The Editors:

David N. Wear, Project Leader, Center for Integrated Forest 

Science, Southern Research Station, U.S. Department of Agriculture 

Forest Service, Raleigh, NC 27695; and John G. Greis, Resource 

Analyst, State and Private Forestry, Southern Region, U.S. 

Department of Agriculture, Forest Service, Tallahassee, FL 32399.

August 2013

Southern Research Station

200 W. T. Weaver Blvd.

Asheville, NC 28804

www.srs.fs.usda.gov

Cover images: top to bottom: (1) Tellico-Robbinsonville Road scenic vista, 

USDA Forest Service; (2) Chipping by David J. Moorhead, University of 

Georgia; (3) Kids playing in the forest, USDA Forest Service; (4) Palmetto and 

hardwood hammock, USDA Forest Service; (5) Forest river scenic vista by 

Chris Evans, Illinois Wildlife Action Plan; (6) Kudzu by R. Kindlund, USDA 

Forest Service; far right: Long leaf pine in the fog, USDA Forest Service.

DISCLAIMER

The use of trade or firm names in this publication is for reader 

information and does not imply endorsement by the U.S. Department 

of Agriculture of any product or service.
The Southern Forest Futures Project: 

Technical Report

David N. Wear and John G. Greis
ii

Acknowledgments

The Southern Forest Futures Project was a cooperative and highly complex effort requiring 

contributions from many organizations, public agencies, universities, and individuals. We wish to 

express our grateful appreciation to all who provided the ideas, guidance, and technical support 

needed to make the project both technically sound and useful, especially the following:

The leaders of the USDA Forest Service, Peter Roussopoulos, Director, Southern Research Station 

(Ret.) and Charles Myers, Regional Forester, Southern Region (Ret.), who recognized the importance 

of looking into the future of southern forests so that they might be best managed and conserved. 

James Reaves and Rob Doudrick, Directors, Southern Research Station and Liz Agpaoa, Regional 

Forester, Southern Region, who continued to support the effort to its conclusion after the retirements 

of their predecessors. Ken Arney, USDA Forest Service, Southern Region, who provided consultation 

and continuous support throughout the process.

The Chairmen of the Southern Group of State Foresters, John Burwell (Ret.) and Charlie Morgan, 

who agreed to co-sponsor the effort and provided strong support on behalf of the 13 State forestry 

agencies.

The Futures Project team, authors, and co-authors of all of the individual chapters of the Technical 

Report and the five subregional reports, who worked tirelessly, cooperatively, and patiently throughout 

the process to make the final product both valuable to the public and one of technical excellence. 

Their work is indeed the foundation of the project. Peer reviewers of chapter and report drafts, who 

contributed technical review and constructive criticism to make the final products as valuable and 

technically sound as possible. 

Nancy Walters, who guided public and team meetings and consulted with the project’s co-leaders to 

build a process that benefitted from and was responsive to public input and issues of concern.

The Science Delivery and Public Affairs teams of the Southern Research Station and Southern 

Region, who worked from project origination to completion to ensure the project’s process and results 

were accessible to the public and the media and reports met high publication standards. In particular, 

Jennifer Plyler, Stevin Westcott, Michael Robinson, Zoe Hoyle, Patricia Matteson, Maureen Stuart, 

Gary Kuhlmann, and Helen Robinson of the Southern Research Station, and Stephanie Johnson and 

Steve Bekkerus of the Southern Region, USDA Forest Service.

Michaela Foster, who maintained and managed process records for the project.

Carol Whitlock, Altamont Professional Services LTD., who expertly edited all chapters and reports. 

Shawna Reid, who patiently coordinated the development of map products across the chapters of the 

Technical Report and various phases of the project.

The interested public, who participated in the 15 public meetings conducted around the South and 

contributed the foundation of their ideas, expertise, and opinions upon which the project was built.

The Wear and Greis families, whose patience, understanding, and support enabled the co-leaders to 

concentrate on the Futures Project and travel widely to ensure its successful conclusion.

David Wear and John Greis

Southern Forest Futures Project Co-Leaders
iii

A second phase of the Futures Project, ongoing as 

this report is being completed, examines the findings 

of the Technical Report from the perspective of each 

of five separate subregions. We contend that because 

of the diversity of resource settings in the South, the 

implications of the Futures Project for management, 

restoration, and potential policy would vary by 

subregion. Separate subregional reports are now being 

completed for the five subregions: (1) AppalachianCumberland, (2) Piedmont, (3) Coastal Plain, (4) 

Mississippi Alluvial Valley, and (5) Mid-South.

In addition to soliciting public input, we have sought 

expert input from the study team (more than 50 

scientists and analysts have been involved) and 

through peer reviews from many specialists across the 

region. In addition, we shared the peer reviewed drafts 

of the summary report and this technical report with 

the public for their review. All of the reports produced 

in the Futures Project have greatly benefited from this 

thorough vetting process.

From its inception, the Southern Forest Futures Project 

has sought to provide an information foundation 

for evaluating the future of the region’s forests and 

consequences for its people, and a platform for 

discussing how to negotiate the future. The ultimate 

measure of our success will be the extent to which our 

findings advance these discussions.

Literature Cited

Wear, D.N.; Greis, J.G., eds. 2002a. The Southern Forest 

Resource Assessment: technical report. Gen. Tech. Rep. 

SRS-53. Asheville, NC: U.S. Department of Agriculture 

Forest Service, Southern Research Station. 635 p.

Wear, D.N.; Greis, J.G. 2002b. The Southern Forest Resource 

Assessment: summary report. Gen. Tech. Rep. SRS54. Asheville, NC: U.S. Department of Agriculture Forest 

Service, Southern Research Station. 103 p. 

Wear, D.N.; Greis, J.G. 2012. The Southern Forest Futures 

Project: summary report. Gen. Tech. Rep. SRS-159. 

Asheville, NC: U.S. Department of Agriculture Forest 

Service, Southern Research Station. 54 p.

Wear, D.N.; Greis, J.G.; Walters, N. 2009. The Southern Forest 

Futures Project: using public input to define the issues. Gen. 

Tech. Rep. SRS–115. Asheville, NC: U.S. Department of 

Agriculture Forest Service, Southern Research Station. 17 p.

Preface

The Southern Forest Futures Project was initiated in 

2008 as an effort to study and understand the various 

forces reshaping forests across the 13 Southeastern 

States. Chartered by the USDA Forest Service 

Southern Region and Southern Research Station along 

with the Southern Group of State Foresters, the effort 

built from the Southern Forest Resource Assessment 

(Wear and Greis 2002a, 2002b), which had identified 

and explored the implications of several forces of 

change reshaping the region’s forests including land 

development, new forest pests, and increasing timber 

demands. These and other changes have continued 

apace or accelerated since 2002, warranting another 

look at the implications for southern forests and the 

many services and values that they provide.

The Futures Project was launched with a set of 15 

public meetings from across the South to gather a 

broad range of insights into ongoing changes and 

anticipated trends affecting forests in the region 

and local areas. In contrast to public involvement 

processes for resource planning or administrative 

decision making—where input focuses on deducing 

values held by the public and their preferences 

regarding outcomes—we simply sought information 

regarding the range of issues, questions, and 

uncertainties regarding the future of forests and their 

various goods, services, and values in the South. We 

sought broad participation from all “stakeholders,” 

including landowners, researchers, nongovernmental 

organizations, and forestry agencies and information 

from the public meetings was used to help organize the 

design of the project (Wear and others 2009).

After compiling public input, Phase I of the Futures 

Project was initiated. This involved constructing 

forecasts of future forest conditions to the extent 

possible using a set of future scenarios describing, 

among other factors, changes in populations, 

economics, and climate to the year 2060. A set of 

resource issues (meta-issues) developed from the 

public input sessions defined a set of questions that 

needed to be addressed. A lead scientist was identified 

for each of these meta-issues, research teams were 

convened, and study plans were developed and 

thoroughly reviewed. The detailed results of the 

forecasting and meta-issue teams are described in this 

technical report. A summary report (Wear and Greis 

2012) provides a synthesis and identifies a set of key 

findings from the technical report.
iv

Contents

Chapter 1. Design of the Southern Forest Futures Project

David N. Wear and John G. Greis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Chapter 2. Constructing Alternative Futures

	 David N. Wear, Robert Huggett, and John G. Greis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 3. Climate Change Summary

	 Steve McNulty, Jennifer Moore Myers, Peter Caldwell, and Ge Sun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Chapter 4. Forecasts of Land Uses

David N. Wear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Chapter 5. Forecasts of Forest Conditions

	 Robert Huggett, David N. Wear, Ruhong Li, John Coulston, and Shan Liu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Chapter 6. Forest Ownership Dynamics of Southern Forests

	 Brett J. Butler and David N. Wear. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Chapter 7. Outdoor Recreation in a Shifting Societal Landscape

	 H. Ken Cordell, Carter J. Betz, and Shela H. Mou. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Chapter 8. Outdoor Recreation

	 J. M. Bowker, Ashley Askew, H. Ken Cordell, and John C. Bergstrom. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Chapter 9. Markets

	 David N. Wear, Jeffrey Prestemon, Robert Huggett, and Douglas Carter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
v

Chapter 10. Forest Biomass-Based Energy

	 Janaki R. R. Alavalapati, Pankaj Lal, Andres Susaeta, Robert C. Abt, and David N. Wear. . . . . . . . . . . . . . . . . . . 213

Chapter 11. Effect of Taxes and Financial Incentives on 

Family-Owned Forest Land 

John L. Greene, Thomas J. Straka, and Tamara L. Cushing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

Chapter 12. Employment and Income Trends and Projections for 

Forest-Based Sectors in the U.S. South

	 Karen L. Abt. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293

Chapter 13. Forests and Water

	 Graeme Lockaby, Chelsea Nagy, James M. Vose, Chelcy R. Ford, Ge Sun, Steve McNulty,

	 Pete Caldwell, Erika Cohen, and Jennifer Moore Myers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309

Chapter 14. Wildlife and Forest Communities

	 Margaret Trani Griep and Beverly Collins. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

Chapter 15. The Invasion of Southern Forests by Nonnative Plants: 

Current and Future Occupation, with Impacts, Management Strategies, 

and Mitigation Approaches

	 James H. Miller, Dawn Lemke, and John Coulston. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397

Chapter 16. Invasive Pests—Insects and Diseases

	 Donald A. Duerr and Paul A. Mistretta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457

Chapter 17. Fire

	 John A. Stanturf and Scott L. Goodrick. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509
vi

ABSTRACT

The Southern Forest Futures Project provides a science-based “futuring” analysis of the

forests of the 13 States of the Southeastern United States. With findings organized in a set of

scenarios and using a combination of computer models and science synthesis, the authors of

the Southern Forest Futures Project examine a variety of possible futures that could shape

forests and the many ecosystem services and values that forests provide. The science findings

and modeling results could inform management and policy analysis of the South’s forests. In

the chapters of this technical report, the authors provide detailed findings and results as well

as sets of key findings and implications.

Keywords: Forest conservation, futuring, integrated assessment, Southern Forest Futures

Project, sustainability.
1

chapter 1. Design of the Southern Forest Futures Project

	 David N. Wear and John G. Greis1

Introduction

The South has a unique human and landscape history and

forests that reflect many episodes of change. Spanning the

13 States from Virginia to Texas, the South still contains

a widely diverse complement of physical, economic, and

ecological conditions; where forests and other native habitats

play an important role not only in supporting diversity of

native plants and animals but also in providing economic,

aesthetic, and cultural values for its residents. Perhaps more

so than in any other region of the United States, southern

forests continue to change in response to direct human uses

and to changes in the physical and biological environment,

raising important questions about their potential future.

Because these and other forces will dictate the long

term sustainability of forest resources, it is important to

scientifically assess their consequences so that society can

make informed policy and management decisions.

The Southern Forest Futures Project represents a response

to this need for a careful, science-based assessment of the

South’s forests. It was chartered by the Southern Research

Station and the Southern Region of the Forest Service, U.S.

Department of Agriculture, in cooperation with the Southern

Group of State Foresters, to forecast potential changes and

to clarify, to the extent possible, how important trends and

potential structural changes might affect a variety of forest

values, conditions, and uses (Wear and others 2009). The

following definition of success shapes the objectives, design,

and conduct of the Futures Project: A successful Southern

Forest Futures Project will provide a credible, objective

information foundation that helps shape sustainable

forests in the South through the informed actions of private

and public forest managers and through the design and

development of effective land use and forest policies.

The Futures Project cooperators anticipate a broad audience,

one that reflects the diverse population of forest owners

and forest users, and recognizes that about 90 percent of

1 David N. Wear is the Project Leader, Center for Integrated Forest Science,

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

Raleigh, NC 27695. John G. Greis is a Resource Specialist, Southern

Region, U.S. Department of Agriculture Forest Service, Tallahassee, FL

32399.

southern forests are privately owned. Within this context,

sustainability starts with understanding current trends,

anticipating changes, and identifying potential future

scarcities; and ends with designing and implementing

management and policy in response.

The first step in developing questions and issues to be

addressed was to engage in discussions with a cross section

of forest owners and forest users in a set of workshops

conducted in all 13 States. With the public’s interests defined

through the workshops, we set out to design an assessment

process that would target the most important questions and

make the most efficient use of available tools and science.

Success also depended on our ability to provide three

different types of information. The first was to take a

comprehensive “systems science” approach in forecasting

change at a scale detailed enough to be meaningful

for the analysis of economic and ecological issues and

their implications, but also capable of addressing major

uncertainties through a set of potential futures. Second

was to collect information about issues that are developing

rapidly, and are not suited for modeling because of their

complexity or their dependence on policy development

or other highly uncertain events. Third was to identify

the management implications of forecasted changes,

which required a tertiary analysis coupling changes and

implications with management science to determine how

forest management choices might affect—or be affected—by

undesirable outcomes.

Satisfying these three sets of information needs defined

distinct and necessary phases of the Futures Project (fig. 1.1):

• A forecasting phase using a simulation modeling

framework to play out the implications of several scenarios

for land use, forest conditions, and forest uses in the South.

We assembled a forecasting team to deploy a modeling

system for forecasting these scenarios of the future.

• A meta-issue phase analyzing and predicting the future

of several key issues of concern to southern forested

landscapes, their functions, and the values that are derived

from them. We recruited experts to address specific

questions about these meta-issues.

Chapter 1.

Design of the Southern Forest 

Futures Project


4 The Southern Forest Futures Project

objective was to shape the first planning step of the Southern

Forest Futures Project.

For the forecasting phase we enlisted a team of experts to

summarize the forces of change and begin shaping a set of

alternative futures for analysis using quantitative models

(chapter 2). The public’s input was the starting point for

a structured workshop in which the team developed a

manageable number of scenarios that address the issues

raised by the public.

For each meta-issue identified through the scoping process,

we continued to use public input as we developed research

questions and associated important elements to be evaluated.

An expert scientist was assigned to manage the analysis

of each meta-issue. We also linked specific concerns and

questions to their applicable subregions. These would be

considered by the team addressing management implications

in each of the five subregions.

The meetings clearly showed that the public anticipates

(and is concerned about) important changes they believe

will occur on the forested landscapes of the South over the

next 50 years (Wear and others 2009). Multiple forces will

drive these changes, and their effects will span ecological,

economic, and social dimensions. The public sessions clearly

validated the need to undertake the Futures Project, and

the public’s input provides the foundation upon which we

designed the three analysis phases of the effort.

Table 1.1—Locations, subregions represented, and schedule of public meetings held for 

the Southern Forest Futures Project in 2008

Meeting location Subregion represented Date

Baton Rouge, LA Coastal Plain/Mississippi Alluvial Valley January 29

Stoneville, MS Coastal Plain/Mississippi Alluvial Valley January 30

Gainesville, FL Coastal Plain February 7

Charleston, SC Coastal Plain February 8

Little Rock, AR Mid-South/Mississippi Alluvial Valley February 13

College Station, TX Mid-South February 11

Stillwater, OK Mid-South February 12

Lexington, KY Appalachian-Cumberland February 19

Nashville, TN Appalachian-Cumberland February 21

Raleigh/Durham, NC Piedmont/Coastal Plain February 25

Blacksburg, VA Appalachian-Cumberland February 26

Asheville, NC Appalachian-Cumberland February 27

Athens, GA Piedmont/Coastal Plain March 6

Auburn, AL Piedmont/Coastal Plain March 7

Webinar #1 All subregions April 8 evening

Webinar #2 All subregions April 16 afternoon

Webinar #3 All subregions April 16 evening
5

chapter 1. Design of the Southern Forest Futures Project

The Future of Southern Forests: An Ongoing Conversation

1969 > South’s Third Forest—Supported by the wood products industry and large private

forest landowners, the third forest report used literature reviews and an evaluation of trends to

evaluate the future of timber supply in light of increased demands for wood products and perceived

underinvestment in private forest lands (Wheeler 1970). Concerned that timber scarcity would limit

growth of the wood products sector, the report recommends policies and strategies to encourage

planting and increase management on private forests; protect forests from insects, diseases, and

fires; and build stronger institutions for forestry training, technology transfer, and research. Its

forecasts of population-driven urbanization and expansion of timber growing and production have

been realized in the South.

1988 > South’s Fourth Forest...alternatives for the future—Nearly 20 years after the

third forest report, the Forest Service asked some of the same questions about the potential future

of the timber-producing sector in the South (USDA Forest Service 1988), this time using a stateof-the-art timber market model and a technical analysis of various policy alternatives for reversing

underinvestment by nonindustrial private forest owners. Their findings anticipate the growth in

timber production realized through 2000 and point to a similar suite of programs and policies to

encourage reforestation, management, and forest protection. While the report dedicates a few pages

to impacts of timber projections on wildlife and water, its emphasis is squarely on the future of

timber management and production.

2002 > Southern Forest Resource Assessment—The growth in forest management and

timber production largely anticipated by the third and fourth forest reports, coupled with the

emergence of satellite chip mills in the late 1990s, raised concerns about the sustainability of

forests in the South (Wear and Greis 2002a, 2002b). An interagency effort let by the Forest Service

and driven by a set of questions developed from extensive public meetings, the Southern Forest

Resource Assessment drew knowledge from extensive literature and data bases to address concerns

ranging from imperiled terrestrial and aquatic species to wetlands; from outdoor recreation to the

influence of policies, regulations, and laws; from air pollution to the future course of timber markets

and land use changes. The assessment identifies urbanization as a key threat to forest sustainability

and raises additional concerns about the effects of increased management intensity on wildlife and

water, and about an increasing scarcity of recreational opportunities in parts of the South.

Today > Southern Forest Futures Project—Six years after the completion of the Southern

Forest Resource Assessment, new issues and questions have arisen. Forest industry has largely

divested its land holdings, science has provided new insights into potential future climates, and

questions about water sustainability are on the horizon. To address these and other questions—

again deriving from extensive public involvement—the Forest Service and Southern Group of

State Foresters commissioned the Southern Forest Futures Project. Where the earlier assessment

relied mostly on literature reviews and stand-alone analysis of future impacts for its forecast of a

most-likely future, this new effort is focused on forecasting the future under a variety of scenarios

and uses these scenarios to integrate findings. The Futures Project builds from the knowledge

foundation of its predecessor, updates some topical areas, and lays out a range of futures for

consideration by policymakers and forest managers.
6 The Southern Forest Futures Project

Forecasting Forest Conditions and Uses 

Forecasting forest conditions and uses required a

determination of the variables that could change the

world surrounding future forests, including the physical

environment (such as climate change), the social environment

(number of people, their relative wealth, and how they use

land), and the economic environment (relative scarcity of

timber and other forest amenities). Following evaluations

of the input from public workshops to sort out the variables

to be addressed, our next step was to conduct a scenario

analysis so that combinations of variables could be projected

and evaluated. In particular, we developed a set of alternative

futures to organize and conduct the forecasting phase of the

project. Derived from scenarios constructed for the 2010

Resources Planning Act Assessment conducted by the Forest

Service, these alternative futures represent a broad range of

internally consistent world possibilities by linking climate,

population, income, and technological advances.

Alternative futures were evaluated using the U.S. Forest

Assessment System, a modeling system designed to forecast

alternative futures for U.S. forests. The Forest Assessment

System is a forward-looking adjunct to the Forest Inventory

and Analysis (FIA) program implemented by the Forest

Service research and development staff. The FIA program

provides nationwide monitoring through repeated inventories

that provide for consistency over time and a high level of

detail. The Forest Assessment System accounts for changes

driven by multiple vectors including biological, physical,

and human factors to generate forecasts of forest inventories.

The modeling approach is designed to address scenarios

changing climate, market-driven timber harvesting, and land

use changes along with changes driven by a successional

transition in forest conditions.

A general schematic of this modeling system (fig. 1.4) starts

with internally consistent combinations of social, economic,

and technology forecasts defined as Cornerstone Futures for

this application of the Forest Assessment System. Linked

to the Cornerstones are various through various general

circulation models (climate change models), each selected

to define a climate forecast that is consistent with the

Cornerstone. Also linked are data from a forest inventory

server, which defines starting conditions for all plots in the

forest inventory.

The modeling framework at the center of this system

shows how future forest conditions are driven by biological

dynamics—such as growth and mortality—that are affected

Figure 1.4–General schematic of the U.S. Forest Assessment System. (Source: Wear and others 2013)

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chapter 1. Design of the Southern Forest Futures Project

by climate factors, allocations among land uses, disposal

of forest land, timber harvesting, and forest management.

The interplay of all of these factors yields a set of outputs,

each of which describes forest projections that are consistent

with the flow of forest products and land uses. Changes in

water, biodiversity, and other ecosystem services can also

be derived from the forecasted changes in forest conditions

and land uses. Many of these are described in subsequent

chapters.

The forest products module of this framework can be

approached in one of two ways. The first is an explicit market

simulation of market-clearing wood products and timber

production in three regions of the United States (North,

South, and West) and all other countries in the world. This

approach accounts for a wide variety of demand drivers

including energy futures, U.S. housing starts, and changes

in paper consumption by households (Ince and others 2011).

The second approach, and one that is used for much of the

analysis of this Futures Project, is to develop price futures

for timber products in the region. Increasing prices are

consistent with increased scarcity of timber; decreasing

prices indicate a lessening of timber scarcity.

The land use module (fig. 1.4) described in chapter 3

simulates changes in all uses of land and is driven by

population and income growth along with the prices of

timber products. Projections of forest area from the land use

module feed into forecasts of future forest conditions and

other analyses described below.

The forest dynamics module of this framework projects the

future of every FIA plot in the forest inventory in a multiple

stage process. The process begins by determining the point

at which the plot is harvested, if ever, and the intensity of

the harvest based on timber prices (from the forest products

module), and the condition of forests on the plot (Polyakov

and others 2010). The age of each plot in the next period

is determined, and if harvested, the plot is determined to

be naturally regenerated or planted. Forecasted climate

including temperature and precipitation is assigned and forest

conditions on the plot are inferred based on the harvest/no

harvest decision, age, and climate selection (chapter 5).

The Forest Assessment System generated simulation results

include forecasts of land use (chapter 4), forest products

(chapter 9), and detailed forest conditions (chapter 5) for the

forecast period (2010 to 2060). In chapter 5, we generate

forecasts on various forest conditions including the volume of

forest biomass, the area of forests by type and age class, and

the carbon contained in the above- and belowground pools

represented. Furthermore, we generate maps forecasting

removals from forests determined by harvesting and land use

changes. Results of these forecasts are used in the analysis

of meta-issues and are summarized for each subregion for

evaluation of management implications.

Evaluating Meta-Issues

We defined a meta-issue as a broad area of concern

that contains a complement of interrelated drivers and

implications. The public input process identified the eleven

meta-issues shown in table 1.2. For each meta-issue, we

synthesized all of the public input into one central question

and an accompanying set of specific issues in need of

resolution. The lead analyst and team member assigned to

each meta-issue designed a study approach. Study plans

are located on the Futures Project Web site (http://www.srs.

fs.usda.gov/futures/process/draftplan/). Reporting of each

meta-issue analysis comprises one or more chapters in this

publication (for example, the meta-issue report for invasive

species is divided into two chapters to address invasive plants

and invasive insects and diseases). The central meta-issue

questions and associated specific issues follow.

Social/economic linkages—How will alternative futures be

affected by changing demographics and values, and how will

these futures alter certain social and economic benefits in the

South?

•	 How are population, demographics, and values changing;

what might these changes mean for forests futures?

•	 How and where will population growth, changing

demographics ownership, and land use affect supply and

demand for different types of forest-based recreation?

• How and where might forest-based employment and

income be affected by anticipated futures?

Table 1.2—Definition of meta-issues for the Southern 

Forest Futures Project

Social and economic factors—recreation, jobs, and income

Timber markets

Wildlife, biodiversity, and forest communities 

Water and forests

Tax influences on forest management and conservation 

Climate change and forest conditions

Fire in southern forests

Forest ownership changes 

Invasive plant species and the integrity of forest ecosystems

Forest insects and diseases

Bioenergy and its potential influence on forests
8 The Southern Forest Futures Project

Wildlife and forest communities—How might changes in

forest environmental and social conditions affect terrestrial

wildlife (birds, mammals, reptiles, and amphibians), their

habitats, and forest vegetation communities in the South?

•	How would anticipated fragmentation and population

growth, urbanization, and related infrastructure affect

wildlife habitats?

•	How might anticipated futures affect wildlife diversity and

where would changes likely be concentrated?

•	What are the implications of anticipated futures for

imperiled, rare, threatened, and endangered wildlife and

plant species?

•	 How will rare forest communities be affected by

anticipated futures?

Water—What roles do forests and forested wetlands play in

producing and protecting water resources in the South, and

how might future land-management and land-use changes

affect these roles?

•	What is the relationship between forests and water timing,

flow, and quality?

•	 How will forest conversion and loss affect these

relationships?

•	What are the implications of intensive forest management

for water?

•	 How do forested wetlands and riparian areas protect water

quality, and what are the potential implications of their

conversion and loss?

•	 How will increased demand interact with forest conversion,

drought, and climate change?

•	What are the known effects of impoundment construction

on forests and associated resources?

Taxes—How might taxation influence retention and

management of forest land in the South?

•	What are the effects of estate, income, severance, and

property taxes for nonindustrial forest owners?

•	 How do differential income taxes affect “C” corporations?

•	 How can/will the tax structure affect conservation

easements or other forms of forest stewardship?

Climate change—How might the environmental conditions

associated with climate change affect forest ecosystem health

and productivity?

•	 What are the critical climate change variables and how are

southern forests likely to be affected by them?

•	Where in the South might forecasted changes in

climate-induced environmental conditions be most/least

significant?

•	 How will potential climate-change outcomes, such as

severe weather events and drought, interact with forest

pests?

•	What are the economic consequences of extreme

weather events for landowners, forest industry, and local

governments?

Fire—How will fire behavior and fire risk change over time,

and what are the likely effects on communities and people?

•	What is the current and potential fire behavior/fire risk

situation in the South, and what factors contribute to

potential changes?

•	What is the likely future of prescribed burning in the

South, including the factors that affect this practice and

alternatives to its use as a management tool?

•	How will restricted or excluded prescribed fire affect

fire-adapted and fire-dependent forest communities and

other dependent species, and where will these effects be

concentrated?

•	What are the economic consequences of reduced

prescribed burning, including potential property and

structural damage and loss, timber devaluation, liability,

and emergency rehabilitation and reforestation costs?

•	Do wildfire and prescribed burning differ in carbon

cycling, air pollution, forest productivity, and forest health?

Forest ownership change—Describe recent and anticipated

changes in forest ownership in the South and the implications

of these changes for forest ecosystem conditions,

management, and productivity.

•	 How much and where has forest land ownership changed

in recent years and where will changes likely to be

concentrated in the future?

•	What are the economic determinants for ownership change

(all ownership categories), and how might they change in

the future?

•	 How will forest-land use and forest uses likely change as a

result of shifts in ownership?

•	 How are forest management practices influenced by

ownership change and what are the ramifications of those

influences?

•	 How will changing forest ownership affect the forest

products industry?

Invasive species—How will invasive plants, insects, and

diseases likely affect southern forests and related ecosystems

in the future?

•	What are the factors influencing historical spread and

forecasting future spread of significant invasive species?

•	What are the expected consequences of the spread of

important invasive species for forest composition, riparian
9

chapter 1. Design of the Southern Forest Futures Project

health, and dependent communities?

•	What is the likelihood of effective invasive species

control in the future, given anticipated fragmentation,

parcelization, and urbanization interactions?

•	What forest species are likely to be completely lost as a

result of the spread of invasive pests?

Bioenergy—What would be the likely effects of the

emergence of a mature bioenergy market on southern forests,

forest owners, and traditional forest product markets?

•	What current and potential technologies are needed to

realize large scale production of biofuels from woody

biomass, including preferred feedstock (if known)?

•	What might be the likely forest management regimes

followed to maximize the production of woody biomass?

•	 How would these regimes affect indicators of forest

ecosystem integrity such as habitat quality, biodiversity,

and soil productivity?

•	 How will the emergence of a bioenergy market affect

competition with traditional forest product markets and

financial returns to landowners?

•	What effects will subsidies or other incentives have on

landowner behavior and wood product markets?

Discerning Management Implications of Futures 

Project Findings

The South’s regional identity sometimes obscures the

substantial diversity of its landscapes. From coastal pine

flatwoods to Blue Ridge escarpments, from cedar swamps

and hardwood hammocks to High Plains mesquite, the

South’s forests are varied across longitude and latitude,

and the region’s diversity of tree species exceeds any other

part of the conterminous United States. The findings of the

Futures Project or any other regional assessment cannot be

meaningfully generalized to the entire South. Rather they

warrant evaluation at finer grains where forecasted change

can be intersected with specific forest and social conditions.

To address the diversity of the South, we include

interpretations of the findings for the subregions shown in

figure 1.2 and, where appropriate, for the 21 sections nested

within these subregions (fig. 1.3). Throughout our analysis

of forecasts and meta-issues, we summarize our findings for

the entire region and for each of the subregions, and follow

up with management implications of forecasts and issue

analyses for each.

The analysis of management implications starts with a

thorough examination of current conditions and a summary

of findings from the forecasts and the meta-issue analyses.

Then a team of experts, coupling forest managers with

scientists, interprets forecasting and analysis results into

implications for management. Management changes

may be directly implied by the forecasts, for example

where intensified management is indicated or where

specialized management is indicated by expected scarcity

of an ecosystem service such as wildlife habitat or water.

Other forecasted changes imply constraints on how forest

management might be practiced in certain places, most

notably where human populations are growing.

By design, the subregional analyses of management

implications are staged for completion after the forecasting

and meta-issue analysis. Accordingly, the results of these

efforts will be published under separate cover at a later

date. We also anticipate that the findings of the Futures

Project will spawn additional studies of the implications for

management and policy across the South. To support such

future study, our findings in all other areas will be readily

accessible online at the Futures Project Web site.

Conclusions

The Southern Forest Futures Project was initiated to

provide interested publics, including forest managers and

policymakers, with insights into the array of possible futures

and a better understanding of what those futures would

mean for forests and their associated values. Anticipating the

future is the first step toward sustainable forest management.

The next is developing management and policy approaches

that lead to desired outcomes in these complex systems. Our

forecasting and meta-issue analyses intend to address the first

step. Our analysis of management implications addresses

the second. The future is uncertain, as is our understanding

of forested ecosystems, the provision of ecosystem services,

and possibilities for management design and effects.

Sustainability is therein a moving target where objectives,

means, and knowledge are all dynamic. The accumulated

data and knowledge arrayed by the Futures Project provides

a starting point for considering the future of southern forests.

Informed management, research, monitoring, and attention

to the mechanisms of change will necessarily define the

region’s path toward forest sustainability.
10 The Southern Forest Futures Project

Wear, D.N.; Greis, J.G., eds. 2002a. Southern forest resource assessment.

Gen. Tech. Rep. SRS-53. Asheville, NC: U.S. Department of Agriculture

Forest Service, Southern Research Station. 635 p.

Wear, D.N.; Greis, J.G. 2002b. Southern Forest Resource Assessment:

summary report. Gen. Tech. Rep. SRS-54. Asheville, NC: U.S.

Department of Agriculture Forest Service, Southern Research Station.

103 p.

Wear, D.N.; Greis, J.G.; Walters, N. 2009. The Southern Forest Futures

Project: using public input to define the issues. Gen. Tech. Rep. SRS-115.

Asheville, NC: U.S. Department of Agriculture Forest Service, Southern

Research Station. 17 p.

Wheeler, P.R. 1970. The South’s third forest. The Journal of Forestry.

March: 142-146.

Literature Cited

Ince, P.J.; Kramp, A.D.; Skog, K.E. [and others]. 2011. U.S. forest products

	 module for RPA forest assessment. Research Paper FPL-RP-662.

Madison, WI: U.S. Department of Agriculture Forest Service, Forest

Products Laboratory. 61 p.

Polyakov, M.; Wear, D.N.; Huggett, R. 2010. Harvest choice and timber

supply models for forest forecasting. Forest Science. 56(4): 344-355.

Rudis, V.A. 1999. Ecological subregion codes by county, coterminous

United States. Gen. Tech. Rep. SRS-36. Asheville, NC: U.S. Department

of Agriculture Forest Service, Southern Research Station. 95 p.

U.S. Department of Agriculture (USDA) Forest Service. 1988. The South’s

Fourth Forest: alternatives for the future. Forest Resource Report No. 24.

Washington, DC: U.S. Department of Agriculture Forest Service. 512 p.

Wear, D.N.; Huggett, R.; Li, R. [and others]. 2013. Forecasts of forest

conditions in U.S. regions under future scenarios: a technical document

supporting the Forest Service 2010 RPA assessment. Gen. Tech. Rep.

SRS-170. Asheville, NC: U.S. Department of Agriculture Forest Service,

Southern Research Station. 101 p.
chapter 2. Constructing Alternative Futures

11

	 David N. Wear, Robert Huggett, and John G. Greis1

Introduction

The desired product of the Southern Forest Futures Project

is a mechanism that will help southerners think about and

prepare for future changes in their forests and the benefits

they provide. Because any single projection of the world’s

(or a region’s) biological, physical, and social systems has a

high probability of being incorrect, the Futures Project instead

examines a range of possibilities–also called scenarios or

futures–that describe the forces influencing forests. Its scope

is defined by an extensive public input process and insights of

an expert panel and then narrowed into a practical number of

futures through modeling and analysis.

This chapter describes the development of the alternative

futures considered for the Southern Forest Futures Project and

the ones selected for detailed analysis. Because they play such

a prominent role, we begin with a definition of what is meant

by a “future:” each future is a comprehensive and coherent

(internally consistent) combination of varying climatic,

demographic, and economic changes in the southern region;

by simulating these changes, we can forecast likely impacts

on the amount and characteristics of forests.

Within the context of this effort, the futures are used to

evaluate how forest conditions and interrelated ecosystem

services might change over time, and how those changes

might affect forest functions, values, management, and

policies. Together, the set of futures represents the full range

of possible trends and changes to southern forests driven by

social, economic, and climatic forces.

1David N. Wear is the Project Leader, Center for Integrated Forest Science,

Southern Research Station, U.S. Department of Agriculture Forest

Service, Raleigh, NC 27695; Robert Huggett is a Research Assistant

Professor, Department of Forestry and Environmental Resources, North

Carolina State University, Raleigh, NC 27695; John G. Greis is a Resource

Specialist, Southern Region, U.S. Department of Agriculture Forest

Service, Tallahassee, FL 32399.

The futures are analyzed with the U.S. Forest Assessment

System, which is also used for national and regional

assessments for modeling responses of land use, forest

harvesting, forest development, and forest disturbance

to changes in key economic, demographic, and climatic

variables. At the national level, the U.S. Forest Assessment

System is the primary tool for evaluating several climate

and timber-market scenarios for the 2010 Resources

Planning Act (RPA) Assessment conducted by the Forest

Service, U.S. Department of Agriculture (USDA Forest

Service 2012). RPA scenarios describe forecasts of

conditions in U.S. counties, most often “downscaled” from

published global scenarios (IPCC 2007).

Some important issues could not be analyzed using our

modeling framework, either because models were not

available within the framework or because available

knowledge could not be formalized into an explicit set of

model variables. We addressed some of these issues by

coupling forecasting work with expert knowledge and results

from previous research; for others, we employed models

that were outside the framework. Although analyses are

informed by the forecasts, these “meta-issues” draw much

of their information from a careful synthesis of the scientific

literature. Each meta-issue is addressed in a separate chapter

of this publication.

Methods

We used a multi-stage process to choose a practical number

of futures that represent a range of factors likely to determine

the future conditions in southern forests. Public meetings,

conducted in 14 locations to gather input on a broad set of

resource issues in the South, drew more than 600 participants

(Wear and others 2009) and identified a large number of

concerns and issues (chapter 1).

Chapter 2.

 Constructing Alternative Futures
The Southern Forest Futures Project

12

A panel of experts distilled and analyzed the public input into

the key driving factors that could plausibly affect the South

(and southern forests) over the next 50 years. The 10 panel

members, all with a broad range of experience in southern

resource issues and forest management, met for two days in a

structured process that followed these steps:

1. The stage was set by an overview of the Futures Project

and its links to other resource assessment efforts [in

particular, RPA and the Intergovernmental Panel on Climate

Change (IPCC) assessments].

2. The panel was briefed on the public input, which had been

organized into broad categories representing seven forces

of change: economic, social, institutional, land use, forest

management, biological, and physical.

3. The panel discussed, clarified, and expanded on the public

input. They were asked to speculate on how each force of

change might play out in the future and to define a list of

alternatives for each of several influential factors (such as

population and bioenergy demands). This list was distilled by

combining similar ideas and forming a short list of what the

panel deemed to be the most important factors likely to drive

change.

We next reviewed the public input and the list of factors from

the expert panel meeting to determine how they could be

addressed within the Futures Project, given data and model

limitations. Some of the factors identified by the expert panel

were not suited for quantitative analysis within the modeling

framework, but could be addressed using science synthesis

and technical analysis through the meta-issue analysis. After

careful deliberation, we specified a strategy for addressing

each of them.

We then examined scenarios that had been developed for

use in other assessments to determine whether any could

be applied to the Futures Project, this in recognition of

the difficulty and high cost, both in dollars and time, of

developing futures for key driving variables. When key

variables interact within a constructed future, such as ours,

coherence is difficult to maintain; for example, economic

futures have implications for energy consumption and

therefore for emissions and climate projections. Based on

these considerations, we selected the 2010 RPA scenarios,

which are in part based on IPCC scenarios, as a starting

point for developing futures. Using these scenarios provided

“downscaled” and detailed forecasts of key driving

variables—including population, income, and climate

changes. In some instances, we modified scenarios to address

specific issues raised for the Futures Project. The resulting

large number of possible futures was impractical for detailed

and timely evaluation, so we conducted a preliminary analysis

to define a smaller, more manageable set—we call these

“Cornerstone Futures”—that represent the range of future

conditions from the full set while eliminating essentially

redundant ones.

Data SouRces

Our primary data source is the set of more than 2,000

comments collected through public meetings, webinars, and

online input. These are described and synthesized in Wear

and others (2009). Raw data and issue aggregations are also

available on the Web at http://www.srs.fs.usda.gov/futures/

input/received/.

Results

Strategies for Analyzing Drivers of Change 

Several key determinants of the future of forests and forest

benefits in the South were identified based on the synthesis

of the public input. For each of them, the expert panel

defined alternative views of how the future might unfold

over time. Each was evaluated as to whether it would be

possible to evaluate the alternative views using inputs to the

U.S. Forest Assessment System and related models. Four

met this criterion: emerging bioenergy demands, land use

changes, forest products markets, and climate change. The

remaining six (forest insects and diseases, invasive plant

species, water, taxes, forest ownership, and fire issues) could

not be addressed using the modeling framework but would

be evaluated as part of meta-issue analysis. The alternatives

considered (where applicable) as well as the analysis

strategies chosen for each are listed below.

Bioenergy—The emergence of new markets for renewable

energy was expected to have great potential for shifting forest

conditions and uses in the South. Driven by various policies,

bioenergy uses of wood could be influenced by demands for

cellulose in biofuel production or for wood chips to be burned

in power plants or to make fuel pellets. Three alternatives

for increased biofuels demand were considered: (1) demand

grows for wood-based cellulose for liquid fuels, (2) demand

for cellulose in liquid fuels is limited to agricultural inputs,

and (3) demand grows for wood chips in power plants and for

wood pellets in heating.

Demand for wood and agricultural feedstocks as renewable

energy sources can be compared within the U.S. Forest

Assessment System by adjusting market activity (harvests)

to reflect emerging markets. However, bioenergy-focused

policies could increase demand to a level that causes broadscale structural changes in timber markets, resulting in

scenarios that exceed the limits of our analytical models.

This suggested a combination of approaches. The first was to

include a moderate expansion in demand for bioenergy as a

part of the package of alternative futures. The second was the
chapter 2. Constructing Alternative Futures

13

use of additional models to address the potential for structural

market changes. In addition, the bioenergy analysis needed

to examine the various production and policy uncertainties

surrounding this issue and to inform additional modeling

with insights into technological constraints and alternative

demands (chapter 10).

Forest products markets—Although bioenergy represents

a new and uncertain element, forest product markets have

long influenced the use and condition of southern forests.

With the South producing about 60 percent of all wood

products in the United States (Prestemon and Abt 2002, Wear

and others 2009), the expert panel saw a need to evaluate

alternative wood products futures based on the possibility

that the retraction of pulp and paper markets and the ongoing

shift from lumber and plywood to engineered solid wood

products will continue. From these possibilities emerged

two alternative scenarios for timber demand: (1) an increase

driven by new technologies in engineered wood products,

biofuels, and a stable pulp and paper sector; or (2) a decrease

driven by declines in paper production or by traditional wood

product uses as wood becomes replaced by other materials.

Alternative futures for wood products demands can be

evaluated within the U.S. Forest Assessment System by

adjusting production levels for the region, using either market

models or simple price forecasts. The evolution of the wood

products industry in the South documented by Prestemon and

Abt (2002) provides a foundation for forecasting alternative

market futures within this well-defined marketplace (chapter 9).

Land uses—The scoping process identified population

growth, development, and changes in the timber and

agricultural sectors as the key driving factors driving land

uses in the South. The expert panel viewed these as dynamic

and advised analyzing a range of future developments in

land use. The analysis yielded two major issues: (1) urban

expansion affected by population growth and income

changes, and (2) changing demand for cropland driven by

bioenergy or food markets.

The land use models in the U.S. Forest Assessment System

are designed for forecasting alternative trajectories of urban

development in response to population and income forecasts.

In addition, changes in rural uses are simulated in response

to changes in the values of agricultural and forest values.

We incorporated land use forecasts as part of the alternative

futures and addressed specific questions regarding the future

of land uses in the South. In particular, questions about

altered urbanization footprints require an analytical future

approach and chapter 4 is dedicated to discussing land use

futures and uncertainties.

Climate—The expert panel identified climate change as a

key driving factor and a source of uncertainty for the future

of southern forests. Alternative climate forecasts from the

IPCC analysis provide a range of futures for the key variables

(temperature and precipitation) that could be considered

within the Futures Project. The panel also raised questions

that may not be precisely captured by the U.S. Forest

Assessment System models: (1) the effects of climate changes

on extreme weather events and fire, and (2) the role of

forests in sequestering carbon as part of a national mitigation

strategy.

A set of climate forecasts defined by existing models can be

examined within our modeling framework: the U.S. Forest

Assessment System was designed to address the impacts of

climate changes, and the RPA scenarios provide a library of

climate scenarios from several general circulation models

(GCMs). We also designated climate change as a meta-issue

so that key uncertainties and implications beyond those

addressed by the U.S. Forest Assessment System could be

fully examined (chapter 3). This publication also addresses

climate change effects on carbon stored in forests (chapter 5),

wildlife (chapter 14), and fire (chapter 17).

Insects, diseases, and invasive plant species—The expert

panel identified insects, diseases, and invasive plant species

as driving factors in determining the future of southern

forests. Insects and diseases have long been the focus of

forest health concerns in the South, with increased threats

from nonnative species defining a key area of uncertainty

for forest sustainability. Over the past 20 years, nonnative

invasive plant species have also become a growing concern.

The spread of cogongrass, paulownia, Chinese privet and

other highly invasive species was raised at public meetings

throughout the South. The rate of spread for these species, the

potential for new species introductions, and the plausibility of

controlling existing and new species are highly uncertain.

Invasive species effects cannot be directly evaluated within

the structure of the U.S. Forest Assessment System. They

may represent novel structural changes to forest systems. We

addressed this issue through two meta-issue analyses: one

addressing nonnative invasive plant species (chapter 15), and

the other addressing insects and diseases (chapter 16), which

updates an earlier analysis of Ward and Mistretta (2002). The

former explores the potential influence of climate changes on

invasive species spread rates and ranges.

Water—Both the demand for and supply of water in the

South was seen as a driving factor for the future, largely

through its impact on acceptable forest management practices.

Water availability could also have an eventual impact on the

scale of urban development and thus would necessarily affect

forest extent and condition. The expert panel raised questions

about the effects of population growth, urban development,

and increased drought frequency and severity; and mitigating

effects of new technologies and conservation.
The Southern Forest Futures Project

14

Although water demands as described by the expert panel

could eventually have an impact on land uses, for example in

metropolitan watersheds, these demands would be localized

and are not amenable to developing alternative futures for

modeling within the U.S. Forest Assessment System. The

primary analysis of these issues is contained in a metaissue analysis (chapter 13), which included linkages of the

U.S. Forest Assessment System model outputs to a water

supply and demand model to predict the effects of land use

conversion and timber harvesting on southern water supplies

(Sun and others 2008).

Taxes—The influences of taxes on forest management,

ownership, and parcelization have long been a concern for

private forest owners, and this issue was raised consistently

at the public meetings. The public asked questions about the

design of “conservation-neutral” tax policy and speculated

about the impacts of changes to inheritance, property, and

income taxes on forest conditions. The future of tax policy

was seen as important in determining future ownership and

management.

The impacts of various tax policies cannot be directly

addressed within the U.S. Forest Assessment System. Instead,

tax issues are examined as a meta-issue (chapter 11) using a

literature synthesis.

Fire—Fire can be an important management tool and an

undesirable occurrence in the forest, depending on timing

and location. The public raised concerns about the future

feasibility of fire management amid the challenges of

urbanization and climate change, and the implications of

climate change for future wildfire extent and patterns.

The complex suite of issues surrounding fire is beyond the

scope of the U.S. Forest Assessment System. However, the

key driving variables of urbanization and climate change,

along with forest-area and forest-inventory outputs from the

U.S. Forest Assessment System, can be explicitly linked to fire

implications for the South. A meta-issue analysis (chapter 17)

of fire issues employs wildfire risk models linked to the

climate forecasts used in the U.S. Forest Assessment System.

Forest ownership dynamics—Recent changes in forest

ownership—especially the shift from forest industry to

Timber Investment Management Organizations (TIMOs) and

Real Estate Investment Trusts (REITs)—portends changes

in forest management and conditions. What is more, several

trends in the demographic makeup of nonindustrial private

forest owners foreshadow potential changes in ownership and

forest uses. All have implications for future forest conditions

and sustainability.

Because of the high degree of uncertainty, forest ownership

is another area that cannot be directly modeled. Instead a

meta-issue analysis examined trends in ownership and owner

attributes, the causes of historical changes in broad owner

categories, and the range of implications from ongoing

changes in ownership (chapter 6). This issue is linked to

forest taxes (chapter 11) and land use changes (chapter 4).

Developing Alternative Futures: Linking Scoping 

Results to Forecasting Models

The expert panel identified nine key driving factors likely

to influence the future development of southern forests and

proposed alternatives for how they might play out over

the next 50 years. Four driving factors (bioenergy, forest

products, land uses, and climate) were suitable for formal

forecasting analysis using the U.S. Forest Assessment

System. Permutations of their alternative projections resulted

in too a large number of alternatives for practicality, so we

winnowed them to eliminate redundancies. In this section,

we describe how we constructed the initial set of futures and

then reduced them to a subset of representative “Cornerstone

Futures.”

The U.S. Forest Assessment System provides the modeling

framework for the analysis, and we considered driving factors

in terms of this model’s inputs. Market futures were driven

by price forecasts with prices increasing as timber products

became more scarce, decreasing with less scarcity. For

constructing the futures we conceptually bundled bioenergy

and forest products market futures. That is, expanding

demands could reflect strengthened markets either for

bioenergy or traditional wood products. Land use models

within the U.S. Forest Assessment System are designed to

be driven by population and income projections. Climate

variables enter the projection of forest conditions affecting

forest type distributions and forest productivity.

Since the U.S. Forest Assessment System had originally

been designed to develop the RPA Assessment, we began

by evaluating whether the existing RPA scenarios were

adequate for addressing the issues of the Futures Project.

Although based on IPCC worldviews, the RPA scenarios also

contain data and detail relevant to conditions in the United

States–specifically, climate and socioeconomic projections,

downscaled to the county level (USDA Forest Service 2012).

The IPCC fourth assessment (IPCC 2007) is the global basis

for the RPA Assessment because it provides an internally

consistent set of scenarios that offer a broad spectrum of

potential futures from which the RPA analysts could select a

subset that was most relevant for U.S. forests.

The IPCC directed a special report to generate scenarios

of greenhouse gas emissions, set within four broad

storylines about future economic, demographic, political,

environmental, and technological change (Nakicenovic and

others 2000). The A1 storyline describes a future of very

rapid economic growth with a global population that peaks in

mid-century, and then declines. The A2 storyline describes
chapter 2. Constructing Alternative Futures

15

a continuously increasing global population and economic

growth that is more regionally oriented. Population growth

for the B1 storyline is the same as A1, but B1’s economic

future describes a rapid change towards a service and

information economy, with a strong emphasis on clean and

resource-efficient technologies. The B2 storyline describes a

growing population and intermediate economic growth, but

a preference for local solutions over global integration. Data

compatibility issues limited the RPA scenarios to the A1,

A2, and B2 storylines. Furthermore, RPA adopted the A1B

storyline with its assumptions regarding energy futures.

For each of the IPCC storylines A1, A2, and B2, the 2010

RPA scenarios provide unique forecasts of population and

economic growth, downscaled to the U.S. county level to the

year 2060. For the A1 storyline, the IPCC developed several

sub-storylines that were used to depict different futures of

energy use and technology. For the 2010 RPA analyses, the

A1B set was chosen. The storylines were used as input to

modeling systems that generate estimates of GHG emissions,

which in turn were used to run GCMs that provide alternative

climate forecasts. The 2010 RPA analyses used results from

three GCMs for each storyline, resulting in nine potential

climate futures. The GCM data were downscaled using a

statistical approach to the 0.5 arc minute and then aggregated

to the county scale (Coulson and others 2010). For the A1B

and A2 storyline, the GCMs are the MIROC, CSIRO, and

CGCM models, while for the B2 storylines they are the

Hadley, CSIRO, and CGCM.

The GCM projections were generated as changes from

simulated historical monthly means (1961–90) for each

GCM (Joyce and others 2011, Price and others 2011). Basing

forecasts on the simulated values corrects for differential

biases of the GCMs at the fine scale used for analysis. Change

forecasts were then applied to historical data to generate the

projections. Monthly mean daily max and min temperatures,

precipitation, along with other variables not used in the

Futures Project’s analysis were generated to the year 2100.

Note, however, that Futures Project impact analysis was

limited to a 50-year analysis (2010–60).

The RPA scenarios, then, provide a set of futures defined by

three GCMs for each of three storylines. Land use models

within the U.S. Forest Assessment System define change

in forest area (and other uses) in response to the economic

variables from the storylines as well as timber and crop prices

(Wear 2011). Forest dynamics models forecast changes in

forest conditions in response to harvesting (which in turn are

influenced by the economic variables) (Polyakov and others

2010), and to changes associated with aging, disturbance,

and climate (Wear and others 2013). These RPA scenarios

therefore directly address a range of futures identified as

important land-use and climate driving factors by the expert

panel.

We decided that the RPA climate data provided an adequate

(and the only practical) range of climate scenarios for the

Futures Project. We also decided that the land use forecasts,

driven by alternative population and income forecasts,

provided an adequate range of land use scenarios for the

Futures Project.

Two other forces of change identified by the expert panel—

forest harvesting and management to supply bioenergy and

other wood products markets—could be evaluated within the

structure of the model. In the U.S. Forest Assessment System,

we can address alternative scenarios for wood production in

two ways. One is to apply alternative projections of prices for

forest products to forecast changes in harvesting. This “priceexogenous” approach, although simple, allows us to simulate

increasing and decreasing scarcity in markets without

specifically addressing market dynamics and wood-products

demand structures. The other approach is to incorporate

explicit models of market demands for various forest

products within the modeling system, which for the RPA

scenarios is the U.S. Forest Products Model (Ince and others

2011). The Forest Products Model was chosen for the RPA

scenarios because it can incorporate demands for all classes

of U.S. wood products within a global marketing framework

(Raunikar and others 2010) and can be driven by variables

taken from the same storylines.

For our development of alternative futures, we used

exogenous price forecasts, in particular, three “priceexogenous” scenarios: constant timber prices, increasing

prices (plus 1 percent per year), and declining prices (minus

1 percent per year). Increasing prices describe an increasing

scarcity of timber products and therefore can be applied to

two possible futures: a shortage in available timber supplies

or an increased demand to satisfy existing uses or emerging

uses such as bioenergy. Decreasing prices reflect decreasing

scarcity consistent with a contraction in demands for products

(such as pulpwood for paper production) or a rapid expansion

in supplies derived from intensive management. We use the

1-percent increase and decrease rates to bookend the analysis

of markets because they are consistent with real price growth

over the expansionary phase of southern timber markets

from the 1980s through the 1990s. Layered on the analysis

of futures, we also conducted an analysis of future wood

products/bioenergy markets to provide insights into how

specific market developments might play out and to define

additional analytical futures for evaluation.

For our initial set of alternative futures, therefore, we

started with the RPA scenarios for forecasts of climatic

and socioeconomic conditions, and then applied the three

alternative timber market scenarios, three socio/economic

storylines, three GCMs, and three timber market scenarios.

None of the resulting 27 initial futures were considered more

likely than the others. Rather, each was judged to be within
The Southern Forest Futures Project

16

the realm of plausibility. While each scenario likely contained

some unique insights into future resource uses and conditions,

it was necessary to select a smaller subset of the futures for

detailed analysis and discussion within the Futures Project.

Selecting the Cornerstone Futures

In this section, we briefly touch on some of the variables

forecasted with the U.S. Forest Assessment System to

describe the logic behind selecting Cornerstone Futures.

Thorough discussions of forecasting approaches and forecast

results are contained in chapters 4 and 5 and in several

supporting documents.

To begin defining the set of Cornerstone Futures to be used

for detailed analysis within the Futures Project, we conducted

the U.S. Forest Assessment System land-use, forest-condition,

and timber-harvesting simulations for the initial set of 27

alternative futures and applied various metrics to compare the

resulting forecasts. This process was complicated because the

alternative futures are ranked differently depending on which

variable is used to construct the ranking—for example, the

same future might forecast the greatest loss of forest land and

a median level of future biomass (estimated as growing stock

volumes).

Timber prices—We started by dropping the set of futures

with constant prices; in every comparison, these futures

yielded forecasts that were intermediate between futures with

increasing and decreasing prices, meaning that the increasing

and decreasing price futures bracketed the constant price

futures for all variables evaluated. This step reduced the

number of alternative futures to 18.

Biomass volume and land use—We next used two highly

aggregate metrics to compare forecasts across the remaining

futures: total volume of biomass by broad forest type and

total area of forestland. We held the GCM constant (CSIRO)

for storyline/timber price scenarios and displayed volume

forecasts to 2060 for all growing stock and then for hardwood

and softwood growing stock. Total volume follows a broad

range of trajectories across futures (fig. 2.1). Increases are

only expected for the B2 storyline with low prices (resulting

in lower harvesting), which has the lowest urbanization

(lower population growth and moderate income growth). All

other futures result in expansion of biomass through 2030

or 2040, followed by declines. The future with the lowest

biomass in 2060 is defined by the A1B storyline (moderate

population growth and high income growth) combined with

high timber prices. Figure 2.2 shows that softwood volumes

increase to 2030 but then either level off (A1B/high prices)

or increase through 2060, with the highest rate of increase for

B2/low prices. Hardwood volumes are projected to decline

after 2030 for all futures except B2/low prices, with the

largest decline for A1B/high prices.

Forest area is forecasted to decline in response to the economic/

population forecasts from the storylines and the timber price

futures (by construction, land use is not directly responsive

to climate). Low population and income growth reduces

urbanization and consumption of forest land. In addition,

high timber prices discourage deforestation. Therefore, the

B2 storyline (moderate income growth) coupled with high

prices yields the smallest loss of forest land by 2060, and the

A1B storyline (rapid economic growth) coupled with low

prices yields the greatest loss of forest land (fig. 2.3). With

the storyline held constant, low prices yield more forest loss

than high prices. Because the A2 storyline is intermediate

to the A1B/high forest loss and B2/low forest loss and was

intermediate in the biomass volume forecasts (for all climate

projections), it was dropped from the cornerstones.

So an analysis of the range of outcomes for these two

variables suggests inclusion of four futures for consideration.

A high economic-growth/increasing timber price future

(A1B/high price) and a low growth/decreasing price future

(B2/low price) bracket the projections of total forest biomass.

For forest area change projections, the brackets are a low

economic-growth/increasing timber price future (B2/

high price), which could reflect less globalization (more

isolated nation economies) and increasing U.S. scarcity of

wood products in the face of less trade; and a high growth/

decreasing price (A1B/low price) future, which could reflect

a shift in timber production offshore to support global

economic growth (or simply a decline in the demand for

forest products).

Climate—The ranking of the futures with respect to total

biomass and total forest area does not vary across GCMs, as

is shown by a follow-up evaluation of the biomass variable.

Figure 2.4 shows forecasts of growing stock volumes for the

three GCMs associated with A1B storyline, low and high

timber prices (MIROC, CSIRO, and CGCM). Clearly, the

trajectories of growing stock volumes cluster strongly around

the price futures with much less variation among the GCMs

within each price cluster.

We concluded that the timber-price and storyline effects

overshadow the effects of climate variation. We were,

however, reluctant to eliminate climate variation from

consideration, primarily to account for any spatial variations

that may be masked by the aggregate outcomes. Accordingly,

we introduced climate variation by assigning different

GCMs to the four cornerstones identified above: MIROC to

A1B/high prices, Hadley to the B2/low prices, and CSIRO

to the A1B/low prices and B2/high prices. These models

were selected to provide a variety of spatial expressions of

the climate projections (MIROC for example, is generally

warmer than the other models, but these differences do

not significantly affect the projections of aggregate forest

conditions). The implications of any spatial variations are


chapter 2. Constructing Alternative Futures

19

discussed in the evaluation of various meta-issues, especially

for wildlife (chapter 14) and fire (chapter 17).

Tree planting—A review of the four remaining futures

indicated that forecasts of forest investment, a key element

in the development of the South’s forests since the 1950s,

did not respond to variations in market futures. This led us to

expand our scope to address this dynamic in a way that was

consistent with our modeled changes in timber markets.

To address the effects of forest planting, we augmented the

U.S. Forest Assessment System with a simple model which

assumes that current plantations will be replanted after

harvesting and that a specified portion of other harvested

forests will be planted. These assumptions derive from

historical rates of planting for each of the 13 States and

from expert advice on the likely path of future planting. The

planting rates adopted for these baseline assumptions are

more moderate than the aggressive expansion of plantations

in the 1990s, and thereby reflect economic conditions

and trends in the 2000s. Because planting rates are tied to

harvesting (which controls the availability of forests for

planting) and to land use changes, the area planted varies

somewhat across simulated futures. We adjusted this baseline

projection approach to introduce broader variation in the

planting rate and to reflect the assumptions about future

timber markets. The result was two additional futures. For the

first, we increased forest planting rates in harvested areas by

50 percent from base rates for the A1B/MIROC/high price

future; this yields planting rates that are higher than for other

futures but not as high as was experienced in the 1990s—they

would be plausible in light of observed nursery capacity and

forest management. For the second, we decreased planting

rates by 50 percent from base rates for the B2/Hadley/low

price future; this yields a very moderate increase in forest

plantations that level off after about 2030.

What the Cornerstone Futures Say and 

How They Compare

Figure 2.5 shows the six Cornerstone Futures in a diagram

that emphasizes their key variables. Cornerstones A through

D are defined by the matrix formed by intersecting RPA/IPCC

storylines A1B and B2 with increasing and decreasing timber

price futures. Cornerstones E and F depart from these four by

either augmenting the planting rates in Cornerstone A (E) or

by decreasing the planting rates in Cornerstone D (F).

Storylines vary in their projections of population density (fig.

2.6). A2, the storyline not used within the Cornerstones yields

the highest population growth with an 80 percent increase

from 2006 to 2060. Lowest population growth is associated

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the 2010 Resources Planning Act (RPA) Assessment, three general circulation models

(GCMs), and two timber price futures; and then expanded by evaluating increased and

decreased forest planting rates.

chapter 2. Constructing Alternative Futures

21

with B2 (40 percent growth) and A1B is bracketed by the two

(60 percent). Because urbanization is also fueled by income

levels, A1B, with its strong economic growth actually results

in the greatest urbanization and greatest losses of forest area

(chapter 4); B2 results in the lowest urbanization and forest

losses.

Figure 2.7 shows population density growth, by subregion,

for the A1B storyline. In 2006, the Piedmont had the greatest

population density (about 250 people per square mile or

ppsm), followed by the Coastal Plain and AppalachianCumberland subregions with intermediate densities (100

to 150 ppsm) and the Mid-South and Mississippi Alluvial

Valley with the lowest densities (75 ppsm). This general trend

continues over the projection period, with growth strongest

on the Piedmont (an additional 150 ppsm). By 2060, the

population density in the Coastal Plain would be as high as

current population densities in the Piedmont.

Even within the subregions, population change is not evenly

spread. Forecasted population growth from 2006 and 2060

(fig. 2.8) shows that several areas are expected to experience

population declines. This includes parts of the High Plains

in Texas and Oklahoma, much of the Mississippi Alluvial

Valley, and parts of southern Alabama and Mississippi.

Population growth in the South is clearly organized around

major metropolitan centers—especially Atlanta; Miami;

Houston; Dallas; Washington, DC; Nashville, TN; and

Charlotte and Raleigh, NC.

Cornerstone Futures are also framed by timber market

projections over the next 50 years. These projections do

not account for short run business cycles or the pattern of

economic recovery from the recent recession but attempts

to capture some long-run potentials for market development

beyond this period of adjustment. Price forecasts defined by

the Cornerstone Futures anticipate an orderly progression,

either increasing or decreasing in real terms at 1 percent per

year from a 2005 base. That year, prices were below their

peak values from the late 1990s, especially for pulpwoodsized material (fig. 2.9). We also held the real returns to

agricultural crops constant through the period. Because

future markets could depart from these assumptions, we used

additional analyses to examine the sensitivity of future forest

conditions to general market conditions (chapter 5) and to

address alternative bioenergy futures (chapter 10). In this case

the Cornerstone Futures define a framework for evaluating

forest product/bioenergy market possibilities.

Also embedded in the Cornerstone Futures is the climate

forecasted using various GCMs, including changes

in temperature, precipitation, and derived potential

evapotranspiration. For example, figure 2.10 displays changes

in the 10-year average annual temperatures from 2000 to

2060 for Cornerstones A through D. Under Cornerstone A

(A1B with MIROC) nearly the entire South is forecasted

to experience an increase of at least a 1° C and the northern

portions of the High Plains and Cross Timbers sections of

Texas and Oklahoma are forecasted to experience 2.5-3° C

increases. Other Cornerstone Futures are consistently warmer

but less warm than A1B/MIROC and show greater spatial

variation in temperature increases. Forecasted changes in

precipitation vary across the South and across the Cornerstone

Futures (fig. 2.11). Under Cornerstone A, precipitation

declines across the entire region while all other Cornerstones

show variation from strong declines to strong increases.

These forecasts vary across the Cornerstones (figs. 2.10 and

2.11) and define variations in future growing conditions

for forests across the South. They may prove important for

determining wildfire impacts over the next 50 years. The

use of three different GCMs in constructing the Cornerstone

Futures should address potential variability in spatial

distributions. More detail on climate inputs to the forecasts is

contained in chapter 3.

Our use of discrete Cornerstone Futures with different climate

futures does not allow for isolating the effects of climate

versus all of the other driving forces behind the scenario, e.g.,

population or land use. The complete factorial analysis of

futures based on emissions scenarios and GCMs required for

this type of analysis was beyond the capacity of the Futures

Project to evaluate secondary and tertiary effects. However,

forest forecasts for the full factorial were the basis for the

selection of the Cornerstone Futures and are described in

detail in Wear and others (2013).

Discussion and Conclusions

Public meetings provided the initial input on issues needing

attention in the Futures Project. These issues were further

synthesized and distilled to define a set of driving factors

to be examined in the course of the study. Some could not

be formalized using quantitative models and have been

examined as meta-issues and through science synthesis. Four

driving factors could be modeled and were used to organize a

set of 27 alternative futures. We reduced this initial set to the

six Cornerstone Futures (shown in table 2.1 and figure 2.5)

that were used to organize our forecasts of forest conditions

and to evaluate long-term implications for a variety of

resource values throughout this publication.

In sum, Cornerstones A-D are defined by the matrix formed

by intersecting low and high population and income forecasts

with increasing and decreasing timber price futures as

described above.

•	Cornerstone A: High population/income growth along

with increasing timber prices and baseline tree planting

rates.

•	Cornerstone B: High population/income growth along with



chapter 2. Constructing Alternative Futures

25

decreasing timber prices and baseline tree planting rates.

•	Cornerstone C: Low population/income growth along with

increasing timber prices and baseline tree planting rates.

•	Cornerstone D: Low population/income growth along with

decreasing timber prices and baseline tree planting rates.

These four Cornerstones use what we label baseline rates

of tree planting following a harvest based on data from the

Forest Inventory and Analysis Program of the Forest Service

to forecast future planting. Cornerstones E and F depart from

these four either by augmenting planting rates by 50 percent

for Cornerstone A (E), where economic growth is strong and

timber markets are expanding, or by decreasing planting rates

by 50 percent for Cornerstone D (F), where economic growth

is reduced and timber markets are declining.

•	Cornerstone E: High population/income growth along with

increasing timber prices and high tree planting rates.

•	Cornerstone F: Low population/income growth along with

decreasing timber prices and low tree planting rates.

The six Cornerstone Futures define a broad range of potential

future conditions within which forests might develop. They

address the set of four change factors identified by the expert

panel using public input: wood products markets, bioenergy,

land uses, and climate changes. They address bioenergy and

wood products markets in a qualitative fashion—through

exogenously defined trajectories of timber prices—that capture

a broad range of market conditions. And they address land

use and climate change in a detailed and spatially explicit way

through projections of population, income, temperatures, and

precipitation downscaled to the county level.

Acknowledgments

Thanks to those who served on the expert panel for

developing alternative futures in June 2008.

Literature Cited

Coulson, D.P.; Joyce L.A.; Price, D.T. [and others]. 2010. Climate scenarios

	 for the conterminous United States at the county spatial scale using

SRES scenarios A1B and A2 and PRISM climatology. Fort Collins,

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

Research Station. http://www.fs.fed.us/rm/data_archive/dataaccess/

US_ClimateScenarios_county_A1B_A2_PRISM.shtml. [Date accessed:

January 10, 2011].

Ince, P.J.; Kramp, A.D.; Skog, K.E. [and others]. 2011. U.S. Forest Products

Module for RPA forest 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. Geneva, Switzerland. http://www.ipcc.ch/

publications_and_data/ar4/syr/en/contents.html. [Date accessed: January

24, 2012].

Joyce, L.A.; Price, D.T.; McKenney, D.W. [and others]. 2011. High

resolution interpolation of climate scenarios for the conterminous United

States and Alaska derived from general circulation model simulations.

Gen. Tech. Rep. RMRS-GTR-263. Fort Collins, CO: U.S. Department of

Agriculture Forest Service, Rocky Mountain Research Station. 87 p.

Nakicenovic, N.; Alcamo, J.; Davis, G. [and others]. 2000. Special report

on emissions scenarios: a special report of working group III of the

Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge

University Press. 599 p. http://www.grida.no/climate/ipcc/emission/index.

htm. [Date accessed: March 25, 2011].

Poylakov, M.; Wear, D.N.; Huggett, R. 2010. Harvest choice and timber

supply models for forest forecasting. Forest Science. 56(4): 344-355.

Prestemon, J.; Abt, R.C. 2002. Timber products supply and demand. In:

Wear, D.N.; Greis, J.G., eds. Southern forest resource assessment. Gen.

Table 2.1—Definition of Cornerstone Futures used in the Southern Forest Futures Project, based on two storylines from 

the 2010 Resources Planning Act (RPA) Assessment and three general circulation models (GCMs)

Cornerstone Futures RPA Storylines Climate 

(GCM) 

model

Timber

prices

Planting

Tag Label Label rates

Economic 

growth

Population 

Growth

A High growth/high prices A1B High Moderate MIROC Increasing Base

B High growth/low prices A1B High Moderate CSIRO Decreasing Base

C Low growth/high prices B2 Low Low CSIRO Increasing Base

D Low growth/low prices B2 Low Low Hadley Decreasing Base

E High growth/high prices/high planting A1B High Moderate MIROC Increasing High

F Low growth/low prices/low planting B2 Low Low Hadley Decreasing Low
The Southern Forest Futures Project

26

Ward, J.D.; Mistretta, P.A. 2002. Impacts of pests on forest health. In: Wear,

D.N.; Greis, J.G., eds. Southern forest resource assessment. Gen. Tech.

Rep. SRS–53. Asheville, NC: U.S. Department of Agriculture Forest

Service, Southern Research Station: 403-428.

Wear, D.N.; Greis, J.G.; Walters, N. 2009. The Southern Forest Futures

Project: using public input to define the issues. Gen. Tech. Rep. SRS–115.

Asheville, NC: U.S. Department of Agriculture Forest Service, Southern

Research Station. 17 p.

Wear, D.N.; Huggett, R.; Li, R. [and others]. 2013. Forecasts of forest

conditions in U.S. regions under future scenarios: a technical document

supporting the Forest Service 2010 RPA assessment. Gen. Tech. Rep.

SRS-170. Asheville, NC: U.S. Department of Agriculture Forest Service,

Southern Research Station. 101 p.

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.

Tech. Rep. SRS–53. Asheville, NC: U.S. Department of Agriculture

Forest Service, Southern Research Station: 299-325.

Price, D.T.; McKenney, D.W.; Joyce, L.A. [and others]. 2011. Highresolution interpolation of climate scenarios for Canada derived from

general circulation model simulations. Information Report NOR-X-421.

Edmonton, AB: Natural Resources Canada, Canadian Forest Service,

Northern Forestry Centre. 104 p.

Raunikar, R.; Buongiorno, J.; Turner, J.A.; Zhu, S. 2010. Global outlook

for wood and forests with the bioenergy demand implied by scenarios

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Economics. 12: 48-56.

Sun, G.; McNulty, S.G.; Moore Myers, J.; Cohen, E.C. 2008. Impacts of

multiple stresses on water demand and supply across the Southeastern

United States. Journal of American Water Resources Association. 

44(6): 1441–1457.

U.S. Department of Agriculture (USDA) Forest Service. 2012. Future

scenarios: a technical document supporting the Forest Service 2010 RPA

assessment. Gen. Tech. Rep. RMRS-GTR-272. Fort Collins, CO: U.S.

Department of Agriculture Forest Service, Rocky Mountain Research

Station. 34 p.
27

chapter 3. Climate Change Summary

	 Steve McNulty, Jennifer Moore Myers,

	 Peter Caldwell, and Ge Sun1

Key Findings

•	 Since 1960, all but two southern capital cities

(Montgomery, AL, and Oklahoma City, OK) have

experienced a statistically significant increase in average

annual temperature (approximately 0.016° C), but none

has experienced significant trends in precipitation.

•	 The South is forecasted to experience warmer temperatures

for the duration of the 21st century; forecasts are mixed for

precipitation changes during the same period.

•	Climate predictions range from wet and warm

	 (1167 mm/19.06° C) to moderate and warm

	 (1083 mm/19.45° C and 1106 mm/19.27° C) to dry and

hot (912 mm/20.22° C).

Introduction

This chapter summarizes the climate predictions that have

been used throughout the Southern Forest Futures Project

(IPCC 2007b). Four distinct combinations of general

circulation models (GCMs) and special report emissions

scenarios were selected as Cornerstone Futures. GCMs are

complex models that provide geographically and physically

consistent estimates of regional climate change (IPCC 2009).

The emissions scenarios are global storylines representing

alternative demographic, socioeconomic, and environmental

futures (Nakicenovic 2000).

The GCMs selected for the Futures Project were the MK2

and MK3.5 from the Australian Commonwealth Scientific

and Industrial Research Organization (CSIRO), the HadCM3

from the United Kingdom Meteorological Center, and

the MIROC 3.2 from the Japanese National Institute for

Environmental Studies.

Two emissions scenarios were selected for the Futures

Project. The A1B scenario is characterized by low population

growth, high energy use, and high economic growth. The

B2 scenario is characterized by medium population growth,

1Steve McNulty is Supervisory Ecologist, Jennifer Moore Myers is a

Resource Information Specialist, and Peter Caldwell and Ge Sun are

Research Hydrologists, Eastern Forest Environmental Threat Assessment

Center, Southern Research Station, U.S. Department of Agriculture Forest

Service, Raleigh, NC 27606.

medium energy use, and medium economic growth (IPCC

2007b). These scenarios represent two levels of global carbon

dioxide (CO2) emissions by 2100: 60 gigatons of CO2-

equivalents (eq) (IPCC 2007a) in the A1B scenario (resulting

in an atmospheric concentration of approximately 700 ppm)

(Solomon and others 2007) and 65 gigatons of CO2-eq

(IPCC 2007a) in the B2 scenario (resulting in an atmospheric

concentration of approximately 600 ppm) (Solomon and

others 2007). The relationship between CO2 equivalent

emissions and atmospheric CO2 concentration is not linear,

and the estimates for 2100 are influenced by emission rates

throughout the 21st century. The A1B scenario peaks higher

around 2050 and tapers off, while the B2 scenario increases

more slowly and steadily. For comparison, carbon dioxide

emissions for 2009 were estimated at 40 gigatons of CO2-eq

(resulting in an atmospheric concentration of 387 parts per

million) (IPCC 2007a, Tans 2011).

The Futures Project combines GCMs and emissions

scenarios into four Cornerstone Futures—

CSIROMK3.5+A1B, MIROC3.2+A1B, CSIROMK2+B2, 

and HadCM3+B2—which are described in this chapter.

Although this chapter does not discuss subregional variations

in detail, the GCM summary data have been provided in both

tabular and graphic formats to allow the reader to examine

climate change impacts for subregions of interest.

Data Sources and Methods

Because the original scale of the GCMs was too coarse for

regional analysis, the Cornerstone Futures were downscaled

from their original resolution of approximately 2 degrees

by the World Climate Research Programme’s Coupled

Model Intercomparison Project phase 3 (CMIP3) (Maurer

and others 2007). Each GCM was spatially downscaled

to one-twelfth degree (5 arc minute) using ANUSPLIN,

a interpolation model that incorporates four dimensions

(climatic variable, latitude, longitude, and elevation) to

produce gridded surfaces for both monthly precipitation and

surface air temperature (Hutchinson 2009).

The CMIP3 data were obtained and processed by Coulson

and others (2010) for use in the 2010 Resources Planning

Act (RPA) Assessment. Monthly precipitation and

Chapter 3.

 Climate Change Summary
28 The Southern Forest Futures Project

temperature data from 2000 to 2100 were scaled to the

county level for the conterminous United States. All chapters

in this assessment use the county level precipitation and

temperature data. All regional and subregional averages

were area-weighted to remove bias that would result from

averaging counties of different areas.

For this chapter, annual and decadal averages were

generated for the South and for its five subregions using

the JMP 8.0 software application (SAS Institute Inc. 2010).

For a historical perspective, trends in air temperature and

precipitation for the 13 southern capital cities from 1960

to 2007 were obtained from the PRISM Climate Group

(Gibson and others 2002). Maps were generated using the

ArcMap version 9.3.1 software application (ESRI 2010). The

decades selected for this chapter were 2010, 2020, 2040,

2060, and 2090. To calculate the decadal averages, the ten

years surrounding each period were summed, in the case of

precipitation, and then averaged. The decadal average for

2010 included data from the years 2005–14, 2020 included

data from 2015–24, 2040 included data from 2035–44, etc.

The results section describes averages and anomalies for

each of the four Cornerstones.

Results

Regional Forecasts

Table 3.1 summarizes precipitation and temperature averages

forecasted for the South through 2100, with historical data for

comparison. Figures 3.1 through 3.4 present graphic and map

displays of precipitation data, and figures 3.5 through 3.8

present graphic and map displays of temperature data.

Characterized by low population growth and high energyuse/economic-growth (MIROC3.2+A1B), Cornerstone A is

forecasted to be dry and hot, with average annual precipitation

of 912 mm and average annual temperature of 20.22° C.

Annual precipitation expected for any southern county ranges

from 103 to 4999 mm, and temperature ranges from -12.01

to 50.24° C. Average maximum monthly temperatures would

exceed the single-day southern maximum of 48.89° C, which

was set in Oklahoma in 1994 (Burt 2007).

Also characterized by low population growth and high

energy-use/economic-growth (CSIROMK3.5+A1B),

Cornerstone B is forecasted to be wet and warm, with

average annual precipitation of 1167 mm and average

temperature of 19.06° C. Annual precipitation expected

for southern counties ranges from 93 to 3912 mm, and

temperature ranges from -11.21 to 44.24° C.

Characterized by moderate population/income growth and

energy use (CSIROMK2+B2), Cornerstone C is forecasted

to be moderate and warm, with average annual precipitation

of 1083 mm and average annual temperature of 19.45° C.

Annual precipitation expected for any southern county ranges

from 35 to 2641 mm. That precipitation minimum would

break the 1956 regional low of 42 mm in Texas (Burt 2007).

Temperature is expected to range from -19.73 to 45.39° C.

Also characterized by moderate population/income

growth and energy use (HadCM3+B2), Cornerstone D is

also forecasted to be moderate and warm, with average

annual precipitation of 1106 mm (higher than Cornerstone

C) and average annual temperature of 19.27° C (lower

than Cornerstone C). Annual precipitation expected for

Table 3.1—Summary statistics for predicted (2010–2100) and historical (2001–09) annual precipitation and temperature 

forecasts for the Southern United States by four Cornerstone Futures A through D 

Cornerstonea

Precipitation (mm) Temperature (°C)

Minimum Maximum Average

Standard 

deviation Minimum Maximum Average 

Standard 

deviation

A 733 1675 912 198 17.29 21.35 20.22 1.05

B 627 1517 1167 138 17.98 23.93 19.06 1.33

C 803 1369 1083 126 17.07 21.74 19.45 1.08

D 724 1383 1106 121 16.76 22.36 19.27 1.10

Average all 

Cornerstones NA NA 1066 NA NA NA 19.57 NA

Historical 

(2001 to 2009) 864 1552 1136 NA 16.97 19.45 17.87 NA

NA = not applicable. aEach Cornerstone represents a general circulation model paired with one of two emission scenarios (A1B represents low-population/

high-economic growth, high energy use; B2 represents moderate growth and use): A is MIROC3.2+A1B, B is CSIROMK3.5+A1B, C is 

CSIROMK2+B2, and D is HadCM3+B2.

Source: Intergovernmental Panel on Climate Change 2007b.






35

chapter 3. Climate Change Summary

any southern county ranges from 102 to 2708 mm, and

temperature ranges from -18.68 to 48.01° C.

Subregional Forecasts

In the Southern United States, forecasted precipitation

(table 3.2) and temperature averages (table 3.3) are not

expected to be uniform, with significant variations across the

five subregions and between seasons (table 3.4). Figures 3.9

and 3.10 present graphic and map displays of precipitation

and temperature data.

Cornerstone A’s high energy-use/economic-growth

(MIROC3.2+A1B) is predicted to result in the least decadal

precipitation by 2060, with an overall average of 810 mm for

all five southern subregions and a low of 525 mm in the MidSouth. This trend is expected to abate only slightly by 2090

to an average of 858 mm for all subregions and 535 mm for

the Mid-South—still much drier than the historical overall

average of 1136 mm.

Although also based on high energy-use/economic-growth,

Cornerstone B (CSIROMK3.5+A1B) predicts more decadal

precipitation than the other Cornerstones by 2060, with

an overall average of 1156 mm. This trend continues into

2090, with an overall average predicted to be 1223 mm.

Cornerstone B also predicts cooler decadal temperatures

than the other Cornerstones by 2060—with an overall

average of 19.39° C—for every subregion except the MidSouth. This trend continues into 2090, with Cornerstone B’s

overall average of 20.14° C, lower than all the others for all

subregions.

Cornerstone A predicts warmer decadal temperatures than

the other Cornerstones by 2060, with an overall average

of 20.83° C for all five southern subregions. This trend

continues into 2090, with Cornerstone A’s overall average of

21.84° C leading all the others for all subregions.

Comparing these predictions with historical trends in air

temperature and precipitation for the 13 southern capital

cities from 1960 to 2007 shows a statistically significant

increase (total of 0.705° C, average of 0.016° C) in air

temperature but no significant change in precipitation

(fig. 3.11). These findings are consistent with a trend of

significant increases in temperature from 1970 to 2008

reported by Karl and others (2009) (table 3.4), but not after

their data from 1901 to 1969 were included.

Discussion and Conclusions

GCMs provide some indication of how climate will

change across the South in coming decades. Each has been

independently developed, often for a specific region, and

frequently calibrated to recreate historical climate on the

assumption that successful modeling of the past increases

the likelihood of accurately forecasting the future. However,

the same calibration that allows an accurate recreation of

historical climate for one region can result in over- or underpredicting climate change for others.

An example of possible over-predicting is Cornerstone A

(MIROC3.2+A1B), which assumes high energy-use and

economic-growth and predicts the warmest conditions,

with monthly averages sometimes exceeding single-day

historical highs (fig. 3.12). Similarly, Cornerstone A’s

average precipitation is about 20 percent lower (fig. 3.13).

For these reasons, it is considered the most severe of the

Cornerstones in terms of extreme events as well as annual

averages. The other GCMs used in this analysis also predict

maximum monthly air temperatures in excess of historically

observed conditions, but by a smaller margin. In particular,

Cornerstone B (CSIROMK3.5+A1B) predicts increases in

average annual precipitation compared to historical averages.

Another caveat is that averaged or summed monthly values

are less able to express climate variability (especially

extremes) than daily values. Monthly average air

temperatures are expected to be much lower than some

of the individual daily highs, and higher than some of the

individual daily lows. For example, if a maximum monthly

air temperature is predicted to be 40° C, then individual daily

air temperatures are likely to exceed 45 or even 50° C.

Likewise, monthly average precipitation does not fully

represent the number or magnitude of individual events.

Although Cornerstone A predicts a reduction in average

precipitation, many of its monthly maximums exceed

historical highs. Similarly, variations among months may not

be captured by monthly averages or annual summaries. For

example, 1000 mm of precipitation during a 5-month period

in winter and spring would produce a very different impact

than if evenly distributed throughout the year or concentrated

during growing-season months. And for monthly level

predictions, a 100-mm average would mask the water quality

and flooding impacts that would result if precipitation were

concentrated in one or two major events.

The GCMs also have limited spatial resolution. Their

one-twelfth degree by one-twelfth degree resolution is a

significant improvement on older model forecasts, but still

coarse for predicting precipitation, which can be highly

variable with adjacent areas receiving drastically different

precipitation amounts from a single event. This variation

is also important for localized flood forecasting and in

estimating water quality.
36 The Southern Forest Futures Project

Table 3.2—Predicted average precipitation for subregions of the Southern United States as forecasted by four 

Cornerstone Futures A through D 

Date Subregion

Cornerstonea prediction of average precipitation (mm)

A B C D

2010

Appalachian-Cumberland 1223 1419 1303 1390

Coastal Plain 1216 1375 1268 1328

Mid-South 721 812 663 784

Mississippi Alluvial Valley 1351 1550 1358 1472

Piedmont 1263 1484 1285 1379

2020

Appalachian-Cumberland 1257 1376 1371 1307

Coastal Plain 1210 1313 1289 1257

Mid-South 677 735 710 659

Mississippi Alluvial Valley 1427 1397 1462 1348

Piedmont 1285 1259 1326 1272

2040

Appalachian-Cumberland 1139 1448 1336 1298

Coastal Plain 1174 1295 1307 1309

Mid-South 579 837 713 725

Mississippi Alluvial Valley 1261 1524 1392 1321

Piedmont 1202 1273 1328 1331

2060

Appalachian-Cumberland 940 1444 1338 1362

Coastal Plain 1037 1370 1309 1370

Mid-South 525 729 650 717

Mississippi Alluvial Valley 1024 1455 1371 1346

Piedmont 1065 1345 1324 1371

2090

Appalachian-Cumberland 999 1434 1271 1417

Coastal Plain 1109 1358 1195 1396

Mid-South 536 884 666 743

Mississippi Alluvial Valley 1110 1582 1303 1456

Piedmont 1164 1395 1231 1388

aEach Cornerstone represents a general circulation model paired with one of two emission scenarios (A1B represents lowpopulation/high-economic growth, high energy use; B2 represents moderate growth and use): A is MIROC3.2+A1B, B is 

CSIROMK3.5+A1B, C is CSIROMK2+B2, and D is HadCM3+B2.

Source: Intergovernmental Panel on Climate Change 2007b.
37

chapter 3. Climate Change Summary

Table 3.3—Predicted average temperature (°C) for subregions of the Southern United States as forecasted 

by four Cornerstone Futures A through D

Date Subregion

Cornerstonea prediction of average temperature (°C) 

A B C D

2010

Appalachian-Cumberland 14.02 13.18 14.31 14.01

Coastal Plain 19.36 18.89 19.49 19.45

Mid-South 18.60 18.02 18.48 18.59

Mississippi Alluvial Valley 19.01 18.54 19.36 19.15

Piedmont 16.16 15.41 16.34 16.24

2020

Appalachian-Cumberland 14.57 13.99 14.67 13.91

Coastal Plain 19.91 19.24 19.84 19.30

Mid-South 19.15 18.40 19.01 19.01

Mississippi Alluvial Valley 19.67 18.95 19.63 19.16

Piedmont 16.73 16.02 16.72 16.05

2040

Appalachian-Cumberland 15.55 14.68 15.46 14.17

Coastal Plain 20.61 19.98 20.27 19.80

Mid-South 19.93 18.91 19.44 19.36

Mississippi Alluvial Valley 20.38 19.63 20.04 19.75

Piedmont 17.59 16.77 17.39 16.41

2060

Appalachian-Cumberland 16.87 15.03 15.91 15.16

Coastal Plain 21.85 20.44 20.80 20.49

Mid-South 21.34 20.11 19.97 19.97

Mississippi Alluvial Valley 21.92 20.27 20.68 20.39

Piedmont 18.79 17.05 17.84 17.26

2090

Appalachian-Cumberland 17.73 15.78 17.29 16.32

Coastal Plain 22.78 21.30 21.96 21.50

Mid-South 22.53 20.74 21.01 20.90

Mississippi Alluvial Valley 22.73 20.94 21.87 21.34

Piedmont 19.74 17.89 19.12 18.46

aEach Cornerstone represents a general circulation model paired with one of two emission scenarios (A1B represents 

low-population/high-economic growth, high energy use; B2 represents moderate growth and use): A is MIROC3.2+A1B, 

B is CSIROMK3.5+A1B, C is CSIROMK2+B2, and D is HadCM3+B2.

Source: Intergovernmental Panel on Climate Change 2007b.

Table 3.4—Average change in temperature and precipitation in the Southeastern United 

States, as recreated from Karl and others (2009) 

Temperature change (°F) Precipitation change (percent)

1901-2008 1970-2008 1901-2008 1970-2008

Annual 0.3 1.6 Annual 6.0 -7.7

Winter 0.2 2.7 Winter 1.2 -9.6

Spring 0.4 1.2 Spring 1.7 -29.2

Summer 0.4 1.6 Summer -4.0 3.6

Autumn 0.2 1.1 Autumn 27.4 0.1





43

chapter 3. Climate Change Summary

However, gaps still exist, both in knowledge and its

implementation. For example, the GCMs from the most

recent assessment incorporate changes in albedo from

polar ice cap melting (IPCC 2007b), an improvement over

previous assessments (Winton 2008) that can offer more

accurate simulations but only if this important feedback is

incorporated into new model runs. Additionally, the positive

feedback between permafrost melting and subsequent release

of carbon dioxide and methane adds important greenhouse

gases to the atmosphere that must be included in the global

warming predictions (Walter and others 2006).

Just as weather forecasts commonly predict from 7 to 10 days

into the future with decreasing accuracy over time, climate

forecasts based on existing and developing global ocean and

atmospheric circulation patterns currently predict 6 to 12

months into the future. Although additional improvement in

the accuracy and forecast length of these seasonal predictions

are likely, accurately predicting specific weather events or

patterns that may occur years or decades in the future is

unlikely anytime soon. The science needed to predict the

impacts of doubling atmospheric carbon dioxide on global

air temperature and precipitation is very different from the

science needed to predict monthly air temperature for a

specific city on a specific date. Given these limitations, land

managers will need to rely on the climate envelopes (ranges

of climatic conditions for specific places and times) as they

develop climate change impact assessments and coping

strategies.

Acknowledgments

We acknowledge the modeling groups, the Program for

Climate Model Diagnosis and Intercomparison and the

World Climate Research Programme (WCRP) Working

Group on Coupled Modeling for their roles in making

available the WCRP CMIP3 multi-model dataset. Support

of this dataset is provided by the Office of Science,

U.S. Department of Energy. We also acknowledge John

Buckley and Erika Cohen, Southern Research Station,

U.S. Department of Agriculture, Forest Service, for their

assistance with reviewing climate summaries.

Literature Cited

Burt, C.C. 2007. Extreme weather: a guide and record book. New York:

	 W.W. Norton and Co. 303 p.

Coulson, D.P.; Joyce, L.A.; Price, D.T. [and others]. 2010. Climate scenarios

for the conterminous United States at the county spatial scale using

SRES scenarios A1B and A2 and PRISM climatology. Fort Collins,

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

Research Station. http://www.fs.fed.us/rm/data_archive/dataaccess/

US_ClimateScenarios_county_A1B_A2_PRISM.shtml. [Date accessed:

January 10, 2011].

Environmental Systems Research Institute (ESRI). 2010. ArcGIS. Version

9.3.1. Redlands, CA: Environmental Systems Research Institute.

Gibson, W.; Daly, C.; Kittel, T. [and others]. 2002. Development of a

103-year high-resolution climate dataset for the conterminous United

States. In: Proceedings of 13th American Meteorological Society

Conference on Applied Climatology. Portland, OR: American

Meteorological Society: 181–183.

Hutchinson, M.F. 2009. ANUSPLIN Version 4.3. http://fennerschool.anu.

edu.au/publications/software/anusplin.php. [Date accessed:

March 17, 2011].

Intergovernmental Panel on Climate Change (IPCC). 2007a. Contribution

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

Intergovernmental Panel on Climate Change. Geneva, Switzerland:

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pdf/assessment-report/ar4/syr/ar4_syr.pdf. [Date accessed:

March 16, 2011].

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assessment report: climate change 2007 (AR4). http://www.ipcc.ch/

publications_and_data/publications_and_data_reports.htm. [Date

accessed: July 7, 2010].

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GCM? http://www.ipcc-data.org/ddc_gcm_guide.html. [Date accessed:

August 18, 2010].

Karl, T.R.; Melillo, J.M.; Peterson, T.C. 2009. Global change impacts

in the United States. Cambridge, UK: Cambridge University Press.

188 p. http://www.globalchange.gov/publications/reports/scientificassessments/us-impacts/full-report. [Date accessed: August 24, 2010].

Maurer, E.P.; Brekke, L.; Pruitt, T.; Duffy, P.B. 2007. Fine-resolution

climate projections enhance regional climate change impact studies.

Eos, Transactions American Geophysical Union. 88(47): 504.

National Assessment Synthesis Team (NAST). 2001. Climate change

impacts on the United States: the potential consequences of climate

variability and change. Foundation Report. Report for the U.S. global

change research program. Cambridge, UK: Cambridge University Press.

154 p.

Nakicenovic, N.; Swart, R., eds. 2000. Special report on emissions

scenarios: a special report of working group III of the Intergovernmental

Panel on Climate Change. Cambridge, UK: Cambridge University Press.

599 p. http://www.grida.no/climate/ipcc/emission/index.htm. [Date

accessed: August 26, 2010].

SAS Institute Inc. © 2010. JMP. SAS/STAT user’s guide. Version 8. Cary,

NC: SAS Institute Inc. [pages unknown].

Solomon, S.; Qin, D.; Manning, M.; Chen, Z. [and others], eds. 2007.

Contribution of working group I to the fourth assessment report of

the Intergovernmental Panel on Climate Change. Cambridge, UK:

Cambridge University Press. 996 p. http://www.ipcc.ch/publications_

and_data/ar4/wg1/en/contents.html. [Date accessed: March 15, 2011].

Tans, P. 2011. Trends in atmospheric carbon dioxide. NOAA/ESRL. http://

www.esrl.noaa.gov/gmd/ccgg/trends. [Date accessed: March 15, 2011].

Walter, K.M.; Zimov, S.A.; Chanton, J.P. [and others]. 2006. Methane

bubbling from Siberian thaw lakes as a positive feedback to climate

warming. Nature. 443(7): 71–75.

Winton, M. 2008. Sea ice albedo feedback and nonlinear Arctic climate

change. In: DeWeaver, E.; Bitz, C.M.; Tremblay, B., eds. Arctic Sea Ice

Decline: Observations, Projections, Mechanisms, and Implications.

Geophysical Monograph Service. Washington, DC: 111–131. Vol. 180.

45

chapter 4. Forecasts of Land Uses

David N. Wear1

Key Findings

•	Between 30 million and 43 million acres of land in the

South are forecasted to be developed for urban uses by

2060 from a base of 30 million acres in 1997. These

forecasts are based on a continuation of historical

development intensities.

•	 From 1997 to 2060, the South is forecasted to lose between

11 million acres (7 percent) and 23 million acres

(13 percent) of forests, nearly all to urban uses. All of the

South’s five subregions are expected to lose at least some

forest acreage under all evaluated futures.

•	 Strong timber markets can ameliorate losses of southern

forest somewhat, but this comes at the expense of cropland

uses.

•	 Among the South’s five subregions, the Piedmont is

forecasted to lose the greatest proportion of its forest

area—21 percent under the highest-loss forecast—by

2060. The Mid-South and Mississippi Alluvial Valley are

forecasted to lose the smallest proportion (between 8 and

9 percent).

•	 At 34 percent, Peninsular Florida is forecast to lose the

most forest land of the 21 sections nested within the South’s

five subregions. All sections within the Piedmont subregion

are forecasted to lose at least 19 percent of their forest land.

•	 The area of cropland in the South is forecasted to decline

by as much as 17 million acres from 1997 to 2060 from a

base of about 84 million acres in 1997. Cropland futures

assume constant real returns to agricultural products.

•	Cropland losses would be highest in North Carolina,

southern Florida, and central Texas.

1David N. Wear is the Project Leader, Center for Integrated Forest Science,

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

Raleigh, NC 27695.

Introduction

Land use patterns define both the extent of human presence

on a landscape and the ability of land to provide a full range

of ecosystem services. The future sustainability of forests in

the South has been and will continue to be largely influenced

by the dynamics of land use. And as the region’s population

grows so too will the area of developed uses. The pattern

of these developments, returns from the various products

of rural land, and the land’s inherent productivity will

determine the distribution of forest, crop, and other rural

land uses, and therein the structure and function of terrestrial

ecosystems (Chen and others 2006, Wear 2002).

The purpose of this chapter is to examine how land use could

respond to the economic and population forecasts associated

with the Cornerstone Futures for the Southern Forest Futures

Project. Our forecasts use empirical models to address the

Cornerstone Futures and to examine some specific questions

about alternative land use futures. Land use forecasts

play a central role in the U.S. Forest Assessment System

(Wear and others 2013), with the information developed

in this chapter providing one of the inputs to the Forest

Assessment System’s forest dynamics model, which in turn

generates forecasts of southern forest conditions (chapter

5). In addition, land use and forest forecasts feed additional

analyses in the Futures Project, including analyses of

timber markets (chapter 9), water (chapter 13), wildlife and

biodiversity (chapter 14), and fire (chapter 17).

Chapter 4.

Forecasts of Land Uses

47

chapter 4. Forecasts of Land Uses

Methods

To forecast land use, we adopt the RPA econometric models

developed by Wear (2011) to reflect variations in land use

patterns and biophysical capability among the U.S. regions.

The land use model for the South addresses all of the

13 States in the Futures Project’s analysis area except for

central and western Texas and Oklahoma, where results

derive from the land use model developed for the Rocky

Mountain/Great Plains region.

Each land use model has two major components: changes

in county-level population and personal income, which are

used to simulate future urbanization; and allocations of rural

land among competing uses that are likely to result from

predicted urbanization and rural land rents. Output from

both components is based on land use data from 1987 and

1997 to ensure that forecasted land use changes are generally

consistent with observed urbanization intensities and rural

land use changes (Wear 2011).

The land use model for the South is driven by county-level

changes in population density, personal income, and timber

and crop prices. In comparison, land use change in the

Rockies/Great Plains model is sensitive only to changes in

population and income, and with changes in rural land uses

forecasted to be proportional to their 1997 levels. Because

tree planting following harvest does not alter total land

use projections, the projections developed in this chapter

are limited to Cornerstone Futures A through D (with

Cornerstone E having forecasts equivalent to Cornerstone

A, and Cornerstone F having forecasts equivalent to

Cornerstone D).

Data Sources

Observations of historical land uses were derived from the

1987 and 1997 surveys conducted by National Resources

Inventory, which provides the only consistent, repeated,

and exhaustive measures of all non-Federal land uses. Uses

include pasture, crops, forest, range, or urban uses (which

includes both urban and lower density developed areas); they

cumulatively define the total “mutable” land for modeling

change in the South (table 4.1). Other land use categories—

including Federal land, water area, enrolled Conservation

Reserve Program lands, and utility corridors—were held

constant within the forecasts.

We applied the population and personal income projections

for the two RPA storylines (A1B for Cornerstones A and

B; B2 for Cornerstones C and D) to drive forecasts of

urbanization. The A1B population forecasts are based

on 2004 Census projections for the entire country; B2

population forecasts are lower than the Census projections.

Zarnoch and others (2010) developed county-level

projections for each scenario; their projections are tied to

spatial econometric/demographic forecasts (Woods and

Poole Economics 2007) that are generally consistent with the

A1B projection for 2000 to 2030. County-level projections

for A1B were disaggregated by extending 2000–30 patterns

of growth from the Woods and Poole projections (Zarnoch

and others 2010). Projections for B2 applied the same spatial

pattern of population change, but were adjusted to yield

county-level projections that added up to the storyline’s total

(chapter 2).

A1B corresponds to mid-range population growth and the

highest per capita disposable personal income level of the

RPA storylines (chapter 2). Under this storyline, the South

can expect to see about 160 million people and a per capita

personal income of around $80,000 (2006 dollars) by 2060.

B2 projects a lower population growth and lower personal

income, predicting a population of 143 million people with

per capita personal income around $60,000 in 2060. A third

storyline, A2 was used in the RPA analysis, but was not

selected for use in the Forest Futures analysis (chapter 2).

A fourth storyline, B1, was not included in either the RPA

or the Forest Futures analysis because of data compatibility

issues.

Population is not forecasted to grow evenly across the South.

Rather, projected growth is concentrated on a number of

existing urban centers. In addition, population declines are

forecasted for many counties (chapter 2). Population loss is

expected to be especially high in the Great Plains portions

of Texas and Oklahoma, the Mississippi Alluvial Valley, and

southern Alabama and Mississippi.

Timber price projections also vary across the Cornerstone

Futures. Cornerstones A and C assume increasing prices

while B and D assume decreasing prices. The land use

model for the South is sensitive to these changes in prices.

Increasing timber prices (relative to crop prices) encourages

higher retention of forest land than price decreases. For all

the Cornerstone Futures, the price of crops was held constant

at current (2006) values.

Results

Percent coverage of the five land uses for non-Federal land

(table 4.1) in 1997 are individually shown at the county level

in figure 4.1 and are compared for the region as a whole in

figure 4.2. Patterns of rural uses reflect biome boundaries

and differences in productivity that are in turn affected by

biophysical conditions. Figure 4.1 shows that forest uses

are predominant across much of the South, cropland is

concentrated in the Mississippi Valley and in northwest

Texas (with areas of moderate concentration in the upper

Atlantic Coastal Plain and along the Gulf of Mexico in Texas

and Louisiana), range is concentrated in the High Plains area









57

chapter 4. Forecasts of Land Uses

(storyline B2) and increasing timber prices. Comparing

forecasts for Cornerstones A and B with those for

Cornerstones C and D shows a 5-million acre difference

between a future of increasing timber prices and a future

of decreasing prices, confirming that the effects of the

economic/population storyline dominate the effects of

timber prices.

Forest losses are especially high in a few areas of the South

(tables 4.4 and 4.5). For all Cornerstone Futures, losses are

concentrated in the Piedmont from northern Georgia through

North Carolina and into parts of Virginia, as figure 4.10

shows for Cornerstone C (selected because it is bracketed by

the other Cornerstones). Other areas of concentrated forest

losses are on the Atlantic Coast, along the Gulf of Mexico,

and in parts of eastern Texas outside of Houston. The

income-fueled development in Cornerstones A and B spreads

low-intensity forest losses across a broader area (fig. 4.11).

Under Cornerstone B, forest losses are highest in the Coastal

Plain, at about 12 million acres by 2060, and lowest in the

Mississippi Alluvial Valley and the Mid-South (fig. 4.12).

Percentage losses are greatest in the Piedmont, where 21

percent of existing forests would be lost, followed by an

Appalachian-Cumberland loss of 13 percent and a Coastal

Plain loss of about 11 percent.

Figures 4.10 and 4.11 show changes in the percentage of each

county that is in forest cover to enable ready comparisons

across counties of variable size. If instead we examine the

percentage change in forest, then different information

is conveyed. Figure 4.13 shows the percentage change in

forest uses for Cornerstone C, where economic growth

is low (storyline B2) but timber prices are increasing, to

highlight areas where slight gains in forest are forecast

in response to increasing timber prices (and stable crop

prices)—most notably in central-western Kentucky and the

Lower Mississippi Alluvial Valley. It also shows where the

percentage loss of forests would be highest, with highest

acreage losses generally at the periphery of urban areas

such as the ring around Atlanta (fig. 4.10), and highest

percentage losses at the core of these urban areas (fig. 4.13).

This means that the percentage forest loss is highest where

current populations is highest and where we might expect the

aesthetic, recreational, and microclimate (cooling) services of

forests to be most needed.

Figure 4.14 displays the loss of forest land by 2060 under

Cornerstone B for each of the sections that comprise the

South’s five subregions. All sections are forecasted to lose

forests, with the highest loss (about 34 percent) expected for

Peninsular Florida. The Deltaic Plain at the mouth of the

Mississippi River is forecasted to lose about 25 percent, but

this is from a very small 1997 base. All three sections in the

Piedmont—Central Appalachian Piedmont, Piedmont Ridge

Valley and Plateau, and Southern Appalachian Piedmont—

would lose between 19 and 24 percent of their forest area.

Cropland Uses

As with forest area, the change in cropland area depends

on the economic conditions defined by each alternative

future. However unlike forest area, which is dominated

by urbanization patterns (driven by the A1B storyline),

cropland change is more heavily influenced by the timber

price futures. Losses range from about 16 million under

Cornerstone A’s high economic growth (A1B) with

increasing timber prices, to only about 5 million acres

under Cornerstone D’s lower economic growth (B2) with

decreasing timber prices (fig. 4.15). The difference in crop

loss between storylines A1B and B2 (holding price futures

constant) is about 3 million acres. The difference between

increasing and decreasing price futures (holding storylines

constant) is about 8 million acres.

Cornerstone D, which predicts the lowest levels of cropland

loss, shows especially high levels in North Carolina, southern

Florida, central Kentucky and Tennessee, and the area in

Texas bordered by Dallas, Houston, and Austin (fig. 4.16).

Cornerstone A, where crop losses are highest (fig. 4.17),

shows losses that are spread across broader areas of North

Carolina, Tennessee, and Kentucky; and additional losses

in southeastern Georgia and the coastal areas of Texas and

Louisiana. Among the five southern subregions, the highest

percentage loss of cropland is in the Piedmont (28 percent

under Cornerstone B and 51 percent under Cornerstone

A), followed by large Coastal Plain and AppalachianCumberland areas (figs. 4.18 and 4.19).

Other Land Uses

Pasture—The pattern of pasture losses across the

Cornerstone Futures is similar to the pattern of forest losses.

The highest loss is forecasted with Cornerstone B (about

7 million acres), and the lowest is forecast with Cornerstone

C (fig. 4.20). Similar to the pattern of cropland forecasts,

pasture area change is more heavily affected by timber price

projections than by the economic growth forecasts. Pasture

losses for all the Cornerstone Futures are concentrated

in three broad zones: the first stretching from northern

Georgia to northern Kentucky and including a large area of

Tennessee, the second in Peninsular Florida, and the third

including the Ozark-Ouachita Highlands and the Cross

Timbers area of eastern Texas and Oklahoma. There is

substantial variation across the five southern subregions.

As is the case for forests and crops, the Piedmont has the

largest percentage loss, about 25 percent for Cornerstone B

(fig. 4.21), followed by Appalachian-Cumberland losses of

15 percent, Coastal Plain losses of 11 percent, and for the

Mid-South losses of 9 percent (fig. 4.22).
58 The Southern Forest Futures Project

Table 4.4—Forecasted area of non-Federal forest land in the South, 1997–2060, based on an expectation of large 

urbanization gains and decreasing timber prices (Cornerstone B)

Area in forest use Change from 1997 to 2060

Subregion Section 1997 2010 2020 2040 2060 Area Percent

- - - - - - - - - - - - - - - - - - - - thousand acres - - - - - - - - - - - - - - - - - - - - - - 

AppalachianCumberland

Blue Ridge 4,312.16 4,192.92 4,077.08 3,847.05 3,536.92 -775.24 -18.0

Cumberland 

Plateau and 

Mountain 8,637.99 8,529.94 8,420.09 8,210.54 7,936.19 -701.80 -8.1

Interior Low 

Plateau 10,309.89 10,013.07 9,752.17 9,249.47 8,660.85 -1,649.04 -16.0

Northern 

Ridge and 

Valley 2,823.01 2,784.61 2,748.56 2,680.78 2,588.49 -234.52 -8.3

Southern 

Ridge and 

Valley 1,836.39 1,783.46 1,734.52 1,633.66 1,508.63 -327.76 -17.8

Total 27,919.43 27,304.00 26,732.42 25,621.50 24,231.08 -3,688.35 -13.2

Coastal Plain

Eastern 

Atlantic 23,265.04 22,705.80 22,184.98 21,209.56 20,033.81 -3,231.23 -13.9

Florida 

Peninsular 3,604.77 3,229.70 3,004.05 2,674.57 2,379.75 -1,225.02 -34.0

Middle Gulfeastern 20,744.52 20,429.34 20,100.35 19,477.49 18,666.28 -2,078.24 -10.0

Middle Gulfwestern 13,700.96 13,555.13 13,404.23 13,118.79 12,727.14 -973.82 -7.1

Northern 

Atlantic 6,443.70 6,287.92 6,134.30 5,857.61 5,538.17 -905.53 -14.1

Southern Gulf 21,693.85 21,342.15 20,987.29 20,314.23 19,479.36 -2,214.49 -10.2

Western Gulf 9,275.35 9,066.80 8,919.50 8,652.13 8,363.80 -911.55 -9.8

Total 98,728.19 96,616.83 94,734.71 91,304.38 87,188.33 -11,539.86 -11.7

Mid-South

Cross Timbers 4,582.04 4,500.57 4,447.78 4,338.32 4,250.32 -331.72 -7.2

High Plains 116.34 116.19 115.91 115.32 114.48 -1.86 -1.6

OzarkOuachita 

Highlands 10,355.32 10,216.25 10,086.52 9,826.35 9,486.59 -868.73 -8.4

West Texas 

Basin and 

Range 0 0 0 0 0 0.00

Total 15,053.70 14,833.01 14,650.22 14,279.99 13,851.39 -1,202.31 -8.0

Mississippi 

Alluvial Valley

Deltaic Plain 707.83 670.05 635.56 587.87 526.93 -180.90 -25.6

Holocene 

Deposits 4,869.42 4,821.75 4,773.78 4,684.08 4,573.39 -296.03 -6.1

Total 5,577.25 5,491.80 5,409.34 5,271.95 5,100.32 -476.93 -8.6

Piedmont

Central 

Appalachian 

Piedmont 12,089.77 11,569.80 11,176.14 10,468.66 9,728.00 -2,361.77 -19.5

Piedmont 

Ridge, Valley 

and Plateau 4,773.56 4,622.42 4,480.43 4,206.27 3,861.46 -912.10 -19.1

Southern 

Appalachian 

Piedmont 11,670.56 10,936.10 10,501.44 9,695.65 8,862.91 -2,807.65 -24.1

Total 28,533.89 27,128.31 26,158.00 24,370.58 22,452.37 -6,081.52 -21.3

Grand total 175,812.46 171,373.95 167,684.68 160,848.40 152,823.49 -22,988.97 -13.1
59

chapter 4. Forecasts of Land Uses

Table 4.5—Forecasted area of non-Federal forest land in the South, 1997 to 2060, based on an expectation of moderate 

urbanization gains and increasing timber prices (Cornerstone C)

Area in forest use Change from 1997 to 2060

Subregion Section 1997 2010 2020 2040 2060 Area Percent

- - - - - - - - - - - - - - - - - - - - thousand acres - - - - - - - - - - - - - - - - - - - - - - 

AppalachianCumberland

Blue Ridge 4,312.16 4,182.00 4,109.46 4,016.85 3,883.87 -428.29 -9.9

Cumberland 

Plateau and 

Mountain 8,637.99 8,541.08 8,493.43 8,448.57 8,352.67 -285.32 -3.3

Interior Low 

Plateau 10,309.89 10,092.69 9,996.56 9,947.65 9,871.77 -438.12 -4.2

Northern Ridge 

and Valley 2,823.01 2,784.17 2,769.88 2,760.96 2,742.38 -80.63 -2.9

Southern 

Ridge and 

Valley 1,836.39 1,782.09 1,745.98 1,703.43 1,653.26 -183.13 -10.0

Total 27,919.43 27,382.03 27,115.30 26,877.46 26,503.95 -1,415.48 -5.1

Coastal Plain

Eastern 

Atlantic 23,265.04 22,712.73 22,422.69 22,129.75 21,740.09 -1,524.95 -6.6

Florida 

Peninsular 3,604.77 3,249.81 3,080.31 2,920.72 2,773.70 -831.07 -23.1

Middle Gulfeastern 20,744.52 20,448.50 20,310.78 20,221.68 20,054.00 -690.52 -3.3

Middle Gulfwestern 13,700.96 13,531.61 13,452.46 13,380.06 13,250.55 -450.41 -3.3

Northern 

Atlantic 6,443.70 6,274.25 6,182.97 6,095.06 5,977.76 -465.94 -7.2

Southern Gulf 21,693.85 21,349.76 21,155.13 20,956.38 20,688.80 -1,005.05 -4.6

Western Gulf 9,275.35 9,062.91 8,966.13 8,857.77 8,733.82 -541.53 -5.8

Total 98,728.19 96,629.56 95,570.48 94,561.42 93,218.72 -5,509.47 -5.6

Mid-South

Cross Timbers 4,582.04 4,510.84 4,478.97 4,449.96 4,410.01 -172.03 -3.8

High Plains 116.34 116.14 115.95 115.84 115.57 -0.77 -0.7

OzarkOuachita 

Highlands 10,355.32 10,215.68 10,135.17 10,037.04 9,900.33 -454.99 -4.4

West Texas 

Basin and 

Range 0 0 0 0 0 0.00

Total 15,053.70 14,842.66 14,730.08 14,602.85 14,425.92 -627.78 -4.2

Mississippi 

Alluvial Valley

Deltaic Plain 707.83 671.81 650.97 642.87 627.54 -80.29 -11.3

Holocene 

Deposits 4,869.42 4,853.69 4,859.30 4,899.07 4,937.64 68.22 1.4

Total 5,577.25 5,525.49 5,510.27 5,541.93 5,565.18 -12.07 -0.2

Piedmont

Central 

Appalachian 

Piedmont 12,089.77 11,528.27 11,240.68 10,911.87 10,584.00 -1,505.77 -12.5

Piedmont 

Ridge, Valley 

and Plateau 4,773.56 4,617.11 4,528.85 4,432.82 4,296.76 -476.80 -10.0

Southern 

Appalachian 

Piedmont 11,670.56 10,902.33 10,613.07 10,239.61 9,862.29 -1,808.27 -15.5

Total 28,533.89 27,047.72 26,382.60 25,584.30 24,743.05 -3,790.84 -13.3

Grand total 175,812.46 171,427.46 169,308.73 167,167.96 164,456.82 -11,355.64 -6.5









69

chapter 4. Forecasts of Land Uses

of population and income. Future models may be enhanced

with more frequent data on observed land uses, and also

by the development of new methods for the combined

forecasting of population change, economic development,

and land use choices.

Literature Cited

Chen, H.; Tian, H.Q.; Liu, M.L. [and others]. 2006. Effect of land-cover

	 change on terrestrial carbon dynamics in the Southern United States.

Journal of Environmental Quality. 35: 1533-1547.

Hardie, I.; Parks, P.; Gottleib, P.; Wear, D.N. 2000. Responsiveness of rural

and urban land uses to land rent determinants in the U.S. South. Land

Economics. 76: 659-673.

Lubowski, R.N.; Plantinga, A.J.; Stavins, R.N. 2006. Land-use change

and carbon sinks: econometric estimation of the carbon sequestration

supply function. Journal of Environmental Economics and Management. 

51: 135-152.

Lubowski, R.N.; Plantinga, A.J.; Stavins, R.N. 2008. What drives land-use

change in the United States? A national analysis of landowner decisions.

Land Economics. 84: 529-550.

U.S. Department of Agriculture (USDA) Forest Service. 2012. Future

scenarios: a technical document supporting the Forest Service 2010 RPA

assessment. Gen. Tech. Rep. RMRS-GTR-272. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky Mountain Research

Station. 34 p.

U.S. Department of Agriculture (USDA). 2009. Summary report: 2007

national resources inventory. Washington, DC: Natural Resources

Conservation Service and Center for Survey Statistics and Methodology.

Ames, IA: Iowa State University. 123 p. http://www.nrcs.usda.gov/

Internet/FSE_DOCUMENTS//stelprdb1041379.pdf. [Date accessed:

January 24, 2012].

Wear, D.N. 2002. Land use. In: Wear, D.N.; Greis, J.G., eds. Southern

Forest Resource Assessment. Gen. Tech. Rep. SRS-53. Asheville, NC:

U.S. Department of Agriculture Forest Service, Southern Research

Station: 153-173.

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.; Huggett, R.; Li, R. [and others]. 2013. Forecasts of forest

conditions in U.S. regions under future scenarios: a technical document

supporting the Forest Service 2010 RPA assessment. Gen. Tech. Rep.

SRS-170. Asheville, NC: U.S. Department of Agriculture Forest Service,

Southern Research Station. 101 p.

Woods and Poole Economics. 2007. Complete economic and demographic

data source (CEDDS) technical documentation. Washington, DC:

Woods and Poole Economics Inc. 101 p.

Zarnoch, S.J.; Cordell, H.K.; Betz, C.J.; Langner, L. 2010. Projecting

county-level populations under 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.
70 The Southern Forest Futures Project

Land Use Models

This appendix provides documentation of the land use

models used to generate forecasts for this report. Wear

(2011) provides details on this modeling approach. We model

changes in land use as a function of independent variables

defined by the RPA scenarios. Population and income

projections, downscaled to counties for each scenario, drive

our forecasts of land development activities. A theoretically

complete analysis of urbanization would jointly address

the mechanics of land supply and demand to determine

both development values and land in developed uses (e.g.,

Lubowski and others 2008). By taking RPA population and

income forecasts as givens, we are adopting an implicit

spatial economic growth solution. As such, the modeling

task is to define the response of land use allocations to

the population and income forecasts from the scenario

framework.

We model changes in the area of land within a county for a

small complement of land use classes in response to these

and other variables. Variation in historical land allocations

reflects differences in the demand for various goods and

services derived from land as well as a number of supply

factors, such as soil characteristics and climate that define

comparative advantage for producing these goods and

services. In a qualitative sense we follow the approach of

Hardie and others (2000) by adopting a model that is a

synthesis of the von Thunen concept of developed land use

organized by steep rent gradients around central business

districts and Ricardo’s model of rural land use allocation

based on rents accruing to competing rural uses (Lubowski

and others 2006). More to the point, we assume that demand

for urban uses follows some pattern of spatial contagion

(defined relative to a single or multiple growth poles) and that

rents associated with new urban uses supersede rents for all

rural land uses—a near vertical rent gradient for the urban

use in von Thunen’s model.

Our modeling approach differs from previous efforts (e.g.,

Lubowski and others 2008, Hardie and others 2000) in

some important ways. These previous models focus on

testing hypotheses regarding land use distributions (e.g.,

interacting Ricardian and von Thunen specifications, Hardie

and others 2000) and conducting counterfactual simulations

regarding policy effects on land use distributions (e.g., for

carbon policies, Lubowski and others 2006). Explaining the

existing distribution of land uses requires extensive data that

account for differences in productivity, including climate,

soil, and topographic variables. We focus here on forecasting

change in land use conditioned on the current distribution

of land uses and based on forecasts of a much smaller set of

exogenous variables.

For each county in the forested area of the South (excluding

central and western Texas and Oklahoma), we model the

urbanization process and changes in four rural uses: forest,

crops, range, and pasture. The data set used for model

estimations is a panel of observed land uses in two years

(1987 and 1997), the most recent comprehensive data

set available for our use derived from the NRI land use

inventory. Models were applied to what we define as the

variable or mutable land base: non-Federal land classified as

developed, crops, pasture, range, or forests. All other land

in the county was held fixed in its current use. We adopted a

two-stage modeling approach which first defines urban-rural

allocations and then allocation for four rural land uses.

We assume that the demand for urban uses dominates all

other land uses. That is, we expect that the amount of urban

land use is determined by demand factors that influence

urban land rents and is unaffected by competition with any

other land use. Consider the following reduced form model:

U = f (Y, Z, X,) (1)

Where U is the area in urban use, Y is a vector of timevarying variables from the RPA scenarios, including the

population contained in the county (pop), and the real per

capita disposable income for the county (inc). These variables

change within each RPA scenario. The vectors Z and X are

vectors of observed and unobserved time-invariant variables

respectively, and describe the land quality attributes of the

county—for example soil productivity, access to markets,

etc.... A linear specification of equation 1 is:

Uit = β0 + β1 popit + β2 popit 2 + β3 incit + (2)

δZ,

i + αX,i + εit

APPENDIX A.

Synopsis of Models
71

chapter 4. Forecasts of Land Uses

Population and income are expected to be positively

associated with the area of urban uses. To model changes in

the area of urban land use, we difference equation 2:

Uit = Ui-1 + β1 (popit - popit-1 ) + (3)

β2 (popit 2 - pop2

it-1 ) + β3 (incit - incit-1 ) ε*it	

Differencing causes observed and unobserved fixed

attributes of the county to fall out of the change equation

(see Wooldridge 2002). Change therefore relies strictly on

time-varying variables that are forecast to change between

periods. Other time-varying variables such as rents accruing

to crop or timber uses are excluded from this model by

assumption—i.e., that urban rents completely dominate all

other rural rents in the area of the county affected by the

shift in demand. We posit that this urban growth difference

equation may differ across subregions of the United States,

due in part to the effects of topography and climate on

the spatial agglomeration of uses (e.g., mountainous areas

and flat areas may reveal different development patterns

determined in part by topographic features). We therefore

estimated separate models for broad regions and within each

regional model we allowed for differences in coefficients by

ecological provinces (Rudis 1999) by interacting dummy

variables for the ecological provinces with each independent

variable.

To complete our model, we address changes in rural land

uses in response to changes in rural land rent determinants in

addition to urbanization. Changes to relative rents could lead

to rural land use switching irrespective of population/income

changes. Consider the equations for current amounts of forest

and crop uses similar to equation (2):

Ft = φ0 + φf f pf,t + φfc pc,t + φfu Ut + δf Z,+ αf X,+ εu (4.1)

Ct = γ0 + γcf pf,t + γcc pc,t + γcu Ut + δc Z,+ αc X,+ εu (4.2)

Here we assume that the areas of land in forest and crops are

determined by the time-varying rents accruing to forests and

crops (p’s) and vectors of observed and unobserved fixed

attributes that influence the suitability of land for various

uses (Z and γ respectively). Pasture area (P) is defined as

a residual land use. Rental values for forest and crop uses

and the area of urban use are considered time-varying. To

account for the urbanization dynamic in the Rent-Biased

Model, we substitute equations (5.1) and (5.2) for urban

change terms in equations (7.1) and (7.2) as follows:

Ct = Ct-1 + [ αc + βcc Pct + βcf Pft] δcu + (5.1)

φcf [ Pf,t - Pf,t-1 ] + φcc [ Pc,t - Pc,t-1 ] + ε*

F

Ft = Ft-1 + [ αf + βfc Pct + βff Pft] δfu + (5.2)

γcf [Pc,t - Pc,t-1] + γff [ Pf,t - Pf,t-1 ] + ε*

c

Pt = Pt-1 - ([ Ut - Ut-1 ] + [ Ft - Ft-1 ] + [ Ct - Ct-1 ]) (5.3)

We estimated equations 3, 5.1 and 5.2 using a weighted

Seemingly Unrelated Estimation approach to account

for cross equation correlations. Coefficient estimates are

described in Wear (2011).

For areas in central and western Texas and Oklahoma, we

use a model developed for the Rocky Mountain region.

This model requires that we address changes in rangeland

and uses the same structure for predicting urbanization.

However, a simpler model is applied to rural land use

changes where forest, crop, range and pasture uses are

forecasted to change in response to urbanization with

proportional change determined by the existing proportion of

each rural land use (see Wear 2011).

Forecasting Algorithm—Our models are designed to

forecast change in the areas of urban, forest, and crop uses

with pasture use as a residual. Because areas in any land use

are not constrained to be positive by the structure of these

equations, nonnegativity constraints and “adding-up” rules

need to be applied to ensure logical forecasts. We forecast

change in land use in response to the driving variables of

each scenario, including population, personal income, and

relative timber prices (indexed by the price of softwood

pulpwood).

(+)

(-) (+)

73

chapter 5. Forecasts of Forest Conditions

Robert Huggett, David N. Wear, Ruhong Li,

	 John Coulston, and Shan Liu1

Key Findings

•	 Among the five forest management types, only planted

pine is expected to increase in area. In 2010 planted pine

comprised 19 percent of southern forests. By 2060, planted

pine is forecasted to comprise somewhere between 24 and

36 percent of forest area.

•	Although predicted rates of change vary, all forecasts

reveal that land use changes and conversion to pine

plantations will result in a continuing downward trend in

naturally regenerated pine types.

•	Changes in forest types are influenced by urbanization

and timber markets: hardwood types are most strongly

influenced by urbanization; softwood types are most

sensitive to future timber market conditions.

•	Reversing a 50-year trend of accumulating about 2.5 billion

cubic feet per year, forest biomass is forecasted to increase

slightly over the next 10 to 20 years and then decline

gradually.

•	 After accounting for harvests, forest growth, land use, and

climate change, the total carbon pool represented by the

South’s forests is forecasted to increase slightly from 2010

to 2020/2030 and then decline.

•	 Urbanization patterns are the dominant determinates of

the size of the future forest carbon pool, although stronger

forest product markets can ameliorate carbon losses.

•	Because of increases in timber supply from 1990 to 2010,

removals of forest biomass (growing stock) are forecasted

to increase for all Cornerstones, including those that project

decreasing prices. This reflects an outward shift in timber

supply associated with forest inventories between 1990 and

2010.

1Robert Huggett is a Research Assistant Professor, Department of Forestry

and Environmental Resources, North Carolina State University, Raleigh,

NC 27695. David N. Wear is the Project Leader, Center for Integrated

Forest Science, Southern Research Station, U.S. Department of Agriculture

Forest Service, Raleigh, NC 27695. Ruhong Li is a Research Associate,

Department of Forestry and Environmental Resources, North Carolina

State University, Raleigh, NC 27695. John Coulston is a Supervisory

Research Forester, Forest Inventory and Analysis Research Work Unit,

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

Knoxville, TN 37919. Shan Liu is a Research Assistant, Department of

Forestry and Environmental Resources, North Carolina State University,

Raleigh, NC 27695.

•	Removals of softwood pulpwood are responsive to futures

for forest planting and product prices. Under a high price

future, softwood pulpwood output would increase by

56 percent, roughly equal to the expansion observed

between 1950 and 2000.

•	 Although the overall loss of upland hardwood acreage is

forecasted to be in the range of 8 to 14 percent, the oakhickory forest type remains essentially constant while

the areas of other forest types decline at higher rates. The

yellow-poplar forest type is forecasted to decline the most,

with the highest losses forecasted for the Piedmont.

•	 The age and species structure of softwood forest types

are most strongly influenced by forest harvesting and

management tied to timber markets. This is not the case for

hardwood forests.

•	 The future structure of hardwood forests is most strongly

affected by urbanization-driven land use changes

(increased population growth and income).

•	Reductions of naturally regenerated pine forests are not

equally distributed among age classes. Mid-age and earlyage forests decline, but old-age forests remain relatively

constant.

•	 The distribution of upland and lowland hardwoods shifts,

with less of these forest management types classified as

early age and more classified as older age.

Introduction

The South’s forests have been shaped by a long history of

harvesting, forest management, and land use conversions.

European settlers began a wave of farming and land

clearing that continued to expand until around 1920 (USDA

Forest Service 1988). Starting in the 1920s, extensive farm

abandonment resulted in widespread forest establishment

and old-field succession. With this period of forest recovery,

forest area and forest biomass grew steadily through much

of the 20th century and forest area peaked in the 1960s. In

the 1970s and 1980s, industrial scale agriculture led to some

losses in forest acreage especially in the Mississippi Alluvial

Valley, and in the 1990s, urbanization became the dominant

dynamic affecting forest area (Wear and Greis 2002). Despite

these losses since the 1970s, the volume of growing stock

(a measure of standing forest biomass) grew by more than

75 percent while industrial output of wood products more

Chapter 5.

Forecasts of Forest Conditions
74 The Southern Forest Futures Project

than doubled over the last half of the 20th century (Wear and

others 2007).

The expansion of timber harvests in the South outpaced

that of all other regions of the United States. As of 2007,

the South’s timber output was more than twice 1952 levels.

About 60 percent of all timber produced in the United States

was from southern forests (Smith and others 2009) compared

to about 40 percent in 1962, indicating a shifting of

production capacity to the region as well as increased output.

This relocation of productive activity reflects the South’s

comparative advantage in growing timber and a new product

mix that favors smaller diameter trees. While softwoods

comprise a majority of harvests (69 percent), hardwood

harvests are also substantial.

This chapter explores the ongoing and potential future

changes affecting southern forests. Several uncertainties

cloud our ability to predict that future. As the South’s

economy expands, population and attendant urbanization

continue to outpace all other regions. At the end of the

20th century, shifts in market demand slowed the steady

progression of timber harvesting. New policies pursue new

demands for timber to provide cellulose-based bioenergy.

Anticipated climate changes raise key questions about the

future productivity and composition of forests. And although

forests are by their nature dynamic, there have been both an

acceleration and an interaction of changes in southern forests

that cannot be understood without comprehensive analysis.

A modeling system designed to forecast the interactions of

various social, economic, and biophysical drivers and the

structure and extent of future forests is used in this chapter

to explore the implications of the changes that will affect

southern forests (see text box on following page, table 5.1,

and fig. 5.1). Key to the system is a set of scenarios, called

Table 5.1—Definition of Cornerstone Futures used in the Southern Forest Futures Project, based on two storylines from 

the 2010 Resources Planning Act (RPA) Assessment and three general circulation models

Cornerstone Futures RPA Storylines General 

circulation 

model

Timber 

prices

Planting 

Tag Label Label rates

Economic 

growth

Population 

growth

A High growth/high prices A1B High Moderate MIROC Increasing Base

B High growth/low prices A1B High Moderate CSIRO Decreasing Base

C Low growth/high prices B2 Low Low CSIRO Increasing Base

D Low growth/low prices B2 Low Low Hadley Decreasing Base

E High growth/high prices/high planting A1B High Moderate MIROC Increasing High

F Low growth/low prices/low planting B2 Low Low Hadley Decreasing Low

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Figure 5.1—The six Cornerstone Futures defined by permutations of two 2010 Resources Planning Act

(RPA) Assessment/Intergovernmental Panel on Climate Change storylines and two timber price futures;

and then extended by evaluating increased and decreased forest planting rates.
75

chapter 5. Forecasts of Forest Conditions

Cornerstone Futures, which were developed to represent

a broad range of plausible futures (chapter 2). For each

Cornerstone Future, the modeling system simulates changes

in forest area, shifts among forest types, the amount and

distribution of forest biomass, harvest removals, and changes

in the forest carbon pool. To the extent possible, we examine

variation across the five subregions of the South and focus on

the spatial distribution in forest conditions. These findings

provide a foundation for further exploration of timber

markets (chapter 9), bioenergy futures (chapter 10), and

impacts on wildlife, biodiversity, and forest communities

(chapter 14).

Methods

The results examined in this chapter are based on the U.S.

Forest Assessment System, a modeling system designed

to forecast alternative futures for U.S. forests.2 The U.S.

Forest Assessment System is a forward-looking adjunct to

the Forest Inventory and Analysis (FIA) Program of the

Forest Service, U.S. Department of Agriculture, and was

implemented by research and development staff of the Forest

Service. The FIA program provides nationwide monitoring

through repeated inventories that provide consistency over

time and a high level of detail. The U.S. Forest Assessment

System accounts for changes driven by multiple vectors

including biological, physical, and human factors to generate

forecasts of forest inventories. The modeling approach is

designed to address changing climate, market-driven timber

harvesting, and land use changes along with changes driven

by successional transitions in forest conditions.

A general schematic of this modeling system (fig. 5.2)

starts with a set of internally consistent combinations of

social, economic, and technology forecasts defined as the

Cornerstone Futures (also called Cornerstones in this book)

for this application of the U.S. Forest Assessment System.

Linked to the Cornerstones are various general circulation

models (climate models, or GCMs), each selected to define a

climate forecast that is consistent with the Cornerstone. Also

linked are data from a forest inventory server, which defines

starting conditions for all plots in the forest inventory.

The modeling framework at the center of this system (middle

column of fig. 5.2) shows how future forest conditions

are driven by biological dynamics—such as growth and

mortality—which in turn are affected by climate factors.

In addition, human choices regarding allocations among

land uses and the disposal of forest land, timber harvesting,

and forest management also affect changes in forests. The

interplay of all of these factors yields a set of outputs, each

2Because of data limitations the U.S. Forest Assessment System does not

yet address changes in forest conditions in Texas and Oklahoma outside of

their eastern survey units that are heavily forested.

The Cornerstone Futures

The Southern Forest Futures Project uses six

Cornerstone Futures (labeled with letters A to F) to

provide alternative scenarios regarding the future of

several exogenous variables (table 5.1). These are based

on a combination of county-level population/income

projections from the Resources Planning Act (RPA) and

Intergovernmental Panel on Climate Change (IPCC)

Assessments, assumptions about future timber scarcity,

and assumptions about tree planting rates (see chapter 2

for details).

Two RPA/IPCC storylines, labeled A1B and B2 are

used for the Cornerstones. B2 provides a lower rate of

population growth (a 40 percent increase between 2010

and 2060), and A1B provides a somewhat higher rate of

growth (60 percent). Income growth is higher with A1B.

Both of these storylines are connected to detailed global

economic/demographic scenarios that are described in

the IPCC and RPA reports (IPCC 2007a, 2007b; USDA

Forest Service 2012).

Timber price futures either address increasing or

decreasing scarcity with an orderly progression of real

prices for timber: either increasing or decreasing in

real terms at one percent per year from a base in 2005

through 2060. We also hold the real returns to crops

constant throughout the forecasts for all Cornerstone

Futures.

Another element of the storylines embedded in these

Cornerstones is the climate forecasted using general

circulation models (GCMs) applied to the assumptions

of the storylines. Forecast variables include changes

in temperature, precipitation, and derived potential

evapotranspiration downscaled to counties. We utilize

three different GCMs in constructing the Cornerstones

to address the potential variability in spatial distribution

of resulting changes to forests. More detail on climate

inputs to the forecasts is contained in chapters 2 and 3.

In figure 5.1, the six Cornerstone Futures are displayed

in a diagram that emphasizes their key variables.

Cornerstones A-D are defined by the matrix formed

by intersecting RPA/IPCC storylines A1B and B2

with increasing and decreasing timber price futures as

described above. These four Cornerstones use historical

tree planting rates following harvests (by State and

forest type) to forecast future planting. Cornerstones

E and F depart from these four by either augmenting

planting rates for Cornerstone A (E) or by decreasing

planting rates for Cornerstone D (F).
76 The Southern Forest Futures Project

of which describes forest projections that are consistent

with the flow of forest products and land uses. Changes in

water, biodiversity, and other ecosystem services can also

be derived from the forecasted changes in forest conditions

and land uses. Many of these are described in subsequent

chapters.

The forest dynamics module of this framework provides

the outputs discussed in the results section of this chapter.

Within this module the future of every plot in the forest

inventory is projected in a multiple stage process (see Wear

and others [2013] for details on modeling approaches). A

harvest choice model assigns a management intensity choice

(no harvest, partial harvest, or final harvest) based on timber

prices (from a forest products module) and the condition of

the plot (Polyakov and others 2010). The age of each plot is

determined for the next period, and if harvested, the plot is

determined to be naturally regenerated or planted. Forecasted

climate including temperature and precipitation is assigned

and forest conditions on the plot are inferred based on the

harvest/no harvest decision, age, and climate selection.

Planting probabilities are based on the frequency of planting

on harvested plots based on the forest type and State for the

most recent inventory period. Given the high planting levels

observed over this period, forecasted rates were adjusted

downward by 50 percent for Cornerstone Futures A-D. The

high-planting Cornerstone Future (E) uses 75 percent of the

historical planting rates while the low-planting Cornerstone

Future (F) uses 25 percent.

To incorporate forest market data, we adopted a simplified

process that specifies a price trajectory for each Cornerstone

Future and provides input both on individual plot-harvest

decisions and on the overall supply of timber. Consistent

with theory, higher prices yield more harvesting and larger

timber supplies; lower prices yield smaller timber supplies.

Harvest choice models are based on empirical analysis and

are consistent with harvesting behavior observed in the late

1990s and early 2000s.

The land use module (fig. 5.2) described in chapter 3 (see also

Wear 2011) simulates changes in all uses of land and is driven

by population and income growth along with the prices of

timber products. Projections of forest area from the land

use module feed into the forest dynamics module and the

projection of future market conditions. Changes forecasted at

the county level for non-Federal land are based on National

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Figure 5.2—Schematic of the U.S. Forest Assessment System.
77

chapter 5. Forecasts of Forest Conditions

Resources Inventory data (chapter 3) and are used to rescale

the area represented by each plot in the county (also known

as plot expansion factors).

The assignment of future plot conditions uses a resampling

of historical plot records called whole-plot imputation (Wear

and others 2013), which involves the selection of a historical

plot with comparable conditions to represent each plot

location in the future. The selected historical plot is as close

as possible to the original plot location to allow for orderly

changes in conditions, e.g., if plot conditions are forecasted to

be warmer, the resampling algorithm would first look within

the same survey unit to find a historical plot with similar

temperature increases. Finding none, the algorithm would

extend the search to adjacent units until an appropriate match

is found. This process is repeated for every time step (or

interval between measurements or projections) to generate

plot forecasts over time. The inventory forecast is completed

by coupling the plot forecast with the land use forecasts,

which are applied to adjust the area represented by each nonFederal plot within each county (through the plot expansion

factor described above).

The forest dynamics model is based on several probabilities,

including probabilistic harvest choice, forest investment, and

forest transition models that are implemented with random

draws from probability distributions, and a whole-plot

imputation that is based on a random selection of a subset

of historical plots with replacement. Forecasts therefore

may vary between runs of the model. The forecasts for the

50-year simulation in this chapter are based on 26 runs, one

of which was selected as representative based on central

tendency across several variables. The full suite of 26 runs

offers information about the uncertainty of the forecasts and

is used whenever confidence intervals are needed.

The time step of the simulation depends on the FIA

inventories that underlie much of the modeling (Wear and

others 2013). Because starting years and time step vary from

State to State, the forecast periods are staggered across the

region, e.g., a State might have a time step of 6 years starting

in 2007, while another State might have a time step of 6 years

starting in 2008. For reporting across the region, we selected

10-year intervals, each beginning with a zero-ending year,

e.g., 1990, and then attached forecasts from the nearest year

to the referenced decade—identical to how FIA assigns years

for aggregate inventories (Smith and others 2007).

The reports and maps in this chapter show changing forest

conditions for the forecast period (2010 to 2060) in enough

detail to depict a complete forest inventory for each State in

the South. We focused on forecasting the volume of forest

biomass, the area of forests by type and age class, the carbon

contained in above- and belowground pools, and removals

from forests determined by forest product harvests and land

use changes. Our approach was to summarize total changes

for the South and their distribution across subregions.

Data Sources

For each State, FIA plot records were available to populate

the forest dynamics model and conduct the resampling. We

required at least two inventory panels of matched plots,

ideally with both derived from the continuous inventory

design that was implemented in 1998 to replace completebut-periodic inventories of the past with annual updates.

Because of changes in the plot measurement approach,

we determined that it was important to use two panels of

comparable design to construct transition models for key

variables. So when faced with the dilemma of only one

available continuous inventory, we opted for panels from two

older (periodic) inventories rather than rely on two different

designs.

From publicly available plot tables located on the FIA Web

site, we extracted raw data on plot characteristics, discrete

landscape features, and measurements of trees larger than

1-inch d.b.h. We were also granted access to confidential

plot location data, allowing us to conduct the plot matching

needed to evaluate transitions over time. Expansion factors,

the area associated with each plot, were attached to the plots

with the appropriate tables. Each plot’s county links it to the

land use model (see chapter 3).

Because the FIA inventory is extensive, we performed some

manipulations on the raw data to allow us to summarize

values for analysis. Biomass variables, such as the volume

and number of growing-stock trees, were calculated on a per

acre basis using algorithms derived from Miles and others

(2001) to account for changes in survey methodology over

the years. For example, plots in the annual inventory panels

were divided into components to capture condition-level

details such as forest type. As a validation step we generated

total values for States and survey units, which could then be

compared with published reports and confirm the accuracy

of the algorithms.

Changes in algorithms, recording, and measurement

techniques created data anomalies in many inventories.

We compared attributes across inventories in each State

to determine where data adjustments were required. This

was especially crucial when conducting the analysis of

transitions.

Carbon estimates are attached to each historical plot using

models developed and applied by FIA using the U.S. Forest

Carbon Calculation Tool (Smith and others 2007). This

tool incorporates estimates derived from field measures

into the FORCARB2 model to provide carbon inventories

that are consistent with standards developed by the

79

chapter 5. Forecasts of Forest Conditions

population growth is moderate but income growth is strong

(resulting in high urbanization), and timber prices are falling

(resulting in shifts of forest land to agricultural uses). Figure

5.3 also shows that price effects dominate the projection

of forest area, with the highest forest loss associated with

Cornerstones that have decreasing prices (B, D, and F);

the three Cornerstones with the lowest forest loss have

increasing prices (A, C, and E).

Forest losses are especially high in a few areas of the South

(fig. 5.4). For all Cornerstone Futures forest losses are

concentrated in the Piedmont from northern Georgia though

North Carolina and parts of Virginia, the Atlantic and Gulf

of Mexico coastal areas, and the area surrounding Houston.

The income-fueled urbanization in Cornerstones A and B

spreads low intensity forest losses across a broader area of

the South (chapter 3). For all Cornerstones, the number of

acres lost is largest in the Coastal Plain and smallest in the

Mississippi Alluvial Valley and the Mid-South. The percent

of acres lost is largest in the Piedmont followed by the

Appalachian-Cumberland highlands and the Coastal Plain.

