

uncommon in the region and generally present only 

in the southern half of the assessment area. American 

chestnut endures as a minor understory component, 

mostly as sprouts from old stumps and root systems, 

since the introduction of chestnut blight in the early 

1900s (Anagnostakis 1995). Although white ash is 

projected to have a marginal increase (21 percent) 

in suitable habitat under PCM B1, no change is 

projected under the hotter and drier GFDL A1FI. 

Additionally, the emerald ash borer is likely to cause 

large declines for this species, so climate change 

model results may have limited value.

Increases in Suitable Habitat

Suitable habitat for 26 species is projected to 

increase under both models by the end of the 

century (Table 10). These species generally 

have more southerly distributions and few are 

currently widespread in the assessment area. The 

more common species with projected increases in 

suitable habitat under future climatic conditions 

include northern red oak, American elm, black oak, 

and white oak. Changes in climate are generally 

projected to extend the range of these species 

northward. Some of these species, including many 

oak and hickory species, are more tolerant of 

drought and fire. These biological traits could benefit 

the species and lead to even greater increases than 

the model results suggest. Of these species, several 

also have multiple positive modifying factors, which 

suggest that the biological characteristics (e.g., shade 

tolerance and seedling establishment) and response 

to various disturbances (e.g., insect pests, drought, 

and fire topkill) may allow the species to succeed 

even more than the model projections suggest. 

Species that are projected to increase under both 

climate scenarios and that have a high capacity to 

adapt to changing conditions include silver maple, 

white oak, chestnut oak, mockernut hickory, yellowpoplar, post oak, and sourwood.

Twenty-two species are projected to increase in 

suitable habitat under the GFDL A1FI scenario but 

with little change projected under PCM B1 (less 

than 20-percent increase or decrease from current 

abundance under PCM B1). These species, many 

of which are relatively infrequent in the assessment 

area―particularly farther north—include American 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

58

basswood, boxelder, bitternut hickory, blackgum, 

bur oak, and sycamore. Several species in this 

category, including bitternut hickory and bur oak, 

have positive modifying factors, such as resistance 

to drought and fire topkill, which suggest that the 

species may be better able to take advantage of new 

conditions than the DISTRIB model would suggest. 

New Suitable Habitat

The DISTRIB model also projects that newly 

suitable habitat will be available in the future under 

at least one of the climate scenarios for 24 species 

that are not currently present in the assessment area 

(Table 10). This does not necessarily mean that 

a given species will be able to migrate to newly 

available habitat and colonize successfully, but 

rather that conditions may be suitable for a species 

to occupy the site if it is established. These species 

are generally hardwood species associated with 

central and southern hardwood forests but also 

include bald cypress and six pine species (e.g., 

loblolly, shortleaf, Virginia). Many of these species 

have ranges that extend close to the assessment area, 

and some, such as blackjack oak, chinkapin oak, and 

shortleaf pine, are present in the southern edge of the 

assessment area but are relatively rare and therefore 

not recognized by the Tree Atlas as currently present 

on the landscape. 

Other species that are not currently present in 

the assessment area would require long-distance 

migration, whether intentional or unintentional, in 

order to establish and occupy suitable habitat in 

the assessment area. Habitat fragmentation and the 

limited dispersal ability of seeds of some species 

could also hinder the northward movement of the 

more southerly species, despite the increase in 

habitat suitability (Ibáñez et al. 2008). Further, 

species are generally expected to migrate more 

slowly than their habitats will shift (Iverson et al. 

2004a, 2004b). Of course, human-assisted migration 

is a possibility for some species or sites, and may be 

explored over the coming decades (Duveneck and 

Scheller 2015, Pedlar et al. 2012). 

Geographic Trends

Projected changes are not uniform across the 

assessment area, and areas of suitable tree habitat 

are governed by latitude, elevation, soils, and other 

factors in addition to climate. The geographic 

and biological complexity of the assessment area 

varied across ecoregion subsections (Fig. 25). 

The DISTRIB model showed a greater number of 

tree species as having current or future suitable 

habitat in Southern and Coastal New England 

(97 species) compared to the Eastern and Coastal 

Maine (84 species) and Northern Forest (89 species) 

subregions. Species diversity is currently greatest in 

the Southern and Coastal New England subregion. 

This subregion is projected to remain the most 

diverse as many other species that are currently 

south of the assessment area but expected to have 

newly suitable habitat may move into the southern 

extent of the assessment area.

Decreases in present suitable habitat are projected 

to be greatest in Southern and Coastal New 

England. Species that are projected to decline in 

all subregions, such as balsam fir, red spruce, and 

paper birch, have the greatest projected decline at 

the southern extent of their ranges. Further, eastern 

white pine and quaking aspen are projected to 

sustain habitat decreases in Southern and Coastal 

New England at the end of the century, although 

suitable habitat generally did not change under PCM 

B1 and only a small decrease was observed under 

GFDL A1FI in other subregions and across the entire 

assessment area.

The habitat of five species is projected to increase 

in all three subregions: black oak, white oak, black 

willow, silver maple, and flowering dogwood. 

Southern and Coastal New England has the most 

species that are projected to increase, largely 

because it currently contains some species that are 

not present in measurable quantities in the other 

subregions, such as sassafras, bitternut hickory, 

and sycamore. Habitat suitability in both the 

Southern and Coastal New England and Northern 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

59

Forest subregions is projected to increase for 

several species that are not present in Eastern and 

Coastal Maine, including eastern redcedar, eastern 

cottonwood, yellow-poplar, and several oak and 

hickory species. The habitat for each of these species 

shows potential for increase in Eastern and Coastal 

Maine in the future.

Many species are also projected to have newly 

suitable habitat and may enter the assessment area 

by the end of the century. The greatest increases 

in potential new habitats for species occurs in 

Eastern and Coastal Maine. In that subregion, 

suitable habitat expands northward and eastward 

for 36 species, many of which are currently present 

elsewhere in New England. Twelve species are 

projected to have new habitat in all three subregions, 

although these species have new habitat in the 

Northern Forest subregion only under the more 

extreme GFDL A1FI scenario. Six species—

common persimmon, loblolly pine, pawpaw, 

shortleaf pine, southern red oak, and sweetgum—

have new habitat in Southern and Coastal New 

England under both climate scenarios and have new 

habitat in the other subregions under GFDL A1FI. 

Southern and Coastal New England is also projected 

under GFDL A1FI to be suitable for some species 

from much farther south, including bald cypress, 

pond pine, and sweetbay.

Outputs from DISTRIB can also be visualized 

spatially, and these results can provide greater 

context for interpreting the projected changes in 

suitable habitat. Maps of six species (red spruce, 

sugar maple, northern red oak, black cherry, 

chestnut oak, and pitch pine) provide examples 

of the potential changes in suitable habitat. These 

maps show that projected changes are not uniform 

across the assessment area, and that areas of suitable 

habitat are also related to projected climate change 

as well as local conditions (Fig. 26). Red spruce 

is projected to retain a large amount of suitable 

habitat in the assessment area under PCM B1, but 

suitable habitat decreases substantially under GFDL 

A1FI, contracting to the most northerly and highest 

elevation locations. Sugar maple is projected to 

have reduced suitable habitat under both climate 

scenarios, with a much greater reduction projected 

under GFDL A1FI. Areas of suitable habitat for 

northern red oak are projected to shift northward 

under both scenarios. Black cherry and chestnut 

oak, which are currently less abundant in the region, 

are expected to have increased habitat suitability in 

the future. Pitch pine habitat is projected to remain 

relatively stable, largely due to the unique soil and 

edaphic conditions associated with this species.

As mentioned earlier, DISTRIB results indicate 

only a change in suitable habitat, not necessarily 

that a given species will be able to migrate to newly 

available habitat. Additionally, these results do not 

incorporate the positive influence of modifying 

factors into the maps for sugar maple or white oak. 

As is the case for interpreting any spatial model 

outputs, local knowledge of soils, landforms, and 

other factors is necessary to determine if particular 

sites may indeed be suitable habitat for a given 

species in the future. These maps serve as an 

illustration of broad trends. Suitable habitat maps 

for all the species addressed in this assessment are 

available online through the Climate Change Tree 

Atlas Web site (www.nrs.fs.fed.us/atlas/tree; see also 

Appendix 4).
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

60

Figure 26.—Modeled importance values from the Climate Change Tree Atlas for six species in the assessment area. Maps show 

current importance values modeled from U.S. Forest Service Forest Inventory and Analysis data (top) and projected for the 

years 2070 through 2099 under the PCM B1 (middle) and GFDL A1FI (bottom) climate scenarios. Importance values can range 

from 0 to 100. An importance value of zero (light yellow) indicates that the species is not present currently (top), or will not 

have suitable habitat at the end of the century (middle, bottom).
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

61

LINKAGES

The LINKAGES model integrates soil, climate, 

and species attributes to simulate ecosystem-level 

changes in species establishment. Projections of 

species establishment probabilities produced by 

the model provide information about the likelihood 

that a particular species will establish and grow in a 

particular place assuming an adequate seed source, 

absent disturbance or competition from other species 

(Table 9). The maximum biomass reached by a 

species for the 30 years starting from bare ground 

was used as a measure of ability to establish from 

seed and grow under a climate at a particular site. 

The first 30 years was used because seedlings are 

most susceptible to climate warming, and stand 

dynamics for longer periods are more realistically 

addressed with the LANDIS PRO model. Results 

for years 2080 through 2099 under the PCM B1 and 

GFDL A1FI climate scenarios were compared to the 

current climate from 1990 through 2009. 

Species establishment projections varied widely 

for the 24 species modeled by LINKAGES 

(Table 12). Across the entire assessment area, five 

species are projected to have substantially reduced 

species establishment at the end of the 21st century 

under climate change when compared to the current 

climate. Four northern conifer species—balsam 

fir, red spruce, northern white-cedar, and black 

spruce—are projected to have large declines under 

both climate change scenarios, with an almost 

total cessation of simulated establishment and 

growth (i.e., projected biomass values near zero) or 

extirpation under GFDL A1FI. Eastern hemlock is 

projected to undergo a similar outcome, although not 

as extreme. Four northern hardwood species (sugar 

maple, quaking aspen, yellow birch, and American 

beech) along with eastern white pine are projected 

under PCM B1 to have establishment similar to 

the current climate, but decreased establishment 

under the harsher GFDL A1FI scenario. Three 

hardwood species more common to transitional 

forests—red maple, northern red oak, and white 

ash—are not projected to have substantial changes in 

establishment. 

Species common south of New England are 

generally expected to have an increased ability 

to establish in the assessment area under the two 

climate change scenarios. Black cherry and white 

oak are projected to have moderate increases under 

both scenarios, whereas pignut hickory and pitch 

pine have more substantial increases under GFDL 

A1FI. Shagbark hickory, yellow-poplar, and scarlet, 

black, and chestnut oak have large increases under 

both scenarios. Two species that are not currently 

present in the assessment area—loblolly and Virginia 

pine—were also modeled. For both species, the 

probability of establishment is projected to increase 

under climate change, although projected biomass 

values for the end of the century are relatively low 

and new habitat is located in the southern portion of 

New England. 

Geographic Trends

Projected species establishment values are 

substantially different in Southern and Coastal New 

England compared to other parts of the assessment 

area (Fig. 27). Under PCM B1, individual species 

are generally projected to have larger decreases or 

less substantial increases in establishment than in 

other parts of the assessment area. More notably, 

the GFDL A1FI scenario projects especially severe 

impacts on establishment: Nearly all species are 

projected to have large decreases under GFDL 

A1FI. Some species, such as quaking aspen, balsam 

fir, red spruce, northern white-cedar, black spruce, 

and yellow birch, have no measurable established 

biomass in Southern and Coastal New England at 

the end of the century. These projected impacts are 

apparently related to modeled changes in regional 

precipitation. Under GFDL A1FI, the number of 

large (greater than 2 inches) rain events increases 

more substantially in Southern and Coastal New 

England, as does the time between rain events (i.e., 

days without precipitation). The result is a “boom 

and bust” cycle, in which most precipitation comes 

during large events and runs off the saturated soils, 

only to be followed by relatively long periods of 

no precipitation. Appendix 5 contains results from 

the LINKAGES model at year 2100 for each of the 

subregions and for the entire assessment area. 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

62

Future climate

Current climate PCM B1 GFDL A1FI

Species

Biomass (metric 

tons/acre)

Biomass (metric 

tons/acre)

Change from 

current climate

Biomass (metric 

tons/acre)

Change from 

current climate

American beech 40.4 38.0 -6% 27.8 -31%

Balsam fir 11.9 5.5 -54% 0.3 -97%

Black cherry 30.8 40.3 31% 37.9 23%

Black oak 7.9 18.4 133% 30.8 290%

Black spruce 2.7 0.9 -67% 0.0 -100%

Chestnut oak 4.0 13.7 243% 34.0 753%

Eastern hemlock 20.0 11.2 -44% 3.4 -83%

Eastern white pine 32.3 29.1 -10% 13.5 -58%

Loblolly pine* 0.0 0.2 >1,000% 1.8 >1,000%

Northern red oak 48.3 52.7 9% 43.3 -10%

Northern white-cedar 8.2 3.9 -52% 0.2 -98%

Pignut hickory 11.4 22.7 99% 29.4 158%

Pitch pine 5.9 10.6 80% 12.6 114%

Quaking aspen 47.6 39.3 -17% 17.4 -63%

Red maple 42.2 45.0 7% 38.3 -9%

Red spruce 9.2 3.7 -60% 0.2 -98%

Scarlet oak 10.2 21.3 109% 29.3 187%

Shagbark hickory 5.8 13.7 136% 24.2 317%

Sugar maple 48.2 44.2 -8% 34.7 -28%

Virginia pine* 0.1 1.5 >1,000% 6.6 >1,000%

White ash 60.6 61.9 2% 50.5 -17%

White oak 30.5 43.1 41% 43.3 42%

Yellow birch 46.0 39.2 -15% 24.6 -47%

Yellow-poplar 15.5 39.3 154% 71.1 359%

*Species is not currently present in the assessment area. Increase in biomass represents new habitat.

Table 12.—Projected change in biomass reached in 30 years of tree growth starting from bare ground projected 

by the LINKAGES model for 24 species in the assessment area under the current climate scenario and two climate 

model-emissions scenario combinations for the period 2070 through 2099
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

63

Figure 27.—Projections of relative amount and direction of change in biomass for six species in the assessment area using the 

LINKAGES model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current climate scenario. Tree species 

growth values modeled by LINKAGES are presented as the biomass reached in 30 years of growth starting from bare ground in 

2070 and ending in 2099. Appendix 5 contains maps of all modeled species.
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

64

LANDIS PRO

Forest landscape change was simulated using the 

LANDIS PRO model to project changes in tree 

abundance (basal area/acre) and density (trees/acre) 

for 24 tree species to the year 2100 and beyond 

(Wang et al. 2017) (Table 9). The LANDIS PRO 

model differs substantially from the Tree Atlas and 

LINKAGES because it simulates tree, stand, and 

landscape dynamics including succession and seed 

dispersal. LANDIS PRO can provide information 

about the projected composition and structure of 

an individual pixel for any point in time during 

the simulation. LANDIS PRO accounts for natural 

stand dynamics, including growth, mortality, 

competition, and succession, in addition to climate 

effects on establishment and growth. 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 under each climate 

scenario (Wang et al. 2017). We simulated landscape 

change out to the year 2300. Because trees are 

long-lived, nearer-term projections of forest change 

are more heavily influenced by the current forest 

conditions and management, and the effects of 

climate change become more pronounced over time 

as forests have time to respond (Duveneck et al. 

2017, Thompson et al. 2011, Wang et al. 2017). This 

section describes the model projections of basal 

area and trees per acre by species for the year 2100; 

however, additional projections for 2040, 2070, and 

2200 were also used to understand the long-term 

response of forests to climate change (Appendix 6). 

All LANDIS PRO simulation models integrate 

current levels of forest harvest based on data 

from the U.S. Forest Service Forest Inventory and 

Analysis program. These simulations do not include 

natural disturbances such as wind, fire, or insects, 

nor do they include effects from carbon dioxide 

(CO2) fertilization or nitrogen deposition (Wang 

et al. 2017).

In this assessment, future forest composition and 

structure are reported as basal area and trees per acre 

for each tree species. Basal area is the area (square 

feet) of tree stems per unit area (acres). High basal 

area can be driven by many large-diameter trees, 

an even greater number of small-diameter trees, 

or a combination of the two. Therefore, trees per 

acre values are also included as another measure of 

abundance, regardless of tree size. A high basal area 

with a low number of trees per acre would indicate a 

higher relative proportion of larger and presumably 

older trees, and a low basal area with a high number 

of trees per acre would indicate a higher relative 

proportion of smaller and presumably younger trees. 

Simulation results using the PCM B1 and GFDL 

A1FI climate scenarios were compared to 

simulations using a current climate scenario, which 

maintained the climate observed during 1960 to 

2010 throughout the simulation. The current climate 

scenario 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 Northeast because 

many forests in the region are still recovering from 

past disturbances, such as land clearing (Chapter 1). 

As forests undergo succession, the tree species that 

are currently present in the region are generally 

expected to have increased basal area throughout the 

century under all climate scenarios (Duveneck et al. 

2017, Wang et al. 2017). 

Because of the existing strong influence of forest 

growth and succession over the next century, climate 

change has a relatively subtle influence on forests 

through 2100. Basal area is projected to increase 

under all three climate scenarios (current climate, 

PCM B1, GFDL A1FI). Climate-related changes 

for individual species are relatively small under the 

climate change scenarios (as compared to the current 

climate) for individual species (Table 13). In general, 

future basal area values for individual species under 

the PCM B1 and GDFL A1FI scenarios are within 

20 percent of the values projected under the current 

climate scenario (Table 13). When basal area is 

summed across all species in the assessment area, 

total basal area is similar between PCM B1 and the 

current climate. This is plausible because the PCM 

B1 scenario projects rather mild changes in climate, 

allowing the trees to respond favorably to slightly 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

65

Table 13.—Basal area (BA) and trees per acre (TPA) projected by the LANDIS PRO model for 24 species in the 

assessment area at year 2100 under the current climate scenario and two climate model-emissions scenario 

combinations

Basal area in year 2100

Current climate PCM B1 GFDL A1FI

Tree species

BA in 2000 

(ft2/acre)

BA in 2100 

(ft2/acre)

Change 

from 2000

BA in 2100 

(ft2/acre)

Change from 

current climate

BA in 2100 

(ft2/acre)

Change from 

current climate

American beech 6.2 11.5 85% 10.3 -10% 13.8 20%

Balsam fir 11.7 7.5 -36% 8.2 9% 9.7 29%

Black cherry 1.9 2.6 37% 2.5 -4% 3.0 15%

Black oak 1.2 1.4 17% 1.5 7% 1.4 0%

Black spruce 1.5 1.4 -7% 1.2 -14% 1.5 7%

Chestnut oak 0.3 0.6 100% 0.6 0% 0.6 0%

Eastern hemlock 9.8 9.1 -7% 8.6 -5% 10.1 11%

Eastern white pine 9.8 11.3 15% 10.6 -6% 13.3 18%

Loblolly pine 0.0 0.0 0% 0.0 0% 0.0 0%

Northern red oak 4.9 5.6 14% 5.3 -5% 6.3 13%

Northern white-cedar 4.1 4.4 7% 4.1 -7% 5.1 16%

Pignut hickory 0.3 0.7 133% 0.7 0% 0.7 0%

Pitch pine 0.2 0.2 0% 0.2 0% 0.2 0%

Quaking aspen 2.0 7.3 265% 7.0 -4% 7.9 8%

Red maple 16.2 15.6 -4% 15.4 -1% 16.8 8%

Red spruce 7.5 11.5 53% 9.1 -21% 11.8 3%

Scarlet oak 0.5 1.1 120% 1.1 0% 1.1 0%

Shagbark hickory 0.2 0.4 100% 0.4 0% 0.4 0%

Sugar maple 10.3 11.0 7% 10.1 -8% 12.5 14%

Virginia pine 0.0 0.0 0% 0.0 0% 0.0 0%

White ash 3.0 6.1 103% 5.5 -10% 7.3 20%

White oak 1.1 2.8 155% 2.8 0% 3.1 11%

Yellow birch 6.1 10.5 72% 9.0 -14% 12.3 18%

Yellow-poplar 0.1 0.1 0% 0.1 0% 0.1 0%

(continued on next page)
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

66

Table 13 (continued).—Basal area (BA) and trees per acre (TPA) projected by the LANDIS PRO model for 24 species 

in the assessment area at year 2100 under the current climate scenario and two climate model-emissions scenario 

combinations

Trees per acre in year 2100

Current climate PCM B1 GFDL A1 FI

Tree species TPA in 2000

TPA in 

2100

Change 

from 2000

TPA in 

2100

Change from 

current climate

TPA in 

2100

Change from 

current climate

American beech 71.1 26.4 -63% 24.4 -8% 34.8 32%

Balsam fir 237.6 15.4 -94% 21.8 42% 18.7 21%

Black cherry 7.4 6.3 -15% 6.0 -5% 9.3 48%

Black oak 1.8 2.2 22% 2.5 14% 1.9 -14%

Black spruce 11.3 12.2 8% 6.4 -48% 5.5 -55%

Chestnut oak 0.4 2.8 600% 3.9 39% 3.1 11%

Eastern hemlock 39.4 13.3 -66% 10.7 -20% 10.2 -23%

Eastern white pine 23.0 50.1 118% 40.0 -20% 65.4 31%

Loblolly pine 0.0 0.0 0% 0.0 0% 0.0 0%

Northern red oak 10.8 17.3 60% 15.7 -9% 20.6 19%

Northern white-cedar 26.2 45.7 74% 25.3 -45% 27.1 -41%

Pignut hickory 0.9 1.1 22% 1.2 9% 0.9 -18%

Pitch pine 0.3 0.3 0% 0.3 0% 0.3 0%

Quaking aspen 10.8 71.9 566% 68.9 -4% 78.2 9%

Red maple 107.2 25.9 -76% 25.7 -1% 34.9 35%

Red spruce 74.2 71.4 -4% 31.8 -55% 29.7 -58%

Scarlet oak 1.2 1.9 58% 2.1 11% 1.4 -26%

Shagbark hickory 0.6 0.8 33% 0.8 0% 0.9 13%

Sugar maple 42.2 20.4 -52% 20.1 -1% 25.8 26%

Virginia pine 0.0 0.0 0% 0.0 0% 0.0 0%

White ash 14.1 19.6 39% 16.3 -17% 28.1 43%

White oak 1.7 17.7 941% 17.2 -3% 18.1 2%

Yellow birch 36.9 46.8 27% 36.4 -22% 59.2 27%

Yellow-poplar 0.1 0.3 200% 0.3 0% 0.3 0%
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

67

longer growing seasons (Chapter 3). Projected basal 

area increases for all species are greater under GFDL 

A1FI, representing approximately 13 percent greater 

basal area than the current climate scenario (Fig. 28). 

This is plausible due to enhanced tree growth of the 

existing forest under warmer conditions and a longer 

growing season with adequate water. These changes 

are relatively consistent across all species projected 

by the LANDIS PRO model as well as by other 

regional forest landscape modeling (Duveneck et al. 

2017). 

Figure 28.—Projections of relative amount and direction of change in basal area at year 2100 for six species in the assessment 

area using the LANDIS PRO model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current climate scenario. 

Appendix 6 contains maps of all modeled species.
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

68

LANDIS PRO projects a greater response in trees 

per acre than in basal area because younger cohorts 

are more responsive to increased growing space 

due to harvest or mortality. Red spruce, northern 

white-cedar, and black spruce show the strongest 

reduction in the number of trees per acre as a result 

of a changing climate. The abundance of these 

species is projected to decrease at least 40 percent by 

2100 under the PCM B1 and GFDL A1FI scenarios, 

compared to a scenario with no climate change. 

These reductions may be due to the natural mortality 

of mature trees combined with the future decreases 

in species establishment under climate warming 

that are projected by the LINKAGES model.The 

number of trees per acre for balsam fir is projected 

to be substantially reduced to less than 10 percent of 

the current value; this decline was seen in all three 

scenarios, including the current climate scenario 

(Table 13).

In contrast, climate change is projected to increase 

tree abundance for several other species by 2100. 

Yellow-poplar, which is at the northern extent of 

its range in the assessment area, is projected to 

undergo greater increases in the number of trees per 

acre under climate change compared to the current 

climate scenario (Table 13). Red maple, American 

beech, sugar maple, white ash, and black cherry are 

projected to be more abundant under the GFDL A1FI 

scenario compared to the current climate scenario. 

Chestnut oak is projected to have modest increases 

in both trees per acre and basal area under the two 

climate change scenarios, but these small increases 

are in addition to much more substantial increases 

that are modeled under the current climate scenario 

(Table 13).

Geographic Trends

LANDIS PRO results point to notable differences in 

how species and forests respond to climate change 

across the assessment area (Fig. 29). For some 

species, basal area or trees per acre are projected to 

increase in some areas while decreasing in others. 

In general, the number of trees per acre decreases 

for many tree species in Southern and Coastal New 

England while increasing in the Northern Forest. 

Responses in Eastern and Coastal Maine tend to be 

more neutral. For example, quaking aspen, sugar 

maple, and scarlet oak are projected to have small 

decreases in abundance in the Southern and Coastal 

New England subregion at the end of the century, 

while abundance is projected to increase in the 

Northern Forest region. Sugar maple abundance is 

also projected to increase in Eastern and Coastal 

Maine. This suggests that the climatic conditions 

may become less favorable for these species farther 

south, leading to increased mortality or reduced 

establishment of regeneration, while tree growth 

and regeneration can increase farther north as 

growing conditions become more suitable for these 

species. The northern conifer species, however, are 

generally projected to have reduced abundance in all 

subregions.

Discussion of Model Results

The three different models used in this assessment 

were selected because of the ability to model and 

represent different facets of potential forest change 

as a result of a changing climate (Iverson et al. 

2017). The ability to compare results from different 

models helps us gain a deeper understanding of 

which parts of a forest ecosystem may be most 

responsive or vulnerable to change. However, the 

differences between the models, in terms of design, 

outputs, strengths, and weaknesses, also make direct 

comparisons among model results difficult. This 

section describes areas where the results from these 

different models point to similar or different future 

trends and provides context for how the results 

from multiple models can be integrated to better 

understand forest change.

Similarities

Despite the differences between the modeling 

approaches, the Tree Atlas, LINKAGES, and 

LANDIS show some strong similarities in forest 

change during the 21st century under a range of 

future climates (Iverson et al. 2017). All three 

models suggest that characteristic boreal species or 

northern species that are currently at their southern 

range limits will face increasing climate stress at 

the end of the century. All three models projected 

the greatest impacts on northern and boreal conifer 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

69

Figure 29.—Projected change in basal area for 24 species across the assessment area as modeled by LANDIS PRO. Appendix 6 

contains values for individual species.
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

70

species, including black spruce, red spruce, and 

northern white-cedar, and to a somewhat lesser 

extent, eastern hemlock. These findings are similar 

to other modeling efforts looking at tree species 

responses to climate change in the Northeast, which 

have projected declines in boreal and northern forest 

communities relative to those more representative of 

warmer climates (e.g., Ollinger et al. 2008, Tang et 

al. 2012). 

Additionally, all three models tend to agree that 

many species within the assessment area may 

fare better under the PCM B1 conditions than 

under GFDL A1FI. These results suggest that 

many temperate species currently present in the 

assessment area could tolerate a mild degree of 

warming with corresponding increases in growing 

season precipitation, as represented by the PCM B1 

scenario, but may be pushed across temperature and 

other thresholds and suffer increased mortality or 

decline under more severe levels of climate change. 

The models also agree that some species have the 

potential to increase under a range of climate futures, 

particularly with the greater change projected under 

the GFDL A1FI scenario (Iverson et al. 2017). 

Species projected to increase across all models are 

typically oaks (e.g., black, white, scarlet, chestnut) 

and hickories (e.g., pignut, shagbark). These species 

are currently common in the central hardwood 

forests located south of the assessment area and 

generally have characteristics that allow them to 

withstand hotter and drier conditions. Results from 

the DISTRIB component of the Tree Atlas and from 

LINKAGES point to increased suitability of site 

conditions for future tree growth for these species 

by the end of the century. The LANDIS PRO 

model, which includes tree growth, mortality, and 

succession in the simulations, also indicates that 

these species are likely to do better in the future. At 

the same time, the long-term nature of forest change 

is evident in LANDIS PRO, and many potentially 

future-adapted species are still relatively rare on the 

landscape at the end of the 21st century (Wang et al. 

2017). 

Differences

Although the three models indicate similar trends 

in tree species response to climate change, there are 

some differences in the projections for individual 

species among the three models. These are generally 

related to differences in how the models project 

change over time, rather than disagreement about 

how individual species are expected to respond 

in the future (Iverson et al. 2017). For example, 

chestnut oak is projected by the Tree Atlas to 

have increased future suitable habitat and is also 

associated with several positive modifying factors, 

such as high seedling establishment rates. The 

LINKAGES model also projects large increases 

in establishment of this species by the end of the 

century, particularly farther north in the assessment 

area. In the LANDIS model, which integrates 

LINKAGES results, notable increases in the number 

of chestnut oak trees per acre are not observed until 

2100, and the basal area of chestnut oak does not 

show a distinct response to climate change until after 

2100 (Fig. 28, Appendix 6).

The Tree Atlas and LINKAGES models provide 

information about future site suitability, with 

the Tree Atlas focusing on suitable habitat and 

LINKAGES, as used here, on potential species 

establishment. These models point to the potential 

for substantial changes in climatic and site 

conditions that may affect many tree species. 

These models tend to provide an early indication 

of forest changes that may first become evident 

in regeneration success or failure, although these 

changes may not be observed in forests for decades. 

In contrast, the LANDIS model simulates forest 

growth, competition, and succession over long time 

periods and instead suggests that much of the change 

in forests over the next 100 years will be due to 

succession and forest management. The LANDIS 

model results suggest that climate-related changes 

may take longer to manifest themselves because 

trees are long-lived and disperse slowly, although 

these model results do not include forest change 

from disturbance events (Wang et al. 2017). 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

71

Limitations

All models are simplified representations of reality, 

and no model can fully consider the entire range 

of ecosystem processes, stressors, interactions, and 

future changes to forest ecosystems. Each model 

omits processes or drivers that may critically 

influence ecosystem change in the future. Future 

uncertainty is not limited to climate scenarios; there 

is also uncertainty associated with future human 

interactions with forests. Examples of factors that 

are not considered in these models are:

• Land management and policy responses to 

climate change or impacts to forests 

• Land-use change or forest fragmentation

• Changes to soil and water acidity from pollutants

• Future changes in forest industry, including 

products and markets

• Changes in phenology and potential timing 

mismatches for key ecosystem processes

• Responses of understory vegetation, soil microorganisms, or soil mycorrhizal associations

• Extreme weather events, which are not captured 

well in climate data or forest impact models

• Future wildfire behavior, fire suppression, and 

ability to apply prescribed fire

• Novel successional pathways for current forest 

ecosystems

• Major insect pests or disease agents

• Invasive species, such as nonnative earthworms 

and nonnative plants

• Future herbivory pressure, particularly from 

white-tailed deer

• Interactions among all these factors.

These factors result in forest ecosystem changes 

throughout the assessment area that may be 

independent of (or exacerbated by) effects from 

a changing climate. The potential for interactions 

among these factors adds layers of complexity and 

uncertainty. Despite these limitations, impact models 

are still the best tools available and can simulate a 

range of possible future outcomes. It is important 

to keep the preceding limitations in mind when 

weighing the results from different models and 

use them to inform an overall assessment. Future 

simulation modeling will broaden to incorporate 

more interacting factors into studies of climate 

change effects on forests. In the following section, 

we draw upon published literature to address other 

factors that may influence how forest ecosystems in 

the assessment area respond to climate change.

SUMMARY OF CURRENT 

SCIENTIFIC KNOWLEDGE

Climate change has the potential to alter the 

distribution, abundance, and productivity of forests 

and their associated species in several ways (Joyce 

et al. 2014, Vose et al. 2012). The results presented 

earlier provide us with important projections of 

suitable habitat and distribution of tree species 

across a range of climate futures, but these models 

do not account for all factors that may influence 

tree species and forest ecosystems under a changing 

climate. The effects of climate change on forests 

can broadly be divided into the direct effects of 

changing temperature, precipitation, and CO2 levels 

on forests and the indirect effects of altered, new, 

and interacting stressors. For the most part, the 

model results just described primarily consider 

the direct effects from changes in temperature, 

precipitation, and other climate variables and their 

influence on competition. The remainder of this 

chapter summarizes the current state of scientific 

knowledge on additional direct and indirect effects 

of climate change on forests in the assessment area 

and throughout the Northeast.

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 taking into account losses from respiration and 

other causes. Forest productivity is an important way 

to assess the condition of a forest because it is the 

rate at which forests sequester carbon and produce 

forest products (e.g., timber). This section describes 

the potential effects of altered temperature and 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

72

precipitation and nonclimatic factors such as CO2

enrichment and ozone damage on forest productivity 

and carbon gain (Table 9). Other complex factors 

that also influence forest growth, such as amplified 

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 2), 

and this trend is expected to continue (Chapter 3). 

Longer growing seasons extend the time available to 

plants for photosynthesis and can lead to enhanced 

forest growth (Loehle et al. 2016, McMahon et al. 

2010, Ollinger et al. 2008). There is evidence both 

worldwide and regionally that increases in growing 

season length during the past century are partially 

responsible for observed increases in forest growth 

and carbon sequestration (Keenan et al. 2014, 

McMahon et al. 2010, Richardson et al. 2010, White 

et al. 1999). For example, one study of increased 

growing season length in northeastern forests found 

that a 1-percent increase in growing season length 

resulted in a 1.6-percent increase in net ecosystem 

productivity (McMahon et al. 2010, White et al. 

1999). Projections of forest growth at four sites 

in the Northeast generally showed increases in 

productivity of up to 25 percent under scenarios of 

mild and moderate climate warming (Ollinger et al. 

2008). Another study of forests across Massachusetts 

also projected increases in biomass from climate 

change due to the positive influence of warmer 

temperatures on photosynthesis (Thompson et al. 

2011).

A high-elevation ecosystem in northern New Hampshire. Photo by Todd Ontl, U.S. Forest Service.
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

73

Temperature influences forest growth through 

effects on both photosynthesis and respiration. 

Plant respiration increases exponentially with 

increasing temperature, although plants are able to 

acclimate to different temperature regimes (Aber 

and Melillo 1991, Aber et al. 1995, Sendall et al. 

2015). Some studies suggest that the increased 

respiration under warmer temperatures is offset 

by increases in growth, resulting in a net gain in 

productivity (Loehle et al. 2016, Richardson et al. 

2010). Conversely, in one simulation of northeastern 

forests, growth rates declined when temperatures 

exceeded the optima for photosynthesis under a 

climate scenario with greater temperature increases; 

this effect was particularly strong in sprucedominated forests because these forests have a lower 

temperature optimum for photosynthesis (Ollinger 

et al. 2008). Likewise, modeling of eastern white 

pine across its range suggests a future contraction 

of its range at the southern extent driven largely 

by temperature thresholds along with temperature 

interactions with moisture balance (Joyce and 

Rehfeldt 2013). 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, Melillo et al. 1995, Rustad et al. 2001), 

although the degree of soil respiration is related to 

the availability of soil moisture and nutrients. 

As temperatures rise throughout the 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 shift earlier in the year also 

accelerate hydrologic cycles (Chapter 3). As peak 

streamflows move earlier into the year, there is an 

increased potential for late-summer soil moisture 

deficits (Chapter 3), which is further compounded 

by changes in the temporal distribution of rain 

throughout the year and increases in extreme 

precipitation events (Campbell et al. 2009, 

Hayhoe et al. 2007). The influence of increased 

evapotranspiration may be greater than the influence 

of decreasing summer precipitation on soil moisture 

(Campbell et al. 2009, Luce et al. 2016b). 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, leading to 

negative impacts on productivity (Campbell et al. 

2009). Shifts in the phenology of leaf emergence in 

response to warmer spring temperatures have the 

potential to increase the vulnerability of northeastern 

forests to late spring frosts when these events do 

occur. For example, early leaf emergence in 

response to warmer spring temperatures followed 

by frost reduced annual gross productivity 7 to 

14 percent across large areas of northern hardwood 

forests in the Northeast during 2010 (Hufkens et 

al. 2012). 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 with 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. 2012b).

Although climate change is expected to increase 

forest growth in many ways, it can be difficult to 

separate these trends from other changes that are 

occurring in forests. Previous land use in the region 

has resulted in second-growth forests that are young 

compared to pre-European settlement conditions 

(Foster et al. 1998, Thompson et al. 2011). Several 

modeling studies demonstrate that forests across 

the region are generally expected to accumulate 

carbon over the next several decades simply due to 

succession and forest maturation (Duveneck et al. 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

74

2017, Thompson et al. 2011, Wang et al. 2017). 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 (Duveneck et al. 2017, Ollinger et al. 

2008, Pan et al. 2009, Thompson et al. 2011, Wang 

et al. 2017). Additionally, land-use changes that 

result in conversion of forest to other land uses have 

the potential to decrease any carbon gained through 

either forest succession or growth from climate 

change (Puhlick et al. 2017, 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 CO2 on plant productivity. Elevated CO2 has a 

direct, positive effect on carbon uptake and can 

increase the water use efficiency of trees (Ainsworth 

and Rogers 2007, Hyvönen et al. 2007, Norby and 

Zak 2011). There is evidence that CO2 fertilization 

has contributed to enhanced tree growth over the 

past two centuries (Cole et al. 2010, Franks et al. 

2013, Loehle et al. 2016, Norby and Zak 2011) and 

has potentially offset some of the effects of drier 

growing seasons (Franks et al. 2013, Wang et al. 

2006).

Modeling studies examining productivity in 

northeastern forests consistently project greater 

increases when the CO2 fertilization effect is 

included in modeling (Aber et al. 1995, Ollinger et 

al. 2008, Pan et al. 2009). This effect is particularly 

strong for deciduous forests; the benefit of CO2 was 

projected to be less in spruce forests because of the 

sensitivity of the northern forests to temperature 

increases (Aber et al. 1995, Ollinger et al. 2008). 

Often, the models suggest that CO2 fertilization 

has a greater effect on forest productivity than does 

climate change (Aber et al. 1995, Ollinger et al. 

2008, Pan et al. 2009). As discussed earlier, warmer 

temperatures and longer growing seasons can lead 

to increased evapotranspiration, respiration, and 

potential for drought stress (Hyvönen et al. 2007). 

Enhanced CO2 can partially offset this effect by 

improving water use efficiency (Ollinger et al. 

2008). 

Although carbon dioxide enrichment experiments 

and models suggest net primary productivity will 

increase under elevated CO2, several factors have the 

potential to limit the CO2 fertilization effect. Other 

environmental change factors, including nutrient and 

water availability, ozone pollution, and tree species, 

age, and size, all influence the ability of trees to 

capitalize on CO2 (Ainsworth and Long 2005, Norby 

and Zak 2011, Norby et al. 2005). For example, a 

future decrease in nitrogen availability may limit 

the otherwise positive effects of CO2 enrichment 

(Templer et al. 2012a). Productivity increases under 

elevated CO2 could be partially offset by reductions 

in productivity from warming-induced drought 

stress or the effects of future disturbances (Dieleman 

et al. 2012, Franks et al. 2013). Furthermore, 

climate change-related disturbance such as fire, 

insects, disease, and management could reduce 

forest productivity independent of CO2 fertilization 

(Couture et al. 2015, Mohan et al. 2009). 

Moreover, no CO2 fertilization experiments have 

been undertaken in the Northeast (Ollinger et 

al. 2008), and observational studies elsewhere 

have generally not evaluated the effects of CO2

fertilization beyond 600 ppm. These limitations 

make it difficult to predict how regional forests may 

respond under even higher levels of atmospheric 

CO2 , such as the 900-ppm levels projected under the 

A1FI emissions scenario for 2100. 

Atmospheric Pollutants

Although warmer temperatures have the potential 

to increase enzymatic activity and nutrient cycling, 

interactions with atmospheric deposition will 

remain an important consideration. Anthropogenic 

emissions of nitrogen and sulfur increased during 

the past century, peaking in the 1970s. Although 

nitrogen and sulfur emissions have declined 

substantially since the Clean Air Act was enacted in 

1970 to control pollution from coal-burning power 

plants (Clean Air Act of 1970, Strock et al. 2014), 

the effects on forests are long-lasting. Nitrogen and 

sulfur emissions undergo chemical transformations 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

75

that produce nitrates and sulfates, which are 

eventually deposited on the ground (Elliott et al. 

2013). Importantly, sulfur and nitrogen compounds 

are deposited at high concentrations in rain and 

snow in the eastern United States, particularly at 

high-elevation locations (Pardo et al. 2011). In forest 

ecosystems, hydrogen ions associated with nitrogen 

and sulfur deposition replace nutrient base cations of 

calcium, magnesium, and potassium, depleting these 

nutrients and allowing them to leach into drainage 

waters. At the same time, toxic cations of aluminum 

are mobilized, and the combined effects of nutrient 

depletion and increased toxicity have been proven 

to reduce the health and productivity of forests and 

streams through acidification (Aber et al. 1989, 

1998; Elliott et al. 2013; Fernandez et al. 2003; Long 

et al. 2013; Schaberg et al. 2006). 

Acid deposition has had dramatic effects on forest 

ecosystems in much of the assessment area (Likens 

et al. 1996), but it is less clear how these impacts 

will interact with the effects of a changing climate. 

Nitrogen saturation has 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 is expected to 

contribute to reduced ability to withstand climatic 

changes (Friedland et al. 1984, McNulty and Boggs 

2010, Pardo et al. 2011). At the same time, some 

research suggests that nitrogen deposition could be 

beneficial and help fuel forest growth in a future 

CO2-rich world (Devaraju et al. 2016, Rustad et 

al. 2012, Thornton et al. 2007). Future rates of 

nitrogen deposition are unknown, but may continue 

to decrease in the future as an indirect outcome of 

reduced greenhouse gas emissions (Driscoll et al. 

2014). The potential impacts of elevated nitrogen 

deposition in a changing climate remain unclear.

Ground-level ozone is a pervasive air pollutant that 

can damage plant tissue and decrease photosynthesis 

(Smith et al. 2012). Ozone levels are relatively low 

in the assessment area compared to other parts of 

the Northeast, although ozone injury was detected 

in sensitive species during most years and most 

states from 1994 through 2010 (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 CO2-

induced 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). Ozone levels have been 

decreasing in recent decades, but it is possible that 

levels may increase with climate change as plants 

produce more volatile organic compounds, which 

then react with nitrogen oxides to produce ozone 

(Rustad et al. 2012).

Nutrient Cycling

As air temperatures warm and precipitation 

patterns change, the way nutrients are cycled 

between plants, soils, and the atmosphere may 

also change. 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. 2012a). The long-term effects of past and 

ongoing acid deposition in the region increase the 

complexity of connected nutrient cycles and their 

interactions, making predictions more difficult. 

Numerous factors, including changes in temperature, 

precipitation, soil moisture, acid deposition, and the 

interaction 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 snowmelt, soil warming, 

and biological production of nutrients happen 

earlier in the spring while the onset of leaf-out, 

photosynthesis, and overstory plant nutrient uptake 

still happen 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).
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

76

Decomposition of vegetation is a major component 

of most nutrient cycles and is largely carried out 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 have examined the effects of extended 

dry periods followed by moisture pulses on nutrient 

cycling, moisture pulses led to a flush of mineral 

nitrogen, but this nutrient flush was not sufficient 

to compensate for the lack of microbial activity 

during dry periods (Borken and Matzner 2009). 

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.

Sea-level Rise

Forest ecosystems along coasts will be affected by 

climate change. Coastal ecosystems—including 

wetlands, salt marshes, estuaries, and forests—

provide many benefits, including water filtration, 

nursery habitat for fish and other species, carbon 

storage, and recreation (Moser et al. 2014, Scavia et 

al. 2002). Additionally, coastal ecosystems help to 

buffer storm surges and waves and reduce impacts 

from flooding. As sea levels rise, land will become 

inundated by water, and areas that are currently 

farther inland or upland will be affected by warmer 

water temperatures, increased water salinity and 

acidity, and other changes in ecosystem dynamics 

(Moser et al. 2014). Further, coastal ecosystems play 

an important role in buffering the effects of extreme 

conditions along the coasts, and impacts on these 

systems can reduce their ability to protect against 

storm surges and flooding (Groffman et al. 2014). 

Part of the extensive Atlantic Ocean coastline within the assessment area. Photo by Maria Janowiak, U.S. Forest Service.
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

77

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, Peterson 2000, Vose et al. 2012), although 

there are challenges in attributing changes in these 

events to a changing climate (Chapter 3). Changes 

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 northeastern 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 result 

of more energy in the atmosphere, resulting in a 

greater amount of precipitation falling in large 

events (Chapter 3). Extreme precipitation can have 

substantial effects on ecosystems, particularly when 

rainfall occurs as part of an extreme storm event. 

As one example, wind- and pressure-driven storm 

surges during hurricanes can result in flooding, 

especially when these events occur in conjunction 

with high tides (Frumhoff et al. 2007). This type 

of interaction occurred during the 1938 hurricane. 

Although nor’easters typically have less energy than 

hurricanes, these events may occur more frequently 

in the region and may be larger in duration and 

extent, leading to a greater overall effect on 

ecosystems. 

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).

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 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. 

Wind Disturbance 

Wind disturbances, including hurricanes, tornadoes, 

downbursts, gales, and intense windstorms, are a 

primary driver of vegetation patterns and succession 

in many regional forests, with both small-scale 

and stand-replacing wind events influencing 

tree species composition, forest structure, and 

landscape complexity (Xi and Peet 2011). These 

disturbance events have historically been an 

important component of the disturbance regime 

for northeastern forests (Boose et al. 1994). The 

effects of individual disturbances can vary greatly, 

occurring at different spatial scales and causing 

different types of damage to forests. The physical 

effects of a given wind event on forests will be 

influenced by numerous factors, such as storm 

severity, forest composition, stand age, soils, and 

topography (Foster and Boose 1992, Peterson 2000, 

Xi and Peet 2011). Impacts that are common across 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

78

most wind disturbances include tree mortality, 

altered forest structure, and altered tree species 

composition and diversity (Xi and Peet 2011). 

To date, the amount of evidence that climate 

change will alter atmospheric circulation patterns 

and processes, leading to increased risk of extreme 

storms, is somewhat limited (Intergovernmental 

Panel on Climate Change 2012, Kunkel et al. 2012, 

Ulbrich et al. 2009). No evidence is available 

to suggest that severe convective storms (e.g., 

thunderstorms, hailstorms) or extreme wind has 

increased in recent decades in the region (Bryan 

et al. 2015, Kunkel et al. 2012). Likewise, the 

infrequency of hurricanes makes it difficult to detect 

change or attribute changes to an altered climate 

(Kunkel et al. 2012). Looking to the future, there 

is some evidence that suggests 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 3).

If wind disturbances do increase in a changing 

climate, forest ecosystem dynamics will also 

change. 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 particularly 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 decisions, 

such as salvage logging, may also compound the 

severity of these events, creating novel regeneration 

environments.

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). Evidence 

suggests that blowdowns 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 yellow-poplar (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). This is especially the case in fire-dependent 

communities where shade-tolerant understories 

have developed in the absence of fire (Abrams and 

Nowacki 1992, Holzmueller et al. 2012). 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.

Ice Storms

Ice storms are especially prevalent in the eastern 

United States, and these storms can cause substantial 

damage to ecosystems and infrastructure (Changnon 

2003, 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. The 

Climatic Data Center’s Storm Data historical records 

document 18 major ice storms in the Northeast 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

79

since the late 1800s, the most devastating of which 

occurred in 1998 (Irland 2000). The 1998 storm 

deposited up to 3.1 inches of freezing rain across 

nearly 24 million acres extending from southcentral New England to northwestern New York and 

southern Quebec, paralyzed power grids, left much 

of the region without power, and caused more than 

$2.2 billion of damage (Irland 2000).

Some studies suggest that the frequency and 

severity of these storms will increase due to climate 

change (Castellano 2012; Cheng et al. 2007, 2011). 

In forests, the accumulation of ice on trees can 

have 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 

are apparently highly susceptible to branch and stem 

breakage, whereas conical species such as spruce 

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 than in pole or sapling size 

classes (Turcotte et al. 2012). Residual trees can 

have reduced photosynthesis due to the loss of crown 

or decreased productivity as resources are used for 

closing the wound or protecting against pathogens. 

Damaged trees 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). 

For example, the severe ice storm of 1998 affected 

canopy structure, regeneration, species composition, 

and nutrient cycling dynamics (Rhoads et al. 2002, 

Weeks et al. 2009) and influenced bird communities 

(Faccio 2003).

A forest that has been subjected to ice storms and other disturbances. Photo by Maria Janowiak, U.S. Forest Service. 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

80

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 vary 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 after wildfire if climatic 

conditions, seed sources, or management decisions 

favor different forest types.

The relationship between temperature, precipitation, 

and evapotranspiration will influence whether future 

conditions are suitable for wildfire. If temperature 

and evapotranspiration increases overwhelm 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 spring and fall where wildfire 

conditions are more common. 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). Drier 

conditions could also increase the frequency of 

wildfires in certain forest types of the Northeast, 

such as northern hardwoods, where fires have 

historically been rare. A change in vegetation, 

particularly an increase in oak systems over time, 

could increase the abundance of fire-associated 

forest types in the region (Mohan et al. 2009).

Relatively few studies have modeled how climate 

change will 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). One recent modeling effort 

suggests that wildfire risk may increase in the 

Northeast (Guyette et al. 2014), whereas another 

suggests that it is not likely to change substantially 

over the next century (Heilman et al. 2015, Tang et 

al. 2015). Projections for adjacent areas of Canada 

point to the potential for increased fire occurrence 

in the future, especially in lightning-caused fires 

(Flannigan et al. 2009). Research on boreal forest 

systems in Quebec projects that the wildfire season 

may shift later into the growing season, with wildfire 

risk doubling in August (Le Goff et al. 2009). 

It is likely that human activities and landscape may 

have a larger influence on wildfire activity than 

biophysical drivers across much of the assessment 

area. Land use and management decisions often 

determine whether a change in fire risk might 

translate to an actual increase in wildfire activity. 

But future policies, decisions, and actions regarding 

wildfire suppression and prescribed fire are 

unknown, adding to the uncertainty of potential 

effects of wildfire on forests in the assessment area.

Intensified Stressors

Moisture Stress and Drought

There is evidence for an increased risk of future 

moisture stress and drought in the assessment 

area (Chapter 3). Temperatures are expected to 

rise over the next century, and evapotranspiration 

in ecosystems is expected to increase as a result 

(Center for Climatic Research 2016, Hayhoe et 

al. 2007, Kunkel et al. 2013). Moisture stress 

and even drought can occur when increases in 

evapotranspiration are not offset by a corresponding 

increase in precipitation and soil moisture (Box 12). 

Within the assessment area, modeling results 

suggest a much greater potential for more frequent 

droughts and moisture stress during the growing 

season under the GFDL A1FI scenario, which 

projects much warmer temperatures and reduced 

summer precipitation (Chapter 3). Under the milder 

PCM B1 scenario, however, warmer temperatures 

may also lead to increased evapotranspiration and 

physiological stress if increases in precipitation 

do not correspond to temperature increases. 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

81

Box 12: What is Drought?

Droughts are among the greatest stressors on forest 

ecosystems, and can often lead to secondary effects 

of insect and disease outbreaks on stressed trees 

and increased fire risk (Clark et al. 2016, Vose et 

al. 2016). 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 is when 

conditions are dry relative to long-term averages in a 

particular 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.

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).

One review of the consequences of precipitation 

variability on forests proposed that the typical 

state of soil moisture in a forest determines 

whether extreme precipitation events with longer 

intervals between events are likely to have positive 

or negative impacts on a system (Knapp et al. 

2008). For example, xeric systems (adapted to dry 

conditions) would generally be less affected by dry 

periods because they are already limited by moisture 

stress, and larger precipitation events could recharge 

soil water levels, allowing for slightly longer periods 

of moisture. On the other end of the spectrum, hydric 

(i.e., wetland) systems are limited by anoxia rather 

than soil moisture, so longer dry periods between 

precipitation events would lower the water table, 

allowing oxygen to reach the roots of aquatic plants 

and increasing biomass productivity. Mesic systems 

(adapted to moderately moist conditions) would be 

the most affected by any increasing duration and 

severity of soil water stress because they are not well 

adapted to prolonged dry periods. This conceptual 

framework does not incorporate modifiers such as 

soil texture and root depth, but the general principles 

are useful.

Moisture availability is a critical dynamic for forests 

in the eastern United States, like other forests 

worldwide (Choat et al. 2012, Clark et al. 2016, 

Pederson et al. 2014, Vose et al. 2016). Early-season 

moisture is essential 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, Millers et al. 1989, Pederson et 

al. 2014). Drought stress has been linked to decline 

of oak and ash trees in the Northeast (Millers et 

al. 1989, Mohan et al. 2009). Further, droughtstressed trees are typically more vulnerable to insect 

pests and diseases (Dale et al. 2001, Millar and 

Stephenson 2015, Ryan and Vose 2012).

The potential effects of drought on forests will 

depend on many factors, including duration and 

severity of the drought and site-level characteristics 

of the forest. High stand density may compound 

susceptibility to moisture stress as high-density 

stands face increased competition for available 

moisture (D’Amato et al. 2011, Magruder et al. 

2012). Tree species also respond differently to 

drought. For example, drought during the past 

century has been linked to dieback in sugar maple 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

82

and some species of birch and ash (Auclair et al. 

2010). Additionally, elevated atmospheric CO2

reduces the rate of water loss from trees through 

evapotranspiration, but as discussed earlier in this 

chapter, it is unclear to what degree enhanced water 

use efficiency may be able to offset the combined 

effects of warmer temperatures and drier conditions 

(Ryan and Vose 2012). 

There may be an increasing potential for exceptional 

drought events with unprecedented severity to occur 

in general, although evidence is lacking specifically 

for the Northeast. Where temperature-driven 

increases in moisture stress combine with extremely 

hot temperatures, the subsequent “hotter droughts” 

can emerge as novel stressors on ecosystems (Allen 

et al. 2015, Millar and Stephenson 2015). The 

effects of these disturbances can make forests more 

vulnerable to insect pests and pathogens, increase 

fire risk, or accelerate shifts to other forest types or 

nonforest vegetation (Millar and Stephenson 2015). 

Invasive Plant Species

Nonnative invasive species are already a major 

threat to many forests in the assessment area. 

