United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical Report

PNW-GTR-802 

September 2010

Advances in Threat 

Assessment and Their 

Application to Forest and 

Rangeland Management

Volume 1

 
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the national forests and national 

grasslands, it strives—as directed by Congress—to provide increasingly greater service to 

a growing Nation.

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Room 1400 Independence Avenue, SW, Washington, DC 20250-9410 or call (800) 795-3272 

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Technical Editors

John M. Pye is an ecologist and program specialist, U.S. Department of Agriculture, Forest 

Service, Southern Research Station, 3041 Cornwallis Road, Research Triangle Park, NC 

27709; H. Michael Rauscher is a forester (retired), U.S. Department of Agriculture, Forest 

Service, Southern Research Station, 200 WT Weaver Boulevard, Asheville, NC 28804; 

Yasmeen Sands is a public affairs specialist, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Pacific Wildland Fire Sciences Laboratory, 400 

N 34th Street, Suite 201, Seattle, WA 98103; Danny C. Lee is Director, U.S. Department 

of Agriculture, Forest Service, Eastern Forest Environmental Threat Assessment Center, 

200 Weaver Boulevard, Asheville, NC 28804; and Jerome S. Beatty is Director (retired), 

U.S. Department of Agriculture, Forest Service, Western Wildland Environmental Threat 

Assessment Center, 3160 NE 3rd Street, Prineville, OR 97754. 

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.

Pesticide Precautionary Statement

This publication reports research involving pesticides. It does not contain recommendations 

for their use, nor does it imply that the uses discussed here have been registered. All uses of 

pesticides must be registered by appropriate state or federal agencies, or both, before they 

can be recommended.

CAUTION: Pesticides can be injurious to humans, domestic animals, desirable plants, 

and fish or other wildlife if they are not handled or applied properly. Use all pesticides 

selectively and carefully. Follow recommended practices for the disposal of surplus 

pesticides and pesticide containers. 
Advances in Threat 

Assessment and Their 

Application to Forest and 

Rangeland Management

Volume 1 

John M. Pye, H. Michael Rauscher, Yasmeen Sands,

 Danny C. Lee, and Jerome S. Beatty, Technical Editors

U.S. Department of Agriculture, Forest Service

Pacific Northwest Research Station

Portland, Oregon 

General Technical Report, PNW-GTR-802

September 2010

Published in cooperation with 

U.S. Department of Agriculture, Forest Service 

Southern Research Station

Asheville, North Carolina
222

GENERAL TECHNICAL REPORT PNW-GTR-802

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Abstract

Pye, John M.; Rauscher, H. Michael; Sands, Yasmeen; Lee, Danny C.; Beatty, Jerome S., 

tech. eds. 2010. Advances in threat assessment and their application to forest and rangeland 

management. Gen. Tech. Rep. PNW-GTR-802. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest and Southern Research Stations. 708 p. 2 vol.

In July 2006, more than 170 researchers and managers from the United States, Canada, 

and Mexico convened in Boulder, Colorado, to discuss the state of the science in environmental threat assessment. This two-volume general technical report compiles peer-reviewed 

papers that were among those presented during the 3-day conference. Papers are organized by 

four broad topical sections—Land, Air and Water, Fire, and Pests/Biota—and are divided into 

syntheses and case studies. Land topics include discussions of forest land conversion and soil 

quality as well as investigations of species’ responses to climate change. Air and water topics 

include discussions of forest vulnerability to severe weather and storm damage modeling. 

Fire topics include discussions of wildland arson and wildfire risk management as well as 

how people precieve wildfire risk and uncertainty. Pests/biota topics include discussions of 

risk mapping and probabilistic risk assessments as well as investigations of individual threats, 

including the southern pine beetle and Phytophora alni. Ultimately, this publication will foster 

exchange and collaboration between those who develop knowledge and tools for threat assessment and those who are responsible for managing forests and rangelands. 

Keywords: Environmental threats, threat assessment, environmental risk analysis, 

disturbance, wildfire, pests, forest and rangeland management.
iv

Preface

Danny C. Lee1 and Jerome S. Beatty2

In July 2006, more than 170 researchers and managers from the United States, Canada, and 

Mexico convened in Boulder, Colorado, to discuss the state of the science in environmental 

threat assessment. The 3-day conference explored the latest information on environmental 

threats, bringing together people who develop knowledge and tools for threat assessment and 

management and those responsible for managing forests and rangelands. The event included 

more than 100 oral and poster presentations on topics ranging from severe weather and 

climate change to risk mapping and forest pests. 

The year preceding the conference, 2005, was one of extremes and provided an ideal 

backdrop for a discussion of environmental threats. More hurricanes were tracked that year 

than had ever before been reported, including Katrina, one of the costliest U.S. hurricanes 

on record and one of the deadliest. Also in 2005, entomologists confirmed the capture of a 

female sirex woodwasp in a sample collected in New York in late 2004. It was the second of 

its kind identified in the United States and has pine managers across the country understandably concerned given the species’ ability to cause severe tree mortality. The wildfire season 

that year also made history, claiming more than 8 million acres and breaking the record that 

had been set in 2000 for total acreage burned. 

Given this context and, more generally, the complexity of assessing and managing 

the myriad threats that face North America’s wildlands, the conference’s organizers were 

committed to continuing the exchange of information and collaboration fostered during the 

event long after its final session. To that end, nearly 50 of the synthesis and case study papers 

presented were adapted to form the initial content of the Encyclopedia of Forest Environmental Threats (http://www.threats.forestencyclopedia.net)—an online resource that promises to deliver to researchers, land managers, and policymakers the scientific knowledge 

about environmental threats they need to achieve their objectives. The threats encyclopedia—like the others in the Forest Encyclopedia Network, of which it is part—connects 

scientific results, conclusions, and impacts with management needs and issues. Designed for 

scientists and practitioners alike, the encyclopedia serves as a growing online compilation of 

scientific knowledge relating to environmental threats and their assessment and management. 

The conference’s content is also preserved here, in a traditional and more permanent 

form. The peer-reviewed papers featured in this volume represent the scope of environmental 

threats and underscore the complexity of their assessment and management. As these papers 

show, environmental threats often act in concert and with no regard for land ownership and 

administrative boundaries, making them as difficult to identify and anticipate as they are 

to manage and control. In response, researchers and managers are developing a growing 

foundation of knowledge, which can help to assess or minimize these threats. This volume 

represents a significant contribution to this effort. 

1

 Director, U.S. Department of Agriculture, Forest Service, Eastern Forest Environmental Threat Assessment Center, 200 Weaver Boulevard, Asheville, NC 28804.

2

 Director (retired), U.S. Department of Agriculture, Forest Service, Western Wildland Environmental 

Threat Assessment Center, 3160 NE 3rd Street, Prineville, OR 97754.
v

Acknowledgments 

The “Advances in Threat Assessment and Their Application to Forest and Rangeland 

Management” conference and this publication were made possible by the contributions 

of many people and organizations. Participating organizations include the USDA Forest 

Service’s Pacific Northwest (PNW) and Southern Research Stations (SRS); the Western 

Wildland Environmental Threat Assessment Center (WWETAC); the Eastern Forest 

Environmental Threat Assessment Center (EFETAC); the Cooperative State Research, 

Education, and Extension Service; Southern Regional Extension Forestry; and the 

Southern Forest Research Partnership. The conference’s organizing committee members 

included John M. Pye, Jerome S. Beatty, Danny C. Lee, H. Michael Rauscher, Yasmeen 

Sands, Gregg DeNitto, Charles G. Shaw, James P. Shephard, and David A. Weinstein. 

Special thanks to the moderators who oversaw the extensive peer-review process—Gregg 

DeNitto, William Bechtold, Charles G. Shaw, Borys Tkacz, William D. Smith, Kurt Riitters, David Weinstein, Becky Kerns, Alan Ager, Charley Luce, and Kerry Overton—and 

to the many peer reviewers who helped to ensure the quality of the science. This publication would not have been possible without the editing expertise of Alan Salmon, Gary 

Benson, Aimee Tomcho, Charmaine Rini, and Sharon DeLaneuville. Matt Howell and 

his University of Georgia team provided excellent Web development and hosting for the 

Web version of this publication’s content, available online at http://www.threats.forestencyclopedia.net. Funding for the conference, this publication, and its Web counterpart was 

provided by WWETAC, EFETAC, PNW, and SRS.
vi

Contents

Volume 1

Land

Syntheses

1 Conversions of Forest Land: Trends, Determinants, Projections, and Policy 

Considerations 

Ralph Alig, Susan Stewart, David Wear, Susan Stein, and David Nowak

27 Soil Quality Is Fundamental to Ensuring Healthy Forests 

Deborah Page-Dumroese, Martin Jurgensen, Dan Neary, Mike Curran, 

and Carl Trettin

37 Assessing the Threat That Anthropogenic Calcium Depletion Poses to Forest Health

and Productivity 

Paul G. Schaberg, Eric K. Miller, and Christopher Eagar

Case Studies

61 Assessment of Oak Wilt Threat to Habitat of the Golden-Cheeked Warbler, 

 an Endangered Species, in Central Texas

David N. Appel and Kim S. Camilli

73 A Methodology for Assessing Annual Risk of Southern Pine Beetle Outbreaks 

Across the Southern Region Using Pheromone Traps

Ronald F. Billings and William W. Upton

87 Using Historical Photography to Monitor and Assess Threats Over Time

Don Evans

93 Integrating Natural Disturbances and Management Activities to Examine Risks 

and Opportunities in the Central Oregon Landscape Analysis

Miles A. Hemstrom, James Merzenich, Theresa Burcsu, Janet Ohmann, 

and Ryan Singleton 

111 Moderate-Resolution Data and Gradient Nearest Neighbor Imputation 

for Regional-National Risk Assessment 

Kenneth B. Pierce, Jr., C. Kenneth Brewer, and Janet L. Ohmann

123 Integration of Population Genetic Structure and Plant Response to Climate Change: 

Sustaining Genetic Resources Through Evaluation of Projected Threats

Bryce A. Richardson, Marcus V. Warwell, Mee-Sook Kim, Ned B. Klopfenstein, 

and Geral I. McDonald

133 Threats to Private Forest Lands in the U.S.A.: A Forests on the Edge Study

Susan M. Stein, Mark H. Hatfield, Ronald E. McRoberts, Dacia M. Meneguzzo, 

and Sara Comas

145 A Spatial Model for Predicting Effects of Climate Change on Swiss Needle Cast 

Disease Severity in Pacific Northwest Forests

Jeffrey K. Stone, Leonard B. Coop, and Daniel K. Manter 

157 Analyzing Risks to Protected Areas Using the Human Modification Framework: 

A Colorado Case Study

David M. Theobald, Alisa Wade, Grant Wilcox, and Nate Peterson 
vii

171 Modeling Species’ Realized Climatic Niche Space and Predicting Their Response 

to Global Warming for Several Western Forest Species With Small Geographic 

Distributions

Marcus V. Warwell, Gerald E. Rehfeldt, and Nicholas L. Crookston

Air and Water

Syntheses

185 The Influence of Forest Management on Vulnerability of Forests to Severe Weather

Robert H. Beach, Erin O. Sills, Tzu-Ming Liu, and Subhrendu Pattanayak

207 Economic Impacts of Hurricanes on Forest Owners

Jeffrey P. Prestemon and Thomas P. Holmes

Case Studies

223 Using Remotely Sensed Data and Elementary Analytical Techniques in 

Post-Katrina Mississippi to Examine Storm Damage Modeling

Curtis A. Collins, David L. Evans, Keith L. Belli, and Patrick A. Glass

237 Evaluating the Vulnerability of Maine Forests to Wind Damage

Thomas E. Perry and Jeremy S. Wilson

Volume 2

Fire

Syntheses

249 Ecological Risk Assessment to Support Fuels Treatment Project Decisions

Jay O’Laughlin

271 Wildland Arson: A Research Assessment

Jeffrey P. Prestemon and David T. Butry

285 Review of Methods for Developing Probabilistic Risk Assessments. 

Part 1: Modeling Fire

D.A. Weinstein and P.B. Woodbury

Case Studies

305 Managing Wildland Fire Risk in Florida

J. Brenner, D. Carlton, S. McLellan, A. Dozier, T. Spencer, D. Buckley, 

and A. Ralowicz

319 Air Pollution Increases Forest Susceptibility to Wildfires: A Case Study 

for the San Bernardino Mountains in Southern California

N.E. Grulke, R.A. Minnich, T. Paine, and P. Riggan 

329 Evaluating Wildland Fire Danger and Prioritizing Vegetation and Fuels Treatments

Paul F. Hessburg, Keith M. Reynolds, Robert E. Keane, Kevin M. James, 

and R. Brion Salter

353 Digital Aerial Sketchmapping and Downlink Communications: A New Tool 

for Fire Managers 

Everett Hinkley, Tom Zajkowski, and Charlie Schrader-Patton
viii

361 Assessing Risks to Multiple Resources Affected by Wildfire and Forest Management 

Using an Integrated Probabilistic Framework 

Steven P. Norman, Danny C. Lee, Sandra Jacobson, and Christine Damiani

371 Probabilistic Risk Models for Multiple Disturbances: An Example of Forest Insects 

and Wildfires

Haiganoush K. Preisler, Alan A. Ager, and Jane L. Hayes 

381 Establishing a Nationwide Baseline of Historical Burn-Severity Data to Support 

Monitoring of Trends in Wildfire Effects and National Fire Policies

Brian Schwind, Ken Brewer, Brad Quayle, and Jeffery C. Eidenshink

397 Information Needs, Acceptability of Risk, Trust, and Reliance: The Case of National 

Predictive Services Customers

Patricia L. Winter and Heidi Bigler-Cole

409 Shared Values and Trust: The Experience of Community Residents in a Fire-Prone 

Ecosystem

Patricia L. Winter and George T. Cvetkovich 

Pests/Biota

Syntheses 

421 Representing Human-Mediated Pathways in Forest Pest Risk Mapping

Frank H. Koch and William D. Smith 

445 Decisionmaking Under Risk in Invasive Species Management: Risk Management 

Theory and Applications

Shefali V. Mehta, Robert G. Haight and Frances R. Homans

469 The Formation of Dense Understory Layers in Forests Worldwide: Consequences 

and Implications for Forest Dynamics, Biodiversity, and Succession

Alejandro A. Royo and Walter P. Carson

497 Methods to Assess Landscape-Scale Risk of Bark Beetle Infestation to Support 

Forest Management Decisions

T.L. Shore, A. Fall, W.G. Riel, J. Hughes, and M. Eng

521 Review of Methods for Developing Regional Probabilistic Risk Assessments, 

Part 2: Modeling Invasive Plant, Insect, and Pathogen Species

P.B. Woodbury and D.A. Weinstein

Case Studies 

541 Developing and Validating a Method for Monitoring and Tracking Changes in 

Southern Pine Beetle Hazard at the Landscape Level

Ronald Billings, L. Allen Smith, Jin Zhu, Shailu Verma, Nick Kouchoukos, and 

Joon Heo

551 Previsual Detection of Two Conifer-Infesting Adelgid Species in North American 

Forests

Stephen Cook, Karen Humes, Ryan Hruska, Christopher Williams, and 

Grant Fraley

559 Estimating the Susceptibility to Phytophthora alni Globally Using Both Statistical 

Analyses and Expert Knowledge

Marla C. Downing, Thomas Jung, Vernon Thomas, Markus Blaschke, 

Michael F. Tuffly, and Robin Reich
ix

571 Assessing Insect-Induced Tree Mortality Across Large Areas With 

High-Resolution Aerial Photography in a Multistage Sample

Randy Hamilton, Kevin Megown, James Ellenwood, Henry Lachowski, 

and Paul Maus 

581 Modeling Potential Movements of the Emerald Ash Borer: the Model Framework

Louis R. Iverson, Anantha Prasad, Jonathan Bossenbroek, Davis Sydnor, and 

Mark W. Schwartz

599 Risk Analysis and Guidelines for Harvest Activities in Wisconsin Oak Timberlands 

to Minimize Oak Wilt Threat

Jennifer Juzwik, Jane Cummings-Carlson, and Kyoko Scanlon 

609 Modeling Current Climate Conditions for Forest Pest Risk Assessment

Frank H. Koch and John W. Coulston 

621 A Multicriteria Framework for Producing Local, Regional, and National Insect and 

Disease Risk Maps 

Frank J. Krist, Jr.; Frank J. Sapio; and Borys M. Tkacz

637 Uncertainty Estimation for Map-Based Analyses

Ronald E. McRoberts, Mark A. Hatfield, and Susan J. Crocker

647 An Aquatic Multiscale Assessment and Planning Framework Approach—Forest 

Plan Revision Case Study

Kerry Overton, Ann D. Carlson, and Cynthia Tait

657 A Landscape-Scale Remote Sensing/GIS Tool to Assess Eastern Hemlock 

Vulnerability to Hemlock Woolly Adelgid-Induced Decline

Jennifer Pontius, Richard Hallett, Mary Martin, and Lucie Plourde

673 Assessment of Habitat Threats to Shrublands in the Great Basin: A Case Study

Mary M. Rowland, Lowell H. Suring, and Michael J. Wisdom

687 Evaluating the Impact of Invasive Species in Forest Landscapes: The Southern Pine 

Beetle and the Hemlock Woolly Adelgid

John D. Waldron, Robert N. Coulson, David M. Cairns, Charles W. Lafon, 

Maria D. Tchakerian, Weimin Xi, Kier Klepzig, and Andrew Birt

699 Spread of Invasive Plants From Roads to River Systems in Alaska: A Network 

Model

Tricia L. Wurtz, Matt J. Macander, and Blaine T. Spellman
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LAND 

Syntheses
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1

Keywords: Deforestation, development, fragmentation, 

land-use change, population growth.

Forest Land Conversion and Recent 

Trends

Forests cover about one-third of the United States and 

range from wildland forests to urban forests. These diverse 

ecosystems provide a variety of habitats for wildlife; help 

to cleanse the air and water; supply timber, fuelwood, and 

other harvested products; serve as places for recreation; help 

to mitigate the effects of global climate change; and provide 

other essential goods and environmental services.

Forests are vulnerable to conversion to other land uses. 

An increasing number of houses and other buildings in and 

near forests portend growing costs and complications in fire 

suppression and potential loss of many values derived from 

forests. Long-term assessment of the condition of forests 

and of the relationships between forest conditions and socioeconomic factors is the key to defining policy questions and 

actions needed to sustain forest-based services. 

In this synthesis, we survey recent trends, determinants, and projections of forest land conversion in the 

United States. Examples with more detailed treatments, supporting tables, and figures are available in Alig and others 

(2003, 2004, 2010) and Alig and Ahearn (2006). Forest land 

conversion is a persistent issue for managers and policymakers; for example, a recent position statement concerning 

loss of forest land by the Society of American Foresters 

(2004) lists ecological effects (e.g., effects on water quality and wildlife habitat) and socioeconomic effects (e.g., 

expansion of the urban-forest interface, reduction of forest 

recreation opportunities, reduction of long-term timber production possibilities, and loss of open space) as important 

implications of forest loss. We examine how socioeconomic 

drivers of land-use change, such as population totals and 

personal income levels, have increased substantially since 

the Second World War and led to changes in forest ecosystem attributes. We summarize determinants of land-use 

changes, focusing on the societal and private tradeoffs of 

retaining land in forests. Our projections reflect population 

Conversions of Forest Land: Trends, Determinants, 

Projections, and Policy Considerations

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Ralph Alig, Susan Stewart, David Wear, Susan Stein, 

and David Nowak

Ralph Alig, research forester, Pacific Northwest Research 

Station, Corvallis, OR 97331; Susan Stewart, research 

social scientist, Northern Research Station, Evanston, IL 

60201; David Wear, project leader and research forester, 

Southern Research Station, Research Triangle Park, NC 

27709; Susan Stein, Forest on the Edge Coordinator, USDA 

Forest Service, Cooperative Forestry Staff, State and Private 

Forestry, Washington, DC 20250; and David Nowak, project leader and research forester, Northern Research Station, 

Syracuse, NY 13210.

Abstract

Forest land conversion leads to ecological effects (e.g., 

changes in water quality and wildlife habitat) and socioeconomic effects (e.g., expanding urban-forest interface, 

reduced long-term timber production possibilities and loss 

of open space). Socioeconomic drivers of land use change 

such as population totals and personal income levels have 

increased substantially since World War II. Human land use 

is the primary force driving changes in forest ecosystem 

attributes. Land use changes affecting forests since 1990 

have been heavily concentrated in the South. Nationwide, 

more than 60 percent of housing units built in the 1990s 

were constructed in or near wildland vegetation. More than 

44 million acres of private forest are projected to experience 

housing density increases between 2000 and 2030, with 

the majority of the most heavily impacted watersheds in 

the East. The United States population is projected to grow 

by more than 120 million people by 2050, and deforestation associated with this growth is projected to exceed 

50 million acres. Fragmentation of remaining forests is 

also projected and expected to be concentrated in distinct 

subregions; in the South, these include urbanizing areas and 

areas close to interstate highway corridors. As urban lands 

expand into surrounding areas, retaining trees can have 

significant benefits. Current benefits of urban vegetation on 

environmental quality nationally are on the order of several 

billion dollars per year.
2

GENERAL TECHNICAL REPORT PNW-GTR-802

growth that spurs demand for land for developed uses at 

the same time that demands for some forest products and 

other forest benefits are increasing. Risk and policy considerations necessitate that creating effective policy in this 

area will require careful deliberation concerning private and 

social viewpoints. For example, some forest benefits (such 

as wildlife habitat and other ecosystem services) can most 

effectively be produced at scales greater than the individual 

private parcel scale and because market imperfections can 

cause some social forest benefits to be undersupplied when 

this is the case (Kline and others 2004a).

Five categories of significant changes affecting forest 

area are:

• Afforestation

• Deforestation

• Forest fragmentation

• Forest parcelization

• Increased numbers of structures, such as 

houses, on forest land

This analysis does not address changes in forest cover 

type. For an example of a national analysis on this topic, 

see Alig and Butler (2004). Examination of intensification 

of land management is illustrated by the 2001 RPA Timber 

Assessment (Haynes 2003).

Table 1 lists supporting major databases, and Table 

2 lists examples of studies that have examined land base 

dynamics in the United States. In the United States, millions of acres of land shift uses each year (USDA NRCS 

2001) reflecting billions of choices made by individuals, 

corporations, nongovernmental organizations, and governments.  Next,  we  look  at  recent  trends  in  those  five 

categories of changes in the land base. Examining historical 

trends provides guidance for identifying key factors that 

are likely to influence forest land condition and associated 

natural resources in future years. Discussion of historical 

trends is a foundation for considering projected changes.

Forest Area Changes in Total

From 1953 to 1997, a majority (26) of States had a loss in 

forest area according to periodic surveys by the USDA Forest Service, e.g., Smith and others (2004). Nine States had 

net losses of at least 1 million acres each, ranging up to 6.3 

million acres. In descending order of net loss amount, the 

States are Texas, Florida, California, Oklahoma, Louisiana, 

Washington, Alaska, Missouri, and Minnesota. Seven 

States had net gains of more than 1 million acres, ranging 

up to 4.1 million acres. In descending order of net gain 

amount, the States are New York, Ohio, Pennsylvania, West 

Virginia, Mississippi, Alabama, and Kentucky. The only 

regions with net gain in forest area were the North, where a 

relatively large amount of pastureland reverted naturally to 

forest, and the Intermountain Region, where a large number 

of acres were reclassified from pasture or rangeland to 

forest over time.

Note that much of the shift from pastureland or 

rangeland to forest use is due to reclassification over time. 

Table 1—Databases used in studies of different types of land base changes

Land base change Coverage Databases

Deforestation 48 contiguous States; 1982 to 1997, USDA NRCS 2001

 with national updates for 2001 and 

 2003

Afforestation Annual tree planting by State, 1980 to 1998; USDA Forest Service Tree planting 

 periodic estimates of reversions to forests reports; periodic and annual FIA 

 surveys

Forest fragmentation 1992, with another in progress National land cover database

Forest parcelization Periodic owner surveys National forest landowner survey

Structure additions to forests Decadal national census; special studies USDC Census Bureau; Oregon 

 structure counts (contact: Dept. of 

 Forestry)

Urban forest changes National USDC Census Bureau
3

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

of deforestation due to urban and developed increased to 55 

percent (USDA NRCS 2001).

Net changes (area into forest minus area out of forest) 

are typically much smaller than total or gross changes (area 

into forest plus area out of forest). Gross change in area of 

non-Federal forests in the contiguous 48 States between 

1982 and 1997 totaled about 50 million acres (USDA NRCS 

2001). The gross change in forest area was 14 times as large 

as the net change in forest area. 

Conversion to Developed Uses—

Development spans a broad range of population density 

associated with settlement patterns, and definitions of 

development can depend on the data source and the purpose 

for which the data are analyzed. Two major data sources 

both show a steady increase in developed uses over recent 

decades. Estimates from the U.S. Census Bureau extend 

furthest back in time and show a 130-percent increase in 

census-defined urban area between 1960 and 2000. Census 

urban area comprises all territory units in urbanized areas 

and in places of more than 2,500 persons outside of urbanized areas. The census measure of urbanization labels as 

“built-up” some land that is still to some extent available for 

rural productive uses, thereby probably erring on the side 

of overgenerous inclusion (Alig and Healy 1987). Although 

the term “paved over” has frequently been used to describe 

As trees grow, they expand to reach the 10-percent canopy 

cover used to define forest land, which changes the pastureland classification to forest use. Even though now classified 

as forest, the land may still be used for grazing. Shifts 

between grazing land and forest uses are common, and 

although they are technically considered as shifts into and 

out of agriculture, they really represent multiple, overlapping uses.

Deforestation

The long-term loss in United States forest area since the 

early 1950s has been due to a combination of factors, but, in 

more recent decades, has been primarily due to conversion 

to urban and developed uses. Deforestation is conversion 

from forest to nonforest use, and between 1982 and 1997, 

23 million acres were deforested on non-Federal land in the 

United States. Here we concentrate on private lands and 

secondarily on other non-Federal lands, for which more data 

are available (e.g., USDA NRCS 2001). The destination of 

about half of the converted forest acres was to urban and 

developed uses (Figure 1). Between 1982 and 1997, more 

than 10 million acres of non-Federal forests were converted 

to developed uses, an area larger than the combined current 

forest area of five Northeastern States (Connecticut, Delaware, Maryland, New Jersey, and Rhode Island). In the most 

recent data remeasurement period (1992–97), the proportion 

Table 2—Example studies of determinants of land base changes involving forest land

Land base change Data Studies Bases

Deforestation Econometric Alig and Healy 1987, Alig

 and others 2004, Kline and

 Alig 2001, Hardie and 

 others 2000

USDA NRCS 2001;

 FIA surveys

Afforestation

Forest fragmentation

Econometric

Econometric and statistical

Lee and others 1992, Kline 

 and others 2002

Butler and others 2004,

 Wear and others 2004,

 Alig and others 2005

USDAForest Service tree

 planting reports

National land cover data-

 base

Forest parcelization Statistical Butler and Leatherberry 

 2004

National forest landowner

 survey

Structure additions to

 forests

Urban forest changes

Statistical

Statistical

Hammer and others 2004,

 Radeloff and others 2005,

 Stewart and others 2003

Nowak and Walton 2005

USDC Census Bureau

 changes

USDC Census Bureau
4

GENERAL TECHNICAL REPORT PNW-GTR-802

urban land, only a small fraction of the land so classified is 

literally paved.

The other major data source is the National Resource 

Inventory (NRI) (USDA NRCS 2001), and it covers a 

shorter period (1982-97). The NRI estimate of U.S. developed area increased 34 percent between 1982 and 1997, with 

an acceleration in the 1990s that was more than 50 percent 

higher than that of the previous 5 years of measurement. 

Between 1982 and 1997, developed area as a percentage 

of the total land area in the 48 contiguous States increased 

from 3.9 percent to 5.2 percent. Forests were the largest 

individual source of developed land.

 One important feature of the NRI data classification in 

contrast to the census urban data is the attempt to exclude 

areas devoted to agricultural crops, forestry, or similar 

purposes when they are within a parcel or contiguous area 

that is otherwise built-up. Outside urban areas, the NRI 

also includes developed land occupied by nonfarm rural 

built-up uses (e.g., rural transportation land), which are not 

included in the census urban category. Including transportation infrastructure can be important in that new roads open 

land to development, alter the environment (e.g., facilitate 

invasion of certain species), can create congestion, and can 

degrade the quality of life. Changes in rural land use have 

historically been and remain connected with changes in 

motor vehicle use, technology, and policy.

A significant amount of low-density development 

has been part of the expansion in developed area. Rural 

America is home to a fifth of the Nation’s people, and rural 

residential lots tend to be larger than housing lots in urban 

areas. One factor in the relatively greater increase in rural 

residential land use is that it is generally land extensive 

compared with the land-intensive residential use in urban 

areas. Rural residential lots, although fewer in number 

than urban lots, tend to be larger, averaging nearly 3 acres 

per household, compared with less than a half-acre per 

household in urban residential areas (USDA ERS 2006). 

Forty-four million acres, 60 percent of all rural residential 

lands, are in the largest lot-size category, over 10 acres. 

Rural land in this category is 3 1/2 times the area of urban 

land in this category. The wide acreage disparity between 

rural and urban large-lot categories is likely attributable to 

relative land values—lower land prices in rural areas make 

large lots more affordable (USDA ERS 2006). 

The low-density housing development in rural areas 

means more people living closer to remaining forest land. 

A measure added in recent periodic Forest Inventory and 

Analysis (FIA) surveys conducted by the USDA Forest 

Service has been the identification of forest lands by ruralurban continuum class. Based on nationwide rural-urban 

continuum classes (Smith and others 2004), 13 percent 

of United States forest land now is located in major 

Figure 1—Conversion of nonfederal forest land by destination, and South vs. 

non-South, 1982-97 (USDA NRCS 2001) (note does not include rangeland and other 

miscellaneous uses). 
5

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

metropolitan counties, and 17 percent in intermediate and 

small metropolitan counties and large towns, together 

making up 30 percent of all U.S. forest land (Smith and 

others 2004, p. 47). Between 1997 and 2002, the forest area 

in major metropolitan areas increased by 5 percent, or more 

than 5 million acres, as the United States developed area 

expanded considerably. Consider that for the whole United 

States, more than one-quarter of counties are currently 

classified as metropolitan. That compares with less than 

one-tenth 50 years ago. 

Amount of urban land per additional person is higher 

for non-metropolitan counties. Many Americans prefer to 

live in less-congested areas and will commute additional 

minutes or hours to realize their goals, taking advantage 

of the United States’ excellent road system. Moreover, 

an increasing population of retirees has augmented outmigration from central cities and suburbs to rural areas that 

offer aesthetic amenities. Natural amenities may be a more 

important determinant of county-level inmigration than 

nearness to metropolitan centers or type of local economy 

(McGranahan 1999).

South

The largest increases in U.S. developed area between 1982 

and 1997 were in the South, a key timber supply region 

(USDA NRCS 2001). There, the amount of land in urban 

and other developed uses increased more than 50 percent 

since the 1960s. The South had one-third of its developed 

area added during those 15 years, equal to about half of 

the U.S. total of developed area added during that period. 

Between 1982 and 1997, the South had 7 of the 10 States 

with the largest average annual additions of developed area 

according to the NRI. The top three—Texas, Florida, and 

North Carolina—each added more developed area than 

did the country’s most populous State, California. Over a 

more recent period, 1992–97, 6 of the 10 States that lost 

the most cropland, forests, and other open spaces to urban 

development were in the South. These six southern States in 

descending order of amount converted were Texas, Georgia, 

Florida, North Carolina, Tennessee, and South Carolina 

(USDA NRCS 2001).

In the Southeast, the concentration of development has 

been in the area of the urban Piedmont Crescent, extending from Richmond to Atlanta. Within this area are the 

Interstate 85 and Interstate 40 corridors, the backbone of 

job growth in the Southeast. Many of the smaller cities 

are adjacent to larger urban areas, resulting in population 

concentrations in larger metropolitan areas. The urban areas 

of the Piedmont are likewise expected to witness the fastest 

growth, whereas the mountains and the Coastal Plain will 

experience most of their growth in nonmetropolitan areas.

Several factors contribute to expansion of developed 

area in the South:

1. Above average county population growth due 

in part to climatic factors and attractiveness to 

immigrants (Glaeser and Shapiro 2001).

2. Above average marginal consumption of land 

per additional resident.

3. Income growth.

The Southern Forest Resource Assessment (Wear and 

Greis 2002) identified urbanization as one of the primary 

threats to forests in the region.

North

Areas of urban and developed uses steadily increased in the 

North since 1982. Between 1992 and 1997, the area of urban 

and developed area in the Northeast increased from 10.4 to 

11.9 percent of the land base. Corresponding increases in 

the North Central subregion were from 6.7 to 7.3 percent. 

The North had about one-third of the total addition to 

U.S. developed area between 1982 and 1997. The North had 

3 of the 10 States with the largest average annual additions 

of developed area according to the NRI. 

West

The West—Great Plains, Southwest, California, and Pacific 

Northwest—accounted for less than one-fifth of the total 

national addition to NRI developed area between 1982 and 

1997. However, recent growth in the region has been above 

the national average. A growing number of “ranchettes” 

and large-lot subdivisions characterize housing growth in 

the Rocky Mountain region, resulting in the highest amount 

of developed area per additional person between 1992 and 

1997 (Alig and others 2004, USDA NRCS 2001).
6

GENERAL TECHNICAL REPORT PNW-GTR-802

or natural forces of non-forest land—has been of a passive 

nature, e.g., reclassification as forest cover increased primarily through natural succession. However, tree planting 

has played a role especially in the South where 25 million 

acres of pine plantations have been established since 1952, 

(Alig and Butler 2004) mostly on land formerly in pasture 

and range use.

Forest Fragmentation

Land use change can lead to forest fragmentation—the 

transformation of a contiguous patch of forest into disjunct 

patches. Forest fragmentation is widely considered to be a 

primary threat to terrestrial biodiversity (Armsworth and 

others 2004), and recent analysis of the fragmentation of 

continental U.S. forests indicates that it is so pervasive that 

edge effects potentially influence ecological processes on a 

majority of forested lands (Riitters and others 2002). 

Definitions of forest fragmentation differ and are 

influenced by the questions or policy issues of interest. 

One major distinction is between treating fragmentation as 

a process and treating it as a pattern (e.g., Alig and others 

2000). Here we discuss forest fragmentation as a pattern. 

Fragmented forests may occur naturally across the landscape (as in the Great Basin, NV), or this pattern may be a 

result of human activities, resulting in edge, core or interior 

habitat, and interspersion changes (Butler and others 2004). 

Forest fragmentation can be quantified spatially using various indices of landscape structure, with different metrics for 

different scales of analysis and measurements of interest. 

Although many fragmentation statistics are available, none 

provide a definitive indicator of landscape fragmentation, 

only a means for comparing the characteristics and relative degree of fragmentation across landscapes or periods. 

Numerous biophysical studies have provided snapshots of 

forest fragmentation, primarily for the East (Table 1).

Forest Parcelization

Forest parcelization is the subdivision of forest tracts 

into smaller ownerships. This phenomenon can have 

profound impacts on the economics of forestry and lead to 

reduced forest management, even when land is not physically altered. Land ownership can influence forest land 

Conversion to Agriculture

More than 8 million acres of forest land were converted to 

agricultural uses between 1982 and 1997 (USDA NRCS 

2001). About half of the converted land has gone to pasture 

use, with the remainder fairly evenly split between crop use 

and rangeland. Forest land contributed 55 percent of the 

land that shifted into agriculture from 1982 to 1997, as land 

continued to be converted from less intensive uses, like forest, to agricultural uses, like cropland and pasture.

As with the conversions of forest land to developed 

uses, the majority of forest to agricultural conversions was 

in the Eastern United States and concentrated in the South. 

The South had the majority of forest land involved in either 

conversion to agriculture or gained from agriculture. In the 

South, land is often suitable for multiple land uses, given 

relatively gentle topography and ease of access.

On net, forestry gained 14.4 million acres from 

agriculture between 1982 and 1997. Of total land shifting 

out of agriculture, 22.7 million acres (46 percent) shifted 

into forest use, with about 17 million acres being former 

pastureland. Much of the shift from pastureland to forest 

use is due to reclassification over time. Factors associated 

with afforestation can differ by region and over time (in 

the North, for example, some land formerly used for dairy 

operations has reverted to forest cover). Most of the afforestation across the Nation—forestation either by human 

The largest percentage of change for a major land use in 

the contiguous three Pacific Coast States (California, Oregon, and Washington) was the 262-percent increase in urban 

area between 1960 and 1997 (Alig and others 2003). Urban 

area as a percentage of total land varies notably by State: 5.9 

percent for California, 3.2 percent for Washington, and 1.0 

percent for Oregon (Vesterby and Krupa 2001). The State 

of Washington illustrates the importance of migration for 

regional population growth and the concentration of growth 

in coastal areas (Alig and White 2007). Between 1990 and 

2000, net migration to western Washington was 180 percent

of the national increase (births minus deaths). Approximately 3.5 million people (59 percent of Washington State 

residents) live within 10 miles of coastline (including the 

Pacific Ocean and sounds).
7

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

management and investment practices. In addition, per 

unit costs of forest management practices will increase if 

economies of scale are lost.

Many of the forest-related increases in population 

density have been on nonindustrial private forest (NIPF) 

lands, the ownership class most subject historically to land 

use changes (e.g., Smith and others 2004). Because NIPF 

owners are aging and have descendants who live farther 

from the forest and for whom timber management is not 

a primary objective, dealing with real estate appreciation 

may be more central to family succession planning now 

than it was in the past. Critical wildlife habitat is often 

provided by NIPF ownership, as in the Pacific Northwest, 

where lowlands and riparian areas critical to threatened and 

endangered species are primarily in NIPF ownership 

(Bettinger and Alig 1996). Family forests are a large component of the NIPF ownership class; the number of family 

forest owners increased from 9.3 million in 1993 to 10.3 

million in 2003, and these owners now control 42 percent of 

the Nation’s forest land (Butler and Leatherberry 2004).

Recent shifts in the ownership of the most intensively 

managed forests in the United States could lead to a 

substantial increase in parcelization. In the sales of large 

forest properties, there often is a spinoff for real estate 

development purposes (highest and best use), and overall, 

the amount of large industrial forest ownership has been 

reduced materially in a relatively short time. A large share 

of the forests long held by consolidated forest products 

companies has recently been sold to institutional investors. 

Many of these transactions have occurred in the South. 

Institutional investors currently hold about 8 percent of 

the investable U.S. timberland (Wilent 2004). By the end 

of 2003, the top 10 timberland investment organizations 

(TIMOs) managed about 9 million acres of U.S.timberland, 

and some analysts predicted that TIMOs and other investor 

groups (e.g., Real Estate Investment Trusts, or REITs) will 

purchase another 10 to 15 million acres in the next decade 

(Wilent 2004).

Two main types of investment models are pursued by 

TIMOs: separate accounts and closed-end funds. Whereas 

separate accounts tend to be managed for the long term, 

closed-end accounts are typically held for a more limited 

period of 10 to 15 years before being sold. One estimate is 

that one-half of all TIMO investments are of the closed-end 

type (SAF 2004). In terms of forest fragmentation and 

conversion, it is the closed-end accounts that may exacerbate rates of deforestation. When TIMOs sell land, they 

pursue the highest value they can receive, which will most 

likely be for development and real estate. Currently, sales 

and acquisitions of forest industry (FI) lands continue to 

be active as market forces, globalization, and consolidation 

impact the forest sector. 

The emergence of timberland holding firms with 

timber production objectives but no link to processing 

facilities has created some difficulties in this traditional 

taxonomy. Lacking processing facilities, these firms would 

be grouped in the NIPF class. Yet their timber management 

behavior is more closely akin to that of the integrated firms 

in FI. Shifts from traditional integrated FI ownership to the 

TIMO/REIT class were extremely rapid in the late 1990s 

and early 2000s, and it is likely that the future will see still 

further decline in traditional, integrated FI ownership.

With a substantial amount of prime U.S. timberland 

shifting from being a personal or industrial asset to being 

a financial one, more frequent turnover in forest ownership 

may be part of a new era in forest ownership. This warrants 

increased attention in data collection and land base monitoring because such changes have implications for a broad 

range of forest-based ecosystem goods and services owing 

to the influence of changing forest ownership patterns on 

forest conversion, fragmentation, and parcelization.

Increased Numbers of Buildings and People 

on Forest Land

A significant proportion of forest land undergoing development each year is used for dispersed residential development in fringe suburbs and smaller cities, commonly 

known as sprawl. Sprawl is characterized by low-density 

residential and commercial settlements, and increases 

in housing density on or adjacent to forests can result in 

changes to the forest’s quality and function and changes 

in forest investment (e.g., Kline and others 2004b). Forest 

lands are very popular as residential building sites; forests 

provide homeowners with shade, screening from neighbors, 
8

GENERAL TECHNICAL REPORT PNW-GTR-802

scenic views, wildlife and bird watching opportunities, and 

often, easy access to forest-based recreation opportunities. New communication and transportation technologies 

reduce the isolation of remote locations and make possible 

long-distance commuting and a wide variety of remote 

work arrangements. These developments effectively reduce 

the costs associated with living far from cities and towns. 

Many areas of the United States are experiencing residential 

growth in the forests and pressure to develop remaining 

forests.

The colocation of houses and forests (as well as other 

wildlands) is captured in the national map of the Wildland 

Urban Interface (WUI), which was created to aid analysis 

of the national wildland fire situation (Figure 2). The WUI 

definition that guided creation of this map and analysis is 

found in the Federal Register (USDA and USDI 2001) and 

specifies minimum housing density of 1 structure per 40 

acres (or 6.17 structures per km2

) and either co-location 

with, or close proximity to, wildland vegetation. National 

Land Cover Data and Census Bureau housing data are used 

Figure 2—Wildland-urban interface in the United States and percentage change in area by region, 1990-2000.
9

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

together to determine where these conditions exist. Two 

main types of WUI—intermix and interface—are identified. Intermix exists where housing and wildland vegetation 

(at least 50 percent of all pixels in the census block are 

forests, grasslands, or shrublands) coincide. Areas that 

meet the housing density minimum but where the wildland 

vegetation is less dense are considered interface if they are 

within 1.5 miles of extensive wildland vegetation (defined 

as an area larger than 5 km2 or 1,235 acres with >75 percent 

wildland vegetation). Together, the intermix and interface 

make up the WUI (Radeloff and others 2005).

In areas where forest conditions, weather, and climate 

make wildfire possible, the WUI is a zone where the threat 

of loss from wildfire is high, because fires can be carried 

into this zone where they will threaten homes and lives. 

Consequently, the WUI has high priority for wildfire hazard 

reduction treatments. The WUI is also the area where wildland fire outreach programs focus their attention. Resource 

managers and their outreach partners work with communities and property owners to mitigate wildfire hazards and 

to plan for evacuation and other emergency measures in the 

event of wildfire.

Across the United States, the 1990s were a period of 

rapid housing growth, with a net gain of 13.5 million housing units, a rate of 13 percent growth. The WUI was clearly 

a preferred setting for new housing; overall WUI growth 

was 22 percent, and intermix growth was 37 percent. The 

growth patterns for the United States were consistent across 

the regions, with growth slower in the non-WUI and faster 

in the intermix WUI (Figure 2). Most of this WUI housing 

growth took place in areas already designated as WUI in 

1990. The growth in WUI area owing to new neighborhoods 

reaching the housing density minimum for WUI was just 

1.5 percent nationally, though it expanded much more in the 

South and North than in the West.

Across the United States as a whole, the distribution of 

housing units across the high-, medium-, and low-density 

interface categories changed little over the decade. Growth 

in the intermix WUI occurred at high, medium, and low 

densities, with biggest gains in medium-density intermix. 

This finding is consistent with adding housing units to 

existing WUI areas (since existing WUI already had at least 

low-density housing in 1990) at a greater rate than adding new areas to the WUI. However, in the West, housing 

growth was strongest in high-density intermix.

In the South, housing increased by 18 percent, almost 

as much as it increased in the Rocky Mountain region 

and presented an even greater contrast between non-WUI 

housing growth (9 percent) and WUI housing growth (29 

percent). Over 3 million housing units were added to the 

WUI during this decade, and the WUI expanded to cover 

17.1 percent of the land area, a greater share than in any 

other region.

Housing grew more slowly in the North than in any 

other region at just 9 percent. However, the intermix WUI 

gained nearly 1.2 million new housing units, an increase 

of 21 percent over the decade. The area of the WUI also 

expanded; by 2000, over 15 percent of the North was WUI.

Housing increased by 23 percent in the Rocky Mountain region more than in any other region. Once again, WUI 

housing growth was even stronger (37 percent), whereas 

intermix WUI housing grew by 75 percent. Although the 

2000 WUI makes up just 1.4 percent of this region’s land 

area, it contains 45.7 percent of the housing units.

Housing growth in the West Coast region was 12 

percent overall, with over 1 million new WUI housing units, 

an 18-percent increase in the number of WUI homes. WUI 

area also expanded from 5.8 to 6.5 percent of the three-State 

area. Of the 16.1 million housing units in this region, over a 

quarter (4.5 million) are located in the interface WUI. 

Analyzing housing growth within the WUI classification provides insight about more than the location and 

density characteristics of recent change; it also indicates 

the impact of this growth on forests, grasslands, and other 

wildland vegetation. More than 60 percent of housing units 

built in the 1990s were constructed in or near wildland 

vegetation. Although the fire management community originated the WUI concept as an approximation of where values 

are at risk from wildland fire, the WUI zone is significant 

for a broader range of ecosystem services. Clean water, 

timber, recreation, and other services and outputs from 

undeveloped land are at risk when development encroaches, 

and encroachment was significant during the 1990s.
10

GENERAL TECHNICAL REPORT PNW-GTR-802

Trees in Urban and Developed Areas

The extent of urban forest has grown appreciably in recent 

decades. As urban lands expand into surrounding areas, 

natural resources are often affected or displaced. Between 

1990 and 2000, most urban expansion in the United States 

was on forested or agricultural land (Nowak and others 

2005). Urban areas in the United States, as defined in the 

2000 census, contain approximately 3.8 billion trees with an 

average tree canopy cover of 27 percent (Nowak and others 

2001). The impact of current urban vegetation on environmental quality nationally is on the order of several billion 

dollars per year (e.g., Nowak and Crane 2002, Nowak and 

others 2006).

Urbanization concentrates people, materials, and 

energy into relatively small geographical areas to facilitate 

the functioning of an urban society. Urbanization often 

degrades local and regional environmental quality as 

natural landscapes are replaced with anthropogenic materials. Byproducts of urbanization (e.g., heat, combustion, and 

chemical emissions) affect the health of local and regional 

landscapes, as well as the health of people who visit or 

reside in and near urban areas. Urban vegetation, through 

its natural functioning, can improve environmental quality 

and human health in and around urban areas, with benefits 

including improvements in air and water quality, building 

energy conservation, cooler air temperatures, and reduction 

in ultraviolet radiation.

Forest-Land Dynamics

Five types of land base changes (afforestation, deforestation, 

forest fragmentation, forest parcelization, and increased 

number of buildings and people on forest land) have 

significantly altered U.S. forests over the last half century. 

Although net changes in total forest area are relatively small 

from a national perspective, many more forest acres are 

actually involved in land use changes as the gross amount 

of change is more than 10 times the net amount. Small net 

changes in forest cover do not necessarily equate to small 

net changes/losses in services provided by forests. The 

gross changes reflect the combined outcome of the five 

types of land-base changes, which often result in spatial 

rearrangement of land uses and land covers. The South, 

which now provides more timber harvest than any other 

region of the country, in particular, has seen many forestrelated land-use changes resulting from population growth 

and economic activity. In addition to deforestation, substantial forest ownership changes in the South include the 

shifting of prime timberland from a personal or industrial 

asset to a financial one. Further, many remaining forest 

acres are affected by addition of houses on them or nearby. 

For some forests that are converted to urban uses, there are 

opportunities to manage urban trees to reduce some of the 

adverse environmental and health effects associated with 

urbanization. The South also has a relatively large number 

of afforestation opportunities, including land suitable for 

biofuels production as part of global change mitigation 

strategies. 

Determinants

Based on land use theory, empirical testing utilizes real 

world data to quantify model parameters and test for consistency with underlying hypothesized behavioral relationships. Empirical models can be used to predict how land use 

will change in response to changes in economic conditions 

and policies. A method increasingly reported in the literature is econometric modeling, which is based on statistical 

methods that are used to quantify relationships between 

land uses and hypothesized determinants such as landowners’ profit from land management. Determinants to be tested 

are drawn from the interaction of biophysical, ecological, 

and socioeconomic processes and forces, often operating 

at a variety of scales. For example, market forces tend to 

operate at much larger scales than biophysical processes 

commonly studied at micro levels such as stands or reaches 

of a stream. Most econometric models of land use developed 

to date have been regional in nature, although Lubowski 

(2002) recently developed a national land use model.

Econometric land use models typically are estimated 

with sample plot data for a random sample of parcels or 

aggregate data such as county-level observations of land 

use (e.g., Ahn and others 2002, Alig 1986, Hardie and Parks 

1997, Kline and Alig 1999, Kline and Alig 2001, Lubowski 

and others 2006, Parks and Murray 1994, Plantinga 1996, 

Wear and others 1996). With the advent of satellite imagery 
11

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

and geographical information systems (GIS), econometric 

land-use models have been estimated using spatially 

referenced plot or parcel-level data (e.g., Bockstael 1996, 

Irwin and Geoghegan 2001, Kline and Alig 2001, Wear and 

Bolstad 1998). Examples of explanatory variables in such 

models are rents (or its proxies) for forestry, agriculture, and 

urban/developed uses. 

Findings from econometric studies indicate that drivers 

of deforestation differ notably from those of afforestation and reforestation activities. Major determinants for 

deforestation associated with conversion to urban and 

developed uses in the United States are population totals 

and personal income levels. The rate and extent of urbanization are typically governed by such determinants, which 

shift demand toward urban and developed uses. Revealed 

behavior by landowners indicates that values for developed 

uses (e.g., residential uses) are generally higher than those 

for rural uses (e.g., forestry and agriculture) (Alig and 

Plantinga 2004, Alig and others 2004). Within the rural land 

base, relative land rents for forestry and agriculture affect 

deforestation (i.e., forest converted to agriculture), afforestation, and reforestation decisions. A number of econometric 

studies offer insights about determinants of afforestation 

(e.g., Plantinga 1996) and reforestation activities (e.g., 

Alig and others 1990, Lee and others 1992, and Kline and 

others 2002), including tests of government subsidies. For 

example, Lubowski (2002) found that rising government 

subsidies for agricultural crops restrained an increase in 

forest area in the Mississippi Delta area by 10 percent from 

1982 to 1997.

Population

A key determinant in land use change is population growth, 

which affects the demand for land. But, population growth 

has potentially contradictory effects on forest land conversion; it can increase demand for land for residential use, 

while also increasing demand for (and thus, price of) wood 

products. Rising prices for wood products will tend to 

increase the relative rents associated with keeping land in 

forest rather than converting it to residential use.

Figure 3 shows population growth for the United States 

since 1950. The population has increased almost fourfold 

since 1900. The distribution of population has also changed 

over time. For example, decentralization of population 

relative to city centers has involved a downward trend in the 

percentage of U.S. population within 3 miles of city centers. 

Around 1900, about 80 percent of the population lived 

within 3 miles of those centers; now less than 30 percent of 

the population does so. However, the proportion of people 

who live in more broadly categorized “urban areas” has 

Figure 3–United States population by urban and nonurban components, 1950 to 2000.
12

GENERAL TECHNICAL REPORT PNW-GTR-802

steadily increased since 1950 and is now about 80 percent. 

The largest increases in population between 1980 and 2000 

have been in metropolitan edges (Heimlich and Anderson 

2001). At the same time, the populations of some nonmetropolitan counties adjacent to metropolitan ones have 

increased as well.

The location of population is important in connection 

with vulnerability of communities and assets (e.g., houses). 

More than half of the U.S. population lives in coastal areas 

(within 50 miles of a coast), part of a growing trend. This 

has implications for vulnerabilities to extreme weather 

events, such as last year’s hurricanes on the Gulf Coast. 

More broadly, coastal ecosystems are increasingly being 

stressed by factors that include lowland development in 

States such as Florida and Texas.

The Census Bureau projects that the Nation’s population will increase by more than 120 million by 2050—more 

than a 40-percent increase over the 2000 population size. 

The two major components driving the United States 

population growth are fertility (births) and net immigration. 

Almost one-third of the current population growth is caused 

by net immigration. Net immigration remains constant at 

880,000 per year, whereas the Census Bureau recognizes 

that there is considerable uncertainty about the future flow 

of migrants. By 2050, the Nation's population is projected to 

be 82 million people larger than it would have been without 

growth through migration. In fact, about 86 percent of the 

U.S. population growth during the year 2050 may be due to 

the effects of post-1992 net immigration. 

Population redistribution (i.e., regional growth and 

decline within the United States) is due in part to amenity 

migration. The National Forests of the United States have 

rich scenic and recreational resources that have induced 

amenity growth in nearby rural areas of the country over 

the past three decades (Garber-Yonts 2004). The 2000 U.S. 

Census showed that national forest (NF) counties (counties 

where >10 percent of land area is national forest, n = 454) 

had higher population growth rates than other counties. 

This is true especially in those NF counties that were 

nonmetropolitan (n = 386), where the 1990 growth rate 

was 18.1 percent versus 10.3 percent in nonmetropolitan, 

non-NF counties. Migration accounted for most growth 

across all NF counties, even metropolitan ones, despite the 

much stronger role of natural increase (i.e., more births than 

deaths) in metropolitan county growth generally.

Personal Income

Average family income (in real or inflation-adjusted dollars) 

increased by more than 150 percent from 1950 to 2000, 

giving individuals more income to spend. The U.S. per 

capita disposable income in 1998 was $22,353, which 

represents more than a 10-percent increase, in real terms, 

during the 1990s alone.

Further increases in personal income are projected, 

but not at the level of increase in the 1990s (USDA Forest 

Service 2001). Even with constant tastes and preferences, a 

larger population base with higher income levels will result 

in greater consumption and demands for developed space. 

For example, consumers may demand more shopping space, 

as between 1990 and 2000 when the United States shopping area increased by 27 percent and number of shopping 

centers by 24 percent (USDC Census Bureau 2001).

Incomes From Rural Land Uses

More than 90 percent of land use changes on non-Federal 

lands in recent decades have been among rural land uses 

(USDA NRCS 2001): forests, crops, pasture, or range. 

Where climate and physiography permit, these rural uses 

can compete for the same land. For example, a reduced 

supply of agricultural land due to urbanization can result in 

“replacement” conversion of forest land to agricultural uses 

(Alig and Healy 1987). Market forces often result in shifts 

in the use of rural lands between agricultural production 

and forest production. Increasingly global markets are also 

affected by technological improvements, and, since World 

War II, increases in cropland yields per acre have generally 

been larger than corresponding increases in forestry yields. 

Enhanced productivity has the effect of concentrating 

agricultural uses on a smaller land base and easing demands 

for conversion of forests to agriculture.

Net income from forestry enterprises is affected by 

prices for products including timber. Over the 50 years from 

1952 to 2002, real prices of softwood lumber, hardwood 

lumber, and paper rose (at compound rates of 0.8 percent, 
13

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

0.4 percent, and 0.3 percent, respectively), whereas prices 

of softwood plywood, oriented strand board (OSB) (since 

1976), and paperboard fell (Haynes and others 2007). 

Recent timber market projections for the USFS Resources 

Planning Act (RPA) Assessment (Haynes and others 2007) 

indicate that prices for some forest products will increase 

over time but at a slower rate. Prices of softwood lumber, 

hardwood lumber, and OSB are projected to rise slowly (at 

compound rates of 0.2 percent, 0.3 percent, and 0.1 percent, 

respectively), and prices of softwood plywood, paper, and 

paperboard are projected to remain stable or fall. Slow product price growth is reflected in many categories of timber 

prices (which determine returns to landowners). Sawtimber 

stumpage prices in the South and interior West are expected 

to decline slowly after 2010, while those in the PNW and 

North are expected to rise at about 0.2 percent and 0.8 

percent per year. Southern hardwood pulpwood prices rise 

in the projection as hardwood inventories contract. Southern 

softwood pulpwood prices oscillate in response to the 

changing fiber mix, ending the projection near recent levels.

On the agricultural side, real prices have declined for 

major agricultural crops grown on land also suitable for 

forestry. Toward the end of the 20th century, farmers and 

ranchers were increasingly caught in a cost-price squeeze. 

The ratio of the Prices Received Index to the Prices Paid 

Index fluctuated considerably over the past hundred years 

(Ahearn and Alig 2006, USDA NASS 2006). Commodity 

prices spiked upward during both World Wars and plunged 

during the Great Depression. Prices again shot upward 

during the early 1970s, spurred on by sharply increased 

world demand. Technological improvements in agriculture, 

such as in yields per acre, have generally outpaced those in 

forestry. Although this has resulted in an increase in the use 

of land for agricultural cropland in some areas, the increase 

in aggregate crop yields and downward pressure on agricultural market prices resulted in land saving for farmers and a 

net switch from agriculture to forestry at the national scale.

The outlook for agricultural income often involves 

substantial uncertainty in land use studies because of the 

cyclical nature of the agricultural economy, effects of 

government programs, and technological developments 

(e.g., genetically modified materials) (Alig and Ahearn 

2006, Alig and others 2003). Emergency aid provided to 

farmers through legislation in 1998 through 2001 suggests 

that the direction of the current policy transition remains 

uncertain. Under the Federal Agricultural Improvement and 

Reform Act of 1996, the Federal Government is moving 

further away from direct involvement in farm commodity 

markets. However, threats of droughts and other elements 

that inject volatility in agricultural and forestry production 

have prompted some annual adjustments in the government 

intervention plans. With lower agricultural prices than 

in the mid-1990s, the 2002 farm bill was debated during 

a period when agricultural prices were low. The debate 

showed that there was some interest in changing the thrust 

of the 1996 farm bill by introducing new countercyclical 

policies. Policy shifts in this area could affect the dynamics 

of the link between land use patterns and market prices.

Projections

Projections from different studies (e.g., Resources Planning Act Assessments) are summarized next and compared 

where appropriate. Land use projections are generally 

prepared by obtaining projections of the independent 

variables and then simulating the impacts of projected 

conditions on future land uses (Table 3). Projections can 

be implemented to contrast the potential effects of policy 

or market changes with historical usage (counterfactual 

simulations, e.g., Lubowski and others 2006) or to project 

future land uses over a range of scenarios (e.g., Alig and 

others 2003). Projection exercises have indicated that future 

land uses are especially sensitive to changes in population 

density, income, and agricultural and timber prices and 

production costs.

Developed Uses

Urban and developed areas are projected to continue to 

grow substantially in line with the projected population 

increase of more than 120 million people over the next 

50 years (Alig and others 2004). This will be part of a 

global increase in population, as the world’s population 

is projected to grow from 6 to 9 billion by 2050. The U.S. 

developed area is projected to increase by 79 percent, raising the proportion of the total land base that is developed 
14

GENERAL TECHNICAL REPORT PNW-GTR-802

  Table 3—Sources of land use projections, by region (note: no large-scale forest parcelization 

 projections were located)

Land base change

Deforestation

Afforestation

Forest fragmentation

Structure additions to 

 forests

Urban forest changes

Approach

Econometric and mathe-

 matical optimization

Econometric and mathe-

 matical optimization

Econometric

Statistical

Statistical

Studies

Alig and Healy 1987,

 Alig and others 2004, 

 FASOM (Alig and others 

 2002) Alig and

 Plantinga 2004

Lee and others 1992, Kline

 and others 2002, FASOM

 (Alig and others 1998, 

 2002), Alig and others 

 2003, Alig and Plantinga 

 2004

Wear and others 2004

Stien and others 2005

Nowak and Walton 2005

Projected amount in U.S.

More than 50 million acres

 deforested by 2050, mostly

 for developed uses

About 20 million

 acres converted

 from agriculture to forest

 by 2050 (FASOM)

South projected to lose 

 about 2 million acres of 

 interior forest by 2020

44 million acres of private 

 forest with substantial 

 increase in housing density 

 by 2030

29 million acres of forest to 

 be urbanized by 2050

an area equal to 38 percent of the current U.S. cropland 

base, or 23 percent of the current U.S. forest land base. In 

line with recent historical trends, the South is projected 

to continue to have the most developed area through 2025 

(Alig and others 2004).

Forest Land

Total forest land area in the United States is projected to 

decrease on net by approximately 23 million acres, or 3 

percent between 1997 and 2050 (Alig and others 2003). 

Projections of forest land area are related to those above for 

the other major land uses. The main reason for the projected 

reduction in forest land area is conversion to urban and 

developed uses associated with the projected increases in 

population and income discussed earlier. 

The projected reduction in forest land is consistent 

with earlier studies (e.g., USDA Forest Service 1988). The 

notable reductions in the South are generally consistent 

with the regional assessment of the southern forest resource 

situation, indicating that urbanization, among all forces 

of change, will have the most direct, immediate, and 

permanent effects on the extent, condition, and health of 

from 5.2 percent to 9.2 percent. Projections based upon 

Census Bureau data indicate similar substantial increases in 

urban area. Urban land in the United States is projected to 

increase from 3.1 percent in 2000 to 8.1 percent in 2050, an 

area of about 97 million acres, which is larger than the state 

of Montana. Most of the urban growth is projected to occur 

around the more heavily urbanized areas, with significant 

expansion in the East and along the west coast.

Population and income pressures on land uses are 

not uniform across the Nation. For example, population 

has shifted from the North to the South and the West in 

recent decades. Because much of the growth is expected 

in sensitive areas already burdened by anthropogenic 

impacts, such as some coastal counties, implications for 

landscape and urban planning include potential impacts on 

sensitive watersheds, riparian areas, wildlife habitat, and 

water supplies. Although providing additional living space 

and infrastructure, added development may also diminish 

agricultural output by reducing farmland and changing 

ecological conditions by converting and fragmenting forests 

and other natural landscapes. The projected developed and 

built-up area of about 175 million acres in 2025 represents 
15

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

forests in the South (Wear and Greis 2002). Projections 

estimate that tens of millions of acres of forests in the 

region will be lost to urbanization from 1992 to 2040. The 

13 States in the South are projected to have an overall net 

decline of only 2 percent in forest area because some farmland will be converted to forests. Timberland area is projected to increase in the South-Central region, where much 

land is suitable for use in either agriculture or forestry, as 

a consequence of conversion of agricultural land to forest. 

Such conversion is to be expected if it is assumed that real 

prices for agricultural commodities fall and agricultural 

subsidies and related programs are reduced.

The largest forest area losses in the South are projected 

for the Southeast. Forest land at the periphery of urban areas 

is likely to be developed. For example, in Georgia about 5.6 

million acres of forests may be converted to developed uses 

by 2010. It is conservatively estimated that as much as 26 

percent of the timber-growing stock measured in the 1997 

Georgia inventory could be affected (Wear and Newman 

2004).

Projections for other regions of the country largely 

follow recent historical trends (Alig and others 2003). Most 

of the losses are projected to be on NIPF lands.

Comparison of Land Use Projections—

We compared land use projections from models by 

Lubowski and others (2006) and Hardie and others (2000). 

We use these models to project land uses in the Southeastern United States to the year 2020. Using two separate 

models allows us to examine potential differences based 

on modeling structure. The Lubowski and others model 

estimates transitions or changes in land uses, whereas the 

Hardie and others model estimates the equilibrium land use 

shares in each period. Both models summarize land uses 

at the county level and are based on measures of land uses 

from the National Resource Inventory (NRI). The 1997 NRI 

survey serves as the base year for projections.

Projections for the South as a whole show a significant 

continuation of urbanization and are consistent with the 

Nowak and others (2006) projections and separate projections by Alig and others (2004). Urban area in the South is 

projected to increase under all scenarios.

Year 2020 forest land area projected by the model of 

Lubowski and others is similar to those generated by the 

model of Hardie and others under a high timber price 

scenario. The Hardie and others model projects that forest 

land area in the South could fall as much as 20 million 

acres under a low timber price scenario. Econometric land 

use models are generally effective tools for projecting forest 

area; but an important consideration is whether there are 

any changes in the underlying structural relationships over 

the historical or projection periods, analyzed using statistical methods to test for changes (e.g., significant policy 

environment alteration) in model parameters over time 

(Ahn and others 2000). 

Projections of Forest Fragmentation—

Relatively few studies have projected forest fragmentation, especially at larger scales. One example of a broader 

scale study is in Wear and others (2004) where changes 

are forecast in interior forest for each county in the South, 

a region where recent trends include significant land use 

change. Wear and others, who based forest fragmentation 

projections on population density forecasts to the year 

2020, assumed that relative returns to agricultural and 

timber production would remain at current levels. Under 

this scenario, the South as a whole is forecast to lose 

747 000 ha (1.85 million acres), or about 2.12 percent of 

interior forest cover.

These changes are not constant across the region. 

Among ecological sections, the Southern Appalachian 

Piedmont would lose the greatest area of interior forest 

cover (173 166 ha or 427,903 acres). The gulf prairies and 

marshes in Texas, which have very little interior forest, 

would lose the greatest proportion of interior forest (56.7 

percent) (Wear and others 2004). The second and fourth 

greatest reductions are projected for the eastern and 

western Florida coastal lowlands, respectively. Aggregation 

to the ecological province level indicates that the Outer 

Coastal Plain would experience the greatest reduction in 

interior forest. All ecological sections with losses forecast 

at greater than 2 percent are located either on the Atlantic 

and Gulf Coastal Plain or in the upland areas of North 

Carolina, Virginia, Tennessee, and Kentucky.
16

GENERAL TECHNICAL REPORT PNW-GTR-802

The distribution of forecast losses of forest interior 

between urban and rural counties was examined further. 

Urban counties were defined as those attached to metropolitan statistical areas (MSAs) by the Office of Management and Budget, and rural counties were defined as the 

remainder. The MSA counties contain 492 690 ha (1,217,463 

acres) or 66 percent of the total forecast loss of 747 744 ha 

(1,847,716 acres). (Heavily impacted MSAs are concentrated 

in Florida. The Tampa-St. Petersburg-Clearwater MSA is 

forecast to lose 34.5 percent of its interior forest, and 7 of 

the 10 MSAs with the highest percentage losses are found in 

Florida. Columbia (South Carolina), Atlanta (Georgia), and 

Raleigh-Durham-Chapel Hill (North Carolina) round out 

the top 10. 

Housing Density—

Projections of housing density increases on forest land were 

made in the Forests on the Edge study discussed in “Forest land Conversion and Recent Trends.” This project has 

ranked watersheds across the conterminous United States 

according to the percentage of each watershed that contains 

private forest projected to experience increased housing 

density (Stein and others 2005, Theobald 2005). Three 

housing density thresholds were identified: rural (no more 

than 15 units for every square mile); urban (at least 64 units 

per square mile); and ex-urban (16 to 63 units per square 

mile). Areas identified as having a substantial increase 

(44 million acres in total) were those where housing was 

projected to increase from either rural or ex-urban to urban 

(22 million acres) or from rural to ex-urban (22 million 

acres). Watersheds included in the assessment had at least 

10 percent forest cover with a minimum of half private land.

Note that for the WUI research, projections of WUI 

growth, 2010 to 2030, will be made by Northern Research 

Station scientists and collaborators as soon as the housing density projections on which they are built have been 

completed. The WUI projections will assume that vegetative cover will remain constant through 2030.

Most watersheds projected to experience the greatest 

amount of change were located in the East, although some 

were located in the Great Lakes area, California, and the 

Pacific Northwest. The greatest change will be in 12 States 

in the Northeast and South (Stein and others 2005). A study 

in progress is identifying watersheds where private forests 

contribute most to water quality, timber, interior forest, and 

at-risk species habitat and determines where these contributions may be most affected by factors such as housing 

increases, fire, air pollution, insect pests, and disease. As 

discussed in a case study at this conference, private forested 

watersheds most affected are generally found in the East 

and along the West Coast (Stein and others, this volume). 

Stresses on forest environmental conditions can be compounded if more people live on the remaining forest land 

as the U.S. population density continues to increase. The 

United States had about 80 people per square mile of land in 

1999 (USDC Census Bureau 2001) in comparison to about 

5 people per square mile in 1790.

Risk and Policy Considerations

A broad complement of research studies is consistent in

projecting continued development of forests or increases 

in the housing density of remaining forests in the future, 

or both. Key assumptions in such studies include projected 

increases in population and income, which are generally 

viewed as more likely to approximate future conditions 

over the next five decades. In contrast, other assumptions 

are viewed as having larger bands of possible outcomes, 

and examples of such assumptions are future changes in the 

agricultural sector, technological changes, changes in forest 

practice regulations, and global climate change. The relative 

sensitivity of projected land use changes to such assumptions has been tested in a number of studies (e.g., Alig and 

others 2003, Haynes 2003, USDA Forest Service 1988).