Forest area change also varies across the five forest

management types: planted pine, natural pine, oak-pine,

upland hardwood, and lowland hardwood. The upland and

lowland hardwood types are forecasted to comprise between

51 and 53 percent of all forests in 2060, a decline from about

54 percent in 2010 (fig. 5.5). The greatest changes however

are found among the softwood types (planted pine, natural

pine, and oak-pine). These forest dynamics are heavily

influenced by management for forest products, which in turn

is driven by timber market conditions, and by the rate of

forest planting.

Planted pine–Of the five forest management types in the

South, only planted pine is forecasted to increase in spite of

overall declines in forested area. The South now contains

39 million acres of planted pine (about 19 percent of total

forest area), the culmination of an upward trend that started

in the 1950s. Our projections of planted pine are driven by

urbanization, timber prices, and planting rates (fig. 5.6).

Cornerstone E, characterized by a relatively high level of

urbanization as well as high timber prices and planting rates,

produces the largest expansion in planted pine (though at

rates lower than those experienced in the 1990s) and yields

an increase of 28.2 million acres by 2060 (about 560,000

acres per year). Under this Cornerstone, 34 percent of forests

would be planted pine in 2060. Conversely, Cornerstone F,

characterized by a relatively low level of urbanization as well

as low timber prices and planting rates, yields the smallest gain

in planted pine area with an increase of 7.8 million acres by

2060 (24 percent of forest area). The remaining Cornerstones

have projections that are intermediate to these results. They

cluster around the forecast for Cornerstone D with its lower

timber prices and slower urbanization: a gain of 16.8 million

acres (about 0.3 million acres per year) with planted pine

comprising 28 percent of the forest acreage in 2060.

Natural pine—Forecasted losses in the area of naturally

regenerated pine forest types mirror the gains in planted

pine forests and are therefore related, albeit inversely, to the

condition of forest products markets (fig. 5.6). The largest

decrease in natural pine—a loss of 58 percent from

31.5 million acres in 2010 to 13.5 million acres in 2060—

is associated with Cornerstone E, which has the highest

planting rates. The smallest decline occurs with Cornerstone

F, with its lower timber prices and planting rates, but losses

are still substantial—7.6 million acres or 25 percent from

2010 to 2060. Regardless of the Cornerstone evaluated,

naturally regenerated pine types are forecasted to decline,

continuing a trend that has dominated forest type dynamics

since the 1960s.

Oak-pine—The area of the oak-pine forest management

type also declines for all Cornerstone Futures, with a similar

pattern of change but smaller acreage and percent changes

than is forecasted for natural pines (fig. 5.6). As with natural

pine, oak-pine is more heavily influenced by timber market

conditions than by urbanization rates. Oak-pine declines

range from 8.5 million acres (38 percent) to 3.9 million acres

(17 percent) by the year 2060.

Upland hardwood—At more than 80 million acres in

2010, upland hardwoods are the predominant forest type

in the South, more than double the area of the next largest

forest type. Upland hardwoods are forecasted to decline

for all Cornerstone Futures (fig. 5.6), and variations in

forecasts are associated more with the rate of urbanization

than with timber market futures. The three Cornerstones

with the lowest upland forest loss are associated with

lower urbanization (Cornerstones C, D, and F), and the

three with the highest loss are associated with the higher

urbanization forecasts (Cornerstones A, B, and E). Loss

of upland hardwood forests ranges from 5.9 million acres

(about 8 percent) for Cornerstone C to 11.2 million acres

(14 percent) for Cornerstone B. For Cornerstone E, which

is also characterized by high timber prices but with even

higher rates of planting, the projected total area of planted

pine forest would be nearly equal to upland hardwood forests

in 2060, as the stimulating effects of price and planting

on the pine type combines with the depressing effects of

urbanization on the hardwood type.

Lowland hardwood—The area of lowland hardwood forests

is also more sensitive to the rate of urbanization and less

sensitive to forest products markets than the softwood types.

For this forest management type (fig. 5.6), forecasts indicate

losses ranging from 1.7 million acres (5 percent) to

4 million acres (12 percent) by 2060 from a base of

32 million acres in 2010. The relative ranking of change


82 The Southern Forest Futures Project

across Cornerstone Futures is identical to the forecasts for

upland hardwood types. Lowland forests lose proportionally

less area than the other forest types that experience losses.

The forecasts of forest type dynamics vary across the Futures

Project subregions (figs. 5.7, 5.8, and 5.9). Forest losses,

especially concentrated in the Piedmont and AppalachianCumberland regions (figs. 5.7), and intensive management

in the Coastal Plain influence forest types differently. The

Coastal Plain, with 82 percent of planted pine in 2010,

experiences the greatest growth in planted pine area, from

32 million acres to 43 million acres. Declines in naturally

regenerated pine are greatest in the Coastal Plain as well.

Upland hardwood losses are greatest in the Piedmont and

in the Appalachian-Cumberland subregion reflecting the

influence of urbanization on these types. Changes in lowland

hardwood types are more evenly spread across the South.

Forest Biomass Forecasts

Of the various metrics available for measuring biomass

changes for a site in a forest inventory, we opted to focus

on the volume of growing stock because it is a useful index

both for timber analysis and for measuring other ecosystem

services. Total growing stock volumes are forecasted to

change in response to both land use changes and timber

harvesting levels (fig. 5.10). From a base of about 292 billion

cubic feet in 2010, inventories increase at most by about

11 percent in 2060 under the low-urbanization/low-timberprice Cornerstone D. The smallest increase in total growing

stock inventories is found with the high-urbanization/hightimber-price Cornerstone A, with an increase in volume to

2030 and then a decline over the remainder of the forecast

period. Under this Cornerstone, the volume in 2060 is about

1 percent higher than values observed in 2010.

Patterns of change differ between hardwood and softwood

components of the inventory and generate generally

countervailing changes. Under all Cornerstones, softwood

growing stock inventories increase (fig. 5.11). For the lowurbanization/low-timber-price Cornerstone D, softwood

inventories increase from a base of about 121 billion cubic

feet in 2010 to as much as 148 billion cubic feet (37 billion

cubic feet or 22 percent). The smallest increase is 15 percent

(18 billion cubic feet) for the high-urbanization/high-timberprices Cornerstone A.

Hardwood growing stock inventories reveal different patterns

of change. Starting from about 171 billion cubic feet in 2010,

hardwood growing stock volumes peak somewhere between

2020 and 2040 for all Cornerstones and then decline to the

year 2060 (fig. 5.11). The most pronounced declines are

for Cornerstones A and E, both of which have high rates

of urbanization and high timber prices. Declines for these

Cornerstones are in the range of 15 billion cubic feet

(9 percent) from 2010 to 2060. Cornerstones F and D result in

increases of 3 billion cubic feet (2 percent) for the same period.

These changes in growing stock volume depart from

historical patterns of volume accumulation in the South.

Between 1963 and 2010 southern forests accumulated

about 2.5 billion cubic feet per year or roughly 70 percent.

Hardwood forests accounted for most of this biomass

accumulation (61 percent). Although growth is projected

to continue over at least the next 10 years, growing stock

volume reaches a maximum and then declines somewhat

over the following 40 years (fig. 5.12), with hardwood

growing stocks declining, especially in response to

urbanization (Cornerstone A), while softwood volumes

increase only slightly.

Figures 5.13, 5.14, and 5.15 show county-level changes in

growing stock volume from 2010 to 2060 for the highurbanization/high-timber-prices Cornerstone A. While

softwood growing stock increases overall, areas with large

increases in planted pine (fig. 5.8) show declines in softwood

growing stock volumes as older naturally regenerated pine

forests are replaced by younger planted pine forests

(fig. 5.14). Declines in hardwood growing stock volumes

between 2010 and 2060 are more widespread and generally

organized by urbanization patterns (fig. 5.15). Eastern

Kentucky and far western Virginia show the greatest gains in

hardwood growing stock volumes.

Forest Carbon Forecasts

We estimate the carbon stored in southern forests in 2010 at

about 12.4 billion tons, including carbon stored in eight pools:

down trees, standing dead trees, litter, soil organic carbon,

live trees aboveground and belowground, and understory

plants aboveground and belowground. Aboveground live trees

and soil organic material comprise 80 percent of the total

carbon stock. Forecasts of future forest carbon stocks reflect

changes in the amount of forest area and the composition

of the forest inventory. However, the model tracks only

the carbon pool in forests and does not account for carbon

transfers to agricultural and other land use pools. Likewise,

the model does not account for carbon that leaves forests as

products and may remain sequestered for long periods of time

in housing or other end uses (e.g., Heath and others 2011).

Changes in forest carbon pools reflect both changes in

growing stock volumes and changes in forest area (figs. 5.16

and 5.17). Under most Cornerstones, tree carbon peaks in

2020 and then levels off or declines; the exception is the

low-urbanization/high-timber-prices Cornerstone C whose

forecast peaks in 2030. At most, the forest carbon pool in

2060 is 5 percent smaller than the pool in 2010 (a net emission

of about 600 million tons). Carbon accumulates as a result

of net biomass growth on forested lands (fig. 5.17.F), but



86 The Southern Forest Futures Project

Figure 5.10—Total growing stock volume, 2010 to 2060, by Cornerstone Future.

these gains are offset by the loss of forested land through

urbanizations (i.e., the loss of soil carbon shown in fig. 5.17.D).

The clustering of carbon forecasts in Figure 5.16 reveals

the interplay of urbanization and timber prices. The

forecasts with the highest amount of carbon in 2060 are

for the low-urbanization Cornerstones (C, D, and F). The

lowest carbon forecasted is for the high-urbanization

Cornerstones (A, B, and E). However, within each of these

triplets the lowest amount of carbon is associated with

low timber prices (Cornerstones B, D, and F) while higher

timber prices and resulting intensive forest management

lead to higher carbon sequestered in the forest pool. This

suggests that urbanization patterns dominate the forecasts

of carbon storage while stronger forest product markets can

ameliorate carbon losses.

Forest Removals Forecasts

Removals from growing stock are forecasted to increase for

all Cornerstone Futures reflecting both land use changes and

timber harvesting (fig. 5.18). From 2010 to 2060, removals are

forecasted to increase by as much as 85 percent (the highurbanization/high-timber-price/more-planting Cornerstone E)

and as little as 35 percent (the low-urbanization/low-timberprice/low-planting Cornerstone F). The dominant factor that

affects the number of removals is the price projection for

timber that is associated with each Cornerstone. The most

removals are associated with high price Cornerstones

(A, C, and E) while the lowest removals are associated with

low price Cornerstones (B, D, and F). Even with declining

prices, harvests are projected to increase, reflecting previous

forest investments, maturation of forest inventories, and

removals associated with forest losses.

The forecasts generate distinctly different removal patterns

for hardwoods and softwoods (fig. 5.19). Softwood removals

are forecasted to increase steadily over the projection period

and at rates comparable to recent history. For hardwoods,

forecasted removals increase from 2010 to 2020 and then level

off for most Cornerstones. The increase in the first decade

reflects the recovery from suppressed removals leading up

to and during the “great recession” (2005 to 2010). Trends

in later decades reflect a decline in hardwood growing stock

inventories, most especially in the pulpwood size classes.

Among products, softwood pulpwood removals are the

most variable across Cornerstones, with forecasts ranging

88 The Southern Forest Futures Project

between 2,800 million cubic feet for the low-planting future

of Cornerstone F and 3,900 million cubic feet for the highplanting future of Cornerstone E by 2060 (see chapter 9 for

a discussion of timber supply and timber market forecasts

related to the Cornerstone Futures). Historically, because the

pines are the dominant plantation species, pine pulpwood has

been the most price-responsive timber product in the South.5

Over the long run, the high-price futures of Cornerstones A

and C produce more pulpwood than the low-price futures of

Cornerstones B and D, and the high-prices/more-planting

future of Cornerstone E produces the most substantial

increase in softwood pulpwood output (about 56 percent).

Softwood sawtimber removals also show strong growth

potential across the Cornerstones and are also strongly

affected by price forecasts (fig 5.19). In contrast to pulpwood

however, sawtimber output levels off over time and is not

strongly influenced by planting rates. Naturally regenerated

sites are maturing in the South, resulting in growth-induced

supply increases. This explains why it is possible for

softwood sawtimber output to remain stable or increase with

5Econometric studies show that sawtimber products are the most priceresponsive in the short run. However, in the long run, investments in new

pine plantations have led to substantial increases in pulpwood production.

decreasing prices. Softwood sawtimber output eventually

levels off as the area of natural pine declines over time.

While still separated by low and high price Cornerstones,

hardwood removal forecasts vary much less than softwood

removals (figs. 5.20). After increasing from 2010 to 2020,

hardwood removals are forecasted to decline for the

low price Cornerstones (B, D, and F). For the high price

Cornerstones (A, C, and E), removals increase until 2030,

when they begin to level off. In contrast to the softwoods,

direct investment in southern hardwood forests has been

negligible, and none is projected for the future. The result

is a steady drop in hardwood forest area and in hardwood

growing stock inventories.

When considered together, growing stock and removal forecasts

indicate a continuation of some important historical trends.

Removals are forecasted to increase substantially in spite of a

leveling off of inventories (fig. 5.21). This reflects an ongoing

transition from a forest mining approach to an agricultural

model of forest production. Planted pine management allows for

more production from a smaller land base with a more frequent

capture of forest growth. This means lower inventories on the

ground compared to naturally regenerated forests.

Figure 5.12—Historical (1962 to 1999) and forecasted (2020 to 2060) hardwood and softwood growing stock

inventories, for Cornerstones A and D.



92 The Southern Forest Futures Project

Forest Condition Forecasts

The U.S. Forest Assessment System is designed to replicate

inventories across broad areas—e.g., aggregates of at least

several counties—and for broad forest type groupings.

However, the resampling/imputation approach does provide

some insights into how drivers of change may alter forest

composition at a finer scale, e.g., figure 5.22 shows forest

forecasts of hardwood types for the high-urbanization/

low-timber-price Cornerstone B, which leads the other

Cornerstones in declines of forest area. For this Cornerstone,

all hardwood types are forecasted to lose 16 million acres

(14 percent) but not all of the forest types lose area at the same

rate. Oak-hickory loses about 1 percent compared to 10 percent

or more for all other forest types. The most substantial losses

are for the other hardwood and yellow-poplar types, which

lose 26 percent (6 million and 4 million acres, respectively).

Digging a bit deeper, losses of the yellow-poplar forest type

are expected to be about 25 percent in the Coastal Plain

(1.4 million acres) and the Appalachian-Cumberland subregion

(1 million acres), compared to a more substantial loss of

34 percent in the Piedmont (1.6 million acres).

The forecasted area of softwood types is shown in

figure 5.23. Loblolly-shortleaf pine is the dominant type

and its area stays roughly level through the forecast period.

Although overall forest area is expected to decline and

softwood removals are forecast to increase, continued

investment in plantations enables this type to maintain its

area. Longleaf-slash pine is forecasted to increase slightly

while oak-pine is expected to decline. All other types exhibit

very little change.

Another element of forest conditions that may be especially

important for wildlife is the age class distribution of forest

types (chapter 6). Figure 5.24 shows forecasts of age classes

for broad forest types for the high-urbanization/high-timberprices/more-planting Cornerstone E. Early-age forests are

those that are less than 20 years old, mid-age forests are

between 20 and 70 years old, and old-age forests are greater

than 70 years old. Because Cornerstone E has the largest

change in forest management types (fig. 5.5) and the most

harvesting (fig. 5.18), it is a “best” case for the production of

early-age forests.

Figure 5.16—Total forest carbon stock, 2010 to 2060, by Cornerstone Future.

94 The Southern Forest Futures Project

Figure 5.18—Forecasts of total removals from growing stock, 2010 to 2060, by Cornerstone Future.

Figure 5.19—Forecasted removals from softwood growing stock, 2010 to 2060, by Cornerstone Future.

Million cubic feet



98 The Southern Forest Futures Project

With the exception of planted pine, all forest types are

forecasted to experience a decline in early-age forests

throughout the forecast period for Cornerstone E.

Planted pine forests are forecasted to shift their age-class

distributions toward more mid-age forests as the rate of

planting declines from its peak in the late 1990s. Mid-age

forest area increases strongly, while early-age forest area

increases at a more moderate rate. There are practically no

acres of old-age forest in the planted pine forest type.

High harvest rates and conversion to planted pine in

Cornerstone E shifts the age-class distribution of the

naturally regenerated pine types. The area of old-age natural

pine stays relatively constant while mid-age forests decline

by 13 million acres (about 64 percent) and early-age forests

decline by about 4.5 million acres (58 percent). Oak-pine

shows a similar pattern of age class changes.

In contrast to the softwoods, hardwood forecasts show an

increase in old-age forests. For upland hardwoods, the area

of mid-age forests is forecasted to decline by 14 million

acres (down from 59 percent in 2010 to 47 percent in 2060).

Over this same period, old-age forests are forecasted to

increase by 12 million acres (up from 20 to 40 percent) and

early-age forests decrease by 8 million acres (down from 21

to 13 percent). Overall, the shift among age classes reveals

that the early-age component of the upland hardwood type

declines by 44 percent at the same time that the old-age

component increases by 71 percent.

The pattern of change for lowland hardwood forest types

is similar to changes in upland hardwood forest types, but

changes occur at different rates. The mid-age component of

lowland hardwood forests declines by about 5 million acres

(26 percent), the old-age component increases by about 4

million acres (77 percent), and the early-age component

declines by about 2 million acres (33 percent). As with the

upland forest types, while the total area of the forest type

declines, the average age of the forest type increases.

Contrary to Cornerstone E, Cornerstone F (fig. 5.25) is

characterized by less planting and a lower harvest rate (fig.

5.18). The results for Cornerstones E and F therefore bracket

the age-class results for all Cornerstones. For Cornerstone

F, the area of early-age planted pine declines over time (in

contrast to increases simulated under Cornerstone E), and

the age class distribution approaches a stasis.

Cornerstone F also results in less dramatic changes in

area of natural pine when compared to Cornerstone E,

with more stability in the early-age class, increases in the

old-age class, and a 33-percent reduction in declines of the

mid-aged class. This pattern is mirrored in the oak-pine age

classes. For hardwood forest types, however, there is little

difference between Cornerstones E and F. This indicates that

management changes strongly influence the age structures

of the softwood forest types but have little influence on the

age structures of hardwood forest types.

Discussion and Conclusions

A number of forces of change will affect the development of

forests over the next 50 years. The U.S. Forest Assessment

System, which accounts for land use changes, climate

change, and forest management, is used here to forecast forest

conditions for six Cornerstone Futures. Each Cornerstone

describes the future in terms of human population, personal

income, climate, and the markets for products from rural

land uses. Across all of these Cornerstones, total forest area

declines and changes among broad forest management types

are substantial. Among the five forest management types,

only planted pine is expected to increase in area from 2010 to

2060. In 2010, planted pine comprised 19 percent of southern

forests. By 2060 planted pine is forecasted to comprise

somewhere between 24 and 36 percent of forest area. The

midpoint of this range of growth (20-72 percent) is consistent

with a recent study by Zhang and Polyakov (2010) that

forecasts a 40 percent expansion in planted pine in the South.

Forest types are influenced differentially by urbanization

and timber market dynamics. In general, hardwoods are

more strongly influenced by urbanization, and softwoods are

more strongly influenced by forest management. Although

predicted rates of change vary, all forecasts reveal that land

use changes and conversion to pine plantations will result

in a continuing downward trend in naturally regenerated

pine types. Hardwood types are most strongly influenced

by urbanization and all Cornerstone Futures show declines

in hardwood area that vary in direct proportion to rates of

urbanization.

Nearly all forests in the South have been harvested at least

once and much of the region’s forests are the result of

reforestation and an accumulation of biomass. After a long

period of accumulating biomass, the South’s forests are

forecasted to reach a maximum by 2030 and then to either

level off or decline.

Southern forests also define a large pool of terrestrial

carbon amounting to 12.3 billion tons. Mirroring changes

in growing stock volumes somewhat, this pool of carbon

is forecasted to reach a peak from 2020 to 2030, and then

decline over time. Urbanization patterns are the dominant

determinates of the size of the future forest carbon pool,

although stronger forest product markets can ameliorate

carbon losses.

Forest forecasts address change in climate variables

by associating forecasted, multiple year averages of

temperatures, precipitation, and potential evapotranspiration

100 The Southern Forest Futures Project

with historical conditions. The potential effects of other

changes, most notably CO2 enrichment do not enter

the analysis. Controlled experiments show increased

productivity with CO2 fertilization but also highlight the

important interaction with nitrogen, precipitation, and

other climate and site variables in determining response

(McCarthy and others 2011). These types of impacts on

productivity are relatively subtle compared to the variability

of field inventory measurements and could not be factored

into the forecasts. The potential effects of atmospheric

carbon on forest biomass at a regional scale remains an

important uncertainty.

The South produces the majority of timber products in the

United States (more than 60 percent) including a diversity of

both hardwood and softwood outputs. Forest management

has increased harvesting throughout the South since the

1960s, and the output per unit of growing stock has increased

as management has intensified. The most important factor

in changing the productivity of southern forests is the

expansion in the amount of planted pine forests. Because of

increases in timber supply from 1990 to 2010, removals of

forest biomass (growing stock) are forecasted to increase for

all Cornerstones, even those that project decreasing prices.

Removals of softwood pulpwood are responsive to futures

for forest planting and product prices. Under a high price

future, softwood pulpwood output would increase by 56

percent, roughly equal to the expansion in output observed

between 1950 and 2000. These projections of removals are

based on the price assumptions of the Cornerstone Futures

and not on forecasts of demand. Timber harvest forecasts

under various market conditions linked to the Cornerstones

are discussed in chapters 9 and 10.

The combination of land use change, management

intensities, and climate result in a number of changes in the

composition and structure of the region’s forests. The forest

type composition of the broad forest management groups are

forecasted to change and the age structure of these forests are

also altered. Although the overall loss of upland hardwood

acreage is forecasted to be in the range of 8 to 14 percent,

the oak-hickory forest type remains essentially constant. The

yellow-poplar forest type is forecasted to decline the most,

with the highest losses forecasted for the Piedmont. The

age and species structure of softwood forest types is most

strongly influenced by forest harvesting and management

tied to timber markets, while the future structure of

hardwood forests is most strongly affected by urbanizationdriven land use changes (increased population growth and

income). Reductions of naturally regenerated pine forests

are not equally distributed among age classes: mid-age and

early-age forests decline but old-age forests remain relatively

constant. For hardwoods, the age distribution of both upland

and lowland hardwood types shift, with less of these forest

types classified as early age and more classified as older age.

Literature Cited

Coulson, D.P.; Joyce L.A.; Price, D.T. [and others]. 2010. Climate scenarios

	 for the conterminous United States at the county spatial scale using

SRES scenarios A1B and A2 and PRISM climatology. Fort Collins,

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

Research Station. http://www.fs.fed.us/rm/data_archive/dataaccess/

US_ClimateScenarios_county_A1B_A2_PRISM.shtml. [Date accessed:

January 10, 2011].

Heath, L.S.; Smith, J.E.; Skog, K.E. [and others]. 2011. Managed forest

carbon estimates for the U.S. greenhouse gas inventory, 1990-2008.

Journal of Forestry. 109(3): 167-173.

Intergovernmental Panel on Climate Change (IPCC). 2007a. Climate

change 2007: synthesis report. Geneva, Switzerland. http://www.ipcc.

ch/publications_and_data/ar4/syr/en/contents.html. [Date accessed:

January 24, 2012].

Intergovernmental Panel on Climate Change (IPCC). 2007b. General

guidelines on the use of scenario data for climate impact and adaptation

assessment. http://www.ipcc-data.org/guidelines/TGICA_guidance_

sdciaa_v2_final.pdf. [Date accessed: January 24, 2012].

Linacre, E.T. 1977. A simple formula for estimating evaporation rates

in various climates, using temperature data alone. Agricultural

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McCarthy, H.R.; Oren, R.; Johnsen, K.H. [and others]. 2011. Re-assessment

of plant carbon dynamics at the Duke free-air CO2 enrichment site:

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over stand development. New Phytologist. 185(2): 343-589.

Miles, P.D.; Brand, G.J.; Alerich, C.L. [and others]. 2001. The Forest

Inventory and Analysis database: database description and users manual.

Version 1.0. Gen. Tech. Rep. NC–218. St. Paul, MN: U.S. Department of

Agriculture Forest Service, North Central Research Station. 130 p.

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practice guidance for land use, land use change, and forestry. Hayama,

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supply models for forest forecasting. Forest Science. 56(4): 344-355.

Smith, J.E.; Heath, L.S.; Nichols, M.C. 2007. U.S. forest carbon calculation

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Forest Service, Northern Research Station. 28 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.

U.S. Department of Agriculture (USDA) Forest Service. 2012. Future

scenarios: a technical document supporting the Forest Service 2010 RPA

assessment. Gen. Tech. Rep. RMRS-GTR-272. Fort Collins, CO: U.S.

Department of Agriculture Forest Service, Rocky Mountain Research

Station. 34 p.

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Fourth Forest: alternatives for the future. For. Res. Rep. 24. Washington,

DC: U.S. Department of Agriculture Forest Service. 512 p.

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sector in 2005: a prospective analysis of recent change. Gen. Tech. Rep.

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Southern Research Station. 29 p.

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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.
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summary report. Gen. Tech. Rep. SRS-54. Asheville, NC

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conditions in U.S. regions under future scenarios:a technical document

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U.S. South. Biomass and Bioenergy. 34(12): 1643-1654.

103

chapter 6. Forest Ownership Dynamics of Southern Forests 

	 Brett J. Butler and David N. Wear1

Key Findings

•	 Private landowners hold 86 percent of the forest area in

the South; two-thirds of this area is owned by families or

individuals.

•	 Fifty-nine percent of family forest owners own between 1

and 9 acres of forest land, but 60 percent of family-owned

forests are in holdings of 100 acres or more.

•	 Two-thirds of family forest land is owned by people who

have harvested and sold trees from their land. Assuming

that corporate owners have harvested timber, then in

all about 8 of every 10 acres of private forest land in the

South is owned by individuals or organizations who have

commercially harvested their timber.

•	 The average size of family forest holdings is 29 acres.

Ongoing parcellation and fragmentation through estate

disposal and urbanization will continue to alter forest

management in the South.

•	 The forest products industry divested about three-fourths

of its timberland holdings between 1998 and 2008, the

largest ownership transition in the last century. The largest

gain in ownership was realized by timber investment

management organizations and real estate investment

trusts.

•	 Forest products industry divestitures were likely driven by

a combination of factors including mergers, alleviation of

timber-scarcity concerns, new technologies for reducing

the cost of fiber acquisition, redeployment of capital, and

desire to reduce tax burdens.

•	 As a result of the transfer of holdings from the forest

products industry to timber investment management

organizations and real estate investment trusts, forest land

held by corporations is now a more liquid asset class and

will likely trade more frequently in the future. If this holds,

individual corporate forest holdings could decline in size

over time.

1Brett J. Butler is a Research Forester, Forest Inventory and Analysis,

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

Amherst, MA 01003; David N. Wear is the Project Leader, Center for

Integrated Forest Science, Southern Research Station, U.S. Department of

Agriculture Forest Service, Raleigh, NC 27695

•	 Although the forest products industry land base was long

perceived to be a stable and predictable component of the

forest landscape in the South, corporate lands may become

less stable and more changeable with implications for both

timber and nontimber values of forest lands.

•	 Increased liquidity of forest assets argues for increased

monitoring of ownership changes and of forest land

transaction values to better understand the conservation

implications of economic trends.

Introduction

Forest ownership in the South has evolved substantially

over the past decade, raising questions about changes in the

landowner objectives and approaches to forest management

and ultimately about the retention of forest lands. How will

ownership change in the future? What are the implications

for forest management and forest sustainability? In

this chapter, we examine the recent dynamics of forest

ownership, develop forecasts of potential future changes,

and identify some implications of these changes for forest

conditions and management.

Although public ownership of forests in the South has grown

slightly over the past 20 years, private owners continue to

dominate, now holding 86 percent of forest lands in the

region. Nevertheless the private group has experienced a

dramatic change among commercial owners and an ongoing

change dynamic within the family-held forest ownership

group.2 Since 2000, the forest products industry—defined

as landowners who also own primary wood processing

facilities, such as sawmills and pulp mills—divested most

of its land base (roughly 40 million acres) either by outright

sale or through a change in organizational structure. New

types of corporate owners, primarily timber investment

management organizations (TIMOs) and real estate

investment trusts (REITs), now manage these lands using

business models that differ from those of previous owners.

At the same time, the area controlled by non-corporate

owners, primarily family forest owners, has increased, and

2The term family forest owners refers to “families, individuals, trusts,

estates, family partnerships, and other unincorporated groups of

individuals that own forest land” (Butler 2008).

Chapter 6.

Forest Ownership Dynamics 

of Southern Forests
104 The Southern Forest Futures Project

this group is continuing to evolve in response to changing

demographics and objectives. The dynamics of corporate

and family forest owners within the context of ongoing

urbanization and changes in landscape configuration may

alter the operability of forested parcels and the desirability of

holding forest lands for long periods of time.

The published literature offers little information about the

factors that influence forest ownership dynamics. Most

studies have been either descriptive, such as general forest

assessments, or they examined timber harvesting or other

specific landowner activities. This paucity of information

has limited forest projections systems to simple sets of

assumptions about the distribution of future forest land

among ownership classes. In the projections presented in

other chapters of this report (e.g., chapter 5), the area of

Federal forest land is held constant and land use changes

are allocated among the State, local, and private ownership

categories according to their current distributions—as has

been the methodology of previous projections systems

(Haynes 2003). Although necessary, this approach ignores

the dynamics of southern ownership patterns and their

resulting impacts on land use and forest management.

Therefore, we have collected and applied available

information from ancillary studies to augment model results

and to provide better insights into potential changes in

private forest ownership.

Most forest assessments include some level of information

on forest ownership patterns. One of the first timber resource

assessments of the United States, conducted in the 1950s,

included a separate chapter on the characteristics of forest

owners (Josephson and McGuire 1958); and landowner

information has become a standard element of other national

(Smith and others 2009), regional (Wear and Greis 2002),

and State-level (Conner and others 2004) forest assessments.

Apart from the general descriptions, there have been many

studies that have examined specific owners and/or activities.

Landowner surveys, focus groups, and personal interviews are

common techniques for developing a deeper understanding

of private forest owners (Hodgden and others 2007). The first

forest ownership studies in the United States were conducted

in the 1940s (Barraclough and Rettie 1950). National

surveys of forest owners were conducted in 1978 (Birch and

others 1982), 1993 (Birch 1996), and 2006 (Butler 2008).

Unfortunately, comparisons across these studies are hampered

by variations in questions asked, subject populations, and/or

methodologies.

Many researchers have studied the management behaviors

of specific private owners, including timber harvesting,

reforestation, and timber stand improvement (Beach

and others 2005); recreational access (Snyder and others

2008); forest certification (Kilgore and others 2007);

biomass harvesting (Joshi and Mehmood 2010); and

carbon sequestration (Fletcher and others 2009). Although

their methods varied, most of the studies have taken

an econometric approach to test hypotheses regarding

landowner decision making.

Of the studies on forest ownership dynamics, parcellation

has been a dominant topic, but most studies have focused on

impacts (Germain and others 2007) rather than empirically

testing the causes of parcellation. The exception was a study

of State-level summaries by Mehmood and Zhang (2001)

that showed correlation of parcel size with death rates,

urbanization, income levels, regulatory uncertainty, and

availability of financial assistance.

We know of no published research that has offered a

theoretical framework for predicting forest ownership

patterns and only a handful of studies that have quantified

historical changes in ownership over time. Therefore, our

approach in this chapter is to use information from existing

studies on landowner behavior to explore the potential

implications of a set of alternative futures (chapter 2).

Methods

The analysis of ownership change relies heavily on surveys

of forests and forest owners. We use forest inventory data

from the Forest Inventory and Analysis (FIA) Program

of the Forest Service, U.S. Department of Agriculture

(Bechtold and Patterson 2005) for foundational data on

broad ownership classes; and the FIA National Woodland

Owner Survey (Butler and others 2005) for insights into

the characteristics, attitudes, and behaviors of family forest

owners. Attributes examined include parcellation and its

impact on resource availability, absentee ownership, public

access to private lands, and intergenerational land transfers.

We rely on existing literature and expert interpretation to

describe potential consequences.

Our examination of the forest products industry divestiture

involved use of an ownership database and geographic

information system output from Lanworth, Inc., to generate

estimates of total acres of ownership classified into the

following categories: forest products industry, TIMOs,

REITs, and other corporations. We use summaries of these

data to evaluate totals and changes by subregion and State

for 1998, 2003, and 2008. Although not entirely consistent

with FIA records, the Lanworth database provides an index

of change that is consistent across all Southern States for the

selected time periods.

We base our discussion of the determinants of landownership

change on a recent analysis3 of timber industry trends in

3Wear, D.N. 2010. The disintegration of timber growing and wood

products manufacturing in the United States. Draft manuscript on file. U.S.

Department of Agriculture, Forest Service, 3041 Cornwallis Rd., Research

Triangle Park, NC, 27713.
105

chapter 6. Forest Ownership Dynamics of Southern Forests 

the South. Our goal is to develop insights on the potential

management and use implications of ownership changes

by evaluating recent and ongoing research on structural

dissimilarities of management by different types of forest

ownership.

Data Sources

Comparisons of forest conditions across ownership types

are derived from FIA inventories, which are coordinated at

the State level by crews measuring conditions on permanent

forest inventory plots (Bechtold and Patterson 2005). The

current design of the FIA inventory in the South involves

a 5-year measurement cycle, with 20 percent of a State’s

plots visited every year.4 We base our analysis on the most

recent surveys within each of the 13 Southern States and on

forecasts of forest inventories (chapter 5).

As a complement to its biophysical information, the FIA

National Woodland Owner Survey collects information on:

who owns the forest, why they own it, how they use it, and

what they intend to do with it. From 2002 to 2006, the years

of the data used in this chapter, 5,517 southern family forest

owners responded to the survey representing a cooperation

rate of 45 percent (Butler and others 2005; www.fia.fs.fed.

us/nwos). The selection procedure for the survey is identical

4 In 1998, FIA’s continuous design of periodic inventories (in place

since the 1930s) was replaced by the annual inventory system. Full

implementation of the annual system requires Federal appropriations as

well as supplemental funds from each State. In practice, the cycle can range

from 5 to 10 years, with more Southern States having approached full

implementation than States in other regions.

to that used to conduct the FIA forest inventory: (1) each

State is divided into 6,000-acre hexagons, and within each

hexagon a sample point is randomly placed; (2) remote

sensing and ground truthing determines whether the point

is forested, and if so, the landowner is identified through

property tax records; (3) if the forest owner is private, then

they are included in the sample, and 20 percent of them are

contacted each year.

Because FIA inventories are conducted on a 5-year or longer

rotation, rapid changes associated with forest products

industry lands divestiture will be reported with a lag. To

track the most recent changes in corporate ownership, we

used the Lanworth proprietary databases of ownership and

ownership transactions to estimate changes in the acreage

owned by various corporate types of owners and the spatial

distribution of those types.

Lanworth created three datasets for the South: (1) total

acres classified into four corporate ownership subgroups by

State for 2008; (2) total acres classified into four corporate

ownership subgroups by State for 2003 and 1998; and (3)

generated area density maps by county for the subgroups in

2008.

The Lanworth definitions of four corporate subgroups are as

follows:

(1) Forest products industry (also known as vertically

integrated timber products companies)— Publicly traded

as well as privately held organizations that produce paper

and/or wood products from forest resources and own or

lease more than 100,000 forest acres across the South.

Definitions of owner types

Private forest owners—Families, individuals,

corporations, and other private entities that own forest land

(Butler 2008).

Corporate (private corporate)—An ownership class

of forest land that is owned by entities that are legally

incorporated (Smith and others 2009). This includes forest

products industry, real estate investment trusts, and timber

investment management organizations.

Family—Families, individuals, trusts, estates, family

partnerships, and other unincorporated groupings of

individuals who own forest land (Butler 2008).

Other private—Private forest owners other than

corporations, families, or individuals. This category

includes Native American lands, unincorporated

partnerships, and clubs.

 

Public forest owners—Forest land managed by

Federal, State, or local government agencies.

Federal—An ownership class of public lands

administered by Federal Government agencies (Smith and

others 2009). Examples include the U.S. Forest Service,

Bureau of Land Management, National Park Service, Fish

and Wildlife Service, and Departments of Defense and

Energy.

State—An ownership class of public lands owned by

States or lands leased by States for more than 50 years

(Smith and others 2009).

Local—An ownership class of public lands administered

by counties or local public agencies, or lands leased by

these governmental units for more than 50 years (Smith

and others 2009).
106 The Southern Forest Futures Project

(2) Timber investment management organizations

(TIMOs)—Management companies that aid institutional

investors in buying, selling, and managing their timberland

investments. This category includes professionally

managed funds which have 100,000 acres or more across

the South.

(3) Real estate investment trusts (REITs)—Corporations

that use the pooled capital of many investors to purchase

and manage property. This category includes public and

privately held REITs with assets of 100,000 acres or more

across the South. Note that these data were compiled

before Weyerhaeuser Corporation transitioned from a

vertically integrated timber products company to a REIT.

(4) Other corporate—All other corporate businesses that

have 100,000 acres or more of forest land across the South.

Results

There are 32 million acres of publicly owned forests

across the Southern United States; these acres represent

14 percent of the total forest land area (figs. 6.1 and 6.2).

The agencies that control these lands include the U.S.

Forest Service, the U.S. Fish and Wildlife Service, and the

U.S. Department of Defense as well as various State and

local government agencies. These forests are not subject

to development pressures as private lands are, but they are

subject to the needs of society and the resulting laws and

regulations that govern their use.

By all measures, the South is dominated by private

ownership. Over 5 million private forest owners across the

region hold 200 million acres of forest land, 86 percent of

the total forest land area. Within this category is a diversity

of owners ranging from large, multi-national corporations

with acreage in the hundreds of thousands to families and

individuals with a few acres.

On average, families and individuals own two out of every

three acres of private forest land. The remaining onethird of the private acreage, 66 million acres, is owned by

corporations, conservation organizations, partnerships, and

tribes; and it is the corporations that own the bulk of these

acres. Within this corporate category are the traditional,

vertically integrated timber products companies, but an

increasing amount of acreage is owned by TIMOs and

REITs. Organizations in the other private ownership

category have a diversity of reasons for owning forest land.

Conservation organizations may own particular parcels to

protect special features of the landscape. Many camps and

churches own land to offer their members rustic and secluded

environments. Hunting and other clubs may own land for

specific recreation purposes.

Family Forest Owners

There are two very distinct ways of looking at family forest

owner statistics: numbers of owners and numbers of acres.

Looking at the distribution of family forest owners by size

of forest holdings (fig. 6.3) shows that nearly 60 percent of

family forest owners own between 1 and 9 acres. But looking

at the distribution of acres shows that 60 percent of all family

forest land is in holdings of 100 acres or more. Both ways of

looking at the data can be useful, depending on the topics

being addressed; often they should be considered together.

On an acreage basis, family legacy, aesthetics, and land

investment are the primary reasons that family forest owners

give for owning forests (fig. 6.4). On an ownership basis,

aesthetics, part of home, and privacy are the most important

reasons. Knowing their reasons for owning is important

for learning what motivates people and leads into what

their concerns are—critical information for understanding,

communicating with, and assisting family forest owners.

When trying to understand owners, it is also important to be

cognizant of the demographics involved. Compared to the

general population, family forest owners tend to be older,

better educated, and have higher incomes. According to the

National Woodland Owner Survey statistics, 69 percent of

family forest owners are men. A caveat needs to be added

about the instructions on the survey, which specified that one

person, the primary decision maker, answer the questions.

If that person was a married man, there would be no way

to indicate if his wife is a co-owner of the land, possibly

skewing the results on gender.

Although timber production is not a primary objective

of most family forest owners, two-thirds of all family

forest land is owned by people who have commercially

harvested some of their trees. But only 18 percent of the

family forest land is owned by someone who has a written

forest management plan. The number of owners who have

received management advice is significantly higher, but at

42 percent, still represents less than half of all family forest

land. The numbers are even less encouraging when viewed

from the number of owners perspective: only 3 percent of the

family forest owners have a written management plan and

only 13 percent have received forest management advice.

These data raise important questions about owners’ abilities

to maximize benefits from their land and decrease the

likelihood that they are leaving it in the best possible shape to

meet future needs.

Much of the family forest land has been owned for relatively

long periods of time (fig. 6.5); related to this fact, and

correlated to the relatively advanced age of many owners



110 The Southern Forest Futures Project

(34 percent of the family owners, who own 47 percent of

the family forest land, are 65 years of age or older), it is not

surprising that 11 percent are planning to pass on their land

(19 percent of total family forest land) in the near future

(fig. 6.6). It is at this point of transfer that land use often

changes, as well as changes in management practices.

Family legacy is an important objective, but it is also a major

concern (fig. 6.7); owners are uncertain about their heirs’

desires to keep the land, whether they can afford to hold it,

and whether an equitable transfer will be possible.

The often stark contrast between acreage and ownership

statistics, as shown by differences in the relative ranking of

ownership objectives or other characteristics, is an indication

that the size of forest holdings is important. Size is the single

most predictive variable collected on the National Woodland

Owner Survey. The average size of family forest holdings

is 29 acres. Economies of scale can make it difficult to

implement traditional forestry tools on small acreages, and

in some circumstances, traditional prescriptions will conflict

with owners’ objectives. Continued parcellation of forest

holdings will likely exacerbate these issues.

Averages can be misleading and are usually not the best way

to understand large, diverse groups. To determine whether

naturally occurring subgroups of family forest owners

exist, the Sustaining Family Forest Initiative conducted

a multivariate, hierarchical analysis that identified four

attitudinal subgroups: woodland retreat, working the land,

supplemental income, and uninvolved owners (Butler and

others 2007). Woodland retreat owners are most interested

in the amenity values their forests provide and more likely to

have their home associated with their forest land. Working

the land owners are multiple objective owners; they are

interested in a combination of amenity and financial values.

Supplemental income owners are interested in earning

money from their land, either through timber harvesting or

land sales, and tend to have larger properties. Uninvolved

owners tend to not have strong ownership objectives.

Understanding the desires and concerns of these subgroups

will improve policies, programs, services, and outreach

efforts aimed at forest sustainability.

Historical Family Ownership Dynamics

Dynamics of family forest ownership patterns happen across

different spatial and temporal scales. At the broadest scales,

family forest ownership is fairly stable. But within this

group, many family forest owners are selling or otherwise

transferring land to other family forest owners, either within

their families or outside of them.

Family forest ownership dynamics are the result of a

combination of personal/familial circumstances and broader

social factors and market conditions. Most family forest

owners have a deep love of their land. They know why they

own it, and many know what they want for the future of their

land, but many will also be confronted with challenges and

opportunities, only some of which are known. Because of

increasing population pressures and ensuing increases in

property values, owners can be faced with increased taxes,

increased offers for their land, and changes in the rural

environment.

Family legacy is an important objective for many family forest

owners, but may also be one of their greatest concerns and

challenges. For many family forest owners, their property is

their largest financial asset. If medical, educational, or other

expenses arise, they may be forced to do things with their land

than they would not do otherwise. And even for those who

have no immediate financial needs or objectives, a (seemingly)

lucrative offer can be difficult to refuse.

Historical Corporate Ownership Dynamics

In the late 1990s, the forest products industry held about 20

percent of the forest land in the South (Conner and Hartsell

2002). Since then, this ownership subgroup has dropped to

less than 5 percent, representing the most rapid recent change

in forest ownership and management. This loss resulted

mainly from companies selling their forest assets and

sometimes changing their corporate structure. Regardless of

approach, however, these transactions fundamentally altered

how the involved forests and land were managed. Separating

forest management from the forest processing industry

changes both the long-term objectives of owners and the

structure of forest investments.

According to the Lanworth data, corporate ownership

changed dramatically from 1998 to 2008 (fig. 6.8) and

has continued to change since then—an example is the

Weyerhaeuser Corporation conversion from a vertically

integrated timber products company to a REIT in 2010.

During that period, the forest products industry ownership

declined by roughly 70 percent from 23.4 million to 7.5

million acres (table 6.1). TIMOs captured most of these lands

as their acreage increased from 2.2 million to 13.4 million

acres. The holdings of REITs increased by about 20 percent

while the “other corporate” ownership remained relatively

constant. The total corporate forest ownership group as

defined by Lanworth declined by about 10 percent over

this period, ostensibly as non-corporate entities acquired

some lands during divestiture. For example, we know that

conservation organizations such as The Nature Conservancy

acquired some of the land offered in these sales.

From 1998 to 2008, TIMO acreage grew from 7 percent to

45 percent of the corporate ownership group (fig. 6.9). This

change mirrors the decline from 71 percent to 25 percent

in forest products industry holdings over the same period.


113

chapter 6. Forest Ownership Dynamics of Southern Forests 

Table 6.1–Corporate forest ownership subgroups by State, 1998 and 2008

State Total corporate

Forest products 

industry

Timber investment 

management organizations

Real estate 

investment trusts Other corporate

1998 2008 1998 2008 1998 2008 1998 2008 1998 2008

---------------------------------------------------------------------------- acres ----------------------------------------------------------------------------

Alabama 4,360,000 4,160,000 3,240,000 1,264,000 772,000 2,115,000 348,000 555,000 0 227,000 

Arkansas 3,931,000 3,671,000 2,440,000 1,545,000 213,000 892,000 1,220,000 1,212,000 57,000 22,000 Florida 2,516,000 2,068,000 1,006,000 0 209,000 496,000 564,000 913,000 737,000 658,000 Georgia 4,348,000 3,889,000 2,478,000 518,000 0 827,000 1,707,000 2,272,000 163,000 272,000 

Kentucky 440,000 334,000 220,000 0 0 333,000 8,000 0 212,000 1,000 Louisiana 4,225,000 4,085,000 3,591,000 1,690,000 126,000 1,739,000 509,000 473,000 0 182,000 Mississippi 3,119,000 2,885,000 2,025,000 875,000 401,000 1,019,000 694,000 686,000 0 304,000 

North Carolina 1,598,000 1,343,000 1,581,000 560,000 0 749,000 17,000 9,000 0 25,000 

Oklahoma 1,172,000 973,000 645,000 509,000 391,000 368,000 15,000 37,000 121,000 59,000 

South Carolina 1,665,000 1,417,000 1,426,000 391,000 43,000 737,000 197,000 189,000 0 100,000 

Tennessee 1,109,000 625,000 1,053,000 0 0 614,000 8,000 0 48,000 10,000 Texas 3,588,000 3,332,000 3,022,000 0 0 3,007,000 56,000 133,000 510,000 193,000 

Virginia 931,000 787,000 687,000 163,000 0 521,000 24,000 0 220,000 103,000 

Total 33,002,000 29,569,000 23,414,000 7,515,000 2,155,000 13,417,000 5,367,000 6,479,000 2,068,000 2,156,000 

Source: Lanworth Inc. On file with: David Wear, U.S. Department of Agriculture, Forest Service, North Carolina State University, PO Box 8008, Raleigh NC, 27695.
114 The Southern Forest Futures Project

These changes are not evenly spread across the South.

Texas had the greatest decline of forest products industry

ownership (about 3 million acres) while Alabama, Georgia

and Louisiana lost about 2 million acres each (fig. 6.10).

Increases in TIMO ownership were similar to regional trends

with gains of 3 million acres in Texas, 1.5 million acres

in Louisiana, and 1.6 million acres in Alabama (fig. 6.11).

The spatial distribution of corporate owners also differs by

type of owner.5 The forest products industry is most heavily

focused in south central areas—in Arkansas, Louisiana,

Mississippi, and Alabama (fig. 6.12). REITs are concentrated

in these same States but their ownership also extends into

Georgia, Florida, and South Carolina (fig. 6.13). TIMO

ownership is even more diffuse but has an especially high

concentration in eastern Texas and the west central parts of

Louisiana (fig. 6.14).

The causes behind the divestiture of forest products

industry lands are debated and likely have varied across

the many corporations involved. Forest-land divestitures

involve dismantling vertically integrated operations in the

wood products industries, in effect disintegrating company

missions by dissolving the bond between timber growing

operations from wood processing operations—indicating a

change in the perceived economic advantages of maintaining

the production of raw materials within the corporate

boundary. We can assume that the vertically integrated

companies had realized benefits that exceeded the costs

incurred by coordinating two very different business models

(also known as transaction costs). To understand the causes

of the divestitures we must understand how the benefits

of holding timberland area diminished relative to these

transaction costs. To be sure, this trend toward disintegration

is not limited to forestry and was especially pronounced in

banking, information technology, and manufacturing sectors

of the economy. The following paragraphs describe some of

the key factors6 that may have influenced decisions to sell.

Consolidation through mergers and divestitures—

Multiple acquisitions and consolidations occurred in the

forest products sector ahead of forest-land divestiture.

Corporations merged and then consolidated their

production around fewer product lines. This shift away

from horizontally integrated manufacturing may have

reduced the amount of timber from corporate land that could

be effectively processed into products. Acquisitions also

5 We defined the concentration of ownership by calculating the ratio of

ownership acres to total forest acres in each county. High concentration

counties are among the top third of these ratios, moderate is defined by the

middle third, and low is defined by the lower third. 6Wear, D.N. 2010. The disintegration of timber growing and wood

products manufacturing in the United States. Draft manuscript on file. U.S.

Department of Agriculture Forest Service, 3041 Cornwallis Rd., Research

Triangle Park, NC, 27713.

generated considerable debt that could be reduced through

land sales.

Alleviation of timber scarcity concerns—A fundamental

change in perceptions about timber supplies occurred during

the 1990s, with focus shifting from impending scarcity

of raw materials to a view that private owners are priceresponsive and reliable in supporting increased demands

for production (Wear and Prestemon 2004). Changes in

production technologies may have also contributed to

alleviating concern about scarcity, as oriented strand board

and other new products can be produced with smaller logs

than required by older products such as plywood. In times of

scarcity, forest products industry land could buffer shortterm market shortages, but in the face of resource abundance,

this insurance value of company timberlands would diminish

considerably.

Transaction technologies—New information technologies

that reduce the costs of transacting and finding sellers of

materials have had far-reaching effects on the structure

of production in many sectors of the economy. The best

example of this ability to reduce transaction costs within the

wood products sector may be the development of Geographic

Information Systems that can sift through satellite imagery

and ground based inventories to “discover” new sources

of timber. These systems can leverage the efforts of

procurement foresters to be quicker at locating owners

with marketable timber and therefore reduce the costs of

procuring timber on open markets. In effect, technology can

make supply less uncertain, thereby reducing the insurance

values of holding timberland.

Globalization—Much has been written about the effects of

globalization on the U.S. economy. Favorable terms of trade,

structural changes in governance, and shifts in comparative

advantage, combined with the developments in transaction

technologies described above, have all contributed to global

shifts in production. But although expanded trade may affect

the optimal scale of a company (by changing the extent of

markets), increased trade alone does not necessarily affect its

optimal structure as much as other aspects of globalization.

To illustrate, globalization increasingly captures what

Grossman and Rossi-Hansberg (2006) call an emerging

“trade in tasks” among regions and countries. They

argue that technological changes (primarily information

technologies that enable communication of precise

specifications coupled with transportation systems that offer

timely delivery) have allowed the production process to

be broken into smaller staged tasks that can be performed

in disparate locations. This encourages the separation of

production stages—such as wood growing and wood product

manufacturing—which has been observed in increased

exports of U.S. hardwood lumber for the manufacturing of

furniture (Wear and others 2007).



118 The Southern Forest Futures Project

What then are the implications for corporate lands of these

transactions and the factors that have driven them? Perhaps

most importantly, changes in production and transaction

technologies suggest that timberland assets will likely be

much more liquid in the future. This trend is reinforced by the

form of new ownerships and the structure of many investment

vehicles for timberland—large shares of timberland portfolios

are commonly held as closed end funds that must be sold

after a specified time period (Zhang and others 2012).7 There

is little reason to believe that we have observed a once-only

episode in timberland sales. Rather, the emerging investment

models for forests indicate that timberland will be traded

much more frequently in the future than ever in the past.

More rapid turnover of timberland might eventually raise

concerns about the stability of timber markets.

It seems clear that the transition from large-block ownership

by the forest products industry is irreversible. Because

technological changes have permanently changed cost

structures, the capacity and desire for internal control

of timberland have likely been permanently eroded.

Furthermore, because divestiture often results in

fragmentation of ownership, it is quite unlikely that any

future owner could stitch together a forest estate comparable

to the pre-divestiture holdings.

Forest land provides many social benefits beyond timber

production, including watershed protection (chapter 13),

biodiversity (chapter 14), and recreation (chapters 7 and 8).

Increased timberland liquidity suggests the possibility of

changes in the availability of these benefits. Large-block

industrial ownerships provided many conservation benefits,

including some of the largest contiguous stretches of forest

habitats in the South (Wear and Greis 2002). They provided

de facto protections for resource sustainability that is

now less certain as timberland ownership becomes more

fragmented.8

TIMOs may seek to diversify the risks associated with

their forest holdings, providing a strong disincentive for

the practice of holding land in the large contiguous blocks

that was typical of forest products industry owners. Risk

management strategies spread investments across space

(e.g., to avoid damage from hurricanes and fires) and perhaps

across species (to mitigate, for example, losses from insect

and disease outbreaks)—both of which would also address

market-based financial risks, but at a cost of increasing forest

parcellation and eventual forest fragmentation.

7 TIMOs also manage open-ended investor accounts without a

predetermined holding period. 8 It is too early to determine how these changes in ownership will be

translated into changes in on-the-ground forest management. Shifts in

management are uncertain and will ultimately hold strong influence over

the future path of forest conditions in the South.

It is important to remember that the investors represented

by TIMOs came very rapidly into forest investments

based on the perception of countercyclical returns and the

attractiveness of land as an investment. Changes in these

perceptions and valuations might just as easily lead them

away from forest investments. Again, the implication of

their actions is increased liquidity of forest assets and more

rapid change in landscape conditions in the future. The

economic downturn of 2008 to 2009 is a reminder that

external conditions can rapidly change the relative position

of various investments.

Future conservation strategies need to anticipate these

changes in corporate owned forests. Divestiture provided

a unique opportunity for conservation purchases of

unprecedented size (Weinberg and Larson 2008). Still the

ability to protect biodiversity, water, and other values that

depend on large blocks of contiguous forest have been and

will continue to be challenged by these forces that fragment

forest ownership. Effective conservation in these dynamic

landscapes may require new approaches, for example,

emerging partnerships between investment and conservation

interests.

The future of family forest lands depends on personal,

familial, social, and market forces that will unfold with the

inevitable land transfers from one owner to the next. It is at

these points of transfer when land use and forest management

are most likely to change. Although many family forest

owners want to keep their land intact for future generations,

they are not certain they will be able do so. Mater and others

(2005) showed that many of the next generation owners are

not attached to the land and have little interest in maintaining

the family legacy, implying another tendency toward liquidity.

That being said there are still many potential heirs and new

owners who are interested in maintaining the land in forest

cover. Continued population pressures and the continued

desire of people to want to live in the country will continue

the parcellation of family forest land. And with parcellation

often comes new development, such as homes and the roads

and other infrastructure that they require. This will create

challenges for wildlife, wildfire control, recreation, and forest

management. Not only will the parcels be smaller, they will

also more likely be surrounded by development, which can

create regulations or other obstructions that further hinder

traditional management practices.

It is uncertain who the future family forest owners will be,

but given historic trends, there will likely be more of them,

the average size of their forest holdings will decrease, and

the importance of amenity values will continue to increase. It

is also likely that more of them will have urban or suburban

backgrounds and be absentee owners. Although many will

still be willing to harvest trees from their land, if current

trends of smaller parcels and changing objectives continue,
119

chapter 6. Forest Ownership Dynamics of Southern Forests 

harvesting is likely to decrease. And it is likely that land

values, either monetary or non-monetary, will continue to

increase for recreation (such as hunting), nontimber forest

products (such as pine straw), water protection, carbon

sequestration, and other nontraditional uses.

Family forest owners will continue to dominate the forested

landscape of the South for the foreseeable future. Many of

the growing pressures on landowners will be felt by family

owners first and most intensively. Therefore it behooves

all who are interested in the future of the South’s forests to

understand this group of owners, their dynamics, and the

factors affecting them. In effect, they hold the future of

southern forests in their hands and need assistance if they are

to continue their tradition of stewardship.

We need to be cognizant of owners’ objectives and

circumstances. Whether they own a 10-acre home lot or a

400-acre investment property will have a large impact on

what issues are important to them, the use/management that

they deem appropriate, and what can be done with their land.

In short, there will be an urgent need to develop programs

and tools that are tailored for specific types of family forest

owners—providing them what they need, when they need

it, and how they want it—so that family forest owners can

continue to provide the goods and services that society has

come to expect.

Discussion and Conclusions

Unlike the western regions of the United States, the South’s

forests are dominated by private owners. Private ownership

is diverse with roughly a third in corporate ownership and

the remainder owned by more than 4 million families or

individuals. Forest holdings vary considerably in size with

most owners (59 percent) holding fewer than 10 acres. Most

forest land (60 percent) is however, in holdings of 100 acres

or more.

Forecasts indicate a loss of 5.5 million to 12.2 million acres

of private forest land in the South by 2060. With expanded

urbanization growing outward from city centers, we expect

an increased fragmentation of remaining forest holdings.

Ongoing parcellation through estate disposal and the tax

increases associated with urbanization will continue to

alter forest management in the South. In particular, areas of

concentrated urbanization could begin to see reductions in

timber harvesting and planting in small inoperable holdings,

and reductions in prescribed burning because of health and

safety concerns and ordinances.

Family forest owners cite a variety of reasons for holding

forests. These include legacy, aesthetics, and land

investment. About two-thirds of family forest land in the

South is held by owners who have harvested timber from

their forests in the past. When combined with corporate

ownership, about 8 of every 10 acres of private forest land in

the South are owned by corporations, families, individuals,

or others who have commercially harvested.

The divestiture of forest lands by the forest products industry

from 1998 to 2008 is the most substantial transition in forest

ownership of the last century. This divestiture substantially

altered the ownership and objective structure of the corporate

ownership group because much of the land shifted to TIMO

and REIT owners. A number of economic factors likely

influenced the decisions of forest products companies to

sell their land. An analysis of these factors suggests that the

transition from large block industry ownership to a more

spatially varied and fragmented ownership is irreversible in

the foreseeable future.

As a result of transition from the forest products industry to

TIMOs, corporate owned forest land is now a more liquid

asset that could trade more frequently in the future, and the

size of individual holdings could continue to decline. While

the forest products industry land base had been a stable and

predictable component of the southern landscape, the “new”

class of corporate forest lands may be less stable and more

changeable with implications for nontimber values, such as

water quality, sensitive plant and animal communities, and

recreation availability. The economic forces that led these

new forest owners to acquire land could cause rapid shifts

in ownership in the future. For example, a sustained decline

in commodity prices, such as the 50 percent reduction in

softwood pulpwood prices since 1998 (chapter 4), could

reduce the profitability of timberland management and

drive away investors. Conversely, policy driven increases in

biomass demands for energy production or the emergence

of additional revenues from markets for ecosystem services

could reverse recent downward trends (chapter 10).

Over the past 2 decades, ownership dynamics have been

largely among owner types within the corporate and family

owner groups (and not from one group to another). Our

analyses of anticipated changes are consistent with this history.

Structural changes in ownership—transferring land among

major groups—might be possible depending on the driving

forces, and would have far reaching effects. For example,

increasing scarcity of recreation and concern for other qualityof-life aspects of forests could lead to public acquisition of

private forest land, especially at State and local levels. A

substantial decline in timberland profitability could lead to

a shift in ownership from corporate to family forest owners.

These are both within the realm of plausibility but have not yet

been observed to any great degree.
120 The Southern Forest Futures Project

Tax policy has had important influence on the structure of

corporate forest ownership, notably the shift from industry

to other forms of corporate owners. Policy in the form of

income and property taxes likewise influences the allocation

of land to forests and other uses and affects the optimal

scale of forest uses. Environmental regulations can affect

the feasibility of forest management thereby affecting the

extent of commercial ownership. What’s more, inheritance

taxes influence the retention of large forest areas through

generational transfers. These and other policy impacts on the

type of ownership and parcellation of forests are discussed in

chapter 11.

Knowledge and Inf ormation Gaps

A number of questions remain about implications of

changes in the corporate forest ownership group for forest

management and forest conditions. In particular we posit

that the change from forest products industry to TIMO

ownership increases the liquidity of forest assets and

that this might affect the long-term conditions of forests.

Frequent sales of timberland could conceivably decrease

the likelihood of long-term investments in forest production

and productivity. Conversely, investments in these assets

could lead to improvements that would be valued in future

transactions because those investments would be fully

capitalized in the sale prices of timberland. If so, efficient

long-term investment would not be impeded by frequent land

sales. This is a crucial question for the future of forests in

the South, and one that could be answered through regular

monitoring and analysis of timberland transactions over

time, perhaps in concert with forest inventories.

Land use (and other land ownership) systems for projecting

ownership dynamics typically use very simplistic

assumptions because they lack solid theoretical modeling

frameworks and empirical data for parameterizing and

validating models. Building on existing land-use change and

forest-management behavior models (Agarwal and others

2002, Beach and others 2005, Pocewicz and Nielsen-Pincus

2008) should pave the way for a first approximation of an

ownership dynamics model. Full implementation of the

newly designed annual FIA inventory system will add data

to help resolve issues surrounding broad-scale ownership

dynamics, but additional work will be needed to examine the

dynamics within ownership groups. Most immediately, the

transfer of lands from vertically integrated timber products

companies to TIMOs and REITs and the intergenerational

transfer of family forest lands are important dynamics that

need to be better understood and monitored.

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land parcelization and ownership transfers on nonindustrial private

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forest land certification opportunities: a Minnesota case study. Journal

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a study of contributing factors. Journal of Forestry. 99(4): 30–34.

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management. Journal of Forestry 110(7): 355-361.

123

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

	 H. Ken Cordell, Carter J. Betz, and Shela H. Mou1

Key Findings

Population

•	 The South grew considerably faster (32.5 percent) in total

population in the 18 years between 1990 and 2008 than

the Nation as a whole (22.2 percent). The region has just

over half of the country’s non-Hispanic African American

population (18.9 million) and is a close second to the

Rocky Mountains in both the size and rate of increase of

the American Indian population. Since 1990, the South

(heavily influenced by Texas and Florida) surpassed

the Pacific Coast (strongly influenced by California) in

Hispanic population to lead the Nation, with growth

especially high in North Carolina and Georgia.

•	 In the South, the baby boomer generation age groups

(44-64 years old) have dominated all others in percent

growth since 1990. The South and the Rocky Mountains

were the only regions to outpace the national growth rate

for every single age group.

•	 The greatest current density of population for the South

is in Florida, in the Piedmont areas of North Carolina to

Georgia, and in eastern Texas. Other high-density areas of

the South include many of the coastal counties, both on the

Gulf of Mexico and along the Atlantic Ocean.

•	The highest growth in density of population (persons

per square mile) has occurred down the Piedmont and

Southern Appalachians from North Carolina to Alabama,

along the coasts of Florida, and around the major cities of

Texas. Some of this growth was substantial and exceeded

the U.S. Census Bureau definition of an urban area,

500 persons per square mile. In areas like eastern Texas,

higher concentrations of people in places near public lands

and bodies of water are likely to put increasing pressures

on these limited resources.

•	With moderate growth, the population of the United States

is projected to exceed 447 million people by 2060, an

increase of more than 47 percent. For the same period,

projected growth for the South is nearly 60 percent. The

1H. Ken Cordell is a Pioneering Research Scientist, Carter J. Betz is an

Outdoor Recreation Planner, and Shela H. Mou is a Computer Assistant

with Pioneering Research Program, Southern Research Station, U.S.

Department of Agriculture Forest Service, Athens, GA 30602.

Atlantic States area in the South ranks second among its

nine U.S. counterparts, at 68 percent forecast increase in

population, followed by the Pacific Northwest with

63 percent. Of the 13 Southern States, Florida, Virginia,

and Texas are projected to grow faster than the South-wide

rate of 59 percent.

Recreation Demand

•	 One overriding recreation trend seems clear—what people

now choose to do for outdoor recreation is different from

choices made by and available to previous generations.

Fishing and hunting, often considered widely popular and

among the more traditional of outdoor activities, are still

somewhat popular but are being replaced by other activities

such as wildlife or bird watching and photography.

•	 For the South, the rate of growth for both the total number

of outdoor recreation participants and total annual activity

participation days exceeded those of the Nation. In the last

decade, participants 16 years old and older increased about

11 percent, from about 68 million to 75 million, with their

number of annual participation days increased by 41 percent.

Average activity participation days per person across the

full list of 60 activities rose from about 310 days per year

to 393 days, a 27-percent increase. Some of the faster gains

can be attributed to a slightly higher population growth

rate than the Nation between 2000 and 2008. The number

of people 16 years old and older increased from just fewer

than 70 million to around 79 million, a 13-percent gain. In

the United States, age 16+ population grew just under 10

percent, from 214 million to about 235 million.

•	 Of the most popular activities in the South (which has

more than 30 million recreation activity participants),

the top six activities were walking for pleasure, family

gatherings outdoors, gardening or landscaping, viewing/

photographing natural scenery, sightseeing, and visiting

outdoor nature centers. Other popular growth activities

included driving for pleasure, viewing/photographing

flowers and trees, viewing/photographing wildlife

(besides birds and fish), swimming in an outdoor pool,

and picnicking. Activities oriented toward viewing and

photographing nature (scenery, flowers/trees, and wildlife)

have been among the fastest growing in popularity.

Chapter 7.

 Outdoor Recreation in a

Shifting Societal Landscape
124 The Southern Forest Futures Project

•	 For moderately popular activities (10–30 million

participants), the most popular were viewing or

photographing birds, bicycling, gathering mushrooms/

berries, warmwater fishing, visiting a wilderness, visiting

a farm or agricultural setting, viewing and photographing

fish, and day hiking. Growth has been especially strong

for off-highway vehicle driving, gathering mushrooms and

berries, and visiting farms or agricultural settings.

•	 Among activities having fewer than 10 million

participants, camping at primitive sites, big game hunting,

waterskiing, using personal watercraft, and equestrian

activities were at the top and showed some growth.

Kayaking was the fastest growing of these activities by

a wide margin, followed by other water-based activities

such as waterskiing and canoeing. Some activities posted

declines during this decade.

Recreation Resources

•	 Less than 5 percent of Federal land, about 30.5 million

acres, is in the South, 44 percent of which is managed by

the Forest Service, U.S. Department of Agriculture. More

than 92 percent of Federal land is located in the Western

United States.

•	While Federal acreage changes little over time, population

changes greatly. In the South, Federal acres per 1,000

persons declined slightly faster than the national rate, with

a 15.4 percent decrease in acres per 1,000 people since

1995.

•	 The South accounts for just 2.5 percent of the area of the

National Wilderness Preservation System (NWPS), about

2.7 million of the over 109 million acres. Due to population

growth, the South’s per capita acreage of NWPS land has

declined nearly 16 percent since 1995.

•	 National Wild and Scenic Rivers and National Recreation

Trails are two specially designated Federal protected

resource systems. Only 810 miles of National Wild and

Scenic River miles are in the South (about 6 percent of

all designated river miles), but the 31 percent increase in

protected Southern river miles since 2000 trailed only the

Rocky Mountains region.

•	There are more than 6,500 National Recreation Trail

System miles in the South, almost 33 percent of the

system nationally. Further, the South led all regions with

84 percent growth in designated trail mileage since 2004,

adding nearly 3,000 new trail miles.

•	 The South has just over 12 Federal recreation facilities

per million people, or about 1 facility per 83,000 people,

according to the Recreation Information Database

maintained by the U. S. Department of the Interior. The

South is fairly well represented in the number of Federal

recreation sites with boating facilities.

•	 State park systems throughout the country have faced

difficult budgetary pressures as a result of the economic

recession of the late 2000s. Two of the most affected State

park systems—Alabama and Georgia—are in the South.

•	State park system areas total more than 2.2 million acres

in the South. Throughout much of the region, especially

in Florida and South Carolina, State park resources are

situated within an hour’s drive of most people.

•	Nationwide, more than 8,800 local governments provide

recreation and park services. Just under 29 percent of these

services (2,552 local units) were in the South. The number

of local parks and recreation departments per million

people was up almost 18 percent in the South since 1997,

higher than the national growth rate of 13 percent. Another

indicator of local government recreation and park resources

is conservation funding, which is tracked through ballot

measures in all 50 States by the Trust For Public Land’s

LandVote database. Since 2000 in the South, more than

80 percent of 226 such measures passed to fund $5 billion

worth of county and municipal government parks and

recreation-related development, improvements, and land

protection.

•	 Among nine outdoor recreation business categories

tracked, five showed a decline in the number of

establishments per million people from 1998 to 2007.

Amusement/theme parks, recreational/vacation camps,

and golf courses posted the largest declines in the South.

Private-sector historical sites, nature parks, and zoos/

botanical gardens showed the greatest gains. Private forest

land, both family forests and that controlled by other

private owners, is a significant outdoor recreation resource

in the South. Each State has a recreational use statute that

limits the liability of private landowners to open their land

to public use, but the interpretation of what constitutes

a recreational user varies by State. Recreation leases,

particularly for hunting, are a common method of allowing

public access to private forest land with mutual benefit to

both land owners and lessees.

•	Residents of most counties in the South have access to

fewer than 1.5 acres of public land per person within

75 miles of their home county, except for relatively

more accessibility in the Ozark Highlands and Virginia

mountains. Within the 75-mile recreation day trip zone,

the greatest water (non-ocean) area per capita is in counties

along the Atlantic Ocean and the Gulf of Mexico.

•	 The pattern of non-Federal forest across counties shows that

much of the South has abundant forest land area. But when

expressed on a per capita basis, some of the metropolitan

areas are found to have relatively little forest land close by.

Parts of Arkansas, Louisiana, Mississippi, Alabama, and

Georgia have relatively abundant per capita non-Federal

forest land within 75 miles of residents’ counties.
125

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Projected Futures 

•	 Federal and State park land area is expected to remain

relatively constant over time. Currently in the South,

5 percent of the total area is Federal or State park land,

less than 0.3 acres per person. By 2060, the Federal or

State park land area per person is projected to decrease to

0.17 acres, about 63 percent of the 2008 level. Because of

population growth, the projected decline is greater for the

South than the Nation.

•	 Total non-Federal forest land area is expected to change

with continuing conversions from forests and farmlands to

cities and suburbs. Currently, more than 30 percent of total

land area in the South is non-Federal forest, or 1.66 acres

per person. By 2060, per capita non-Federal forest is

predicted to decline to 0.95 acres per person, or 57 percent

of the 2010 level. The projected decline is greater for the

South than the Nation due to both population growth and

increased development.

•	 Like Federal and State park land, total water area is

expected to stay mostly constant. Currently, water area in

the South is slightly more than 5 percent of the region’s

total surface area, or 0.28 acres per person. By 2060, per

capita water area is predicted to decline to 0.18 acres per

person, or 63 percent of the 2008 level. Similar to the other

resources, the projected decline in water resources per

capita is greater for the South than the Nation.

Introduction

During the scoping phase of the Southern Forest Futures

Project (chapter 1), input from a cross-section of forest

owners, forest users, and forestry professionals was analyzed

to identify issues relating to the socioeconomic aspects of

forest policy and management. The issues addressed in this

chapter include:

•	 How are population and demographics changing?

•	Where and how do population growth, changing

demographics, changing land ownership, and other factors

affect supply and demand for different types of outdoor

recreation?

Described are recent trends, forecasts of population numbers,

population demographic makeup, recreation participation

of the population, and resources available in the South. The

materials presented are adapted from the data, analyses, and

reporting developed for the Forest Service 2010 Renewable

Resources Planning Act Assessment (Cordell 2012). The

Forest and Rangeland Renewable Resources Planning Act

(RPA) of 1974 mandates a periodic RPA assessment of

the Nation’s renewable resources on all public and private

ownerships. Each RPA Assessment provides a snapshot of

current U.S. forest and rangeland conditions and trends,

identifies drivers of change, and projects 50 years into the

future through analyses of recreation, water, timber, wildlife

(biodiversity), and urban forest and range resources. As well,

land-use and climate change are included. The 2010 RPA

Assessment stresses the importance of climate change and

has adapted three socioeconomic scenarios based on the

framework for the fourth world assessment of climate change

done by the Intergovernmental Panel for Climate Change

(IPCC).

Historical Context

The agrarian way of life up through the middle of the 20th

century meant that the majority of people in the United States

worked out-of-doors and had little desire for leisure outdoors.

After the Great Depression and World War II, however,

Americans in large numbers shifted to manufacturing

and other forms of livelihood. With shifting work lives,

Americans took to the open road to see and experience “the

great outdoors.” This led to mounting pressures on recreation

facilities and most public lands. Consequently, major efforts

ensued beginning in the 1960s to study and understand

Americans’ growing pursuit of outdoor recreation.

The interest in better understanding trends in outdoor

recreation continues into the 21st century (Cordell 2008).

In our earlier publication, Outdoor Recreation for 21st

Century America, we reported that Americans’ participation

in outdoor activities, including nature-based recreation

activities, had been rising up through the early part of

the first decade of this century (Cordell and others 2004).

Overall, since the first nationwide assessment of outdoor

recreation trends conducted by the Outdoor Recreation

Resources Review Commission 1962, almost all forms

of outdoor activity and public land visitation have been

observed to be growing. Cordell (2008) observed as well that

there were signs of shifts in Americans’ outdoor recreation:

“Both the NSRE (National Survey on Recreation and

the Environment) and the National Survey on Fishing,

Hunting, and Wildlife-Associated Recreation show

that participation in some nature-based activities

has declined. However, for many other activities

there seems to be growing popularity. Some outdoor

recreation activities have even demonstrated rather

strong popularity growth. One such activity is visiting

wilderness and other primitive areas (Cordell, Betz, and

Green 2008).”

Because trends in nature-based and other outdoor recreation

have far reaching implications, a close look at those trends

and projected futures for the South is important.
126 The Southern Forest Futures Project

Outdoor Recreation Defined

Outdoor recreation is recreation activity done out-of-doors,

which can, of course, take many forms. Those many forms

occur with different activities, settings, types of social

engagements, equipment, and times which are chosen by the

recreation participant. Recreation can be physically active or

sedentary. Nature-based recreation participation as reported

in this chapter and summarized for the South includes:

•	Visiting recreation and historic sites: Visiting the beach,

prehistoric sites, and historic sites; developed camping;

swimming in lakes, ponds, and other bodies of water;

visiting watersides besides beaches

•	Viewing/photographing nature: Viewing or

photographing birds, other wildlife, fish, natural scenery,

and wildflowers, trees, and other plants; visiting “nature

center” type facilities; sightseeing; gathering mushrooms,

berries, and other plants; taking boat tours or excursions

•	Backcountry activities: Backpacking, day hiking,

horseback riding on trails, mountain climbing, visiting a

wilderness or primitive area, primitive camping, mountain

biking, caving, rock climbing, orienteering

•	Motorized activities: Motorboating, off-highway vehicle

driving, snowmobiling, using personal watercraft,

waterskiing

•	Hunting and fishing: Anadromous fishing (salt-to-freshwater migratory fish, for example salmon, which does not

occur in the South but does draw some participants from

the region), coldwater fishing, warmwater fishing, saltwater

fishing, big game hunting, small game hunting, and

migratory bird hunting

•	Non-motorized boating and diving: Canoeing, kayaking,

rafting, rowing, sailing, surfing, windsurfing, snorkeling,

scuba diving

•	Snow skiing and other winter activities: Cross-country

skiing, downhill skiing, snowboarding, snowshoeing, ice

fishing

Methods and Data Sources

Population and Demographic Trends and Futures 

for the South

U.S. Census Bureau historical data from the 1990 Decennial

Census through the 2008 national population estimates

from Census were analyzed to examine recent trends in

population and demographic makeup. National and regional

population totals and proportions are presented as tables.

As well, maps are presented showing the distribution of

the population among counties. Included in this chapter

are data on population by race/ethnicity, population by age

groups, current population density (persons per square mile),

population density change since 1990, percent change in

Hispanic population, percent change in non-Hispanic White

population, and projected changes in population density

from 2008 to 2060. For comparison with the South, selected

statistics are also shown for the Northern, Rocky Mountain/

Great Plains, and Pacific Coast regions. The Southern Region

consists of Alabama, Arkansas, Florida, Georgia, Kentucky,

Louisiana, Mississippi, North Carolina, Oklahoma, South

Carolina, Tennessee, Texas, and Virginia. The Census

Bureau provides updated annual population estimates

for States and counties each year between the decennial

population censuses. Based on these updates, maps at county

scale were produced for this chapter reporting on change in

Hispanic and other segments of the South’s population. Data

consulted included:

•	 U.S. Census Bureau (2008a), SC-EST2008-alldata6:

Annual State Resident Population Estimates for 6

Race Groups (5 Race Alone Groups and One Group

with Two or more Race Groups) by Age, Sex, and

Hispanic Origin: April 1, 2000, to July 1, 2008 (http://

www.census.gov/popest/states/asrh/files/SC-EST2008-

alldata6-ALL.csv)

•	 U.S. Census Bureau (2008b), CC-EST2008-ALLDATA-

[ST-FIPS]: Annual County Resident Population

Estimates by Age, Sex, Race, and Hispanic Origin:

April 1, 2000, to July 1, 2008 (http://www.census.gov/

popest/counties/asrh/CC-EST2008-alldata.html)

•	 State and county population from the 1990 Census were

derived from Woods and Poole Economics, Inc. (2009).

Working from Census Bureau estimates, county-scale

forecasts of population change were developed for three of

the future scenarios defined by the IPCC’s fourth climate

change assessment. These three IPCC scenarios were

adapted for use in both the national RPA Assessment and

as Cornerstones for the Southern Forest Futures Project

(chapter 2). They provided a useful framework for evaluating

the sensitivity of forest and other resource trends to a range

of feasible population growth futures. The IPCC scenario

designations were labeled A1B, A2, and B2. County-level

population growth projections were developed for each of

these three scenarios for the 2010 RPA Assessment and are

used for this Southern Forest Futures chapter (Zarnoch and

others 2010). Percent change over the 50-year assessment

period is shown only for the A1B moderate-level scenario.

Under this scenario, total population in the United States is

projected to exceed 447 million people by 2060, a growth of

more than 47 percent.

Recreation Activity Trends

The primary source of data for recreation activity trends

for this chapter is the National Survey on Recreation and

the Environment (NSRE). NSRE is a general-population
127

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

random sample telephone survey that asks Americans age

16 and older about their participation in outdoor recreation

activities. The data presented in this chapter are from

surveys conducted continuously from 1999 to 2009, with a

brief interruption during 2004.

This chapter updates earlier estimates of trends in outdoor

recreation overall (Cordell and others 2008) and in naturebased outdoor recreation in particular (Cordell 2008). Period

trends are reported for 2000 (the midpoint year for the

1999–2001 data used) and 2008 (the midpoint year for the

2005–09 data). A general picture of Americans’ participation

in outdoor recreation was constructed by defining a

“participant” as any person who engaged in at least one of

60 outdoor recreation activities being tracked one or more

times during the 12 months prior to the date they were

interviewed. A binary variable was created with a “yes”

value assigned to respondents if they reported participation

in one or more of these 60 activities. A similar indicator was

used to determine nature-based activity participation using

a shorter list of 50 activities typically occurring in natural

settings. Previous estimates from the 1994-to-1995 period

are included to indicate overall trends across two decades.

Recreation Resources 

Federal resources—Federal outdoor recreation resources

in the South are described in this chapter based on the latest

available data from Federal land managing agencies. Nearly

all Federal land in the South is open for public recreation.

The four largest Federal land-managing agencies—the Forest

Service (U.S. Department of Agriculture), the Bureau of

Land Management (U.S. Department of the Interior), the

U.S. Fish and Wildlife Service (U.S. Department of the

Interior), and the National Park Service (U.S. Department

of the Interior)—have real estate offices that maintain real

property records on the size, location, and boundaries of

agency holdings. The three Federal water management

agencies—the U.S. Army Corps of Engineers, the Bureau of

Reclamation (U.S. Department of the Interior),

and the Tennessee Valley Authority—have much smaller

land holdings.

Specially designated Federal land systems that are described

in this chapter include the NWPS, the National Wild and

Scenic River System, and the National Trails System.

Current and past data from each of these systems was

examined for trends in per capita availability.

Federal recreation sites and facilities are cataloged in

an online database called the Recreation Information

Database, better known through its portal as Rec.gov. An

interagency coalition coordinated by the U.S. Department

of the Interior gathers information on Federal recreation

sites and/or facilities across all agencies. A standardized

list of 22 separate recreation activities or attractions with

binary (yes/no) availability appears on Rec.gov. Trend

data are not available for the database because it is fairly

new, originating around 2002. Moreover, it is an evolving

source of information, which is populated by the various

Federal agencies with varying levels of completeness and

comprehensiveness.

State resources—State park system data that appear

in this chapter are from two sources. The first source is

the National Association of State Park Directors Annual

Information Exchange survey which collects land, facilities,

visitation, and other data from all 50 State park systems

(parks, recreation areas, natural areas, historical areas,

environmental education areas, scientific areas, forests,

wildlife and fish areas, and other miscellaneous areas) and

assesses the status of each State park system’s resources,

operations, and visits. The Exchange does not have

individual State park unit information, such as size and

location, but rather State summaries, with information about

parks and recreation area classes tending to be the most

consistent over time.

The second source is a State park database developed from

printed and online sources (available from the lead author

of this chapter), and includes acreage data and latitude/

longitude geo-locations. The database focuses on the three

most common types of State park system areas: parks,

recreation areas, and historic sites.

Local government resources—Tracking these resources

is complicated by the sheer number and variety of local

jurisdictions that provide park and recreation services. The

emphasis of many local agencies is as much on providing

indoor leisure programs and services, as it is on outdoor

recreation resources. For this chapter, it is assumed that all

local government agencies listed as providing recreation

services include management of outdoor recreation

resources. The data source is the Census of Governments

done every 5 years by the Census Bureau. This census

classifies governments by the type of governmental unit

and by services provided. Another data source for local

government resources is the Trust For Public Land’s

LandVote database, which monitors conservation and

parks-related funding nationwide via ballot measures and

initiatives, particularly at the municipal and county levels of

government.

Private recreation businesses and land—The Census

Bureau’s County Business Patterns (part of the Economic

Census) provides data on the number of recreation business

establishments (in addition to data on payroll and number

of employees) for the full range of businesses as described

in the North American Industry Classification System. Nine

of these business classes are related to outdoor recreation
128 The Southern Forest Futures Project

and are summarized in this chapter. Number of business

establishments per capita with percent change from the

previous 1998 survey to their most recent survey of 2007 are

provided.

The 2006 National Woodland Owner Survey, conducted by

the Northern Research Station of the Forest Service, provides

the most recent data on family forest owners in the United

States. The survey assessed landowners’ objectives, practices,

and reason for owning their private forest land. Another

Forest Service data source is the NSRE, sponsored by the

Southern Research Station. The NSRE, a general population

household telephone survey, included a section which asked

respondents about their participation in several nature-based

recreation activities and the number of days annually that

were spent on private forest lands.

County Pattern Maps

Also included in this chapter are county-level maps for

2008 that depict patterns of recreation resource availability

per capita across counties in the South and as well the

Nation. Shown are the recreation resources per capita

within a 75-mile radius of each county. The 75-mile zone

includes a home county plus all surrounding counties whose

centroids (geographic centers) are within a 75-mile straightline distance from the home county centroid (roughly the

equivalent of a recreation day trip). The three basic recreation

resources summarized in this chapter are combined Federal

and State-park land area, non-Federal forest land, and water

area (from Census Tiger geographic data).

Projected Futures

The future change measure used in this chapter is the ratio of

per capita acres predicted for 2060 relative to the per capita

acres in 2008. This statistic indicates the proportion of the

area existing in 2008 that is forecast to remain by 2060. The

per capita resources forecast (Federal and State park land

area, non-Federal forest land, and water) are summarized

by region and for the Nation as a whole. Also reported is the

percentage of total surface area in each region represented by

the resource.

Results

Current Population Trends for the South 

Based on official Census Bureau population data, the race

and ethnic composition by region, along with the percent

change trend from 1990 to 2008, are summarized in table 7.1.

Race and ethnicity are important determinants of what people

choose as outdoor recreation activities and the settings they

use for those activities (Cordell and others 2004).

The race and ethnic makeup of the U.S. population changed

dramatically in the 18 years since the 1990 Census. Although

all races have been growing in number, generally, Asian or

Pacific Islander and Hispanic components have been growing

fastest. Non-Hispanic Whites have been growing much

slower than the other groups. The highest growth for the total

population has been in the Rocky Mountains, and lowest

has been in the North. Highest percentage growth of any

group has been the Asian or Pacific Islanders in the Rocky

Mountains and South. Non-Hispanic Whites experienced

population losses in the North and Pacific Coast.

The population of the South grew considerably faster

(32.5 percent) than the Nation as a whole (22.2 percent).

The Rocky Mountains and South are the only regions that

outpaced the national rate for all race/ethnic groups. The

lowest percentage increase in the South by a large margin

was for Non-Hispanic Whites. But the 14 percent growth

rate of this group still was more than double the national

rate. Although the rate of increase (35.4 percent) for African

Americans in the South was slightly more than half that of

the Rocky Mountains, this population of almost 19 million

was nearly 20 times larger than the Rocky Mountain

population and more than half of the national total

(37.2 million).

The South is a close second to the Rocky Mountains in both

the size and rate of increase for American Indians. Although

Asian or Pacific Islander population is much smaller than

it is in the North and the Pacific Coast, the growth rate of

this group in the South was considerably larger than it was

in either of those regions. Since 1990, the South (heavily

influenced by Texas and Florida) surpassed the Pacific Coast

(strongly influenced by California) in Hispanic population

to lead the Nation. Just over a third of all Hispanics now live

in the South. The South’s 143 percent growth in Hispanic

population trails only the Rocky Mountains, but the South

outnumbers the other region by nearly 3-to-1 in total

Hispanic population. Growth of the Hispanic population was

especially high in North Carolina and Georgia.

Age distribution—Age also has a strong effect on recreation

activity choices (Cordell and others 2004). Similar to

other demographic aspects, the age distribution of the U.S.

population has been changing over time, as table 7.2 shows.

Nationally, the fastest growing age group since 1990 (in

percentage terms) has been the 44-54 age bracket followed

by the 55-64 bracket. Next fastest growing is age 65 or older.

Age 44 to 54 is the fastest growing group in all regions. The

25-34 age group has declined nationally, led by steep declines

in the North and to a lesser extent in the Pacific Coast. The

10-and-under age group has declined in the North, but has

experienced its fastest growth in the Rocky Mountains.
129

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Table 7.1—Population (thousands) in 2008 by race/ethnicity and region with percent change since the 1990 Census 

Race/ 

ethnicity North

Percent 

change South

Percent 

change

Rocky 

Mountain

 Percent 

change

Pacific 

Coast

 Percent 

change

United 

States

Percent 

change

White 92,246.8 -0.2 63,478.5 14.0 19,479.6 25.3 24,286.6 -1.4 199,491.5 5.9

African 

American 14,780.5 18.7 18,866.8 35.4 952.9 69.4 2,571.6 8.9 37,171.8 26.8

American 

Indian 416.7 23.2 704.0 36.4 768.9 38.3 439.3 13.7 2,329.0 29.6

Asian or 

Pacific 

Islander 4,670.3 116.4 2,481.3 170.6 690.5 171.1 5,830.2 59.0 13,672.3 95.4

Two or 

more 

racesa 1,492.0 – 1,261.5 – 426.6 – 1,271.6 – 4,451.7 –

Hispanicb 10,761.7 94.6 16,013.4 143.2 5,497.2 157.8 14,671.3 80.4 46,943.6 109.8

Total 124,368.0 10.1 102,805.6 32.5 27,815.7 46.0 49,070.4 25.2 304,059.7 22.2

aPercent change for two or more races is missing because U.S. citizens could not select more than one race until the 2000 Census.

bHispanics may be of any race, but are included in the Hispanic category only. 

Source: U.S. Census Bureau 1990, 2008a.

Table 7.2—Population (thousands) in 2008 by age group and region with percent change since 1990

Age 

group North 

Percent 

change South 

Percent 

change

Rocky 

Mountains

Percent 

change

Pacific 

Coast

Percent 

change

United 

States

Percent 

change

Under 6 9,503.9 -3.0 8,825.9 27.1 2,555.8 37.7 4,196.7 10.4 25,082.3 12.0

6 to 10 7,793.1 -1.2 6,939.6 21.7 1,941.7 24.1 3,222.9 11.4 19,897.3 10.2

11 to 15 8,206.8 10.9 6,864.0 27.6 1,897.9 34.5 3,377.4 31.9 20,346.1 21.5

16 to 24 15,645.9 3.7 12,740.3 19.2 3,544.3 41.8 6,442.8 18.6 38,373.4 13.8

25 to 34 15,928.0 -17.6 14,037.8 5.6 3,965.7 22.7 7,000.0 -4.3 40,931.6 -5.2

35 to 44 17,416.9 2.7 14,349.8 25.2 3,679.9 28.7 7,054.5 14.4 42,501.1 13.5

45 to 54 18,933.9 63.2 14,586.3 86.8 3,861.1 111.2 6,990.7 82.6 44,372.1 77.0

55 to 64 14,246.1 42.1 11,307.9 71.4 2,989.5 96.1 5,142.7 73.3 33,686.2 59.5

65+ 16,693.5 12.4 13,153.9 35.2 3,379.6 48.6 5,642.7 33.6 38,869.7 25.0

Total 124,368.0 10.1 102,805.0 32.5 27,815.7 46.0 49,070.4 25.2 304,059.0 22.2

Source: U.S. Census Bureau 1990, 2008a.
130 The Southern Forest Futures Project

Similar to the Nation, in the South, the baby boomer

generation age groups (44-64) dominated all other age

groups in percent growth. In fact, the nearly 87 percent

growth rate for 44-54 age group was higher than any other

in any region or in the Nation as a whole. As with race and

ethnicity, the South and the Rocky Mountains were the

only regions to outpace the national growth rate for every

single age group. Two related trends stand out. The South

and Rocky Mountains grew much faster than the North and

Pacific Coast in the 10-and-under age group and in the 25-

44 prime childbearing age group. Because these increases

cannot be attributed to natural birth-over-death rate increases

alone, they are likely related to the large number of younger

families migrating into these regions. The South’s 25-34 age

group was the only group not to experience double-digit

growth, but its 5.6 percent increase still outpaced overall

national losses.

Population density—Figure 7.1 shows the distribution of

the U.S. population density (persons per square mile) across

counties. The greatest density of population is in Florida,

in the Piedmont areas of North Carolina to Georgia, along

the coast of the northern Atlantic States, in the Great Lakes,

in eastern Texas, in the Denver-Front Range area, and in

scattered areas along the Pacific Coast and into Arizona.

Greatest density in Alaska (not shown) is in the Anchorage

area.

The South’s other high-density areas include many coastal

counties, both on the Gulf of Mexico and the Atlantic Ocean,

especially near the metropolitan areas in Louisiana (New

Orleans), Arkansas (Little Rock), Mississippi (Jackson),

Oklahoma (Oklahoma City), Alabama (Birmingham,

Montgomery, and Huntsville), South Carolina (Columbia),

and Tennessee (Nashville, Knoxville, and Memphis). With

the exception of a handful of counties scattered throughout

the Eastern United States, most of the lowest-density

counties are in the Plains area of western Texas.

Figure 7.2 shows that much of the overall growth in

concentration of population (growth in persons per square

mile) has occurred along the northern Atlantic coast,

down the Piedmont and Southern Appalachians from

North Carolina to Alabama, along both Florida coasts, and

around the major cities of Texas. Elsewhere in the United

States, growth occurred in the Chicago and Minneapolis/

St. Paul areas, in the Denver and Salt Lake City areas, in the

southwest and coastal California areas, and in the Portland

and Seattle areas. Growth in some areas like eastern Texas

and the greater Los Angeles area is substantial—in amounts

exceeding 500 persons per square mile, which is the Census

Bureau definition of an urban area. Greater concentrations

of people in places near public lands and bodies of water are

likely to put increasing pressures on these limited resources.

In the South, population density increased throughout nearly

all of Florida and is notable in northern Virginia and the

metropolitan areas of Tennessee, and in a band of counties

that follows the I-85 corridor through the Piedmont in the

States of Georgia, South Carolina, and North Carolina.

Hispanics—Figure 7.3 shows the distribution of percentage

growth of the Hispanic population of the United States from

1990 to 2008. Much of the fastest growth has been in the

States bordering the Atlantic Ocean and Mississippi River.

High rates of growth have also occurred through the upper

Midwest and through southern Nevada, Arizona, Utah,

and Wyoming. Substantial percentage growth can also be

seen in coastal Oregon and Washington counties. The rate

of Hispanic growth throughout much of the South has been

high. North Carolina stands out, with growth exceeding

376 percent in all but a handful of its 100 counties. Hispanic

populations more than tripled in large portions of Georgia,

South Carolina, Alabama, Arkansas, and Mississippi.

Because most Texas counties already had a substantial

Hispanic population base in 1990, their growth of Hispanic

populations did not reach the high rates of many other

southern counties. In Florida, the largest increases were

concentrated in central Florida, which includes the Orlando

and Tampa-St. Petersburg metropolitan areas.

Non-Hispanic Whites—The non-Hispanic White population

has been growing in metropolitan areas such as Atlanta,

Washington, DC, Minneapolis/St. Paul/Duluth, Phoenix,

Salt Lake City, and Albuquerque (fig. 7.4). It appears to be

growing fastest in areas rich in natural amenities, such as the

Rocky Mountains and Florida, Arizona, Colorado, Utah, and

Nevada. In the South, the State of Florida and nearly every

Atlantic coastal county experienced high rates of growth.

Central and eastern Tennessee, northwestern Arkansas, and

the metropolitan counties of eastern and southeastern Texas

also were among the fastest-growing counties.

Population Projections for Three Levels 

of Change (2008 to 2060)

Similar to the historic trends in population growth and

composition since 1990, the RPA regions likely to lead the

Nation in future projected rate of change under the moderate

growth scenario are the Rocky Mountains at 79 percent and

the South at 59 percent (table 7.3). The Pacific Coast follows

closely, at about 56 percent. The North trails the other regions

by a wide margin, with just 27 percent expected growth. The

Rocky Mountains’ Intermountain area far exceeds all others

with projected growth of 92 percent (nearly three times the

rate of its Great Plains area of this region).

The Atlantic States area in the South ranks second among

its nine RPA subregion counterparts, at a 68 percent forecast

increase in population, followed by the Pacific Northwest

with 63 percent. Of the Atlantic coastal States, Florida leads




135

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Table 7.3—Estimated population (thousands of people) for 2008, projections to 2060 by region and State 

for three levels of population growth, and percent change from 2008 to 2060 for the moderate growth 

projection 

Region, State

Population 

estimate (2008)

Population growth projections (2060) Percent 

change, 

moderate 

Low Moderate High growth level

Southern States 102,805.6 145,360.3 163,673.8 184,909.9 59.2

Alabama 4,661.9 5,988.3 6,742.8 7,617.6 44.6

Arkansas 2,855.4 3,869.2 4,356.7 4,921.9 52.6

Florida 18,328.3 30,496.7 34,338.9 38,794.3 87.4

Georgia 9,685.7 13,156.1 14,813.6 16,735.6 52.9

Kentucky 4,269.2 5,131.1 5,777.5 6,527.1 35.3

Louisiana 4,410.8 5,269.1 5,932.9 6,702.7 34.5

Mississippi 2,938.6 3,773.1 4,248.5 4,799.7 44.6

North Carolina 9,222.4 12,723.6 14,326.7 16,185.5 55.3

Oklahoma 3,642.4 4,446.7 5,006.9 5,656.5 37.5

South Carolina 4,479.8 6,257.6 7,045.9 7,960.1 57.3

Tennessee 6,214.9 8,384.0 9,440.3 10,665.1 51.9

Texas 24,327.0 34,689.9 39,060.4 44,128.4 60.6

Virginia 7,769.1 11,174.8 12,582.7 14,215.3 62.0

Northern States 124,368.0 139,964.2 157,597.9 178,045.6 26.7

Rocky Mountains States 27,760.9 44,135.2 49,695.6 56,143.5 79.0

Pacific Coast States 49,070.4 67,798.9 76,340.6 86,245.5 55.6

U.S. total 304,004.9 397,258.6 447,308.0 505,344.5 47.1

Source: Cordell 2012; U.S. Census Bureau 2008a.
136 The Southern Forest Futures Project

by a wide margin with 87 percent projected growth, followed

by Virginia and Texas, each at more than 60 percent. These

three are the only States projected to grow faster than the

South-wide rate. Louisiana, Kentucky, and Oklahoma rank

lowest among the Southern States, each with less than 40

percent projected growth.

Figures 7.5 through 7.7 show the geographic patterns of

projected changes in population density by 2060—ranging

from lowest density (fewer than two people per square mile)

to the highest density (more than 190 persons per square

mile). These projected changes are shown for the low (fig.

7.5), moderate (fig. 7.6), and high (fig. 7.7) population growth

projection scenarios. For the purposes of this analysis, all

counties are assumed to have constant land area between

2008 and 2060.

Immediately apparent in the low growth projection scenario

(fig. 7.5) is the presence of numerous lower density counties

distributed throughout the South, especially in western

Texas, the Mississippi Alluvial Valley, and southern portions

of Arkansas, Alabama, and Georgia. Counties with the

highest projected growth under this low growth scenario,

with projection of more than 190 persons per square mile, are

mostly limited to suburban areas, both coasts of Florida, and

a few other scattered coastal counties. The moderate growth

projection scenario (fig. 7.6), which closely approximates

the Census Bureau State projections, has fewer low growth

counties as expected, and more counties in the intermediate

ranges (between 1.8 and 190.7 additional persons per square

mile). The highest-growth counties under the moderate

growth scenario are mostly centered around the major

metropolitan areas of Atlanta, Charlotte, Nashville, DallasFort Worth, San Antonio, coastal South Carolina, and the

Atlantic coast of central Florida.

Under the high growth scenario (fig. 7.7), more counties in

the South shift by 2060 from the lowest to the two moderate

population density growth categories. Also, more of the

counties that are in metropolitan areas are expected to add

significant population density of more than 190 persons per

square mile, especially around greater Atlanta, Charlotte,

and central Florida. Smaller metropolitan regions such as

the Triangle and Triad areas of North Carolina, Knoxville,

New Orleans, and the Florida Panhandle are also projected

to grow significantly. Only most of western Texas, the

Mississippi Alluvial Valley, and portions of southern

Alabama and Georgia are expected to lose population or

grow very slowly, resulting in lower or about the same

population density by 2060.

Trends in Outdoor Recreation

Between 2000 and 2008 (midpoint data years for two data

collection periods of 1999 to 2004, and 2005 to 2009),

the number of people in the Nation who participated in

one or more of 60 outdoor activities grew by 7.3 percent,

from an estimated 208.5 million to 223.8 million (fig. 7.8).

Included in the list of 60 was a wide range of activities from

visiting beaches and visiting farms to rock climbing and

backpacking. Across the 60 activities, the indexed number

of annual activity days of participation (measured as the

product of the average number of days per activity times the

number of participants and then summed across all activities)

increased 31 percent from 67.1 billion to 87.8 billion. Average

annual days of participation per person increased about

21 percent, from roughly 322 to about 390 total activity days

per year.

For the South, the rate of growth for both total number of

outdoor recreation participants and total annual activity

days exceeded the national rate. Participants increased about

11 percent, from about 68 million to 75 million people age

16 and older, but this was overshadowed by a 41 percent

increase in their total number of annual days (the sum of all

individual activity days, which assumes that more than one

activity can occur during any single day). Average activity

participation days per person across the full list of activities

rose from about 310 per year to 393, a 27-percent increase.

Some of these faster gains can be attributed to slightly higher

population growth than the national rate. The number of

people age 16 and older increased 13 percent in the South

(from just under 70 to around 79 million) compared to just

under 10 percent (from 214 million to about 235 million) for

the United States as a whole.

Results from comparison of percentages of the national

number of participants by region for seven activity groups

with the regional percentages of the U.S. population are

shown in table 7.4. Also listed is the participation rate

(percent of the region’s population participating) for the

four regions. Observations about regional differences in the

participation rates are noted below by activity group name.

Visiting recreation and historic sites—Generally, regional

differences are modest with participation in activities at

recreation and historic sites slightly greater in the North

and slightly lower in the South. The South is the only region

where participation is less than the 81 percent national rate

(not shown), though only about two percentage points less.

Viewing and photographing nature—Participation rates

are a few percentage points higher in the Rocky Mountains

and Pacific Coast, and a few points lower in the South. The

North participation rate of 75.6 percent is identical to the

national rate.

Backcountry activities—The participation rate in

backcountry activities is substantially higher in the Rocky

Mountains and Pacific Coast than the national rate, and is




141

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Table 7.4—Participants for seven activity groups in four U.S. regions 

Activity group 

(activities that comprise the group) Region

Percent of 

participantsa

Percent 

of populationa

Percent 

participating

Visiting recreation and historic sites

(Family gatherings, picnicking, visiting 

the beach, visiting historic or prehistoric 

sites, and camping)

North 42.0 40.7 82.7

South 29.7 31.4 78.9

Rocky Mountains 10.1 10.1 81.9

Pacific Coast 18.2 17.8 81.4

Viewing/photographing nature 

(View/photograph birds, natural scenery, 

other wildlife besides birds, and 

wildflowers, trees, and other plants)

North 40.8 40.7 75.6

South 30.7 31.4 73.2

Rocky Mountains 10.5 10.1 78.1

Pacific Coast 17.9 17.8 75.8

Backcountry activities

(Backpacking, day hiking, horseback 

riding on trails, mountain climbing, and 

visiting a wilderness or primitive area)

North 40.1 40.7 43.1

South 26.0 31.4 37.4

Rocky Mountains 13.0 10.1 57.4

Pacific Coast 20.9 17.8 51.4

Motorized activities 

(Motorboating, off-highway vehicle 

driving, snowmobiling, using personal 

watercraft, and waterskiing)

North 40.8 40.7 36.4

South 31.1 31.4 37.1

Rocky Mountains 10.7 10.1 39.1

Pacific Coast 17.4 17.8 35.6

Hunting and fishing

(Anadromous fishing, coldwater fishing, 

warmwater fishing, saltwater fishing, big 

game hunting, small game hunting, and 

migratory bird hunting)

North 38.6 40.7 32.4

South 35.5 31.4 38.8

Rocky Mountains 10.9 10.1 37.1

Pacific Coast 15.0 17.8 28.8

Non-motorized boating

(Canoeing, kayaking, rafting, rowing, 

and sailing)

North 45.6 40.7 23.0

South 27.5 31.4 18.0

Rocky Mountains 9.2 10.1 18.7

Pacific Coast 17.7 17.8 20.4

Snow skiing and boarding

(Cross-country skiing, downhill skiing, 

and snowboarding)

North 49.6 40.7 14.0

South 14.5 31.4 5.5

Rocky Mountains 12.6 10.1 14.7

Pacific Coast 23.3 17.8 15.1

aPercentages sum down to 100 within the four regions of each activity group. 

Source: U.S. Department of Agriculture Forest Service 2009a.
142 The Southern Forest Futures Project

especially higher than the South. The South’s participation

in backcountry activities is about 7 percent lower than

the national rate of 44.3 percent. Thus, Southerners are

less likely to be backcountry activity participants than are

residents of any other region.

Motorized activities—The South’s participation rate is just

slightly above the national rate (36.9 percent), but exceeds

those of the North and the Pacific Coast. Participation

in motorized activities is slightly higher in the Rocky

Mountains than in any other of the three regions, and

the Rocky Mountains is the only region more than a few

percentage points higher than the national participation rate.

Hunting and fishing—The South leads all regions in

hunting and fishing participation, followed by the Rocky

Mountains. Both are higher than the national rate of 34.3

percent. Thus, hunting and fishing are somewhat more likely

to occur in the South than they are in the other regions.

Hunting and fishing participation is lowest in the Pacific

Coast.

Non-motorized boating activities—Participation in nonmotor boating is highest in the North and Pacific Coast, and

lowest in the South. At 18 percent participation, the South

trails the national rate of 20.8 percent.

Snow skiing and boarding—It is no surprise that snow

skiing and boarding participation are by far the lowest by

residents of the South. Participation is highest in the Pacific

Coast and Rocky Mountains, followed by the North. Every

region but the South exceeds the national participation rate

of 11.6 percent. Participation in the South is less than half

the national rate, and whereas the South has about 31 percent

of the national population, it has less than 15 percent of its

skiers and snow boarders.

The South’s Participation in Nature-Based 

Activities 

Tables 7.5–7.8 summarize the trends in activity participation

(number of people and percent of population age 16 and older

in the South) in nature-based activities, such as bird watching

or camping, from the mid-1990s to the present time.

Activities that had 30 million or more people participating

at least once during a single year are shown in table 7.5.

Walking for pleasure, family gatherings outdoors, gardening

or landscaping, viewing/photographing natural scenery,

sightseeing, and visiting outdoor nature centers occupy the

top six slots, each with over 40 million participants in the

South. Between 30 million and 40 million participants are

shown for the activities of driving for pleasure, viewing/

photographing flowers and trees, viewing/photographing

wildlife (besides birds and fish), swimming in an outdoor

pool, picnicking, visiting historic sites, swimming outdoors

(besides pools), and visiting a beach. With the exception

of picnicking, which was essentially constant, all of these

activities have shown growth. Activities oriented toward

viewing and photographing nature (scenery, flowers/trees,

and wildlife) have been among the fastest growing.

Sixteen activities have between 10 million and 30 million

participants (table 7.6). The activities of viewing or

photographing birds, bicycling, gathering mushroom/berries,

warmwater fishing, visiting a wilderness, visiting a farm or

agricultural setting, viewing and photographing fish, and

day hiking all have more than 20 million participants. All of

the 16 activities, with the exception of developed camping,

showed growth in numbers of participants during this decade

(1999 to 2009), though a few grew modestly, under 10

percent. Fastest growing between the 1999 to 2001 and 2005

to 2009 periods were off-highway vehicle driving, gathering

mushrooms and berries, and visiting farms or agricultural

settings.

There are 17 activities with 3 to 10 million participants

(table 7.7). Camping at primitive sites, big game hunting,

waterskiing, using personal watercraft, and equestrian

activities top this list for the South. All of the activities

in table 7.7 have shown growth, though many at less than

10 percent. Kayaking was the fastest growing activity by

a wide margin, but it began with a small base of just 1.4

million people in the late 1990s. Other relatively fast growing

activities were also water-based, such as waterskiing and

canoeing.

Table 7.8 shows activities with fewer than 3 million

participants. At the top, with 2 or more million participants,

are anadromous fishing, sailing, rowing, and rock climbing.

Snowboarding and orienteering have showed significant

growth since the late 1990s. (Much if not most of some

activity participation, for example anadromous fishing and

snowboarding, undoubtedly occurs in other regions of the

country.) However, many activities with fewer than 3 million

participants posted declines over the past decade. Given

their low participation rates, nearly all the activities in table

7.8 represent niche activities that appeal to small market

segments. Many require substantial investments in time,

equipment, and skill.

The data shown in tables 7.5–7.8 may not fully describe all

dimensions of activity trends, some of which may reflect

the rapid rise in gasoline prices from 2007 to 2008 and

the recession that began in 2007 and continues to impact

incomes as of the writing of this chapter. However, viewed

overall, the data presented in these four tables clearly show

that what people in the South choose as activities is changing
143

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Table 7.5—For activities with more than 30 million participants (2005–2009), trends in the number and percentage 

of Southerners 16 years old and older participating in nature-based activities from 1994 to 2009 

Activity

1994 to 1995a 1999 to 2001b 2005 to 2009c 1999 to 2009

Number of 

participants 

(millions ) 

Number of 

participants 

(millions ) 

Portion of 

population 

(percent)

Change 

(percent)

Walk for pleasure 42.4 55.9 66.3 83.9 18.5

Gathering of family/friends 39.2 50.7 58.4 73.9 15.2

Gardening/landscaping for pleasure – 45.9 54.8 69.3 19.2

View/photograph natural scenery – 38.2 47.1 59.6 23.4

Sightseeing 35.9 35.2 41.8 52.8 18.8

Visit outdoor nature center/zoo 33.3 36.8 41.5 52.5 12.8

Driving for pleasure – 34.5 39.7 50.3 15.1

View wildflowers/trees – 28.9 39.1 49.5 35.3

View wildlife besides birds and fish 18.3 29.4 38.9 49.2 32.2

Outdoor pool swimming 32.2 29.0 35.1 44.5 21.0

Picnicking 32.0 34.7 35.0 44.3 0.8

Visit historic sites/monuments 28.5 29.4 33.4 42.3 13.6

Swimming (other than pools) 26.7 27.0 31.2 39.4 15.3

Visit a beach 39.9 26.2 31.0 39.3 18.5

– = Missing data indicate that participation was not asked during this time period.

aBased on 64.01 million people age 16 and older (Woods and Poole Economics, Inc. 2009).

bBased on 69.88 million people age 16 and older (U.S. Census Bureau 2008a).

cBased on 79.02 million people age 16 and older (U.S. Census Bureau 2008a).

Source: U.S Department of Agriculture Forest Service 2009a.

Table 7.6— For activities with 10 million to 30 million participants (2005–09), trends in the number and percentage 

of Southerners 16 years old and older participating in nature-based activities from 1994 to 2009 

Activity

1994 to 1995a 1999 to 2001b 2005 to 2009c 1999 to 2009

Number of 

participants 

(millions)

Number of 

participants 

(millions)

Portion of 

population 

(percent)

Change 

(percent)

Viewing or photographing birds 16.9 21.0 27.0 34.2 28.7

Bicycling for fun/exercise 21.1 23.3 26.1 33.1 12.3

Gathering mushrooms/berries – 18.1 25.1 31.8 38.7

Warmwater fishing 19.2 19.6 23.6 29.9 20.3

Visiting a wilderness – 19.4 23.6 29.9 21.4

Visiting a farm/agricultural setting – 17.7 23.1 29.2 30.4

Viewing salt/freshwater fish 8.8 17.9 22.9 28.9 27.9

Day hiking 13.0 17.8 20.0 25.2 12.4

Motor boating 20.1 16.8 19.1 24.2 13.4

Visiting waterside besides beach – 17.4 18.3 23.1 5.0

Off-highway vehicle driving 11.9 11.8 16.8 21.3 42.1

Camping at developed sites 12.3 15.3 15.3 19.4 -0.2

Visiting archaeological sites 10.6 13.4 15.1 19.1 12.5

Boat touring or excursions – 12.4 14.8 18.8 19.6

Saltwater fishing 10.3 9.7 12.1 15.3 24.3

Bicycling (mountain/hybrid bike) – 10.9 11.6 14.7 7.3

– = Missing data indicate that participation was not asked during this time period.

aBased on 64.01 million people age 16 and older (Woods and Poole Economics, Inc. 2009).

bBased on 69.88 million people age 16 and older (U.S. Census Bureau 2008a).

cBased on 79.02 million people age 16 and older (U.S. Census Bureau 2008a).

Source: U.S Department of Agriculture Forest Service 2009a.
144 The Southern Forest Futures Project

Table 7.7— For activities with 3 million to 10 million participants (2005–09), trends in the number and percentage 

of Southerners 16 years old and older participating in nature-based activities from 1994 to 2009 

Activity

1994 to 1995a 1999 to 2001b 2005 to 2009c 1999 to 2009

Number of 

participants 

(millions ) 

Number of 

participants 

(millions)

Portion of 

population 

(percent)

Change 

(percent)

Camping at primitive site 9.1 9.2 9.3 11.8 1.7

Big game hunting 6.7 6.4 8.0 10.1 25.2

Waterskiing 7.8 5.4 7.7 9.7 43.3

Using personal watercraft 4.6 6.8 7.7 9.7 13.7

Horseback riding/equestrian 6.9 6.7 7.6 9.6 12.9

Coldwater fishing 5.7 6.8 7.4 9.4 8.0

Canoeing 5.4 5.1 7.1 9.0 39.3

Small game hunting 6.7 5.9 6.8 8.6 16.0

Rafting 6.4 5.9 6.3 7.9 5.3

Backpacking on trails 4.2 5.0 6.1 7.8 22.5

Horseback riding on trails 4.7 5.4 5.6 7.1 3.7

Snorkelingd 5.4 4.2 4.7 5.9 10.7

Sledding 3.7 3.7 3.8 4.8 2.9

Kayaking 0.9 1.4 3.6 4.6 154.3

Mountain climbing 2.3 3.0 3.3 4.1 8.4

Caving 2.8 3.1 3.3 4.1 5.3

Downhill skiing 4.3 2.9 3.2 4.0 8.2

aBased on 64.01 million people age 16 and older (Woods and Poole Economics, Inc. 2009).

bBased on 69.88 million people age 16 and older (U.S. Census Bureau 2008a).

cBased on 79.02 million people age 16 and older (U.S. Census Bureau 2008a).

dSnorkeling in 1994-1995 included scuba diving.

Source: USDA Forest Service 2009a.

Table 7.8— For activities with fewer than 3 million participants (2005–09), trends in the number and percentage 

of Southerners 16 years old and older participating in nature-based activities from 1994 to 2009 

Activity

1994 to 1995ab 1999 to 2001c 2005 to 2009d 1999 to 2009

Number of 

participants 

(millions ) 

Number of 

participants 

(millions)

Portion of 

population 

(percent)

Change 

(percent)

Anadromous fishing 2.8 2.0 2.7 3.4 33.8

Sailing 3.0 2.6 2.6 3.2 -3.2

Rowing 2.4 1.9 2.4 3.1 24.2

Rock climbing 1.8 2.2 2.2 2.7 -0.9

Migratory bird hunting 2.2 1.8 1.9 2.5 9.7

Snowboarding 0.9 1.2 1.8 2.2 48.2

Orienteering 1.4 1.0 1.5 1.9 47.4

Ice skating 1.3 1.5 1.5 1.9 -1.9

Surfing 0.9 1.0 1.3 1.6 32.4

Snowmobiling 1.0 0.9 1.1 1.4 15.2

Scuba divingb – 1.4 1.0 1.3 -25.7

Cross-country skiing 0.5 0.7 0.6 0.8 -10.6

Ice fishing 0.2 0.2 0.3 0.4 83.3

Windsurfing 0.8 0.4 0.3 0.3 -33.6

Snowshoeing – 0.3 0.2 0.2 -36.0

– = Missing data indicate that participation was not asked during this time period.

aBased on 64.01 million people age 16 and older (Woods and Poole Economics, Inc. 2009).

bScuba diving was included as part of snorkeling in 1994-1995.

cBased on 69.88 million people age 16 and older (U.S. Census Bureau 2008a).

dBased on 79.02 million people age 16 and older (U.S. Census Bureau 2008a).

Source: USDA Forest Service 2009a.
145

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

over time. Some of the activities that dominated in the 1960s,

1970s, and 1980s no longer dominate as generations, society,

lifestyles, information, and technology are shifting (Cordell

2008).

Federal Recreation Resources 

Federal land—The almost 640 million acres of Federal

land provide vast areas for outdoor recreation. Such areas

are as important in the South as they are throughout the

country. With the exception of some national wildlife

refuges, areas reserved for science and research, dams,

and other administrative and operational sites, nearly all

Federal land is open and available to the public. However,

access is sometimes inhibited by in-holdings and ownership

fragmentation.

Less than 5 percent of Federal land, about 30.5 million acres,

is in the South, about 44 percent of which is managed by

the Forest Service. More than 92 percent of Federal land

is located in the Western United States. Even excluding

Alaska—which has 36 percent of the national Federal total,

Federal land is still predominantly western at 88 percent. The

regional distribution of acreage in the three water resource

agencies (Army Corps of Engineers, Bureau of Reclamation,

and Tennessee Valley Authority), however, is much more

evenly split between the West and East. Of the land and

water area in the East, about 37 percent is in the South and

12 percent is in the North.

Federal acreage changes very little over time. What does

change however, particularly by region, is the per capita

amount of Federal land as population changes, mostly

through growth. In 2008, the 2,105 acres per 1,000 U.S.

residents (or about 2.1 acres per person) represented a

5.6 percent decrease from the 2002 level. Declines were

largest in the Rocky Mountains (8.8 percent) and Pacific

Coast (7.7 percent), reflecting greater population growth. In

the South, the 296.3 Federal acres per 1,000 persons was a

4.8 percent decline since 2002.

The decline in per capita Federal acres nationally was even

more pronounced when compared to 1995 levels, mirroring

the 14 percent population increase (table 7.9): a decrease of

21.2 percent in the Rocky Mountains, 17.8 percent in the

Pacific Coast, 15.4 percent in the South, and 8.2 percent

in the North. These figures suggest that the pressure for

recreation space may well grow as population grows.	

Wilderness—As with Federal land in general, the Federal

National Wilderness Preservation System is mostly in the

western regions (96 percent), in particular in Alaska, which

has more than 52 percent largely managed by the National

Park Service and Fish and Wildlife Service. The South

accounts for just 2.5 percent, or about 2.7 million of the

109.5 million acres. Even with Alaska’s acres removed, the

South’s share rises to just 5.2 percent of the Nation’s total.

Since 1995, Wilderness area has grown about 6 percent, but

with population increases, per capita acres have declined

3 percent (table 7.10) for the Nation and nearly 16 percent

for the South compared to 8 percent Rocky Mountains and

10 percent for Oregon, California, and Washington on the

Pacific Coast. Per capita Wilderness acres decreased across

all agencies, except for Bureau of Land Management.

Protected rivers and trails—The National Wild and Scenic

Rivers and National Recreation Trails were established by

Congress in 1968 to designate high-value linear land areas

that are important for resource protection and outdoor

recreation. The more than 12,500 miles of wild and scenic

rivers in the United States represent an 11 percent increase

since 2000 (table 7.11); 3,000 miles are in the East and the

remaining 75 percent are in the West. Classified as wild,

scenic, or recreational, these rivers range from the most

primitive and undeveloped (wild) to the most accessible

which may have been impounded in the past (recreational).

The South has only 810 miles (about 6 percent of the national

total), despite an increase of more than 31 percent since 2000.

Just under 100 miles were added to each of the wild and

scenic classifications in the South, but no miles were added

as recreational rivers.

The National Trails System consists of three categories of

nationally significant, mostly long-distance trails: national

scenic trails, national historic trails, and national recreation

trails. Similar to the Federal designated rivers, national

trails protect linear land resources that are judged to have

significant value for the entire country. Scenic and historic

trails are typically overland trails that are remote from

population centers, but national recreation trails tend to be

located near or within urban areas for the express purpose

of providing accessible recreation opportunities. There

were more than 20,000 miles of national recreation trails in

the United States as of 2009 (table 7.12), nearly 69 percent

of which are located in populous eastern areas. More than

6,500 miles are in the South, almost a third of the system.

Further, the South led all regions with 84 percent growth in

designated trail mileage since 2004, adding nearly 3,000 new

trail miles.

Recreation facilities—The U.S. Department of the Interior

coordinates the Recreation Information Database, an

interagency effort to provide data to the public on Federal

recreation sites and facilities through the website, www.

recreation.gov. Table 7.13 shows that the Nation’s estimated

9,075 Federal facilities translate into just under 30 facilities

per million people (or about 1 per 33,500), with the West

leading in all categories. With just 12.1 facilities per million

people overall (or about 1 per 83,000), the South is fairly

well represented in boating facilities, although still trailing
146 The Southern Forest Futures Project

Table 7.9—Federal agency acres per 1,000 people (including Alaska) in 1995a and percent change from 1995 to 2008b

North South Rocky Mountains Pacific Coast United States

Federal Agency

Acres 

1995c

Percent 

change 

2008

Acres 

1995c

Percent 

change 

2008

Acres 

1995c

Percent 

change 

2008

Acres 

1995c

Percent 

change 

2008

Acres 

1995c

Percent 

change 

2008

U.S. Forest Service 101.9 -3.4 151.7 -14.6 4,600.3 -22.3 1,581.1 -12.7 719.6 -11.9

National Park Service 11.0 -1.8 58.3 -13.4 482.5 -17.5 1,447.3 -13.8 292.0 -11.2

U.S. Fish and Wildlife 

Service 10.3 34.0 44.8 -5.4 330.6 7.6 1,855.6 -13.7 339.7 -8.5

Bureau of Reclamation 0.0 0.0 2.3 -17.4 251.4 -21.8 20.3 -14.3 24.5 -12.7

Bureau of Land 

Management 3.3 -100.0 9.4 -95.7 6,629.1 -22.5 2,898.7 -22.4 1,005.1 -17.1

Tennessee Valley 

Authority 0.0 0.0 2.9 -17.2 0.0 0.0 0.0 0.0 0.9 -11.1

U.S. Army Corps of 

Engineers 24.8 -16.9 66.2 4.4 113.8 11.9 12.8 -13.3 43.4 4.1

All Federal agencies 156.4 -8.2 350.2 -15.4 12,422.9 -21.2 7,911.8 -17.8 2,448.6 -14.0

a1995 U.S. population estimate is 266.28 million (Woods and Poole Economics, Inc. 2009). 

b2008 U.S. population estimate is 304.06 million (U.S. Census Bureau 2008a).

cResource data years for earlier period vary by agency; expressed as 1995 because 1995 population estimates were used in per 

capita measures. 

Sources: USDA Forest Service 1995, 2008; U.S. Department of the Interior National Park Service 1995, 2008; U.S. Department of the 

Interior Fish and Wildlife Service 1995, 2008; U.S. Department of the Interior Bureau of Reclamation 1993, 2008; Tennessee Valley 

Authority 2008; U.S. Army Corps of Engineers 2006.

Table 7.10—Federal acres in the National Wilderness Preservation System by region and Federal agency (excluding 

Alaska) per 1,000 people in 1995a, and percent change from 1995 to 2009b

North South Rocky Mountains Pacific Coast United States

Agency

Acres 

1995

Percent 

change 

2009

Acres 

1995

Percent 

change 

2009

Acres 

1995

Percent 

change 

2009

Acres 

1995

Percent 

change 

2009

Acres 

1995

Percent 

change 

2009

Bureau of Land 

Management 0.0 0.0 0.0 0.0 74.8 121.4 89.5 -3.5 20.0 44.0

U.S. Fish and Wildlife 

Service 0.5 0.0 5.5 -16.4 67.3 -21.7 0.3 -33.3 7.6 -11.8

U.S. Forest Service 11.5 0.0 8.3 -12.0 823.1 -20.5 237.8 -9.1 111.5 -9.5

National Park Service 1.1 27.3 17.5 -17.1 36.0 34.2 200.9 -13.8 39.0 -6.9

U.S. total 13.2 1.5 31.3 -15.7 1,001.2 -8.0 505.2 -9.8 176.9 -3.0

a1995 U.S. population estimate is 265.67 million, excluding Alaska (Woods and Poole Economics, Inc. 2009). 

b2008 U.S. population estimate is 303.37 million, excluding Alaska (U.S. Census Bureau 2008a).

Source: Wilderness.net 2009.

Table 7.11—Miles of river in the National Wild and Scenic River System by classification and region, 2000 and 2009 

Wild rivers Scenic rivers Recreational rivers Total

Region 2000 2009

Percent 

change 2000 2009

Percent 

change 2000 2009

Percent 

change 2000 2009

Percent 

change

North 172 174 1.5 935 1,014 8.5 964 1,007 4.4 2,070 2,195 6.0

South 187 284 51.8 318 414 30.2 112 112 0.0 617 810 31.3

Rocky 

Mountains 710 1,328 87.1 288 380 31.9 532 587 10.5 1,530 2,295 50.0

Pacific 

Coast 4,280 4,370 2.1 911 936 2.7 1,886 1,946 3.2 7,077 7,252 2.5

U.S. total 5,349 6,156 15.1 2,452 2,743 11.9 3,493 3,652 4.6 11,294 12,552 11.1

Source: Interagency Wild and Scenic Rivers Council 2009.
147

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Table 7.12—Number and miles of National Recreation Trails in 2004 and 2009 by 

region and percent change in 2009 

National Recreation Trails

Number Miles

Region 2004 2009

Percent 

change 2004 2009

Percent 

change

North 226 312 38.1 4,119 7,319 77.7

South 220 264 20.0 3,578 6,577 83.8

Rocky Mountains 254 292 15.0 2,969 3,380 13.8

Pacific Coast 198 209 5.6 2,622 2,944 12.3

U.S. total 898 1,077 19.9 13,288 20,220 52.2

Source: American Trails 2010.

Table 7.13—Federal recreation facilities and activities supported per 1 million 

people by region in 2009 

Ratio (per 1 million people)a

Activity or facility North South

Rocky 

Mountains

Pacific 

Coast

United 

States

Camping 8.3 11.2 121.3 63.8 28.6

Hiking 1.5 1.8 65.6 19.9 10.4

Fishing 1.3 2.4 64.0 18.3 10.2

Boating 1.9 4.3 22.7 10.2 5.9

Picnicking 0.1 0.1 43.6 9.3 5.6

Recreational vehicle camping 0.0 0.0 38.0 11.7 5.4

Biking 0.4 0.4 32.4 5.7 4.2

Horseback riding 0.1 0.4 27.5 4.7 3.5

Hunting 0.4 0.8 24.8 4.2 3.4

Wildlife viewing 0.1 0.1 20.1 7.5 3.1

Auto touring 0.0 0.0 13.4 2.4 1.6

Water sports 0.0 0.0 6.4 3.9 1.2

Interpretive programs 0.8 0.7 4.7 1.3 1.2

Visitor centers 0.9 0.8 4.0 1.1 1.2

Riding off highway vehicles 0.0 0.0 9.3 1.2 1.0

Wildernesses 0.0 0.0 6.0 2.3 0.9

Winter sports 0.0 0.0 6.3 0.8 0.7

Swimming sites 0.0 0.0 2.2 2.8 0.6

Historic and cultural sites 0.2 0.0 4.0 0.5 0.5

Fish hatcheries 0.1 0.2 0.7 0.3 0.2

Day use areas 0.0 0.0 1.4 0.4 0.2

Climbing 0.0 0.0 1.5 0.2 0.2

All activities and facilities 9.5 12.1 124.2 65.2 29.8

aBased on 2008 U.S. population estimate of 304.06 million (U.S. Census Bureau 2008a).

Source: U.S. Department of the Interior 2009.
148 The Southern Forest Futures Project

the West in per capita availability by a wide margin due to

its larger population. The Rocky Mountains has more than

10 times the number of available Federal facilities per capita

than both the South and the North and nearly twice as many

as the Pacific Coast. Camping facilities dominate the list;

they are offered at nearly 96 percent of facilities nationwide.

Non-Federal Recreation Resources

State parks—State park systems exist in all 50 States,

usually as a division or agency within a State department of

natural resources or conservation. They are usually closer

to population centers and more developed than their Federal

counterparts, and although most manage a significant

number of backcountry acres, these holdings are not nearly

as extensive as those in Federal systems. State parks have

been called “intermediate” resources because they represent

a middle ground between the sometimes vast and distant

Federal lands and the usually much smaller and more highly

developed parks managed by local governments (Clawson

and Knetsch 1966).

Every State system is built around flagship “State parks,”

but also typically consists of recreation areas and several

types of other areas such as natural areas, historic sites,

environmental education and science areas, State forests, and

wildlife and fish areas. Although these other types of areas

exist within State natural resource departments, in many

States they are not managed within the State park system.

This is the case for most, but not all, State forests and State

wildlife and fish areas.

State park systems’ accessibility is evident in their

distribution across counties of the United States (fig. 7.9).

The majority of U.S. counties have one or more acres of State

park system lands. Although many of the largest holdings are

found in western counties, representation is also substantial

throughout the Eastern United States (particularly the

Atlantic States and Florida for the South). With the exception

of some areas in the Great Plains, and a few other scattered

regions across the country, it is rare to travel across more

than just a few counties without encountering State park

system lands.

State parks typically provide a diversity of recreation

opportunities, so many of the activities that people enjoy on

Federal lands can also be enjoyed on the State park system

lands. Although figure 7.9 shows Southern States having

less extensive coverage as States in the Northeast and

Midwest, State parks are located within less than an hour’s

drive regardless of where one is located in Florida and South

Carolina, and throughout much of the South.

In 2009, the National Association of State Park Directors

reported more than 13.9 million acres in State park systems,

an increase of about 3 percent in acres per 1,000 people since

1995 (table 7.14).2 Southern States reported about 2.2 million

acres (16 percent of the national total or 21 percent if Alaska’s

large State parks are removed from the Pacific Coast total).

State park acreage per capita grew 40 percent between 1995

and 2008 in the South. State recreation area acreage per

capita dropped by nearly 29 percent during this period. The

South’s 8.8 percent growth in per capita acreage across all

categories of areas under State park system management was

more than twice that of the Nation, but was dwarfed by the

North, which increased a full one-third in size. It should be

noted, however, that most of the North’s increase was likely

due to reclassification of other State properties into the State

park system jurisdictions. This was particularly prevalent in

New York.

State park systems have faced difficult budgetary pressures

since the onset of the 2007 recession, occasionally resulting

in closure of some facilities (four in Arizona), transfer of

others to other government and quasi-government entities,

and/or reduced hours, services, and staffing (table 7.15).

Two of the most affected States—Alabama and Georgia—

are located in the South. The location and status of State

park units throughout the 48 States is shown in figure 7.9.

Substantial numbers are particularly evident throughout

much of the Northeast, Midwest, Florida, and along the

Pacific Coast. Although there are many fewer parks in the

West, they tend to be larger than those in the North and

South.

State facilities—Table 7.16 lists eight major types of

facilities provided by State park systems and trends in these

facilities since 1995. Campsites are by far the most plentiful

resource per capita, although a comparison with the numbers

of other facilities listed is not appropriate since each of those

represents a much larger investment of resources. Nationally,

improved campsites, cabins, golf courses, and marinas held

steady on a per capita basis (per one million people) since

1995, but primitive campsites fell about 12 percent. The

opposite was true in the South, where primitive campsites

increased nearly 29 percent, accompanied by decreases in

cabins, golf courses, and marinas. The drop in the number

of swimming pools per capita at Southern State parks was

in the same direction as the national trend. The South’s

large percent gain in stables per capita may represent just a

handful of stables in the region, as the base number in 1995

was relatively small.

2 Information derived from series of reports: National Association of State

Park Directors. 2009. Annual Information Exchange for the Period July

1, 2007 through June 30, 2008, and National Association of State Park

Directors. 1996. Annual Information Exchange for the Period July 1, 1994

through June 30, 1995. Available from North Carolina State University.

Department of Parks, Recreation and Tourism Management, Jordan

Hall 5107, Box 7106, Raleigh, NC 27697-7106. Yu-Fai Leung, Principal

Investigator.

150 The Southern Forest Futures Project

Table 7.14—State park system area by region and type of area per 1,000 people in 1995a and percent change from 

1995 to 2008b

North South Rocky Mountains Pacific Coast United States

Type 

Acres 

1995

Percent 

change 

2008

Acres 

1995

Percent 

change 

2008

Acres 

1995

Percent 

change 

2008

Acres 

1995

Percent 

change 

2008

Acres 

1995

Percent 

change 

2008

State parks 18.0 -2.3 10.1 40.2 37.0 -12.8 95.3 -8.1 29.4 -1.1

Recreation areas 1.5 49.7 1.3 -28.8 8.9 -22.9 18.1 -28.1 4.7 -15.0

Historic sites 0.1 123.1 0.3 -8.8 1.2 -59.8 0.4 51.2 0.3 12.1

Natural areasc 0.9 77.6 0.1 9066.7d 0.3 2907.4d 0.0 – 0.4 804.9

Other areas 9.4 111.6 0.6 -7.9 14.4 -87.1 1.2 7.5 5.7 52.5

All areas 31.2 33.2 19.7 8.8 70.5 -29.7 117.4 -10.1 44.4 3.2

– = unavailable.

a1995 U.S. population estimate is 266.28 million (Woods and Poole Economics, Inc. 2009). 

b2008 U.S. population estimate is 304.06 million (U.S. Census Bureau 2008a).

cIncludes environmental education sites and areas classified as scientific sites. 

dIncludes forests, fish and wildlife areas, and other miscellaneous State park system sites. Changes are likely the result of system 

reclassifications and not additions.

Source: National Association of State Park Directors 1996, 2009 (see footnote 2).

Table 7.15—State park systems affected by closure or reduction in services by State, 2009 

State

Number 

of system 

areas

Number of 

closures Reduction in services

Alabama 23 0 One park transferred to county government

Arizona 28 Two parks and 

two historic sites

Hours open were cut for two State parks and five historic 

parks

Georgia 63 0

One park changed to outdoor recreation area; six historic 

parks/sites have cut hours; and three historic sites are now 

operated by the counties within which they reside

Hawaii 50 0 One park transferred to a development corporation

Massachusetts 136 Two State forests Two areas will not be staffed

Michigan 93 0 One site cut hours for the summer

Source: USDA Forest Service 2009b.

Table 7.16—State park system facilities per 1 million people by region and type of facility in 1995a and percent change 

from 1995 to 2008b

North South Rocky Mountains Pacific Coast United States

Facilities

Number 

1995

Percent 

change 

2008

Number 

1995

Percent 

change 

2008

Number 

1995

Percent 

change 

2008

Number 

1995

Percent 

change 

2008

Number 

1995

Percent 

change 

2008

Improved 

campsites 608.1 15.0 361.1 -2.4 837.2 4.7 514.8 -40.4 533.2 0.3

Primitive campsites 144.4 -31.3 60.6 28.7 855.1 -5.4 215.0 -30.5 186.9 -11.7

Cabins 23.3 11.7 30.1 -11.2 17.3 50.4 9.7 29.0 22.8 5.5

Golf courses 0.4 19.5 0.6 -3.2 0.2 38.9 0.1 -42.9 0.4 4.8

Golf holes 6.4 28.2 9.8 3.3 2.5 108.9 1.7 -46.2 6.4 15.1

Marinas 0.8 29.8 1.0 -22.9 2.5 -10.7 0.3 0.0 0.9 3.3

Swimming pools 1.4 -8.1 1.5 -19.9 0.4 8.1 0.1 100.0 1.1 -12.6

Stables 0.3 -10.0 0.3 92.6 0.6 -40.0 0.1 -77.8 0.3 14.3

a1995 U.S. population estimate is 266.28 million (Woods and Poole Economics, Inc. 2009). 

b2008 U.S. population estimate is 304.06 million (U.S. Census Bureau 2008a).

Source: National Association of State Park Directors 1996, 2009 (see footnote 2).
151

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Local government services—The 2007 Census of

Governments tallied 8,852 local governments nationwide that

provide recreation and park services, with just fewer than

29 percent of these (2,552 units) in the South. Ona per capita

basis (per million people) the South experienced almost

18 percent growth since 1997—slightly higher than the

national rate of 13 percent and higher than any other single

region (table 7.17). Municipal recreation departments grew

much faster than county departments in the South. Fastest

of all was special recreation and park districts, which grew

more than 53 percent per capita. These numbers, however,

were very small in 1997, with onlya single special district

department for every2 million Southern residents. Further,

the Trust For Public Land’s LandVote database tracks ballot

measures throughout the country that finance recreation and

park-related protection, development, and improvements

(TPL 2011). Since 2000, TPL reports 184 ballot measures

passing (out of 226 or 81 percent) for municipal and county

governments in the South. These initiatives summed to

just over $5 billion worth of funding through bond and tax

measures.

Local government resources are important for the basic fact

that they provide ‘close-to-home’ recreation opportunities,

especially in urban and urbanizing areas. Many of the

most popular outdoor recreation activities (based on the

proportion of people who participate in them), such as

walking and bicycling for pleasure, picnicking, and outdoor

family gatherings, occur most often in accessible places

near where people live, hence the importance of local parks

and recreation areas. Though most local parks tend to be

highly developed in terms of facilities and infrastructure,a

significant proportion of local resources also provide more

natural environments that are suitable for dispersed forms

of recreation. Nature centers, natural areas, trails, and

greenways are examples of resources that offer nature-based

recreation opportunities. The TPL’s Center for City Park

Excellence estimates that almost exactly one-half of the local

government parkland in the Nation’s 77 largest cities is in its

natural state, as opposed to being designed or developed for

human use (Harnik and others 2012).

Private businesses and land—Among the nine outdoor

recreation business categories tracked, five showeda decline

in the number of establishments per million people from

1998 to 2007 (table 7.18). Skiing facilities (very few of which

were located in the South in 1998), amusement/theme parks,

recreational/vacation camps, golf courses, and marinas all

posted double-digit declines in the South. Private-sector

historical sites, nature parks, and zoos/botanical gardens—

all in the viewing/learning/photography group of activities—

posted the greatest gains. Historical sites and zoos/gardens

exceeded that national trend, and nature parks were not far

from the national growth rate of 42 percent.

Table 7.17—Number of local government parks and recreation departments by governmental unit and region per million people in 1997 and 2007, and percent change from 1997a to 2007b

North South Rocky Mountains Pacific Coast United States

Type of government unit 1997 2007

Percent 

change 1997 2007

Percent 

change 1997 2007

Percent 

change 1997 2007

Percent 

change 1997 2007

Percent 

change

County 3.5 3.5 0.3 5.4 5.7 6.2 5.8 5.1 -12.6 2.3 2.2 -5.6 4.1 4.2 1.7

Municipal 15.0 18.4 22.7 15.5 18.8 20.7 21.0 29.4 40.3 12.9 14.0 8.0 15.3 18.8 22.6Town or township 8.5 9.4 11.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 3.7 3.9 5.1

Special district 3.4 3.2 -5.6 0.5 0.7 53.2 7.6 4.8 -37.1 3.8 3.4 -11.3 2.9 2.5 -11.5All local units 30.3 34.5 13.6 21.4 25.2 17.7 34.4 39.5 14.8 19.1 19.5 2.5 26.0 29.4 13.0

a1997 U.S. population estimate is 272.65 million (Woods and Poole Economics, Inc. 2009). b2007 U.S. population estimate is 301.29 million (U.S. Census Bureau 2008a).

Source: U.S. Census Bureau 2007a.
152 The Southern Forest Futures Project

Privately-owned land, particularly forest, isa significant

outdoor recreation resource, especially in the eastern

United States. The 2006 National Woodland Owner Survey

estimated that 10.4 million family forest owners hold about

264 million acres (35 percent) of all U.S. forest land and

62 percent of private forest land area (Butler 2008). Most

of the private forest land is in the southern and northern

regions (Butler 2008). The South has 44 percent of both

the Nation’s private forest land and private forest owners.

Besides family forest owners, most of the other private

forest land in the South is controlled by forest industry and

forest management companies, including timber investment

management organizations (TIMOs) and timber-oriented real

estate investment trusts (REITs). More than half of family

forests are owned primarily for their beauty and scenery,

but also for nature protection, and other reasons. Among

secondary reasons are fora vacation home or cabin and

recreation, primarily for the landowner, their family, and

friends. While only 15 percent of family forest land is open

to the general public for recreation, all private forest land

tracts may be considered available to be used by someone for

recreation. Each State hasa recreational use statute which

limits the liability of private landowners to allow access to

their land, thus ostensibly encouraging public use. However,

the interpretation of what constitutesa recreational ‘user’

varies by State. An online copy of each State’s recreational

use statute appears on the University of Arkansas’s National

Agricultural Law Center Web site (NALC 2011).

Recreation leases on private land in th

e South, especially

for hunting, area popular and mutually beneficial means of

allowing users access to private property while providing

income to the land owner. Hunters and other recreationists

who are willing to paya fee in effect compensate the

land owner for providing the necessary wildlife habitat to

maintain wildlife populations. Yarrow (2009) has provided

an online primer on hunting leases, includinga description

of four different types of lease-hunting arrangements anda

copy ofa sample lease. In addition to hunting, lessees may

use private property for fishing, observing or photographing

wildlife, and other compatible nature-based recreation

activities. Timber and other forest industry companies are

also in the business of offering recreation leases. Plum Creek

Timber Company, Inc. is the largest private landowner in

the United States with approximately 6.8 million acres of

timberland in 19 States (including every Southern State

except Tennessee, Virginia, and Kentucky). They are

certainly one of the largest private providers of recreational

opportunities (Plum Creek Timber Company, Inc. 2011).