The invasion of nonnative plant species into 

new environments is related to both the species 

and environmental conditions in question. It is 

generally expected that many invasive plants will 

“disproportionately benefit” under climate change 

due to broad environmental tolerances, extended leaf 

phenology, more effective exploitation of changed 

environments, and more aggressive colonization 

of new areas (Dukes et al. 2009, Fridley 2012, 

Hellmann et al. 2008, Willis et al. 2010). 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). Across a variety of ecosystems 

and land uses, increases in CO2 have been shown 

to have positive effects on growth for many plant 

species, including some of the most invasive weeds 

in the United States (Ziska 2003). Experiments 

with CO2 fertilization on kudzu seedlings have 

indicated increased growth, increased competition 

with native species, and range expansion (Sasek and 

Strain 1988, 1989). Models have also projected that 

increased CO2 emissions and subsequent warmer 

winter temperatures are likely to expand the northern 

ranges of bush honeysuckles, privet, and kudzu 

(Bradley et al. 2010).

Further, as discussed throughout this chapter, many 

potential effects of climate change are expected 

to increase stress and disturbance within forest 

ecosystems, thereby raising 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). Once established, invasive plant species can 

also limit regeneration of native tree species through 

increased competition. Nonnative species may 

facilitate the invasion and establishment of other 

nonnative species. Evidence suggests that such an 

interaction occurs with European earthworms and 

buckthorn, which apparently have a co-facilitating 

relationship (Heimpel et al. 2010). Similarly, studies 

in northern Minnesota found that a combination of 

invasive earthworms and warming conditions could 

benefit nonnative understory plant species (Eisenbies 

et al. 2007). 

Insect Pests and Forest Pathogens

The response of forest insect pests and pathogens 

to a warmer future will vary widely by modes of 

infection, transmission, survival, and tree response 

(Dukes et al. 2009). Pests and pathogens are 

generally expected to become more damaging in 

forest ecosystems as the climate changes, because 

they will 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 possible climate change impacts highlight the 

potential for interactions involving other stressors 

that heighten susceptibility to these agents (Sturrock 

et al. 2011, Trotter 2013, Weed et al. 2013). Threats 

from insect pests and forest disease outbreaks, 

including those of native species (e.g., forest tent 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

83

caterpillar and spruce budworm), become more 

problematic when trees are stressed by factors such 

as drought (Babin-Fenske and Anand 2011, Gray 

2008, Manion 1991).

Although the effect of climate on specific forest 

insects remains uncertain in many cases, information 

about some agents suggests the potential for 

negative impacts on forests in the assessment 

area. Research on the hemlock woolly adelgid 

indicates that its range is generally limited by cold 

winter temperatures with mortality occurring at 

temperatures below –20 °F (–29 °C) and that the 

ability of the insect to tolerate cold temperatures 

decreases as the winter progresses (Dukes et al. 

2009, Paradis et al. 2008, Skinner et al. 2003). 

Less severe winters may increase insect survival 

and spread (Dukes et al. 2009, Paradis et al. 2008, 

Trotter 2013). Similarly, a warmer climate may also 

allow the balsam woolly adelgid to increase in the 

region (Kanoti 2006). Cold temperatures below 

about 0 °F (–18 °C) kill the southern pine beetle, 

and this species has been observed as expanding 

its range northward into the pinelands of New 

Jersey (Weed et al. 2013). The fungal pathogen 

Armillaria is already widespread, but could expand 

further or become more aggressive due to a longer 

active season, enhanced colonization under warmer 

and drier conditions, and increased stress on host 

trees, particularly in response to drought (Dukes 

et al. 2009, Kliejunas 2011, Sturrock et al. 2011). 

Although many forest insect pests are expected to 

have increased habitat in the region, populations of 

some species such as the native spruce budworm 

may be reduced under the warmer conditions 

expected in the future (De Grandpré and Pureswaran 

2013, Pureswaran et al. 2015, Régnière et al. 2012). 

Herbivory

As mentioned earlier, changes in snowfall amount 

and duration throughout the assessment area 

may change the wintertime foraging behavior for 

herbivores such as moose, white-tailed deer, and 

snowshoe hare. In particular, there is mounting 

evidence that climate change will influence both 

moose and white-tailed deer populations (Frelich 

et al. 2012, New Hampshire Fish and Game 

Department [FGD] 2013, Rempel 2011). Moose 

are expected to be negatively affected by numerous 

changes in the future, including heat stress from 

warmer temperatures and an increase in parasitism 

from winter ticks (Rempel 2011, Rodenhouse 2009). 

In contrast, changes in climate may benefit deer 

in many parts of the region. Warmer winter 

temperatures and reduced snow depth are expected 

to reduce the energy requirements for deer and 

increase access to forage during winter months 

(New Hampshire FGD 2013, Wisconsin Initiative 

on Climate Change Impacts 2011). Where warmer 

temperatures and less severe winters enable deer 

populations to expand into areas currently dominated 

by moose, deer may spread brainworm—a common 

deer parasite that does not affect the species, but 

which causes mortality when spread to moose 

(Frelich et al. 2012).

Dynamics between moose and deer populations 

are tightly linked to forest composition, and any 

changes in the distribution or abundance of deer 

and moose will have a strong influence on forest 

composition in the future (Frelich et al. 2012, 

Rodenhouse 2009). The ability of either species to 

thrive will depend in part on the tree species that are 

available for browse. Research has found that deer 

browsing pressure may limit the ability of forest 

ecosystems to respond to climate change (Fisichelli 

et al. 2012). Northern white-cedar, in particular, 

has been negatively affected by excessive deer 

browsing within the assessment area (Boulfroy et 

al. 2012, Larouche and Ruel 2015), and expanded 

deer herbivory could affect recruitment of northern 

white-cedar, especially where snowpack and winter 

severity are reduced (Kenefic et al. 2015). Tree 

species that are anticipated to expand their ranges 

northward in the assessment area, such as many 

hardwood species, are browsed much more heavily 

than boreal fir and spruce species (Andreozzi et al. 

2014). Deer herbivory may also favor species which 

are not preferred browse species, such as eastern 

hophornbeam and black cherry, or invasive species 

such as buckthorn or Japanese barberry. 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

84

Changes in Forest Composition

Trees and other plant species have responded to past 

climate change in a variety of ways. The ranges of 

tree species in eastern North America have shifted 

in response to climate since the last ice age (Davis 

1983), and tree species are expected to shift in 

response to future 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), with some species migrating at 

very high rates (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 

their range (Murphy et al. 2010, Zhu et al. 2011). 

However, these changes are less visible at smaller 

spatial scales in New England, where cold-adapted 

conifer species are still recovering from historical 

declines (Foster and D’Amato 2015). Forest 

composition changes slowly due to the long-lived 

nature of trees (Davis 1989). 

The modeling results presented earlier in this chapter 

describe projected changes, negative and positive, in 

future tree species distribution. In general, trees that 

are at the range boundary for the species 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 

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 suffer a decline in seedset or 

declines in successful germination or establishment, 

whereas others could find it difficult to grow into 

maturity (Ibáñez et al. 2007, 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 grow into maturity and reproduce 

(Ibáñez et al. 2008). 

Ecosystem models project that tree species currently 

near their northern range limits in the region may 

become more abundant and more widespread under 

a range of climate futures. As discussed earlier in 

this chapter, it is possible that some tree species 

that are not currently common or even present in 

New England or northern New York will establish 

in the region. However, it is expected that species 

establishment will substantially lag changes in 

climate (Dobrowski et al. 2013, Iverson and 

McKenzie 2013, Iverson et al. 2004a, Renwick 

and Rocca 2015). The northward expansion of 

tree species ranges is constrained by many factors, 

including seed dispersal dynamics and landscape 

fragmentation (Ibáñez et al. 2008, Scheller and 

Mladenoff 2008). Catastrophic disturbances, such as 

wildfire, could facilitate establishment of colonizing 

species from the south if environmental conditions 

promote germination and vigor of establishing 

seedlings, but also have the potential to reduce the 

ability of an area to maintain forest cover at any 

scale (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 colonization (Duveneck and Scheller 

2015, Iverson and McKenzie 2013, Pedlar et al. 

2012).

Interactions 

Although this chapter focuses on the potential 

effects of climate change on forests, there are 

substantial interactions between climate change 

and other changes occurring within the landscape 

of New England and northern New York. Climate 

change has the potential to alter an array of complex 

ecosystem processes and interactions, and the 

interactions among these impacts will be critically 

important in determining the resulting changes to 

forest ecosystems across the assessment area. Just 

as there are typically several interacting drivers for 

individual tree mortality (Dietze and Moorcroft 

2011), overall ecosystem shifts will be influenced by 

multiple factors (Frelich and Reich 2010, Millar and 

Stephenson 2015). Although many of these potential 

interactions have been described in this chapter, 
CHAPTER 4: FUTURE CLIMATE CHANGE IMPACTS ON FORESTS

85

many have not. Examples of additional community 

interactions that could alter forest ecosystems 

include 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). Across the landscape of New England 

and northern New York, factors related to land use 

and management affect how climate change will 

influence natural systems (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. 

Recent research has found that drought stress leads 

to more-damaging forest tent caterpillar outbreaks 

(Babin-Fenske and Anand 2011). Earthworm 

invasion tends to create warmer, drier soil surface 

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 subjected to multiple interacting 

stressors may reach thresholds that fundamentally 

change ecosystem character and function (Manion 

1991, Millar and Stephenson 2015). To date, much 

of the literature on this subject focuses on global and 

national analyses (Allen et al. 2010, 2015; Anderegg 

et al. 2015; Millar and Stephenson 2015).

SUMMARY

Models are useful for exploring potential 

future changes, but all models are simplified 

representations of reality and each has limitations. 

The Tree Atlas (DISTRIB), LINKAGES, and 

LANDIS PRO models suggest that conditions for 

some species will become less favorable during 

the 21st century, particularly farther south in the 

region and under the harsher climate of the GFDL 

A1FI scenario. Additionally, the Tree Atlas and 

LINKAGES tend to agree that the habitat for many 

species will remain stable or increase under the PCM 

B1 scenario and decrease under GFDL A1FI. These 

results support the idea that GFDL A1FI future 

climate is beyond the tolerance of many species, 

but that many currently present species could 

tolerate or would benefit from a mild degree of 

warming with a corresponding increase in growing 

season precipitation. All three models suggest 

that conditions in the Northeast will become more 

favorable for more-southerly species by the end of 

the century.

Generally, the changing climate tends to intensify the 

stressors that may already exist for many species and 

increases susceptibility to drought, pests, diseases, or 

competition from other species. It is the interaction 

among all these factors that will drive the response 

of forests to climate change. All of these factors 

need to be taken into account when evaluating the 

vulnerability of regional forests to climate change. 

The vulnerability of eight forest systems is described 

in the next chapter.
86

CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

Climate change is expected to drive significant 

changes in fundamental ecosystem processes, 

alter the effects of current stressors, and influence 

species composition (Joyce et al. 2014, Rustad et 

al. 2012, Ryan and Vose 2012, Vose et al. 2016). 

This chapter describes the vulnerability of the forest 

ecosystems of New England and northern New 

York to climate change, drawing on the information 

presented in previous chapters. It is organized into 

two sections. First, we present an overall synthesis 

of the climate change vulnerability of the assessment 

area, 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 3 and 4). In the second section, we 

present individual vulnerability determinations for 

the eight forest systems that are currently common 

in this assessment area, as developed through an 

expert elicitation process (Brandt et al. 2016, 2017) 

(Appendix 7). 

Vulnerability is the susceptibility of a system to the 

adverse effects of climate change (Glick et al. 2011, 

Intergovernmental Panel on Climate Change [IPCC] 

2007a). It is a function of potential climate change 

impacts and the adaptive capacity of the system 

(Fig. 30). In this assessment, we consider a forest 

system to be vulnerable if it is at risk of a shift in 

composition that leads to a substantially different 

character for the system, or if the system is expected 

to suffer substantial declines in extent, health, or 

productivity. Although economic and social values 

affect the way a system 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 ecosystems 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 systems, allow them to shift to an 

alternate state, or direct their transformation—will 

depend on the individual objectives and actions of 

land managers and stakeholders.

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 the level of agreement 

among information (Fig. 31). “Evidence” refers to 

the body of information available based on theory, 

data, models, expert judgment, and other sources. 

Evidence was considered robust when multiple 

observations or models, as well as an established 

Figure 30.—Key components of vulnerability, illustrating 

the relationship among exposure, sensitivity, and adaptive 

capacity (adapted from Glick et al. [2011]).
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

87

theoretical understanding to support a statement, 

were available. “Agreement” refers to the agreement 

among the multiple lines of evidence. If theories, 

observations, and models tended to suggest similar 

outcomes, then agreement was high. Agreement does 

not refer to the level of agreement among the authors 

of this assessment. 

SYNTHESIS OF CLIMATE CHANGE 

IMPACTS ON FOREST ECOSYSTEMS

Climate change will continue to cause wideranging direct and indirect impacts on 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 

system if the changes result in improved health or 

productivity, a greater area occupied by the system, 

or a tendency to maintain the current identity of the 

system. 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 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 assessment area 

over the 21st century. This synthesis is based on the 

current scientific knowledge in published literature 

and described in more detail in the preceding 

chapters.

Figure 31.—Confidence determination diagram 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 assessment area. These factors include drivers, 

which are the most fundamental forces that shape 

a particular ecosystem, and stressors, which are 

agents that can reduce forest health or productivity 

or impair ecosystem functions. Some factors may 

be drivers in one situation and stressors in another. 

For example, forest pests and pathogens of species 

such as American chestnut, American beech, 

and American elm are initially stressors but may 

eventually become drivers after being present in 

the forest system for a long time. Similarly, some 

disturbances, such as flooding or fire, act as drivers 

in certain systems, but can cause additional stress 

on ecosystems 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 1). Temperatures across the assessment 

area have already increased (Chapter 2). Continued 

temperature increases are projected for the 

assessment area even under the most conservative 

future climate scenario (Chapter 3).

Growing seasons will lengthen (robust evidence, 

high agreement). There is strong agreement that 

projected temperature increases will lead to longer 

growing seasons in the assessment area.

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 this century (Chapters 2 and 

3). Longer growing seasons have the potential to 

affect the timing and duration of ecosystem and 

plant physiological processes across the Northeast 

(Dragoni and Rahman 2012, Rustad et al. 2012). 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

88

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, 

Hufkens et al. 2012, Richardson et al. 2006, Willis 

et al. 2010). 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 4).

Winter processes will change (robust evidence, 

high agreement). There is strong evidence that 

temperatures will increase more in winter than in 

other seasons across the assessment area, leading 

to changes in snowfall, soil frost, and other winter 

processes.

Climate models indicate that winter temperatures, 

particularly minimum temperatures, will increase 

more than temperatures in other seasons (Chapter 3). 

Projected temperature increases indicate that an 

increasing proportion of moisture will generally be 

delivered as rain rather than snow during the winter. 

Additionally, total snowfall, the duration of snow 

cover, and snow depth are expected to decrease 

across the assessment area by the end of the 21st 

century (Bryan et al. 2015, Ning and Bradley 2015, 

Notaro et al. 2014). The assessment area is also 

expected to experience complex changes in patterns 

of soil frost by the end of the century (Chapter 3). 

Decreases in snow cover and alteration of frozen 

soil conditions may affect a variety of ecosystem 

processes, including microbial activity, nutrient 

cycling, and the onset of the growing season 

(Campbell et al. 2010, Groffman et al. 2012).

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.

Sea levels have risen approximately 1 foot along the 

coastline of the assessment area since 1900 (Boon 

2012, Horton et al. 2014). All evidence points to a 

continuation of this trend due to the expansion of 

warming ocean waters and melting land ice flowing 

into the ocean (Church et al. 2008, Kopp et al. 

2014). Although model projections differ, sea levels 

may rise another 2 feet or more during this century 

(Chapter 3). Coastal forests and ecosystems will 

be further threatened by inundation, more frequent 

coastal erosion, flooding, and saltwater intrusion 

(Kane et al. 2015, Manomet Center for Conservation 

Sciences and National Wildlife Federation [NWF] 

2013c). Additionally, severe storms are more 

destructive with higher sea levels, causing increased 

damage from storm surges and flooding (Buonaiuto 

et al. 2010, Horton et al. 2014).

The amount and timing of precipitation will 

change (robust evidence, high agreement). There 

is strong agreement that precipitation patterns 

will change across the assessment area. Total 

precipitation is generally expected to increase 

during winter and spring, but summer and fall 

projections are more uncertain.

For the climate projections used in this assessment 

(Chapter 3) and other publications, there is greater 

uncertainty about projected changes in precipitation 

than for temperature across the assessment area 

(Bryan et al. 2015, Center for Climatic Research 

2017, Kunkel et al. 2013). Although individual 

model projections for the assessment area differ, 

there is general agreement that total annual 

precipitation is projected to increase during the 21st 

century, largely due to more-intense precipitation 

events (Bryan et al. 2015, Hayhoe et al. 2007). 

Models also tend to agree that precipitation patterns 

between seasons may shift substantially (Kunkel 

et al. 2013). Precipitation increases are generally 

expected for winter and spring, whereas summer and 

fall precipitation projections are much more variable, 

ranging from very slight increases to moderate 

decreases (Bryan et al. 2015, Center for Climatic 

Research 2017, Kunkel et al. 2013). The two climate 

scenarios presented in this assessment reflect this 

range of potential outcomes (Chapter 3).
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

89

Intense precipitation events will continue to 

become more frequent (robust evidence, high 

agreement). Climate models generally project 

that the number of heavy precipitation events will 

continue to increase in the assessment area. If they 

do increase, damage from flooding and soil erosion 

may also become more severe.

Since the mid-20th century, heavy precipitation 

events have increased in number and severity in 

the Northeast more than in any other part of the 

United States (Brown et al. 2010, Horton et al. 2014, 

Walsh et al. 2014), and many models agree that this 

trend will continue during the 21st century (Center 

for Climatic Research 2017, Kunkel et al. 2013). 

Most heavy precipitation events in the assessment 

area 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 retention of water 

vapor 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 will 

ultimately depend on local geologic and topographic 

conditions, current infrastructure, and land use, as 

well as future decisions about infrastructure and 

land use. 

Soil moisture patterns will change in response to 

temperature and precipitation (medium evidence, 

high agreement). Warmer temperatures and altered 

precipitation will interact to change soil moisture 

patterns throughout the year, but there is uncertainty 

about the direction and magnitude of the changes.

Soil moisture is expected to change in response 

to warmer temperatures and seasonal changes in 

precipitation, although uncertainty remains regarding 

the specific amount, direction, and seasonality of 

precipitation changes (Huntington et al. 2009). 

Changes are likely to vary seasonally as well as 

geographically (Center for Climatic Research 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 (Bryan et al. 

2015). Wetter conditions may become more frequent 

during parts of the year; 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 (Groffman et al. 2012, Hayhoe et al. 

2007, Huntington et al. 2009). Locations where soils 

and landforms cannot retain the water from intense 

precipitation events may be more likely to have drier 

conditions during the growing season.

Forest vegetation may face increased risk of 

moisture deficit and drought during the growing 

season (medium evidence, medium agreement).

Studies show that climate change will affect soil 

moisture, but there is some disagreement among 

climate and impact models on how soil moisture and 

drought will change during the growing season. 

The uncertainty of future precipitation patterns 

makes it difficult to determine whether conditions 

may become dry enough to increase moisture stress 

for plants in the Northeast. Forests that are affected 

by moisture deficits and drought are more likely to 

have reduced tree vigor and increased tree mortality, 

both of which can affect forest composition and 

structure (Clark et al. 2016). Further, warmer 

temperatures can drive or exacerbate droughtinduced 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).

Certain insect pests and pathogens will increase 

in occurrence or become more damaging 

(medium evidence, high agreement). Evidence 

indicates that increases in temperature will lead to 

increased threats from insect pests and pathogens, 

but research to date has examined relatively few 

species.

A warming climate may allow some pests and 

pathogens to become a greater threat (Régnière et 

al. 2012, Sturrock et al. 2011, Trân et al. 2007). The 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

90

loss of a historically cold climate and short growing 

season in the region may allow some insect pests 

and pathogens, such as hemlock woolly adelgid 

and southern pine beetle, to expand their ranges 

northward (Chapter 4). Forest pests and pathogens 

are generally able to respond rapidly to changes 

in climate. Forest impacts from insect pests and 

pathogens are generally more severe in ecosystems 

that are affected by drought and other stressors 

(Manion 1991, Weed et al. 2013). 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 (Régnière et al. 2012, Weed et 

al. 2013). Further, it is not possible to predict which 

new pests or pathogens will enter the assessment 

area during the 21st century. 

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 (Rebbeck 

2012). Increases in carbon dioxide have been shown 

to increase growth for many plant species, including 

some of the most invasive weeds in the United States 

(Ziska 2003). Warmer conditions may allow invasive 

plant species such as bush honeysuckles, privet, and 

kudzu to expand their ranges northward (Bradley 

et al. 2010, Dukes et al. 2009). Future increases 

in drought, fire, or flooding are likely to benefit 

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 nonnative plant species 

that may enter the assessment area during the 21st 

century. 

Potential Impacts on Forests

Shifts in drivers and stressors mentioned earlier 

are expected to alter forest ecosystems throughout 

the assessment area. Indirect impacts of climate 

change may be manifested through shifts in suitable 

habitat, species composition, or function of forest 

ecosystems. 

Many northern and boreal tree species will face 

increasing stress from climate change (medium 

evidence, high agreement). Ecosystem models 

agree that northern and boreal tree species will 

have reduced suitable habitat and biomass across 

the assessment area, and that they may be less able 

to take advantage of longer growing seasons and 

warmer temperatures than warm-adapted temperate 

forest species.

Across northern latitudes, warmer temperatures are 

expected to become less favorable to tree species 

near the southern extent of their range (Gauthier et 

al. 2014, Iverson et al. 2008, Mohan et al. 2009). 

Results from climate impact models project a decline 

in suitable habitat and landscape-level biomass for 

northern species such as red spruce, paper birch, 

black spruce, and tamarack, as well as spruce-fir 

forest systems (Chapter 4). These northern species 

may persist in the assessment area through the 21st 

century, although with declining vigor (Mohan et 

al. 2009). Boreal and northern forest communities 

may persist in areas where climatic changes do 

not exceed thresholds for individual species. For 

example, topographic or hydrologic position may 

create favorable microclimates that are buffered 

from some changes (Morelli et al. 2016, Raney et al. 

2016).

Habitat will become more suitable for southern 

species (medium evidence, high agreement). 

Ecosystem models agree that longer growing 

seasons and warmer temperatures will increase 

suitable habitat for many temperate species across 

the assessment area. 

Model results project that tree species currently near 

their northern range limits in the assessment area 

may become more abundant and more widespread 

under a range of climate futures (Chapter 4). Species 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

91

that are currently present and projected to increase in 

both suitable habitat and biomass in the assessment 

area include black cherry, chestnut oak, and yellowpoplar (Chapter 4). Results from the Climate 

Change Tree Atlas and, to a lesser extent, LANDIS 

PRO, project that habitat may become available 

for species not currently found in the assessment 

area by the end of the century (Chapter 4). The 

fragmentation of forests by other land uses and the 

dispersal limitations of individual species could 

hinder the northward movement of southern tree 

species, despite increases in habitat suitability. Most 

species can be expected to colonize new areas more 

slowly than their suitable habitats will shift (Iverson 

et al. 2004a, 2004b; Woodall et al. 2009). Pests 

and diseases such as emerald ash borer, beech bark 

disease, and Dutch elm disease are also expected 

to limit some species that are projected to increase 

according to tree species models.

Forest composition will change across the 

landscape (medium evidence, high agreement). 

Although few models have specifically examined 

how forest ecosystems may change, model results 

from individual species and ecological principles 

suggest that recognized forest community 

assemblages will change. 

Species will respond individually to climate change, 

and this may lead to new species assemblages 

(Davis et al. 2005, Root et al. 2003). The model 

results presented in Chapter 4 raise the possibility of 

large changes in tree species distribution across the 

assessment area. Evidence suggests that although 

dominant conifer species are currently expanding 

on the landscape, future climatic conditions will 

generally favor many hardwood species by the 

end of the century. Changes in distribution for 

individual species are expected to lead to shifts in 

forest assemblages from south to north (Iverson et 

al. 2008, Lenihan et al. 2008), and novel community 

types may emerge. Some studies have suggested that 

forest species may be prone to range contraction at 

southern limits and also less able to expand ranges 

northward into new areas at a pace that tracks 

changes in climate (Murphy et al. 2010, Treyger 

and Nowak 2011, Woodall et al. 2013, Zhu et al. 

2011). Major shifts in overstory species composition 

may not be observable until well into this century 

because of the long timeframes associated with 

many ecosystem processes and responses to climate 

change (Chapter 4). These shifts, however, may 

become more apparent along ecotonal boundaries 

where boreal species reach the southern edge of 

their range (Fisichelli et al. 2014b, Legaard et al. 

2015). Major stand-replacing disturbance events, 

forest management, and human activities all have the 

potential to strongly influence how forests change in 

response to changing climatic conditions (Duveneck 

et al. 2014, Millar and Stephenson 2015, Thompson 

et al. 2011). Additionally, nonnative species may be 

able to take advantage of shifting forest communities 

and unoccupied niches if native forest species are 

limited (Hellmann et al. 2008).

Shifts in forest composition will take at least 

several decades to occur in the absence of 

major disturbance (medium evidence, medium 

agreement). Although some models indicate 

major changes in habitat suitability, results from 

spatially dynamic forest landscape models indicate 

that a major shift in forest composition across 

the landscape may take 100 years or more in the 

absence of major disturbances. 

Model results from the Tree Atlas and LINKAGES 

(Chapter 4) indicate substantial changes in habitat 

suitability or establishment probability for many 

species on the landscape, but do not account for seed 

source constraints, longevity of current species, or 

differences among age classes. Forest landscape 

models such as LANDIS PRO can incorporate 

spatial configurations of current forest ecosystems, 

seed dispersal, and potential interactions among 

native species and the invasion and establishment 

of nonnative plant species (Wang et al. 2014, 2015) 

(Chapter 4). In addition, forest landscape models can 

account for differences among age classes, and have 

generally found mature trees to be more tolerant of 

warming (He and Mladenoff 1999). Because mature 

trees are expected to remain on the landscape, 

and recruitment of new species is expected to be 

limited, major shifts in species composition are not 

likely to be observed by the middle of the century, 
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92

except along ecotonal boundaries and in areas that 

undergo major stand-replacing disturbance events. 

However, climate change may increase the intensity, 

scope, or frequency of some stand-replacing 

events such as windstorms, ice storms, and insect 

outbreaks, possibly promoting rapid shifts in species 

composition where these events occur.

Conditions affecting 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. 

Evidence of climate change impacts on forest 

ecosystems is more likely to be seen in seedlings and 

early growth than in mature individuals (Fisichelli 

et al. 2014a, 2014b). Temperature and moisture 

requirements for seed dormancy and germination are 

often much more critical than habitat requirements 

of an adult tree (Kitajima and Fenner 2000). 

Projected changes in temperature, precipitation, 

growing season onset, and soil moisture may 

alter the duration or manifestation of germination 

conditions, and regeneration responses will be highly 

variable based on both environmental conditions and 

species characteristics. For example, regeneration 

failure in balsam fir populations has been attributed, 

at least partially, to climate change (Abrams et al. 

2001). For species with high dispersal capabilities, 

these changes may result in redistribution on the 

landscape as seeds germinate only where conditions 

are favorable (Walck et al. 2011). Other species may 

fail to regenerate under altered future conditions, or 

may germinate without having optimal conditions 

for development. Warmer winters may promote 

the establishment of more southerly species, 

although warmer temperatures alone are unlikely 

to drive their establishment (Abrams 2003). Even 

after establishment, advance regeneration (i.e., 

saplings) is still more sensitive than mature trees 

to disturbances such as drought, heat stress, fire, 

flooding, and herbivory (Kitajima and Fenner 2000). 

Changes in tree regeneration and recruitment will 

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 assessment 

area, but they disagree on how multiple factors 

may interact to influence productivity. The diversity 

of forest conditions across the assessment area 

suggests that changes will be spatially variable.

Regional forests are still recovering from historical 

disturbance (Foster et al. 1998, Thompson et al. 

2013), and continued forest recovery and succession 

following these disturbances are generally expected 

to be the dominant drivers of forest growth for 

the next several decades (Duveneck et al. 2017, 

Thompson et al. 2011, Wang et al. 2017) (Chapter 

4). Changes in forest productivity are likely to be 

spatially variable. 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, Tang 

et al. 2014, Thompson et al. 2011). Further, many 

studies also point to the beneficial effects of carbon 

dioxide fertilization on forest productivity (Loehle 

et al. 2016, Ollinger et al. 2008, Tang et al. 2014, 

Thompson et al. 2011), 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). There is 

some evidence that deciduous forests will be better 

able to take advantage of warmer temperatures than 

coniferous forests in the region (Ollinger et al. 2008; 

Tang et al. 2012, 2014); however, one study found 

that modeled productivity was lower in southern 

New England as a result of moisture stress and 

increased soil respiration (Tang et al. 2014). Further, 

increasing disturbance, such as fires, windstorms, 

and pest outbreaks, and changes in land use could 

substantially reduce forest productivity, but are 

beyond the scope of most modeling efforts.
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93

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 2007a). It is strongly related 

to the concept of ecological resilience (Holling 1973, 

Stein et al. 2014). In this section, we summarize 

factors that could reduce or increase the adaptive 

capacity of forest systems within the assessment 

area. Higher adaptive capacity tends to reduce 

vulnerability to climate change, and lower adaptive 

capacity tends to increase vulnerability. 

Low-diversity systems are at greater risk 

(medium evidence, high agreement). Studies have 

consistently shown that high-diversity systems are 

more resilient to disturbance. Low-diversity systems 

are expected to be more vulnerable to climate 

change.

In general, species-rich communities have exhibited 

greater resilience to extreme environmental 

conditions and greater potential to recover from 

disturbance than less diverse ecosystems (Isbell 

et al. 2015, Tilman et al. 2014). This makes less 

diverse ecosystems inherently more susceptible to 

future changes and stressors. Within an ecosystem, 

the range of potential responses of a system to 

environmental change is a critical component of 

ecosystem 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; in contrast, pitch pinedominated 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). 

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 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 move to more suitable habitat on the 

landscape; the degree to which fragmentation limits 

dispersal depends on the level of fragmentation, 

land cover and use, and the dispersal characteristics 

of individual species (Ibáñez et al. 2006, 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 for 

seedling distributions, it has been estimated that 

many northern tree species could possibly disperse 

seedlings northward at a rate of 60 miles per century 

(Woodall et al. 2009), and other evidence indicates 

that natural migration rates could be far slower for 

some species (McLachlan et al. 2005, Murphy et al. 

2010). Fragmentation creates additional challenges 

by making the landscape less permeable to dispersal 

(Jump and Peñuelas 2005, Scheller and Mladenoff 

2008). The potential for humans to assist in the 

migration of species to newly suitable areas (Pedlar 

et al. 2012, Schwartz et al. 2012) is beyond the 

scope of this 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 will be particularly difficult for tree species 

and forest communities with narrow habitat 

requirements.

Several species and forest types in the assessment 

area are confined to certain habitats on the 

landscape, whether through particular requirements 

for hydrologic regimes or soil types, or other 

reasons. Similar to species occurring only in 

fragmented landscapes, isolated species and systems 

face additional barriers to migration (Jump and 

Peñuelas 2005). Species restricted to riparian forests 

are not expected to migrate to upland areas because 

many species depend on seasonal flood dynamics 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

94

for regeneration and a competitive advantage. 

Similarly, lowland conifer systems often contain a 

unique mix of species that are adapted to low pH 

values, peat soils, and particular water table regimes. 

These systems face greater challenges in migration 

than more widespread species with broad ecological 

tolerances. Even widespread species may have 

particular site requirements for optimal growth. For 

example, sugar maple does best on soils that are rich 

in nutrients such as calcium, so this species may 

not respond as predicted to new suitable habitat in 

the assessment area projected from temperature and 

precipitation patterns.

Ecosystems that have greater tolerance to 

disturbance have less risk of declining on the 

landscape (medium evidence, high agreement).

Basic ecological theory and other evidence support 

the idea that systems adapted to more frequent 

disturbance will be at lower risk.

Disturbances such as wildfire, flooding, hurricanes, 

and pest outbreaks are expected to increase in 

the assessment area (Chapters 3 and 4). Northern 

hardwoods in particular are adapted to gap-phase 

disturbances, with stand-replacing events occurring 

over hundreds or thousands of years. Therefore, 

these systems may be less tolerant of more frequent 

widespread disturbances. Mesic systems can create 

conditions that could buffer against fire and drought 

to some extent, but these systems are not expected 

to do well if soil moisture declines significantly 

(Nowacki and Abrams 2008). Forest systems in the 

assessment area that are more tolerant of drought, 

flooding, or fire may be better able to withstand 

climate-driven disturbances. This principle is 

limited, however, because it is also possible for 

disturbance-adapted systems to be subjected to 

excessive disruption. For example, pitch pine 

systems might cover a greater extent under drier 

conditions with more frequent fire, but these systems 

might also convert to barrens or open grasslands if 

fire becomes too frequent or drought becomes too 

severe. 

VULNERABILITY DETERMINATIONS 

FOR INDIVIDUAL FOREST SYSTEMS

Climate-induced shifts in drivers, stressors, and 

dominant tree species will result in different impacts 

to forest systems within the assessment area. Some 

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 systems.

We assessed the vulnerability of eight forest systems 

(described in Chapter 1) to climate change impacts, 

drawing on the information presented in previous 

chapters, as well as an expert panel assembled from 

a variety of organizations and disciplines across 

the assessment area. The 20 panelists evaluated 

anticipated climate trends for the assessment area 

and ecosystem model projections (Chapters 2, 

3, and 4) and used their expertise to interpret the 

information. For each forest system, panelists 

considered the potential impacts and adaptive 

capacity to assign a vulnerability determination and 

a level of confidence in that determination using the 

confidence scale described earlier in this chapter. 

For a complete description of the methods used to 

determine vulnerability, see Appendix 7.

Vulnerability determinations for the forest systems 

ranged from low to moderately high based on the 

interaction between impacts and adaptive capacity 

(Table 14). These vulnerability determinations 

were associated with medium levels of confidence 

for most forest systems. Ratings of evidence were 

limited-medium or medium, generally reflecting 

unknown interactions among dominant tree species 

and potential stressors. The ratings of agreement 

among information sources also tended to be 

medium. Contrasting information related to future 

precipitation patterns was a major factor that limited 

the level of agreement. More importantly, there was 

reduced agreement where potential climate change 

impacts varied geographically across the assessment 

area (e.g., high elevations in the north vs. southern 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

95

and coastal areas), especially for widely distributed 

forest systems. In some instances, use of broad forest 

systems for this assessment also limited agreement 

where species-level impacts and vulnerability varied 

widely within the forest system. 

In the sections that follow, we summarize the 

climate-related impacts on drivers, stressors, and 

dominant tree species that were major contributors 

to the vulnerability determination for each forest 

system across the assessment area. In addition, 

we summarize the main factors contributing to 

the adaptive capacity of each system. Importantly, 

climate change impacts and the adaptive capacity 

of a forest system were evaluated across the 

entire assessment area. Because forest systems 

vary widely at a local level due to differences in 

elevation, climate, landform, soils, disturbance, 

past management, and numerous other factors, the 

vulnerability in a given location may 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.

Forest system Potential impacts Adaptive capacity Vulnerability Evidence Agreement

Central hardwood-pine Neutral-Positive Moderate-High Low Medium Medium-High

Low-elevation spruce-fir Neutral-Negative Moderate Moderate-High Medium Medium

Lowland and riparian hardwood Positive and Negative Moderate-High Moderate Limited Limited

Lowland mixed conifer Neutral-Negative Low-Moderate Moderate-High Limited-Medium Medium

Montane spruce-fir Neutral-Negative Moderate Moderate-High Medium Medium

Northern hardwood Positive and Negative Moderate-High Low-Moderate Medium Medium

Pitch pine-scrub oak Neutral-Positive Moderate Low Medium Medium

Transition hardwood Positive and Negative Moderate-High Low-Moderate Medium Medium-High

Table 14.—Summary of vulnerability determination for the forest systems considered in this assessment evaluated 

through the end of the 21st century 
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96

Central Hardwood-Pine

Low Vulnerability (medium evidence, medium-high agreement) 

These forests support a diversity of tree species and occur over a wide range of habitats. Many species 

are tolerant of dry soil conditions and fire, although young trees may be sensitive to severe drought and 

fire. Several oak and hickory species are likely to benefit from projected changes in climate.

Neutral-Positive Potential Impacts

Drivers: The central hardwood-pine forest system 

is at the northern extent of its range in the region, 

occurring on a wide variety of dry-mesic sites 

and coarse, well-drained soils. These fire-adapted 

ecosystems are generally expected to be able to cope 

with increased risk of wildfire under warmer and 

drier conditions (Manomet Center for Conservation 

Sciences and Massachusetts Division of Fisheries 

and Wildlife [DFW] 2010a, Manomet Center for 

Conservation Sciences and NWF 2013b), although 

it is unclear whether fire occurrence will actually 

increase due to suppression by humans in this 

relatively densely populated region (Chapter 4). 

Dominant Species: The many tree species found 

in these locations, including black, chestnut, 

scarlet, and white oak and pignut and shagbark 

hickory (Chapter 4), are generally expected to be 

able to persist on these sites into the future. These 

species may also potentially expand to new areas as 

conditions become suitable. At the same time, when 

extremely hot and dry conditions prevail, species 

establishment may be impaired for these trees in 

southern and coastal New England (Chapter 4). 

Stressors: Climate change could amplify several 

stressors to central hardwood-pine forests. Insect 

pests, such as winter moth and southern pine beetle, 

are expected to cause more frequent and severe 

damage under climate change, and new pests present 

unknown risks. White-tailed deer populations may 

also increase with warmer winters, which may 

hinder regeneration as well as the expansion of this 

forest type. Invasive species such as buckthorn, 

honeysuckles, and garlic mustard can also hinder 

regeneration, and are poised to increase in the future.

Moderate-High Adaptive Capacity

Central hardwood forests are generally expected to 

fare better than other forest systems under climate 

change, in part because of an ability to thrive under 

relatively warm and dry conditions. These forests 

also contain a variety of oak and hickory species 

with diverse traits, including drought tolerance and 

varied reproductive strategies. This diversity may 

increase the number of ways in which the ecosystem 

can adjust to changing conditions while maintaining 

important ecosystem functions. This forest type, 

however, is often found in areas that have a high 

degree of historical or current human disturbance, 

and fragmentation, invasive species, or other threats 

that can reduce the adaptive capacity of certain 

locations. A history of fire suppression and reduced 

light reaching the forest floor has facilitated a shift 

to more mesic conditions and associated hardwood 

species (e.g., red and sugar maple, American beech, 

yellow-poplar). In many forests, regeneration of 

drought-tolerant oak and hickory trees is currently 

reduced due to fire suppression and competition 

from more shade-tolerant mesic species. 
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A central hardwood-pine forest in central Massachusetts. Photo by Anthony W. D’Amato, 

University of Vermont, used with permission. 

A central hardwood-oak forest in southern New England, marked for forest harvest. Photo by 

Maria Janowiak, U.S. Forest Service. 
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98

Low-Elevation Spruce-Fir

Moderate-High Vulnerability (medium evidence, medium agreement) 

The future climate is expected to be less suitable for the northern and boreal conifer species, while 

more favorable for many hardwood species that are a component of these forests. Heightened stress 

and changes in competitive relationships among tree species are likely to alter forest composition. 

Forest vulnerability is expected to vary widely across the assessment area, with forests in some locations 

undergoing substantial changes.

Neutral-Negative Potential Impacts

Drivers: The low-elevation spruce-fir forest system 

is widespread across the northern portion of the 

assessment area and will undergo varying levels 

of impacts. These forests may be particularly 

susceptible to warmer temperatures and longer 

growing seasons (Manomet Center for Conservation 

Sciences and NWF 2013b). Forest productivity 

may increase in areas that maintain adequate 

soil moisture, but substantially warmer and drier 

conditions could disrupt nutrient cycling, cause 

organic soils to break down, and increase stress. 

Warmer and drier conditions could also increase the 

likelihood of wildfire, which is currently rare in this 

forest type. Increases in extreme storm events may 

lead to greater tree mortality from disturbance.

Dominant Species: Red spruce, balsam fir, and 

other dominant species are projected to suffer 

substantial declines in suitable habitat, although 

these declines are most substantial under greater 

warming in the southern portion of the assessment 

area (Chapter 4). As a temperate conifer, red spruce 

may have a greater ability to adapt to a warmer 

climate than the boreal species black spruce and 

balsam fir due to its capacity for year-round 

photosynthesis and its greater phenotypic plasticity 

in response to temperature variations (Schaberg and 

DeHayes 2000). Impacts from climate change are 

projected to be less severe in the northern part of 

the assessment area, such as in Maine, allowing the 

dominant species to persist in some areas (Whitman 

et al. 2014). Species such as white pine or several 

northern hardwood species may become more 

competitive under future conditions (Chapter 4). 

Stressors: Insect pests such as eastern spruce 

budworm and balsam woolly adelgid currently affect 

this system and are likely to respond to temperature 

changes. Warmer temperatures may allow balsam 

woolly adelgid to increase, while dampening 

the effects of the eastern spruce budworm in the 

assessment area (De Grandpré and Pureswaran 

2013, Pureswaran et al. 2015, Régnière et al. 

2012) (Chapter 4). Hemlock woolly adelgid may 

cause even greater mortality for hemlock under 

warmer conditions. Likewise, changes in herbivore 

populations may also have substantial effects on 

forest growth and composition. Although moose 

are generally projected to have reduced habitat 

suitability under warmer conditions, white-tailed 

deer populations may expand in some areas 

(Chapter 4). 

Moderate Adaptive Capacity

Low-elevation spruce-fir forests are present across 

a variety of sites, representing varying levels 

of adaptive capacity. These forests tend to have 

relatively low diversity and be dominated by a 

relatively small number of northern boreal species; 

forests with lower species diversity may be more 

vulnerable. Past and current management often 

favors the hardwood component in these forests, 

and additional stress or disturbance may continue to 

shift forest composition toward hardwood species. 

This forest type also has some factors that increase 

adaptive capacity, such as the presence of multiple 

species that can regenerate prolifically when 

conditions allow, including red maple and balsam fir.
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99

Spruce-fir forest in coastal Maine. Photo by Nicholas 

Fisichelli, Schoodic Institute at Acadia National Park, used 

with permission.

Red spruce and white pine regeneration in the forest 

understory. Photo by Maria Janowiak, U.S. Forest Service.

Dense spruce regeneration in lowland spruce-fir forest. 

Photo by Nicholas Fisichelli, Schoodic Institute at Acadia 

National Park, used with permission. 
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100

Lowland and Riparian Hardwood

Moderate Vulnerability (limited evidence, limited agreement)

Climate change is expected to alter the hydrologic regimes in riparian and lowland systems, and may 

amplify the effects of insect pests and invasive species. High diversity and the presence of southern 

species raise the adaptability of these forests. There is high uncertainty regarding future precipitation 

patterns and associated hydrologic change.

Positive and Negative Potential Impacts

Drivers: Climate change has the potential to 

substantially alter the hydrologic regimes in the 

lowland and riparian hardwood forest system. These 

hardwood forests are adapted to annual and seasonal 

water table fluctuations; however, more intense and 

variable precipitation events may present risks to 

this system through excessive flooding, inundation, 

streambank erosion, or prolonged droughts between 

heavy precipitation events. Extended droughts 

could cause significant damage to shallow-rooted 

species, but increased winter and spring precipitation 

may buffer summer droughts in low-lying areas on 

the landscape. Groundwater-fed systems may be 

less sensitive where cooler, wetter soil conditions 

can be maintained over time. There is substantial 

uncertainty about future precipitation patterns 

(Chapter 3), and impacts may be greater in areas that 

are subject to changes in water level, such as vernal 

pools, floodplain systems, and wetlands.

Dominant Species: This forest system contains 

many tree species that are tolerant of warmer 

temperatures, and the assessment area is in the 

central to northern portion of their range. Species 

such as American elm, silver maple, and eastern 

cottonwood are expected to gain suitable habitat 

across the assessment area under a range of climate 

futures. Species in these ecosystems that are 

expected to undergo decreased habitat suitability 

include balsam fir, paper birch, and northern whitecedar.

Stressors: There are many invasive plant species, 

insect pests, and forest diseases that have negative 

impacts on lowland and riparian hardwoods; many 

of these stressors are expected to increase through 

the direct and indirect effects of climate change. 

Invasive species such as Japanese stiltgrass and 

buckthorn are existing threats to these forests, 

and invasive species are expected to increase in 

abundance under climate change, particularly where 

forests are disturbed (Chapter 4). Likewise, insect 

pests such as hemlock woolly adelgid may also 

increase in response to warmer temperatures. 

Moderate-High Adaptive Capacity

Many riparian and lowland forests can tolerate 

intermittent wet and dry conditions, including 

periodic floods and drought. Where these forests 

include a diverse array of tree species occupying 

a variety of microsites, there may be less risk of 

ecosystem transition under future conditions. Some 

of these forests are dominated by one or two canopy 

tree species (e.g., red maple or black ash), creating 

the potential for large alterations to ecosystem 

structure if these species are selectively affected by 

climate or other stressors.
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101

Silver maple in a riparian forest. Photo by Anthony W. D’Amato, University of Vermont, used 

with permission. 

Silver maple in a riparian forest. Photo by Anthony W. D’Amato, University of Vermont, used 

with permission. 
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102

Lowland Mixed Conifer

Moderate-High Vulnerability (limited-medium evidence, medium agreement)

Lowland conifer forests have limited tolerance to hydrologic change, including altered precipitation 

patterns and water table depth. Additionally, the dominant species in these forests are expected to 

decline across a range of climate scenarios. Future precipitation and hydrologic conditions are the primary 

uncertainties for this forest system.

Neutral-Negative Potential Impacts

Drivers: The lowland mixed-conifer forest 

system functions in a relatively narrow window of 

hydrologic and soil conditions. These conditions 

are expected to be perturbed in a variety of ways, 

including through increased severe precipitation 

events and flooding, increased risk of drought, and 

changes in the water table or relative influence of 

precipitation versus groundwater. In general, drier 

conditions would be expected to have a greater 

negative impact than excess moisture. Organic 

soils could decompose more quickly under warmer 

and drier conditions. Drier conditions in peatland 

systems could also impair tree regeneration or shift 

tree species composition toward the few hardwood 

species found in these areas. Wetland communities 

may be affected by increased storm events because 

shallow-rooted trees on saturated soils may be more 

prone to windthrow.

Dominant Species: Most of the dominant tree 

species in this system, including balsam fir, red 

spruce, northern white-cedar, and black spruce, are 

expected to undergo significant declines in future 

habitat (Chapter 4). There are already concerns 

about sustainability of northern white-cedar in some 

parts of the assessment area, due to the negative 

effects from harvesting and deer browse (Chapter 4). 

Impacts from climate change are projected to be less 

severe in the northern part of the assessment area, 

such as in Maine, allowing the dominant species 

to persist in some areas (Whitman et al. 2014). 

Similarly, forests dominated by species with more 

southerly distributions, such as Atlantic white-cedar, 

may be less susceptible to changes in climate.

Stressors: Insect pests such as eastern spruce 

budworm and balsam woolly adelgid currently affect 

this system and are likely to respond to temperature 

changes. Warmer temperatures may allow balsam 

woolly adelgid to increase, while dampening the 

effects of eastern spruce budworm in the assessment 

area (Chapter 4). Changes in herbivory could have 

important effects on these ecosystems. Expanded 

deer herbivory could affect recruitment of northern 

white-cedar, especially where snowpack and winter 

severity are reduced.

Low-Moderate Adaptive Capacity

Although all forests will be uniquely affected 

by climate change, impacts on lowland mixed 

conifer forests are more likely to vary with site 

conditions (e.g., surface geology, hydrology, soils), 

dominant tree species, and local changes in climate. 

Nevertheless, these ecosystems, like other forested 

wetlands in the region, tend to have a very limited 

suite of tree species physiologically adapted to the 

moisture and nutrient conditions found in these 

areas, making them particularly vulnerable to the 

loss of a given species due to climate change and 

associated impacts. 
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103

Lowland spruce-fir forest in Downeast Maine. Photo by 

Nicholas Fisichelli, Schoodic Institute at Acadia National 

Park, used with permission. 

Lowland conifer forest regeneration in autumn. Photo by 

Nicholas Fisichelli, Schoodic Institute at Acadia National 

Park, used with permission. 

Northern white-cedar in a lowland mixed conifer forest. Photo by Maria Janowiak, 

U.S. Forest Service. 
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104

Montane Spruce-Fir

Moderate-High Vulnerability (medium evidence, medium agreement)

These cold-adapted forests are susceptible to warmer temperatures, increased disturbance, and declining 

habitat suitability for the dominant species. Interactions among these effects may exceed the ecological 

tolerances of the defining species in this system, particularly at lower elevations and more southerly 

locations. Ecosystems may persist in areas that remain relatively cool.

Neutral-Negative Potential Impacts

Drivers: The montane spruce-fir forest system 

is strongly associated with the coldest and most 

extreme climates in New England, making these 

ecosystems particularly susceptible to warmer 

temperatures and longer growing seasons (Cogbill 

and White 1991, Manomet Center for Conservation 

Sciences and NWF 2013b). Reductions in snowpack 

may decrease soil moisture availability in the spring 

or increase the probability of root damage during 

freeze-thaw events. Increases in extreme storm 

events may lead to greater tree mortality from 

disturbance. 

Dominant Species: The dominant species, including 

balsam fir and red spruce, are projected to have 

substantial declines in suitable habitat, particularly 

under greater warming (Chapter 4). Montane 

spruce-fir forests are expected to be especially 

vulnerable at the southern extent of their range, 

where a temperature increase of 5 °F (–2.8 °C) could 

eliminate all habitat from Massachusetts (Manomet 

Center for Conservation Sciences and Massachusetts 

DFW 2010a). Impacts are projected to be less in 

the northern part of the assessment area, such as in 

Maine, where the dominant species are expected to 

persist in some areas (Whitman et al. 2014). 

Stressors: Insect pests, such as eastern spruce 

budworm and balsam woolly adelgid, currently 

affect these ecosystems in many parts of the 

assessment area and are likely to respond to 

temperature changes. Warmer temperatures may 

allow balsam woolly adelgid to increase, while 

dampening the effects of eastern spruce budworm in 

the assessment area (Chapter 4). Likewise, changes 

in herbivore populations may also have substantial 

effects on forest growth and composition. Moose 

are generally projected to have reduced habitat 

suitability under warmer conditions, although higher 

elevations may provide cooler habitats where the 

species will persist for longer. White-tailed deer 

populations may expand in some areas, particularly 

at lower elevations.

Moderate Adaptive Capacity

Several factors contribute to a lower capacity for 

these forests to adapt to climate change, including 

relatively low species and genetic diversity and 

slow rates of recovery in response to disturbance. 

Additionally, the location at high latitudes and 

elevations presents little opportunity for this forest 

system to move to new habitats. This is particularly 

true in southern New England, where this forest 

system is highly fragmented and exists primarily on 

isolated mountaintops. At the same time, this system 

does have some adaptive capacity due to its ability 

to outcompete many other species, its capacity to 

persist on cold and nutrient-poor sites, and recent 

signs of its recovery from past stressors such as acid 

rain and logging that previously reduced its extent. 

This current rebound of the montane spruce-fir 

forest system (Foster and D’Amato 2015, Kosiba et 

al. 2013) on the landscape may increase its adaptive 

capacity in the coming decades.
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105

Montane ecosystems, as seen from Mount Jefferson on the White Mountain National Forest. 

These ecosystems are particularly vulnerable to climate change. Photo by Toni Lyn Morelli, 

U.S. Geological Survey. 

A montane spruce-fir forest. Photo by Anthony W. D’Amato, 

University of Vermont, used with permission.

A montane spruce-fir forest. Photo by Anthony W. D’Amato, 

University of Vermont, used with permission. 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

106

Northern Hardwood

Low-Moderate Vulnerability (medium evidence, medium agreement)

Climate change poses several threats to these forests, although the widespread and diverse nature of 

this system suggests that impacts will be highly variable across the region and dependent on local site 

conditions.

Positive and Negative Potential Impacts

Drivers: Climate change is expected to alter 

precipitation patterns and hydrology, leading to the 

potential for drier summer conditions (Chapters 3 

and 4) for the northern hardwood forest system. 

Changes in soil temperature and moisture that 

increase risk of drought and alter nutrient availability 

or soil processes could have substantial effects on 

sugar maple and other dominant species (Groffman 

et al. 2012). Disturbance dynamics in these forests 

may also change, and increases in extreme weather 

events could lead to more frequent or widespread 

windthrow, affecting the gap-phase dynamics that 

foster regeneration of shade-tolerant species. The 

greatest changes in northern hardwood forests are 

expected to occur at lower elevations and latitudes. 

Dominant Species: Many species common to 

these forests are projected to have a broad range of 

habitat changes under different climate scenarios 

(Chapter 4). Eastern hemlock, yellow birch, and 

quaking aspen, and to a lesser extent, sugar maple, 

have substantial projected declines in habitat 

suitability and biomass under the warmer and 

drier GFDL A1FI scenario than under the PCM 

B1 scenario, suggesting that greater changes in 

climate will lead to more-negative consequences. 

Red maple occurrence is not modeled to change 

substantially, but its current abundance, biological 

traits, and ability to respond to disturbance suggest 

that it may increase. Common associate species, 

such as American beech, American elm, and white 

ash, may not be able to increase as projected due to 

substantial impacts from insects and diseases. Some 

southerly-distributed hardwood species that are 

currently infrequent or absent in the assessment area 

are projected to gain suitable habitat, including white 

oak, sassafras, and yellow-poplar.

Stressors: Climate change may amplify several 

major stressors, including invasive plant species, 

herbivory by white-tailed deer, and forest pests 

such as the hemlock woolly adelgid. Confidence in 

the vulnerability of this system is somewhat lower 

due to the likelihood of different responses across 

the landscape as well as uncertainty about how 

climate change will interact with numerous other 

current threats to these ecosystems. Unanticipated 

interactions may also occur between multiple 

stressors, such as drought, invasive species, and 

forest pests and diseases. Overall, it is expected that 

these impacts may be greatest on currently marginal 

sites or on sites where soil conditions become 

substantially drier.

Moderate-High Adaptive Capacity

Northern hardwood forests are widespread across 

the region and occur on a variety of soils and 

landforms. These forests often contain many tree 

species, representing a broad mix of tolerances and 

reproductive strategies. In general, areas that are 

north-facing, at higher elevations, or farther north 

in the region are expected to undergo less change 

and may continue to support northern hardwoods 

in the future. In these areas, it is also possible that 

sites that are currently too wet or cold to support 

northern hardwoods may become suitable over time 

and be colonized by these species. At the same time, 

northern hardwood forests that are farther south 

may have a reduced capacity to cope with future 

conditions, particularly where past land use, land 

development, fragmentation, invasive species, or 

other factors have already impaired the system.
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

107

A relict white pine growing above a northern hardwood 

forest. Photo by Maria Janowiak, U.S. Forest Service.

An old and unmanaged northern hardwood forest. Photo by 

Maria Janowiak, U.S. Forest Service. 