It appears that tens of millions of acres of forest are at 

risk of being converted to nonforest and that many more 

acres remaining in forest cover will have houses and other 

structures added over the next several decades. To the 

extent that this loss of forest land may have detrimental 

impacts on the social values derived from forests, it seems 

reasonable to ask how changes in policy might affect this 

outcome. We next discuss several types of policies that 

could have some influence on the future of forests in the 

United States.
17

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Government Policies

Government policies that can contribute to development 

include Federal/State expansion of highways, income tax 

subsidy for home ownership, and extension of public utilities. Policy responses to any perceived problem can involve 

local, regional, State, and national responses. An example 

of local influence is zoning and development impact fees. 

Regional responses may include regional governance or 

tax sharing. State policies include urban growth boundary 

approaches as in Oregon. Conservation easements have 

been increasing in popularity as a tool for encouraging the 

protection of forests and other lands. Landowners receive 

tax benefits or are paid a lump sum in exchange for restricting the type and amount of development and other uses that 

may take place on their property. Easement restrictions are 

identified in a legal agreement signed by the landowner and 

a conservation recipient (usually a public agency or land 

trust). To qualify for tax benefits, an easement must be perpetual, with future owners bound by the same restrictions. 

National Level—

At a national level, the USDA (1983) is an example of 

a Federal agency with a major land use responsibility. 

Departmental policy is to promote land use objectives 

responsive to current and long-term economic, social, and 

environmental needs. This policy recognizes the rights and 

responsibilities of State and local governments for regulating the uses of land under their jurisdiction. It also reflects 

the department’s responsibility to:

1. Assure that the United States retains a farm, 

range, and forest land base sufficient to produce 

adequate supplies, at reasonable production 

costs, of high-quality food, fiber, wood, and 

other agricultural products that may be needed.

2 Assist individual landholders and State and 

local governments in defining and meeting 

needs for growth and development in such 

ways that the most productive farm, range, 

and forest lands are protected from unwarranted 

conversion to other uses.

3. Assure appropriate levels of environmental 

quality.

Contemporary land use policies as a whole are multiobjective in nature, as is evident in the policy directive of 

USDA (1983). Implementation of multiobjective policies is 

laden with tensions. For example, although the USDA policy 

directive was written nearly two decades ago, major challenges still exist in attaining a balance that satisfies USDA’s 

many constituents. One challenge involves a major contemporary focus of land use policies—the management of 

the direction of development. Urban sprawl has been cited 

as one of the leading concerns of Americans (Pew Center 

2000). According to the Pew report, approximately 1,000 

measures aimed at changing planning laws and at making 

United States development more orderly and conserving 

were introduced in State legislatures in the late 1990s. 

Concerns about sprawl originate from both the disamenities 

associated with increased congestion as well as the loss of 

productive land for agriculture and forestry uses. Although 

the recent and current situation in agriculture is one of 

surpluses and depressed markets, agriculture is historically 

cyclical in the long run. With a rapidly growing world 

population, food and fiber demand is likely to increase in 

the future. There has not been sufficient confidence that the 

current land market is capable of appropriately discounting 

the future value of farmland to account for this eventuality. 

Long-term loss of prime agricultural land and forest land 

to urban uses arises in part because lands that are highly 

suitable for agriculture or forestry and for urban expansion 

are often one and the same—gently sloped, fertile valleys, 

and flood plains. Urban conversion is generally one way; the 

land is usually irretrievably lost for less intensive use within 

typical planning horizons. 

State and Local Levels—

State and local governments use a variety of tools to 

protect farm and forest lands as productive resource bases. 

These tools include agricultural zoning, differential farm 

tax assessments, right to farm laws, agricultural districts, 

purchase of development rights, transfer of development 

rights, comprehensive land use planning, and urban growth 

boundaries.

At a State level, Oregon has a statewide land use policy 

involving urban growth boundaries (e.g., Kline and Alig 

1999). For example, Portland, Oregon, in the 1990s had 
18

GENERAL TECHNICAL REPORT PNW-GTR-802

30 percent of new housing as infill and redevelopment. 

Boulder, Colorado, has an urban containment policy. The 

city restricts new development, and the majority of the 

workforce lives outside city limits.

Two USDA programs, the Farmland Protection Program for agricultural lands and the Forest Legacy Program 

for forest lands, complement State and local government 

programs that purchase development rights. The purchase 

of development rights gives government agencies the option 

of conserving open space for future use in farm or forest 

production without government acquisition. The land will 

not necessarily be required to stay in a current farm or 

forestry use, but under a program that purchases development rights, a landowner will not be allowed to develop 

the parcel. Because the cost of cultivating undeveloped 

land is considerably less than the expense associated with 

reversing development, purchasing development rights 

is viewed as an investment in food and forestry security 

for future generations. Conservation easements and other 

partial interests in land have also been increasingly used to 

accomplish particular natural resource protection goals such 

as maintaining open space that provides scenic beauty and 

wildlife habitat.

Most land use protection programs are designed to 

conserve urban open space. Few are focused directly on 

working forests. Managed by the USDA Forest Service 

in partnership with State governments, the Forest Legacy 

Program is designed to encourage the protection of privately 

owned forest land and promote sustainable forestry practices by purchasing development rights, including conservation easements. Legacy parcels continue to produce forest 

commodities and noncommodity ecological values such 

as healthy riparian areas and fish and wildlife, as well as 

scenic, aesthetic, cultural, and recreational resources, on 

landscapes otherwise likely to be shifted to nonforest use. 

As of 2006, the program has protected 1.15 million acres in 

33 States. Interest in the program has grown, with 46 States 

and Territories now enrolled in the program, up from 24, 5 

years ago, and with over $200 million in requests each year. 

In 2006, Federal appropriations for the program were $56 

million, with 91 percent of this money directed to conservation projects. Conservation easements in general have 

been increasing in popularity as a tool for encouraging the 

protection of forests and other lands, and are also used by 

NGOs, such as the Pacific Forest Trust. Landowners receive 

tax benefits or are paid a lump sum in exchange for signing 

a legal document that restricts the use of their land. These 

restrictions might include development as well as certain 

other forms of land use.

Risk, Hazard, and Land Use Change

The focus of this conference is primarily on threats to 

ecosystems, and we discuss the role of land use change 

and housing growth in creating and amplifying threats to 

ecosystems. A related issue worth exploring is the relationship between human settlement patterns and vulnerabilities 

to natural disasters. Natural disasters have many varied 

consequences, including damage to ecosystems and human 

communities. Recent trends in land use and housing growth 

not only create stresses on natural ecosystems, they also 

increase society’s vulnerability to natural hazards.

Housing growth is perhaps the single most important 

factor behind increasing economic losses from natural 

disasters. The threat posed by most natural disasters has 

not changed significantly over time. Wildland fire is an 

exception, to some extent; many ecologists argue that forest 

management policies contribute directly and indirectly to 

increasing the severity of wildland fires. Global climate 

change has also been indicted in recent catastrophic weather 

events, and although scientific opinion is mixed regarding 

its role in current patterns, scientists agree that there is 

potential for significant change in the future. However, in 

the short run, i.e., over the past 50 years, the likelihood of 

natural hazards has been relatively stable, but losses in the 

United States have increased because our vulnerability to 

these hazards has increased. More houses and more wealth 

concentrated in regions of the country facing significant 

hazard levels describe the trend in the United States over 

the past 50 years (Cutter and Emrich 2005, De Souza 2004, 

van der Vink and others 1998).

Regional patterns of growth and decline in the United 

States have shifted population and property value to more 

vulnerable areas (van der Vink and others 1998). By 1970, 

population and housing growth had shifted away from the 
19

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

cities of the Northeastern United States and into two regions 

facing considerable natural hazards: the Southeast, with its 

exposure to hurricanes from both the Atlantic and the Gulf; 

and the West where a wide range of hazards are present. 

Colorado and California stand out as States where population and housing growth have been substantial (California 

earlier in the period, Colorado later) and both face the threat 

of catastrophic wildland fires. In California, earthquakes 

and landslides are also major threats to heavily populated 

cities. Coastal Oregon and Washington are exposed to tsunami risks; Southwestern States (Arizona, and later in the 

period, New Mexico and Nevada) have grown tremendously 

and have active fire regimes.

Selective urban deconcentration, which has been the 

overarching pattern of settlement change in the late 20th 

century (Johnson and others 2005), has brought growth to 

many rural communities and to suburbs more distant from 

the urban core. This change from the centuries-long urban 

concentration pattern contributes to vulnerability in two 

ways. First, isolated communities and especially unincorporated areas have less infrastructure (e.g., roads and 

water supply systems) and fewer resources for providing 

protection services (e.g., police and fire protection). Rapid 

growth exacerbates the difficulties of providing adequate 

infrastructure. Second, wildland fire is a meaningful threat 

to homes in the wildland urban interface, which is typically 

found around the outer edges of metropolitan areas and 

throughout the countryside, the same areas where housing 

growth has been most dramatic.

United States society’s response to natural disasters 

has been more oriented to reaction than to planning. When 

losses to human communities are substantial, the outcome 

is often new policy, reallocation of public spending, and 

regulation. To take an example familiar to the resource 

management community, the Healthy Forests Restoration 

Act can be seen as a policy response to the bad fire seasons 

of 2000 and 2002. With human community vulnerability 

and loss comes the prospect of more changes such as these. 

To date, few incentives or policies have addressed the root 

cause—the development of new housing units without 

regard to landscape patterns, ecological processes, or 

hazard exposure. 

Research to Improve Analyses of Risk of

Forest Land Conversion: Expected Benefits and 

Costs

Improved analyses of threats from conversions of forest 

land require additional data and research pertaining to:

1. Expected benefits and costs regarding likelihood 

of land use conversion for a particular unit of 

land.

2. Environmental impacts, losses in commodity 

production, and other costs that may arise if a 

unit of land is developed, such as increased costs 

for fire suppression with houses in the 

wildland-urban interface. 

3. Estimates of opportunity costs of retaining 

forest land, such as land values, to provide a sense 

of what it may cost to transfer development rights, 

implement a conservation easement, or undertake 

some other policy action.

Here we point to several studies as examples where 

improved information is being pursued, with a more 

detailed discussion outside the scope of this paper. 

At a national scale, the RPA Assessments have examined costs and benefits associated with land conversion for 

several decades (e.g., Alig and others 2003, Nowak and 

Walton 2005, USDA Forest Service 1989). With a growing wildland-urban interface, other national studies have 

focused on housing growth (e.g., Stein and others 2005, 

Stewart and others 2003). Other national studies have 

focused on global climate change and relationships to land 

use and land cover changes (e.g., Alig and others 2002). 

At a regional scale, two studies for the South illustrate the 

changing nature of the informational requirements and 

analytical approaches: the study of the South’s “fourth forest” (USDA Forest Service 1988) and the Southern Forest 

Resource Assessment (Wear and Greis 2002).

Forest conditions have received increasing attention in 

recent years, with bioregional assessments implemented to 

examine conditions at an ecoregional rather than a jurisdictional level: e.g., the Southern Appalachian Assessment 

(USDA Forest Service 1996), the Southern Forest Resource 

Assessment (Wear and Greis 2002), and the Coastal 
20

GENERAL TECHNICAL REPORT PNW-GTR-802

Landscape Analysis and Modeling Study (Kline and others 

2003). One part of improving such resource-centric studies 

is better tracking of ecological structures that are changing 

in response to human population growth and economic 

developments at broad scales across a region (Wear and others 2004). The identification of specific conservation targets 

within these broad areas would require additional work at 

a finer scale. Nevertheless, the methods described by Wear 

and others (2004) could provide a mechanism for defining conservation priorities for the region at a broad scale. 

Forecasts of interior forest change can be viewed as a risk 

measure indicating where human activities are more likely 

to change ecosystem structure, similar to the risk indicators 

developed at a much finer scale by Theobald (2003). This 

forecast provides a first step in setting priorities: defining 

where the structure is likely to change and where it is likely 

to be relatively stable. Wear and others (2004) identified 

four ecological sections of the South where 5 percent or 

more of existing interior forest is forecast to be lost by 2020, 

but they also identified 16 ecological sections, or about half 

of the sections in the Southeastern United States, where less 

than 1.5 percent would be lost. Areas that are found to be 

essentially stable could be excluded from further detailed 

analysis, allowing analysts to focus their efforts on that portion of the landscape that is more likely to change without 

some intervention.

A second step in defining conservation priorities 

involves examining the ecological condition of the broad 

areas with relatively high threat levels. Indicators such as 

numbers of imperiled species highlight where ongoing 

change may have the most impact on biodiversity. Although 

a detailed assessment of ecological scarcity is beyond the 

scope of this paper, the Southern Forest Resource Assessment (Wear and Greis 2002) illustrates the approach. For 

lands that are urbanized, a tool developed to assess urban 

forests structure and functions is the Urban Forest Effects 

(UFORE) model (Nowak and Crane 2000; www.itreetools.

org).

Different types of land base changes can result in 

different forest ecosystem conditions because acres exiting (e.g., through deforestation) or entering (e.g., through 

afforestation) the forest land base can represent quite 

different forest conditions. This change becomes even more 

important when there is a relative acceleration in one type, 

as in deforestation, as occurred in the 1990s, when about 1 

million acres of forests were converted to developed uses 

per year (USDA NRCS 2001).

With a projected increase of more than 120 million 

people in the United States over the next 50 years, the 

different projections of additional developed land area and 

housing growth all point to significant increases that represent threats of forest conversion. Demand for wood products 

is expected to keep growing, driven by the same population 

increases and economic development that affect demands 

for other major land uses. Given dynamics of the changing 

population and social values, some forest conversion can 

adversely impact provision of public goods by forests, such 

as the environmental service of storing terrestrial carbon 

to mitigate climate change, which falls outside private 

decisionmaking. Measuring and evaluating multiple forest 

benefits associated with public goods can be difficult owing 

to a general lack of information describing forest outputs 

and their values. This lack of information is especially true 

when it comes to valuing benefits accruing from ecosystem 

services, a set of values clearly needed to fully value open 

space and other ecosystems services provided by private 

forests. Efforts to better align commercial uses of forests 

with conservation objectives have led to increased interest 

in what is being called sustainable forestry, although there 

are similar efforts tied to other major competing interests 

in the land, such as sustainable agriculture or sustainable 

communities. Land use will continue to change as private 

decisionmakers and society examine options to adjust to 

changing demands for and supplies of renewable resources 

(e.g., biofuels for energy security and to address climate 

change) (e.g., White 2010) and ecosystem services from 

the Nation’s forest and aquatic ecosystems. Sustainability 

analyses will be enhanced if both land use and land investment options are examined. Analyses should be explicit 

as to timing of tradeoffs and market-level impacts, to help 

promote enhanced integrated macro analyses of land base 

changes using a balanced mixture of spatially explicit data 

and other information. 
21

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Deborah Page-Dumroese, Martin Jurgensen, 

Dan Neary, Mike Curran, and Carl Trettin

Deborah Page-Dumroese, research soil scientist, Rocky 

Mountain Research Station, Moscow, ID 83843; Martin 

Jurgensen, professor, forest soils, Michigan Technological 

University, School of Forestry and Environmental Sciences, 

Houghton, MI 49931; Dan Neary, research soil scientist, 

Rocky Mountain Research Station, Flagstaff, AZ 86001; 

Mike Curran, research soil scientist, BC Forest Service, 

Southern Interior Forest Region, Kootenay Lake Forestry 

Centre, Nelson, BC V1L 6K1; and Carl Trettin, research 

soil scientist, Southern Research Station, Charleston, SC 

29414.

Abstract

Government agencies, industrial landowners, and private 

landowners often strive to maintain soil quality after site 

management activities in order to maintain site productivity, 

hydrologic function, and ecosystem health. Soil disturbance 

resulting from timber harvesting, prescribed fire, or site 

preparation activities can cause declines, improvements, or 

have no effect on site productivity and hydrologic function. In many cases, detailed soil resource data can be 

used to determine the stress level and ecosystem health of 

stands and may be one method used to determine the risk 

of disease or insect outbreak. Currently, organic matter 

accumulations in many forests exceed historical levels. 

Fire suppression or fire exclusion has produced numerous 

overstocked stands. When this condition is combined with 

increased climatic variation, drought, and type conversion, 

these stands have a high risk for catastrophic wildfire. The 

resulting large, high-intensity, and high-severity fires could 

contribute to changes in soil quality and lead to outbreaks 

of insects and diseases in many ecosystems. Changes in 

ecosystem processes can also be associated with changes 

in overstory properties that alter forest stand resilience. For 

example, loss of western white pine to blister rust infection in the Northwestern United States has caused a type 

conversion to forest species that are not tolerant of root 

diseases, are not fire resistant, and sequester nutrients in 

the surface mineral soil and tree crown that can later be 

lost through logging or fire. These relationships, and others, 

can be used in conjunction with soil resource data bases to 

assess susceptibility to threats and to help develop management strategies to mitigate disturbances. Development of 

monitoring strategies that use common methods that can 

be utilized by a variety of land management agencies and 

specialists is a key component for relating forest health to 

soil changes after fire or other land management activities.

Keywords: Fire suppression, forest health, soil indicators, sustainable forestry.

Soil Quality

Soil quality and function are interrelated concepts that 

represent the range of soil properties and their associated 

ecological processes. The National Forest Management 

Act of 1976 and related legislation direct U.S. Department 

of Agriculture Forest Service managers to maintain the 

productivity potential of national forest land. The British 

Columbia Ministry of Forests uses professional assessment 

to evaluate the impacts of management practices on organic 

matter (OM) losses (British Columbia Ministry of Forests 

1997). Even with these mandates and laws, the concept of 

soil productivity has not been well defined, and the impact 

of timber removal or fire on the productive potential of 

soils is not well understood or easily measured (Powers and 

others 2005). Soil quality has been defined as the capacity 

of a soil to function within an ecosystem to sustain biological productivity, maintain environmental quality, and 

promote plant and animal health (Doran and others 1996). 

In addition, soil health definitions include maintaining the 

integrity of nutrient cycling and resilience to disturbance or 

stress (O’Neill and others 2005). Tree or stand growth has 

often been used as an indicator of soil productivity changes, 

but growth reductions attributable to management practices 

may take >20 years to become manifest in many North 

American ecosystems (Morris and Miller 1994). The forest 

floor is likely a key element in maintaining healthy ecosystems, but it is also the one most impacted by fire and forest 

management (Tiedemann and others 2000). Maintaining 

site organic matter at or near the ecosystem baseline levels 

may help reduce nutrient losses (McNabb and Cromack 

1990), insect (Fellin 1980a) and disease (McDonald and 

Soil Quality Is Fundamental to Ensuring Healthy Forests
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GENERAL TECHNICAL REPORT PNW-GTR-802

others 2000) outbreaks, and may ultimately reduce many 

forest health problems. For example, Page-Dumroese and 

Jurgensen (2006) described baseline organic matter levels 

in 13 undisturbed forests around the Northwestern United 

States. The levels include measurements of downed wood, 

forest floor and mineral soil organic matter (OM), carbon 

(C) and nitrogen (N), and they can be used to determine 

when a site has excess or deficient organic matter stores. 

Carbon accumulation, as measured by forest floor depth or 

amounts of downed wood, can be a useful indicator of forest 

health because forests with OM levels above their historical 

baseline levels are at risk from increased insect and disease 

activities or high-intensity fires (Oliver and others 1994).

Wildfire Impacts

Active fire suppression during the 20th century has 

increased OM volume on the soil surface in forest stands 

that historically had supported a regular fire-return interval 

(Oliver and others 1994). It has been suggested that active 

fire suppression, together with selective harvesting of seral 

species, has resulted in a shift in dominance to shadetolerant Douglas-fir (Pseudotsuga menziesii) and grand fir 

(Abies grandis) in many western forests (Mutch and others 

1993, Swetnam and others 1995), and a build-up of fuels in 

other forest types such as ponderosa pine (Covington and 

Sackett 1984, DeBano and others 1998). A consequence of 

the advance in succession in some western forests and the 

suppression of fire in others is the increased accumulation 

of aboveground biomass and nutrients in standing live 

trees, standing dead trees, downed wood, and forest floor 

(Keane and others 2002, Major 1974). Particularly in the 

Western United States, this increase in OM biomass in firesuppressed or fire-excluded forests has led to forest floor 

C accumulation far in excess of normal conditions. In the 

absence of fire, critical nutrients are tied up in this excess 

plant debris, possibly causing the site to become nutrient 

limited (Harvey 1994). Accumulations of woody residue 

and surface OM from fire suppression activities are also 

undesirable because of the increased risk from high-severity 

wildfires and slower OM decomposition rates (Covington 

and Sackett 1984).

Deep accumulations of organic material (those in 

excess of decomposition) are generally lost through fires 

(Oliver and others 1994). If the fires are frequent and 

of low severity, few organic matter (or nutrient) losses 

occur (Neary and others 1999, Page-Dumroese and 

Jurgensen 2006), but infrequent, high-severity fires can 

be catastrophic to soil productivity and forest health if 

significant amounts of biomass have accumulated (Habeck 

and Mutch 1973). Neary and others (1999) outlined the 

threshold temperatures for biological disruptions in soils. 

The cumulative impact of a catastrophic fire may directly 

affect belowground processes because it can alter nutrient 

inputs (soil macro- and microfauna), increase soil temperatures, increase erosion, alter evapotranspiration rates, and 

decrease moisture availability (Neary and others 1999). 

These detrimental impacts may also exacerbate insect and 

disease outbreaks (Harvey and others 1989, Jurgensen and 

others 1990).

Management Impacts

The Healthy Forest Restoration Act of 2003 was designed 

to help alleviate the accumulation of OM by using partial 

cuts and prescribed fires to remove small-diameter trees and 

surface OM from many forest stands. Prescribed fire and 

harvesting operations are important variables in determining soil OM losses because they both influence the removal 

of organic matter, C, and N on the soil surface and influence 

the amount of OM within the mineral soil profile. However, 

frequent repeated burns and multiple entries by mechanical 

equipment to reduce wildfire risk may impact ecosystem 

processes, soil quality and productivity, and site sustainability at a variety of scales (e.g., a cutting unit or an entire 

watershed).

Prescribed Fire

Prescribed fire, as a site preparation method or for underburning intact stands, is a major component of the restoration effort to reduce fuel levels in many forested ecosystems 

(McIver and Starr 2001). Prescribed fires produce a wide 

range of fire intensities, depending on fuel loads, fuel moisture content, slope position, and slope aspect (Brown and 

others 1991, Huffman and others 2001, Little and Ohmann 
29

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

1988, Oswald and others 1999, Vose and others 1999). Fire 

severity is a term used to describe the impact of fire on both 

above- and belowground stand components (DeBano and 

others 1998, Keane and others 2002). Various burn indices 

have been developed to evaluate fire effects on ecosystem 

processes and soil productivity (Neary and others 1999), but 

three classes are commonly used: low severity—a nonlethal, low-intensity surface fire in which patches of surface 

OM are lost, moderate severity—a patchy fire that creates 

a mixed mosaic of fire intensities and all small-diameter 

(<7.6 cm) OM is consumed, and high severity—a standreplacement fire that kills more than 90 percent of the trees, 

consumes most surface OM, and some OM in the mineral 

soil has been lost (mineral soil changes color) (Keane 

and others 2002). Information is needed on the impact of 

prescribed fire on soil OM content and distribution to evaluate the effects of fire management practices on residual fuel 

loads, soil erosion potential, and long-term site productivity 

(Elliot 2003, Neary and others 2000).

Because of the range of fire conditions possible in 

any given stand, the range of soil conditions will also be 

variable (Landsberg 1994). Often, reduced productivity or 

health of the remaining stand is influenced by the amount 

of injury to remaining trees, crowns, and roots, reductions 

in microorganisms in the surface mineral soil, and changes 

in C and other nutrient pools (Klemmedson and Tiedemann 

1995, Page-Dumroese and others 2003). In addition, the 

impacts of prescribed fire are dependent on forest type and 

past management (Schoennagel and others 2004). Historically, dry forests of the Western United States (e.g., Pinus

ponderosa, dry Pseudotsuga menziesii, etc.), which had a 

relatively short fire return interval and low fire intensity 

(Agee 1998), did not usually develop disease problems (like 

Armillaria spp.) when the fire return interval remained 

short (McDonald and others 2000). However, because these 

dry stands are water limited and have relatively shallow 

forest floors (Page-Dumroese and Jurgensen 2006), as fire 

exclusion and suppression increase, so does the stress and 

competition between trees for limited water and nutrient 

resources (McDonald and others 2000).

The combined effects of harvest operations and prescribed burning on the remaining forest slash may severely 

impact mesofauna living in the forest floor by either directly 

killing them or removing their desired food source (Fellin 

1980b). This includes both pests and beneficial insects. 

For instance, predators and parasites of spruce budworm 

that live in the forest floor may help to regulate budworm 

numbers at low levels so that existing populations do not 

reach epidemic proportions (Fellin 1980b). Stands affected 

by low-severity fires, which can leave many unburned 

areas of the forest floor, may provide a favorable location 

for maintaining important insects in the forest floor and 

ensuring that decomposition and nutrient cycling processes 

continue (Fellin 1980b).

Results of studies of repeated prescribed fires on soil 

quality and forest health are mixed. Annual or biannual 

prescribed fires have been shown to reduce pools of C, N, 

and sulfur (S) in the forest floor after 30 years (Binkley 

and others 1992). Burning on a 2-year interval for 20 years 

reduced N from both the forest floor and mineral soil 

(Wright and Hart 1997). However, a study of prescribed fire 

at intervals of 1, 2, 4, 6, 8, and 10 years in ponderosa pine 

resulted in an increase of soil C and N (Neary and others 

2002). These long-term changes in nutrient status may or 

may not affect long-term site productivity (Jurgensen and 

others 1997), but on some sites they could affect insect and 

disease outbreaks (Harvey and others 1989). For example, 

the introduction of white pine blister rust (Cronartium 

ribicola) in the Western United States has reduced the 

number of 5-needled pines (e.g., Pinus monticola or P.

lambertiana) in many ecosystems (Monnig and Byler 1992). 

These pines, along with ponderosa pine (P. ponderosa) 

and western larch (Larix occidentalis) tend to be broadly 

adapted species (Rehfeldt 1990) and are relatively tolerant 

to many native pests. However, in the absence of fire, they 

are strongly reduced and are more susceptible to nonnative 

pest outbreaks (Harvey 1994). This shift from more tolerant 

species has also reduced nutrient cycling within the surface 

organic matter and mineral soil (Harvey 1994). Typically, 

more carbon is held in aboveground biomass when there is 

a compositional shift toward less tolerant species, and this 

can result in more plant stress when available moisture is 

low (McDonald 1990).
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GENERAL TECHNICAL REPORT PNW-GTR-802

Mechanical Soil Disturbance—

Soil displacement (removal of surface organic and mineral soil) is most often measured by the amount of forest 

floor removed. Loss of surface OM either by equipment 

or through accelerated erosion may produce detrimental 

changes if it is moved off site, is unavailable to tree roots, or 

if mineral soil removal results in exposing subsoil horizons. 

However, careful placement of harvesting and yarding 

layout in ground-based units could mitigate some detrimental displacement. For instance, McIver and others (2003) 

noted that when displacement along the edge of trails is 

included (i.e., displacement caused by the harvester moving 

close to trails to cut logs) in the inventory, displacement 

can be extremely variable (5 to 43 percent). However, if soil 

displacement along trail edges was excluded from the site 

inventory, displacement was no greater than USDA Forest 

Service guidelines of 15 percent. The report by McIver and 

others (2003) and the information in Curran and others 

(2005) both stress that it is imperative to have uniform 

terms for describing soil disturbance to improve our 

techniques for tracking the consequences of forest practices 

on soil productivity and forest health. 

Often soil erosion is not a significant problem on slopes 

that have some soil cover. Using information about slope, 

amount of the hillside with some soil cover, and local 

precipitation values, Page-Dumroese and others (2000) 

noted that in many cases, soils with at least 50-percent soil 

cover did not produce more than 2 to 4 mg ha-1 of soil erosion. In some cases, removal of soil from the upper slope to 

somewhere downslope may reduce upslope productivity, but 

increase downslope productivity. However, if soil is moved 

off site, productivity is reduced permanently (Elliot and 

others 1998). Both onsite and offsite soil movement results 

in lower soil productivity for part of the slope because of 

loss of nutrients, water-holding capacity, and rooting depth, 

and it may also impair forest health on that portion of the 

landscape. Combining soil-cover loss with compaction can 

accelerate erosion rates above the natural soil formation 

rates (Elliot and others 1998). Soil loss is only one problem 

associated with accelerated erosion. Often N, C, and cation 

exchange capacity are also moved offsite with the moving 

soil (Page-Dumroese and others 2000). Erosion rates are 

usually highest immediately after soil is disturbed mechanically or as a result of fire (Robichaud and Brown 1999).

Minimizing soil compaction during harvesting and 

mechanical site preparation operations on forested lands 

is critical for maintaining the productive capacity of a site 

(Powers and others 2005). Compaction increases soil bulk 

density and soil strength, decreases water infiltration and 

aeration porosity, restricts root growth, increases surface 

runoff and erosion, and alters heat flux (Greacen and Sands 

1980, Williamson and Neilsen 2000). These changes can 

lead to substantial declines in tree growth and forest health 

(Froehlich and others 1986, Gomez and others 2002) or, 

conversely, have little impact (Powers and others 2005). 

Significant changes in soil physical properties occur more 

often on fine-textured soils than on coarse-textured soils 

(Page-Dumroese and others 2006), and knowing basic site 

conditions like texture and soil moisture content along with 

designating skid trails or providing operator training may 

help reduce undesirable soil conditions and maintain longterm productivity (Quesnel and Curran 2000).

Most forests are dependent on a variety of biological 

processes to regulate nutrients and cycle organic matter. For 

instance, forest diseases such as Armillaria and Annosus

root diseases are a key ecosystem process to recycle carbon 

(Harvey 1994), but they can also expand to epidemic 

proportions if conditions are favorable. Trees not adapted 

to a site, wounded during thinning operations, growing on 

compacted soils, or in areas of disturbed hydraulic function, 

are at risk from both disease infection (Goheen and Otrosina 1998, Wiensczyk and others 1997) and insect outbreaks 

(Larsson and others 1983). In a study in loblolly pine (Pinus 

taeda) plantations, Annosum was positively correlated with 

areas of higher bulk density that had stressed numerous 

trees (Alexander and others 1975). Low vigor, stressed 

forest stands are also susceptible to insect attacks (Larsson 

and others 1983). The recent outbreak of mountain pine 

beetle (Dendroctonus ponderosae) in British Columbia, 

Canada, and the Pacific Northwest of the United States have 

caused significant changes in water flow, soil moisture, and 

groundwater levels in many forest stands (Uunila and others 

2006). These changes in water abundance and timing can 

affect the health of subsequent stands by changing annual 
31

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

water yields, times of low flow and peak flows (Uunila and 

others 2006).

Acidic Deposition and Forest Health

The risks posed by atmospheric deposition to forest health 

are complex because of distinct regional patterns of deposition and temporal variations in air quality (McLaughlin 

and Percy 1999). However, air pollution stresses in North 

American forests and their impact on insect and disease 

incidence are a major consideration in both the United 

States and Canada (McLaughlin and Percy 1999), particularly in the eastern part of North America. One potential 

threat from changes in air quality on many forested lands is 

the altered nutrient balance and soil acidity (Adams and 

others 2000), which then affect insect and disease outbreaks. Soil impacts and threats to forest health can be 

caused by nitrogen (N) saturation (Aber and others 1989), 

depletion of basic cations due to increased leaching (Federer 

and others 1989), and altering nutrient availability (Robarge 

and Johnson 1992). Combined, all these nutrient stresses 

may cause long-term declines in site productivity (Likens 

and others 1996) and may increase the number and severity of disease or insect outbreaks (McLaughlin and Percy 

1999). Soil processes altered by deposition of air pollutants 

also alter the way forests grow and respond to biotic and 

abiotic stresses within their regional environments. The 

potential for large-scale forest health changes becomes 

greater over time (McLaughlin and Percy 1999).

Monitoring

Current soil monitoring efforts primarily address changes 

in soil quality through measures of compaction, pH, water 

infiltration, hydrologic function, water availability, and 

plant-available nutrients, but do not address ecological function of a site (van Bruggen and Semenov 2000). Ecological 

function may be difficult to assess because of season, 

management, or climate variables. Potential approaches 

to assess ecological function can include techniques that 

extract DNA to determine microbial species composition 

(Zhou and others 1996), determine substrate utilization (i.e., 

Biolog) to trace microbial communities (Bossio and Scow 

1995), or decomposition of a wood substrate (Jurgensen and 

others 2006). In addition, levels of root pathogens or disease 

suppression organisms could also be considered as potential 

bioindicators of soil health (Hoeper and Alabouvette 1996, 

Visser and Parkinson 1992).

In North America, several forest monitoring programs, 

[i.e., Forest Health Monitoring (FHM, U.S.A.), Acid Rain 

National Early Warning System (ARNEWS, Canada), 

North American Maple Project (NAMP, joint United States 

and Canada), Forest Inventory and Analysis (FIA, U.S.A.)] 

have been developed as a result of studies that indicated 

widespread changes in forest health (McLaughlin and Percy 

1999). Many of these “tree” monitoring programs also 

include soil indicators in their protocols (O’Neill and others 

2005). In addition, the Montréal Process (an international 

effort to develop criteria and indicators of forest sustainability) is working to provide a common framework for describing soil changes by using two important soil characteristics: 

significantly diminished organic matter and significant 

compaction (Montréal Process Working Group 1997). The 

USDA Forest Service is mandated to monitor management 

impacts on soil productivity, site resiliency, and long-term 

productivity (Johnson and Todd 1998, Jurgensen and others 

1997, Page-Dumroese and others 2000) and is part of an 

international, multiagency effort to pursue correlation 

and common definitions of practical standards for soil 

disturbance and maintenance of forest productivity capacity 

(Curran and others 2005).

There are numerous tools available to monitor a forest 

site or soil for changes over time or after wildfire, prescribed fire, or mechanical disturbances. Together, forest 

and soil monitoring data can provide a backdrop for current 

conditions and trends in forest health. Successful forest and 

soil monitoring programs must:

• Be simple to use.

• Have repeatable methods that are useable by 

nonspecialists.

• Provide meaningful results.

• Comprise collaborative efforts between both 

scientists and various government agencies.

• Provide a compelling link between soil properties 

and forest growth or health.
32

GENERAL TECHNICAL REPORT PNW-GTR-802

Summary and Management Implications

Solutions to the current forest health problems are neither 

clearcut nor easily managed (Tiedeman and others 2000). 

However, maintenance of the forest floor during prescribed 

fire or mechanical forest treatments will help to maintain 

soil quality, soil health, and ultimately forest health (Fellin, 

1980a, Page-Dumroese and Jurgensen 2006). The indirect 

influence of changing microclimatic conditions of the 

forest floor will depend on the kinds of site treatment and 

the condition of the previous stand. Overcrowded and 

fire-suppressed stands will likely differ in their response to 

treatment than more open-grown stands. Similarly, stands 

with seral species will respond differently to nutrient, water, 

insect, and disease stressors than those stands occupied by 

less tolerant, narrowly adapted species (Harvey 1994).

Forest health can be successfully managed if we shift the 

focus from just “tree health” to entire ecosystem health. 

Current levels of pest problems are not due only to a single 

organism, but to the complex of environmental conditions 

and stand histories that determine stand resilience to pest 

outbreaks. Healthy and resilient forest soils can also help 

limit the extent of forest pest problems.

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37

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Assessing the Threat That Anthropogenic Calcium Depletion 

Poses to Forest Health and Productivity

Paul G. Schaberg, Eric K. Miller, and Chistopher Eagar

Paul G. Schaberg, research plant physiologist, USDA Forest Service, Northern Research Station, South Burlington, 

VT 05403; Eric K. Miller, President and senior scientist, 

Ecosystems Research Group, Ltd., Norwich, VT 05055; and 

Christopher Eagar, project leader and ecologist, USDA 

Forest Service, Northern Research Station, Durham, NH, 

03824.

Abstract

Growing evidence from around the globe indicates that 

anthropogenic factors including pollution-induced acidification, associated aluminum mobility, and nitrogen saturation 

are disrupting natural nutrient cycles and depleting base 

cations from forest ecosystems. Although cation depletion 

can have varied and interacting influences on ecosystem 

function, it is the loss of calcium (Ca) that may be particularly limiting to tree health and productivity. Calcium plays 

unique roles in plant cell function, including environmental 

signal transduction processes that allow cells to sense and 

respond to stress. Considering this, Ca depletion could 

impair plant response systems and predispose trees to 

reduced growth and increased decline. Controlled experiments with red spruce (Picea rubens Sarg.) and other tree 

species provide mechanistic support for the hypothesis that 

Ca deficiencies predispose trees to decline. Importantly, 

several examples of species declines in the field also suggest 

that injury is often greater when Ca depletion and stress 

exposure co-occur.

Connections between contemporary species declines 

and Ca depletion highlight the need for monitoring forests 

for indicators of change, including Ca loss. Direct measures 

of soil and plant Ca concentrations provide one traditional 

means of assessing the Ca status within forests. Although 

these measures are often valuable, substantial variation 

among soils and species and a lack of comparative historical data provide obstacles to the use of these measures for 

evaluating Ca depletion across the landscape. An alternative 

approach for assessing Ca depletion is to model critical 

loads and exceedances of pollutant additions that lead to 

net losses in Ca pools and likely disrupt ecosystem Ca 

cycles within forests. For example, spatial associations of 

Ca cycling and loss to broad-scale data on forest health 

and productivity were recently conducted for portions of 

the Northeastern United States. A steady-state ecosystem 

process model was coupled to extensive spatial databases 

and used to generate maps identifying forest areas likely to 

experience Ca depletion. Sustainable Ca supplies in forest 

ecosystems are functions of forest type, timber extraction 

intensity, prior land use, atmospheric deposition rates, 

and site factors including climate, hydrology, soil mineral 

type and weathering rates. Considering the unique vulnerability of Ca to leaching loss and its vital role in supporting 

tree stress response systems, the model focused on how 

changes in Ca pools may influence forest health conditions. 

Initial comparisons within New England indicated that the 

model-based Ca deficiency metric was a good predictor 

of field-based indicators of forest health and productivity. 

Models such as this show promise for evaluating the threat 

Ca depletion poses to forest health and productivity in an 

integrated and spatially explicit manner in North America. 

This approach has already proven valuable to policymakers 

and managers in Europe when evaluating alternative pollution reduction or mitigation options. 

Keywords: Anthropogenic depletion, calcium, critical 

loads, stress response, sustainable nutrient supply, tree 

health.

Anthropogenic Cation Depletion 

Cations are naturally occurring, positively charged elements that are important constituents of soils and surface 

waters and play unique and critical roles in biological 

systems. Among many functions, cations serve as important 

co-factors influencing the activity of biomolecules, act to 

modify charge balances within cells and organelles, and 

serve as signaling agents that help regulate cell physiology 

(Buchanan and others 2000, Marschner 2002). In forested 

ecosystems, the presence and availability of cations is governed through the interplay of numerous natural processes, 

including atmospheric additions, mineral weathering, soil 
38

GENERAL TECHNICAL REPORT PNW-GTR-802

production of acids from pollutant constituents, N inputs 

can lead to N saturation (the availability of N in excess of 

biological demand), which can deplete cations as excess 

N leaches from forest soils (Aber and others 1998). It has 

even been hypothesized that pollution-associated climatic 

warming could enhance rates of natural acidifying process, 

further exacerbating soil cation loss (Tomlinson 1993). In 

addition to pollution-associated cation loss, a side effect of 

existing pollution controls has been the reduced emission 

of particulates that contain base cations such as Ca (Hedin 

and others 1994). Reduced inputs and increased removals of 

cations from forests have resulted in net depletions that have 

been documented in a variety of ways, including long-term 

changes in stream chemistry, the analysis of archived soils, 

and the chemical analysis of tree xylem cores.

Evidence From the Field

Long-term data of stream water chemistry at watersheds 

such as those at the Hubbard Brook Experimental Forest, 

New Hampshire, have documented changes consistent with 

the pollution-induced leaching of base cations from soils 

(Likens and others 1996, 1998). Early stream monitoring 

revealed an increasing flush of base cations (Ca and Mg) 

that paralleled SO4

2- and NO3̄ concentrations—evidence 

that pollutant inputs were leaching stored soil cations into 

surface waters (Likens and others 1996, 1998). However, 

after 1970, mass balance calculations identified ever-reducing concentrations of cations, particularly Ca, coincident 

with decreases in SO4

2- and NO3̄—a pattern suggesting the 

depletion of available cations following long-term leaching 

(Likens and others 1996, 1998). The connection between 

cation losses and pollutant inputs was reinforced by data 

indicating that these same trends in stream chemistry occur 

in old-growth forests where the potentially confounding 

effects of land use disturbance (and associated acidification) were avoided (Martin and others 2000). Furthermore, 

European data indicate that the largest losses of Ca and 

Mg occur at sites with the most acid loading (Kirchner and 

Lydersen 1995). 

Calculated reductions in soil cation storage inferred 

from the chemical analysis of stream water have recently 

formation, plant uptake and growth, forest stand dynamics, 

and leaching losses (Likens and others 1998). However, 

mounting evidence indicates that a variety of anthropogenic 

factors are altering biogeochemical cycles and depleting 

base cations such as calcium (Ca) and magnesium (Mg) 

from terrestrial ecosystems. Chief among these drivers of 

cation loss are processes directly or indirectly associated 

with atmospheric pollution.

Pollutant Drivers

Through industrial activity and the increased combustion of fossil fuels over the past century, humans have 

dramatically increased gaseous emissions of sulfur dioxide 

(SO2), nitrogen oxides (NOx), and ammonia (NH3) and 

particulate emissions of acidifying compounds (Driscoll 

and others 2001). Recent pollution controls have reduced 

emissions of sulfur (S) -based compounds in Europe and 

North America, resulting in moderate reductions in S 

deposition, but there has been little change in nitrogen (N) 

deposition (Driscoll and others 2001, UNECE 2005). In 

contrast to North America and Europe, with rapid economic 

development and economic growth, Asia—and most notably China—has significantly increased fossil fuel combustion in recent years (Liu and Diamond 2005). As a result, 

emissions of SO2, NOx, NH3, and associated compounds 

have increased greatly in the region (Carmichael and others 

2002, Liu and Diamond 2005, Richter and others 2005). 

In fact, pollutant deposition of S and N compounds now 

affects a quarter of China’s land area, making China one of 

the countries most influenced by these pollutants (Feng and 

others 2002, Jianguo and Diamond 2005). 

Through the atmospheric conversions of SO2 and NOx

to the acids H2SO4 (sulfuric) and HNO3, (nitric) as well as 

the release of hydrogen ions (H+) during the oxidation of 

ammonium (NH4

+

) by soil microbes, S- and N-based pollutants act to acidify forest systems (Driscoll and others 2001). 

Among other impacts, this acidification increases the leaching of base cations from soils (Kirchner and Lydersen 1995; 

Likens and others 1996, 1998; Schulze 1989), and enhances 

the availability of aluminum (Al), which reduces base cation 

availability for plant uptake (Cronan and Scholield 1990, 

Lawrence and others 1995). In addition to the atmospheric 
39

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

been bolstered by studies from the United States and 

Europe that directly measured reductions in soil Ca storage 

following long-term exposures to acidic deposition. Bailey 

and others (2005) measured the cation concentration of soils 

at four forested sites in the Allegheny Mountains of Pennsylvania in 1997 and compared these to data from archived 

soil samples from these same sites collected in 1967. At all 

four sites there were significant reductions in Ca and Mg 

concentrations and pH over the two sample periods, and, 

at most sites, documented losses of Ca and Mg were much 

larger than could be accounted for by biomass accumulation—suggesting leaching losses as a more likely cause. In 

a separate analysis, Lawrence and others (2005) measured 

the cation contents of soil samples collected in 1926, 1964, 

and 2001 near St. Petersburg, Russia. They found that concentrations of exchangeable Ca in the upper 30 cm of soil 

decreased about tenfold from 1926 to 1964 but remained 

stable thereafter. In contrast, exchangeable Al showed a 

small decrease in the upper 10 cm of soil from 1926 to 1964, 

but a tenfold increase in the upper 30 cm from 1964 to 2001. 

They interpreted these results as reflecting a two-stage 

acidification process: (1) from 1926 to 1964 when inputs of 

acidity were neutralized by the replacement of exchangeable 

Ca by H, and (2) from 1964 to 2001 when the neutralization 

of continued acidic inputs shifted from cation exchange to 

weathering of solid phase Al (Lawrence and others 2005). 

Here, too, changes in soil Ca concentrations were not 

attributable to biomass accumulation of Ca, but appeared 

better related to pollution-induced soil Ca depletion. 

Consistent with measured reductions in soil Ca, 

several studies have noted reduced Ca concentrations in the 

stemwood of trees following the advent of elevated pollution 

loading (Bondietti and others 1990, Likens and others 1996, 

Shortle and others 1995). An initial increase in Ca concentration is often noted within wood for the decades with the 

greatest increases in acidic deposition that likely mobilized 

soil cations, increasing their availability for root uptake 

and leaching loss (e.g., Shortle and others 1995). However, 

the reduction in stemwood Ca in recent decades may better 

reflect the long-term depletion of Ca from soils (Shortle and 

others 1995). Importantly, reductions in Ca concentrations 

within wood also suggest that pollution-induced changes in 

soil Ca levels are being transferred to living organisms.

Potential Contributions from Harvesting 

In addition to pollution-associated depletion, tree harvests 

have the potential to exacerbate cation depletion within 

forests if they contribute to net cation losses that exceed 

long-term inputs (Adams 1999, Federer and others 1989, 

Huntington 2000, Mann and others 1988, Nykvist 2000). 

Sequestration in aboveground woody biomass is an 

important cation sink within forest systems (Federer and 

others 1989, Mann and others 1988), and this is particularly 

true for Ca, which is highly concentrated in woody cell 

walls (Marschner 2002). Because of this, tree harvests 

can lead to the disproportionate removals of Ca relative to 

other cations (e.g., Adams 1999, Federer and others 1989). 

Harvests can also affect nutrient cycling through increased 

site acidification and leaching (Federer and others 1989), 

and reduced stocking following harvest may diminish 

stand-level transpiration and associated Ca uptake, further 

promoting Ca loss via leaching (Hornbeck and others 1993). 

In addition, varying methods of harvest can differentially 

alter Ca loss. For example, in one study, whole-tree (stems 

and branches) harvests removed up to 530 kg/ha, whereas 

sawtimber sales (bole wood only) removed about 442 kg/ha 

(Mann and others 1988). The frequency of tree harvest may 

also influence overall cation removal. Calculations from one 

study estimated a 15-percent loss of Ca from leaching even 

with no harvest, a 28-percent loss of Ca with one harvest 

(at 80 years), and a 41-percent Ca loss for an equal-intensity 

harvest performed in two stages: once at 40 years and once 

at 80 years (Adams 1999). 

Calcium Depletion as a Biologically 

Unique Threat

Although the depletion of any of the essential base cations 

can have varied and interacting influences on ecosystem 

function, the loss of Ca may be particularly limiting to tree 

health because the unique distribution and physiology of Ca 

suggests that the depletion of this key cation could specifically weaken plant stress response systems and predispose 

trees to decline.
40

GENERAL TECHNICAL REPORT PNW-GTR-802

conducted studies have concluded that Ca plays a critical 

message perception and transduction role in response to an 

array of environmental stresses, including low temperature 

(DeHayes and others 1997, 1999; Monroy and others 1993), 

drought (Sheen 1996), fungal infections (Hebe and others 

1999), and insect infestations (McLaughlin and Wimmer 

1999). 

Implications to Forest Health

Given the fundamental role Ca plays in plant stress response 

systems, biological Ca depletion could create a scenario 

analogous to the suppression of animal immune systems 

(Schaberg and others 2001). For example, there are numerous circumstances (e.g., HIV infection, chemotherapy 

treatment, etc.) that impair the normal function of human 

immune systems. An immuno-compromised person may 

appear, feel, and ostensibly function as if they were healthy. 

Nonetheless, when exposed to a disease agent, they can 

experience declines in health that are exaggeratedly large 

relative to a person with a fully functioning immune 

system. In this same way, it is possible that depletions 

of biologically available Ca could suppress the ability of 

plants to adequately sense and respond to changes in their 

surroundings and make them more vulnerable to decline 

(Figure 1). This suppression would predispose plants to 

disproportionate decline following exposure to perhaps 

even normal levels of stress (e.g., pathogens or drought) that 

would otherwise pose no catastrophic threat if biological 

response systems were fully functional. Importantly, under 

this scenario plants might initially appear to be normal and 

healthy even though their biological response systems were 

compromised (Schaberg and others 2001).

Experimental Evidence of Tree Health 

Impacts

Although based on basic understandings of the distribution 

and physiology of Ca in plants, experimental evidence that 

Ca deficiencies could reduce stress tolerance in trees has 

only recently surfaced. This evidence was first documented 

for the well-studied phenomenon of winter injury in red 

spruce, but was later shown to be relevant to other tree 

species and stresses other than freezing injury.

Calcium Distribution and Physiology

In contrast to many cations, Ca is highly compartmentalized within plant cells and tissues, and this partitioning 

is a defining characteristic of its physiological function. 

Although Ca is an essential micronutrient, it is toxic in its 

free form within the cell cytoplasm because it precipitates 

with inorganic phosphate (e.g., Bush 1995, Knight 2000). 

Thus, in order to assure phosphate availability for energy 

metabolism and other essential processes, Ca is actively 

pumped from the cytoplasm and is sequestered in inaccessible locations and chemical forms, including insoluble 

oxalate crystals outside the plasma membrane (Fink 1991). 

Because Ca can only exist in very low concentration in the 

cytoplasm, it is functionally immobile in the phloem (which 

relies on cytoplasmic transport). Thus, unique to other cations, Ca cannot be redistributed within plants to overcome 

localized deficiencies.

Localized concentrations of Ca support at least two 

important functions: (1) they add to the structural stability of cell walls and membranes, and (2) labile Ca is a key 

constituent in the pathway that allows cells to sense and 

respond to environmental stimuli and change (Marschner 

2002). This second function appears particularly relevant to 

tree health concerns related to Ca depletion. Ca serves as an 

important second messenger in the perception and transduction of environmental and stress signals (Bush 1995, Pandey 

and others 2000, Roos 2000, Sanders and others 1999). 

Because extremely little free Ca exists in the cytoplasm 

of cells, environmental stimuli that temporarily alter the 

permeability of the plasma membrane allow labile Ca to 

flow into cells along a steep concentration gradient (Sanders 

and others 1999). Once in the cytoplasm, Ca quickly binds 

to Ca-specific proteins such as calmodulin, which then 

initiate a chain of physiological modifications (e.g., changes 

in enzyme activity, gene transcription, etc.) that help cells 

adjust to the environmental conditions that triggered the 

response cascade. This entry of Ca into the cytoplasm 

acts as a messenger of environmental information for 

cells and appears to be an essential first step in triggering 

a wide range of physiological responses needed by plants 

to successfully adjust to environmental change or defend 

against pests and pathogens. Numerous independently 
41

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Mechanism of Red Spruce Winter Injury

Red spruce winter injury is the reddening and mortality of 

the foliage in late winter followed by its abscission in late 

spring (DeHayes 1992). Injury is caused by freezing and is 

likely the result of various stresses, including low temperatures (DeHayes and others 1990), freeze-thaw cycles 

(Hadley and Amundson 1992, Lund and Livingston 1998), 

and rapid freezing (Perkins and Adams 1995). The currentyear foliage of red spruce is more vulnerable to injury than 

older foliar age classes or foliage from sympatric species 

because it is less cold tolerant (DeHayes and others 1990). 

In addition, certain anthropogenic inputs such as acidic 

or prolonged N deposition can further reduce foliar cold 

tolerance and increase the risk of freezing injury (Schaberg 

Figure 1—Schematic representation of how biological calcium (Ca) depletion may suppress plant stress 

response systems and predispose trees to decline. Environmental stimuli (e.g., a stress event) triggers 

the movement of Ca from areas of high concentration (i.e., extra-cellular and organelle pools) into the 

cytoplasm where Ca concentrations are 100 to 1,000 times lower. Once in the cytoplasm, Ca can bind 

to and activate Ca-specific protein complexes that interact with other cellular components (existing 

enzymes, DNA, etc.) to modify cell physiology in response to the instigating environmental cue. Depletion of biologically labile Ca may perturb signal transduction and diminish the ability of plants to sense 

and respond to environmental change/stress (Schaberg and others 2001).
42

GENERAL TECHNICAL REPORT PNW-GTR-802

recent evidence indicates that the same basic mechanism of 

physiological disruption documented for this species and 

syndrome are pertinent to other tree species and stressors. 

For example, Schaberg and others (2001) treated red spruce, 

eastern hemlock (Tsuga Canadensis (L.) Carr.), balsam fir 

(Abies balsamea (L.) Mill.), and eastern white pine (Pinus

strobus L.) seedlings with acid mist and compared the nutritional and physiological responses of the newly evaluated 

species to those well-documented for red spruce. Although 

there was insufficient tissue to make all measurements on 

each species, results showed that acid mist reduced mCa 

levels (in eastern hemlock), decreased cell membrane 

stability (in balsam fir), and reduced foliar cold tolerance 

(in white pine) similar to red spruce (Schaberg and others 

2001). In a separate experiment with red spruce, Borer and 

others (2005) examined the influence of acid mist exposure 

on stomatal closure following tissue desiccation—a stress 

response to drought that is also dependent on Ca signaling 

(Knight 2000). Red spruce seedlings were exposed to pH 3 

or 5 mists and then measured for foliar Ca concentrations 

and rates of stomatal closure as foliage desiccated following 

shoot harvest. As with past experiments, acid mist exposure 

reduced the Ca available in foliage, but here the loss of Ca 

was also accompanied by a 15 percent slower rate of stomatal closure as tissues desiccated (Borer and others 2005). 

Results of experimental trials like this support the theory 

that anthropogenic Ca depletion could deplete biological 

Ca pools enough to suppress stress response systems and 

predispose trees to decline.

Field Evidence of Influences on Tree 

Health

Controlled experiments like the ones outlined above have 

provided valuable insights into the biological mechanism 

through which Ca depletion may influence tree physiology 

and health. However, such studies by themselves do little 

to inform us of the threat Ca depletion may pose to native 

forests. Instead, evidence from numerous field studies has 

supported experimental findings and implicated Ca depletion as a contributing factor in the real-world decline of tree 

species in the United States and abroad.

and DeHayes 2000). Heavy foliar loss and potential bud 

mortality from winter injury disrupts the carbon economies 

of trees, leading to growth declines and potential mortality 

(DeHayes 1992, Lazarus and others 2004). Winter injury 

was linked to the widespread decline of red spruce observed 

in the Northeastern United States from the 1960s through 

the 1980s (Friedland and others 1984, Johnson 1992), 

and severe winter injury events persist within the region 

(Lazarus and others 2004).

Beginning in the late 1980s, a series of studies were 

published showing that acid mist exposure significantly 

reduced the cold tolerance of red spruce current-year 

foliage, increasing the risk of foliar winter injury (e.g., 

DeHayes and others 1991, Fowler and others 1989, Vann and 

others 1992). The physiological mechanism for this acidinduced reduction in cold tolerance remained unresolved, 

however, until a new method for measuring Ca specifically 

associated with cellular membranes was used in conjunction with controlled acid mist exposure experiments. Using 

these new methods for measuring membrane-associated Ca 

(mCa), a series of experiments documented that acid mists 

preferentially leached mCa from the outside of mesophyll 

cells, whereas other cations and forms of Ca were leached 

less, presumably because they were concentrated within 

the protective membrane barrier of cells (DeHayes and 

others 1999, Jagels and others 2002, Jiang and Jagels 1999, 

Schaberg and DeHayes 2000, Schaberg and others 2000). 

Furthermore, these studies showed that this loss of mCa 

destabilized membranes, depleted a source of Ca needed for 

stress signaling, reduced foliar cold tolerance, and predisposed trees to the secondary freezing injury responsible for 

decline (DeHayes and others 1999, Schaberg and DeHayes 

2000, Schaberg and others 2000). Later work verified that 

soil-based Ca depletion initiated the same mechanistic 

sequence of physiological disruptions documented for foliar 

Ca leaching (Schaberg and others 2002).

Pertinence to Other Tree Species and Stressors

The bulk of experimental evidence elucidating the influence 

of Ca depletion on tree nutrition and stress response has 

involved winter freezing injury of red spruce. However, 
43

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Winter Injury of Red Spruce

The role that foliar winter injury has played in red spruce 

decline in Northeastern United States and adjacent Canada 

has long been understood (see DeHayes 1992). In addition, 

experimental evidence has provided a detailed understanding of the mechanism through which acid deposition can 

deplete biologically available Ca and predispose foliage to 

damage (see “Mechanism of Red Spruce Winter Injury”). 

However, it was only following the severe winter injury 

event of 2003 that evidence indicated that acid deposition 

exposure in the field influences winter injury expression 

across the landscape. Lazarus and others (2006) measured 

the degree of foliar winter injury among dominant and 

codominant red spruce trees at multiple elevations (plots) at 

23 sites in Vermont and adjacent States and used regression 

analyses to evaluate how injury varied with plot elevation, 

latitude, longitude, slope, and aspect. They found that 

injury was significantly greater in western portions of the 

study area, west-facing slope, and higher elevations—areas 

that have historically received higher levels of acidic and 

N deposition (Lazarus and others 2006). Although these 

findings support the hypothesis that acidic or N deposition or both act on a landscape scale to exacerbate winter 

injury, it was an ancillary evaluation that more specifically 

implicated Ca depletion as a modifier of injury expression 

in 2003. Hawley and others (2006) measured foliar nutrition 

and winter injury of red spruce on two watersheds at the 

Hubbard Brook Experimental Forest in New Hampshire: 

one a reference watershed that has undergone considerable 

Ca loss attributed to acid deposition-induced leaching 

(Likens and others 1996, 1998), and another watershed 

that was fertilized with CaSiO3 in 1999 to replace lost Ca. 

Dominant and codominant red spruce on the Ca-addition 

watershed had significantly more Ca in their foliage and 

experienced about one-third the foliar injury of comparable 

trees on the reference watershed (Hawley and others 2006). 

Sugar Maple Decline

Sugar maple (Acer saccharum Marsh.) decline has been 

documented throughout parts of the Northeastern United 

States and Quebec over many recent decades (Allen and 

others 1992a, Kelley 1988, Mader and Thompson 1969, 

Wilmot and others 1995). These declines have been characterized using various measures, including crown deterioration, increased leaf chlorosis, and reduced growth. Stress 

factors such as drought (Payette and others 1996), freezing 

(Robitaille and others 1995), and insect defoliation (Allen 

and others 1992b) have been implicated with the decline and 

mortality of sugar maple. Regardless of stressor, decline 

has also been associated with deficiencies or imbalances of 

various elements including N, phosphorous (P), potassium 

(K), Mg, manganese (Mn), or Ca (Bernier and Brazeau 

1988, Horsley and others 2000, Mader and Thompson 1969, 

Ouimet and Fortin 1992, Paré and Bernier 1989, Wilmot and 

others 1995). Although the specific elements associated with 

decline can differ among sites, deficiencies in Ca have been 

highlighted as a potential contributor to sugar maple decline 

in recent studies throughout the region (e.g., Ellsworth and 

Liu 1994, Ouimet and Camiré 1995, Wilmot and others 

1996), in part because experimental additions of Ca or lime 

or both have been shown to reduce decline symptoms (Long 

and others 1997, Moore and others 2000, Wilmot and others 

1995). 

Consistent with these observations, Schaberg and 

others (2001) hypothesized that sugar maple decline may be 

another example of Ca depletion’s influence on tree stress 

response systems and health (Figure 2). Variations in maple 

decline symptoms (crown condition and basal area growth) 

coinciding with differences in soil Ca status across a range 

of sites are consistent with this hypothesis (Schaberg and 

others 2006). However, as with red spruce, controlled additions of Ca at the Hubbard Brook Experimental Forest have 

provided a more specific test of the influence of Ca depletion on sugar maple health. For example, Juice and others 

(2006) compared the nutrition, reproductive success, and 

physiology of sugar maple seedlings on the reference and 

Ca-addition watersheds there. They found that seedlings 

on the Ca-treated watershed had higher root and foliar Ca 

concentrations, experienced greater survivorship, existed in 

greater densities, and had higher foliar chlorophyll concentrations than seedlings on the reference watershed (Juice 

and others 2006). Mycorrhizal colonization of seedlings 

was also greater in the treated than the reference watersheds (Juice and others 2006). In another study, Huggett 
44

GENERAL TECHNICAL REPORT PNW-GTR-802

Anthracnose Damage to Flowering Dogwood

Acidic deposition exposure significantly increases the 

susceptibility of flowering dogwood (Cornus florida L.) 

trees to injury by the fungal pathogen dogwood anthracnose 

(Discula destructive Redlin) (Anderson and others 1993, 

Britton and others 1996). Furthermore, controlled studies 

have implicated acid-induced nutrient deficiencies in this 

altered disease susceptibility (Britton and others 1996). 

Elevated disease susceptibility has also been associated 

with environmental conditions (e.g., shade, low temperatures, wet cool summers, etc.) that result in low transpiration rates and thereby reduce the accumulation of Ca in 

plants (McLaughlin and Wimmer 1999). The association of 

Ca deficiency and anthracnose susceptibility is also supported by an increased resistance to this disease following 

lime application (USDA FS 1991). Based on this and other 

evidence, McLaughlin and Wimmer (1999) proposed that 

Ca deficiencies driven by low soil Ca concentrations, low 

transpiration rates or accelerated foliar leaching, or both, 

reduce the natural resistance of dogwood to anthracnose 

infection. 

Emerging Examples in the United States and 

Europe

In addition to the well-established connections between 

Ca depletion and tree health outlined above, new associations between the Ca status and health of trees periodically 

emerge—particularly in regions that experience continued 

pollution-induced Ca leaching. Differences in the susceptibility of eastern hemlock trees to damage by the hemlock 

woolly adelgid (HWA; Adelges tsugae Annand) in the 

Northeastern United States may provide an example of 

this. The HWA is a small, aphid-like insect that was likely 

introduced to the mid-Atlantic States from Asia in the 1950s 

and has since expanded its range and influence, devastating 

hemlock forests over an ever-widening portion of eastern 

hemlock’s native range (Orwig and Foster 1998). Although 

it was first believed that little or no variation in susceptibility to HWA damage existed among hemlock trees (McClure 

1995), recent work has shown that differences in site 

conditions and the presence of other stressors are associated 

with differential damage and decline (Orwig and Foster 

and others (2007) surveyed and wounded forest-grown 

sugar maple trees in a replicated Ca manipulation study at 

Hubbard Brook. Similar to past studies, this study found 

that Ca addition increased foliar Ca levels and resulted in 

improved crown vigor, reduced branch dieback, and greater 

basal area growth among trees (Huggett and others 2007). 

However, new were findings that Ca addition particularly 

improved the growth release of trees following a severe ice 

storm and significantly increased stem wound closure—a 

capacity particularly important to a species that is regularly 

wounded as part of maple sugar production (Huggett and 

others 2007). These new findings are particularly noteworthy because they more specifically test the influence of Ca 

nutrition on the ability of sugar maple trees to respond to 

environmental change (release from competition) and stress 

(wounding).

Figure 2—Proposed mechanism of sugar maple decline. This 

depicts how Ca deficiency may suppress stress response systems 

and predispose trees to decline following exposure to stress factors reported to contribute to sugar maple decline in the Eastern 

United States and Canada (Schaberg and others 2001).
45

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

1998, Sivaramakrishnan and Berlyn 1999). In particular, 

Pontius and others (2006) recently concluded that foliar 

chemistry was linked to the infestation and susceptibility of 

eastern hemlock to HWA. Among other evidence, results of 

a regional monitoring effort showed that concentrations of 

Ca, K, N, and P were strongly correlated with HWA densities (a driving factor in hemlock decline). From this and 

other findings, they hypothesized that, whereas foliar N and 

K concentrations may influence hemlock decline through 

an alteration in insect behavior due to palatability issues, 

Ca and P concentrations may deter severe HWA damage 

through an alteration in tree physiology (Pontius and others 

2006). Experimental tests are needed to assess whether 

these changes in physiology involve Ca-induced alterations 

in plant stress response systems.

Evidence of acid deposition damage to forest health 

and productivity in Europe is anything but new or emerging. Indeed, media attention and resulting public concern 

about the possible connection between pollution exposures 

and forest death (Waldsterben) helped spur initial efforts to 

control acidifying pollution additions (Kakebeeke and others 2004). Research eventually identified pollution-induced 

cation depletion (particularly Mg and Ca) as an instigating 

component of forest decline (Schulze 1989). Although the 

mechanism through which acid deposition influences the 

health of European forests has not been explicitly defined, 

it is generally agreed that it acts as a predisposing agent, 

weakening forests and making them more susceptible to 

damage by other stresses including insect attack, extreme 

climate events, or storm damage (Materna and Lomský 

2002, UNECE 2005). This scenario is strikingly similar to 

the Ca depletion and stress response suppression hypothesis 

developed primarily using evidence from declines in the 

United States (Schaberg and others 2001). In fact, a recent 

example of forest damage in Europe that is thought to be 

predisposed by pollution exposure involves the reddening 

and abscission of foliage of Norway spruce in the late winter, presumably due to freezing injury (Lomský and Šrámek 

2002, Materna 2002). Patterns of injury (preferentially 

impacting the youngest foliage with the intensity and extent 

of damage increasing with elevation, Lomský and Šrámek 

2002, Materna 2002) are identical to those documented 

for red spruce winter injury (DeHayes 1992, Lazarus and 

others 2004), which has been mechanistically linked to Ca 

depletion (see “Mechanism of Red Spruce Winter Injury”). 

Limitations of Current Indicators of 

Calcium Depletion

Evidence from laboratory experiments and a growing 

number of field assessments indicate that anthropogenic 

Ca depletion may pose a unique threat to forest health and 

productivity. In particular, connections between contemporary species declines and Ca depletion highlight the need 

to monitor forests for indicators of change, including Ca 

loss. Direct measures of soil and plant Ca concentrations 

provide one traditional means of assessing the Ca status of 

forests. Although often valuable, these measures alone may 

not support a comprehensive and practical assessment of the 

biological threat posed by Ca depletion across the landscape 

for several reasons. Chief among these are difficulties 

associated with the high spatial variability of Ca storage and 

availability within forest soils, substantial differences in Ca 

uptake and nutritional requirements among tree species, and 

a lack of historical data to serve as a reference to gauge the 

timing and extent of Ca depletion.

Soils 

As noted in “Evidence From the Field” (p. 38), direct 

evidence of cation depletion is limited to a few retrospective studies that were able to use archived soil samples and 

contemporary measurements of the exact field locations to 

determine changes in soil chemical properties (Bailey and 

others 2005, Lawrence and others 2005). These studies, in 

combination with a 9-year watershed acidification study 

in Maine that showed sizable reductions in exchangeable 

Ca and Mg compared to the control watershed (Fernandez 

and others 2003), and long-term watershed mass balance observations (Likens and others 1996), support the 

theoretical basis of cation depletion due to acid deposition 

(Reuss 1983). However, application of these results to larger 

landscapes is highly problematic because of the large spatial 

variability of soil properties. Plant-available Ca status 

within soils is primarily determined by the mineralogy of 

the parent material from which the soil was formed and the 
46

GENERAL TECHNICAL REPORT PNW-GTR-802

Obviously, not all tree species access or require Ca in equal 

amounts. Depending on tree rooting habits (e.g., depth of 

roots, possible mycorrhizal association, etc.), access to soil 

Ca can differ greatly. Furthermore, perhaps because Ca 

cycling within forests has historically been adequate to 

amply supply this critical nutrient, specific thresholds of Ca 

depletion have been developed for only a few key species 

(such as red spruce and sugar maple). Even for well-studied 

species, internal chemical sequestration of Ca (e.g., the 

precipitation of Ca as insoluble oxalate crystals within cell 

walls; Fink 1991), may mask biological Ca deficiencies 

and complicate the establishment of universally relevant 

deficiency thresholds. Indeed, discovery of the mechanism 

through which acid deposition reduces the cold tolerance of 

red spruce foliage relied on the differentiation of biologically labile Ca from total foliar pools in order to remove the 

confounding influence of foliar Ca sequestration (DeHayes 

and others 1999, Schaberg and others 2000). Thus, owing to 

great species-to-species variation in Ca nutrition and use, 

combined with questions of tissue sequestration and biological availability, foliar assessments of Ca are not universally 

valuable in assessing Ca deficiency. However, despite 

limitations, measurements of foliar Ca concentrations may 

have value if conducted as part of monitoring efforts to 

gauge spatial and temporal changes in Ca nutrition, thereby 

assessing trends in Ca accrual or depletion. 