Much of Plum Creek’s land in the northern and western

States is open to the public, but access in the South is limited

to lease arrangements. Data on industry recreation leases

are not released for proprietary reasons and although some

recent studies have examined economic issues regarding

hunting leases in parts of the South (e.g., see Hussain and

Table 7.18—Number of selected private recreation business establishments by type of business and region per million people in 1998a and percent change from 

1998 to 2007b

North South Rocky Mountains Pacific Coast United States

Recreation business

Number 

1998

Percent 

change 

2007

Number 

1998

Percent 

change 

2007

Number 

1998

Percent 

change 

2007

Number 

1998

Percent 

change 

2007

Number 

1998

Percent 

change 

2007

Golf courses and country clubs 49.8 -4.3 40.1 -11.7 47.1 -6.0 26.0 -8.8 42.6 -7.7

Recreational vehicle parks and campgrounds 13.3 4.1 11.4 0.4 26.9 -1.3 17.3 -3.1 14.5 1.4

Marinas 17.7 -3.5 16.5 -18.6 7.5 -24.9 10.6 -12.3 15.3 -11.5

Recreational and vacation camps (not campgrounds) 14.0 -18.1 8.5 -20.7 22.5 -28.4 11.3 -12.2 12.5 -19.6

Historical sites 4.5 6.1 2.3 23.9 3.8 -15.2 1.8 3.4 3.3 6.7Nature parks and similar institutions 1.9 31.7 1.5 35.6 2.9 20.7 1.3 118.5 1.7 42.5Amusement and theme parks 3.7 -47.8 3.6 -36.8 2.9 -16.6 2.4 -12.9 3.4 -37.7Zoos and botanical gardens 1.4 33.8 1.3 44.8 1.7 15.5 1.5 49.3 1.4 37.8

Skiing facilities 1.7 0.6 0.4 -40.5 4.4 -7.7 1.5 -17.6 1.5 -8.3

a1998 U.S. population estimate is 272.65 million (Woods and Poole Economics, Inc. 2009). b2007 U.S. population estimate is 301.29 million (U.S. Census Bureau 2008a).

Source: U.S. Census Bureau 2007b.
153

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

others 2007, Liu and others 2010, Zhang and others 2006), no

region-wide assessment or summary statistics on recreation

leases in the South exists.

The NSRE estimated the number and percent of annual

recreation activity days in the United States that occur on

private land (Cordell 2012). Based on a national sample of

about 5,400 people age 16 and older, the estimates for seven

activity groups were split into East and West regions. (Forest

Service Regions Northern and Southern were combined to

form the East group because of an insufficient sample for

the South alone for some less popular activities.) Hunting led

all activities with 47 percent of all annual days nationwide

occurring on private forest lands in the East. Motorized

activities ranked second with 43 percent followed by 34

percent of nature viewing and photography estimated to

occur on eastern private forest lands. Backcountry activities,

such as backpacking, day hiking and visiting a primitive

area, were least likely to occur on eastern private forest

lands.

Nearby Recreation Resources (Current)

Figure 7.10 shows the county-level pattern of four levels

of Federal and State-park land acreage within the 75-mile

distance zone (considered to be suitable for a day trip with

no overnight stay necessary). Whereas some counties have

no Federal or State land within their boundaries, all have

some access to public acreage when surrounding counties are

considered. Most counties in the South have from 0.07 acres

to 1.46 acres of public land per person, with a high between

1.46 acres and 18.31 acres in the Ozark Highlands and the

Virginia mountains and a low of less than 0.1 acres per

person in central Florida and the southeastern Piedmont.

Counties in much of the drier parts of western Texas and in

some parts of Nevada and California lack non-Federal forest

land within their 75-mile recreation day trip zone

(figure 7.11). Although much of the South has abundant

forest land area, when population density is considered in

calculating per capita acreage, some of the metropolitan

areas are found to have relatively little. Parts of Arkansas,

Louisiana, Mississippi, Alabama, and Georgia have relatively

abundant per capita non-Federal forest land within 75 miles.

As with public land area, all counties have access to some

water area when the 75-mile zone for each county is

considered (fig. 7.12). Water as defined here is all water area

with the exception of open ocean. For the South, greater

water (non-ocean) area per capita is in coastland counties

along the Atlantic Ocean and Gulf of Mexico. Moderate

levels of water per capita are found inland throughout the

South. Relatively low water per capita is found in Texas

metropolitan and the Atlanta metropolitan areas (because

of greater populations in these areas), as well as large

portions of North Carolina, Virginia, and Kentucky. On a

per capita basis, the greatest water area is the Great Lakes

area, the Dakotas, and Montana (because of low population

density), and in a few counties in the West.

Nearby Recreation Resources (Projected)

Federal and State park lands—Federal and State park

acreage is expected to be (or almost be) constant through

time. Forecasts of future areas of Federal and State park

lands were not attempted for this report. Historically, the net

area of federal land has changed very little in recent decades.

State park systems, despite growth in per capita acreage

since 1995, are not expected to continue expanding at recent

historical rates. Given the recent State political and budget

trends nationwide which have resulted in many closures,

downsizing, and understaffing of State park units, a nogrowth scenario for State park resources might be considered

a reasonable future projection. For the country as a whole,

about 26 percent of the total area is in Federal or State

management or slightly more than 2 acres per person

(table 7.19). By 2060, per capita Federal and State park

acreage is predicted to decline to 1.4 acres per person or

about 68 percent of the 2008 amount.

Federal or State-park land is much less in the East, with 5

percent of total area in the South or less than 0.3 acres per

person. By 2060, the Federal or State park land area per

person is projected to decrease to 0.17 acres, about 63 percent

of the 2008 level. Percentage of total area that is Federal or

State-park land is somewhat higher for the Atlantic States

than for southern States further west (7.4 versus 4 percent),

but higher population growth is expected to result in lower

per capita acreages of the public land acres for these States.

Non-Federal forest—Non-Federal forest land area is

expected to change with continuing conversions from forests

and farmlands to cities and suburbs. For the country as a

whole (excluding Alaska and Hawaii), about 19 percent of

total land area is non-Federal forest (table 7.20), or 1.27 acres

per person. By 2060, per capita non-Federal forest area is

predicted to decline to 0.8 acres per person, or 63 percent of

the 2010 amount.

For the South, more than 30 percent of total land area is nonFederal forest, or 1.66 acres per person. By 2060, per capita

non-Federal forest is predicted to decline to 0.95 acres per

person, or 57 percent of the 2010 level. The percentage

of total area that is non-Federal forest is considerably higher

for the Atlantic States than for States farther west (about

43 versus 26 percent), but higher projected population growth

is expected to result in lower per capita non-Federal forest

acreages for these States compared to those farther west.





159

chapter 7. Outdoor Recreation in a Shifting Societal Landscape

Concurrent with population growth and shifting recreation

demands is increasing pressure on forest and other natural

lands. In the South, this can pose a challenge. For example,

State and Federal lands are often the places people choose for

nature-based outdoor recreation. However, less than

5 percent of Federal land, just over 30 million acres, is in the

South where almost 105 million people live (about one-third

of the Nation’s population). Over the last decade and half,

Federal acres per 1,000 persons in the South declined slightly

faster than the national rate, a decrease of over 15 percent.

Similar patterns can be seen for per capita State lands and

for most other resources used for outdoor recreation in the

South. Residents of most counties in the South have access to

fewer than 1.5 acres of public land per person within

75 miles of their county of residence, with a high of up to

18.3 acres in the Ozark Highlands and Virginia mountains.

In all likelihood, there will be little to no increases in Federal

and State land in the South. In fact, some States have recently

closed some State parks.

Water and forests will continue to be important recreation

resources. Like water, across the region there is abundant

forest land area. But when expressed on a per capita basis,

many of the major metropolitan areas are found to have

relatively little non-Federal forest land nearby. Water area

per capita is abundant in coastal areas, but throughout the

rest of the South there is increasing scarcity. Like public

lands, total water area is fairly static over time translating to

a decreasing per capita acreage over the next few years, and

likely decreasing even more in future years.

In 2008, 5 percent of the South’s total area was in Federal

or State-park ownership, less than 0.3 acres per person.

By 2060, the Federal or State-park land area per person

is projected to decrease to 0.17 acres, about 63 percent of

the 2008 per capita area. In 2010, more than 30 percent

of total land area in the South was non-Federal forest, or

1.66 acres per person. By 2060, per capita non-Federal

forest is predicted to decrease to 0.95 acres per person, or

to 57 percent of the 2010 level. Not only is per capita forest

area projected to decline, but also the actual total area of

non-Federal forest land area is expected to decline due to

conversions from forests and farmlands to cities and suburbs.

In 2008, water area in the South was slightly more than

5 percent of the total surface area, or 0.28 acres per person.

By 2060, water acres per capita are predicted to decline to

0.18 acres per person, or to 63 percent of the 2008 level.

Population, recreation, and resource trends and futures

all are headed in directions that leave one wondering

“who, where, and how?” Who will the future recreation

participants be from among the South’s growing population?

Will participants of the future be representative of the

growing diversity of our population? Or, could there be a

demographic narrowing of who the participants will be

as a result of shrinking per capita availability of places

and resources for outdoor recreation? Where will outdoor

recreation occur? As land and water resources in rural

areas are increasingly pressured by expanding urban and

other development, private land and water may become less

available for outdoor recreation for some segments of the

population. This raises the question of how future residents

of the South will gain access to outdoor recreation venues.

It seems that the importance of easily accessible, nearby

public or publicly accessible private areas will only increase

in the future. Perhaps one key to an outdoor recreation future

for coming generations of Southerners would be to include

recreation benefits in the calculation of the value of forest

lands, especially those close to populated areas. Without

inclusion of recreation and other ecosystem services in

land value calculations, very often the development value

outweighs all other considerations. Including recreation and

other ecosystem service values could open an opportunity

for local governments and other public service organizations

to find concrete ways to encourage private owners to keep

more land in forest and make it more accessible.

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22, 2010].

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Hussain, A.; Zhang, D.; Armstrong, J.B. 2004. Willingness to pay for

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acreage for public recreational use in southern and central Appalachia.

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center research publication. http://www.nationalaglawcenter.org/assets/

recreationaluse/index.html. [Date accessed: December 14, 2011].

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plumcreek.com/Recreation/HuntingPrograms/tabid/142/Default.aspx.

[Date accessed: December 15, 2011].

Tennessee Valley Authority. 2008. Recreation resources inventory

database. Available at: Tennessee Valley Authority, 400 West Summit

Hill Drive, Knoxville, TN 37902.

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facts. http://www.corpsresults.us/recreation/recfastfacts.cfm. [Date

accessed: April 1, 2009].

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summary tape file 1. http://www2.census.gov/census_1990/1990STF1.

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census.gov/govs/apes/. [Date accessed: March 31, 2009].

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asrh/2008/SC-EST2008-03.html. [Date accessed: September 23, 2009].

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report as of September 30, 1995. Unpublished report. On file with:

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report as of September 30, 2008. http://www.fs.fed.us/land/staff/lar/.

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survey on recreation and the environment (NSRE dataset). Unpublished

report. On file with: U.S. Department of Agriculture Forest Service,

Southern Research Station, RWU-4953, 320 Green St. Athens, GA

30602. [pages unknown].

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systems database compiled from published State literature and State

park Web sites. Unpublished report. On file with: U.S. Department of

Agriculture, Forest Service, Southern Research Station, RWU-4953, 320

Green St. Athens, GA 30602. [Pages unknown].

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land statistics 2008. http://www.blm.gov/public_land_statistics/pls08/

pls08.pdf. [Date accessed: June 23, 2009].

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of Wildlife. 11: 1–14.
161

chapter 8. Outdoor Recreation 

J. M. Bowker, Ashley Askew, H. Ken Cordell,

	 and John C. Bergstrom1

Key Findings

•	By 2060, the number of southern adults participating in

each of 10 different popular outdoor recreation activities is

projected to increase. Depending on future demographic,

economic, land use, and population changes, the activity

demonstrating the least growth in participants is hunting

(8–25 percent). The activity projected to demonstrate the

most growth is day hiking (70–113 percent).

•	For many activities, participation will grow similarly

to the population growth rate. However, the number

of participants in fishing, hunting, and motorized offroading will grow slower than the regional population,

as a smaller proportion of adults are projected to engage

in these activities. Conversely, the growth in the number

of participants in birding and day-hiking is projected to

exceed that of the population.

•	By 2060, the number of days that southern adults will

participate annually in each of 10 different outdoor

recreation activities is projected to increase. The smallest

increase in days of participation will be for hunting (8–24

percent), while the largest increase in days of participation

will be for day hiking (77–116 percent).

•	 Days of annual participation for each of the 10 activities

are projected to increase at rates similar to the growth in

participant numbers.

•	 Acres of southern forest and rangeland per recreation

participant will decline by up to 50 percent across the

various activities by 2060. Acres per participant in hiking

will shrink the most, while acres per participant for hunting

will shrink the least.

•	 Annual user days per acre of forest and rangeland for

recreation activities will increase most by 2060 for

horseback riding on trails (up to 151 percent) and hiking

(up to 118 percent) and least for motorized off-road use (up

to 59 percent) and hunting (up to 34 percent).

1J.M. Bowker is a Research Social Scientist, U.S. Department of

Agriculture, Forest Service, Forest Sciences Laboratory, Athens,

GA 30602; Ashley E. Askew is a Doctoral Candidate, University of

Georgia, Department of Statistics, Athens, GA 30602; H. Ken Cordell

is a Pioneering Research Scientist, U.S. Department of Agriculture,

Forest Service, Forest Sciences Laboratory, Athens, GA 30602; John

C. Bergstrom is a Professor, Department of Agricultural and Applied

Economics, University of Georgia, Athens, GA 30602.

•	 Depending on social and economic factors, southern

national forest recreation visits are projected by 2060 to

increase across all site types: Wilderness (38–72 percent),

day use developed sites (35–70 percent), overnight use

developed sites (30–64 percent), and general forest area

(22–55 percent).

•	Because southern national forest acreage is expected to

stay approximately constant to 2060, visits per acre across

the various site types will grow at same rate as visits.

Introduction

In this chapter, we address a small component of the

question posed by Wear and others (2009) in defining

the Southern Forest Futures Project, namely, “How will

changing demographics influence associated demand for

esthetic settings, recreation, and second homes?” We focus

our analysis on projecting natural resource-based outdoor

recreation demand at a broad regional scale, as well as at

the regional national forest scale. In doing so, we hope to

provide relevant information to “[e]valuate how population

growth and changing demographics will affect changes in

demands for different types of recreation activities, and

explore implications for forest land uses” and “[e]xamine

the potential for increased congestion and conflict among

recreational users of forests as a result of changing supply

and demand factors” (Wear and others 2009). Specifically,

for the southern region through 2060, we develop and present

projections of (a) the number of adult participants in 10

traditional outdoor recreation activities, (b) the number of

days of adult participation in the same 10 outdoor recreation

activities, and (c) the number of recreation visits to national

forests in the southern region by national forest setting.

An individual is said to have participated in an outdoor

recreation activity if the individual reported engaging in that

activity at least once in the preceding 12 months. Participation

is a general indicator of the size of a given market and can

also be indicative of relative public interest. For example, if

over 80 percent of the population goes day hiking, whereas

only 4 percent participate in snowmobiling, public resource

management agencies and private land managers may be

more concerned with providing hiking trails rather than

snowmobiling opportunities. It is important, therefore, for

Chapter 8.

 Outdoor Recreation 
162 The Southern Forest Futures Project

land managers and legislators to know how many people

participate in a given recreation activity, and how this

measure could change over time. Measures of participation,

either per capita or absolute numbers of participants, provide

the broadest measure of a recreation market.

A second measure of recreation use or demand is

consumption. Consumption can be measured in such units

as number of times, days, or trips in a given year. The

Forest Service, U.S. Department of Agriculture, has used

such consumption measures as recreation visitor days and

national forest visits. A consumption measure is important

because it adds another dimension to participation. Although

resource managers providing recreation opportunities

need to know how many people participate, many of their

decisions depend even more upon knowing how often and

for how long people engage in an activity. Such information

can be critical to the allocation of existing resources, such as

campsites, and is also useful in planning the development of

new venues. Participation and consumption at the regional

level together provide the broadest measures of a recreation

market. The regional consumption measure used in this

study [item (b) above] is the number of days in the previous

year that an individual, residing in the South, reported

engaging in a specific activity. A day in this case follows

the definition given by the National Survey on Recreation

and the Environment (NSRE) definition of an activity day,

i.e., any amount of time spent on an activity on a given day,

whether less than an hour or for several hours, whether that

activity was the primary reason for recreating outdoors or

not (USDA Forest Service 2009).

The preceding two metrics are origin based, i.e., resulting

from household-level surveying. There is no additional

information as to where the respondent engaged in the

participation for any activity. Research has shown, however,

that the vast majority of outdoor recreation takes place within

a few hours’ drive of one’s residence. Another metric, based

on the destination rather than origin, is the on-site visit. In

addressing item (c) above, southern national forest recreation

visits by each of four different site types, Wilderness, day use

developed sites, overnight use developed sites, and general

forest area sites are used.

Past outdoor recreation trends, as well as recent ones, are

important indicators of what may happen with outdoor

recreation in the near future (chapter 7, Hall and others

2009). However, simple descriptive statistics or trends do

not formally explore underlying factors and associations

which may be driving these trends. Thus a trend may be

of limited value if the time horizon is long and factors

driving the trend are expected to deviate substantially

from their historic levels. Trend analysis can therefore be

supplemented by development of projection models which

attempt to relate recreation participation directly to factors

known to influence this behavior. The projection models can

then be used to simulate future participation by combining

external projections of relevant factors, including population

growth, with estimated model parameters. Such modeling

allows changes in recreation participation and consumption

behavior over time to be assessed in light of previously

unseen changes in factors driving this behavior, e.g., large

changes in demographic, economic, and land use factors.

Previous research (Bowker and others 1999, Bowker

and others 2006, Cicchetti 1973, Hof and Kaiser 1983b,

Leeworthy and others 2005) has established that factors

including race, ethnicity, gender, age, income, and supply or

proximity to settings affect outdoor recreation participation

as well as the participation intensity or consumption.

Similarly, these factors along with others, including distance

and quality descriptors, have been used to explain visitation

to specific sites (Bowker and others 2009, Bowker and others

2007, Englin and Shonkwiler 1995). Reliable information

about these factors is often available from external sources,

like the U.S. Census or parallel research efforts aimed at

modeling and simulating influential variables into the future.

Such information can thus be available long before recreation

survey results can be obtained.

A two-step approach was used to develop projections for

participation and consumption of 10 traditional outdoor

recreation activities (see table 8.1). The first step, or model

estimation step, focused on the development of statistical

models of southern adult per capita participation and days of

participation (conditional on being a participant) for each of the

activities. The statistical models first represent the probability

that one participated in an activity. Then, if one participated,

the number of days is modeled. These models can be used

to estimate the total number of participants and the total

number of days of participation for each activity by regional

residents. This information is important as it allows a better

understanding of the factors that influence individual recreation

choices or behavior. In addition, it allows one to examine

how, under the assumption of static tastes and preferences,

individual behavior changes over time as underlying factors

change. Statistical models were also developed explaining the

demand for visits to national forests in the South across the four

different types of settings listed above.

The second step, or simulation step, combines the estimated

models with external projections of relevant explanatory

variables to generate estimated per capita participation

probabilities and conditional expected days of participation

for each activity at 10-year intervals to 2060. Per capita

estimates are in turn combined with population projections

to derive regional estimates of adult participants and days

of participation for each activity. These estimates are then

used to create indices by which 2008 baseline estimates

of participants for the various activities, found in table 8.1,
163

chapter 8. Outdoor Recreation 

can be scaled. Indices of estimated adult participants for

each of the 10 activities and days of annual participation

are presented across the three 2010 Resources Planning

Act (RPA) Assessment scenarios described below. For

discussion, the activities are grouped into the broader

categories as follows: land-based activities, water-based

activities, and wildlife (see table 8.1).

For southern national forest visits, the simulation step

consists of combining estimated visitation models for each

site type with relevant projected explanatory variables to

derive estimates of conditional expected visits at

10-year intervals to 2060. These estimates are combined

with projected population changes to derive indices by which

2008 baseline estimates of visits per site type can be scaled.

Indices of estimated visits for each forest site type across

each of the RPA Assessment scenarios are presented below.

The remainder of the chapter proceeds as follows. First,

we present a brief discussion of the statistical methods and

previous research upon which our per capita participation,

consumption models, and national forest visitation models

are based. Next, we describe the data used in the estimation

step including projections of covariates for the three

assessment scenarios and relevant assumptions. We then

present the results of our estimation and simulation steps

for regional participation and days projections by activity

and assessment scenario to 2060. Similarly, we present

projections for visits by each of the national forest site types

and assessment scenarios to 2060. Finally, we discuss some

of the key findings within and across categories as well as

with respect to demographics.

Methods and Data

Models used to assess recreation demand decisions can be

grouped into three basic categories: site-specific user models,

site-specific aggregate models, and population-level models

(Cicchetti 1973). Population-level modeling is used to address

items (a) and (b) above, while site-specific user models are

developed to address item (c).

Cicchetti (1973) pioneered the use of cross-sectional

population-level models with the household-based 1965

National Survey on Recreation and the Environment to

estimate annual participation and use nationally for many

outdoor recreation activities. Estimated models and Census

Bureau projections of socio-demographic variables and

population were then used to forecast participation and use

to 2000. The cross-sectional population-level approach has

subsequently been used to estimate and project participation

and use for recreation activities at national and regional

levels (Bowker 2001, Bowker and others 1999, Hof and

Kaiser 1983a, 1983b, Leeworthy and others 2005, Walsh and

others 1992). Alternative approaches, wherein population

data were combined with individual site-level data to project

Table 8.1—Outdoor recreation activities in the South, 2008, by participation rate, number of participants, and 

number of days

Activity

Participation

Rate

Participants

(thousands)

Days

(millions)

Land-based

Developed Site Use—Family gathering, picnicking, developed camping .799 63,157.5 672.2

Horseback riding on trails .071 5,649.2 99.0

Hiking–Day hiking .252 20,283.2 462.7

Motorized Off-road–Off-road driving .213 16,907.4 561.6

Primitive–Visiting a Wilderness, primitive camping, backpacking .353 28,158 412.3

Water-based

Motorized Water–Motor boating, water skiing, personal water craft .270 21,268.7 384.2

Non-motorized–Canoeing, kayaking, rafting .154 12,201.6 80.0

Wildlife

Birding–Viewing or photographing birds .342 26,975.5 2,862.4

Fishing .357 28,038.5 572.5

Hunting .137 10,785.9 230.3

Source: NSRE 2005-2009 (n=30,394), U.S. Department of Agriculture Forest Service 2009.
164 The Southern Forest Futures Project

participation or consumption have also been used (Bowker and

others 2006, Cordell and Bergstrom 1991, Cordell and others

1990, Englin and Shonkwiler 1995, English and others 1993).

A major drawback of cross-sectional models, imposed by the

nature of the data, is that the structure of the estimated models

remains constant over the forecast period. For example, the

factors that influence participation or use are assumed to have

the same effects throughout the projection period. Hence,

barring major shifts in demographics, the results are primarily

driven by population growth. This assumption can be tenuous.

For example, new sports brought about by technological

changes or shifts in tastes and preferences, such as mountain

biking, jet skiing, snowboarding, flat-water kayaking, and

orienteering are unlikely to be correctly represented in the

models. Moreover, if data are collected while activities are in

a new or rapid growth phase, recent trends can be potentially

very misleading. For example, Cordell and others (chapter 7)

report a recent increase in kayaking of 154 percent, something

unlikely to be sustained into the future. Nevertheless, without

appropriate time-series data, researchers are left with the use

of cross-sectional models and their inherent limitations, as a

second-best alternative to estimate and forecast participation

and use. A further drawback of these models is that it is

difficult to account for the dampening effects of future

congestion, supply limitations, and price changes on growth in

participation and use.

Regional cross-sectional population-level logistic models are

used to describe the probability of adult participation in each

of the 10 activities as:

Pij = [1 + exp (− XijB)] (1)

where, Pij is the probability that the jth individual participated

in the ith recreation activity in the preceding year. The vector

Xij contains socio-demographic characteristics unique to

activity i for individual j and relevant supply variables for

activity i pertaining to individual j’s location (table 8.2), and

B represents a vector of parameters which are estimated

using NLOGIT 3.0 (Greene 2003).

Logistic models for each activity, based on NSRE data from

1999–2008, were combined with 2008 baseline populationweighted sample means for the explanatory variables to

create an initial predicted per capita participation rate

for each activity. The per capita participation rates were

recalculated at 10-year intervals using projected external

data. Indices were then created for the participation rates

by which the NSRE 2005-09 average population-weighted

participation frequencies (baselines) were scaled, leading

to indexed per capita participation rates for each of the 10

activities. Indexing the 2005-2009 averages by changes in

model-predicted rates was judged to be superior in terms

of mitigating potential non-linearity biases associated with

complete reliance on logistic predicted values (Souter and

Bowker 1996). The indexed participation rate estimates

were then combined with projected changes in population,

according to each of the three 2010 RPA Assessment

scenarios, to yield indexed values for total adult participants

across the 10 activities.

Participation intensity or consumption models are similar to

the participation models listed above except that an integer

metric represents use, e.g., times, days, trips, modeled

rather than the binary (yes/no) choice to participate. The

general specification for the population-level consumption

model is,

Yij = f (Xj, Qj) + uj (2)

where, Yij represents the annual number of times or days that

individual j participates in activity i, Xj is a vector of sociodemographic characteristics associated with individual j, Qj

is a vector of supply relevant variables, and uj is a random

disturbance term. These integer or count data models are

often estimated using negative binomial specifications with

a link function of semi-logarithmic form (Bowker 2001,

Bowker and others 1999, Zawacki and others 2000).

Alternatively, if one thinks that observed zeros for the

dependent variable are excessive or not entirely caused by the

same data generating process as the positive values, a hurdle

model structure can be employed (Cameron and Trivedi

1998, p. 124). The hurdle model combines the probability of

participation (threshold) with the estimated number of days

for those participating, i.e.,

Yij = Pij * Yij, y>0 (3)

The hurdle model allows different vectors of explanatory

variables for the respective probability and conditional

days portions of the model, here estimated as a truncated

negative binomial, and thus leads to two unique sets of

estimated parameters. Model parameters for each of the

10 models for regional activity days were estimated with

NLOGIT 3.0 (Greene 2003) using NSRE data for southern

households from 1999 to 2008. Similar to the procedure

with the participation models and indices, hurdle model

parameter estimates are combined with 2008 NSRE baseline

participation and days estimates, projected explanatory

variables, and projected population changes under each of the

RPA Assessment scenarios (A1B, A2, B2) to provide indices

of projected growth of annual days of participation for the

10 activities listed in table 8.1.

Site specific user models were developed to describe the

demand for recreation visits to southern national forests by

each of four forest settings: Wilderness, day use developed

sites, overnight use developed sites, and general forest area

1
165

chapter 8. Outdoor Recreation 

sites. These settings correspond to the sampling strata used

to collect all visitor use information in the national forest

system (English and others 2002, USDA Forest Service

2010). The data used for the on-site visitation models were

obtained from the Forest Service’s National Visitor Use

Monitoring Program database for round one and pooled

across all national forests in the South.

Pooled, on-site, individual travel cost models are commonly

used to examine forest recreation visitation (Bowker and

others 2007, Englin and Shonkwiler 1995, Ovaskainen and

others 2001). The modeling approach follows Englin and

Shonkwiler (1995) wherein a truncated negative binomial

specification, adjusted to account for endogenous stratification,

is used with a semi-logarithmic link function. For each of the

site types, the models can be generally specified as:

Ln Yik = f (TCik, SEi, QSk)	 (4)

where, Ln Yik is the natural log of annual visits by the ith

group to the kth forest, TCik is the travel cost for the ith group

to the kth forest, SEi is a vector of characteristics of the ith

group, and QSk is a vector of characteristics related to the

kth forest. For a more complete discussion of the modeling

approach and procedures see Sardana (2010).

Similar to the simulation step in the participation and days

projections, model parameter estimates were first combined

with projected values for relevant explanatory variables to

obtain projections of per-group visits to each of the four site

types at 10-year intervals to 2060. These estimates were

in turn adjusted for average group size (number in travel

party) by setting type to obtain projections of conditional

site visits per group. Englin and Shonkwiler (1995) applied

such conditional site visit means across a general population

to forecast Wilderness visits in the State of Washington.

However, this approach is misguided if the unknown

population of potential visitors is not identical to the overall

State or regional population. To address this problem, and

to create appropriate growth indices, we assume that the

proportion of the population who would be in the market to

visit southern national forests is an unknown constant, K, of

Table 8.2—Socioeconomic and supply variables for participation and days projections for outdoor recreation activities 

in the South

Variable Description 

Gender 1=male

American Indian 1=American Indian, non-Hispanic, 0=otherwise

Asian/Pacific Islander 1=Asian/Pac Islander, 0=otherwise

Hispanic 1=Hispanic, 0=otherwise

Black 1=Black, non-Hispanic, 0=otherwise

Bachelor’s 1=Bachelor’s degree, 0=otherwise

Below High School 1=Less than high school, 0=otherwise

Post Graduate 1=Post-graduate degree, 0=otherwise

Some College 1=Some college or technical school, 0=otherwise

Age Respondent age in years

Age Squared Respondent age squared

Income Respondent household income (2007 dollars)

Population Density County area divided by population. Base 1997.

Coastal 1=County on coast, 0 otherwise

for_ran_pcap Sum of forest land acres and rangeland acres divided by population at county level and at 50, 100, 

200-mile radii. Base 1997. 

water_pcap Water acres divided by population at county level and at 50, 100, 200-mile radii. Base 1997. 

mtns_pcap Acres in mountainous divided by population. Base 1997.

pct_mtns_pcap Percentage of county acres in mountains divided by population (x100000). Base 1997. 

natpark_pcap Number of nature parks and similar institutions divided by population (x100000). Base 1997 

fed_land_pcap Sum USFS, NPS, USFWS, BLM, USBR, TVA, and USACE acreage divided by population. Base 1997.

avg_elev Average elevation in meters at county level and 50, 100, 200-mile radii. Base 1997.
166 The Southern Forest Futures Project

some magnitude less than one. Therefore, the total change in

visits for a given site type between any two time periods can

be represented as:

dTVt,t+1 = [Kt+1*E(Y)t+1*POPt+1 − Kt*E(Y)t*POPt]	 (5)

where, d represents the total differential operator, TVt is the

total annual visits in time t, E(Y)t is mean visits per group

from the truncated negative binomial model, and POPt is the

overall population in time t. As the K’s in (5) are assumed

constant, an index of change can be created dividing the

first term by the second term in (5) and using the model

parameters along with projections of explanatory variables

and population for the given time period. Estimates of

southern national forest visits by site type for 2008 derived

from the National Visitor Use Monitoring Program database

can then be scaled by the estimated growth index to derive

projections of visits to 2060.

Results

The results reported in this section combine the models and

indexing procedures discussed above with projections of

population change, economic growth, and land use change

common to three future scenarios from the Forest Service’s

2010 RPA Assessment. The goal of the Assessment is to

characterize the common demographic, socioeconomic,

and technological driving forces underlying changes in

resource conditions in order to evaluate the sensitivity of

resource trends to a feasible future range of these driving

forces. The use of scenarios links underlying assumptions

of the individual analyses and frames the future uncertainty

in these driving forces within the integrated modeling and

analysis framework of the 2010 RPA Assessment.

Three scenarios, considered equally likely, were

chosen that are linked to globally consistent and welldocumented scenarios used in the Fourth Assessment by

the Intergovernmental Panel on Climate Change (IPCC

2007). The scenarios include a range of future global and

U.S. socioeconomic and climate conditions that are likely

to have different effects on future U.S. resource conditions

and trends (USDA Forest Service 2012). The IPCC scenario

“names” have been maintained in both the RPA Assessment

and in this chapter of the Southern Forest Futures Project

documentation for continuity: A1B, A2, and B2. The IPCC

global data were scaled to the U.S. national and sub-national

levels, here the South, to facilitate the resource analyses

for the 2010 RPA Assessment. U.S. gross domestic product

(GDP) and population projections used in IPCC analyses

were updated, and the updated U.S. population and

disposable personal income data were then downscaled to the

southern county level (USDA Forest Service 2010, Zarnoch

and others 2010).

As shown in figures 8.1 and 8.2, A1B corresponds to midrange population growth and the highest household income

level of the three IPCC scenarios. Under this scenario, the

South can expect to see about 164 million people (135 million

adults) and an average household income of $129,000 by

2060. Scenario A2 projects the highest population growth,

reaching about 185 million people (152 million adults) by

2060, and the lowest projected household income, around

$91,000. Scenario B2 projects the lowest population growth

and mid-level personal income, predicting a population

of 145 million people (120 million adults) with average

household income about $96,000.

In accordance with the assessment scenarios A1B, A2,

and B2, projected land use changes are incorporated from

Wear (2011) to develop supply variables listed in table 8.2.

Nationally, Wear’s projections indicate an increase in urban

area of 1-1.4 million acres per year between 1997 and 2060,

with a decline in forest area of 24-37 million acres, and

decline in cropland of 19-28 million acres by 2060. Wear also

projects that about 90 percent of forecasted forest land losses

are found in the Eastern United States with more than half

in the South. For the South, Wear (chapter 4) forecasts forest

acreage losses of 11-23 million acres or about 7-13 percent for

Cornerstones based on scenarios A1B and B2. Based on the

forecasts of land use change from Wear (chapter 4) across the

three scenarios adopted in this chapter, forest and rangeland

per capita across the South between 2008 and 2060 is

expected to decline about 45 percent under A1B, 50 percent

under A2, and about 37 percent under B2. Federal lands

and areas covered by water are assumed static throughout

the projection period. Further details regarding explanatory

variables can be found in chapter 8-appendix A (retrievable

at http://www.srs.fs.usda.gov/trends/research/sffpa2010.

html).

Participants and Days of Recreation Participation

Estimation results for the participation and days models

and related projections for A1B, A2, and B2 are reported in

chapter 8-appendix A (retrievable at http://www.srs.fs.usda.

gov/trends/research/sffpa2010.html). Reported results include

model estimates for each activity, values and definitions for

explanatory variables by scenario and year, odds ratios which

indicate the odds of participation occurring in one group

to the odds of it occurring in another group, and graphics

of overall participant growth by activity and assessment

scenario. Throughout the remainder of this sub-section,

we present the results for per capita and overall changes

in participation and days of participation by activity and

scenario at 10-year intervals from 2010 to 2060.

Land-based activities—Land-based activities include

developed site use, hiking, horseback riding on trails,

motorized off-roading, and visiting primitive areas (table

168 The Southern Forest Futures Project

8.1). Developed site use is the most popular of the land-based

outdoor recreation activities, both nationally and in the

South. This composite activity includes family gatherings,

picnicking, and developed camping. On average, between

2005 and 2009, this activity was practiced by about 80

percent of southern adults, or more than 63 million people,

accounting for 672 million days of participation in 2008.

Moreover, because our projections only relate to adults

and many kids participate in these activities, participation

including all age groups should be much higher. As table

8.3 indicates, per capita participation growth in this activity

is expected to be static over the next 50 years across each

of the assessment scenarios with A1B showing the most

change at less than a 3-percent change from 2008. However,

as this composite activity is highly popular to begin with,

the static participation rate means that overall participants

in this activity will grow by the rate at which the population

increases for each scenario (see table 8.3). Thus A2, which

has the greatest expected population growth, demonstrated an

increase in participants of nearly 90 percent to approximately

121 million adults per year. Days per participant in developed

site use is projected to remain constant across each of the

three scenarios. Hence, total days of developed site use will

follow growth in participant numbers and range from 53 to

90 percent over the next 5 decades.

Hiking is perhaps the most popular single land-based

backcountry activity. In 2008 about 33 percent of adults

nationally participated in hiking. In the South, 25 percent

of adults participated in hiking totaling about 20 million

participants and 463 million days of hiking annually

(table 8.4). Among the three assessment scenarios, hiking

participation per capita is expected to increase by 12 to 15

percent by 2060, increasing the most under A1B (table 8.4).

As the participation rates are similar across scenarios, A2’s

higher population growth leads to the greatest increase in

hiking participants over the time span, nearly 113 percent,

resulting in about 44 million hikers by 2060. Scenarios B2

and A1B led to hiking participant increases from 2008 of

about 70 percent and 96 percent, respectively. A notable

model result for hiking is that it is the only activity in this

chapter for which Hispanic ethnicity is associated with a

higher participation rate and higher days per participant than

whites (see chapter 8-appendix A retrievable at http://www.

srs.fs.usda.gov/trends/research/sffpa2010.html).

Horseback riding on trails, while the least popular of the

land-based activities in this chapter, is nevertheless engaged

in by seven percent of southern adults annually (table 8.5).

Unlike developed use and hiking, per capita participation

in horseback riding on trails is projected to decrease by five

to nine percent in two of the three scenarios (B2, A2). In

scenario A1B, however, per capita participation is expected

to increase by about nine percent over the next 50 years.

The number of participants in this activity increases under

scenarios A1B and A2 from about 5.6 million in 2008 to

about 10 million by 2060. Despite A1B’s lower population

growth, greater income growth under this scenario yields

more participants. Annual riding days per participant is

static under A2, but increases by 8 to 26 percent under B2

and A1B, respectively. Combined with the participation rate

changes and population growth, horseback riding on trails by

Southerners is projected to increase from a total of about

100 million days in 2008 to 150-230 million days annually

by 2060 (table 8.5).

Motorized off-road driving increased in popularity among

Southerners by 42 percent from 1999 to 2009 (chapter 7,

table 8.6). In 2008, approximately 21 percent or 17 million

adults took part in off-road driving, accounting for more than

560 million days region-wide. This total makes motorized

off-roading second only to visiting developed sites for

days of use among the land-based activities. However,

participation rates are projected to decline by about eight

percent across scenarios A1B, A2, and B2 over the next five

decades resulting in participant numbers growing less than

the population growth rate (table 8.6). By 2060, the number

of participants in motorized off-roading is projected to

increase 26-50 percent, depending on the scenario. Annual

days per participant is expected to decline by up to three

percent; therefore the total number of days for this activity

will grow slightly less than participant numbers, or from

24 to 48 percent during the same time frame, which is less

than population growth.

Visiting primitive areas is the final land-based activity

examined in this chapter. It is an aggregate, which consists

of participating in NSRE activities such as backpacking,

primitive camping, and visiting a wilderness, either

designated or undesignated. This composite accounted for

28 million participants in 2008, or about 35 percent of all

adults in the South (table 8.1). These participants visited

primitive areas on 412 million days in 2008. Annual per

capita participation in this category is expected to decline by

up to 7 percent over the next 50 years (table 8.7). Increased

population density and declines in forest and rangeland per

capita appear to be factors influencing the participation

rate decline (see chapter 8-appendix A retrievable at http://

www.srs.fs.usda.gov/trends/research/sffpa2010.html).

However, overall participation is expected to increase by

43 to 76 percent across the three scenarios by 2060 because

population growth offsets the small decline in participation

rates. Annual days of visiting primitive areas per participant

is projected to remain nearly constant throughout the

simulation period, therefore the growth in total days per year

will closely follow adult population growth and range from

44 to 79 percent across the three future alternatives used in

this chapter.
169

chapter 8. Outdoor Recreation 

Table 8.3—Developed site use (family gatherings, picnicking, or developed camping) as part of outdoor recreation 

activities in the South for 2010 and projected to 2060 by participation rate, number of participants, and days per 

participant for Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, 

and E. RPA scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.799 A1B 1.00128 1.004375 1.006045 1.0103 1.016267 1.02331

0.799 A2 0.99992 1.000946 1.000462 1.001864 1.004499 1.007731

0.799 B2 1.00276 1.003951 1.002652 1.004684 1.008491 1.010542

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

63157.5 A1B 1.02711 1.172944 1.33204 1.474788 1.607101 1.738229

64252.81 A2 1.02846 1.184915 1.359355 1.525197 1.70001 1.900895

62992.86 B2 1.02821 1.169015 1.292198 1.374478 1.452541 1.528463

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

10.61488 A1B 1.00016 1.000722 1.001326 1.001467 1.001553 1.001659

10.61488 A2 1.00016 1.000699 1.001287 1.001411 1.001479 1.001569

10.61488 B2 1.00017 1.000712 1.00131 1.001452 1.001525 1.00159

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

672.2 A1B 1.02728 1.173791 1.333806 1.476952 1.609596 1.741113

683.8577 A2 1.02863 1.185743 1.361105 1.527348 1.702524 1.903877

670.4477 B2 1.02838 1.169847 1.293891 1.376473 1.454756 1.530894

Table 8.4—Hiking as part of outdoor recreation activities in the South for 2010 and projected to 2060 by participation 

rate, number of participants, and days per participant for Resources Planning Act (RPA) scenarios. (RPA scenario A1B 

corresponds to Cornerstone Futures A, B, and E. RPA scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.252 A1B 1.00605 1.031514 1.055053 1.084983 1.119007 1.15579

0.252 A2 1.00371 1.025849 1.046013 1.071215 1.099834 1.130571

0.252 B2 1.00879 1.030516 1.046885 1.069836 1.097007 1.120834

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

20283.2 A1B 1.03201 1.204638 1.396929 1.583806 1.769571 1.963266

20634.96 A2 1.03236 1.214396 1.421246 1.630774 1.861355 2.13261

20230.33 B2 1.03439 1.199947 1.349204 1.463611 1.580032 1.695281

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

22.93241 A1B 1.00131 1.005806 1.011284 1.016951 1.023362 1.030894

22.93241 A2 1.00104 1.004255 1.008036 1.011268 1.013613 1.014911

22.93241 B2 1.00135 1.006119 1.014205 1.024453 1.034866 1.045992

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

462.7 A1B 1.03336 1.211632 1.412692 1.610654 1.810911 2.023918

470.7244 A2 1.03344 1.219563 1.432668 1.649149 1.886693 2.16441

461.4938 B2 1.03579 1.20729 1.368369 1.4994 1.635121 1.77325

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html
170 The Southern Forest Futures Project

Table 8.5—Horseback riding on trails as part of outdoor recreation activities in the South for 2010 and projected 

to 2060 by participation rate, number of participants, and days per participant for Resources Planning Act (RPA) 

scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA scenario B2 corresponds to 

Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.071 A1B 1.00612 1.003754 0.985406 0.999309 1.035266 1.087135

0.071 A2 0.99265 0.969614 0.931027 0.915953 0.914336 0.918211

0.071 B2 1.02113 0.999758 0.953196 0.945632 0.957732 0.952997

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

5649.2 A1B 1.03208 1.172219 1.304713 1.458744 1.637146 1.846645

5747.172 A2 1.02099 1.147825 1.265012 1.39441 1.547419 1.732034

5634.474 B2 1.02113 0.999758 0.953196 0.945632 0.957732 0.952997

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

17.67102 A1B 1.01537 1.051192 1.075871 1.114747 1.175211 1.264171

17.67102 A2 0.99742 1.002701 0.99473 0.987054 0.984611 0.987825

17.67102 B2 1.03525 1.046258 1.034731 1.048706 1.079023 1.085742

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

99 A1B 1.04795 1.232227 1.403703 1.626131 1.923993 2.334475

100.7169 A2 1.01835 1.150925 1.258345 1.376358 1.523606 1.710946

98.74192 B2 1.08396 1.217983 1.271125 1.356701 1.48844 1.565016

Table 8.6—Motorized off-road driving as part of outdoor recreation activities in the South for 2010 and projected 

to 2060 by participation rate, number of participants, and days per participant for Resources Planning Act (RPA) 

scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA scenario B2 corresponds to 

Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.213 A1B 0.99766 0.957862 0.906493 0.884038 0.879865 0.886648

0.213 A2 0.98756 0.930879 0.863019 0.818057 0.784222 0.75284

0.213 B2 1.0082 0.955955 0.891692 0.865497 0.855411 0.835686

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

16907.4 A1B 1.0234 1.118624 1.20023 1.290476 1.391398 1.506091

17200.62 A2 1.01575 1.10197 1.172607 1.245377 1.327214 1.420091

16863.33 B2 1.03379 1.113128 1.149195 1.18406 1.232058 1.26399

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

33.29748 A1B 0.99909 0.994347 0.987165 0.983684 0.981891 0.980326

33.29748 A2 0.99905 0.993779 0.985774 0.980973 0.976998 0.971912

33.29748 B2 0.99912 0.99424 0.987199 0.984712 0.984311 0.984114

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

561.6 A1B 1.02247 1.1123 1.184825 1.269421 1.366202 1.47646

571.3396 A2 1.01478 1.095115 1.155925 1.221681 1.296686 1.380204

560.136 B2 1.03287 1.106717 1.134484 1.165959 1.212728 1.24391

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html
171

chapter 8. Outdoor Recreation 

Water-based activities—Two water-based activity

aggregates are examined in this chapter, motorized and

non-motorized (table 8.1). Motorized water use includes

motor boating, waterskiing, and personal watercraft use.

Twenty-seven percent of southern adults, or about 21 million

participants, accounted for approximately 384 million

days of motorized water use in 2008. Taken separately,

these activities all experienced relatively strong growth in

participants from 1999 to 2009 (chapter 7). The participation

rate for motorized water use is projected to increase by

10 percent to 2060 under scenario A1B, while decreasing

by up to five percent under scenarios A2 and B2 (table 8.8).

The difference can be attributed to A1B’s higher growth rate

for household income which is an important driver of this

activity (see chapter 8- appendix A retrievable at http://www.

fs.usda.gov/trends/research/sffpa2010.html). Combining

the participation rate change with population growth yields

projected increases in total participants by 2060 of 48 to 87

percent. Annual days per participant is expected to be stable

at 18 days per year under A1B, but decline slightly under

A2 and B2. Total days of motorized water use will therefore

grow faster than adult population under A1B, but somewhat

less than the population under scenarios A2 and B2. By

2060, annual days of motorized water use are expected to

grow from 2008 levels by 38 to 86 percent, totaling 528-714

million days annually.

Non-motorized water use is an aggregate which includes

canoeing, kayaking, and rafting. In 2008 approximately

15 percent or 12 million adults in the South participated in

this activity resulting in 80 million days of use (table 8.1).

Although rafting grew by just 5 percent between 1999 and

2009, canoeing (39 percent) and kayaking (154 percent) grew

dramatically during the same period (chapter 7, table 8.7).

Despite rapid growth over the past decade, per capita adult

participation in non-motorized water activities is projected

to be stable out to 2060, resulting in participant numbers

growing at the same rate as the population, or 5-81 percent

(table 8.9). This activity is less affected by income than its

motorized counterpart. Hence, A2 with greater population

growth yields the biggest increase in participants. Days per

participant is expected to remain about constant over time at

about seven, meaning that the current 80 million days for this

activity will increase to 115-141 million days by 2060.

Wildlife-based activities—Three wildlife activities are

assessed in this chapter: birding, fishing, and hunting.

Birding, a non-consumptive activity, consists of viewing

Table 8.7—Primitive activities (visiting a wilderness, primitive camping, backpacking) as part of outdoor recreation 

activities in the South for 2010 and projected to 2060 by participation rate, number of participants, and days per 

participant for Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, 

and E. RPA scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.353 A1B 1.00011 0.990617 0.975478 0.972364 0.97673 0.985278

0.353 A2 0.99614 0.980348 0.95872 0.9467 0.939954 0.934888

0.353 B2 1.00443 0.989538 0.966515 0.957866 0.956245 0.949698

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

28158.1 A1B 1.02592 1.156877 1.291568 1.41941 1.544578 1.673628

28646.43 A2 1.02458 1.160532 1.302639 1.441217 1.590774 1.763491

28084.7 B2 1.02992 1.152233 1.245625 1.310427 1.37729 1.436435

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

14.5468 A1B 1.00011 1.001487 1.00081 1.003275 1.006743 1.009793

14.5468 A2 1.00025 1.002073 1.001824 1.004803 1.009072 1.013173

14.5468 B2 1.00009 1.001351 0.999549 1.000399 1.002826 1.005186

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

412.3 A1B 1.02603 1.158598 1.292615 1.424059 1.554994 1.690018

419.4503 A2 1.02483 1.162938 1.305015 1.448139 1.605205 1.786722

411.2252 B2 1.03001 1.153789 1.245064 1.310951 1.381182 1.443884

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html
172 The Southern Forest Futures Project

Table 8.8—Motorized water activities (motor boating, waterskiing, or using personal watercraft) as part of outdoor 

recreation activities in the South for 2010 and projected to 2060 by participation rate, number of participants, and days 

per participant for Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures 

A, B, and E. RPA scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.27 A1B 1.00734 1.014868 1.010267 1.026023 1.057717 1.102462

0.27 A2 0.99533 0.984415 0.960997 0.950488 0.949452 0.953869

0.27 B2 1.0207 1.011029 0.979127 0.973127 0.982065 0.975312

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

21268.7 A1B 1.03333 1.185198 1.337631 1.497739 1.672649 1.87268

21637.55 A2 1.02374 1.165345 1.305732 1.446985 1.606848 1.799296

21213.26 B2 1.0466 1.177257 1.26188 1.331306 1.41448 1.475177

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

18.20546 A1B 1.00211 0.995464 0.983224 0.979496 0.983172 0.993332

18.20546 A2 0.99588 0.979454 0.957283 0.939857 0.926315 0.914961

18.20546 B2 1.009 0.993569 0.968414 0.955711 0.950136 0.937173

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

384.2 A1B 1.03551 1.179822 1.31519 1.467029 1.644502 1.860192

390.863 A2 1.01952 1.141402 1.249956 1.359958 1.488447 1.646286

383.1985 B2 1.05602 1.169686 1.222022 1.272343 1.343948 1.382495

Table 8.9—Participation in whitewater activities (canoeing, kayaking, or rafting) as part of outdoor recreation 

activities in the South for 2010 and projected to 2060 by participation rate, number of participants, and days per 

participant for Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, 

and E. RPA scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.154 A1B 1.00312 0.999375 0.982134 0.993721 1.022044 1.0596

0.154 A2 0.99461 0.978312 0.948653 0.942407 0.948409 0.958434

0.154 B2 1.01282 0.996408 0.958152 0.951875 0.961969 0.960829

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

12201.6 A1B 1.029 1.167105 1.300382 1.450587 1.616236 1.799874

12413.21 A2 1.02301 1.158121 1.28896 1.434682 1.605084 1.807906

12169.79 B2 1.03853 1.160231 1.234847 1.302232 1.385535 1.453271

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

6.576664 A1B 0.99965 0.996812 0.994198 0.989302 0.983887 0.978904

6.576664 A2 0.99956 0.996113 0.992653 0.986506 0.979073 0.970976

6.576664 B2 0.99968 0.996803 0.994834 0.991485 0.987636 0.984023

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

80 A1B 1.02865 1.163385 1.292836 1.435069 1.590194 1.761904

81.38741 A2 1.02256 1.15362 1.27949 1.415322 1.571494 1.755434

79.79145 B2 1.0382 1.156522 1.228468 1.291143 1.368404 1.430052

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html
173

chapter 8. Outdoor Recreation 

or photographing birds. This activity is very popular in

the South, involving 34 percent of the adult population

or 27 million people. Among all the activities in this

chapter, birding, at 106, has the highest annual days per

participant (table 8.1) accounting for about 2,900 million

days annually. This extremely high value is likely reflective

of the many levels or intensities of birding, from watching

feeders to pursuing sightings in remote forests or along the

coast. Cordell and others (chapter 7) report that birding

participation increased by nearly 30 percent from 1999

to 2009. Per capita participation in birding is projected to

increase 7 to 10 percent over the next five decades meaning

that birders will increase faster than the adult population at

large across each of the future scenarios used herein

(table 8.10). By 2060 birding participants are projected to

number 44-56 million in the South. Days per participant

are expected to decline by 9-13 percent over the same time

period. This decline will mean that the total number of days

per year of birding by Southerners will increase marginally

less than the population, or 47-75 percent by 2060.

Fishing, as defined here, is a composite including various

types of saltwater and freshwater pursuits. Fishing has

the second highest participation rate (36 percent) for

Southerners among the activities examined in this chapter.

In 2008, approximately 28 million anglers accounted for 572

million days of participation (table 8.1). Fishing participants

increased in the South by over 20 percent in the past decade

(chapter 7, table 8.7). Across each of the futures scenarios

used in this chapter, the fishing participation rate is projected

to decline by 10 to 18 percent over the next five decades

(table 8.11). Thus, the number of Southern anglers will grow

slower than the regional population. Projected growth rates

for participants of 32-54 are expected. Days per participant

will remain at about 20 per year across A1B, A2, and B2.

Therefore, the number of days of fishing is expected to grow

considerably slower than the population, or 30-51 percent.

Nevertheless, fishing will remain among the top recreation

activities in the South accounting for 742-859 million days

in 2060.

Hunting is the final activity examined in this section. Here,

hunting consists of an aggregate including all types of legal

hunting, including big game, small game, waterfowl, and

varmint. Approximately 10 to 11 million adults in the South,

or over 13 percent, reported hunting in 2008 on a total of

230 million days (table 8.1). Cordell and others (chapter 7)

report that small game hunting participants increased by

16 percent, and big game hunters increased by 25 percent

from 1999 to 2009. Findings from our models, suggest

that per capita participation by southerners in hunting has

peaked and will decline by 26-42 percent over the next

five decades (table 8.12). A number of factors appear to be

driving the participation rate decline including: increasing

population density, growth in Asian and Hispanic population

proportions, increasing levels of education, and declining

forest and rangeland per capita (chapter 8-appendix A

retrievable at http://www.fs.usda.gov/trends/research/

sffpa2010.html). Despite the declining participation rate,

the number of southern hunters is expected to increase

out to 2060 by 8 to 25 percent for scenarios B2 and A1B,

respectively (table 8.12). Days of participation per hunter,

currently around 22, is projected to remain relatively

constant regardless of the selected future scenario. Total

days of hunting are forecast to grow at about the same rate

as hunter numbers, by 8-24 percent. By 2060 the southern

adults will account for 248-286 million days of hunting

annually.

Visits to Southern National Forests

Estimation results for visits to southern national forests

and related projections for A1B, A2, and B2 are reported

in appendix B (retrievable at http://www.srs.fs.usda.gov/

trends/research/sffpb2010.html). Reported results include

on-site visitation model estimates for each of the four site

types, values for explanatory variables by scenario and year,

population projections and site visit indexes. Beginning with

over 30 million site visits annually in 2008 (table 8.13), we

present the results for annual visits per group and overall

changes in total Southern national forest visitation by site

type and scenario at 10-year intervals, from 2010 to 2060.

Developed use day sites—Visitation to developed use day

sites is the second most popular of site types encountered

in Southern national forests accounting for 6.5 million

recreation visits in 2008. Depending on the specific national

forest, these sites include some combination of built

structures including picnic areas, playgrounds, shelters, boat

ramps, toilets, parking lots, and the like. Groups visiting

developed use day sites averaged more than 12 person-trips

per year (e.g., a family of four traveling to the site 3 times

per year). Annual visits per group are projected to decline

5-8 percent by 2060 across the three scenarios used in this

chapter (table 8.14). However, as population is forecast to

increase with each scenario, visits to developed use day sites

are projected to rise by 35 percent under the lower population

growth of B2, 47 percent under mid-population growth A1B,

and by approximately 70 percent under scenario A2 wherein

population is expected to increase by 77 percent.

Overnight use developed sites—As the name implies,

overnight use developed sites have facilities which

accommodate overnight stays such as cottages, recreation

vehicle hook-ups, camp sites, electricity, and running water.

Southern national forests experienced about 2.3 million visits

to these sites in 2008 (table 8.13). Annual visits per group

averaged around 10, and are projected to decline by 7 percent

under A2 and by 14 percent under scenario A1B. However,

because of forecasted population growth, an increase of
174 The Southern Forest Futures Project

Table 8.11—Fishing (cold water, warm water, Saltwater, or anadromous fishing) as part of outdoor recreation activities 

in the South for 2010 and projected to 2060 by participation rate, number of participants, and days per participant for 

Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA 

scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.357 A1B 0.99853 0.973791 0.942967 0.921708 0.90907 0.903249

0.357 A2 0.99265 0.957615 0.915961 0.88001 0.848306 0.818184

0.357 B2 1.00464 0.972593 0.933503 0.909384 0.892704 0.86999

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

28038.5 A1B 1.0243 1.137227 1.248522 1.345465 1.437583 1.53429

28524.76 A2 1.02099 1.13362 1.24454 1.339691 1.43567 1.543351

27965.41 B2 1.03013 1.132501 1.203081 1.2441 1.285772 1.315875

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

20.58497 A1B 0.99983 0.996436 0.99389 0.989476 0.98509 0.981589

20.58497 A2 0.9996 0.99517 0.991378 0.985243 0.978098 0.970506

20.58497 B2 0.99988 0.996576 0.995829 0.994575 0.992771 0.991327

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

572.5 A1B 1.02412 1.133173 1.240893 1.331306 1.416148 1.506043

582.4286 A2 1.02057 1.128145 1.23381 1.319922 1.404226 1.497831

571.0076 B2 1.03001 1.128624 1.198063 1.237351 1.276477 1.304462

Table 8.10—Participation in birding (viewing or photographing birds) as part of outdoor recreation activities in 

the South for 2010 and projected to 2060 by participation rate, number of participants, and days per participant for 

Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA 

scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.342 A1B 1.00618 1.032559 1.061017 1.076363 1.087937 1.101538

0.342 A2 1.00422 1.02759 1.052804 1.063612 1.069752 1.076769

0.342 B2 1.00846 1.031673 1.054197 1.06431 1.071119 1.075097

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

26974.5 A1B 1.03214 1.205858 1.404826 1.571223 1.720438 1.871112

27442.31 A2 1.03289 1.216456 1.430473 1.6192 1.810444 2.031123

26904.18 B2 1.03406 1.201295 1.358629 1.45605 1.542745 1.626103

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

106.6459 A1B 0.9974 0.981444 0.968683 0.942633 0.913481 0.886459

106.6459 A2 0.99705 0.979323 0.964218 0.934968 0.900713 0.866154

106.6459 B2 0.99745 0.981866 0.972521 0.952338 0.92803 0.905072

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

2862.4 A1B 1.02946 1.183483 1.36083 1.481086 1.571588 1.658663

2862.4 A2 1.02984 1.191303 1.379288 1.5139 1.63069 1.759266

2854.938 B2 1.02708 1.174842 1.319549 1.385743 1.429886 1.47403

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html
175

chapter 8. Outdoor Recreation 

Table 8.12—Hunting as part of outdoor recreation activities in the South for 2010 and projected to 2060 by 

participation rate, number of participants, and days per participant for Resources Planning Act (RPA) scenarios. (RPA 

scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA scenario B2 corresponds to Cornerstone Futures 

C, D, and F.)

Participation rate Per capita participation index

2008 Scenario 2010 2020 2030 2040 2050 2060

0.137 A1B 0.99305 0.922444 0.843038 0.790214 0.757181 0.737596

0.137 A2 0.98094 0.88952 0.790927 0.713447 0.648339 0.588152

0.137 B2 1.00519 0.921013 0.832986 0.785025 0.753318 0.714254

Adult participants (thousands) Participants index

2008 Scenario 2010 2020 2030 2040 2050 2060

10785.9 A1B 1.01867 1.077262 1.116213 1.153516 1.197389 1.252906

10972.96 A2 1.00894 1.053009 1.074654 1.086123 1.097246 1.109438

10757.78 B2 1.0307 1.07244 1.073536 1.073968 1.085012 1.080322

Days per participant Per capita days index

2008 Scenario 2010 2020 2030 2040 2050 2060

21.68489 A1B 0.99986 0.998123 0.996155 0.994609 0.993631 0.993092

21.68489 A2 0.99973 0.997374 0.994599 0.9919 0.989004 0.985531

21.68489 B2 0.99988 0.998275 0.997554 0.998172 0.999059 1.000172

Total days (millions) Total days index

2008 Scenario 2010 2020 2030 2040 2050 2060

230.3 A1B 1.01853 1.07524 1.111921 1.147297 1.189763 1.24425

234.294 A2 1.00867 1.050244 1.068849 1.077326 1.085181 1.093386

229.6996 B2 1.03057 1.07059 1.07091 1.072005 1.083992 1.080508

Table 8.13—National forest visits in the South by site type 

as counted in 2008 and as projected for 2060 (averages per 

Resources Planning Act scenarios A1B, A2, and B2). 

Site type 2008 visits

2060 visits

(avg. A1B, A2, B2)

Developed use day sites 6,522,780 9,828,167

Overnight use developed sites 2,297,810 3,305,599

Wilderness 826,883 1,217,358

General forest areas 22,858,446 30,390,127

Source: U.S. Department of Agriculture Forest Service 2010. 

Source: http://www.srs.fs.usda.gov/trends/research/sffpa2010.html
176 The Southern Forest Futures Project

Table 8.15—Overnight use developed site visits to national forests in the South in 2008 and 2010 and projected to 2060 

for Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA 

scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Annual person visits per group Person visit index

2008 Scenario 2010 2020 2030 2040 2050 2060

10.28 A1B 0.99 0.96 0.94 0.91 0.89 0.86

10.28 A2 1.00 0.98 0.97 0.96 0.94 0.93

10.28 B2 0.97 0.96 0.95 0.94 0.92 0.91

Annual site visits (thousands) Total site visit index

2008 Scenario 2010 2020 2030 2040 2050 2060

2,298 A1B 1.01 1.09 1.18 1.26 1.32 1.37

2,298 A2 1.02 1.13 1.25 1.37 1.50 1.64

2,298 B2 1.00 1.09 1.16 1.21 1.25 1.30

Table 8.16—Wilderness site visits to national forests in the South in 2008 and 2010 and projected to 2060 for Resources 

Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA scenario B2 

corresponds to Cornerstone Futures C, D, and F.)

Annual person visits per group Person visit index

2008 Scenario 2010 2020 2030 2040 2050 2060

14.52 A1B 0.99 0.98 0.98 0.97 0.96 0.95

14.52 A2 1.00 0.99 0.99 0.98 0.98 0.97

14.52 B2 0.99 0.98 0.98 0.98 0.97 0.97

Annual site visits (thousands) Total site visit index

2008 Scenario 2010 2020 2030 2040 2050 2060

827 A1B 1.02 1.12 1.23 1.33 1.42 1.51

827 A2 1.02 1.15 1.27 1.41 1.56 1.72

827 B2 1.01 1.12 1.20 1.26 1.31 1.38

Table 8.14—Developed day use site visits to national forests in the South in 2008 and 2010 and projected to 2060 for 

Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, and E. RPA 

scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Annual person visits per group Person visit index

2008 Scenario 2010 2020 2030 2040 2050 2060

12.87 A1B 0.99 0.98 0.96 0.95 0.94 0.92

12.87 A2 1.00 0.99 0.98 0.98 0.97 0.96

12.87 B2 0.99 0.98 0.97 0.96 0.95 0.95

Annual site visits (thousands) Total site visit index

2008 Scenario 2010 2020 2030 2040 2050 2060

6,523 A1B 1.01 1.11 1.21 1.31 1.39 1.47

6,523 A2 1.02 1.14 1.27 1.40 1.54 1.70

6,523 B2 1.01 1.11 1.19 1.24 1.29 1.35
177

chapter 8. Outdoor Recreation 

30 percent (B2) to 64 percent (A2) in visitors to these sites is

projected (table 8.15).

Wilderness—Recreation visits to designated Wilderness

sites in southern national forests totaled 826,883 in 2008 (table

8.13). Annual visits per group averages over 14 and is expected

to decline slightly 3 to 5 percent over the next five decades

(table 8.16). This relatively stable per group annual visitation

rate, when combined with population growth, suggests that

Wilderness visits to southern national forests will grow the

most among the four site types, between 38 percent (B2)

and 72 percent (A2). Despite faster relative growth in visits,

Wilderness visits will remain the smallest in absolute terms,

totaling 1.1-1.4 million visits annually by 2060.

General forest areas—Visits to general forest areas in

southern national forests, at almost 23 million in 2008,

greatly exceed recreation visits to the other three site types

combined. General forest areas are probably the most like

private forest lands in that while they often have trails and

forest roads, they generally lack maintained facilities of any

type. Like Wilderness, the average annual visits per group to

general forest areas is approximately 14. Unlike Wilderness,

though, this average per group is projected to decline by

12-24 percent by 2060 (table 8.17). The biggest decline comes

with scenario A1B and this is primarily driven by the effect

of increased household income. Like the other national forest

settings, the increase in population over the next five decades

offsets the drop in average visits per group yielding increases

in annual visits by 2060 of 22 percent for A1B and B2, and

55 percent for A2. By scenario, the relative increases are less

than for each of the other site types. However, in total, the

average visits across the various site types in 2060 is still

substantially more than the combination of the other three

(table 8.13, figure 8.3).

Discussion and Conclusions

This chapter developed models to explain outdoor recreation

participation and days of participation for residents of the

Southern United States. Models were also developed to

examine visits to national forests in the South. These models,

combined with population, socioeconomic, and land use

projections from alternative futures were employed to project

the number of outdoor recreation participants and days of

participation regionally to 2060, and to project the number of

southern national forest recreation visits by site type to 2060.

The objectives were to first, evaluate how population growth

and changing demographics will affect changes in demand

for different types of recreation activities, and second, to

examine the potential for increased congestion and conflict

among recreational users of forests as a result of changing

supply and demand factors.

Regarding the first objective, the preceding results section

indicates that in general, despite continued losses in forest

and rangeland across the region and changing demographics,

outdoor recreation activity will continue to grow in both

numbers of participants and days of participation. Generally,

speaking, the number of projected participants and days of

participation will increase at a rate near or somewhat below

the rate of growth of the regional population.

For a few activities, such as developed site use, hiking, and

birding, participant numbers as well as days of participation

are projected to grow faster than the rate at which the

regional population grows. Other activities typically

associated with higher income, like horseback riding on

trails, motorized water use, and non-motorized water use,

will grow faster than the population if the higher income

conditions of scenario A1B eventuate. Otherwise, they will

grow at rates slightly less than the population.

A few activities, such as fishing, hunting, and motorized

off-road use are projected to experience substantial declines

in participation rates and thus, while increasing, will grow

much slower than the growth rate of the regional population.

Hunting and motorized off-road use, being relatively land

intensive, are adversely affected by the expected decline in

available forest and rangeland. Moreover, these activities are

essentially counter-cultural to the growing numbers of ethnic

minorities in the region.

Annual visits per group to southern national forests will

decline across all site type and scenario combinations. For

day use developed sites, overnight use developed sites, and

Wilderness sites, these declines are virtually all less than

10 percent. Thus, when visits per group are combined with

an increasing number of groups due to population growth,

overall national forest visits will grow, slightly lagging

regional population growth. However, visits to general forest

areas, which comprise about 70 percent of all national forest

visits in the South will grow noticeably slower than the

regional population, albeit still increasing by 22-55 percent.

The second objective was to examine the potential for

increased congestion and conflict among recreational

users of forests as a result of changing supply and demand

factors. While modeling use for specific activity and site

combinations was beyond the scope of this chapter, tables

8.18 and 8.19 can be used to broadly assess the potential

for congestion and conflict facing forest recreationists as

demand and supply factors change over the next five decades.

Two measures of density and change are reported in table

8.18—forest and range acres per participant, and days of

participation per forest and range acre. With the exception of

hunting, forest and range acres per participant are projected

to decline over the next decade by 33-50 percent. For hunting

179

chapter 8. Outdoor Recreation 

Table 8.18—Forest-based recreation use densities in the South in 2008 and projected for 2060 for 

Resources Planning Act (RPA) scenarios. (RPA scenario A1B corresponds to Cornerstone Futures A, B, 

and E. RPA scenario B2 corresponds to Cornerstone Futures C, D, and F.)

Forest and range acres per participant Days per forest/range acre

Activity Scenario 2008 2060

Percent

change 2008 2060

Percent

change

Birding

A1B 10.5 5.2 -50 10.1 18.0 79

A2 10.4 4.8 -54 10.3 19.2 87

B2 10.5 6.2 -41 10.1 15.5 54

Developed site use

A1B 4.5 2.4 -47 2.4 4.4 88

A2 4.4 2.2 -51 2.4 4.9 102

B2 4.5 2.8 -37 2.4 3.8 60

Hiking

A1B 14.0 6.6 -53 1.6 3.6 118

A2 13.8 6.1 -51 1.7 3.8 130

B2 14.0 7.9 -43 1.6 3.0 85

Horseback on trails

A1B 50.2 25.2 -50 0.3 0.9 151

A2 49.4 26.8 -46 0.4 0.6 82

B2 50.2 33.4 -33 0.3 0.6 63

Hunting

A1B 26.3 19.5 -26 0.8 1.1 34

A2 25.9 21.9 -15 0.8 1.0 16

B2 26.3 23.3 -11 0.8 0.9 13

Motorized off-road

A1B 16.8 10.3 -38 2.0 3.1 59 

A2 16.5 10.9 -34 2.0 3.0 47

B2 16.8 12.7 -24 2.0 2.6 30

Primitive area use

A1B 10.1 5.6 -45 1.5 2.6 82

A2 9.9 5.3 -47 1.5 2.8 90

B2 10.1 6.7 -33 1.5 2.2 51
180 The Southern Forest Futures Project

the decline will be from 11 to 26 percent. For developed

site use and hiking, this could begin to strain existing

infrastructure necessary for such activities. For birding and

hiking, as defined in the NSRE, these may or may not require

expansive areas for quality experiences. The activities

currently taking place on the largest amount of space, e.g.,

horseback riding on trails, hunting, and motorized off-road

use, while experiencing somewhat smaller changes in acres

per participant, may actually “feel” more congested given

the nature of the activity, particularly hunting. It should also

be noted that across the three futures scenarios (A1B, A2,

and B2), A1B which has the highest income growth, middle

land conversion, and middle population growth led to the

most “congestion” by the loss in forest and range land per

participant measure.

An alternative measure of congestion or land impact—

annual days of use per forest and range land acre—is also

presented in table 8.18. This measure is perhaps better to

assess the impact of activities on nature as it combines

participant number and participant intensity per unit of land

area. Congestion per unit of land is will rise most over the

next five decades for horseback riding on trails (151 percent)

and hiking (118 percent). Hunting will see the smallest

increase (13-34 percent). These measures are not intended

to be comparable across activities, and some may actually

have a social component, and thus increase user utility with

increased congestion—up to a point. Nevertheless, for those

activities which may be near biological carrying capacity or

infrastructure carrying capacity, the large increases in use

per acre could be a concern, both for the land and for the

user.

A final measure of congestion—visits per acre to southern

national forests by site type—is reported in table 8.19.

Wilderness visits per acre appear to be facing the biggest

increases in potential congestion with visits per acre

increasing from 38 to 72 percent. Increases across the

various scenarios suggest that the density of Wilderness

visits in 2060 will exceed that of general forest area visits

today. This increase could present difficult challenges to

Wilderness and protected area land managers. For example,

it is generally understood that an important motivation

for visiting Wilderness is to “get away from civilization”

or experience nature “untrammeled by man.” Having this

type of experience will be challenging if Wilderness visitor

density continues to increase. In order to accommodate

visitor satisfaction, and to comply with Wilderness

legislation, managers may be faced with the potentially

unappealing prospect of regulating access in the future.

Table 8.19—National forest site type use densities in the South in 2008 and 

projected for 2060 for Resources Planning Act (RPA) scenarios. (RPA scenario 

A1B corresponds to Cornerstone Futures A, B, and E. RPA scenario B2 

corresponds to Cornerstone Futures C, D, and F.)

Visits per national forest acre

Site type Scenario 2008 2060

Percent

change

Developed day use

A1B 0.49 0.72 47

A2 0.49 0.88 70

B2 0.49 0.66 35

Overnight developed use

A1B 0.17 0.24 37

A2 0.17 0.28 64

B2 0.17 0.22 30

General forest area

A1B 1.72 2.10 22

A2 1.72 2.66 55

B2 1.72 2.10 22

Visits per national forest wilderness acre

Site type Scenario 2008 2060

Percent

change

Wilderness

A1B 1.26 1.91 51

A2 1.26 2.17 72

B2 1.26 1.74 38
181

chapter 8. Outdoor Recreation 

General forest area use density is expected to rise by

22 percent (A1B, B2) to 55 percent (A2) as national forests

likely become even more of a substitute destination as private

forest and range land is reduced by further development.

Because general forest area recreation use including hunting,

motorized off-road use, and horseback riding on trails

generally require more space between users for high-quality

(and safe) experiences, this increase in use density should

also be of concern to national forest managers. For example,

conflicts due to congestion may increase not just within

activities (e.g., motorized off-road users running into each

other figuratively and literally), but across activities (e.g.,

motorized off-road users scaring away game sought by

hunters and spooking horses). As in the case of Wilderness,

managers could be faced with choosing among potentially

unpopular access regulation schemes to mitigate congestion

conflicts. Managers may also need to consider sectioning

general forest areas into special use areas for specific

activities such as motorized off-road use, horseback riding on

trails and hunting in order to reduce cross-activity congestion

conflicts. Regardless, the increased congestion can only

increase the impacts of recreation on the forest environment.

Across all activities and venues, private and public, this

chapter provides strong evidence to suggest that the number

of southern outdoor recreation participants and their annual

days of use will continue to grow over the next five decades

putting increasing pressure on existing infrastructure, both

built and natural, thus stressing the recreation carrying

capacity of the forest and range land resources. In some

cases, it may be possible to relieve congestion problems

by investing in and building more infrastructure, e.g.,

constructing more hiking trails on public lands. Private

land owners may also help to meet increased demand by

increasing built recreation infrastructure on private lands. In

the South, there has historically been a fairly large market for

consumptive recreational activities (e.g., hunting) provided

on private lands. In the future, owners of remaining private

land may also be able to “cash in” on increased demand

for non-consumptive recreational activities by investing in

recreation infrastructure traditionally provided by public

lands (e.g., hiking trails, bird-watching facilities).

Because of increasing visitors per acre resulting from

increasing population and decreasing private forest and

range land, remaining private land will likely become

more valuable for other recreation uses including overnight

developed site use and developed site day use. A portion

of private land in the South is already devoted to private

developed campgrounds which have sprung up over the

past several decades in response to increased demand for

camping opportunities and limited public campgrounds,

particularly public campgrounds with RV hook-ups.

Increasing congestion on public lands in the future may

provide increased demand for private campgrounds and

incentives for private land owners to invest in private,

overnight developed site infrastructure.

NSRE data suggest that some ethnic minority populations

in the South participate in activities occurring at public day

use developed sites such as family gatherings and picnicking

in greater proportions than traditional visitors. Thus, as the

ethnic minority populations in the South increase to 2060,

public day use developed sites and hiking venues are likely to

become even more congested leading to visitor competition

and conflict over limited facilities (e.g., limited picnic

pavilions). Public land managers can respond by devoting

more public land and management budgets to day use

developed sites. Because increasing scarcity of public day use

developed sites will increase the value of private land for day

use developed sites, private land owners may have incentives

in the future to invest in day use developed site infrastructure

such as picnic area pavilions to rent out for profit.

Knowledge and Inf ormation Gaps

It is impossible to see exactly how changes in income,

socioeconomic factors, and economic development will

affect the supply and demand for forest-based outdoor

recreation. The models, results, and conclusions presented

in this chapter are predicated on a number of assumptions

and relationships that are likely to change with time.

People’s preferences change over time. New technology

will bring changes to how people enjoy the outdoors—and

how they avoid the outdoors. Activities like snowboarding,

mountain biking, flat water kayaking, and orienteering did

not appear on the radar when Cicchetti’s seminal forecasting

work on national recreation use was published in 1973,

nor did activities like video gaming and DVD renting.

Despite differences in outdoor recreation and consumption

across ethnic lines today, acculturation factors may mask

such differences over the course of the next five decades.

Regardless, as the population grows, it is likely that outdoor

recreation pressure on the natural environment will become

more prevalent and management will need to find ways of

creatively mitigating this pressure.

Acknowledgments

We are grateful to Carter Betz, Don English, Shela Mou,

Meghan Starbuck, Kavita Sardana, and Stan Zarnoch for

various helpful insights and technical assistance.
182 The Southern Forest Futures Project

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183

chapter 9. Markets

David N. Wear, Jeffrey Prestemon, Robert Huggett,

	 and Douglas Carter1

Key Findings

•	 Although timber production in the South more than

doubled from the 1960s to the late 1990s, output levels have

declined over the last 10 years, signaling structural changes

in timber markets.

•	 For softwood products, production declines are most clearly

related to demand issues. Demand for softwood solid wood

products is strongly linked to housing markets, and a sharp

decline in construction beginning in 2007 reduced timber

demand, a short run adjustment. Demand for pulpwood in

paper manufacturing has declined as the production capacity

has dropped in the South, a long run adjustment.

•	 As demand declined, investments in softwood production

continued to expand, leading to supply growth for all

softwoods, but especially for softwood pulpwood. The net

result was a substantial reduction in softwood pulpwood

prices.

•	 In contrast to softwood products, hardwood pulpwood

output declined and its price increased in the 2000s,

indicating a contraction of supply, especially in the Coastal

Plain where paper production is concentrated.

•	 Several forecasts of timber markets show expanding

supplies of softwood timber, especially softwood

pulpwood, as new plantations mature and additional

plantations accumulate across the South. Across all

forecasts, softwood pulpwood supply expands through the

next 40 years, while softwood sawtimber supply grows

over the next decade and then stabilizes.

•	 Forecasts of hardwood supplies indicate a gradual

contraction as urbanization shrinks inventories.

•	 If timber product demand returns to and stays at the 2006

levels, total timber production is forecasted to expand

by about 25 percent over the next 50 years, with little

impact on the price of softwood sawtimber and hardwood

pulpwood. Softwood pulpwood prices would decline by

about 50 percent.

1David N. Wear is the Project Leader, Center for Integrated Forest Science,

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

Raleigh, NC 27695; Jeffrey Prestemon is a Research Forester at the Forest

Economics and Policy Research Work Unit, Southern Research Station,

U.S. Department of Agriculture Forest Service, Research Triangle Park,

NC 27709; Robert Huggett is a Research Assistant Professor, Department

of Forestry and Environmental Resources, North Carolina State University,

Raleigh, NC 27695; Douglas Carter is a Professor, School of Forest

Resources and Conservation, University of Florida, Gainesville, FL 32611.

•	 If demand growth returns to 1980s and 1990s levels, total

timber production could expand by about 40 percent over

the next 50 years, with the greatest gains in softwood

pulpwood output. Softwood pulpwood prices stabilize

at 2006 levels while softwood sawtimber and hardwood

pulpwood prices would increase at an average annual rate

of slightly less than 1 percent.

•	Growth in demand, coupled with gains in the productivity

of planted pine forests, would likely expand total timber

production by about 70 percent, with the production of

softwood pulpwood more than tripling. The price of

softwood sawtimber would stabilize, the price of softwood

pulpwood would fall at less than 1 percent per year, and the

price of hardwood pulpwood would increase by less than 1

percent per year.

•	 Forecasts indicate that the South’s timber supply could

expand if moderate rates of future forest investments are

added to investments in forests made over the past 20

years. Forecasts for 2055 show that annual production of

softwood pulpwood could increase beyond 2006 levels

by an additional 2.4 billion to 3.7 billion cubic feet (36.6

million to 57.9 million green tons) without substantial price

effects.

•	 Timber production has the potential to expand substantially

in the South, but future markets are likely to be limited

by demand levels. Bioenergy is a potential but highly

uncertain source of demand. Recovery of housing-related

demand for wood products remains a key uncertainty in the

short run.

•	Without an expansion in timber demand, private forest

owners would be expected to eventually experience a

strong shift away from forest management as investment

returns diminish to the point where continued investments

cannot be justified.

Introduction

Timber production from the South grew substantially and

steadily from 1950 to the late 1990s. Although production

has declined from 1997/1998 peak levels, the region still

provides a majority of the timber products produced in

the United States (Smith and others 2009). Rapid growth

in production from 1970 to 1998 did not, however, deplete

standing inventories of biomass because high growth rates

and investments in agricultural-style forestry increased

Chapter 9.

 Markets
184 The Southern Forest Futures Project

forest productivity—planted pine forests now represent 19

percent of southern forest land. Recent harvest declines raise

questions about the future of timber markets, and recent

policy discourse about the use of wood to produce energy on

large scales suggests potential for uncertainty and structural

changes in these markets.

The objectives of this chapter are to examine the history of

change in forest products markets and to consider potential

futures. We use historical records of harvest quantities and

timber prices to test general hypotheses about changes in

supply and demand. Supply forecasts and trends in product

demand are then used to analyze market potential and

to construct alternative forecasts of harvests and timber

prices. Throughout these analyses, the chapter addresses the

following specific questions:

•	 How have markets for forest products changed, especially

in the past decade?

•	What are the implications of these changes for the future of

timber markets?

•	 How is timber supply projected to change?

•	What factors influence the future of forest product demand

and what are the implications for timber markets?

•	 How might markets develop in response to alternative

scenarios for future supply and demand?

Influences on Timber Supply

Timber supply defines how landowners deliver timber to

market in response to timber prices and, in the longer run,

to a variety of other signals. Several factors make it difficult

to analyze the timber supply situation, including the long

production period involved in growing trees, the multiple

benefits that landowners can derive from standing forests,

and constant changes in the land base from which timber

is produced. It is common to think of supply as simply the

relationship between harvests and prices but these other

factors need to be accounted for, especially when considering

long run supply dynamics.

Supplies of timber are ultimately determined by the

intersection of the biological production capacity of forests

and the preferences of forest land owners. This chapter

describes alternative production possibilities by evaluating

alternative assumptions about productivity. It also considers

a range of producer behavior by considering alternative

projections of forest investments (plantation replacement and

establishment), based on the historical behavior of private

forest landowners.

Influences on Timber Demand

Demand is an economic concept that relates the consumption

of a commodity to its price. Economic theory indicates

that less of a commodity is consumed at a higher price

and that charting all the possible price-consumption

combinations defines a demand curve. This curve, however,

can be repositioned based on many factors other than the

commodity’s price—such as income, prices of substitutes

for the commodity, and changing tastes. In this chapter,

we examine demand for timber products by analyzing the

various factors that could alter demand relationships. We

look closely at substitution possibilities, production capacity,

and international trade as indicators of changes in domestic

demand for timber products.

Perhaps the most important uncertainty about the future of

timber demand is the development of new markets for fiber

in the production of bioenergy. As a renewable resource,

forest biomass may play an important role in meeting goals

established through renewable portfolio standards, and

cellulosic feedstocks for liquid fuels have been targeted in

2008 Farm Bill and other policies aimed at increasing the

use of renewable energy. Demands for wood for co-firing in

coal fired electricity plants and for production of fuel pellets

have already emerged, although biofuel production on a

large scale would require technological advances. Chapter 10

addresses potential bioenergy futures in detail.

Scope of Analysis

Chapter 10 examines how demand for wood in the

production of bioenergy could develop in the future, and

we refer to that chapter in examining a full range of market

futures. While evaluating market futures, we do not attempt

to forecast the business cycle, in particular, the recovery

from the 2007 recession and the return to historical trends in

product demand. Rather, our focus is on long run trends and,

ultimately, the implications for forest sustainability and the

capacity to adapt to changing demand for fiber in the coming

years and decades.

Methods

The analysis of historical changes in timber markets presented

here starts by updating the data from a report (Wear and

others 2007) that examined basic price- and harvest-quantity

indicators and interpreted patterns of change to provide

general insights into market direction and trends in demand

and supply. A set of explanatory factors that have affected the

demand for timber products—including domestic conditions,

technology changes, and international trade—places demand

trends in context. An analysis of timber supply fundamentals

focuses on land use, forest investment, and timberland

ownership and their effects on the future provision of timber

from private lands.

We use empirical models of timber supply and demand to

explore alternative futures for timber markets. Future timber
185

chapter 9. Markets

supply relationships are derived from simulation runs of the

U.S. Forest Assessment System’s Forest Dynamics Model

described in chapters 4 and 5. The model simulates change

in the South on all plots of the Forest Inventory and Analysis

(FIA) Program of the Forest Service, U.S. Department of

Agriculture, including harvest choices made in response

to the future market conditions described by the price

projections of the six Cornerstone Futures (chapter 2):

•	Cornerstone A describes a future of very rapid economic

and technological growth, combined with increasing

timber prices.

•	Cornerstone B is also based on rapid economic and

technological growth but combined with decreasing timber

prices.

•	Cornerstone C is based on moderate levels of economic

development and less rapid but more diverse technological

change, combined with increasing timber prices.

•	Cornerstone D is also based on moderate levels of

economic development and less rapid technological change,

but combined with decreasing timber prices.

•	Cornerstone E is based on Cornerstone A but allows for an

increased rate of planting following the harvest of naturally

regenerated forests.

•	Cornerstone F is based on Cornerstone D but with a

decreased rate of forest planting following harvests.

Harvest choice models are based on empirical models of

historical harvesting linked to FIA plots in the South. The

models are sensitive to changing forest productivity and

prices that affect net revenues from harvest/no harvest

alternatives. Using the Cornerstone Futures, simulations of

harvests for a range of prices are summed across all plots to

define the timber supply function (defined as the relationship

between aggregate timber harvest quantities and their

respective timber prices within a forecast period). Prices for

softwood sawtimber, other softwoods, hardwood sawtimber,

and other hardwoods enter the calculations (Polyakov and

others 2010), and a set of empirical supply functions are

derived for these four product classes.

Supply Scenarios

We used a modified version of the method outlined by

Polyakov and others (2010) to construct aggregate supplies.

For a set of related Cornerstone Futures—for example,

Cornerstones A, B, and E, that share the same population

and economic growth futures but apply different price

projections—we use the simulations to generate multiple

supply realizations, specifically, a bootstrapping approach

(employing random sampling with replacement) of

simulations for each State in each time period that generates

1000 observations of supply. These realizations provide the

data for regression equations where the harvest quantity

for each product is modeled as the function of its price,

and cumulative results for all products provide estimates

of supply models in each period. We set up the equations

so that the coefficient on price is the own-price elasticity

of supply (the ratio of proportional change in harvest to the

proportional change in price), and so that supply for each

period reflects forecasts of land use change and responses to

climate, disturbances, and forest succession.

The U.S. Forest Assessment System models the supply

of total removals from inventory, but our questions target

specific product markets. Estimated quantities of products

obtained from numbers of removals derive from utilization

coefficients that translate sawtimber-sized removals and

other removals into what we label sawlogs and pulpwood.

Sawlogs are used in the production of lumber and veneer for

panels. Pulpwood is defined as material delivered for use in

the paper manufacturing and in other industrial processes—

especially for fuelwood and for the manufacture of oriented

strand board. The timber product output database (Johnson

and others 2010) provides estimates of these conversion

factors, which we adjusted to reflect the difference between

chip-and-saw sawlogs from plantations and sawtimber

products from naturally regenerated forests.

Basic supply scenarios—We constructed two supply

scenarios from the Cornerstone Futures, one labeled

“High GDP” to reflect the strong economic and moderate

population growth projections of Cornerstones A, B,

and E; and the other labeled “Low GDP” to reflect the

weak economic and low population growth projections of

Cornerstones C, D, and F.

These forecasts of changes in forests are contingent on

projections of timber harvests across private and public

forested plots in the FIA inventory using market-driven

harvest probability models (Polyakov and others 2010).

Harvest predictions are driven by the price projections

that are part of the assumptions that structure each of the

Cornerstone Futures. We use these projections of harvests to

estimate supply functions for the two fundamental economic

storylines they embody. Associated forest condition forecasts

and land use forecasts are described in chapters 5 and 4,

respectively.

Effects of productivity increases—The imputation

approach adopted for the U.S. Forest Assessment System

that undergirds our supply projections uses current

observed forest productivity to construct forecasts. This

is appropriate for short run supply forecasts, but recent

research indicates that the productivity of pine plantations

could expand over the next several years (McKeand

and others 2003). Tree improvement programs have

yielded genotypes with large gains in productivity and

newly planted forests are expected to have even larger

productivity gains (with additional crossing of superior
186 The Southern Forest Futures Project

parents). Tissue culture propagation along with other

advanced genetic techniques may increase output per

acre by even greater amounts. The rate of deployment of

improved planting stock and the proportion of established

plantations receiving intensive management throughout

their rotation is unclear and compounds the uncertainty of

any attempt to forecast productivity growth.

To examine the potential contributions of this enhanced

productivity, we adopted a straightforward simulation

approach using an additive formula that increases

productivity by 10 percent each decade, so that by the 2050s

average productivity of planted pine forests is 50 percent

higher than the current level. Although we expect increased

productivity to eventually alter planting decisions, and

therefore skew some of the decision models that undergird our

analysis, we believe that this simulation approach provides a

first approximation of long run production potential.

Demand Scenarios

We examine two different demand scenarios. The first—

labeled Constant Demand—holds the demand relationships

for timber products in 2006 constant over the 50-year

projection period. This is consistent with demand stability for

both paper and solid-wood products and would be consistent

with some substitution within product lines. In effect, it is

consistent with moderate (long run average) housing demand

and the stability observed in pulp and paper markets in the

late 2000s. Demand was modeled using a constant elasticity

equation by intersecting the harvest-price observation for

2006 and applying exogenously determined own-price

elasticity, always -0.5, as was consistent with the literature.

Note that constant demand does not imply constant harvests.

Rather it holds the demand relationship between price and

harvest constant, so prices and harvests can vary over time in

response to supply shifts.

The second demand scenario—labeled Expanding

Demand—examines a return to demand growth in the South.

Under this scenario, product demand is assumed to return to

1996 levels by 2015 and then expand 10 percent per decade

through the end of the projection period. This is roughly

consistent with demand growth in the 1980s and 1990s.

We did not construct these demand scenarios to address

changes in world trade of forest products explicitly,

but instead assumed that they capture range of market

realizations that is useful for our projections, i.e., they should

provide useful insights into the potential range of market

responses over the next 50 years.

Market Forecasts

For market forecasts defined by permutations of the supply

and demand scenarios, we report forecasted harvests

and prices for every decade. Inventory and removals are

constructed on a decadal basis, with inventory reflecting the

conditions at the end of the period and removals reflecting

the average removals over the previous decade—for example,

the 2030 inventory reflects removals occurring over the

years 2021–30. All prices are in real 2009 dollars and harvest

forecasts are, after applying conversion factors, comparable

to the historical timber product output data and reported in

summary reports (Johnson and others 2010) for the 2010

Resources Planning Act (RPA) Assessment.

Data Sources

Historical harvest quantity data are derived from the timber

product output system of the Forest Service, U.S. Department

of Agriculture. Reports of roundwood output by region have

been developed for the RPA National Inventory Database

for the years 1952, 1962, 1977, 1981, 1996, 2001, and 2006

(Smith and others 2001, 2004, 2009). Comparable annual

data for softwood and hardwood pulpwood harvests have

been compiled for the South (Johnson and Steppleton 2005).

We also constructed an annual series of softwood sawlog

production by interpolating between the RPA reporting years

based on the production of softwood lumber in the South as

reported by the Southern Forest Products Association.

To examine price trends we constructed regional price

indices based on prices reported by Timber-Mart South for

all subregions of the South. We constructed price indices by

product class based on prices reported for intra-State areas

by Timber Mart-South, with each index representing an

average weighted by the inventory volumes of its associated

geographic area. Throughout this paper we report prices in

real terms, adjusted for inflation using the Consumer Price

Index price deflator, with 2009 as the value basis. Indices

of timber prices were also used to allow easier comparisons

among product types. When indices were used, we defined

1977 as the base year (the index is set equal to 1 in 1977) and

applied the indexing to the real prices described above.

Trade data were taken largely from the database compiled

by Daniels (2008), which summarizes extensive records on

imports and exports from the U.S. Department of Commerce

through 2005. Other secondary sources were tapped to

provide data on exports/imports of selected products beyond

2005, wood products capacity, and various price indices.
187

chapter 9. Markets

Results

We start this section by examining how timber markets have

changed in the South since detailed records have been kept

(with emphasis on the most recent changes) using timber

harvests and prices as summary indicators of development

over time. We begin by examining how harvest quantities

and prices have changed, and where possible, deconstructing

those changes into implied shifts in supply and demand to

add context.

Historical Timber Markets

Southern forests yield a wide variety of hardwood and

softwood timber products. Softwood products constituted

71 percent of harvest output in 2006, the latest year for which

comprehensive timber product output data are available

(fig. 9.1). Forty-two percent of total harvest was for sawlogs

and 38 percent was for pulpwood products. Softwood

sawlogs comprised the largest product class (31 percent),

followed by softwood pulpwood (26 percent) and hardwood

pulpwood (12 percent); the three represented roughly

69 percent of harvests, continuing a trend that began in the

1970s (fig. 9.1).

Timber harvests from southern forests trended strongly

upward during the last half of the 20th century (fig. 9.1).

From 1962 to 1996, annual harvesting more than doubled

from about 4 to almost 10 billion cubic feet, with a relatively

constant product mix. Production ranged from 39 to

44 percent from pulpwood and 64 to 71 percent from all

softwoods, with no consistent trends.

Growth in harvests for all products was steady from one

year to the next with only a few exceptions (fig. 9.2), the

most notable being a dip in output during a brief recession

in the mid-1970s. Growth in harvests was at its strongest

from 1982 through 1998, with output expanding at a

compound rate of 3.3 percent per year. After this long

period of strong growth, total harvest quantity fell by

approximately 23 percent from 1997 to 2008, returning

total harvest quantity to 1987 levels. This represents

the largest and longest downturn in harvesting over the

historical period (1952 to 2008).

Trends in the three largest product classes (fig. 9.3) show

that the harvest decline was led by reductions in hardwood

pulpwood (a loss of 42 percent), followed by 27 percent for

softwood sawtimber and 7 percent for pulpwood. Most of

the decline in softwood sawtimber production occurred

since 2005 (fig. 9.3). We were unable to construct an annual

time series of hardwood sawlog production (the fourth

largest product class) using a comparable technique, but the

periodic data (fig. 9.1) suggest that hardwood sawtimber

harvests were relatively stable at least through 2006, with

incomplete data suggesting substantial declines beginning

in 2007 in association with the housing-related recession

that began that year.

Timber prices are an indicator of the scarcity of timber as

an input to production, and they reflect the interaction of

supply and demand: if stumpage prices increase, then timber

becomes relatively scarcer. Conversely, falling stumpage

prices indicate that timber is becoming more abundant

relative to demand for its use. Prices for various wood

products demonstrated a variety of trends from 1977 to 2009,

the period for which we have comprehensive data, indicating

that scarcity or abundance of these resources is a complex

and evolving story.

From 1977 to the late 1980s, timber prices were flat-todeclining for all hardwood and softwood products (fig. 9.4).

Compared to 1977, softwood sawtimber prices declined

very slightly through 1991, softwood pulpwood prices were

essentially flat through 1989, and hardwood pulpwood prices

were flat through 1988. Harvesting grew at moderate rates

(fig. 9.3), with no indications of increasing scarcity through

the late 1980s.

Price patterns began changing between 1989 and 1992

(fig. 9.4). Real-dollar prices turned upward for all four

products and increased through 1997 or 1998, when

production peaked. From 1988 to 1998, hardwood pulpwood

prices increased at an average annual rate of 12 percent,

followed by softwood pulpwood at 5 percent and softwood

sawtimber at 8 percent. Hardwood sawtimber prices increased

by 6 percent from 1992 to 1998. These price data indicate

increasing scarcity for all timber products over the decade.

From 1998 to 2009, hardwood pulpwood and sawtimber

prices stabilized, and softwood sawtimber prices declined

from their near-peak 1998 level (only exceeded in 1979) and

from 2005 to 2009 reached their lowest level of the historical

period. Softwood pulpwood prices have, however, followed

a decidedly different pattern. From 1998 to 2001, prices for

this product fell to about half of their 1998 value, their lowest

level of the historical period, and have remained at this level

through 2009.

Changes in harvest quantities and timber prices since 1998

suggest that timber markets have been and continue to be

dynamic. Softwood product prices have declined from their

peak levels, but hardwood product prices have remained

relatively constant. These price changes, combined with

harvest patterns, suggest that returns available to most

timberland owners are now substantially lower than they

were in the peak years of the 1990s. For softwood pulpwood,

these patterns suggest a contraction in pulpwood demand

coupled with stable-to-expanding supply of standing

pulpwood-sized timber. In contrast, hardwood pulpwood


190 The Southern Forest Futures Project

seems to have become somewhat scarcer; softwood

pulpwood prices were about twice as high as hardwood

pulpwood prices in the early 1990s, but the two products are

now roughly equal in price (fig. 9.4).

Looking jointly at price and harvest changes for the three

largest product classes in the South (fig. 9.5), we can define

three distinct periods of development from 1977 to 2008.

Moderate growth phase (1977 to 1986)—During this

period, harvests of all products increased at a moderate rate

while timber prices stayed constant or even declined for all

three of the major products. These trends are consistent with

expansion of both supply and demand for the products—that

is, forest investments generated additional wood supply and

kept prices from increasing with output.

Rapid growth phase (1986 to 1998)—During this period,

harvests of hardwood pulpwood, softwood pulpwood, and

softwood sawtimber continued to increase but at faster

rates than the earlier period. Prices for these products also

increased, and at a higher rate than for harvests. This pattern

is consistent with a strong expansion in timber demand but

does not provide conclusive evidence of change in timber

supply. It is consistent, however, with demand expanding

faster than supply. In contrast, production was stable

but price increased for hardwood sawtimber, signaling a

tightening of hardwood sawlog supply.

Adjustment phase (1998 to 2009)—Following the

production peaks on 1997 through 1998, fundamental

changes in output and price trends suggest important changes

in timber markets. For hardwood pulpwood, prices initially

fell and then increased again from 2001 to 2009, and harvests

declined steadily throughout the period, falling by about

60 percent. Falling output with increasing prices indicates

a contraction in supply for hardwood pulpwood over the

period, irrespective of demand changes. For softwood

pulpwood, harvests fell about 7 percent from 1997 to 2000,

and then stabilized, but prices fell by about 50 percent

between 1998 and 2001 and have remained at this level

through 2009. Decreasing prices with a stable output is

consistent with a strong expansion in the supply of softwood

pulpwood. For softwood sawtimber, simultaneous declines in

harvest and prices indicate that markets were dominated by

a contraction of demand from 2005 to 2009, coincident with

strong declines in the demand for U.S. housing construction.

Demand Trends for Pulp and Paper Products

For several decades, the United States has produced more

wood pulp than any other nation. In 2006, hardwood and

softwood pulpwood made up 36 percent of the timber

consumed in the South. The region’s paper mills are

concentrated in the few areas where plentiful water is

available. These areas include southeastern Georgia,

northeastern Florida, and southern Alabama and Mississippi.

Concentration of paper production capacity organizes

the demand for pulpwood within the South: demand for

pulpwood is strongest in the vicinity of mills and weakens

with distance from the mill gate (fig. 9.6). Although satellite

chipmills distributed the demand for pulpwood over a wider

area in the 1990s, pulpwood markets are still much more

concentrated geographically than are markets for solid wood.

The raw material for production of paper products

comes from pulpwood-grade trees, from wood product

manufacturing residuals, and increasingly from recycled

fiber. Ince (2000) shows that recycled material comprised

37.9 percent of U.S. paper products in 1998, up from 23.9

percent in 1985. This has resulted in a drop in the demand

for virgin wood fiber. The amount of recycled material used

in U.S. paper manufacturing may have reached a maximum,

especially given strong export demand for recovered paper.

So it is likely that expanding use of recycled material

mitigated demand and price increases during the rapid

growth phase (1986 to 1998), but that changes in demand

for recycled material have not been a major influence in the

adjustment phase (since 1998).

Pulping capacity within the region defines the upper limit

for pulpwood demand, at least in the short run. Because

expanding capacity through construction requires a large

commitment of capital (typically in the $2 billion range),

trends in capacity provide a strong indicator of current and

anticipated demand for pulpwood. Through 1998, both U.S.

and southern pulpmill capacity trended upward (fig. 9.7).

Since then, U.S. capacity has decreased only slightly, while

Southern capacity decreased by 16 percent before stabilizing

in 2003 (fig. 9.8). The rate of decrease in southern capacity

was much lower than decreases in the number of paper

mills, reflecting an increased concentration of production in

remaining plants.

Accompanying these declines in domestic capacity

was an expansion in capacity by other countries such a

Sweden, Finland, Chile, and Brazil (fig. 9.9). Although the

United States and the South continue to lead in pulpwood

production, their share of worldwide capacity has declined

since 1991. By 2003, pulp capacity in the South had returned

to its 1985 level (well short of the 1998 level), where it

remained through 2008.

New pulpmill capacity and pulp production is feeding

increased worldwide (and especially Asian) demand for paper

products. With level-to-declining capacity in the United

States, it is clear that the new capacity is being developed

elsewhere. These changes are likely explained by shifts

in comparative advantage resulting from several factors,

including labor costs, raw materials costs, and proximity to



194 The Southern Forest Futures Project

final product markets, which controls transportation costs.

Other contributing factors include the shrinkage of U.S.

manufacturing, which requires paper for packaging, and the

demand for pulpwood in products like oriented strand board.

Manufacturing costs for kraft linerboard (fig. 9.10) provide

an example of differences in comparative advantage among

regions and countries. The South is competitive in this

market compared to the Western United States, Canada, and

Europe, but its cost structure lags behind Latin American

countries (primarily Brazil and Chile), mainly because fiber

and labor costs are significantly lower in less industrialized

countries. The South retains comparative advantage because

of its proximity to U.S. demand centers (thereby lowering

transportation costs), but labor and wood cost differentials

make Latin American producers viable competitors.

In 1995, 1999, and 2004, both Brazilian and Chilean

producers could deliver softwood and hardwood (mostly

Eucalyptus) pulpwood to mills at substantially lower cost

than producers in the South (fig. 9.11). In 2004, delivered

southern softwood pulpwood was 24 percent higher than in

Brazil (21 percent for hardwood pulpwood) and 27 percent

higher than in Chile (27 percent for hardwood pulpwood).

Price differentials are not static however, and prices in Brazil

and Chile have risen since 1999. The comparative advantage

held by these nations would decrease if this trend were to

continue.

Demand Trends for Solid Wood Products

The large majority of the solid wood produced in the South

goes into lumber and panel products, comprising about

52 percent in 2006. The region’s lumber mills, unlike its pulp

and paper mills, are widely dispersed (fig. 9.12). Southern

softwood sawmill capacity grew steadily from 1995 to 2005

and then declined slightly through 2009 (Spelter and others

2009), mirroring a strong decline in lumber production

associated with the decline in U.S. housing construction

(fig. 9.13). Comparable data are not available for hardwood

lumber capacity in the South.

McKeever and Spelter (1998) report that southern panel

capacity expanded significantly in the 1990s (fig. 9.14). From

1998 to 2009, oriented strand board capacity nearly doubled

(APA-The Engineered Wood Association 2010) from 7,900 to

13,840 square feet (3/8-inch basis), representing 81 percent of

total U.S. capacity. In contrast, southern pine plywood, which

dominated panel production through the 1970s, peaked in the

1990s and has since declined (APA-The Engineered Wood

Association 2010). At the 1996 peak, plywood capacity was

14,530 million square feet (3/8-inch basis) but fell to 9,190

square feet by 2009 (APA-The Engineered Wood Association

2010). Capacity for medium density fiberboard production

grew strongly through the 1990s.

More recent data indicate that although southern panel

production remained stable from 1996 to 2007 and fell

precipitously in 2008/ 2009 because of the 2007 recession

and housing market collapse, oriented strand board as a share

of production has continued to grow (fig. 9.15). Expanding

oriented strand board capacity coupled with declining

plywood capacity suggests increasing demand for less

expensive, small-diameter timber, especially when compared

to the veneer logs used in plywood production.

Unlike the demand for paper products, which is most

clearly linked to general levels of economic activity, notably

manufacturing activity, demand for solid wood products is

strongly linked to the construction industry. Housing starts

in particular provide a strong correlate to the consumption

of solid wood products, and recent economic developments

are a strong reminder that the housing market is cyclical.

Peaks in housing starts in the early 1970s, in the late 1970s,

in the mid-1980s, and in 2006 have all been succeeded by

rapid declines of at least 30 percent (fig. 9.16), with these

cycles centering on a base level of about 1.5 million units

per year. Within this context, the most recent decline and

continuing stagnation of housing markets is unprecedented.

After exceeding 2 million units in 2005, housing starts fell

to 554,000 units in 2009 and (as projected) 619,000 units in

2010 (fig. 9.16), compared to lows that had not dipped below

1 million from 1959 to 2007.

The Congressional Budget Office has constructed alternative

forecasts of construction activity recovery from the current

housing trough that incorporate existing housing stocks,

population growth, household formation, depreciation, and

employment. The forecasts predicted that housing starts

could return to between 1.2 and 1.5 million units by 2012

(Congressional Budget Office 2008), a trend that longer term

forecasts predicted would continue. From the perspective of a

long run analysis, this forecasted stability suggests recovery

and subsequent stability in demand for solid wood products

used in construction. In addition, the expansion in the overall

number and age of existing residences may bring increased

upkeep and repairs, stimulating a gradual expansion in

demand for wood.

More recent data indicate that the recovery of construction

activity projected by the Congressional Budget Office has yet

to be realized. In March 2011, the U.S. Census Bureau (2011)

estimated new private housing starts at a seasonally adjusted

479,000 units, considerably lower than the housing starts

recorded in 2009 and 2010. The time-path of a recovery

in housing influences the future of solid wood products

demand. With a sustained suppression of housing demand,

solid wood processing would likely shrink, eventually

resulting in structural changes in these markets. Although

necessarily difficult to predict, the implications of sustained

suppression might include sustained declines in production








203

chapter 9. Markets

defines a ceiling for domestic hardwood stumpage prices in

certain areas of the South.

From the beginning of the data series (1989) to 2002, the

United States has had a large trade surplus in wood chips

(fig. 9.22)—with exports far exceeding imports. Since 1999,

however, the trade surplus has fallen steadily, from around

3 million tons in the mid-1990s to less than 0.1 million tons

in 2003. From 1991 to 2002, nearly all of the wood chips

exported from U.S. southern ports were shipped to Japan.

By 2002, chip exports from southern ports essentially

ceased. In 2003, the reduction in southern chip exports—

primarily hardwood chips—to Japan was equivalent to 5

percent of total southern pulpwood production and nearly 16

percent of southern hardwood pulpwood production. With

most of the trade in wood chips moving through Mobile, we

might expect the economic impacts of reduced demand to be

strongest in Alabama and to decline in an outward radiating

pattern.

Lumber—Since the late 1980s, the United States has been

a large net importer of softwood lumber, primarily from

Canada (fig. 9.23). Lumber imports from South America,

although relatively small from 1989 to 2004, have been rising

steadily. Although the United States exports some lumber,

the balance of trade favors imports, and the trade deficit is

growing.

Imports of lumber from Canada have an important influence

on all U.S. timber markets, but the effects on southern

markets are likely to be indirect. Lumber from Western

Canada more directly substitutes for lumber of species that

grow in the Western United States (Nagubadi and others

2004), and imports are generally not directly substitutable for

the treated lumber produced in the South.

In 2004, the United States led all other temperate countries

in producing (60 percent) and consuming (52 percent)

hardwood lumber, with about 8 percent of domestic

production exported. Hardwood lumber is a much more

heterogeneous commodity than softwood lumber, so its

production and trade serves a wide variety of end uses—

from flooring to furniture to shipping pallets—and aggregate

data provide only a very general description of trends. Note

that about 10 percent of U.S. hardwood exports are from the

Pacific Northwest (especially red alder) compared to about 90

percent from the Eastern United States.

Exports of hardwood lumber from the South increased from

about 0.4 million m3 in 1989 to just over 1.2 million m3 in

2004 (fig. 9.23)—mostly to other North American countries,

followed by East Asia and the 27 countries of the European

Union (see fig. 9.24), and with about 10 percent going to

all other countries combined. The distribution of exports

among these destinations has changed somewhat since

1989, with shipments to Europe declining and shipments

to other Canada and Mexico increasing substantially (fig.

9.24). Shipments to East Asia have been essentially constant

in aggregate, with a changing mix of individual country

destinations and large increases in shipments to China offset

by decreases in shipments to other Asian countries. The 2007

recession led to a strong decline in total hardwood exports

with the distribution among destinations remaining relatively

constant (fig. 9.24).

Southern exports of softwood lumber have been relatively

small and have declined over the last decade (fig. 9.25),

falling to about a third of 1992 levels in 2004 and now

representing only 1 to 2 percent of total production.

Panels—Trade in panel products is weighted toward imports,

with about 15 percent of plywood consumption and 38

percent of oriented strand board consumption imported

from Canada and other countries in 1999 (Spelter 2001).

Particleboard, waferboard, and oriented strand board

imports from Canada grew strongly through the mid2000s, increasing from $1.53 billion in 1999 to $3.16 billion

in 2004, before decreasing substantially at the end of the

decade (APA-The Engineered Wood Association 2010) U.S.

exports of panels cannot be considered negligible, although

they are substantially lower than imports. For example, in

2009, plywood exports were 482 million square feet (3/8-

inch basis) compared to 616 million square feet for imports.

Oriented strand board trade has been significantly more

imbalanced, tilted toward imports (APA-The Engineered

Wood Association 2010).

Oriented strand board markets expanded through the mid2000s. North America will likely continue to dominate

World production in this commodity class, but the trade

balance within North America—especially between Canada

and the United States—could change with market expansion.

In addition, a decline in demand for southern pulpwood

could offer a competitive mill-siting advantage to U.S.

manufacturers.

Overall, we see no dramatic change in international markets

that would strongly affect southern timber demand in the

short run. At the national level, the value of wood products

imports exceeds exports so the wood products balance of

trade is negative. For southern ports, the wood products

balance of trade is positive, but a small share of total

production.

Timber Supply Trends

Overall, changes during the adjustment phase (1997 to

2009) indicate some important changes in supply. An

expanded supply of softwood pulpwood timber coupled with







211

chapter 9. Markets

forests still in production but with a very different set of

owners. Forecasts of the impacts of this ownership change on

investment can only be speculative at this point, but will play

an important part in determining future supply.

Forecasts of supply indicate a substantial expansion in

softwood supply over the next decade as new pine plantations

mature. This portends continued low prices for softwood

products, especially softwood pulpwood. Beyond 2020,

supply depends on a much lower rate of expansion in forest

plantations—generally the rate of planting harvested forests

is assumed to be about half of what it was in the 1990s. Even

at these lowered levels, the supply of timber would grow

and the price of products would generally decline if demand

does not grow over the next decades. While supply growth

could also be affected by policy changes affecting future

management options—e.g., potential restrictions on the use

of herbicides for site preparation—our analysis focuses on

a future with no substantial changes in policy environment.

Policy changes could lead to different outcomes.

Growth in harvesting can be supported by the forest land

of the South. A return to 1990s demand levels would

result in a price stabilization for softwood pulpwood

prices and an increase of less than 1 percent per year for

softwood sawtimber and hardwood pulpwood, as well as

an increase in total output of about 40 percent from 2006

to 2055. If, in addition, productivity in pine plantations

grows by 50 percent, then output could increase even more

substantially—up to 70 percent for softwood pulpwood.

Demand is perhaps the most crucial uncertainty in this

analysis. Current demand is suppressed by the unprecedented

fall in housing construction in 2008, and by long run

phenomena, such as the decline in paper production

capacity in the South in line with broader economy-wide

shifts that are impacting the timber products industry and

global capacity shifts. Recovery from the 2007 recession

will strongly affect the course of future demand, but policy

developments may also play a role. Incentives for using

renewable biomass in various bioenergy operations could

provide a potentially large new demand for timber products

in the South (chapter 10).

What is clear from our analysis is that, absent renewed

growth in demand for traditional southern forest products,

production growth could be sustained in support of new

markets without substantial increases on timber prices,

although regional stability could coincide with important

scarcities in local timber markets, for example if some

individual States develop their own Renewable Portfolio

Standards. The question that remains is, “How much?”

Without productivity gains, the largest projections of

demand for wood-based bioenergy products (under strong

economic and moderate population growth projections

of Cornerstones A and B, and the A1B storyline in the

2010 RPA Assessment) outlined by the U.S. Department

of Energy and described in chapter 10 would lead to large

price increases (as much as 400 percent by 2055). With the

50-percent productivity growth for plantations, this demand

could more readily be accommodated without strong price

increases, even with existing industries consuming their

current levels of timber products. Under the Expanding

Demand scenario and holding pulpwood consumption for

existing industries at 2006 levels, an additional 2.4 billion

cubic feet or 36.6 million green tons per year of softwood

pulpwood harvesting are forecasted for 2055. Combining

Enhanced Productivity to the above scenario would

increase softwood pulpwood harvesting to 3.7 billion cubic

feet or 57.9 million green tons per year.

In summary, the South has the capacity to expand production

well into this century, but demand for forest products seems

to be a limiting factor. Timber supply has continued to grow

while demand has slackened over the past decade, inducing

disinvestment in pulp and paper manufacturing and slower

investment in other wood products by the forest products

industry. Given this reality, the future of timber markets will

largely be determined by demand growth that would emerge

primarily from the requirements of forest fiber inputs to

supply bio-based energy.

Knowledge and Inf ormation Gaps

Our market models are based, to the extent possible, on

empirical models of biological changes and management

behavior. One area where empirical models have not proved

sufficient is in forest investments. Better information on how

various owner and investor groups adjust their management

plans, particularly by expanding tree planting in response

to market signals, could reduce the uncertainty of market

projections. Better models of the demand for final wood

products and timber inputs to their production could also

improve market projections.

Change in the ownership of forests is another key source

of uncertainty. Given the information at hand, we assume

that the management objectives and management models

of timber investment management organizations are

similar to those of the vertically integrated forest products

companies that they have replaced over the past 10 years

(chapter 6). Little is known about the broader implications

of these changes in ownership and associated changes

in management strategies for the land that has been

transferred. For example, the productivity of planted forests

derives from other treatments, including fertilization, weed

control, and thinning which have not been modeled here.

We have assumed that management strategies have not

been greatly impacted by these changes, but this remains an

untested hypothesis.
212 The Southern Forest Futures Project

Past attempts to model southern timber markets have

been successful because of the dominance of private

owners. Our models indicate that forest harvesting can be

modeled as a function of market signals and is therefore

predictable. However, an important uncertainty may well

be the development of new demands for bioenergy and

biofuels that are driven, not by markets, but by new State

and Federal policies, which are unknowable at this point.

In addition, the spatial scope of our models addresses the

region’s timber markets as one entity, given the current

distribution of production demands and forest management

types. However, policies at the State level, especially

State Renewable Portfolio Standards, may create local

demands that could result in local scarcities and a spatial

realignment of production; these we cannot address with

our models.

ACKNOWLEDGMENTS

Thanks to Shan Liu, Southern Research Station, U.S.

Department of Agriculture Forest Service; Ruhong Li,

Department of Forestry and Management, University of

Wisconsin, Madison; and Robert C. Abt, Department of

Forestry and Environmental Resources, North Carolina State

University.

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	 engineered wood yearbook. APA Economics Report E176. Tacoma,

WA: American Plywood Association. 80 p.

Congressional Budget Office. 2008. The outlook for housing starts, 2009 to

2012. Background Paper. U.S. Congress, Congressional Budget Office.

23 p. http://www.cbo.gov/ftpdocs/98xx/doc9885/11-17-HousingStarts.

pdf. [Date accessed: October 28, 2010].

Daniels, J.M. 2008. United States trade in wood products, 1978–2005.

Gen. Tech. Rep. PNW-GTR-738. Portland, OR: U.S. Department of

Agriculture Forest Service, Pacific Northwest Research Station. 124 p.

Fleishman, S.J.; Eastin, I.L.; Shook, S.R. 1999. Material substitution trends

in residential construction, 1995 vs. 1998. Center for International Trade

in Forest Products Working Paper Number 73. Seattle: University of

Washington. 76 p.

Ince, P. 2000. Industrial wood productivity in the United States, 1900-1998.

Res. Note FPL-RN-0272. Madison, WI: U.S. Department of Agriculture

Forest Service, Forest Products Laboratory. 14 p.

Johnson, T.G.; Steppleton, C.D. 2005. Southern pulpwood production,

2003. Resour. Bull. SRS-101. Asheville, NC: U.S. Department of

Agriculture Forest Service, Southern Research Station. 38 p.

Johnson, T.G.; Steppleton, C.D.; Bentley, J.W. 2010. Southern pulpwood

production, 2008. Resour. Bull. SRS–165. Asheville, NC: U.S. Department

of Agriculture Forest Service, Southern Research Station. 42 p.

McKeand, S.; Mullin, T.; Byram, T.; White, T. 2003. Deployment of

	 genetically improved loblolly and slash pines in the South. Journal

of Forestry. 101(3): 32-37.

McKeever, T.; Spelter, H. 1998. Wood-based panel plant locations and

timber availability in selected U.S. States. Gen. Tech. Rep. FPLGTR-103. Madison, WI: U.S. Department of Agriculture Forest Service,

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Nagubadi, R.; Zhang, D.; Prestemon, J.; Wear, D.N. 2004. Softwood lumber

products in the United States: substitutes, complements, or unrelated?

Forest Science. 50(4): 416-426.

Norris Foundation. 1977–present. Timber Mart-South State Timber Price

Reports. Center for Forest Business, Warnell School of Forestry and

Natural Resources, University of Georgia, Athens, GA.

Poylakov, M.; Wear, D.N.; Huggett, R. 2010. Harvest choice and timber

supply models for forest forecasting. Forest Science. 56(4): 344-355.

Smith, B.R.; Rice, R.W.; Ince, P.J. 2003. Pulp capacity in the United States,

2000. Gen. Tech. Rep. FPL–GTR–139. Madison, WI: U.S. Department

of Agriculture Forest Service, Forest Products Laboratory. 23 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.

Smith, W.B.; Miles, P.D.; Vissage, J.S.; Pugh, S.A. 2004. Forest resources

of the United States, 2002. Gen. Tech. Rep. NC-241. St. Paul, MN: U.S.

Department of Agriculture Forest Service, North Central Research

Station. 137 p.

Smith, W.B.; Vissage, J.S.; Darr, D.R.; Sheffield, R.M. 2001. Forest

resources of the United States, 1997. Gen. Tech. Rep. NC-219. St. Paul,

MN: U.S. Department of Agriculture Forest Service, North Central

Research Station. 190 p.

Spelter, H. 2001. Wood-based panels—supply, trade and consumption. In:

ECE/FAO Forest Products Annual Market Review 1999-2000:113-130.

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Spelter, H.; McKeever, D.; Toth, D. 2009. Profile 2009: softwood sawmills

in the United States and Canada. Res. Pap. FPL-RP-659. Madison,

WI: U.S. Department of Agriculture Forest Service, Forest Products

Laboratory. 55 p.

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newresconstindex.html. [Date accessed: April 5, 2011].

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of forest product exports from the United States to Europe and Japan.

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sector in 2005: a prospective analysis of recent change. Gen. Tech. Rep.

SRS-99. Asheville, NC: U.S. Department of Agriculture Forest Service,

Southern Research Station. 29 p.

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demand for paper and paperboard packaging. Scandinavian Journal

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213

chapter 10. Forest Biomass-Based Energy

	 Janaki R. R. Alavalapati, Pankaj Lal,

	 Andres Susaeta, Robert C. Abt, and David N. Wear1

Key Findings

•	 Harvesting woody biomass for use as bioenergy is

projected to range from 170 million to 336 million green

tons by 2050, an increase of 54 to 113 percent over current

levels.

•	Consumption projections for forest biomass-based energy,

which are based on Energy Information Administration

projections, have a high level of uncertainty given the

interplay between public policies and the supply and

investment decisions of forest landowners.

•	 It is unlikely that the biomass requirement for energy

would be met through harvest residues and urban wood

waste alone. As consumption increases, harvested timber

(especially pine pulpwood) would quickly become the

preferred feedstock.

•	The emergence of a new woody biomass-based energy

market would potentially lead to price increases for

merchantable timber, resulting in increased returns for

forest landowners.

•	While woody biomass harvest is expected to increase with

higher prices, forest inventories would not necessarily

decline because of increased plantations of fast growing

species, afforestation of agricultural or pasturelands, and

intensive management of forest land.

•	Because it would allow more output per acre of forest land

and dampen potential price increases, forest productivity is

a key variable in market futures.

•	The impacts that increased use of woody biomass for

energy would have on the forest products industry could

be mitigated by improved productivity through forest

management and/or by increased output from currently

unmanaged forests.

1 Janaki R.R. Alavalapati is a Professor and Department Head of Forest

Resources and Environmental Conservation, Virginia Tech, Blacksburg,

VA 24061. Pankaj Lal is an Assistant Professor, Department of Earth and

Environmental Studies, Montclair State University, Montclair, NJ 07043.

Andres Susaeta is a Postdoctoral Scholar, Department of Forest Resources

and Environmental Conservation, Virginia Tech, Blacksburg, VA 24061.

Robert C. Abt is a Professor, Department of Forestry and Environmental

Resources, North Carolina State University, Raleigh, NC 27695. David N.

Wear is the Project Leader, Center for Integrated Forest Science, Southern

Research Station, U.S. Department of Agriculture Forest Service, Raleigh,

NC 27695.

•	 Price volatility associated with increased use of woody

biomass for energy is expected to be higher for pulpwood

than for sawtimber.

•	 The impacts of wood-based energy markets tend to be

lower for sawtimber industries, although markets for all

products would be affected at the highest levels of projected

demand.

•	Different types of wood-based energy conversion

technologies occupy different places on the cost feasibility

spectrum. Combined heat and power, co-firing for

electricity, and pellet technologies are commercially viable

and are already established in the South. Biochemical and

thermochemical technologies used to produce liquid fuels

from woody biomass are not yet commercially viable.

•	Current research does not suggest which woody species

and what traits would likely be most successful for energy

production. The future of conversion technologies is

uncertain.

•	 In the absence of government support, research, pilot

projects, and incentives for production, woody bioenergy

markets are unlikely to grow substantially.

•	Under a high demand scenario for bioenergy, the resulting

intensity of woody biomass harvests could have deleterious

effects on stand productivity, biodiversity, soil fertility, and

water quality.

•	 Although research provides some guidelines for the

design of management to protect various forest ecosystem

services, forest sustainability benchmarks are not well

defined for a high bioenergy demand future and existing

certification systems may need modifications to address

multiple resource values.

Introduction

The United States is the largest consumer of petroleum

products, consuming about 19.5 million barrels per day in

2008 (Energy Information Administration 2009), with a

significant portion imported from politically unstable regions

of the world. This reliance on imported fossil fuels, coupled

with their associated greenhouse gas emissions, has led to

economic, social and environmental concerns. Bioenergy

may offset fossil fuel use, diversify energy sources, reduce

emissions, and provide socioeconomic benefits in the form

Chapter 10.

Forest Biomass-Based Energy
214 The Southern Forest Futures Project

of additional income and new jobs. Bioenergy from woody

biomass could contribute by increasing U.S. renewable

energy resources, reducing competition between agricultural

crops destined for food and those for fuel production (Hill

and others 2006), and perhaps improving the condition of

some forests. Some analysts, for example the Manomet

Center of Conservation Sciences (2010) in their analysis of

wood-based bioenergy in Massachusetts raise doubts about

the greenhouse gas mitigation potential of forest bioenergy.

Others, e.g., Lucier (2010) and O’Laughlin (2010), challenge

these findings. In the South, some studies, e.g., Dwivedi and

others (2011), indicate that southern pine based energy could

reduce greenhouse gas emissions as compared to using fossil

fuels.

Although historically limited to residues from the production

of wood products, biomass could be sourced from logging

residues, stands damaged by natural disturbances (such as

wildfire, pest outbreaks, and hurricanes), small-diameter

trees thinned from plantations and other forests, and energy

crops such as eucalyptus and poplar; these sources would

likely be tapped as woody bioenergy markets become

competitive. At high enough prices, even merchantable

timber could be diverted to bioenergy uses. Hughes (2000)

suggests that the combination of forest bioenergy plantations

and continued use of wood residues from forest product

industries could supply 7 to 20 percent of the U.S. electricity

generation in the future.

Many pine plantations established to supply pulpwood for

paper and engineered wood products are overstocked and

therefore susceptible to wildfires and pest attacks (Gan and

Mayfield 2007a). For example, nearly half of over

1.1 million acres of nearly pure pine stands are at risk from

southern pine beetle in Oklahoma (Oklahoma Department

of Agriculture Food And Forestry 2008). Wood-based

bioenergy markets could increase thinning and removals,

thereby reducing these risks (Belanger and others 1993, Gan

and Mayfield 2007a, Neary and Zieroth 2007, Speight 1997).

Schmidt and others (2002) estimated that 2.7 billion dry

tons of forest biomass needs to be removed through forest

fuel reduction treatments in the South, about 20 million

dry tons annually. Furthermore, wood-based bioenergy

markets would improve profitability for landowners in the

South (Nesbit and others 2011, Susaeta and others 2009).

Furthermore, southerners appear willing to pay more for

cleaner sources of energy such as wood based biofuels

(Susaeta and others 2010).

Federal policies such as the 2002 Farm Bill, 2005 Energy

Policy Act, 2007 Energy Independence Security Act, and

2008 Farm Bill have specifically encouraged the production

of cellulosic biofuels such as those produced from wood,

ranging from grants and loans to the establishment of

renewable fuel standards (15.5 billion gallons in 2012, and

36 billion gallons by 2022 of which 21 billion gallons must

be cellulosic). Federal law provides differing definitions of

acceptable forest biomass for bioenergy. For example, under

the 2007 Energy Independence Security Act biomass from

public lands, municipal solid waste, plantations established

after the enactment of the Act, ‘old growth’ or ‘mature’

forests, and most other woody biomass (except for slash

and pre-commercial thinning) is excluded from private

and non-industrial forests (NIPFs) landowners. The 2008

Farm Bill on the other hand is less restrictive, as it allows

for biomass derived from Federal lands and other forests

(i.e., not tree plantations) as biofuels. The American Clean

Energy and Security Act of 2009 (H.R. 2454), as passed by

the House of Representatives, sought to create a broadened

universal definition of renewable biomass that applies to

the Renewable Fuel Standard, and a national Renewable

Electricity Standard. We followed a non-restrictive definition

of biomass while simulating supply variations and southern

forests and considered that aboveground biomass on private

forestlands in the South could be used for energy production.

This is based on the assumption that policy would not restrict

the allocation of forest biomass to bioenergy uses.

This chapter analyzes the potential effects of the emergence

of a bioenergy market on southern forests, forest owners,

traditional forest product industries, and ecosystem integrity

and services; with emphasis on the following key issues:

•	How markets for wood for energy production might evolve

and potential implications for traditional forest product

industries and landowners

•	The status of current and potential technologies that can

help realize large-scale production of woody bioenergy

•	 How bioenergy policies could impact forest landowners

and forest industry

•	 Effects of woody bioenergy markets on forest ecosystems

health; benchmarks for sustainability

Methods

We surveyed the literature to address questions about

technology development, bioenergy policies, and

sustainability, and we developed detailed modeling to project

market changes and incorporate an analytical component

into the results of the literature survey.

To assess tradeoffs between the traditional forest

product industry and the woody bioenergy industry, we

evaluated woody biomass supply variation through time

and associated price, inventory, and removal responses

following Rossi and others (2010). In the face of future

competition for raw materials and the potential competitive

advantage that policy incentives would provide to woody

bioenergy sector, this tradeoff analysis was considered
215

chapter 10. Forest Biomass-Based Energy

critical for the future of southern forests (Wear and others

2009). Many authors have explored this issue; what has

been lacking is a systematic analysis of regional trends that

assesses woody biomass supply in response to variation in

future consumption for energy.

We modified the Subregional Timber Supply (SRTS) model

(Abt and others 2000), to assess the potential effects of

bioenergy consumption on wood products markets. The

model provided price, inventory, and removal responses

for different wood-for-energy consumption and supply

scenarios; and allowed us to estimate impacts on traditional

forest industries and landowners.

Of the large-scale macro models available for conducting

our analysis (Adams and others 1996; De La Torre Ugarte

and Ray 2000; De La Torre Ugarte and others 1998, 2006),

the SRTS model is the only one that treats standing timber

as a potential supply of bioenergy and defines regions in

a way that is congruent with Forest Inventory Analysis

(FIA) survey units. Because it incorporates an inventory

projection model into a timber market model framework, its

projections are based on supply and demand interactions. It

allows of larger diameter sawtimber to be downgraded for

nonsawtimber (largely pulpwood uses) in response to price

signals and is familiar to many forest industry analysts and

State forestry agencies, having been used to model timber

supply and prices in the Northeast (Sendek and others

2003) as well as the South (Bingham and others 2003,

Prestemon and Abt 2002). It has also been used to assess the

influence of nonmarket values on timber market decisions

by nonindustrial private forest landowners (Pattanayak and

others 2005), the effects of wood chip mills on timber supply

in North Carolina (Schaberg and others 2005), the impacts of

Renewable Energy Standards policy implemented in North

Carolina (Galik and others 2009), and bioenergy demands in

South (Abt and Abt, in press).

The SRTS model estimates two forest products, sawtimber

and pulpwood product allocations for softwoods and

hardwoods. Its equations—defined through supply, demand,

and inventory elasticity values—are used to project the

market-clearing price and quantity levels, which in turn

are used to allocate subregional harvesting and to project

the next period’s inventory values. A Goal Program then

categorizes the total wood requirement by management type

and age class and makes allocations to subregions, owners,

and products.

The separation of products and inventory in terms of

sawtimber and pulpwood is based on user-specified

definitions that allocate most of the largest diameter wood

to saw mills, a percent of the largest diameter and all of

the medium diameter wood to pulpwood, and the smallest

diameter wood to the forest floor. With these allocations, a

product mix is calculated for harvest in any management

type and age class with the objective of defining the projected

removal mix for the region/owner in a way that follows

historical harvest patterns of existing removal-to-inventory

intensities. For partial harvests, the model defines a stocking

target (volume per acre) for each management type and age

class; if the current stocking is greater than the target, the

harvest is considered a thinning. After the volume-per-acre

target is reached, the harvest considered final and acres are

returned to age class zero. Under most circumstances, this

approach ensures that average stocking is close to target

(historical) levels throughout the projection (Abt and Abt

2010; Abt and others 2000; Abt and others 2009, 2010;

Prestemon and Abt 2002; Rossi and others 2010).

We made a number of modifications to the SRTS model

(fig. 10.1) to assess the effects of woody bioenergy industry

on future prices, harvests, and inventories of four wood

product categories—softwood sawtimber, other softwoods,

hardwood sawtimber, and other hardwoods—derived from

private owners of forest land (public forest lands have

been excluded from the study, because public land harvest

decisions are not necessarily price-responsive). Appendix B

contains descriptions of these products and the allocation of

consumption of each for woody bioenergy production. The

model allocates woody biomass consumption among product

groups based on the price variations. Pine plantations can

be harvested for pulpwood as early as 10 years of age. To

determine the availability of harvest residuals, we applied

utilization percentages that are consistent with timber

product output data for the South (Johnson and others 2009).

Alternative runs of the model allowed us to examine

how management or genetic improvements would affect

productivity. Rather than applying identical responses

across the five forest management types (pine plantation,

natural pine, oak-pine, upland hardwood, and lowland

hardwood), we modified the model so that responses can be

disaggregated across them.

Within the SRTS model, the area of timberland will change

in response to the relative rents of crop and forest uses. We

defined timber rents as weighted averages of sawtimber

and nonsawtimber prices, with weighting specified by the

present value difference in income between the two products

while agricultural rents are held constant. Because woody

bioenergy markets are expected to impact the nonsawtimber

sector more than the high valued sawtimber sector (Aulisi

and others 2007), the model allocates less weight to

sawtimber prices.

We used the aggregate demand information gathered from

each southern wood-based industry—forest products, woody

biomass-based electricity, woody biomass-based liquid fuels,

and wood pellets—to project the allocation of harvested
216 The Southern Forest Futures Project

timber. The modified SRTS model defines a market

simulation model based on empirical relationships—demand

and supply, price, land use, reforestation and inventory—

for woody biomass and traditional forest products. A key

assumption is that forest owners are price responsive and

decisions to invest or harvest are made accordingly.

Consumption/Demand Scenarios

Our consumption scenarios were based on the three principal

uses of woody biomass for energy: as power for electricity

generation through combustion or gasification processes,

co-firing with coal, or in combined heat and power systems

in industrial facilities (Energy Information Administration

2010b); as liquid fuel (cellulosic ethanol) that can be blended

with conventional transportation fuels (Energy Information

Administration 2010b); and as bioproducts such as highly

compact wood pellets used for heating purposes (Spelter and

Toth 2009, appendix B).

The amount of wood consumed for electricity, liquid

fuels, and pellets defines the total requirement for meeting

bioenergy consumption projections. This can be met

with wood from additional harvesting or with residuals

and other wood waste. Although harvesting unutilized

residues (discarded tree tops and limbs generated during

the harvesting process) might provide a portion of woody

biomass-based energy consumption, recent analysis

(Galik and others 2009, Rossi and others 2010) indicates

that merchantable timber is also likely to be required. In

addition, woody biomass-based energy demand figures

need to account for urban wood wastes that could be used

for energy production (Rossi and others 2010). Because the

SRTS model deals only in harvested wood, we backed urban

Figure 10.1—Methodology diagram for the modified Subregional Timber Supply model used to project levels and effects of woody biomass consumed for

energy for the South.

Forest inventory 

plot data

Growth regressions 

Inventory and harvest 

data for plots

Timberland 

acreage change 

Price sensitive land 

use model

inventory proportion 

in term of d.b.h.

Species, product, 

percentage degrade 

from sawtimber to 

nonsawtimber

Productivity increase

Subregion, product, 

owner, age class

Goal program

Equilibrium harvest 

by region, owner and 

product are allocated 

to forest type, age 

class

harvest 

Subregion, product, 

owner

Starting acres, 

growth and harvest 

species, subregion, 

owner, age class, 

management type

inventory accounting 

Growth and removals

inventory available for harvest 

Subregion, product, owner

Product price 

Determined by 

interaction of aggregate 

demand and supply

Woody biomass for energy 

consumption scenarios 

Net of urban wood waste 

Demand scenarios

Downward sloping 

forest industry 

demand plus vertical 

demand curve for 

bioenergy

elasticity Values

Supply elasticities based on 

Uinited States Forest Product 

model runs and demand 

elasticities (Abt and Abt 2010, 

Abt and others 2010)

2013,
217

chapter 10. Forest Biomass-Based Energy

waste and other sources of nonharvested woody biomass

out of the consumption estimates, and defined the remainder

as harvested-wood consumption (including harvesting

residues) for woody biomass-based energy; appendix B

shows the method used to estimate the harvesting residues

and urban wood waste that can be diverted for energy

production. Demand price elasticity, which like inventory

supply elasticity can vary by product (Liao and Zhang 2008,

Pattanayak and others 2002), was assumed to be -0.5 for all

four SRTS products (softwood/hardwood sawtimber and

nonsawtimber), the same assumption used by Abt and Abt

(2010) for their Southwide timber supply analysis.

Demand for woody biomass for energy can also be met with

fast-growing short rotation woody crop species, among them

yellow-poplar (Populus spp.), willow (Salix spp.), cottonwood

(Populus fremontii L.), sweetgum (Liquidambar styraciflua),

sycamore (Platanusoccidentalis), black locust (Robinia

pseudoacacia), silver maple (Acer saccharinum L.), and

eucalyptus (Eucalyptus cinerea); these species have been

identified by the U.S. Department of Energy as potentially

viable for energy production. We followed the approach

outlined by the Energy Information Administration (2010a)

and assumed that short rotation woody crops would grow

largely on nonforested lands (agricultural or pasture lands)

and partially offset increased future wood requirements. We

assumed of the offset to be 10 percent by 2050 and removed

this material from woody biomass demands for bioenergy

(in effect, treating short rotation woody crops as a part of the

agricultural sector).

Although we describe our assumptions as consumption

scenarios, it is important to understand that they are

not demand projections, as we have not specified priceresponsive demand relationships for woody biomass. The

consumption projection is essentially a vertical demand

curve added to the downward sloping demand curves for

traditional forest products for each period using modified

Energy Information Administration (2010b) projections. As

a counterfactual, we also introduced a constant consumption

scenario with no forest biomass-based energy market and

ran the SRTS model to define the amount of woody biomass

that would be required by traditional forest industry absent

a bioenergy market. Subsequent years are held constant at

the original 2010 level on the assumption that the traditional

forest product industry will not increase wood consumption

beyond what would be expected at the constant price level

estimated by SRTS.

To account for uncertainty in bioenergy technologies,

demands, and policies, we considered three consumption

scenarios that we label high, medium, and low. The lowconsumption scenario assumes that 7.74 percent of total

electricity will derive from renewable sources based on

Energy Information Administration (2010b) reference case

projections. The medium-and high-consumption scenarios

assume that 20 percent of total electricity consumption derives

from renewable sources; in the high-consumption scenario,

woody biomass is assigned a higher percentage of the total

electricity generation from renewable sources (table 10.1).

Biomass Supply

The SRTS model accounts for forest inventory changes

and timber removals based on historical forest inventory

(FIA) data. However, southern forest productivity has seen

a three-fold over the last 50 years from advancements in

management and genetic improvements (Fox and others

2007). Siry and others (2001) projected that productivity

gains for pine plantations could be as high as 100 percent

of empirical FIA data (using data from the late 1990s) over

the next 50 years. Prestemon and Abt (2002) assumed a

75-percent productivity gain in southern pine plantations

from 2000 to 2040. With strong markets, other forest

Table 10.1—Allocation of woody biomass for energy production under woody biomass consumption scenarios by 2050

Woody biomass 

consumption scenario Electricity Liquid fuels Wood pellets

Low Based on Energy Information 

Administration (2010b) projections

Provides 30 percent of renewable 

energy sources 

Based on Spelter and 

Toth (2009)

Medium Increases to 20 percent of renewable 

energy sources by 2050, with share 

of total electricity sources remaining 

the same as in the low-consumption 

scenario 

Increases to 50 percent of 

renewable energy sources by 

2050, with 30 percent of total 

liquid energy coming from woody 

sources 

Increases by 25 percent, 

2015–50

High Increases to 40 percent of renewable 

energy sources by 2050, with 20 

percent of total electricity coming from 

woody sources

Increases to 50 percent of 

renewable energy sources by 

2050, with 40 percent of total 

liquid fuel coming from woody 

sources

Increases by 50 percent, 

2015–50
218 The Southern Forest Futures Project

management types might experience productivity gains

due to silvicultural improvements or improvements in

management, although not as high as pine plantations.

We developed supply projections to examine alternative

trajectories of productivity increases through 2050. In these

projections, productivity growth is applied to every acre

every year, so that over time the improved silvicultural

practices on existing or new forest stands or genetic

improvements of new plantations result in an aggregate

growth response. For the “pine productivity” strategy,

we assumed that pine plantation productivity increases

steadily until it reaches 100 percent, while the productivity

of other forest management types is held constant. For the

“all productivity” strategy, we assumed a 100-percent pine

plantation productivity increase and a 25-percent increase

for other types. For the “low productivity” strategy, pine

plantation productivity increases by 50 percent and the

productivity of other types increases to 25 percent

(tables 10.2 and 10.3). These assumptions are in line with

hardwood field trials that report growth responses between

17 and 33 percent after stem density reduction, herbaceous

competition control, and fertilization (Siry and others 2004).

Within SRTS, removals are treated as a function that

responds to changes in the product price and the total

biomass inventory. The timber supply elasticity with respect

to inventory has been assumed to be 1.0 for all products

and owners. For own-price elasticities of timber supplies

(elasticity of product demand with respect to their own price),

we used the average bootstrapped values for A1B and B2

cornerstone futures described in chapter 9, which vary across

products and years and range from 0.18 to 0.32.

Results

Market Analysis

By 2050, woody biomass consumption is projected to range

from 150.16 million green tons for the low-consumption

scenario to 235.88 million for the medium-consumption

scenario and 316.12 million for the high-consumption

scenario (fig. 10.2). The amount of urban wood waste

amounts to about 12.72 million in 2010 and trends slightly

upward throughout the projection period to reach

20.08 million by 2050. In contrast, the projection of biomass

requirement for the forest products industry (held constant

through the projection period) is about 278.46 million. By

2050, the biomass requirement for energy reaches about

54 percent of the forest products requirement for the lowconsumption scenario and 85 percent for the medium

scenario. For the high-consumption scenario, the bioenergy

requirement exceeds the forest products requirement by 2045

and is 13 percent greater than the forest product requirement

in 2050.

Adding urban wood waste and the forest biomass

consumption requirement in 2050 would bring demand to

170 million tons for the low-consumption scenario,

256 million for the medium scenario, and 336 million for

the high scenario. These estimates are comparable to other

estimates in the literature if we assume that that supply of

wood from the South mirrors the national harvest share—i.e.,

approximately 57 percent of national harvest as per Hanson

and others (2010).

Without accounting for milling residues, Milbrandt (2005)

estimated that just 86 million tons of woody biomass is

readily available for energy production (roughly half of the

projection for the low-consumption scenario). Walsh (2008)

estimated that approximately 121 million tons of forest and

mill residues could be supplied at a price of $100 per dry

short ton, compared to estimates of 154 million tons by

Kumarappan and others (2009). The Energy Information

Administration (2007) estimated that approximately 414

million tons of wood from South might be required to

meet the Federal goal of 25 percent of renewable fuel and

electricity standards. Sample (2009) suggested that this

demand figure could be much higher, estimating the yearly

requirement at 992 million green tons. Perlack and others

(2005) estimated that 420 million green tons of wood

resources could be annually made available for energy

production from southern forests.

