F u t u r e F o r e s t s

of the 

N o r t h e r n U n i t e d S t a t e s

U.S. Department of Agriculture
Suggested Citation

Shifley, Stephen R.; Moser, W. Keith, 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.

Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance Implementation Plan,

which requires technical and policy review for all scientific publications produced or funded by the 

Station. The process included a blind technical review by at least two reviewers, who were selected by 

the Assistant Director for Research and unknown to the author. This review policy promotes the Forest 

Service guiding principles of using the best scientific knowledge, striving for quality and excellence, 

maintaining high ethical and professional standards, and being responsible and accountable for 

what we do.

Published by:

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Newtown Square, PA 19073

February 2016

Manuscript received for publication: May 2014

For additional copies:

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Visit our homepage at: http://www.nrs.fs.fed.us
Future Forests of the Northern United States

Stephen R. Shifley and W. Keith Moser, editors

Chapter Authors

francisco x. aguilar, associate professor

School of Natural Resources

203 Anheuser-Busch Natural Resources Building

University of Missouri-Columbia

Columbia, MO 65211

aguilarf@missouri.edu

j. m. bowker, research social scientist

Southern Research Station, U.S. Forest Service

320 Green Street

Athens, GA 30602

mbowker@fs.fed.us

brett j. butler, research forester

Northern Research Station, U.S. Forest Service

160 Holdsworth Way

Amherst, MA 01003

bbutler01@fs.fed.us

peter v. caldwell, research hydrologist

Southern Research Station, U.S. Forest Service

Center for Watershed Research 

Coweeta Hydrological Lab

3160 Coweeta Lab Road

Otto, NC 28736

pcaldwell02@fs.fed.us

ryan d. desantis, postdoctoral research associate

Department of Forestry, University of Missouri and 

Northern Research Station, U.S. Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

Currently: Forestry/Natural Resources Advisor

University of California Cooperative Extension

1851 Hartnell Avenue

Redding, CA 96002

rdesantis@ucanr.edu 

brent dickinson, former research fellow

Family Forest Research Center

University of Massachusetts Amherst

160 Holdsworth Way

Amherst, MA 01003

bdickinson@som.umass.edu

i
marla r. emery, research geographer

Northern Research Station, U.S. Forest Service

Aiken Center, Room 303E

81 Carrigan Drive

Burlington, VT 05405

memery@fs.fed.us

james d. garner, wildlife biologist

Northern Research Station, U.S. Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

jamesdgarner@fs.fed.us 

michael e. goerndt, postdoctoral fellow

School of Natural Resources

203 Anheuser-Busch Natural Resources Building

University of Missouri-Columbia

Columbia, MO 65211

Currently: Assistant Professor

Missouri State University

William H. Darr School of Agriculture

901 S. National Avenue

Springfield, MO 65897

mgoerndt@missouristate.edu 

dale d. gormanson, forester

Northern Research Station, U.S. Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

dgormanson@fs.fed.us

eric j. greenfield, forester

Northern Research Station, U.S. Forest Service

c/o SUNY ESF, 5 Moon Library

1 Forestry Drive

Syracuse, NY 13210

ejgreenfield@fs.fed.us

robert j. huggett, jr., research assistant professor

North Carolina State University

College of Natural Resources

Department of Forestry and Environmental Resources

Raleigh, NC 27695

Currently: 128 Benedict Lane 

Raleigh, NC 27614 

bobby.huggett@gmail.com

ruhong li, research associate

North Carolina State University

College of Natural Resources

Department of Forestry and Environmental Resources

Raleigh, NC 27695

rnli@ncsu.edu 

patrick d. miles, research forester

Northern Research Station, U.S. Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

pmiles@fs.fed.us 

w. keith moser, research forester and scientist-in-charge

Fort Valley Experimental Forest

Rocky Mountain Research Station, U.S. Forest Service

2500 South Pine Knoll Drive

Flagstaff, AZ 86001

wkmoser@fs.fed.us 

mark d. nelson, research forester

Northern Research Station, U.S. Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

mdnelson@fs.fed.us

david j. nowak, research forester

Northern Research Station, U.S. Forest Service

c/o SUNY ESF, 5 Moon Library

1 Forestry Drive

Syracuse, NY 13210

dnowak@fs.fed.us

ii
charles h. perry, research soil scientist

Northern Research Station, U.S. Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

charleshperry@fs.fed.us

rachel riemann, research forester/geographer

Northern Research Station, U.S. Forest Service

c/o U.S. Geological Survey

425 Jordan Road

Troy, NY 12180

rriemann@fs.fed

titus s. seilheimer, fisheries specialist

University of Wisconsin-Manitowoc

705 Viebahn Street, Room F103

Manitowoc, WI 54220

tseilheimer@aqua.wisc.edu 

stephen r. shifley, research forester

Northern Research Station, U.S. Forest Service

202 Anheuser-Busch Natural Resources Building

University of Missouri-Columbia

Columbia, MO 65211

sshifley@fs.fed.us 

kenneth e. skog, supervisory research forester

Forest Products Laboratory, U.S. Forest Service

One Gifford Pinchot Drive

Madison, WI 53726

kskog@fs.fed.us 

nianfu song, research associate

School of Natural Resources

203 Anheuser-Busch Natural Resources Building

University of Missouri-Columbia

Columbia, MO 65211

songn@missouri.edu

aimee stephens, forester

Northern Research Station, U.S. Forest Service

21235 Alberta Avenue Suite 1

L’anse, MI 49946

alstephens@fs.fed.us

ge sun, research hydrologist

Southern Research Station, U.S. Forest Service

920 Main Campus Drive

Venture Center 2, Suite 300

Raleigh, NC 27606

gesun@fs.fed.us

brian g. tavernia, postdoctoral fellow

School of Natural Resources, University of Missouri-Columbia

and Northern Research Station, U.S. Forest Service

Currently: Spatial Scientist

The Nature Conservancy, Colorado Field Office

2424 Spruce Street

Boulder CO 80302

brian.tavernia@tnc.org 

david n. wear, project leader

Southern Research Station, U.S. Forest Service

PO Box 8008, College of Natural Resources

North Carolina State University

Raleigh, NC 27695

dwear@fs.fed.us 

b. tyler wilson, research forester

Northern Research Station, U.S. Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

barrywilson@fs.fed.us 

sherri wormstead, sustainability and planning coordinator

Northeastern Area, State and Private Forestry

U.S. Forest Service

271 Mast Road

Durham, NH 03824

swormstead@fs.fed.us

iii
iv
Abstr a c t v

ABSTRACT

The U.S. North—the 20 states bounded by Maine, Maryland, Missouri, and Minnesota—have a greater 

forest cover (42 percent of land area) and population density (194 people per square mile) than other 

large regions of the nation. Ecological, social, and economic changes anticipated over the next 50 years 

will profoundly affect future forest management needs and opportunities in the North. This report 

draws on data from numerous sources to analyze 13 scenarios that consider future forest change 

in response to assumptions about land-use change, economic change, greenhouse gas emissions, 

climate change, forest growth, forest harvest, and other factors. Results are organized using the 

Montréal Process Criteria and Indicator framework. Important trends for the U.S. North over the next 

50 years include the following:

• Forest area is projected to decrease between 

3.5 and 6.4 percent with losses concentrated 

around existing urban and suburban areas.

• Forest area is currently concentrated in the 

40-to-80-year age class and is expected to 

increase in mean age over time, resulting in 

a paucity of early-successional habitats and 

low structural forest diversity. 

• Closed-canopy habitat classes are expected 

to gain acreage at the expense of open-canopy 

habitat classes.

• The historical trend of steadily increasing 

live wood volume over time is projected 

to level off or decline under all scenarios, 

with little variation attributable to 

differing assumptions about 

future climate conditions.

• The area of the maple-beechbirch forest-type group is 

expected to increase relative 

to nearly all other groups. 

• Projected forest removals resulting from 

land-use changes are likely to average 

about 13 percent of total removals, with the 

remainder resulting from harvesting; in some 

populous Eastern States, removals resulting 

from land-use changes could exceed 50 percent 

of all removals.

• Forests are under the expanding negative 

influence of numerous invasive species 

including emerald ash borer, Asian longhorned 

beetle, spruce budworm, Sirex woodwasp, 

winter moth, hemlock woolly adelgid, beech 

bark disease, gypsy moth, and dozens of 

invasive plants. 

• Across the North, 76 million people 

depend on public and private forests 

for high quality water supplies.

• On average, the amount of 

water needed to meet societal 

demands is projected to 

increase and the supply of 
vi FUTURE FORESTS OF THE NORTHERN UNITED STATES

high quality water is projected to decline, but 

water supplies for most watersheds would 

remain adequate.

• Aboveground forest biomass is expected to 

increase by about 4 percent, but total carbon 

sequestered by northern forests (including 

soils) is expected to decrease by about 2 percent, 

primarily as the result of reduced forest 

acreage combined with slower tree growth 

that is typical for aging forests.

• By 2030, U.S. electrical power generated 

from all biomass is expected to increase 

350 percent over 2010 levels, but in Northern 

States biomass will still be used for less than 

6 percent of total electric production, often 

by co-firing biomass and coal.

• Forest-associated employment has declined 

in the past decade, and real-dollar wages in 

forestry have been relatively constant, but 

productivity per employee is increasing.

• The North is the location of nearly all commercial 

U.S. maple syrup production—about 2.5 million 

gallons valued at about $90 million annually.

• Projected population increases in the North 

are expected to cause Federal and State 

park land area per capita to decrease by 

19 percent and non-Federal forest land area 

per capita to decrease by 26 percent. 

• Of all tax policies, those governing property 

taxes have the greatest potential to influence 

the behavior of family forest owners. 

However, future tax policies will likely hinge 

on issues that are larger than forestry.

• Partnerships among various institutions 

and organizations have become increasingly 

common for addressing large-scale, complex 

issues. This trend is likely to continue.

• U.S. urban tree cover is estimated at 35 percent 

but is declining in many cities.

• By 2050, urban land is expected to increase 

to about 8 percent in the conterminous 

United States and to 14 percent in the North.

• Practices that can support future management 

for sustainable, resilient northern forests 

include strengthening connections between 

rural and urban forest habitats and people; 

developing mutually beneficial partnerships 

among all stakeholders to support shared 

conservation goals; developing measurable 

State and regional goals for forest diversity 

and monitoring progress toward achieving 

them; promoting forward-looking forest 

management across all forest ownerships; 

and working to understand the many dimensions 

of expected forest change. 

Key Words: climate change, land use change, biodiversity, 

Montréal Process, forest health, forest industry, invasive 

species, biomass, bioenergy, sustainable timber products, 

nontimber forest products, private forest owners, 

watershed, urban forest
A c k n o w l e d g m e n t s vii

Acknowledgments

This document draws on data and maps from many sources. Principal among these sources is 

the U.S. Forest Service, Forest Inventory and Analysis data set, which spans 60 years of forest 

change. These data have been collected by numerous field-going personnel, often working in 

adverse conditions. We have drawn inspiration from the Southern Forest Futures Project of the 

U.S. Forest Service, Southern Research Station. We have utilized methods and data prepared by 

the many contributors to the Forest Service 2010 Resources Planning Act Assessment. 

We owe special thanks to Michael Rains, Director of the Northern Research Station and the Forest 

Products Laboratory of the U.S. Forest Service. He is a visionary leader in the Forest Service and among 

the broader natural resource community where for nearly five decades he has been caring for the 

land and serving people where they live. It was his vision and drive that initiated the Northern Forest 

Futures Project—a collaboration that laid the foundation for this report and many associated products.

We have been the beneficiaries of encouragement, advice, and constructive reviews from the 

administrators of State forestry and natural resource agencies, including their expert staffs and the 

Northeastern Area Association of State Foresters. Tom Schmidt, Dennis May, and Deb Dietzman of 

the U.S. Forest Service Northern Research Station consistently provided financial support, insight, 

and encouragement over many years. Susan Wright, Carol Whitlock, Amy Scherzer, and Rhonda Cobourn 

provided the editorial expertise and polish necessary to turn our technical manuscript into an 

attractive, coherent, and highly readable finished publication. Jim Lootens-White developed the 

Web interface that elegantly provides interactive access to the data and results.

Publication design was by Red, Inc. Communications, Idaho Falls, Idaho.

We thank them all
viii FUTURE FORESTS OF THE NORTHERN UNITED STATES
C o n t e n t s ix

CONTENTS

Chapter 1. Assessing and Forecasting Change in Northern Forests 1

Chapter 2. Projection Methods 1 3

Chapter 3. Conservation of Biological Diversity 4 3

Chapter 4. Maintenance of Productive Capacity of Forest Ecosystems 7 7

Chapter 5. Maintenance of Forest Ecosystem Health and Vitality 107

Chapter 6. Conservation and Maintenance of Soil and Water Resources 1 4 5 

Chapter 7. Projections of Forest Contributions to Global Carbon Cycles 1 7 7 

Chapter 8. M a i n t e n a n c e a n d E n h a n c e m e n t o f L o n g - t e r m M u l t i p l e S o c i o e c o n o m i c B e n e f i t s 

to Meet the Needs of Societies 2 0 7 

Chapter 9. L e g a l , I n s t i t u t i o n a l , a n d P o l i c y F r a m e w o r k f o r Fo r e s t C o n s e r v a t i o n 

and Sustainable Management 2 4 3 

C h a p t e r 10 . Urban Forests 2 7 1 

Chapter 11. The Outlook for Northern Forests 2 9 9 

Glossary 315

Appendices 331
x FUTURE FORESTS OF THE NORTHERN UNITED STATES
C h a p t e r O n e 1

A s s e s s i n g a n d Fo r e c a s t i n g C h a n g e 

in Northern Forests

Stephen R. Shifley, W. Keith Moser, Sherri Wormstead

Introduction

HE PURPOSE OF this report is to describe how yesterday’s trends and today’s choices might change 

the future forest landscape of the Northern United States from 2010 (the baseline year) to 2060. 

Its results are intended to help resource managers and policy makers identify actions that will sustain 

the health, productivity, diversity, and resilience of these forests, which provide for the well-being 

of the 125 million people who currently live in the 20 Northern States (Fig. 1.1) and for 32 million 

additional people expected by 2060. 

FIGURE 1.1

Forested area and population of the Northern 

United States compared with other U.S. regions 

(Homer et al. 2004, Shifley et al. 2012, 

Smith et al. 2009, U.S. Census Bureau 2010). 

Timberland is defined as forest land that 

maintains a minimum potential for growth of 

20 cubic feet per acre per year and is not 

restricted from active forest management by 

statute or regulation. Hawaii

42% of land is forested

32% of U.S. timberland

41% of U.S. population

NORTH

Forest

Nonforest

37% of land is forested

14% of U.S. timberland

16% of U.S. population

PACIFIC COAST

Alaska

Puerto Rico

40% of land is forested

40% of U.S. timberland

34% of U.S. population

SOUTH

INTERIOR WEST

20% of land is forested

9% of U.S. population

14% of U.S. timberland
2

Our attention is on issues that affect forest 

resources in the broadest sense, including urban 

and rural trees and forests; forest-associated 

plants and animals; tree and forest health; 

water and soil conservation; carbon sequestration; 

timber and nontimber forest products; forestbased employment; attitudes, laws, and policies 

affecting forest use and management; land-use 

change; and forest fragmentation. 

A large component of forest change will be 

the result of normal forest growth, aging, 

natural regeneration, and species succession. 

In addition, we expect that a range of external 

forces will also drive forest change: 

• Population increases will cause millions of 

acres of forest land to be converted to urban 

and other land uses.

• Economic conditions will affect forest 

products consumption, production, and 

harvesting rates.

• The spread of invasive species will 

exacerbate the effects of other disturbances 

on forest ecosystems.

• Changes in population, economic activity, 

energy consumption, and energy production 

will affect future climate change.

• Climate change will, in turn, affect dynamics 

within forested ecosystems, including patterns 

of vegetation growth and species succession.

Because we cannot know with certainty how 

the economy, population, or climate will 

change in the future, this report presents 

several alternative scenarios that cover a 

range of different assumptions about these and 

other driving forces. The assumptions were 

incorporated into analytical models that were 

used to estimate how northern forests are likely 

to change under each alternative scenario.

NORTHERN FORESTS IN CONTEXT

The U.S. North consists of 20 Central and 

Northeastern States (Fig. 1.1) that together 

constitute the most heavily forested and most 

densely populated quadrant of the Nation 

(Shifley et al. 2012). Maintaining diverse, 

healthy, productive forests requires managers 

and policy makers to anticipate future changes 

and respond proactively to avert negative 

consequences of those changes. 

Past and current forest conditions and forest 

management issues for the North are presented 

in detail in previous products of the Northern 

Forest Futures Project. Northern forests today 

bear the imprint of land-use practices over prior 

centuries (Thompson et al. 2013). As European 

settlers migrated westward into forested areas 

in the 1700s and 1800s, they set in motion the 

following general sequence of processes that 

shaped the current condition of northern forests: 

• Exploitive timber harvesting 

• Forest clearing for agriculture

• Burning forests to open the understory and 

benefit livestock grazing 

FUTURE FORESTS OF THE NORTHERN UNITED STATES
C h a p t e r O n e 3

• Gradual abandonment of marginal agricultural 

lands with reversion to forest cover 

• Aggressive forest fire suppression 

• Urbanization 

• Forest fragmentation 

• Maturing timber resources

• Aging private forest owners

• Low management intensity among family 

forest owners

The cumulative consequences of human activities 

on northern forests have been dramatic over 

the past 50 years. Some changes have been 

remarkably positive—an increase of 11 million 

acres of forest land (7 percent) and an increase 

of 144 million cubic feet of timber (240 percent), 

both despite a population increase of 26 million 

people (27 percent). Other changes have been of 

great concern—the expanding impact of invasive 

species, loss of species diversity, increasing 

numbers of areas in which urban expansion is 

shrinking forest acreage, fragmentation of forest 

land, parcellation of forest ownerships, loss of 

forest-based employment, effects of burgeoning 

white-tailed deer (Odocoileus virginianus) 

populations on tree regeneration and forest 

composition, and increasing atmospheric 

carbon emissions. 

We expect equally dramatic changes in northern 

forests over the next 50 years. Some will be 

the consequences of the natural forest processes 

that over time affect every 

forest acre. 

Other changes will be determined by human 

activities including: 

• Conversion of forests to other land uses

• Introduction and continued spread of 

invasive species

• Emphasis on management practices that focus 

on forest health and species diversity

• Consumption of forest products 

• Production of wood-based bioenergy

• Extraction of gas and minerals beneath forest land

• Competing pressures and changing preferences 

among outdoor recreation enthusiasts

• Valuation of forest ecosystem services 

and commodities 

• Federal and State policy and legislation 

• Effectiveness of hunting as a tool for controlling 

white-tailed deer populations 

Still other changes will be determined by emerging 

global-scale phenomena. An example is climate 

change, which is expected to gradually increase 

in impact over the next 50 years and beyond. It 

is a phenomenon that we have limited ability to 

control locally and for which mitigation through 

forest management is still in early stages. 

The processes of natural regeneration, species 

succession, growth, inter-tree competition, aging, 

senescence, and weather-related disturbances—

when coupled with vast forest acreage—result in 

gradual forest changes at a landscape scale that 

is measured in thousands or millions of acres. 

The cumulative effects of human activities, when 

combined with normal forest dynamics, can take 

decades to unfold at State and regional scales. 
Northern Forest Futures Project

The Northern Forest Futures Project is a multi-year, collaborative undertaking by the Northern Research 

Station of the U.S. Forest Service to provide a window on tomorrow’s forests, demonstrating how today’s 

trends and choices might change tomorrow’s landscapes. The intent of this research is to provide forest 

owners, managers, and other decisionmakers with information to guide sustainable management of the 

public and private forests in the Northern United States.

Some previous outputs from the Northern Forest Futures Project include:

Northern Forest

Futures Project 

Scoping of Issues in the Forests of

the Northeast and Midwest United States 

Version 3.0, June 2011 

Debra Dietzman, Katie LaJeunesse, and Sherri Wormstead 

USDA Forest Service, Northern Research Station 

Northeastern Area Association of State Foresters 

Previous Versions: Version 2.0 November 19, 2010, Deb Dietzman & Katie LaJeunesse, 

Version 1.2, March 29, 2010, Deb Dietzman 

f o r e s t s

of the 

N o r t h e r N U N i t e d s t a t e s

in the Northern

United States

O u t d O O r r e c r e a t i O n

in the Northern

United States

Ou t l O O k f O r Ou t d O O r re c r e at i O n

A Technical Document Supporting the 

Northern Forest Futures Project 

with Projections through 2060

• “Northern Forest 

Futures Project: Scoping 

of Issues in the Forests 

of the Northeast 

and Midwest United 

States,” by Debra 

Dietzman et al. (2011), 

offers a broad-scale 

summary and analysis 

of widespread issues 

affecting northern 

forests. 

• “Forests of the 

Northern United 

States,” by Stephen R. 

Shifley et al. (2012), 

reports on current 

baseline (2010)

conditions of 

northern forests.

• Detailed trends and 

projections for participation in 

dozens of outdoor recreation 

activities in the North are 

published in “Outdoor Recreation 

in the Northern United States,” 

by H. Ken Cordell et al. (2012).

• “Outlook for 

Outdoor Recreation 

in the Northern 

United States...,” 

by Bowker and 

Askew (2013), is a 

technical document 

supporting the 

Northern Forest 

Futures Project with 

recreation projections through 2060. 

• Web-based 

tools for an 

interactive 

summary, 

mapping, and 

graphical 

visualization of 

forest-associated data for the North are available 

at http://www.nrs.fs.fed.us/futures/dashboard/.

4
C h a p t e r O n e 5

For example, local disturbances, such 

as forest clearing resulting from urban 

development or violent weather, can 

produce obvious changes in forest 

conditions at a particular location. 

However, such disturbances need to 

cumulatively affect millions of acres to 

represent a significant change in the 

collective condition of 174 million acres 

of northern forest land. Consequently 

this report takes a long-term, largescale perspective in describing how 

scenario analysis and other forwardlooking analytical tools will help society 

understand the magnitude of cumulative 

forest changes anticipated over the next 

50 years.

SCENARIO ANALYSES

Subsequent chapters of this report 

present a methodology for and 

results of our efforts to describe how 

the conditions represented by the 

alternative scenarios would change 

northern forests over the next 

50 years. The scenarios all start with 

forest conditions as of 2010, and they 

all incorporate predictable patterns 

of forest growth, mortality, aging, and 

species succession. Scenarios differ in 

assumptions about land-use changes, 

population growth, forest product 

output levels, invasive species impacts, 

and climate change. 

For each scenario, a system of models 

was applied to make detailed, quantitative 

projections of forest area, age structure, 

volume, biomass, and forest-type group; 

and of characteristics that are linked to 

changes in forest condition, land use, 

and human demographics including 

measures of biodiversity, surface water 

quality, forest fragmentation, insect 

and disease impacts, and demand for 

outdoor recreation. 

The analyses presented in this report 

drew on data and methods that had 

been developed for other national and 

regional analyses. Those prior efforts 

provided a context, data, and analytical 

techniques that helped us compare 

alternative scenarios. Collectively, 

the results of prior analyses and those 

described in later chapters of this 

report provide a window on future forest 

conditions in the North and insight 

into the choices that will be available 

for directing forest conditions along a 

path that society considers desirable. 

Ultimately it is the work of society to 

choose among options, make tradeoffs 

among desirable outcomes, and find 

effective and equitable solutions. We 

hope that our analyses provide society 

with a better understanding of possible 

futures for northern forests and 

therefore serve as a basis for making 

wise choices.
Related National and Regional Reports

The Northern Forest Futures Project benefited 

from several national and regional analyses, 

which provided guidance, data, and analytical 

methods that could sometimes be modified and 

applied to similar analyses for individual Northern 

States. Throughout the course of the project, 

prior publications helped set the ecological and 

socioeconomic context for the North. 

The 2010 national report on sustainable forests 

(USDA FS 2011) is the most recent synthesis of U.S. 

forest resources for each of the seven Montréal 

Process criteria and their associated indicators. It 

provided the framework both for this report and 

for the report on past and current forest conditions 

(Shifley et al. 2012). Some of the data sources 

compiled for the national report were also used here. 

The scenario analysis methods developed for the 

2010 Resources Planning Act (RPA) assessment 

(USDA FS 2012c, Wear et al. 2013) provided a 

quantitative framework that was modified for the 

northern state-scale scenario analyses of forest 

change. Dozens of RPA technical reports document 

the estimation methods and underlying climate and 

socioeconomic assumptions used in the national 

scenario analyses of future forest conditions (USDA 

FS 2012d). To the extent possible, the Northern 

Forest Futures Project followed the assumptions and 

analytical methods for scenario analysis that were 

used for the national RPA analyses.

Individual State forest action plans (NAASF 2012), 

a regional collective summary of all the State action 

plans (Wormstead et al. 2011), and output from 

the Forest Sustainability Indicators Information 

System (USDA FS 2012b) provided detailed, statescale information about forest resources, issues, 

and management priorities. This information sets 

the context for projecting conditions of individual 

Northern States. 

State forest inventories repeated through time by 

the Forest Inventory and Analysis (FIA) Program of 

the U.S. Forest Service (USDA FS 2012a) provided 

the core descriptive forest data for individual States 

and for the region as a whole. The scenario analysis 

methods used in this report projected conditions 

for each FIA inventory plot forward in time, subject 

to the assumptions and conditions of each scenario. 

The highly refined FIA database summary and analysis 

tools simplified additional efforts to analyze and 

interpret the projection results (Miles 2013, USDA 

FS 2013).

The Southern Forest Futures Project (USDA FS 2012e, 

Wear and Greis 2013) is the forerunner of and 

inspiration for the Northern Forest Futures Project. 

Although the two projects differed somewhat in 

approach, the basic projection methodology that 

was used for the Northern States was developed 

as part of Southern Forest Futures Project and the 

national RPA. 

6
C h a p t e r O n e 7

As detailed in Chapter 2, the scenarios analyzed 

are closely tied to those used nationally for 

the Resources Planning Act assessment (USDA 

FS 2012c, 2012d). Moreover the format of 

the Montréal Process Criteria and Indicators 

(described on page 8) was followed when 

reporting results. The approach and methodology 

can provide a context for landscape-scale 

conservation and land-use planning across 

various scales, including national forest plans 

and State forest action plans. The projections 

of future forest conditions for the North and 

for the 20 individual States it encompasses are 

provided for use in the preparation of a range of 

assessment and planning documents. 

Although the focus here is on forest conditions 

within the U.S. North, there are related ecological, 

social, and economic flows with the U.S. South and 

Canada. Those are not addressed in this report, 

but complimentary material for those regions 

is available from many sources including the 

Southern Forest Futures Project (Wear and Greis 

2013) and Natural Resources Canada (2015).
8

CRITERIA AND INDICATORS

We use the format of the Montréal Process 

Criteria and Indicators for the Conservation 

and Sustainable Management of Temperate 

and Boreal Forests (Montréal Process Working 

Group 2013) to organize information about the 

expected future condition of northern forests. 

This organization matches the format used in 

the report on current conditions of northern 

forests (Shifley et al. 2012), a choice intended to 

simplify comparison of current and anticipated 

future forest conditions. The format is based on 

seven criteria that characterize the essential 

components of sustainable forest management 

and 54 indicators (metrics) that describe the 

criteria in terms of forest conditions. 

Montréal Process Criteria and Indicators

As part of the Montréal Process Working Group 

the United States has joined with 11 other member 

nations to define seven criteria (essential components) 

that are described by 54 indicators (measures)—

these collectively provide a structured approach 

for assembling and organizing information relevant 

to an assessment of sustainable management of 

temperate and boreal forests (Montréal Process 

Working Group, n.d.). This report is organized 

around the seven Montréal Process criteria plus an 

eighth that describes urban forest resources. 

Individual indicators address resource conditions 

such as forest area, canopy cover, wood volume, 

forest health, and biodiversity as well as processes 

such as land-use change, tree growth, tree removals, 

and recreation. The Montréal Process Criteria and 

Indicators are scalable and have been the foundation 

for national (USDA FS 2011), regional (USDA FS 2012b), 

State (USDA FS 2011, Wormstead et al. 2011) and 

county assessments (Baltimore County Maryland 

Department of Environmental Protection and 

Resource Management 2007).

1 Conservation of biological diversity 9 3

2 Maintenance of productive capacity of forest ecosystems 5 4

3 Maintenance of ecosystem health and vitality 2 5

4 Conservation and maintenance of soil and water resources 5 6

5 Maintenance of forest contribution to global carbon cycles 3 7

6 Maintenance and enhancement of long-term multiple 

socioeconomic benefits to meet the needs of societies 

20 8

7 Legal, institutional, and economic framework for forest 

conservation and sustainable management 

10 9

8 Urban forests n/a 10

a

The first seven criteria are taken from the Montréal Process Working Group (2013).

Criterion namea

Number of Montréal Process 

indicators used to describe 

the criterion 

Corresponding 

chapters of this 

publication
C h a p t e r O n e 9

Some of the 54 indicators of forest conditions 

included in the Montréal Process are well defined 

and readily quantified, but others are not. For 

example, indicators that report on forest area, 

wood volume, carbon, insects, diseases, species 

diversity, fragmentation, products, employment, 

recreation, or best management guidelines can 

be quantified with existing information and can 

sometimes be projected forward in time. Other 

indicators are ambiguously defined, or they 

lack the metrics needed for quantitative—or 

even qualitative—definition. Examples include 

measures of resilience in forest-dependent 

communities, the importance of forests to people, 

whether trade policies for forest products are 

discriminatory, or the success of efforts to 

conserve genetic diversity. Chapters 3 through 

9 address the seven Montréal Process criteria, 

focusing on indicators that can be described 

quantitatively and offer some capacity for 

examining past trends and future expectations. 

In addition to the seven Montréal Process 

criteria, our analysis included one additional 

criterion—urban forests—which addresses a 

forest component that is relatively small in 

area but expanding. Urban trees and forests 

are of high impact because they affect the 

quality of life of urban dwellers that constitute 

the majority of the 

North’s population.

REPORT ORGANIZATION

Chapter 2 provides detailed descriptions of the 

scenarios, assumptions, and forecasting methods 

used to project future forest conditions from 

2010 to 2060. Chapters 3 through 9 describe 

current trends and anticipated future changes 

for selected indicators that are associated 

with each Montréal Process criterion. Chapter 

10 gives additional focus to urban trees and 

forests. Finally, Chapter 11 summarizes the 

major drivers of northern forest change and 

offers some of the associated implications. 

The appendices provide additional quantitative 

details for individual Northern States. 

The Northern Forest Futures Project Web site 

(http://www.nrs.fs.fed.us/futures/) and the 

Northern Forest Futures database (online and 

included with the printed version of this report) 

provide an opportunity for users to interactively 

explore data and results related to these analyses. 
10

Literature

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2007. The state of our forests – 2007. Baltimore, MD. 70 p.

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. 

Cordell, H.K.; Betz, C.J.; Mou, S.H.; Gormanson, D.D. 2012. Outdoor recreation in the Northern 

United States. Gen. Tech. Rep. NRS-100. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 74 p. 

Dietzman, D.; LaJeunesse, K.; Wormstead, S. 2011. Northern forest futures project: scoping of 

issues in the forests of the Northeast and Midwest United States. Version 3.0. Newtown 

Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 

42 p. http://www.nrs.fs.fed.us/futures/local-resources/downloads/NFFPScopingDoc.pdf 

(accessed August 19, 2014).

Homer, C.; Huang, C.; Yang, L.; Wylie, B.; Coan, M. 2004. Development of a 2001 national land-cover 

database for the United States. Photogrammetric Engineering and Remote Sensing. 70(7): 

829-840. 

Miles, P.D. 2013. Forest inventory EVALIDator Web application. Version 1.5.1.04. 

St. Paul, MN: U.S. Department of Agriculture, Forest Service, Northern Research Station. 

http://apps.fs.fed.us/Evalidator/evalidator.jsp (accessed May 21, 2013).

Montréal Process Working Group. n.d. The Montréal Process. http://www.montrealprocess.org/ 

(accessed May 20, 2013).

Natural Resources Canada. 2015. State of Canada’s forest report. Ottawa, ON, Canada: Natural 

Resources Canada. http://www.nrcan.gc.ca/forests/report/16496 (accessed January 12, 2015). 
C h a p t e r O n e 11

Northeastern Area Association of State Foresters [NAASF]. 2012. Forest action plans. 

http://www.forestactionplans.org/regions/northeastern-region (accessed November 8, 2012).

Shifley, S.R.; Aguilar, F.X.; Song, N. [et al.]. 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.

Smith, W.B.; Miles, P.D.; Perry, C.H.; Pugh, S.A. 2009. Forest resources of the United States, 2007. 

Gen. Tech. Rep. WO-78. Washington, DC: U.S. Department of Agriculture, Forest Service. 336 p.

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. 

U.S. Census Bureau. 2010. Population estimates. Washington, DC: U.S. Department of Commerce. 

http://www.census.gov/popest/estimates.html (accessed March 26, 2011). 

U.S. Department of Agriculture, Forest Service. 2011. National report on sustainable forests – 2010. 

FS–979. Washington, DC: U.S. Department of Agriculture, Forest Service. 212 p. 

http://www.fs.fed.us/research/sustain/2010SustainabilityReport/ (accessed June 11, 2012).

U.S. Department of Agriculture, Forest Service. 2012a. Forest inventory and analysis national 

program. Washington, DC: U.S. Department of Agriculture, Forest Service, Forest Inventory 

and Analysis. http://www.fia.fs.fed.us/ (accessed November 8, 2012).

U.S. Department of Agriculture, Forest Service. 2012b. Forest sustainability indicators information 

system. Washington, DC: Department of Agriculture, Forest Service. http://apps.fs.fed.us/

fsiis/index.jsp (accessed February 13, 2012). 

U.S. Department of Agriculture, Forest Service. 2012c. Future of America’s forest and rangelands: 

Forest Service 2010 Resources Planning Act assessment. Gen. Tech. Rep. WO-87. 

Washington, DC: U.S. Department of Agriculture, Forest Service. 198 p.

U.S. Department of Agriculture, Forest Service. 2012d. Resources Planning Act (RPA) assessment. 

Washington, DC: U.S. Department of Agriculture, Forest Service. http://www.fs.fed.us/

research/rpa/ (accessed November 8, 2012).
12

U.S. Department of Agriculture, Forest Service. 2012e. Southern forest futures project. Research 

Triangle Park, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 

http://www.srs.fs.usda.gov/futures/ (accessed November 6, 2012).

U.S. Department of Agriculture, Forest Service. 2013. Forest inventory and analysis national 

program: data and tools. Washington, DC: U.S. Department of Agriculture, Forest Service, 

Forest Inventory and Analysis. http://www.fia.fs.fed.us/tools-data/default.asp 

(accessed May 21, 2012).

Wear, D.N.; Greis, J.G., eds. 2013. The southern forest futures project: technical report. 

Gen. Tech. Rep. SRS–GTR-178. Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station. 542 p.

Wear, D.N.; Huggett, R.; Li, R.; Perryman, B.; Liu, S. 2013. Forecasts of forest conditions in regions 

of the United States under future scenarios: a technical document supporting the 

Forest Service 2012 RPA assessment. Gen. Tech. Rep. SRS–GTR-170. Asheville, NC: 

U.S. Department of Agriculture, Forest Service, Southern Research Station. 101 p.

Wormstead, S; Luther, T.; Barnes, M. 2011. 2010 statewide forest resource assessments and 

strategies in the Northeast and Midwest: a regional summary. Newtown Square, PA: 

U.S. Department of Agriculture, Forest Service, Northeastern Area State and 

Private Forestry; Northeastern Area Association of State Foresters. 51 p. 

http://www.northeasternforests.org/FRPC/files/1308144846RegSummary-StateAssess

Strategy-2011-04-08.pdf (accessed November 8, 2012).
Chapter T w o 13

Projection Methods

Michael E. Goerndt, W. Keith Moser, Patrick D. Miles, David N. Wear, Ryan D. DeSantis, 

Robert J. Huggett, Jr., Stephen R. Shifley, Francisco X. Aguilar, Kenneth E. Skog

Introduction

NE PURPOSE OF the Northern Forest Futures Project is to predict change in future forest attributes 

across the 20 States in the U.S. North (see Fig. 1.1) for the period that extends from 2010 to 2060. 

The forest attributes of primary interest are the 54 indicators of forest sustainability identified in 

the Montréal Process Criteria and Indicators (Montréal Process Working Group, n.d.; USDA FS 2011). 

However, some indictors are virtually impossible to forecast for future decades, so we concentrated 

on the forest characteristics that have quantitative or experiential bases for forecasting future change. 

When possible, future forest attributes for individual States were projected and summarized, and then 

aggregated to estimate change for the North. 

The projections of forest change are based on 

underlying assumptions about future land-use 

change, population change, climate change, 

and rates of disturbance (primarily from timber 

harvesting). We focused on three primary 

alternative scenarios that have different 

assumptions about population change, land-use 

change, economic activity, timber harvesting, 

forest products consumption, and energy use 

and its association with climate change. 

To the extent possible, methods followed those 

proven successful for projections at the national 

scale such as those from the Resources Planning 

Act (RPA) assessment of future forest conditions 

(USDA FS 2012a, 2012c). RPA forecasts generally 

report results for four regions: North, South, 

Rocky Mountain, and Pacific Coast (Fig. 2.1). 

For some forest attributes, the RPA proved to 

be the best available source of science-based 

information for the North. For other attributes, 

however, we needed to increase the spatial 

or temporal detail of the RPA data or add 

information from new sources. 
Summary of Projection Methods

• We examined three storylines, or coordinated 

groups of assumptions that describe future 

population, economic activity, land use, bioenergy 

use, and associated greenhouse gas emissions. 

• We coupled the storylines with general 

circulation models that predict and map future 

climate conditions. 

• We combined storylines and general circulation 

models to examine 13 alternative future scenarios 

of forest change for the Northern States; the 

scenarios differ in assumptions about economic 

growth, population change, climate change, 

and disturbances (such as harvesting, conversion 

of forest to urban areas, or spread of emerald 

ash borers). 

• For each of 13 scenarios, we used a statistical 

imputation model to forecast change over time 

from 2010 to 2060 for all survey plots within a 

given State, group of States, or section within 

a State; the model accounted for the effects of 

climate change, land-use change, harvesting, 

growth, and species succession. 

• Results were saved in a database (Miles and Wear 

2015) to summarize predicted survey plot conditions 

and to interpolate those results into predictions 

of forest conditions, by decade from 2010 to 

2060, for States and for the region as a whole. 

• The forest conditions that we projected directly 

for each scenario include forest area, volume, 

biomass, and forest type. 

• For one scenario, we included algorithms to 

model the potential impacts of emerald ash borer 

(Agrilus planipennis), a nonnative, invasive insect 

that is spreading through northern forests, killing 

virtually all the ash trees (Fraxinus spp.) in areas 

of infestation. 

• We created an interactive, Web-based tool that 

can be used to summarize and tabulate a range 

of projected forest conditions, by State and from 

2010 to 2060, for a wide range of possible futures.

• We used the projected forest conditions as 

input to auxiliary models to estimate associated 

attributes such as wildlife habitat suitability and 

water quality; those techniques and their results 

are described in subsequent chapters. 

14
Pacific Northwest

North Central

South Central

Northeast

Southeast

Pacific Southwest

Intermountain

ROCKY MOUNTAIN

NORTH

SOUTH

PACIFIC COAST

Great Plains

FIGURE 2.1

Four regions and eight subregions 

of the United States.

15
16

Forecasts from 2010 to 2060 of forest 

attributes for the Northern States were based 

on the following four general procedures, which 

were also used to project future changes for 

numerous indicators of forest condition and 

sustainable management:

1. Statistical imputation was used to project 

changes over time for long-term survey plots 

established by the Forest Inventory and 

Analysis (FIA) program of the U.S. Forest 

Service (Appendix 2) in a process that is 

similar to the procedures used for RPA 

projections (USDA FS 2012a, Wear et al. 

2013) but that downscales data for individual 

States or groups of States. This process was 

used to develop 13 future scenarios that 

have different assumptions about future 

conditions—harvesting rates, land-use 

change, insect pests, and climate change—

and to select seven scenarios for detailed 

analysis. The predicted future FIA plot 

conditions for all 13 scenarios are published 

in a Northern Forest Futures database (Miles 

and Wear 2015) that summarizes estimates 

of future values for many forest attributes 

by States or for the entire region (further 

described in this chapter). The database is on 

a DVD found in the back cover of this report 

and is also available from the Northern Forest 

Futures Web site at http://www.nrs.fs.fed.

us/futures/. 

2. Some forest attributes, such as wildlife 

habitat diversity or water quality, can be 

at least partially discerned from projected 

changes in forest area, tree species 

composition, and age class. Consequently, 

the second analysis used the predicted 

changes in forest trees and stands in 

statistical models that estimated future 

values for nontimber forest attributes. Often 

this required the use of additional maps or 

supplementary data sets. 

3. Analyses of some forest attributes relied solely 

on external maps and data sets for estimates 

of future conditions. For example, future 

outdoor recreation activity was explicitly 

modeled from projected socioeconomic trends 

(Bowker and Askew 2013).

4. The data and insights needed for detailed 

forecasts were not available for some 

attributes of interest. In these cases, a 

procedure was used that draws inferences 

from current conditions and recent trends. 

The remainder of this chapter describes 

in detail: assumptions about changes in 

population, economic activity, land use, 

climate, and utilization of woody biomass 

and bioenergy; how those assumptions were 

incorporated in imputation procedures used 

to forecast change in FIA inventories for 

individual States and the region as a whole; 

and availability of data and tools for access to 

additional summaries and analyses. 
Chapter T w o 17

STORYLINES, SCENARIOS, 

AND CLIMATE MODELS

Population, economic conditions, new technologies, 

wood removals for products and energy, land 

use, greenhouse gas emissions, and climate 

change all influence future forest conditions. 

The Intergovernmental Panel on Climate Change 

(IPCC 2007) described four global “storylines” 

or combinations of plausible assumptions about 

future socioeconomic conditions that would 

affect greenhouse gas emissions, which would 

in turn affect climate. The IPCC refined global 

scenarios within each storyline and ultimately 

developed estimates of future greenhouse gas 

emissions for a range of alternative futures 

(Nakic’enovic’ et al. 2000). 

Climatologists have applied multiple models 

(competing hypotheses) to estimate and map 

future change in temperature and precipitation 

for a given future trend in greenhouse gas 

emissions. These general circulation models vary 

considerably in the rate of climate change that 

they predict. For example, all circulation models 

indicate increasing average temperature with 

increasing greenhouse gas emissions, but they 

differ in how much and how rapidly temperature 

will rise at a given location. Some models tend to 

be consistently “hotter” or “cooler” than others. 

Combinations of a predicted future pattern 

of greenhouse gas emissions (based on IPCC 

storylines) with a specific general circulation 

model have been used to map future patterns 

of precipitation and temperature over time. 

The number of possible combinations of future 

greenhouse gas emission storylines with alternative 

circulation models can quickly become overwhelming. 

Therefore, for the analyses used in this report, 

we focused primarily on results from two 

versions of the Canadian Global Circulation 

Model (Canadian Centre for Climate Modelling 

and Analysis 2012a, Canadian Centre for Climate 

Modelling and Analysis 2012b), which predicts 

neither the highest nor the lowest temperature 

levels, and has been widely applied across 

multiple greenhouse gas emission storylines. 

Greenhouse gas emission scenarios and climate 

change based on three of the IPCC storylines (IPCC 

2012, Nakic’enovic’ et al. 2000, Solomon et al. 2007) 

were selected. The storylines (Table 2.1) define 

combinations of assumptions about population 

growth, land-use change, gross domestic product 

growth, energy use, technological change, bioenergy 

use, and projected greenhouse gas emissions. 

The labeling system for these storylines uses 

a combination of letters and numbers and was 

maintained here to provide a reference to other 

analyses that use it, such as the RPA reports. 
18

A1, A1B Globalization and economic 

growth; introduction of new 

and more efficient energy 

technologies; emphasis on 

capacity building. Storyline 

variation A1B assumes moderate 

greenhouse gas emission and 

a balanced use of fossil and 

renewable fuels. 

High Medium

Global: 1.3x

United States: 1.5x

Global: Very High (6.2x)

United States: medium (3.3x)

A2 A more differentiated world with 

less trade compared to storyline 

A1B; uneven economic growth; 

slower technological change.

Moderate High

Global: 1.7x

United States: 1.7x

Global: medium (3.2x)

United States: low (2.6x)

B2 Sustainable development 

and diversified technologies; 

increased concern for 

environmental and 

social sustainability. 

Low Medium

Global: 1.4x

United States: 1.3x

Global: medium (3.5x)

United States: low (2.2x)

Table 2.1—Overview of the Intergovernmental Panel on Climate Change greenhouse-gas emissions storylines 

(Ince et al. 2011, Nakic’enovic’ et al. 2000, USDA FS 2012b).

Storyline Description

Characteristics

Land-use 

change Population growth Economic activity and growth in gross domestic product

The key point for readers is that the labels identify 

relatively high (A2), medium (A1B), and low 

(B2) future emissions of greenhouse gases:

Storyline A1—A future in which the rest of 

the world approaches the United States in 

terms of per capita wealth, technology use, and 

population growth. Storyline A1 is characterized 

by rapid economic growth, increasing global 

trade, and population peaking in mid-century 

and then declining. A variation of storyline A1, 

storyline A1B, predicts moderate greenhouse 

gas emissions, a balanced use of fossil and 

renewable fuels, with an early dependence on 

fossil fuel followed by a relatively rapid increase 

in renewable energy sources.

FUTURE FORESTS OF THE NORTHERN UNITED STATES
Chapter T w o 19

A1, A1B Very high High Rapid: gas, biomass, 

and other renewables

Storyline A1B: 

Balanced use 

of fossil fuel 

and renewable 

energy

High 

(highest for the United States)

Medium

A2 High Low Slow: coal and gas Medium High

B2 Medium Medium Medium: gas, oil, 

 and biomass

Medium 

(lowest for United States)

Low

Table 2.1 continued 

Storyline

Characteristics

Global 

energy use

Oil and gas 

availability

Energy technological 

pace and sources

Global expansion of primary 

biomass energy production

Relative increase in 

greenhouse gasses

Storyline A2—A future in which the focus is 

regional, in contrast to the global approach of 

storyline A1. Storyline A2 assumes relatively 

high greenhouse gas emissions, continuously 

increasing global population, and more 

regionally centered economic growth. Among 

the storylines, it has the highest total global 

population growth but the lowest long-term 

economic growth.

Storyline B2—A future of global sustainable 

development, with some regional economic 

convergence. The B2 storyline assumes relatively 

low greenhouse gas emissions. It is similar to 

A2 in that regional and local institutions and 

economies are emphasized over global integration. 

Economic growth is intermediate, but population 

growth is significantly lower than for storylines 

A1 and A2. Thus, per capita income is closer to 

A2 than to A1. Storyline B2 also has the lowest 

projected growth in biomass energy for the United 

States and its neighbors.
20

After evaluating 13 different scenarios that 

combined general circulation models and 

storylines (with and without the effects of bioenergy 

and invasive species), seven were selected for 

further analysis (Table 2.2); projections of forest 

conditions for the remaining alternate six scenarios 

can be summarized using the Northern Forest 

Futures database (Miles and Wear 2015). Each 

of the selected scenarios has three components: 

• A general storyline and associated assumptions 

as described above; for consistency with 

prior Forest Service research products, 

variations of the assumptions adapted for the 

2010 RPA Assessment were used (USDA FS 

2012a, 2012b, 2012c; Wear et al. 2013).

• A general circulation model that estimates 

by geographic area the temperature and 

precipitation change associated with the 

different levels of greenhouse gas emissions 

projected under alternative storylines (Meehl 

et al. 2007, USDA FS 2012b); part of the RPA 

analyses was to downscale the global climate 

change estimates from the IPCC (Figs. 2.2, 2.3) 

to a finer grid so that current and projected 

conditions could be associated with individual 

FIA plots (USDA FS 2013).

• A set of future harvesting assumptions, 

with one including forecasts of insectinduced mortality.

Table 2.2 summarizes the combinations for each 

scenario selected for the Northern Forest Futures 

Project. The scenarios differ from the IPCC/RPA 

efforts in two ways: assumptions about future 

harvesting and the scenario naming convention 

used. Scenarios labeled A1B-C, A2-C, and B2-C 

include the effects of climate change on forest 

growth and composition and include projected 

land-use change, but rather than using harvesting 

projections that reflect increases in wood used 

for energy, they project future harvesting based 

on the recent past probability of harvesting—

thus they represent a continuation of recent 

harvesting rates. 

Scenarios labeled A1B-BIO, A2-BIO and B2-BIO 

include the same respective climate and land-use 

change effects, but incorporate the projections of 

increasing future harvesting (with more wood 

biomass used for energy) that Wear et al. (2013) 

applied in RPA projections of northern forest 

conditions. Projected harvesting levels for scenarios 

A1B-BIO, A2-BIO, and B2-BIO are similar to the 

IPCC market-based projections of future biomass 

utilization for energy (projections of forest markets 

including production, consumption, trade, and prices 

of timber and wood products by storyline). Those 

projections (Ince et al. 2011) include very large 

increases in wood energy use under storyline 

A1B. For our scenario A1B-BIO, the market-based 

harvesting levels would eventually have exceeded 

any realistic harvesting expectations for the 

Northern States. Consequently, the post-2030 rate 

of harvesting was set below the projections of 

Ince et al. (2011). 
Chapter T w o 21

CGCM3.1d Scenario A1B-C

Scenario A1B-BIO

 (Primary scenarios)

Scenario A2-C

Scenario A2-BIO

Scenario A2-EAB

(Primary scenarios)

No scenario developed

MIROC3.2 Alternate scenarioe Alternate scenarioe No scenario developed

CSIRO-Mk3.5 Alternate scenarioe Alternate scenarioe No scenario developed

CGCM2d No scenario developed No scenario developed Scenario B2-C

Scenario B2-BIO

(Primary scenarios)

CSIRO-Mk2 No scenario developed No scenario developed Alternate scenarioe

UKMO-HadCM3 No scenario developed No scenario developed Alternate scenarioe

a

The models that were developed into scenarios were chosen because their climate sensitivities were above or near the mean of all general 

circulation models assessed (Randall et al. 2007). The choice of models was also governed by the desire to maintain compatibility among 

projections for the Northern Forest Futures Project and those for the national Resources Planning Act assessment (USDA FS 2012b). 

Sources of models: CGCM3.1 = Canadian Centre for Climate Modeling and Analysis Coupled Global Climate Model, medium resolution (T47) 

(http://www.ec.gc.ca/ccmac-cccma); MIROC3.2 = Center for Climate System Research, University of Tokyo, National Institute for Environmental 

Studies and Frontier Research Center for Global Change (Japan), model for Interdisciplinary Research on Climate Version 3.2, medium resolution 

(http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdf); CSIRO-Mk3.5 = Commonwealth Scientific and Industrial Research 

Organization (Australia), CSIRO Mk3 Climate System Model (http://www.cawcr.gov.au/publications/technicalreports/CTR_021.pdf); CGCM2 = 

Coupled Global Climate Model, medium resolution (T47), Canadian Centre for Climate Modelling and Analysis (http://www.ec.gc.ca/ccmac-cccma); 

CSIRO-Mk2 = Australia’s Commonwealth Scientific and Industrial Research Organization Australia (http://www.cmar.csiro.au/e-print/open/

hennessy_1998a.html#ccm); UKMO-HadCM3 = Hadley Centre for Climate Prediction and Research UK (http://wwwpcmdi.llnl.gov/ipcc/model_

documentation/HadCM3.pdf).

b

Variations of the A1B-C, A2-C, and B2-C scenarios predict the impact of increased harvest and utilization of woody biomass for energy. They 

are referred to as scenarios A1B-BIO, A2-BIO, and B2-BIO. A variation of scenario A2-C, A2-EAB, examines the potential impact of continued 

spread of the emerald ash borer with associated mortality of all ash trees in the affected areas. 

c

A variation of the A1 storyline. Other variations predict different energy portfolios.

d

Note that CGCM3.1 is actually the third generation of the same model group as CGCM2. The primary difference for CGCM3.1 is updated data 

and predictors. 

e

Additional projections of forest conditions from 2010 to 2060 were made by pairing IPCC storylines A1B, A2, and B2 with different general 

circulation models. Those alternate scenarios are not discussed in this report, but the projected plot conditions by decade for those 

combinations are available in Miles and Wear (2015).

General 

circulation modela

Storylineb

A1Bc A2 B2

Table 2.2—Scenarios used to project future forest conditions in the North; scenarios combine a greenhouse gas 

storyline (IPCC 2007) and a general circulation model with additional assumptions about future harvest levels.

Potential impacts of the invasive emerald 

ash borer (EAB) (Agrilus planipennis) were 

modeled in a variation of scenario A2-C and 

relabeled A2-EAB. Details for that scenario 

are provided later in this chapter, in Chapter 5, 

and in Appendix 2. 
22

FIGURE 2.2

Annual precipitation in the Northern United 

States (A) in 2010; (B) projected for 2060 

under emissions storyline A2, which assumes 

high greenhouse gas emissions and large 

gains in population and energy consumption 

with moderate gains in income; and 

(C) projected for 2060 under emissions 

storyline B2, which assumes low greenhouse 

gas emissions with moderate gains in 

population growth, income, and energy 

consumption (USDA FS 2013). 

A

B

C High: 70

Low: 16

PRECIPITATION (inches per year)
Chapter T w o 23

A

B

C FIGURE 2.3

Maximum June temperature in the Northern 

United States (A) for 2010; (B) projected for 

2060 under emissions storyline A2, which 

assumes high greenhouse gas emissions and 

large gains in population growth and energy 

consumption with moderate gains in income; 

and (C) projected for 2060 under emissions 

storyline B2, which assumes low greenhouse 

gas emissions with moderate gains in 

population growth, income, and energy 

consumption (USDA FS 2013). 

High: 104

Low: 63

TEMPERATURE (degrees fahrenheit)
24

ASSUMPTIONS FOR PRIMARY SCENARIOS

Population Projections 

We used U.S. population projections from 2000 

to 2060 that were developed for the RPA analyses 

(USDA FS 2012b), which in turn followed IPCC 

trends that were originally based on 1990 

census data. The RPA values were subsequently 

updated to incorporate the 2004 census 

estimates, projected forward to 2060, and then 

allocated spatially among the States and their 

individual counties (Zarnoch et al. 2010). The 

RPA projected a nationwide population increase 

from the 2010 census (309 million) to 2060: 

397 million (29 percent) under scenario B2-C, 

447 million (45 percent) under scenario A1B-C, 

and 505 million (64 percent) under scenario 

A2-C (USDA FS 2012b, Zarnoch et al. 2010). 

Population in the Northern States (125 million 

in 2010) is expected to follow the same trends 

(Fig. 2.4), with lower rates of increase: 140 million 

(12 percent) under storyline B2; 158 million 

(25 percent) under storyline A1B; and 178 million 

(39 percent) under storyline A2 (Zarnoch et al. 

2010). The projected change varies spatially 

(Fig. 2.5), with the largest increase in population 

density in areas that are nearest to urban centers 

(Cordell et al. 2012, Zarnoch et al. 2010). 

Under storyline A1B, the Northern States account 

for 41 percent of the total U.S. population in 

2010, but that proportion is projected to drop to 

35 percent of the total by 2060. 

Under storyline A1B, the rate of population 

increase from 2010 to 2060 would be larger than 

the U.S. average (45 percent) for New Hampshire 

(64 percent), Maryland (55 percent), Vermont 

(49 percent), and Minnesota (49 percent). States 

with the lowest rate of growth would be New York 

(13 percent), West Virginia (12 percent), and 

Ohio (12 percent). Population in Washington, 

D.C., is projected to decrease by 12 percent 

(Appendix 2). 

FIGURE 2.4

Historical and projected population growth 

in the Northern United States under three 

greenhouse gas storylines (U.S. Census 

Bureau 2004, USDA FS 2012b): A1B assumes

moderate greenhouse gas emissions, 

moderate gains in population, and large 

gains in income and energy consumption 

(but with a balanced renewable/fossil fuel 

portfolio); A2 assumes high greenhouse gas

emissions, large gains in population and 

energy consumption, and moderate gains in 

income; and B2 assumes low greenhouse gas 

emissions with moderate gains in population, 

income, and energy consumption. 

1960 1980 2000 2020 2040 2060

200

180

160

140

120

100

80

60

POPULATION (millions)

Historical (baseline)

A1B

A2

B2
Chapter T w o 25

Under 3

3 to 30

31 to 175

Over 175

PROJECTED POPULATION DENSITY 

CHANGE (people per square mile)

Counties with the highest growth, which are 

expected to add >175 persons per square mile, 

would be concentrated mainly in the Washingtonto-Boston urban corridor and in other suburban 

areas throughout the region. Second-tier growth 

counties would either be found adjacent to counties 

with the fastest growth rates, or in lower Michigan, 

Wisconsin, and southern Missouri. 

Economic Projections

Projections of U.S. gross domestic product were 

developed for the RPA analyses (USDA FS 2012b) 

and used to estimate future forest product demand 

and production by region (Ince et al. 2011). 

Projections of total personal income and disposable 

personal income from 2010 to 2060 were also 

developed by region as part of the RPA analyses 

and then disaggregated to State and county levels 

(Zarnoch et al. 2010) for subsequent use as 

predictor variables for modeled trends in land use, 

water use, and recreation use. 

At the time that the economic projections were 

constructed, the 2006 economic data were the 

most recent available. Thus, the projections do 

not explicitly capture the U.S. economic downturn 

that began at the end of 2007, address annual 

variations in economic indicators, or anticipate 

periods of economic recession. Instead, their 

focus is on long-term averages. Figure 2.6 shows 

projections to 2060 for gross domestic product, 

personal income, and disposable personal 

income for storylines A1B, A2, and B2 (Ince et al. 

2011, Zarnoch et al. 2010). 

FIGURE 2.5

Pattern of projected increases in population 

density from 2010 to 2060 under storyline 

A1B which assumes moderate greenhouse gas 

emissions, moderate gains in population, 

and large gains in income and energy 

consumption (but with a balanced renewable/

fossil fuel portfolio) (Cordell et al. 2012).
26

FIGURE 2.6

Comparison of economic activity projections 

assumed under three global greenhouse gas 

storylines: (A) U.S. gross domestic product 

historically and projected, (B) projected per 

capita personal income for the North, 

(C) projected total personal income for the 

North (USDA FS 2012b, Zarnoch et al. 2010). 

1960 1980 2000 2020 2040 2060

2010 2020 2030 2040 2050 2060

2010 2020 2030 2040 2050 2060

45

40

35

30

25

20

15

10

5

0

16

14

12

10

8

6

4

2

0

90

80

70

60

50

40

30

20

10

GROSS DOMESTIC PRODUCT (trillion dollars)

PERSONAL INCOME (trillion dollars) PERSONAL INCOME (thousand dollars per person)

Historical (baseline)

A1B

A2

B2

A1B

A2

B2

A1B

A2

B2

Land-use Projections

Future land use for each scenario was projected 

to 2060 for categories of forest, urban areas, 

cropland, pastureland, and rangeland as part 

of the RPA assessment (USDA FS 2012b). 

Projected areas for land-use categories were 

used as predictor variables in several auxiliary 

resource analysis models. Land-use change 

was assumed to be a function of the population 

and economic forecasts for each scenario, but 

not to be directly affected by climate change 

(for instance, it ignores potential conversion of 

marginal cropland to other uses in a warmer or 

drier climate). Wear (2011) describes in detail 

the methodology used to project and map land-use 

change at the county scale. 

A B

C
Chapter T w o 27

Projected losses of non-Federal forest acreage 

in the Northern States from 2010 to 2060 range 

from 6 to 11 million acres, depending on the 

scenario considered (Fig. 2.7). The focus is on 

non-Federal land because it is assumed that 

Federal land (for example, national forests 

and national parks) will remain forested and in 

Federal ownership. Losses in the North would 

likely be about half the losses predicted for the 

South, but more than twice what is predicted 

for regions farther west. In all scenarios and for 

all regions of the United States, urban area is 

projected to increase and all other land uses are 

projected to decrease. 

Under all scenarios, urban areas in the North 

would increase at the expense of forest, cropland, 

and pastureland land (Wear 2011). The rate 

of change would be highest for scenario A1B 

followed by A2 and then B2, but the pattern of 

change is similar among all three (Fig. 2.8). 

When forest is converted to other land uses, any 

biomass, wood volume, or carbon associated 

with those acres is considered to be lost from the 

forest. Thus, projected changes in attributes such 

as total forest biomass, volume, or carbon have 

two components: (1) change attributed to growth 

and/or disturbance on land that remains forested, 

and (2) decreases attributed to conversion of 

forest to nonforest uses. Projected changes 

in land-use area by scenario are reported in 

Appendix 2, as is projected forest area by State 

and scenario.

Forest acreage decreases under every storyline 

for every Northern State. The States with the 

highest percentage decreases are those with high 

population density along the Atlantic seaboard 

such as Delaware, Maryland, Rhode Island, and 

New Jersey. For almost all States, A1B predicts 

the largest decrease in forest acreage over the 

50-year period (almost twice that of B2).

South North Pacific Rocky Mountain

0

-2

-4

-6

-8

-10

-12

-14

-16

-18

CHANGE IN FOREST AREA (million acres)

FIGURE 2.7

Projected loss of non-Federal forest area, 

2010 to 2060, for the regions of the 

conterminous United States under three 

global greenhouse storylines (USDA FS 2012b, 

Wear 2011). Storyline A1B assumes moderate 

greenhouse gas emissions, moderate gains 

in population, and large gains in income and 

energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes 

high greenhouse gas emissions, large gains 

in population and energy consumption, 

and moderate gains in income; and B2 

assumes low greenhouse gas emissions with 

moderate gains in population, income, 

and energy consumption. 

A1B

A2

B2
28

100

90

80

70

60

50

40

30

20

100

100

90

80

70

60

50

40

30

20

100

100

90

80

70

60

50

40

30

20

100

LAND AREA (percent) LAND AREA (percent) LAND AREA (percent)

FIGURE 2.8 Percent of area in primary land uses (forest, urban areas, 

cropland, and pastureland) for the North, 1997 to 2010 

with projections to 2060, under three global greenhouse 

storylines: A1B assumes moderate greenhouse gas emissions, 

moderate gains in population, and large gains in income 

and energy consumption (but with a balanced renewable/

fossil fuel portfolio); A2 assumes high greenhouse gas 

emissions, large gains in population and energy consumption, 

and moderate gains in income; and B2 assumes low 

greenhouse gas emissions with moderate gains in population, 

income, and energy consumption. 

1997 2010 2020 2030 2040 2050 2060

1997 2010 2020 2030 2040 2050 2060

1997 2010 2020 2030 2040 2050 2060

Pasture

Cropland

Urban

Forest

A1B

A2

B2
Chapter T w o 29

Removals

Projected removals for scenarios A1B-C, A2-C, 

and B2-C were based on a continuation of observed 

harvesting patterns from the recent past (generally 

the 10-year period that started in the mid-1990s) 

as observed in the FIA State data sets that were 

used to calibrate the Forest Dynamics Model. 

As described by Wear et al. (2013): 

[A] two-step empirical harvest probability 

model was estimated for each forest type 

in each of the Northern States. In the first 

step, harvest probability was modeled as a 

function of a set of biophysical attributes in 

the inventory, including sawtimber volumes 

observed at the beginning and predicted 

for the end of the period, stand age, slope, 

ownership class, average stand diameter, 

and an index that gauges the diversity of 

the tree species on the plot. In the second 

step, harvests were defined as full or partial, 

based on the frequency of the harvest types 

observed for the paired inventories for each 

State [by] forest type permutation. 

For each State, the harvesting probability model 

was applied to each FIA plot at each projected 

time interval to calculate the probability and type 

of harvesting for that plot. Modeled harvesting 

probabilities were compared to a random draw 

from a uniform probability distribution (think of 

the computer rolling the harvesting dice). Based 

on the outcome, a given plot was either selected 

for harvesting in that interval or it was not selected. 

For each plot that was “harvested,” additional 

stochastic algorithms determined the type of 

harvesting and selected a suitable replacement 

plot representing post-harvesting conditions.

A simple scaling function permitted the plot 

harvesting probabilities to be increased or 

decreased, so that the predicted cumulative 

harvest volume could be increased or decreased 

to adjust the State total harvest volume without 

changing the relative priority of harvesting 

among the individual plots. That scaling function 

was used to ensure that the predicted harvest 

volumes matched the observed harvest volumes 

for the recent past, and it was used to model 

alternative future harvesting scenarios. The 

following section describes how the scaling 

function was used to model scenarios A1B-BIO, 

A2-BIO, and B2-BIO, with increased harvesting 

associated with increased utilization of biomass 

for energy.

VARIATIONS ON PRIMARY SCENARIOS

Woody Biomass and Bioenergy Projections

Projected removals for scenarios A1B-BIO, 

A2-BIO, and B2-BIO (Table 2.2) were based on 

projected harvesting (including increasing biomass 

use for energy) in the IPCC storylines (Nakic’enovic’

et al. 2000). Our modeled removals from 2010 

through 2060 for scenarios A2-BIO and B2-BIO 

were very close to the market-based projections 

made using the U.S. Forest Products Module in 

combination with the Global Forest Products 

Model (Ince et al. 2011, USDA FS 2012b).
30

Scenario A1B-BIO results are similar to the 

market-based harvesting projections for 2010 

to 2030 but are lower than the market-based 

projections for the remainder of the forecasting 

period. The scope of the market-based harvesting 

projections is large-scale (multi-national), with 

the U.S. results reported as part of the Organization 

for Economic Cooperation and Development (OECD) 

macro-region along with Japan, Canada, Australia, 

New Zealand, and Western Europe (Ince et al. 

2011). The projections from 2010 to 2060 identify 

three major sources of biomass: cropland crops 

and residues, dedicated biomass energy plantations, 

and roundwood fuelwood from forests (Fig. 2.9).

Under scenarios A1B-C and A2-C, the wood 

component (from forests and plantations) of 

bioenergy is expected to be largest from 2030 to 

2060 (Fig. 2.9). Because projections are based 

on broad-scale assumptions for the United States 

and several other countries combined, we were 

unable to delineate this information specifically for 

the Northern States. Consumption of roundwood 

from forests is projected to increase rapidly from 

2030 to 2050 and then begin to stabilize as energy 

plantations mature. Agricultural crops and energy 

plantations dominate biomass energy consumption 

under scenario B2-C. For all scenarios, biomass 

from agricultural crops and residue is expected to 

vary less over time than biomass from roundwood 

or energy plantations. In contrast to other areas 

of the macro-region, however, the U.S. North 

is not expected to dramatically increase energy 

plantations because the value of biomass will 

likely be lower than other wood uses.

The U.S. Forest Products Module (Ince et al. 

2011) was used to project harvesting levels in 

response to roundwood demand for traditional 

products and increasing wood energy demand 

for the entire United States and for the northern, 

southern, and western regions under scenarios 

A1B-C, A2-C, and B2-C. We synchronized the 

projected wood removal levels for scenarios 

A2-BIO, B2-BIO, and A1B-BIO (through 2030) 

with the harvesting levels from the U.S. Forest 

Products Module projections (Ince et al. 2011) 

by adjusting the scaling function in the harvesting 

probability model within the Forest Dynamics 

Model (Wear et al. 2013). A comparison with the 

corresponding market-based removals estimates 

by Ince et al. (2011) showed that scenario A1BBIO was 21 percent lower, scenario A2-BIO was 

7 percent lower, and scenario B2-BIO was virtually 

the same (Ince et al. 2011, Wear et al. 2013).
2500

2000

1500

1000

5000

1600

1400

1200

1000

800

600

400

2000

900

800

700

600

500

400

300

200

1000

BIOMASS CONSUMPTION (million tons) BIOMASS CONSUMPTION (million tons) BIOMASS CONSUMPTION (million tons)

Cropland Crops and Residues

Energy Plantations

Roundwood Fuelwood (forests)

A1B

A2

B2

FIGURE 2.9 Projected annual biomass consumption for energy for 

the macro-region composed of the United States, Japan, 

Canada, Australia, New Zealand, and Western Europe 

under three global greenhouse storylines (Ince et al. 

2011): A1B assumes moderate greenhouse gas emissions, 

moderate gains in population, and large gains in income 

and energy consumption (but with a balanced renewable/

fossil fuel portfolio); A2 assumes high greenhouse 

gas emissions, large gains in population and energy 

consumption, and moderate gains in income; and B2 

assumes low greenhouse gas emissions with moderate 

gains in population, income, and energy consumption. 

2000 2010 2020 2030 2040 2050 2060

2000 2010 2020 2030 2040 2050 2060

2000 2010 2020 2030 2040 2050 2060

31
Figure 2.10 illustrates the modeled removals 

over time for all seven scenarios discussed in 

the remainder of this chapter. Scenarios that 

are coded with the same initial characters (for 

example, A2-C, A2-BIO, and A2-EAB) share 

the same assumptions about future population 

increase, land-use change, economic change, 

and greenhouse gas emissions, but they differ in 

removals assumptions. Scenarios A1B-C, A2-C, 

and B2-C assume future removals will follow 

the trends observed in the recent past—roughly 

3.5 billion cubic feet annually for the Northern 

States. Scenarios A1B-BIO, A2-BIO, and B2-BIO 

assume increased harvesting removals over time 

to satisfy increased bioenergy demands. 

Scenario A2-EAB is similar to scenario A2-C 

and assumes that future removals will follow the 

trends observed in the recent past. However, 

in scenario A2-EAB all live ash trees within the 

expanding projected range of the emerald ash 

borer are converted to dead trees to simulate 

the potential impact of that invasive insect. 

Additional information about scenario A2-EAB is 

presented later in this chapter and in Chapter 5.

2010 2020 2030 2040 2050 2060

12

10

8

6

4

2

0

ANNUAL HARVEST REMOVALS (billion cubic feet)

A1B

A2

B2

A1B-BIO 

A2-BIO

B2-BIO

A2-EAB

FIGURE 2.10

Projected harvest removals by scenario for North, 2010 to 2060, for 

seven scenarios, each representing a global greenhouse storyline 

(IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, 

and large gains in income and energy consumption (but with a 

balanced renewable/fossil fuel portfolio); A2 assumes high greenhouse 

gas emissions, large gains in population and energy consumption, 

and moderate gains in income; and B2 assumes low greenhouse gas 

emissions with moderate gains in population, income, and energy 

consumption. Scenario projections assume harvest will continue at 

recently observed levels (labeled –C) or increase to reflect increased 

harvest for bioenergy production (labeled –BIO). Scenario A2-EAB 

is a variation of scenario A2-C that also assumes all ash species will 

gradually succumb to an expanding zone of infestation by the nonnative 

emerald ash borer.

32
Chapter T w o 33

Modeling Emerald Ash Borer Effects 

The nonnative, invasive emerald ash borer 

continues to spread relatively unimpeded 

across North America, causing considerable 

damage to the North American ash (Fraxinus

spp.) resource. Further ash decline appears 

likely considering that mortality in infested 

black (Fraxinus nigra), green (Fraxinus 

pennsylvanica), and white ashes (Fraxinus 

americana) is ≥99 percent for trees >1 inch 

diameter at breast height (d.b.h.) (Herms 

et al. 2010). To examine the extremes in its 

anticipated effects on tree species composition 

in northern forests, we created a variation of 

scenario A2-C that modeled patterns of ash 

mortality from 2010 to 2060. This scenario 

is labeled A2-EAB, and it assumes that the 

emerald ash borer expands from currently 

infested areas at a rate of 12 miles per year 

(Prasad et al. 2010), that it kills all infested 

ash trees, and that it prevents successful 

ash regeneration in affected areas. We used 

data from the U.S. Department of Agriculture, 

Animal and Plant Health Inspection Service, 

Plant Protection and Quarantine program 

(USDA APHIS, n.d.) and the Canadian Food 

Inspection Agency (2011) to identify the core 

infested areas of United States counties and 

Canadian municipalities where emerald ash 

borer was detected at the end of December 

2010. We simulated complete ash mortality 

within each FIA plot after the plot was completely 

engulfed by the projected area of spread. 

Projections commenced in simulation year 

2015, calculated using the 2010 range data. 

The spread of emerald ash borer was modeled 

spatially and used to simulate locations of 

affected FIA plots in future decades. More 

information on the modeling methods can be 

found in DeSantis et al. (2013). Additional 

details on the projected spread and impact of 

emerald ash borer in the Northern States can 

be found in Chapters 4 and 5 and in Appendix 2.

MODELING FOREST CHANGE 

Model Design

Changes in tree and stand conditions for each 

scenario were projected using the Forest Dynamics 

Model developed by Wear et al. (2013). Their 

model is a set of interlinked submodels that uses 

FIA forest plot results to produce predictions 

of future inventories, given assumptions about 

growth, succession, climate, timber market 

conditions, and land-use changes. The principal 

advantage of this modeling framework is that it 

can be calibrated and applied using the existing 

FIA forest sampling design. Climate, market, and 

land-use assumptions link the forest forecasts 

to alternative scenarios (Wear et al. 2013). 

Modeling for northern forests was carried out 

for broad scale analysis units, generally whole 

States, but sometimes multiple small States 

or sections within large States: Minnesota, 

Michigan, Wisconsin, Missouri, Iowa, New York, 

Maine, Connecticut, New Jersey plus Delaware, 

Massachusetts plus Rhode Island, Vermont plus 

New Hampshire, West Virginia plus western 
34

Maryland, eastern Maryland, Illinois and Indiana 

plus western Ohio, eastern Ohio, western 

Pennsylvania, and eastern Pennsylvania. Additional 

information about the Forest Dynamics Model 

can be found in Appendix 2. 

The FIA database provides the source data for 

the Forest Dynamics Model. FIA inventories 

for each analysis unit were summarized by plot 

(technically by the FIA condition variable). Only 

plots classified as forest were included, and FIA 

data for the base year of 2010 were used as the 

starting point for model projections. For each 

plot for each inventory:

• The major forest-type group was assigned 

based on observed forest cover type.

• Variables for physical characteristics (such 

as slope and aspect) were retained.

• Biophysical attributes (such as basal area, 

growing-stock volume, and number of trees) 

were calculated for each 5-year model 

time step.

• Transition, partitioning, and imputation 

submodels were used to predict changes in 

forest plot conditions through time.

Transition submodel—Based on linked plot 

observations from the two most recent FIA 

inventories for Northern States, the transition 

submodel calibrates a regression equation that 

predicts changes in plot age over time and a 

probability transition matrix that predicts whether 

the species composition will change over time. 

The average remeasurement period between 

the two linked inventories (generally 5 years 

for this analysis) defines the time step for the 

simulation algorithm. Plots were examined 

for evidence of harvesting activity over the 

observed remeasurement interval and were 

classified as unharvested, partially harvested, 

or clearcut. Harvesting probability models were 

calibrated and separate forest-type transition 

matrices were estimated for harvested plots to 

model probabilities of post-harvesting changes 

in species composition. 

Partitioning submodel—The partitioning 

submodel groups plots with similar attributes 

based on a set of biological, physical, stand 

age, and climate characteristics. Partitions of 

similar plots are constructed using regression 

tree analysis applied to FIA data. Growing stock 

per acre (softwood and hardwood) was selected 

as the appropriate biophysical attribute for 

measuring similarity within each group. The plot 

characteristics that defined the groups included 

stand age, slope, aspect, ownership category, 

and a set of climate variables that reflect 

average temperature, precipitation, and aridity 

over the life of the plot. 

Imputation submodel—After (1) the transition 

submodel projects changes in forest age and 

harvesting/regeneration, and (2) the climate 

models project changes in key climate variables 

by geographic area, the imputation submodel 

finds (or imputes) the subset of observed FIA 

inventory plots that best matches the conditions 
Chapter T w o 35

that are projected for each plot location—

the “bucket” of suitable plots (termed donor 

plots) that the partitioning submodel grouped 

by matching age class, predominant species 

grouping, hardwood/softwood mix, and climate 

conditions. Then a random donor plot (with 

replacement) is drawn from the appropriate 

group (bucket). For example, if the transition 

probabilities state that a 50-year-old oakhickory (Quercus spp. – Carya spp.) plot will 

become a 55-year-old oak-hickory plot—instead 

of a elm-ash-cottonwood (Ulmus spp. – Fraxinus

spp. – Populus spp.) plot, for instance—then 

the imputation model would describe what 

it would look like in 5 years by randomly 

selecting an oak-hickory plot from the group 

of existing 55-year-old plots whose ecological 

characteristics and climate conditions are 

similar to those anticipated for the future year. 

The selected plot would then be substituted 

for the 50-year-old plot during the next 5-year 

time step.

The completed simulation projects the results 

for future decades by summarizing the conditions 

of the rearranged FIA plots in the same way 

that one would summarize current or past forest 

conditions using FIA plots inventoried in the 

field (Miles 2012). These forecasts are further 

refined by adjusting the per-acre expansion 

factors to reflect the projected land-use changes 

that are generated by an external land-use 

model (for instance, a smaller plot area expansion 

factor would be applied to model the loss of 

forest acreage). 

Removals of wood products are estimated from 

the Forest Dynamics Model (described earlier), 

summarized by hardwood and softwood major 

species groups, and “imputed” by replacing 

some FIA plots with representative harvested 

(and subsequently regenerated) plots from 

the appropriate “bucket” of harvested FIA 

plots. Additional forest dynamics information, 

estimation of harvesting removals, and 

calculation of change variables can be found in 

Appendix 2. 

Although complex, the imputation model 

provides results that can be stored, distributed, 

and summarized for many different attributes 

using the desktop database interface described 

in the next section. 

Predictions of Forest Conditions

Output from the Forest Dynamics Model was 

combined with data from the FIA database 

(Woudenberg et al. 2010) to produce the Northern 

Forest Futures database (Miles and Wear 2015, 

Miles et al. 2015), which in turn was the basis 

for reporting current forest conditions as well 

as a range of possible futures through the year 

2060 for the 20 Northern States. 

Future projections were made for 13 scenarios 

that reflect varying climatic and socioeconomic 

assumptions, including the A1B-C, A2-C, B2-C, 

A1B-BIO, A2-BIO, B2-BIO, and A2-EAB scenarios 

that are the focus of this report. 
36

Programs were developed to enable customized 

futures reporting using the Northern Forest 

Futures database (Miles and Wear 2015). 

The underlying plot data for each scenario and 

the programs used to access and summarize 

scenario results are available on the DVD 

(included in the back cover of this report or 

on the Northern Forest Futures Web site at 

http://www.nrs.fs.fed.us/futures/), enabling 

users to conduct additional data summaries 

and analyses for any scenario.