Lack of Historical Data

One-time measures of soil or plant Ca concentrations 

can provide useful information of the current status of a 

site, particularly if Ca concentrations are clearly aberrant 

relative to limited established standards. However, short 

of this, they provide little insight into trends in Ca availability or potential disruptions in Ca cycling. In contrast, 

repeated measures (and the archiving of samples to allow 

for reanalysis should measurement protocols change over 

time) provide baseline data needed to make necessary trend 

assessments. To date, comparatively few such databases and 

archives have been established. Long-term research at the 

Hubbard Brook and other experimental forests (e.g., Adams 

1999, Likens and others 1996, 1998), as well as a few recent 

studies (notably Bailey and others 2005, Lapenis and others 

period of time that the rooting zone has been exposed to 

weathering. Thus, the underlying parent material and soil 

classification information are useful in identifying regions 

that may be at risk to Ca depletion (e.g., areas of the White 

Mountains in New Hampshire or the Adirondacks in New 

York). However, within susceptible regions, soil properties 

are highly variable, both vertically and horizontally, which 

make it challenging to conduct site-specific evaluations. 

This spatial variability can be dealt with by using a large 

sample size and sampling the soil by genetic horizon (Bailey and others 2005); however, this requires the assistance 

of trained soil scientists and incurs high costs for analysis. 

Consequently, collection of soil nutrition data to support 

regional assessments would be expensive and requires 

the expenditure of considerable time and effort. In part to 

overcome the inherent difficulties in assessing soil nutrition, 

but also considering the theoretical value of assessing living 

organisms when considering biological deficiency thresholds, vegetation chemistry has also been monitored to test 

for Ca deficiencies.

Vegetation

Ca deficiency thresholds for trees in native forests exist for 

some species with established nutritional vulnerabilities. 

For example, based on greenhouse (Swan 1971) and field 

studies (Borer and others 2004, DeHayes and others 1999), 

minimal sufficiency and deficiency Ca thresholds have 

been determined for red spruce foliage (1200 and 800 

μg g

-1, respectively). The deficiency threshold is associated 

with reduced cold tolerance and growth and increased 

winter injury of trees (Borer and others 2004, DeHayes 

and others 1999, Swan 1971). Similarly, based on surveys 

of forest-grown trees, a foliar Ca deficiency threshold of 

about 5000 μg g-1 has been established for sugar maple (e.g., 

see Kolb and McCormick 1993). Foliar Ca concentrations 

below this threshold have been associated with increases 

in crown dieback and reduced growth of trees (Schaberg 

and others 2006), reduced growth following overstory 

release, and impaired stem wound closure (Huggett and 

others 2007). However, an analysis of these thresholds 

reveals that threshold concentrations are not uniform among 

species and can differ greatly (here more than fivefold). 
47

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

2004, Lawrence and others 2005) highlight the unique benefit of repeated measures for detecting meaningful changes 

in Ca nutrition. Recognition of the value of such data sets 

has bolstered efforts to compile broad nutritional data sets 

that encompass a variety of sites, tree species, and time 

periods (e.g., see the foliar chemistry database compiled by 

the Northeastern Ecosystem Research Cooperative: http://

www.folchem.sr.unh.edu/), and establish sample archives as 

described by Lapenis and others (2004) and Lawrence and 

others (2005). However, given practical (especially funding) limitations, even if such data sets and archives were 

dramatically expanded, appropriate historical data would 

exist for just a fraction of the land area impacted by pollution loading. Thus, alternatives for assessing the location 

and extent of Ca depletion across the landscape must be 

developed, tested, and employed.

Critical Loads as an Alternative Approach 

Given the limitations of static measures of Ca status, an 

alternative approach has been developed that models the 

level of pollutant additions that will likely lead to net losses 

in Ca pools within forests, therefore disrupting Ca cycling 

and leading to reduced plant Ca availability, and the physiological consequences described above. 

Description and Overview

The critical load of a pollutant is classically defined as a 

quantitative estimate of an exposure to one or more pollutants below which significant harmful effects on specified 

sensitive elements of the environment do not occur according to present knowledge (Nilsson and Grennfelt 1998). 

This definition implies some sort of measurable threshold 

value of ecosystem condition (termed the critical limit) that 

is demonstrably influenced by the pollutant(s) in question. 

The critical load is the pollutant deposition load, which can 

be shown to perturb a system such that the critical limit 

condition is attained. In the context of the adverse impacts 

of S and N deposition, many different critical limits have 

been proposed based on values of the Ca/Al ratio of soil 

water, the Ca/Al ratio of soil exchange sites, soil percentage 

base saturation, soil water nitrate concentrations, and soil 

and plant carbon (C)/N ratios. The relationship between 

pollutant loading and the value of the ecosystem condition 

being evaluated can be established empirically or by mechanistic modeling. For mechanistic modeling, both steadystate and dynamic-process models have been employed. 

Important modeling considerations are appropriate data 

availability at the spatial scale desired and the existence or 

lack of data that can be used for dynamic model calibration 

and evaluation.

Examples from Europe and Canada

In 1984, a protocol to the UNECE 1979 Convention on 

Long-Range Transboundary Air Pollution was established 

providing for long-term financing of the Cooperative 

Program for Monitoring and Evaluation of the Long-Range 

Transmission of Air-Pollutants in EUROPE (EMEP) (http://

www.unece.org/env/lrtap/welcome.html). This protocol 

permitted the development of the scientific and administrative structure necessary for coordinated evaluation of 

critical loads and exceedances for acidifying pollutants in 

Europe. For over a decade, European countries have used 

the critical loads concept to set emissions reduction targets 

for S and N under the UNECE Convention on Long-Range 

Transboundary Air Pollution (Posch and others 1995). 

Initially, the critical loads for S and N were developed for 

effects on freshwater aquatic ecosystems. This work was 

rapidly expanded to include the developing understanding 

of the role of S and N deposition in producing European 

forest declines. 

Canada followed Europe’s lead and developed critical 

loads modeling programs first for aquatic and then forest 

ecosystems (Ouimet and others 2001, 2006) that have been 

incorporated into the periodic Canadian Acid Rain Assessments (Morrison and others 2005). Until recently, few have 

attempted to apply the critical loads approach to large forest 

landscapes in the United States.

Application to New England

One approach for applying the concept of critical loads to 

assessing the sensitivity of forests to Ca depletion grew 

out of the work of the Conference of New England Governors and Eastern Canadian Premiers (NEG/ECP). In 1998, 

the NEG/ECP developed an Acid Rain Action Plan that 
48

GENERAL TECHNICAL REPORT PNW-GTR-802

deposition rates, and site factors including climate, hydrology, and soil mineral weathering rates (NEG/ECP 2001). 

The ecosystem model and several submodels simulate these 

processes. The crucial determinant of an ecosystem’s ability 

to tolerate S and N deposition without declining Ca supplies 

is the rate at which primary minerals (e.g., hornblende, 

plagioclase, calcite) chemically decompose, liberating the 

nutrient cations Ca2+, Mg2+, and K+ to plant-available pools 

and, thus, replenishing nutrients lost via timber removals 

and acid-induced leaching. A geochemical model based on 

the work of Sverdrup and Warfvinge (1993) was used to 

estimate the weathering rate of primary minerals. Considerable field and modeling efforts were required to develop 

the spatial data layers needed to apply this model to the 

New England region. The annual demand for nutrients 

required to regrow the biomass exported via harvesting was 

estimated from timber extraction rates and wood-nutrient 

content. This information was generally available for the 

New England States from combinations of State and Federal 

sources. Atmospheric deposition of S, N, chloride (Cl), Ca, 

Mg, sodium (Na), and K was estimated for a 5-year period 

(1999-2003) in order to provide some smoothing of yearto-year variations in climate and patterns of atmospheric 

transport. Total deposition, including precipitation, cloud 

droplet interception, and dry deposition, was estimated 

using atmospheric chemistry data from the US NADP, 

CASTNet, and NOAA-AirMon deposition monitoring 

networks and Ecosystems Research Group, Ltd.’s HighResolution Deposition Model (Miller 2000, Miller and 

others 2005, NEG/ECP 2001). 

Although only results for Vermont and New Hampshire 

are presented here as examples, this assessment methodology has been applied to all of New England. Critical loads 

of S plus N ranged widely in New Hampshire and Vermont 

(0 to 21 keq ha-1

 y

-1) as a result of the diverse geology and 

climate of the region (Figure 3). Areas of Ca-rich rocks and 

soil materials scattered throughout the region support the 

highest critical loads, often in excess of 3 keq ha-1 y

-1. The 

lowest critical loads were found primarily in northern New 

Hampshire where soils are developed in thin and patchy tills 

derived from base-poor rocks. The range of S deposition 

was between 3.2 and 18.9 (average 5.1) kg ha-1

 y

-1 and N 

called for (among 44 action items) a regional assessment 

of the sensitivity of Northeastern North American forests 

to current and projected S and N emissions levels. This 

assessment was intended to identify the location and extent 

of forested areas most sensitive to continued S and N deposition and estimate deposition rates required to maintain 

forest health and productivity under current rates of forest 

resource utilization.

Conference of New England Governors and 

Eastern Canadian Premiers

Considering the unique vulnerability of Ca to leaching loss 

and its vital role in supporting tree stress response systems, 

the model focuses on how atmospherically deposited S and 

N act to produce changes in Ca pools that may influence 

forest health conditions. Review of the literature (discussed 

above) indicated that the most appropriate critical limit that 

could be modeled as a function of S and N deposition was 

whether an ecosystem could maintain a sustainable supply 

of the nutrient base cations Ca, Mg, and K or if the system 

was experiencing long-term depletion of these elements. 

Systems in a chronic state of cation depletion will eventually exhibit the Ca deficiency-related problems discussed 

above. Because the goal of the assessment was to provide 

estimates of the forest area potentially impaired if pollution 

remained at current S and N deposition levels, a steadystate modeling approach was selected. It was determined 

that adequate estimates of the parameters required for 

steady-state modeling could be developed regionwide at an 

appropriate spatial scale, whereas the data requirements for 

dynamic modeling could only be met in a few locations. It 

is anticipated that the results of the steady-state modeling 

assessment will direct future data collection efforts to highvalue, high-risk areas where the cost of data collection for 

dynamic modeling may be justified. 

A steady-state ecosystem process model was coupled to 

extensive spatial databases and used to generate maps identifying forest areas likely to experience Ca depletion (Miller 

2005, 2006; Ouimet and others 2006). Sustainable Ca 

supplies in forest ecosystems are functions of forest type, 

timber extraction intensity, prior land use, atmospheric 
49

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

deposition (ammonium + nitrate) ranged between 3.3 

and 25.2 (average 8.4) kg ha-1 y

-1, producing an aggregate 

acidifying and nutrient-leaching potential of 0.43 to 2.7 

(average 0.92) keq ha-1 y

-1 (Figure 4). The highest elevation 

areas received the highest S + N deposition from orographically enhanced precipitation and cloud water inputs. 

Deposition was also high in the southern and western parts 

of the region owing to proximity to emission sources. 

Using critical load and atmospheric deposition 

estimates, a deposition index can be calculated to help 

evaluate the relative risk for ecosystem health problems 

resulting from Ca limitation (Figure 5). The deposition 

index is calculated as the ecosystem critical load minus the 

atmospheric deposition. In this index, positive values reflect 

the capacity of a forest ecosystem to tolerate additional 

acidic deposition, whereas negative values correspond to 

the reduction in S and N deposition required to eliminate 

present or deter the development of nutrient limitations. 

Atmospheric deposition of S and N during 1999-2003 

exceeded the critical load in approximately 18 percent of the 

forested area of NH and 30 percent of the forested area in 

VT (Figure 5 and Figure 6). Critical loads were frequently 

exceeded where deposition was moderate (Northeast) to 

high (South) and where critical loads are low. An additional 

10 percent of the forested area in each State experienced 

deposition rates during 1999-2003 that were less than 0.2 

keq ha-1

 y

-1 below the critical load. Some locations within 

these areas with shallower soils and more intense harvesting 

than the average values used in this assessment are likely to 

also be at risk of Ca depletion.

Forest tree species occupy different portions of the 

landscape as a function of climate, soil conditions, and land 

use history. This distribution results in some types of forests 

being more severely impacted than others by the nutrient 

Figure 3—Critical loads of sulfur plus nitrogen to upland forests; 

S + N atmospheric deposition rates higher than the critical load 

result in greater exports of nutrient cations (Ca2+, Mg2+, K+

) than 

inputs of these nutrients, leading to the eventual deterioration of 

soil fertility, forest health, and forest productivity. Critical loads 

are expressed in kilo-equivalents per hectare per year in this map 

of New Hampshire and Vermont; N deposition includes both 

ammonium + nitrate forms. White areas depict locations with 

nonforested land or water.

Figure 4—Average annual atmospheric deposition of sulfur 

and nitrogen (1999-2003). Total deposition (particle + SO2 + 

precipitation + cloud water) expressed in terms of kilo-equivalents 

of charge per hectare per year in this map centered on New 

Hampshire and Vermont; N deposition includes both ammonium 

+ nitrate forms. This represents the total base-neutralizing and 

cation-leaching power of S and N atmospheric deposition.
50

GENERAL TECHNICAL REPORT PNW-GTR-802

cation depletion caused by S + N deposition. For example, 

critical loads are exceeded in 49.6 percent of New Hampshire’s central hardwood forests, but in just 2 percent of the 

State’s northern hardwood forests. This discrepancy exists 

because the central hardwood forests (6.1 percent of total 

forest area) tend to occur more frequently on poor sandy 

soils. The northern hardwood forest (18.9 percent of forest 

area) occurs on somewhat richer sites. Stands dominated 

by sugar maple (11.4 percent of forest area) occupy midelevation sites and have the highest Ca requirement of the 

northern hardwood forest variants. We estimate that the 

critical load is exceeded in 39.8 percent (88 167 ha) of New 

Hampshire’s sugar maple stands, and deposition is within 

10 percent of the critical load in an additional 3.6 percent 

(7826 ha). 

Relationships to Forest Health

The model-based nutrient deficiency metric appears to be 

a good predictor of independent, on-the-ground indicators 

of current forest health and productivity. For oak and pine 

forests in Massachusetts, tree height and canopy transparency were significantly related to foliar Ca levels and to the 

modeled rate of base cation depletion. Canopies were more 

transparent and grew to lower heights where base cation 

depletion rates were higher, and foliar Ca status was lower. 

At these same sites, root-zone soil base saturation, pH, and 

Ca/Al ratio were correlated with modeled base cation depletion rates. A separate evaluation also showed promising 

results: a comparison of model results with multiple-year 

aerial surveys of forest damage in Vermont indicated that 

both the frequency of damage and size of damaged areas 

were related to modeled base cation depletion. Forests in 

Vermont also showed greater canopy transparency and 

higher percentages of trees with chronic dieback where 

modeled base cation depletion rates were higher.

Figure 6—Areas with sulfur plus nitrogen deposition in excess of 

the critical load. Red-shade shows areas in New Hampshire and 

Vermont where S+N deposition exceed the critical load, and forests would be likely to experience Ca depletion under 1999–2003 

deposition rates.

Figure 5—The deposition index for atmospheric sulfur and 

nitrogen deposition (1999-2003) with respect to Ca depletion. 

Positive values of the deposition index reflect the capacity of a 

forest ecosystem to tolerate additional acidic deposition. Negative 

values of the index correspond to the reduction in S and N deposition required to eliminate present or deter the development of 

nutrient limitations. Red-orange-yellow areas in this map of New 

Hampshire and Vermont indicate current S and N atmospheric 

deposition rates greater than the critical load. The deposition 

index is expressed in terms of kilo-equivalents of charge per 

hectare per year. N deposition includes both ammonium + nitrate 

forms. White areas represent areas with nonforested land or water.
51

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

The critical loads framework can also be used to assess 

the recovery of ecosystems owing to changes in pollution 

levels and management strategies. Miller (2005) estimated 

that the cation depletion that likely previously occurred in 

126 000 ha of forest in New Hampshire and 133 775 ha in 

Vermont was probably reversed by the 36-percent reduction in S deposition that occurred in the region from the 

late 1990s to the early 2000s. These forest areas should be 

experiencing recovery of base cation pools.

Development and Relevance of Future 

Models

Initial results like those described for New Hampshire and 

Vermont indicate that models of critical loads and forest 

sensitivity show real promise in helping to empirically 

define the threat posed to forests in an integrated and 

spatially explicit manner. Risk assessment will be assisted 

by the expansion and refinement of existing data sets 

and models, as well as the development of more detailed 

dynamic models applicable to specific, high-interest sites. 

Ultimately, refined models should help policymakers and 

managers improve their analyses and recommendations 

regarding possible options to prevent or mitigate anthropogenic Ca depletion across the forested landscape. 

Steady State versus Dynamic Models 

Steady-state models are most appropriate for regional 

studies because of their lower data requirements. Steadystate models can provide reasonable estimates of longterm average Ca depletion rates covering large areas at a 

reasonable cost. These models can provide estimates of 

the extent, location, and average intensity of base cation 

depletion. Where additional measures of base cation pools 

are available or can be obtained, comparison of steady-state 

depletion rates with base cation pools can provide first-order 

estimates of the time to ecosystem degradation or recovery 

(Miller 2005). 

Dynamic models offer important insight into how and 

when ecosystem changes will occur in response to changes 

in pollution loadings and management activities. However, 

dynamic models are data intensive and require calibration 

and evaluation data that take years to obtain at considerable 

expense. Calibrated dynamic models are typically only 

applicable to the specific locations for which they were 

calibrated. Thus, dynamic models seem most appropriate 

for assessment of high-value or high-interest sites where the 

additional cost of data gathering can be justified. Dynamic 

models may be used opportunistically where suitable 

pre-existing data exist; however, great care must be taken 

to understand how representative such opportunistic studies 

are of the broader landscape.

Policy Implications

First and foremost, modeling approaches such as those 

involving the estimation of steady-state or dynamic critical 

loads could be of benefit to policymakers when evaluating 

the possible consequences of various pollution mitigation 

options as they relate to Ca cycling, forest health, and 

productivity. Analyses of trends in data from the field have 

been used to evaluate whether implementation of Phase I of 

the Clean Air Act Amendments of 1990 resulted in sufficient reductions in pollutant inputs to protect environmental 

resources (e.g., Butler and others 2001, Likens and others 

2001). However important, this retrospective approach 

does little to inform decisionmakers of the possible future 

or long-term impacts of pollutant deposition. In contrast, a 

noted benefit of critical loads and associated forest sensitivity modeling is the flexibility provided to project trends 

forward and estimate the location and extent (steady state) 

and timing (dynamic models) of pollution-induced impacts. 

Furthermore, these models can be used to estimate differences in pollution loads and impacts among various pollution reduction plans, thereby allowing for a more detailed 

and reasoned analysis among policy alternatives. 

Management Implications

Results from modeling efforts may also inform resource 

managers regarding the need for possible intervention and 

protective strategies to reduce or mitigate the impacts of 

air pollution. For example, forest sensitivity maps could 

be used to identify forest tracts where N or S loads remain 

above critical loads and where liming may be needed to 

sustain the health and productivity of forests. In recent 

decades, lime has been routinely applied to forests in 
52

GENERAL TECHNICAL REPORT PNW-GTR-802

Europe to counter pollution-induced losses of base cations 

(e.g., Materna 2002, Meiwes 1995, Saarsalmi and Mälkönen 

2001), and experimental trials in the United States have 

shown reductions in decline symptoms when sugar maple 

forests received lime applications (Long and others 1997, 

Moore and others 2000, Wilmot and others 1996). However, 

lime addition can be costly and difficult to apply over forest 

landscapes. Modeling results could help identify areas of 

particular vulnerability to Ca depletion and better target 

regions where interventions such as lime addition may be 

most beneficial and cost effective. Another management 

option is to modify harvesting intensity in particularly 

vulnerable areas by extending rotation lengths or limiting 

the biomass removed (stem only vs. whole-tree harvesting). 

A reduction in harvesting intensity could be used to lower 

the export of Ca from the forest during the time required for 

air pollution reductions to be achieved.

Worldwide Relevance

Concerns about the influence of Ca depletion on forest 

health exist for industrialized regions around the world 

including Europe, Eastern North America, and increasingly China (Driscoll and others 2001, Duan and others 

2000, Kirchner and Lydersen 1995, Likens and others 1996, 

Schulze 1989, Tao and Feng 2000). Growing experimental 

evidence and examples from the field indicate that the threat 

posed to forest ecosystems from anthropogenic Ca depletion 

is real and potentially widespread. Knowledge of the influence of pollution loading on the cation pools that sustain 

forest health and productivity provides further scientific 

grounding and impetus for policymakers to modify existing pollution control measures. In addition, an increased 

recognition of the potential consequences of Ca depletion 

has functional relevance to managers in the field. Especially 

in regions with low inherent soil fertility or high precipitation leaching, or both, management options that either add 

Ca to systems or decrease its removal are increasingly being 

examined and employed. Indeed, in some tropical forests 

where native precipitation-based leaching has historically 

been high, and pollution-induced acidity is on the increase, 

sustainable forestry is not currently possible without the 

addition of Ca after logging (Nykvist 2000). The output of 

tested models that simulate the complexities of Ca cycling 

could greatly enhance the capabilities of policymakers and 

managers to integrate the influences of factors adding to or 

removing Ca from forest ecosystems. As such, these models 

could play a critical role in guiding adaptive policy and 

management decisions that prevent or mitigate pollutioninduced damage to forest ecosystems.

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Case Studies
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Assessment of Oak Wilt Threat to Habitat of the 

Golden-Cheeked Warbler, an Endangered Species, 

in Central Texas

David N. Appel and Kim S. Camilli

David N. Appel, professor, Texas A&M University, Department of Plant Pathology and Microbiology, College Station, 

TX 77843; and Kim S. Camilli, forest pathologist, University of Nevada, Reno, NV 89557.

Abstract

A major epidemic of oak wilt, caused by Ceratocystis 

fagacearum (Bretz) Hunt, has been killing trees in Central 

Texas for at least 40 years. This has created large and 

expanding canopy gaps in the vast, homogenous live oak 

woodlands (Quercus fusiformis Small) in the Edwards 

Plateau region of Texas. The changes in stand structure may 

have detrimental consequences for an endangered migratory 

songbird, the golden-cheeked warbler (GCW, Dendroica 

chrysoparia Sclater & Salvin). More information is needed 

to assess the direct impact of oak wilt on the GCW and how 

oak wilt control measures might affect bird populations. 

In our study, two surveys with different objectives were 

conducted at the Fort Hood Military Installation in Central 

Texas. In 2001, IKONOS 1-meter pan-sharpened satellite 

imagery was used to assess the incidence and severity of 

oak wilt. The disease was found to be the cause of mortality 

in 69 percent of the sampled plots. Only a small proportion 

of the oak wilt centers (12 percent) were located in designated GCW habitat. A second survey was conducted in 

2003-04 to determine the key characteristics of GCW nesting sites and how they compare to those of oak wilt centers. 

This systematic survey was based on randomly selected 

cluster sample plots stratified in five resource categories 

based on the presence or absence of oak wilt, GCW habitat, 

or GCW nesting sites, or both. Stand densities ranged from 

90 trees/ha (GCW habitat, no oak wilt) to 1,298 trees/ha 

(GCW habitat, nesting site). Juniper (Juniperus ashei

Buchh.) to oak ratios ranged from 0.24:1 (GCW habitat, no 

oak wilt) to 6.57:1 (GCW habitat, no oak wilt). Classification tree analysis was conducted to identify independent 

variables associated with the presence of nesting sites in 

GCW habitat. Key variables in the resulting model included 

road density, selected Landsat and SPOT 10 satellite imagery bands, elevation, and distance to roads. In terms of tree 

mortality, the impact of oak wilt on GCW home ranges may 

be minimal. Further analyses are needed to evaluate the 

impacts of other site disturbances caused by oak wilt, such 

as fragmentation and alterations in stand composition. The 

results of this project will be used to aid natural resource 

managers when conflicts occur between endangered species 

management and oak wilt control.

Keywords: Ceratocystis fagacearum, classification tree 

analysis, endangered species, Fort Hood, golden-cheeked 

warbler, oak wilt.

Introduction

Woodlands were sampled on Fort Hood Military Installation that were typical of the oak-juniper savanna ecosystem 

in central Texas. The tree disease oak wilt, caused by Ceratocystis fagacearum (Bretz) Hunt, is a common disturbance 

throughout the region. Oak wilt management is a viable 

option for reducing losses from the disease, but the decision 

to implement control options is not always obvious. Further 

information is needed to assess the benefits of controlling 

oak wilt when compared to the costs of deploying expensive 

and disruptive management tactics. Specifically, the objective of this project was to determine whether oak wilt is 

having a detrimental impact on endangered species habitat. 

This information would presumably be useful to natural 

resource managers responsible for oak wilt management 

decisions. The following topics describe the study site, the 

status of an endangered species that may be influenced by 

oak wilt management decisions, and the disease.

Characterization of Central Texas Ecosystem

Central Texas is a unique, fragile ecosystem increasingly 

pressured by multiple land use objectives. A description of 

this ecosystem is important for understanding the complex 

issues being faced by natural resource managers throughout 

the region. Central Texas is dominated by the Edwards 
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tourism, retirement urbanization, wildlife and hunting 

leases, and farming. In the past few decades, relatively 

low land costs have encouraged a doubling of population 

size owing to light industrial development and subdividing 

formerly large ranches into small ranchettes for retirement 

and tourism. The Edwards Plateau is a region of biological 

transition at the limits of the natural ranges of numerous 

plant and animal species. Many of these are threatened and 

endangered species.

Characterization of the Study Site: Fort Hood, 

Texas

There are no significant Federal parks or reserves in the 

Texas Hill Country. Opportunities for large-scale conservation management are limited. One exception is the largest 

Army installation in the United States, Fort Hood, covering 88,500 acres in Bell and Coryell Counties (Figure 2). 

Plateau, a limestone-layered tableland lying between latitudes 29°-32° N and longitudes 97°30’-102°30’ W (Figure 

1). The region is known as the Texas Hill Country because 

the topography is highly dissected by canyons separated 

by flat or sloping divides. There are prodigious outcrops of 

Cretaceous limestone, and the thin soils are mostly stony 

clay loam (Davis and others 1997, Jordan 1970). Rainfall is 

sparse at around 35 in per year, resulting in a low, semiarid 

temperate, semievergreen forest interspersed with grassland 

savanna. Historically, the Edwards Plateau was on the 

southern range of the Great Plains grassland prairie, but 

fire control and overgrazing have significantly changed 

the landscape (Reisfeld 1992). The dominant trees occur 

in large expanses and include live oak (Quercus fusiformis

Small), Ashe juniper (Juniperus ashei Buchh.), and mesquite (Prosopis glandulosa Torr.) (Burns and Honkala 1990, 

Hayden and others 2001). Primary land uses are ranching, 

Figure 1—This map shows the region in Texas called the Edwards Plateau. Fort Hood is located with a red star, and the hill country is 

outlined in green.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

available for public recreation, including water sports, hunting leases, mountain biking, off-road vehicles, and hiking. 

Soil compaction, vegetation damage, and erosion are just 

a few of the disturbances having an impact on the fragile 

Fort Hood topography (Chenault 2005). Land management 

activities at Fort Hood are conducted under plans designed 

to protect and mitigate effects on the habitats of a number of 

endangered species while repairing rangeland and adapting 

sites for military training activities. These multiple land 

use objectives often conflict, providing an ideal location for 

studying the consequences of controlling oak wilt within 

endangered species habitat.

Fort Hood, the home to two U.S. Army divisions, has the 

advantage of being under a single management authority 

and operates under the auspices of the Endangered Species 

Act. Fort Hood was originally established on privately held 

ranchland, consisting of 65 percent perennial grassland and 

30 percent forest/woodlands (Hayden and others 2001). A 

full range of military training operations is conducted at 

Fort Hood, including large-scale troop and vehicle movements, live-fire weapons exercises on extensive training 

ranges, and realistic air attack and air transport missions. 

Also, there are large expanses of cattle grazing under lease 

through cattlemen’s associations. Sections of Fort Hood are 

Figure 2—Photointerpreted polygon, training areas, and golden-cheeked warbler habitat at Fort 

Hood, Texas.
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Association between Golden-Cheeked Warbler 

and Oak Wilt

A migratory songbird inhabiting Fort Hood, the endangered 

golden-cheeked warbler (GCW, Dendroica chrysoparia), 

is of particular interest to conservation specialists, wildlife 

experts, and military planners. The original listing of the 

GCW as threatened and endangered was in 1990 (USFWS 

1990). The breeding and nesting requirements of the GCW 

are particularly dependent upon certain characteristics of 

the oak/juniper savannas of central Texas (Kroll 1980). 

As a migrating species, the GCW overwinters in Central 

America and southern Mexico and returns to Texas in the 

spring for 3 to 4 months (Ladd and Gass 1999). While in 

Texas, the warbler inhabits woodlands comprising mature 

junipers with shedding bark that is used for nesting. Oaks 

are required for foraging because they support high populations of Lepidopteran insects during the breeding season 

(Kroll 1980). Feeding and breeding GCW habitats may be 

considered one and the same. These warblers forage for 

insects in oak tree canopies within their home ranges (Kroll 

1980, Pulich 1976, Simmons 1924, Smith 1916, Wahl and 

others 1990), and nests have been found in Ashe juniper, 

Texas red oak (Quercus buckleyi Buckl.), post oak, (Q. 

stellata (Wangenh.)), Texas ash (Fraxinus texensis (Gray) 

Sarg.), and live oak trees in Fort Hood (Hayden and others 

2001). Suitable habitat usually consists of steep canyon 

slopes or rugged terrain (Moses 1996) with some proximity 

to a source of water. About 21 850 ha, or 24.7 percent of the 

total installation, is designated GCW habitat (Dearborn and 

Sanchez 2001). Urbanization, fragmentation of breeding 

habitats for agricultural purposes, and predators are the 

primary reasons given for the decline in GCW numbers 

throughout its northern range (Moses 1996, USFWS 1990). 

Although the additional woodland disturbance caused by 

oak wilt is mentioned in the GCW recovery plan as a factor 

with the potential to impact GCW populations, it needs 

further study (Keddy-Hector 1992).

Ceratocystis fagacearum is a destructive pathogen 

causing enormous losses of oaks throughout Central Texas 

(see Web site of the Texas Forest Service http://www.

texasoakwilt.org). In terms of numbers, live oak is the 

species most severely affected by oak wilt. The fungus 

grows through connected root systems of live oak resulting in large, expanding patches of dead and dying trees. In 

red oaks (gen. Quercus, subgenus Erythrobalanus) such as 

Texas red oak, the pathogen can grow briefly as a saprophyte forming fungal pads under the bark, thus making 

spores available to insect vectors. These two modes of 

transmission, through roots and by insect vectors, strongly 

influence the spatial distribution of the disease. Root transmission in live oaks kills larger numbers of trees, but insect 

transmission initiates new disease centers.

Oak Wilt Management Concerns and Objectives

Oak wilt control on a landscape scale involves removal 

of large numbers of trees, both healthy and diseased, and 

digging deep trenches on the perimeters of disease centers. 

These measures prevent inoculum formation and spread of 

the pathogen through root connections (Appel 1995). They 

are expensive and result in a great deal of environmental 

disruption in order to successfully control the disease. 

Resource managers must therefore be able to assess the 

potential impact of the disease and the benefits resulting 

from costly control measures. Given the conflicting land 

management objectives throughout the central Texas region, 

the decision to undertake oak wilt control on the landscape 

level can be difficult to make. We have initiated long-term 

studies on Fort Hood to assess the impact of oak wilt on 

GCW habitat and to contribute this knowledge in the oak 

wilt management decision process.

Methods and Materials

Separate surveys were conducted to assess the incidence 

of oak wilt on Fort Hood and to determine the effects of 

the disease on GCW populations. Each of the surveys 

incorporated satellite imagery into a geographic information system, ground surveys, and data analysis with various 

statistical approaches.

2001 Survey

The goal of the 2001 survey was to estimate the incidence of 

oak wilt at Fort Hood. In order to complete a survey of the 

entire installation with a minimum of personnel, IKONOS 

1-m pan-sharpened satellite imagery was obtained for Fort 
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Hood that included a buffer area extending 1 mi beyond 

the boundary (Pacific Meridian Resources, Emeryville, CA 

94608). Survey lines separated by 330 m were transposed 

on the images utilizing the geographic information system 

ArcView (ESRI, 380 New York St., Redlands, CA 92373). 

Fort Hood is parceled by training areas (Figure 2), which 

were also used to aid photointerpretation. Images of the 

entire post were interpreted by a trained technician to 

select and map live and red oak mortality, presumably from 

oak wilt. Attempts were made to exclude brush clearing, 

wildfire, and obvious sources of mortality other than oak 

wilt. The imagery was coregistered to Orthophoto Quarter 

Quadrangles (DOQQS), and the mortality polygons were 

transposed to maps for ground truthing. A random sample 

of 10 percent of the photointerpreted polygons was selected 

for diagnosis. Oak wilt was diagnosed according to recognized symptoms of the disease in the field and laboratory 

isolation of the pathogen when necessary (Appel 2001).

2003–04 Survey

One of the goals of the second survey was to characterize typical GCW nesting sites and assess the threat posed 

by oak wilt to GCW populations. This goal was part of a 

larger project conducted in cooperation with the USDA 

Forest Service (USFS) Forest Health Technology Enterprise 

Team (FHTET, Fort Collins, Colorado) that focused on 

methodology to model and predict oak wilt incidence and 

severity. The tool being tested for these purposes was 

binary classification and regression tree analysis (CART) 

(Baker and others 1993, De’ath and Fabricius 2000). This 

nonparametric statistical technique results in a classification 

tree intended to explain variation of a dependent or response 

variable by a collection of independent, or explanatory 

variables. The dependent variable for the FHTET model 

was the presence or absence of oak wilt. Cluster sample 

plots (n = 80) were randomly selected using a Sample Points 

Generator (SPGen), an ArcView application, from four 

land classification categories: (1) GCW habitat, non-oak 

wilt; (2) GCW habitat, oak wilt; (3) non-GCW habitat, 

oak wilt; and (4) non-GCW habitat, non-oak wilt. A fifth 

category consisting of known nesting sites (GCW/NS) was 

subsequently added as an additional dependent variable 

for a separate CART analysis. Plots in this category were 

known to have been occupied by GCW nesting pairs during 

2002–03, in contrast to plots in the other four categories. In 

these latter plots, GCW habitat was designated according 

to stand characteristics based on aerial photography and 

ground surveys. Designated GCW habitat is characterized 

as having high densities of mature junipers, the availability 

of deciduous hardwoods (primarily oaks), and a proximity 

to water (Hayden and others 2001). Independent variables 

for the model were derived from two sources of satellite 

imagery (2003 Spot 10 and Landsat TM satellite imagery) 

and geographic information system (GIS) files in the 

format of grid themes to be used in the ArcView program, 

e.g., slope, elevation, aspect, soils, distance to roads, road 

density, distance to streams, streams density, distance to 

lake, forest savanna, and landform. Ancillary data for Fort 

Hood were obtained from the Natural Management Branch 

office at Fort Hood. The classification tree was fitted to the 

spatial information database using the S-PLUS© statistical 

software package (Insightful Corp., Seattle, WA 98109).

Systematic surveys by ground crews from the USFS, 

the Nature Conservancy, the Texas Forest Service, and 

Texas A&M University were conducted in the summers 

of 2003 and 2004. Cluster sample plots were distributed 

throughout the four sampling categories. Each sample 

consisted of a 20-m by 20-m fixed plot subdivided into four 

subplots. Data collected for plots and subplots consisted 

of tree diameters, tree species identification, symptom 

development of infected trees, dominant overstory and 

understory species, and average tree height. In 2003, 80 

systematic fixed plots were surveyed with equal numbers of 

plots in each of the four categories. In 2004, an additional 

33 fixed plots were surveyed to increase sampling intensity, 

bringing the total to 28, 21, 32, and 32, in each of the four 

categories, respectively. Unlike the 2001 survey, all plots 

were located within the boundaries of Fort Hood. For the 

present study, the response or dependent variable was the 

presence of a GCW nesting site. The 24 GCW nesting 

site locations from 2002 and 2003 were obtained from the 

Nature Conservancy and surveyed in 2004 in the manner 

identical to the procedure described for the sample cluster 
66

GENERAL TECHNICAL REPORT PNW-GTR-802

variables until all independent variables have been included. 

The result of the cross validation is plotted with the x-axis 

as the number of terminal nodes and the y-axis as the misclassification error. The best model chosen is the one with 

the greatest number of terminal nodes with the least amount 

of misclassification error. The original tree was then pruned 

to the best model chosen from the cross validation results.

S-PLUS© statistical software was used to fit the 

classification tree to the Spatial Information Databases for 

each model (TREE, S-PLUS©, Statistical Sciences 2000). 

Twenty-two independent variable grid themes and 25 field 

data categories were used to construct the classification 

tree to describe the GCW nesting sites in Fort Hood. The 

independent variable grid themes consisted of each of the 

seven bands exported as grid themes from Landsat 5 TM 

and each of the four bands exported as grid themes from 

SPOT 5. The remaining 11 variables included slope, elevation, aspect, soils, distance to roads, road density, distance 

to streams, streams density, distance to lake, forest savanna, 

and landform. ERDAS Imagine Software Grid Export 

function was used to create the individual grid themes from 

each band in the imagery (ERDAS Inc. ERDAS Imagine 

V8.5. 2001, Atlanta, GA). The classification trees were run 

comparing the nesting site data with the data from GCW 

habitat with no oak wilt present (GCW/non-OW). The 

comparison was run with both the grid-theme data and 

plots. These plots were treated as a separate category resulting in five different categories for the analysis.

A preliminary analysis of the total survey data was 

conducted to determine frequencies of species, their 

sizes, and their densities in the various habitat categories. 

Tree species included deciduous hardwoods other than 

oaks (DH), live oaks (LO), red oaks (RO), shin oaks (Q. 

sinuata Walt.) and other white oaks (SO/WO), and junipers 

(J). Typical DHs at Fort Hood include cedar elm (Ulmus 

crassifolia Nutt.), walnut (Juglans spp.), hackberry (Celtis

spp.), and Texas ash (Fraxinus texensis (Gray) Sarg.). The 

dominant red oaks are Texas red oak and blackjack oak (Q. 

marilandica Muenchh.). The primary white oak, other than 

shin oak, is post oak. The ratio of juniper to oak was also 

calculated for each habitat category.

Classification Tree Analysis

Tree-based modeling is an exploratory technique for 

uncovering structure in data (Clark and Pregibon 1992) 

and has been used with ecological data that are complex, 

unbalanced, and contain missing values. Classification 

trees explain variation of a single response variable by 

one or more explanatory variables. The response variable 

can either be categorical (classification trees) or numeric 

(regression trees) (De’ath and Fabricius 2000). The categorical response variable used in this analysis of Fort Hood data 

was the presence or absence of GCW nesting sites. The tree 

is constructed by repeatedly splitting the data in two mutually exclusive groups, each of which are as homogeneous as 

possible. The objective for classification trees is to partition 

the response variable (GCW nesting sites) into subsets of 

homogeneous groups while also keeping the tree reasonably small (De’ath and Fabricius 2000). The tree is shown 

graphically in Figure 3 and consists of three parts: the root 

node, branches, and leaves. The root node represents the 

undivided data at the top, the branches and leaves each 

represent one of the final groups beneath. To keep the trees 

as accurate as possible, cross validation is a widely used 

technique to look at the independent variables from the tree 

and calculate the amount of error produced by iteratively 

combining the independent variables. It starts with one 

variable, then adds another, and keeps adding independent 

Figure 3—Classification tree model of golden-cheeked warbler 

(GCW) nesting-site characteristics.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

field-collected data (total data), grid-theme data 

only (independent data), and field-collected data 

only (field data).

Results

2001–Survey

There were 1,164 polygons delineated as dying 

oak trees on the IKONOS satellite imagery. A 

sample of 119 polygons, or 10 percent of the 

total, was randomly selected for ground truthing 

(Table 1). Oak wilt was found to be the cause of 

mortality in 82 (69 percent) of the centers. The 

major factor, other than oak wilt, delineated 

as dying or dead trees on the imagery, was 

brush-clearing operations (19.3 percent), where 

piles of dead trees resemble the crowns of dead, 

standing live oaks. With two exceptions, all of 

the brush piles consisted of Ashe juniper cut 

and stacked during land-clearing operations. 

Relatively few other causes of mortality were 

found, including fire, wind damage, and damage 

to trees caused by military operations. At eight 

of the sites, trees identified as oaks were actually some other species, or causes of mortality 

were not readily identified.

Of the 1,164 polygons, 821 fell within the 

perimeter of Fort Hood. Of those 821 polygons, 

144 or 18 percent were located in designated 

GCW habitat. Of the total 82 oak wilt centers 

Table 1—Diagnostic results for a sample 

of tree mortality locations randomly 

selected from photointerpretation of 

satellite imagery of Fort Hood, Texas

Cause of No. of

mortality centers Total

Percent

Oak wilt 82 69

Military ops 1 0.8

Unknown 8 6.7

Brush piles 23 19.3

Blow down 1 0.8

Fire 4 3.3

Table 2—Species composition and average diameters of trees 

located in sample cluster plots for the 2003—04 survey

Total Total 

no. Trees Tree no. Proportion Ave.

Habitata plots / ha typeb trees of total d.b.h.c

Inches

GCW/non- 28 886 DH 115 0.12 3.8

 OW LO 38 0.04 6.6

RO 58 0.06 5.5

SO/WO 20 0.02 3

J 762 0.78 6.9

GCW/OW 21 639 DH 55 0.11 5.3

LO 103 0.21 10.6

RO 72 0.15 6.6

SO/WO 6 0.01 2.3

J 301 0.62 5.6

non- 32 570 DH 190 0.26 5

 GCW/OW LO 260 0.33 11.5

RO 165 0.23 8.1

SO/WO 10 0.01 3.3

J 105 0.13 5.1

non- 32 90 DH 42 0.37 4.4

 GCW/non- LO 8 0.07 9.0

 OW RO 13 0.11 4.5

SO/WO 0 0 0

J 52 0.45 6.1

GCW/NS 24 1,298 DH 185 0.15 5.36

LO 34 0.02 6.76

RO 122 0.1 7.7

SO/WO 99 0.08 3.53

J 806 0.65 6.98

a GCW/non-OW = golden cheeked warbler habitat, no oak wilt; GCW/OW = golden 

cheeked warbler habitat with oak wilt present; non-GCW/OW = non-habitat, oak wilt 

present; GCW/NS = habitat with nesting site present.

b

 DH = deciduous hardwood, LO = live oak, RO = red oak, SO/WO = shin oak or other 

white oak.

c

 d.b.h. = diameter at breast height.

identified in the survey (including the 1-mi buffer), 60 were located 

within the post perimeter. Only 7, or 12 percent of the oak wilt centers 

found within the Fort Hood perimeter, were located in designated GCW 

habitat.

2003–04 Survey

The highest stand densities, 1,298 trees/ha, were found at the nesting 

sites within GCW habitat (GCW/NS) (Table 2). Stand densities were 
68

GENERAL TECHNICAL REPORT PNW-GTR-802

also relatively high in habitat where there was no oak wilt 

(GCW/non-OW = 886 trees/ha). At oak wilt locations both 

within and outside of habitat, the stand densities were relatively lower. There were also notable trends in the species 

among the various categories. The proportion of juniper was 

far lower outside GCW habitat where oak wilt was present 

(13 percent) than in the plots located within GCW habitat 

(62 percent) (Table 2). Live oak density was greater in oak 

wilt locations, whether they were within (21 percent) or 

outside of habitat (33 percent), than in the uninfected plots 

within habitat (4 percent) or nesting sites (2 percent).

The juniper to oak ratios (J:O) varied widely among the 

four sampling categories. The highest J:O ratio was 6.57:1 

in the GCW habitat where there was no oak wilt (Table 3). 

The lowest was 0.24:1 in oak wilt centers outside of GCW 

habitat.

Classification Tree Model

The classification tree model was developed using the plot 

survey data from nesting sites (GCW/NS) in 2004 and 

tested with plot survey data from one of the four habitat 

categories (GCW/non-OW) collected in 2003 and 2004. The 

nesting sites were assigned a value of 1, and the plots used 

to test against were assigned the value of 0. The analysis 

included the total data (field and independent data) from 

both the nesting-site plots and from the GCW/non-OW 

plots. Preliminary analysis indicated that the accuracy of 

the classification tree could be increased by modeling the 

soil types separately from the other independent data. The 

resulting classification tree had an accuracy of 98.2 percent 

with 8 terminal nodes (Figure 3). Discriminating variables 

included road density, Landsat band 6, elevation, distance 

to roads, and Spot band 3. Deviance was calculated for 

each variable by dividing the total deviance of the model 

variance by each of the variables produced (Kelly 2002). 

Road density explained the most variance (62 percent) in 

nesting site habitat location, followed by Landsat band 6 (43 

percent), elevation (30 percent), road density (16 percent), 

distance to roads (6 percent), and Spot band 3 (3 percent). 

Plots that had a road density of less than 969.5 m per mi2

had a higher probability of being in a site for GCW nestingsite habitat. Of all combinations of forest habitat conditions 

for this test, GCW habitat nesting sites are more likely to 

occur in areas having low road density (roadense < 969.5 m 

per mi2

), an elevation greater than 247.5 m, and a distance 

from roads of less than 91.5 m.

Discussion

Photointerpretation of the satellite imagery for the 2001 

survey proved to be fairly accurate in identifying oak wilt. 

Brush piles resulting from roguing Ashe juniper were the 

features most often confused with oak wilt. Improved training would probably reduce many of these errors, but may 

not eliminate them altogether. Juniper clearing is a common 

practice in the Hill Country, and, in many cases, piles were 

visually indistinguishable from the crowns of dead live 

oaks.

Oak wilt was found to be a prominent feature and a 

major cause of oak mortality throughout Fort Hood. No 

other cause of mortality came close to the level found 

for oak wilt. However, the survey was not designed to 

determine the volume or extent of the other major cause of 

tree mortality—fire. It was noted that in certain locations, 

fire was a dominant cause of mortality and probably far 

exceeded the extent of oak wilt as a disturbance. A detailed 

analysis of the comparative effects of fire and oak wilt 

on habitat is warranted and will be considered in future 

studies.

Habitat requirements and tree species composition 

associated with GCW populations have been addressed in 

previous studies (Kroll 1980). The dependency of GCW on 

Ashe juniper bark as a source of nesting materials is 

Table 3—Juniper to oak ratios for each 

of the four sampling categories at Fort 

Hood, Texas

Habitata J:O ratio

GCW/non-OW 6.57:1

GCW/OW 1.66:1

non-GCW/OW 0.24:1

non-GCW/non-OW 2.48:1

GCW/NS 3.16:1

a

 GCW/non-OW = golden-cheeked warbler habitat, no 

oak wilt; GCW/OW = golden-cheeked warbler habitat 

with oak wilt present; non-GCW/OW = no goldencheeked warbler habitat, oak wilt present; non-GCW/

non-OW = no golden-cheeked warbler habitat, no oak 

wilt present; GCW/NS = habitat with nesting site present.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

a well-described phenomenon (Kroll 1980, Pulich 1976). 

There appear to be preferences for the sizes and densities of 

junipers. Kroll (1980) characterized good habitats as those 

with juniper-oak ratios of 1.35 to 1, and poor habitats with 

ratios of 2.27 to 1. We also found trends for the selection of 

nesting sites at Fort Hood. The juniper-oak ratios ranged 

from a high of 6.57:1 to a low of 1.66:1 in designated GCW 

habitat (Table 3). But, our results indicate that preferred 

nesting sites were in areas with a juniper-oak ratio of 3.16:1. 

Other variables determined to be characteristic of good 

habitats by Kroll (1980) were older Ashe junipers at wider 

spacing and relatively lower densities than those of poor 

habitats. In a previous study conducted at Fort Hood, Dearborn and Sanchez (2001) made pairwise comparisons of 13 

vegetation variables between nest locations and nearby nonuse vegetation patches. The only significant variable was 

canopy closure, which was greater at nesting sites than at 

the paired nest-free location. A stand density equivalent to 

487 stems per ha for junipers and hardwoods combined was 

found at nesting sites, and junipers dominated hardwoods 

in all size classes. Nesting sites were characterized as having dense vegetation and nearly complete canopy closure 

dominated by junipers (Dearborn and Sanchez 2001). The 

nesting sites in our survey appear to have higher stand 

densities than those surveyed by Dearborn and Sanchez 

(2001), but we included smaller diameter stems in the 

survey protocol. The trends in both surveys are consistent. 

Of all habitats surveyed, our results confirm that GCWs 

prefer dense vegetation with high juniper densities.

Live oaks dominated sites where oak wilt occurred 

outside of GCW habitat. Within GCW habitat, the highest 

levels of live oaks also occurred in oak wilt centers, with 

a large decrease of live oak density at nesting sites and 

randomly selected habitat sites. The average diameters 

of live oak were larger in oak wilt centers than in healthy 

plots. A similar trend, although not as pronounced, was 

observed for the deciduous oaks. Oak wilt appears to be 

less likely to occur in places where the proportion of oaks 

is relatively low, such as GCW nesting sites. The incidence 

of live oak depends, for the most part, on availability of 

susceptible hosts, availability of inoculum, occurrence of 

infection courts (fresh wounds), and existence of nitidulid 

vectors (Appel 2001). Red oak density was fairly consistent 

among the different plot types, so inoculum availability in 

the form of fungal mats was potentially the same. The most 

likely explanation relates to vector behaviors in the live 

oak–dominated stands, but this suggestion would need to be 

confirmed with trapping studies. Our results also suggest 

that the site requirements for oaks may not coincide with 

sites preferred by GCW. The oak wilt threat to critical habitat may therefore be less than anticipated. A comparison of 

the GCW classification tree model developed in the present 

study for nesting sites, with the oak wilt model developed 

by FHTET, should be useful in determining whether oak 

wilt is a threat to GCW breeding habits.

Classification tree modeling proved to be a useful 

technique for establishing the site factors influential in 

determining the habitat for GCW nesting sites. When the 

comparison was made between designated habitats classified by The Nature Conservancy and nesting sites, the classification tree revealed that low road density was needed for 

ideal nesting-site locations. This agrees with other research 

findings that GCW prefers to have large unfragmented 

habitat for breeding and territory ranges (Kroll 1980, Ladd 

1985, Moses 1996). There are, however, conflicting opinions 

on the GCW preferences for large blocks of unfragmented 

habitat or for sites bounded by edges of different vegetative 

composition (Moses 1996). In one study in Travis County 

(Texas), the estimated territory required per breeding pair 

of GCWs was 1.9 to 2.7 ha/pair (Ladd 1985). Kroll (1980) 

estimated ranges for breeding pairs were 4.49 to 8.48 ha/

pair in a Texas state park. These estimates were noted to be 

larger than those from previous research, which resulted in 

estimates of territory sizes ranging from 0.81 ha to 2.55 ha 

per breeding pair (Kroll 1980). One effect of oak wilt is to 

fragment contiguous tree stands into treeless patches and 

expanding edges (Appel and others 1989). Dispersal distances for adult males averaged 223 m in a study conducted 

at Fort Hood during 1991–96, whereas juvenile dispersal 

distances were greater, averaging 4040 m (Jette and others 

1998). These dispersal distances, patch expansion, and the 

creation of edges by oak wilt requires further research to 

determine how the disease relates to the GCW beyond the 

consequences of direct loss of trees.
70

GENERAL TECHNICAL REPORT PNW-GTR-802

The effects of oak wilt on the landscape go beyond 

the destruction of trees. Gaps and edges are created, tree 

composition is changed, and woodland stand structure is 

altered. All of these effects may influence GCW populations and will require further analysis to confidently 

decide whether to manage the disease in the vicinity of 

GCW nesting sites. Oak wilt control activities need not be 

disruptive because they can be implemented when the birds 

are migrating. However, because oak wilt appears to fall in 

areas where oak densities are greater than those found in 

preferred GCW habitats, the simple loss of trees may not be 

sufficient justification to undertake expensive and disruptive 

oak wilt control methods.

Acknowledgments

This work was supported by grants from the Texas Nature 

Conservancy and from the USDA Forest Service Forest 

Health Technology Enterprise Team, Fort Collins, Colorado 

The authors would like to thank Marla Downing, USDA 

Forest Service, Vernon Thomas of INTECS International 

Inc., and Robin Reich from Colorado State University for 

technical assistance and surveying plots. Additional survey 

support was provided by Cruz Torres and Coy Crane, 

technicians with the Department of Plant Pathology and 

Microbiology, Texas A&M University; Sheila Jackson with 

The Nature Conservancy at Fort Hood; and Dale Starkey, 

USDA USFS in Pineville, Louisiana. John Cornelius with 

the Environmental Resources Branch at Fort Hood was a 

valuable source of information regarding golden-cheeked 

warbler biology and habitat. We also thank Steve Jester and 

Tom Greene with the Fort Hood Nature Conservancy office 

for their advice and guidance during the course of the study.

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73

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Ronald F. Billings and William W. Upton

Ronald F. Billings, Manager, Forest Pest Management, 

Texas Forest Service, College Station, TX 77840; and 

William W. Upton, staff forester II, Forest Pest Management, Texas Forest Service, Lufkin, TX 75901.

Abstract

An operational system to forecast infestation trends 

(increasing, static, declining) and relative population levels 

(high, moderate, low) of the southern pine beetle (SPB), 

Dendroctonus frontalis, has been implemented in the 

Southern and Eastern United States. Numbers of dispersing SPB and those of a major predator (the clerid beetle, 

Thanasimus dubius) are monitored with multiple-funnel 

traps baited with the SPB aggregation pheromone frontalin 

and host volatiles. One to three traps are placed in each 

county or national forest ranger district to be surveyed for 

4 consecutive weeks during the spring, to coincide with the 

long-range dispersal of SPB. The average number of SPB 

per trap per day and the ratio of SPB to total catch of SPB 

and clerids in the current and previous year for the same 

trapping location are the variables used for predicting infestation trends and population levels for the remainder of the 

year. An analysis of predicted and actual SPB infestation 

trends and population levels for 16 States and up to 19 consecutive years (1987-2005) documents the accuracy of the 

annual prediction system. From 1987 to 1998, predictions at 

the State level, validated by subsequent infestation detection 

records for the specific year, proved accurate 68 percent 

of the time (range 42 to 83 percent) for SPB infestation 

trend and 69 percent of the time (range 42 to 92 percent) 

for population level. From 1999 to 2005, the mean accuracy 

of predictions of SPB infestation trend improved to 82 

percent for all States combined (range 71 to 100 percent); 

mean predictions for population level for States increased 

in accuracy to 74 percent (range 43 to 100 percent). Despite 

system limitations, forest managers have come to depend 

on this early warning system to predict pending outbreaks 

A Methodology for Assessing Annual Risk of Southern 

Pine Beetle Outbreaks Across the Southern Region Using 

Pheromone Traps

or collapses of SPB populations. This represents the first 

effective and validated prediction system for outbreaks of a 

bark beetle species.

Keywords: Bark beetles, Dendroctonus frontalis, 

prediction, surveys, Thanasimus dubius.

Introduction

Forest managers and pest control specialists have long 

needed a reliable and practical early warning system to 

detect developing outbreaks of the southern pine beetle 

(SPB), Dendroctonus frontalis (Coleoptera: Curculionidae: 

Scolytinae), the most destructive forest pest of southern pine 

forests (Thatcher and others 1980). Because aerial surveys 

to detect SPB infestations do not become effective until 

late spring or summer (Billings and Doggett 1980), a more 

efficient system for monitoring SPB populations early in 

the season has value. Early detection of pending outbreaks 

gives forest pest managers valuable insight for scheduling 

detection flights and planning suppression programs.

In 1986, the Texas Forest Service began development of 

an operational system using pheromone-baited traps for predicting SPB infestation trends (increasing, static, declining) 

and population levels (high, moderate, low). In cooperation 

with State and Federal pest management specialists, the 

system was implemented throughout the South beginning in 

1987 (Billings 1988, 1997). Validation and refinement of the 

system have been conducted periodically with use of yearend SPB infestation detection records. Here, we evaluate the 

accuracy of this prediction system, based on results from 16 

States, most of which have now deployed the system for 20 

consecutive years (1987-2006). 

The southern pine beetle’s range extends from New 

Jersey to Florida and west to Texas, coinciding with the 

distribution of its major hosts, loblolly (Pinus taeda L.) 

and shortleaf pine (P. echinata Mill.). SPB populations 

also are found from southern Arizona south to Nicaragua. 

Outbreaks of this insect tend to occur somewhere within its 

range every year, with peak populations occurring every 6 

to 9 years in many Southern States (Price and others 1998). 
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GENERAL TECHNICAL REPORT PNW-GTR-802

The location and intensity of SPB outbreaks may vary 

greatly from year to year. Unlike most other destructive 

bark beetles of the genus Dendroctonus, SPB completes up 

to seven generations per year in Gulf Coastal States and 

infests host trees in distinct and predictable patterns that 

vary with the seasons. For example, most new multiple-tree 

SPB infestations (spots) are initiated during the spring, 

following long-range dispersal of overwintering beetle 

populations (Hedden and Billings 1979, Thatcher and Pickard 1964). In the late spring and summer, adult beetles tend 

to attack host trees on the periphery of the same spots from 

which they emerged (Gara 1967; Hedden and Billings 1977, 

1979; Thatcher and Pickard 1964). This behavior leads to 

expansion of previously established spots, rather than the 

initiation of new ones. These expanding infestations (Figure 

1) are easily recognized in summer detection flights by the 

presence of pines in various phases of crown discoloration 

(Billings and Doggett 1980). Emerging SPB adults tend 

to disperse again in the fall, redistributing the population 

among scattered single trees, nonexpanding spots, and 

established infestations prior to winter.

Materials and Methods

Standardized procedures for conducting the annual 

SPB prediction survey are available online at http:// 

texasforestservice.tamu.edu/pdf/forest/pest/tutorial_for_

predicting_spb.pdf. Basically, from one to three multiplefunnel traps (Lindgren 1983), available from Phero Tech, 

Inc., Delta, British Columbia, and other sources (e.g., 

Synergy Semiochemicals Corp., Burnaby, British Columbia, Canada) are placed in pine forests within a county or 

national forest ranger district in early spring. The traps 

consist of 12 funnels superimposed over a collection cup 

(Figure 2A) that is partially filled with soapy water. The 

number of counties or ranger districts surveyed differs from 

State to State, depending on availability of pest management personnel, supplies, travel distances, and other factors. 

The survey is initiated when flowering dogwood 

(Cornus florida L.) begins to bloom or loblolly pine pollen 

appears. These physiological events coincide with the longrange dispersal of SPB (Billings, unpublished data). Each 

spring, when ambient temperatures consistently exceed 59 

°F, (the flight threshold for SPB and its predators) (Moser 

Figure 1—Small (A) and large (B) expanding southern pine beetle (SPB) infestations (spots) in east Texas, as seen from the air. Beetles 

emerging from the yellow-crowned host trees (faders) will tend to attack uninfested, green-crowned trees on the spot periphery in 

response to aggregation pheromones, leading to continuous spot growth during summer months. 

R. Billings
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

and Dell 1979), populations of SPB disperse from overwintering sites in search of weakened pine trees in which they 

initiate new infestations (Billings and Kibbe 1978). Because 

of regional variations in climate, trapping usually occurs 

from early March to mid-April in the Gulf Coastal States 

and in April or May in States in the northern portions or 

mountain regions of the beetle’s range. It is during this 

spring dispersal period that airborne populations of adult 

SPB and associated insects can be most effectively monitored with pheromone-baited traps for predictive purposes 

(Billings 1988).

Each trap is baited with a single pheromone packet 

containing two 400 microliter “bullet” capsules of racemic frontalin (Phero Tech, Inc., Delta, British Columbia) 

(Figure 2B), the SPB aggregation pheromone (Kinzer and 

others 1969, Payne and others 1978), and a rapid-release 

dispenser of steam-distilled southern pine turpentine (Billings 1985) (Figure 2C). From 1987 to 2005, the turpentine 

dispenser consisted of a 250 ml amber Boston round bottle 

(Fisher Scientific Company, Pittsburgh, PA) with a cotton 

wick. Various brands of commercial-grade southern pine 

turpentine (Klean-stripTM (1987-1997), Star-texTM (1998), 

and HerculesTM (1999-2006)) were used, depending on 

availability. These insect- and host-produced volatiles 

attract in-flight adult SPB (Figures 2D, E) as well as a major 

predator, the clerid beetle Thanasimus dubius (Coleoptera: 

Cleridae) (Figure 2F) (Billings and Cameron 1984, Moser 

and Dell 1980, Payne and others 1978, Vite and Williamson 

1970). More than any other associated insect, T. dubius is 

believed to play a major role in the population dynamics of 

SPB (Moore 1972; Reeve and Turchin 2002; Thatcher and 

Pickard 1966; Turchin and others 1991, 1999). 

Each trap is installed in a pine-forested area, preferably in stands having sawtimber trees (> 30 cm in diameter 

at breast height) with a sparse hardwood understory. The 

traps are purposely placed outside of SPB infestations to 

Figure 2—The Southern Pine Beetle Prediction System uses 12-funnel survey traps. The traps are (A) placed in the field for 4 weeks in 

the spring starting when dogwoods bloom. Each trap is baited with the SPB pheromone frontalin (B) and a bottle of steam-distilled pine 

turpentine (C), an attractive bait combination that draws in flying adult SPB (D) and the clerid beetle, Thanasimus dubius (F), a major 

SPB predator. Both the number of SPB (E) and number of clerids caught in traps are used to forecast SPB infestation levels for the current year. (Photos A-C by R. Billings; photo D by Erich G. Vallery, USDA Forest Service, Bugwood.org; photo E by Texas Agricultural 

Extension Service Archive, Texas A&M University, Bugwood.org; photo F by Gerald J. Lenhard, Bugwood.org) 
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GENERAL TECHNICAL REPORT PNW-GTR-802

plotted onto the SPB prediction chart (Figure 3). This chart 

was developed and refined over the years by comparing the 

relationship between the two independent variables (SPB 

per trap per day and percentage SPB) and actual numbers of 

SPB spots detected in a given county or ranger district for 

the current year to ascertain SPB population level. Actual 

SPB infestation trend is obtained by comparing the number 

of spots reported in a given locality or State in the current 

year with the number reported for the same locality or State 

in the previous year.

Where trapping data are available for the previous year 

from the same county or ranger district (preferably from the 

same specific trap location), a comparison of mean SPB per 

trap per day and percentage SPB for the current year with 

that for the previous year provides further insight into the 

direction of infestation trends (increasing, static, or declining). In turn, the number of SPB spots detected in a given 

county, ranger district, or State in the previous year is useful 

for predicting population levels (high, moderate, or low) in 

attract long-range dispersing SPB and its predators. Traps 

are suspended from a 3-m metal pole or from a cord strung 

between two hardwood trees, at least 10 m from any live 

pine (Figure 2A). If multiple traps are deployed in a county 

or ranger district, they should be at least 1 mi apart. The 

turpentine bottle is placed within the top funnel of the trap, 

and the frontalin packet is hung from a funnel stanchion 

near the middle of the trap. 

Insects are collected from traps weekly for 4 consecutive weeks, and the numbers of adult SPB and clerids are 

counted and recorded. Upon completion of the survey, 

the data from each State and Federal cooperator are sent 

to the authors for compiling. With these data, the authors 

or cooperators or both make predictions of SPB infestation trend and relative population level for the current 

year, based on mean numbers of SPB per trap per day and 

percentage SPB. The latter is defined as the number of SPB 

x 100 percent divided by the combined number of SPB 

plus clerids caught per trap (Billings 1988). The data are 

Figure 3—Southern pine beetle prediction chart. The prediction of SPB infestation trend and level for the current year is derived by 

plotting the mean number of SPB per trap per day and percent SPB for the current year and comparing these data to that for the previous 

year (if available) for a given locality. Percentage SPB = (No. SPB x 100)/(No. SPB + No. Clerids).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

the current year. For purposes of this paper, the severe 

outbreak level shown in Figure 3 was considered the 

same as the increasing or high level. If no spots were 

detected in a given county the previous year, and few 

SPB are caught this year suggesting a declining trend, 

the prediction would be static or low (rather than declining or low, since infestation levels cannot decline below 

0). Trap catch data for all individual counties or ranger 

districts monitored within a State are averaged to make 

SPB predictions at the State level. Once all the data are 

received and processed, the Southwide predictions at 

the local and State levels are sent to each cooperator and 

also are made available on the Texas Forest Service Web 

page (http://texasforestservice.tamu.edu). 

The accuracy of State-level predictions made 

since 1987 are summarized in this paper. The Southwide SPB Prediction System was initiated in 1987 

in 11 Southern States (Arkansas, Texas, Louisiana, 

Mississippi, Alabama, Georgia, Tennessee, Virginia, 

Florida, South Carolina, and North Carolina). Federal 

and State pest managers in several other States joined 

at a later date (Maryland in 1988, Oklahoma in 1996, 

Kentucky in 2000, Delaware in 2001, and New Jersey 

in 2002) and have participated ever since. Florida did 

not conduct SPB pheromone surveys from 1989 to 1994, 

but returned as a cooperator from 1995 to the present. 

At the end of each calendar year, SPB cooperators 

provide the authors with total numbers of SPB spots 

detected from aerial surveys in each county or ranger 

district monitored with pheromone traps, as well as 

for the entire State. These data are used to evaluate the 

accuracy of each year’s predictions and provide valuable 

information for making predictions for the next year.

For purposes of the SPB Prediction System and 

to evaluate its accuracy, the following variables are 

defined:

Trend:

• Increasing = > 25-percent increase in total spots 

in a county, ranger district, or State from previous year.

• Static = ≤ 25-percent change in total spots from 

previous year.

• Declining = > 25-percent decrease in spots from the 

previous year.

Level:

• Low = < 0.40 spots per 1,000 acres of host type in a 

county, ranger district, or State during a given year.

• Moderate = 0.40-1.50 spots per 1,000 acres of host 

type.

• High = > 1.50 spots per 1,000 acres of host type.

• Severe outbreak = combined with increasing or high 

level for purposes of this paper.

Error:

• Minor = discrepancy between prediction and actual 

trend or population level was a single category (e.g., 

prediction was for static trend whereas actual trend 

was increasing; prediction was for low level whereas 

actual level was moderate, etc.).

• Major discrepancy between prediction and actual 

trend or level was two categories (e.g., prediction 

was for declining trend whereas actual trend was 

increasing or outbreak, prediction was for low population level whereas actual level was increasing or 

outbreak, etc.).

Host Type:

• Total acres of loblolly, shortleaf, and slash pine 

(Pinus elliottii Engelm.) from 1990 to 2000 Forest 

Inventory and Analysis data for a specific State, 

county, parish, or national forest ranger district.

Results

Actual SPB Infestation Levels: 1987 to 2005

Total numbers of SPB spots detected in 16 States in the 

Southeastern United States for the period 1987-2005 are 

shown in Figure 4. Infestation levels on a regional basis 

varied significantly by year from a high of >90,000 spots in 

2002 to a low of <5,000 spots in 2005. During the 12-year 

interval from 1987 to 1998, at least one SPB outbreak cycle 

(increasing, peak, declining) occurred in every participating 

State. SPB populations reached peak levels in Arkansas in 

1995; in Texas in 1989 and 1992; in Louisiana in 1992; in 

Mississippi in 1988, 1991, and 1995; in Alabama in 1992 
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GENERAL TECHNICAL REPORT PNW-GTR-802

and 1995; in Georgia in 1988; in Tennessee in 1988; in Virginia in 1993; in Florida in 1995 and 1997; in South Carolina in 1995; in North Carolina in 1995; and in Maryland in 

1993. Interestingly, 9 of the 11 States that had participated 

in the system since 1987 reported SPB infestations in every 

year from 1987 to 1998. Only Florida and Maryland had at 

least 1 year with 0 SPB spots during this time. 