A large beech tree in a northern hardwood forest. Photo by 

Maria Janowiak, U.S. Forest Service. 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

108

Pitch Pine-Scrub Oak

Low Vulnerability (medium evidence, medium agreement)

A major threat to pitch pine-scrub oak forests is the potential for greater pest and disease activity, along 

with the potential for interactions among stressors. Tolerance for drought and disturbance increases the 

adaptive capacity of these forests, and the future fire regime is a primary uncertainty.

Neutral-Positive Potential Impacts

Drivers: Warmer temperatures are generally not 

expected to have major effects on the pitch pinescrub oak system because it is at the northern 

extent of its range in the region and occurs on 

particularly warm and dry sites (Manomet Center 

for Conservation Sciences and Massachusetts 

DFW 2010a, Manomet Center for Conservation 

Sciences and NWF 2013b, New Hampshire Fish 

and Game Department [FGD] 2013). Fire is the 

dominant natural disturbance in this system; 

increases in fire occurrence would generally be 

expected to favor pitch pine over more mesic oak 

and hardwood species that encroach in the absence 

of fire (Manomet Center for Conservation Sciences 

and Massachusetts DFW 2010a, Manomet Center 

for Conservation Sciences and NWF 2013b, New 

Hampshire FGD 2013). It is uncertain how climate 

change would affect the low-lying frost pockets 

common in this forest type, which generally 

support cold-hardy scrub oak. Over the long term, 

substantially warmer temperatures could reduce 

the occurrence of these unique microclimates (New 

Hampshire FGD 2013). Although pitch pine-scrub 

oak forests are generally expected to benefit under 

warmer and drier conditions, the close association 

of this forest system with sandy, nutrient-poor soils 

suggests that it may be unable to expand its extent 

appreciably.

Dominant Species: Considering the range of 

possible climate futures, scrub oak is projected to 

have little change in future habitat, and pitch pine 

habitat may increase slightly. Likewise, other oak 

species that are minor components of this forest 

system are generally expected to have similar or 

increased habitat and growth in the future. 

Stressors: Insect pests and diseases affecting this 

forest type, such as red pine shoot blight, may 

become more damaging under a warmer climate. 

Additionally, species such as southern pine beetle 

have been observed expanding northward, and these 

movements are expected to continue due to higher 

temperatures (Weed et al. 2013). The continued shift 

toward mesic species in these forests may continue 

if fire suppression activities remain constant and 

broadleaf species such as red maple and black cherry 

increase under climate change. 

Moderate Adaptive Capacity

This is a fire-dependent forest type that is generally 

expected to respond favorably to disturbance. 

Although the individual species common to this 

system are expected to fare better under warmer 

conditions, these ecosystems have low species 

diversity. Additionally, fire suppression has 

created denser forests in many locations, and it 

is unclear how human response to increased fire 

risk from climate change may alter future forest 

fire conditions. Further, some pitch pine-scrub oak 

forests may have reduced adaptive capacity where 

land use and development have increased forest 

fragmentation, fire suppression, or abundance of 

invasive species.
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

109

A southern New England forest containing pitch pine and oak trees. Photo by Maria Janowiak, 

U.S. Forest Service. 

A forest containing pitch pine and scrub oak, marked for 

forest harvest. Photo by Anthony W. D’Amato, University of 

Vermont, used with permission. 

Pitch pine trees in the forest overstory. Photo by Maria 

Janowiak, U.S. Forest Service. 
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

110

Transition Hardwood

Low-Moderate Vulnerability (medium evidence, medium-high agreement)

The transitional nature of this system suggests a variety of potential outcomes depending on the 

interaction of climate impacts and local conditions. Over the next several decades, change in these forests 

is expected to be driven primarily by a number of current stressors rather than climate change. 

Positive and Negative Potential Impacts

Drivers: Longer growing seasons would be expected 

to increase productivity of the transition hardwood 

forest system where there is adequate moisture, but 

altered precipitation may increase the potential for 

drier summer conditions (Chapters 3 and 4). Further, 

changes in soil temperature, moisture, nutrient 

availability, freeze-thaw cycles, or belowground 

processes could have substantial effects on sugar 

maple and other dominant species. Disturbances that 

affect this forest system, such as blowdowns or ice 

storms, may become more frequent or severe. The 

widespread and diverse nature of this forest type 

suggests that impacts will be highly variable across 

the region and dependent on local site conditions, 

with systems in more southerly or warm locations 

expected to undergo greater impacts.

Dominant Species: Model projections vary by 

species but generally suggest decline under scenarios 

projecting greater climatic changes for many species 

common to these forests, such as eastern white pine 

and sugar maple (Chapter 4). Red maple occurrence 

is not modeled to change substantially, but its current 

abundance, biological traits, and ability to respond 

to disturbance suggest that it may increase. Species 

composition may change over time to reflect future 

conditions, and may ultimately make a transition to 

oak- and pine-dominated forests in some locations 

(Manomet Center for Conservation Sciences and 

Massachusetts DFW 2010a). At the same time, 

several species that are at the middle or northern 

edge of their range, such as black cherry and yellowpoplar, are present in lower abundances and may 

increase. This suggests the potential for changes in 

the relative abundance of different species within 

this system, altering the “character” of forest 

composition.

Stressors: Climate change may amplify several 

major stressors that are already affecting this 

forest system. Several pests, including beech 

bark disease, gypsy moth, and hemlock woolly 

adelgid, currently affect many forests, and there is 

a disproportionately large impact on forest systems 

where eastern hemlock is lost. Pests such as Asian 

longhorned beetle may present new risks as they 

expand. Invasive plants already affect many of 

these ecosystems and may increase due to warmer 

temperatures and exacerbated disturbance. Browsing 

by white-tailed deer may increase if populations 

expand under less severe winters. 

Moderate-High Adaptive Capacity

These forests are found across a variety of landforms 

and local conditions and contain a mix of shade 

and moisture tolerances and reproductive strategies 

(e.g., seeding, sprouting). In general, areas that 

are north-facing, at higher elevations, or farther 

north in the region are expected to undergo less 

change compared to forests in warmer, drier, or 

more southerly locations (Manomet Center for 

Conservation Sciences and NWF 2013b, New 

Hampshire FGD 2013, Tetra Tech 2013). Likewise, 

forests containing a wide diversity of tree species 

as well as tree species that are expected to be better 

adapted to future conditions are likely to be able 

to respond more favorably to future conditions. In 

comparison with northern hardwoods, transition 

hardwood forests are more likely to be located in 

areas that have (or have had) higher levels of human 

disturbance; fragmentation, invasive species, or 

other stressors may have reduced the capacity of 

forests in some locations to cope with changing 

conditions (Manomet Center for Conservation 

Sciences and NWF 2013b). Adaptive capacity may 

also be reduced by ecosystem shifts toward moremesic tree species and a lack of regeneration of 

oaks and other species tolerant of warmer and drier 

conditions.
CHAPTER 5: FOREST ECOSYSTEM VULNERABILITIES

111

A transition hardwood forest with coniferous and deciduous tree species. Photo by Anthony 

W. D’Amato, University of Vermont, used with permission.

A transition hardwood forest with a mix of deciduous tree species. Photo by Anthony W. 

D’Amato, University of Vermont, used with permission.
112

CHAPTER 6: 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 systems 

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 managers of public and 

private land. These topics, and the descriptions of 

climate change implications, are not comprehensive. 

Some topics have received less scientific attention 

or entail 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 specific 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 are beginning 

to address the issue, however. For example, the 

recently updated U.S. 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 (36 CFR 219; U.S. Forest Service 

2012). In fact, climate change was among the stated 

purposes for revising the rule (U.S. Forest Service 

2012). When the Green Mountain National Forest 

in Vermont and the White Mountain National 

Forest in New Hampshire and western Maine revise 

their management plans in the future, they will be 

required to address climate change under this new 

rule. Similarly, the state-level management plans 

have not historically addressed climate change, but 

recent statewide forest strategies generally identify 

climate change as an issue that could influence the 

long-term sustainability of forests. 

Incorporating climate change considerations 

into natural resource planning will always be a 

challenging 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 for private lands, are typically written to guide 

management for a 10- to 25-year period, and it may 
CHAPTER 6: MANAGEMENT IMPLICATIONS

113

be difficult to address the potential long-term effects 

of climate change within a relatively short planning 

horizon. Management plans developed by Native 

American tribes look across longer timeframes, but 

still face the challenge of uncertainty about future 

conditions. 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 be more difficult 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. 

Private landowners, who own about 80 percent 

of the forest land in the assessment area, are also 

beginning to consider the implications of climate 

change for their planning and management. This 

may be out of a desire to anticipate the material 

risk posed to their forest land. For corporate or 

industrial owners, it may also be due to questions 

from outside funding, investment, and certification 

agencies regarding their “climate preparedness.” 

In the near term, the biggest 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 4). In the long term, 

adjustments may need to be made for suboptimal 

growing conditions induced by shifts in the climate 

envelope for commercially important tree species. 

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 dioxide 

fertilization, and the ability to grow entirely new 

species. Managers are increasingly thinking of 

climate change as a new lens through which to view 

management activities.

More Information

• More information on the U.S. Forest Service’s 

2012 Planning Rule can be found here: 

www.fs.usda.gov/planningrule 

• 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 more actively 

manage their forest and related resources, offers 

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. 

▪ 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. 

www.nrs.fs.fed.us/pubs/52760 
CHAPTER 6: MANAGEMENT IMPLICATIONS

114

▪ 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, 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 for the network has publicly available 

bulletins synthesizing a wide range of topics, as 

well as additional information about its services. 

www.climatesmartnetwork.org/ 

• The National Wildlife Federation has developed 

a guide to provide to 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 

will in turn lead to habitat changes for a variety of 

plant and animal species (Groffman et al. 2014; 

Manomet Center for Conservation Sciences and 

National Wildlife Federation [NWF] 2013b, 2013c; 

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). Many organizations are 

beginning to address the effects of climate change by 

gaining a better understanding of which species and 

habitats are most vulnerable and identifying actions 

to maintain or improve critical habitats.

The complex effects of climate change on plant and 

animal species and their management is an area of 

active research. A short synthesis of some research 

is included in Appendix 8, but numerous climate 

change assessments have been developed for the 

Northeast as a whole and for individual states that 

provide a more thorough analysis of the subject. 

Many species are expected to respond to climatic 

changes by moving northward, upslope, or upstream, 

while others may adapt in place or be unable to cope 

with the changes (Staudinger et al. 2013). Climate 

change will affect species differently, such that some 

species may decline while others expand under 

future conditions. For example, although conditions 

in some areas of the region are currently favorable 

for moose (Wattles and DeStefano 2013), this coldadapted species is considered to be highly vulnerable 

to climate change (Hoving et al. 2013, Whitman et 

al. 2013). Warmer temperatures can increase heat 

stress and infestations of winter tick, both of which 

are expected to cause moose populations to decline 

in the future (Dou et al. 2013, Murray et al. 2006, 

Musante et al. 2007). Future conditions are expected 

to make habitat conditions more suitable for whitetailed deer, which could further increase moose 

mortality due to spread of the brainworm parasite 

(Frelich et al. 2012, New Hampshire Fish and Game 

Department 2013, Rodenhouse et al. 2009).

It is hard to determine which species and habitats 

may be most vulnerable to climate change, but 

evidence and biological principles suggest some 

characteristics increase risk. For example, species 

with small geographic ranges, narrow physiological 

tolerance, fragmented distributions, or limited 

dispersal ability may be more restricted to certain 

A dead hemlock tree that is providing benefits for wildlife. 

Photo by Maria Janowiak, U.S. Forest Service. 
CHAPTER 6: MANAGEMENT IMPLICATIONS

115

locations and less likely to move in response to 

changing conditions (Trani Griep and Manley 2012). 

Threatened and endangered species often face 

population declines due to a variety of nonclimatic 

factors, such as habitat loss, competition from 

invasive species, and disease, all of which can 

be exacerbated by climate-related stress. Some 

evidence suggests that aquatic systems and waterdependent 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 (Manomet Center for 

Conservation Sciences and NWF 2013c, Staudinger 

et al. 2013).

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 state-level wildlife action plans. These plans 

identify wildlife species and associated habitats that 

are in 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 nonclimate 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 will 

depend on the needs and scope of a given 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/integratingclimate-change-state-wildlife-action-plans 

• The Climate Change Bird Atlas, developed by 

the U.S. 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 Massachusetts Wildlife Climate Action Tool 

is designed to inform and inspire local action to 

protect the Commonwealth’s natural resources in 

a changing climate. 

www.climateactiontool.org 

• The U.S. 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 

• The Designing Sustainable Landscapes project 

has projected future habitat suitability for some 

regional forest wildlife under climate change. 

www.umass.edu/landeco/research/dsl/documents/

dsl_documents.html

• 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/CCVInortheast-spp/CCVI-northeast-spp

• The National Wildlife Federation has developed 

a guide to provide to 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 
CHAPTER 6: MANAGEMENT IMPLICATIONS

116

WILDERNESS MANAGEMENT

Land may gain wilderness status by federal 

or state designation. The Green Mountain and 

White Mountain National Forests contain several 

federally designated Wilderness areas, and New 

York designated and manages the Adirondack 

Forest Preserve. These areas play a special role in 

the regional landscape because of their remote and 

unmanaged character and their scenic, recreational, 

and ecological value. 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. U.S. Forest Service 

policy directs the agency to “manage the wilderness 

resource to ensure its character and values are 

dominant and enduring” (U.S. Forest Service 2007). 

Since the early 20th century, lands in New York’s 

forest preserve have been required by Article 14 of 

the state constitution to be kept “forever wild”. The 

potential for extensive ecosystem change resulting 

from climate change raises difficult questions about 

the future management of these and other wilderness 

areas. 

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. Wilderness areas also provide 

important benefits for wildlife species within 

the region. Some evidence suggests that climate 

change may have the greatest impacts on species 

that are confined to protected areas, largely because 

populations would not be able to migrate in response 

to changing range limits for species (Peters 1985). 

Additionally, species within protected areas may 

potentially face new competitors, predators, or 

diseases as many native and nonnative species move 

around on the landscape. 

It is uncertain how climate-related impacts 

will 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 pros and cons.

More Information

• The Wildlife Conservation Society’s Adirondack 

Communities and Conservation Program includes 

climate change in this boreal forest wilderness as 

an area of emphasis. 

http://programs.wcs.org/northamerica/wildplaces/adirondacks/adirondack-climate-change.

aspx

• The Wilderness.net Climate Change Toolbox 

provides information about climate change and 

wilderness, including management guidelines and 

strategies. 

www.wilderness.net/climate

• The U.S. 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/

A view of the Adirondack Mountains and lakes in northern 

New York. Photo by Maria Janowiak, U.S. Forest Service. 
CHAPTER 6: MANAGEMENT IMPLICATIONS

117

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 

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 and Moser 2016). Climate 

change trends and ecosystem 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 will 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. 

When designing 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 potential effects of projected stressors 

on the property in the future, and then to integrate 

those considerations into project design. Private 

nonprofit organizations, government agencies, 

landowners, and entities that may provide funding 

will increasingly need research-based results on 

anticipated climate trends and impacts, including 

spatially explicit information on how these shifts 

will play out over the land. This science can enable 

effective use of funding, staff time, and other 

resources that are essential to advancing “climateinformed” conservation of forests in the region and 

shaping conservation efforts to promote a more 

resilient landscape. 

More Information

• The Open Space Institute has developed the 

Resilient Landscapes Initiative to protect habitats 

that will 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 has identified places that will 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

FOREST PRODUCTS

The forest products industry is important to the 

local and regional economy of the assessment area 

(Chapter 1). Tree species and forest composition are 

projected to change over the 21st century (Chapters 

4 and 5). Changes in forest composition across the 

landscape will be influenced by forest management, 

and in turn will influence forest management and 

the forest products industry (Moser et al. 2016). 

Several commercially important species such as 

quaking aspen are projected to suffer significant 

declines under a range of possible climate futures 

over the next century. Conversely, hardwood species 

such as white oak and yellow-poplar are projected 

to increase in the assessment area. Large potential 

shifts in commercial species availability may pose 

risks for the forest products sector if the shifts are 

rapid and the industry is unprepared. The forest 

products industry may benefit from awareness 
CHAPTER 6: MANAGEMENT IMPLICATIONS

118

of anticipated climate trends and shifts in forest 

species. In many cases, forest managers can take 

actions to reduce potential risks associated with 

climate change or proactively encourage species 

and forest types anticipated to fare better under 

future conditions (Stein et al. 2014, Swanston and 

Janowiak 2016). There may be regional differences 

in forest responses, as well as potential opportunities 

for new merchantable species to gain suitable habitat 

in the assessment area. 

Overall, the effects of climate change on the forest 

products industry depend not only on ecological 

responses to the changing climate, but also on 

socioeconomic factors that will undoubtedly 

continue to change throughout the 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). Large uncertainties 

are associated with each of these factors. The 

forest products industry has adjusted to substantial 

changes over the past 100 years, and continued 

responsiveness can help the sector remain viable. 

More Information

• The U.S. Forest Service 2010 Resources Planning 

Act Assessment includes future projections for 

forest products and other resources through the 

year 2060 and examines social, economic, land 

use, and climate change influences. 

www.fs.fed.us/research/rpa/

• The U.S. Forest Service’s Northern Forest 

Futures Project uses the latest inventory data and 

scientific projections to understand how forests in 

the Midwest and Northeast will change as climate 

and other stressors change. 

www.nrs.fs.fed.us/futures/

• The Climate Change Tree Atlas, developed by the 

U.S. Forest Service, provides information on the 

projected suitable habitat for tree species under 

climate change. 

www.nrs.fs.fed.us/atlas/

A red pine plantation in central Vermont. Photo by Maria Janowiak, U.S. Forest Service. 
CHAPTER 6: MANAGEMENT IMPLICATIONS

119

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 available time 

window for conventional forest management 

operations. Most management in lowland areas 

and on soils prone to compaction or erosion is 

accomplished during the winter. Climate change 

is projected to result in shorter seasons of frozen 

ground, more midwinter thaws, less snowpack, and 

more rain during winter months (Chapter 3). Frozen 

ground facilitates timber harvest and transport, 

and snowpack provides protection for soils during 

harvest operations. 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 

might increase the need to build roads to facilitate 

winter harvest, which would entail additional costs 

compared to conventional practices.

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. Extended or severe 

droughts could present problems in sandy areas, 

however, if it becomes necessary to install gravel 

over logging roads. 

Projected changes in severe weather patterns 

could increase the need for salvage harvests. 

Harvesting green timber allows resource managers 

to strategically achieve desired objectives and 

outcomes. In contrast, salvage harvesting after a 

wind event or pest or disease outbreak 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 financial 

return as a green timber sale.

Analysis of timber harvest records in northern 

Wisconsin has identified some consequences of 

the changes in frozen ground condition (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 curtail the spread of forest diseases can further 

reduce the annual harvest window. Additionally, 

ongoing stressors of overcapitalization, loan and 

insurance payments, and high fuel prices increased 

pressure on loggers to harvest year-round. 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 the current trend 

Winter landscape. Winters are becoming shorter and milder 

as temperatures warm. Photo by Maria Janowiak, U.S. 

Forest Service.
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120

of intensifying precipitation has put additional 

strains on old and fragile infrastructure. 

Heavy precipitation events, which are already 

increasing and projected to increase more in 

the future (Chapter 3), 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 will need to last up to 100 years 

into the future and be able to withstand 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. Additionally, 

forest managers may find it necessary to take further 

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 populations (Baumflek et al. 

2010, Robbins et al. 2008). Many of these products 

will be affected by climatic changes; each product 

will 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). Similarly, climate change is 

expected to have impacts on tribal traditional food, 

such as the use of several berry species by tribal 

communities in the Northeast (Lynn et al. 2013, 

Saint Regis Mohawk Tribe 2013).

Maple syrup is a nontimber forest product that 

is largely synonymous with the forests of the 

Northeast. Maple syrup and sugar, made from 

boiling sugar maple sap, provided treasured sources 

of sweetness and a critical source of late winter 

nourishment well before the arrival of the first 

Europeans on the continent. Fur traders’ 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 

continues to have profound cultural and economic 

importance in the region. Commercial production of 

maple syrup and related products provides millions 

of dollars of revenue in the assessment area, and this 

total does not include production of maple syrup 

that never enters the market. Gathering and boiling 

sap and sharing syrup knits together families and 

communities. Sap flow necessary for maple syrup 

production requires a combination of warm days 

and freezing nights that is highly seasonal. Climate 

records show such conditions now occur earlier than 

in the past and this trend is projected to continue; 

however, it is unclear whether the season will be 

shortened or sap yield reduced (Groffman et al. 

2012, Matthews et al. 2017, Skinner et al. 2012). 

Maple producers report that their current ability 

to adapt to changing climatic conditions is largely 

related to the health of the forest and the ability of 

producers to adopt new technologies (Kuehn et al. 

2016). 

FIRE AND FUELS

Weather and climate are major drivers of fire 

behavior. Across the region, the fire season is 

controlled by a combination of day length, weather, 

and fuel conditions. 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 the 

potential for fire ignition and spread. Live trees drop 

leaves and go dormant in the fall, but most forests 

become increasingly fire-prone around the same 

time that short days and cool temperatures return. 
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121

Projected changes in climate could affect fire and 

fuels management in the assessment area. Climate 

change is generally expected to increase annual 

precipitation in the assessment area, but there is the 

potential for drier conditions during the growing 

season (Chapter 3). Drier conditions later in the 

growing season following wet springs could cause 

some tree mortality, increasing forest fuel loads and 

the potential for more-intense fires. High-intensity 

wildfire can result in species 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). Under intense fire weather conditions, 

wildfires could also become a hazard and safety 

risk to the public, firefighters, and infrastructure. 

More resources may be needed to reduce fuel loads 

to prevent these catastrophic wildfires, fight them 

when they do occur, and restore ecosystems after a 

catastrophic event. 

A prescribed fire in a pitch pine-scrub oak forest in the 

Montague Plains of western Massachusetts. Photo by 

Matthew Duveneck, Harvard Forest, used with permission. 

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-scrub oak forests, for example. Projected 

changes in climate could also affect the ability of 

public, tribal, and private land managers to apply 

prescribed fire in the assessment area. 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. 

http://www.firesciencenorthatlantic.org

• The U.S. 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 in the assessment 

area to store carbon. A longer growing season and 

carbon dioxide fertilization may lead to increased 

productivity and carbon storage in forests in the 

assessment area (Chapter 4). Several modeling 

studies suggest that forests are likely to continue to 

sequester additional carbon over the next several 

decades as relatively young forests continue to 

mature and forests benefit from slightly warmer 

conditions (Duveneck et al. 2017, Thompson et al. 

2011, Wang et al. 2017). Over time, this increase 

could be offset by climate-related physical and 
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122

biological disturbances (Gough et al. 2008, Hicke 

et al. 2011, Tang et al. 2014), 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. Regional modeling studies have examined the 

effects of species composition changes on landscapescale carbon stocks, and they suggest that some 

forests may have increases in biomass and overall 

productivity, despite declines of some northern and 

boreal species (Duveneck et al. 2017; Tang et al. 

2010, 2014). For example, one study projected that 

aboveground biomass would continue to increase 

over the next century, even though the contribution 

of biomass provided by different forest types 

would change as forest communities changed on 

the landscape. 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 U.S. 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/carbon-land-mgmt

• A review report, Considering Forest and 

Grassland Carbon in Land Management, 

summarizes the key issues related to forest 

management and carbon.

www.treesearch.fs.fed.us/pubs/54316

RECREATION

Forests are the centerpieces of outdoor recreation in 

the assessment area (Chapter 1). People throughout 

the Northeast enjoy a variety of recreational 

activities, including hunting, fishing, camping, 

wildlife watching, skiing, and snowboarding. People 

also explore trails on foot, bicycles, skis, snowshoes, 

horseback, and snowmobiles, 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, 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 will continue 

toward shorter, milder winters and longer, hotter 

summers, which could reduce opportunities for 

popular winter-based recreation activities in the long 

term. Climate change has already caused reductions 

in the duration of lake ice in the assessment area 

(Chapter 3), and there may be fewer opportunities 

for activities such as ice fishing and pond hockey 

as conditions continue to change (Fairley et al. 

2015). It is expected that much of the assessment 

area 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, 

and downhill skiing (Bowker and Askew 2013, 

McBoyle et al. 2007, Scott et al. 2008). Because 

impacts on winter recreation activities will be 

closely tied to winter temperatures, southern parts 

of the assessment area are at greater risk in coming 

decades and will be less able to provide recreational 

opportunities such as downhill skiing (Dawson and 

Scott 2013, 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 et al. 2013). 
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123

It is also expected that recreational activities during 

the spring, summer, and fall will shift in response to 

warmer and more variable climatic 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; this could have 

implications for staffing, especially for recreationrelated businesses that rely on student labor, which 

will be unavailable during the school year (Nicholls 

2012). Regional increases in average temperatures 

and heat waves during summer months could shift 

visitor behavior, depending on the magnitude of 

changes, and visitor use is expected to decrease 

where hot weather becomes more frequent (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 coincide 

with 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 2013b). As 

mentioned earlier, climate change may also result in 

substantial changes in habitat availability and quality 

for wildlife and fish species. Big game species, such 

as moose, 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.

More Information

• The U.S. Forest Service Northern Forest 

Futures Project uses the latest inventory data 

and scientific projections to understand how 

recreational opportunities in the Midwest and 

Northeast will change as climate and other 

stressors change. 

www.nrs.fs.fed.us/futures/ 

A forest trail in the Adirondack Forest Preserve. Photo by Maria Janowiak, U.S. Forest Service.
CHAPTER 6: MANAGEMENT IMPLICATIONS

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ARCHAEOLOGICAL 

AND HISTORIC RESOURCES

Managing cultural resources may become more 

challenging as a result of the direct and indirect 

impacts of climate change. The remnants of past 

human activity, such as paintings, sculptures, and 

objects from everyday life, are present within the 

assessment area. These resources date to both 

prehistoric and historic time periods, and exist 

both above and below the ground surface. Climate 

change impacts on the physical environment have 

the potential to affect the character and condition 

of these cultural resources. 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 flood terraces. 

Floodwaters can further threaten the integrity of 

historic structures in low-lying areas by eroding 

the foundation, or adding moisture. The increased 

moisture can generate more mold and fungus 

growth, thereby hastening deterioration of wooden 

and other constructed features (Schiffer 1996). 

FOREST-ASSOCIATED 

TOWNS AND CITIES

The ability of human communities to respond 

to environmental changes is directly related 

to their adaptive capacity—resources that can 

be leveraged by the 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. Many 

towns and cities in the assessment area are closely 

tied to the health and functioning of surrounding 

forests, whether for economic, cultural, recreational, 

or other reasons. 

Every forest-associated community has certain 

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. Some communities (e.g., indigenous 

communities with forest-dependent cultural 

practices, tourism-dependent communities) and 

social groups within communities (e.g., individuals 

working in the forest products industry) may be 

more vulnerable to these impacts or less able to 

adapt.

State and municipal agencies as well as private 

companies are also responsible for maintaining 

infrastructure in the assessment area, including 

roads, power lines, sewer lines, dams, drainage 

ditches, and culverts. Storms, extreme temperatures, 

longer growing seasons, and warmer winters 

can pose particular challenges for infrastructure. 

Extreme heat and longer growing seasons can result 

in rising costs associated with roadside and power 

line vegetation management. Extreme cold and 

freeze-thaw cycles can accelerate road deterioration. 

Intense rainfall could increase the potential for 

erosion on dirt and gravel roads common in forest 

landscapes, logging projects, gas development, and 

rural areas. Water resource infrastructure such as 

bridges, sewers, major culverts, low-water crossings, 

and dams may have to be redesigned and rebuilt 

to accommodate flows of increased duration and 

intensity. Projected increases in average temperature 

and summer heat waves can increase demand for 

cooling and place additional strain on electrical 

infrastructure.

If resource professionals, community leaders, 

and local organizations are to help communities 

adapt to changes, they must identify community 

vulnerabilities and sensitivities to climate-related 

impacts and also build community 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 
CHAPTER 6: MANAGEMENT IMPLICATIONS

125

adaptation plans has focused on coastal communities 

and infrastructure (Olmstead et al. 2016, Woodruff 

and Stults 2016, Zimmerman and Faris 2010). When 

planning for climate change, decisionmakers can 

consider how ecological events or changes (e.g., 

floods, droughts, wildfire, windstorms, introduced 

species, outbreaks of insects or pathogens) will 

affect their communities and community members 

by asking several questions (Davenport et al. 2013). 

These include: 

• Is access to healthy ecosystems at risk? 

• Is there a potential for resource scarcity?

• Are cultural practices or recreational 

opportunities at risk?

• Is there potential for loss of social connectedness 

or increased social or cultural conflict?

• Is there potential for disproportionate impacts to 

certain populations?

• Is there potential for human health problems 

including stress, anxiety, despair, or sense of 

powerlessness?

More Information

• The Resilience Alliance has created Assessing 

Resilience in Social-Ecological Systems: 

Workbook for Practitioners 2.0. 

www.resalliance.org/resilience-assessment 

• The U.S. Department of Energy has examined 

current and potential future impacts of these 

climate trends on the U.S. energy sector. 

www.energy.gov/articles/climate-change-effectsour-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: 

http://nca2014.globalchange.gov/report/

sectors/urban

▪ Rural communities: 

http://nca2014.globalchange.gov/highlights/

regions/rural-communities

▪ Indigenous peoples, lands, and resources: 

http://nca2014.globalchange.gov/report/

sectors/indigenous-peoples 

URBAN FORESTS

Climate change will also affect urban forests in 

the assessment area. Urban environments can pose 

additional stresses that trees do not encounter 

in natural environments, such as pollution from 

vehicle exhaust, confined root environments, and 

road salts. The altered vegetation structure and 

impervious surfaces in urban environments can 

also create microclimates different from those of 

the surrounding natural areas (Hall et al. 2016). 

Impervious surfaces can make urban environments 

more susceptible to flash floods, placing floodintolerant species at risk. Responses of trees to 

disturbances such as drought may vary by land use 

within an urban area, so it is difficult to understand 

how the forest will respond as a whole (Fahey et 

al. 2013). All of these abiotic stressors can make 

urban forests more susceptible to nonnative species 

invasion, and insect and pathogen attack, especially 

because only a limited range of species and 

genotypes is typically planted in urban areas. Urban 

settings are also the most likely places for exotic 

insect pests to be introduced. 

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. 2017, 

Ordóñez and Duinker 2015). Shifts in temperature 

and changes in extreme events may have effects on 

selection of species for planting (Yang 2009). Native 

species projected to decline under climate change 

are not expected to tolerate the even more extreme 

conditions presented by urban settings. Conversely, 

urban environments may favor heat-tolerant or 

drought-tolerant native species or new migrants. 

Determining appropriate species for planting may 

be a challenge, but community foresters are already 

familiar with the practice of planting species novel 

to an area. Because of urban effects on climate, 

many community forests already contain species that 

are from planting zones south of the area or cultivars 

that tolerate a wide range of climatic conditions. 

Large disturbance events may also become more 

frequent or intense in the future, necessitating 
CHAPTER 6: MANAGEMENT IMPLICATIONS

126

informed decisions in response. For example, wind 

events or pest outbreaks may be more damaging 

to already stressed trees. If leaf-out dates advance 

earlier in the spring due to climate change, 

community forests may be increasingly susceptible 

to early-season frosts or snowstorms. More people 

and larger budgets may be required to handle an 

increase in the frequency or intensity of these events, 

which may become more difficult in the face of 

reduced municipal staffing and budgets.

More Information

• The U.S. 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 

A riparian forest containing hemlock, which is more 

susceptible to the hemlock woolly adelgid under warmer 

winter conditions. Photo by Todd Ontl, U.S. Forest Service. 

• Several urban areas have developed adaptation 

guides to help communities use urban forests to 

reduce climate change impacts and adapt urban 

forests to future conditions. 

▪ For urban forests in British Columbia: 

www.toolkit.bc.ca/Resource/Urban-ForestsClimate-Adaptation-Guide 

▪ For Toronto’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 has important implications for the 

health of the people who live, work, or recreate in 

the forests of the assessment area. Climate change 

can influence a wide array of human health issues 

through complex interactions in the environment 

and the human body (Patz et al. 2011, Portier et 

al. 2013). 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 (Portier et al. 2013, 

Ziska et al. 2011). Extremely high temperatures 

can lead to heat stress, which can exacerbate 

cardiovascular disease or induce heat-related illness 

and death.

Vector-borne diseases, such as Lyme disease and 

West Nile virus, pose an ongoing risk to natural 

resource managers, local residents, and visitors 

alike, and this issue may become increasingly 

important over 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—usually 

mammals or birds. Humans are typically infected 

incidentally when they are bitten instead of animal 

hosts. Climate is one of many key interacting 
CHAPTER 6: MANAGEMENT IMPLICATIONS

127

variables that affect people’s risk for vector-borne 

diseases (Portier et al. 2013). Changes in climate can 

influence vector-borne disease risk by affecting the 

abundance and distribution of ticks or mosquitoes, 

the percentage of vectors infected, the abundance 

and distribution of animal hosts, the presence of 

suitable habitat for these vectors, and the behaviors 

that bring humans into contact with infected vectors. 

Most arthropod vectors of disease are sensitive to 

physical conditions, such as levels of humidity, 

daily high and low temperatures, rainfall patterns, 

and winter snowpack. For example, blacklegged 

ticks (i.e., “deer ticks”) are the vector for Lyme 

disease and several other diseases, and these ticks 

are most active on warm, humid days. They are most 

abundant in wooded or brushy habitats that contain 

abundant small mammals and deer. Projected 

expansion of mesic hardwoods with changing 

climatic conditions may increase the incidence 

of Lyme disease and other tick-borne diseases if 

humans frequently visit those habitats. 

More Information

• The National Climate Assessment provides a 

summary of how climate change may affect 

human health. 

http://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/ 

SUMMARY

The breadth of these topics highlights the wide 

range of effects that climate change may have on 

forest management in the assessment area. 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 Northeast will 

be affected by economic, political, ecological, and 

societal factors. These factors will be specific to 

each land owner and agency, and are unpredictable. 

Confronting the challenge of climate change 

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 will be a useful foundation for land 

managers in that process, to be further enriched by 

local knowledge and site-specific information. 
128

GLOSSARY

adaptive capacity

the general ability of institutions, systems, and 

individuals to moderate the risks of climate change, 

or to realize benefits, through changes in their 

characteristics or behavior. Adaptive capacity can be 

an inherent property or it could have been developed 

as a result of previous policy, planning, or design 

decisions.

agreement

the extent to which evidence is consistent in support 

of a vulnerability statement or rating (see also 

confidence, evidence). 

biomass

the mass of living organic matter (plant and 

animal) in an ecosystem; also 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.

carbon dioxide (CO2) fertilization 

increased plant uptake of CO2 through 

photosynthesis in response to higher concentrations 

of atmospheric CO2 .

climate change 

a change in the state of the climate that can be 

identified (for example, by using statistical tests) 

by changes in the mean and/or the variability of its 

properties, and that persists for an extended period, 

typically decades or longer. Climate change may 

be due to natural internal processes or external 

factors, or to persistent anthropogenic changes in the 

composition of the atmosphere or in land use. 

climate model

see general circulation model.

climate normal

the arithmetic mean of a climatological element 

computed over three consecutive decades.

community

an assemblage of plants and animals living together 

and occupying a given area.

confidence

a qualitative assessment of uncertainty as determined 

through evaluation of evidence and agreement (see 

also evidence, agreement). 

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.

downscaling

methods for obtaining high-resolution climate or 

climate change information from coarse-resolution 

general circulation models; involves examining the 

statistical relationship between past climate data and 

on-the-ground measurements. 
GLOSSARY

129

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 province

climatic subzones, controlled primarily by 

continental weather patterns such as length of dry 

season and duration of cold temperatures. Provinces 

are also characterized by similar soil orders and are 

evident as extensive areas of similar potential natural 

vegetation. 

ecoregion

a region characterized by a repetitive pattern of 

ecosystems associated with commonalities in climate 

and landform that characterize that larger region.

ecosystem 

a volumetric unit of the Earth’s surface that includes 

air (climate), land (landform, soil, water), and biota. 

Ecosystems are defined by land area, and contain all 

the interactions between living organisms and their 

physical environment. 

emissions scenario

a plausible representation of the future development 

of emissions of greenhouse gases and aerosols 

that are potentially radiatively active, based on 

demographic, technological, or environmental 

developments.

evapotranspiration

the sum of evaporation from the soil and 

transpiration from plants.

evidence

mechanistic understanding, theory, data, models, 

or expert judgment used to determine the level of 

confidence in a vulnerability statement or rating (see 

also agreement, confidence). 

exposure 

the nature and degree to which a system is exposed 

to significant climate variations.

fire-return interval

the number of years between two successive fire 

events at a specific location.

forest land

land that is at least 10 percent stocked by forest trees 

of any size, or land formerly having such tree cover, 

and not currently developed for a nonforest use.

forest type

a classification of forest vegetation based on the 

dominant species present, as well as associate 

species commonly occurring with the dominant 

species.

forest-type group 

based on FIA definitions, a combination of forest 

types that share closely associated species or site 

requirements and are generally combined for brevity 

of reporting.

fragmentation

a disruption of ecosystem or habitat connectivity, 

caused by human or natural disturbance, creating a 

mosaic of successional and developmental stages 

within or between forested tracts of varying patch 

size, isolation (distance between patches), and edge 

length.

fundamental niche

the total habitat available to a species based on 

climate, soils, and land cover type in the absence of 

competitors, diseases, or predators.

general circulation model (GCM)

numerical representation of the climate system 

based on the physical, chemical, and biological 

properties of its components, their interactions, and 

their feedback processes, and accounting for all or 

some of its known properties (also called climate 

model).
GLOSSARY

130

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 temperature is 

favorable for plant growth.

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). 

hydric

pertaining to sites or habitats with abundant moisture 

throughout the year, frequently including saturation, 

ponding, or flooding.

impact

the direct and indirect consequences of climate 

change on systems, particularly those that would 

occur without adaptation.

impact model

simulations of impacts on trees, animals, and 

ecosystems. It uses projections from general 

circulation models as inputs, and includes additional 

inputs such as tree species, soil types, and lifehistory traits of individual species.

importance value

in the Climate Change Tree Atlas model, an index 

of the relative abundance of a species in a given 

location or pixel cell (0 = least abundant, 100 = most 

abundant).

invasive species

any species that is nonnative (or alien) to the 

ecosystem under consideration and whose 

introduction causes or is likely to cause damage, 

injury, or disruption to ecosystem processes or other 

species within that ecosystem.

mesic

referring to sites or habitats where soil moisture is 

available to plants throughout the growing season.

model reliability score

in the Climate Change Tree Atlas model, a “trimodel” approach to assess reliability of model 

predictions for each species, classified as high, 

medium, or low.

modifying factor

in the Climate Change Tree Atlas model, an 

environmental variable (for example, site conditions, 

interspecies competition, disturbance, dispersal 

ability) that influences the way a tree may respond to 

climate change.

parcelization

the subdivision of a single forest ownership into 

two or more ownerships. Parcelization may result 

in fragmentation if habitat is altered under new 

ownership. 

peak flow

the maximum instantaneous discharge of a stream or 

river at a given location. 

phenology

the timing of natural events such as the date that 

migrating birds return, the first flower dates for 

plants, and the date on which a lake freezes in the 

autumn or opens in the spring; also refers to the 

study of this subject.

prairie 

a natural community dominated by perennial grasses 

and forbs with scattered shrubs and very few trees 

(less than 10 percent canopy cover). 

precipitation

the process where water vapor condenses in the 

atmosphere to form water droplets that fall to the 

Earth as rain, sleet, snow, hail, etc.

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

131

productivity 

the rate at which biomass is produced per unit area 

by any class of organisms, or the rate of energy 

utilization by organisms.

projection 

a potential future evolution of a quantity or set of 

quantities, often computed with the aid of a model. 

Projections are distinguished from predictions 

in order to emphasize that projections involve 

assumptions concerning, for example, future 

socioeconomic and technological developments that 

may or may not be realized, and are therefore subject 

to substantial uncertainty. 

pulpwood

roundwood, whole-tree chips, or wood residues used 

for the production of wood pulp for making paper 

and paperboard products. 

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.

refugia

locations and habitats that support populations of 

organisms that are limited to small fragments of their 

previous geographic range.

runoff

that part of the precipitation that appears in surface 

streams. It is the same as streamflow unaffected by 

artificial diversions or storage.

savanna

fire-maintained grasslands with open-grown, 

scattered, orchard-like trees or groupings of trees 

and shrubs. 

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.

scenario 

a plausible and often simplified description of 

how the future may develop, based on a coherent 

and internally consistent set of assumptions about 

driving forces and key relationships. Scenarios 

may be derived from projections, but are often 

based on additional information from other sources, 

sometimes combined with a narrative storyline (see 

also emissions scenario). 

sensitivity

the degree to which a system is affected, either 

adversely or beneficially, by climate-related stimuli.

severity

the proportion of aboveground vegetation killed and 

the degree of forest floor and soil disruption.

significant trend

least-squares regression p-values of observed climate 

trends. In this report, trends are significant when 

p < 0.10. For trends where p > 0.10, observed trends 

have a higher probability of being due to chance 

alone. 

snow water equivalent 

the amount of water contained in snowpack. It 

is a way of measuring the amount of snow while 

accounting for differences in density.

snowpack

layers of accumulated snow that usually melts during 

warmer months.

softwood

a coniferous tree, usually evergreen, having needles 

or scale-like leaves.

species distribution model

a model that uses statistical relationships to project 

future change.
GLOSSARY

132

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

involving or containing a random variable or 

process.

streamflow 

discharge that occurs in a natural surface stream 

course whether or not it is diverted or regulated.

stressor 

an agent, condition, change in condition, or other 

stimulus that causes stress to an organism.

suitable habitat

in the Climate Change Tree Atlas model, the areaweighted importance value, or the product of tree 

species abundance and the number of cells with 

projected occupancy.

tension zone

a transitional band that corresponds to a number of 

climatic factors. Vegetation north and south of the 

tension zone reflects varied habitat conditions as a 

result of climatic differences.

timberland

forest land that is producing or capable of producing 

more than 20 cubic feet per acre per year of wood. 

topkill

death of aboveground tree stem and branches.

transpiration

liquid water phase change occurring inside plants 

with the vapor diffusing to the atmosphere.

troposphere

the lowest part of the atmosphere from the surface 

to about 6 miles in altitude in mid-latitudes (ranging 

from 5.6 miles in high latitudes to 9.9 miles in 

the tropics on average) where clouds and weather 

phenomena occur.

uncertainty 

an expression of the degree to which a value (such as 

the future state of the climate system) is unknown. 

Uncertainty can result from lack of information or 

from disagreement about what is known or even 

knowable. It may have many types of sources, 

from quantifiable errors in the data to ambiguously 

defined concepts or terminology, or uncertain 

projections of human behavior. Uncertainty can 

be described using quantitative measures or by 

qualitative statements.

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 

the degree to which a system is susceptible to, and 

unable to cope with, adverse effects of climate 

change, including climate variability and extremes. 

Vulnerability is a function of the impacts and 

adaptive capacity of a system. For this assessment, 

a system may be considered to be vulnerable if it 

is at risk of a composition change leading to a new 

identity, or if the system is anticipated to suffer 

substantial declines in health or productivity.

weather 

the state of the atmosphere at a given time and 

place, with respect to variables such as temperature, 

moisture, wind velocity, and barometric pressure. 

windthrow 

trees uprooted or broken by wind.

xeric

pertaining to sites or habitats characterized by 

decidedly dry conditions.
133

LITERATURE CITED

Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; 

Berntson, G.; Kamakea, M.; McNulty, S.; Currie, 

W.; Rustad, L.; Fernandez, I. 1998. Nitrogen 

saturation in temperate forest ecosystems.

BioScience. 48(11): 921-934. 

https://doi.org/10.2307/1313296.

Aber, J.D.; Melillo, J.M. 1991. Terrestrial 

ecosystems. Philadelphia, PA: Saunders College 

Publishing. 429 p.

Aber, J.D.; Nadelhoffer, K.J.; Steudler, P.; Melillo, 

J.M. 1989. Nitrogen saturation in northern 

forest ecosystems. BioScience. 39(6): 378-286. 

https://doi.org/10.2307/1311067.

Aber, J.D.; Ollinger, S.V.; Fédérer, C.A.; Reich, 

P.B.; Goulden, M.L.; Kicklighter, D.W.; Melillo, 

J.M.; Lathrop, R.G. 1995. Predicting the effects 

of climate change on water yield and forest 

production in the northeastern United States.

Climate Research. 5: 207-222. 

https://doi.org/10.3354/cr005207.

Abrams, M.D. 2003. Where has all the white oak 

gone? BioScience. 53(10): 927-939. 

https://doi.org/10.1641/0006-

3568(2003)053[0927:whatwo]2.0.co;2. 

Abrams, M.D.; Copenheaver, C.A.; Black, B.A.; van 

de Gevel, S. 2001. Dendroecology and climatic 

impacts for a relict, old-growth, bog forest 

in the Ridge and Valley Province of central 

Pennsylvania, USA. Canadian Journal of Botany. 

79(1): 58-69. https://doi.org/10.1139/cjb-79-1-58.

Abrams, M.D.; Nowacki, G.J. 1992. Historical 

variation in fire, oak recruitment, and postlogging accelerated succession in central 

Pennsylvania. Bulletin of the Torrey Botanical 

Club. 119(1): 19-28. 

https://doi.org/10.2307/2996916.

Abrams, M.D.; Nowacki, G.J. 2008. Native 

Americans as active and passive promoters of 

mast and fruit trees in the eastern USA. The 

Holocene. 18(7): 1123-1137. 

https://doi.org/10.1177/0959683608095581.

Abrams, M.D.; Ruffner, C.M.; DeMeo, T.E. 1998. 

Dendroecology and species co-existence in 

an old-growth Quercus-Acer-Tilia talus slope 

forest in the Central Appalachians, USA.

Forest Ecology and Management. 106(1): 9-18. 

https://doi.org/10.1016/S0378-1127(97)00234-X.

Abrams, M.D.; Scott, M.L. 1989. Disturbancemediated accelerated succession in two 

Michigan forest types. Forest Science. 

35(1): 42-49.

Adaptation Subcommittee to the Governor’s 

Steering Committee on Climate Change. 2010. 

The impacts of climate change on Connecticut 

agriculture, infrastructure, natural resources, 

and public health. 195 p. Available at http://

www.ct.gov/deep/lib/deep/climatechange/

impactsofclimatechange.pdf (accessed July 22, 

2014).

Ainsworth, E.A.; Long, S.P. 2005. What have 

we learned from 15 years of free-air CO2

enrichment (FACE)? A meta-analytic review 

of the responses of photosynthesis, canopy 

properties and plant production to rising CO2.

New Phytologist. 165(2): 351-372. 

https://doi.org/10.1111/j.1469-8137.2004.01224.x.

Ainsworth, E.A.; Rogers, A. 2007. The response of 

photosynthesis and stomatal conductance to 

rising [CO2]: mechanisms and environmental 

interactions. Plant, Cell & Environment. 30(3): 

258-270. 

https://doi.org/10.1111/j.1365-3040.2007.01641.x.
LITERATURE CITED

134

Allen, C.D.; Breshears, D.D.; McDowell, N.G. 2015. 

On underestimation of global vulnerability to 

tree mortality and forest die-off from hotter 

drought in the Anthropocene. Ecosphere. 6(8): 

art129. https://doi.org/10.1890/ES15-00203.1.

Allen, C.D.; Macalady, A.K.; Chenchouni, H.; 

Bachelet, D.; McDowell, N.; Vennetier, M.; 

Kitzberger, T.; Rigling, A.; Breshears, D.D.; 

Hogg, E.H.; Gonzalez, P.; Fensham, R.; Zhang, 

Z.; Castro, J.; Demidova, N.; Lim, J.-H.; Allard, 

G.; Running, S.W.; Semerci, A.; Cobb, N. 2010. 

A global overview of drought and heat-induced 

tree mortality reveals emerging climate 

change risks for forests. Forest Ecology and 

Management. 259(4): 660-684. 

https://doi.org/10.1016/j.foreco.2009.09.001.

Anagnostakis, S.L. 1995. The pathogens and pests 

of chestnuts. Advances in Botanical Research. 

21: 125-145. 

https://doi.org/10.1016/S0065-2296(08)60011-7.

Anderegg, W.R.; Hicke, J.A.; Fisher, R.A.; Allen, 

C.D.; Aukema, J.; Bentz, B.; Hood, S.; Lichstein, 

J.W.; Macalady, A.K.; McDowell, N. 2015. 

Tree mortality from drought, insects, and 

their interactions in a changing climate. New 

Phytologist. 208(3): 674-683. 

https://doi.org/10.1111/nph.13477.

Anderson, M.G.; Clark, M.; Ferree, C.; Jospe, A.; 

Olivero Sheldon, A.; Weaver, K. 2013. Northeast 

habitat guides: a companion to the terrestrial 

and aquatic habitat maps. Boston, MA: The 

Nature Conservancy, Eastern Conservation 

Science, Eastern Regional Office. 394 p. 

Available at http://rcngrants.org/sites/default/

files/news_files/Northeast%20Aquatic%20and%

20Terrestrial%20Habitat%20Guide.pdf (accessed 

August 3, 2017).

Anderson, M.G.; Clark, M.; Sheldon, A.O. 2012. 

Resilient sites for terrestrial conservation 

in the Northeast and Mid-Atlantic region.

Boston, MA: The Nature Conservancy, Eastern 

Conservation Science. 168 p. Available 

at http://www.conservationgateway.org/

ConservationByGeography/NorthAmerica/

UnitedStates/edc/reportsdata/terrestrial/resilience/

Pages/default.aspx (accessed August 3, 2017).

Anderson, M.G.; Ferree, C.E. 2010. Conserving 

the stage: climate change and the geophysical 

underpinnings of species diversity. PLoS ONE. 

5(7): e11554. 

https://doi.org/10.1371/journal.pone.0011554.

Andreadis, K.M.; Lettenmaier, D.P. 2006. Trends 

in 20th century drought over the continental 

United States. Geophysical Research Letters. 

33(10): L10403. 

https://doi.org/10.1029/2006GL025711.

Andreozzi, H.A.; Pekins, P.J.; Langlais, M.L. 

2014. Impact of moose browsing on forest 

regeneration in northeast Vermont. Alces. 

50: 67-79.

Andresen, J.; Hilberg, S.; Kunkel, K. 2012. 

Historical climate and climate trends in the 

midwestern USA. In: Winkler, J.; Andresen, J.; 

Hatfield, J.; Bidwell, D.; Brown, D., coords. U.S. 

National Climate Assessment midwest technical 

input report. 18 p. Available at http://glisa.umich.

edu/media/files/NCA/MTIT_Historical.pdf

(accessed August 2, 2017). 

Auclair, A.N.; Heilman, W.E.; Brinkman, B. 2010. 

Predicting forest dieback in Maine, USA: a 

simple model based on soil frost and drought.

Canadian Journal of Forest Research. 40(4): 

687-702. https://doi.org/10.1139/X10-023.

Aust, W.M.; Blinn, C.R. 2004. Forestry best 

management practices for timber harvesting 

and site preparation in the eastern United 

States: an overview of water quality and 

productivity research during the past 20 years 

(1982-2002). Water, Air and Soil Pollution: 

Focus. 4(1): 5-36. https://doi.org/10.1023/b:

wafo.0000012828.33069.f6.
LITERATURE CITED

135

Austin, J.A.; Colman, S.M. 2007. Lake Superior 

summer water temperatures are increasing 

more rapidly than regional air temperatures: 

a positive ice-albedo feedback. Geophysical 

Research Letters. 34(6): L06604. https://doi.

org/10.1029/2006GL029021.

Babin-Fenske, J.; Anand, M. 2011. Agent-based 

simulation of effects of stress on forest tent 

caterpillar (Malacosoma disstria Hübner) 

population dynamics. Ecological Modelling. 

222(14): 2561-2569. 

https://doi.org/10.1016/j.ecolmodel.2011.04.014.

Bachelet, D.; Neilson, R.P.; Lenihan, J.M.; Drapek, 

R.J. 2001. Climate change effects on vegetation 

distribution and carbon budget in the United 

States. Ecosystems. 4(3): 164-185. 

https://doi.org/10.1007/s10021-001-0002-7.

Bartomeus, I.; Ascher, J.S.; Wagner, D.; Danforth, 

B.N.; Colla, S.; Kornbluth, S.; Winfree, R. 2011. 

Climate-associated phenological advances 

in bee pollinators and bee-pollinated plants.

Proceedings of the National Academy of 

Sciences. 108(51): 20645-20649. 

https://doi.org/10.1073/pnas.1115559108.

Baumflek, M.J.; Emery, M.R.; Ginger, C. 2010. 

Culturally and economically important 

nontimber forest products of northern Maine.

Gen. Tech. Rep. NRS-68. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 74 p.

Beier, C.M.; Signell, S.A.; Luttman, A.; DeGaetano, 

A.T. 2012a. High-resolution climate change 

mapping with gridded historical climate 

products. Landscape Ecology. 27(3): 327-342. 

https://doi.org/10.1007/s10980-011-9698-8.

Beier, C.M.; Stella, J.C.; Dovčiak, M.; McNulty, 

S.A. 2012b. Local climatic drivers of changes 

in phenology at a boreal-temperate ecotone 

in eastern North America. Climatic Change. 

115(2): 399-417. 

https://doi.org/10.1007/s10584-012-0455-z.

Berg, A.; Sheffield, J.; Milly, P.C.D. 2017. 

Divergent surface and total soil moisture 

projections under global warming. Geophysical 

Research Letters. 44(1): 236-244. 

https://doi.org/10.1002/2016gl071921.

Birdsey, R.A. 1996. Regional estimates of timber 

volume and forest carbon for fully stocked 

timberland, average management after final 

clearcut harvest. In: Sampson, R.N.; Hair, D., 

eds. Forests and global change, vol. 2: Forest 

management opportunities for mitigating carbon 

emissions. Washington, DC: American Forests: 

309-334.

Bishop, D.A.; Beier, C.M. 2013. Assessing 

uncertainty in high-resolution spatial climate 

data across the US Northeast. PLoS ONE. 8(8): 

e70260. 

https://doi.org/10.1371/journal.pone.0070260.

Bliss, J.C.; Kelly, E.C.; Abrams, J.; Bailey, C.; Dyer, 

J. 2009. Disintegration of the U.S. industrial 

forest estate: dynamics, trajectories, and 

questions. Small-scale Forestry. 9(1): 53-66. 

https://doi.org/10.1007/s11842-009-9101-7.

Blunden, J.; Arndt, D.S. 2012. State of the climate 

in 2011. Bulletin of the American Meteorological 

Society. 93(7): S1-S282. https://doi.org/10.1175/

2012BAMSStateoftheClimate.1.