Consumption increases of this magnitude (at a minimum,

a 54 percent increase in timber harvesting) could imply a

structural change in forest products markets. Analysis of

traditional wood products markets (chapter 9) indicates that

the supply of biomass could grow by about 43 percent under

current levels of productivity without increased scarcity,

largely because of declining demands for wood products.

With plantation productivity growth at about 50 percent by

2060, forest biomass output could expand by as much as

70 percent without substantial impacts on market scarcity.

To identify the market implications of the three consumption

scenarios, we ran the SRTS model, which provides

projections of the removals from growing stock resulting

from timber harvesting but does not distinguish among final

products. To deduce the implications of increased woody

biomass requirement for the traditional wood products

industry, we disaggregated the removals projections into

harvesting residues, additional removals that could not

have occurred without woody bioenergy markets, and/or

displacement from traditional wood product industry.

To ensure that some slash is left on the ground, we

constrained the SRTS model so that no more than
219

chapter 10. Forest Biomass-Based Energy

Table 10.2—Simulations of supply responses when woody biofuels at three consumption levels are matched with four 

productivity strategies, 2050

Woody biomass

consumption scenario Productivity strategy Details

Medium Only improve pine plantation productivity Productivity of pine plantations doubles; no change in other 

forest management types

Medium Improve productivity on all management 

types

Productivity of pine plantations doubles by 2050 and 

productivity of other forest management types increases by 

50 percent

High Only improve pine plantation productivity Productivity of pine plantations doubles; no change in other 

forest management types 

High Improve productivity on all management 

types

Productivity of pine plantations doubles and productivity of 

other forest management types increases by 50 percent

Short rotation woody crops 

Improve productivity on all management 

types and expand short rotation woody 

crops 

Short rotation woody crops growing on agricultural or 

pasture land offset 10 percent of wood energy demand; 

productivity of pine plantations doubles and productivity of 

other forest management types increases by 25 percent

High Low productivity 

Productivity of pine plantations increases by 50 percent and 

productivity of other forest management types increases by 

25 percent

Table 10.3—Modified subregional timber supply model assumptions

Assumption Scenario/Strategies Details

Woody biomass consumption 

for electricity and biofuels

Low,Medium. High Demand values in million green tons (Energy Information Administration 

2010b)

Urban wood waste Low,Medium. High Per capita availability (Carter and others 2007)

Harvest residues Low,Medium. High SRTS model run based on Johnson and others (2009) data 

Forest industry demand Low,Medium. High Auxiliary SRTS run for constant prices

Demand elasticity Low,Medium. High -0.5 for all products (Abt and others 2010)

Supply elasticity Low,Medium. High Different annual values for products based on RPA storylinesa

Pine productivity Pine productivity strategy Pine productivity increases by 100 percent by 2050

All productivity values All productivity strategy Pine productivity increases by 100 percent and other forest type 

increases by 50 percent by 2050

Low productivity values Low productivity strategy Pine productivity increases by 50 percent and other forest type increases 

by 25 percent by 2050

Short rotation woody crops Short rotation woody 

crops

Short rotation woody crops take care of 10 percent of total woody 

biomass for energy demand by 2050

Forest management type 

acreage

All scenarios and 

strategies 

Forest land change as compared to agriculture and pasture land, in turn 

impacting acreage of pine plantations, natural pines, oak-pines, upland 

hardwoods, and lowland hardwoods (Abt and Abt 2013, Hardie and 

others 2001)

Timber rent All scenarios and 

strategies

Weighted average of pulp and sawtimber prices. Model allocates weights, 

with pulpwood gaining more weight in total rent calculations

Degradation of sawtimber for 

pulp use

All scenarios and 

strategies

Percentage allocation of sawtimber that can be used as pulp (Abt and 

others 2010)

Pulp diameter range All scenarios and 

strategies

<9 inch softwood

<13 inch hardwood

Sawtimber diameter range All scenarios and 

strategies

>9 inch softwood

>13 inch hardwood

Forest products All scenarios and 

strategies

Sawtimber softwoods, other softwoods, sawtimber hardwoods, and other 

hardwoods

aPersonal communication. 2010. David N. Wear, Project Leader, Center for Integrated Forest Science, Southern Research Station, 

U.S. Department of Agriculture Forest Service, Raleigh, NC 27695. 






226 The Southern Forest Futures Project

reaching five times the 2007 level for softwoods, and eight

times the 2007 level for hardwoods. Inventory and harvest

levels for softwoods are higher compared to low- or noconsumption scenarios, but lower than the medium scenario;

for hardwoods, inventory levels are much higher than the

no- or low-consumption scenarios and removals are higher

than all other scenarios. The pulp industry is adversely

impacted as significant supplies are diverted to energy

production (fig. 10.12). The bioenergy requirement is not met

by new removals, pulpwood, or harvesting residues, resulting

in a complete elimination of forest industry demand for

hardwoods by 2037 followed by softwoods in 2043.

The prices, inventory, and removal levels of sawtimber are

similar to the other consumption scenarios. The industry

would experience a significant impact as 91 million green

tons of sawtimber is diverted to energy production. The

increased acreage of pine plantations might result in some

of the softwood timber moving to sawtimber diameters.

Significant amounts of hardwood sawtimber are also diverted

to energy production.

Private forest acreage increases by 9 percent from

175.39 million acres in 2010 to 191.6 million acres in 2050

(fig. 10.13), 21 percent higher than the no-consumption

scenario. All forest management types except natural pines

increase in area by 2050, led by a 33 percent increase in pine

plantation acreage. Initial acreage declines for upland and

lowland hardwoods and oak-pines are reversed after 2027,

resulting in a 2-percent net increase by 2050.

Supply Adjustment Strategies

Increased consumption for wood by a new woody bioenergy

industry can be expected to result in the supply side

adjustments such as the use of short rotation woody crops

and the increased productivity strategies described below.

Productivity increases limited to pine plantations—An

increase in pine plantation productivity would do more

to dampen nonsawtimber softwood price increases in the

medium- and high-consumption scenarios (fig. 10.14) than

in the no- and low-consumption scenarios (which do not

stimulate productivity gains), with prices falling until the

late 2020s before beginning to increase again. Inventory and

removals levels are also higher. The increase in productivity

of pines also lowers price responses for hardwoods, largely

because increased softwood inventories fulfill the demands

for bioenergy.

Figure 10.15 shows price, inventory, and removal projections

for sawtimber under medium-and high-consumption

scenarios. For softwood sawtimber, productivity increases

in pine plantations also result in lower prices and higher

inventory and removals under both increased productivity

strategies, with the medium-consumption scenario providing

a greater price dampening effect than the high-consumption

scenario. Price trends are the same for hardwood sawtimber

but the decreases are less extreme. Higher inventory levels

result from the increase in productivity, which reduces

prices. The impact on the sawtimber-using industry is also

reduced. For example, in the high-consumption scenario

54.5 million green tons of sawtimber from both hardwoods

and softwoods is diverted to energy use in the pine

productivity strategy as compared to 91 million green tons

associated with no productivity increases. The decreased

impact on the forest industry is due to expanded removals

supported by increased productivity.

Productivity increases result in higher removals and less

displacement from forest industry (fig. 10.16). The softwoods

being used by forest industry are still completely diverted for

energy production in the high-consumption scenario, but this

occurs later.

Forest management type trends are similar for the mediumand high-consumption scenarios, with increases in pine

productivity resulting in lower levels of private forest acreage

for both scenarios (fig. 10.17)—9.6 percent for the mediumand 10.2 percent for the high-consumption scenario—albeit

much higher than for the no-consumption scenario. Because

productivity gains are limited to softwoods, a higher share

of the wood requirements for woody bioenergy markets is

met by softwoods than hardwoods. Acreage declines across

all five management types, with the highest rate of decline in

pine plantations.

Productivity increase extended to all management 

types—A productivity increase for all forest types results

in price, inventory, and removal responses that are similar

to those observed for increases in pine plantations alone,

the only difference being in the magnitude of change.

Softwood price is lower and inventory and removal levels are

higher (fig. 10.18). Hardwood trends for medium- and highconsumption scenarios are similar to the softwoods, with

lower prices and higher inventories and removals than was

projected for planted forest types alone (fig. 10.19).

Nonsawtimber softwoods used by forest industry are still

completely diverted to energy production in the highconsumption scenario, but the impact on the sawtimber-using

industry is reduced. For example, in the high-consumption

scenario, 36.38 million green tons of sawtimber from is

diverted to energy use as compared to 53.5 million green

tons with pine productivity alone and 91 million green

tons with no productivity (fig. 10.20). Higher removals of

sawtimber are attributed to unharvested pulpwood timber

moving into the higher diameter sawtimber class. The

productivity increases therefore result in higher acreage and

higher inventory at the aggregate level.








235

chapter 10. Forest Biomass-Based Energy

Compared to planted-pine-alone productivity strategy,

this approach increases total forest area for both mediumconsumption scenario (165.52 million acres) and the highconsumption scenario (175.01 million acres), with acreage

increases for all forest management types except pine

plantations (fig. 10.21).

Low productivity increase—Lower productivity increases

combined with medium- and high-consumption scenarios

result in price, inventory, and removal responses similar to

the all productivity increase strategies (figs. 10.22 and 10.23).

The supply response of the low productivity strategy

fails to offset the woody biomass requirements, with

all nonsawtimber softwood being diverted from forest

industry to energy production under the high-consumption

scenario and a significant amount diverted under the

medium-consumption scenario (fig. 10.24). The impact

on the sawtimber-using industry is higher than for the all

productivity or pine productivity strategies, but lower than

if no productivity measures were taken. For example, in

the high-consumption scenario, 57.18 million green tons of

sawtimber is diverted to energy use as compared to 36.38

million green tons for the all productivity strategy, 53.5

million green tons for the pine productivity strategy and 91

million tons if no productivity measures were taken.

Private forest acreage is higher than for the other two

productivity strategies. Forest land decreases from

175.39 million acres in 2010 to 172.47 million acres for the

medium-consumption scenario, but increases to 181.85

million acres for the high-consumption scenario (fig. 10.25).

Planted pine acreage increases more and other forest type

acreage declines less as compared to the pine productivity or

all productivity strategies.

Productivity increases on short rotation woody 

crops—We ran the model to simulate the results of a high

productivity strategy coupled with the emergence of short

rotation woody crops in the South. Inventories and removals

(fig. 10.26) are higher than for the all productivity strategy

coupled with high consumption (similar to results from

a subsequent run combining a low productivity strategy

with short rotation woody crops). Softwood and hardwood

inventories are higher compared to the no-consumption

scenario. Price increases for all products are dampened.

These results also suggest that the pulp industry would still

face adverse impacts, as merchantable wood from forest

industry would be diverted to energy production (fig. 10.27).

However, the combination of increased supplies from short

rotation plantations and from productivity gains on existing

forests would provide most of the ‘additional’ sawtimber

needed for energy production, resulting in just 26.7 million

tons diverted from forest industry. The higher levels of

aggregate inventory and removals counter the notion

that diverting wood for energy would necessarily lead to

inventory declines. Forest acreage is lower than for the other

productivity strategies, but higher than the no-consumption

scenarios (fig. 10.28).

Technologies

Considering the potential availability of wood that could

be used in the traditional forest product industries and

woody bioenergy industries, it is important to determine

how current and likely suitable wood-to-energy conversion

technologies can potentially impact the future of southern

forests (for example, how technological preferences towards

a particular species might increase its price, producing

changes in inventory and removal). Dwivedi and Alavalapati

(2009) found that a broad spectrum of stakeholders view

conversion technologies as one of the main weaknesses for

the development of forest biomass-based energy in the South.

In addition, Nesbit and others (2011) found that under current

levels of technology, slash pine ethanol is not a financially

viable competitor for fossil fuels. They found that unit

cost of producing ethanol from slash pine (Pinus elliottii)

through a two-stage dilute sulfuric acid conversion process,

and a synthesis gas ethanol catalytic conversion process

was estimated to be $2.39 per gallon and $1.16 per gallon

respectively. If adjustments are based on the lower energy

content of ethanol relative to gasoline (Oak Ridge National

Laboratory 2008), the cost of an energy equivalent gallon of

ethanol increases to $3.55 and $1.74 per gallon for the two

conversion processes, respectively.

Woody biomass can be converted into energy using a

number of different processes. Broadly speaking, wood-toenergy conversion technologies can be grouped into two

main categories: thermal technologies—such as co-firing

and combined heat and power, direct combustion using wood

pellets and wood chips, gasification and pyrolysis—and

biochemical processes.

Co-firing and combined heat and power—Combustion

of woody biomass can be applied to produce heat and

electricity, particularly in industrial and residential sectors.

Three major technology options are being developed

for producing electricity and heat. These are: setting up

dedicated cellulosic power plants, co-firing biomass in

existing coal plants, and developing combined heat and

power plants. All these options are being explored in the

South, ranging from a dedicated power plant that will use

urban wood waste, wood processing wastes, and logging

residues in Gainesville, Florida to plants that blend biomass

with coal or inject biomass separately into boilers. Currently,

27 co-firing plants supply a biomass/coal co-firing capacity

of 2,971 megawatts. Virginia is the leader in the number of

co-firing plants and capacity in the South, followed by North






242 The Southern Forest Futures Project

are more expensive and have lower ethanol yields, but they

allow each to be carried out at its optimal temperature

(Jackson and others 2010).

Although several hydrolysis techniques have gained

momentum in the last decade, efficiency and cost issues have

hindered commercial viability. An integrated enzymatic

process could contribute to cost reductions, but it has not yet

moved out of the laboratory stage.

The Department of Energy set 2012 commercialization

targets for research and development which included

reducing the selling price of ethanol by 2012 to $1.07 rather

than $1.61 per gallon, increasing ethanol yield per dry ton

from 56 gallons in 2005 to 67 gallons in 2012, and reducing

installed 2005 capital and operational costs by 35.5 percent

and 65.3 percent respectively. For fermentation based ethanol

production, the target is to increase yield from 65 gallons

per ton in 2005 to 90 gallons per ton in 2012. The target also

sets feedstock cost target for 2012 as $35 per dry ton. Efforts

are ongoing to achieve these targets, but no technological

breakthrough has yet achieved these large-scale production

targets. The Range Fuel plant in Soperton, Georgia produced

waste wood methanol in August 2010, and currently

producing its first batch of cellulosic ethanol. However, the

plant is shutting down operations after demonstrating its

cellulosic production technology. The scale of bioenergy

plant in terms of capital and biomass demands from the

forest landscape are issues that need further attention. If a

large plant is set up, then the transportation cost of procuring

biomass from areas farther from the plant site might

increase per unit cost and/or lead to procuring lower quality

feedstock. The scale of the plant not only depends on cost

issues, but also on the purpose for which it is being built.

For example, Van Loo and Koppejan (2008) suggest that

small combined heat and power plant facilities with lower

conversion efficiency (10 percent) can be used where heat is

the primary product with power as the secondary product,

while facilities (more than ten megawatts) generally have

higher efficiency (25 percent) as they produce electricity as

the primary product.

The Policy Environment 

A number of current and proposed policies and programs

may influence the future of woody biomass-based energy

markets in the South. Some of these policies are directed

specifically at the expansion of woody biomass use for

energy, and others influence indirectly by focusing on

reductions of greenhouse gas emissions.

Incentive-based policies provide financial support such as

cost-shares, tax reductions, subsidies or grants, and low- or

no-interest loans for project financing. The Database of State

Incentives for Renewables and Efficiency (2010) reports that

policies for renewable energy (including woody biomass

for energy) in the Southern States are generally in the form

of tax rebates, grants, loans, industry support, bonds, and

performance-based incentives.

Regulatory and support mechanisms include policies that

set goals, targets, and limits; and compel certain types of

behavior, as well as creating supportive infrastructure and

facilitating public educational outreach. Rules, regulations,

and policies (regulatory and support policies) are in the form

of public benefit funds, renewable portfolio standards, net

metering, interconnection standards, contractor licenses,

equipment certification, access laws, construction and design

rules, green power purchasing guidelines, and green power

policies.

Incentive-based policies—In an effort to support marketbased solutions, Federal and State governments have

introduced a number of incentive-based policies. This

generally results in altering prices by assigning a monetary

value to something that was previously external to market

forces (Shrum 2007). Subsidies are intended to encourage

planting and management activities that might promote

feedstock availability, and tax support encourages the use of

renewables. Support in the form of grants and loans are also

provided to encourage clean technology development and

adoption.

Incentives for liquid biofuels were first instituted in the

Energy Tax Act of 1978, which provided a $0.40 per gallon

exemption from the gasoline excise tax for blends with at least

10 percent ethanol. Then it was increased to $0.51 per gallon

by the 1998 Transportation Equity Act of the 21st Century.

The American Jobs Creation Act of 2004 replaced the excise

tax exemption with a volumetric ethanol excise tax credit of

$0.51 per gallon until 2010 (reduced to $0.45 per gallon by the

Farm Bill of 2008). The Energy Independence Security Act

(2007) provided a production tax credit of $1.01 per gallon

for cellulosic biofuels through 2012. The following section

summarizes the current bioenergy policies in the South.

The 2008 U.S. Farm Bill created a new Biomass Crop

Assistance Program (BCAP) to encourage development of

large-scale energy crops that can support commercial-scale

bioenergy production. BCAP provides incentives to farmers,

ranchers, and forest landowners to establish, cultivate and

harvest biomass for heat, power, bio-based products, and

biofuels. The program shares the establishment cost and

matches cost related to transportation and logistics up to

$45 per ton to producers with user facilities contracts. The

program reduces the financial risk to farmers and forest

landowners to supply eligible biomass materials to qualifying

facilities, and can reduce the cost of raw materials to the

facility. These also promote conservation and stewardship

by emphasizing that biomass is collected and harvested
243

chapter 10. Forest Biomass-Based Energy

according to an approved conservation, or similar plan

to protect soil and water quality and preserve future land

productivity.

Rebates followed by loans are the most popular financial

incentives in the South (table 10.4). Federal financial

incentives are mainly comprised of corporate tax rebates,

research and development grants, and loans. Loans and

performance-based incentives are the policies most

frequently used in the 76 State financial incentive programs

in the South. North Carolina has the largest number of State

financial incentives (eight), and Texas has the smallest (two).

Few State programs are specifically aimed at increasing

woody biomass stock for energy use, partly because woodfor-energy markets have not yet been established. However,

more often than not, improvement in forest biomass

availability and sustainable use is an offshoot although

not the overarching goal of these programs. Although

the minimum acreage and stocking levels for property

tax calculations vary across Southern States, the general

objective of all these taxes is to provide an incentive for

managing land on a sustained yield basis and a disincentive

for converting forest land to other uses. The objectives of

State cost-share programs are to reforest cutover land, plant

open land, or improve woodlands; and many States offer to

share the costs of other forest management activities. For

example, South Carolina has forestry commission cost-share

programs and North Carolina has forest agriculture costsharing programs. These programs lead to higher availability

of feedstocks for energy conversion.

Several Federal programs provide incentives for conservation

of forestlands and maintaining sustainable forest

management practices. For example, the Environmental

Quality Incentives Program (EQIP) provides cost shares for

installing greenhouse gas mitigating technologies and the

Landowners Incentive Program provides financial assistance

to landowners for a variety of conservation goals including

carbon sequestration. The Forest Land Enhancement

Program promotes additional carbon sequestration and other

ecosystem services through cost shares with landowners.

These programs help to reduce land use change away from

forests, in turn indirectly maintaining the forest stock that

can be used for energy production at a later date. Incentive

programs for reforestation have long been established in

a number of States. For example, Mississippi provides tax

credits for reforestation.

Table 10.4—Number of financial incentives for renewable energy at Federal and State levels (blanks indicate no 

incentives): Number in the parentheses means whether incentives are State governments (S), utility companies (U), local 

governments (L), or nonprofit organizations (N)

State(s)

Personal 

tax

Corporate 

tax

Sales 

tax

Property 

tax Rebates Grants Loans

Industry 

support

Performancebased 

incentive

All States 

(Federal 

incentives) 3 4 3 5 1 1

Alabama 1(1S) 3(3U) 1(1S) 3(1S,2U) 1(1U)

Arkansas 2(1S,1U) 1(1U) 1(1S)

Florida 2(2S) 2(2S) 12(1S,10U,1L) 6(1S,5U) 1(1L) 2(2U)

Georgia 1(1S) 1(1S) 1(1S) 10(1S,9U) 1(1S) 2(2U)

Kentucky 1(1S) 2(2S) 1(1S) 11(1S,10U) 1(S) 4(1S,1U,1L,1N) 1(1S)

Louisiana 1(1S) 1(1S) 1(S) 2(2S)

Mississippi 5(1S,4U) 4(1S,3U) 1(S)

North Carolina 1(1S) 1(1S) 1(1S) 2(2S) 6(6U) 1(1S) 4(3S,1U) 4(3S,1N)

Oklahoma 1(1S) 3(3U) 6(4S,2(U) 1(S)

South Carolina 1(1S) 2(2S) 1(1S) 6(6U) 6(1S,5U) 4(1S,2U,1N)

Tennessee 1(S) 2(1S,1U) 2(2S) 3(2S,1U) 1(S) 1(S)

Texas 1(1S) 1(1S) 27(25U,2L) 2(2S) 2(2S) 1(1S) 2(2U)

Virginia 1(1S) 1(1S) 1(1S) 1(1S) 1(1U)

Total 9 15 6 6 88 10 48 7 20

Source: Database of State Incentives for Renewables and Efficiency (2010).
244 The Southern Forest Futures Project

Regulations and support programs—At the Federal

level, the Energy Policy Act of 2005 established Renewable

Fuel Standards, which mandated that transportation fuels

contain a minimum volume of renewable fuels, starting

with 4 billion gallons in 2006 and 7.5 billion gallons by

2012. The Energy Independence Security Act (2007) called

for production of 36 billion gallons of biofuels by 2022, of

which 21 billion gallons must be cellulosic biofuel. The 2008

Farm Bill authorized mandatory funding of $1.1 billion for

the 2008 to 2012, providing grants and loans to promote

alternative feedstock resources including woody biomass.

Interconnection standards and green power purchasing have

also been formulated at the Federal level.

Construction and design support for establishment of

bioenergy production facilities and net metering available

to biomass based energy facilities so they can sell power

back to the grid are the most employed State-level policies

in the South and 10 Southern States have also formulated

renewable portfolio standards as targets for using cleaner

sources of energy in utilities and industries.

Extension and support activities have facilitated knowledge

transfers, technology demonstrations, and information

sharing sessions; and have developed multi-stakeholder

partnerships to reduce greenhouse gas emissions. Extension

agents and specialists at land-grant universities and

government institutions transfer knowledge about natural

resource management (including woody biomass-based

energy) to client groups, such as forest owners, foresters

and other natural resource managers, tree growers, loggers,

and forest workers. Non-State efforts aimed at landowners

include a State Tree Farm program that recognizes

landowners who are doing a good job of managing their

land with a certificate, subscription to Tree Farm magazine,

and Tree Farm sign to display on their property. Regular

interaction between landowners and professional foresters is

facilitated through periodic visits by foresters.

There have been number of efforts by policymakers in the

United States to create markets as a mechanism to regulate

GHG emissions, although no bill has yet become law. For

example, the House passed the American Clean Energy and

Security Act (a.k.a. Waxman-Markey) on June 26, 2009,

and three other bills were submitted to the Senate in 2009

and 2010: the Clean Energy Jobs and American Power Act

(Kerry-Boxer), the American Power Act (Kerry-Lieberman),

and the Carbon Limits and Energy for America’s Renewal

Act (Cantwell-Collins). Waxman-Markey, Kerry-Boxer,

and Kerry-Lieberman would create markets for emitting

and offsetting carbon dioxide and permit the purchase of up

to 2 billion metric tons of carbon offsets annually (Mercer

and others 2011). Gorte and Ramseur’s (2008) estimate

that at a CO2e price of $50 per metric ton, more than 800

million metric ton of CO2e could be sequestered through

afforestation activities, and approximately 380 million metric

ton through improved forest management activities. Since

2010, little congressional effort has focused on climate in

general and carbon sequestration policies in particular.

Forestry offset projects including mitigation of green house

gases through bioenergy production can potentially accrue

carbon credits but the accounting is challenging. Assuming

that energy crops do not lead to land use changes, life cycle

analyses of different biofuels (including woody biomass)

suggest overall green house gas reductions (Blottnitz and

Curran 2006, Eriksson and others 2007, Gustavsson and

others 2007). Searchinger and others (2008) argue that life

cycle studies have failed to factor in indirect land use change

effects, and suggest that using U.S. croplands or forestlands

for biofuels results in adverse land use effects elsewhere,

thus harming the environment rather than helping it. Indirect

land use change effects are difficult to assess, and today

there is no generally accepted methodology for determining

such effects. Fritsche and others (2006) argue for assessing

indirect influence of bioenergy on land use change through

measures such as land prices and rents. However, conducting

such assessments at the site level and translating these

to operational indicators is quite costly. A satisfactory

methodology for incorporating the effects of indirect land

use changes into the lifecycle greenhouse gas emissions of

fuels remains an important challenge.

There are also policies and regulations that could limit

development of a bioenergy industry in the South. The

Environmental Protection Agency’s final Greenhouse Gas

Tailoring Rule, does not exempt biomass power producers

from greenhouse gas permitting requirements, and might

act to limit the establishment of bioenergy conversion plants

(Mendell and others 2010). This rule treats carbon emissions

from biomass combustion identically to fossil fuels emissions

and increases costs associated with obtaining permits and

costs associated with technology requirements, such as

Best Available Control Technology. Mendell and others

(2010) suggest that regulatory uncertainty created due to

this regulation could affect establishment of 130 renewable

energy projects, and $18 billion in capital investment

across the country. Similarly, the Environmental Protection

Agency’s air quality permitting for biomass boilers impacts

biomass based electricity producers adversely.

Assessing efficacy of policies—A number of researchers

suggest that private landowners are by and large unresponsive

to property tax and capital gains provisions, and that forest

property tax programs are only modestly successful in

achieving their goals (Greene and others 2005, Jacobson

and others 2009, Kilgore and others 2007). Many authors

have found that landowners are largely unaware of the

existence of incentives or do not understand how incentives

might apply to them. For example, Butler (2008) based
245

chapter 10. Forest Biomass-Based Energy

on landowner responses to the Forest Service’s National

Woodland Owner Survey, concluded that not all landowners

are price-responsive. Factors such as maintaining forest land

for aesthetics or wildlife conservation, as well a movement

towards smaller ownerships, might be responsible for this

price unresponsiveness. Nevertheless, at aggregate level,

these incentive based policies result in increased welfare, as

shown by Huang (2010) who found that when combined with

investment in technology, they can result in overall positive

outcomes for the South’s economy and household welfare.

Beach and others (2005) and Greene and others (2005)

found that nonindustrial private forest owners more often

respond to targeted government programs than to market

prices or other financial incentives. They also suggest that

technical assistance, cost-share payments, and direct contact

with professional foresters or natural resource specialists

more often than not succeed in changing forest management

decisions. Authors like Haines (2002) and Arnold (2000)

have proposed integrating land use planning (and woody

biomass-based energy use) into extension programs.

Educating landowners and the general public about the

benefits derived from cleaner energy sources such as woody

biomass will improve and increase interest in forest biomass

utilization. Mayfield and others (2008) indicated that

education and community engagement play important roles

in the development of cleaner technology like wood-based

energy. Joshi and Arano (2009) agree that landowners are

largely unaware of incentive programs available to them,

and thus argue that much remains to be done to encourage

private investment in forestry activities. In light of these

findings, extension and outreach support programs become

important for increasing the acceptability of wood-forenergy technology options and improving forest and land

management practices.

Sustainability

The development of forest bioenergy systems presents

new opportunities as well as risks. Many sustainability

concerns are being raised about wood biomass utilization for

energy. These concerns range from production processes to

consumption processes—feedstock production, harvesting,

transport, conversion, distribution, consumption, and waste

disposal—and include issues of job creation and societal

benefit distribution.

Forests provide not only wood for traditional uses but

also several ecosystem services such as clean water and

biodiversity (Amacher and others 2008, Neary 2002, Stupak

and others 2007). These potential impacts—grouped into

productivity, water quality, and biodiversity categories—are

described in detail below.

Productivity—The forest floor accumulates nitrogen,

phosphorus, calcium, and other nutrients that are essential

for tree growth. Unlike traditional timber harvests, biomass

harvests for energy production could impact regeneration and

site productivity unless productivity reductions associated

with site quality are offset by fertilization. Studies of forest

biomass based energy production raise concerns regarding

soil compaction and rutting (Reijnders 2006), decreased

amounts of decaying wood on forested landscapes, changes

in the chemical and physical environment of soils (Astrom

and others 2005), increased use of agrochemicals (Fritsche

and others 2006), increased soil erosion (Burger 2002), and

nutrient loss (Burger 2002). These issues suggest a need

for intensified site and off-site monitoring where forest

management is intensified.

The machinery used to build roads and infrastructure for

biomass harvesting biomass for energy might be different

from what was used in traditional timber harvesting and

harvesting might take place in areas where timber harvesting

is traditionally not undertaken, resulting in new roads or

pathways (Lal and others 2011, Smith and Lattimore 2008).

Frequency of harvests for biomass removal could also be

generally higher than for traditional harvests, and second

operations or harvest residue collections might result in

vehicle re-entry at the site (Lal and others 2009). Intensive

removals of forest biomass for bioenergy might reduce soil

carbon and organic matter to levels that are inadequate for

sustaining forest productivity. Hope (2007) through their

site experiments in British Columbia observed that stump

removal decreases the soil stock of carbon by 53 percent,

nitrogen by 60 percent, and phosphorus by 50 percent; and

that the forest floor depth was decreased by 20 to 50 percent.

Peng and others (2002) through their study in Central Canada

reported that whole-tree harvesting produces an additional

32 percent loss of soil carbon compared to conventional tree

harvesting. Smith and Lattimore (2008), while discussing

potential environmental impacts of bioenergy harvesting on

biodiversity list contributing activities such as mechanical

damage to residual trees; expanded road networks; increased

removals and land use changes that might impact productive

and diverse ecosystems. Scott and Dean’s (2006) Long Term

Site Productivity Study found that whole-tree harvesting

reduced productivity on over 75 percent of the study blocks

in South by an average of 18 percent. However, they also

found that a one-time application of nitrogen and phosphorus

fertilizer maintained productivity and increased productivity

by an additional 47 percent above the stem-only harvest level.

Harvesting slash remaining after conventional harvesting

of loblolly pine (Pinus taeda) in the Coastal Plain along the

Gulf of Mexico reduced site productivity, decreasing soil

organic matter and associated nutrients by 18 percent (Scott
246 The Southern Forest Futures Project

and Dean 2006). Reductions of jack pine (P. banksiana)

height growth of 18 percent on whole-tree harvested plots in

sites of Quebec region of Canada were attributed to lower

soil moisture and nutrient availability (Thiffault and others

2006). To avoid decreased productivity from soil compaction

during biomass harvesting, Janowiak and Webster (2010),

after reviewing the state of knowledge regarding the impacts

of intensive forestry with respect to issues relevant to

bioenergy production, recommended using machinery that is

similar to what is used in conventional harvesting.

Water quality—Increased biomass harvesting activities for

a wood-to-energy market might have adverse impacts on

water quality in streams, rivers, and lakes. Increased road

construction required for woody biomass harvesting might

lead to soil erosion, high soil moisture, and increased runoff

and sediments from forest roads and landings (Janowiak and

Webster 2010). Increased machinery use might also impact

the water table at the harvest site, leading to impermeable

soils from compaction. Removal of younger trees and

lopping and topping during biomass harvests might decrease

leaf surface area, resulting in decreased transpiration and

interception (Lal and others 2009).

Machine re-entry at harvest sites might increase

sedimentation and flow levels in waterways, increasing

the chances of sediment movement into wetlands through

damaged erosion control features. Frequent harvests might

increase suspended solids and aluminum levels in water,

raising acidification levels and negatively impacting fish

and other aquatic organisms (Grigal 2000). In addition,

woody biomass harvesting adjacent to waterways might

increase the probability of higher water temperatures,

disturbed chemistry, and reduced clarity that would damage

biological communities and alter ecological processes

(Janowiak and Webster 2010). Aust and Blinn (2004)

reviewed best management practices for timber harvesting

and site preparation in the eastern United States in terms of

water quality and productivity research during for the time

period between 1982 and 2002, and concluded that effects

of harvesting on forest hydrology are highly variable across

sites and time periods. However, harvesting impacts on forest

hydrology are likely to be greater immediately following

harvest, with the recovery to preharvest conditions taking up

to 5 years

Biodiversity—The extraction of additional biomass for

bioenergy could degrade habitats beyond the range of

natural variability and produce negative effects on some

species (Janowiak and Webster 2010). Increased access and

intensity of harvest can also fragment habitats and adversely

impact wildlife corridors (Fletcher and others 2011, Lal and

others 2009). Natural disturbances such as fire, wind, and

pest outbreaks permit a continuous supply of deadwood in

unmanaged forests. Intensive forest management leading to

removal of stumps might reduce the amount of deadwood

that is considered essential to forest ecosystems and provides

habitats for different organisms (Humphrey and others 2002).

The removal of residues and stumps might negatively

alter the entire soil fauna community and structure of

the food web, harming small mammals, and reducing

ecological niches, thereby lowering diversity and numbers of

invertebrates such as spiders and predatory insects (Ecke and

others 2002). There is also a chance of insects or other woodcolonizing species getting trapped in wood burnt for fuel.

However, intensive forest management practices controlling

pests and disease can also improve forest habitats. For

example, certain fungi species cause root and butt rot

disease to conifers worldwide. Stump removal associated

with whole-tree harvesting generally leads to significant

reductions in the area of the stump colonized by these

fungi, reducing the risk of attack (Thor and Stenlid 2005).

Conversely, the harvesting of forest residues and stumps

would also favor pioneering species of flora that are also

more tolerant of exposure and soil moisture levels. When

all biomass is removed, growth these species is more

vigorous, particularly the invasive nonforest field vegetation,

which—if it is not managed—might lead to a reduction in

timber productivity (Walmsley and Godbold 2010). Scott

and Dean (2006) also suggest that in the Gulf Coastal Plain,

soil analyses could be used to identify harvesting sites at

risk of harvesting-induced productivity loss, and fertilization

treatment could be used to avoid productivity loss caused by

whole-tree harvesting.

Meta-analysis by Fletcher and others (2011) of studies on

crops being used or considered in the United States, found

that vertebrate diversity and abundance are generally lower

in biofuel crop habitats relative to the non-crop habitats.

They found diversity effects are lower for pine and poplar

than for corn, and birds of conservation concern experience

lower negative effects. However, for minimizing impacts

of biofuel crops on biodiversity, they suggest practices that

reduce chemical inputs, increase heterogeneity within fields,

and delay harvests until after bird breeding. Many of these

practices might already be incorporated under intensive

management regimes in South and could be incorporated

into biomass production systems and management planning

used to avoid adverse impact on forested landscapes.

Results of direct and indirect land use change to agricultural

row systems can also cause habitat loss (Jonsell 2007). The

land use change from natural forests to forest plantations,

including short rotation woody crops, is of the greatest

concern from an ecological point of view (Wear and others

2010). Interventions focused on ecological restoration or fuel

reduction activities associated with woody biomass would

also benefit wildlife habitat (Janowiak and Webster 2010).
247

chapter 10. Forest Biomass-Based Energy

However, biomass production might also have negative

consequences unless coordinated with breeding and nesting

seasons and maintaining cover for overwintering small

mammal species (Bies 2006).

Just as important to southerners, but less quantifiable, are the

potential impacts of increased woody biomass removals on

quality-of-life issues: aesthetics, community relationships,

and appreciation of forest land as an integral part of the

social and physical landscape (Wear and others 2010).

Discussion and Conclusions

Markets

Our demand analysis shows that the consumption

requirements for wood from bioenergy markets would not

likely be met by urban wood waste alone, and that demands

for woody biomass would require harvesting residues or

biomass from timber markets by 2013 (fig. 10.2). Prices

for all forest products would likely increase, resulting in

increased returns to forest landowners. Price changes are

greater than changes in removals or inventory, consistent

with an inelastic market response. Although removals are

responsive to price changes (higher removals at higher

prices), forest inventories will also depend on factors like

forest growth, afforestation of agricultural or pasture

lands, intensive management of forest land, and increased

plantations of fast growing species. The models used for

our analysis attempt to account for these factors, but future

conditions are clouded by large uncertainties about demand

and supply factors. Consistent with chapter 4, the market

model indicates that increased prices under bioenergy

futures would mitigate the loss of forest land in the future.

Planted pine forest area is the most responsive to these price

trends. Bioenergy demands would result in declining use of

timber by forest industry, with impacts more pronounced for

pulp-based industries than for sawtimber industries.

With high demand for woody biomass, sawtimber industries

could also be impacted, although at lower levels. This

projection is consistent with studies by Aulisi and others

(2007) and Galik and others (2009), who found that

pulpwood markets are more likely to be impacted by an

emerging wood-based energy industry. Furthermore, Aulisi

and others (2007) suggest that sawmills might benefit from

the higher prices paid by bioenergy markets for secondary

products such as sawdust and chips. Our simulation indicates

that at high levels of bioenergy demands, the softwood

sawtimber industry would eventually be adversely impacted.

Forest industry might also face increased feedstock prices for

their pulp and sawtimber operations. In the long run, price

increases for softwood nonsawtimber are less severe than for

hardwood nonsawtimber because pine plantation area can

respond quickly, and hardwood plantations are not common

in the South.

Increased forest productivity could moderate price growth

and result in higher rates of removals and inventories.

Although productivity has grown substantially in the

South as a response to intensive management and genetic

improvements, productivity effects are not limited to

softwoods. Price increases are smallest with productivity

growth strategies that extend to all management types along

with an increase in short rotation plantations. Expanding

demands for bioenergy would not necessarily reduce the

levels of forest inventories. Our simulations show that an

increase in demand from the energy industry, coupled with

productivity increases, could lead to higher levels of both

removals and inventory.

With management and technological advancements, woody

bioenergy markets could result in increases in inventory,

removals, forest acreage, and returns to landowners.

Southern forests could be managed to produce substantially

more timber for bioenergy and other forest products

consistent with the projections shown in chapter 9.

These results indicate that the future trajectory of southern

forests will depend on the state of wood-based energy

markets as influenced by technological developments and

cost considerations. Markets will also be shaped by other

unknowns, including the amount of renewable energy that

will come from solar, wind, and other sources of renewable

energy. Similar to any nascent industry, the future of woodbased energy will depend on a number of uncertainties,

including the costs of production, technological

breakthroughs, the government policies that support

renewable technologies, forest productivity decisions, and the

expansion of short rotation woody crops. Along these lines,

if carbon markets emerge and carbon credits for displacing

fossil fuels with woody bioenergy are considered, more

changes in forest management and short rotation woody

crops might be expected, but inclusion of these details is

beyond the scope of this chapter.

Technologies

On the woody biomass-based energy technology front, there

is no emergent favorite. Even supposedly “low-hanging

fruits” such as co-firing face significant challenges, such

as boiler ash deposition, corrosion, and feedstock selection.

Federal and State governments, along with forest industry,

are investing research dollars into these technologies with

hopes of commercial success. Different types of woody

bioenergy occupy different places on the cost feasibility

spectrum. Wood pellets are already feasible under current
248 The Southern Forest Futures Project

markets, while biofuels are not economically competitive at

the current level of technology.

Advantages of wood pelletization include high energy-toweight ratio, lower capital requirements, ability to operate

production facilities at a variety of scales based on demand

or wood supply, lower costs of shipping the final product,

easier handling, and, most of all, high demand in European

countries. Conversely, preferred conversion technologies

for wood-based fuels remain largely uncertain because of

the high cost of production, project-specific factors, and

environmental standards (McKendry 2002). The high unit

cost of woody biomass-based energy is largely attributed

to high harvesting and transport costs; for example,

making woody biomass-based ethanol competitive with

starch-based ethanol or gasoline would require reduced

capital costs through technology improvements, reduced

feedstock costs (primarily from yield improvement), and

densification of wood at the harvest site to lower harvesting

costs (Alavalapati and Lal 2009, Dwivedi and others 2009,

Jackson and others 2010). The cost of transport from the

supply source (for example, the forest) to the conversion plant

also determines the viability of the manufactured product

(electricity, heat, or liquid fuels). Overcoming this significant

challenge requires that plants have easy access to the wood

supply and to distribution markets.

No species group has emerged as a favorite for woody

bioenergy. Both softwoods and hardwoods can be co-fired

with coal, used in combined heating and power plants, and

compressed for wood pellet production (Spelter and Toth

2009). Evidence supporting a clear preference for hardwood

or softwood species for wood-based liquid fuel is lacking as

well. Zhu and Pan (2010) suggest that sulfite pretreatment

to overcome lignocelluloses recalcitrance process holds

promise for woody biomass conversion, especially for

softwood species. However, softwoods contain more

lignin than hardwoods (Galbe and Zacchi 2002), meaning

that the conversion to liquid fuels might be less efficient

in softwoods because lignin needs to be removed during

the pretreatment process. Even Zhu and Pan (2010) noted

that in one of the most common pretreatment processes

(acid catalyzed steam explosion) sugar was successfully

recovered from hardwoods (for example, 65 to 80 percent

recovery from poplars) compared to less encouraging

results for softwood species.

Regardless of the conversion technology employed, a

continuous long-term flow of wood would be needed as raw

material. Because many Southern States are emphasizing

renewable technologies, new co-firing and combined heat

and power plants and ethanol biorefineries are likely to be

established in the future. Expansion of this sector—more

woody biomass-based energy plants or expansion of existing

facilities to achieve economies of scale—will be associated

with an increase in the demand for wood fiber. To meet the

burgeoning demand for woody biomass for energy estimated

by SRTS simulation runs, merchantable timber and smalldiameter wood would be required in addition to logging

residues or wood waste such as sawdust, shavings, and chips

from other wood product manufacturing processes.

Technological advancements are essential for making wood

energy competitive with other sources such as gasoline

and coal. Policy support for woody biomass-based energy,

a nascent industry, might help in attaining commercial

viability and developing a mature market.

Policies

Available policy instruments have advantages and

disadvantages (Aguilar and Saunders 2010, Alavalapati

and others 2009). Financial incentives allow directly

measurements of their impact on prices. Moreover, they

can promote sustained demand for and supply of energy

feedstocks, and can lower the capital costs of investments.

However, funding for these programs is vulnerable during

hard economic times. Regulations such as renewable

portfolio standards are easy to adopt, and producers

generally bear incurred costs. However, these types of

policies might suffer from inflexibility, and information

needed for effective targeting can be elusive. A better

option might be to develop a suite of policy options geared

towards woody biomass-based energy. For example,

an Environmental and Energy Study Institute proposal

(2010) suggests that in uncertain times, an integrated

policy approach for bioenergy might include: inventorying

bioenergy resources and markets and developing a long

range bioenergy plan; developing sustainable feedstock

production guidelines; developing locally appropriate

feedstocks and conversion technologies; creating easement

programs for sustainable feedstock production; establishing

minimum renewable fuel standards; enacting a low carbon

fuel standard; promoting interagency cooperation and

cooperation with other States; providing tax incentives

for producers and retail distributors; and leveraging State

resources through Federal and private partnerships.

Given current logistical and technological challenges,

developing a mature woody biomass-based energy market

would likely depend on some level of government support that

includes financial incentives and other regulatory and support

policies. Indeed, such policies have emerged in various forms,

including research and development, consumption incentives

(such as fuel tax reductions), production incentives (such as

tax incentives, direct subsidies, and loan guarantees), and

mandatory consumption requirements. These and future

policies for production, conversion technologies, and markets
249

chapter 10. Forest Biomass-Based Energy

and distribution can potentially impact the production and

commercialization of woody biomass for energy, but might

also alter the ecosystem services provided by forests.

Financial incentives might facilitate the increased production

and diversion of woody biomass, likely increasing wood

demand and adding to the profitability of landowners

and those engaged in wood-to-energy conversion. Stand

improvement and restoration activities prioritized by States,

such as land recovery and cost share programs, might help

landowners make the long-term investments. Support for

weed and pest management, such as the pine bark beetle

prevention program in Virginia, might also increase biomass

availability. Best management practices and harvesting

guidelines developed especially for bioenergy could restrict

wood availability by reducing harvesting impacts through

minimum tillage and reduced applications of fertilizers

and pesticides; protecting wildlife corridors, riparian

zones, and other sensitive areas; and adopting wildlife

habitat enhancement measures such as leaving patches of

undisturbed areas, promoting certain species mixtures and

crop rotations, and retaining quantities of harvest residues,

litter, deadwood, snags, and den trees.

Research and technology grants, coupled with subsidies,

could help develop current and future wood-for-energy

markets. Other financial incentives targeting energy

producers might also favor the progress of new conversion

technologies and the integration of new technologies with

existing ones. Policy efforts geared towards development

of gasification techniques or an integrated process

with biomass-based electricity generation would likely

increase the production of woody biomass-based energy.

Technological innovations channeled towards reducing

feedstock production costs are significant, as they are likely

to spike the demand of wood, luring away some share from

traditional forest industries.

A wide array of policy instruments geared towards

improving the marketing and distribution of woody biomassbased bioenergy—such as appliance efficiency standards,

mandatory utility green power options, and renewable

portfolio standards—could play a pivotal role in deciding

where the wood–to-energy conversion plants and distribution

centers are set up. Because location of infrastructure

translates to increased demand for forest biomass, the

conditions of nearby forests might change.

Economic and technological uncertainties might influence

the impacts that current and future policies have on southern

forests. However, the great variety of policies—and the

multitude of ways in which the can interact—confounds

efforts to predict their potential effects. Policies addressing

other environmental and societal benefits associated with

forests and wood-to-energy markets might also alter the

impacts of bioenergy policies. In particular, emergence of

carbon markets could spur further growth in the wood-toenergy industry, but formulating a policy mechanism to

realize carbon payments is a huge challenge. For example,

under the Carbon Cap and Trade Bill currently in the U.S.

Congress, many forest landowners would not qualify for

carbon market benefits because they would not get credit for

existing levels of carbon sequestration, nor could they meet

sequestration permanence standards.

Sustainability Issues

Production of woody biomass for bioenergy can help

meet energy goals, but can also stimulate accelerated

harvesting, with potentially negative implications for

forest ecosystems. Reduction of soil nutrients as well as

soil compaction would likely decrease forest productivity.

Intensive biomass removal might affect aquatic communities

by increasing erosion, runoff, and waterway sedimentation.

Intensive forest management might also degrade forest

habitat conditions, negatively affecting flora and fauna and

reducing biodiversity. Land use changes from natural forest

to managed plantations might adversely affect imperiled

species in certain locations (see chapter 14). However,

changes from agricultural systems to forests might improve

habitat conditions. Further, the highgrading of stands

generally observed during some timber harvesting might be

eliminated with biomass harvesting.

Intensive woody biomass removal might also have some

negative implications for community relationships,

aesthetics, and public perceptions about forest land as

an integral component of southern ecosystems. Potential

impacts on forest ecosystems at local and regional levels is

most likely to challenge the forestry community to consult

new research findings like those summarized below and

update existing certification systems with guidelines on

how, when, and where woody biomass removals should be

conducted:

Janowiak and Webster (2010) provide a framework that

includes adapting management to site conditions, increasing

forested land where feasible, using biomass harvests as a

restoration tool, evaluating the possibility of fertilization and

wood ash recycling, and retaining deadwood and structural

heterogeneity for biodiversity.

Hennenberg and others (2009) suggest creating protected

areas that can be used to conserve relevant portions of

biodiversity.

Lal and others (2011) similarly report a set of nine criteria

that are necessary to the pursuit of sustainable woody

biomass extraction: reforestation and productive capacity,

land use change, biodiversity conservation, soil quality
250 The Southern Forest Futures Project

and erosion prevention, hydrologic processes, profitability,

community benefits, stakeholder participation, and

community and human rights.

Fletcher and others (2011) recommend the following

strategies to ensure habitat for biodiversity: reducing

harvesting impacts through minimum tillage and reduced

fertilizers and pesticides; protecting wildlife corridors,

riparian zones, and other sensitive areas; and adopting

wildlife habitat enhancement measures such as leaving

patches of undisturbed areas, promoting certain species

mixtures and crop rotations, and retaining quantities of

harvest residues, litter, deadwood, snags, and den trees.

Multi-stakeholder efforts such as the Roundtable on

Sustainable Biofuels and the Global Bioenergy Partnership

for biomass harvesting are already underway. The

Roundtable on Sustainable Biofuels and Global Bioenergy

Partnership are in the process of developing global principles

and criteria for developing a set of global, science-based

criteria and indicators coupled with field examples and

best practices (including benchmarks) for bioenergy

sustainability.

In addition to the overall scale of biomass production, the

location and methods of woody biomass harvests would

affect the health, vitality, and ecological function of southern

forests. Existing certification systems such as the Forest

Stewardship Council, American Tree Farm System, and

Sustainable Forestry Initiative have criteria and indicators to

safeguard site productivity, water quality, and biodiversity

but some additional indicators may be required for woody

biomass harvests. For example, an indicator might be needed

to address harvest residues left on site to maintain habitat for

small mammals, insects, reptiles, and amphibians. Levels of

necessary residues would depend on site-specific conditions,

although general guidelines could be formulated at State or

Southwide levels. Similarly, erosion-preventing indicators

(such as those prohibiting harvests on shallow and nutrientpoor soils) would need to consider specific soil conditions

such as depth of soils, nutrient conditions, and regeneration

potential.

Biomass harvesting at the levels explored in this chapter

could have negative implications for future forest conditions

and ecosystem services flowing from southern forests

including water (chapter 13) and wildlife/biodiversity

(chapter 14). These outcomes depend on the amount and

location of harvesting, but perhaps more critically on

the management strategies used. The research described

above indicates that management systems can be designed

to mitigate damages to various ecosystem services. Of

course, this requires management planning that addresses

management objectives in the context of local conditions.

The need for additional best management practices or

other guidelines will depend on the rate of development

of the bioenergy sector, which is highly uncertain. The

acceptability of these approaches would depend on the

process of updating best management practices, which would

ideally combine public involvement with a science-based

process at appropriate scales (Alavalapati and Lal 2009).

SUMMARY

Wood-based energy markets have been proposed as a means

to ensure sustainable forests, enhance energy security,

promote environmental quality, and realize social benefits.

However, several complex issues are influencing the

ability to develop these markets in economically efficient,

environmentally benign, and socially desirable ways.

These issues include biomass availability or supply, market

competitiveness and technology development, supportive

Federal and State policies, tradeoffs with traditional forest

product industries, sustainability, and ecosystem integrity.

This chapter has focused on four interrelated dimensions

of bioenergy futures related to southern forests: markets,

technologies, policies, and sustainability. Across the various

bioenergy scenarios, these new demands would affect the

markets for all wood products and lead to price increases for

timber products and higher returns to private landowners.

The degree to which other wood consumers are impacted

would depend on expansion in supply, which in turn depends

on intensification of forest management and changes in land

use (primarily from agricultural to forestry).

New demands for bioenergy will be determined by

expansion of existing technologies—for example, pellets and

co-firing with coal—but more critically on the emergence

of new technologies that are not yet economically viable.

Accelerated technological developments and reduced

production costs might be achieved through various

policies at Federal and State levels. The sustainability

issues surrounding bioenergy are defined by the negative

externalities associated with accelerated harvesting in the

South. Research indicates that management systems and

standards can be designed to protect these values, defining

another interface with future policy.

All of these dimensions are fraught with uncertainty. Market

futures depend on demands for traditional wood products

and on energy prices. Technology development depends on

research funding but also on unknowable limits to technical

feasibility and the prospect of economic returns. Policy

development is highly uncertain and fundamentally engages

tradeoffs among energy, environment, community, and other
251

chapter 10. Forest Biomass-Based Energy

societal objectives. The relationship between harvesting at

unprecedented levels and forest ecosystem services is not

fully known.

This chapter lays out a broad range of potential developments

and management options. Clearly the path to sustainable

bioenergy futures will involve enhancing knowledge,

monitoring changes, updating expectations, and narrowing

the overall uncertainty about future prospects. These issues

will likely be the focus of forest assessments for years to

come.

Knowledge and Inf ormation Gaps

The future of woody bioenergy markets depends on a

multitude of factors such as supply and availability of

wood biomass; advancements in conversion technologies;

improvements in harvesting, collection, storage,

densification, preprocessing, and transportation; product

prices and elasticities; infrastructure; and productivity

increases.

Determining many such factors with confidence was

difficult, and our analysis tools were limited. The

bioeconomic model that we employed for market analysis

calculates harvest levels, related prices, inventory, and

acreage as functions of input demands, productivity

increases, and various assumed parameters. These

relationships are not known with high precision, and the

market analysis cannot account for every economic variable

and strategic response to the impacts on energy markets.

Applying the models to a large number of scenarios provides

insights into the range of potential market responses in the

future. Improved estimates of the various supply, demand,

and production relationships would enhance forecasts of

future market developments. What’s more, high-demand

bioenergy futures imply important trades between wood

products sectors with implications for employment, income,

and rural economies that warrant additional study.

Our stylized approach to constructing consumption

projections for the region leaves certain aspects of bioenergy

futures unaddressed. Importantly, questions regarding

interregional and international trade in wood products that

could affect the ultimate expression of regional demands

were not directly addressed. Trade modeling was beyond the

scope of the Futures Project and explains why a broad range

of futures was evaluated—i.e., to capture a reasonable range

of futures regardless of the mechanisms leading to demand

outcomes. The national assessment of timber and wood

products markets contained in the 2010 RPA Assessment

(Ince and others 2011) explores trade under similar scenarios

and serves as a useful reference.

Woody bioenergy production might be more cost competitive

under a greenhouse gas reduction strategy that assigns

a market value to carbon emissions, in effect allowing

social and environmental benefits to be accrued to woody

bioenergy. This approach could monetize the benefits gained

through greenhouse gas reduction, and those gains could be

traded in a carbon market. Although likely to spur further

growth in a bioenergy industry, the carbon market approach

has yet to formulate a viable mechanism for realizing carbon

payments to forest landowners.

The legal definitions of what qualifies as ‘forest biomass’

under different policy descriptions would generate large

variations in forest biomass utilization and therefore require

research attention. For example, the Energy Independence

Security Act (2007) provides a restricted definition by

excluding biomass from public forests and naturally

regenerated private forests. Conversely, the 2008 Farm Bill

provides a comprehensive definition for forest biomass.

Estimates of the volume of woody material that can

used for energy production at secondary wood products

manufacturing facilities are imprecise and based on varying

assumptions about production facilities and per-unit

production potential. Also needing research attention is

comprehensive analyses of short rotation woody crops that

can be made available for energy use; land use tradeoffs

of short rotation woody crops with agriculture, pastures,

and forest land; and potential for pine-switchgrass and

other agroforestry systems to expand. Productivity gains

from changing the geographic range of agriculture and

woody biomass feedstocks and improving management is

another research area that warrants further attention, as is

documenting landowner willingness to participate in forest

biomass markets and incorporating this information into

woody biomass supply functions.

Additional research is needed to identify sustainability issues

surrounding woody biomass utilization for energy. The

focus of these concerns ranges from production processes to

consumption processes (feedstock production, harvesting,

transport, conversion, distribution, consumption, and waste

disposal) to job creation and societal benefit distribution.

Future research would necessarily focus on the tradeoffs

arising from woody biomass diversion for energy use,

and the level at which woody bioenergy might become

ecologically, economically, and socially undesirable.

Acknowledgments

The authors wish to thank Robert J. Huggett, Jr., and Karen

L. Abt, U.S. Department of Agriculture, Forest Service,

Southern Research Station; and Frederick J. Rossi, School of
252 The Southern Forest Futures Project

Forest Resources and Conservation, University of Florida,

for their support and insights. Thanks are also due to Carol

Whitlock and Shawna Reid from the U.S. Department of

Agriculture Forest Service, Southern Research Station, for

technical edits and help in preparing GIS maps respectively.

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257

chapter 10. Forest Biomass-Based Energy

We assigned 36 percent of the grid’s output to the Southern

States—Texas, Oklahoma, Arkansas, and Louisiana.

An East Central Area Reliability Coordination Agreement

state, now merged into Reliability First Corporation, serves

portions of Kentucky and Virginia. We assigned 18 percent

of the grid’s output to those States.

Western Texas also receives some electricity from the

Western Electricity Grid. Rather than apportioning part of

the Western Grid supply, we inflated the electricity supply of

the major supplier in the State (Electric Reliability Council

of Texas) by 6 percent.

Using the percentage apportioning described above, we

scaled data on the total U.S. annual electricity sales outlined

in Energy Information Administration (2010) reference case

scenario to the Southern States.

We estimated the share of woody biomass-based

electricity, using the same data source (Energy Information

Administration 2010) for the electricity grids. These data are

broken down by the type of renewable energy—including

conventional hydroelectric, geothermal, wood and other

biomass, biogenic municipal waste, wind, photovoltaic, and

solar thermal sources; but excluding ethanol, net electricity

imports, and nonmarketed renewable energy consumption

for geothermal heat pumps, buildings photovoltaic systems,

and solar thermal hot water heaters—and scaled down

according to the percentage factors used to derive total

output for the South. Using total electricity demanded, total

renewable electricity, and total woody and other biomassbased electricity data, we derived the share of renewables

in the total electricity portfolio of the region, as well as

the share of wood-based biomass electricity within the

renewables. Following Galik and others (2009), we assumed

that all energy from wood and other biomass sources

outlined by the Energy Information Administration (2010)

is completely composed of wood. The woody biomass

demand specified as electricity in billion kWh was converted

to woody biomass in thermal energy terms of trillion Btu.

Following Rossi and others (2010), we used a conversion

factor of 13,648 Btu per kWh, which is the standard

electricity to thermal energy conversion factor (3,412 Btu per

This appendix accompanies chapter 10. Estimation of the

woody biomass required for electricity production began

with Energy Information Administration (2010) data on

electricity generation, in billion kilowatt hours (kWh), for

the electricity grids that supply customers in the Southern

United States. The grid-based sales data is available only

until 2035, but we extrapolated it to 2050 by applying the

average growth rate calculated over the five preceding years.

Determining the amount of electricity consumed in the 13

Southern States is challenging because the electric grid

networks do not track the volume of power flowing to or

from individual areas, nor do they break out the electricity

sales information by States.1

We assumed that a fixed percentage of individual grid 

electricity caters to the South (Galik and others 2009, Rossi 

and others 2010) and based estimates of that percentage on 

expert opinions. Our underlying assumption was that the 

electricity demand storyline will not drastically change, with 

little alterations in percentages. 

The Florida Reliability Coordinating Council and Electric

Reliability Council of Texas grids only serve customers in

the South, so we assumed that all of their sales are within

the South. Other electric grids cater to customers outside the

South as well:

The Southeast Reliability Corporation serves customers

throughout Missouri, Alabama, Tennessee, North Carolina,

South Carolina, Georgia, and Mississippi; as well as portions

of Iowa, Illinois, Kentucky, Virginia, Oklahoma, Arkansas,

Louisiana, Texas, and Florida. To account for supplies going

outside the South—most of Missouri and portions of Iowa

and Illinois—we subtracted 16 percent of the grid total

electricity.

The Southwest Power Pool serves customers throughout

Kansas as well as portions of New Mexico, Texas,

Oklahoma, Arkansas, Louisiana, Missouri, and Nebraska.

1 Personal communication, 2010. R.J. Robertson, Manager, Customer

Relations. Southwest Power Pool, 415 North McKinley, #140 Plaza West,

Little Rock, AR 72205; and Teresa Glaze, Data Analyst, SERC Reliability

Corporation, 2815 Coliseum Centre Drive, Suite 500, Charlotte, NC 28217.

APPENDIX B.

Total Wood Demand for Energy Estimation
258 The Southern Forest Futures Project

kWh) at a 25 percent level of efficiency. This is congruent

with the Wiltsee (2000) study of biomass-fuelled power

plants, which reported the typical higher heating value to be

approximately 14,000 Btu per kWh (24.4 percent efficiency).

To account for conversion efficiency increases resulting

from factors such as increased use of co-firing with coal

in the future, replacing older combustion steam turbines

with gasification combined cycle plants, and technological

advances to all types of biomass power plants, we assumed

a gradual increase in thermal efficiency after 2020 to a

maximum of 40 percent in 2050. We converted Btu values

to mass in green tons by applying a conversion factor of 8.6

million green tons per Btu outlined by the Forest Service

(2004) for green wood (50 percent moisture content). Next

we needed to allocate how much of the total biomass used

for energy is sourced from softwoods versus hardwoods.

This is challenging as weight-to-volume conversion factors

vary with stem size and specific gravity of species. Galik

and others (2009) estimated conversion factors for trees of

average diameters based on Timber Mart-South 2007 data.

We followed their conversion factors—34.44 green tons per

thousand cubic feet for softwoods and 35.98 green tons per

thousand cubic feet for hardwoods.

Estimating Demand

Wood-Based Liquid Fuels

Estimation of the woody biomass required for liquid fuels

production began with Energy Information Administration

(2010) projections of energy consumption by sector and

source. We used this information to determine the share

of cellulosic ethanol with respect to the total domestic

ethanol production. While extrapolating ethanol production

from 2036 to 2050, we pegged the corn and starch ethanol

production value at the 2035 level and assumed that

increased ethanol production will come from cellulosic

sources alone. This is in sync with current Renewable

Fuel Standard target of pegging corn and starch ethanol

production at a fixed level and allows for increase in ethanol

production through cellulosic sources alone, although the

Energy Information Administration (2010) projections

assume that the Renewable Fuel Standard target of cellulosic

ethanol will not be met by 2022.

We estimated total domestic cellulosic ethanol production

(in million barrels per day) based on percentage share

data on estimates of liquid fuels supply and disposition,

and added data for other biomass-derived liquids such as

pyrolysis oils, biomass-derived Fischer-Tropsch liquids,

and renewable feedstocks used for the production of green

diesel and gasoline, gathered from the same source, to get

total liquid fuels that can be produced from wood or other

cellulosic sources (Energy Information Administration 2010).

We scaled cellulosic liquid fuels demand at the national

level down to southern levels based on the assumption that

55 percent of the national demand will be met by the 13

Southern States. Because wood is a high-volume low-value

product, transportation costs limit its transport to conversion

plants far from harvesting areas. In this light, 55 percent is

conservative, as 57 percent of wood harvesting occurs in the

South (Hanson and others 2010).

A suite of feedstocks (including wood, paper and pulp

liquors, algae, switch grass, and agricultural residue) can be

used to produce cellulosic ethanol or other bio-oils. Because

the future of liquid fuel from biomass sources is uncertain

and we do not know how much cellulosic ethanol and other

bio-oils will come from wood sources, we assumed that 30

percent of the total cellulosic fuels and bio-oils are from

wood. We converted barrel-per-day demand to gallons per

day according to Oak Ridge National Laboratory (2010)

protocols, which defined 1 barrel as 42 gallons. We converted

daily consumption data to annual levels by multiplying by a

factor of 365.242 and converted gallons of ethanol and biooils to green tons of wood using an ethanol yield calculator

(http://www1.eere.energy.gov/biomass/ethanol_yield_

calculator.html), establishing that a green ton of softwoods

produces 40.75 gallons, 50.4 gallons for hardwoods. We

further converted the wood demand in thousand cubic feet by

applying the volume-to-weight conversion factors developed

by Galik and others (2009).

Wood Pellets 

The U.S. wood pellet industry is already established, in

contrast to the wood electricity and wood fuels industries

(Alavalapati and Lal 2009, Spelter and Toth 2009). However,

to a large extent, it is being driven by European demand

(Gold 2009). This along with the use of wood pellets for

domestic heating rather than grid electricity might result

in incomplete accounting by the Energy Information

Administration (2010), where renewable electricity

production estimates are based solely on electricity grid

sales. This prompted us to account for wood pellet demand

separate from wood based electricity demand. Spelter and

Toth (2009) estimated southern pellet plant capacity to be

1.85 million tons in 2009; based on their assessment that

U.S. plants operate at an average efficiency of 66 percent, we

estimated the demand for wood for pellets in the southern

region to be 1.22 million tons. Because many States are

encouraging the use of renewables, domestic demand is

likely to increase in future. To account for expected demand

increase in future, we assumed a 0.5-percent annual increase

in the capacity of pellet plants from 2011 onwards. The

current capacity utilization of U.S. pellet plants is lower than

countries like Canada, which have utilization efficiency of

81 percent. Spelter and Toth (2009) attributed this to reasons

such as newer plants, normal startup problems, and limits
259

chapter 10. Forest Biomass-Based Energy

on fiber availability. However, they also say that as pellet

plants become older, the U.S. capacity utilization is expected

to increase. To account for technological advancements,

we assumed that overall capacity utilization increases by

1 percent per year from 2015 until it reaches 85 percent.

Harvesting Residues and Urban Wood Waste 

Current literature (Perlack and others 2005, Galik and others

2009, Energy Information Administration 2010) indicates

that harvesting residues—discarded tree tops and limbs

generated during the harvesting process—currently being

left on the ground can be used as woody biomass-based

energy feedstocks. Recent analyses (Galik and others 2009,

Rossi and others 2010) suggest that harvesting residues

could be used to avoid diverting some merchantable timber

for energy production. Rossi and others (2010) also argue

that the projections of woody biomass demand for energy

production need to be scaled down further to account for

urban wood waste. For this reason, we dropped urban wood

waste from the total amount of woody biomass consumption.

This essentially gives us the merchantable timber that will be

required for energy production (total woody biomass minus

urban wood waste). Note that the residue from additional

harvesting was handled endogenously. The model calculates

softwood and hardwood harvesting residues along with the

merchantable timber that can be harvested in a particular

year. For each year, the amount of harvesting residues that

can be made available is estimated along with the harvest

levels of softwood and hardwood pulpwood and sawtimber.

Because it ignores urban wood waste, we netted out urban

wood waste from total woody biomass consumption and fed

the remainder into the model.

The harvest residue that can be used for energy production

depends on total harvest as well as the residue utilization

factor (the percentage of harvest residue that can be

converted to energy). Increased harvesting efficiency can

reduce the availability of forest residues (Grushecky and

others 2007). Rather than having a constant harvesting

residue utilization factor—40 percent for Walsh and others

(2008), 45 percent for Rossi and others (2010), and 50 percent

for Galik and others (2009)—we assume 45 percent in 2010,

increasing to 67 percent in 2025, and remaining pegged at

this level until 2050. We believe that this trend characterizes

the effects of current harvest efficiencies and technology

improvements along with potential developments of the

forest residues market. Because forest residue removal also

has the potential for adverse impacts on site productivity

and biodiversity (Lal and others 2009), some State biomass

harvesting guidelines are aimed at retaining of 10 to 33

percent forest residues on harvesting sites (Lal and others

2011). Consequently, we assume that not more than 67

percent of harvest residues are removed and utilized for

energy production.

Total harvest information for different time periods is

estimated through an auxiliary Subregional Timber Supply

model, (Abt and others 2000), which calculates gross harvest

residues. The modified model uses residual factors (Johnson

and others 2009) to estimate softwood and hardwood

harvesting residues produced for different woody biomass

consumption scenarios. For the forest survey units in this

study, the harvesting residual factors for softwood ranges

from 0.049 to 0.161 (per cubic foot of removals) for growing

stock, and 0.091 and 0.357 for nongrowing stock, compared

to 0.106 to 0.247 for growing stock and 0.1945 and 0.3783 for

nongrowing stock for hardwoods.

The subregional supply model run allocates harvest residues

based on species (hardwood versus softwood) rather than

to hardwood and softwood products (sawtimber versus

nonsawtimber). To distribute residues to the four products,

we used average product shares of these products as initial

parameter values and calculated the average product shares

based on the Timber Product Outputs data (Bentley 2003;

Johnson and others 2006, 2008, 2009). We estimated the

residues at 55.51 percent for softwood sawtimber, 44.48

percent for softwood nonsawtimber, 39.74 percent for

hardwood sawtimber, and 60.25 percent for hardwood

nonsawtimber.

Wiltsee (1998) estimated per capita urban wood waste at

0.203 green tons per year, which we used—along with the

yearly estimates of future population of the Southern States

through 2050 from the U.S. Census Bureau States Interim

Population Projections by Age and Sex data sets (http://www.

census.gov/population/www/projections/projectionsagesex.

html)—to calculate the annual amount of urban wood waste

generated in the region. Because some urban wood waste

will not be diverted for energy use, we scaled the per capita

urban wood waste estimation by a utilization factor of 60

percent (Carter and others 2007).

Allocating Merchantab le Timber

into Four Products

To determine the percentage share within a species group,

we allocated woody biomass requirement (minus harvesting

residues) only to the pulpwood market, as sawtimber and

other higher value forest resources might be too expensive

to be used for bioenergy production. The nonsawtimberbased feedstock preference can also be observed in a recent

study by Rossi and others (2010) in Florida, which assumed

that 88 percent of the total timber diverted for energy comes

from nonsawtimber sources. However, Perlack and others

(2005) outline the possibility that high oil prices and low

timber prices may create conditions whereby pulpwood or

even small sawtimber resources could be used for bioenergy.

We assumed that nonsawtimber will be used for energy

production earlier than high value sawtimber. However, at
260 The Southern Forest Futures Project

higher level of woody biomass consumption, we determined

how much of the woody biomass requirement exceeds

what can be met by nonsawtimber. We posit that this

extra requirement of biomass (over and above the harvest

levels depicted by the model runs) is sourced by displacing

softwoods and hardwoods sawtimber from forest industries.

The subregional supply model utilizes diameter distributions

for each subregion, owner, management type, and age class

to calculate product removals and inventory volumes by

age class. We modified age class in the subregional supply

model from a five-year period to annual levels so that the

supply response could be consistent with consumption data.

Furthermore, the model requires a specific cull factor and

a diameter range that determines how much volume (in

each product category) contributes to nonsawtimber. We

used the cull factor outlined in Abt and others (2009, 2010)

and demarcated sawtimber versus nonsawtimber based on

diameter at breast height (d.b.h.) definitions from the Forest

Service Forest Inventory and Analysis program: 5–8.9 inches

for softwood nonsawtimber, 5–10.9 inches for hardwood

nonsawtimber, >9.0 inches for softwood sawtimber, and

≥11.0 inches for hardwood sawtimber. Trees <5 inches d.b.h.

are considered to be saplings.

The modified subregional supply model requires that

consumption be input according to the softwood and

hardwood categories specified by the user. We apportioned

total woody biomass consumed (both for energy and for

traditional forest industry requirements) for a particular

scenario among the hardwood and softwoods as the starting

point. Looking at roundwood output data for the past decade

(Bentley 2003; Johnson and others 2006, 2008, 2009), we

observed that the softwoods comprise approximately 70

percent of the total timber output, compared to 30 percent for

hardwoods.

As most of the technology for woody bioenergy production

is in nascent stage and no species group has been established

as favored, we followed this generic timber output trend and

parameterized the initial model run by assuming that 70

percent of wood used for energy or by industry is sourced

from softwood, and the remaining 30 percent is from

hardwoods.
261

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

John L. Greene, Thomas J. Straka,

	 and Tamara L. Cushing1

Key Findings

•	 Federal and State taxes reduce the pre-tax value of familyowned forest land in the South by amounts ranging from

little more than one-quarter to nearly half, with the greatest

share of the reduction attributable to the Federal income tax

and State property taxes.

•	Most family forest owners are aware of some general

business provisions of the Federal income tax, but half or

fewer are aware of provisions specifically for forests and

other working lands, such as the reforestation incentives

and special treatment of qualifying cost-share payments.

•	 For family forest owners who do not grow timber for sale,

State property taxes are of greater concern than any other

tax, because they occur annually and are perceived as

being high in relation to the value of the land.

•	 State-to-State variability in property taxes produces

relative disadvantages to holding forest land and likely

contributes to conversion of family-owned forest land in

States that tax property at higher rates.

•	 Owners of family forests and other working lands are many

times more likely than U.S. taxpayers in general to incur

the Federal estate tax. Of the forest estates that owe estate

tax, 40 percent sell timber or land to pay part or all of the

tax, with roughly one-quarter of the acres sold converted to

other uses.

•	 Financial incentive programs are generally successful in

promoting sustainable practices among the family forest

owners who participate in them, but funding levels and

owner confusion about the requirements to apply for and

participate in the programs limit the number of acres that

are treated.

1John L. Greene is an Emeritus Scientist, Forest Economics and Policy

Research Work Unit, Southern Research Station, U.S. Department of

Agriculture Forest Service, Research Triangle Park, NC 27709; Thomas

J. Straka is a Professor of Forestry and Natural Resources and Tamara

L. Cushing an Assistant Professor and Extension Forestry Specialist,

Department of Forestry and Natural Resources, Clemson University,

Clemson, SC 29634.

Introduction

Taxes on forest-related income, forest land, and forest

products can encourage or inhibit private investment in forest

resource management. In financial analyses, taxes rank with

harvest returns and rotation length as a key determinant

of the viability of forest management investments. As

such, they constitute an important part of the operating

environment for owners and managers of private forest land,

and a critical factor in determining the level of stewardship

practiced and the types of products and services provided.

For units of government, taxes represent a significant source

of funding and a powerful tool for pursuing societal goals.

These characteristics combine to make effective integration

of tax considerations both problematic and essential for forest

owners, managers, investors, elected officials, and natural

resource policymakers.

Of the 751 million acres of forest land in the United States,

35 percent (264 million acres) is owned by families—defined

to include individuals, married couples, estates, trusts, and

other unincorporated groups of individuals—and 18 percent

(138 million acres) is owned by forest industry (Butler 2009).

Private forest ownership is even more prevalent in the South,

with 59 percent of forest land (128 million acres) held by

families (Butler and Leatherberry 2004) and 27 percent

(57 million acres) held by forest industry (Smith and others

2009).

This chapter addresses the effect of Federal, State, and local

taxes on family-owned forests in the South. The effect of

taxes on land owned by forest industry and the comparative

advantages of different business organizational models are

discussed in chapter 6.

The Federal Income Tax

The Federal income tax was established in 1913, under the 16th

amendment to the U.S. Constitution. The earliest provisions

that recognize the unique character of forest management

date to 1918 (Dana and Fairfax 1980). The Federal income

tax has the greatest potential of any tax to affect family forest

owners, because it applies to income from all sources and the

rates are high compared with other taxes. The economic effect

Chapter 11.

 Effect of Taxes and Financial Incentives

on Family-Owned Forest Land
262 The Southern Forest Futures Project

of an income tax is to increase the variable cost of owning

or managing forests. It therefore influences how intensively

owners manage their holdings (Gregory 1972).

Since its institution, a number of provisions have been

added to the Federal income tax that help family forest

owners keep their land in forest and manage it sustainably.

Some are general business provisions, while others are

specifically for owners of forests and other working lands.

Among the most important of the general provisions are

(Greene and others 2013):

Long-term capital gain treatment of qualifying income—

Income from the sale of timber held for more than 12 months

generally qualifies as a “long-term capital gain.” Long-term

capital gains currently are taxed to individuals at a maximum

rate of 20 percent (compared with 39.6 percent for ordinary

income) and a minimum rate of 0 percent (compared with

10 or 15 percent for ordinary income; CCH 2013). Capital

gains enjoy other advantages over ordinary income: they

are not subject to self-employment taxes, at rates up to 15.3

percent, and they do not count toward the amount of income

retired persons may earn before their Social Security benefits

are reduced. Further, large losses in capital investments may

only be applied against $3,000 of ordinary income per year,

but against any amount of capital gains.

Depletion deduction—Owners who sell or dispose of

timber or such other natural resources as oil or minerals

can recover their investment in the resource sold through a

depletion deduction. The deduction is equal to the owner’s

“basis” (a measure of investment in a capital asset) in each

unit of the resource sold. This deduction is available to all

owners who hold their forest for the production of income,

whether as an investment or part of a trade or business.

Annual deduction of management costs—Owners may

deduct the cost of forest management practices annually, as

they occur. This deduction does not apply to reforestation,

which has its own provisions (see below), but does apply

to fees paid to a consulting forester or the cost of brush

control, thinning, mid-rotation fertilization, timber stand

improvement, control of insects and diseases, maintenance

of roads and firebreaks, and similar practices, as long as

they are ordinary and necessary for timber management and

related to the income potential of the forest. This deduction

also is available to all owners who hold their forest for the

production of income. Investors, however, must take it as

a “miscellaneous itemized deduction,” which combined

with other such expenses is deductible only to the extent it

exceeds 2 percent of their “adjusted gross income.”

Depreciation deductions—Owners can recover investments

in qualifying income-producing property—including

machinery, buildings, equipment, fences, culverts, and

bridges—as it loses value over time due to wear and tear,

age, deterioration, or obsolescence. Depreciation deductions

are available to all owners who hold their forests to produce

income, although investors again must report them as

miscellaneous itemized deductions subject to the 2 percent of

adjusted gross income floor. Under current law, owners also

may elect to take a first-year “bonus” depreciation deduction

equal to 100 percent of the cost of new property “placed in

service” (available and ready for use) between Sep. 9, 2009,

and the end of 2011. The bonus depreciation deduction for

new property placed in service before Sep. 9, 2009, or during

2012 or 2013, is 50 percent of its cost (CCH 2010, CCH 2013).

The section 179 deduction—Owners who hold their

forest as part of a trade or business may elect to deduct

part or all of the cost of certain types of property instead

of capitalizing and depreciating it. This deduction is

not available to investors, trusts, or estates. Qualifying

property includes tangible personal property, but not

improvements to land, buildings, or components of

buildings. For 2010 through the end of 2013, the maximum

amount of the deduction is $500,000, reduced by $1 for

each dollar over $2 million of section 179 property placed

in service during the year (CCH 2010, CCH 2013).

Loss deductions—All owners who hold their forest land

to produce income may recover the amount of their basis

in timber or other property lost in a casualty event, theft,

or condemnation. Owners who hold their forest as part of

a trade or business also may recover their basis in property

lost in a noncasualty event. (Owners who hold their forest for

personal use, without a profit motive, can recover their basis

in timber or other property lost in a casualty event, theft, or

condemnation only to the extent that all losses in a year, minus

$100 per event, exceed 10 percent of their adjusted gross

income.) If income-producing property is damaged rather than

destroyed, the owner must make an effort to salvage it. Since

owners’ basis in their timber typically is lower than its actual

value, a salvage harvest of damaged timber often results in a

taxable gain rather than a loss. But the owner can postpone

recognition of the gain, and the tax on it, by using the gain to

restore or replace the damaged property within the allowable

replacement period, usually 2 years.

The provisions for owners of forests and other working lands

include (Greene and others 2013):

Reforestation incentives—All owners who hold forest land

for the production of income may deduct outright qualifying

reforestation costs up to $10,000 per year and “amortize”

(write off over a set period) any additional amount over 8

tax years.

Special treatment of qualifying cost-share payments—

Landowners may elect to exclude a calculated portion of
263

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

qualifying public cost-share payments from their gross

income. Currently, cost-share payments from nine Federal

programs—the Conservation Reserve Program, Emergency

Forest Restoration Program, Emergency Watershed

Protection Program, Environmental Quality Incentives

Program, Forest Health Protection Program, Longleaf Pine

Initiative, State Acres for Wildlife Enhancement, Wetlands

Reserve Program, and Wildlife Habitat Incentives Program

(table 11.1)—as well as a number of State programs are

approved for exclusion. Because of the way the excludable

portion is calculated, it is likely that the full amount of a

cost-share payment will be excludable if the affected area

has been harvested in the past 3 years, but only a fraction

will be excludable it if has not.

Enhanced charitable deduction for a qualifying donation 

of interest in land—Landowners may take a charitable

contribution deduction for donation of an interest in land.

To qualify for a deduction, the donation must consist of

a qualified real property interest, made to a government

agency or qualified publicly-supported organization, for

one of four conservation purposes (see “Incentives for

Conservation Easements”). Under current law, the annual

limit for this deduction is 100 percent of adjusted gross

income for owners who earn more than half of their gross

income from farming (defined to include forest land) or

ranching, and 50 percent of adjusted gross income for other

owners (Land Trust Alliance Web site 2011).

The tax rates and deduction limits for four of the above

provisions are temporary, and were originally put in place by

laws enacted between 2001 and 2008, collectively called the

Bush tax cuts:

•	 The reduced tax rates for long-term capital gains were put

in place by the Jobs and Growth Tax Relief Reconciliation

Act of 2003 (P.L. 108-27), and were scheduled to sunset at

the end of 2010.

•	Bonus depreciation, previously available only to taxpayers

affected by a Presidentially-declared disaster, was made

generally available by the Economic Stimulus Act of

2008 (P.L. 110-185) and extended through the end of

2009 (through the end of 2010 for certain property with a

long production period) by the American Recovery and

Reinvestment Act of 2009 (P.L. 111-5).

•	 The increased section 179 deduction was put in place by The

Economic Stimulus Act of 2008 (P.L. 110-185), then increased

further and extended through the end of 2011 by the Hiring

Incentivesto Restore Employment and Small BusinessJobs

Acts of 2010 (P.L. 111-147 and 111-240, respectively).

•	 The enhanced charitable deduction for a qualifying

donation of interest in land was put in place by the Pension

Preservation Act of 2006 (P.L. 109-280) and extended

through the end of 2009 by the 2008 Farm Bill (P.L.

110-246).

As a result, the provisions either expired at the end of 2009

or were set to expire at the end of 2010 or 2011. The Tax

Relief, Unemployment Insurance Reauthorization, and Job

Creation Act (2010 Tax Relief Act, P.L. 111-312), signed into

law Dec. 17, 2010, reinstated all four provisions and extended

them through the end of 2012 (CCH 2010). The American

Taxpayer Relief Act of 2012 (P.L. 112-249), signed into law

Jan. 2, 2013, replaced the first three temporary provisions

with permanent ones and extended the enhanced charitable

deducation for donation of an interest in land through the end

of 2013, preventing all four provisions from returning to pre2002 law (CCH 2013).

State Income Taxes

All but three of the Southern States tax income to individuals.

The exceptions are Florida and Texas, which do not levy

an individual income tax, and Tennessee, which taxes only

dividend and interest income (table 11.2). Most States that tax

income to individuals use Federal adjusted gross income or

Federal taxable income as the starting point for calculating

the State tax (Cushing 2006); however, they differ widely

in how they set tax rate schedules, incorporate personal

exemptions and itemized deductions, and treat retirement

income and capital gains (Butler and others 2010). Because of

the close link between Federal and State income taxes, most

Federal tax provisions that benefit family forest owners flow

through to the State income tax (Siegel and others 1996).

State income tax rate schedules generally ramp up quickly.

In all Southern States except Kentucky and Georgia, the

threshold for the top tax rate is below the ceiling for the

15-percent Federal tax bracket, the second-lowest bracket

(Bankrate.com 2011). But the top marginal State tax rates

range only from 5 percent to 8 percent, well below the top

Federal rates for either capital gains (20 percent) or ordinary

income (39.6 percent). For this reason, although State income

taxes have the same economic effect as the Federal tax, their

qualitative impact is smaller.

State Property and Harvest Taxes

State and local governments have levied taxes on land and

other forms of property since the colonial period, with

provisions which recognize that taxing land and timber

together encourages deforestation dating to the 1860s (Dana

and Fairfax 1980). Property taxes have the greatest impact

of any State tax on family forest owners, because they occur

annually and are based on the value of the land. The economic

effect of a property tax is to increase the fixed cost of owning

land. It therefore influences forest owners’ decisions about

whether to continue holding their land (Gregory 1972).

All of the Southern States assess or tax family-owned forest

land in its current use rather than its highest and best use
264 The Southern Forest Futures Project

Table 11.1—Federal incentive programs of interest to family forest owners

Biomass Crop Assistance Program (BCAP)—Authorized in 2008 to provide financial incentives for the collection, harvest, 

storage, and transportation of biomass material by qualified conversion facilities and the establishment and production of biomass 

crops. Payments to landowners under the biomass material provisions were suspended early in 2010 pending issuance of final 

rules and the biomass crop provisions are to be implemented in the future. All program payments must be included in adjusted 

gross income. BCAP is administered by the Farm Service Agency.a,b

Conservation Reserve Program (CRP)—Established in 1985 to help safeguard environmentally sensitive agricultural land by 

converting it to a long-term, resource-conserving cover. Participants receive annual rental payments under a 10 to 15 year contract. 

They also may receive incentive payments, and cost-share payments to cover up to 50 percent of the cost of establishing a suitable 

long-term cover. A calculated portion of cost-share payments may be excluded from adjusted gross income, but all other program 

payments must be included. CRP is administered by the Farm Service Agency.c

Conservation Stewardship Program (CSP)—Authorized in 2008 to assist owners of agricultural and forest land to adopt and 

maintain practices to conserve soil, water, air, and related resources. Participating owners receive annual payments under a 5-year 

contract to install and maintain new conservation practices; they also may receive supplemental payments to adopt a resourceconserving crop rotation. All program payments must be included in adjusted gross income. CSP is administered by the Natural 

Resources Conservation Service.d

Emergency Forest Restoration Program (EFRP)—Created in 2008 as a new part of the Emergency Conservation Program. 

EFRP provides participating forest owners a cost-share of up to 75 percent of the cost of restoring land damaged by a natural 

disaster, such as a flood, hurricane, tornado, or wildfire. A calculated portion of EFRP cost-share payments may be excluded from 

adjusted gross income. EFRP is administered by the Farm Service Agency.e

Emergency Watershed Protection Program (EWP)—Established to assist in implementing emergency recovery measures from 

natural disasters that impair or damage watersheds. Affected landowners may receive technical assistance and a cost-share of up 

to 75 percent (90 percent in limited resource areas) of the cost of clearing or restoring the damage; the administering agency also 

may elect to purchase perpetual floodplain easements from willing owners. A calculated portion of EWP cost-share payments have 

been excludable from adjusted gross income since 1978. EWP is administered by the Farm Service Agency.d

Environmental Quality Incentives Program (EQIP)—Established in 1996 to help farm, ranch, and forest-landowners address 

management practices that pose a significant threat to soil or water resources. Participating landowners receive technical 

assistance, incentive payments, and cost-share payments for 1 to 10 years that cover up to 75 percent (90 pecent for new, limited 

resource, or socially disadvantaged owners) of the cost of implementing conservation practices. A calculated portion of EQIP costshare payments may be excluded from adjusted gross income. EQIP is administered by the Farm Service Agency.d

Forest Land Enhancement Program (FLEP)—Established in 2002, FLEP combined aspects of two earlier programs. It promoted 

sustainable management of family forest land by providing technical, educational, and cost-share assistance to owners. A written 

forest management plan was required to participate. A calculated portion of FLEP cost-share payments could be excluded from 

adjusted gross income. Administered by the U.S. Forest Service in cooperation with State forestry agencies, the program was not 

reauthorized in the 2008 Farm Bill.

Forest Legacy Program (FLP)—Created in 1990 to protect environmentally important private forest land threatened with 

conversion to non-forest uses. FLP is not a cost-share program. It operates primarily through the purchase of permanent 

conservation easements. Up to 75 percent of the total cost of protecting forest land may be Federally funded. FLP is administered 

by the Forest Service in partnership with the individual States.f

Forest Stewardship Program (FSP)—Established in 1990 to encourage and enable active long-term management of familyowned forest land and increase the economic and environmental benefits it provides. FSP is not a cost-share program. State 

forestry agency partners use the program to promote forest owner adoption of stewardship practices, for example, by offering a 

State Forest Stewards program or providing technical assistance to develop Forest Stewardship plans. FSP is administered by the 

U.S. Forest Service in partnership with the individual States.g

Healthy Forest Reserve Program (HFRP)—Authorized in 2003 to restore and enhance forest ecosystems to promote the 

recovery of at-risk species, improve biodiversity, and enhance carbon sequestration. Participating owners receive assistance 

to develop a Forest Stewardship Plan, then may elect either a 10-year agreement, which pays 50 percent of the cost of the 

conservation practices, or a permanent easement, which pays the easement value of the land plus 100 percent of the cost of the 

practices. All payments must be included in adjusted gross income. HFRP is administered by the Natural Resources Conservation 

Service.d

Landowner Incentive Program (LIP)—Established in 2003 to help private landowners protect and restore habitat for at-risk plant 

and animal species. LIP provides funding for States to offer technical assistance and grants to participating landowners to develop 

and implement habitat management plans. All LIP payments must be included in adjusted gross income. LIP is administered by the 

U.S. Fish and Wildlife Service in cooperation with the individual States. To participate, States must provide a minimum 25 percent 

match for Federal funding.h

(Continued)
265

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

Table 11.1—(continued) Federal incentive programs of interest to family forest owners

Longleaf Pine Initiative (LPI)—Initiated in 2006 as a conservation practice under CRP, with the goal of restoring up to 250,000 

acres of longleaf pine forest in nine Southern States. Participating landowners receive annual rental payments under a 10 to 15 

year contract. They also may receive incentive payments, and cost-share payments to cover up to 50 percent of the cost to plant, 

protect, and manage longleaf pine stands on suitable sites. A calculated portion of LPI cost-share payments may be excluded from 

adjusted gross income, but all other payments must be included. LPI is administered by the Farm Service Agency.i

Partners for Fish and Wildlife (PFW)—Established in 1987 to help restore wetlands and other important fish and wildlife 

habitats on private lands. Participating owners receive technical assistance and a cost-share of up to 100 percent of the cost of 

implementing conservation practices. Funds for cost-share payments come from Federal, State, and local units of government, soil 

and water conservation districts, and private conservation organizations. All program payments must be included in adjusted gross 

income. PFW is administered by the U.S. Fish and Wildlife Service in cooperation with the individual States.j

Red-Cockaded Woodpecker (RCW) Recovery Program—Created under the Endangered Species Act of 1973 to help public and 

private landowners in 11 Southern States conserve red-cockaded woodpeckers and the habitat upon which they depend. Program 

specifics for private landowners vary by State. In most States, participants receive technical assistance in habitat improvement, but 

in some States cost-share funding also is available. Any program payments must be included in adjusted gross income. RCW is 

administered by the Fish and Wildlife Service in cooperation with the individual States.k

Southern Pine Beetle Prevention Program (SPBP)—Established in 2003 to help public and private forest-landowners in the 

Southern States reduce the susceptibility of their holdings, restore affected areas, and fund research. Program specifics vary 

by State, but private landowners can receive technical assistance and cost-share payments to cover part of the cost of such 

treatments as thinning and hazard fuel reduction. A calculated portion of SPBP cost-share payments may be excluded from 

adjusted gross income. SPBP is administered by the U.S. Forest Service in cooperation with the individual States.l

State Acres for Wildlife Enhancement (SAFE)—Initiated in 2008 as a conservation practice under CRP to protect and restore 

habitat for high-priority wildlife species. Participating landowners receive annual rental payments under a 10 to 15 year contract. 

They also may receive incentive payments, and cost-share payments to cover up to 50 percent of the cost to establish habitatenhancing natural covers on suitable land. A calculated portion of program cost-share payments may be excluded from adjusted 

gross income, but all other payments must be included. SAFE is administered by the Farm Service Agency.m

Wetlands Reserve Program (WRP)—Established in 1985 to encourage conservation of wetlands on privately owned lands. 

Participating owners elect one of three program options: a permanent easement, which pays 100 percent of the easement value of 

the land and the cost of wetland restoration practices; a 30-year easement, which pays 75 percent of the easement value and the 

cost of restoration practices; or a cost-share option, which pays 75 percent of the cost of restoration practices. A calculated portion 

of WRP cost-share payments may be excluded from adjusted gross income, but all other payments must be included. WRP is 

administered by the Farm Service Agency.d

Wildlife Habitat Incentives Program (WHIP)—Established in 1996 to encourage development and improvement of wildlife habitat 

on private land. Participating landowners receive technical assistance, incentive payments, and cost-share payments under an 

agreement lasting 1 to 10 years that cover up to 75 percent (90 percent for new, limited resource, socially disadvantaged owners, 

or Indian tribes) of the cost of implementing conservation practices. A calculated portion of program cost-share payments may be 

excluded from adjusted gross income. WHIP is administered by the Farm Service Agency.d

aFSA BCAP Fact Sheet: http://www.fsa.usda.gov/Internet/FSA_File/bcap09.pdf. 

bBiomass Magazine: http://biomassmagazine.com/article.jsp?article_id=3793.

cFSA CRP Fact Sheet: http://www.fsa.usda.gov/Internet/FSA_File/crpcont06.pdf.

dNRCS Conservation Programs Web page: http://www.nrcs.usda.gov/PROGRAMS/.

eFSA EFRP Fact Sheet: http://www.fsa.usda.gov/Internet/FSA_File/2008fbemergencyforestsummary.pdf. 

f

USFS FLP Web page: http://www.fs.fed.us/spf/coop/programs/loa/flp.shtml. 

gUSFS FSP Web page: http://www.fs.fed.us/spf/coop/programs/loa/fsp.shtml. 

hUSFWS LIP Web page: http://wsfrprograms.fws.gov/Subpages/GrantPrograms/LIP/LIP.htm.

i

FSA LPI Fact Sheet: http://www.fsa.usda.gov/Internet/FSA_File/crplongleaf06.pdf. 

j

USFWS PFW Southeast Region Web page: http://www.fws.gov/southeast/es/partners/. 

kUSFWS Red-Cockaded Woodpecker Recovery Web page: http://www.fws.gov/rcwrecovery/., 

l

USFS SPBP Web page: http://www.fs.fed.us/r8/foresthealth/programs/spb_prevention/spb_prevention.shtml. 

mFSA SAFE Fact Sheet: http://www.fsa.usda.gov/Internet/FSA_File/safe08.pdf. 
266 The Southern Forest Futures Project

(table 11.3). The States vary substantially, however, in the

approaches they use and the methods by which they apply

them. Some States determine the current use value of land

using soil type and productivity, which involves using a

specified capitalization rate to discount prospective future

returns from the land back to the present; others use fair

market value in the land’s current use, which emphasizes

recent sales of comparable properties. The States also vary in

the goals for their preferential property tax programs, and the

requirements to participate in or withdraw from the program

(table 11.3).

Three States—Alabama, North Carolina, and Tennessee—

expressly exempt standing timber from property taxes

(National Timber Tax Website 2011a). Another three states

restrict deduction of property taxes on State income tax

returns: Louisiana does not permit deduction of property

taxes, Virginia does not allow individual taxpayers to deduct

property taxes, and Tennessee does not allow corporations to

deduct property taxes (Cushing 2006).

Property taxes are set and levied at the county level, making

them the most diverse of the taxes that family forest owners

face and the most difficult to track. Some county officials

in the Southern States have expressed interest in developing

property tax provisions that discourage urban sprawl and

encourage provision of ecosystem services from rural land,

but little is known about how many such provisions have

been put in place or the level of their success.

Seven Southern States also impose a severance tax on

timber when it is harvested. Three of the States—Georgia,

Louisiana, and Mississippi—levy the tax on forest owners,

while the other four—Alabama, Arkansas, North Carolina,

and Virginia—levy it on timber processors. All seven States

use at least part of their severance tax receipts to support a

forestry incentive program or another forest-related purpose

(Cushing 2006, National Timber Tax Website 2011a). The

economic effect of a severance tax mirrors that of an income

tax, but at the rates used its impact is minor, having little

effect on an owner’s management decisions.

The Federal Estate and Gift Taxes

The Federal government has taxed transfers of estates since

1916 and lifetime gifts since 1932 (Siegel and others 2009).

The U.S. Congress periodically redefines what constitutes

a taxable transfer of wealth; the most recent changes came

with passage of the Economic Growth and Tax Relief

Reconciliation Act of 2001 (EGTRRA, P.L. 107-16) and the

Tax Relief, Unemployment Insurance Reauthorization, and

Job Creation Act of 2010 (2010 Tax Relief Act, P.L. 111-312).

The economic effect of estate and gift taxes is difficult to

quantify because they occur at irregular intervals. They

do, however, increase risk and put a premium on keeping

planning options open.

The Federal tax code includes numerous provisions that

reduce or eliminate the impact of the Federal estate and

gift taxes. These provisions help family forest owners keep

their holdings intact through a transfer from one generation

to another and reduce the likelihood that heirs will need to

liquidate timber or fragment the holding. As with the Federal

income tax, some are general provisions available to all

taxpayers, while others are specifically for owners of forests

and other working lands. Among the most important general

provisions are (Siegel and others 2009):

Gifting—Individuals may make lifetime gifts up to the

annual exclusion amount, currently $14,000 (Internal

Revenue Service 2012), to as many different recipients each

year as they wish without using the effective exemption

amount for gifts (see below) or incurring a gift tax. Married

couples may make “split gifts” of double the annual exclusion

amount. In addition, there is an unlimited exclusion for

gifts to qualifying charitable organizations and qualifying

gift payments of educational or medical costs. There is no

“step-up” in basis (see below) for gifts, but gifting enables

Table 11.2—State income tax provisions applicable to 

family forest owners in the South, 2010

State

State-level

income tax

Preferential

treatment of

long-term

capital gains

Deduction or

credit for

conservation

Alabama Yes – –

Arkansas Yes Yes Credit

Florida – – –

Georgia Yes – Credit

Kentucky Yes – –a

Louisiana Yes – –

Mississippi Yes – Deduction

North Carolina Yes – Credit

Oklahoma Yes – –

South Carolina Yes Yes Creditb

Tennessee – – –

Texas – – –

Virginia Yes – Creditb

– = indicates no applicable provision.

aSome family forest owners may qualify for an income tax 

deduction Kentucky provides for donation of a conservation 

easement on farmland or open space land for agricultural use.

bThe credit is transferrable; that is, any unused portion may be 

sold to others.

Sources: Butler and others 2010, Private Landowner Network 

Web site 2011.
267

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

owners to remove from their estate assets that are rapidly

appreciating in value.

“Step-up” in basis for bequests—A recipient’s basis in

an asset received through a bequest generally is its fair

market value on the valuation date, either the date of the

decedent’s death or the earlier of 6 months after death or

the date any estate asset is sold. This usually results in a

“step-up” in the basis compared with what it was in the

decedent’s hands. EGTRRA repealed the Federal estate tax

for 2010, and placed limits on the value of estate assets that

could receive a step-up in basis. The 2010 Tax Relief Act

extended and enhanced the Federal estate tax provisions for

decedents dying in 2011 or 2012 (see below), with all estate

assets eligible for a step-up in basis. For decedents dying in

2010, the executor could elect to use either the provisions

of EGTRRA or the 2010 Tax Relief Act (CCH 2010, CCH

2013).

The marital deduction—The Federal tax code allows an

unlimited deduction for the value of all property passed from

one spouse to the other through a lifetime gift or bequest.

This provision recognizes the role of both spouses in

building up a family’s assets. It does not eliminate or reduce

the estate tax, however, but merely postpones it to the time

of the surviving spouse’s death. This can be a considerable

disadvantage if the assets—land or standing timber, for

example—appreciate greatly in value during the time

between the deaths.

Effective exemption amount for gifts—This is a credit

against the tentative gift tax due, which shields part or all of

gifts over the annual exclusion amount from tax. The 2010

Tax Relief Act extended the $1 million exemption amount

for gifts made in 2010; for gifts made in 2011 or 2012, the

Act established a $5 million unified exemption amount for

gifts and estates (CCH 2010). Under a unified exemption,

an owner can transfer assets up to the exemption amount to

recipients other than their spouse, either as lifetime gifts or

bequests, without incurring a tax.

Effective exemption amount for estates—This is a credit

against the tentative estate tax due, which shields part or all

of an owner’s estate from tax. As just described, the 2010

Tax Relief Act combined the effective exemption amounts

for gifts and estates into a $5 million unified exemption

amount. It applied to the estates of decedents dying in 2011,

and where the executor elected to use the provisions of the

2010 Tax Relief Act rather than EGTRRA, to the estates of

decedents dying in 2010 (CCH 2010). The American Tax

Relief Act of 2012 indexed the unified exemption amount

for inflation after Dec. 31, 2011, so that it increased to $5.12

million for decedents dying in 2012 and $5.25 million for

decedents dying in 2013 (CCH 2013). The 2010 Tax Relief

Table 11.3—Property tax provisions applicable to family-owned forest land in the South, by State, 2010 

State

Property tax

type of

programa

Primary

goalsb

Minimum

acreage to

enrollc

Requires a

management

planc

Enrollment

period

in yearsc

Withdrawal

penalty

Alabama PT AG 5 Varies Varies No

Arkansas PA – – – – –

Florida PT AG/OS Varies Varies Continuous No

Georgia PT OS Varies No 10 Yes

Kentucky PA – – – – –

Louisiana PA – – – – –

Mississippi PA – – – – –

North Carolina PT HAB 20 Yes Continuous Yes

Oklahoma PA – – – – –

South Carolina PT AG/FOR 5 Varies Continuous Yes

Tennessee PT OS 15 Yes Continuous Yes

Texas PT FOR Varies Varies Continuous Yes

Virginia PT FOR/OS 20 No Continuous Yes

– = indicates no applicable provision.

aPA=Preferential assessment; PT=Preferential tax. 

bAG=Sustain agriculture; FOR=Sustain forestry; HAB=Habitat conservation; OS=Maintain open space.

cVaries=Varies from county to county.

Source: Butler and others 2010.
268 The Southern Forest Futures Project

Act further made the unified exemption amount “portable”

between spouses. This means that in families led by a

married couple, any part of the unified exemption amount

not used by the estate of the first spouse to die may be added

to the unified exemption amount for the estate of the second

spouse. Portability effectively doubles the unified exemption

amount, allowing family assets of $10 million or more in

value to pass untaxed from one generation to another (CCH

2010, CCH 2013).

Deferral and extension of estate tax—If an interest in a

closely-held business accounts for more than 35 percent of

a decedent’s estate, the Federal estate tax on the business

portion of the estate may be deferred for 4 years after the

estate tax return is filed, with only interest payments due,

then paid in up to 10 annual installments. Although this

provision does not reduce the amount of estate tax due, it can

reduce the need to disrupt an established forest management

plan in order to pay tax.

The provisions for owners of forests and other working lands

include (Siegel and others 2009):

Special use valuation—Under specific conditions, an

executor may elect to reduce the taxable value of an estate

by valuing assets used for farming (defined to include forest

land) or a trade or business according to their value in

actual use rather than their fair market value. The maximum

amount of the reduction has been indexed for inflation since

1998 and reached $1.07 million in 2013 (Internal Revenue

Service 2012). There are, however, stringent requirements

to qualify for and remain under the provision, including a

restriction against harvesting special use-valued timber for

10 years.

Exclusion for land in a qualified conservation easement—

An executor may elect to exclude from the taxable value of

an estate up to 40 percent of the value of land subject to a

qualified conservation easement (see below). The benefit is

capped at $500,000 and the 40 percent maximum exclusion

is reduced if the value of the easement is less than 30 percent

of the value of the land. As with the charitable deduction for

donation of an interest in land, the easement must consist

of a qualified real property interest, made to a government

agency or qualified publicly-supported organization, for

one of four conservation purposes (see “Incentives for

Conservation Easements”). This provision offers many of

the benefits of special use valuation with fewer restrictions.

There is, however, no step-up in basis for the excluded land.

Estate planning professionals have developed additional

strategies, not specifically provided in the Federal tax code,

to facilitate intergenerational transfers of family assets.

These include (Siegel and others 2009):

Forms of business—Two forms of business organization,

the Family Limited Partnership (FLP) and Limited Liability

Company (LLC), are popular among family forest owners

as means to transfer ownership of a forest enterprise to other

family members and engage them in its management. The

FLP is a type of limited partnership. In an FLP, the general

partners (typically the parents) retain management rights but

can transfer ownership to the limited partners (typically the

children) through gifts, which can be discounted for minority

interest and/or lack of control. The LLC is a hybrid between a

partnership and a corporation. Like a partnership, an LLC is

a pass-through entity for tax purposes, but like a corporation,

individual members’ liability is limited to the amount of their

investment in the business. Forest owners should be aware

that these forms of business have two drawbacks: first, there

is no step-up in basis for land or timber transferred to others

through the business, and second, both FLPs and LLCs are

under scrutiny by the Internal Revenue Service as potential

tax avoidance devices that lack economic substance. To help

avoid difficulties, owners should ensure that their FLP or LLC

has a clear business purpose, is held completely separate from

personal assets, and is set up and run entirely as a business.

Trusts—A trust is an arrangement in which a person or

institution called the trustee holds legal title to designated

property and manages it for the benefit of one or more

beneficiaries. A trust is a separate legal entity from its

donor. A “lifetime trust” is created during the donor’s life

and may be revocable or irrevocable. Only an irrevocable

lifetime trust removes the trust property from the donor’s

estate. A “testamentary trust” is created at the donor’s death,

according to instructions in his or her will. The full value of

the trust property is included in the donor’s estate, but the

trust then can provide income to successive generations of

beneficiaries while shielding the trust property from further

estate tax. Trusts may be used for a variety of purposes. For

example, an “irrevocable life insurance trust” removes a life

insurance policy from the donor’s ownership and prevents

its full face value from entering his or her estate at death.

A “qualified terminal interest property trust” is a type of

marital deduction trust that may be useful with “blended”

families; it provides for the needs of the surviving spouse

while controlling disposition of the trust property remaining

after his or her death.

Conservation easements—This is the donation or sale

of one or more attributes of land ownership, for example,

the right to subdivide the land. The easement removes the

attribute(s) of ownership from the land. This typically lowers

the value of the land and reduces the tax consequences of

transferring it to heirs; however, the easement passes with the

land and is binding on future owners. To qualify for either

of the tax provisions discussed above, an easement must

involve the transfer in perpetuity, by means of an outright
269

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

gift or “bargain sale” (a sale at a price below the property’s

fair market value), of a qualified real property interest,

to a government agency or qualified publicly-supported

organization, for one of four conservation purposes (see

“Incentives for Conservation Easements”).

The Bush tax cuts temporarily set separate effective

exemption amounts for gifts and estates. Between 2001 and

2009 they increased the effective exemption amounts for

estates from $1 million to $3.5 million and decreased the top

rate for gift and estate taxes from 55 percent to 45 percent.

For 2010, they repealed the Federal estate tax, placing limits

on the value of estate assets that could receive a step-up in

basis and setting the top gift tax rate at 35 percent, equal to

the top Federal income tax rate. These provisions were set

to sunset at the end of 2010 (Siegel and others 2009). But as

noted above, the 2010 Tax Relief Act extended and enhanced

the estate tax, with all estate assets eligible for a step-up in

basis. As well, it reunified the effective exemption amounts

for gifts and estates, with the maximum exemption increased

to $5 million and portability between married spouses, and

reduced the top estate tax rate to 35 percent (CCH 2013).

The American Taxpayer Relief Act of 2012 further enhanced

the Federal estate and gift tax provisions and made them

permanent, preventing them from returning to pre-2002 law

after the end of 2012 (CCH 2013).

State Estate, Inheritance, and Gift Taxes

The States again vary widely in how they tax

intergenerational transfers of assets. Some States tax the

right to transfer property through an estate tax, while

others tax the right of heirs to receive property through an

inheritance tax. A handful of States tax gifts over specified

annual or lifetime exemption amounts. As well, State transfer

taxes differ in their filing requirements, exclusion amounts,

and rate schedules; whether they are stand-alone taxes or

tied to the Federal tax code; whether they are “flat-rate”

(one tax rate applies regardless of the amount transferred),

“graduated” (the tax rate increases in steps with the amount

transferred) or “layered” (the tax rate varies with the heir’s

relation to the decedent); and whether certain closely-related

heirs are exempt (Siegel and others 2009).

Before the enactment of EGTRRA, every State had on its

books at least one tax that was a “pick-up” or “piggy-back”

tax designed to use the full available amount of the Federal

credit for State transfer taxes. This approach apportioned

part of what would have been the Federal estate or gift tax

to the State, with no additional tax burden on the estate or

the beneficiaries. EGTRRA phased out the Federal credit

for State transfer taxes between 2002 and 2005, replacing it

with a deduction. This eliminated State transfer taxes that

were tied to the Federal credit, throwing State tax law and

tax planning into turmoil. Individual States responded very

differently to the change. Many “decoupled” their transfer

taxes from current Federal law, tying them to the Federal tax

code as it existed before EGTRRA. Others made no change,

allowing their estate, inheritance, and gift taxes to phase out

with the Federal credit. A few States took EGTRRA as an

opportunity to repeal transfer taxes or to craft stand-alone

taxes on transfers of assets (Siegel and others 2009).

Only five Southern States currently levy transfer taxes:

Kentucky and Louisiana each have a stand-alone inheritance

tax, North Carolina has a stand-alone gift tax and an estate

tax that is decoupled from current law and tied to the Federal

tax code as of the end of 2001, Oklahoma has a standalone estate tax, and Tennessee has stand-alone inheritance

and gift taxes (table 11.4). Virginia repealed its estate tax

effective during 2007.

Except for North Carolina and Virginia, however, all of the

Southern States still have pick-up or piggy-back taxes on their

books which will come back into effect if the Federal credit

for State transfer taxes is reinstated (table 11.4). If that occurs,

seven additional Southern States—Alabama, Arkansas,

Florida, Georgia, Mississippi, South Carolina, and Texas—

will have an estate tax; Kentucky and Louisiana will have an

estate tax as well as an inheritance tax; Oklahoma will have a

second estate tax; and Tennessee will have estate, inheritance,

and gift taxes (Siegel and others 2009).

Incentives for Conservation Easements

Conservation easements are one of the most powerful tools

available to family forest owners who wish to preserve the

conservation value of their land over time. A conservation

easement involves the donation or sale of one or more

attributes of land ownership—the right to build additional

structures on the land, for example, or develop it for

commercial or industrial use—to a government agency or

organization that shares the owner’s vision for the land. The

easement removes those attributes of ownership from the

land and helps ensure that it remains in forest (Greene and

others 2013).

A conservation easement does not involve the donation or

sale of an owner’s entire interest in the land. The owner

can retain the right to live on the land, manage it for timber

or other forest products, and use it for other benefits. The

easement also can apply only to part of the property, with

the owner retaining all attributes of ownership for the rest.

The owner can pass the full remaining interest in the land

to heirs or sell it to others, although the easement passes

with the land and is binding on future owners (Greene and

others 2013).

The terms for conservation easements are not standardized,

but can be tailored to reflect the values of the owner and
270 The Southern Forest Futures Project

the receiving organization, as well as the characteristics of

the land itself. For example, an easement on property that

contains habitat for rare plant or wildlife species might

prohibit any development, while an easement on working

forest land might permit continued management for forest

products and the construction of roads and improvements

consistent with that use. The purchaser or recipient of the

easement is responsible for ensuring the easement’s terms

are followed (Land Trust Alliance Web site 2011).

Federal provisions—Income from the sale of a conservation

easement is taxable at the Federal level. The donation

or bargain sale of an easement, however, can provide a

charitable contribution deduction on the donor’s income

tax and a future estate tax deduction (Greene and others

2013). These deductions are discussed above, but generally

require the transfer in perpetuity, by means of an outright

gift or bargain sale, of a qualified real property interest,

to a government agency or qualified publicly-supported

organization, for one of four conservation purposes. The

qualified real property interest may be the owner’s entire

interest (but not solely a mineral interest), a remainder

interest, or a perpetual restriction on how the property

may be used, as with a conservation easement. The four

conservation purposes are: outdoor recreation by or

education of the general public; protection of a relatively

natural habitat for fish, wildlife, or plants; preservation of

open space for scenic enjoyment by the general public or

pursuant to a clear conservation policy of the Federal, State,

or local government; and conservation of an historically

important land area or certified historic structure.

Four of the Federal incentive programs available to family

forest owners involve conservation easements. The Forest

Legacy Program funds up to 75 percent of the cost of placing

forest land under an easement. Under the Healthy Forest

Reserve and Wetlands Reserve Programs, owners may elect

to receive payments for the easement value of their land as

well as the cost of conservation practices they implement.

Under the Emergency Watershed Protection Program, the

administering agency may elect to address impairment or

damage to watersheds caused by a natural disaster through

the purchase of floodplain easements from willing owners

(SRS Forest Economics and Policy Web site 2011). The

provisions of these programs are summarized in table 11.1.

State provisions—Because of the close link between Federal

and State income taxes, most Federal tax provisions that

benefit family forest owners flow through to the State income

tax (Siegel and others 1996). Individual States, however, offer

their own incentives for conservation easements. Property

tax relief proportional to the decrease in value of land placed

in an easement generally is available in every State, but is

required by law in Florida, Georgia, Kentucky (for land

dedicated to the State Nature Preserves System), South

Carolina, Tennessee, and Virginia. Arkansas, Georgia, North

Table 11.4—State estate, inheritance, and gift tax provisions applicable to family forest owners 

in the South

State

Currently has

a State-level

estate tax

Currently has

a State-level

inheritance tax

Currently has

a State-level

gift tax

Special use

valuation for

estate tax

Phased-out

tax still on

the books

Alabama – – – – Estate tax

Arkansas – – – – Estate tax

Florida – – – – Estate tax

Georgia – – – – Estate tax

Kentucky – Yes – – Estate tax

Louisiana – Yes – – Estate tax

Mississippi – – – – Estate tax

North Carolina Yes – Yes No No

Oklahoma Yes – – No Estate tax

South Carolina – – – – Estate tax

Tennessee – Yes Yes – Estate tax

Texas – – – – Estate tax

Virginia – – – – No

– = indicates no applicable provision.

Sources: Butler and others 2010, Siegel and others 2009.
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chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

Carolina, South Carolina, and Virginia each have enacted an

income tax credit for donation of a qualifying conservation

easement. In Virginia, sales of easements over 30 years in

duration are exempt from the State capital gain tax. As well,

Alabama, Florida, Georgia, and Texas operate conservation

trusts to preserve working agricultural and forest land

(Private Landowner Network Web site 2011).

Incentives for Forest Sustainability

Forest sustainability is one aspect of sustainable development

(USDA Forest Service 2004). In a broad sense, forest

sustainability can be described as involving:

“... the continued existence and use of forests to meet

human physical, economic, and social needs; the desire

to preserve the health of forest ecosystems in perpetuity;

and the ethical choice of preserving options for future

generations while meeting the needs of the present.”

(USDA Forest Service 2002)

At a more specific level, it can be defined as:

“The stewardship and use of forests and forest lands in

a way, and at a rate, that maintains their biodiversity,

productivity, regeneration capacity, vitality, and

potential to fulfill, now and in the future, relevant

ecological, economic, and social functions at local,

national, and global levels, and that does not cause

damage to other ecosystems.” (Helms 1998)

Federal programs—The Federal government sponsors a

wide range of incentive programs to encourage sustainable

management of family-owned forests and other rural lands.

Most are administered by agencies of the Department of

Agriculture—the Farm Service Agency, Forest Service, or

Natural Resources Conservation Service. Three programs

are administered by the Fish and Wildlife Service within the

Department of Interior. Virtually all of the programs provide

technical assistance to help owners select and implement

conservation practices that will be effective on their land.

Many also provide financial incentives, such as cost-share

payments to cover part or all of the cost of conservation

practices, land rental payments over a term of years, or other

types of payments.

Table 11.1 provides a brief description of the Federal

incentive programs of particular interest to family forest

owners. As noted above, owners can elect to exclude

a calculated portion of cost-share payments from nine

programs from their adjusted gross income, making the

payments tax-free (Greene and others 2013), and four

programs involve use of conservation easements.

State programs—State agencies participate in the on-theground management of six Federal incentive programs: the

Forest Legacy, Forest Stewardship, and Southern Pine Beetle

Prevention Programs administered by the Forest Service

and the Landowner Incentive, Partners for Fish and Wildlife,

and Red-cockaded Woodpecker Recovery Programs

administered by the Fish and Wildlife Service. As well,

educational and technical assistance for forest management

generally is available in every Southern State, through State

forestry and cooperative extension personnel.

In addition, many States offer their own incentive programs

for family forest owners. Louisiana, Mississippi, North

Carolina, South Carolina, and Virginia offer reforestation

cost-share programs, while Texas offers technical assistance

and cost-share payments for practices to suppress oak wilt.

Owners may exclude a calculated portion of payments from

all six programs from adjusted gross income in calculating

their Federal income tax. Mississippi and Texas also provide

tax incentives for reforestation, Mississippi through a

reforestation tax credit and Texas through a 50 percent

property tax reduction for reforesting following a harvest.

And through its Forest Health Program, Mississippi informs

forest owners if they have pest damage on their property and

provides technical assistance on how to salvage damaged

timber and reduce or prevent further damage (Private

Landowner Network Web site 2011, SRS Forest Economics

and Policy Web site 2011).

Provision of Ecosystem Services

Ecosystem services are commonly defined as the benefits

people obtain from ecosystems. Ecosystem services include

basic services—provisioning services like delivery of food,

fresh water, wood and fiber, and medicine—as well as

services that are equally critical but less tangible and harder

to measure: regulating services like carbon sequestration,

erosion control, and pollination; cultural services like

recreation, ecotourism, and educational and spiritual values;

and supporting services like nutrient cycling, soil formation,

and primary productivity (USDA Forest Service Valuing

Ecosystem Services Web site 2011).

Federal programs—To ensure that the full range of

ecological, social, and economic benefits from family-owned

lands is maintained in quantity and quality over time, all

of the Federal-sponsored incentive programs summarized

in table 11.1 have at least one objective that focuses on

conservation of natural resources, protection of natural

systems, enhanced stewardship, or sustainable management.

Consequently, all of the programs, as well as most of the

Federal income and estate tax provisions summarized above,

promote the provision of ecosystem services.
272 The Southern Forest Futures Project

State programs—It is the States, however, that most directly

address provision of ecosystem services. Educational and

technical assistance for management of wildlife habitat or

riparian areas, water quality, resource conservation, and

protection from invasive species generally is available in

all States, through their forestry, wildlife, and cooperative

extension personnel. Additionally, individual States offer

a wide range of programs that directly address ecosystem

services (Private Landowner Network Web site 2011, SRS

Forest Economics and Policy Web site 2011):

•	 Alabama sponsors TREASURE Forest, a voluntary

program promoting sound and sustainable multiple-use

forest management, and the Alabama Agricultural and

Conservation Development Commission Program, which

provides cost-share payments for soil conservation,

water quality improvement, reforestation, and forest

improvement practices.

•	 Arkansas has enacted an income tax credit for creating or

restoring wetland or riparian zones.

•	 Through its Rural and Family Lands Protection Program,

Florida purchases perpetual easements on working

agricultural or forested lands that contain significant

natural areas or water resources.

•	 Georgia sponsors Georgia GROWS, a recognition program

promoting sound management and stewardship of familyowned forest land.

•	 The Kentucky Soil Erosion and Water Quality Cost Share

and Soil Stewardship Programs help farm and forest

owners address soil erosion, water quality, and other

environmental issues.

•	 Louisiana provides relief from State, parish, and district

property taxes for owners who enter a contract over

25 years in duration that allows the State to use their land

as a wildlife management area.

•	Mississippi offers a property tax exemption for owners of

coastal wetlands.

•	 The North Carolina Agricultural Cost-Share Program

reimburses up to 75 percent of the cost of controlling

runoff of sediment, nutrients, animal wastes, and pesticides

from working lands.

•	 The Oklahoma Conservation Cost-Share Program

reimburses the cost of applying soil and water

conservation practices.

•	 The Tennessee Farm Wildlife Habitat Program reimburses

up to 75 percent of the cost of improving habitat for

declining grassland and shrubland wildlife species,

including bobwhite quail, cottontail rabbits, and songbirds.

•	Texas sponsors the East Texas Wetlands Project, which

reimburses up to 50 percent of the cost of restoring,

enhancing, or creating wetlands on land subject to a 30-

year or perpetual easement; the Lone Star Land Steward

Award Program, a recognition program that promotes

wildlife conservation and habitat management; the Wildlife

Grant Program for habitat improvement projects closely

tied to the Texas Wildlife Action Plan; and property tax

reductions for aesthetic timber management, protection of

critical wildlife habitat, and streamside management.

•	 Virginia has enacted tax credits for a portion of the value

of timber retained in a riparian buffer and the cost of

approved equipment used to implement Best Management

Practices (BMPs); as well, the Virginia BMP CostShare Program reimburses up to $50,000 of the cost

of implementing practices to address nonpoint source

pollution.

Privately sponsored programs available in the Southern

States include State Tree Farm programs coordinated by the

American Forest Foundation (American Tree Farm System

Web site 2011) and the Longleaf Restoration Program

sponsored by The Longleaf Alliance (The Longleaf Alliance

Web site 2011).

Methods and Data Sources

From the time private forest owners first became interested

in long-term management, researchers have been suggesting

ways to improve the management and sustainability of

family forest holdings: financial incentives for owners who

demonstrate interest in managing their forest (Folweiler

and Vaux 1944); technical assistance, leveraged through

coordinated management of neighboring forest ownerships

(Cloud 1966); reduced property, estate and inheritance taxes,

more favorable tax credits and deductions, more favorable

capital gain tax treatment of timber income, and cost-sharing

of forest management expenses (Fecso and others 1982);

incentives linked to specific management practices, such

as reforestation (Greene 1998); and incentive programs for

ecosystem services, such as wildlife habitat or protection of

water quality (Greene and Blatner 1986, Koontz 1999).

Family Forest Owner Awareness and Use of 

Federal Income Tax Provisions

Most of the literature on income taxes concerns Federal

taxation of forest-related income and focuses on the tax law

itself. It consists of tax guides for forest owners (see Greene

and others 2013, Hoover and Koontz 2010), popularized

descriptions of how particular income tax provisions affect

forest owners (see Haney 2011, Wang and Greene 2011), or

background papers prepared for policymakers (see Dialog

Group on Forested Lands and Taxation 2001, Granskog

and others 2002). A small number of studies have analyzed

the effect of current or proposed income tax provisions on

returns to hypothetical family forest owners (Bailey and

others 1999; Klemperer 1989; Smith and others 2007, 2008;

Straka and Greene 2007).

In a 2001 study conducted in South Carolina, researchers

with the Clemson University Department of Forest
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chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

Resources and the Forest Service Southern Research Station

investigated whether family forest owners were aware of

Federal income tax provisions that provide incentives for

following sound management practices, whether they had

used provisions they were aware of, and their reasons for

using or not using each one. The provisions examined were:

long-term capital gain treatment of qualifying income,

annual deduction of management costs, depreciation and

the section 179 deduction, loss deductions, special treatment

of qualifying cost-share payments, and the reforestation

incentives. At the time the study was conducted, the

reforestation incentives consisted of a 10 percent investment

tax credit on up to $10,000 per year of qualifying expenses to

establish or reestablish trees, plus the ability to amortize up

to $10,000 per year of qualifying costs over 8 tax years (the

amount an owner could amortize was reduced by half of any

reforestation tax credit taken).

Data for the study were collected by means of a mailed

questionnaire sent to family forest owners randomly selected

from a list of current, past, and prospective members

maintained by the State chapter of a national forest owner

organization, using the Dillman (2000) tailored design

method. In addition to knowledge and use of each income

tax provision, several demographic characteristics were

surveyed: total acres owned; forested acres owned; primary

reason for owning forest land; whether the owner belonged to

a forest owner organization; whether the owner had a written

forest management plan; owner occupation; and owner

education, age, and household income, by level. The response

categories for primary reason for owning forest land and for

owner occupation, education, and age corresponded closely

to those used by Birch (1996).

State Property Taxes

As with income taxes, most of the literature on State

property taxes consists of landowner guides (see Baughman

and Reichenbach 2009, Kays and Schultz 2002) and

summaries of State tax provisions (see Chang 1996,

Rodenberg and others 2004). State property tax studies by

Hickman and others in Tennessee, Virginia, and Texas are

distinguished by their inclusion of economic analyses as well

as summaries of the law (Gayer and others 1987, Hickman

1982, Hickman and Crowther 1991).

Hibbard and others conducted a study that examined

the use, structure, and effectiveness of forest property

taxes throughout the U.S. In a survey of State program

administrators conducted as part of the study, the researchers

found that State property tax programs only marginally

conformed to accepted attributes of a “good” tax (equity,

efficiency, simplicity, stability, adequacy, and visibility), and

only modestly accomplished program objectives (Hibbard

and others 2003).

Other researchers have found that property tax program

requirements can be at odds with family forest owner

objectives for their land. One example, identified in studies

in Pennsylvania (Jacobson and McDill 2003) and New York

(Kernan 2004), is an overemphasis on timber management

and production.

In a national study completed in 2010, researchers from

six institutions documented the Federal, State, and local

tax policies that affect family forest owners and evaluated

their impact on owners’ decisions regarding their land.

The collaborating institutions were the Forest Service

Northern and Southern Research Stations, the University

of Massachusetts Amherst Family Forest Research Center,

University of Minnesota Department of Forest Resources,

Utah State University Department of Environment and

Society, and Yale School of Forestry and Environmental

Studies. Data for the study were collected using several

methods: a review of the existing literature; systematic

documentation and verification of the Federal, State, and local

tax provisions that affect family forest owners; a survey of

State property tax program administrators; and focus groups

of family forest owners and natural resource professionals

in selected States. The data were quantitatively analyzed,

then synthesized with the assistance of a panel of forestry,

conservation, and tax professionals and family forest owners.

The literature review initially focused on peer-reviewed

publications from the past 10 to 15 years, but was expanded

to include earlier, seminal works and non-peer reviewed

publications. Federal tax provisions affecting family forest

owners were documented using sources from the National

Timber Tax Website (2011b). The web site also was the

starting point for documenting State tax provisions; gaps were

filled in using State government web sites and other sources,

then verified using a key informant in each State, an employee

of either the State forestry agency or department of revenue.

The survey of State property tax administrators focused on

preferential property tax programs, defined as voluntary

programs that reduce the property tax burden on owners

in return for requiring them to restrict use of their land,

have a written forest management plan, or pay a penalty for

removing land from the program. The survey was conducted

using a mailed questionnaire sent to a selected department of

revenue employee in each State. The questionnaire asked the

respondents to use a 5-point Likert scale to rate their State’s

preferential property tax program according to the eight policy

effectiveness criteria used by Hibbard and others (2003):

•	 The program has clearly articulated goals;

•	 The magnitude of the tax break is significant;

•	 The program complements other State forestry incentive

programs;

•	 The forest land valuation mechanisms, eligibility
274 The Southern Forest Futures Project

requirements, withdrawal penalties, and minimum

enrollment periods reflect program goals;

•	 The program is administered consistently from county to

county;

•	 Funding for the program has been stable and predictable;

•	 The program is periodically reviewed to ensure that

objectives are being met; and

•	 Guidance through the application process is available to

forest owners.

The respondents also were asked to estimate the average

savings for enrollees in the preferential property tax

program, the percentage of eligible forest owners enrolled,

and the overall effectiveness of the program in protecting

forest resources in areas highly susceptible to development.

Administrators from 33 of the 38 States that have a

preferential property tax program applicable to family forest

owners returned a completed questionnaire, for a response

rate of 87 percent.

Ten 2-hour focus groups of 8 to 10 family forest owners

were held, two each in New Hampshire, Wisconsin, South

Carolina, Alabama, and Washington. The States were selected

to represent a broad range of property, income, and estate or

inheritance tax policies. Participants were selected from local

property tax rolls. Owners who held between 10 and 999 acres

of forest land were screened to provide a mix of holding sizes,

harvesting experience, acquisition history (inherited or noninherited), estate planning status (formal plan or no formal

plan), and demographics (gender, age, and formal education).

Parallel focus groups of 6 to 10 forestry and conservation

professionals also were held in New Hampshire, Wisconsin,

South Carolina, and Washington (logistical problems

precluded a professionals focus group in Alabama).

Participants included members of State forestry agencies,

university extension systems, and nongovernmental

organizations as well as private consulting foresters. These

groups covered the same topics as the forest owner focus

groups.

Combined Impact of Federal and State Taxes

Family forest owners face combinations of Federal and State

taxes on their forest-related income and forest land, yet with

few exceptions researchers have studied taxes in isolation

from one another. Only two studies were identified in the

past 25 years that considered Federal and State taxes in

combination. Following passage of the Tax Reform Act of

1986 (P.L. 99-514), Bettinger and others calculated the effect

of Federal and State income taxes on hypothetical family

forest owners in the South (Bettinger and others 1989) and

West (Bettinger and others 1991). Smith and others updated

this work following passage of the Economic Growth and

Tax Relief Reconciliation Act of 2001 and Jobs and Growth

Tax Relief Reconciliation Act of 2003, the first Bush tax cuts,

calculating the effect of Federal and State income taxes for

family forest owners In the North (Smith and others 2007)

and West (Smith and others 2008).

In a study initiated in 2003, researchers with the University

of Georgia Warnell School of Forest Resources and the

Forest Service Southern Research Station quantified the

effect of Federal and State taxes on private forest owners by

calculating land expectation value (LEV) for typical forest

management regimes in 22 timber-producing States in the

South, North, and Northwest. The calculations were made

pre-tax and again after each Federal or State tax was applied.

Using this approach, it was possible to determine the relative

effect of each type of tax as well as the combined effect.

Separate calculations were made for family, corporate, and

institutional forest owners.

The study was framed by several assumptions: that forest

owners of all types are profit-oriented and employ timber

management practices appropriate to that objective and

“typical” for their region; the owners meet the requirements

for current use property tax valuation; they deduct property

taxes annually against both the Federal, and as allowed, State

income taxes; and they capitalize reforestation expenditures

and offset them against harvest returns. The last assumption

was based on studies such as Greene and others (2004)

and Smith and others (2007, 2008), which found that many

forest owners are unaware of Federal income tax provisions

developed for owners of forests and other working lands.

Spreadsheets were developed to perform the LEV calculations

based on user input for State, management expenses, timber

prices, property tax per acre, Federal income tax rate, State

income tax rate, harvest tax per unit of timber, and discount

rate. Following Klemperer (1988) and Chang (1996), pre-tax

LEV was used as the base reference, and reduction in LEV as

each tax was added as the measure of the economic effect of

that tax. A discount rate of 5 percent, real (with no adjustment

for inflation), was used for all LEV calculations. Because it

is uniform across the nation, the effect of the Federal income

tax was calculated first, followed by the State property tax,

harvest tax, and income tax.

For each study State, data were collected on typical timber

management practices, including species or species mix,

rotation length, and harvest volumes; typical costs for stand

establishment and timber management; average stumpage

prices for the products obtained; applicable property tax per

acre; harvest taxes per unit of timber; and applicable Federal

and State income tax rates.

The data were gathered from numerous sources,

including published price reports; State tax web sites; and

correspondence with consulting foresters, State agency and
275

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

cooperative extension foresters, and university faculty. For

the Coastal Plain States of the South, management practices

and harvest volumes were determined using the SiMS 2003

growth and yield model (ForesTech International 2003).

Timber product prices were taken from Timber Mart-South

(2003), using the regional pine sawtimber and pulpwood

market segments defined by Yin and others (2002). The

Federal income tax rates and the income, property, and harvest

tax rates used for each State were those in effect in 2003.

Effect of the Federal Estate Tax

Most of the literature on the effect of taxes on transfers of

assets from one generation to another relates to the Federal

estate tax and concerns the tax law itself. It consists of estate

planning guides for forest owners (see Becker and Jacobson

2008, Siegel and others 2009) and popularized descriptions of

how particular estate tax provisions affect forest owners (see

Siegel 2010; Tufts and others 2003a, 2003b). A handful of case

studies have used hypothetical family forest owners to analyze

aspects of intergenerational transfers of forest land, including

the effect of form of forest ownership and assets used to pay

the estate tax on net returns from the forest (Howard 1985) and

the interaction between Federal and State death taxes (Peters

and others 1998; Walden and others 1987, 1988).

With the many strategies available to reduce or eliminate the

impact of the estate tax, one might expect that only owners

who fail to plan would owe tax. Many owners, however,

fail to take advantage of the estate planning tools available

to them because they are unaware of the full value of their

holdings, overwhelmed by the complexity and ever-changing

nature of estate tax law, unable to confront their personal

mortality, or unwilling to accept the loss of control that

most estate planning strategies entail. Further, the stringent

requirements for special use valuation make it difficult for

managed forest land to qualify for or remain under that

provision (Peters and others 1998, Siegel and others 2009).

In a study initiated in 1999, researchers with the Mississippi

State University College of Forest Resources and the Forest

Service Southern Research Station investigated the effect of

the Federal estate tax on owners of family forests and other

working lands. The study represented the first attempt to

quantify the effect of the Federal estate tax on family forests.

Data for the study were collected by means of a mailed

questionnaire, using the Dillman (1978) total design method.

A draft version of the questionnaire was pretested using

members of the Mississippi Forest Association. The revised

questionnaire was sent to landowners randomly selected

from the membership lists of the National Woodland

Owners Association and American Tree Farm System and a

nationwide database of farm and ranch owners maintained

by J.D. Esseks at Northern Illinois University. Questionnaire

recipients were first asked whether they had been involved in

the transfer of an estate between 1987 and 1997, a period when

the unified exemption amount shielded a constant $600,000

of estate value from tax. Those who responded affirmatively

were asked a series of questions about the characteristics of the

estate, whether special use valuation had been used, and what

assets were used to pay any Federal estate tax due.

The number of family forest holdings affected was estimated

by multiplying the percent of positive responses by Birch’s

(1996) estimate of the number of “individual” and “other”

private forest ownership units in the United States. The

number of acres affected was estimated by multiplying that

figure by the mean acreage response for the question. Chisquare tests at the 5 percent level of significance were used to

test for differences between the responses from forest owners

and other owners of working lands.

Effectiveness of Financial Incentive Programs in 

Promoting Sustainable Practices

Research has shown that a large percentage of family

forest owners are unaware that financial and tax incentive

programs exist or what the programs can do for them

(Anderson 1960, Christensen and Grafton 1966, Farrell

1964, Greene and others 2004, Perry and Guttenberg 1959);

that many owners who participate in an incentive would

have done the supported practice anyway (Brockett and

Gerhard 1999, James and others 1951), although the incentive

generally enables the owners to treat additional acres (Bliss

and Martin 1990, Royer 1987); and that favorable property

and capital gain tax provisions have little short-term effect on

forest owner behavior (Brockett and Gerhard 1999, Kluender

and others 1999, Stoddard 1961).

Three approaches, however, have consistently been found to

influence family forest owners to apply sustainable practices

on their land: technical assistance, cost-share payments, and

programs that put owners in direct contact with a forester or

other natural resource professional. James and others (1951)

found that owners prefer technical assistance to financial or

tax incentives. Greene and Blatner (1986) further found that

direct contact with a professional is associated with owners

becoming forest managers. Egan and others (2001) found that

the aspects of the Forest Stewardship Program that involve

contact with a professional—getting a management plan

and technical assistance—were the things owners liked best

about the program.

In a nationwide study conducted in 2005, researchers from

five institutions identified and assessed the success of public

and private incentive programs in encouraging family forest

owners to use sustainable practices on their lands. The

collaborating institutions were the Forest Service Southern

Research Station, the Clemson University Department
276 The Southern Forest Futures Project

of Forestry and Natural Resources, Pennsylvania State

University School of Forest Resources, University of

Minnesota Department of Forest Resources, and Utah State

University Department of Sociology, Social Work, and

Anthropology.

The study was conducted in three phases: a systematic

review of the research literature on the tax, cost-share, and

other financial incentives available to family forest owners;

a nationwide survey of selected forestry officials; and focus

groups with family forest owners in the South, North, and

West.

Publications for the literature review were identified

through a search of databases including the University of

Minnesota Social Sciences in Forestry web site and CABI

Publishing’s Forestry Abstracts. The identified publications

were summarized and analyzed for their conclusions about

the effectiveness of the various incentive programs and their

apparent effect on forest owner motivations and practices.

The survey of forestry officials was done by means of a

mailed questionnaire, using the Dillman (2000) tailored design

method. One official in each State was selected to receive the

questionnaire, based on their overall knowledge of financial

incentive programs. The appropriate person in each State was

identified using peer recommendations; in most cases it was

the individual in the State forestry agency who managed the

Forest Stewardship Program. The draft questionnaire was

pre-tested with the identified official in each of the researchers’

home state and refined using their feedback.

The questionnaire asked the officials to name and describe

the public and private financial incentive programs available

to family forest owners in their State. In follow-up questions

they were asked to use a 4-point Likert scale to assess forest

owners’ awareness of each program they had identified,

its overall appeal among the owners aware of it, and its

effectiveness in encouraging sustainable forestry and

enabling owners to meet their objectives of forest ownership.

The officials also were asked to estimate the percent of

program practices that remained in place and enrolled

acres that remained in forest over time, and to suggest

ways to improve owner participation in the program and its

administrative effectiveness.

Nine Federal financial incentive programs were examined:

the Forest Stewardship Program, Conservation Reserve

Program, Environmental Quality Incentives Program,

Forest Land Enhancement Program, Forest Legacy

Program, Landowner Incentive Program, Southern Pine

Beetle Prevention Program, Wetlands Reserve Program,

and Wildlife Habitat Incentives Program (table 11.1). Three

types of non-Federal financial incentive programs also were

examined: preferential property tax programs for forest land,

other State-sponsored incentive programs, and programs

sponsored by private entities.

Although the questionnaire was extensive—89 questions on

30 pages—follow-up e-mails and telephone calls produced

a 100 percent useable response. The Likert scale ratings

and the officials’ written comments were compiled and

summarized. Tukey tests at the 5 percent level of significance

were used to identify statistically significant differences

between the officials’ ratings for each program attribute.

The final study phase consisted of focus groups with family

forest owners in South Carolina, Pennsylvania, Minnesota,

and Oregon. Two focus groups were conducted in each

State, one with members of forest owner organizations and

one with other family forest owners. The participants in

each group were identified through an approach similar to

that used for the survey of forestry officials. The number

of participants in the focus groups ranged from 7 to 17 and

averaged 11.

The focus group sessions were conducted using the protocol

described by Daniels and Walker (2001), with a moderator

guiding discussion by means of a chart mounted on the

meeting room wall and verbal prompts from a prepared

guideline. Data were collected by recording the sessions and

by taking notes. The recordings and notes for each session

were qualitatively analyzed, again following Daniels and

Walker (2001), first by a single researcher, then in discussion

among the entire research team. The results for each region

were coded in terms of themes without consideration for

what might be themes in other regions. Once the regionspecific themes were identified, they were compared across

regions to identify emergent patterns. The data were then

re-analyzed to look specifically for the presence or absence

of the emergent patterns in each region.

Results

Family Forest Owner Awareness and Use of 

Federal Income Tax Provisions

In the study of South Carolina family forest owners, 87

percent of the survey respondents were aware of at least one

Federal income tax provision. Nearly 80 percent were aware

of two provisions available to taxpayers in general: treatment

of qualifying income as a long-term capital gain and annual

deduction of management costs. In contrast, just over 40

percent were aware of special treatment of qualifying costshare payments, one of the provisions available to owners of

working lands (Greene and others 2004).

Long-term capital gain treatment of qualifying income—

Some 78 percent of the respondents were aware that income

from the sale or disposal of timber can qualify as a long-term
277

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

capital gain. Of those who were aware of the provision,

85 percent had used it (table 11.5). Respondents who were

aware of the provision tended to own more acres of land and

more forested acres than those who were not; they also were

more likely to belong to a forest owner organization and to

have a written forest management plan, and tended to have

higher levels of formal education and household income. As

shown in Table 11.6, most respondents who were aware of

the provision but had not used it believed it did not apply to

their situation (36 percent) or that the benefit was too small to

bother with (21 percent).

Annual deduction of management expenses—Overall,

78 percent of the respondents were aware they could deduct

ordinary and necessary forest management expenses

annually, and of those who were aware of the provision,

85 percent had used it (table 11.5). Respondents who were

aware of the provision differed from those who were not in

the same ways as above: they tended to own more acres of

land and more forested acres, were more likely to belong

to a forest owner organization and to have a written forest

management plan, and tended to have higher levels of formal

education and household income. Most respondents who

were aware of the provision but had not used it believed it did

not apply to their situation (35 percent) or that the benefit was

too small to bother with (33 percent; table 11.6).

Depreciation and the section 179 deduction—About half

of the respondents (51 percent) were aware they could recover

the cost of equipment and other property purchased for the

production of income on their forests through depreciation

or the section 179 deduction. Of those who were aware of

the provisions, 66 percent had used one or both (table 11.5).

Respondents who were aware of the provisions differed from

those who were not in that they tended to own more acres of

land and more forested acres, were more likely to own their

forest land primarily for timber production, were more likely

to belong to a forest owner organization and to have a written

forest management plan, were more likely to be salaried

professionals, and tended to have higher levels of formal

education and household income. Most respondents who were

aware of the provisions but had not used them believed the

provisions did not apply to their situation (57 percent) or that

the benefit was too small to bother with (21 percent; table 11.3).