From 1999 to 2005, SPB activity fluctuated widely 

across the South. SPB infestations totally disappeared 

west of the Mississippi River: Louisiana, Texas, Arkansas, 

and Oklahoma reported 0 spots for these years and few 

if any SPB were captured in pheromone traps during this 

7-year period. In sharp contrast, the worst SPB outbreak 

on record within a single State occurred in South Carolina 

in 2002, when 67,127 SPB spots were detected (> 14 spots 

per 1,000 acres of host type). Peak populations occurred in 

Alabama and Virginia in 2000; in Kentucky, Tennessee, 

and Florida in 2001; and in Georgia, North Carolina, and 

New Jersey in 2002. Throughout this period, SPB trapping 

procedures were standardized with all cooperators using 

the same turpentine (HerculesTM) and dispenser (amber 

bottle and wick). Also, in 1998 and again in 2002, the SPB 

prediction chart was slightly modified based on results 

of pheromone trapping and SPB detection records for the 

period 1987–2001 [see Figure 3 versus the chart shown in 

Billings (1988)]. Accordingly, in certain cases, results for 

the period 1987–1998 were evaluated separately from those 

for the period 1999–2005 to determine whether accuracy 

of the prediction system has improved since 1999 following 

standardization of field trapping procedures and modifications to the SPB prediction chart.

Overall Prediction Accuracy at the State Level, 

Based on State Averages 1987–2005

Over the 19-year period (1987–2005), 245 State-level 

predictions of SPB infestation trend and population level 

were made by averaging the mean SPB per trap per day 

and percentage SPB for all locations monitored within each 

State for each year of participation in the Southwide survey. 

To determine overall accuracy of predictions at the State 

level, a 9 by 9 matrix was prepared to compare predicted 

versus actual infestation trends and population levels for 

every possible trend or level category (declining to low 

level, declining to moderate level, declining to high level, 

static at low level, static at moderate level, static at high 

level, increasing to low level, increasing to moderate level, 

increasing to high or outbreak level). Overall, the infestation 

trend and population level were both predicted accurately 

Figure 4—Total numbers of southern pine beetle infestations (spots) detected in 16 States for 

the period 1987-2005. This figure shows marked differences in infestation levels between those 

States located east and those located west of the Mississippi River (MSR).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

in 57 percent of the cases (140 of 245). The category of 

static or low was correctly predicted 85 percent of the time 

(45 of 53 cases) whereas the declining or low category was 

correctly predicted 73 percent of the time (60 of 82 cases). 

These two categories were the most common situations 

experienced across the South from 1987 to 2005, occurring 

in 55 percent of the total cases. 

A 3 by 3 matrix was used to evaluate predicted and 

actual SPB trends alone (Figure 5). SPB infestations that 

increased by more than 25 percent from the previous year 

(increasing trend) occurred in 30 percent of the 245 cases 

and were correctly predicted 62 percent of the time. Those 

that declined by at least 25 percent from the previous year 

occurred in 43 percent of the cases and were correctly 

predicted 80 percent of the time. Static infestation trends 

(≤ 25 percent change from previous year) occurred in 27 

percent of the cases and were correctly predicted 76 percent 

of the time.

A separate matrix was used for SPB population levels 

(Figure 6). Low SPB population levels occurred in 64 

percent of the 245 cases and were correctly predicted 85 

percent of the time. Moderate levels occurred in 26 percent 

of the cases and were correctly predicted 54 percent of the 

time. In contrast, SPB populations reached high levels in 10 

percent of the cases but were correctly predicted to do so 

only 32 percent of the time. However, 88 percent of those 

States that experienced high SPB infestation levels were 

predicted to have at least moderate levels. Major errors 

in both infestation trend and population level predictions 

occurred in only 3 percent of the cases. Major errors (those 

cases within red boxes in Figures 5 and 6) in trend predictions alone occurred in 11 percent of the cases (28 of 245 

cases) whereas major errors in population level predictions 

alone occurred in only 2 percent of the cases (5 of 245 

cases).

Accuracy of Predictions for Individual States, 

Based on State Averages: 1987–2005

Accuracy of SPB predictions at the State level for individual 

States for the period 1987–2005, determined by averaging 

data for all counties and ranger districts surveyed within the 

given State for a given year, is shown in Table 1. State-level 

trend predictions proved most accurate in Oklahoma, Texas, 

Mississippi, and Kentucky where accuracy exceeded 80 

Figure 5—Matrix showing accuracy of SPB trend predictions at the State level, based on 245 

predictions over the 19-year period 1987–2005 in 16 States.
80

GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 6—Matrix showing accuracy of SPB population level predictions at the State level, based 

on 245 predictions over the 19-year period 1987–2005 in 16 States.

Table 1—Accuracy of the SPB Prediction System by State: 1987–2005

Major Major

Years Spots Correct Error Correct Error

State Surveyed Locations Detected Trend Trend Level Level

--------------------------Percent--------------------------

OK 9 15 0 89 0 100 0

AR 19 123 17,004 79 5 84 0

TX 19 357 27,236 89 11 89 5

LA 19 340 30,182 79 0 79 5

MS 19 156 44,123 84 11 53 11

AL 19 97 99,956 68 5 58 0

KY 6 17 5,137 83 17 67 0

GA 19 173 50,121 58 21 63 5

TN 19 79 46,301 68 26 53 5

VA 19 61 28,706 79 11 89 0

FL 13 240 6,957 69 15 77 0

SC 19 390 156,058 58 16 47 0

NC 19 160 37,831 68 21 68 0

MD 18 51 455 78 17 83 0

DE 5 5 3 60 0 100 0

NJ 4 21 637 50 25 50 0

Total 245 2,285 550,707

Mean 72.4 12.6 72.5 1.9

±SE 2.96 2.17 4.39 0.82

Note: Based on State averages, with Locations column indicating counties, parishes, and National Forest Ranger Districts surveyed.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

percent. Accuracy of trend prediction was lowest for New 

Jersey (50 percent), South Carolina (58 percent), and Georgia (58 percent). Accuracy of predictions of SPB population 

level was greatest for Oklahoma (100 percent), Delaware 

(100 percent), Texas (89 percent), Virginia (89 percent), 

Arkansas (84 percent), and Maryland (83 percent). The 

least accurate predictions of population level were those 

for South Carolina (47 percent), New Jersey (50 percent), 

Tennessee (53 percent), and Mississippi (53 percent). 

Correct predictions for all States combined averaged 72.4 

percent for trend and 72.5 percent for level. It should be 

noted that Oklahoma, Delaware, Kentucky, and New Jersey 

have participated in the annual survey for less than 10 years.

Accuracy of Predictions at the State Level: 1987–

1998 versus 1999–2005

We summarized State-level predictions for 12 States for 

the periods 1987–1998 and 1999–2005. We then compared 

the summarized State-level predictions for these periods to 

ascertain whether prediction accuracy at the State level has 

improved in recent years as a result of system modifications 

described above. Tables 2 and 3 show that the accuracy of 

SPB trend predictions for these States combined increased 

from 68 percent prior to 1999 to 82 percent in subsequent 

years. Prediction of population levels increased in accuracy 

to a lesser extent, from 69 percent to 74 percent. Trend 

predictions declined in accuracy during the last 7 years 

only in Virginia. The accuracy of predictions of population 

level declined slightly for Mississippi, Tennessee, Virginia, 

Florida, and South Carolina and increased in the other 

States surveyed.

Discussion and Conclusions

The Southwide SPB Prediction System has proven to be 

a practical and relatively reliable means to forecast SPB 

activity early in the season. Three traps per county, parish, 

or ranger district appear sufficient to predict SPB infestation 

trends and probable severity at the local level. By averaging trap catch data for all counties and ranger districts 

monitored in a given year (20 to 100 traps per State), a 

reasonably reliable prediction of SPB trend and level for a 

given State can be achieved. There is a distinct relationship 

between the numbers of both SPB and clerids caught in 

early-season traps and subsequent SPB infestation trends 

during the same year, providing further evidence that Thanasimus dubius populations are SPB-density dependent and 

Table 2—Accuracy of the SPB Prediction System for 12 States from 1987 to 1998

Major Major

Years Spots Correct Error Correct Error

State Surveyed Locations Detected Trend Trend Level Level

--------------------------Percent--------------------------

AR 12 65 17,004 67 8 75 0

TX 12 229 27,236 83 17 83 8

LA 12 181 30,182 75 0 67 8

MS 12 89 41,691 83 8 58 0

AL 12 52 47,193 67 8 42 0

GA 12 83 32,402 50 25 67 0

TN 12 39 12,710 58 17 58 8

VA 12 33 26,244 83 0 92 0

FL 6 69 1,632 67 17 83 0

SC 12 155 33,471 42 17 50 0

NC 12 70 25,978 67 25 67 0

MD 11 29 323 73 0 82 0

Total 1,094 296,066

Mean 67.9 11.8 68.7 2.0

±SE 3.75 2.64 4.33 1.04

Note: Based on State averages, with Locations column indicating counties, parishes, and National Forest Ranger Districts surveyed.
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GENERAL TECHNICAL REPORT PNW-GTR-802

are likely to be a major regulating factor in SPB population 

dynamics, as suggested by Moore (1972) and Turchin and 

others (1991, 1999).

Declining SPB populations tend to be more consistently 

predicted than are increasing populations, presumably 

because SPB and clerid populations are distributed across 

the pine-forested landscape more uniformly following an 

outbreak than they are at the beginning of the outbreak. 

Nevertheless, since its implementation in 1987, the prediction system has been used by State and Federal pest 

management specialists and foresters as a basis for scheduling aerial detection flights and identifying the need for 

suppression projects. Knowing when endemic SPB populations are about to explode or outbreak populations are soon 

to collapse is valuable information for more effectively 

managing this major forest pest.

Although 16 States currently participate in the system, 

levels of participation and accuracy differ from State to 

State. One State (Louisiana) prefers to use a single trap per 

parish and distribute available traps among more parishes. 

Interestingly, this approach doesn’t seem to have substantially affected the accuracy of State-level predictions 

(Tables 1, 2, 3). Presumably, surveying more locations or 

using more traps per location or both will lead to more 

reliable predictions, particularly in the early stages of a 

developing SPB outbreak when beetle populations are more 

scattered. 

Continual validation using end-of-the-year detection 

records has enabled refinement of the system over the years. 

The prediction chart (Figure 3) has been modified since 

1987 to better define the dividing lines between increasing 

or high, static or moderate, and declining or low infestations 

as more data points (both trap catch data and corresponding 

infestation trends and population levels) became available. 

Single sources of pheromone and turpentine have been 

used by all cooperators since 1999. In 2006, the standard 

turpentine dispenser (amber bottle and wick) was replaced 

in many cases by a polyethylene bag of turpentine from the 

same source (HerculesTM). Since 2006, the standard turpentine dispenser (amber bottle and wick) has been replaced by 

a polyethylene bag containing 200 gm of 75 percent alphapinene: 25 percent beta-pinene (Synergy Semiochemicals 

Corp., Burnaby, British Columbia, Canada). This new 

dispenser is more practical for field application and has been 

found to be equally effective in eluting host volatiles and 

attracting SPB and clerids.

Table 3—Accuracy of the SPB Prediction System for 12 States from 1999 to 2005

Major Major

Years Spots Correct Error Correct Error

State Surveyed Locations Detected Trend Trend Level Level

--------------------------Percent-------------------------

AR 7 58 0 100 0 100 0

TX 7 133 0 100 0 100 0

LA 7 159 0 86 0 100 0

MS 7 64 2,432 86 14 43 29

AL 7 42 52,763 71 0 86 0

GA 7 85 17,719 71 14 57 14

TN 7 34 33,591 86 14 43 0

VA 7 28 3,048 71 14 86 0

FL 7 168 4,953 71 0 71 0

SC 7 233 122,587 86 14 43 0

NC 7 90 11,853 71 14 71 0

MD 7 22 10 86 0 86 0

Total 1,170 254,733

Mean 7 82.1 7.0 73.8 3.6

±SE 3.18 2.11 6.54 2.59

Note: Based on State averages, with Locations column indicating counties, parishes, and National Forest Ranger Districts surveyed.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

finally, maintaining a reliable source of steam-distilled 

southern pine turpentine has been a problem over the years. 

Despite these limitations, the system has been deemed a 

success. This is reflected in the fact that Federal and State 

cooperators continue to use it. 

Nevertheless, improvements in the SPB Prediction 

System are continually being sought. For example, monitoring SPB and clerid populations in the fall months for 

predictive purposes using the same protocol is currently 

being tested. Since 2003, the national forests in Mississippi 

have been conducting fall pheromone trapping during the 

secondary dispersal phase of SPB. These studies, though 

limited, have yielded relatively accurate predictions similar 

to those generated from the following spring survey 

(James Meeker, U.S. Forest Service FHP, 2500 Shreveport 

Highway, Pineville, LA 71360, personal communication). 

If effective, fall trapping would provide forest managers 

with additional lead time to prepare for peak infestations 

in the summer. The accuracy of forecasts based on fall 

insect numbers, however, may be dependent on weather 

conditions the following winter. Severe winter conditions 

such as freezes, prolonged cold temperatures, etc., could 

curtail the growth of SPB populations that were increasing 

in the fall, for example. In turn, SPB populations that are 

in the process of declining following a severe summer may 

be identified several months earlier if pheromone traps are 

deployed in the fall. Additional fall trials are needed across 

the Southeastern United States over multiple years to fully 

evaluate this approach. Indeed, a combination of spring and 

fall trapping may prove most effective for tracking seasonal 

and annual SPB population shifts.

Recent field research has demonstrated significantly 

enhanced attraction of SPB to trap sites additionally baited 

with the pheromone (+)endo-brevicomin (B. Sullivan, 

personal communication, East Texas Forest Entomology 

Seminar, Fall 2005). This finding offers a potentially new 

and alternative means of improving the precision, accuracy, 

and timeliness of the system. A bonus of the current SPB 

Prediction System is the wealth of historical trap-catch 

data from across the Southern United States over a 20-year 

period, which can help researchers better understand 

SPB and clerid dynamics (Tran and others 2007). Finally, 

As with any prediction system, the SPB prediction protocol has strengths and weaknesses. The strengths include 

its simplicity, practicality, and relative accuracy. Data from 

a few strategically placed traps in early spring provide forest pest managers with insight into SPB infestation trends 

and levels for the remainder of the year at both local and 

large geographical scales. Currently, the standardized system is being implemented annually in 16 States, providing 

a consistent means to monitor SPB populations throughout 

the range of this pest in the Southern and Eastern United 

States. Limitations include the following:

The process of installing and monitoring traps and 

counting beetles is tedious and time-consuming. Numerous 

field crews with varying levels of training and experience 

are involved in setting out and monitoring the traps and 

identifying the beetles captured, which influences the quality of the data.

Pheromone traps placed too close to host trees may 

initiate infestations and inflate trap catches, leading to 

erroneous predictions, although such high trap catches are 

eliminated from the prediction data prior to analysis when 

infested trees are reported adjacent to a trap or trap catches 

exceed ca. 1,200 SPB per trap.

Variations in climate among States means that trapping 

data from Northern States is often not available until early 

June, 1 or 2 months after the results from Gulf Coastal 

States are completed.

In certain years, atypical weather patterns may accelerate or delay emergence of overwintering SPB and clerid 

populations, leading to erroneous predictions. For example, 

in 1989, an SPB outbreak occurred in Texas but was not 

forecasted. In that year, beetles emerged in large numbers 

in late April and May, after survey traps had been removed 

from the field (Billings, unpublished data). Of course, 

SPB outbreaks may originate in counties or locations not 

monitored with pheromone traps, a potential problem in 

those States that survey a limited number of counties (e.g., 

Mississippi, Alabama) or a single locality within each 

county or parish (e.g., Louisiana) each year. Also, predictions made in the spring for increasing SPB populations 

will prove wrong if adverse summer temperatures or other 

unanticipated factors limit SPB population growth. And, 
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GENERAL TECHNICAL REPORT PNW-GTR-802

incorporation of SPB predictions into area-wide hazard 

maps (Billings and Bryant 1983; Billings and others 1985; 

Billings and others, this volume; Cook and others 2007) 

may lead to the development of more effective SPB risk 

maps. This would provide yet another tool for more effectively managing SPB.

Acknowledgments

The authors acknowledge the many cooperating State, 

university, and Federal pest management specialists for 

their dedicated contributions. The SPB Prediction System 

would not exist were it not for their willingness to set out 

and monitor traps, collect and count beetles annually in 

numerous counties and national forest ranger districts, and 

provide the data to the authors in a timely manner.

Literature Cited

Billings, R.F. 1985. Southern pine bark beetles and 

associated insects: effects of rapidly-released host 

volatiles on response to aggregation pheromones. 

Zeitschrift Angew Entomologia. 99: 483–491.

Billings, R.F. 1988. Forecasting southern pine beetle 

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pine beetle habitats in east Texas. Zeitschrift Angew 

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large area hazard- and risk-rating system for southern 

pine beetle. In: Branham, S.J.; Thatcher, R.C., eds. 

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proceedings. Gen. Tech. Rep. SO-56. Asheville, NC: U.S. 

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Billings, R.F.; Cameron, R.S. 1984. Kairomonal responses 

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between southern pine beetle brood development and 

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changes in southern pine beetle hazard at the landscape 

level. In: Pye, John M.; Rauscher, H. Michael; Sands, 

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Southern Research Stations: 539–547. Vol. 2. 

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factors influencing the growth and decline of summer 

infestations in east Texas. Forest Science. 25: 547–556.

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others]. 1969. Bark beetle attractants: identification, 

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beetles (Coleoptera). Canadian Entomologist. 

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North Carolina caused by parasites and predators. 

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Moser, J.C.; Dell, T.R. 1980. Weather factors predicting 

flying populations of a clerid beetle and its prey, the 

southern pine beetle. In: Berryman, A.A.; Safranyik, 

L., eds. Dispersal of forest insects: evaluation, theory, 

and management implications: Proceedings of the 2nd

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Payne, T.L.; Coster, J.E.; Richerson, J.V.; [and others]. 

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behavioral chemicals. Environmental Entomology. 

7: 578–582.

Price, T.S.; Doggett, C.; Pye, J.M.; Smith, B. 1998.

A history of southern pine beetle outbreaks in 

the southeastern United States. Georgia Forestry 

Commission, Macon, GA. 71 p.

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Population cycles: the case for trophic interactions. 

Oxford University Press, Inc., New York, NY: 92–108.

Thatcher, R.C.; Pickard, L.S. 1964. Seasonal variations in 

activity of the southern pine beetle in Texas. Journal of 

Economic Entomology. 57: 840–842.

Thatcher, R.C.; Pickard, L.S. 1966. The clerid beetle, 

Thanasimus dubius, as a predator of the southern pine 

beetle. Journal of Economic Entomology. 59: 955–957.

Thatcher, R.C.; Searcy, J.L.; Coster, J.E.; Hertel, G.D. 

eds. 1980. The southern pine beetle. Tech. Bulletin 1631. 

Washington, DC: U.S. Department of Agriculture, Forest 

Service. 266 p.

Tran, J.K.; Ylioja, T.; Billings, R.; [and others]. 

2007. Impact of minimum winter temperatures on 

the population dynamics of Dendroctonus frontalis 

(Coleoptera: Scolytinae). Ecological Applications. 

17(3): 882–899.

Turchin, P.; Lorio, P.L., Jr.; Taylor, A.D.; Billings, R.F. 

1991. Why do populations of southern pine beetles 

(Coleoptera: Scolytidae) fluctuate? Environmental 

Entomology. 20: 401–409.

Turchin, P.; Taylor, A.D.; Reeve, J.D. 1999. Dynamical 

role of predators in population cycles of a forest insect: 

an experimental test. Science. 285: 1068–1071.

Vite, J.P.; Williamson, D.L. 1970. Thanasimus dubius: 

prey perception. Journal of Insect Physiology. 

16: 233–239.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Using Historical Photography to Monitor and Assess Threats 

Over Time

Don Evans

Service, Remote Sensing Applications Center, Salt Lake 

City, UT 84119.

Abstract

Analysis of aerial photography is perhaps the best way 

to assess changes in landcover conditions. In the United 

States, most national forests have repeat photography on 

approximately a 10-year cycle. Analysis of this rich photo 

record can reveal changes in insect damage, fuels buildup, 

unmanaged off-highway vehicle use, loss of open space, 

and other land-cover conditions. Current technologies 

now allow Forest Service employees to input photos into a 

geographic information system (GIS) accurately and easily, 

and important changes can be documented, analyzed, and 

evaluated within the GIS. This analysis discusses selected 

photo characteristics, available software solutions, and 

techniques for orthocorrection of historical photos. It also 

discusses two cases in which historical imagery was used to 

document selected changing threat levels over time.

Keywords: Aerial photography, geographic information systems, loss of open space, orthocorrection software, 

pseudocamera reports, unmanaged recreation.

Introduction

Remote sensing—aerial photography in particular—provides perhaps the best means to assess landcover changes 

over time. This is especially true when assessing changes 

over long periods of time. Aerial photography provides 

an invaluable record of land use and landcover conditions—frequently dating back to the 1930s. No other 

source of remotely sensed imagery is available prior to the 

1970s. Most national forests have repeat photography on 

approximately a 10-year cycle. This rich photo record can 

indicate changing threat conditions through time, including 

threats such as insect damage, fuels buildup, unmanaged 

off-highway vehicle (OHV) use, and loss of open space.

We’ve long recognized and valued the quality information content of photos; however, we’ve also long struggled 

with how to get our photos into our Geographical Information Systems (GIS). Current technologies now allow Forest 

Service employees to input photos into a GIS accurately 

and easily. Within the GIS, we can evaluate, analyze, and 

document important changes.

This analysis discusses selected photo characteristics, 

available software solutions, and techniques for historical 

photo orthocorrection—along with examples using historical imagery to document selected changing threat levels 

over time.

The methods described in this paper are available 

throughout the Forest Service and provide the most consistent and objective method for detecting, assessing, and 

monitoring land-cover changes through time.

Why Photography?

Aerial photography, like all remote sensing technologies, 

has advantages and disadvantages compared with other 

remote sensing tools. The advantages of using photography 

to assess threats over time include a long history of repeat 

coverage, it is well understood, it is intuitive to the user 

community, it has excellent resolution, and it provides a 

superb stereo view.

The Forest Service has been systematically collecting aerial photography since the 1930s. Since that time, 

considerable effort and expense by developers have resulted 

in significant improvements, including faster and higher 

resolution films and film types, faster and higher resolution 

lenses and cameras, and more precise camera geometries 

and calibrations. On the users’ side, there is a long history 

of effective use and technique development. These developments include a thorough understanding of photo geometry, 

stereoscopy, photogrammetry, and interpretation. Thus, 

the complexities of photography are well understood in the 

professional community.

Although photography is complex, it is simultaneously 

intuitive—both to the professional community, and to the 

at-large community. We’ve all grown up with photographs 

as part of our daily lives. We may not always understand the 

complexities and properties of the photography we see, but 

we still know how to interpret photographic images. Pho-
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tography’s intuitiveness promotes a greater understanding 

and acceptance of analytic results by the public—especially 

when the analysis techniques are easily understood.

Photography has excellent spatial resolution that is 

only now being challenged by other imaging systems. Even 

early aerial photography (1930s and 1940s), though not as 

resolute as post-WWII photography, has very good resolution. This allows easy comparison of vastly different dates 

of imagery.

An under-appreciated property of aerial photography 

is the ability to view the imagery in stereo. The stereo view 

drastically increases our ability to discern what we are 

viewing in the image and to perceive the topography and 

the varying heights of features on the ground. The stereo 

view has been the domain of the stereoscope and hardcopy 

photographs but is now becoming easily accessible in the 

digital domain—a trend that is certain to continue.

Why Not Photography?

Photography has its weaknesses, too. The major disadvantages of using photography to assess threats over time 

include: photography usually has to be converted from 

hardcopy (analog) to a digital format, it has non-map 

geometry, and it has a relatively small footprint.

All historical photography (meaning all photography 

before the current cycle of acquisition) is analog. Thus, one 

of the first steps required to get this imagery into a GIS is 

to scan each photo. This is a time-consuming operation 

affected by scanner speed, scan resolution, and the number 

of photographs. Time and cost of this step must be considered for each project.

The same geometry that allows for stereoscopic 

viewing is often viewed as a disadvantage when trying to 

get our photo-derived information into a GIS. Photography 

has single-point-perspective geometry. This means that a 

vertical photograph may appear map-like, but, features at 

differing elevations are displaced relative to one another, 

and the scale changes with each change in elevation. Time 

and money are required to orthocorrect the photography 

(make the digital photo imagery accurately conform to a 

map coordinate system and projection).

A common trade-off with the various remote sensing 

systems is between footprint (area covered) and spatial 

resolution—photography is no exception. In general, the 

larger the area that is covered, the coarser the resolution. 

This holds true when comparing photography of different 

scales, and it is generally true when comparing photography 

to most satellite imagery. Compared with satellite imagery, 

photography is usually higher resolution but covers a much 

smaller area. Given the time and cost required for scanning 

and orthocorrection of historical photography, the small 

footprint constrains the applicability of using photography 

to relatively small areas or to sampling over very large areas 

for detecting, assessing, and monitoring changes.

Tools for Getting Historical Photos in GIS

There is a fortunate convergence of technologies that makes 

it easier than ever to get historical photography into our 

GIS. The most important technologies are the newly available orthocorrection software tools and the online data and 

camera reports. Also of value are the simple techniques for 

creating pseudocamera reports.

Although image orthocorrection is not required to 

detect changes, orthocorrection and subsequent analysis in 

GIS provides two major advantages: 

1. Changes are much easier to detect using the 

built-in image display options in widely used 

GIS mapping applications.

2. Documenting the magnitude of change is much 

easier and can usually be objectively quantified.

Orthocorrection Software Tools

Commercially available orthocorrection software makes 

powerful softcopy photogrammetry tools available to all 

Forest Service personnel. This software has a significant 

learning curve, but it allows users to efficiently orthocorrect 

large blocks (or sets) of overlapping photos.

Online Data (DOQs, DEMs, and Camera Reports)

In addition to the scanned photography, orthocorrection 

requires horizontal and vertical reference data and information about the camera that acquired the photography. 

Typically, digital orthophotoquads (DOQs) are used for the 

horizontal reference, digital elevation models (DEMs) are 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

used for the vertical reference, and a USGS Optical Sciences Lab Camera Report is used for the camera information. All of this data is available online, allowing for far 

more efficient data preparation for orthocorrection.

Creating Pseudo-Camera Reports

The USGS camera reports are not available for any resource 

photography prior to 1973. When one is needed, it is fairly 

easy to create your own pseudocamera report. The vital 

information needed from an official USGS camera report 

includes the principal point, the fiducial measurements, 

the fiducial orientation (with respect to the data strip), 

and the lens focal length. If we know the scan resolution 

of the scanned photo, the dimension of one pixel in the 

image is known (e.g., if the image was scanned at 600 dpi, 

each resulting pixel is 1/600 inch) and distances between 

fiducials on the image can be measured. We have created 

a simple spreadsheet that converts imaged fiducial file 

coordinates to millimeters in a camera coordinate system 

similar to USGS camera reports. For these pseudocamera 

reports, we have to assume the principal point is at coordinate 0,0 and we initially assume the focal length is 6 inches 

(152.4 mm). The fiducial orientation can be defined by the 

user—but then has to be applied consistently.

Obviously, these pseudocamera reports are not as 

accurate as official camera reports. The spatial accuracy of 

the resulting orthocorrected photography will likely be less 

accurate as well. However, for many resource applications, 

satisfactory accuracies can be obtained.

Analysis Options

Once the multiple dates of photography have been orthocorrected, there are few limits to analysis options. The imagery 

itself, with little or no analysis, may be sufficient for public 

awareness, although some interpretation is usually helpful. 

To detect and document spatially continuous changes over 

time (e.g., vegetation cover), we have used a digital dot grid 

technique that provides percentage of cover by cover type 

for each cycle of imagery. For discrete feature changes, such 

as OHV trails, we have used two different techniques. Our 

first method used a digital grid that required the interpreter 

to simply click a cell of the grid if an OHV trail was in the 

cell. The grid was then put through a kriging process, and 

a map of OHV hot spots was created. The second method 

consisted of manually digitizing the trails as they were 

interpreted from the imagery. The kriging method has the 

advantage of being very fast. The digitizing method has the 

advantages of being simple, easily verifiable, and producing 

a map of actual OHV trails. In addition, it was a simple 

matter to construct graphs of the rate of change from the 

digitized trail’s attribute tables. These methodologies have 

worked well for us, but the optimum methodologies may yet 

be awaiting discovery.

Only the most basic interpretations should be attempted 

from the monoscopic imagery. Most interpretations should 

be accomplished in stereo, which allows users to visualize 

relief within the image and heights of features. This can 

be achieved using a stereoscope and the analog (hardcopy) 

imagery or digitally using commercially available applications.

Examples

Following are two brief examples of using historical 

photography to detect and document changing conditions 

over time.

Loss of Open Space

Aerial photography from 1969, 1977, 1988, and 1993 of a 

small portion of the Black Hills National Forest boundary 

were orthocorrected using the orthocorrection tools in ArcMap Image Analysis. Roads and buildings were digitized 

from each cycle of photography (within the chosen subset, 

there are no roads or buildings visible in 1969). Features 

digitized from each cycle of photography were symbolized 

with different colors. The results (Figure 1) clearly depict 

significant human-made changes that fragment the open 

space around the national forest boundary.

Unmanaged Recreation

Leica Photogrammetry Suite (LPS) was used to orthocorrect photography from 1967, 1976, 1988, and 1995 over a 

small portion of the Fishlake National Forest. In addition, 

we added orthocorrected NAIP imagery from 2004. The 

1967 photography did not have a camera report, so we 
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GENERAL TECHNICAL REPORT PNW-GTR-802

created one using methods described in above. Once all of 

the photography was orthocorrected, we added the existing 

travel management plan roads layer. Then we digitized 

new OHV trails as they became apparent on each cycle 

of photography (Figure 2). This methodology was simple, 

easily verifiable, and produces a map of actual OHV trails. 

In addition, it was a simple matter to construct graphs of 

the rate of change from the digitized trail’s attribute tables 

(Figure 3).

Resources

Where to Find Old Photos

Typically, each national forest receives copies of every 

photo acquisition over that national forest. However, there 

is no standard for archiving historical aerial photography. 

In too many cases, these old photographs are discarded by 

those who don’t see their continued value. Start your search 

locally and expand to the regional geospatial or remote 

sensing leader.

If photos are not available at the regional level, the 

USDA Aerial Photography Field Office (APFO) in Salt 

Lake City archives over 50,000 rolls of film. Their holdings include nearly all USDA photo acquisitions since the 

mid-1950s. When searching for historical photography 

within the APFO Web site, be aware that they have separate 

listings for Forest Service photography and all other USDA 

agencies.

Older photography is held in the National Archives in 

College Park, MD.

Training/Help

Countless sources for training and assistance have public 

access, but the following suggestions are tailored for the 

Forest Service and provided behind the Forest Service 

firewall.

Figure 1—Aerial photography from 1969, 1977, 1988, and 1993 over a portion of the Black Hills National Forest was orthocorrected. 

New roads and buildings were identified and digitized for each cycle of photography. The result (bottom-right of graphic) was a compelling graphical documentation of 24 years of loss of open space.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

The USDA Forest Service Remote Sensing Applications Center (RSAC) is an excellent source for training and 

help. The RSAC provides instructor-led courses in using the 

Leica Photogrammetry Suite—including a section on creating and using pseudocamera reports. There is no tuition for 

the course. Course offerings and registration are available 

from the Forest Service GeoTraining FS Web site (intranet) 

(http://fsweb.geotraining.fs.fed.us/). Online help for Imagine 

and Image Analysis is also available on this site.

The RSAC staffs the Remote Sensing Helpdesk. Access 

is through the Forest Service End User Support Center 

(EUSC). Specify that you have a remote-sensing-related 

question to be forwarded to the Remote Sensing Helpdesk.

Additional Reading

Chou, Y. 1997. Exploring spatial analysis in geographic 

information system. Santa Fe, NM: OnWard Press: 

334–336.

Jensen, J. 1996. Introductory digital image processing—a 

remote sensing perspective. Upper Saddle River, NJ: 

Prentice-Hall. 316 p.

Lachowski, H.; Maus, P.; Golden, M. [and others]. 1996. 

Guidelines for the use of digital imagery for vegetation 

mapping. Rep. No. EM-7140-25. Washington, DC: U.S. 

Department of Agriculture, Forest Service, Engineering 

Staff (Remote Sensing Applications Center). 125 p.

Newman, P. 2001. Off-highway vehicle (OHV) trail 

digitizing. [Online]. Available: http://www.or.blm.gov/

gis/projects/ohv.asp. [Date accessed: March 25, 2004].

Paine, D.P.; Kaiser, J. 2003. Aerial photography and image 

interpretation. Hoboken, NJ: John Wiley. 632 p.

Figure 3—This bar graph documents the increase in unmanaged 

OHV trails for our project area on the Fishlake National Forest, 

1967-2004. Official managed trails are graphed in cyan (and highlighted with the yellow line), newly interpreted unmanaged trails 

are shown in solid red, and the cumulative unmanaged trails are 

shown in the red gradient. Note: in 2004 there were approximately 

9 miles of cumulative unmanaged recreation trails—a 50-percent 

increase over the official managed trails.

Figure 2—Aerial photography from 1967 (Figures 2a and 2b), 

1976 (Figure 2c), 1988 (Figure 2d), and 1995 (Figure 2e) was 

orthocorrected, and 2004 NAIP photography (Figure 2f) was 

obtained to assess unmanaged recreation over a portion of 

the Fishlake National Forest. The official managed trails were 

obtained and overlaid on each image (light green). Unmanaged 

recreation trails were identified on each cycle of photography and 

digitized from the orthocorrected imagery.
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Timchak, L. 2003. Four threats to the health of the Nation’s 

forest and grasslands [Online]. Available: http://www.

fs.fed.us/r6/centraloregon/news/2003/06/030624timchak.

shml [Date accessed: April 19, 2004].

U.S. Department of Agriculture, Forest Service. 2002. 

Draft Environmental Impact Statement Duck CreekSwains Access Management Project. Cedar City, UT: 

U.S. Department of Agriculture, Forest Service, Dixie 

National Forest. 297 p.

U.S. Department of Agriculture, Forest Service. 2004. 

USDA Forest Service Chief Charters National OHV 

Teams. Washington, DC: National OHV Policy & 

Implementation Teams, On the Right Trail! Newsletter, 

January 7, 2004:1 [Online]. Available: http://www.fs.fed.

us/recreation/programs/ohv/ohv_use.pdf [Date accessed: 

April 19, 2004].

Werstak, C.; Johnson, V.; Lachowski, H. [and others]. 

2004. Mapping and monitoring non-system trail change 

with remote sensing and GIS. Rep. No. 0063-RPT3. Salt 

Lake City, UT: U.S. Department of Agriculture, Forest 

Service, Remote Sensing Applications Center. 16 p.

Williamson, M.; Evans, D.; Lachowski, H. [and 

others]. 2002. Technology evaluation for mapping 

roads and trails in the Ocala National Forest, Florida. 

RSAC-41-RPT1. Salt Lake City, UT: U.S. Department 

of Agriculture, Forest Service, Remote Sensing 

Applications Center. 12 p.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

very-large-tree forests and decreases in high-severity 

wildfire and insect outbreaks. All three scenarios resulted 

in conversion of most WUI to open grass, shrub, and forest 

conditions.

Keywords: Forests, landscape ecology, management, 

modeling, natural disturbances, Oregon.

Introduction

Management of diverse landscapes in the interior Pacific 

Northwest requires consideration of the integrated effects of 

natural disturbances and management activities on natural 

resource conditions. The opportunities for managing lands 

depend on widely varying objectives of owners, vegetation 

conditions, environmental settings, natural disturbances, 

and other factors. Likewise, the risks that land managers encounter include natural disturbances, unforeseen 

consequences of management activities, changing political, social, and economic environments, and others. Land 

managers and those who influence or set land management 

policy need to examine the short- and long-term potential 

effects of different management approaches using methods 

that (1) integrate the effects of natural disturbances and 

management activities on vegetation and resource conditions; (2) consider landscapewide characteristics and trends 

across all ownerships; (3) maximize the effects of limited 

budgets and personnel through cooperation across agencies 

and ownerships; (4) use a modeling approach that is flexible, powerful, easy to understand, and integrative.

A partnership of Federal and State agencies and 

nongovernment organizations developed a shared effort to 

generate landscapewide vegetation data, landscape models, 

and related information. The Interagency Mapping and 

Assessment Project (IMAP) addresses several landscape 

assessment and analysis issues, including (1) limited 

and declining funds to perform landscape assessments 

and analyses of potential effects of various management 

options on resources of interest; (2) an increasing lack of 

highly skilled people to perform landscape analyses; (3) a 

desire to avoid conflicting answers to broad questions that 

Integrating Natural Disturbances and Management Activities 

to Examine Risks and Opportunities in the Central Oregon 

Landscape Analysis

Miles A. Hemstrom, James Merzenich, Theresa Burcsu, 

Janet Ohmann, and Ryan Singleton

Miles A. Hemstrom, research ecologist, Portland Forestry 

Sciences Laboratory, Portland, OR 97208; James

Merzenich, planning analyst, USDA Forest Service, 

Portland, OR 97208; Theresa Burcsu, research ecologist, 

Portland Forestry Sciences Laboratory, Portland, OR 97208; 

Janet Ohmann, research forester, Corvallis Forestry 

Sciences Laboratory, Corvallis, OR 97331; and Ryan 

Singleton, research assistant, Department of Forest Science, 

Oregon State University, Corvallis, OR 97331-5752.

Abstract

We used state and transition models to integrate natural 

disturbances and management activities for a 275 000-ha 

landscape in the central Oregon Cascades. The landscape 

consists of a diverse mix of land ownerships, land use 

allocations, and environments. Three different management 

scenarios were developed from public input: (1) no management except wildfire suppression on federally managed 

lands, (2) manage Federal lands to increase multistory 

forests of large and very large trees, and (3) manage Federal 

lands to move toward historical conditions. All scenarios 

treated privately owned lands as if they were wildlandurban interface (WUI) areas and all recognized wilderness, 

reserves, and general forests within federally managed 

lands. Models were run for 200 years and 30 Monte Carlo 

simulations to include variability in fire years and other 

natural disturbances. Passive management on federally 

managed lands resulted in small increases in single-story 

and multistory large-tree forests and increases in highseverity wildfire and insect outbreaks. Managing toward 

multistory large- and very-large-tree forests resulted in 

minor increases in those forest types and increased wildfire 

and insect outbreaks. Contrary to intent, this scenario did 

not generate appreciable increases in multistory large- and 

very-large-tree forests. Managing toward historical conditions resulted in strong increases in single-story large- and 
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GENERAL TECHNICAL REPORT PNW-GTR-802

wildlife habitats, old forests, and timber products. In addition, policymakers and others want to consider long-term 

sustainability of landscape resources and conditions given 

various management approaches.

Landscape simulation models may be used to assist in 

understanding the potential reaction of large landscapes to 

various management and policy approaches (e.g., Bettinger 

and others 2005, Hann and others 1997, Hemstrom and 

others 2004, Mladenoff and He 1999, USDA and USDI 

2000). Advances in modeling techniques, computer technology, and geographic information systems (GIS) have made 

cross ownerships and interests; (4) the need for integrated 

analyses that include many management and natural 

disturbances across a broad range of ownerships, vegetation 

conditions, and environments; (5) a consistent basis for 

monitoring the effectiveness of management activities at 

achieving policy goals across large landscapes; and (6) the 

desire for relatively simple and understandable approaches 

to landscape analysis and policy evaluation. Key issues 

for all these landscape analysis, planning, and assessment 

activities include, among others, fire risks, forest conditions, 

Figure 1—The Five Buttes study area in central Oregon, U.S.A. WUI = wildland-urban interface.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

it possible to model large landscapes at increasingly finer 

scales of spatial and temporal resolution (Barrett 2001, 

Bettinger and others 2005). In much of the Pacific Northwest of North America, resource planning models have 

focused primarily on conifer succession and management 

while representing other ecosystem elements as byproducts 

(e.g., Alig and others 2000, Johnson and others 1986). 

Although progress has been made in the formulation of 

multiobjective goals in landscape simulations (Sessions and 

others 1999, Wedin 1999), there remain many challenges 

to building landscape planning models that include all of 

the important disturbance processes that influence change. 

The net, synergistic effects of various disturbances (e.g., 

drought, fire, insects, and management activities) across 

a large, ecologically diverse landscape are of particular 

interest to policymakers, scientists, land managers, and 

others. Our approach treats vegetation as discrete types 

and management activities and natural disturbance as 

transitions among those types to project the long-term 

net effects of alternative management scenarios across a 

large landscape, building on the work of Hann and others 

(1997) and Hemstrom and others (2004). Although we do 

not specifically include drought and other climatic effects, 

their impacts are manifest in our annual wildfire and insect 

probabilities.

Study Area

The study area consisted of about 276 000 ha in seven 

watersheds in the southern portion of the upper Deschutes 

subbasin (Figure 1). Vegetation ranged from low-elevation 

shrublands, meadows, ponderosa pine and lodgepole pine 

forest to high-elevation parkland and spruce-fir and mountain hemlock forests. Ownerships were mixed and include 

about 142 000 ha of Federal general forest, 24 000 ha of 

Federal late-successional forest reserves established by the 

Northwest Forest Plan (USDA and USDI 1994), 51 000 ha 

of wilderness and similar areas, and 59 000 ha of private 

lands.

Private lands constituted about 28 percent of the area 

(about 64 000 ha). These could be managed with a wide 

variety of treatments. For the purposes of this exercise, 

however, we assumed that private lands were a proxy for 

wildland-urban interface areas (WUI). WUI was an important stratification because fuel treatments were generally 

the highest priority management activity on private lands in 

this landscape. A consequence of our use of private lands as 

a surrogate for WUI was a potential overestimate of the rate 

of fuel treatments and an underestimate of other treatments 

on private lands.

Reserves were publicly owned lands (usually managed by the Forest Service or BLM) designated for special 

consideration and management (3 percent or about 7000 

ha). These were usually late-successional reserves under 

the Northwest Forest Plan (USDA and USDI 1994) or other 

similar areas. Under some conditions, they may be managed 

with thinning or other fuel-reduction treatments. However, 

they were generally designated to maintain old forest structure and similar conditions. Wilderness was legally designated land managed for natural characteristics and included 

wilderness, state parks, and similar areas (15 percent or 

about 34 000 ha). Only natural disturbances (wildfire and 

insect/disease activity) were modeled in wilderness.

We recognized eight vegetation types based on maps 

provided by the Deschutes National Forest, and, for gaps 

in those data, information gathered during the Interior 

Columbia Basin Ecosystem Management Project (Hann 

and others 1997). These ranged from the lowest elevation 

juniper (Juniperus occidentalis Hook.) woodlands to alpine 

parklands:

1. Juniper woodland—shrub steppe areas generally 

capable of supporting grass, shrubs, and juniper 

but not closed forest.

2. Dry ponderosa pine (Pinus ponderosa)—

areas capable of supporting ponderosa pine forests 

but generally not Douglas-fir (Pseudotsuga 

menziesii) or other tree species. These were 

transitional between forest and juniper woodland 

or shrub/steppe.

3. Mixed conifer dry—grand fir (Abies grandis) 

and Douglas-fir forests at lower elevations and 

in relatively dry environments. Historically, these 

areas consisted mostly of large, open, ponderosa 

pine stands maintained by frequent ground fire 

(average 10- to 20-year fire return interval).
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GENERAL TECHNICAL REPORT PNW-GTR-802

4. Mixed conifer moist—forests dominated by a 

variety of conifer species, including ponderosa 

pine, Douglas-fir, grand fir, sugar pine (Pinus 

lambertiana Dougl.), incense-cedar (Calocedrus 

decurrens (Torr.) Florin), western larch (Larix 

occidentalis Nutt.) and others. Under historical 

conditions, these somewhat wetter areas had less 

frequent natural fire than the dry mixed conifer type 

and were often dominated by large, widely spaced 

ponderosa pine.

5. Lodgepole pine (Pinus contorta) dry—lodgepole 

pine stands growing primarily on pumice soils. Soil 

and microsite conditions restricted other conifer 

species.

6. Upper montane cold—high-elevation forests dominated by Engelmann spruce (Picea engelmannii), 

mountain hemlock (Tsuga mertensiana), grand fir, 

subalpine fir (Abies lasiocarpa), lodgepole pine, and 

other species. This type occurred mostly within 

reserves or wilderness.

7. Upper montane moist—high-elevation forests that 

largely reflected westside climatic influences. Pacific 

silver fir (Abies amabilis), noble fir (Abies procera), 

Douglas-fir, and other species generally dominated.

8. Subalpine parkland—high-elevation mosaics of 

tree islands, alpine shrublands, and grasslands 

largely within reserves or wilderness.

Methods

We developed 337 combinations of vegetation structure 

classe (Table 1) and cover type (Table 2) to represent 

existing and potential future vegetation conditions. Cover 

types were based on the dominant species in the uppermost 

canopy layer and included several categories of developed 

land (e.g., urban, agriculture, etc.). Structure class depended 

on the size, tree density per unit area, and canopy layering 

for forests or on the dominant life form and canopy cover 

for shrublands and grasslands. Our structure classification 

was carefully designed to address important issues regarding wildlife habitats, fire and fuels, and various commercial 

forest products. Combinations of cover type and structure 

Table 1—Forest structure class definitions for the Five Buttes study area, 

central Oregon, U.S.A.

Tree canopy Overstory Dominant

Structure class layers canopy cover tree d.b.h.

Percentage cm

Grass forb None Tree <10, shrub <15 NA

Shrub None Tree <10, shrub >15 < 2.5

Seedlings/saplings 1 ≥ 10 ≥ 2 to 13

Pole tree – open 1 ≥ 10 to 40 ≥ 1 3 to 25

Pole tree – medium 1 ≥ 40 to 70 ≥ 13 to 25

Pole tree – closed 1 ≥ 70 ≥ 13 to 25

Small tree – open 1 ≥ 10 to 40 ≥ 25 to 38

Small tree – medium 1+ ≥ 40 to 70 ≥ 25 to 38

Small tree – closed 1+ ≥ 70 ≥ 25 to 38

Medium tree – open 1 ≥ 10 to 40 ≥ 38 to 51

Medium tree – medium 1+ ≥ 40 to 70 ≥ 38 to 51

Medium tree – closed 1+ ≥ 70 ≥ 38 to 51

Large tree – open 1 ≥ 10 to 40 ≥ 51 to 76

Large tree – medium 1+ ≥ 40 to 70 ≥ 51 to 76

Large tree – closed 1+ ≥ 70 ≥ 51 to 76

Very large tree – open 1 ≥ 10 to 40 ≥ 76

Very large tree – medium 1+ ≥ 40 to 70 ≥ 76

Very large tree – closed 1+ ≥ 70 ≥ 76
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

class within potential vegetation types formed the basic 

vegetation state-classes in our models.

Current vegetation data was developed using Gradient 

Nearest Neighbor (GNN) methods as described by Ohmann 

and Gregory (2002). This process imputed approximately 

1,600 inventory plots to 30-m pixels using a statistical 

relationship between LANDSAT-TM imagery and other 

geographic data and inventory plots. In general, GNN 

methods are best at predicting forest structure (e.g., diameter of dominant and codominant trees) but less accurate 

for canopy tree species (Ohmann and Gregory 2002). 

Correlation between predicted and observed quadratic 

mean diameter of dominant and codominant trees in the 

Oregon Coast Range was about 0.8, whereas that for tree 

species richness was about 0.53 (Ohmann and Gregory 

2002). Where GNN data were unavailable, we used vegetation composition and structure attributes from Oregon 

GAP (2006). Cover and structure data were summarized 

to state-classes within strata of watershed, ownership/land 

allocation, and potential vegetation type. These estimates of 

area by state-class by stratum were the initial conditions for 

our modeling process.

We used state-and-transition models to project the 

integrated effects of natural disturbances and management 

treatments on vegetation. Vegetation composition and 

structure within plant association strata defined each state. 

States were connected by transitions that indicated either 

the effect of successional vegetation development over time, 

or the effect of disturbance (Hemstrom and others 2004). 

This approach expanded transition matrix methods and 

represented vegetation development as a set of transition 

probabilities among various vegetative states (Cattelino 

and others 1979, Hann and others 1997, Horn 1975, Keane 

and others 1996, Laycock 1991, Noble and Slatyer 1980, 

Westoby and others 1989 ). For example, grass/forb communities might be dominated by closed forest following 

tree establishment over a period of time or might remain 

as grass/forb communities following wildfire. Alternatively, management activities or low-severity wildfire 

may generate more open forest conditions. State changes 

along the successional, time-dependent paths were usually 

deterministic, and, without disturbance or management, all 

the vegetation could ultimately accumulate in one state. Different management scenarios were developed to represent 

alternative landscape objectives and, hence, management 

treatments.

We developed and ran our models with the Vegetation Dynamics Development Tool (VDDT) (Beukema and 

others 2003). VDDT has been used in several landscape 

assessments and land management planning efforts in the 

Interior Northwestern United States (e.g., Hann and others 

1997, Keane and others 1996, Merzenich and others 2003) 

Table 2—Forest cover type classes used in the Five Buttes study area, central Oregon, U.S.A.

Cover type Dominant species

Not vegetated None – rock, water, ice, etc.

Developed land Variable – agriculture, suburban, urban, etc.

Grass/shrub Various grass, forb, and shrub species

Juniper Western juniper (Juniperus occidentalis Hook.)

Ponderosa pine Ponderosa pine (Pinus ponderosa P. & C. Lawson)

Douglas-fir/white fir Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) and white fir (Abies concolor (Gord. &

 Glend.) Lindl. ex Hildebr.)

Grand fir Grand fir (Abies grandis (Dougl. ex D. Don) Lindl.), Douglas-fir, and other conifers

Lodgepole pine Lodgepole pine (Pinus contorta Dougl. ex Loud.)

Pacific silver fir Pacific silver fir (Abies amabilis (Dougl. ex Loud.) Dougl. ex Forbes), noble fir (Abies procera

 Rehd.), and Douglas-fir

Mixed conifer Variable mixtures of white fir, Douglas-fir, Engelmann spruce (Picea engelmannii Parry 

 ex Engelm.), mountain hemlock (Tsuga mertensiana (Bong.) Carr.), and other conifers at 

 upper elevations

Subalpine parkland Mosaic of subalpine fir (Abies lasiocarpa (Hook.) Nutt.), mountain hemlock, and Engelmann 

 spruce at high elevations
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GENERAL TECHNICAL REPORT PNW-GTR-802

and elsewhere (Hann and Bunnell 2001, Merzenich and Frid 

2005). Although VDDT is a nonspatial model, managers 

and others often need to understand the spatial distribution 

of vegetation conditions and disturbances. Consequently, 

we ran models using strata of land ownership and allocation 

and potential vegetation types within watersheds so that 

we could display results about the spatial distribution of 

landscape characteristics without implying pixel or standlevel accuracy. All scenarios were run for 200 years with 

30 Monte Carlo simulations to allow the occurrence of rare 

events and generate estimates of long-term disturbance 

variability and forest development trends. We compared 

decadal average area treated with different treatments and 

disturbances across our three scenarios to examine trends 

that would have been more difficult to visualize in highly 

variable annual outputs. Average annual area in various 

forest types, however, was not as variable and was displayed 

on a yearly basis.

Forest Growth and Management Treatments

Our models include a set of assumptions and definitions that 

form the basis of transition rates and directions. In general, 

transition rates and directions were developed from a combination of inventory data and the Forest Vegetation Simulator (FVS) (Dixon 2002), the published literature, and, where 

necessary, expert opinion. The inventory data were tree 

lists from plots collected as part of the Forest Inventory and 

Analysis (FIA) (Barrett 2004) and Continuous Vegetation 

Survey (CVS) (Max and others 1996) inventories collected 

by the USDA Forest Service. There are over 1,600 inventory 

plots in the larger landscape study area. Each of these plots 

was assigned to one of our VDDT model state-classes, and 

FVS was used to project the rate and direction of growth 

transitions. We also modeled a set of management activities 

using FVS and the inventory data to estimate yield streams 

from management activities (Hemstrom and others 2006). 

We used a fixed set of silvicultural treatments to model 

our scenarios. The treatments we used were simplified in 

terms of timing, exact effects at the stand level, and other 

factors compared to the full suite of treatments that might 

be applied. However, based on discussions with local land 

managers and silviculturists, our treatments represent typical, commonly implemented kinds of activities that might 

occur on the various ownerships and allocations in the study 

area. Management treatments included:

1. Regeneration harvests on private lands only.

2. Salvage following stand-replacement wildfire or

insect outbreaks on Federal general forest and

 private land, but not in reserves or wilderness.

3. Tree planting in areas that had been regeneration 

harvested or salvaged.

4. Precommercial thinning from closed to open 

condition at age 15.

5. Commercial thinning of trees across all diameter 

classes to reduce stand density to open structure. 

We assumed that trees greater than 51 cm in 

diameter at breast height (d.b.h.) could be 

harvested on private lands but not on lands 

administered by the UDSA Forest Service, a 

reflection of current management policy.

6. Partial harvest was commercial thinning from 

below in closed stands to reduce stand density, 

favor fire-resistant tree species (e.g., ponderosa 

pine) and increase average tree diameter.

7. Mechanical treatments to reduce fuels were 

applied to closed stands beyond the age of 

precommercial thinning. Closed stands were 

converted to open, low-density conditions.

8. Prescribed fire was underburning applied to 

low-density stands of fire-tolerant species (e.g., 

ponderosa pine) to maintain open stands of firetolerant tree species. We assumed a small portion 

of these inadvertently became mixed or highseverity fires.

For the purposes of reporting, we combined regeneration, partial harvest, and commercial thinnings into a 

commercial harvest category that might produce enough 

saw-log-sized material to be of commercial interest. We 

combined precommercial thinning and mechanical fuel 

treatment into noncommercial harvest because the majority 

of material available from treatment would likely be too 

small to be used for sawtimber.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Wildfire

We used wildfire probabilities for historical or reference conditions from the interagency LANDFIRE effort 

(LANDFIRE 2006). Reference conditions were assumed 

by LANDFIRE (2006) to be the disturbance and vegetation 

characteristics that existed over a long period of time prior 

to about 1850 and, consequently, prior to wildfire suppression (Table 3). Current wildfire probabilities were estimated 

from discussions with local fire managers and other experts. 

In keeping with the estimates provided by LANDFIRE 

(2006), we split wildfire into three severity levels, depending on the degree of mortality in aboveground vegetation: 

(1) 0- to 25-percent mortality was low severity, (2) 25- to 

75-percent mortality was mixed severity, and (3) more than 

75-percent mortality was high severity. We recognized 

that wildfire probabilities, and those of insect outbreaks, 

reflected larger externalities such as regional drought and 

Table 3—Average wildfire return intervals under reference conditions (prior to 1850) for potential 

vegetation groups in the Five Buttes study area, central Oregon, U.S.A. (from LANDFIRE 2006)

Average

Potential Fire severity Average annual fire 

vegetation type class fire interval probability LANDFIRE rapid assessment model

Years

Juniper Replacement 1000 0.001 R#JUPIse Western Juniper Pumice

Mixed 500 0.002

Surface NA

All 333 0.003

Ponderosa pine dry Replacement 125 0.008 R#PIPOm Dry Ponderosa Pine - Mesic

Mixed 50 0.02

Surface 8 0.125

All 7 0.153

Mixed conifer dry Replacement 115 0.0087 R#MCONdy Mixed Conifer - Eastside Dry

Mixed 75 0.0133

Surface 25 0.04

All 16 0.062

Mixed conifer moist Replacement 200 0.005 R#MCONms Mixed Conifer - Eastside Mesic

Mixed 150 0.0067

Surface 400 0.0025

All 71 0.0142

Lodgepole pine dry Replacement 125 0.008 R#PICOpu Lodgepole Pine - Pumice Soils

Mixed 450 0.0022

Surface NA

All 98 0.0102

Upper montane cold Replacement 185 0.00541 R#ABLA Subalpine Fir

Mixed 800 0.0013

Surface NA

All 150 0.0067

Upper montane moist Replacement 500 0.002 R#ABAMup Pacific Silver Fir--High Elevation

Mixed 1100 0.0009

Surface NA

All 344 0.0029

Subalpine parkland Replacement 350 0.0029 R#ALME Alpine and Subalpine Meadows and 

Mixed 750 0.0013 Grasslands

Surface NA

All 239 0.00420
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GENERAL TECHNICAL REPORT PNW-GTR-802

ignition patterns but did not explicitly include drought 

effects.

We used random streams of fire years to model yearto-year variability in area burned owing to the effects of 

weather, fuel conditions, and chance. For example, regional 

climate may produce a series of dry years with abundant 

lightning ignitions. In this case, wildfire probabilities would 

be substantially higher compared to average years. Our 

modeling process included randomly drawn sequences of 

normal, high, and extreme wildfire years to simulate annual 

variability in natural disturbances. We also assumed that 

wildfire variability changes with landscape scale, being 

higher in small areas (e.g., watersheds of 14 000 to 90 000 

ha) and lower in large areas (e.g., the Deschutes subbasin of 

over 800 000 ha). The LANDFIRE (2006) wildfire probabilities were developed for very large landscapes with, 

consequentially, relatively low annual variability. After 

discussions with local fire experts and examination of the 

relatively few data available, we assumed that 80 percent 

of years experience normal or average amounts of wildfire, 

15 percent experience high amounts of wildfire, and 5 

percent experience extreme amounts of wildfire at the scale 

of the entire upper Deschutes subbasin. Essentially, for 

every hectare burned in a normal year, 16 ha burned in a 

high year, and 200 ha burned in a severe year. At the scale 

of watersheds, however, we assumed that even in severe 

years, most wildfire would occur in large fires that impact 

only a few watersheds but burn most of the area within 

affected watersheds. We assumed that 95 percent of years 

produced normal amounts of wildfire, 4 percent produced 

high amounts, and 1 percent produced extreme amounts at 

the watershed scale (Table 4). In essence, for every hectare 

burned in a normal year at the watershed scale, 65 ha 

burned in a high year, and 810 ha burned in a severe year.

Wildfire probabilities were set by vegetation cover, 

structure, and potential vegetation type. However, wildfire 

probability is not just a function of the vegetation conditions in a single state class. We used landscape condition 

feedback to increase overall wildfire probabilities when the 

landscape contained abundant dense forests and to decrease 

them when overall forest conditions were more open. We 

assumed that abundant dense forests meant highly contiguous fuels and increased landscape susceptibility to large, 

difficult-to-suppress wildfires.

Management Scenarios

In 2005, we held meetings in Bend and Klamath Falls, 

Oregon, to develop a set of management scenarios. Local 

members of the public and representatives from government land management agencies were invited to help us 

develop reasonable alternatives that might address differing 

perspectives about how Federal lands in the area might be 

managed. We used the results of these meetings to design 

four management scenarios for modeling. Because fuel 

and fire hazards in WUI were an overriding concern, fuel 

treatments in WUI described for scenario one were included 

in all three scenarios.

Scenario 1—

Active Fuel Treatment in Wildland/Urban Interface, 

No Management on Federal Lands

The primary emphasis was to actively treat fuels on private 

land (WUI). At least 25 percent of the dry lodgepole pine 

area was treated with partial harvests, precommercial 

thinning, mechanical treatment, or commercial thinning per 

decade. We assumed that mechanical treatments to maintain 

reduced fuel levels would be used on private lands, rather 

than prescribed fire. The long-term objective was to maintain the level of medium and dense stands on private lands 

at less than 10 percent of the total. No treatments other than 

continued fire suppression occurred on public lands.

Table 4—Fire-year sequences in the entire upper 

Deschutes subbasin (approximately 800,000 ha) and 

for individual watersheds (approximately 50,000 ha).

Analysis Size Fire-year type

area Normal High Severe

Upper Fire-year 80% 15% 5%

 Deschutes frequency

 subbasin Area multiplier 1 40 500

Huc5 Fire-year 95% 4% 1%

 watersheds frequency

Area multiplier 1 160 2000
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Scenario 2—

Active Fuel Treatment in Wildland/Urban Interface, 

Maximize Multistory Large Tree Forests on Federal 

Lands

Federal lands were managed to produce large trees and 

increase habitat for wildlife species associated with multistoried stands that contained many large and very large 

trees (more than 51 cm in d.b.h.). With fire suppression, 

an understory tree layer will develop naturally on most 

forested environments in the study area. In these areas, 

early management activities included thinning prescriptions 

to create and maintain open stands of fire-tolerant tree species that could grow to a large size relatively quickly while 

reducing risk of loss to high-severity wildfire. In addition, stands dominated by large and very large trees were 

infrequently thinned from below to reduce stand density 

while retaining some smaller trees. Our treatment regime 

on federally managed general forest lands included:

1. Precommercial thinning of all stands at age 15.

2. Treating 5 percent of high-density and 2.5 

percent of medium- and high-density stands in 

ponderosa pine, mixed conifer dry, and mixed 

conifer moist environments each year after the 

initial precommercial thinning to maintain open 

conditions until trees reached large size. After 

stands reached large-tree size, thinning ceased 

to allow development of understory trees until 

the stands became very-large-tree sized.

3. Lightly thinning from below in dense stands of 

very large trees in mixed conifer dry and mixed 

conifer moist types at an annual rate of 10 

percent to reduce fire and insect losses while 

maintaining most of the multistory structure.

4. Alternately thinning and underburning open 

stands of smaller trees in ponderosa pine dry, 

mixed conifer dry, and mixed conifer moist 

types to reduce fuels.

5. Mechanically thinning lodgepole pine dry 

stands at a rate of 4 percent annually.

6. Salvaging dead wood in 25 percent of the stands 

that had experienced wildfire and insect outbreaks.

7. Treating reserves at one-half these rates and 

wilderness not at all.

Scenario 3—

Active Fuel Treatment in Wildland/Urban Interface, 

Move Federal General Forest Lands Toward Historical 

Conditions

Federal general forest lands outside wilderness were managed to reduce fuels and high-severity wildfire risks while 

moving forests toward historical conditions, i.e., conditions 

assumed to be typical prior to about 1850. Management 

in reserves was designed to reduce stand density and fuel 

levels while maintaining large and very large trees in generally open forest conditions.

We used the reference condition VDDT models 

developed by LANDFIRE Rapid Assessment (LANDFIRE 

2006) as a basis for historical disturbance regimes, including wildfire return intervals and insect outbreaks on Federal 

general forest lands. We added state classes to the reference 

condition models to reflect the variety of structural conditions required by our issues, but retained the overall wildfire 

return intervals by fire-severity class (Table 3). We used a 

variety of treatments to mimic the reference disturbance 

regimes and favor single-storied forests of fire-tolerant 

conifers, especially in the drier potential vegetation types. 

We applied prescribed fire to mimic historical wildfire 

Figure 2—Percentage of the landscape in multistory large and 

very large tree structure classes by scenario in the Five Buttes 

area, central Oregon, U.S.A.
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GENERAL TECHNICAL REPORT PNW-GTR-802

frequencies on Federal general forest lands. Reserves were 

treated with half the intensity of Federal general forests 

because the late-successional reserves are intended to 

provide more abundant large- and very-large-tree multistory 

forest habitat than Federal general forest lands. Wilderness 

areas were not treated except with wildfire suppression.

 Multistory large- and very-large-tree forests declined 

over the 200-year simulation period under scenario one 

owing to a combination of wildfire and insect outbreaks 

(Figure 2). This suggests, based on our modeling assumptions, that passive management on Federal lands in the 

study area might produce no more than about the current 

abundance of large- and very-large-tree forests in the study 

area and that those forest conditions might decline on a 

long-term basis.

Single-story large- and very-large-tree forests remained 

relatively constant at about 2 percent of the landscape area 

under scenario 1, on average, over 200 years (Figure 3). 

The relatively low levels of single-story large- and verylarge-tree forests that did occur resulted from an uncommon 

coincidence of slow regeneration of small trees and random 

low- or moderate-severity wildfire.

High-severity wildfires burned more landscape 

area under scenario 1 than scenarios 2 and 3 (Figure 4). 

High-severity wildfire was proportionately greatest in 

WUI areas dominated by grass, forb, and shrub communities. Although these communities are highly susceptible 

to wildfires that kill most of the aboveground vegetation, 

wildfires in such vegetation are much more easily controlled 

than those burning in dense forests. The other ownership/

allocation categories were largely forested throughout our 

Figure 3—Percentage of the landscape in single-story very large 

and large tree structure classes by scenario in the Five Buttes area, 

central Oregon, U.S.A.

 Figure 4—Percentage change in average decadal area affected 

by high-severity wildfire under scenarios 2 and 3 compared to 

scenario 1 in the Five Buttes study area, central Oregon, U.S.A.

Figure 5—Average decadal area treated with mechanical fuel 

treatments and thinnings by scenario in the Five Buttes area, 

central Oregon, U.S.A.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

developing multistory large-tree forests on Federal general 

forests could be formulated and might be more successful than our scenario 2. This also suggests, at least given 

assumptions in our models, that current levels of multistory 

large- and very-large-tree forests in the study area are 

perhaps an artifact of fire suppression and other factors and 

may not be sustainable in the study area over the long run.

Single-story large- and very-large-tree forests substantially increased across the entire landscape under scenario 2 

(Figure 3). Much of the increase occurred in Federal general 

forest, and WUI was due to thinning to produce large trees 

quickly in scenario 2 and very active fuel treatments that 

produced open stands in scenario 3. Single-story largeand very-large-tree forests remained at very low levels in 

reserves and in the wilderness.

Scenario 2 produced lower levels of high-severity 

wildfire compared to scenario 1 (Figure 4). Even though 

fuel treatments were not extensive in scenario 2, some did 

occur on Federal general forests and in reserves to foster 

early development of large trees. As a result, the area 

burned in high-severity wildfires was, on average, about 20 

percent to 30 percent less than in scenario 1, especially after 

the first two decades. As in scenario 1, the highest proportion of high-severity wildfire in scenario 2 was in open 

simulations. High-severity wildfire affected Federal general 

forests somewhat more than other ownership/allocation 

classes because Federal general forests were mostly in lower 

elevation, drier environments subject to higher fire probabilities.

Mechanical fuel treatments and stand thinnings only 

occurred on private lands under scenario 1 and remained 

below 1000 ha treated per decade (Figure 5). Likewise, prescribed fire treatments only occurred on Federal lands and 

were absent under scenario 1 (Figure 6). Treatments that 

might produce at least some commercial timber products 

averaged less than 10 000 ha per decade under scenario 1 

and slowly declined to about 5000 ha per decade in the last 

ten decades (Figure 7).

Scenario 2 produced moderate amounts of multistory 

large- and very-large-tree forests (Figure 2). Contrary to 

our design objectives, scenario 2 did not increase multistory 

large tree forest by much compared to current conditions. 

In fact, scenario 2 produced lower amounts of multistory 

large- and very-large-tree forest than scenario 1 for the first 

100 years. Both scenarios 1 and 2 simulations produced, 

on average, about half the current amount of multistory 

large-tree and very-large-tree forest at the end of 200 

years. Perhaps alternative approaches to protecting and 

Figure 6—Average decadal area treated with prescribed fire by 

scenario in the Five Buttes area, central Oregon, U.S.A.

Figure 7—Average decadal area treated with management activities that may produce commercial timber products by scenario in 

the Five Buttes study area, central Oregon, U.S.A.
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GENERAL TECHNICAL REPORT PNW-GTR-802

forests dominated by grass/forb/shrub communities in WUI. 

High-severity wildfires are relatively easy to control in open 

grass/forb/shrub communities compared to high-severity 

wildfire in dense forest. From a wildfire protection perspective, active fuel treatment changed potential fire behavior 

rather than eliminating wildfire. Federal general forests 

experienced considerably lower amounts of high-severity 

wildfire compared to scenario 1, owing to fuel treatment 

effects, but amounts in reserves and wilderness were similar 

to those under scenario 1.

Management activity levels were higher in scenario 2 

than in scenario 1 due to thinnings to promote large tree 

development in Federal general forests. Scenario 2 produced about 25 000 ha of commercial treatment activities 

per decade over 20 decades (Figure 7). Mechanical fuel 

treatment rates declined slightly after the first decade, then 

varied over the remaining 19 decades. Mechanical fuels and 

thinning were highest in the first decade as the initial round 

of mechanical fuel treatments peaked (Figure 5). Prescribed 

fire rose to about 10 000 ha per decade as fire replaced 

mechanical fuel treatment for fuel reduction, then remained 

at relatively stable levels (Figure 6).

Scenario 3 produced the lowest overall abundance of 

multistory large- and very-large-tree forests (Figure 2). 

Multistory large- and very-large-tree forests declined from 

about 13 percent of the study area to a minimum of about 

4 percent at the end of the simulation. Much of the decline 

occurred in the first 100 years as dense forests burned or 

were killed by insect outbreaks. Initial declines on Federal 

general forests were due to thinnings designed to quickly 

move dense forests to more open conditions followed by 

thinnings and fuel treatments at maintenance levels.