Reporting Using the Northern Futures Database

To simplify reporting (Fig. 2.11), three programs 

were developed in Microsoft® Access: the NFF_

CurrentReports form to generate baseline 

estimates (up to 2008), the NFF_ProjectionReports 

form to generate projections of forest conditions 

under the 12 scenarios without emerald ash borer, 

and the NFF_ProjectionReportsEABscenario 

form to generate projections of forest conditions 

under a scenario (A2-EAB, a version of scenario 

A2-C) that models the emerald ash borer threat 

(as described earlier in this chapter). Additional 

documentation and example retrievals are 

provided on the enclosed DVD (Miles and Wear 

2015, Miles et al. 2015).

FIGURE 2.11

Screenshot of the three reporting forms 

available in the Northern Forest Futures 

database (Miles and Wear 2015).
Chapter T w o 37

The NFF_ProjectionReports form offers 

four types of projection retrieval: (1) single 

scenario for a single year, (2) single scenario 

for multiple years, (3) multiple scenarios 

for a single year, and (4) multiple scenarios 

for multiple years. Each type of retrieval 

represents a tradeoff between summarization 

detail and level of confidence (Fig. 2.12).

The single scenario/single year retrieval, though 

capable of providing state-level estimates, is 

best used for broad scale retrievals (multiple 

States). If used for a single State, the data would 

likely be stretched very thin, depending on the 

forest attributes of interest. For example, only 12 

plots were used to generate the 2060 projection 

of 48,821 acres of overstocked white-red-jack 

pine (Pinus strobus–P. resinosa–P. banksiana) 

forests in New Hampshire (Table 2.3). This kind 

of retrieval is best used to study a very specific 

resource over a large geographic area at a 

single point in time. Note that when <50 plots 

are used to generate a projection in a northern 

futures database summary table, that projection 

is printed in red to indicate a potentially large 

sampling error.

The Northern Forest Futures database (Miles and 

Wear 2015) was created to assist with the creation 

of the assessment and reporting tools for the 

Northern Forest Futures Project. All were developed 

in an effort to promote transparency and to 

provide access for further review and analysis, 

thereby providing a framework for evaluating 

potential forest futures given various scenarios.

FIGURE 2.12

Tradeoffs between detail and level 

of confidence for the Northern Forest 

Futures database (Miles and Wear 

2015) reporting forms.
-------------------------------------------------------------- All live stocking (acres) --------------------------------------------------------------

Total 4,400,903 466,007 2,555,042 1,141,830 223,750 14,274

White-red-jack pine 423,663 48,821 165,164 172,166 37,512 0

Spruce-fir 399,182 67,410 182,181 117,824 31,767 0

Oak-hickory 497,596 6,046 387,981 81,124 22,446 0

Maple-beech-birch 2,839,496 305,601 1,693,942 717,076 122,876 0

Aspen-birch 41,964 17,055 6,543 14,414 3,952 0

Other 199,002 21,074 119,231 39,226 5,197 14,274

---------------------------------------------------------- Number of FIA plots included ----------------------------------------------------------

Total 943 106 534 248 49 6

White-red-jack pine 96 12 40 37 7 0

Spruce-fir 78 12 33 25 8 0

Oak-hickory 110 2 85 17 6 0

Maple-beech-birch 603 72 347 158 26 0

Aspen-birch 7 3 1 2 1 0

Other 49 5 28 9 1 6

Forest-type group Total Overstocked Fully stocked Medium 

stocked

Poorly 

stocked Nonstocked

Table 2.3—Forecasts for forest area by stocking class in New Hampshire, 2060, under scenario A1B-C that 

assumes moderate greenhouse gas emissions, moderate gains in population growth with large gains in income 

and energy consumption (but with a balanced renewable/fossil fuel portfolio), and a continuation of 2010 

harvesting levels.

38
Chapter T w o 39

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effects of emerald ash borer on forest composition in the Midwest and Northeast United 

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forests of eastern North America. Biological Invasions. 12: 389-405.
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RP–662. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products 

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ipcc_fourth_assessment_report_synthesis_report.htm (accessed August 19, 2014).

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University Press. 996 p.

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Forest Service 2010 Resources Planning Act assessment. Gen. Tech. Rep. WO-87. Washington, DC: 

U.S. Department of Agriculture, Forest Service. 198 p. 

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Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 34 p.
42

U.S. Department of Agriculture, Forest Service. 2012c. Resources Planning Act (RPA) assessment 

online. Washington, DC: U.S. Department of Agriculture, Forest Service. http://www.fs.fed.

us/research/rpa/ (accessed July 27, 2012).

U.S. Department of Agriculture, Forest Service. 2013. 2010 RPA assessment historical climate data 

and climate projections. Washington, DC: U.S. Department of Agriculture, Forest Service. 

http://www.fs.fed.us/research/rpa/assessment/climate–data.php (accessed March 28, 2013).

Wear, D.N. 2011. Forecasts of county-level land uses under three future scenarios: a technical document 

supporting the Forest Service 2010 RPA assessment. Gen. Tech. Rep. SRS-141. Asheville, NC: 

U.S. Department of Agriculture, Forest Service, Southern Research Station. 41 p.

Wear, D.N.; Greis, J.G., eds. 2013. The southern forest futures project: technical report. Gen. Tech. 

Rep. SRS–GTR-178. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 542 p.

Wear, D. N.; Huggett, R.; Li, R.; Perryman, B.; Liu, S. 2013. Forecasts of forest conditions in regions 

of the United States under future scenarios: a technical document supporting the Forest 

Service 2012 RPA assessment. Gen. Tech. Rep. SRS-170. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research Station. 101 p.

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of Agriculture, Forest Service, Rocky Mountain Research Station. 336 p.

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three future scenarios: a technical document supporting the Forest Service 2010 RPA 

assessment. e–Gen. Tech. Rep. SRS-128. Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station. 8 p.
Chapter T h r e e 43

Conservation of Biological Diversity

EOPLE ENJOY A variety of ecosystem services, or benefits, from forests, including water purification, 

recreation, income from tourism, timber products, and the cultural and economic benefits from 

hunting, fishing, and gathering (Shvidenko et al. 2005). Across the Northern United States, growing 

human populations will place increased service demands on forests for the foreseeable future. The 

type, magnitude, and stability of future services from northern forests will depend in part on the 

level of biological diversity in those forests. 

Biodiversity and Climate 

Biological diversity, or biodiversity, is broadly 

defined as the variety of life, which encompasses 

diversity within species, diversity among species, 

and diversity among organisms. As defined by 

the Convention on Biological Diversity (2010), 

forest biodiversity consists of the multiplicity 

and genetic diversity of plants, animals, and 

micro-organisms that inhabit forests. Biodiversity 

can be an important asset by itself, for instance 

when an infusion of genetic material from wild 

stocks increases crop productivity (Myers 1997). 

A high level of biodiversity in forests increases 

resiliency and helps ensure an uninterrupted flow 

of services, even when resources are subjected 

to natural or human-caused disturbances that 

inevitably occur (Bengtsson et al. 2000, Tilman 

et al. 2006). Shifley et al. (2012) describe current 

contributions of northern forests to biological 

diversity. Because of the links among biodiversity, 

ecosystem health and function, and ecosystem 

services, assessments of the potential effects of 

alternative futures on forest biodiversity are a 

useful source of information for policy makers 

and natural resource managers.

Because climate affects the spatial distribution 

and diversity of forest ecosystems and species 

across the landscape, changing climate and 

land-use patterns can be expected to affect 

levels of forest biodiversity during the coming 

decades and centuries (Iverson and Prasad 

1998, Schwartz et al. 2006). As climate 

changes, some forest ecosystems and forestassociated species can shift their distributions 

to track the conditions that favor them, and 

other species are able to adapt to their new 

Brian G. Tavernia, Mark D. Nelson, Rachel Riemann, Brent Dickinson, 

W. Keith Moser, B. Tyler Wilson, James D. Garner

Introduction
Present Day

• Forests cover 174 million acres in the North, or 

41 percent of the total land area.

• Current tree species richness by State ranges from 

49 species sampled in Rhode Island to 99 species 

sampled in West Virginia, with 160 tree species 

recorded regionwide.

• Northern forests provide habitat for more than 

363 terrestrial vertebrate species, with richness 

varying among habitat classes and wildlife taxa; 

the highest level of richness is associated with 

birds, and with forests of open-canopy structure 

and hardwood dominance. 

• With one exception—amphibians in closed-canopy 

hardwoods—most forest wildlife species also 

frequent one or more nonforest habitats.

Projected 2010 to 2060

• Forest area is projected to decrease slightly across 

the North, with the rate ranging from 3.5 to 6.4 

percent and with losses concentrated around 

existing urban and suburban areas.

• Under all scenarios, area is projected to decrease 

for four forest-type groups (aspen-birch, elmash-cottonwood, oak-hickory, and spruce-fir) 

and increase for one group (maple-beech-birch); 

white-red-jack pine, which is projected to increase 

under current rates of biomass utilization, would 

decrease under high biomass-utilization scenarios.

• Under all scenarios, the forest area in the large 

diameter size class is projected to increase by 

3.5 to 5.7 percent and the medium diameter size 

class is projected to decrease; the small diameter 

size class is projected to decrease under scenarios 

that assume no increase in biomass harvesting for 

energy, but increase or remain stable under high 

biomass utilization scenarios.

• Closed-canopy habitat classes are projected to 

gain acreage whereas open-canopy habitat classes 

are projected to lose acreage; intensive biomass 

harvesting rates for bioenergy are projected to 

result in losses for closed-canopy classes and to a 

lesser degree for open-canopy classes.

• Increasing fragmentation and parcellation are 

expected to reduce high-density core forests within 

large patches and the average size of family forest 

landholdings in every State; these changes would 

decrease the ecological services and socioeconomic 

benefits of core forests and diminish the ability to 

manage large patches of forest.

• Overall tree species richness is projected to 

decline through 2060; projected reductions in 

early successional habitats such as oak-hickory 

could have a large effect on tree species richness 

in such ecosystems.

Key Findings

environment (Parmesan 2006). But if a species 

is unable to move or adapt, its geographic range 

may shrink or it may become extirpated from 

portions of its former range (Parmesan 2006). 

44
Chapter T h r e e 45

Human Impacts on Forests

Within the large-scale patterns established by 

climate, land-use decisions further modify the 

extent and configuration of forests ecosystems, 

ecosystem functioning, species diversity, and 

genetic diversity (Opdam and Wascher 2004). 

Landscape patterns, landowner objectives, and 

the area, composition, and structure of forests 

all affect the amount and quality of habitat for 

wildlife. Conversion to urban or other uses 

reduces forest area and potentially fragments 

forest ecosystems; this increases the proximity 

and exposure of remaining forests to human 

influences. Public and private land management 

decisions affect forest structure and composition. 

Species and genetic diversity can be reduced 

by forest loss, fragmentation, and urbanization, 

or can be increased by forest management 

strategies (Battles et al. 2001, Pimm and 

Askins 1995). 

Assessments of forest biodiversity under a 

range of plausible climate and land-use change 

scenarios can help answer questions about 

current actions and future outcomes: How will 

the areas of forest ecosystem types change in 

the future? What are the potential consequences 

of climate and land-use changes for species in 

protected areas? What changes will occur in the 

area of forest habitats and how will changes 

affect at-risk wildlife? Answers to these questions 

will be valuable input for informing policy and 

management decisions that influence the trajectories 

of climate change and land-use patterns. 

Modeling Change

The projections for changes in forest condition 

used three greenhouse gas emissions storylines 

developed by the Intergovernmental Panel on 

Climate Change (IPCC 2007, Chapter 2): A1B 

assumes moderate greenhouse gas emissions 

associated with moderate gains in population 

and large gains in income and energy 

consumption—but with a balanced renewable/

fossil fuel portfolio; A2 assumes high 

greenhouse gas emissions associated with large 

gains in population, high energy consumption, 

and moderate gains in income; and B2 assumes 

low greenhouse gas emissions associated 

with moderate gains in population, income, 

and energy consumption. General circulation 

models (climate models) provide estimates 

of temperature and precipitation changes by 

geographic area. The scenarios discussed in 

this chapter refer to combinations of storylines 

with general circulation models (climate change 

models) and assumptions about future forest 

harvest rate—either a continuation of harvest 

rates observed in the recent past or greatly 

increased biomass harvesting for energy. See 

Chapter 2 for detailed descriptions of storylines, 

climate models, and scenarios.
46

PREDICTED CHANGES IN FOREST 

CONDITIONS AND OWNERSHIP

In 2010, forest area in the North was estimated 

at 174 million acres, accounting for 41 percent 

of total land cover. Projections of future conditions 

suggest a decrease from 2010 to 2060 under 

all three storylines (Fig. 3.1), with the largest 

decrease projected for A1B (-6.4 percent), the 

smallest decrease for B2 (-3.5 percent), and 

an intermediate decrease for A2 (-5.4 percent) 

(Wear 2011). 

Forest Composition

Under the three scenarios that predict a 

continuation of current harvesting levels, two 

major groups—maple-beech-birch (Acer spp.–

Fagus spp.–Betula spp.) and white-red-jack pine 

(Pinus strobus – P. resinosa – P. banksiana)—

are projected to increase from 2010 to 2060 

and four others1 are projected to decrease 

(Fig. 3.2). Under scenarios of increased 

harvesting to support high biomass utilization, 

similar trends are expected with the exception 

of white-red-jack pine, which is expected to 

decrease, albeit slightly (Fig. 3.2). The whitered-jack pine group is projected to experience 

a large increase in harvesting rates under 

the high biomass utilization scenarios, which 

suggests a potential explanation for its switch 

from increasing to decreasing forest area 

among the alternative scenarios, although 

other processes and model uncertainty may 

also be at play.

2010 2020 2030 2040 2050 2060

175

170

165

160

FOREST LAND AREA (million acres)

FIGURE 3.1

Forest land area estimates under baseline conditions (2010) and 

projections (2020 to 2060) under greenhouse gas emission storylines A1B, 

A2, and B2 (IPCC 2007) for the North (Wear 2011). Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and 

large gains in income and energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes high greenhouse gas 

emissions, large gains in population and energy consumption, and 

moderate gains in income; and B2 assumes low greenhouse gas emissions 

with moderate gains in population, income, and energy consumption.

A1B

A2

B2

spruce-fir (Picea spp.—Abies spp.); oak-hickory (Quercus spp.—

Carya spp.); elm-ash-cottonwood (Ulmus spp.–Fraxinus spp.–

Populus spp.); and aspen-birch (Populus spp.—Betula spp.).

1
4

2

0

-2

-4

-6

-8

-10

-12

-14

A1B-C A1B-BIO A2-C A2-BIO B2-C B2-BIO

CHANGE (million acres)

Maple-beech-birch

White-red-jack pine

Spruce-fir

Oak-hickory

Elm-ash-cottonwood

Aspen-birch

Unavailable

FIGURE 3.2

Projected change in northern forest area by forest-type group, 2010 to 2060, under six scenarios, 

each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B 

assumes moderate greenhouse gas emissions, moderate gains in population, and large gains in income and 

energy consumption (but with a balanced renewable/fossil fuel portfolio); A2 assumes high greenhouse gas 

emissions, large gains in population and energy consumption, and moderate gains in income; and B2 assumes 

low greenhouse gas emissions with moderate gains in population, income, and energy consumption. Scenario 

projections assume harvest will continue at recently observed levels (labeled –C) or increase to reflect 

increased harvest for bioenergy production (labeled –BIO).

47
48 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Tree diameter provides a reliable measure of 

forest stand structural stage, which in turn is 

indicative of successional stage. Under the three 

scenarios that predict a continuation of current 

harvesting levels (A1B-C, A2-C, and B2-C), 

the forest area in the large diameter size class 

(≥11 inches d.b.h. for hardwoods and ≥9 inches 

d.b.h. for softwoods) is projected to increase

by 3.5 to 5.7 percent from 2010 to 2060.

The forest area is projected to decrease for 

both the medium diameter class (≥5 inches d.b.h. 

and smaller than the large diameter class) 

and the small diameter class (<5 inches d.b.h.) 

(Fig. 3.3). However, under scenarios of high 

biomass utilization, forest area in the smalldiameter size classes is projected to increase 

under A1B-BIO and A2-BIO, and to remain 

unchanged under B2-BIO.

20

15

10

5

0

-5

-10

-15

-20

-25

-30

A1B-C A1B-BIO A2-C A2-BIO B2-C B2-BIO

CHANGE (million acres) FIGURE 3.3

projected change in northern forest area by stand-size class, 2010 to 2060, for six scenarios, each

representing a global greenhouse storyline (IpCC 2007) paired with a harvest regime. Storyline 

a1B assumes moderate greenhouse gas emissions, moderate gains in population, and large 

gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio); 

a2 assumes high greenhouse gas emissions, large gains in population and energy consumption,

and moderate gains in income; and B2 assumes low greenhouse gas emissions with moderate 

gains in population, income, and energy consumption. Scenario projections assume harvest will 

continue at recently observed levels (labeled –C) or increase to reflect increased harvest for 

bioenergy production (labeled –BIO). Small diameter class <5 inches d.b.h.; medium diameter 

class ≥5 inches d.b.h. and smaller than large diameter class; large diameter class ≥11 inches 

d.b.h. (hardwoods) or ≥9 inches d.b.h. (softwoods).

Large diameter

Medium diameter

Small diameter

Nonstocked
Chapter T h r e e 49

Although trends in forest tree species 

composition for the entire North are likely to 

be relatively consistent across scenarios, the 

direction and magnitude of change would vary 

dramatically among States, with projected 

increases in some States offset by decreases in 

others. For example, the elm-ash-cottonwood 

(Ulmus spp.–Fraxinus spp.–Populus spp.) group 

is projected to decrease by 12 to 18 percent from 

2010 to 2060. On a per-State basis, however, 

this group would decrease by ≥25 percent in a 

number of States and actually increase by ≥25 

percent in several other States within the same 

scenario, suggesting subregional variations 

(Fig. 3.4). These trends do not address additional 

threats from Dutch elm disease (Ophiostoma 

ulmi) nor threats from the emerald ash borer 

(Agrilus planipennis), a nonnative insect pest 

discussed in detail in Chapter 5.

Forest Structure

Unlike forest composition, forest structure 

projections vary widely among States, both in 

response to the greenhouse gas storylines and 

in response to levels of harvesting. Some States 

would experience increases in large or small 

diameter classes under continued harvesting 

levels, but trends would reverse under high 

biomass utilization scenarios. For instance, 

a continuation of current forest harvesting 

rates (scenarios A1B-C, A2-C, and B2-C) would 

result in modest or major reductions in forest 

acreage in the small diameter class, for the vast 

majority of States. However under high biomass 

utilization scenarios (A1B-BIO, A2-BIO, and 

B2-BIO), many of those States would experience 

moderate or major increases in small diameter 

forest area (Fig. 3.5).
50

 A1B-C A1B-BIO A2-C A2-BIO

White-red-jack pine

Spruce-fir

Oak-hickory

Elm-ash-cottonwood

Maple-beech-birch

Aspen-birch

Other

Under -25

-25 to -5

-5 to 5

5 to 25

Over 25

No data

FIGURE 3.4 CHANGE (percent)

Projected change in northern forest area by forest-type group, 2010 to 2060, under four scenarios, 

each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline 

A1B assumes moderate greenhouse gas emissions, moderate gains in population, and large gains in 

income and energy consumption (but with a balanced renewable/fossil fuel portfolio); A2 assumes high 

greenhouse gas emissions, large gains in population and energy consumption, and moderate gains in 

income. Scenario projections assume harvest will continue at recently observed levels (labeled –C) or 

increase to reflect increased harvest for bioenergy production (labeled –BIO). 
Chapter T h r e e 51

FIGURE 3.5

projected change in northern forest area by stand-size class, 2010 to 2060, under four scenarios, each

representing a global greenhouse storyline (IpCC 2007) paired with a harvest regime. Storyline a1B 

assumes moderate greenhouse gas emissions, moderate gains in population, and large gains in income

and energy consumption (but with a balanced renewable/fossil fuel portfolio); a2 assumes high

greenhouse gas emissions, large gains in population and energy consumption, and moderate gains in income.

Scenario projections assume harvest will continue at recently observed levels (labeled –C) or increase to 

reflect increased harvest for bioenergy production (labeled –BIO). Small diameter class <5 inches d.b.h.; 

medium diameter class ≥5 inches d.b.h. and smaller than the large diameter class; large diameter class

≥11 inches d.b.h. (hardwoods) or ≥9 inches d.b.h. (softwoods).

Large diameter

Medium diameter

Small diameter

Nonstocked

 A1B-C A1B-BIO A2-C A2-BIO

Under -25

-25 to -5

-5 to 5

5 to 25

Over 25

No data

CHANGE (percent)

THREATS TO BIODIVERSITY IN PROTECTED AREAS

The Convention on Biological Diversity defines a protected area as 

“...a geographically defined area which is designated or regulated 

and managed to achieve specific conservation objectives” (UNEP 

1992). Protected areas are classified under a system used by 

the International Union for Conservation of Nature (2012) and 

include (from most to least protected): strict nature reserves, 

wilderness areas, national parks, natural monuments or features, 

habitat and species management areas, protected landscapes, 

and managed resource protection areas. 
52

Protected areas differ in their permanence 

and level of protection. For example, federally 

established wilderness areas are unlikely to 

experience high levels of resource extraction, 

but lands voluntarily enrolled in conservation 

programs (such as the Conservation Reserve 

Program) may only be temporarily protected. 

Forest land that is withdrawn from harvesting 

through statute, administrative regulation, or 

designation without regard to productive status 

is labeled “reserved forest,” which is usually 

publicly owned (Bechtold and Patterson 2005). 

Of the 174 million acres of northern forests, 

<6.4 million acres (3.7 percent) are designated 

as reserved forest (Fig. 3.6); they align with four 

of the protected-area classes described above: 

strict nature preserves, wilderness 

areas, national parks, and natural 

monuments or features.

All forests

Protected forests

Reserved forests

National Forests

FIGURE 3.6

Spatial distribution of land covered by forests—including reserved, 

protected, and national forests—across the North in 2001. Data 

sources: ForestOwn_v1 geospatial database (Nelson et al. 2010); 

and Protected Area Database (CBI 2012). 
Chapter T h r e e 53

Much of the public land base (including most 

national forests) does not meet the strict definition 

for protected areas. But these lands do provide 

protection through long-term stability in both 

ownership and land use (Chapter 6). In this 

report, projections of land-use change excluded 

Federal land because it is assumed that forested 

Federal land (such as national forests and 

national parks) will remain forested and in 

Federal ownership (Chapter 2).

Protected areas can act as important refuges 

for biodiversity by excluding or controlling 

factors (such as habitat loss or invasive species) 

that cause loss of biodiversity outside their 

boundaries. Forest biodiversity in protected 

areas can be an important source of ecosystem 

services, or benefits, especially in landscapes 

where land cover is predominantly nonforest. 

These benefits include erosion control, carbon 

sequestration, recreation, and economic returns 

from tourism and other rural enterprises. 

Changing climate and land-use patterns may 

threaten the ability of protected areas to 

effectively conserve biodiversity (Hannah 2008, 

Radeloff et al. 2010, Stein et al. 2007). Although 

many protected areas are geographically fixed, 

distributions of forest ecosystems and species 

are dynamic and depend partly on climate 

conditions and the conditions in surrounding 

landscapes. For example, some species could 

disappear from protected areas that lose 

suitable conditions, but these losses could be 

offset if species are able to take advantage 

of an increase in suitable climate conditions 

in other protected areas (Hannah 2008). For 

other species, distributions could expand if the 

overall area with suitable climate and landuse conditions increases. The ability of forestassociated plants and animals to move with 

changing climate conditions would be reduced 

in landscapes with isolated protected forests 

that border nonforested areas, lack connectivity 

to other forested acres, or are subject to 

interspersed urbanization (Hannah 2008, 

Opdam and Wascher 2004). This is not likely 

to be an issue in landscapes with relatively 

contiguous forest cover, such as northern 

Minnesota. If changing climate conditions cause 

one species to be an overall gainer or loser of 

territory, the result could be a change in species 

representation, which is the proportion of range 

in protected areas that each species commands 

(Hannah 2008). Some proposed management 

strategies to maintain species representation 

include creating additional protected areas, 

increasing connectivity of protected areas, 

assisting migration by relocating species to new 

areas (Hannah 2008), and reducing human/

urban pressures on remaining areas to increase 

their resiliency and ability to recover from 

disturbances (Giordano and Boccone 2010, 

Zipperer 2002).

Across the North, housing densities in and 

around protected areas are expected to 

increase at the expense of forest and other 

land uses (Radeloff et al. 2012, Radeloff et al. 

2010, Stein et al. 2007, Theobald 2010). 
54

The natural amenities offered by protected areas 

attract people who want to live in relatively 

undeveloped areas and are willing to commute long 

distances or can take advantage of telecommuting 

(Radeloff et al. 2010). Purchasers of seasonal 

homes and an increasing number of retirees are 

also contributing to housing growth in and around 

protected areas. In 2000, 35 million housing units 

were located within 31 miles of national forests, 

and this number is projected to increase by 16 

million units by 2030. And within national forests, 

the number of housing units on private inholdings 

was projected to increase from 1.8 million in 2000 

to 1.9 million by 2030 (Radeloff et al. 2010).

FOREST FRAGMENTATION

The value of forest habitats depends not only on 

attributes of composition and structure but also 

on the size and arrangement of forest fragments 

within the larger landscape. Growth of residential 

development and other land-use changes can 

threaten forest biodiversity (Radeloff et al. 2010, 

Stein et al. 2007) by reducing habitat abundance, 

increasing fragmentation, changing ecological 

processes (e.g., suppressing wildfires), and 

altering biotic interactions (e.g., introducing 

nonnative species or predatory pets). Such changes 

can also isolate forested areas, reducing the 

ability of species to respond to changing climate. 

Policies such as promotion of clustered developments 

and financial mechanisms such as tax benefits 

offered by land trusts, could help reduce the 

impact of housing growth on the biodiversity of 

protected areas (Radeloff et al. 2010). 

Various metrics of forest fragmentation offer 

useful indicators of habitat suitability for species 

that require large fragments, forest interiors, 

ecotones (forest edges), proximity to nonforested 

land covers, or corridors for movement between 

fragments. Landscape metrics typically are 

derived from maps of current and projected 

forest cover that delineate individual fragments 

of land cover. These maps are needed to assess 

potential future changes in landscape patterns.

A raster map showing baseline forest cover 

(distribution and density) of the North in 2009 

(Fig. 3.7A) was developed following the 

methodology of Wilson et al. (2012), having 250-m 

spatial resolution. The forest cover data set 

was combined with a model of future nighttime 

illuminated areas, based on a satellite-image 

time series recorded over the past decade. For 

future projections, it was assumed that nighttime 

illumination is associated with developed areas 

and that recent trends in the growth of illuminated 

areas will continue through the projection 

period. The 2009 forest cover map was adjusted 

using a map of projected illuminated areas, 

with the appropriate adjustment factor supplied 

by projections of forest cover in 2060 under 

storylines A1B, A2, and B2 from an econometric 

model developed by Wear (2011). This analysis 

lacked sufficient resolution to include reversion to 

forest from pastureland, cropland, or rangeland. 

Map results for storyline A1B were visually similar 

to A2, but differed from B2; for simplicity, map 

results are only portrayed for A1B (Fig. 3.7B), 

but quantitative results are portrayed for both 

A1B and B2 (see subsequent figures).

FUTURE FORESTS OF THE NORTHERN UNITED STATES
Chapter T h r e e 55

A

FIGURE 3.7

Spatial distribution and density 

of northern forests (A) in 2009, 

and (B) projected for 2060 under 

storyline A1B that assumes 

moderate greenhouse gas emissions, 

moderate gains in population, and 

large gains in income and energy 

consumption (but with a balanced 

renewable/fossil fuel portfolio) 

(IPCC 2007).

B

High

Low

PROPORTION FOREST

Aerial view of vacation homes perforating 

a forest landscape in Nashville, IN

Imagery ©2015 Google
56

Projected reductions in forest extent and density 

are portrayed as differences between the 2009 and 

2060 maps (Fig. 3.8). Not surprisingly, given that 

area of nighttime illumination was used as a proxy 

for development, forest losses are projected to be 

concentrated around existing urban and suburban 

areas and major transportation corridors (Fig. 3.8).

To quantify current and projected future forest 

fragmentation, a spatial integrity index was 

adapted that integrates forest patch size, 

local forest density (which picks up likelihood 

of edge conditions), and patch connectivity 

to core forest areas (Kapos et al. 2000). 

Because values for the index are calculated from 

pixels that are labeled or classified as forest, 

pixels with high forest canopy density were 

reclassified as forest, and those with low density 

as nonforest, for current and future conditions, 

and then spatial analysis was performed to 

determine spatial integrity values. A value of 

10 is defined as core forest, and the lowest 

values (approaching zero) are defined as highly 

fragmented forest, with intermediate values 

representing varying degrees of opportunity 

for rehabilitating landscapes to connect core 

forest areas.

FIGURE 3.8

Projected reduction of forest extent in the North, 2009 to 2060, 

under a greenhouse gas emissions storyline A1B (IPCC 2007) that

assumes moderate greenhouse gas emissions, moderate gains in 

population, and large gains in income and energy consumption 

(but with a balanced renewable/fossil fuel portfolio).

Greatest change

Least change

SPATIAL INTEGRITY INDEX
Chapter T h r e e 57

FIGURE 3.9

Forest spatial integrity index values, adjusted 

for housing densities associated with the 

wildland-urban interface or urbanized areas, 

2009, across the North. Index values range 

from less than 1 (highly fragmented forest) 

to 10 (core forest with large patches and 

high local forest density).

Pixels having high tree canopy cover (inferred 

as representing high forest density) can occur 

in areas that also contain residential urban 

development. To determine the effects of such 

development on ecological processes and biotic 

interactions, two housing density classes were 

overlaid on the spatial integrity map: (1) forest 

pixels with a local housing density >15 houses per 

square mile, which is considered the threshold 

for the wildland-urban interface; and (2) pixels 

with >41 houses per square mile, which represent 

more urbanized areas. Assuming that this level 

of residential development contributes some 

characteristics of fragmented land, such as edge 

conditions and barriers to biotic movement, 

the spatial integrity value was reduced by two 

classes for the wildland-urban interface and 

one class for urbanized areas in the following 

summary (Fig. 3.9). Following procedures similar 

to those used for the 2009 spatial integrity index 

map, but excluding wildland-urban interface 

data which are not available for future projections, 

spatial integrity index values were estimated 

for 2060 and used to estimate the State-level 

changes in the spatial integrity index discussed 

later in this chapter. 

 Under 1

1

2

3

4

5

6

7

8

9

SPATIAL INTEGRITY VALUE

10

add object

Photo by Mariko Yamasaki, 

U.S. Forest Service
58

Areas with the highest level of spatial integrity 

are characterized by forests with (1) a patch 

size >1,544 acres; (2) a local forest density 

of >90 percent in the surrounding 1,213-acre 

neighborhood; and (3) a housing density of <15 

houses per square mile. These forests are thus 

considered to be core for this scale of analysis, 

with an index value of 10. An index value of 1 

translates into a forest that is highly fragmented, 

with low or no connectivity to the core, small 

patch sizes, and ≤10 percent forest density. 

Values between 1 and 10 correspond to increasing 

proximity to core forest areas, increasing patch 

size, and increasing local forest density (Fig. 3.9), 

with local arrangement and interspersion of 

spatial integrity values for the wildland-urban 

interface more easily seen when the map focus is 

zoomed to a finer scale, as is shown for a subset of 

the North (Fig. 3.10). 

This analysis combined fragmentation estimates 

from 2009 and housing densities from 2000 to 

demonstrate future challenges to maintaining 

biodiversity. This was done for two reasons. 

First, because fragmentation and urbanization 

have a delayed impact on forest ecosystems, a 

phenomenon known as extinction-debt trajectory, 

their existence portends areas where we can 

expect future changes in biodiversity that are 

not yet apparent (Malanson 2002, Tilman et al. 

1994). Second, predicting the extent, intensity, 

location, and very importantly, the spatial 

distribution of future development depends 

on many factors, and thus comes with many 

uncertainties. This, combined with the high 

sensitivity of most measures of fragmentation 

to spatial variations in the data sources used, 

prompted the use of current information. 

 Under 1

1

2

3

4

5

6

7

8

9

SPATIAL INTEGRITY VALUE

10

FIGURE 3.10

Forest spatial integrity index values, adjusted 

for housing densities associated with the 

wildland-urban interface or urbanized areas, 

2009, in one area of the North. Index values 

range from less than 1 (highly fragmented 

forest) to 10 (core forest with large patches 

and high local forest density).
Chapter T h r e e 59

Figure 3.11 shows the current distribution of 

forest spatial integrity categories, adjusted for 

housing densities associated with the wildlandurban interface or urbanized areas. Maine supports 

the greatest percentage of core forest (88 percent), 

and Iowa has the least (1 percent). The pattern 

is reversed for unconnected forest, with 87 percent 

for Iowa and less than 1 percent for Maine. The 

majority of forests (56 percent) in Rhode Island 

are in neither of these two extreme categories, 

but are characterized by intermediate categories 

of low, medium, or high integrity forest.

All States are projected to lose some forest 

acreage by 2060. Losses outweigh gains, but 

losses vary by State and are generally larger 

under storyline A1B than B2 (Fig. 3.12). 

Under both storylines, some States and spatial 

integrity class combinations would increase, 

most of which would occur in unconnected, 

low integrity, and intermediate integrity 

forests. Such increases typically are linked to 

fragmentation of core and high integrity forests, 

resulting in losses to those classes. In contrast, 

decreases in unconnected, low integrity, and 

intermediate integrity forests typically are not 

balanced by increases in core and high integrity 

forest, suggesting that acreage would convert to 

another use. No State is projected to gain forest 

in the core forest class.

100

90

80

70

60

50

40

30

20

10

0

FOREST LAND (percent)

Maine

New Hampshire

Vermont

West Virginia

Michigan

Minnesota

New York

Pennsylvania

Wisconsin

Massachusetts

Missouri

Connecticut

Ohio

New Jersey

Rhode Island

Maryland

Indiana

Illinois

Delaware

Iowa

FIGURE 3.11

Forest area by spatial integrity index class 

in the North, adjusted for housing densities 

associated with the wildland-urban interface 

or urbanized areas, 2009.

Unconnected

Low integrity

Intermediate integrity

High integrity

Core
60

300

200

1000

-100

-200

-300

-400

-500

-600

-700

-800

5000

-500

-1000

-1500

-2000

NET CHANGE (acres in thousands) NET CHANGE (acres in thousands)

New York

Pennsylvania

Michigan

Ohio

Massachusetts

Wisconsin

New Jersey

Missouri

Maryland

Minnesota

Illinois

Indiana

Connecticut

West Virginia

New Hampshire

Maine

Iowa

Vermont

Rhode Island

Delaware

New York

Pennsylvania

Michigan

Ohio

Massachusetts

Wisconsin

New Jersey

Missouri

Maryland

Minnesota

Illinois

Indiana

Connecticut

West Virginia

New Hampshire

Maine

Iowa

Vermont

Rhode Island

Delaware

Unconnected

Low integrity

Intermediate integrity High integrity

Core

FIGURE 3.12 Projected change in forest area 

and spatial integrity index class 

in the North, 2009 to 2060, 

adjusted for housing densities 

associated with the wildlandurban interface or urbanized 

areas, under two greenhouse 

gas emissions storylines (IPCC 

2007): (A) storyline A1B assumes 

moderate greenhouse gas 

emissions, moderate gains in 

population, and large gains in 

income and energy consumption 

(but with a balanced renewable/

fossil fuel portfolio); and 

(B) storyline B2 assumes low 

greenhouse gas emissions with 

moderate gains in population, 

income, and energy consumption.

BA
Chapter 

T h r e e 61

100

90

80

70

60

50

40

30

20

100

AREA (percent of acreage in 2009) AREA (percent of acreage in 2009)

Maine

New Hampshire

Vermont

West Virginia

Michigan

Minnesota

New York

Pennsylvania

Wisconsin

Massachusetts

Missouri

Connecticut

Ohio

New Jersey

Rhode Island

Maryland

Indiana

Illinois

Delaware

Iowa

Maine

New Hampshire

Vermont

West Virginia

Michigan

Minnesota

New York

Pennsylvania

Wisconsin

Massachusetts

Missouri

Connecticut

Ohio

New Jersey

Rhode Island

Maryland

Indiana

Illinois

Delaware

Iowa

FIGURE 3.13 Projected forest area in the North, 

2060, and the spatial integrity index 

class, adjusted for housing densities 

associated with the wildlandurban interface or urbanized 

areas, under two greenhouse gas 

emissions storylines (IPCC 2007): 

(A) storyline A1B assumes moderate 

greenhouse gas emissions, 

moderate gains in population, and 

large gains in income and energy 

consumption (but with a balanced 

renewable/fossil fuel portfolio); 

and (B) storyline B2 assumes low 

greenhouse gas emissions with 

moderate gains in population, 

income, and energy consumption.

100

90

80

70

60

50

40

30

20

100

Unconnected

Low integrity

Intermediate integrity High integrity

Core

BA
62

Figure 3.13 shows two projected distributions of 

future spatial integrity values, calculated as the 

percentage of the 2009 forest acreage in each 

spatial integrity category, sorted from highest 

to lowest percentage of core forest. New Jersey, 

Massachusetts, Rhode Island, Connecticut, and 

Delaware are all projected to lose ≥25 percent 

of forest acreage by 2060 under storyline A1B, 

but under B2 these five States are projected to 

lose much less acreage (10 to 16 percent).

FOREST PARCELLATION

One factor affecting forest fragmentation and 

management is forest parcellation (sometimes 

called parcelization). Haines et al. (2011) defines 

parcellation as the division of large landholdings 

into smaller ones. Parcellation does not necessarily 

imply that ecological services from affected 

forests will be lost, but Haines et al. (2011) have 

shown evidence of a possible relationship between 

parcellation and fragmentation. Although this 

relationship has not yet been studied further, it 

is plausible enough to be of concern, especially 

because of the growing trend in forest parcellation 

over the past several decades (DeCoster 1998, 

Mehmood and Zhang 2001, Zhang et al. 2009).

The distinction between fragmentation and 

parcellation is shown in Figure 3.14. Two of 

the landscapes are fragmented—one with all 

fragments under one ownership and the other 

divided in multiple ownerships. A third forest 

landscape does not appear to be fragmented 

at all; it is a contiguous forest, but one that 

is divided into 20 different ownerships and so 

is parcelized. Although a small portion of the 

fourth forest landscape is divided into many 

parcels, the biggest portion is not parcelized 

at all; 80 percent of it is under one ownership. 

The distinction between the two parcelized 

forest landscapes demonstrates the potential 

weakness of using average parcel size as a 

parcellation measurement. Both landscapes 

have the same average ownership size (same 

area and same number of ownerships), but these 

two forests will most likely face very different 

sets of development pressures. Nonetheless 

average ownership size was used as the best 

proxy for detecting parcellation because it is the 

only measurement available to use at the scale 

required by the data set. 

Parcellation was correlated with estimated 

average family forest ownership size using 

National Woodland Owner Survey data from 

2006 (Butler 2008) at the State and county 

levels. Estimates were based on the average 

number of forested acres in the State for 

families that had some holdings in the county 

of interest. Because respondents indicate how 

many acres they own, not in a county, but in 

the State, an ownership may be more than one 

parcel, with multiple parcels across different 

counties. We regressed the natural log of 

those estimates on the log of current (2010) 

U.S. housing density data as well as several 

significant State categorical variables (true/false 

categories). The county-level model has very 

low explanatory power, with an R2

 of 0.08, but 

county estimates were retained to illustrate 
Chapter T h r e e 63

spatial pattern in cartographic outputs (Fig. 3.15). 

In contrast, the state-level model has an R2

 of 

0.77. These models projected average parcel 

size for 2060 using U.S. housing density predictions. 

In all, eight sets of projections were created: 

the two described above and those based on the 

county and state-level housing density prediction 

sets for storylines A1B, A2, and B2.

FIGURE 3.14

Schematic illustrating hypothetical combinations of parcellation and 

fragmentation: (A) no parcellation on a fragmented landscape; 

(B) parcellation into uniform ownerships on a fragmented landscape; 

(C) parcellation into uniform ownerships on a contiguous landscape; 

and (D) parcellation into ownerships of varying sizes on a contiguous 

landscape. Hypothetical ownership parcel boundaries are illustrated by 

dashed lines. Imagery: ©Google 2015.

B

D

A

C
64

Figure 3.15 shows projected changes in average 

family forest ownership size at the county level 

for storyline A1B. Differences in projections 

across the scenarios are not visually discernible, 

so the other scenarios are not displayed. Overall, 

each county and each State is expected to see 

a reduction in average ownership size, which 

indicates an increase in parcellation.

The projected changes represent the difference 

between using fitted or actual current ownership 

size in projections. The reason the differences 

shown do not use actual current estimated 

average ownership size is that the map would 

be misleadingly noisy. Indeed, some of the 

projected future sizes are larger than actual 

current sizes. This is because some actual 

current sizes are much smaller than the fitted 

current values. To indicate gains in average 

ownership size would suggest that the model 

somehow predicts reverse parcellation in 

those counties, which is certainly not what is 

occurring. The map is designed simply to give an 

understanding of how parcellation is expected to 

follow increasing housing density predictions.

FIGURE 3.15

County-level projected reductions in average family forest parcel size, 

2006 to 2060, under greenhouse gas emissions storyline A1 that 

assumes moderate greenhouse gas emissions, moderate gains in 

population, and large gains in income and energy (Mondal et al. 2013).

No data

Large city

100%

80%

60%

40%

20%

0%

PARCEL SIZE REDUCTION (percent)
Chapter T h r e e 65

Much of the extreme parcellation (>70-percent 

reduction in average size) is projected to occur 

far from city centers, most notably in northern 

Maine, New York, and Minnesota. However, 

a relatively low proportion of northeastern 

Minnesota forest acreage is in family forest 

ownership; in two counties <10 percent of forest 

acreage is estimated to be held by families. 

Thus, although parcellation of family forests is 

projected to be quite severe in that area, it may 

be less of a concern than in the Adirondacks of 

New York or in northern Maine.

These projections are simplistic and should be 

interpreted with caution. They are meant only to 

offer an idea of the direction of change that can 

be expected by 2060. With this caveat in mind, 

we anticipate a much more parcelized family 

forest landscape, regardless of which of the 

anticipated scenarios play out. Insofar as forest 

fragmentation follows this projected parcellation, 

the forest landscape would be much more 

fragmented by 2060 as well. With that level of 

fragmentation, losses in ecological services and 

socioeconomic benefits can be expected.

TREE SPECIES AND GENETIC DIVERSITY

One of the most basic measures of biological 

diversity is the total number of species within 

a given geographic area. This is referred to as 

species richness. In general, large areas are 

likely to contain more species than smaller 

areas. Different measures of diversity are 

used to account for these differences. Alpha 

diversity is the number of species observed 

within a small, homogeneous area; and gamma 

and epsilon diversity estimate species richness 

over increasingly larger, heterogeneous areas. 

Average species richness is reported for trees 

on survey plots (alpha diversity) established 

by Forest Inventory and Analysis (FIA), within 

States (gamma diversity), and across the entire 

region (epsilon diversity). Numbers of future 

species are not projected because this report 

addresses only potential habitat abundance 

within the region; species distributions also are 

affected by many nonhabitat factors, including 

invasive pests, disease, and competition.

Across the 20 Northern States, 160 tree species 

were recorded on FIA plots during the baseline 

period (2010). An average of 76 tree species 

was recorded in each State, ranging from 47 

species in Rhode Island to 99 species in West 

Virginia. The number of species per FIA plot also 

varied among States and among major species 

groups (Fig. 3.16).
66

White-red-jack pine

Spruce-fir

Oak-hickory

Elm-ash-cottonwood

Maple-beech-birch

Aspen-birch

Other

FIGURE 3.16

Number of trees (≥5 inches d.b.h), by forest-type group and tree 

frequency, 2010, per survey plot in the North; data from FIA. Error bars 

indicate 68-percent sampling error associated with the estimated 

number of trees for each column.

6

5

4

3

2

1

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

NUMBER OF TREE SPECIES PER PLOT

NUMBER OF TREES (billions)

Given such a large geographic extent and the 

high number of FIA plots, it is not surprising that 

the number of tree species per plot presents 

such a classically normal distribution. What 

is notable, however, is the prominent shift to 

the right (more species per plot) in the maplebeech-birch (the northern hardwoods) and 

the oak-hickory (Quercus spp.–Carya spp.) 

groups. These two groups demonstrate different 

underlying processes. Maple-beech-birch is 
primarily composed of shade-tolerant species 

and regenerates largely by gap-phase processes. 

Oak-hickory forests have a more disturbancemediated origin and so their regeneration and 

development dynamic reflects a more even-age 

initiation and release; they can be characterized 

as the climax forests where disturbance has 

“reset the clock.”

Biological diversity refers not only to species 

diversity but also to genetic diversity. To illustrate, 

consider the variation in characteristics associated 

within cultivated plants of a single species, like 

apples (Malus spp.). Similarly, genetic diversity 

within a single tree species results in a variety of 

traits, resulting in different physical and functional 

characteristics that help trees adapt to local 

conditions. A species under threat of rapid, widespread 

mortality is at risk of losing genetic diversity, even 

if individual trees persist in some locations. 

Such is the concern for ash trees, which are under 

serious threat from emerald ash borer, a nonnative 

insect pest that causes rapid mortality in several ash 

species (Chapter 5). In response, work is underway 

to preserve germplasm by collecting ash seeds 

for long-term storage under a combined effort 

by three U.S. Department of Agriculture agencies 

(Agricultural Research Service, Forest Service, 

and Natural Resources Conservation Service) the 

U.S. Department of the Interior’s Bureau of Land 

Management, and more than 50 cooperators. 

The goal of this effort is to preserve 

seeds from many individual trees of each ash 

species, evenly spaced across the range. To date, 

samples have been collected from more than 3,000 

individual ash trees, including white (Fraxinus 

americana), green (Fraxinus pennsylvanica), black 

(Fraxinus nigra), blue (Fraxinus quadrangulata), and 

pumpkin (Fraxinus profunda) ash species. Collection 

priority is given to portions of the range that are 

under current infestation. Working collections of 

seeds (samples made available to researchers and 

breeders) are held at national seed laboratories 

with duplicate samples held as a security backup at 

alternate locations (Karrfalt and Carstens 2013). 

Genetic Diversity

Collecting ash tree seeds 

Photo by Tom Arbour, Ohio Department of Natural Resources, 

used with permission

67
68

The implications for biodiversity are several. 

The disturbance patterns that initiated many 

oak-hickory forests—such as land clearing 

(and subsequent reforestation), large fires, and 

even-age forest management—are declining in 

extent and intensity. Furthermore, oak-hickory 

forests, being primarily located in the southern 

reaches of the region, are more likely to adjoin 

major metropolitan areas and thus more 

likely to lose acreage to urban and suburban 

encroachment. Oak-hickory is the largest group 

by area (Chapter 4, Appendix 4) and is projected 

to lose the most acreage; on a relative basis, 

elm-ash-cottonwood is projected to be the 

biggest loser (Chapter 4). Maple-beech-birch 

and white-red-jack pine are projected to gain 

acreage. The implication of this projected trend 

is that overall, tree species diversity would 

decline through 2060, primarily because of the 

decrease in acreage of oak-hickory, a group that 

benefits from some forms of canopy disturbance.

FORECASTS OF CHANGES IN HABITAT 

AND AT-RISK WILDLIFE SPECIES 

Some species require forest habitats that 

can have unique characteristics such as the 

species composition, age, and size of trees 

(Patton 2011). For example, Canada warblers 

(Cardellina canadensis) breed in young forests 

(6 to 20 years) whereas Acadian flycatchers 

(Empidonax virescens) breed in areas 

dominated by mature forests. Conservation and 

management of species with divergent habitat 

requirements cannot be successful unless 

they are based on a landscape approach that 

focuses on simultaneously providing different 

types of habitats and successional stages 

in separate areas (Hamel et al. 2005) and 

managing changes in habitat as forests grow 

and are subjected to disturbances. Efforts 

to conserve diverse groups of wildlife can 

benefit from assessments of habitat responses 

to changes in climate and land-use change. 

This report considered species richness and 

projected changes in area for six forest habitat 

classes—closed-canopy and open-canopy 

hardwoods, conifers, and mixed forests—using 

a NatureServe (2011) data set to project habitat 

classes, species richness, and species-habitat 

class associations.

Northern forests provide habitat for 363 terrestrial 

vertebrate wildlife species, including mammals, 

reptiles, amphibians, and birds. Many of these 

species—such as the coyote (Canis latrans), 

American crow (Corvus brachyrhynchos), 

common garter snake (Thamnophis sirtalis), 

and American toad (Anaxyrus americanus)—

are common generalists, meaning that 

they inhabit a wide variety of habitats (not 

necessarily forested), do not depend on any 

one type of habitat for their survival, and are 

mostly plentiful across their geographic range. 

Many other species are more specialized and 

spend their entire lives within forests; they 

include the northern hawk owl (Surnia ulula), 

hoary bat (Lasiurus cinereus), fisher (Martes 

pennanti), wood frog (Lithobates sylvaticus), 

and coal skink (Plestiodon anthracinus). 
Chapter T h r e e 69

A few species are even more specialized and 

depend on a specific forest habitat for their 

survival, such as the open canopies of mixed 

pine and oak-pine barrens required by the 

treefrog (Hyla andersonii) or the closed-canopy 

hardwood forests required by the streamside 

salamander (Ambystoma barbouri). Other 

species may frequent numerous nonforest 

habitat types but rely on northern forests for 

critical life stages. An example is the wood 

duck (Aix sponsa), which spends most of its 

life on water but selects tree cavities for 

nesting sites. For other species, primarily 

migratory birds, northern forests make up 

only part of their geographic range, either 

as a seasonal destination as with Kirtland’s 

warbler (Dendroica kirtlandii) and the northern 

shrike (Lanius excubitor), or as a stop along 

a migratory route as with the gray-cheeked 

thrush (Catharus minimus). Northern forests 

are also home to two known endemic species 

of salamander, the Cheat Mountain salamander 

(Plethodon nettingi), and the blue-spotted 

salamander (Ambystoma laterale), which can 

be found nowhere else on earth.

Abundance of terrestrial vertebrate wildlife 

species varied among habitat classes and among 

major taxa (Fig. 3.17). Species richness is 

highest in open-canopy forests and hardwood 

habitats. Bird species accounted for the highest 

richness, with mammals second in all forests. 

Bird species numbered >150 species in opencanopy hardwoods, the greatest richness for any 

taxon-habitat combination. Amphibians were the 

least rich taxon in open-canopy habitats, and 

reptiles were the least rich in closed-canopy 

habitats. Species that limit their habitats to 

forests (defined as specialists) were in the 

minority across all habitats except closed-canopy 

hardwoods, where amphibian specialists were 

more abundant than amphibian generalists.

160

140

120

100

80

60

40

20

0

Conifer Hardwood Mixed Conifer Hardwood Mixed

CLOSED CANOPY OPEN CANOPY

NUMBER OF SPECIES

FIGURE 3.17

Distribution of terrestrial vertebrate 

species among closed- and 

open-canopy conifer, hardwood, 

and mixed forest habitats across 

the North in 2010; note that 

species associated only with forest 

habitats are termed “specialists” 

and species associated with both 

forest and nonforest habitats are 

termed “generalists.”

Amphibian generalist 

Amphibian specialist

Bird generalist 

Bird specialist

Mammal generalist 

Mammal specialist

Reptile generalist 

Reptile specialist
70

Forest projection models were used to assess 

climate and land-use driven changes in forest 

habitat classes under the assumption that current 

harvesting levels will continue without the influence 

of increased bioenergy demands (scenarios A1B-C, 

A2-C, and B2-C). Results showed that total forest 

habitat area is expected to decrease from a 2010 

value of 173 million acres to 162 million acres for 

A1B-C, 164 million acres for A2-C, and 168 million 

acres for B2-C. Across all scenarios, closed-canopy 

habitat classes would gain acreage but open-canopy 

classes would lose (Fig. 3.18, Table 3.1). We also 

projected the potential influence of intensive 

biomass harvesting rates for bioenergy (scenarios 

A1B-BIO, A2-BIO, and B2-BIO) (Fig. 3.18); most 

open-canopy classes would lose some acreage 

and closed-canopy classes would lose even more 

(Tavernia et al. 2013).

FIGURE 3.18

Projected change in closed- and 

open-canopy conifer, hardwood, 

and mixed forest habitats of 

the North, 2010 to 2060, for 

six scenarios, each representing 

a global greenhouse storyline 

(IPCC 2007) paired with a 

harvest regime. Storyline A1B 

assumes moderate greenhouse 

gas emissions, moderate gains 

in population, and large gains in 

income and energy consumption 

(but with a balanced renewable/

fossil fuel portfolio); A2 assumes 

high greenhouse gas emissions, 

large gains in population and 

energy consumption, and 

moderate gains in income; and 

B2 assumes low greenhouse gas 

emissions with moderate gains in 

population, income, and energy 

consumption. Panel A scenario 

projections assume harvesting 

will continue at recently observed 

levels (scenarios labeled –C); 

panel B scenario projections 

assume increased harvest for 

bioenergy production (scenarios 

labeled –BIO).

25

20

15

10

5

0

-5

-10

-15

-20

15

10

5

0

-5

-10

-15

-20

-25

-35

Hardwood Conifer Mixed Hardwood Conifer Mixed

Hardwood Conifer Mixed Hardwood Conifer Mixed

CHANGE IN AREA (percent) CHANGE IN AREA (percent)

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A

B

CLOSED CANOPY OPEN CANOPY

CLOSED CANOPY OPEN CANOPY
Chapter T h r e e 71

Table 3.1—Projected area, 2060, and percent change from the 2010 baseline of closed-canopy and opencanopy forest habitat classes (NatureServe 2011, Tavernia et al. 2013) across the Northern United States under 

three scenarios; note that changes were driven by projected climate and land-use changes, forest succession, and 

forest harvesting.

--------------------------------------------------Million acres (percent change in acreage)--------------------------------------------------

2010 173.4a 70.4 4.3 10.8 59.5 16.7 11.7

A1B-Cb 162.4 (-6) 73.1 (4) 5.2 (21) 11.4 (6) 48.5 (-19) 14.3 (-14) 9.9 (-15)

A2-C 164.1 (-5) 74.3 (6) 5.0 (16) 11.6 (7) 48.7 (-18) 14.5 (-13) 10.1 (-14)

B2-C 167.5 (-3) 74.9 (6) 5.0 (16) 12.1 (12) 50.6 (-15) 14.6 (-13) 10.2 (-13)

a

0.6 million acres of the total 174 million acres of forest area could not be associated with habitat classes (Tavernia et al. 2013).

b

Under the three scenarios, A1B-C assumes moderate levels of greenhouse gas emissions associated with moderate gains in population 

growth and large gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio); A2-C assumes high levels of 

greenhouse gas emissions associated with large gains in population growth and energy consumption with moderate gains in income; and B2-C 

assumes low levels of greenhouse gas emissions associated moderate gains in population growth, income, and energy consumption.

Scenerio Total 

habitat

Closed canopy forests Open canopy forests

Hardwood Conifer Mixed Hardwood Conifer Mixed

Early successional forest habitats in the North 

have experienced decades-long decreases in 

acreage (Trani et al. 2001). Researchers attribute 

these decreases to maturation of forests on 

abandoned farmland, altered forest management 

practices, forest ownership patterns that 

discourage harvesting, fire suppression and other 

disruptions to natural disturbance regimes, and 

land-use conversion (Askins 2001, Lorimer and 

White 2003, Trani et al. 2001). Given that young 

forests characteristically have small diameter 

trees and relatively open canopies, decreases 

can be expected to continue under 

all scenarios. 

If so, a broad segment of the wildlife community 

would be affected. For example, many species that 

typically inhabit mature, closed-canopy forests 

relocate to early successional habitat at certain 

times of the year (Vitz and Rodewald 2006). 

Wildlife managers can use information about 

historical habitat conditions and wildlife population 

dynamics to identify a mix of closed- and opencanopy habitats capable of supporting diverse 

wildlife communities (Askins 2001, Litvaitis 2003, 

Lorimer 2001, Thompson and DeGraaf 2001). As 

illustrated by the intensive harvesting scenarios, 

policy and management decisions can alter forest 

habitat conditions and, consequently, help achieve 

goals for habitat improvement.
72

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C h a p t e r F o u r 77

M a i n t e n a n c e o f P r o d u c t i v e C a p a c i t y 

o f Fo r e s t E c o s y s t e m s 

Several alternative scenarios were used that 

cover a range of different assumptions about the 

economy, population, climate and other driving 

forces. The assumptions were incorporated into 

analytical models that estimate how northern 

forests are likely to change under each alternative 

scenario. The seven scenarios (A1B-C, A1B-BIO, 

A2-C, A2-BIO, A2-EAB, B2-C, and B2-BIO) are 

based on a storyline and storyline variation. 

They are labeled by their storyline identifier 

(A1B, A2, or B2) followed by a hyphen and the 

storyline variation (C, BIO, or EAB).

The storylines were developed by the 

Intergovernmental Panel on Climate Change 

(IPCC 2007, Chapter 2): A1B assumes moderate 

gains in population growth with large gains in 

income and energy consumption—but with a 

balanced renewable/fossil fuel portfolio; A2 assumes 

large gains in population growth and energy 

consumption with moderate gains in income; and 

B2 assumes moderate gains in population growth, 

income, and energy consumption. All projections 

are estimated using two versions of a single global 

circulation model, the CGCM 3.1 (Canadian Centre 

for Climate Modelling and Analysis 2012a) for A1B 

and A2 and the CGCM 2.0 (Canadian Centre for 

Climate Modelling and Analysis 2012b) for B2.

W. Keith Moser, Patrick D. Miles, Aimee Stephens, Dale D. Gormanson, 

Stephen R. Shifley, David N. Wear, Robert J. Huggett, Jr., Ruhong Li

Introduction

HIS CHAPTER REPORTS projected changes in forest area, age, volume, biomass, number of trees, 

and removals from 2010 to 2060 for alternative scenarios that bracket a range of possible 

future socioeconomic and climate conditions in the Northern United States, which consists of 20 

central and northeastern States (see Fig. 1.1). As described in Chapter 2, the scenarios incorporate 

different assumptions about population growth, economic development, land-use change, carbon 

emissions, and climate change. Changes in forest attributes over time for each scenario were 

estimated using the Forest Dynamics Model (Wear et al. 2013). The information presented in this 

chapter, more than any other, is directly derived from projections of changing forest conditions for 

each scenario as modeled by the Forest Dynamics Model. 
• If harvesting rates observed in the recent past 

continue into the future, differences in projections 

of forest conditions in the northern region would 

be small. 

• Under all projections, the trend of steadily 

increasing live wood volume that characterized 

northern forests in the past century would level 

off from 2010 to 2050; after 2050, volume is 

projected to decrease if harvesting increases to 

satisfy demand for bioenergy.

• The levels of increased biomass harvesting for 

energy assumed in three scenarios appear to be 

too large to be sustainable through 2060; lower 

levels of harvesting for energy or projections 

that include wood-energy plantations could have 

different outcomes. 

• Forest area by age class is concentrated in the 

40- to 80-year age category, resulting in a lack 

of structural forest diversity that would take 

decades to alter.

• Under a projection of large gains in population 

and energy consumption and moderate gains in 

income, total ash mortality within the expanding 

infestation forecasted for 

the emerald ash borer 

forest conditions would have little effect on total 

volume, area by species group, or area by age 

class in northern forests. 

• Under all projections for the North, the area of 

the maple-beech-birch forest-type group would 

increase and the area of nearly all other foresttype groups would decrease; projections are 

mixed for the white-red-jack forest-type group. 

• For the North as a whole, projected forest 

removals resulting from land-use changes are 

likely to average about 13 percent of total 

removals, with the remainder resulting from 

harvesting; in populous eastern States—including 

Massachusetts, Maryland, New Jersey, and Rhode 

Island—removals resulting from land-use changes 

would be >50 percent in some decades. 

• Under all projections for northern forests, 

the growth-to-removals ratio would be <1.0 

(indicating an unsustainable situation over the 

long term) from 2035 to 2055; by 2060, the 

ratio would increase to 1.2 if harvesting rates 

observed in the recent past (2003 to 2008) 

continue into the future.

• The large increases in harvesting 

northern forests required to satisfy 

a robust demand for bioenergy 

would not be sustainable over 

the long term because it 

would result in decreasing 

forest volume after 2050; 

this does not suggest that 

lower rates of bioenergy 

harvesting would 

be unsustainable. 

Key Findings

78
C h a p t e r F o u r 79

The three storyline variations are as follows:

• C—the standard variation continuing recent 

removals trends 

• BIO—increased harvest and utilization of 

woody biomass for energy 

• EAB—potential impact of continued spread 

of the emerald ash borer (EAB) (Agrilus 

planipennis) with associated mortality of all 

ash trees in the affected areas.

A baseline assessment (Shifley et al. 2012) 

characterized the North as the most densely 

forested and most densely populated region of the 

country—a place where people and forests meet 

and mingle. The past influences of this juxtaposition 

of people, forests, and associated interactions have 

culminated in the forests of today, and expectations 

about future interactions strongly shape projections 

of forest conditions. Over the next 50 years, 

population is expected to increase across all three 

storylines (Chapter 2) resulting in an increase in 

urban areas at the expense of all other land uses 

(Chapter 10).

FOREST AREA 

Over Time 

The anticipated decreases in forest area reverse 

the long-term trend of increasing forest area in 

the region (Fig. 4.1). Over the next 50 years, 

forest land area is projected to decrease from an 

estimated 174 million acres in 2010 to 163 million 

acres (-6.4 percent) in 2060 under A1B; to 165 

million acres (-5.4 percent) under A2; and to 168 

million acres (-3.5 percent) under B2. Although 

decreasing by about 1 percent per decade across 

all scenarios, the anticipated losses would still 

be relatively small compared to the cumulative 

increase in forest area since the start of the 20th 

century. However, the anticipated loss paired with 

the expected increases in population would mean 

a substantial decrease in forest area per capita 

(Appendix Chapter 2). Northern forests will have 

to work harder to meet the needs of a growing 

population, which is expected to increase by 

somewhere between 15 and 50 million people from 

2010 to 2060 (Chapter 2). 

FIGURE 4.1

Forest area in the North under historical conditions (Smith 

et al. 2009) and projected from 2010 to 2060 under three 

greenhouse gas storylines (IPCC 2007)—A1B assumes 

moderate greenhouse gas emissions, moderate gains 

in population, and large gains in income and energy 

consumption (but with a balanced renewable/fossil fuel 

portfolio); A2 assumes high greenhouse gas emissions, 

large gains in population and energy consumption, 

and moderate gains in income; and B2 assumes low 

greenhouse gas emissions with moderate gains in 

population, income, and energy consumption. Forest area 

in 1630 has been estimated at about 300 million acres. 1900 1920 1940 1960 1980 2000 2020 2040 2060

200

150

100

50

0

FOREST LAND AREA (million acres)

Historical

A1B

A2

B2

Observed

Projected
80 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Projected population increases are expected 

to occur in and around existing urban centers 

(Chapter 2). Consequential losses in forest 

area are expected to follow a similar pattern 

(Chapter 3). As a result, forest land losses 

would be concentrated in the more densely 

populated States along the Atlantic seaboard 

(Fig. 4.2). With the loss of forest land and 

with the clustering of losses around urban 

areas would come additional concerns 

about fragmentation and parcellation, the 

consequences of which could outweigh the 

relatively small percentage loss in total forest 

area (Chapter 3).

By Forest-type Group 

The current distribution of forest area by foresttype groups is spatially depicted in Figure 4.3 

and displayed in Figure 4.4. Under all three 

scenarios the oak-hickory (Quercus spp.–Carya spp.) 

and maple-beech-birch (Acer spp.–Fagus spp.–

Betula spp.) groups would continue to dominate 

through 2060. These two types, which together 

comprised 62 percent of the total forest area in 

2010, are projected to account for about 64 percent 

of total forest area under all scenarios in 2060 

(Appendix Chapter 4). 

A1B A2 B2

Under 3

3 to 4

5 to 9

10 plus

FIGURE 4.2 FOREST LOSS (percent)

State-level projected decreases in forest area, 

2010 to 2060, under three greenhouse gas 

emissions storylines: A1B assumes moderate 

greenhouse gas emissions, moderate gains 

in population, and large gains in income and 

energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes 

high greenhouse gas emissions, large gains 

in population and energy consumption, 

and moderate gains in income; and B2 

assumes low greenhouse gas emissions with 

moderate gains in population, income, and 

energy consumption.
C h a p t e r F o u r 81

Nonforest

White-red-jack pine

Spruce-fir

Oak-hickory

Elm-ash-cottonwood

Maple-beech-birch

Aspen-birch

Other

FOREST-TYPE GROUP

FIGURE 4.3

Forest-type groups in the North 

(Wilson et al. 2012, Wilson et al. 2013).

Forest area loss will likely be one of the biggest 

contributors to composition changes in northern 

forests from 2010 through 2060. Figure 4.5 shows 

the percent change in forest area by forest-type 

group. Four of the six groups—spruce-fir 

(Picea spp.—Abies spp.), oak-hickory, elm-ashcottonwood (Ulmus spp.–Fraxinus spp.–Populus

spp.), and aspen-birch (Populus spp.—Betula

spp.)— are projected to decrease under all 

three scenarios. The white-red-jack pine group 

(Pinus strobus – Pinus resinosa – Pinus banksiana) 

is expected to increase under some scenarios, and 

the maple-beech-birch group is expected to 

increase under all scenarios. 

These changes reflect the differential impacts 

of projected stand development, succession, 

growth, harvest, and mortality on areas that 

would remain forested (for example, more 

maple-beech-birch and less oak-hickory). Also, 

projected increases in urban areas would affect 

some groups more than others, in large part 

reflecting their relative abundance in locations 

near major population centers.
82 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Only 52 percent of forest area is projected to 

be in the same forest-type group in 2060 as it 

was in 2010 (Fig. 4.6), with changes stemming 

from a variety of causes including succession, 

harvesting, and diversion to nonforest land uses. 

For example, only 40 percent of the land that 

was in the white-red-jack pine group in 2010 

is projected to remain in that group in 2060; 

21 percent is projected to be converted to the 

late successional maple-beech-birch group; 

and 5 percent to nonforest uses (Fig. 4.6). 

Conversion to nonforest land uses is projected 

to be highest in the “other” forest-type group 

(10 percent) and the oak-hickory group (9 percent), 

both of which are often located in close proximity 

to urban areas. Conversions to nonforest would 

be lowest (2 percent) for the spruce-fir group, 

which is usually located far from large urban 

areas. The elm-ash-cottonwood group—especially 

under the emerald ash borer scenario—and the 

aspen-birch groups would experience the largest 

percentage decrease (Fig. 4.5).

By Age Class

Because of past patterns of natural and human 

disturbance that occurred in the first half of the 

20th century, northern forests are predominantly 

“middle aged,” with over half of current acres 

between 40 and 80 years and relatively few acres 

<20 years or >100 years (Pan et al. 2011) 

(Fig 4.7). This lack of age-class diversity has 

implications for biodiversity and wildlife habitat 

suitability now and into the future (Chapter 3). 

In forests undergoing normal patterns of 

succession without large disturbances, the ageclass distribution naturally shifts toward older 

forests. Certain harvesting practices or severe 

insect, disease, or fire effects will regenerate 

forest area and move it to the youngest age 

class. Over the past five decades, however, 

rates of forest regeneration have been relatively 

low, and northern forests have aged. 

Three of the scenarios (A2-C, A1B-C, and B2-C) 

assume continuation of the levels of forest 

harvesting, regeneration, and disturbance that 

have contributed to the current compressed 

age-class distribution. As a result, northern 

forests would continue to age, with the bulk of 

acres shifting into the 60- to 100-year classes 

over the next 50 years (Fig 4.7). Chapter 3 

reports decreasing potential habitat for species 

that depend on early successional forest 

ecosystems, primarily because of changes in 

forest succession and conversion of forests 

to nonforest uses. Under A2-EAB, a similar 

shift in the age-class distribution would occur, 

despite the assumed loss of ash species to the 

continued spread of emerald ash borer. For all 

of these scenarios, forest maturation over the 

next five decades is expected to decrease the 

area of forest land <60 years and increase area 

for older forests (Fig. 4.8). Longer projection 

periods would likely exacerbate this trend.
Area of forest land in the North by forest-type group, 2010 and 2060 

(projected), for seven scenarios, each representing a global greenhouse gas 

storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large 

gains in income and energy consumption (but with a balanced renewable/

fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains 

in population and energy consumption, and moderate gains in income; 

and B2 assumes low greenhouse gas emissions with moderate gains in 

population, income, and energy consumption. Scenario projections assume 

harvest will continue at recently observed levels (labeled –C) or increase to 

reflect increased harvest for bioenergy production (labeled –BIO). Scenario 

A2-EAB is a variation of scenario A2-C that also assumes all ash species will 

eventually succumb to an expanding zone of infestation by the nonnative 

emerald ash borer.

60

50

40

30

20

10

0

FOREST LAND AREA (million acres)

FOREST-TYPE GROUP

Estimate in 2010

A1B-C in 2060

A2-C in 2060

B2-C in 2060

A1B-BIO in 2060

A2-BIO in 2060

B2-BIO in 2060

A2-EAB in 2060

White-red-jack 

pine

SprucefirOakhickoryElm-ashcottonwoodMaple-beechbirchAspenbirchOther

FIGURE 4.4

83
10

5

0

-5

-10

-15

-20

CHANGE IN FOREST LAND AREA (percent)

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

White-redjack pine

Spruce-fir Oak-hickory Aspen-birch Other

Elm-ashcottonwood

Maple-beechbirch

Projected decrease in forest area by forest-type group in the North, 2010 and 2060 (projected), for seven scenarios, 

each representing a global greenhouse gas storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption 

(but with a balanced renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in 

population and energy consumption, and moderate gains in income; and B2 assumes low greenhouse gas emissions 

with moderate gains in population, income, and energy consumption. Scenario projections assume harvest will continue 

at recently observed levels (labeled –C) or increase to reflect increased harvest for bioenergy production (labeled –BIO). 

Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will eventually succumb to an expanding 

zone of infestation by the nonnative emerald ash borer.

FIGURE 4.5

84
C h a p t e r F o u r 85

The pattern of age-class changes for A2-EAB, 

which assumes expanding zones of ash mortality, 

would mirror that of the A2-C scenario. Because 

ash species are rarely the majority component 

of forest stands, the loss would not have an 

effect on the stand age of affected forest acres. 

Scenarios with enhanced removals to meet 

the demand for bioenergy (A1B-BIO, A2-BIO, 

and B2-BIO) are projected to have the largest 

impacts on the age structure of northern 

forests. All three scenarios assume an increase 

in harvesting that results in regenerated stands. 

The projected total forest area would be the same 

for a paired set of scenarios (for example, A2-C 

versus A2-BIO), but the proportion of forest area 

in younger age classes would increase under 

the one that assumes increased harvesting for 

bioenergy. The result would be an increase in 

young (early successional) forests and a shift 

in distribution toward the younger age classes 

(Fig. 4.7). Under all scenarios with increased 

harvesting for bioenergy, forest area in the 

youngest age class (<20 years) would increase. 

FOREST-TYPE GROUP, 2010

100

80

60

40

20

10

0

PROJECTED AREA IN 2060 FOR EACH 2010 FOREST-TYPE GROUP (percent) 

FIGURE 4.6

Projected change in the proportionate 

distribution of forest-type groups in the North, 

2010 to 2060, under scenario A2-C that 

assumes high greenhouse gas emissions, large 

gains in population and energy consumption, 

moderate gains in income, and a continuation 

of recently observed harvest rates.

Nonforest

White-red-jack pine

Spruce-fir

Other

Oak-hickory

Maple-beech-birch

Elm-ash-cottonwood

Aspen-birch

White-red-jack pine

Spruce-fir

Oak-hickory

Elm-ash-cottonwood

Maple-beech-birch

Aspen-birch

Other
86 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Scenario A1B-BIO would result in double the 

area of young forests by 2060 while also reducing 

forest area in the 40- to 100-year classes. With 

increased harvesting, fewer acres would progress 

into older age classes. Thus, the projected 

increases for age classes >60 years would be 

smaller for scenarios with increased biomass 

harvesting than for no increase in harvesting 

(Figs. 4.7, 4.8). 

All three scenarios with increased harvesting 

are expected to result in a more even 

distribution of forest area by age class, most 

notably for forests <80 years, which would 

improve both landscape-scale forest structural 

diversity and habitat diversity (Hunter and 

Schmiegelow 2011). However, as discussed in 

later sections of this chapter and in Chapter 5, 

the rate of harvesting associated with scenarios 

A1B-BIO, A2-BIO, and B2-BIO has other, less 

desirable, ramifications. 

70

60

50

40

30

20

10

0

FOREST LAND AREA (million acres) 

0 20 40 60 80 100 120 140 160 180 200

FOREST AGE (years; summarized at 20-year age-class midpoints)

Baseline

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

FIGURE 4.7

Forest land area by age class in the North is estimated for 2010 and projected for 2060 under seven scenarios, each 

representing a global greenhouse gas storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes moderate 

greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption (but with 

a balanced renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in population and 

energy consumption, and moderate gains in income; and B2 assumes low greenhouse gas emissions with moderate gains 

in population, income, and energy consumption. Scenario projections assume harvest will continue at recently observed 

levels (labeled –C) or increase to reflect increased harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is a 

variation of scenario A2-C that also assumes all ash species will gradually succumb to an expanding zone of infestation by 

the nonnative emerald ash borer.
C h a p t e r F o u r 87

FIGURE 4.8

Proportionate area of early 

successional forest (aged 40 years 

or younger) and late successional 

forest (aged more than 100 

years) in the North in 2010 

and 2060 (projected) under 

three scenarios that assume a 

continuation of recently observed 

harvest levels. Furthermore, A1B 

assumes moderate greenhouse 

gas emissions, moderate gains 

in population, and large gains in 

income and energy consumption 

(but with a balanced renewable/

fossil fuel portfolio); A2 assumes 

high greenhouse gas emissions, 

large gains in population and 

energy consumption, and moderate 

gains in income; and B2 assumes 

low greenhouse gas emissions 

with moderate gains in population, 

income, and energy consumption 

(IPCC 2007).

2010

A1B-C

A2-C

B2-C

Early successional Late successional

Under 6

6 to 10

11 to 20

Over 20

PERCENT OF FOREST LAND

Under 3

3 to 5

6 to 7

Over 7

PERCENT OF FOREST LAND
88 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Many northern forests currently classified as >100 

years will experience gap-phase regeneration and 

gradually move into an uneven-age structure with 

multiple age cohorts. Although uneven-aged stands 

do not fall neatly into a single age class or size 

class, these forests generally retain a cohort of 

mature, dominant overstory trees. Consequently, 

from a biodiversity perspective, uneven-aged 

stands are considered to be mature forests that 

are in the understory reinitiation stage of stand 

development—or as Oliver and Larson (1990) 

reported, in the old-growth stage of development.

NUMBER OF TREES

The number of trees in the region is projected 

to decrease by 12 to 20 billion, or roughly 10 

to 17 percent, from 2010 to 2060 (Fig. 4.9). 

As forests age, fewer but larger trees are the 

expected outcome. The largest decrease would 

occur under the A2-EAB scenario, in which the 

emerald ash borer infestation would permanently 

reduce the total number of ash trees. The growing 

space released by dying ash trees is presumed 

to be captured by adjacent trees of other species 

rather than by new reproduction. This projected 

outcome—the long-lasting reduction in the total 

number of trees—is one of the most notable 

results from the A2-EAB scenario. 

VOLUME

Live Tree Volume

The total volume of all live trees is a measure 

of historical productivity because it represents 

the wood volume that has accumulated up to 

the year of measurement. It is a function of site 

factors (productivity of the soil and availability 

of soil moisture), climatic factors (growing 

season length, averages and variations in 

temperature and rainfall), ecological factors 

(competing species and disturbances), and 

stand history (forest age, past management 

actions, and past disturbances). The estimates 

of all live volume include the volume from cull 

trees and noncommercial tree species, which are 

excluded in estimates of growing-stock volume 

discussed in the next section. Typically, estimates 

of all live volume are highly correlated with 

aboveground tree biomass, even though they 

exclude tree stumps, tops, branches, and bark, all 

considered part of the aboveground biomass pool. 

The projected total live tree volume for the 

northern forests is somewhat similar across 

all scenarios until 2050 (Appendix Chapter 2, 

Fig. 4.10). The scenarios with increased biomass 

harvesting (A1B-BIO, A2-BIO, and B2-BIO) or 

increased mortality attributed to emerald ash 

borer (A2-EAB) would have less volume than 

those assuming no changes in harvesting 

(A1B-C, A2-C, and B2-C), but the trends for 

all scenarios would follow a similar pattern. 
C h a p t e r F o u r 89

Starting in 2050, however, biomass removals 

would increase rapidly under scenarios A1BBIO, A2-BIO, and B2-BIO with a corresponding 

decrease in volume. For those scenarios, 

bioenergy demand would exceed what can be 

provided by alternative sources such as residues 

from wood production and agriculture, and require 

that harvesting increase dramatically (Ince et al. 

2011a,b; Chapter 2). 

Only scenarios with increased biomass 

harvesting (A1B-BIO and A2-BIO and B2-BIO) 

projected a net decrease in live tree volume 

from 2010 to 2060. But as illustrated in the 

following section on growing-stock volume, even 

the scenarios that project volume increases 

estimate that the rate of increase would be 

lower than the rate observed over the previous 

five decades.

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

FIGURE 4.10

Projected live tree volume in forests of the North, 2010 to 2060, under 

seven scenarios, each representing a global greenhouse gas storyline 

(IPCC 2007) paired with a harvest regime. 

FIGURE 4.9

Projected number of trees in forests of the North, 2010 to 2060, under 

seven scenarios, each representing a global greenhouse gas storyline 

(IPCC 2007) paired with a harvest regime. 

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

120

115

110

105

100

90

340

320

300

280

260

240

NUMBER OF TREES (billions)

LIVE TREE VOLUME (billion cubic feet)

Storyline A1B assumes moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption 

(but with a balanced renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in population and energy 

consumption, and moderate gains in income; and B2 assumes low greenhouse gas emissions with moderate gains in population, income, and 

energy consumption. Scenario projections assume harvest will continue at recently observed levels (labeled –C) or increase to reflect increased 

harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will 

gradually succumb to an expanding zone of infestation by the nonnative emerald ash borer.
Live tree volume projections are markedly different 

across the region, with most of the States in 

western areas increasing in volume and most 

of the States farther east experiencing smaller 

increases or even decreases (Fig. 4.11). Many of 

the eastern States that are expected to experience 

losses are the same States expected to lose forest 

area to urbanization. Projected differences vary 

greatly by State under A1B-C, A2-C, and B2-C 

(based on historical removals by product class from 

2003 to 2008) versus scenarios with increased 

biomass utilization. Examples are Minnesota and 

Wisconsin, both heavily forested and both expected 

to experience significant volume decreases under 

the biomass scenarios, particularly A1B-BIO, 

and increases under the other scenarios. Under 

all enhanced biomass scenarios, heavily forested 

States where current harvesting rates are high are 

projected to supply most of the additional biomass. 