Boer, G. 2009. Changes in interannual variability 

and decadal potential predictability under 

global warming. Journal of Climate. 22(11): 

3098-3109. 

https://doi.org/10.1175/2008JCLI2835.1.

Bohlen, P.J.; Scheu, S.; Hale, C.M.; McLean, M.A.; 

Migge, S.; Groffman, P.M.; Parkinson, D. 2004. 

Non-native invasive earthworms as agents 

of change in northern temperate forests.

Frontiers in Ecology and the Environment. 

2(8): 427-435. https://doi.org/10.1890/1540-

9295(2004)002[0427:nieaao]2.0.co;2.
LITERATURE CITED

136

Boon, J.D. 2012. Evidence of sea level acceleration 

at US and Canadian tide stations, Atlantic 

coast, North America. Journal of Coastal 

Research. 28(6): 1437-1445. https://doi.

org/10.2112/JCOASTRES-D-12-00102.1.

Boose, E.R.; Chamberlin, K.E.; Foster, D.R. 2001. 

Landscape and regional impacts of hurricanes 

in New England. Ecological Monographs. 

71(1): 27-48. https://doi.org/10.1890/0012-

9615(2001)071[0027:LARIOH]2.0.CO;2.

Boose, E.R.; Foster, D.R.; Fluet, M. 1994. 

Hurricane impacts to tropical and temperate 

forest landscapes. Ecological Monographs. 

64(4): 369-400. https://doi.org/10.2307/2937142.

Borken, W.; Matzner, E. 2009. Reappraisal 

of drying and wetting effects on C and N 

mineralization and fluxes in soils. Global 

Change Biology. 15(4): 808-824. 

https://doi.org/10.1111/j.1365-2486.2008.01681.x.

Bormann, F.H.; Likens, G.E. 1979. Catastrophic 

disturbance and the steady state in northern 

hardwood forests: A new look at the role 

of disturbance in the development of forest 

ecosystems suggests important implications 

for land-use policies. American Scientist. 67(6): 

660-669.

Boucher, D.; Rodick, C.; Bailey, J.; Snitzer, J.; 

Kyde, K.; Prudden, B. 2005. Hurricane Isabel 

and the forests of the Mid-Atlantic Piedmont 

and Blue Ridge: short-term impacts and 

long-term implications. In: Sellner, K.G., ed. 

Hurricane Isabel in perspective. CRC Publ. 

05-160. Edgewater, MD: Chesapeake Research 

Consortium: 201-208. http://www.chesapeake.

org/pubs/Isabel/Boucher%20et%20al.pdf.

Boulfroy, E.; Forget, E.; Hofmeyer, P.V.; Kenefic, 

L.S.; Larouche, C.; Lessard, G.; Lussier, J.-

M.; Pinto, F.; Ruel, J.-C.; Weiskittel, A. 2012. 

Silvicultural guide for northern white-cedar 

(eastern white cedar). Gen. Tech. Rep. 

NRS-98. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 74 p. 

https://doi.org/10.2737/NRS-GTR-98.

Bowker, J.M.; Askew, A.E. 2013. Outlook for 

outdoor recreation in the northern United 

States. A technical document supporting 

the Northern Forest Futures project with 

projections through 2060. Gen. Tech. 

Rep. NRS-120. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, 

Northern Research Station. 62 p.

Bradley, B.A.; Wilcove, D.S.; Oppenheimer, 

M. 2010. Climate change increases risk of 

plant invasion in the eastern United States.

Biological Invasions. 12(6): 1855-1872. 

https://doi.org/10.1007/s10530-009-9597-y.

Bradley, N.L.; Leopold, A.C.; Ross, J.; Huffaker, 

W. 1999. Phenological changes reflect climate 

change in Wisconsin. Proceedings of the 

National Academy of Sciences of the United 

States of America. 96(17): 9701-9704. 

https://doi.org/10.1073/pnas.96.17.9701.

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.

Brandt, L.A.; Derby Lewis, A.; Scott, L.; Darling, 

L.; Fahey, R.; Iverson, L.; Nowak, D.J.; Bodine, 

A.; Bell, A.; Still, S.; Butler, P.R.; Dierich, A.; 

Handler, S.D.; Janowiak, M.K.; Matthews, S.; 

Miesbauer, J.; Peters, M.; Prasad, A.M.; Shannon, 

P.D.; Stotz, D.; Swanston, C.W. 2017. Chicago 

Wilderness region urban forest vulnerability 

assessment and synthesis: a report from the 

Urban Forestry Climate Change Response 

Framework Chicago Wilderness pilot project.

Gen. Tech. Rep. NRS-168. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 142 p. 

https://doi.org/10.2737/NRS-GTR-168.

Brown, P.J.; Bradley, R.S.; Keimig, F.T. 2010. 

Changes in extreme climate indices for the 

northeastern United States, 1870-2005. Journal 

of Climate. 23(24): 6555-6572. 

https://doi.org/10.1175/2010jcli3363.1.
LITERATURE CITED

137

Brown, P.J.; DeGaetano, A.T. 2011. A paradox 

of cooling winter soil surface temperatures 

in a warming northeastern United States.

Agricultural and Forest Meteorology. 

151(7): 947-956. 

https://doi.org/10.1016/j.agrformet.2011.02.014.

Bryan, A.M.; Karmalkar, A.; Coffel, E.; Ning, L.; 

Horton, R.; Demaria, E.; Fan, F.; Bradley, R.S.; 

Palmer, R. 2015. Chapter 1: Climate change in 

the northeast and midwest United States. In: 

Staudinger, M.D.; Morelli, T.L.; Bryan, A.M., 

eds. Integrating climate change into northeast and 

midwest state Wildlife Action Plans. Amherst, 

MA: U.S. Department of the Interior, U.S. 

Geological Survey, Northeast Climate Science 

Center: 1-57.

Brzostek, E.R.; Blair, J.M.; Dukes, J.S.; Frey, S.D.; 

Hobbie, S.E.; Melillo, J.M.; Mitchell, R.J.; 

Pendall, E.; Reich, P.B.; Shaver, G.R. 2012. 

The effect of experimental warming and 

precipitation change on proteolytic enzyme 

activity: Positive feedbacks to nitrogen 

availability are not universal. Global Change 

Biology. 18(8): 2617-2625. 

https://doi.org/10.1111/j.1365-2486.2012.02685.x.

Buonaiuto, F.; Patrick, L.; Gornitz, V.; Hartig, E.; 

Leichenko, R.; Vancura, P. 2010. Chapter 5: 

Coastal zones. In: Rosenzweig, C.; Solecki, 

W.; DeGaetano, A.; O’Grady, M.; Hassol, S.; 

Grabhorn, P., eds. Integrated assessment for 

effective climate change adaptation strategies in 

New York State. Albany, NY: New York State 

Energy Research and Development Authority: 

122-162.

Burakowski, E.A.; Wake, C.P.; Braswell, B.; Brown, 

D.P. 2008. Trends in wintertime climate in the 

northeastern United States: 1965-2005.

Journal of Geophysical Research: Atmospheres 

(1984-2012). 113(D20).

Butler, B.J. 2008. Family forest owners of the 

United States, 2006. Gen. Tech. Rep. NRS-27. 

Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research 

Station. 72 p. 

https://doi.org/10.2737/NRS-RB-27.

Butler, B.J.; Barnett, C.J.; Crocker, S.J.; Domke, 

G.M.; Gormanson, D.; Hill, W.N.; Kurtz, C.M.; 

Lister, T.; Martin, C.; Miles, P.D.; Morin, R.; 

Moser, W.K.; Nelson, M.D.; O’Connell, B.; 

Payton, B.; Perry, C.H.; Piva, R.J.; Riemann, R.; 

Woodall, C.W. 2011. The forests of southern 

New England, 2007: a report on the forest 

resources of Connecticut, Massachusetts, and 

Rhode Island. Resour. Bull. NRS-55. Newtown 

Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 48 p. 

https://doi.org/10.2737/NRS-RB-55.

Butler, B.J.; Crocker, S.J.; Domke, G.M.; Kurtz, 

C.M.; Lister, T.W.; Miles, P.D.; Morin, R.S.; 

Piva, R.J.; Riemann, R.; Woodall, C.W. 2015. 

The forests of southern New England, 2012.

Resour. Bull. NRS-97. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 42 p. 

https://doi.org/10.2737/NRS-RB-97.

Butler, B.J.; Hewes, J.H.; Dickinson, B.J.; 

Andrejczyk, K.; Butler, S.M.; MarkowskiLindsay, M. 2016. Family forest ownerships 

of the United States, 2013: findings from the 

USDA Forest Service’s National Woodland 

Owner Survey. Journal of Forestry. 114(6): 

638-647. https://doi.org/10.5849/jof.15-099.

Butler, C.J. 2003. The disproportionate effect of 

global warming on the arrival dates of shortdistance migratory birds in North America.

Ibis. 145: 484-495. 

https://doi.org/10.1046/j.1474-919X.2003.00193.x.

Camill, P.; Clark, J.S. 2000. Long-term 

perspectives on lagged ecosystem responses 

to climate change: permafrost in boreal 

peatlands and the grassland/woodland 

boundary. Ecosystems. 3(6): 534-544. 

https://doi.org/10.1007/s100210000047.

Campbell, J.L.; Driscoll, C.T.; Pourmokhtarian, A.; 

Hayhoe, K. 2011. Streamflow responses to past 

and projected future changes in climate at the 

Hubbard Brook Experimental Forest, New 

Hampshire, United States. Water Resources 

Research. 47(2): W02514. 

https://doi.org/10.1029/2010wr009438.
LITERATURE CITED

138

Campbell, J.L.; Mitchell, M.J.; Groffman, P.M.; 

Christenson, L.M.; Hardy, J.P. 2005. Winter in 

northeastern North America: a critical period 

for ecological processes. Frontiers in Ecology 

and the Environment. 3(6): 314-322. https://

doi.org/10.1890/1540-9295(2005)003[0314:

WINNAA]2.0.CO;2.

Campbell, J.L.; Ollinger, S.V.; Flerchinger, 

G.N.; Wicklein, H.; Hayhoe, K.; Bailey, A.S. 

2010. Past and projected future changes in 

snowpack and soil frost at the Hubbard Brook 

Experimental Forest, New Hampshire, USA.

Hydrological Processes. 24(17): 2465-2480. 

https://doi.org/10.1002/hyp.7666.

Campbell, J.L.; Rustad, L.E.; Boyer, E.W.; 

Christopher, S.F.; Driscoll, C.T.; Fernandez, 

I.J.; Groffman, P.M.; Houle, D.; Kiekbusch, 

J.; Magill, A.H.; Mitchell, M.J.; Ollinger, 

S.V. 2009. Consequences of climate change 

for biogeochemical cycling in forests of 

northeastern North America. Canadian Journal 

of Forest Research. 39(2): 264-284. 

https://doi.org/10.1139/x08-104.

Castellano, C.M. 2012. Synoptic and mesoscale 

aspects of ice storms in the northeastern US.

Albany, NY: State University of New York. 

Master’s thesis.

Center for Climatic Research. 2016. Dynamical 

downscaling for the Midwest and Great Lakes 

basin. Madison, WI: University of WisconsinMadison, Center for Climatic Research, the 

Nelson Institute for Environmental Studies. 

Available at http://www.nelson.wisc.edu/ccr/

resources/dynamical-downscaling/index.php.

Center for Climatic Research. 2017. LCC statistical 

downscaling. Madison, WI: University of 

Wisconsin-Madison, Center for Climatic 

Research, the Nelson Institute for Environmental 

Studies. Available at http://nelson.wisc.edu/ccr/

resources/LCC/index.php (accessed July 10, 

2017).

Changnon, S.A. 2003. Characteristics of 

ice storms in the United States. Journal of 

Applied Meteorology. 42(5): 630-639. 

https://doi.org/10.1175/1520-

0450(2003)042<0630:COISIT>2.0.CO;2.

Cheng, C.; Auld, H.; Li, G.; Klaassen, J.; Li, Q. 

2007. Possible impacts of climate change on 

freezing rain in south-central Canada using 

downscaled future climate scenarios. Natural 

Hazards & Earth System Sciences. 7(1). 

https://doi.org/10.5194/nhess-7-71-2007.

Cheng, C.S.; Li, G.; Auld, H. 2011. Possible 

impacts of climate change on freezing rain 

using downscaled future climate scenarios: 

updated for eastern Canada. AtmosphereOcean. 49(1): 8-21. 

https://doi.org/10.1080/07055900.2011.555728.

Cherkauer, K.A.; Sinha, T. 2010. Hydrologic 

impacts of projected future climate change 

in the Lake Michigan region. Journal of Great 

Lakes Research. 36: 33-50. 

https://doi.org/10.1016/j.jglr.2009.11.012.

Choat, B.; Jansen, S.; Brodribb, T.J.; Cochard, 

H.; Delzon, S.; Bhaskar, R.; Bucci, S.J.; Feild, 

T.S.; Gleason, S.M.; Hacke, U.G. 2012. Global 

convergence in the vulnerability of forests to 

drought. Nature. 491: 752-756. 

https://doi.org/10.1038/nature11688.

Church, J.A.; White, N.J.; Aarup, T.; Wilson, W.S.; 

Woodworth, P.L.; Domingues, C.M.; Hunter, J.R.; 

Lambeck, K. 2008. Understanding global sea 

levels: past, present and future. Sustainability 

Science. 3(1): 9-22. 

https://doi.org/10.1007/s11625-008-0042-4.

Clark, J.S.; Iverson, L.R.; Woodall, C.W.; Allen, 

C.D.; Bell, D.M.; Bragg, D.; D’Amato, A.W.; 

Davis, F.W.; Hersh, M.; Ibanez, I.; Jackson, S.T.; 

Matthews, S.; Pederson, N.; Peters, M.; Schwartz, 

M.; Waring, K.; Zimmermann, N.E. 2016. 

The impacts of increasing drought on forest 

dynamics, structure, and biodiversity. Global 

Change Biology. 

https://doi.org/10.1111/gcb.13160:24.
LITERATURE CITED

139

Clean Air Act of 1970, as amended August 1977;

42 U.S.C. s/s 7401 et seq.

Cleland, D.T.; Freeouf, J.A.; Keys, J.E., Jr.; 

Nowacki, G.J.; Carpenter, C.; McNab, W.H. 

2007. Ecological subregions: sections and 

subsections for the conterminous United 

States. Sloan, A.M., cartog. Gen. Tech. Report 

WO-76D. Washington, DC: U.S. Department of 

Agriculture, Forest Service, Washington Office. 

[1:3,500,000]. [CD-ROM]. Available at 

https://www.treesearch.fs.fed.us/pubs/48672.

Climate Wizard. 2014. Climate Wizard 

Custom analysis application. In: The Nature 

Conservancy, University of Washington, 

University of Southern Mississippi, Climate 

Central, and Santa Clara University. Available at 

www.climatewizardcustom.org (accessed 2014).

Cogbill, C.V.; Burk, J.; Motzkin, G. 2002. The 

forests of presettlement New England, 

USA: spatial and compositional patterns 

based on town proprietor surveys. Journal of 

Biogeography. 29(10-11): 1279-1304. 

https://doi.org/10.1046/j.1365-2699.2002.00757.x.

Cogbill, C.V.; White, P.S. 1991. The latitudeelevation relationship for spruce-fir forest 

and treeline along the Appalachian Mountain 

chain. Vegetatio. 94(2): 153-175. 

https://doi.org/10.1007/BF00032629.

Cole, C.T.; Anderson, J.E.; Lindroth, R.L.; 

Waller, D.M. 2010. Rising concentrations of 

atmospheric CO2 have increased growth in 

natural stands of quaking aspen (Populus 

tremuloides). Global Change Biology. 16(8): 

2186-2197. 

https://doi.org/10.1111/j.1365-2486.2009.02103.x.

Collins, M.J. 2009. Evidence for changing flood 

risk in New England since the late 20th 

century. JAWRA Journal of the American Water 

Resources Association. 45: 279-290. 

https://doi.org/10.1111/j.1752-1688.2008.00277.x.

Comer, P.; Faber-Langendoen, D.; Evans, R.; 

Gawler, S.; Josse, C.; Kittel, G.; Menard, S.; 

Pyne, M.; Reid, M.; Schulz, K.; Snow, K.; 

Teague, J. 2003. Ecological systems of the 

United States: a working classification of U.S. 

terrestrial systems. Arlington, VA: NatureServe. 

Available at http://explorer.natureserve.org/

servlet/NatureServe?init=Ecol (accessed 

January 30, 2015).

Coniglio, M.C.; Stensrud, D.J. 2004. Interpreting 

the climatology of derechos. Weather and 

Forecasting. 19(3): 595-605. https://doi.

org/10.1175/1520-0434(2004)019<0595:

ITCOD>2.0.CO;2.

Cook, J.; Oreskes, N.; Doran, P.T.; Anderegg, 

W.R.L.; Verheggen, B.; Maibach, E.W.; Carlton, 

J.S.; Lewandowsky, S.; Skuce, A.G.; Green, 

S.A.; Nuccitelli, D.; Jacobs, P.; Richardson, 

M.; Winkler, B.; Painting, R.; Rice, K. 2016. 

Consensus on consensus: a synthesis of 

consensus estimates on human-caused global 

warming. Environmental Research Letters. 11(4). 

https://doi.org/10.1088/1748-9326/11/4/048002.

Coumou, D.; Rahmstorf, S. 2012. A decade of 

weather extremes. Nature Climate Change. 2: 

491-496. https://doi.org/10.1038/nclimate1452.

Couture, J.J.; Meehan, T.D.; Kruger, E.L.; Lindroth, 

R.L. 2015. Insect herbivory alters impact of 

atmospheric change on northern temperate 

forests. Nature Plants. 1: 15016. 

https://doi.org/10.1038/nplants.2015.16.

D’Amato, A.W.; Bradford, J.B.; Fraver, S.; Palik, 

B.J. 2011. Forest management for mitigation 

and adaptation to climate change: insights 

from long-term silviculture experiments. Forest 

Ecology and Management. 262(5): 803-816. 

https://doi.org/10.1016/j.foreco.2011.05.014.

Dai, A.; Trenberth, K.E.; Qian, T. 2004. A global 

dataset of Palmer Drought Severity Index for 

1870-2002: relationship with soil moisture 

and effects of surface warming. Journal of 

Hydrometeorology. 5(1117-1129). 

https://doi.org/10.1175/JHM-386.1.
LITERATURE CITED

140

Dale, V.H.; Joyce, L.A.; McNulty, S.; Neilson, 

R.P.; Ayres, M.P.; Flannigan, M.D.; Hanson, 

P.J.; Irland, L.C.; Lugo, A.E.; Peterson, C.J.; 

Simberloff, D.; Swanson, F.J.; Stocks, B.J.; 

Wotton, B.M. 2001. Climate change and 

forest disturbances. BioScience. 51(9): 

723-734. https://doi.org/10.1641/0006-

3568(2001)051[0723:CCAFD]2.0.CO;2.

Daly, C.; Halbleib, M.; Smith, J.I.; Gibson, 

W.P.; Doggett, M.K.; Taylor, G.H.; Curtis, J.; 

Pasteris, P.P. 2008. Physiographically sensitive 

mapping of climatological temperature and 

precipitation across the conterminous United 

States. International Journal of Climatology. 

28(15): 2031-2064. 

https://doi.org/10.1002/joc.1688.

Danco, J.F.; DeAngelis, A.M.; Raney, B.K.; 

Broccoli, A.J. 2016. Effects of a warming 

climate on daily snowfall events in the 

Northern Hemisphere. Journal of Climate. 

29(17): 6295-6318. 

https://doi.org/10.1175/JCLI-D-15-0687.1.

Davenport, M.A.; Sames, A.; Bussey, J.; Pradhanaga, 

A.; Emery, M.; Jakes, P. 2013. Community 

readiness for environmental change: a 

pilot study in a Minnesota forest-associated 

community. Staff Paper Series No. 223. St. Paul, 

MN: University of Minnesota, College of Food, 

Agricultural and Natural Resource Sciences, 

Department of Forest Resources. 22 p. Available 

at http://www.forestry.umn.edu/prod/groups/

cfans/@pub/@cfans/@forestry/documents/asset/

cfans_asset_455326.pdf.

Davis, M.B. 1983. Quaternary history of 

deciduous forests of eastern North-America 

and Europe. Annals of the Missouri Botanical 

Garden. 70(3): 550-563. 

https://doi.org/10.2307/2992086.

Davis, M.B. 1986. Climatic instability, time, lags, 

and community disequilibrium. New York, NY: 

Harper & Row.

Davis, M.B. 1989. Lags in vegetation response to 

greenhouse warming. Climatic Change. 15(1-2): 

75-82. https://doi.org/10.1007/BF00138846.

Davis, M.B.; Shaw, R.G.; Etterson, J.R. 2005. 

Evolutionary responses to changing climate.

Ecology. 86(7): 1704-1714. 

https://doi.org/10.1890/03-0788.

Dawson, J.; Scott, D. 2013. Managing for 

climate change in the alpine ski sector.

Tourism Management. 35: 244-254. 

https://doi.org/10.1016/j.tourman.2012.07.009.

Dawson, J.; Scott, D.; Havitz, M. 2013. Skier 

demand and behavioural adaptation to climate 

change in the US Northeast. Leisure/Loisir. 

37(2): 127-143. 

https://doi.org/10.1080/14927713.2013.805037.

De Grandpré, L.; Pureswaran, D. 2013. Spruce 

budworm: Heading north? Branching out from 

the Canadian Forest Service, Laurentian Forestry 

Centre. No. 88. Available at http://www.cfs.nrcan.

gc.ca/publications/?id=35411 (accessed February 

1, 2016).

DeGaetano, A.T. 2009. Time-dependent changes in 

extreme-precipitation return-period amounts 

in the continental United States. Journal of 

Applied Meteorology & Climatology. 48(10). 

https://doi.org/10.1175/2009JAMC2179.1.

DeGaetano, A.T.; Allen, R.J. 2002. Trends in 

twentieth-century temperature extremes 

across the United States. Journal of Climate. 

15(22): 3188-3205. https://doi.org/10.1175/1520-

0442(2002)015<3188:TITCTE>2.0.CO;2.

Delworth, T.L.; Broccoli, A.J.; Rosati, A.; Stouffer, 

R.J.; Balaji, V.; Beesley, J.A.; Cooke, W.F.; 

Dixon, K.W.; Dunne, J.; Dunne, K.A.; Durachta, 

J.W.; Findell, K.L.; Ginoux, P.; Gnanadesikan, 

A.; Gordon, C.T.; Griffies, S.M.; Gudgel, 

R.; Harrison, M.J.; Held, I.M.; Hemler, R.S.; 

Horowitz, L.W.; Klein, S.A.; Knutson, T.R.; 

Kushner, P.J.; Langenhorst, A.R.; Lee, H.C.; 

Lin, S.J.; Lu, J.; Malyshev, S.L.; Milly, P.C.D.; 

Ramaswamy, V.; Russell, J.; Schwarzkopf, 

M.D.; Shevliakova, E.; Sirutis, J.J.; Spelman, 

M.J.; Stern, W.F.; Winton, M.; Wittenberg, A.T.; 

Wyman, B.; Zeng, F.; Zhang, R. 2006. GFDL’s 

CM2 global coupled climate models. Part I: 

Formulation and simulation characteristics.

Journal of Climate. 19(5): 643-674. 

https://doi.org/10.1175/JCLI3629.1.
LITERATURE CITED

141

Demaria, E.M.C.; Palmer, R.N.; Roundy, J.K. 

2016. Regional climate change projections of 

streamflow characteristics in the northeast and 

midwest U.S. Journal of Hydrology: Regional 

Studies. 5: 309-323. 

https://doi.org/10.1016/j.ejrh.2015.11.007.

Devaraju, N.; Bala, G.; Caldeira, K.; Nemani, R. 

2016. A model based investigation of the 

relative importance of CO2-fertilization, 

climate warming, nitrogen deposition and 

land use change on the global terrestrial 

carbon uptake in the historical period.

Climate Dynamics. 47(1): 173-190. 

https://doi.org/10.1007/s00382-015-2830-8.

Didham, R.K.; Tylianakis, J.M.; Hutchison, M.A.; 

Ewers, R.M.; Gemmell, N.J. 2005. Are invasive 

species the drivers of ecological change? Trends 

in Ecology & Evolution. 20(9): 470-474. 

https://doi.org/10.1016/j.tree.2005.07.006.

Dieleman, W.I.J.; Vicca, S.; Dijkstra, F.A.; 

Hagedorn, F.; Hovenden, M.J.; Larsen, K.S.; 

Morgan, J.A.; Volder, A.; Beier, C.; Dukes, J.S.; 

King, J.; Leuzinger, S.; Linder, S.; Luo, Y.; Oren, 

R.; De Angelis, P.; Tingey, D.; Hoosbeek, M.R.; 

Janssens, I.A. 2012. Simple additive effects are 

rare: a quantitative review of plant biomass 

and soil process responses to combined 

manipulations of CO2 and temperature. Global 

Change Biology. 18(9): 2681-2693. 

https://doi.org/10.1111/j.1365-2486.2012.02745.x.

Dietze, M.C.; Moorcroft, P.R. 2011. Tree mortality 

in the eastern and central United States: 

patterns and drivers. Global Change Biology. 

17(11): 3312-3326. 

https://doi.org/10.1111/j.1365-2486.2011.02477.x.

Diffenbaugh, N.S.; Pal, J.S.; Trapp, R.J.; Giorgi, 

F. 2005. Fine-scale processes regulate the 

response of extreme events to global climate 

change. Proceedings of the National Academy 

of Sciences of the United States of America. 

102(44): 15774-15778. 

https://doi.org/10.1073/pnas.0506042102.

Dobrowski, S.Z.; Abatzoglou, J.; Swanson, A.K.; 

Greenberg, J.A.; Mynsberge, A.R.; Holden, Z.A.; 

Schwartz, M.K. 2013. The climate velocity of 

the contiguous United States during the 20th 

century. Global Change Biology. 19(1): 241-251. 

https://doi.org/10.1111/gcb.12026.

Dou, H.; Jiang, G.; Stott, P.; Piao, R. 2013. Climate 

change impacts population dynamics and 

distribution shift of moose (Alces alces) in 

Heilongjiang Province of China. Ecological 

Research. 28: 625-632. 

https://doi.org/10.1007/s11284-013-1054-9.

Dragoni, D.; Rahman, A.F. 2012. Trends in fall 

phenology across the deciduous forests of 

the eastern USA. Agricultural and Forest 

Meteorology. 157: 96-105. 

https://doi.org/10.1016/j.agrformet.2012.01.019.

Drever, C.R.; Bergeron, Y.; Drever, M.C.; Flannigan, 

M.; Logan, T.; Messier, C. 2009. Effects of 

climate on occurrence and size of large fires 

in a northern hardwood landscape: historical 

trends, forecasts, and implications for climate 

change in Témiscamingue, Québec. Applied 

Vegetation Science. 12(3): 261-272. https://doi.

org/10.1111/j.1654-109X.2009.01035.x.

Driscoll, C.T.; Buonocore, J.; Fakhraei, H.; Lambert, 

K.F. 2014. Co-benefits of carbon standards. 

Part 1: Air pollution changes under different 

111d options for existing power plants.

A report of the Science Policy Exchange.

Syracuse, NY: Syracuse University and 

Cambridge, MA: Harvard University. 34 p.

Ducey, M.J.; Johnson, K.M.; Belair, E.P.; Mockrin, 

M.H. 2016. Forests in flux: the effects of 

demographic change on forest cover in New 

England and New York. National Issue Brief 

#99. Durham, NH: University of New Hampshire, 

Carsey School of Public Policy. 10 p.
LITERATURE CITED

142

Dukes, J.S.; Pontius, J.; Orwig, D.; Garnas, 

J.R.; Rodgers, V.L.; Brazee, N.; Cooke, B.; 

Theoharides, K.A.; Stange, E.E.; Harrington, R.; 

Ehrenfeld, J.; Gurevitch, J.; Lerdau, M.; Stinson, 

K.; Wick, R.; Ayres, M. 2009. Responses of 

insect pests, pathogens, and invasive plant 

species to climate change in the forests of 

northeastern North America: What can we 

predict? Canadian Journal of Forest Research. 

39(2): 231-248. https://doi.org/10.1139/X08-171.

Duveneck, M.J.; Scheller, R.M. 2015. Climatesuitable planting as a strategy for maintaining 

forest productivity and functional diversity.

Ecological Applications. 25(6): 1653-1668. 

https://doi.org/10.1890/14-0738.1.

Duveneck, M.J.; Scheller, R.M.; White, M.A.; 

Handler, S.D.; Ravenscroft, C. 2014. Climate 

change effects on northern Great Lake 

(USA) forests: a case for preserving diversity.

Ecosphere. 5(2): art23. 

https://doi.org/10.1890/ES13-00370.1.

Duveneck, M.J.; Thompson, J.R.; Gustafson, 

E.J.; Liang, Y.; Bruijn, A.M. 2017. Recovery 

dynamics and climate change effects to future 

New England forests. Landscape Ecology. 32(7): 

1385-1397. 

https://doi.org/10.1007/s10980-016-0415-5.

Easterling, D.R.; Evans, J.; Groisman, P.Y.; Karl, 

T.; Kunkel, K.E.; Ambenje, P. 2000. Observed 

variability and trends in extreme climate 

events: a brief review*. Bulletin of the American 

Meteorological Society. 81(3): 417-425. https://

doi.org/10.1175/1520-0477(2000)081<0417:

OVATIE>2.3.CO;2.

Eisenbies, M.; Aust, W.; Burger, J.; Adams, M.B. 

2007. Forest operations, extreme flooding 

events, and considerations for hydrologic 

modeling in the Appalachians—a review.

Forest Ecology and Management. 242(2): 77-98. 

https://doi.org/10.1016/j.foreco.2007.01.051.

Eisenhauer, N.; Fisichelli, N.A.; Frelich, L.E.; 

Reich, P.B. 2012. Interactive effects of global 

warming and ‘global worming’ on the initial 

establishment of native and exotic herbaceous 

plant species. Oikos. 121(7): 1121-1133. 

https://doi.org/10.1111/j.1600-0706.2011.19807.x.

Elkinton, J.; Liebhold, A. 1990. Population 

dynamics of gypsy moth in North America.

Annual Review of Entomology. 35(1): 

571-596. https://doi.org/10.1146/annurev.

en.35.010190.003035.

Elliott, K.J.; Vose, J.M.; Jackson, W.A. 2013. 

Effects of future sulfate and nitrate deposition 

scenarios on Linville Gorge and Shining Rock 

Wildernesses. Gen. Tech. Rep. SRS-GTR-181. 

Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 30 p. 

Ellwood, E.R.; Temple, S.A.; Primack, R.B.; 

Bradley, N.L.; Davis, C.C. 2013. Recordbreaking early flowering in the eastern United 

States. PLoS ONE. 8(1): e53788. 

https://doi.org/10.1371/journal.pone.0053788.

Elmqvist, T.; Folke, C.; Nyström, M.; Peterson, 

G.; Bengtsson, J.; Walker, B.; Norberg, J. 

2003. Response diversity, ecosystem change, 

and resilience. Frontiers in Ecology and 

the Environment. 1(9): 488-494. https://doi.

org/10.1890/1540-9295(2003)001[0488:

RDECAR]2.0.CO;2.

Emery, M.R. 2002. Historical overview of 

nontimber forest product uses in the 

northeastern United States. In: Jones, E.T.; 

McLain, R.J.; Weigand, J., eds. Nontimber forest 

products in the United States. Lawrence, KS: 

University Press of Kansas: 3-25.

Emery, M.R.; Barron, E.S. 2010. Using local 

ecological knowledge to assess morel decline in 

the US Mid-Atlantic region. Economic Botany. 

64(3): 205-216. 

https://doi.org/10.1007/s12231-010-9127-y.
LITERATURE CITED

143

Faccio, S.D. 2003. Effects of ice storm-created 

gaps on forest breeding bird communities 

in central Vermont. Forest Ecology and 

Management. 186(1-3): 133-145. 

https://doi.org/10.1016/S0378-1127(03)00232-9.

Fahey, R.T.; Bialecki, M.B.; Carter, D.R. 2013. 

Tree growth and resilience to extreme 

drought across an urban land-use gradient.

Arboriculture & Urban Forestry. 39(6).

Fairley, S.; Ruhanen, L.; Lovegrove, H. 2015. On 

frozen ponds: the impact of climate change 

on hosting pond hockey tournaments. Sport 

Management Review. 18(4): 618-626. 

https://doi.org/10.1016/j.smr.2015.03.001.

Fan, F.; Bradley, R.S.; Rawlins, M.A. 2014. Climate 

change in the northeastern US: Regional 

climate model validation and climate change 

projections. Climate Dynamics. 43(1-2): 

145-161. 

https://doi.org/10.1007/s00382-014-2198-1.

Fernandez, I.J.; Rustad, L.E.; Norton, S.A.; 

Kahl, J.S.; Cosby, B.J. 2003. Experimental 

acidification causes soil base-cation depletion 

at the Bear Brook watershed in Maine. Soil 

Science Society of America Journal. 67(6): 

1909-1919. 

https://doi.org/10.2136/sssaj2003.1909.

Fernandez, I.J.; Schmitt, C.V.; Birkel, S.D.; 

Stancioff, E.; Pershing, A.J.; Kelley, J.T.; Runge, 

J.A.; Jacobson, G.L.; Mayewski, P.A. 2015. 

Maine’s climate future: 2015 update. Orono, 

ME: University of Maine. 22 p. Available at 

http://climatechange.umaine.edu/research/

publications/climate-future (accessed 

December 15, 2017).

Fernholz, K.; Bowyer, J.L.; Howe, J. 2007. TIMOs 

& REITs: what, why & how they might impact 

sustainable forestry. Minneapolis, MN: Dovetail 

Partners, Inc.

Finzi, A.C.; Sinsabaugh, R.L.; Long, T.M.; Osgood, 

M.P. 2006. Microbial community responses to 

atmospheric carbon dioxide enrichment in a 

warm-temperate forest. Ecosystems. 9: 

215-226. 

https://doi.org/10.1007/s10021-005-0078-6.

Fisichelli, N.; Frelich, L.E.; Reich, P.B. 2012. 

Sapling growth responses to warmer 

temperatures ‘cooled’ by browse pressure.

Global Change Biology. 18(11): 3455-3463. 

https://doi.org/10.1111/j.1365-2486.2012.02785.

Fisichelli, N.; Wright, A.; Rice, K.; Mau, A.; 

Buschena, C.; Reich, P.B. 2014a. First-year 

seedlings and climate change: species-specific 

responses of 15 North American tree species.

Oikos. 123(11): 1331-1340. 

https://doi.org/10.1111/oik.01349.

Fisichelli, N.A.; Frelich, L.E.; Reich, P.B. 2014b. 

Temperate tree expansion into adjacent 

boreal forest patches facilitated by warmer 

temperatures. Ecography. 37(2): 152-161. 

https://doi.org/10.1111/j.1600-0587.2013.00197.x.

Fisichelli, N.A.; Schuurman, G.W.; Monahan, W.B.; 

Ziesler, P.S. 2015. Protected area tourism 

in a changing climate: Will visitation at US 

national parks warm up or overheat? PLoS 

ONE. 10(6): e0128226. 

https://doi.org/10.1371/journal.pone.0128226.

Fitzhugh, R.D.; Likens, G.E.; Driscoll, C.T.; 

Mitchell, M.J.; Groffman, P.M.; Fahey, T.J.; 

Hardy, J.P. 2003. Role of soil freezing events 

in interannual patterns of stream chemistry 

at the Hubbard Brook Experimental Forest, 

New Hampshire. Environmental Science & 

Technology. 37(8): 1575-1580. 

https://doi.org/10.1021/es026189r.

Flannigan, M.D.; Krawchuk, M.A.; de Groot, 

W.J.; Wotton, B.M.; Gowman, L.M. 2009. 

Implications of changing climate for global 

wildland fire. International Journal of Wildland 

Fire. 18(5): 483-507. 

https://doi.org/10.1071/WF08187.
LITERATURE CITED

144

Foster, D.; Donahue, B.; Kitteredge, D.B.; Lambert, 

K.F.; Hunter, M.L.; Hall, B.R.; Irland, L.C.; 

Lilieholm, R.J.; Orwig, D.A.; D’Amato, A.W.; 

Colburn, E.A.; Thompson, J.R.; Levitt, J.N.; 

Ellison, A.M.; Keeton, W.S.; Aber, J.D.; Cogbill, 

C.V.; Driscoll, C.T.; Fahey, T.J.; Hart, C.M. 

2010. Wildlands and woodlands: a vision for 

the New England landscape. Petersham, MA: 

Harvard University, Harvard Forest. 36 p.

Foster, D.R. 1992. Land-use history (1730-1990) 

and vegetation dynamics in central New 

England, USA. Journal of Ecology. 753-771. 

https://doi.org/10.2307/2260864.

Foster, D.R. 2006. Forests in time: the 

environmental consequences of 1,000 years of 

change in New England. New Haven, CT: Yale 

University Press.

Foster, D.R.; Boose, E.R. 1992. Patterns of forest 

damage resulting from catastrophic wind in 

central New England, USA. Journal of Ecology. 

80(1): 79-98. https://doi.org/10.2307/2261065.

Foster, D.R.; Motzkin, G.; Slater, B. 1998. Land-use 

history as long-term broad-scale disturbance: 

regional forest dynamics in central New 

England. Ecosystems. 1(1): 96-119. 

https://doi.org/10.1007/s100219900008.

Foster, D.R.; O’Keefe, J.F. 2000. New England 

forests through time: insights from the 

Harvard Forest dioramas. Petersham, MA: 

Harvard University, Harvard Forest.

Foster, J.R.; D’Amato, A.W. 2015. Montane forest 

ecotones moved downslope in northeastern 

USA in spite of warming between 1984 and 

2011. Global Change Biology. 21(12): 

4497-4507. https://doi.org/10.1111/gcb.13046.

Francis, J.A.; Vavrus, S.J. 2012. Evidence linking 

arctic amplification to extreme weather in 

mid-latitudes. Geophysical Research Letters. 

39(6). https://doi.org/10.1029/2012GL051000.

Franks, P.J.; Adams, M.A.; Amthor, J.S.; Barbour, 

M.M.; Berry, J.A.; Ellsworth, D.S.; Farquhar, 

G.D.; Ghannoum, O.; Lloyd, J.; McDowell, 

N.; Norby, R.J.; Tissue, D.T.; von Caemmerer, 

S. 2013. Sensitivity of plants to changing 

atmospheric CO2 concentration: from the 

geological past to the next century. New 

Phytologist. 197(4): 1077-1094. 

https://doi.org/10.1111/nph.12104.

Fraver, S.; Seymour, R.S.; Speer, J.H.; White, A.S. 

2007. Dendrochronological reconstruction 

of spruce budworm outbreaks in northern 

Maine, USA. Canadian Journal of Forest 

Research. 37(3): 523-529. 

https://doi.org/10.1139/x06-251.

Frelich, L.E.; Peterson, R.O.; Dovčiak, M.; Reich, 

P.B.; Vucetich, J.A.; Eisenhauer, N. 2012. 

Trophic cascades, invasive species and bodysize hierarchies interactively modulate climate 

change responses of ecotonal temperate-boreal 

forest. Philosophical Transactions of the Royal 

Society B: Biological Sciences. 367(1605): 

2955-2961. 

https://doi.org/10.1098/rstb.2012.0235.

Frelich, L.E.; Reich, P.B. 2010. Will environmental 

changes reinforce the impact of global 

warming on the prairie-forest border of 

central North America? Frontiers in Ecology 

and the Environment. 8(7): 371-378. 

https://doi.org/10.1890/080191.

Frey, S.; Ollinger, S.; Nadelhoffer, K.; Bowden, R.; 

Brzostek, E.; Burton, A.; Caldwell, B.; Crow, 

S.; Goodale, C.; Grandy, A. 2014. Chronic 

nitrogen additions suppress decomposition 

and sequester soil carbon in temperate forests.

Biogeochemistry. 121(2): 305-316. 

https://doi.org/10.1007/s10533-014-0004-0.

Fridley, J.D. 2012. Extended leaf phenology 

and the autumn niche in deciduous forest 

invasions. Nature. 485(7398): 359-362. 

https://doi.org/10.1038/nature11056.
LITERATURE CITED

145

Friedland, A.J.; Gregory, R.A.; Karenlampi, L.; 

Johnson, A.H. 1984. Winter damage to foliage 

as a factor in red spruce decline. Canadian 

Journal of Forest Research. 14(6): 963-965. 

https://doi.org/10.1139/x84-173.

Frumhoff, P.C.; McCarthy, J.J.; Melillo, J.M.; 

Moser, S.C.; Wuebbles, D.J. 2007. Confronting 

climate change in the U.S. Northeast: science, 

impacts, and solutions. Synthesis report of 

the Northeast Climate Impacts Assessment 

(NECIA). Cambridge, MA: Union of Concerned 

Scientists. 160 p. Available at http://www.

northeastclimateimpacts.org/pdf/confrontingclimate-change-in-the-u-s-northeast.pdf (accessed 

April 28, 2014).

Fry, J.A.; Xian, G.; Jin, S.; Dewitz, J.A.; Homer, 

C.G.; Limin, Y.; Barnes, C.A.; Herold, N.D.; 

Wickham, J.D. 2011. Completion of the 

2006 National Land Cover Database for the 

conterminous United States. Photogrammetric 

Engineering and Remote Sensing. 77(9): 

858-864.

Fuller, J.L.; Foster, D.R.; McLachlan, J.S.; Drake, 

N. 1998. Impact of human activity on regional 

forest composition and dynamics in central 

New England. Ecosystems. 1(1): 76-95. 

https://doi.org/10.1007/s100219900007.

Galbraith, H. 2010. NEAFWA regional 

vulnerability assessment project—report 

1: Forming the expert panel. Plymouth, 

MA: Manomet Center for Conservation 

Sciences. Available at http://rcngrants.org/

sites/default/files/final_reports/RCN%202009-

01%20Final%20Report%20-%20NEreport%201

%20developing%20the%20expert%20panel.pdf

(accessed April 11, 2014).

Galbraith, H. 2011a. NEAFWA regional 

vulnerability assessment project—report 2: 

The habitat vulnerability model. Plymouth, 

MA: Manomet Center for Conservation 

Sciences. Available at http://rcngrants.org/

sites/default/files/final_reports/RCN%202009-

01%20Final%20Report%20-%20NEreport%20

2%20THE%20MODEL.pdf (accessed April 11, 

2014).

Galbraith, H. 2011b. Report to the NEAFWA 

vulnerability assessment expert panel: 

exposure information. Plymouth, MA: 

Manomet Center for Conservation Sciences. 

Available at http://rcngrants.org/sites/

default/files/final_reports/RCN%202009-

01%20Final%20Report%20-%20Northeastern%2

0exposure%20data.pdf (accessed April 11, 2014).

Galford, G.L.; Hoogenboom, A.; Carlson, S.; Ford, 

S.; Nash, J.; Palchak, E.; Pears, S.; Underwood, 

K.; Baker, D.V., eds. 2014. Considering 

Vermont’s future in a changing climate: the 

first Vermont climate assessment. Burlington, 

VT: University of Vermont, Gund Institute for 

Ecological Economics. 219 p. Available at http://

dev.vtclimate.org/wp-content/uploads/2014/04/

VCA2014_FullReport.pdf (accessed July 22, 

2014).

Gange, A.; Gange, E.; Sparks, T.; Boddy, L. 2007. 

Rapid and recent changes in fungal fruiting 

patterns. Science. 316(5821): 71. 

https://doi.org/10.1126/science.1137489.

Gauthier, S.; Bernier, P.; Burton, P.J.; Edwards, 

J.; Isaac, K.; Isabel, N.; Jayen, K.; Le Goff, 

H.; Nelson, E.A. 2014. Climate change 

vulnerability and adaptation in the managed 

Canadian boreal forest 1. Environmental 

Reviews. 22(3): 256-285. 

https://doi.org/10.1139/er-2013-0064.

Gavier-Pizarro, G.I.; Radeloff, V.C.; Stewart, S.I.; 

Huebner, C.D.; Keuler, N.S. 2010. Housing 

is positively associated with invasive exotic 

plant species richness in New England, USA.

Ecological Applications. 20(7): 1913-1925. 

https://doi.org/10.1890/09-2168.1.

Geisler, E.; Rittenhouse, C.D.; Rissman, A.R. 2016. 

Logger perceptions of seasonal environmental 

challenges facing timber operations in the 

upper Midwest, USA. Society & Natural 

Resources. 29(5): 540-555. 

https://doi.org/10.1080/08941920.2015.1107789.
LITERATURE CITED

146

Gellesch, E.; Hein, R.; Jaeschke, A.; Beierkuhnlein, 

C.; Jentsch, A. 2013. Biotic interactions in 

the face of climate change. In: Lüttge, U.; 

Beyschlag, W.; Francis, D.; Cushman, J., 

eds. Progress in botany 74. Berlin, Germany: 

Springer: 321-349. 

https://doi.org/10.1007/978-3-642-30967-0_12.

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.

Gillett, N.P.; Weaver, A.J.; Zwiers, F.W.; Flannigan, 

M.D. 2004. Detecting the effect of climate 

change on Canadian forest fires. Geophysical 

Research Letters. 31(18). 

https://doi.org/10.1029/2004gl020876.

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.

Glick, P.; Stein, B.A.; Edelson, N.A., eds. 2011. 

Scanning the conservation horizon: a guide 

to climate change vulnerability assessment.

Washington, DC: National Wildlife Federation. 

168 p. Available at http://www.nwf.org/

vulnerabilityguide.

Gough, C.M.; Vogel, C.S.; Schmid, H.P.; Curtis, 

P.S. 2008. Controls on annual forest carbon 

storage: lessons from the past and predictions 

for the future. Bioscience. 58(7): 609-622. 

https://doi.org/10.1641/B580708.

Gray, D.R. 2008. The relationship between climate 

and outbreak characteristics of the spruce 

budworm in eastern Canada. Climatic Change. 

87(3-4): 361-383. 

https://doi.org/10.1007/s10584-007-9317-5.

Grimm, E.C.; Jacobson, G.L., Jr. 1992. Fossilpollen evidence for abrupt climate changes 

during the past 18,000 years in eastern North 

America. Climate Dynamics. 6(3-4): 179-184. 

https://doi.org/10.1007/BF00193530.

Groffman, P.M.; Kareiva, P.; Carter, S.; Grimm, 

N.B.; Lawler, J.; Mack, M.; Matzek, V.; Tallis, H. 

2014. Chapter 8: Ecosystems, biodiversity, and 

ecosystem services. In: Melillo, J.M.; Richmond, 

T.C.; Yohe, G.W., eds. Climate change impacts 

in the United States: the Third National Climate 

Assessment. Washington, DC: U.S. Global 

Change Research Program: 195-219.

Groffman, P.M.; Rustad, L.E.; Templer, P.H.; 

Campbell, J.L.; Christenson, L.M.; Lany, N.K.; 

Socci, A.M.; Vadeboncoeur, M.A.; Schaberg, 

P.G.; Wilson, G.F. 2012. Long-term integrated 

studies show complex and surprising effects 

of climate change in the northern hardwood 

forest. BioScience. 62(12): 1056-1066. 

https://doi.org/10.1525/bio.2012.62.12.7.

Guilbert, J.; Betts, A.K.; Rizzo, D.M.; Beckage, 

B.; Bomblies, A. 2015. Characterization 

of increased persistence and intensity of 

precipitation in the northeastern United States.

Geophysical Research Letters. 42(6): 1888-1893. 

https://doi.org/10.1002/2015gl063124.

Guyette, R.P.; Thompson, F.R.; Whittier, J.; 

Stambaugh, M.C.; Dey, D.C. 2014. Future fire 

probability modeling with climate change data 

and physical chemistry. Forest Science. 60(5): 

862-870. https://doi.org/10.5849/forsci.13-108.

Hale, C.M.; Frelich, L.E.; Reich, P.B. 2006. 

Changes in hardwood forest understory 

plant communities in response to European 

earthworm invasions. Ecology. 87(7): 

1637-1649. https://doi.org/10.1890/0012-

9658(2006)87[1637:CIHFUP]2.0.CO;2.
LITERATURE CITED

147

Hall, S.J.; Learned, J.; Ruddell, B.; Larson, K.L.; 

Cavender-Bares, J.; Bettez, N.; Groffman, P.M.; 

Grove, J.M.; Heffernan, J.B.; Hobbie, S.E. 2016. 

Convergence of microclimate in residential 

landscapes across diverse cities in the United 

States. Landscape Ecology. 31(1): 101-117. 

https://doi.org/10.1007/s10980-015-0297-y.

Hallett, R.A.; Bailey, S.W.; Horsley, S.B.; Long, 

R.P. 2006. Influence of nutrition and stress 

on sugar maple at a regional scale. Canadian 

Journal of Forest Research. 36(9): 2235-2246. 

https://doi.org/10.1139/x06-120.

Hamburg, S.P.; Vadeboncoeur, M.A.; Richardson, 

A.D.; Bailey, A.S. 2013. Climate change at 

the ecosystem scale: a 50-year record in New 

Hampshire. Climatic Change. 116(3-4): 

457-477. 

https://doi.org/10.1007/s10584-012-0517-2.

Hansen, J.; Sato, M.; Ruedy, R. 2012. Perception 

of climate change. Proceedings of the National 

Academy of Sciences. 109(37): E2415-E2423. 

https://doi.org/10.1073/pnas.1205276109.

Hanson, P.J.; Weltzin, J.F. 2000. Drought 

disturbance from climate change: response of 

United States forests. The Science of the Total 

Environment. 262(3): 205-220. 

https://doi.org/10.1016/s0048-9697(00)00523-4.

Hardy, J.; Groffman, P.; Fitzhugh, R.; Henry, K.; 

Welman, A.; Demers, J.; Fahey, T.; Driscoll, 

C.; Tierney, G.; Nolan, S. 2001. Snow depth 

manipulation and its influence on soil frost 

and water dynamics in a northern hardwood 

forest. Biogeochemistry. 56(2): 151-174. 

https://doi.org/10.1023/A:1013036803050.

Hayhoe, K. 2011. 1/8 degree CONUS 

daily downscaled climate projections 

*PROVISIONAL*. USGS Geodata Portal 

[Online]: Texas Tech University, Center for 

Integrated Data Analytics. Available at 

http://cida.usgs.gov/thredds/catalog.html

(accessed July 11, 2011).

Hayhoe, K.; Wake, C.P.; Huntington, T.G.; Luo, 

L.F.; Schwartz, M.D.; Sheffield, J.; Wood, E.; 

Anderson, B.; Bradbury, J.; DeGaetano, A.; Troy, 

T.J.; Wolfe, D. 2007. Past and future changes in 

climate and hydrological indicators in the US 

Northeast. Climate Dynamics. 28(4): 381-407. 

https://doi.org/10.1007/s00382-006-0187-8.

He, H.S.; Mladenoff, D.J. 1999. Spatially explicit 

and stochastic simulation of forest-landscape 

fire disturbance and succession. Ecology. 

80(1): 81-99. https://doi.org/10.1890/0012-

9658(1999)080[0081:seasso]2.0.co;2.

Heilman, W.E.; Tang, Y.; Luo, L.; Zhong, S.; 

Winkler, J.; Bian, X. 2015. Potential climate 

change impacts on fire weather in the United 

States. Fire Management Today. 74(3): 22-27.

Heimpel, G.E.; Frelich, L.E.; Landis, D.A.; Hopper, 

K.R.; Hoelmer, K.A.; Sezen, Z.; Asplen, M.K.; 

Wu, K.M. 2010. European buckthorn and 

Asian soybean aphid as components of an 

extensive invasional meltdown in North 

America. Biological Invasions. 12(9): 

2913-2931. 

https://doi.org/10.1007/s10530-010-9736-5.

Hellmann, J.J.; Byers, J.E.; Bierwagen, B.G.; 

Dukes, J.S. 2008. Five potential consequences 

of climate change for invasive species.

Conservation Biology. 22(3): 534-543. 

https://doi.org/10.1111/j.1523-1739.2008.00951.x.

Herring, S.C.; Hoerling, M.P.; Peterson, T.C.; Stott, 

P.A. 2014. Explaining extreme events of 2013 

from a climate perspective. Bulletin of the 

American Meteorological Society. 95(9): 

S1-S104. 

https://doi.org/10.1175/1520-0477-95.9.s1.1.

Hicke, J.A.; Allen, C.D.; Desai, A.R.; Dietze, M.C.; 

Hall, R.J.; Hogg, E.H.; Kashian, D.M.; Moore, 

D.; Raffa, K.F.; Sturrock, R.N.; Vogelmann, J. 

2011. Effects of biotic disturbances on forest 

carbon cycling in the United States and 

Canada. Global Change Biology. 18(1): 7-34. 

https://doi.org/10.1111/j.1365-2486.2011.02543.x.
LITERATURE CITED

148

Hicke, J.A.; Johnson, M.C.; Hayes, J.L.; Preisler, 

H.K. 2012. Effects of bark beetle-caused tree 

mortality on wildfire. Forest Ecology and 

Management. 271: 81-90. 

https://doi.org/10.1016/j.foreco.2012.02.005.

Hodgkins, G.; Dudley, R.; Huntington, T. 2003. 

Changes in the timing of high river flows in 

New England over the 20th century. Journal of 

Hydrology. 278(1): 244-252. 

https://doi.org/10.1016/S0022-1694(03)00155-0.

Hodgkins, G.A. 2013. The importance of record 

length in estimating the magnitude of climatic 

changes: an example using 175 years of lake 

ice-out dates in New England. Climatic Change. 

119(3): 705-718. 

https://doi.org/10.1007/s10584-013-0766-8.

Hodgkins, G.A.; Dudley, R.W. 2005. Changes 

in the magnitude of annual and monthly 

streamflows in New England, 1902-2002.

Scientific Investigations Report 2005-5135. 

Reston, VA: U.S. Department of the Interior, U.S. 

Geological Survey. 37 p.

Hodgkins, G.A.; Dudley, R.W.; Schalk, L.F. 2012. 

Relations between winter climatic variables 

and April streamflows in New England 

and implications for summer streamflows.

Scientific Investigations Report 2012-5092. 

Reston, VA: U.S. Department of the Interior, U.S. 

Geological Survey. 19 p.

Hodgkins, G.A.; James, I.C.; Huntington, T.G. 2002. 

Historical changes in lake ice-out dates as 

indicators of climate change in New England, 

1850-2000. International Journal of Climatology. 

22(15): 1819-1827. 

https://doi.org/10.1002/joc.857.

Holland, G.J.; Webster, P.J. 2007. Heightened 

tropical cyclone activity in the North Atlantic: 

Natural variability or climate trend?

Philosophical Transactions of the Royal Society 

A: Mathematical, Physical and Engineering 

Sciences. 365(1860): 2695-2716. 

https://doi.org/10.1098/rsta.2007.2083.

Holling, C.S. 1973. Resilience and stability 

of ecological systems. Annual Review of 

Ecology and Systematics. 4(1): 1-23. https://doi.

org/10.1146/annurev.es.04.110173.000245.