Conversely, scenario 3 produced abundant single-story 

large- and very-large-tree forests (Figure 3). Single-story 

large- and very-large-tree forests initially occupied less 

than 5 percent of the study area, but increased fourfold to 

over 20 percent by the end of the simulation. Increases were 

nearly greatest on Federal general forest lands due to active 

thinning and fuel treatment. Smaller increases occurred in 

WUI and reserves that were treated at lower rates. Though 

the trend was flattening after 200 years, single-story largeand very-large-tree forests were still increasing across the 

landscape as a whole. Landscape levels (about 20 percent) 

at 200 years were lower than those estimated by Hann and 

others (1997) for historical conditions in the southern Cascades area in Oregon and Washington (about 57 percent).

Scenario 3 also produced the lowest overall rates 

of high-severity wildfire (Figure 4). After the first five 

Figure 8—Variation in amounts of multi- and single-story large- and very-large-tree forests under scenario 2 for 30 Monte Carlo simulations in the Five Buttes area, central Oregon, U.S.A. Upper and lower lines are plus and minus one standard deviation from the mean.

A B
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

decades, the proportion of area burned in high-severity 

wildfires was generally 30 percent or more below that in 

scenario 1 and 5 percent to 10 percent lower than that in 

scenario 2. WUI areas experienced the highest proportion 

of high-severity wildfire, again in grass/forb/shrub-dominated open forests where wildfire is most easily controlled. 

Of the other three ownership/allocation classes, wilderness 

areas were most highly impacted by high-severity wildfires, 

in contrast to scenarios 1 and 2. This resulted from fuel 

treatments that reduced wildfire outside wilderness. Federal 

general forests, on the other hand, experienced lower levels 

of high-severity wildfire compared to both scenarios 1 and 

2 owing to fuel treatment effects.

Scenario 3 produced about 35 000 ha of commercial 

timber harvest per decade (Figure 7). Mechanical fuel 

treatments and thinnings occurred on about 9000 ha in the 

first decade, then varied between 6000 and 7000 ha per 

decade after that (Figure 5). Not surprisingly, given the 

emphasis on reducing fire risks and generating open forests 

on Federal general forests, scenario 3 produced the high 

levels of prescribed fire (Figure 6). The initial ramp-up in 

prescribed fire took place over the first four decades after 

initial mechanical fuel treatments reduced fuel levels so that 

prescribed fire could be used for subsequent fuel treatments.

Variability

Results for several important landscape characteristics were 

highly variable over 30 Monte Carlo simulations in our 

study area. For example, whereas the multistory large- and 

very-large-tree forests under scenario 2 averaged about 

8 percent of the landscape area at year 100, one standard 

deviation above and below the mean ranged from about 12 

percent to less than 4 percent of the landscape area (Figure 

8a). The same scenario produced lower variability for 

single-story large- and very large-tree-forests (Figure 8b). 

In this case, the mean at 200 years was about 16 percent and 

the standard deviation, plus or minus 2 percent. In our study 

area, and given the assumptions in our model, multistory 

large- and very-large-tree forests seem to be potentially 

less abundant and subject to more variability than singlestory large- and very-large-tree forests, even in a scenario 

designed to increase multistory large- and very-large-tree 

forests.

Variability patterns for large- and very-large-tree 

forests under scenario 3 were similar to those in scenario 

2 (Figures 9a and 9b). As one might expect given scenario 

3 objectives, multistory large- and very-large-tree forests 

were much less abundant than single-story large- and 

very-large-tree forests overall. Single-story large- and 

Figure 9—Variation in amounts of multi- and single-story large- and very-large-tree forests under scenario 3 for 30 Monte Carlo simulations in the Five Buttes area, central Oregon, U.S.A. Upper and lower lines are plus and minus one standard deviation from the mean.

A B
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GENERAL TECHNICAL REPORT PNW-GTR-802

very-large-tree forests increased steadily to an average of 

about 22 percent of the landscape area with relatively narrow variation. Large- and very-large-tree multistory forests 

steadily declined from current conditions to less than 6 

percent of the landscape area by the end of the simulations. 

Our interpretation, based on our modeling assumptions, 

was that single-story large- and very-large-tree forests were 

relatively stable and might be sustained at high abundance 

for many decades in much of the study area given fuel 

and thinning treatments, as others have suggested for 

similar environments (e.g., Agee 2003, Hann and others 

1997, Hessburg and Agee 2003). In addition, managing 

to increase multistory large- and very-large-tree forests 

in this landscape might not succeed, and future variation 

Figure 10—One randomly selected example simulation run showing amounts of multi- and single-story large- and very-large-tree forests 

under scenario 2 in the Five Buttes area, central Oregon, U.S.A.

Figure 11—Variation in amounts of multistory large- and very-large-tree forests under Scenario 3 in the Five Buttes area, central Oregon, 

U.S.A.

A B

A B
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

might produce very small amounts even with management 

designed to increase them.

In fact, none of the individual simulation runs that 

make up the 30 Monte Carlo set for multistory large- and 

very-large-tree forests under scenarios 2 or 3 looked 

anything like the mean trend (Figures 10a, 10b, 11a, 11b). In 

these examples, multistory large- and very-large-tree forests 

experience occasional crashes during a sequence of years 

with abundant high-severity wildfire or insect outbreaks. 

Simulated patterns, however, suggest that multistory largetree forests may be subject to boom-and-bust abundance in 

the study area. Single-story large-tree forests also experienced occasional sharp drops, but to a lesser degree, and 

recovery was quicker. Judging from patterns in individual 

simulations and variation in many simulations, single-story 

large- and very-large-tree forest structures were the most 

stable older forest structure in general Federal forests in the 

study area.

Conclusions

Our model results may indicate some interesting landscape 

hypotheses in this and similar areas:

1. Fuel treatments in WUI may shift wildfire behavior 

as fires burn in grass, shrubs, and open forests, but 

not reduce overall wildfire probability. However, 

shift in behavior could be important because 

wildfires in grass, shrub, and open forest fuels are 

easier to control than those in closed, dense forests.

2. Efforts to increase multistory, dense forest 

habitats in these drier environments for particular 

wildlife habitats may prove difficult because 

increased wildfire and insect outbreak probabilities 

might offset gains from silvicultural manipulation. 

In our simulations, multistory large-tree forests 

didn’t exceed about 15 percent of the landscape on 

average, and amounts declined from current 

conditions. Most individual model runs in our 

Monte Carlo set experienced boom and bust 

conditions such that this forest type occasionally 

crashed to less than 5 percent of the landscape area.

3. Scenario 3, which moved Federal general forests 

toward historical conditions, generated the most stable 

landscape conditions, but individual simulations 

still produced occasional sharp declines in largetree forests because of severe wildfire years or insect 

outbreaks.

The models we used and the assumptions they embody 

reflect how we think the landscape disturbance and management processes might work to control landscape characteristics in the study area. Our models were based on expert 

opinion, the existing literature, and calibration by finer 

scale, stand-level silvicultural models. Calibration of annual 

wildfire year and insect outbreak sequences with historical 

drought and other climatic influences with empirical data 

from other sources (e.g., dendroclimatology) is an area 

where future model improvements could be made. Stand 

treatment prescriptions we used need to be tested in the field 

to determine whether desired outcomes are achieved. In 

addition, processes in the future may produce results much 

different than our estimates because: (1) odd or unusual 

events could occur, (2) we may not understand the system 

sufficiently well, (3) there may be some undetected logical 

error in our models, (4) climate change may alter fire, 

insects/disease, and other disturbances, and (5) management 

direction may change.

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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Moderate-Resolution Data and Gradient Nearest Neighbor 

Imputation for Regional-National Risk Assessment

Kenneth B. Pierce, Jr.; C. Kenneth Brewer; 

and Janet L. Ohmann 

Kenneth B. Pierce, Jr., research ecologist, USDA Forest 

Service, Forestry Sciences Lab, Corvallis, OR 97331 (now 

Department of Fish and Wildlife, Olympia, WA); C. 

Kenneth Brewer, IAAA Program Leader, USDA Forest 

Service, Remote Sensing Applications Center, Salt Lake 

City, UT 84119; and Janet L. Ohmann, research ecologist, 

USDA Forest Service, Forestry Sciences Lab, Corvallis, 

OR 97331.

Abstract

This study was designed to test the feasibility of combining 

a method designed to populate pixels with inventory plot 

data at the 30-m scale with a new national predictor data 

set. The new national predictor data set was developed by 

the USDA Forest Service Remote Sensing Applications 

Center (hereafter RSAC) at the 250-m scale. Gradient 

Nearest Neighbor (GNN) imputation was designed by the 

USDA Forest Service Pacific Northwest Research Station 

(hereafter PNW) to assign a plot identifier, and, therefore, 

a link to associated plot data, to each pixel within a target 

raster. Gradient Nearest Neighbor was implemented at 30-m 

resolution in three separate multimillion-hectare regions of 

the Western United States. Concurrently, RSAC developed 

a set of spatial predictor surfaces at 250-m resolution for 

use in producing nationally consistent data products. These 

data have been used for modeling forest types and forest 

biomass for the conterminous United States and Alaska. 

These predictor data have also been used for large regional 

applications.

In this study, we substituted the 250-m predictor data 

for the 30-m predictor data used thus far in GNN. Our 

objective was to quantify the difference in performance 

using the lower spatial resolution predictors. We remodeled 

the same three regions that were mapped at 30 m with the 

250-m data set and compared the error structure of the two 

modeling efforts. For species presence/absence models in 

the two areas with large environmental gradients, the Sierra 

Nevada and northeastern Washington, the species models 

performed substantially the same at the two resolutions. 

For the region with reduced environmental heterogeneity 

and moderate environmental gradients, coastal Oregon, species models did not work well with either the 30-m or 250-m 

studies. Models geared towards mapping forest structure 

did not perform as well as the 30-m models and may be 

insufficient for risk-assessment use.

Keywords: Gradient Nearest Neighbor, imputation, 

regional analysis, species distributions, vegetation mapping.

Introduction

A great wealth of resources has been expended to inventory 

our Nation’s forests, and an equally substantial amount of 

effort has gone into acquiring remotely sensed data. As 

such, these two data types make up the ends of a continuum 

of detail. Plot inventory data are extremely sparse geographically but have a high level of information content regarding 

the resources at the inventory plot locations. Conversely, 

remotely sensed data cover the entire globe but with comparatively limited information at any single location. The 

common approach to leverage these two forms of data has 

been to create thematic maps of vegetation-related classes 

as well as response surfaces for other target variables of 

interest. With regard to vegetation mapping, these thematic 

maps typically describe dominant vegetation and include 

physiognomic, floristic, or structural characteristics, or all. 

These thematic maps often include some additional land use 

classes or map land use as a separate theme. The variables 

available for analysis are limited to the classes included in 

the map, and once the analysis is complete there is no ability 

to develop new attributes without a new mapping effort. 

Recently, a more flexible approach, single neighbor imputation, has been utilized to provide sample tree lists and plotcalculated variables for all the unsampled pixels in raster 

maps. Although not replacing traditional mapping methods, 

imputed maps can greatly enhance analytical flexibility 

and provide information in a familiar context that is often 

supported by extensive simulation modeling capability. 

Imputed maps are not intended to suggest that each pixel is 

in fact occupied by the imputed plot data, but rather given 
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current information, what do we expect. However, developing and mapping 30-m products over broad spatial extents 

is a lengthy process. This project was conducted in order 

to evaluate the differences in using a new national spatial 

predictor database at a coarser resolution (250 m) instead of 

the 30-m data used in the previous study.

Existing Methods for Regional-National 

Vegetation Mapping and Risk Assessment

Traditional Methods—

In recent years, many regions within the USDA Forest 

Service have implemented midlevel classification and 

mapping programs to provide thematic maps of existing 

vegetation for a wide variety of analysis applications. These 

programs are becoming more similar as they implement the 

USDA Forest Service direction established by the Existing 

Vegetation Classification and Mapping Technical Guide

(hereafter technical guide) (Brohman and Bryant 2005). 

The vegetation classifications and mapping methodologies 

of these programs follow the technical guide’s midlevel 

direction (Brewer and others 2005), and most use satellite 

remote sensing approaches to provide synoptic coverage of 

the mapping areas (e.g., Mellin and others 2004). Some of 

these programs also utilize summary databases populated 

by the USDA Forest Service Forest Inventory and Analysis 

(hereafter FIA) data to develop quantitative map unit 

descriptions including estimates of common inventory 

variables. This approach provides thematic map products 

with statistically sound estimates of inventory variables. 

These estimates are explicitly connected to the vegetation pattern depicted in the thematic map products. The 

approach is designed to support midlevel and broad-level 

analysis applications, as well as some project-level cumulative effects analyses.

As suggested in the technical guide, these midlevel 

map products can be used as is or rescaled for a variety of 

base-level analysis applications including project support, 

risk analysis, and watershed analyis at the scale of 4th and 

5

th Hydrologic Units, (USDA FS 1995). Unfortunately, the 

geographic extent of these base-level analysis applications is 

normally too small to effectively use FIA data as the inventory data source. Given the extensive design of the FIA 

program without spatial intensification, each plot represents 

approximately 6,000 acres. This leaves forests and ranger 

districts faced with the difficult choice of using the midlevel 

map product as is with an inadequate sample size of associated FIA data or reverting to the use of often biased and 

outdated stand-exam data that cannot provide defensible 

statistical estimates and have no explicit relationship to the 

midlevel map data used for forest plan revision and project 

cumulative effects analyses. Alternatives to this untenable 

choice include several expensive and logistically difficult 

inventory approaches including intensifying the base grid 

to provide an adequate sample size or implementing a new 

traditional two-stage sample of the map features depicting 

vegetation pattern.

Imputation Methods—

A primary information need of land managers is consistent 

and continuous current vegetation data on each and every 

parcel of land in an analysis area sufficient to address the 

principal issues and resource concerns. As discussed above, 

where these data do exist they are normally based on a sampling inference procedure rather than wall-to-wall inventory 

data. Many of the analyses needed to address multiple 

resource issues at the project level are essentially analyses 

of vegetation pattern and process relationships through time 

and space. Inventory data based on traditional two-stage 

sampling or quantitative map unit descriptions from a systematic random grid are not sufficient to address the spatial 

or the temporal dimensions or both of these analyses. These 

data are not spatially explicit enough to identify important 

vegetation pattern relationships and do not provide adequate 

thematic detail (i.e., plot-level tree list data) for simulating 

vegetation change through time.

The ability to simulate these vegetation pattern relationships through space and time, particularly with a variety 

of management and disturbance alternatives, is important 

for effective land and resource planning. Despite the 

capability of simulation models and decision-support tools, 

comprehensive landscape-level planning is still difficult to 

implement because the inventory data are rarely complete 

or current or both. For planning purposes, it would be 

convenient to be able to operate as if detailed inventory 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

stand data to unsampled stands. The second of these 

approaches, Gradient Nearest Neighbor (GNN) developed by Ohmann and Gregory (2002), follows the same 

general analytical logic, but is designed to use vegetation 

information from regional grids of field plots (similar to 

an intensified FIA grid) with remotely sensed imagery and 

other spatial data to produce a continuous raster surface by 

imputing data from sampled grid cells to unsampled grid 

cells.

Study Objectives

Our primary objective was to quantify the difference in 

GNN model performance using the lower spatial resolution 

predictors. We remodeled the same three regions that were 

mapped at 30 m with the 250-m data set and compared the 

error structure of the two modeling efforts. As explained 

below, two effects occur when 30-m data are replaced with 

250-m data, and both involve averaging across multiple 

pixels.

One of the reasons for implementing this study is that 

development of spatial products and modeling at 30 m could 

take several years to complete a large portion of the Western 

United States. We anticipate much faster turnaround if 

250-m modeling proves sufficient. The 250-m data products 

could potentially be available for large areas such as the 

Western United States in 1 to 2 years of production.

Methods

The Study Area

Three western regions covering temperate steppe, coastal 

forest, and Mediterranean ecosystems were mapped using 

GNN imputation for a Joint Fire Sciences Program study 

(Figure 1). The original study examined the feasibility of 

mapping wildland fuels and vegetation structure to provide 

data for fire and fuels management planning (Pierce and 

others 2009, Wimberly and others 2003). The 2.86-millionha coastal forest site was located in the coast range of 

Oregon extending as far inland as the western edge of the 

Willamette Valley (Figure 1). The forests are primarily 

coniferous with hardwoods occupying riparian and disturbed areas. The 4.1-million-ha Mediterranean site was 

located in the central Sierra Nevada occupied by savannah, 

information were available for all units in the planning area 

(Moeur and Stage 1995).

As an alternative to historically common statistical 

approaches (e.g., regression estimates or stratum averages) 

to populating unsampled units with data, imputation can be 

used. Imputation involves estimating values for variables 

of interest (Y variables) by supplying realistic measurements from one or more sampled units to unsampled 

units with similar characteristics in auxiliary (X) variable 

space (Hassani and others 2004, LeMay and Temesgen 

2005, McRoberts and others 2002, Moeur and Stage 1995, 

Ohmann and Gregory 2002, Temesgen and Gadow 2004). 

These auxiliary (X) variables typically include biophysical 

characteristics such as slope, aspect, precipitation, etc., as 

well as data from remotely sensed imagery such as aerial 

photography or satellite imagery.

Imputation of inventory data from sampled areas to 

similar unsampled areas produces data sets that function 

like wall-to-wall data for planning purposes. There are 

many methods and variations of imputation, both univariate and multivariate; however, multivariate approaches 

that impute a single plot tend to produce more realistic 

data sets for simulation modeling because they retain the 

original covariance structure of actual sample units. LeMay 

and Temesgen (2005) provided a brief summary of common imputation approaches and a detailed comparison of 

variable-space nearest neighbor (NN) methods for estimating basal area and stems per hectare using aerial auxiliary 

variables. LeMay and Temesgen (2005) also summarized 

variable-space nearest neighbor methods and compared 

them to other estimation methods. These summaries were 

the most comprehensive available in current literature when 

this paper was written.

In recent years, two modeling approaches have been 

developed that could potentially address this critical need 

through the imputation of inventory data. The first of these 

approaches, Most Similar Neighbor (MSN), was developed 

by Moeur and Stage (1995) to impute attributes measured 

on some sample units (e.g., stand polygons) to sample units 

where they were not measured. The MSN was originally 

designed to use a traditional two-stage inventory of forest 

stands, as described by Stage and Alley (1972), imputing 
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R5, and CVS plots were installed on systematic grids. The 

CVS/R6 and R5 plots covered the national forests whereas 

FIA installed plots on all ownerships. The FIA and CVS 

inventory plots used five subplot arrays within a 1-ha area. 

Small trees, snags, coarse woody debris line-intercept 

transects, and ground cover were sampled on each subplot.

Because vegetation data were derived from multiple 

inventories with different sampling protocols, all individual 

tree records were converted to per-hectare values. For plots 

with multiple vegetation or land cover conditions, only the 

forested portion was used with expansion factors adjusted 

accordingly. Plot-level summary variables were calculated 

for each plot.

Stand-summary variables included total basal area, 

basal area by species, trees per hectare, quadratic-mean 

diameter, snags per hectare, percentage of tree canopy 

cover, and down-wood volume. Different inventories collected down-wood data using different sampling schemes 

chaparral, mixed conifer, and alpine woodlands vegetation types. The site stretches from the northern border of 

Sequoia National Park north through the Plumas National 

Forest. The 5-million-ha temperate steppe in northeastern 

Washington was bounded on the west by the Cascade crest 

and on the south by the Columbia and Spokane Rivers 

(Figure 1). The temperate steppe site is dominated by a 

combination of mixed coniferous forest and extensive shrub 

steppe.

Vegetation Data from Field Plots

Vegetation data from regional inventories were derived in 

each of the three regions from multiple sources including 

Forest Inventory and Analysis (FIA) plots, Current Vegetation Survey/Pacific Northwest Region (R6) plots (CVS), 

Pacific Southwest Region (R5), Bureau Land Management 

(BLM), research Ecology Plots in North Cascades National 

Park (NCNP) (provided by Dave L. Peterson) and Yosemite 

National Park (provided by Jan Van Wagtendonk). The FIA, 

Figure 1—The Joint Fire Sciences project covered three western sites in 

contrasting ecosystems.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

and minimum sizes. As a result, we focused primarily on 

species basal area.

Moderate-Resolution Predictor Data

Beginning in 2003, RSAC, in cooperation with the FIA 

remote sensing band, developed a national predictors database to support FIA national mapping efforts (e.g., national 

forest type maps, and national biomass maps) (Blackard 

and others 2008, Ruefenacht and others 2008). The original 

database included about 60 layers consisting primarily of 

MODIS imagery. The national predictors database has been 

extensively used with additional data layers added each 

year. The current version has more than 700 layers, which 

includes DAYMET climate data, additional MODIS imagery, derived MODIS-based vegetation indices, STATSGO 

soil layers, topography, and several derived thematic products (Table 1). These data cover the entire conterminous 

United States as well as Alaska and Puerto Rico. 

To prepare these data for Gradient Nearest Neighbor 

analysis, all layers were sampled with all plot locations. 

With 30-m data, 13-pixel footprints were used to sample the 

spatial data to account for plots occupying more area than a 

single 30-m pixel. With 250-m data, only a single pixel was 

necessary to represent each plot because the plot area is less 

than the area of one 250-m pixel. All predictor images were 

split into separate bands, comprising over 700 individual 

predictor layers. For example, some STATSGO soil layers 

comprised 11 soil horizons. Those 11 horizons were split 

into separate predictor layers. Before statistical modeling, 

the 700 bands were reduced to those without a correlation of 

> 0.90 with any other remaining predictor. For each of the 

three study areas, this left about 100 to 150 predictor layers. 

These predictors were entered as the predictor matrix in a 

stepwise canonical correspondence analysis. In each case, 

10 to 15 predictors were sufficient to explain the primary 

gradients in vegetation composition.

Satellite Imagery

For the 30-m study, Landsat Thematic Mapper imagery 

(TM) was mosaiced and histogram matched. For the 250-m 

study, several products derived from MODIS imagery were 

used (Table 1). These products have a spatial extent covering our entire study regions, so no image matching was 

necessary.

Table 1—Comparison of spatial data between the 30-m and 250-m analyses

250-m resolution 30-m resolution (Pierce and others 2009)

Elevation, slope, and aspect Elevation, slope, and aspect

DAYMET shortwave radiation Potential Relative Radiation

Ecological region layers NA

USGS mapping zones, Bailey’s ecoregions, and unified NA

 ecoregions for Alaska

STATSGO data layers (lower 48) NA

Available water capacity, soil bulk density, soil permeability, 

 soil PH, soil porosity, soil plasticity, depth to bedrock, rock

 volume, soil types, and soil texture None

MODIS Vegetation indices such as EVI, NDVI Landsat TM band ratios, 3/4, 4/5, 5/7 

MODIS Vegetation continuous fields NA

MODIS fire points developed from the MODIS active fire maps NA

MODIS 8-day composites, multiple dates from the spring, TM-5 and ETM-7 data

 summer, and fall

NA Within footprint spectral variability

All MODIS 32-day composite imagery that is cloud-free NA

 between the years 2001–2003

USGS NLCD layers, percentage tree cover, percent herbaceous NA

 cover, and percentage bare ground

DAYMET temperature and precipitation, mins, maxs, aves, SD DAYMET temperature and precipitation, mins,

 (all, yearly, monthly) maxs, aves (all) 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Biophysical Environment Data

For both studies, biophysical data were derived from digital 

elevation models, including slope, aspect, and elevation. 

Both studies used climate data derived from DAYMET, 

although the 250-m data set used many more calculated 

variables. These variables included year-to-year variability 

by month. The DAYMET data are provided at 1-km resolution, so both the 30-m and 250-m DAYMET data were 

interpolated or resampled to their respective resolutions.

The 250-m data set also included STATSGO data 

layers, such as available water-holding capacity, soil bulk 

density, soil permeability, and soil pH. No analog for these 

data existed in the 30-m data set.

The Gradient Nearest Neighbor (GNN) Method of 

Predictive Vegetation Mapping

Imputation is a process in which values are assigned to 

unmeasured locations from either measured values or a 

statistical summary of a few selected measured values such 

as a mean (Moeur and Stage 1995). Unlike regression-based 

predictions, the assigned value is not a product of predictor variables and coefficients. The predictor variables are 

used to rank sample plots as to their similarity to a target 

location (30-m or 250-m-square pixel). In Gradient Nearest 

Neighbor imputation (GNN) we used the loadings for the 

ordination axes and their eigenvalues from a Canonical 

Correspondence Analysis (CCA) to relate the target locations to the locations of sample plots. This is achieved by 

calculating a Euclidean distance in eight-axis ordination 

space between the target pixel and each plot using the 

ordination loadings to weigh the spatial variables and the 

eigenvalues to weigh each axis. The distance in gradient 

space between the target location and each plot is used to 

rank the sample plots for potential assignment at each target 

pixel in our study regions (Ohmann and Gregory 2002). 

Because we use a multivariate response, which is analogous 

to a community representation of a plot, the variables 

selected must be of similar type. Our primary modeling 

scenarios have been species modeling, which involves using 

the total basal area of each individual species, and structure 

modeling, which has used basal area in different size classes 

of hardwoods and conifers, snag density, coarse woody 

debris volume, and canopy cover.

When GNN is run as a single neighbor imputation, the 

closest plot in gradient space is assigned to each pixel in the 

landscape. Once the assignment has been made, any attribute calculated for all plots can be mapped, maintaining the 

original covariance structure for any/all other attributes to 

be mapped. Additionally, the ranking of potential neighbors 

provides a sample neighborhood of similar plots from which 

natural variability and sample sufficiency can be evaluated 

(Pierce and others 2009).

Gradient Nearest Neighbor Results Summary 

from Previous Studies

In the 30-m study, coastal Oregon had favorable results 

for structure models but substandard results for species 

models. This pattern was reversed in both Washington and 

California, where species models worked well, but structure 

models did a poor job with attributes such as quadratic 

mean diameter and trees per hectare. The purpose of the 

original study was to map wildland fuels and vegetation 

structure. Wildland fuel components, such as coarse woody 

debris and snag density, were not mapped adequately in any 

of the three sites (However, canopy-related fuels variables 

were more satisfactory.) This was partially due to remote 

sensing not directly detecting wildland fuels, which, in 

general, are below the canopy. Another factor is that coarse 

woody debris data are collected on only a relatively short 

transect on FIA plots such that the resulting sample size 

is too small to characterize individual plots. The original 

design of the coarse woody debris sampling was to create 

estimates for a region.

We expect that species models will perform more 

favorably than structure models with 250-m data. In the 

previous study, remote sensing imagery was very important 

for mapping structure and has a much finer spatial grain 

than our climate data. Although we had climate data at 30 

m, it was interpolated from 1-km-resolution DAYMET data, 

which are interpolated from weather stations plus higher 

resolution covariates (elevation, topography, etc.). Therefore, climate data would change gradually over the course of 

a kilometer whereas TM data can change abruptly from one 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Results

Gradients in Species Composition

Daubenmire (1952) noted the axiomatic relationship 

between climate and vegetation. Our CCA modeling 

results are consistent with this observation and suggest 

that climatic variables, as well as topographic and edaphic 

variables indirectly related to temperature and moisture, 

strongly influence the patterns of species composition. The 

gradients described by the three CCA models were composed of the dominant patterns in temperature and precipitation. In Washington, elevation, precipitation frequency, 

and brightness in MODIS 8-day composites separated 

warmer drought-tolerant conifers from both high-elevation 

wet and dry forests. In California, species were separated 

by September growing degree days, September average air 

temperature, April cooling degree days, and water vapor 

pressure variability in July. In Oregon, the dominant environmental variables were August mean precipitation, June 

cooling degree days, soil permeability, and June standard 

deviation of water vapor pressure. 

Species Mapping Performance of GNN

Species performance from confusion matrices are listed for 

all species occurring with a frequency of at least 5 percent 

in the plot data set (Tables 2 and 3). California had the highest average Kappa statistics at 0.53 for the 30-m study and 

0.48 for the 250-m study (Table 2). Washington was second 

with 0.46 and 0.43 (Table 3), followed by Oregon with 0.32 

and 0.25 (Table 4). Patterns for producers and users accuracy were similar across sites, as were the actual values. In 

each case, the 30-m study had higher producers accuracy 

than the 250-m study by about 22 percent, whereas for users 

accuracy, the 250-m study was actually about 3 percent 

higher with an average across sites of 55 percent.

Structure Mapping Performance of GNN

To date we have only developed structure models for the 

Washington and Oregon study sites. Second nearest neighbor correlations for structure variables were generally low 

in both sites. In Washington, total basal area had an r-square 

of 0.06 compared to 0.17 for the 30-m analysis, 0.04 for 

snags per hectare compared to 0.16, and 0.01 for quadratic 

30-m pixel to the next. Therefore, our change in resolution 

loses much more information for predictions relying heavily 

on imagery than those relying on climate.

Model Evaluation and Accuracy Assessment

To evaluate the results of the 30-m study compared with the 

250-m study results, we used two primary approaches. For 

continuous variables, we calculated 2nd nearest neighbor 

correlations (analogous to r-squares from cross-validation), 

(see Ohmann and Gregory 2002). For discrete variables, 

such as species presence/absence, we used standard 

confusion matrices. Producers accuracy, users accuracy, 

and Kappa statistics were calculated for each species and 

compared for the two studies (Wilkie and Finn 1996). The 

Kappa statistic accounts for the probability of randomly 

assigning a plot to its correct class. As such, the random 

probability of assigning a species with a frequency of 0.9 

to any plot is very high; Kappa statistics tend to be low as 

the probability of improving upon randomness decreases. 

Producers accuracy is the proportion of sample plots with 

a species present in which the species is predicted to occur. 

Users accuracy is the proportion of plots with the predicted 

species occurrence that actually had the species in the 

inventory.

Any discrepancies derived from changing the scale of 

the analysis stems from two primary effects, which are both 

aspects of spatial averaging. First, with the 30-m product, 

the predicted value for a plot is the average of the 13 pixels 

imputed to the plot footprint. In this way, the predicted 

value is subject to averaging over those 13 imputations. 

With the 250-m imputation, only a single plot is imputed for 

the same ~1 ha space. Therefore, the 250-m results will be 

slightly more variable. The second effect involves the spatial 

averaging of the predictor data set. The predictor variables 

are the averages of the 13 pixels within the individual predictor layers. In addition, we calculate two texture indices 

for the remote sensing data, which provides an estimate 

of variability within the footprint. With the 250-m data, a 

single pixel covers an area considerably larger than a 1-ha 

plot (62 500 m2

 vs. 11 700 m2). Thus, the spectral values 

are averaged over a larger area, and we have no estimate 

of within-plot variability.
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GENERAL TECHNICAL REPORT PNW-GTR-802

mean diameter compared to an almost equally random 0.05 

for the 30-m analysis. In Oregon, where we had quite good 

results for structure with 30-m data, we mapped basal area 

with an r-square of 0.09 compared to 0.59 for the 30-m 

analysis, 0.03 for snags per hectare compared to 0.09, and 

0.08 for quadratic mean diameter compared to 0.69.

Discussion

Accuracy of GNN Vegetation Maps

For the Washington and California study sites, species 

distributions were modeled equally well with the 250-m 

data and the 30-m data. Both the Kappa statistics and visual 

inspection of species maps indicated essentially the same 

Table 3—Washington species presence/absence results, N = 763 plots for species of 

≥5 percent frequency on plots

 Kappa Producers Users

Species 30 m 250 m 30 m 250 m 30 m 250 m Plots

Pseudotsuga menziesii 0.43 0.43 0.97 0.89 0.85 0.86 1,835

Pinus ponderosa 0.53 0.52 0.91 0.74 0.65 0.69 971

Pinus contorta 0.32 0.41 0.82 0.63 0.50 0.61 827

Larix occidentalis 0.54 0.51 0.89 0.70 0.61 0.67 809

Abies lasiocarpa 0.52 0.50 0.87 0.65 0.58 0.64 707

Picea engelmannii 0.41 0.35 0.81 0.54 0.49 0.52 643

Abies grandis 0.57 0.55 0.87 0.68 0.58 0.64 587

Thuja plicata 0.53 0.51 0.82 0.61 0.51 0.58 393

Tsuga heterophylla 0.49 0.44 0.78 0.53 0.45 0.48 277

Pinus monticola 0.35 0.27 0.68 0.37 0.33 0.33 240

Abies amabilis 0.67 0.62 0.86 0.66 0.60 0.66 219

Tsuga mertensiana 0.58 0.49 0.81 0.52 0.50 0.52 179

Pinus albicaulis 0.55 0.47 0.77 0.50 0.47 0.51 152

Populus tremuloides 0.15 0.16 0.40 0.23 0.16 0.20 146

Betula papyrifera 0.29 0.26 0.51 0.30 0.25 0.29 119

Average 0.46 0.43 0.78 0.57 0.50 0.55

Table 2—California species presence/absence results, N = 1,835 plots for species of 

≥5 percent frequency on plots

 Kappa Producers Users 

Species 30 m 250 m 30 m 250 m 30 m 250 m Plots

Abies concolor 0.51 0.47 0.88 0.67 0.64 0.68 761

Calocedrus decurrens 0.51 0.52 0.86 0.69 0.61 0.67 643

Pinus ponderosa 0.56 0.52 0.88 0.70 0.63 0.66 632

Quercus kelloggii 0.56 0.48 0.86 0.61 0.59 0.61 510

Pinus jeffreyi 0.49 0.46 0.82 0.61 0.54 0.60 488

Pinus lambertiana 0.42 0.36 0.80 0.54 0.49 0.50 468

Abies magnifica 0.63 0.55 0.85 0.65 0.63 0.65 389

Pseudotsuga menziesii 0.59 0.52 0.86 0.61 0.58 0.62 370

Pinus contorta 0.60 0.42 0.84 0.59 0.58 0.48 340

Quercus chrysolepis 0.54 0.43 0.78 0.50 0.52 0.51 264

Pinus monticola 0.52 0.39 0.70 0.48 0.49 0.43 189

Quercus wislizeni 0.53 0.53 0.71 0.59 0.47 0.52 108

Juniperus occidentalis 0.40 0.42 0.64 0.50 0.34 0.42 102

Quercus douglasii 0.53 0.67 0.65 0.69 0.48 0.69 89

Average 0.53 0.48 0.79 0.60 0.54 0.57
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

pattern when moving from 30-m to 250-m data. Because 

the gradient models were largely composed of climate 

variables, there is actually little loss in predictor data 

information when using the 250-m data. This is because 

the climate data for both the 250-m and 30-m studies were 

interpolated from the same 1-km-resolution data. Species 

performance in Oregon was not as good, though it was 

also less accurate for the 30-m data. In both the 30-m and 

250-m studies, we saw some definite differences between 

the results for Oregon and the results for Washington and 

California. Both California and Washington are precipitation limited, receive most of their precipitation during 

winter, and have large elevation gradients. The Coast Range 

in Oregon has much higher precipitation, milder temperatures, and lower overall topographic variation resulting in 

less orographic precipitation. Coastal Oregon has also had 

a long history of timber management and, therefore, has a 

large patchwork of even-aged stands.

Sources of Error in GNN—

The GNN and all nearest neighbor techniques, are particularly susceptible to errors introduced by natural variability 

at spectrally and environmentally similar sites. Whereas 

regression techniques model a trend and the departure 

from that trend, imputation retains the full range of variability within a data set. As such, for a certain location, a 

regression model with little predictive capability will predict the mean plus some small departure based on predictor 

variables and coefficients, whereas imputation will find 

the most environmentally similar site and select it. The 

tendency for imputation to impute values similar to the 

actual target values is constrained by the strength of the 

relationship between available spatial predictor variables 

and the target response variables.

Other sources of error include (1) residual spatial error 

of predictor data sets and plot locations as well as plot 

registration, (2) temporal mismatches between inventory 

dates and imagery dates, and (3) the lack of adequate spatial 

data on disturbance and management history across large 

regions.

Advantages of GNN for Risk Assessment—

There are several advantages to GNN for risk assessment. 

The GNN technique retains the covariance structure for 

multiple attributes by imputing whole plots and provides 

mapped estimates of natural variability and sample 

sufficiency (Pierce and others 2009). Comparative risk 

assessment requires spatially explicit data with estimates 

of variability (Borchers 2005) in order to create probability 

surfaces for different management scenarios. For instance, 

what is the probability of the desired outcome given two 

different management choices, and are they statistically 

different? Without an estimation of uncertainty, this type 

of analysis can’t be performed. By using a set of multiple 

potential neighbors, the variability in potential neighbors 

for a selected attribute can be mapped. In addition, by 

using the frequency distribution of all interplot distances in 

gradient space, thresholds for closeness in gradient space 

can be assigned and the number of candidate plots within a 

threshold calculated. This gives an indication as to whether 

Table 4—Oregon species presence/absence results, N = 2,325 plots for species of ≥5 

percent frequency on plots

 Kappa Producers Users 

Species 30 m 250 m 30 m 250 m 30 m 250 m Plots

Pseudotsuga menziesii 0.22 0.19 0.97 0.95 0.91 0.91 684

Alnus rubra 0.32 0.18 0.89 0.70 0.67 0.66 440

Tsuga heterophylla 0.27 0.27 0.85 0.63 0.52 0.60 320

Acer macrophyllum 0.25 0.26 0.78 0.49 0.42 0.47 228

Thuja plicata 0.11 0.06 0.56 0.24 0.26 0.25 150

Picea sitchensis 0.52 0.42 0.83 0.50 0.50 0.53 128

Abies grandis (concolor) 0.39 0.29 0.63 0.34 0.34 0.33 52

Quercus garryana 0.47 0.33 0.62 0.32 0.43 0.38 40

Average 0.32 0.25 0.77 0.52 0.51 0.52
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GENERAL TECHNICAL REPORT PNW-GTR-802

or not the inventory can provide adequate information for 

a certain pixel, and, as such, a map depicting the sampling 

support can be created.

Species Response Models in Multispecies Mapping—

One of the key areas of interest in natural resource risk 

assessment is the interactions among species. The location 

of invasive species and the presence of host species are 

two data surfaces of interest to managers. Mapping with 

single neighbor imputation ensures that the assemblages of 

tree species mapped are consistent with actual inventoried 

assemblages. This has both benefits and limitations. The 

benefit is robust assemblages of species as they currently 

exist. The limitation is that prediction for new interactions 

can not be inferred on the basis of these maps. Singlespecies models are probably best suited for predicting suitable habitat for an individual species, or rather the present 

distribution of habitat consistent with currently occupied 

habitat.

Risk Assessment Applications of GNN Predictions—

Single-neighbor imputation using GNN provides a very 

flexible wall-to-wall data set that includes any variable 

that can be derived from those measured on all inventory 

plots. This includes the ability to derive new variables 

or vegetation classifications after the initial modeling. A 

GNN imputation also provides a link to the full tree lists 

allowing for almost any kind of ecological modeling. The 

inclusion of multiple neighbors provides uncertainty data 

for Monte Carlo simulations or analyses seeking to show 

the uncertainty associated with different scenarios. As new 

risks or identification of new data needs arise, imputation 

maps are ready to adapt to new needs without the necessary 

production of a new model. However, at the 250-m scale, 

the variables that are correlated with broad climate patterns, 

specifically species distributions, will probably be characterized the best. Structure attributes, such as coarse woody 

debris and quadratic mean diameter, can be mapped, but the 

mapped variability will likely overwhelm the utility of such 

products.

Acknowledgments

We would like to thank Jerry Beatty and Terry Shaw for 

support and comments on this project. We also thank Matt 

Gregory and Bonnie Rufenacht for technical assistance in 

conducting these analyses.

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accurately predicted for both species based on presence/

absence of field observations. General circulation models 

were used to predict areas of suitable habitat for both 

species under the current climate and the projected climate 

around the year 2030. These models predict that these 

species will respond differently to projected climate change. 

Suitable habitat (i.e., climate space) for whitebark pine is 

predicted to decline dramatically by ca. 2030. Populations 

in the lower latitudes (below 45° N) and those persisting at 

the low elevation limits show the greatest threat to extinction from climate change. Predictions also indicate that 

suitable habitat for western white pine will be reduced in 

some southern latitudes, whereas the suitable habitat will 

be increased in the northern latitudes of its distribution. For 

whitebark and western white pine, both molecular markers 

and quantitative traits frequently reveal congruent genetic 

structure for species conservation. The combination of 

genetic studies with climate modeling can provide base-line 

tools that will enable managers to focus genetic conservation efforts on populations at highest risk while restoring 

areas that have the lowest risk for predicted climatic 

extirpation. The ability to define forest populations and 

predict landscape-level effects of climate change is critical 

for sustaining future forest health.

Keywords: Bioclimatic models, biogeography, genetic 

conservation, western white pine, whitebark pine.

Introduction

Climate change and associated glacial/interglacial cycles 

have had a profound impact on the biogeography of plant 

communities. Paleoecological data from pollen sediment 

cores and packrat middens have been used to uncover many 

of the past plant-climate associations (e.g., Thompson and 

Anderson 2000, Thompson and others 1993, Whitlock 

1993). These climatic oscillations have also affected intraspecies genetic relationships that are recorded in changes in 

gene frequencies or adaptive traits, or both (Davis and Shaw 

2001). In western North America, most forest trees have 

Bryce A. Richardson, Marcus V. Warwell, Mee-Sook Kim, 

Ned B. Klopfenstein, and Geral I. McDonald

Bryce A. Richardson, research geneticist, Rocky Mountain 

Research Station, Provo, UT; Marcus V. Warwell, geneticist; and Ned B. Klopfenstein and Geral I. McDonald, 

research plant pathologists, USDA Forest Service, Rocky 

Mountain Research Station, Moscow, ID 83843; Mee-Sook 

Kim, assistant professor, Kookmin University, Seoul, 

Korea. 

Abstract

To assess threats or predict responses to disturbances, 

or both, it is essential to recognize and characterize the 

population structures of forest species in relation to changing environments. Appropriate management of these genetic 

resources in the future will require (1) understanding the 

existing genetic diversity/variation and population structure 

of forest trees, (2) understanding climatic change and its 

potential impacts on forest species and populations, and (3) 

development and use of new tools to identify populations at 

risk and geographic areas that will provide suitable habitat 

in the future. Forest trees exist within distinct geographic 

populations created by climatic shifts, evolutionary 

processes, the availability of suitable habitats, and other 

environmental factors. These processes have occurred 

over millennia and continue to shape the biogeography and 

genetic structure of these species. Forest trees in Western 

North America are being defined on the basis of molecular 

markers and quantitative traits. For example, studies of 

whitebark pine (Pinus albicaulis) and western white pine 

(P. monticola) demonstrate the existence of several distinct populations that likely developed via the long-term 

processes described above. These studies and others have 

shown that distinct populations exist within western conifer 

species and further indicate that the biogeographies of 

forest species are quite dynamic over time and space. Here, 

we present a case study using genetic data from whitebark 

pine and western white pine coupled with landscape-based, 

plant-climate modeling. Suitable contemporary habitat is 

Integration of Population Genetic Structure and Plant Response 

to Climate Change: Sustaining Genetic Resources Through 

Evaluation of Projected Threats
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GENERAL TECHNICAL REPORT PNW-GTR-802

broad, disjunct distributions that are associated with variable environmental conditions. The combination of disjunct 

intra-species distributions and environmental variables can 

contribute to barriers in gene flow and changes in adaptive 

or neutral gene frequencies, or both, caused by selection 

or genetic drift (Richardson and others 2005). Population 

genetics typically assess neutral genetic variation and provide estimates of genetic diversity and partitioning among 

a spatial hierarchy. Population genetic studies can estimate 

both contemporary and historical gene flow, and thereby aid 

in elucidating past intraspecies relationships and distributions. Such genetic data, coupled with paleoecological data, 

provide inferences to historical changes in plant biogeography and postglacial expansion/contraction (e.g., Godbout 

and others 2005, Jackson 2006, Magri and others 2006, Petit 

and others 2004, Richardson and others 2002, Thompson 

and Anderson 2000). Knowledge of the population genetic 

structure and adaptation is critical for successful forest 

health management and conservation/restoration efforts.

Five-needled pines are keystone species in numerous 

ecosystems in western North America. This paper focuses 

on two case studies: whitebark pine (Pinus albicaulis 

Engelm.) and western white pine (P. monticola Dougl. ex 

D. Don). Whitebark pine occupies subalpine habitats across 

the northern Rocky, Cascade, and Sierra Nevada Mountains and mountains of northern Nevada. Seed dispersal 

is dependent on a coevolved mutualism with Clark’s 

nutcracker (Nucifraga columbiana) that deposits seeds 

in caches for later consumption (Tomback 2001). Flight 

distances with seeds vary but have been reported as far as 

22 km (Richardson and others 2002, Vander Wall and Balda 

1981). Whitebark pine has been recognized as critical for 

wildlife (Mattson and others 2001) and watershed stability 

(Arno and Hoff 1989).

Western white pine occupies a wider ecological niche 

than whitebark pine, occurring from sea level in Washington and British Columbia to a predominately subalpine 

distribution from Sierra Nevada northward to the Cascades. 

In the northern Rocky Mountains, this species occupies wet 

montane to subalpine habitats (Wellner 1962). It is valued 

as a timber species because of its rapid growth, straight 

bole, and ability to regenerate as a seral species from winddispersed seeds. Since the early 1900s, the abundance 

of both five-needled pines has dropped precipitously, 

mainly from white pine blister rust caused by the fungus 

Cronartium ribicola (McDonald and Hoff 2001). Major 

efforts and resources have been directed toward restoration of five-needled pine ecosystems. Unfortunately, most 

restoration efforts have been operating on the assumption 

that the climate will remain stable. For successful management and restoration of five-needled pine ecosystems, future 

climate change must be considered and integrated into current efforts and plans for species management/conservation.

Research corroborated by diverse sciences provides 

strong evidence that the Earth is experiencing a warming 

process driven by increased greenhouse gas concentrations. 

Records indicate that mean global temperature increased 

0.6 °C during the 20th century, and global temperature is 

expected to further increase 1.4 to 5.8 °C over the next 100 

years (IPCC 2001). This predicted rate of climate change 

is unprecedented within the available historical records. 

Because of the extremely fast rate of predicted climate 

change, determining the responses of forest tree species and 

populations is one of the major challenges for future threat 

assessment (Jump and Peñuelas 2005). Recent studies have 

developed plant-climate models that are highly accurate 

in predicting a suitable contemporary climate envelope 

for several western North American species (Rehfeldt and 

others 2006). These models serve as means to project future 

climate scenarios using general circulation models (GCMs).

It is well known that historical climatic oscillations 

have dramatically shifted species distributions. However, 

these historical climate changes typically occurred at a relatively slow rate over the course of millennia. The predicted 

rate of climate change, due to the anthropogenic release of 

greenhouse gases, will greatly exceed historical rates (IPCC 

2001). Studies based on several climate models have shown 

the predicted rate of climate warming could jeopardize plant 

taxa with limited seed dispersal distances or disjunct habitats (Malcolm and others 2002). In this analysis, we develop 

an approach to evaluate potential impacts of future climate 

change on genetic diversity and biogeography of whitebark 

pine and western white pine. Our objectives include:
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

1. Assess the existing genetic diversity and population structure of whitebark pine and western 

white pine. 

2. Model each species climate space (i.e., suitable 

habitat) for contemporary and year 2030 

climate. 

3. Use the predicted biogeographical changes to 

prioritize genetic conservation efforts by 

identifying populations at risk and determining 

areas suitable for restoration.

Material and Methods

Population Genetic Analyses

Sampling of needle and bud tissue was conducted across the 

range of both whitebark pine and western white pine. For 

whitebark pine, 28 sites were sampled and were combined 

into 6 regions (n = 21 to 72, Richardson and others 2002). 

Western white pine was sampled from 15 sites (n = ~30) 

across the range (Kim and others unpublished). Plant 

tissue was used to extract genomic DNA using previously 

published protocols (Kim and others 2003, Richardson and 

others 2002).

For whitebark pine, two types of DNA markers were 

used: mitochondrial DNA (mtDNA) and chloroplast 

microsatellites (cpSSR) haplotypes. These markers are 

uniparentally inherited enabling separate estimates of gene 

flow via pollen by cpSSR and via seed dispersal by mtDNA. 

Protocols for DNA typing and analyses are described by 

Richardson and others (2002). For western white pine, an 

amplified fragment length polymorphism (AFLP) approach 

was used to provide high-resolution markers that are typically biparentally inherited (i.e., nuclear markers). Western 

white pine AFLP protocols and analyses are described in 

Kim and others (2003).

Plant-Climate Modeling

Whitebark pine and western white pine bioclimatic models 

used species presence/absence data for more than 118,000 

point locations described geographically by latitude, 

longitude, and elevation (see “Acknowledgments”). Climate 

was estimated for each of these locations using a spline 

climate model (Rehfeldt 2006). This procedure yielded a 

data set that described species presence or absence based 

on 33 climate variables representing simple interactions of 

Figure 1—(A) The predicted contemporary distribution of 

whitebark pine (Pinus albicaulis) in the Western United States 

and southwestern Canada using a plant-climate model (Rehfeldt 

and others 2006). Pixel colors represent votes for whitebark pine 

occurrence; votes between 0 and 50 are not shown. The black 

drawn lines represent predicted metapopulations of whitebark pine 

based on chloroplast microsatellite molecular markers (Richardson 

and others 2002). (B) The predicted 2030 distribution of whitebark 

pine using the average of two general circulation models.
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GENERAL TECHNICAL REPORT PNW-GTR-802

temperature or precipitation, or both, in relation to time, all 

of which have some relationship to plant responses. Random 

forests multiple classification tree analysis (Breiman 2001) 

was then used to identify climate variables important for 

predicting species presence and to build decision-tree-based 

bioclimatic models.

Arc map software (ESRI Inc.) was used to project the 

models predictions on a map. For this approach, the spline 

climate model was used to estimate climate in each 1-sq-km 

grid across the Western United States (more than 5.9 million 

pixels). Each pixel was then assessed using 100 independent 

decision trees. For each decision tree, a vote for or against 

species presence was cast. Pixels receiving vote tallies of 50 

percent or greater were defined as having suitable climate 

space. These pixels are represented by the colored areas in 

Figures 1 and 2. These procedures are described in depth 

elsewhere (Rehfeldt and others 2006).

The response of a species contemporary suitable 

climate space under climate change was then assessed. 

The spline climate model was updated to the predicted 

climate of beginning in ca. 2030 using an average from the 

HadCM3GGa1 of the Hadley Centre (Gordon and others 

2000) and CGCM2 of the Canadian Centre for Modeling 

and Analysis (Flato and Boer 2001) general circulation 

models. The procedure described in the previous paragraph 

was then repeated using the updated climate model to 

predict future climate for each pixel space.

Results

Whitebark Pine

Population Genetics—

Analysis of mtDNA reveals three haplotypes that are 

discretely partitioned among regions with two introgression 

zones. One introgression zone apparently occurs across a 

broad area in central Idaho, and another introgression zone 

occurs across a narrow area in the northern Cascades near 

Mount Rainier, Washington. The introgression of mtDNA 

haplotypes in the northern Cascades has been proposed to 

have resulted from the colonization of previously glaciated 

habitat during Holocene warming (Richardson and others 

2002).

Figure 2—(A) The predicted contemporary distribution of western 

white pine (Pinus moniticola) in the Western United States and 

southwestern Canada using a plant-climate model (Rehfeldt 

and others 2006). Pixel colors represent votes for western white 

pine occurrence. The black drawn lines represent the predicted 

metapopulations of western white pine based on AFLP analyses. 

(B) The predicted 2030 distribution of western white pine using 

the average of two general circulation models.

Analysis of pollen gene flow using cpSSRs reveals 

broader population boundaries that include mtDNA 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

introgression zones. These data circumscribe three metapopulations: the Sierra Nevada Mountains, the Yellowstone 

region, and a large Pacific Northwest region that includes 

the northern Rocky Mountains and the Cascade (Figure 1A. 

Private alleles were typically found at low frequency within 

each region; however, one notable exception was the Sierra 

Nevada metapopulation that had a private allele at high 

frequency. Genetic diversity has been described as moderately high for cpSSR markers (Richardson and others 2002) 

to moderately low using allozyme analysis (reviewed in 

Bruederle and others 2001). Furthermore, genetic characterization of isolated populations in southern Oregon, northern 

California, and Nevada are still needed to assess genetic 

conservation efforts in these areas.

Plant-Climate Modeling—

The contemporary plant-climate model for whitebark pine 

fits its present distribution with remarkable precision. Comparisons between ground-truthed whitebark pine locations 

and predicted areas of suitable habitat showed an overall 

error rate of only 2.47 percent (Figure 1A). The mapped 

predictions for the occurrence of whitebark pine were more 

accurate than Little’s (1971) published range maps (Warwell 

and others 2006). Based on two GCMs (Hadley and Canadian Centre for Climate Change), the projected climatic 

space for whitebark pine in ca. 2030 shows a dramatic 

change in comparison with the contemporary prediction. 

An estimated 70 percent of the current climate space for 

whitebark pine will be lost by 2030. Most of predicted loss 

occurs in the southern latitudes of the whitebark pine range, 

including the Oregon Cascades, Siskiyou, and northern 

Nevada mountain ranges, where current suitable habitats 

are limited to mountaintops in many locations (Figure 1B). 

The elevation of suitable climate space is also expected 

to rise 330 m, thereby leaving only the Sierra Nevada 

Mountains as a potential refuge with suitable climate space 

below 40o N latitude. In contrast, the model does predict 

suitable climate space will occur in central Colorado, which 

may represent a fundamental climate niche for whitebark 

pine. In the northern Rockies and Cascades, whitebark pine 

climate space is predicted to persist only among the highest 

elevations found in Yellowstone, south-central Idaho, Glacier National Park, and the northern Cascades. Areas north 

of 50o N will also likely fit climatic space for whitebark 

pine, but these areas fell outside of the geographic window 

and are not included in the analyses.

Western White Pine

Population Genetics—

Analyses of AFLP loci discern three metapopulations in 

western white pine. These include a Pacific Northwest 

metapopulation that extends from the northern Rockies 

west to the Pacific and south along the Cascade Mountains 

to central Oregon. A distinct metapopulation exists in 

the Siskiyou Mountains of southern Oregon and northern 

California, and another metapopulation extends into the 

Sierra Nevada Mountains (Figure 2A). Like most other 

pines, western white pine contained moderate to high levels 

of heterozygosity in all three metapopulations (Kim and 

others unpublished).

Plant-Climate Modeling—

For western white pine, the contemporary climate space 

also fits with the current distribution with a small overall 

error of 3.85 percent. The projected current distribution of 

western white pine is shown in Figure 1A. The predicted 

2030 climate space for western white pine provides a strong 

contrast with that of whitebark pine. Western white pine 

climate space is expected to increase 29 percent by 2030 

(Figure 2B). Overall, suitable climate space for western 

white pine is expected to increase 157 m in elevation by 

2030. This predicted expansion occurs in the northern latitudes, with notable expansion into northwestern Montana 

and northward into the Canadian Rocky Mountains. In the 

more southern latitudes of California and southern Oregon, 

populations are projected to persist at higher elevations. 

Predicted climate space is reduced in southern Oregon, 

thereby rendering the projected Sierra Nevada population 

more disjunct from the southern Cascades.

Discussion

Whitebark Pine

Past climate change has shaped the biogeography of whitebark pine and, hence, genetic relationships and potential 

adaptive traits. Since the last glacial maximum, whitebark 
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GENERAL TECHNICAL REPORT PNW-GTR-802

pine has apparently responded to the warming climate after 

the last glacial maximum by colonizing new habitat opened 

up by receding glaciers of the Canadian Rocky Mountains 

and the northern Cascades. Holocene expansion into the 

northern Cascade Mountains likely originated from source 

populations to the south and east of the north Cascades, 

based on the mtDNA at the contact zone (Richardson and 

others 2002). In contrast, a wider distribution of whitebark 

pine in more southern latitudes during the last glacial 

maximum was probably constricted to higher elevations by 

subsequent Holocene warming. This constriction in suitable 

habitat appears particularly dramatic in northern Nevada, 

where whitebark pine currently persists only on the highest 

mountaintops.

Population genetics of whitebark pine have identified 

distinct regional metapopulations occupying the Pacific 

Northwest (i.e., northern Rockies and Cascade Mountains), 

the greater Yellowstone region, and the Sierra Nevada. 

Other populations in the Great Basin and central Oregon 

have not been analyzed with mtDNA or cpSSR markers. 

However, allozyme analyses have shown distinct genetic 

structure among the Great Basin whitebark pine populations (Yandell 1992). Further studies to characterize genetic 

diversity and structure of whitebark pine in Washington 

and Oregon are in progress (Personal communication. C. 

Aubry. 2006. Area Geneticist, Olympia National Forest, 

1835 Black Lake Blvd. Suite A, Olympia, WA 98512), and 

studies are assessing rangewide and regionwide adaptive 

traits in whitebark pine (Bower and Aitken 2006, Warwell 

and others unpublished). These studies and population 

genetic analyses are essential to determine appropriate 

genetic conservation efforts and proactive management that 

consider predicted climate change.

The contemporary climate space predicted using the 

plant-climate model is extremely precise for the distribution 

of whitebark pine. This precision has also been demonstrated with other western plant species (Rehfeldt and others 

2006). The predicted suitable climate space for whitebark 

pine shows a dramatic reduction in the year 2030. At the 

highest risks are populations that currently only exist on 

mountaintops, where projected suitable climate space will 

be entirely lost from the region. Loss of local whitebark pine 

populations is predicted to occur throughout the Oregon 

Cascades and mountaintops of northern Nevada, and further result in disjunct populations in the Sierra Nevada that 

will persist only at the highest elevations. These populations 

that face the highest risk of extirpation from predicted 

climate change and the white pine blister rust fungus should 

take priority for seed bank collections. Besides the Sierra 

Nevada, the plant-climate model predicts four major regions 

where climate space remains in the Western United States. 

These regions include the highest mountain ranges: the 

northern Cascades, Glacier National Park, south-central 

mountain ranges in Idaho, and the greater Yellowstone 

region. These regions that contain suitable climate space in 

2030 predictions should be considered as priority regions 

for restoration efforts.

Western White Pine

Like whitebark pine, western white pine populations were 

also shaped by past climate change. This process created 

genetic structure with similarities to whitebark pine: a 

large Pacific Northwest metapopulation with partitions in 

southern Oregon and northern California (Kim and others 

unpublished). This population structure is also supported by 

an earlier study of isozyme markers (Steinhoff and others 

1983). In addition, similar findings were reported in a study 

of adaptive traits for western white pine, showing a relationship between latitude and shoot elongation with a transition 

zone in southern Oregon (Rehfeldt and others 1984).

Again, the bioclimatic model predicted an excellent fit 

to the known distribution of western white pine. However, 

predicted 2030 climate space for western white pine shows 

a stark contrast to whitebark pine. The predicted increase 

in climate space is evident as an expansion in the northern 

Rocky Mountains that apparently relates to a slight increase 

in moisture. In the Sierra Nevada and Siskiyou Mountains, 

predicted climate space in 2030 does not show expansion. 

In these areas, moisture levels are predicted to increase 

more dramatically relative to the Pacific Northwest, and 

predicted climate becomes less suitable for western white 

pine, possibly owing to increased competition. Whereas 

losses of genetic diversity seem relatively small compared 

to whitebark pine, some areas of western white pine may 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

be at risk. Reductions in the abundance or extirpation of 

small isolated populations are conceivable for the Siskiyou 

Mountains and other areas of northern California where the 

western white pine populations exhibit a unique and diverse 

genetic background. Such populations should be considered 

as a priority for genetic conservation efforts.

Model Refinements and Uncertainties

Further refinements in the plant-climate model can be 

achieved by integrating knowledge from molecular and 

quantitative genetics. An important point is that this model 

currently utilizes ground-truthed locations for the entire 

distribution of each species for current and future predictions, ignoring genetic adaptation to local/regional climate. 

This model assumes that each climate-adapted population 

will be able to occupy its climate space in the future regardless of the geographic distance. For example, it is clear that 

the Sierra Nevada metapopulation of western white pine is 

genetically different and occupies a climate that is different 

from western white pine in the more northern latitudes. 

However, it is unclear where the populations occupying current climate space in Sierra Nevada may move in the future. 

Future plant-climate model improvements should be able to 

correlate climate variables and genetic data. Ongoing studies are focused on the delimiting population-based adaptive 

traits and refining molecular genetic data from western 

white pine populations to project suitable climates for these 

populations under current and future climate scenarios. 

Similar studies can be conducted with whitebark pine and 

other tree species, but this will require investing in efforts 

to conduct thorough, rangewide genetic studies.

Most uncertainty in the plant-climate model is 

associated with future climate scenarios and the GCMs. 

Precipitation is a major factor for predicting suitable climate 

space of plant species. For the Western United States and 

Canada, models display consistency for increased winter 

precipitation; however, summer precipitation predictions 

remain inconsistent (IPCC 2001). In addition, the time 

scale between loss of suitable climatic space and extirpation of local populations remains uncertain. Much of this 

uncertainty comes from site-specific biotic interactions. For 

example, two potential scenarios can be envisioned: (1) slow 

attrition from succession where whitebark pine persists for 

decades or (2) stress from maladaptation leading to insect 

(e.g., bark beetle) outbreaks or disease (e.g., root rots) epidemics resulting in local extirpation within years. Projected 

scenarios are further complicated by mortality and loss 

of cone crops to white pine blister rust and insect attack. 

Continued studies are needed to address these uncertainties and improve predictions of climate-change impacts on 

distribution and population genetic structure of whitebark 

pine and western white pine.

Summary

Case studies were presented for whitebark pine and western white pine using plant-climate modeling of current 

and future climate predictions and genetic studies. These 

studies indicate dramatic differences in these two species’ 

responses to climate change from the past and the future. 

These responses are largely dependent on the breadth of the 

ecological niches and life-history characteristics of each 

species. For whitebark pine, specialization for subalpine 

habitat is predicted to severely limit its distribution under 

future climate scenarios. Alternatively, coevolved relationships with Clark’s nutcracker have enabled this species to 

colonize new habitat following Holocene warming. This 

characteristic may also aid its migration northward in 

the future, but successful migration to newly established 

suitable climate space is contingent upon the rate of climate 

change and the severity of impact from white pine blister 

rust and insect attack. Because western white pine has 

broader ecological range, it may be better suited to buffer 

effects of climate change. This ecological range is reflected 

in its predicted resilience under the future climate scenario. 

The creation of the plant-climate models and determining 

the impact from future climate scenarios for a particular 

species are dependent upon basic genetic research, both ecological and population genetics. Current research is focused 

on integration of genetic and climate data for these species. 

This approach represents a synthesis of multidisciplinary 

research to provide useful guidelines for forest management 

plans for genetic conservation and restoration that consider 

future climate change projections.
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Acknowledgments

The authors thank Dr. Gerald Rehfeldt and Nick Crookston 

for their creative development of the plant-climate model 

used in this manuscript. Thanks to Drs. Carol Aubry and 

Dennis Ferguson for reviewing this manuscript. We also 

thank the following people and organizations for collections 

of whitebark pine and western white pine location data:

• Forest Inventory and Analysis (FIA)

• Andy Bower, Ph.D. Dissertation, University of 

British Columbia

• CalFlora Database (BLM: Eagle Lake Herbarium)

• CalFlora Database (Yosemite National Park 

Natural Resource Inventory)

• Donna Dekker (Lynn Wells and Ray Wiseman 

1991 Report)

• John King and Mike Carlson, British Columbia 

Ministry of Forests

• Jodie Krakowski, Master’s Thesis, University 

of British Columbia

• RMRS Field Data

• RMRS Northern Idaho Habitat Type Data

• Ulla Geralyn Yandell, Master’s Thesis, University 

of Nevada, Reno

• USFS Whitebark Limber Pine Information 

System

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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Threats to Private Forest Lands in the U.S.A.: A Forests 

on the Edge Study

Susan M. Stein, Mark H. Hatfield, Ronald E. McRoberts, 

Dacia M. Meneguzzo, and Sara Comas

Susan M. Stein, Forests on the Edge Coordinator, USDA 

Forest Service, Cooperative Forestry Staff, State and Private 

Forestry, Washington, DC 20250; Mark H. Hatfield, 

forester, Ronald E. McRoberts, mathematical statistician, 

and Dacia M. Meneguzzo, forester, USDA Forest Service, 

Northern Research Station, St. Paul, MN 55108; and Sara 

Comas, wildlife specialist, USDA Forest Service, Cooperative Forestry Staff, State and Private Forestry, Washington, 

DC 20250.

Abstract

The Forests on the Edge project, sponsored by the USDA 

Forest Service, uses geographic information systems 

to construct and analyze maps depicting threats to the 

contributions of America’s private forest lands. For this 

study, watersheds across the conterminous United States 

are evaluated with respect to the amount of their private 

forest land. Watersheds with at least 10 percent forest land, 

of which 50 percent is privately owned, are then ranked relative to the contributions of their private forest lands to water 

quality, timber supply, at-risk species habitat, and interior 

forest. In addition, threats from housing development, fire, 

air pollution, and insect pests and disease to private forest 

land contributions are assessed. Results indicate that private 

forest lands contributions and threats are concentrated in the 

Eastern and Southeastern United States but are also distributed throughout the north-central, central hardwoods, and 

Pacific Northwest regions. 

Keywords: Ecological services, forest contributions, 

geographic information systems, fourth-level watershed, 

land use change, private forest, sustainable forest management.

Introduction

America’s forest lands contribute in a myriad of ways to 

the economic, ecological, and social well-being of the 

Nation. Increasingly, however, forest lands are threatened 

from a variety of sources including urbanization, lowdensity housing development, climate change, invasive flora 

and fauna, wildfire, pollution, fragmentation, and parcelization. The increasing emphasis on sustainable forest management and loss of open space requires quantitative and 

spatial assessments of the impacts of threats to forest lands 

and their contributions. The Forests on the Edge (FOTE) 

project, sponsored by the Cooperative Forestry Staff, State 

and Private Forestry, USDA Forest Service, conducts mapbased assessments of threats to the private forest lands of 

the United States using spatial data layers and geographic 

information systems. The objectives of the study described 

here are threefold: (1) to construct nationally consistent 

data layers depicting the spatial location of private forest 

lands and their contributions; (2) to construct similar layers 

depicting threats to the contributions of private forest land 

from sources such as conversion to urban and exurban uses, 

wildfire, and pollution; and (3) to identify watersheds whose 

private forest lands simultaneously make the most important 

contributions and face the greatest threats.

The Montreal process criteria and indicators provide 

an appropriate context for framing and conducting these 

assessments (McRoberts and others 2004). For example, 

criterion 2, maintenance of the productive capacity of forest 

ecosystems, includes indicators related to forest area and 

timber production; criterion 3, maintenance of forest ecosystem health and vitality, includes indicators related to fire, 

wind, disease, and insects; and criterion 4, conservation and 

maintenance of soil and water resources, includes indicators 

related to the contributions of forests to water quality.

Data Layers

All data layers were obtained as or constructed to be 

nationally consistent and were summarized at the spatial 

scale of fourth-level watersheds (Steeves and Nebert 1994). 

Watersheds were selected as the analytical units because 

they highlight the important connections between private 

forests and ecological processes.
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Private Forest Contributions

Area of Private Forest Land—

A 100-m-resolution forest ownership layer was constructed 

by aggregating the classes of the National Land Cover Dataset (NLCD) (Vogelmann and others 2001) into forest and 

nonforest classes and using the Protected Areas Database 

(PAD) (DellaSalla and others 2001) to distinguish ownership and protection categories. The emphasis for this study 

was private forest land, which includes tribal, forest industry, and nonindustrial ownerships. Stein and others 2005a, 

2005b) provide detailed information on this layer. Only 

watersheds with at least 10 percent forest land, of which 50 

percent is privately owned, qualified for subsequent analyses. Figure 1 depicts the percentile rankings of qualifying 

watersheds relative to the percentage of privately owned forest land; for example, a watershed in a 91 to 100-percentile 

category has a higher percentage of private forest land than 

at least 90 percent of the qualifying watersheds.

Water Quality—

Private forest lands provide nearly 60 percent of all water 

flow from U.S. forests and nearly 30 percent of the water 

flow originating on land in the Lower 48 States (Personal 

communication. Thomas C. Brown. 2004. Hydrologist, 

Rocky Mountain Research Station, 2150 Centre Ave. Bldg. 

A, Suite 376, Fort Collins, CO 80526). Water flow from 

private forests is generally considered clean relative to water 

flow from other land uses and, therefore, makes a positive 

contribution to water quality. The water quality layer depicts 

the contribution of private forest land to the production of 

clean water and is based on three underlying assumptions: 

(1) water bodies near the heads of hydrologic networks 

are more sensitive to the loss of forest buffers than water 

bodies near the bases of the networks, (2) the presence or 

absence of upstream forest buffers influences water quality 

downstream in the networks, and (3) forest lands throughout 

watersheds, not just those in immediate proximity to water 

bodies, are important when considering the contributions of 

private forest land to water quality (FitzHugh 2001).