This outcome is in part due to model assumptions 

about future biomass harvesting that are based on 

historical rates of harvest removals and land use. A 

simple scaling function is used to increase removals 

under the biomass scenarios (Chapter 2). 

Differences in projected volume among forest-type 

groups are illustrated by comparing A2-C with 

corresponding scenarios that assume enhanced 

biomass utilization (A2-BIO) and expanding 

emerald ash borer impact (A2-EAB). 

Volume differences among these scenarios were 

generally <10 percent by 2060 (Fig. 4.12) and 

proportional to volumes in 2010. As expected, 

scenarios with increased biomass harvesting or 

increased ash mortality predict less live volume 

than A2-C. 

Further comparisons by major species group 

indicated that under A2-EAB, ash trees would 

essentially disappear from northern forests by 2040 

(Fig. 4.13), but the loss of ash volume would be 

partially offset by volume increases in other species 

(Chapter 5). 

Growing-stock Volume on Timberland

In the North, 95 percent of forest land is classified 

as timberland (forest land that maintains a 

minimum potential for growth of 20 cubic feet per 

acre per year and is not restricted from active 

forest management by statute or regulation); 

and 92 percent of the timber on timberland is 

classified as growing stock (merchantable timber 

volume, or all live volume reduced by defect and 

nonmerchantable species). Consequently, patterns 

and trends observed for growing-stock volume on 

timberland are usually similar to corresponding 

measures of total volume of all live trees, albeit 

with lower volume totals. 

90
FIGURE 4.11

Projected change in live tree volume 

in forests of the North, 2010 to 

2060, under seven scenarios, each 

representing a global greenhouse 

gas storyline (IPCC 2007) paired 

with a harvest regime. Storyline 

A1B assumes moderate greenhouse 

gas emissions, moderate gains 

in population, and large gains in 

income and energy consumption 

(but with a balanced renewable/

fossil fuel portfolio); A2 assumes 

high greenhouse gas emissions, 

large gains in population and energy 

consumption, and moderate gains 

in income; and B2 assumes low 

greenhouse gas emissions with 

moderate gains in population, 

income, and energy consumption. 

Scenario projections assume harvest 

will continue at recently observed 

levels (labeled –C) or increase 

to reflect increased harvest for 

bioenergy production (labeled –BIO). 

Scenario A2-EAB is a variation of 

scenario A2-C that also assumes all 

ash species will gradually succumb 

to an expanding zone of infestation 

by the nonnative emerald ash borer.

Harvesting unchanged Harvesting increases for 

bioenergy demand

A1B-C

A2-C

B2-C

A2-EAB

A1B-BIO

A2-BIO

B2-BIO

Over 15

11 to 15

2 to 10

+_ 1 percent

-2 to -14

-15 to -25

Under -25

PROJECTED VOLUME CHANGE (percent)

91
92 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Trends and projections of volume—Historical 

data from Smith et al. (2009) show that volume 

increases of growing stock have been steady, 

doubling from about 125 billion cubic feet in the 

early 1960s to current levels of 263 billion cubic 

feet. This trend is projected to level off by 2050 

(Fig. 4.14), when differences in assumptions 

would result in decreases under the scenarios 

with accelerated biomass harvesting and modest 

increases under the other scenarios. Even under 

the scenarios that project the largest increases in 

growing-stock volume, the rate of increase would 

be much smaller than that observed in previous 

decades. Projected growing-stock volume in 2060 

would be 6 percent larger than the 2010 estimate 

under A1B-C, 7 percent larger under A2-C, and 

9 percent larger under B2-C. Decreases in growing 

stock volume from 2010 to 2060 under scenarios 

that assume accelerated biomass removals would 

be 0.2 percent (B2-BIO), 8 percent (A2-BIO) and 

19 percent (A1B-BIO); clearly A2-BIO and A1B-BIO 

would be unsustainable in the long run. 

120

100

80

60

40

20

0

VOLUME (billion cubic feet)

FOREST-TYPE GROUP

Inventory in 2010

A2-C in 2060

A2-BIO in 2060

A2-EAB in 2060

White-red-jack 

pine

SprucefirOakhickoryElm-ashcottonwoodMaple-beechbirchAspenbirchOther

FIGURE 4.12

Live tree volume on forests in the North estimated for 2010 and projected through 2060 under three scenarios, all sharing 

one greenhouse gas emission storyline, A2, that assumes large increases in greenhouse gas emissions, large gains in 

population and energy consumption, and moderate gains in income (IPCC 2007), but with varying assumptions about 

future removals. Scenario projections assume harvest will continue at recently observed levels (labeled –C) or increase to 

reflect increased harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also 

assumes all ash species will gradually succumb to an expanding zone of infestation by the nonnative emerald ash borer.
C h a p t e r F o u r 93

A2-C

A2-BIO

A2-EAB

2010 2020 2030 2040 2050 2060 1960 1980 2000 2020 2040 2060

250

200

150

100

50

0

300

250

200

150

100

50

VOLUME OF LIVE TREES ON FOREST LAND 

(billion cubic feet)

GROWING-STOCK VOLUME 

(billion cubic feet)

Hardwoods, not Ash

Softwoods

Ash

FIGURE 4.13

Projected change in live tree volume of ash, other hardwoods, and softwoods in the 

North under three scenarios, all sharing one greenhouse gas emission storyline, A2, 

that assumes large increases in greenhouse gas emissions, large gains in population 

and energy consumption, and moderate gains in income (IPCC 2007); but with 

varying assumptions about future removals. Scenario projections assume harvest 

will continue at recently observed levels (labeled –C) or increase to reflect increased 

harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is a variation 

of scenario A2-C that also assumes all ash species will gradually succumb to an 

expanding zone of infestation by the nonnative emerald ash borer.

FIGURE 4.14

Net volume of growing stock on timberland in the North, both historical (back to 

1963) and projected through 2060 under seven scenarios, each representing a 

global greenhouse gas storyline (IPCC 2007) paired with a harvest regime. Storyline 

A1B assumes moderate greenhouse gas emissions, moderate gains in population, 

and large gains in income and energy consumption (but with a balanced renewable/

fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains 

in population and energy consumption, and moderate gains in income; and B2 

assumes low greenhouse gas emissions with moderate gains in population, income, 

and energy consumption. Scenario projections assume harvest will continue at 

recently observed levels (labeled –C) or increase to reflect increased harvest for 

bioenergy production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C 

that also assumes all ash species will gradually succumb to an expanding zone of 

infestation by the nonnative emerald ash borer.

Historical

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

Despite the expected impact of emerald ash borer 

upon ash species, regional growing-stock volumes 

in 2060 projected under A2-EAB would increase 

by 8 percent over the 2010 estimates and would 

mirror projections for A2-C.

The change from a historical trend of rapidly 

increasing growing-stock volume to a projected 

trend of nearly constant growing-stock volume 

can be attributed to the combined effects of (1) 

an aging forest resource with a normal decrease 

in the annual rate of volume growth; and (2) an 

accelerated conversion of forest to urban land 

with a corresponding loss of timberland area and 

associated losses in timberland volume.

Net volume growth predictions—Collectively, the 

annual rates of volume growth, mortality, and 

removals determine the net change in total 

growing-stock volume as illustrated in Figure 4.14. 

When net growing-stock volume (gross growth 

less mortality) exceeds growing-stock removals, 

the result is an overall increase in growing-stock 

volume. Likewise, a high rate of mortality or of 

removals can result in a decrease in total volume, 

for example, under A1B-BIO (Fig.4.15). 
94 FUTURE FORESTS OF THE NORTHERN UNITED STATES

This section reports net growth of growing-stock 

volume, which has been adjusted to exclude 

volume losses not associated with harvesting or 

land-use removals (harvesting and land-use 

removals are addressed in subsequent sections). 

Net growth is usually a positive value; a negative 

value would indicate abnormally high mortality 

attributed to insect infestations, disease 

outbreaks, weather events, wildfire, or other 

severe disturbances.

Predicted net annual volume growth for growing 

stock on timberland would be essentially the 

same under A1B-C, A2-C, B2-C, and A2-EAB 

(Fig. 4.16) for the entire projection period. 

Conversely, the scenarios with increased biomass 

harvesting (A1B-BIO, A2-BIO, B2-BIO) projected 

much higher increases in volume growth from 

2030 to 2060, primarily because increased 

harvesting intensity regenerates new, young 

forest areas that typically have more trees, 

2010 2020 2030 2040 2050 2060

2010 2020 2030 2040 2050 2060

2000

1800

1600

1400

1200

1000

11

9

7

5

3

1

GROWING-STOCK VOLUME PER ACRE (cubic feet)

ANNUAL VOLUME GROWTH (billion cubic feet)

FIGURE 4.15

Projected change in per-acre growing stock in the North, 2010 to 2060, under seven 

scenarios, each representing a global greenhouse gas storyline (IPCC 2007) paired 

with a harvest regime. Storyline A1B assumes moderate greenhouse gas emissions, 

moderate gains in population, and large gains in income and energy consumption 

(but with a balanced renewable/fossil fuel portfolio); A2 assumes high greenhouse 

gas emissions, large gains in population and energy consumption, and moderate 

gains in income; and B2 assumes low greenhouse gas emissions with moderate 

gains in population, income, and energy consumption. Scenario projections assume 

harvest will continue at recently observed levels (labeled –C) or increase to reflect 

increased harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is a 

variation of scenario A2-C that also assumes all ash species will gradually succumb 

to an expanding zone of infestation by the nonnative emerald ash borer.

FIGURE 4.16

Projected average annual increase in growing-stock volume for the North, 2010 to 

2060, under seven scenarios, each representing a global greenhouse gas storyline 

(IPCC 2007) paired with a harvest regime. Storyline A1B assumes moderate 

greenhouse gas emissions, moderate gains in population, and large gains in income 

and energy consumption (but with a balanced renewable/fossil fuel portfolio); 

A2 assumes high greenhouse gas emissions, large gains in population and energy 

consumption, and moderate gains in income; and B2 assumes low greenhouse gas 

emissions with moderate gains in population, income, and energy consumption. 

Scenario projections assume harvest will continue at recently observed levels 

(labeled –C) or increase to reflect increased harvest for bioenergy production 

(labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes 

all ash species will gradually succumb to an expanding zone of infestation by the 

nonnative emerald ash borer.

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB
C h a p t e r F o u r 95

smaller trees, and less volume per acre—but a 

faster rate of volume increase—than the mature 

forest they replace. Thus, the enhanced biomass 

scenarios would create a temporary bubble of 

growth related to the intensity of the harvesting 

removals. As shown in subsequent sections, 

much of this accelerated growth would be offset 

by the accelerated removals, resulting in little 

net change in volume.

REMOVALS 

Volume removals occur when trees are 

harvested or when land is converted to urban 

or other uses. In the northern forests, partial 

harvesting of the overstory can accelerate the 

growth of the remaining trees. Harvesting that 

removes much or all of the forest overstory 

usually regnerates land back to young forest. 

Thus, harvesting mature forest can sometimes 

stimulate increased rates of volume growth in 

the residual or replacement forests. However, 

land that is converted to urban use is almost 

always permanently lost from the forest 

resource base, and its associated wood volume 

no longer contributes to the timberland volume 

total. In contrast, land-use conversions between 

timberland and agricultural land are more likely 

to be bidirectional; for example, abandoned 

agricultural land in one location often reverts 

to timberland through natural succession at 

the same time that forests are cleared for 

agriculture elsewhere. 

Removals, whether by harvesting or by conversion 

of forests to another use, were modeled in 5-year 

intervals and then summarized by decade from 

2020 to 2060 (Appendix Chapter 4). As described 

in Chapter 2, removals predictions were based 

on assumptions that the patterns of harvesting 

removals observed in the recent past will continue 

into the future (A1B-C, A2-C, B2-C, A2-EAB) or 

will increase to meet bioenergy demand (A1B-BIO, 

A2-BIO, B2-BIO). 

Procedurally, the volume of removals attributable to 

land-use change for a given period was calculated 

as acreage converted to nonforest use multiplied 

by average volume per acre. Removals attributable 

to harvesting were then calculated by subtracting 

estimated land-use removals from the total 

removals projected by the Forest Dynamics Model 

(Chapter 2). The following sections separately 

discuss the projected volume changes for both types 

of removals.

Harvesting

Annual removals in northern forests are projected 

to double or triple from 2010 to 2060 under the 

enhanced biomass utilization scenarios, with 

increases ranging from 2.9 to 6.8 billion cubic feet 

(Appendix Chapter 4, Fig. 4.17).

In contrast, annual removals would be essentially 

flat and nearly identical under the other four 

scenarios, rising slightly from 3.09 billion cubic 

feet in 2010 to 3.33 billion cubic feet under 

A1B-C, 3.39 billion cubic feet under A2-C, 3.12 

billion cubic feet under B2-C, and 3.58 billion 

cubic feet under A2-EAB by 2060.
With the exception of the densely populated 

areas on the Atlantic seaboard, projected 

removals are expected to increase or stay 

relatively constant at the State level, as shown 

for A2-C in Figure 4.18. An example is Maine, 

which had the highest projected removals of 

any Northern State based on removals that 

increased by <5 percent from 2010 to 2060. 

The other heavily forested States of Michigan, 

Minnesota, and Wisconsin also are projected to 

have increases, as is the less heavily forested 

Indiana, and Pennsylvania is expected to remain 

about the same. With the exception of Rhode 

Island, decreases in volume harvested are projected 

for the States along the Atlantic seaboard. 

Modeling the extent of salvage harvesting that 

would accompany ash mortality was beyond 

the scope of this chapter; doing so would likely 

have produced noticeable increases in projected 

removals under the A2-EAB scenario.

2010 2020 2030 2040 2050 2060

10

8

6

4

2

0

ANNUAL HARVEST REMOVALS (billion cubic feet)

FIGURE 4.17

Annual harvest volumes for the North for 2010 and projected through 2060, under 

seven scenarios, each representing a global greenhouse gas storyline (IPCC 2007) 

paired with a harvest regime. Storyline A1B assumes moderate greenhouse gas 

emissions, moderate gains in population, and large gains in income and energy 

consumption (but with a balanced renewable/fossil fuel portfolio); A2 assumes high 

greenhouse gas emissions, large gains in population and energy consumption, and 

moderate gains in income; and B2 assumes low greenhouse gas emissions with 

moderate gains in population, income, and energy consumption. Scenario projections 

assume harvest will continue at recently observed levels (labeled –C) or increase to 

reflect increased harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is 

a variation of scenario A2-C that also assumes all ash species will gradually succumb 

to an expanding zone of infestation by the nonnative emerald ash borer.

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

96
C h a p t e r F o u r 97

Conversion to Nonforest Land Uses

Land-use change removals are those volume 

losses solely attributable to a decrease in forest 

area when forest land is converted to urban 

or agricultural uses. The woody material on 

converted lands may or may not be harvested, 

but the assumption is that the entire volume 

is lost from the forest inventory. Projections of 

land-use removals were driven by assumptions 

about loss of forest land under the greenhouse 

gas emissions storylines; scenarios with 

accelerated biomass harvesting or expanding 

emerald ash borer mortality were very similar 

to the analogous scenarios that projected a 

continuation of past harvesting rates (Appendix 

Chapter 4, Fig. 4.19). The only appreciable 

exception is A1B-BIO/A1B comparison: the 

volumes removed under the enhanced biomass 

removal assumptions would be so large that 

they would reduce the metric—average standing 

volume per acre—used to calculate land-use 

change removals. 

Land-use removals are projected to increase 

for all variations of the A1B and A2 scenarios 

and are projected to decrease for the B2-C and 

B2-BIO scenarios (Fig. 4.19). Removals would 

primarily come from hardwood stands, reflecting 

the composition of forests in the counties that 

would be most impacted by the loss of forest land. 

For the northern forests as a whole, removals 

resulting from land-use change would be a small 

component (about 12 percent in 2010) of total 

removals (Fig. 4.20); the exception would be six 

States along the Atlantic seaboard (Fig. 4.21) 

where land-use change would be a large—or 

even the largest—source of removals. Compared 

to the midwestern States, most of the States 

farther east would have higher percentages of 

total removals resulting from land-use change 

(examples include New Hampshire, Massachusetts, 

and Pennsylvania but not Maine) with the highest 

among them being the small States along the 

Atlantic seaboard. Situated along the Interstate-95 

corridor, this highly urbanized area is more 

likely to lose forest land to population growth 

and associated development; harvesting is 

expected to be comparatively minimal because 

ownerships are small and the value of ecosystem 

services from forests is relatively high. In Iowa 

and Illinois, land-use removals are predicted to be 

high in the first decade of the projection period, 

but then decrease rapidly in subsequent decades.
0 100 200 300 400 500 600

ANNUAL HARVEST REMOVALS (million cubic feet)

State-level harvest volume in forests of the North in 2010 and projected for 2060 

under scenario A2-C that assumes high greenhouse gas emissions, large gains in 

population and energy consumption, moderate gains in income, and a continuation 

of recently observed harvest rates.

2060

2010

Rhode Island

Delaware

Massachusetts

New Jersey

Illinois

Iowa

Connecticut

Maryland

New Hampshire

Indiana

Vermont

Missouri

Ohio

Minnesota

New York

West Virginia

Wisconsin

Michigan

Pennsylvania

Maine

FIGURE 4.18

98
C h a p t e r F o u r 99

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

Removals due to land-use change

Harvest removals

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

600

500

400

300

200

100

4500

3500

2500

1500

500

0

VOLUME LOSS (million cubic feet per year)

ANNUAL RECORDS (billion cubic feet)

FIGURE 4.19

Projected annual volume losses resulting from land-use changes in forests of the 

North, 2010 to 2060, under seven scenarios, each representing a global greenhouse 

gas storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains 

in income and energy consumption (but with a balanced renewable/fossil fuel 

portfolio); A2 assumes high greenhouse gas emissions, large gains in population 

and energy consumption, and moderate gains in income; and B2 assumes low 

greenhouse gas emissions with moderate gains in population, income, and energy 

consumption. Scenario projections assume harvest will continue at recently observed 

levels (labeled –C) or increase to reflect increased harvest for bioenergy production 

(labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes 

all ash species will gradually succumb to an expanding zone of infestation by the 

nonnative emerald ash borer.

FIGURE 4.20

Comparison of projected volume losses resulting from harvesting and land-use changes 

in the North, 2010 to 2060, under scenario A2-C that assumes high greenhouse 

gas emissions, large gains in population and energy consumption, moderate gains in 

income, and a continuation of recently observed harvest rates.
2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

2010 2020 2030 2040 2050 2060

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

120

100

80

60

40

20

0

120

100

80

60

40

20

0

120

100

80

60

40

20

0

600

500

400

300

200

100

0

24

20

16

12

8

4

0

240

200

160

120

80

40

0

240

200

160

120

80

40

0

600

500

400

300

200

100

0

2010 2020 2030 2040 2050 2060

240

200

160

120

80

40

0

Connecticut Illinois

Maryland

Minnesota

Delaware

Indiana

Missouri

2010 2020 2030 2040 2050 2060

120

100

80

60

40

20

0

Iowa

2010 2020 2030 2040 2050 2060

120

100

80

60

40

20

0

Massachusetts Michigan

New Hampshire

2010 2020 2030 2040 2050 2060

600

500

400

300

200

100

0

Maine

REMOVALS (million cubic feet per year)

100
2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

120

100

80

60

40

20

0

600

500

400

300

200

100

0

600

500

400

300

200

100

0

600

500

400

300

200

100

0

24

20

16

12

8

4

0

600

500

400

300

200

100

0

240

200

160

120

80

40

0

600

500

400

300

200

100

0

New Jersey

Pennsylvania

New York

Rhode Island

Wisconsin

Ohio

Vermont

West Virginia

FIGURE 4.21

State-level comparison of projected volume 

losses resulting from harvesting and land-use 

changes in the North, 2010 to 2060, under 

scenario A2-C that assumes high greenhouse 

gas emissions, large gains in population 

and energy consumption, moderate gains 

in income, and a continuation of recently 

observed harvest rates.

Removals due to land-use change

Harvest removals

REMOVALS (million cubic feet per year)

101
102 FUTURE FORESTS OF THE NORTHERN UNITED STATES

RATIO OF NET GROWTH TO REMOVALS 

The ratio of net volume growth to harvest removals 

is an important indicator of forest sustainability 

(Fig. 4.22). For periods when the ratio is >1.0, 

wood volume is accumulating faster than it is being 

harvested, and total live volume on timberland 

is increasing. Ratios <1.0 indicate periods when 

harvest removals exceed net growth and total live 

volume on timberland is being depleted; over the 

long run, such ratios are not sustainable. 

Figure 4.22 shows ratios predicted for harvesting 

removals but excludes removals that would result 

from the land-use changes discussed above. Thus, 

in some situations where the ratio of growth to 

harvest removals is >1.0 and volume per acre 

is increasing, total volume for a State or region 

could still decrease if large areas of forest are 

converted to urban or agricultural uses and the 

trees on the converted land no longer contribute 

to estimates of total volume. 

1.4

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

RATIO OF NET GROWTH TO HARVEST REMOVALS

FIGURE 4.22

Ratio comparing projected average annual net cubic foot volume growth to harvest, by decade, under seven scenarios, 

each representing a global greenhouse gas storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption 

(but with a balanced renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in 

population and energy consumption, and moderate gains in income; and B2 assumes low greenhouse gas emissions 

with moderate gains in population, income, and energy consumption. Scenario projections assume harvest will continue 

at recently observed levels (labeled –C) or increase to reflect increased harvest for bioenergy production (labeled –BIO). 

Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will gradually succumb to an expanding 

zone of infestation by the nonnative emerald ash borer.

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

2020 2030 2040 2050 2060
C h a p t e r F o u r 103

Total volume is projected to increase from 2010 

to 2030, decrease slightly until 2050, and then 

increase from 2050 to 2060 under A1B-C, A2-C, 

B2-C, and A2-EAB (Fig. 4.14). The decrease is 

attributable in part to conversion of forest land 

to other uses and in part to a reduced rate of 

growth associated with an aging forest resource. 

As forests age, the annual rate of volume growth 

typically decreases even though total volume per 

acre may increase gradually. When coupled with 

constant or increasing harvesting (Fig. 4.17), 

the outcome would be a decreasing growth-toremovals ratio from 2020 to 2040 (Fig. 4.22). 

The projected ratio in 2030 would be <1.0 for 

all scenarios, with harvesting exceeding growth 

for a decade or more. 

By 2060, the growth-to-removals ratio would 

increase to about 1.2 under scenarios A1B-C, 

A2-C, B2-C, and A2-EAB. In contrast, the ratio 

would continue to decrease for the scenarios 

with accelerated biomass removals, both for 

hardwoods and softwoods, with decreases 

reaching precipitous levels by 2060. These 

results suggest that the modeled harvesting levels 

associated with scenarios A1B-BIO, A2-BIO, and 

B2-BIO are not sustainable over the long term. 

This is not to suggest that in all situations and 

at all levels of removal, increased harvesting of 

biomass for energy is unsustainable, but simply 

that it is not sustainable at the rate assumed in 

the modeled scenarios over the long term. 

CONCLUSIONS

Differences among projected forest conditions 

from 2010 to 2060 are small under A1B-C, A2-

C, and B2-C. Although these scenarios are based 

on different assumptions—population growth, 

land-use change, greenhouse gas emissions, 

and climate response (see Chapter 2)—their 

projections of forest area, species composition, 

volume, volume growth, and removals are most 

notable for their similarities. The projected 

outcomes for A2-EAB, which models the 

elimination of ash species from northern forests 

as a consequence of the expanding range of 

emerald ash borer, are similar to those for A2-C 

in most other respects. Despite the anticipated 

impacts from conversion of forest land to urban 

uses, forest land area is projected to decrease 

by only about 5 percent over the next 50 years. 

The models predict that existing forests will get 

older and removals resulting from harvesting will 

remain nearly constant under scenarios A1B-C, 

A2-C, B2-C, and A2-EAB. The consequence for 

the region as a whole is that forest growth and 

succession rather than climate change or land 

conversion would be the primary forces driving 

forest changes. This is not to suggest that 

stability will prevail in all locales. For example, 

forests that adjoin urban areas will certainly 

be impacted more than rural forests, and tree 

species at the edge of their natural ranges would 

be the most vulnerable to changes in climate. 
104 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Scenarios with accelerated biomass harvesting 

stand in sharp contrast, because the greatly 

accelerated rates of harvesting would dominate 

projected future changes in volume, volume 

growth, and age-class diversity. 

Two other highly consequential outcomes emerge 

from these projections. First, the normal aging 

of northern forests over the next 30 to 40 years 

will likely end the rapid increase in standing 

volume that has characterized the past 50 years. 

As forests mature, the rate of volume increase 

slows. When this dynamic is coupled with small 

but persistent losses of forest land to land-use 

changes, the result would be wood volumes 

that remain relatively constant from 2010 to 

2050. This appears to be the outcome under all 

scenarios, although larger distinctions among 

alternative scenarios would emerge after 2050. 

The potential consequences of this trend are 

far reaching, and they are discussed further in 

Chapter 11. 

The second consequence, as illustrated by the 

scenarios with accelerated biomass harvesting, 

is potential for human intervention—through 

harvesting—to transform future outcomes 

over the long run. Changes in harvesting 

rates could take decades to alter the northern 

forest landscape; but eventually as new 

patterns of disturbance (increased biomass 

harvesting) accumulate over time, they can 

create substantial shifts in standing volume, 

volume growth rates, and the regional age-class 

structure. Thus, the future condition that is 

deemed desirable for northern forests may well 

require intensive management intervention over 

many decades to markedly alter the trajectories 

of change from the status quo. Implicit in the 

scenario projections are opportunities for 

improving forest vitality and diversity, supplying 

large quantities of wood to forest industries, and 

still sustaining a stable volume of wood.
C h a p t e r F o u r 105

Literature

Cited

Canadian Centre for Climate Modelling and Analysis. 2012a. CGCM2-coupled global climate 

model, medium resolution (T47). Ottawa, ON, Canada: Environment Canada. 

http://www.cccma.bc.ec.gc.ca/models/cgcm2.shtml (accessed August 20, 2014).

Canadian Centre for Climate Modelling and Analysis. 2012b. CGCM3.1-coupled global climate 

model (CGCM3), medium resolution (T47). Ottawa, ON, Canada: Environment Canada. 

http://www.cccma.bc.ec.gc.ca/models/cgcm3.shtml (accessed August 20, 2014).

Hunter, M.L., Jr.; Schmiegelow, F.K.A. 2011. Wildlife, forests and forestry. New York: Prentice Hall. 

259 p.

Ince, P.J.; Kramp, A.D.; Skog, K.E.; Spelter, H.N.; Wear, D.N. 2011a. U.S. Forest Products module: 

a technical document supporting the Forest Service 2010 RPA assessment. Res. Pap. 

FPL–RP-662. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products 

Laboratory. 61 p.

Ince, P.J.; Kramp, A.D.; Skog, K.E.; Yoo, D.; Sample, V.A. 2011b. Modeling future U.S. forest sector 

market and trade impacts of expansion in wood energy consumption. Journal of Forest 

Economics. 17(2): 142-156.

Intergovernmental Panel on Climate Change [IPCC]. 2007. Climate change 2007: synthesis 

report. Contribution of working groups I, II and III to the fourth assessment report of the 

Intergovernmental Panel on Climate Change. Geneva, Switzerland: Intergovernmental 

Panel on Climate Change. 104 p. http://www.ipcc.ch/publications_and_data/publications_

ipcc_fourth_assessment_report_synthesis_report.htm (accessed August 19, 2014).

Oliver, C.D.; Larson, B.C. 1990. Forest stand dynamics. New York: McGraw-Hill. 467 p.

Pan, Y.; Chen, J.M.; Birdsey, R.; McCullough, K.; He, L.; Deng, F. 2011. Age structure and disturbance 

legacy of North American forests. Biogeosciences. 8: 715-732.
106 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Shifley, S.R.; Aguilar, F.X.; Song, N. [et al.]. 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.

Smith, W.B.; Miles, P.D.; Perry, C.H.; Pugh, S.A. 2009. Forest resources of the United States, 2007. 

Gen. Tech. Rep. WO-78. Washington, DC: U.S. Department of Agriculture, Forest Service. 

336 p.

Wear, D.N.; Huggett, R.; Li, R.; Perryman, B.; Liu, S. 2013. Forecasts of forest conditions in regions 

of the United States under future scenarios: a technical document supporting the Forest 

Service 2012 RPA assessment. Gen. Tech. Rep. SRS–GTR-170. Asheville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research Station. 101 p.

Wilson, B.T.; Lister, A.J.; Riemann, R.I. 2012. A nearest-neighbor imputation approach to mapping 

tree species over large areas using forest inventory plots and moderate resolution raster data. 

Forest Ecology and Management. 271: 182-198.

Wilson, B.T.; Lister, A.J.; Riemann, R.I.; Griffith, D.M. 2013. Live tree species basal area of the 

contiguous United States (2000-2009). Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. http://dx.doi.org/10.2737/RDS–2013–0013 

(accessed September 23, 2013).
M a i n t e n a n c e o f Fo r e s t E c o s y s t e m 

Health and Vitality

OREST HEALTH WILL likely be threatened by a number of factors—including fragmentation, fire 

regime alteration, and a variety of diseases, insects, and invasive plants—along with global 

climate change (Krist et al. 2007, Tkacz et al. 2008). By itself, global climate change could dramatically 

and rapidly alter forest composition and structure (Allen and Breshears 1998, Allen et al. 2010). 

In conjunction with other threats, global climate change poses unique challenges to forest management 

by influencing forest dynamics at virtually all levels: disturbance regimes in forest ecosystems; rates 

of resource availability and utilization; canopy gap formation and woody debris dynamics; fire regimes; 

community composition; and forest distribution, structure, biodiversity, and biogeochemistry. Global 

climate change and other threats, in turn, could favor the establishment of invasive species. The 

following pages discuss effects of the most pertinent threats to the future health of forests in the 

Northern United States.

Ryan D. DeSantis and W. Keith Moser

Introduction

OVERVIEW OF THREATS 

The major biotic threats affecting northern 

forests (USDA FS, n.d.a) include:

• Oak decline

• Asian longhorned beetle 

(Anoplophora glabripennis)

• Gypsy moth (Lymantria dispar)

• Emerald ash borer (Agrilus planipennis)

• Hemlock woolly adelgid (Adelges tsugae)

• Beech bark disease 

• Sirex woodwasp (Sirex noctilio)

• Spruce budworm (Choristoneura fumiferana)

• Winter moth (Operophtera brumata) 

The threats vary considerably in the amount 

of forest area they affect and in their rate of 

spread, but collectively the North surpasses 

other regions of the country in the number of 

invasive forest pests per county (Liebhold 

et al. 2013). Some threats are not yet broadly 

established but pose serious concern for 

future forests nonetheless (Tables 5.1, 5.2). 

Although some have been successfully mitigated, 

contained, controlled, or even eradicated, others 

continue to spread unimpeded throughout 

northern forests (Fig. 5.1).

CHAPTER FIVE 107
• Forest threats like emerald ash borer, hemlock 

woolly adelgid, beech bark disease, and gypsy 

moth are causing major changes to forests.

• Insects such as the Asian longhorned beetle, 

spruce budworm, Sirex woodwasp, and winter 

moth have exhibited the potential to decimate 

a variety of tree species and could become more 

formidable to forests in urban and rural areas.

• Emerald ash borer could decimate the entire U.S. 

population of ash, which is culturally significant to 

Native American populations, valuable for specialty 

products, and valued in urban landscapes. 

• Decline-disease complexes such as oak decline are 

causing negative ecological and economic effects.

• Fire regime changes have substantially decreased 

the abundance of fire-tolerant and fire-adapted 

vegetation like oaks and have contributed to 

invasion by fire-intolerant mesic species.

• Invasive plants are contributing to habitat 

loss, ecosystem degradation, and decreasing 

species diversity.

• Other threats such as deforestation and fragmentation 

contribute to worsening forest health conditions, 

which provide increased opportunities for invasions 

to spread.

• Although forest threats have always existed, 

present-day challenges to forest ecosystem 

health, diversity, and resilience are unprecedented.

Key Findings

Nonforest

Forest

Forest insect and disease risk

FIGURE 5.1

Areas in the North predicted to 

have ≥25-percent mortality risk 

from oak decline, southern pine 

beetle, gypsy moth, pine engraver 

beetle, or hardwood decline by 

2030 (USDA FS, n.d.e). 

108 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Table 5.1—Occurrence of the major invasive insect and disease threats affecting forests in the North

Connecticut X X X X X X X

Delaware X X X

Illinois X X X

Indiana X X X

Iowa X X X

Maine X X X X X X

Maryland X X X X X

Massachusetts X X X X X X X

Michigan X X X X X X

Minnesota X X X X

Missouri X X

New 

Hampshire

X X X X X X X

New Jersey X X X X X X X

New York X X X X X X X X X

Ohio X X X X X X

Pennsylvania X X X X X X

Rhode Island X X X X X

Vermont X X X X X X

West Virginia X X X X X

Wisconsin X X X X X

a

USDA FS, n.d.a.

b

Slow the Spread Project, n.d.a.

c

USDA 2014

d

Purdue University, n.d.

State

Threat (X indicates presence)

Oak 

decline

Asian 

longhorned 

beetlea

Gypsy 

mothb

Emerald 

ash borerc

Hemlock 

woolly 

adelgida

Beech 

bark 

diseasea

Sirex 

woodwaspd

Spruce 

budworma

Winter 

motha

CHAPTER FIVE 109
Table 5.2—Major invasive insect and disease threats affecting forests of the Northern United States (Purdue University, 

n.d.; USDA FS, n.d.a.)

Oak decline Native 

decline 

complex

NA NA NA NA NA Combination of tree and site factors, drought, 

fungi, and insects. Periodic widespread mortality 

is triggered when all factors occur on sites 

with a high proportion of mature trees in the 

red oak group.

Asian 

longhorned 

beetle

Nonnative 

invasive 

insect

9 10 11 11 11 Currently infests a very small area, but has 

the potential to cause substantial regionwide 

damage. Feeds on a variety of species but 

prefers maples.

Gypsy moth Nonnative 

invasive 

insect

122 123 54 36 89 Currently found in 19 of 20 States, gradually 

spreading west from eastern Massachusetts. 

Feeds on a variety of species but prefers oaks.

Emerald 

ash borer

Nonnative 

invasive 

insect

187 228 282 330 403 Currently found in 16 of 20 States, spreading 

at approximately 12 miles per year from 

southeastern Michigan. Infests ash (Fraxinus

spp.) exclusively.

Hemlock 

woolly 

adelgid

Nonnative 

invasive 

insect

181 202 204 217 228 Currently found in 13 of 20 States, spreading 

northeast and southwest from Virginia. Infests 

eastern and Carolina hemlocks.

Beech bark 

disease

Nonnative 

decline 

complex

246 254 254 257 261 Currently found in 14 of 20 States and infects 

>90 percent of American beech trees >6 inches 

d.b.h. Affects American beech exclusively.

Sirex 

woodwasp

Nonnative 

invasive 

insect

1 6 2 1 5 Currently infests a very small area, but has 

the potential to cause substantial regionwide 

damage. Feeds on a variety of species but 

prefers pines.

Spruce 

budworm

Native 

invasive 

insect

23 38 29 33 24 Biggest defoliator of spruce and fir in eastern 

forests of North America. Periodic outbreaks 

are part of natural cycle in spruce-fir forests, 

but fire suppression and other anthropogenic 

disturbances increase the likelihood of outbreaks.

Winter 

moth

Nonnative 

invasive insect

8 13 13 14 17 Currently found in 6 of 20 States. Feeds on a 

variety of species.

Threat Type 

Counties (out of 1037) where severe 

outbreaks occurred Characteristics

2009 2010 2011 2012 2013

110 FUTURE FORESTS OF THE NORTHERN UNITED STATES
DECLINE-DISEASE COMPLEXES

Oak Decline

Oak decline is a disease complex recognized 

in North America since the middle of the 19th 

century (Hopkins 1902, Oak et al. 1996). 

Characterized by progressive branch dieback 

(Fig. 5.2) and eventual mortality, oak decline 

has been described as the combination of 

predisposing, inciting, and contributing factors 

primarily affecting trees from the red oak group 

(Manion 1991, Wargo et al. 1983).

Oak decline could be responsible for the 

loss of more than 2 billion square feet of oak 

basal area over the next 15 years, causing 

substantial economic losses throughout North 

America (Krist et al. 2007). In addition, 

considering that oak-hickory (Quercus spp. – 

Carya spp.) is the largest species group in the 

United States and accounts for 35 percent of 

all land in northern forests (Smith et al. 2009), 

the consequences of oak decline could be 

widespread (Fralish 2004). 

FIGURE 5.2

Oaks showing symptoms of oak decline. 

Photo by USDA Forest Service, Forest Health 

Protection, St. Paul Archive, Bugwood.org.

CHAPTER FIVE 111
Although studies have described relatively 

localized incidences of oak decline and probable 

primary causes (Fan et al. 2006) and have 

detected shifts in species composition away 

from oak (Rogers et al. 2008), broad-scale 

information on oak decline is lacking. Identifying 

the controlling factors and extent of oak decline 

across a broader scale will be critical for the 

management of oaks throughout northern forests.

A variety of long-term factors related to tree 

characteristics and growing site conditions 

predispose forests to oak decline:

• Affected tree species are primarily from 

the red oak group (Fan et al. 2008, Haavik 

and Stephen 2010) and include black oak 

(Quercus velutina), red oak (Q. rubra) and 

scarlet oak (Q. coccinea). 

• The average age of affected trees is 

>70 years (Oak et al. 1996).

• The size of affected trees is >10 inches d.b.h. 

(Johnson et al. 2002).

• Growth of affected trees is <0.08 inches per 

year (Shifley et al. 2006).

• The canopy position of affected trees is 

classified as intermediate or suppressed 

crown class (Fan et al. 2006).

• Stand basal area of affected stands is 

>56 square feet per acre (Fan et al. 2006).

• Affected trees are generally on poor-quality 

sites characterized by low productivity, 

shallow and rocky soils, dry ridges or slopes, 

and southerly aspect (Kabrick et al. 2008).

Short-term factors that increase susceptibility 

to oak decline (Houston 1987, Staley 1965, 

Wargo et al. 1983) by increasing tree stress include: 

drought (Huddle and Pallardy 1996, Jenkins and 

Pallardy 1995); structural damage from wind 

and ice (Shirakura et al. 2006); and defoliation 

by frost, ice, and insects such as gypsy moth 

and oak leaf roller (Archips semiferanus).

A number of factors contribute to the oak 

decline complex, including: fungal pathogens, 

such as Armillaria mellea and Hypoxylon spp. 

(Wargo et al. 1983), and insects from the 

family Buprestidae, such as two-lined chestnut 

borer (Agrilus bilineatus) and red oak borer 

(Enaphalodes rufulus) studied by Starkey et 

al. (2004). Both are opportunistic organisms 

that inhabit weakened trees. In combination 

with predisposing and inciting factors, these 

organisms increase stress and damage to trees, 

leading to mortality (Manion 1991, Wargo 1996).

Identifying areas that are particularly susceptible 

to oak decline can help land managers mitigate 

negative economic and ecological consequences. 

Although some factors such as drought are 

beyond the control of land managers, others can 

help identify conditions presently conducive to 

oak decline (for example species, age, and stand 

basal area). This information can help guide 

management strategies for reducing incidence 

and impacts of oak decline.

112 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Beech Bark Disease

Beech bark disease, a complex caused by several 

species of native and nonnative fungi (Nectria spp.) 

that are vectored by a nonnative scale insect 

(Shigo 1972), has caused 50- to 85-percent 

mortality of American beech (Fagus grandifolia) 

in the heaviest infected areas (Fig. 5.3) of northern 

forests (Houston 1994, Houston et al. 1979).

High

Low

BEECH DISTRIBUTION

Beech bark disease detected, 2013

FIGURE 5.3

Range of American beech and counties with 

beech bark disease in the North (USDA FS, 

n.d.a; Wilson et al. 2012).

Research suggests that <1 percent of beech trees 

exhibit genetic resistance to the disease complex 

and that beech bark disease infects >90 percent 

of American beech trees >6 inches d.b.h. (Latty 

et al. 2003, Sage 1996). Although smaller trees 

have lower susceptibility, beech bark disease 

has the potential to cause substantial basal-area 

loss of this species throughout its range. Because 

American beech is a major component of northern 

forests, the ecological effects of its demise would 

be far-reaching and could have negative effects on 

associated wildlife (Rosemier and Storer 2010).

Other Decline, Disease, and Pathogen Threats

Other threats to northern forest hardwoods 

include butternut canker (Sirococcus clavigignentijuglandacearum); thousand cankers disease 

(Geosmithia morbida) of walnut (Juglans spp.); 

oak wilt (Ceratocystis fagacearum); sudden oak 

death (Phytophthora ramorum); and decline of ash 

(Fraxinus spp.), maple (Acer spp.), and basswood 

(Tilia americana). As with oak decline, tree, site 

and stand conditions, abiotic factors, insects, and 

pathogens have been implicated as contributing 

agents (Manion 1991). In addition to decline, ash 

is susceptible to pathogens that cause chlorosis, 

witches’ brooms, and abundant epicormic branching 

(PSU CAS 1987).

CHAPTER FIVE 113
INVASIVE INSECT SPECIES

Asian Longhorned Beetle

Among the most serious of nonnative insect pests 

in northern forests is the Asian longhorned 

beetle. This species is capable of causing 

extensive damage to many hardwood species 

(Poland et al. 1998, Solomon 1995). As with 

emerald ash borer (described in a subsequent 

section of this chapter), Asian longhorned beetle 

larvae (Fig. 5.4) cause extensive damage to 

phloem and xylem in both healthy and declining 

host trees of all age classes (Haack et al. 1997).

Asian longhorned beetles feed on a variety of 

species, but they prefer maples (Acer spp.), a 

major concern given the importance of maples 

in northern forests and urban areas (Haack et al. 

2010). An estimated 111 million acres, 64 percent 

of all northern forest land (Fig. 5.4), are at risk 

from infestation. 

Although quarantines and eradication programs 

have been somewhat successful since the discovery 

of Asian longhorned beetle in the mid-1990s, they 

have come at a high economic cost (Hu et al. 2009). 

Adult Asian longhorned beetle on log 

showing damage. 

Photo by E. Richard Hoebeke, Cornell University, 

Bugwood.org

Asian longhorned beetle larva.

Photo by Kenneth R. Law, USDA APHIS PPQ, 

Bugwood.org

FIGURE 5.4

Spatial distribution of Asian longhorned beetle occurrence and 

susceptibility in the North; susceptibility is defined as the potential for 

introduction and establishment of a forest pest within a tree species 

or group (USDA FS, n.d.a; USDA FS, n.d.b; Wilson et al. 2012). 

High

Low

SUSCEPTIBILITY POTENTIAL

Asian longhorned beetle detected, 2013

114 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Consequently, the long-term and broad-scale 

effectiveness of inspection, interception, and 

eradication programs is uncertain. As of 2013, 

Asian longhorned beetle infestations were 

confined to Massachusetts, New Jersey, New York, 

and Ohio (Fig. 5.4). Because spread rates and 

host mortality probabilities have not yet been 

established, accurately projecting the future 

range of Asian longhorned beetle is challenging. 

However, it clearly poses a serious threat to 

northern forests for two reasons: (1) it can 

utilize numerous tree species as hosts; and 

(2) new introductions associated with imported 

goods are highly likely.

Gypsy Moth

Gypsy moth was brought to North America and 

accidentally released in 1869. Since then it has 

continued to spread across northern forests from 

east to west (Liebhold et al. 1995). Although it 

prefers white oak (Q. alba), host trees can include 

other oak species as well as aspen (Populus spp.), 

birch (Betula spp.), willow (Salix spp.), beech, 

hemlock (Tsuga spp.), pine (Pinus spp.), and 

spruce (Picea spp.). During population outbreaks, 

foliage of nearly all forest trees is at least partially 

consumed (Liebhold et al. 1995). Outbreaks can 

cause extensive defoliation, reducing tree growth 

and vigor. Repeated defoliation (Fig. 5.5) can result 

in mortality, often in conjunction with secondary 

fungal and insect invaders that attack weakened 

trees (Liebhold et al. 1995).

Susceptibility is defined as the potential for 

introduction and establishment, over a 15-year 

period, of a forest pest within a tree species or 

species group (USDA FS 2014; USDA FS, n.d.e). 

Potential for introduction considers the import of 

commodities (such as bricks, stones, metal, and glass 

materials) that are shipped with wood packing or 

pallets from countries where invasive insects are 

native, as well as the businesses and individuals 

engaged in importing and handling commodities of 

concern (represented as polygon ZIP code centroids). 

Potential for establishment considers the mean basal 

area for all host trees (Wilson et al. 2012), by ZIP 

code centroids with 18-mile buffers, to include the 

potential cumulative spread of the invasive insect 

over a 15-year period at the rate of 1 mile per year 

(Bancroft and Smith 2001). 

Susceptibility of Host Trees 

to Nonnative Invasive Insects

CHAPTER FIVE 115
A 2005 study suggested that the infested area is 

only 23 percent of the total area susceptible to 

the gypsy moth (Morin et al. 2005). Thus far, 

management consists of slowing the spread of 

its advancing border (Figs. 5.5, 5.6, 5.7) along 

a line from central Wisconsin southward and 

eastward to West Virginia (Tkacz et al. 2008).

FIGURE 5.5

Gypsy moth spread 

by year throughout 

Northern U.S. counties 

(USDA FS, n.d.a).

Gypsy moth larva feeding on foliage.

Photo by John H. Ghent, USDA Forest Service, 

Bugwood.org

Adult male (brown) and female (white) 

gypsy moths. 

Photo by USDA APHIS PPQ Archive, 

Bugwood.org

Before 1902

GYPSY MOTH SPREAD

1902 to 1914

1915 to 1949

1950 to 1974

1975 to 1989

1990 to 2000

2001 to 2010

Foliage consumed by late instar 

gypsy moth larvae. 

Photo by E. Bradford Walker, Vermont Department 

of Forests, Parks and Recreation, Bugwood.org

116 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Because females (Fig. 5.5) are generally 

incapable of flight, the rate of gypsy moth spread 

has been fairly slow (Slow the Spread Project, 

n.d.b). Given the slow spread rate and the fact 

that the gypsy moth is a resource generalist, 

modeling the expansion of the core infested area 

is important. The Gypsy Moth Slow the Spread 

Project has monitored the gypsy moth population 

expansion and has been successful at slowing its 

spread for more than 20 years; the average 

spread rate in northern forests since the 

project’s inception is about 3 miles per year 

(Tobin and Blackburn 2007), a gradual decrease 

from previous decades. Nevertheless, gypsy 

moth could occupy most of the northern forest 

by 2060 based on the current spread rate. This 

means that the continued success of the Gypsy 

Moth Slow the Spread Project will likely be 

crucial for sustaining the health of northern 

forests over the next 50 years.

FIGURE 5.6

Occurrence of gypsy moth throughout its 

range in the North in 2010; occurrence 

levels for areas in red range from “populated” 

to “infested” (Tobin and Blackburn 2007).

FIGURE 5.7

Occurrence and future risk of gypsy moth infestation in the North, 

estimated by volume of host tree species (Slow the Spread Project, 

n.d.a; USDA FS, n.d.c).

Low

2

3

4

High

GYPSY MOTH RISK LEVEL

Gypsy moth detected, 2010

Low

High

GYPSY MOTH OCCURRENCE

CHAPTER FIVE 117
Emerald Ash Borer

The nonnative invasive emerald ash borer (EAB) 

kills trees when damage from larval phloem 

galleries (Fig. 5.8) and outer sapwood cavities 

girdles their stems, disrupting water and 

nutrient transport (Cappaert et al. 2005).

In Asia, the beetles feed on ash throughout its 

native range but do not usually cause extensive 

damage because their hosts have developed 

defensive mechanisms, including production of 

volatile compounds (Eyles et al. 2007, Jendek 

1994, Rebek et al. 2008). Because North American 

ash species lack these defenses and are suitable 

hosts, infestations are usually fatal to affected 

trees (Poland and McCullough 2006). 

Since its introduction to North America 

in the 1990s, emerald ash borer has spread 

across 23 percent of the range of ash (Fig. 5.8) 

and killed millions of ash trees in northern forests 

(Gandhi and Herms 2010, Haack et al. 2002, 

Pureswaran and Poland 2009). It is spreading 

at about 12 miles per year, which suggests that 

it will occupy the entire range of northern ash 

(Fig. 5.9) before 2050 (DeSantis et al. 2013b). 

High

Low

SUSCEPTIBILITY 

POTENTIAL

Emerald ash borer detected, 2014

Emerald ash borer larva in phloem gallery. 

Photo by David Cappaert, Michigan State University, 

Bugwood.org 

FIGURE 5.8

Spatial distribution of emerald ash borer occurrence and susceptibility as a function of preferred 

host range, presence of urban ash trees, proximity of urban ash trees to natural forests, and 

past rates of phloem insect interceptions at U.S. ports of entry; susceptibility is defined as the 

potential for introduction and establishment of a forest pest within a tree species or group 

(USDA FS, n.d.b; Wilson et al. 2012).

Adult emerald ash borer. 

Photo by David Cappaert, Michigan State University, 

Bugwood.org

118 FUTURE FORESTS OF THE NORTHERN UNITED STATES
2015

2020

2025

2030

PROJECTED 100% ASH MORTALITY

FIGURE 5.9

Projected ash mortality caused by emerald ash borer in forests of the North by survey reporting unit within each State 

(source: FIA data) and based on the following assumptions: (1) emerald ash borer spread leads to 100-percent ash 

mortality once the spread subsumes each survey unit, (2) the spread in New York, Vermont, New Hampshire, and Maine 

will be influenced by present infestations in the Canadian municipalities of Ontario and Québec but not from infestations in 

other Canadian or U.S. locations, (3) the spread rate is 12 miles per year, with the initial extent of the insect based on 

detection in U.S.counties and Canadian regional municipalities (CFIA 2014; DeSantis et al. 2013b; Personal communication, 

2011, Paul Chaloux, Emerald Ash Borer National Program Manager, U.S. Department of Agriculture, APHIS, Riverdale, MD). 

Therefore, given enough time, nearly all ash 

trees in northern forests will likely be killed 

(Herms et al. 2010, Prasad et al. 2010).

Modeling results suggest infestations will 

contribute to a small decrease in the total number 

of trees and saplings in survey units measured by 

Forest Inventory and Analysis (FIA) from 2010 to 

2060, but also that the effects of ash mortality on 

the elm-ash-cottonwood (Ulmus spp.–Fraxinus

spp.–Populus spp.) group will differ among FIA 

inventory units. Although the loss of ash trees 

will alter forest species composition in non-urban 

forests measured by FIA, modeling results 

suggest EAB effects will not cause substantial 

forest-type group changes. In many areas, the 

impacts of emerald ash borer on ecosystem 

functioning could be minimal because increases 

in associated non-ash species may offset the 

loss of ash trees and their associated volume. 

Despite the assumption of 100-percent ash 

mortality, the transition from ash to other 

species could progress slowly as it is replaced 

by a variety of associated species. The emerald 

ash borer infestation could also produce canopy 

gaps that would facilitate an increase in native 

and nonnative invasive plant species (Gandhi 

and Herms 2010).

2035

2040

2045

2050

CHAPTER FIVE 119
Because this analysis focused on species groups, 

the geographic differences in the composition of 

each group are important including: 

• In Maine inventory units, ash is not present 

in significant amounts in any species groups.

• In Minnesota inventory units, the elm-ashcottonwood group is mainly composed of green 

ash (Fraxinus pennsylvanica) and black ash 

(F. nigra) whereas white ash (F. americana) 

is a very small component of the oak-hickory 

forest-type group and is largely absent from 

the elm-ash-cottonwood forest-type group. 

• In most northern inventory units, the ash 

component of the elm-ash-cottonwood foresttype group is larger than the ash component 

of the oak-hickory forest-type group. 

Furthermore, this modeling was not designed 

for urban areas not measured by FIA; therefore, 

urban areas could experience larger impacts 

because they support an extensive distribution 

of ash (Chapter 10).

Emerald ash borer infestation will likely have 

negative economic consequences for the wood 

products industry, in urban areas where ash has 

been widely used for landscaping and street trees, 

and for Native American tribes that value ash as 

a culturally important forest resource (Cappaert 

et al. 2005). Infestation could also affect wildlife 

and ecosystem functioning, especially on wet sites 

where black ash and pumpkin ash (F. profunda) 

are common (Burns and Honkala 1990b).

Hemlock Woolly Adelgid

Hemlock woolly adelgid was first discovered 

in North America in the Pacific Northwest in 

1924, but the insect was not reported to cause 

extensive hemlock damage until its 1951 

discovery in Virginia (Annand 1924, Gouger 

1971, McClure 1987). It has subsequently 

spread northeast and southwest from the initial 

Virginia infestation to its present range (Fig. 5.10) 

and has caused considerable damage to eastern 

hemlock (Tsuga canadensis) and Carolina 

hemlock (T. caroliniana).

Throughout the native range of hemlock woolly 

adelgid in Asia, as well as in western areas 

of North America, hemlocks appear to be 

resistant (McClure and Cheah 1999). However, 

mortality in northern forests can be >95 percent 

of infested hemlock trees, and defoliation of 

surviving trees can range from 50 to 75 percent. 

This means that the probability of hemlock 

mortality in infested stands can eventually 

reach 100 percent (Orwig and Foster 1998, 

2000). Mortality commonly occurs 4 to 10 

years after infestation in the northern range 

of hemlock woolly adelgid, but in its southern 

range mortality occurs more rapidly (Georgia 

Forestry Commission, n.d.). Because defoliated 

hemlock trees lose the ability to produce seed or 

to sprout, changes in tree species composition 

are common following infestation (Orwig and 

Foster 1998, Spaulding and Rieske 2010).

120 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Hemlock woolly adelgid infestation. 

Photo by Chris Evans, Illinois Wildlife Action Plan, 

Bugwood.org

FIGURE 5.10

Range of hemlock and counties with hemlock woolly adelgid in the 

North (USDA FS, n.d.a; Wilson et al. 2012).

Hemlock woolly adelgid detected, 2013

High

Low

HEMLOCK DISTRIBUTION

Establishing the spread rate of hemlock woolly 

adelgid and the probability of hemlock mortality 

is necessary for the management of hemlock in 

northern forests. However, this determination 

is complicated by (1) an asymmetric pattern 

of adelgid spread that is influenced by host 

abundance and (2) geographical variation in 

lethal cold winter temperatures (Parker et al. 

2002, Skinner et al. 2003). Although hemlock 

woolly adelgid can succumb to low winter 

temperatures, it appears to be more susceptible 

to extreme low temperature events than to 

long periods of exposure to typical winter low 

temperatures. The spread rate ranges from 

5 to 18 miles per year, and southwest spread is 

more rapid than to the northeast (where winter 

temperatures are lower). Thus, if winter low 

temperatures rise with climate change, the rate 

of hemlock woolly adelgid spread could increase 

across northern landscapes.

CHAPTER FIVE 121
FIGURE 5.11

Spatial distribution of Sirex woodwasp occurrence and susceptibility; 

susceptibility is defined as the potential for introduction and 

establishment of a forest pest within a tree species or group (Purdue 

University, n.d.; USDA FS, n.d.d ; Wilson et al. 2012). 

High

Low

SUSCEPTIBILITY POTENTIAL

Sirex woodwasp detected, 2011

Sirex Woodwasp 

Sirex woodwasp, which attacks both living and 

dead trees, was first detected in North America in 

2004 and has been increasingly present in solid 

wood packing materials at U.S. ports of entry 

(de Groot et al. 2006, Haugen and Hoebeke 2005). 

Native to Asia, Europe, and northern Africa, Sirex 

woodwasp was accidentally introduced to Australia, 

New Zealand, South Africa, and parts of South 

America, where it now causes ≤80-percent tree 

mortality in pine plantations (Haugen and Hoebeke 

2005). Although it currently infests a very small 

area of northern forests, it has the potential to 

cause substantial regionwide damage (Fig. 5.11).

122 FUTURE FORESTS OF THE NORTHERN UNITED STATES
FIGURE 5.12

Counties with spruce budworm and range of 

four host tree species—spruce, fir, pine, and 

larch—in the North (USDA FS, n.d.a; Wilson 

et al. 2012). 

Spruce budworm detected, 2013

High

Low

DISTRIBUTION OF CONIFER HOST

Spruce Budworm

Eastern spruce budworm is one of the most 

destructive defoliators in North America and is 

the major defoliator of spruce and fir (Abies spp.) 

throughout eastern Canada and the eastern 

United States (Kucera and Orr, n.d.; PSU CAS 

1987; Williams and Liebhold 2000). Over-mature, 

fire-suppressed stands of balsam fir (Abies 

balsamea), white spruce (Picea glauca), red 

spruce (Picea rubens), and black spruce (Picea 

mariana) are particularly susceptible to spruce 

budworm outbreaks, but other softwoods are 

also vulnerable (Fig. 5.12).

Spruce budworm feeds on foliage and new shoots, 

causing extensive root mortality (Burns and 

Honkala 1990a). Although periodic budworm 

outbreaks are part of a natural cycle in spruce-fir 

forests, fire suppression and other human 

disturbances leading to unhealthy stand conditions 

can predispose forests to spruce budworm outbreaks 

(Boulanger and Arsenault 2004). 

CHAPTER FIVE 123
Winter Moth

Winter moth was first introduced in eastern 

Canada in the 1930s and is now established 

throughout coastal New England and New York 

(Childs et al. 2011). Native to Europe, this 

insect became a serious pest in eastern Canada, 

western Canada, and the U.S. Pacific Northwest; 

it now threatens to infest northern forests. Winter 

moth larvae defoliate a variety of woody species, 

including oaks, maples, cherries (Prunus spp.), 

basswood (Tilia americana), ash, elms, apples 

(Malus spp.), and spruces (Childs et al. 2011). 

Other Invasive Insects 

Other major pests of North American conifers 

include the nonnative balsam woolly adelgid 

(Adelges piceae) and the native jack pine 

budworm (Choristoneura pinus), which could 

cause considerable ecological and economic 

damage (McCullough et al. 1994). 

Southern pine beetle (Dendroctonus frontalis) 

and pine engraver beetle (Ips spp.) cause 

extensive damage to yellow pines throughout 

the southeastern and western United States. 

Although damage is not as widespread in the U.S. 

North, both beetles are established and remain 

a threat. Other biotic agents that could pose 

serious problems to forests in the region include 

European spruce bark beetle (Ips typographus) 

and Mediterranean pine engraver 

beetle (Orthotomicus erosus).

NONNATIVE INVASIVE PLANTS

Invasive plants can cause considerable damage 

to ecosystem function, productivity, and 

species diversity (Lavoie 2010, McCullough 

et al. 2006). Other than habitat loss, invasive 

plants could pose the greatest threat to 

already imperiled North American plants and 

wildlife, with research showing that invasive 

plants contributed to the endangerment of 

49 percent of all imperiled wildlife species 

and 57 percent of the imperiled plant species 

in the United States (Wilcove et al. 1998). 

In northern forests, invasive plants of major 

concern include tree, shrub, vine, herbaceous, 

and grass species (Olson and Cholewa 2009). 

Among the most widespread are garlic mustard 

(Alliaria petiolata), common buckthorn 

(Rhamnus cathartica), Morrow’s honeysuckle 

(Lonicera morrowii), Japanese honeysuckle 

(Lonicera japonica), reed canarygrass (Phalaris 

arundinacea), Canada thistle (Cirsium arvense), 

Amur honeysuckle (Lonicera maackii), and 

autumn olive (Elaeagnus umbellata). Multiflora 

rose (Rosa multiflora), the most common 

invasive plant (Table 5.3) throughout 

northern forests (Kurtz 2013, Moser et al. 

2009, Webster et al. 2006), 

is described here.

124 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Table 5.3—Known occurrence of the major invasive plant threats in Northern States, 2005-2010 (Kurtz 2013)

Connecticut X X X X X X X X

Delaware X X X X X X

Illinois X X X X X X X X X

Indiana X X X X X X X X

Iowa X X X X X X X X

Maine X X X X X X

Maryland X X X X X X X X

Massachusetts X X X X X X

Michigan X X X X X X X X X

Minnesota X X X X X X X X X

Missouri X X X X X X X X X

New Hampshire X X X X

New Jersey X X X X X X X X

New York X X X X X X X X

Ohio X X X X X X X X X

Pennsylvania X X X X X X X X X

Rhode Island X X X

Vermont X X X

West Virginia X X X X X X X X X

Wisconsin X X X X X X X X X

Percenta 17 5 4 4 4 3 3 3 3

a

State

The percentage of FIA plots where the monitored species was recorded.

Threat (marked by an X)

Multiflora 

rose

Garlic 

mustard

Common 

buckthorn

Morrow’s 

honeysuckle

Japanese 

honeysuckle

Reed 

canarygrass

Canada 

thistle

Amur 

honeysuckle

Autumn 

olive

CHAPTER FIVE 125
A native of Japan, Korea, and eastern China, 

multiflora rose was introduced to the eastern 

United States as rootstock for ornamental 

roses in 1866 (Plant Conservation Alliance 

2005). This nonnative plant is extremely 

invasive in human-disturbed prairies, old fields, 

savannas, forest edges, and woodlands, forming 

dense thickets that can exclude native plant

species, thereby decreasing species diversity.

Multiflora rose has a broad ecological niche,

tolerating various light, moisture, and soil

conditions ranging from open fields to stream

banks and roadsides to dense woods. It also

produces abundant fruits that are widely consumed

by birds, enhancing seed distribution.

COMPOUNDING EFFECTS OF ABIOTIC AGENTS

Forest Fragmentation

Forest land constitutes nearly 172 million acres, 

or 42 percent of the total land area in the U.S. 

North (Smith et al. 2009). In recent decades, U.S. 

forests have experienced a net increase in area. 

However, this increase includes conversions of 

land from agriculture to forest land, and from 

forest land that is contiguous to forest land that 

is fragmented by urbanization. As forest land 

becomes increasingly fragmented, the plants and 

wildlife that depend on forest ecosystems are 

subjected to increased competitive interactions 

among populations (O’Neill et al. 1988, 1992; 

Riitters et al. 2002). Fragmentation not only 

decreases the amount of habitat suitable for 

species adapted to the forest interior but also 

increases forest edge, thereby raising the chances 

of invasion from other land cover types (for 

example, agricultural, urban, barren, grassland, 

and shrub land) or water (Gardner et al. 1993, 

Tkacz et al. 2008).

In this manner, fragmentation can alter forest 

composition and structure and reduce forest 

health by predisposing the spread of nonnative 

and invasive species, reducing biodiversity, 

and dramatically changing ecological processes 

(Boulinier et al. 2001, Smith et al. 2009). 

Multiflora rose in May. 

Photos by James H. Miller, 

U.S. Forest Service, 

Bugwood.org.

126 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Human-caused fragmentation involves more 

frequent disturbances that lack the randomness 

of natural fragmentation, and often is more 

permanent than natural fragmentation because 

it is frequently the result of conversion to 

urban uses (Best 2002). Northern forest area is 

projected to decrease 5 percent by 2060 (Miles 

and Wear 2015). However, this includes both 

losses and gains, with much of the new acreage 

resulting from conversion of agricultural land 

to fragmented forest. Forest fragmentation is 

discussed in more detail in Chapter 3; conversion 

of urban and rural forest land is discussed in 

Chapter 10.

Climate Change

Climate is a major force that influences the 

composition of plant communities. Disturbances 

such as drought or fire often influence forests by 

promoting early successional, light-demanding 

vegetation (Abrams 1996). By changing successional 

pathways, severe or prolonged droughts can 

increase tree mortality and contribute to shifts 

in species composition (Allen et al. 2010). Although 

decreased available precipitation, increased 

temperature, and increased extent and severity 

of drought can have substantial impacts on 

forest ecosystems, the importance of these 

factors drastically increases when coupled with 

other forest threats, such as invasive species. 

The Intergovernmental Panel on Climate Change 

generally suggests future global mean temperature 

will increase (Solomon et al. 2007). As with 

other invasive insect ranges, climate change 

may create warmer conditions favorable to 

nonnative invasive plant and insect habitat 

in areas that are currently unsuitable. For 

example, while the northward spread of 

insects has historically been limited by winter 

conditions, insects such as the emerald ash 

borer have rapidly spread to the north recently 

thanks to increasing winter temperatures 

(Chown and Terblanche 2006). However, the 

northern spread of emerald ash borer can be 

affected by complex weather relationships 

(DeSantis et al. 2013a).

Biotic and abiotic forces have altered forest 

ecosystems for millennia. Research suggests 

that climate has always played an important 

role in determining forest composition and 

structure (Allen 2009, Swetnam and Betancourt 

1998, van Mantgem et al. 2009). One example 

is the decline of eastern hemlock in North 

America during the period that started 4700 

years ago (and ended 3800 years ago), which 

one study (Filion and Quinty 1993) attributes 

to increasingly wet conditions and another 

(Allison et al. 1986) attributes to a pathogen. 

Both studies reported an eventual increase 

in eastern hemlock following the decline, and 

taken together they suggest that the interaction 

of biotic and abiotic forces is important to 

long-term changes in forest composition and 

structure. Along with evidence that climate is 

changing at an increasingly rapid pace (Solomon 

et al. 2007), there is the growing concern 

that background rates of forest change could 

increase and substantially alter the composition 

and structure of forests globally.

CHAPTER FIVE 127
Fire

Wildfires pose several concerns, including 

risks to human life and property, species loss, 

adverse effects on forest productivity and health, 

carbon-cycle alteration, and air pollution. With 

a few exceptions, the annual amount of acreage 

burned in the North from 2004 to 2013 did 

not substantially change (Table 5.4). Although 

prescribed fire was used, most of the acreage that 

burned in northern forests was caused by wildfire.

Both the presence and absence of fire can 

have serious negative implications for forest 

ecosystems. The long-term effects of fire can be 

especially important for wildlife because of the 

potential for substantial changes to habitat and 

food sources (Bendell 1974). The long-term 

effects of fire suppression have indirectly altered 

forest health by influencing stand structure and 

composition (Lorimer 1985). For example, in 

northern forests that were once dominated by 

fire-adapted and fire-tolerant woody species 

or herbaceous vegetation, the suppression or 

decreased use of fire has enabled encroachment 

by other woody plants (Mutch 1970). When human 

population pressures and forest fragmentation 

decreased opportunities for safe burning, 

ecosystems historically maintained by humancaused fire often experienced shifts in species 

composition (Guyette et al. 2002, Nowacki and 

Abrams 2008).

The result was a decrease in fire-tolerant and 

fire-adapted vegetation such as oaks, which 

have substantially decreased in abundance in 

northern forests (Abrams 1992). Similar trends 

have occurred with yellow pine species in 

southern forests (Aldrich et al. 2010, Lafon et al. 

2007, South and Buckner 2003). Given the 

amount of oak and pine in the region (30 percent 

of volume according to 2010 FIA data), such 

changes in species composition have broad-scale 

economic and ecological implications (Beisner 

et al. 2003, Scheffer et al. 2001). In addition, 

a decrease in fire is thought to have played 

an important role in oak decline throughout 

the range of northern oaks (Brose et al. 2001, 

Lorimer et al. 1994, Van Lear 2001).

Forests play an essential role in the global 

carbon cycle by acting as carbon sinks or 

sources. To address concerns about the effects 

of increasing atmospheric carbon dioxide on 

global climate, policy makers need accurate 

quantification of forest ecosystem carbon 

stocks, carbon fluxes between forests and the 

atmosphere, and effects of disturbances on 

forest carbon stocks and fluxes (Schulze et al. 

2000). Depending on the forest type and 

stage of stand development, forests can act as 

carbon sinks or sources, but human-caused and 

natural disturbances can quickly change forest 

carbon sinks into sources. For example, standreplacing wildfires that consumed jack pine 

(Pinus banksiana) forests in northern Michigan 

caused the rapid conversion of a carbon sink 

to a carbon source due to the consumption of 

respirable substrate (Rothstein et al. 2004). 

128 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Table 5.4—Acres burned by wildfire in North (NIFC, n.d.) 

Connecticut 94 263 419 288 893 246 262 244 417 238 336

Delaware 0 231 126 153 36 44 32 0 0 17 64

Illinois 476 733 1,040 611 16 1,155 630 2,019 1,848 55 858

Indiana 3,351 2,568 3,468 5,730 1,064 4,706 132 530 1,846 806 2,420

Iowa 585 10,566 827 684 2,217 8,565 2,722 2,145 349 14,704 4,336

Maine 960 822 1,794 423 535 481 318 116 489 743 668

Maryland 3,147 4,350 6,396 5,790 2,421 4,956 1,577 8,310 838 182 3,797

Massachusetts 1,534 2,530 2,821 2,687 2,918 1,144 2,117 545 1,027 879 1,820

Michigan 2,754 4,684 8,128 23,344 2,675 3,899 11,441 1,865 28,377 940 8,811

Minnesota 42,881 24,944 66,540 163,111 19,712 13,858 33,969 135,650 64,682 22,107 58,745

Missouri 32,542 19,621 24,308 10,537 9,765 42,241 17,620 55,395 26,456 1,660 24,015

New Hampshire 289 172 472 212 195 159 145 48 221 137 205

New Jersey 549 1,532 2,514 20,849 3,322 1,131 10,630 794 3,095 1,430 4,585

New York 390 656 2,508 855 3,629 1,404 2,066 232 2,127 1,073 1,494

Ohio 3,126 2,920 3,866 1,578 1,078 10,962 3,995 1,203 1,250 152 3,013

Pennsylvania 3 1,100 6,822 1,047 7,694 6,132 3,203 636 3,157 1,788 3,158

Rhode Island 81 84 120 61 132 45 23 27 41 27 64

Vermont 244 574 232 230 168 192 86 14 405 281 243

West Virginia 6,007 2,762 13,663 7,149 10,296 13,591 14,319 2,680 6,703 8,577 8,575

Wisconsin 2,385 6,311 2,287 4,713 1,050 3,746 2,185 719 2,880 9,196 3,547

Total 101,398 87,423 148,351 250,052 69,816 118,657 107,472 213,172 146,208 64,992 130,754

State

Area burned (acres) 

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Average

CHAPTER FIVE 129
However, the extraordinary regeneration abilities 

of jack pine—producing seed from serotinous 

cones (Fig. 5.13)—enabled rapid establishment 

and conversion of a carbon source to a carbon 

sink. In other cases, the depletion of carbon sinks 

can sometimes be long term or even permanent 

(Balshi et al. 2009, Metsaranta et al. 2010).

FUTURE THREATS

In 1900, American chestnut (Castanea dentata) 

could be found throughout northern forests. 

Little did people know that one of the most 

dominant hardwood species would be devastated 

within 50 years. Since that time, Dutch elm 

disease (Ophiostoma nova-ulmi) has decimated 

American elm (U. americana), hemlock 

woolly adelgid has begun to decimate hemlock, 

and more recently, emerald ash borer has 

begun to decimate ash in northern forests. 

In addition, plants such as multiflora rose, 

intentionally introduced, have rapidly spread, 

competitively excluding native plant species. 

Asian longhorned beetle, Sirex woodwasp, 

and other recently introduced forest pests 

threaten to devastate maples and pines. Over 

the coming decades, new threats, including 

those from climate change and extreme storms, 

will inevitably materialize and negatively 

impact northern forests. Unfortunately, as has 

previously occurred, some destructive threats 

will emerge for which we lack the capacity to 

predict. Keeping forests intact and managing 

for forest health are the best defenses for 

resilience to these forest threats.

FIGURE 5.13

Serotiny of jack pine cones. 

Photo by Paul Wray, Iowa State 

University, Bugwood.org.

130 FUTURE FORESTS OF THE NORTHERN UNITED STATES
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CHAPTER FIVE 141
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142 FUTURE FORESTS OF THE NORTHERN UNITED STATES
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CHAPTER FIVE 143
144 FUTURE FORESTS OF THE NORTHERN UNITED STATES
C h a p t e r S i x 145

C o n s e r v a t i o n a n d M a i n t e n a n c e 

of Soil and Water Resources

Soil

Human society depends on soils for many essential 

services, which Blum (2005) categorized into six 

environmental, social, and economic functions: 

(1) biomass production, (2) protection of humans 

and the environment, (3) gene reservoir, (4) physical 

basis for human activities, (5) source of raw 

materials, and (6) soil and cultural heritage. 

Of these, the first three are most important from 

an environmental perspective: 

• Biomass production is the aspect of soils that 

addresses the production of food, fiber, and 

fodder (Foley et al. 2005, Matson et al. 1997). 

• The protection of humans and the environment 

includes those functions where soils filter, 

buffer, and transform water and gases among 

the terrestrial, hydrologic, and atmospheric 

systems—described narrowly by Gilliam 

(1994) and more broadly by Lowrance et al. 

(1984). Not all transformations are positive; 

for example, soil microbes convert elemental 

mercury to methyl mercury which bioaccumulates 

in the food chain (Jeremiason et al. 2006, 

Mitchell et al. 2008). 

• As a gene reservoir, soil biota are critical to 

nutrient mineralization, which supports plant 

productivity (Van Der Heijden et al. 2008). 

Brian G. Tavernia, Mark D. Nelson, Titus S. Seilheimer, Dale D. Gormanson, 

Charles H. Perry, Peter V. Caldwell, Ge Sun

Introduction

OREST ECOSYSTEM PRODUCTIVITY and functioning depend on soil and water resources. But the 

reverse is also true—forest and land-use management activities can significantly alter forest 

soils, water quality, and associated aquatic habitats (Ice and Stednick 2004, Reid 1993, Wigmosta 

and Burges 2001). Soil and water resources are protected through the allocation of land for that 

purpose or through appropriate management regimes and best management practices (Blinn 

and Kilgore 2001, Young 2000). Because the biophysical linkage between soils and hydrological 

functions is strong, conservation land-use designations and best practices for forest management 

usually combine soil and water conservation objectives (California Department of Forestry and 

Fire Protection 2014, Minnesota Forest Resources Council 2013). In the absence of widespread, 

long-term direct measures of water and soil condition, information on land use, management activity, 

and application of best practices can serve as useful indicators of efforts to conserve soil and 

water resources. 
146 FUTURE FORESTS OF THE NORTHERN UNITED STATES

• Forest ecosystem productivity and function 

depend on soil and water resources. Forest and 

land-use management activities can significantly 

alter forest soils, water quality, and associated 

aquatic habitats. 

• Across the Northern States, 76 million people 

depend on public and private forests for high 

quality water supplies.

• Thirty-six million acres (21 percent) of northern 

forest land is under Federal and State management. 

• For private forest lands, which account for most 

of the forest land area in the North, Forest 

Stewardship Management Plans are of particular 

value in helping forest owners manage their 

resources sustainably; excluding land held by the 

forest products industry, 10 percent of private 

acreage is managed under such plans, but the 

covered area for individual States ranges from 

<5 percent to >30 percent.

• All Northern States except Minnesota have 

suboptimal conditions for one or more soil 

attributes on ≥50 percent of their survey plots; 

Delaware and Maryland have the largest number 

of plots with soil conditions that limit potential 

tree growth.

• The 10 States with the highest levels of suboptimal

soil conditions are all located in the East.

• Only 2 of 551 northern watersheds have water 

supplies that currently are inadequate to meet 

societal demands, a number that is projected to 

increase substantially by 2060.

• Watersheds with the potential to produce high 

quality water tend to be found in forested regions 

of the upper Midwest, New England, and along 

the Appalachian Mountains; watersheds with 

relatively low potential to produce high quality 

water are located in the Midwest.

• State and Federal forest land area is expected to 

remain relatively constant for the next 50 years; 

however, expanding human populations are 

expected to place more pressure on public lands 

for freshwater supplies and other benefits.

• Under a future of moderately growing population 

and rapidly growing income, urban areas are 

projected to increase by 78 percent and forest 

areas are projected to shrink by 6 percent.

• Although total area of Federal and State forest 

land in the North is projected to remain stable 

through 2060, the relative area of 0.29 acres per 

person is projected to decrease to 0.23 acres per 

person due to increasing human population.

• On average for all northern watersheds, the 

amount of water used to meet societal demands is 

projected to increase under most future scenarios; 

for most watersheds, supplies would remain 

adequate, but the number of watersheds with the 

potential for shortfalls likely would increase. 

• By 2060, the potential to supply high quality 

water is projected to decline for most watersheds 

in the North.

• At a regional scale, climate projections 

had more impact on future water supply 

and stress than did land-use or population 

projections, but there were some exceptions 

for individual watersheds.

Key Findings 
C h a p t e r S i x 147

Because soil is a limited resource, its six 

functions need to occur in appropriate spatial 

and temporal combinations to ensure its 

sustainability (Blum 2005). 

Soils are dynamic and are commonly described 

as the result of five factors: climate, organisms, 

topographic relief, geologic parent material, and 

the passage of time (Jenny 1941). Understanding 

which of these factors are likely to remain stable 

and which are subject to change is critical to 

projecting future forest soil resources. Of the five 

soil-forming factors, global climate change and 

land-use change—two factors that are highly 

variable even in the current environment—will 

most strongly influence climate and organisms. 

Topographic relief and parent material can 

both change over geologic time, but they are 

relatively stable over human time scales, except 

when altered by direct human activities (such 

as surface mining or construction). One notable 

exception is the formation of soil along riparian 

corridors, where changes in river flow and 

sediment delivery resulting from land-use or 

climate change would influence the formation 

and character of future soils.

Climate influences soil formation and properties 

in two primary ways: temperature and precipitation. 

Temperature influences the physical, chemical, 

and biological processes that first transform 

raw parent material (such as sand, clay, and 

rock) into soil and then guide the development 

of the soil profile. Precipitation, both the total 

amount and the intensity of individual events, 

influences the transport of minerals through the 

soil profile to deeper horizons and eventually to 

local streams and other surface waters. Also, 

excessive water can lead to anaerobic conditions 

that again influence the physical, chemical, and 

biological processes in soil. Some soil properties 

change along a continuous temperature and 

precipitation gradient, but others strongly respond 

to thresholds, as reported by Dahlgren et al. 