Loss deductions—Only 60 percent of the respondents

were aware they could take a deduction for timber or

other income-producing assets lost in a casualty, theft,

condemnation, or for owners who held their forest as a trade

or business, in a noncasualty event. Further, only 23 percent

of those who were aware of the provision had used it (table

11.5). Respondents who were aware of the provision differed

from those who were not on nearly all of the demographic

characteristics tested: they tended to own more acres of

land and more forested acres, were more likely to own their

Table 11.5—Number and percent of forest owner reporting awareness and use of beneficial Federal income tax provisions 

Response to survey

Long-term

capital gains

treatment

Management

expense 

deduction

Depreciation,

Section 179a

deduction

Loss

deductions

Reforestation incentives

Cost-share

payment 

exclusion Tax credit Amortization

Aware of the provision 364 77.8% 363 77.6% 235 51.4% 277 60.2% 255 54.8% 260 56.4% 194 42.1%

Had used the provision 308 84.6% 308 84.8% 155 66.0% 64 23.1% 199 78.0% 207 79.6% 137 70.6%Had not used the provision 56 15.4% 53 14.6% 80 34.0% 213 76.9% 56 22.0% 53 20.4% 57 29.4%

Not aware of the provision 104 22.2% 105 22.4% 222 48.6% 183 39.8% 210 45.2% 201 43.6% 267 57.9%

aAllows a taxpayer to deduct the cost of certain types of property as an expense, rather than requiring the cost to be capitalized and depreciated.

Source: Greene and others 2004.
278 The Southern Forest Futures Project

forestland primarily for recreation or timber production, were

more likely to belong toa forest owner organization and to

havea written forest management plan, were more likely to

be salaried professionals, and tended to have higher levels of

formal education and household income. Most respondents

who were aware of the provision but had not used it believed

it did not apply to their situation (49 percent) or that the

benefit was too small to bother with (16 percent; table 11.6).

Reforestation incentives—Just over half of the respondents

(55 percent) were aware of the reforestation tax incentives,

but among those who were aware, 80 percent had used

one or both incentives (table 11.5). Respondents who were

aware of the reforestation tax credit tended to own more

acres of land and more forested acres, were more likely to

belong toa forest owner organization and to havea written

forest management plan, and tended to havea higher level

of household income. Respondents who were aware of the

reforestation amortization deduction tended to own more

acres of land and more forested acres, were more likely

to own their forest land primarily for recreation or timber

production, were more likely to belong toa forest owner

organization and to havea written forest management plan,

were more likely to bea salaried professional ora farmer,

and tended to have higher levels of formal education and

household income. Most respondents who were aware of the

provisions but had not used them believed they did not apply

to their situation (51 percent) or that the benefit was too small

to bother with (31 percent; table 11.6).

Special treatment of qualifying cost-share payments—

Only 42 percent of the respondents were aware they could

excludea calculated portion of qualifying public cost-share

payments from their gross income (table 11.5), making it the

least-known provision surveyed. Of those who were aware of

the provision, 71 percent had used it. Respondents who were

aware of the provision were more likely than those who were

not to belong toa forest owner organization and to havea

written forest management plan, and tended to havea higher

level of household income. Most respondents who were aware

of the provision but had not used it believed the benefit was too

small to bother with (29 percent) or that it did not apply to their

situation (22 percent; table 11.6).

State Property Taxes

Survey of State property tax administrators—Estimates

of family forest owner participation in State preferential

property tax programs varied widely. Just 48 percent of the

administrators who responded to the survey estimated that

half or more of eligible family forest owners were enrolled

in their State’s program. Administrators who indicated the

greatest percent of eligible forest land enrolled generally

were from States in the West or South.

Table 11.6—Reasons forest owners who were aware of beneficial Federal income tax provisions cited for not using the provisions 

Response to survey

Long-term

capital gains

treatment

Management

expense 

deduction

Depreciation,

Section 179a

deduction

Loss

deductions

Reforestation incentives Cost-share payment 

exclusion Tax credit Amortization

It’s too complicated 2 3.8% 3 5.5% 3 3.9% 11 5.8% 1 2.0% 0 0.0% 5 10.2%Benefit is too small to bother with 11 20.8% 18 32.7% 16 21.1% 31 16.2% 16 31.4% 10 23.3% 14 28.6%It doesn’t apply to my situation 19 35.8% 19 34.5% 43 56.6% 93 48.7% 20 39.2% 22 51.2% 11 22.4%

I don’t want to use it 1 1.9% 1 1.8% 2 2.6% 5 2.6% 2 3.9% 0 0.0% 7 14.3%

Other 20 37.7% 14 25.5% 12 15.8% 51 26.7% 12 2.35% 11 25.6% 12 24.5%

aAllows a taxpayer to deduct the cost of certain types of property as an expense, rather than requiring the cost to be capitalized and depreciated.

Source: Greene and others 2004. 
279

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

Of the administrators who provided a response, 83 percent

estimated that participating in their State’s program reduced

the annual property tax burden by half or more. On the

one hand, some administrators expressed regret that forest

owners could not qualify for their State’s agricultural

preferential property tax program, which typically provides

greater tax relief, while others expressed frustration at “tax

dodgers” and “loopholes” that allowed their State’s program

to be misused.

Only a third of the administrators responded that their State’s

program had all of the attributes of an effective property

tax policy. The most commonly noted shortcomings were

lack of consistency from county to county and lack of stable

funding, followed by lack of complementarity with other

programs (Butler and others 2010).

Family forest owner focus groups—Property taxes were

by far the tax of greatest concern to the family forest owners

in this study, coming up unprompted as a concern in all 10

focus groups. This is not a surprise, since property taxes

occur on an annual basis as opposed to being a rare event, as

with taxes on timber income, or once-in-a-lifetime, as with

an estate or inheritance tax. Particularly outside the South,

owners perceived their property taxes as high, out of sync

with what their land was worth, and inevitably increasing.

Some forest owners had never heard about their State’s

preferential property tax program, while others were

uncertain about whether they were enrolled in a program.

The latter was particularly the case in the South, where

program requirements are the least restrictive; owners

in States with more rigorous programs were more likely

to know what program they were enrolled in and its

requirements. The primary means for finding out about tax

programs was conversations with neighbors, friends, and

relatives, followed by county assessors, foresters or loggers

working on the land, and community meetings.

At the same time, many owners who were enrolled

their State’s program were highly positive about it and

recommended it to those who were not enrolled. They

cited benefits including that the reduced property taxes

were helping them keep their land, and the program

promoted open space and sustainability, encouraged tree

planting and growth, and improved forest management.

Some owners became interested at this point, while others

remained wary.

Reasons for wariness about participating in a preferential

property tax program included uncertainty about penalties

for withdrawing land and what happened if the land was sold

or passed to heirs. Privacy and freedom of action were major

objectives for many owners, with the result that some opted

not to enroll in their State’s program due to fear of losing

managerial control to the government or being required to

allow public access on their land (Butler and others 2010).

Combined Impact of Federal and State Taxes

The full study reported here estimated the effect of Federal

and State taxes on privately-owned forest land in 22 States

in the South, North, and Northwest, by calculating preand after-tax land expectation value (LEV) under typical

management regimes for family, corporate, and institutional

forest owners (Cushing 2006). This section, however,

summarizes only the results for family forest owners in the

Southern States.

Pre-tax LEV—Among the Coastal Plain States, pre-tax

LEV ranged from $373 per acre for Texas to $796 per acre

for Alabama, with a mean of $585 per acre and a median of

$539 per acre (table 11.7). Oklahoma was not included in the

analysis. The spreadsheets for all 10 included States were

built around the same loblolly pine management plan and

assumed the same costs for stand establishment and timber

management. The only source for differences in pre-tax LEV

was variation in the stumpage prices for the pulpwood, chipn-saw, and sawtimber produced.

In the States of the Appalachian-Cumberland highlands,

pre-tax LEV was just $271 per acre for Kentucky, due

primarily to long rotation lengths and low harvest yields for

mixed upland hardwood timber. Pre-tax LEV for Tennessee

was comparable to that for the Coastal Plain States, at $579

per acre for uneven-age management of mixed oak-hickory

timber (table 11.7). There was no statistically significant

difference in the results for the two subregions (Cushing

2006).

Effect of the Federal income tax—Although family forest

owners in every State paid the same 15 percent Federal

capital gain tax on their net harvest returns, the economic

effect of the tax varied with the size and frequency of

harvest returns and the amount of capitalized reforestation

expenses. In the Coastal Plain States, LEV decreased by

amounts ranging from $91 per acre for Texas to $153 per

acre for Alabama, in roughly the same order as pre-tax LEV

(table 11.6). The absolute and relative changes were inversely

related to one another, however, with the $91 per acre change

for Texas equating to a 24 percent reduction in LEV and the

$153 per acre change for Alabama equating to a 19 percent

reduction.

In the Appalachian-Cumberland highlands, the Federal

income tax had a similar but smaller effect, decreasing LEV

by $48 per acre (18 percent) in Kentucky and $87 per acre

(15 percent) in Tennessee (table 11.6). The difference in the

results for the two subregions was statistically significant

(Cushing 2006).

281

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

Effect of property tax—Property tax rates on family-owned

forest land varied widely across the Coastal Plain States,

from just over $1 per acre per year in Arkansas to nearly

$5 per acre per year in Georgia. As a result, the amount by

which property tax decreased LEV also varied widely, from

$18 per acre (4 percent) for Arkansas to $71 per acre (10

percent) for Georgia and $51 per acre (14 percent) for Texas.

The results for the States of the Appalachian-Cumberland

highlands were at the low end of the same range (table 11.6).

There was no statistically significant difference in the results

for the two subregions (Cushing 2006).

Effect of harvest tax—Only harvest taxes levied on forest

owners were included in the study; taxes levied on timber

processors were excluded. Of the three Southern States that

levy a harvest tax on forest owners, Louisiana and Georgia

expressed the tax as a percentage of timber stumpage

price, while Mississippi expressed it as a flat rate per unit

harvested. In all three States the tax rate was quite low,

resulting in a decrease in LEV ranging from $6 per acre

(1 percent) for Mississippi to $35 per acre (7 percent) for

Louisiana (table 11.6).

Effect of State income tax—As discussed above, Florida

and Texas do not tax income to individuals and Tennessee

taxes only dividend and interest income. In most of the

Southern States that tax income to individuals the top

marginal tax rate for 2003 fell between 5 and 6 percent;

the exceptions were North and South Carolina, with top

marginal tax rates of 8.25 percent and 7 percent, respectively.

In the Coastal Plain States, State income tax decreased

LEV by amounts ranging from $41 per acre (8 percent) for

Virginia to $57 per acre (11 percent) for North Carolina. In

the Appalachian-Cumberland highlands, State income tax

decreased LEV by $17 per acre (6 percent) in Kentucky (table

11.6). There was no statistically significant difference in the

results for the two subregions (Cushing 2006).

Effect of the Federal Estate Tax

The research results summarized in this section are from a

national survey (Greene and others 2006). The sample size

precludes segmenting the findings into regional estimates;

however, it is known that the Southern States account for

about two-fifths (41 percent) of family forest holdings and

half (51 percent) of family-owned forest land in the United

States (Butler and Leatherberry 2004).

Family forest land transferred—During the survey period,

9 percent of the forest owner respondents had been involved

in the transfer of an estate. Among these respondents,

84 percent were family members of the decedent; the

remaining 16 percent were friends, business associates,

or professionals who had served the decedent. Roughly

half of the estates (49 percent) had been held in individual

ownership by the decedent, with another 27 percent held

jointly with other individuals, and the remaining 24 percent

held by partnerships, corporations, or such other forms of

business as Family Limited Partnerships or Limited Liability

Companies. Some 64 percent of the decedent owners had

used a financial or legal professional to help them plan their

estate (table 11.8).

The value of the decedents’ gross taxable estates ranged from

below the $600,000 unified credit amount to over $3 million.

The total area of the forest estates ranged from 10 to

20,000 acres, with a mean of 1,225 acres and a median of

200 acres; the forest area ranged from 8 to 20,000 acres,

with a mean of 1,024 acres and a median of 156 acres (table

11.8). Expanded to family-owned forest lands throughout

the United States, these findings mean an estimated 77,200

forest estates, with 79.1 million acres of forest land, were

transferred each year at the death of their owners (Greene

and others 2006).

Special use valuation—With forest land, special use

valuation (see above) can be applied to the land only or to

both the land and timber. Just 33 percent of forest estates

qualified for and 26 percent elected to use special use

valuation. Of the estates that used special use valuation,

26 percent applied it to the land only and 74 percent applied

it to both the land and timber.

Applying special use valuation reduced the taxable value

of forest estates by amounts ranging from $0 to $750,000,

with a mean of $325,000 and a median of $250,000, both

well under the $750,000 maximum for the provision during

the study period. Expanded nationally, these findings mean

an estimated 20,000 forest estates elected to use special use

valuation each year, resulting in a combined total reduction

in their taxable estate values on the order of $6.5 billion

(Greene and others 2006).

Assets used to pay the Federal estate tax—A substantial

majority of survey respondents (62 percent) reported that no

Federal estate tax was due in the transfers they were involved

with. In most instances where estate tax was due, insurance

or other assets were used to pay it. But in 42 percent of the

transfers, timber or land was sold to pay part or all of the tax.

In 22 percent of all transfers, timber was sold to pay estate

tax, with 75 percent of the sales necessary because other

assets were not sufficient to pay the tax. The forest size

of ownerships that needed to sell timber ranged from 79

to 10,000 acres, with a mean of 3,035 acres and a median

of 670 acres. The area harvested ranged from 5 to 1,100

acres, with a mean of 498 acres and a median of 430 acres. 

Expanded nationally, these findings mean an estimated
282 The Southern Forest Futures Project

Table 11.8—Characteristics of the estates of forest owners and other owners of working lands

Survey question Response

Forest owners Other rural owners

Number Percent Number Percent

Involved in an estate transfer?a No 1,110 91.3 578 86.1

Yes 106 8.7 93 13.9

Relationship of respondent to 

the decedent 

Family member 85 84.2 84 94.4

Friend or business associate 9 8.9 4 4.5

Professional advisor/trustee 7 6.9 1 1.1

Form of ownership in which land 

was held 

Individual 51 48.6 54 58.1

Joint 28 26.7 26 28.0

Partnership 11 10.5 1 1.1

Corporation 8 7.6 4 4.3

Otherb 7 6.7 8 8.6

Value of gross taxable estate, 

Southern regionc

Less than $600,000 21 60.0 17 73.9

$600,000 to $999,999 4 11.4 4 17.4

$1,000,000 to $1,999,999 5 14.3 0 0.0

$2,000,000 to $2,999,999 1 2.9 0 0.0

$3,000,000 or more 4 11.4 2 8.7

Value of gross taxable estate, 

Northern and Western regionsc

Less than $600,000 26 40.6 40 67.8

$600,000 to $999,999 16 25.0 8 13.6

$1,000,000 to $1,999,999 9 14.1 7 11.7

$2,000,000 to $2,999,999 6 9.4 1 1.7

$3,000,000 or more 7 10.9 3 5.1

Total area transferred 

1 to 99 acres 24 23.3 23 28.4

100 to 499 acres 48 46.6 35 43.2

500 acres or more 31 30.1 23 28.4

Forested area transferreda

0 acres 0 0.0 58 71.6

1 to 99 acres 38 36.9 16 19.8

100 to 499 acres 38 36.9 7 8.6

500 acres or more 27 26.2 0 0.0

Area converted to croplanda

0 acres 69 67.0 22 27.2

1 to 99 acres 25 24.3 26 32.1

100 to 499 acres 7 6.8 23 28.4

500 acres or more 2 1.9 10 12.3

Area converted to grazinga

0 acres 62 60.2 32 39.5

1 to 99 acres 27 26.2 21 25.9

100 to 499 acres 10 9.7 13 16.0

500 acres or more 4 3.9 15 18.5

Estate planning helped by a 

professional? 

Yes 67 64.4 64 71.1

No 34 32.7 26 28.9

Don’t know 3 2.9 0 0.0

Did professional help reduce 

taxes due?a

Yes 41 61.2 48 75.0

No 21 31.3 8 12.5

Don’t know 5 7.5 8 12.5

aThe samples differ statistically at the α = 0.05 level of significance.

bTest results are based on a small sample.

cSuch as a Family Limited Partnership or a Limited Liability Company.
283

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

4,900 forest estates needed to sell a total of 2.4 million

acres of timber each year to pay part or all of the Federal

estate tax.

In 19 percent of all transfers, land was sold to pay estate

tax, with 57 percent of the sales necessary because other

assets were not sufficient to pay the tax. The forest size of

ownerships that needed to sell land ranged from 100 to

2,000 acres, with a mean of 770 acres and a median of 490

acres. The amount of land sold ranged from 160 to 780 acres,

with a mean of 387 acres and a median of 220 acres. Further,

in 29 percent of the cases where land was sold to pay estate

tax, the land was developed or converted to another use.

Expanded nationally, these findings mean an estimated 3,300

forest estates needed to sell a total of 1.3 million acres of land

each year to pay the Federal estate tax, of which on the order

of 400,000 acres were developed or converted to other uses

(Greene and others 2006).

Comparison with owners of other working lands—The

questionnaire responses from owners of other working lands,

largely farmers and ranchers, were more remarkable for

their similarities to forest owners than their differences. The

groups differed statistically in just 6 of the 20 characteristics

surveyed, with most differences stemming from the different

uses the two groups make of their land: whether it is mostly

forest or mostly crop or grazing land, and whether special

use valuation was applied to both land and timber or to the

land only. Also, a lower percentage of forest owners had been

involved in the transfer of an estate during the survey period,

and forest owners were less likely than other landowners to

believe the decedent’s use of an estate planning professional

had reduced the amount of estate tax due (Greene and others

2006).

Effectiveness of Financial Incentive Programs 

in Promoting Sustainable Practices

This section also reports findings from a national study. But

the results for the survey of forestry officials, summarized

from Jacobson and others (2009), are for the Southern States.

And while the results for the forest owner focus groups,

summarized from Daniels and others (2010) are for the entire

United States, points where the South differs from other

regions are noted.

Survey of State forestry officials—Table 11.9 summarizes

the results for Federal financial incentive programs as ranked

by the State forestry officials. None of the officials responded

about the Landowner Incentive Program, which for that reason

was excluded from the analysis. Section a of the table shows

the officials’ mean rankings for forest owner awareness of

each program and its overall appeal among owners aware of it.

All of the programs were ranked in the middle ranges for both

awareness and appeal, with appeal generally rated higher than

awareness. There were no statistically significant differences

between the ratings for any of the programs (Table 11.9).

Section b of Table 11.9 summarizes the officials’ mean

rankings for the programs in terms of their effectiveness

in encouraging sustainable forestry among participating

owners. The Forest Legacy Program (FLP) was

ranked highest overall, scoring well in all attributes of

sustainability. Ranked next-highest were the Conservation

Reserve Program (CRP), Forest Stewardship Program

(FSP), and Forest Land Enhancement Program (FLEP).

CRP scored particularly well for protecting soil

productivity and water quality and preventing conversion

of forest land. FSP scored well for protecting water quality,

encouraging forest management, and protecting wildlife

and fish, while FLEP scored quite well for encouraging

forest management.

The Wetlands Reserve Program (WRP) was ranked nexthighest overall, receiving its best scores for protecting

water quality. The lowest rankings went to the Wildlife

Habitat Incentives Program (WHIP), Environmental

Quality Incentives Program (EQIP), and Southern Pine

Beetle Prevention Program (SPBP), although WHIP

scored quite well for protecting wildlife and fish, EQIP for

protecting water quality and soil productivity, and SPBP for

encouraging forest management (Table 11.9).

Section c of Table 11.9 summarizes the officials’ mean

rankings for the programs in terms of their effectiveness in

helping owners meet their objectives of forest ownership.

The officials generally scored the programs less effective

in this area than in encouraging sustainable forestry. FLP

again was ranked highest, scoring particularly well for

helping owners meet objectives related to soil and water

conservation, wildlife, and aesthetics. FSP and FLEP were

ranked next-highest; FSP received high marks for objectives

related to wildlife and timber production, while FLEP scored

well for objectives related to timber production and soil and

water conservation.

CRP and WHIP were ranked next-highest. CRP scored well

for owner objectives related to soil and water conservation

and wildlife, while WHIP received the highest possible score

for objectives related to wildlife. None of the remaining

programs rated above the moderately ineffective range,

although EQIP received solid scores for objectives related to

soil and water conservation, WRP for objectives related to

wildlife, and SPBP for objectives related to timber production

and soil and water conservation (Table 11.9).

The final section of Table 11.9 summarizes the officials’

mean rankings for program practices remaining in place

and enrolled acres remaining in forest over time. All

eight Federal programs scored in the moderately to very
284 The Southern Forest Futures Project

Table 11.9—State forestry officials’ evaluations of Federal forestry incentive programs

Likert rating of incentive programa

Attribute FSP CRP EQIP FLEP FLP SPBP WRP WHIP

Owner awareness and appeal

Awareness 2.69A 2.62A 2.40A 2.58A 1.89A 2.00A 1.75A 2.14A

Appeal 3.31AB 3.38AB 2.50AB 3.50A 3.00AB 2.75AB 2.13B 2.86AB

Effectiveness in encouraging sustainable forestry

Prevents conversion 3.00ABC 3.70A 2.11C 3.36AB 3.89A 2.83ABC 3.00AB 2.50BC

Prevents parcelization 2.85ABC 3.27ABC 2.11C 3.18ABC 3.89A 2.67BC 3.38AB 2.50BC

Maintains forest type 3.00AB 3.40AB 2.40B 3.27AB 3.63A 2.60AB 3.25AB 2.71AB

Protects wildlife/fish 3.77A 3.31A 3.30A 3.36A 3.67A 2.17B 3.38A 3.86A

Protects water quality 3.92A 3.77A 3.70A 3.36AB 3.78A 2.57B 3.50A 3.29AB

Protects soil productivity 3.54AB 3.92A 3.50AB 3.45AB 3.78A 2.43C 3.25ABC 2.86BC

Encourages forest management 3.85A 3.46ABC 2.50CD 3.91A 3.56AB 3.57AB 2.25D 2.71BCD

Overall average 3.42AB 3.44AB 2.82CD 3.42AB 3.74A 2.70D 3.14BC 2.92 CD

Effectiveness in helping owners meet their objectives

Timber production 3.54A 3.00AB 2.30BC 3.82A 3.13AB 3.57A 2.38AB 1.86C

Recreation 3.23A 2.67A 2.30A 3.00A 3.25A 2.17A 2.75A 3.29A

Wildlife 3.69A 3.31A 3.20AB 3.55A 3.50A 2.43B 3.38A 4.00A

Aesthetics 3.38AB 2.69AB 2.70AB 2.91AB 3.50A 2.43B 3.00AB 3.14AB

Soil/water conservation 3.38AB 3.92A 3.50AB 3.64A 3.75A 2.86B 3.25AB 2.86B

Invasive species control 2.62A 2.50A 3.10A 2.91A 3.00A 2.67A 2.00A 2.71A

Overall average 3.31AB 3.11ABC 2.85BC 3.30AB 3.36A 2.70C 2.80C 2.98ABC

Over time

Practices remain in place 3.38A 3.69A 3.50A 3.50A 3.89A 3.71A 3.63A 3.17A

Acres remain in forest 3.54A 3.46A 3.00A 3.50A 3.89A 3.71A 3.63A 3.00A

aTukey’s grouping across incentive programs for each respective program attribute. α = 0.05. Means with the same 

superscript letter (A, B, or C) are not significantly different.

Forest Stewardship Program (FSP); Conservation Reserve Program (CRP); Environmental Quality Incentives Program 

(EQIP); Forest Land Enhancement Program (FLEP); Forest Legacy Program (FLP); Southern Pine Beetle Prevention Program 

(SPBP); Wetlands Reserve Program (WRP); Wildlife Habitat Incentives Program (WHIP).Likert Scale awareness ratings: 

1 = very low, 2 = moderately low, 3 = moderately high, 4 = very high; Likert ratings for effectiveness: 1 = very ineffective, 

2 = moderately ineffective, 3 = moderately effective, 4 = very effective. 
285

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

effective range for these characteristics, with no statistically

significant differences between the scores (Table 11.9).

The State forestry officials also ranked the success of State

and private financial incentive programs. The questionnaire

sections relating to private incentive programs were

streamlined, however, to request only ratings for their

effectiveness in encouraging sustainable forestry and helping

owners meet their objectives of forest ownership; no data

were collected for owner awareness and appeal, or for

practices remaining in place and acres remaining in forest

over time. For owner awareness, State property tax and

incentive programs generally were rated higher than Federal

programs; for owner appeal, they were rated about the same

(table 11.10).

In terms of effectiveness in encouraging sustainable forestry,

State incentive programs were ranked higher overall than

property taxes, although both types of programs received

high scores for preventing conversion of forest land. Among

the private programs, incentives offered by nongovernmental

organizations were ranked higher overall than those offered

by industry firms and State forestry associations, scoring

highest among all State and private programs for maintaining

forest type and protecting wildlife and fish. Programs offered

by firms and associations scored highest for encouraging

forest management (table 11.10).

There were no statistically significant differences among the

programs in the officials’ mean rankings for effectiveness

in helping family forest owners meet their objectives of

ownership. State incentive programs again scored higher

overall than property taxes, however, and programs offered

by nongovernmental organizations again scored higher

overall than those offered by industry firms and State

forestry associations. Both types of State programs received

their highest scores for helping owners meet objectives

related to timber production and soil and water conservation.

Programs offered by firms and associations scored best for

objectives related to timber production, while programs

offered by nongovernmental organizations received their

highest marks for objectives related to soil and water

conservation (table 11.10).

Property tax programs were ranked moderately to very

effective for both practices remaining in place and enrolled

acres remaining in forest over time. Other State incentives

were ranked moderately effective for practices remaining

in place, but moderately ineffective for acres remaining

in forest. The differences, however, were not statistically

significant (table 11.10; Jacobson and others 2009).

Focus group sessions with family forest owners—The

focus group sessions were designed to foster discussion

about family forest owners’ experience with financial

incentive programs, what objectives of forest ownership

the programs helped them to meet, and what additional

program approaches would help them meet other objectives.

The actual responses were much wider in scope, however,

comprising four broadly shared themes (Daniels and others

2010):

•	 Forest ownership is more strongly linked to self-identity

and lifestyle than to profit. Despite marked differences in

time of ownership, there was a broadly shared commitment

to long-term stewardship and appropriate management.

Land ownership seemed much more tied to self-identity

and lifestyle than to financial return, and in many groups

there were clear statements that financial return was not a

major driver for management behavior. Of the eight focus

groups, the one made up of forest owner organization

members in the South was the most focused on timber

management to generate financial return, but even in this

group there was a strong intergenerational component in

their motivations for land ownership.

•	 A strong ethic of conservation. A readily verbalized

commitment to a strong conservation ethic appeared

to be interwoven with the self-identity theme for forest

ownership and management. Rather than saying they

intended to sell off land or liquidate standing timber,

participants emphasized a desire to pass the land to future

generations, or to buy more land if they had the money.

•	 Landowners have heard about sustainable forestry, but

generally are not clear as to its meaning. Many focus group

participants said they knew about sustainable forestry, but

when asked to articulate what the term meant to them, the

responses became more hesitant or vague. In many cases,

the participants offered statements resonant of sustained

yield concepts—such as harvesting at a rate no greater

than growth—or referred to the program of a particular

group—for example, stating, “That is what Tree Farm is

promoting.”

•	 Landowners have a high interest in face-to-face technical

assistance. Participants in every focus group said they would

do a management practice they thought was important even

if there was no incentive program, but needed someone

to walk their land with them and guide them through the

decision about what they should do. The need for on-theground help in understanding what was happening on their

land was strongly expressed in every region.
286 The Southern Forest Futures Project

Table 11.10—State forestry officials’ evaluations of State tax and incentive 

programs, industry and State association programs, and nongovernmental 

organization (NGO) programs

Attribute

Likert rating provision or programa

Property 

tax

provisions

State

incentive

programs

Industry 

programs

NGO

programs

Owner awareness and appeal

Awareness 3.00A 2.70A N/A N/A

Appeal/effectiveness 3.25A 3.14A N/A N/A

Effectiveness in encouraging 

sustainable management

Prevents conversion 3.08A 3.71A 3.00A 2.66A

Prevents parcelization 2.91A 3.28A 2.87A 3.00A

Maintains forest type 3.00A 3.28A 3.14A 3.33A

Protects wildlife/fish 2.81A 3.14A 2.50A 3.33A

Protects water quality 3.00A 3.42A 3.12A 3.33A

Protects soil productivity 2.83A 3.43A 2.87A 3.33A

Encourages forest management 2.91A 3.71A 3.25A 3.00A

Overall average 2.94B 3.43A 2.96B 3.14AB

Effectiveness in helping owners 

meet their objectives

Timber production 3.08A 3.85A 3.86A 3.00A

Recreation 2.72A 3.00A 2.37A 3.33A

Wildlife 2.75A 3.28A 2.62A 3.33A

Aesthetics 2.82A 2.85A 2.50A 3.33A

Soil/water conservation 3.00A 3.57A 3.25A 3.66A

Invasive species control 2.30A 3.14A 2.43A 2.67A

Overall average 2.79A 3.28A 2.85A 3.22A

Effectiveness over time

Practices remain in place 3.66A 3.00A N/A N/A

Acres remain in forest 3.66A 2.25A N/A N/A

N/A = not applicable.

aTukey’s grouping across incentive programs for each respective program attribute. α = 0.05. 

Means with the same superscript letter (A or B) are not significantly different. 

Likert Scale awareness ratings: 1 = very low, 2 = moderately low, 3 = moderately high, 

4 = very high; Likert ratings for effectiveness: 1 = very ineffective, 2 = moderately ineffective, 

3 = moderately effective, 4 = very effective.
287

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

Discussion and Conclusions

Because of the sources used for lists of family forest owners

to receive survey questionnaires, the results of the some

of the studies summarized in this chapter may be more

representative of owners who are active and financially

motivated than family forest owners in the South in

general. For this reason, the findings should be considered

conservative.

Family forest owner awareness and use of Federal income 

tax provisions—Owner awareness of the provisions available

to taxpayers in general ranged widely, from nearly 80 percent

for treatment of qualifying income as a long-term capital gain

and annual deduction of management costs to between 50 and

60 percent for depreciation, the section 179 deduction, and

loss deductions. In comparison, awareness of the provisions

intended for owners of forests and other working lands—the

reforestation incentives and special treatment of qualifying

cost-share payments—was substantially lower, at roughly 50

percent or less.

Three demographic characteristics were associated with

owner knowledge of each of the beneficial tax provisions:

membership in a forest owner organization, having a written

forest management plan, and a high level of household income.

None of the demographic characteristics were associated

across-the-board with owner use of beneficial tax provisions.

The study findings confirm the need for additional efforts

to improve family forest owner awareness of beneficial tax

provisions, particularly the provisions designed specifically

for them. Historically, the tax handbooks, short courses,

popularized articles, and extension workshops available to

owners have focused on tax aspects of timber production.

This approach has been beneficial and certainly needs to

be continued. It seems likely, however, that approaches

aimed at informing owners of the tax implications of other

forest uses—nontimber forest products, recreation, and

stewardship, for example—would appeal to the interests of

additional owners (Greene and others 2004).

State property taxes—Most of the property tax

administrators surveyed believed their State’s program was

effective at achieving its primary goal—reducing property

tax—and approximately half believed it was effective

at retaining forest land in areas highly susceptible to

development. The findings from the survey indicate, however,

that only a fraction of family forest land in the United States

is enrolled in a preferential property tax program. Property

taxes were of greater concern to the forest owners than any

other type of tax, because they occur on an annual basis, are

due whether or not the property produced income during

the year, and are perceived as being high in relation to the

value of the land. A common theme from the focus groups

was that property taxes may be forcing some owners to sell

timber or land when they would rather not. These decisions

often are compounded by other factors, such as the loss of

a job or the rigors of living on a fixed income. A number of

owners had stories of relatives, friends, or friends of friends

who had been forced to sell timber or land, and some feared

they would be forced into the same position in the future. As

well, several owners enrolled in a preferential property tax

program stated that the program had enabled them to hold on

to their land.

The study findings suggest that property tax policies should

be simple, flexible enough to address the various threats

to maintaining forest land that exist across a State, and

appropriate to the challenges that the owners currently face.

The preferential property tax programs in many States

were put in place decades ago, when forest owners faced a

different set of challenges. It needs to be determined whether

these programs adequately address the current situation. If

the primary objective of a property tax program is to keep

forests as forests, it should focus primarily on discouraging

conversion to other uses; promoting timber production or

public access should be secondary.

The New Hampshire Forest Land tax program is one

example of a flexible preferential property tax program that

meets the needs of different types of owners. The basic

program provides a property tax reduction for keeping land

undeveloped. Forest owners who desire to manage their

land according to a plan developed by a licensed forester

are eligible for an additional reduction in tax. Owners who

are willing to permit non-motorized recreation by the public

on their property may be eligible for a further “recreational

adjustment” in the assessment and taxation of their land

(Butler and others 2010).

Combined impact of Federal and State taxes—Research

has consistently shown that most family forest owners do

not take taxes into consideration when making management

decisions for their land. Nonetheless, Federal and State

taxes affect the level of stewardship owners can practice and

whether they are able to continue holding their land.

Federal and State taxes were found to reduce the pre-tax

land expectation value of family-owned forest land in the

South by amounts ranging from just over 25 percent to

nearly 50 percent. Much of the reduction, but only a small

part of the variation, is attributable to Federal income tax.

All family forest owners in the United States face the same

Federal capital gain tax rates on their net returns from timber

harvests. The economic effect of the tax varies with the

frequency and value of harvest returns and the amount of

capitalized reforestation expenses, but within a defined area

the variation falls within a fairly narrow range. In the Coastal

Plain States, for example, Federal income tax decreased the
288 The Southern Forest Futures Project

pre-tax expectation value by 19 to 24 percent, and in the

Appalachian-Cumberland highlands by 15 to 18 percent

(table 11.7).

In contrast, the cumulative burden of State and local

taxes varied widely across the South, from 4 percent of

pre-tax land expectation value in Tennessee to 23 percent

in Louisiana. Some of the variation can be explained by

the number of different taxes a State imposes: Tennessee

essentially levies only a property tax, while Louisiana

levies harvest and income taxes as well as a property tax.

The number of taxes levied did not, however, explain all of

the variation. Like Tennessee, Texas levies only a property

tax, while like Louisiana, Mississippi, levies property,

income, and harvest taxes. Yet for Texas and Mississippi,

the cumulative effect of State and local taxes was near

the median for the region, and relative to pre-tax land

expectation value, nearly identical (table 11.7).

Most variation in the cumulative effects stemmed from

State-to-State variability in property tax rates. Property

taxes occur annually and carry the greatest economic burden

of any State and local tax. At the same time, property tax

is the tax that family forest owners can do the most about;

for example, they can ensure that their forest land meets the

requirements to be assessed or taxed at its current use. Some

States require a written management plan to qualify for this

benefit, which encourages forest stewardship while providing

tax relief. Owners also can seek other sources of income

from their forest land—through, for example, a hunting

lease, fee recreation, or nontimber forest products—to offset

the annual tax levy.

The Federal income tax and State property taxes carry costs

in addition to their economic impact. The Federal tax law

changes continually. Although some changes are designed to

benefit owners of forests and other working lands, they have

the effect of increasing the complexity of the law and the

cost of complying with it. As well, State-to-State variation

in property taxes produces relative disadvantages to holding

forest land and may have the unintended consequence of

contributing to differential rates of development among

States, particularly at the urban-rural interface or in areas

undergoing gentrification (Cushing 2006).

Effect of the Federal estate tax—An estimated 77,200

forest estates, with 79.1 million acres of family-owned

forest land, were transferred each year at the death of their

owners. The median forest area transferred was 156 acres.

Only a third of forest estates qualified for and one-quarter

applied for special use valuation to reduce the Federal

estate tax due. In three-fourths of the transfers where it was

used, special use valuation was applied to both the land and

timber. Although this may have been necessary to meet the

requirements for the provision, it precluded the harvesting

of timber for 10 years. The reduction in the gross value of

forest estates from applying special use valuation averaged

$325,000, well under the $750,000 maximum benefit in effect

during the study period.

Owners of family forests and other rural lands were many

times more likely than other taxpayers to incur the Federal

estate tax. In about two-fifths of the transfers where Federal

estate tax was due, timber or land was sold to pay part or all

of the tax. Some three-fourths of the timber sales and nearly

three-fifths of the land sales occurred because other estate

assets were not sufficient to pay the tax. The need to sell

timber or land to pay the estate tax was not limited to small

holdings and the areas affected were not inconsequential.

The mean forest size of ownerships that needed to sell timber

was 3,035 acres and the mean area harvested was 498 acres;

the mean forest size of ownerships that needed to sell land

was 770 acres and the mean area sold was 387 acres.

The responses from forest owners and other owners of

working lands were more remarkable for their similarities

than their differences. The groups differed statistically in

just 6 of the 20 characteristics surveyed, with most of the

differences stemming from the different uses members of the

two groups make of their land.

The results of this study provide insight into the magnitude

of the effect of the Federal estate tax on family-owned

forests and other rural lands. As well, they suggest avenues

for development of an estate tax relief policy that would

benefit both forest and other owners of working lands. Some

elements of such a policy might include:

•	 A targeted increase in the effective exemption amount for

estates that consist largely of working lands, such as farms,

ranches, or forest land

•	Revision of the requirements for special use valuation

to permit timber harvests made in accordance with

a management plan developed in consultation with a

qualified professional forester

•	Recognition of a business entity for family farms and

forests, to help ensure that they qualify for businessoriented provisions in the tax code and to facilitate the

transfer of working lands from one generation to another

(Greene and others 2006).

Effectiveness of financial incentive programs in promoting 

sustainable practices—The results of the survey of State

forestry officials indicate there are clear differences among

the incentive programs available to family forest owners.

The Forest Stewardship Program, Forest Land Enhancement

Program, and Forest Legacy Program—all administered

by the Forest Service—were among the top rated Federal

programs by all measures, both overall and for individual

attributes. All three programs stress multiple objectives, but
289

chapter 11. Effect of Taxes and Financial Incentives on Family-Owned Forest Land

their clientele is limited to forest owners. The other Federal

incentive programs have forestry emphases, but their clientele

includes farmers and ranchers as well as forest owners.

Regardless of their orientation or administrative agency,

however, all of the Federal programs scored in or near the

very effective range for practices remaining in place and

acres remaining in forest over time (table 11.9). This finding

speaks to the participating owners’ long-term commitment to

the supported practices as well as the long-term effectiveness

of the programs themselves.

Programs sponsored by States, industry firms and State

forestry associations, and nongovernmental organizations

generally were more narrowly targeted than Federal

programs, and scored higher for specific attributes. Such

targeted programs have the potential to outperform general

conservation programs for regional concerns, emerging

issues (for example, invasive species control) or where

program funding is constrained.

The findings from the survey of forestry officials must

be interpreted with respect to acres enrolled in incentive

programs, rather than by all acres held by family forest

owners. The results of the forest owner focus groups clearly

showed that public and private financial incentive programs

play only a limited role in promoting sustainable practices on

family-owned forest land. One reason is that funding of the

programs limits the number of acres that may be enrolled.

Another is that many forest owners remain unaware that

the programs exist. Owner awareness of Federal financial

incentive programs, for example, peaked in the moderately

ineffective range (table 11.9; Jacobson and others 2009).

Southern forest owners share four strongly held sentiments

with family forest owners nationwide:

•	Their reasons for owning forest land are more strongly

linked to self-identity than to profit;

•	They have a strong ethic of conservation toward their land;

•	The concept of sustainable forestry resonates with them,

although they are not entirely clear as to its meaning; and

•	They are more interested in face-to-face technical

assistance than incentive programs or beneficial tax

provisions.

Forest owner organization members in the South were more

focused on managing timber for profit than owners in other

regions, but still operated within these broad shared themes

(Daniels and others 2010).

Since the research described in this chapter was completed,

funding and legislative changes have occurred in the

financial incentive programs available for family forest

owners. The Forest Land Enhancement Program, among the

top-rated programs, received no funding beyond its initial

allocation. Forest Service distributions to States ended in

2006 and the program was not reauthorized in the 2008

Farm Bill (P.L. 110-246). As well, the Farm Bill modified

provisions of the Environmental Quality Incentives Program

and other programs administered by the Farm Service

Agency and Natural Resources Conservation Service to

include management and conservation practices on familyowned forest land as eligible for assistance. It also added

protection of forests from threats such as invasive species,

insects, and disease as a national priority for Federal

assistance and established the Emergency Forest Restoration

Program to address the new priority (Gorte 2008, Greene

and others 2010).

The effect of these changes has largely been to shift incentive

programs for family forest owners from the Forest Service

to sister agencies within the Department of Agriculture

whose traditional focus has been farmers and ranchers. The

challenge for the Forest Service will be to find new ways to

deliver direct assistance to landowners and to coordinate

program delivery with other Federal and State agencies.

Kilgore and others (2007) proposed nine 

recommendations for financial incentive 

programs:

• Increase funding and availability of one-on-one 

technical assistance from both extension foresters 

and State service foresters; 

• Approach the concept of forest sustainability 

through technical assistance that addresses 

owners’ long-term stewardship and family legacy 

objectives rather than through certification; 

• Make a written forest management plan a 

requirement to participate in all incentive 

programs;

• Design incentive programs to put forest owners 

in direct contact with a forester or other natural 

resource professional;

• Design some incentive programs with sufficient 

flexibility to address regional differences in forest 

characteristics, forest health concerns, or forest 

owner objectives;

• Link incentives directly to stewardship practices 

instead of general forest management practices;

• Fund cost-share applications according to 

their expected environmental benefit instead of 

first-come-first-served;

• Maintain adequate funding and stable program 

requirements for financial incentives over the long 

term; and

• Make the requirements for owners to participate 

in financial incentive programs more uniform, and 

coordinate program administration and delivery 

more closely.
290 The Southern Forest Futures Project

Knowledge and Inf ormation Gaps

Additional research is needed to update and validate the

findings of each of the studies discussed above for current

legislation, and to obtain larger and broader samples of

family forest owners. Additional work also is needed to

assess the policy implications that arise from the studies,

including:

•	 Identify and evaluate program approaches for improving

family forest owner awareness and use of beneficial income

tax provisions, including assisting owners to develop

written forest management plans, encouraging them

to participate in forest owner organizations, and better

informing them of the tax aspects of nontimber forest uses.

•	 Identify and evaluate approaches to develop an estate tax

relief policy for owners of forests and other working lands,

including a targeted increase in the exemption amount

for estates that consist largely of working land, revising

the requirements for special use valuation to permit

timber harvests made in accordance with an approved

management plan, and developing a business entity tailored

for owners of family farms and forests.

•	Monitor the development of property tax provisions

intended to reduce urban sprawl and encourage provision

of ecosystem services from rural land, and examine the

level of their success.

•	 Determine whether property taxes on family forest land at

the urban-rural interface remain stable over time or rise in

response to development pressures.

•	 Determine whether property tax differentials in

neighboring States continue or diminish over time.

Little is known about the effect of the changes to financial

incentive programs made by the 2008 Farm Bill, which

shifted major responsibility for program administration

from the Forest Service to sister agencies in the Department

of Agriculture. Research is needed to determine the effects

of this shift on State forestry agency partners, family forest

owners, and the number of family forest acres treated.

And finally, the period 2002–2012 has provided a veritable

laboratory on the effects of continually changing tax

provisions. Research is needed to determine the effect of

such tax uncertainty on the management decisions of family

forest owners.

Acknowledgments

Our thanks to Robert J. Dee and Heather Irwin, Department

of Forestry and Natural Resources, Clemson University;

David H. Newman, Department of Forest and Natural

Resource Management, State University of New York; Ted

Beauvais, Cooperative Forestry, U.S. Forest Service; Steven

H. Bullard, Temple College of Forestry and Agriculture,

Stephen F. Austin State University; Stephen E. Daniels,

Department of Sociology, Social Work, and Anthropology,

Utah State University; Michael G. Jacobson, School of Forest

Resources, Pennsylvania State University; and Michael L.

Kilgore, Department of Forest Resources, University of

Minnesota.

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293

chapter 12. Employment and Income Trends and Projections for Forest-Based Sectors in the U.S. South

Karen L. Abt

1

Key Findings

•	 The southern logging sector is expected to experience

small increases in both industry output (3 percent) and

jobs (2 percent) from 2008 to 2018. Increased demand

from bioenergy is expected to counteract increasing trends

toward mechanization and reduced demand from some

traditional wood-using industries.

•	 Southern wood products manufacturing is expected to

increase in industry output (2.2 percent) in conjunction

with the housing recovery after the 2007–09 recession.

Technical change is expected to continue—with capital

substituting for labor—leading to continued declines in

jobs through 2018 (8 percent).

•	 The southern paper manufacturing sector is expected to

continue contracting, with industry output declining by

17 percent through 2018. Output declines and continued

technical change are expected to reduce jobs by 26 percent

from 2008 to 2018.

•	 Forest-based recreation is expected to increase following

the 2007–09 recession, but at lower rates than overall

travel and tourism. Increases in output may be limited

because forest-based recreation per capita is not expected

to increase at the same rate as other travel and tourism. In

addition, technical change is expected to continue to reduce

labor demand for the same level of output.

•	Bioenergy demands resulting from State and Federal

policies are expected to lead to increases in logging sector

jobs and output. If competition occurs between bioenergy

demands and traditional wood products demands, additional

losses in jobs and output in the wood products and paper

manufacturing sectors would be expected. Output and

employment gains from bioenergy development and

production would be offset by losses in conventional energy,

including mining, drilling, transport, and fuel and electricity

generation and distribution. The overall effects on output and

employment in the South are expected to be small.

1Karen L. Abt is a Research Economist at the Forest Economics and Policy

Research Work Unit, Southern Research Station, U.S. Department of

Agriculture Forest Service, Research Triangle Park, NC 27709.

Introduction

Southern forests are used for recreation, provide wood

inputs to manufacturing, create scenery, and enhance the

quality of life. In addition to providing jobs and income to

the local and regional economy, forests are now considered

a potential source of woody biomass for bioenergy. This

chapter addresses the short-term future output and jobs

in forest-using sectors of the southern economy. Specific

sectors addressed include forestry and logging, wood

products manufacturing, paper manufacturing, forest-based

recreation, and the new bioenergy sectors.

Economists represent the regional economy through

production functions. A production function is a stylized

model that expresses industry or business outputs (typically

measured in dollars) as a function of the inputs needed to

generate the outputs. For example, a generic production

function would represent output as a function of capital,

labor, energy, materials (such as wood), and other inputs.

Over time, we expect this production function to change as

technology reduces the amount of inputs needed to produce

the same level of output by substituting capital for labor,

energy, and other inputs.

Assuming that companies are profit maximizing and riskneutral, the optimal output level and the optimal combination

of inputs needed to achieve it will be driven by the prices of

the inputs and outputs. We typically expect the inputs to be

complements (an increase in one input requires an increase

in the other) or substitutes (an increase in one input leads to a

decrease in the other). Changes in input use levels can be the

result of changes in the output level or changes in input price

or quality. Thus, an industry or business can decrease its

demand for labor (jobs) because of decreased demand for its

outputs, or because capital or another input is substituting for

labor in the production function.

A conundrum of economic analysis is that a positive outcome

in one area is likely offset by a negative outcome elsewhere.

For example, increasing wages is usually perceived as a

positive because it leads to a higher standard of living for

workers, but it also leads to increased labor costs, which

Chapter 12.

 Employment and Income Trends and 

Projections for Forest-Based Sectors 

in the U.S. South
294 The Southern Forest Futures Project

will result in substituting capital for labor in the production

function. The result is a loss of jobs, which is typically seen

as a negative outcome. Similarly, gains in one sector (such

as logging) are likely offset by losses in a different sector

(such as coal mining); and gains in one geographic area (such

as the South) may be offset by losses in other regions (such

as the Northern Appalachians). These outcomes illustrate

that the definition of sectors, inputs, and areas of interest are

likely to influence the outcome of any economic analysis

of a region’s economy, which should be kept in mind while

reading the following assessment.

Throughout this chapter, we use some specific economic and

modeling terms. They are defined below:

Recession: an economic term implying, generally, a decline

in economic activity that is between a peak and trough

of economic activity. The National Bureau of Economic

Research is the accepted arbiter of when recessions begin

and end (see http://www.nber.org/cycles/recessions.html).

The recession referred to in this chapter began in December

2007 and continued until June of 2009.

Technical change: Technical change is an economic term

representing any change in the relationship between inputs

to a production process and outputs from a production

process. This is often an improvement in capital use,

leading to a reduction in labor use, but can also result from

administrative or policy changes.

Jobs (also employment): These are not full-time

equivalents, and so represent any continuous employeremployee relationship for wages and salary, whether full- or

part-time.

Output (also total industry output): Output is an economic

term representing the total dollar value of a firm or sector or

economy. This value “double counts” the contribution of a

sector when adding up the totals for an economy—the value

of a log would be counted in the output of the logging sector,

and, for example, in the output of the sawmill sector. When

adding up the sector values across an economy, the total

value added should be used in place of output; however in

this chapter we use output because the models and forecasts

were developed for output and not for total value added.

Gross regional product (also gross domestic product): an

economic term representing the total value of the production

of goods and services for a region (or State or country).

Total value added: an economic term that nets out the

cost of inputs (such as logs) that are counted as another

firm’s outputs and is nearly equivalent to gross domestic

(or regional) product (indirect taxes are excluded). Other

components of total value added include proprietor’s income

and property-type income.

Income: represents, in this chapter, wage and salary income

from a job. Wage and salary income is a large component

of total value added, and thus is a large component of gross

regional product.

Forestry and logging: a sector that includes both the growing

and management of forests [forestry is part of North American

Industry Classification System (NAICS) sector 115] and the

harvesting and transportation of timber (NAICS 113).

Forest-based recreation: a sector that represents all

expenditures made to participate in forest-based recreation,

including hiking, hunting, winter sports, water sports,

fishing, nature study and other recreation activities taking

place in forests. This sector is not defined separately in

NAICS, but could include portions of other sectors including

transportation (NAICS 48), accommodations (NAICS 721),

eating and drinking places (NAICS 722), recreation and

entertainment (NAICS 713) as well as parts of other sectors.

Bioenergy: a sector that represents current (or potential)

uses of wood to produce energy (pellets, liquid fuels, and

electricity). This sector is not defined separately in NAICS,

but could include portions of miscellaneous wood products

(NAICS 321999), electricity generation (NAICS 237130) and

ethanol sectors (NAICS 325193).

Wood products manufacturing: a sector that includes

primary sawmills as well as manufacturers of veneer and/

or plywood, engineered wood members, and reconstituted

wood products (NAICS 321). These companies manufacture

and/or use solid wood products such as lumber, millwork,

pallets, mobile homes, and trusses.

Paper manufacturing: a sector (NAICS 322) that includes

firms that make pulp, paper and/or converted paper products.

Input-output models: models used to represent static,

detailed production relationships between inputs and outputs,

and jobs and income.

Computable general equilibrium models: models used to

represent changes in an economy using estimated or assumed

equations and parameters.

Methods

To address the future of jobs, income, and contributions

of forest-using sectors to the regional or local economy,

we first evaluated historical trends and current conditions

in the forestry and logging, wood products, and paper

manufacturing sectors, as well as forest-based recreation and

future bioenergy sectors. We then developed projections, to

the extent possible given the data limitations, for forest-using

sectors.
295

chapter 12. Employment and Income Trends and Projections for Forest-Based Sectors in the U.S. South

The forest-based recreation and bioenergy sectors have

inadequate data and/or analyses at the national level and

for the South that limits our ability to project these sectors.

Forest-based recreation is not specifically tracked in the

national economic accounts, and even the data available

nationally have not been subset for the South, limiting our

ability to provide Southwide trends. The bioenergy sector

(distinct from by-products of wood products and paper

manufacturing) is fairly new and does not have separate data

for historical analysis.

Forecasts for the logging, wood products manufacturing,

and paper manufacturing sectors were developed for a

single decade, using trends in the southern component of

each sector to downscale national forecasts. These national

and southern trends were developed from the IMPLAN

(MicroImplan Group, Inc. 2010) database (percent of each

sectors’ outputs that were from the South) and from the 2007

Resources Planning Act database (percent of each sectors’

inputs that were from the South). Forecasts of economic

activity at the sector level were not available beyond 2018.

The national forecasts were developed by the Bureau of

Labor Statistics, Department of Labor using methods

outlined in chapter 13 of the BLS Handbook of Methods

(U.S. Department of Labor BLS, 2011). These forecasts are

developed every two years, for a 10-year forecast period.

There are six separate modeling components, each of which

builds on the previous outputs. These six components are:

1.	 Forecast of Labor Supply: developed from forecasts of

labor participation rates and population.

2.	 Forecast of Aggregate Economic Growth: BLS contracts

with Macroeconomic Advisers, LLC to develop these

forecasts, using the forecast of labor supply as an input.

3.	 Forecast of Commodity Final Demand: this modeling

component disaggregates the economic growth into

growth in the components of final demand (personal

consumption expenditures, gross private domestic

investment, foreign trade, and government demand) for

each commodity.

4.	 Forecast of Sectoral Industry Outputs: using an inputoutput model with inputs from above and production

relationships from the Bureau of Economic Analysis, the

commodity final demands are converted into industry

total outputs.

5.	 Forecasts of Industry Employment: jobs are estimated

as a function of total industry output, wages, prices and

time, as a system of equations. Wages derived from

the Current Employment Statistics and the Current

Population Survey, prices are adjusted using forecast

inflation, and time accounts for changes in labor

productivity.

6.	 Forecasts of Occupational Employment and Job

Openings: a matrix of 300 industries by 750 occupations

is used to calculate how forecasts of industry

employment result in particular jobs.	

The national forecasts for the tourism-related sectors are

also presented, as are the expected changes in jobs and

income that could result from these sectors. Forecasting the

bioenergy sector is complicated because the technologies are

under development and the markets are not well established.

In addition, there is considerable uncertainty regarding the

policies and technologies that will drive the industries and

resulting outputs and jobs.

Data Sources

Data used in this analysis are primarily from the U.S.

Department of Commerce, Bureau of Economic Analysis

(BEA) (2010), Woods (2009), and the Travel and Tourism

Satellite Account (Griffith and Zemanek 2009). In addition,

data from IMPLAN (MicroImplan Group, Inc. 2010) and

from the 2007 Resources Planning Act database were used to

downscale the national labor forecasts to the South.

Results

Past and Current Role of Forest-Based Sectors in 

the Southern Economy

Wood-related manufacturing, including logging and

forestry comprised less than 1 percent of southern jobs

and employment income in 2008 (figs. 12.1 and 12.2) (U.S.

Department of Commerce 2010). This was down from 1.2

percent in 1990 (figs. 12.3 and 12.4), and resulted from

both the growth of the entire southern economy as well as

a decline in wood-related employment (figs. 12.5 and 12.6).

Wood-related income (in constant 2008 dollars) increased

from 1990 to 2000, but fell back to 1990 levels after 2000,

with most of the variation coming from the wood products

and paper manufacturing sectors (fig. 12.6). Wood-related

manufacturing comprised 10 percent of all southern

manufacturing employment (fig. 12.7) and 8 percent of all

southern manufacturing income (fig. 12.8). This compares to

food manufacturing with 13 percent of employment and 10

percent of income, and textiles manufacturing with 6 percent

of employment and 4 percent of income (fig. 12.7 and fig. 12.8).

Of the three wood-related manufacturing sectors’

employment, wood products manufacturing is the largest

component (47 percent of wood-related employment) and

forestry and logging is the smallest (14 percent) (fig. 12.9).

The paper manufacturing sector, however, provides a much

larger proportion of wood-related income (51 percent)




300 The Southern Forest Futures Project

reflecting the higher wages and more full-time employment

in this sector (fig. 12.10).

The direct and total contribution of the wood-related

manufacturing sectors was also assessed using the IMPLAN

(MicroImplan Group, Inc. 2010) input-output model. The

thirteen Southern States were aggregated, and then the

forestry and logging, wood products manufacturing, and

paper manufacturing sectors were aggregated. Table 12.1

shows the direct contributions of these sectors to southern

employment, employee compensation, wage and salary

income, total value added, and total industry output in 2009.

Table 12.2 shows the total contribution of wood-related

manufacturing, which is the sum of the direct contribution

plus the multiplier effects, for each sector. For details

about the calculation of these results, see Abt and others

(2002). Using the IMPLAN data, which is derived from

different sources than the BEA data, we found that the direct

contribution of the wood-related manufacturing sectors

was between 0.6 (employment) to 1.4 percent (total industry

output) of the southern economy. The total contribution of

these sectors ranged from 1.9 percent (employment) to 2.8

percent (total industry output), with the total contribution to

employee compensation and total value added in between.

The U.S. travel and tourism sector has increased since 1990

(fig. 12.11) (Griffith and Zermanek 2009, Kern and Kocis

2007). However, because a comparable analysis has not been

done for the South alone, and because forest-based recreation

comprises only a portion of the total travel and tourism

sector, we cannot determine precisely what portion of jobs

and income in the tourism-related sectors can be attributed to

southern forest-based recreation.

Logging

Concerns over a shortage of loggers have been voiced in the

South for more than 50 years (Pikl 1960, Wollf and Nolley

1977). More recently, surveys of loggers indicate increases

in average age, a reluctance to encourage children to enter

the field, and increasing financial concerns, all of which

could signal a future shortage of loggers (Baker and Greene

2008; Egan and Taggart 2004a, 2004b; Egan and Taggert

2007; Luppold and others 1998). At the same time, increasing

mechanization could lead to reduced need for loggers as

more of the work is accomplished by machinery.

Past surveys have indicated that insurance was a primary

concern for sustainability of logging companies, but

respondents to more recent surveys reported that fuel prices

and timber prices are more critical today (Baker and Greene

2008, Moldenhauer and Bolding 2009). Issues that were

not reported as significant barriers to sustainability include

taxes and regulations (Baker and Greene 2008) and tract size

and development (Egan and others 2007, Moldenhauer and

Bolding 2009).

The future is likely to bring increasing mechanization and

the substitution of equipment for jobs in the logging sector.

This mechanization, as well as regulations and laws, has

also led to increased safety and less strenuous work, which

may serve to make logging a more attractive career choice.

However, many current loggers indicate that their preference

for logging work is derived in part from the hard, physical

nature of the work (Egan and Taggart 2004a, 2004b); making

the work safer and easier might lead to fewer (or different)

new entrants into the field.

Although current loggers report that wages are low, they do

not view wage increases as a priority. However, economic

theory would imply that an increase in wages would result in

an increase in numbers of loggers. Even so, the risky nature

of both the work and the business may prevent a sufficient

number of workers from choosing logging as a profession.

If shortages do occur, other market solutions are expected;

for example, arrangements could be made between woodusing companies and loggers that could include long-term

contracts, immigrant labor contracts, loans for equipment, or

other solutions.

The national projection shows a slight increase in the number

of logging jobs and in output for the logging industry in 2018

(Woods 2009). Scaling this to the South shows increases of

2 percent in jobs and 3 percent in output for 2008 to 2018,

reversing the trend from 1998 to 2008 (fig. 12.12). This

increase is attributed, in part, to a slight increase in the

expected use of wood for energy. Income per logging job

in the South has increased and now surpasses the national

average (fig. 12.13). This increase results from a combination

of both increasing hourly wages and increasing hours per job

(more full time employment). Beyond the projection (2018),

we expect the number of logging jobs to correlate strongly

with changes in harvest levels, while also continuing to

respond to technical change by declining as mechanization

continues to increase.

Wood Products Manufacturing

The sector is strongly linked to the housing market, and

the decline in output and jobs from 1998 to 2008

(fig. 12.14) reflects the decline in housing starts during

the 2007 recession (Woods 2009). As the housing market

recovers, output is expected to rise by 2.2 percent through

2018, but technical change and a change in the product mix

is expected to cause employment to continue declining by

8 percent (fig. 12.14). These values were downscaled from

the national labor forecasts (Woods 2009) by proportioning

the national trends to the South’s share of total output and

employment for this sector.
301

chapter 12. Employment and Income Trends and Projections for Forest-Based Sectors in the U.S. South

Table 12.1—Direct contributions of three wood-related sectors to employment, compensation, value added, and total 

industry output of the South in 2009, in millions of 2009 U.S. dollars and by percent of totals for the South

NAICS 

sector Sector name Employment

Employee 

compensation

Total value 

added

Total industry 

output

percent

millions 

of 2009$ percent

millions 

of 2009$ percent

millions 

of 2009$ percent

131 Forestry and 

logging 57,676 0.10 1,454 0.06 3,776 0.09 9,613 0.12

321 Wood products 

manufacturing 145,936 0.26 6,468 0.27 10,168 0.25 28,065 0.34

322 Paper 

manufacturing 143,984 0.25 11,440 0.48 23,390 0.57 79,991 0.98

All wood-related 

manufacturing 347,596 0.61 19,363 0.81 37,333 0.92 117,668 1.44

Table 12.2—Total contribution of three wood-related sectors to employment, compensation, value added, and total 

industry output of the South in 2009, in millions of 2009 U.S. dollars and by percent of totals for the South

NAICS 

sector Sector name Employment

Employee 

compensation

Total value 

added

Total industry 

output

percent

millions 

of 2009$ percent

millions 

of 2009$ percent

millions 

of 2009$ percent

131 Forestry and 

logging 137,461 0.24 4,104 0.17 9,000 0.22 19,350 0.24

321 Wood products 

manufacturing 348,001 0.61 15,180 0.63 26,803 0.66 58,474 0.72

322 Paper 

manufacturing 591,934 1.04 31,995 1.34 63,240 1.55 152,075 1.86

All wood-related 

manufacturing 1,077,396 1.89 51,279 2.14 99,044 2.43 229,900 2.82



305

chapter 12. Employment and Income Trends and Projections for Forest-Based Sectors in the U.S. South

(Woods 2009). Any recovery from the recession is masked by

overall declines in this sector. Downscaling these forecasts

to the South resulted in projected declines of 26 percent in

jobs and 14 percent in output (fig. 12.15). This larger decline

in jobs in both the United States and the South in the paper

sector represents expectations of continued technical change

involving the substitution of capital for labor.

Forest-Based Recreation

Forests in the South are used for a variety of recreational

opportunities, ranging from whitewater kayaking to nature

study. Forest-based recreation is not recorded as a single

economic sector, but is instead part of several sectors,

including transportation, entertainment, accommodations,

sporting goods manufacturing, and eating and drinking

places. This is also true for overall travel and tourism,

where expenditures are recorded in other sectors, but then

consolidated and tracked nationally as a separate account

(Griffith and Zemanek 2009, Kern and Kocis 2007). Forestbased recreation is expected to be only a part of this much

larger travel and tourism sector.

In a separate chapter in this study, Bowker and others

(2013) developed projections for forest-based recreation

participation by southern residents. They concluded that per

capita forest-based recreation would stay fairly constant for

most activities, while declining slightly for hunting, fishing,

and motorized off-road activities. This contrasts with the

projections for national forests, where the per capita rate

of recreation visits declines for all activities. Their chapter

also shows that national forest per capita recreation visits

are expected to decline more for overnight and general

visits than for day use developed visits. Further, they

conclude that recreation pressures are likely to increase

proportionately more near urban areas. A separate study

shows that expenditures per visit per party are three to

four times higher for overnight visits than for day visits,

and are 40 to 80 percent higher for non-local visits (Stynes

and White 2006). These results, taken together, provide

some, although weak, support for somewhat lower per

capita expenditures on forest-based recreation in the South,

resulting in forest-based recreation becoming a smaller

part of total travel and tourism expenditures in the future.

Expenditures, and thus output, are expected to grow,

however, although below the rate of population growth,

which will reduce the percentage of the economy deriving

from forest-based recreation.

Alternatively, we could assume that the projected demand for

forest-based recreation aligns with the demand for all travel

and tourism and that the South follows the national trends.

In this case, forest-based recreation total industry output in

the South would increase slightly through 2018. Even this

small increase in output, however, will likely not keep jobs

in tourism, and thus in forest-based recreation, from rising at

a rate slower than the rate of rise in output. Even the servicebased sectors in travel and tourism have experienced, and

are expected to continue to experience, technical change

that reduces the jobs even if output were to stay the same.

Franchising, low-service accommodations, self- and lowservice restaurants, and central offices for management

and marketing are all expected to reduce labor demand in

the travel and tourism sectors (Griffith and Zemanek 2009,

Woods 2009).

Bioenergy

Small amounts of wood are currently used to produce liquid

transportation fuels or electricity in the South, although

there is a potential for significant increases in one or both of

these uses of southern wood in the near future, depending on

policies and markets. In this chapter, we discuss the potential

for changes in jobs and output that might result from an

increase in the use of wood for energy and previous literature

in this area.

Introduction of policies to reduce the amount of carbon

in the atmosphere (including Federal and State renewable

fuel standards and renewable electricity standards) or the

imposition of a carbon tax or carbon cap-and-trade could

shift production of energy to renewable sources, including

wood. Thus, economic theory would imply that output

and jobs in the conventional liquid fuels and conventional

electricity sectors would decrease, with an offsetting increase

in output and jobs in the bioenergy sectors, all other things

held constant. This means that increases in the demand for

non-renewable energy could lead to increases in output and

jobs in the conventional liquid fuels and electricity sectors,

provided that technology is held constant. If markets fail to

account for the costs of carbon disposal to the atmosphere

and if all other aspects of the economy are held constant, we

expect that any imposition of new standards and regulations

would cause an overall decrease in output and jobs in the

economy (Huang 2010).

All of the studies conducted to date indicate that economic

activity (including output and jobs) will increase in the

logging sector. These increases are likely at the expense of

jobs and output in the coal-mining sector, which is often

excluded from the smaller regional analyses (English and

others 2009, Faaij and others 1998, Gan and Smith 2007,

Hodges and others 2010, Perez-Verdin and others 2008).

Depending on the variations in wage rates and in full-time/

part-time employment rates, net jobs may be increased

or decreased slightly as a result of bioenergy-feedstock
306 The Southern Forest Futures Project

procurement policies. In the bioenergy sector, jobs and

output are expected to increase, with a corresponding

decrease in jobs and output in the conventional energy sector

(Huang 2010, Hodges and others 2010, Winston 2009).

Previous studies of the conversion to bioenergy typically

use either a computable general equilibrium model or an

input-output model to evaluate the impacts on jobs and

output. Input-output models are simple and rich in data,

providing a snapshot of the economy and clearly illuminating

the linkages among sectors in the system. Computable

general equilibrium models, often using the data developed

for the input-output models, are more complex and can

provide either a snapshot or a dynamic view of the economy.

Although they have the advantage of allowing input

substitution to adjust over time, their complexity often makes

explaining results and outcomes difficult.

In the development of input-output and computable general

equilibrium models, the designation of the regions of

importance has a significant effect on outcomes: the smaller

the region the greater the likelihood of excluding areas

where losses would occur, while including areas where

gains would occur. Input-output models may also overstate

impacts because dynamic adjustment is not part of the

modeling framework. English and others (2009), Faaij and

others (1998), Gan and Smith (2007), and Perez-Verdin and

others (2008) all conducted studies in States, regions, or

countries without coal, and thus do not address the negative

effects on coal mining. Many of these studies also fail to

account for the negative effects of bioenergy production

on the conventional electricity sector. Only English and

others (2009) address the negative effects that a utility rate

increase would have on households. Forecasts of increased

economic activity from the conversion to bioenergy result

from some or all of the following: (1) large multiplier effects

from increases in bioenergy feedstock production, as well as

increases in power and fuel production; (2) smaller multiplier

effects from costs to households; (3) analysis of small regions

that may not fully capture effects on sectors such as coal

mining; and (4) excluding the coal feedstock, conventional

power and fuel sectors. The incomplete nature of these

analyses limits their usefulness in evaluating economy-wide

effects of a conversion to bioenergy or indeed, any renewable

energy, from conventional energy.

Computable general equilibrium models typically include

the effects on households (increasing utility and fuel costs),

conventional energy providers, wood-products companies

(increasing wood costs), and bioenergy providers (Hodges

and others 2010, Huang 2010, Winston 2009). These

studies predict losses to conventional energy providers and

households, gains to bioenergy providers, and varying effects

on the wood-products sector. One reason for the discrepancy

in the wood-products predictions may be Huang’s (2010)

assumption of a large increase in biofuels, which may exceed

the model’s ability to correctly represent the sectors. Huang

provides no explanation for the counterintuitive results that

sawmill output and jobs increase when cellulosic ethanol

production increases, or that jobs decrease and output

increases for woody electricity with implementation of the

bioelectricity policies. Hodges and others (2010) show small

increases in economic activity while Huang shows small

decreases. As these studies use the same model and data,

one possible explanation for discrepancies is the geographic

scale of their analyses as Huang analyzes the southeast while

Hodges and others analyze only Florida.

The most complete studies, Hodges and others (2010)

and Huang (2010), indicate small future changes overall

(reductions in conventional sectors and increases in the

bioenergy sector) with the changes occurring in the power

sectors. For the South, economic theory would imply an

increase in logging jobs and output, which may be offset

at larger regional and national levels by declines in coal

production and transport but would nonetheless provide

increases in local jobs and income. Depending on the

specific policies implemented, competition for wood between

traditional wood-using companies and bioenergy companies

may increase wood costs and thus decrease jobs and output

in the traditional sectors, although these changes will likely

be masked by larger structural changes in the wood products

and paper manufacturing sectors. Finally, a shift to bioenergy

on a large scale would require the construction of facilities

with accompanying growth, albeit temporary, in jobs and

income. It is unclear how much of this construction will

substitute for decreases in construction and/or maintenance

of conventional energy facilities.

Discussion and Conclusions

The future of forest-related jobs and income in the South is

uncertain. Forecasting is complicated by large recent changes

in these sectors, combined with the effects of the 2007–09

recession and the potential for bioenergy. The logging sector

is expected to respond to changes in the demand for timber

products at paper mills, sawmills, and bioenergy plants.

Unknowns include how the evolution to a more highly

mechanized and less family-firm oriented sector will affect

timber production. Shortages of workers have been noted in

Maine, although contract loggers from Canada have readily

filled the void. Concern is frequently voiced, but shortages

have not been documented.

The wood products manufacturing sector, which includes

sawmills, is expected to recover to pre-recession levels of

output, although jobs per unit of output is likely to continue

to decline due to technical change, which will influence

overall sector employment. Beyond the next decade, we do

not know precisely how wood will continue to be used in

housing, or how technical change will affect the production

process.
307

chapter 12. Employment and Income Trends and Projections for Forest-Based Sectors in the U.S. South

The paper manufacturing sector is expected to continue to

contract slightly, even after recovering from the recession. A

reduction in fine paper production in the South and declining

overall demand for virgin paper are likely to reduce output.

And continued technical change is likely to further reduce

employment in this sector over the next decade.

Future changes in jobs, income, and output deriving from

forest-based recreation in the South will depend on changes

in the demand for recreation and the level of technical

change in the service sector. Recreation demand is a positive

function of population and income, so increases in these

factors would be expected to lead to increases in jobs and

income in the sectors that provide recreation services.

However, it is likely that forest-based recreation will increase

at rate slower than the rate of increase in population.

Considerable uncertainty surrounds the potential for wood

use in bioenergy, including the success of commercial

conversion technologies for cellulosic ethanol, policy

requirements for renewable energy, carbon emissions control

schemes, and even the future employment and output profiles

of specific activities such as co-firing and ethanol production.

Under standard economic theory, implementation of policies

to correct a nonmarket externality, such as unpriced carbon

emissions, would be expected to lead to short-run monetary

losses in an economy. By sector, an increase in wood use for

bioenergy could lead to (1) increases in logging accompanied

by decreases in coal mining, (2) increases in bioenergy

production accompanied by decreases in conventional energy

production, (3) decreases in household income because

of increases in electricity and fuel costs, (4) increases in

construction activity, and (5) a potential loss in traditional

wood products sectors if increased demand for timber results

in higher timber costs.

Knowledge and Inf ormation Gaps

Considerable information is lacking in the literature and

in the data that complicate the development of industry

and employment forecasts for the forest-based sectors. The

bioenergy sector is new and currently untracked in national

data as a distinct sector and thus is lacking historical data.

As in any developing industry, technologies and industry

structures are likely to change significantly over the next

decade. And this assumes the bioenergy sector does

develop—there is a chance this sector will not become a

major wood user. Interactions between existing sectors and

this new sector are also unknown.

Assessing the future of employment and output in the

forest-based recreation sector is likewise hampered by the

lack of data, although this is neither a new nor developing

sector. The only data available on forest-based recreation

are collected by individual land management agencies.

For example, the National Visitor Use Monitoring Survey

conducted by the U.S. Forest Service (Stynes and White

2006), collects information on recreational activities on

national forests only and a comparable study is not available

for private- and State-owned forest-based recreation.

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309

chapter 13. Forests and Water

	 Graeme Lockaby, Chelsea Nagy, James M. Vose,

	 Chelcy R. Ford, Ge Sun, Steve McNulty,

	 Pete Caldwell, Erika Cohen,

	 and Jennifer Moore Myers1

Key Findings

•	 Forest conversion to agriculture or urban use consistently

causes increased discharge, peak flow, and velocity of

streams. Subregional differences in hydrologic responses to

urbanization are substantial.

•	 Sediment, water chemistry indices, pathogens, and

other substances often become more concentrated after

forest conversion. If the conversion is to an urban use,

the resulting additional increases in discharge and

concentrations will produce even higher loads.

•	 Although physiographic characteristics such as slope and

soil texture play key roles in hydrologic and sediment

responses to land use conversion, land use (rather than

physiography) is the primary driver of water chemistry

responses.

•	Conversion of forest land to urban uses may decrease the

supply of water available for human consumption and

increase potential threats to human health.

•	 Increases in urbanization by 2060 in the Appalachians,

Piedmont, and Coastal Plain will increase imperviousness

and further reduce hydrologic stability and water quality

indices in the headwaters of several major river basins and

in small watersheds along the Atlantic Ocean and Gulf of

Mexico.

1Graeme Lockaby is a co-lead author and Associate Dean and Professor,

Auburn University School of Forestry and Wildlife Sciences, Auburn, AL

36849. Chelsea Nagy is a Graduate Research Assistant, Brown University,

Department of Ecology and Evolutionary Biology, Providence, RI 02912.

James M. Vose is a co-lead author and Project Leader, U.S. Department

of Agriculture Forest Service, Southern Research Station, Center for

Integrated Forest Science, College of Natural Resources, North Carolina

State University, Raleigh, NC 27695. Chelcy R. Ford is an Ecologist, U.S.

Department of Agriculture Forest Service, Southern Research Station,

Coweeta Hydrologic Laboratory, Otto, NC 28763. Ge Sun is a Research

Hydrologist, U.S. Department of Agriculture Forest Service, Southern

Research Station, Eastern Forest Environmental Threats Assessment

Center, Raleigh, NC 27606. Steve McNulty is a Supervisory Ecologist,

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

Eastern Forest Environmental Threats Assessment Center, Raleigh,

NC 27606. Pete Caldwell is a Research Hydrologist, U.S. Department

of Agriculture Forest Service, Southern Research Station, Eastern

Forest Environmental Threats Assessment Center, Raleigh, NC 27606.

Erika Cohen is a Resource Information Specialist, U.S. Department of

Agriculture Forest Service, Southern Research Station, Eastern Forest

Environmental Threats Assessment Center, Raleigh, NC 27606. Jennifer

Moore Meyers is a Resource Information Specialist, U.S. Department of

Agriculture Forest Service, Southern Research Station, Eastern Forest

Environmental Threats Assessment Center, Raleigh, NC 27606.

•	 On average, water supply model projections indicate that

water stress due to the combined effects of population and

land use change will increase in the South by 10 percent by

2050.

•	Water stress will likely increase significantly by 2050

under all four climate change scenarios, largely because

higher temperatures will result in more water loss by

evapotranspiration and because of decreased precipitation

in some areas.

•	 Approximately 5,000 miles of southern coastline are highly

vulnerable to sea level rise.

Introduction

Compared to all other land uses, southern forests provide

the cleanest and most stable water supplies for drinking

water, recreation, power generation, aquatic habitat, and

groundwater recharge (Brown and others 2008, Jackson

and others 2004, Sun and others 2004). Forests are unique

among land covers because they are long-lived and relatively

stable. However, they are subject to substantial structural

and functional alterations by management practices and/

or natural disturbances, the intensity of which determines

whether alterations are short or long-term. Water resources

in the South are at risk of degradation from a growing

population, continued conversion of forests to other land

uses, and climate change. Urban and agricultural lands can

impair water resources by introducing nutrients, sediment,

bacteria, and other pollutants to streams. Additionally,

altered hydrology—including higher peak flows, and lower

baseflows and hydroperiods (Amatya and others 2006)—is

common with forest conversion to other land uses. Together,

these changes can modify the habitat and consequently the

composition of aquatic (and riparian) communities.

Historical land use practices have dramatically changed

the landscape of the South. Soil erosion and sedimentation

were prevalent throughout the region during the period

of agricultural expansion in the 18th and 19th centuries.

Evidence can still be seen today in the sediment deposits

of floodplains. This massive topsoil erosion and depleted

soil productivity was followed by a period of agricultural

abandonment and reforestation throughout much of the

South. Today major land uses include forestry, agriculture,

Chapter 13.

Forests and Water



313

chapter 13. Forests and Water

HadCM3B2. It should be noted that climate models are

often calibrated to best address climate within a specific

geographic region (e.g., MIROC32A1B was specifically

developed for Japan). The application of any general

circulation model (GCM) will not be universally reliable

(i.e., accurately able to predict future climate). In this study

the MIROC32A1B predicts climatic conditions for the

Southern United States that are extreme for most parts of

the globe. Other models could be considered more moderate

in their predictions of future climate. The results of all for

model predictions are presented in this chapter but further

discussion of the GCM can be found in chapter 3.

Population and land use change data—The U.S. Census

Bureau (2001) records indicate that population increased

about 30 percent from 1980 to 2000. Population projections

at the census block level were aggregated to the 8-digit

HUC watershed scale for each year from 1967 to 2050 (NPA

Data Services Inc. 1999). For the WaSSI model simulation,

we selected the land use data (chapter 4) belonging to

Cornerstone A (Intergovernmental Panel on Climate Change

storyline A1B, level crop prices, high timber prices). For

model simulations, the land use classes described in chapter

4 were grouped into eight categories as: crop, deciduous

forest, evergreen forest, mixed forest, grassland, shrub land,

savanna, and water/urban/barren. In addition, the land use

data were rescaled from the county to the 8-digit HUC

watershed for model input. The representative year was

2000 for historic baseline model simulations, and 2050 was

selected for future model simulations.

Sea Level Rise

We used the analysis of Titus and Richman (2001) to identify

land area 1.5 m, 1.5 to 3.5 m, and >3.5 m above sea level

and generated 66 maps (based on 1-degree digital elevation

models) to outline coastal areas on the Gulf of Mexico and

the southern Atlantic States from Virginia to Florida. The

vulnerability of coastal regions (coastal vulnerability index,

or CVI) was calculated using the analyses of HammarKlose and Thieler (2001), who incorporated geomorphology,

coastal slope, rate of relative sea-level rise (mm yr-1),

shoreline erosion and accretion rates (m yr-1), mean tidal

range (m), and mean wave height (m) into calculations of

CVI for the Atlantic, Pacific, and Gulf of Mexico coasts. A

ranking was applied to each variable for ~5 km segments (or

3 minutes) of coastline and then combined to form an index

of risk using the following equation:

CVI =

Where a is geomorphology, b is coastal slope, c is relative

sea-level rise, d is shoreline erosion/accretion rate, e is mean

tide range, and f is mean wave height, and the final CVI was

broken into to four risk categories: low (<8.7), moderate

(8.7 to 15.6), high (15.6 to 20.0), and very high (>20.0).

Data sources used to calculate CVI included state geologic

maps and 1:250,000-scale topographic maps to determine

geomorphology; a combination of U.S. Navy ETOPO5 and

National Geophysical Data Center digital topographic and

bathymetric elevation databases to determine coastal slope;

National Ocean Service data to determine relative sea-level

rise and mean tidal range; a combination of the May and

others (1982) Coastal Erosion Information System dataset

and more recent State and local regional studies to estimate

shoreline erosion and accretion rates; and the U.S. Army

Corps of Engineers Wave Information Study to estimate

mean wave height (Hammar-Klose and Thieler 2001). After

all data were compiled and rescaled to a 5 km grid, each

variable was ranked from 1 (very low vulnerability to sea

level rise) to 5 (very high vulnerability to sea level rise).

Results

Physical Environment of the Southern Region

The southern climate is predominantly humid subtropical;

however, the western most areas, such as Texas and

Oklahoma, are semi-arid. The average annual temperature

range is 15 to 21 °C and the precipitation range is 1010 to

1520 mm yr-1 (Bailey 1980). Ultisols, the predominant

soil order of the South, are strongly leached and nutrient

poor with a subsurface accumulation of clay (Bailey 1980,

USDA Natural Resource Conservation Service 2009). The

relief is mostly level along much of the Atlantic and Gulf

of Mexico, however, the upper Coastal Plain of Alabama

and Mississippi is moderately to gently rolling (Martin

and Boyce 1993). Coastal Plain soils are sandy. The

Piedmont has gently rolling to steep terrain with clayey

surface and subsurface soils. Consequently, the potential

for erosion is high throughout the Piedmont and even

higher toward the Blue Ridge subregion of the Southern

Appalachians (Trimble 2008). The Southern Appalachians

have steep topography and elevation ranges from 225 to

900 m. The three major river basins of the South are the

Mobile, Tennessee, and Cumberland (World Wildlife Fund

2010). Southern streams support a diversity of freshwater

species and are thus a high conservation priority (World

Wildlife Fund 2010). Physiographic subregions, and the

landscape components of watersheds within them, are

connected through the flow of energy and materials,

the movement of species, and the movement of insects,

disease, and other disturbance agents. Unlike many of

the exchanges, the movement of water across most of the

South is fairly predictable because water follows hydrologic

flowpaths that are primarily driven by elevation gradients.

Exceptions occur in the lower Coastal Plain and other

√ a*b*c*d*e*f

6 ( )
314 The Southern Forest Futures Project

systems where hydrology is dominated by groundwater

hydrology. Understanding how changing landscapes will

alter the quantity, quality, and value of surface water and

groundwater requires analyses at expanding spatial scales

to examine how rapid urbanization affects forest practices

such as cutting, road building, and drainage.

Functions of Forested Wetlands and 

Riparian Forests

Forested wetlands can be described by hydrogeomorphic

considerations such as landscape position, water source,

and hydrodynamics are dominant process regulators

(Ainslie 2002). The three most common classes of southern

forested wetlands are riverine, depressional, and flat with

mineral or organic soil (table 13.3). In general, forests and

hydrological cycles are connected through the processes of

evapotranspiration (Amatya and others 2008). Hydrological

functions of southern forested wetlands may include flood

mitigation or short-term surface water storage; and to a

lesser extent than forested wetlands in other regions of the

United States, they abate storms and recharge groundwater

(National Research Council 1995, Walbridge 1993).

Biogeochemical functions of wetlands, including cycling of

elements and retention and removal of dissolved substances,

serve to improve surface, subsurface, and ground water

quality (Blevins 2004, National Research Council 1995).

Regardless of type, all forested wetlands contribute to food

web maintenance by providing habitat for plants and animals

(Faulkner 2004, Walbridge 1993). Some forested wetlands

may provide unique functions based on their distinctive

characteristics and structure. For example, Carolina Bays

may contain rare and endangered plants and also provide

desirable breeding sites and habitat for birds and wildlife

(Ainslie 2002). And mineral soil flats can have very high

herbaceous species richness in part because of their unique

fire regime (Ainslie 2002).

Riparian forests also provide hydrological, biogeochemical,

and habitat functions. Many studies have shown that riparian

forests help to stabilize stream banks and trap pollutants such

as sediment, nutrients, bacteria, fertilizers, and pesticides

from runoff (Anderson and Masters 1992, Binkley and

Brown 1993, de la Crétaz and Barten 2007, Klapproth and

Johnson 2000, Naiman and others 2005, USDA National

Agroforestry Center 2008, Vellidis 1999). In particular,

Naiman and others 2005 found that “The hydraulic

connectivity of riparian zones with streams and uplands,

coupled with enhanced internal biogeochemical processing

and plant uptake, make riparian zones effective buffers

against high levels of dissolved nutrients from uplands and

streams, while geomorphology and plant structure make

them effective at trapping sediments.” However, an intact

riparian corridor does not ensure stream protection as this

relationship is dependent on other factors including residence

time of pollutants in the buffer, depth and variation of water

table, upland land use practices, climate, and watershed

characteristics such as topography, hydrology, soils, and

vegetation (de la Crétaz and Barten 2007, Groffman and

others 2003, Tomer and others 2005, Walsh and others 2005).

Habitat functions provided by riparian forests include lower

water temperatures for aquatic animals due to shading from

trees, along with shelter for birds and wildlife (Anderson and

Masters 1992, Binkley and Brown 1993, Naiman and others

2005, Vellidis 1999). Riparia are sources of large woody

debris, which creates habitat heterogeneity, acts as a substrate

for colonization, and provides nutrients to the aquatic (and

Table 13.3—Southern wetland types and characteristics

Wetland type Characteristics Sources

Riverine 

(bottomland 

hardwood 

wetlands)

Occurring in floodplains or riparian corridors

Ainslie 2002, Brinson 1993, Palmer 

1994, Childers and Gosselink 1990, 

National Research Council 1995, 

Naiman and others 2005, Walbridge 

1993, Meyer 1992, Dennis 1988

Many connections between wetland and stream channel 

(overbank flow and subsurface connections)

Including cypress stands, sloughs, and hardwood swamps 

associated with brown-, black-, and red-water streams in 

Atlantic and Gulf Coastal Plains

Depressional

Named for their depressional topography which promotes 

surface water accumulation

Ainslie 2002, Brinson 1993, Duryea 

and Hermansen 1997, Dennis 1988 Cypress trees predominant

Including cypress domes (Gulf Coastal Plain) and Carolina 

bays (Atlantic Coastal Plain)

Wet flats

Can have either organic soils (pocosins) on plateaus or 

mineral soils in the Atlantic Coastal Plain between rivers or 

floodplain terraces Ainslie 2002, Brinson 1993, 

Pocosins characterized by dense evergreen shrub vegetation Gresham 1989

Mineral flats characterized by a closed canopy of hardwoods 

or an open savannah with some pines
315

chapter 13. Forests and Water

riparian) community (Naiman and others 2005). Inputs of

organic matter from riparian forests supply an allochthonous

energy source to stream ecosystems, thereby linking

the riparian and aquatic foodwebs. Additionally, if Best

Management Practices are implemented appropriately, riparian

forests can also provide wood products, pasture for livestock,

and recreational opportunities (Anderson and Masters 1992).

Hydrologic Effects of Forest Conversion 

to Other Land Uses

Harvesting forests reduces evapotranspiration and

infiltration; creating impervious surfaces increases

overland flow (Paul and Meyer 2001). Similarly, forest

conversion to agricultural land may compact soils, reduce

evapotranspiration and infiltration, and increase overland

flow. Regardless of post-harvesting use, characteristic

changes in hydrology following forest removal include

greater streamflow and peak flows (Bosch and Hewlett 1982,

Crim 2007, de la Crétaz and Barten 2007, Hibbert 1967,

McMahon and others 2003, Schoonover and others 2006).

Representative hydrographs for typical forested, agricultural,

and urban watersheds are shown in figures 13.2 to 13.4. A

study of the 13 Southern States showed that streamflow

increased by 69 to 210 mm yr-1 following forest harvesting

(Grace 2005). Stednick (1996) found that a 20 percent change

in forest cover produces a quantifiable change in water yield

in the Appalachians, but that the threshold is about

25 percent higher for the Piedmont and Coastal Plain. Since

the 1970s in Houston, impervious surfaces were responsible

for 32 percent of the 159 percent increase in peak flows,

and 77 percent of the 146 percent increase in annual runoff

(Olivera and DeFee 2007). Similarly since the 1960s in

the White Rock Creek watershed of northeastern Texas,

peak flows increased by 20 to 118 percent with varying

precipitation intensities in response to dramatic increases

in impervious cover (Vicars-Groening and Williams 2007).

Stream hydrographs of urban watersheds reflect a flashy

hydrology with greater pulses and faster attainment of

peak flows during storm events (Beighley and others 2003,

Boggs and Sun 2011, Calhoun and others 2003, Crim 2007,

Schoonover and others 2006). However, as arid regions

naturally have flashy hydrology due to inherent precipitation

regimes, urban effects may be obscured on the hydrographs

of streams in those parts of the South (Grimm and others

2004). Flow-duration curves also depict changes in

hydrology by displaying the percentage of time streamflow

equals or exceeds a particular value. The pre-urbanization

flow duration curve exhibits more gradual variations while

the post-urbanization curve is much steeper (fig. 13.5).

In urban and agricultural watersheds, decreased infiltration

produces less groundwater recharge, possibly reducing

baseflows (Calhoun and others 2003, Rose and Peters 2001,

Wang and others 2001). As an example, in tributaries of

Figure 13.2—Representative hydrograph of a forested watershed (Crim

2007). Discharge units are liters per second per ha.

Figure 13.3—Representative hydrograph of a pastoral watershed (Crim

2007). Discharge units are liters per second per ha.

Figure 13.4—Representative hydrograph of an urban watershed (Crim

2007). Discharge units are liters per second per ha.
316 The Southern Forest Futures Project

the upper Chattahoochee River, Calhoun and others (2003)

estimated that every 1 percent increase in impervious surface

reduces baseflow by 2 percent. However, this is not always

the case as illustrated by the lack of baseflow response to

increases in impervious surface in the Florida Panhandle

(Nagy and others 2012); and by higher median baseflow in

pastoral watersheds compared to forested watersheds in the

Georgia Piedmont (Schoonover and others 2006). The less

responsive baseflow in the Coastal Plain may be explained by

differences between the extent of surface water and baseflow

recharge zones in very flat terrains where baseflow zones

may extend beyond surface catchment boundaries. In the

case of the pastoral vs. forested watersheds in the Piedmont,

apparently reduced ET and adequate surface infiltration rates

in the pastures accounted for the higher baseflows there.

Although historically the South has not experienced a great

deficiency of water supply compared to other regions in the

United States, with continued forest loss and expanding

urbanization, water supply may become a more pressing

issue in this region. When modeling land use effects only,

Sun and others (2008) predicted reduced water deficits due

to increased water yield following conversion of forest to

urban land uses. However, they found that water resources

would likely be under greater pressure in the future when

the effects of climate change and population growth are

also taken into account. Additionally, it should be noted

that despite increased streamflow following forest removal,

available surface water might decline due to unstable flow

regimes. After increasing withdrawal rates from Alabama

streams near Birmingham, surface water available for

human use ranged from about 20 to 45 percent of discharge

for urban watersheds and 20 to 60 percent for forested

watersheds; and at higher withdrawal rates water availability

was significantly higher in forested than urban watersheds.2

Therefore, although total water yield is often reduced in

forested watersheds compared to urban watersheds, forested

watersheds may have a greater percentage of water available

for use, suggesting that increasing urbanization contributes

to greater stress. Lastly, degradation of water quality from

point- and non-point source pollution can also reduce the

amount of available water (Sun and others 2008).

Effects of Forest Conversion on Sediment 

Forests stabilize soils (Jackson and others 2004); therefore soil

is more readily eroded following removal of vegetation, and

is transported as sediment into floodplains and other areas of

lower topography (Jackson and others 2005a, Trimble 2008)

and/or directly into stream channels. The effects of historical

agricultural use, in particular row-crop agriculture, on soil

erosion and subsequent sediment deposition throughout the

South were profound (Casarim 2009, Jackson and others

2005a, Trimble 2008). For example, in the Georgia Piedmont,

sediment deposition from historical agriculture was as much

as 1.6 m in the Murder Creek floodplain (Jackson and others

2005a) and averaged 1.8 m in Bonham Creek and Sally

Branch watersheds (Casarim 2009). It can be difficult to

differentiate sediment contributions from current land use

versus historical agricultural use within a watershed because

the legacy effects of historical land use can be observed

decades later. Jackson and others (2005a) estimated that 25

to 30 percent of the sediment load of Murder Creek consisted

of re-suspended legacy sediment. This means that land use

conversions in the Piedmont have the potential to re-suspend

legacy sediment that accumulated in the stream beds decades

ago in addition to generating sediment export from terrestrial

sources.

The combined effects of altered hydrology, removal of

vegetation, and an increase in impervious surface often cause

urban watersheds to exhibit stream sediment concentrations

much higher than forested watersheds (Clinton and Vose

2006, Lenat and Crawford 1994, Schoonover and others

2005). In the Southern Appalachians, total suspended

solids concentrations were 4 to 5 times greater in an urban

2L. Kalin, unpublished data; Auburn University, School of Forestry and

Wildlife Sciences, 602 Duncan Dr., Auburn, Alabama, 36849; kalinla@

auburn.edu.

Figure 13.5—Stream flow-duration curves in cubic feet per second before

and after urbanization. (Source: L. Kalin, unpublished data; Auburn

University, School of Forestry and Wildlife Sciences, Auburn, AL, 36849;

latif@auburn.edu)
317

chapter 13. Forests and Water

compared to a reference stream (Clinton and Vose 2006). In

the Georgia Piedmont, total dissolved solids concentrations

were twice as high in urban streams compared to forested

streams (Crim 2007, Schoonover and others 2005), but total

suspended solids did not increase significantly under urban

cover. In the Coastal Plain, Wahl and others (1997) found a

twofold increase in total suspended solids in urban compared

to forested streams (up to 200 mg/L during stormflow in the

urban stream). Erosion associated with urban land uses can

be particularly high at construction sites and areas of new

development (Novotny 2003, Paul and Meyer 2001). For

example 100- to 10,000-fold increases over nondeveloped

conditions were reported by Paul and Meyer (2001),

especially in areas with greater topographic variation such as

the Southern Appalachians.

Effects of Forest Conversion on Water Chemistry

Undisturbed forested watersheds are generally associated

with low stream-water concentrations of most ions. Since

most forests are deficient in one or more elements, forested

systems are generally effective in retaining inputs of

nutrients. Consequently, net export of macronutrients, or

nutrients required in large quantities such as N, P, and K,

from undisturbed forested catchments is often negative,

indicating an accretion of forest biomass (Likens and

Bormann 1995, Swank and Douglass 1977). As an example,

the 3600 ha Table Rock Reservoir watershed in the Southern

Appalachians of South Carolina has been highly restricted

in terms of any human activity since 1930, and water quality

there remains unchanged since that time.3

Increased nutrient concentrations and loads have been

observed in urban and agricultural streams compared to

forested streams. Excess nutrients may arise from fertilizers,

wastewater effluent, and industrial waste in urban areas;

and from animal waste and fertilizers in agricultural areas.

Concentrations of NO3

-

, Cl-, Na+, K+, Ca2+, and Mg2+ were

all higher in an urban stream than a reference stream in the

Southern Appalachians during baseflow and stormflow both

(Clinton and Vose 2006). Nitrates had the most pronounced

increase with mean concentrations of about 0.01 mg/L in the

reference stream and 0.7 mg/L in the urban stream (Clinton

and Vose 2006). However, ammonium concentrations were

higher in the forested stream during stormflow. Bolstad and

Swank (1997), working in the same physiographic subregion,

found no increases in nitrate, ammonium, or phosphate

concentrations during baseflow as urbanization indices

increased. However, during stormflow, slight increases were

noted for nitrate (from 0.05 to 0.07 mg/L with a significant

regression relationship) and ammonium.

3 Okun, D.A. 1992. Properties of the Table Rock and Poinsett Reservoirs:

their future. Unpublished report. 24 p. On file with: the Greenville

Watersheds Study Committee, P.O. Box 728, Greenville, SC 29602.

In the Georgia Piedmont, Schoonover and Lockaby (2006)

found results similar to those of Clinton and Vose (2006)

for dissolved organic carbon, NO3

-

, Cl-, and K+, as well

as SO4

2- when comparing streams with <5 percent and

>24 percent impervious surface. The concentrations in

streams of watersheds with >24 percent impervious surface

were generally two-to-four times higher than those of less

developed catchments. For instance, median baseflow nitrate

concentrations were 0.61 mg/L for streams with <5 percent

impervious surface and 1.64 mg/L for streams with

>24 percent impervious surface; stormwater concentrations

were 0.36 and 1.93 mg/L respectively for the same

comparison (Schoonover and Lockaby 2006). Although

ammonium concentrations have been reported to be higher

in forested than in urban streams (Clinton and Vose 2006,

Tufford and others 2003), two Piedmont studies produced

different results (Crim 2007, Schoonover and Lockaby 2006).

Similarly, dissolved organic carbon concentrations are often

higher in forested watersheds than in urban streams (Wahl

and others 1997) although there are exceptions (Schoonover

and Lockaby 2006).

Unlike most studies of land-use/land-cover impacts on

water quality, which have substituted space for time, Weston

and others (2009) evaluated water quality changes within

the Altamaha River Basin of the Georgia Piedmont for

more than 30 years. The increases in population during

that period exceeded 100 percent in some of the basin’s

watersheds. During that period, agricultural land use

declined as populations rose, producing decreases in stream

ammonium and organic carbon concentrations but increases

in total nitrogen and nitrogen oxide concentrations and

loads. Phosphorus concentrations did not increase with

urbanization, which the authors suggest may reflect the

elimination of phosphates in detergents after 1972. They

also suggest that for the Piedmont, elevated total nitrogen

and nitrogen oxides may serve as water quality signatures

of urbanization and elevated ammonium, and that organic

carbon may be associated with agriculture. Ammonium and

organic carbon are also often linked with forest cover but the

effects of changes in forest cover were beyond the scope of

this chapter.

There are fewer studies of land-use/land-cover associated

with the Coastal Plain than with the Piedmont. Wahl and

others (1997) compared water quality within two coastal

watersheds: one with increasing urbanization (18 percent

impervious surface) and the other with predominately forest

cover (no impervious surface). Nitrate was consistently

higher in the urban stream: 130 ug/L in winter (90 ug/L in

summer) versus 42 ug/L in winter (29 ug/L in summer).

Ammonium was higher in the forested stream regardless of

season (159 ug/L versus 70 ug/L) as were dissolved organic

carbon concentrations (27 ug/L versus 13 ug/L). In a study

within the Florida Panhandle (Nagy and others 2012),
318 The Southern Forest Futures Project

median concentrations of nitrate, ammonium, calcium,

potassium, and sulfate were higher in watersheds with

more urbanization (impervious surface up to 16 percent)

than in their forested counterparts. However, nitrate

concentrations in the Coastal Plain were well below those

generally observed in some studies of urban watersheds

of the Piedmont (0.35 mg/L versus 1.78 mg/L). Median

concentrations of total phosphorus were high and increased

from 0.31 mg/L in watersheds with <5 percent impervious

surface to 0.43 mg/L in watersheds with >10 percent

impervious surface. Similarly, total suspended solids

increased from 1.50 to 2.40 mg/L for impervious-surface

levels above 10 percent. In contrast, dissolved organic carbon

declined from 36 mg/L in watersheds with low impervious

surface to 30 mg/L in watersheds with >10 percent

impervious surface. Tufford and others (2003) found that

total phosphorus was significantly higher in urban streams

than forested streams in the Coastal Plain (concentrations of

roughly 0.06 mg/L versus 0.03 mg/L).

In general, increases in stream concentrations of several

elements within urbanized watersheds are very common

although the magnitude of increase and sometimes the

particular ions involved vary considerably within and among

physiographic subregions. While nitrate and potassium

ions commonly increase (probably due to their mobility

in water), responses of total potassium or phosphate are

much more variable and may not occur at all. Responses

of the other major elements fall in between those of

nitrate and phosphate. Since discharge usually increases

with urbanization, loads increase as well regardless of

physiographic subregion. Consequently, there do not seem

to be clear distinctions among subregions in terms of stream

chemistry responses, which may indicate that the influence

of land use overrides that of physiography in the South.

Higher loads of base cations (K+, Ca2+, and Mg2+), Cl-,

and total nitrogen were found in agricultural streams than

in forested streams in the Coastal Plain (Lowrance and

others 1985). Increased nitrate concentrations and loads

in agricultural streams compared to forested streams are

common in the Appalachians (Hagen and others 2006),

Piedmont (Crim 2007), and Coastal Plain (Lehrter 2006,

Lowrance and others 1985). For example, nitrate loads

were 1.5 to 4.4 times higher in watersheds with greater

agricultural land than watersheds with less agricultural

land (Lowrance and others 1985) and nitrate concentrations

were 2.1 to 4.4 times higher in agricultural versus forested

watersheds (Hagen and others 2006).

Effects of Forest Conversion on Human Health 

Urban and agricultural land uses contribute to increased

bacterial concentrations in stream waters. Connected

stormwater and sewer overflow systems or failures in

sewer systems (such as broken pipes, mechanical failures,

and blockages from tree roots) can directly or indirectly

release raw sewage into surface waters. Additionally, pet

and wildlife feces can be transported in runoff over lawns

and impervious surfaces in urban areas. Fecal coliform

bacteria counts were higher in urban than forested or

reference streams in the Coastal Plain (DiDonato and

others 2009, Holland and others 2004, Mallin and others

2000), Piedmont (Crim 2007, Schoonover and others 2005),

and Southern Appalachians (Clinton and Vose 2006). For

example, Mallin and others (2000) report that fecal coliform

is highly correlated with impervious surface (r=0.975,

p=0.005), percent development (r=0.945, p=0.015), and

population (r=0.922, p=0.026). Similarly, concentrations

of E. coli can be much greater in urban watersheds than

forested watersheds (Crim 2007, Mallin and others 2000).

Urban watersheds in the Georgia Piedmont had the highest

median E. coli concentrations (as measured in MPN or most

probable number), ranging from 135 to 1255 MPN/100 mL,

compared to median ranges of 94 to 169 MPN/100 mL for

pine covered watersheds and 59 to 170 MPN/100 mL for

oak-pine covered watersheds (Crim 2007). Becker (2006)

reported that residential areas (2.2 percent of the watershed)

did not appear to be a source of bacteria to Travertine Creek

subbasin in Oklahoma, but attributed increased bacterial

concentrations in the nearby Rock Creek basin to livestock

grazing and sewage effluent in periods of high precipitation.

Andrews and others (2009) reported fecal coliform counts as

high as 10,000 colonies/100 mL with the highest counts in

stormflow for the portions (42.17 percent) of the Illinois River

basin in Oklahoma and Arkansas that are in agricultural

use (pastures for cattle and confined feeding operations

for poultry and swine); they also observed rapid urban

development on the upper portion of this basin that may

further impair water resources.

In addition to the danger of elevated concentrations of

bacteria in surface waters, other health risks from urban

and agricultural land uses include metals, pesticides, and

personal care products (Klapproth and Johnson 2000, Paul

and Meyer 2001). Trace metal sediment concentrations can

be 2 to 10 times higher in streams near urban and industrial

areas than in forested watersheds or suburban watersheds

(Holland and others 2004); they tend to accumulate, rather

than degrade, over time in sediments and plant and animal

tissue (Klapproth and Johnson 2000). Metal concentrations

may be inversely related to sediment particle size (Paul and

Meyer 2001) and thus we might expect high concentrations of

metals to be more problematic in the Southern Appalachians

or Piedmont with more silty and clayey soils than in the

Coastal Plain. Pesticides enter streams through runoff from

agricultural and urban areas (Klapproth and Johnson 2000,

Paul and Meyer 2001) again underscoring the importance of

riparian buffers and other forested areas in slowing runoff

and enhancing infiltration before contaminants can reach
319

chapter 13. Forests and Water

the stream. Personal care products including deodorants,

perfumes, and pharmaceuticals may not be removed by

traditional water treatment methods and are not as widely

regulated as other substances (Kolpin and others 2002).

Effects of Forest Conversion on Aquatic 

Communities

Altered hydrology and channel-morphology and higher

stream temperatures caused by forest conversion can

dramatically affect aquatic communities. With increasing

urban and/or agricultural uses, species richness and

abundance often decline as reported for algae (Sponseller

and others 2001), macroinvertebrates (Helms 2008, Lenat

and Crawford 1994, Maloney and Feminella 2006, Paul and

Meyer 2001, Roy and others 2003), fish (Onorato and others

1998, Walsh and others 2005), and amphibians (Houlahan

and Findlay 2003, Orser and Shure 1972, Price and others

2006, Wang and others 2001). These effects may be offset

somewhat for algae by additional stream nutrients (Biggs

1996, Chessman and others 1999) and for macroinvertebrates

by perennial flows (Chadwick and others 2006). Mussels

have virtually disappeared from some southern streams as a

result of increased conversion of land to urban uses (Gangloff

and Feminella 2007, Gillies and others 2003). These

detrimental effects on aquatic organisms are particularly

evident in the Southern Appalachians (Scott and others 2002,

Walters and others 2003) where both diversity and endemism

are very high (Wallace and others 1992). Cuffney and

others (2010) found a high correlation of macroinvertebrate

assemblages with urban metrics in eastern metropolitan

areas (including Raleigh, NC; Atlanta; and Birmingham,

AL), but not in central metropolitan areas such as DallasFort Worth. Additionally, species composition may shift

as sensitive species are replaced by more tolerant species

or species better suited for the new conditions (Lenat and

Crawford 1994, Onorato and others 1998, Price and others

2006, Roy and others 2005, Sutherland and others 2002,

Walters and others 2003, Weaver and Garman 1994). For

example, in urban streams of the western Georgia Piedmont,

reptile species richness increased at the same time that

the richness of salamanders and other amphibian species

decreased (Barrett and Guyer 2008).

Fish communities may also be strongly affected by changes

in hydrology and water quality that are derived from land

use changes. Higher velocities and discharge as well as

increased sediment loads may degrade stream habitat to

a significant degree (Nagy and others 2011). In particular,

higher deposition of sediment in stream channels may reduce

diversity of habitat with negative implications for some fish

species. Reduced abundance of benthic feeders and lower

spawning success in general may accompany such changes

(Nagy and others 2011). In addition, near Columbus, GA,

Helms and others (2005) noted indicators of reduced fish

health such as occurrence of lesions and tumors in fish from

urban streams and a negative correlation between biotic

integrity of fish and the proportion of impervious surface

within watersheds.

Implications of Land Use Change Projections 

on Water Quality

The decreases in forest cover and increases in urbanization

that are projected by 2060 carry important implications

for water resources in the region. Losses of forest cover

across much of the Piedmont of North Carolina, South

Carolina, Georgia, and, to a lesser extent, Alabama (chapter

5) imply that further degradation of water quality and

destabilization of surface water hydrology are likely in

localized catchments within that subregion. In addition, it

is likely that the alterations in the hydrologic cycles within

the headwaters of major river basins (fig. 13.6) will affect

conditions downstream. River basins and watersheds will

undergo reductions in evapotranspiration due to lower leaf

area indices as well as increases in impervious surfaces.

Consequently, responses to deforestation—reduced

infiltration, increased runoff, reduced baseflow, and

increased discharge and velocity—that already exist on the

Piedmont to some extent (but are more often associated with

steeper terrains) will likely be exacerbated.

These hydrologic responses, combined with the increased

quantities of potential pollutants in urbanizing watersheds,

will increase streamwater concentrations and/or loads of

sediment, nutrients, pathogens, and various chemicals. The

result could be significant degradation of water quality

within river basins and stream systems, and concurrent

negative effects on diversity of aquatic organisms. Although

responses will be manifested throughout river basins,

cumulative effects will magnify the trends along their lower

reaches. If streams remain connected to the floodplain forests

that lie below the physiographic fall line, some fraction of

pollutant loads may be filtered as sediment is deposited by

spreading floodwaters. However, although upper coastal

forests are projected to be cleared to a lesser extent than

Piedmont forests, they remain at some risk of conversion

with subsequent reduction of pollutant filtration potential on

floodplains. In addition, the increased velocity of streams and

rivers draining highly urbanized upper reaches will tend to

increase channel incisement, thereby reducing the filtering

benefits of overbank flooding and sediment deposition.

Although reservoirs created by dams may trap significant

amounts of sediment and other substances, they have a

finite capacity for sediment filling, which is already being

approached in some areas. Consequently, loads of sediment,

nutrients, and pathogens will likely increase in lower reaches

of river basins, with exports of these materials expected to

elevate levels in coastal estuaries as well.

321

chapter 13. Forests and Water

forest land (15 to 27 million acres) that would be required

to substantially reduce nonpoint source pollutant exports

(Mitsch and others 2001). Increased forest coverage will

also be characteristic on many inland Coastal Plain areas

of Virginia, North Carolina, South Carolina, Georgia,

Alabama, Mississippi, and Louisiana (chapter 5), a trend

that could protect floodplain forests and maintain water

quality of those systems.

Another location that is anticipated to undergo major

increases in urbanization is the southern half of Florida,

where forested wetlands (both riparian and depressional) are

prevalent and associated water quality functions are at risk.

At a time when additional nonpoint source pollutant exports

are originating from newly urbanized landscapes, a portion

of the natural systems with potential to filter pollutant loads

will disappear with the demise of forested wetlands.

Effects of Expanded Intensive Forest 

Management on Water

The establishment of pine plantations has resulted in vast

acreages of intensively managed pine forests in the South.

Plantation based forestry–using pine and fast growing

hardwood species–is likely to increase in the future (chapter

9), and demand from a shrinking land base and emerging

wood fiber markets for bioenergy is likely to increase

management intensity on new and established plantations.

Considerable information is available on the impacts of forest

management on streamflow throughout the United States

(Brown and others 2005, Jones and Post 2004). For example,

removing the forest canopy increases streamflow for the first

few years, but the magnitude, timing, and duration of the

response varies considerably among ecosystems. In some,

streamflow returns to preharvest levels within 10 to 20 years;

whereas in others, streamflow remains higher for several

decades after cutting, or can even drop lower than preharvest levels (Jackson and others 2004). This wide variation

in responses is attributable to the complex interactions

between climate, which can vary considerably from dry to

wet regimes, and vegetation, which can vary in structure and

phenology (coniferous versus deciduous forest).

Information on the relationships between specific ecosystems

or forest types and streamflow can be inferred from studies

quantifying annual evapotranspiration. At annual time

scales, streamflow is approximated by the difference between

precipitation (PPT) and evapotranspiration (ET), streamflow

= PPT - ET. Therefore, for a given amount of precipitation,

management actions that alter evapotranspiration will also

alter streamflow. It is well established that coniferous forests,

with their greater capacity for interception and transpiration,

have higher evapotranspiration (and hence lower

streamflow) than deciduous hardwood forests (Ford and

others 2011, Swank and Douglass 1974). Averaged across

several climate regimes and forest types, the difference

between coniferous and hardwood forests is about 55

percent at 1200 mm yr-1 precipitation and increasing

precipitation widens this difference in the two forest

types. Evapotranspiration also varies considerably between

managed and unmanaged southern forests (table 13.4).

This variation is important for evaluating the implications

Table 13.4—Mean annual transpiration (mm yr-1) for southern forest types 

Vegetation type Transpiration Source

Longleaf pine savanna 244 Ford and others 2008

Old field 250 Stoy and others 2006

Oak-pine-hickory forest 278 Oren and Pataki 2001

Upland oak forest 313 Wullschleger and others 2001

Mixed pine hardwood 355 Phillips and Oren 2001

Mixed pine hardwood 442 Stoy and others 2006

Planted loblolly pine 490 Stoy and others 2006

Mixed pine hardwood 523 Schafer and others 2002

Slash pine flatwoods

Eucalyptus hybrid plantation

563

882

Powell and others 2005

Estimated for Baker County, southwestern Georgia in 

2006 for an average climate and rainfall yeara

Planted loblolly pine (early rotation) 328 Domec and others 2012; Sun and others 2010

Planted loblolly pine (mid-rotation) 777 Domec and others 2012; Sun and others 2010

aDerived from a model that used data collected in 2006 by the Joseph W. Jones Ecological Research Center, 3988 

Jones Center Drive, Newton, GA 39870. Model assumed no soil water limitation; all trees at age 5; 1,111 trees ha-1; 

and a leaf area index of 6 m2 m-2 (Mielke and others 1999).
322 The Southern Forest Futures Project

of increasing pine plantation forests in the South because

the magnitude of the effects on streamflow depends on the

species, forest type, or land use being replaced. For example,

pine plantations may consume nearly twice the water

consumed by longleaf pine savannas (table 13.4).

Implications of increasing management intensity on water

resources will depend on the specific management activity.

Increasing acreages of fast growing species for bioenergy

production or carbon sequestration may have negative

consequences for water yield (Farley and others 2005,

Jackson and others 2005b). To illustrate, a mature eucalyptus

plantation (age 5, 1,111 trees ha-1, leaf area index of 6 m2 m-2)

growing in southwestern Georgia could potentially consume

882 mm yr-1 of water, exceeding other forest types by a

factor of 2.5 (table 13.4). Nitrogen fertilization improves

productivity primarily though increased leaf area (Vose and

Allen 1988), and evapotranspiration is highly correlated

with leaf area index (Sun and others in press). Shortening

rotation times usually increases streamflow by decreasing

the amount of time that the stand is at canopy closure, when

leaf area index is highest and streamflow is lowest. For any

given leaf area, younger or shorter trees also have higher

stomatal conductance than older or taller trees (Moore and

others 2004, Novick and others 2009, Schafer and others

2000). Although transpiration per unit leaf area is less

than for younger forests, older forests have larger leaf area

and can intercept more water, and therefore have greater

evapotranspiration. This means that managing for older

forests is likely to decrease streamflow.

Impacts on water quality will depend on the type of

management activity and the effectiveness of established

Best Management Practices, which were originally

developed for less intensive management. For example,

in review of the impacts of forests fertilization, Fox and

others (2007) concluded that correctly applied fertilizer

rarely degrades water quality. In contrast, increasing the

frequency of harvest for shorter rotations may have impacts

on sediment yield, especially if the harvests result in greater

soil disturbance (Ursic 1986) or require more roads and more

frequent road usage (Swift 1988).

Implications of Climate Change, Land Use Change, 

and Population on Water Resources 

Climate change impacts on water resources—Because

of the combination of biological and physical controls on

hydrologic processes, climate change will both directly and

indirectly impact southern water resources (Brian and others

2004, Sun and others 2008). The direct impacts will depend

on how the amount and timing of precipitation are altered

and how this influences baseflow, stormflow, groundwater

recharge, and flooding. Long-term U.S. Geological Survey

streamflow data suggest that average annual streamflow

has increased and that this increase has been linked to

greater precipitation in eastern States over the past 100 years

(IPCC 2007, Karl and Knight 1998, Lins and Slack 1999);

however, fewer than 66 percent of all General Circulation

Models can agree on the direction of predicted precipitation

change, whether wetter or drier (IPCC 2007). Annual

precipitation within a year or from one year to the next is

a natural phenomenon related to large-scale global climate

teleconnections, such as El Niño Southern Oscillation,

Pacific Decadal Oscillation, and North Atlantic Oscillation

cycles. Many regions of the United States have experienced

an increased frequency of precipitation extremes over the

last 50 years (Easterling and others 2000, Huntington,

2006, IPCC 2007). As the climate warms in most General

Circulation Models, the frequency of extreme precipitation

events increases across the globe (O’Gorman and Schneider

2009); however, the timing and spatial distribution of

extreme events are among the most uncertain aspects of

future climate scenarios (Allen and Ingram 2002, Karl and

Knight 1998). Despite this uncertainty, recent experience

with droughts and low flows in many areas of the United

States indicate that even small changes in drought severity

and frequency will have a major impact on society, among

them a reduction in drinking water supplies (Easterling and

others 2000, Luce and Holden 2009).

The indirect impacts of climate change are related to

changes in temperature and atmospheric carbon dioxide.

In the short term, higher temperatures have the potential to

increase evaporation and plant water use via transpiration

(as temperatures increase, the energy available for

evapotranspiration increases), and therefore decrease excess

precipitation available for streamflow or groundwater

recharge. Warmer temperatures will also influence the

duration and timing of snowmelt, a critical factor in

ecosystems where snowmelt dominates hydrologic processes.

The impacts of temperature may be offset (or exacerbated)

by changes in other factors that influence evapotranspiration

such as vapor pressure (warm air holds more water), wind

patterns (which impact boundary layer resistance), increases

in carbon dioxide (which decrease stomatal conductance),

and changes in net radiation (influenced by changes in cloud

cover and aerosols). In the longer term, a warmer climate

in combination with changes in precipitation will likely

shift distributions of tree species, which differ considerably

in the amount of annual and seasonal water they use via

transpiration and interception (Ford and others 2011, Sun

and others 2011). For example, in some geographic areas,

a shift from hardwood to pine forests may result in yearround transpiration and interception and greater water

use. Controlled studies have demonstrated that increased

atmospheric carbon dioxide reduces transpiration in many

tree species, which may translate into increased streamflow




327

chapter 13. Forests and Water

On average, water stress due to the combined effects of

population and land use change will increase in the South

by 10 percent.

6.	All climate change scenarios predicted that the South

would likely see increases in air temperature in the next

50 years but differed in predictions of precipitation change

across the region. The combined effects of changing

temperature and precipitation will generally decrease

streamflow across the South (fig. 13.13). In addition,

streamflow will likely become more variable with lower

flows during drought periods and higher flows during wet

periods than experienced in the past.

7. Water supply stress would likely increase significantly

under all four climate change scenarios (fig. 13.14), largely

caused by increases in water loss by evapotranspiration

resulting from higher air temperatures, and also because

of decreasing precipitation in some areas. The effects of

changing climate on water stress will vary significantly

across the region (fig. 13.15). For example, the WaSSI

model projects that Frankfort, KY will have negligible

change in water stress across the four future climate

scenarios, while Oklahoma City, OK, Little Rock, AR, and

Austin, TX are projected to have significant increases in

water stress.

Implications of Sea Level Change on Coastal Areas

Sea-level may rise from 0.4 to 2.0 m by the end of the 21st

century (table 13.5) (McMullen and Jabbour 2009, Rahmsorf

2007, Solomon and others 2009). Along the Atlantic Coast in

the study region there is approximately 7,297 square miles

(~4.6 million acres) of coastal land below an elevation of

1.5 meters (North Carolina and Florida have the most coastal

area below 1.5 m), with an additional 5,573 square miles

(~3.5 million acres) of coastal land between 1.5 and 3.5 m.

Along the Gulf Coast there is approximately 13,605 square

miles (~8.7 million acres) of land below an elevation of

1.5 m (Louisiana and Texas have the most coastal area below

1.5 m), with an additional 6,430 square miles (~4.1 million

acres) of coastal land between 1.5 and 3.5 m (fig. 13.16). If

sea level rose 1.5 m we estimate that 2,633 square miles

(~1.6 million acres) of forests could be affected along the

Atlantic Coast, and 3,352 square miles (~2.1 million acres)

of forests could be impacted along the Gulf Coast. When

physical processes are considered by the coastal vulnerability

index, along the Atlantic Coast North Carolina and Virginia

have the most coastline in the very high-risk class, and along

the Gulf Coast, Louisiana and Texas have the most coastline

in the very high-risk class (fig. 13.17).

Projections of sea level changes can help managers identify

portions of the coastline that could be monitored more closely.

For example, figure 13.18 shows that the entire Louisiana

coastline is in the high risk category with coastal area below

1.5 m, but the Gulf coast portion of southern Florida is ranked

in the moderate risk category even though its coastal area is

also below 1.5 m, suggesting that its response to a rising sea

may be slower than if predicted from elevation alone (Thieler

and Hammar-Klose 2000). Figure 13.19 shows that portions of

the North Carolina coastline and the Atlantic coast of Florida

are in the high risk category, but because those coastal areas

are between 1.5 and 3.5 m, a sea-level rise of 1 m may not

affect those higher elevation areas.

Discussion and Conclusions

Forest conversion to agriculture or urban land uses

consistently causes increases in discharge, peak flow, and

velocity of streams. Differences in the nature of hydrologic

responses to urbanization among subregions are substantial.

As examples, the pronounced effect of urban development

on peak flows and stream hydrographs found in the

Appalachians and Piedmont may be obscured by natural

precipitation regimes in arid regions, such as western Texas

where hydrographs from less disturbed streams resemble

those of urban streams (Grimm and others 2004). Similarly,

the reductions in baseflow that are often observed following

increases in impervious area in the Piedmont may not occur

in the flatter terrain of the Coastal Plain.

Forest conversions also result in increases in sediment, water

chemistry indices, fecal coliform and E. coli, and other

substances. Because discharge and concentrations increase

after urbanization, loads are generally higher. Physiographic

characteristics such as slope and soil texture strongly

influence hydrology and sediment export, but their impact

on water chemistry is less than the impact of urbanization.

Conversion of forest land to urban uses may result in health

risks for humans as evidenced by large increases in fecal

coliform and E. coli, heavy metals, pharmaceuticals, and

other substances in stream water. While effective water

treatment may overcome this risk to drinking water, there

remains significant potential for direct contact with polluted

water as streams flow through residential areas prior to

treatment.

Each river basin has a unique land use history that may have

long-lasting effects. Cuffney and others (2010) reported

that the conversion of forest to urban land had more

pronounced effects on benthic macroinvertebrates in Atlanta;

Birmingham, AL; and Raleigh, NC than the conversion

of agriculture to urban land had in Dallas, where natural

grassland had already been degraded by agriculture in the

recent past (an example antecedent land use impacts taking

precedent over historical land use). In fact, their study found

that antecedent agricultural land use masked the effects of

urbanization in areas of historic forest use as well.

Physiographic characteristics could determine the threshold,

or the resilience to change, that each subregion displays in





333

chapter 13. Forests and Water

response to changes in land use. For this reason, McMahon

and Harned (1998) recommended incorporating measures

of both natural physiographic variation and effects of

human activity in watershed studies and management

plans. Additionally, there have been some indications that

impervious surface increases may have higher thresholds for

significant water degradation in the Coastal Plain than the

Piedmont or Southern Appalachians (Helms and others 2009,

Morgan and Cushman 2005, Roy and others 2003, Stednick

1996, Utz and others 2009); but this does not appear to be

true for all measures (Nagy and others 2012).

The concept of thresholds of imperviousness beyond which

significant degradation of water quality occurs is vague

and had previously been reported at 10-20 percent (Arnold

and Gibbons 1996, Bledsoe and Watson 2001). However,

some reports have noted significant changes in water quality

at even lower levels of development, such as <5 percent

impervious surface (Crim 2007, Cuffney and others 2010,

Nagy and others 2012). For instance, at 5 percent impervious

surface, Cuffney and others (2010) estimated a 13 to

23 percent degradation of macroinvertebrate assemblages

compared to background conditions. This suggests that care

must be taken from the first stages of development to limit

impacts on water resources. Boggs and Sun (2011) suggest

that maintaining high ET of vegetation in the growing

season is key to reducing stormflow in urban watersheds.

Furthermore, once impervious surface cover exceeds 30

percent, deterioration of water quality becomes severe

(Calhoun and others 2003, Paul and Meyer 2001). In areas

where development is planned or about to begin, it would

be very useful to identify key bioindicators for detecting the

onset of significant degradation.

Among the most dramatic impacts associated with forest

conversion to urban or agriculture are changes in aquatic

populations. The higher velocity and channel scouring

associated with urban hydrology creates unstable habitat, and

this is compounded by the effects of degraded water quality.

Species richness and abundance generally decline; and some

groups, such as mussels, may be eliminated from particular

locations. These impacts tend to be most severe in the

Appalachians. Also, species that are tolerant of the altered

conditions may replace those that are intolerant. An example

was observed in the Georgia Piedmont as reptile species

richness increased after urbanization, while amphibian

richness decreased (Barrett and Guyer 2008).

Increased intensification of forest management on a smaller

land base could have impacts on quantity and quality of

water, especially at local scales. In general, an increase in

pine plantations or fast growing hardwood species may

result in greater water use via transpiration (Ford and

others 2011); however, the magnitude and significance of

greater transpiration on water resources will depend on the

community type that is being replaced and on site specific

hydrologic processes. In addition, the impact of greater water

use may be offset by a net reduction of forest cover. Increased

intensification of forest management activities that create more

severe or frequent soil disturbance—such as site preparation,

increased harvest frequencies, a larger road network, or more

traffic—may result in increased sediment and reduced water

quality if Best Management Practices are bypassed.

Based on WaSSI model results under the four future climate

scenarios considered in this chapter, streamflows and water

supply will generally decrease and become more variable

over the next 50 to 100 years. However, magnitudes and even

the signs of changes in streamflows resulting from climate

change will vary considerably across the region, with some

small areas, such as western Texas, experiencing increases

in water supply. Other areas will likely experience decreases

in supply, particularly in Florida, Oklahoma, and northern

Texas. Overall, climate-induced decreases in water supply

and increased demand from a growing human population

will likely result in an increase in water supply stress into the

next century.

Considerable variability of water resource predictions among

the future climate scenarios and the absence of overlapping

predictions for any particular subregion confound the

certainty of future projections. Despite these uncertainties,

the importance of water resources for human and aquatic life

argues for further research and active management.

Our projections indicate a greater risk of sea-level rise for

many coastal areas in this century. Thermal inertia dictates

that once the waters rise, curbs in future greenhouse gas

emissions will not produce a quick reversal. Therefore,

unlike precipitation driven flood events, flooding due to

sea-level rise will have long-term consequences. Coastal

inundation is one of the most visible impacts of rising sea

levels. Areas that were once dry further inland will gradually

shift to episodically inundated (during high tides and storms)

and then to permanently inundated. The impact of sea level

rise to the point of inundation is obvious, but other impacts

may be less visible, such as the salt water marshes that

exemplify an ecosystem in balance between fresh water and

saline environments. These unique places provide important

breeding habitat for many terrestrial and aquatic animal

species. However, rapidly rising sea levels will permeate

non-saline forests and grasslands, causing losses of existing

vegetation without the possibility of replacement by more

salt tolerant species. Once the existing vegetation is dead,

the root structure that binds the soil system together and

provides a buffer from incoming tides will also be lost,

and coastal erosion is likely to accelerate. Although coastal

erosion is a naturally occurring process in barrier islands

and many other areas, the increase in rate and severity
334 The Southern Forest Futures Project

that is likely with rising sea levels could result in a greatly

accelerated loss of valuable coastal property.

Finally, a combination of pressure on water resources from

increasing human populations and rising sea levels could

severely reduce fresh water supplies along coastal areas. As

fresh water is drawn out of shallow ground water systems,

adjacent brackish water would likely fill the void, thus

raising the risk of salt water contamination to drinking water

supplies. Rises in sea level will further increase the risk of

contamination as saline water levels rise. The loss of ground

water supplies in places like Florida would have enormous

social and economic implications and may be more significant

than coastal inundation in the near to medium future.

Knowledge and Inf ormation Gaps

Past studies on forest-water relations that have been

conducted primarily in forested watersheds are not sufficient

to address issues in more complex, human dominated

landscapes. A key issue is the relationship between

increasing urbanization and diminishing available water

supply for humans in the South. We need to understand

more about the nature of this relationship and how it

may change across the array of southern physiographic

features. Complexity increases with the interactive effects

of multiple drivers including land use change, climate

change, population growth, and the natural variability in the

hydrologic cycle. A better understanding is critically needed

in advance of the next major drought, whose impacts may be

exacerbated by expected increases in human populations and

impervious surfaces in many areas of the South (chapter 4).

The ramifications of urbanization on surface water and

subsequently, human health is another topic that deserves

greater attention. Very high counts of fecal coliform and

E. coli have been documented in urban streams, but the

potential risks to human health have not yet been assessed.

Research is needed on coastal areas, which have not been

adequately studied and are expected to undergo high

population growth and development rates in coming years.

Also, the sensitivities of aquatic organisms to urbanization

have been demonstrated but not quantified, and should be

more fully understood so that they can serve as bioindicators

of impending degradation to surface water resources. The

focus of this chapter was on surface water impacts associated

with land use conversion, but literature searches produced

little information on the relationships of groundwater to land

use and land cover. Because many southern communities

are considering expanded use of aquifers, believing them to

be “drought proof,” they will need to understand the extent

to which changes in land use might affect groundwater

resources.

The WaSSI model provides a general summary of water

supply and demand dynamics across large regions over

extended periods of time, requiring assimilation and

integration of large datasets and the use of extensive GIS

and computing resources. These large data requirements

necessitated development of simplifying assumptions to

simulate water resource changes in response to climate

change. For example, the WaSSI model used for this chapter

does not include provisions for water supply reservoir

storage or interbasin water transfers; it also assumes that

all in-stream surface water is available for human use

(no ecological flow is reserved) and that river flows are

routed through the river network instantaneously during

a given month. It is important to keep in mind that these

assumptions may impact water supply stress predictions for

some areas across the region. Future land use changes are

likely to affect water quality and extreme hydrology such

as peakflow rate, issues that are not addressed yet by the

WaSSI model. The tradeoffs between water resources and

carbon sequestration are not well understood and need to be

quantified before embarking on bioenergy development and

forest management to mitigate climate warming.

Compared to the physics of oceanic thermal expansion,

relatively little is known about the rate of global warming,

the changes in ocean surface albedo, or the input of water

from snow and ice melts on land. Many of these unknowns

are not a function of science gaps, but rather uncertainty

about future increases in greenhouse gas emissions.

Conversely, the physics of thermal expansion are well

understood. As predictions of global warming rates improve,

the accuracy of sea-level rise will also improve significantly.

Finally, demographic changes and associated pressures on

ground water resources are also unknown. These knowledge

gaps need to be addressed before a more complete

assessment of climate change on sea-level rise is possible.

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341

chapter 14. Wildlife and Forest Communities

Margaret Trani Griep and Beverly Collins1

Key Findings

•	 The South has 1,076 native terrestrial vertebrates: 179

amphibians, 525 birds, 176 mammals, and 196 reptiles.

Species richness is highest in the Mid-South (856) and

Coastal Plain (733), reflecting both the large area of these

subregions and the diversity of habitats within them.

•	The geography of species richness varies by taxa.

Amphibians flourish in portions of the Piedmont and

Appalachian-Cumberland highlands and across the Coastal

Plain. Bird richness is highest along the coastal wetlands of

the Atlantic Ocean and Gulf of Mexico, mammal richness

is highest in the Mid-South and Appalachian-Cumberland

highlands, and reptile richness is highest across the

southern portion of the region.

•	 The South has 142 terrestrial vertebrate species

considered to be of conservation concern (e.g., global

conservation status rank of critically imperiled, imperiled,

or vulnerable), 77 of which are listed as threatened or

endangered by the U.S. Fish and Wildlife Service. More

than 900 plant species are of concern, 141 of which are

threatened or endangered. Threats to biodiversity are

occurring throughout the region.

•	 The proportion of species at risk varies among taxonomic

groups: 46 percent of imperiled vertebrate species are

amphibians, followed by reptiles (25 percent), mammals (16

percent), and birds (13 percent). The Coastal Plain (64) and

Mid-South (55) lead in the numbers of imperiled vertebrate

species, followed by the Appalachian-Cumberland

highlands (31), Piedmont (29), and Mississippi Alluvial

Valley (9).

•	Hotspots of vertebrate species of conservation concern

include the Atlantic and Gulf coasts, Peninsular Florida,

and Southern Gulf. Emerging areas of concern include

sections within the Appalachian-Cumberland highlands

(Blue Ridge, Southern Ridge and Valley, Cumberland

Plateau and Mountain, Interior Low Plateau) and MidSouth (Ozark-Ouachita Highlands, West Texas Basin and

Range, and Cross Timbers).

1Margaret Trani Griep is the Regional Wildlife Ecologist, Southern Region,

U.S. Department of Agriculture Forest Service, Atlanta, GA 30309. Beverly

Collins is an Associate Professor, Department of Biology, Western Carolina

University, Cullowhee, NC 28734.

•	 Hotspot areas for plants of concern are Big Bend National

Park; the Apalachicola area of the Southern Gulf Coast;

Lake Wales Ridge and the area south of Lake Okeechobee

in Peninsular Florida; and coastal counties of North

Carolina in the Atlantic Coastal Plain. The AppalachianCumberland highlands also contain plants identified by

States as species of concern.

•	 Species, including those of conservation concern, are

imperiled by habitat alteration, isolation, introduction of

invasive species, environmental pollutants, commercial

development, human disturbance, and exploitation.

Conditions predicted by the forecasts will magnify these

stressors. Each species varies in its vulnerability to

forecasted threats, and these threats vary by subregion. Key

areas of concern arise where hotspots of vulnerable species

coincide with forecasted stressors.

•	There are 614 species that are presumed extirpated from

selected States in the South; 64 are terrestrial vertebrates

and 550 are vascular plants. Over 50 percent of the

terrestrial vertebrates are new to this list since the Southern

Forest Resource Assessment. Factors contributing to their

demise include urban growth, industrial development,

incompatible agricultural practices, degradation of

wetlands, alteration of natural hydrological conditions,

pesticide contamination, natural and human-caused

disturbance, and destruction of locally unique habitats.

•	Mid-South: Forest loss and urban growth in the OzarkOuachita Highlands threatens concentrations of plant and

animal species. Urban development along southern borders

of Texas and Louisiana in the Cross Timbers and Western

Gulf sections could impact a large number of reptiles and

birds.

•	Appalachian-Cumberland highlands: Forecasted

changes in the Interior Low Plateau of central Kentucky

and Tennessee threaten bats and plants associated with

limestone glades. Urban development in the Southern

Appalachians could imperil the diversity of salamanders.

Recreational use may add additional pressure on rare

communities, and climate change threatens species

endemic to high elevation areas.

• Piedmont: Substantial urban growth and forest loss

could degrade the diversity of amphibians, mammals, and

plants, although species in inaccessible sites (such as rock

outcrops) may be less at risk. Management on public land

Chapter 14.

Wildlife and Forest Communities
342 The Southern Forest Futures Project

may become difficult due to the population pressure in

surrounding counties. Species in areas transitional to other

subregions may also be threatened by climate change.

•	Mississippi Alluvial Valley: Urban growth forecasts for

the Deltaic Plain could degrade the richness of shorebirds

and waterfowl in the wetlands of the Mississippi Flyway as

well as habitat for the Louisiana black bear. Sea level rise

could inundate the coastal habitat inhabited by numerous

species.

•	Coastal Plain: Urban development could threaten species

along both coasts and within the Florida Peninsula, which

serves as stopover habitat in the Atlantic Flyway and

nesting habitat for imperiled sea turtles. The flora of inland

ecosystems is threatened by changing fire regimes. Projected

inundation of mangrove and coastal live oak forests from sea

level rise would reduce habitat for several taxa.

•	High elevation forests: Spruce-fir forests in the Southern

Appalachians are subject to air pollution, acid deposition,

and natural disturbances. Climate warming and further

housing development may result in the loss of endemic

species or changes in species ranges.

•	Upland hardwood forests: Declines are predicted at 14

percent throughout the region under the Cornerstone that

forecasts higher levels of urbanization and lower timber

prices. Predicted northward shifts in species distributions

could threaten forest interior species and reassemble forest

types, including the widely distributed oak-hickory forest.

•	Longleaf pine forests: Portions of the Coastal Plain

are expected to lose acreage under the Cornerstone that

forecasts higher urbanization and higher timber prices,

while south-central Florida and northwest Alabama are

predicted to gain acreage of this forest type.

•	Early successional forests: Under the Cornerstone that

forecasts higher urbanization and higher timber prices,

the greatest losses are expected in the Northern Ridge

and Valley section, southern Florida and associated Keys,

and scattered locations in coastal Virginia and North

Carolina. Gains are expected in the Ridge and Valley

of east Tennessee, Cumberland Plateau and Mountains,

Apalachicola region of Florida, Ozark-Ouachita Highlands,

and adjacent northern area of the Mississippi Alluvial Valley.

•	Climate change is an additional source of stress on

terrestrial species and ecosystems. Projections of

temperature increase and variability in precipitation

patterns may change the future distribution of many

species, influencing seasonal movement, recruitment, and

mortality. Species may move into the habitats of others,

creating new assemblages; changes in phenology will affect

the timing of resource availability.

•	Species at risk from climate change include those with

restricted geographic ranges, patchy distributions, and

those that occur at the margins of their ranges. Other

characteristics include limited dispersal ability, low genetic

diversity, affinity to aquatic habitats, narrow physiological

tolerance, and late maturation.

•	Communities at high elevations, grassland communities,

and wetland ecosystems may be particularly susceptible

to climate change. Species whose ranges are limited to

coastal areas will be vulnerable to projected changes in sea

level. Sea level rise may inundate barrier islands, coastal

wetlands, and marshes of the Coastal Plain, as well as

along the eastern Atlantic and Gulf coasts.

•	The forecasts pose challenges on how best to implement

future conservation and management strategies. New tools

and approaches to managing uncertainty (e.g., scenario

planning, sensitivity analysis, or ecological risk analysis)

may become routine.

•	Integrating climate science into management planning

will be important, accompanied by monitoring strategies

that identify patterns in disturbance, phenology, and range

changes. As future impacts occur across large areas, the

appropriate decision-making level may shift to cover

landscape or regional scales; temporal scales will be longer

than typically considered.

•	 An awareness of the relationship between the forecasts and

the geographic pattern of species occurrence will foster

planning efforts. The implications for the conservation of

southern species are significant: in the midst of a growing

region, the provision of biological diversity will become a

critical conservation issue.

Introduction

The diversity of plant and animal communities in the South

ranges from high elevation forests to coastal wetlands,

barrier islands, and arid regions of west Texas. Factors

contributing to the diversity of these communities include

regional gradients in climate, geologic and edaphic site

conditions, topographic variation, and natural disturbance

processes (Boyce and Martin 1993, Delcourt and others

1993, Healy 1985). These factors have contributed to the

diversity of several species groups: salamanders, snakes, and

turtles (White and others 1998). Throughout the South, the

evolution of plants and animals combines with the isolation

that characterizes some habitats to produce many pockets of

endemism. Endemic species are unique to a given geographic

area or locale (http://en.wikipedia.org/wiki/Cosmopolitan_

distribution); physical, climatic, and biological factors can

contribute to endemism.

Centuries of land use change have modified the southern

landscape, resulting in the disappearance and endangerment

of species communities. Habitat loss and degradation have

become serious threats (Buckner 1989, Noss and others

1995, Williams 1989). Rapid population growth has resulted

in land-use conversion (such as wetland drainage and

channelization), urban sprawl, and habitat fragmentation

(White and others 1998). Landscape modification has led

to habitat isolation, water and air pollution, and altered

disturbance regimes (Lorimer 2001, Trani and others 2001).
343

chapter 14. Wildlife and Forest Communities

The introduction of nonnative invasive species (Wilcove and

others 1998) is a major concern, as is the proliferation of the

illegal pet trade (Bailey and others 2006).

The fragmentation of forests that occurs with the conversion

of forest habitats often eliminates or displaces species from

a site simply because less habitat occurring in smaller and

more isolated patches supports fewer species (MacArthur

and Wilson 1967). This effect has been shown in fragments

of globally imperiled pine rockland forest scattered within

urban South Florida (Possley and others 2008), where the

result has been fewer plant species and high variance in

species richness. Reduced population size can decrease

genetic diversity and outcrossing rates (Aguilar and others

2008, Godt and others 1996), while microclimate gradients

from edge to interior habitats alter species composition

(Honu and others 2009, Matlack 1994). Forest edge provides

habitat for invasive species, and decreases habitat for interior

species (Fridley and others 2009, Guirado and others 2006).

Another concern is the effect of changing climate on plant

and animal communities. Species that are rare because of

restrictive or specialized needs are especially at risk. Climate

change is one of the factors attributed to amphibian declines

(Trani 2002b) and is a special concern for high elevation

communities. Along with suffering the direct effects of sea

level rise—changes in temperature, precipitation, and coastal

inundation—species are indirectly affected by changes in

fire regimes and species interactions.

Although the future of these species and the communities

they inhabit is uncertain, human population expansion over

the next five decades raises the possibility of substantial

impacts. The objective of this chapter is to examine how

changes in forest environmental and social conditions affect

terrestrial wildlife, their habitats, and forest vegetation

communities in the South. It is organized into six major

discussion topics:

•	The geographic patterns of richness for amphibian, bird,

mammal, and reptile species, along with a description of

the differences in richness among taxa and subtaxa

•	 The geographic patterns of terrestrial wildlife species

formally listed as threatened or endangered under the

Endangered Species Act of 1973 (Flather and others 2008)

along with a discussion of the environmental factors that

imperil them

•	The geographic patterns of other at-risk plant and

terrestrial wildlife species—those ranked as species of

conservation concern by State Heritage Agencies (Trani

2002b)—along with a discussion of the environmental

factors that imperil them

•	 The extent of species extirpation that have already

occurred, along with a discussion of the factors that

contributed to their extirpation. Comparisons are made

with the state lists of extirpated wildlife species presented

in the Southern Forest Resource Assessment

•	 The potential impact on southern species from forecasts of

urban development, forest loss, and climate change, and the

key areas of concern that coincide with forecasted changes

•	 The potential effects of anticipated futures on selected

forest communities: longleaf pine forests, high elevation

forests, early successional communities, and upland

hardwood forests

Each topic is addressed for the region as a whole and by

subregion and section. The focus is on terrestrial vertebrate

species, vascular plants, and select forest communities

identified during public meetings held throughout the region

(Wear and others 2009). Additional information on forest

communities is provided in chapter 4 (land uses), chapter

5 (forest conditions), chapter 3 (climate change), chapter

16 (invasive insects and diseases), and chapter 15 (invasive

plant species). Terrestrial species of non-forest habitats, such

as arid west Texas, are included. However, because aquatic

species were examined in extensive detail in Herrig and

Shute (2002) and were not identified as a concern during the

public meetings, they are not covered here.