The water quality layer was constructed from two 

underlying layers, the forest ownership layer and the 

National Hydrography Dataset (NHD) (USGS 2000), which 

depicts water bodies in the 48 contiguous States. The 

Figure 1—Percentile rankings of watersheds with respect to percentage of private forest land.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

layer was constructed in four steps: (1) 30-m buffer was 

constructed around all water bodies, (2) the buffers were 

intersected with the private forest land class of the forest 

ownership layer to quantify the amount of private forest 

land in proximity to water bodies, (3) each buffer segment 

was assigned to one of four categories based on the relative position of the segment to the head of its hydrologic 

network, and (4) for each watershed, the percentage in each 

of the four categories was determined. Water quality index 

(WQI) was then calculated for each watershed as,

WQI = 0.6[A1 + (A1A2)] + 0.4 (0.48B1 + 0.24B2 + 0.16B3 + 0.12B4

where A1 = percentage of watershed in private forest land, 

A2 = percentage of total forest land in watershed that is 

privately owned, B1 = percentage of private forest land 

buffer in the first category (nearer head of hydrologic 

network headwater), B2 = percentage of buffer in the second 

category, B3 = percentage of buffer in third category, and B4 

= percentage of buffer in fourth category (farthest downstream from the head of hydrologic network). Variables A1

and A2 represent private forest coverage throughout the 

watershed, and variables B1 through B4 represent private 

forest coverage in the buffers. In WQI, A1 and A2 are 

collectively weighted 0.6, whereas variables B1 through B4

are collectively weighted 0.4 to reflect the third assumption 

above. Watershed boundaries for this and all other layers 

were determined using Steeves and Nebert (1994). 

Timber Supply—

Private forest lands make a substantial contribution to 

America’s timber resources, accounting for 92 percent of 

all timber harvested in the United States in 2001 (Smith and 

others 2004). The timber supply layer depicts the ranking of 

watersheds relative to an index of the importance of private 

timberland and is based on Forest Inventory and Analysis 

(FIA) plot data (http://www.fia.fs.fed.us/tools-data/default.

asp.). The private timberland importance index (TI) is based 

on three subindices of contributions of the timberland subset 

of private forest land. Timberland is defined by the FIA 

program as forest land that has not been withdrawn from 

production and that is capable of producing 20 cubic feet 

per year of industrial wood. For each watershed, the three 

subindices are calculated as follows: (1) growth index (GI) 

is the average growing stock volume growth rate on private 

timberland in a watershed relative to the average across all 

private timberland, (2) volume index (VI) is the average net 

growing stock volume per acre on private timberland in a 

watershed relative to the average on all private timberland, 

and (3) area index (AI) is the ratio of private timberland 

area to total private land area in a watershed relative to the 

same ratio across all watersheds. TI was calculated for each 

watershed as, TI = AI(GI + VI).

At-Risk Species—

Private forests provide the key to conservation for many 

species. In the Pacific Northwest, they provide significant 

habitat for the spotted owl (Holthausen and others 1995). 

NatureServe and its member Natural Heritage Programs and 

Conservation Data Centres, prepared a geographic data set 

depicting the number of at-risk species occurring on private 

forested lands within fourth-level watersheds in the Lower 

48 States of the United States. At-risk species are defined 

as species with element occurrences (EO) that have been 

observed by an authoritative source within at least the last 

50 years, and are either: (1) federally designated under the 

Endangered Species Act (Endangered, Threatened, Candidate, Proposed), or (2) designated as critically imperiled, 

imperiled, or vulnerable according to the NatureServe 

Conservation Status Ranking system (G1/T1- G3/T3) (http://

www.natureserve.org/explorer/ranking.htm). An EO is an 

area of land or water, or both, in which a species or natural 

community is, or was, present.

NatureServe selected populations that only occur on 

private forested lands by conducting a geographic analysis 

comparing the location of at-risk populations with private 

forest locations (both protected and nonprotected). These 

species are labeled as forest-associated as opposed to forestobligated because a separate analysis to refine this species 

list using knowledge of species habitat requirements and 

preferences was not conducted. Known data gaps include: 

(1) no at-risk species data available in Arizona, Massachusetts, and the District of Columbia, (2) no at-risk fish data 

available for Idaho, and (3) at-risk animal data in Washington are incomplete. Private forested lands were determined 

using the data layer described in the “Area of Private Forest 

Land” section. 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Interior Forest and Habitat Contiguity—

Habitat contiguity is an index of the structural integrity of 

forests, an important conservation concern (Wear and others 

2004). Habitat contiguity can be measured in terms of the 

amount of interior forest cover that is functionally distinct 

from forest edge. The interior forest layer was created 

using three steps. First, the forest cover layer described in 

the section “Area of Private Land” was used to identify 

forested pixels in each watershed. Second, forested pixels 

were labeled interior forest if 90 percent of the pixels in a 

surrounding 65-ha window were also forested. Third, the 

proportion of interior forest pixels in each watershed was 

determined, and all watersheds were assigned a percentile 

ranking based on this proportion. Note that a watershed 

could have very little forest land but a high proportion that 

satisfied the interior forest criteria.

Threats to Private Forest Lands

Development—

The development layer depicts predicted threats to private 

forest lands resulting from conversion to urban or exurban 

uses. The layer is based on estimates of current population and housing density data obtained from the 2000 U.S. 

Census, and predictions of housing density increases. A 

spatially explicit model was used to predict the full urbanto-rural spectrum of housing densities (Theobald 2005). 

The model uses a supply-demand-allocation approach and 

is based on the assumption that future growth patterns will 

be similar to those in the past decade. Future patterns are 

forecast on a decadal basis in four steps:

1. The number of new housing units in the next 

decade is forced to meet the demands of the 

predicted populations. 

2. A location-specific average population growth

rate from the previous to current time step was 

computed for each of four density classes: urban, 

suburban, exurban, and rural.

3. The spatial distribution of predicted new housing 

units was adjusted with respect to accessibility 

to the nearest urban core area.

4. Predicted new housing density was added to the 

current housing density under the assumption 

that housing densities do not decline over time. 

For these analyses, predicted new housing was not 

permitted to occur on protected private land as indicated 

by PAD (DellaSalla and others 2001). The spatially explicit 

housing density predictions were combined with the forest 

ownership layer to identify watersheds with the greatest 

predicted conversion of private forest land to urban and 

exurban uses. Stein and others (2005b) provided detailed 

information on this layer. 

Wildfire—

Although wildfire is one of the most compelling threats 

to forest land, particularly in the Western United States, 

predicting the threat of wildfire incidence is extremely 

complex and relies on a variety of regional models and 

regional variables. Further, even if the models could be 

readily implemented to construct a national layer, the 

geographic consistency of the layer would be questionable. 

Therefore, as a surrogate for wildfire risk, FOTE used the 

1-km by 1-km-resolution current fire condition class (CFCC) 

data, which depict deviations of fire incidence from historical natural fire regimes and estimated efforts necessary to 

restore stands to historical regimes (Schmidt and others 

2002). These data reflect the assignment of forest lands to 

one of three CFCC classes:

1. CFCC1—forest lands with fire regimes that are 

within or near historical ranges and that can be 

maintained by treatments such as prescribed fire 

or fire use.

2. CFCC2—forest lands with fire regimes that have 

been moderately altered from historical ranges and 

that may require moderate levels of prescribed fire, 

fire use, hand or mechanical treatment, or a combination to restore to historical fire regime. 

3. CFCC3—forest lands with fire regimes that have 

been substantially altered from historical ranges and 

that may need high levels of hand or mechanical 

treatment before fire is used to restore historical fire 

regimes.

The appeal of the CFCC classes is that they are objective, nationally consistent, and are assumed to correlate well 
137

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

with the threat of wildfire incidence. Although these classes 

reflect the widely varying State-level commitments to 

wildfire mitigation efforts, they do not reflect ease of access 

to forest lands experiencing wildfire or the availability of 

resources to combat wildfires. 

For each watershed, all private forest lands were 

assigned to one of the three CFCC classes, and a watershedlevel index was calculated as,

CC = CC1 + 2CC2 + 4CC3,

where CCi

 is the area of private forest land in class CFCCi. 

The wildfire layer used for this study depicts the percentile 

ranking of each watershed with respect to its CC index 

value.

Ozone

Ozone affects forest ecosystems by causing foliar lesions 

and rapid leaf aging, altering species compositions, and 

weakening pest resistance (Chappelka and others 1997, 

Miller and others 1996). It is the only gaseous air pollutant 

that has been measured at known phytotoxic levels at both 

remote and urbanized forest locations (US EPA 1996). 

The ozone layer depicts private forest land threatened by 

ground-level ozone and was based on late summer observations by FIA field crews of ozone damage to bioindicator 

species known to be sensitive to ground-level ozone. Data 

for more than 2,500 FIA plots were available for the study. 

Each plot was assigned a biosite value based on a subjective assessment by trained observers of the quantity and 

severity of damages (Coulston and others 2003, Smith and 

others 2003). Inverse distance weighted interpolation was 

used to create a map of ozone damage. This map was then 

combined with the forest ownership layer to identify private 

forest land with elevated levels of ozone damage. For each 

watershed, the percentage of private forest land in moderate 

or high-damage categories was calculated. 

Nitrate and Sulfate Deposition—

Acidic deposition, the transfer of strong acids and acidforming substances from the atmosphere to the Earth, 

has become a critical stress affecting forested landscapes 

across the United States. Effects can include increased 

sulfate and nitrate levels in soils and waters, which, in 

turn, can alter soil and water chemistry and affect trees and 

other living organisms that depend upon affected soils and 

waters (Driscoll and others 2001). The nitrate and sulfate 

layers were created from National Atmospheric Deposition 

Program data. The data were used to interpolate yearly wet 

sulfate and wet nitrate deposition maps using gradient plus 

inverse distance interpolation (Nalder and Wein 1998). This 

technique adjusts for elevational, longitudinal, and latitudinal gradients when present in the data based on local regression of the 20 nearest neighbors. The wet sulfate deposition 

maps (2000-2004) were then averaged to produce a map of 

average annual deposition 2000-2004 (kg/ha per year). The 

same was done for wet nitrate deposition. Cross-validation 

(Issaks and Srivastava 1989) was performed to estimate the 

bias and precision of the yearly map.

Insect Pests and Disease—

Forest insect pests and diseases can affect forest health, 

including the reduction of tree basal area. The Forest Health 

Monitoring Program of the USDA Forest Service formed 

a Risk Map Integration Team (RMIT) to coordinate the 

development of a nationally consistent database for mapping insect pest and disease risk. The RMIT developed a 

GIS-based multicriteria risk modeling framework based 

on Eastman’s risk assessment process (Eastman and others 

1997).

A five-step multicriteria process was used to construct 

a 1-km by 1-km-resolution map depicting risk (Krist and 

others 2006): (1) Identify a list of forest pests (risk agents) 

and their target host species; this is conducted at the regional 

level with certain models constrained to select geographic 

areas. (2) Identify, rank, and weight criteria (factors and 

constraints) that determine the susceptibility and vulnerability to each risk agent. (3) Standardize risk agent criteria 

values, and combine the resultant criteria maps in a final 

risk assessment using a series of weighted overlays. Users 

assign a level of potential to values within GIS layers that 

represent criteria. (4) Convert modeled values of potential 

risk of mortality for each pest to predicted basal area (BA) 

loss over a 15-year period. This is accomplished for each 

risk agent/forest host species pair included in the national 

risk assessment. (5) Compile the resultant values from step 4 

and identify areas (1-km raster grid cells) on a national base 
138

GENERAL TECHNICAL REPORT PNW-GTR-802

Contributions

Watersheds with the greatest percentages of private forest 

land are generally in New England, the Southeast, and the 

Pacific Northwest (Figure 1). The concentration in the East 

is not surprising because much of the forest land in the West 

is in public ownership. Watersheds whose private forests 

make the greatest contributions to water quality, timber 

supply, at-risk species habitat, and interior forest closely 

align with the watersheds with greatest amounts of private 

forest land. 

Threats

Development threats to private forest land area are concentrated in southern New England and the Southeast, although 

some are also found in the Pacific Northwest; wildfire 

threats to private forest land (as indicated by the surrogate 

CC layer), are primarily in the Northeastern quadrant of the 

country. Data for these two layers are shown in conjunction 

with the “contributions” data in the next paragraphs. Threats 

to private forests from ozone are found scattered throughout 

the Eastern United States (Figure 2). Loss of basal area on 

map that are at risk of encountering a 25-percent or greater 

loss of total basal area in the next 15 years. 

Methods

For each contribution and threat layer, the distribution of 

watershed index values was determined, and a percentile 

ranking was assigned to each watershed. Threats to particular contributions were evaluated in two steps. First, the 

averages of the contribution and threat percentile rankings 

were calculated on a watershed-by-watershed basis. Second, 

the distribution of the averages was determined and used 

to assign a new percentile ranking to each watershed. The 

results are depicted using percentile-based categories similar to those used for individual contributions and threats. 

Results

The results are briefly discussed. Because of space limitations, only a few maps presenting data on individual layers 

of contributions or threats are displayed here. Instead, this 

paper focuses on some of the more interesting intersections 

of contributions and threats in the “Threats” section. 

Figure 2—Percentile ranking of watersheds with respect to ozone threat.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

private forest land from insect pests and diseases is most 

likely to occur in the East, but also in the lake States, the 

Southwest, California, and the Northwest (Figure 3).

Development threat to at-risk species associated with 

private forest is highest in the East, including Tennessee, 

North Carolina, and States immediately north and south of 

these States, as well as coastal California and the Pacific 

Northwest (Figure 4). Development threats to private 

interior forests are found throughout the Eastern United 

States and the Great Lakes area and are concentrated in the 

Southeast and Maine (Figure 5). Development threats to the 

contributions of private forest land to both water quality and 

timber supply are concentrated in southern New England 

and the Southeast (due to space limitations, these maps are 

not shown here).

Wildfire threats to both water quality and timber 

supply contributions are distributed throughout the East, the 

lake States, the central hardwoods region, and the Pacific 

Northwest (Figures 6 and 7). 

Conclusions

Four primary conclusions may be drawn from this study:

1. The FOTE spatial approach to assessing threats to

the contributions of private forest lands produces

useful, visual information that is relatively easy to 

obtain. The only serious impediment is the 

difficulty in obtaining or constructing nationally 

consistent data layers that depict the contributions 

and threats of interest.

2. The watersheds making the greatest private forest

contributions to water quality, timber supply, at-risk 

species, and interior forest are generally the 

watersheds with the greatest percentages of private 

forest land (i.e., those in the Eastern United States,

particularly New England and the Southeast, and 

some watersheds in the Pacific Northwest). Two 

exceptions are noted. Some watersheds in western 

California and Florida do not have large amounts 

of private forest but do have large numbers of 

at-risk forest-associated species. In addition, some 

Figure 3—Percentile rankings of watersheds with respect to loss of basal area of private forests associated 

with insect pests and diseases.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 4—Percentile rankings of watersheds with respect to housing development threat to contribution of 

private forest land to at-risk species habitat.

Figure 5—Percentile rankings of watersheds with respect to housing development threat to interior forest 

on private lands.
141

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 6—Percentile rankings of watersheds with respect to wildfire threat to contribution of private forest 

land to water quality.

Figure 7—Percentile rankings of watersheds with respect to wildfire threat to contribution of private forest 

land to timber supply.
142

GENERAL TECHNICAL REPORT PNW-GTR-802

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[and others]. 1995. The contribution of Federal and 

non-Federal habitat to persistence of northern spotted 

owl on the Olympic Peninsula, Washington. Report of 

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watersheds in eastern Texas, the Southwest, and 

Washington State also do not have large amounts 

of private forest but do have high proportions of 

private forest classified as interior forest.

3. Watersheds with the greatest development threat 

to the contributions to water quality, timber supply, 

at-risk species, and interior forest are also generally 

the watersheds with the greatest percentages of 

private forest land. The exceptions for at-risk 

species and interior forest noted in the previous 

paragraph apply here as well.

4. The CC surrogate for wildfire depicts the greatest 

threats to watersheds in the central part of the 

Eastern United States and the Pacific Northwest 

(although watersheds in the central part of the 

United States have relatively small percentages of 

private forest land). Percentile rankings of 

watersheds based on wildfire threat to private 

forest contribution to water quality and timber 

supply follow this pattern as well.

Future Forests on the Edge work will include assessment of additional contribution and risk intersections and 

the construction of an Internet-based system that permits 

users to select particular contribution and threat layers, 

options for combining them, and options for depicting the 

results. In addition, an assessment of national forests and 

grasslands most likely to experience increased pressures 

from housing development on adjacent lands is nearing 

completion. For more information on Forest on the Edge, 

go to our Web site at http://www.fs.fed.us/openspace/fote.

Acknowledgments

The authors acknowledge the contributions of the following 

individuals who constructed layers or provided assistance 

in data processing, coordination ofcontributions, and 

consultations: Xiaoping Zhou and John Mills of the Pacific 

Northwest Research Station, USDA Forest Service; Lisa 

Mahal, University of Nevada, Las Vegas; Greg Liknes, 

Northern Research Station, USDA Forest Service; John W. 

Coulston, Southern Research Station, USDA Forest Service; 

Frank Krist, Jr., Forest Health Technology Enterprise Team, 

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wildland fire and fuel management. Gen. Tech. Rep. 

RMRS-GTR-87. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research 

Station. 41 p.

Smith, G.C.; Coulston, J.W.; Jepsen, E.; Prichard, 

T. 2003. A national ozone biomonitoring program: 

results from field surveys of ozone sensitive plants 

in northeastern forests (1994–2000). Environmental 

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Tech. Rep. NC-241. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central Research 

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nc241.pdf. [Date accessed: February 28, 2005].

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projecting housing development on private forests. In: 

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and technology symposium: Unifying knowledge for 

sustainability in the Western Hemisphere. Proceedings 

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on America’s private forests. Gen. Tech. Rep. PNWGTR-636. Portland, OR: U.S. Department of Agriculture, 

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growth in the U.S.A. from 1980 to 2020. Ecology and 

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Wear, D.; Pye, J.; Riitters, K. 2004. Defining conservation 

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145

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Jeffrey K. Stone, Leonard B. Coop, and Daniel K. Manter

Jeffrey K. Stone and Leonard B. Coop, Department of 

Botany and Plant Pathology, Cordley Hall 2082, Oregon 

State University, Corvallis, OR 97331; Daniel K. Manter, 

USDA-ARS Soil Plant Nutrient Research Unit, Fort Collins, 

CO 80526. 

Abstract

Swiss needle cast disease of Douglas-fir (Pseudotsuga 

menziesii) is caused by the ascomycete fungus Phaeocryptopus gaeumannii. Symptoms of the disease are foliage 

chlorosis and premature needle abscission due to occlusion 

of stomata by the ascocarps of the pathogen, resulting 

in impaired needle gas exchange. Severe defoliation 

and growth losses of 20 to 50 percent because of Swiss 

needle cast have been reported for about 150 000 ha of 

Douglas-fir plantations in western Oregon since 1996. 

Because the physiological effects of the disease (impaired 

CO2 uptake and photosynthesis) are quantitatively related 

to the abundance of the pathogen (proportion of stomata 

occluded by ascocarps), pathogen abundance is directly 

related to disease and is a suitable response variable for 

assessing effects of climatic factors on disease. Climate 

factors most highly correlated with pathogen abundance are 

winter temperature and spring leaf wetness, and a model 

for prediction of disease severity based on these factors 

accounts for 77 percent and 78 percent of the variation in 

1- and 2-year-old needles, respectively, for western Oregon 

sites. A trend of temperatures increasing by 0.2 to 0.4 °C 

during the winter months and spring precipitation increasing by 1.6 to 2.6 cm per decade since 1966 suggests that 

regional climate trends are influencing the current distribution and severity of Swiss needle cast disease. Forecasts 

of climate change in the Pacific Northwest region predict 

continued increases in temperatures during winter months 

of about 0.4 °C per decade through 2050, suggesting that 

the severity and distribution of Swiss needle cast is likely 

to increase in the coming decades as a result of climate 

change, with significant consequences for Pacific Northwest 

A Spatial Model for Predicting Effects of Climate Change 

on Swiss Needle Cast Disease Severity in Pacific Northwest 

Forests

forests. A climate-based disease prediction model is being 

developed as an online, interactive tool that can be used to 

guide further research, conduct extended model validations, 

perform climate change scenario analyses, and eventually 

to provide short- and long-term disease risk predictions and 

management cost/benefit analyses. The model will be useful 

for prediction of disease development trends under different 

climate change scenarios and temporal scales.

Keywords: Douglas-fir, foliage disease, forest pathology, Phaeocryptopus gaeumannii, Pseudotsuga menziesii.

History of the Problem

Swiss needle cast disease of Douglas-fir (Pseudotsuga 

menziesii (Mirb.) Franco) is caused by the ascomycete Phaeocryptopus gaeumannii (Rohde) Petrak. The disease, and 

the fungus that causes it, were first described from Douglasfir plantations in Switzerland and Germany in 1925, and 

soon afterward reported from various locations in Europe, 

the British Isles, and Northeastern North America (Boyce 

1940, Peace 1962). The causal agent, P. gaeumannii, was 

found to be abundant on foliage of diseased trees and was 

determined to be distinct from any previously described 

foliage fungi from coniferous hosts. Subsequent surveys of 

Douglas-fir in the Western United States found the pathogen 

was widespread throughout the Pacific Northwest region, 

where it had escaped notice because of its inconspicuous 

habit and negligible effect on its host. Boyce (1940) considered P. gaeumannii widespread but harmless on Douglas-fir 

in Western North America, and probably indigenous to the 

Pacific Northwest, where “...the fungus has been found at 

such widely separated localities in British Columbia, Washington, and Oregon that it must be considered generally 

distributed, although harmless, in the Douglas-fir region of 

the Pacific coast.”

Douglas-fir is the only host of P. gaeumannii, which 

has accompanied cultivation of its host to various localities 

worldwide (Hood 1997, Temel and others 2003). Diseased 

trees have chlorotic foliage and may lose all but the current year’s complement of needles (Figure 1). The earliest 
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GENERAL TECHNICAL REPORT PNW-GTR-802

confirmed records of P. gaeumannii in western North 

America are herbarium specimens collected by J.S. Boyce 

from Oregon and California in 1916 (Boyce 1940). The 

fungus occurs on both coastal (Pseudotsuga menziesii var. 

menziesii) and interior (P. menziesii var. glauca (Beissn.) 

Franco) forms of Douglas-fir throughout the natural range 

of the host (Boyce 1940, Hood 1982). The fungus has also 

been reported from P. macrocarpa (Vasey) Mayr in New 

Zealand (Gadgil 2005), although it has not been reported on 

this host in North America. 

Although Swiss needle cast disease in Europe first 

brought attention to Phaeocryptopus gaeumannii, the 

fungus has long been considered a pathogen of negligible 

significance in western North American forests. Peace 

(1962) wrote of Swiss needle cast disease in Europe: “This 

is a classic case of a disease, of no importance in its native 

haunts, which has become damaging when transported to 

other areas.” With the growth of a commercial Douglas-fir 

Christmas tree industry in the Pacific Northwest in the 

1970s, and the practice of shearing trees to promote a 

more compact growth form, Swiss needle cast first began 

to attract serious notice in its native region (Michaels and 

Chastagner 1984). Although episodic outbreaks of brief 

duration in forest plantations in the Pacific Northwest were 

noted in the 1970s and 1980s (Russell 1981), Swiss needle 

cast was not considered a significant forest pathogen. The 

occurrence of Swiss needle cast disease primarily in forest 

plantations outside the native range of Douglas-fir, or on 

sheared Christmas tree plantations, has contributed to the 

perception of the disease as being primarily associated with 

inappropriate planting stock or stressed trees. 

Current Extent and Impacts of Swiss 

Needle Cast in Pacific Northwest Forests

Since around 1990, unusually severe and persistent symptoms of Swiss needle cast have been observed in Douglas-fir 

forest plantations in western Oregon, particularly near the 

town of Tillamook (Hansen and others 2000). Annual aerial 

surveys conducted since 1996 by the Oregon Department 

of Forestry have documented the disease on about 150 000 

ha of forest land in the Oregon Coast Range (Figure 2). 

Unlike Boyce’s (1940) characterization of the pathogen on 

native Douglas-fir as being inconspicuous and harmless, the 

fungus is abundant, trees frequently are defoliated of all but 

current-year needles, and attached foliage is often severely 

chlorotic (Figure 1). Growth reductions of 20 to 50 percent 

Figure 1—Symptoms of Swiss needle cast, premature defoliation, loss of older needles.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

from Swiss needle cast have been measured in the affected 

area (Maguire and others 2002). The severity of the problem 

in Oregon has brought renewed interest in understanding 

the biology of the pathogen and epidemiology of Swiss 

needle cast disease. In particular, research has focused on 

understanding why an inconspicuous, insignificant native 

pathogen has become a significant forest health problem.

Aerial surveys for Swiss needle cast conducted by the 

Oregon Department of Forestry have classified patches of 

Swiss needle cast severity based on foliage discoloration, 

characterizing the discoloration as being severe or moderate. The affected area as determined by the aerial survey 

lies along the entire length of the Oregon coast, extending 

inland about 40 km, with most symptoms occurring within 

30 km of the coast. The crest of the Coast Range forms the 

approximate eastern edge of the affected area. The aerial 

survey covers about 1.2 million ha of coastal forest, with the 

symptomatic area comprising between 50 and 160 000 ha 

(Figure 2). 

Disease severity (foliage retention, discoloration, crown 

sparseness, pathogen abundance) has also been monitored 

annually in permanent plots in the Oregon Coast Range 

(Hansen and others 2000) equipped with temperature and 

leaf wetness dataloggers. Foliage retention, and abundance 

Figure 2—Annual variation in surveyed Swiss Needle Cast (SNC) area in western Oregon 1996–2006 (A) and combined disease severity 

survey scores for western Oregon (severity scores for all 11 years summed and superimposed on map B). Legend signifies 1 = disease 

moderate for single year to 22 = disease severe all 11 years of survey. Circled areas highlight regions where chronic SNC has been 

observed. Source data: Oregon Department of Forestry.
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GENERAL TECHNICAL REPORT PNW-GTR-802

management practices that favor the cultivation of 

Douglas-fir over other species (Hansen and others 2000). 

Swiss needle cast itself may be one such environmental 

determinant of the natural distribution of Douglas-fir in 

the western Coast Range, making it an inferior competitor 

where Swiss needle cast is severe (Hansen and Stone 2005). 

But what factors affect the distribution and abundance 

of P. gaeumannii? It has long been suspected that local 

climate plays a key role in the pathogenicity of P. gaeumannii. Boyce (1940) suggested that seasonal patterns in 

local climate could differentially affect fungal growth and 

development, and this might explain the greater virulence 

of P. gaeumannii in Europe and the Eastern United States 

compared to the area where both P. gaeumannii and 

Douglas-fir are native. Within the Douglas-fir zone of the 

Pacific Northwest, there is also a relationship between 

disease severity and local climate. Hood (1982) found more 

P. gaeumannii in southern British Columbia and western 

Washington in coastal forests of Vancouver Island and the 

Olympic Peninsula, with lower levels in the rain shadow of 

eastern Vancouver Island and the interior, and attributed 

the difference mainly to precipitation patterns. More severe 

disease symptoms and greater fungal colonization are 

commonly observed on lower elevation sites near the coast, 

suggesting the possible involvement of maritime fog (Rosso 

and Hansen 2003).

Rosso and Hansen (2003) used a visual index of stand 

disease to investigate the relationship between several environmental variables and Swiss needle cast severity. The best 

model for predicting stand disease rating (a composite index 

of several factors) was a regression model that included 

July temperature, precipitation, and fog occurrence, which 

explained about 60 percent of the variation. Manter and 

others (2005) found a strong relationship between winter 

(Dec.–Feb.) mean daily temperature, spring (May–July) leaf 

wetness, and P. gaeumannii abundance. 

Understanding the Disease

To better understand which environmental factors might be 

important regulators of P. gaeumannii abundance and how 

they affect the epidemiology of Swiss needle cast, it was 

essential to investigate the infection cycle of the pathogen 

of P. gaeumannii ascocarps on 1- and 2-year-old needles 

have been monitored annually since 1996 in 9 to 12 

Douglas-fir stands, initially aged 12 to 15 years. Study sites 

were selected to represent a range of elevations, distance 

from maritime influence, and disease severity. Within the 

area of severe disease, symptom severity is variable, but 

all Douglas-fir show some effects of the disease compared 

to healthy stands on the eastern slope of the Coast Range 

and in the Cascade Range. Normal needle retention in 

healthy coastal form Douglas-fir is about 4 years. Within 

the epidemic area, needle retention varies from about 1.5 

to 2.6 years (Hansen and others 2000). Although symptom 

severity (e.g., foliage discoloration, needle retention) for all 

sites varies from year to year, relative disease severity (i.e., 

abundance of P. gaeumannii ascocarps on 1- and 2-year-old 

foliage) is fairly consistent. Disease tends to be more severe 

nearer the coast, at lower elevations, and on southern aspect 

slopes, gradually diminishing to the east (Hansen and 

others 2000, Manter and others 2003b, Rosso and Hansen 

2003). 

The Swiss needle cast epidemic area appears to correspond approximately to the Sitka spruce (Picea sitchensis

(Bong.) Carr.) vegetation zone, a narrow strip of coastal 

forest characterized by elevations generally below 150 m, 

proximity to the ocean, a moderate climate, and a distinct 

forest type (Franklin and Dyrness 1973). Although 

Douglas-fir is considered the early seral dominant in the 

western hemlock vegetation zone, which borders the Sitka 

spruce zone to the east, its occurrence within the Sitka 

spruce zone is more sporadic. There Douglas-fir occurs 

mainly in mixtures with Sitka spruce and western hemlock 

(Tsuga heterophylla (Raf.) Sarg.), but normally not as 

pure stands as is typical of early postfire succession in the 

western hemlock zone (Franklin and Dyrness 1973).

The correspondence between the Sitka spruce zone 

and the main Swiss needle cast epidemic area suggests 

that environmental factors that influence the development 

of different plant associations in western Oregon may also 

influence the severity of Swiss needle cast. Irruption of 

the disease in the coastal zone may be owing changes in 

abundance of Douglas-fir in coastal forests owing to forest 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

and its mechanism of pathogenicity. Ascospores of P. gaeumannii mature and are released during early May through 

late July, coinciding with bud break and shoot elongation of 

Douglas-fir. Ascospores are the only infective propagule; 

there is no conidial anamorph. Newly emerging foliage of 

Douglas-fir is most susceptible to infection. Infection of 

1-year-old needles during the same period also occurs but 

is much less frequent. Germinating ascospores produce 

appressoria above stomata, and penetration pegs enter 

needles via stomata. Colonization within needles is exclusively intercellular; no intracellular hyphae or haustoria are 

produced. Internal colonization occurs gradually during the 

fall and winter. Ascocarp (pseudothecia) primordia begin 

to form in substomatal chambers at 4 to 9 months following 

infection. Concurrently with the formation of pseudothecial 

primorida, epiphytic hyphae emerge from the periphery of 

developing pseudothecia. These hyphae grow across the 

needle surface, form numerous anastomoses, and reenter 

the needle by producing appressoria above unoccupied stomata (Stone and others 2008). The importance of epiphytic 

hyphae in needle colonization and epidemiology of Swiss 

needle cast is not fully understood (Figure 3).

Internal colonization of needles continues as long as 

they remain attached, so numbers of ascocarps increase as 

needles age. Normally, fruiting bodies of the fungus are 

more abundant on needles aged 3 years or older and are 

sparse or absent on younger foliage (Boyce 1940, Hood 

1982). In recent years, however, trees having abundant fruiting bodies on current-year needles have been commonly 

observed in forest plantations along the Oregon coast, with 

older foliage being prematurely abscised due to the disease 

(Hansen and others 2000). 

The ascocarp primordia completely occupy the substomatal space, thereby rendering the stoma nonfunctional. 

Occlusion of the stomata by pseudothecia of P. gaeumannii 

impedes gas exchange and regulation of transpiration, 

causing impaired photosynthetic activity and is considered 

the primary mechanism of pathogenicity (Manter and others 

2000, 2003). Disruption of host cells by hyphal penetration 

has not been observed in infected needles examined in 

SEM and TEM preparations (Stone and others 2008), and 

attempts to find evidence of fungal phytotoxins to date have 

been inconclusive. Estimates of the effect of P. gaeumannii

on CO2 assimilation indicate that occlusion of 25 percent 

of stomata results in negative needle carbon budgets, i.e., 

respiration exceeds assimilation, on an annual basis (Figure 

4) (Manter and others 2003a). 

The abundance of pseudothecia is also highly correlated with needle abscission because of the effect on CO2

assimilation. It has been suggested that foliage abscission 

occurs when needles switch from being carbon sources to

carbon sinks (Cannell and Morgan 1990). The mechnism of 

Figure 3—Phaeocryptopus gaeumannii on the underside of Douglas-fir needles with pseudothecia (fruiting bodies) emerging through 

stomata. A, B. Pseudothecia aligned along stomatal rows. C. Cross section through a stoma showing obstruction of the opening by the 

pseudothecium (p) between the guard cells (gc).
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GENERAL TECHNICAL REPORT PNW-GTR-802

pathogenicity of P. gaeumannii, therefore, can be accounted 

for by the physical blockage of the stomata and interference with photosynthetic gas exchange. The proportion 

of stomata occupied by pseudothecia on attached needles 

seldom exceeds 50 percent, suggesting that most needles are 

abscised before more than half the stomata are occluded by 

pseudothecia, regardless of needle age (Hansen and others 

2000). Because the physiological effects of the disease 

(impaired CO2 uptake and photosynthesis) are quantitatively 

related to the abundance of the pathogen (proportion of 

stomata occluded by ascocarps), pathogen ascocarp abundance is a suitable response variable for assessing effects of 

climatic factors on disease.

Experimental Approaches to Understanding 

Climate-Disease Interactions

Because the most severe disease has been observed in sites 

within the low-elevation coastal fog zone (so called because 

of the frequency of summer maritime fog), the presence 

of free water on needle surfaces during the summer has 

been considered a possible factor affecting disease severity 

(Hansen and others 2000, Rosso and Hansen 2003). Other 

investigators have noted a relationship between precipitation patterns and Swiss needle cast severity in the Pacific 

Northwest (Hood 1982, McDermott and Robinson 1989). 

Manter and others (2005) attempted to investigate the relative effects of individual climate factors on P. gaeumannii 

abundance experimentally. A factorial design was used to 

compare the effect of post-inoculation incubation conditions 

under two levels each of drip irrigation, shade, and intermittent mist on P. gaeumannii colonization. Seedling trees 

were exposed to inoculum in a diseased forest stand, then 

randomized and maintained under the different postinoculation treatments. Abundance of P. gaeumannii

ascocarps on foliage was determined monthly. The different postinoculation conditions resulted in significant 

differences in P. gaeumannii development. Abundance 

of P. gaeumanii was negatively correlated with shade and 

mist—this unexpected outcome was interpreted as a result 

of indirect effects of shade and mist on temperature and not 

as a direct effect of shade or leaf wetness—and irrigation 

had no effect (Figure 5).

Figure 4—Relationship of net carbon dioxide uptake (Anet) per unit leaf area to [A] Phaeocryptopus gaeumannii pseudothecia abundance (disease index, percent) and [B] needle retention (percent). Taken from Manter and 

others (2003a).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Modeling Swiss Needle Cast

The finding that small differences in temperature could 

affect rates of needle colonization and fungal development 

over the 11-month incubation period of P. gaeumannii

prompted us to examine the effect of temperature in relation 

to infection data from field sites in western Oregon. Average 

daily temperature and cumulative leaf wetness hours were 

separated into 3-month groups, corresponding to major 

phases in the infection cycle, and subjected to stepwise 

regression against P. gaeumannii infection data to identify 

climate factors for use in a disease prediction model. 

Consistently strong correlations were found between winter 

(Dec.-Feb.) mean daily temperature and infection in both 1- 

and 2-year-old needles (R2

 = 0.75 to 0.92), and this was the 

only climate variable with R2

 values above 0.5. The best-fit 

climate model included winter mean daily temperature 

and cumulative spring leaf wetness hours, R2 = 0.78 and 

0.77 for 1- and 2-year-old needles, respectively. When this 

model was tested against infection data for different sites in 

different years, a significant 1:1 relationship was found (R2

= 0.79, Figure 6).

Figure 6—Best-fit model for predicting Phaeocryptopus gaeumannii abundance using only climate variables (winter temperature and spring leaf wetness), predicted vs. observed values. From 

Manter and others (2005).

Figure 5—Final Phaeocryptopus gaeumannii disease index values for seedlings incubated under shade, 

mist, and irrigation treatments following inoculum exposure. Sun = 100 percent ambient, shade = 50 percent 

ambient, mist = three 2-hour overhead mist treatments per day, no mist = 0 overhead mist treatments per day, 

low irr. = 0.5 L day-1 drip irrigation, and high irr. = 1.9 L day-1 drip-irrigation. Bars are the arithmetic means 

and individual standard errors. Treatment effects were analyzed using a split-randomized complete block 

design, and bars with different letters are significant at p < 0.05. A. Monthly measurements of pseudothecia 

abundance by treatment. B. Final disease index by treatment. Taken from Manter and others (2005).
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GENERAL TECHNICAL REPORT PNW-GTR-802

A more general model was sought that could be used to 

predict geographic variation in Swiss needle cast severity 

with the aid of spatial climate models. However, because 

leaf wetness is not readily available in public meteorological databases or climate models, alternative models were 

tried. Winter temperature was the best single predictor of P. 

gaeumannii abundance, but its relationship with infection 

level varied by year. The abundance of P. gauemannii and 

severity of Swiss needle cast for a particular site in a given 

year are not independent of previous year disease severity 

for the site. Abundance of P. gaeumannii in 1-year-old 

needles is partly determined by the amount of inoculum 

present and the number of ascospores that initiate infection 

on a needle. Because ascospore infection of needles in their 

second year is negligible (Stone and others 2008, Hood and 

Kershaw 1975), abundance of P. gaeumannii on 2-year-old 

needles is partly determined by the degree of colonization 

in needles at the beginning of their second year. Therefore, 

two infection components, the amount of P. gaeumannii

in 2-year-old and 1-year-old needles for the previous year, 

were added to the temperature model to predict infection 

in 1- and 2-year-old needles for the year of interest. Disease 

predictions generated by this model had a significant 1:1 

relationship (R2

 = 0.812) when compared with observed 

values in the validation data set (Manter and others 2005), 

(Figure 7). 

The temperature/infection model described above 

was then run over several iterations with temperature held 

constant over a range of values (2 to 10 °C). After five iterations, the infection level reaches a stable asymptote for any 

value of winter temperature. The maximum infection value 

is a function of winter temperature, regardless of initial 

infection level, and represents the point of equilibrium 

between colonization and inoculum production. A plot of 

the equilibrium infection level against temperature shows 

that between 3.77 and 8.90 °C, the range of average winter 

temperatures measured at coastal study sites, infection 

levels vary from zero to about 15 percent in 1-year-old 

needles and between 10 to about 40 percent in 2-year-old 

needles (Figure 8). 

This model also was accurate for predicting disease 

levels for sites in the Western Coast Range (R2

 = 0.85) 

but was less accurate when infection data from sites from 

the east slope of the Coast Range and Willamette Valley 

were included (R2 = 0.70). Observed infection levels for 

the inland sites were less than predicted by the equilibrium 

temperature model, and this is likely due to the comparative 

dryness of the inland sites. The model that included a term 

for spring leaf wetness was more accurate in predicting 

infection levels for the inland sites than the equilibrium 

Figure 7—Plots of predicted vs. observed values for the best-fit 

model for predicting Phaeocryptopus gaeumannii abundance. 

Terms in the model were average daily winter temperature (Dec.-

Feb.) and preceding year disease index for the corresponding 

needle cohort. From Manter and others (2005). Panel A, 1-year-old 

needles; panel B, 2-year-old needles.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 8—A. Simulation of Phaeocryptopus gaeumannii infection index over time as predicted by the combined winter temperatureinfection model, with temperature held constant (5.13 oC) and initial infection index of 1 percent; B. Relationship between final equilibrium infection level and winter temperature for 1- (solid circles) and 2-year-old (open circles) needles.

temperature model, illustrating that the relative importance of different climate factors can vary spatially. The 

simple equilibrium temperature model appears suitable 

for predicting disease levels in the Western Coast Range, 

where spring surface moisture on foliage is probably 

rarely limiting, but additional parameters are necessary 

for disease prediction on more inland sites. 

The equilibrium property of the model was then 

used to generate a disease prediction map for a portion 

of western Oregon based on the DAYMET climate 

model, a continuous surface at 1-km resolution base on 

18-year (1980-1997) average temperatures (Figure 9). The 

model appears to correspond well with the distribution 

of disease symptoms as observed by aerial surveys and 

permanent monitoring plots. Research is in progress to 

compare disease distribution and severity predicted by 

different models with observed aerial survey data.

The sensitivity of P. gaeumannii to relatively small 

temperature differences helps to explain patterns of 

spatial variation in Swiss needle cast severity, and suggests that recent increases in Swiss needle cast severity 

have been influenced by regional climate trends. Over the 

past century, average temperatures in the Pacific Northwest 

region have increased by about 0.8° C, with more warming 

occurring during winter months (Mote and others 2003). 

Average temperatures for the period January-March have 

increased by approximately 0.2 to 0.4 °C per decade since 

1966 in coastal Oregon and Washington (USDC NOAA 

2005). An increase in average winter temperature of 1 °C 

corresponds to an increase in infection index (proportion of 

stomata occluded) of 3 percent for 1-year-old needles and 

6 percent for 2-year-old needles, based on the equilibrium 

temperature model. Spring precipitation has also increased 

on average by about 1.6 to 2.6 cm per decade since 1966 

(USDC NOAA 2005). These climate trends suggest that 

over the past few decades, conditions have become more 

favorable for growth of P. gaeumannii and have contributed 

to increasing Swiss needle cast severity. Predictions for 

continued regional warming of about 0.4 °C per decade 

to 2050, together with increasing (+2 to 4 percent) spring 

precipitation (Mote and others 2003), suggest that conditions in the region will continue to be favorable for Swiss 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 9—Disease prediction map for northwest Oregon as predicted by the equilibrium disease model. Infection indices were predicted 

from winter average daily temperatures from the DAYMET climate model. From Manter and others (2005).

needle cast development, and could result in expansion of 

the area affected by the disease beyond the Western Coast 

Range of Oregon. A goal of ongoing research is to develop 

an improved disease prediction model to investigate the 

interactions between climate and Swiss needle cast. An 

expanded disease prediction model will be designed to 

incorporate long-term climate trend forecasts to enable 

site-specific short- and long-term disease risk predictions, 

growth impact predictions, and incorporate climate change 

models to allow examination of disease development trends 

under different climate scenarios.

Conclusions

Improved understanding of the effects of climate factors 

on P. gaeumannii abundance now helps to clarify the 

underlying causes for recently observed increases in Swiss 

needle cast in the Western Coast Range. Previous observations on the regional distribution of P. gaeumannii in the 

Pacific Northwest have suggested a connection between P. 

gaeumannii abundance and spring rainfall (Hood 1982). In 

the Western Coast Range of Oregon, where spring precipitation is abundant, winter temperature has been found to be 

a highly reproducible predictor of the spatial variation in 

abundance of P. gauemannii and resulting Swiss needle 

cast severity, presumably because of its effect on fungal 

growth. Winter temperature alone is not a satisfactory 

predictor of P. gauemannii abundance regionwide or in 

areas where spring precipitation is not as abundant, such 

as the Willamette Valley, or Oregon Cascade Range, as 

shown by Manter and others (2005). The predictive disease 

model described here, therefore, is applicable for predicting 

spatial variation in P. gaeumannii abundance and Swiss 

needle cast severity only for the western slope of the Oregon 

Coast Range. Within this area, there have been few historical reports of Swiss needle cast, and the disease has been 

considered an insignificant forest health issue. The natural 

distribution of Douglas-fir in the Western Coast Range has 

undoubtedly been influenced by P. gaeumannii and Swiss 

needle cast, along with other disturbance agents. The effect 

of chronic, profuse P. gaeumannii colonization of Douglasfir foliage is to reduce growth rates of affected trees relative 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

to competing species, such as spruce and hemlock. Normally faster growing than western hemlock, Douglas-fir 

is an inferior competitor where Swiss needle cast disease 

pressure is high. In the coastal lowlands and interior valleys 

of the Western Coast Range, seasonal climatic conditions 

are the most favorable for P. gaeumannii growth and 

reproduction. In these areas, a distinct natural forest type 

has historically been dominated by western hemlock and 

Sitka spruce, with Douglas-fir occurring only sporadically. 

Douglas-fir gradually becomes more abundant in natural 

forests at higher elevations and further inland, as the Sitka 

spruce zone gradually merges into the western hemlock 

vegetation zone, where Douglas-fir is a successional 

dominant, and where climatic conditions are less favorable 

for P. gaeumannii growth. This leads to the conclusion that 

the Sitka spruce vegetation zone occurs as a consequence 

not only of favorable habitat for Sitka spruce and western 

hemlock, but also because of the inhibition of their main 

competitor, Douglas-fir, due to Swiss needle cast disease. 

This scenario also suggests that the severity of Swiss 

needle cast in the region may be the result of recent forest 

management in the Western Coast Range, where Douglasfir has been strongly favored in forest plantations because 

of its greater economic value, increasing the abundance of 

the host species in the area most favorable for growth and 

reproduction of the pathogen. As noted above, however, 

recent climate trends also are likely to have contributed to 

current Swiss needle cast severity. Furthermore, forecasts 

of future climate trends for the Pacific Northwest suggest 

a probable expansion of the area affected by severe Swiss 

needle cast beyond the Western Coast Range as winter 

temperatures and spring precipitation continue to increase, 

resulting in greater disease pressure on Douglas-fir stands 

further inland.

Acknowledgments

This research was supported by the Office of Science, 

Biological and Environmental Research Program (BER), 

U.S. Department of Energy, through the Western Regional 

Center of the National Institute for Global Environmental 

Change (NIGEC) under Cooperative Agreement Nos. 

DE-FC03-90ER61010 and DE-FCO2-03ER63613. Financial 

support does not constitute an endorsement by DOE of the 

views expressed in this article/report. Funding was also 

provided by the Swiss Needle Cast Cooperative of Oregon 

State University.

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93: 790–798.

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279. Olympia: Washington Department of Natural 

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of infection and colonization of Douglas-fir needles by 

Phaeocryptopus gaeumannii. Mycologia. 

100(3): 431-444.

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Atmospheric Administration [USDC NOAA]. 2005. 

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Weather Service, Climate Prediction Center. U.S. http://

www.cpc.noaa.gov/trndtext.shtml. [Date accessed 

unknown].
157

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

David M. Theobald, Alisa Wade, Grant Wilcox, 

and Nate Peterson

David M. Theobald, research scientist, Natural Resource 

Ecology Lab, and associate professor, Department of 

Human Dimensions of Natural Resources, Alisa Wade, 

graduate student, Department of Forest, Range, and Watershed Stewardship, and Grant Wilcox, research scientist, 

Natural Resource Ecology Lab, Colorado State University, 

Fort Collins, CO 80523-1499; and Nate Peterson, GIS 

scientist, Institute of Sustainable Resources, Queensland 

University of Technology, Brisbane, Queensland 4001, 

Australia.

Abstract

A framework that organizes natural and protected areas is 

often used to help understand the potential risks to natural 

areas and aspects of their ecological and human dimensions. 

The spatial (or landscape) context of these dynamics is also 

a critical, but, rarely considered, factor. Common classification systems include the U.S. Geological (USGS) Gap 

Analysis Program GAP) stewardship coding scheme, the 

International Union for the Conservation of Nature (IUCN) 

Protected Area Management Categories, and the American 

Planning Association (APA) Land-Based Classification 

Standards. The GAP and IUCN frameworks are coarse 

classifications (four to eight categories), whereas the APA 

focuses primarily on private land uses. To address these 

limitations, we develop here more refined implementation 

methods based on the human modification framework, 

which conceptually is rooted in characterizing the degree 

to which natural processes are free or controlled, and the 

degree to which landscape patterns are natural or artificial. 

To provide useful and tighter coupling of specific threats 

and spatial data surrogates, we refine the conceptual basis 

by identifying three primary types of human activities that 

cause modification of natural systems and patterns. These 

are land uses categorized as urban/built-up, recreation, 

and production/extraction. We detail specific metrics and 

common data used as surrogates that can provide a stronger 

basis for characterizing the degree of human modification. 

We illustrate our methods by presenting analysis results for 

a Colorado case study.

Keywords: Human modification, protected areas, threat 

assessment, landscape pattern, land use.

Introduction

Overview of Conceptual Frameworks

A number of general wilderness frameworks have been 

established to provide a conceptual basis to understand protected areas. One of the earliest was provided by Brown and 

others (1978) who developed the Recreational Opportunity 

Spectrum (ROS), an approach that was based on characterizing resource, social, and managerial conditions for parks 

and recreation areas. The ROS has less well-developed 

guidance for characterizing threats and conservation on 

working and private lands. In Australia, Lesslie and Taylor 

(1985) developed the wilderness continuum concept that 

provided the basis for developing an inventory of highquality wilderness areas. More recently, Cordell and others 

(2005) provided a concise review of the two most widely 

accepted attributes of wildlands: naturalness and wildness.

A number of efforts have built on these conceptual 

frameworks to provide detailed characterizations of 

wilderness through fine-grained mapping. Lesslie and 

others (1988) provided an early mapping technique based on 

overlaying distance classes from settlement and road infrastructure. Kliskey (1998) developed a method to better link 

perception of wilderness to the ROS in a spatially explicit 

mapping framework. Aplet and others (2000) developed a 

nationwide map of wilderness by distinguishing managed 

from natural landscapes and mapping a multidimensional 

index at 1-km resolution. Building on earlier work in Australia and the United States, Sanderson and others (2002) 

mapped the human footprint for the entire globe at 1-km 

resolution.

More recently, scientists have been moving from 

employing general indicators of wilderness (or degree of 

human modification) to recognizing specific indirect and 

direct threats (e.g., to wilderness ecosystems by Cole and 

Landres 1996) and the relative degree to which different 

Analyzing Risks to Protected Areas Using the Human 

Modification Framework: A Colorado Case Study
158

GENERAL TECHNICAL REPORT PNW-GTR-802

types of protected areas function to protect biodiversity. 

A common approach to categorizing or organizing different types of protections or threats has been to define 

categories based on whether land is managed for permanent 

biodiversity maintenance through some legal or institutional 

mechanism, or both. Both the World Conservation Union 

(IUCN) and the U.S. Geological Survey’s (USGS) Gap 

Analysis Program (GAP) use this approach, though their 

protection categories differ somewhat (Davey 1998, Scott 

and others 1993). For example, the IUCN contains eight 

categories (IUCN 1994): I–scientific reserve/strict nature 

reserve; II–national park; III–natural monument/natural 

landmark; IV–managed nature reserve/wildlife sanctuary; 

V–protected landscapes; VI–resource reserve; VII–natural 

biotic area/anthropological reserve; and VIII–multiple-use 

management area/managed resource area.

The USGS GAP program found these too vague for 

their uses, and so they devised a stewardship classification with four status classes (Csuti and Crist 2000): (1) 

permanent protection from conversion of natural land cover, 

with natural disturbance events allowed; (2) permanent 

protection and some suppression of natural disturbance; (3) 

some extractive uses permitted; and (4) no protection from 

conversion of natural land cover.

The Land-Based Classification Standards (APA 2003) 

provide a multidimensional land use classification model 

based on five characteristics: activities (land use based on 

observable characteristics), functions (economic purpose), 

building types (structures), site development character 

(physical characteristics such as land cover types), and ownership constraints (private vs. public, or other legal devices 

to constrain use). They are richly developed for private 

land but are less directly applicable to public protected or 

conserved areas.

The IUCN categories have been revised substantially 

in the Conservation Measures Partnership, a collaborative 

effort mostly among nongovernmental organizations that 

includes The Nature Conservancy, World Wildlife Fund, 

and Conservation International, in addition to the IUCN. 

They recognize 11 classes of direct threats (IUCN-CMP 

2006): residential and commercial development; agriculture and aquaculture; energy production and mining; 

transportation and service corridors; biological resource 

use; human intrusions and disturbance (e.g., recreation, war, 

etc.); natural system modifications (e.g., fire suppression); 

invasive species and genes; pollution; geologic events; and 

climate change and severe weather.

Several alternative approaches have recently emerged. 

For example, the human footprint (Sanderson and others 2002) represents, in a general way, the total footprint 

of all the human population's influence as a continuum 

stretched across the land surface. Implicit in nearly all 

these approaches is that if an area is of high value, and it is 

unprotected, then it is vulnerable. However, incorporation 

of threat directly into these frameworks is underdeveloped, 

and these approaches have been critiqued because they 

neglect a means to assess the relative imminence of the 

threat or urgency of conservation action need—Are some 

gaps more important than others, and, if so, which ones 

(Margules and Pressey 2000, Theobald 2003)?

Objectives

We find four main limitations of these existing frameworks. 

First, they are often based on ad hoc categories or classes, 

with little or minimal attention to establishing explicit 

measures of threat. Second, they are based on discrete 

categories or classes, and so cannot resolve small differences. Also, because they are based on class (or nominal) 

data, they cannot be easily integrated at different spatial 

scales to examine landscape context. Third, the surrogate 

spatial variables that are used as a basis for classifying are 

generally too coarse grained to capture fine-grain patterns, 

ultimately limiting detailed characterization. Finally, and, 

especially, most approaches examine the in situ landscape 

content of a park or protected area (PPA) using levels of 

threat or protection. Typically, an individual PPA receives 

a single score, regardless of size or the types of adjacent 

land uses. For example, although national parks often have 

an intensely used, built-up portion (e.g., Yosemite Valley in 

Yosemite National Park) directly adjacent with designated 

wilderness, typical protocols and methods result in only 

a single value being applied to the entire PPA. In addition 

to content, we believe that the landscape context of a PPA 

is important as well. This aligns analytical methods with 
159

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

the growing concerns of land managers over these external 

threats. For example, two of the main threats to the National 

Forest System (loss of open space due to housing development and invasive species) are external (Bosworth 2004), 

and changes in surrounding land use are a leading threat to 

resources in United States national parks (GAO 1994).

We aim to address these limitations by building on 

the human modification framework (HMF) (Theobald 

2004). The HMF is based on two dimensions: the degree 

to which natural processes are free or controlled, and the 

degree to which landscape patterns are natural or artificial. 

To provide useful and tighter coupling of specific threats 

and spatial data surrogates, we refine the conceptual 

framework by identifying three primary types of human 

activities that cause human modification of natural systems 

and patterns. These are land uses categorized as urban/

built-up, recreation, and production/extraction. We detail 

specific metrics and common data used as surrogates that 

can be used to implement each of these factors to provide 

a stronger basis for characterizing the degree of human 

modification. Our overall goal in this paper is to develop a 

general yet simple method to characterize threats associated 

with human land use activities for managing landscapes 

and to provide metrics and more directly capture threats in 

a detailed, spatially explicit manner. Our objectives are to 

describe factors that can be used to refine the HMF, identify 

common surrogates of these factors that can be mapped at 

relatively fine grain, conduct a spatially explicit analysis of 

the landscape context, and illustrate these methods using a 

case study example for the State of Colorado.

Methods

Human Modification Framework

Here we further refine the HMF to emphasize three primary 

factors that can be used to characterize land use based on 

what human activities occur at a given location. Principally, humans go to an area to produce or remove natural 

resources (production/extraction), visit but do not extract 

significant resources (recreation/tourism), or concentrate 

or intensify resources by reconfiguring and constructing 

buildings and other infrastructure (urban/built-up). Each of 

these three factors begins with a value of 0.0 denoting no 

human influence (i.e., a wild location), and 1.0 representing 

an area that is strongly influenced (developed) by human 

activities. We seek to fully characterize the spatial heterogeneity of threats by characterizing land uses at a relatively 

fine grain (<1 km2 cells or minimum mapping unit), rather 

than attributing an average or overall value across a broader 

spatial extent (e.g., a GAP stewardship value for an entire 

national park). We then can overlay our threat maps with a 

map of protected areas to assess gaps and opportunities.

For each factor, we explicitly place values for different 

levels of threat along a 0 to 1.0 axis (Table 1). Although 

these values are either assigned arbitrarily, based on expert 

opinion, mapped through surrogate variables, or quantified 

Table 1—Example land uses along human modification gradient for each factor. Each row provides 

an example of a possible human use along the “wild” (0.0) to human-dominated (1.0) gradient for 

each of the three factors (columns)

Value Convert Extract Visit

(urban/built-up) (production/extraction) (recreation/work)

0.0 Wilderness Wilderness Municipal watershed (no access)

0.1 Wilderness with trails Gathering Private preserve (guide d access)

0.2 Wilderness w/trails, bridges Public grazing allotments Wilderness area

0.3 Multiuse forest - Backcountry forest

0.4 Rural housing density Timber selective cut Multiuse forest

0.5 Campground Hay meadow Highuse trail/destination

0.6 Exurban housing density Timber clearcut Motorized recreation

0.7 Visitor center Dryland crop Cross-country ski resort

0.8 Suburban housing density Oil/gas well Downhill ski resort

0.9 Urban residential Mine Urban park

1.0 Commercial/industrial Irrigated cropland Sports stadium/complex
160

GENERAL TECHNICAL REPORT PNW-GTR-802

using some combination of these, it is critical that we make 

our assumptions quantitatively explicit, at the very least to 

communicate how threats may affect a location and their 

relative importance to other threats. Also note that this 

approach is agnostic to land ownership—that is, land use 

activities for each of these factors can occur on both private 

and public lands (though some types may be more dominated than others). For example, although built-up is often 

associated with urban areas, some localized areas within 

protected areas (e.g., a national park visitor center) can be 

highly built-up as well. Table 2 proceedes a short, preliminary listing of surrogate maps that are commonly used to 

estimate these activities for the three factors—yet much 

work needs to be done to fill these critical data gaps. Again, 

we emphasize that often these values are arbitrary, but 

providing explicit values is a useful and important exercise.

Urban/Built-Up—

This factor characterizes the intensity to which humans 

occupy a given location. Human activities often concentrate 

Table 2—Possible surrogate spatial data for urban/built-up factor 

Data Source Scale (extent/grain) Notes

Housing density Census Bureau; SERGoM Nationwide/block Better indicator of landscape change

 v2 (Theobald 2005) Nationwide/1 ha than population density

Population density Census Bureau Nationwide/block Commonly used

Road density Census TIGER; GDT; Nationwide/1:100K Need to identify the radius or size 

 NavTec of the moving window in 

 computing density.

 Major road types are often distin-

 guished and weighted when com-

 puting road density. Need to 

 extend to characterize road use 

 (i.e., traffic volume) as well.

 Poor representation of rural, 

 forest, park areas

Structures (houses, Parcel-level County by county Critical data for fine-grained

 buildings, bridges) National forest/park unit analysis, but very limited avail-

 ability etc.

Trail density National forest/PPA PPA/1:24K, GPS Need to specify radius of moving 

 window when computing density 

 (~ 500 m)

 Can major differences in trail 

 types, such as modes of use, use 

 level be distinguished?

 Standardized, consistent data on 

 trails are not available

Campground density USFS PPA/1:24K Location of campgrounds, number 

 of units/sites, and type of use

Dam density National Inventory of Dams Nationwide/ For major dams (> 4 m in height);

 1: 1,000,000 spatial precision issues; incom- 

 plete attributes such as year built, 

 storage volume, etc.

Land cover National Land Cover Data Nationwide/900 m2 Urban/built-up classes (21, 22, 23)– set (NLCD) update with NLCD 2001 when 

 available

Each row provides an example of a possible spatial datum that is commonly used as a surrogate for this factor.

PPA = park or protected area.
161

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

or intensify resources by reconfiguring resources or constructing buildings and other infrastructure such as roads, 

dams, bridges, etc. Table 2 provides a list of useful data 

layers that can be used to measure the degree of the urban/

built-up factor. Common surrogate spatial variables include 

population density and housing density. We prefer housing 

density because it more directly characterizes landscape 

and ecological changes on the ground, whereas population 

density is attributed typically only for primary residences. 

Data on residential land cover (e.g., from National Land 

Cover Data (NLCD) are often used to represent built-up 

areas, though additional data from the Census Bureau 

(or parcel level) are needed to characterize lower density 

development beyond the urban fringe. Commercial, 

industrial, and transportation land uses also are associated 

with high levels of human modification (e.g., high levels of 

impervious surface), population density (less direct), etc. 

Data on transportation infrastructure, such as road density 

(e.g., Theobald 2003), are also strong measures of land uses 

associated with built-up areas. We believe that housing 

density and road density provide complementary information—high density of roads can occur not only in urban 

areas but also in remote rural areas (e.g., forest logging 

roads). Recently, researchers are beginning to distinguish 

major road types (e.g., interstate vs. secondary roads) when 

computing road density, though more work to characterize road use rather than the existence of roads is needed. 

Improved data sets are needed to better identify the location and intensity of structures in rural areas, particularly 

bridges, culverts, roadway fences, utility corridors (e.g., 

power lines, pipelines, etc.).

Production/Extraction—

This factor characterizes the intensity of human activities 

associated with the amount and intensity of extracting or 

removing resources. Agricultural production, especially 

cropland, but, also, grazing and mining and timber activities are common forms of this activity type (Table 3). It is 

common to use land cover data to map production land uses 

that have caused land cover conversion, such as agricultural 

croplands and orchards. The intense changes in pattern 

and the high degree of modification of ecological processes 

mean that this is a critical component to map. A variety of 

also additional activities also contribute to human modification for which data are less commonly available. This would 

include spatially explicit information about grazing (intensity and animal density) through permits on public lands 

and stocking rates on private lands. Extraction of natural 

resources in the form of quarries and mines that cause 

significant land surface modification can be readily identified. However, activities with extensive but more diffuse 

disturbance (e.g., oil and gas wells or selective harvesting/ 

thinning of forest products) are more difficult to identify 

and generate metrics from typical satellite imagery, though 

increasingly specialized databases (e.g., well locations) are 

being developed.

Recreation and Tourism—

This factor characterizes the intensity of human activities 

associated with use or visitation of an area. This is best 

represented by recreation and tourism activities, but can 

also include work-related activities. It includes the amount 

and type (mode) of recreational use (i.e., pedestrian only, 

passive, motorized, etc.). There are a large number of 

important data gaps here—particularly whether areas are 

publicly accessible, motorized vs. nonmotorized use, visitor 

use levels, etc. (Table 4).

Spatially explicit data on this aspect of human modification are particularly underdeveloped. Although general 

visitor use levels may be available for whole parks or 

national forests, more detailed data to map the visitation 

levels (and the timing) are particularly poor. Use has been 

estimated based on trail density (Schumacher and others 

2000), though this suffers from the same limitation as road 

density methods—namely the assumption that areas close 

to trail heads receive the same amount of use as those more 

distant. Methods to estimate visitor use via spatial models 

of accessibility are increasingly common (Geertman and 

van Eck 1995).

Assessing Landscape Context—

To characterize risks within the landscape, we assign each 

factor a value along a 0 to 1.0 axis based on explicit criteria 

(Table 5). We use a fuzzy average approach to combine 

our individual surrogate layers (in GIS) into a composite 

value layer for each factor. This is calculated simply as 
162

GENERAL TECHNICAL REPORT PNW-GTR-802

Table 3—Possible surrogate spatial data for production/extraction factor

Data Source Scale (extent/grain) Notes

Grazing U.S. Forest service; BLM; National/1:100K Characterize by animal unit months, 

 private permit locations—but these data are 

 mostly unavailable in electronic, 

 spatial format

Oil & gas well Colorado Oil & Gas Statewide/1:100K Point locations of wells, need to define 

 density Conservation Commission “footprint”

Cropland, grazing USGS National Land National/0.09 ha Cropland (NLCD classes 81-85)

 Cover Data set Grazing estimated from grassland, 

 shrubland, and forested land

Mining (gravel, Colorado Division of Statewide/1:100k -

 hard-rock, etc.) Minerals and Geology

Groundwater wells State water engineer Statewide/1:100K Poor availability of data on recharge 

 rate, spatial location often tied to 

 center of Public Land Survey 

 Section

Table 4—Possible surrogate spatial data for recreation/visitation factor 

Data Source Scale (extent/grain) Notes

Parks and protected World Protected Areas Nationwide/block Inconsistent; poor data for State, 

 areas Database Nationwide/1 ha local (city and county), private 

 Protected Areas Database; (reserves, conservation easements)

 COMaP

Visitor use levels National Forest Service Nationwide/? Very coarse (State/forest) data, need 

 survey? data to be tied to trailheads

Recreational Census TIGER; GDT; Nationwide/1:100K Use levels (visitor days per year) would 

 facilities (golf NavTec be useful

 courses, ski areas,

 etc.)

Accessibility from Cost distance weighting Nationwide/1:100K -

 urban populations

Each row provides an example of a possible spatial datum that is commonly used as a surrogate for this factor.

Each row provides an example of a possible spatial datum that is commonly used as a surrogate for this factor.

provides or computes estimates of any given factor. Here 

we summarize the spatial context using circular moving 

windows with radii of 1 and 5 km. These distance values 

are arbitrary, but attempt to capture the relatively local and 

midscale spatial context pattern. Again, our goal here is to 

provide an illustration of the broader method, and users will 

likely want to employ different values that reflect the species, process, or patterning that they are concerned about, 

which likely varies across ecoregions.

the maximum value (at every cell) of any single input. 

These ratio values are then spatially averaged over different neighborhood sizes and shapes to establish a measure 

of landscape context. A challenging part of developing 

estimates along the three factors is to be explicit about the 

spatial characterization. That is, for example, when a value 

for wilderness is prescribed, does this apply to an entire 

wilderness area (as a political designation), or within some 

defined neighborhood (e.g., within a 1-km circular window)? The spatial grain needs to be considered before one 
163

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Table 5—Surrogate variables used for human modification framework factors for Colorado

Factor

Surrogate Convert Extract Visit

variable urban/built-up Production/extraction Recreation/work

Land cover Comm/ind (23) = 1.0 Orchards (61) = 0.6 -

 (NLCD)1 High dens. res. (22) = 0.9 Pasture (81) = 0.5

Low dens. res. (21) = 0.8 Row crops (82) = 1.0

Barren (33) = 0.6 Nonrow crops (83) = 0.7

Grass/parks (85) = 0.5 Other ag (84) = 0.8

Quarries/mines (32) = 0.9

2000 housing Urban (13-15) = 0.9 - -

 density2 Suburban (12) = 0.8

Exurban (7-11) = 0.6

Rural (4-6) = 0.4

Roadedness3 Interstates (CFC A1x) = 1.0, 60-m buffer - -

Highways (CFCC A2x) = 1.0, 30-m buffer

Secondary (CFCC A3x) = 1.0, no buffer

Local (CFCC 4x) = 0.5, no buffer

4WD (CFCC 5x) = 0.2, no buffer

Accessibility - - <0.5 hrs = 1.0

 (only public 0.5-1 hr = 0.8

 lands)4 1-2 hrs = 0.5

2-4 hrs = 0.3

4-8 hrs = 0.1

>8 hrs = 0.0

Oil & gas wells5 - W/in 60-m radius = 1.0, -

 w/in 100 m = 0.7, w/in 

 200 m = 0.3, w/in 300 m 

 = 0.1

Mining - Coal exploration and -

 operations6 unknown = 0.3 m,

 underground = 0.5,

 heap leach = 0.8,

 in-situ = 0.8, surface and 

 underground = 0.9

COMaP (NF - - Ski resorts = 0.8

 management Motorized = 0.6

 types) Multiuse = 0.4

Backcountry = 0.3

Wilderness = 0.2

1

 USGS National Land Cover Dataset, 0.09-ha resolution, ~1992-94.

2

 U.S. Census Bureau 2000 http://www.census.gov, Theobald 2005 www.asri.com/data, 1-ha resolution.

3

 Created from ESRI Streetmap 2006 data.

4

 Created from travel time along major roads, weighted by population size to nearest urban area, for urban areas with population > 100k and < = 100k, 

data from ESRI Streetmap 2006.

5

 Colorado Oil and Gas Conservation Commission, Well and facility shapefiles, downloaded 16 July 2006 from http://www.oil-gas.state.co.us.

6

 Mining data converted points to circles with area equal to permitted area ( sqr( [Permit_acr] * 4048 / 3.1415 )).

Note: CFCC = census feature class code; 4WD = four-wheel drive.
164

GENERAL TECHNICAL REPORT PNW-GTR-802

oil and gas wells, particularly in the northern Front Range 

area and western slope.