(1997) in a study that placed the threshold along 

the effective winter snow line for soils in the 

California Sierra Nevada, thus reinforcing Jenny’s 

(1941) conclusion that changes in climate can 

produce changes in soil properties. Such changes 

in soil properties could result in trees becoming 

nutrient stressed as changes also occur in the 

underlying cycles and interactions of essential 

mineral cycles (St. Clair et al. 2008). Of particular 

concern is the availability of soil minerals to 

trees, particularly in landscapes that experience 

precipitation decreases along with temperature 

increases. Oren et al. (2001) reported that soil 

nutrient and water deficits could limit tree growth 

responses to carbon dioxide enrichment. In 

addition, Saxe et al. (1998) reported that nutrient 

uptake by roots occurs primarily in the soil 

solution, and uptake can be greatly reduced with 

increasing drought events.
148 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Organisms are important to soils. Not only do 

animals and plants modify the landscape in which 

they live, but they are also subject to climate 

and land-use changes, further exacerbating 

direct influences of climate and land use on 

soils. Observing forests developing on similar 

parent material, Finzi et al. (1998) found that 

community dynamics significantly influence 

surface soil acidity and base cation availability, 

thus altering potential plant productivity. 

Another important contributor to forest soils is 

the glaciation that covered most of the northern 

landscape; these events essentially restarted the 

process of soil formation and plant succession. 

Many plants and animals can migrate across 

great distances, an ability that some important 

soil forming organisms lack. Earthworms, which 

are nonnative throughout much of the North, are 

very slow colonizers. But these species have a 

profound influence on forest soils. Holdsworth et 

al. (2007) found that one of the best predictors of 

nonnative earthworm invasion is the distance to 

nearest road (Wisconsin) or cabin (Minnesota). 

Humans clearly play a role in the introduction 

of these nonnative “ecosystem engineers.” 

Hale et al. (2005) found that the introduction 

of European worms to northern forests greatly 

reduces forest floor thickness, even to the point 

of complete removal. The invasion of nonnative 

earthworms could increase the pressure placed 

on forests already stressed by changing climates 

(Bohlen et al. 2004). 

One of the concerns about land-use and climate 

change impacts on soils is the potential for 

feedback loops. According to Eswaran et al. 

(1993), the amount of organic carbon stored in 

soil is three times larger than what is stored 

in aboveground biomass, and double what is 

stored in the atmosphere. Although little is 

known about the potential complex feedbacks 

between a changing climate and soil carbon 

stocks, Davidson and Janssens (2006) reported 

that wetland, peatland, and permafrost soils are 

the most vulnerable to changing land-use and 

climate patterns.

Water

Adequate and reliable water supplies are critical 

to the proper functioning of social, economic, 

and ecological systems, all of which depend on 

forested watersheds for a broad range of aquatic 

benefits (or ecosystem services): freshwater 

supplies, stream-flow regulation, water-quality 

maintenance, and habitat for fish and other 

organisms. To varying degrees across the North, 

76 million people rely on public and private 

forests for high quality water supplies (USDA FS 

2005). Understanding the links among forests, 

water resources, and people is critical both for 

developing forest protection and management 

policies across the United States (Steen 2004). 

Many forest management policies and practices 

are designed to reduce negative impacts 

on water resources or improve conditions in 

places where disturbances have occurred 

(Aust and Blinn 2004). 
C h a p t e r S i x 149

As populations continue to grow in the region 

(Zarnoch et al. 2010), policy makers and managers 

need information about the potential effects of 

changing climate and land-use patterns on the 

ability of forested watersheds to supply sufficient 

water to this burgeoning population (Chapter 2). 

Changing climate and land-use patterns have 

direct impacts on the quantity and quality of 

water resources at multiple scales. As temperatures 

increase, increasing rates of water loss through 

evapotranspiration can reduce the surface-water 

volume levels (Gleick 2000, Levin et al. 2002, 

National Assessment Synthesis Team 2000). 

However, volume will also be impacted by 

changes in the amount, timing, and variability 

of precipitation in addition to the frequency and 

intensity of storm events (Gleick 2000, Levin et 

al. 2002, National Assessment Synthesis Team 

2000). Assessments of future water supplies 

also need to account for changes in land cover 

and land-use patterns, which have been shown 

to greatly affect the water balance in long-term 

watershed studies (Ford et al. 2011, Piao et al. 

2007, Swank and Douglass 1974). 

Changing land-use patterns can also affect 

water quality. Land-use conversions can change 

landscape-level forest composition and structure 

and can increase loading of sediments and other 

potential pollutants (Osborne and Kovacic 1993). 

In particular, urbanization changes the quantity 

and character of trees and forests and increases 

the area of impervious surfaces (such as roads 

and parking lots). This reduces the ability of 

precipitation to infiltrate the soil (Sun and Lockaby 

2012), resulting in an increase in the amount of 

surface runoff and, consequently, in the amount 

of sediment being delivered to streams (Booth 

and Jackson 1997). Other pollutants could also 

increase with an increase in urbanization. For 

example, wastewater and fertilizers increase 

phosphorous concentrations in urban streams 

(Paul and Meyer 2001), and in the western Great 

Lakes, turbidity is correlated with tree canopy 

disturbances and phosphorus is correlated with 

the amount of urban acreage in a watershed 

(Seilheimer et al. 2013). 

Forest management practices can affect both the 

quantity and quality of water (National Research 

Council 2008), with impacts varying across areas, 

forest and treatment types, soils, climate, and 

harvesting intervals. As an example of effects on 

quantity, canopy removal during harvesting can 

reduce evapotranspiration levels, increasing water 

yield. This effect is usually transient, however, as 

the regrowth of young forest eventually results 

in a decrease of water yield. With respect to 

water quality, disturbances to soil during forest 

management activities could result in greater 

sediment delivery to surface waters. Recognition 

of the links between forest management practices 

and water resources has led to the development of 

best management practices (page 152) that focus 

on minimizing or eliminating deleterious effects 

(Aust and Blinn 2004).
150 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Across the region, providing for future population 

and economic growth while meeting biodiversity 

conservation goals will require sufficient quantities 

of high quality water. Assessments of water 

quantity and quality under a range of plausible 

future climate and land-use scenarios answer a 

range of questions, including how levels of water 

quantity and quality might change in the future, 

where these changes might be the greatest, and 

whether water supplies will be sufficient to meet 

human demands. Answers to these questions will 

help inform policy and management decisions that 

could alter the trajectories of changing climate and 

land-use patterns. 

INDICATORS OF SOIL AND WATER 

CONSERVATION AND MAINTENANCE

Some indicators of soil and water conservation 

and maintenance can be addressed using available 

data, for both current conditions and future 

projections, but others are much more difficult to 

project. For example, quantifying current acreage 

of lands enrolled in conservation easements and 

incentive programs is feasible, but predicting the 

future direction of legal, institutional, and policy 

conditions that affect these programs is much 

more difficult; so for these and other indicators, 

future conditions cannot be reliably predicted 

(Chapter 9). 

Where Forest Management Focuses on Soil 

and Water Protection 

The extent to which soil (land) and water 

resources in forested areas are protected by 

legislative or administrative designation or where 

their protection is the primary management 

focus is one indicator of forest sustainability. 

Such designations or management protections 

guard against degradation of soil resources, 

maintain soil quality, and prevent impairment of 

water supplies intended for public consumption 

(USDA FS 2011). A closely related indicator 

examines the proportion of forest management 

activities that meet best management practices 

or other relevant legislation to protect soil and 

water resources. Chapter 9 provides additional 

information about voluntary certification 

programs designed to protect soil and water 

resources, as well as the legal, institutional, and 

economic framework for forest conservation and 

sustainable management. 

Because of the strong biophysical link between 

soils and hydrological functions, conservation 

land-use designations and best practices for 

forest management usually combine soil and 

water conservation objectives. Because data for 

these indicators was not available, information 

from State forestry reports of management 

activity and land-use designations was used. 
C h a p t e r S i x 151

The lack of consistency among reporting protocols 

presented considerable challenges in addressing the 

indicators across an entire region (Carpenter 2007) 

and complicated efforts to predict public and private 

forest land-use patterns, practices, laws, and 

regulations and their associated impacts over the 

next 50 years. However, this is not to downplay the 

challenges of expanding human populations with 

resulting pressures on land and water resources. 

We simply describe best management practices 

and stewardship plans. Using a moderate 

population forecast, we show that increases in the 

forested land base over the last century are coming 

to an end, and per capita decreases are projected 

for the future. The public land ownership footprint 

is relatively small on the northern landscapes, and 

the private sector is where most land-use change 

and forest management will happen. The magnitude 

of forest area losses can have implications for the 

natural processes critical to ecosystem health as 

well as the environmental services that derive 

from clean air, water quality, and carbon storage 

(Carpenter 2007).

Stewardship Challenges for Private Forests

The northern forest landscape has been shaped 

by the stewardship ethic observed by generations 

of private landowners, local conservation and 

recreation groups, and public agencies. Lessons 

were learned from overzealous harvesting at 

the end of the 19th century. Now the lands and 

waters that will be needed to meet current and 

future conservation and recreation demands are 

under pressures unknown a generation ago. 

Private lands that once supported high-quality 

family farmlands, working forests, and wetlands 

are now being converted to a variety of other uses 

(Chapter 10). The rural countryside is being divided 

into smaller parcels as an increasing number of 

people can afford a 10-, 20- or 40-acre private 

getaway. Often, this parcellation (Chapter 3) is selffulfilling—the more that areas become fragmented, 

the more pressure there is to subdivide remaining 

parcels (Wisconsin DNR 2010). 

Because most northern forest land is in private 

ownership (74 percent), private forest lands may 

well provide the bulk of wood products and other 

accessible natural resource raw materials like 

sands, gravels, oil, and gas minerals. Thus, they 

deserve careful consideration and thoughtful 

conservation measures to preserve soil and water 

benefits into the future. Partnerships involving 

government and nongovernment organizations, 

companies, and individuals are not only desirable 

but also necessary for the conservation and 

maintenance of forest ecosystems and the 

economic and social benefits that derive from 

them. Laws, regulations, and voluntary approaches 

all have a place in efforts to achieve sustainability 

(Carpenter 2007, Chapter 9). 
152 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Unlike governments that manage their forests 

with specialized staffs and resources, private 

citizens with small parcels often lack resources 

for developing their own plans for resource 

management. State and Federal agencies 

make an effort to provide technical assistance, 

help landowners recognize special areas, and 

support them in managing these areas for 

special values. In today’s environment, progress 

toward sustainability requires continuous public 

awareness and discourse. 

Based on a review of public programs and options 

for private forestry, Sampson and DeCoster (1997) 

concluded that Federal support is not keeping 

pace with changing demographics. In effect, the 

mosaic of U.S. forests, including those in the 

North, is managed by owners who have diverse 

cultural backgrounds and objectives and who 

operate within a complex framework of Federal, 

State, and local laws and regulations, privateproperty rights, and public land-management 

policies. Understanding the linkages among these 

factors and their aggregate impact is critical to 

understanding future forest sustainability. 

Sampson and DeCoster (1997) recommend 

expanding service to rapidly developing 

communities and suggest more sophisticated 

marketing by policy leaders to target the needs of 

specific audiences. Implementing such a strategy 

would require more detailed information on 

forest landowners and the relationship between 

communities and the forest resource; however, 

inadequate funding for investments in marketing 

is cited as the major impediment to adopting 

these changes (Carpenter 2007). 

More detailed information about forest ownership, 

management, and stewardship can be found in 

Chapter 9.

Best Management Practices

Best management practices are recommendations 

for working on the land. Forestry best management 

practices include a set of preventative measures 

designed to control or reduce the movement of 

sediment, nutrients, pesticides, or other pollutants 

from soils to surface waters (Aust and Blinn 2004). 

They capture and maintain a collective wisdom 

about how to protect the environment during 

operations such as harvesting and road building. 

They are meant to guide and regulate daily routine 

activities undertaken in the course of the (often) 

small projects that, taken together, alter stands, 

landscapes, and regions. They are designed to 

avoid excessive loss of productive soils from the 

landscape and to protect receiving surface waters 

from the excess sediment loads that result from 

accelerated erosion. 

Best management practice categories include 

preharvest, stream management, logging roads, 

stream crossings, site preparation, chemical 

use, roads-to-bed, and wetlands (USDA FS 

2011). All Northern States have some form of 

best management standards and guidelines 

across three general areas of management: 

silviculture, water (wetlands) and soils, and 

wildlife or biodiversity (Table 6.1). They can be 

voluntary or mandatory and can take the form 

of recommendations, guidelines, or standards. 

Once established, their effectiveness depends on 

whether they are maintained and whether their 

use is promoted (USDA FS 2011). 
C h a p t e r S i x 153

Table 6.1—Forest management standards and guidelines and their monitoring across ownership types for the North 

(Shifley et al. 2012); whether standards and guides are mandatory or voluntary; and whether the purpose of monitoring is 

for compliance with the standard or guideline or for effectiveness of the standard or guideline. (Note that information for 

Connecticut was missing from Shifley et al. [2012], but is included here.)

State forests Private forests State forests Private forests State forests Private forests

Connecticut Voluntary; 

no monitoring

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

Voluntary; 

no monitoring

Voluntary; 

no monitoring

Delaware Mandatory; 

monitor for 

compliance

None Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

None None

Iowa Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Illinois Monitor for 

effectiveness

Voluntary; 

no monitoring

Monitor for 

effectiveness

Voluntary; 

no monitoring

None None

Indiana Mandatory; 

no monitoring

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Mandatory; 

no monitoring

Voluntary; 

no monitoring

Massachusetts Mandatory; 

monitor for 

compliance, 

effectiveness

None Mandatory; 

monitor for 

compliance, 

effectiveness

None Mandatory; 

monitor for 

compliance, 

effectiveness

None

Maryland Voluntary; 

monitor for 

compliance, 

effectiveness

None Mandatory; 

monitor for 

compliance, 

effectiveness

Mandatory; 

no monitoring

None None

Maine Voluntary; 

monitor for 

compliance, 

effectiveness

None Voluntary; 

monitor for 

compliance, 

effectiveness

Voluntary; 

monitor for 

compliance, 

effectiveness

Voluntary; 

monitor for 

compliance, 

effectiveness

None

Michigan Monitor for 

compliance

None Mandatory; 

monitor for 

compliance

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance

None

Minnesota Voluntary; 

monitor for 

compliance

Voluntary; 

monitor for 

compliance

Voluntary; 

monitor for 

compliance

Voluntary; 

monitor for 

compliance

Voluntary; 

monitor for 

compliance

Voluntary; 

monitor for 

compliance

State Standard or guideline

Silviculture Water, soils Wildlife, biodiversity
154 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Table 6.1 continued 

State forests Private forests State forests Private forests State forests Private forests

Missouri Monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

New 

Hampshire

Voluntary; 

no monitoring

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

None None

New Jersey Monitor for 

compliance, 

effectiveness

Voluntary; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

Monitor for 

compliance

Voluntary; 

monitor for 

compliance

New York Monitor for 

compliance

Voluntary; 

no monitoring

Monitor for 

compliance

Voluntary; 

no monitoring

Monitor for 

compliance 

Voluntary; 

no monitoring

Ohio Mandatory; 

monitor for 

compliance

Mandatory; 

no monitoring

Mandatory; 

monitor for 

compliance

Mandatory; 

no monitoring

Mandatory; 

monitor for 

compliance

Mandatory; 

no monitoring

Pennsylvania Mandatory; 

monitor for 

compliance

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Rhode Island Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

Voluntary; 

no monitoring

Vermont Voluntary; 

no monitoring

Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary; 

no monitoring

Voluntary; 

no monitoring

Wisconsin Voluntary; 

monitor for 

compliance

Voluntary and 

mandatory; 

monitor for 

compliance

Voluntary and 

mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary and 

mandatory; 

monitor for 

compliance, 

effectiveness

Voluntary and 

mandatory; 

monitor for 

compliance

Voluntary and 

mandatory; 

monitor for 

compliance

West Virginia None Voluntary; 

no monitoring

Mandatory; 

monitor for 

compliance

Mandatory; 

monitor for 

compliance

None Voluntary; 

no monitoring

State Standard or guideline

Silviculture Water, soils Wildlife, biodiversity
C h a p t e r S i x 155

Forest Stewardship Management Plans

Forest stewardship management plans are 

particularly valuable in helping private forest 

owners manage their resources sustainably. 

Consulting foresters or State forestry staffs work 

with landowners to assess resources, determine 

management goals that include best management 

practices, and develop plans for operations and 

activities (including harvesting, timber stand 

improvement work, and wildlife habitat protection 

and maintenance). Ideally, the consulting forester 

is both a sounding board to help owners clarify 

their goals and intentions, and a source of expert 

and practical information about the potential 

for ensuring sustainability while pursuing other 

desired outcomes (Shifley et al. 2012). To meet 

the Federal standards, forest stewardship 

management plans must include consideration of 

soil stability and water quality. 

As a case in point, many different methods are 

available for harvesting and removing trees from 

forest areas, and the way they are applied affects 

the kinds and degrees of impacts on ecosystems. 

For example, timber harvesting is known to 

remove soil nutrients—especially the base 

cations calcium, magnesium, and potassium—

as part of the fiber removed from the site, and to 

increase the exposure of soil and residues to light, 

heat, and moisture. This results in accelerated 

decomposition of organic matter and some 

small acceleration of chemical weathering of 

inorganic minerals in soils and rocks, increasing 

the availability of nutrients for transport. 

Nitrogen, which normally would be taken up by 

living trees in the form of ammonium, is converted 

by soil microbes to nitrate, a form of nitrogen 

easily transported in water. This conversion results 

in a temporary flush of hydrogen atoms that 

replace base cations on soil particles and cause 

nutrients to move into the soil solution where 

they become susceptible to leaching. Stewardship 

plans that target soil organic matter content and 

nutrient stores would address issues related to 

timber harvesting (timing and intensity), land-use 

history, and the effects of potential nutrient losses 

resulting from soil erosion (Carpenter 2007).

Variations in the number of forest stewardship 

plans among States largely reflect differences 

in the amount of forest acreage and the number 

of owners, but States also differ in the degree of 

emphasis they place on stewardship planning in 

relation to other priorities. Because technical 

assistance from professional foresters is central 

to plan development, the number of plans and 

the number of acres covered are both sensitive 

to changes in the Federal and State funding 

that determines their availability (Shifley et al. 

2012). An increasing number of landowners 

(with increasingly smaller landholdings) is 

also a critical challenge for those providing the 

needed technical assistance. About 10 percent 

of all northern nonindustrial private forest 

acreage (acreage not owned by forest products 

companies) is managed under stewardship plans, 

but the covered area for individual States ranges 

from <5 percent to >30 percent (Table 6.2).
156 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Table 6.2—Cumulative area of private forest land in the North that is covered by active forest stewardship plans, 2010 

(Shifley et al. 2012).

------------------------------------------(thousand acres)------------------------------------------ (percent)

Wisconsin 9,674 2,985 31

New Hampshire 2,844 634 22

Maryland 1,462 324 22

Illinois 3,509 628 18

Delaware 244 39 16

Minnesota 5,921 860 15

New Jersey 805 115 14

Massachusetts 1,998 276 14

Indiana 3,588 463 13

Iowa 2,511 295 12

Ohio 6,064 520 9

New York 12,190 975 8

Pennsylvania 9,603 531 6

West Virginia 7,174 270 4

Vermont 3,109 110 4

Connecticut 1,148 39 3

Maine 6,261 210 3

Missouri 11,755 343 3

Rhode Island 251 7 3

Michigan 9,458 203 2

Northern U.S. total 99,569 9,828 10

a

Includes family farms, real estate investment trusts, and timber investment management organizations but does not include land owned by the forest products industry.

b

From Smith et al. (2009).

c

From State data in the Performance Measurement Accountability System, provided by Michael Huneke, 

U.S. Forest Service.

State

Area

Total forest areaab Cumulative area under forest stewardship plansc
C h a p t e r S i x 157

Other Conservation Efforts

Many Federal, State, and local laws are in place 

to conserve and protect special environmental, 

cultural, social, or scientific values. Agencies 

regulate against harm to special resources 

like soil, fisheries, water quality, watershed 

protection, air quality, and species at risk. 

They acquire or administer lands for special 

purposes, or provide technical assistance on 

how to mitigate the adverse impacts of forest 

management on special resources. For example, 

maintenance of soil and site quality is written 

into the management plan of every national 

forest, with monitoring protocols designed to 

detect forest-productivity losses that are 

≥15 percent (Powers et al. 1990).

FOREST LAND WITH SIGNIFICANT 

SOIL DEGRADATION

Trees grow in response to available light, 

water, and nutrients. Tree stress increases 

when nutrient levels are suboptimal. Cronan 

and Grigal (1995) demonstrated that calcium/

aluminum ratios measured in soil solution 

are useful indicators of tree stress, and PageDumroese et al. (2010) summarized the science 

of soil monitoring for managers of national 

forests and rangelands. In this analysis of 

suboptimal soil conditions, indicators of soil 

quality developed by Amacher et al. (2007) were 

applied to survey plots maintained by Forest 

Inventory and Analysis (Table 6.3).
158 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Soil Quality IndicatorConnecticutDelawareIllinoisIndianaIowaMaineMarylandMassachusettsMichigan

At 0- to 10-cm depth (percent)

Phosphorusa < 15 mg/kg 62 84 70 91 64 78 100 73 73

Phosphorusb < 10 mg/kg NAc NA 60 75 25 NA NA NA 33

Exchange aluminum > 100 mg/kg 92 83 14 35 0 81 88 81 27

Exchange calcium < 100 mg/kg 46 66 7 4 0 37 75 50 17

Exchange potassium < 100 mg/kg 69 76 41 65 19 79 88 69 81

Exchange magnesium < 50 mg/kg 54 62 10 17 0 60 75 69 45

Organic carbon < 1 percent 0 0 0 6 0 1 0 0 6

Total nitrogen < 0.1 percent 8 14 7 11 5 4 25 13 23

Water pH less than 4.0 0 59 0 0 0 25 25 31 8

At 10- to 20-cm depth (percent)

Phosphorusa < 15 mg/kg 69 90 75 91 53 84 83 82 70

Phosphorusc < 10 mg/kg NA NA 60 75 40 100 NA NA 77

Exchange aluminum > 100 mg/kg 85 83 37 54 5 74 88 81 22

Exchange calcium < 100 mg/kg 77 90 11 24 0 61 75 81 40

Exchange potassium < 100 mg/kg 100 97 63 85 32 95 100 94 95

Exchange magnesium < 50 mg/kg 85 90 11 39 3 81 100 88 65

Organic carbon < 1 percent 23 10 26 20 11 5 0 0 34

Total nitrogen < 0.1 percent 46 55 49 44 22 14 88 19 57

Water pH < 4.0 0 28 0 0 0 7 0 6 2

a

Test performed according to Bray and Kurtz (1945).

b

Test performed according to Olsen et al. (1954).

c

NA = no data available.

Table 6.3—Percentage of forest inventory plots, 2000-2005, in the North that were reported to have suboptimal 

soil conditions (FIA data).
C h a p t e r S i x 159

Table 6.3 continued 

Soil Quality IndicatorMinnesotaMissouriNew HampshireNew JerseyNew YorkOhioPennsylvaniaRhode IslandVermontWest VirginiaWisconsin

At 0- to 10-cm depth (percent)

Phosphorusa < 15 mg/kg 44 88 77 100 76 88 80 50 89 96 49

Phosphorusb < 10 mg/kg 13 88 NAc NA 50 100 NA NA NA NA 22

Exchange aluminum > 100 mg/kg 24 20 85 50 67 35 85 100 63 58 32

Exchange calcium < 100 mg/kg 1 9 35 63 20 5 43 0 40 14 8

Exchange potassium < 100 mg/kg 48 49 74 88 64 49 72 0 90 54 74

Exchange magnesium < 50 mg/kg 11 14 65 88 42 19 70 0 63 31 26

Organic carbon < 1 percent 3 4 0 0 1 0 5 0 3 0 4

Total nitrogen < 0.1 percent 13 11 3 50 4 2 4 0 10 17 21

Water pH less than 4.0 2 0 18 50 33 2 30 0 23 10 5

At 10- to 20-cm depth (percent)

Phosphorusa < 15 mg/kg 52 90 86 80 83 90 81 100 86 90 56

Phosphorusc < 10 mg/kg 64 80 NA NA 0 100 100 NA NA NA 71

Exchange aluminum > 100 mg/kg 28 28 67 38 64 49 89 100 67 65 36

Exchange calcium < 100 mg/kg 3 24 70 88 42 28 61 50 50 29 17

Exchange potassium < 100 mg/kg 77 74 100 88 86 78 93 100 100 67 90

Exchange magnesium < 50 mg/kg 31 22 94 88 59 39 84 50 83 47 48

Organic carbon < 1 percent 31 40 3 13 4 38 22 0 0 10 27

Total nitrogen < 0.1 percent 54 55 21 88 13 47 35 0 17 53 55

Water pH < 4.0 2 0 6 25 13 0 8 0 10 2 1
160 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Federal and State Land

A number of programs and legal instruments focus 

on preserving specific forest-soil-water interactions 

and conditions at State and Federal levels. An 

example is the 1891 Forest Reserve Act, which 

established the first forest reserves (designated as 

national forests in 1907). Its original purpose was 

to preserve water resources—that is, to protect 

watersheds from erosion and flooding—and to 

protect timber supplies from overexploitation; but it 

later became the basis for a growing conservation 

movement to preserve natural resources for future 

generations by applying multiple-use and sustainedyield principles to forest management. These 

principles stress the need to balance the uses of the 

major resources and benefits of forests—timber 

and soil productivity, water supplies, recreation, 

livestock forage, wildlife and fish, and minerals—in 

the best public interest.

To forecast changes in amount of Federal and 

State land area per person, it is assumed 

that State and Federal land area will remain 

relatively constant for the next 50 years, but 

projected increases in human populations will 

likely place more pressure on public land and 

water resources.

An examination of Federal and State forest land 

area illustrates differences in rankings among 

U.S. regions (Table 6.4). Thirty-six million acres 

(or 21 percent) of northern forest land is managed 

by Federal and State agencies. Federal and 

State forest land was 0.29 acres per person in 

2007 and is expected to decrease to 0.23 acres 

per person by 2060 under an assumption of 

increasing urbanization (moderate population 

and high economic growth) and relatively stable 

to slightly decreasing forest area. 

Table 6.4—Combined area of all Federal and State forest land in the United States, 2007, and projected change in acres 

per capita, 2007 to 2060, assuming constant forest area in these ownerships, with increasing population (Cordell et al. 

2012, Smith et al. 2009).

(thousands of acres) (percent of total) (acres per capita) (acres per capita)

North 36,345 21 0.29 0.23

South 26,541 12 0.25 0.16

Rocky Mountains 112,597 75 3.99 2.27

Pacific Coast 141,762 66 2.87 1.86

U.S. total 317,244 42 1.03 0.71

Region Federal and State forested area 

2007 2060
C h a p t e r S i x 161

Private Forest Land

Private forests are expected to continue being 

converted to developed uses. Smith et al. (2009) 

reported that the bulk of northern forest land 

(58 percent) is in private ownership (defined 

here as all private owners, excluding forest 

products companies); the remainder is held by 

the Federal government (8 percent), State and 

local governments (17 percent), and corporate 

forest ownership (17 percent). 

Although lands in public ownership are expected 

to form the base for most “legislatively” protected 

or reserved forest, management and land-use 

change will have the biggest impacts on private 

lands. The increase in number of humans in the 

next 50 years could bring different demands on 

forest resources and other natural resources, 

particularly on private lands near the urban fringe. 

A variety of programs and legal instruments are 

in place to preserve specific forest-soil-water 

interactions and conditions regardless of ownership. 

These include establishing conservation easements, 

placing lands in private and public land trusts, 

and marketing rights that are traditionally 

associated with property.

PROJECTIONS OF WATER SUPPLY 

AND QUALITY

Water Quantity

Baseline conditions from 2010 and projected 

climate, land-use, and human population change 

were used to assess the ability of water supplies 

to meet societal demands from 2010 to 2060. 

Projections were based on unique combinations 

of IPCC (2007) storylines and general circulation 

models (Table 6.5). The projections used 

greenhouse gas emissions storylines developed by 

the Intergovernmental Panel on Climate Change 

(IPCC 2007, Chapter 2): A1B assumes moderate 

greenhouse gas emissions associated with 

moderate gains in population growth and large 

gains in income and energy consumption—but 

with a balanced renewable/fossil fuel portfolio, 

A2 assumes relatively high greenhouse gas 

emissions associated with large gains in 

population growth and energy consumption with 

moderate gains in income, and B2 assumes 

relatively low greenhouse gas emissions 

associated with moderate gains in population 

growth, income, and energy consumption. 

Storylines A1B and A2 storylines were used 

for our water resource assessments; storyline 

B2 was omitted because projected emissions 

under the B2 storyline may underestimate actual 

greenhouse gas emissions (Raupach et al. 2007). 

See Tavernia et al. (2013) for detailed description 

and assumptions.
162 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Table 6.5—Water supply and stress index (WaSSI) in the 551 watersheds of the North, estimated for 2010 and projected 

for 2060, under two IPCC (2007) greenhouse gas emissions storylines—A1B assumes moderate greenhouse gas emissions 

associated with large urbanization gains and moderate population growth and rapid income growth, and A2 assumes 

moderate greenhouse gas emissions associated with moderate urbanization gains, rapid population growth, and moderate 

income growth—combined with three general circulation models, CGCM-3.1MR, CSIRO-Mk 3.5, and MIROC3.2MR.

Stress 

index

Number of 

watersheds 

(percent)

Million persons 

(percent)

Number of 

watersheds 

(percent)

Million persons 

(percent)

Number of 

watersheds 

(percent)

Million 

persons 

(percent)

Estimate, 2010 0.07 538 (98) 121.3 (98) 11 (2) 2.1 (2) 2 (0) 0.2 (0)

A1B+CGCM3.1c 0.08 536 (97) 150.6 (96) 13 (2) 6.0 (4) 2 (0) 0.2 (0)

A1B+CSIROMk3.5c 0.13 521 (95) 142.9 (91) 21 (4) 12.5 (8) 9 (2) 1.4 (1)

A1B+MIROC3.2c 0.19 504 (92) 135.5 (86) 29 (5) 18.1 (12) 18 (3) 3.2 (2)

A2+CGCM3.1 0.18 505 (92) 151.5 (86) 31 (6) 20.6 (12) 15 (3) 5.1 (3)

A2+CSIROMk3.5 0.09 532 (97) 155.5 (88) 15 (3) 19.5 (11) 4 (1) 2.2 (1)

A2+MIROC3.2 0.14 516 (94) 156.5 (88) 24 (4) 18.3 (10) 11 (2) 2.4 (1)

aThe proportion of the water supply that is used to meet societal demands; values represent an average of annual values for watersheds across the region.

bWatersheds are classified into three categories based on their WaSSI values: low (<0.50), moderate (0.50 to 1), and high (>1).

cSources of models: CGCM3.1 = Canadian Centre for Climate Modeling and Analysis Third Generation Coupled Global Climate Model (T47 medium resolution version)

(http://www.ec.gc.ca/ccmac-cccma); MIROC3.2 = University of Tokyo Center for Climate System Research, National Institute for Environmental Studies, and Frontier

Research Center for Global Change, Japan, Model for Interdisciplinary Research on Climate version 3.2 (medium resolution) (http://www-pcmdi.llnl.gov/ipcc/model_

documentation/MIROC3.2_hires.htm); CSIRO-Mk3.5 = Commonwealth Scientific and Industrial Research Organization, Australia, Mark 3.5 Global Climate Model 

(http://www.cawcr.gov.au/publications/technicalreports/CTR_021.pdf).

Estimate/scenario Overall Watersheds and populations in each category of water stressb

WaSSIa Low WaSSI Moderate WaSSI High WaSSI
C h a p t e r S i x 163

For these assessments, we used a water supply 

and stress index (WaSSI), defined by Sun et al. 

(2008) as the proportion of a watershed’s water 

supply that is used to meet societal demands. 

When WaSSI values are >1, water supplies are 

insufficient and external sources could be needed 

to meet the deficit. Watersheds were classified 

into three categories based on their WaSSI values: 

• <0.50—low stress

• 0.50 to 1—moderate stress

• >1—high stress

The working scale for WaSSI is the U.S. Geologic 

Survey 8-digit Hydrologic Unit Code, which describes 

approximately 2100 watersheds in the lower 

48 States (USDA NRCS 2009). See Tavernia et al. 

(2013) for a detailed description of methods.

The 551 watersheds in the North currently 

experience little water stress (Fig. 6.1, Table 6.5). 

Across watersheds, the average annual WaSSI 

value was 0.07, and only two watersheds had 

high WaSSI values, indicating high stress. About 

121 million people reside in watersheds with 

low stress values, 2 million in watersheds with 

moderate stress, and 200,000 people in two 

watersheds with high stress, one located in 

western Iowa, the other in central Illinois.

By 2060, changes in climate, land-use patterns, 

and human populations could individually and 

collectively alter water stress levels. Temperature 

levels across the region are expected to increase 

under all scenarios considered, although the 

magnitude of projected increase varies (Chapter 2). 

An increase in temperature could lead to 

increased evaporative loss and decreased 

water availability, thus increasing water stress. 

However, water abundance is primarily driven 

by precipitation (McCabe and Wolock 2011). 

Scenarios differ with respect to precipitation, 

with some suggesting that regional precipitation 

will increase and others suggesting that it will 

decrease (Chapter 2). For a specific location, 

projected changes in precipitation also vary 

in magnitude and direction. Urban land use is 

projected to increase across the region with 

variations in magnitude across scenarios and 

locations (Chapter 10). Because urban areas 

are less vegetated, are less permeable, and 

have lower evapotranspiration rates than other 

land-use types, water surface abundance would 

increase in some areas, but at a cost to water 

quality (Lull and Sopper 1966, 1969; Sun and 

Lockaby 2012). Although projections indicate 

that the overall human population in the region 

would increase under all scenarios considered, 

the rate of growth would vary among scenarios 

(see Chapter 2) and across the region, with some 

locations expected to have smaller populations 

by 2060. Increases in human populations can be 

expected to increase water demand and place an 

additional burden on water supplies.
164 FUTURE FORESTS OF THE NORTHERN UNITED STATES

FIGURE 6.1

Average annual water supply and stress index values—the proportion of the water supply 

being used to meet societal demands—for watersheds in the North (A) in 2010 and 

(B) projected for 2060 under the MIROC3.2MR general circulation model from the 

Japanese National Institute for Environmental Studies combined with a greenhouse gas 

emissions storyline A1B (IPCC 2007) that predicts moderate greenhouse gas emissions, 

large urbanization gains, moderate population growth and rapid income growth. 

Maps for five additional projections (storyline-GCM combinations) can be viewed in 

Tavernia et al. (2013).

a)A

b)B

Under 0.5 (low stress)

0.5 to 1.0 (moderate stress)

Over 1.0 (high stress)

WATER STRESS INDEX
C h a p t e r S i x 165

Regionally, assessments using the WaSSI model 

(Caldwell et al. 2012) suggest that WaSSI values 

would increase under most future scenarios 

(Fig. 6.1, Table 6.5). Although average annual 

WaSSI values would remain low under all 

scenarios, the number of watersheds classified 

as having moderate or high WaSSI values would 

increase. Increases in the average annual 

WaSSI value and the number of watersheds with 

moderate or high WaSSI values would be highest 

for storyline A1B with climate projected by 

the MIROC 3.2MR general circulation model 

(Table 6.5, Chapter 2). Under this scenario by 

2060, approximately 136 million people are 

expected to live in watersheds with low water 

stress, 18 million in watersheds with moderate 

stress, and 3 million in watersheds with high 

stress. At a regional scale, climate projections 

had more impact than land-use or population 

projections, but there were some exceptions 

for individual watersheds. The results of WaSSI 

assessments could differ if conducted for localized 

areas within a watershed or at finer temporal 

scales, such as monthly (Tavernia et al. 2013). 

Other researchers have used lower WaSSI 

thresholds for labelling watersheds as stressed. 

Ultimately, local water management strategies 

affect the level at which a watershed becomes 

stressed. See Tavernia et al. (2013) for 

additional discussion.

Water Quality

The suitability of water for a given use (such 

as drinking, industrial processes, or recreation) 

depends on its quality; for this reason, 

assessments of future water supplies need to 

account for the effect of changing land use on 

water quality. Projections for 2060 suggest that 

urban areas will increase at the expense of 

forest and agricultural land (Chapters 2, 10). 

Projected increases are expected to be highest 

under storyline A1B (resulting from moderate 

population growth and high economic growth). 

Under A1B, urban areas are projected to grow 

from 37 to 66 million acres (78-percent increase) 

whereas forest areas are projected to shrink from 

174 to 163 million acres (6-percent decrease). 
166 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Conversion of forests to urban areas would 

negatively impact water quality in a variety of ways, 

including changing stream temperatures and 

increasing the load and concentration of sediments, 

nutrients, pesticides, and other pollutants (Paul 

and Meyer 2001, Sun and Lockaby 2012).

The potential for the biophysical and land-use 

characteristics of a watershed to support 

production of high quality water resources 

under the 2010 baseline and the land-use 

conditions projected for 2060, was assessed 

using a Clean Water Potential Index (CWPI), 

based on an index developed by Barnes et al. 

(2009) but modified to substitute some attributes 

for which future projections are unavailable. 

Watersheds were assigned individual values 

based on soil erodibility and the percentage 

cover of forest, riparian-forest, agricultural, 

and urban areas. We computed CWPI by 

combining these values into a composite value 

that ranged from 5 (low potential for clean 

water) to 20 (high potential for clean water), 

assuming constant percentage cover of riparian 

forest and constant soil erodibility over time. 

CWPI values are useful for ranking the ability 

of watersheds to support the production of high 

quality water resources based on landscape 

conditions, but realized water quality also 

depends on additional factors, such as water 

treatment capacity, that are beyond the scope 

of this assessment. 

Currently, watersheds with relatively high CWPI 

values tend to be found in forested areas of the 

upper Midwest, New England, and along the 

Appalachian Mountains. Relatively low CWPI is 

found in midwestern watersheds that have low 

forest use, high agricultural use, and sometimes 

high urban use (Fig. 6.2). By 2060, land-use 

changes, predominantly by increases in urban 

land use, are projected to decrease CWPI values 

for the majority of watersheds in the region. 

Under storyline A1B, 362 of 551 (66 percent) 

watersheds would have lower CWPI values 

compared to 293 (53 percent) under A2 (Fig. 6.2). 

Agreement between storylines A1B and A2 was 

high (Fig. 6.3) with both storylines predicting 

losses for 288 watersheds and only a few 

watersheds predicted to experience losses 

under one or the other alone—74 watersheds 

under A1B and 5 watersheds under A2. Declines 

in CWPI values are expected to occur for 

watersheds scattered throughout the region, 

suggesting that landscapes within these 

watersheds will be less able to produce high 

quality water. 

For the vast majority of watersheds, projections 

indicate that the amount of high-quality water 

available for human use will be adequate, 

but some could face shortages, leading to water 

stress or increasing water treatment costs. 

Policies including zoning regulations and financial 

incentives such as conservation easements could 

help safeguard the role of forests in providing 

high quality water to human populations. 
C h a p t e r S i x 167

FIGURE 6.2

Clean water potential index values for watersheds 

in the North (A) in 2010; (B) projected for 2060 

under storyline A1B that predicts moderate 

greenhouse gas emissions, large urbanization 

gains, moderate population growth and rapid 

income growth; and (C) projected for 2060 

under storyline A2 that predicts high greenhouse 

gas emissions, moderate urbanization gains, 

rapid population growth, and moderate income 

growth. Index values are a composite of soil 

erodibility and percentage cover of forest, 

riparian-forest, agricultural, and urban areas. 

A

B

C

5 10 15 20

CLEAN WATER POTENTIAL

Low High
168 FUTURE FORESTS OF THE NORTHERN UNITED STATES

FIGURE 6.3

Changes in clean water potential index values projected under IPCC (2007) 

greenhouse gas emissions storyline A1B that predicts moderate greenhouse 

gas emissions, large urbanization gains, moderate population growth, and 

rapid income growth; and under storyline A2 that predicts high greenhouse 

gas emissions, moderate urbanization gains, rapid population growth, and 

moderate income growth. Index values are a composite of soil erodibility and 

percentage cover of forest, riparian-forest, agricultural, and urban areas.

No change

Decrease under storyline A1B

Decrease under storyline A2

Decrease under A1B and A2

CHANGE IN CLEAN WATER POTENTIAL
C h a p t e r S i x 169

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176 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Projections of Forest Contributions

 to Global Carbon Cycles

 

ORESTS COVER 42 PERCENT of the Northern United States, and collectively they store 13 billion 

tons of carbon in live trees (29 percent), roots (6 percent), forest floor (9 percent), dead 

trees (6 percent), and soils (50 percent). About half the biomass of a live tree (dry weight basis) 

is sequestered carbon (Woodall et al. 2011)—not the largest but the most dynamic source of 

sequestered forest carbon over time. Through photosynthesis, live trees emit oxygen in exchange 

for the carbon dioxide that they pull from the atmosphere, storing the carbon in wood above 

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rapidly through combustion (McKinley 2011, Woodall et al. 2011).

Michael E. Goerndt, Stephen R. Shifley, Patrick D. Miles, David N. Wear, and Francisco X. Aguilar

Introduction

Society emits enormous amounts of carbon dioxide 

during electricity production, transportation, and 

other activities. Emissions for each U.S. resident 

average about 18 tons of carbon dioxide per year; 

this corresponds to 5 tons of carbon (USDOE EIA 

2010, US EPA 2011). Increases in atmospheric 

carbon dioxide and other greenhouse gasses 

have been implicated in global climate change. 

Forests, because of their geographical extent 

and their capacity to sequester additional carbon 

or to release stored carbon, serve as important 

sinks or sources of carbon, thereby playing an 

important role in regional and global carbon 

cycles (McKinley et al. 2011, Pan et al. 2011).

CHAPTER SEVEN 177
PRESENT DAY

• In the United States, the total amount of 

sequestered carbon in forests is equal to about 27 

years of carbon dioxide emissions.

• The yearly accumulation of carbon in U.S. forests 

is estimated to offset 13 percent of total annual 

carbon emissions.

• In 2007, 2 percent of the energy consumed in the 

United States came from wood firing by industry 

(1.3 percent), residential units (0.4 percent), 

utilities (0.2 percent), and other (0.1 percent) users.

• Less than 1 percent of the U.S. electric power is 

generated from wood.

PROJECTED

• Total carbon sequestered by northern forests is 

expected to decrease by about 2 percent from 

2010 to 2060, primarily the result of reduced 

forested acreage combined with the slower 

growth that is typical for mature trees.

• Aboveground forest biomass is expected to 

increase by 3.9 percent from 2010 to 2060.

• Both sequestered forest carbon and aboveground 

woody biomass are expected to increase in the 

Midwest and decrease in many eastern States.

• By 2030, U.S. electrical power generated from all 

biomass is expected to increase 350 percent over 

2010 levels; in the North, biomass will still be 

used in <6 percent of total electric production. 

• The potential for increased woody biomass use 

in co-firing (with coal) is highest in States that 

border the Great Lakes and in the Northeast; the 

potential increase for co-firing across the region 

would improve if power plants were upgraded to 

higher efficiency co-generation status.

Key Findings

178 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Global climate change associated with changes 

in atmospheric carbon dioxide can have 

significant impacts on the condition of future 

forests, and consequently on the plant and 

animal (and human) communities that depend 

on forest habitats and services (Kurz et al. 

2008). Some forest management activities can 

increase carbon sequestration to partially offset 

the human activities that emit carbon dioxide 

and associated greenhouse gases. For example, 

afforestation and certain thinning protocols can 

increase the quantity of carbon sequestered 

in trees (Hoover and Stout 2007). Energy 

production from woody biomass can offset 

carbon that otherwise would be released by 

burning fossil fuels, provided that a comparable 

amount of the carbon is reincorporated into 

the new trees that regenerate to replace those 

harvested (Malmsheimer et al. 2008). In 

contrast, the energy production from fossil fuels, 

such as coal and oil, emits carbon that has been 

sequestered underground for millennia.

The projections of forest biomass and carbon 

presented in this chapter are juxtaposed with 

projections of energy consumption at State, 

regional, and national scales (USDOE EIA 2010). 

This facilitates the comparison of three important 

carbon-cycle considerations for the North: 

(1) projected carbon sequestration in forests, 

(2) projected woody biomass growth and utilization, 

and (3) projected total and renewable energy 

consumption and likely consequences for carbon 

emissions. Together these provide insight into 

the dynamics and interactions among forest 

biomass, forest carbon, and energy utilization. 

The following sections present:

• Total quantity of carbon stored in forests 

• How forest carbon is expected to change 

over time 

• The role of woody biomass for energy production

• Current and projected energy profiles for the 

Northern States

• Current and projected capacity to curb carbon 

emissions by substituting woody biomass for 

fossil fuels in electricity production 

• Current status and future potential of co-firing 

biomass with coal for energy production

CARBON SEQUESTRATION

Through photosynthesis, trees sequester (absorb 

and store) carbon in wood, bark, leaves, flowers, 

roots, and seeds. Carbon is found in cellulose, 

hemicellulose, lignin, and other compounds that 

form the wood and other parts of the tree, all of 

which are defined as woody biomass. When a tree 

or some part of a tree dies, the carbon it contains 

is released during decomposition. Carbon in 

decomposing roots can remain in the soil and 

gradually add to the large store of sequestered 

carbon in forest soils. Leaves are short-lived and 

release carbon back to the atmosphere quickly as 

they decompose, but carbon can be sequestered 

for centuries in the wood of long-lived trees. 

Large dead and down trees can sequester carbon 

for decades as they decompose slowly and gradually 

release carbon dioxide back to the atmosphere. 

CHAPTER SEVEN 179
When trees are harvested, the carbon they 

contain is removed from the forest, but it 

can continue to be sequestered for decades 

as long-lived wood products or as wood 

and paper products in landfills. Harvested 

forests and those that suffer heavy mortality 

will typically regenerate to new forests and 

accumulate carbon over future decades as 

they grow, provided the land is not converted 

to agricultural, urban, suburban, developed, 

or other uses. When forests are converted 

to other land uses, the carbon sequestered 

in aboveground woody biomass is typically 

released. Soil carbon can be retained following 

the conversion, but that soil carbon is no longer 

classified as forest carbon and therefore is 

excluded in future estimates of forest carbon 

storage and sequestration.

The 2010 per capita carbon emissions in the 

United States, largely resulting from combustion 

of fossil fuels, amounted to 5 tons of carbon 

which equates to 18 tons of carbon dioxide gas 

(USDOE EIA 2010). This is roughly equivalent to 

the carbon in 20 tons of green wood (i.e., wood 

weight when harvested). Based on storyline A1B 

(Chapter 2; IPCC 2007) that assumes a moderate 

level of future greenhouse gas emissions, the 

per capita emissions in the United States would 

decrease by about 15 percent to 4.5 tons of 

carbon (16.5 tons of carbon dioxide) by 2030. 

However, the U.S. population is expected to 

increase by 55 million (18 percent), resulting 

in a net increase of 0.2 percent in total 

carbon emissions. 

Comparing estimates of carbon from different 

sources requires attention to details. Typically, 

roughly half the weight of green, freshly cut biomass 

is water, and half of the remaining dry biomass is 

elemental carbon. Carbon is customarily reported 

in metric units (metric tons/hectare) rather than 

English units (tons per acre). Carbon sequestration in 

forests and carbon emissions from human activities 

or natural causes are usually reported in metric tons 

of carbon dioxide gas rather than as elemental 

carbon alone. Thus if the equivalent of 

a metric ton of carbon dioxide is 

sequestered in a forest, 12/44th 

of that ton is actual carbon 

based on the proportional 

weight of carbon in a carbon 

dioxide molecule.

Carbon in Wood

180
For the same time period the population in the 

North is expected to increase by only 10 percent 

(12 million people), potentially leading to a 

small net decrease in carbon emissions in the 

North relative to the 2010 level (U.S. Census 

Bureau 2009, USDOE EIA 2012).

The total amount of sequestered forest carbon 

in the region (including carbon in forest soils) 

is expected to decrease slightly in the next five 

decades, falling to about 97 percent of the 2010 

total by 2060 (Fig. 7.1). This overall decrease in 

carbon occurs even though the total dry weight 

of live aboveground forest biomass (which is 

50 percent carbon) increases slightly under 

all storylines (Table 7.1). The decrease in total 

forest carbon occurs primarily because of a 

decrease in forest area and a resulting decrease 

in carbon levels associated with forest soils. 

Thus, the predicted gradual decrease in 

sequestered carbon in northern forests from 

2010 to 2060 (Fig. 7.1) is primarily attributable 

to conversion of forest area to other land uses. 

Forest conversion results in a corresponding 

decrease in forest area and the “loss” of 

aboveground and soil carbon that was previously 

considered part of the forest carbon pool. This 

does not imply that the carbon stored in forest 

soils is eliminated through conversion of forest 

land to other uses, but rather that it ceases to 

be part of the forest carbon pool.

In addition to forest carbon losses resulting 

from land-use changes, sequestered carbon in 

aboveground woody biomass is affected by changes 

associated with forest age, species composition, 

and location (Nowak and Crane 2002). 

Dead trees

Roots

Forest floor

Live trees

Soil

2010 2020 2030 2040 2050 2060

14

12

10

8

6

4

2

0

CARBON (billion tons)

FIGURE 7.1

Sources of forest carbon sequestration in the 

North, 2010 to 2060, under a greenhouse 

gas emissions storyline A2 that assumes 

high levels of greenhouse gas emission 

associated with moderate urbanization gains 

(rapid population growth and moderate 

income growth) combined with high energy 

consumption (Miles et al. 2015, USDA 

FS 2012b).

CHAPTER SEVEN 181
Table 7.1—Aboveground biomass on timberland in the North, 2010 to 2060, under three scenarios—A1B-C assumes 

moderate levels of greenhouse gas emissions associated with moderate gains in population growth and large gains 

in income and energy consumption (but with a balanced renewable/fossil fuel portfolio), A2-C assumes high levels of 

greenhouse gas emissions associated with large gains in population growth and energy consumption with moderate gains 

in income, and B2-C assumes low levels of greenhouse gas emissions associated with moderate gains in population growth, 

income, and energy consumption—combined with the assumption that harvesting will continue at rates observed from 

1995 to 2005 and not increase substantially to meet future demand for bioenergy.

--------------------------(million dry tons)----------------------------- -----------(million dry tons)-------- Change from 2010 (percent)

Connecticut 119 123 123 120 117 107 113 113 -9.8 -4.7 -5.3

Delaware 23 23 22 21 20 20 20 22 -12.4 -14.9 -6.1

Illinois 229 236 234 232 230 231 223 241 1.1 -2.5 5.4

Indiana 250 261 261 257 253 271 259 268 8.4 3.7 7.2

Iowa 111 126 129 128 127 134 125 134 20.3 12.4 20.9

Maine 647 670 679 676 674 666 651 672 2.9 0.5 3.8

Maryland 168 160 147 136 126 117 120 126 -30.0 -28.4 -24.7

Massachusetts 198 194 195 190 184 178 172 184 -10.0 -12.9 -7.1

Michigan 774 794 796 789 780 812 805 840 4.9 4.1 8.5

Minnesota 428 443 472 468 464 487 481 471 13.7 12.2 10.0

Missouri 606 640 648 643 638 675 668 689 11.3 10.2 13.7

New 

Hampshire

270 281 282 277 272 265 264 273 -1.6 -2.0 1.3

New Jersey 101 89 89 82 75 70 70 82 -31.3 -30.8 -19.1

New York 880 936 955 949 941 962 962 985 9.3 9.4 12.0

Ohio 452 493 497 492 487 505 525 521 11.8 16.2 15.2

Pennsylvania 980 962 972 957 939 951 932 983 -3.0 -4.9 0.3

Rhode Island 22 22 19 18 17 16 17 19 -27.1 -22.0 -12.8

Vermont 270 272 284 282 280 284 271 277 5.3 0.4 2.5

West Virginia 766 756 748 742 734 744 714 750 -2.9 -6.9 -2.1

Wisconsin 595 640 664 659 654 690 676 691 15.9 13.6 16.0

Total for North 7,890 8,121 8,215 8,119 8,011 8,184 8,069 8,342 3.7 2.3 5.7

State

Initial 

for all 

scenarios

Projected for 2020 to 2050 

(A2-C only) Projected for 2060 (all storylines)

2020 2030 2040 2050 A2-C A1B-C B2-C A2-C A1B-C B2-C 

182 FUTURE FORESTS OF THE NORTHERN UNITED STATES
National assessments based on greenhouse gas 

emissions storylines predict that sequestered 

carbon in forests will decrease by 2.3 to 2.8 

billion tons—half of which is expected to occur 

in the North—from 2040 to 2060 (USDA FS 2012a). 

The expected decrease for the North as a whole 

would be unevenly distributed among the States 

(Fig. 7.2).

FIGURE 7.2

Projected change in sequestered carbon on forest 

land including soil, 2010 to 2060, in the North under 

greenhouse gas emissions storyline A2 that assumes 

high levels of greenhouse gas emission associated with 

moderate urbanization gains (rapid population growth 

and moderate income growth) with (A) harvest rates 

continuing to follow historical trends (scenario A2-C), 

and (B) increased harvesting to satisfy greater demands 

for bioenergy (scenario A2-BIO).

-35 to -25

-25 to -10

-10 to 0

0 to 5

CHANGE IN SEQUESTERED CARBON 

(percent) A

B

CHAPTER SEVEN 183
Three scenarios examined for the North resulted 

from adding accelerated woody biomass utilization 

to the assumptions for storylines A1B, A2, and 

B2 (Chapter 2). These scenarios (A1B-BIO, 

A2-BIO, and B2-BIO) were based on the implications 

of increasing oil prices (peak oil) and corresponding 

increases in demand for energy from woody 

biomass (Chapter 2). The assumed increased 

biomass utilization for energy would have a 

noticeable effect on sequestered carbon, as 

demonstrated by the substantial decreases in 

sequestered carbon for scenario A2-BIO compared 

to A2-C (Fig. 7.2). This change would primarily 

result from increased removals of aboveground 

biomass, which would decrease the total carbon 

sequestration capacity of the live trees (Figs. 7.1, 

7.2; Appendix 2).

Most sequestered carbon in northern forests 

(Fig. 7.1) is primarily stored in soil organic 

matter and aboveground biomass (Shifley et al. 

2012). Second only to soil organic matter, 

aboveground biomass is a highly dynamic 

component of the carbon sequestration profile 

and is constantly changing as trees grow, 

die, or are removed. As a forest ages in the 

absence of major disturbances, the quantity 

of aboveground biomass and carbon typically 

increases. Cut, dead, dying, and down trees 

serve as reservoirs of sequestered carbon that 

will be emitted to the atmosphere gradually 

as the trees decompose. When cut trees are 

converted to forest products, the carbon in 

those products will continue to be sequestered 

until the products decompose or are burned. 

For perspective, a cubic foot of wood in a living 

or freshly harvested oak tree (Quercus spp.) 

weighs about 60 pounds (green weight), with 

roughly half that weight in water. This means 

that the dry weight of a cubic foot of oak is 

about 30 pounds, about half of which is carbon 

(i.e., 15 pounds or roughly a quarter of the 

green weight). Fifteen pounds of carbon is 

equivalent to the amount of carbon in 55 pounds 

of carbon dioxide gas. 

PROJECTED WOODY BIOMASS 

IN NORTHERN FORESTS

Projected changes in forest area, tree size 

distribution, and tree species composition that 

are expected to result from the climate change 

and disturbance scenarios described in Chapters 

2 and 4 were further analyzed to predict 

changes in forest biomass. As with the forest 

changes analyzed in other chapters, the primary 

focus here is on storylines A1B, A2, and B2 as 

well as variants of those storylines assuming 

increased biomass utilization. For consistency 

with other chapters of this document, biomass 

is reported in United States short tons (2,000 

pounds) and acres rather than in metric tons 

and hectares, as is typical for international 

reporting. Because carbon constitutes about half 

the dry weight of biomass, or roughly a quarter 

the green weight of biomass, readers may prefer 

to visualize tons of woody biomass in the form of 

boles, limbs, branches, bark, roots, or residue; 

and to think of carbon as a proportion of that 

biomass (Fig. 7.3). 

184 FUTURE FORESTS OF THE NORTHERN UNITED STATES
FIGURE 7.3

Schematic of the combined 

forest tree components that constitute 

woody biomass and the elemental carbon that 

is contained therein (stump, bole, branches).

Although woody biomass is a collective term for 

the mass (weight) of all the material in trees, it is 

often used to summarize the components that are 

relevant to wood harvesting and merchandising: 

boles above stump height, tops and limbs, 

cull trees versus those that can be utilized for 

wood products, and composition by species or 

diameter class. Biomass can be reported in 

green tons or in equivalent dry weight, which 

is used to compute the energy value in wood 

and to compare biomass yields among different 

harvesting treatments or different geographic 

locations. Total biomass per tree is often 

computed using an equation that is based 

on tree diameter, with the biomass of 

various tree components estimated 

as a proportion of total biomass. 

Aboveground woody biomass per 

acre is usually proportional to the 

cubic volume of wood or the basal 

area per acre.

CHAPTER SEVEN 185
Coinciding with the slight increase (4 percent) 

projected for aboveground sequestered carbon 

(Fig. 7.1) is a projected 4-percent increase 

in aboveground northern forest biomass from 

2010 to 2060 for scenario A2-C (Fig. 7.4, 

Table 7.1). The variations among predictions 

of aboveground biomass are small; the largest 

difference of 277 million dry tons in 2060 is 

only 3.3 percent of the 2010 estimate (Fig. 7.4, 

Table 7.1). But the contrast between the 

expected increase in aboveground biomass and 

the expected decrease in the total amount of 

carbon sequestration merits further discussion. 

Total timberland1 in the North is expected to 

decrease by nearly 6 percent by 2060 (Chapter 4). 

Because aboveground biomass and soil (with its 

considerable quantity of sequestered carbon) 

are considered “removed” from the forest when 

forest land is converted to other uses, gradual 

losses of forest land can result in a net decrease 

in total sequestered carbon—due primarily to 

removal of the carbon in the associated forest 

soils—even though aboveground biomass and 

aboveground carbon sequestered increases.

2010 2020 2030 2040 2050 2060

9.0

8.5

8.0

7.5

7.0

6.5

BIOMASS (billion dry tons)

FIGURE 7.4

Projected total aboveground biomass on forest land in the North, 

2010 to 2060, under six scenarios, each representing a global 

greenhouse gas storyline (IPCC 2007) paired with a harvest regime. 

Storyline A1B assumes moderate greenhouse gas emissions, 

moderate gains in population, and large gains in income and energy 

consumption (but with a balanced renewable/fossil fuel portfolio); 

A2 assumes high greenhouse gas emissions, large gains in population 

and energy consumption, and moderate gains in income; and B2 

assumes low greenhouse gas emissions with moderate gains in 

population, income, and energy consumption. Scenario projections 

assume harvest will continue at recently observed levels (labeled –C) 

or increase to reflect increased harvest for bioenergy production 

(labeled –BIO). 

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

Timberland is defined as forest land that maintains a minimum 

potential for growth of 20 cubic feet per acre per year and is not 

restricted from active forest management by statute or regulation.

1

186 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Lichen on stump

187
A closer examination of aboveground woody 

biomass estimated for 2010 (Fig. 7.5) and 

projected for 2060 (Fig. 7.6) reveals substantial 

variation among the Northern States. Under 

scenario A2-C, aboveground woody biomass is 

expected to decrease in several of the States 

along the Atlantic seaboard (Table 7.1). New 

Jersey, Maryland, Delaware, and Rhode Island 

are all projected to experience decreases of 

>15 percent. Additionally, the increase of 

aboveground forest biomass for the region does 

not completely reflect some of the interim trends 

that occur within the 50-year time period. Under 

all three scenarios (A2-C, A1B-C, B2-C), total 

aboveground forest biomass would actually 

decrease from 2030 to 2050, and then increase 

again in the last decade of the projection 

period (Fig. 7.4). This is a sharp contrast to the 

enhanced biomass-utilization scenarios (A1B-BIO, 

A2-BIO, and B2-BIO), which all show an 

overall decrease in aboveground forest biomass 

from 2030 to 2060 (Figs. 7.4, 7.6). The large 

decreases in aboveground forest biomass for 

the enhanced biomass utilization scenarios are 

projected to take place primarily from 2020 

to 2030 and from 2050 to 2060. This trend 

reflects both the current rise in woody biomass 

utilization for energy and the assumption that 

limited supplies and higher costs of fossil fuels 

(peak oil) will result in increased biomass 

utilization in about 40 years. From 2010 to 

2060, aboveground forest biomass is expected to 

increase in 11 Northern States under scenario 

A2-C compared to only two States (Missouri and 

New York) under A2-BIO (Appendix 7).

FIGURE 7.5

Total aboveground live-tree biomass on 

forest land in the North, 2010; note that 

aboveground forest carbon in million tons is 

estimated as half the biomass dry weight.

0 to 200

200 to 400

400 to 600

600 to 800

800 to 1,000

Over 1,000

ABOVEGROUND BIOMASS 

(million dry tons)

188
FIGURE 7.6

Change in total aboveground live tree 

biomass on timberland for the North, 2010 

to 2060, under greenhouse gas emissions 

storyline A2 that assumes high levels 

of greenhouse gas emission associated 

with moderate urbanization gains (rapid 

population growth and moderate income 

growth) combined with high energy 

consumption under the assumption of 

(A) harvest rates continuing to follow 

historical trends (scenario A2-C), or 

(B) increasing harvest rates to satisfy 

demands for bioenergy (scenario A2-BIO).

-40 to -30

-30 to -20

-20 to -10

-10 to -5

-5 to 0

0 to 5

5 to 10

10 to 20

CHANGE IN ABOVEGROUND 

BIOMASS (percent)

In a few Northern States, the conversion of 

forest land to other uses would decrease both 

total aboveground woody biomass and timberland 

area from 2010 to 2060; however, for most 

States, average aboveground biomass per acre 

is expected to increase on the forested acres 

that remain after conversion. Aboveground 

woody biomass per acre of retained forest 

would increase for almost every State in the 

region, even though total aboveground woody 

biomass would decrease in nine States (Fig. 7.7) 

primarily due to a decline in total forest area. 

This is an indication of the large influence that 

projected land-use change has in projections 

of woody biomass. The increase in woody 

biomass for acres that remain forested is also 

an important factor for the future of energy from 

woody biomass in the region, as discussed below.

A

B

CHAPTER SEVEN 189
FIGURE 7.7

Change in aboveground woody biomass 

per acre of timberland, 2010 to 2060, in the 

North under scenario A2-C, which assumes 

high greenhouse gas emissions, large gains 

in population and energy consumption, 

moderate gains in income, and harvest rates 

that continue at recently observed levels. 

-1 to 0

0 to 10

10 to 15

15 to 20

20 to 25

CHANGE IN ABOVEGROUND WOODY 

BIOMASS PER ACRE (percent)

Projections of biomass increases for northern 

forests under scenario A2-C are expected to 

differ from national-level projections (USDA 

FS 2012a, USDA FS 2012c), with aboveground 

volume of growing stock (a surrogate for 

aboveground biomass or carbon) increasing more 

slowly than the national rate from 2020 to 2030 

and then continuing to increase from 2050 to 

2060 in contrast to a national trend of growingstock volume decreases. Over the 50-year period, 

the result would be a small decrease in growingstock volume at the national scale but a modest 

increase for the Northern States.

ENERGY CONSUMPTION 

Current and future patterns of energy consumption 

affect the quantity of carbon emissions and the 

quantity of biomass utilized for energy. Patterns of 

biomass utilization affect rates of forest harvesting, 

forest age structure, and the associated capacity of 

forests to sequester carbon. 

Annual energy consumption per capita in the 

United States is expected to decrease by 9 percent, 

from 318 million Btu in 2010 to 290 million Btu 

in 2030 (USDOE EIA 2012). However, population 

in the North (based on storyline A2) is expected 

to increase by 16 million from 2010 to 2030 and 

by 36 million by 2050 (Appendix Table A2.1). 

Thus, even with declining per capita energy 

consumption, total energy consumption in the 

region is likely to increase from 41 quadrillion 

Btu in 2010 to 42 quadrillion Btu in 2030 and 

would continue to increase because the projected 

rate of population increase exceeds the rate 

of decrease in per capita energy consumption 

(USDOE EIA 2012, USDA FS 2012a). 

190
In 2010, about 4 percent of all U.S. energy 

consumption came from wood combustion. Less 

than 1 percent of electrical power consumed 

in the United States comes from wood or wood 

byproducts (USDOE EIA 2010). In the North, 

this percentage is slightly higher (Fig. 7.8), 

with electric utilities throughout the region using 

wood for part of their energy (USDA FS 2011).

Electricity consumption alone makes up 

approximately 41 percent of the annual national 

energy consumption, with the Northern States 

accounting for 40 percent of the national electricity 

consumption total. About 55 percent of northern 

electricity consumption is fueled by coal, followed 

by 27 percent fueled by nuclear energy (Fig. 7.8, 

Table 7.2). Individual States vary greatly in 

the source of the electricity consumed, with coalgenerated electricity ranging from 0 to 97 percent 

and nuclear energy ranging from 0 to 66 percent 

(Table 7.2). 

Two percent of total annual electricity consumption 

in the region is derived from a combination of woody 

and herbaceous biomass (Fig. 7.8); this is about 

25 percent of total annual electricity consumption 

from all renewables including hydroelectric and 

wind sources (Table 7.2). Among the different 

biomass feedstocks for electricity, woody biomass 

accounts for the largest share (53 percent), meaning 

that about 1 percent of all electric production is 

from woody biomass. 

FIGURE 7.8

Electricity consumption in 2010 by source in 

the North; note that wood and wood residue 

constitute about half of all biomass, or about 

1 percent of all electricity consumption.

Other 

Biomass 5%

2%

Nuclear 

27%

Natural gas 

10%

Coal

55%

USING WOODY BIOMASS FOR ENERGY

Burning fossil fuels to produce energy 

releases carbon dioxide that was previously 

sequestered for millennia as carbon in coal, 

oil, or natural gas. By converting to woody 

biomass, society may be able to reduce the 

amount of carbon emissions from fossil fuels. 

Burning woody biomass also releases carbon 

into the atmosphere, but when forests harvested 

for biomass regenerate and regrow, carbon is 

again sequestered in the trees growing in the 

regenerated forest. Thus, some carbon would 

be cycled from the forest to the atmosphere 

and eventually sequestered back into forest 

regrowth. Methods to account for carbon 

emissions and sequestration when using woody 

biomass for energy are still evolving. 

CHAPTER SEVEN 191
Table 7.2—Electricity consumption by source in the North, 2010 (USDOE EIA 2010). 

----------------------------------------------------------------------(percent)---------------------------------------------------------------------- (trillion Btu)

Pennsylvania 49.3 10.0 0.4 37.2 1.2 1.3 0.5 2,172

Illinois 46.6 1.7 0.1 49.7 0.1 0.5 1.4 2,009

Ohio 84.2 2.8 1.0 11.4 0.4 0.2 0.0 1,390

New York 9.8 27.9 2.0 33.8 20.0 2.3 1.6 1,344

Indiana 95.0 3.1 0.1 0.0 0.4 0.3 1.1 1,193

Michigan 64.6 8.0 0.3 21.6 1.2 2.1 0.3 1,057

Missouri 81.9 3.3 0.1 11.8 1.9 0.1 0.5 909

West Virginia 97.2 0.2 0.3 0.0 1.4 0.0 1.0 716

Wisconsin 64.6 6.9 1.1 22.0 2.1 1.6 1.7 602

New Jersey 9.9 28.2 0.1 59.9 0.1 1.8 0.0 600

Minnesota 54.2 4.2 0.1 23.0 1.2 3.7 8.8 563

Iowa 73.1 1.9 0.2 9.3 1.8 0.3 13.7 528

Maryland 54.9 4.3 0.9 34.2 4.1 1.7 0.0 445

Massachusetts 25.2 43.2 2.5 15.7 3.2 5.8 0.0 360

Connecticut 8.7 23.7 1.1 57.6 1.7 4.5 0.0 302

New Hampshire 16.1 19.3 0.9 45.2 8.0 8.5 0.3 204

Maine 0.8 33.1 2.8 0.0 29.0 26.0 2.5 116

Vermont 0.0 0.1 0.0 66.1 16.8 6.7 0.1 85

Rhode Island 0.0 92.6 0.2 0.0 0.0 2.9 0.0 61

Delaware 70.5 23.8 2.3 0.0 0.0 3.4 0.0 47

Total for North 55.3 9.9 0.6 26.9 3.2 1.6 1.5 14,702

U.S. total 47.8 18.5 1.0 21.9 7.0 1.2 1.9 38,094

a

All biomass

State

Energy source

Coal Natural gas Petroleum Nuclear Hydroelectric Biomassa Wind Total 

consumption

192 FUTURE FORESTS OF THE NORTHERN UNITED STATES
The carbon and energy balance associated 

with broader use of woody biomass and other 

plant-derived biomass for energy production 

depends on numerous assumptions used in the 

computations (Malmsheimer et al. 2008). 

Because feedstocks are relatively abundant and 

infrastructure for utilization is already in place, 

woody biomass represents an opportunity for 

replacing some portion of fossil fuels used for 

energy production in the Northern States. 

Nevertheless, long-term projections by the U.S. 

Department of Energy’s Energy Information 

Administration (EIA) indicate the vast majority 

of electricity generation will continue to come 

from coal, natural gas, and nuclear fuels. These 

projections indicate that biomass from all 

sources will continue to be only a small part of 

the total energy profile (USDOE EIA 2010), but 

also that total electricity derived from biomass 

will increase from 2010 to 2030 (Fig. 7.9). 

Although small in absolute terms compared 

to other feedstocks, this would represent a 

350-percent increase in biomass utilization from 

2010 to 2020, the greatest percentage increase 

of any electricity source (Fig. 7.10).

FIGURE 7.9

Projected annual electricity consumption in 

the North, 2010 to 2030, based on nationallevel rates of change (USDOE EIA 2014).

Hydroelectric

Coal

Petroleum

Natural gas

Wind

Nuclear

Biomass

2010 2020 2030

16

14

12

10

8

6

4

2

0

CONSUMPTION (trillion BTU)

400

300

200

100

0

-100

CHANGE (percent)

FIGURE 7.10

Projected change in annual electricity consumption by electricity 

source, 2010 to 2030, in the United States; note that the projections 

were derived using national-level projections and adjustment factors 

that were based on 2010 proportions of electricity consumption 

(USDOE EIA 2014).

Hydroelectric

Coal

Petroleum

Natural gas

Wind

Nuclear

Biomass

2010 2020 2030

CHAPTER SEVEN 193
The increase in biomass utilization for electricity 

production is part of a more gradual overall 

increase in bioenergy consumption that started 

at the turn of this century in response to a 

developing market for corn-based ethanol (USDOE 

2011). However, unlike ethanol producers, 

which primarily serve the transportation sector, 

the forest products industry has traditionally 

been both the primary supplier and the primary 

consumer of woody biomass for energy production 

(USDOE 2011). In 2010, annual consumption of 

harvested roundwood fuelwood was 1.6 million 

cubic feet, and consumption is expected to 

increase by 230 percent in 2060 (Ince et al. 2011, 

USDA FS 2012a). Fuelwood utilization extends 

beyond electricity production to include industrial 

production of steam and heat in paper and woodprocessing mills and residential heating. 

CO-FIRING OF BIOMASS FOR ELECTRICITY

Because wood biomass is likely to remain a 

relatively small component of total energy 

production through 2030, future increases in 

its use for electrical energy could be clustered 

around locations that have a comparative 

advantage in the quantity of raw materials, 

the transportation network, or existing energy 

infrastructures. Spatial analyses were used to 

identify places where an increase in the use of 

biomass may be practical in co-firing boilers 

that combine wood with coal. 

The use of biomass with coal during electric 

generation could be a low-risk, short-term 

option for rapidly increasing renewable energy 

production because adding wood as a feedstock 

to existing coal-fired electric plants would be 

relatively inexpensive. Furthermore, unlike wind 

and solar energy, woody biomass can be readily 

stored as chips or on the stump until needed. 

With an abundant, widely dispersed biomass 

resource base, a large number of coal-fired 

power plants (360), and a long history of woody 

biomass use, the North has a relatively high 

potential for future utilization of biomass for 

electricity production (USDOE 2011). 

One way to assess the feasibility of replacing 

fossil fuels with biomass is to examine the 

regional electric energy footprint with respect to 

biomass resources. Clearly, total replacement of 

fossil fuels with biomass would not be feasible. 

Doing so would consume 11 percent of all standing 

woody biomass in the North to generate the 

electricity consumed in a single year (Table 7.3); 

after 9 years, virtually all the wood in the region 

would have been used to generate electricity. 

However, increasing woody biomass used for 

electricity production above the 0.2 percent 

level would not have dire ecological consequences 

(Table 7.3).

194 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Table 7.3—Proportion of electricity produced from coal and biomass by State with the proportion of the State’s total aboveground 

woody biomass that would be required annually to instead generate electricity from woody biomass.

---------------------------------------------------------------------------------(percent)---------------------------------------------------------------------------------

Rhode Island 0.0 0.5 0 16

Vermont 0.0 0.1 0 2

Maine 0.8 0.3 < 1 1

Connecticut 8.7 0.7 2 15

New Jersey 9.9 0.8 6 36

New York 9.8 0.2 1 10

New Hampshire 16.1 0.4 1 5

Massachusetts 25.2 0.6 3 12

Illinois 46.6 0.2 25 153

Pennsylvania 49.3 0.2 7 13

Minnesota 54.2 0.2 5 8

Maryland 54.9 0.3 10 17

Wisconsin 64.6 0.1 4 6

Michigan 64.6 0.2 5 9

Delaware 70.5 0.5 15 19

Iowa 73.1 <0.1 22 28

Missouri 81.9 <0.1 7 9

Ohio 84.2 <0.1 17 20

Indiana 95.0 <0.1 30 31

West Virginia 97.2 0.0 7 7

All States 55.0 0.2 6 11

State

Electricity 

produced from 

coal

Electricity 

produced from 

biomass

Proportion of State’s total aboveground woody biomass 

needed annually to replace:

All coal-generated electricity Electricity from all sources

CHAPTER SEVEN 195
Although energy footprints are a good way to 

calculate the limitations of woody biomass as an 

energy source based on energy consumption, they 

are relatively uninformative as indicators of the 

actual resource potential for woody biomass as an 

energy feedstock in the Northern States. Annual 

net forest inventory change is more informative 

because it accounts for annual growth minus 

annual removals from harvesting, mortality, and 

land-use change, thus showing which States have 

(or will have) annual woody-biomass increases 

(after accounting for removals) that could satisfy 

energy demands without perpetually depleting 

existing forests. The projected mean annual net 

inventory change over the preceding decade was 

calculated for 2020, 2030, 2040, and 2050 for 

multiple scenarios (Table 7.4). At 2060, mean 

annual net inventory change was calculated for the 

2050 to 2060 interval, and over the entire 50-year 

interval (2010 to 2060) for comparison.

Lake and midwestern States would generally have 

the highest positive net inventory change from 

2010 to 2060 under all scenarios (Table 7.4). 

In one 20-year period (2030 to 2050) all States 

in the North are expected to have a net annual 

decrease in aboveground biomass. States along the 

Atlantic seaboard (such as Maryland, New Jersey, 

and Massachusetts) would have negative values 

throughout most of the projection period, reflecting 

an expected decrease in total aboveground biomass 

over the next 50 years. However, this does not 

imply that these States have no potential for 

biomass energy. Most of the projected 6-percent 

loss of timberland in the region (Chapter 4) is 

expected to take place in the States farthest east, 

which are projected to experience net annual 

decreases in woody biomass due to loss of forest 

through urbanization. However, most of these 

States would also experience increases in average 

aboveground woody biomass per acre of forest 

land retained as forest (Figure 7.7). Therefore, 

negative values for annual inventory change do not 

necessarily mean that woody biomass will not be 

available from existing timberland in the future. 

Nevertheless, increases in demand for woody 

biomass for energy could potentially lead to 

decreases in available woody biomass on 

timberland. The enhanced woody biomass utilization 

scenarios offer a hypothetical assessment of this 

contingency based on two primary assumptions: 

(1) the adjustment for harvesting probability was 

applied using the same predetermined proportions 

across the entire region, and (2) conversions from 

forest/timberland into other land uses were not 

reflected, but rather higher utilization on timberland 

currently existing and expected to exist in the future 

was simulated. 

Overall, the enhanced biomass utilization 

scenarios (A1B-BIO, A2-BIO, B2-BIO) predict 

decreases in woody biomass from 2010 to 2060 

when compared to the non-enhanced counterparts 

(Fig. 7.4). With northern timberland projected 

to decrease by about 6 percent from 2010 to 

2060, levels assumed by the enhanced biomass 

utilization scenarios in eastern States could 

perpetually reduce the amount of available 

woody biomass from timberland (Appendix 7).

196 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Table 7.4—Annual net forest biomass change for the North under three scenarios— A1B-C assumes moderate levels of greenhouse gas 

emissions associated with moderate gains in population growth and large gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio), A2-C assumes high levels of greenhouse gas emissions associated with large gains in population growth and energy consumption with moderate gains in income, and B2-C assumes low levels of greenhouse gas emissions associated with moderate gains in population growth, income, and energy consumption—combined with the assumption that harvesting will continue at rates observed from 1995 to 2005 and not increase substantially to meet future demand for bioenergy. 

------------------------------------------------------------------------------------------(thousand dry tons)-------------------------------------------------------------------------------------------

Maryland 3,406 -772 -1,333 -1,076 -955 -630 -891 5 -953 -1,006 -826New Jersey 2,155 -1,232 -27 -692 -683 -494 -547 698 -626 -636 -387Pennsylvania 10,095 -1,795 1,018 -1,522 -1,816 -683 1,186 4,433 -960 -586 64West Virginia 9,013 -1,047 -770 -653 -801 -1,994 999 1,661 -1,053 -454 -322Massachusetts 3,465 -376 48 -508 -538 -1,172 -614 -34 -509 -398 -282Connecticut 459 449 -34 -292 -346 -334 -941 -402 -112 -233 -125Rhode Island 565 -6 -245 -105 -116 -4 -87 196 -95 -112 -55New Hampshire 2,708 1,116 86 -450 -505 -796 -690 107 -110 -89 71Delaware 772 -39 -61 -85 -95 -60 -4 141 -68 -57 -28Illinois 5,612 751 -233 -164 -186 -743 95 1,071 -115 53 248Vermont 1,184 179 1,231 -223 -256 -832 480 -263 20 282 134Maine -930 2,286 865 -242 -290 -2,285 -749 -179 67 374 488Indiana 6,930 1,101 7 -357 -432 594 1,792 1,479 183 422 360Iowa 2,274 1,479 268 -67 -80 -224 657 724 275 452 465Michigan 9,713 1,970 291 -772 -919 2,566 3,248 6,037 627 764 1,322Ohio 7,555 4,054 375 -448 -560 3,891 1,859 3,465 1,462 1,056 1,377Minnesota 4,166 1,510 2,827 -359 -390 1,632 2,293 706 1,044 1,176 859Missouri 11,209 3,394 765 -477 -576 3,086 3,743 5,191 1,238 1,370 1,660New York 10,362 5,592 1,911 -616 -740 2,114 2,042 4,413 1,652 1,638 2,112Wisconsin 6,784 4,441 2,407 -440 -528 2,211 3,553 3,661 1,618 1,887 1,908Total for region 97,497 23,056 9,396 -9,549 -10,814 5,843 17,425 33,111 3,586 5,903 9,040

a

State

Calculated using volume growth and removals from the most current data from Forest Inventory and Analysis (FIA).