Holzmueller, E.J.; Gibson, D.J.; Suchecki, P.F. 2012. 

Accelerated succession following an intense 

wind storm in an oak-dominated forest. Forest 

Ecology and Management. 279: 141-146. 

https://doi.org/10.1016/j.foreco.2012.05.036.

Hooper, D.U.; Chapin, F.S.; Ewel, J.J.; Hector, A.; 

Inchausti, P.; Lavorel, S.; Lawton, J.H.; Lodge, 

D.M.; Loreau, M.; Naeem, S.; Schmid, B.; Setälä, 

H.; Symstad, A.J.; Vandermeer, J.; Wardle, D.A. 

2005. Effects of biodiversity on ecosystem 

functioning: a consensus of current knowledge.

Ecological Monographs. 75(1): 3-35. 

https://doi.org/10.1890/04-0922.

Horton, R.; Solecki, W.; Rosenzweig, C., eds. 

Climate change in the Northeast—a 

sourcebook: draft technical input report 

prepared for the U.S. National Climate 

Assessment. 2012. Washington, DC: U.S. Global 

Change Research Program. 313 p. Available at 

http://www.globalchange.gov/engage/processproducts/NCA3/technical-inputs (accessed 

August 3, 2017).

Horton, R.; Yohe, G.; Easterling, W.; Kates, R.; 

Ruth, M.; Sussman, E.; Whelchel, A.; Wolfe, D.; 

Lipschultz, F. 2014. Chapter 16: Northeast. In: 

Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds. 

Climate change impacts in the United States: the 

Third National Climate Assessment. Washington, 

DC: U.S. Global Change Research Program: 

371-395.

Hoving, C.L.; Lee, Y.M.; Badra, P.J.; Klatt, B.J. 

2013. Changing climate, changing wildlife: 

a vulnerability assessment of 400 species of 

greatest conservation need and game species 

in Michigan. Wildlife Division Report 3564. 

Lansing, MI: Michigan Department of Natural 

Resources, Wildlife Division. 82 p.
LITERATURE CITED

149

Hufkens, K.; Friedl, M.A.; Keenan, T.F.; Sonnentag, 

O.; Bailey, A.; O’Keefe, J.; Richardson, A.D. 

2012. Ecological impacts of a widespread frost 

event following early spring leaf-out. Global 

Change Biology. 18(7): 2365-2377. 

https://doi.org/10.1111/j.1365-2486.2012.02712.x.

Huntington, T.; Hodgkins, G.; Dudley, R. 2003. 

Historical trend in river ice thickness and 

coherence in hydroclimatological trends in 

Maine. Climatic Change. 61(1-2): 217-236. 

https://doi.org/10.1023/A:1026360615401.

Huntington, T.G.; Hodgkins, G.A.; Keim, B.D.; 

Dudley, R.W. 2004. Changes in the proportion 

of precipitation occurring as snow in New 

England (1949-2000). Journal of Climate. 

17(13): 2626-2636. https://doi.org/10.1175/1520-

0442(2004)017<2626:CITPOP>2.0.CO;2.

Huntington, T.G.; Richardson, A.D.; McGuire, K.J.; 

Hayhoe, K. 2009. Climate and hydrological 

changes in the northeastern United States: 

recent trends and implications for forested and 

aquatic ecosystems. Canadian Journal of Forest 

Research. 39: 199-212. 

https://doi.org/10.1139/X08-116.

Hyvönen, R.; Ågren, G.I.; Linder, S.; Persson, T.; 

Cotrufo, M.F.; Ekblad, A.; Freeman, M.; Grelle, 

A.; Janssens, I.A.; Jarvis, P.G.; Kellomäki, 

S.; Lindroth, A.; Loustau, D.; Lundmark, T.; 

Norby, R.J.; Oren, R.; Pilegaard, K.; Ryan, 

M.G.; Sigurdsson, B.D.; Strömgren, M.; van 

Oijen, M.; Wallin, G. 2007. The likely impact 

of elevated [CO2], nitrogen deposition, 

increased temperature and management on 

carbon sequestration in temperate and boreal 

forest ecosystems: a literature review. New 

Phytologist. 173(3): 463-480. 

https://doi.org/10.1111/j.1469-8137.2007.01967.x.

Ibáñez, I.; Clark, J.S.; Dietz, M.C. 2008. Evaluating 

the sources of potential migrant species: 

implications under climate change. Ecological 

Applications. 18(7): 1664-1678. 

https://doi.org/10.1890/07-1594.1.

Ibáñez, I.; Clark, J.S.; Dietze, M.C.; Feeley, 

K.; Hersh, M.; LaDeau, S.; McBride, A.; 

Welch, N.E.; Wolosin, M.S. 2006. Predicting 

biodiversity change: outside the climate 

envelope, beyond the species-area curve.

Ecology. 87(8): 1896-1906. https://doi.

org/10.1890/0012-9658(2006)87[1896:

PBCOTC]2.0.CO;2.

Ibáñez, I.; Clark, J.S.; Shannon, L.; Lambers, 

J.H.R. 2007. Exploiting temporal variability 

to understand tree recruitment response to 

climate change. Ecological Monographs. 77(2): 

163-177. https://doi.org/10.1890/06-1097.

Intergovernmental Panel on Climate Change [IPCC]. 

2000. Special report on emission scenarios.

Cambridge, UK: Cambridge University Press. 

Available at http://www.grida.no/publications/

other/ipcc_sr/?src=/climate/ipcc/emission/.

Intergovernmental Panel on Climate Change 

[IPCC]. 2007a. Climate change 2007: the 

physical science basis: summary for policy 

makers. [Core Writing Team, Pachauri, R.K. and 

Reisinger, A. (eds.)]. Geneva, Switzerland. 

104 p. Available at http://www.ipcc.ch/

publications_and_data/ar4/wg1/en/contents.html.

Intergovernmental Panel on Climate Change 

[IPCC]. 2007b. Climate change 2007: synthesis 

report: contribution of Working Groups I, 

II and III to the Fourth Assessment Report 

of the Intergovernmental Panel on Climate 

Change. [Core Writing Team, Pachauri, R.K. and 

Reisinger, A. (eds.)]. Geneva, Switzerland.

Intergovernmental Panel on Climate Change [IPCC]. 

2012. Managing the risks of extreme events 

and disasters to advance climate change 

adaptation. Cambridge, UK and New York, NY: 

Cambridge University Press. 582 p.
LITERATURE CITED

150

Intergovernmental Panel on Climate Change 

[IPCC]. 2013. Summary for policymakers. 

Climate change 2013: the physical science 

basis. In: Stocker, T.F.; Dahe, Q.; Plattner, 

G.-K., Tignor, M.; Alpen, S.K.; Boschung, J.; 

Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M., eds. 

Summary for policymakers. Working Group I 

contribution to the Fifth Assessment Report of 

the Intergovernmental Panel on Climate Change. 

Cambridge, UK and New York, NY: Cambridge 

University Press. 29 p. 

Irland, L.C. 2000. Ice storms and forest impacts. 

Science of The Total Environment. 262(3): 

231-242. https://doi.org/10.1016/S0048-

9697(00)00525-8.

Irland, L.C.; Adams, D.; Alig, R.; Betz, C.J.; 

Chen, C.-C.; Hutchins, M.; McCarl, B.A.; 

Skog, K.; Sohngen, B.L. 2001. Assessing 

socioeconomic impacts of climate change on 

US forests, wood-product markets, and forest 

recreation. BioScience. 51(9): 753-764. https://

doi.org/10.1641/0006-3568(2001)051[0753:

asiocc]2.0.co;2. 

Isbell, F.; Craven, D.; Connolly, J.; Loreau, M.; 

Schmid, B.; Beierkuhnlein, C.; Bezemer, T.M.; 

Bonin, C.; Bruelheide, H.; De Luca, E. 2015. 

Biodiversity increases the resistance of 

ecosystem productivity to climate extremes.

Nature. 526(7574): 574-577. 

https://doi.org/10.1038/nature15374.

Iverson, L.; Thompson, F., III; Matthews, S.; Peters, 

M.; Prasad, A.M.; Dijak, W.; Fraser, J.S.; Wang, 

W.J.; Hanberry, B.B.; He, H.; Janowiak, M.K.; 

Butler, P.R.; Brandt, L.A.; Swanston, C.W. 2017. 

Multi-model comparison on the effects of 

climate change on tree species in the eastern 

U.S.: results from an enhanced niche model 

and process-based ecosystem and landscape 

models. Landscape Ecology. 32(7): 1327-1346. 

https://doi.org/10.1007/s10980-016-0404-8.

Iverson, L.R.; McKenzie, D. 2013. Tree-species 

range shifts in a changing climate: detecting, 

modeling, assisting. Landscape Ecology. 28: 

879-889. 

https://doi.org/10.1007/s10980-013-9885-x.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, 

M. 2008. Estimating potential habitat for 

134 eastern US tree species under six climate 

scenarios. Forest Ecology and Management. 

254(3): 390-406. 

https://doi.org/10.1016/j.foreco.2007.07.023.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; 

Peters, M.P. 2010. Merger of three modeling 

approaches to assess potential effects of 

climate change on trees in the eastern United 

States. Proceedings of the 2010 IUFRO 

Landscape Ecology Working Group International 

Conference: 135-140.

Iverson, L.R.; Schwartz, M.W.; Prasad, A.M. 2004a. 

How fast and far might tree species migrate 

in the eastern United States due to climate 

change? Global Ecology and Biogeography. 

13(3): 209-219. 

https://doi.org/10.1111/j.1466-822X.2004.00093.x.

Iverson, L.R.; Schwartz, M.W.; Prasad, A.M. 2004b. 

Potential colonization of newly available 

tree-species habitat under climate change: an 

analysis for five eastern US species. Landscape 

Ecology. 19(7): 787-799. 

https://doi.org/10.1007/s10980-005-3990-5.

Iwata, Y.; Hayashi, M.; Suzuki, S.; Hirota, T.; 

Hasegawa, S. 2010. Effects of snow cover on 

soil freezing, water movement, and snowmelt 

infiltration: a paired plot experiment. Water 

Resources Research. 46(9): W09504. 

https://doi.org/10.1029/2009wr008070.

Jacobson, G.L.; Fernandez, I.J.; Mayewski, P.A.; 

Schmit, C.V., eds. 2009. Maine’s climate future: 

an initial assessment. Orono, ME: University 

of Maine. 72 p. http://climatechange.umaine.

edu/files/Maines_Climate_Future.pdf (accessed 

June 24, 2014).

Jensen, O.P.; Benson, B.J.; Magnuson, J.J.; Card, 

V.M.; Futter, M.N.; Soranno, P.A.; Stewart, K.M. 

2007. Spatial analysis of ice phenology trends 

across the Laurentian Great Lakes region 

during a recent warming period. Limnology 

and Oceanography. 52(5): 2013-2026. 

https://doi.org/10.4319/lo.2007.52.5.2013.
LITERATURE CITED

151

Jeong, D.I.; Sushama, L.; Naveed Khaliq, M. 2014. 

The role of temperature in drought projections 

over North America. Climatic Change. 127(2): 

289-303. 

https://doi.org/10.1007/s10584-014-1248-3.

Jin, S.; Sader, S.A. 2006. Effects of forest 

ownership and change on forest harvest rates, 

types and trends in northern Maine. Forest 

Ecology and Management. 228(1-3): 177-186. 

https://doi.org/10.1016/j.foreco.2006.03.009.

Johnson, S.; Stefan, H. 2006. Indicators of climate 

warming in Minnesota: lake ice covers and 

snowmelt runoff. Climatic Change. 75(4): 

421-453. 

https://doi.org/10.1007/s10584-006-0356-0.

Joyce, D.G.; Rehfeldt, G.E. 2013. Climatic niche, 

ecological genetics, and impact of climate 

change on eastern white pine (Pinus strobus

L.): guidelines for land managers. Forest 

Ecology and Management. 295: 173-192. 

https://doi.org/10.1016/j.foreco.2012.12.024.

Joyce, L.A.; Running, S.W.; Breshears, D.D.; Dale, 

V.H.; Malmsheimer, R.W.; Sampson, R.N.; 

Sohngen, B.; Woodall, C.W. 2014. Forests. In: 

Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds. 

Climate change impacts in the United States: the 

Third National Climate Assessment. Washington, 

DC: U.S. Global Change Research Program: 

175-194. Chapter 7.

Jump, A.S.; Peñuelas, J. 2005. Running to stand 

still: adaptation and the response of plants to 

rapid climate change. Ecology Letters. 8(9): 

1010-1020. 

https://doi.org/10.1111/j.1461-0248.2005.00796.x.

Kane, H.H.; Fletcher, C.H.; Frazer, L.N.; Anderson, 

T.R.; Barbee, M.M. 2015. Modeling sea-level 

rise vulnerability of coastal environments 

using ranked management concerns. Climatic 

Change. 131(2): 349-361. 

https://doi.org/10.1007/s10584-015-1377-3.

Kanoti, A.M. 2006. Relationship between balsam 

woolly adelgid damage, radial growth, climate 

and stand characteristics in eastern Maine.

Orono, ME: University of Maine. 103 p. 

M.S. thesis.

Karl, T.R.; Knight, R.W.; Easterling, D.R.; Quayle, 

R.G. 1996. Indices of climate change for 

the United States. Bulletin of the American 

Meteorological Society. 77(2): 279-292. https://

doi.org/10.1175/1520-0477(1996)077<0279:

ioccft>2.0.co;2.

Karl, T.R.; Meehl, G.A.; Miller, C.D.; Hassol, S.J.; 

Waple, A.M.; Murray, W.L. 2008. Weather 

and climate extremes in a changing climate; 

regions of focus: North America, Hawaii, 

Caribbean, and U.S. Pacific Islands.

Washington, DC: U.S. Climate Change Science 

Program. 162 p.

Karmalkar, A.V.; Bradley, R.S. 2017. Consequences 

of global warming of 1.5 °C and 2 °C for 

regional temperature and precipitation 

changes in the contiguous United States. PLoS 

ONE. 12(1): e0168697. 

https://doi.org/10.1371/journal.pone.0168697.

Karnosky, D.F.; Zak, D.R.; Pregitzer, K.S.; Awmack, 

C.S.; Bockheim, J.G.; Dickson, R.E.; Hendrey, 

G.R.; Host, G.E.; King, J.S.; Kopper, B.J.; 

Kruger, E.L.; Kubiske, M.E.; Lindroth, R.L.; 

Mattson, W.J.; McDonald, E.P.; Noormets, 

A.; Oksanen, E.; Parsons, W.F.J.; Percy, K.E.; 

Podila, G.K.; Riemenschneider, D.E.; Sharma, 

P.; Thakur, R.; Sôber, A.; Sôber, J.; Jones, W.S.; 

Anttonen, S.; Vapaavuori, E.; Mankovska, B.; 

Heilman, W.; Isebrands, J.G. 2003. Tropospheric 

O3 moderates responses of temperate 

hardwood forests to elevated CO2: a synthesis 

of molecular to ecosystem results from the 

Aspen FACE project. Functional Ecology. 

17(3): 289-304. 

https://doi.org/10.1046/j.1365-2435.2003.00733.x.

Kauserud, H.; Stige, L.C.; Vik, J.O.; Økland, R.H.; 

Høiland, K.; Stenseth, N.C. 2008. Mushroom 

fruiting and climate change. Proceedings of the 

National Academy of Sciences. 105(10): 

3811-3814. 

https://doi.org/10.1073/pnas.0709037105.
LITERATURE CITED

152

Keenan, T.F.; Gray, J.; Friedl, M.A.; Toomey, M.; 

Bohrer, G.; Hollinger, D.Y.; Munger, J.W.; 

O’Keefe, J.; Schmid, H.P.; Wing, I.S. 2014. 

Net carbon uptake has increased through 

warming-induced changes in temperate forest 

phenology. Nature Climate Change. 4(7): 

598-604. 

https://doi.org/10.1038/nclimate2253.

Kenefic, L.; Ruel, J.-C.; Tremblay, J.-P. 2015. 

Sustainable management of white-tailed deer 

and white-cedar. The Wildlife Professional. 

(Fall): 33-36.

Kitajima, K.; Fenner, M. 2000. Ecology of seedling 

regeneration. In: Fenner, M., ed. Seeds, the 

ecology of regeneration in plant communities. 

Wallingford, UK: CABI Publishing: 331-359. 

https://doi.org/10.1079/9780851994321.0331.

Kliejunas, J.T. 2011. A risk assessment of climate 

change and the impact of forest diseases on 

forest ecosystems in the western United States 

and Canada. Gen. Tech. Rep. PSW-GTR-236. 

Albany, CA: U.S. Department of Agriculture, 

Forest Service, Pacific Southwest Research 

Station. 70 p.

Kling, G.W.; Hayhoe, K.; Johnson, L.B.; Magnuson, 

J.J.; Polasky, S.; Robinson, S.K.; Shuter, B.J.; 

Wander, M.M.; Wuebbles, D.J.; Zak, D.R.; 

Lindroth, R.L.; Moser, S.C.; Wilson, M.L. 2003. 

Confronting climate change in the Great 

Lakes region: impacts on our communities 

and ecosystems. Union of Concerned Scientists; 

Ecological Society of America. Available at 

http://ucsusa.org/assets/documents/global_

warming/greatlakes_final.pdf.

Knapp, A.K.; Beier, C.; Briske, D.D.; Classen, 

A.e.T.; Luo, Y.; Reichstein, M.; Smith, M.D.; 

Smith, S.D.; Bell, J.E.; Fay, P.A. 2008. 

Consequences of more extreme precipitation 

regimes for terrestrial ecosystems. BioScience. 

58(9): 811-821. https://doi.org/10.1641/B580908.

Knutti, R.; Sedláček, J. 2013. Robustness and 

uncertainties in the new CMIP5 climate model 

projections. Nature Climate Change. 3(4): 

369-373. https://doi.org/10.1038/nclimate1716.

Kopp, R.E.; Horton, R.M.; Little, C.M.; Mitrovica, 

J.X.; Oppenheimer, M.; Rasmussen, D.; Strauss, 

B.H.; Tebaldi, C. 2014. Probabilistic 21st and 

22nd century sea-level projections at a global 

network of tide-gauge sites. Earth’s Future. 2(8): 

383-406. https://doi.org/10.1002/2014EF000239.

Kosiba, A.M.; Schaberg, P.G.; Hawley, G.J.; Hansen, 

C.F. 2013. Quantifying the legacy of foliar 

winter injury on woody aboveground carbon 

sequestration of red spruce trees. Forest 

Ecology and Management. 302: 363-371. 

https://doi.org/10.1016/j.foreco.2013.03.006.

Kuehn, D.; Chase, L.; Sharkey, T.; Powers, S. 

2016. Perceptions of maple producers 

towards climate change. SUNY College of 

Environmental Science and Forestry. 38 p. 

http://www.esf.edu/for/kuehn/documents/

mapleproducersreportfinal_001.pdf (accessed 

June 14, 2016).

Kunkel, K.; Bromirski, P.; Brooks, H.; Cavazos, 

T.; Douglas, A.; Easterling, D.; Emanuel, K.; 

Groisman, P.Y.; Holland, G.; Knutson, T. 2008. 

Observed changes in weather and climate 

extremes. In: Karl, T.R.; Meehl, G.A.; Miller, 

C.D.; Hassol, S.J.; Waple, A.M.; Murray, W.L., 

eds. Weather and climate extremes in a changing 

climate; regions of focus: North America, Hawaii, 

Caribbean, and U.S. Pacific Islands. Washington, 

DC: U.S. Climate Change Science Program: 

35-80.

Kunkel, K.E.; Andsager, K.; Easterling, D.R. 1999. 

Long-term trends in extreme precipitation 

events over the conterminous United States 

and Canada. Journal of Climate. 12(8): 

2515-2527. https://doi.org/10.1175/1520-

0442(1999)012<2515:lttiep>2.0.co;2.
LITERATURE CITED

153

Kunkel, K.E.; Karl, T.R.; Brooks, H.; Kossin, 

J.; Lawrimore, J.H.; Arndt, D.; Bosart, L.; 

Changnon, D.; Cutter, S.L.; Doesken, N.; 

Emanuel, K.; Groisman, P.Y.; Katz, R.W.; 

Knutson, T.; O’Brien, J.; Paciorek, C.J.; 

Peterson, T.C.; Redmond, K.; Robinson, D.; 

Trapp, J.; Vose, R.; Weaver, S.; Wehner, M.; 

Wolter, K.; Wuebbles, D. 2012. Monitoring 

and understanding trends in extreme storms: 

state of knowledge. Bulletin of the American 

Meteorological Society. 94(4): 499-514. 

https://doi.org/10.1175/bams-d-11-00262.1.

Kunkel, K.E.; Stevens, L.E.; Stevens, S.E.; Sun, 

L.; Janssen, E.; Wuebbles, D.; Rennells, J.; 

DaGaetano, A.; Dobson, J.G. 2013. Regional 

climate trends and scenarios for the U.S. 

National Climate Assessment. Part 1. Climate 

of the northeast U.S. NOAA Technical 

Report NESDIS 142-1. Washington, DC: U.S. 

Department of Commerce, National Oceanic and 

Atmospheric Administration. 79 p. Available at 

http://www.nesdis.noaa.gov/technical_reports/

NOAA_NESDIS_Tech_Report_142-1-Climate_

of_the_Northeast_U.S.pdf (accessed April 30, 

2014).

Landerer, F.W.; Jungclaus, J.H.; Marotzke, J. 2007. 

Regional dynamic and steric sea level change 

in response to the IPCC-A1B scenario. Journal 

of Physical Oceanography. 37(2): 296-312. 

https://doi.org/10.1175/JPO3013.1.

Larouche, C.; Ruel, J.-C. 2015. Development of 

northern white-cedar regeneration following 

partial cutting, with and without deer 

browsing. Forests. 6(2): 344-359. 

https://doi.org/10.3390/f6020344.

Larson, E.R.; Kipfmueller, K.F.; Hale, C.M.; 

Frelich, L.E.; Reich, P.B. 2010. Tree rings 

detect earthworm invasions and their effects 

in northern hardwood forests. Biological 

Invasions. 12(5): 1053-1066. 

https://doi.org/10.1007/s10530-009-9523-3.

Le Goff, H.; Flannigan, M.D.; Bergeron, Y. 2009. 

Potential changes in monthly fire risk in the 

eastern Canadian boreal forest under future 

climate change. Canadian Journal of Forest 

Research. 39(12): 2369-2380. 

https://doi.org/10.1139/X09-153.

Leathers, D.J.; Luff, B.L. 1997. Characteristics 

of snow cover duration across the northeast 

United States of America. International Journal 

of Climatology. 17(14): 1535-1547. 

https://doi.org/10.1002/(sici)1097-0088(1997113

0)17:14<1535::aid-joc215>3.0.co;2-7.

Lee, T.D.; Barrett, J.P.; Hartman, B. 2005. 

Elevation, substrate, and the potential for 

climate-induced tree migration in the White 

Mountains, New Hampshire, USA. Forest 

Ecology and Management. 212(1-3): 75-91. 

https://doi.org/10.1016/j.foreco.2005.03.007.

Legaard, K.R.; Sader, S.A.; Simons-Legaard, E.M. 

2015. Evaluating the impact of abrupt changes 

in forest policy and management practices on 

landscape dynamics: analysis of a LANDSAT 

image time series in the Atlantic northern 

forest. PLoS ONE. 10(6): e0130428. 

https://doi.org/10.1371/journal.pone.0130428.

Lenihan, J.M.; Bachelet, D.; Neilson, R.P.; Drapek, 

R. 2008. Simulated response of conterminous 

United States ecosystems to climate change 

at different levels of fire suppression, CO(2) 

emission rate, and growth response to CO(2).

Global and Planetary Change. 64(1-2): 16-25. 

https://doi.org/10.1016/j.gloplacha.2008.01.006.

Likens, G.E.; Driscoll, C.T.; Buso, D.C. 1996. 

Long-term effects of acid rain: response 

and recovery of a forest ecosystem. Science. 

272(5259): 244. 

https://doi.org/10.1126/science.272.5259.244.

Lindbladh, M.; Jacobson, G.L., Jr.; Schauffler, M. 

2003. The postglacial history of three Picea

species in New England, USA. Quaternary 

Research. 59(1): 61-69. 

https://doi.org/10.1016/S0033-5894(02)00023-6.
LITERATURE CITED

154

Lins, H.F.; Slack, J.R. 1999. Streamflow trends in 

the United States. Geophysical Research Letters. 

26(2): 227-230. 

https://doi.org/10.1029/1998gl900291.

Loehle, C.; Idso, C.; Wigley, T.B. 2016. 

Physiological and ecological factors 

influencing recent trends in United States 

forest health responses to climate change.

Forest Ecology and Management. 363: 179-189. 

https://doi.org/10.1016/j.foreco.2015.12.042.

Long, R.P.; Horsley, S.B.; Hall, T.J. 2013. Longterm impact of liming on growth and vigor 

of northern hardwoods. Canadian Journal of 

Forest Research. 41(6): 1295-1307. 

https://doi.org/10.1139/x11-049.

Lorimer, C.G. 1977. The presettlement forest and 

natural disturbance cycle of northeastern 

Maine. Ecology. 58(1): 139-148. 

https://doi.org/10.2307/1935115.

Lorimer, C.G.; White, A.S. 2003. Scale and 

frequency of natural disturbances in the 

northeastern US: implications for early 

successional forest habitats and regional age 

distributions. Forest Ecology and Management. 

185(1): 41-64. 

https://doi.org/10.1016/S0378-1127(03)00245-7.

Luce, C.; Pederson, N.; Campbell, J.; Millar, C.; 

Kormos, P.; Vose, J.M.; Woods, R. 2016a. 

Characterizing drought for forested 

landscapes and streams. In: Vose, J.; Clark, 

J.S.; Luce, C.; Patel-Weynand, T., eds. Effects of 

drought on forests and rangelands in the United 

States: a comprehensive science synthesis. 

Gen. Tech. Rep. WO-93b. Washington, DC: 

U.S. Department of Agriculture, Forest Service, 

Washington Office: 13-48.

Luce, C.H.; Vose, J.M.; Pederson, N.; Campbell, 

J.; Millar, C.; Kormos, P.; Woods, R. 2016b. 

Contributing factors for drought in United 

States forest ecosystems under projected 

future climates and their uncertainty. Forest 

Ecology and Management. 380: 299-308. 

https://doi.org/10.1016/j.foreco.2016.05.020.

Lundgren, M.R.; Small, C.J.; Dreyer, G.D. 2004. 

Influence of land use and site characteristics 

on invasive plant abundance in the Quinebaug 

Highlands of southern New England.

Northeastern Naturalist. 11(3): 313-332. 

https://doi.org/10.1656/1092-

6194(2004)011[0313:IOLUAS]2.0.CO;2.

Lynch, C.; Seth, A.; Thibeault, J. 2016. Recent and 

projected annual cycles of temperature and 

precipitation in the northeast United States 

from CMIP5. Journal of Climate. 29(1): 347-

365. https://doi.org/10.1175/jcli-d-14-00781.1.

Lynn, K.; Daigle, J.; Hoffman, J.; Lake, F.; Michelle, 

N.; Ranco, D.; Viles, C.; Voggesser, G.; Williams, 

P. 2013. The impacts of climate change on 

tribal traditional foods. Climatic Change. 

120(3): 545-556. 

https://doi.org/10.1007/s10584-013-0736-1.

Magnuson, J.J.; Robertson, D.M.; Benson, B.J.; 

Wynne, R.H.; Livingstone, D.M.; Arai, T.; Assel, 

R.A.; Barry, R.G.; Card, V.; Kuusisto, E.; Granin, 

N.G.; Prowse, T.D.; Stewart, K.M.; Vuglinski, 

V.S. 2000. Historical trends in lake and river 

ice cover in the Northern Hemisphere. Science. 

289(5485): 1743-1746. 

https://doi.org/10.1126/science.289.5485.1743.

Magruder, M.; Chhin, S.; Monks, A.; O’Brien, 

J. 2012. Effects of initial stand density and 

climate on red pine productivity within Huron 

National Forest, Michigan, USA. Forests. 3(4): 

1086-1103. https://doi.org/10.3390/f3041086.

Malmsheimer, R.W.; Bowyer, J.L.; Fried, J.S.; Gee, 

E.; Izlar, R.L.; Miner, R.A.; Munn, I.A.; Oneil, 

E.; Stewart, W.C. 2011. Managing forests 

because carbon matters: integrating energy, 

products, and land management policy. Journal 

of Forestry. 109(7): S7-S48.

Manion, P.D. 1991. Tree disease concepts.

Englewood Cliffs, NJ: Prentice-Hall, Inc.
LITERATURE CITED

155

Manomet Center for Conservation Sciences 

and Massachusetts Division of Fisheries and 

Wildlife [DFW]. 2010a. Climate change and 

Massachusetts fish and wildlife: habitat and 

species vulnerability (volume 2). Westborough, 

MA: Massachusetts Division of Fisheries and 

Wildlife. 59 p. Available at http://www.mass.

gov/eea/agencies/dfg/dfw/wildlife-habitatconservation/climate-change-and-massachusettsfish-and-wildlife.html (accessed April 29, 2014).

Manomet Center for Conservation Sciences 

and Massachusetts Division of Fisheries and 

Wildlife [DFW]. 2010b. Climate change and 

Massachusetts fish and wildlife: habitat 

management (volume 3). Westborough, MA: 

Massachusetts Division of Fisheries and Wildlife. 

23 p. Available at https://www.manomet.org/

publications-tools/climate-services.

Manomet Center for Conservation Sciences 

and Massachusetts Division of Fisheries and 

Wildlife [DFW]. 2010c. Climate change and 

Massachusetts fish and wildlife: introduction 

and background (volume 1). Westborough, MA: 

Massachusetts Division of Fisheries and Wildlife. 

19 p. Available at https://www.manomet.org/

publications-tools/climate-services.

Manomet Center for Conservation Sciences and 

National Wildlife Federation [NWF]. 2012. 

Climate change and cold water fish habitat 

in the Northeast—a vulnerability assessment: 

a report to the Northeastern Association 

of Fish and Wildlife Agencies and to the 

North Atlantic Landscape Conservation 

Cooperative. Plymouth, MA: Manomet Center 

for Conservation Sciences. 

Manomet Center for Conservation Sciences and 

National Wildlife Federation [NWF]. 2013a. 

Implementing climate smart conservation 

in northeastern upland forests: a report 

to the Wildlife Conservation Society and 

Northeastern Association of Fish and Wildlife 

Agencies. Montpelier, VT: Manomet Center for 

Conservation Sciences. 

Manomet Center for Conservation Sciences and 

National Wildlife Federation [NWF]. 2013b. The 

vulnerabilities of fish and wildlife habitats 

in the Northeast to climate change: a report 

to the Northeastern Association of Fish and 

Wildlife Agencies and to the North Atlantic 

Landscape Conservation Cooperative.

Plymouth, MA: Manomet Center for 

Conservation Sciences. 188 p. Available at http://

rcngrants.org/sites/default/files/final_reports/

Galbraith%20et%20al%20-%20TerrestrialWetland%20Vulnerabililty%20Assessment.pdf

(accessed April 11, 2014).

Manomet Center for Conservation Sciences and 

National Wildlife Federation [NWF]. 2013c. The 

vulnerabilities of northeastern fish and wildlife 

habitats to sea level rise: a report to the 

Northeastern Association of Fish and Wildlife 

Agencies and to the North Atlantic Landscape 

Conservation Cooperative. Plymouth, MA: 

Manomet Center for Conservation Sciences. 49 p. 

Available at http://rcngrants.org/sites/default/

files/final_reports/Galbraith%202014%20-%20T

he%20vulnerabilities%20of%20northeastern%20

fish%20and%20wildlife%20habitats%20to%20se

a%20level%20rise.pdf (accessed April 11, 2014).

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).

Matthews, S.N.; Iverson, L.R. 2017. Managing 

for delicious ecosystem service under climate 

change: Can United States sugar maple (Acer 

saccharum) syrup production be maintained 

in a warming climate? International Journal 

of Biodiversity Science, Ecosystem Services & 

Management. 13(2): 40-52.
LITERATURE CITED

156

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.

Mawdsley, J.R.; O’Malley, R.; Ojima, D.S. 2009. 

Una revisión de las estrategias de adaptación 

al cambio climático para el manejo de vida 

silvestre y conservación de la biodiversidad [A 

review of climate-change adaptation strategies 

for wildlife management and biodiversity 

conservation]. Conservation Biology. 23(5): 

1080-1089. https://doi.org/10.1111/j.1523-

1739.2009.01264.x.

McBoyle, G.; Scott, D.; Jones, B. 2007. Climate 

change and the future of snowmobiling in nonmountainous regions of Canada. Managing 

Leisure. 12(4): 237-250. 

https://doi.org/10.1080/13606710701546868.

McDowell, N.; Pockman, W.T.; Allen, C.D.; 

Breshears, D.D.; Cobb, N.; Kolb, T.; Plaut, J.; 

Sperry, J.; West, A.; Williams, D.G.; Yepez, 

E.A. 2008. Mechanisms of plant survival and 

mortality during drought: Why do some plants 

survive while others succumb to drought? 

New Phytologist. 178(4): 719-739. https://doi.

org/10.1111/j.1469-8137.2008.02436.x.

McKinley, D.C.; Ryan, M.G.; Birdsey, R.A.; 

Giardina, C.P.; Harmon, M.E.; Heath, L.S.; 

Houghton, R.A.; Jackson, R.B.; Morrison, J.F.; 

Murray, B.C. 2011. A synthesis of current 

knowledge on forests and carbon storage in the 

United States. Ecological Applications. 21(6): 

1902-1924. https://doi.org/10.1890/10-0697.1.

McLachlan, J.S.; Clark, J.S.; Manos, P.S. 2005. 

Molecular indicators of tree migration 

capacity under rapid climate change. Ecology. 

86(8): 2088-2098. 

https://doi.org/10.1890/04-1036.

McLaughlin, S.B.; Nosal, M.; Wullschleger, S.D.; 

Sun, G. 2007. Interactive effects of ozone 

and climate on tree growth and water use in 

a southern Appalachian forest in the USA.

New Phytologist. 174(1): 109-124. https://doi.

org/10.1111/j.1469-8137.2007.02018.x.

McMahon, S.M.; Parker, G.G.; Miller, D.R. 2010. 

Evidence for a recent increase in forest 

growth. Proceedings of the National Academy of 

Sciences. 107(8): 3611-3615. 

https://doi.org/10.1073/pnas.0912376107.

McNab, W.H.; Avers, P.E. 1994. Ecological 

subregions of the United States: section 

descriptions. Washington, DC: U.S. Department 

of Agriculture, Forest Service, Ecosystem 

Management. 267 p. 

McNab, W.H.; Cleland, D.T.; Freeouf, J.A.; Keys, 

J.E., Jr.; Nowacki, G.J.; Carpenter, C.A. 2007. 

Description of ecological subregions: sections 

of the conterminous United States. Gen. Tech. 

Rep. WO-768. Washington, DC: U.S. Department 

of Agriculture, Forest Service, Washington 

Office. 80 p. 

McNulty, S.G.; Boggs, J.L. 2010. A conceptual 

framework: redefining forest soil’s critical acid 

loads under a changing climate. Environmental 

Pollution. 158(6): 2053-2058. 

https://doi.org/10.1016/j.envpol.2009.11.028.

Meinshausen, M.; Smith, S.J.; Calvin, K.; Daniel, 

J.S.; Kainuma, M.L.T.; Lamarque, J.F.; 

Matsumoto, K.; Montzka, S.A.; Raper, S.C.B.; 

Riahi, K.; Thomson, A.; Velders, G.J.M.; van 

Vuuren, D.P.P. 2011. The RCP greenhouse gas 

concentrations and their extensions from 1765 

to 2300. Climatic Change. 109(1-2): 213-241. 

https://doi.org/10.1007/s10584-011-0156-z.

Melillo, J.M.; Kicklighter, D.; McGuire, A.; 

Peterjohn, W.; Newkirk, K. 1995. Global change 

and its effects on soil organic carbon stocks.

In: Dahlem conference proceedings. New York, 

NY and Chichester, UK: John Wiley and Sons: 

175-189.
LITERATURE CITED

157

Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds. 

2014. Climate change impacts in the United 

States: the Third National Climate Assessment.

Washington, DC: U.S. Global Change Research 

Program. 841 p. Available at http://nca2014.

globalchange.gov/downloads (accessed May 7, 

2014).

Meyer, S.R.; Cronan, C.S.; Lilieholm, R.J.; Johnson, 

M.L.; Foster, D.R. 2014. Land conservation in

northern New England: historic trends and 

alternative conservation futures. Biological 

Conservation. 174: 152-160. 

https://doi.org/10.1016/j.biocon.2014.03.016.

Millar, C.I.; Stephenson, N.L. 2015. Temperate 

forest health in an era of emerging 

megadisturbance. Science. 349(6250): 823-826. 

https://doi.org/10.1126/science.aaa9933.

Miller-Rushing, A.J.; Primack, R.B. 2009. The 

impact of climate change on the flora of 

Thoreau’s Concord. Arnoldia. 66: 2-9.

Millers, I.; Shriner, D.S.; Rizzo, D. 1989. History 

of hardwood decline in the eastern United 

States. Gen. Tech. Rep. NE-126. Broomall, PA: 

U.S. Department of Agriculture, Forest Service, 

Northeastern Forest Experiment Station. 75 p. 

Mitchell, M.J.; Raynal, D.J.; Driscoll, C.T. 1996. 

Biogeochemistry of a forested watershed in 

the central Adirondack Mountains: temporal 

changes and mass balances. Water, Air, and Soil 

Pollution. 88(3-4): 355-369. 

https://doi.org/10.1007/BF00294111.

Mohan, J.E.; Cox, R.M.; Iverson, L.R. 2009. 

Composition and carbon dynamics of forests 

in northeastern North America in a future, 

warmer world. Canadian Journal of Forest 

Research. 39: 213-230. 

https://doi.org/10.1139/X08-185.

Morelli, T.L.; Daly, C.; Dobrowski, S.Z.; Dulen, 

D.M.; Ebersole, J.L.; Jackson, S.T.; Lundquist,

J.D.; Millar, C.I.; Maher, S.P.; Monahan, W.B.;

Nydick, K.R.; Redmond, K.T.; Sawyer, S.C.;

Stock, S.; Beissinger, S.R. 2016. Managing

climate change refugia for climate adaptation.

PLoS ONE. 11(8): e0159909.

https://doi.org/10.1371/journal.pone.0159909.

Morin, R.S.; Barnett, C.J.; Butler, B.J.; Crocker, 

S.J.; Domke, G.M.; Hansen, M.H.; Hatfield,

M.A.; Horton, J.; Kurtz, C.M.; Lister, T.W. 2015.

Forests of Vermont and New Hampshire 2012.

Resour. Bull. NRS-95. Newtown Square, PA:

U.S. Department of Agriculture, Forest Service,

Northern Research Station. 80 p.

https://doi.org/10.2737/NRS-RB-95.

Moritz, M.A.; Parisien, M.A.; Batllori, E.; 

Krawchuk, M.A.; Van Dorn, J.; Ganz, D.J.; 

Hayhoe, K. 2012. Climate change and 

disruptions to global fire activity. Ecosphere. 

3(6). https://doi.org/10.1890/ES11-00345.1.

Moser, S.C.; Davidson, M.A.; Kirshen, P.; 

Mulvaney, P.; Murley, J.F.; Neumann, J.E.; Petes, 

L.; Reed, D. 2014. Chapter 25: Coasts. In: 

Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds. 

Climate change impacts in the United States: the 

Third National Climate Assessment. Washington, 

DC: U.S. Global Change Research Program: 

579-618.

Moser, S.C.; Kasperson, R.E.; Yohe, G.; Agyeman, 

J. 2008. Adaptation to climate change in

the northeast United States: opportunities,

processes, constraints. Mitigation and

Adaptation Strategies for Global Change. 13(5):

643-659.

https://doi.org/10.1007/s11027-007-9132-3.

Moser, W.K.; Miles, P.D.; Stephens, A.; Gormanson, 

D.D.; Shifley, S.R.; Wear, D.; Huggett, R.J.; Li,

R. 2016. Maintenance of productive capacity of

forest ecosystems. In: Shi ley, S.R.; Moser, W.K.,

eds. Future forests of the Northern United States.

Gen. Tech. Rep. NRS-151. Newtown Square, PA:

U.S. Department of Agriculture, Forest Service,

Northern Research Station: 77-106.
LITERATURE CITED

158

Murphy, H.T.; VanDerWal, J.; Lovett-Doust, J. 2010. 

Signatures of range expansion and erosion in 

eastern North American trees. Ecology Letters. 

13(10): 1233-1244. https://doi.org/10.1111/

j.1461-0248.2010.01526.x.

Murray, D.L.; Cox, E.W.; Ballard, W.B.; Whitlaw, 

H.A.; Lenarz, M.S.; Custer, T.W.; Barnett, 

T.; Fuller, T.K. 2006. Pathogens, nutritional 

deficiency, and climate influences on 

a declining moose population. Wildlife 

Monographs. 166: 1-30. 

https://doi.org/10.2193/0084-0173(2006)166[1:

PNDACI]2.0.CO;2.

Musante, A.R.; Pekins, P.J.; Scarpitti, D.L. 2007. 

Metabolic impact of winter tick infestations on 

calf moose. Alces. 43: 101-110.

National Oceanic and Atmospheric Administration 

[NOAA] National Centers for Environmental 

Information. 2017. State climate summaries.

Available at https://statesummaries.ncics.org/

(accessed January 31, 2017).

National Oceanic and Atmospheric Administration 

[NOAA] National Climatic Data Center. 2014. 

Climate at a glance. Available at http://www.

ncdc.noaa.gov/cag/ (accessed July 22, 2014).

National Wildlife Federation. 2013a. Nowhere to 

run: big game wildlife in a warming world.

36 p. http://www.nwf.org/Sportsmen/ClimateChange/Big-Game-In-a-Warming-World.aspx

(accessed June 14, 2016).

National Wildlife Federation. 2013b. Shifting 

skies: migratory birds in a warming world.

43 p. https://www.nwf.org/pdf/Reports/

NWF_Migratory_Birds_Report_web_Final.pdf

(accessed June 14, 2016).

Nearing, M.; Pruski, F.; O’Neal, M. 2004. Expected 

climate change impacts on soil erosion rates: a 

review. Journal of Soil and Water Conservation. 

59(1): 43-50.

New Hampshire Fish and Game Department [FGD]. 

2013. Ecosystems and wildlife climate change 

adaptation plan: amendment to the New 

Hampshire Wildlife Action Plan. Concord, NH. 

133 p. http://www.wildnh.com/Wildlife/Wildlife_

Plan/climate_change/Eco_Wildlife_CC_Adapt_

Plan.pdf (accessed April 16, 2014).

New York Department of Environmental 

Conservation. 2016. Adirondack Forest 

Preserve. Available at http://www.dec.ny.gov/

lands/5263.html (accessed December 29, 2016).

New York State Energy Research and Development 

Authority. 2011. Responding to climate change 

in New York State. Final report No. 11-18.

Albany, NY. 652 p. Available at http://www.

nyserda.ny.gov/climaid (accessed April 28, 2014).

Nicholls, S. 2012. Outdoor recreation and 

tourism. In: Winkler, J.; Andresen, J; Hatfield, 

J.; Bidwell, D.; Brown, D., coords. U.S. National 

Climate Assessment midwest technical input 

report. 12 p. Available at http://glisa.umich.

edu/media/files/NCA/MTIT_RecTourism.pdf

(accessed August 4, 2017).

Ning, L.; Bradley, R.S. 2015. Snow occurrence 

changes over the central and eastern United 

States under future warming scenarios.

Scientific Reports. 5: 17073. https://doi.

org/10.1038/srep17073http://www.nature.com/

articles/srep17073#supplementary-information.

Ning, L.; Riddle, E.E.; Bradley, R.S. 2015. 

Projected changes in climate extremes over the 

northeastern United States. Journal of Climate. 

28(8): 3289-3310. 

https://doi.org/10.1175/jcli-d-14-00150.1.

Norby, R.J.; DeLucia, E.H.; Gielen, B.; Calfapietra, 

C.; Giardina, C.P.; King, J.S.; Ledford, J.; 

McCarthy, H.R.; Moore, D.J.P.; Ceulemans, 

R. 2005. Forest response to elevated CO2

is conserved across a broad range of 

productivity. Proceedings of the National 

Academy of Sciences. 102(50): 18052-18056. 

https://doi.org/10.1073/pnas.0509478102.
LITERATURE CITED

159

Norby, R.J.; Zak, D.R. 2011. Ecological lessons 

from free-air CO2 enrichment (FACE) 

experiments. Annual Review of Ecology, 

Evolution, and Systematics. 42: 181-203. https://

doi.org/10.1146/annurev-ecolsys-102209-144647.

Notaro, M.; Lorenz, D.; Hoving, C.; Schummer, 

M. 2014. Twenty-first-century projections of 

snowfall and winter severity across centraleastern North America. Journal of Climate. 

27(17): 6526-6550. 

https://doi.org/10.1175/JCLI-D-13-00520.1.

Nowacki, G.J.; Abrams, M.D. 2008. The demise 

of fire and “mesophication” of forests in the 

eastern United States. BioScience. 58(2): 

123-138. https://doi.org/10.1641/b580207.

Ollinger, S.; Goodale, C.; Hayhoe, K.; Jenkins, 

J. 2008. Potential effects of climate change 

and rising CO2 on ecosystem processes in 

northeastern U.S. forests. Mitigation and 

Adaptation Strategies for Global Change. 

13(5): 467-485. 

https://doi.org/10.1007/s11027-007-9128-z.

Olmstead, S.M.; Fisher-Vanden, K.A.; Rimsaite, R. 

2016. Climate change and water resources: 

some adaptation tools and their limits. Journal 

of Water Resources Planning and Management. 

142(6): 01816003. https://doi.org/10.1061/

(ASCE)WR.1943-5452.0000642.

Olofsson, P.; Holden, C.E.; Bullock, E.L.; 

Woodcock, C.E. 2016. Time series analysis of 

satellite data reveals continuous deforestation 

of New England since the 1980s. Environmental 

Research Letters. 11(6): 064002. 

https://doi.org/10.1088/1748-9326/11/6/064002.

Ordonez, A.; Martinuzzi, S.; Radeloff, V.C.; 

Williams, J.W. 2014. Combined speeds 

of climate and land-use change of the 

conterminous US until 2050. Nature Climate 

Change. 4: 811-816. 

https://doi.org/10.1038/nclimate2337.

Ordóñez, C.; Duinker, P. 2015. Climate change 

vulnerability assessment of the urban forest in 

three Canadian cities. Climatic Change. 131(4): 

531-543. 

https://doi.org/10.1007/s10584-015-1394-2.

Orwig, D.A.; Foster, D.R.; Mausel, D.L. 2002. 

Landscape patterns of hemlock decline in New 

England due to the introduced hemlock woolly 

adelgid. Journal of Biogeography. 

29(10-11): 1475-1487. https://doi.org/10.1046/

j.1365-2699.2002.00765.x.

Oswalt, S.N.; Smith, W.B.; Miles, P.D.; Pugh, S.A. 

2014. Forest resources of the United States, 

2012: a technical document supporting 

the Forest Service 2010 update of the 

RPA assessment. Gen. Tech. Rep. WO-91. 

Washington, DC: U.S. Department of Agriculture, 

Forest Service, Washington Office. 218 p.

Pan, Y.; Birdsey, R.; Hom, J.; McCullough, K. 2009. 

Separating effects of changes in atmospheric 

composition, climate and land-use on carbon 

sequestration of US Mid-Atlantic temperate 

forests. Forest Ecology and Management. 

259(2): 151-164. 

https://doi.org/10.1016/j.foreco.2009.09.049.

Paradis, A.; Elkinton, J.; Hayhoe, K.; Buonaccorsi, 

J. 2008. Role of winter temperature and 

climate change on the survival and future 

range expansion of the hemlock woolly adelgid 

(Adelges tsugae) in eastern North America.

Mitigation and Adaptation Strategies for Global 

Change. 13(5-6): 541-554. 

https://doi.org/10.1007/s11027-007-9127-0.

Pardo, L.H.; Robin-Abbott, M.J.; Driscoll, C.T., eds. 

2011. Assessment of nitrogen deposition effects 

and empirical critical loads of nitrogen for 

ecoregions of the United States. Gen. Tech. Rep. 

NRS-80. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 291 p. 

https://doi.org/10.2737/NRS-GTR-80.
LITERATURE CITED

160

Parmesan, C.; Yohe, G. 2003. A globally coherent 

fingerprint of climate change impacts across 

natural systems. Nature. 421: 37-42. 

https://doi.org/10.1038/nature01286.

Parshall, T.; Foster, D.R. 2002. Fire on the New 

England landscape: regional and temporal 

variation, cultural and environmental controls.

Journal of Biogeography. 29(10-11): 1305-1317. 

https://doi.org/10.1046/j.1365-2699.2002.00758.x.

Patterson, W.A., III; Sassaman, K.E. 1988. Indian 

fires in the prehistory of New England. In: 

Nicholas, G.P., ed. Holocene human ecology 

in northeastern North America. New York, NY: 

Springer: 107-135. 

https://doi.org/10.1007/978-1-4899-2376-9_6.

Patz, J.A.; Malecki, K.; McLellan, S.; Shaw, S.; 

Vavrus, S. 2011. Human health working group 

report. Wisconsin Initiative on Climate Change 

Impacts. 46 p. Available at http://www.wicci.

wisc.edu/report/Human-Health.pdf.

Pease, A.A.; Paukert, C.P. 2014. Potential impacts 

of climate change on growth and prey 

consumption of stream-dwelling smallmouth 

bass in the central United States. Ecology of 

Freshwater Fish. 23(3): 336-346. 

https://doi.org/10.1111/eff.12084.

Pederson, N.; D’Amato, A.W.; Dyer, J.M.; Foster, 

D.R.; Goldblum, D.; Hart, J.L.; Hessl, A.E.; 

Iverson, L.R.; Jackson, S.T.; Martin-Benito, D.; 

McCarthy, B.C.; McEwan, R.W.; Mladenoff, 

D.J.; Parker, A.J.; Shuman, B.; Williams, J.W. 

2014. Climate remains an important driver 

of post-European vegetation change in the 

eastern United States. Global Change Biology. 

21(6): 2105-2110. 

https://doi.org/10.1111/gcb.12779.

Pedlar, J.H.; McKenney, D.W.; Aubin, I.; 

Beardmore, T.; Beaulieu, J.; Iverson, L.; O’Neill, 

G.A.; Winder, R.S.; Ste-Marie, C. 2012. Placing 

forestry in the assisted migration debate.

Bioscience. 62(9): 835-842. 

https://doi.org/10.1525/bio.2012.62.9.10.

Peters, M.P.; Iverson, L.R.; Matthews, S.N. 2014. 

Spatio-temporal trends of drought by forest 

type in the conterminous United States, 1960-

2013. Res. Map NRS-7. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. [1:12,000,000]. 

https://doi.org/10.2737/NRS-RMAP-7.

Peters, R.L. 1985. The greenhouse effect and 

nature reserves. BioScience. 35(11): 707-717. 

https://doi.org/10.2307/1310052.

Peterson, C.J. 2000. Catastrophic wind damage 

to North American forests and the potential 

impact of climate change. The Science of the 

Total Environment. 262: 287-311. 

https://doi.org/10.1016/S0048-9697(00)00529-5.

Peterson, C.J.; Krueger, L.M.; Royo, A.A.; Stark, 

S.; Carson, W.P. 2013a. Disturbance size and 

severity covary in small and mid-size wind 

disturbances in Pennsylvania northern 

hardwoods forests. Forest Ecology and 

Management. 302: 273-279. 

https://doi.org/10.1016/j.foreco.2013.03.045.

Peterson, T.C.; Heim, R.R.; Hirsch, R.; Kaiser, 

D.P.; Brooks, H.; Diffenbaugh, N.S.; Dole, 

R.M.; Giovannettone, J.P.; Guirguis, K.; Karl, 

T.R.; Katz, R.W.; Kunkel, K.; Lettenmaier, D.; 

McCabe, G.J.; Paciorek, C.J.; Ryberg, K.R.; 

Schubert, S.; Silva, V.B.S.; Stewart, B.C.; 

Vecchia, A.V.; Villarini, G.; Vose, R.S.; Walsh, J.; 

Wehner, M.; Wolock, D.; Wolter, K.; Woodhouse, 

C.A.; Wuebbles, D. 2013b. Monitoring and 

understanding changes in heat waves, cold 

waves, floods, and droughts in the United 

States: state of knowledge. Bulletin of the 

American Meteorological Society. 94(6): 

821-834. https://doi.org/10.1175/BAMS-D-12-

00066.1.

Peterson, T.C.; Zhang, X.; Brunet-India, M.; 

Vázquez-Aguirre, J.L. 2008. Changes in North 

American extremes derived from daily 

weather data. Journal of Geophysical Research: 

Atmospheres. 113(D7): D07113. https://doi.

org/10.1029/2007jd009453.
LITERATURE CITED

161

Portier, C.J.; Tart, K.T.; Carter, S.R.; Dilworth, 

C.H.; Grambusch, A.E.; Gohlke, J.; Hess, J.; 

Howard, S.; Luber, G.; Lutz, J. 2013. A human 

health perspective on climate change: a report 

outlining the research needs on the human 

health effects of climate change. Journal of 

Current Issues in Globalization. 6(4): 621.

Puhlick, J.; Woodall, C.; Weiskittel, A. 2017. 

Implications of land-use change on forest 

carbon stocks in the eastern United States.

Environmental Research Letters. 12(2): 024011. 

https://doi.org/10.1088/1748-9326/aa597f.

Pureswaran, D.S.; De Grandpré, L.; Paré, D.; 

Taylor, A.; Barrette, M.; Morin, H.; Régnière, J.; 

Kneeshaw, D.D. 2015. Climate-induced changes 

in host tree–insect phenology may drive 

ecological state-shift in boreal forests. Ecology. 

96(6): 1480-1491. 

https://doi.org/10.1890/13-2366.1.

Rahbek, C.; Gotelli, N.J.; Colwell, R.K.; 

Entsminger, G.L.; Rangel, T.F.L.; Graves, G.R. 

2007. Predicting continental-scale patterns 

of bird species richness with spatially explicit 

models. Proceedings of the Royal Society B: 

Biological Sciences. 274(1607): 165-174. 

https://doi.org/10.1098/rspb.2006.3700.

Raney, P.A.; Leopold, D.J.; Dovciak, M.; Beier, 

C.M. 2016. Hydrologic position mediates 

sensitivity of tree growth to climate: 

Groundwater subsidies provide a thermal 

buffer effect in wetlands. Forest Ecology and 

Management. 379: 70-80. 

https://doi.org/10.1016/j.foreco.2016.08.004.

Raupach, M.R.; Marland, G.; Ciais, P.; Le Quere, 

C.; Canadell, J.G.; Klepper, G.; Field, C.B. 2007. 