Perhaps the most unique contribution of this map 

is to portray the distribution of the extent and types of 

recreational uses found in Colorado (Figure 3). Compared 

with the typical map of public land ownership, portraying 

national forests, grasslands, and parks as being largely the 

same, this map clearly differentiates the heavy influence 

that accessibility to large populations (like the Front Range) 

can have. Accessibility here is computed as the one-way 

travel time (along roads) from urban areas. Also differentiated are the types of land management activities on public 

lands, distinguishing the heavily-recreated resort areas such 

as Aspen, Vail, and Breckenridge. This map also reflects 

Results

The composite map of the degree to which an area is urban/

built-up shows a relatively familiar pattern of urban areas, 

particularly the cities along the Colorado Front Range 

(Figure 1). Including information on slightly lower density 

of houses provides a fairly large expansion of locations that 

are developed beyond those locations identified in NLCD, 

particularly along mountain valleys.

The composite map of production and extractionoriented land use provides a fairly different picture of 

Colorado (Figure 2). Extensive lands have been converted to 

cropland agriculture in the eastern plains and San Luis Valley. Another factor included is the extensive distribution of 

Figure 1—This composite map for urban/built-up areas in Colorado shows the degree to which locations have been converted, or builtup. This is a composite of commercial, industrial, and residential development (from National Land Cover Data Set); a “roadedness” layer 

that depicts the footprint of interstates, highways, and roads; and housing density (including exurban and rural densities) from SERGoM 

v2 (Theobald 2005). Darker shades show a higher level of built-up, grey shows absence, and grey lines show county boundaries. Note 

that the Front Range (Fort Collins, Denver, Colorado Springs) is clearly visible, as are many of the mountain valleys.
165

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

locations that are dominated by motorized vs. non-motorized 

recreation (future improvements should incorporate specific 

travel management plans).

The degree of human modification values vary across 

the State, with fine-grained differences particularly noticeable in riparian zones (Figure 4). The average HMF value 

is 0.392 (SD = 0.231). The range and distribution of HMF 

values can also be summarized by various ecological system types, such as lower montane, mixed, and upper montane zone (Table 6). Our results show that the midmontane 

zone has the largest average HMF score (0.428), indicating 

the highest degree of human activities that may modify or 

affect ecological systems in this zone. The upper montane 

zone (0.329) is the lowest of the conifer-dominated zones 

(lower montane = 0.405), but aspen forests have the overall 

lowest degree of human modification (0.313). In addition to 

summarizing by counties, different analytical units such as 

watersheds, ecoregions, ecosystems, etc., can be supported. 

It is important to note that although one can summarize 

values over fairly large areas, as we have done by major 

ecological system type, the fine-grained data will allow 

differentiation of more localized areas that are particularly 

impacted or have particularly low HMF values.

Commonly, population density is used as a surrogate 

indicator of the degree of impact on ecological systems. 

Although we found a loose relationship between population 

density and HMF score (Figure 5), there is a fair amount of 

variation as well, showing the additional information that 

Figure 2—This map of Colorado shows the degree to which locations have some production or extraction-oriented land use. This is a 

composite of agricultural lands (from National Land Cover Data Set); mining activities; and oil and gas wells. Darker shades of orange 

show a higher level of activity, grey shows absence, and grey lines show county boundaries. Note that the productive agricultural lands 

of Weld and Adams County are clearly visible, as are the northeastern plains counties, whereas the San Luis Valley is visible in the 

south-central part of Colorado.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 3—This composite map of Colorado for recreation/visitation shows the degree to which locations are likely to have recreation 

or tourism-oriented land use. This is a composite of an accessibility map that depicts the one-way travel time (along roads) from urban 

areas, and the type of public recreation and visitation targeted for different land management categories (especially for national parks and 

forests—data from the Colorado Ownership Management & Protection project). Darker shades of maroon show a higher level of activity 

and grey lines show county boundaries. Note that the public lands directly adjacent (mostly west of) the Front Range are highly affected, 

as are downhill ski resorts, and motorized recreation areas.

Table 6—Summary statistics for forested 

ecosystems in Colorado 

Mean HMF

Ecological systemsa (SD)

Upper montane (spruce-fir; 24, 26, 28) 0.329 (0.159)

Midmontane (Douglas-fir, lodgepole 

 pine; 29, 30, 32) 0.428 (0.192)

Lower montane (ponderosa pine, 

 pinyon-juniper; 34, 35, 36) 0.405 (0.203)

Aspen (22, 38) 0.313 (0.135)

a

 Data from the Southwest ReGap program.

The mean and standard deviation (SD) of the final human modification 

(HMF) score for upper, mid, and lower montane, and aspen forests.

Table 7—Summary statistics of human modification 

framework (HMF) scores as summarized by GAP 

status level in Colorado 

Minimum and 

GAP statusa Mean HMF (SD) maximum HMF

1 0.244 (0.149) 0.1, 0.893

2 0.295 (0.166) 0.098, 1.0

3 0.421 (0.133) 0.1, 1.0

4 0.397 (0.192) 0.1, 1.0

a

 Data from the Southwest ReGap program.

The mean, standard deviation (SD), minimum, and maximum of the final 

human modification score summarized for GAP status levels.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 4—This map of Colorado shows the final map of degree of human modification (darker shade depicts higher degree of modification). This is a composite map of the urban/built-up, production/extraction, and recreation factor maps.

Figure 5—Population and degree of modification are compared in this graph of the county population for 2000 

against the mean degree of human modification score. Although some counties fall along the linear relationship 

line, supporting the notion that larger populations lead to larger effects, there is a fair amount of variation as well.
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GENERAL TECHNICAL REPORT PNW-GTR-802

need to move from using general surrogates to factors that 

are more closely linked to the threats, ideally in a mechanistic way. In addition, we believe that finer-grained data are 

increasingly required to address concerns about fragmentation and other pattern issues. Finally, the spatial (or perhaps 

landscape) context is critical to incorporate. Further work is 

needed to develop methods for generating landscape context 

that more directly reflect ecological processes. For example, 

neighborhoods for freshwater should use a series of flowconnected watersheds. Similarly, the flow or movement of 

processes through other transport vectors such as air, soil 

transport, animal movement, seed dispersal, etc., should be 

explicitly represented as well.

There are a number of data gaps that need to be filled. 

Some of these gaps could be filled by collecting data from 

field offices, standardizing the data and entering them into 

an electronic database (with spatial coordinates recorded). 

For example, grazing permit data have been collected for 

many decades, but much of it remains in an unstandardized, unmapped, and nonelectronic format. The location of 

timber harvests (year, harvest method) data are available, 

but not in a consistent, easy-to-use format. Another type of 

data gap would simply require acquisition and compilation 

of existing spatial databases (e.g., highway structures such 

as fencing, culverts, bridges, etc., or hiking trails that would 

include structures, tread type, width, use, etc.). Spatial data 

about water infrastructure, in addition to large dams, would 

be useful as well for structures such as ditches, levees, 

rip-rap, and channelized segments.

Acknowledgments

This research was supported in part by a grant from the 

USDA National Research Initiative (Award No. 2003-35401-

13801, NRI, CSREES, USDA).

Literature Cited

APA (American Planning Association). 2003. Land-based 

classification standards. [online] URL: http://www.

planning.org/lbcs/. (December 7, 2006).

this approach provides. By the same token, HMF provides 

a finer degree of detail than housing density—although 

housing density had a strong relationship.

We also compared HMF to GAP stewardship categories 

from the Southwest Regional GAP study (Table 7). We 

found that status 1 lands had lower average HMF values 

than status 2 (0.244 vs. 0.295), which were less than status 

3 and 4. Interestingly, we found that status 4 had a lower 

mean HMF value than level 3 (0.397 vs. 0.421). Also, all 

status levels had very large maximum values (0.89 for level 

1 and 1.0 for 2, 3, and 4).

Conclusion

In summary, we offer the human modification framework as 

an explicit approach to better quantify the spatial patterning 

and degree to which locations have been altered by human 

activities. We found that in our case study area, HMF 

showed markedly different results, particularly in the spatial 

patterns and distribution of disturbed areas, as compared to 

GAP status levels. An important, nonintuitive result is that 

we found that level 4 had less evidence of alteration than did 

level 3. This can occur because the HMF relies on surrogate 

variables that characterize current land use activities that 

have been shown to have effects on biodiversity. In contrast, 

GAP status levels characterize the legal protection from 

land use conversion (or management plans)—not the current 

condition per se. It is important to note that the HMF, as 

employed in this paper, shows existing (or at least currently 

known) modifications to the landscape, whereas the GAP 

and IUCN approach estimate long-term protection (in perpetuity). Although we did not document these trends here, 

the HMF methods could be easily applied to future growth 

or potential land use scenarios to examine the differences 

between their effects. The fine-grained approach and spatial 

context analysis in particular would be sensitive to differences in patterns of potential future land uses.

We believe that current approaches to estimating the 

degree of human modification can be improved in three 

ways. First, as understanding and elicitation of threats to 

protected areas matures, we believe that approaches will 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Aplet, G.; Thomson, J.; Wilbert, M. 2000. Indicators 

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stewardship. Version 2.1.0. A handbook for conducting 

gap analysis. Moscow, ID: University of Idaho. [Not 

paged].

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protected areas. Gland, Switzerland: World Conservation 

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GAO (U.S. General Accounting Office). 1994. Activities 

outside park borders have caused damage to resources 

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models of accessibility potential: an application in 

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of Nature). 1994. Guidelines for protected area 

management categories. Gland, Switzerland: 

IUCN. 261 p.

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2006].

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mapping concept to the recreation opportunity spectrum. 

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computer-based method of wilderness evaluation. 

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continuum concept and its implications for wilderness 

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32: 309–333.

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conservation planning. Nature. 405: 243–253.

Sanderson, E.W.; Jaiteh, M.; Levy, M.A. [and others]. 

2002. The human footprint and the last of the wild. 

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Jensen, M.E. 2000. Mapping patterns of human 

use and potential resource conflicts on public lands. 

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Theobald, D.M. 2003. Targeting conservation action 

through assessment of protection and exurban threats. 

Conservation Biology. 17(6): 1624–1637.

Theobald, D.M. 2004. Placing exurban land use change in 

a human modification framework. Frontiers in Ecology 

and Environment. 2(3): 139–144.

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growth in the U.S.A. from 1980 to 2020. Ecology 

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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Modeling Species’ Realized Climatic Niche Space and 

Predicting Their Response to Global Warming for Several 

Western Forest Species With Small Geographic Distributions 

Marcus V. Warwell, Gerald E. Rehfeldt, 

and Nicholas L. Crookston

Marcus V. Warwell, geneticist, Gerald E. Rehfeldt, 

plant geneticist (emeritus), and Nicholas L. Crookston, 

operations research analyst, USDA Forest Service, Rocky 

Mountain Research Station, Forestry Sciences Laboratory, 

Moscow, ID 83843.

Abstract

The Random Forests multiple regression tree was used 

to develop an empirically based bioclimatic model of the 

presence-absence of species occupying small geographic 

distributions in western North America. The species 

assessed were subalpine larch (Larix lyallii), smooth 

Arizona cypress (Cupressus arizonica ssp. glabra), Paiute 

cypress (syn. Piute cypress) (Cupressus arizonica ssp. 

nevadensis), and Macfarlane’s four-o’clock (Mirabilis macfarlanei). Independent variables included 33 simple expressions of temperature and precipitation and their interactions. 

These climate variables were derived from a spline climate 

model for the Western United States that provides point 

estimates (latitude, longitude, and altitude). Analyses used 

presence-absence data largely from the Forest Inventory 

and Analysis, USDA Forest Service database. Overall errors 

of classification ranged from 1.39 percent for Macfarlane’s 

four-o’clock to 3.55 percent for smooth Arizona cypress. 

The mapped predictions of species occurrence using the 

estimated realized climatic niche space were more accurate 

than published range maps. The Hadley and Canadian 

general circulation models (scenario IS92a for 1 percent 

increase GGa/year) were then used to illustrate the potential 

response of the species’ contemporary realized climatic 

niche space to climate change. Predictions were mapped at a 

1-km2

 resolution. Concurrence between species’ geographic 

distribution and their contemporary realized climatic niche 

rapidly disassociates through the century. These models 

demonstrate the heightened risk for species occupying 

small geographic ranges of displacement into climatic 

disequilibrium from rapid climate change and provide tools 

to assist decisionmakers in mitigating the threat.

Keywords: Bioclimatic models, climatic distributions, climatic niche, global warming, Random Forests 

multiple-regression tree, response to climate change, narrow 

endemic.

Introduction

Climate is a principle factor that controls where species 

occur in nature (Woodward 1987). As the climate changes 

so then does the distribution of species. Long-lived plant 

species have adapted repeatedly to past climate change (see 

Ackerly 2003). When climate change exceeds species’ tolerance limits, continued survival is dependent on the species 

ability to genetically adapt or migrate, or both, to suitable 

climate. These processes have contributed successfully to 

the persistence of long-lived plant species in response to 

past climate change (Davis and Shaw 2001). Their effectiveness under future climate change may be exacerbated by the 

increased rate of change predicted to occur over the present 

century. Projections for change in global climate rival 

historical periods of climate change at an accelerated rate 

(Houghton and others 2001). The accelerated rate of change 

threatens to displace current plant species distributions into 

climatic disequilibrium resulting in an increased potential 

for extinction of all or portions of species’ ranges (Thomas 

and others 2001).

The objective for these analyses was to develop bioclimatic models that predict the occurrence of species with 

small natural distributions in the Western United States and 

project where suitable climates for the natural occurrence of 

these species may occur in the future in response to global 

warming. Three species were selected for the purposes of 

this analysis. The first was subalpine larch (Larix lyallii 

Parl.), a high-elevation, deciduous conifer inhabiting the 

Pacific Northwest. The second included two subspecies of 

Arizona cypress (Cupressus arizonica Greene), the smooth 

Arizona cypress (C. arizonica var. glabra (Sudw.) Little), 

which is endemic to central Arizona, and the Paiute cypress 
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GENERAL TECHNICAL REPORT PNW-GTR-802

(syn. Piute cypress) (C. arizonica ssp. nevadensis Abrams 

E. Murray), which is endemic to the southern tip of the 

Sierra Nevada. The third was Macfarlane’s four-o’clock 

(Mirabilis macfarlanei Constance & Rollins), a long-lived, 

deep-rooted perennial species that inhabits mid-to low-level 

canyon grasslands near the Oregon-Idaho northern border 

and is listed as endangered by the Bureau of Land Management (BLM) (USFWS 1996).

Methods

Study Area

The area of study consisted of the region supported by the 

climate surfaces of Rehfeldt (2006). This area encompasses 

the Western United States and southwestern Canada, latitudes 31° to 51° N and longitudes 102° to 125° W (Figure 1).

Climate Estimates

Spline climate model (Rehfeldt 2006) was used to estimate 

climate at point locations (latitude, longitude, and altitude). 

The model is based on monthly minimum, maximum, and 

average temperatures and precipitation normalized for the 

period of 1961-90. This data was obtained from more than 

3,000 weather stations dispersed throughout the conterminous Western United Sates and southwestern Canada. 

Hutchinson’s (1991, 2000) thin plate splines were used to 

develop geographic surfaces for monthly data. The result 

produced 48 surfaces representing minimum, maximum, 

and average temperature and precipitation for each month 

Figure 1—This shows the Western United States and southwestern Canada (from Rehfeldt and 

others 2006).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

of the year. For our analyses, these surfaces were used 

to estimate 33 climate variables that represent a range in 

climate variables from simple sums to complex interactions 

of temperature and precipitation (Table 1).

An updated version of the spline climate model was 

used to predict the effects of global warming for decades 

beginning in 2030, 2060, and 2090. The model used a 

regional summary of the IS92a scenario (1 percent per year 

increase in greenhouse gases after 1990) of the International 

Panel on Climate Change (Houghton and others 2001) from 

General Circulation Models (GCMs) produced by the Hadley Centre (HadCM3GGa1) (Gordon and others 2000) and 

Table 1—Climate variables and their acronyms used 

as independent variables in regression analyses 

(from Rehfeldt and others 2006)

Acronym Definition

MAT Mean annual temperature

MTCM Average temperature in the coldest month

MMIN Minimum temperature in the coldest month

MTWM Mean temperature in the warmest month

MMAX Maximum temperature in the warmest month

MAP Mean annual precipitation

GSP Growing season precipitation, April through 

 September

TDIFF Summer-winter temperature differential, 

 MTWM-MTCM

DD5 Degree-days > 5 °C

DD0 Degree-days < 0 °C

GSDD5 Accumulated growing-season degree days 

 > 5 °C, April through September 

MINDD0 Minimum degree-days < 0 °C

SDAY Julian date of the last freezing date of spring

FDAY Julian date of the first freezing date of autumn

FFP Length of the frost-free period

D100 Julian date the sum of degree-days > 5 °C 

 reaches 100

AMI Annual moisture index, DD5/MAP

SMI Summer moisture index, GSDD5/GSP

PRATIO Ratio of summer precipitation to total 

 precipitation, GSP/MAP 

Interactions: MAP x DD5, MAP x MTCM, GSP x MTCM, 

GSP x DD5, DD5 x MTCM, MAP x TDIFF, GSP x TDIFF, 

MTCM/MAP, MTCM/GSP, AMI x MTCM, SMI x MTCM, 

TDIFF/MAP, TDIFF/GSP, PRATIO x MTCM, PRATIO x 

DD5

the Canadian Centre for Climate Modeling and Analysis 

(CGCM2_ghga) (Flato and Boer 2001). These two models 

are generally well respected, and averages of their predictions should provide an illustration of the potential effects 

of global warming.

Vegetation Data

Species were selected for this analysis based on their small 

geographic range and the availability of point location 

data to the authors. Species presence data were acquired 

from Barnes (1996) for Macfarlane’s four-o’clock, Arno 

(1970), Khasa and others (2006), and Forest Inventory and 

Analysis, USDA Forest Service (FIA) database for subalpine larch, and unpublished field data from Forest Service 

RMRS, Moscow Forestry Sciences Laboratory, for smooth 

Arizona cypress and Paiute cypress (see Rehfeldt 1997). 

The FIA plot data were used to identify locations where the 

species did not occur. Their database was developed from a 

systematic sampling of woody vegetation permanent plots 

on forested and nonforested lands (Alerich and others 2004, 

Bechtold and Patterson 2005) within the United States. In 

accordance with FIA proprietary restrictions, plot locations 

are not available for publication. However, we were given 

access to their database to generate climate estimates for 

each plot directly from uncompromised field data. The 

resulting data sets include a tabulation of the presenceabsence of species at approximately 117,000 locations.

Statistical Procedures

The Random Forests classification and regression tree 

package (Breiman 2001, Liaw and Wiener 2002, R Development Core Team 2004) was used to model species presence 

and absence. This tree-based method of regression uses a 

nonparametric approach and is resistant to overfitting, as 

multicolinearity and spatial correlation of residuals are not 

issues (Breiman 2001). Consequently, the algorithm was 

well suited for our analyses, which used variables among 

which intercorrelations could be pronounced.

An analysis data set was constructed for each species 

that initially included all predictor variables. Observations 

in this data set include all the observations with presence = 

yes weighted by a factor of 3. These observations made up 
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GENERAL TECHNICAL REPORT PNW-GTR-802

40 percent of the total for a species (Table 2). The remaining 

observations (60 percent) of presence = no were selected 

by a stratified random sample of locations from two strata 

constructed using threshold values of the 33 predictor 

variables that define a climatic envelope for the species. The 

first stratum is the space formed by an expanded climatic 

envelope, where the expansion is defined by increasing the 

range for the threshold values of each climatic variable. The 

climate envelopes were expanded by factors large enough 

to produce about 20 times the number of locations sampled. 

The second stratum included locations outside the expanded 

envelope.

Regression analysis used 10 independent forests of 100 

independent regression trees. Random Forests builds each 

tree using a separate boot-strap sample of the analysis data 

resulting in about 36 percent of the observations being used 

to compute classification error.

A set of regressions were run. The first regression used 

33 climate predictors. Random Forest produces indices of 

variable importance (mean decrease in accuracy). The 12 

least important variables were dropped after the first run. 

The regression procedure was then rerun nine more times 

with the remaining predictors, whereby the least important 

1 to 3 predictors were dropped at each run until classification errors began to increase. The Random Forests run with 

the fewest variables selected prior to detecting an increase 

in classification error was considered the most parsimonious 

bioclimatic model for the species.

Mapping Procedure

Rehfeldt’s climate surfaces (2006) and those updated to 

convey global warming were used to estimate the climate 

for nearly 5.9 million pixels (1 km2 resolution) representing 

the terrestrial portion of the study area. The average altitude 

was made available from Globe (1999). The estimated 

climate and projected climates of each pixel were run down 

the 100 regression trees in the final set and the number of 

trees that predict the species is present and the number predicting absence were tabulated. A single-tree prediction is 

termed a vote. The votes were grouped into six categories: 

< 50, 50-60, 60-70, 70-80, 80-90, 90-100 percent. We 

consider any pixel in the first group as not having suitable 

climate for the species and define pixels in the other five 

categories as the species’ realized climatic niche space. 

The fit of the mapped projections were assessed visually 

by comparing them with locations where the species were 

observed or Little’s range maps (1971, 1976) that are available as digitized files (USGS 2005), or both.

Results

We found that reasonably parsimonious bioclimatic models 

are driven by either three or four climate variables (Table 

3). Overall out-of-bag classification errors from fitting of 

the Random Forests algorithm ranged from 1.39 percent 

for Macfarlane’s four-o’clock to 2.2 percent for subalpine 

larch. The models predicted species occurrence where species were known to occur with perfect accuracy (0 errors 

of omission); the error therefore is due to commission, 

Table 2—Number of field plots in which each species was present, the number of 

locations contained within the 33-variable climatic envelope, the number of standard 

deviations for each climate variable in which the envelope was expanded, and the 

number of locations within the expanded envelope

Number of plots

Standard Expanded

Species Present Envelope deviations envelope

Subalpine larch (Larix lyallii) 168 683 ± 4.2 12,809

Smooth Arizona cypress

 (Cupressus arizonica ssp. glabra) 22 156 ± 3.5 1,782

Paiute cypress

 (Cupressus arizonica ssp. Nevadensis) 49 537 ± 11.6 3,811

Macfarlane’s four-o’clock

 (Mirabilis macfarlanei) 27 107 ± 3.0 1,910
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

predicting the presence of a species when, in fact, it was 

absent. The use of digital elevation model (DEM) and GIS 

to make predictions on a 1-km grid introduces additional 

error and uncertainty. The use of Little’s range maps to 

validate the models’ mapped projections also introduced the 

possibility of error. Range maps are known to contain errors 

(see Rehfeldt and others 2006) but perhaps more importantly, they only provide two-dimensional limits of the 

species distribution and do not indicate where species actually occur within the species boundaries. Even so, a visual 

comparison between these range maps and our predictions 

can be used for general validation of the models.

Subalpine Larch

A visual comparison of Little’s range map shows that 

the projection of the bioclimatic model overestimates the 

distribution of subalpine larch in the western portion of 

southwestern British Columbia and portions of the central 

Rocky Mountains (Figure 2A). Error associated with Canadian projections may have been exacerbated by the lack of 

species presence-absence data for that region. Nonetheless, 

the model does an excellent job of predicting the United 

States distribution. Here the majority of the pixels receiving 

the greatest proportion of votes occur well within the range 

limits.

Mapped projections for the effect of global warming 

on distribution of subalpine larch’s realized climatic niche 

space in decades 2030 (Figure 2B), 2060 (Figure 2C) and 

2090 (Figure 2D) indicate a rapid decline in distribution (Table 4). For area studied, about 62 percent of the 

contemporary realized climatic niche space is projected to 

disappear by the end of decade 2030 (Table 4). Only about 

one-third of the remaining proportion is expected to occur 

in pixels suitable for the species today. By decade 2030, the 

mean elevation of the realized climatic niche space is projected to occur 100 m higher in altitude, and the minimum 

elevation is expected to rise almost 300 m by the end of the 

century. Also, by the end of the century, only 3 percent of 

the contemporary realized climatic niche space is expected 

to remain and should be relegated to a few high mountain 

peaks in south-central Idaho, southwestern Montana, the 

northern Cascade Range in Washington and to the Grand 

Teton Range in northwestern Wyoming. Note that suitable 

climate space in the Grand Teton Range is projected to first 

occur in decade 2030 and persist in decades 2060 and 2090.

Smooth Arizona Cypress and Paiute Cypress

The predicted contemporary realized climatic niche space 

for smooth Arizona cypress and Paiute cypress better 

described actual distributions than Little’s range map (Figures 3A, 4A). Although a few populations were not mapped 

in the realized climatic niche, we suspect that this omission 

was due to either a mapping resolution that was too coarse 

or to field locations that were not accurate enough.

In response to global warming under the IS92a scenario, the realized climatic niche space of Arizona smooth 

Table 3—Statistical output from bioclimatic models: classification errors from the confusion matrix, and the 

important predictors according to mean decrease in accuracy; variable acronyms are keyed to Table 1

Classification errors (%)

Omissiona Commissionb Over

Species allc	 Important variables

Subalpine larch (Larix lyallii) 0 3.94 2.2 GSP × TDIFF, MAP × MTCM, MMIN

Smooth Arizona cypress

 (Cupressus arizonica ssp. glabra) 0 6.37 3.55 PRATIO, GSP × TDIFF, TDIFF

Paiute cypress (Cupressus arizonica ssp. 0 3.09 1.73 MMIN, MAP × DD5, MTCM × GSP, 

 Nevadensis) PRATIO

Macfarlane’s four-o’clock (Mirabilis 0 2.55 1.39 MINDD0, MAP × TDIFF, TDIFF

 macfarlanei)

a

 Errors of omission (species presence = yes but model prediction presence = no).

b

 Error of commission (species presence = no but model prediction = yes).

c

 Combined overall error from omission and commission statistics.
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GENERAL TECHNICAL REPORT PNW-GTR-802

cypress should shift about 200 to 350 km northwest of 

its contemporary location (Figures 3B, 3C, 3D). The area 

occupied should increase by about 1.5 and 2 times its 

contemporary size in decades 2030 and 2060, respectively 

(Table 4). In decade 2090, the area decreases to 1.2 times 

the contemporary size as the distribution shifts to northern 

Nevada and southwestern Colorado. In all three future 

decades, the contemporary realized climatic niche space 

is expected to be prominent in valleys where the Arizona, 

Nevada, and Utah borders meet. This includes the Virgin 

Mountains in Nevada, an area where naturalized populations of the subspecies have been observed (Charlet 1996). 

Only 14 percent of the climate space is expected to remain 

in place in its contemporary location through the decade 

of 2030, and none is to remain in place for decades 2060 

and 2090. By the end of the century, average elevation is 

projected to increase 611 m.

By decade 2030, the Paiute cypress realized climatic 

niche space will lie outside its contemporary distribution. 

The realized climatic niche space is projected to shift nearly 

40 km to the east along the southeastern slopes of the 

Sierra Nevada (Figure 4B). Overall, the area of its distribution should shrink about 86 percent (Table 4). Altitudes, 

however, should increase by about 100 m. The realized 

Figure 2—Modeled realized climatic niche for subalpine larch (Larix lyallii) and its predicted response to 

climate change for decades beginning in (B) 2030, (C) 2060, and (D) 2090. Areas in (A) outlined in black 

depict species boundaries defined by Little (1971). Colors code the proportions of votes received by a pixel in 

favor of being within the climate profile: no color 0%–50%; yellow 50%–60%; green 60%–70%; light blue 

70%–80%; dark blue 80%–90%; and red 90%–100%.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Table 4—Projected change in area of species realized climatic niche space in response 

to climate change during three decades, and, in parenthesis, the percentage realized 

climatic niche space expected to match their contemporary distribution

 Year of projected change

Species 2030 2060 2090

 Percent

Subalpine larch (Larix lyallii) -62 (26) -76 (21) -97 (3)

Smooth Arizona cypress (Cupressus arizonica ssp. glabra) +158 (14) +210 (0) +115.7 (0)

Paiute cypress (Cupressus arizonica ssp. nevadensis)   -86 (0) -91 (0) -95 (0)

Macfarlane’s four-o’clock (Mirabilis macfarlanei) +807 (23) +1912 (2) +3343 (0)

Figure 3—(A) Modeled realized climatic niche for smooth Arizona cypress (Cupressus arizonica ssp. glabra) 

and its predicted response to climate change for decades beginning in (B) 2030, (C) 2060, and (D) 2090. 

Areas in (A) outlined in black depict species boundaries defined by Little (1971) and squares indicate sites 

known to be inhabited. Colors code the proportions of votes received by a pixel in favor of being within 

the climate profile: no color 0%–50%; yellow 50%–60%; green 60%–70%; light blue 70%–80%; dark blue 

80%–90%; and red 90%–100%.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 4—(A) Modeled realized climatic niche for Paiute cypress (Cupressus arizonica ssp. nevadensis) and 

its predicted response to climate change for the decade beginning in (B) 2030, in California. Areas in (A) 

outlined in black depict species boundaries defined by Little (1971) and squares indicate sites known to be 

inhabited. Colors code the proportions of votes received by a pixel in favor of being within the climate profile: 

no color 0%–50%; yellow 50%–60%; green 60%–70%; light blue 70%–80%; dark blue 80%–90%; and red 

90%–100%.

Figure 5—Predicted response for Paiute cypress 

(Cupressus arizonica ssp. nevadensis) contemporary realized niche to climate change for decades 

beginning in (A) 2030, (B) 2060, and (C) 2090 in 

Oregon and Washington. Colors code the proportions of votes received by a pixel in favor of being 

within the climate profile: no color 0%–50%; yellow 

50%–60%; green 60%–70%; light blue 70%–80%; 

dark blue 80%–90%; and red 90%–100%.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

climatic niche space should also appear nearly 1100 km to 

the north near The Dalles, Oregon, by the decade of 2030 

(Figure 5A) where it is projected to persist throughout the 

century (Figures 5B, 5C). In fact, by 2060, the realized 

climatic niche space of this subspecies occurs exclusively in 

Oregon and should occupy an area less than 90 percent of 

its contemporary distribution. The mean elevation for these 

new sites in Oregon and Washington are about 200 to 300 m 

lower in elevation than the contemporary distribution.

Macfarlane’s Four-O’clock

Through most of the century, climate suitable to Macfarlane’s four-o’clock should remain near its contemporary 

location (Figures 6B, 6C). The realized climatic niche, 

however, is expected to climb the canyon’s slopes along the 

Snake, Imnaha, and lower Salmon Rivers in decade 2030 

(Figure 6B). In the decade of 2060, it should occur in these 

canyons at altitudes nearly 142 m higher than today (Figure 

6C), but, by the end of the century, it should disappear in 

these particular canyons but reappear within the Snake 

River watershed in southern Idaho (Figure 6D). Elsewhere 

in the West, however, the profile of this species should 

expand rapidly and increase in altitude (Table 4, Figures 

6B, 6C, 6D), increasing by a factor of 8, 19, and 34 times 

the contemporary realized climatic niche space in decades 

2030, 2060, and 2090, respectively (Figure 6, Table 4).

Figure 6—(A) Modeled realized climatic niche for Macfarlane’s four-o’clock (Mirabilis macfarlanei) and its 

predicted response to climate change for decades beginning in (B) 2030, (C) 2060, and (D) 2090. Squares in 

(A) indicate sites known to be inhabited. Colors code the proportions of votes received by a pixel in favor of 

being within the climate profile: no color 0%–50%; yellow 50%–60%; green 60%–70%; light blue 70%–80%; 

dark blue 80%–90%; and red 90%–100%.
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Discussion

Our bioclimatic models predict the occurrence of species 

and attempt to identify suitable habitats. As shown, these 

models can also be updated with predicted future climates 

to project the redistribution of species’ contemporary 

realized climatic niche. The validity of these projections are 

dependent on the accuracy of the bioclimatic models as well 

as how closely the predicted future climate using the IS92a 

scenario from the Canadian and Hadley GCMs matches the 

actual climate of the future.

The climate variables identified as important for predicting species occurrence differed among the species and 

subspecies analyzed. These differences suggest that they 

will respond uniquely to climate change. Despite these differences, it appears that their small geographic distributions 

predispose them to a shared threat of more rapid onset of 

climatic disequilibrium in comparison with species occupying large-scale distributions. Analyses of species occupying 

larger scale distributions tend to report greater areas of 

unaffected distribution and show less separation between 

existing distributions and the geographic occurrence of 

suitable climate (Bakkenes and others 2002, Iverson and 

Prasad 1998, Rehfeldt and others 2006). The extent of disequilibrium predicted by these studies may, however, be an 

underestimate owing to the potential for maladaptation to 

climate within species (Rehfeldt 2004, Rehfeldt and others 

1999, Rice and Emery 2003).

As natural systems attempt to regain equilibrium with 

the novel distribution of climates, distributions will shift. 

Redistribution rates, however, will be influenced by genetic 

structure, autecology, life history, reproductive capabilities, and ecophysiology (see, for instance, Ackerly 2003). 

Consequently, all of these factors should be taken into 

consideration in interpreting projections from bioclimatic 

models. The physiology of subalpine larch, for instance, 

appears to be consistent with the climatic profile described 

by our models. Its realized niche seems dependent on a 

superior hardiness and resistance to winter desiccation 

(Burns and Honkala 1990). Subalpine larch, however, does 

not compete well with other evergreen species (Arno and 

Hammerly 1984). Hence, the continued warming trend will 

likely eliminate subalpine larches from the Western 

United States through competitive exclusion. Common 

garden studies with smooth Arizona cypress and Paiute 

cypress by Rehfeldt (1997) revealed genetic variability 

equivalent to that conveyed by broadly distributed 

conifer species. This indicates populations within the 

species may only be adapted to a portion of its present or 

future realized climate space. In addition, both smooth 

Arizona cypress and Paiute cypress are fire dependent. 

Fire management practices may have had a substantial 

influence on limiting their distribution (Marshall 1963). 

Consequently, for these subspecies of Cupressus arizonica, our estimate of the realized climatic niche space 

may underestimate the breadth of climatically suitable 

area. Finally, Macfarlane’s small distribution is attributed to reduced compatibility between its floral biology 

and pollinators (Barnes 1996). The rates that the range 

may expand would depend, therefore, on the presence of 

compatible pollinators.

Bioclimatic models represent tools that can be used 

to assist decisionmakers in managing threats associated 

with global warming. These models can be updated 

with predicted future climate to identify locations where 

suitable climate should occurs. These predictions can 

be used by managers to assist the natural processes by 

transferring species to the future location of climate 

suitable for their survival. Ideally the application of 

bioclimate models for this purpose should use multiple 

general circulation models and climate change scenarios 

to reduce uncertainty associated with predicted future 

climate (see Rehfeldt and Jaquish 2010). To be sure, 

additional modeling is needed that integrates a general 

understanding of climate, climate change, and plantclimate relationships. Interpretation of results by 

resource managers also requires integration of additional layers of information such as land use (see 

Hannah 2006).

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AIR and WATER

Syntheses
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Robert H. Beach, Erin O. Sills, Tzu-Ming Liu, 

and Subhrendu Pattanayak

Robert H. Beach, senior economics, Enviromental, 

Technology, and Economics, Program, RTI International, 

Research Triangle Park, NC 27709-2194; Erin O. Sills, 

associate professor, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, 

NC 27695; Tzu-Ming Liu, assistant professor, Kainan University, Luchu, Taiwan; Subhrendu Pattanayak, associate 

professor, Duke University, Durham, NC. 

Abstract

Excessive wind, ice, and snow regularly cause major 

disturbances to forests in many parts of the world, significantly impacting both ecological conditions and economic 

returns to forest landowners. These events cause immediate 

losses for landowners, and the broken and uprooted trees 

left in the wake of a storm increase the risk that wildfires, 

disease, and pest outbreaks will cause secondary damage to 

the surviving trees. Although weather severity (e.g., windspeed and duration, or form and amount of precipitation) 

is clearly an important factor in the occurrence and severity of forest damage, site conditions, tree characteristics, 

and stand characteristics play a major role in determining 

resistance of a forest stand to wind, ice, and snow loading. 

However, the relationships between site, tree, and stand 

characteristics and weather damage are complex and vary 

spatially and temporally. In this article, we review and 

synthesize the literature on the risk of forest damages from 

severe weather—focusing on wind, ice, and snow—and 

the factors that influence vulnerability. Forest management 

decisions are found to play an important role in influencing 

risk associated with severe weather events. The risk of damages can be managed through strategies such as selection of 

planting site and species, stocking, and selection and timing 

of silvicultural treatments. Optimal management strategies 

under endogenous risk vary based on the probability of 

damage and management objectives.

The Influence of Forest Management on Vulnerability 

of Forests to Severe Weather

Keywords: Damage mitigation, ice, natural disturbances, forest policy, risk management, wind.

Introduction

Natural disturbances play an important role in forest 

stand dynamics and ecology, with effects that are highly 

dependent on characteristics of the specific disturbance 

(Foster and Boose 1995). Severe weather events that bring 

high winds or heavy precipitation or both (e.g., hurricanes 

and ice, snow, and hailstorms), drought, flooding, lightning 

strikes, wildfires, and disease and pest outbreaks are a 

regular feature of forest landscapes. Although these disturbances may serve important ecological functions, they also 

significantly—and usually negatively—affect economic 

returns to forest landowners. Wind, ice, and snowstorms 

affect large areas of forest relatively frequently in some 

important timber production regions. These storms impact 

forest landowners by causing the loss of merchantable 

timber, increased risk of secondary damages to wildlife and 

from disease and pests in damaged stands, and depressed 

timber prices in the immediate aftermath of events that 

cause widespread damage. These risks have led to considerable interest in identifying factors that influence damage 

levels as well as ways to mitigate damages.

To optimally manage risk, it is necessary to identify 

and characterize the hazards faced, assess the risk associated with these hazards, identify options to mitigate risk, 

choose the optimal combination of options given the 

manager’s objectives (e.g., producing amenities or maximizing profitability of timber production), and implement 

the strategy selected. In the case of forested landscape 

management, these tasks are complicated by long planning 

horizons, which lead to substantial uncertainty concerning 

future prices, production costs, forest policies and regulations, and objectives, in addition to uncertainty regarding 

severe weather impacts (Wilson and Baker 2001). Spatial 

and temporal heterogeneity combined with the infrequency 

of major storms impacting a given area (compared with a 

human lifespan) make it relatively difficult to learn from 

previous storms. Damage severity depends on the complex 
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interaction of numerous tree, stand, and site characteristics. 

For instance, although windspeed is likely to be the most 

important determinant of timber losses, topography, soil 

conditions, planting density, species, stand age, the presence 

of gaps and edges, and other factors determine a stand’s 

resistance to wind loading.

Many studies focus almost exclusively on biophysical 

properties of forest plots or individual trees to explain differences in damages without explicitly examining the role 

of landowners’ forest management decisions in influencing 

those properties. However, forest management decisions 

can change susceptibility to wind damage through effects 

on tree and stand characteristics such as tree species, 

tree height, tree diameter, crown area, rooting depth and 

width, and stand density (Dunham and Cameron 2000, 

Kerzenmacher and Gardiner 1998, Peltola and others 1999, 

Peltola and others 2000). Stand age and forest structure 

may also contribute to vulnerability of forests to high winds 

(Everham and Brokaw 1996, Francis 2000, Mitchell 1995, 

Ruel 1995). Silvicultural treatments such as thinning and 

clearcutting adjacent stands are important as well (Foster 

1988, Lohmander and Helles 1987, Mitchell 2000, Wilson 

and Baker 2001). Similar factors have been related to the 

degree of ice damage to forests (Bragg and others 2003, Van 

Dyke 1999), though with different magnitudes and, in some 

cases, opposite direction of effect. Overlooking the impacts 

of management decisions on the vulnerability of a stand to 

weather damages may lead to inefficient decisionmaking by 

policymakers and private landowners.

In this article, we review and synthesize the literature 

on the risk of forest damages from severe wind, snow, and 

ice, the factors influencing the extent of damages in the 

event of severe weather, alternatives for mitigating weather 

damages, and policy implications. This type of information 

can inform forest management decisions and increase the 

efficiency of policy for managing public forest land as well 

as public policy that encourages timber stand management 

and compensates landowners for weather damages.

Severe Weather and Forest Impacts

This section describes characteristics of severe weather 

storms and selected major events that have taken place in 

recent years as well as resulting impacts on forests. These 

impacts include a variety of damages to trees because of 

Figure 1—Simulated peak hurricane windspeed associated with hurricanes in the Southeast United States over 10- and 50-year periods 

using FEMA’s HAZUS-MH model. In (a), which is for a 10-year period, only southern Florida and a few coastal census tracts in Alabama, 

South Carolina, and North Carolina are expected to experience hurricane-force winds (greater than 119 km per hour) within this timeframe. Over a 50-year period, however, the entire region is expected to have winds of at least tropical storm strength, with much higher 

winds in some coastal areas, as shown in (b). Source: FEMA HAZUS-MH model.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

excessive loadings by high winds, ice storms, and snow 

accumulation. In addition to immediate uprooting and 

stem breakage, heightened tree mortality can continue over 

time as a result of substantial crown loss. Even trees that 

recover may have suffered permanent internal damage that 

reduces wood quality. In addition, damaged stands are more 

prone to secondary damages from fire, disease, insects, and 

competing plants.

High Winds

Hurricane-strength winds can cause severe defoliation and 

directly damage and kill trees through uprooting, breakage 

and loss of minor and major branches, and stem breakage. 

Biodiversity is affected through an increase in gaps of 

broken and uprooted trees and reallocation of light, water, 

nutrients, and other resources (Nowacki and Kramer 1998). 

The amenity value of a forest can decrease because of the 

higher proportion of treefall gaps. Wind damage imposes 

costs on commercial forestry owing to reduction in timber yields, unscheduled and costly thinnings, and added 

uncertainty for forestry planning (Quine and others 1995). 

In addition, broken and uprooted trees left unharvested 

can lead to additional costs by increasing the probability of 

disease outbreaks, insect infestations, and wildfires in the 

remaining growing stock as well as increasing the costs of 

containment.

Between 1900 and 2005, an average of 1.6 hurricanes 

per year made landfall between Texas and Maine, with an 

average of 0.6 major hurricanes (category 3 or above on the 

Saffer-Simpson scale) annually. In 2004 and 2005 alone, 

seven major hurricanes had an impact on the United States 

(Blake and others 2006). Figure 1 shows peak windspeeds 

associated with hurricanes in the Southeastern United 

States that are expected in 10-year and 50-year periods 

based on estimates from the Federal Emergency Management Agency’s HAZUS-MH model. Within a 50-year 

period, the entire Southeast is expected to have winds of at 

least tropical storm strength.

Hurricanes resulting in the greatest loss of timber 

from 1900 to 2000 were the Hurricane of 1938 (in New 

England) and Hurricanes Camille (1969), Frederic (1979), 

Hugo (1989), Opal (1995), and Fran (1996) (Lutz 2005). For 

example, Hurricane Hugo swept through South Carolina on 

September 21, 1989, damaging more than one-third of the 

State’s timberland. The damaged volume of timber was estimated to be 36.8 million m3

 (Remion 1990). More recently, 

Hurricanes Katrina and Rita damaged more than 2 million 

ha of forest in the gulf coast States, leading to timber losses 

of almost $6 billion for affected landowners (though an estimated one-third of these losses may be recovered through 

salvage efforts) (SAF 2005).

Other regions are also affected by high winds. On 

average, there are almost 1,000 tornadoes per year in the 

United States, mostly in the continental interior. Although 

many are relatively small and weak and cause little damage, a small proportion do considerable damage to forests 

(see Peterson 2000). Dale and others (2001) estimate that 

tornado damage in the United States averages $154 million 

annually. Other windstorms also occasionally result in 

major damages, such as the July 4, 1999, windstorm that 

severely impacted approximately 193 000 ha of Minnesota 

forest (Mattson and Shriner 2001). The hurricane-force 

winds that affect the coastal Pacific Northwest have a return 

interval of about 20 years (Wilson 2004).

Ice and Snow Storms

Recurring ice and snow storms are a significant hazard to 

forests in temperate climates. Snow or ice causes damage 

when the weight of frozen precipitation exceeds the buckling load of the portion of the tree bearing the load, causing 

bending or breakage. Bending can result in permanent 

internal damage without external signs of damage. There 

are important interactions between winter precipitation 

and wind because windspeeds required to cause damage 

are lower for trees loaded with snow and ice, all else equal. 

Every winter, ice and snow storms affect portions of eastern 

North America and Europe to some degree, with occasional 

major events covering millions of hectares and causing 

millions of dollars in damages. The majority of research on 

ice and snow damage has been conducted in these regions. 

Below, we focus on ice storms, recognizing that heavy snow 

can produce similar damages, particularly in the case of 

unusually wet snow.
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Ice storms require a layer of warm air, with temperatures above freezing, between layers of air that are below 

freezing. As frozen precipitation falls through the layer of 

warm air, it melts and reaches the cold lower layer as rain. 

As the water droplets fall through this cold surface layer, 

they become supercooled, which means that the water in 

the droplets is cooled to temperatures below 0 °C without 

freezing. When this supercooled liquid strikes a cold 

surface, the impact triggers an almost instant transformation to a smooth thin layer of ice. Although ice storms can 

result in significant accumulation and major damages when 

conditions for ice formation are favorable, ice accumulation 

depends critically on characteristics of the layers of air and 

the water droplets. Small temporal or spatial differences in 

air temperature and droplet size frequently result in freezing rain mixed with sleet, snow, or nonfreezing rain, which 

substantially complicates area-specific forecasting of ice 

formation and accumulation (Heidorn 2001).

Ice accumulation is one of the most frequent forest 

disturbances in temperate regions (Irland 2000). However, 

most ice storms produce minimal ice accumulation and do 

not result in substantial losses. Some locations in North 

America experience at least some ice accumulation every 1 

to 2 years on average, though most areas have return intervals of 5 years or more (Bragg and others 2003). Ice storms 

are estimated to occur about every 5 years in Northern New 

England States (Smith and Musser 1998), while ice storms 

have been estimated to occur every 6 years in the natural 

range of loblolly pine (Pinus taeda L.) (Shultz 1997) and 

every 5 to 12 years inland from the Gulf of Mexico (Bragg 

and others 2003).

Storms resulting in major damages are estimated to 

have a return time of 20 to 100 years in Northern North 

America (Van Dyke 1999). Several recent ice storms have 

caused catastrophic damages over very large areas. For 

instance, a 1998 ice storm in southeastern Canada and 

the Northeastern United States damaged over 10 million 

forested hectares (Irland 1998, Miller-Weeks and others 

1999). The Southern United States is less prone to major ice 

storms, but the region has experienced a number of major 

events such as the December 2000 ice storms in Arkansas 

that damaged an estimated 40 percent of the 7.4 million ha 

of forest in the State (Bragg and others 2003). 

Tree Damage and Mortality

Wind can cause severe defoliation and can directly injure 

and kill trees through obvious damage such as uprooting (see Figure 2), breakage and loss of minor and major 

branches, and stem breakage (see Figure 3). Trees can be 

permanently damaged by the bending caused by wind, ice, 

and snow (see Figures 2 and 4), and compression injuries 

may be present even in seemingly undamaged trees. Severe 

bole injuries can lead to sap rot, slower growth, formation of 

compression wood, unusual growth rings, and intercellular 

voids between rings (Bragg and others 2003). Winds often 

carry saltwater inland, and foliage may die on trees that 

receive salt spray, although most affected trees will grow 

new leaves and recover (Gresham 2004).

The type of damage suffered tends to differ by tree 

size. Younger trees are more likely to bend with the wind 

without suffering serious damage, whereas older trees 

are more likely to uproot or break. Similarly, saplings 

and small-diameter trees can become seriously bent by 

ice accumulation, although these trees often recover. As 

diameter increases, the proportion of bending to breakage 

decreases. The majority of ice damage in larger trees is loss 

of branches and breakage of the main stem within the crown 

Figure 2—Bending and windthrow due to a major windstorm. 

Illustrated in this picture is wind damage in quaking aspen 

(Populus tremuloides) in Chequamegon National Forest, northern 

Wisconsin, resulting from a windstorm in July 1999. The majority 

of damage is in the form of bending and windthrow rather than 

stem breakage. 

Steven Katovich, USDA Forest Service, Bugwood.org.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

(Van Dyke 1999). Understory trees may be damaged by 

windthrown trees or broken portions of trees. In addition, 

tree species differ in their ability to withstand high winds, 

ice, and snow. Key factors include shape and size of the 

crown, rooting depth, and wood strength.

Whether a tree recovers from damage depends significantly on crown loss. Hardwood trees are not usually killed 

by breakage, but those with greater than 75 percent crown 

loss are likely to die. Conifers broken below the live crown 

or that have lost a majority of the crown will generally not 

survive (Van Dyke 1999).

Increased Risk of Secondary Damages

The immediate loss of timber is not the only damage 

imposed by weather events. Uprooting of trees also causes 

significant soil turnover and increases risk of soil erosion (Lutz 1940, Lyford and MacLean 1966, Meyers and 

McSweeney 1995). In addition, the increased number of 

broken and uprooted trees raises the risk of wildfires as 

well as disease and pest outbreaks for the surviving trees, 

whereas newly created canopy gaps provide opportunities 

for plant invasions. The presence of damage and decay may 

also increase vulnerability to future wind, ice, and snow 

(Bragg and others 2003).

Fire hazard may be increased following major storms 

that result in increased dead tree biomass and low-growing 

plants (Myers and Van Lear 1998). Federal and State forest 

resource departments frequently mention the heightened 

fire risk following major storm events as a key consideration 

for short-term forest recovery and management. In addition 

to increased fuel loading, access may be limited by downed 

trees, which further increases risk (Irland 2000). Myers 

and Van Lear (1998) examined the historical interaction 

between hurricanes and fires and concluded that these 

interactions had a major impact on the composition and 

structure of ecosystems in the Southern United States, 

although modern fire suppression efforts may have obscured 

this relationship in recent years.

Young trees can suffer bark damage following severe 

bending, which may increase susceptibility to disease. Any 

wounds permit disease fungi to infect damaged trees. Stain 

fungi may infect exposed wood within weeks, leading to 

wood quality degradation. Also, sap rot has been linked to 

sudden exposure of the stem to intense sunlight following 

canopy damage (Bragg and others 2003).

Broken and uprooted trees left unharvested also can 

lead to detrimental insect attacks on the remaining stand 

(Bakke 1989, Ravn 1985, Schroeder and Eidmann 1993). 

For example, severe outbreaks of spruce bark beetle 

(Ips typographus) occurred in Norway in 1971–1981, damaging trees equivalent to 5 million cubic meters of timber as 

Figure 3—Stem breakage from a tornado. Widespread stem 

breakage is pictured in loblolly pine (Pinus taeda) from tornado 

winds in Texas. The damage appears very similar to that caused 

by major hurricanes.

Figure 4—Tree bending from ice accumulation. The bending 

that can result from the weight of ice accumulation brought by a 

significant ice storm is shown in this picture. Although many of 

these trees will survive and may appear undamaged after they 

have straightened themselves, permanent internal damage may be 

present. 

 Ronald F. Billlings, Texas Forest Service, Bugwood.org.

USDA Forest Service Archives, Bugwood.org.
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GENERAL TECHNICAL REPORT PNW-GTR-802

an indirect consequence of extensive wind damage (Bakke 

1989). Cain and Shelton (1996) reported that an ice storm 

in southern Arkansas contributed to a southern pine beetle 

(Dendroctonus frontalis) outbreak. Hurricane Hugo caused 

large areas of loblolly pine (Pinus taeda) to be broken or 

uprooted, and these trees were susceptible to insect infestations (Yates and Miller 1996). Within months of Hurricane 

Katrina, virtually all of the downed material in De Soto 

National Forest in Mississippi had been infested by bark 

beetles and woodborers, and bark beetles (Ips engravers) 

had begun to infest and kill root-sprung trees (Meeker and 

others 2006).

Gaps resulting from tree fall and defoliation present 

opportunities for establishment of invasive plants (Burke 

and Grime 1996, D’Antonio 1993, Davis and others 2000, 

Hobbs 1989, Hobbs and Huenneke 1992, Réjmánek 1989), 

which may change forest structure and decrease populations 

of indigenous species. Increased light levels from large 

canopy gaps may also encourage the growth of invasive 

plant species, which could influence forest succession and 

alter ecosystem function. Snitzer and other (2005) found 

that mean cover percentage of invasive plants increased by 

47.8 percent in high-light canopy gaps created by Hurricane 

Isabel and by only 4.2 percent in less-damaged forest. 

Invasive species such as cogongrass (Imperata cylindrica 

(L.) P. Beauv.) can also contribute to heightened wildfire 

risk (SAF 2005).

Economic Impacts

Because major severe weather events can cause the direct 

loss of great quantities of timber, large numbers of landowners can suffer revenue losses and the entire forest 

products sector can be affected. Revenues from salvage 

operations are generally lower than those from typical 

harvests because saleable volume is reduced, particularly 

in cases where there is extensive stem breakage, but also 

because fungal decay reduces wood quality. At the same 

time, costs of salvaging damaged stands are usually higher 

than costs of conventional harvesting. Manley and Wakelin 

(1989), who took into account the effects of increased costs 

and reduced revenues, calculated that the discounted net 

present value of an impacted forest decreases by as much 

as 11 percent for a 1-percent annual level of damage. Forest 

landowners also may be affected by depressed timber 

market prices (at least in the short run) because of the large 

volume of salvaged timber on the market. Other things 

being equal, greater production risk unambiguously reduces 

optimal rotation length and decreases expected returns to 

forest land and land values, although the effects of price risk 

are ambiguous (Prestemon and Holmes 2000, Prestemon 

and others 2001). Prestemon and Holmes (this volume) 

provide more information on the economic impacts of hurricanes on landowners.

Factors That Influence Vulnerability

The severity of wind, ice, and snow damage depends on 

the interaction of numerous biological, topographic, stand, 

tree, and management factors. Before reviewing forest 

management activities to mitigate damage, we need to know 

what factors most strongly influence the severity of damage 

caused by wind, ice, and snow. Many empirical studies 

have indicated that the susceptibility of forests to damage 

depends on tree and stand characteristics such as species, 

height, diameter, crown area, rooting depth and width, stand 

density, and presence of forest gaps or edges (Dunham and 

Cameron 2000: Kerzenmacher and Gardiner 1998; Nykänen 

and others 1997; Peltola and others 1999, 2000; Quine 1995; 

Solantie 1994; Valinger and others 1993; Zeng and others 

2004). However, the statistical significance and even the 

direction of the effect of individual variables differ across 

studies. The natural systems involved and the interactions 

among factors are complex, so these apparent inconsistencies may be a result of nonlinearities in response and the 

omission of individual variables in some studies.

Weather Severity

The intensity and duration of severe weather events are 

among the most important determinants of forest damage 

associated with severe weather. The greater the intensity 

and duration of loading placed on trees, the higher the 

likelihood this loading will exceed critical thresholds 

and cause damage. However, few empirical studies have 

explicitly incorporated spatially differentiated, quantitative, 

physical measures of severity or duration or both (e.g., peak 
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wind gusts or ice accumulation). Storm conditions can vary 

considerably within a relatively small region, so local storm 

severity must be gauged accurately if researchers are to 

properly evaluate the impact of local management activities 

on damages.

Greater windspeed and duration mean that more force 

is exerted on trees. Thus, the probability of uprooting, 

stem breakage, or other damages is expected to increase 

with windspeed and duration. However, forests in areas 

where strong winds are common may be less susceptible to 

catastrophic damage because they have developed higher 

wind resistance in response to greater normal loading. For 

related reasons, wind direction also influences damage. 

Storm winds that come from a direction other than the 

typical prevailing winds may cause greater damage (Ruel 

1995). For winds of a given velocity, wind direction affects 

the loading placed on objects.

Similarly, the greater the accumulation of ice or snow 

on trees, the greater the likelihood that the trees will suffer 

structural failure. The National Weather Service declares an 

ice storm when there is at least 0.6 centimeters of ice accumulation, but minor glazing (0.6 to 1.3 centimeters) may 

cause little damage (Lemon 1961). Major events may result 

in accumulations as high as 10 to 20 centimeters (Bragg and 

others 2003). Depending on weather conditions, ice may 

remain on trees for some time before melting or being shed. 

As ice is retained on the trees for longer periods, damages 

increase (Schultz 1997).

Numerous mechanistic simulation models have been 

used to simulate the probability of structural failure in 

response to increased wind and ice, or snow loading or both 

(Blennow and Sallnäs 2004, Dunham and others 2000, 

Miller and others 2000, Peltola and others 1999, Talkkari 

and others 2000). These models generally predict critical 

windspeeds and ice or snow loading or both required for 

uprooting and breakage as a function of site, tree, and stand 

characteristics based on biomechanical properties. Several 

models incorporate both sources of risk to account for 

interactions between ice and snow loadings and wind risk 

(e.g., Miller and others 2000, Talkkari and others 2000).

A few empirical studies have included windspeed or 

ice and snow accumulation as explanatory variables in their 

regression models. Ramsey and others (2001) incorporated 

windspeed and duration to explain the variation in hurricane damage among forest types along the Atchafalaya 

River basin of Louisiana. Using forest-type distribution 

from Landsat Thematic Mapper image data, hurricaneimpact data from very high-resolution radiometer images, 

and windspeed and duration calculated from a wind field 

model, they found that the estimated impact for each forest 

type was strongly related to the duration and speed of 

extreme winds from Hurricane Andrew. Proulx and Greene 

(2001) found higher damage in northern hardwoods associated with greater ice accumulation, but Olthof and others 

(2004) did not find a direct relationship between ice storm 

precipitation and forest damage.

Site Conditions

Site conditions are commonly identified as influencing 

severe weather damage. These are physical properties of a 

location, such as topography and soil conditions, that cannot 

typically be directly modified in a particular stand through 

management actions.

Numerous studies have shown that local topography 

is a key determinant of risk of wind damage (e.g., Boose 

and others 2004, Foster and Boose 1992, Kulakowski and 

Veblen 2002, Quine 1995, Ruel 1995), but the relationship 

is complex, and specific topographic factors identified 

as important differ from study to study. Impacts may be 

increased when there is a barrier behind the stand (Talkkari 

and others 2000). Stands at higher elevations, on easterly 

slopes, and near ridges were more likely to suffer wind 

damage in Colorado (Kulakowski and Veblen 2002). The 

key issue for topography seems to be its influence on the 

probability of local windspeeds reaching critical speeds 

(Quine 1995). In many cases, the topography variables are 

serving as proxies of local wind intensity in the absence of 

direct measures of windspeed. Links between topography 

and ice and snow damages are less studied, but ice damages 

are increased at greater elevations (Olthof and others 2004, 

Rhoads and others 2002, Van Dyke 1999) as well as on 

steep slopes (Proulx and Greene 2001, Van Dyke 1999).

Findings about relationships between soil conditions 

and wind damage are relatively consistent (Wilson 2004). 
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Soil saturation is an important factor affecting the probability of windthrow. When hurricanes or other wind storms 

pass through an area following periods of heavy rainfall, 

blowdown is more likely (Ruel 1995). Deeper, well-drained 

soils lower the risk of wind damage. Beese (2001) found 

wet soil to be one of the largest contributing factors to 

wind damage. Similarly, damages from ice and snow are 

greater for wet soils (Van Dyke 1999). Other conditions that 

prevent establishment of deep roots, such as the presence 

of boulders, bedrock, or other barriers close to the surface, 

also increase the probability of uprooting (Rhoads 1999).

Tree Characteristics

A number of studies have focused on relationships between 

wind, ice, and snow damage and various tree characteristics. Tree characteristics of interest include species, health, 

age, size, height-to-diameter (H/D) ratio, crown shape, and 

canopy position.

Conifers tend to be more susceptible than hardwoods 

to hurricane damage (Foster 1988, Foster and Boose 1992, 

Jalkanen and Mattila 2000), though there are differences in 

susceptibility between taxa related to several species characteristics, including crown form, branching, rooting depth, 

center of gravity, and wood strength. Species susceptibility 

to hurricane-related damages also varies across types of 

damage, including breakage, uprooting, exposure to saltwater, and secondary insect and disease damage (Barry and 

others 1998). In addition, trees planted outside their natural 

range may be more susceptible to damage.

Differences in susceptibility to ice storms are related 

to species characteristics such as crown form, crown 

size, branch thickness, branch angle, root depth, and 

wood strength. Trees that have an excurrent form (apical 

dominance) are better able to shed ice and snow than those 

with decurrent form, and the vase-shaped form of many 

elms and oaks makes these trees especially vulnerable to 

ice and snow accumulation (Van Dyke 1999). Trees with 

branches that are pliable may better withstand ice and snow 

accumulation because they can shed ice or transfer the force 

to other parts of the tree, the ground, or neighboring trees. 

However, wood that is cold, “green,” or less dense has lower 

resistance to breakage than warm seasoned wood of the 

same species (Bragg and others 2003).

Root and stem rots, cankers, insect infestation, or any 

form of prior injury increases the probability of wind damage (Wilson and Baker 2001) and ice damage (Van Dyke 

1999) (with the possible exception of defoliating insects 

or other agents that reduce crown surface area). Trees are 

likely to break at the point of decay when exposed to excessive loading by wind, ice, and snow. For instance, trees with 

advanced beech bark disease experienced more ice damage 

(Rhoads and others 2002). Vine coverage may also increase 

susceptibility to glaze damage by increasing the surface 

area available for ice accumulation (Bragg and others 2003). 

Boose and others (2001) examined hurricanes affecting 

New England since European settlement in 1620 and found 

that forest damage from an event was strongly dependent on 

natural disturbance history. High proportions of compression wood, which can form in response to tree bending in 

past weather events, may increase storm damage (Dunham 

and Cameron 2000).

Stand age and tree size may also contribute to forest 

vulnerability to high winds. Although age and size are 

generally correlated, different studies alternately focus on 

age, height, diameter, or H/D ratio. Many studies indicate 

that susceptibility to wind damage increases with increasing 

stand age during the initial decades of forest development 

(Everham and Brokaw 1996, Francis 2000, Jalkanen and 

Mattila 2000). Foster (1988) found that conifers’ susceptibility to wind damage increases rapidly after age 15, but hardwood damage increases more gradually with age. Foster and 

Boose (1992) found wind damage increased approximately 

linearly with stand height. Ameteis and Burkhart (1996), on 

the other hand, argued that older, larger trees are stronger 

and have greater resistance to damage.

Francis (2000) and Greenberg and McNab (1998) also 

reported increasing uprooting with increasing tree height. 

Francis (2000) showed that tree height strongly influenced 

wind damage in San Juan during Hurricane Georges. 

Other authors suggested that height relative to the height 

of surrounding trees is the factor that most strongly 

determines susceptibility to wind damage (Asner and 

Goldstein 1997, Francis and Gillespie 1993, Hedden and 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

others 1995). Jalkanen and Mattila (2000) and Francis and 

Gillespie (1993) both showed that the susceptibility of a 

stand to wind damage was increased by larger mean diameter. Achim and others (2005) found that stem mass was the 

factor most highly correlated with wind damage. Francis 

and Gillespie (1993) related gust speed to tree damage in 

Hurricane Hugo, and their results indicate that the probability of a tree suffering some form of damage increases with 

increasing tree diameter.

Findings were mixed for ice damage. Bragg and 

others (2004) found the lowest levels of damage from ice 

in the oldest plantations. They found that the smallest trees 

were much more likely to bend severely in response to ice 

loading, that intermediate-sized trees had both bending and 

stem breakage, and that the largest trees had little of either 

but had more crown damage and branch loss. Rhoads and 

others (2002) found heavy ice damage in intermediate-sized 

trees that were 24 to 28 years old but little ice damage in 

younger stands in the affected forest. Proulx and Greene 

(2001) found that very small trees (3.2 to 9.5 cm in diameter) were more likely to suffer damage, that small and 

intermediate trees (<17.8 cm in diameter) were more likely 

to bend or snap, and that large trees were more likely to lose 

branches. Van Dyke (1999) indicated that older trees may be 

more susceptible to injury from ice because they have larger 

crowns, greater likelihood of internal decay, and decreased 

branch flexibility.

A number of studies have examined H/D ratios rather 

than either height or diameter individually and have typically found that larger H/D ratios tend to increase the risk 

of damage from high-wind events (Wilson and Baker 2001), 

snow (Kato and Nakatani 2000), and ice accumulation 

(Van Dyke 1999). However, Valinger and Fridman (1997) 

found that the ratio of the H/D ratio to the upper diameter 

(measured at 3 or 5 meters) was the best predictor of the 

probability of snow and wind damage, and that for a given 

upper diameter, the probability of damage was higher for a 

site with trees of low H/D.

In addition to looking just at tree height, diameter, or 

H/D ratio, some studies examine the influence of crown 

shape and canopy position. Mechanistic models of critical 

wind, ice, and snow loading generally include crown size 

and shape because greater crown surface area increases the 

drag coefficient and potential for loading from accumulation 

(Peltola and others 1999, Talkkari and others 2000). Empirical observations and models have generally supported this 

assumption. Foliated trees and trees with large crowns 

have more surface area for ice and snow accumulation and 

for wind to act on, increasing vulnerability to damage; 

however, damage is lowered by greater crown symmetry 

(Bragg and others 2003, Kato and Nakatani 2000, Van Dyke 

1999). Beese (2001) found greater wind damages in trees 

with larger crowns in montane coastal forests in British 

Columbia. Hardwoods are less susceptible than conifers 

to damage from ice or snow storms that occur during the 

winter months, when the hardwoods do not have foliage. 

Mitchell (1995) and Ruel (1995) reported that wind damage 

depends on canopy density and tree position in the canopy. 

Van Dyke (1999) indicated that dominant trees are more 

likely to suffer ice damage.

Stand Characteristics

Disturbances that open the canopy may increase the 

severity of damage from subsequent windstorms (Everham 

and Brokaw 1996). Stand density, thinning regime, and 

the presence of edges and gaps each have been found to 

influence forest damage from severe weather. However, the 

effects of density on forest damage depend on the interplay 

of a complex set of factors. Higher densities may lead to 

greater H/D ratios and trees that are less wind resistant. 

However, higher stand density may increase the probability 

of stem bending but decrease the probability of stem breakage because neighboring trees provide support for bending 

stems (Bragg and others 2004). Wind may move through 

low-density stands more freely, so that energy is dissipated 

and wind loadings are lowered. In addition, trees in the 

interior areas of low-density stands may normally receive 

higher winds and develop greater wind resistance than 

those in high-density stands. However, ice damage may be 

expected to increase at lower stand densities because trees 

in lower density stands develop larger crowns, and these 

tend to accumulate more ice and snow (Gardiner and Quine 

2000).
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It is also possible that bending or breakage of trees 

results in damage to other trees. When windthrow or breakage occurs in lower density stands, there is less chance that 

neighboring trees will be damaged (Baker and others 2002). 

Beese (2001) found that striking of trees by other trees was 

one of the most common categories of wind damage in their 

study region.

Recent thinning can increase vulnerability to wind, 

ice, and snow. Thinning removes structural support of 

proximate trees, and recently exposed pines tend to have 

relatively weak stems and ice-accumulating surfaces more 

concentrated in the crown (Bragg and others 2003). Burner 

and Ares (2004) found that thinning increases susceptibility to ice damage. Jalkanen and Mattila (2000) suggested 

that thinning will increase wind damage but decrease snow 

damage. Peltola and others (1999) simulated critical snow 

loading for both uprooting and stem breakage for managed 

and unmanaged stands of Scots pine (Pinus sylvestris L.) in 

southern Finland and concluded that critical snow loading 

for both uprooting and breakage is lower for unthinned 

stands than thinned stands.

Proximity to forest edge is highly related to wind (Wilson 2004) and ice storm damage (Olthof and others 2004). 

Zeng and others (2004) integrated a mechanistic wind 

damage model and an airflow model with a forest database 

containing information at the tree, stand, and regional levels 

and simulated damages for current forest edges and situations where new edges might be created through clearcutting. They found that clearcuttings increased windspeeds at 

forest edges and increased risk of damage for cases where 

vulnerable stands remained at the newly created edges. 

Others have found reduced damage at stand edges, however, 

especially when the edge has been established for a long 

time. This is likely due to trees on the edges facing greater 

wind loading on a normal basis and responding by increasing strength to resist wind loading (Foster 1988).

Management under Endogenous Risk

The probability of a severe weather event occurring is 

exogenous to the landowner; that is, landowners are unable 

to influence this probability through their own actions. 

However, although these events cannot be prevented, their 

potential impact can be mitigated through forest management, so the extent of severe weather damages to a particular forest area is partially under the landowner’s control. 

Recognizing this endogeneity of risk and understanding 

the factors that influence damage patterns are of key 

importance to efficient forest management. Although there 

is substantial variation in the empirical findings on the most 

important factors influencing weather damages, there are 

some common management recommendations. Landowners 

can incorporate expectations of severe weather damages 

into their decisions regarding site selection, species selection, silvicultural treatments, and planning for damage 

recovery to decrease expected impacts (Bragg and others 

2003, Lohmander and Helles 1987, Olofsson and Blennow 2005, Persson 1975, Zeng and others 2004). Another 

strategy for the forest industry is to diversify spatially to 

manage risk across total forest holdings thereby avoiding 

the loss of a large share of their standing timber to a single 

weather event.