Estimated Projected mean annual changes (A2-C only) Projected mean annual changes (all scenarios)

for 2010a 2010 to 2020 to 2030 to 2040 to 2050 to 2060 2010 to 2060

2020 2030 2040 2050 A1B-C A2-C B2-C A1B-C A2-C B2-C

CHAPTER SEVEN 197
Net annual change in forest inventory is only 

one of many factors to consider when assessing 

resource potential for woody biomass as a 

feedstock. Another way is net annual woody 

biomass increase, which simply assumes a value 

of zero for negative estimates of net annual 

inventory change (Goerndt et al. 2012) and 

focuses purely on the extra woody biomass that 

is available every year (if any) after accounting 

for growth and removals. 

Other forest-derived sources of woody biomass for 

bioenergy include residues from wood processing 

mills and logging residues (tops and limbs) 

from roundwood, snags (standing dead trees), 

harvesting, and downed woody material from 

natural mortality or human activity. Volume of 

logging-residue availability was calculated as 

30 percent of the harvesting removals reported by 

Forest Inventory and Analysis (USDOE 2011). Of 

the total logging residue, it is assumed 35 percent 

will be retained on site (USDOE 2011) as coarse 

woody material providing ecological values. 

Figure 7.11 shows 2010 estimates for 

potential sources of woody biomass feedstocks 

including annual woody biomass increase, 

logging residues, standing dead trees, and mill 

byproducts that are currently unused (Miles 

2010, USDA FS 2012d). Unused mill byproducts 

are the smallest component of available woody 

biomass for bioenergy (Figs 7.11, 7.12)—

primarily because most are already being 

used, often as a feedstock to supplement power 

for wood-processing mills. Annual woody 

biomass increase is the largest (Fig. 7.12). 

Even assuming a 35 percent retention rate, 

the biomass of dead standing material is only 

slightly larger than logging residues, probably 

because salvaging standing dead trees is 

traditionally rare in northern forests. However, 

if an energy-driven market develops for such 

material and if procurement regimes such as 

integrated harvesting of roundwood and biomass 

are implemented, standing dead trees could 

become an available source of woody biomass. 

FIGURE 7.11

Availability of aboveground woody biomass and wood mill byproducts, by state, 

2010, for the North; note that a 35-percent rate of onsite biomass retention was 

assumed for logging residues and standing dead trees (USDOE 2011).

Net annual woody biomass increase

Logging residues

Standing dead

Unused mill byproducts

650,000 dry tons

19.2 million dry tons

198 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Net annual 

woody 

biomass 

increase 

55%

Logging 

residues 

20%

Standing 

dead 

24%

Unused mill 

byproducts 1%

FIGURE 7.12

Availability of aboveground 

woody biomass and wood mill 

byproducts for the North in 

2010; note that a 35-percent 

rate of onsite biomass retention 

was assumed for logging 

residues and standing dead trees 

(USDOE 2011).

199
States with the highest availability of logging 

residues also tend to have higher estimates for 

mill byproducts and lower estimates for annual 

woody biomass increase (Appendix Table A7.3). 

This illustrates the symbiotic relationship 

between wood processing and harvesting, as well 

as the reduction in extra available timberland 

biomass in States with intensive harvesting. 

Based on the 35 percent retention rate for both 

logging residues and dead standing material, the 

northern region would have a combined potential 

to generate approximately 303 million megawatt 

hours of electricity annually from all sources of 

woody biomass feedstock. 

Opportunities for co-firing of coal and woody 

biomass for electricity production appear to be 

abundant in the Northern States. Of the electricity 

currently produced from biomass (Fig. 7.13), 

approximately 83 percent comes from dedicated 

plants compared to only 17 percent from co-firing 

plants (USDOE EIA 2010). However, Figure 7.13 

shows that biomass electricity production is 

expected to increase dramatically; moreover, 

co-firing is likely to be the primary means of 

biomass-fuel electricity production by 2020 

(USDOE EIA 2014). A key consideration is the 

location of exising coal-fired plants that could 

incorporate woody biomass as a fuel. This is a 

first step in identifying northern locations that 

would have a relative advantage for co-firing.

We evaluated the effects of several physical, 

environmental, and socioeconomic factors that 

would influence adoption of co-firing of biomass 

and coal in the North. The analysis was performed 

using logistic regression models that accounted 

for location of existing coal-fired power plants, 

condition of infrastructure such as roads and 

railways, implementation of renewable energy 

portfolio standards, and available biomass feedstocks 

(such as wood processing residues). Data consisted 

of projected energy consumption values from the 

U.S. Energy Information Administration (USDOE 

EIA 2012) and projected biomass resources under 

scenario A2-C. Results from the analysis were 

summarized at the county level (Aguilar et al. 2012) 

for 2010 and 2030 (Fig. 7.14).

FIGURE 7.13

Electricity consumption by co-firing plants and 

plants that burn biomass only in the North, 

2010 to 2030, based on national-level rates 

of change (USDOE EIA 2012); note that in 

2010, biomass burned in co-firing plants 

accounted for 17 percent of the total.

2010 2020 2030

80

70

60

50

40

30

20

10

0

CONSUMPTION (million dry tons)

Co-firing

Biomass only

200 FUTURE FORESTS OF THE NORTHERN UNITED STATES
FIGURE 7.14

Co-firing wood with coal for electricity 

production in the North (A) in 2010 

and (B) projected for 2030 under 

greenhouse gas emissions scenario 

A2-C that assumes high levels of 

greenhouse gas emission associated 

with moderate urbanization gains 

(rapid population growth and moderate 

income growth) combined with high 

energy consumption.

Under 10

10 to 20

20 to 30

30 to 40

Over 40

PROBABILITY OF CO-FIRING

A

B

Many of the counties that appear to have high 

potential for biomass and coal co-firing are in 

the Lake States (Wisconsin, Minnesota, and 

Michigan) and in the mid-Atlantic States of 

West Virginia and Pennsylvania. This is not 

surprising, as these States tend to have either 

a large amount of available woody biomass, a 

large number of coal-fired power plants, or both. 

Total co-firing probability would not increase 

dramatically across the region from 2010 to 

2030. The States that in the future are expected 

to have more counties with a high probability of 

co-firing are primarily in the Midwest (Aguilar 

et al. 2012), and that increase is attributable 

to projected increases in available woody mill 

residues. The overall number of counties with 

high probability of co-firing is not expected 

to increase significantly from 2010 to 2030. 

CHAPTER SEVEN 201
Although increases in population and electricity 

demand in the States farther east would lead 

to increased probability of co-firing, projected 

decreases in timber harvesting, and therefore 

wood mill byproducts, would counteract this 

effect. Though projected increases in potential of 

co-firing in the North are relatively low, this could 

change with the conversion of some power plants 

to higher efficiency of woody biomass use through 

co-generation of both heat and electricity. 

CONCLUSIONS

The total amount of carbon sequestered in forest 

biomass and forest soils in the United States is 

about 27 times the total annual carbon emissions 

from energy production. However, the carbon 

sequestered annually by tree growth is only about 

13 percent of total annual U.S. carbon emissions 

(USDA FS 2011, Vose et al. 2012). Thus, the 

capacity of forests to offset greenhouse gas 

emission through carbon sequestration, though 

important, is relatively small compared to the total 

quantity of emissions. In the Northern States, this 

shortfall represents an ecological carbon footprint 

(biomass equivalent of atmospheric carbon) of 

approximately 24 million acres or 14 percent of 

existing forest land. For perspective, consider 

that to sequester enough carbon to offset northern 

carbon dioxide emissions from fossil fuels, northern 

forest land would have to increase by 14 percent 

every year (i.e., double every 7 years). Total fossil 

fuel emissions will continue to increase with 

increasing population even though per capita 

emissions will decrease, both for the United States 

as a whole and for the Northern States. 

In the past, woody biomass has been a relatively 

small part of the total energy profile for the 

United States and likely will remain small, but 

increase proportionally. Nevertheless, as a 

percentage of current levels of utilization, woody 

biomass is expected to experience the largest 

relative increase in use of any power source 

(350 percent) by 2030 (USDOE EIA 2014). 

Assuming a future of moderate urbanization 

(high population growth and moderate income 

growth) combined with high energy consumption 

(storyline A2), use of forest-derived roundwood 

fuelwood as the sole energy source (for heat 

and power at pulp mills, residences, and other 

venues) is projected to increase by 230 percent 

from 2010 to 2060. One form of woody biomass 

energy that will likely see a dramatic increase is 

electricity produced in power plants that co-fire 

coal and wood. This is the result of advances in 

biomass energy technology as well as a shift 

from viewing biomass as a localized fuel source 

(primarily for energy at pulp and paper mills 

or primary wood processing plants) to a more 

universally accepted biofuel that can be combined 

with other feedstocks. 

202
Making biomass utilization feasible for the North 

would require resolution of barriers associated 

with infrastructure, economics, and compatibility 

with other conservation goals. An example is 

efforts to find markets for small diameter trees 

that must be removed during savannah and 

woodland restoration. Residential fuel wood 

and other biomass energy used by individuals 

could also have a measurable impact on the 

future energy profile of the region. Residential 

heating with wood can be highly efficient, and the 

potential users (demand) are widely dispersed 

among the abundant woody biomass resource 

(Song et al. 2012). Additionally, increased use 

of urban wood waste can provide an additional 

source of fuel for bioenergy in the future.

Although the projections used to describe future 

trends in both carbon and woody biomass for 

the Northern States are based on intensive 

study of past trends, the projected energy future 

of the region is subject to alterations from 

unpredictable—and unknowable—factors. A 

recent example is the sharp increase in availability 

of natural gas that could lead to falling energy 

prices and thereby reduce economic incentives 

for biomass use. As assumed in all scenarios that 

are based on enhanced woody biomass utilization, 

demand for woody biomass energy feedstocks 

could increase dramatically if fossil fuels become 

scarce or cost prohibitive. Implementation 

of practical cellulosic ethanol technologies 

would be a game-changer, leading to increased 

consideration of woody biomass beyond the realm 

of fuelwood and co-firing and thereby increasing 

the market base, but perhaps also placing 

unsustainable demand on the northern woody 

biomass resource. Carbon emissions regulations 

will also be influential. Decisions about the carbon 

or bioenergy future of the North will need to 

address the woody biomass resource situation 

within the context of larger energy issues. 

CHAPTER SEVEN 203
Aguilar, F.X.; Goerndt, M.; Shifley, S.; Song, N. 2012. Internal, external, 

and location factors influencing cofiring of biomass with coal 

in the U.S. northern region. Energy Economics. 34: 1790-1798.

Goerndt, M.E.; Aguilar, F.X.; Miles, P.; Shifley, S.; Song, N.; Stelzer, H. 2012. Regional assessment 

of woody biomass as an energy feedstock for combined combustion in the U.S. northern 

region. Journal of Forestry. 110(3): 138-148.

Hoover, C.; Stout, S. 2007. The carbon consequences of thinning techniques: stand structure makes 

a difference. Journal of Forestry. July/August: 266-270.

Ince, P.J.; Kramp, A.D.; Skog, K.E.; Spelter, H.N.; Wear, D.N. 2011. U.S. Forest Products module: 

a technical document supporting the Forest Service 2010 RPA assessment. Res. Pap. 

FPL–RP–662. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products 

Laboratory. 61 p.

Intergovernmental Panel on Climate Change [IPCC]. 2007. Climate change 2007: synthesis report. 

Contribution of working groups I, II and III to the fourth assessment report of the 

Intergovernmental Panel on Climate Change. Geneva, Switzerland: Intergovernmental Panel 

on Climate Change. 104 p. http://www.ipcc.ch/publications_and_data/publications_ipcc_

fourth_assessment_report_synthesis_report.htm (accessed August 21, 2014).

Kurz, W.A.; Stinson, G.; Rampley, G.J.; Dymond, C.C.; Neilson, E.T. 2008. Risk of natural 

disturbances makes future contribution of Canada’s forests to the global carbon cycle highly 

uncertain. Proceedings National Academy of Sciences. 105: 1551-1555.

Malmsheimer, R.W.; Heffernan, P.; Brink, S. [et al.]. 2008. Forest management solutions for 

mitigating climate change in the United States. Journal of Forestry. 106(3): 119-171.

McKinley, D.C.; Ryan, M.G.; Birdsey, R.A. [et al.]. 2011. A synthesis of current knowledge on forests 

and carbon storage in the United States. Ecological Applications. 21: 1902-1924.

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(accessed November 15, 2012).

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Gen. Tech. Rep. NRS–GTR-150. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 48 p.

Nowak, D.J.; Crane, D.E. 2002. Carbon storage and sequestration by urban trees in the USA. 

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Rep. NRS–90. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 202 p.

Song, N.; Aguilar, F.X.; Shifley, S.; Goerndt, M. 2012. Analysis of U.S. residential wood energy 

consumption: 1967-2009. Energy Economics. 34(6):2116-2124.

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Washington, DC: U.S. Department of Agriculture, Forest Service. 198 p.

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Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 34 p.

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and bioproducts industry. ORNL/TM–2011/224. Oak Ridge, TN: U.S. Department of Energy, 

Oak Ridge National Laboratory. 227 p. 

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energy report. Washington, DC: U.S. Department of Energy, Energy Information Administration. 

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206 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Maintenance and Enhancement of

L o n g - t e r m M u l t i p l e S o c i o e c o n o m i c 

Benefits to Meet the Needs of Societies

Nianfu Song, Stephen R. Shifley, J. M. Bowker, Marla R. Emery, 

Francisco X. Aguilar, Kenneth E. Skog

Introduction

 

ORESTS ARE AN important source of timber and nontimber products, recreation, ecological 

services, and employment in the Northern United States. Timber products are primarily 

used for building homes; manufacturing flooring, furniture, and cabinets; and making paper and 

paperboard. Residues from forest harvesting operations and wood processing are used for bioenergy, 

charcoal, and landscaping mulch. The employment opportunities in forestry range from research 

and development to logging and manufacturing. Maple syrup is the most prominent nontimber 

forest product in the North in terms of annual quantity produced and market value. The quantity 

and value of nontimber products are dwarfed by the wood products and paper industries, 

but gathering nontimber products has considerable cultural and economic importance for 

local communities. The dollar value of forest-based recreation is hard to quantify, but the 

vast majority of people who participate is a strong indicator of its importance to 

society. In addition, forest-based recreation generates demand for travel and 

equipment and creates employment for recreation managers and service providers.

In addition to provisioning and cultural 

ecosystem services provided by forest products 

and recreation, regulating ecosystem services 

from forests includes clean water, carbon 

sequestration, and diversity of plant and animal 

species (Chapters 3, 4, 6, and 7). As with 

recreation, the value of these services is difficult 

to quantify, but maintaining them is an essential 

part of sustainable forest management. 

This chapter describes indicators of tangible 

socioeconomic benefits from northern forests. 

To the extent possible, the indicators are 

described quantitatively, but some 

are limited to qualitative descriptions 

because of large data gaps. For many 

indicators, descriptions are limited to trends 

rather than projections. The methods described 

in Chapter 2 were used to project growing-stock 

roundwood removals from 2010 to 2060 for 

the seven alternative scenarios. Forecasts of 

forest products were based on a forest products 

model described by Ince et al. (2011). Modeling 

of future recreation participation was based 

on assumptions about changes in population 

demographics, land use, and other factors. 

CHAPTER EIGHT 207
• Overall employment in forestry is declining, 

but productivity (total value of shipments per 

employee) is increasing.

• Capital investments by government in both 

production and research are negatively affected 

by the recent economic recession that 

followed the 2007 U.S. financial crisis.

• Real-dollar wages in forestry have been 

relatively constant from 2001 to 2010.

• Under scenarios that assume a constant rate of 

harvesting without added demands for bioenergy 

feedstocks, the average annual projected removal 

of growing stock on timberland from 2010 to 2060 

would be 1.3 percent, about half of the projected 

rate of forest growth. 

• Under scenarios that assume greater biomass 

consumption for energy, the average annual 

projected removals of growing stock on timberland 

from 2010 to 2060 would range from 1.7 to 2.7 

percent of growing stock. 

• Production of lumber and wood panels is expected 

to increase under a scenario that assumes large 

gains in urbanization but would decrease 

under scenarios that assume smaller 

gains in urbanization. 

• Paper and paperboard production is projected to 

be variable in the next decade and is expected to 

decrease before 2060 under scenarios that assume 

a constant rate of harvesting without added 

demand for bioenergy feedstocks. 

• The low rates of wood recovered from construction 

and demolition waste can be attributed to high 

cost and low returns, but up to 70 percent of wood 

waste from construction and demolition could 

potentially be reused.

• The wholesale value of wild-harvested nontimber 

products is estimated at $682 million for the entire 

United States. 

• Nearly all commercial U.S. maple syrup production 

is from the North; total annual production in 2013 

was about 3.5 million gallons and was valued at 

$132 million. 

• The total volume of wood and paper products 

from northern forests is about 40 percent of the 

quantity consumed by residents in this region; 

that percentage is expected to decrease under all 

scenarios by 2060. 

• Projected population increases from 2008 to 2060 

in the North are expected to cause per capita 

Federal and State park land to decrease by 19 

percent and per capita non-Federal forest land to 

decrease by 26 percent. 

• Under a scenario that assumes large gains 

in urbanization, increases in participation 

(compared to current participation) are projected 

for horseback riding on trails, downhill skiing, 

motor boating, and visiting interpretative sites; 

decreases are expected for hunting, back-country 

skiing, and snowmobiling. 

Key Findings

208
WOOD AND WOOD PRODUCTS 

Production Volumes

As described in Chapter 4, volume removals 

attributable to harvesting and land-use change 

are projected to increase moderately under 

scenarios that assume a constant rate of 

harvesting without added demands for bioenergy 

feedstocks, but could more than double under 

scenarios that assume enhanced biomass 

consumption for energy (Chapter 7). 

Removals on timberland in the North were 

about 3.6 billion cubic feet in 2007, and the 

removal rate as a proportion of total growingstock volume was 1.4 percent, or roughly half 

the 2.5-percent net annual growth rate (Shifley 

et al. 2012). The projected 1.3 percent mean 

annual removals rate from 2010 to 2060 under 

scenarios that assume a continuation of current 

harvest rates (i.e., scenarios A1B-C, A2-C, 

B2-C, A2-EAB) are close to the 2007 baseline 

or about half of estimated net annual growth, 

but removals rates under the scenarios with 

enhanced biomass consumption would range 

from 1.7 to 2.7 percent (Table 8.1). When 

coupled with projected growth and mortality, 

the projected removals for the three scenarios 

with accelerated biomass harvesting are large 

enough to result in a net decrease in total 

standing volume over the period. Chapter 4 

describes the volume growth, mortality, and 

removals patterns in detail for all scenarios. 

CHAPTER EIGHT 209
Table 8.1—Projected average annual growing-stock removal rates on timberland in the North, 2010 to 2060. States are 

ordered from greatest to least growing-stock volume in 2012.

(billion ft3) (percent) -----------------------------------------------------(percent)-------------------------------------------------------- 

Pennsylvania 32.0 1.3 1.3 1.3 1.1 1.3 2.6 1.9 1.6

New York 29.3 1.0 0.7 0.7 0.7 0.7 1.4 1.0 0.9

Michigan 28.4 1.2 1.3 1.3 1.2 1.2 2.8 2.1 1.7

West Virginia 25.0 1.3 1.3 1.3 1.3 1.3 3.0 2.2 1.7

Maine 23.1 2.4 2.3 2.3 2.3 2.3 4.7 3.8 3.1

Wisconsin 20.9 1.6 1.7 1.7 1.8 1.7 3.8 2.9 2.4

Missouri 16.8 1.0 0.9 0.9 0.9 0.9 2.0 1.4 1.2

Minnesota 14.5 2.0 1.8 1.8 1.7 1.9 3.7 2.5 2.4

Ohio 14.1 1.3 1.4 1.5 1.4 1.4 3.3 2.4 1.9

New Hampshire 9.5 1.0 1.2 1.1 1.1 1.1 2.8 2.0 1.6

Vermont 9.0 1.3 1.2 1.0 1.1 1.1 2.5 1.8 1.4

Indiana 8.6 0.9 1.3 1.2 1.1 1.1 2.8 2.1 1.6

Illinois 7.2 0.8 0.7 0.6 0.7 0.7 1.3 1.1 0.9

Massachusetts 6.9 0.5 0.4 0.4 0.4 0.5 0.6 0.6 0.4

Maryland 5.9 1.1 1.5 1.5 1.4 1.5 2.6 2.1 1.7

Connecticut 3.8 1.4 0.6 0.7 0.5 0.7 0.8 0.7 0.6

New Jersey 3.5 0.8 1.5 1.5 1.1 1.6 2.3 2.1 1.4

Iowa 3.0 1.5 1.4 1.3 1.7 1.3 3.4 2.3 2.1

Delaware 0.8 0.9 1.0 1.1 1.0 1.1 1.8 1.4 1.2

Rhode Island 0.7 0.3 0.7 0.8 0.4 0.7 0.9 0.8 0.4

Total 263.4 1.3 1.3 1.3 1.3 1.3 2.7 2.1 1.7

a

Removal rates are projected under six scenarios, each representing a global greenhouse storyline (IPCC 2007)—A1B assumes moderate greenhouse gas emissions, 

moderate gains in population growth with large gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio), A2 assumes high greenhouse 

gas emissions, large gains in population growth and energy consumption with moderate gains in income, or B2 assumes low greenhouse gas emissions, moderate gains in 

population growth, income, and energy consumption—combined with the assumption that bioenergy demands will stimulate an increase in roundwood harvesting (-BIO) 

or that roundwood harvesting will stay constant (-C); a seventh scenario—A2-EAB—assumes that roundwood harvesting will stay constant and that all ash species will 

succumb to an infestation by the nonnative emerald ash borer.

States

Estimated, 2010 Projecteda annual removal rate, 2010 to 2060

Total 

growing-stock

volume

Removal 

rate A1B-C A2-C B2-C A2-EAB A1B-BIO A2-BIO B2-BIO

210 FUTURE FORESTS OF THE NORTHERN UNITED STATES
The removals in Pennsylvania, Michigan, Maine, 

and Wisconsin are expected to account for 

about half of the total removals in the North. 

Predictions suggest that hardwoods will continue 

to dominate timber production and account for 

about three-fourths of total removals. 

The future production of lumber, wood panels, 

and paper and paperboard in the North (Fig. 

8.1) was estimated by Ince et al. (2011) using 

global demand and supply factors that affect 

wood product markets for all U.S. regions. 

Projected changes in production reflect 

anticipated market demand and therefore are 

greatly affected by the economic assumptions 

that underlay the greenhouse gas emissions 

FIGURE 8.1

Projected (A) lumber and wood panels and (B) paper and paperboard production in the North, 2010 to 2060, based on the U.S. Forest Products 

Module (Ince et al. 2011) applied with three global greenhouse gas storylines (IPCC 2007). Storyline A1B assumes moderate greenhouse 

gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced renewable/fossil fuel 

portfolio); A2 assumes high greenhouse gas emissions, large gains in population and energy consumption, and moderate gains in income; and 

B2 assumes low greenhouse gas emissions with moderate gains in population, income, and energy consumption. Additional assumptions of the 

Forest Products Module are described by Ince et al. (2011).

2000 2010 2020 2030 2040 2050 2060 2000 2010 2020 2030 2040 2050 2060

1.6

1.4

1.2

1.0

0.8

0.6

0.4

35

30

25

20

15

10

5

ROUNDWOOD EQUIVALENT (billion cubic feet)

ROUNDWOOD EQUIVALENT (million cubic feet)

A1B

A2

B2

A1B

A2

B2

storylines and additional assumptions utilized 

by Ince et al. (2011). Under assumptions for 

storyline A1B, the production of primary solid 

wood products—the total of lumber and wood 

panels production—in the North could increase 

68 percent from 870 million cubic feet in 2006 

to 1,461 million cubic feet in 2060. However, 

in the same period under the assumptions for 

storylines A2 and B2, the production of primary 

solid wood products could decline by 28 percent 

(Fig. 8.1). The 50-year predictions for paper 

and paperboard production eventually trend 

downward under all storylines. 

A B

CHAPTER EIGHT 211
Timber removals in the North totaled 3,045 

million cubic feet in 2007, and the population 

was 120 million people at that time (Shifley et 

al. 2012, Smith et al. 2009). Thus, the average 

annual timber removals per capita in the North 

(including removals associated with conversion 

of forest to other land use categories) were 

only 25 cubic feet. Total wood production in the 

region is expected to increase by 2060 under all 

scenarios, but due to increased population, the 

removals per capita between 2010 and 2060 

are expected to decline slightly for all scenarios 

except those with large increases in biomass 

harvest for energy (i.e., scenarios A1B-BIO, 

A2-BIO, and B2-BIO) (Fig. 8.2). 

FIGURE 8.2

Projected per capita wood growing-stock removals on timberland in the North, 2010 to 2060 under seven scenarios, each representing a global 

greenhouse gas storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes moderate greenhouse gas emissions, moderate 

gains in population, and large gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio); A2 assumes high 

greenhouse gas emissions, large gains in population and energy consumption, and moderate gains in income; and B2 assumes low greenhouse 

gas emissions with moderate gains in population, income, and energy consumption. Scenario projections assume harvest will continue at 

recently observed levels (labeled –C) or increase to reflect increased harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is 

a variation of scenario A2-C that also assumes all ash species will gradually succumb to an expanding zone of infestation by the nonnative 

emerald ash borer.

2010 2020 2030 2040 2050 2060

70

60

50

40

30

20

ANNUAL REMOVALS (cubic feet per capita)

A1B-C

A2-C

B2-C

A1B-BIO

A2-BIO

B2-BIO

A2-EAB

Total and Per Capita Consumption

U.S. annual timber product consumption 

averaged 69 cubic feet per capita from 1965 

to 2011. Due to weak market demand since 

2007, per capita consumption hit a low of 

41 cubic feet in 2009 and 2010 but rebounded 

modestly to 47 cubic feet by 2011(Howard 

and Westby 2013, Howard et al. 2010). With 

recovery of the U.S. economy, housing starts 

and per capita timber product consumption 

will likely increase.

212 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Future total consumption of wood products in 

the North can be estimated from projections of 

population growth and per capita consumption. 

Based on the population projections for each 

of the 1,037 counties in the U.S. North and 

Washington, D.C., the predicted population 

in the region for 2060 would be 158 million 

people (+26 percent) under storyline A1B, 178 

million people (+42 percent) under storyline 

A2, and 140 million people (+12 percent) under 

storyline B2 (Zarnoch et al. 2010). Assuming 

that future per capita wood consumption in 

the North would mirror the 46-year national 

average (69 cubic feet per year), the projected 

2060 consumption reaches 10.9 billion cubic 

feet under storyline A1B, 12.3 billion cubic feet 

under storyline A2, or 9.7 billion cubic feet 

under storyline B2 (Fig. 8.3).

Exports and Imports of Wood Products 

The net import of wood products into the 

U.S. North can be estimated by calculating 

the difference between future consumption 

(assumed 69 cubic feet per capita) and the 

predicted production for scenarios A1B-C, A2-C, 

or B2-C. Table 8.2 suggests that a net equivalent 

of >60 percent of the wood products consumed 

in the region will have to be imported from other 

regions or foreign countries. The net import of 

wood products is predicted to increase over 

time under scenarios A1B-C, A2-C, and B2-C 

(Fig. 8.3). The assumption of 69 cubic feet per 

capita consumption does not apply to A1B-BIO, 

A2-BIO, and B2-BIO, which assume greatly 

increased woody biomass demand. 

2010 2020 2030 2040 2050 2060

14

12

10

8

6

4

ROUNDWOOD EQUIVALENT (billion cubic feet)

FIGURE 8.3

Projected roundwood equivalent volumes of wood products 

consumed and imported in the North, 2010 to 2060, under three 

scenarios, each representing a global greenhouse gas storyline (IPCC 

2007) paired with a harvest regime. Storyline A1B assumes moderate 

greenhouse gas emissions, moderate gains in population, and large 

gains in income and energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes high greenhouse gas 

emissions, large gains in population and energy consumption, and 

moderate gains in income; and B2 assumes low greenhouse gas 

emissions with moderate gains in population, income, and energy 

consumption. These scenarios assume harvest will continue at 

recently observed levels, and when paired with a specific storyline 

these scenarios are labeled A1B-C, A2-C, and B2-C. Import values 

are estimated by subtracting the projected production from the 

assumed per capita consumption multiplied by projected population.

A1B-C consumed

A2-C consumed

B2-C consumed

A1B-C imported

A2-C imported

B2-C imported

CHAPTER EIGHT 213
Many of the timber and nontimber products 

from northern forests are exported to other 

regions or other countries. However, available 

trade statistics are insufficient to track trade 

into and out of the region for specific products. 

NONTIMBER FOREST PRODUCTS 

Nontimber forest products include hundreds 

of plant materials and fungi collected for food, 

medicine, and other purposes. While much of 

the nontimber forest products harvested are 

used for subsistence or personal consumption, 

or are traded in local markets, some are traded 

in national and global markets. The quantity 

and dollar value of northern nontimber forest 

products entering commercial markets is 

typically small compared to timber harvested 

for lumber, pulp, fuel, and similar commodities. 

Table 8.2—Predicted proportion of total wood product consumption in the North, 2010 to 2060, that is 

expected to be met by net imports from other regions and countries. 

----------------------------------------------------------------------(percent)---------------------------------------------------------------------

A1B-C 61 63 65 64 66 66

A2-C 62 66 66 68 70 70

B2-C 61 64 65 64 63 64

A2-EAB 62 65 67 69 70 71

a

Removal rates are projected under six scenarios, each representing a global greenhouse storyline (IPCC 2007)—A1B assumes moderate greenhouse gas 

emissions, moderate gains in population growth with large gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio), 

A2 assumes high greenhouse gas emissions, large gains in population growth and energy consumption with moderate gains in income, or B2 assumes 

low greenhouse gas emissions, moderate gains in population growth, income, and energy consumption—combined with the assumption that bioenergy 

demands will stimulate an increase in roundwood harvesting (-BIO) or that roundwood harvesting will stay constant (-C); a seventh scenario—A2-EAB—

assumes that roundwood harvesting will stay constant and that all ash species will succumb to an infestation by the nonnative emerald ash borer.

Scenarioa 2010 2020 2030 2040 2050 2060

Nonetheless, nontimber products can be very 

important to local peoples and economies. 

Information about the type and quantity of 

nontimber forest products harvested and 

consumed is limited, but Alexander et al. (2011) 

estimated the 2007 wholesale value of U.S. 

wild-harvested nontimber forest products 

entering commercial markets at $682 million 

(Fig. 8.4) and the corresponding value to 

nontimber forest product gatherers at their first 

point of sale (cash value to gatherers if selling 

to a wholesaler) at $272 million or 40 percent of 

the wholesale value. The estimated total value of 

commercially traded nontimber forest products 

varies from year to year but has gradually 

decreased by 23 percent from 1998 to 2007. 

Product categories that increased in value were 

herbal and medicinal plants, craft and floral 

items, and grass and forage. 

214 FUTURE FORESTS OF THE NORTHERN UNITED STATES
U.S. exports of major nontimber forest 

products tracked by the U.S. Department 

of Agriculture were >$400 million in 2007, 

compared to >$700 million in imports (Table 

8.3). Although current data are not sufficient 

FIGURE 8.4

Estimated wholesale value 

(in nominal dollars) of commercially 

traded nontimber forest products 

in the United States, 1998 to 2007 

(Alexander et al. 2011). 

Grass and forage

Posts and poles

Regeneration seeds 

and cones

Landscaping materials

Herbs and medicinal

Edible fruits, nuts, and sap

Wild-grown Christmas trees

Crafts and floral

Fuelwood

900

800

700

600

500

400

300

200

100

0

WHOLESALE VALUE (million dollars)

‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07

Table 8.3—Estimated annual value of U.S. imports and exports of selected nontimber forest products, 2007 (Alexander et al. 2011). 

(million dollars) (million dollars)

Pecans 187 Pecans 147

Ginseng, wild or cultivated 52 Maple syrup 142

Foliage, branches 48 Wild blueberries 109

Wild blueberries 48 Foliage, branches 96

Other fruits 32 Pine nuts 54

Mushrooms, truffles 14 Cranberries 49

Pine oil 12 Vanilla beans 43

Maple syrup, sugar 8 Mushrooms, truffles 34

Oil of cedarwood, clove 

or nutmeg

5 Ginseng, wild or cultivated 25

Gum, turpentine 5 Mosses and lichens 4

Total 411 Total 703

Exports Imports 

Product Value Product Value

to justify comprehensive regional or State-level 

analyses, they suggest that imports substantially 

exceed exports for several important northern 

nontimber forest products 

including maple syrup, wild 

blueberries, mushrooms, 

and decorative foliage. 

CHAPTER EIGHT 215
Common wild-harvested herbal and medicinal 

plants include 15 species found in northern 

forests (Table 8.4), but none are exclusive 

to the region. Harvesting estimates are not 

available separately for northern forests. 

However, large-scale field cultivation of ginseng 

occurs almost exclusively in Wisconsin, and 12 

of the 19 States meeting Federal requirements 

for export of wild and wild-simulated ginseng 

are in the region: Illinois, Indiana, Iowa, 

Massachusetts, Minnesota, Missouri, New York, 

Ohio, Pennsylvania, Vermont, West Virginia, and 

Wisconsin (USDI FWS 2011). 

The value of per capita consumption of 

commercially marketed nontimber forest 

products, adjusted for imports and exports, was 

estimated at $2.46 for 2007, down from $3.31 

in 2003 (Alexander et al. 2011). These overall 

totals mask the economic importance of products 

such as ginseng, maple syrup, or balsam boughs 

to some individual households and communities. 

Nearly all U.S. commercial maple syrup 

production occurs in the North. Ten 

Northern States (Fig. 8.5) produced 

an average of 2 million gallons of 

syrup per year from 2006 to 2012 

(USDA NASS 2014). Although 

production has gradually 

increased over the last 

20 years, there can be 

substantial year-to-year 

variation. In 2012, 

maple syrup production 

216
Table 8.4—Wild-harvested plants found in northern U.S. forests 

that are used for herbal and medicinal purposes, 1997 to 2005 

(Alexander et al. 2011); entries are sorted from greatest to least 

total harvest weight for the United States as a whole. 

(pounds)

Black cohosh root, rhizome 259,617

Slippery elm bark 189,208

Goldenseal root, rhizome, leaf 106,728

Wild yam tuber 42,400

Bloodroot root 28,798

Eastern purple 

coneflower

root, herb 20,208

Pale purple coneflower root, herb 9,759

Blue cohosh root 6,460

Fairywand root 4,529

Red trillium whole plant 1,234

Beard lichen whole plant 1,094

White colicroot root 1,019

Virginia snakeroot root 149

Lady’s slipper whole plant 58

Arnica whole plant 21

Total 671,282

Plant Part(s) used

Estimated 

average annual 

U.S. harvest

and value dropped sharply throughout the North, 

attributable to prevailing high temperatures 

that limited sap flow. Weather conditions were 

favorable in 2013; consequently annual production 

in the North increased by 85 percent relative to 

2012. Output in 2013 totaled 3.5 million gallons, 

which is 0.7 million gallons more than the highest 

output reported for the previous 30 years. The 

average value of maple syrup produced from 2006 

to 2012 was $75 million annually, up from $39 

million during the prior 5-year period. The value of 

production reached $132 million in 2013. Vermont 

leads all States in maple syrup production 

(valued at $49 million in 2013) and 

accounts for about 40 percent 

of total U.S. output. 

FIGURE 8.5

Volume and value 

(in nominal dollars) 

of maple syrup production 

in the North, 1992 to 2013 

(USDA NASS 2014).

Connecticut

Massachusetts

Pennsylvania

Michigan

New Hampshire

Ohio

Wisconsin

Maine

New York

Vermont 

Maple syrup value

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

MAPLE SYRUP PRODUCTION (million gallons)

‘92 ‘94 ‘96 ‘98 ‘00 ‘02 ‘04 ‘06 ‘08 ‘10 ‘12 ‘13

MAPLE SYRUP VALUE 

(million dollars)

80

60

40

20

0

140

120

100

CHAPTER EIGHT 217
The annual variations in sap and syrup 

production attributed to weather conditions have 

heightened concerns about future impacts of 

climate change. In addition to concerns about 

unfavorable temperature for sap production, 

climate change is expected to reduce the extent 

of favorable habitat for sugar maple trees in the 

region (Iverson et al. 2008, USDA FS 2014) with 

the additional concern that maples are highly 

valued for autumn color and important to the 

recreation and tourism industries discussed in 

the Outdoor Recreation section of this chapter. 

As is true for any commodity, markets and 

economic values for nontimber forest products 

can be expected to fluctuate in the future. 

Economic downturns will depress demand for 

discretionary items such as floral greens and 

decoratives, and research about adverse effects 

will continue to eliminate markets for individual 

medicinal species. However, growing interest 

in local foods and medicinal supplements can 

be expected to drive overall growth in demand 

for species that fill these niches. Economic 

downturns can also increase harvesting of 

desirable nontimber forest products for personal 

consumption and sale. As markets for herbal 

supplements and medicinal products develop, 

harvesting volumes can be expected to increase. 

For some species, markets will be short lived, 

but for those with enduring markets, cultivation 

efforts and offshore sourcing could fill a growing 

portion of demand, resulting in less harvesting 

from northern forests. 

RECOVERY OR RECYCLING 

OF FOREST PRODUCTS 

Recycling of forest products includes the reuse 

of any wood and paper product except wood 

waste used for energy. Many wood products 

such as furniture, pallets, boxes, and wood 

from demolition are reused. The recycling rate 

for some types of wood products is already 

high. For example, most pallets in the United 

States are reused or repaired for reuse (Bush 

et al. 2007). Manufacturing wood residues 

are used as fuel wood or recycled for making 

engineered wood products, landscaping 

products, and animal bedding.

Paper and wood account for about a third of 

total U.S. municipal solid waste by weight (Fig. 

8.6). In the Northeastern States (Connecticut, 

Maine, Massachusetts, New Hampshire, 

New Jersey, New York, Rhode Island, and 

Vermont), paper and paperboard make up 

about 29 percent of municipal solid waste, and 

wood makes up about 6 percent of municipal 

solid waste (Northeast Waste Management 

Officials’ Association 2009). With the growth of 

opportunities and requirements for recycling, 

an increasing proportion of wood and paper 

products will be reused in increasingly 

efficient ways. The national rate of paper and 

paperboard recycling was 67 percent in 2010, 

about twice the 1999 rate of 34 percent (Paper 

218 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Industry Association Council 2012). The recovery 

rate of wood from the municipal waste stream 

has also increased but is low compared to other 

reusable wastes (Fig. 8.6). For example, the U.S. 

recovery rate of wood from municipal solid waste 

in 2006 was only 12 percent.

Wood was an estimated 10 percent 

of construction demolition waste, 

and its estimated recovery rate 

is about 15 percent, with 

large variation among 

Northeastern 

FIGURE 8.6

Comparison of wood-products and other materials in municipal wastes, 1960 to 2010, in the Northern United States by (A) volume 

generated and (B) percent recycled (US EPA 2011).

1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010

300

250

200

150

100

50

0

70

60

50

40

30

20

10

MUNICIPAL SOLID WASTES (

0

million tons per year)

RECYCLING RATE (percent)

Paper and paperboard waste

Other municipal solid waste

Wood waste

Paper and paperboard recycling rate

Other municipal solid recycling rate

Wood recycling rate

States (Northeast Waste Management Officials’ 

Association 2009). Corresponding recovery 

rates in other Northern States have not been 

reported, but Falk and McKeever (2004) 

suggest that low rates of recovery of wood in 

construction demolition waste can be attributed 

to high costs and low returns, and also 

that up to 70 percent of such wood 

waste could be reused. 

A B

CHAPTER EIGHT 219
FORESTRY INVESTMENTS AND RETURNS 

Private Landowner Investments

Historically, investments in tree planting in 

northern forests have been small compared 

to forests in other regions. This is partially 

because hardwoods, the dominant component, 

can regenerate naturally. The planted forest area 

in the North is <4 percent of total forest land 

compared to 8 percent for all U.S. forest land, 

and tree planting area has decreased to <0.2 

million acres each year in recent decades (USDA 

FS 2009). The acreage planted is expected to 

remain at that level or slowly decrease 

unless there are large 

investments in wood 

bioenergy plantations. 

The degree to which an assisted migration 

strategy for tree species could affect future rates 

of tree planting is unknown. Assisted migration 

uses planting to expand the occurrence of a 

species outside its natural range into locations 

that are presumed to be better suited to its growth 

and survival under a changing climate. Assisted 

migration is a widely discussed climate change 

adaptation strategy that remains controversial 

(McLachlan et al. 2007, Vose et al. 2012). 

The increasing number of forest owners and 

the decreasing average size of private forest 

ownerships in the region contribute to low 

investments in forest management (Chapter 9). 

Nevertheless, participation of landowners in 

conservation programs in recent decades has 

increased as measured by tax deductions and 

increased enrollment in easements and other 

incentive programs (Land trust Alliance 2011, 

Song et al. 2014). Recreational investment 

is expected to increase in anticipation of an 

increasing number of people participating in 

outdoor activities (Bowker and Askew 2013, 

Bowker et al. 2012, Cordell 2012).

220 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Wood Products Industry Investments

Capital investments in wood products—the 

North American Industry Classification System 

(NAICS) category 321—increased to $1.2 billion 

in 2006 in the U.S. North (Shifley et al. 2012). 

After the 2007 U.S. financial crisis, capital 

investment in wood products decreased to a 

low of $0.5 billion in 2011 (U.S. Census Bureau 

2012), a rate that is expected to increase as the 

overall economy continues to recover.

In contrast, investment in pulp and paper 

production in the North has been declining 

since 1997 due to weak demand and intensive 

competition. Total investments in the pulp and 

paper industry in the North decreased from 

$2.9 billion in 2006 to $2.2 billion in 2010. 

It is unknown when the decline of investments 

in this industry will stop.

2006 2008 2010 2012

300

250

200

150

100

50

FOREST SERVICE APPROPRIATIONS (

0

million dollars) Northern Research Station

Northeastern Area State and Private Forestry

National Forest System, Region 9

FIGURE 8.7

U.S. Forest Service discretionary appropriations in the North, 2006 to 

2012, for the National Forest System (Region 9), Northeastern Area 

State and Private Forestry, and the Northern Research Station.

Federal Investments

Federal government investment in forest 

management in the North has remained 

relatively flat. A modest increase in combined 

funding for the National Forest System and the 

State and Private Forestry Program of the U.S. 

Forest Service has been more than offset by the 

effects of the Federal budget sequestration 

(Fig. 8.7). 

CHAPTER EIGHT 221
Table 8.5—Comparison of forest production and service employment, 

2001 to 2010, in the United States and in the North (U.S. Department of Labor, Bureau of Labor Statistics 2012).

(113) (1142) (321) (322)

(337110, 337122, 

337211, 337212) (71219)

Northern States

2001 (employees) 12,243 809 205,516 300,389 125,389 11,993 656,339

2010 (employees) 9,495 407 125,390 197,905 78,626 15,121 426,944

Change (percent) -22 -50 -39 -34 -37 26 -35

United States

2001 (employees) 82,526 3,619 646,964 577,030 313,308 69,934 1,693,381

2010 (employees) 60,065 1,665 473,495 392,853 169,898 80,642 1,178,618

Change (percent) -27 -54 -27 -32 -46 15 -30

Employment North American Industry Classification System category (NAICS code)

Forestry 

and logging

Hunting and 

trapping

Wood 

products

Paper and 

paperboard

Wood 

furniture

Parks and 

recreation Total

The total Federal government investment 

in forest research, landowner support, and 

national forest management decreased slightly 

in nominal dollars from 2005 to 2011 (Fig. 8.7). 

However, in 2012, the combined expenditure 

was $280 million, down 5 percent from 2005 

and down 15 percent from the 2011 high. 

EMPLOYMENT IN FOREST INDUSTRIES 

Numbers of Employees

Employment in six major NAICS categories is 

forest-related (Table 8.5). In addition, forestassociated employment includes jobs in forest 

administration offices, forest research stations, 

universities, consulting firms, and conservation 

organizations, but statistics for these jobs are 

indirectly included in employment numbers of 

government, education, research, conservation, 

and other job categories and cannot be 

separated for forestry related employment. 

Direct forest production and service sectors in 

the North provided a combined total 427,000 

jobs in 2010, which was 36 percent of the U.S. 

total for these sectors (1.2 million jobs). Most 

jobs (94 percent) were in manufacturing: wood 

products manufacturing, paper and paperboard 

products manufacturing, and wood furniture 

manufacturing (Table 8.5). 

222 FUTURE FORESTS OF THE NORTHERN UNITED STATES
The job data used in this report are from the Quarterly 

Census of Employment and Wages (U.S. Department 

of Labor, Bureau of Labor Statistics 2012) for private 

establishments and government installations with one 

or more paid employees. Survey units without paid 

employees are excluded, as are government agencies, 

education, research, and conservation employees.

Forestry and logging (NAICS 113)—Establishments 

and government installations growing and harvesting 

timber on a long production cycle (≥10 years). 

Consequently, Christmas tree production and other 

production involving production cycles <10 years 

are not in this subsector. Silvicultural activities, 

harvesting timber, and gathering forest products 

(such as gums, barks, balsam needles, rhizomes, 

fibers, Spanish moss, ginseng, and truffles) are 

included in this subsector.

Hunting and trapping (NAICS 1142)—Establishments 

that do commercial hunting and trapping or operate 

commercial hunting and game preserves.

Wood products (NAICS 321)—Establishments 

manufacturing wood products (from logs and bolts) 

that are sawed and shaped, and establishments 

purchasing sawed lumber and making wood 

products. Products of this subsector include lumber, 

plywood, veneers, wood containers, wood flooring, 

wood trusses, manufactured homes (mobile homes), 

and prefabricated wood buildings.

Paper and paperboard (NAICS 322)—Establishments 

making pulp, paper, or converted paper products. 

Converted paper products are made from paper and 

other materials by various cutting, shaping, coating, 

and laminating activities. Products of converted 

paper products include paperboard containers, paper 

bags, coated and treated paper, stationery, sanitary 

paper, tissue paper, and disposable diapers. Excluded 

from this subsector are photosensitive papers. 

Wood furniture—Establishments manufacturing 

wood kitchen cabinet and countertop (NAICS 337110), 

non-upholstered wood household furniture 

(NAICS 337122), wood office furniture (NAICS 337211), 

and custom architectural woodwork and millwork 

(NAICS 337212).

Parks and recreation (NAICS 71219)—Establishments 

or government installations primarily engaged in 

the preservation and exhibition of natural areas 

or settings. National and State parks, reserves, 

conservation areas, wildlife sanctuary, and waterfalls 

are all included in this subsector.

Forest-related Job Categories in the 

North American Industry Classification System

223
Employment in the North from 2001 to 2010 

declined in all the forest-associated categories 

except parks and recreation (NAICS 71219). 

During these years, total forest-associated 

employment in the region dropped by 35 

percent; jobs in forestry and logging (NAICS 

113) dropped by 22 percent, from about 

12,000 to 9,000 jobs (Fig. 8.8); jobs in hunting 

and trapping dropped by 50 percent; jobs in 

wood products manufacturing dropped by 39 

percent; jobs in paper and paperboard products 

manufacturing dropped by 34 percent; and 

700

600

500

400

300

200

100

0

JOBS (thousand)

Parks and recreation

Wood furniture

Paper and paperboard

Forest products

Forestry and logging

FIGURE 8.8

Forest production and service job 

categories that employed >1,000 workers 

in the North, 2001 to 2010.

2002 2004 2006 2008 2010

jobs in wood furniture manufacturing dropped 

by 37 percent. Although they make up a small 

proportion of the total, jobs in parks and 

recreation increased by 26 percent. 

More than half of the decreases in forestassociated employment occurred after the 2007 

U.S. financial crisis (Fig. 8.8). A long-term trend 

of declining employment in the forest-associated 

industries appears to have been exacerbated 

by the economic downturn. Future changes in 

employment will likely be linked to economic 

conditions, and future rates of decline could 

be smaller as the economy recovers. Table 8.5 

shows employment numbers by job category 

for 2001 and 2010, and Table 8.6 shows 

employment numbers, by State, from 2001 

to 2010. 

224 FUTURE FORESTS OF THE NORTHERN UNITED STATES
The economic recession that followed the 2007 

financial crisis helps explain the particularly 

large drop in forest-related employment in 

2009, as does the weak demand for U.S. 

paper products since 1990. An additional 

factor was an overall increase in productivity 

per employee (average dollars of product 

shipment per employee), which was a major 

reason for employment declines in the forest 

products industry and one of several reasons 

for declines in the paper and paperboard 

industry. From 1997 to 2011, per employee 

shipments increased from $0.13 million to 

$0.20 million in the forest products 

industry and nearly doubled from 

0.24 to 0.45 million dollars in 

the paper and paperboard 

industry (Fig. 8.9). 

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

VALUE OF ANNUAL SHIPMENTS PER 

EMPLOYEE (million dollars)

Paper and paperboard (United States)

Paper and paperboard (North)

Forest products (United States)

Forest products (North)

All U.S. manufacturing

FIGURE 8.9

Comparison of forest-industry employee productivity 

(defined as shipment value per employee) in 

the United States and the North, 1997 to 2011 

(U.S. Census Bureau 2012).

1996 2000 2004 2008 2012

Average productivity values measured in million 

dollars per job per year for these two industries 

generally increased from 1997 to 2011 but 

showed considerable variation among States 

(Fig. 8.10). The increasing value of shipments 

per employee suggests a trend of higher 

productivity in these industries (Maine Forest 

Products Council 2013). Similar trends exist 

for the northern logging industry, where the 

number of workers has decreased although total 

timber production has increased over the last 

half century. 

CHAPTER EIGHT 225
Table 8.6—Numbers of forest production and service jobs, reported for the U.S. North by State and year, 

2001 to 2010, [NAICS categories 113, 321, 1142, 71219, 337110, 337122, 337211, and 337212] (U.S. 

Department of Labor, Bureau of Labor Statistics 2012). 

------------------------------------------------------(number of workers)----------------------------------------------------------

Connecticut 11,211 10,666 10,432 10,017 9,369

Delaware 2,519 2,661 2,455 2,022 1,367

District of Columbiaa 33 14 17 NA NA

Illinois 49,626 47,589 46,325 44,248 44,786

Indiana 52,365 50,357 48,638 49,056 49,395

Iowa 21,720 20,887 20,979 20,684 22,061

Maine 23,478 22,332 19,849 19,835 20,011

Maryland 13,033 12,302 11,898 11,653 12,018

Massachusetts 24,463 22,878 21,488 21,169 20,251

Michigan 42,640 39,780 38,341 36,777 36,995

Minesotta 41,207 40,466 40,048 39,916 40,184

Missouri 29,397 27,962 26,778 26,720 26,012

New Hampshire 8,593 7,814 7,093 7,298 7,092

New Jersey 28,076 25,818 25,034 24,388 23,534

New York 50,826 47,126 45,082 44,626 43,990

Ohio 69,032 65,722 63,185 61,478 60,012

Pennsylvania 79,135 76,632 74,337 72,829 73,442

Rhode Island 3,685 1,011 3,337 2,488 2,234

Vermont 7,149 6,498 5,766 5,593 5,587

West Virginia 12,548 12,541 12,597 12,806 12,650

Wisconsin 85,603 83,141 76,576 74,452 73,691

North total 656,339 624,197 600,255 588,055 584,681

U.S. total 1,693,381 1,620,104 1,547,753 1,505,504 1,484,624

North/U.S. (percent) 39 39 39 39 39

a

Record keeping stopped in 2004, designated as NA.

State 2001 2002 2003 2004 2005

226 FUTURE FORESTS OF THE NORTHERN UNITED STATES
--------------------------------------------(number of workers)-------------------------------------------- (percent)

Connecticut 9,366 9,537 9,163 7,725 7,148 -36

Delaware 1,622 1,574 1,472 1,337 1,329 -47

District of Columbiaa NA NA NA NA NA NA

Illinois 44,488 43,290 41,474 36,320 34,022 -31

Indiana 48,814 46,410 42,548 34,630 33,551 -36

Iowa 22,043 21,203 19,095 15,932 15,175 -30

Maine 19,151 18,170 17,196 15,013 14,888 -37

Maryland 11,717 10,586 9,545 8,035 7,432 -43

Massachusetts 19,139 18,780 17,145 14,682 14,251 -42

Michigan 35,523 34,095 31,768 27,248 26,597 -38

Minesotta 39,202 36,987 34,253 29,629 28,797 -30

Missouri 26,043 24,849 22,917 19,786 18,974 -35

New Hampshire 6,447 5,869 5,233 4,434 4,124 -52

New Jersey 22,670 21,533 20,747 17,910 17,654 -37

New York 46,827 45,634 43,753 38,240 33,177 -35

Ohio 58,221 55,932 52,711 45,009 43,402 -37

Pennsylvania 73,656 70,499 65,716 56,469 55,013 -30

Rhode Island 2,137 2,880 2,428 2,779 2,867 -22

Vermont 5,308 5,227 5,147 4,310 3,976 -44

West Virginia 12,501 11,552 10,147 8,465 8,091 -36

Wisconsin 72,145 69,775 66,337 58,360 56,476 -34

North total 577,020 554,382 518,795 446,313 426,944 -35

U.S. total 1,453,356 1,408,614 1,351,194 1,224,524 1,178,618 -30

North/U.S. (percent) 40 39 38 36 36 NA

a

Record keeping stopped in 2004, designated as NA.

State 2006 2007 2008 2009 2010

Change 

2001 

to 2010

Table 8.6 continued

CHAPTER EIGHT 227
1995 2000 2005 2010 2015 1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

Connecticut

Indiana

Maryland

Minnesota

Delaware

Iowa

Massachusetts

Missouri

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015

PRODUCTIVITY 

(thousand dollars per employee per year)

FIGURE 8.10

Employee productivity (defined as shipment value 

per employee) by States in the North, 1997 to 

2011, forest products industry and the paper and 

paperboard industry (U.S. Census Bureau 2012).

Paper and 

paperboard manufacturing

Wood products manufacturing

228 FUTURE FORESTS OF THE NORTHERN UNITED STATES
1995 2000 2005 2010 2015

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

1995 2000 2005 2010 2015

700

600

500

400

300

200

100

0

Illinois

Maine

Michigan

New Hampshire

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

700

600

500

400

300

200

100

0

New Jersey

Ohio

Rhode Island

New York

Pennsylvania

Vermont

West Virgina Wisconsin

C hapter E ight 229
The historical trends of increasing productivity 

and declining employment in manufacturing 

industries, where a majority of forest-related 

jobs were created, implies that future forestrelated employment will decrease if wood 

demand and removals follow historical trends in 

the North (e.g., under scenarios A1B-C, A2-C, 

and B2-C). However, if biomass energy demand 

grows rapidly (e.g., under scenarios A1B-BIO, 

A2-BIO, and B2-BIO), wood removal will be much 

greater (Chapter 4), and gains in employment in 

woody biomass energy production can help offset 

a potential decline in the manufacturing sectors 

(Woodall et al. 2012) or result in increasing 

forest-related employment in the future. With 

bioenergy-feedstock demands being increasingly 

sourced from urban areas, employment in 

urban forestry is expected to increase. Parks 

and recreation jobs will also increase over time 

should the current trend in this industry remain. 

Income 

From 2001 to 2010, workers in the paper and 

paperboard industry earned on average $45,000 

annually (in 2001 dollar value), the highest wage 

rate among the six forest related industries; 

workers in wood furniture and forest products 

manufacturing earned the second highest annual 

wages (about $30,000) and workers in other 

forest related industries earned <$30,000.

Despite the recent economic recession, the 

average wages of forest related production 

workers in the North increased by 1 percent (in 

real dollars discounted by consumer price index) 

from 2001 to 2010 (Fig. 8.11). The average 

annual wage rate, weighted by total workers 

in 2010, increased by $441 (in 2001 dollar 

value) in the same period. Although average 

nominal wage rates increased in five of the six 

job categories, the real value of the annual wage 

rate in the wood products category (NAICS 321) 

decreased by 4 percent from 2001 to 2010. In 

the same period, the real value of wage rates 

increased by 3 percent in paper and paperboard, 

2 percent in wood furniture manufacturing, 5 

percent in park and recreation services, 10 

percent in forestry and logging, and 16 percent 

in hunting and trapping (Fig. 8.11). If the U.S. 

economy continues to grow as it did at the turn 

of the century, the average wage rate 

of forest production workers is 

expected to grow as well. 

230 FUTURE FORESTS OF THE NORTHERN UNITED STATES
OUTDOOR RECREATION

Most public forests in the North are open 

to all, meaning that 44 million acres or 26 

percent of northern forest land is available 

for recreation. In recent decades the area of 

publically owned forests has increased slightly, 

and that component of forest-based recreation 

land appears relatively stable. Private forests 

in the hands of families or individuals—

approximately 100 million acres in total—are 

used for recreation primarily by their owners. 

An estimated 18 million acres (18 percent) of 

those forests are open for public recreation. In 

addition, most forest-based U.S. skiing facilities 

are in the North, as are the majority of forestbased nature parks. Most northern forest-based 

recreation, sightseeing, and tourism businesses 

are concentrated in the Northeast and the Great 

Lakes States (USDA FS 2011). 

Cordell (2012) compared northern recreation 

trends and resources to those in other U.S. 

regions. That summary of participation rates 

and trends for 61 outdoor recreation activities 

describes the diversity of available recreation 

activities and participants, and it identifies 

activities in which participation rates have 

increased or decreased (Table 8.7). 

Population demographics are expected to affect 

future participation in outdoor recreation. For 

example, the projected population increase 

from 2010 to 2060 in the North is expected to 

range from 15 to 50 million people (Chapter 2). 

This could result in a net increase in the total 

number of recreation participants, even for 

activities that are expected to experience 

constant or slightly decreasing participation 

rates. Increases in population would also 

reduce the area of potential recreation land and 

water per capita (Table 8.8). However, the per 

capita decreases in the North are expected to 

be lower than the national average because the 

rate of population increase will be lower. 

50

45

40

35

25

20

15

10

ANNUAL WAGE PER WORKER (thousand dollars) Paper and paperboard (45%)

Wood furniture (18%)

Park and recreation (4%)

Forest products (29%)

Forestry and logging (2%)

Hunting and trapping (<1%)

U.S. all industries

FIGURE 8.11

Average annual wage of workers in 2001 dollars 

for six forest production and service industries in 

the Northern United States, 2001 to 2010 

(U.S. Department of Labor, Bureau of 

Labor Statistics 2012).

2000 2002 2004 2006 2008 2010

CHAPTER EIGHT 231
Table 8.7—Rates of 

participation in naturebased activities by people 

aged ≥16 years in the 

Northern United States 

(adapted from Cordell 

et al. 2012). 

---------------------------------------(percent)------------------------------------

More than 30 million participants

Walking for pleasure 85 6

Attending family gatherings 75 7

Gardening or landscaping 67 3

Viewing or photographing natural scenery 64 12

Visiting an outdoor nature center or zoo 57 5

Picnicking 55 1

Viewing or photographing wildlife 

(other than birds and fish)

51 21

Viewing or photographing flowers or trees 51 23

Sightseeing 51 5

Driving for pleasure 50 4

Visiting beaches 45 15

Visiting historic sites 44 0

Swimming in lakes, ponds or streams 44 8

Swimming in outdoor pools 43 11

Bicycling 40 4

Viewing or photographing birds 38 18

Gathering mushrooms or berries 36 26

Visiting farm or agricultural settings 36 28

Day hiking 33 15

Visiting wilderness areas 31 11

10 to 30 million participants 

Viewing/photographing fish 25 13

Warm water fishing 25 17

Motor boating 24 5

Visiting watersides (besides beaches) 24 2

Sledding 21 5

Developed camping 21 -10

Mountain biking 20 -6

Participating in boat tours or excursions 19 -2

Visiting prehistoric sites 19 3

Off-road driving 18 25

Canoeing 12 8

Primitive camping 12 -3

Activity 

Proportion of 

population 

participating, 2007a

Change in total number 

of participants, 

2000b to 2007

232 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Activity 

Proportion of 

population 

participating, 2007a

Change in total number 

of participants, 

2000b to 2007

---------------------------------------(percent)------------------------------------

3 to 10 million participants

Big game hunting 9 17

Backpacking 9 -2

Ice skating 8 -10

Saltwater fishing 8 2

Waterskiing 8 29

Horseback riding 8 6

Using personal watercraft 8 4

Downhill skiing 8 -15

Rafting 7 -13

Snowmobiling 7 -3

Kayaking 7 89

Small game hunting 7 15

Snorkeling 6 4

Horseback riding on trails 6 0

Snowboarding 6 36

Rowing 5 1

Sailing 5 -12

Mountain climbing 4 -12

Caving 4 30

Rock climbing 4 10

Cross country skiing 4 -21

Ice fishing 4 9

Anadromous fishing 4 6

Fewer than 3 million participants

Orienteering 3 91

Snowshoeing 2 -17

Migratory bird hunting 2 13

Scuba diving 2 7

Surfing 1 43

Windsurfing 1 4

a

Spanned the years 2005 to 2009.

b

Spanned the years 1999 to 2001.

Table 8.7 continued

CHAPTER EIGHT 233
Table 8.8—Per capita park, forest, and water area in the United States and the North with projections to 2060 

(Cordell et. al 2012); projected declines in acreage per capita result from a combination of increasing population 

and decreasing forest area, particularly for private forest land.

----------------------------(acres per capita)---------------------------- (percent)

Federal and state park land

North 0.16 0.13 -19

United States 2.06 1.40 -32

Private forest land

North 1.19 0.88 -26

United States 1.27 0.80 -37

Surface waterb

North 0.46 0.36 -22

United States 0.54 0.37 -31

a

2010 for private forest land, 2008 for all other categories.

b

Includes inland, coastal, territorial, and Great Lakes water surface area.

Category of land or water Estimate, 2008a Projected, 2060 Change from 

2008 to 2060

From 2000 to 2007, outdoor recreation participation 

increased for 48 activities and decreased for 13 

activities in the North (Cordell 2012). Bowker 

and Askew (2013) modeled annual participation 

rates and participation days for 17 categories 

of recreation activities using data from 1999 to 

2009. They then applied the models to project the 

number of participants and participation days for 

each activity from 2010 to 2060. The models take 

into account expected changes in socioeconomic 

factors such as population demographics, 

education, and income; as well as county-scale 

recreation supply indicators such as per capita 

public land, forest, water, and mountain acreage. 

Projected recreation participation rates were 

indexed as increases or decreases compared to 

observed participation rates for 2008 (Fig. 8.12). 

Predicted changes in number of participants 

and participation days generally follow the 

trends plotted for the indexed per 

capita participation rates shown 

in Figure 8.12. 

234 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Future changes to forest ecosystems may 

impact outdoor recreation activities and local 

economies. An example is the expected change 

to the maple-beech-birch (Acer spp.-Fagus spp.-

Betula spp.) forest-type group. Because sugar 

maples are valued for their brilliant autumn 

color, they are a key resource for the tourism 

and outdoor recreation industries at many 

northern locations. Projected decreases in the 

future area of the maple-beech-birch group are 

variable across the region (Chapter 3), but the 

projections do not specifically address gains 

or losses of sugar maple trees. Other analyses 

focused specifically on sugar maple indicate 

that its range and relative abundance are 

likely to decrease across the region under 

a wide range of climate scenarios (Iverson 

et al. 2008, USDA FS 2014) with potentially 

negative consequences for recreation and 

tourism industries.

235
2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

Birding

Whitewater activities

Visiting interpretative sites

Horseback riding on trails

Fishing

Visiting developed sites

Day hiking

Hunting

Swimming

INDEXED PER CAPITA PARTICIPATION RATE

236 FUTURE FORESTS OF THE NORTHERN UNITED STATES
2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

2000 2020 2040 2060

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

1.25

1.00

0.75

0.50

0.25

Downhill skiing

Motor boating

Participating in challenge 

activities

Back-country skiing

Snowmobiling

Visiting primitive areas

Off-roading driving

Viewing nature

FIGURE 8.12

Change in per capita participation rates, 2008 to 2060, in the North for 17 

categories of outdoor recreation activities under storyline A1B; a value of 

1.0 was assigned to participation in 2008—values >1.0 indicate a relative 

increase and values <1.0 indicate a relative decrease (adapted from 

Bowker and Askew 2013). 

INDEXED PER CAPITA PARTICIPATION RATE

CHAPTER EIGHT 237
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Agriculture, Forest Service, Southern Research Station. 8 p.

CHAPTER EIGHT 241
242
Legal, Institutional, and Policy

 F r a m e w o r k f o r Fo r e s t C o n s e r v a t i o n 

a n d S u s t a i n a b l e M a n a g e m e n t 

Brett J. Butler

Introduction

NE CONCLUSION FROM the 50-year forest projections described in Chapters 2 through 8 

is that some of the most dramatic changes to the forests of the North will be the 

direct result of human activities, including land-use change, forest management, 

greenhouse gas emissions, and invasive species introductions. The tools that 

society will have available to influence the type and intensity of these and 

related human activities will draw from a complex mix of legal, institutional, 

and policy frameworks.

The concepts in this chapter provide a context 

for the prior chapters of this report and for 

future policies and decisions about forest 

conservation and management. As described by 

the Montréal Process Working Group (n.d.): 

Legislation, institutional capacity, and 

economic arrangements, with associated 

policy measures at both national and 

subnational levels, create an enabling 

environment for the sustainable 

management of forests. Reporting against 

these indicators contributes to raising 

public and political awareness of issues 

affecting forests and builds support for 

their sustainable management. 

Unlike previous chapters, 

the indicators for the legal, 

institutional, and policy 

frameworks are largely 

descriptive (Table 9.1). For 

example, predicting how forestassociated laws, regulations, and policies 

will change with any degree of certainty is 

not possible. This chapter describes the forest 

ownership patterns that have a direct impact 

on legal, institutional, and policy frameworks, 

and then summarizes salient recent 

developments for each indictor and the trends 

that will likely influence their future direction. 

CHAPTER NINE 243
• The impact of the legal, institutional, and 

policy framework differs enormously between 

publically managed forests and those held by 

private owners; three-quarters of the forest 

land in the North is privately owned, mostly by 

families and individuals.

• Numerous laws, policies, and regulations affecting 

forest-land management have been enacted 

at national and State levels; Best Management 

Practices, which are in place across the North, are 

periodically reviewed and updated. 

• More than 30 million acres of forest land 

across the North are certified under one or 

more forest certification systems; the future 

of these programs depends on the demand for 

certified products, the requirements imposed 

by the various certification programs, and the 

willingness of landowners to participate.

• An estimated 7.2 million acres of forests and 

nonforested (primarily agricultural) land are 

enrolled in conservation easement programs across 

the North; enrolled acreage has been increasing 

substantially and will continue to do so as long 

as owners see this as a viable option and 

organizations see value in acquiring and 

holding the easements. 

• Of all tax policies, those 

governing property taxes 

have the greatest potential 

to influence the behavior 

of family forest owners; 

although all Northern 

States have programs that 

can help defer or reduce 

annual property taxes 

on forest land, the future 

of tax policies is unknown and will likely hinge 

largely on issues that are larger than forestry.

• For owners that are primarily interested in 

financial gain, taxes can have an important 

impact—tax policies are one factor that led to the 

shift from vertically integrated forest companies 

(companies that own both forest land and the 

mills that processed timber from that land) to real 

estate investment trusts and timber investment 

management organizations.

• To augment the private natural resource 

professionals across the North, numerous 

programs and services have been established 

including cost sharing; technical assistance; 

financial incentives; and delivery of tools, 

training, and publications. For example, 9 percent 

of the nonindustrial private forest land—land 

not owned by forest products companies—is 

managed under a management plan associated 

with the Forest Stewardship Program of the U.S. 

Forest Service. Forestry assistance programs will 

continue to evolve to meet changing needs and 

the reality of decreasing budgets, continuing 

the trend of prioritizing where and how limited 

resources are spent.

• Partnerships among various institutions and 

organizations have become increasingly common 

for addressing large-scale, complex issues; this 

trend is likely to continue in the future. 

• Public input on strategic-level forest management 

decisions is common on public lands and is 

increasingly common on large private holdings, 

especially those enrolled in certification 

programs; a continuing push for transparency in 

decisionmaking will likely cause an increases in 

public participation.

Key Findings

244
Table 9.1—Indicators of legal, institutional, and economic framework for forest conservation and sustainable 

management of temperate and boreal forests under Montréal Process Criterion 7 (Montréal Process Working 

Group 2015).

7.1.a Legislation and polices supporting 

the sustainable management of forests

This indicator provides information on legislation and policies, 

including regulations and programs, which govern and guide 

forest management, operations, and use. Legislation and 

policies designed to conserve and improve forest functions 

and values are prerequisite to achieving the sustainable 

management of forests.

7.1.b Cross-sectoral policy and program 

coordination

This indicator provides information on the extent to which 

policies and programs are coordinated across sectors to 

support the sustainable management of forests. Nonforest 

sector land use and development decisions may have a 

significant impact on forests and their use. Cross-sector 

coordination of forest and nonforest related policies and 

programs can promote improved forest management by 

helping to minimize adverse impacts and by strengthening the 

ability of countries to respond to national and global issues.

7.2.a Taxation and other economic 

strategies that affect the sustainable 

management of forests

This indicator provides information on the economic 

strategies that affect the sustainable management of forests. 

Government policies and strategies on investment, taxation, 

and trade may influence both forest management and the 

level of long-term investment in forestry.

7.3.a Clarity and security of land and 

resource tenure and property rights

This indicator provides information on land, forest, and 

resource tenure, laws, and rights. Clear title identifies rights 

and responsibilities under the law with respect to land and 

resources, while due process ensures that these rights can be 

protected or disputed. Lack of clear ownership or due process 

may hinder the active engagement of stakeholders in the 

sustainable management of forests, or leave forests vulnerable 

to illegal or unsustainable use.

7.3.b Enforcement of laws related to forests This indicator provides information on the extent to which 

forest related laws and regulations are enforced. The ability 

to successfully prosecute offenders is essential in combating 

harmful activities that may threaten forests and their 

sustainable management (e.g., illegal forest conversion and 

illegal logging).

Indicator Rationale

CHAPTER NINE 245
Indicator Rationale

7.4.a Programs, services, and other 

resources supporting the sustainable 

management of forests

This indicator provides information on the capacity of both 

government and private organizations to deliver programs and 

services, to maintain and develop infrastructure, and to access 

the financial and human resources necessary to support the 

sustainable management of forests.

7.4.b Development and application 

of research and technologies for the 

sustainable management of forests

This indicator provides information on the capacity to develop 

and incorporate new science, research, and technologies into 

forest management. Continuous improvement in the depth 

and extent of knowledge and its application will help ensure 

advances in the sustainable management of forests.

7.5.a Partnerships to support the 

sustainable management of forests

This indicator provides information on partnerships and 

their contribution to the sustainable management of forests. 

Partnerships may help create a shared purpose and are 

important tools in building capacity; leveraging financial, 

technical, and human resources; strengthening political 

commitment; and in developing public support to advance the 

sustainable management of forests.

7.5.b Public participation and conflict 

resolution in forest-related decisionmaking

This indicator provides information on the processes that 

promote public participation in forest-related decisionmaking 

and reduce or resolve conflict amongst forest stakeholders. 

Public participation in decisionmaking processes and conflict 

resolution efforts can lead to decisions that are widely 

accepted and result in better forest management.

7.5.c Monitoring, assessment, and 

reporting on progress toward sustainable 

management of forests

This indicator provides information on the capacity to monitor, 

assess, and report on forests. An open and transparent 

monitoring and reporting system that provides up-to-date and 

reliable forest-related information is essential for informed 

decisionmaking, in generating public and political awareness 

of issues affecting forests, and in the development of policies 

to underpin the sustainable management of forests.

Table 9.1 continued 

246 FUTURE FORESTS OF THE NORTHERN UNITED STATES
INFLUENCE OF FOREST OWNERSHIP PATTERNS

The impact of the legal, institutional, and policy 

framework differs enormously depending on 

whether one is considering public or private 

forest lands. Expectations for lands within these 

two broad ownership categories are different, 

and often different laws and regulations apply 

to each. Indeed, the legal, institutional, and 

policy framework is a major influence on the 

distribution of forest land between public 

and private ownerships and the protection of 

basic private ownership rights. As discussed 

in Chapter 3, these ownership patterns also 

influence, among other patterns and processes: 

• Which lands are protected from development

• Areas where timber harvesting is prohibited

• Forest parcellation and 

fragmentation patterns

• The location and makeup 

of the wildlandurban interface.

FIGURE 9.1

Forest ownership across the North 

(Hewes et al. 2014).

Current Status

As described in Chapter 3, forest ownership 

patterns vary substantially across the United 

States (Butler 2008) and across the North 

(Fig. 9.1). Three-quarters (128 million acres) 

of forest land in the Northern United States is 

privately owned; families and individuals are the 

dominant, private ownership category (Fig. 9.2). 

Other private ownerships include forest products 

companies and other corporations, Native 

American tribes, and other private groups, such 

as conservation organizations. Of the 44 million 

acres of public forest land across the region, 

half are managed by State governments, usually 

within forestry, wildlife, parks, or water agencies; 

a third by Federal agencies, including the U.S. 

Forest Service, the U.S. Fish and Wildlife Service, 

and the U.S. National Park Service; and the 

remainder by county and municipal governments, 

often for water protection (Chapter 6) 

or recreation (Chapter 8).

Federal

FOREST OWNERSHIP CATEGORY

State

Local

Family

Corporate

Other private

CHAPTER NINE 247
From 1977 to 2007, the net area of forest 

land across the North increased by 7 percent, 

but this increase was not evenly distributed 

among ownership categories (Fig. 9.3). Some 

States experienced net decreases in private 

forest land, even though total forest area 

increased. The largest percentage increase in 

forest land was for State and local governments 

that acquired additional forest land through 

purchases, donations, and other transfers. 

Predicted Trends

Forest land area is projected to decrease 

between 2010 and 2060 (Chapters 2, 3, 4). 

The projection methods do not explicitly model 

changes in forest ownership, but they assume 

that future losses of forest land will be from 

the private rather than public ownership 

holdings. Indeed historical trends (Fig. 9.3), 

combined with pressures from development and 

other human activities (Mondal et al. 2013), 

suggest that any losses of forest acreage will 

be concentrated on private holdings. Divesture 

of public forest land has been rare, with recent 

trends showing increases, especially for State 

and local governments. An exception to the 

net loss in private forest land could occur in 

areas where continued conversion of marginal 

agricultural land to forest land offsets forest 

land lost to development (Chapter 2). The net 

forest gain from or loss to agricultural lands 

is heavily influenced by the relative prices of 

agricultural commodities (Alig et al. 2010). 

During periods when agricultural markets are 

depressed, unused cropland and pastureland 

will naturally revert to forests across most of 

100

80

60

40

20

0

AREA OF FOREST LAND (million acres)

Family

Other private

Federal

State

Local

FIGURE 9.2

Forest land area by ownership 

category for the North, 2006 

(Butler 2008).

Ownership

248
the region. Conversely, 

when markets rise, forest land 

is more likely to be cleared and converted 

to agriculture. Over the long term as these 

cycles occur, the specific location of forest and 

agriculture lands can shift even though total 

forest area remains relatively stable. And as 

the “green revolution” and other efforts have 

allowed more food to be produced on fewer 

acres, millions of acres have reverted (or been 

actively converted) to open space, often forest 

land—a primary driver of the increase in 

forest land observed in the United States over 

the past century.

LEGISLATION AND POLICES SUPPORTING THE 

SUSTAINABLE FOREST MANAGEMENT 

Numerous laws, policies, and regulations have 

been enacted by Federal and State governments 

to support sustainable forest management. 

At the broadest level, these policies have 

influenced the distribution of forest land 

between public and private forest ownerships. 

Federal laws such as the Weeks Act have 

enabled governments to acquire forest land; and 

others, such as the National Forest Management 

Act, have prescribed how Federal forest 

holdings can be managed and used (Brown 

2004, Cubbage et al. 1993). Analogous statelevel laws or executive orders are in place for 

most, if not all, of the 20 Northern States.

1.5

1.4

1.3

1.2

1.1

1.0

0.9

RELATIVE FOREST AREA (1977=1)

FIGURE 9.3

Relative changes in forest ownership for the 

North, 1977 to 2007 (Smith et al. 2009).

1970 1980 1990 2000 2010

State

Local

Federal

Private

CHAPTER NINE 249
Current Status

Best management practices—Best management 

practices (BMPs) are common tools used by 

States to outline acceptable practices for forest 

operations. Many guidelines are focused on 

minimizing soil erosion and sustaining water 

quality (Chapter 6), but they can also include 

guidance on timber harvesting practices, forest 

regeneration, wildlife habitat, and aesthetic 

values. The specifics vary by State as to whether 

BMPs are voluntary or mandatory (Ellefson 

et al. 2001, Shifley et al. 2012). Shifley et al. 

(2012) summarize the general attributes of such 

management standards and guidelines for State 

and private ownerships across the region. In 

addition to BMPs, some States (such as Maine 

and Connecticut) have implemented more 

comprehensive forest practices laws. 

Certification—Through forest certification, the 

private sector has initiated its own incentives 

for encouraging sustainable forest management. 