Methods

Species Criteria 

The major species groups included in this analysis consist

of the following: birds, mammals, reptiles, amphibians, and

vascular plants. The analysis includes forest or non-forest

dwelling species that are native to the South.

Species with a conservation status rank of G1-G5 were

selected for the richness analyses; G1-G3 and federal status

species for the areas of conservation concern analyses; and

SX-SH for the State extirpation analyses (table 14.1). Species

that were not assessed, unranked or not yet ranked were

not included due to the incompleteness of location data for

those species. The following filters were applied to the global

species data (McNees 2010):

•	 The species occurs in one or more of the 13 Southern

States;

•	The species has a rounded G-Rank of G1, G2, G3, G4, or

G5, creating a full-species analysis;

•	 Infrataxa records were rolled up to the full species level

for the G-Rank counts. However, infrataxa were tallied

individually in the Federal status analyses if that was the

relevant taxonomic level that the listing applied to (i.e.,

often a subspecies has Federal listing status but not the

species in entirety);

•	A data record was excluded if it was not mappable or had a

last observed date prior to 1970;

•	 For analyses using range maps, the following were

excluded: historic, introduced, and extirpated/extinct

portions of a species range.
344 The Southern Forest Futures Project

Geographic analysis 

Geographic shapefiles for each section, subregion, and

region boundary used for the Futures analyses (fig. 14.1)

were obtained from the Forest Service, U.S. Department

of Agriculture. (Further description of these areas, and the

process of their delineation, can be found in chapter 1).

Shapefiles of the occurrence and range records were

extracted from NatureServe’s central databases for species

matching project criteria. The species shapefiles were

separately intersected against the county, section, and

subregion GIS layers using a series of spatial join processes

to attribute each individual occurrence record and range

polygon to appropriate county, section, and subregion

polygons (McNees 2010). The county boundaries layer was

downloaded from the U.S. Geological Survey.

The attribute table from the output layer of each spatial

join process in the step above was imported into Microsoft

Access. The results tables were combined so that there was

a single table for the county, section, and subregion results;

these were then summarized to create unique lists of species

within each area. Crosstab queries generated counts by

taxonomic groupings and conservation rank categories.

A series of map image files were produced in .gif format

(200 dpi resolution) using ArcMap showing the various

counts of species by county for the South. Areas of unique

species richness or rarity were identified and representative

species occurring in these areas described. Legend

categories were determined using the natural breaks method

for dividing a range of numeric values into categories, an

iterative process to minimize within-category variance.

Biodiversity-forecast analyses 

Cornerstone scenarios were selected for the analysis of

biodiversity-forecast stressors based on their potential for

future impacts in the South. The spatial products created

during the initial geographic analysis (patterns of species

richness and rarity) were then analyzed in contrast with the

Table 14.1—Conservation status ranks used by NatureServe and its network of Natural Heritage Programs 

(NatureServe 2011) 

Status Rank Definition

G1 Critically imperiled—At a very high risk of extirpation due to extreme rarity (often five or fewer occurrences), very 

steep declines, or other factors. Critically imperiled globally because of extreme rarity or because of some factor(s) 

the organism especially vulnerable to extinction. Typically 5 or fewer occurrences or very few remaining individuals 

(<1,000) or acres (<2,000) or linear miles (<10).

G2 Imperiled—At high risk of extirpation due to very restricted range, very few populations (often 20 or fewer), steep 

declines, or other factors. Imperiled globally because of rarity or because of some factor(s) the organism very 

vulnerable to extinction or elimination. Typically 6 to 20 occurrences or few remaining individuals (1,000 to 3,000) or 

acres (2,000 to 10,000) or linear miles (10 to 50).

G3 Vulnerable—At moderate risk of extirpation due to a restricted range, relatively few populations (often 80 or fewer), 

recent and widespread declines, or other factors. Vulnerable globally either because the organism is very rare and 

local throughout its range, found only in a restricted range (even if abundant at some locations), or because of other 

factors making it vulnerable to extinction or elimination. Typically 21 to 100 occurrences or between 3,000 and 

10,000 individuals.

G4 Apparently secure—Uncommon but not rare; some cause for long-term concern due to declines or other factors—

although the organism may be rare in parts of its range, particularly on the periphery—and usually widespread.

Apparently not vulnerable in most of its range, but possibly cause for long-term concern. Typically more than 100 

occurrences and more than 10,000 individuals.

G5 Secure—Common, widespread, and abundant—although the organism may be rare in parts of its range, 

particularly on the periphery. Not vulnerable in most of its range. Typically with considerably more than 100 

occurrences and more than 10,000 individuals.

SH Possibly Extirpated—Known from only historical records; evidence that the species may no longer be present, but 

not enough to state this with certainty. A species has been searched for unsuccessfully, but not thoroughly enough 

to presume that it is no longer present.

SX Presumed Extirpated—Species is believed to be extirpated from the state. Not located despite intensive searches 

of historical sites and other appropriate habitat, and virtually no likelihood that it will be rediscovered.

346 The Southern Forest Futures Project

forecast maps generated for urban growth (chapter 4), forest

loss (chapter 5), and climate change (chapter 3). Patterns of

coincidence were identified and examined; selected areas

of particular concern were described where forecasted

stressors coincided with species richness and rarity by

subregion and section. A synthesis of the published literature

further described how anticipated land use change, human

population growth, urbanization, and related infrastructure

development could affect species in the South.

GIS maps of special forest communities under selected

forecasts were developed from Forest Inventory and Analysis

data (chapter 5). Distributions for 2010 and 2060 and

percentage changes were described for longleaf pine, early

successional forest, and upland hardwood forest along with

a discussion of potential impacts on the species that inhabit

these communities.

Data Sources

The foundation of this analysis consists of global (rangewide) tracking data developed by NatureServe (2010, 2011)

and State-level tracking data provided by Natural Heritage

Programs across the South. Use of standard ranking criteria

and definitions makes Natural Heritage ranks comparable

across taxa groups and across jurisdictions. Standardized

criteria include population size, area of occupancy,

population trends, suspected threats, environmental

specificity, and viability of extant populations. Species

data are updated annually, incorporating new information

provided by field surveys, monitoring activities, and

literature reviews. Systematic surveys for imperiled and

other species occurrence vary across counties in the South.

These data gaps and other limitations are discussed in the

Knowledge and Information Gaps section.

The species locations were derived from element of

occurrence and range map data sources. For species

considered at-risk (G1-G3) or those having Federal listing

status, the data were based on NatureServe’s element of

occurrence database which is based on observed locations of

species (for G3 species that lack occurrence records, range

map data were substituted). The threatened or endangered

species listed by the U.S. Fish and Wildlife Service were

verified from the U.S. Department of the Interior (2011).

For species considered secure or apparently secure (G4-G5),

the data were based on NatureServe’s range maps for birds,

mammals, reptiles, and amphibians. (Because NatureServe

does not maintain range maps for plant species, data were not

available for G4 to G5 plants.) Unlike element of occurrence

data, this information is coarsely mapped and intended to

represent the entire range of a species. While often based

on element occurrence data, the ranges for species are

also based on published literature, expert opinion, and

consultations with other organizations. The following are the

sources of the range map data specific to this analysis:

•	 Digital Distribution Maps of the Birds of the Western

Hemisphere (Ridgely and others 2007). ArcView

shapefiles contain the known range of each species

depicted as polygons where a species is widespread,

or as points where there are isolated records. Not all

vagrant occurrences are depicted. Data were provided

by NatureServe in collaboration with Robert Ridgely,

James Zook, The Nature Conservancy Migratory Bird

Program, Conservation International Center for Applied

Biodiversity Science (CABS), World Wildlife Fund US,

and Environment Canada WILDSPACE.

•	 Digital Distribution Maps of the Mammals of the Western

Hemisphere (Patterson and others 2007). ArcView

shape files contain the known range of each species

depicted as polygons where a species is widespread, or

as points where there are isolated records. Data were

provided by NatureServe in collaboration with The

Nature Conservancy, Conservation International CABS,

World Wildlife Fund US, and Environment Canada

WILDSPACE.

•	 Digital Distribution Maps of the Reptiles of the United

States and Canada (NatureServe 2007). This dataset

contains distribution information for terrestrial and aquatic

reptiles, crocodilians, and turtles occurring in the United

States and Canada. Distribution maps accompany Red List

Assessments and species accounts in NatureServe Explorer

(www.natureserve.org/Explorer/). Annotated maps indicate

scale, sources, taxonomic decisions, current range, origin,

and island distributions where applicable.

•	Digital Distribution Maps of the International Union

for Conservation of Nature Red List of Threatened

Species: Amphibian Range Maps (International Union for

Conservation of Nature 2009). Part of a global biodiversity

assessment, the dataset contains spatial data for

approximately 20,000 species including amphibians. The

data are held in shapefiles; the known range of each species

is depicted in polygon format.

Results

Geographic Patterns of Vertebrate Richness

The terrestrial vertebrates of the South consist of 1,076 native

species (NatureServe 2011): 179 amphibians, 525 birds,

176 mammals, and 196 reptiles. Species richness is highest

in the Mid-South (856) and Coastal Plain (733). It is

evident that species richness is influenced by a species-area

relationship among the subregions. Richness reflects the

large area of these subregions (chapter 1) and the diversity

of habitats within them. The remaining, smaller subregions

support fewer vertebrate species: 528 for the Piedmont,

348 The Southern Forest Futures Project Table 14.2—Vertebrate species richness by ecological subregion and section (NatureServe 2011). Subregion Section Subtaxa

Frogs and toads

Salamanders

er birds thO

Perching birds

Raptors

Shorebirds

Wading birds

Waterfowl

Bats

nivores ar

C

er mammals th

O

Rodents

codilians roC

ards iz

L

Snakes

Turtles

Worm lizards

Appalachian-Cumberland Blue Ridge 17 54 42 132 13 17 11 21 16 11 18 26 9 26 13

Cumberland Plateau and Mountain 20 35 43 135 14 17 11 22 16 11 15 25 9 24 12

Interior Low Plateau 25 32 45 139 15 16 11 24 16 12 16 23 9 29 15

Northern Ridge and Valley 16 41 42 130 14 17 7 24 11 12 16 23 7 21 13

Southern Ridge and Valley 15 41 43 130 12 16 8 20 14 11 16 26 9 21 11

Coastal Plain Eastern Atlantic 29 31 70 131 16 35 16 28 14 10 14 20 1 13 42 23 1

Florida Peninsular 21 18 81 128 20 33 19 25 13 12 13 18 2 14 40 21 1Middle Gulf-Eastern 32 35 52 139 16 22 15 24 15 13 11 21 1 11 40 23

Middle Gulf-Western 26 21 54 140 14 24 13 24 13 13 12 21 1 13 38 17

Northern Atlantic 29 22 75 134 17 35 13 32 11 11 13 20 1 10 35 18Southern Gulf 34 33 71 136 15 32 19 27 13 11 11 25 1 12 48 34 1Western Gulf 27 16 80 142 18 37 16 31 13 15 13 22 12 41 18

Mid-South Cross Timbers 38 26 86 184 21 37 17 34 16 17 18 41 25 58 24

High Plains 37 18 92 208 28 38 17 31 25 20 16 57 38 64 21

Ozark-Ouachita Highlands 29 29 49 143 15 20 12 25 16 12 14 27 1 13 38 17

West Texas Basin and Range 19 4 66 171 21 22 10 25 24 14 15 46 35 43 10

Mississippi Alluvial Valley Deltaic Plain 25 18 69 127 15 32 15 27 8 10 10 14 10 36 16

Holocene Deposits 30 29 61 141 16 31 14 24 14 12 11 23 1 11 38 17

Piedmont Central Appalachian Piedmont 29 48 61 133 17 25 10 27 13 12 20 24 10 33 13

Piedmont Ridge, Valley, and Plateau 24 36 43 127 13 16 11 22 14 10 11 23 11 30 17

Southern Appachian Piedmont 31 43 43 128 13 17 13 20 15 12 17 22 11 38 21



352 The Southern Forest Futures Project

Alluvial Valley (306), Piedmont (283), and AppalachianCumberland highlands (257).

Bird richness along the coastal areas and wetlands of the

Atlantic Ocean and Gulf of Mexico points to the importance

of this habitat (fig. 14.6). The pattern across the southernmost

portions of Texas and Peninsular Florida reflects those

species typical of Latin America and the Caribbean (Stein

and others 2000). Of particular prominence are the Southern

Gulf; the portions of the Cross Timbers and High Plains that

form the Texas eastern coastline; and the Western Gulf and

Deltaic Plain at the mouth of the Mississippi River. The two

Mid-South sections each support habitat for over 360 species;

these include Cooper’s hawk (Accipiter cooperii), golden

eagle (Aquila chrysaetos), white-tailed kite (Elanus

leucurus), Le Conte’s sparrow (Ammodramus leconteii),

juniper titmouse (Baeolophus ridgwayi), and canyon wren

(Catherpes mexicanus).

The Northern Atlantic and Western Gulf provide habitat for

a diversity of waterfowl including American wigeon (Anas

americana), blue-winged teal (Anas discors), bufflehead

(Bucephala albeola), hooded merganser (Lophodytes

cucullatus), and lesser scaup (Aythya affinis). The salt

marshes of the Northern Atlantic also support important

breeding and wintering populations of the American black

duck (Anas rubripes) and black rail (Laterallus jamaicensis).

Numerous wading birds inhabit the Southern Gulf and

Florida Peninsula including the great egret (Ardea alba),

little blue heron (Egretta caerulea), and white-faced ibis

(Plegadis chihi). Thirty-three species of shorebirds occur in

the Eastern Atlantic, Northern Atlantic, and Western Gulf;

characteristic species include the American oystercatcher

(Haematopus palliatus), dunlin (Calidris alpina), greater

yellowlegs (Tringa melanoleuca), and upland sandpiper

(Bartramia longicauda).

Mammals—Terrestrial, marine, and freshwater habitats in

the South are home to 176 native mammals (NatureServe

2011) including rodents, bats, and carnivores. Rodents

(79 species) are the largest group, with representative species

including squirrels, pocket gophers, voles, jumping and

harvest mice, and muskrats. There are 39 species of bats

inhabiting the region. Foxes, weasels, canids, and skunks

are among the 25 species of carnivores. The relative absence

of large, native carnivores reflects the history of European

settlement (Trani and others 2007). The American black

bear (Ursus americanus) is the largest carnivore currently

inhabiting the South. The NatureServe category of “other

mammals” includes 33 species represented by ungulates,

lagomorphs, shrews, moles, and others.

Mammals are associated with specific habitats that offer

suitable forage and refuge; patterns of use vary with seasonal

food availability. Areas are diverse in composition, structure,

and ecological succession stage; mosaics of cover types

and the ecotones between them enhance prey density and

other food opportunities. Most hollow logs, snags, brush

piles, or rock outcrops are acceptable dens for rodents and

carnivores, but the caves used by some bats must meet

precise temperature and humidity conditions. Mammals

associated with aquatic habitats use estuaries, marshes, and

streams. Terrestrial habitats include desert, prairie, savanna,

and agricultural fields. In the mountains, high-elevation

habitats (such as spruce-fir and northern hardwood forests)

are important to the long-tailed shrew (Sorex dispar); in

coastal areas, bottomland hardwoods and cypress swamps

support the swamp rabbit (Sylvilagus aquaticus).

The peak number of mammal species (148) occurs in the

Mid-South, where rodent (68), bat (38), and carnivore (22)

diversity occur in highest numbers (fig. 14.7). Second is

the Coastal Plain (103 species), which supports the most

species categorized as “other mammals” (24 species) by

NatureServe. The next tier is led by the AppalachianCumberland highlands (77 species), Piedmont (76 species),

and Mississippi Alluvial Valley (61 species).

The distribution of mammal diversity across the region

highlights patterns in two quite different subregions: the

Mid-South and the Appalachian-Cumberland highlands

(fig. 14.8). Of particular importance is the West Texas

Basin and Range section, which is located on the Mexican

border and provides habitat for 99 mammal species.

Together, the four Mid-South sections support the highest

richness of rodents ranging from 57 species in the High

Plains to 27 species in the Ozark-Ouachita Highlands.

Characteristic rodents from these sections include the

cactus deermouse (Peromyscus eremicus), Chihuahuan

pocket mouse (Chaetodipus eremicus), Mexican ground

squirrel (Spermophilus mexicanus), Southern Plains

woodrat (Neotoma micropus), and Texas antelope squirrel

(Ammospermophilus interpres). Bat richness is also greatest

in the Mid-South, with the High Plains (25 species) and West

Texas Basin and Range (24 species) supporting the southern

yellow bat (Lasiurus ega), spotted bat (Euderma maculatum),

Yuma myotis (Myotis yumanensis), and other species.

Carnivore richness is highest in the band from southcentral

Texas (17 species) expanding westward through the Western

Gulf (15 species). Unique western carnivores include

the ocelot (Leopardus pardalis), western spotted skunk

(Spilogale gracilis), and white-nosed coati (Nasua narica).

Each remaining area in the South supports a range of 10 to	

12 carnivores.

Mammal richness is also notable in the AppalachianCumberland highlands, which encompasses a much

smaller area than the Mid-South but supports 16 bat species

including the eastern small-footed myotis (Myotis leibii),

gray myotis (Myotis grisescens), and Virginia big-eared

354 The Southern Forest Futures Project

bat (Corynorhinus townsendii virginianus). There are 28

rodent species within mountainous areas providing habitat

for the Allegheny woodrat (Neotoma magister), red squirrel

(Tamiasciurus hudsonicus), and southern bog lemming

(Synaptomys cooperi). The Central Appalachian Piedmont

(20 species) and Blue Ridge (18 species) support the largest

number of “other mammals,” which include the American

water shrew (Sorex palustris), Appalachian cottontail

(Sylvilagus obscurus), hairy-tailed mole (Parascalops

breweri), and long-tailed shrew among others.

Reptiles—The South supports 196 reptile species

(NatureServe 2011), including snakes (90), lizards (53),

turtles (50), crocodilians (2), and worm lizards (1). The major

subgroups of snakes are nonvenomous snakes, coral snakes,

and pit vipers; species that inhabit the water are especially

prevalent. Two of the largest snakes in North America

occur in the region: the eastern indigo snake (Drymarchon

couperi) and eastern diamond-backed rattlesnake (Crotalus

adamanteus). The four lizard subgroups include anole

lizards, fence lizards, collared lizards, horned lizards,

whiptails, skinks, and glass lizards. The turtle group consists

of sea turtles, snapping turtles, box turtles, mud and musk

turtles, tortoises, and soft-shell turtles. The two crocodilians

are quite well-known: the American alligator (Alligator

mississippiensis) and American crocodile (Crocodylus

acutus). The fossorial worm lizard (Rhineura floridana),

despite its name and appearance, is an Amphisbaenian and

does not belong in either the snake or the lizard group.

With the exception of lizards, the all reptiles reach their

maximum species richness in the South (Bailey and others

2006). As with amphibians, ecological importance of lizards

has become recognized in the past decade as resource

objectives focus on biodiversity conservation, landscape

perspectives, and their role in ecosystem functioning.

Reptiles occupy a variety of habitats including mesic and xeric

hardwood forests, sandhills, grasslands, prairies, barrens,

outcrops, beaches and dunes, agricultural and urban areas

(Bailey and others 2006). Rivers, streams, swamps, lakes,

and marshes figure prominently in aquatic turtle occurrence.

Groups such as mud turtles (Kinosternon spp.) use terrestrial

habitat for nesting and winter dormancy, spending the summer

in wetland areas. The forested mountain areas support an

abundance of reptiles including the bog turtle (Glyptemys

muhlenbergii), while the longleaf pine-wiregrass community

is vital habitat for the gopher tortoise (Gopherus polyphemus)

and glass lizard (Ophisaurus spp.). Cypress-gum swamps

support several species (Gibbons and Buhlmann 2001)

including the rainbow snake (Farancia erytrogramma) and

striped crawfish snake (Regina alleni). Leaf litter and fallen

logs provide shelter and foraging opportunities; friable soils

are an important habitat component for many.

The Mid-South (148 species) leads in reptile richness

(fig. 14.9), where snake (74) and lizard (45) species occur

in the highest numbers. The diversity of this subregion

reflects its large size and strategic location at the crossroads

of several distinct reptilian fauna (Stein and others 2000).

Many eastern reptiles reach their westernmost distribution

in the Mid-South, while the converse is also true for western

reptiles. Second in reptile richness is the Coastal Plain (129),

which supports the most turtle species (43) in its abundant

coastal and freshwater habitats. The third tier is comprised

of the Piedmont (75), Mississippi Alluvial Valley (72), and

Appalachian-Cumberland highlands (60).

The distribution of reptile diversity is concentrated across

the southern portion of the region, with notable differences

among the various groups (fig. 14.10). Lizard richness is

highest in the western sections of three Mid-South sections—

High Plains (38), West Texas Basin and Range (35), and Cross

Timbers (25)—reflecting availability of arid habitats. These

three sections provide habitat for the Texas spotted whiptail

(Aspidoscelis gularis), eastern collared lizard (Crotaphytus

collaris), round-tailed horned lizard (Phrynosoma modestum),

and canyon lizard (Sceloporus merriami). Snakes are quite

diverse in both the eastern and western portions of the region.

The High Plains (64), Cross Timbers (58), and West Texas

Basin and Range (43) sections support the Chihuahuan hooknosed snake (Gyalopion canum), prairie rattlesnake (Crotalus

viridis), and Texas threadsnake (Leptotyphlops dulcis).

The Southern Gulf (48), Eastern Atlantic (42), and Florida

Peninsular (40) sections are inhabited by the cottonmouth

(Agkistrodon piscivorus), Florida crowned snake (Tantilla

relicta), and southern watersnake (Nerodia fasciata). The

Southern Gulf supports the maximum diversity of turtles

(34) including the Alabama map turtle (Graptemys pulchra),

Pascagoula map turtle (Graptemys gibbonsi), and Peninsula

cooter (Pseudemys peninsularis). Other notable Coastal

Plain areas inhabited by a variety of turtles include the

Eastern Atlantic (23), Middle Gulf-Eastern (23), and Florida

Peninsular (21). Characteristic species include the common

musk turtle (Sternotherus odoratus), eastern box turtle

(Terrapene carolina), southern painted turtle (Chrysemys

dorsalis), and spiny softshell (Apalone spinifera).

Geographic Patterns of Species Listed as 

Threatened or Endangered

Figure 14.11 displays the distribution of 77 vertebrate species

listed as threatened or endangered by the U.S. Fish and

Wildlife Service in the South. There is an evident pattern

of endangerment along the Atlantic Ocean coast extending

from North Carolina to Florida and along the Gulf of Mexico

westward to Louisiana, with pockets along the southern coast

of Texas.



358 The Southern Forest Futures Project Table 14.3—(continued) Terrestrial vertebrate species that are federally listed as threatened or endangered in the South (U.S. Department of Interior 2011) Scientific namea Common name ESAb Subregion name Sectionc,d Picoides borealis Red-cockaded woodpecker E Coastal Plain, Piedmont, Appalachian- Cumberland, Mississippi Alluvial Valley, Mid-South 1_1, 1_2, 1_3, 1_4, 1_5, 1_6, 1_7, 2_1, 2_2, 2_3, 3_3, 3_4, 3_5, 4_1, 5_1, 5_2 Sternula antillarum Least tern E Coastal Plain, Mid-South 1_6, 5_1, 5_2, 5_3 Sternula antillarum athalassos Interior least tern E Coastal Plain, Appalachian- Cumberland, Mississippi Alluvial Valley. Mid-South 1_5, 1_6, 1_7, 3_5, 4_1, 4_2, 5_1, 5_2, 5_3 Sterna dougallii dougallii Roseate tern E Coastal Plain 1_2, 1_3 Strix occidentalis lucida Mexican spotted owl T Mid-South 5_4 Tympanuchus cupido attwateri Attwater’s greater prairie chicken E Coastal Plain, Mid-South, Mississippi Alluvial Valley 1_7, 5_2, 4_2, 5_2, 5_3 MAMMALS Bats Corynorhinus townsendii ingens Ozark big-eared bat E Mid-South 5_1 Corynorhinus townsendii virginianus Virginia big-eared bat E Appalachian-Cumberland 3_1, 3_2, 3_4, 3_5 Leptonycteris nivalis Mexican long-nosed bat E Mid-South 5_4 Myotis sodalis Indiana myotis E Coastal Plain, Piedmont, Appalachian- Cumberland, Mid-South 1_5, 2_1, 2_2, 2_3, 3_1, 3_2, 3_3, 3_4, 3_5, 5_1 Myotis grisescens Gray myotis E Coastal Plain, Piedmont, Appalachian- Cumberland, Mississippi Alluvial Valley, Mid-South 1_2, 1_4, 1_5, 2_1, 2_3, 3_1, 3_2, 3_3, 3_4, 3_5, 4_1, 5_1 Rodents Glaucomys sabrinus coloratus Carolina northern flying squirrel E Appalachian-Cumberland 3_1 Glaucomys sabrinus fuscus Virginia northern flying squirrel E Appalachian-Cumberland 3_2 Microtus pennsylvanicus dukecampbelli Duke’s salt marsh vole E Coastal Plain 1_4 Neotoma floridana smalli Key Largo woodrat E Coastal Plain 1_3 Oryzomys palustris natator Key Oryzomys E Coastal Plain 1_3 Peromyscus gossypinus population 1 Key Largo cotton deermouse E Coastal Plain 1_3 Peromyscus polionotus allophrys Choctawhatchee beach deermouse E Coastal Plain 1_4 Peromyscus polionotus ammobates Alabama beach deermouse E Coastal Plain 1_4 Peromyscus polionotus niveiventris Southeast beach deermouse T Coastal Plain 1_3 Peromyscus polionotus peninsularis St. Andrews beach deermouse E Coastal Plain 1_4 Peromyscus polionotus phasma Anastasia beach deermouse E Coastal Plain 1_3 Peromyscus polionotus trissyllepsis Perdido Key beach deermouse E Coastal Plain 1_4 Sciurus niger cinereus Delmarva fox squirrel E Coastal Plain 1_1 Carnivores Canis rufus Red wolf E Coastal Plain 1_2, 1_4 Leopardus pardalis Ocelot E Mid-South 5_3 Puma concolor coryi Florida panther E Coastal Plain 1_3 Puma concolor (all except coryi) Mountain lion SAT Mid-South 5_2, 5_3, 5_4 (Continued)


361

chapter 14. Wildlife and Forest Communities

Table 14.4—Vascular plant species that are Federally listed as threatened or endangered in the South (U. S. Department 

of Interior 2011) 

Scientific namea Common name ESAb Subregion name Sectionc,d

Ferns and Relatives

Asplenium scolopendrium var. americanum Hart’s-tongue Fern T Appalachian-Cumberland 3_4, 3_5

Isoetes louisianensis Louisiana Quillwort E Coastal Plain 1_4

Isoetes melanospora Black-spored Quillwort E Piedmont 2_2

Isoetes tegetiformans Merlin’s-grass E Piedmont 2_2

Conifers and Relatives

Torreya taxifolia Florida Torreya E Coastal Plain, Piedmont 1_4, 2_2

Flowering Plants

Graminoids

Carex lutea Sulphur Sedge E Coastal Plain 1_2

Scirpus ancistrochaetus Northeastern Bulrush E Appalachian-Cumberland 3_2

Zizania texana Texas Wild Rice E Mid-South 5_3

Cacti

Astrophytum asterias Star Cactus E Mid-South 5_3

Coryphantha scheeri var. robustispina Pima Pineapple Cactus E Mid-South 5_4

Echinocereus chisoensis var. chisoensis Chisos Hedgehog Cactus T Mid-South 5_2, 5_3, 5_4

Echinocereus viridiflorus var. davisii Davis’ Green Pitaya E Mid-South 5_4

Escobaria minima Nellie Cory Cactus E Mid-South 5_4

Escobaria sneedii var. sneedii Sneed Pincushion Cactus E Mid-South 5_4

Harrisia fragrans Fragrant Prickly-apple E Coastal Plain 1_3

Pilosocereus robinii Key Tree Cactus E Coastal Plain 1_3

Sclerocactus brevihamatus ssp. tobuschii Shorthook Fishhook 

Cactus E Mid-South 5_3, 5_4

Sclerocactus mariposensis Lloyd’s Mariposa Cactus T Mid-South 5_4

Vines

Apios priceana Price’s Potato-bean T Coastal Plain, Piedmont, 

Appalachian-Cumberland

1_5, 2_3, 3_4, 

3_5

Bonamia grandiflora Florida Lady’s-nightcap T Coastal Plain 1_3

Clematis morefieldii Morefield’s Leatherflower E Appalachian-Cumberland 3_4, 3_5

Clematis socialis Alabama Leather-flower E Piedmont 2_3

Cucurbita okeechobeensis Okeechobee Gourd E Coastal Plain 1_3

Galactia smallii Small’s Milkpea E Coastal Plain 1_3

Jacquemontia reclinata Reclined Clustervine E Coastal Plain 1_3

Herbs

Abronia macrocarpa Large-fruit Sand-verbena E Mid-South 5_2

Aeschynomene virginica Sensitive Joint-vetch T Coastal Plain, Piedmont 1_1, 2_1

Amaranthus pumilus Seabeach Amaranth T Coastal Plain 1_1, 1_2

Ambrosia cheiranthifolia South Texas Ragweed E Mid-South 5_2

Amorpha herbacea var. crenulata Crenulate Leadplant E Coastal Plain 1_3

Amphianthus pusillus Little Amphianthus T Piedmont 2_1, 2_2

Arabis perstellata Braun’s Rockcress E Appalachian-Cumberland 3_5

Arabis serotina Shalebarren Rockcress E Appalachian-Cumberland 3_2

Astragalus bibullatus Pyne’s Ground-plum E Appalachian-Cumberland 3_5

Baptisia arachnifera Hairy Rattleweed E Coastal Plain 1_2

Callirhoe scabriuscula Texas Poppy-mallow E Mid-South 5_3

(Continued)
362 The Southern Forest Futures Project

Table 14.4—(continued) Vascular plant species that are Federally listed as threatened or endangered in the South (U. S. 

Department of Interior 2011)

Scientific namea Common name ESAb Subregion name Sectionc,d

Campanula robinsiae Robins’ Bellflower E Coastal Plain 1_3

Cardamine micranthera Small-anther Bittercress E Piedmont 2_1

Chamaesyce deltoidea ssp. adhaerens Wedge Spurge E Coastal Plain 1_3

Chamaesyce garberi Garber’s Spurge T Coastal Plain 1_3

Chrysopsis floridana Florida Goldenaster E Coastal Plain 1_3

Clitoria fragrans Pigeon Wings T Coastal Plain 1_3

Crotalaria avonensis Avon Park Rabbit-bells E Coastal Plain 1_3

Cryptantha crassipes Terlingua Creek Cat’s-eye E Mid-South 5_4

Dalea foliosa Leafy Prairie-clover E Coastal Plain, 

Appalachian-Cumberland 1_5, 3_5

Echinacea laevigata Smooth Purple Coneflower E Coastal Plain, Piedmont, 

Appalachian-Cumberland

1_2, 2_1, 2_2, 

3_1, 3_2

Echinacea tennesseensis Tennessee Coneflower E Appalachian-Cumberland 3_5

Eriogonum longifolium var. gnaphalifolium Scrub Wild Buckwheat T Coastal Plain 1_3

Eryngium cuneifolium Wedgeleaf Buttonsnakeroot E Coastal Plain 1_3

Euphorbia telephioides Telephus Spurge T Coastal Plain 1_4

Geocarpon minimum Tiny Tim T Coastal Plain, Mid-South 1_6, 1_7, 5_1

Geum radiatum Spreading Avens E Appalachian-Cumberland 3_1

Halophila johnsonii Johnson’s Sea-grass T Coastal Plain 1_3

Harperocallis flava Harper’s Beauty E Coastal Plain 1_4

Helenium virginicum Virginia Sneezeweed T Appalachian-Cumberland 3_2

Helianthus paradoxus Pecos Sunflower T Mid-South 5_4

Helianthus schweinitzii Schweinitz’s Sunflower E Piedmont 2_1, 2_2

Helonias bullata Swamp-pink T Coastal Plain, Piedmont, 

Appalachian-Cumberland

1_1, 2_2, 3_1, 

3_2

Hexastylis naniflora Dwarf-flower Heartleaf T Piedmont, AppalachianCumberland 2_1, 2_2, 3_1

Hoffmannseggia tenella Slender Rushpea E Mid-South 5_2

Houstonia purpurea var. montana Mountain Bluet E Appalachian-Cumberland 3_1

Hymenoxys texana Prairie Dawn E Coastal Plain, Mid-South 1_7, 5_2

Hypericum cumulicola Highlands Scrub St. 

John’s-wort E Coastal Plain 1_3

Iliamna corei Peters Mountain Mallow E Appalachian-Cumberland 3_2

Isotria medeoloides Small Whorled Pogonia T Coastal Plain, Piedmont, 

Appalachian-Cumberland

1_1, 2_1, 2_2, 

3_1, 3_2, 3_3, 

3_4

Justicia cooleyi Cooley’s Water-willow E Coastal Plain 1_3

Lesquerella filiformis Missouri Bladderpod T Mid-South 5_1

Lesquerella lyrata Lyrate Bladderpod T Coastal Plain, 

Appalachian-Cumberland 1_5, 3_5

Lesquerella pallida White Bladderpod E Coastal Plain 1_7

Lesquerella perforata Spring Creek Bladderpod E Appalachian-Cumberland 3_5

Lesquerella thamnophila Zapata Bladderpod E Mid-South 5_3

Liatris helleri Heller’s Blazingstar T Appalachian-Cumberland 3_1

Liatris ohlingerae Florida Gayfeather E Coastal Plain 1_3

Lupinus westianus var. aridorum Scrub Lupine E Coastal Plain 1_3

(Continued)
363

chapter 14. Wildlife and Forest Communities

Table 14.4—(continued) Vascular plant species that are Federally listed as threatened or endangered in the South (U. S. 

Department of Interior 2011)

Scientific namea Common name ESAb Subregion name Sectionc,d

Lysimachia asperulifolia Roughleaf Loosestrife E Coastal Plain 1_1, 1_2

Manihot walkerae Walker’s Manihot E Mid-South 5_3

Marshallia mohrii Mohr’s Barbara’s-buttons T Coastal Plain, Piedmont 1_5, 2_3

Minuartia cumberlandensis Cumberland Sandwort E Appalachian-Cumberland 3_4, 3_5

Nolina brittoniana Britton’s Bear-grass E Coastal Plain 1_3

Oxypolis canbyi Canby’s Dropwort E Coastal Plain 1_2, 1_4

Phlox nivalis ssp. texensis Texas Trailing Phlox E Coastal Plain 1_7

Pinguicula ionantha Violet-flowered Butterwort T Coastal Plain 1_4

Pityopsis ruthii Ruth’s Silk-grass E Appalachian-Cumberland 3_1

Platanthera leucophaea Eastern Prairie Whitefringed Orchid T Appalachian-Cumberland 3_2

Platanthera praeclara Western Prairie Whitefringed Orchid T Mid-South 5_2

Polygala lewtonii Lewton’s Polygala E Coastal Plain 1_3

Polygala smallii Tiny Polygala E Coastal Plain 1_3

Polygonella basiramia Wireweed E Coastal Plain 1_3

Polygonella myriophylla Small’s Jointweed E Coastal Plain 1_3

Potamogeton clystocarpus Little Aguja Pondweed E Mid-South 5_4

Ptilimnium nodosum Harperella E Coastal Plain, Piedmont, 

Mid-South

1_2, 2_1, 2_2, 

2_3, 5_1

Sagittaria fasciculata Bunched Arrowhead E Piedmont, AppalachianCumberland 2_2, 3_1

Sagittaria secundifolia Little River Arrowhead T Piedmont 2_2, 2_3

Sarracenia oreophila Green Pitcherplant E Piedmont, AppalachianCumberland 2_3, 3_1, 3_4

Sarracenia rubra ssp. alabamensis Alabama Canebrake 

Pitcherplant E Coastal Plain 1_5

Sarracenia rubra ssp. jonesii Mountain Sweet 

Pitcherplant E Piedmont, AppalachianCumberland 2_2, 3_1

Schwalbea americana Chaffseed E Coastal Plain 1_2, 1_4, 1_7

Scutellaria floridana Florida Skullcap T Coastal Plain 1_4

Scutellaria montana Large-flower Skullcap T Piedmont, AppalachianCumberland 2_3, 3_3, 3_4

Silene polypetala Fringed Campion E Coastal Plain, Piedmont 1_2, 1_4, 2_2

Sisyrinchium dichotomum Reflexed Blue-eyed-grass E Piedmont, AppalachianCumberland 2_1, 2_2, 3_1

Solidago albopilosa White-haired Goldenrod T Appalachian-Cumberland 3_4, 3_5

Solidago shortii Short’s Goldenrod E Appalachian-Cumberland 3_5

Solidago spithamaea Blue Ridge Goldenrod T Appalachian-Cumberland 3_1

Spigelia gentianoides Gentian Pinkroot E Coastal Plain 1_4

Spiranthes parksii Navasota Ladies’-tresses E Coastal Plain, Mid-South 1_7, 5_2

Thalictrum cooleyi Cooley’s Meadowrue E Coastal Plain 1_2, 1_4

Thymophylla tephroleuca Ashy Dogweed E Mid-South 5_3

Trifolium stoloniferum Running Buffalo Clover E Appalachian-Cumberland 3_4, 3_5

Trillium persistens Persistent Trillium E Piedmont, AppalachianCumberland 2_2, 3_1

Trillium reliquum Relict Trillium E Coastal Plain, Piedmont 1_2, 1_4, 1_5, 

2_2

(Continued)
364 The Southern Forest Futures Project

Table 14.4—(continued) Vascular plant species that are Federally listed as threatened or endangered in the South (U. S. 

Department of Interior 2011)

Scientifi c namea common name eSAb Subregion name Sectionc,d

Warea amplexifolia Wide-leaf Warea E Coastal Plain 1_3

Warea carteri Carter’s Mustard E Coastal Plain 1_3

Xyris tennesseensis Tennessee Yellow-eyedgrass E Coastal Plain, Piedmont, 

Appalachian-Cumberland 1_5, 2_3, 3_5

Trees and Shrubs

Asimina tetramera Four-petal Pawpaw E Coastal Plain 1_3

Ayenia limitaris Texas Ayenia E Mid-South 5_3

Betula uber Virginia Roundleaf Birch T Appalachian-Cumberland 3_2

Chionanthus pygmaeus Pygmy Fringetree E Coastal Plain 1_1

Conradina brevifolia Shortleaf Rosemary E Coastal Plain 1_3

Conradina etonia Etonia Rosemary E Coastal Plain 1_3

Conradina glabra Apalachicola Rosemary E Coastal Plain 1_4

Conradina verticillata Cumberland False 

Rosemary T Appalachian-Cumberland 3_4

Deeringothamnus pulchellus Beautiful Pawpaw E Coastal Plain 1_3

Deeringothamnus rugelii Rugel’s Pawpaw E Coastal Plain 1_3

Dicerandra christmanii Yellow Scrub Balm E Coastal Plain 1_3

Dicerandra cornutissima Longspurred Mint E Coastal Plain 1_3

Dicerandra frutescens Scrub Mint E Coastal Plain 1_3

Dicerandra immaculata Lakela’s Mint E Coastal Plain 1_3

Frankenia johnstonii Johnston’s Frankenia E Mid-South 5_3

Hudsonia montana Mountain Golden-heather T Appalachian-Cumberland 3_1

Lindera melissifolia Pondberry E Coastal Plain, Mississippi 

Alluvial Valley

1_1, 1_2, 1_4, 

1_6, 4_1

Prunus geniculata Scrub Plum E Coastal Plain 1_3

Quercus hinckleyi Hinckley’s Oak T Mid-South 5_4

Rhododendron chapmanii Chapman’s Rhododendron E Coastal Plain 1_3, 1_4

Rhus michauxii Michaux’s Sumac E Coastal Plain, Piedmont 1_2, 2_1, 2_2, 

Ribes echinellum Miccosukee Gooseberry T Coastal Plain, Piedmont 1_4, 2_2

Spiraea virginiana Virginia Spiraea T Piedmont, AppalachianCumberland

2_3, 3_1, 3_3, 

3_4, 3_5

Styrax platanifolius ssp. texanus Texas Snowbell E Mid-South 5_3, 5_4

Ziziphus celata Scrub Ziziphus E Coastal Plain 1_3

aSpecies names follow USDA NRCS Plants Database (2010).

bT = Threatened; E = Endangered; SAT = Similarity of Appearance to a threatened taxon.

cLocation data from NatureServe (2010).

d1_1 (Northern Atlantic); 1_2 (Eastern Atlantic); 1_3 (Florida Peninsular); 1_4 (Southern Gulf); 1_5 (Middle Gulf-Eastern); 1_6 (Middle 

Gulf-Western); 1_7 (Western Gulf); 2_1 (Central Appalachian Piedmont); 2_2 (Southern Appalachian Piedmont); 2_3 (Piedmont 

Ridge, Valley and Plateau); 3_1 (Blue Ridge); 3_2 (Northern Ridge and Valley); 3_3 (Southern Ridge and Valley); 3_4 (Cumberland 

Plateau and Mountain); 3_5 (Interior Low Plateau); 4_1 (Holocene Deposits); 4_2 (Deltaic Plain); 5_1 (Ozark-Ouachita Highlands); 

5_2 (Cross Timbers); 5_3 (High Plains); 5_4 (West Texas Basin and Range).



368 The Southern Forest Futures Project

Table 14.5—(continued) Amphibian species of global conservation concern within the South (NatureServe 2011)a

Scientific name Common name Subregion name Sectionb

Plethodon caddoensis Caddo Mountain salamander Coastal Plain, Mid-South 1_6, 5_1

Plethodon cheoah Cheoah Bald salamander Appalachian-Cumberland 3_1

Plethodon fourchensis Fourche Mountain salamander Mid-South 5_1

Plethodon hubrichti Peaks of Otter salamander Piedmont, Appalachian-Cumberland 2_1, 3_2

Plethodon kiamichi Kiamichi slimy salamander Mid-South 5_1

Plethodon ouachitae Rich Mountain salamander Mid-South 5_1

Plethodon petraeus Pigeon Mountain salamander Piedmont 2_3

Plethodon sequoyah Sequoyah slimy salamander Mid-South 5_1

Plethodon sherando Big Levels salamander Piedmont 2_1

Plethodon shermani Red-legged salamander Appalachian-Cumberland 3_1

Plethodon virginia Shenandoah Mountain salamander Appalachian-Cumberland 3_2

G3

Amphiuma pholeter One-toed amphiuma Coastal Plain 1_3, 1_4

Aneides aeneus Green salamander Coastal Plain, Piedmont, 

Appalachian-Cumberland

1_5, 2_1, 2_2, 

2_3, 3_1, 3_3, 

3_4, 3_5

Cryptobranchus 

alleganiensis Hellbender Coastal Plain, Piedmont, 

Appalachian-Cumberland

1_5, 2_2, 2_3, 

3_1, 3_2, 3_3, 

3_4, 3_5

Desmognathus aeneus Seepage salamander Coastal Plain, Piedmont, 

Appalachian-Cumberland

1_5, 2_2, 2_3, 

3_1, 3_3

Desmognathus 

apalachicolae Apalachicola dusky salamander Coastal Plain 1_4

Desmognathus imitator Imitator salamander Appalachian-Cumberland 3_1

Desmognathus santeetlah Santeetlah dusky salamander Appalachian-Cumberland 3_1

Desmognathus wrighti Pygmy salamander Appalachian-Cumberland 3_1, 3_2, 3_3

Eurycea junaluska Junaluska salamander Appalachian-Cumberland 3_1, 3_3

Eurycea latitans Cascade Caverns salamander Mid-South 5_3

Eurycea troglodytes Eurycea troglodytes complex Mid-South 5_3

Eurycea tynerensis Oklahoma salamander Mid-South 5_1

Necturus lewisi Neuse River waterdog Coastal Plain, Piedmont 1_1, 2_1

Plethodon jordani Red-cheeked salamander Appalachian-Cumberland 3_1

Plethodon kisatchie Louisiana slimy salamander Coastal Plain, Mississippi Alluvial 

Valley 1_7, 4_1

Plethodon metcalfi Southern gray-cheeked salamander Piedmont, Appalachian-Cumberland 2_2, 3_1

Plethodon montanus Northern gray-cheeked salamander Piedmont, Appalachian-Cumberland 2_2, 3_1

Plethodon punctatus White-spotted salamander Appalachian-Cumberland 3_2

Plethodon teyahalee Southern Appalachian salamander Piedmont, Appalachian-Cumberland 2_2, 3_1

Plethodon websteri Webster’s salamander Coastal Plain, Piedmont 1_4, 1_5, 2_2, 

2_3

Plethodon welleri Weller’s salamander Appalachian-Cumberland 3_1, 3_2

aG1 = Critically imperiled; G2 = Imperiled; G3 = Vulnerable.

b1_1 (Northern Atlantic); 1_2 (Eastern Atlantic); 1_3 (Florida Peninsular); 1_4 (Southern Gulf); 1_5 (Middle Gulf-Eastern); 1_6 (Middle 

Gulf-Western); 1_7 (Western Gulf); 2_1 (Central Appalachian Piedmont); 2_2 (Southern Appalachian Piedmont); 2_3 (Piedmont 

Ridge, Valley and Plateau); 3_1 (Blue Ridge); 3_2 (Northern Ridge and Valley); 3_3 (Southern Ridge and Valley); 3_4 (Cumberland 

Plateau and Mountain); 3_5 (Interior Low Plateau); 4_1 (Holocene Deposits); 4_2 (Deltaic Plain); 5_1 (Ozark-Ouachita Highlands); 

5_2 (Cross Timbers); 5_3 (High Plains); 5_4 (West Texas Basin and Range).

370 The Southern Forest Futures Project

species) lead in the number of species of concern followed by

the Appalachian-Cumberland highlands (31 vertebrate and

207 plant species) and Piedmont (29 vertebrate and

188 plant species). Nine imperiled vertebrate and 20 plant

species inhabit the Mississippi Alluvial Valley. Although

several of these hot spots are shared by several species, there

are interesting differences among the taxa that are described

below.

Amphibians—Sixty-six amphibian species are of

conservation concern (table 14.5). Salamanders dominate

with 62 listings, followed by frogs and toads with 4 listings.

Amphibians at risk occur in heaviest concentrations across

the Appalachian-Cumberland highlands, Coastal Plain, and

Mid-South (fig. 14.16). Numbers of imperiled amphibians are

prominent in the Blue Ridge (15 salamanders) where they are

affected by habitat degradation, water pollution, drought, and

acid rain. Characteristic species include the Blue Ridge graycheeked salamander (Plethodon amplus), Tellico salamander

(Plethodon aureolus), and hellbender (Cryptobranchus

alleganiensis).

Also important in supporting species at risk are the High

Plains (12 species) and Southern Gulf (11 species). These

areas provide important habitat for the dusky gopher frog

(Rana sevosa), Florida bog frog (Rana okaloosae), and

striped newt (Notophthalmus perstriatus). Threats to these

species include loss of longleaf pine, agricultural and urban

development, fire exclusion, contamination of springs,

introduction of nonnative fish into breeding ponds, and

stream impoundment.

Birds—Eighteen avian species are of conservation concern

(table 14.6). The breakdown along subtaxa is 3 wading and

shorebirds, 7 perching birds, and 8 others. Species include

the Kirtland’s warbler (Dendroica kirtlandii), goldencheeked warbler (Dendroica chrysoparia), Fea’s petrel

(Pterodroma feae), and lesser prairie chicken (Tympanuchus

pallidicinctus).

Bird species at risk occur predominately along the Atlantic

Ocean extending from southern Virginia to Florida and

continuing along the Gulf of Mexico across Louisiana to

the southernmost tip of eastern Texas (fig. 14.17). Highest

numbers occur in the Cross Timbers (7 species) and High

Plains (6 species) sections of the Mid-South. Species such

as the piping plover (Charadrius melodus) are vulnerable to

disturbance of nesting areas, declining fish populations, oil

spills, and extreme weather conditions. The black-capped

vireo (Vireo atricapilla) is vulnerable to cowbird parasitism

and loss of nesting habitat from housing development, road

construction, and over-browsing by domestic livestock.

Peninsular Florida supports six birds at risk, including the

Florida scrub jay (Aphelocoma coerulescens) and wood

stork. Conservation concerns center on rapidly growing

population centers and habitat conversion to urban and

agricultural uses (such as sugarcane and citrus production).

Imperiled birds also occur in the Eastern Atlantic, Southern

Gulf, and Western Gulf sections; these species include

the whooping crane (Grus americana) and red-cockaded

woodpecker (Picoides borealis). Threats to birds in these

areas include conversion of longleaf pine and upland

hardwoods to other uses, hydrological alteration, and coastal

development.

Mammals—Twenty-two mammal species are imperiled or

vulnerable (table 14.7). Rodents dominate with 8 listings,

followed by bats (7), carnivores (4), and others (3). Species

include the Texas kangaroo rat (Dipodomys elator),

Strecker’s pocket gopher (Geomys streckeri), red wolf (Canis

rufus), and jaguar (Panthera onca).

Although the Coastal Plain (10 species) and Mid-South

(9 species) support the largest number of imperiled

mammals (fig. 14.18), it is the Appalachian-Cumberland

highlands where the majority of hot spots occur. Numbers

of imperiled mammals are particularly prominent in

Oklahoma and Tennessee in the Interior Low Plateau,

Cumberland Plateau and Mountain, Southern Ridge and

Valley, and in the Blue Ridge of North Carolina. Species

occurring in these sections include the eastern smallfooted myotis, Carolina and Virginia northern flying

squirrels, and Virginia big-eared bat. The Ozark-Ouachita

Highlands are also notable, supporting the Ozark big-eared

bat, southeastern myotis (Myotis austroriparius), and

several other bat species. Cave disturbance, vandalism,

and destruction of roost sites imperil these species, as

does habitat loss stemming from deforestation and stream

channelization. Threats to other mammals in these areas

include insect pests (such as balsam wooly adelgid,

gypsy moth), acid rain which contaminates mycorrhizal

food sources, and heavy metals in forest litter. Habitat

fragmentation has resulted in population isolation and the

loss of dispersal and travel corridors.

Peninsular Florida and the northern portion of the Eastern

Atlantic are also important for mammals at risk, supporting

the round-tailed muskrat (Neofiber alleni), Florida

deermouse (Podomys floridanus), and West Indian manatee

(Trichechus manatus). Threats to these species include loss

of wetlands, marsh drainage, and salt water intrusion—

all of which reduce available habitat and further isolate

populations. For the manatee, the potential loss of warmwater refugia from residential and commercial development

of coastal land remains a problem.




375

chapter 14. Wildlife and Forest Communities

Table 14.8—(continued) Reptile species of global conservation concern within the South (NatureServe 2011)a

Scientific name Common name Subregion name Sectionb

G2

Dermochelys coriacea Leatherback Coastal Plain 1_1, 1_2, 1_3

Graptemys barbouri Barbour’s map turtle Coastal Plain, Piedmont 1_2, 1_4, 2_2

Graptemys ernsti Escambia map turtle Coastal Plain 1_4

Graptemys flavimaculata Yellow-blotched map turtle Coastal Plain 1_4, 1_5

Graptemys gibbonsi Pascagoula map turtle Coastal Plain 1_4

Graptemys oculifera Ringed map turtle Coastal Plain 1_4, 1_5

Sternotherus depressus Flattened musk turtle Coastal Plain, Piedmont 1_5, 2_3

G3

Caretta caretta Loggerhead Coastal Plain, Mississippi 

Alluvial Valley, Mid-South

1_1, 1_2, 1_3, 1_4, 

4_2, 5_2, 5_3

Chelonia mydas Green turtle Coastal Plain 1_1, 1_2, 1_3, 1_4

Eretmochelys imbricata Hawksbill Coastal Plain 1_1, 1_3

Glyptemys muhlenbergii Bog turtle Piedmont, AppalachianCumberland 2_1, 2_2, 3_1

Graptemys caglei Cagle’s map turtle Mid-South 5_3

Gopherus polyphemus Gopher tortoise Coastal Plain, Piedmont 1_2, 1_3, 1_4, 1_5, 2_2

Graptemys nigrinoda Black-knobbed map turtle Coastal Plain, Piedmont 1_4, 1_5, 2_2

Macrochelys temminckii Alligator snapping turtle

Coastal Plain, Piedmont, 

Mississippi Alluvial Valley, 

Mid-South, AppalachianCumberland

1_2, 1_3, 1_4, 1_5, 

1_6, 1_7, 2_2, 2_3, 

3_5, 4_1, 5_1, 5_2, 5_3

Pseudemys gorzugi Rio Grande River cooter Mid-South 5_3

Trachemys gaigeae Mexican plateau slider Mid-South 5_4

aG1 = Critically imperiled; G2 = Imperiled; G3 = Vulnerable.

b1_1 (Northern Atlantic); 1_2 (Eastern Atlantic); 1_3 (Florida Peninsular); 1_4 (Southern Gulf); 1_5 (Middle Gulf-Eastern); 

1_6 (Middle Gulf-Western); 1_7 (Western Gulf); 2_1 (Central Appalachian Piedmont); 2_2 (Southern Appalachian 

Piedmont); 2_3 (Piedmont Ridge, Valley and Plateau); 3_1 (Blue Ridge); 3_2 (Northern Ridge and Valley); 3_3 (Southern 

Ridge and Valley); 3_4 (Cumberland Plateau and Mountain); 3_5 (Interior Low Plateau); 4_1 (Holocene Deposits); 

4_2 (Deltaic Plain); 5_1 (Ozark-Ouachita Highlands); 5_2 (Cross Timbers); 5_3 (High Plains); 5_4 (West Texas Basin and 

Range).

377

chapter 14. Wildlife and Forest Communities

five that are threatened—among them Florida skullcap

(Scutellaria floridana) and Telephus spurge (Euphorbia

telephioides). Two other regions in Florida, the central

ridges and uplands, including Lake Wales Ridge, and much

of the area south of Lake Okeechobee, have a number of

sensitive species. Sandhills, scrub, flatwoods, bayheads,

and hammocks of the Florida central uplands have 21

species listed as endangered or threatened. These include

pygmy fringetree (Chionanthus pygmaeus), false rosemary

(C. brevifolia), and scrub balm (Dicerandra frutescens).

Upward along the Atlantic Coastal Plain, at-risk plants in

North Carolina include wet-site species. Examples include

the endangered pondberry (Lindera melissifolia) and

Canby’s dropwort (Oxypolis canbyi), as well as American

chaffseed (Schwalbea americana) and other species of fireprone ecosystems that occupy pine savannas, bottomland

and swamp forests, and scattered pocosin wetlands.

Species Extirpation within Selected States 

of the South

Terrestrial vertebrates—The Southern Forest Resource

Assessment (Trani 2002b) presented 32 terrestrial species

that were classified as extinct or extirpated from the South.

In the years following that effort, the databases from State

Heritage Agencies (NatureServe 2011) indicate this list has

expanded to 65 species (table 14.9). The degree of extirpation

species varies among taxonomic groups, with birds

comprising 61 percent, followed by mammals (28 percent),

reptiles (6 percent), and amphibians (5 percent).

Recent extirpation was most prominent in the perching bird

and wading bird groups. Nine perching species have been

lost from six States. Henslow’s sparrow (Ammodramus

henslowii) has experienced population and range reductions

due to habitat alteration (urban growth and industrial

development), while the bobolink (Dolichonyx oryzivorus)

decline has been attributed to incompatible agricultural

practices (NatureServe 2011). There appears to be a pattern

among extirpated wading birds that reflects the continuing

loss and modification of wetland habitat in the South. The

American bittern (Botaurus lentiginosus) is threatened by

the degradation of wetlands due to drainage, siltation, and

conversion to agriculture; the wood stork has been negatively

impacted by human alteration of natural hydrological

conditions that affect both nesting and feeding areas. The

white-faced ibis is also vulnerable to fluctuating water levels,

habitat alteration, and pesticide contamination.

Also notable is the first appearance of extirpated bat species

on the list since the Southern Forest Resource Assessment.

Populations of the Northern myotis (Myotis septentrionalis)

in the South are small and widely dispersed; the philopatry

displayed by this bat for winter roosts may lead to local

extirpation if a hibernaculum is modified or destroyed

(Chapman 2007). The Indiana myotis is quite vulnerable to

natural and human-caused disturbance due to concentrated

populations in few winter hibernacula. Population declines

have also been attributed to destruction of summer

foraging and roosting habitat by deforestation and stream

channelization (Ford and Chapman 2007).

Carnivore species (10) remain the largest group of extirpated

mammals in the South. The extirpation of large carnivores

such as the gray wolf (Canis lupus) reflects the history of

European settlement (Trani 2002b) where they were regarded

as threats to livestock and personal safety. The decline of

the red wolf has been attributed to indiscriminate predator

control, extensive land clearing, and coyote (Canis latrans)

hybridization (Trani and Chapman 2007). The disappearance

of the jaguar reflects habitat destruction, illegal hunting,

and exploitation by the fur industry (NatureServe 2011).

Other carnivores such as the cougar (Puma concolor) were

relegated to relatively remote areas. 

Recent extirpation of reptile species occurred in four States.

The mimic glass lizard has a disjunct distribution in the

South; it is vulnerable to habitat loss from development,

conversion to pine plantations, and road mortality. Snakes

comprise three-fourths of the State-level reptile extirpations.

The Southern hog-nosed snake is declining throughout most

of its range in the Coastal Plain; potential threats include

fire ants, intensive agricultural/silvicultural activities,

widespread pesticide application, and road mortality.

Frogs and toads are new to the list of extirpated amphibian

species since the Southern Forest Resource Assessment. The

dusky gopher frog formerly occurred in the Coastal Plain

from Alabama to Louisiana; it is now known from a small

area in Mississippi and threatened by habitat degradation

(NatureServe 2011).

Vascular plants—The 550 extirpated plant species listed

by NatureServe (2011) databases include those within

74 vascular plant families (table 14.10). Habitat loss has

reduced the range of species associated with unique plant

communities or those found in areas subject to development.

Climate change may cause further reduction in species range.

For example, seaside heliotrope (Heliotropium curassavicum

var. curassavicum), a plant of salt flats and marshes in the

South, has disappeared from North Carolina, likely due

to past coastal development, and may be threatened in the

future by sea level rise in other Southern States (chapter 13).

Other obligate and facultative freshwater wetland plants,

including four bladderwort (Utricularia) species, have

disappeared from selected States. Plants of locally unique

areas such as glades, savannas, and prairie-like sites (e.g.,

yellow flax (Linum sulcatum), entire leaf Indian paintbrush

(Castilleja indivisa), and American columbo (Frasera

caroliniensis), have been extirpated from some States,


380 The Southern Forest Futures Project

Table 14.10—Number of vascular plant species by family considered to be extirpated 

from selected States in the South (NatureServe 2011)

Family No. Species Former Area of Occurrence

Acanthus 3 GA(2), SC(2)

Amaranth 3 AR(1), OK(2)

Aster 56 AL(7), AR(7), FL(2), GA(10), KY(8), LA(3), NC(13), 

OK(1), TN(5), TX(1), VA(4)

Barberry 1 AL(1)

Beech 5 AL(1), AR(1), GA(1), FL(1), TX(1)

Bladderwort 4 AR(1), NC(2), VA(1)

Blazingstar 2 AR(1), OK(1)

Borage 4 FL(1), KY(1), NC(2)

Broom-Rape 2 KY(2), TN(1)

Buckthorn 1 LA(1)

Buckwheat 3 GA(1), NC(2)

Buttercup 13 AL(2), KY(5), NC(3), OK(1), SC(1), TN(2), VA(2)

Cactus 1 TX(1)

Carrot 11 AR(2), GA(2), KY(3), LA(2), OK(1), VA(2)

Currant 1 TN(1)

Dodder 1 GA(12)

Dogbane 1 MS(1)

Elm 1 AL(1)

Evening-Primrose 8 AL(2), AR(2), FL(1), KY(2), TX(1), VA(1)

Ferns and Relatives 29 AL(1), AR(3), FL(4), GA(1), KY(4), LA(6), MS(1), NC(1), 

OK(3), SC(1), TN(3), VA(3)

Fig-Marigold 1 TX(1)

Figwort 30 AL(1), AR(4), GA(3), KY(5), LA(6), MS(2), NC(5), 

OK(2), TN(4), TX(1), VA(4)

Flax 3 FL(2), GA(1), NC(2)

Four-O’clock 2 KY(1), TX(1)

Gentian 8 AL(1), KY(1), LA(3), NC(2), OK(1), TX(1)

Geranium 2 AR(1), TN(1)

Goosefoot 1 NC(1)

Grape 1 GA(1)

Grass 37 AL(1), AR(5), FL(2), GA(6), KY(5), LA(2), MS(2), NC(8), 

OK(3), TN(4), VA(7)

Greenbrier 3 AR(1), KY(1), VA(1)

Heath 5 GA(2), NC(1), SC(1), TN(1)

Holly 1 GA(1)

Honeysuckle 7 AR(1), GA(2), LA(1), OK(1), TN(2)

Iris 2 AR(1), OK(1)

Laurel 2 AR(1), FL(1), LA(1)

Loosestrife 1 VA(1)

Lily 17 AR(2), GA(3), KY(2), LA(2), NC(5), OK(1), SC(2), VA(1)

Madder 1 FL(1)

(Continued)
381

chapter 14. Wildlife and Forest Communities

Table 14.10—(continued) Number of vascular plant species by family considered to be 

extirpated from selected States in the South (NatureServe 2011)

Family No. Species Former Area of Occurrence

Mallow 6 KY(1), LA(2), NC(1), TN(1), TX(1)

Meadowfoam 1 LA(1)

Melastome 2 GA(1), TX(1)

Morning-Glory 1 NC(1), OK(1), VA(1)

Milkweed 7 AR(1), GA(1), LA(1), MS(1), NC(1), OK(1), TX(1)

Milkwort 4 KY(1), NC(1), VA(2)

Mint 16 AR(1), FL(1), GA(2), KY(5), NC(6), TN(1), TX(1), VA(2)

Mustard 13 AL(3), GA(2), KY(3), LA(2), MS(1), NC(2), TN(1), VA(1)

Nettle 1 NC(1), OK(1)

Orchid 30 AR(1), FL(10), GA(4), KY(4), NC(5), OK(2), SC(3), 

TN(2)

Other Flowering 

Plants 24 AL(1), AR(1), FL(1), GA(2), KY(3), LA(6), OK(3), SC(2), 

TX(1), VA(5)

Pea 25 AL(3), AR(2), FL(2), GA(2), KY(2), LA(1), NC(6), SC(1), 

TN(2), TX(3), VA(3)

Pepper 1 FL(1)

Pink 11 AL(1), FL(1), LA(2), OK(1), SC(2), TN(1), VA(5)

Pipewort 2 SC(1), TN(1)

Pitcherplant 2 LA(1), TN(1)

Plantain 1 FL(1), KY(1), VA(1)

Pondweed 5 KY(1), LA(2), NC(2), VA(1)

Potato 7 GA(2), MS(1), OK(4)

Primrose 4 GA(1), KY(3), VA(2)

Rock-Rose 3 AR(1), FL(1), TN(2)

Rose 11 AR(3), GA(1), MS(1), NC(2), TN(1), TX(1), VA(2)

Rue 1 FL(1)

Rush 4 AL(1), KY(1), NC(1), TN(1)

Saxifrage 4 AL(1), KY(1), LA(1), NC(1), TN(1)

Sedge 63 AL(1), AR(8), FL(2), GA(6), KY(11), LA(4), NC(11), 

OK(2), SC(3), TN(11), TX(1), VA(12)

Spurge 3 AR(2), FL(1)

St. John’s Wort 7 AL(1), KY(3), NC(2), VA(2)

Stonecrop 1 NC(1), TN(1)

Sumac 2 OK(1), SC(1) 

Valerian 1 OK(1)

Verbena 3 KY(1), NC(1), OK(1), VA(1)

Violet 1 OK(1)

Water-Lily 1 MS(1), NC(1)

Water-Milfoil 1 KY(1)

Water-Plantain 4 AR(1), LA(1), NC(1), VA(1)

Willow 3 AR(1), KY(2), NC(1)

TOTAL 550
382 The Southern Forest Futures Project

especially where the habitat is sparse or at the edge of the

range.

Plant species that were initially known from single sporadic

locations, or are inconspicuous may easily be overlooked in

field surveys. This includes the southern (Listera australis)

and heartleaf (L. cordata) twayblades orchids, possibly

extirpated from Kentucky and North Carolina, respectively.

On the other hand, showy or specialized plants such as

selected orchids may be lost through habitat reduction and

exploitation. These include species in the genus Platanthera:

Chapman’s fringed orchid (P. chapmanii - Presumed extinct

Georgia); white fringeless orchid (P. integrilabia - Presumed

extinct North Carolina); eastern prairie white-fringed orchid

(P. leucophaea - Presumed extinct Oklahoma); snowy orchid

(P. nivea - Presumed extinct Arkansas), and purple fringeless

orchid (P. peramoena - Presumed extinct South Carolina).

Harvesting from the wild may lead to increasing rates of

extirpation of economically important plants if market

demands increase faster than the supplies from garden

populations.

Forecasts of Urban Growth, Forest Loss, 

and Climate Change

Potential sources of future threats to wildlife and plant

communities include forest and range loss, coastal

inundation, forest fragmentation with land development,

and growing urban centers. Because the forecasts of urban

growth vary across the South, species may be impacted

disproportionally. Forecast changes in forest cover reflect, for

the most part, the pattern forecast for urbanization (chapter

4). Among the possible futures described in chapter 2, the

one that predicts the highest loss of forest and the greatest

urban growth, Cornerstone B, will be discussed below.

Urbanizing areas overlap with several areas of conservation

concern in the following subregions:

Mid-South—In Arkansas, the Ozark-Ouachita Highlands

region around Hot Springs and Little Rock is predicted

to experience 10 to 20 percent forest loss and an equal

percentage of urban growth. This area includes Hot Springs

National Park and the Ouachita National Forest. Forest and

glade plants that could be threatened on unprotected lands

in this westernmost area include the vulnerable southern

lady’s slipper (Cypripedium kentuckiense) reported by Case

and others (1998), the clasping twistflower (Streptanthus

maculatus), and least trillium (Trillium pusillum) reported by

Timmerman-Erskine and others (2003).

Urban growth in this subregion is forecast for counties

that support the Caddo Mountain salamander (Plethodon

caddoensis), the Ozark big-eared bat (Corynorhinus

townsendii ingens), and the Oklahoma salamander (Eurycea

tynerensis). Additional Mid-South development along the

southern borders shared by Cross Timbers and Western Gulf

could impact a numerous reptiles, especially Cagle’s map

turtle (Graptemys caglei), loggerhead, and other turtles. This

area also lies within a band of especially high avian richness

that occurs along the Texas Gulf Coast of the Central Flyway.

Appalachian-Cumberland highlands—Ten to twenty-five

percent urban growth and 10 to 20 percent forest loss near

Nashville in the Interior Low Plateau and around Knoxville

and Asheville in the Blue Ridge section could threaten bats,

salamanders, and concentrations of sensitive plant species.

The central Tennessee basin, adjacent escarpment, and

highland rim around Nashville have plants of limestone

glades, prairie-like areas, and forests. These include the

endangered Pyne’s ground plum (Astragalus bibullatus) and

Tennessee coneflower (Echinacea tennesseensis), which

are endemic to central basin limestone glades (Baskin and

Baskin 2005, Snyder and others 1994).

In eastern Tennessee and western North Carolina, forest

loss, increased recreational use and residential development

near Knoxville and Asheville threaten to reduce the high

biodiversity of the Southern Appalachian Mountains.

Warmer temperatures may allow migration of southern

species into lower elevation sites. Even though large public

land holdings (Great Smoky Mountain National Park, Blue

Ridge Parkway, and Nantahala, Pisgah, and Cherokee

National Forests) buffer and protect these habitats, residential

development and growing recreational use threaten plant

species such as the endangered spreading avens (Geum

radiatum), which is endemic to high elevation rock outcrops,

grassy balds, and cliff faces (Murdock 1993). Warmer

temperatures, changes in precipitation or fire regime, or

climate-change induced competition from offsite plants

may threaten spreading avens (Godt and Hamrick 1995;

Murdock 1994), along with other species of high elevation

and unique habitats such as the vulnerable false dandelion

(Krigia montana) on cliffs, outcrops, and grassy balds; the

vulnerable Rugel’s ragwort (Rugelia nudicaulis) of spruce

forests; and the endangered Smoky Mountains mannagrass

(Glyceria nubigena) of high elevation seeps. The Blue Ridge

supports a notable 53 species of salamanders, 15 of which

are imperiled or vulnerable: dwarf black-bellied salamander

(Desmognathus folkertsi), red-legged salamander (Plethodon

shermani), and South Mountain gray-cheeked salamander

(Plethodon meridianus). Any loss of habitat connectivity will

make migration difficult for the amphibians that occur there.

Piedmont—Forecasts of substantial urban growth (10 to

25 percent), with substantial losses of forest habitat, could

impair the relatively high richness of amphibians (59 to

76 species/section) and mammals (49 to 58 species/section)

that inhabit this subregion. Several species of concern

occur in the Central Appalachian Piedmont (14), Southern

Appalachian Piedmont (18), and Piedmont Ridge and
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chapter 14. Wildlife and Forest Communities

Valley (17), including the black warrior waterdog (Necturus

alabamensis), gray myotis, Peaks of Otter salamander

(Plethodon hubrichti), and Shenandoah Mountain

salamander. The greater than 25 percent urban growth

predicted for Atlanta, particularly expansion along Interstate

85 northward toward Greenville, SC, could threaten plants

of upland forests and openings, such as American ginseng

(Panax quinquefolius). However, more than 75 percent of atrisk plant species in the fast-growing DeKalb and Gwinnett

counties around Atlanta are either associated with protected

lands or with areas that are otherwise inaccessible for

development (e.g., granite outcrops).

Areas with concentrations of sensitive plant species or plant

communities—including the Blue Ridge escarpment and

foothills (Southern Appalachian Piedmont), and southern

extensions of the Cumberland Plateau and adjacent Valley

and Ridge (Piedmont Ridge, Valley and Plateau)—are

predicted to have 3 to 20 percent increase in urban area and

forest loss. The escarpment and foothills area, primarily in

northern South Carolina, includes mountain outcrops such

as Table Rock State Park, gorges, lakes (such as Jocassee,

Keowee, and Hartwell), the Chattooga Wild and Scenic

River, and the growing urban area around Greenville.

Beyond protected public lands, development threatens plants

such as the imperiled Oconee-bells (Shortia galacifolia) in

ravines and shady streambanks. Plants at risk from habitat

loss in the Piedmont Ridge and the Valley and Plateau

section of northern Alabama and Georgia include the

endangered Alabama leather-flower (Clematis socialis) that

occurs along roadsides and recently logged forests (Trusty

and others 2009).

Coastal Plain—The forecast of a 3 to 25 percent forest loss

with subsequent urban development, especially along the

Atlantic and Gulf coasts, threatens wildlife and their habitats.

Areas close to the coast also are at risk of storm surges and

the greater salinity that accompanies sea level rise (chapter

13). For example, seabeach amaranth (Amaranthus pumilus)

on barrier island dunes is threatened by beach erosion and

inundation as well as construction (U.S. Department of the

Interior 2011). More extensively, loss of freshwater emergent

marsh and pool habitat threatens wildlife such as the marsh

rabbit (Sylvilagus palustris) and several waterbirds that

depend on these habitats (Erwin and others 2006).

Climate change-induced inundation of mangrove forests

(e.g., Rhizophora mangle, Avicennia germinans, or

Laguncularia racemosa) along the coast of the Coastal Plain

may reduce available habitat and nesting substrate for birds

such as the frigatebird (Fregata magnificens), mangrove

cuckoo (Coccyzus minor), reddish egret (Egretta rufescens),

and roseate spoonbill (Ajaia ajaia). Loss of this habitat may

also impact the diamondback terrapin (Malaclemys terrapin)

and salt marsh snake (Nerodia clarkii). Live oak (Quercus

virginiana) maritime forests that occur on Atlantic and Gulf

coast barrier islands may also be degraded by predicted

sea level rise. These forests serve as important nesting

habitat for many birds that feed in aquatic habitats as well as

supporting a diversity of winter avifauna. Birds affected by

the loss of live oak maritime forests include the boat-tailed

grackle (Quiscalus major), fish crow (Corvus ossifragus),

great crested flycatcher (Myiarchus crinitus), and northern

parula (Parula americana). Other characteristic species of

this forest are the broadhead skink (Eumeces laticeps), green

treefrog (Hyla cinerea), golden mouse (Ochrotomys nuttalli),

and southeastern shrew (Sorex longirostris).

Coastal areas also have a mixture of vegetation types, such

as fire-maintained wet pine savannas and flatwoods, seeps

and pocosins, marshes, swamps, and bottomlands—that are

home to diversity of species and are at risk from changing

fire regimes and other indirect effects of climate change

(chapter 3). One area of at-risk plant diversity is the Cape

Fear Arch region of North and South Carolina: LeBlond

(2001) lists 22 endemic and another 22 near-endemic plant

species such as coastal goldenrod (Solidago villosicarpa) in

coastal edge forests and roughleaf loosetrife (Lysimachia

asperulifolia) in the ecotone between upland pine forest and

pocosin (Sorrie and others 2006) in this region. Along the

Eastern Atlantic and Florida coastlines that coincide with

portions of the Atlantic Flyway, extensive development will

likely eliminate important stopover habitat for spring and

fall migrating birds as well as habitat for resident species.

This coastline is also an important nesting area for sea turtles

such as the leatherback and Kemp’s Ridley (Lepidochelys

kempii). This forecast will impact the habitat for 25 species

of conservation concern including the red wolf, round-tailed

muskrat, and short-tailed snake (Lampropeltis extenuata).

The Florida Peninsula, especially around Palm Beach and

Miami, is threatened by projected 10 to 25 percent urban

growth and also by sea level rise (chapter 13). This area is

ecologically diverse and unique; Monroe and Miami-Dade

counties include part of the Everglades and are a mix of

pine forests, hammocks, beach dune and strand, prairies,

cypress swamps, mangroves, and freshwater and saltwater

marshes. These counties contain seven plant species listed as

threatened or endangered (U.S. Department of the Interior

2011); while most of them may be further threatened by

urban growth and sea level rise, marine species such as

Johnson’s seagrass (Halophila johnsonii) could expand

their range (Virnstein and Hall 2009). The habitat of

several aquatic and marsh species in the Everglades may

be vulnerable to sea level rise. This includes the common

yellowthroat (Geothlypis trichas), greater siren (Siren

lacertina), northern harrier (Circus cyaneus), squirrel

treefrog (Hyla squirella), and American mink (Mustela

vison). The Florida Peninsula also includes the inland Lake

Wales Ridge. Although this area is projected to have a
384 The Southern Forest Futures Project

moderate (3 to 10 percent) increase in urban area and forest

loss, its diverse fire-maintained ecosystems may be more

threatened by changing fire regimes that could accompany

climate change.

The Southern Gulf, which includes the Apalachicola region

westward to the tip of Louisiana, is projected for 3 to

25 percent urban growth and forest loss. Near-coastal areas

and the southern part of Louisiana also are threatened by

direct and indirect effects of sea level rise. Off protected

lands, upland plant species such as Apalachicola false

rosemary are threatened by disturbances caused by

urbanization and other land use changes. Some rare species

such as Florida nutmeg are also at risk from pathogens

(Schwartz and others 2000). The projected urban growth

of the Southern Gulf is coincident with the highest turtle

diversity in the region and with especially rich areas of

habitat for species such as the Mississippi sandhill crane

(Grus canadensis pulla).

Forecasts of increased urbanization in Peninsular Florida

would affect bird habitat on the Gulf and Atlantic Coasts, as

well as the 38 amphibians that occur in the northern areas of

the State. Numerous species of conservation concern could be

imperiled by future habitat losses: 38 species in the Southern

Gulf and 30 species in the Florida Peninsula. Included are

the Florida grasshopper sparrow (Ammodramus savannarum

floridanus), Florida panther, Key Oryzomys (Oryzomys

palustris natator), ringed map turtle (Graptemys oculifera),

and yellow-blotched map turtle (Graptemys flavimaculata).

Mississippi Alluvial Valley—Portions of the Deltaic Plain

are forecast for 10-25 percent urban growth, while the

Holocene Deposits are predicted to expand to a lesser degree

(3-10 percent). This growth could negatively influence the

current richness of shorebirds and waterfowl occurring

within the Mississippi Flyway that runs through these

sections; the wetlands of Louisiana and Mississippi provide

critical stopover habitat for migrating birds crossing the

Gulf from South America. Twenty-five species of frogs and

toads that inhabit the Deltaic Plain could also be impacted.

The projected areas of urban development/forest loss are

adjacent to conservation priority areas designated for habitat

enhancement for the threatened Louisiana black bear, which

are intended to promote bottomland forest connectivity

within the landscape (Lower Mississippi Valley Joint

Venture Forest Resource Conservation Working Group

2007). Other forest-dependent vertebrate species of concern

in this subregion include the American woodcock (Scolopax

minor), ivory-billed woodpecker (Campephilus principalis),

and several forest interior songbirds including the Swainson’s

warbler (Limnothlypis swainsonii).

The sea level rise predicted for the Deltaic Plain would

inundate the coastal wetland habitat inhabited by numerous

species including the American bittern, king rail (Rallus

elegans), least bittern (Ixobrychus exilis), and southern

cricket frog (Acris gryllus).

Overall, southern ecosystems will continue to be threatened

by forest loss and urban growth, as well as effects of climate,

such as altered fire regimes, sea level rise, and spread of

pathogens. Changes in forest communities may occur due

to warming and precipitation patterns. Coastal regions,

high elevation areas, species of fire-maintained systems

(especially near growing urban centers) are especially

at-risk. The value of public and private forest lands for the

preservation and conservation of these species and their

communities will continue to increase in the future.

Forecasts for Selected Forest Communities

High elevation forests—These forests are distributed

above 4,000 feet elevation on the peaks of the Southern

Appalachians and northward in the Appalachian–Cumberland

highlands. Species include red spruce (Forest Type 123), red

spruce/balsam fir (Forest Type 124), eastern hemlock (Forest

Type 105), and northern hardwoods consisting of sugar

maple/beech/yellow birch (Forest Type 801; Woudenberg

and others 2010). They occur in the Allegheny Mountains of

the Central Appalachians in east-central West Virginia and

west-central Virginia, the northern Blue Ridge of central and

northern Virginia, and the southern Blue Ridge of eastern

Tennessee, western North Carolina, and limited areas of

northern Georgia.

High elevation communities are characterized by cool

temperatures, relatively high moisture levels within forests,

short growing seasons, exposed rock and acidic soils, and

extreme weather events. Canopy trees are often misshapen

by persistent strong winds. Open (sparse-to-no tree canopy)

communities such as heath or grassy balds and rock outcrops

are scattered throughout. The distinctive flora includes the

vulnerable Rugel’s ragwort, which is restricted to a few

counties in the Great Smoky Mountains (USDA National

Resource Conservation Service Plants Database 2010), and

the imperiled Fraser fir (Abies fraseri), which is recovering

from infestation (Moore and others 2008) by the balsam

woolly adelgid (Adelges piceae).

High elevation forests support several mammals including

the fisher (Martes pennanti), snowshoe hare (Lepus

americanus), northern flying squirrel, and rock vole

(Microtus chrotorrhinus). The golden-crowned kinglet

(Regulus satrapa), red crossbill (Loxia curvirostra), sawwhet owl (Aegolius acadicus), and yellow-bellied flycatcher
385

chapter 14. Wildlife and Forest Communities

(Empidonax flaviventris) also inhabit this community.

Although few reptiles can tolerate these harsh conditions

(Bailey and others 2006), there are locally high populations

of several salamanders, some of which are endemic habitat

specialists with restricted ranges. Species include the

Allegheny Mountain dusky (Desmognathus ochrophaeus),

imitator salamander (D. imitator), pigmy salamander

(D. wrighti), shovel-nosed salamander (D. marmoratus),

Southern Appalachian salamander (Plethodon teyahalee),

and Weller’s salamander (P. welleri).

High elevation forests are threatened by air pollution, heavy

metal deposition, acid precipitation (which influences soil and

stream chemistry), natural disturbances such as hurricanes

and landslides, and housing development on unprotected

lands (Moore and others 2008; Turner and others 2003;

Wear and Bolstad 1998; White and others, in preparation).

Recent pressures include drier, warmer conditions normally

associated with climate change (Ibanez and others 2007)

and recreational activity that results in soil compaction

and physical damage to young trees (Trani 2002b). These

forest types occur infrequently in the Forest Inventory and

Analysis data that provided the baseline for Forest Future

modeling, precluding any accurate predictions of future

changes. Nevertheless, if population centers expand and

air temperatures warm by 2060 as predicted by several of

the forecasts, the pressures that are currently affecting high

elevation forests are likely to continue. For example, the

Carolina northern flying squirrels that inhabit high elevation

spruce, northern hardwoods, and hemlock forests are ceding

territory to expanding populations of southern flying squirrels

(Glaucomys volans) as well as suffering from human impacts

on the size, quality, and connectedness of their habitat (Weigl

2007) and from the infestation of eastern hemlock (Tsuga

canadensis) by the hemlock woolly adelgid (A. tsugae).

Upland hardwood forests—Cornerstone B forecasts the

greatest loss in upland hardwood forests caused by moderate

population growth, high urbanization due to strong income

growth, and falling timber prices. The forecast is a 14 percent

decrease South-wide, although the dominant forest type, oakhickory forest, is forecast to lose only 1 percent of its area

(chapter 5).

Upland hardwood forests of the South were established in

the 1800s and early 1900s (Lorimer 2001). These forests are

aging and, like forests in all subregions except the newly

planted Mississippi Alluvial Valley, will see a decrease in

acreage of midsuccessional forest and concomitant increase

in late successional forest (chapter 5). Forest aging, with

increasing tree sizes and canopy development, could benefit

interior species that are sensitive to forest fragmentation and

habitat patch size; examples include the gray fox, black and

white warbler (Mniotilta varia), hooded warbler (Wilsonia

citrina), scarlet tanager (Piranga olivacea), and worm-eating

warbler (Helmitheros vermivorus).

Over longer time intervals, oak-hickory forest species

are predicted to increase (Dale and others 2010) or shift

northward, decreasing abundance of this forest type in the

South (Iverson and others 2008, Prasad and others 2009). In

addition, continued forest fragmentation in this heavily-used

forest type, microclimate changes associated with climate

warming, and greater recreational use of the forest with

increasing human population growth, could threaten forest

interior species, thereby offsetting the benefits of forest aging.

Longleaf pine forests—These forests historically dominated

Coastal Plain sites from southern Virginia to eastern Texas.

The fire-maintained longleaf pine–grassland ecosystem

currently occupies less than 5 percent of its original 30

million acres (Van Lear and others 2005). Now highly

fragmented, this diverse, open-canopied ecosystem occurs

primarily in the Coastal Plain over gradients from bogs

through flatwoods to sand ridges. Community composition

varies with soil moisture and geography. Wiregrass and

bluestem dominate the herbaceous layer of longleaf pine

savanna. The herb layer of wet longleaf pine forests is diverse

and includes grasses, wildflowers, and carnivorous plants. In

mature communities, the trees are thinly distributed, flattopped, and have limbless lower trunks.

Rare plant species (including 27 plants listed as threatened

or endangered by the U.S. Fish and Wildlife Service)

occur in embedded wetlands, wetland-upland ecotones,

pine flatwoods, savannas, and dry ridges (Van Lear and

others 2005). The threatened or endangered plants include

the Canby’s dropwort in wetlands and the vulnerable

sandhills milkvetch (Astragalus michauxii) in longleaf

pine–wiregrass savannas. The longleaf community supports

several vertebrates. The red-cockaded woodpecker occurs

in the open pinewoods; Bachman’s sparrow (Aimophila

aestivalis) breeds in dense, grassy areas with scattered trees.

Other avifauna include Henslow’s sparrow, brown-headed

nuthatch (Sitta pusilla), and pine warbler (Dendroica pinus).

Characteristic mammals include the southern short-tail

shrew (Blarina carolinensis), eastern mole (Scalopus

aquaticus), Seminole bat (Lasiurus seminolus), nine-banded

armadillo (Dasypus novemcinctus), fox squirrel (Sciurus

niger), marsh rice rat (Oryzomys palustris), and long-tailed

weasel (Mustela frenata; Trani and others 2007). Longleaf

pine communities support 74 amphibians and 96 reptiles

(Dodd 1995), including the eastern spadefoot (Scaphiopus

holbrookii), pine snake (Pituophis melanoleucus), pine

woods treefrog (Hyla femoralis), sand skink, and southern

hognose snake (Heterodon simus).


388 The Southern Forest Futures Project

rufus), common gray fox (Urocyon cinereoargenteus), and

least weasel (Mustela nivalis) rely on prey associated with

early successional habitats.

The focus in this section is on young forest habitat (10 years

or less). The high-urbanization/high-timber-prices of

Cornerstone A forecasts the greatest loss of young forest

habitat in the Northern Ridge and Valley section of Virginia,

southern Florida and associated Keys, and scattered locations

in the Northern Atlantic, Southern Appalachian Piedmont,

Blue Ridge, northern Interior Low Plateau, and Mississippi

Alluvial Valley (fig. 14.21).

The greatest gain in young forest is predicted in the

Cumberland Plateau and Mountains and adjacent Southern

Ridge and Valley, Apalachicola region of Florida, OzarkOuachita Highlands and adjacent northern area of the

Mississippi Alluvial Valley, and scattered areas throughout

Mississippi and Louisiana. Disturbances that create opencanopy habitat could benefit some forest plant species,

such as the imperiled Lesquereux’s mustard (Lesquerella

globosa) as reported by the Center for Plant Conservation

(2010b). Gain of young forest, especially if accompanied

by loss of mature forest, could threaten plants and animals

of forest interior and specialized habitats. For example,

Lucy Braun’s white snakeroot (Ageratina luciae-brauniae)

which lives on wet, shaded cliff ledges and overhangs

(Kral 1983), and the endangered Braun’s rockcress (Arabis

perstellata) occurring on moist calcareous forest slopes

(Center for Plant Conservation 2010a) could be threatened

by localized canopy opening, indirect effects of logging, or

land clearing.

Discussion and Conclusions

Patterns of Species Richness, Imperilment, 

and Extirpation

The richness of species in the South is impressive, with 1,076

native terrestrial vertebrates: 179 amphibians, 525 birds, 176

mammals, and 196 reptiles. Species richness is highest in the

Mid-South (856 species in 115 ecosystems) and the Coastal

Plain (733 species in 153 ecosystems), reflecting both the

large area of these subregions and the diversity of habitats

within them.

The pattern of species richness varies by taxon. The

distribution of amphibians encompasses mountains,

highlands, and coastal areas along the Atlantic Ocean and

Gulf of Mexico. Bird richness along the coastal areas and

wetlands of the Atlantic Ocean and Gulf of Mexico points

to the importance of these areas, while mammal richness

highlights patterns in the Mid-South and AppalachianCumberland. In contrast, the distribution of reptiles is

greatest across the southern portion of the region, with

notable differences among the various subtaxa.

There are 142 terrestrial vertebrate species considered to

be of conservation concern in the South; 77 of these are

threatened or endangered. However, they are overshadowed

by at-risk plants—more than 900 are species of concern and

141 are threatened or endangered. Threats to biodiversity are

occurring throughout the region, particularly in the Coastal

Plain, Mid-South, and Appalachian-Cumberland highlands.

The distribution of threatened or endangered species

coincides with areas of serious conservation concern

along the Atlantic and Gulf coasts, Peninsular Florida, and

Southern Gulf. This pattern has remained relatively stable

and has been observed by others using different data sources

and criteria (Chaplin and others 2000, Dobson and others

1997, Flather and others 2008, Rutledge and others 2001),

which suggests that the geographic extent of identified

endangerment locations is not an artifact of any particular

data set (Flather and others 2008).

There is also a pattern of geographic coincidence between

the Federal status and NatureServe ranking schemes for both

plants and vertebrates with regard to the Atlantic and Gulf

coastal areas and that of Peninsular Florida. However, the

NatureServe rankings provide an additional perspective on

the geography of risk, pointing to locations that are emerging

as new areas of concern: the Appalachian-Cumberland

highlands (Blue Ridge, Southern Ridge and Valley,

Cumberland Plateau and Mountain, Interior Low Plateau

sections) and the Mid-South (Ozark-Ouachita Highlands,

West Texas Basin and Range sections, Edwards Plateau in

central Texas).

The NatureServe ranking scheme also identified five hotspot

areas representing threatened or endangered plant diversity:

Big Bend National Park in the West Texas Basin and Range,

the Apalachicola area of the Southern Gulf Coast, Lake

Wales Ridge and the area south of Lake Okeechobee in

Peninsular Florida, and coastal counties of North Carolina

in the Atlantic Coastal Plain. The Appalachian-Cumberland

highlands also contain plant species of concern at the State

level.

Across all taxa groups, habitat loss and degradation remain

the primary threats to maintaining the current number of

plant and animal species. Degradation can take the form of

environmental contamination (such as water pollution, acid

rain, and pesticides) and agricultural, urban, and commercial

development (such as channel modification, impoundments,

and habitat fragmentation and isolation). Species are also
389

chapter 14. Wildlife and Forest Communities

impacted by many other factors such as introduction of

forest pests and nonnative plants, disruption of fire regimes,

collection, indiscriminant killing, driving off-road vehicles

through rare plant communities, caving in maternity bat

caverns, and building road networks, power lines, and cell

towers. Each species varies in its vulnerability to these

threats, and the severity of threats often varies by subregion.

Numerous plants and vertebrates are presumed extirpated

from selected States in the South; over 50 percent of the

terrestrial vertebrates have been added to this list since

the time of the Southern Forest Resource Assessment.

The causes that factored in species extirpations are, in the

majority of cases, the same as those that jeopardize species

of conservation concern today. Although the wide-spread

land clearing of European settlement is not occurring,

dramatic urban growth with accompanying infrastructure

development in the South is projected for all subregions. In

addition, sea level rise may further reduce the range of plants

in coastal estuaries and marshes.

Prioritizing conservation and management efforts on areas

with concentrations of species of concern may be needed

to avert future species losses. New long term strategies

are required that focus upon (and mitigate) multiple

environmental stressors by incorporating ways to promote

landscape connectivity, facilitate species movement, reduce

mortality, and increase species viability.

Forecasts of Urban Growth, Forest Loss, and 

Climate Change

Forecasts of human population growth and urban expansion

(chapter 4) raise the possibility of a substantial impact on

species and the communities that support them over the next

several decades. As the South continues to grow, so also

will the number of threats associated with infrastructure

development, water development, land conversion, and

other effects of an urbanizing population. The number of

species negatively affected by the loss of forest is expected to

increase. The geographic pattern of richness and imperilment

indicates that many species in the South are clustered into

identifiable areas of unique richness. Analyzing the overlap

of these areas with hot spots of imperiled species under the

Cornerstone B projections of urban growth and associated

forest loss suggests that several subregions may experience

conflicts between development and species conservation and

management:

•	 In the Mid-South, forest loss and urban growth in the

Ozark-Ouachita Highlands threatens forest plant and

animal species. Urban development along the southern

borders shared by Cross Timbers and Western Gulf could

impact a large number of reptiles. In addition, the area lies

within a band of especially high avian richness.

•	 In the Appalachian-Cumberland highlands, urban growth

in the Interior Low Plateau of central Kentucky and

Tennessee may threaten wildlife and associated plant

species. Forest loss may degrade forest connectivity,

hindering migration of amphibians that are at-risk for

elimination or displacement. In addition, successful

management of the wildland-urban interface will be

needed to balance species conservation with anticipated

increases in residential development and recreation. And

finally, a warming climate threatens species endemic to

high elevation outcrops and forests.

•	Substantial urban growth in the Piedmont could reduce

the richness of amphibians and mammals. Management of

species on public lands may be hindered by the pressure

of expanding human populations in surrounding counties,

while the smaller (and shrinking) tracts typical of private

ownership provide little opportunity for sustainable forest

management. Plants in transitional communities, such as

the escarpment and foothills of northern South Carolina

or southern extensions of the Plateau in northern Alabama

and adjacent Georgia, also are at-risk from habitat loss and

climate change.

•	 Urban growth in the Deltaic Plain Section at the mouth

of the Mississippi River could negatively impact the

richness of shorebirds and waterfowl occurring within the

wetlands of the Mississippi Flyway as well as habitat for

the Louisiana black bear. Sea level rise could inundate the

coastal habitat of the American alligator and numerous

species of frogs and toads. Ongoing reforestation programs

such as restoration of bottomland hardwoods will remain of

especial importance in the light of this forecast.

•	 Urban development and the effects of sea level rise

threaten wildlife habitat and plant species in the Atlantic

and Gulf Coastal Plain and Peninsular Florida. The

projected inundation and loss of mangrove and coastal

live oak forests would reduce nesting habitat for several

birds, snakes, and reptiles. Forecasted development along

the coastline portion of the Atlantic Flyway will likely

eliminate important stopover habitat, as well as nesting

areas for several imperiled sea turtles. Inland, the diversity

of flora in fire-maintained Coastal Plain ecosystems

is threatened by urban development and changing fire

regimes.

•	 Urban development forecasted for the South will place

continued demands on natural ecosystems, species, and

their habitats. Biodiversity often declines as development

proceeds: habitats for native species are replaced, while

other habitats are modified or degraded. The forecasts

also raise concern for conservation of imperiled species,

bringing unique management challenges in areas becoming

increasingly urbanized such as Peninsular Florida, the Blue

Ridge, and the Piedmont.
390 The Southern Forest Futures Project

Losses of forests on the southern landscape would affect

the persistence of species by changing the distribution and

availability of spatial resources. Isolated populations in

fragmented habitat are prone to inbreeding depression and

genetic drift; this is especially true for those species that

cannot disperse long distances. Strategic land acquisition

may improve habitat quality by promoting connectivity

and enabling movement of habitat-restricted species,

especially in the face of climate change (Haddad and Baum

1999, Rosenburg and others 1998). There are a number of

policies and programs that promote habitat conservation.

These approaches include collaborative conservation plans,

landowner incentive programs, and conservation easements.

Biodiversity and Climate Change

Climate change represents an additional source of stress on

terrestrial species and ecosystems (Lovejoy and Hannah

2005). Climate scenarios are incorporated into the forest

condition and land use futures analyses described previously.

Chapter 3 also presents projections for temperature increase

and variability in precipitation patterns over the next century;

this may change the future distribution of many species.

Species respond to environmental conditions based on

habitat needs and physiological tolerances, which in turn

influences community composition, structure, and resilience.

A rise in temperature could influence seasonal movement,

recruitment, and mortality (Inouye and others 2000).

Changes in phenology (e.g., timing of resource availability,

advances in flowering or nesting dates) may alter predatorprey, competitive interaction, and herbivore-vegetation

dynamics.

Characteristics of species at risk from climate change

include those with restricted geographic range, fragmented

distributions, and those that occur at the margins of their

ranges. Other characteristics include limited dispersal ability,

low genetic diversity, strong affinity to aquatic habitats,

narrow physiological tolerance, and late maturation (Manley

and Trani-Griep 2012, Midgley and others 2002). For

example, the Southern Appalachian Mountains and Piedmont

have an exceptionally high diversity of salamanders

whose ecology is strongly influenced by temperature and

precipitation; there is significant projected loss of high

elevation habitat for these and other species existing at

their thermal maxima (Milanovich and others 2010). Forest

amphibians associated with cool, moist conditions may be

subject to microclimates beyond their tolerance. Ephemeral

streams and ponds may be especially vulnerable to drying

with variable precipitation patterns; this may affect habitat

limitations of several taxa.

Matthews and others (2004) modeled the potential future

distribution of eastern bird species under global climate

change (table 14.11). Climate change has influenced the

geographic range of species along environmental gradients;

temperate birds have shifted their ranges to higher latitudes,

affecting migration strategies and community composition

(LaSorte and Jetz 2010). Successful migration will depend on

the rate of climate change relative to essential habitat needs

and key community interactions. Species may move into the

habitats of others, creating new assemblages. The effect of

this migration is unknown at this time.

Climate warming (ranging between 0.14–0.49 °C and

2.0–2.6 °C) is projected across the South by 2050 (chapter 3).

Warmer temperatures could decrease the winter cold period,

which limits some species, but is tolerated by others, such as

high-elevation plants (Larcher 2010) and is required for seed

germination in others (Walck and Hidayati 2004). Although

moderate change in average annual precipitation is projected,

warmer temperatures could increase summer drought and

fire potential, or allow less cold-tolerant plant species to

establish. It is unlikely, however, that the large-scale shifts

in forest communities predicted under longer-term climate

warming scenarios (e.g., Dale and others 2010) will occur

by 2060; fifty years is a short time for widespread dispersal

and growth of long-lived species (such as trees). In addition,

more immediate factors such as disturbance (e.g., trampling)

and land use can override climate change effects on species

distributions (Feeley and Silman 2010).

Plant communities at high elevations may be particularly

susceptible (Currie 2001, Malcolm and others 2006), where

warming temperatures can lengthen the growing season.

Forest communities in the Piedmont and Coastal Plain may

be influenced by changes in fire frequency. Although some

species of the fire-maintained longleaf pine – grassland

ecosystem of the Coastal Plain Subregion might benefit from

frequent fire, urban growth around major cities may override

climate change effects on much of this ecosystem. Species

whose ranges are limited to coastal areas will be vulnerable

to projected changes in sea level as well as beach erosion.

Sea level rise may inundate barrier islands, coastal wetlands,

and marshes of the Coastal Plain, as well as along the eastern

Atlantic and Gulf coasts.

Communities that support threatened or endangered species

are currently at-risk from a variety of environmental

stressors. The small or disjunct populations that often

accompany species of conservation concern are likely

to be impacted by stochastic climatic events. Sensitive

species, influenced by a number of stressors discussed in

this chapter, may not have the ability to adapt to a changing
391

chapter 14. Wildlife and Forest Communities

climate. Thus, climate change projections pose important

questions about future challenges for biological diversity in

the South.

Forecasts of Special Communities 

High elevation forests, which occur above 4,000 feet, are too

infrequent to be captured by Forest Inventory and Analysis

data for this assessment. This provided the baseline for Forest

Future modeling, precluding any accurate predictions of

future changes. These forests traditionally have been subject

to air pollution, acid deposition, and natural disturbances.

Climate warming and housing development may result in the

loss of endemic species or changes in species ranges.

Upland hardwood forests are forecast to decline 14 percent

over the region under the high urbanization and low timber

demand predictions of Cornerstone B. The dominant forest

type, oak-hickory, is forecasted to lose only 1 percent.

However, distributions of oak-hickory forest species are

predicted to shift northward, which could threaten forest

interior species of this widespread and heavily used forest

type.

Although some areas of the Coastal Plain are forecasted

to lose acreage of longleaf pine forest under the high

urbanization and high timber demand predictions of

Cornerstone A, other areas such as south-central Florida

and northwest Alabama, are predicted to gain acreage of

this forest type and are potential sites for expansion of the

numerous vertebrates that inhabit this community.

Maturation of southern forests raises concern about the loss

of early successional habitat. Cornerstone A projects the

Table 14.11—Predicted influence on selected bird species based on climate change scenarios (Matthews and others 2004)

Scientific name Common name Influence on speciesa

Aimophila aestivalis Bachman’s sparrow Extensive loss in abundance. 

Ammodramus savannarum Grasshopper sparrow Decrease in abundance and range.

Archilochus colubris Ruby-throated hummingbird Population losses in the South.

Buteo jamaicensis Red-tailed hawk Substantial increase in abundance.

Buteo platypterus Broad-winged hawk Shift in range with losses in northern areas.

Caprimulgus vociferus Whip-poor-will Range expands northward; decrease in overall abundance.

Catharus fuscescens Veery Substantial decrease in abundance and contraction in range.

Chaetura pelagica Chimney swift Little change in abundance.

Coccothraustes vespertinus Evening grosbeak Near extirpation. 

Coccyzus erythropthalmus Black-billed cuckoo Contraction to the north.

Coragyps atratus Black vulture Extensive expansion northward and increase in abundance.

Colinus virginianus Northern bobwhite Expansion northward.

Dendroica cerulea Cerulean warbler Decrease in abundance and shift in range northward.

Dendroica dominica Yellow-throated warbler Decrease in abundance in southern range.

Hylocichla mustelina Wood thrush Reductions in numbers over its range in eastern forests.

Ictinia mississippiensis Mississippi kite Increase in range from Tennessee northward.

Passerculus sandwichensis Savannah sparrow Decrease in abundance and range northward. 

Picoides pubescens Downy woodpecker Loss in abundance in the south; population gains to the north.

Progne subis Purple martin Increase in abundance.

Sayornis phoebe Eastern phoebe Shift in eastern population to northwest.

Sitta carolinensis White-breasted nuthatch Increase in abundance; expansion in range southward.

Spinus tristis American goldfinch Extensive loss in abundance. 

Tyrannus tyrannus Eastern kingbird Decrease in abundance.

Vermivora chrysoptera Golden-winged warbler Contraction in range northward.

aCanadian Climate Center Model and Hadley Center for Climate Prediction and Research Model.
392 The Southern Forest Futures Project

greatest loss of young forest habitat in the Northern Ridge

and Valley section of western Virginia, southern Florida

and associated Keys, and scattered locations in the Northern

Atlantic. Gains are forecasted for the Cumberland Plateau

and Mountains and adjacent Southern Ridge and Valley,

Apalachicola region of Florida, Ozark-Ouachita Highlands

and adjacent northern area of the Mississippi Alluvial Valley.

Management Challenges

Finally, our analysis of biodiversity and the Southern

Forest Futures projections underscores the challenges that

resource managers face to conserve the rich species legacy

of the South. The potential implications described herein

bring uncertainty about how best to implement future

conservation and management strategies. Although there is

an existing framework of regulations and programs which

promote species conservation (e. g., Endangered Species Act,

sustainable forestry certification standards, among others),

preparing for future growth will require new strategies to

prepare for the changes in land use, forest conditions, and

urbanization that are expected. For example, extinctions

in longer lived species are expected to lag behind climate

change; adaptation strategies across land ownerships will

require anticipatory measures to ensure the future of the

South’s biodiversity.

New tools and approaches to managing uncertainty will

become essential. Scenario planning, sensitivity analysis,

or ecological risk analysis may become incorporated into

resource planning for areas of concern. Integrating climate

science into land management planning will be important,

accompanied by monitoring strategies that identify patterns

in disturbance, phenology, and species range changes.

Furthermore, static management can no longer be assumed

(Hayward and others 2009); that is, the environment will

change in a directional way rather than varying around a

mean condition (Milly and others 2008). The planning focus

will be on spatial and temporal scales that are broader and

longer than typically considered. As future impacts occur

across large areas, the appropriate decision-making level may

shift to cover landscape or regional scales.

The conservation focus on species of at-risk will continue

until we understand the relationship between the loss of

biodiversity and ecosystem function, resilience, and stability

(Flather and others 2008). It may become commonplace for

management to consider:

•	Implementation of vulnerability assessments to identify

species and communities at risk, including strategies to

maximize species persistence and dispersal;

•	Examination of landscape connectivity and infrastructure

barriers to migration, incorporating mitigation measures

into planning efforts; and

•	 Enhancement of genetic diversity to provide resilience

against environmental stressors.

The geographic area managed by the Forest Service in the

South makes it one of a few land stewards that can have a

significant impact on the conservation and management of

biodiversity. The agency will play a substantial role in the

development and implementation of adaptation strategies.

Nevertheless, there is a need for strong collaboration

with State and Federal agencies, private landowners, and

nongovernmental organizations to successfully implement

management across landscapes at scales necessary to make

substantive impacts on species and their habitats (Hayward

and others 2009). A collaborative approach increases the

scale of restoration and conservation on both public and

private lands.

Each species differs in its ability to tolerate climate change

and other environmental stressors. An awareness of the

relationship between the forecasts and the geographic pattern

of species occurrence will foster planning efforts that arise

from the Southern Forest Futures effort. The implications

for the conservation of southern species are significant: in

the midst of a growing region, the provision of biological

diversity will become a critical conservation issue.

Knowledge and Inf ormation Gaps

The forecasts of biodiversity response were based on countylevel patterns of coincidence for GIS maps of forecasted

stressors, special forest communities, and species richness

and rarity. This scale of analysis may over- or under-predict

threats to species that occur at finer scales, such as rare

species that occur at only a few locations within a county,

or those that occur in scattered locations. Because this was

a regional assessment, a selection was made early on by the

Futures team as to a suitable, manageable scale. This turned

out to be the county scale for the forecast and other analyses.

The absence of species data in a county does not necessarily

mean the species does not occur there; the area may not have

been intensively inventoried or there may be an uneven level

of scientific knowledge on the identification of uncommon

species or subspecies, particularly herpetofauna. The

following describes known data gaps that State Heritage

Programs have provided to NatureServe for species-at-risk

(G1 to G3 or federal status):

•	Florida: Access restrictions in some areas have precluded

thorough surveys on corporate timberlands across north

Florida and on several large (over 10,000 acres) private

ranches in central Florida.
393

chapter 14. Wildlife and Forest Communities

•	Kentucky: Limited access has precluded survey on the

Ft. Campbell military installation (14,000 acres).

•	North Carolina: Eighteen counties have not been

systematically inventoried or are currently being inventoried:

Alexander, Alleghany, Anson, Caldwell, Caswell, Cherokee,

Clay, Dare, Graham, Macon, Mitchell, Robeson, Stanly,

Swain, Tyrrell, Union, Wilkes, and Yancey.

•	South Carolina: A comprehensive survey has not been

done; the majority of gaps are on private lands.

•	Tennessee: Data are limited for the Great Smoky

Mountains National Park in east Tennessee due to data

sensitivity; no data are available for Ft. Campbell in northcentral Tennessee.

•	Texas: Extensive areas of privately owned land have not

been surveyed.

Acknowledgments

Appreciation is extended to David Schmid and the Biological

and Physical Resources Unit in the Southern Regional

Office of the Forest Service for their ongoing support and

encouragement. The editing efforts of Carol Whitlock

and suggestions from four anonymous reviewers greatly

improved the quality of this chapter.

Our appreciation also is extended to Jason McNees at

NatureServe for his tireless efforts supplying the biodiversity

data and related spatial products. Shawna Reid, Southern

Research Station, provided GIS support on the forecasts of

special forest communities. Gary Wein, Highlands-Cashiers

Landtrust, mapped initial draft maps of species richness and

concern areas.

Special thanks to Bobby Huggett, Dave Wear, and John

Greis for their invaluable help while providing interpretation

of forecast and Forest Inventory and Analysis data. Steve

McNulty provided the sea level rise data described in the

forecast section. Ed Laurent, American Bird Conservancy,

contacted several Joint Venture and Bird Conservation

Region coordinators for information on the current status and

habitat concerns for priority birds in several subregions.

Finally, our appreciation is offered to Ampaporn Mattox

who assisted with the Internet searches, status rank updates,

production of tables and figures, and species proofing; and

to Haven Cook for selected table creation and miscellaneous

checks.

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397

chapter 15. The Invasion of Southern Forests by Nonnative Plants

	 James H. Miller, Dawn Lemke, and John Coulston1

Key Findings

•	 Invasive plants continue to escape into and spread through

southern forests to eventually form exclusive infestations,

and replace native communities to the detriment of forest

productivity, biodiversity, ecosystem services, and human

use potential.

•	 Over a 300-year period, invasive plants have been

increasingly imported into the South, despite public

policies and warnings by professional ecologists and plant

experts of long-term irreversible ecosystem damage.

•	 The invasion process is accelerated by greater forest

disturbance, fragmentation, parcelization, and urbanization

needed to accommodate and support an increasing

population and is being accelerated by climate warming.

Approximately 9 percent of southern forests or about 19

million acres are currently occupied by one or more of the

300 invasive plants in the region.

•	 The annual spread of invasive plants in southern forests

is conservatively estimated at a 145,000 forested acres;

accelerated by a warming climate and by increasing

numbers of forest disturbances that accommodate and

support growing human populations.

•	 Given the current occupation and spread of invasive plants

and the increasingly common infestations by multiple

species, eradication appears only probable on specific

lands unless awareness and strategic programs are greatly

enhanced.

•	 Over a 20 year period, research has developed effective

control treatments and integrated approaches that can

eradicate or replace invasive plants, while a more robust,

coordinated, and focused effort will be required to stem

and turn the tide of invasion.

•	Model projections show high-threat invasive plants have

not reached their potential range or density limits within

the region under current conditions. A predicted warming

climate will permit northward range extensions for some,

1James H. Miller is an Emeritus Research Ecologist, Southern Research

Station, U.S. Department of Agriculture Forest Service, Auburn, AL

36849-5418; Dawn Lemke is a Research Associate, Department of Natural

Resources and Environmental Sciences, Alabama A&M University,

Normal, AL 35762; John Coulston is a Supervisory Research Forester,

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

Knoxville, TN 37919.

while range extensions can be restricted by a simultaneous

drier climate. Losses in forest production, recreation, and

wildlife habitat would have quality-of-life implications for

future generations that would continue to be exacerbated if

not mitigated.

•	 Increased occupation by invasive plants would diminish

the variety and abundance of current wood-based products

from the “wood basket” of the United States. Some

invasive species may find use in biomass and composite

products if harvesting and processing become more

efficient.

•	Most plants escaping into southern forests have been

imported, hybridized, sold, and planted for yard and

garden beautification, soil stabilization, wildlife habitat

enhancement, and livestock production.

•	 Stricter controls for importing species are pending, but

their effectiveness will be hampered as long as garden

centers continue to market invasive plants as ornamentals.

•	 Limiting the degree of occupation and impact depends on

the development of adaptive management programs and

actions that are coordinated across political boundaries and

engage all ownerships. Piecemeal and splintered actions by

agencies and ownerships, if continued, cannot dwarf the

destructive impacts of this invasion.

•	 Public awareness campaigns, cooperative spread abatement

networks, collaborative programs of detection and

eradication, dedicated research and extension programs,

and employment of new land restoration options have been

found to slow the spread of invasive plants and prevent

them from destroying critical habitats.

Introduction

Invasive plants pose one of the most immediate threats and

socio-ecological challenges we face to present and future

forests, especially in the South (Miller and others 2010b,

Moser and others 2009). These alien plants increasingly

infiltrate landscapes to erode and replace native communities

while irreversibly degrading critical human-sustaining

ecosystems (Mack and others 2000, Pimentel 2002, U.S.

Congress OTA 1993). The replacement of diverse native

plant communities by dense infestations with limited

Chapter 15.

The Invasion of Southern Forests by Nonnative Plants: 

 Current and Future Occupation, with Impacts, 

 Management Strategies, and Mitigation Approaches
398 The Southern Forest Futures Project

species is becoming widespread—altering forest ecosystem

structure and function, threatening all forest communities

and agricultural systems, and imposing human economic

and cultural costs (Holmes and others 2009, Mack and others

2000, Pfeiffer and Voeks 2008).

The U.S. Congress Office of Technology Assessment

(1993) reported that the need for a more restrictive national

policy on introductions of nonnative species was widely

acknowledged but impeded by historical divisions among

agencies and constituencies. Most plants that have escaped

into wildlands have gained entry into the United States

through the vast and complex plant production industry or

by other deliberate introductions (Bryson and Carter 2004,

Drew and others 2010). Invasive species continue to be both

accidentally and intentionally introduced through relatively

porous entry points (Carrete and Tella 2008, Conn and others

2008, Mack and others 2000). The ever increasing volume

of trade, including international Internet sales, will continue

this trend unless surveillance improves (Britton and others

2005, Mack and others 2000, Simberloff and others 2005).

Of the 20,000 species of nonnative plants now living in the

United States, about 4,500 have invasive tendencies (Devine

1989), and thousands more reside in gardens, moving with

the expanding urban fringe, with unknown consequences to

adjoining lands (Pimentel 2002).

Of the 380-plus recognized invasive plants in southern

forests and grasslands (more than 330 terrestrials and 48

aquatics) 53 are ranked as high-to-medium risk to natural

communities (Morse and others 2004, USDA Forest Service

2008b). Only recently has the extent of invasive plant

occupation in the Southern United States and elsewhere

in the world been realized (Colton and Alpert 1998, Miller

2003). Colton and Alpert (1998) report that the extent and

spread of nonnative plant species over the past several

decades has taken most people by surprise, and is still not

comprehended by most citizens and policymakers.

In 1999, President Clinton issued Executive Order 13112

defining an “invasive species” as a species: (1) that is

nonnative (or alien) to the ecosystem under consideration

(such as the South) and (2) whose introduction causes or is

likely to cause economic or environmental harm or harm

to humans. Thus, a plant invader is a species that occurs

outside its area of origin and has become established, can

reproduce, and can spread without cultivation to cause

harmful impacts. The Executive Order established the

National Invasive Species Council to coordinate a counter

offensive and instructed Federal agencies to direct available

funding toward prevention, detection, response, monitoring,

restoration, research, and public awareness efforts.

The National Invasive Species Council involves Federal

agency heads as members. They were mandated to establish

the National Technical Advisory Council and to write

a national invasive species strategic plan with reviews

and revisions every five years (National Invasive Species

Council 2001). Since then, most agencies have drafted their

own strategic plans (such as those prepared by the USDA

Forest Service and its Southern Region). As instructed by

the Executive Order, agency implementation of invasive

programs has been in collaboration with regional efforts and

subject to the availability of appropriated funding. Much

networking among Federal and State agencies has occurred

owing to the Executive Order along with non-governmental

partners. Control activities and treatments have been

initiated during this period, while these efforts in every

Southern State are only coordinated in Florida, with formal

partnerships among governmental and non-governmental

collaborators.

Not all nonnative plants are invasive. Some 128 crop species

have been intentionally introduced and a few are among

our most valued crops, including wheat, barley, rice, oats,

and corn, which was a Native American introduction from

Mesoamerica (Pimentel 2002). Other nonnative grasses

are mainstays for forage; however, many of them are often

invasive in regenerated forests or forest openings. For

example, pasture grasses are highly competitive when

invading loblolly pine (Pinus taeda) plantings, reducing early

survival and growth (Smith 1989). Plant breeding programs

over the past 150 years have yielded numerous crop and

forage varieties that have improved productivity, useable

yields, and tolerance to a wider range of growing conditions

and predators. And over the past 50 years, the rapid increase

in turf, ornamental and horticultural species and varieties

has improved many aspects of modern life, such as landscape

beautification and fast growing shade trees and shrubs. But

the accumulation of all these introductions and “improved”

varieties have taken its toll on our natural ecosystems

(Burton and Samuelson 2008, Mack and others 2000); as

well as in parks, green spaces, and rights-of-way, where the

cost of controlling invasive species has skyrocketed (Perrings

and others 2002, Pimentel 2002, U.S. Congress Office of

Technology Assessment 1993).

Invasive plants can in general outcompete native species

and reproduce rapidly in the absence of predators from their

native lands to form dense infestations that exclude most

other plants (Randall and Marinelli 1996). These infestations

decrease forest productivity, threaten forest health and

sustainability, and limit biodiversity and wildlife habitat on

millions of acres including protected habitats (Westbrooks

1998). Alterations to forest structure and natural succession

result in changes in functions and processes that threaten

vital ecosystem services, like soil formation, water yield, and

air rejuvenation (Ehrenfeld and others 2001, Gomez-Aparicio

and Canham 2008, Martin and others 2009). Some invasives

alter natural fire regimes and increase risk of wildfire
399

chapter 15. The Invasion of Southern Forests by Nonnative Plants

Existing Collaborative Invasive Plant Efforts in the South

Nonnative invasive plant collaboration networks in the South are presently organized for horizontal connectivity, usually

State centered. The leading collective efforts involved in managing nonnative invasive plants are voluntary State exotic

pest plant councils linked via the Internet through university centers with State land management agencies, individual

and corporate land owners, and vegetation management associations. Fledgling cooperative weed management areas

are becoming organized within States and among groups of counties. The first council was established in 1984 to bring

together numerous agencies combating severe outbreaks in the Everglades and tropical Florida. With significant State

and Federal funding, strong leadership, a dedicated and inclusive membership, and a focus on natural areas, the Florida

council has developed a mission statement, bylaws, invasive list with threat categories, and identification and control

publications (Langeland and others 2008).

The Tennessee council was established in 1994 with assistance from Florida council members and support from the

Great Smoky Mountains National Park, which has had an invasive plant control program under way since the 1970s. The

Tennessee Council’s mission and goals, like Florida’s, focuses on raising public awareness, facilitating the exchange of

information on identification and control, convening forums and workshops to share information, advising on all aspects of

nonnative invasive plants, and launching campaigns to prevent future introductions. Among its accomplishments has been

an exotic plant management manual for the State (Tennessee Exotic Pest Plant Council 1996) and leadership in organizing

both a South-wide council to fulfill a regional mission to one that now includes a national council in 1997. From 1999

to 2008, exotic plant councils were organized in Georgia (1999), Kentucky (2000), Alabama (2002), Mississippi (2002),

South Carolina (2003), North Carolina (2005) and Texas (2008). Most of these broadened the scope of their partnerships to

include right-of-way managers, gardeners, native plant enthusiasts, and stakeholders from all components of the intricate

modern landscape. State and regional annual meetings share current developments in research, policy, new nonnative

invasive plant arrivals, and council activities that have helped to propel invasive management efforts in the region.

Although their 501(c)(3) Federal tax status allows some collaborative lobbying, this avenue has not yet been pursued.

The South-wide and State Web sites are hosted at the Center for Invasive Species and Ecosystem Health (an offshoot

of the Bugwood Network organized in 1994), which was given official status by the University of Georgia in 2007. In

cooperation with Federal agencies, the Center provides critical information and services such as the invasive plants listed

by each State, identification and control guides for these invasive plants, details and Web hosting for cogongrass (www.

cogongrass.org) and other severe invasive plants, and annual meeting proceedings. The Center maintains an image

database system containing over 145,000 high-resolution images of native and nonnative species (Bargeron and others

2006) and with The Invasive Plant Atlas of the United States provides identification, distribution, and management

methods for most recognized invasive plants of the United States (Bargeron and others 2007). Regional connectivity for

council members on nonnative invasive plant matters is also being provided by the Center’s managing a listserv, blog,

Facebook page, and Twitter site. The Center also hosts a national reporting and mapping Web site entitled EDDMapS,

Early Detection and Distribution Mapping System (Bargeron and Moorhead 2007). It is publically accessible, provides

for voluntary inputs by State councils, and assigns a verifier to review data submitted on invasive species infestations

and distribution. This mapping site has in 2010 been expanded to cover the entire United States. In 2008, the Woodrow

Wilson School at Princeton University created another voluntary mapping database—the Invasive Species Mapping

Program—that focuses on the southern distribution of Chinese privet, kudzu, and cogongrass (Marvin and others 2008).

Additionally, a parallel mapping project, the Invasive Plant Atlas of the MidSouth, is under construction at the Mississippi

State University GeoSpatial Institute. It will combine information from the U.S. Geological Survey and other agencies

with the Invasive Plant Atlas of New England; and it will incorporate mechanisms and procedures to transmit data both

upward (nationally) and downward to the local level for rapid assessment and response. These databases are being linked

and projected to map most nonnative invasive plants in the region and eventually provide an effective and efficient early

detection and rapid response network for identifying and locating new high-risk introductions (Westbrooks 2004).

State vegetation management associations and societies focused on right-of-way management and their regional and

national organizations represent potentially invaluable collaborative partners for pest plant councils. These associations

recently added nonnative invasive plant management to their certified training curriculum. Because rights-of-way are

major conduits for some invasive plant spread, this increased awareness and added management approaches should

greatly aid further containment. Most pest plant councils have a transportation department employee on their boards and

many professional right-of-way managers are members of pest plant councils. These interactions have contributed to new

regulations ensuring that right-of-way projects use fill-dirt and rock uncontaminated by invasive plant seeds and plants.
400 The Southern Forest Futures Project

occurrence and damage (Brooks and others 2004, Lippincott

2000). Exotic plant bio-pollution is one of the greatest threats

to biodiversity, second only to habitat destruction, and

continues to attack our highly valued nature preserves and

wetlands (Wilcove and others 1998).

To date, successful efforts to combat and contain invasive

nonnative plants have taken an integrated approach to

vegetation management (Miller 2003, Miller and others

2010b). This approach incorporates all effective control

methods, which may include preventive measures (such as

quarantines, border inspections, and embargoes), biocontrol

using natural predators, herbicide technology, prescribed

fire, and mechanical and manual removal. Most preventive

measures and biocontrol programs are only effective when

organized on a regional basis because cooperation among

States is necessary for their success (Moran and others 2005,

Pimentel and others 2000, Westbrooks 2004).

This chapter summarizes pertinent information for the most

damaging trees, shrubs, vines, grasses, and forbs that have

invaded forests and natural areas as well as pastures, rightsof-way, orchards, grasslands, wetlands, and yards in the

South (Langeland and others 2008, Miller and others 2010a).

Our objective is to provide useful information on species

descriptions, traits that make them invasive, and current

management procedures and strategies for 56 threatening

invasive plants in 31 groups. The chapter also covers

principles of invasion and the value of organization, planning,

prevention, and management programs in slowing their spread

(with the caveat that eradication of widespread invasions

appears only possible on specific lands). Finally, the current

occupation and impacts of the invasive plants are presented,

with projections of potential spread for the next 50 years.

Methods and Data Source

Biological and ecological traits are summarized from the

literature for the major invasive plants in the South to guide

specific and general management actions. Recognized

concepts, impacts, strategies, policies, and program

elements regarding invasive plants and their management

are synthesized from the literature. The influences and

impacts relative to the other meta-issue areas are discussed

using current literature and inputs derived from their

chapters. Linear regression was used to calculate the

mean annual spread rate in forests using the approximate

date of introduction, major planting campaigns or escape

documented in the literature, and current occupation

estimates from Forest Service survey results. The linear

regression models provided conservative estimates of

future occupation under past and current climate conditions

and no expected major control programs. Other modeling

approaches used the climate change cornerstones and

landscape data bases to forecast current and future potential

habitat for five high-threat species.

In 2001, the Southern Research Station of the Forest Service,

U.S. Department of Agriculture began surveying 53 invasive

plant groups on all forest ownerships in partnership with

State forestry agencies (USDA Forest Service 2008a). This

survey of 33 regional species (Miller 2003) and 20 species

particular to Florida (Langeland and Burks 1998) has

become part of the traditional timber data collections that

have been conducted by the Station’s Forest Inventory and

Analysis unit (FIA) since the 1930s. Maps of occupation for

the most occupying species and tabular coverage estimates

were derived from these data.

The species selected for survey are regionally recognized

nonnative plants known to invade interior forest stands,

some forest edges, gaps, roadsides, and stream-sides less

than 120 feet wide. The 13 State inventories commenced in

different years; although they have varying rates of progress

and cycle completions, the expectation is that at least a fifth

of the plots within a State will be inventoried every year

(Oklahoma’s data has yet to be verified for posting). Percent

cover by species is recorded on existing FIA clusters of four

permanent 1/24-acre subplot that are located across forested

landscapes on an approximately 3-mile grid. Each subplot

represents about 1,500 acres. Invasive plant cover is recorded

in five categories: 1 = trace to <1 percent; 2 = 1 to 10 percent;

3 = 11 to 50 percent; 4 = 51 to 90 percent; and 5 = 91 to

100 percent. For each category, midpoint values are used

to calculate an estimate of cover by species for the region

and for each county within a State. Our methods combine

analyses of FIA data to display current occupation by county,

State, and subregion, and then follow up with analyses to

understand the nature of occupations.

We focused our predictive modeling on five species of high

concern in the South using the Cornerstone Futures. These

species were selected because they had sufficient datasets,

represented a range of plant growth forms and invasion

patterns, and varied by subregional occurrence. The modeled

species were Japanese climbing fern (Lygodium japonicum),

Nepalese browntop (Microstegium vimineum), nonnative

roses (Rosa spp.), silktree (Albizia julbrissin), and tallowtree

(Triadica sebifera). Human and environmental landscape

variables were extracted from available digital information

for each FIA datapoint, including national resource inventory

land-use categories, distance to roads and rivers, human

population census with projections, elevation, and climate

information (Gesch and others 2002, PRISM Group 2008,

U.S. Census Bureau 2000). All variables were converted into

295 feet by 295 feet (90 m by 90 m) cells across the South.
401

chapter 15. The Invasion of Southern Forests by Nonnative Plants

Two modeling techniques, logistic regression (Hosmer

and Lemeshow 2000) and maximum entropy (Phillips and

others 2006), were used to develop a potential distribution

for each species. Logistic regression is a generalized linear

model that is used to investigate the relationship between a

set of explanatory variables for prediction of the probability

of occurrence of an event by fitting data to a logistic curve.

It makes use of several predictor variables that may be

either numerical or categorical. Logistic regression makes

no assumptions about the distribution of the independent

variables. MaxEnt (Phillips and others 2006) is based on

maximum entropy probability distribution. It is a probability

distribution whose entropy is at least as great as that of all

other members of a specified class of distributions. The

MaxEnt approach is to estimate the probability distribution,

such as the spatial distribution of a species that is the greatest

extent subject to constraints such as the known locations of

the species. It is a machine learning technique that predicts

species distributions using detailed geospatial data sets

together with species occurrence information, conducted

using a specialized program package of MaxEnt (Phillips

and others 2006). It generally performs as well or better

than other algorithms in tests of model performance (Elith

and others 2006, Phillips and others 2006). The important

difference between the two techniques is that logistic

regression uses information on both presence and absence

to estimate a predictive linear model, whereas maximum

entropy (MaxEnt) uses information from presence-only

and is a nonparametric approach. In developing models for

species, variables were eliminated using a manual backward

selection method to delete those having little or no impact.

Impact on the model was measured as percent contribution

and with a jackknife test on gain and influence on area under

the curve (AUC). This allowed identification of key variables

in determining the occurrence of each species.

To identify the key variables in determining each species

occurrence, we calculated the contribution of each variable

to the model. The omission rate and area under the Receiver

Operator Characteristic (AUC) were used to assess the

reliability and validity of models. To assess models, FIA

data were split spatially with 50 percent used as a test dataset

and 50 percent used as a training dataset. The omission

rate is the false negative or the proportion of sites where

the species was present but the model predicted absence.

To calculate this, a cut-off criterion is required to convert

continuous model predictions to binary classifications. We

used a threshold value that maximized the sum of sensitivity

and specificity. The sensitivity is the proportion of actual

presence correctly identified and the specificity is the

proportion of absences correctly identified. Sensitivity and

specificity for each potential cut-off were added together and

the cut-off with the greatest combined number was selected

for further work. This has the advantage of giving equal

weights to the probability of success of both presences and

absences (Manel and others 2002). This is one of the most

appropriate methods to correctly derive a binary variable

from continuous probabilities when species presence-absence

distribution data are unbalanced (Jiménez-Valverde and Lobo

2006, Liu and others 2005). AUC provides a single measure

of model performance, independent of any particular choice

of threshold but is sensitive to the method in which absences

in the evaluation data are selected (Lobo and others 2008).

It is most applicable to data with true absences (JiménezValverde 2011), thus it needs to be used with caution with

datasets that do not have absence data. The models run with

FIA data were measured absences, but as invasions are not at

their full potential, these were not necessarily true absences.

We used the following classes of AUC to assess model

performance: 0.50 to 0.75 = fair, 0.75 to 0.92 = good, 0.92 to

0.97 = very good, and 0.97 to 1.00 = excellent (Hosmer and

Lemeshow 2000). AUC is useful when used in conjunction

with other validation statistics because invasive species are

often not at equilibrium with their environment and their

current realized distribution is much smaller than their

potential distribution, thus even field absence data can be

temporary with a time dimension.

Although logistic and MaxEnt models may be compared

individually to select the best overall model for a particular

dataset, combining the two (Araújo and New 2007) can

reduce the uncertainty associated with dependence on one or

the other. We identified areas where both models predict high

potential of invasion, areas where just one model predicts

moderate potential of invasion, and areas where both models

predict low potential of invasion. Variable contribution to

the models was calculated as an average of the two models

and direction was assessed in combination. The directions

were either a linear positive or negative, or a binomial

(two peaks) or polynomial (one valley) relationship. The

percentage of forest invaded was calculated by overlaying

the final occurrence map with a binary layer of forest for

each Cornerstone (chapter 2), producing percentages at high

and moderate potential levels. For comparison, each was

converted to a current and future percentage of forested FIA

plots invaded for each species and for each Cornerstone,

based on projected forest acreage (chapter 5). Using these

same models, a likewise probability for nonforested lands

was calculated for display on the same maps. Because the

datasets of landscape variables used for modeling were

extracted for the entire South and FIA from forested area,

predictions for nonforest lands using forest plot occurrence

may be less accurate. However, because all invasive species

have large populations on nonforested lands, their projected

occupation on nondeveloped lands provides a depiction of

interconnectivity that has been lacking. This interconnectivity

of the forest and nonforest land invasions under way and

projected should not be ignored, however limited the strength

of the models on the nonforested portion may be.
402 The Southern Forest Futures Project

Results

The South’s Most Pervasive Invasive Plants

The 31 groups (taxa) discussed here qualify as the first

targets for proactive management because they account for

much of the lands occupied by invasive plants. Although

they cumulatively pose the greatest threat for the region,

priorities vary for specific subregions, depending on current

and potential occupations. Most are able to spread from

one subregion to another if not contained, because most are

limited by spread vectors and not environmental factors

(Pattison and Mack 2009). In this section we summarize

invasive plant descriptions, current and future occupation

with country of origin (table 15.1), and the community layers

they mostly impact (table 15.2) in five categories: trees,

shrubs, vines, grasses and bamboos, and forbs. Descriptions

are mostly derived from Miller and others (2010a) and

Langeland and others (2008), while specific traits that lead

to their success as invasives are derived from the wider

literature.

Invasive Trees

In addition to dramatically altering habitats, nonnative

trees hinder reforestation and management of rights-of-way

and natural areas. Some species occur initially as scattered

individuals and then eventually form dense stands if not

controlled. Almost all invasive trees are hardwoods. Most

spread by prolific seed production and abundant root sprouts,

and all are still sold as ornamentals unless prohibited by

State laws. Because they tolerate an exceptionally wide

range of soil and site conditions, they are popular as low

maintenance ornamentals. Depending on conditions, invasive

trees can be eliminated with herbicides by stem injection,

cut-treat, soil spots, basal sprays, and foliar sprays (Miller

and others 2010b). Although bulldozers with root rake blades,

mulchers, chainsaws, and prescribed burning will eliminate

or reduce standing trees, only herbicides are effective in

controlling roots. Total elimination requires surveillance and

treatment of resprouts and plant germinants that originate

from the soil seed bank.

Tallowtree—Tallowtree or popcorntree (Triadica sebifera)

forms nearly pure stands in former wet prairies and is

more likely on low and flat lands, areas adjacent to water

and roadways, sites recently harvested or disturbed, young

stands, and private forestlands (Bruce and others 1995, Gan

and others 2009). Tallowtree was originally introduced from

China presumably through France into coastal South Carolina

near Charleston and Georgia as early as the 1770s (Hunt

1947). It is a deciduous tree growing to 60 feet tall with leaves

that are broadly ovate to diamond-shaped and turn bright

yellow and scarlet in the autumn, which makes it an attractive

and widely planted yard tree (Jubinsky and Anderson 1996).

Also, the plant has a high tolerance to insect defoliation

(Rogers and Siemann 2003) and all parts of the plant are

considered toxic to humans, especially the inner seeds

(Everest and others 1996). Although not pollinated by bees

(but wind), the tree is prized and planted by honey producers

because of its abundant nectar glands (Lieux 1975). Bundles

of white waxy popcorn-like seeds appear on branchlets in

the autumn and remain into winter. Seeds are high in fat

and protein, and birds and possibly mammals consume the

waxy seed coat and then pass and spread the seeds (Conway

and Smith 2002; Renne and others 2000, 2002). Because

they float, seeds are also spread by water around lake and

bog margins as well as along drainage ditches, streams, and

rivers. Tallowtree is shade intolerant, which limits seedling

establishment in intact forests (Pattison and Mack 2009).

Trees as young as 3 years can produce viable seed and remain

reproductive for 100 years, capable of producing 100,000

seeds per year (Bruce and others 1997, Gray 1950). Seed

viability in the soil is 2 to 7 years, and germination rate

varies by State from 6 to 52 percent reported (Cameron and

others 2000). Infestations intensify seeding and surface root

sprouts, and foliage and roots release chemicals that inhibit

other vegetation, causing an eventual collapse of biodiversity

following invasion (Conway and others 2002).

Occupation occurs mostly in the Coastal Plain along the

Gulf of Mexico and Atlantic Ocean, with the greatest

concentration on invaded coastal prairies surrounding

Houston (fig. 15.1). Reports of China’s fourteen centuries

of uses prompted the U.S. Department of Agriculture to

establish trials and promote Gulf Coastal Plain plantings

in Texas during the early 1900s, resulting in the current

Texas epicenter (Howes 1949). Tallowtree has the highest

regional occupation of any nonnative tree invader, with

more than a half million acres covered and a 45 percent

increase projected during the next 50 years (table 15.1)

under current climate conditions. Floodwaters from multiple

hurricanes in the past two decades have facilitated spread

into damaged forests, wetlands, and wet prairies (Chapman

and others 2008). Oswalt (2010) reported that the numbers

of Chinese tallowtree in Louisiana, Mississippi, and eastern

Texas increased by about 370 percent from the 1990s to

2005. Because its sole limitation is dispersal vectors, this

invasive has yet to occupy the full extent of its range in

North America (Pattison and Mack 2008, 2009). Increases in

both range and severity have been predicted with a warming

climate (Gan and others 2009), and we investigated this

further and provide results in a later section.

Tree-of-heaven—Tree-of-heaven (Ailanthus altissima) or

ailanthus occurs mostly along forest roads where it spreads

into recently harvested or disturbed sites and displays

exceptional competitive capabilities as a new player in stand

development (Landerberger and others 2007, Miller 1990).

Tree-of-heaven was imported into the Eastern U.S. as an


405

chapter 15. The Invasion of Southern Forests by Nonnative Plants

ornamental in 1785 and was a common nursery species

by the mid-1800s (Davies 1942, Hu 1979). Favorable early

tests paved the way for widespread plantings on surface

mined lands in Appalachia (Plass 1975). Tree-of-heaven is a

shallow-rooted deciduous tree that can grow to 80 feet tall. It

has long compound leaves that have rows of non- opposing

leaflets on both sides of the stalk with two circular glands

under small lobes at leaflet bases (Miller and others 2010a).

Large terminal clusters of tiny greenish flowers in early

summer yield persistent clusters of wing-shaped fruit with

twisted tips on female trees. Viable seeds are produced at 2

to 3 years, and mature trees can produce more than 100,000

seeds per year (Renne and others 2002). Even light-green

seeds in midsummer are capable of germination. Renne

and others (2002) reported that 40 percent of a seed crop

was dispersed by 16 bird species in coastal South Carolina.

Seeds can be also blown up to 330 feet from a parent tree

(Landenberger and others 2007) and can also float causing

long-distance dispersal and infestations along streams

and rivers (Kowarik and Saumel 2008). If the main stem

is deadened, root sprouts will appear afterward, and root

segments left in soil after pulling treatments will sprout

(Miller and others 2010b). Sprouts can grow 10 to 14 feet

tall the first year (Swingle 1916). This vigorous growth

can continue for 4 or more years. Leaves and roots release

allelopathic chemicals that inhibit other plants, decreasing

biodiversity (Gomez-Aparicio and Canham 2008, Lawrence

and others 1991).

The area of highest occupation is around Nashville in the

Cumberland Plateau of Tennessee, followed by another one

along the Shenandoah Valley where the Piedmont meets the

Northern Ridge and Valley Province in Virginia (fig. 15.2).

Southward spread is expected since scattered infestations

occur even as far south as Florida, with a predicted increase

of 24 percent more cover in 50 years (table 15.1).

Chinaberrytree—Chinaberrytree (Melia azedarach),

a traditional widely escaped ornamental introduced into

South Carolina and Georgia from Asia in 1830 (Gordon and

Thomas 1997), is increasingly becoming established within

forests. It is deciduous, growing to about 50 feet tall with

multiple trunks that tend to arch outward. It has lacy, many

divided leaves that are dark green, sometimes turning bright

yellow in autumn (Miller and others 2010a). Showy panicles

of tiny blue flowers in spring yield abundant round yellow

pulpy fruits that persist during winter. Some seeds will

germinate even when the fruit coats are green. If the main

stem is deadened, stump sprouts, root sprouts, and seedlings

will eventually emerge. Viable seed can be produced at 4 to

5 years, while the longevity of seed viability in the soil has

not been reported. This species spreads by abundant birdand animal-dispersed seeds (Vines 1960), which are toxic to

humans and some mammals (Everest and others 1996).

Occupation is highest across the Coastal Plains with

scattered outliers elsewhere in the South (fig. 15.3).

Occurrences in the cooler climates of northern Virginia

indicate that range is not limited by temperature and that

further spread can be expected. Chinaberrytree is the third

most abundant invasive tree in the region (table 15.1). Regionwide spread has already occurred (USDA Natural Resource

Conservation Service 2010), and an additional 28 percent of

occupation is forecasted by 2060.

Silktree—Silktree or mimosa (Albizia julbrissin) is a small

legume tree 10 to 50 feet tall imported into the South from

central Asia (Cothran 2004) and traditionally planted as

an ornamental owing to abundant showy pink and white

flowers in spring and throughout summer. It reproduces by

abundant seeds and root sprouts. It has feathery deciduous

leaves and smooth light-brown bark. Profuse dangling flat

pods containing 5 to 10 seeds are released during winter

and can float to spread along waterways and ditches, where

seeds remain viable for many years. All subregions have

scattered silktree stands, mostly along highways, with the

heaviest infestations in north central Alabama surrounding

Birmingham (fig. 15.4). Silktree is currently the fourth most

abundant invasive tree (table 15.1). Forest occupation is

expected to increase by 22 percent over the next 50 years

under current climate, resulting in extra roadside

maintenance costs to prune jutting limbs. Various diseases

attack silktree and may restrict future range and density

(Dirr 1998). Estimates of spread rates with climate change

scenarios are reported in a later section.

Brazilian peppertree—Brazilian peppertree (Schinus

terebinthifolius) was initially imported in the mid- and

late 1800s (Barley 1944), while it was made popular as

an ornamental near Miami in the 1930s where it initially

escaped (Morton 1978). Brazilian peppertree completely

replaces native vegetation with its tangled infestations

that reach heights of 40 feet (Langeland and others 2008).

It is an evergreen shrub or small tree and has many short

trunks or arching stems of contorted branches. Drooping,

odd pinnately-compound leaves smell of turpentine when

crushed. It produces many multi-branched clusters of small

whitish flowers in summer and autumn that yield abundant

clusters of spherical red pepper-smelling fruit in winter

(only on female plants). Plants can produce seeds as early

as 3 years. Germination mainly occurs from November to

April, with seed viability ranging from 30 to 60 percent.

Drought appears to be the main cause of seedling mortality.

Allelopathic chemicals are released by fallen leaves that

inhibit other plants, decreasing biodiversity (Morgan and

Overholt 2005). Chemicals are produced in leaves, flowers,

fruits, and sap that can irritate human skin and respiratory

passages (Morton 1978).


408 The Southern Forest Futures Project

Princesstree—Princesstree or paulownia (Paulownia

tomentosa) was introduced from Europe to America for

ornamental purposes in about 1844, although originally from

China (Hu 1961). It was considered naturalized in Georgia by

1896 (Harper 1900). Princesstree is a deciduous tree to

60 feet tall with large heart-shaped leaves that are fuzzy

on both surfaces. Before leaves appear in spring, trees are

covered with showy pale-violet flowers that produce persistent

pecan-like capsules in clusters in autumn and winter. Each

capsule splits to release thousands of tiny winged seeds

that are spread by wind and water. Abundant flower buds

are present on erect stalks over winter. Plants can produce

viable seed at 5 to 7 years. In the mountains, seeds can be

dispersed as far as 2 miles by wind (Langdon and Johnson

1994). Because germination requires bare soil, princesstree

invades widely after wildfire, timber harvesting, and other

disturbances, forming colonies from prolific root sprouts

(Langdon and Johnson 1994). This ornamental is still widely

marketed as royal paulownia or royal empress and planted as

an “instant” shade tree. Princesstree occurs as scattered forest

infestations in all States except Texas. Heaviest infestations

are associated with surface mine plantings (Tang and others

1980) and those cities with numerous ornamental plantings,

such as Lexington-Lynchburg (TN), Forest City (NC),

Florence and Tuscaloosa (AL), and Vicksburg (MS) (fig. 15.5).