Global and regional drivers of accelerating 

CO2 emissions. Proceedings of the National 

Academy of Sciences. 104(24): 10288-10293. 

https://doi.org/10.1073/pnas.0700609104.

Rebbeck, J. 2012. Fire management and woody 

invasive plants in oak ecosystems. In: Dey, 

D.C.; Stambaugh, M.C.; Clark, S.L.; Schweitzer, 

C.J., eds. Proceedings of the 4th fire in eastern 

forests conference. Gen. Tech. Rep. NRS-P-102. 

Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research 

Station: 142-155. 

Régnière, J.; St-Amant, R.; Duval, P. 2012. 

Predicting insect distributions under climate 

change from physiological responses: spruce 

budworm as an example. Biological Invasions. 

14(8): 1571-1586. 

https://doi.org/10.1007/s10530-010-9918-1.

Reinmann, A.B.; Templer, P.H. 2016. Reduced 

winter snowpack and greater soil frost reduce 

live root biomass and stimulate radial growth 

and stem respiration of red maple (Acer 

rubrum) trees in a mixed-hardwood forest.

Ecosystems. 19(1): 129-141. 

https://doi.org/10.1007/s10021-015-9923-4.

Rempel, R.S. 2011. Effects of climate change on 

moose populations: exploring the response 

horizon through biometric and systems 

models. Ecological Modelling. 222(18): 

3355-3365. 

https://doi.org/10.1016/j.ecolmodel.2011.07.012.

Rennenberg, H.; Dannenmann, M.; Gessler, A.; 

Kreuzwieser, J.; Simon, J.; Papen, H. 2009. 

Nitrogen balance in forest soils: nutritional 

limitation of plants under climate change 

stresses. Plant Biology. 11(s1): 4-23. 

https://doi.org/10.1111/j.1438-8677.2009.00241.x.

Renwick, K.M.; Rocca, M.E. 2015. Temporal 

context affects the observed rate of climatedriven range shifts in tree species. Global 

Ecology and Biogeography. 24(1): 44-51. 

https://doi.org/10.1111/geb.12240.
LITERATURE CITED

162

Reusch, T.B.; Ehlers, A.; Hämmerli, A.; Worm, 

B. 2005. Ecosystem recovery after climatic 

extremes enhanced by genotypic diversity.

Proceedings of the National Academy of Sciences 

of the United States of America. 102(8): 

2826-2831. 

https://doi.org/10.1073/pnas.0500008102.

Rhoads, A.G.; Hamburg, S.P.; Fahey, T.J.; Siccama, 

T.G.; Hane, E.N.; Battles, J.; Cogbill, C.; Randall, 

J.; Wilson, G. 2002. Effects of an intense ice 

storm on the structure of a northern hardwood 

forest. Canadian Journal of Forest Research. 

32(10): 1763-1775. 

https://doi.org/10.1139/x02-089.

Ricciardi, A.; Hoopes, M.F.; Marchetti, M.P.; 

Lockwood, J.L. 2013. Progress toward 

understanding the ecological impacts of 

nonnative species. Ecological Monographs. 

83(3): 263-282. 

https://doi.org/10.1890/13-0183.1.

Rich, R.L.; Frelich, L.E.; Reich, P.B. 2007. Windthrow mortality in the southern boreal forest: 

effects of species, diameter and stand age.

Journal of Ecology. 95: 1261-1273. https://doi.

org/10.1111/j.1365-2745.2007.01301.x.

Richardson, A.D.; Bailey, A.S.; Denny, E.G.; 

Martin, C.W.; O’Keefe, J. 2006. Phenology of 

a northern hardwood forest canopy. Global 

Change Biology. 12(7): 1174-1188. https://doi.

org/10.1111/j.1365-2486.2006.01164.x.

Richardson, A.D.; Black, T.A.; Ciais, P.; Delbart, 

N.; Friedl, M.A.; Gobron, N.; Hollinger, D.Y.; 

Kutsch, W.L.; Longdoz, B.; Luyssaert, S. 2010. 

Influence of spring and autumn phenological 

transitions on forest ecosystem productivity.

Philosophical Transactions of the Royal Society 

of London B: Biological Sciences. 365(1555): 

3227-3246. 

https://doi.org/10.1098/rstb.2010.0102.

Rissman, A.R.; Owley, J.; Shaw, M.R.; Thompson, 

B. 2015. Adapting conservation easements to 

climate change. Conservation Letters. 8(1): 

68-76. https://doi.org/10.1111/conl.12099.

Rittenhouse, C.D.; Rissman, A.R. 2015. Changes in 

winter conditions impact forest management 

in north temperate forests. Journal of 

Environmental Management. 149: 157-167. 

https://doi.org/10.1016/j.jenvman.2014.10.010.

Robbins, P.; Emery, M.; Rice, J.L. 2008. Gathering 

in Thoreau’s backyard: nontimber forest 

product harvesting as practice. Area. 40(2): 

265-277. https://doi.org/10.1111/j.1475-

4762.2008.00794.x.

Robinson, E.D.; Trapp, R.J.; Baldwin, M.E. 2013. 

The geospatial and temporal distributions of 

severe thunderstorms from high-resolution 

dynamical downscaling. Journal of Applied 

Meteorology and Climatology. 52(9): 2147-2161. 

https://doi.org/10.1175/JAMC-D-12-0131.1.

Rodenhouse, N.L. 2009. Climate change effects on 

native fauna of northeastern forests. Canadian 

Journal of Forest Research. 39(2): 249-263. 

https://doi.org/10.1139/X08-160.

Rodenhouse, N.L.; Christenson, L.M.; Parry, D.; 

Green, L.E. 2009. Climate change effects on 

native fauna of northeastern forests. Canadian 

Journal of Forest Research. 39: 249-263. 

https://doi.org/10.1139/X08-160.

Root, T.L.; Price, J.T.; Hall, K.R.; Schneider, S.H.; 

Rosenzweig, C.; Pounds, J.A. 2003. Fingerprints 

of global warming on wild animals and plants.

Nature. 421(6918): 57-60. 

https://doi.org/10.1038/nature01333.

Ruefenacht, B.; Finco, M.V.; Nelson, M.D.; 

Czaplewski, R.; Helmer, E.H.; Blackard, 

J.A.; Holden, G.R.; Lister, A.J.; Salajanu, 

D.; Weyermann, D.; Winterberge, K. 2013. 

Conterminous U.S. and Alaska forest type 

mapping using forest inventory and analysis 

data. Washington, DC: U.S. Department of 

Agriculture, Forest Service Forest Inventory 

and Analysis and Remote Sensing Applications 

Center. Available at https://data.fs.usda.gov/

geodata/rastergateway/forest_type/ (accessed 

October 17, 2013).
LITERATURE CITED

163

Rustad, L.; Campbell, J.; Dukes, J.S.; Huntington, 

T.; Fallon Lambert, K.; Mohan, J.; Rodenhouse, 

N. 2012. Changing climate, changing forests: 

the impacts of climate change on forests of 

the northeastern United States and eastern 

Canada. Gen. Tech. Rep. NRS-99. Newtown 

Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 48 p. 

Rustad, L.; Campbell, J.; Marion, G.; Norby, 

R.; Mitchell, M.; Hartley, A.; Cornelissen, J.; 

Gurevitch, J. 2001. A meta-analysis of the 

response of soil respiration, net nitrogen 

mineralization, and aboveground plant 

growth to experimental ecosystem warming.

Oecologia. 126(4): 543-562. 

https://doi.org/10.1007/s004420000544.

Rustad, L.E.; Campbell, J.L. 2012. A novel ice 

storm manipulation experiment in a northern 

hardwood forest. Canadian Journal of Forest 

Research. 42(10): 1810-1818. 

https://doi.org/10.1139/x2012-120.

Ryan, M.G.; Harmon, M.E.; Birdsey, R.A.; Giardina, 

C.P.; Heath, L.S.; Houghton, R.A.; Jackson, R.B.; 

McKinley, D.C.; Morrison, J.F.; Murray, B.C.; 

Pataki, D.E.; Skog, K.E. 2010. A synthesis of the 

science on forests and carbon for US forests.

Ecological Society of America: Issues in Ecology. 

13: 1-16.

Ryan, M.G.; Vose, J.M. 2012. Effects of climatic 

variability and change. In: Vose, J.M.; Peterson, 

D.L.; Patel-Weynand, T., eds. 2012. Effects 

of climatic variability and change on forest 

ecosystems: a comprehensive science synthesis 

for the U.S. forest sector. Gen.Tech. Rep. 

PNW-GTR-870. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest 

Research Station: 7-95.

Saint Regis Mohawk Tribe. 2013. Climate change 

adaptation plan for Akwesasne. Draft. 63 p. 

Available at http://www.srmt-nsn.gov/_uploads/

site_files/ClimateChange.pdf (accessed June 15, 

2016).

Sallenger, A.H, Jr.; Doran, K.S.; Howd, P.A. 2012. 

Hotspot of accelerated sea-level rise on the 

Atlantic coast of North America. Nature 

Climate Change. 2(12): 884-888. 

https://doi.org/10.1038/nclimate1597.

Sasek, T.W.; Strain, B.R. 1988. Effects of carbon 

dioxide enrichment on the growth and 

morphology of kudzu (Pueraria lobata). Weed 

Science. 36(1): 28-36.

Sasek, T.W.; Strain, B.R. 1989. Effects of carbon 

dioxide enrichment on the expansion and 

size of kudzu (Pueraria lobata) leaves. Weed 

Science. 37(1): 23-28.

Scavia, D.; Field, J.; Boesch, D.; Buddemeier, R.; 

Burkett, V.; Cayan, D.; Fogarty, M.; Harwell, 

M.; Howarth, R.; Mason, C.; Reed, D.; Royer, 

T.; Sallenger, A.; Titus, J. 2002. Climate 

change impacts on U.S. coastal and marine 

ecosystems. Estuaries. 25(2): 149-164. 

https://doi.org/10.1007/bf02691304.

Schaberg, P.G.; DeHayes, D.H. 2000. Physiological 

and environmental causes of freezing injury 

in red spruce. In: Mickler, R.A.; Birdsey, R.A.; 

Hom, J., eds. Responses of northern US forests 

to environmental change. Ecological Studies 139. 

New York, NY: Springer: 181-227. 

https://doi.org/10.1007/978-1-4612-1256-0_6.

Schaberg, P.G.; Tilley, J.W.; Hawley, G.J.; DeHayes, 

D.H.; Bailey, S.W. 2006. Associations of 

calcium and aluminum with the growth and 

health of sugar maple trees in Vermont. Forest 

Ecology and Management. 223(1): 159-169. 

https://doi.org/10.1016/j.foreco.2005.10.067.

Scheller, R.M.; Mladenoff, D.J. 2008. Simulated 

effects of climate change, fragmentation, 

and inter-specific competition on tree species 

migration in northern Wisconsin, USA.

Climate Research. 36: 191-202. 

https://doi.org/10.3354/cr00745.

Schiffer, M.B. 1996. Formation processes of the 

archaeological record. Salt Lake City, UT: 

University of Utah Press. 488 p.
LITERATURE CITED

164

Schulte, L.A.; Mladenoff, D.J. 2005. Severe wind 

and fire regimes in northern forests: historical 

variability at the regional scale. Ecology. 86(2): 

431-445. 

https://doi.org/10.1890/03-4065.

Schwartz, M.D.; Ahas, R.; Aasa, A. 2006. Onset 

of spring starting earlier across the Northern 

Hemisphere. Global Change Biology. 12(2): 

343-351. https://doi.org/10.1111/j.1365-

2486.2005.01097.x.

Schwartz, M.W.; Hellmann, J.J.; McLachlan, J.M.; 

Sax, D.F.; Borevitz, J.O.; Brennan, J.; Camacho, 

A.E.; Ceballos, G.; Clark, J.R.; Doremus, H. 

2012. Managed relocation: integrating the 

scientific, regulatory, and ethical challenges.

Bioscience. 62(8): 732-743. 

https://doi.org/10.1525/bio.2012.62.8.6.

Scott, D.; Dawson, J.; Jones, B. 2008. Climate 

change vulnerability of the US Northeast 

winter recreation–tourism sector. Mitigation 

and Adaptation Strategies for Global Change. 

13(5): 577-596. 

https://doi.org/10.1007/s11027-007-9136-z.

Sendall, K.M.; Reich, P.B.; Zhao, C.; Jihua, 

H.; Wei, X.; Stefanski, A.; Rice, K.; Rich, 

R.L.; Montgomery, R.A. 2015. Acclimation 

of photosynthetic temperature optima of 

temperate and boreal tree species in response 

to experimental forest warming. Global Change 

Biology. 21(3): 1342-1357. 

https://doi.org/10.1111/gcb.12781.

Shifley, S.R.; Aguilar, F.X.; Song, N.; Stewart, S.I.; 

Nowak, D.J.; Gormanson, D.D.; Moser, W.K.; 

Wormstead, S.; Greenfield, E.J. 2012. Forests 

of the northern United States. Gen. Tech. Rep. 

NRS-90. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research 

Station. 202 p.

Shifley, S.R.; Moser, W.K., eds. 2016. Future 

forests of the northern United States. Gen. 

Tech. Rep. NRS-151. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 388 p. 

https://doi.org/10.2737/NRS-GTR-151.

Skinner, C.B.; DeGaetano, A.T.; Chabot, B.F. 2010. 

Implications of twenty-first century climate 

change on northeastern United States maple 

syrup production: impacts and adaptations.

Climatic Change. 100(3-4): 685-702. 

https://doi.org/10.1007/s10584-009-9685-0.

Skinner, M.; Parker, B.L.; Gouli, S.; Ashikaga, T. 

2003. Regional responses of hemlock woolly 

adelgid (Homoptera: Adelgidae) to low 

temperatures. Environmental Entomology. 

32(3): 523-528. 

https://doi.org/10.1603/0046-225x-32.3.523.

Smith, G.; Morin, R.; McCaskill, G.L. 2012. 

Ambient ozone injury to forest plants in 

northeast and north central USA: 16 years 

of biomonitoring. Gen. Tech. Rep. NRS-103. 

Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research 

Station. 46 p. 

https://doi.org/10.2737/NRS-GTR-103.

Sommers, W.T.; Coloff, S.G.; Conard, S.G. 2011. 

Synthesis of knowledge: fire history and 

climate change. Project 09-2-01-09 Report 

submitted to the Joint Fire Science Program. 

283 p. http://www.firescience.gov/projects/09-2-

01-9/project/09-2-01-9_Final_Report_JFSP_09-

2-01-09_Fire_History_and_Climate_Change_by_

Chapter_111013.pdf.

Spierre, S.G.; Wake, C.P. 2010. Trends in extreme 

precipitation events for the northeastern 

United States 1948-2007. Durham, NH: Carbon 

Solutions New England. 14 p. Available at http://

www.amwa.net/galleries/climate-change/2010_

NortheastExtremePrecip.pdf (accessed July 3, 

2017).

Spurr, S.H. 1956. Natural restocking of forests 

following the 1938 hurricane in central New 

England. Ecology. 37(3): 443-451. 

https://doi.org/10.2307/1930166.

St. Clair, S.B.; Sharpe, W.E.; Lynch, J.P. 2008. 

Key interactions between nutrient limitation 

and climatic factors in temperate forests: 

a synthesis of the sugar maple literature.

Canadian Journal of Forest Research. 38(3): 

401-414. https://doi.org/10.1139/X07-161.
LITERATURE CITED

165

Stager, J.C.; Thill, M. 2010. Climate change in 

the Champlain basin: what natural resource 

managers can expect and do. Montpelier, 

VT: The Nature Conservancy. 44 p. Available 

at http://www.nature.org/ourinitiatives/regions/

northamerica/unitedstates/vermont/howwework/

champlain-climate-report-5-2010-2.pdf (accessed 

April 16, 2014).

Staudinger, M.D.; Carter, S.L.; Cross, M.S.; 

Dubois, N.S.; Duffy, J.E.; Enquist, C.; Griffis, 

R.; Hellmann, J.J.; Lawler, J.J.; O’Leary, J.; 

Morrison, S.A.; Sneddon, L.; Stein, B.A.; 

Thompson, L.M.; Turner, W. 2013. Biodiversity 

in a changing climate: a synthesis of current 

and projected trends in the US. Frontiers in 

Ecology and the Environment. 11(9): 465-473. 

https://doi.org/10.1890/120272.

Stein, B.A.; Glick, P.; Edelson, N.; Staudt, A., 

eds. 2014. Climate-smart conservation: 

putting adaptation principles into practice.

Washington, DC: National Wildlife Federation. 

262 p.

Stoner, A.M.K.; Hayhoe, K.; Yang, X.; Wuebbles, 

D.J. 2012. An asynchronous regional 

regression model for statistical downscaling of 

daily climate variables. International Journal of 

Climatology. 33(11): 2473-2494. 

https://doi.org/10.1002/joc.3603.

Stott, P.A.; Gillett, N.P.; Hegerl, G.C.; Karoly, D.J.; 

Stone, D.i.ı.A.; Zhang, X.; Zwiers, F. 2010. 

Detection and attribution of climate change: a 

regional perspective. WIREs Climate Change. 

1(March/April): 192-211. 

https://doi.org/10.1002/wcc.34.

Strock, K.E.; Nelson, S.J.; Kahl, J.S.; Saros, J.E.; 

McDowell, W.H. 2014. Decadal trends reveal 

recent acceleration in the rate of recovery 

from acidification in the northeastern U.S.

Environmental Science & Technology. 48(9): 

4681-4689. 

https://doi.org/10.1021/es404772n.

Sturrock, R.; Frankel, S.; Brown, A.; Hennon, P.; 

Kliejunas, J.; Lewis, K.; Worrall, J.; Woods, 

A. 2011. Climate change and forest diseases.

Plant Pathology. 60(1): 133-149. https://doi.

org/10.1111/j.1365-3059.2010.02406.x.

Sullivan, T.J.; Lawrence, G.; Bailey, S.W.; 

McDonnell, T.C.; Beier, C.; Weathers, K.C.; 

McPherson, G.T.; Bishop, D.A. 2013. Effects 

of acidic deposition and soil acidification 

on sugar maple trees in the Adirondack 

Mountains, New York. Environmental Science 

& Technology. 47(22): 12687-12694. 

https://doi.org/10.1021/es401864w.

Sun, Q.; Miao, C.; Duan, Q. 2015. Comparative 

analysis of CMIP3 and CMIP5 global climate 

models for simulating the daily mean, 

maximum, and minimum temperatures and 

daily precipitation over China. Journal of 

Geophysical Research: Atmospheres. 120(10): 

4806-4824. 

https://doi.org/10.1002/2014jd022994.

Swanson, F.J.; Johnson, S.L.; Gregory, S.V.; Acker, 

S.A. 1998. Flood disturbance in a forested 

mountain landscape. BioScience. 48(9): 

681-689. 

https://doi.org/10.2307/1313331.

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. 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.

Tang, G.; Beckage, B.; Smith, B. 2012. The 

potential transient dynamics of forests in New 

England under historical and projected future 

climate change. Climatic Change. 114(2): 

357-377. 

https://doi.org/10.1007/s10584-012-0404-x.
LITERATURE CITED

166

Tang, G.; Beckage, B.; Smith, B. 2014. Potential 

future dynamics of carbon fluxes and pools 

in New England forests and their climatic 

sensitivities: a model-based study. Global 

Biogeochemical Cycles. 28(3): 286-299. 

https://doi.org/10.1002/2013gb004656.

Tang, G.; Beckage, B.; Smith, B.; Miller, P.A. 2010. 

Estimating potential forest NPP, biomass and 

their climatic sensitivity in New England using 

a dynamic ecosystem model. Ecosphere. 1(6): 

1-20. https://doi.org/10.1890/es10-00087.1.

Tang, Y.; Zhong, S.; Luo, L.; Bian, X.; Heilman, 

W.E.; Winkler, J. 2015. The potential impact

of regional climate change on fire weather in

the United States. Annals of the Association of

American Geographers. 105(1): 1-21.

https://doi.org/10.1080/00045608.2014.968892.

Taylor, K.E.; Stouffer, R.J.; Meehl, G.A. 2012. 

An overview of CMIP5 and the experiment 

design. Bulletin of the American Meteorological 

Society. 93(4): 485-498. 

https://doi.org/10.1175/BAMS-D-11-00094.1.

Templer, P.H.; Pinder, R.W.; Goodale, C.L. 2012a. 

Effects of nitrogen deposition on greenhousegas fluxes for forests and grasslands of 

North America. Frontiers in Ecology and the 

Environment. 10(10): 547-553. 

https://doi.org/10.1890/120055.

Templer, P.H.; Schiller, A.F.; Fuller, N.W.; Socci, 

A.M.; Campbell, J.L.; Drake, J.E.; Kunz, T.H.

2012b. Impact of a reduced winter snowpack

on litter arthropod abundance and diversity

in a northern hardwood forest ecosystem.

Biology and Fertility of Soils. 48(4): 413-424.

https://doi.org/10.1007/s00374-011-0636-3.

Tetra Tech. 2013. Climate change adaptation 

framework: full report. Montpelier, VT: 

Vermont Agency of Natural Resources. 157 p. 

Available at http://www.anr.state.vt.us/anr/

climatechange/Pubs/2013.0610.vtanr.NR_CC_

Adaptation_Framework_Report.pdf (accessed 

April 16, 2014).

Thibeault, J.M.; Seth, A. 2014a. Changing climate 

extremes in the northeast United States: 

observations and projections from CMIP5.

Climatic Change. 127(2): 273-287. 

https://doi.org/10.1007/s10584-014-1257-2.

Thibeault, J.M.; Seth, A. 2014b. A framework for 

evaluating model credibility for warm-season 

precipitation in northeastern North America: 

a case study of CMIP simulations and 

projections. Journal of Climate. 27(2): 493-510. 

https://doi.org/10.1175/JCLI-D-12-00846.1.

Thompson, J.R.; Carpenter, D.N.; Cogbill, C.V.; 

Foster, D.R. 2013. Four centuries of change in 

northeastern United States forests. PLoS ONE. 

8(9): e72540. 

https://doi.org/10.1371/journal.pone.0072540.

Thompson, J.R.; Foster, D.R.; Scheller, R.; 

Kittredge, D. 2011. The influence of land use 

and climate change on forest biomass and 

composition in Massachusetts, USA. Ecological 

Applications. 21(7): 2425-2444. 

https://doi.org/10.1890/10-2383.1.

Thornton, P.E.; Lamarque, J.-F.; Rosenbloom, 

N.A.; Mahowald, N.M. 2007. Influence of

carbon-nitrogen cycle coupling on land model

response to CO2

 fertilization and climate

variability. Global Biogeochemical Cycles.

21(4). https://doi.org/10.1029/2006gb002868.

Tilman, D.; Isbell, F.; Cowles, J.M. 2014. 

Biodiversity and ecosystem functioning. 

Annual Review of Ecology, Evolution, and 

Systematics. 45(1): 471. https://doi.org/10.1146/

annurev-ecolsys-120213-091917.

Trân, J.K.; Ylioja, T.; Billings, R.F.; Régnière, J.; 

Ayres, M.P. 2007. Impact of minimum winter 

temperatures on the population dynamics of 

Dendroctonus frontalis. Ecological Applications. 

17(3): 882-899. 

https://doi.org/10.1890/06-0512.

Trani Griep, M.; Manley, P. 2016. Biodiversity and 

climate change. U.S. Department of Agriculture, 

Forest Service, Climate Change Resource Center. 

Available at www.fs.usda.gov/ccrc/topics/

biodiversity (accessed December 30, 2016).
LITERATURE CITED

167

Trapp, R.J.; Diffenbaugh, N.S.; Brooks, H.E.; 

Baldwin, M.E.; Robinson, E.D.; Pal, J.S. 2007. 

Changes in severe thunderstorm environment 

frequency during the 21st century caused by 

anthropogenically enhanced global radiative 

forcing. Proceedings of the National Academy 

of Sciences of the United States of America. 

104(50): 19719-19723. 

https://doi.org/10.1073/pnas.0705494104.

Trapp, R.J.; Robinson, E.D.; Baldwin, M.E.; 

Diffenbaugh, N.S.; Schwedler, B.R.J. 2011. 

Regional climate of hazardous convective 

weather through high-resolution dynamical 

downscaling. Climate Dynamics. 37(3): 677-688. 

https://doi.org/10.1007/s00382-010-0826-y.

Trenberth, K.E.; Fasullo, J.T.; Shepherd, T.G. 2015. 

Attribution of climate extreme events. Nature 

Climate Change. 5(8): 725-730. 

https://doi.org/10.1038/nclimate2657.

Trotter, T. 2013. Relationships among climate, 

forests, and insects in North America: 

examples of the importance of long-term and 

broad-scale perspectives. In: Camp, A.E.; Irland, 

L.C.; Carroll, C.J.W., eds. Long-term silvicultural 

and ecological studies. Results for science and 

management: volume 2. New Haven, CT: Yale 

University, School of Forestry and Environmental 

Studies, Global Institute of Sustainable Forestry: 

161-177.

Tu, J. 2009. Combined impact of climate and land 

use changes on streamflow and water quality 

in eastern Massachusetts, USA. Journal of 

Hydrology. 379(3-4): 268-283. 

https://doi.org/10.1016/j.jhydrol.2009.10.009.

Tu, M.; Callie, H.; Randall, J.M. 2001. Weed 

control methods handbook: tools and 

techniques for use in natural areas. Davis, 

CA: The Nature Conservancy Wildland Invasive 

Species Team. 291 p. https://www.invasive.org/

gist/products/handbook/methods-handbook.pdf

Turcotte, R.M.; Elliott, T.R.; Fajvan, M.A.; Park, 

Y.-L.; Snider, D.A.; Tobin, P.C. 2012. Effects of 

ice storm damage on hardwood survival and 

growth in Ohio. Northern Journal of Applied 

Forestry. 29(2): 53-59. 

https://doi.org/10.5849/njaf.10-053.

U.S. Forest Service. 2007. USDA Forest Service 

Manual 2300: recreation, wilderness, and 

related resource management. Chapter 2320: 

Wilderness Management. Washington, DC: 

U.S. Department of Agriculture, Forest Service, 

National Headquarters. 65 p. http://www.fs.usda.

gov/Internet/FSE_DOCUMENTS/fsbdev3_

053277.pdf.

U.S. Forest Service. 2012. 36 CFR Part 219. 

National Forest System land management 

planning. Final rule and record of decision.

Federal Register. 77(68): 21162-21276. Available 

at https://www.fs.fed.us/emc/applit/includes/

20120409FinalPlanningRule.pdf (accessed 

August 2, 2017).

U.S. Forest Service. 2013. Forest Inventory and 

Analysis DataMart: FIADB version 5.1.

Washington, DC: U.S. Department of Agriculture, 

Forest Service. Available at http://apps.fs.fed.

us/fiadb-downloads/datamart.html.

U.S. Forest Service. 2016. Forest pest conditions.

U.S. Department of Agriculture, Forest Service, 

Forest Health Technology Enterprise Team. 

Available at http://foresthealth.fs.usda.gov/portal/

Flex/FPC.

U.S. Forest Service [N.d.]. Climate change atlas.

Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research 

Station. http://www.nrs.fs.fed.us/atlas.

Ulbrich, U.; Leckebusch, G.; Pinto, J. 2009. Extratropical cyclones in the present and future 

climate: a review. Theoretical and Applied 

Climatology. 96(1-2): 117-131. 

https://doi.org/10.1007/s00704-008-0083-8.
LITERATURE CITED

168

Vadas, R.L., Jr.; Sanger, J.E. 1997. Lateral zonation 

of trees along a small Ohio stream. Ohio 

Journal of Science. 97(5): 107-112.

Vavrus, S.; Walsh, J.; Chapman, W.; Portis, D. 2006. 

The behavior of extreme cold air outbreaks 

under greenhouse warming. International 

Journal of Climatology. 26(9): 1133-1147. 

https://doi.org/10.1002/joc.1301.

Vose, J.; Clark, J.S.; Luce, C.; Patel-Weynand, 

T., eds. 2016. Effects of drought on forests 

and rangelands in the United States: a 

comprehensive science synthesis. Gen. Tech. 

Rep. WO-93b. Washington, DC: U.S. Department 

of Agriculture, Forest Service, Washington 

Office. 289 p. Available at www.treesearch.fs.fed.

us/pubs/50261.

Vose, J.M.; Peterson, D.L.; Patel-Weynand, T., 

eds. 2012. Effects of climatic variability and 

change on forest ecosystems: a comprehensive 

science synthesis for the U.S. forest sector.

Gen. Tech. Rep. PNW-GTR-870. Portland, OR: 

U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 265 p. 

https://doi.org/10.2737/PNW-GTR-870.

Waite, T.A.; Strickland, D. 2006. Climate change 

and the demographic demise of a hoarding 

bird living on the edge. Proceedings of the 

Royal Society B: Biological Sciences. 273(1603): 

2809-2813. 

https://doi.org/10.1098/rspb.2006.3667.

Wake, C.P.; Burakowski, E.; Wilkinson, P.; Hayhoe, 

K.; Stoner, A.; Keeley, C.; LaBranche, J. 2014a. 

Climate change in northern New Hampshire: 

past, present, and future. Durham, NH: 

Sustainability Institute of New Hampshire. 76 p. 

Available at http://ddc-climate.sr.unh.edu/pages/

resources/publications/ (accessed July 3, 2017).

Wake, C.P.; Burakowski, E.; Wilkinson, P.; Hayhoe, 

K.; Stoner, A.; Keeley, C.; LaBranche, J. 2014b. 

Climate change in southern New Hampshire: 

past, present, and future. Durham, NH: 

Sustainability Institute of New Hampshire. 86 p. 

Available at http://ddc-climate.sr.unh.edu/pages/

resources/publications/ (accessed July 3, 2017).

Walck, J.L.; Hidayati, S.N.; Dixon, K.W.; 

Thompson, K.; Poschlod, P. 2011. Climate 

change and plant regeneration from seed.

Global Change Biology. 17(6): 2145-2161. 

https://doi.org/10.1111/j.1365-2486.2010.02368.x.

Walsh, J.; Wuebbles, D.; Hayhoe, K.; Kossin, J.; 

Kunkel, K.; Stephens, G.; Thorne, P.; Vose, R.; 

Wehner, M.; Willis, J.; Anderson, D.; Doney, S.; 

Feely, R.; Hennon, P.; Kharin, V.; Knutson, T.; 

Landerer, F.; Lenton, T.; Kennedy, J.; Somerville, 

R. 2014. Chapter 2: Our changing climate. In: 

Melillo, J.M.; Richmond, T.C.; Yohe, G.W., eds. 

Climate change impacts in the United States: the 

Third National Climate Assessment. Washington, 

DC: U.S. Global Change Research Program: 

19-67.

Walther, G.R.; Post, E.; Convey, P.; Menzel, A.; 

Parmesan, C.; Beebee, T.J.C.; Fromentin, 

J.M.; Hoegh-Guldberg, O.; Bairlein, F. 2002. 

Ecological responses to recent climate change.

Nature. 416(6879): 389-395. 

https://doi.org/10.1038/416389a.

Wang, G.; Chhin, S.; Bauerle, W.L. 2006. Effect 

of natural atmospheric CO2 fertilization 

suggested by open-grown white spruce in a dry 

environment. Global Change Biology. 12(3): 

601-610. 

https://doi.org/10.1111/j.1365-2486.2006.01098.x.

Wang, W.J.; He, H.S.; Fraser, J.S.; Thompson, F.R.; 

Shifley, S.R.; Spetich, M.A. 2014. LANDIS 

PRO: a landscape model that predicts forest 

composition and structure changes at regional 

scales. Ecography. 37(3): 225-229. 

https://doi.org/10.1111/j.1600-0587.2013.00495.x.

Wang, W.J.; He, H.S.; Thompson, F.R., III; Fraser, 

J.S.; Dijak, W.D. 2017. Changes in forest 

biomass and tree species distribution under 

climate change in the northeastern United 

States. Landscape Ecology. 32(7): 1399-1413. 

https://doi.org/10.1007/s10980-016-0429-z.
LITERATURE CITED

169

Wang, W.J.; He, H.S.; Thompson, F.R., III; 

Fraser, J.S.; Hanberry, B.B.; Dijak, W.D. 2015. 

Importance of succession, harvest, and climate 

change in determining future composition in 

US central hardwood forests. Ecosphere. 6(12): 

1-18. https://doi.org/10.1890/es15-00238.1.

Washington, W.M.; Weatherly, J.W.; Meehl, 

G.A.; Semtner, A.J., Jr.; Bettge, T.W.; Craig, 

A.P.; Strand, W.G., Jr.; Arblaster, J.; Wayland, 

V.B.; James, R.; Zhang, Y. 2000. Parallel 

climate model (PCM) control and transient 

simulations. Climate Dynamics. 16(10): 755-

774. https://doi.org/10.1007/s003820000079.

Wattles, D.W.; DeStefano, S. 2013. Moose habitat 

in Massachusetts: assessing use at the southern 

edge of the range. Alces. 49: 133-147.

Weed, A.S.; Ayres, M.P.; Hicke, J.A. 2013. 

Consequences of climate change for biotic 

disturbances in North American forests.

Ecological Monographs. 83(4): 441-470. 

https://doi.org/10.1890/13-0160.1.

Weeks, B.C.; Hamburg, S.P.; Vadeboncoeur, M.A. 

2009. Ice storm effects on the canopy structure 

of a northern hardwood forest after 8 years.

Canadian Journal of Forest Research. 39(8): 

1475-1483. https://doi.org/10.1139/X09-076.

White, M.; Running, S.; Thornton, P. 1999. The 

impact of growing-season length variability on 

carbon assimilation and evapotranspiration 

over 88 years in the eastern US deciduous 

forest. International Journal of Biometeorology. 

42(3): 139-145. 

https://doi.org/10.1007/s004840050097.

White, M.A.; Mladenoff, D.J. 1994. Old-growth 

forest landscape transitions from preEuropean settlement to present. Landscape 

Ecology. 9(3): 191-205. 

https://doi.org/10.1007/BF00134747.

Whitman, A.; Cutko, A.; deMaynadier, P.; Walker, 

S.; Vickery, B.; Stockwell, S.; Houston, R. 

2014. Climate change and biodiversity in 

Maine: vulnerability of habitats and priority 

species. Brunswick, ME: Manomet Center 

for Conservation Sciences. 95 p. Available at 

https://www.manomet.org/sites/default/files/

publications_and_tools/ 2013%20BwH%20Vuln

erability%20Report%20CS5v7_0.pdf (accessed 

December 19, 2017).

Whitman, A.; Vickery, B.; deMaynadier, P.; 

Stockwell, S.; Walker, S.; Cutko, A.; Houston, 

R. 2013. Climate change and biodiversity in 

Maine: a climate change exposure summary 

for species and key habitats (revised). Report 

NCI-2013-01. Brunswick, ME: Manomet Center 

for Conservation Sciences. 36 p.

Whitney, G.G. 1994. From coastal wilderness 

to fruited plain: a history of environmental 

changes in temperate North America, 1500 to 

the present. Cambridge, UK and New York, NY: 

Cambridge University Press. 451 p.

Wilderness Act of 1964; 16 U.S.C. 1121 (note), 

1131-1136. Available at http://www.fs.usda.gov/

Internet/FSE_DOCUMENTS/stelprdb5253940.

pdf.

Willis, C.G.; Ruhfel, B.R.; Primack, R.B.; MillerRushing, A.J.; Losos, J.B.; Davis, C.C. 2010. 

Favorable climate change response explains 

non-native species’ success in Thoreau’s 

woods. PLoS ONE. 5(1): e8878. 

https://doi.org/10.1371/journal.pone.0008878.

Windberg, L.A. 1995. Demography of a highdensity coyote population. Canadian Journal of 

Zoology. 73: 942-954. 

https://doi.org/10.1139/z95-110.

Wisconsin Initiative on Climate Change Impacts. 

2011. Wildlife working group report. Madison, 

WI: University of Wisconsin-Madison, Nelson 

Institute for Environmental Studies, and the 

Wisconsin Department of Natural Resources. 

52 p. Available at www.wicci.wisc.edu/report/

Wildlife.pdf.
LITERATURE CITED

170

Wolfe, D.W.; Schwartz, M.D.; Lakso, A.N.; Otsuki, 

Y.; Pool, R.M.; Shaulis, N.J. 2005. Climate 

change and shifts in spring phenology of three 

horticultural woody perennials in northeastern 

USA. International Journal of Biometeorology. 

49(5): 303-309. 

https://doi.org/10.1007/s00484-004-0248-9.

Woodall, C.W.; Oswalt, C.M.; Westfall, J.A.; 

Perry, C.H.; Nelson, M.D.; Finley, A.O. 2009. 

An indicator of tree migration in forests of 

the eastern United States. Forest Ecology and 

Management. 257(5): 1434-1444. 

https://doi.org/10.1016/j.foreco.2008.12.013.

Woodruff, S.C.; Stults, M. 2016. Numerous 

strategies but limited implementation guidance 

in US local adaptation plans. Nature Climate 

Change. Advance online publication. 

https://doi.org/10.1038/nclimate3012.

Woudenberg, S.W.; Conkling, B.L.; LaPoint, E.B.; 

Turner, J.A.; Waddell, K.L. 2010. The Forest 

Inventory and Analysis database: database 

description and users manual version 4.0 for 

Phase 2. Gen. Tech. Rep. RMRS-GTR-245. Fort 

Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research 

Station. 336 p. 

https://doi.org/10.2737/RMRS-GTR-245.

Wuebbles, D.; Meehl, G.; Hayhoe, K.; Karl, T.R.; 

Kunkel, K.; Santer, B.; Wehner, M.; Colle, B.; 

Fischer, E.M.; Fu, R.; Goodman, A.; Janssen, E.; 

Kharin, V.; Lee, H.; Li, W.H.; Long, L.N.; Olsen, 

S.C.; Pan, Z.T.; Seth, A.; Sheffield, J.; Sun, L.Q. 

2014. CMIP5 climate model analyses: climate 

extremes in the United States. Bulletin of the 

American Meteorological Society. 95(4): 571-

583. https://doi.org/10.1175/bams-d-12-00172.1.

Wuebbles, D.J.; Fahey, D.W.; Hibbard, K.A.; 

Dokken, D.J.; Stewart, B.C.; Maycock, T.K., 

eds. 2017. Climate science special report: 

Fourth National Climate Assessment, vol. 1.

Washington, DC: U.S. Global Change Research 

Program. 470 p. https://doi.org/10.7930/J0J964J6.

Xi, W.; Peet, R.K. 2011. Chapter 27: The 

complexity of catastrophic wind impacts on 

temperate forests. In: Lupo, A., ed. Recent 

hurricane research—climate, dynamics, and 

societal impacts. InTech: 503-534. 

https://doi.org.10.5772/16167. 

Yang, J. 2009. Assessing the impact of climate 

change on urban tree species selection: a case 

study in Philadelphia. Journal of Forestry. 

107(7): 364-372.

Yin, J.; Schlesinger, M.E.; Stouffer, R.J. 2009. 

Model projections of rapid sea-level rise on 

the northeast coast of the United States. Nature 

Geoscience. 2(4): 262-266. 

https://doi.org/10.1038/ngeo462.

Zhao, T.; Dai, A. 2016. Uncertainties in historical 

changes and future projections of drought. 

Part II: Model-simulated historical and future 

drought changes. Climatic Change. 1-14. 

https://doi.org/10.1007/s10584-016-1742-x.

Zhu, K.; Woodall, C.W.; Clark, J.S. 2011. Failure 

to migrate: lack of tree range expansion in 

response to climate change. Global Change 

Biology. 18(3): 1042-1052. 

https://doi.org/10.1111/j.1365-2486.2011.02571.x.

Ziegler, S.S. 2000. A comparison of structural 

characteristics between old-growth and 

postfire second-growth hemlock–hardwood 

forests in Adirondack Park, New York, U.S.A.

Global Ecology and Biogeography. 9(5): 

373-389. 

https://doi.org/10.1046/j.1365-2699.2000.00191.x.

Zimmerman, R.; Faris, C. 2010. Chapter 4: 

Infrastructure impacts and adaptation 

challenges. Annals of the New York Academy of 

Sciences. 1196(1): 63-86. 

https://doi.org/10.1111/j.1749-6632.2009.05318.x.

Zimova, M.; Mills, L.S.; Lukacs, P.M.; Mitchell, 

M.S. 2014. Snowshoe hares display limited 

phenotypic plasticity to mismatch in seasonal 

camouflage. Proceedings of the Royal Society B. 

281: 20140029. 

https://doi.org/10.1098/rspb.2014.0029.
LITERATURE CITED

171

Ziska, L.; Knowlton, K.; Rogers, C.; Dalan, D.; 

Tierney, N.; Elder, M.A.; Filley, W.; Shropshire, 

J.; Ford, L.B.; Hedberg, C.; Fleetwood, P.; 

Hovanky, K.T.; Kavanaugh, T.; Fulford, G.; 

Vrtis, R.F.; Patz, J.A.; Portnoy, J.; Coates, F.; 

Bielory, L.; Frenz, D. 2011. Recent warming 

by latitude associated with increased length 

of ragweed pollen season in central North 

America. Proceedings of the National Academy 

of Sciences. 108(10): 4248-4251. 

https://doi.org/10.1073/pnas.1014107108.

Ziska, L.H. 2003. Evaluation of the growth 

response of six invasive species to past, present 

and future atmospheric carbon dioxide.

Journal of Experimental Botany. 54(381): 

395-404. https://doi.org/10.1093/jxb/erg027.

Zuckerberg, B.; Woods, A.M.; Porter, W.F. 2009. 

Poleward shifts in breeding bird distributions 

in New York State. Global Change Biology. 

15(8): 1866-1883. https://doi.org/10.1111/j.1365-

2486.2009.01878.x.
172

APPENDIX 1: COMMON AND SCIENTIFIC NAMES 

OF SPECIES MENTIONED IN THIS REPORT

Plants and Fungi

(continued on next page)

Common name Scientific name

American basswood Tilia americana

American beech Fagus grandifolia

American chestnut Castanea dentata

American elm Ulmus americana

American holly Ilex opaca

American hornbeam 

(musclewood)

Carpinus caroliniana

American mountain-ash Sorbus americana

Asiatic bittersweet 

(Oriental bittersweet)

Celastrus orbiculatus

Atlantic white-cedar Chamaecyparis thyoides

bald cypress 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

Common name Scientific name

boxelder Acer negundo

buckthorn Rhamnus spp.

bur oak Quercus macrocarpa

bush honeysuckles Lonicera spp.

butternut Juglans cinerea

cedar elm Ulmus crassifolia

cherrybark oak Quercus pagoda

chestnut oak Quercus prinus

chinkapin oak Quercus muehlenbergii

chokecherry Prunus virginiana

common persimmon Diospyros virginiana

eastern cottonwood Populus deltoides

eastern hemlock Tsuga canadensis

eastern hophornbeam 

(ironwood)

Ostrya virginiana

eastern redbud Cercis canadensis

eastern redcedar Juniperus virginiana

eastern white pine Pinus strobus

flowering dogwood Cornus florida

garlic mustard Alliaria petiolata

gray birch Betula populifolia

green ash Fraxinus pennsylvanica

hackberry Celtis occidentalis

highbush blueberry Vaccinium corymbosum

honeylocust Gleditsia triacanthos

jack pine Pinus banksiana

Japanese barberry Berberis thunbergii
APPENDIX 1

173

Common name Scientific name

Japanese knotweed Polygonum cuspidatum

Japanese stiltgrass Microstegium vimineum

kudzu Pueraria lobata

lilac Syringa spp.

loblolly pine Pinus taeda

longleaf pine Pinus palustris

mockernut hickory Carya alba

morel mushroom Morchella spp.

mountain maple Acer spicatum

northern pin oak Quercus ellipsoidalis

northern red oak Quercus rubra

northern white-cedar Thuja occidentalis

Ohio buckeye Aesculus glabra

Osage-orange Maclura pomifera

paper birch (white birch) Betula papyrifera

pawpaw Asimina triloba

pignut hickory Carya glabra

pin cherry Prunus pensylvanica

pin oak Quercus palustris

pitch pine Pinus rigida

pond pine Pinus serotina

post oak Quercus stellata

privet Ligustrum vulgare

quaking aspen Populus tremuloides

red maple Acer rubrum

red mulberry Morus rubra

red pine Pinus resinosa

red spruce Picea rubens

river birch Betula nigra

rock elm Ulmus thomasii

sand pine Pinus clausa

sassafras Sassafras albidum

Common name Scientific name

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

slender yellow woodsorrel Oxalis dillenii

slippery elm Ulmus rubra

sourwood Oxydendrum arboreum

southern red oak Quercus falcata var. falcata

striped maple Acer pensylvanicum

sugar maple Acer saccharum

sugarberry Celtis laevigata

swamp chestnut oak Quercus michauxii

swamp white oak Quercus bicolor

sweet birch Betula lenta

sweetbay Magnolia virginiana

sweetgum Liquidambar styraciflua

sycamore Platanus occidentalis

tamarack Larix laricina

Virginia pine Pinus virginiana

water oak Quercus nigra

white ash Fraxinus americana

white oak Quercus alba

white spruce Picea glauca

willow oak Quercus phellos

winged elm Ulmus alata

yellow birch Betula alleghaniensis

yellow-poplar (tuliptree) Liriodendron tulipifera
APPENDIX 1

174

Common name Scientific name

American woodcock Scolopax minor

Asian longhorned beetle Anoplophora glabripennis

balsam woolly adelgid Adelges piceaea

big brown bat Eptesicus fuscus

black bear Ursus americanas

black-legged tick Ixodes scapularis

blackpoll warbler Setophaga striata

brook trout Salvalinus fontinalis

coyote Canis latrans

dwarf wedgemussel Alasmidonta heterodon

eastern box turtle Terrapene carolina carolina

eastern cottontail Sylvilagus floridanus

eastern pearlshell Margaritifera margaritifera

emerald ash borer Agrilus planipennis

European earthworm Dendrobaena octaedra, 

Lumbricus rebellus, 

and L. terrestris

grouse family Tetraonidae 

(order Galliformes)

gypsy moth Lymantria dispar

hemlock woolly adelgid Adelges tsugae

hoary bat Lasiurus cinereus

map turtle Graptemys geographica

moose Alces alces

mosquito family Culicidae

mussel order Bivalvia

Nashville warbler Vermivora ruficapilla

New England cottontail Sylvilagus transitionalis

northern flying squirrel Glaucomys sabrinus

northern long-eared bat 

(northern myotis)

Myotis septentrionalis

rainbow trout (coast rainbow 

trout)

Oncorhynchus mykiss

ruffed grouse Bonasus umbellus 

smallmouth bass Micropterus dolomieu

snowshoe hare Lepus americanus

Animals

Pathogens, Viruses, and Other Diseases

Common name Scientific name

southern flying squirrel Glaucomys volans

southern pine beetle Dendroctonus frontalis

spruce budworm Choristoneura fumiferana

white-tailed deer Odocoileus virginianus

wild turkey Meleagris gallopavo

winter moth Operophtera brumata

winter tick (moose tick) Dermacentor albipictus

wood duck Aix sponsa

wood turtle Glyptemys insculpta

Common name Scientific name

beechbark disease A complex of the scale insect

Cryptococcus fagisuga and 

the fungus Neonectria spp.

brainworm Parelaphostrongylus tenuis

butternut canker Sirococcus clavigignentijuglandacearum 

chestnut blight Cryphonectria parasitica

Dutch elm disease Ophiostoma ulmi

Lyme disease Borrelia burgdorferi

red pine shoot blight Diplodia pinea or Sirococcus 

conigenus

West Nile virus Flavivirus spp.

white-nose syndrome Pseudogymnoascus 

destructans
175

APPENDIX 2: HISTORICAL CLIMATE DATA 

AND TREND ANALYSIS

HISTORICAL CLIMATE DATA

To examine historical trends in precipitation and 

temperature for the assessment area, we used the 

Climate Wizard Custom Analysis application 

(Climate Wizard 2014, Girvetz et al. 2009). Data 

for Climate Wizard are derived from PRISM 

(Parameter-elevation Regressions on Independent 

Slopes Model) (Gibson et al. 2002). The PRISM 

model interpolates historical data from the National 

Weather Service cooperative stations, the Midwest 

Climate Data Center, and the Historical Climate 

Network, among others. Data undergo strict quality 

control procedures to check for errors in station 

measurements. The PRISM model finds linear 

relationships between these station measurements 

and local elevation by using a digital elevation 

model (digital gridded version of a topographic 

map). Temperature and precipitation are then derived 

for each pixel on a continuous 2.5-mile grid across 

the conterminous United States. The closer a station 

is to a grid cell of interest in distance and elevation, 

and the more similar it is in its proximity to coasts 

or topographic features, the higher the weight of the 

station observations on the final, predicted value for 

that cell. More information on PRISM can be found 

at: www.prism.oregonstate.edu. 

BASELINE CLIMATE CONDITIONS: 

1971 TO 2000

Temperature and precipitation data were used to 

derive annual, seasonal, and monthly values for the 

30-year average (also referred to as the “climate 

normal”) for 1971 through 2000 (Table 15; 

Figs. 32, 33).

OBSERVED CHANGES: 

1901 TO 2011

Linear trend analysis for 1901 through 2011 was 

performed 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 Fourth Assessment Report 

and are considered an effective way to identify 

trends in climate data over time (Trenberth et al. 

2007). A first-order autoregression was assumed 

for the residuals, meaning that values one time step 

away from each other are assumed to be correlated. 

This method was used to examine trends for every 

2.5-mile grid cell. The slope and p-values for the 

linear trend over time were calculated annually, 

seasonally, and monthly for each climate variable, 

and then mapped. An overall trend for an area is 

based on the trend analysis of the average value for 

all grid cells within the area over time. 

The developers of the Climate Wizard application 

advise users to interpret the linear trend maps 

in relation to the respective map of statistical 

confidence (Figs. 34, 35). In this case, statistical 

confidence is described 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 alone. For 

this assessment, p-values of less than 0.10 were 

considered to have sufficient statistical confidence. 

Areas with low statistical confidence in the rate of 

change (gray areas on the map) should be interpreted 

with caution. 
APPENDIX 2

176

Mean temperature (°F)

Mean minimum 

temperature (°F)

Mean maximum 

temperature (°F)

Mean precipitation 

(inches)

Winter 20.1 10.0 30.2 10.1

December 23.3 14.3 32.4 3.7

January 17.2 6.9 27.4 3.7

February 19.8 8.9 30.6 2.8

Spring 41.3 30.3 52.4 11.1

March 29.5 19.1 39.9 3.6

April 41.2 30.6 51.9 3.7

May 53.3 41.4 65.3 3.9

Summer 64.9 53.5 76.3 11.9

June 62.2 50.6 73.9 4.0

July 67.2 55.8 78.6 4.0

August 65.4 54.2 76.6 4.0

Fall 46.1 36.2 56.1 12.0

September 57.0 45.9 68.0 4.0

October 46.0 35.5 56.5 3.9

November 35.5 27.0 43.9 4.1

Annual 43.1 32.5 53.7 45.2

Table 15.—Mean annual, seasonal, and monthly temperature and precipitation for the assessment area from 1971 

through 2000 (data source: Climate Wizard [2014])

A mosaic of northern hardwood forest and farmland in northern Vermont. Photo by Todd Ontl, U.S. Forest Service.
APPENDIX 2

177

Figure 32.—Thirty-year annual and seasonal averages of mean, minimum, and maximum temperature across the assessment 

area, 1971 through 2000. Data source: Climate Wizard (2014).
APPENDIX 2

178

Figure 33.—Thirty-year averages of mean annual and seasonal precipitation across the assessment area. Data source: Climate 

Wizard (2014).
APPENDIX 2

179

Figure 34.—Statistical confidence (p-values for the linear regression) for trends in temperature across the assessment area, 

1901 through 2011. Gray values represent areas of low statistical confidence. Data source: Climate Wizard (2014).
APPENDIX 2

180

Figure 35.—Statistical confidence (p-values for the linear regression) for trends in precipitation across the assessment area, 

1901 through 2011. Gray values represent areas of low statistical confidence. Data source: Climate Wizard (2014).
APPENDIX 2

181

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). The comparison of a gridded 

PRISM dataset and another gridded climate product 

available in the Northeast showed localized biases 

in both products, signaling a need for caution when 

using these products (Beier et al. 2012, Bishop and 

Beier 2013). Several factors appear to contribute to 

the limitations of these products. Areas that have 

few weather stations lack data for interpolation and 

therefore have greater uncertainty. These issues are 

particularly pronounced at high elevations and in 

areas with complex topography, as well as along 

coasts (Beier et al. 2012). Therefore, interpretations 

should be based on many grid cells showing regional 

patterns of climate change with high statistical 

confidence. For those interested in understanding 

trends in climate at a certain location, it is best to 

refer to weather station data for the closest station 

in the Global Historical Climatology Network from 

the National Climatic Data Center (www.ncdc.noaa.

gov/). 

We selected the period 1901 through 2011 because 

it was sufficiently long to capture interdecadal and 

intradecadal variation in climate for the assessment 

area. 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

Beier, C.M.; Signell, S.A.; Luttman, A.; DeGaetano, 

A.T. 2012. High-resolution climate change 

mapping with gridded historical climate 

products. Landscape Ecology. 27(3): 327-342. 

https://doi.org/10.1007/s10980-011-9698-8.

Bishop, D.A.; Beier, C.M. 2013. Assessing 

uncertainty in high-resolution spatial climate 

data across the US Northeast. PLoS ONE. 

8(8): e70260. 

https//doi.org/10.1371/journal.pone.0070260.

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).
182

APPENDIX 3: ADDITIONAL CLIMATE PROJECTIONS

In this document, we primarily use statistically 

downscaled climate projections for two general 

circulation model (GCM)-emissions scenario 

combinations: GFDL A1FI and PCM B1. Both 

models and both scenarios were included in the 

Intergovernmental Panel on Climate Change (IPCC) 

Fourth Assessment Report (IPCC 2007). The latest 

version of the National Climate Assessment (in 

development) also draws on statistically downscaled 

data based on IPCC models and scenarios but uses 

the A2 scenario as an upper bound. The A2 scenario 

projects lower emissions compared to A1FI. The 

IPCC assessment includes about 20 other models, 

which are represented as a multi-model average in 

its reports. The National Climate Assessment takes a 

similar approach in using a multi-model average. For 

this assessment, we instead selected two models that 

had relatively good skill at simulating climate in the 

eastern United States and that bracketed a range of 

temperature and precipitation futures. This approach 

gives readers a better understanding of the range of 

projected changes in climate and provides a set of 

alternative scenarios that can be used by managers in 

planning and decisionmaking. 

The National Oceanic and Atmospheric 

Administration’s Geophysical Fluid Dynamics 

Laboratory model (GFDL) is considered 

moderately sensitive to changes in greenhouse gas 

concentrations (Delworth et al. 2006). In other 

words, any change in greenhouse gas concentration 

would lead to a change in temperature that is 

higher than some models and lower than others. 