Voluntary certification programs require forest 

owners and managers to adhere to specific 

protocols intended to ensure sustainable 

management and to submit to periodic audits 

to ensure compliance. They include programs 

sponsored by the Forest Stewardship Council 

covering 27.7 million acres1

, the Sustainable 

Forestry Initiative covering 25.2 million acres 

(Sustainable Forestry Initiative, n.d.), and the 

American Tree Farm System covering 7.8 million 

acres2

 (Fig. 9.4), with some acres certified 

under more than one program. In addition to 

regulating field operations, required protocols 

can include long-term planning, monitoring, 

consideration of endangered species, mitigation 

of invasive species, and sustaining aesthetic 

and cultural resources. Participation affords 

forest owners assurances and recognition that 

they are managing sustainably as well as the 

opportunity to market their products as such. 

Research has shown that these systems are 

making a significant difference in how forest 

owners manage their land (Moore et al. 2012). 

Although few direct market incentives, such as 

price premiums for certified forest products, 

have been received, certification can provide 

access to markets that require certified wood 

and assurances to investors that corporations 

are managing sustainably. 

Easements and land trusts—Conservation 

easements are another voluntary tool for 

influencing the uses of private lands. Each 

easement can have unique features, but in 

general, they are crafted to ensure that natural 

environments are protected from future 

development. The owner retains the rights to 

sell the land and use it within the confines of 

the agreement, and the easement is held by 

a government agency or a nongovernmental 

organization such as a land trust, defined as 

a nonprofit organization that, as all or part of 

its mission, actively works to conserve land by 

undertaking or assisting in land or conservation 

easement acquisitions, or by its stewardship of 

such lands or easements (Byers and Marchetti 

Ponte 2005). An estimated 902 State and 

local land trusts operate across the region 

250 FUTURE FORESTS OF THE NORTHERN UNITED STATES
(Chang 2011), each attached to a property 

deed and remaining effective in perpetuity, 

although some groups are experimenting 

with term easements. Owners can donate 

the development rights or can sell them for 

an agreed-upon price. The Federal tax 

code and some State tax codes provide 

incentives in the form of income tax 

deductions for donating conservation easements 

(Butler et al. 2012). Currently, thousands of 

conservation easements across the region cover 

an estimated 7.2 million acres (1.7 percent 

of the total area) of forests and nonforested 

(primarily agricultural) land (Fig. 9.5), which 

is an increase of 48 percent over the last decade. 

FIGURE 9.4

Proportion of forest land in the North, 2012, under certification systems sponsored 

by the (A) Forest Stewardship Council [Forest Stewardship Council, n.d.]; 

(B) Sustainable Forestry Initiative [Sustainable Forestry Initiative, n.d.]; and 

(C) American Forest Foundation-American Tree Farm System [American Tree Farm 

System, n.d.] ( Personal communication: B. Kahn1

, 2012.; and C. Kuebler2, 2012.)

Under 50

50-99

100-199

200 and over

ACRES CERTIFIED (per 1,000 forested acres)

Personal communication: B. Kahn. 2012. Communications Director, 

Forest Stewardship Council U.S. 212 Third Avenue North, Suite 445, 

Minneapolis, MN 55401.

Personal communication: C. Kuebler, 2012. Senior Manager of Woodland 

Owner Engagement, American Forest Foundation. 2000 M Street, NW, 

Suite 550, Washington, D.C. 20036.

1

2

A B

C

CHAPTER NINE 251
Through the Forest Legacy Program, the U.S. 

Forest Service provides funding to encourage 

the establishment of conservation easements 

(USDA FS 2013b). From 1993 (the first year 

of activity in the North) to 2012, a total of 458 

tracts covering 1.6 million acres have been 

protected across the region. In addition, several 

state-level programs foster the establishment of 

conservation easements. For example, in 2008 

Minnesota passed the Clean Water, Land, and 

Legacy amendment that added 0.375 percent 

to the sales tax, a third of which is dedicated 

to protecting, restoring, and enhancing lands 

and waters through conservation easements 

and other activities (Minnesota Legislative 

Coordinating Commission, n.d.). 

Other government activities—Numerous 

laws and regulations have been enacted 

that hinder, intentionally or unintentionally, 

the implementation of forest management 

practices. For example, some municipalities 

require permits that some foresters and 

loggers consider overly onerous and choose to 

not operate in those areas. In response, some 

States have passed “right to practice 

forestry” legislation.

FIGURE 9.5

Approximate location of conservation 

easements across the U.S. North, 2012. 

Easements include both forests and nonforested 

land. Area covered by individual easements ranges 

from less than 1 acre to more than 100,000 acres (National 

Conservation Easement database, n.d.).

252 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Predicted Trends

BMPs are subject to periodic reviews and 

updates. For example, many State BMPs have 

recently incorporated guidelines for biomass 

harvesting. The technical specifications in 

BMP guidelines and implementation strategies 

are influenced by professional resource 

managers. However, anything more stringent 

than voluntary BMP implementation ultimately 

depends on the will of the public officials and 

the influence exerted by their constituents.

Private sector solutions, such as certification 

and conservation easements, are influencing 

management on an increasing number of acres 

and are likely to continue to do so in the future. 

The ultimate fate of certification depends on the 

demand for certified products, the requirements 

imposed by the various certification programs, 

and the willingness of landowners to participate. 

Few, if any, price premiums have been obtained 

for certified wood produced in the United 

States to date, but certification has provided 

added market access (e.g., being able to supply 

companies that sell certified paper, certified 

lumber, and other products). If price premiums 

emerge or market access increases, certification 

will likely expand as well.

The acreage under conservation easements has 

been increasing dramatically. Assuming private 

owners continue to accept easements as a 

viable option and that organizations continue to 

express interest in acquiring and holding them, 

the area under easements will likely increase, 

but at what rate is unknown. Tax policies and 

the availability of public and private funds 

to purchase easements will likely influence 

these rates. A lack of resources to monitor the 

easements is a growing problem that will be 

exacerbated by increased area in easements, 

unless more is done to address this issue.

Other policies currently being debated, 

such as those intended to increase carbon 

sequestration, could have important 

implications for forest management (Chapter 7). 

Mitigation of greenhouse gas emissions through 

carbon sequestration is a topic of international 

interest (Heath et al. 2011). Programs such 

as the Regional Greenhouse Gas Initiative 

(http://www.rggi.org ) or the California Climate 

Action Registry (http://www.climateregistry.

org ) provide opportunities to quantify and sell 

carbon sequestered in forests. Some policies 

allow only carbon credits from afforestation, but 

others include incremental carbon sequestration 

from existing or modified practices. Although 

public trading of carbon credits in the United 

States ceased with the shuttering of the Chicago 

Climate Exchange (Gronewold 2011), voluntary 

over-the-counter trading still occurs as private 

corporations hedge future policies and “green” 

their portfolios.

CHAPTER NINE 253
CROSS-SECTOR POLICY 

AND PROGRAM COORDINATION

Agricultural policies have strong links with forest 

policies. For example, many agricultural policies 

affect agricultural commodity prices, and those 

in turn affect the propensity of farm owners to 

convert forest land to agricultural land (Alig et 

al. 2010). The agencies that deal with forestry 

and agriculture are often administratively 

separated, thus decreasing the potential for 

cross-communication and increasing the potential 

for competition for limited resources. Likewise, 

many individuals and corporations working in the 

agriculture community operate separately from 

those who work in the forestry community.

Some national cost-share programs such as the 

Conservation Reserve Program (CRP), which 

supported the planting, mostly in Southern 

States, of 2.8 million acres of trees in the 1980s 

(Moulton and Hernandez 2000), are aimed 

explicitly at converting unproductive or erosionprone agricultural lands to forests. Federal 

landowner assistance programs have gradually 

merged through legislation commonly known as 

the Farm Bill3, the most recent being formally 

titled the Agriculture Reform, Food, and Jobs 

Act of 2012. For example, the Environmental 

Quality Incentives Program (EQIP), overseen 

by the USDA Natural Resource Conservation 

Service, now contains more explicit instructions 

for inclusion of forestry activities in cost-share 

assistance (USDA NRCS, n.d.). Many statelevel initiatives either operate on funding from 

or otherwise mirror those established at the 

Federal level.

The current trend within governments is for 

increased cross-sector cooperation. Recently, 

the America’s Great Outdoors initiative was 

launched to unite all areas of conservation and 

strengthen public engagement with natural 

resources (Council on Environmental Quality, 

n.d.), but neither this initiative, nor few, if any, 

government initiatives aimed at cross-sector 

communication, has been fully implemented.

TAXATION AND OTHER ECONOMIC STRATEGIES 

Policy tools that influence forest owners include 

technical assistance, educational programs, 

financial incentives, regulations, and delivery 

of tools and publications (Jacobson et al. 

2009). Taxation is considered one of the most 

effective of these tools. Property tax policies, 

in particular, have a high potential to influence 

the behavior of family forest owners (Butler 

et al. 2012). This is partially due to the fact 

that property taxes are due regardless of how 

much, if any, revenue is earned from a piece of 

land, and revenues from forest land are few and 

sporadic for most family forest owners. 

The Agricultural Act of 2014 (H.R. 2642; Pub. L. 113-79, also known 

as the 2014 Farm Bill).

3

254
Current Status

All Northern States have programs that can 

help defer or reduce annual property taxes 

for landowners with forest holdings, with 

estimated enrollment rates ranging from <25 

percent to >75 percent of eligible owners 

(Butler et al. 2012). Higher enrollment rates 

are likely a function of higher tax savings, lower 

barriers to enrollment, better alignment with 

owners’ objectives, and better dissemination 

of information about the programs. Conversely, 

further expansion of programs is inhibited by 

enrollment barriers (such as minimum acreage 

requirements) and other requirements, and 

the “fog of low awareness, confusion, and 

misinformation that enshrouds tax policies” 

that render the programs either nonapplicable, 

unpalatable, or unknown to many family 

forest owners (Butler et al. 2012). In addition, 

legislation can change program requirements, 

benefits, and penalties, causing further 

confusion for landowners and administrators.

For owners whose primary ownership objective 

is financial gain—not the objective of most 

family forest owners but a paramount objective 

for most businesses—taxes can have an 

important impact. The treatment of profits is 

one factor that has created dramatic changes 

in the forest products industry. Historically, 

many large companies owned both forest land 

and the mills that processed timber from that 

land (hence the term, vertically integrated 

companies). Beginning in the late 1980s, 

many companies sold their forest holdings or 

converted their businesses into real estate 

investment trusts (REITs). Many of these 

lands were acquired by timber investment 

management organizations (TIMOS; often 

managed on behalf of pension funds or other 

institutional investors) or other REITs. The 

advantage for such groups is that the tax burden 

on earnings falls to its investors rather than 

the investment group itself. This, combined 

with new ways of assigning financial value to 

timber assets, increased globalization, increased 

corporate consolidations; thus changing 

business strategies has created an enormous 

shift away from industry-owned forest land 

(Butler and Wear 2013).

At a broader level, government policies 

that affect trade with other countries can 

have large impacts on U.S. timber markets and 

consequently on U.S. forests. The Softwood 

Lumber Agreement of 1996 is one such example 

(Zhang 2001). In this ruling, the World Trade 

Organization determined that Canadian timber 

production had an unfair competitive advantage 

over U.S. production, and therefore imposed 

trade restructuring and substantial fines.

Predicted Trends

Changes to tax policies are a perennial topic for 

legislators, but the changes that ultimately get 

enacted and the consequences of those changes 

are notoriously difficult to predict. Future 

policies depend on the goals of the elected 

officials and the pressure placed on them by the 

public and special interest groups.

CHAPTER NINE 255
RESOURCE TENURE AND PROPERTY RIGHTS

There are different ways of explaining land 

ownership in the United States, but one useful 

way to view it is as a bundle of rights (Fig. 

9.6), with the degree of landowner control 

over a specific right varying by the resource 

being considered (Banner 2011). For example, 

private owners have the right to sell their land 

and the right of usage according to the general 

laws and any easements or specific 

property restrictions in effect; 

however, wildlife, whether found 

on private land or on land managed 

by any level of government, is 

considered State property, and the extraction 

rights for belowground mineral rights are 

often not included with land ownership.

Current Status

In general, land ownership rights are well 

established across the United States and are 

well supported by legal precedent. Among the 

handful of contentious topics is eminent domain, 

Across much of the Alleghany Plateau of 

Pennsylvania and nearby States—Kentucky, Ohio, 

and West Virginia—extraction of natural gas 

from shale by hydraulic fracturing, also known as 

“fracking,” has become a topic of much debate. For 

many years, two basic methods were available for 

extracting gas: (1) the traditional method involves 

the drilling of a vertical shaft; and (2) in some types 

of rock formations, liquid under high pressure is 

sent down the shaft to “fracture” the rock and 

release additional gas—this is a technology that has 

been in use for a long time. 

Recently a new method, hydraulic fracturing, 

was introduced whereby a well is drilled 

first vertically, after which the drill makes a 

90-degree turn and continues, 

sometimes for a mile or more. Gas developers then 

fracture along the horizontal pipes. 

Hydraulic fracturing can yield large quantities of 

natural gas but has also raised concerns about 

water quality (Soeder and Kappel 2009) and the 

impacts on forests (Drohan et al. 2012). The shale 

formations being drilled across the Alleghany 

Plateau and other areas across the North often 

underlie private forest land, but the ownership 

of this subterranean resource is separate from 

aboveground resources. The policies that govern this 

type of extraction are being debated and will have 

important consequences for the forests and the 

people of the region.

Gas Drilling in the Marcellus Shale

Marcellus Shale rig and gas well operation in 

Butler County, PA.; Photo by WCN24/7 via flickr.com

256
the capacity of a government to 

take land (with some monetary 

compensation provided) if the 

land is deemed necessary to 

build a highway or satisfy some 

other public good.

The longest enduring land 

ownership conflicts in the United 

States are related to Native 

Americans (Banner 2011), who 

used large swathes of land before 

European settlement but did 

not have a land ownership system that 

mirrored that of the European colonists. Either 

voluntarily or involuntarily, tribes left their 

ancestral grounds and many were relocated to 

distant reservations. Many of their descendants 

have subsequently fought to regain access to 

ancestral lands, a fight that continues.

Predicted Trends

Land tenure is fairly secure in the United States 

and will likely remain so for the foreseeable 

future. Issues related to Native American land 

claims are the major exception. The rights 

to subsurface gas, oil, and minerals are well 

established, but issues related to access and 

environmental impacts of extraction will continue 

to be debated.

ENFORCEMENT OF LAWS RELATED TO FORESTS 

Numerous laws are in place to prevent illegal 

activities, such as timber theft, trespassing, 

illegal dumping, arson, and poaching, in

forests, but enforcement is often difficult. 

Units within Federal and State governments 

are specifically tasked with enforcing forest, 

fish, and game laws and ensuring public 

safety. Most efforts focus on public lands, 

with enforcement of laws on private lands 

often falling to local law enforcement officials. 

Enforcement is complicated by the vast area 

that forests cover and the relative difficulty of 

either identifying individuals who break the law 

Timber rights

Right to build homes

Mineral rights

Access rights

Right to sell

FIGURE 9.6

Land ownership rights conceptualized as 

a bundle with different owners possibly 

controlling different rights for the same 

parcel of land (King County, n.d.).

CHAPTER NINE 257
or obtaining sufficient evidence for a successful 

prosecution. Existing laws cover a wide range 

of forest related problems, but enforcement 

can be difficult, primarily because of the vast 

areas that enforcement officers oversee. New 

technologies may help to reduce the cost of 

some enforcement.

RESOURCES TO SUPPORT SUSTAINABLE 

FOREST MANAGEMENT

Current Status

To augment the private foresters, wildlife 

biologists, and other natural resource 

professionals, numerous public and nonprofit 

organizations and programs have been 

established in pursuit of sustainable forest 

management. Both publicly funded and privatesector resources are available to help forest 

owners and managers meet their needs and the 

needs of society. Public agencies offer technical 

assistance and financial incentives, enforce 

regulations, and deliver tools, training, and 

publications (Jacobson et al. 2009). Service 

foresters in many Northern States engage in 

one-on-one visits with private forest owners, 

help owners write management plans, and 

provide other management advice. Extension 

foresters implement programs to help owners, 

natural-resource professionals, and community 

leaders through workshops, master owner 

programs (akin to master gardener programs), 

and similar mechanisms. Cost sharing of 

management practices, such as developing 

management plans, planting trees, or improving 

timber stands, is periodically available through 

Federal and State programs.

The Forest Stewardship Program (Chapter 6) is 

an important source of U.S. Forest Service funding 

for many of these activities, particularly those 

implemented by State agencies. Although the 

funds can be used for various forms of landowner 

assistance, the cost sharing or full subsidization 

of forest management plans is a key component 

in many States. By 2011, stewardship plans were 

in place for 9 percent of northern nonindustrial 

private forest land (land not held by forest products 

companies), with individual State participation 

varying from <2 percent to nearly 30 percent 

of eligible forest lands (Fig. 9.7). The 2008 

reauthorization of the Forest Stewardship Program 

required States to create forest action plans 

(www.forestactionplans.org) to be eligible 

for funding. These plans help States 

identify specific threats to their 

forests and develop mitigation 

strategies, including 

identification of priority 

forest landscapes.

In addition to public agencies, some 

nongovernmental organizations 

assist landowners with forest 

management. For example, forest 

certification, described on page 250, 

is a tool that helps identify, apply, and 

document sustainable forest 

management practices.

258 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Predicted Trends

Funding levels for many public programs have 

been decreasing. For example, the Forest 

Stewardship Program, which supports many 

State agency activities including the writing of 

forest management plans, decreased 50 percent 

from 2005 to 2010 in terms of inflation-adjusted 

funding (Fig. 9.8).

FIGURE 9.7

Proportion of nonindustrial 

private forest land (land not owned by forest products 

companies) in the North that is covered by management 

plans prepared with U.S. Forest Service funding in 

accordance with the Forest Stewardship Program 

(Butler et al. 2013).

FIGURE 9.8

Funding levels for the Forest Stewardship Program of the U.S. Forest 

Service, 1991 to 2010; (Personal communication: K. Dalla Rosa. 2012. 

Forest Stewardship Program Manager, U.S. Forest Service-Cooperative 

Forestry, Washington, DC).

Under 5

5 to 9

10 to 19

Over 19

FORESTED AREA (percent)

60

50

40

30

20

10

0

DOLLARS (in thousands)

‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10

Current dollars

Constant (1991) 

dollars

Forestry assistance programs will continue to 

evolve to meet changing needs and the realities of 

budget changes. Some programs now take a more 

prioritized approach to place limited resources 

in those areas deemed to provide the greatest 

returns. Others are shifting the delivery of services 

to private providers and seeking alternatives to 

traditional one-on-one interactions.

CHAPTER NINE 259
RESEARCH AND TECHNOLOGIES FOR 

SUSTAINABLE FOREST MANAGEMENT 

Research organizations throughout the North 

conduct studies and develop new technologies 

to improve sustainable management of forests 

throughout the region and beyond. The U.S. Forest 

Service, which employs the largest cadre of forest 

researchers in the world, has a strong presence 

across the region (USDA FS 2013a.) Scientists 

at land grant universities and other universities 

and colleges conduct research and train future 

generations of natural resource managers and 

scientists. Many nongovernmental organizations 

also employ experts to address specific natural 

resource issues. Traditionally, forest products 

companies had substantial research staffs, 

but with the forest land divestitures to timber 

management investment organizations and real 

estate investment trusts, many of those companies 

have lost their research capacities, and many 

of the new groups have opted to discontinue 

research on forest resources. Nevertheless some 

cooperative research programs that fund and 

direct private landowners and nongovernmental 

organizations remain intact and effective (e.g., 

University of Maine Cooperative Forestry 

Research Unit 2015). 

The collective forest research budget across the 

region has been substantial; the average U.S. 

Forest Service annual research appropriation 

for the region has been $60 million over the 

past 6 years (Chapter 8). Anticipated near-term 

reductions in the Federal appropriations would 

likely diminish research capacity. Long-term, 

forest research funding levels in the region are 

unknown but would likely mirror national trends. 

PARTNERSHIPS TO SUPPORT SUSTAINABLE 

FOREST MANAGEMENT 

Current Status

Partnerships play a pivotal role in shaping 

and implementing the legal, institutional, 

and policy frameworks for sustainable forest 

management. Partnerships at the national level 

among Federal agencies and nongovernmental 

organizations help advance the collective forest 

policy agenda. Analogous partnerships at more 

local levels often form around specific topics 

or geographic locations. Some partnerships 

are temporary by design or circumstance, but 

others have proven to be long lasting.

Through partnerships, such as the Forests in 

the Farm Bill Coalition4

, the interests of forest 

owners and the forestry community are voiced. 

Many forestry programs are administered 

through collaborations among multiple groups 

that often include Federal and State agencies, 

universities, and nongovernmental organizations. 

For example, the Forest Stewardship Program 

involves funding from the Federal government 

that goes to State governments that in 

turn often contract with private consultants. 

For a list of member organizations and more information on the 

Forests in the Farm Bill Coalition, see http://www.americanforests.

org/wp-content/uploads/2012/01/fifb_coalition_web_version.pdf.

4

260 FUTURE FORESTS OF THE NORTHERN UNITED STATES
The Wisconsin Driftless Network is an example of 

a group of partners coming together to address 

a common goal—increasing the engagement of 

family forest owners in forest conservation (http://

mywisconsinwoods.org). The geographic focus of the 

partnership is an area of southwestern Wisconsin 

that is topographically and geologically distinct 

because it escaped the most recent episode of 

glaciation that smoothed the surrounding landscape. 

The partnership includes the Aldo Leopold 

Foundation, the American Forest Foundation, the 

Wisconsin Department of Natural Resources, the 

University of Wisconsin, the U.S. Forest 

Service, and others. Its approach is to develop 

collaborative, innovative, science-based methods 

that will increase the number of family forest 

owners who heed its conservation message and 

opt to participate in conservation activities. The 

breadth and diversity of its membership allow for 

coordinated outreach efforts, sharing of costs, and 

sharing of combined intellect. Plans are underway to 

document their experiences so that others can learn 

from what they have done.

The Wisconsin Driftless Network

Similarly, the North American Bird Conservation 

Initiative (www.nabci-us.org) brings together 

numerous agencies and nongovernmental 

organizations to identify regional activities 

needed to sustain diverse, viable bird populations.

Predicted Trends

As natural resource issues become more 

complex and oriented toward landscape-scale 

solutions, coordination among owners and 

cooperation across agencies will be increasingly 

important (Harper et al. 2006). A broad view of 

sustainable forests, as embodied in the criteria 

used to organize this report, requires increased 

reliance on effective partnerships to coalesce 

multiple interests as well as multiple fields 

of expertise to address the large-scale, longterm issues that affect forest conservation and 

sustainability. Because of increasingly scarce 

resources and other challenges mentioned 

throughout this chapter, cooperation among 

partners is more imperative than 

ever and is not expected 

to abate in the future. 

261
PUBLIC PARTICIPATION AND CONFLICT 

RESOLUTION IN DECISIONMAKING

Current Status

The degree to which the public participates in 

forest-related decisionmaking depends on the 

policies being considered and on the ownership 

of land that will be affected. For most public 

lands, the public has many opportunities to 

offer input. For example, during national forest 

management planning, the public is encouraged 

to provide input, both on strategic issues and on 

proposed specific management activities. Dayto-day, on-the-ground decisions are typically 

the domain of natural resource professionals 

and only occasionally capture the interest of 

neighbors and visitors.

Some large, private forest ownerships, either 

by choice or by necessity (as when required 

by enrollment in a certification program), also 

interact with the general public, primarily 

local residents, on forest management issues. 

Formal public input for smaller scale private 

forest ownerships is uncommon, but neighbors 

often informally influence each other’s 

management decisions.

Predicted Trends

The past several decades have been marked 

by efforts to make the decision processes of 

agencies more transparent and to increase 

public input into these processes. If this trend 

continues, options for public participation in 

forest management decisions on public lands 

are likely to increase. New technologies have 

improved communications with stakeholders. 

Increasingly, proposed forest plans are 

distributed electronically rather than as (often 

massive) printed documents. Maps, which are 

usually an integral component of forest plans, 

are now shared online with applications that 

allow readers to interactively adjust the map 

scale and to select and combine map layers. 

On private lands, concern for ownership rights 

will continue to make public involvement 

optional. But as forest certification systems 

expand to encompass more acres of privately 

owned forest lands, increased public 

involvement appears likely on those lands. 

Right-to-practice forestry laws have been 

enacted across many areas to (1) recognize 

the benefits of forestry to the economy and 

ecology of the State, (2) provide protection 

from nuisance complaints against landowners 

conducting forestry operations, and (3) limit the 

power of local governments to enact ordinances 

and zoning regulations restrictive to forestry 

(Granskog et al. 2002). However in many places, 

local laws, norms, or attitudes make traditional 

forestry practices difficult to implement.

262 FUTURE FORESTS OF THE NORTHERN UNITED STATES
PROGRESS TOWARD SUSTAINABLE 

FOREST MANAGEMENT

Current Status

Monitoring, assessment, and reporting of 

progress occur at multiple scales. Nationally, 

regionally, and at individual State levels, 

the Forest Inventory and Analysis program 

(FIA) provides information on trends in forest 

resources that is valuable for strategic planning 

(Bechtold and Patterson 2005). FIA inventories, 

which have been conducted on a periodic basis 

since the 1930s, now provide annual updates 

for all Northern States. FIA data are readily 

available online as are tools that allow users 

to summarize the data for their individual 

situations (USDA FS 2014b). These data are 

reported in national and regional reports (Smith 

et al. 2009) and numerous FIA-produced State 

reports. They are also combined with other data 

in State forest action plans compiled by the 

National Association of State Foresters (http://

www.forestactionplans.org); these plans provide 

snapshot assessments of forest resources, help 

identify key issues, and assist in setting forest 

policies and implementing management actions. 

Another important national monitoring effort, 

the Forest Health Monitoring Program of the 

U.S. Forest Service, facilitates the surveying 

and monitoring of forest pests and other threats 

(USDA FS, n.d.). 

At a finer scale, most natural resource 

agencies and many large, private ownerships 

conduct their own monitoring, which provides 

information, often at the stand level, that 

is useful for writing specific treatment 

prescriptions. At present, these data cannot 

be easily combined nor summed to draw 

conclusions beyond the boundaries of the lands 

on which the inventories are conducted because 

of differences in data collection protocols and 

issues related to data sharing. 

Predicted Trends

The fate of public inventory and analysis 

programs depends on funding levels and the 

desire of governments and the public for this 

information. At the national level, forest inventory 

and forest health monitoring have had strong 

support, but funding has been flat in recent years; 

from 2007 to 2011, the FIA budget decreased by 

0.5 percent in terms of inflation-adjusted dollars 

(USDA FS 2014a). Among individual States, 

support for inventory and monitoring varies 

significantly. Nevertheless, unless resource 

decisionmaking ceases to be data driven, the 

need for inventory data will continue.

263
New and emerging technologies will continue 

to influence the collection, analysis, and 

dissemination of inventory data. Remotely 

sensed data are now routinely combined with 

field inventories to decrease the cost and 

increase the utility and precision of inventory 

and monitoring products. Online tools, such 

as FIA’s Forest Inventory Data Online tool 

(USDA FS 2014b) and EVALIDator tool (Miles 

2014) have increased data access and utility.

Over the past few decades, the objectives of the 

inventories have shifted dramatically. Initially 

they were focused on estimating timber volume 

by species and location. However, in recognition 

of the vast array of goods and services that 

forests provide—ranging from the intrinsic 

value of biodiversity to the role that forests play 

in water recharge and purification—the scope 

of inventoried forest characteristics has 

expanded and will likely continue to do so.

CONCLUSIONS

The role of the legal, institutional, and policy 

framework in sustainable management of the 

forests of the Northern United States has been 

important in the past and is likely to become 

more so in the future. Ultimately the will of 

the people, as expressed through politicians, 

nongovernmental organizations, and markets, 

will determine the reach of those laws, policies, 

and institutions. As forest policy shifts (Schelhas 

2003), understanding the complex, multifaceted 

issues and interactions affecting the region’s 

forests has become more important. The 

availability of resources will continue to be a 

major issue that influences all aspects of the 

legal, institutional, and policy framework. 

264 FUTURE FORESTS OF THE NORTHERN UNITED STATES
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CHAPTER NINE 269
270 FUTURE FORESTS OF THE NORTHERN UNITED STATES
 U r b a n 

Fo r e s t s 

David J. Nowak 

and Eric J. Greenfield

Introduction

REES AND FORESTS are resources that significantly affect the health and well-being of people 

who live in urban areas where more than 80 percent of the U.S. population resides. These 

trees within our cities and communities provide many ecosystem services and values 

to both urban and rural populations. Healthy urban and rural forests are critical for 

sustaining quality of life for people in urban areas, but many forces could change the 

quantity and quality of urban trees and forests in the coming decades.

Urban forests are all of the trees 

and forests found in urban areas. 

This includes forested stands as 

well as trees along streets, in 

residential lots, and in parks. 

These forests provide a range of 

public services, in cities and beyond 

(Nowak and Dwyer 2007) by: 

Societal and Ecological Benefits from Urban Forests

• Sequestering and storing carbon 

• Removing air pollution and improving 

overall air quality

• Reducing air temperature 

• Reducing building energy use

• Absorbing ultraviolet radiation

• Reducing runoff from precipitation 

and improving water quality

• Reducing noise pollution

• Improving human comfort

• Increasing property values

• Improving human physiological 

and psychological well-being

• Improving aesthetics

• Improving community cohesion

271
Although urban forests may seem to be isolated, 

they actually have many links to rural forests 

across the landscape. For example, destructive 

insects, diseases, and invasive species (Chapter 

5) affect both urban and rural forests. Sometimes 

these destructive agents spread from rural forest 

landscapes to urban centers. At other times, 

people inadvertently move insects, diseases, 

or invasive plants to urban centers where they 

escape and spread into surrounding rural forests. 

The predicted increases in urban area that would 

result from increasing human populations would 

convert millions of acres of rural forest land 

(Chapters 3, 4) to urban uses. In contrast to land 

valuation in rural areas, the proximity of trees 

and people in urban areas increases the value of 

those trees and forests but can also increase the 

cost of caring for individual trees.

This chapter describes the potential changes in 

urban area and the consequences for forest land 

area and tree cover that would likely result from 

urban expansion over the next 50 years. To assess 

the potential impact of future urbanization, four 

indicators are analyzed: (1) change in urban 

area, (2) conversion of forest area to urban land, 

(3) change in percentage of tree cover resulting 

from urban-land expansion, and (4) change 

in percentage of tree cover in urban areas. In 

addition, the potential impact of insects, diseases, 

and invasive species in urban areas is discussed.

CHANGE IN URBAN AREA

Urban land in the conterminous United States 

increased from 2.5 percent of total land area 

in 1990 to 3.1 percent in 2000 (Table 10.1), 

a small percentage increase that nevertheless 

added 11 million acres in urban land—

an area about the size of Massachusetts 

and New Hampshire combined (Fig. 10.1). 

• From 1990 to 2000, urban land 

increased from 2.5 percent to 3.1 percent 

in the conterminous United States, and from 

4.9 percent to 5.9 percent in the North.

• By 2050, urban land is expected to increase to 8.1 

percent in the conterminous United States and to 

13.7 percent in the North.

• Most of the urban expansion across the 

conterminous United States occurred in forests 

(33 percent) or agricultural land (33 percent), 

compared to 42 percent in agricultural land and 

37 percent in forests for the North.

Key Findings

• Based on projected land-use changes, overall tree 

cover would decrease by as much as 1.6 percent 

in 2060 in the conterminous United States and by 

as much as 1.9 percent in the North.

• Projected changes in tree cover 

resulting from land-use change would be variable 

across the conterminous United States, with 

decreases in forested areas and increases in 

deserts, grasslands, and agricultural areas. 

• Although U.S. urban tree cover is estimated at 35 

percent, a recent study revealed that 17 out of 

20 cities have recently experienced statistically 

significant decreases in tree cover.

272
Table 10.1—U.S. urban land growth estimated for 1990 to 2000 and projected for 2050 (Nowak and Walton 2005, Nowak et al. 2005). 

(thousand 

acres)

(percent of 

total)

(thousand 

acres)

(percent of 

total)

(thousand 

acres)

(percent) (thousand 

acres)

(percent of 

total)

Ranking based 

on percent

Connecticut 975 30.6 1,135 35.5 159 5.0 1,942 60.9 4

Delaware 141 10.9 195 15.0 53 4.1 512 39.5 5

Maine 202 1.0 228 1.1 26 0.1 792 3.8 37

Maryland 957 14.3 1,157 17.3 199 3.0 2,512 37.5 6

Massachusetts 1,537 29.2 1,797 34.2 261 5.0 3,206 61.0 3

New Hampshire 259 4.4 362 6.1 103 1.7 1,018 17.1 13

New Jersey 1,552 31.2 1,805 36.2 253 5.1 3,167 63.6 2

New York 2,266 7.2 2,540 8.1 274 0.9 5,824 18.5 11

Ohio 2,205 8.3 2,568 9.7 364 1.4 6,058 22.9 8

Pennsylvania 2,175 7.5 2,730 9.4 555 1.9 6,408 22.1 9

Rhode Island 213 30.2 254 35.9 41 5.7 498 70.5 1

Vermont 81 1.3 95 1.5 14 0.2 328 5.3 31

West Virginia 292 1.9 361 2.3 70 0.5 1,193 7.7 26

All Northeast States 12,854 8.2 15,225 9.7 2,370 1.5 33,457 21.2 NR

Illinois 1,939 5.4 2,304 6.4 366 1.0 5,279 14.6 17

Indiana 1,136 4.9 1,424 6.1 287 1.2 3,857 16.7 14

Iowa 468 1.3 523 1.5 55 0.2 1,780 4.9 33

Michigan 1,797 4.8 2,179 5.8 382 1.0 5,111 13.7 19

Minnesota 859 1.6 1,010 1.9 151 0.3 2,577 4.8 34

Missouri 1,006 2.3 1,168 2.6 163 0.4 3,078 6.9 29

Wisconsin 875 2.4 1,061 3.0 186 0.5 2,980 8.3 25

All North Central States 8,079 3.0 9,669 3.6 1,590 0.6 24,662 9.2 NR

All Northern States 20,934 4.9 24,894 5.9 3,960 1.0 58,119 13.7 NR

State and region totals

State land in urban use, baseline State land in urban use, 

Projected 2050 1990 2000 Change

CHAPTER TEN 273
(thousand 

acres)

(percent of 

total)

(thousand 

acres)

(percent of 

total)

(thousand 

acres)

(percent) (thousand 

acres)

(percent of 

total)

Ranking based 

on percent

Florida 3,093 8.3 4,018 10.8 925 2.5 10,355 27.9 7

Georgia 1,702 4.5 2,397 6.4 695 1.8 5,392 14.3 18

North Carolina 1,624 5.0 2,278 7.1 654 2.0 6,140 19.1 10

South Carolina 907 4.6 1,194 6.0 287 1.4 3,647 18.3 12

Virginia 1,253 4.8 1,522 5.9 270 1.0 3,280 12.6 20

All Southeast States 8,580 5.6 11,409 7.5 2,829 1.8 28,815 18.8 NR

Alabama 910 2.8 1,141 3.4 231 0.7 3,533 10.7 22

Arkansas 469 1.4 582 1.7 114 0.3 1,972 5.8 30

Kentucky 644 2.5 779 3.0 135 0.5 2,271 8.8 24

Louisiana 902 3.0 1,066 3.5 164 0.5 3,348 11.1 21

Mississippi 491 1.6 600 2.0 109 0.4 2,135 7.0 27

Oklahoma 648 1.4 743 1.7 96 0.2 2,123 4.7 35

Tennessee 1,198 4.4 1,558 5.8 360 1.3 4,118 15.3 15

Texas 3,704 2.2 4,575 2.7 871 0.5 11,840 7.0 28

All South Central States 8,965 2.3 11,044 2.8 2,079 0.5 31,340 7.9 NR

All Southern States 17,545 3.2 22,453 4.1 4,908 0.9 60,154 10.9 NR

Kansas 464 0.9 554 1.1 90 0.2 1,682 3.2 39

Nebraska 251 0.5 293 0.6 41 0.1 902 1.8 44

North Dakota 80 0.2 93 0.2 13 0.0 442 1.0 45

South Dakota 95 0.2 108 0.2 13 0.0 481 1.0 46

All Great Plains States 890 0.5 1,048 0.5 157 0.1 3,507 1.8 NR

Table 10.1 continued 

State and region totals

State land in urban use, baseline State land in urban use, 

Projected 2050 1990 2000 Change

274 FUTURE FORESTS OF THE NORTHERN UNITED STATES
(thousand 

acres)

(percent of 

total)

(thousand 

acres)

(percent of 

total)

(thousand 

acres)

(percent) (thousand 

acres)

(percent of 

total)

Ranking based 

on percent

Arizona 766 1.0 1,074 1.5 308 0.4 3,702 5.1 32Colorado 650 1.0 815 1.2 165 0.2 2,609 3.9 36Idaho 202 0.4 261 0.5 59 0.1 982 1.8 43Montana 142 0.2 167 0.2 25 0.0 755 0.8 47Nevada 216 0.3 348 0.5 132 0.2 1,578 2.2 41New Mexico 352 0.5 482 0.6 130 0.2 1,611 2.1 42Utah 352 0.6 442 0.8 90 0.2 1,379 2.5 40Wyoming 96 0.2 108 0.2 12 0.0 401 0.6 48All Intermountain States 2,776 0.5 3,696 0.7 921 0.2 13,018 2.4 NRAll Rocky Mountain States 3,666 0.5 4,744 0.6 1,078 0.2 16,525 2.2 NROregon 539 0.9 658 1.1 119 0.2 2,199 3.5 38Washington 1,092 2.5 1,368 3.1 276 0.6 4,018 9.2 23All Pacific Northwest States 1,631 1.6 2,026 1.9 395 0.4 6,216 5.8 NRCalifornia 4,349 4.3 5,086 5.0 737 0.7 15,160 15.0 16All Pacific Southwest States 4,349 4.3 5,086 5.0 737 0.7 15,160 15.0 NRAll Pacific Coast States 5,980 2.9 7,112 3.5 1,132 0.5 21,376 10.3 NR48 State totala 48,163 2.5 59,242 3.1 11,079 0.6 156,214 8.1 NRU.S. totalb 48,473 2.1 59,635 2.6 11,163 0.5 N.d. N.d. NR

NR = no rankingN.d. = no dataaIncludes Washington,D.C.b

Table 10.1 continued 

State and region 

Includes Alaska, Hawaii, and Washington,D.C.

totals

State land in urban use, baseline State land in urban use, 

Projected 2050 1990 2000 Change

CHAPTER TEN 275
Urban land, 2000

61.3 million acres (3.2 percent), 

about the area of Michigan

Urban area increase, 1990 to 2000

11 million acres (0.6 percent), 

about the area of Massachusetts and 

New Hampshire

Projected urban land, 2050

156 million acres (8.1 percent), about 

the area of the northeast states

In the Northern United States, from 1990 to 

2000, urban area increased by 4 million acres 

or 1.0 percent of the land area. The States 

with the largest percentage increase were 

in the Northeast: Rhode Island (urbanization 

Urban land, 2000

25 million acres (5.9 percent), 

about the area of Ohio

Urban area increase, 1990 to 2000

4 million acres (1.0 percent), about the 

area of Connecticut and Rhode Island

Projected urban land, 2050

58 million acres (13.7 percent), 

about the area of Illinois and Indiana

FIGURE 10.1

Relative change in urban land area for the 

(A) conterminous United States and 

(B) the North (Nowak et al. 2005). 

subsumed 5.7 percent of the State land area), 

New Jersey (5.1 percent), Connecticut (5.0 

percent), and Massachusetts (5.0 percent). 

Nationally, States with the largest acreage 

increase were Florida (925,000 acres), Texas 

(871,000 acres), and California (737,000 acres). 

Of the ten U.S. States with the largest percentage 

increase, seven were in the Northeast, and 

A

B

276 FUTURE FORESTS OF THE NORTHERN UNITED STATES
three were in the Southeast. In aggregate, 

from 1990 to 2000, the Southeast had the 

largest increase in area and percent of urban 

land: 2.8 million acres (1.8 percent of total 

land area). In comparison, the Northeast States 

added 2.4 million acres of urban land 

(1.5 percent of total land area) (Table 10.1).

Based on the urban growth patterns of the 

1990s, the expectation is that urban land area 

in the conterminous United States will increase 

substantially—from 3.1 percent in 2000 to 8.1 

percent in 2050 (Fig. 10.2, Table 10.1). In the 

Northern States, urban land is expected to more 

FIGURE 10.2

County-level estimates of U.S. urban land 

(A) in 2000 and (B) projected for 2050 

(Nowak and Walton 2005).

than double, from 25 to 58 million acres. 

As discussed in Chapter 3, roughly a third 

of the total urban area expansion would be 

into forest land, but the proportion of forest 

land affected would vary greatly depending on 

geographic location.

From 2000 to 2025, the area of developed land 

in the United States is expected to increase by 

79 percent (Alig et al. 2004). The landscape 

changes resulting from urbanization and other 

types of development will have significant 

impacts on land management and efforts to 

sustain environmental quality in urban and 

urbanizing areas.

(A)

(B)

0 to 1

2 to 5

6 to 10

11 to 20

20 to 40

41 to 60

61 to 80

81 to 100

URBAN AREA 

(percent of all county land)

A

B

CHAPTER TEN 277
The National Resource Inventory (NRI) defines 

developed land as urban and built-up land and 

rural transportation land (USDA NRCS 2001). 

The developed land category includes: (1) large 

tracts of urban and built-up land; (2) small tracts 

of built-up land (<10 acres); and (3) land outside 

of these built-up areas that is used for roads, 

railroads, and associated rights-of-way (USDA 

NRCS 2001). NRI defines urban and built-up areas 

as land uses “consisting of residential, industrial, 

commercial, and institutional land” as well as 

several public infrastructure land-use categories 

such as railroads, landfills, and sewage treatment 

plants. Within NRI inventories of urban and built-up 

areas, highways and other transportation facilities 

are included if they are surrounded by urban areas 

(Alig et al. 2004).

Urban areas are defined as all the territory, 

population, and housing units that are located within 

either urbanized areas or urban clusters. Urban areas 

have a core population density of 1,000 people or 

more per square mile, but they also include some 

surrounding areas with lesser population density. As 

defined by the U.S. Census Bureau (2007), urbanized 

areas have a total population of 50,000 people or 

more and urban clusters have a population of 2,500 

to 49,999 people. 

Developed versus Urban Areas

Future urban growth patterns will be affected by 

population growth and other demographic shifts, 

changes in personal income, and other economic 

and socioeconomic factors (Alig et al. 2004). 

In the North, most urban growth is projected 

to occur around areas that are already heavily 

urbanized, with significant expansion expected 

along the east coast from Massachusetts to 

Maryland. In four States in the Northeast, 

more than half of the land area is projected 

to be urban by 2050 (Table 10.1): Rhode 

Island (71 percent), New Jersey (64 percent), 

Massachusetts (61 percent), and Connecticut 

(61 percent). 

CONVERSION OF FOREST AREA TO URBAN LAND

Past Rates of Conversion

From 1990 to 2000, most of the urban 

expansion across the United States occurred 

in forests (33 percent of the expansion) or 

agricultural land (33 percent), with wide 

variations among States. In the Northern 

States (Appendix 10, Fig. 10.3), more urban 

expansion occurred in agricultural land 

(42 percent) than in forests (37 percent). 

The Northern States with the largest percentage 

of urban expansion into forests (Table 10.2) 

were Rhode Island (65 percent), Connecticut 

(64 percent), and Massachusetts (63 percent). 

278
FIGURE 10.3

Percentage of land within various 

land-cover categories converted 

to urban land in the North, 1990 

to 2000 (Nowak et al. 2005).

Pasture, hay, row crops, small 

grains, or fallow (75 to 100 

percent of the cover); or orchard, 

vineyards, or other planted 

woody vegetation (25 to 100 

percent of the cover).

Deciduous, evergreen, or mixed 

forests,with 25 to 100 percent 

tree cover.

Areas characterized by a high 

percentage (30 percent or more) 

of constructed materials or 

vegetation (primarily grasses) 

planted in developed settings 

for recreation, erosion control, 

or aesthetic purposes (75 to 100 

percent of the cover).

Areas where forest or shrubland 

vegetation accounts for 25 

to 100 percent of the cover 

and the soil or substrate is 

periodically saturated with or 

covered by water.

Bare, rock, sand, clay, quarries, 

strip mines, gravel pits, 

transitional areas, or shrubland 

(25 to 100 percent of the 

cover); or natural, seminatural 

grasslands and herbaceous 

plants (75 to 100 percent of 

the cover).

Areas where perennial 

herbaceous vegetation accounts 

for 75 to 100 percent of the 

cover and the soil or substrate 

is periodically saturated with or 

covered by water.

Forest land 37% Agricultural land 42%

Developed land 14.7%

Woody wetlands 3.3% Other 1.7%

Herbaceous wetlands 

1.4%

279
Table 10.2—U.S. States with the greatest percentage and largest acreage of urban land expansion within various land-cover 

categories, 1990 to 2000 (Nowak et al. 2005, 2010).

Area 

(thousand acres)

Change 

(percenta

)

Area 

(thousand acres)

Change 

(percenta)

Forests Rhode Island 26 65 Georgia 444 64

Connecticut 102 64 North Carolina 366 56

Georgia 444 64 Pennsylvania 237 43

Massachusetts 164 63 Texas 184 21

West Virginia 43 62 Florida 174 19

Agricultural areas Nebraska 28 69 Texas 357 41

Indiana 192 67 Pennsylvania 252 46

Illinois 237 65 Illinois 237 65

Wisconsin 116 62 Indiana 192 67

Idaho 32 55 Ohio 185 51

Woody wetlands Florida 133 14 Florida 133 14

New Jersey 22 9 North Carolina 33 5

Rhode Island 3 8 Georgia 28 4

Massachusetts 16 6 Michigan 23 6

Michigan 23 6 New Jersey 22 9

Herbaceous wetlands Minnesota 11 7 Florida 57 6

Maine 2 6 Minnesota 11 7

Florida 57 6 Massachusetts 11 4

Massachusetts 11 4 Georgia 9 1

Delaware 2 4 Texas 8 1

a

Percent of total urban expansion within land-cover type.

Land-cover category Top 5 States based on percentage gain Top 5 States based on acreage gain

State Urban land increase State Urban land increase

280
The Northern States with the largest total 

acreage of urban expansion into forests (Table 

10.2) were Pennsylvania (237,000 acres) and 

Massachusetts (164,000 acres), States already 

known for their vast forests and large urban 

areas. In other States, urban areas expanded into 

other land-use types in differing proportions.

Projected Rates of Conversion

Nationwide by 2050, about 5.3 percent of 

forest land that is currently outside urban 

areas is projected to be subsumed by urban 

growth. This amount can be substantially 

higher at the individual State level, with Rhode 

Island (48 percent), New Jersey (40 percent), 

Massachusetts (37 percent), Connecticut (36 

percent), and Delaware (33 percent) projected 

to lead all States (Fig. 10.4).

Although the Northeastern States are expected 

to have the highest percentage of forest land 

converted to urban uses, the Southern States 

would have more total acreage converted to 

urban areas (Fig. 10.5): 2.2 million acres in 

North Carolina, followed by 1.9 million acres 

in Georgia, 1.7 million acres in New York, 1.6 

million acres in Pennsylvania, and 1.5 million 

acres in Texas. The total additional U.S. forest 

land projected to be subsumed by urbanization 

from 2000 to 2050 would be about 29 million 

acres, an area about the size of Pennsylvania 

(Fig. 10.6).

FIGURE 10.4

Percentage of U.S. non-urban 

forest land projected to be 

converted to urban land, 2000 to 

2050 (Nowak and Walton 2005).

0 to 1

2 to 5

6 to 10

11 to 20

21 to 40

41 to 50

PERCENT OF FOREST LAND 

TO CONVERT TO URBAN

CHAPTER TEN 281
FIGURE 10.5

Area of U.S. non-urban forest 

land projected to be converted 

to urban land, 2000 to 2050 

(Nowak and Walton 2005).

5 to 10

11 to 100

101 to 250

251 to 500

501 to 750

751 to 1,000

1,001 to 1,500

1,501 to 2,200

AREA OF FOREST LAND 

TO CONVERT TO URBAN 

(thousand acres)

Forest land subsumed by urban land 

growth, 1990 to 2000

4.1 million acres, slightly greater than 

the area of Connecticut and Rhode Island

Forest land subsumed by urban land 

growth, 1990 to 2000

1.6 million acres, slightly greater than 

the area of Delaware

Projected forest land subsumed by 

urban land growth, 2050

29.2 million acres, slightly greater than 

the area of Pennsylvania

Projected forest land subsumed by 

urban land growth, 2050

11.6 million acres, slightly greater than 

the area of New Hampshire, 

Massachusetts, and Rhode Island

FIGURE 10.6

Forest land converted and projected to be 

converted to urban land, 1990 to 2050, 

for the (A) conterminous United States and 

(B) the North (Nowak and Walton 2005).

A B

282 FUTURE FORESTS OF THE NORTHERN UNITED STATES
EFFECTS OF URBAN LAND EXPANSION 

ON TREE COVER 

Expansion of urban land affects the tree cover in 

a landscape, but not always as one would imagine. 

For example, although expansion of urban 

areas into forests reduces tree cover as roads 

and buildings are constructed, substantial tree 

cover often still remains. Urban expansion into 

agricultural areas can increase tree cover as trees 

are planted in proximity to new homes or as trees 

naturally migrate into former agricultural fields 

that have not yet been developed. 

Land-use change forecasts were used to 

estimate potential changes in tree cover for 

counties of the conterminous United States 

through 2060 (USDA FS 2012, Wear 2011). 

Tree cover predictions under three storylines 

(Fig. 10.7, Table 10.3) were developed by 

applying the land-use change forecasts to 

adjusted tree cover values in the 2001 National 

Land Cover Database (Nowak and Greenfield 

2010) and then projecting estimated tree cover 

forward to 2060 (Greenfield and Nowak 2013). 

FIGURE 10.7

Change in U.S. tree cover, 2000 to 2060 under three greenhouse 

storylines (IPCC 2007)—(A) A1B assumes moderate greenhouse gas 

emissions, moderate gains in population growth with large gains 

in income and energy consumption, but with a balanced renewable/

fossil fuel portfolio; (B) A2 assumes moderate greenhouse gas emissions, 

large gains in population growth and energy consumption with 

moderate gains in income; and (C) B2 assumes low greenhouse 

gas emissions, moderate gains in population growth, income, and 

energy consumption (USDA FS 2012). 

No data

Under -10

-10 to -6

-5 to 0

Over 0

CHANGE IN PERCENT TREE COVER

A

B C

CHAPTER TEN 283
--------------------------------------------------------(percentd

)-----------------------------------------------------

Connecticut -2.8 -3.9 -3.3 -4.5 -1.7 -2.3

Delaware -2.4 -5.9 -2.6 -6.4 -1.6 -4.0

Maine -1.5 -1.7 -1.2 -1.4 -0.9 -1.0

Marylande -6.3 -12.2 -5.9 -11.6 -4.5 -8.8

Massachusetts -4.1 -5.9 -4.4 -6.4 -2.7 -3.9

New Hampshire -3.4 -4.0 -3.0 -3.6 -2.3 -2.7

New Jersey -7.3 -13.1 -8.4 -15.1 -4.9 -8.8

New York -1.4 -2.1 -1.2 -1.8 -0.8 -1.1

Ohio -0.9 -2.3 -0.8 -2.0 -0.5 -1.3

Pennsylvania -2.6 -4.0 -2.2 -3.4 -1.4 -2.2

Rhode Island -9.2 -14.8 -10.0 -16.1 -5.5 -8.9

Vermont -3.5 -4.5 -2.5 -3.2 -2.3 -3.0

West Virginia -4.6 -5.6 -2.9 -3.6 -2.7 -3.3

All Northeast states -2.6 -3.9 -2.2 -3.4 -1.6 -2.4

Illinois -0.5 -3.3 -0.3 -1.9 -0.4 -2.6

Indiana -2.4 -8.6 -1.6 -5.7 -1.4 -5.2

Iowa -0.2 -1.5 -0.1 -0.8 -0.1 -1.1

Michigan -1.9 -3.3 -1.6 -2.7 -1.2 -2.1

Minnesota -1.6 -4.6 -1.3 -3.7 -1.0 -3.1

Missourif -3.0 -7.1 -2.2 -5.3 -1.9 -4.7

Wisconsin -0.9 -1.9 -0.7 -1.5 -0.6 -1.2

All North Central States -1.5 -4.4 -1.1 -3.3 -1.0 -2.8

All Northern States -1.9 -4.1 -1.5 -3.3 -1.2 -2.6

Florida -4.6 -8.5 -4.9 -9.1 -3.6 -6.7

Georgia -6.6 -9.9 -5.1 -7.6 -4.5 -6.8

North Carolina -3.9 -6.1 -3.3 -5.1 -2.7 -4.2

South Carolina -4.3 -6.6 -3.7 -5.8 -2.9 -4.4

Virginiag -3.1 -4.6 -2.4 -3.5 -2.2 -3.2

All Southeast States -4.7 -7.4 -4.0 -6.4 -3.3 -5.2

Alabama -3.2 -4.5 -2.4 -3.4 -2.0 -2.8

Arkansas -4.7 -8.5 -3.6 -6.5 -3.2 -5.8

Kentucky -5.0 -9.0 -3.9 -7.0 -3.1 -5.6

Louisiana -3.0 -5.9 -2.3 -4.4 -1.9 -3.6

Mississippi -3.0 -4.9 -2.2 -3.5 -1.9 -3.1

Oklahoma -1.3 -4.8 -1.0 -4.0 -0.9 -3.3

Tennessee -4.3 -7.3 -3.9 -6.5 -2.8 -4.7

Table 10.3—Projected change in U.S. tree cover, by State, under three greenhouse gas emissions storylines (IPCC 

2007), 2000 to 2060 (Greenfield and Nowak 2013, USDA FS 2012).

State and region

Storyline A1Ba Storyline A2b Storyline B2c

Change Relative 

change Change Relative change Change Relative change

284 FUTURE FORESTS OF THE NORTHERN UNITED STATES
--------------------------------------------------------(percentd

)-----------------------------------------------------

Texas -1.2 -5.4 -0.8 -3.6 -0.8 -3.8

All South Central States -2.4 -6.1 -1.8 -4.6 -1.6 -4.0

All Southern States -3.0 -6.6 -2.4 -5.3 -2.1 -4.5

Kansas -0.1 -1.0 -0.1 -1.0 -0.1 -0.8

Nebraska -0.2 -3.6 -0.1 -2.4 -0.1 -2.1

North Dakota -0.1 -1.7 0.0 -1.5 0.0 -1.6

South Dakota 0.3 6.1 0.2 4.4 0.2 4.3

All Great Plains States 0.0 -0.3 0.0 -0.3 0.0 -0.2

Arizona 0.1 0.3 0.1 0.4 0.0 0.2

Coloradoh -1.7 -6.8 -1.5 -6.2 -1.2 -4.7

Idaho -0.4 -0.9 -0.3 -0.7 -0.2 -0.5

Montana -1.2 -4.5 -1.0 -3.6 -0.8 -3.0

Nevada -0.4 -2.8 -0.3 -2.5 -0.3 -2.2

New Mexico -0.2 -1.0 -0.1 -0.8 -0.1 -0.7

Utah -1.2 -6.4 -1.2 -6.2 -0.9 -4.9

Wyoming -0.2 -1.6 -0.2 -1.2 -0.2 -1.1

All Intermountain States -0.7 -3.0 -0.6 -2.6 -0.5 -2.1

All Rocky Mountain States -0.5 -2.8 -0.4 -2.4 -0.3 -2.0

Oregon -1.0 -2.4 -1.0 -2.4 -0.7 -1.7

Washington -1.6 -3.3 -1.7 -3.6 -1.1 -2.4

All Pacific Northwest States -1.2 -2.8 -1.3 -2.9 -0.9 -2.0

Californiai -1.4 -4.0 -1.5 -4.1 -1.0 -2.9

All Pacific Southwest States -1.4 -4.0 -1.5 -4.1 -1.0 -2.9

All Pacific Coast States -1.3 -3.3 -1.4 -3.4 -0.9 -2.4

48 State total -1.6 -4.7 -1.3 -3.9 -1.1 -3.2

State and region

Storyline A1Ba Storyline A2b Storyline B2c

Change Relative 

change Change Relative change Change Relative change

a

A1B-Moderate greenhouse gas emissions, moderate gains 

in population growth with large gains in income and energy 

consumption, but with a balanced renewable/fossil fuel portfolio.

b

A2-High greenhouse gas emissions, large gains in population growth 

and energy consumption with moderate gains in income.

c

B2-Low greenhouse gas emissions, moderate gains in population 

growth, income, and energy consumption.

d

Change = percent tree cover in 2060 minus percent tree cover in 

2000. Relative change = 100 X (tree cover in 2060 minus tree cover 

in 2000) divided by tree cover in 2000. 

e

Maryland analysis covers 23 of the State’s 24 counties (Wear 2011).

f

Missouri analysis covers 114 of the State’s 115 counties (Wear 2011).

g

Virginia analysis covers 97 of the State’s 135 counties (Wear 2011).

h

Colorado analysis covers 62 of the State’s 63 counties (Wear 2011).

i

California analysis covers 57 of the State’s 58 counties (Wear 2011).

Table 10.3 continued 

CHAPTER TEN 285
The simulations were derived from 

demographic, macroeconomic, and 

climate change models and data 

compiled in the United States Forest 

Assessment System (Wear 2011). 

Forecasts of land-use allocations 

represent the competing economic values 

(land rents) of forests versus developed, 

agricultural, and other land uses as 

influenced by the storyline population and 

income dynamics. The assumptions and 

projection methodology for tree cover are 

consistent with, but separate from, those 

used to forecast change in forest conditions 

(Chapter 2). 

All three projections predicted similar changes 

in percentage of tree cover from 2000 to 2060. 

Nationally, tree cover would decrease by 

1.6 percent under storyline A1B, by 1.3 percent 

under A2, and by 1.1 percent under B2. Note 

that changes are expressed in absolute rather 

than relative terms; that is, if tree cover drops 

from 20 percent to 18 percent, the change 

would be expressed as 2-percent reduction.

With the exception of several countly-level 

increases in midwestern areas, tree cover 

is projected to decrease across all three 

storylines in the Northern States (Table 10.3). 

The largest decrease would be in Rhode Island: 

5.5 percent under storyline B2, 9.2 percent 

under A1B, and 10.0 percent under A2. The 

State with the smallest decrease would be 

Iowa: 0.1 percent under storylines A2 and 

B2, and 0.2 percent under A1B.

The effects of expanding urbanization on 

surrounding rural land would be different 

for forests than for agricultural land. Tree 

cover would decrease in the more forested 

counties east of the Mississippi River 

and along the northwestern Pacific coast, 

but would increase in many agricultural, 

grassland, and desert counties of midwestern 

and western areas. Percentage tree cover 

tends to be greater in urban areas built on 

grassland, deserts, and agricultural land 

than in the rural areas that surround those 

urban areas; the opposite is true for urban 

areas built in counties dominated by forests 

(Nowak and Greenfield 2012b). The projected 

increases in tree cover for urban areas in 

counties dominated by desert, grassland, 

and agricultural areas are attributable to 

landscaping associated with urbanization. 

Projections of increases in these areas 

assume favorable conditions for sustaining 

tree populations; increases would not occur 

if necessary resources, such as water for 

irrigation, become scarce.

286 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Providence, RI

287
(percent of total urban area)

Connecticut 67

Delaware 38

Maine 54

Maryland 33

Massachusetts 65

New Hampshire 64

New Jersey 50

New York 41

Ohio 29

Pennsylvania 34

Rhode Island 54

Vermont 53

West Virginia 47

All Northeast states 44

Illinois 26

Indiana 22

Iowa 24

Michigan 35

Minnesota 31

Missouri 31

Wisconsin 29

All North Central States 29

All Northern States 38

Florida 35

Georgia 52

North Carolina 48

South Carolina 47

Virginia 35

All Southeast States 42

Alabama 53

Arkansas 43

Kentucky 27

CHANGE IN TREE COVER IN URBAN AREAS

Urban tree cover in the conterminous United 

States has been estimated at 27 percent 

(Dwyer et al. 2000, Nowak et al. 2001) based 

on Advanced Very High Resolution Radiometer 

data and Landsat thematic mapper data from 

the early 1990s (Zhu 1994). Urban tree cover 

tended to be greatest in urban areas that were 

developed in forested ecoregions (34 percent), 

followed by grasslands (18 percent) and deserts 

(9 percent). These results are consistent with 

earlier estimates of urban tree cover by ecoregion 

types (based on aerial photo interpretation of 

58 U.S. cities), where tree cover averaged 

31 percent in forest cities, 19 percent in 

grassland cities, and 10 percent in desert 

cities (Nowak et al. 1996). The percentage of 

tree cover in urban areas tends to decrease 

as population density increases and can vary 

within a city based on the distribution of landuse types and ecoregions; for example, vacant 

land in forested regions often supports tree 

cover, but vacant land in desert regions is 

often devoid of trees (Nowak et al. 1996, 2001; 

Nowak and Greenfield 2012b). Understanding 

the factors most associated with urban tree 

cover can improve projections of tree cover and 

inform policies that modify urban tree cover. 

Based on photo-interpretation of the 

conterminous States from circa 2005, U.S. 

urban tree cover is estimated at 35 percent 

(Nowak and Greenfield 2012b). In the North 

Table 10.4—U.S. urban tree cover, circa 2005, by 

State (Nowak and Greenfield 2012b).

State and region Tree cover

288 FUTURE FORESTS OF THE NORTHERN UNITED STATES
State and region Tree cover

Table 10.4 continued (Table 10.4), tree cover in urban areas is 

highest in Connecticut (67 percent) and 

Massachusetts (65 percent) and lowest in 

Indiana (22 percent) and Iowa (24 percent). 

Because of the different methods used and 

the limitations of the analyses, the increases 

in estimated urban tree cover in 2005 from 

previous years do not necessarily indicate a 

real increase or a temporal trend in urban tree 

cover. To measure temporal trends in city and 

urban tree cover more accurately, a pairedphoto-interpretation was conducted in 20 U.S. 

cities (Nowak and Greenfield 2012a). Results 

revealed that urban tree cover is declining 

nationally by about 0.2 percent (4 million trees) 

per year and that eight of the nine northern 

cities analyzed had statistically significant 

declines in tree cover averaging about 0.9 

percent per year (Table 10.5). Because tree 

cover is an indicator of the extent to which 

trees and forests are providing critical services 

to local urban residents, assessing the size of 

this resource and whether it is increasing or 

decreasing is critical. Even though tree cover 

seems to have declined, more data are needed 

to determine if this is a trend that will continue 

into the future. 

(percent of total urban area)

Louisiana 32

Mississippi 41

Oklahoma 19

Tennessee 39

Texas 32

All South Central States 35

All Southern States 39

Kansas 28

Nebraska 19

North Dakota 15

South Dakota 21

All Great Plains States 24

Arizona 16

Colorado 17

Idaho 13

Montana 9

Nevada 12

New Mexico 12

Utah 15

Wyoming 9

All Intermountain States 15

All Rocky Mountain States 17

Oregon 40

Washington 33

Alaska 38

All Pacific Northwest States 35

California 20

Hawaii 30

All Pacific Southwest States 20

All Pacific Coast States 25

48 State total 35

U.S. total 35

CHAPTER TEN 289
------------------------------------(percent)------------------------------------

New Orleans, LA 2005 to 2009 -9.6 0.4 -29.2 0.9

Houston, TX 2004 to 2009 -3.0 1.3 -9.8 3.5

Albuquerque, NM 2006 to 2009 -2.7 1.8 -6.6 5.1

Baltimore, MD 2001 to 2005 -1.9 2.1 -6.3 4.7

Atlanta, GA 2005 to 2009 -1.8 1.7 -3.4 6.5

Miami, FL 2003 to 2009 -1.7 1.0 -7.1 1.7

Tacoma, WA 2001 to 2005 -1.4 3.6 -5.8 8.9

Kansas City, MO 2003 to 2009 -1.2 2.0 -4.2 11.2

Nashville, TN 2003 to 2008 -1.2 1.1 -2.4 6.2

New York, NY 2004 to 2009 -1.2 1.4 -5.5 2.3

Minneapolis, MN 2003 to 2008 -1.1 0.8 -3.1 1.8

Boston, MA 2003 to 2008 -0.9 1.7 -3.2 3.6

Los Angeles, CA 2005 to 2009 -0.9 1.8 -4.2 3.4

Detroit, MI 2005 to 2009 -0.7 1.2 -3.0 2.6

Portland, OR 2005 to 2009 -0.6 1.5 -1.9 3.5

Spokane, WA 2002 to 2007 -0.6 2.0 -2.5 5.8

Chicago, IL 2005 to 2009 -0.5 0.0 -2.7 0.0

Pittsburgh, PA 2004 to 2008 -0.3 0.0 -0.8 0.0

Denver, CO 2005 to 2009 -0.3 1.4 -3.1 3.6

Syracuse, NY 2003 to 2009 1.0 -0.5 4.0 -1.0

20 city average -1.5 1.3 -5.0 3.7

18 city averagee -1.1 1.4 -3.8 3.9

Table 10.5—Changes in tree and impervious cover change in 20 U.S. cities (Nowak and 

Greenfield 2012a). Values in boldface type indicate the change is statistically significant from 

zero at alpha=0.1.

City Analysis 

period

Urban cover change for the analysis period

Absolute changea Relative changeb

Treec Imperviousd Treec Imperviousd

290 FUTURE FORESTS OF THE NORTHERN UNITED STATES
------------------------------------(percent)------------------------------------

New Orleans, LA 2005 to 2009 -2.49 0.09 -8.27 0.21

Houston, TX 2004 to 2009 -0.60 0.26 -2.03 0.69

Albuquerque, NM 2006 to 2009 -0.91 0.60 -2.26 1.67

Baltimore, MD 2001 to 2005 -0.48 0.51 -1.62 1.16

Atlanta, GA 2005 to 2009 -0.46 0.43 -0.85 1.58

Miami, FL 2003 to 2009 -0.28 0.16 -1.22 0.27

Tacoma, WA 2001 to 2005 -0.36 0.89 -1.49 2.15

Kansas City, MO 2003 to 2009 -0.20 0.34 -0.71 1.78

Nashville, TN 2003 to 2008 -0.24 0.22 -0.48 1.21

New York, NY 2004 to 2009 -0.23 0.27 -1.13 0.45

Minneapolis, MN 2003 to 2008 -0.22 0.15 -0.63 0.35

Boston, MA 2003 to 2008 -0.19 0.35 -0.65 0.71

Los Angeles, CA 2005 to 2009 -0.23 0.45 -1.06 0.85

Detroit, MI 2005 to 2009 -0.18 0.30 -0.77 0.64

Portland, OR 2005 to 2009 -0.15 0.38 -0.49 0.87

Spokane, WA 2002 to 2007 -0.11 0.39 -0.50 1.14

Chicago, IL 2005 to 2009 -0.13 0.00 -0.69 0.00

Pittsburgh, PA 2004 to 2008 -0.08 0.00 -0.19 0.00

Denver, CO 2005 to 2009 -0.08 0.35 -0.78 0.88

Syracuse, NY 2003 to 2009 0.17 -0.09 0.65 -0.17

20 city average -0.37 0.30 -1.29 0.82

18 city averagee -0.27 0.31 -0.90 0.87

a

Absolute change = percent of city land in first year of analysis minus percent of city land in the last year.

b

Relative percent change = absolute change (percent of city in first year minus percent of city in second year) divided 

by percent of city in first year.

c

Tree and shrub cover includes shrub/scrub/chaparral cover in Albuquerque.

d

Impervious surfaces include building, roads, and other impervious combined. 

e

Two cities were excluded from this average to factor out effects of losses associated with extreme disturbances: the 

storm damage from Hurricane Katrina in New Orleans and mortality from the emerald ash borer infestation in Detroit.

City Analysis 

period

Average change per year

Absolute changea Relative changeb

Treec Imperviousd Treec Imperviousd

Table 10.5 continued 

CHAPTER TEN 291
Numerous natural and societal factors affect 

tree cover, both in positive and negative ways. 

For example, trees naturally regenerate and 

are planted, but they also succumb to storm 

damage, insects and diseases, old age, and 

land clearing for development. Continued 

monitoring of U.S. urban tree cover is needed to 

identify local and national changes. Because of 

numerous factors that can influence tree cover 

in cities and urban areas over the long term, 

the ability to predict future urban tree cover 

over the next 50 years is extremely limited. 

Nevertheless the recent, short-term trend is 

slightly downward.

POTENTIAL IMPACTS OF EMERALD ASH 

BORER AND INVASIVE TREE SPECIES

In addition to urban development and climate 

change, two other dominant factors will 

shape urban forests of the future: (1) insects 

and diseases, and (2) invasive tree species. 

Numerous insects and diseases are affecting 

tree health in urban areas across the United 

States (Chapter 5). One of the more recent 

and devastating insects introduced into 

the United States is the emerald ash borer 

(Agrilus planipennis), an exotic beetle that 

was discovered in southeastern Michigan near 

Detroit in 2002. This insect kills ash trees and 

has the potential to substantially alter urban 

forest composition in some cities in the North. 

The emerald ash borer is prevalent throughout 

much of the Northern States (USDA 2014) 

and may kill at least 50 percent of ash trees 

(Fraxinus spp.) within 15 years in infested 

areas (Krist et al. 2014). Based on field data 

from various northern cities collected between 

1996 and 2009 (Nowak et al. 2013a), this insect 

could kill up to 11 percent of some city tree 

populations within the next 15 years, which 

will equate to millions of urban trees killed in 

northern cities (Table 10.6).

Hartford, CT

292
----------------------(percent)----------------------

Minneapolis, MN 105,900 11.0 5.1 12.4

Milwaukee, WI 286,650 9.2 5.7 6.9

Chicago, IL 201,350 6.0 4.2 5.8

Baltimore, MD 106,150 4.7 2.8 3.0

Philadelphia, PA 62,800 3.2 2.5 2.3

Morgantown, WV 15,350 2.9 0.9 1.3

Woodbridge, NJ 24,800 2.7 3.0 3.3

Boston, MA 15,550 1.3 0.9 0.5

Scranton, PA 13,000 1.3 1.4 0.9

Washington, DC 20,050 1.1 1.1 1.0

Hartford, CT 5,200 1.0 0.5 1.1

Moorestown, NJ 5,300 1.0 0.6 0.9

Freehold, NJ 450 1.0 0.4 0.4

Syracuse, NY 9,350 0.9 0.4 1.1

New York, NY 13,800 0.3 0.1 0.2

Jersey City, NJ 0 0.0 0.0 0.0

Table 10.6—Estimated number of trees, percent of total tree population, 

percent carbon storage, and percent leaf area lost within the next 15 years 

due to emerald ash borer in select northern cities. All cities are within emerald 

ash borer infested areas except for New York (USDA 2014). For city sampling 

information, see Nowak et al. (2013a).

City Number of 

trees 

Tree 

population

 Carbon 

storage

Leaf 

area

Philadelphia, PA

Chicago, IL

Boston, MA

CHAPTER TEN 293
Invasive trees and shrub species (Chapter 5) 

also have the potential to substantially alter 

urban forest composition in the coming years 

through prolific regeneration. The invasive 

characteristics of some species pose problems 

associated with their spreading into the 

surrounding landscape, displacing native 

species and altering local ecosystems (e.g., 

Pimentel et al. 2000). In Syracuse, NY, only 

35 percent of new trees (plantings or natural 

regeneration) occurring between 2001 and 

2009 were native species, with 52 percent 

classified as invasive species (Nowak 2012). 

Regeneration was dominated by European 

buckthorn (Rhamnus cathartica), which tripled 

in population during this period (Nowak et al. 

2013b). Species classified as invasive (based on 

State invasive species lists) can reach one-third 

of the total tree population in some northern 

cities (Table 10.7). These invasive species 

have and will continue to alter urban tree 

composition in the coming years, and thereby 

alter urban forest structure and ecosystem 

services derived from these forests.

Table 10.7—Characteristics of trees classified as invasive in select northern cities showing 

numbers of trees; numbers of species; and their relative proportion of the total population, 

carbon storage, and leaf area. For city sampling information, see Nowak et al. (2013a).

City Number 

of trees 

Number 

of tree 

species

Tree 

population

 Carbon 

storage

Leaf 

area

--------------------(percent)--------------------

Milwaukee, WI 1,112,000 12 33.3 20.6 30.7

Syracuse, NY 326,300 5 29.8 13.9 21.4

Freehold, NJ 14,000 7 29.4 27.4 30.2

Jersey City, NJ 38,300 7 28.0 25.6 28.4

Boston, MA 267,400 5 22.6 14.6 25.5

Chicago, IL 570,400 10 16.1 17.6 14.7

New York, NY 778,600 2 15.0 9.3 10.7

Philadelphia, PA 294,300 6 13.8 10.4 12.4

Woodbridge, NJ 126,000 9 12.8 7.3 10.7

Hartford, CT 60,500 4 10.7 5.5 7.0

Moorestown, NJ 52,000 8 8.8 6.1 8.5

Baltimore, MD 178,900 4 7.2 4.1 4.8

Washington, DC 130,900 7 6.8 3.0 5.4

Minneapolis, MN 57,800 2 5.9 8.3 10.9

Scranton, PA 71,000 7 5.8 11.4 14.1

Morgantown, WV 19,500 1 3.4 1.2 1.7

Minneapolis, MN

Milwaukee, WI

294 FUTURE FORESTS OF THE NORTHERN UNITED STATES
CONCLUSIONS

Four trends are evident: 

(1) urban land is expanding at the 

expense of rural land, particularly forest 

and agricultural land; (2) urbanization will 

change tree cover nationally with results varying 

by region, with some regions gaining tree cover 

and others, including the Northern States, losing 

tree cover; (3) tree cover in urban areas has 

been on the decline in recent years; and (4) 

insects, disease, and invasive species will alter 

urban forest structure. 

These trends indicate that urban land is 

subsuming rural forest land at the same time 

that the importance of urban trees and forests 

is increasing. Although urban forests will be 

a minor component of the forest landscape 

nationally, they are critical in terms of their 

influence on rural forests, both environmentally 

and politically. Many insects, invasive plants, 

pollutants, and human disturbances emanate 

from urban areas and spread into rural forests. 

In addition, because of their close proximity 

to more than 80 percent of the northern 

population, urban forests will directly affect 

the vast majority of people by influencing their 

perspectives, votes, and decisions about natural 

resources. Thus, urbanization will likely be 

the most dominant forest influence, and urban 

forests will be the most influential forest of the 

21st century. 

As populations and urban areas increase, urban 

forest benefits become more significant. From 

1990 to 2000, urban land in the United States 

increased by 11 million acres and the urban 

population increased by 39 million people. 

Urban land is projected to almost triple in 

size by 2050 but will still remain a relatively 

small proportion of the total land area of 

the conterminous United States (8 percent). 

Northern urban land expanded by 4 million 

acres from 1990 to 2000, and it is projected 

to be 14 percent of total northern land area by 

2050. The highest levels of urbanization have 

been and are projected to occur in Northeastern 

States, where much of the urban land is 

converted from forests and results in decreased 

tree cover. In the Northeast, 48 percent of 

the converted urban land from 1990 to 2000 

came at the expense of forests. Urbanization in 

desert, grassland, and agricultural areas can 

increase tree cover relative to the surrounding 

landscape, but by 2060, a net decrease in tree 

cover for all land is predicted, ranging from 

1.1 to 1.6 percent for the conterminous United 

States and from 1.2 to 1.9 percent for the 

Northern States. 

Urban tree cover in the conterminous United 

States is currently estimated at 35 percent, 

with higher levels found in forested areas and 

lower levels found in deserts, grasslands, and 

agricultural areas. However, a recent study that 

included nine northern cities found that tree 

cover decreased in eight of them. Numerous 

natural and societal factors affect tree cover 

change, both positively and negatively. Continued 

monitoring and management of urban forests is 

necessary to sustain their essential benefits for 

current and increasing future urban populations. 

CHAPTER TEN 295
Literature

Cited

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21st century. Landscape and Urban Planning. 69(2–3): 219-234.

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connecting people with ecosystems in the 21st century. Gen. Tech. Rep. PNW–460. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station. 540 p.

Greenfield, E.J.; Nowak, D.J. 2013. Tree cover and aridity projections to 2060: a technical document 

supporting the Forest Service 2010 RPA assessment. Gen. Tech. Rep. NRS–125. Newtown 

Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 35 p.

Intergovernmental Panel on Climate Change [IPCC]. 2007. Climate change 2007: the physical 

science basis. Summary for policymakers. Geneva, Switzerland: IPCC Secretariat. 18 p. 

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Forest Health Technology Enterprise Team. 199 p.

Nowak, D.J. 2012. Contrasting natural regeneration and tree planting in 14 North American cities. 

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photo-interpreted estimates. Environmental Management. 46(3): 378-390.

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doi: 10.1007/s11252-013-0326-z.

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Measuring and analyzing urban tree cover. Landscape and Urban Planning. 36: 49-57.

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Gen. Tech. Rep. NRS–62. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

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urban environments on U.S. forest management. Journal of Forestry. 103(8): 377-382.

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nonindigenous species in the United States. BioScience. 50 (1): 53-65.

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EAB detections in North America. Washington, DC: U.S. Department of Agriculture. [map]. 

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Forest Service 2010 Resources Planning Act assessment. Gen. Tech. Rep. WO–87. 

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U.S. Department of Agriculture, Natural Resources Conservation Service; Ames, IA: 

Iowa State University, Statistical Laboratory. 89 p.

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document supporting the Forest Service 2010 RPA assessment. Gen. Tech. Rep. SRS–141. 

Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 41 p.

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Experiment Station. 11 p.

298 FUTURE FORESTS OF THE NORTHERN UNITED STATES
Chapter E l e v e n 299

T h e O u t l o o k 

for Northern Forests

Stephen R. Shifley, W. Keith Moser, Sherri Wormstead, Francisco X. Aguilar

Introduction

HE FORESTS OF the Northern United States have been remarkably resilient to nearly four centuries 

of immigration and settlement with associated anthropogenic disturbances caused by farming,

logging, burning, urbanization, trade, and many other factors. Although resilient, forests conditions have 

been significantly impacted by humans as is illustrated by the extent of forest land-use changes or 

the introduction of exotic, invasive species. Unquestionably, society has shaped the composition 

and structure of Northern Forests and will continue to do so in the future. The future holds many

uncertainties, but identification of clear trends and analyses of alternative future scenarios can help

managers, decisionmakers, and the public at large prioritize the most pressing challenges to sustaining

healthy, resilient forests that produce a desirable blend of commodities and ecosystem services.

In many respects, northern forests are faring 

better than they did a century ago when 

widespread, exploitive logging was common. 