Some occurrences are probable escapes from commercial

princesstree plantations owing to the promotion by the

American Paulownia Association, Inc. The relatively few

straight trees produced by these plantations have a high value

in Japan but nowhere else (Tang and others 1980). Because of

continued sales and plantings along with a naturalized range

that covers all Southern States (USDA Natural Resource

Conservation Service 2010), spread and intensification is

expected to increase by at least 31 percent over the next

50 years (table 15.1).

Invasive Shrubs

Nonnative shrubs often occur as dense understory layers

that prevent natural regeneration of the native overstory trees

(table 15.2). Herbicide control options resemble those for

trees, with a few exceptions: foliar sprays are more often the

control of choice for shrubs; cutting shrub stems close to the

soil surface and treating the stump with an herbicide is easier

with shrubs; and because shrub stems are smaller, basal

sprays are usually more effective (Miller and others 2010b).

All invasive shrubs are shade tolerant and are spread by birddispersed seeds, initially resulting in scattered plants under

existing forest canopies that require interior surveillance

strategies. All species described below are still produced,

sold, and planted as ornamentals and wildlife food plants,

except tropical soda apple.

Invasive privets—There are at least eight species of invasive

privets (Ligustrum spp.) that have been introduced from

Asia and Europe into the South as ornamentals from 1794 to

1899 (Dirr 1998, Maddox and others 2010, USDA Natural

Resource Conservation Service 2010). They are the second

most abundant invasive plants in the South and they form

dense stands in the understory of bottomland hardwood

forests and exclude most native plants and replacement

regeneration (Merriam and Feil 2002). These privets are

also increasing in upland forests, fencerows, rights-of-way,

and special habitats throughout the region. They drastically

change habitat and critical wetland processes. Abundance of

common birds is sustained in privet thickets, but abundance

of specialist birds and diversity of native plants and bees is

decreased (Hanula and others 2009, Wilcox and Beck 2007).

Chinese privet (L. sinense) is the most common invasive

privet across the South, while European privet (L. vulgare),

Amur privet (L. amurense), California privet (L. ovalifolium),

waxyleaf privet (L. quihoui), and border privet (L.

obtusifolium, only in VA, KY, TN, and NC) are confined to

subregions (USDA Natural Resource Conservation Service

2010). These privet species are most often semi-evergreen to

evergreen being multi-stemmed shrubs to 30 feet tall and just

as wide due to arching tops. They are difficult to distinguish

to species since all have leafy stems with opposite leaves less

than 3 inches long. The evergreen privets include Japanese

privet (L. japonicum) that grows to 12 feet tall and just as

wide and glossy privet (L. lucidum) that grows up to 50 feet

in height, with an upward spreading canopy. They have thick

leathery opposite leaves 4 to 6 inches long that are glossy,

and stems that are hairless. Terminal sprays of small showy

white flowers bloom on all privets in spring, except waxyleaf

in autumn; abundant clusters of small, green-turning-purple

fruit appear in autumn and often persist through winter.

Birds spread seeds (Greenberg and Walter 2010), which

produce abundant seedlings and are thought to be viable for

only a year (Shelton and Cain 2002). Privets also increase

in density by stem and root sprouts. If the parent shrub is

deadened, many shallow surface roots will produce sprouts

(Harrington and Miller 2005). In Georgia, privet has been

reported as an important autumn and winter browse for

white-tailed deer (Odocoileus virginianus) (Stromayer and

others 1998).

Occupation is widespread throughout the South, with the

most occurrences in an epicenter around Birmingham, AL

(fig. 15.6) and the least in Kentucky, Florida, and western

Texas (USDA Natural Resource Conservation Service 2010).

Thirty-seven percent more privet cover is predicted by 2060,

which would amount to 1.2 million more acres, second only

to Japanese honeysuckle for potential spread (table 15.1).

Invasive roses—There are over 21 nonnative roses (Rosa

spp.) invading ecosystems in the South, while multiflora rose

(R. multiflora) is the most pervasive in the Eastern United

States (Miller and others 2010a, USDA Natural Resource

Conservation Service 2010). With their dense infestations

410 The Southern Forest Futures Project

of thorny shrubby entanglements, invasive roses occur

increasingly along forest margins, within interior forests, and

along stream banks where they disrupt forest regeneration,

wildlife movement, and land access (Honu and Gibson 2008,

Merriam 2003). With the exception of multiflora, all invasive

roses are evergreen. Multiflora was imported from Japan

and Korea in 1886 (Dirr 1998). Roses can be erect, arching,

or trailing shrubs to 30 feet in height or long and clump

forming. They have compound leaves with three to nine

leaflets and frequent recurved or straight thorns along the

stems. Clustered or single white-to-pink flowers appear in

early summer to yield red rose hips in autumn to winter. The

fruit are consumed by birds resulting in wide seed dispersal

(Greenberg and Walter 2010). Roses colonize by prolific

sprouting and rooted trailing stems. It has been estimated

that an average multiflora rose plant may produce a million

seeds per year, which may remain viable in the soil for up to

20 years (Bergmann and Swearingen 2009). Nonnative roses

invade along stream banks and forest road edges to extend

into open forests. Nonnative roses resemble native Carolina

rose (R. carolina), swamp rose (R. palustris), and climbing

rose (R. setigera), all of which have pink flowers in spring

and nonbristled leafstalk bases, but none form extensive

infestations except swamp rose.

Occupation varies by species (fig. 15.7). Supported by

government programs that promoted and funded plantings

until the 1950s, multiflora rose has been planted for “living

fences” to confine livestock, wildlife habitat, and in highway

medians as a crash barrier (Bergmann and Swearingen

2009). Its range is the entire Eastern United States and

Canada, although declines have been reported in central

New Jersey as a forest stand developed over a 40-year period

(Banasiak and Meiners 2009). Multiflora occurs in most

Southern States with heaviest infestations in Kentucky and

Virginia throughout the Cumberland Plateau and Mountains

and the Appalachian Mountains. Other infestations are

common in the Ozark-Ouachita Highland, while Cherokee

rose (R. laevigata) principally occurs across the Coastal

Plain most notably in the Black Belt Prairie area of southern

Alabama. Together, invasive roses occupy almost 700,000

acres of forests—making them the second most common

invasive shrub—and they are predicted to increase their

coverage by 37 percent over the next 50 years (table 15.1)

because of continued spread along highway-forest margins,

which go largely untreated. Estimates of spread rates with

climate change scenarios are reported in a later section.

Invasive lespedezas—Invasive lespedezas (Lespedeza

spp.) were introduced in the United States from 1837 to

1896, originally from China, Japan, or Korea (Dirr 1998,

Donnelly 1954). All three species have been widely planted

for wildlife food plots and forage for more than a century

and continue to be used for soil stabilization projects along

highways and on surface mines. All were reported to have

escaped from plantings in the 1940s (Allard and Leonard

1943, Davison 1945, Gunn 1959). If allowed to escape from

planted stands, they form dense exclusive infestations that

remain standing during winter dormancy to prevent forest

regeneration, wildlife movement, prescribed burning,

and land access. All have nitrogen-fixing bacteria on root

nodules. Shrubby lespedeza (L. bicolor) and its look-alike,

Thunberg’s lespedeza (L. thunbergii), are perennial muchbranched semi-woody shrubs 3 to 10 feet in height. Chinese

lespedeza (L. cuneata) occupies the most lands of the

invasive lespedezas and has already spread westward into

the Great Plains. A subshrub that grows to 6 feet in height

with allelopathic chemicals in the foliage that inhibit other

plants (Kalburtji and Mosjidis 1992), it has many tiny creamcolored flowers during summer compared to the pinkishto-white flowers of shrubby and Thunberg’s lespedeza. All

three species yield abundant single flat seeds in autumn and

winter that are spread by birds, ants, and rodents. Seeds have

low germination, but because they are long-lived in the soil

seed bank control treatments must be followed by longterm monitoring (Logan and others 1969). These invasives

resemble two native species, the slender lespedeza (L.

virginica) that grows in tufted clumps instead of infestations

and the native roundhead lespedeza (L. capitata Michx.)

that has similar leaves but whitish flowers in round-topped

clusters. Superior strains of invasive lespedezas that were

developed at Federal plant materials centers (Pieters 1950)

have the potential to take over diminishing native grasslands

and prairie communities to the detriment of biodiversity

(Brandon and others 2004). Chinese lespedeza varieties were

developed with lower lignin and tannin concentrations to

overcome forage limitations (Donnelly 1954, Hawkins 1955).

All subregions have invasive lespedeza infestations, with

an epicenter around Greenville and Spartanburg, SC, while

the least are along the Mississippi River Delta and in Florida

(figs. 15.8). Occurrence appears to be matched to areas of

past planting programs for soil stabilization and wildlife food

plots. Region-wide spread mainly along highways and roads

is occurring at an estimated annual rate of 7,600 acres, and

71 percent more cover is predicted over the next 50 years

(table 15.1).

Bush honeysuckles—There are at least six species of

invasive bush honeysuckles (Lonicera spp.) that have been

repeatedly imported from eastern Asia into the United

States over a 100-year period from 1752 to 1860 followed

by plant breeding programs. Widespread distribution has

occurred through continued nursery sales and Federal

programs from 1960s to 1984 (Dirr 1998, Luken and

Thieret 1996). They are still planted as ornamentals and

for wildlife habitat and soil stabilization, while botanists

proclaim their biological threat. Bush honeysuckles now

occur as frequent shrubs along forest margins and in

openings in many Southern States, and as solid understory


413

chapter 15. The Invasion of Southern Forests by Nonnative Plants

are green above and silvery scaly beneath, and it produces

many silvery-scaled red berries in autumn. Silverthorn is

evergreen, shade tolerant, and densely bushy to 25 feet in

height, with long limber projecting shoots that can eventually

climb into tree crowns. Its leaves are simple, both silver and

tawny, scaly, and its tiny cream-colored flower clusters appear

in late autumn, producing oblong, red olive-like brown-scaled

fruit in spring. Russian olive is deciduous with a single bole

that grows to 35 feet tall, silvery-scaled leaves, and fruit

that is produced in autumn. All have scattered thorn like

short branches along their stems. The fruit are consumed by

wildlife followed by widely dispersed seeds. Plants initially

occur as scattered individuals, both in the open and under

forest shade, and intensify by abundant arching basal sprouts

(Munger 2003).

Occurrence is greatest in the Piedmont of Georgia and

South Carolina, where government nurseries once supplied

elaeagnus for wildlife plantings (fig. 15.10). Surface mine

reclamation using autumn olive plantings has resulted

in another epicenter in the northern Appalachian and

Cumberland highlands. The more recent popularity of

silverthorn as an ornamental is likely to result in regionwide escapes into urban forests and then into the broader

landscapes. Invasive elaeagnus species are in their early

“lag” phase of forest invasion when occurrences are scattered

and populations are low, which means the spread of the

current 96,421 acres will be at least 60 percent more acres in

50 years (table 15.1).

Sacred bamboo—Sacred bamboo or nandina (Nandina

domestica) was an early imported ornamental from 1804

due to its evergreen foliage, spring flower clusters, and

bunches of red berries that persist during winter. It was only

in the 1960s (150 years after introduction) that escapes into

forests of North Carolina were recognized (Radford and

others 1964). It is still widely sold and cultured to yield new

hybrids, some of which are seedless. It is now replacing the

shrub layer in deciduous forests due its continuous evergreen

growth habit (Langeland and others 2008).

Nandina is an erect shrub that can grow up to 8 feet in

height, with multiple bushy jointed stems, somewhat

resembling bamboo (Kaufman and Kaufman 2007). Glossy

multiple divided leaves are green turning to red or pink in

winter. Abundant berries in autumn and winter are a favorite

food for birds, spreading seeds from back yards to forests.

Nandina is widely escaped to varying degrees in all States

(fig. 15.11). Continued production and sale in the plant trade

have resulted in occupation on 24,000 acres of forest land,

with as much as an 11-fold increase expected by 2060 (table

15.1).

Tropical soda apple—Tropical soda apple (Solanum

viarum) was listed as a Federal Noxious Weed soon after

its accidental introduction from South America into Florida

in the late 1980s (Mullahey 1996). Spread was exceedingly

rapid, predominantly through intra-State and interstate

transportation of cattle with tropical soda apple fruit in their

rumens. It infests pastures in at least eight other States.

Wildlife now feed on the fruit in pastures and spread its

seeds to many land-use areas, including forest margins

and gaps and open forests (Akanda and others 1996). Once

established, it forms exclusive thorny infestations.

Tropical soda apple is an upright, perennial subshrub or

shrub, 3 to 6 feet in height, which remains green year-round

in most southern locations. It has thorny oak-shaped and

sized leaves, clusters of tiny white flowers, and golf-ball

sized fruit that are mottled green-white turning to yellow in

late summer to autumn. Even immature fruits can contain

viable seeds, which adhere to machinery, wildlife, clothing,

and boots. Fruits have a sweet smell that is attractive to

livestock and wildlife, and each can contain 400 seeds.

Shoots increase in numbers and size annually from the

rootcrowns. Most infestations are in the Coastal Plains with

migration occurring into the southern Piedmont (fig. 15.12).

Research has shown that tropical soda apple will grow and

reproduce as far north as Illinois (Patterson and others

1997), indicating that further spread in the South is probable.

Forested acres occupied in the region are predicted to triple

by 2060, from 9,570 to 31,320 (table 15.1). One biocontrol

insect for tropical soda apple has been released with several

others undergoing tests (Medal and others 2010).

Winged burning bush—Winged burning bush (Euonymus

alatus) is an ornamental imported from China around

1860 due to its brilliant pink to red autumn foliage, thus

its common name (Dirr 1998). Only recently has it been

observed as infestations under forest canopies and along

rights-of-way (Ebinger 1983). Winged burning bush is a

deciduous, wing-stemmed, bushy shrub to 12 feet in height

(Miller and others 2010a). Leaves are opposite, oval with

elongated bases and tips, and thin, less than 2 inches long

with both surfaces smooth and hairless. Plants are densely

branched with a broad leafy canopy. Abundant tiny orange

fruit appear in late summer as stemmed pairs in leaf axils

and turn purple in autumn.

Winged burning bush has been used extensively as an

ornamental in the Northeastern United States and upper

reaches of Southern States, and has many cultivars (Dirr

1998). It escapes and spreads by bird-carried seeds and

colonizes by root suckers. Along with occupying forest

openings and rights-of-way, it forms dense infestations

that replace understory shrub layers in deciduous forests

because of its tolerance to shade (table 15.2). It resembles

the threatened and endangered native burning bush (E.

atropurpureus), which has erect hairs covering the lower leaf

surfaces. Although infestations of winged burning bush are






420 The Southern Forest Futures Project

dense, blocking introduction of native species (table 15.1

and 15.2). All are still widely produced, sold, and planted

as ornamentals. Continued planting by landscapers and

developers would accelerate spread at the same time that

older plantings reach fruiting age.

Vincas, periwinkles—Periwinkles (Vinca spp.) were

brought by European colonialists for their many medicinal

purposes, but can also be poisonous if used inappropriately

(Schittler 1973). Common periwinkle (V. minor), introduced

in about 1711, and bigleaf periwinkle (V. major) in 1789

(Wells and Brown 2000) are evergreen, somewhat woody,

trailing or scrambling vines to 3 feet long and upright to 1

foot, which form dense ground cover to exclude all native

plants. They have thick opposite lance-to-heart-shaped leaves

and five-petaled pinwheel-shaped violet single flowers. They

can form mats and extensive infestations by vines rooting at

nodes even under forest canopies, especially under deciduous

hardwoods, usually near the site of original planting around

farm houses. Viable seeds appear to be produced only

rarely. Infestations originate from prior plantings and these

species are still widely sold and planted as evergreen ground

cover. Invasive vincas occur as scattered infestations that

vary across States, probably because of historical sources

and gardening practices. The highest concentrations are in

Virginia, Kentucky, North Carolina, and Mississippi (fig.

15.17). Spread is expected to be slow, unless fertile hybrids

appear (table 15.1).

Invasive climbing yams—One species of nonnative

climbing yams—Chinese yam or cinnamon vine (Dioscorea

oppositifolia)—is invading southern forests from the north

and two others—air yam (D. bulbifera) and water yam

(D. alata)—are moving northward from the Coastal Plain

and Florida. All threaten forested parks and preserves by

covering native plants (table 15.2). Water yam was introduced

in the 17th century and may have arrived on slave ships from

Africa while it became widely cultivated. It was noted as an

escape in 1897 (Austin 1999). Air yam is believed to have

been introduced before 1777 and was observed by the famous

explorer botanist William Bartram in Mobile (Harper 1958).

It escaped about 1905 (Morton 1976). Chinese yam was

introduced in the 1800s (Flora of North America Association

2009) and believed escaped in the Carolinas after cultivation

around 1900 (Rodgers and Shake 1965). Invasive climbing

yams are herbaceous vines to 65 feet that cover shrubs and

small trees in infestations (Langeland and others 2008,

Mueller and others 2003). They have twining and sprawling

stems with long-petioled smooth heart-shaped or shieldshaped leaves and dangling potato-like tubers (bulbils) that

appear at leaf axils and drop to form new plants. Their aerial

tubers spread down slope and by water, sprouting to form

new plants, and they also have large underground tubers that

hinder eradication. All three species are thought to rarely

produce seeds. All vines die back during winter but can

completely cover small trees the following year, with old

vines providing trellises for regrowth.

Chinese yams are found scattered throughout the region with

most common infestations occurring in western Tennessee

and less common ones in Virginia; air and water yams

occur along the Gulf of Mexico and throughout Florida

(USDA Forest Service Natural Resources Conservation

Service 2010) (fig. 15.17). All are difficult to control and

contain because aerial tubers can persist in the soil. All were

traditionally sold as unique ornamentals and readily escaped

when fruits were discarded. Air yam and Chinese yams are

still sold and their tubers and bulbils are prized as herbal diet

supplements. Invasive vines with less than 21,000 acres of

current forest cover, climbing yams are projected to increase

by 45 percent in 50 years (table 15.1).

Winter creeper—Winter creeper or climbing euonymus

(E. fortunei) has been planted as an ornamental since the

1907 introduction into the United States from China (Dirr

1998). It is an evergreen shrub to 3 feet in height or woody

trailing vine to 40 to 70 feet that forms a dense ground cover,

using aerial roots at nodes along stems to colonize and to

cling to trees and rocks. Sensitive forest habitats, forested

parks and preserves, and unmanaged forests are threatened

by loss of diversity after invasion by winter creeper

(table 15.2). It has thick leaves less than 2.5 inches long that

are opposite, dark-green or green-white-variegated on green

stems that become woody and brown with age. Clusters of

small, inconspicuous flowers in summer yield pinkish-to-red

fruit capsules that open in autumn to expose orange fleshycovered seeds that are spread by birds, other animals, and

water. Many cultivars are still widely produced, sold, and

planted as ornamentals in a range of foliar colors, increasing

the likely spread rate (table 15.1). Markets have traditionally

been confined to the Northern States, although expansion

into a more southern range should not be hindered under

current climate conditions (Dirr 1998). The Cumberland

Plateau in Kentucky and Tennessee has the most recorded

occurrences of this species, and escapes from rural

population centers continue to promote its spread across

the region (fig. 15.17).

Old World climbing fern—Old World or small-leaf

climbing fern (L. microphyllum), like Japanese climbing

fern, is a climbing and twining, perennial viney fern to 90

feet in length, which only occurs in central Florida, escaping

about 1960 (Langeland and Burks 1998). It covers shrubs and

trees of all sizes and forms mats that are several feet deep

(Volin and others 2004). Like Japanese climbing fern, it has

lacy but not finely divided leaflets along green-to-orangeto-black wiry vines. Vines arise as long branches from

underground wiry and black rhizomes that must be killed for

eradication. Dead vines from previous years serve as trellises

for reestablishment. This species persists and colonizes by
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chapter 15. The Invasion of Southern Forests by Nonnative Plants

rhizomes and spreads rapidly by wind-dispersed spores and

spores carried on contaminated clothing or wildlife fur. Old

World climbing fern has blanketed entire tree islands in the

Everglades. Currently confined to central and south Florida,

it is likely to steadily spread northward (Violin and others

2004), with coverage projected to almost double over the next

50 years (table 15.1). Several biological control insects are in

various stages of testing.

Oriental bittersweet—Oriental bittersweet (Celastrus

orbiculatus) is a popular ornamental vine in the Northeast

and upper South, introduced in 1860 (Rehder 1940). The first

noted escape in North Carolina was 1895 (Merriam 2003).

It is a deciduous, twining, and climbing woody vine to 60

feet high with drooping branches in tree crowns. It forms

thicket and arbor infestations (table 15.2) on disturbed sites

mainly in the southern Appalachians (McNab and Loftis

2002). It has alternate elliptic-to-rounded leaves 1.2 to 5

inches long. Female plants have axillary dangling clusters

of inconspicuous yellowish flowers that yield spherical fruit

capsules that are green maturing to yellow. The capsules split

in autumn to reveal abundant scarlet fleshy fruits, each with

five seeds (Miller and others 2010a), that remain through

winter at most leaf axils. It colonizes by prolific vines that

root at nodes and seedlings from prolific seeds that have been

spread throughout the winter, mainly by birds and possibly

by other animals (Greenberg and Walter 2010). Wreaths

made of vines covered in the showy fruit have been a

traditional Appalachian folk craft item but, when discarded,

they are often a source of new infestations.

Seeds are highly viable, germinating immediately even

under dense shade and growing rapidly when exposed

to light (Greenberg and others 2002) but only remaining

viable in the soil for a single year (Ellsworth and others

2004). Oriental bittersweet resembles American bittersweet

(C. scandens), which has terminal white flower clusters

that produce orange fruit capsules and leaves that are

usually twice as large but not among the flowers and fruit.

Hybridization is occurring between the two species (Pooler

and others 2002, White and Bowden 1947). At present,

escape of oriental bittersweet can only be found around

small towns and cities in North Carolina and Virginia with

outliers in Mississippi (fig. 15.17). The widest occupation by

this species is in Northern States. Spread projections for the

South are based upon the current occupation and an estimate

of date of escape. This estimate could be deceptively low

because it is clear that oriental bittersweet is still in its

early “lag” phase of forest invasion when occurrences are

scattered and populations are low (table 15.1).

Invasive Grasses and Bamboos

Nonnative grasses and bamboos continue to spread along

highway rights-of-way and gain access to adjoining lands.

Because herbicide treatments of southern highways do not

extend to the outer margins, they become an invasive plant

“free-zone” and a conduit for rapid spread. Most invasive

grasses are highly flammable, increasing fire intensity and

subjecting firefighters to higher risk; and then spreading

rapidly after a wildfire or prescribed burn. Invasive grasses

have compromised wildlife management efforts because

they have low general nutritive value and leave little room

for native plants (Barnes 2007). Repeated applications

of herbicides are required for control of invasive grass

infestations often followed by establishment of native plants

to suppress the grasses that survive.

Nepalese browntop—Nepalese browntop or Japanese

stiltgrass (Microstegium vimineum) is the most widely

distributed invasive grass in eastern forests. The earliest

herbarium specimen collected for this species in the United

States was found in 1919 by G.G. Ainslee along a creek

bank at Knoxville, TN (Fairbrothers and Gray 1972). This

is a sprawling, dense mat-forming annual grass even under

forest canopies, 0.5 to 3 feet long with stems growing to

1 to 3 feet in height. It bends over and roots at nodes to

form extensive entangled infestations that remain during

winter dormancy. It has alternate, lanceolate leaf blades to

4 inches long with off-center veins and thin seed heads in

summer and autumn. Hidden, self-pollinated seeds within

leaf sheaves are produced in early summer. Each plant

produces 100 to 1,000 seeds that can remain viable in the

soil for up to 3 years (Barden 1987). It is flood tolerant and

flourishes on the alluvial floodplains and streamsides where

its seeds have been dispersed, mostly colonizing floodscoured banks (Touchette and Romanello 2010). It is also

common in forest edges, roadsides, and trailsides, as well as

damp fields, swamps, lawns, and ditches. It spreads along

trails and recreational areas by seeds hitchhiking on hikers’

and visitors’ shoes and clothes. It occurs up to 4,000 feet

elevation and is very shade tolerant to invade partly shaded

and fully shaded habitats (Flory and others 2007).

Nepalese browntop has been emigrating from the

Northeastern States and therefore occurs mostly in the

Appalachian-Cumberland and Piedmont subregions

(fig. 15.18). Infestations also are concentrated in the deep

silt bluffs west of the Mississippi River Alluvial Flood

Plain where westward spread currently stops. Scattered

infestations are popping up all across the region in every

State. Linear spread projections are 10,000 plus acres per

year (table 15.1). Estimates of spread rates with climate

change scenarios are reported in a later section.

Tall fescue—Tall fescue (Schedonorus phoenix) is one of

the region’s most important forage crops for cattle and sheep

with the discovery of the Kentucky 31 variety in 1931, even

though it is a severe invasive in all other land uses. Tall

fescue (formerly S. arundinaceus, Lolium arundinaceum,


424 The Southern Forest Futures Project

the Florida Experiment Station at Gainesville and planted

in northwest Florida for pastures and surface reclamation

(Tabor 1949). In the 1990s red varieties were developed

as ornamentals with cold tolerance for northern gardens

and sold as “Japanese Blood Grass,” “Rubra,” and “Red

Baron” (Greenlee 1992). Although no viable seeds have been

reported for these red varieties, their viable pollen can impart

cold tolerance to nearby invasive populations and reversion

to aggressive green plants have been observed as far north as

Idaho. In response, several Southern States prohibit the sale

of these red varieties (Miller and others 2010b).

Cogongrass is a dense erect perennial grass 1 to 6 feet

high with tufts of long leaves from short stems, yellowgreen leaf blades (each with an off-center whitish midvein and finely saw-toothed margins), and silver-plumed

flowers and seeds in spring (sporadically year-round after

disturbance) (MacDonald 2004). Cogongrass leaf and

inflorescence dimensions vary widely (Bryson and others

2010). Abundant plumed seeds are dispersed by wind and

on contaminated clothing, equipment, and products like

pinestraw mulch and fill materials. Seed viability is up to

a year (Brook 1989). Cogongrass was found to occur on a

full range of soils in Mississippi, which indicates that most

southern soils can support this invasive plant unless they

are permanently flooded (Bryson and others 2010, King and

Grace 2000). Dense stands of dried plants remain standing

during winter to prevent natural succession and present

a severe fire hazard. Cogongrass can remain green yearround in central and southern Florida where infestations

have been estimated at over a million acres (MacDonald

2004). Infestations form dense mats of underground stems

with buds every one-half inch, making eradication difficult,

because abundant shoot and rhizome buds usually sprout

after treatment or lay dormant to sprout within months

(Williard and others 1996). New invasions occur as circular

patches; they are thought to become more difficult to control

as they mature (Miller 2003). Federal and State funded

control programs have been under way in all infested States

for several years. These programs were upgraded in 2010

with Recovery Act funding and are aimed at stopping the

spread by eradication of outliers and treating the advancing

fronts and selected epicenter infestations in South Carolina,

Georgia, Alabama, Mississippi, Tennessee, and Texas.

Through these cooperative efforts all known infestations in

Tennessee, Texas, and South Carolina are thought to have

been eradicated in 2011.

The epicenter of cogongrass infestations remains near the

point of initial introductions in coastal Alabama and nearby

Mississippi with another in central Florida (fig. 15.20). These

multiple introductions have gradually hybridized in South

Alabama where fertile seeds are most common (Capo-chichi

and others 2008). Because it thrives in the wide range of

climates and habitats, northward spread is likely unless

dramatic eradication efforts are undertaken (MacDonald

2004). The 60,000 acres infesting forest lands (table 15.1)

is just a small percent of the total southern occupation on

pastures, hay fields, natural preserves, and urban and rural

home landscapes.

Golden and other invasive bamboos—Nonnative

bamboos (Phyllostachys spp. and Bambusa spp.) form

exclusive dense stands scattered throughout the region

from past plantings. Golden bamboo, the mostly widely

occurring species in the South, was first planted in

Alabama in 1882 (Lady Bird Johnson Wildflower Center

2007). Invasive bamboos are perennial infestation-forming

canes 16 to 40 feet in height. They have jointed cane

stems and bushy tops of grass-like leaves in fan clusters

on jutting branches, often golden-green. Plants rise from

large branched rhizomes (underground stems) that must

be killed for eradication. Infestations rapidly expand after

disturbance through rhizome extensions. Seeds rarely, if

ever are produced—potentially once every 50 to 100 years.

Bamboos are still sold and planted as ornamentals and

golden bamboo stems have value in Asia for construction,

paper, fishing equipment, ski poles, javelins, irrigation

pipes, musical instruments, furniture, and handles for

umbrellas and fans (Barkworth and others 2007). Rivercane

or switchcane (Arundinaria gigantea and other Arundinaria

spp.) are the only native bamboo-like canes in the South,

and are distinguished by a lower height—usually only 6

to 8 feet—persistent sheaths on the stem, and absence of

long opposite horizontal branches. Invasive bamboos are

actively being promoted as a potential biomass crop, but

supporting research has yet to appear in scientific literature.

Invasive bamboos occur throughout the region in scattered

dense infestations (fig. 15.21) on the edges of forests, fields,

and rights-of-way—the result of past plantings over a 130-

year period for various structural uses, fishing poles, and

more recently as managed roosting sites for migrant black

bird species that have been shown to be vectors for the

human respiratory disease, histoplasmosis (Glahn and others

1994). The potential exists for a broad general flowering and

seeding, which characterizes bamboo forests in their native

ranges.

Chinese silvergrass—A locally invasive plant, Chinese

silvergrass (Miscanthus sinensis) is a tall, densely tufted,

perennial grass, 5 to 10 feet in height that grows from a

perennial root crown. It has long, slender, and upright-toarching slender leaves with whitish upper mid-veins and

many loosely plumed panicles turning silvery-to-pink

in autumn. Dried stalks, some with seed heads, remain

standing with during winter, but seed viability is variable

depending on cultivar and location. This species requires

pollination by another cultivar to produce viable seeds and

fertile offspring. This results in extensive infestations that






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chapter 15. The Invasion of Southern Forests by Nonnative Plants

in the mid-Mississippi valley, Florida and along the Atlantic

Coast under Cornerstone B would allow tallowtree to exist in

these locations. A predicted drier zone in Coastal Alabama

and along northern Florida would nullify invasion potential

in these areas. The areas of increase potential are related

to rainfall. Cornerstone C would have the largest potential

distribution of tallowtree with high or moderate potential

of occurrence on 43 percent of forestland (fig. 15.26), a

range that could expand westward from the Atlantic Coast

and northward from the Gulf of Mexico, and an increase in

inland pockets with medium potential. Cornerstones D and

F, with decreasing minimum temperatures and a slightly

drier Gulf Coastal Plain would have the lowest potential

distribution (13 percent, combined higher and moderate

potential), but still higher than the current occupation.

Similar local conditions as now would confine tallowtree to

Florida and the coastal areas of Alabama, Mississippi, and

Louisiana.

Silktree—Our model for silktree was one of the weakest,

but was still statistically significant with AUC of 0.75 and

0.80 and omission rates of 0.15 and 0.26. Seven variables

had moderate contribution (table 15.3), ranging from 6 to

25 percent. A number of these variables, such as proximity

to roads, were not addressed when the Cornerstones were

developed, reducing the confidence in predictions. The

two strongest variables were temperature (24 percent)

and distance to interstates (25 percent). Silktree has some

tolerance to cold weather but cannot withstand winters below

-5 oF (Dirr 1998). Herbarium specimens have been collected

from throughout the region except south Florida, which

supports the current potential range (fig. 15.27). Its current

preference to roadways is widely evident, both visually when

driving and by its web-like pattern of potential occurrence on

the maps (fig. 15.27). The next most dominant variables are

rainfall at 14 percent and elevation at 13 percent contribution

(fig. 15.28). These variables have a binomial relationship,

with silktree preferring intermediate levels for both. Under

all Cornerstones, silktree showed much greater potential

(16 to 28 percent at high potential) than its current

occupation of 2 percent (fig. 15.28). Compared to the status

quo prediction, potential would diminish under Cornerstones

A, E, and B, primarily of increasing temperatures and

decreasing rainfall, The current potential distribution range

would push, but not extend, the range northward. Under the

minimal decreases in rainfall of Cornerstones C, D, and F

the potential of silktree increases the most compared to the

current potential, although by only a few percentage points.

It is clear that silktree thrives with human habitation and the

roadway systems that will likely increase.

Invasive roses—Invasive roses currently predominate in

the upper portion of the South, with a few areas scattered

through the Coastal Plain and in the Black Belt Prairie across

central Alabama and northwestern Mississippi (fig. 15.7). Of

the five species modeled, roses had the highest occurrence

on forested FIA plots (5 percent). They include a number of

species with the principal one being multiflora rose (Miller

and others 2010a), which is also one of the most pervasive

invasive plants in the Midwest and Northeast United States

and Ontario. The model drops out Macartney and Cherokee

roses, which are concentrated in Louisiana and the Black Belt

Prairie across central Alabama and northwestern Mississippi

(figs. 15.7 and 15.29). The statistics on the models show they

are reasonable with AUCs of 0.86 and omission rates of

around 0.15. The model is highly influenced by minimum

temperature, which is different for multiflora and the more

southern adapted Macartney and Cherokee roses which were

selected, bred, and widely planted for this reason. The status

quo model predicts that 40 percent of the forests in the South

have a high to moderate potential of habitats suitable for

invasive roses (fig. 15.30). In the Appalachian-Cumberland

highlands, this potential increases to 90 percent of forests.

The model is dominated by a strong negative relationship

(63 percent) to minimum temperature and a weaker negative

relationship (12 percent) to elevation (table 15.3). Field

observations show heavy infestations in high-elevation plots

along the Blue Ridge Parkway in North Carolina, raising

questions about the true elevational relationships. The only

other variable with a reasonable contribution to the model is

the positive relationship to the amount of pasture in a county

(table 15.3). This is also supported by findings of Glasgow

and Matlack (2006) that a higher percentage of pasture lands

increases the expectation for invasive roses. Federal and

State programs from 1930 to 1950 promoted the planting of

multiflora rose as a “living fence” around pastures, where it

eventually spread into pastures (Bergmann and Swearingen

2009). It is possible that such planting at high elevations

predated the construction of the Blue Ridge Parkway on

lands reclaimed from subsistence farming (and presumably

grazing), explaining the difference between model output

and field results.

Under the cooling of minimum winter temperatures of

Cornerstones D and F, the potential for occupation gets as

high as 87 percent (fig. 15.30). The potential distribution of

invasive roses is smaller than the current potential under all

other Cornerstones. With the warmer Cornerstone C, the

potential (4 percent) all but disappears, falling below the

current occupation level (fig. 15.30).

Japanese climbing fern—Japanese climbing fern currently

occurs on 4 percent of FIA plots in northern Florida and

southern Louisiana, Mississippi, Georgia, and Alabama

(figs. 15.15 and 15.32). The prediction of current potential

shows moderate expansion into Florida with a few isolated

areas further up the Atlantic Coast (fig. 15.31), a sizable

increase of 18 percent at the high potential and 10 percent

at the moderate potential (fig. 15.32). This model was one

of the strongest (AUCs of over 90 and omission rates near






438 The Southern Forest Futures Project

0.10), with rainfall, temperature, and elevation dominating

(table 15.3). Potential for Japanese climbing fern becomes

higher as temperature and rainfall increase, although this

effect diminishes at the extremes of both. The negative

relationship to elevation may be an artifact of the dataset

limitations, as populations currently only occur at low

elevations. The potential distributions under the varying

climate forecasts seem primarily driven by rainfall, with

reduced rainfall within the current potential distribution and

in Cornerstones A, E, and B limiting distribution (fig. 15.31).

Under Cornerstone A, B, and E the areas of high potential

are lower than the status quo prediction (fig. 15.32). Under

Cornerstone C the area of moderate potential extends to as

far as Tennessee—the result of sustained rainfall patterns

from the central Gulf to Appalachia coupled with a minimal

increase in temperature. Under Cornerstones D and F cooler

temperatures in winter are expected to reduce the potential

significantly, pushing the distribution mainly into Florida.

Nepalese browntop—The potential habitat range for

Nepalese browntop under current conditions is projected

to occupy up to 41 percent more forest land than its current

distribution of 3 percent (fig. 15.33 and 15.34). The models

had reasonable statistics with AUCs around 0.85 and

omission rates of 0.24 and 0.14. The dominant variables in

the model are minimum temperature, rainfall, elevation, and

proportion of forest (table 15.3). Temperature is the biggest

contributor at 62 percent, with a bimodal relationship of

minimum temperature preferences ranging from 21 oF to

32 oF. The other dominant variables suggest the highest

potential occurs at lower elevations with intermediate forest

cover and moderate rainfall (table 15.3). Under Cornerstones

A, E, and C, Nepalese browntop all but disappears in the

South (fig. 15.33), driven predominantly by the higher

temperatures pushing its range northward. Cornerstone

B, with minimal warming, has some reduction but is most

similar to the current potential. Under Cornerstones D and F,

with reduced winter temperatures and slightly less rainfall,

the potential for southern expansion is greatly increased (91

percent).

South-wide projections—Overall, the current proportion

of forest invaded (2 to 5 percent) is substantially lower

than the current potential (28 to 44 percent) for all five

species, and predictions of potential suitable habitats for

most Cornerstones are also lower than the status quo

predictions. Nevertheless, in only 2 of the 20 high-moderate

projections (figs. 15.26, 15.28, 15.30, 15.32, and 15.34) are the

Cornerstones lower than the current portion of forest invaded

(Cornerstone C for invasive roses and Nepalese browntop).

Both of these species have current distributions in the upper

reaches of the South, and warming temperatures that extend

to the northern part of the South (less so with Cornerstone A

or B) will push their ranges further north.

With these models at the regional level, the dominant

variables were temperature, rainfall, and elevation. Overall,

these models suggest that the probable distribution of suitable

habitats for these invasive species increases under most

climate forecasts.

Treatments for Integrated Management 

of Invasive Plants

A successful program for invasive plant management usually

involves a combination of treatment methods integrated into

an approach that considers the invader and the site. Many

methods are available to manage invasive plants leading to

site rehabilitation, and more are under development. Current

treatment options for specific areas involve herbicides,

prescribed fire, prescribed grazing, mechanical, and

manual removal (Miller and others 2010b). Fire, grazing,

and mechanical cutting treatments usually control only the

above-ground plant parts, resulting in reduced height, but not

resulting in permanent suppression.

Herbicidal Control Methods

Most nonnative invasive plants in the South are perennials

with extensive roots, tubers, or rhizomes. This means that

effective herbicide applications offer the best means of

containment or eradication, because herbicides can kill roots

without baring soil, protecting the site from reinvasion and

erosion and leaving the soil seed bank in place for native

plant reestablishment. Research has found that herbicides

tested and registered with the U.S. Environmental Protection

Agency are safe for humans and animals when stored,

transported, and applied according to label directions. For

successful herbicide treatments:

•	 Use the herbicide most effective for the targeted species

and appropriate for safety to non-target species and

situation.

•	 Follow, in detail, the application methods prescribed on

the label. Adhere to all label prohibitions, precautions, and

safety requirements during herbicide transport, storage,

mixing, and application.

•	Choose the optimum time for applications. For foliarapplied herbicides to non-evergreen woody plants, the

best time is usually midsummer to early autumn and not

later than a month before expected frost. Evergreens and

semi-evergreens with leaves can be treated effectively

until they lose their leaves (Frey and others 2007). The

optimum time of application for each specific herbicide

on each specific invasive has not been fully researched;

future findings should greatly improve prescription

efficacy.

•	After application, watch for herbicidal activity—

detectable as yellowing of foliage or as leaves with dead

spots or margins—which may take a month or longer.


441

chapter 15. The Invasion of Southern Forests by Nonnative Plants

sprayers, tractor-mounted sprayers, roadside sprayers, and

helicopter sprayers.

Other Treatments

Manual treatments—Manual methods include hand

pulling and using a wide array of tools for cutting, chopping,

wrenching, and girdling. Manual methods are most effective

on woody invasive plants when they are small. Eradication

is only possible when the root crown or roots that can

sprout are completely extracted and seedlings are pulled or

eliminated following germination. Because it is difficult and

even impossible to extract all of the shallow roots, stolons,

and rhizomes of many invasives, re-sprouting usually occurs.

Unless an herbicide is applied to cut surfaces, merely pulling

small plants and cutting top growth will result only in shortterm control before stump or root sprouting occurs. Manual

treatments are labor-intensive and can only be used on smallsized plants, resulting in limited but effective use on special

habitats (such as recreational trails or nature preserves).

Mechanical treatments—In many situations, hand labor

is unavailable or cost prohibitive and more horsepower

is needed. Specific machines developed for forestry and

land clearing operations are available to clear large or

dense infestations (Klepac and others 2007). Skid-steer

loaders, mulchers, mowers, and tractors and bulldozers

having special attachments can be used to reduce invasive

woody plants (Miller and others 2010b). Tree shears, rootrakes, and harrows have been used to cut and dislodge

woody and rhizomatous plants, but can leave soil bare for

probable reinvasion and possible erosion. These methods

can complement and increase the efficiency of herbicide

treatments, but merely cutting above ground parts can

aggravate plants—such as cogongrass (Willard and others

1996) and Chinese lespedeza (Brandon and others 2004)—

that have surviving rootcrowns or rhizomes. Although highly

disturbing, mechanical treatments have been used to clear

dense infestations of multi-species of invasive woody plants

and prepare the way for other more selective followups.

Cultural treatments—Several cultural practices, including

prescribed burning and water level manipulation, can reduce

or control nonnative invasive plant populations. However, if

not applied with care, these practices may have undesirable

impacts to soils, animal habitat, and native species, so care

in planning and enactment must be exercised. Burning to

weaken woody invasives is most effective in the late spring

after plants begin using their root reserves for early growth.

Burning in late winter or spring leaf-out can minimize the

period of bare soil, while summer burns are the hottest and

can maximize consumption of standing plants. Burning can

predispose a forest stand or opening to invasion, even though

prescribed burning increasingly is favored for native plant and

longleaf pine ecosystem restoration as well as fuel reduction.

A close evaluation of the benefits and risks is demanded

before applying prescribed burning to avoid unexpected

consequences (Brooks and Lusk 2008, Glasgow and Matlack

2006). A propane spot burner can be used to kill individual or

small groups of herbaceous or woody invasives. Commercial

kits are available for attaching propane cylinders to a backpack

frame and fitting the cylinder with a flame nozzle. Other units

are available for mounting propane cylinders on tractors.

When plant and wet fuel conditions permit, the flame is

directed at herbaceous and woody invasives.

In areas where water level can be manipulated, flooding or

drawdowns can reduce invasive plant populations in aquatic

and wetland habitats but these are species- and site-specific

and usually not effective as stand-alone treatments (Allen

and others 2007). They require an understanding of the

biology of both invasive and native plants in the treatment

area.

Mulching and solarization—Mulching involves covering

the soil surface with materials that block light, thereby

preventing weed germination and growth. Although

application of mulches and landscape fabrics is common for

reseeding and soil stabilization in restoration operations,

mulching for control of tough invasive plants will not be

effective unless adequate amounts of materials are applied.

Mulching is most effective on small seeded species and

marginally effective on established re-sprouting perennials

such as kudzu. Many types of mulches are available, including

natural ones such as straw, bark, sawdust, crop residues, and

grass clippings; and artificial ones such as paper, cardboard,

and plastic. Although mulch applications are not commonly

used to control invasives on a large scale, they are still useful.

Tallowtree and Chinese privet suppression have been achieved

by chipping standing trees and dense shrubs into a deep mulch

layers (Donahue and others 2006, Klepac and others 2007). In

soil solarization, polyethylene sheeting covers low-growing,

cultivated, mowed, or chopped invasive infestations and traps

solar energy to kill and suppress invasive plants by heating

the soil and air underneath the sheeting. Two or more years of

summer cover are needed to suppress most invasive plants by

90 percent. Other, more desirable, plants are also killed by this

method—it is not selective.

Biological Controls

Biological control methods range from prescribed grazing to

the introduction of insects, pathogens, and other agents that

feed solely on target species. Classical bio-control involves

finding agents from the home range (or similar habitat) of the

invasive plant, followed by intensive research on feeding habits

and reproduction and a planned introduction into infested

areas. The goal is to identify predators that are host-specific to

the target invasives, will avoid attacking native plants, and will

increase and spread in the new range to permanently suppress
442 The Southern Forest Futures Project

the invasive species. The process is usually expensive, often

involving lengthy searches for the right agent, extensive

feeding tests in special quarantine facilities, coordinated

releases that are strictly controlled and documented under

Federal oversight, and long-term monitoring. Following

release, non-target damage is very rare but has occurred

(Moran and others 2005). In general, scientific evaluations of

past releases have shown that the benefits from bio-control

over a region outweigh the threats (Messing and Wright 2006).

Prescribed or targeted grazing is an approach that relies

on cattle, sheep, goats, or horses to reduce infestations.

Grazing is a potential control treatment only if the invasive

is palatable and not poisonous to the animal. Grazing can

either promote or reduce plant abundance at a particular site.

By itself, grazing will rarely, if ever, completely eradicate

invasive plants. However, when combined with other control

techniques, such as herbicides or bio-control, grazing can

reduce severe infestations and eliminate small ones. Grazing

by cattle and horses is limited to herbaceous invasive plants.

Sheep and goats both feed on woody plants as well, but

goats can also eat bark and thorny vegetation and are able to

reach higher areas of shrubs, saplings, and small trees. The

animal species is important, as is the breed, with the most

effective being larger and able to handle difficult situations,

such as hair goats and range cattle. Best results come from

leaving an appropriate number of animals on a site long

enough to reduce the infestation, and then reintroducing

them at intervals when invasive regrowth appears

(Luginbuhl and others 1999).

Rehabilitation, Restoration, and Reclamation

The promotion and establishment of desirable vegetation

during the latter phases of control and eradication

treatments is one of the most important phases of an

integrated invasive plant management program (Hartman

and McCarthy 2004). The severity of infestation, site

degradation, and desired future outcome determine

whether a rehabilitation, restoration, or more stringent

reclamation effort is appropriate. Rehabilitation is effective

when soil, stream, and wetland damage is minimal and

native plants are present or will enter from surrounding

areas. Genetically improved loblolly pine seedlings or

other fast-growing native tree species can be planted to

suppress regrowth of invasive plants. Restoration is a much

more involved process that combines soil and streambank

stabilization methods with planting and seeding of desirable

species to create a planned landscape. Reclamation

is appropriate for surface-mined lands, large road

construction projects, and other severely altered sites to

reshape landform, replace surface soils, and establish fastgrowing plants—often in conjunction with mulches and

fertilizers. Invasive plants have been most often planted on

reclamation sites and now warrant control efforts. Native or

noninvasive, nonnative plants are substitutes now available

and recommended for reclamation operations.

The goal of all three approaches is to establish and/or

release fast-growing native plants that can outcompete and

outlast any surviving nonnative plants while stabilizing and

protecting soil and water (Hartman and McCarthy 2004,

Kaeser and Kirkman 2010). At times, nonnative plants

must be used to suppress invasives—then eradicated—

to facilitate native plant establishment; an example is

planting bahiagrass after herbicide treatments to suppress

cogongrass regrowth (Ewel and Putz 2004). The ultimate

goal is to replace invasive plants with native alternatives

(Burrell and others 2006).

If the soil seed bank remains intact, native plant communities

will naturally become established and regenerate during

eradication of nonnative plants (Barnes 2007). Light-seeded

native species are usually present in the seed bank, and

heavier seeded plants will gradually be deposited on a site by

birds and other animals. Continued surveillance and follow

up treatments are often required to control nonnative plant

infestations. Select herbicides and other treatments such as

mowing and prescribed burning can play a role in continued

suppression (Barnes 2007).

Discussion and Conclusions

Examination of survey data, literature, and modeling

shows that invasive plants are not an issue that is going

away. Using dates of introduction and current levels of

occupation we predict that in the next 50 years the acres of

invasives will increase from the current 19 million acres to

27 million acres. This conservative estimate does not take

fully into account the growing amounts of land disturbance,

fragmentation, parcelization, and urbanization that foster

invasive plant spread, or effects of potential climate changes.

Of the five species evaluated with modeling, none were

close to their full potential of occupation. This is supported

by other research (Bryson and Carter 2004, Gan and others

2009, Simberloff 1996). Also, we have only considered those

species already present in the South that have already been

identified as high threats.

Common Traits of Invasive Plants

Nonnative plants when they escape cultivation usually remain

at low levels as scattered occurrences of small infestation

size with low populations and numbers of individuals, known

as the “lag phase.” Further generations are better adapted to

their new environment through cross pollination and selection

pressures and then reproduce and spread more successfully at

increasing speed (fig. 15.35). Invasive traits can be enhanced


445

chapter 15. The Invasion of Southern Forests by Nonnative Plants

banks (Gan and others 2009), and in abandoned crop and

pasture lands (Stapanian and others 1998).

Having more of these traits increases the likelihood that a

nonnative plant species will succeed in establishing itself,

spreading, and resisting control and eradication.

Damages Caused by Invasive Plants

The damages and impacts of nonnative invasive plants are

numerable and have not been completely determined. The

costs of damage and control of invasive species has been

estimated in the billions of dollars (Pimentel and others

2000, U.S. Congress OTA 1993) while no economics exist

to evaluate the more widespread ecological and sociological

effects of invasive plants (Holmes and others 2009, Perrings

and others 2002).

•	 Dense infestations of most invasive plants limit or

stop productive land management and especially

regeneration of forests, such as cogongrass’s severe

competition with planted pines (Jose and others 2002),

Japanese honeysuckle’s allelopathic impacts to young

pine regeneration (Shulman and others 2004), Nepalese

browntop’s physical completion with natural hardwood

regeneration (Oswalt and others 2007), and Chinese privet’s

suppression of tree regeneration in mixed hardwood forests

(Merriam and Feil 2002). Infestations replace native plant

strata in forest communities (table 15.2).

•	 Invasions may initially increase species richness and

diversity upon first entry (Gan and others 2009). However,

with intensified infestations, they displace and permanently

decrease biodiversity, even among insects (Ulyshen and

others 2010), they are especially harmful to rare plants

and animal habitat, and they hybridize with native plants

to dilute genetic traits (Heywood 1989, Pooler and others

2002, Stohlgren and others 1999, Wilcove 1998). A few

harbor plant diseases (Swearingen 2000) and several are

toxic to people, wildlife, and livestock (Everest and others

1996, Turner and von Aderkas 2009, Williams 1980).

Flowering Brazilian peppertree and English ivy can cause

respiratory difficulties and contact dermatitis in sensitive

individuals (Morton 1978).

•	 Some invasive plants produce overabundant pollen that

causes widespread allergenic reactions in humans.

•	 Invasive plant occupation alters vital ecological processes

such as soil formation and wetland function (Ehrenfeld

2003). For example, tallowtree changes soil and litter

chemical properties (Cameron and Spencer 1989),

cogongrass lowers soil pH and potassium (Collins and Jose

2009), and melaleuca greatly alters soil microbial processes

and litter in ways to maintain site dominance (Martin and

others 2009). Soils invaded by kudzu have exceptionally

high levels of nitrogen through nitrogen fixation by root

bacteria; they release excess nitrogen as minerals in soil

water and as gases that contribute to ground level ozone

(Hickman and others 2010). In an opposite manner, soils

invaded by Nepalese browntop have slower nitrogen

internal cycling (DeMeester and Richter 2010).

•	 Invasive-plant alterations to wildlife habitat favor more

common species over those that are at-risk, complicating

restoration efforts (Asland and Rejmanek 2010, Cipollini

and others 2009, Schmidt and Whelan 1999).

•	Most invasive plants grow formidable barriers of vegetation

that limit land access for recreation such as hiking, fishing,

hunting, and bird watching. They can cause psychological

anxiety though a sense of the inability to control one’s

surroundings (Blaustein 2001).

•	 Some invasive plants present extreme fire hazards to

forests, preserves, and homes, such as cogongrass and giant

reed (Arundo donax).

The damages and costs are both ecological and societal,

with many complex linkages that stymie economic

analysis. Mack and others (2000) concluded that the threat

of large nonnative infestations is largely the unintended

consequences of uninformed decisionmaking, and Perrings

and others (2002) added that the threat is compounded by

societal resistance to change.

Potential Uses of Invasive Plants

Because of their increasing replacement of usable forest

species, we are faced with the challenge to discover uses

for the widespread or abundant invasive plants, determine

how to efficiently harvest and process wild populations, or

learn how to cultivate them in plantations or farms to lower

harvesting and transportation costs.

Many invasive plants are being proposed, considered, bred,

propagated, and experimentally planted in plantations for

biomass and potential biofuel production due to their rapid

juvenile growth and pest resistance: tallowtree (Scheld and

Cowels 1981), Chinese and giant silvergrass (M. floridus)

(Jessup 2009), melaleuca (Wang and others 1982), giant reed

(Lewandowski and others 2003), golden bamboo (Scurlock

and others 2000), princesstree, and kudzu (Sage and others

2009). The Invasive Species Advisory Council (2009)

considers this a high-risk use with escapes to surrounding

natural and productive landscape eventually inevitable. The

same concerns exist for the existing commercial plantations

of princesstree in the southern region, recognizing that

mature straight trees, rarely produced in the region, only

have a high value in Japan at this time (Tang and others

1980). On another front, research has found that tallowtree

can be used for all three composite panel types meeting

various American National Standards Institute grades (Shupe

and others 2006) and has value as a source of drying oil

(Howes 1949). The current coverage of all these invasive

species, with the possible exception of tallowtree, would have
446 The Southern Forest Futures Project

to be increased in plantations to make economically viable

operations, bringing a high probability of additional escapes.

Extracts from kudzu, Brazilian peppertree, and Chinese

yam have traditional medicinal uses, while the leaves of

garlic mustard and kudzu and the root starch from kudzu

are valued Asian cooking ingredients (Shurtleff and Aoyagi

1985). Dietary root extracts from kudzu have been found to

regulate blood pressure, high cholesterol, and blood glucose

in lab rats predisposed to these conditions (Peng and others

2009). Autumn olive fruits have 3 to 15 times more of the

lycopene anti-oxidant than tomatoes (Fordham and others

2001). Thus, utilization of invasive plants is in its infancy

with probable increases likely.

General Concepts for Managing Nonnative 

Invasive Plants

How we manage invasive plants will ultimately determine

the severity of damages and costs. The most effective and

efficient strategy is early detection and effective early

treatment of initial invaders. Three overarching concepts

provide powerful ways to get organized and counter invasive

plant takeovers: collaboration, adaptation, and restoration

(Buck and others 2001, Schelhas and others 2001, Miller

and Schelhas 2008). Collaboration with adjacent and area

landowners is essential because invasive plant infestations

most often occur across ownership and political boundaries.

Communication networks can link local, county, State, and

regional programs (Meyerson and Reaser 2003). Adaptation,

or adaptive resource management, is a community shared

cyclical process of learning by doing (Foxcroft 2004) that

consists of goal setting, learning from the experience and

research findings of others, monitoring actions and outcomes,

and then rapidly incorporating new knowledge into refined

goals and actions. This process is useful for decisionmaking

in the face of uncertainty, because it reduces uncertainty over

time by monitoring results of actions and making careful

adjustments. Its effectiveness can be enhanced by monitoring

print and Web resources for new and forthcoming

information (Jordan and others 2003). Restoration of infested

lands to healthy and productive ecosystems is the guiding

objective, involving the establishment and monitoring of

desirable and useful plants that protect soil, produce needed

resources and habitats, and safeguard to prevent a resurgence

of invasive plants. Restoration approaches for most invasive

plants are just being developed and will require adaptive

management cycles in order to perfect them (Hartman and

McCarthy 2004, Sauer 1998).

Organizing Across Borders

The common elements of successful regional, State,

and local invasive plant management programs include

developing:

•	 Scientifically based and coordinated invasive plant lists that

recognize that priorities will differ by subregion as well as

frequency and severity of infestations

•	Multi-level, cooperative knowledge networks that link

stakeholders, land managers, scientists, and policymakers,

provide real-time information and connectivity (Jordan

and others 2003), and encourage timely actions and

communication by all participants regardless of their roles

•	Collaborative prevention strategies and programs,

including legislative, policies, and public outreach

components (Britton and others 2005), “good neighbor”

programs among rights-of-way managers and adjacent

landowners (Randall 2007), sanitization protocols

(Fleming 2005), and safeguards against the spread of

contaminated products (Evans and others 2006)

•	 Early detection and rapid response networks to identify

and map high-risk sites and new introductions, verify the

invasive species, and facilitate communication, eradication,

and restoration (Westbrooks 2004)

•	 A Web-accessible interactive survey, inventory, and

mapping system to corporately track existing and

spreading invasions (Bargeron and Moorhead 2007)

• Coordinated control, containment, and eradication

programs that establish cycles of integrated treatments,

share successes and mistakes (Miller and Schelhas 2008),

raise public awareness, close known pathways, and

facilitate regional biological control programs (Messing

and Wright 2006)

• Restoration treatments that suppress new invasions,

maintain ecosystem functions and services, incorporate

adaptive information cycles, and provide for continued

surveillance and monitoring with timely re-intervention if

necessary (Miller and others 2010b)

•	 A continuous cycle of research, research syntheses,

practical application of findings, and a feedback

mechanism for communicating additional research needs

(Miller and Schelhas 2008)

The spread of invasive plants from State to State means that

State-level plans need to include common elements that

assure regional protection, including working elements and

programs for adaptive collaborative restoration. The most

effective strategies for constraining invasions and restoring

ecological services are unified and readily shared through

collaborative networks that define zones of occupation

severity and show areas where different strategies should be

employed (fig. 15.37).

Managing outlier areas—Outlier (or satellite) infestations

exist beyond highly infested areas due to long distance

movement of plants or plant reproductive parts. Outlier

infestations must be detected and eradicated early if

containment is to be successful. Early detection rests

with public awareness as well as organized search and

surveillance efforts and strong reporting networks.
447

chapter 15. The Invasion of Southern Forests by Nonnative Plants

Movement of contaminated equipment and materials must

be effectively prevented to stop new outlier infestations from

being established.

Managing the advancing front—All infestations along the

advancing front must be found, mapped, and documented

through intensive search and surveillance programs.

The search and surveillance programs must include all

ownerships. To stop seed dispersal from worsening the

situation, treatments must be timely and persistent. For all

work near or inside infested areas, extra care must be taken

to ensure sanitation of equipment and personnel to prevent

spread. Special habitats of rare plants and animals within

the advancing front zone should be carefully treated to save

them from ultimate loss. The front must be held and then

pushed back.

Managing severely infested areas—Surveys employing

sampling techniques are required to quantify the acres

of infestation. Concerted programs in cooperation with

landowners including funding assistance are needed to fully

implement, support, and maintain management programs

in severely infested zones. Equipment and personnel

sanitation as well as quarantines of product movement

out of severely infested areas must be strictly regulated to

prevent both short- and long-distance movement of plants

and reproductive parts. Any forest and nursery product

movement must be monitored for contamination. Special

habitats of rare plants and animals must be safeguarded from

destruction and restored using special techniques. People’s

homes must be safeguarded against wildfire by highly

flammable invasive plants.

Small Scale Stewardship

Invasive plant strategies and programs ultimately depend

upon the eradication and restoration of one infestation at a

time at the local level and preventing new entries. Following

these principles will greatly increase the chances of success.

Prevent entry and spread—Do not plant invasives such

as those covered in this book, others listed in the appendix

in Miller and others 2010b, entitled “Nonnative Invasive

Plant Species Not to be Used or Recommended for Wildlife

Food Plots and Bird Viewing Plots,” and those on your

State’s noxious and invasive plant lists. For wildlife food

plots, soil stabilization, and ornamentals, plant only native

plants of local origin when possible or choose noninvasive

alternatives. Employ sanitation practices to avoid introducing

or spreading invasive plants.

Make a plan—Base your planned treatments on stated

objectives and the best information, then schedule and

acquire resources that support your plan. Devise a timeline

for implementation of your plan’s action items, and add some

“wiggle room” for contingencies. Devise both a short- or

long-term plan to include both specific infestation treatment

regimes and ideas for how these fit into a general land

management plan. Your maps of infestation locations and

priority ratings of invasive species will assist the planning

process.

An eradication and rehabilitation program for specific

invasive plant infestations usually requires several years of

treatments and many more years of surveillance to check for

rhizome and root sprouts, seed germinates, or new invaders.

Newer infestations and smaller plants require much less time

than extensive and dense infestations.

Prioritize treatments by targeting the worst of the plants first.

Remember that the worst plant may not be the one with the

highest level of infestation but the one that has the greatest

potential for spreading. Balance eradication of first entries

of high-priority invasive plants with persistent treatment of

extensive infestations (see www.invasive.org/south/ for a

regional list of High Priority Invasive Species of Southern

Forests and Grasslands). Monitor the effectiveness of

treatments, retreating as needed.

During the treatment and retreatment phase, take steps to

safeguard, promote, or establish desirable vegetation. To

effectively combat plant invasions and restore lands, you

will need to carefully plan for each step in the program

by incorporating primary and contingency schedules of

enactment. You should project a minimum of 4 years and up

to 10 years for older infestations when less than maximum

effective treatments are used. You can use short-term

plans to target specific areas, but you will need a long-term

management plan for an increasingly invaded landscape. You

must consider surrounding lands, particularly the degree of

current infestation in those lands as well as the invasive plant

management programs the owners and managers of those

lands have in place. Also consider emerging State funding

assistance programs.

Make a map and monitor results of locations—Detect

invasive plants early through active surveillance of your

lands. Map and mark locations of the invasive plants you

find. Identify invasive plant location sites at risk, and denote

treatments and their outcomes. You must positively identify

those invasive plants that are present and those poised to

enter from adjacent lands, determine their locations and

abundance, and record this information on a sketch map

or Geographic Information System (GIS) map. Gain their

Global Positioning System (GPS) locations when possible.

Make the locations easy to find again by marking them

with flagging. Monitor the locations through repeated visits

and record progress or the lack of it. Agencies should map

as many acres as possible with the dollars available before
448 The Southern Forest Futures Project

investing in unorganized treatments of extensive invasions,

while new entries of severe invasives should be tackled early.

The five-option Search, Survey, Inventory, Monitor, and

Surveillance method, can help map, monitor, and track

treatments with their results at several scales: (1) Look at the

most likely points of entry, like along roads and especially

near bridges, and record any occurrence of invasive plants

you find in such areas. Then widen your search as time and

resources permit. (2) Systematically locate plots or conduct

band sampling across the landscape to determine the extent

of occupation and acres covered. By mapping the survey plot

findings, areas of highest infestation density and multiple

invasive species can be identified. (3) Prepare an inventory

by recording the location and area of every infestation and

the treatments that you apply. This is the best approach for

individual land ownerships. Inventories can map individual

patches and plants or circle them as a group when they

occur in close proximity to one another. The GPS locations

can be taken and mapped, or a sketch map made to plan

the program of treatment and restoration. (4) Monitor the

site by revisiting inventoried points at scheduled times or

resurveying tracks with scattered infestation to record and

track treatment effectiveness and any further invasions. (5)

Practice surveillance, a constant task for all those who work

on and/or otherwise use your land. Everyone should be alert

for new infestations and know how to report these when and

where sighted.

A Time to Reflect, Rethink, Redouble Efforts

In an article about introduction of new species into the

United States, Simberloff (2001) admonishes, “What is

needed is a change in philosophy, away from innocent

until proven guilty. The very nature of introduced species

makes current risk assessments unreliable documents,

that introductions are generally irrevocable once they are

established, and that the harm some species can cause is

not only staggering in economic terms but incalculable in

ecological ones.”

Now More Than Ever: Sanitation Is the Key

•	 Educate yourself, employees, and other users of your land about the invasive plants that pose major threats and how to

prevent their entry. Learn how to identify both invasive and native plants in your area. The more native plants that you

can identify, the easier you will spot the “plants out of place.”

•	Require or instruct those who work, hunt, and recreate on your lands, to minimize invasive plant spread by:

(1) inspecting the site and infestation before operations especially noting the presence or absence of invasive plant fruit,

seed heads, or spore clusters under climbing fern (Lygodium spp.) leaves; (2) when possible, avoiding driving vehicles,

mowers, all-terrain vehicles (ATVs), or spray equipment through infestations bearing seed or fruit, especially of lateflowering cogongrass and musk thistle (Carduus nutans); (3) removing all seeds and debris from clothes, boots, socks,

boot laces, soles, and personal protective equipment, avoiding cuffed pants, carrying contractor-size refuse bags to stand

in while brushing and removing seeds or place contaminated gear within the bag for careful cleaning at a designated

location; (4) when working in infestations, thoroughly cleaning motorized equipment (especially the undercarriage and

tire surfaces, radiator front, and engine compartments), removing excess grease and oil, and modifying vehicles and

equipment to prevent buildup of debris or selecting vehicles that have the least potential for contamination.

•	When moving cut fruiting or seeding invasive plants offsite such as to a burn pile, always cover loads or bag before

transport.

•	Monitor burn pile areas for new seedlings as the fire may not consume or kill all seeds. Also, monitor any designated

decontamination sites for seedlings.

•	 Avoid entering or working in spore-forming species such as invasive climbing fern infestations when spore clusters

are present (October to November in temperate climates); if entry is unavoidable, complete sanitation of all equipment,

clothing, and workers is necessary to prevent potential spread.

•	 Use only non-contaminated fill materials, mulches, and seeds. Inspect material sources at the site of origin for

indications of contamination by invasive plants growing on or near the area. Regularly inspect areas where offsite fill

materials have been used and areas used by visitors and lessees.

•	Be careful not to disturb areas where there is a high probability of invasion. Most land disturbing activities raise the

potential for establishment of aggressive plant invaders, especially when the invaders occur nearby.

•	 Practice search and surveillance at these likely points of entry: lands adjacent to yours that you do not own (such as

highways, county roads, and utility rights-of-way and their edges and fencerows) especially after new construction or

maintenance activities; internal roads, trails, and fire lines; lands next to streams, rivers, and lake shores, especially

after recent flooding or high-flow periods; recently prepared and seeded wildlife food plots; harvested, thinned, burned,

or storm-damaged areas during the years following disturbance.
449

chapter 15. The Invasion of Southern Forests by Nonnative Plants

Successful management of nonnative invasive plants

requires recognition that the number of species, their area

of occupation, and their spread are drastically increasing—

and that new knowledge, approaches, and cooperation

are needed. Management of undesirable plants has been a

growing science and practice in intensive agriculture and

horticulture, at the same time that invasive plant populations

toughened by hybridization and new introductions, are

spreading across land uses. Forestry, right-of-way, park,

and preserve managers can borrow and modify control

techniques from agriculture and from one another. Accurate

identification skills of both invasive and native plants are

required for precise management, as are new tools, machines,

products, and techniques.

Management of invasive plants would be more effective

if augmented by integrated planning, better and timelier

preparation, and heightened resolve and persistence.

Ownership, area, and site management plans (long range

and for specific activities such as timber harvesting, stand

thinning, prescribed burning, and road and firebreak

maintenance) would benefit from goals and actions addressing

prevention, eradication, and control of invasive infestations,

especially those that minimize entry and spread of invasive

plants and anticipate the possibility of new infestations.

It is important to remember, however, that such plans

are incomplete if they do not lead to site rehabilitation or

restoration.

Preparation always has been critical to forest, roadway, and

natural area managers and landowners. As invasive plant

populations increase in size and density, new concepts,

tools, and materials will be needed. Preparation includes

having the very latest information as well as using reliable

sources of uncontaminated fill dirt and rock, seed, and

mulch for soil stabilization. Preparing for rehabilitation

and restoration may involve extra expenditures for newly

available native seeds, planting tools and equipment,

landscape fabrics and fiber mats for stabilization, or

consultations with professionals.

Without persistence, all efforts to control and rehabilitate

infested lands will be lost. Nurturing a healthy native or

noninvasive community of plants usually requires a regime

that includes timed treatments and retreatment, tenacious

follow through, and years of site monitoring for reappearance

or new introductions.

Knowledge and Inf ormation Gaps

There is a critical need for research and policy action to

address many aspects of invasive plants in the southern

forests and elsewhere (Sieg and others 2010, Simberloff and

others 2005). Specific gaps include the absence of data on

the degree that invasive plants impact tree and stand growth

and structure for any forest type. There are essentially no

data in the southern region on relationships among invasive

plants, hydrology, and changes in water quality and

quantity. There is a critical need for new approaches that

will help managers avoid marked and permanent alterations

of forest, agricultural, and conservation lands and waters as

invasive plants spread from urban, suburban, and exurban

lands and connecting rights-of-way (Liebhold and others

1995, NRC 2002, Simberloff 1996, Von der Lippe and

Kowarik 2006).

Invasive plants thus represent a complex and perplexing

societal dilemma, with need for a more comprehensive

awareness, management strategies, coordinated programs,

and effective laws if we are to avoid bequeathing future

generations degraded ecosystems and ecosystem services.

A concerted, holistic effort that integrates science with

management in new ways will be required for predicting,

managing, and mitigating the spread of invasive species

(McPherson 2004), as will the involvement of the wider

society in new approaches (Miller and Schelhas 2008).

Acknowledgments

We gratefully acknowledge the following individual for his

contributions to data analysis, map and table construction,

and reviews: Erwin Chambliss, U.S. Department of

Agriculture Forest Service, Southern Research Station.

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457

chapter 16. Invasive Pests—Insects and Diseases

	 Donald A. Duerr and Paul A. Mistretta1

Key Findings

•	 Nonnative pest species have increasing impacts in the

South regardless of climate change, patterns of land

ownership, or changes in the composition of vegetation.

•	 “New” nonnative invasive insects and diseases will have

serious impacts on southern forests over the next 50 years.

Some species such as emerald ash borer, laurel wilt, and

thousand cankers disease are expanding rapidly; they

threaten the ecological viability of their hosts throughout

large areas of the South.

•	Given the trend in introductions of nonnative insect pests

and plant pathogens over the last 100 years, we can expect

additional introductions of previously undocumented pests

(insects, fungal pathogens, plant parasitic nematodes, etc.)

from foreign countries that will have serious consequences

for some native forest plant species.

•	When host material for a given insect or disease is

projected to increase over the next 50 years as a result of

climate change or management choice, we can expect more

pest activity; for example, more pine acreage enables more

southern pine beetle damage. Conversely, if host material

decreases, the overall impact of pests utilizing that host

material will likely decrease.

•	Very few indisputable projections can be made about the

effects of climate change on native or naturalized pests.

Although climate-change-induced host abundance is

expected to increase the activity of some pests, others

(such as gypsy moth) may become less active with warmer

temperatures despite relatively similar levels of host

availability.

•	 The scientific literature and the body of expert opinion

are inconclusive in predicting the effects of climate

change on many pests’ activity levels, often even lacking

historic trend data. However, based on anecdotal reports

from professionals, and in the absence of other data, we

generally assume that pest activity levels over the next 50

years will be similar to the past 50 years with respect to

impact on preferred hosts.

1Donald A. Duerr is the Staff Entomologist and Paul A. Mistretta is a

Staff Pathologist and Regional Pesticide Specialist, Southern Region, U.S.

Department of Agriculture Forest Service, Atlanta, GA 30309.

•	 A significant source of uncertainty in projecting pest

impacts is the adequacy of prevention and suppression

methods: how effective are existing methods, compared

with those that might be available in the future; how

willing and able are land managers or landowners

to adopt management/control methods; how much

funding is available compared to the amount needed for

implementation.

•	 Under the influence of climate warming host plants, pests

and pest complexes are expected to migrate northward

and to higher elevations. Because migration rates differ

among the affected species, migrating plants are expected

to form new associations, which will then affect the pests,

their host populations, and the interactions among them.

Unexpected pests very likely will become important, while

some that are currently active will be less severe in their

new habitats. As host plants “migrate” to the north an

increase in the incidence of decline syndrome of plants in

their previous range is expected.

•	 Although not expected to be a significant problem in the

next 50 years, the migration of lower elevation plants to

higher elevations could ultimately eliminate or at least

severely restrict the host ranges of current high elevation

plant associations. Pests that act on a restricted host base,

such as the balsam woolly adelgid and butternut canker,

could become far more significant ecologically in areas of

relict host populations.

•	Climate change will lead to extra uncertainty in decision

making, especially in areas where the changes cause

increased variability in local (fragmented) climate regimes

that exceed historical variability of local weather patterns.

Introduction

An important part of the southern forested landscape is the

array of insect and disease pests that significantly affect

the management of forest resources on a relatively broad

scale. The list of 21 key pests that were documented less

than a decade ago (Ward and Mistretta 2002) has already

expanded to 30.

The goal of this chapter is to project the behavior of insect

and disease pests that we anticipate will affect forest

resources over the next 50 years, based on changing climate,

Chapter 16.

 Invasive Pests—Insects and Diseases
458 The Southern Forest Futures Project

human activity, and biologic factors. Our primary focus is on

climate change and its sub-elements of temperature régime

(dominated by temperature extremes), overall pattern of

solar radiation, and rainfall pattern. All available climate

change scenarios predict an environment in which we expect

vegetation changes to occur (Iverson and others 1999).

Concurrent with ecological changes will be a shift in the

pests that function within an altered vegetative landscape

under changed temperature, rainfall, and other climatic

conditions. The impacts on pest activity, in turn, may

influence the direction or scope of other changes in forest

type and structure.

The focus of this chapter is the 30 species of pest insects or

fungal pathogens that cause diseases projected to be of future

concern, with emphasis on the following key issues:

•	 The historical and forecasted future spread of high threat

insects and diseases

•	 Other pests invading southern forests and other high threat

species poised to enter the region

•	 Expected consequences of the spread of high-threat pest

species for forest productivity, ecosystem composition and

biodiversity, threatened and endangered species and their

habitats, watershed and soil health, carbon storage, and fire

dynamics

•	Potential severity of pest species threats relative to other

threats and to future forest sustainability

•	 Forest species or populations that are likely to be lost or

dramatically degraded by pests; the resulting changes in

the composition of southern forests over the next 50 years;

and the degree of certainty in these outcomes

•	 Adaptive strategies and methods for invasive pest

management that could mitigate the effects of predicted

future outbreaks

Methods

In response to the issues developed above, we present a brief

extract of relevant information about the pests that are well

established in southern forests (Ward and Mistretta 2002); we

add more detailed descriptions of several new pests or pest

complexes that have emerged in the past few years; we apply

the results of past research on pests and pest management to

expected changes in southern forests over the next 50 years;

we identify management strategies for responding to pests

in a changing environment; and we identify research needed

to improve our knowledge about pests with their hosts and

their interactions with their changing environment, thereby

enabling a more quantitative approach to forecasting in the

future.

Data Sources

Information for this chapter is derived from two primary

sources, selected items from the extensive body of published

scientific literature, and the experience of the authors and

their colleagues in State and Federal agencies, universities,

and other public or private organizations that are engaged

either in research or field-based pest management activities.

Additional information about forest pests and their control

is readily available from State and Federal forestry agencies

or on the Internet (two good starting points are http://fhpr8.

srs.fs.fed.us/ and http://www.na.fs.fed.us/spfo/pubs/fth_

pub_pages/fidl.htm). Also, appendix C contains additional

resources (References) not cited here but which provide

valuable additional background for understanding the

biology and ecology of the pests discussed.

Results

The scientific literature on climate change and other

environmental considerations is summarized in appendix

C, which also provides the background information on our

approach to pest modeling and future projection of impacts.

Below we address the pests projected to influence the forests

of the South over the next 50 years, their potential damage,

potentially effective management strategies, and research

needed to better understand and manage them (table 16.1).

Of the 30 forest pests in the South discussed below, 21 are

well established and 9 are relative newcomers. Pests are

roughly evenly divided between those affecting softwoods

and those affecting hardwoods.

Insect Pests of Softwoods

Balsam woolly adelgid—Impacts of balsam woolly adelgid,

Adelges piceae, were first documented in 1957 on Fraser fir

in the Southern Appalachians. The five major areas of highelevation, spruce-fir forest in North Carolina, Tennessee, and

Virginia are highly valued for their scenic and recreation

values, attracting several million visitors annually (Ward

and Mistretta 2002). In addition, several species of flora

and fauna rely on mature spruce-fir habitat for survival, and

many are found only in this environment. The balsam woolly

adelgid has infested Fraser fir in all five areas. Damage

caused by the adelgid has degraded scenery and recreation

value and put this habitat of dependant tree species at great

risk.
459

chapter 16. Invasive Pests—Insects and Diseases

Table 16.1—Important insect and disease pests of southern forests

Pest Pest’s scientific name

Type of 

pests / 

abiotic 

factors Origin

Forest type 

or species 

affected

Annosum root disease Heterobasidion annosum Fungus Native Pines in the loblollyshortleaf and longleaf–slash 

forest types

Asian longhorned beetle Anoplophora glabripennis Insect China Most hardwoods, but 

especially maples.

Baldcypress leafroller Archips goyerana Insect Native Baldcypress in oak-gumcypress forest type

Balsam woolly adelgid Adelges piceae Insect Europe Fraser fir in the spruce-fir 

forest type

Bark beetles (other than 

southern pine beetle)

Ips avulsus, I. calligraphus, 

I. grandicolli, & Dendructonus 

terebrans

Insect Native Pine in the loblolly-shortleaf 

and longleaf–slash forest 

types

Beech bark disease Nectria coccinea var. faginata, 

N. galligena (fungi); 2 (at least) 

insect vectors

Complex of 

insects and 

fungi

Unknown American beech in the oakhickory forest type

Brown spot needle 

disease 

Scirrhia acicola Fungus Native Longleaf pine in the 

longleaf–slash forest type

Butternut canker Sirococcus clavigignentijuglandacearamFungus Unknown Butternut in the oak-hickory 

forest type

Chestnut blight Cryphonectria parasitica Fungus Asia American chestnut, 

chinquapins, several 

species of oak in the oakhickory forest type

Dogwood anthracnose Discula destructiva Fungus Unknown Dogwood in the oak-hickory 

forest type

Dutch elm disease Ophiostoma ulmi (formerly 

called Ceratocystis ulmi) &

Ophiostoma novo-ulmi (fungi); 

two bark beetles

Complex of 

fungi and 

insects

Europe All elm species

Emerald ash borer Agrilus planipennis Insect Asia All ash species

Forest tent caterpillar Malacosoma disstria Insect Native Hardwoods in the oak-gumcypress forest type

Fusiform rust Cronartium fusiforme f. sp. 

fusiforme

Fungus Native Loblolly and slash pines in 

the loblolly-shortleaf and 

longleaf slash types

Gypsy moth Lymantria dispar Insect Europe and 

Asia

Hardwoods (all types)

(Continued)
460 The Southern Forest Futures Project

Table 16.1—(continued) Important insect and disease pests of southern forests

Pest Pest’s scientific name

Type of 

pests / 

abiotic 

factors Origin

Forest type 

or species 

affected

Hardwood borers Various Insect Native All species of hardwoods

Hemlock woolly adelgid Adelges tsugae Insect Asia Hemlocks 

Laurel wilt Raffiella lauricola (fungus),

Xyleborus glabratus (insect)

Complex of 

an insect 

and fungus

Asia Lauraceae, especially 

Redbay

Littleleaf disease Phytophthora cinnamomi, 

Pythium sp. 

Tree decline 

complex; 

fungi and 

site factors

Southeast 

Asia (likely)

Shortleaf and loblolly pines 

in the loblolly-shortleaf 

forest type

Loblolly pine decline As a minimum: various fungi 

(Lophodermium spp.) and 

insects (Hylastes spp.) 

Tree decline 

complex; 

insect and 

fungi

Unknown Pines

Nantucket pine tip moth Rhyacionia frustrana Insect Native Pines

Oak decline Armillaria sp., and other 

secondary fungi

Tree decline 

complex; site 

conditions 

and fungi

Mixed Oaks 

Oak wilt Ceratocystis fagacearum Fungus Native Oaks in the oak-hickory 

forest type

Pine reproduction weevils Hylobius pales, Pachylobius 

picivorus

Insect Native Pines

Sirex woodwasp Sirex noctilio (insect),

Amylostereum areolatum 

(fungus)

Complex of 

an insect 

and fungus

Europe, Asia, 

northern 

Africa

Pines

Soapberry borer Agrilus prionurus Insect Mexico Western soapberry

Southern pine beetle Dendroctonus frontalis Insect Native Pines

Sudden oak death Phytophthora ramorum Fungus Unknown Oaks 

Texas leafcutting ant Atta texana Insect Central 

and South 

America

Pine (reproduction)

Thousand cankers 

disease

Geosmithia sp. (fungus), 

Pityophthorus juglandis (insect)

Complex of 

an insect 

and fungus

Unknown Black walnut

462 The Southern Forest Futures Project

hemlock to serve as cover and nesting habitat for birds and

small mammals.

Given the adelgid’s current rate of spread, it could infest

nearly the entire southern range of eastern hemlock and

Carolina hemlock within the next 50 years. Some isolated

areas within the infested range and some areas of hemlocks

that are separated from the main range (in northwestern

Alabama, for example) may escape infestation. In all

likelihood, within the next 50 years hemlock woolly adelgid

will kill most of the hemlocks that are alive today in the

South. The loss of hemlock will be one of the major impacts

caused by nonnative invasive species to Southern forests in

the next 50 years.

A number of suppression tactics show some promise for

preventing the loss of significant numbers of hemlocks over

the next 50 years. Treatment of trees with imidacloprid

effectively controls hemlock woolly adelgids for several

years (Cowles and others 2006). Distribution of the

insecticide into tree crowns is more effective with soil drench

or injection than with stem injection (Dilling and others

2010). Dinotefuran is also being used with success. Current

insecticide treatments are applied to individual trees and

function primarily as a temporary protection measure for

a relatively small number of trees. At this time, insecticide

application over large areas is neither logistically feasible

nor cost-effective. Several biological control agents (beetle

predators) have been and are being released, and some are

successfully establishing (Mausel and others 2010). More

time is likely needed before conclusive impacts of biological

control agents on the health of hemlock forests can be shown.

Establishment of a complex of natural enemies in a given

area is desired to achieve long-term success. In June 2009,

researchers and forest health professionals began evaluating

the efficacy of Lecanicillium muscarium, an insect-killing

fungus that is registered as a bio-pesticide in Europe

(Grassano 2008).

Research and work is being done on hemlock host resistance

and ex-situ conservation of hemlock seedlings and genetic

diversity (Bentz and others 2002, Jetton and others 2008,

Jetton and others 2010, Montgomery and others 2009, Pooler

and others 2002). These efforts may allow scientists and land

managers to reintroduce adelgid-resistant hemlocks in the

future.

Climate change is unlikely to reverse the spread of hemlock

woolly adelgids. In the northern part of the hemlock woolly

adelgid range, low minimum winter temperatures can

significantly knock back populations and appear to limit

spread. Therefore, we can assume that climate warming

would likely promote a northward expansion of the

adelgid (Paradis and others 2008). The southern range of

hemlock is currently not benefitting from much cold winter

knockback—a warming climate would presumably only

exacerbate the situation.

Nantucket pine tip moth—The Nantucket pine tip moth,

Rhyacionia frustrana, is one of the most common forest

insects in the South (Berisford 1988). Although it is usually

considered a southern pest, its range includes most of the

eastern half of the United States.

Most commercial pine species are susceptible to attack

by the Nantucket pine tip moth, but there are considerable

differences in relative susceptibility. Among the southern

pines, longleaf nursery seedlings and all ages of shortleaf,

loblolly, and Virginia pines are highly susceptible, while

slash and older longleaf pines are highly resistant.

Damage is normally transitory or negligible in forest stands

but can be severe for seedlings and saplings younger than 5

years, resulting in deformities and loss of growth.

Based on the warmer and possibly drier climate that is

expected over the next 50 years, the activity and damage

levels of Nantucket pine tip moth are likely to increase in the

South and extend to northern areas (Midwest, New England)

where tip moth has not been much of a management concern.

Activity may increase and continue into the winter months, as

could the number of generations per year. Nantucket pine tip

moths are primarily a problem in young loblolly monocultures.

To the extent that land managers increase the planting of

loblolly monocultures in the next 50 years, damage from the

Nantucket pine tip moth is likely to increase.

A number of effective, chemical control options exist for

this pest (Asaro and others 2003). If population levels are

monitored in a timely and regular fashion, and are followed

up by appropriate insecticide applications, tip moth damage

can be minimized. Chemical control options are effective,

especially the new systemic insecticides. However, they

are often prohibitively expensive and will probably not be

adopted under most commonly accepted climate scenarios

unless tip moth population pressure becomes quite high.

Other bark beetles—Although the southern pine beetle

is the most damaging insect in southern pine forests, it is

only one of five pine bark-beetle species of concern for

forest managers in the South. The others are the six-spined

engraver, Ips calligraphus, the southern pine engraver, Ips

grandicollis, the small southern pine engraver, Ips avulsus,

and the black turpentine beetle, Dendructonus terebrans.

These beetles are usually considered secondary pests

because they normally infest only stressed, weakened,

damaged, or downed pines. They also colonize pines that

have been attacked by southern pine beetles or another bark

beetle species. Host species in the South include loblolly,

shortleaf, Virginia, longleaf, eastern white, pitch, slash (P.
463

chapter 16. Invasive Pests—Insects and Diseases

elliotii), and sand (P. clausa) pines. Both pure pine and oakpine stands may be affected (Conner and Wilkinson 1983,

Smith and Lee 1972, USDA Forest Service 1985a).

Attacks by black turpentine beetles may continue for several

months but infestation is not always fatal. Multiple attacks

around the entire circumference of the tree are required to

cause mortality (Smith and Lee 1972, Staeben and others

2010, USDA Forest Service 1985a).

The small southern pine engraver and the six-spined

engraver are the most aggressive and may kill small groups

of trees. Losses may be extensive during periods of drought

(Conner and Wilkinson 1983, USDA Forest Service 1985a).

The secondary bark beetles play a vital role in shaping forest

structure and may have a greater impact on regulating pine

stands than southern pine beetles (Paine and others 1981,

Thatcher 1960a). They attack weakened or severely stressed

trees and those reaching senescence. Large infestations

develop only occasionally, usually after widespread

environmental stress, such as that caused by drought, storm

damage, or wildfire. Their action serves to thin the pine

forests, reducing competition, leaving the stronger trees, and

decreasing the risk of southern pine beetle (SPB) outbreaks.

The impact of these beetles depends largely on management

activities (Coulson and others 1986). Engravers also breed

in downed material, so it is difficult to substantially reduce

populations, but prevention methods (such as lowering

planting densities, thinning stands, and cutting and removing

groups of infested trees) can reduce damage.

In unmanaged stands, they attack single trees or small

groups of pines and reduce pine basal area. They provide

openings for pine reproduction or for established hardwoods

to grow. The effects are often not noticeable except during

periods of extended drought, after storm damage, or at the

end of SPB epidemics.

Increased temperature and decreased precipitation would

stress pines and could therefore increase the impacts of these

bark beetles, but it is unlikely that they will become primary

pests that kill large areas of trees. These four bark beetle

species may move northward as winters become warmer.

Pine reproduction weevils—Pales weevil (Hylobius pales)

and pitch-eating weevil (Pachylobius picivorus) are two

of the most damaging insect pests of pine seedlings in the

Southeastern United States. In southern forests, they are

found wherever pine occurs. Adult weevils of both species

are attracted to newly harvested sites, where they breed

in logging slash, stumps, and old root systems; they cause

economic losses by feeding on the bark and often killing

planted seedlings. If seedlings are planted on or adjacent to

sites with fresh stumps or damaged trees, it is common to

have 30 to 60 percent weevil-caused mortality among firstyear seedlings, with instances of 90 percent or more mortality

recorded (Thatcher 1960b). A third species, the eastern pine

weevil (Pissodes nemorensis), is generally less common but

is known to kill terminal and lateral branches and girdle

the stems of small trees (Doggett and others 1977, Nord and

others 1984). The reproduction weevils are almost never a

problem in forest management unless seedlings have been

planted on or adjacent to sites with fresh stumps or damaged

trees. Foresters usually avoid this problem on cutover sites by

delaying planting or by planting treated seedlings.

Pales and pitch-eating weevils prefer loblolly, shortleaf, pitch,

and eastern white pines. They almost never attack longleaf

and slash pines, but on rare occasions have been observed

feeding on hardwoods. Although the eastern pine weevil

prefers cedar, it also attacks most southern yellow pines,

such as loblolly, slash, and shortleaf. Pales and eastern pine

weevils may serve as vectors for various pathogenic fungi.

The future outlook for the activity and damage levels of

reproduction weevils is similar to the recent past. A warmer

climate may allow these insects to extend their ranges north

into Canada. Warmer southern winter months may allow

them to increase and/or prolong activity and to produce more

generations per year. Decreased precipitation may reduce

their activity. The impacts of these pests are more dependent

on stand management (and whether seedlings were treated

with insecticides) than on climatic conditions. If pines are

planted and then left unmanaged over the next 50 years, we

can expect increased damage from pine reproduction weevils.

Sirex woodwasp—Sirex woodwasp, Sirex noctilio, is

native to Europe, Asia and northern Africa and has been

introduced to North America, South America, New Zealand,

Australia, and South Africa. In Australia, South Africa, and

South America, it is considered an important pest, causing

significant mortality (Oliveira and others 1998) in stands

planted with North American pines, especially Monterey

pine (P. radiata) and loblolly pine. Haugen and Hoebeke

(2005) report that other known susceptible pines include

slash, shortleaf, ponderosa (P. ponderosa), lodgepole (P.

contorta), and jack (P. banksiana).

Females can produce up to 450 eggs and deposit them

(mostly singly) below the surface of the bark close to

the cambium. The female also deposits mucus and a

basidiomycete symbiotic fungus, Amylostereum areolatum,

which grows rapidly and excretes wood-digesting enzymes.

When the larvae hatch they bore into the wood, but feed

on wood already colonized by the fungus. The fungus and

mucus act together to kill the tree and create an environment

suitable for the development of the larvae.
464 The Southern Forest Futures Project

Sirex woodwasp has not caused widespread mortality in the

North American areas where it is established, nor have any

populations been reported in the South. However within the

next 50 years, it is very likely that natural or human-aided

spread will introduce this pest to southern forests. Many

of the South’s most important pine species are susceptible

to Sirex and many trees will succumb if attacks are as

aggressive as they are in South America and Australia.

Although this scenario could result in catastrophic ecological

and economic losses, the complexity of southern forests

(mixed stands, high biodiversity, many possible competitors,

predators and parasitoids) contrasts with the monoculture

pine plantations in other countries where the pest has been

most damaging. Many studies are underway to assess the

potential level of danger to southern forests. A national

risk map for Sirex has been developed (see http://www.

fs.fed.us/foresthealth/technology/invasives_sirexnoctilio_

riskmaps.shtml) and risk maps specific to the South are in

development.

If the Sirex woodwasp becomes established in the South

and acts as a primary “tree killer,” effective prevention

and suppression techniques are available, including the

current practice of thinning stands to increase growth and

vigor and reduce susceptibility to bark beetles. In other

countries, Sirex woodwasp has been successfully managed

using biological control agents. The key agent is a parasitic

nematode, Deladenus siricidicola, which infests Sirex

woodwasp larvae, and ultimately sterilizes the adult females.

Infested adult females lay infertile eggs that are filled with

nematodes, which further spreads the nematode population.

The nematodes can effectively regulate the woodwasp

population below damaging levels. As Sirex woodwasp

establishes in new areas, this nematode can be easily massreared in the laboratory and introduced by inoculating

it into infested trees. Biological control employing these

nematodes is being evaluated for use in U.S. forests. If

effective, it should provide a good control option for southern

landowners and land managers.

The effects of changes in temperature, carbon dioxide, and

precipitation on Sirex woodwasp activity and aggressiveness

are unknown. If pine acreage increases throughout the South

or in certain areas of the South, susceptibility of these areas

to attack will increase.

Southern pine beetle—Southern pine beetle (SPB),

Dendroctonus frontalis, is the most destructive insect pest

of pine forests in the South (Thatcher and Conner 1985).

Populations build rapidly during periodic outbreaks and kill

large numbers of trees. For example, during the outbreak

of 1999 to 2002, SPB killed more than a million acres of

pines valued at greater than $1.5 billion. However, during

periods of low activity, SPB populations may be so low that

it is difficult to locate a single infested tree (Thatcher and

Barry 1982, Thatcher and others 1980) or capture beetles in

pheromone traps (Billings and Upton 2010).

The SPB, which attacks all species of pines, prefers loblolly

(Pinus taeda), shortleaf (P. echinata), Virginia (P. virginiana),

slash (P. elliottii) pond (P. serotina), and pitch (P. rigida) pines

but seldom attacks longleaf pine (P. palustris). SPB has been

observed to successfully infest eastern white (P. strobus) and

Table Mountain (P. pungens) pines. Mature trees in pure,

dense stands have long been considered most susceptible to

SPB attack, but in recent years unthinned pine plantations

have increasingly supported SPB infestations (Cameron and

Billings 1988). Attacks are rare for trees younger than 5 years

or smaller than 2 inches in diameter at breast height (d.b.h.).

During outbreaks, SPB activity peaks in early summer in

States on the Gulf of Mexico and in late summer and early

autumn farther north.

In the last five decades, large acreages of pine plantations

have been established in the South. Even-aged, singlespecies plantations become increasingly susceptible to SPB

infestations as they age. Millions of acres of pine across the

South are at high hazard for SPB attack as shown by regional

and State maps (Nowak [N.d.]). SPB hazard maps and

information about their development can be viewed at: http://

www.fs.fed.us/foresthealth/technology/nidrm_spb.shtml.

SPB impacts over the next 50 years are expected to be

significant, especially if the pine acreage increases in the

South, high-susceptibility species are planted in dense

plantations, and the plantations are left unthinned. A warmer,

drier climate is likely to increase SPB activity and impacts.

Warmer temperatures will likely allow an increase in the

number of SPB generations per year as well as the portion

of the year that the beetles are active. The northern edges

of the southern region and pine stands that are farther north

than the historical SPB range (such as in the Lake States,

New England, and Canada) are almost certain to experience

SPB activity and impacts that are unprecedented or at least

significantly greater than in the past.

There is some uncertainty and debate about the potential

effects of a warmer climate on SPB (Tran and others 2007),

and general predictions are difficult to make. An increase in

temperature (particularly warmer winters) would allow more

generations per year. Gan (2004) and Rivera Rojas and others

(2010) predict outbreaks to become more frequent as climate

changes, although lack of landscape-scale data on host

abundance and distribution may have led Gan to overestimate

future SPB activity. Very high summer temperatures may

increase brood mortality, reduce spot growth rates, and

hinder predation. Warmer winter temperatures may disrupt
465

chapter 16. Invasive Pests—Insects and Diseases

synchronization of the life cycles required for concentrated

spring emergence that favors initiation of large, new

infestations (Billings and Kibbe 1978).

The impact of outbreaks in the 1980s was magnified by an

abundance of contiguous mature stands of sawtimber, many of

which have been replaced with young plantations, at least on

non-Federal lands. If increased forest fragmentation, a younger

age class distribution, and more thinning of plantations occur

in the next 50 years, SPB impacts could be lower in the

future, despite increases in temperatures. And although it is

generally accepted that increased temperatures will increase

SPB activity and damage, other factors (for example forest

composition, forest management, direct suppression, etc.) may

be more meaningful in determining future SPB activity and

damage (Friedenberg and others 2008).

Similar to temperature’s effect on SPB, the potential of

moisture regime to increase or decrease SPB problems is open

to conjecture and not fully understood. Some experts believe

that drought is a major enhancer of SPB outbreaks, whereas

others point to too much moisture as a primary facilitating

factor. If the frequency of precipitation extremes (years of

extreme wetness or dyness) increases throughout the South

over the next 50 years, it is probable that pines will become

stressed and increased SPB activity and damage will result.

In addition to the effects that forest composition,

temperature, and moisture will have on the SPB outlook,

forest management will play a defining role. Planting the

proper species for a given site, lower planting densities,

and thinning of pine stands can increase stand vigor and

resiliency and possibly reduce SPB damage. When outbreaks

do occur, damage can be minimized by early detection and

monitoring of spots, followed by prompt direct suppression

of active spots (Billings 1980).

Texas leafcutting ant—The Texas leafcutting ant, Atta

texana, targets first- and second-year pine plantations in

eastern Texas and west central Louisiana. In local areas

where the ants are abundant, it is nearly impossible to

establish pine plantations unless the ant colonies are

eliminated. The annual loss of pine seedlings to Texas

leafcutting ants is nearly 12,000 acres (Cherret 1986, Texas

Forest Service 1982).

A warmer climate may lead to an increase and/or continuation

of leafcutting ant activity during winter months. Decreased

precipitation would likely have the opposite effect. Because

this ant has a strong preference for well-drained, deep

sandy soils (Moser 1984, Vilela 1986), climate-induced

spread beyond its current distribution is unlikely. Although

leafcutting ants are limited by average low temperatures

(warmer temperatures would lessen this limiting factor), their

spread into new, northern areas is going to be limited due to

the lack of preferred soils for the ant. There is a possibility that

a warmer climate would allow northward movement into areas

of Oklahoma and Arkansas that have deep, sandy soils. A new

fipronil control product, PTMTM was registered in 2009, and

an insecticidal bait is on the horizon. Regular and consistent

application of these products has the potential to reduce the

impacts of Texas leafcutting ants from historical levels.

Insect Pests of Hardwoods 

Asian longhorned beetle—Asian longhorned beetle,

Anoplophora glabripennis, was discovered attacking

hardwood trees in the United States in the mid-1990s.

Tunneling by beetle larvae girdles tree stems and branches.

Repeated attacks lead to dieback of the tree crown and,

eventually, death of the tree. The beetle probably travelled

to the United States inside solid wood packing material

from China. This pest beetle has been intercepted at ports

and found in warehouses throughout the United States and

is currently infesting trees in New York City, New Jersey,

Worcester (MA), and Toronto (Ontario, Canada). It was

successfully eradicated from the Chicago area following a

lengthy and aggressive campaign of detection and removal of

infested trees (Antipin and Dilley 2004).

This beetle is a serious pest in China, where it kills hardwood

trees in roadside plantings, shelterbelts, and plantations. In

the United States the beetle prefers maple species, including

boxelder (A. negundo), Norway (A. platanoides), red (A.

rubrum), silver (A. saccharinum), and sugar (A. saccharum)

maples. Other preferred hosts are birches (Betula spp.), Ohio

buckeye (Aesculus glabra), elms (Ulmus spp.), horsechestnut

(Aesculus hippocastanaeum), and willows (Salix spp.).

Occasional-to-rare hosts include ashes, European mountain

ash (Sorbus sp.), London planetree (Platanus sp.), mimosa

(Albizia julebrissin), and poplars (Populus spp.). A complete

list of host trees in the United States has not been compiled.

Asian longhorned beetles produce one generation per year.

Adult beetles are usually present from July to October, but

can be found later in the fall if temperatures are warm.

Adults usually stay on the trees from which they emerged or

disperse short distances to a new host to feed and reproduce.

Each female usually produces 35 to 90 eggs (or more) during

her lifetime. Eggs hatch in 10 to 15 days. The larvae feed

under the bark in the living tissue of their host and then bore

deep into the wood to pupate. Adults emerge by boring a

tunnel and creating a large round exit hole in the tree (USDA

Forest Service and Animal and Plant Health Inspection

Service 2008).

Currently, the only effective means to eliminate Asian

longhorned beetle is to remove infested trees and destroy
466 The Southern Forest Futures Project

them by chipping or burning. To prevent further spread of the

insect, quarantines are established to prevent transportation

of infested trees and branches from the area. Early detection

of infestations and rapid treatment response are crucial.

Systemic insecticides can provide protection for individual

trees or small numbers of trees, but individual tree treatment

is not feasible in forested settings.

The future impact of Asian longhorned beetles on southern

forests is unknown for several reasons. First, the pest may

or may not spread into the South over the next 50 years.

Significant eradication and containment efforts are being

pursued in areas where trees are under attack. Although the

beetle disperses slowly—it does not fly great distances and

tends to remain in the same area until hosts are exhausted—

it may be spread great distances in firewood or by movement

of other infested material.

A wide variety of southern hardwood trees (especially

maples) is at risk. It is unlikely, however, that vast areas of

hardwoods would be killed within the next 50 years because

the beetle takes several years to kill host trees and it is a slow

disperser. If spot infestations are discovered early enough,

the beetle can be eradicated before it becomes widely

established. Successful eradication efforts require much

time, funding, personnel, and strength of will.

Effects of southern climate on Asian longhorned beetle are

completely unknown. Extreme heat in some parts of the

South may inhibit activity and success. However, there is

also the possibility that warmer temperatures would lead to

quicker completion of the beetle’s life cycle, which would

mean larger populations and more damage to southern trees.

Baldcypress leafroller—Formerly named the fruittree

leafroller, the baldcypress leafroller, Archips goyerana,

periodically defoliates baldcypress in Louisiana and

Mississippi. Kruse (2000) describes the baldcypress

leafroller, and summarizes its biology and its effects on its

host. This native insect causes growth reduction and dieback,

but only causes mortality when multiple other stressors are

at work.

The baldcypress leafroller was first recorded in 1983 in

Louisiana, where it feeds almost exclusively on baldcypress.

It annually defoliates an average of 35,000 acres in the oakgum-cypress forest type. Although this insect is mainly a

pest of flooded baldcypress, it can move into drier upland and

urban settings during periods of heavy infestation.

Baldcypress trees of all sizes display canopy dieback and

significant reductions in diameter growth resulting from

repeated annual defoliation. Pole-sized to small sawtimbersized trees growing on forest edges or in dense stands are

most severely affected. In areas where chronic saltwater

intrusion is a problem, trees die after as few as two

consecutive years of defoliation.

Temperature and precipitation changes are unlikely

to directly affect baldcypress leafroller’s activity and

impacts. However, higher sea levels resulting from warmer

temperatures would further stress baldcypress trees because

of increased saltwater intrusion, significantly increasing the

likelihood that defoliation would damage and kill host trees.

Human alterations to southern Louisiana’s hydrology, greater

saltwater intrusion, nutria feeding, defoliation by baldcypress

leafroller, and other stressors are all combining to threaten

the baldcypress resource in southern Louisiana. Although

unlikely to disappear in the next 50 years, this resource is

expected to continue to be compromised.

Emerald ash borer—Emerald ash borer, Agrilus

planipennis, is a devastating, wood-boring beetle native to

Asia. It was first found infesting trees in North America

in southeastern Michigan and adjacent areas of Ontario,

Canada, in 2002 (Various 2010). Within the core infested

area of Michigan, Indiana, and Ohio, more than 50 million

ash trees are estimated to be dead, dying, or infested (Smith

and others 2009). Elsewhere, the emerald ash borer already

has killed tens of millions of ash trees, and continues to pose

a serious threat to the ash resource of North America.

The emerald ash borer was first found in the United States in

2002, but it was likely introduced into the area around Detroit

in the early 1990s (Kovacs and others 2009), probably in solid

wood packing material from Asia. Soon after detection, five

counties in Michigan were placed under quarantine. However,

in the years before detection, infested material—such as

nursery stock, unprocessed ash logs, firewood, and other ash

commodities—was most likely moved to many areas around

the United States. Inadvertent movement by humans continues

into the present in spite of Federal and State quarantines

restricting the export of potentially infested materials once the

borer is detected in a county (U.S. Department of Agriculture

Animal and Plant Health Inspection Service 2003, 2006).

Surveys made in 2003 found infestations in 12 counties in

Michigan and 3 counties in northern Ohio. By early 2011

infestations were located in an additional 13 States: Indiana,

Illinois, Iowa, Maryland, Pennsylvania, Missouri, Virginia,

West Virginia, Wisconsin, Kentucky, Minnesota, and New

York (fig. 16.2). In Canada, infestations now occur in several

areas of Ontario and Quebec (USDA Animal and Plant Health

Inspection Service 2011).

Since its introduction, the emerald ash borer has had a

significant negative impact on the ecology and economy of

infested areas, with all 16 species of North American ash

appearing to be susceptible. Ash trees are an important part

of the rural and urban forests of the United States, valued

at more than $282 billion (USDA Animal and Plant Health

468 The Southern Forest Futures Project

detect infestations early enough to effectively control them

and prevent their spread.

There are a number of effective chemical control options

available to protect individual trees from infestation (Herms

and others 2009). Unfortunately, available time, funding,

equipment, and expertise limit the number of trees that can be

protected to urban/suburban settings and a very small number

of high value trees in forested settings. With the emerald ash

borer destroying every ash in its path, one practical option may

be to delineate and protect small pockets of exceptional ash

resource as “ash conservation areas.”

Several larval and egg parasitoids are being investigated for

use as biological control agents (USDA Animal and Plant

Health Inspection Service and others 2010). Although results

are preliminary, it is reasonable to expect that biological

control agents would mitigate populations but would not

control or completely stop the spread and impacts of this

insect invader.

The effects of changes in climate—such as increases in

temperature, precipitation, and carbon dioxide— on emerald

ash borer are uncertain. Warmer temperatures would

likely result in more rapid life cycle completion resulting

in increased population growth and impacts. However, the

extreme heat of southern summers could actually inhibit

activity and reduce the amount of ash mortality. The range

of ash trees in the South is expected to shrink as the climate

warms; between climate stress and the emerald ash borer

infestations, the South is likely to lose millions of ash trees in

the next 50 years.

Forest tent caterpillar—Forest tent caterpillar, Malacosoma

disstria, occurs throughout most of the United States and

Canada, where it defoliates a variety of hardwoods (Batzer

and Morris 1978, Drooz 1985, Fitzgerald 1995, USDA

Forest Service 1985b). In the South, it heavily defoliates

water tupelo (Nyssa aquatica), sweetgum (Liquidambar

styraciflua), blackgum (N. sylvatica), and various oak

species (Quercus spp.). The most persistent and extreme

outbreaks in the South occur on host trees in bottomlands,

forested wetlands, and riparian areas. When populations

reach epidemic levels, the caterpillars often spread to urban

and suburban areas where they defoliate shade trees and

ornamental plants.

Outbreaks occur in several Southern States, where more than

500,000 acres can be defoliated in a single season; defoliation

does not cause significant amounts of tree mortality and

therefore control practices are rarely cost effective. However,

significant loss of tree growth is often an outcome, and

repeated, heavy defoliation of stands may cause significant

dieback. If needed, control techniques are available and

have proven effective but depend on the availability of both

funding and technical expertise.

Tent caterpillar impacts occur mainly in the bottomland

hardwood-cypress forest types (mapped as oak-gum-cypress

and elm-ash-cottonwood), but they occasionally occur in

upland northern hardwood forest types (mapped as maplebeech-birch, oak-hickory, and oak-pine).

Changes in temperature and precipitation are unlikely to

increase defoliation by forest tent caterpillars. If climate

change significantly stresses the forest types most vulnerable

to tent caterpillar defoliation, the additive effect of multiple

stressors could mean hastened or increased tree mortality.

Gypsy moth—Gypsy moth, Lymantria dispar, is native to

Europe and Asia. In 1869, Leopold Trouvelot introduced

the European strain of the gypsy moth. Since then, it has

spread across the landscape of the eastern United States,

defoliating vast acreages of forest (USDA Animal and Plant

Health Inspection Service 2010b). The insect was found in

northeastern Virginia in the early 1980s. At its current rate

of spread, specialists predict that a significant portion of the

South will be infested in the next 50 years.

The impact of repeated gypsy moth defoliation on the health

of oak forests is significant (Campbell and Sloan 1977).

Repeated severe defoliation of oaks weakens trees to such

an extent that they may be attacked and killed by secondary

pest organisms, such as the two-lined chestnut borer (Agrilus

bilineatus) and Armillaria root rot (caused by Armillaria

mellea). Extended drought intensifies the death rate.

Gypsy moth caterpillars feed on a wide range of trees and

shrubs (Liebhold and others 1995, Zhu 1994) but prefer

oaks. Species are attacked preferentially without respect

to forest type. Highly favored species include sweetgum,

northern red oak (Quercus rubra), and American basswood

(Tilia americana). Species of limited suitability include

pines, maples (Acer spp.), ash (Fraxinus spp.), American

beech (Fagus grandifolia), and cherry (Prunus serotina).

Species that are not favored or are avoided include blackgum,

yellow-poplar (Liriodendron tulipifera), black locust (Robinia

pseudoacacia), baldcypress (Taxodium distichum), magnolia

(Magnolia grandiflora), and tupelo (Nyssa sylvatica). As

gypsy moth moves south and west, it will encounter lower

concentrations of oak and cove hardwoods, and forest

susceptibility will decrease in many but not all areas.

However, with its wide host range it should still persist.

The most important disease agents affecting gypsy moths are

the gypsy moth nucleopolyhedrosis virus (LdMNPV) and

the gypsy moth fungus, Entomophaga maimaiga (Andreadis

and Weseloh 1990, Hajek and others 1990).
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chapter 16. Invasive Pests—Insects and Diseases

The Slow the Spread Program decreases the gypsy moths’ rate

of spread from approximately 25 miles a year to 7 to 10 miles

per year (Sharov and others 2002). If the program continues,

we can expect the gypsy moth to move 350 to 500 miles

farther into the South over the next 50 years, compared to total

infestation within 25 to 30 years without the program.

Gypsy moths can also be artificially spread by human

activities; continued vigilance to detect and eradicate the

resulting small infestations help to prevent the moth’s rapid

spread into all areas of the South. In addition, methods exist

to suppress areas of high populations in infested areas and

to eradicate “satellite” infestations in advance of the moth’s

moving front; these methods include aerial applications

of Bt (Bacillus thuringiensis) or dimilin (insecticides), or

pheromone flakes (to disrupt mating).

Temperature changes alone are unlikely to have a dramatic

effect on gypsy moth movement or impacts. The range of

gypsy moth infestation is expected to expand regardless of

changes in climate, and at a rate faster than can be attributed

to any potential climate change-caused host range expansion.

If warmer temperatures cause the oak-hickory forest type

to displace boreal forests at higher elevations in the South,

gypsy moth impacts will likely increase in these areas.

However, one hypothesis is that gypsy moth spread and

damage will decrease as temperatures warm, thereby

reducing the extent of southward spread. Gypsy moths need

a cold snap to synchronize hatches (avoids different life

stages from occurring at the same time) and thus improve

mating efficiency.2 If this hypothesis is correct, as the moth

moves farther south and as the temperatures warm, winters

would not be cold enough or the necessary cold snap would

come too late in the year to synchronize the spring hatch.

A drier climate would likely increase gypsy moth impacts

because it would stress host trees and discourage build-up

of the moth’s fungal predator, which thrives during wetter

springs.

Because the gypsy moth is still spreading into the South,

barring unforeseen circumstances we can say with certainty

that its impacts will increase over the next 50 years. How

severe and widespread the impacts will be, however, is

dependent on many factors including: the continuation of

active programs to slow the spread, suppress and eradicate

gypsy moth; the amount and health of hardwood forests

the moth encounters in the future; and potential unknown

temperature and moisture effects on the moth, its hosts, and

its natural enemies.

2John Ghent, USDA Forest Service, Forest Health Protection, 200 W.T.

Weaver Blvd., Asheville, NC 28804, 828-257-4328, jghent@fs.fed.us.

Personal communication: May 11, 2010.

Hardwood borers—Insect borers are important pests

of hardwood trees throughout the South. They tunnel in

the bark, trunks, terminals, and roots, causing a variety

of defects in wood, stem deformity, reduction of seed

production, and tree decline.

Some of the major damaging borers in the South (Solomon

1995) are the carpenterworm (Prionoxystus robiniae), red oak

borer (Enaphalodes rufulus), white oak borer (Goes tigrinus),

redheaded ash borer (Neoclytus acuminatus), poplar borer

(Saperda calcarata), oak timberworm (Arrhenodes minutus),

Columbian timber beetle (Corthylus columbianus), and

ambrosia beetle (Xyleborus celsus). Borers that are endemic

to an area do not normally cause dieback and mortality, but

in abnormally large numbers they contribute to tree decline

and stand degradation. Excessive numbers of growth defects

caused by borers affect between 25 and 88 percent of all

hardwood logs (Ward and Mistretta 2002).

In the early 2000s, prolonged droughts compromised the

vigor of oaks in northern Arkansas, leading to a massive

red oak borer outbreak. Although they were not the primary

cause of the oak mortality in that area, the borers soon

became the most destructive agent in the decline complex.

More than 340,000 acres of oak and mixed-oak-pine forest

were severely impacted, with an estimated loss of 500

million board feet (more than $29 million) of oak.

Temperature change by itself is unlikely to have much

effect on hardwood borer populations. As secondary insect

pests, these borers are expected to have increased impact as

populations of hardwood age and decline, especially during

periods of drought stress. Hardwood borer activity and

damage is likely to increase throughout the South over the

next 50 years if current predictions of future climate change

prove accurate.

Soapberry borer—Soapberry borer, Agrilus prionurus,

a native of Mexico, was first confirmed in eastern Travis

County, Texas, in 2003. It infests and kills western soapberry

(Sapindus saponaria var. drummondii), its only known host.

Reports by landowners and arborists indicate that the insect

had probably been infesting soapberry trees for several years

prior to being identified. Infested trees were observed in Travis

and McLennan counties as early as 1998. By January 2009,

infestations had been reported in 18 Texas counties, including

areas near Fort Worth, Dallas, Waco, College Station, Austin,

Houston, and Corpus Christi. By December 2010, the number

of counties had increased to 43 (Billings 2011).3 To date no

infestations have been observed in adjacent States, although

infestations in Roberts County in the Texas panhandle and

3R. Billings, Texas Forest Service, Forest Health unit, 200 Technology Way,

Suite 1281, College Station, TX 77845-3424, 979-458-6650, rbillings@tfs.

tamu.edu. Personal communication: March 8, 2011.

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chapter 16. Invasive Pests—Insects and Diseases

Wichita County on the Texas-Oklahoma border suggest that

the insect may already be in Oklahoma (fig. 16.3).

As soapberry borer populations expand rapidly in Texas, this

wood-boring beetle is killing all soapberry trees larger than

2 inches d.b.h. Methods of prevention and control are being

investigated. Among the most promising is injection of a

systemic insecticide (emamectin benzoate, registered for the

control of insects on conifers and hardwoods, including the

prevention of emerald ash borer) into uninfested soapberry

trees or those in early stages of attack. Test-injection trees are

still being monitored, but early results look promising.

Regardless of climate change, it is likely that within 50

years the insect will threaten western soapberry populations

throughout the tree’s entire range, which extends from

northern Mexico to Missouri, and west to Arizona.

Diseases of Softwoods

Annosum root disease—Annosum root disease (ARD),

caused by the fungus Heterobasidion annosum (recently

proposed to be renamed H. irregulare (Otrosina and

Garboletto 2010), produces significant losses of conifers

across the South. On sandy, well-drained sites, this disease

causes growth loss and mortality. It is most often associated

with thinning of loblolly, longleaf, shortleaf, slash, and white

pine plantations. The fungus commonly infects fresh stumps

and then grows through root grafts (roots that come into

physical contact and then grow together, sharing water and

nutrients) and infects residual trees on the site. Slash and

loblolly pines are the most commonly planted species in the

South and are both very susceptible to ARD (Robbins 1984,

Stambaugh 1989).

A survey in the South documented: 44 to 60 percent

occurrence of this root disease; and 2 to 3 percent mortality

in planted pine. Radial and height growth are significantly

less for diseased pines (Applegate 1971, Froelich and others

1977, Morris 1970).

The primary risk factors associated with ARD are the

amount of host type available, the soil type and condition,

and the timing and degree of management activity. Risk

decreases as clay content in the surface layer of soil

increases, a condition that enables risk mapping (Ward

and Mistretta 2002). In the South, risk of ARD is high or

moderately high on an estimated 163.5 million acres, not all

currently forested (Hoffard and others 1995).

The range of ARD already extends throughout southern

forests and into the boreal forests of the North, making

spread unlikely. Indeed, its range could decrease with

efforts by many land management agencies to restore

the less susceptible longleaf pine to its previous range

while concurrently potential drought/temperature related

dieback in the southernmost part of the loblolly/slash pine

range further decrease its range. Increased temperatures,

reduced rainfall, and increased host growth (from more

carbon dioxide in the atmosphere) would all produce some

increases in disease activity resulting from increased host

susceptibility, but would not significantly increase fungus

virulence. It is improbable that climate warming/drying

would affect pine susceptibility on well-drained, sandy sites

and forested old farm fields since on these sites potentially

affected pines are already highly susceptible to the disease.

Management for disease prevention using borax as a

stump treatment in uninfected stands should continue to be

effective. Depending on the rate of temperature increase,

insolation (thermal treatment of the stumps by the sun)

may be effective in preventing infection via stumps further

north than the 35th parallel, which is the currently accepted

northern limit of its effectiveness.

Loss of area by host species favored by H. annosum should

lead to a slight overall loss of the negative impact of this

disease over the next 50 years.

Brown spot needle disease—Brown spot needle disease,

caused by the fungus Scirrhia acicola, is considered the

most damaging disease of longleaf pine. It primarily affects

seedlings by delaying the onset of height growth and causing

loss of potential wood production and mortality (if infection

is severe). Brown spot is somewhat a disease of opportunity:

the grasses that compete with longleaf seedlings also

maintain a humid microclimate that contributes significantly

both to infection of the seedling and to the general success of

the disease.

This disease occurs from Virginia to Texas, primarily on the

Coastal Plain. It is more severe in certain geographic areas

(Ward and Mistretta 2002). Use of controlled fires to remove

competing grasses and eliminate dampness is highly effective

for controlling the disease and encouraging early growth

of seedlings, provided steps are taken to avoid subsequent

colonization by competing non-natives such as cogongrass.

At present, longleaf pine occupies only about 5 million

acres of its former 60 million acre range. Recent restoration

efforts have led to the production of healthier seedlings for

planting and planting success has improved on sites where

longleaf was once the dominant species (Cordell and others

1989, Kais 1989). Over the next 50 years, the emphasis on

longleaf pine restoration should have a greater impact on this

disease than climate warming. Longleaf pine is well adapted

to summer temperatures in the South and it is unclear that

increases even as high as 1 oC would have significant impact

on the southern extent of the longleaf pine range. Higher

temperatures might slightly favor increase in growth and
472 The Southern Forest Futures Project

longer summer heat spells might trigger early onset of height

growth from the grass stage to the candle stage, ending

the potential for brown-spot damage sooner. Reductions in

rainfall, dew, and fog should favor the longleaf pine over

the fungal pest. No shift in aggressiveness of infection or

virulence of the pathogen is foreseen.

We anticipate a significant increase in the incidence of brown

spot disease. This expectation is based more on increased

out-planting of longleaf pine seedlings than on climate

influences. Thus, although climate change is not expected to

significantly change the disease profile (its virulence or host

spectrum), human intervention to increase the quantity of

host trees could result in increased incidence.

Fusiform rust—Fusiform rust, caused by the fungus

Cronartium fusiforme f. sp. fusiforme, occurs primarily on

slash and loblolly pines. It is considered the most destructive

disease of southern pines, causing the production of cigarshaped galls that are generally fatal if formed on the main

stem of the host (Anderson and others 1980, Czabator 1971).

Extensive planting of susceptible slash and loblolly pines

since the 1930s has resulted in an epidemic of fusiform rust,

which now extends throughout its available host range in the

South; infected trees being found throughout the southern

pine region (Ward and Mistretta 2002). Losses are most

serious on Coastal Plain sites from Louisiana to southeastern

South Carolina. Several variables including weather, amount

of inoculum, abundance of oaks (the alternate host), and

susceptibility of the individual pine species govern incidence

of the disease. Effective strategies are available for managing

fusiform rust impact in plantations and forests including

avoidance of over-fertilizing seedlings in the nursery,

silvicultural manipulation of young stands to favor healthy

saplings, and favoring the deployment of genetically screened

resistant seedlings in areas of historic high rust incidence.

Increase in disease range in this region under the influence

of a warmer, drier climate change scenario is not a concern

since the disease is already distributed host-range wide

within the region. However, increased temperature and

carbon dioxide in the atmosphere could cause the pathogen

to become more virulent on its current host base. Although

there is some disagreement on the effect of projected warmer,

drier climate regimes on the geographic ranges for the pine

hosts, it is anticipated that any losses of pine in coastal areas

would be matched by gains in the Piedmont and in the lower

reaches of the Appalachian Mountains.

Although research on rust fungi is inconclusive and

primarily based on cereal grains and other field crops, results

suggest that there would be greater incidence of fusiform

rust simply as a function of healthier fungus and host trees

(Chakraborty and others 1998). We also anticipate that

loblolly pine at least will be planted in areas north of its

current range; and that the rust, which infects juvenile tissue,

will rapidly follow into these newly planted areas.

Over the next 50 years given the general availability of oak

alternate hosts for the fungus and the only slight predicted

migration of pine from coastal areas upward into the

Appalachian Mountains, we expect that the pathogen will

successfully fully colonize the extended range of its hosts.

The potential effect of outplanting rust resistant seedlings

in conjunction with potential geographic range and climate

shifts is uncertain at the present time. If the resistance is

maintained in the face of changing conditions, a reduction of

the impact of this disease would be expected to occur.

Littleleaf disease—Littleleaf disease is the most serious

pest of shortleaf pines in the South. It is caused by a complex

of factors including a nonnative fungus (Phytophthora

cinnamomi), low soil nitrogen, eroded soils, poor internal soil

drainage, and a plow pan—a compacted layer of soil that has

become less porous than the soil above or below, generally

the result of tilling or other farming operations (Campbell

and Copeland 1954). Often, native nematodes (microscopic

roundworms) and native species of Pythium (also a fungus)

are associated with the disease. Infected trees have reduced

growth rates and commonly die within 12 years of symptom

onset.

P. cinnamomi is distributed throughout (and well beyond)

the range currently occupied by shortleaf and loblolly

pine in the South. Shortleaf pine is the most seriously

damaged softwood host, with loblolly pine affected to a

lesser extent; American chestnut was its primary hardwood

host. Littleleaf disease has also been reported on Virginia,

pitch, slash, and longleaf pines. Affected pine stands are

found on the Piedmont from Virginia to Mississippi. The

disease has its greatest impact in Alabama, Georgia, and

South Carolina (Ward and Mistretta 2002, fig. 17.10), with

additional scattered pockets occurring in eastern Tennessee

and southeastern Kentucky. Note that, although the fungus’

range exceeds the range of its pine hosts, littleleaf disease is

further restricted in within that larger range generally by site

conditions.

The fungus has a mobile spore and needs water to spread

from and infected host to uninfected potential hosts;

however, the disease thrives under dry conditions that stress

the host. Control strategies are available but most—such

as sanitation thinning and salvaging dead materials—rely

on treatment after infection when damage is imminent or

already occurring.

Because of its specific site requirements, spread into

uninfected southern forests is not expected. Further,

rehabilitating sites by breaking up of the plow pans that
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chapter 16. Invasive Pests—Insects and Diseases

favor this disease should result in better water relations and

a reduction in infections. An increase in atmospheric carbon

dioxide would result in increased growth of the host and

greater disease expression in affected trees. Losses to this

disease should continue at the same rate on affected sites.

However, its range should contract if increased temperatures

cause its hosts to migrate north, and its impact should

decrease over time as sites are rehabilitated.

Loblolly pine decline—Reports of sparse, yellowing

crowns, and low annual wood production in the pines of

central-to-northern Alabama date back to the late 1960s

(Brown and McDowell 1968, Brown and others 1969). Since

the early 1990s, localized incidents of declining pines have

been occurring throughout Alabama and into southwestern

Georgia, with additional symptoms including root mortality

and discoloration of many of the surviving rootlets (Hess

and others 2003). Recent literature suggests the presence of

fungi—including Leptographium serpens, L. terebrantis,

and L. lundbergii–in the roots of affected trees (Eckhardt

and others 2004b); but whether they are primary pathogens

or simply taking advantage of already significantly weakened

trees is still uncertain. A bark beetle, Hylastes sp., has

been found in the root systems of many declining pines,

and is suspected of vectoring the fungus from infected to

uninfected trees (Eckhardt and others 2004a). Information

is lacking on whether they select weakened trees to attack

or are indiscriminate in their attacks (which would suggest

that healthy trees may be able to overcome successful

inoculation).

The symptoms of the decline primarily occur in loblolly

pines older than 40 years, first becoming apparent in

trees in the 40 to 50 year age class. Mortality can occur

beginning as little as two to three years after first symptom

expression. Little is known about the potential range and

severity beyond that from field surveys in central northern

Alabama (Hess and others 2005) and Fort Benning, Georgia

(Menard and others 2006). Nevertheless, there is strong

speculation that both abiotic and biotic factors are involved in

predisposing affected stands to decline. These factors include

climate, wildfire, and human disturbances such as previous

agriculture. Coincidently, many upland sites in northern and

central Alabama were originally converted from subsistence

farming to loblolly pine plantation because of loblolly’s outplanting success rate and its rapid growth. One theory is that

many of these sites are simply unable to sustain such rapid

growth over the long-term.

Despite the uncertainties about the causes and progression

of this disease complex, management strategies are in place

that can be implemented with the expectation of improving

resistance of future stands on affected sites. These strategies

start with applying a risk rating model that uses digital

elevation maps and mapped shape files for the sites in

question combined with data on landform and root health of

the trees in the stand. If the model predicts hazard to loblolly

pine, the recommended alternative species is longleaf pine.

For existing loblolly pine stands on high hazard sites, the

recommendation is to thin them between ages 20 and 40

(Hess and others 2003). A previous recommendation, to

allow a high- risk site to revert back to native hardwoods

(Loomis 1976, Miller 1979), is still a viable (but seldom

adopted) management option.

Tree decline is likely to increase in a warmer and drier

climate, regardless of inputs from disease and insect vectors.

This response to changing climate is a major factor in the

northward movement projected for the southern pines.

Increasing incidence of decline should eventually diminish

as new adapted ecosystems form in the region, but this is not

expected to occur within the next 50 years.

Diseases of Hardwoods

Beech bark disease—Beech bark disease is caused by a

complex of two or more agents working in concert. The

beech scale, Cryptococcus fagisuga, attacks the bark

of American beech, creating infection courts which are

subsequently colonized by the fungus Nectria coccinea var.

faginata. This fungus causes cankers that grow together and

girdle host trees.

While the beech scale is now a common pest of the

American beech, it is nonnative, having been introduced

through the Canadian Province of Nova Scotia in the

late 1800s. There is speculation that the fungus is also an

introduced species. Discussion on that point is somewhat

pointless since a native fungus, N. galligena, is also capable

of inciting cankers and killing hosts after entering through

scale-damaged bark. The scale is considered the pivotal

introduction that allowed the invasive spread of this disease

complex (Houston and O’Brien 1983, Southern Appalachian

Man and the Biosphere 1996).

This disease complex, first identified in southern forests

in the early 90s, continues to spread along a broad front

and is expected to occupy the range of its host (Ward and

Mistretta 2002). In the early phase of its cycle, more than half

of the American beech trees 10 inches d.b.h. or larger are

killed. Openings created by death or removal of the beech

result in dense stands of root-sprouts, which produce stands

dominated by beech but lacking any of its normal associates.

In the second phase of the cycle, revegetated beech stands are

attacked less severely, resulting in cankered survivors rather

than in extensive mortality. Trees infected in this phase are

rarely girdled, but they are generally severely deformed.

Since this disease complex affects only American beech,

there is a direct relationship between the amount of beech
474 The Southern Forest Futures Project

in a stand and the intensity of the disease. Houston (1997)

reports that stand age and density, tree size, and species

composition affect disease severity, especially in forests

affected for the first time.

Beech bark disease is enabled by an insect vector, so the

projection of future condition is complicated beyond that

of a simple pathogen or insect driven pest system. Vector

mediation corresponds to availability of spores and host

susceptibility, and is expected to maintain synchronicity

sufficient to cause a slight increase in infection. Temperature

intolerance of the host should reduce the host’s geographic

range in the face of climate change. Increases in carbon

dioxide should increase host growth allowing a slight

increase in disease virulence.

Ultimately, however, the reduction in available host trees

should result in an overall decrease of significance of beech

bark disease in southern forests despite the probability that

individual trees will experience a slight increase in disease

severity.

Butternut canker—Butternut is being killed throughout

its range in North America by a fungus, Sirococcus

clavigignenti-juglandacearum, which causes multiple

cankers on the main stem and branches of host trees.

Butternut canker has been found in 55 counties in the

South extending north from northern Alabama along the

Appalachian Mountains into North Carolina, Tennessee,

Virginia, and Kentucky, with scattered occurrences

throughout Kentucky and Tennessee (Ward and Mistretta

2002). Butternut numbers have been dramatically reduced

and the species is now listed as a species of Special Concern

in Kentucky and as Threatened in Tennessee (USDA

Natural Resources Conservation Service 2011). In both

states the species is listed as G4/S3. G4 indicates a plant

which is “...apparently secure globally, though it may be

quite rare in parts of its range...” while S3 indicates “...rare

and uncommon in the state...” (USDA Natural Resources

Conservation Service 2008, 2009).

Detailed examination of cankers indicates that butternut

canker has been present in the United States since the early

1960s. Its origin is unknown but its rapid spread throughout

the butternut range, its highly aggressive nature on infected

trees, the scarcity of resistant trees, the lack of genetic

diversity in the fungus, and the age of the oldest cankers

(40 years) support the theory that it is a recent introduction.

Data from forest inventories show a dramatic decrease in

the number of live butternut trees in the United States (77

percent loss in North Carolina and Virginia).

Because butternut makes up less than 0.5 percent of the

trees in the South, the overall current impact of its loss to the

forested ecosystem in the South is considered by some to be

minor. However, as butternut trees die, they are replaced by

other already present species, contributing to a reduction of

biodiversity.

Climate change would likely raise temperatures at the higher

elevations of the Appalachians and the Cumberland Plateau.

This coupled with drier conditions would significantly reduce

the range of butternut at its southern edge. Although the

higher temperatures and predicted increases in atmospheric

carbon dioxide could increase the host trees’ growth, drier

conditions resulting from reduced precipitation would

act against this increase. Overall we expect to see more

cankering and mortality occurring on fewer butternut trees

in the South.

Chestnut blight—Introduction of the chestnut blight

fungus, Cryphonectria parasitica, from Asia, probably in

the middle-to-late 1890s, led to a permanent change in forest

ecosystems. The American chestnut (Castanea dentata) was

essentially lost, not only as a valuable timber species but also

as the most important producer of hard mast for wildlife.

Oaks and other species filled the voids in forest stands left by

the death of chestnut (Hepting 1974, Oak and others 1998).

The fungus continues to survive on infected sprouts from old

chestnut rootstock, various oaks, and some other hardwoods

(Boyce 1961).

No control was found to stop the rapid devastation

caused by this blight, and there is little chance that the

pathogen will disappear or that the American chestnut will

naturally recover its preeminent position in eastern forests.

Researchers into hypovirulence have discovered a disease

that weakens the blight fungus, resulting in less damage

to the infected tree (Anagnostakis 1978). Field-testing is

underway on a genetically engineered virus that causes a

hypovirulent reaction and has the potential to efficiently

spread hypovirulence throughout the fungal population.

Attempts to cross American chestnuts with oriental varieties

and then backcross to the American parent appear to

offer a viable method of maintaining resistant chestnut in

forests (Schlarbaum 1988). Selectively breeding chestnuts

as described has produced chestnut hybrid clones that are

undergoing field evaluation by the American Chestnut

Foundation. If the seedlings overcome both the blight and

another disease (caused by Phytophthora cinnamomi) that

was devastating chestnuts at the time chestnut blight was

introduced, a serious effort can be made to reintroduce

chestnut into the American forests. It is too early yet to

predict the outcome of this effort. However, even if the

hybrids are resistant to the disease, large areas of forest land

cannot be restored to chestnut in the next 50 years because

the seedlings that would be needed for that effort are not

expected to be available in large enough quantities. Further,

if climate change is considered, the impacts on chestnut
475

chapter 16. Invasive Pests—Insects and Diseases

deployed in the restoration effort would probably be similar

to those predicted for oaks suffering from oak decline.

Dogwood anthracnose—Dogwood anthracnose is caused

by an introduced fungus, Discula destructiva. It was first

reported in the United States on flowering dogwood, Cornus

florida, in 1978 and on western flowering dogwood, C.

nuttallii, in 1979. For the past three decades, flowering

dogwoods have been declining at a rate that threatens

important cultural aspects of southern society. In some areas,

they have been all but eliminated from the forest ecosystem

above 3,000 feet (Ward and Mistretta 2002).

The eastern flowering dogwood is a small tree valued

both as a sign of spring for rural communities and forest

visitors, and as an important source of soft mast for over 100

different species of wildlife that feed on its berries (Kasper

2000). It is typically an understory tree found growing with

other hardwoods such as oak and hickory. Severe infection

is restricted to fully shaded understory trees at higher

elevations (above 3,000 feet) and to those on shaded sites

with a northern exposure. The hazard of severe infection and

mortality is greatest in shaded, moist, and cool areas.

The range of this disease stretches southward into

South Carolina and Alabama and westward into central

Tennessee and scattered western Kentucky counties (Ward

and Mistretta 2002) with activity concentrated in the

Appalachian Mountains. The southernmost limit of the

dogwood anthracnose range relative to available host trees

suggests that this disease is temperature limited in the

South. Whether this limitation functions at the time of spore

propagation or dissemination and host infection, or whether

it acts directly to limit disease success is unclear.

Any projected increase in the incidence or virulence of

dogwood anthracnose based on increased host and fungal

growth resulting from higher carbon dioxide levels in

the atmosphere should be eclipsed by the temperature

increases and possible rainfall reductions projected to

occur under climate change. Increased temperature and

aridity encroaching at higher-than-current elevations in the

Appalachian Mountains should diminish the importance

of this disease in the region, especially if it has reached a

temperature barrier farther south. A recolonization of some

areas currently denuded of dogwood by this disease might be

possible.

Dutch elm disease—The Dutch elm disease pathogen is

vectored by one of two bark beetles and can be caused by

either of two closely related species of fungi: Ophiostoma

ulmi (formerly called Ceratocystis ulmi); and Ophiostoma

novo-ulmi, which is more aggressive in causing disease

(Brasier 1991). These fungi were first introduced to the

United States on diseased elm logs from Europe prior to

1930. It is unknown when the more aggressive species

became established; however it was possibly present as

early as the 1940s to 1950s, and most likely caused much of

the devastating elm mortality through the 1970s. The less

aggressive species is becoming increasingly rare in nature,

and the aggressive species is thought to be the primary cause

of current mortality. Although some local resurgence has

been observed, there is no evidence that the pathogen has

further changed. Localized resurgence is more likely the

result of decreased monitoring and sanitation vigilance, a

buildup in populations of the insect vectors, or high densities

of susceptible host trees in the wild (French and others 1980,

Haugen 2007, Hubbes 1999).

Native species of North American elms vary in their

susceptibility to Dutch elm disease. American elm (Ulmus

americana) is generally highly susceptible To the disease

while winged elm (U. alata), September elm (U. serotina),

slippery elm (U. rubra), rock elm (U. thomasii), and cedar

elm (U. crassifolia) range from susceptible to somewhat

resistant. No native elms are immune, but some individuals

or cultivars have a greater resistance or a higher tolerance

to infection (and therefore may recover or at least survive).

Many European and Asiatic elms are less susceptible than

American elm (Haugen 2007).

In addition to genetic factors present in some cultivars and

species, physical factors affect tree susceptibility. These

factors include season of the year, climatic conditions (such

as drought), and vitality of the tree. Water conducting

elements are most susceptible to infection because they

are produced in the spring, making susceptibility highest

from first leafing to midsummer and lowest during drought

conditions. Vigorously growing trees are generally more

susceptible than slower growing trees (D’Arcy 2005).

Roots of the same or closely related tree species growing

in close proximity often cross each other in the soil and

eventually fuse (become grafted). The fungus can move

from infected trees to adjacent trees through these grafted

roots. Infections that occur through root grafts can spread

very rapidly throughout the tree, because the fungus is

carried upward in the sap. Root graft spread is a significant

cause of tree death in urban areas where elms are closely

spaced (French and others 1980, Haugen 2007).

Current management options in urban, suburban, and

other high value settings include sanitizing to reduce

insect vectors, applying insecticides to kill insect vectors,

disrupting root grafts; injecting trees with fungicide,

eradicating the fungus from newly infected trees (pruning),

and planting resistant or tolerant trees (French and others

1980, Haugen and Stennes 1999, Newhouse and others 2007,

Scheffer and others 2008).
476 The Southern Forest Futures Project

Although the most effective action is prompt removal of

stressed, dead, and dying elms, this intensity of treatment is

often not feasible (Haugen 2007).

Despite the presence of several elm species (American elm,

winged elm, and slippery elm, at least) very little Dutch

elm disease can be found in areas below northern North

Carolina, Tennessee, and Arkansas. It appears that either

the beetles or the fungi involved in transmitting/causing the

disease are temperature limited. Barring significant changes

in its pathogen/vector combination, increasing temperature

and migration of the host slightly to the north is expected to

diminish the disease’s overall impact in the South.

Laurel wilt—Laurel wilt is an insect-vectored disease

that is currently decimating the redbay (Persea borbonia)

population of the southern Coastal Plain. This disease was

first identified near Port Wentworth, Georgia, in 2003 and

has subsequently spread north, south, and inland (west) from

that location (fig. 16.4). It is caused by an introduced and only

recently classified fungus, Raffaelea lauricola, (Harrington

and others 2008) that is vectored from host to host by an

ambrosia beetle (Xyleborus glabratus, also an introduced

species). The beetle carries the fungus in pouches located

near its mandibles. When the beetle bores into the sapwood

the fungus inoculates the xylem. Once inoculated, the host

rapidly develops a vascular wilt; its leaves die generally

downward from the top, and the wood beneath the bark

becomes discolored from streaking (Fraedrich and others

2008). Infected hosts display rapid dieback (wilted leaves and

discolored sapwood) and may or may not exhibit extrusion of

frass (the fine powdery sawdust and excrement that insects

pass as waste after digesting plant material) from the insect’s

entry holes.

Several additional hosts have been identified for this

vectored disease including swampbay (Persea palustris),

sassafras (Sassafras albidum), avocado (Persea americana),

camphor (Cinnamomum camphorate), pondberry (Lindera

melissifolia), and pondspice (Litsea aestivalis). Redbay,

however, is the favored host for the ambrosia beetle and to

the present the severest damage has been limited to redbay

(Hanula and others 2008).

At the present time there is no effective control known for

this disease for forest and woodland use. While preliminary

results using propiconazole (a fungicide) show promise

for preventing the disease in treated trees, the necessity

of retreating them and the cost of treatment suggests that

in the future use may be limited to the protection only of

high value trees (Mayfield 2008). Research into chemical

treatment, centered on control of the vector, is ongoing but

has yet to identify a chemical effective for this purpose.

Management recommendations emphasize early sanitation

(removal) of killed material but with the strong concurrent

recommendation that the dead materials not be moved

offsite, or if moved offsite then not out of the known infested/

infected area. Further, it is recommended that whenever

possible material that has been cut down should be chipped

or buried rather than left intact (Mayfield 2008).

Based on the current rate of spread (estimated to be about

20 miles per year), the known distribution of redbay, and

regional climate projections, Koch and Smith (2008b)

have extrapolated probable spread of this disease through

2040 (fig. 16.5). According to their projection, the disease

complex will have reached its northern extent (host based)

by 2020, and will reach the western extent of its host range

in eastern Texas by 2040. The basis of their projections is the

combination of redbay’s natural range and climatic barriers

that affect the vector and fungus, which will likely stall

further progress of the disease in the South. Their caveat is

that projections are limited to the redbay host.

Unanswered at this point in time is whether this fungus/

vector complex could become established in other parts of

the country on other lauraceous hosts (such as the California

laurel) should fungus-carrying beetles be introduced into

potential new host ranges. Further, potential for affecting the

spread of and possibly controlling some of the loss through

implementation of the Recovery Plan for Laurel Wilt on

Redbay and Other Forest Species (Mayfield and others 2009)

is as yet an unknown factor in the management of this disease.

Unfortunately, in 2009, laurel wilt was detected in the

Sand Hill Crane National Wildlife Reserve in southern

Mississippi—a location that was not predicted by Koch and

Smith (2008b) for infection until about 2017—apparently

through human introduction. Regardless whether this is a

new introduction or movement from the east coast infected

area, it has reduced by 8 years the disease’s expected arrival

in Texas.

Of concern is whether the disease might expand its host

range under the influence of climate change or through a

modification of the fungus/vector complex that would allow a

new insect vector to become involved. If either occurs, there

is strong potential for currently unpredicted involvement of

new hosts and unpredicted spread; newness of this complex

in the South leads to extreme uncertainty when attempting to

project future behavior.