The National Center for Atmospheric Research’s 

Parallel Climate Model (PCM), in contrast, is 

considered to have low sensitivity to greenhouse gas 

concentrations (Washington et al. 2000). Together 

the GFDL A1FI and PCM B1 scenarios span a large 

range of possible futures. Although both projections 

are possible, the GFDL A1FI scenario is closer to 

current trends in greenhouse gas emissions (Raupach 

et al. 2007). It is important to note that actual 

emissions and temperature increases could be lower 

or higher than these projections. 

This assessment relies on a statistically downscaled 

climate dataset (Hayhoe 2014). Daily mean, 

minimum, and maximum temperature and total daily 

precipitation were downscaled to an approximately 

7.5-mile grid across the United States. This dataset 

uses a modified statistical asynchronous quantile 

regression method to downscale daily GCM output 

and historical climate data (Stoner et al. 2012). 

This approach is advantageous because GCM 

and historical data do not need to be temporally 

correlated, and it is much better at capturing extreme 

temperatures and precipitation events than a linear 

regression approach (Hayhoe et al. 2013). This is 

a different statistically downscaled dataset than 

used in the National Climate Assessment, which 

relies on a simpler “delta” approach (Kunkel et al. 

2013). This dataset was chosen for several reasons. 

First, the dataset covers the entire United States, 

and thus allows a consistent dataset to be used in 

this and other regional vulnerability assessments. 

Second, it includes downscaled projections for the 

A1FI emissions scenario, which is the scenario 

that most closely matches current trends in global 

greenhouse gas emissions (Raupach et al. 2007, 

Hayhoe et al. 2013). Third, the dataset includes daily 

values, which are needed for some impact models 

used in this report and provide the opportunity to 

examine questions related to growing season length, 

heavy precipitation events, and droughts. Fourth, 

the statistical technique used is more accurate at 

reproducing extreme values at daily time steps than 
Appendix 3

183

simpler statistical downscaling methods (Hayhoe 

et al. 2013). Finally, the 7.5-mile resolution of 

the downscaled data is useful for informing land 

management decisions. 

To show projected changes in temperature and 

precipitation, we calculated the average daily 

mean, minimum, and maximum temperature and 

mean precipitation for each month for three 30-

year periods (2010 through 2039, 2040 through 

2069, and 2070 through 2099). The use of 30-year 

periods reduces the influence of natural year-toyear variation that may bias calculations of change. 

The use of 30-year periods also allows for more 

direct comparison with the 1971 through 2000 

historical data, highlighting longer-term trends over 

annual fluctuations. Monthly averages were used 

to calculate seasonal and annual values. We then 

subtracted the corresponding 1971 through 2000 

average from these values to determine the departure 

from current climate conditions (Figs. 36 through 

43, Tables 16, 17; Figs. 19, 20, 21, 23 in Chapter 

3). Historical climate data used for the departure 

analysis were taken from Climate Wizard (Girvetz 

et al. 2009). 

LITERATURE CITED

Delworth, T.L.; Broccoli, A.J.; Rosati, A.; Stouffer, 

R.J.; Balaji, V.; Beesley, J.A.; Cooke, W.F.; 

Dixon, K.W.; Dunne, J.; Dunne, K.A.; Durachta, 

J.W.; Findell, K.L.; Ginoux, P.; Gnanadesikan, 

A.; Gordon, C.T.; Griffies, S.M.; Gudgel, 

R.; Harrison, M.J.; Held, I.M.; Hemler, R.S.; 

Horowitz, L.W.; Klein, S.A.; Knutson, T.R.; 

Kushner, P.J.; Langenhorst, A.R.; Lee, H.C.; 

Lin, S.J.; Lu, J.; Malyshev, S.L.; Milly, P.C.D.; 

Ramaswamy, V.; Russell, J.; Schwarzkopf, 

M.D.; Shevliakova, E.; Sirutis, J.J.; Spelman, 

M.J.; Stern, W.F.; Winton, M.; Wittenberg, A.T.; 

Wyman, B.; Zeng, F.; Zhang, R. 2006. GFDL’s 

CM2 global coupled climate models. Part I: 

Formulation and simulation characteristics.

Journal of Climate. 19(5): 643-674. 

https://doi.org/10.1175/jcli3630.1.

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. 

Hayhoe, K. 2014. Statistical asynchronous 

regional regression daily downscaled climate 

projections. U.S. Geological Survey, Center for 

Integrated Data Analytics. Available at 

http://cida.usgs.gov/thredds/catalog.html 

(accessed December 18, 2017).

Hayhoe, K.; Stoner, A.; Yang, X.; Crow, C.; 

Swaminathan, R.; Scott-Fleming, I.; Ryu, J.; 

Gelca, R.; Swain, S. 2013. Development and 

dissemination of a high-resolution national 

climate change dataset. Texas Tech University. 

https://cida.usgs.gov/thredds/fileServer/dcp/files/

Hayhoe_USGS_downscaled_database_final_

report.pdf (accessed December 18, 2017).

Intergovernmental Panel on Climate Change [IPCC]. 

2007. Climate change 2007: the physical 

science basis. Summary for policy makers.

[Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; 

Marquis, M.; Averyt, K.B.; Tignor, M.; Miller, 

H.L., eds.] Cambridge, UK and New York, NY. 

Available at http://www.ipcc.ch/publications_

and_data/ar4/wg1/en/contents.html.

Kunkel, K.E.; Stevens, L.E.; Stevens, S.E.; Sun, 

L.; Janssen, E.; Wuebbles, D.; Rennells, J.; 

DeGaetano, A.; Dobson, J.G. 2013. Regional 

climate trends and scenarios for the U.S. 

National Climate Assessment. Part 1. Climate 

of the Northeast U.S. Washington, DC: U.S. 

Department of Commerce, National Oceanic and 

Atmospheric Administration. 79 p. Available at 

http://www.nesdis.noaa.gov/technical_reports/

NOAA_NESDIS_Tech_Report_142-1-Climate_

of_the_Northeast_U.S.pdf (accessed April 30, 

2014).
APPENDIX 3

184

Raupach, M.R.; Marland, G.; Ciais, P.; Le Quere, 

C.; Canadell, J.G.; Klepper, G.; Field, C.B. 2007. 

Global and regional drivers of accelerating 

CO2 emissions. Proceedings of the National 

Academy of Sciences. 104(24): 10288-10293. 

https://doi.org/10.1073/pnas.0700609104. 

Stoner, A.M.K.; Hayhoe, K.; Yang, X.; Wuebbles, 

D.J. 2012. An asynchronous regional 

regression model for statistical downscaling of 

daily climate variables. International Journal of 

Climatology. 33(11): 2473-2494. 

https://doi.org/10.1002/joc.3603. 

Washington, W.M.; Weatherly, J.W.; Meehl, 

G.A.; Semtner, A.J., Jr.; Bettge, T.W.; Craig, 

A.P.; Strand, W.G., Jr.; Arblaster, J.; Wayland, 

V.B.; James, R.; Zhang, Y. 2000. Parallel 

climate model (PCM) control and transient 

simulations. Climate Dynamics. 16(10): 755-774. 

https://doi.org/10.1007/s003820000079. 
APPENDIX 3

185

Figure 36.—Projected difference in mean daily mean temperature across the assessment area at the beginning of the century 

(2010 through 2039) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

186

Figure 37.—Projected difference in mean daily minimum temperature across the assessment area at the beginning of the 

century (2010 through 2039) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

187

Figure 38.—Projected difference in mean daily maximum temperature across the assessment area at the beginning of the 

century (2010 through 2039) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

188

Figure 39.—Projected difference in precipitation across the assessment area at the beginning of the century (2010 through 

2039) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

189

Figure 40.—Projected difference in mean daily mean temperature across the assessment area for the middle of the century 

(2040 through 2069) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

190

Figure 41.—Projected difference in mean daily minimum temperature across the assessment area for the middle of the 

century (2040 through 2069) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

191

Figure 42.—Projected difference in mean daily maximum temperature across the assessment area for the middle of the 

century (2040 through 2069) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

192

Figure 43.—Projected difference in precipitation across the assessment area for the middle of the century (2040 through 

2069) compared to baseline (1971 through 2000) for two climate scenarios.
APPENDIX 3

193

Baseline temperature (0F) Temperature departure from baseline (°F)

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

Mean temperature

Annual PCM B1 42.8 1.1 2.2 2.6

GFDL A1FI 42.8 1.8 5.3 7.6

Winter (Dec.-Feb.) PCM B1 19.6 1.5 3.0 3.2

GFDL A1FI 19.6 2.1 5.8 7.7

Spring (Mar.-May) PCM B1 41.1 -0.1 1.5 1.9

GFDL A1FI 41.1 0.9 4.1 6.1

Summer (June-Aug.) PCM B1 64.8 1.2 2.3 2.6

GFDL A1FI 64.8 2.3 5.9 8.3

Fall (Sept.-Nov.) PCM B1 45.8 1.9 2.1 2.6

GFDL A1FI 45.8 1.7 5.3 8.0

Minimum temperature

Annual PCM B1 32.1 1.2 2.3 2.7

GFDL A1FI 32.1 1.8 5.5 8.0

Winter (Dec.-Feb.) PCM B1 9.5 1.8 3.6 4.1

GFDL A1FI 9.5 2.6 7.3 9.8

Spring (Mar.-May) PCM B1 30.0 0.6 1.9 2.2

GFDL A1FI 30.0 1.2 4.6 6.8

Summer (June-Aug.) PCM B1 53.3 0.9 1.9 2.1

GFDL A1FI 53.3 2.0 5.4 7.9

Fall (Sept.-Nov.) PCM B1 35.8 1.6 2.0 2.5

GFDL A1FI 35.8 1.5 4.7 7.5

Maximum temperature

Annual PCM B1 53.5 1.0 2.1 2.5

GFDL A1FI 53.5 1.7 5.1 7.1

Winter (Dec.-Feb.) PCM B1 29.8 1.2 2.4 2.5

GFDL A1FI 29.8 1.5 4.4 5.7

Spring (Mar.-May) PCM B1 52.2 -0.8 1.0 1.7

GFDL A1FI 52.2 0.6 3.5 5.4

Summer (June-Aug.) PCM B1 76.3 1.6 2.7 3.2

GFDL A1FI 76.3 2.6 6.5 8.7

Fall (Sept.-Nov.) PCM B1 55.9 2.1 2.3 2.8

GFDL A1FI 55.9 2.0 5.9 8.6

Table 16.—Projected change in mean daily mean, minimum, and maximum temperature under two future climate 

scenarios for the assessment area through the end of the century (°F)
APPENDIX 3

194

Baseline precipitation (inches) Precipitation departure from baseline (inches)

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

Annual PCM B1 45.1 -0.2 -0.6 0.9

GFDL A1FI 45.1 0.7 1.1 2.9

Winter (Dec.-Feb.) PCM B1 10.0 0.3 0.8 0.8

GFDL A1FI 10.0 1.3 1.2 2.3

Spring (Mar.-May) PCM B1 11.1 0.2 -0.3 -0.1

GFDL A1FI 11.1 0.1 0.4 1.0

Summer (June-Aug.) PCM B1 12.0 0.3 -0.5 0.1

GFDL A1FI 12.0 -0.3 -0.1 -1.8

Fall (Sept.-Nov.) PCM B1 12.0 -0.9 -0.7 0.1

GFDL A1FI 12.0 -0.4 -0.3 1.4

Table 17.—Projected change in precipitation under two future climate scenarios for the assessment area through the 

end of the century
195

APPENDIX 4: SUPPLEMENTARY 

CLIMATE CHANGE TREE ATLAS RESULTS

This appendix contains additional model results 

and details from the Climate Change Tree Atlas, 

including the results of the DISTRIB model and 

additional modifying factors.

The DISTRIB model was used to simulate changes 

in suitable habitat during the 21st century for 

102 tree species for this assessment, and model 

outputs for these species are provided in Table 18. 

More information about the modeling approach is 

available online through the Climate Change Tree 

Atlas Web site (www.nrs.fs.fed.us/atlas), including 

detailed methods, maps of changes in importance 

values, and additional statistics. Publications 

describing the Tree Atlas tools also include key 

definitions and methods descriptions (Iverson et al. 

1999, 2008, 2011; Matthews et al. 2011). 

For this assessment, current area-weighted 

importance values (IVs) were derived from U.S. 

Forest Service Forest Inventory and Analysis (FIA) 

data. Using the DISTRIB model, these were used 

to develop modeled current (1961-1990) IVs, as 

well as future IVs for three time periods (2010 

through 2039, 2040 through 2069, 2070 through 

2099) under the PCM B1 and GFDL A1FI climate 

scenarios. 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) across the assessment area, it 

was deleted from the list because the species has 

either no current or no future suitable habitat in the 

region, or there were not enough data. This step 

resulted in the list of 102 tree species for which data 

are shown.

A set of rules was established to determine change 

classes for 2070 through 2099, which was used to 

create tables in Chapter 4. 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 to 0.8 small decrease

>0.8 to <1.2 no change

1.2 to 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. Habitat for a species was considered to be 

extirpated if the future IV was 0 and FIA values 

were greater than 3. 

Special rules were created for rare species. A species 

was considered rare if it had a current modeled areaweighted IV that equaled less than 10 percent of 

the number of 12.5-mile by 12.5-mile pixels in the 

assessment area. The change classes are calculated 

differently for these species because their current 

infrequency tends to inflate the projected percentage 

change. The cutoff for the assessment area was 

66 pixels, or 10 percent of the total number of pixels 

(657).

When a species was below the cutoff, “extirpated” 

was not used in this case due to low confidence, and 

the following rules applied:

Future:Current modeled IV Class

<0.2 large decrease

0.2 to <0.6 small decrease

0.6 to <4 no change

4 to 8 small increase

>8 large increase 

(not used when current 

modeled IV ≤3)
APPENDIX 4

196

DISTRIB Resultsa

Current IV Modeled IV Future:Current suitable habitat Change class

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2090

Common name FIA Modeled Model reliability 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

American basswood 255 311 Medium 254 413 287 529 301 637 0.82 1.33 0.92 1.70 0.97 2.05 No change Large increaseAmerican beech 3543 3659 High 3670 3333 3554 2609 3513 2086 1.00 0.91 0.97 0.71 0.96 0.57 No change Small decreaseAmerican chestnut 27 9 Medium 15 12 15 8 16 7 1.67 1.33 1.67 0.89 1.78 0.78 No change No changeAmerican elm 581 662 Medium 657 942 724 1344 823 1662 0.99 1.42 1.09 2.03 1.24 2.51 Small increase Large increaseAmerican holly 7 4 High 5 8 5 40 5 55 1.25 2.00 1.25 10.00 1.25 13.75 No change Large increaseAmerican hornbeam 188 248 Medium 250 286 273 470 291 546 1.01 1.15 1.10 1.90 1.17 2.20 No change Large increaseAmerican mountain-ash 77 33 Medium 43 22 28 5 21 2 1.30 0.67 0.85 0.15 0.64 0.06 No change Large decreaseAtlantic white-cedar 31 36 Low 35 34 33 29 35 28 0.97 0.94 0.92 0.81 0.97 0.78 No change No changeBald cypress 0 24 Medium 25 40 24 53 41 91 1.04 1.67 1.00 2.21 1.71 3.79 New habitat New habitatBalsam fir 5334 5255 High 4501 3445 3713 1457 3313 1175 0.86 0.66 0.71 0.28 0.63 0.22 Small decrease Large decreaseBalsam poplar 131 125 High 82 73 68 33 73 39 0.66 0.58 0.54 0.26 0.58 0.31 Small decrease Large decreaseBigtooth aspen 507 613 High 550 628 589 562 606 461 0.90 1.02 0.96 0.92 0.99 0.75 No change Small decreaseBitternut hickory 46 21 Low 36 116 36 280 54 420 1.71 5.52 1.71 13.33 2.57 20.00 No change Large increaseBlack ash 313 436 High 298 394 290 322 296 297 0.68 0.90 0.67 0.74 0.68 0.68 Small decrease Small decreaseBlack cherry 1355 1561 High 1630 1889 1752 2160 1807 2175 1.04 1.21 1.12 1.38 1.16 1.39 No change Small increaseBlack hickory 0 0 High 0 6 0 188 0 588 0.00 New 0.00 New 0.00 New ― New habitatBlack locust 40 79 Low 101 234 143 508 182 768 1.28 2.96 1.81 6.43 2.30 9.72 Large increase Large increaseBlack maple 5 1 Low 1 1 1 1 1 0 1.00 1.00 1.00 1.00 1.00 0.00 No change Small decreaseBlack oak 473 563 High 654 964 803 1627 900 2059 1.16 1.71 1.43 2.89 1.60 3.66 Small increase Large increaseBlack spruce 475 537 High 345 294 266 30 222 5 0.64 0.55 0.50 0.06 0.41 0.01 Large decrease Large decreaseBlack walnut 9 31 Medium 40 204 99 587 138 801 1.29 6.58 3.19 18.94 4.45 25.84 Small increase Large increaseBlack willow 124 136 Low 135 243 153 448 181 623 0.99 1.79 1.13 3.29 1.33 4.58 Small increase Large increaseBlackgum 37 62 High 107 130 139 336 165 500 1.73 2.10 2.24 5.42 2.66 8.07 No change Large increaseBlackjack oak** 0 2 Medium 1 7 2 122 3 413 0.50 3.50 1.00 61.00 1.50 206.50 ― New habitatBoxelder 49 99 Medium 77 235 84 350 104 543 0.78 2.37 0.85 3.54 1.05 5.49 No change Large increaseBur oak 12 32 Medium 13 100 14 217 21 397 0.41 3.13 0.44 6.78 0.66 12.41 No change Large increaseButternut 81 40 Low 50 50 46 32 48 21 1.25 1.25 1.15 0.80 1.20 0.53 No change Small decreaseCedar elm 0 1 Low 0 0 0 0 0 49 0.00 0.00 0.00 0.00 0.00 49.00 ― New habitatCherrybark oak 0 2 Medium 7 8 6 10 7 42 3.50 4.00 3.00 5.00 3.50 21.00 New habitat New habitatChestnut oak 91 191 High 240 287 323 740 351 964 1.26 1.50 1.69 3.87 1.84 5.05 Small increase Large increaseChinkapin oak** 0 2 Medium 1 5 2 127 1 280 0.50 2.50 1.00 63.50 0.50 140.00 ― New habitatChokecherry 214 181 Low 174 170 167 88 180 65 0.96 0.94 0.92 0.49 0.99 0.36 No change Large decreaseCommon persimmon 0 0 Medium 0 48 1 338 8 797 0.00 New ― New New New New habitat New habitatEastern cottonwood 51 78 Low 64 259 80 682 119 1057 0.82 3.32 1.03 8.74 1.53 13.55 Small increase Large increaseEastern hemlock 2804 2810 High 2891 2877 2909 2503 2957 2136 1.03 1.02 1.04 0.89 1.05 0.76 No change Small decreaseEastern hophornbeam 679 738 Medium 666 762 706 887 740 1053 0.90 1.03 0.96 1.20 1.00 1.43 No change Small increaseEastern redbud 1 1 Medium 1 31 14 302 24 615 1.00 31.00 14.00 302.00 24.00 615.00 Small increase Small increaseEastern redcedar 93 150 Medium 227 562 317 1593 399 2371 1.51 3.75 2.11 10.62 2.66 15.81 Large increase Large increaseEastern white pine 3608 3525 High 3303 3092 3273 2613 3254 2448 0.94 0.88 0.93 0.74 0.92 0.69 No change Small decreaseFlowering dogwood 49 71 High 133 307 219 941 275 1372 1.87 4.32 3.09 13.25 3.87 19.32 Large increase Large increaseGray birch 649 603 Medium 584 566 555 483 555 475 0.97 0.94 0.92 0.80 0.92 0.79 No change Small decreaseGreen ash 70 108 Medium 75 101 79 142 96 300 0.69 0.94 0.73 1.32 0.89 2.78 No change Large increaseHackberry 4 16 Medium 9 166 19 614 43 952 0.56 10.38 1.19 38.38 2.69 59.50 No change Large increaseHoneylocust 5 9 Low 9 107 12 449 20 742 1.00 11.89 1.33 49.89 2.22 82.44 No change Large increaseJack pine 14 111 High 37 136 41 142 50 163 0.33 1.23 0.37 1.28 0.45 1.47 Small decrease Small increaseLoblolly pine 0 37 High 50 90 55 141 104 499 1.35 2.43 1.49 3.81 2.81 13.49 New habitat New habitatLongleaf pine 0 12 High 9 7 19 8 24 10 0.75 0.58 1.58 0.67 2.00 0.83 New habitat ―Mockernut hickory 41 74 High 68 107 105 340 116 520 0.92 1.45 1.42 4.60 1.57 7.03 Small increase Large increaseMountain maple 294 281 High 223 188 176 38 153 18 0.79 0.67 0.63 0.14 0.54 0.06 Small decrease Large decreaseNorthern pin oak 3 13 Medium 2 58 5 45 6 60 0.15 4.46 0.39 3.46 0.46 4.62 No change Small increaseNorthern red oak 1618 1715 High 1961 2255 2127 2671 2267 2656 1.14 1.32 1.24 1.56 1.32 1.55 Small increase Small increaseNorthern white-cedar 1641 1726 High 1222 1193 1031 651 948 574 0.71 0.69 0.60 0.38 0.55 0.33 Small decrease Large decreaseOhio buckeye 1 0 Low 0 6 0 14 4 17 0.00 6.00 0.00 14.00 4.00 17.00 Small increase Small increaseOsage-orange 0 8 Medium 8 15 7 59 9 94 1.00 1.88 0.88 7.38 1.13 11.75 ― New habitatPaper birch 1902 1933 High 1653 1601 1469 773 1402 435 0.86 0.83 0.76 0.40 0.73 0.23 Small decrease Large decreasePawpaw** 0 1 Low 2 20 15 72 14 169 2.00 20.00 15.00 72.00 14.00 169.00 New habitat New habitatPignut hickory 170 217 High 251 351 308 731 349 981 1.16 1.62 1.42 3.37 1.61 4.52 Small increase Large increase

Table 18.—Complete Climate Change Tree Atlas results for the 102 tree species in the assessment area

(continued on next page)
APPENDIX 4

197

DISTRIB Resultsa

Current IV Modeled IV Future:Current suitable habitat Change class

2010-2039 2040-2069 2070-2099 2010-2039 2040-2069 2070-2099 2070-2090

Common name FIA Modeled Model reliability 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

Pin cherry 383 397 Medium 343 349 327 286 335 235 0.86 0.88 0.82 0.72 0.84 0.59 No change Small decreasePin oak 3 12 Medium 16 80 31 241 55 346 1.33 6.67 2.58 20.08 4.58 28.83 Small increase Large increasePitch pine 168 144 High 157 166 162 163 168 191 1.09 1.15 1.13 1.13 1.17 1.33 No change Small increasePond pine 0 3 High 5 7 4 4 6 14 1.67 2.33 1.33 1.33 2.00 4.67 New habitat New habitatPost oak 1 2 High 1 45 10 624 15 1787 0.50 22.50 5.00 312.00 7.50 893.50 Small increase Small increaseQuaking aspen 1278 1488 High 1249 1419 1249 967 1267 699 0.84 0.95 0.84 0.65 0.85 0.47 No change Large decreaseRed maple 7631 7772 High 8003 7573 8121 6921 8126 5899 1.03 0.97 1.05 0.89 1.05 0.76 No change Small decreaseRed mulberry 7 6 8 217 23 781 49 1163 1.33 36.17 3.83 130.17 8.17 193.83 Large increase Large increaseRed pine 221 315 Medium 262 381 286 558 325 656 0.83 1.21 0.91 1.77 1.03 2.08 No change Large increaseRed spruce 2750 2651 Low 2327 1606 1809 945 1692 915 0.88 0.61 0.68 0.36 0.64 0.35 Small decrease Large decreaseRiver birch 1 7 Low 3 16 2 5 3 16 0.43 2.29 0.29 0.71 0.43 2.29 ― No changeRock elm 0 0 Low 0 1 0 4 1 22 0.00 ― 0.00 New ― New ― New habitatSand pine 0 11 Medium 27 24 21 27 23 32 2.46 2.18 1.91 2.46 2.09 2.91 New habitat New habitatSassafras 56 103 High 143 208 208 569 236 880 1.39 2.02 2.02 5.52 2.29 8.54 Large increase Large increaseScarlet oak 217 208 High 300 345 351 634 377 846 1.44 1.66 1.69 3.05 1.81 4.07 Small increase Large increaseScrub oak 6 14 Low 10 14 12 17 13 16 0.71 1.00 0.86 1.21 0.93 1.14 No change No changeServiceberry 320 302 Medium 303 361 347 387 352 370 1.00 1.20 1.15 1.28 1.17 1.23 No change Small increaseShagbark hickory 110 128 Medium 158 274 195 511 225 651 1.23 2.14 1.52 3.99 1.76 5.09 Small increase Large increaseShellbark hickory 8 1 Low 2 9 2 87 3 161 2.00 9.00 2.00 87.00 3.00 161.00 No change Small increaseShingle oak 0 1 Medium 0 25 1 167 2 289 0.00 25.00 1.00 167.00 2.00 289.00 ― New habitatShortleaf pine** 0 5 High 4 8 16 107 14 384 0.80 1.60 3.20 21.40 2.80 76.80 New habitat New habitatShumard oak 0 0 Low 0 0 0 0 0 23 0.00 0.00 0.00 0.00 0.00 New ― New habitatSilver maple 135 184 Medium 152 493 208 986 280 1372 0.83 2.68 1.13 5.36 1.52 7.46 Small increase Large increaseSlippery elm 52 70 Medium 71 213 100 472 132 649 1.01 3.04 1.43 6.74 1.89 9.27 Small increase Large increaseSourwood 1 1 High 8 4 10 5 24 29 8.00 4.00 10.00 5.00 24.00 29.00 Small increase Small increaseSouthern red oak 0 0 High 3 3 11 82 11 261 New New New New New New New habitat New habitatStriped maple 1084 1056 High 1017 860 929 589 892 475 0.96 0.81 0.88 0.56 0.85 0.45 No change Large decreaseSugar maple 4620 4825 High 4768 4563 4624 3958 4628 3628 0.99 0.95 0.96 0.82 0.96 0.75 No change Small decreaseSugarberry 0 13 Medium 5 11 6 63 10 255 0.39 0.85 0.46 4.85 0.77 19.62 ― New habitatSwamp chestnut oak 1 0 Medium 1 0 0 0 2 18 1.00 0.00 0.00 0.00 2.00 18.00 No change Small increaseSwamp white oak 25 18 Low 21 39 24 74 29 115 1.17 2.17 1.33 4.11 1.61 6.39 No change Small increaseSweet birch 611 662 High 819 785 933 1073 957 1052 1.24 1.19 1.41 1.62 1.45 1.59 Small increase Small increaseSweetbay** 0 7 High 1 2 4 1 11 1 0.14 0.29 0.57 0.14 1.57 0.14 New habitat ―Sweetgum** 0 32 High 91 182 115 422 198 938 2.84 5.69 3.59 13.19 6.19 29.31 New habitat New habitatSycamore 9 13 Medium 16 41 24 210 37 360 1.23 3.15 1.85 16.15 2.85 27.69 No change Large increaseTamarack (native) 256 259 High 214 210 183 178 160 161 0.83 0.81 0.71 0.69 0.62 0.62 Small decrease Small decreaseVirginia pine 0 13 High 27 17 41 109 44 246 2.08 1.31 3.15 8.39 3.39 18.92 New habitat New habitatWater oak 0 0 High 0 0 0 2 0 31 0.00 0.00 0.00 ― 0.00 New ― New habitatWhite ash 2077 2135 High 2358 2403 2466 2515 2590 2538 1.10 1.13 1.16 1.18 1.21 1.19 Small increase No changeWhite oak 404 564 High 666 1073 847 2112 961 2716 1.18 1.90 1.50 3.75 1.70 4.82 Small increase Large increaseWhite spruce 491 524 Medium 379 384 300 229 286 185 0.72 0.73 0.57 0.44 0.55 0.35 Small decrease Large decreaseWillow oak 0 7 Medium 5 3 5 24 5 77 0.71 0.43 0.71 3.43 0.71 11.00 ― New habitatWinged elm 1 0 High 0 8 0 122 3 471 0.00 8.00 0.00 122.00 3.00 471.00 No change Small increaseYellow birch 2344 2314 High 2252 1919 2037 1101 1941 844 0.97 0.83 0.88 0.48 0.84 0.37 No change Large decreaseYellow-poplar 25 69 High 129 189 183 543 206 805 1.87 2.74 2.65 7.87 2.99 11.67 Large increase Large increasea Current importance values (Current IV) are based on modeled results. Modeled early-century, mid-century, and late-century importance values are average values for the indicated years. Future:Current suitable habitat is a ratio of projected importance value to current importance value. Species are assigned to change classes based on the comparison between end-of-century (2070 through 2099) and current figures for area-weighted importance value. Descriptions of the change classes are given later in this appendix. Data for the three subregions are available at www.fs.fed.us/nrs/atlas/products/#ra.

Table 18 (continued).—Complete Climate Change Tree Atlas results for the 102 tree species in the assessment area

**Not observed in the Forest Inventory and Analysis data, but other data (e.g., State surveys) suggest species is present though rare.
APPENDIX 4

198

Special rules also applied to species that were known 

to be present (current FIA IV >0) but not modeled 

as present (current modeled = 0). In these cases, the 

FIA IV was used in place of the current modeled IV 

to calculate ratios. Then, change class rules were 

applied based on the FIA IV.

MODIFYING FACTORS 

AND ADAPTABILITY SCORES

Modifying factors for the species modeled in the 

Tree Atlas (Table 19) were developed by using 

a literature-based scoring system to capture the 

Modifying factors1 Adaptability scores2

Common name DISTRIB model reliability Positive traits Negative traits DistFact BioFact Adapt Adapt Class

American basswood Medium COL FTK 0.31 0.16 4.6 ○

American beech High COL INS FTK -1.14 0.03 3.6 ○

American chestnut Medium COL DISE FTK 0.13 0.30 4.5 ○

American elm Medium ESP DISE INS -0.80 0.30 4.0 ○

American holly High COL ESP FTK -0.10 0.47 4.5 ○

American hornbeam Medium COL SES FTK DRO 0.56 0.62 5.1 ○

American mountain-ash Medium --- FTK COL ESP -0.23 -1.62 3.1 ―

Atlantic white-cedar Low DISP FTK DRO ESP -0.61 -1.21 3.0 ―

Bald cypress Medium DISP FTK 0.38 -1.02 3.9 ○

Balsam fir High COL INS FTK DRO -3.00 -0.35 2.7 ―

Balsam poplar High FRG VRE COL DRO 0.13 -0.59 4.0 ○

Bigtooth aspen High FRG DISP COL DRO FTK 1.01 0.16 5.1 ○

Bitternut hickory Low DRO COL 2.17 -0.83 5.6 +

Black ash High --- INS COL DISP 

DRO SES FTK 

ESP

-1.31 -3.00 1.7 ―

Black cherry High DRO ESP INS FTK COL -1.56 -0.32 3.0 ―

Black hickory High --- ESP COL 1.04 -2.27 4.1 ○

Black locust Low --- COL INS 0 -0.59 3.8 ○

Black maple Low COL ESP FTK 0.48 0.90 5.2 ○

Black oak High DRO ESP INS DISE 0.51 0.42 4.9 ○

Black spruce High COL ESP DISP FTK INS DRO -2.14 1.24 4.3 ○

Black walnut Medium SES COL DRO 0.35 -0.83 4.0 ○

Black willow Low --- COL FTK DRO -0.31 -2.13 2.8 ―

Blackgum High COL FTK --- 1.46 0.83 5.9 +

Blackjack oak** Medium DRO SES FRG 

VRE

COL FTK 1.56 0.21 5.6 +

Boxelder Medium SES DISP DRO 

COL SES 

FTK 2.39 2.06 7.4 +

Bur oak Medium DRO FTK --- 2.77 -0.16 6.4 +

(continued on next page)

potential adaptability of species to changes in 

climate that cannot be adequately captured by the 

DISTRIB model (Matthews et al. 2011). The Tree 

Atlas modelers used this approach 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 pest outbreaks, and fire. This 

information distinguishes between species likely 

to be more tolerant (or sensitive) to environmental 

changes than the habitat models alone suggest. 

Table 19.—Modifying factor and adaptability information for the 102 tree species in the assessment area modeled 

using the Climate Change Tree Atlas
APPENDIX 4

199

Modifying factors1 Adaptability scores2

Common name DISTRIB model reliability Positive traits Negative traits DistFact BioFact Adapt Adapt Class

Butternut Low --- FTK COL DRO 

DISE

-1.41 -1.27 2.3 ―

Cedar elm Low --- DISE -0.27 -1.17 3.3 ○

Cherrybark oak Medium --- INS FTK -0.51 0.06 3.9 ○

Chestnut oak High SES VRE ESP 

FTK

INS DISE 1.39 1.29 6.1 +

Chinkapin oak** Medium SES --- 1.18 -0.66 4.8 ○

Chokecherry Low --- COL 0.18 -0.86 3.8 ○

Common persimmon Medium COL ESP --- 1.18 0.95 5.8 +

Eastern cottonwood Low SES INS COL DISE 

FTK

0.22 -0.75 3.9 ○

Eastern hemlock High COL INS DRO -1.34 -0.88 2.7 ―

Eastern hophornbeam Medium COL ESP SES --- 1.72 1.29 6.4 +

Eastern redbud Medium --- --- 0.90 -0.03 4.9 ○

Eastern redcedar Medium DRO FTK COL INS 0.56 -1.48 3.9 ○

Eastern white pine High DISP DRO FTK INS -1.97 0.13 3.3 ○

Flowering dogwood High COL --- 0.07 0.95 5.0 ○

Gray birch Medium DISP ESP FTK COL INS 

DISE

-1.14 0.03 3.6 ○

Green ash Medium --- INS FTK COL -0.13 -0.25 4.0 ○

Hackberry Medium DRO FTK 1.66 0.30 5.7 +

Honeylocust Low --- COL 1.91 -0.54 5.5 +

Jack pine High DRO COL INS 1.87 -1.24 5.2 ○

Loblolly pine High ESP INS INP DRO 

COL

-0.51 -0.66 3.4 ○

Longleaf pine High FTK COL 0.96 -1.74 4.2 ○

Mockernut hickory High --- FTK 1.69 -0.28 5.4 +

Mountain maple High COL VRE ESP DRO FTK 0.80 1.48 5.9 +

Northern pin oak Medium DRO FTK COL 2.52 -0.56 6.0 +

Northern red oak High --- INS 1.39 0.13 5.4 +

Northern white-cedar High COL FTK -0.70 0.49 4.2 ○

Ohio buckeye Low COL SES FTK 0.38 -1.92 3.5 ○

Osage-orange Medium ESP ESP --- 2.32 0.33 6.3 +

Paper birch High FRG DISP ESP FTK COL INS 

DRO

-1.72 0.18 3.4 ○

Pawpaw** Low COL DRO -0.48 -0.32 3.7 ○

Pignut hickory High ESP INS DRO 0.22 0.40 4.7 ○

Pin cherry Medium SES FRG FTK COL 0.45 -0.68 4.2 ○

Pin oak Medium --- FTK COL INS 

DISE

-0.65 -1.39 2.8 ―

Pitch pine High --- COL INS 0.56 -1.79 3.8 ○

Pond pine High --- DRO COL INS 

DISP

-1.06 -1.51 2.4 ―

Post oak High DRO SES FTK COL INS DISE 2.17 -0.59 5.7 +

Quaking aspen High SES FRG ESP COL DRO FTK 0.56 0.01 4.7 ○

Red maple High SES ESP ESP 

COL DISP 

--- 3.00 3.00 8.5 +

(continued on next page)

Table 19 (continued).—Modifying factor and adaptability information for the 102 tree species in the assessment area 

modeled using the Climate Change Tree Atlas
APPENDIX 4

200

Modifying factors1 Adaptability scores2

Common name DISTRIB model reliability Positive traits Negative traits DistFact BioFact Adapt Adapt Class

Red mulberry Low COL DISP FTK 0.07 0.59 4.7 ○

Red pine Medium --- INS COL DISP 0.86 -2.42 3.9 ○

Red spruce High ESP COL FTK SES -1.28 -0.62 2.9 ―

River birch Low DISP FTK COL DRO -0.45 -0.32 3.7 ○

Rock elm Low --- ESP ESP SES -0.20 -2.61 2.8 ―

Sand pine Medium --- DRO INS FTK -1.09 -1.14 2.7 ―

Sassafras High --- COL FTK 0.48 -0.64 4.2 ○

Scarlet oak High VRE ESP ESP INS DISE FTK -0.35 0.71 4.6 ○

Scrub oak Low FRG VRE COL FTK 1.04 -0.81 4.6 ○

Serviceberry Medium COL SES DRO -0.38 0.98 4.8 ○

Shagbark hickory Medium --- INS FTK -0.20 0.37 4.4 ○

Shellbark hickory Low COL FTK ESP -0.48 -0.30 3.7 ○

Shingle oak Medium ESP COL 1.31 -0.74 4.9 ○

Shortleaf pine** High ESP COL INS DRO 0 -0.97 3.6 ○

Shumard oak Low DRO SES COL 2.45 -1.02 5.8 +

Silver maple Medium DISP SES COL DRO FTK 0.13 1.63 5.6 +

Slippery elm Medium COL FTK DISE 0.03 0.68 4.8 ○

Sourwood High COL ESP --- 2.59 0.98 6.9 +

Southern red oak High SES --- 1.21 0.21 5.3 +

Striped maple High COL SES DRO 0.96 0.25 5.1 ○

Sugar maple High COL ESP --- 0.86 1.34 5.8 +

Sugarberry Medium COL SES FTK -0.17 0.64 4.6 ○

Swamp chestnut oak Medium SES COL INS 1.08 -0.81 4.6 ○

Swamp white oak Low --- --- 1.04 -0.30 4.9 ○

Sweet birch High DISP FTK COL INS 

DISE

-1.28 -0.32 3.2 ―

Sweetbay** High FTK INS 1.39 -0.47 5.1 ○

Sweetgum** High VRE ESP FTK COL DRO -0.41 0.18 4.1 ○

Sycamore Medium --- --- 1.28 -0.90 4.8 ○

Tamarack High --- FTK COL INS -0.48 -1.24 3.1 ―

Virginia pine High --- COL POL 0.10 -0.81 3.8 ○

Water oak High SES FTK COL -0.17 -0.59 3.7 ○

White ash High --- INS FTK COL -2.01 -0.54 2.7 ―

White oak High ESP ESP SES 

FTK

INS DISE 1.66 1.00 6.1 +

White spruce Medium --- INS 0.07 -0.62 3.9 ○

Willow oak Medium SES SES COL 0.63 -0.01 4.7 ○

Winged elm High --- INS DISE -0.58 -0.30 3.6 ○

Yellow birch High DISP FTK INS DISE -1.38 -0.03 3.4 ○

Yellow-poplar High SES DISP ESP INP 0.13 1.26 5.3 +

1

Modifying factor codes are described in Table 20. 

2

Adaptability scores are described in the appendix text. Scores of 3.2 and less are considered low (–), and scores of 5.3 and above are considered 

high (+). Scores between 3.2 and 5.3 are denoted by ○.

**Not observed in the Forest Inventory and Analysis data, but other data (e.g., State surveys) suggest species is present though rare.

Table 19 (continued).—Modifying factor and adaptability information for the 102 tree species in the assessment area 

modeled using the Climate Change Tree Atlas
APPENDIX 4

201

For each biological and disturbance factor, a species 

was scored on a scale from –3 to +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 (Matthews et al. 2011). 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 4 indicated highly relevant 

and a 1 indicated not highly relevant to climate 

change. Means for individual biological scores and 

disturbance scores were then calculated to arrive at 

an overall biological and disturbance score for the 

species (Table 20). 

To arrive at an overall adaptability score for the 

species that could be compared across all modeled 

tree species, the mean, rescaled (0-6) values for 

biological and disturbance characteristics were 

plotted to form two sides of a right triangle; the 

hypotenuse was then a combination (disturbance and 

Code Description (if positive) Description (if negative)

COL Tolerant of shade or limited light conditions Intolerant of shade or limited light conditions

DISE --- Has a high number and/or severity of known pathogens 

that attack the species

DISP High ability to effectively produce and distribute 

seeds

---

DRO Drought-tolerant Susceptible to drought

ESP Wide range of soil tolerance Narrow range of soil requirements 

FRG Regenerates well after fire ---

FTK Resistant to fire topkill Susceptible to fire topkill

INP --- Strong negative effects of invasive plants on the species, 

either through competition for nutrients or as a pathogen

INS --- Has a high number and/or severity of insects that may 

attack the species

SES High ability to regenerate with seeds to maintain 

future populations

Low ability to regenerate with seeds to maintain future 

populations

VRE Capable of vegetative reproduction through stump 

sprouts or cloning

---

*These codes describe positive or negative modifying factors used in Table 19. 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.

biological characteristics) metric, ranging from 0 to 

8.5 (Fig. 44). For this assessment, adaptability scores 

of 3.2 and less are considered low, and scores of 5.3 

and greater are considered high (Table 20).

Figure 44.—Schematic showing how adaptability was 

determined for information for the 102 tree species in the 

assessment area modeled using the Climate Change Tree 

Atlas. Modifying factor codes are described in Table 20. 

Adaptability scores are described in the appendix text.

Table 20.—Description of Tree Atlas modifying factor codes*
APPENDIX 4

202

Note that modifying factors and adaptability scores 

are calculated for a species across its entire range. 

Many species may have higher or lower adaptability 

in certain areas. For example, a species with a low 

flooding tolerance may have higher adaptability in 

areas not subject to flooding. Likewise, local impacts 

of insects and disease may reduce the adaptability of 

a species in that area.

LITERATURE CITED

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, 

M. 2008. Estimating potential habitat for 

134 eastern US tree species under six climate 

scenarios. Forest Ecology and Management. 

254(3): 390-406. 

https://doi.org/10.1016/j.foreco.2007.07.023.

Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, 

M.P. 2011. Lessons learned while integrating 

habitat, dispersal, disturbance, and lifehistory traits into species habitat models under 

climate change. Ecosystems. 14(6): 1005-1020. 

https://doi.org/10.1007/s10021-011-9456-4.

Iverson, L.R.; Prasad, A.; Schwartz, M.W. 1999. 

Modeling potential future individual treespecies distributions in the eastern United 

States under a climate change scenario: a 

case study with Pinus virginiana. Ecological 

Modelling. 115(1): 77-93. 

https://doi.org/10.1016/s0304-3800(98)00200-2.

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/.
203

APPENDIX 5: SUPPLEMENTARY LINKAGES RESULTS

This appendix contains additional model details and 

results from the LINKAGES model, which was used 

to simulate changes in tree species establishment 

probability during the 21st century for 24 common 

tree species within the assessment area. 

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; however, here the 

model was initialized from bareground conditions 

and modeled the establishment and growth of 

individual tree species over 30 years. It also 

modeled ecosystem functions such as soil water 

balance, litter decomposition, nitrogen cycling, 

soil hydrology and evapotranspiration. Inputs to 

the model include climate variables (e.g., daily 

temperature, precipitation, wind speed, and solar 

radiation), soil characteristics (e.g., soil moisture 

capacity and 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). Model results include number 

of stems, biomass, leaf litter, available nitrogen, 

humus, and organic matter. Similar to the Climate 

Change Tree Atlas, the LINKAGES model provides 

the unconstrained response in fundamental niche, 

or the habitat a species could occupy, to climate 

change. LANDIS PRO uses this fundamental 

niche information provided by LINKAGES and 

constrains the distribution through competition 

into the realized niche (the habitat that a species 

may actually occupy). Unlike the LANDIS PRO 

model, LINKAGES is not spatially dynamic, and 

does not simulate tree dispersal or any other spatial 

interaction among grid cells. 

For this assessment, LINKAGES projected changes 

in forest distribution by using downscaled daily 

mean temperature and precipitation under GFDL 

A1FI and PCM B1 for the end of the century (2080 

to 2099), and compared these projections with 

end-of-century projections under a current climate 

scenario (i.e., the climate during 1980 through 

2009).

Future biomass projections for the 24 tree species 

are presented for the assessment area as a whole and 

by subregion (Table 21). Early growth potential (first 

30 years) was also mapped for each species modeled 

by LINKAGES (Fig. 45). Change in early growth 

was calculated by dividing the modeled future 

biomass by the current climate biomass. Change was 

classified according to the following ratios:

Modeled:Current biomass Class

<0.5 large decrease

0.5 to 0.8 small decrease

>0.8 to <1.2 no change

1.2 to 2.0 small increase

>2 large increase

current climate = 0 and 

future climate model = 0 not present

current climate >0 and 

future climate model = 0 extirpated

LITERATURE CITED

Dijak, W.D.; Hanberry, B.B.; Fraser, J.S.; He, H.S.; 

Wang, W.J.; Thompson, F.R. 2016. Revision 

and application of the LINKAGES model to 

simulate forest growth in central hardwood 

landscapes in response to climate change.

Landscape Ecology. 32(7): 1365-1384. 

https://doi.org/10.1007/s10980-016-0473-8.
APPENDIX 5

204

Pastor, J.; Post, W. 1985. Development of a linked 

forest productivity-soil process model. Oak 

Ridge, TN: Oak Ridge National Laboratory.

Wullschleger, S.D.; Gunderson, C.A.; Tharp, 

M.L.; West, D.C.; Post, W.M. 2003. Simulated 

patterns of forest succession and productivity 

as a consequence of altered precipitation. In: 

Hanson, P.J.; Wullschleger, S.D., eds. North 

American temperate deciduous forest responses 

to changing precipitation regimes. New York, 

NY: Springer: 433-446.

Table 21.—Biomass reached in 30 years of tree growth starting from bare ground projected by the LINKAGES model 

for 24 species in the assessment area under the current climate scenario and two climate model-emissions scenario 

combinations for the period 2070 through 2099, by entire assessment area and subregion

Current climate PCM B1 GFDL A1FI

Species

Biomass in 

2100 (metric 

tons/acre)

Biomass 

in 2100 

(metric 

tons/acre)

Change from 

current climate 

biomass in 

2100 Change class

Biomass 

in 2100 

(metric 

tons/acre)

Change from 

current climate 

biomass in 

2100 Change class

Entire Assessment Area

American beech 40.4 38.0 -6% No change 27.8 -31% Small decrease

Balsam fir 11.9 5.5 -54% Large decrease 0.3 -97% Large decrease

Black cherry 30.8 40.3 31% Small increase 37.9 23% Small increase

Black oak 7.9 18.4 133% Large increase 30.8 290% Large increase

Black spruce 2.7 0.9 -67% Large decrease 0.0 -100% Extirpated

Chestnut oak 4.0 13.7 243% Large increase 34.1 753% Large increase

Eastern hemlock 20.0 11.2 -44% Small decrease 3.4 -83% Large decrease

Eastern white pine 32.3 29.1 -10% No change 13.5 -58% Large decrease

Loblolly pine 0.0 0.2 >1,000% New habitat 1.8 >1,000% New habitat

Northern red oak 48.3 52.7 9% No change 43.3 -10% No change

Northern white-cedar 8.2 3.9 -52% Large decrease 0.2 -98% Large decrease

Pignut hickory 11.4 22.7 99% Small increase 29.4 158% Large increase

Pitch pine 5.9 10.6 80% Small increase 12.6 114% Large increase

Quaking aspen 47.6 39.3 -17% No change 17.4 -63% Large decrease

Red maple 42.2 45.0 7% No change 38.3 -9% No change

Red spruce 9.2 3.7 -60% Large decrease 0.2 -98% Large decrease

Scarlet oak 10.2 21.3 109% Large increase 29.3 187% Large increase

Shagbark hickory 5.8 13.7 136% Large increase 24.2 317% Large increase

Sugar maple 48.2 44.2 -8% No change 34.7 -28% Small decrease

Virginia pine 0.1 1.5 >1,000% New habitat 6.6 >1,000% New habitat

White ash 60.6 61.9 2% No change 50.5 -17% No change

White oak 30.5 43.1 41% Small increase 43.3 42% Small increase

Yellow birch 46.0 39.2 -15% No change 24.6 -47% Small decrease

Yellow-poplar 15.5 39.3 154% Large increase 71.1 359% Large increase

(continued on next page)
APPENDIX 5

205

Current climate PCM B1 GFDL A1FI

Species

Biomass in 

2100 (metric 

tons/acre)

Biomass 

in 2100 

(metric 

tons/acre)

Change from 

current climate 

biomass in 

2100 Change class

Biomass 

in 2100 

(metric 

tons/acre)

Change from 

current climate 

biomass in 

2100 Change class

Southern and Coastal New England

American beech 22.3 21.6 -3% No change 3.0 -87% Large decrease

Balsam fir 0.7 0.2 -71% Large decrease 0.0 -100% Extirpated

Black cherry 26.3 30.6 16% No change 6.1 -77% Large decrease

Black oak 15.2 28.0 84% Small increase 7.1 -53% Large decrease

Black spruce 0.3 0.0 -100% Extirpated 0.0 -100% Extirpated

Chestnut oak 10.6 34.5 225% Large increase 10.9 2% No change

Eastern hemlock 1.3 1.1 -15% No change 0.6 -54% Large decrease

Eastern white pine 8.0 4.0 -50% Small decrease 1.4 -83% Large decrease

Loblolly pine 0.0 1.0 >1000% New habitat 2.9 >1000 New habitat

Northern red oak 47.5 45.9 -3% No change 15.9 -67% Large decrease

Northern white-cedar 0.8 0.1 -88% Large decrease 0.0 -100% Extirpated

Pignut hickory 14.9 22.2 49% Small increase 4.3 -71% Large decrease

Pitch pine 9.8 11.1 13% No change 2.4 -76% Large decrease

Quaking aspen 31.6 15.2 -52% Large decrease 0.0 -100% Extirpated

Red maple 34.4 32.8 -5% No change 8.3 -76% Large decrease

Red spruce 0.6 0.2 -67% Large decrease 0.0 -100% Extirpated

Scarlet oak 17.5 27.9 59% Small increase 6.8 -61% Large decrease

Shagbark hickory 10.6 18.3 73% Small increase 3.8 -64% Large decrease

Sugar maple 36.2 31.9 -12% No change 7.0 -81% Large decrease

Virginia pine 0.4 4.7 >1000% New habitat 3.9 875% New habitat

White ash 47.6 45.5 -4% No change 14.1 70% Large decrease

White oak 43.7 44.0 1% No change 22.4 -49% Small decrease

Yellow birch 31.0 19.9 -36% Small decrease 0.0 100% Extirpated

Yellow-poplar 36.0 75.4 109% Large increase 30.1 -16% No change

Eastern and Coastal Maine

American beech 54.6 53.6 -2% No change 51.1 -6% No change

Balsam fir 17.6 6.8 -61% Large decrease 1.2 -93% Large decrease

Black cherry 44.5 56.8 28% Small increase 68.8 55% Small increase

Black oak 5.3 22.2 319% Large increase 51.6 874% Large increase

Black spruce 4.4 0.6 -86% Large decrease 0.1 -98% Large decrease

Chestnut oak 0.0 8.7 >1,000% New habitat 53.4 >1,000% New habitat

Eastern hemlock 30.2 13.9 -54% Large decrease 10.5 -65% Large decrease

Eastern white pine 45.8 39.3 -14% No change 35.2 -23% Small decrease

(continued on next page)

Table 21 (continued).—Biomass reached in 30 years of tree growth starting from bare ground projected by the 

LINKAGES model for 24 species in the assessment area under the current climate scenario and two climate modelemissions scenario combinations for the period 2070 through 2099, by entire assessment area and subregion
APPENDIX 5

206

Current climate PCM B1 GFDL A1FI

Species

Biomass in 

2100 (metric 

tons/acre)

Biomass 

in 2100 

(metric 

tons/acre)

Change from 

current climate 

biomass in 

2100 Change class

Biomass 

in 2100 

(metric 

tons/acre)

Change from 

current climate 

biomass in 

2100 Change class

Loblolly pine 0.0 0.0 0% ― 1.1 >1,000% New habitat

Northern red oak 60.9 63.4 4% No change 64.0 5% No change

Northern white-cedar 13.0 4.2 -68% Large decrease 0.7 -95% Large decrease

Pignut hickory 13.7 32.1 134% Large increase 52.0 280% Large increase

Pitch pine 6.1 14.6 139% Large increase 21.7 256% Large increase

Quaking aspen 57.3 56.7 -1% No change 28.2 51% Large decrease

Red maple 55.3 58.8 6% No change 62.6 13% No change

Red spruce 13.4 4.4 -67% Large decrease 0.7 -95% Large decrease

Scarlet oak 9.2 27.3 197% Large increase 49.0 433% Large increase

Shagbark hickory 4.0 17.7 343% Large increase 43.8 995% Large increase

Sugar maple 56.3 56.6 1% No change 55.7 -1% No change

Virginia pine 0.0 0.0 0% ― 12.9 >1,000% New habitat

White ash 77.4 79.5 3% No change 80.7 4% No change

White oak 36.9 51.9 41% Small increase 60.0 63% Small increase

Yellow birch 54.0 53.7 -1% No change 49.8 -8% No change

Yellow-poplar 6.1 42.8 602% Large increase 106.2 1,641% Large increase

Northern Forest

American beech 42.1 38.5 -9% No change 28.6 -32% Small decrease

Balsam fir 14.1 7.1 -50% Small decrease 0.0 -100% Extirpated

Black cherry 27.4 38.0 39% Small increase 38.7 41% Small increase

Black oak 6.1 13.4 120% Large increase 32.1 426% Large increase

Black spruce 2.9 1.3 -55% Large decrease 0.0 -100% Extirpated

Chestnut oak 2.9 7.7 166% Large increase 35.8 1,134% Large increase

Eastern hemlock 23.4 14.1 -40% Small decrease 1.9 -92% Large decrease

Eastern white pine 36.5 35.0 -4% No change 10.1 -72% Large decrease

Loblolly pine 0.0 0.0 0% ― 1.7 >1,000% New habitat

Northern red oak 44.0 51.3 17% No change 46.1 5% No change

Northern white-cedar 9.3 5.2 -44% Small decrease 0.1 -99% Large decrease

Pignut hickory 9.3 19.4 109% Large increase 30.6 229% Large increase

Pitch pine 4.4 8.9 102% Large increase 13.2 200% Large increase

Quaking aspen 50.1 42.0 -16% No change 20.0 60% Large decrease

Red maple 40.4 44.6 10% No change 40.8 1% No change

Red spruce 10.9 4.7 -57% Large decrease 0.0 -100% Extirpated

Scarlet oak 7.8 16.6 113% Large increase 30.6 292% Large increase

Shagbark hickory 4.6 10.5 128% Large increase 24.8 439% Large increase

Sugar maple 49.8 44.4 -11% No change 37.5 -25% Small decrease

Virginia pine 0.0 0.8 >1,000% New habitat 5.2 >1,000% New habitat

White ash 59.3 61.6 4% No change 53.4 -10% No change

White oak 23.1 39.5 71% Small increase 45.2 96% Small increase

Yellow birch 48.7 41.2 -15% No change 24.7 -49% Small decrease

Yellow-poplar 11.1 24.2 118% Large increase 73.9 566% Large increase

Table 21 (continued).—Biomass reached in 30 years of tree growth starting from bare ground projected by the 

LINKAGES model for 24 species in the assessment area under the current climate scenario and two climate modelemissions scenario combinations for the period 2070 through 2099, by entire assessment area and subregion
APPENDIX 5

207

Figure 45.—Projections of relative amount and direction of change in biomass for 24 species in the assessment area using the 

LINKAGES model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current climate scenario. Tree species 

growth values modeled by LINKAGES are presented as the biomass reached in 30 years of growth starting from bare ground in 

2070 and ending in 2099.
APPENDIX 5

208

Figure 45 (continued).—Projections of relative amount and direction of change in biomass for 24 species in the assessment 

area using the LINKAGES model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current climate scenario. 