Although there are many reasons to celebrate 

the current conditions of northern forests, 

significant challenges lie ahead. This chapter 

focuses on the potential threats to northern 

forests and the opportunities that society has to 

shape these forests for the future. Large-scale, 

strategic analyses of future conditions can help 

focus attention on improving the resiliency, 

health, and diversity of northern forests, making 

them more economically, socially, and ecologically 

sustainable and able to continue supporting the 

quality of life for the 125 million people who live 

in the region now, as well an additional 15 to 50 

million people expected by 2060. 

Chapters 3 through 10 report details of projected 

forest conditions for future alternative scenarios. 

The material is organized using the Montréal 

Process Criteria and Indicators (Montréal Process 

Working Group, n.d.) in an effort to provide 

substantial breadth in the material presented 

and consistency with many other State, regional, 

and national assessments. Breadth of information, 

although essential, does not necessarily lead 

to clarity in identifying necessary management 

actions or policy decisions. That requires 

additional work by society.
300 FUTURE FORESTS OF THE NORTHERN UNITED STATES

ANTICIPATED CHANGES IN FORESTS 

AND SOCIETY

Previous chapters of this report analyzed future 

scenarios projected for northern forests. The 

scenarios were based on assumptions about 

changes in population, land-use patterns, public 

policy, demand for wood resources, and emissions 

of greenhouse gases and associated climate 

effects. The projections are not certainties, and to 

emphasize that point, alternative scenarios were 

compared. The future reality will differ from all 

the projections, and the further we look into the 

future, the more uncertainty we face from events 

that cannot be foreseen. Consider that 50 years 

ago, in the early 1960s, personal computing and 

cell phones were unknown, gasoline was about 

30 cents a gallon, homes were commonly heated 

with coal, no one was concerned with greenhouse 

gas emissions, and the National Environmental 

Policy Act did not exist. 

Drivers of Change

The next 50 years will be greatly affected 

by expected and unforeseeable changes 

in societies, economies, technologies, and 

regulations that will influence the interactions 

of people and forests. Populations in the United 

States and around the world are expected to 

increase, and the cumulative demand for forest 

products and services will accordingly grow. 

Despite many uncertainties, based on 

historical and recent tendencies, some 

dominant trends emerge for northern forests. 

Relative to today, these projected trends and 

their consequent effects presented next seem 

fairly certain for the North over the next 

several decades.

More people, but a slower rate of population 

growth than for the United States as a whole, 

leading to: 

• A larger proportion of people, up to 85 percent, 

living in urban areas 

• Greater energy demand and likely more 

greenhouse gas emissions

• Increased fuelwood and energy consumption 

(even though per capita energy consumption 

is decreasing) 

• More urban and suburban expansion into 

private forest land

• More forest fragmentation and parcellation, 

especially near urban areas

• Decreased size of private forest ownerships 

and increased number of owners

• Greater threat for the spread of invasive species 

• A fundamental shift from the prior century-long 

trend of gradually increasing forest area to a 

trend of gradually decreasing forest area

• Fewer acres of forest per person with 

increased pressures on forests to provide 

ecosystem products and services, including 

recreation opportunities, diverse wildlife 

habitats, and watershed protection.
Chapter E l e v e n 301

Increased attention to the effects of climate on 

forests and forests on climate, leading to:

• Increased emphasis on carbon sequestration 

as an ecosystem service provided by forests

• Improved methods to quantify forest 

carbon dynamics

• Increased emphasis on sustainable forest 

management though certification and other 

stewardship programs for private landowners

• Policy changes that reduce per capita 

greenhouse gas emissions, although total 

greenhouse gas emissions may continue to 

increase with increasing population 

• Greater attention to managing forests 

for resilience to climate change impacts, 

including extreme weather events

Less emphasis on timber production for the 

majority of private forest owners and privately 

owned forest acres, leading to:

• Modest changes in forest tree 

species composition 

• Increased global trade in wood products, 

with more reliance on imports

• Fewer jobs in the forestry, logging, wood 

products, and paper products industries, 

especially as productivity per worker increases

• Stagnant, slow, or even declining economic 

activity associated with wood product 

manufacturing, particularly in rural areas

• An aging forest resource resulting from 

low harvesting rates and limited capacity 

for private forest management

• Limited timber-derived revenues available 

to support proactive management goals such 

as increasing forest diversity and improving 

resilience to threats such as climate change 

Forest growth and succession along with relatively 

low rates of management, leading to:

• Aging forests, with most forest area falling 

in the 60- to 100-year-old age class

• Low forest age-class diversity resulting in 

reduced habitat diversity for wildlife

• Fewer issues with water quality and quantity 

compared to other U.S. regions because of 

large water yields coupled with low rates of 

forest disturbance; localized water issues, 

particularly near urban areas or in watersheds 

with substantial loss of forest cover

• Reduced growth rate of wood volume 

and biomass

• Reduced net rate of carbon sequestration 

annually, since older forests accumulate 

carbon at lower rates than younger forests
302 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Less Certain Future Outcomes 

Impacts of a changing climate—Despite the broad 

scientific consensus that increased greenhouse 

gas emissions are changing the climate, uncertainty 

continues as to what the local and regional 

impacts will be on forests, wildlife, and people. 

Concern continues to mount about the potential 

for climate change—including increased frequency 

of extreme weather events—to cause declining 

forest health, increased tree mortality, decreased 

growth of wood, reduced output of timber and 

nontimber products (such as maple syrup), 

and loss of recreation opportunities (such as 

those due to loss of vibrant foliage from autumn 

landscapes and reduced snowpack for skiing).

How bioenergy and emerging wood-using 

industries will affect resource use—The North 

has high potential for future utilization of biomass 

for energy and advanced bio-based products 

(ranging from co-firing woody biomass with 

coal to cellulose nanotechnology); however, 

forest conditions, and social, financial, and 

logistical barriers have hindered large increases 

in wood-based energy production. Wood is a 

relatively low-value, widely-dispersed energy 

feedstock, and options for using it for energy 

production are unevenly distributed across the 

North. Electricity generation could use millions 

of tons of low-value wood annually, but the 

number of locations where electric production 

infrastructures coincide with economically 

advantageous supplies of woody biomass 

is limited. 

Moreover, burning biomass to generate electricity 

is not the most efficient energy conversion path 

for woody biomass unless used in high efficiency 

systems (such as combined heat-and-power 

plants). In contrast, opportunities to use wood 

for residential and commercial heat are spatially 

dispersed (Song et al. 2012) and contemporary 

wood stove designs have energy conversion 

efficiencies that rival the best electric utilities 

(Alliance for Green Heat 2014). Residential 

wood heating in the United States has declined 

in recent years with little to no indication that 

consumption will grow in the future, but foreign 

markets could sustain local bioenergy projects 

as has been the case for wood pelletized and 

exported to Europe. The amount of woody 

biomass harvested for energy production or 

other products affects the amount of carbon 

retained in forests, the level of carbon emissions 

from competing fossil fuels, the associated 

quantity of roundwood harvest, and future forest 

age structure. The three scenarios with greatly 

increased woody biomass utilization for energy 

(A1B-BIO, A2-BIO, and B2-BIO) all predicted 

large reductions in forest volume by 2060.
Chapter E l e v e n 303

How other factors will affect future forest 

conditions—Many other factors have the 

potential to affect the future trajectory of 

forest change or to be affected by future forest 

conditions, but the magnitude of those interactions 

is uncertain. Some of these uncertainties include: 

• How expanding urban areas along with changes 

in tree cover will impact the ability for urban 

and community trees and fragmented forests 

to meet life-supporting ecosystem services of 

an increasing population

• How the forest products industry and forestbased rural economies will fare if rates of 

forest volume growth decrease as predicted; 

any significant changes in growth rates could 

accumulate to cause significant disruption 

in the current supply of wood resources to 

the industry

• How macroeconomic conditions will change 

and impact domestic and global demand for 

wood and other forest products

• How public policy changes and cumulative 

management decisions or indecision by 

millions of private forest owners will affect 

future forest diversity, fragmentation, and 

the impact of invasive species 

• How attitudes and management priorities 

directed toward urban and rural forests will 

change; for example, a majority of all land 

area will be urban in several eastern 

States by 2060 

• How effective public land managers and 

private conservation organizations, such as 

land trusts, will be in addressing landscapescale conservation issues such as maintenance 

of forest biodiversity

• How forest certification will affect 

stand-scale and landscape-scale forest 

management practices

• How new or migrating invasive insects, 

diseases, and plants will affect tree composition 

and forest structure 

Effects of Spatial and Temporal Scales 

on Findings

Projected (and past) forest changes are not 

uniform across the North. Differences among 

subregions and among states occur in:

• Forest area, vegetation types, and 

product utilization 

• Forest resource policies, programs, 

and attention to forest-resource issues

• Spatial patterns of population density 

and land-use change (especially forest 

versus urban)
Thus, the forest resource problems anticipated 

for the North must be considered in the context 

of subregional, State, and local conditions as 

policy makers, landowners, and managers work 

to identify appropriate on-the-ground management 

actions. Throughout this report State-level and 

regional summaries are provided, because State 

natural resource agencies and legislatures will 

be at the forefront of coping with many of the 

potential problems and opportunities associated 

with northern forests. The appendices provide 

additional details. The Northern Forest Futures 

database (Miles and Wear 2015) and guide 

(Miles et al. 2015) (available on the DVD included 

with this book and available online) provide 

maximum flexibility to summarize projected forest 

conditions at State and subregional scales, for a 

number of scenarios, including those discussed 

in this report. 

The natural processes of forest growth, mortality, 

regeneration, species succession, nutrient cycling, 

and water cycling are dominant forces that drive 

forest change. However, those processes can be 

greatly modified by human choices about forest 

land conversion, harvesting, habitat restoration, 

fire management, energy use, greenhouse gas 

emissions, forest policies, energy policies, 

economic policies, and more. Across large spatial 

scales, forests and the processes within them are 

subject to great inertia. Clear evidence that forests 

are responding to policy or management changes 

can take decades to become evident in State 

or regional statistics, simply because so many 

acres are involved. Nevertheless, current forest 

conditions in the North clearly bear the imprint 

of past human choices, and northern forests 

will gradually come to bear the imprint of future 

human choices.

• Strengthen people’s connections to rural and 

urban forests

• Develop mutually beneficial partnerships among 

forest owners, managers, industry, and the larger 

society to support conservation goals

• Develop measurable state and regional goals 

for forest diversity and monitor progress toward 

achieving them 

• Promote forward-looking forest management 

across all forest ownerships

• Estimate the types and number of forestry jobs 

that could be sustainably supported, now and in 

the future 

• Work to understand the many dimensions of 

forest change 

Actions to Consider

304
Chapter E l e v e n 305

ACTIONS TO CONSIDER

The challenges facing northern forests are large, 

complicated, intertwined, and enduring. New 

challenges will emerge in the coming decades. 

The following actions (Shifley et al. 2014) are 

among many that will be needed to move from 

thoughtful consideration of the issues to on-theground management: 

Strengthen people’s connections to rural 

and urban forests 

Forests and cities are intertwined across the 

northern landscape. Over time, the region is 

expected to become more populous and more 

urbanized; by 2060 urban areas will be the primary 

habitat for 85 percent of residents. Clearly, 

attention to the health and sustainability of urban 

trees and forests is important to human well-being. 

Rural forests near urban land will be heavily 

impacted by inevitable urban expansion. These 

forests provide critical ecosystem services to large 

numbers of people and can serve as places to foster 

greater appreciation for natural resources. 

Nearly 12 million acres of forest land will likely 

be converted to urban uses by 2050. Although 

an enormous area, this still amounts to a loss 

of only about 7 percent of the region’s current 

forest land. By 2060, rural forests will likely still 

cover about 40 percent of the land. Since there 

are already indications where urbanization will 

take place, it might be important to focus less on 

how much forest will be converted and more on 

how to accentuate potential benefits and mitigate 

undesirable consequences in the specific places 

where urbanization is most likely to occur. 

By definition urban areas are heavily populated, 

but many also contain substantial tree and 

forest resources. Trees cover an estimated 

38 percent of urban land in the North and 

are an essential component of human habitats. 

Current forest inventories separate rural forest 

descriptions from those for urban trees and 

forests, even though rural and urban forests 

fall along an ecological continuum that is 

characterized by a changing ratio of the number 

of trees to the number of people. Tree inventory, 

monitoring, and valuation methods that span the 

gradient from rural to urban areas could evolve 

with increasingly sophisticated, standardized, 

and informative forest resource inventory systems. 

Urban forest managers have excelled in estimating 

the value of the ecosystem services that trees 

provide in terms of cooling neighborhoods, 

mitigating pollution, sequestering carbon, 

providing habitat, and contributing to overall 

human well-being. Valuation of those and other 

ecosystem services can be extended to rural 

forests where commodities have been the 

primary indicators of value. The net result 

would be better knowledge of the spatial 

distribution of the multiple values derived 

from rural and urban trees. 
306 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Develop mutually beneficial partnerships among 

forest owners, managers, industry, and the 

larger society to support conservation goals

Collaboration is required for planning and 

management to create forest landscapes 

that are diverse, that are resilient, and that 

are economically, socially, and ecologically 

sustainable. The long-term trends of an 

increasing number of forest owners coupled 

with decreasing forest parcel size adds to the 

complexity of managing for landscape-scale 

forest health and diversity across multiple 

ownerships. Large, publicly owned forest tracts 

with long-term management plans can serve as 

focal points for forest landscape management, 

but significant progress in landscape-scale 

management ultimately requires participation of 

private forest owners who collectively control 

74 percent of the North’s forest land. State forestry 

agencies, in cooperation with the U.S. Forest 

Service and other agencies, are leaders in 

identification of state-scale forest management 

priorities, but actual on-the-ground management 

demands the cooperation of public and private 

owners. Conservation easements can be effective 

mechanisms to coordinate management among 

private owners. Those instruments are well 

suited to implement forest management plans 

that will survive through ownership changes, 

intergenerational transfers, and parcellation. 

Other collaborative partnerships, such as the 

migratory bird joint ventures (USDI FWS 2014) 

and corporations that choose sustainably 

produced forest products or ecosystem services, 

are expected to continue to be highly influential 

in the future. 

Some of the most widely applied practices used 

to regenerate, restore, or increase resilience 

in forest landscapes include monitoring and 

planning, writing prescriptions, harvesting, 

burning, planting, applying herbicides, controlling 

deer, and controlling invasive species. All 

management practices cost money and require 

labor to implement. Consequently, market 

values of forest products and services are an 

essential consideration in management decisions. 

Traditionally, the sale of forest products has been 

an economic engine that helped offset management 

costs for nontimber conservation goals. Shifting 

the management focus from forest products to 

other ecosystem services can alter the economic 

balance. Failure to adequately measure and 

monetize the value of ecosystem services can 

be a barrier to managing them. Forests that are 

valued for multiple commodities and services, 

such as timber, water, soil, carbon sequestration, 

and wildlife habitat, will be better positioned to 

cope with disruption in a single market (such as 

a housing slump) and therefore will more likely 

be sustainably managed and conserved over time. 

Effective partnering with foresters, loggers, and 

other woods workers will be essential to design 

and implement economically viable management 

prescriptions for ecosystem services as well as 

for forest products. 

As a natural resource community, we are getting 

better at understanding how to manipulate 

forest ecosystems to favor a particular plant 

community or wildlife species. In addition, best 

management practices are available to guide 

protection of forest-associated soil and water 

resources during management activities. 
Chapter E l e v e n 307

We know much about restoring degraded habitats 

and adding diversity to forest landscapes. Still, 

the best laid plans for conservation can fail if they 

are not also socially acceptable and economically 

viable. As a natural resource community we need 

to ask new questions, such as: 

• What configuration of forest products industries 

can effectively implement the on-the-ground 

forest management required to sustain healthy, 

diverse, and resilient urban and community 

trees and rural forested landscapes? 

• What is the role that publicly owned forests 

should play to support timber markets in 

addition to nonconsumptive activities?

• What are the associated implications for 

economic sustainability of rural communities? 

• Will society in general, and private forest 

land owners in particular, participate in 

collaborative management practices that 

result in desirable cumulative effects across 

large forest landscapes?

• How can we build stronger support for 

forward-looking forest management from the 

5 million private forest owners and the 

120 million other residents of the North that 

derive products and services from forests?

Develop measurable state and regional goals 

for forest diversity and monitor progress toward 

achieving them 

Diversity of forest age classes is fundamental to 

sustaining resilience and long-term forest habitat 

diversity (Hunter and Schmiegelow 2011). Past 

disturbance history has resulted in low age-class 

diversity in northern forests with the majority 

of acres clustered in the 40- to 80-year age 

classes (Shifley et al. 2012, 2014). This is true 

for forest land in each State, as well as for the 

North as a whole. Efforts directed at increasing 

forest age-class diversity would increase other 

measures of forest diversity, expand habitat 

diversity for wildlife, and increase forest resiliency 

to undesirable consequences from stressors 

such as climate change and invasive species. 

Failure to address this issue has long-term 

implications for future forest diversity and 

resilience. Current deficits in the 10-year-old 

age class will eventually become deficits in the 

20- and the 30-year-old age classes—creating 

a trend that will persist for decades. Although 

forests >100 years are also underrepresented 

in the North, the proportion of area in this age 

class would increase as existing forests mature 

if current disturbance regimes continue. Many 

other indicators of forest diversity are important, 

but forest age-class diversity is a simple 

indicator that provides a good starting point 

for discussions about increasing all aspects of 

forest diversity and resiliency. Forest age class 

diversity is typically evaluated at the scale of tens 

of thousands or even millions of acres. Making 

changes at those scales requires shared goals 

across multiple ownerships. 
308 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Refine/adjust 

 and repeat

Monitor and 

evaluate indicators 

for desired outcomes

Plan actions and set 

goals to move 

toward desirable 

conditions

Implement 

appropriate actions

Secure 

the resources 

required to 

take appropriate 

actions

2

3

5 4

6

7

1

Develop indicators of desirable, 

sustainable, resilient future forest 

conditions at the appropriate 

scale(s): stand, landscape, state, 

regional, national

Measure current 

conditions using quantitative 

and qualitative indicators

Promote forward-looking forest management 

across all ownerships

As is true for much of the United States, active 

forest management is a low priority among 

northern private forest owners, and management 

intensity is low on much—but not all—of the 

public land. Northern forests face significant 

challenges over the next 50 years: climate 

change, invasive species, decreasing diversity, 

decreasing productivity, decreasing area, 

and increased demands for commodities and 

ecosystem services. Some, like the infestation 

of emerald ash borer (Agrilus planipennis), 

present an immediate crisis; others, 

including declining diversity and species 

responses to climate change, build gradually 

but often inexorably. 

The processes for dealing with future forest 

resource issues, individually or collectively, 

in forest ecosystems usually follow the same 

pattern (see diagram below): 
Chapter E l e v e n 309

This is forest management, plain and simple. 

The details of how to tailor management activities 

to achieve a desired outcome acre-by-acre 

may be complex and will certainly differ from 

place to place, but management procedures 

can generally be designed to avert or reduce 

the undesirable consequences of multiple 

stressors. Often, the limitation will not be a 

lack of understanding about which prescriptions 

will be successful in the woods, but rather the 

challenges of developing societal consensus and 

finding practical ways to invest in the selected 

treatments, monitor the outcomes, and repeat 

as needed. Many of the major stressors that will 

affect northern forests over the next 50 years—

climate change, introduction of invasive species 

through global trade, and population growth—are 

largely outside the control of the natural resource 

community, but managing northern forests to be 

sustainable, productive, and resilient in the face 

of whatever comes along is squarely within the 

influence of the region’s forest owners and the 

broader natural resource community. 

Estimate the types and number of forestry jobs 

that can be sustainably supported, now and in 

the future

The number of jobs has been decreasing steadily 

in the traditional fields of forestry and logging, 

wood products production, and pulp and paper 

production—in part caused by higher production 

efficiency of the remaining employees, either 

through technological enhancements or higher 

skilled workers—but the nature of woods work 

is also changing, as attention increasingly 

shifts to management of ecosystem services 

(including the provision of wildlife habitat), 

certification of sustainable timber production, 

and urban-tree management. 

Despite centuries of timber harvesting in 

northern forests, there is a lack of understanding 

about the number of new jobs that would be 

supported for every additional 10 million cubic 

feet of timber that is sold and processed. Even 

less is known about the capacity of other 

rural and urban forest management activities 

to create jobs and generate economic activity. 

For example, how many people with what skills 

would be required for restoring and maintaining 

each additional 10,000 acres of woodland, or for 

managing the latest invasive species? The types 

of work activities would likely include:

• Planning

• Inventorying 

• Prescribing treatments

• Planting

• Cutting and thinning

• Pruning

• Applying herbicides 

and pesticides

• Prescribed burning

• Suppressing wildfires

• Managing fuels and fire risk

• Managing invasives

• Monitoring effectiveness of operations

• Communicating plans and outcomes

By their very nature, most of these activities 

support local jobs that cannot be outsourced to 

other locations. 
310 FUTURE FORESTS OF THE NORTHERN UNITED STATES

The forest products industry and public land 

management agencies in the northern region 

appear to be undergoing a period of realignment. 

Despite two decades of increased emphasis on 

landscape-scale conservation and decreased 

emphasis on timber production from public 

lands, the demand for raw materials and 

processing capacities of local forest products 

companies does not always align with the 

vegetation manipulation prescribed by forest 

managers. Woodland restoration is a case in 

point. Early stages of restoration can require 

harvesting of many small-diameter, virtually 

unmarketable trees, thereby creating an 

overwhelming management expense. In addition, 

repeated prescribed burning is used to encourage 

vegetation diversity but can degrade the quality 

of merchantable timber that could otherwise be 

sold to traditional timber markets. The forest 

management community is extremely adept at 

prescribing on-the-ground activities required 

to bring a stand or a landscape to a desired 

future condition, but less adept at forecasting 

the related implications in terms of jobs and 

economic activity. 

Work to understand 

the many dimensions of forest change 

Following the general outline of the Montreal 

Process Criteria and Indicators in this report 

forced a broad examination of the current 

conditions and expected changes for many 

elements of northern forests: biodiversity; 

productivity; health; soil and water resources; 

biomass and carbon; socioeconomic benefits; 

legal and institutional frameworks for sustainable 

management; and urban forest resources. 

Our comprehensive format for reporting and 

discussing results provides a broad base 

for thinking about future forest conditions. 

Moreover, the modeling described in this report 

specifically addresses potential impacts of a 

changing climate. Given the broad consensus 

that climate will change in the North, the 

scenarios we examined were designed to 

capture the potential effects of those changes 

on northern forests in a way that complements 

past research conducted by U.S. Forest Service 

researchers and partners (e.g., Iverson et al. 

2008, Prasad et al. 2007, Vose et al. 2012) and 

summarized by the Northern Institute of Applied 

Climate Science (USDA FS 2012). 
One of the surprising results of the scenario

analyses is the similarity of the projected forest

outcomes for the three storylines developed by

the Intergovernmental Panel on Climate Change

(IPCC 2007) to estimate climate change associated

with relatively low (B2), medium (A1B) or high

(A2) greenhouse gas emissions. Differences were 

found to be modest until at least 2040 among

the scenarios that continued with current rates

of harvest, and there was no evidence that over

this period the effects of climate change would

overwhelm the changes resulting from forest 

aging, species succession, harvest, and land-use 

conversion. However, results from the scenarios

assuming large increases in the amount of woody 

biomass that could be needed to satisfy increased 

demand for bioenergy (A1B-BIO, A2-BIO, and

B2-BIO) confirmed that human influence through 

harvesting could substantially alter the trajectory 

of forest change in future decades.

Climate change will affect forest diversity, 

health, and ecosystem services in the North, 

but so will forest aging, species succession, 

logging, weather, fire, insects, disease, invasive 

plants, land-use change, recreation use, and 

management actions or inaction. Successful 

forest management requires society to consider 

which of these forces are most influential in 

the short-run and which are the most amenable 

to manipulation in pursuit of long-term, largescale forest conservation objectives. We will 

learn more about climate effects on forests in 

the coming years and more about management 

practices necessary to increase resiliency and 

reduce unwanted consequences. Climate change 

appears to be a factor that will complicate—

rather than dominate—change in northern forests 

over the next 50 years. 
312 FUTURE FORESTS OF THE NORTHERN UNITED STATES

Literature

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wood_stoves.html (accessed February 13, 2014).

Hunter, M.L., Jr.; Schmiegelow, F.K.A. 2011. Wildlife, forests and forestry. New York: Prentice Hall. 259 p.

Intergovernmental Panel on Climate Change [IPCC]. 2007. Climate change 2007: synthesis report. 

Contribution of working groups I, II and III to the fourth assessment report of the Intergovernmental 

Panel on Climate Change. Geneva, Switzerland: Intergovernmental Panel on Climate Change. 

104 p. http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_

report_synthesis_report.htm (accessed August 19, 2014). 

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: 390-406. 

Miles, P.D.; Huggett, R.J., Jr.; Moser, W.K. 2015. Northern Forest Futures reporting tools and database 

guide. Gen. Tech. Rep. NRS-150. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 48 p.

Miles, P.D.; Wear, D.N. 2015. Northern Forest Futures database. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service Research Data Archive. http://dx.doi.org/10.2737/RDS-2015-0009.

Montréal Process Working Group. N.d. Criteria and indicators. http://www.montrealprocess.org/

Resources/Criteria_and_Indicators/index.shtml (accessed May 20, 2013).

Prasad, A.M.; Iverson, L.R.; Matthews, S.N.; Peters, M. 2007. A climate change atlas for 134 forest 

tree species of the eastern United States. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. http://www.nrs.fs.fed.us/atlas/tree 

(accessed August 6, 2013).

Shifley, S.R.; Aguilar, F.X.; Song, N. [et al.]. 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.
Chapter E l e v e n 313

Shifley, S.R.; Moser, W.K.; Nowak, D.J. [et al.]. 2014. Five anthropogenic factors that will radically 

alter forest conditions and management needs in the northern United States. Forest Science. 

60(5): 914-925.

Song, N.; Aguilar, F.X.; Shifley, S.R.; Goerndt, M.E. 2012. Factors affecting wood energy consumption 

by US households. Energy Economics. 34: 389-397.

U.S. Department of Agriculture, Forest Service. 2012. Forest management for an uncertain 

climate future: tools and training. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. http://www.nrs.fs.fed.us/niacs/climate/

uncertainfuture/ (accessed February 11, 2015).

U.S. Department of Interior, Fish and Wildlife Service [USDI FWS]. 2014. Migratory bird joint 

ventures. Washington, DC: U.S. Department of interior, Fish and Wildlife Service. 

http://www.fws.gov/birdhabitat/JointVentures/index.shtm (accessed February 11, 2015).

Vose, J.M.; Peterson, D.L.; Patel-Weynand, T. 2012. Effects of climatic variability and 

change on forest ecosystems: a comprehensive science synthesis 

for the U.S. Gen. Tech. Rep. PNW–GTR–870. Portland, OR: 

U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 265 p.
314
G l o s s a r y 315

A1B storyline—a set of assumptions developed by the Intergovernmental Panel on Climate Change 

describing a future of robust growth: greenhouse gas emissions (medium); population growth 

(medium); gross domestic product increase (high); energy consumption growth (high, but 

with a balanced renewable/fossil fuel portfolio); loss of forest land (high); greenhouse gas 

emissions (medium); and pace of energy technology developments (fast); among other 

characteristics. See Chapter 2 for details.

A1B-BIO scenario—the combination of the assumptions from (1) the A1B storyline with (2) a general 

circulation model estimating future climate conditions based on expected greenhouse gas 

emissions for the A1B storyline, and (3) projected increasing harvest level based on a 

relatively high rate of woody biomass utilization for energy production. See Chapter 2 

for details.

A1B-C scenario—the combination of the assumptions from (1) the A1B storyline with (2) a general 

circulation model estimating future climate conditions based on expected greenhouse gas 

emissions for the A1B storyline, and (3) projected future harvest rates that are a continuation 

of harvest rates observed for the prior 5 years. See Chapter 2 for details.

Glossary
316

A2 storyline—a set of assumptions developed by the Intergovernmental Panel on Climate Change 

describing a future of asymmetric growth: greenhouse gas emissions (high); population 

growth (high), gross domestic product increase (moderate), energy consumption growth 

(high), loss of forest land (moderate), greenhouse gas emissions (high), and pace of energy 

technology developments (slow), among other characteristics. See Chapter 2 for details.

A2-BIO scenario—the combination of the assumptions from (1) the A2 storyline with (2) a general 

circulation model estimating future climate conditions based on expected greenhouse 

gas emissions for the A2 storyline, and (3) projected increasing harvest level based on a 

relatively moderate rate of woody biomass utilization for energy production. See Chapter 2 

for details.

A2-C scenario—the combination of the assumptions from (1) the A2 storyline with (2) a general 

circulation model estimating future climate conditions based on expected greenhouse 

gas emissions for the A2 storyline, and (3) projected future harvest rates that are a 

continuation of harvest rates observed for the prior 5 years. See Chapter 2 for details.

A2-EAB scenario—a variation of the A2-C scenario that also models the death of all ash trees 

within the expanding zone of projected emerald ash borer infestation over time. 

See Chapter 2 for details. 

activity day—a day that an individual (or a collective group of individuals) participates for 

any period of time in a particular recreational activity. This metric is commonly used 

to measure recreational activities. Multiple recreation activities (such as hiking, 

photographing nature, and fishing) can be recorded for one person on the same day. 

afforestation—planting trees to establish a forest in an area where the prior land use was not 

forest (as in planting trees on former agricultural land).

annual growth—the average annual net increase in the volume of trees during the period between 

surveys. Components include the increment in net volume of trees at the beginning of the 

specific year surviving to its end, plus the net volume of trees reaching the minimum size 

class during the year, minus the volume of trees that died during the year, and minus the 

net volume of trees that became cull trees.
G l o s s a r y 317

B2 storyline—a set of assumptions developed by the Intergovernmental Panel on Climate Change 

describing a future of moderate growth: greenhouse gas emissions (low); population 

growth (medium), gross domestic product increase (medium), energy consumption growth 

(medium), loss of forest land (low), greenhouse gas emissions (low), and pace of energy 

technology developments (moderate), among other characteristics. See Chapter 2 for details.

B2-BIO scenario—the combination of the assumptions from (a) the B2 storyline with (b) a general 

circulation model estimating future climate conditions based on expected greenhouse 

gas emissions for the B2 storyline, and (c) projected increasing harvest level based on a 

relatively low rate of woody biomass utilization for energy production. See Chapter 2 

for details.

B2-C scenario—the combination of the assumptions from (1) the B2 storyline with (2) a general 

circulation model estimating future climate conditions based on expected greenhouse 

gas emissions for the B2 storyline, and (3) projected future harvest rates that are a 

continuation of harvest rates observed for the prior 5 years. See Chapter 2 for details.

biomass—the weight of organic matter in a tree, stand, or forest. Biomass is commonly categorized 

into live or dead material and into bole, branch, bark, or root components. Weight can 

be reported in green tons (field weight at harvest) or dry tons (the oven-dry equivalent 

biomass). For a given sample, the green weight can be as much as twice the corresponding 

dry weight. 

carbon emissions—quantity (typically tons or metric tons) of carbon emitted to the atmosphere 

from combustion of fuels, decomposition of organic matter, and other processes; often 

expressed as quantity of carbon dioxide (CO2) with the mass of oxygen molecules included 

and sometimes expressed as carbon dioxide equivalent (CO2e) where the quantity of 

greenhouse gasses other than carbon dioxide (for example methane) are included in the 

total by converting those gasses to the amount of carbon dioxide expected to have a 

comparable greenhouse effect. 

carbon sequestration (in forests)—the quantity of carbon, or carbon dioxide (CO2) equivalent, 

held in plant tissues and soils in a forest ecosystem. The amount of sequestered carbon is 

often estimated by components (including live trees, dead trees, aboveground tree parts, 

belowground tree parts, soil, or forest floor). 
318

challenge recreation activities—physically challenging recreation such as caving, mountain climbing, 

and rock climbing. These activities are often associated with affluent young adults.

chlorosis—abnormal reduction or loss of the normal green coloration of leaves, typically caused by 

iron deficiency in lime-rich soils, by disease, or by lack of light.

climate change—long-term trends or fluctuations in temperature, precipitation, wind, and other 

climate variables. In contemporary usage, the term often is used to indicate changes and 

fluctuations associated with increased atmospheric carbon dioxide and other greenhouse 

gasses (Helms 1998). 

community land—places that have geopolitical boundaries (such as cities, towns, or unincorporated 

named places) that may include all, some, or no urban land within their boundaries.

composite products—wood products composed of a resin matrix in combination with a wood fiber 

reinforcement. Composite wood pertains to, but is not limited to, particleboard, plywood, 

medium-density fiberboard, composite veneer, and oriented strandboard. 

cull tree—a tree >_5 inches d.b.h. that is unmerchantable as a saw log because it is rotten, poorly 

formed, or of an undesirable species.

d.b.h.—tree diameter at breast height; tree diameter at breast height measured as inches of tree 

diameter at a height of 4.5 feet above ground level.

d.o.b.—tree diameter outside the bark measured at a specified height (other than d.b.h.), often 

used to indicate the upper (or small) diameter of a merchantable tree bole.

early successional forest habitat—a young forest (typically no more than 10 years old) that has 

regenerated following a large disturbance (such as a regeneration harvest) or by planting 

or natural succession on other open land.

ecological region—see ecoregion.

econometric model—a statistical or mathematical model used to describe or predict economic 

quantities or relationships. 

FUTURE FORESTS OF THE NORTHERN UNITED STATES
G l o s s a r y 319

ecoregion—a contiguous geographic area with relatively uniform macroclimate, altitude, predominant 

natural vegetation, or other features that influence ecosystem function. Ecoregions can 

be split into finer divisions or aggregated into coarser categories based on macroclimate, 

landform, geology, hydrology, native vegetation, or other factors that affect plants and 

animals (Bailey 1995).

ecosystem services—the benefits that people derive from ecosystems. These include provisioning 

services such as food and water; regulating services such as flood and disease control; 

cultural services such as spiritual, recreational, and cultural benefits; and supporting 

services such as nutrient cycling that maintain the conditions for life on Earth. Ecosystems 

provide food, clothes, fuels, and many of the commodities used to manufacture the products 

used in daily life. Ecosystems also purify and store water, help clean air, regulate climate, 

provide protection from natural hazards such as hurricanes, pollinate crops, and regulate 

pests. They can process and detoxify wastes, provide places to play and reflect, and 

contribute to a person’s cultural, inspirational, and intellectual well-being by providing a 

sense of place. 

edge—the transition between forest and nonforest land, between mature forest and young forest, 

or between two other contrasting forest conditions.

employees (number of)—all the full-time and part-time employees who are on the payrolls of 

establishments and worked or received pay for any part of a pay period that includes 

the 12th of March, June, September, or December. Included are employees on paid sick 

leave, paid holidays, and paid vacations; not included are proprietors and partners of 

unincorporated businesses. Employment data for prior years represent the average 

number of production workers for the payroll periods that include the 12th of March, May, 

August, and November. The “all employees” number is the average number of production 

workers plus the number of other employees in mid-March. 

epicormic branching—branches growing from a previously dormant bud on the trunk or a limb 

of a tree.

family forest owners—families, individuals, trusts, estates, family partnerships, and other 

unincorporated groups of individuals that own forest land. This group is a subset of 

nonindustrial private forest owners.
320

FIA—see Forest Inventory and Analysis. 

forest associated birds, mammals, and vascular plants—birds, mammals, and vascular plants 

that normally spend part of their lives in a forest habitat.

Forest Inventory and Analysis (FIA)—the U.S. Forest Service research and development group 

charged with conducting and reporting inventories of forest vegetation, soils, health, and 

product outputs.

forest land—land >_120 feet wide and 1 acre in area with >_10 percent cover (or equivalent stocking) 

by live trees of any size; includes land that formerly had such tree cover and that will be 

naturally or artificially regenerated. Forest land includes transition zones, such as areas 

between forests and nonforest lands that have >_10 percent cover and forest areas adjacent 

to urban and built-up lands. Roadside, streamside, and shelterbelt strips of trees must 

have a crown width of >_120 feet and continuous length of >_363 feet to qualify as forest 

land. Unimproved roads and trails, streams, and clearings in forest areas are classified as 

forest land if they are <120 feet wide or 1 acre in area. Tree-covered areas in agricultural 

production settings, such as fruit orchards, or tree-covered areas in urban settings, such 

as city parks, are not considered forest land. 

forest stand—a contiguous group of trees of similar age structure and size structure. In the North, 

stands are typically 5 to 40 acres in extent. 

forest stewardship plan—a forest management plan that addresses the management objectives of 

the forest owner while maintaining the forest in a healthy condition for future generations.

forest type—a classification of forest land based on the tree species that presently form the plurality 

of live tree stocking, for example, the white oak (Quercus alba) forest type or the red pine 

(Pinus resinosa) forest type.

forest-type group—a combination of forest types that share closely associated species or site 

requirements and generally are combined for brevity of reporting, for example the 

oak-hickory (Quercus spp. – Carya spp.) forest-type group or the white-red-jack pine 

(Pinus strobus – Pinus resinosa – Pinus banksiana) forest-type group.

fragmentation (forest)—the process of breaking up contiguous forest cover into various nonforest 

land uses.
G l o s s a r y 321

fuelwood—wood used for conversion to some form of energy. 

GCM—see general circulation model.

general circulation model (GCM)—numerical models that represent the physical processes in the 

atmosphere, oceans, and land surface and have been applied to predict and map climate 

response to increased greenhouse gas emissions. The numerous GCMs are competing 

hypotheses about how climate will change in response to increased greenhouse gas emissions. 

global circulation model—see general circulation model (GCM).

greenhouse gas (GHG) emissions—atmospheric emission of carbon dioxide (CO2) and other gasses 

known to trap heat in the atmosphere.

growing stock—a classification of the timber inventory that excludes cull trees and includes 

live trees of commercial species meeting specified standards of quality or vigor. When 

associated with volume, growing stock includes only trees >_5.0 inches d.b.h. 

habitat restoration—returning forest species composition, size structure, or ecosystem processes 

to a desired range of conditions, typically conditions with increased diversity and/or 

resilience. In some cases the desired state may be representative of a former period of 

time (for example before European settlement). 

hardwood—trees belonging to the botanical group Angiospermae. These broad-leaved trees typically 

drop their leaves in autumn. Examples include oaks (Quercus spp.), maples (Acer spp.), 

elms (Ulmus spp.), cottonwood (Populus spp.), quaking aspen (Populus tremuloides), and 

birch (Betula spp.). Although the wood of hardwoods physically ranges from hard or soft, 

it is usually hard compared to the wood of softwoods.

indicator—qualitative or quantitative variable that functions as a signal to relay complex ecological 

information in a simple and useful manner. Effective indicator variables must be indicative 

of a larger ecological process, easily measured, cost effective, and repeatable. 

instar—a phase between two periods of molting in the development of an insect larva or other 

invertebrate animal.

integrated harvesting—a logging operation that combines harvesting of small-diameter trees for 

energy production with harvesting of large-diameter trees for pulping and milling.
322

Intergovernmental Panel on Climate Change (IPCC)—the international body established in 1988 

by the United Nations Environment Programme and the World Meteorological Organization 

(WMO) and charged with providing a clear scientific view on the current state of knowledge 

about climate change and its potential environmental and socioeconomic impacts 

(http://www.ipcc.ch/ [accessed August 19, 2014]). 

interior—locations within mature forest cover that are removed by some minimum distance 

(such as 50 yards) from a forest edge.

invasive plants and animals—see invasive species.

invasive species—a species whose introduction causes, or is likely to cause, economic or 

environmental harm or harm to human health. Only a small proportion of nonnative 

species are invasive. 

IUCN protected status—see protected area.

land-use change—change in the classification of land; for example from forests to urban land. 

lumber—timber sawn into boards, planks, or other structural members of standard or specified length.

national forest—an ownership class of Federal lands, designated by Executive Order or statute 

as national forests or purchase units, and other lands under the administration of the 

U.S. Forest Service.

net annual forest growth—the average annual net increase in the volume of trees during the 

period between surveys. Components include the increment in net volume of trees at the 

beginning of the specific year surviving to its end, plus the net volume of trees reaching the 

minimum size class during the year, minus the volume of trees that died during the year, 

and minus the net volume of trees that became cull trees during the year.

nonindustrial private forest—all private forest owners excluding forest products companies.
G l o s s a r y 323

nontimber forest products—products not commonly listed as outputs of the forest products 

industry, but that are used in the herbal medicine, culinary, crafts, and floral industries. 

They range from edible products (such as fruits, nuts, mushrooms, ramps, and maple 

syrup) to medicinal type products (such as ginseng and bloodroot) to ornamental products 

(such as pine cones, boughs, and grapevines) to landscape products (such as native plants) 

to specialty woods (such as burl and crotch wood for fine crafts) to wildgrown Christmas 

trees. As used here the category also includes post, poles, or residential fuelwood sold in 

small quantities. 

old-growth forest—a subjective term describing forests that are relatively old for their forest 

cover type and relatively undisturbed by humans. Old-growth forests are characterized 

by the presence of large trees of late-successional (climax) species, multiple age cohorts, 

decaying trees, large snags, down wood in multiple stages of decay, and large-diameter 

down trees.

other Federal—an ownership class of Federal lands other than those administered by the Forest 

Service or Bureau of Land Management. This category includes land managed by the 

National Park Service, Fish and Wildlife Service, Department of Defense, and Department 

of Energy.

paper and paperboard products—products from pulp or recycled pulp and paper.

parcellation—the division of a property (individual ownership) into smaller parcels split among 

multiple owners. 

phloem—the food-conducting tissues of a vascular plant that transport sugars produced by 

photosynthesis in the leaves. In trees the phloem is the innermost layer of the bark. 

primary forest—see old-growth forest.

private forest—forest land held by private individuals, forest products companies, corporations, 

or other nongovernmental entities.

private forest owners—owners other than Federal, State, county, or municipal governments. 

This category includes private corporate (forest products industry and other incorporated) 

and private noncorporate entities.
324

private noncorporate owners—owners of private lands excluding lands owned by corporate 

interests. This category includes land owned by individuals, Native American lands, 

unincorporated partnerships, clubs, and lands leased by corporate interests.

protected area—an area that is especially dedicated to the protection and maintenance of 

biological diversity and natural and associated cultural resources, and is managed through 

legal or other effective means (see also reserved forest). Although all protected areas 

meet the general purposes contained in this definition, in practice the precise purposes for 

which protected areas are managed differ greatly. The International Union for Conservation 

of Nature (IUCN) has defined a series of six protected area management categories, based 

on primary management objective (DellaSalla et al. 2001, Dudley 2008, International 

Union for Conservation of Nature 2015).

Category I: an area possessing some outstanding or representative ecosystems, geological 

or physiological features, or species that is available primarily for scientific research or 

environmental monitoring; or a large unmodified or slightly modified area, which retains 

its natural character and influence without permanent or significant habitation, that is 

protected and managed so as to preserve its natural condition. Strict nature reserves (Ia) 

are distinguished from wilderness areas (Ib).

Category II: a natural area designated to (1) protect the ecological integrity of one or more 

ecosystems for present and future generations; (2) exclude exploitation or occupation 

inimical to the purposes of designation of the area; and (3) provide a foundation for spiritual, 

educational, recreational, and visitor opportunities, all of which must be environmentally 

and culturally comparable.

Category III: an area containing one or more specific natural or natural/cultural features 

that are of outstanding or unique value because of their inherent rarity, representative or 

esthetic qualities, or cultural significance.

Category IV: an area of land and/or sea subject to active intervention for management 

purposes so as to ensure the maintenance of habitats and/or to meet the requirements of 

specific species.
G l o s s a r y 325

Category V: an area of land with coast and sea as appropriate, where the interaction of 

people and nature over time has created a distinct character with significant esthetic, 

ecological, or cultural value, and often with high biological diversity. Safeguarding the 

integrity of this traditional interaction is vital to the protection, maintenance, and evolution 

of such an area.

Category VI: an area containing predominantly unmodified natural systems, managed to 

ensure long-term protection and maintenance of biological diversity, while at the same time 

providing a sustainable flow of natural products and services to meet community needs. 

protected forest—see protected area. 

public forest land—forest managed by Federal, State, county, municipal, or other governmental entities.

pulpwood—roundwood, whole-tree chips, or wood residues that are used for production of wood pulp.

removals—the net volume of growing-stock trees removed from the inventory by harvesting, 

cultural operations such as timber stand improvement, or land clearing during a specified 

period of time (usually expressed as an annual average for a specified period of time).

reserved forest land—forest land withdrawn from timber extraction through statute, administrative 

regulation, or designation without regard to productive status. This is usually public forest land. 

See also protected area. 

roundwood products—logs, bolts, and other round timber generated from harvesting trees for 

industrial or consumer use.

scenario—as used here it is the combination of the assumptions from (1) one of the IPCC storylines 

with (2) a general circulation model estimating future climate conditions based on expected 

greenhouse gas emissions for the selected storyline, and (3) an estimated pattern of future 

forest harvesting. In this study future harvest rates were modeled as either a continuation 

of harvest rates for the prior 5 years or one of three levels of increased biomass harvesting 

for energy production. 

serotinous—able to release seeds in response to a fire or other disturbance.
326

softwood—trees belonging to the botanical group Gymnospermae. These needle-bearing trees 

typically produce seeds in cones. Examples include pines (Pinus spp.), spruces (Picea spp.), 

firs (Abies spp.), hemlocks (Tsuga spp.), and eastern redcedar (Juniperus virginiana). 

Although the wood of softwoods physically ranges from soft or hard, it is usually soft 

compared to the wood of hardwoods. 

soil horizon—a specific layer in the land area that is parallel to the soil surface and possesses 

different physical characteristics from the layers above and beneath.

spatial scale—the degree of aggregation or disaggregation that one chooses to examine an issue. 

Spatial scales are hierarchical, and each scale can be split into finer units or combined 

into coarser units. Examples of spatial scale include national > State > county > township 

> section > woodlot (about 20 acres) < all woodlots along same south-facing ridge 

(about 100 to 500 acres) < small landscape (about 1,000 acres) < cluster of landscapes 

(thousands to millions of acres). Other relevant spatial scales might include the area 

around an individual tree, a forest stand, a group of forest stands, the typical home range 

of a wildlife species, an ecoregion, a watershed, a community, or an urban area.

statistical imputation—a process used to determine and assign replacement values for missing 

data or missing observations. As used here, future FIA plot conditions are “imputed” by 

drawing a replacement plot from a pool of candidate plots with the appropriate conditions. 

During a 10-year simulation time step, for example, an undisturbed 50-year-old oak-hickory 

(Quercus spp. – Carya spp.) forest plot would be replaced by a different 60-year-old 

oak-hickory forest plot chosen at random from among many dozens of candidate plots. 

But if the original plot were instead predicted to be harvested, it would be replaced by a 

newly regenerated forest plot, selected at random from a large pool of candidate plots. 

Imputation schemes can be quite complex. For example, climate change effects can be 

modeled by selecting future replacement plots from locations with warmer temperature 

regimes, similar to those expected in future decades. 

stewardship—the administration of land and associated resources in a manner that enables their 

passing on to future generations in a healthy condition.

stewardship plan—see forest stewardship plan.
G l o s s a r y 327

storyline—as used here, a set of assumptions about future changes in population, gross domestic 

product, land use, and greenhouse gas emissions. The projected forest conditions 

described in this report are based on the A1B, A2, or B2 storylines developed by the 

Intergovernmental Panel on Climate Change.

successional pathway—as a forest area progresses from pioneering community to climax 

community after a stand-replacing disturbance, the series of vegetation types that follow 

one another as the new stand matures (all depending on site and climate conditions).

susceptibility—the potential for introduction and establishment, over a 15-year period, of a forest 

pest within a tree species or tree-species group.

timberland—forest land that is producing or is capable of producing crops of industrial wood and 

not withdrawn from timber utilization by statute or administrative regulation. Areas qualifying 

as timberland are capable of producing >20 cubic feet per acre per year of industrial wood 

in natural stands. Currently inaccessible and inoperable areas are included.

total wages—the annual payroll or gross earnings paid in each calendar year to employees at the 

establishment. Total wage includes all forms of compensation (such as salaries, wages, 

commissions, dismissal pay, bonuses, vacation and sick leave pay, and compensation in 

kind) before such deductions as employees’ Social Security contributions, withholding taxes, 

group insurance, union dues, and savings bonds purchases. The total includes salaries of 

officers of corporations; it excludes payments to proprietors or partners of unincorporated 

entities. Also excluded are payments to members of Armed Forces and pensioners carried 

on the active payrolls of manufacturing establishments. Also included are fringe benefits 

for employees, including the employer’s costs for Social Security taxes, unemployment 

taxes, workmen’s compensation insurance, State disability insurance pension plans, 

stock purchase plans, union-negotiated benefits, life insurance premiums, and insurance 

premiums on hospital and medical plans for employees. Also included are the employer’s 

costs for benefits to individual employees such as stock purchase plans, deferred profit-sharing 

plans, and defined benefit and defined contribution retirement plans. Also excluded are 

such benefits as company-operated cafeterias, in-plant medical services, free parking lots, 

discounts on employee purchases, and uniforms and work clothing.
328

turbidity—a measure of water clarity measured by how much the material suspended in water 

decreases the passage of light through the water. Higher turbidity increases water temperatures 

because suspended particles absorb more heat. This, in turn, reduces the concentration of 

dissolved oxygen because warm water holds less dissolved oxygen than cold.

urban or community land—all territory, population, and housing units located within urbanized 

areas or urban clusters, which is based on population density (areas with core population 

density of 1,000 people per square mile) but also includes surrounding areas with lesser 

population density. Community lands are places that have geopolitical boundaries (for 

example, cities, towns, or unincorporated named places) and may include all, some, or 

no urban land within their boundaries. As urban land defines the more heavily populated 

areas (population density-based definition) and community land defines both urban 

and rural (nonurban) communities that are recognized by their geopolitical boundaries 

(political definition), both definitions provide information related to human settlements 

and the forest resources within those settlements. Because some urban land exists beyond 

community boundaries and not all community land is urban (communities are often a mix 

of urban and rural land), the category of “urban or community” was created to understand 

forest attributes accumulated by the union of these two definitions (U.S. Census Bureau 2015).

urban or community population—total population within a region that resides within urban or 

community land.

urban land—all the territory, population, and housing units located within urbanized areas or urban 

clusters, each with a core population density of 1,000 people per square mile and including 

surrounding areas that have lower population densities (U.S. Census Bureau 2015). 

vertically integrated company—a company that is engaged in different parts of the production 

process, such as growing, transporting, and processing raw materials; and manufacturing, 

transporting, and marketing products.

volume of timber—the gross volume in cubic feet of wood for standing trees or for cut trees 

>_ 4.0-inch top d.o.b. less deductions for rot, roughness, and poor form. 

vulnerability—the potential for experiencing mortality of a tree species, over a 15-year period, 

if a forest pest were to become established.
watershed—as defined in nature by topography, the land area that drains to a waterbody and 

affects its flow, water level, loadings of pollutants, and other processes. In both a real and 

figurative sense, a lake or river is a reflection of its watershed. The U.S. Geological Survey 

has developed a hydrologic unit classification (HUC) system of watersheds at various 

scales and mapped these watersheds. The HUCs depicted in the figures of this report are 

at the “eight-digit scale” (USGS 2015).

wildland-urban interface—where houses meet or intermingle with wildland vegetation. The 

wildland-urban interface is composed of both interface and intermix communities. In both 

interface and intermix communities, housing must meet or exceed a minimum density of 

one structure per 40 acres. Intermix communities are places where housing and vegetation 

intermingle; wildland vegetation is continuous, covering >_ 50 percent of the land, in areas 

>1 house per 40 acres. Interface communities are areas with housing in the vicinity of 

contiguous vegetation; they have >1 house per 40 acres, have <50 percent vegetation, and 

are within 1.5 miles of an area (made up of one or more contiguous census blocks) that is 

>1,325 acres (500 ha) and >75 percent vegetated (this minimum size limit ensures that 

areas surrounding small urban parks are not classified as interface).

witches’ broom—dense twiggy growth in a tree caused by infection with fungus (especially rusts), 

mites, or viruses.

wood panels—engineered wood board such as plywood, veneer, oriented strandboard (OSB), 

particle board, medium-density board, and materials that are made from wood and glue. 

xylem—the water conducting tissues of a vascular plant. Xylem transports water absorbed by roots 

throughout a tree. 

329
330

Bailey, R.G. 1995. Description of the ecoregions of the United States. Misc. Pub. 1391. Washington, DC: 

U.S. Dept. of Agriculture, Forest Service. 108 p.

DellaSalla, D.A.; Staus, N.L.; Strittholt, J.R.; Hackman, A.; Iacobelli, A. 2001. An updated protected 

areas database for the United States and Canada. Natural Areas Journal. 21: 124-135.

Dudley, N., ed. 2008. Guidelines for applying protected area management categories. Gland, Switzerland: 

International Union for Conservation of Nature. 86 p. http://cmsdata.iucn.org/downloads/guidelines_

for_applying_protected_area_management_categories.pdf (accessed January 15, 2015).

Helms, J.A. 1998. The dictionary of forestry. Bethesda, MD: Society of American Foresters. 210 p.

International Union for Conservation of Nature. 2015. The International Union for 

Conservation of Nature. Gland, Switzerland: International Union for Conservation of Nature. 

http://www.iucn.org/ (accessed January 15, 2015).

U.S. Census Bureau. 2015. Geography: urban and rural classification.Washington, DC: 

U.S. Department of Commerce, Census Bureau. http://www.census.gov/geo/reference/

urban-rural.html (accessed January 15, 2015).

U.S. Geological Survey. 2015. Water resources of the United States: hydrologic unit maps. 

Washington, DC: U.S. Department of Interior, Geological Survey. http://water.usgs.gov/GIS/huc.

html (accessed January 15, 2015).

Literature

Cited
A p p e n d i c e s 331

Glossary

Appendices

The appendices consist of supplemental text and tables related 

to Chapters 2, 4, 7, 8, and 10. Content is organized by chapter 

and labeled to indicate the chapter from which it is introduced. 

For example, Tables A2.1 and A2.2, are introduced in Chapter 2. 

References in the appendices are listed with the citations in the 

related chapter.
332

Appendix Chapter 2

DETAILS OF THE FOREST DYNAMICS MODEL 

AND SUBMODELS 

The simulation algorithm links the Forest Dynamics 

Model (FDM) submodels together and produces 

predictions of future forest inventories, given a 

beginning inventory and assumptions about future 

land use and climate. In preparation for the 

simulation, the beginning inventory is screened 

for major forest-type groups that include less 

than 2 percent of the total plots established 

by the Forest Inventory and Analysis (FIA) 

Program of the U.S. Forest Service. Any such 

groups are considered too sparsely populated 

to model future forest type transitions directly, 

so a different method (described later in this 

section) is applied to those groups. The FIA 

plots in each remaining forest-type group form 

the bucket of suitable donor plots for that foresttype group. Plots are further subdivided by the 

partitioning submodel, which assigns each plot 

to a subgroup based upon the type of harvesting 

(none, partial, or full) that occurred on the plot 

in the previous inventory cycle. 

Assuming that the beginning inventory of plots 

is a snapshot of forest conditions for 2010, the 

2010-2015 simulation continues applying the 

harvest submodel to estimate the probability of 

each management level (unharvested, partially 

harvested, clearcut) for each plot in the screened 

2010 inventory. A random draw from a uniform 

distribution assigns plots to the various management 

categories based on the probabilities programmed 

into the harvesting submodel. 

Following assignment, the appropriate transition 

model for type of harvesting is applied. Change 

in the group assigned to each plot in 2015 is 

determined by another random draw from a 

uniform distribution applied to the correct 

group transition matrix (unharvested, partially 

harvested, clearcut). A plot designated as 

clearcut has its age reset to 2.5 years for 2015, 

and the other plots retain their plot age as 

of 2015. Based on the assumptions of future 

climate for each scenario, the plot location, and 

the forecast of plot age, each plot is assigned a 

climate profile for temperature, precipitation, 

and aridity. The predicted major forest-type 

group, harvesting type, forecasted age, and 

climate (along with the inventoried slope, 

aspect, and ownership) are then used to find 

the corresponding “bucket” of plots from which 

the partitioning submodel can randomly draw 

a replacement plot to represent that location’s 

forest conditions for 2015.

The FDM submodels operate stochastically. 

Many modeled events in the transition, harvesting, 

partitioning, and imputation submodels (for 

example, whether or not a plot is harvested or shifts 

to a different forest type) draw a random number 

for the purposes of selecting a specific outcome 

from among all possible outcomes represented in 

a probability matrix (in effect, a computerized roll 

of the dice determines the outcome for numerous 

events modeled at each time step). Because of 

the random element in choosing outcomes at 

each time step, repeated projections based on 

the same scenario will differ in their outcomes. 
A p p e n d i c e s 333

Therefore, each scenario was repeated 26 times 

to produce a range of possible outcomes and to 

capture variations among simulations (limits of 

computing time restricted the number of repeated 

simulations). Wood volumes were summed for 

each of the 26 estimates for each time step, and 

an index that reflects the difference from the 

central tendency of growing-stock volume for 

each time step was calculated for each of the 26 

inventory projections and summed over all time 

steps. For each scenario, the single inventory 

projection with the lowest summed index (the 

single inventory projection that is closest to 

the central tendency of growing-stock volume 

projected for all 26 runs of a single scenario) 

is the outcome summarized in subsequent 

analyses. The representative predicted inventory 

selected for each scenario has the same number 

of forested plots as the 2010 inventory.

The land-use submodel was used to forecast 

forest area. The assumed percentage of change 

in forest area for non-Federal land for each 

5-year time step was applied to adjust the plot 

area expansion factors associated with each 

FIA plot. For example, in the first time step 

the plot area expansion factors in the 2010 

beginning inventory were adjusted (e.g., reduced 

to indicate loss of forest land) to arrive at the 

2015 expansion factors, and then were adjusted 

again in the next time step to arrive at the 2020 

expansion factors, and so forth. Because the 

land-use submodel predicts change in forest area 

by county, every plot on non-Federal land within 

a county receives the same proportional change 

in forest area for a given 5-year time step. 

Plots on Federal land retain their 2010 expansion 

factors because Federal forest land area is 

assumed to be unchanged over the forecast period.

Harvesting removals, land-use removals 

(or additions), and growth and mortality can 

be calculated for each plot to arrive at a full 

bookkeeping of projected change in growing-stock 

volume on timberland. Harvesting removals 

were obtained by applying the removal intensities 

from the harvest submodel to the beginning 

inventory volume. Changes to volume resulting 

from land-use change (e.g., conversion of 

forest land to urban land) were calculated by 

multiplying the per-acre volume at the start of 

the period by the area of forest converted to 

other uses. Volume of total removals is the sum 

of harvesting removals and land-use change 

removals. Note that any increase in volume 

due to an increase in expansion factors is not 

included here. The FDM does not consider 

growth and mortality explicitly, but the net 

sum (net growth or other net change) can be 

estimated from the change in inventory. The 

total change in inventory between two periods 

is the ending inventory less the beginning 

inventory and is equal to the sum of harvesting 

removals and changes resulting from land use, 

growth, and mortality. Therefore, net growth 

= change in inventory + harvest removals + 

land-use removals. This formulation retains 

land-use additions in net growth. Subtracting 

land-use additions from the right-hand side of 

the equation accounts for net growth explicitly. 
334

Note that total removals are divided by the time 

step of the simulation (5 years) to produce an 

annual removals estimate. Each element in 

this bookkeeping (beginning and ending volume, 

removals, and net growth) can be summed over 

all plots to obtain state-level totals.

SEPARATION AND SIMULATION 

OF HARVEST REMOVALS 

The Northern Forest Futures Project relied on 

the removals forecasts provided by the Forest 

Dynamics Model for the 2010 Resources Planning 

Act (RPA) assessment (Wear 2011, Wear et al. 

2013). These forecasts did not separate harvest 

removals (volume of cut trees) and land-use 

removals (volume of trees removed from the 

forest inventory when forest land is converted 

to other uses); only state-level total removals 

on an annual basis were available from the RPA 

assessment. A simple “top down” procedure 

was employed to split these state-level totals 

into separate estimates of land use removals 

and harvest removals. For a given time interval, 

subtracting ending timberland area from beginning 

timberland area provides an estimate of total 

forest area lost (or gained) through land use 

change, and a rough estimate of the sum, over 

all timberland plots, of the change in plot 

area expansion factors. Dividing the beginning 

inventory volume by beginning timberland area 

yields an approximation of per-acre beginning 

growing-stock volume on timberland. 

Multiplying the change in timberland area 

by this per-acre volume gives an estimate of 

total removals resulting from land-use change 

over the 5-year time step (in these analyses 

projected forest area decreased in all time 

periods for all States). Dividing this estimate by 

the number of years in the time step produces 

an annual estimate of the removals volume due 

to land-use change. Subtracting this annual 

estimate of state land-use change removals from 

the total annual removals in the RPA assessment 

provides an estimate of annual harvest removals 

as the remainder. 

Calculating change variables—The Forest 

Dynamics Model (Wear et al. 2013) was applied 

to calculate inventory summaries (volumes, 

number of trees, forest-type groups) based 

on imputed future inventories of tree species 

and tree size. The removals submodel is 

a component of the projection model that 

estimates the probability of a tree being 

harvested based on its species and diameter. 

This information aids in estimating annual 

harvest removals. The removals estimates were 

converted to an annual basis. An additional 

factor was included in the total removals 

estimate—the estimate of trees that are not 

harvested on forested parcels, but instead are 

removed from the inventory due to conversion 

of forest to other land uses. 
A p p e n d i c e s 335

Calculations for determining volume changes 

between succeeding periods are shown in 

Equations 1-3. This change in volume is made 

up of three components: growth, mortality, and 

removals. The Forest Dynamics Model does 

not model mortality directly. All of the change 

variables are modeled based on growing stock on 

timberland, so the volume equations do so as well.

ΔVol12= Vol2 – Vol1 (1)

ΔVol12 = Net growth12 – Removals(total)12 (2)

Net growth12 = ΔVol12 + Removals(total)12 (3)

Where:

Vol is growing-stock volume on timberland 

ΔVol is periodic change in growingstock volume

Net growth is growing-stock volume growth 

less mortality 

Removals(total) is combined removals due 

to harvest and land-use change

subscript 1 represents variables from time 

period 1 

subscript 2 represents variables from time 

period 2

subscript 12 represents change from the 

time period 1 to 2.

Calculating removals—Because two types of 

removals (removals resulting from harvesting 

and removals from land-use change [LUC]) 

are included in the total removals estimate, 

removals can be calculated as in Equation 4. 

Removals(total)12 = Removals(harvest)12 + 

Removals(LUC)12 (4)

where the removals component is indicated 

within the parentheses.

Periodic removals resulting from land-use change 

were calculated as the product of the land 

area converted from forest to another land use 

and the mean volume per acre as shown in 

Equations 5 and 6. 

Vol/acre1

 = Vol1/Area1 (5)

ΔArea12 = Area1 – Area2 (6)

Where:

Vol/acre is mean growing-stock volume 

per acre

Area is timber land area.

Multiplying the change in timberland area (acres) 

over a given time interval by the mean volume 

per acre produces an estimate of periodic 

total volume removals due to land use change. 

Dividing this estimate by the number of years 

in the interval gives an annual estimate of the 

volume removals due to land-use change 

(Eq. 7), and harvest removals were estimated 

by subtraction (Eq. 8).
336

Removals(LUC)12 = (Vol1/Area1 * ΔArea12)/

Time12 (7)

Removals(harvest)12 = Removals(total)12 – 

Removals(LUC)12 (8)

where Time is the number of years in the 

specified interval.

Note that for forest area estimates (Eq. 6, 7), 

in all cases forest land area declined at the State 

level between two estimation periods, but at 

smaller spatial scales (which were not evaluated), 

forest land area may occasionally increase.

MODELING EFFECTS OF THE 

EMERALD ASH BORER

The effects of the emerald ash borer (Agrilus 

planipennis) on the number and volume of ash 

trees (Fraxinus spp.) were summarized for the 

broad category of forest-type groups and at the 

large scale of FIA inventory units. Our modeling 

results suggest that effects will contribute to a 

small decrease in the total number of trees and 

saplings in many FIA inventory units from 2010 

to 2060, but changes in the elm-ash-cottonwood 

(Ulmus spp.–Fraxinus spp.–Populus spp.) 

forest-type group will vary among FIA inventory 

units. Forest species composition in forests 

measured by FIA will change when ash trees are 

killed by the emerald ash borer, but modeling 

results suggest that effects will not cause 

substantial changes in the number of plots in 

any given forest-type group. 

For example, the area of forest in the elmash-cottonwood group is expected to show 

little change over time even though emerald 

ash borer is expected to kill all the ash trees 

in many locations. Some of the impacts of 

infestation on ecosystem functioning may be 

modest because associated non-ash species 

not prone to infestation have the potential to 

offset the loss of ash trees and their associated 

volume. Consequently, despite the assumption 

that emerald ash borer will cause 100 percent 

ash mortality, the transition from ash to other 

species may progress slowly as ash is replaced 

by a variety of associated species. However, 

the effects of infestation could lead to canopy 

gaps and facilitate an increase in native and 

nonnative invasive plant species (Gandhi and 

Herms 2010). Because changes were analyzed 

by forest-type groups, considering geographic 

differences in the composition of each foresttype group was important. For example, ash is 

not a significant component of any forest-type 

group in Maine FIA inventory units, but the elmash-cottonwood forest-type group in Minnesota 

FIA inventory units is composed mainly of 

green ash (Fraxinus pennsylvanica) and black 

ash (F. nigra). It is also important to keep in 

mind that this modeling may not apply to urban 

areas not measured by FIA, where the extensive 

distribution of planted ash trees could result in 

a greater impact.
A p p e n d i c e s 337

----------------------------------------- (1,000 people) ----------------------------------------------- (percent)

Connecticut A1B 3,535 3,654 3,792 3,947 4,115 4,281 21

A2 3,606 3,768 3,959 4,188 4,481 4,836 34

B2 3,524 3,634 3,681 3,690 3,738 3,802 8

District of Columbia A1B 570 548 531 518 507 500 -12

A2 581 566 554 549 553 565 -3

B2 568 545 515 484 461 444 -22

Delaware A1B 884 969 1,057 1,145 1,229 1,309 48

A2 902 999 1,103 1,215 1,338 1,478 64

B2 881 964 1,026 1,070 1,116 1,162 32

Iowa A1B 3,005 3,100 3,212 3,339 3,473 3,613 20

A2 3,065 3,197 3,353 3,543 3,781 4,082 33

B2 2,996 3,083 3,118 3,121 3,155 3,209 7

Illinois A1B 13,042 13,717 14,451 15,160 15,794 16,365 25

A2 13,304 14,145 15,087 16,084 17,196 18,488 39

B2 13,002 13,641 14,030 14,170 14,347 14,534 12

Indiana A1B 6,402 6,698 7,025 7,383 7,758 8,148 27

A2 6,531 6,908 7,334 7,833 8,447 9,205 41

B2 6,383 6,661 6,820 6,901 7,048 7,236 13

Massachusetts A1B 6,498 6,731 6,997 7,272 7,539 7,801 20

A2 6,629 6,941 7,305 7,715 8,209 8,813 33

B2 6,479 6,694 6,793 6,798 6,849 6,928 7

Maryland A1B 5,877 6,579 7,290 7,964 8,571 9,120 55

A2 5,995 6,785 7,611 8,450 9,332 10,303 72

B2 5,860 6,543 7,077 7,445 7,786 8,100 38

Maine A1B 1,342 1,407 1,479 1,562 1,654 1,756 31

A2 1,369 1,451 1,544 1,657 1,801 1,983 45

B2 1,338 1,399 1,436 1,460 1,503 1,559 17

Table A2.1—Projected population from 2010 to 2060 in North under three greenhouse gas emissions storylines 

developed by the Intergovernmental Panel on Climate Change (IPCC 2007, Zarnoch et al. 2010). 

State Storylinea

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
338

----------------------------------------- (1,000 people) ----------------------------------------------- (percent)

Michigan A1B 10,165 10,465 10,821 11,236 11,688 12,173 20

A2 10,369 10,792 11,298 11,921 12,725 13,753 33

B2 10,134 10,407 10,506 10,503 10,617 10,811 7

Minnesota A1B 5,358 5,884 6,423 6,962 7,483 7,988 49

A2 5,465 6,068 6,706 7,387 8,147 9,024 65

B2 5,342 5,852 6,236 6,508 6,798 7,094 33

Missouri A1B 5,960 6,320 6,704 7,133 7,597 8,091 36

A2 6,080 6,518 6,999 7,568 8,271 9,141 50

B2 5,942 6,285 6,509 6,668 6,901 7,186 21

New Hampshire A1B 1,379 1,549 1,722 1,900 2,078 2,256 64

A2 1,406 1,598 1,798 2,016 2,263 2,548 81

B2 1,375 1,541 1,672 1,776 1,888 2,003 46

New Jersey A1B 8,948 9,601 10,287 10,928 11,484 11,969 34

A2 9,127 9,901 10,740 11,594 12,503 13,522 48

B2 8,921 9,548 9,987 10,215 10,432 10,630 19

New York A1B 19,382 19,820 20,371 20,934 21,448 21,929 13

A2 19,771 20,440 21,267 22,211 23,352 24,774 25

B2 19,323 19,711 19,776 19,568 19,484 19,475 1

Ohio A1B 11,484 11,653 11,893 12,183 12,491 12,811 12

A2 11,715 12,017 12,416 12,926 13,599 14,473 24

B2 11,450 11,589 11,546 11,388 11,347 11,378 -1

Pennsylvania A1B 12,555 12,994 13,498 14,058 14,640 15,236 21

A2 12,807 13,400 14,092 14,916 15,940 17,213 34

B2 12,517 12,922 13,104 13,141 13,300 13,531 8

Table A2.1 continued 

State Storylinea

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
A p p e n d i c e s 339

----------------------------------------- (1,000 people) ----------------------------------------------- (percent)

Rhode Island A1B 1,089 1,154 1,223 1,289 1,349 1,404 29

A2 1,111 1,190 1,277 1,367 1,469 1,586 43

B2 1,086 1,147 1,188 1,204 1,226 1,247 15

Vermont A1B 643 696 751 813 881 956 49

A2 656 717 784 863 960 1,080 65

B2 641 692 729 760 801 849 32

Wisconsin A1B 5,687 6,073 6,481 6,922 7,383 7,861 38

A2 5,801 6,263 6,767 7,344 8,038 8,881 53

B2 5,670 6,040 6,292 6,470 6,707 6,981 23

West Virginia A1B 1,818 1,840 1,873 1,919 1,972 2,034 12

A2 1,854 1,898 1,956 2,036 2,147 2,298 24

B2 1,812 1,830 1,819 1,793 1,792 1,806 0

All Northern States A1B 125,621 131,454 137,881 144,565 151,134 157,598 25

A2 128,141 135,562 143,948 153,380 164,550 178,045 39

B2 125,242 130,727 133,860 135,132 137,294 139,964 12

U.S. total A1B 308,936 335,805 363,584 391,946 419,854 447,308 45

A2 315,135 346,297 379,581 415,845 457,122 505,344 60

B2 308,004 333,948 352,980 366,372 381,404 397,259 29

a

Storyline A1B assumes moderate greenhouse gas emissions associated with moderate population growth and large gains in income and energy 

consumption but with a balanced renewable/fossil fuel portfolio; storyline A2 assumes high greenhouse gas emissions associated with high population 

growth, high energy consumption, and moderate gains in income; and storyline B2 assumes low greenhouse gas emissions associated with moderate 

increases in population, income, and energy consumption.

Table A2.1 continued 

State Storylinea

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
340

------------------------------------------- (percent of total land area) -------------------------------------------

A1B 2010 41 9 39 10 < 1

2020 41 11 39 10 < 1

2030 40 12 38 10 < 1

2040 40 13 38 10 < 1

2050 39 14 37 9 < 1

2060 39 16 37 9 < 1

A2 2010 41 9 39 10 < 1

2020 41 10 39 10 < 1

2030 41 11 39 10 < 1

2040 40 12 38 10 < 1

2050 40 13 38 10 < 1

2060 39 14 37 9 < 1

B2 2010 41 10 39 10 < 1

2020 41 11 39 10 < 1

2030 41 11 39 10 < 1

2040 40 12 38 10 < 1

2050 40 12 38 10 < 1

2060 40 13 38 10 < 1

a

Storyline A1B assumes moderate greenhouse gas emissions associated with moderate population growth and large gains in income and energy 

consumption, but with a balanced renewable/fossil fuel portfolio; storyline A2 assumes high greenhouse gas emissions associated with high 

population growth, high energy consumption, and moderate gains in income; and storyline B2 assumes low greenhouse gas emissions associated 

with moderate increases in population, income, and energy consumption.

Table A2.2—Projected land use from 2010 to 2060 in the North under three greenhouse gas emissions 

storylines developed by the Intergovernmental Panel on Climate Change (IPCC 2007, Wear 2011); note that 

percentages may not sum to 100 due to rounding and that the total area is 413,586,000 acres or 646,228 

square miles. 

Storylinea Year Forest Urban Cropland Pasture Range
A p p e n d i c e s 341

Table A2.3—Projected forest area, 2010 to 2060, and percentage change in forest area in the North under three greenhouse gas 

emissions storylines developed by the Intergovernmental Panel on Climate Change (IPCC 2007, Wear 2011).

---------------------------------------------------(1,000 acres) --------------------------------------------------- (percent)

Connecticut A1B 1,724 1,691 1,657 1,620 1,580 1,537 -11

A2 1,724 1,689 1,653 1,613 1,567 1,512 -12

B2 1,724 1,694 1,676 1,663 1,644 1,626 -6

Delaware A1B 352 339 327 315 301 287 -18

A2 352 340 328 315 301 285 -19

B2 352 340 332 326 318 311 -12

Illinois A1B 4,798 4,703 4,648 4,597 4,539 4,472 -7

A2 4,798 4,722 4,683 4,651 4,614 4,570 -5

B2 4,798 4,712 4,677 4,645 4,607 4,582 -5

Indiana A1B 4,744 4,654 4,569 4,475 4,364 4,235 -11

A2 4,744 4,678 4,617 4,554 4,478 4,388 -7

B2 4,744 4,671 4,636 4,596 4,537 4,493 -5

Iowa A1B 3,033 3,012 2,993 2,971 2,945 2,915 -4

A2 3,033 3,018 3,005 2,991 2,974 2,953 -3

B2 3,033 3,017 3,009 3,000 2,986 2,977 -2

Maine A1B 17,657 17,598 17,541 17,477 17,404 17,319 -2

A2 17,657 17,606 17,558 17,507 17,444 17,369 -2

B2 17,657 17,607 17,580 17,554 17,515 17,481 -1

Maryland A1B 2,493 2,314 2,142 1,977 1,826 1,680 -33

A2 2,493 2,328 2,167 2,019 1,883 1,746 -30

B2 2,493 2,330 2,216 2,119 2,022 1,952 -22

Massachusetts A1B 3,019 2,941 2,861 2,779 2,700 2,627 -13

A2 3,019 2,939 2,858 2,774 2,687 2,597 -14

B2 3,019 2,949 2,904 2,870 2,831 2,796 -7

Michigan A1B 19,821 19,624 19,431 19,219 18,977 18,701 -6

A2 19,821 19,655 19,491 19,317 19,110 18,856 -5

B2 19,821 19,655 19,559 19,466 19,337 19,225 -3

Minnesota A1B 16,990 16,872 16,760 16,644 16,518 16,389 -4

A2 16,990 16,888 16,793 16,697 16,591 16,487 -3

B2 16,990 16,887 16,822 16,761 16,687 16,626 -2

Missouri A1B 15,397 15,262 15,132 14,987 14,819 14,625 -5

A2 15,397 15,290 15,186 15,075 14,941 14,774 -4

B2 15,397 15,285 15,219 15,147 15,053 14,976 -3

New Hampshire A1B 4,803 4,727 4,654 4,577 4,493 4,401 -8

A2 4,803 4,736 4,671 4,604 4,529 4,441 -8

B2 4,803 4,736 4,691 4,649 4,599 4,557 -5

State Storylinea Year Change (2010 

2010 2020 2030 2040 2050 2060 to 2060)
342

---------------------------------------------------(1,000 acres) --------------------------------------------------- (percent)

New Jersey A1B 2,002 1,859 1,722 1,597 1,483 1,379 -31

A2 2,002 1,849 1,706 1,572 1,441 1,309 -35

B2 2,002 1,869 1,778 1,717 1,658 1,611 -20

New York A1B 18,951 18,804 18,681 18,547 18,394 18,219 -4

A2 18,951 18,824 18,719 18,608 18,476 18,312 -3

B2 18,951 18,826 18,770 18,717 18,647 18,591 -2

Ohio A1B 8,044 7,964 7,887 7,802 7,703 7,591 -6

A2 8,044 7,975 7,907 7,835 7,746 7,633 -5

B2 8,044 7,978 7,943 7,911 7,869 7,835 -3

Pennsylvania A1B 16,652 16,368 16,092 15,787 15,439 15,044 -10

A2 16,652 16,418 16,191 15,949 15,661 15,310 -8

B2 16,652 16,417 16,292 16,173 16,002 15,864 -5

Rhode Island A1B 348 323 306 290 273 257 -26

A2 348 323 305 288 269 248 -29

B2 348 325 315 309 302 296 -15

Vermont A1B 4,588 4,538 4,492 4,442 4,385 4,322 -6

A2 4,588 4,551 4,518 4,484 4,446 4,400 -4

B2 4,588 4,547 4,525 4,500 4,466 4,439 -3

West Virginia A1B 11,974 11,802 11,642 11,463 11,249 10,992 -8

A2 11,974 11,859 11,758 11,656 11,529 11,370 -5

B2 11,974 11,840 11,784 11,713 11,601 11,524 -4

Wisconsin A1B 16,697 16,562 16,432 16,291 16,126 15,936 -5

A2 16,697 16,589 16,488 16,381 16,253 16,096 -4

B2 16,697 16,584 16,522 16,456 16,366 16,291 -2

All Northern States A1B 174,090 171,958 169,967 167,857 165,520 162,925 -6

A2 174,090 172,276 170,601 168,891 166,938 164,656 -5

B2 174,090 172,270 171,250 170,291 169,047 168,053 -3

a

Storyline A1B assumes moderate greenhouse gas emissions associated with moderate population growth and large gains in income and energy consumption 

but with a balanced renewable/fossil fuel portfolio; storyline A2 assumes high greenhouse gas emissions associated with high population growth, high 

energy consumption, and moderate gains in income; and storyline B2 assumes low greenhouse gas emissions associated with moderate increases in 

population, income, and energy consumption. 