Given the rapid and severe damage done to the infected

hosts coupled with predicted shifts in coastal vegetation

resulting from projected temperature increases and possibly

decreasing precipitation, the potential of this disease to

spread beyond its projected range is highly uncertain.

Oak decline—Because of the history of woods grazing,

widespread wildfire, and exploitive logging for wood


479

chapter 16. Invasive Pests—Insects and Diseases

oak decline episodes continue to occur in the region (primarily

in Arkansas and Virginia) where predisposing conditions,

inciting events, and contributing factors are coincident (Gysel

1957, Oak and others 1988, Starkey and others 2000).

With increased temperature and (possibly) less rainfall

being predicted, oak decline is expected to increase, possibly

significantly. Decline resulting from the stresses imposed

should be contributory to elimination of oak in some drier

areas, and it is uncertain what community of plants would

replace the oak on these sites.

Oak wilt—Oak wilt is a vascular wilt disease of oaks

that is found only in North America. The causal fungus,

Ceratocystis fagacearum, was first identified in Wisconsin

in 1942. Scientists believed the disease to be native to North

America and to have been present long before its discovery

(MacDonald 1995, Tainter and Baker 1996). Recently, strong

speculation has been voiced that the fungus is actually a

nonnative introduction, possibly from South America where

it occurs without causing disease (Juzwick and others 2008).

Oak wilt occurs in 21 Central and Eastern States (Rexrode

and Brown 1983); 9 of the 13 Southern States are known to

harbor the disease, but severe mortality is limited to a recent

outbreak in central Texas (Ward and Mistretta 2002).

Oak wilt causes affected trees to wilt and usually to die. All

species of oak are susceptible, but species in the red oak

group—northern red (Quercus rubra), scarlet (Q. coccinea),

and black (Q. velutina) oak—are most readily killed. Oaks

in the white oak group—white (Q. alba), post (Q. stellata),

and chestnut (Q. prinus) oaks—are infected but mortality

occurs much less frequently and more slowly. Live oaks (Q.

virginiana) die at a rate generally intermediate between red

and white oaks.

Sap-feeding beetles can carry fungal spores to nearby

healthy trees, the fungus can colonize neighboring

uninfected trees by growing through root grafts, and human

mediated transmission is also possible (moving infected

firewood with intact bark allows fruiting of the fungus in

areas currently not infected).

It is unclear whether the north-to-south progress of the

disease was halted by a temperature barrier that limits

migration of the fungus. The existence of such a barrier

could mean that the Texas outbreak is the result of a relatively

recent adaptation of the fungus to a higher temperature

regime or an adaptation to the hosts (live oak) attacked in

that area. Regardless of what caused the recent surge in oak

wilt activity in Texas, its rapid spread raises the practical

question of whether the fungus can now spread throughout

the uninfected areas from Louisiana to Georgia and Florida.

We anticipate that this question may be answered within the

next 10 to 20 years as the disease appears to be spreading (or

being spread by humans) at a fairly rapid rate.

Increasing soil temperature might provide a further

barrier to spread, if indeed temperature has been a

barrier. Predicting the direct effects of temperature and

atmospheric carbon dioxide on this disease will require an

understanding of the pathogen-host mechanisms at play:

whether damage to the root system is sufficient to cause

symptoms and death, or whether the fungus must grow

from the root system (where most of the transmission

is occurring) into and throughout the vascular system

aboveground to cause the same effect.

Little can be said with any degree of certainty about

possible insect transmission of this disease. Consistent but

inefficient transmission by sap-feeding beetles (Nitidulids

and Scolytids) is an accepted mode of spread. Shothole

borers have also been suggested, but these, and other possible

insects, are less accepted. Longer periods of activity of these

insects, resulting from the lengthening of summers (already

being observed), could greatly increase transmission.

However, this increase could only occur if fruiting mats of

the fungus (which, in Texas, is associated with cooler and

moister fall, winter and spring conditions; not the anticipated

conditions) were present during the time in which the

insects are active. Unless increased temperature triggers

more mat formation than has been historically reported in

Central Texas (unlikely), it is not expected that additional

insects would become significant carriers of the fungus

to uninfected trees. Possible loss of some coastal forest to

savanna should have only a slight impact: simply reducing

the number of hosts lessens disease incidence.

Management of this disease has proven to be expensive

and is generally reserved for high value (aesthetically

desirable) trees. Given the apparent adaptation of the fungus

to warmer temperatures and relatively dry conditions, and

the limitations of control tactics available, there is a high

probability of significant oak loss in previously unaffected

areas along the Gulf of Mexico and in Georgia within 50

years. However, if the apparent adaptation to warmer and

drier conditions proves inadequate for continued disease

spread, we would expect an overall slight lessening of the

impact of oak wilt in the South.

Sudden oak death—First reported in California in 1995,

sudden oak death (SOD) is now a well-established pest with

a fairly limited range in California and Oregon. However,

despite this relatively limited current range, it is believed that

if introduced into the eastern oak forest the consequences

could be dire.

Literature relating to this disease is extensive, but has

recently been reviewed (Kliejunas 2010) and much of what
480 The Southern Forest Futures Project

follows has been extracted from or cross checked with

that review to limit the number of citations included here.

This publication, which includes a 58 page bibliography of

relevant literature, is available on the internet at http://www.

fs.fed.us/psw/publications/documents/psw_gtr234/.

Sudden oak death is caused by Phytophthora ramorum,

a fungus, which causes several nonspecific symptoms

depending on the host and host part affected. Symptoms

include stem or bole cankers, twig blight (dieback), and

leaf blight. Individual plant species can display more than

one or only one symptom type (see http://rapra.csl.gov.uk/

background/hosts.cfm for links to images of symptoms on a

variety of hosts).

Cankers appear in the phloem (tissues that carry sugars away

from the leaves of a tree) which may be discolored a bright

red, and spread until they reach the xylem (tissues that carry

water and minerals up from the root; wood fiber). Cankers are

sunken, “bleed” sap, and are generally restricted to the lower

portion of the tree trunk. The amount of bleeding is variable

even on a single tree and may be related to environmentally

available water and the age of the canker. Decline symptoms

(loss of leaves) and crown death first appear at the top of

the tree and spread rapidly down through the crown often

resulting in tree death (Garbelotto and others 2001).

The list of hosts currently reported for this pest is extensive.

As of 2010 the list includes 45 proven regulated hosts plus

another 82 associated hosts regulated in the nursery trade

(USDA Animal and Plant Health Inspection Service 2010a).

Hosts with stem or branch cankering include California tanoak

(Lithocarpus densiflora), coast live oak (Quercus agrifolia),

California black oak (Quercus kelloggii), and Shreve’s

oak (Quercus parvula var. shrevei). In addition, field and

greenhouse inoculation experiments (Rizzo and others 2002)

confirm that the fungus can cause a variety of leaf and branch

symptoms, but generally not stem cankering, on rhododendron

and azalea (Rhododendron spp.), madrone (Arbutus menziesii),

huckleberry (Vacinium ovatum), manzanita (Arctostaphylos

sp.), California bay laurel (Umbellularia californica), buckeye

(Aesculus californica), bigleaf maple (Acer macrophyllum),

toyon (Heteromeles arbutifolia), California coffeeberry

(Rhamnus californica), honeysuckle (Lonicera hispidula), and

a long list of other plants.

Although few of these species occur in eastern forests,

several of them can be found in significant numbers. Early

results by Rizzo and others (2002) show that northern red

oak and pin oak (Q. palustirs) are susceptible to infection.

In California greenhouse tests, seedlings of both eastern

oak species developed lesions almost twice as long as those

formed on the oak seedlings from Pacific coastal areas and

roughly equal to those formed on tanoak (considered the

most susceptible species in California). These results suggest

that, all conditions being equal, these species should be

highly susceptible to sudden oak death.

Kliejunas (2003) rated the risk posed by this disease as very

high, but cautions that the degree of uncertainty related to

future disease risk is also high based on lack of knowledge

about the host range. Noting the absence of control measures,

his risk assessment predicts rapid spread by wind, water,

and human transport of infected plants; and suggests the

potential for severe economic and ecologic losses, reductions

in biodiversity, and indirect impacts on sensitive or critical

habitat for at-risk plant and animal communities.

Based on past history with invasive species, it is easy to

project that it is not a matter of “if,” but “when,” sudden

oak death will gain a foothold in eastern oak forests (see

alternative hypothesis below as “Note”). If the disease

reaches southern forests, the role that climate would

play is far from certain. Also uncertain, lacking basic

epidemiological research, is the potential effects on eastern

species; these could range from insignificant to potentially

catastrophic (rivaling the effects of chestnut blight).

Sudden oak death appears to have the potential to devastate

the eastern oak population, even absent climate change

considerations (Kliejunas 2010, chapter 4). Increased

temperatures and atmospheric carbon dioxide could be

expected to increase growth of both the pathogen and

its host, at least in the short term. That effect would be

somewhat counteracted by reductions in precipitation

and increased ozone in conjunction with the warmer

temperatures. Nevertheless, once acclimated to the eastern

forest, the disease would probably spread even faster than it

has in California.

Using the distribution of known or likely hosts, climate

conditions adequate for the survival and propagation of

the pathogen, and probable pathways of introduction of the

disease outside of its current range Koch and Smith (2008a;

fig. 16.6) project a potential range for this disease. Very

similar potential range is indicated by DEFRA, Fowler and

others, and Margary and others. Kelly and others and Venette

and Cohen propose somewhat different potential ranges but

both include significant Southern forest areas (Kliejunas

2010, chapter 4).

Climate-induced losses of native oaks at their southern

margins (Iverson and others 1999) would reduce the potential

incidence of disease, but only slightly, and would not slow the

progress of the disease in other parts of its potential range.

Sturrock and others (2011) state that, based on CLIMEX

projections, changing climate will decrease substantially the

area in the Eastern United States favorable or very favorable

for P. ramorum.

482 The Southern Forest Futures Project

counties (with suspect trees occurring in similar settings

in an additional 10 counties); as yet no woodland or forest

surveys have been conducted. Although the Tennessee

infections were the first reported east of the Great Plains,

they may have been occurring since the 1990s. The full

extent of this infection is as yet to be determined.

Symptoms of the disease include a foliage wilt in which

the leaves progress rapidly from green through yellow and

then to brown. Wilting progresses from the top of the crown

downward as branches die. In the West, the dieback and

ultimate death of infected trees takes about three years.

Symptoms at first (and certainly when observed at a distance)

resemble those caused by drought. Closer inspection of

dead branches reveals many beetle entry holes through the

bark and many (often small) cankers just under the bark. As

cankers increase in number and each grows bigger until the

areas of dead tissue coalesce and girdle the branch. In the

later stages of dieback the beetles may attack the bole of the

tree accelerating its death (Seybold and others 2010).

Control measures for thousand cankers disease have been

proposed but not yet evaluated. Because the current range of

the fungal pest is generally hotter and drier than Tennessee’s

climate, the disease is highly unlikely to encounter

temperature barriers that would limit its spread into southern

forests. And predicted a warming climate is likely to have

little effect; the pathogen and vector originated in a hot dry

area of the Southwest but both have moved into the cooler,

moister climate of central Tennessee. Finding no barriers

to spread, thousand cankers disease could occupy the

entire range of black walnut within 50 years, similar to the

projected spread of laurel wilt.

Additional Concerns

Although we can make relatively uncertain predictions about

the future of known pests, predicting currently endemic

organisms that may become pests or organisms from other

locations that may invade the South is virtually impossible.

Lovett and others (2006) predict that forest pests will be the

primary source of changes in eastern forests but cautioned

against speculation on specific changes or specific pest

introductions.

An important consideration is human caused change in the

forest land base driven by increasing and shifting populations

and economic conditions. As shown in chapter 5, all of the

Cornerstone Futures forecast that total forest acreage will

decline over the next 50 years, only planted pine is expected

to expand, the oak-hickory type is expected to remain

relatively stable, but the three other forest types considered

are expected to decline. Additionally, total forest biomass is

expected to increase at first but then decline somewhat.

A generalized implication of these potential shifts is

relatively straightforward. Because pest activity appears to

be basically a linear response to availability, less biomass

would indicate less (in absolute terms) loss of biomass to

pests. However, planted softwoods would be expected to

show an increase in absolute loss proportional to the increase

in planted acreage.

The possible effects of fragmentation, parcelization, and

urbanization on pest impacts and management are so

complex (and largely unknown) that it is not prudent or

feasible to attempt to identify specific interactions. Generally,

parcelization (greater number of landowners on smaller units

of land) may complicate pest prevention and/or suppression

by making it more difficult to attain effective management on

significant acreages due to the greater number of landowners

involved. On the other hand, fragmentation and urbanization

would interrupt or decrease the amount and continuity of

host species, thereby potentially decreasing the spread and

impacts of pests.

We expect continuing introduction (through international and

domestic commerce and tourism) of nonnative insects and

diseases which could become pests of forest trees, despite

imposition of inspections and quarantines. Which organisms

might be introduced, and then which of these might become

pest species is the source of significant speculation, but is

relatively unpredictable.	

Discussion and Conclusions

Future Considerations for Pest-Host Relationships

Planned adaptation (Spittlehouse and Stewart 2003) should

reduce vulnerability for commercial tree species at selected

sites. However, many forest species will have to adapt

autonomously and society will have to adjust to the result

(Winnett 1998). Forest pest distribution changes caused

by climate change are likely closely tied to shifts in host

distribution (Sturrock 2007).

Some ecosystems are expected to be new: new communities

of tree and plant species with different suites of insects

and pathogens. If forests do remain on a particular site,

similar functional types of insects and pathogens are likely

to remain, although they may be include different species

than at present (Beukema and others 2007). Pathogens

expanding their ranges and contacting ‘new’ hosts and

vectors may mean that new pathosystems probably will

emerge. Interactions between pathogens may change

(Sturrock 2007).
483

chapter 16. Invasive Pests—Insects and Diseases

Climate change may amplify the impact and aggressiveness

of pathogens or alter the balance between pathogens and

their natural enemies; it may also change the status of weak/

opportunistic pathogens such that they are able to infect and

damage stressed tree hosts (Sturrock 2007).

Tree decline is likely to increase in a warmer and drier

climate, regardless of inputs from diseases and insects. The

effect of warmer and drier climate is to stress trees used

to a cooler and moister regime. This stress alone should

cause an increase in the incidence of declining trees, but

compounded by the presence of opportunistic insects and

pathogens, there is a strong possibility that this increase in

declining trees could be significant. Increasing incidence

of decline should eventually diminish as new adapted

ecosystems form in the region, but this is not expected to

occur within the next 50 years.

Almost every study and review of climate change effects

on forests has a common caveat—the complexity of the

ecosystems and pest systems, about which relatively little

is known (Sturrock 2007). The difficulty in predicting

the future of plant disease is highlighted by Woods and

others (2005), who report on an endemic needle blight

fungus (Mycosphaerella pini) that previously had only

minimal impact on native forest trees in British Columbia.

However, recently, in apparent response to a local increase

in summer precipitation, this disease has been causing

extensive mortality of lodgepole pines. While admitting

that establishing causality of the increased virulence of this

endemic pathogen is fraught with risk of misinterpretation

of the evidence, they indicate the link to precipitation

(while dismissing warmer temperatures) appears to be far

greater than “circumstantial.” No prior indication of this

shift to virulence appears in the literature—the event was

unprecedented, unpredicted, and possibly unpredictable.

In partial confirmation, Sturrock (2007) notes that wetter

springs in some regions may result in increased foliage

diseases without venturing to predict subsequent possible

host/pest scenarios.

Endemic root rot fungi (Inonotus schweinitzeii, I. tomentosus,

or Ganoderma spp.), which currently cause limited damage,

or insects such as engraver beetles or species of wood borers

could become important management concerns or could fade

into obscurity from a management standpoint. The fungi that

cause littleleaf disease, sudden oak death (Brasier and Scott

1994), and other infections are predicted to increase their

activity in temperate zones in the Northern and Southern

Hemispheres as they migrate away from the tropics. Under

changing climatic conditions these fungi are expected to

cause more damage to existing urban and forest tree hosts

in the South and to expand the number of species they can

infect. Expected to be especially prevalent and damaging

are those, like the littleleaf disease fungus, that can grow in

temperatures higher than 28 oC (Broadmeadow 2005).

Increased drought stress on hosts may mean increased

mortality from root pathogens. Pathogenic Armillaria spp.

fungi may be assisted by the impairment of host tolerance

caused by climate change-induced stress: this may enable

less pathogenic fungi to become more successful on

stressed trees (Sturrock 2007). Incidence of oak and beech

decline, highly complex disorders, is likely to increase if the

predicted frequency and severity of summer drought stress

prove accurate (Broadmeadow 2005).

A changing climate with increased temperatures, increased

evapotranspiration, and extreme weather events would

increase the frequency and severity of stress factors, which

may lead to more frequent forest declines (Sturrock 2007).

Pathogen evolution could be accelerated by mutation

resulting from increased sunlight or increased reproduction

rates (shorter life cycles under higher temperatures) that

could lead to host resistance being overcome more rapidly

(Coakley and Scherm 1996).

Based on these occurrences and trends, the following

basic patterns have emerged on which we have built our

projections of future impacts of pests:

•	 The current emphasis on longleaf pine restoration, coupled

with increasing temperature and decreasing rainfall should

result in a measurable shift in the population distribution of

southern yellow pine types, both spatially and numerically.

•	 Boreal forest species are expected to have reduced ranges

in the South due to the combined effects of increased

temperature and decreased available water.

•	 Pests associated with southern host species are expected to

migrate with their hosts with few exceptions. The exceptions

are those pests that already occur throughout the South and

extend into the northern part of the United States.

•	Although long-term projections suggest that coastal

savannah will replace forests in many coastal and coastalplain locations, the progress of this change within the next

50 years is not expected to be severe.

•	Most root rotting diseases are expected to respond

aggressively to the combination of warmer soil temperature

and reduced precipitation. This combination of heat and

drought is expected to result in an increase in dieback and

decline among many tree species, often providing further

stress that could act as a precursor to successful invasion/

colonization by root rotting fungi. Newly stressed trees

also may become the focus of insect attack.

•	 Trees suffering long-term stress may prove to be more

resistant to secondary pest attack because of lower

physiological activity and reduced availability of resources

needed by pest organisms.
484 The Southern Forest Futures Project

•	 Tree diseases which affect primarily stem and branch

tissue are subject directly to the potential effects of

warmer temperatures and a drier environment. At first,

warmer temperatures and increased carbon dioxide in the

atmosphere are expected to have a stimulatory effect on

both host and pathogen. However, the anticipated lower

availability of water should generally function more

against the host plant than the fungi infecting it, favoring

an increase in disease. This assumes that the temperature

increase does not exceed the thermal death point of the

fungus or its spores.

•	 Foliage attacking fungi are subject to significant pressure

from light and the microclimate in the host’s leaves.

Although significant loss of spore viability is common

on the upper surface of leaves, any change in the amount

of sunlight will normally alter the survival rate; more

sunlight results in lower spore survival and less successful

infection and vice versa. The microclimate of the underside

of leaves is also critical to the success of foliar pathogens.

Lower atmospheric moisture resulting from less rainfall,

fog, and dew (with a secondary effect of reduced secretion

of liquids) is expected to reduce the effectiveness of

colonization by leaf-infecting fungi.

•	 Longer and warmer summertime temperatures are expected

to increase pathogen and insect activity. Insect populations

may show simple increases in number due to the availability

of additional host material on which to browse, or may be

able to produce an additional generation each year.

Managing Pests Under Changing Conditions

Many land-management decisions made today are based on

the assumption that the climate will remain relatively stable

throughout a forest’s life—an assumption that may have

worked well in the past but is being challenged by climate

change. Even without a clear view of the future climate

and forest, it is possible to develop adaptive strategies now.

Adaptation in forest management requires a planned response

well in advance of the impacts of climate change (Spittlehouse

and Stewart 2003). This is especially important when the

rotation periods are long (Lemmen and Warren 2004).

Changes in climate, especially if they lead to greater

variability among and within regions, tend to add extra

uncertainty to decision making (Garrett and others 2006).

Burton and others (2002) appear to contest the conclusion

of Spittlehouse and Stewart (2003) cited above with their

conclusion that development of adaptation measures for

some time in the future, under an uncertain climate, in an

unknown socioeconomic context is bound to be highly

speculative. Not so; reconciling the apparent contradiction

here is the necessity that best professional judgment rather

than proven science be brought to bear on planning for an

uncertain, but generally predicted future.

Adaptive strategies include resilience options and response

options. Mitigation options include options to sequester

carbon and reduce overall greenhouse gas emissions (Millar

and others 2007). Coping strategies for one disturbance

type are often appropriate management responses to other

disturbance types. Before disturbance occurs forests can

be managed to reduce vulnerability or to enhance recovery.

Trees can be planted that are less susceptible to disturbance.

Species that promote disturbance can be removed (Dale and

others 2001). Millar and others (2007) propose the following

generalized strategies:

•	Improve resistance in hosts: From high-value plantations

near to harvest to high-priority endangered species with

limited available habitat, maintaining the status quo for a

short time may be the only or the best option. Resistance

practices seek to improve forest defenses against direct and

indirect effects of rapid environmental changes by reducing

the undesirable or extreme effects of fires, insects, and

diseases. Because they may require intensive intervention,

these options are best applied only in the short-term.

•	Promote resilience to change: Resilient forests are those

that not only accommodate gradual changes related to

climate but also tend to return toward a prior condition

after disturbance, either naturally or with management

assistance. Promoting resilience is the most commonly

suggested adaptive option discussed in a climate change

context. This process may also become intensive as

changes in climate accumulate over time.

•	Enable forests to respond to change: These adaptation

options intentionally accommodate change rather than

resist it. Treatments implemented would mimic, assist,

or enable ongoing natural adaptive processes such as

species dispersal and migration, population mortality and

colonization, community composition and dominance within

communities, and disturbance regimes. Some potential

practices include: (1) Increase redundancy and buffers,

manage for asynchrony, realign significantly disrupted

conditions, and use establishment phase to reset succession;

(2) Establish “neo-native” forests, experiment with

refugia, and promote connected landscapes; (3) Develop

indicators as a prerequisite for any kind of decisionmaking

and surveillance networks to assess spatial and temporal

evolution of diseases and improve epidemiological models;

(4) Take an anticipatory and preventive approach based

on risk analysis when addressing disease management in

forest ecosystems (even more so than for crops), avoid total

reliance on one or two control strategies (as Hain [2006]

recommended when discussing the unsatisfactory results

of balsam woolly adelgid control efforts), and anticipate

surprises and threshold effects.

•	 Disease management options could be altered (Coakley and

others 1999) or imposed. For example, although it is known

that movement of firewood, nursery stock, and even family

trailers and boats is responsible for the transport of many
485

chapter 16. Invasive Pests—Insects and Diseases

species, there is no cohesive strategy for addressing this

problem (Moser and others 2009). Other actions proposed

for managing insects and diseases include:

•	Avoid dissemination of pests into climatically favorable

zones where they could find naïve host populations by

practicing strict hygiene measures, based on the most

probable dissemination pathways of organisms (in seeds,

wood, and plants).

•	Reduce vulnerability to future disturbance by managing

tree density, species composition, forest structure, and

location and timing of activities (Dale and others 2001).

•	Increase light, water, and nutrient availability to the

uninfected/uninfested trees and decrease susceptibility

to pest attack by practicing precommercial thinning,

sanitation removal, or selective removal of suppressed,

damaged, or poor quality individuals (Gottshalk 1995,

Papadopol 2000, Wargo and Harrington 1991).

•	 Underplant with other species or genotypes in forests

where the current composition is unacceptable as a source

of regeneration (Spittlehouse and Stewart 2003).

•	 Shorten rotations to reduce the period of stand

vulnerability to insect or disease attack, and replant to

speed the establishment of better-adapted forest types

(Gottshalk 1995; Parker and others 2000).

•	Use pesticides in situations where silvicultural or other

means of pest management are ineffective (Parker

and others 2000); however, because morphological or

physiological changes in the host resulting from increased

carbon dioxide uptake could affect uptake, translocation,

and metabolism of systemic fungicides (Coakley and

others 1999), incorporate integrated pest management

practices.

•	 Expand and improve existing monitoring efforts to include

an expected increase in the number of new, introduced

plant diseases (Sturrock 2007).

•	Assist in the migration of forests, by introducing carefully

selected tree species (including using biotechnology

techniques in some situations) in regions beyond their

current ranges, being mindful of the potential for

unforeseen consequences.

With respect to nonnative invasive species management,

Moser and others (2009) recommend five priorities:

(1) promoting education and awareness, (2) expanding

early detection and active management and intensifying

enforcement of quarantines, (3) building the capacity to

increase understanding of and treatments for NNIS control,

(4) strengthening the basic forest health curriculum, and,

(5) encouraging cross agency collaboration and investment.

Although the process of planning and acting to prepare for a

future most probably affected by climate change is fraught

with uncertainty, not planning and acting will likely result in

greater economic and social disruption. Success can only be

achieved if those in environmentally sensitive management

roles are well informed and exercise their best judgment.

The single consistent theme throughout the literature

on pest impacts and climate change is that minimizing

ecological change (and disruption) requires maximum

possible biodiversity, either through a system of protected

refugia or by direct adaptive management for specific

characteristics.

Differing perceptions of risk and adaptation may lead

to increased tension among various groups. Conflicting

priorities and mandates could also lead to future problems

(Lemmen and Warren 2004). In these situations, care must

be taken to adopt a decisionmaking process that identifies

and evaluates all issues and employs the best ecological

science.

Knowledge and Inf ormation Gaps

As should be clear from the above discussion of current

knowledge and from our projections of the future activity

of known pests, huge uncertainty dominates the subject of

pest management and climate change, with significant gaps

existing in baseline knowledge making any generalized

quantitative modeling of future conditions impossible.

Although some specific pest behaviors have been projected,

most of them are qualitative. Lacking generalized and

often specific baseline data leaves modeling (quantitative

projection) a desired tool whose time has yet to come.

Currently unavailable data that would contribute to a

generalized projection of potential future pest activity in

forests (Beukema and others 2007; Chakraborty and Datta

2003; Hain 2006; Lemmen and Warren 2004; Logan and

others 2003; Mamlstrom and Raffa 2000; Rogers and others

1994; Scherm 2004; and, Seem 2004) include:

Information on host biology and response to pests: the role

of changing secondary metabolites (primarily phenols or

phenol-like) under changing environmental conditions;

the functional components of respiration (construction,

maintenance, and ion uptake) as well as carbon costs due to

root exudation; the role of water in tree health; the genotypic

variability and plasticity of hosts; water balance threshold

as it affects direct mortality of host plants, the effects of

climate change on host defensive mechanisms (physiological,

morphological, or other); the impact of climate change

on biodiversity and the role of biodiversity in ecosystem

functions and pest management/prevention; and, projections
486 The Southern Forest Futures Project

of host migration and availability under the influence of

climate change.

Information on forest pests: current distributions and ranges

of pests; influence of mycorrhizae on plant health under

climate change; direct and indirect effects of carbon dioxide,

ozone, and UV-B on roots and root-surface microfloras under

natural conditions; knowledge of insects and pathogens

from outside the area such as Mexican bark beetles and

various Asian insects; mechanisms by which changes in

carbon dioxide and precipitation alter pest survival, growth,

susceptibility and interactions

Information to add clarity and specificity on pest/

host interactions: dispersal structure and distance and

interconnectedness of temperature, phenology and pest

population growth rate; phenological relationships among

trees and pests; role of climate on insects and pathogens

in relation to available water; baseline data on pests of

natural populations that identify the separate of multiple

climate variables and problems they cause (including

forecasts of epiphytotics or epizootics, and evaluations the

role of evolution); pest/predator interactions and responses,

relationships among climate, pests, and their parasites;

minimum and maximum temperature preferences of pests

and pest/host interactions and response to temperature

extremes; protocol for identifying the “drivers” that

transform new insects and diseases into pests; disturbance

regimes and their interactive impacts; and, synergies among

fire, insects, and pathogens.

Models and modeling protocols needed: models that

incorporate local meteorological data; improved spatiallyexplicit climate predictions at finer scales (average daily

patterns and projected variations from the average); effects

of down-scaling or up-scaling data from various models

and appropriate linking tools for increasing the accuracy of

these predictive processes to be more accurate predictors;

functional group rather than single-species models; and,

predictive models that incorporate data on disturbances and

disturbance impacts.

Management information needed: a new protocol for

addressing the research needs of invasive forest pests that

involves all stakeholders in a coordinated partnership; and

management action plans developed in the face of no-analog

vegetation systems and climate change.

Acknowledgments

Our appreciation is extended to Christopher Asaro, Virginia

Department of Forestry; Matthew P. Ayres, Biological

Sciences Department, Dartmouth College; Fred P. Hain,

Department of Entomology and Frank Koch, Department

of Forestry and Environmental Resources, North Carolina

State University; Edward L. Barnard, Florida Division of

Forestry; Ronald F. Billings and Donald M. Grosman, Texas

Forest Service; Scott Cameron and James Johnson, Georgia

Forestry Commission; Stephen R. Clarke, John A. Ghent,

William E. Jones, Bruce D. Moltzan, Derek Puckett, James

D. Smith, Dale A. Starkey, and Borys M. Tkacz, Forest

Health Protection, Susan J. Frankel, Pacific Southwest

Research Station, and Kier Klepzig, Albert E. ‘Bud’

Mayfield, III, Daniel Miller, William J. Otrosina, and A.

Daniel Wilson, Southern Research Station, U.S. Department

of Agriculture, Forest Service.

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chapter 16. Invasive Pests—Insects and Diseases

and all vegetation. New plant communities will organize

themselves and will replace plants that are unable to adapt

to new climates. New communities could include current

tree species, other tree species (e.g., hardwoods or strongly

dispersing species from warmer areas) or could become

dominated by grass and shrub species.	

MODELING CLIM ATE CHANGE

Available Models

Major efforts are underway to create and use models that can

project potential scenarios describing both the impacts of

climate change on ecological conditions and the subsequent

responses resulting from and possibly then influencing

those conditions. Modeling can contribute to our projections

of future conditions “...but requires sound knowledge

of the causal factors determining spatial distribution,

survival, reproduction, dispersal, and infliction of damage”

(Goudriaan and Zadocks 1995).

Selection of broad-scale model types (such as general

circulation models, process-based models, and empirical

models) depends on the specific questions being analyzed

and the available relevant data. The application of general

circulation models is limited; the finest scale used for global

climate simulation is far too coarse for meaningful ecological

applications (Logan and others 2003). At a smaller scale, gap

models, biogeography models, and biogeochemistry models

are among those being used to refine probable broad-scale

model projections to reflect conditions at a more local scale

(Winnett 1998).

Current model projections of future conditions that will

affect forest composition and productivity vary over a

wide range of plausible scenarios (Logan and others 2003,

National Assessment Synthesis Team 2000, Scherm 2000).

Scherm (2000) supports Millstein’s (1994) contention that

uncertainties in model input can compromise the credibility

of the output because of error perpetuation or propagation.

These scientists are not alone in their concern. Others add the

concern that data selection can also significantly influence

INTRODUCTION

This appendix contains a generalized summary of the

relevant literature related to climate change, vegetation

change (species and geographic range changes), and pest

activity scenario classification as reflected in the current

literature.

Information for this appendix was derived from published

science literature, along with a selection of literature

about the biology and ecology of forest pests. Additional

information about forest pests and their control is readily

available from State and Federal forestry agencies or online

(two good starting points are http://na.fs.fed.us/pubs/index.

shtm and http://www.fs.fed.us/r8/foresthealth/).

Many scientists believe that climate change in the form

of global warming has occurred over the last century

and will continue to occur into the immediate future

(Intergovernmental Panel on Climate Change 2007,

Kleijunas and others 2009, Malcolm and Pitelka 2000,

McNulty and Aber 2001, National Assessment Synthesis

Team 2000). The primary factors of climate noted as driving

observed ecological effects are temperature and available

water. In addition atmospheric gasses (carbon dioxide and

air pollutants such as nitrogen oxides and sulfur dioxide)

in excess of their ‘normal’ ranges are often identified as

additional drivers of the change being observed. Climate

change is also linked, at least in part, to human activity

(Malcolm and Pitelka 2000, Sturrock 2007, Winnett 1998).

These changes are expected to impact crops and their pests

individually, as well as impacting the interactions between

crops and pests (Runion 2003).

Reporting the results of a workshop attempting to understand

the potential interactions among forests, insects, diseases,

and climate change, Beukema and others (2007) report that:

Participants agreed that things will change. Most vegetation

communities will not simply migrate from one location to

another. Instead, many communities will be completely new,

with new combinations of trees, understory plants, insects,

and diseases. At the same time it is important to bear in

mind that we are not going to completely lose all forests

APPENDIX C.

Climate Change and Its Impacts on Forests
494 The Southern Forest Futures Project

model output. The use of a crisp data set versus a “fuzzy

number” set will have additional major impacts on outputs

(Coakley and others 1999).

Scherm and Coakley (2003) have identified three continuing

problems with the application of models for predicting

climate change effects:

•	 Model inputs have a high degree of uncertainty.

•	 Nonlinear relationships and thresholds in the

relationship between climatic variables and

epidemiological responses complicate efforts to collect

sufficient data for clear predictive understanding.

•	 Modeling often ignores the potential for adaptation by

plants and the insects and diseases that attack them.

Physical Impacts of Climate Change

Temperature—Increase in average temperature is

consistently shown in results from a variety of models

as being of concern. The Intergovernmental Panel on

Climate Change (2007) stated that the data supporting an

ongoing warming of the climate are unequivocal, pointing

to observations of increases in global air and ocean

temperatures, widespread melting of snow and ice, and

rising sea levels. The panel found a linear trend in average

temperature which had increased by 0.74 oC (0.56 to 0.92 oC)

from 1906 to 2005, higher than the earlier reported increase

of 0.6 oC (0.4 to 0.8 oC) for 1901 to 2000 (Intergovernmental

Panel on Climate Change 2001); that land areas have warmed

faster than oceans; and that temperature increases appear to

be larger in northern latitudes.

Overall, climate change is predicted to lead to increasing

temperature. Mean global surface air temperatures are

predicted to increase from 1.4 to 5.8 oC by the end of

the century. Both night-day and winter-summer average

temperature ranges are likely to shrink as minimum

temperatures increase more than maximum ones, and

continental and high-latitude areas will tend to warm more

than coastal and lower-latitude areas (Burdon and others

2006, Harvell and others 2002). The magnitude of these

changes is expected to vary both temporally and spatially

(McNulty and Boggs 2010).

Water regime—Water is reported to be of great significance,

second only to temperature, when projecting potential effects

of climate change. Overabundance of water, lack of it, and

seasonality of its availability all have significant impacts

on the forest processes that govern the overall health of

individual organisms. Projections of overall responses to

rainfall pattern vary greatly. Generalizations found in the

literature include the following:

•	 In the South, intense precipitation events have increased

over the past 100 years (National Assessment Synthesis

Team 2000).

•	 Rising sea levels have already had significant impacts

on coastal areas, and these impacts will likely increase

(National Assessment Synthesis Team 2000).

Malcolm and Pitelka (2000) summarized the effects of water

as follows: future regional-scale precipitation changes remain

particularly difficult to predict, and changes in the frequency

and severity of storms and other extreme weather events

are uncertain (Wigley 1999). Overall these changes will

appear as a shift of climatic zones towards the poles; warmer

temperatures will reach further north in the United States.

This last observation introduces a critical concern when

discussing climate change. Ecological factors do not function

in isolation, they interact and influence each other. This is

a fact easily forgotten when reading the literature, much of

which discusses single factor effects at a variety of scales.

Carbon dioxide and trace gases—Carbon dioxide is

routinely cited as a primary cause of global warming. The

consensus within the scientific community (Coakley 1988)

is that the increase in carbon dioxide and shifting percentage

of trace gases (ozone, chlorofluorocarbons, nitrogen oxides,

sulfur oxides, and methane) will combine to bring about

continuing global warming. Although this is generally

agreed to be an accurate projection of future condition, the

spatial relationships involved are extremely uncertain, as are

predictions of where the effect will be significant.

Light—Solar radiation is the source of energy for most

terrestrial processes, and anything that alters the amount

of radiation reaching the earth’s surface may alter climate.

Fluctuations in solar output, volcanic eruptions, and other

natural perturbations influence solar input to the earth’s

energy engine, as do changes in land use and industry. The

quality of light and the duration of photoperiod have been

shown to affect plants in a variety of ways. Yet except to note

that greenhouse gasses can affect the quality of light, little

is said in the literature about possible future shifts in light

quality. Photoperiod is seldom discussed as changing for a

given area. Effects of photoperiod only appear to be noted as

significant within the context of other factors that influence

plant migrations as described below.

Wind—In the early 1950s, Hepting (1963) found that

wind, not temperature or rainfall, was the primary driver

of climate change in Great Britain. More recently, Lemmen

and Warren (2004), also discussing climate change in

Great Britain, suggest that a warmer climate may be more

conducive to extreme wind events and that these may in turn

have consequences for other forest disturbances. Yarwood

(1959) suggested that wind has significant impacts, both
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chapter 16. Invasive Pests—Insects and Diseases

directly or indirectly, on plants and the pests that attack

them. Unfortunately, with the exception of discussions in

the context of storm events, wind is little discussed in the

literature, and we found no projections of future wind events

in the South.

Soil—Soil chemical properties do not appear to be directly

affected by climate change, their only contribution to climate

change being a complex of secondary effects. However, it

is generally recognized that as air temperature increases so

does soil temperature. Soil warming in conjunction with

drought is a major concern because it predisposes roots

and rootlets to mortality, whether or not root rotting fungi

are involved. Localized and often short-term shifts in the

albedo are predicted if soil warming results in the failure

of vegetative cover, but predictions are not spatially explicit

either as to size or location.

Rates of soil mineralization, acidification, nitrification,

and carbon sequestration are all processes that are clearly

influenced by climate change, but generally these effects are

more affected by (and subsequently influence) the local biota.

Mixed edaphic effect projections—A variety of projections

have been made for compounded edaphic factors; four are

briefly noted below:

•	 Increased frequency of extreme weather events (Scherm

2003)

•	 Increased frequency and intensity of drought occurring

under warmer temperatures (Breshears and others 2005)

•	 More frequent winter waterlogging resulting from

increased winter rainfall (Broadmeadow and Ray 2005)

•	 Increased duration of sunshine resulting from changes in

temperature and humidity which in turn lead to reduced

summer cloud cover (Broadmeadow and Ray 2005)	

IMPACTS ON PESTS AND INDIVIDUAL

HOST PLANTS 

Climate is the single most important factor determining the

distribution of major vegetation types and individual species

(Malcolm and Pitelka 2000).

Extrapolating the physical effects of climate change to the

potential biological/ecological effects that they engender

is often problematic. The simple description is that as the

climate warms, southern forests will migrate northward

and upward (assuming that higher elevation sites become

available), and will displace a portion of the temperate

mixed hardwood forest. The temperate mixed hardwood

forest in its turn will migrate, displacing part of the northern

boreal forest. Although this presents an easy to understand

generalization, it masks an extremely complex reality.

Forests are not expected to migrate as cohesive units.

Although driven by a set of individual physical parameters,

migration will more likely respond directly at the species

and individual plant level, not at the association, ecosystem,

or other ecological level of organization. Different species

(and different individuals even within a species) will react

in potentially very different ways to the various stimuli

generated by climate change. The responses of ecosystems

can only be predicted by understanding the behavior of their

convergent properties and the unique characteristics and

responses of individual species (Malcolm and Pitelka 2000).

On the positive side, increasingly sophisticated computer

models have been developed that incorporate more

fundamental ecological mechanisms. However, even these

newer models cannot yet predict with accuracy what happens

as the climate is changing (Malcolm and Pitelka 2000).

Nevertheless, we have some clear reports of observed

responses to climate change. An average 1°C increase in

average temperature is reported to increase plant growth

and lengthen the growing season. Budbreak of quaking

aspen (Populus tremuloides) is reported to be 26 days earlier

than a century ago in Alberta, Canada; and budbreak of

white spruce (Picea glauca) is earlier in Ontario (Lemmen

and Warren 2004). Ground-based monitoring efforts in

Europe documented an 11-day increase in growing season

length over a 34-year period (Malcolm and Pitelka 2000).

Because temperature can affect ecosystems in many

different ways and because there are multiple pathways for

feedback and interaction, evaluating or predicting the effects

of temperature increases is not simple. Not surprisingly,

published results have been mixed (Malcolm and Pitelka

2000).

The size of plant organs at multiple scales may increase as a

response to elevated levels of carbon dioxide. Increased area

per leaf, leaf thickness, number of leaves, leaf area per plant,

and diameter of stems and branches have all been observed

under increased carbon dioxide. Enhanced photosynthesis,

increased water use efficiency, and reduced damage from

ozone are also reported as responses to increased carbon

dioxide (Garrett and others 2006).

Disease and Insect Risks, Absent Climate Change

The second periodic National Insect and Disease Risk

Map, completed in 2006, presents a strategic assessment

of potential tree mortality resulting from major insects

and diseases. This is the definitive source at the present

time for projected insect- and disease-caused mortality

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chapter 16. Invasive Pests—Insects and Diseases

Zadocks 1995). Although the number of recent attributions

of pathosystem shifts resulting from climate change is

increasing, field research is plagued by the long-term nature

of climate change, which is much more complicated than the

shifts in weather that have been more commonly studied in

the past (Coakley 1988).

Host-pest interactions will be affected by climate change

in similar ways as other plants and animals. In the most

simplistic scenario, pest species migrations will generally

follow the migration of their preferred hosts. All of the

same ecological elements affecting the hosts in the new

environment will impact the pests themselves. Temperature,

available water, quality and duration of light, air quality, soil

condition, and other factors will affect their physiological and

ecological responses. In addition, the condition and possibly

altered physiology of the host in its new environment will

influence the new host-pest interaction.

Disease evolution is another factor that presents

complications when predicting the migration of diseases into

new areas; rates are determined by the number of generations

of reproduction per time interval, along with the heritability

of traits related to fitness under the new climate scenario

(Garrett and others 2006).

A few recent publications have focused on the need to

consider microclimate factors as being immediately relevant

when describing pest-host interactions. This is a little studied

area due to the complexity inherent in isolating micro-effects

in a macro-scale ecosystem.

Temperature effects on diseases—Gradual warming

would probably lead to a general northerly shift in seasonal

climatic regimes, which in turn would affect the range of

oak (Quercus spp.), sometimes adversely and sometimes

favorably (Brasier and Scott 1994). New disease complexes

may arise and some diseases may cease to be economically

important if warming causes a poleward shift of agroclimatic

zones and host plants migrate beyond their current ranges.

Pathogens would follow the migrating hosts and may

infect remnant vegetation of natural plant communities not

previously exposed (Coakley and others 1999).

The geographic range of fungal pathogens are to some extent

determined by the temperature ranges over which they can

grow (Lonsdale and Gibbs 1994). Brasier and Scott (1994)

found that the growth and development of many fungi

within the host may often be favored by climate warming,

and the conditions that prevail when fungi arrive at the

host surface are often critical for disease establishment;

they also observed that the effects of temperature on the

development and population dynamics of many potential

oak diseases have been little researched and they identified

the difficulties involved. Nevertheless, they predicted that

as warming increases in Europe, a root rot disease (caused

by Phytophthora cinnamomi) will extend its northward

range, survive winters better in root systems, show increased

spread within the host, have greater infection frequency of

new hosts, and cause markedly more rapid host decline and

mortality.

Other authors concur with the predictions of Brasier and

Scott (1994). Chakraborty and others (1998) point out that

changes in temperature will alter host-plant physiology

and thus host resistance to pests. Broadmeadow and Ray

(2005) add that increased temperatures will result in higher

evapotranspiration. And Burdon and others (2006) reiterate

that when we turn to the impact of the more unpredictable

aspects of global climate change on the pathogens

themselves, we will likely see significant changes in hostpathogen interactions over time, which are likely in both

directions (increase and decreased activity).

Increased soil temperature has been shown to have negative

effects on plant roots. Redmond (1955) reported that in

a 55-year old stand, yellow birch (Betula alleghaniensis)

rootlets with a normal background mortality rate of about 6

percent suffered 19 percent root mortality when average soil

temperature increased 1°C and 60 percent root mortality if

the temperature average increased 2°C. They also reported

a change in microbial population and a change in the

development of mycorrhizae, the symbiotic associations

between fine feeder roots of plants and root-inhabiting fungi.

Because of their rapid response to small environmental

changes, pathogens may provide good early warning of

impending climate change. The damage threshold from a

disease may also change in a new geographical location

(Chakraborty and others 1998).

Temperature effects on insect pests—Higher air

temperatures commonly enhance the general activity,

population size, and potential for dispersal of insect pests.

Higher temperatures could lead to greater overwintering

population size, increased length of flight season, and

length of daily flight periods (Brasier and Scott 1994).

Continued climate change, and particularly warming, would

have a dramatic impact on pest insect species. As coldblooded organisms, they have a life history that hinges on

temperature; thermal habitat largely sets the boundaries of

their geographic distribution (Logan and others 2003).

Extended periods of warm weather can favor the

development of insect pests both directly and indirectly.

Warm temperatures can accelerate the development of

insect populations by reducing the time needed for lifecycle completion. Indirect effects can be the result of

changes in the host plant, or can be produced by decoupling

relationships with natural enemies (Mamlstrom and Raffa
498 The Southern Forest Futures Project

2000). In some circumstances, warmer temperatures could

actually inhibit insect activity or disrupt the buildup of

populations: although warmer winters would increase

overwinter survival of some insect pests, reduced snow cover

could increase the winter mortality of others (Burdon and

others 2006). Enemies of insect pests would also be affected

by climate change, but these effects are generally unknown

and require more research. If warmer temperatures favor

predators and parasitoids, these natural enemies of pests

will exhibit greater control of those pest species. Conversely,

if warmer temperatures disrupt or decrease predator and

parasitoid populations, pest populations will grow more

quickly and will persist at higher levels for longer periods of

time.

Available water effects—Gilmour (1960) identified two

opposite water related conditions that cause significant

impacts on trees. Drought conditions have been shown

to be the cause of various disorders with or without any

associated fungal pathogen. And, saturated soil has been

found to cause disorders in many plants. Thus, both extremes

in water availability have been shown to negatively affect

trees. Saturated soils, although being somewhat deficient

in oxygen, appear also to have altered chemistry from

similar drier soils. Garrett and others (2006) found that even

without the added impetus of climate change the interaction

of precipitation and disease is of primary importance for

predicting disease severity.

Broadmeadow and Ray (2005) found that increased winter

rainfall leads to more frequent winter waterlogging of soil

and, in some circumstances, to fine root death extending

into the soil surface horizons. This in turn exacerbates the

effects of subsequent summer drought. Black and others

(2010) associated Swiss needle cast disease (Phaeocryptopus

gäumannii) with spring and summer needle wetness, as well

as wintertime temperatures.

Because most plant parasitic fungi are believed to require

free water for spore germination, microclimate of leaf

surfaces is an important consideration. The important

sources of free water for foliage diseases are rain, fog,

condensed water, and guttation water. Yarwood (1959)

found little germination when the relative humidity fell

below 95 percent and categorized foliage diseases by

their requirements for water in the phyllosphere during

the infection stage; but, instead of presenting a broad

categorization of this effect, focused attention on rust fungi

(specifically their urediospore stage).

Lemmen and Warren (2004) emphasize that forest

characteristics and age-class structure also affect how

forests respond to changes in moisture, noting that mature

forests (with well established root systems) are less

sensitive to changes in moisture than younger forests and

post-disturbance stands—at least in the short run. They

add that different species have different drought tolerance,

which also must be considered. And Lonsdale and Gibbs

(1994) remind us that climate change with its associated

change in frequency of summer droughts would alter the

stability of associations between tree species and various

members of their non-disease fungal associations—resulting

in an outbreak of disease in place of coexistence, or in some

circumstances mutualism.

Hanson and others (2001) found that the impact of potential

changes in drought or precipitation regimes will not only

depend on the predicted scenario of change, but also on the

type of forest ecosystem and the climate conditions to which

it is currently adapted. They conclude by summarizing six

reasons why forests would not exhibit catastrophic dieback

under the influence of climate change (including drought)

and the prediction that the replacement of forests by faster

growing trees will be gradual (Loehle 1996).

Generally speaking, any precipitation regime that stresses

host trees (whether it is too little or too much moisture) will

make them more susceptible to insect attack.

Wind effects—Yarwood (1959) cites wind as being a serious

modifier of water relations and suggests that wind commonly

prevents the formation of dew, and causes raindrops or

dew to evaporate more rapidly than they would in still air.

Broadmeadow and Ray (2005) note that an increase in the

number of storms may make woodlands more vulnerable to

wind damage.

Light effects—Fungi preferentially grow when the sky

is cloudy and are therefore active mainly on shaded parts

of the plant or in non-irradiated angles of the ecosystem.

Pathogenic fungi are additionally protected when growing

partly or completely within the host’s tissue (Manning and

von Tiedemann 1995).

The great significance of light especially in the near

ultraviolet band (UV-A) on fungal sporulation has been

recognized since the first studies were performed on this

phenomenon in the 1960s. Humphrey (1941) reports that

exposure to light stimulated sporulation in 62 of 75 species

of fungi tested; most required light for the initiation of

sporulation. Sporulation was not inhibited in any of the

417 fungal strains tested when exposed to light. However,

enhanced UV-B radiation may increase, decrease, or

leave unaffected the severity of biotic diseases. A serious

comparison of this contradictory information is not possible

since, in the underlying studies, the ranges of light qualities,

light intensities, and light exposures were too large and too

variable as were the experimental designs and time courses

applied (Manning and von Tiedemann 1995).
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chapter 16. Invasive Pests—Insects and Diseases

If some parts of the disease life cycle are photoperiod

sensitive, populations might need to undergo extensive

adaptation to make use of extended seasons in temperate

areas (Garrett and others 2006).

UV-B has positive and negative effects on fungal

development; its effect on diseases is mainly through altered

physiology and morphology (Chakraborty and others 1998).

Air quality effects—As noted above, increased carbon

dioxide in the atmosphere is generally cited as being a

primary factor in driving physiological changes in plant

populations. Working with a pasture legume and a fungus

(Coletotricum gloeosporioides) at two times ambient carbon

dioxide concentration, Runion (2003) reported an increase

of virulence of the disease against resistant cultivars of the

legume (no change with respect to susceptible cultivars)

and a significant increase in fecundity (more pronounced in

the aggressive fungal cultivars being tested). Chakraborty

and Datta (2003) focused particular concern on whether

this increased fecundity at elevated carbon-dioxide levels

could rapidly erode the usefulness of disease resistance.

Altering the predisposition of the host to disease may be

the predominant effect of rising levels of carbon dioxide

(Manning and von Tiedemann 1995).

Charkrabotory and others (1998) report an increase of

disease severity in response to increased carbon dioxide for

6 of 10 biotrophic fungi and 9 of 15 necrotrophic fungi; and

observe that predicting effects for unstudied pathosystems

will be challenging, and even more challenging when

including the combined effects on diseases and their host

plants.

Burdon and others (2006) suggest that the effect of carbon

dioxide may be to increase the efficiency of carbon fixation

with a resultant increase in growth and improvement in

the carbon status of the plant. This increase would lead to

morphological change generally expressed as enhanced

growth; the combined changes in nutrition and morphology,

in turn, could affect the suitability of the plant as host

material for a variety of diseases. This having been said, the

authors caution that the reported research on the subject is

limited and end the discussion with this further caution: “...

the predictability of the impact of these factors as on whole

communities is even more uncertain with both indirect and

direct effects of varying magnitude being likely.”

Mirroring this concern Lemmen and Warren (2004) report

that although numerous studies have investigated the impacts

of elevated carbon dioxide on forest growth and health, the

results are neither clear nor conclusive.

Manning and Keane (1988) conclude that “in a theoretical

sense, air pollution can increase, decrease or not affect the

course of development of a disease epidemic,” based on

new and existing observations about air pollution and pest

behavior including:

•	 Bacterial diseases are generally inhibited by sulfur

dioxide, which limits lesion size and often increases

latent periods.

•	 Fungal diseases have been reported to be enhanced,

inhibited, or not affected at all by air pollutants.

•	 The little that is known about the effects of pollution

on root diseases indicates that virus-affected plants are

usually less affected by air pollutants than virus-free

plants.

•	 According to James and others (1980a), inoculated

stumps of ozone-stressed pines (Pinus spp.) were more

readily invaded by annosum root disease (caused by

Heterobasidion annosum).

•	 According to Skelly and others (1983), ozone stressed

eastern white pine (Pinus strobus) in the Blue

Ridge Mountains of Virginia were more subject to

Leptographium root disease (caused by Verticicladiella

procera).

•	 According to Mahoney and others (1985), loblolly pine

seedlings with ectomycorrhizae (Pisolithus tinctorius)

were not adversely affected by ozone, sulfur dioxide, or

a combination of both.

•	 According to Keane and Manning (1987), ozone caused

significant decreases in ectomycorrhizae of white birch

(Betula pendula) and white pine seedlings.

Soil environment effects—Carbon dioxide concentration

in soil is expected to be far less impacting to diseases than

atmospheric carbon dioxide. Soil microflora is routinely

exposed to levels 10 to 20 times higher than atmospheric

carbon dioxide levels (Coakley and others 1999; Manning

and von Tiedemann 1995). Colonization and persistence of

mycorrhizae appears to be dependent, in part, on the nutrient

status (primarily nitrogen) and carbon dioxide concentration

in soil, although observed responses do not show a consistent

pattern. Not much more can be said here because the

influence of mycorrhizae on plant disease is still not well

understood.

Ozone does not penetrate the soil surface and therefore

affects roots only indirectly by altering photosynthesis.

Damage caused by several tree root disease pathogens

became more severe when the host plant was stressed by

ozone (Fenn and others 1990; James and others 1980b, 1982;

Skelly and others 1983).
500 The Southern Forest Futures Project

O’Neill (1994) presents a detailed review of the potential

effects of elevated levels of carbon dioxide on the rhizosphere

(the region of soil that is directly influenced by root

secretions and associated soil microorganisms), observing

that ecosystems are largely constrained by the rates at which

soil processes occur. Much more data will be needed to

begin the process of generalized modeling of effects on the

rhizosphere.

Effects of soil saturation have already been briefly discussed

above. Both the amount of water and timing of flooding

affect the degree of negative impact on cover plants.

Soil characteristics, nutrient availability, and disturbance

regimes may prove to be more important than temperature

in controlling future ecosystem dynamics (Lemmen and

Warren 2004). Climate and vegetation interact to determine

the characteristic soils of an area, and different climatic

zones are characterized by different soil types—except

where the presence of unusual rock, such as serpentine,

results in unique soils (Malcolm and Pitelka 2000).

Effects on Host Biology

Little is known about how environmental effects on tree

physiology influence the inducible responses that are relevant

to pathogens (signal recognition, generation of phytoalexins

and reactive oxygen species, hypersensitive responses,

callus growth, and systemic acquired resistance) (Ayres and

Lombardero 2000).

Carbon dioxide is a primary input to growth and

development of all plant life, providing both a fertilization

effect and an increase in the efficiency with which plants

use water. The fertilization effect may be affected by the

availability of water and other nutrients. It may also diminish

after an initial period of adjustment by the plant. Increased

carbon dioxide levels may also trigger changes in the

chemical composition of vegetation such as affecting the

carbon-to-nitrogen ratio in leaves (Winnett 1998). Positive

response to carbon dioxide appears to occur under a wide

range of nutrient availability (Rogers and others 1994). In

addition, Bazzaz and others (1994) stress that the differential

responses of species to elevated levels of carbon dioxide

indicate potential shifts in the competitive relationships

among plants. Partial closure of the guard cells forming

stomates has been proposed as the mechanism by which

plants slow transpiration (Jones and Mansfield 1970), which

in turn may be one mechanism of adaptive resistance to

elevated carbon dioxide levels.

Other factors to consider include the following:

•	 In soils, some fungi can use carbon dioxide as an

additional source of carbon, which is incorporated into

organic acids and eventually enters the Krebs cycle as an

additional energy supply (Manning and von Tiedemann

1995); this increase tends to increase root growth more

than aboveground growth (Rogers and others 1994).

•	 Ozone effects on plant diseases are host mediated.

•	 The principal mechanism for UV-B effects on plant

diseases would be through alteration of host plants

(Manning and von Tiedemann 1995).

•	 Host-pathogen relationships, defense against physical

stressors, and the capacity to overcome resource

shortages could be impacted by rises in carbon dioxide

(Rogers and others 1994).

•	 During winter dormancy, direct effects of climate on the

host are generally less important than those involving a

pathogen (Lonsdale and Gibbs 1994).

Combined Effects

Increased summer temperatures and droughtiness would be

expected to help shift the distributions of fungi northwards

within the range of potential hosts, or at least to increase

the geographic range over which they behave as pathogens

(Lonsdale and Gibbs 1994).

Fungi appear to be largely tolerant of current ozone levels.

However, a strong negative correlation exists between

rainfall or relative air humidity and photochemical

ozone generation in the atmosphere: on wet days that are

appropriate for fungal growth on plant surfaces, ozone

levels are usually low. Consequently, biologically harmful

concentrations of ozone are unlikely to coincide with

germinating spores or actively growing mycelium (Manning

and von Tiedemann 1995).

Expected increases in growth from elevated carbon dioxide

levels will almost certainly aggravate problems with

diseases. However, this effect would likely be offset by

growth reductions caused by increased ozone and UV-B

(Manning and von Tiedemann 1995). Because carbon

dioxide may greatly alter ecosystem structure and function

(Bazzaz and Fajer 1992), unmanaged forest ecosystems

may be seriously impacted by carbon dioxide acting in

combination with drought, compared to intensively managed,

monoculture tree farms where species composition has

been altered. Overall the interaction of carbon dioxide and

temperature is not well understood and the experimental data

have been inconsistent (Rogers and others 1994).

At higher temperatures, an increase in the availability of all

major nutrients (nitrogen, phosphorus, calcium, magnesium,

potassium, and sulfur) can be expected as a result of
501

chapter 16. Invasive Pests—Insects and Diseases

increased water fluxes through soil and higher organic matter

decomposition rates, which would increase the circulation

of nutrients in the soil-nutrition system. Also, nutrient

circulation would increase because of higher growth rates

of forest species at increasing atmospheric carbon dioxide

concentration and warmer temperature (Nilson and others

1999).

Stressed trees are more susceptible to insect pests and

diseases (Broadmeadow and Ray 2005), enabling some

level of assessment by forest pathologists and entomologists.

However, firm projections of future pest activity cannot

be made and considerable caution should be exercised in

extrapolating analysis to a future climate. For some insects

and diseases, likely trends cannot be predicted even on the

basis of expert judgment (Broadmeadow and Ray 2005).

Climate change will directly influence infection,

reproduction, dispersal, and survival among the seasons and

other critical stages in the life cycle of a disease (Coakley

and Scherm 1996). Observed outcomes include modifications

in host resistance, altered stages and rates of disease

development, and changes in the physiology of host-pathogen

interactions (Scherm 2003).

EFFECTS ON ECO SYSTEMS

Because individual species will respond to climate change

differently, ecosystems will not necessarily shift as cohesive

units. The most vulnerable species are expected to be

those with narrow temperature tolerances, slow growth

characteristics, and limiting dispersal mechanisms such

as heavy seeds (Lemmen and Warren 2004). How well

plant and animal species adapt to or move with changes in

their potential habitat is strongly influenced both by their

dispersal abilities and by the characteristics and severity

of disturbances to these environments. Nonnative and

invasive species that disperse rapidly are likely to find

opportunities in newly forming communities (Joyce and

others 2001). However, if climate change causes a gradual

shift of cropping regions, pathogens will follow their hosts

(Goudriaan and Zadocks 1995) into less changed new

communities.

The pattern of disturbance imposed on a landscape by a

particular biotic agent is determined both by the structure

and condition of the landscape and by the characteristics of

the agent and its responsiveness to environmental conditions

(Mamlstrom and Raffa 2000). Factors such as changes in

land use or increases in resistant strains of diseases may

underlie range expansions (Harvell and others 2002).

Dale and others (2001) point out that many disturbances are

cascading. For example, insect infestations and diseases

promote forest fires by creating fuels, and the fires in turn

promote future infestations and infections by compromising

the resistance of surviving trees to insects and diseases.

Invasive nonnative species are sometimes able to modify

existing disturbances or introduce entirely new ones. Under

climate change, these compounded interactions may be

unprecedented and unpredictable. They are likely to appear

slowly and be difficult to detect because of tree longevity.

Climate change could represent a new form of disturbance

to unmanaged ecosystems and thus could provide new

opportunities for invasive species to flourish and displace

native species. An important feature of many invasive

species is their dispersal effectiveness and their high

reproductive rates (Malcolm and Pitelka 2000). Changes

in phonological synchronicity of hosts and native pests, as

well as their relative abundance and physiological condition,

may affect the frequency and consequences of outbreaks

(Malcolm and others 2006).

EFFECTS ON DISTRIBUTION OF 

SPECIE S

As climate shifts, climatically sensitive species will

eventually die out, and only a subset of the potential pool of

incoming plants may actually migrate sufficiently quickly to

keep up with the shifting climate. Thus, plant communities

could become progressively composed of the more

adaptable and faster moving species, especially if warming

is rapid. This change in plant communities, especially tree

communities, is of considerable concern. Expansion of the

warm-temperature mixed-evergreen forests of the South

would be at the expense of other kinds of forests. In some

scenarios, parts of the South become drier and grasslands or

savannahs replace the current forest (Malcolm and Pitelka

2000).

The forest area impacted by insects and diseases in the

United States is approximately 45 times that impacted by

fire, with an economic impact that is almost 5 times larger

(Dale and others 2001). If this trend continues, pests and

diseases are likely to be the primary cause of species change

in eastern forests over the next few decades. Forecasting the

trajectory of those changes is nearly impossible because we

cannot predict with any certainty which pests or diseases

will be established (Lovett and others 2006). Given the

complexities of climate change, and biotic responses

to it, prediction of the future impact of climate change

on emerging infectious diseases is difficult except on a

broad scale. Climate change can lead to the emergence of

preexisting pathogens as major disease agents or can provide

the climatic conditions required for nonnative diseases to

flourish (Anderson and others 2004). Because climate change

will allow plants and diseases to survive outside their historic

ranges, Harvell and others (2002) have projected an increase

in the number of invasive diseases.
502 The Southern Forest Futures Project

The following discussion and analysis is excerpted with only very minor changes from Régnière and Bentz (2008) and 

provides an example for consideration of a pest present and destructive in the western United States which and its 

potential impact in the East and South under the influence of climate change. 

The mountain pine beetle (Dendroctonus ponderosae) is a native insect of pine forests in Western North America. 

Although it has a broad geographical distribution, it has been historically confined in the United States, by the 

distribution of its pine hosts, and in the northern half of British Columbia, by the geoclimatic barrier of the Rocky 

Mountains. Since the early to mid-1990s, an outbreak has reached unprecedented levels in terms of acreage and 

number of pine trees attacked. Lodgepole pine (Pinus contorta) is being killed throughout its range, most notably 

in Colorado and British Columbia. The beetle is also causing very high mortality among whitebark pine (Pinus 

albicaulis) and limber pine (Pinus flexilis) at high elevations. Historical records from the past century suggest that these 

ecosystems have had pulses of infestation and mortality but not at the levels currently being observed. Since 2006, 

the range of infestation has expanded into the Peace River area of north-central Alberta. Climate change may well be 

involved in this recent northeastward and upward range expansion. Evidence of similar shifts in insect distributions is 

ample and mounting throughout the world, much of it convincingly linked to climate change. 

The primary concern at this time is the likelihood that the infestation will continue spreading eastward into the pines of 

the Canadian boreal forest, eventually reaching the eastern provinces and threatening the pines growing on the Atlantic 

side of the continent and then spreading into the Southern United States. Because of its recent incursion to the edges of 

the Canadian boreal forest, mountain pine beetle is viewed as a potential invading species in eastern pine ecosystems. 

Three well-understood links connect climate and mountain pine beetles and form the basis for the concern that 

changing climate (temperature and precipitation) has had—and will continue to have—a role in the recent outbreaks and 

range expansion of this insect. 

1. A well-synchronized adult emergence pattern is a prerequisite for successful mass attack of healthy pine trees. 

Such highly synchronized emergence is most likely to occur where (and when) the insect has a strictly univoltine 

(one generation per year) life cycle. 

2. Cold winter temperature is the major cause of mortality in mountain pine beetles. For more than 20 years, processbased models describing responses to temperature have been under development; they show that a hemivoltine 

life cycle (one generation every 2 years) entails exposure to two winters, leading to lower population performance. 

3. Drought affects the ability of pine trees to defend themselves against insect attack. 

Three model components are available to study the impact of weather on mountain pine beetle populations: a 

phenology model that predicts life stage-specific developmental timing, a cold-tolerance model that predicts probability 

of larval mortality resulting from cold temperature, and a drought-stress model that predicts fluctuations of tree 

susceptibility. All three models have been implemented within BioSIM to make landscape-scale predictions of mountain 

pine beetle performance under climate change scenarios. BioSIM is a generic modelling tool that uses available 

knowledge about the responses of particular species (usually pests) to key climatic factors to predict their potential 

geographic range and performance. 

The phenology model is very good at predicting the portions of the continent where the insect has a high likelihood 

of being univoltine. This model predicts the northward and upward shift of infestation. Under a conservative climate 

change scenario, it also predicts that by the end of the 21st century, the area at risk will shift considerably northward, 

to a point that the insect may be maladapted over much of its current distributional range. The cold tolerance model 

suggests that winter survival is very low and will remain so in the foreseeable future throughout the boreal pine forests 

from Alberta to Ontario. Although drought stress is, and is predicted to be, more common in that same area, there is not 

a very large change in this risk factor predicted in the near future. 

Thus, with our current understanding of the insect’s physiology and host plant interactions, the risk of seeing the 

mountain pine beetle spread across the northern forests of Canada into the eastern pine forests seems rather low. This 

prediction, of course, is contingent on failure of the insect to adapt (evolve) and change its thermal responses, and on a 

relatively stable distribution of pines over the time range under consideration.
503

chapter 16. Invasive Pests—Insects and Diseases

Describing similar effects for pest insects in climate change

scenarios, Logan and others (2003) indicate that there is a

historic trend to intensification in all aspects of outbreak

behavior, based on assessments of individual species’

responses to date; this certainly characterizes modeling work

with the mountain pine beetle (Dendroctonus ponderosae),

gypsy moth, spruce beetle (Dendroctonus rufipennis), and

spruce budworm (Choristoneura fumiferana).

Walther and others (2002) link climate change to changes in

a variety of known springtime life-cycle events in European

organisms (including earlier annual bird breeding, migrant

bird arrival, the appearance of butterflies, choruses and

spawning of amphibians, and shoot growth and flowering of

plants); these changes in event timing suggest a lengthening

of growing season by 8 to 16 days. Anderson and others

(2004), citing grey leaf spot (Pyricularia grisea) disease of

corn (Zea spp.), suggest that the ranges of several important

crop insects, nonnative plants, and plant diseases have

already expanded northward. They also note that autumn

life-cycle events (leaf color change and leaf fall) are not as

clearly defined in their response to the extension of growing

season as springtime events.

Plants have historically responded to climate change by

migration and adaptation. Fragmentation and rate of seedling

establishment may hinder some plant populations from

successful migration to higher latitudes. Persistence of these

populations may depend heavily on adaptive evolution, but

predicted rates of evolutionary response are much slower

than the predicted rate of climate change. Historical climate

changes were generally much slower (by one or more orders

of magnitude) than those predicted for the future (Etterson

and Shaw 2001). This observation leads to concern that

historical response patterns to climate change may not prove

to be effective as predictors of future change.

Adding to the concerns expressed above is a critical

consideration that has not yet been emphasized enough—

climate change cannot be viewed in isolation; its effects on

ecosystems must be considered in the context of a range of

human-caused impacts on ecosystems, such as air pollution,

water pollution, habitat destruction and fragmentation, and

the nonnative species that thrive and have their most serious

effects in ecosystems already disturbed by human activities

(Malcolm and Pitelka 2000).

Many unpredictable, unforeseen pest problems may

arise as a result of changing temperatures, changing

precipitation regimes, or both in combination. Previously

minor or infrequent pests may become significant causes

of tree mortality. Some current major pests may decline

in importance. In addition to new nonnative invasive

pests arriving from overseas, the ranges of insects

and diseases native to North America may expand or

contract dramatically. Because these changes are largely

unpredictable yet bound to occur, land managers and

scientists in forestry-related disciplines will need to practice

early detection and monitoring of “new” problems and

follow up with research and creative, adaptive management

strategies.

PATHOSYSTEMS

The subtle changes in conditions attributed to climate change

can affect plant-disease development. These changes are not

easily determined, and, consequently, the ability to forecast

how disease changes under altered growth conditions is not

simple (Seem 2004).

Less stable relationships tend to occur in the simpler

ecosystems that initially exist in planted forests, often

involving new combinations of host and pathogen species

that have been transported beyond their natural geographic

ranges. In such situations, climate change would likely

encourage major changes in disease incidence and severity

(Lonsdale and Gibbs 1994).

Tree-disease problems cannot be fully understood without a

thorough appreciation of the part played by environmental

factors, particularly climate, as a precursor to fungal attack.

The manifestation of many diseases often merely reflects

unfavorable site factors, the presence of the fungus being

the result of an unhealthy condition rather than the primary

cause of the tree’s debility (Gilmour 1960).

During unusual weather events or biologically induced

stress periods, the competitive dominant may be the most

vulnerable. Its large size has stretched its limits to coordinate

uptake, transport, storage, and photosynthesis (Manion and

Lachance 1992).

The timing of the stress event is also very important. Early

season stress is frequently overcome although later stressors

are not so, often simply because of sufficient time remaining

in the growing season (Lundquist and Hamelin 2005).

Nonnative insect pests and diseases pose the most serious

threat to the forests of eastern North America. The litany

of pest and disease introductions is long: chestnut blight

(Cryphonectria parasitica), Dutch elm disease (Ophiostoma

ulmi), beech bark disease (Nectria coccinea var. faginata),

balsam woolly adelgid (Adelges piceae), hemlock woolly

adelgid (A. tsugae), dogwood anthracnose (Discula

destructiva), and gypsy moth (Lovett and others 2006).

According to Lovett and others (2006), ecologists need

adequate information in only six categories of knowledge

about nonnative pests and their hosts to make rough

predictions of the type and magnitude of potential ecosystem
504 The Southern Forest Futures Project

impacts. Pest information is needed concerning: (1) mode

of action, (2) host specificity—such as species and age

class, and (3) virulence. With respect to the host, the

information needed is: (4) ecological importance—position

or bio-production values in the system, (5) uniqueness, and

(6) phytosociology—such as pure versus mixed stands,

effectiveness of regeneration.

When climate change has a significant and direct effect on

plants, changes in composition may ensue. Given differential

responses across plant species, this may lead to relative

changes in community composition. When coupled with

range extensions or contractions of individual species, the

result may be increased or decreased diversity of whole

plant communities. Diseases of one host species may thus be

brought into intimate contact with new hosts, although the

likelihood of spatial movements necessary for this to occur is

perhaps low in the immediate future; they may benefit from

increasing overlap of obligate alternate host distributions;

or they may suffer significant reductions in population size

as a consequence of allopatric distributions or incomplete

congruence in the distribution of obligate alternate hosts

(Burdon and others 2006).

Boland and others (2004) summarize research on the

potential impact of climate change on plant diseases and

list 143 plant diseases, only 18 of which are forest tree

diseases. Despite this they tabulate data for climate change

effects with respect to forest pathosystems under these

categories: primary inoculum or disease establishment, rate

of disease progress, potential duration of epidemic, reasons

for effects, and net effect of the disease. Although predicting

the effects on the diseases is relatively intuitive to plant

pathologists, the authors argue that extending the intuitive

knowledge pathosystems or disease mechanisms requires

more knowledge about how the host’s physiology and thus

the host-pathogen interaction will be affected. They also

cite a specific need for further knowledge about the effects

of elevated carbon dioxide, UV radiation, and ground level

ozone, as well as the effects of environmental changes on

insect vectors of diseases.

An interesting sidebar to pathosystems activity is reflected

in the capacity of fungi to perform their cleanup function

(woody and leaf litter decomposition) under the influence

of climate change. Yin (1999) makes several points. First,

the decay rate of forest woody debris is a key missing link

in our quantitative understanding of carbon dynamics and

the global carbon budget of forests. And, in the context of

global climate change, a 2 °C warming in air temperature in

January and July would accelerate stem woody debris decay

(in density loss); accelerated decay would decrease in the

presence of increased precipitation (and vice-versa); but, the

magnitude of increase would be smaller when adjusted for

the detrimental effect of elevated carbon dioxide as part of

climate change.

For many fungal diseases that rely on biotic vectors for

dispersal, the effects of climate change and weather on the

development of outbreaks or epidemics have not been studied

in detail. In areas where a pathogen already occurs, weather

conditions may favor outbreaks of its vectors in certain

years, suggesting that climate change could influence longterm prevalence of the disease (Lonsdale and Gibbs 1994).

The introduction of new vector species, changes in vector

overwintering and oversummering (Garrett and others 2006),

and other effects of change on insects may have important

effects on pathogen survival, movement, and reproduction

(Garrett and others 2006). Pathogens that rely on vectors

may see significant shifts in their distribution or intensity

if environmental changes affect the behavior or viability of

their vector (Burdon and others 2006).

However, in some circumstances, warmer temperatures

could actually inhibit insect activity or disrupt the build-up

of populations. Enemies of insect pests will also be affected

by climate change, but these effects are unknown and

require more research. If warmer temperatures positively

affect predators and parasitoids, natural enemies will exhibit

greater control of pest species. Conversely, if warmer

temperatures disrupt or decrease predator and parasitoid

populations, pest populations will grow more quickly and

will persist at higher levels for longer periods.

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509 chapter 17. Fire 

	 John A. Stanturf and Scott L. Goodrick1

Key Findings

•	Climate forecasts indicate that the South’s spring and fall

wildfire seasons will be extended.

•	Prescribed fires, currently conducted on roughly a 3 to

5 year rotation across much of the South, would need to

become more frequent if conditions become drier.

•	 Major wildfire events,such asthe 2007 Okefenokee wildfires,

2008 Evans Road Fire in eastern North Carolina, and recent

west Texas fire seasons, are also likely to occur more often.

Such events currently occur once every 50 years; however

they could be more frequent in a warmer/drier climate.

•	Land use change will have the most immediate effects

on fuels and wildland fire management by constraining

prescribed burning and increasing suppression complexity

and cost.

•	Air quality issues will likely increase restrictions on

prescribed burning over large areas, not just in the

wildland-urban interface.

•	Potential health and safety concerns, in addition to air

quality restrictions, will add to the regulatory constraints

on use of prescribed burning.

•	Alternatives to prescribed burning are generally not costeffective and do not provide the ecological benefits of

fire to adapted ecosystems; nor do they provide adequate

protection for structures and human communities.

•	Restrictions on use of prescribed burning to manage

fuels will exacerbate potential climate change effects,

particularly in the Coastal Plain and on the western

Appalachian Mountains, where models predict an increase

in wildfire potential.

•	Fuels buildups combined with more intense wildfires

under a warmer, drier climate could severely degrade

fire-dependent communities that often support one or more

threatened, endangered, or sensitive species.

•	In addition to increasing the severity of wildfire events, the

drier conditions and increased variability in precipitation

that are associated with climate change could hamper

successful forest regeneration and cause shifts in vegetation

types over time.

1John A. Stanturf is a Research Ecologist and Scott L. Goodrick is a

Project Leader/Supervisory Research Meteorologist at the Center for Forest

Disturbance Science, Southern Research Station, U.S. Department of

Agriculture Forest Service, Athens, GA 30602.

Introduction

Fire is an integral part of the southern landscape. The

pervasive role of fire predates human activity in the South

(Lafon 2010, Stanturf and others 2002), and human society

has magnified that role. The South leads the nation in

number of wildfires per year, averaging approximately

45,000 wildfires per year from 1997 through 2003 (Gramley

2005). Continued population growth in this region increases

the potential threat that wildfires pose to life and property.

In addition, forestry and forestry related industry represent

a significant portion of the region’s economy, making each

wildfire a potential loss to a local economy.

Prescribed fire is an important tool used in the South to

manage hazardous fuels and provide other ecological and

economic benefits (Wade and Lunsford 1989). Each year

approximately 8 million acres (3.2 million ha) of land are

treated with prescribed fire in the South — more than in

all other regions combined (Wade and others 2000). Most

of this acreage is burned for hazardous fuel reduction,

wildlife management, and range management; although

an increasing number of acres are burned for ecosystem

restoration and maintenance. Most prescribed burning is

carried out in the Coastal Plain and Piedmont; however, its

use is increasing in the Southern Appalachians and Ozark/

Ouachita Highlands as historic fire regimes are reintroduced

into these physiographic regions. Of increasing importance

is the use of prescribed burning in landscape restoration, in

particular for longleaf pine (Pinus palustris; see Brockway

and others 2005). In March 2009, the Regional Working

Group for America’s Longleaf published a “Range-wide

conservation plan for longleaf pine” that calls for increasing

the extent of longleaf forests from 3.4 million acres to 8

million acres over 15 years (online report available at http://

www.americaslongleaf.net/resources/the-conservationplan/

Conservation%20Plan.pdf, last accessed on 9 December

2010). Because periodic burning is essential to maintain

the longleaf ecosystem, successful restoration will require

a significant increase in the area burned annually in the

South (Southern Regional Partnership for Planning and

Sustainability 2011).

Chapter 17.

Fire 
510 The Southern Forest Futures Project

In the United States, the popular notion of wildfires

often focuses on the large conflagrations common in the

western States. However, wildfires occur more frequently

in the Southeast, where rapid vegetation growth and fuel

accumulation combine with frequent ignitions from lightning

and humans. Wildfires in the Southeast have the potential to

develop into large, dangerous conflagrations, as epitomized

by the Volusia Fire (111,130 acres) and the Flagler/St. John

Fire (94,656 acres) that occurred in Florida in 1998 and more

recently the Bugaboo Fire/Big Turn Around/Sweat Farm

Road Fires (Okefenokee) Fires of 2007 (over 600,000 acres),

which occurred in Georgia and Florida and the 2008 Evans

Road Fire in North Carolina (over 41,000 acres). Despite

the annual wildfire acreage typically being relatively small

compared to the West, a disproportionate number of the

structures destroyed nationally by wildfires are located in the

Southeast (Monroe 2002). For example, in 2008 the Highway

31 Fire in South Carolina burned 19,000 acres, destroyed or

damaged 176 homes and caused economic losses in excess of

$50 million.

Wildland fire is an integral component of southern

ecosystems across a range of climatic conditions, including

recent warming associated with greenhouse gas emissions.

Westerling and Swetnam (2003) have linked annual areas

burned in the Southwest to similar large-scale patterns

favoring unusually dry conditions. Their reconstructed

paleo-fire records reveal that the drought-producing, yearto-year variability in the atmospheric circulation patterns of

the past are still a driving force in the variability of wildfire

season severity. Wildfires continue to exhibit significant

variability from one year to the next. For example, the

burned area in the United States increased from 1.3 million

acres (0.5 million ha) in 1998 to 5.6 million acres (2.3

million ha) the next year (National Interagency Fire Center

2010). This mainly results from the inter-annual variability

of atmospheric condition, which is a determinant for

wildfires along with fuel properties and topography (Pyne

and others 1996).

The close relationship between droughts and wildfires

provides a basis for evaluating and predicting wildfire

potential. Several studies have linked long-term atmospheric

anomalies and wildfire activities in the South (Brenner 1991,

Dixon and others 2008, Goodrick and Hanley 2009), using

atmospheric teleconnection patterns to predict wildfire

season severity and help establish a strong tie between

wildfire activity and the global climate system. Using the

Keetch-Byram Drought Index to forecast changes in wildfire

potential at a global scale, Liu and others (2009) found that

wildfire potential in the United States is likely to increase

by the end of this century, although the magnitude of this

increase varied widely, depending on the climate model and

emissions scenario selected for the projection.

The remainder of this chapter examines how wildland fire

conditions could evolve over the next 50 years, and how

these changing conditions may impact prescribed fire in the

South. Our examination of changing wildland fire conditions

builds upon the methodology of Liu and others (2009) by

using a simple water balance-based wildfire potential index

to relate changes in temperature and precipitation patterns

across the South to changes in fire potential. We evaluate

four possible futures (chapter 2) each of which represent

a different combination of general circulation model and

greenhouse gas emission scenario (IPCC 2007). For each of

these Cornerstone Futures, we examine potential changes in

the duration and severity of future wildfire seasons and how

these changes may impact prescribed burning.

The issues affecting continued use under current conditions

of prescribed burning will be presented, along with a

discussion of alternatives and their efficacy. Prescribed

burning is used routinely to reduce fuel loads and decrease

the risk of catastrophic wildfires, improve forest health,

and manage habitat for threatened and endangered species.

Increasingly, one of the most effective tools in the manager’s

kit, fuel reduction by frequent understory burning, is offlimits because of safety and liability risks (Achtemeier and

others 1998, Wade and Brenner 1995) or public dislike for

the inconvenience of smoke (Macie and Hermansen 2002).

The concluding section will describe the effects of potential

climate change on prescribed fire practice.

Methods

To address questions regarding future wildfire potential, we

examine the response of a drought index to a set of simulated

future conditions. A description of these methods follows.

Questions regarding the future of prescribed burning are

addressed using a synthesis of the scientific literature linked

to these forecasts.

Climate Scenarios 

Four climate scenarios are used in evaluating potential

changes in wildfire potential over a 50 year period from

2010 and 2060. These four scenarios represent four of the

six Cornerstone Futures presented in chapter 2 and represent

different combinations of general circulation model and

IPCC greenhouse gas emission scenario. Cornerstone A uses

the MIROC model developed by the University of Tokyo’s

Center for Climate System Research (National Institute

for Environmental Studies) and forced by the IPCC’s A1B

emissions scenario. Also using the A1B emissions scenario,

Cornerstone B uses the CSIRO mk3.5 model developed

by the Commonwealth Scientific and Industrial Research

Organization of Australia. Cornerstone C employs an older

version of the CSIRO model (mk2) forced by the IPCC’s
511 chapter 17. Fire 

B2 emissions scenario. Cornerstone D uses version 3 of the

Hadley Centre Coupled Model forced by the IPCC’s B2

emissions scenario.

IPCC emissions scenarios combine two sets of divergent

tendencies: one set varies between strong economic values

and strong environmental values, the other set between

increasing globalization and increasing regionalization

(Nakicenovic and others 2000). The A1 scenario family

describes a future of very rapid economic growth, global

population that peaks in mid-century and declines thereafter,

and the rapid introduction of new and more efficient

technologies. Within that family, A1B represents a balance

between fossil fuels and alternative energy sources. The

B2 scenario describes a world with continuously increasing

global population, moderate levels of economic development,

and less rapid but more diverse technological change than in

the A1B scenario.

The climate and wildfire potential information presented

in this chapter is based on decadal averages, rather than on

individual years. Therefore, data for 2010 represents the

average of all the years from 2001 to 2010. Monthly data is

also expressed as a decadal average, for example, April 2060

would represent the average of the 10 Aprils from 2051 to

2060.

Measuring Wildfire Potential 

Wildfire potential is a complex function of recent weather

conditions, vegetation and topography. Of these three

components, weather exhibits the most variability at any

given spot. Wildfire potential is often determined using a

system such as the National Fire Danger Rating System

(Burgan 1988) that utilizes afternoon weather observations

of temperature, humidity, wind speed, and precipitation

amount/duration. In general, the output from general

circulation models does not include all the information that

would be required by such a system to project future changes

in wildfire potential.

The Keetch-Byram Drought Index (KBDI) is a rather

simple drought index designed specifically for assessing

wildfire potential in the South (Keetch and Byram 1968).

The KBDI is a cumulative measure of the balance between

evapotranspiration and rainfall; and only requires three

inputs: daily high temperature, daily rainfall and annual

average rainfall. The high temperature and annual rainfall

are used to estimate daily evapotranspiration (annual rainfall

acts as a surrogate for the amount of vegetation as higher

annual rainfall supports more vegetation which leads to

increased evapotranspiration).

The KBDI has two potential limitations for climate change

work. First, because the function defining evapotranspiration

was derived for historical rainfall and temperature regimes,

the fit may not be as good under climate change conditions.

Secondly, the index scale is fixed to be from 0 (very wet) to

800 (extremely dry) with a nonlinear, asymptotic approach

to this maximum value. For a changing climate where

conditions could potentially get much drier than they are

currently, use of the KBDI could underestimate the potential

drought conditions by compressing the changes into the

asymptotic portion of the curve.

As an alternative index, referred to as simply the potential

drought index (PDI), we use the balance between 0.75 times

the potential evapotranspiration minus precipitation.

The 0.75 scaling is designed to reflect the fact that the

potential evapotranspiration is an overestimate of the actual

evapotranspiration (Eagleman 1967). The exact value of this

scaling coefficient is not critical; the primary requirement

is that it provides reasonable estimates of the current water

balance conditions to serve as a basis for evaluating future

changes. The slight change in how evapotranspiration is

calculated compared to the KBDI will cause the PDI to

accentuate drought conditions and thus highlight areas of

potential increases in wildfire potential. The PDI has an

open-ended scale with units of millimeters. Positive values of

the PDI indicate drought conditions.

Results

Future Wildfire Potential Changes 

Annual fire potential—Wildfire reports compiled as part of

the Southern Wildfire Risk Assessment (Buckley and others

2006) reveal three primary areas of wildfire activity from

1997 to 2002: the Coastal Plain, the western Appalachian

Mountains (eastern parts of Kentucky and Tennessee) and

eastern Oklahoma/Arkansas (fig. 17.1). Other areas may be

important locally but are of limited geographic extent, such

as the Coastal Plain sandhills, where longleaf pine burns

regularly. Care must be taken when examining this figure

as not all States provided wildfire records with latitude/

longitude for each fire; some States located all wildfires

at the geographic center of counties. This is especially

noticeable in Texas, where counties are larger.

All four Cornerstone Futures provide a consistent view of

the current annual fire potential as expressed by the PDI

(fig. 17.2). On these maps brown areas define regions where

evapotranspiration exceeds precipitation (positive PDI)

while in blue regions precipitation dominates (negative PDI).

White areas show a balanced moisture budget (PDI near

zero). Areas farthest west are dominated by the highest PDI

values because of lower precipitation and higher summer

temperatures; areas farther east are dominated by higher

precipitation, leading to negative PDI values. The primary

differences among the Cornerstone Futures are primarily


514 The Southern Forest Futures Project

focused in the Ohio River Valley where Cornerstone B is the

wettest, and along a band just inland of the coast where the

PDI is near zero. This band is most evident for Cornerstones

C and D.

Comparing these PDI maps to the map of acres burned

in figure 17.1 shows that areas with the driest conditions

(highest PDI) do not necessarily have the highest

acres burned. The Coastal Plain, whose annual PDI in

Cornerstones B, C and D is near zero has some of the highest

amounts of burned area. The areas with highest positive

PDI values are not productive enough to support sufficient

build up of fuels to support frequent wildfires. The eastern

Oklahoma/Arkansas region is another area of transition

in the PDI reflecting near balance between rainfall and

precipitation. The western Appalachians shows significant

areas burned despite having the lowest PDI values.

In 50 years, all Cornerstone Futures depict drier conditions

(fig. 17.3). Cornerstone A depicts the most severe conditions

with an eastward expansion of the western dry area and

the development of a similar area in southern Georgia and

Florida; only the Appalachians maintain a negative PDI.

The other Cornerstone Futures are very consistent in their

depiction of drier conditions, though the magnitude of the

drying is far less than in Cornerstone A. The central part of

the region shifts from negative PDI values to a more balanced

condition and the band of near zero PDI in the Coastal Plain

becomes better defined. All three of the primary fire areas

depicted in figure 17.1 experience an increase in wildfire

potential, with Cornerstone A showing the most dramatic

increase and B, C and D showing more modest increases.

Seasonal variation of wildfire potential—These annual

numbers provide a glimpse of future wildland fire

conditions, but examination of PDI changes at the seasonal

scale provides more information. Splitting the area burned

information presented in figure 17.1 by season provides

insight into the current wildfire season. Figure 17.4 shows

the number of acres burned during the winter months

(December, January and February). South Florida and

the western Appalachians are the areas showing highest

wildfire activity; although wildfire activity is present at a

low level across much of the South. For southern Florida,

the heart of the dry season is the winter months, when

natural ignitions are uncommon, but human ignitions are

sufficient to support significant winter wildfire activity. In

the Appalachians, much of the winter wildfire season is

tied to either the start or end of the season reflecting either

a prolonged fall wildfire season or an early start to a spring

wildfire season.

Spring (March, April, May) brings more wildfire activity,

particularly to the Coastal Plain and Piedmont (fig. 17.5).

Along the Coastal Plain, sea-breeze induced thunderstorms

provide a natural ignition source along with the ever

present human ignition component. By summer (June,

July, August), wildfire activity decreases throughout the

Appalachians while a low level of wildfire activity persists

in the Coastal Plain, where continuing thunderstorms

produce sufficient rainfall to reduce the probability ignition

by late June or early July (fig. 17.6). Fall brings a return of

wildfire activity to the Appalachians and a great reduction

in the Coastal Plain, particularly Florida (fig. 17.7). For

much of the Appalachians the input of litter to the forest

floor provides the fuel to support the spread of wildfires

when coupled with dry conditions.

Although wildfires are possible in any season, the two

areas discussed above have distinct wildfire seasons. For

the Coastal Plain, wildfire activity is lowest in the fall and

highest in the spring, with some activity spilling over into

summer and winter. For the Appalachians, activity is lowest

in the summer and highest in the fall, with spring providing

a secondary peak in wildfire activity. Winter wildfire activity

in the Appalachians is considerably more than during

summer, but is largely tied to either an extended fall wildfire

season or an early spring season. Although no other area

shows a seasonal peak in wildfire activity as pronounced as

the Coastal Plain or Appalachians, the eastern Oklahoma/

Arkansas region experiences wildfire activity in all seasons.

For current conditions under Cornerstone A, winter is the

primary rainy season, although the areal extent of this wet

area is restricted to the Appalachians as reflected by the PDI

(fig. 17.8). During the summer, Cornerstone A is dominated

by pronounced drying and fails to capture the summer rains

in Florida and along the Coastal Plain. Over the course of

50 years, this drying is further reinforced and virtually

eliminates all areas of negative PDI values (fig. 17.9).

Cornerstone B offers a better representation of current

conditions compared to Cornerstone A (fig. 17.10); especially

in capturing the evolution of the spring/fall wildfire season

of the Coastal Plain. Key features of note are the improved

flow of moisture from the Gulf of Mexico northward across

the Appalachians and dry conditions across Florida during

winter. The area of moist conditions shifts northward during

spring as dry conditions expand across the Coastal Plain.

Summer brings dry conditions to much of the South, with

the exception of the Coastal Plain where precipitation from

afternoon thunderstorms balances the dry conditions. During

fall, dry conditions return to the Coastal Plain.

Compared to Cornerstone A, the changes in wildfire

potential in 50 years are much more subtle under Cornerstone

B (fig. 17.11), which shows substantial drying along the Gulf

of Mexico during winter and areas of dryness in spring and

summer that are similar but smaller than in Cornerstone A.

Unlike the domination by strong, widespread drying under







522 The Southern Forest Futures Project

Cornerstone A; Cornerstone B shows a much smaller area of

change that is strongest during winter rather than summer.

Wintertime drying could adversely affect prescribed burning

by favoring conditions that promote escaped prescribed

fires. Drier conditions would also promote increased fuel

consumption on prescribed burns, increasing the likelihood

of air quality problems. Cornerstones C and D resemble

Cornerstone B in spatial patterns but their magnitudes of

changes after 50 years are smaller.

Monthly variation in wildfire potential—To get a better

feel for the spatial extent of these changes in wildfire

potential as described by the PDI, we examine the changes in

areal extent of wet and dry conditions within each State by

month. What constitutes wet versus dry conditions for each

Cornerstone is determined by taking all PDI estimates for

2010 and splitting this collection of values into thirds. The

third with the highest PDI values represents dry conditions

and the lowest third wet conditions. The breakpoints defining

dry versus wet conditions are shown in table 17.1 along with

maximum/minimum values for each Cornerstone.

For current conditions, Cornerstones A, C and D have

many of the States predominantly in the wettest category

for November through March, then transitioning to the

driest category for June through August (tables 17.2 to

17.5). Cornerstone B has a much more prolonged and

gradual transition in the spring for many of the States.

These transition periods in spring and fall are typical of the

southern wildfire season, and they largely depend on the

annual evolution of live fuel moisture conditions. In spring,

live fuel moisture values are low until the start of green up.

Periods of drought during this time create periods of high fire

danger. When live fuel moisture peaks, the moisture content

acts as a heat sink, reducing the fire danger. In the fall, live

fuel moistures begin to decline in many species, which along

with drying from high summer temperatures brings about

the fall wildfire season. The onset of winter rains typically

signals the end of the fall wildfire season.

Notable Exceptions to this Pattern are Florida, 

Texas, and Oklahoma 

Florida has a complex climate as the northern part of the

State has both a summer and winter rainy season, while the

southern part exhibits only a single summer rainy season.

In Florida the primary wildfire season is in the spring as

this is the time of year when most of the acres burn. For

the southern part of Florida, spring marks the peak of dry

conditions prior to the start of the summer rainy season.

During May and June, the summer rainy season begins

with isolated thunderstorms. Lightning from these storms

provides a major ignition source until the rainy season

progresses to a point where most areas are receiving rain on

a regular basis.

Texas and Oklahoma represent the dry western portion of

the region. During winter, the storms that move eastward

out of the Rocky Mountains are dry and must begin

rebuilding their moisture levels from southerly winds

coming from the Gulf of Mexico. This process is just getting

started as the storms move across Texas and Oklahoma, only

reaching significant moisture levels in those States’ eastern

parts (hence the very low acreage in the wet category).

In 50 years, Cornerstone A has almost every acre of the

South in the driest category during the summer (table 17.6).

This scenario completely erases Florida’s summer rainy

season. This reveals a possible flaw in the downscaling used

to generate the Cornerstone Futures. Florida’s summer rains

are small-scale local events, far below the resolution of the

underlying general circulation models. These storms are

forced by the difference in temperature between the land and

ocean, which is not going to disappear due to climate change.

Cornerstones B, C and D show only subtle differences (tables

17.7 to 17.9). The gradual transition from winter rainy season

to summer dry season in Cornerstone B is largely erased

which brings the 2060 conditions into much closer alignment

with Cornerstones C and D.

Impacts of climate change—Results from the four

Cornerstone Futures indicate that wildfire potential is likely

to increase over the next 50 years. The magnitude of that

increase is likely to be fairly slight, although one scenario

(Cornerstone A) predicts a significant increase. Predicted

results for Cornerstone B are much more aligned with

Cornerstones C and D despite being forced with the same

emissions scenario as Cornerstone A (A1B). This suggests

that the simulated severe drying of Cornerstone A may be

more closely tied to the general circulation model used for

the simulation than any forcing from the emissions scenario.

From Cornerstone B we can expect both the spring and fall

wildfire seasons to increase in duration across the Coastal

Plain. Drier conditions in winter, spring, and summer will

likely both extend and worsen the spring wildfire season.

Although the results presented above reflect average

conditions, it is likely that we will see shifts in variability

that will result in the bad wildfire seasons being worse than

they currently are. Winter and summer drying will likely

extend the fall wildfire season, but the overall fall magnitude

is little changed from current conditions. Outside of the

Coastal Plain, the western Appalachians would see drier

summers, resulting in a prolonged spring and earlier fall

wildfire season.

These changes in wildfire potential in the South would lead

to longer fire seasons, but for the elevated fire potential

to translate to increased acres burned requires ignitions.

Because the vast majority of southern wildfires are human

caused, not natural; changes in ignitions will be more
523 chapter 17. Fire 

Table 17.1—Breakpoints defining the wettest and driest thirds of potential drought 

index (PDI) values for current conditions in the South for each Cornerstone Future

Scenario Wet breakpoint Dry breakpoint Wettest value Driest value

Cornerstone A 95 562 -585 1162

Cornerstone B -3 530 -708 1169

Cornerstone C 133 634 -510 1222

Cornerstone D 19 500 -582 1083

Average 61 556 —a —

aAverages are not provided for extreme values.

Table 17.2—Percent of area in dry and wet classes for current conditions (2010) by State and 

month for Cornerstone A

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 0 100 32 85 0 0 0 0

Arkansas 0 0 0 0 0 100 75 100 36 0 0 0

Florida 0 0 0 32 48 33 6 0 0 37 0 0

Georgia 0 0 0 0 0 97 73 56 1 1 0 0

Kentucky 0 0 0 0 0 91 16 30 0 0 0 0

Louisiana 0 0 0 0 0 100 0 20 51 0 0 0

Mississippi 0 0 0 0 0 100 19 61 30 0 0 0

North Carolina 0 0 0 0 0 63 0 2 0 0 0 0

Oklahoma 0 0 0 0 0 100 100 100 54 0 0 0

South Carolina 0 0 0 0 0 100 28 12 0 0 0 0

Tennessee 0 0 0 0 0 88 20 49 0 0 0 0

Texas 0 0 19 55 45 100 73 93 96 18 0 0

Virginia 0 0 0 0 0 63 6 10 0 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 100 100 9 0 0 0 0 0 0 99 100

Arkansas 96 100 97 0 0 0 0 0 0 14 100 100

Florida 21 41 22 0 0 5 0 18 18 0 8 22

Georgia 84 100 82 4 1 0 0 0 0 1 31 84

Kentucky 100 100 100 0 0 0 0 0 0 0 100 100

Louisiana 100 100 82 0 0 0 18 34 0 0 100 100

Mississippi 100 100 100 27 0 0 0 0 0 0 100 100

North Carolina 100 100 100 4 3 0 0 0 2 11 45 100

Oklahoma 3 12 4 0 0 0 0 0 0 36 5 16

South Carolina 98 100 100 0 0 0 0 0 0 0 19 96

Tennessee 100 100 100 5 0 0 0 0 0 0 100 100

Texas 12 8 0 0 0 0 8 1 0 3 7 15

Virginia 100 100 100 0 0 0 0 0 0 12 97 100
524 The Southern Forest Futures Project

Table 17.3—Percent of area in dry and wet classes for current conditions (2010) by State and 

month for Cornerstone B

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 0 0 0 94 17 0 0 0

Arkansas 0 0 0 0 0 17 61 100 73 0 0 0

Florida 0 0 0 0 0 0 0 1 0 0 0 0

Georgia 0 0 0 0 0 0 7 71 20 0 0 0

Kentucky 0 0 0 0 0 0 0 26 3 0 0 0

Louisiana 0 0 0 0 0 85 0 58 34 0 0 0

Mississippi 0 0 0 0 0 11 0 93 73 0 0 0

North Carolina 0 0 0 0 0 0 0 19 0 0 0 0

Oklahoma 0 0 0 0 0 26 100 100 52 8 0 0

South Carolina 0 0 0 0 0 0 0 47 0 0 0 0

Tennessee 0 0 0 0 0 0 0 92 30 0 0 0

Texas 0 0 25 32 65 80 97 99 75 64 0 0

Virginia 0 0 0 0 0 0 0 4 0 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 100 66 61 39 0 0 0 0 0 26 100

Arkansas 100 89 100 97 9 0 0 0 0 0 100 100

Florida 49 39 0 0 3 45 17 57 7 0 0 11

Georgia 100 100 32 21 16 0 0 0 1 0 11 55

Kentucky 100 100 100 100 100 0 0 0 0 0 96 100

Louisiana 100 100 48 38 88 0 26 0 0 0 32 100

Mississippi 100 100 83 89 91 0 5 0 0 0 43 100

North Carolina 100 100 87 22 86 2 1 0 15 0 16 96

Oklahoma 39 13 32 10 0 0 0 0 0 0 20 62

South Carolina 100 100 26 6 17 0 0 0 0 0 6 53

Tennessee 100 100 100 100 93 0 0 0 1 0 92 100

Texas 30 32 3 4 0 0 0 0 0 0 93 33

Virginia 100 100 100 45 51 0 0 0 0 0 35 100
525 chapter 17. Fire 

Table 17.4—Percent of area in dry and wet classes for current conditions (2010) by State and 

month for Cornerstone C

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 0 64 0 92 0 0 0 0

Arkansas 0 0 0 0 0 93 98 100 1 0 0 0

Florida 0 0 0 9 0 0 0 0 0 2 0 0

Georgia 0 0 0 0 0 23 0 64 25 0 0 0

Kentucky 0 0 0 0 0 14 17 63 0 0 0 0

Louisiana 0 0 0 0 0 84 48 67 11 0 0 0

Mississippi 0 0 0 0 0 97 39 93 10 0 0 0

North Carolina 0 0 0 0 0 0 0 8 0 0 0 0

Oklahoma 0 0 0 15 10 96 100 100 49 24 0 0

South Carolina 0 0 0 0 0 0 0 21 0 0 0 0

Tennessee 0 0 0 0 0 35 17 83 0 0 0 0

Texas 0 0 28 60 63 99 99 100 73 50 0 0

Virginia 0 0 0 0 0 0 10 0 0 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 100 100 1 0 0 0 0 0 0 26 100

Arkansas 99 96 100 0 0 0 0 0 0 0 99 100

Florida 32 32 20 0 7 17 25 16 17 0 0 16

Georgia 99 99 62 5 0 0 0 0 0 0 15 67

Kentucky 100 100 100 0 0 0 0 0 0 0 100 100

Louisiana 100 100 88 0 0 0 0 0 0 0 58 100

Mississippi 100 100 100 0 0 0 0 0 0 0 64 100

North Carolina 100 100 100 9 3 0 0 0 1 4 28 76

Oklahoma 12 5 25 0 0 0 0 0 0 0 6 6

South Carolina 100 100 81 0 0 0 0 0 0 0 6 43

Tennessee 100 100 100 4 0 0 0 0 0 0 99 100

Texas 13 7 3 0 0 0 0 0 0 0 4 13

Virginia 100 100 100 0 0 0 0 0 0 2 92 99
526 The Southern Forest Futures Project

Table 17.5—Percent of area in dry and wet classes for current conditions (2010) by State and 

month for Cornerstone D

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 0 96 0 79 7 0 0 0

Arkansas 0 0 0 0 0 71 70 70 87 0 0 0

Florida 0 0 0 0 1 8 0 0 0 24 0 0

Georgia 0 0 0 0 5 72 0 47 19 0 0 0

Kentucky 0 0 0 0 0 7 7 45 17 0 0 0

Louisiana 0 0 0 0 0 83 7 36 44 0 0 0

Mississippi 0 0 0 0 0 100 2 68 53 0 0 0

North Carolina 0 0 0 0 0 0 0 2 0 0 0 0

Oklahoma 0 0 0 0 0 76 100 94 71 26 0 0

South Carolina 0 0 0 0 0 6 0 15 0 0 0 0

Tennessee 0 0 0 0 0 39 8 72 16 0 0 0

Texas 0 0 7 40 35 96 97 99 77 64 1 0

Virginia 0 0 0 0 0 0 0 7 0 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 100 95 0 0 0 0 0 0 0 56 100

Arkansas 99 97 100 2 0 0 0 0 0 0 100 100

Florida 32 35 5 0 4 22 15 15 35 0 0 22

Georgia 100 100 36 1 0 0 0 0 0 0 21 74

Kentucky 100 100 100 0 0 0 0 0 0 0 100 100

Louisiana 100 100 100 0 0 0 0 0 0 0 82 100

Mississippi 100 100 100 0 0 0 0 0 0 0 93 100

North Carolina 100 100 97 5 3 0 11 0 0 0 21 100

Oklahoma 6 5 16 0 0 0 0 0 0 0 29 32

South Carolina 100 100 32 0 0 0 0 0 0 0 6 82

Tennessee 100 100 100 2 0 0 0 0 0 0 100 100

Texas 16 9 12 0 0 0 0 0 0 0 18 20

Virginia 100 100 98 0 0 0 0 0 0 2 42 100
527 chapter 17. Fire 

Table 17.6—Percent of area in dry and wet classes for future conditions (2060) by State and 

month for Cornerstone A

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 46 100 100 100 100 0 0 0

Arkansas 0 0 0 0 0 100 100 100 100 0 0 0

Florida 0 0 0 81 60 93 96 95 93 59 4 0

Georgia 0 0 0 44 80 100 100 100 98 52 0 0

Kentucky 0 0 0 0 0 100 100 100 100 0 0 0

Louisiana 0 0 0 0 0 100 83 100 88 0 0 0

Mississippi 0 0 0 0 1 100 100 100 100 0 0 0

North Carolina 0 0 0 0 8 98 93 95 88 0 0 0

Oklahoma 0 0 1 60 26 100 100 100 100 23 0 0

South Carolina 0 0 0 10 83 100 100 100 100 0 0 0

Tennessee 0 0 0 0 0 100 100 100 99 0 0 0

Texas 0 0 68 59 89 100 96 100 100 66 21 0

Virginia 0 0 0 0 0 100 100 97 98 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 100 8 0 0 0 0 0 0 0 0 98

Arkansas 38 93 0 0 0 0 0 0 0 0 65 100

Florida 20 8 0 0 0 0 0 0 0 0 0 5

Georgia 74 58 15 0 0 0 0 0 0 0 2 32

Kentucky 100 100 12 0 0 0 0 0 0 0 0 100

Louisiana 98 52 0 0 0 0 0 0 0 0 0 93

Mississippi 100 100 0 0 0 0 0 0 0 0 16 100

North Carolina 100 100 16 0 0 0 0 0 0 1 7 86

Oklahoma 0 3 0 0 0 0 0 0 0 0 8 0

South Carolina 89 84 6 0 0 0 0 0 0 0 0 26

Tennessee 100 100 35 0 0 0 0 0 0 0 14 100

Texas 3 0 0 0 0 0 0 0 0 0 1 1

Virginia 100 100 8 0 0 0 0 0 0 0 0 100
528 The Southern Forest Futures Project

Table 17.7—Percent of area in dry and wet classes for future conditions (2060) by State and 

month for Cornerstone B

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 0 0 93 100 21 0 0 0

Arkansas 0 0 0 0 0 10 100 100 50 0 0 0

Florida 0 0 0 0 0 0 3 11 12 0 0 0

Georgia 0 0 0 0 0 30 83 65 77 1 0 0

Kentucky 0 0 0 0 0 30 66 0 30 0 0 0

Louisiana 0 0 0 0 0 0 45 55 0 0 0 0

Mississippi 0 0 0 0 0 2 91 90 0 0 0 0

North Carolina 0 0 0 0 0 0 64 41 8 0 0 0

Oklahoma 0 0 0 8 14 36 100 100 87 55 0 0

South Carolina 0 0 0 0 0 18 68 45 58 0 0 0

Tennessee 0 0 0 0 0 0 59 34 3 0 0 0

Texas 0 0 40 59 85 50 97 100 63 78 11 0

Virginia 0 0 0 0 0 3 76 32 0 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 94 45 0 6 0 0 0 0 0 0 100

Arkansas 76 46 0 8 0 0 0 0 0 0 25 61

Florida 16 1 0 0 14 57 33 42 4 0 0 32

Georgia 71 42 19 4 4 0 0 0 0 0 2 100

Kentucky 100 100 16 43 0 0 0 0 0 0 0 100

Louisiana 43 89 0 0 21 17 12 0 22 0 11 93

Mississippi 100 100 24 4 15 0 0 0 0 0 0 100

North Carolina 100 71 59 10 7 0 0 0 0 3 6 100

Oklahoma 0 0 0 0 0 0 0 0 0 0 0 0

South Carolina 76 32 13 0 0 0 0 0 0 0 0 100

Tennessee 100 100 74 43 0 0 0 0 0 0 0 100

Texas 0 5 0 0 0 7 0 0 3 0 12 2

Virginia 100 100 66 6 0 0 0 0 0 0 2 100
529 chapter 17. Fire 

Table 17.8—Percent of area in dry and wet classes for future conditions (2060) by State and 

month for Cornerstone C

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 65 75 17 89 9 0 0 0

Arkansas 0 0 0 0 12 100 100 100 12 0 0 0

Florida 0 0 0 23 67 0 0 0 0 0 0 0

Georgia 0 0 0 0 73 52 28 60 41 0 0 0

Kentucky 0 0 0 0 0 38 38 100 0 0 0 0

Louisiana 0 0 0 0 0 48 54 61 17 0 0 0

Mississippi 0 0 0 0 36 87 73 92 28 0 0 0

North Carolina 0 0 0 0 0 1 31 30 0 0 0 0

Oklahoma 0 0 0 27 78 100 100 100 78 23 0 0

South Carolina 0 0 0 0 19 13 27 32 0 0 0 0

Tennessee 0 0 0 0 0 61 42 98 0 0 0 0

Texas 0 0 37 71 64 94 99 100 75 37 12 0

Virginia 0 0 0 0 0 9 64 76 0 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 100 100 15 0 0 0 0 0 0 15 100

Arkansas 21 82 27 0 0 0 0 0 0 0 65 99

Florida 27 8 21 0 0 12 0 25 4 0 0 25

Georgia 97 46 60 6 0 0 0 0 0 0 8 88

Kentucky 100 100 96 5 0 0 0 0 0 0 72 100

Louisiana 96 75 40 0 0 0 0 0 0 0 0 100

Mississippi 100 100 100 18 0 0 0 0 0 0 32 100

North Carolina 100 79 99 9 0 0 0 0 3 4 13 100

Oklahoma 0 0 0 0 0 0 0 0 0 0 0 3

South Carolina 100 29 68 0 0 0 0 0 0 0 4 96

Tennessee 100 100 100 35 0 0 0 0 0 0 91 100

Texas 4 0 0 0 0 0 0 0 1 0 0 11

Virginia 100 100 60 0 0 0 0 0 0 0 3 100
530 The Southern Forest Futures Project

Table 17.9—Percent of area in dry and wet classes for future conditions (2060) by State and 

month for Cornerstone D

Percent area in driest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 0 0 0 0 0 91 83 94 3 0 0 0

Arkansas 0 0 0 0 0 100 100 100 100 0 0 0

Florida 0 0 0 0 0 0 0 0 0 0 0 0

Georgia 0 0 0 0 6 63 56 61 2 0 0 0

Kentucky 0 0 0 0 0 85 82 100 34 0 0 0

Louisiana 0 0 0 0 0 91 78 88 54 0 0 0

Mississippi 0 0 0 0 0 99 94 96 73 0 0 0

North Carolina 0 0 0 0 0 53 15 14 0 0 0 0

Oklahoma 0 0 0 36 11 100 100 100 100 44 0 0

South Carolina 0 0 0 0 0 37 33 29 0 0 0 0

Tennessee 0 0 0 0 0 83 81 98 42 0 0 0

Texas 0 0 25 75 55 97 100 100 98 76 17 0

Virginia 0 0 0 0 0 77 44 47 0 0 0 0

Percent area in wettest class

State JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Alabama 100 100 100 0 0 0 0 0 0 0 22 100

Arkansas 51 74 99 0 0 0 0 0 0 0 82 100

Florida 23 39 25 0 4 20 1 39 6 0 0 22

Georgia 91 100 89 1 1 0 0 0 0 0 11 74

Kentucky 100 100 100 38 0 0 0 0 0 0 50 100

Louisiana 100 100 85 0 0 0 0 0 0 0 49 100

Mississippi 100 100 100 0 0 0 0 0 0 0 58 100

North Carolina 100 100 100 8 3 0 0 0 0 1 13 100

Oklahoma 0 3 8 0 0 0 0 0 0 0 8 6

South Carolina 100 100 100 0 0 0 0 0 0 0 6 57

Tennessee 100 100 100 2 0 0 0 0 0 0 91 100

Texas 3 8 3 0 0 0 0 0 0 0 3 9

Virginia 100 100 100 2 0 0 0 0 0 0 2 100
531 chapter 17. Fire 

closely tied to social issues than to climate. As the population

in the South continues to increase and the wildland-urban

interface continues to expand, ignitions caused by human

carelessness are likely to increase, creating wildfire

conditions that quickly exceed local suppression capabilities.

Future of Prescribed Fire 

Prescribed fire is an important tool used in the South to

manage hazardous fuels. The potential for an extended

wildfire season will magnify the importance of effective

fuels management. However, the same drying that is

extending the wildfire season could also limit the ability to

use prescribed fire as the dry conditions will likely increase

the potential for escaped fires and also increase the potential

for the fires to harm resources. Dry conditions will promote

increased fuel consumption and consequently increased

emissions. With air quality standards continually being

tightened, these added emissions could result in further

constraints on prescribed fire usage to help protect the health

of the growing population. Air quality issues could have the

largest impact on prescribed fire, as air quality restrictions

would restrict burning over large areas, not just within the

wildland-urban interface.

The rapid expansion of the U.S. population since World War

II into formerly rural areas has caused significant shifts in

land use and land cover. Natural resource managers must

cope with constraints on traditional tools as well as a new

class of resource and societal problems in the interface zone

where urban and wildland uses must coexist. A history

of extensive clearing, farming, or grazing has left many

legacies, including an extensive road system (fig. 17.12).

Population growth since the middle of the last century has

caused increasing urbanization and fragmentation of the

forested landscape (Stanturf and Wimberly, in press; Wear

2002), increasing the size and importance of the wildlandurban interface. More people now live at the interface and

the transportation system is expanding, becoming denser and

more pervasive (Riitters and Wickham 2003).

Aside from the physical aspects of urbanization, changing

demographic profiles and cultural values (Cordell and

others 2004) have altered attitudes towards natural resource

management in general (Bliss and others 1997, Hull and

Stewart 2002, Jacobson and others 2001) and prescribed

burning in particular (Duryea and Hermansen 2002, Loomis

and others 2001). More than 50,000 U.S. communities on the

wildland-urban interface have been designated as “at risk”

for fire, and most of them (70 percent) are in the Southern

States (Blue Ribbon Panel 2008). The values at risk are

substantial: recent wildfire seasons have been expensive with

suppression costs in 2002 at $1.5 billion nationwide (National

Interagency Fire Center 2001) and damage estimates from

the 1998 wildfires in Florida alone costing close to $800

million (Butry and others 2001).

The growing wildland-urban interface increases both

the risk of wildfire occurring and the cost of wildfire by

placing higher values at risk than in wildland areas. Use of

prescribed burning in the wildland-urban interface is still

practical but requires more planning and preparedness, safe

conduct, and communication with landowners and local

officials (Miller and Wade 2003, Wade and Mobley 2007).

In addition to the increased complexity of fire management,

State agencies are faced with a dwindling workforce as the

number of firefighters dropped by 24 percent between 2004

and 2010.2 Declining budgets impact more than just staffing

as agencies incur increased costs for training their staff and

cooperators to work in the interface (State of Georgia 2010).

High rates of arson in some states add to the fire risk (USDA

Forest Service 2011).

2Personal communication with David Frederick, Fire Director, Southern

Group of State Foresters, P.O. Box 680235, Prattville, AL 36068-0235. Feb.

2011.

Figure 17.12—Legacy of roads in the South as compared to the West: (A) roads in an approximately 26,000 km2 area of southwestern Georgia, the Flint

River Valley, compared to (B) a similar area of the Bitterroot Valley in Montana. (Source: Stanturf and Wimberly, in press)

(A) (B)
532 The Southern Forest Futures Project

The South exemplifies the problems of mixing urbanized

land uses with fire-adapted natural vegetation. Urbanization

constrains traditional forest management and use of

prescribed burning even at the wildland end of the

urban-wildland gradient because of concerns for liability

from escaped prescribed fire, transportation safety, and

regional air quality. Moving toward the urban end of the

gradient, these concerns greatly increase often resulting

in abandonment of fuel management and increased risk of

occurrence and severity of inevitable wildfire. Because of

an extensive road system, the entire South may be regarded

as a wildland-urban interface, at least in terms of managing

smoke from prescribed burning.

Even when continued forest management is feasible, there

will likely be further constraints on use of prescribed

burning in the wildland-urban interface due to smoke.

Smoke from prescribed burning is a critical issue in the

South due to a combination of physical (meteorology,

climate, topography), biological (fire-affected vegetation

and hazardous fuels), and social (population density, road

network) factors. In fact, smoke is probably the key issue

in suitability of prescribed burning as a way to manage

fuel loads in the interface. Concerns with smoke are

several: local and regional air quality (Achtemeier 2003,

Achtemeier and others 2001, Monroe 2002), visibility

on roads (Mobley 1989), and health impacts especially

on sensitive segments of the population with respiratory

problems (Sorenson and others 1999).

Threat of escapes—Potential liability from escaped

prescribed fire is often cited as a constraint on the use

of prescribed burning (Brenner and Wade 2003, Haines

and Busby 2001, Haines and Cleaves 1999). Even when

the best available practices are applied, the possibility

of an escape exists. Potential damage to neighboring

properties, endangerment of human lives, and smoke-caused

transportation accidents pose liability risk, along with

litigation costs (Sun 2006).

Following the lead of Florida, all Southern States except

Tennessee have revised their liability laws to limit liability

unless negligence is involved (Brenner and Wade 2003,

Sun 2006); some differentiate between simple and gross

negligence. In the 10 States with simple negligence rules

(Alabama, Arkansas, Kentucky, Louisiana, Mississippi,

North Carolina, Oklahoma, South Carolina, Texas, and

Virginia), a landowner who does not exercise the care that

would be exercised by a “reasonable prudent person” could

be held liable for damage from an escaped prescribed fire.

In Florida and Georgia, where the gross negligence rule

holds, the burden on the landowner or agent is even lower

(Sun 2006). Thus State legislatures in the South offer

legal protection for managers who use prescribed burning,

provided they follow relevant laws and regulations, and

exercise care in planning and execution.

Smoke—Smoke is produced when wood and other organic

material combusts (Urbanski and others 2009) and produces

a mixture of gases, solid particles, and droplets. Because

wood fires are generally inefficient, they produce a large

number of chemicals. Emissions from wildland fire are

usually expressed as emission factors, defined as the mass

of compound released per mass of dry fuel consumed

(Urbanski and others 2009). Emission factors are influenced

by fuel moisture and whether combustion is smoldering or

flaming (Naeher and others 2007). In the South, the preferred

time for prescribed burning is when fuel moisture is high and

meteorological conditions favor low-intensity fires with lower

fuel consumption as compared to wildfires that typically

occur under drier conditions that favor high-intensity fires

with more complete fuel consumption. Prescribed burning

generally results in lower emissions than wildfire (Urbanski

and others 2009). Typical emission factors from prescribed

burns in a variety of southern forest ecosystems are given

in table 17.10; the dominant compounds emitted are carbon

dioxide, carbon monoxide, and particulates (Urbanski and

others 2009).

Smoke is a problem when it in some way negatively impacts

human habitation or activity (Achtemeier and others 2001).

Smoke is a health problem when it invades the habitation of

those with respiratory problems and other smoke-sensitive

illnesses (Naeher and others 2007). Smoke is a nuisance

when it irritates the eyes and mucus membranes of the nose

and throat. Smoke is a nuisance when it deposits soot on

clothes hung out to dry. Smoke is a safety problem when

it impedes local visibility to create hazards to drivers of

motor vehicles. The enormous wildland—urban interface

and dense road network located in a region where up to six

million acres of forest land per year are subject to prescribed

fire combine to make problem smoke the foremost forestryrelated air quality problem in the South. During the daytime,

smoke becomes a problem when it drifts into areas of human

habitation. At night, smoke can become entrapped near

the ground and, in combination with fog, create visibility

reductions that cause roadway accidents. Public complaints

about smoke-related problems usually begin at levels well

below national ambient air quality standards.

Air quality—One of the key indicators of air quality is

whether monitoring shows that an area complies with

the national air quality standards established by the

Environmental Protection Agency (EPA). Although EPA

does not directly regulate the use of wildland fire, it is

responsible for enforcing the sections of the Clean Air Act

that requires States and Tribes to attain and maintain the

national ambient air quality standards (NAAQS). The EPA

also must develop “primary” and “secondary” standards
533 chapter 17. Fire 

for six pollutants: ozone, particulate matter, sulfur dioxide,

carbon monoxide, nitrogen dioxide and lead (table 17.11).

Primary standards are for human health and secondary

standards for public welfare, which includes damage to

vegetation and crops as well as effects on visibility. Of these

six pollutants, only two—sulfur dioxide and lead—are of

little concern for prescribed burning. As a result of rapid

dilution and its instability, carbon monoxide emissions from

prescribed burning are not a concern to the general public

(National Coalition of Prescribed Fire Councils 2007).

However, carbon monoxide emissions may be a concern to

firefighters and prescribed burning crews.

Although nitrogen oxides from prescribed burning are not

of concern on a local level (National Coalition of Prescribed

Fire Councils 2007), they combine with other emissions

(volatile organic carbon, particulates, and carbon monoxide)

in a photochemical process (Urbanski and others 2009) and

contribute to ozone formation that may be a concern in some

areas (National Coalition of Prescribed Fire Councils 2007).

Figure 17.13 shows the current status of non-attainment areas

in the South for ozone and highlights the relationship of

urban areas to non-attainment status. Ozone and particulate

levels are generally at their lowest ambient levels during the

prescribed burning season in the South, winter and early

spring (Southeast Regional Partnership for Planning and

Sustainability 2010). But occasionally summer burns are

recommended for ecological reasons (Brockway and others

2005), a practice that would be limited in an area designated

as non-attainment for ozone and particulates.

After carbon dioxide and carbon monoxide, particulates

account for the greatest share of emissions from wildland

burning (Urbanski and others 2009) and because particulates

are a criteria pollutant, currently they are the greatest

concern from prescribed burning. Wood smoke particulates

are relatively small but their size distribution can vary

greatly, depending on the rate of energy release.

Because of their size (generally, 70 percent are smaller

than 2.5 microns in aerodynamic diameter or PM2.5),

wood smoke particulates scatter light and reduce visibility

(National Coalition of Prescribed Fire Councils 2007).

Standards for particulate matter have been on a trend of

increasing stringency since 1971 (Southeast Regional

Partnership for Planning and Sustainability 2011)—with

current thresholds of 35 μg m-3 averaged for any 24 hourperiod and 15 μg m-3 averaged over a full year—and there

is little evidence to suggest that standards will loosen in

future reviews. Recent annual and 24-hour ambient PM2.5

Table 17.10—Modeled ranges of emission factors (g kg-1) for prescribed burning in several southern forest ecosystems 

(developed for illustrative purposes and not intended to be definitive because numbers of fires in each ecosystem varied 

and were conducted under varying conditions); these are fire-weighted average factors comparing compound emitted to 

dry fuel consumed

Vegetation type MCE a CO2 CO CH4 C2H6 C2H4 C2H2 C3H8 C3H6 C3H4 PM2.5

Longleaf pine, palmetto 0.934-

0.952

1681-

1712

55.3-

75.2

1.26-

1.45

0.13-

0.18

0.94-

1.34

0.40-

0.74 .01 0.35-

0.37

0.00-

0.09

10.0-

11.3

Sandhills longleaf pine 0.918 1653 94.0 3.39 0.39 0.95 0.30 0.11 0.50 0.05 11.5

Loblolly pine, wiregrass 0.928-

0.942

1657-

1687

66.5-

81.5

1.78-

2.31

0.26-

0.28

1.19-

1.27

0.33-

0.42

0.10-

0.11

0.45-

0.46

0.05-

0.07

13.2-

15.6

Mixed pine, wax myrtle 0.904 1621 109.4 3.00 0.23 0.83 0.28 0.06 0.38 0.04 10.4

Oak, pine, grass 0.921-

0.942

1647-

1688

65.9-

90.2

1.75-

2.26

0.21-

0.28

0.97-

1.17

0.28-

0.36

0.08-

0.10

0.40-

0.49

0.05-

0.06

14.1-

14.5

Mixed pine, wiregrass 0.936 1682 73.1 1.99 0.22 0.86 0.23 0.09 0.37 0.09 11.4

Sandhill shrub 0.921 1652 89.7 2.62 0.32 1.01 0.23 0.11 0.47 0.03 11.9

Palmetto, turkey oak 0.938 16.95 71.1 1.65 0.18 1.13 0.49 0.02 0.31 0.05 6.9

Palmetto 0.933 1665 76.4 2.13 0.23 1.12 0.35 0.08 0.45 0.05 15.7

Pocosin 0.935-

0.943 1683 64.2-

76.4

1.84-

2.13 0.23 1.12-

1.35 0.36 0.08-

0.11 0.46 0.06 15.7-

16.7

Sawgrass 0.914-

0.97

1635-

1752

34.7-

98.3

0.90-

4.12

0.07-

0.59

0.52-

1.60

0.21-

0.49

0.02-

0.23

0.10-

0.79

0.02-

0.08

9.9-

9.1

Wiregrass 0.912-

0.936

1626-

1681

73.5-

99.5

2.16-

3.34

0.21-

0.44

1.15-

1.42

0.25-

0.64

0.06-

0.20

0.42-

0.64

0.05-

0.07

9.7-

15.3

aMCE is modified combustion efficiency, calculated as the ΔCO2/(ΔCO+ΔCO2).

Source: Adapted from Urbanski and others 2009.

535 chapter 17. Fire 

levels for the States east of the Mississippi River and south

of Virginia (EPA Region 4) are displayed in figures 17.14 and

17.15. Although current levels for most of the Coastal Plain

are below national standards (both the current standards and

those being evaluated), the same cannot be said for areas in

the Piedmont and Southern Appalachian Mountains.

EPA also monitors visibility in Federal Class I areas (Fox

and others, in press), which consist of all international parks,

national wilderness areas larger than 5,000 acres, national

memorial parks larger than 5,000 acres, and national parks

larger than 6,000 acres that were established before 1977.

EPA’s 1999 Regional Haze Rule (64 FR 35714) provides

specific guidance on wildland fire for many Western

States but takes a more general approach for the rest of the

country (National Coalition of Prescribed Fire Councils

2007), requiring that all States with Class I areas consider

the impacts of prescribed burning on visibility. Five

Regional Planning Organizations were established to help

States develop visibility protection programs; the Central

Regional Air Planning Association serves Oklahoma, Texas,

Arkansas, and Louisiana and the Visibility Improvement

State and Tribal Association of the Southeast for all other

Southern States. Their goal for each Class I area is to

improve the 20 percent haziest days and ensure that no

degradation occurs on the cleanest days.

The Regional Haze Rule requires all States and participating

Tribes to develop State Implementation Plans for reducing

emissions of visibility degrading aerosols, relative to

“natural background conditions.” Natural background

haze is a complex concept that reflects contemporary, not

pre-European settlement conditions (Fox and others, in

press). One central issue is whether wildland fire is natural

or anthropogenic. The policy developed for the Western

States is that any wildfire or any fire being managed to

the natural fire frequency is classified as natural; any fire

ignited or managed to restore the natural fire frequency

is anthropogenic (National Coalition of Prescribed Fire

Councils 2007). This policy, which has not been applied

beyond the West, would have serious implications for the

South, especially in the mountains where prescribed burning

for restoration objectives is increasing.

Transportation safety—The extensive transportation system

in the South presents a formidable challenge to prescribed

burners. Although most burns are carried out without

incident, smoke and smoke/fog visibility obstructions on

southern highways cause numerous accidents with loss of life

and personal injuries. Mobley (1989) reported 28 fatalities,

more than 60 serious injuries, numerous minor injuries,

and millions of dollars in lawsuits from 1979 to 1988.

Comparing three years of accident reports in Florida, Lavdas

and Achtemeier (1995) found accidents are more closely

associated with local ground radiation fogs (cooling of land

after sunset) than with widespread advection fogs (formed

when moist air passes over a cool surface) and that most

serious accidents occur at night or near sunrise when smoke

from smoldering fires is entrapped near the ground and

carried by local drainage winds into shallow basins. Near

sunset, under clear skies and near calm winds, temperatures

in shallow stream basins can drop up to 20 °F in an hour

(Achtemeier 1993) and strong, shallow valley inversions can

develop. Weak nighttime drainage winds of approximately

1 mile per hour (0.5 m sec-1 ) can carry smoke more than 10

miles, far enough to carry smoke/fog over a roadway in many

areas. An example is the smoke from wildfires in 2000 that

drifted across Interstate 10 and caused at least 10 fatalities,

five in Florida and five in Mississippi.

Achtemeier (2006, 2008, 2009) demonstrated that under

certain conditions, fog combined with smoke from

prescribed burning can produce a “superfog” that reduces

visibility to less than 10 feet (3 m, the definition of zero

visibility). Motorists have no defense when driving from

unlimited visibility to zero visibility in a manner of seconds.

Because most prescribed burns take place in the winter

when dry surface fuels overlay wet fuels, they often provide

considerable moisture release both from the combustion

and from heated soil and underlying wet fuels that do not

ignite. At night, moisture from residual smoke can increase

ambient relative humidity to 100 percent and contribute to

the formation of superfog (Achtemeier 2009). Because we

are just beginning to recognize the conditions for superfog

formation, the full significance of this extremely hazardous

phenomenon is yet to be realized or mitigated by the public

safety community.

Human health—The greatest health threat from wood

smoke appears to come from fine particles although a

number of other constituents have health effects (Naeher

and others 2007). Fine particles in wood smoke (less

than 100 μg m3) that penetrate far into lung tissue have

toxic effects (Naeher and others 2007). Because ultra-fine

particles (PM2.5) can be transported long distances from the

combustion site and may form later through condensation

and atmospheric chemical reactions, they can pose a health

hazard to vulnerable populations at considerable distance

from a prescribed burn. According to the World Health

Organization, vulnerable groups are the very young,

pregnant women, the elderly, and individuals with preexisting respiratory (asthma, chronic obstructive pulmonary

diseases) and cardiac diseases (Schwela and others 1999).

Other groups may be more susceptible due to higher

exposures: outdoor workers, firefighters and emergency

response workers (“Guidelines on vegetation fire

emergencies for public health protection” also contains

a review of studies linking health effects to biomass

burning. Available online at http://www.who.int/docstore/

537 chapter 17. Fire 

peh/Vegetation_fires/Health_Guidelines_final_3.pdf , last

accessed on 9 December 2010). Recent studies have shown

that wildfires and prescribed burns expose fire personnel to

smoke levels high enough to present potential occupational

health concerns (Carlton and others 2004, Naeher and others

2007, Yanosky 2001). Naeher and others (2006) also found

that current exposure standards for dust inhalation, although

not intended to apply to wildland fire personnel, would be

inadequate if applied to protect fire personnel from harmful

particulate exposures.

A number of other wood smoke constituents have health

effects (Naeher and others 2007). Although carbon

monoxide’s instability and rapid dilution preclude any threats

to the general public (National Coalition of Prescribed

Fire Councils 2007), carbon monoxide emissions may

be a concern to firefighters and persons on prescribed

burning crews. At least five chemical groups with known

carcinogenic properties are present in wood smoke along

with 26 chemicals considered hazardous air pollutants by

EPA (Naeher and others 2007). Currently EPA is focusing

on acetaldehyde, acrolein, 1,3 butadiene, formaldehyde, and

polycyclic organic matter (Southeast Regional Partnership

for Planning and Sustainability 2010). Naeher and others

(2007) found that even limited exposure to wood smoke can

reduce resistance against infections, that most effects are

associated with the particle phase, and that an associations

exists between wildfires and increased emergency room

visits for upper and lower respiratory illnesses and decreased

lung functioning (Naeher and others 2007).

Alternatives to Prescribed Burning 

Various mechanical and chemical alternatives to prescribed

burning are used or have been proposed and recent reviews

provide details (Guldin 2010, Marshall and others 2008,

Mercer and Prestemon 2008, O’Brien and others 2010,

Outcalt 2009, Reilly and others 2009, Schwilk and others

2009). Equipment such as mowers, mulchers and choppers

are used to cut, chop, or sever mostly midstory and

understory fuel layers (Outcalt 2009). This equipment is

most effective where large stems are widely spaced and is

often used in areas with high fuel loads. Mechanical methods

change fuel configurations but do not remove fuels from the

site and may not completely mitigate the wildfire threat. Most

often they are used as a pre-treatment prior to prescribed

burning. Although slope limitations have traditionally

hindered usage of mechanical methods in the mountains,

increasingly smaller crawler units are now available for steep

slopes (Reilly and others 2009). Harvesting with mechanized

equipment is a normal forestry operation and clear-cutting or

thinning for fuels management or restoration is increasingly

utilized especially in pine types (Guldin 2010, Outcalt 2009).

Harvesting to remove unwanted species or to reduce stem

density is often followed by prescribed burning to maintain

stand structure and composition.

Herbicides that target broadleaved trees have been a standard

treatment in pine plantation management for more than

30 years. Managers also use herbicides for fuel reduction

(Outcalt 2009). Similar to mechanical fuel reduction

methods, herbicides are often the precursor to prescribed

burning in stands with dense shrub-layer vegetation.

Herbicide application followed by burning can be more

effective than burning alone (Outcalt 2009).

Prescribed burning remains the most widely used fuel

treatment in the South although significant acres are treated

with mechanical means, mostly on Federal lands in the

wildland-urban interface zone (Outcalt 2009). Each method

has benefits and drawbacks (table 17.12) with prescribed

burning often costing the least and providing the most

ecosystem benefits (Glitzenstein and others 2003, Kirkman

and others 2004a, b).

Carbon and Climate 

Wildfire can affect climate through emitting carbon dioxide

and aerosol particles into the atmosphere (National Academy

of Sciences 2010). The greenhouse gas effect is one of the

major contributors for climate change at long-term (decade

and century) scale. Greenhouse gases in the atmosphere

can absorb long-wave radiation emitted from the ground,

which prevents heat energy from radiating into space. As

a result, the temperature of the earth-atmosphere system

increases. A number of atmospheric general circulation

models have projected that greenhouse gases will increase

global temperature by 4 to 6 °C by the end of this century,

accompanied by significant changes in precipitation. It is

estimated that average annual global fire carbon emissions

were about 2 Pg (petagrams) in the recent decade, about a

third of all carbon emissions. This indicates that wildfire

emission is one of the major sources of atmospheric carbon

dioxide and therefore an important contributor to future

climate change, even though they comprised only 4 to 6

percent of anthropogenic emissions in the United States

(Wiedinmyer and Neff 2007).

Charlson and others (1992) showed that smoke from wildfires

can affect global climate by scattering and absorbing

short-wave (solar) radiation (direct radiative forcing) and

modifying cloud microphysics (indirect radiative forcing).

These processes can further modify clouds and precipitation

and atmospheric circulation (Ackerman and others 2000,

Liu 2005a). In contrast, smoke aerosols (including black

carbon or soot) have a shorter life span, but greater spatial

variability and the potential for long-range transport (Kopp

and Mauzerall 2010). Thus, they mainly affect short-term

(daily, monthly, or seasonal) regional climate variability.
538 The Southern Forest Futures Project

For example, figure 17.16 shows the role of the smoke

aerosols from the Yellowstone National Park wildfires in

the development of the 1988 drought in the Northern United

States (Liu 2005b). The precipitation change in response to

radiative forcing of smoke aerosols was mostly negative in

the Northwest, with the largest negative response of about -30

mm found in the northeastern portion of the Midwest. This

was accompanied by positive responses in the Southwest,

Northeast, and southeastern portion of the Midwest; and

negative response in the South. This simulated pattern was

similar to the observed pattern of precipitation anomalies,

suggesting that the smoke particles from the wildfire might

have exacerbated the drought.

Although much about the interaction between wildfire and

climate has yet to be understood and great uncertainty

surrounds U.S. policy and regulatory approaches, smoke

from prescribed burning clearly will receive increased

attention from the scientific and policy communities. Recent

studies have called for a more complete accounting of fire

in carbon budgets (Hurteau and others 2008) and have

emphasized the need to consider black carbon in climate

change projections (Kopp and Mauzerall 2010). If climate

change increases the potential for wildfire and alters fire

regimes (Running 2006), the ability of forests to sequester

carbon as a mitigation strategy could be compromised;

instead of a carbon sink, forests could become a carbon

source. Although it is generally agreed that fuel management

through prescribed burning emits less carbon into the

atmosphere compared to more intense wildfires, only a few

studies have quantified this comparison (Wiedinmyer and

Hurteau 2010) or demonstrated how forest management

techniques can significantly alter the emissions from

prescribed burning (Tian and others 2008).

Conclusions and Discussion

The potential for an extended wildfire season magnifies

the importance of effective fuels management. However,

the same drying that is extending the wildfire season could

also limit the ability to use prescribed fire because the dry

conditions will likely increase the potential for escaped

fires and harm to resources. Dry conditions promote

increased fuel consumption and consequently increased

emissions. If air quality standards continue to tighten,

these added emissions could result in further constraints on

use of prescribed fire to protect the health of the growing

population. Air quality issues could have the largest impact

on prescribed fire by restricting burning over large areas, not

just within the wildland-urban interface.

Prescribed burning is an important forest management tool

in the South, used to manage fuels and promote wildlife

habitat. Because natural wildfires have been limited both

by effective fire suppression to protect other resources and

by forest fragmentation, prescribed burning plays a critical

ecological role in restoring and maintaining the integrity of

fire-dependent forest and grassland communities.

Nevertheless, the near-term future of prescribed burning

in the South is problematic. Changing land use and

demographics have increased the numbers of people and

value of structures in close proximity to wildlands, the

so-called wildland-urban interface. In this interface zone,

Table 17.12—Advantages, disadvantages, and costs of fuel treatment options in use in the South 

Attributes Treatment

Prescribed burn Mechanical Manual Harvesting

Pros

Low cost Facilitates burning Selective Selective

Ecological benefits Use in urban areas Use in urban areas Produces revenue

Minimal soil disturbance

Cons

Smoke Can be costly Can be costly Fuel created

Potential escapes Fuel created Fuel created Potential site damage

Resource damage Equipment breakage

Potential site damage

Cost (dollars 

per acre)

23 to 121a 120 to 350b

35 to 1000c

aCleaves and others 2000.

bRummer and others 2002.

cWolcott and others 2007. 

Source: Outcalt 2009.

540 The Southern Forest Futures Project

prescribed burning requires greater skill and more attention

to communication with the public, both of which increase

costs (State of Georgia 2010). State legislatures have

established limits on liability from responsibly conducted

burns that escape, but laws can be changed. The greatest

threat to continued use of prescribed burning comes from

the effects of smoke on public health, transportation safety,

and air quality; and from new regulations on carbon and

greenhouse gas emissions to mitigate climate change. Air

quality issues, including caps on carbon and greenhouse

gas emissions, would have the greatest impact as they could

restrict prescribed burning over large areas, not just the

wildland-urban interface. Alternatives to prescribed burning

are neither cost-effective nor do they provide the ecological

benefits of fire in adapted ecosystems, (Glitzenstein and

others 2003, Kirkman and others 2004a, 2004b) and do not

achieve the same level of health and safety benefits to human

communities.

Over the longer term and factoring in the effects of climate

change, the need for prescribed burning will likely grow

at the same time that obstacles, complexity and cost will

increase. Restrictions on the use of prescribed burning to

manage fuels would exacerbate potential climate change

effects, particularly in the Coastal Plain and western

Appalachian Mountains where wildfire potential is expected

to increase. Fuels buildups combined with more intense

wildfires under a changed climate potentially would have

drastic consequences for fire-dependent communities

that often support one or more threatened, endangered, or

sensitive species. Drier conditions with more variability in

precipitation could cause vegetation ranges to begin shifting,

which could be initially resisted by active management,

particularly in production conifer forests where reforestation

through planting currently is the norm. Over longer time

than the projections used here, the combination of climate

change, extreme weather events, and severe wildfires could

disrupt successful regeneration and result in new species

assemblages, so-called novel ecosystems, with possibly novel

fire regimes (Williams and Jackson 2007).

Acknowledgments

The Atmospheric Science and Fire Science teams of the

Center for Forest Disturbance Science (Gary Achtemeier,

Yongqiang Liu, Tom Waldrop, and Joe O’Brien) contributed

to this chapter both by contributing their knowledge and

ideas. We would also like to thank three anonymous

reviewers for their helpful comments and recommendations.

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Wear, David N.; Greis, John G., eds. 2013. The Southern Forest Futures 

Project: technical report. Gen. Tech. Rep. SRS-178. Asheville, NC: U.S. 

Department of Agriculture Forest Service, Southern Research Station. 542 p. 

The Southern Forest Futures Project provides a science-based “futuring” 

analysis of the forests of the 13 States of the Southeastern United States. With 

findings organized in a set of scenarios and using a combination of computer 

models and science synthesis, the authors of the Southern Forest Futures Project 

examine a variety of possible futures that could shape forests and the many 

ecosystem services and values that forests provide. The science findings and 

modeling results could inform management and policy analysis of the South’s 

forests. In the chapters of this technical report, the authors provide detailed 

findings and results as well as sets of key findings and implications.

Keywords: Forest conservation, futuring, integrated assessment, Southern 

Forest Futures Project, sustainability.

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