Tree species growth values modeled by LINKAGES are presented as the biomass reached in 30 years of growth starting from 

bare ground in 2070 and ending in 2099.
APPENDIX 5

209

Figure 45 (continued).—Projections of relative amount and direction of change in biomass for 24 species in the assessment 

area using the LINKAGES model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current climate scenario. 

Tree species growth values modeled by LINKAGES are presented as the biomass reached in 30 years of growth starting from 

bare ground in 2070 and ending in 2099.
210

APPENDIX 6: SUPPLEMENTARY LANDIS PRO RESULTS

This appendix contains additional model details and 

results from the LANDIS PRO model, which was 

used to simulate changes in tree abundance (basal 

area per acre) and density (trees per acre) through 

the year 2200 for 24 common tree species within the 

assessment area (Wang et al. 2017). 

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

a spatially dynamic process model that simulates 

biological and ecosystem processes at the scale 

of individual species, stands, and landscapes. It is 

derived from the LANDIS model (Mladenoff 2004), 

but has been modified extensively from its original 

version. The LANDIS PRO model can simulate very 

large landscapes (millions of acres) at relatively 

fine spatial and temporal resolutions (typically 200 

to 300 feet and 1- to 10-year time steps). One new 

feature of the model compared to previous versions 

is that inputs and outputs of tree species data 

include tree density and volume and are compatible 

with U.S. Forest Service Forest Inventory and 

Analysis (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 observed on the ground 

and allows the modelers to quantify the uncertainties 

inherent in the initial data. 

The LANDIS PRO model stratifies the landscape 

into land types based on environmental 

characteristics. Within a land type, species 

establishment and resource availability are assumed 

to be similar. Basic inputs to the LANDIS PRO 

model include maps of species composition, land 

types, stands, management areas, and disturbance 

areas. In addition, species characteristics such as 

longevity, maturity, shade tolerance, average seed 

production, and maximum diameter at breast height 

are given as inputs into the model. A software 

program, Landscape Builder, is used to assign the 

number of trees by age cohort and species to each 

grid cell to generate the species composition map 

(Dijak 2013). 

Species-level processes are simulated from known 

life-history traits and empirical equations and can 

include seedling germination and establishment, 

growth, vegetative reproduction, and tree mortality. 

Basic outputs in LANDIS PRO for a species or 

species cohort include biomass, age, and carbon. 

Stand-level processes include competition and 

succession. The model can simulate landscape-level 

processes including fire, wind, insect outbreaks, 

disease, invasive species, harvesting, silviculture, 

and fuels treatments, but only current forest 

harvest levels were a component of the simulations 

presented in this assessment. 

Projections of future tree abundance and density for 

24 tree species are shown for the assessment area 

as a whole for four time periods under three climate 

scenarios: a current climate scenario (in which the 

climatic conditions for 1980 through 2009 are used 

as the projected climate), PCM B1, and GFDL A1FI 

(Table 22). Relative amount and direction of change 

in projected tree abundance at year 2100 was also 

mapped for each species modeled by LANDIS PRO 

(Fig. 46). Estimated and projected abundance are 

graphed in Figure 47. 

LITERATURE CITED

Dijak, W. 2013. Landscape Builder: software for 

the creation of initial landscapes for LANDIS 

from FIA data. Computational Ecology and 

Software. 3(2): 17-25. 
APPENDIX 6

211

Mladenoff, D.J. 2004. LANDIS and forest landscape models. Ecological Modelling. 180(1): 

7-19. https://doi.org/10.1016/j.ecolmodel.2004.0

3.016.

Wang, W.J.; He, H.S.; Fraser, J.S.; Thompson, F.R.; 

Shifley, S.R.; Spetich, M.A. 2014. LANDIS 

PRO: a landscape model that predicts forest 

composition and structure changes at regional 

scales. Ecography. 37: 225-229. 

https://doi.org/10.1111/j.1600-0587.2013.00495.x

Wang, W.J.; He, H.S.; Thompson, F.R.; Fraser, J.S.; 

Dijak, W.D. 2017. Changes in forest biomass 

and tree species distribution under climate 

change in the northeastern United States.

Landscape Ecology. 32(7): 1399-1413. 

https://dx.doi.org/10.1007/s10980-016-0429-z.
APPENDIX 6

212Table 22.—Basal area (BA) and trees per acre (TPA) projected by the LANDIS PRO model for 24 species in the assessment area in the years 2040, 2070, 2100, and 2200 under the current climate scenario and two climate model-emissions scenario combinations

(continued on next page)

Basal area Trees per acre

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

BA in 2000 

(ft2/acre)

BA (ft2/

acre)

Change from 

2000

BA (ft2/

acre)

Change from 

current climate

BA (ft2/

acre)

Change from 

current climate

TPA in 

2000 TPA

Change from 

2000 TPA

Change from 

current climate TPA

Change from 

current climate

American beech 6.2 2040 8.6 39% 8.1 -6% 9.2 7% 71.1 27.2 -62% 24.8 -9% 29.5 8%

2070 10.5 69% 9.5 -10% 11.8 12% 23.1 -68% 20.3 -12% 27.3 18%2100 11.5 85% 10.3 -10% 13.8 20% 26.4 -63% 24.4 -8% 34.8 32%2200 11.7 89% 11.4 -3% 15.4 32% 42.7 -40% 38.9 -9% 53.1 24%

Balsam fir 11.7 2040 11.1 -5% 11.1 0% 12.1 9% 237.6 44.0 -81% 42.3 -4% 45.7 4%

2070 9.2 -21% 9.5 3% 11.0 20% 21.9 -91% 23.8 9% 26.0 19%2100 7.5 -36% 8.2 9% 9.7 29% 15.4 -94% 21.8 42% 18.7 21%2200 2.2 -81% 2.4 9% 1.5 -32% 10.3 -96% 8.4 -18% 3.9 -62%

Black cherry 1.9 2040 2.2 16% 2.2 0% 2.3 5% 7.4 7.7 4% 7.4 -4% 8.2 6%

2070 2.4 26% 2.3 -4% 2.6 8% 6.1 -18% 5.8 -5% 7.2 18%2100 2.6 37% 2.5 -4% 3.0 15% 6.3 -15% 6.0 -5% 9.3 48%2200 2.4 26% 2.7 13% 3.7 54% 6.9 -7% 7.8 13% 13.4 94%

Black oak 1.2 2040 1.1 -8% 1.1 0% 1.1 0% 1.8 3.0 67% 3.1 3% 3.0 0%

2070 1.2 0% 1.2 0% 1.2 0% 2.1 17% 2.3 10% 2.0 -5%2100 1.4 17% 1.5 7% 1.4 0% 2.2 22% 2.5 14% 1.9 -14%2200 1.4 17% 1.6 14% 1.0 -29% 3.4 89% 4.2 24% 2.5 -26%

Black spruce 1.5 2040 1.6 7% 1.6 0% 1.7 6% 11.3 12.2 8% 10.9 -11% 12.3 1%

2070 1.6 7% 1.5 -6% 1.8 13% 12.3 9% 8.5 -31% 10.0 -19%2100 1.4 -7% 1.2 -14% 1.5 7% 12.2 8% 6.4 -48% 5.5 -55%2200 0.8 -47% 0.5 -38% 0.3 -63% 8.6 -24% 3.1 -64% 0.6 -93%

Chestnut oak 0.3 2040 0.5 67% 0.5 0% 0.5 0% 0.4 2.4 500% 2.5 4% 2.4 0%

2070 0.5 67% 0.5 0% 0.5 0% 1.8 350% 2.1 17% 1.9 6%2100 0.6 100% 0.6 0% 0.6 0% 2.8 600% 3.9 39% 3.1 11%2200 1.8 500% 2.2 22% 2.1 17% 5.3 1,225% 8.0 51% 8.0 51%

Eastern hemlock 9.8 2040 10.1 3% 9.8 -3% 10.6 5% 39.4 16.8 -57% 15.3 -9% 16.6 -1%

2070 10.0 2% 9.5 -5% 10.8 8% 12.7 -68% 10.6 -17% 11.4 -10%2100 9.1 -7% 8.6 -5% 10.1 11% 13.3 -66% 10.7 -20% 10.2 -23%2200 4.3 -56% 3.5 -19% 3.6 -16% 17.2 -56% 10.0 -42% 7.7 -55%

Eastern white pine 9.8 2040 11.8 20% 11.6 -2% 12.4 5% 23.0 45.3 97% 43.0 -5% 46.3 2%

2070 11.7 19% 11.3 -3% 13.0 11% 42.6 85% 36.3 -15% 44.0 3%2100 11.3 15% 10.6 -6% 13.3 18% 50.1 118% 40.0 -20% 65.4 31%2200 7.9 -19% 7.3 -8% 8.1 3% 39.6 72% 36.7 -7% 56.2 42%
APPENDIX 6

213

Table 22 (continued).—Basal area (BA) and trees per acre (TPA) projected by the LANDIS PRO model for 24 species in the assessment area in the years 2040, 2070, 2100, and 2200 under the current climate scenario and two climate model-emissions scenario combinations

(continued on next page)

Basal area Trees per acre

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

BA in 2000 

(ft2/acre)

BA (ft2/

acre)

Change from 

2000

BA (ft2/

acre)

Change from 

current climate

BA (ft2/

acre)

Change from 

current climate

TPA in 

2000 TPA

Change from 

2000 TPA

Change from 

current climate TPA

Change from 

current climate

Loblolly pine 0.0 2040 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%

2070 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0%2100 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0%2200 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0%

Northern red oak 4.9 2040 5.4 10% 5.3 -2% 5.5 2% 10.8 14.5 34% 14.1 -3% 15.2 5%

2070 5.7 16% 5.5 -4% 6.0 5% 14.2 31% 13.3 -6% 15.5 9%2100 5.6 14% 5.3 -5% 6.3 13% 17.3 60% 15.7 -9% 20.6 19%2200 7.8 59% 7.3 -6% 8.4 8% 21.3 97% 19.2 -10% 25.0 17%

Northern white-cedar 4.1 2040 4.3 5% 4.2 -2% 4.7 9% 26.2 36.2 38% 31.5 -13% 36.5 1%

2070 4.4 7% 4.2 -5% 5.0 14% 41.2 57% 28.4 -31% 35.2 -15%2100 4.4 7% 4.1 -7% 5.1 16% 45.7 74% 25.3 -45% 27.1 -41%2200 3.2 -22% 2.1 -34% 2.0 -38% 41.0 56% 14.8 -64% 12.3 -70%

Pignut hickory 0.3 2040 0.5 67% 0.5 0% 0.5 0% 0.9 1.2 33% 1.2 0% 1.1 -8%

2070 0.6 100% 0.6 0% 0.6 0% 1.1 22% 1.1 0% 1.0 -9%2100 0.7 133% 0.7 0% 0.7 0% 1.1 22% 1.2 9% 0.9 -18%2200 0.9 200% 0.9 0% 0.7 -22% 1.5 67% 1.6 7% 0.9 -40%

Pitch pine 0.2 2040 0.3 50% 0.3 0% 0.3 0% 0.3 1.9 533% 1.9 0% 1.8 -5%

2070 0.2 0% 0.2 0% 0.2 0% 0.6 100% 0.7 17% 0.7 17%2100 0.2 0% 0.2 0% 0.2 0% 0.3 0% 0.3 0% 0.3 0%2200 0.0 -100% 0.0 0% 0.0 0% 0.1 -67% 0.1 0% 0.1 0%

Quaking aspen 2.0 2040 3.2 60% 3.2 0% 3.3 3% 10.8 29.0 169% 27.6 -5% 30.2 4%

2070 4.5 125% 4.3 -4% 4.9 9% 41.5 284% 38.3 -8% 42.4 2%2100 7.3 265% 7.0 -4% 7.9 8% 71.9 566% 68.9 -4% 78.2 9%2200 23.6 1,080% 26.3 11% 19.2 -19% 179.6 1,563% 212.9 19% 163.1 -9%

Red maple 16.2 2040 19.3 19% 19.2 -1% 19.8 3% 107.2 60.9 -43% 60.1 -1% 63.6 4%

2070 19.0 17% 18.9 -1% 20.0 5% 38.0 -65% 37.2 -2% 43.3 14%2100 15.6 -4% 15.4 -1% 16.8 8% 25.9 -76% 25.7 -1% 34.9 35%2200 3.5 -78% 3.6 3% 4.2 20% 10.1 -91% 10.5 4% 13.0 29%

Red spruce 7.5 2040 10.0 33% 9.5 -5% 10.7 7% 74.2 64.9 -13% 55.5 -14% 63.3 -2%

2070 10.8 44% 9.6 -11% 11.9 10% 65.5 -12% 41.4 -37% 49.9 -24%2100 11.5 53% 9.1 -21% 11.8 3% 71.4 -4% 31.8 -55% 29.7 -58%2200 8.6 15% 4.8 -44% 4.4 -49% 47.1 -37% 13.3 -72% 5.0 -89%
APPENDIX 6

214Table 22 (continued).—Basal area (BA) and trees per acre (TPA) projected by the LANDIS PRO model for 24 species in the assessment area in the years 2040, 2070, 2100, and 2200 under the current climate scenario and two climate model-emissions scenario combinations

Basal area Trees per acre

Current climate PCM B1 GFDL A1FI Current climate PCM B1 GFDL A1FI

Tree species

BA in 2000 

(ft2/acre)

BA (ft2/

acre)

Change from 

2000

BA (ft2/

acre)

Change from 

current climate

BA (ft2/

acre)

Change from 

current climate

TPA in 

2000 TPA

Change from 

2000 TPA

Change from 

current climate TPA

Change from 

current climate

Scarlet oak 0.5 2040 0.8 60% 0.8 0% 0.8 0% 1.2 1.9 58% 2.0 5% 1.9 0%

2070 1.0 100% 1.0 0% 1.0 0% 1.5 25% 1.6 7% 1.4 -7%2100 1.1 120% 1.1 0% 1.1 0% 1.9 58% 2.1 11% 1.4 -26%2200 1.4 180% 1.5 7% 0.8 -43% 2.9 142% 3.6 24% 1.4 -52%

Shagbark hickory 0.2 2040 0.3 50% 0.3 0% 0.3 0% 0.6 0.8 33% 0.8 0% 0.8 0%

2070 0.4 100% 0.4 0% 0.4 0% 0.7 17% 0.7 0% 0.7 0%2100 0.4 100% 0.4 0% 0.4 0% 0.8 33% 0.8 0% 0.9 13%2200 0.5 150% 0.5 0% 0.6 20% 1.0 67% 1.2 20% 1.2 20%

Sugar maple 10.3 2040 11.4 11% 10.9 -4% 11.8 4% 42.2 18.9 -55% 17.7 -6% 20.4 8%

2070 11.6 13% 10.7 -8% 12.6 9% 15.2 -64% 14.4 -5% 18.5 22%2100 11.0 7% 10.1 -8% 12.5 14% 20.4 -52% 20.1 -2% 25.8 26%2200 12.9 25% 12.7 -2% 14.1 9% 38.2 -9% 37.6 -2% 44.5 16%

Virginia pine 0.0 2040 0.0 0% 0.0 0% 0.0 0% 0.0 0.0 0% 0.0 0% 0.0 0%

2070 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0%2100 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0%2200 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0%

White ash 3.0 2040 4.2 40% 4.1 -2% 4.4 5% 14.1 13.4 -5% 12.7 -5% 14.3 7%

2070 5.1 70% 4.8 -6% 5.7 12% 14.2 1% 12.7 -11% 16.8 18%2100 6.1 103% 5.5 -10% 7.3 20% 19.6 39% 16.3 -17% 28.1 43%2200 10.1 237% 8.5 -16% 15.3 51% 34.1 142% 26.5 -22% 66.0 94%

White oak 1.1 2040 2.2 100% 2.2 0% 2.2 0% 1.7 9.2 441% 9.2 0% 9.4 2%

2070 2.2 100% 2.2 0% 2.3 5% 10.4 512% 10.3 -1% 10.8 4%2100 2.8 155% 2.8 0% 3.1 11% 17.7 941% 17.2 -3% 18.1 2%2200 5.2 373% 5.0 -4% 7.5 44% 21.2 1,147% 18.9 -11% 29.6 40%

Yellow birch 6.1 2040 7.6 25% 7.4 -3% 8.0 5% 36.9 30.6 -17% 28.5 -7% 31.9 4%

2070 9.1 49% 8.5 -7% 10.1 11% 36.5 -1% 31.1 -15% 40.1 10%2100 10.5 72% 9.0 -14% 12.3 17% 46.8 27% 36.4 -22% 59.2 27%2200 18.0 195% 15.6 -13% 20.3 13% 72.4 96% 57.0 -21% 90.6 25%

Yellow-poplar 0.1 2040 0.1 0% 0.1 0% 0.1 0% 0.1 0.9 800% 0.9 0% 0.9 -2%

2070 0.1 0% 0.1 0% 0.1 0% 0.4 300% 0.4 0% 0.4 0%2100 0.1 0% 0.1 0% 0.1 0% 0.3 200% 0.3 0% 0.3 0%2200 0.1 0% 0.1 0% 0.2 100% 0.5 400% 0.7 40% 1.2 140%
APPENDIX 6

215

Figure 46.— Projections of relative amount and direction of change in basal area at year 2100 for 24 species in the assessment 

area using the LANDIS PRO model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current climate scenario.
APPENDIX 6

216

Figure 46 (continued).— Projections of relative amount and direction of change in basal area at year 2100 for 24 species in 

the assessment area using the LANDIS PRO model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current 

climate scenario.
APPENDIX 6

217

Figure 46 (continued).— Projections of relative amount and direction of change in basal area at year 2100 for 24 species in 

the assessment area using the LANDIS PRO model under the PCM B1 and GFDL A1FI climate scenarios, relative to the current 

climate scenario.
APPENDIX 6

218

Figure 47.—Projected change in basal area for 24 species in the assessment area using the LANDIS PRO model under the 

current climate and two climate change scenarios. Note that the panels have different Y-axis values.
APPENDIX 6

219

Figure 47 (continued).—Projected change in basal area for 24 species in the assessment area using the LANDIS PRO model 

under the current climate and two climate change scenarios. Note that the panels have different Y-axis values.
220

APPENDIX 7: VULNERABILITY 

AND CONFIDENCE DETERMINATION

EXPERT PANEL PROCESS

To assess vulnerabilities to climate change for each 

forest system, we elicited input from a panel of 20 

experts from a variety of land management and 

research organizations across the assessment area 

(Table 23). We sought a team of panelists who would 

be able to contribute a diversity of subject area 

expertise, management history, and organizational 

perspectives. Most panelists had extensive 

knowledge about the ecology, management, and 

climate change impacts on forests in the assessment 

area. This panel was assembled at an in-person 

workshop in Burlington, Vermont, in December 

2015. During that workshop, we used a structured 

discussion process to assess the vulnerability of 

eight forest types; the methodology is described in 

greater detail by Brandt et al. (2017). 

Participant Organization

Diane Burbank Green Mountain National Forest

Patricia Butler* Northern Institute of Applied Climate Science and Michigan Technological University

John Campbell U.S. Forest Service, Northern Research Station

Charlie Cogbill Independent scientist

Tony D’Amato University of Vermont

Bill Dijak U.S. Forest Service, Northern Research Station

Matt Duveneck Harvard Forest

Marla Emery U.S. Forest Service, Northern Research Station

Nick Fisichelli National Park Service

Jane Foster University of Vermont/Northeast Climate Science Center

Jennifer Hushaw Manomet Center for Conservation Sciences

Louis Iverson U.S. Forest Service, Northern Research Station

Maria Janowiak* Northern Institute of Applied Climate Science and U.S. Forest Service

Laura Kenefic U.S. Forest Service, Northern Research Station

Amanda Mahaffey Forest Stewards Guild

Steve Matthews Ohio State University

Toni Lyn Morelli Northeast Climate Science Center

Nick Reo Dartmouth College

Paul Schaberg U.S. Forest Service, Northern Research Station

K. Rogers Simmons White Mountain National Forest

Aaron Weiskittel University of Maine

Sandy Wilmot Vermont Agency of Natural Resources

* Workshop facilitators

Table 23.—Participants in the December 2015 expert panel workshop
APPENDIX 7

221

FOREST SYSTEMS ASSESSED

The authors of this assessment used the forest 

ecosystem classification described in Chapter 1. For 

each forest type, we collected information related 

to the major system drivers, dominant species, 

and stressors that characterize that system from 

the relevant ecological literature. The panel was 

asked to comment on and suggest modifications 

to the descriptions of the forest systems, and those 

suggestions were incorporated into the descriptions. 

POTENTIAL IMPACTS

To examine potential impacts, the panel was given 

several sources of background information on 

past and future climate change in the Northeast 

(summarized in Chapters 2 and 3) and projected 

impacts on dominant tree species and forest 

productivity (summarized in Chapter 4). The panel 

was 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 end-of-century period. 

The panel assessed impacts by considering a range 

of climate futures bracketed by two scenarios: GFDL 

A1FI and PCM B1. Panelists were then led through 

a structured discussion process to consider this 

information for each of the eight forest systems in 

the assessment. 

Potential impacts on community drivers and 

stressors were summarized based on climate 

model projections, the published literature, and 

insights from the panelists. Impacts on drivers 

were considered positive or negative if they would 

alter system drivers in a way that would be more 

or less favorable for that forest system. Impacts 

on stressors were considered negative if they 

increased the influence of that stressor or positive 

if they decreased the influence of that stressor on 

the forest system. Panelists were also asked to 

consider the potential for climate change to facilitate 

new stressors in the assessment area over the next 

century. 

To assess potential impacts on dominant tree species, 

the panelists examined 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 panel evaluated how much agreement existed 

within the available information, between climate 

scenarios, and across space and time. Finally, 

panelists were asked to consider the potential for 

interactions among anticipated climate trends, 

species impacts, and stressors. Input on these 

future ecosystem interactions relied primarily on 

the panelists’ expertise and judgment because there 

are not many examples of published literature on 

complex interactions, nor are future interactions 

accurately represented by ecosystem models. 

ADAPTIVE CAPACITY

Panelists discussed the adaptive capacity of each 

forest system based on their ecological knowledge 

and management experience with the community 

types in the assessment area. Panelists were told 

to focus on the characteristics of that forest system 

that could increase or decrease the adaptive capacity 

of that system. Factors that the panel considered 

included characteristics of dominant species within 

each forest system (e.g., dispersal ability, genetic 

diversity, range limits) as well as comprehensive 

ecosystem characteristics (e.g., functional 

and species diversity, tolerance to a variety of 

disturbances, distribution across the landscape). The 

panelists were directed to base their considerations 

on the current condition of the system given past and 

current management regimes, with no consideration 

of potential adaptation actions that could take place 

in the future. 

VULNERABILITY

After extensive group discussion, each panelist 

evaluated the potential impacts and adaptive 

capacity of each forest system to arrive at a 

vulnerability rating. Participants were provided with 

individual worksheets and asked to list the impacts 

they felt were most important to that system in 

addition to the major factors that would contribute to 

the adaptive capacity of that system. 
APPENDIX 7

222

Panelists were directed to mark their rating in 

two-dimensional space on the individual worksheet 

and on a large group poster (Fig. 48A). 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 system 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. 48B). 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 eight forest systems 

considered in this assessment are displayed in 

Figures 49 through 56. 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 systems were identified and 

developed into vulnerability statements (boldface 

text) and supporting text in Chapter 5. 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 panel for review. Panelists were asked 

to review each statement for accuracy, whether 

the confidence determination should be raised 

or lowered, if there was additional literature that 

was overlooked, and if there were any additional 

statements that needed to be made. Any changes that 

were suggested by a single panelist were brought 

forth for discussion. Changes to vulnerability 

statements required approval by the entire panel. 

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).
APPENDIX 7

223

Figure 48.—Figure used for (A) vulnerability determination by expert panelists (described by Brandt et al. [2017]), and (B) 

confidence rating among expert panelists (adapted from Mastrandrea et al. [2010]).

Figure 49.—Vulnerability and confidence determinations for the central hardwood-pine forest system. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.
APPENDIX 7

224

Figure 50.—Vulnerability and confidence determinations for the low-elevation spruce-fir forest system. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 51.—Vulnerability and confidence determinations for the lowland and riparian hardwood forest system. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.
APPENDIX 7

225

Figure 52.—Vulnerability and confidence determinations for the lowland mixed-conifer forest system. Circles indicate 

individual determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 53.—Vulnerability and confidence determinations for the montane spruce-fir forest system. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.
APPENDIX 7

226

Figure 54.—Vulnerability and confidence determinations for the northern hardwood forest system. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.

Figure 55.—Vulnerability and confidence determinations for the pitch pine-scrub oak forest system. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.
APPENDIX 7

227

Figure 56.—Vulnerability and confidence determinations for the transition hardwood forest system. Circles indicate individual 

determinations by each panelist and squares indicate the group determination after consensus was reached.
228

APPENDIX 8: CLIMATE CHANGE IMPACTS 

ON BIODIVERSITY

Climate change will have profound effects on the 

species associated with forest ecosystems. Several 

detailed assessments have been developed to 

evaluate the vulnerability of species and habitats 

across the Northeast (see Introduction). This 

appendix provides a short review of some effects 

on wildlife, aquatic organisms, and plant species of 

concern in the assessment area.

WILDLIFE

Climate change is likely to have both direct and 

indirect impacts on wildlife populations and their 

habitats, and managers will increasingly need to 

consider these effects (Mawdsley et al. 2009). 

Changes to habitats discussed in Chapter 6 are likely 

to result in range expansion for some species and 

the reduction or complete loss of available suitable 

habitat for others. Wildlife populations may adapt to 

new conditions by shifting the timing of life cycle 

events, altering their physiology (e.g., developing 

tolerance to warmer and drier conditions), and 

changing their behavior (e.g., foraging for new food 

sources), or they may migrate to follow shifts in 

suitable habitat (Bellard et al. 2012). Species that 

are unable to adapt or have limited dispersal ability, 

particularly those that are already rare, may face the 

most substantial challenges in a changing climate. 

Climate change is expected to shift the ranges of 

other boreal and subalpine specialists northward, 

which is expected to result in the fragmentation 

and loss of southern populations (Cheng et al. 

2014). Snowshoe hares, for example, are likely to 

have mismatches between coat color and ground 

cover for longer as the number of snow-free 

days increases, leading to increased vulnerability 

to predators (Zimova et al. 2014). The moose 

is another cold-adapted species considered to 

be highly vulnerable to climate change in the 

Northeast (Hoving et al. 2013, Whitman et al. 2013) 

although conditions in some areas of the region, 

such as Massachusetts, are currently favorable for 

moose (Wattles and DeStefano 2013). Extreme 

infestations of winter tick, exacerbated by milder 

winters, can cause substantial mortality to moose 

(Musante et al. 2007), and increasing white-tailed 

deer populations can spread pathogens such as 

brainworm. Thermoregulatory stress associated 

with rising temperatures may also play an important 

role in moose population dynamics (Dou et al. 

2013, Murray et al. 2006). The northern flying 

squirrel is an example of a species threatened by 

the northward shift of spruce-fir and northern 

hardwood ecosystems, which may reduce access to 

habitat and to the fungi and lichen that are important 

food sources for this squirrel species. Habitat and 

temperature changes are already allowing southern 

flying squirrels to expand northward; the subsequent 

decline of northern flying squirrels is associated with 

disease transmission and competition (Smith 2012).

Not all effects of climate change will be negative. 

The New England cottontail may benefit from 

decreased snow cover and forest disturbance in the 

Northeast. But indirect effects through changing 

relationships with other species such as predators 

and competitors are hard to predict. For example, if 

climate change affects eastern cottontails positively, 

there may be increased competition for New 

England cottontails (Fuller and Tur 2012). Milder 

winters may reduce the time that black bears spend 

in hibernation; increased bear activity combined 

with the potential for low food availability during 

winter months may increase the potential for 

human-bear conflict, as bears are more likely to visit 

urbanized areas in search of food during shortages 

(Baruch-Mordo et al. 2014, Obbard et al. 2014). 
APPENDIX 8

229

Generalist species such as the coyote may benefit 

from the greater food availability in milder winters 

(Knowlton and Gese 1995, O’Donoghue et al. 1997, 

Todd and Keith 1983, Windberg 1995). Likewise, 

milder winters may be advantageous to wild turkeys 

(Porter et al. 1983) and wood ducks (Oli et al. 2002). 

White-tailed deer are likely to benefit from milder 

winters and longer growing seasons (Mech et al. 

1987, Post and Stenseth 1998).

With declines in bat populations due to white-nose 

syndrome, many managers are now thinking about 

how management might affect bat species. Bats may 

be particularly sensitive to climate change because 

many aspects of their ecology and life history are 

closely tied to temperature and precipitation, and 

many species in the assessment area have already 

suffered catastrophic declines as a result of whitenose syndrome. For example, the northern longeared bat (northern myotis) is highly affected by 

white-nose disease while overwintering, but it 

is unclear whether climate change will increase 

overwintering stress (by decreasing access to 

insects or by causing bats to arouse more frequently 

from hibernation) or decrease overwintering stress 

(because a longer growing season may translate to 

a short hibernation time) (Rodenhouse et al. 2009). 

Hoary bats in the Northeast have been known to 

roost exclusively in eastern hemlock trees (Veilleux 

et al. 2009), which face increased risk of mortality 

from the hemlock woolly adelgid as a result of 

climate change (Paradis et al. 2008). On the other 

hand, increases in precipitation at the right time may 

bode well for insectivorous bat species (Moosman 

et al. 2012). Moreover, climate change may increase 

riparian habitat, which has been shown to be 

important for foraging by bats (e.g., hoary bats and 

big brown bats), in some areas of the Northeast in 

coming decades (Menzel et al. 2005). 

Many bird species may be less vulnerable to climate 

change impacts than other taxonomic groups 

because they tend to have less habitat specificity, 

are able to disperse long distances, and are not as 

hindered by natural and anthropogenic obstacles on 

the landscape. American woodcocks are common 

in early successional forests and have already been 

found to be arriving earlier in Massachusetts and 

New York (Butler 2003) and breeding earlier in 

Maine (Wilson et al. 2000). However, bird species 

that are dependent on specific habitat types (e.g., 

high-elevation conifer forest) may be unable to 

meet their habitat requirements in a new location, 

or habitat shifts may introduce new competitors 

and predators (Matthews et al. 2011). Blackpoll 

warblers breed in the spruce-fir forests of the 

Northeast. As that habitat contracts northward 

and upslope, the species is likely to become less 

common. Some modeling studies have projected 

breeding populations of blackpoll warblers will 

be greatly reduced or extirpated from New York, 

Vermont, and New Hampshire by 2080, depending 

on the climate scenario used (Ralston and Kirchman 

2013). Adequate snow cover and quality can also 

be important for overwinter survival in grouse 

(Whitaker and Stauffer 2003) and climate change 

might greatly reduce the proportion of the New 

England landscape that is capable of supporting 

ruffed grouse by 2080 (McGarigal et al. 2016). 

Other potential climate change impacts include 

changes in the timing of migration for some birds, 

or the resources (e.g., flowers, seeds, larvae) upon 

which they depend. Birds arriving either too early 

or too late could face suboptimal conditions (e.g., 

limited food resources or difficulty finding mates), 

resulting in adverse impacts to fitness and survival 

(Fraser et al. 2013).

Freshwater turtles will be affected by climate 

change in a variety of ways, mostly acting 

through effects on water temperature and flow. 

Wood turtles are particularly sensitive to flooding 

events (Jones and Sievert 2009). In contrast, map 

turtle hatchlings emerge later in the season with 

increasing temperatures and rain events, resulting in 

higher survival (Nagle et al. 2004). Population sex 

ratio determination is an important consideration 

in turtles, as it is driven by temperature. Thus, 

there is concern that populations will begin to be 

artificially skewed toward more females or more 

males, depending on the life history of the particular 

species and location of the population. Experimental 

manipulation has shown a lack of adaptive capacity 
APPENDIX 8

230

to compensate for sex ratio bias resulting from 

warming nest temperatures, at least in some species 

(Refsnider et al. 2013). However, other studies have 

pointed out that the amount of atmospheric warming 

required to raise nest temperatures enough to affect 

sex ratio is not expected until late in the century, at 

least for eastern box turtles (Savva et al. 2010).

AQUATIC ORGANISMS

Aquatic organisms are expected to be affected by 

more-intense precipitation events, water quality 

changes, and other changes to the hydrology of the 

assessment area. Thermal habitat in cold-water lakes 

and streams may be further impaired as temperatures 

continue to warm. Altered precipitation patterns 

may influence streamflows, and more frequent 

and intense storms, rain-on-snow events, and other 

hydrologic changes may promote streambank and 

shoreline erosion, leading to increased turbidity and 

reduced water quality. These impacts may not occur 

equally across species or even across life stages of a 

given organism.

Warming water temperatures could influence activity 

levels, demand for food, growth rates, interspecific 

interactions, and the amount of suitable habitat 

available for freshwater fish. Cold-water and 

cool-water fish species, particularly populations 

inhabiting small, high-elevation streams that may 

encounter elevated water temperatures or drying of 

streambeds, are highly vulnerable to climate change 

impacts. Brook trout are sensitive to increased 

water temperatures, and riparian cover can locally 

buffer the effects of increasing temperatures 

(Argent and Kimmel 2013), potentially allowing for 

adaptive capacity in the species (Stitt et al. 2014). 

Competition for prey and thermal refugia typically 

constrains brook trout growth (Petty et al. 2014). 

Although cold-water species, such as brook and 

rainbow trout, may be adversely affected by impacts 

on their reproduction and exposure to more lowflow conditions in summer, evidence suggests that 

smallmouth bass and other warm-water species 

may show increased growth rates and northward 

expansion as temperatures rise (Groffman et al. 

2014, Pease and Paukert 2014).

Freshwater mussels are already under threat from 

development, urbanization, and pollution. Many 

are nonmigratory with limited vertical movement 

and rely on flood events to make large distribution 

shifts (Furedi 2013). Increased flooding from 

climate change may decrease water quality as well 

as displace individuals from suitable habitat, while 

summer drought could slow or eliminate critical 

flows (Santos et al. 2015). For example, the dwarf 

wedgemussel is considered extremely vulnerable to 

climate change and especially increased flooding in 

the Northeast; populations are highly localized in 

areas within a narrow band of precipitation (Furedi 

2013). Another species, the eastern pearlshell, is 

considered extremely vulnerable to climate change 

as it is found in cold, nutrient-poor, unpolluted 

streams and smaller rivers with moderate flow rates 

(Furedi 2013). Another study, however, found that 

it might have some capacity to adapt to increasing 

temperatures and shifting flows (Hastie et al. 2003). 

It may also be sensitive to sea-level rise. Cascading 

effects could result from shifts by its host species.

PLANT SPECIES OF CONCERN

As discussed in Chapter 6, it is expected that plant 

or animal species that are already rare, threatened, 

or endangered may be especially vulnerable to 

shifts in temperature and precipitation. Rare plants 

and rare plant communities often rely on very 

specific combinations of environmental and habitat 

conditions, in many cases as relict populations 

from previous climatic conditions (Devall 2009). 

Threatened and endangered species often face 

population declines due to a variety of other factors, 

including habitat loss, competition from invasive 

species, and disease. As temperatures become 

warmer and the precipitation regime changes, 

already rare or declining species may therefore be 

among the first to undergo climate-related stress. 

The limited range of rare species makes it difficult 

to model the effects of climate and climate change 

on distribution and abundance (Schwartz et al. 

2006). In the absence of human intervention, rare 

or threatened species may face greater extinction 

risks. Alternatively, rare species that live in habitats 
APPENDIX 8

231

that are buffered from climate shifts (i.e., refugia) 

may be able to persist. Vulnerability assessments 

for some states in the region, such as Massachusetts 

(Manomet and Massachusetts Division of Fisheries 

and Wildlife 2010) and Maine (Whitman et al. 

2014), include consideration of rare or threatened 

plant species. 

LITERATURE CITED

Argent, D.G.; Kimmel, W.G. 2013. Potential 

impacts of climate change on brook trout 

(Salvelinus fontinalis) populations in streams 

draining the Laurel Hill in Pennsylvania.

Journal of Freshwater Ecology. 28(4): 489-502. 

https://doi.org/10.1080/02705060.2013.784880.

Baruch-Mordo, S.; Wilson, K.R.; Lewis, D.L.; 

Broderick, J.; Mao, J.S.; Breck, S.W. 2014. 

Stochasticity in natural forage production 

affects use of urban areas by black bears: 

implications to management of human-bear 

conflicts. PLoS ONE. 9(1): e85122. 

https://doi.org/10.1371/journal.pone.0085122.

Bellard, C.; Bertelsmeier, C.; Leadley, P.; Thuiller, 

W.; Courchamp, F. 2012. Impacts of climate 

change on the future of biodiversity. Ecology 

Letters. 15(4): 365-377. 

https://doi.org/10.1111/j.1461-0248.2011.01736.x. 

Butler, C.J. 2003. The disproportionate effect of 

global warming on the arrival dates of shortdistance migratory birds in North America.

Ibis. 145: 484-495. 

https://doi.org/10.1046/j.1474-919x.2003.00193.x. 

Cheng, E.; Hodges, K.E.; Melo-Ferreira, J.; 

Alves, P.C.; Mills, L.S. 2014. Conservation 

implications of the evolutionary history and 

genetic diversity hotspots of the snowshoe 

hare. Molecular Ecology. 23: 2929-2942. 

https://doi.org/10.1111/mec.12790. 

Devall, M.S. 2009. Effect of global climate change 

on rare trees and shrubs. Unasylva (English 

edition). 60(231/232). http://www.fao.org/

docrep/011/i0670e/i0670e06.htm (accessed 

August 3, 2017).

Dou, H.; Jiang, G.; Stott, P.; Piao, R. 2013. Climate 

change impacts population dynamics and 

distribution shift of moose (Alces alces) in 

Heilongjiang Province of China. Ecological 

Research. 28(4): 625-632. 

https://doi.org/10.1007/s11284-013-1054-9. 

Fraser, K.C.; Silverio, C.; Kramer, P.; Mickle, N.; 

Aeppli, R.; Stutchbury, B.J. 2013. A transhemispheric migratory songbird does 

not advance spring schedules or increase 

migration rate in response to record-setting 

temperatures at breeding sites. PLoS ONE. 

8(5): e64587. 

https://doi.org/10.1371/journal.pone.0064587. 

Fuller, S.; Tur, A. 2012. Conservation 

strategy for the New England cottontail 

(Sylvilagus transitionalis). 143 p. http://

www.newenglandcottontail.org/sites/default/

files/conservation_strategy_final_12-3-12.pdf

(accessed July 3, 2017).

Furedi, M. 2013. An examination of the effects 

of climate change on species in Pennsylvania.

Pittsburgh, PA: Pennsylvania Natural Heritage 

Program-Western Pennsylvania Conservancy.

Groffman, P.M.; Kareiva, P.; Carter, S.; Grimm, 

N.B.; Lawler, J.; Mack, M.; Matzek, V.; Tallis, H. 

2014. Chapter 8: Ecosystems, biodiversity, and 

ecosystem services. In: Melillo, J.M.; Richmond, 

T.C.; Yohe, G.W., eds. Climate change impacts 

in the United States: the Third National Climate 

Assessment. Washington, DC: U.S. Global 

Change Research Program: 195-219.

Hastie, L.C.; Cosgrove, P.J.; Ellis, N.; Gaywood, 

M.J. 2003. The threat of climate change to 

freshwater pearl mussel populations. AMBIO: 

A Journal of the Human Environment. 32(1): 40-46. 

https://doi.org/10.1579/0044-7447-32.1.40.

Hoving, C.L.; Lee, Y.M.; Badra, P.J.; Klatt, B.J. 

2013. Changing climate, changing wildlife: 

a vulnerability assessment of 400 Species of 

Greatest Conservation Need and game species 

in Michigan. Wildlife Division Report 3564. 

Lansing, MI: Michigan Department of Natural 

Resources.
APPENDIX 8

232

Jones, M.T.; Sievert, P.R. 2009. Effects of 

stochastic flood disturbance on adult wood 

turtles, Glyptemys insculpta, in Massachusetts.

The Canadian Field-Naturalist. 123(4): 313-322. 

https://doi.org/10.22621/cfn.v123i4.1000. 

Knowlton, F.F.; Gese, E.M. 1995. Coyote 

population processes revisited. In: Rollins, 

D.; Richardson, C.; Blankenship, T.; Canon, 

K.; Henke, S., eds. Symposium proceedings—

Coyotes in the Southwest: a compendium of our 

knowledge. Austin, TX: Texas Parks and Wildlife 

Department.

Manomet Center for Conservation Sciences and 

Massachusetts Division of Fisheries and Wildlife. 

Climate change and Massachusetts fish and 

wildlife: habitat and species vulnerability 

(volume 2). Westborough, MA: Massachusetts 

Division of Fisheries and Wildlife. 59 p. 

Available at http://www.mass.gov/eea/agencies/

dfg/dfw/wildlife-habitat-conservation/climatechange-and-massachusetts-fish-and-wildlife.html

(accessed April 29, 2014).

Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; 

Peters, M.P. 2011. Changes in potential habitat 

of 147 North American breeding bird species 

in response to redistribution of trees and 

climate following predicted climate change.

Ecography. 34(6): 933-945. 

https://doi.org/10.1111/j.1600-0587.2011.06803.x. 

Mawdsley, J.R.; O’Malley, R.; Ojima, D.S. 2009. 

Una revisión de las estrategias de adaptación 

al cambio climático para el manejo de vida 

silvestre y conservación de la biodiversidad [A 

review of climate-change adaptation strategies 

for wildlife management and biodiversity 

conservation]. Conservation Biology. 23(5): 

1080-1089. 

https://doi.org/10.1111/j.1523-1739.2009.01264.x.

McGarigal K.; Deluca, W.V.; Compton, B.W.; 

Plunkett, E.B., Grand J. 2016. Designing 

sustainable landscapes: modeling focal species.

Report to the North Atlantic Conservation 

Cooperative, U.S. Fish and Wildlife Service, 

Northeast Region. Available at http://jamba.

provost.ads.umass.edu/web/lcc/DSL_

documentation_species.pdf (accessed August 2, 

2017).

Mech, L.; McRoberts, R.; Peterson, R.; Page, 

R. 1987. Relationship of deer and moose

populations to previous winters’ snow. The

Journal of Animal Ecology. 56(2): 615-627.

https://doi.org/10.2307/5072.

Menzel, J.M.; Menzel, M.A.; Kilgo, J.C.; Ford, 

W.M.; Edwards, J.W.; McCracken, G.F. 2005.

Effect of habitat and foraging height on bat

activity in the Coastal Plain of South Carolina.

Journal of Wildlife Management. 69(1): 235-245.

https://doi.org/10.2193/0022-541x(2005)069<023

5:eohafh>2.0.co;2.

Moosman, P.R.; Thomas, H.H.; Veilleux, J.P. 2012. 

Diet of the widespread insectivorous bats 

Eptesicus fuscus and Myotis lucifugus relative 

to climate and richness of bat communities.

Journal of Mammalogy. 93(2): 491-496. 

https://doi.org/10.1644/11-mamm-a-274.1.

Murray, D.L.; Cox, E.W.; Ballard, W.B.; Whitlaw, 

H.A.; Lenarz, M.S.; Custer, T.W.; Barnett,

T.; Fuller, T.K. 2006. Pathogens, nutritional

deficiency, and climate influences on a

declining moose population. Wildlife

Monographs. 166(1): 1-30. https://doi.

org/10.2193/0084-0173(2006)166[1:pndaci]2.0.

co;2.

Musante, A.R.; Pekins, P.J.; Scarpitti, D.L. 2007. 

Metabolic impact of winter tick infestations on 

calf moose. Alces. 43: 101-110.

Nagle, R.D.; Lutz, C.L.; Pyle, A.L. 2004. 

Overwintering in the nest by hatchling map 

turtles (Graptemys geographica). Canadian 

Journal of Zoology. 82(8): 1211-1218. 

https://doi.org/10.1139/z04-096.
APPENDIX 8

233

O’Donoghue, M.; Boutin, S.; Krebs, C.J.; Hofer, E.J. 

1997. Numerical responses of coyotes and lynx 

to the snowshoe hare cycle. Oikos. 80(1): 150-167. 

https://doi.org/10.2307/3546526. 

Obbard, M.E.; Howe, E.J.; Wall, L.L.; Allison, B.; 

Black, R.; Davis, P.; Dix-Gibson, L.; Gatt, M.; 

Hall, M.N. 2014. Relationships among food 

availability, harvest, and human-bear conflict 

at landscape scales in Ontario, Canada. Ursus. 

25(2): 98-110. 

https://doi.org/10.2192/ursus-d-13-00018.1. 

Oli, M.K.; Hepp, G.R.; Kennamer, R.A. 2002. 

Fitness consequences of delayed maturity 

in female wood ducks. Evolutionary Ecology 

Research. 4(4): 563-576.

Paradis, A.; Elkinton, J.; Hayhoe, K.; Buonaccorsi, 

J. 2008. Role of winter temperature and 

climate change on the survival and future 

range expansion of the hemlock woolly adelgid 

(Adelges tsugae) in eastern North America.

Mitigation and Adaptation Strategies for Global 

Change. 13(5-6): 541-554. 

https://doi.org/10.1007/s11027-007-9127-0.

Pease, A.A.; Paukert, C.P. 2014. Potential impacts 

of climate change on growth and prey 

consumption of stream-dwelling smallmouth 

bass in the central United States. Ecology of 

Freshwater Fish. 23(3): 336-346. 

https://doi.org/10.1111/eff.12084. 

Petty, J.T.; Thorne, D.; Huntsman, B.; Mazik, P. 

2014. The temperature-productivity squeeze: 

constraints on brook trout growth along an 

Appalachian river continuum. Hydrobiologia. 

727(1): 151-166. 

https://doi.org/10.1007/s10750-013-1794-0.

Porter, W.F.; Nelson, G.C.; Mattson, K. 1983. 

Effects of winter conditions on reproduction in 

a northern wild turkey population. The Journal 

of Wildlife Management. 47(2): 281-290. 

https://doi.org/10.2307/3808500. 

Post, E.; Stenseth, N.C. 1998. Large-scale climatic 

fluctuation and population dynamics of 

moose and white-tailed deer. Journal of Animal 

Ecology. 67(4): 537-543.

Ralston, J.; Kirchman, J.J. 2013. Predicted range 

shifts in North American boreal forest birds 

and the effect of climate change on genetic 

diversity in blackpoll warblers (Setophaga 

striata). Conservation Genetics. 14(2): 543-555. 

https://doi.org/10.1007/s10592-012-0418-y.

Refsnider, J.M.; Bodensteiner, B.L.; Reneker, 

J.L.; Janzen, F.J. 2013. Nest depth may not 

compensate for sex ratio skews caused by 

climate change in turtles. Animal Conservation. 

16(5): 481-490. 

https://doi.org/10.1111/acv.12034. 

Rodenhouse, N.L.; Christenson, L.M.; Parry, D.; 

Green, L.E. 2009. Climate change effects on 

native fauna of northeastern forests. Canadian 

Journal of Forest Research. 39(2): 249-263. 

https://doi.org/10.1139/x08-160. 

Santos, R.M.B.; Sanches Fernandes, L.F.; Varandas, 

S.G.P.; Pereira, M.G.; Sousa, R.; Teixeira, A.; 

Lopes-Lima, M.; Cortes, R.M.V.; Pacheco, F.A.L. 

2015. Impacts of climate change and landuse scenarios on Margaritifera margaritifera, 

an environmental indicator and endangered 

species. Science of The Total Environment. 

511(0): 477-488.

Savva, Y.; Swarth, C.W.; Gupchup, J.; Szlavecz, K. 

2010. Thermal environments of overwintering 

eastern box turtles (Terrapene carolina 

carolina). Canadian Journal of Zoology. 88(11): 

1086-1094. https://doi.org/10.1139/z10-076. 

Schwartz, M.W.; Iverson, L.R.; Prasad, A.M.; 

Matthews, S.N.; O’Connor, R.J. 2006. 

Predicting extinctions as a result of climate 

change. Ecology. 87(7): 1611-1615. https://

doi.org/10.1890/0012-9658(2006)87[1611:

peaaro]2.0.co;2. 

Smith, W.P. 2012. Sentinels of ecological 

processes: the case of the northern flying 

squirrel. BioScience. 62(11): 950-961. 

https://doi.org/10.1525/bio.2012.62.11.4. 
APPENDIX 8

234

Stitt, B.C.; Burness, G.; Burgomaster, K.A.; 

Currie, S.; McDermid, J.L.; Wilson, C.C. 

2014. Intraspecific variation in thermal 

tolerance and acclimation capacity in brook 

trout (Salvelinus fontinalis): physiological 

implications for climate change. Physiological 

and Biochemical Zoology. 87(1): 15-29. 

https://doi.org/10.1086/675259.

Todd, A.W.; Keith, L.B. 1983. Coyote demography 

during a snowshoe hare decline in Alberta. The 

Journal of Wildlife Management. 47(2): 394-404. 

https://doi.org/10.2307/3808512. 

Veilleux, J.P.; Moosman, P.R.; Reynolds, D.S.; 

LaGory, K.E.; Walston, L.J. 2009. Observations 

of summer roosting and foraging behavior of a 

hoary bat (Lasiurus cinereus) in southern New 

Hampshire. Northeastern Naturalist. 16(1): 

148-152. https://doi.org/10.1656/045.016.0113.

Wattles, D.W.; DeStefano, S. 2013. Moose habitat 

in Massachusetts: assessing use at the southern 

edge of the range. Alces. 49: 133-147.

Whitaker, D.M.; Stauffer, D.F. 2003. Night roost 

selection during winter by ruffed grouse in the 

central Appalachians. Southeastern Naturalist. 

2(3): 377-392. https://doi.org/10.1656/1528-

7092(2003)002[0377:NRSDWB]2.0.CO;2.

Whitman, A.; Cutko, A.; De Maynadier, P.; Walker, 

S.; Vickery, B.; Stockwell, S.; Houston, R. 

2014. Climate change and biodiversity in 

Maine: vulnerability of habitats and priority 

species. Report SEI-2013-03. Brunswick, ME: 

Manomet Center for Conservation Sciences (in 

collaboration with Maine Beginning with Habitat 

Climate Change Working Group). 96 p. 

https://www.manomet.org/sites/default/files/

publications_and_tools /2013%20BwH%20Vuln

erability%20Report%20CS5v7_0.pdf (accessed 

June 24, 2014).

Wilson, W.H., Jr.; Kipervaser, D.; Lilley, S.A. 2000. 

Spring arrival dates of Maine migratory 

breeding birds: 1994–1997 vs. 1899–1911.

Northeastern Naturalist. 7(1): 1-6. 

https://doi.org/10.2307/3858427.

Windberg, L.A. 1995. Demography of a highdensity coyote population. Canadian Journal of 

Zoology. 73(5): 942-954. 

https://doi.org/10.1139/z95-110. 

Zimova, M.; Mills, L.S.; Lukacs, P.M.; Mitchell, 

M.S. 2014. Snowshoe hares display limited 

phenotypic plasticity to mismatch in seasonal 

camouflage. Proceedings of the Royal Society B. 

281: 20140029. 

https://doi.org/10.1098/rspb.2014.0029. 
Printed on recycled paper

Janowiak, Maria K.; D’Amato, Anthony W.; Swanston, Christopher W.; Iverson, Louis; Thompson, Frank R., III; Dijak, William 

D.; Matthews, Stephen; Peters, Matthew P.; Prasad, Anantha; Fraser, Jacob S.; Brandt, Leslie A.; Butler-Leopold, 

Patricia; Handler, Stephen D.; Shannon, P. Danielle; Burbank, Diane; Campbell, John; Cogbill, Charles; Duveneck, 

Matthew J.; Emery, Marla R.; Fisichelli, Nicholas; Foster, Jane; Hushaw, Jennifer; Kenefic, Laura; Mahaffey, Amanda; 

Morelli, Toni Lyn; Reo, Nicholas J.; Schaberg, Paul G.; Simmons, K. Rogers; Weiskittel, Aaron; Wilmot, Sandy; Hollinger, 

David; Lane, Erin; Rustad, Lindsey; Templer, Pamela H. 2018. New England and northern New York forest ecosystem 

vulnerability assessment and synthesis: a report from the New England Climate Change Response Framework 

project. Gen. Tech. Rep. NRS-173. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern 

Research Station. 234 p. https://doi.org/10.2737/nrs-gtr-173.

Forest ecosystems will face direct and indirect impacts from a changing climate over the 21st century. This assessment 

evaluates the vulnerability of forest ecosystems across the New England region (Connecticut, Maine, Massachusetts, 

New Hampshire, northern New York, Rhode Island, and Vermont) 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 vegetation impact models, which 

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

panel of scientists and natural resource professionals familiar with the forests of this region to assess ecosystem vulnerability 

through a formal consensus-based expert elicitation process. 

Observed trends in climate over the historical record from 1901 through 2011 show that the mean annual temperature 

has increased across the region by 2.4 °F, with even greater warming during winter. Precipitation patterns also changed 

during this time, with a slight trend toward greater annual precipitation and a substantial increase in extreme precipitation 

events. Projected climate trends using downscaled global climate model data indicate a potential increase in mean annual 

temperature of 3 to 8 °F for the assessment area by 2100. Projections for precipitation indicate an increase in fall and winter 

precipitation, and spring and summer precipitation projections vary by scenario. We identified potential impacts on forests 

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

Model projections suggest that many northern and boreal species, including balsam fir, red spruce, and black spruce, may 

fare worse under future conditions, but other species may benefit from projected changes in climate. Published literature 

on climate impacts related to wildfire, invasive species, and forest pests and diseases also contributed to the overall 

determination of climate change vulnerability. 

We assessed vulnerability for eight forest communities in the assessment area. The assessment was conducted through 

a formal elicitation process with 20 scientists and resource managers from across the area, who considered vulnerability 

in terms of the potential impacts and the adaptive capacity for an individual community. Montane spruce-fir, low-elevation 

spruce-fir, and lowland mixed conifer forests were determined to be the most vulnerable communities. Central hardwoods, 

transition hardwoods, and pitch pine-scrub oak forests were perceived as having lower vulnerability 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, 

 Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, Vermont

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www.nrs.fs.fed.us