State Storylinea Year Change (2010 

2010 2020 2030 2040 2050 2060 to 2060)

Table A2.3 continued 
A p p e n d i c e s 343

-------------------------------------------------(1,000,000 tons)-------------------------------------------------

Roundwood 

fuelwood (forests)

A1B 93.1 105.3 240.4 405.3 628.9 1,022.5 1,297.1

A2 93.1 98.8 154.4 356.8 544.2 748.2 746.1

B2 93.1 98.9 133.0 67.1 45.7 145.4 263.8

Energy plantations A1B 0.0 0.0 82.8 198.2 353.4 625.0 784.2

A2 0.0 0.0 62.4 193.7 338.4 467.6 458.9

B2 0.0 0.0 19.6 91.9 159.2 215.2 289.0

Cropland crops 

and residues

A1B 257.4 210.7 209.8 209.4 207.5 205.7 203.0

A2 257.4 216.8 220.6 224.6 229.2 233.9 239.5

B2 257.4 216.7 221.0 225.1 228.3 231.4 234.7

a

Storyline A1B assumes moderate greenhouse gas emissions associated with moderate population growth and large gains in income and energy 

consumption but with a balanced renewable/fossil fuel portfolio; storyline A2 assumes high greenhouse gas emissions associated with high 

population growth, high energy consumption, and moderate gains in income; and storyline B2 assumes low greenhouse gas emissions associated 

with moderate increases in population, income, and energy consumption..

Table A2.4—Underlying large-scale assumptions about future biomass consumption for bioenergy in the multinational macro-region Organization for Economic Cooperation and Development (OECD 90) (Ince et al. 2011, 

USDA FS 2012b) under three greenhouse gas emissions storylines developed by the Intergovernmental Panel on 

Climate Change (IPCC 2007). 

Source Storylinea Year

2000 2010 2020 2030 2040 2050 2060
344

--------------------------------------(billion cubic feet)--------------------------------------

(Mill) fuel residue A1B 2.0 2.6 2.8 3.5 3.4 3.4

A2 2.4 2.3 2.2 2.3 2.1 1.2

B2 2.4 2.4 2.4 2.3 2.4 2.6

(Recovered) harvest 

residue

A1B 0.0 3.6 5.9 8.9 12.5 16.3

A2 0.0 2.8 4.1 5.8 7.7 10.0

B2 0.0 1.6 3.7 4.2 4.7 5.6

Hardwood pulpwood A1B 0.0 0.0 1.0 4.1 9.5 14.3

A2 0.0 0.0 0.0 0.4 2.6 4.5

B2 0.0 0.0 0.0 0.0 0.0 0.0

Mill (fiber) residue A1B 0.1 0.3 0.5 1.3 2.2 3.3

A2 0.1 0.4 1.0 2.1 2.1 1.2

B2 0.0 0.0 0.2 0.6 1.7 2.4

Roundwood fuelwood 

harvesting

A1B 1.8 2.2 3.3 4.8 7.0 9.0

A2 1.7 1.9 2.5 3.1 4.2 5.7

B2 1.7 1.7 2.0 2.5 2.9 3.4

Softwood pulpwood A1B 0.2 1.1 3.4 6.9 11.2 16.5

A2 0.1 0.1 2.3 5.0 8.5 15.1

B2 0.0 0.0 0.0 0.1 0.2 0.6

a

Storyline A1B assumes moderate greenhouse gas emissions associated with moderate population growth and large gains in income and 

energy consumption but with a balanced renewable/fossil fuel portfolio; storyline A2 assumes high greenhouse gas emissions associated with 

high population growth, high energy consumption, and moderate gains in income; and storyline B2 assumes low greenhouse gas emissions 

associated with moderate increases in population, income, and energy consumption.

Table A2.5—Projected biomass production for bioenergy from roundwood and fuelwood, 2010 to 2060, 

under three greenhouse gas emissions storylines developed by the Intergovernmental Panel on Climate 

Change (IPCC 2007).

Source Storylinea Year

2010 2020 2030 2040 2050 2060

Please refer to the Literature Cited section at the end of Chapter 2 for the references.
A p p e n d i c e s 345

Table A4.1—Projected forest land area by forest-type group for the North, 2010 to 2060, under three 

greenhouse gas emissions storylines developed by the Intergovernmental Panel on Climate Change (IPCC 2007).

Year

Forest-type group 

and storylinea 2010 2020 2030 2040 2050 2060

----------------------------------------------------------(1,000 acres)----------------------------------------------------------------

Aspen-birch

A1B 17,255 15,964 15,324 15,213 15,087 14,451

A2 17,255 16,012 15,365 15,280 15,182 14,203

B2 17,255 16,129 15,339 15,288 15,220 14,687

Elm-ash-cottonwood

A1B 12,147 10,976 10,222 10,068 9,903 10,213

A2 12,147 10,923 10,503 10,363 10,205 9,966

B2 12,147 10,740 10,541 10,467 10,375 10,346

Maple-beech-birch

A1B 44,982 46,104 46,668 46,216 45,704 46,441

A2 44,982 46,191 46,805 46,467 46,063 46,635

B2 44,982 46,703 47,339 47,147 46,882 47,623

Oak-hickory

A1B 63,184 62,851 62,459 61,455 60,342 57,730

A2 63,184 63,125 62,276 61,463 60,538 59,329

B2 63,184 62,782 62,215 61,758 61,167 60,197

Other 

A1B 12,746 12,209 12,012 11,775 11,527 11,307

A2 12,746 12,347 12,128 11,917 11,695 11,556

B2 12,746 12,271 12,020 11,909 11,778 12,361

Spruce-fir 

A1B 15,279 15,037 14,342 14,276 14,199 13,659

A2 15,279 15,039 14,682 14,632 14,573 13,836

B2 15,279 14,839 14,763 14,732 14,688 13,676

White-red-jack pine

A1B 8,497 8,817 8,940 8,855 8,758 9,123

A2 8,497 8,639 8,843 8,769 8,682 9,131

B2 8,497 8,806 9,033 8,991 8,937 9,163

a

Storyline A1B assumes moderate greenhouse gas emissions associated with moderate population growth and large gains in income and 

energy consumption but with a balanced renewable/fossil fuel portfolio; storyline A2 assumes high greenhouse gas emissions associated with 

high population growth, high energy consumption, and moderate gains in income; and storyline B2 assumes low greenhouse gas emissions 

associated with moderate increases in population, income, and energy consumption.

Appendix Chapter 4
346

-------------------------------------------------------(million cubic feet)------------------------------------------------------ (percent)

Connecticut A1B-C 4,127 4,232 4,669 4,564 4,450 4,065 -2

A2-C 4,127 4,354 4,357 4,255 4,134 3,767 -9

B2-C 4,127 4,080 4,024 3,990 3,944 4,101 -1

A2-EAB 4,127 4,222 4,128 4,030 3,913 3,585 -13

A1B-BIO 4,127 4,225 4,369 4,271 4,165 3,943 -4

A2-BIO 4,127 4,247 4,308 4,205 4,083 3,914 -5

B2-BIO 4,127 4,243 4,022 3,988 3,942 3,850 -7

Delaware A1B-C 862 867 897 860 822 738 -14

A2-C 862 876 841 808 771 766 -11

B2-C 862 872 889 869 848 806 -7

A2-EAB 862 888 765 734 700 744 -14

A1B-BIO 862 835 857 821 783 675 -22

A2-BIO 862 928 906 869 828 712 -17

B2-BIO 862 858 841 823 803 825 -4

Illinois A1B-C 8,652 9,238 8,830 8,735 8,627 8,308 -4

A2-C 8,652 8,877 8,871 8,810 8,740 8,656 0

B2-C 8,652 9,083 8,554 8,491 8,418 9,087 5

A2-EAB 8,652 9,337 8,473 8,415 8,347 8,311 -4

A1B-BIO 8,652 8,694 8,874 8,775 8,663 7,680 -11

A2-BIO 8,652 8,804 8,966 8,896 8,817 7,829 -10

B2-BIO 8,652 9,147 8,961 8,896 8,821 8,262 -5

Indiana A1B-C 9,785 10,527 10,468 10,255 10,005 10,117 3

A2-C 9,785 10,254 10,231 10,095 9,931 10,585 8

B2-C 9,785 10,253 10,597 10,508 10,377 10,228 5

A2-EAB 9,785 9,564 10,144 10,010 9,849 9,833 0

A1B-BIO 9,785 10,383 10,139 9,926 9,676 8,799 -10

A2-BIO 9,785 10,329 10,506 10,369 10,204 9,499 -3

B2-BIO 9,785 10,136 10,793 10,703 10,571 9,840 1

Table A4.2—Projected volume of live trees on forest land in the North, 2010 to 2060, for seven future scenarios based on 

greenhouse gas emissions storylines developed by the Intergovernmental Panel on Climate Change (IPCC 2007).

State Scenarioa

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
A p p e n d i c e s 347

-------------------------------------------------------(million cubic feet)------------------------------------------------------ (percent)

Iowa A1B-C 4,256 5,019 5,027 4,992 4,950 4,780 12

A2-C 4,256 4,800 4,921 4,894 4,862 5,095 20

B2-C 4,256 4,922 5,085 5,070 5,048 5,185 22

A2-EAB 4,256 4,893 5,175 5,088 5,022 4,605 8

A1B-BIO 4,256 4,708 4,679 4,646 4,607 3,747 -12

A2-BIO 4,256 4,728 4,910 4,883 4,851 4,059 -5

B2-BIO 4,256 4,724 5,003 4,986 4,962 4,569 7

Maine A1B-C 25,474 26,738 27,130 27,013 26,877 26,046 2

A2-C 25,474 26,551 27,544 27,446 27,329 27,043 6

B2-C 25,474 26,804 27,084 27,035 26,965 27,191 7

A2-EAB 25,474 26,632 26,933 26,367 26,070 25,980 2

A1B-BIO 25,474 25,864 24,703 24,597 24,475 14,939 -41

A2-BIO 25,474 26,168 25,782 25,686 25,571 18,810 -26

B2-BIO 25,474 26,627 26,525 26,478 26,409 22,367 -12

Maryland A1B-C 6,469 6,275 5,883 5,377 4,926 4,708 -27

A2-C 6,469 6,248 5,802 5,373 4,990 4,576 -29

B2-C 6,469 6,221 5,704 5,425 5,158 4,913 -24

A2-EAB 6,469 6,466 6,050 5,606 5,216 4,531 -30

A1B-BIO 6,469 6,044 5,399 4,952 4,563 3,735 -42

A2-BIO 6,469 6,070 5,728 5,306 4,927 4,128 -36

B2-BIO 6,469 6,404 5,751 5,486 5,227 4,617 -29

Massachusetts A1B-C 7,754 7,863 7,359 7,175 6,991 6,630 -14

A2-C 7,754 7,637 7,500 7,302 7,092 6,765 -13

B2 7,754 7,577 7,490 7,418 7,331 7,021 -9

A2-EAB 7,754 7,651 7,521 7,021 6,822 6,333 -18

A1B-BIO 7,754 7,717 7,523 7,327 7,135 6,696 -14

A2-BIO 7,754 7,700 7,494 7,291 7,077 6,422 -17

B2-BIO 7,754 7,880 7,749 7,675 7,588 7,398 -5

State Scenarioa

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060

Table A4.2 continued 
348

-------------------------------------------------------(million cubic feet)------------------------------------------------------ (percent)

Michigan A1B-C 31,553 32,857 33,405 33,024 32,590 33,934 8

A2-C 31,553 32,844 33,505 33,194 32,824 34,290 9

B2-C 31,553 32,967 34,196 34,026 33,791 35,574 13

A2-EAB 31,553 31,510 32,143 31,847 31,494 32,742 4

A1B-BIO 31,553 32,515 32,146 31,787 31,379 25,620 -19

A2-BIO 31,553 33,008 32,650 32,344 31,978 29,003 -8

B2-BIO 31,553 32,791 33,930 33,763 33,533 32,368 3

Minnesota A1B-C 18,099 19,232 20,498 20,314 20,116 22,013 22

A2-C 18,099 19,569 21,021 20,843 20,649 22,403 24

B2-C 18,099 19,292 20,487 20,387 20,268 20,968 16

A2-EAB 18,099 19,767 20,874 19,812 18,847 20,901 15

A1B-BIO 18,099 19,218 18,906 18,730 18,540 12,807 -29

A2-BIO 18,099 19,520 20,591 20,428 20,253 18,504 2

B2-BIO 18,099 19,340 20,361 20,261 20,142 17,933 -1

Missouri A1B-C 20,143 21,930 22,529 22,309 22,054 23,298 16

A2-C 20,143 21,625 22,161 21,996 21,798 23,451 16

B2-C 20,143 21,075 22,396 22,290 22,152 24,064 19

A2-EAB 20,143 21,489 22,396 21,972 21,609 23,341 16

A1B-BIO 20,143 21,156 21,718 21,510 21,269 19,779 -2

A2-BIO 20,143 21,707 21,859 21,700 21,507 21,649 7

B2-BIO 20,143 21,402 22,430 22,325 22,187 22,731 13

New 

Hampshire

A1B-C 10,670 11,298 11,247 11,045 10,826 10,618 0

A2-C 10,670 11,378 11,367 11,183 10,977 10,697 0

B2-C 10,670 11,232 11,418 11,309 11,181 11,069 4

A2-EAB 10,670 11,033 11,114 10,735 10,535 10,289 -4

A1B-BIO 10,670 10,995 10,930 10,725 10,505 8,300 -22

A2-BIO 10,670 11,203 10,944 10,768 10,571 9,409 -12

B2-BIO 10,670 10,779 11,256 11,145 11,014 10,002 -6

Table A4.2 continued 

State Scenarioa

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
A p p e n d i c e s 349

-------------------------------------------------------(million cubic feet)------------------------------------------------------ (percent)

New Jersey A1B-C 3,919 3,835 3,557 3,308 3,078 2,768 -29

A2-C 3,919 3,497 3,439 3,176 2,917 2,790 -29

B2-C 3,919 3,878 3,842 3,707 3,579 3,253 -17

A2-EAB 3,919 3,559 3,215 2,957 2,707 2,593 -34

A1B-BIO 3,919 3,690 3,426 3,171 2,937 2,634 -33

A2-BIO 3,919 3,726 3,668 3,399 3,134 2,567 -35

B2-BIO 3,919 3,738 3,577 3,455 3,339 3,155 -19

New York A1B-C 39,609 41,756 43,115 42,796 42,433 42,506 7

A2-C 39,609 41,519 42,696 42,434 42,120 42,281 7

B2-C 39,609 41,229 43,210 43,084 42,914 43,418 10

A2-EAB 39,609 41,448 40,560 40,316 40,023 40,687 3

A1B-BIO 39,609 40,856 42,585 42,276 41,924 40,247 2

A2-BIO 39,609 40,999 42,463 42,206 41,899 41,474 5

B2-BIO 39,609 41,208 43,145 43,021 42,854 42,156 6

Ohio A1B-C 15,992 17,576 17,884 17,682 17,449 18,780 17

A2-C 15,992 17,587 17,504 17,345 17,145 17,944 12

B2-C 15,992 17,102 18,021 17,942 17,837 18,526 16

A2-EAB 15,992 15,765 16,719 16,560 16,365 16,944 6

A1B-BIO 15,992 17,007 16,884 16,696 16,478 13,948 -13

A2-BIO 15,992 16,604 17,422 17,254 17,048 16,027 0

B2-BIO 15,992 16,691 17,876 17,802 17,701 17,172 7

Pennsylvania A1B-C 35,386 35,318 35,040 34,364 33,594 34,093 -4

A2-C 35,386 34,921 35,469 34,922 34,269 34,847 -2

B2-C 35,386 35,349 36,492 36,215 35,821 35,978 2

A2-EAB 35,386 35,221 35,125 34,602 33,978 34,758 -2

A1B-BIO 35,386 35,794 34,662 34,006 33,260 27,624 -22

A2-BIO 35,386 35,806 35,770 35,244 34,615 31,090 -12

B2-BIO 35,386 35,472 35,824 35,562 35,186 33,514 -5

Table A4.2 continued 

State Scenarioa

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
350

-------------------------------------------------------(million cubic feet)------------------------------------------------------ (percent)

Rhode Island A1B-C 786 707 742 702 663 581 -26

A2-C 786 778 695 658 616 573 -27

B2-C 786 675 710 696 680 658 -16

A2-EAB 786 735 725 672 629 560 -29

A1B-BIO 786 748 812 769 726 613 -22

A2-BIO 786 734 678 641 600 623 -21

B2-BIO 786 739 701 688 672 633 -19

Vermont A1B-C 10,174 10,540 10,558 10,438 10,303 10,346 2

A2-C 10,174 10,266 10,727 10,644 10,548 10,770 6

B2-C 10,174 10,705 10,786 10,727 10,645 10,366 2

A2-EAB 10,174 10,404 9,908 9,834 9,750 9,830 -3

A1B-BIO 10,174 10,544 10,473 10,352 10,217 8,288 -19

A2-BIO 10,174 10,234 10,817 10,737 10,646 9,296 -9

B2-BIO 10,174 10,526 10,688 10,629 10,547 10,561 4

West Virginia A1B-C 27,102 26,327 26,897 26,487 25,995 25,546 -6

A2-C 27,102 26,686 26,435 26,209 25,932 26,544 -2

B2-C 27,102 26,092 26,286 26,133 25,892 26,299 -3

A2-EAB 27,102 26,686 27,426 27,199 26,920 27,332 1

A1B-BIO 27,102 26,534 25,579 25,198 24,740 20,069 -26

A2-BIO 27,102 26,808 26,112 25,892 25,620 23,223 -14

B2-BIO 27,102 26,032 26,338 26,181 25,934 24,856 -8

Wisconsin A1B-C 23,242 25,818 27,208 26,973 26,699 28,378 22

A2-C 23,242 25,810 27,293 27,114 26,899 28,855 24

B2-C 23,242 25,673 27,102 26,995 26,847 28,895 24

A2-EAB 23,242 25,608 25,402 25,236 25,038 27,074 16

A1B-BIO 23,242 25,263 25,057 24,835 24,577 17,722 -24

A2-BIO 23,242 25,695 26,403 26,226 26,014 22,068 -5

B2-BIO 23,242 25,280 26,670 26,562 26,413 24,985 8

Table A4.2 continued 

State Scenarioa

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
A p p e n d i c e s 351

-------------------------------------------------------(million cubic feet)------------------------------------------------------ (percent)

All Northern 

States

A1B-C 304,055 317,951 322,940 318,412 313,447 318,255 5

A2-C 304,055 316,077 322,381 318,700 314,544 322,699 6

B2-C 304,055 315,081 324,374 322,317 319,695 327,599 8

A2-EAB 304,055 312,877 314,795 309,013 303,833 310,974 2

A1B-BIO 304,055 312,790 309,721 305,370 300,619 247,866 -18

A2-BIO 304,055 315,018 317,979 314,345 310,242 280,307 -8

B2-BIO 304,055 314,018 322,441 320,428 317,845 301,793 -1

a

There are seven scenarios, each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes moderate greenhouse 

gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio); A2 assumes 

high greenhouse gas emissions, large gains in population and energy consumption, and moderate gains in income; and B2 assumes low greenhouse gas emissions with 

moderate gains in population, income, and energy consumption. Scenario projections assume harvest will continue at recently observed levels (labeled –C) or increase 

to reflect increased harvest for bioenergy production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will gradually 

succumb to an expanding zone of infestation by the nonnative emerald ash borer.

Table A4.2 continued 

State Scenarioa

Year Change 

2010 to 

2060 2010 2020 2030 2040 2050 2060
352

 --------------------------------------------(million cubic feet)---------------------------------------------

Connecticut A1B-C 54 22 18 16 18 15

A2-C 54 24 17 18 20 18

B2-C 54 17 13 5 6 8

A2-EAB 54 31 17 11 16 15

A1B-BIO 54 21 29 24 24 24

A2-BIO 54 21 19 25 18 23

B2-BIO 54 20 22 12 12 15

Delaware A1B-C 7 6 7 12 7 9

A2-C 7 10 7 7 9 11

B2-C 7 9 8 7 10 7

A2-EAB 7 8 9 6 7 10

A1B-BIO 7 10 8 15 21 19

A2-BIO 7 7 9 10 13 18

B2-BIO 7 7 9 10 10 13

Illinois A1B-C 58 49 39 64 48 50

A2-C 58 54 38 30 57 43

B2-C 58 54 36 71 53 39

A2-EAB 58 51 44 33 52 55

A1B-BIO 58 59 60 97 132 162

A2-BIO 58 61 47 72 96 142

B2-BIO 58 56 59 55 83 76

Indiana A1B-C 80 115 106 142 143 115

A2-C 80 113 115 89 111 132

B2-C 80 97 90 104 126 110

A2-EAB 80 94 89 95 93 133

A1B-BIO 80 122 168 282 371 423

A2-BIO 80 111 155 194 259 331

B2-BIO 80 125 97 164 217 220

Table A4.3—Projected volume of total annual removals in the North, 2010 to 2060, for seven future scenarios 

based on greenhouse gas emissions storylines developed by the Intergovernmental Panel on Climate Change 

(IPCC 2007).

State Scenarioa Year

2010 2020 2030 2040 2050 2060
A p p e n d i c e s 353

State Scenarioa Year

2010 2020 2030 2040 2050 2060

 --------------------------------------------(million cubic feet)---------------------------------------------

Iowa A1B-C 46 39 50 69 47 48

A2-C 46 47 64 40 39 46

B2-C 46 48 74 56 86 69

A2-EAB 46 39 50 53 35 42

A1B-BIO 46 49 105 134 144 169

A2-BIO 46 51 63 78 101 120

B2-BIO 46 49 56 98 87 98

Maine A1B-C 562 549 558 556 560 575

A2-C 562 524 549 567 503 581

B2-C 562 586 575 565 566 500

A2-EAB 562 541 541 519 515 564

A1B-BIO 562 630 890 1,106 1,172 1,157

A2-BIO 562 631 709 830 1,020 1,079

B2-BIO 562 574 645 718 836 880

Maryland A1B-C 67 103 74 76 67 79

A2-C 67 85 80 81 81 68

B2-C 67 81 65 69 81 60

A2-EAB 67 87 70 72 84 80

A1B-BIO 67 107 126 113 144 125

A2-BIO 67 96 90 113 118 120

B2-BIO 67 104 75 72 105 104

Massachusetts A1B-C 35 34 32 29 18 21

A2-C 35 37 32 23 20 21

B2-C 35 40 22 17 14 16

A2-EAB 35 34 36 24 22 25

A1B-BIO 35 53 40 34 36 36

A2-BIO 35 46 42 29 30 31

B2-BIO 35 41 27 24 20 19

Table A4.3 continued 
354

State Scenarioa Year

2010 2020 2030 2040 2050 2060

 --------------------------------------------(million cubic feet)---------------------------------------------

Michigan A1B-C 339 357 344 408 400 408

A2-C 339 365 358 372 390 437

B2-C 339 354 374 370 383 418

A2-EAB 339 320 328 371 353 407

A1B-BIO 339 399 650 845 1,114 1,188

A2-BIO 339 363 490 615 803 939

B2-BIO 339 389 427 508 668 725

Minnesota A1B-C 297 249 288 291 289 321

A2-C 297 241 303 275 318 338

B2-C 297 230 250 298 293 293

A2-EAB 297 257 294 295 274 348

A1B-BIO 297 304 435 605 680 622

A2-BIO 297 249 296 421 512 542

B2-BIO 297 267 322 373 456 470

Missouri A1B-C 175 142 171 179 171 170

A2-C 175 156 143 141 160 172

B2-C 175 153 169 170 176 190

A2-EAB 175 157 179 183 135 178

A1B-BIO 175 177 250 349 466 623

A2-BIO 175 195 195 259 333 417

B2-BIO 175 147 173 224 270 312

New Hampshire A1B-C 92 113 103 121 144 110

A2-C 92 90 99 108 130 125

B2-C 92 111 90 134 127 126

A2-EAB 92 109 108 113 104 132

A1B-BIO 92 118 208 284 366 427

A2-BIO 92 115 170 172 259 308

B2-BIO 92 117 118 160 220 226

Table A4.3 continued 
A p p e n d i c e s 355

State Scenarioa Year

2010 2020 2030 2040 2050 2060

 --------------------------------------------(million cubic feet)---------------------------------------------

New Jersey A1B-C 29 35 61 50 51 40

A2-C 29 52 44 41 46 39

B2-C 29 44 32 31 42 33

A2-EAB 29 47 46 38 48 42

A1B-BIO 29 49 52 65 91 91

A2-BIO 29 54 47 63 82 74

B2-BIO 29 43 38 47 58 47

New York A1B-C 300 172 181 234 219 263

A2-C 300 170 192 180 227 234

B2-C 300 175 169 188 197 248

A2-EAB 300 161 206 188 210 234

A1B-BIO 300 200 303 489 601 767

A2-BIO 300 173 248 333 400 506

B2-BIO 300 161 194 261 363 388

Ohio A1B-C 189 251 204 218 227 252

A2-C 189 213 223 244 244 253

B2-C 189 212 229 198 243 204

A2-EAB 189 219 186 212 230 219

A1B-BIO 189 284 390 514 656 734

A2-BIO 189 253 319 375 472 570

B2-BIO 189 220 244 330 376 384

Pennsylvania A1B-C 415 388 386 398 454 465

A2-C 415 361 407 389 402 425

B2-C 415 341 376 378 376 355

A2-EAB 415 387 359 404 454 412

A1B-BIO 415 395 577 798 1,132 1,308

A2-BIO 415 397 421 587 741 993

B2-BIO 415 374 363 503 696 748

Table A4.3 continued 
356

 --------------------------------------------(million cubic feet)---------------------------------------------

Rhode Island A1B-C 2 6 4 6 4 4

A2-C 2 5 5 5 4 7

B2-C 2 4 3 2 2 1

A2-EAB 2 6 5 3 4 5

A1B-BIO 2 7 9 5 7 6

A2-BIO 2 6 6 4 6 6

B2-BIO 2 6 2 1 2 1

Vermont A1B-C 113 111 123 107 96 106

A2-C 113 96 99 85 93 101

B2-C 113 109 89 89 107 117

A2-EAB 113 86 104 89 110 104

A1B-BIO 113 109 164 220 311 367

A2-BIO 113 102 144 162 195 249

B2-BIO 113 89 96 128 147 190

West Virginia A1B-C 322 330 277 350 345 321

A2-C 322 296 280 330 311 328

B2-C 322 319 312 344 334 304

A2-EAB 322 355 338 330 325 325

A1B-BIO 322 382 518 741 967 1,025

A2-BIO 322 346 443 478 668 815

B2-BIO 322 356 324 429 561 520

Wisconsin A1B-C 327 394 421 449 439 486

A2-C 327 392 417 463 446 471

B2-C 327 408 396 447 469 492

A2-EAB 327 383 380 413 425 448

A1B-BIO 327 436 666 937 1,119 1,132

A2-BIO 327 456 537 708 874 968

B2-BIO 327 443 478 590 755 773

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.3 continued 
A p p e n d i c e s 357

 --------------------------------------------(million cubic feet)---------------------------------------------

All Northern States A1B-C 3,509 3,466 3,445 3,776 3,747 3,858

A2-C 3,509 3,332 3,475 3,485 3,612 3,851

B2-C 3,509 3,393 3,370 3,543 3,692 3,590

A2-EAB 3,509 3,372 3,389 3,454 3,497 3,781

A1B-BIO 3,509 3,910 5,650 7,656 9,555 10,403

A2-BIO 3,509 3,731 4,451 5,528 7,001 8,251

B2-BIO 3,509 3,587 3,770 4,707 5,943 6,210

a

There are seven scenarios, each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in population and energy consumption, and moderate 

gains in income; and B2 assumes low greenhouse gas emissions with moderate gains in population, income, and energy consumption. Scenario 

projections assume harvest will continue at recently observed levels (labeled –C) or increase to reflect increased harvest for bioenergy 

production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will gradually succumb to an 

expanding zone of infestation by the nonnative emerald ash borer.

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.3 continued 
358

 --------------------------------------------(million cubic feet)---------------------------------------------

Connecticut A1B-C 51 14 10 5 6 3

A2-C 51 16 7 8 8 4

B2-C 51 10 8 2 2 3

A2-EAB 51 23 7 1 4 2

A1B-BIO 51 13 20 14 14 12

A2-BIO 51 12 10 15 6 9

B2-BIO 51 13 18 9 7 11

Delaware A1B-C 7 3 4 9 4 6

A2-C 7 7 4 4 6 6

B2-C 7 6 6 5 8 5

A2-EAB 7 5 6 3 3 6

A1B-BIO 7 7 5 12 18 16

A2-BIO 7 4 6 7 9 14

B2-BIO 7 5 7 8 8 11

Illinois A1B-C 33 40 29 55 37 38

A2-C 33 48 31 24 50 35

B2-C 33 46 31 66 46 35

A2-EAB 33 44 37 27 45 47

A1B-BIO 33 50 50 88 122 150

A2-BIO 33 55 41 65 89 133

B2-BIO 33 48 54 49 77 71

Indiana A1B-C 72 98 87 122 119 87

A2-C 72 101 102 75 95 113

B2-C 72 83 83 95 114 101

A2-EAB 72 81 76 81 76 113

A1B-BIO 72 105 149 261 347 395

A2-BIO 72 98 142 181 243 311

B2-BIO 72 111 89 155 204 210

Table A4.4—Projected volume of annual harvest removals in the North, 2010 to 2060, for seven future 

scenarios based on greenhouse gas emissions storylines developed by the Intergovernmental Panel on Climate 

Change (IPCC 2007).

State Scenarioa Year

2010 2020 2030 2040 2050 2060
A p p e n d i c e s 359

State Scenarioa Year

2010 2020 2030 2040 2050 2060

 --------------------------------------------(million cubic feet)---------------------------------------------

Iowa A1B-C 35 37 47 66 43 44

A2-C 35 45 62 37 35 42

B2-C 35 46 73 54 83 68

A2-EAB 35 38 48 51 33 39

A1B-BIO 35 47 102 131 140 164

A2-BIO 35 50 61 76 98 116

B2-BIO 35 47 55 96 84 96

Maine A1B-C 542 539 547 544 546 559

A2-C 542 515 540 557 491 567

B2-C 542 578 569 560 559 494

A2-EAB 542 532 532 509 503 550

A1B-BIO 542 619 880 1,095 1,160 1,143

A2-BIO 542 622 700 821 1,008 1,065

B2-BIO 542 565 640 713 829 874

Maryland A1B-C 54 62 25 28 24 38

A2-C 54 47 34 40 45 31

B2-C 54 44 35 43 55 42

A2-EAB 54 49 25 29 46 42

A1B-BIO 54 66 79 71 108 89

A2-BIO 54 58 47 73 82 83

B2-BIO 54 66 43 46 81 86

Massachusetts A1B-C 13 19 14 12 < 1 3

A2-C 13 21 14 4 0 0

B2-C 13 26 12 11 6 8

A2-EAB 13 18 18 6 3 4

A1B-BIO 13 37 23 16 18 18

A2-BIO 13 30 24 10 10 9

B2-BIO 13 28 17 17 12 12

Table A4.4 continued 
360

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.4 continued 

 --------------------------------------------(million cubic feet)---------------------------------------------

Michigan A1B-C 318 327 310 370 357 359

A2-C 318 339 330 341 354 392

B2-C 318 328 358 353 360 398

A2-EAB 318 294 299 340 316 360

A1B-BIO 318 368 616 809 1,073 1,142

A2-BIO 318 337 462 584 767 894

B2-BIO 318 363 410 491 645 706

Minnesota A1B-C 257 236 275 274 271 302

A2-C 257 229 290 260 302 323

B2-C 257 218 242 289 283 285

A2-EAB 257 246 281 282 259 334

A1B-BIO 257 291 422 590 663 605

A2-BIO 257 237 283 405 496 527

B2-BIO 257 256 314 363 445 461

Missouri A1B-C 141 125 153 158 146 141

A2-C 141 141 128 125 140 148

B2-C 141 138 159 159 162 179

A2-EAB 141 143 165 166 115 153

A1B-BIO 141 160 232 329 442 595

A2-BIO 141 180 181 243 314 394

B2-BIO 141 132 164 214 257 301

New Hampshire A1B-C 71 96 83 101 123 87

A2-C 71 74 81 90 110 101

B2-C 71 95 78 123 114 115

A2-EAB 71 93 91 95 84 109

A1B-BIO 71 100 189 263 344 404

A2-BIO 71 100 152 155 239 285

B2-BIO 71 101 107 149 207 215
A p p e n d i c e s 361

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.4 continued 

 --------------------------------------------(million cubic feet)---------------------------------------------

New Jersey A1B-C 21 12 35 26 29 20

A2-C 21 29 19 16 22 14

B2-C 21 22 14 18 30 24

A2-EAB 21 24 20 12 23 16

A1B-BIO 21 26 27 41 69 71

A2-BIO 21 30 19 38 58 48

B2-BIO 21 21 22 36 47 38

New York A1B-C 274 150 157 207 188 227

A2-C 274 150 172 158 200 201

B2-C 274 157 159 178 183 237

A2-EAB 274 142 186 165 182 200

A1B-BIO 274 177 280 463 570 732

A2-BIO 274 154 227 311 373 473

B2-BIO 274 142 184 251 349 377

Ohio A1B-C 181 237 187 199 205 227

A2-C 181 201 208 229 225 229

B2-C 181 200 221 191 233 196

A2-EAB 181 207 173 195 210 193

A1B-BIO 181 270 375 496 635 710

A2-BIO 181 241 306 360 453 546

B2-BIO 181 209 237 323 366 376

Pennsylvania A1B-C 362 332 327 333 379 380

A2-C 362 315 358 336 338 349

B2-C 362 295 349 351 338 324

A2-EAB 362 340 309 350 389 334

A1B-BIO 362 339 517 734 1,059 1,226

A2-BIO 362 351 371 536 680 919

B2-BIO 362 327 335 477 660 718
362

 --------------------------------------------(million cubic feet)---------------------------------------------

Rhode Island A1B-C 1 3 < 1 2 < 1 < 1

A2-C 1 2 1 1 < 1 2

B2-C 1 1 1 < 1 < 1 < 1

A2-EAB 1 3 1 < 1 < 1 < 1

A1B-BIO 1 3 5 1 3 2

A2-BIO 1 3 2 < 1 2 1

B2-BIO 1 3 < 1 < 1 < 1 < 1

Vermont A1B-C 105 100 113 95 82 91

A2-C 105 88 91 76 84 90

B2-C 105 100 84 83 99 110

A2-EAB 105 78 96 82 101 94

A1B-BIO 105 98 153 208 297 352

A2-BIO 105 94 136 154 186 238

B2-BIO 105 80 91 122 139 184

West Virginia A1B-C 286 294 243 309 296 263

A2-C 286 272 258 308 283 293

B2-C 286 291 300 329 310 287

A2-EAB 286 331 317 307 297 290

A1B-BIO 286 346 484 703 922 970

A2-BIO 286 322 421 456 641 781

B2-BIO 286 328 312 413 537 503

Wisconsin A1B-C 311 375 401 426 412 454

A2-C 311 377 401 445 425 445

B2-C 311 392 386 436 454 480

A2-EAB 311 368 365 396 405 423

A1B-BIO 311 418 646 915 1,094 1,102

A2-BIO 311 442 521 690 853 943

B2-BIO 311 427 469 580 741 761

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.4 continued 
A p p e n d i c e s 363

 --------------------------------------------(million cubic feet)---------------------------------------------

All Northern States A1B-C 3,133 3,097 3,046 3,340 3,268 3,327

A2-C 3,133 3,018 3,133 3,133 3,213 3,387

B2-C 3,133 3,078 3,169 3,346 3,439 3,389

A2-EAB 3,133 3,058 3,052 3,096 3,093 3,309

A1B-BIO 3,133 3,541 5,255 7,240 9,099 9,899

A2-BIO 3,133 3,418 4,113 5,179 6,607 7,790

B2-BIO 3,133 3,272 3,566 4,513 5,694 6,012

a

There are seven scenarios, each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in population and energy consumption, and moderate 

gains in income; and B2 assumes low greenhouse gas emissions with moderate gains in population, income, and energy consumption. Scenario 

projections assume harvest will continue at recently observed levels (labeled –C) or increase to reflect increased harvest for bioenergy 

production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will gradually succumb to an 

expanding zone of infestation by the nonnative emerald ash borer.

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.4 continued 
364

Table A4.5—Projected volume of total annual removals attributed to land-use change in North, 2010 to 2060, 

for seven future scenarios based on greenhouse gas emissions storylines developed by the Intergovernmental 

Panel on Climate Change (IPCC 2007).

State Scenarioa Year

2010 2020 2030 2040 2050 2060

 --------------------------------------------(million cubic feet)---------------------------------------------

Connecticut A1B-C 3 8 9 10 11 12

A2-C 3 8 10 10 12 14

B2-C 3 7 5 3 5 4

A2-EAB 3 8 9 10 12 14

A1B-BIO 3 8 9 10 10 11

A2-BIO 3 8 9 10 12 14

B2-BIO 3 7 5 3 4 4

Delaware A1B-C 0 3 3 3 4 4

A2-C 0 3 3 3 4 4

B2-C 0 3 2 2 2 2

A2-EAB 0 3 3 3 3 4

A1B-BIO 0 3 3 3 3 4

A2-BIO 0 3 3 3 4 4

B2-BIO 0 3 2 2 2 2

Illinois A1B-C 24 9 10 9 10 12

A2-C 24 6 6 6 7 8

B2-C 24 8 5 5 6 4

A2-EAB 24 6 7 6 7 8

A1B-BIO 24 9 10 9 11 12

A2-BIO 24 6 7 7 8 9

B2-BIO 24 8 5 6 7 4

Indiana A1B-C 8 17 18 21 25 29

A2-C 8 13 13 13 16 19

B2-C 8 14 7 9 13 10

A2-EAB 8 13 13 14 17 20

A1B-BIO 8 17 19 21 24 29

A2-BIO 8 13 13 13 16 19

B2-BIO 8 14 8 9 13 10
A p p e n d i c e s 365

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.5 continued 

 --------------------------------------------(million cubic feet)---------------------------------------------

Iowa A1B-C 11 3 3 3 4 5

A2-C 11 2 2 3 3 4

B2-C 11 2 1 1 2 2

A2-EAB 11 2 2 2 3 3

A1B-BIO 11 3 3 3 4 4

A2-BIO 11 2 2 3 3 4

B2-BIO 11 2 1 2 2 2

Maine A1B-C 21 10 11 12 13 15

A2-C 21 9 9 10 12 14

B2-C 21 9 5 5 7 6

A2-EAB 21 9 9 10 11 14

A1B-BIO 21 10 10 10 12 14

A2-BIO 21 9 9 9 11 14

B2-BIO 21 9 5 5 7 6

Maryland A1B-C 13 40 49 48 43 41

A2-C 13 38 46 40 36 36

B2-C 13 37 30 27 26 18

A2-EAB 13 38 45 44 38 38

A1B-BIO 13 40 47 42 36 35

A2-BIO 13 38 44 40 36 37

B2-BIO 13 37 32 26 25 18

Massachusetts A1B-C 22 15 18 18 18 17

A2-C 22 16 18 19 20 22

B2-C 22 14 10 7 8 7

A2-EAB 22 16 18 18 19 21

A1B-BIO 22 15 17 19 19 18

A2-BIO 22 16 19 19 20 22

B2-BIO 22 14 10 7 8 7
366

 --------------------------------------------(million cubic feet)---------------------------------------------

Michigan A1B-C 21 31 33 38 43 49

A2-C 21 26 28 31 37 45

B2-C 21 26 17 17 23 20

A2-EAB 21 26 28 31 37 46

A1B-BIO 21 31 33 36 41 46

A2-BIO 21 26 28 31 36 44

B2-BIO 21 26 17 17 23 20

Minnesota A1B-C 40 13 13 17 18 19

A2-C 40 11 13 15 16 15

B2-C 40 11 8 9 11 9

A2-EAB 40 11 13 13 15 14

A1B-BIO 40 13 14 15 16 17

A2-BIO 40 11 12 15 16 15

B2-BIO 40 11 8 9 11 9

Missouri A1B-C 33 18 18 22 25 29

A2-C 33 14 15 16 19 24

B2-C 33 15 9 10 14 11

A2-EAB 33 14 15 17 20 25

A1B-BIO 33 18 18 20 24 27

A2-BIO 33 14 14 16 19 24

B2-BIO 33 15 9 10 14 11

New Hampshire A1B-C 21 18 20 20 22 24

A2-C 21 16 18 18 20 23

B2-C 21 16 12 11 13 11

A2-EAB 21 16 17 18 20 24

A1B-BIO 21 18 19 20 22 24

A2-BIO 21 16 17 17 19 22

B2-BIO 21 16 12 11 13 11

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.5 continued 
A p p e n d i c e s 367

 --------------------------------------------(million cubic feet)---------------------------------------------

New Jersey A1B-C 8 23 26 24 22 20

A2-C 8 23 25 25 24 25

B2-C 8 21 18 12 12 10

A2-EAB 8 23 26 26 26 26

A1B-BIO 8 23 25 24 22 20

A2-BIO 8 23 28 25 25 26

B2-BIO 8 21 16 12 11 9

New York A1B-C 26 22 24 27 31 36

A2-C 26 19 21 22 27 33

B2-C 26 19 10 11 14 11

A2-EAB 26 19 21 23 28 34

A1B-BIO 26 22 23 26 30 35

A2-BIO 26 19 21 22 27 33

B2-BIO 26 19 10 10 14 11

Ohio A1B-C 8 14 16 19 22 25

A2-C 8 12 15 15 19 24

B2-C 8 12 7 8 10 8

A2-EAB 8 12 13 17 21 25

A1B-BIO 8 14 16 18 21 23

A2-BIO 8 12 13 16 19 24

B2-BIO 8 12 7 7 10 8

Pennsylvania A1B-C 54 56 59 66 75 85

A2-C 54 47 49 53 63 77

B2-C 54 47 27 27 38 31

A2-EAB 54 47 50 54 65 78

A1B-BIO 54 56 61 64 72 82

A2-BIO 54 47 50 51 61 74

B2-BIO 54 47 28 25 36 30

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.5 continued 
368

 --------------------------------------------(million cubic feet)---------------------------------------------

Rhode Island A1B-C 2 3 4 4 4 4

A2-C 2 4 4 4 4 5

B2-C 2 3 2 1 2 1

A2-EAB 2 4 4 4 4 5

A1B-BIO 2 3 4 4 4 4

A2-BIO 2 4 4 4 4 5

B2-BIO 2 3 2 1 2 1

Vermont A1B-C 8 11 11 12 13 15

A2-C 8 8 8 8 10 11

B2-C 8 9 5 6 8 6

A2-EAB 8 8 8 8 9 11

A1B-BIO 8 11 11 12 13 15

A2-BIO 8 8 7 8 9 11

B2-BIO 8 9 5 6 8 6

West Virginia A1B-C 36 35 34 41 49 58

A2-C 36 24 22 22 27 35

B2-C 36 28 12 15 24 16

A2-EAB 36 24 21 23 28 36

A1B-BIO 36 35 34 38 45 54

A2-BIO 36 24 22 22 27 34

B2-BIO 36 28 12 16 24 17

Wisconsin A1B-C 16 18 21 23 27 31

A2-C 16 15 16 18 21 26

B2-C 16 15 10 11 15 12

A2-EAB 16 15 15 17 21 25

A1B-BIO 16 18 20 22 26 30

A2-BIO 16 15 16 17 21 26

B2-BIO 16 15 10 11 15 12

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.5 continued 
A p p e n d i c e s 369

 --------------------------------------------(million cubic feet)---------------------------------------------

All Northern States A1B-C 375 369 399 436 479 531

A2-C 375 314 341 352 398 464

B2-C 375 315 201 197 252 201

A2-EAB 375 314 337 358 404 472

A1B-BIO 375 369 395 416 455 505

A2-BIO 375 314 338 349 395 461

B2-BIO 375 315 203 194 249 198

a

There are seven scenarios, each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in population and energy consumption, and moderate 

gains in income; and B2 assumes low greenhouse gas emissions with moderate gains in population, income, and energy consumption. Scenario 

projections assume harvest will continue at recently observed levels (labeled –C) or increase to reflect increased harvest for bioenergy 

production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will gradually succumb to an 

expanding zone of infestation by the nonnative emerald ash borer.

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.5 continued 
370

 --------------------------------------------(million cubic feet)---------------------------------------------

Connecticut A1B-C 79 15 55 -4 -4 -39

A2-C 79 30 11 -2 -3 -32

B2-C 79 4 2 -2 -2 11

A2-EAB 79 31 8 -8 -7 -33

A1B-BIO 79 15 35 5 4 -11

A2-BIO 79 23 9 5 -5 -7

B2-BIO 79 24 -6 6 3 -1

Delaware A1B-C 31 1 8 5 < 1 -1

A2-C 31 7 2 < 1 2 6

B2-C 31 4 9 4 6 3

A2-EAB 31 7 -4 1 < 1 11

A1B-BIO 31 3 7 8 14 7

A2-BIO 31 8 4 4 6 4

B2-BIO 31 1 7 6 6 14

Illinois A1B-C 226 110 -2 47 28 23

A2-C 226 95 33 18 44 30

B2-C 226 104 -21 61 41 104

A2-EAB 226 81 38 22 39 81

A1B-BIO 226 77 75 80 112 41

A2-BIO 226 81 69 60 82 41

B2-BIO 226 83 59 44 71 46

Indiana A1B-C 318 165 104 102 96 91

A2-C 318 154 103 63 80 171

B2-C 318 131 120 87 102 94

A2-EAB 318 127 142 68 60 104

A1B-BIO 318 174 120 242 325 317

A2-BIO 318 156 168 168 228 235

B2-BIO 318 147 154 147 193 142

Table A4.6—Projected net annual growth of growing stock in the North, 2010 to 2060, for seven future 

scenarios based on greenhouse gas emissions storylines developed by the Intergovernmental Panel on Climate 

Change (IPCC 2007).

State Scenarioa Year

2010 2020 2030 2040 2050 2060
A p p e n d i c e s 371

 --------------------------------------------(million cubic feet)---------------------------------------------

Iowa A1B-C 103 98 47 63 40 31

A2-C 103 93 73 35 33 66

B2-C 103 106 93 53 82 79

A2-EAB 103 91 77 50 31 10

A1B-BIO 103 89 102 128 137 100

A2-BIO 103 87 72 74 95 68

B2-BIO 103 89 82 95 82 65

Maine A1B-C 573 634 598 533 534 476

A2-C 573 596 645 549 481 523

B2-C 573 672 606 556 553 521

A2-EAB 573 613 560 500 492 519

A1B-BIO 573 635 769 1,086 1,149 240

A2-BIO 573 666 656 812 998 434

B2-BIO 573 643 627 709 823 498

Maryland A1B-C 176 40 -8 -18 -17 18

A2-C 176 27 -5 1 11 -11

B2-C 176 16 -6 17 30 16

A2-EAB 176 47 7 -13 9 -25

A1B-BIO 176 24 14 31 74 16

A2-BIO 176 17 22 34 47 3

B2-BIO 176 56 -14 22 57 30

Massachusetts A1B-C 155 21 -27 -4 -16 -41

A2-C 155 12 1 -14 -19 -36

B2-C 155 6 4 4 -1 -21

A2-EAB 155 4 11 -11 -15 -52

A1B-BIO 155 32 3 -1 1 -26

A2-BIO 155 22 -3 -8 -9 -51

B2-BIO 155 39 1 11 4 -9

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.6 continued 
372

 --------------------------------------------(million cubic feet)---------------------------------------------

Michigan A1B-C 697 445 348 336 318 494

A2-C 697 446 383 313 320 542

B2-C 697 444 469 337 338 572

A2-EAB 697 397 372 311 282 493

A1B-BIO 697 450 573 776 1,036 600

A2-BIO 697 458 434 556 733 617

B2-BIO 697 459 523 476 624 589

Minnesota A1B-C 421 306 391 260 255 475

A2-C 421 325 412 247 288 486

B2-C 421 280 367 282 273 367

A2-EAB 421 355 399 270 247 523

A1B-BIO 421 352 409 577 650 119

A2-BIO 421 306 418 392 482 390

B2-BIO 421 333 405 355 436 282

Missouri A1B-C 510 268 229 139 124 245

A2-C 510 263 199 111 124 295

B2-C 510 220 284 150 151 340

A2-EAB 510 259 256 152 98 307

A1B-BIO 510 248 279 312 422 482

A2-BIO 510 304 234 229 298 402

B2-BIO 510 227 269 205 245 365

New Hampshire A1B-C 188 153 81 82 102 59

A2-C 188 144 69 73 91 78

B2-C 188 157 85 113 102 96

A2-EAB 188 132 106 78 65 98

A1B-BIO 188 133 181 244 324 220

A2-BIO 188 160 122 139 221 174

B2-BIO 188 123 144 139 195 118

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.6 continued 
A p p e n d i c e s 373

 --------------------------------------------(million cubic feet)---------------------------------------------

New Jersey A1B-C 95 3 15 3 9 -15

A2-C 95 -11 18 -7 -1 -5

B2-C 95 20 1 6 19 4

A2-EAB 95 -14 13 -13 -2 10

A1B-BIO 95 4 3 18 49 39

A2-BIO 95 11 11 15 34 -5

B2-BIO 95 2 11 25 36 18

New York A1B-C 627 335 324 182 159 278

A2-C 627 352 283 137 176 263

B2-C 627 320 357 168 170 316

A2-EAB 627 389 323 143 157 315

A1B-BIO 627 326 459 439 542 604

A2-BIO 627 321 362 290 349 453

B2-BIO 627 297 402 241 336 338

Ohio A1B-C 410 377 233 181 185 373

A2-C 410 361 210 216 207 312

B2-C 410 314 317 184 224 273

A2-EAB 410 304 282 180 191 239

A1B-BIO 410 361 379 480 616 487

A2-BIO 410 305 390 345 435 461

B2-BIO 410 301 335 316 357 342

Pennsylvania A1B-C 746 354 313 270 308 436

A2-C 746 305 415 286 279 412

B2-C 746 307 465 326 301 362

A2-EAB 746 434 387 299 328 396

A1B-BIO 746 402 421 673 991 700

A2-BIO 746 428 356 488 622 609

B2-BIO 746 352 374 454 626 581

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.6 continued 
374

 --------------------------------------------(million cubic feet)---------------------------------------------

Rhode Island A1B-C 19 -2 2 -2 -4 -9

A2-C 19 4 -9 -3 -4 -3

B2-C 19 -7 3 -1 -1 -2

A2-EAB 19 < 1 1 -4 -4 -9

A1B-BIO 19 2 12 -3 -2 -11

A2-BIO 19 < 1 -3 -3 -2 2

B2-BIO 19 < 1 -3 -2 -1 -5

Vermont A1B-C 192 132 122 84 70 88

A2-C 192 92 138 69 75 116

B2-C 192 149 92 78 92 89

A2-EAB 192 99 104 74 93 106

A1B-BIO 192 132 145 197 285 170

A2-BIO 192 98 198 146 178 98

B2-BIO 192 121 99 117 131 174

West Virginia A1B-C 609 226 303 271 250 223

A2-C 609 233 238 287 257 352

B2-C 609 187 333 315 287 333

A2-EAB 609 325 413 285 270 346

A1B-BIO 609 299 387 667 879 523

A2-BIO 609 298 350 436 616 560

B2-BIO 609 223 346 399 514 401

Wisconsin A1B-C 586 618 544 405 387 603

A2-C 586 613 539 429 406 629

B2-C 586 619 534 427 441 672

A2-EAB 586 581 485 380 386 625

A1B-BIO 586 603 643 894 1,070 467

A2-BIO 586 675 585 674 833 596

B2-BIO 586 612 608 570 727 648

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.6 continued 
A p p e n d i c e s 375

 --------------------------------------------(million cubic feet)---------------------------------------------

All Northern States A1B-C 6,759 4,299 3,680 2,936 2,825 3,809

A2-C 6,759 4,144 3,758 2,807 2,845 4,194

B2-C 6,759 4,052 4,113 3,164 3,206 4,227

A2-EAB 6,759 4,260 3,978 2,763 2,719 4,065

A1B-BIO 6,759 4,358 5,017 6,855 8,679 5,083

A2-BIO 6,759 4,424 4,455 4,856 6,241 5,085

B2-BIO 6,759 4,133 4,424 4,334 5,465 4,638

a

There are seven scenarios, each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in population and energy consumption, and moderate 

gains in income; and B2 assumes low greenhouse gas emissions with moderate gains in population, income, and energy consumption. Scenario 

projections assume harvest will continue at recently observed levels (labeled –C) or increase to reflect increased harvest for bioenergy 

production (labeled –BIO). Scenario A2-EAB is a variation of scenario A2-C that also assumes all ash species will gradually succumb to an 

expanding zone of infestation by the nonnative emerald ash borer.

State Scenarioa Year

2010 2020 2030 2040 2050 2060

Table A4.6 continued 

Please refer to the Literature Cited section at the end of Chapter 4 for the references.
376

-------------------------------------------------(million dry tons)-------------------------------------------------

New York 880 921 957 950 943 933

Missouri 606 643 641 637 631 625

Ohio 452 465 490 485 479 450

Iowa 111 122 126 125 124 109

Minnesota 428 442 464 460 456 416

Indiana 250 264 268 264 260 242

Illinois 229 233 239 237 235 211

Vermont 270 273 282 280 277 247

Wisconsin 595 636 645 640 635 544

Connecticut 119 119 121 118 115 108

Michigan 774 794 783 775 766 701

Pennsylvania 980 987 983 969 951 848

New Hampshire 270 276 273 269 264 232

Massachusetts 198 194 193 188 183 169

West Virginia 766 759 737 731 723 651

Delaware 23 24 23 22 21 19

Rhode Island 22 20 19 18 17 17

Maine 647 653 639 636 634 505

New Jersey 101 96 92 85 78 64

Maryland 168 156 146 135 126 106

All Northern States 7,890 8,079 8,119 8,024 7,917 7,196

Table A7.1—Projections of total aboveground biomass on timberland for enhanced biomass utilization scenarios 

in the North, 2010 to 2060, for three future scenarios based on greenhouse gas emissions storylines developed 

by the Intergovernmental Panel on Climate Change (IPCC 2007).

State

Initial for all 

scenarios

Scenarioa

A2-BIO A2-BIO A2-BIO A2-BIO A2-BIO

2010 2020 2030 2040 2050 2060

Appendix Chapter 7
A p p e n d i c e s 377

State

Scenarioa

A1B-BIO B2-BIO A2-BIO A1B-BIO B2-BIO

2060 2060

------------(million dry tons)------------ ---------------(percent change, 2010 to 2060)---------------

New York 903 948 6.1 2.6 7.7

Missouri 573 653 3.2 -5.4 7.8

Ohio 388 481 -0.6 -14.2 6.3

Iowa 99 119 -1.9 -10.4 7.3

Minnesota 303 410 -2.8 -29.3 -4.3

Indiana 226 254 -3.0 -9.7 1.5

Illinois 207 223 -8.0 -9.8 -2.5

Vermont 219 277 -8.6 -18.9 2.6

Wisconsin 447 605 -8.6 -24.9 1.7

Connecticut 111 109 -9.4 -6.8 -8.4

Michigan 625 772 -9.4 -19.3 -0.3

Pennsylvania 748 911 -13.5 -23.7 -7.1

New Hampshire 207 248 -14.1 -23.5 -8.0

Massachusetts 173 191 -14.5 -12.6 -3.3

West Virginia 553 701 -15.0 -27.9 -8.4

Delaware 18 21 -18.9 -21.8 -8.8

Rhode Island 17 18 -21.3 -23.5 -18.4

Maine 430 577 -22.0 -33.6 -10.8

New Jersey 65 80 -36.7 -36.0 -21.2

Maryland 95 119 -36.9 -43.4 -29.3

All Northern States 6,405 7,717 -8.8 -18.8 -2.2

Table A7.1 continued 

a

There are three scenarios, each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes 

moderate greenhouse gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced 

renewable/fossil fuel portfolio); A2 assumes high greenhouse gas emissions, large gains in population and energy consumption, and moderate 

gains in income; and B2 assumes low greenhouse gas emissions with moderate gains in population, income, and energy consumption. Scenario 

projections (labeled –BIO) assume harvest will increase to reflect increased harvest for bioenergy production.
378

----------------------------------------------------------------(thousand dry tons)----------------------------------------------------------------

Maine -930 569 -1,433 -239 -287 -12,892

Pennsylvania 10,095 701 -364 -1,465 -1,753 -10,310

West Virginia 9,013 -680 -2,228 -637 -784 -7,200

Michigan 9,713 2,063 -1,179 -770 -917 -6,459

Maryland 3,406 -1,116 -1,057 -1,054 -952 -1,979

Wisconsin 6,784 4,111 847 -430 -517 -9,141

New Hampshire 2,708 599 -283 -440 -493 -3,165

New Jersey 2,155 -551 -433 -691 -684 -1,391

Massachusetts 3,465 -387 -106 -507 -535 -1,333

Vermont 1,184 245 912 -210 -241 -3,049

Illinois 5,612 392 630 -189 -214 -2,426

Minnesota 4,166 1,343 2,195 -356 -386 -4,051

Connecticut 459 47 192 -292 -345 -712

Indiana 6,930 1,391 365 -354 -429 -1,724

Rhode Island 565 -128 -143 -107 -117 63

Delaware 772 70 -49 -90 -101 -258

Ohio 7,555 1,312 2,403 -454 -560 -2,987

Iowa 2,274 1,119 317 -68 -81 -1,527

Missouri 11,209 3,627 -151 -466 -564 -567

New York 10,362 4,166 3,523 -619 -742 -974

All Northern States 97,497 18,893 3,959 -9,439 -10,701 -72,083

Table A7.2—Estimates of annual net forest biomass change based on enhanced biomass utilization scenarios in the North, 2010 to 

2060, for three future scenarios based on greenhouse gas emissions storylines developed by the Intergovernmental Panel on Climate 

Change (IPCC 2007). Note that estimates for 2060 are based on both a 10-year increment (2050 to 2060) and a 50-year increment 

(2010 to 2060), and the initial 2010 estimate was calculated using volume growth and removals from the current FIA inventory panel.

State Estimated 

for 2010

Scenarioa

A2-BIO A2-BIO A2-BIO A2-BIO A1B-BIO

2010 to 2020 2020 to 2030 2030 to 2040 2040 to 2050 2050 to 2060
A p p e n d i c e s 379

State

Scenarioa

A2-BIO B2-BIO A1B-BIO A2-BIO B2-BIO

2050 to 2060 2050 to 2060 2010 to 2060 2010 to 2060 2010 to 2060

----------------------------------------------------------------(thousand dry tons)----------------------------------------------------------------

Maine -18,649 -7,587 -2,856 -4,350 -1,401

Pennsylvania -16,377 -5,266 -2,638 -4,636 -1,381

West Virginia -14,292 -3,505 -2,306 -4,274 -1,297

Michigan -12,563 -2,743 -1,452 -2,986 -38

Maryland -1971 -1,505 -1,232 -1,449 -976

Wisconsin -15,832 -4,328 -1,026 -2,965 198

New Hampshire -5,327 -2,433 -756 -1,265 -428

New Jersey -1,106 -713 -750 -736 -437

Massachusetts -928 -259 -573 -499 -129

Vermont -5,177 -107 -469 -1,029 133

Illinois -2,537 -1,382 -361 -444 -112

Minnesota -11,761 -4,310 -251 -2,512 -376

Connecticut -514 -256 -222 -160 -200

Indiana -2,260 -1,653 -150 -484 77

Rhode Island -295 -112 -86 -96 -74

Delaware -248 5 -86 -99 -40

Ohio -7,743 -1,771 -57 -1,290 566

Iowa -2,228 -1,107 -48 -235 157

Missouri -5,001 369 376 -663 935

New York -4,073 -2,077 1,071 471 1,357

All Northern States -128,882 -40,740 -13,874 -29,702 -3,465

Table A7.2 continued 

a

There are three scenarios, each representing a global greenhouse storyline (IPCC 2007) paired with a harvest regime. Storyline A1B assumes moderate greenhouse 

gas emissions, moderate gains in population, and large gains in income and energy consumption (but with a balanced renewable/fossil fuel portfolio); A2 assumes 

high greenhouse gas emissions, large gains in population and energy consumption, and moderate gains in income; and B2 assumes low greenhouse gas emissions with 

moderate gains in population, income, and energy consumption. Scenario projections (labeled –BIO) assume harvest will increase to reflect increased harvest for 

bioenergy production.
380

-------------------------------------------------------------(1,000 dry tons)------------------------------------------------------------

Connecticut 459 921 599 935 608 13

Delaware 772 113 74 124 81 0

Illinois 5,612 539 350 2,415 1,570 58

Indiana 6,930 1,227 798 1,970 1,281 34

Iowa 2,274 522 339 981 638 1

Maine 0 9,178 5,966 7,030 4,570 141

Maryland 3,406 931 605 1,191 774 57

Massachusetts 3,465 200 130 1,603 1,042 0

Michigan 9,713 5,368 3,489 6,888 4,477 46

Minnesota 4,166 4,166 2,708 6,179 4,016 89

Missouri 11,209 2,520 1,638 3,959 2,573 259

New Hampshire 2,708 1,261 820 2,517 1,636 23

New Jersey 2,155 355 231 650 422 5

New York 10,362 4,733 3,076 7,337 4,769 91

Ohio 7,555 3,241 2,107 3,631 2,360 75

Pennsylvania 10,095 6,416 4,170 6,810 4,427 570

Rhode Island 565 10 7 137 89 1

Vermont 1,184 1,799 1,170 1,584 1,030 0

West Virginia 9,013 5,048 3,281 4,892 3,180 394

Wisconsin 6,784 5,284 3,435 5,307 3,449 212

All Northern States 98,427 53,831 34,990 66,141 42,992 2,068

Table A7.3—Values for annually available woody biomass for bioenergy by net annual woody biomass increase 

(NAWI), logging residues, standing dead material, and unused mill byproducts by State, 2010. Note that logging 

residues and standing dead material were assessed for both 100 percent availability and 65 percent availability 

to account for an assumed 35 percent retention rate for residues and dead material for ecological stability and 

physical inaccessibility.

State NAWI Logging Residues Standing Dead Unused mill 

 100 percent 65 percent 100 percent 65 percent byproducts

Please refer to the Literature Cited section at the end of Chapter 7 for the references.
A p p e n d i c e s 381

Table A8.1—Estimated value of shipment of forest products, 2001 to 2011, in the North (U.S. Census

Bureau 2012).

State

Year

1997 2002 2005 2006 2007 2008 2009 2010 2011

Appendix Chapter 8

-------------------------------------------------------------(million dollars)------------------------------------------------------------

Connecticut 191 215 242 251 244 -- -- -- 156

Delaware 90 89 -- -- -- -- -- -- --

Illinois 1,138 1,301 1,500 1,515 1,729 1,695 1,294 1,423 1,322

Indiana 3,167 3,121 3,558 3,434 3,243 2,823 2,173 2,328 2,502

Iowa 1,063 1,464 1,884 1,979 1,802 1,654 1,322 1,434 1,411

Maine 925 1,079 1,290 1,150 1,149 1,100 702 776 797

Maryland 567 905 1,118 1,166 802 589 457 464 441

Massachusetts 398 506 732 768 617 538 385 442 451

Michigan 2,034 2,022 2,501 2,535 2,410 2,027 1,645 1,930 1,903

Minnesota 2,499 3,002 3,697 3,643 3,585 2,977 2,228 2,415 2,398

Missouri 1,030 1,169 1,264 1,240 1,289 1,150 893 908 998

New Hampshire 476 629 606 599 544 437 383 387 401

New Jersey 406 484 715 810 806 683 558 498 455

New York 1,334 1,439 1,607 1,713 1,622 1,482 1,032 1,163 1,114

Ohio 2,386 2,505 2,749 2,784 2,559 2,352 1,818 1,909 2,019

Pennsylvania 3,293 4,090 5,539 5,401 5,027 4,359 3,475 3,386 3,261

Rhode Island 101 137 -- -- -- -- -- --

Vermont 420 429 424 416 383 344 277 239 239

West Virginia 1,074 1,620 1,999 1,887 1,808 1,522 1,034 1,191 1,185

Wisconsin 3,748 4,302 5,281 5,169 4,756 4,184 3,159 3,297 3,324

All Northern States 26,341 30,507 36,704 36,460 34,376 29,917 22,837 24,190 24,378

U.S. total 88,470 89,436 112,095 112,403 102,029 87,765 65,440 -- --

--- Value is zero or not available.
382

-------------------------------------------------------------(million dollars)------------------------------------------------------------

Connecticut 1,839 1,736 1,675 1,700 1,791 1,678 1,554 1,501 1,560

Delaware 804 647 549 -- -- -- -- -- --

Illinois 5,873 5,290 5,801 5,782 5,768 5,829 5,239 5,615 5,281

Indiana 2,765 3,158 3,305 3,416 3,834 3,777 3,517 3,872 3,901

Iowa 1,531 1,345 1,407 1,522 1,759 1,714 1,467 1,611 1,638

Maine 4,460 4,131 3,840 3,476 3,914 4,491 4,080 3,512 4,028

Maryland 1,166 1,216 1,371 1,314 1,210 1,063 1,005 1,091 1,056

Massachusetts 4,042 3,412 3,340 3,267 3,546 3,257 2,829 2,988 2,896

Michigan 4,878 4,632 4,802 4,965 4,944 5,189 4,482 4,848 4,968

Minnesota 4,050 4,050 5,067 4,384 5,204 5,420 4,791 5,504 5,853

Missouri 2,986 3,660 4,256 4,579 4,647 4,574 4,374 4,513 4,886

New Hampshire 882 511 496 415 -- -- -- -- --

New Jersey 3,211 3,255 3,476 3,241 3,216 3,198 2,760 2,427 2,582

New York 5,140 4,512 4,947 5,059 4,996 5,190 5,022 5,397 5,659

Ohio 6,844 6,858 6,904 7,422 8,119 7,785 6,653 7,188 7,415

Pennsylvania 8,901 9,236 9,793 10,935 10,285 11,057 10,503 11,336 11,554

Rhode Island 303 259 264 257 247 254 158 -- --

Vermont 382 390 302 -- -- -- -- -- --

West Virginia 130 200 -- -- -- -- -- --

Wisconsin 12,652 13,603 13,261 13,806 14,193 14,258 13,042 14,221 14,465

All Northern 

States

72,840 72,103 74,855 75,540 77,675 78,734 71,476 75,624 77,742

U.S. total 150,296 153,223 161,929 169,033 176,108 179,249 161,816 -- --

-- Value is zero or not available.

Table A8.2—Estimated value of shipment of paper and paperboard in the North, 2001 to 2011 (U.S. Census 

Bureau 2012).

State

Year

1997 2002 2005 2006 2007 2008 2009 2010 2011

Please refer to the Literature Cited section at the end of Chapter 8 for the references.
A p p e n d i c e s 383

-----------------------------------------------------(percent)-----------------------------------------------------

Connecticut 64.1 11.5 0.9 16.2 5.8 1.7

Delaware 28.4 45.6 1.4 15.3 5.2 4.0

Maine 54.8 7.7 1.3 26.1 3.7 6.3

Maryland 43.5 40.7 2.6 9.5 2.7 0.9

Massachusetts 62.9 7.6 1.4 17.7 6.1 4.2

New Hampshire 61.3 10.2 1.3 20.7 4.2 2.4

New Jersey 48.4 28.0 1.0 12.7 8.6 1.3

New York 51.2 28.1 0.5 17.5 1.9 0.7

Ohio 31.6 50.8 0.4 14.3 2.3 0.6

Pennsylvania 42.7 45.5 1.4 9.7 0.4 0.2

Rhode Island 64.8 5.7 0.8 19.0 7.9 1.9

Vermont 39.7 28.1 1.7 22.4 5.5 2.6

West Virginia 62.2 25.4 1.8 10.4 0.2 0.1

Northeast total 47.9 32.0 1.2 14.1 3.4 1.4

Illinois 15.2 64.8 1.8 15.2 2.4 0.7

Indiana 15.2 66.8 0.8 14.9 1.9 0.5

Iowa 12.1 52.3 8.0 25.4 1.7 0.6

Michigan 31.2 47.5 2.1 12.2 6.1 1.0

Minnesota 17.7 52.4 1.1 17.6 3.7 7.4

Missouri 28.6 44.7 6.5 19.0 0.8 0.3

Wisconsin 18.3 62.0 2.2 14.5 2.2 0.6

North Central total 20.9 56.7 2.5 15.5 3.0 1.3

All Northern States 37.0 42.0 1.7 14.7 3.3 1.4

Florida 18.8 17.9 13.8 28.9 14.4 6.1

Georgia 64.0 18.8 3.1 8.9 4.0 1.3

North Carolina 55.9 22.8 1.1 14.5 5.0 0.6

South Carolina 52.9 21.4 2.3 15.4 6.0 1.9

Virginia 48.4 28.1 3.4 13.9 4.5 1.7

Southeast total 44.9 20.6 6.1 17.8 7.8 2.8

Table A10.1—Percentage and area of additional urban development, 1990 to 2000, in various land-cover 

classes in the conterminous United States (Nowak et al. 2005).

State and region Foresta Agricultureb Otherc Developedd Woody 

wetlande

Herbaceous 

wetlandf

Appendix Chapter10
384

-----------------------------------------------------(percent)-----------------------------------------------------

Alabama 55.5 30.2 1.1 8.8 4.2 0.2

Arkansas 36.8 44.8 0.6 16.3 1.1 0.4

Kentucky 38.1 46.1 0.4 13.3 1.9 0.2

Louisiana 36.3 38.7 1.1 16.5 4.4 3.1

Mississippi 41.9 39.5 1.0 11.6 5.4 0.6

Oklahoma 16.9 38.5 24.4 19.8 0.3 0.1

Tennessee 48.0 37.6 0.6 12.4 1.4 0.1

Texas 21.2 40.9 22.1 14.3 0.5 0.9

South Central total 33.8 39.3 10.6 13.7 1.8 0.7

All Southern States 39.9 29.0 8.1 16.0 5.1 1.9

Kansas 8.3 46.8 23.7 19.6 0.1 1.4

Nebraska 3.2 68.9 12.4 14.4 0.2 0.8

North Dakota 3.3 32.7 19.5 42.9 0.1 1.5

South Dakota 5.5 50.6 19.9 22.2 0.4 1.4

All Great Plains States 6.3 51.3 20.1 20.8 0.2 1.3

Arizona 8.4 19.2 60.7 11.6 0.0 0.0

Colorado 10.3 26.1 48.6 15.0 0.0 0.0

Idaho 3.1 54.6 26.0 15.5 0.5 0.2

Montana 5.5 37.4 23.9 30.9 1.9 0.3

Nevada 0.8 7.7 77.8 13.5 0.1 0.1

New Mexico 3.2 11.4 73.8 11.0 0.4 0.1

Utah 6.8 40.5 41.1 11.1 0.5 0.1

Wyoming 1.8 11.1 60.2 24.5 0.9 1.5

Intermountain total 6.2 22.5 57.6 13.4 0.2 0.1

All Rocky Mountain States 6.3 27.0 51.7 14.5 0.2 0.3

Oregon 26.3 41.3 14.2 17.3 0.3 0.5

Washington 46.5 20.4 13.9 18.4 0.7 0.1

Pacific Northwest total 40.3 26.9 14.0 18.0 0.6 0.2

California 9.3 20.1 58.9 11.3 0.1 0.4

Pacific Southwest total 9.3 20.1 58.9 11.3 0.1 0.4

All Pacific Coast States 19.1 22.2 44.7 13.4 0.2 0.3

Conterminous U.S. total 33.4 32.7 14.0 15.1 3.5 1.4

State and region Foresta Agricultureb Otherc Developedd Woody 

wetlande

Herbaceous 

wetlandf

Table A10.1 continued 
A p p e n d i c e s 385

State and region Foresta Agricultureb Otherc Developedd Woody 

wetlande

Herbaceous 

wetlandf

Table A10.1 continued 

----------------------------------------------------------(acres)----------------------------------------------------------

Connecticut 108,600 19,400 1,500 27,500 9,800 2,800

Delaware 15,800 25,500 800 8,500 2,900 2,200

Maine 22,800 3,200 600 10,800 1,600 2,600

Maryland 98,800 92,500 6,000 21,600 6,100 2,100

Massachusetts 198,600 24,000 4,400 55,900 19,400 13,300

New Hampshire 66,100 11,000 1,400 22,300 4,500 2,500

New Jersey 130,200 75,300 2,600 34,200 23,300 3,400

New York 152,500 83,700 1,500 52,200 5,700 2,000

Ohio 131,600 211,500 1,800 59,400 9,700 2,600

Pennsylvania 251,800 268,600 8,500 57,500 2,600 1,500

Rhode Island 26,400 2,300 300 7,700 3,200 700

Vermont 7,000 5,000 300 4,000 1,000 500

West Virginia 47,600 19,500 1,400 8,000 100 100

Northeast total 1,257,900 841,400 30,800 369,600 89,800 36,400

Illinois 59,900 254,800 7,000 59,600 9,300 2,900

Indiana 45,900 201,100 2,300 44,800 5,600 1,400

Iowa 9,500 41,200 6,300 20,100 1,300 500

Michigan 124,800 190,100 8,400 48,700 24,500 4,100

Minnesota 30,000 88,700 1,800 29,900 6,300 12,600

Missouri 61,800 96,500 14,100 40,900 1,800 700

Wisconsin 39,000 131,700 4,700 30,900 4,800 1,400

North Central total 370,800 1,004,200 44,600 274,800 53,600 23,400

All Northern States 1,628,700 1,845,600 75,500 644,400 143,400 59,800

Florida 182,300 173,200 133,500 279,700 139,400 59,300

Georgia 472,100 138,400 22,700 65,700 29,300 9,800

North Carolina 381,900 155,900 7,700 99,400 34,100 4,000

South Carolina 161,800 65,500 6,900 47,200 18,300 6,000

Virginia 141,800 82,400 9,900 40,700 13,000 5,100

Southeast total 1,340,000 615,500 180,700 532,700 234,100 84,100
386

----------------------------------------------------------(acres)----------------------------------------------------------

Alabama 163,800 89,200 3,400 25,900 12,300 700

Arkansas 51,200 62,400 800 22,700 1,500 600

Kentucky 63,000 76,100 700 21,900 3,200 200

Louisiana 73,600 78,400 2,200 33,400 8,900 6,200

Mississippi 57,900 54,500 1,300 16,100 7,500 800

Oklahoma 22,200 50,400 31,900 25,900 400 100

Tennessee 190,000 148,600 2,200 48,900 5,400 300

Texas 209,600 405,300 218,800 141,800 5,300 9,000

South Central total 831,300 964,900 261,300 336,600 44,400 18,000

All Southern States 2,171,200 1,580,400 442,000 869,300 278,500 102,100

Kansas 8,700 48,900 24,700 20,500 100 1,500

Nebraska 1,500 31,600 5,700 6,600 100 400

North Dakota 600 5,700 3,400 7,500 0 200

South Dakota 1,100 10,200 4,000 4,500 100 300

All Great Plains States 11,800 96,300 37,800 39,100 300 2,300

Arizona 28,400 65,500 206,600 39,600 0 0

Colorado 18,000 45,700 85,200 26,200 0 0

Idaho 2,000 34,200 16,300 9,700 300 100

Montana 1,700 11,300 7,200 9,300 600 100

Nevada 1,200 10,800 109,100 18,900 100 100

New Mexico 4,600 16,300 105,800 15,700 600 100

Utah 7,100 42,000 42,700 11,600 500 100

Wyoming 300 2,000 10,700 4,300 200 300

Intermountain total 63,300 227,800 583,600 135,400 2,400 900

All Rocky Mountain States 75,100 324,200 621,400 174,400 2,700 3,300

State and region Foresta Agricultureb Otherc Developedd Woody 

wetlande

Herbaceous 

wetlandf

Table A10.1 continued 
A p p e n d i c e s 387

----------------------------------------------------------(acres)----------------------------------------------------------

Oregon 33,700 52,900 18,200 22,200 400 700

Washington 133,400 58,600 40,000 52,600 1,900 200

Pacific Northwest total 167,100 111,400 58,100 74,800 2,300 800

California 83,600 181,200 530,400 101,500 600 3,500

Pacific Southwest total 83,600 181,200 530,400 101,500 600 3,500

All Pacific Coast States 250,700 292,600 588,600 176,200 2,900 4,300

Conterminous U.S. total 4,125,700 4,042,800 1,727,600 1,864,400 427,500 169,500

a

Deciduous, evergreen, or mixed forests, with 25 to 100 percent tree cover.

b

Pasture, hay, row crops, small grains, or fallow (75 to 100 percent of the cover); or orchard, vineyards, or other planted woody vegetation (25 

to 100 percent of the cover).

c

Bare, rock, sand, clay, quarries, strip mines, gravel pits, transitional areas, or shrubland (25 to 100 percent of the cover); or natural, 

seminatural grasslands and herbaceous plants (75 to 100 percent of the cover).

d

Areas characterized by a high percentage (30 percent or more) of constructed materials or vegetation (primarily grasses) planted in 

developed settings for recreation, erosion control, or aesthetic purposes (75 to 100 percent of the cover). 

e

Areas where forest or shrubland vegetation accounts for 25 to 100 percent of the cover and the soil or substrate is periodically saturated 

with or covered by water.

f

Areas where perennial herbaceous vegetation accounts for 75 to 100 percent of the cover and the soil or substrate is periodically saturated 

with or covered by water.

State and region Foresta Agricultureb Otherc Developedd Woody 

wetlande

Herbaceous 

wetlandf

Table A10.1 continued 

Please refer to the Literature Cited section at the end of Chapter 10 for the references.
388

“Best of all he loved the fall, 

the leaves yellow on the cottonwoods, 

leaves floating on the trout streams 

and above the hills, 

 the high blue windless skies.

Now he will be 

 a part of them forever.”

–Ernest Hemingway

FUTURE FORESTS OF THE NORTHERN UNITED STATES
In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the 

USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from 

discriminating based on race, color, national origin, religion, sex, gender identity (including gender expression), sexual orientation, 

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Remedies and complaint filing deadlines vary by program or incident.

Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, 

American Sign Language, etc.) should contact the responsible Agency or USDA’s TARGET Center at (202) 720-2600 (voice and TTY) 

or contact USDA through the Federal Relay Service at (800) 877-8339. Additionally, program information may be made available in 

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To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at 

http://www.ascr.usda.gov/complaint_filing_cust.html and at any USDA office or write a letter addressed to USDA and provide in 

the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your 

completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 

1400 Independence Avenue, SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov.
DE P A R TMENT OF AGRICU LT U RE

FOREST SERVICE

U S

U.S. Forest Service

General Technical Report

NRS-151

2016