Moore and Quine (2000) presented one example of 

the ability of forest management to mitigate weather risk. 

They compared wind risk in Sitka spruce (Picea sitchensis 

(Bong.) Carr.) plantations in Great Britain with that in 

radiata pine (Pinus radiata D. Don) plantations in New 

Zealand using the FORESTGALES simulation model. They 

reported that the plantations in New Zealand were at greater 

risk of damage than those in Great Britain because of differences in management even though the New Zealand plantations were subjected to a less severe wind regime. In Great 

Britain, silvicultural practices have been adopted to reduce 

density, conduct more careful site preparation, and reduce 

thinning. This comparison demonstrates the influence of 

forest management on the risk of weather damage, but it 

also demonstrates that mitigation options adopted should 

reflect the level of risk faced. Managers of plantations in 

New Zealand have been able to focus on maximizing profit 

under a less constraining wind risk, and plantations in New 

Zealand are more profitable than those in Great Britain.

Of course, appropriate response depends on the degree 

of risk. In cases where the exogenous risk of damages is 

low, incorporation of weather risk into strategy may result 

in minimal or no change in optimal forest management. 
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In contrast, there may be no feasible strategy for reducing damages when the exogenous risk of damages is high 

other than simply limiting investments and therefore the 

expected cost of damages. In the intermediate case, where 

the exogenous risk of damages is significant but there are 

cost-effective strategies for mitigation, the incorporation of 

weather risk into decisionmaking may substantially alter 

optimal forest management practices (Gardiner and Quine 

2000).

This implies that any evaluation comparing weather 

damages on managed and unmanaged sites will be inaccurate if it does not properly account for selection bias. 

Selection bias arises because the forest management strategies observed reflect decisions made by forest managers 

based in part on the perceived vulnerability of their stands 

to weather-related damages and the perceived effectiveness 

of potential silvicultural actions in those stands. Thus, the 

value of managing weather risk could be understated if 

these actions are selectively being undertaken in stands 

with high expected damages and the outcomes following a 

storm are compared with those in areas with low expected 

damages where managers chose to adopt fewer mitigation 

options. On the other hand, the effectiveness of damage 

reduction strategies may be overstated if little or no action 

to reduce impacts is being taken in certain stands because 

the expected damages in those locations are so large that 

there are few feasible mitigation options. Thus, careful and 

systematic program evaluation is needed to account for 

endogenous selection and to ensure that the effectiveness of 

damage mitigation options is being assessed after controlling for selection of mitigation options based on perceived 

baseline risk (Butry 2006).

Damage Mitigation Options

The primary categories of damage mitigation options are 

site and stand selection, selection and timing of silvicultural 

treatments, and stand recovery activities. Forest management is unlikely to significantly alter climate, topography, or 

soils in a given site. However, forest managers establishing 

a new stand can affect these conditions for forests they manage through site selection. In addition, owners with multiple 

sites can manage overall risk by selecting a portfolio of sites 

that offers the desired risk-return profile. Typically, weather 

risk is not the overriding consideration in the selection of 

sites for forest investments, but taking the risk of forest 

damages into account when selecting a site may improve the 

ability to meet management objectives.

Similarly, among numerous other factors affecting 

choice of species (e.g., productivity, market forces, objectives of the forest manager), the differential weather risk 

associated with different species should be taken into 

account. Some species are more vulnerable to weather 

damage than others based on crown configuration, rooting, 

wood strength, and many other factors. In addition, planting trees outside of their natural range may increase their 

vulnerability to damage (Bragg and others 2003). Selecting 

a tree species that is less productive or has a lower market 

price but also provides a reduced risk of severe weather 

damage may result in a higher expected present value of the 

stand if the baseline risk of severe weather and the speciesto-species difference in expected damages in the event of 

severe weather are sufficiently large.

The effects of management decisions about stocking, 

thinning, and pruning on storm damage risk have been 

identified as key issues in a number of studies. A number 

of trade-offs are associated with these activities, partly 

because short-term and long-term effects on risk may differ 

substantially. For instance, low-density stocking, thinning, 

or pruning may increase the risk of damage in the short 

term, but expected damages are reduced farther into the 

future as trees respond by growing stronger and more 

resistant to damages. Limited tree-size variation increases 

susceptibility to developing high H/D ratios in the dominant 

trees (Wilson 1998). There is typically less variation in tree 

size in plantations than in naturally regenerated stands, 

and this may lead to greater H/D ratios of dominant trees, 

reducing the stability of these trees. To keep H/D ratios 

down, it is necessary to conduct thinning operations before 

competition that leads to increased H/D (Wilson and Baker 

2001). Similarly, Wilson and Oliver (2000) found that 

plantation H/D ratios can be lowered by reducing planting 

density or early thinning to encourage development of 

strong stems and crowns; later thinning did not appear to 

be as effective. Also, if the rotation length is reduced 
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sufficiently, the reduction of time at risk of damage may 

more than offset the increase in expected damages if an 

event does occur. One of the best defenses against weather 

damages may be to grow the trees out of vulnerable intermediate sizes as quickly as possible through the application 

of silvicultural practices such as reduced density and proper 

thinning and pruning to increase site productivity.

The standard Faustmann equation maximizes discounted net revenue from an infinite series of rotations, 

accounting for the costs of maintaining land in forest production. The optimal rotation under certainty occurs where 

the increase in stand value from one additional period of 

growth is equal to the costs of delaying current and future 

harvests by one period. Incorporating an exogenous positive 

probability of damages into the equation unambiguously 

yields a reduction in optimal rotation length (Martell 1980, 

Reed 1984, Reed and Errico 1985, Routledge 1980). Under 

some conditions, increased production risk may also lead 

to the use of lower intensity production methods such as 

naturally regenerated stands (Haight and others 1986). 

Accounting for endogeneity of expected forest damages 

alters optimal management strategies by explicitly recognizing effects of management decisions on the expected 

present value of the stand through accounting for changes in 

expected damages.

Stocking influences, in several ways, damages that 

result from weather events. Less densely stocked stands 

may be more vulnerable in the short term because they 

will have less support from their neighbors. However, less 

dense stocking increases the growth of individual trees and 

reduces the time trees require to reach marketable size. In 

addition, low stand density will result in trees that are more 

resistant to loading because they are larger and subject to 

greater loading on a normal basis. One caveat is that low 

stand density commonly results in trees with larger, less 

tapered crowns that will tend to accumulate more ice and 

snow, although their increased strength may offset this 

effect.

Thinning may result in stronger trees that are more 

able to withstand wind, ice, and snow (Kato and Nakatani 

2000) a few years after thinning but increase the likelihood 

of damage in the interim. Coates (1997) found that partial 

cutting doubled the percentage of trees with wind damage in the 2 years after thinning, from 1.1 percent to 2.2 

percent but concluded that this increase in damage was too 

small to warrant changes in management (this increased 

damage may also be offset by increased tree growth rates 

that reduce the time spent at risk). Similarly, pruning can 

increase wind damage if a storm passes through the area 

soon after the treatment but can reduce damage in the 

future after trees have strengthened. Pruning was found 

to result in an increased resistance to snow damage in the 

short term, though, because the reduced surface area of the 

crown decreases accumulation. However, to the extent that 

heavy pruning reduces diameter growth more than height 

growth, resistance to snow loading may decline in the years 

after pruning because of increased H/D ratios (Kato and 

Nakatani 2000).

Thus, in areas that receive severe weather very frequently, managers may be less willing to conduct thinning 

or pruning because of the high probability that a storm will 

occur during the period when the stand is more vulnerable. In addition to removing support for remaining trees, 

thinning operations often cause minor damage to individual 

trees, and the damage sites can serve as entry points for 

insects and disease that then become established and infest 

the stand (Lohmander and Helles 1987). However, there 

is a reduced probability of trees damaging their neighbors 

following thinning because the spacing between trees is 

increased (Bragg and others 2003). In addition, identifying and removing trees most vulnerable to damage during 

thinning operations may be advantageous. Cameron and 

Dunham (1999) found that trees that were damaged by wind 

and snow had significantly more compression wood than 

did undamaged trees, and Dunham and Cameron (2000) 

therefore recommended preferentially removing trees likely 

to contain compression wood.

Because of the complexity of the interactions between 

various factors affecting expected forest damages, the 

relative costs and benefits of a particular action will vary 

spatially and temporally. Thus, optimal management strategies regarding planting density and whether, when, and how 

intensely thinning and pruning should be conducted need to 

be determined on a site-specific basis.
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If a plan for stand recovery is formulated before a 

damaging event takes place, this can help mitigate the 

damage. Salvage efforts after ice storms should concentrate 

on minimizing economic losses from damage to timber. 

Timber subject to losing the greatest value to degradation in 

the short term should be salvaged first, although timber of 

all ages and sizes may require some remediation to lessen 

fire danger and insect outbreaks. Salvage of severely damaged pine is more time sensitive than salvage of severely 

damaged hardwoods because pine is more susceptible to 

infestation by bark beetles and wood borers, but downed 

hardwoods should also be salvaged as soon as possible 

to avoid degradation (Van Dyke 1999). Remedial actions 

following hurricanes, such as staking saplings upright, are 

not clearly effective and are feasible only on small areas. 

In one study, saplings left in a leaning position did not lose 

measurably more growth than those staked upright, and 

many righted themselves (Gresham 2004), although it is 

important to recognize that bent trees may have permanent 

internal damage. In some cases, severely bent trees will 

not fully recover and may die or grow at greatly reduced 

rates. Managers may wish to explore selective harvesting of 

damaged trees.

Optimal Thinning and Rotation Decisions

As an illustrative example of the implications of treating 

weather damage risk as endogenous, we examine optimal 

thinning and rotation decisions. The implications of 

production risk for optimal rotations have been examined in 

several previous studies, including Martell (1980), Routledge (1980), Reed (1984), and Reed and Errico (1985), all 

of which evaluated changes in optimal rotation length under 

the exogenous risk of forest fires. Each found that optimal 

rotation age unambiguously declined where production risk 

was assumed to exist. Thorsen and Helles (1998) modifed 

these earlier models to treat risk as endogenous, focusing 

on the timing and intensity of thinning activities to influence the risk of damage. Thinning is one of the silvicultural 

practices that is most frequently mentioned in the literature 

as playing an important role in damage risk and as being 

readily applied.

Thinning offers the benefit of immediate income, and 

it promotes faster diameter growth of remaining trees, 

which increases net price received and reduces the risk of 

windthrow. Thinning also reduces the remaining volume 

that could potentially be damaged by storms. However, 

thinnings are costly because of fixed costs of thinning 

operations and increased risk of storm damage for several years after thinning. These dynamics are complex, 

but important to consider in determining optimal forest 

management strategies.

Thorsen and Helles (1998) examined an even-aged 

forest stand managed to maximize expected present value. 

They modeled the selection of an optimal thinning strategy 

and optimal rotation age (at which time the stand is clearcut), taking into account the stand growth function, net 

timber price, and risk of severe weather damage. Stand 

density, age, site, tree, and stand characteristics, including 

time since thinning and thinning intensity, all affect the risk 

of damage. The damage risk functions describe how the 

stand’s stability is impacted by current and past stand management decisions. The less the risk functions are affected 

by management decisions, the more resistant the stand is 

to disturbances such as thinning, and the less important it 

is to consider the implications of management decisions 

for weather damage risk. Thorsen and Helles (1998) used 

numerical optimization procedures to determine optimal 

management in the case of Norway spruce (Picea abies L. 

Karst.), which they identified as one of the species most 

often suffering from health or windthrow problems that 

necessitate salvage harvest of entire stands. Based on their 

assumptions regarding growth, risk dynamics, storm probability, and other variables, they found that optimal rotation 

age is reduced from 74 years under no risk to 69 years if 

storm risk is treated as exogenous, but 71 years when risk is 

assumed to be endogenous. In addition, the optimal number 

of thinnings increases from 10 to 11 between the case of 

no risk and exogenous risk. When risk is considered to be 

endogenous, the optimal number of thinnings increases to 

18, but the intensity of each thinning is reduced. In addition, 

they found that incorporating endogeneity in determining 

optimal management increases expected present value of 

the stand.
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The findings of this study are dependent on the 

growth, risk, and other dynamic relationships assumed. 

Optimal management will differ across stands depending 

on baseline damage risk, effects of thinning on growth 

rates and damage risk, prices and costs, and other variables 

(Straka and Baker 1991). In addition, this example assumes 

profit-maximizing behavior, which may be appropriate 

for forest industry but not for many nonindustrial private 

forest (NIPF) landowners or public forest managers. NIPF 

landowners and public forest mangers are expected to 

consider the amenity value of standing forests, which would 

add an additional term measuring amenity value to the 

optimal control problem discussed above. This term would 

depend on site, tree, and stand characteristics. Nevertheless, 

treating severe weather risk as endogenous may enable both 

private and public managers to reduce damage risk while 

increasing the expected present value of their stands.

Policy Implications

To the extent that weather damage risk is endogenous, 

private and public forest managers may be able to improve 

ecological and economic returns by allowing for the effects 

of their decisions on risk. However, this does not tell us 

whether any particular strategy should be adopted in the 

case of any specific stand. Options to be adopted depend on 

managers’ objectives and the cost of the option relative to 

the reduction in risk provided. Many key factors interact in 

a very complex manner to influence weather risk, making it 

vital to consider case-specific conditions in making decisions regarding mitigation of weather risk. It is important 

to consider joint risk of different types of weather events, 

which may reinforce or contradict each other.

Policy implications are expected to differ among forest 

industry, NIPF landowners, and public forest managers 

because these groups have different objectives. Forest 

industry managers are expected to be profit maximizing, 

whereas NIPF landowners likely have more varied objectives and value nontimber amenities of standing forests 

more highly. Damage mitigation options that would be optimal for forest industry may be less suitable for NIPF lands 

or vice versa, depending on the joint effects of the strategies 

on damage risk, expected present value, and stand amenity 

values. Similarly, public land managers are expected to take 

various nontimber objectives into account. The presence 

of endogenous storm risk may be an opportunity for the 

public sector to provide incentives for private landowners 

to take actions to mitigate storm damage, especially where 

private inaction could result in secondary insect and disease 

infestations or other conditions that could have negative 

effects on neighboring properties.

Private Forest Management Decisions

For forest industry, management of weather risk should 

be selected to maximize expected profitability, assuming 

risk neutrality. Under risk neutrality, firms are interested 

in maximizing the expected value of profits without regard 

for variability, whereas risk-averse firms would be willing 

to accept lower expected returns in exchange for reduced 

variability of profits. Decisionmaking under risk aversion 

could be analyzed using similar methods but would have 

to account for risk tolerance. One strategy when operating 

under risk is to reduce rotation age. Accounting for damage risk reduces the optimal rotation age relative to that 

obtained by solution of the standard Faustmann equation 

under certainty (Gardiner and Quine 2000, Haight and 

others 1996). However, if risk is truly endogenous, then 

managers may be able to alter practices to mitigate risk and 

potentially even increase expected present value of a stand, 

as described in “Optimal Thinning and Rotation Decisions.” 

Selective removal of specific high-risk trees when thinning, 

as well as adjustment of thinning timing and intensity could 

reduce risk of storm damage. However, it must be possible 

to identify and remove these trees cost effectively. Heavily 

damaged or dead trees are obvious candidates for removal, 

but it may be worth selectively removing those trees with 

features that increase their risk, such as large, asymmetric 

crowns, and trees that have been heavily bent.

Numerous other management strategies, including site 

selection, species selection, and planting density, could be 

modified to mitigate risk and should be adopted if they offer 

expected benefits exceeding their costs. Each of these strategies and combinations could be examined through empirical simulations using a model similar to that described in 

“Optional Thinning and Rotation Decisions.”
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

To optimally incorporate these strategies into forest 

management practices, it is necessary to understand the 

implications of each for the dynamic risk of storm damage 

in the event of a storm at different points in time, forest 

growth rates, and stand value.

For NIPF landowners, behavior may be more complex 

because the perceived impacts on aesthetics, wildlife, and 

other nontimber amenities provided by standing forests 

are a more important part of their management objectives. 

These landowners are likely to have substantially different 

optimal management policies as a result, probably tending 

toward fewer thinnings and increased rotation age relative 

to land managed by the forest industry, although this may 

vary depending on the primary objectives of particular 

NIPF landowners.

Public Policy and Forest Stand Management—

Management of public lands to mitigate weather risk also is 

likely to differ from the profit-maximizing forest industry 

case. Wilson and Baker (2001) argued that managing stands 

to allow greater future flexibility may be more important in 

the case of public lands than in that of private lands because 

objectives of public land management are more likely to 

change over time with shifting political pressures. It is 

important to develop stands that are capable of producing 

multiple outputs, from timber to older forest habitat, when 

future objectives are uncertain. Like NIPF landowners, public land managers are likely to consider aesthetics, wildlife, 

recreation, and other nontimber products in determining 

optimal forest management strategies.

In addition, the presence of endogenous forest risk and 

the extensive losses that could result from a major event 

could lead public agencies to become involved in providing incentives for improved private forest management, 

especially to avoid secondary impacts such as insect and 

disease infestations that may negatively affect neighbors. 

Private landowners will not fully account for the damages 

that may result on neighbors’ lands and may underinvest 

in storm damage mitigation relative to the socially optimal 

level. This negative externality may be an argument for government intervention to correct this market failure through 

subsidies of mitigation practices, by establishing penalties 

for not following recommended practices, or by establishing regulations requiring specific practices. However, it is 

important to construct such programs with care to ensure 

that they result in net increases in forest investment and 

improvements in forest health and do not simply offset 

private investment that would occur in the absence of the 

policy.

In Switzerland, short-term damage resulting from 

storm Lothar was similar across cantons (states) even 

though different cantons employed significantly different 

forest protection measures (Bisang and Zimmerman 2006). 

More expensive measures adopted in some cantons were 

not much more effective in preserving forests and preventing forest damages than much less expensive options. In 

addition, forest owners’ salvage decisions appeared to be 

independent of the financial incentives provided by cantons. 

Forest owners spent more of their private resources in 

cantons that provided minimal financial support, and their 

marketing of salvaged timber was less successful in these 

cantons than in those that provided greater public resources. 

Thus, the varying strategies for public involvement in 

providing financial incentives for forest management had 

important distributional effects for forest owners and 

government spending but not for forest health (Bisang and 

Zimmerman 2006).

Conclusions

Our review characterizes primary influences on damage 

risk as site, tree, or stand characteristics, each of which is 

influenced by forest management decisions. Findings about 

key factors differ from study to study, and this indicates 

both the complexity of storm damage and the substantial 

variation in data and methods used to conduct analyses. 

A few studies simultaneously incorporate many of the 

factors identified in the literature as having an important 

influence on damages; most focus on only a few variables 

and do not necessarily report statistical significance. Thus, 

it is unclear just how broadly applicable the findings of 

many studies are. This review reveals a need for additional 

empirical research to better quantify the effects of various 

factors influencing the risk of forest damages, particularly 

in the case of complex stands that are unlikely to be 
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captured adequately through standard mechanistic models. 

Future studies should place greater emphasis on empirical 

analyses that examine the effects of variables believed 

to affect damages and their interactions simultaneously. 

Another important issue is the need for more systematic 

program evaluation that accounts for endogenous selection 

of damage mitigation options to better identify the effects 

of management actions while controlling for differences in 

underlying conditions.

Our review indicates that some general management 

practices are likely to affect risk and that forest managers 

must consider the influence of their management decisions 

on the extent of weather damages. Site and species selection, as well as choice and timing of silvicultural treatments, 

affect expected damages. In the event of damage, the 

effectiveness of management plans for storm recovery is 

another key determinant of economic impacts and secondary damages, although management planning for storm 

recovery is not a focus of this chapter. Thus, optimal stand 

management where damage risk is treated as endogenous 

is likely to differ from management where risk is treated as 

completely exogenous. Treating risk as endogenous results 

in more frequent, less intense thinnings and a longer rotation age than when risk is treated as exogenous, though both 

cases have shorter optimal rotation ages than when there is 

assumed to be no weather risk (Thorsen and Helles 1998). 

This finding depends on a number of assumptions regarding 

the change in expected damages associated with thinning, 

however, and may differ across stands depending on their 

baseline risk of damage, effects of thinning on tree growth 

rates, timber price, thinning costs, and other variables. 

Developing better parameterizations of these relationships is an important area for additional research to better 

understand optimal decisionmaking under endogenous risk 

for a variety of climate and forest characteristics.

Management practices should be selected on the basis 

of the risk faced and management objectives. In the case of 

the forest industry, risk-neutral firms should undertake damage mitigation measures only if the increase in the expected 

present value of the stand exceeds the cost of the mitigation 

option. For NIPF landowners, management decisions under 

endogenous risk are generally expected to be more complex 

because of objectives other than profit maximization. 

Thinning regime and rotation age have implications for 

nontimber amenities provided by forests. Therefore, to the 

extent that NIPF landowners have objectives that include 

the production of such amenities, management by NIPF 

landowners will differ from management where the goal is 

profit maximization. Incorporating the nontimber amenities 

provided by standing forest will probably tend to reduce 

the optimal number of thinnings and increase rotation age, 

although this may vary depending on the primary objectives 

of particular NIPF landowners (Foster and Orwig 2006). 

Similarly, the implications for public land management 

under endogenous risk are expected to differ from the 

profit-maximizing case based on the varied objectives of 

public agencies managing forest land.

Recognition of the endogenous nature of forest damages and the large potential losses could induce public agencies to provide incentives for private landowners to engage 

in improved management practices. This is particularly 

true when damages may lead to secondary impacts such as 

insect infestation or forest fires that could spread to neighbors’ land. Landowners do not fully account for impacts 

on neighbors or invest at socially optimal levels in damage 

mitigation and stand recovery measures, and this provides a 

rationale for potential government intervention. However, it 

is important to construct such programs with care to ensure 

that they result in net increases in forest investment and 

improvements in forest health rather than simply offsetting 

private investment.

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Jeffrey P. Prestemon and Thomas P. Holmes

Jeffrey P. Prestemon and Thomas P. Holmes, research 

foresters, USDA Forest Service, Southern Research Station, 

Research Triangle Park, NC 27709.

Abstract

We present a conceptual model of the economic impacts 

of hurricanes on timber producers and consumers, offer a 

framework indicating how welfare impacts can be estimated 

using econometric estimates of timber price dynamics, and 

illustrate the advantages of using a welfare theoretic model, 

which includes (1) welfare estimates that are consistent 

with neo-classical economic theory, and (2) wealth transfers 

among various market participants that can be evaluated. 

Timber producers in the Southern United States are faced 

with the regular risk of damages from intense hurricanes. 

Individual events can kill several million cubic feet of 

standing timber, with attendant losses for forest owners. 

One result of using a welfare theoretic model that is not 

apparent using simpler models is that timber producers with 

undamaged timber suffer economic losses in the short term 

because of a price depression, and they may be compensated in the long term by an enhancement of market prices 

owing to the loss of standing inventory. Catastrophic storms 

induce losses to timber producers holding damaged timber 

owing to quality degrade, price depression, and the inability 

to salvage all of the damaged timber. To minimize decayrelated losses, owners of damaged timber should salvage 

as quickly as possible and favor salvage of higher value 

trees. Owners of undamaged timber should delay harvesting 

until salvage wood is exhausted from the market. Evidence 

suggests that timberland investors under hurricane threat 

could benefit by diversifying their holdings geographically, 

favoring areas far enough from coastal counties to minimize catastrophic losses from such storms but close enough 

to benefit from market-level price enhancements resulting 

from regional inventory losses.

Keywords: Economic welfare, Hurricane Hugo, 

Hurricane Katrina, timber salvage, tropical cyclone.

Introduction

This paper provides a description of the hurricane process 

in the United States in the context of economic impacts on 

forest land owners, presents the welfare theoretic model 

used to quantify timber market impacts, describes and 

compares the impacts of a few large and recent hurricanes 

on timber markets in the South, and concludes with some 

recommendations for landowners based on the research. 

This section focuses on measurement of catastrophic storm 

impacts on landowners.

More than 150 hurricanes have struck the U.S. Atlantic 

and Gulf of Mexico coasts during the past century, and 

these storms have caused economic damages totaling 

billions of dollars (Blake and others 2007). Human loss of 

life and suffering, destruction of housing, and disruptions 

in economic activities are the most obvious impacts of 

catastrophic storms, and recent, large hurricanes are proof 

of their ability to economically and politically transform 

affected regions. States vulnerable to the most intense 

storms include Florida, Texas, North Carolina, and South 

Carolina, but all States in the South and some States in the 

mid-Atlantic region and the Northeastern United States are 

also vulnerable to hurricanes.

Less appreciated, perhaps, are the effects that catastrophic storms have on forests and the people who own 

them. The effects of hurricanes on landowners are a 

complex function of many factors, including storm severity 

(windspeeds), prior rainfall activity (whose effect in turn is 

modified partially by soils), the strength of the timber market, tree species affected, and timber decay rates. Overall 

effects on landowners from hurricanes depend on hurricane 

frequencies and intensities. Given these variables affecting 

damages, the long-term effect of hurricanes will depend on 

long-term trends in climate and the timber market.

Research has shown that individual storms can create 

forest damages into the billions of dollars, and those damages create widespread wealth redistributions. The timing, 

scale, and implications of these losses and benefits have 

been the subject of research by the authors of this paper 

and by others. We have learned that measuring the impacts 

Economic Impacts of Hurricanes on Forest Owners
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on landowners can be accomplished using standard economic concepts of supply and demand in combination with 

econometric analysis of timber price dynamics following a 

catastrophic event. This approach is consistent with welfare 

economic methods described by Just and others (1982) and 

as applied to forest impacts by Holmes (1991). These concepts have been used by the authors to measure the effects 

of Hurricane Hugo on prices, producers, and consumers 

(Prestemon and Holmes 2000, 2004). These techniques have 

also been used to quantify the timber market impacts of 

large-scale fires (e.g., Butry and others 2001, Prestemon and 

others 2006). The implications of these findings suggest that 

landowners can take steps before and after such events to 

reduce exposure to the risk process and to mitigate damages 

when subjected to it.

The Biophysical Risk Process of Atlantic Basin 

Hurricanes in the United States

This section provides a discussion of biophysical risk 

processes associated with timberland ownership in the 

Eastern and Southern States of the United States. Atlantic 

hurricane arrival rates are available from the National Oceanic and Atmospheric Administration, which are reported 

here to provide the scope of the issue for forest landowners. 

Table 1 and Figure 1 show the number of hurricanes, by 

Saffir-Simpson scale category, striking each State in the 

Atlantic Basin of the United States, 1851-2004. Table 1 can 

be used to infer the probability of a hurricane striking a 

specific State by dividing the number of years represented 

by the number of landfalls (although a spatially weighted 

probability listing would look different). This table shows 

that there is a coincidence of high hurricane frequency and 

high timber volumes. This coincidence is apparent when the 

Table 1—Hurricane landfalls, 1851–2006, and timber growing-stock volumes 

and production, 2002 

All Major

categories hurricanes Standing growing- Timber,

State (1-5) (3-5) stock volume, 2002 production 2002

Million ft3 Thousand ft3

Florida 113 37 15,366 560,475

Texas 60 19 12,939 769,501

Louisiana 52 20 18,844 958,981

North Carolina 50 12 32,742 957,675

South Carolina 30 6 17,702 682,901

Alabama 26 6 27,847 1,298,533

Georgia 23 3 31,704 1,447,941

Mississippi 16 9 20,611 1,149,880

Virginia 10 1 26,487 654,829

New York 12 5 21,831 141,068

Connecticut 11 3 3,192 11,691

Massachusetts 11 3 5,732 26,437

Rhode Island 9 4 496 1,742

Maine 6 0 20,891 441,729

Maryland 2 0 5,092 40,507

Delaware 2 0 695 7,654

New Jersey 2 0 2,819 10,549

New Hampshire 2 0 9,015 140,282

Pennsylvania 1 0 24,903 215,912

Note: State-by-State information about the number and intensity ranges of hurricanes, where they made landfall 

(1851–2006), and the total current (nondamaged) amount of growing stock and timber production by State (for 2002) 

are given. Hurricane intensities are represented using Saffir-Simpson categories.

Sources: Blake and others (2007, p. 18); Smith and others (2004).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

States are ranked by hurricane landfall. The States with the 

greatest number of hurricanes are Florida, Texas, Louisiana, 

and, then, North and South Carolina. The strongest timber 

markets are found along the gulf coast, so the southern 

timber resource is particularly threatened by severe losses 

from large-scale wind events. In other words, timber owners 

in these high-frequency States are at particularly high risk 

of timber loss. See Stanturf and others (2007) for additional 

details about timber and hurricane frequencies and the 

United States Department of Commerce, National Hurricane Center (2007) for information on hurricane return 

intervals.

Data from the National Oceanic and Atmospheric 

Administration (2006) show that the number of hurricanes 

in the United States has recently demonstrated an upward 

trend. These data are consistent with Emanuel (2005) who 

indicated that hurricane intensities in many parts of the 

world appear to have risen over the past several decades. 

Both of these trends are consistent with some predictions 

of anticipated effects of climate change (e.g., Knutson and 

Tuleya 2004) and with recent global ocean temperature 

rises (Barnett and others 2001). Whether or not the recent 

increase in hurricane activity is due to climate change (Gray 

1984a, 1984b; Gray and others 1996) or primarily associated with multidecadal Atlantic Ocean sea surface temperature oscillations is still being debated. What is clear is 

that the Southern United States is facing a period in which 

substantial damage to timber resources from hurricanes can 

be anticipated, along with the associated consequences for 

forest landowners.

Timber Market Dynamics Following 

Hurricanes

This section provides information on (1) timber market 

shifts following natural disturbances, (2) resulting timber 

price movements through time, (3) welfare movements 

through time, and (4) salvage recovery values following 

natural disturbances. This will provide the reader with an

overall picture of timber market behaviors and offer 

intuition about expected price movements and production 

Figure 1—Atlantic hurricane strikes, 1851–2004, by State by Saffir-Simpson Category. Source: National Oceanic 

and Atmospheric Administration (2006).
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shifts in the aftermath of large-scale catastrophes affecting 

timber.

Graphical Overview of Market Shifts

Following a large hurricane (or any natural disaster), a 

timber market may undergo substantial changes in supply 

and demand. In Figure 2, supply of timber is represented 

as S0 initially and is a function of price (P) and available 

inventory (I0). Demand, also a function of price, is the curve 

sloping down from the left, and initial levels are represented 

by the curve D0. The initial (prestorm) price is designated 

as P0, and quantity is Q0. Following the storm, the timber 

supply curve shifts inward to S1 due to a reduction in 

available standing live inventory of undamaged timber (I1). 

However, in the immediate aftermath of the storm, a pulse 

of timber salvage, V1, enters the market. This supply is zero 

when price is zero—i.e., it is a single point—but jumps to 

a large positive amount when price exceeds zero (Holmes 

1991; Prestemon and Holmes 2000, 2004). In other words, 

the supply curve parallels the horizontal axis between 0 and 

the quantity V1. Any stand with fatally damaged timber 

with nonzero stumpage value is part of salvage supply. The 

combination of the inventory effect and the salvage effect is 

Figure 2—A representation of the timber market following a natural disaster such as a hurricane. The primary effect of hurricane damage to timber is to temporarily shift supply outward (from S0 to Ss,1) during salvage and then shift back to S1 due to salvage exhaustion 

and inventory loss. As inventory (I) regrows, supply shifts outward toward S0, and an intermediate position may be St

. Demand, initially 

at D0, may shift backward to D1 because of mill closures and higher market prices; intermediate demand level is at Dt

, during inventory regrowth. Price paths over time can be traced to these market shifts. Lower-case letters have been added at intersection points to 

facilitate detailed discussion in the text.
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to create a total market supply of Ss,1. Again, as in the case 

of the salvage supply, Ss,1 consists of a single point at (0,0) 

but jumps to V1 when price slightly exceeds zero. During 

this salvage period, the equilibrium price is P1, lower than 

the prestorm price. When the pulse of salvaged timber is 

exhausted, the supply curve reverts to S1, with a higher 

price (Pt) than prior to the storm. As inventory grows over 

time, the supply curve gradually shifts back out to the original position of S0, achieving the original price and quantity 

at equilibrium. At that point, assuming no investment shifts 

owing to the storm, the effects of the hurricane disappear 

from the market.

In addition to impacts on timber supply, hurricanes 

might also impact timber demand. Many mills were closed 

because of either hurricane damage to mill facilities or 

anticipated long-term timber shortages following salvage 

operations after the passage of Hurricane Hugo. Syme and 

Saucier (1992) uncovered some evidence of short-term mill 

closures. In that case, demand might shift back (to D1), as 

capital is reallocated to other, undamaged regions or out of 

the wood processing sector. The degree of backward shift 

in demand identified by Prestemon and Holmes (2004) was 

slight. Such a backward demand shift tended to dampen 

any price swings following Hugo. However, it could be that, 

with time, demand expands in response to timber inventory 

regrowth, allowing for prices that are not as low (e.g., Pt) as 

they would be if demand did not respond.

Timber Price Movements Through Time

Timber prices following a hurricane proceed through three 

phases: (1) price depression, (2) price enhancement, and (3) 

the gradual return of timber prices to prestorm levels, as 

implied by the discussion of the graphical representation 

of the hurricane’s impact on a timber market. First, immediately following the storm, a price depression occurs, as 

was identified by Yin and Newman (1999) and Prestemon 

and Holmes (2000) following Hurricane Hugo. The finding 

of a price depression is consistent with what Holmes (1991) 

found in the case of southern pine beetle (Dendroctonus 

frontalis Zimmermann), and in all cases it is due to timber 

salvage activity, which drives down the market price owing 

to the supply expansion.

Prestemon and Holmes (2000) also found that there was 

a second stage involving a higher set of prices for timber 

products owing to the shortage of timber inventory following Hugo’s destruction. This is the enhancement effect 

identified by them statistically, consistent with theory and 

with local markets where timber is expensive to move. Such 

an effect has been observed in other circumstances, as well 

(Berck and Bentley 1997, Olson and others 1988). In the 

third stage, timber returns to the prestorm level, when the 

inventory returns to normal levels.

Figure 3 illustrates timber price dynamics following a 

large-scale natural catastrophe. This figure shows a brief, 

negative price spike for timber because of salvage, a longer 

term price enhancement owing to inventory shortages, and 

then a gradual price reduction to precatastrophe levels as 

inventory regrows. Both a timber price index (where the 

precatastrophe timber is normalized at an index value of 

100) and a timber inventory index (where the precatastrophe 

inventory volume is normalized at an index value of 1.00) 

are shown, indicating that 25 percent of timber inventory 

is killed. The figure is built upon assumptions of a constant 

demand elasticity with respect to timber price of –0.5 

and constant supply elasticities with respect to price and 

inventory of 0.3 and 1.0, respectively. (These elasticities 

represent the percentage change in timber supplied to the 

market, given a 1-percent change in price or inventory 

volume, respectively). It also assumes that (1) 30 percent of 

timber killed is salvaged, (2) it takes 4 quarters (a quarter is 

3 consecutive months) to exhaust the salvage, (3) the salvage 

quantity is highest immediately following the event and 

tapers off linearly following the event, and (4) the salvage 

quality discount factor is 5 (i.e., a unit of salvage timber has 

a quality discount of 80 percent).

Prestemon and Holmes (2000) conducted an intervention analysis of southern pine sawtimber and pulpwood 

stumpage (timber) prices, modeled after a similar analysis 

conducted by Holmes (1991). The approach was to model 

timber prices as autoregressive-integrated-moving average 

processes, including an intervention component that captured the effect of the hurricane on South Carolina timber 

prices. Comparison of the South Carolina price processes 

to price processes for the same product in other locations 
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GENERAL TECHNICAL REPORT PNW-GTR-802

in the South allowed identification of the full price effect 

of the storm, in both the short and long terms. As Holmes 

(1991) pointed out, statistical identification of the market 

price effects of such an event requires a sufficiently long 

series before and after the modeled event in order to identify 

derivative patterns in the prices owing to the event. Prestemon and Holmes (2000) found in their intervention analysis 

that salvage induced an average 30-percent decline in green 

(undamaged) pine pulpwood and pine sawtimber stumpage 

prices during the salvage period and that the inventory 

loss induced a persistent 10- to 30-percent enhancement 

(increase) in those prices after salvage was exhausted. This 

finding relied on tests that confirmed the nonstationarity 

of timber prices in South Carolina and identification of 

significant co-integration relations between South Carolina 

prices and those of other States. Nonstationarity of the 

State’s timber prices has been also confirmed using longer 

time series, inclusion and exclusion of time trends, alternative deflation methods, and alternative tests of stationarity 

by Prestemon and others (2004). Using a different approach 

that relied on an assumption of trend-stationary timber 

prices in South Carolina, Yin and Newman (1999) identified 

no long-run price enhancement in South Carolina.

Welfare Movements through Time

This section describes how timber damages, salvage, and 

price shifts result in effects on different segments of the 

timber market. Results from published research are used to 

illustrate these shifts. Prestemon and Holmes (2004) traced 

out the economic equity (welfare, or economic surplus) 

impacts of hurricanes and similar large-scale catastrophes 

in an analysis that is based on methods applied by Holmes 

(1991) and those of Just and others (1982). Thurman and 

Easley (1992) showed how, when an event occurs in a 

Figure 3—A hypothetical price path over time for timber following a large-scale natural catastrophe. In this 

figure, prices in the quarter immediately preceding the catastrophe (quarter -1) are at 100 and inventory is at 1.00. 

In the quarter of the catastrophe, inventory drops to about 0.75 and prices fall to 60 as salvage volume moves 

into the timber market. With salvage exhaustion, the inventory shortage effect is felt in timber prices, as they are 

temporarily elevated to as high as about 140. With time, prices recover to their original level as inventory returns 

to its initial volume.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

primary product market (such as timber), a welfare analysis 

of the economic impacts of the event on the primary market 

will capture the effects on all higher levels of processing 

deriving from that market. Given this, in our study, the full 

timber market economic impacts can be partitioned across 

owners of damaged timber, owners of undamaged timber, 

and timber consumers. This requires definition, however. 

Consumer surplus is defined, in Figure 2, as the area below 

the demand curve, above the equilibrium market price, 

and to the left of the equilibrium quantity supplied to the 

market. For example, before the hurricane, the consumer 

surplus is area P0ch, while in the immediate aftermath of 

the storm, it was area P1di. Producer surplus of undamaged 

producers is the area above and to the left of the supply 

curve and below price. Before the storm, producer surplus 

was P0c0. In the immediate aftermath of the storm, producers of undamaged timber receive surplus represented by 

the area P1e0, but as timber salvage is exhausted, say in 

period t, this subset of producers actually gains, with their 

surplus valued as 0fmPt. The producer surplus gained by 

damaged producers upon salvage of their timber by the 

storm is equivalent to salvage volume (e.g., V1) times the 

price received during the salvage period (P1). This is the 

area represented by the rectangle P1dQ10. In the immediate 

aftermath of the storm, then, these undamaged producers, 

because they cannot produce undamaged timber, also lose 

an area represented by a curved shape bounded by 0, S1, S0, 

and demand curve, D1, the shape represented by the shape 

0cg.

Figure 4 illustrates how changes in southern pine timber market economic surplus, salvage revenues, and timber 

Figure 4—Identified price and welfare shifts in the Timber Mart-South (Norris Foundation, 2004–2006) Region 2 market, South 

Carolina, immediately before and after Hurricane Hugo, for southern pine sawtimber, by quarter. Figure based on the base case 

scenario data from Prestemon and Holmes (2004). [mbf = million board feet].
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price varied over time following hurricane Hugo, as identified by Prestemon and Holmes (2004). In their simulations, 

inventory regrowth is projected, based on Forest Inventory 

and Analysis surveys in 1993 and 1999 in South Carolina. 

The rate of regrowth is a key determinant of the quantity 

and timing of post-hurricane welfare impacts. The figure 

shows that consumer welfare increased briefly following 

the hurricane because of lower prices and greater quantity 

consumed. Undamaged producers were briefly harmed from 

this salvage, but damaged producers captured some value 

for a few quarters. Consumers were harmed over the long 

term, undamaged producers gained over the long term, and 

damaged producers lost in the long term. In this figure, 

prices were determined to return to prestorm levels in the 

sawtimber market by 2012, about 90 quarters (23 years) 

after the storm. A similar price and welfare path was also 

traced out by Prestemon and Holmes (2004) for the southern 

pine pulpwood market there.

As shown, all of these losses and gains proceed into 

the future until the timber inventory returns to the prestorm 

inventory volume and timber production and price levels. 

The long-term discounted economic surplus lost and gained 

by the various market participants is tracked by tallying 

these changes for all periods into the future, discounting the 

values using an economic discount rate to the present. In a 

later section, we describe those changes for Hurricane Hugo 

in a table.

Salvage

This section describes how the value of salvage is calculated 

and some of the factors influencing the value recovered by 

salvaging timber. Readers interested in more detail about 

the economics of the salvage decision should review Haight 

and others (1996), whereas those interested in operational 

aspects of salvage should see Stanturf and others (2007). 

Our research and research conducted by others (e.g., Lowell 

and others 1992), indicate that the value of salvage depends 

on several factors: (1) the severity of initial damage (function of wind, prestorm rain); (2) the amount of timber 

decay, which is a function of tree species, the post-event 

ambient air temperature, the post-event ambient humidity, 

the dimensions of the materials, and time; (3) the aggregate 

volume of salvage entering the timber market; and (4) 

market supply and demand sensitivities with respect to price 

and market supply sensitivity with respect to inventory. The 

species, temperature, humidity, log dimension, and time all 

govern the current value of the salvage discount factor, K. K

is used to translate the salvage volume into a value-equivalent “green” timber volume for market valuation purposes 

(see Holmes 1991):

Qs = QD K -1,

where QD is the raw quantity damaged or killed and QS

is the value-equivalent of the timber in “green” timber 

volume. As K increases, the value-equivalent reduction in 

timber volume increases.

What the research implies is that hurricanes (which 

occur generally in warm, humid areas of the United States 

and cause a great amount of physical damage to the internal 

log structure from wind) produce values of K that are large. 

Prestemon and Holmes (2004) found that K likely averaged 

about 4.5 for southern pine sawtimber following Hugo and 

6.6 for southern pine pulpwood. Events such as fires (e.g., 

Lowell and others 1992) and southern pine beetle outbreaks 

(e.g., de Steiguer and others 1987) produce smaller values 

for K (in the range of 1.1 to 1.5 in the months immediately 

following the event).

Forest and Timber Impacts of Recent 

Hurricanes

The need for landowners and policymakers to plan for 

effects of increased hurricane activity is discussed in this 

section, and comparative information about damage levels 

of recent vs. older hurricanes is provided. Recent hurricanes, including Ivan and Frances in 2004, and Katrina and 

Rita in 2005, provide evidence of the timber market impacts 

of these events, including their impacts on landowners. 

These can also be compared with the impacts of older hurricanes, such as Hugo in 1989 and Camille in 1969, to help us 

understand how changes in timber prices as well as volumes 

affected may be leading to rising economic impacts. Below, 

we focus on the kinds of losses experienced by owners of 

timber in terms of hardwood and softwood, pulpwood, and 

sawtimber-product categories.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Table 2—Timber damage volume and dollar impacts from six severe hurricanes affecting selected 

States in the United States Atlantic Basin 

Camille, Hugo, Frances, Ivan, Katrina, Rita, 

Units 19691 19891 20042 20042 20051 20052

Softwood timber mortality

 South Carolina million ft3 1,008

 Florida million ft3 87 208

 Mississippi million ft3 216 619

 Louisiana million ft3 287 296

 Alabama million ft3 603 126

 Texas million ft3 239

Hardwood timber mortality

 South Carolina million ft3 319

 Florida million ft3 58 94

 Mississippi million ft3 74 426

 Louisiana million ft3 193 177

 Alabama million ft3 414 91

 Texas million ft3 293

Total timber mortality

 South Carolina million ft3 1,327

 Florida million ft3 145 302

 Mississippi million ft3 290 1,044

 Louisiana million ft3 480 473

 Alabama million ft3 1,017 216

 Texas million ft3 532

 Total, measured affected million ft3 290 1,327 145 1,319 1,740 1,005

 States, all species

Softwood portion of Percentage 74 76 60 61 54 53

 damaged volume

Timber value damaged 

 (price × quantity)

 Sawtimber value lost Millions of 177 4943 153 2,094 1,600 504

 2005 dollars

 Pulpwood value lost Millions of 25 1363 9 83 113 32

 2005 dollars 

 All products Millions of 202 6303 162 2,177 1,713 536

 2005 dollars

Timber economic surplus

 Producer surplus lost, all Millions of 1213

 owners and periods 2005 dollars

 Consumer surplus Millions of 1,4023

 lost, all periods 2005 dollars

 All market groups Millions of 1,5233

 2005 dollars

1 Final USDA Forest Service Forest Inventory and Analysis estimates of volumes of timber mortality. Economic impacts in dollars based on 

these final estimates.

2 Initial USDA Forest Service Forest Inventory and Analysis estimates of volumes of timber mortality. Economic impacts in dollars based on 

these initial estimates.

3 Dollar values shown for Hurricane Hugo are for softwood timber only.

Sources: Surplus values for Hurricane Hugo are from Prestemon and Holmes (2004); volumes for Camille are from Van Hooser and Hedlund 

(1969); pine values are from Mississippi Forestry and Agricultural Experiment Station (2006); baldcypress and hardwood values are from and 

Louisiana Department of Agriculture (1969); volumes for Frances, Ivan, Katrina, and Rita are from USDA Forest Service Forest Inventory and 

Analysis (2004a, 2004b, 2005a, 2005b), respectively; and values are from Norris Foundation (2005–2006). 
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GENERAL TECHNICAL REPORT PNW-GTR-802

The 2005 hurricane season was the worst, in terms 

of timber damages, in a generation—much bigger than 

the largest in the last two decades, Hurricane Hugo (Table 

2) (USDA Forest Service, Forest Inventory and Analysis 

2005a, 2005b). Hurricane Katrina damaged 1.74 billion 

cubic feet of timber, and about 41 percent of the volume was 

hardwood. Other estimates, not reported here, put longterm damages much higher than this. Hurricane Rita added 

to those damages, so when combined, these two storms 

damaged at least twice as much timber in the affected 

States of Alabama, Louisiana, Mississippi, and Texas as 

was damaged by Hugo in South Carolina in 1989 (although 

those South Carolina damages exclude damages in North 

Carolina, another affected State).

The market value of these timber inventory losses for 

Katrina and Rita was quantified in a special report provided 

to the Forest Service, expressed in terms of 2005 dollars 

to account for inflation. For this review, we also quantified 

the same values for Hurricane Frances (which affected 

Florida) and Hurricane Ivan (which affected both Florida 

and Alabama) in 2004. These economic effects are reported 

at the bottom of Table 2, principally in market-value terms. 

In Table 2, we also include the economic effects of two 

other large Hurricanes, Camille (1969) and Hugo (1989). At 

the bottom of Table 2, we include information for Hurricane 

Hugo, addressing both market value and economic surplus, 

as calculated by Prestemon and Holmes (2004). Market values can be termed timber value losses, whereas economic 

surpluses can be termed economic losses. We note that the 

welfare impacts for softwood calculated by Prestemon and 

Holmes (2004) depend on assumptions regarding market 

supply and demand parameters and timber regrowth rates, 

and they depend on the validity of the price paths identified 

by Prestemon and Holmes (2000). Prestemon and Holmes 

(2004) conducted Monte Carlo and sensitivity analyses on 

these damages, which report ranges of possible impacts. 

The welfare amounts shown in Table 2 for Hugo are average 

estimates.

Hurricane Frances is the smallest hurricane impact 

shown in the table, with 145 million cubic feet of damage. 

Camille damaged twice as much timber as Frances, but it 

still is small compared to the rest of the hurricanes shown 

in the table. For example, Hurricane Ivan created timber 

damages that are comparable to those measured for Hugo. 

Rita created damages that totaled a billion cubic feet, about 

75 percent the size of Hugo. The amount of softwood lost 

in these storms varied, mainly according to the amount of 

pre-existing softwood in the timber inventory. In the South, 

the amount generally exceeds 50 percent.

The values of timber damaged or killed in the storm are 

variable, as documented in Table 2. One surprising finding 

from this comparison is that Ivan’s timber damages exceed 

the value of those found for Katrina. This is explained by 

the substantially higher timber prices in effect in Florida 

and Alabama prior to the hurricane, in comparison with the 

timber mainly damaged in Mississippi and Louisiana. It is 

also partially explained by the former hurricane’s higher 

proportion of damage to softwood and sawtimber-sized 

trees. Still, when Rita’s damages, which totaled over $536 

million, are added to those of Katrina, those two storms 

damaged volumes that are more than twice those of Hugo 

or Ivan.

Hugo’s timber value lost in the southern pine market, 

measured as price times quantity, was $630 million. The 

analysis by Prestemon and Holmes (2004) showed, however, 

that the market impacts in welfare terms were 2.4 times 

larger. This magnitude of difference between market value 

and welfare impact should be viewed with caution when 

attempting to translate between market-value losses to 

welfare losses for other storms or catastrophic events. Even 

for Hugo, this translation depended on many assumptions, 

especially regarding (1) timber salvage rates, market supply 

and demand sensitivities; (2) rates of timber inventory 

regrowth; (3) the reality of a long-term inventory-induced 

price enhancement; and (4) the scale of timber market 

demand and supply. Nonetheless, a rough calculation could 

be attempted that would suggest the economic surplus 

impacts of those other storms are much larger—maybe two 

to three times larger—than the timber value impacts shown. 

In short, recent hurricanes have created market losses 

totaling into the many billions of dollars. As Prestemon 

and Holmes (2004) and Holmes (1991) pointed out, these 

losses represent a net redistribution of wealth in the market, 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

in favor of undamaged producers and to the detriment of 

consumers and damaged producers.

There are obviously other potential outcomes of hurricanes on the broader economy as they relate to the timber 

market. Structure (e.g., housing) losses are quite high when 

large events such as Hurricane Katrina sweep through heavily populated areas. The National Oceanic and Atmospheric 

Administration (2006) indicated that hurricanes Katrina, 

Rita, and Wilma in 2005 combined to create nearly 2.8 million insurance claims, totaling $40 billion in insured losses. 

Data provided by the American Red Cross, as cited by the 

National Association of Home Builders (2005), compiled 

preliminary estimates indicating that Hurricane Katrina 

damaged 275,000 homes in the Gulf States of Louisiana, 

Mississippi, and Alabama, 10 times the second-largest loss 

in homes, which was caused by Andrew in 1992 in south 

Florida. The National Association of Home Builders (2005) 

report stated that Katrina, coupled with Rita and Wilma (in 

Florida), would drive up prices for southern pine framing 

lumber, plywood, and other sheathing products. This price 

effect on forest products should filter back to the timberland, helping to support prices in the Southern United States 

during the reconstruction phase. The implication here is 

that landowners whose timber was unaffected by the storms 

should enjoy some price support, increasing the chances for 

a post-hurricane price enhancement, even if that enhancement does not emanate directly from lost timber inventory 

in the region.

Management Implications

We now discuss the future of hurricane activity in the 

United States and the implications of the existence of 

hurricane risk on land management strategies, with a focus 

on timber salvage. What we can glean from the previous 

discussion is that the impacts of hurricanes on landowners 

will be partitioned according to the location of the timberland. Hence, owners closer to the impact zone—typically, 

closer to the ocean—will face a higher likelihood of timber 

damages and long-term negative impacts. Those farther 

from the coast may face lower impacts and a greater chance 

that they could enjoy a long-term price enhancement owing 

to timber inventory losses and, potentially, output effects on 

the construction market. Due at least partly to the species 

mixes and particular species vulnerabilities, it appears also 

that owners of softwood timber may face greater economic 

risks than owners of hardwood timber, but this is a hypothesis that remains to be tested in the literature. Hardwood 

damages frequently occur as degrade, rendering the stand 

lower in value in the long term and potentially vulnerable to 

a higher rate of mortality (Sheffield and Thompson 1992).

As we appear to be in a period (or riding a trend) 

of higher hurricane activity, we might expect that the 

frequency and magnitude of large-scale catastrophic 

hurricanes will increase or remain high for several years 

or decades. In response to damage, timberland owners 

would do well to prepare for such storms by assessing the 

value of the timber before the storm. Creditable casualty 

losses require that the current basis of affected timber be 

assessed. Following such storms, timberland owners whose 

timber is unaffected would benefit from withholding their 

mature timber from the timber market until after salvage 

material has been recovered from damaged forests and 

then consumed at area mills. As well, owners of damaged 

timber should endeavor to quickly arrange for the salvage of 

their timber, owing to the high likelihood of timber decay 

for killed timber. Here, some colloquial evidence suggests 

that hardwoods should receive lower priority, as they may 

remain alive and hence valuable for a longer period, compared to softwoods, which are more likely to die following a 

storm (Sheffield and Thompson 1992). Because of the rapid 

decay rate of killed timber, then, it is especially beneficial to 

quickly salvage such timber following the event, especially 

timber stocks with the least amount of damage, to justify 

salvage.

Government actions following catastrophic windstorms 

may have an effect on the value of salvaged timber. For 

example, timber salvage on publicly owned lands has a 

negative impact on private landowners but a positive impact 

on consumer welfare (Prestemon and others 2006). There 

are, however, government actions that can produce broader 

benefits. Governments can facilitate salvage activities by 

(1) rapidly clearing roads, (2) relaxing weight limits on 

roads to allow logging trucks to carry heavier loads, (3) 

permitting larger than normal log storage volumes at mills, 
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GENERAL TECHNICAL REPORT PNW-GTR-802

(4) subsidizing or providing tax breaks for mills to produce 

water storage facilities for logs, (5) aiding in post-hurricane 

damage assessment, and (6) assisting private landowners 

and nongovernmental organizations with salvage planning 

(Freeman 1996, Lupold 1996). However, we would caution 

that research is currently not available to help evaluate 

either the form or the scale of public interventions that 

would be economically or socially optimal.

Research Needs

We conclude our synthesis of the impacts of hurricanes on 

forest landowners with a discussion of the research needed 

to further advance our understanding of these impacts 

and to identify alternative strategies for coping with these 

storms. This assessment reveals a long list of potential 

research needs. A primary need in all timber market assessments of hurricanes is up-to-date and precise information 

on green timber supply, salvage supply, and demand function parameters. Our results for Hurricane Hugo depend 

critically on assumed market sensitivities, and conclusions 

about the ultimate economic (especially welfare) impacts 

of other events depend on similar assumptions that need 

to be evaluated. We would note that for published salvage 

studies (starting with Holmes 1991), assumptions were 

made about the perfect inelasticity of the salvage supply 

function. This function is unlikely to be perfectly inelastic 

(unresponsive to prices). Although sensitivity analysis is 

useful for establishing plausible bounds on potential economic impacts, up-to-date studies of southern pine supply 

and demand functions would reduce the need for making 

assumptions regarding market conditions. Additionally, new 

studies are needed to identify how hurricanes affect markets 

for products other than southern pine. A large component 

of total timber impact results from damages to hardwoods, 

yet hardwood markets are notoriously under-researched. 

Such studies could reveal not only the hardwood market 

economic impacts but also potentially identify new and 

innovative strategies that owners of hardwoods could apply 

before and after hurricanes and other catastrophic events.

Timber salvage is a means of recovering value following such storms, yet little is known about how various 

factors affect the size of the timber salvage discount, 

particularly how it varies over time and among species. 

Such studies could provide valuable information regarding 

optimal salvage strategies following major hurricanes. Our 

economic calculations—and, hence, recommended salvage 

strategies—could be improved and changed with better 

information on this segment of the post-hurricane timber 

market.

Other analysts in these proceedings provide evaluations 

of the timber management consequences of holding timber 

assets that are subject to large-scale natural disturbances. 

However, research to date has not addressed whether longterm price enhancements emanating from a hurricane or 

other large natural disturbance can generate expected profits 

that are, in aggregate, greater than those expected in regions 

without such events. Timber losses occur in a subset of the 

timber market—consumers and damaged producers—but 

the gains could be significant for some landowners. Identifying the circumstances under which timberland owners 

gain, in aggregate, from these storms is an area worthy of 

further study.

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AIR and WATER

Case Studies
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Curtis A. Collins, David L. Evans, Keith L. Belli, 

and Patrick A. Glass

Curtis A. Collins, research associate II, and David L. 

Evans, professor, Department of Forestry, Mississippi State 

University, Mississippi State, MS 39762; Keith L. Belli, 

Head, Department of Forestry, Wildlife and Fisheries, 

University of Tennessee, Knoxville, TN 37996; and Patrick 

A. Glass, Director of Operations, Mississippi Institute 

for Forest Inventory, Mississippi Forestry Commission, 

Jackson, MS 39201. 

Abstract

Hurricane Katrina’s passage through south Mississippi on 

August 29, 2005, which damaged or destroyed thousands 

of hectares of forest land, was followed by massive salvage, 

cleanup, and assessment efforts. An initial assessment by 

the Mississippi Forestry Commission estimated that over $1 

billion in raw wood material was downed by the storm, with 

county-level damage percentages ranging from 50 percent 

to 60 percent across Mississippi’s three coastal counties. 

Remotely sensed data were used to provide a more complete 

picture of the damage inflicted by Katrina. Moderate (56- to 

29-m) and high (1- to 0.3-m)-resolution data were acquired 

from spaceborne and airborne platforms in natural color 

and MultiSpectral (MS) formats. Transformed data such 

as Normalized Difference Vegetation Index (NDVI) and 

Normalized Difference Moisture Index (NDMI), along with 

damage estimates obtained by interpreting aerial photography, were also used as variables in a linear modeling 

process. This continuous damage prediction process demonstrated the effect of incorporating forest condition thematic 

information, prestorm moderate-resolution imagery with 

transforms, and poststorm moderate-resolution imagery 

with transforms. The resulting models, all of which used a 

large number of regressors, had overall fit values of R2

adj = 

0.708 and RMSE = 0.130 with all variable types used, R2

adj 

= 0.492 and RMSE = 0.172 with all variables except the 

forest condition data, and R2

adj = 0.599 and RMSE = 0.153 

with all variables except the poststorm imagery data.

Using Remotely Sensed Data and Elementary Analytical 

Techniques in Post-Katrina Mississippi to Examine Storm 

Damage Modeling

Keywords: AWiFS, damage modeling, forest damage, 

hurricane damage, Katrina damage, timber damage.

Introduction

Overview

Hurricane Katrina made landfall in Mississippi near the 

outlet of the Pearl River on August 29, 2005, as a category 3 

storm on the Saffir-Simpson scale (Knabb and others 2005). 

Loss of life and damage to property were catastrophic, as 

New Orleans was flooded, and many towns and cities along 

the Louisiana and Mississippi Gulf Coasts were destroyed 

or severely affected. Rural areas whose economy depends 

on agriculture and forest industry were devastated also. 

Accordingly, preliminary damage estimates obtained 

through aerial surveys of the affected region by the Mississippi Forestry Commission (MFC) exceeded $1 billion 

in damaged wood and timber stumpage. These estimates 

underscore the need for more continuous damage estimates 

that can be developed when remotely sensed, storm, and 

pre-existing thematic data are employed in the modeling 

process.

Moderate-resolution remotely sensed data, from 

sources such as Landsat, have been used in modeling various forest parameters related to timber harvesting (Healey 

and others 2005), canopy closure (Butera, 1986, Cohen 

and others 2001, Larsson 1993), and other forest attributes 

(Cohen and others 2001, 2003). Cohen and others (2001) 

modeled percentage green canopy cover in a predominantly 

evergreen softwood region of the Pacific Northwest, similar 

to south Mississippi, with a coefficient of determination 

(R2

) of 0.74 and a root mean squared error (RMSE) of 12 

percent. Similarly, Healey and others (2005) used a series 

of transformations on independent variables as well as a 

natural logarithm transformation on percentage cover, the 

dependent variable, in a series of simple linear regressions 

to determine adequate univariate models. The results of 

their work were promising with regard to using single Short 

Wave InfraRed (SWIR) bands, as well as the Normalized 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) transformations described 

in the methods section of this work.

Damage assessments of past catastrophic tropical 

storms in the Southeast United States were not able to use 

the large number of image sources and processing techniques that are presently available. The use of geographic 

information systems (GISs) and remote sensing techniques 

in these assessment activities were, for the most part, 

restricted to a minor role in the wake of Hurricane Hugo 

in South Carolina (Nix and others 1996) with an expanded 

role noted for Hurricane Andrew (Jacobs and EggenMcIntosh 1993, Ramsey and others 1997, 2001). With these 

two storms and the studies mentioned, a progression of 

technology and techniques can be noted. In Nix and others 

(1996), remote Hugo forest damage assessments were made 

in a GIS through aerial photointerpretation and digitization. Jacobs and Eggen-McIntosh (1993) also used visual 

image interpretation to perform assessments of Hurricane 

Andrew-induced damage with the imagery taking the form 

of airborne digital video frames. The two works led by 

Ramsey (Ramsey and others 1997, 2001) show a final evolution to satellite acquired moderate (Landsat)- and coarse 

(Advanced Very High Resolution Radiometer or AVHRR)- 

resolution imagery, along with storm data, in identifying 

Andrew’s damage in a largely hardwood area in south 

Louisiana.

Objectives

Because the MFC assessment was performed rapidly 

through aerial viewing using expert approximation, a 

more definitive and continuous damage assessment model 

was sought. In our work here, we studied the viability 

and possible methods needed to develop predictive storm 

damage assessment models. The acquisition and analysis of 

remotely sensed data acquired before and after hurricane 

Katrina, along with various storm data and pre-existing 

thematic data created for the Mississippi Institute for Forest 

Inventory (MIFI), were used to determine the feasibility of 

mapping storm impacts in a more accurate and continuous 

form.

Beyond characterization of Katrina-induced forest 

resource damage, we explored model development to 

predict the likely scope and severity of damage from future 

hurricanes. This procedure involved use of MIFI forest 

thematic data, storm data, prestorm imagery (which can 

be simulated to note the effects from different size and 

intensity storms), and poststorm imagery to determine their 

relative importance in producing predictive damage models. 

The implications here involve two aspects: (1) model 

performance without poststorm data so that predictive 

equations can be used to forecast future hurricane damage, 

and (2) level of predictive model improvement afforded by 

use of forest type and age thematic layers that accompany 

inventory protocol employed by MIFI.

Methodology

Remotely Sensed Data

Acquiring Remotely Sensed Data—

Moderate-Resolution Data

Indian Remote Sensing (IRS) Advanced Wide Field Sensor 

(AWiFS) data were acquired for use in pre- versus poststorm damage assessment as a moderate spatial resolution 

(56 m at nadir) data source. These data were selected for 

several reasons:

• Relatively high visibility, although minor cloud 

coverage was noted in both pre- and poststorm 

images.

• Compatibility of spatial resolution with Landsat 

Thematic Mapper (TM) data, which were used in 

the creation of thematic data also used in this study. 

• Acquisition dates of June 19 (prestorm) and 

September 4 (poststorm), less than one week after 

Katrina’s landfall in Mississippi.

Spectral attributes associated with the AWiFS sensor 

included four bands representing the following reflected 

energy wavelengths, respectively: green (520-590 nm), 

red (620-680 nm), near-infrared (NIR) (770-860 nm), and 

shortwave infrared (SWIR) (1550-1700 nm).

High-Resolution Data

We used prestorm digital imagery taken throughout the 

summer of 2004 (before Katrina’s landfall) that was 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

acquired through the United States Department of Agriculture’s (USDA) National Agriculture Inventory Program 

(NAIP) and made available via the internet by the Mississippi Automated Resource Information System (MARIS). 

These data were acquired in natural color, sampled at 

a spatial resolution of 1 m, and presented as mosaiced 

county-level images. Within 2 months of Katrina’s landfall, 

a private contractor using a Leica ADS40 sensor provided 

post-Katrina digital imagery for the U.S. Army Corps of 

Engineers (USACE) over south Mississippi from 31° N to 

the Gulf Coast. These poststorm data were made available by the United States Geological Survey (USGS) via a 

disaster-support Web site. They were acquired in natural 

color with an approximate 0.3-m spatial resolution.

Preprocessing Remotely Sensed Data—

Using the existing thematic MIFI data as a georegistration 

base, the AWiFS data were georegistered in Leica’s ERDAS 

Imagine 8.7 using first- or second-order polynomial models 

(ERDAS 2003) in order to achieve subpixel spatial root 

mean squared (RMS) values. The resulting products were 

thus projected into the Mississippi Transverse Mercator 

(MSTM) (MARIS 2005), as this was the native projection 

of the MIFI base data. This procedure, as recommended 

by Lillesand and Kiefer (2000) and Lu and others (2004) to 

analyze multidate imagery, ensured highly aligned overlapping pixel registration so that data extraction for later 

modeling purposes would use correctly sampled reflectance 

and thematic values. In contrast to AWiFS data, visual 

inspection of the spatial orientation of high-resolution data 

sets appeared to match the MIFI base data, so no georegistration was required.

Cloud cover and corresponding shade, although 

minimal, was present in the AWiFS data sets and required 

removal to reduce the possibility of sampling erroneous 

reflectance data. To achieve this removal, both pre- and 

poststorm rectified AWiFS imagery was clustered using 

the Iterative Self-Organizing Data Analysis Techniques 

(ISODATA) algorithm in Imagine (ERDAS 2003) with 

250 clusters, 12 maximum iterations, and a convergence 

threshold of 0.95. The resulting two thematic layers were 

next interpreted, in a heads-up fashion, coded, and recoded 

in order to create two cloud and cloud shadow (code = 1) 

versus noncloud (code = 0) masks. These masks were then 

used to remove all cloud-tainted spectral information from 

the rectified AWiFS images by recoding the eight image 

layers to zero (four per pre- and poststorm imagery) in these 

problem areas.

Transforming Remotely Sensed Data—

In exploring various simple band differences, general 

trends were noted to be unique but subtle for the green and 

red, NIR, and SWIR bands across the anticipated stormdamaged region. Visually, the red and green bands appeared 

highly correlated with each other. Because the red appeared 

more contrasting moving orthogonally from the anticipated 

center of damage just east of the eye’s track (Boose and 

others 1994), it was chosen for further examination along 

with NIR and SWIR bands.

Previous studies confirmed the choice in directly using 

the red, NIR, and SWIR bands (Hame 1991), along with 

their use in two transformations. These transformations 

employed two band ratios proven to work in forest change 

detection: Normalized Difference Vegetation Index (NDVI) 

(Healey and others 2005; Jin and Sader 2005; Mukai and 

Hasegawa 2000; Ramsey and others 1997, 2001; Sader and 

others 2003) and Normalized Difference Moisture Index 

(NDMI) (Healey and others 2005, Jin and Sader 2005, 

Sader and others 2003). These indices not only use the visible and infrared bands of interest through proven functions, 

they also serve to reduce the dimensionality of the data to 

be analyzed. The formulae for NDVI and NDMI are:

NIR band – Red band

NDVI = (1)

NIR band + Red band

and

NIR band – SWIR band

NDVI = (2) NIR band + SWIR band

Land Cover and Type Thematic Data

In Collins and others (2005), the creation of thematic data 

for use by MIFI in a statewide forest inventory, per the 

inventory’s procedural pilot study (Parker and others 2005), 

was outlined and resulted in forest age and type thematic 

layers. The forest-age layer used an approximate 5-year 

temporal resolution back to the genesis of the Landsat 
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GENERAL TECHNICAL REPORT PNW-GTR-802

program in the early 1970s and covered the entire State. In 

other words, this data set attempted to identify the year of 

regeneration for areas harvested between 1972 and 2003 

in 5-year increments. The data were created using Landsat 

Multi-Spectral Scanner (MSS) and TM data with the 

finished products’ resolution taking on the finer TM resolution (29 m). The forest-type layer mapped the entire State 

into water, other nonforest, regenerating forest, softwood, 

mixed softwood-hardwood, and hardwood classes using 

2002–03 TM imagery. Data were georegistered to USGS 

Digital Ortho Quarter Quadrangles (DOQQs) county-level 

mosaics, making this the base resolution and orientation for 

all subsequent analyses involved in this study.

Ancillary Storm Data

Four data set types were obtained from the Internet for use 

in this study as storm attribute layers. The first two types 

were acquired from the Atlantic Oceanographic and Meteorological Laboratory (AOML), a subunit of the National 

Oceanic and Atmospheric Administration’s (NOAA’s) 

Hurricane Research Division (HRD), in 14, 3-hour-interval 

gridded surface wind data sets depicting conditions from 

21:00 CDT, August 28, 2005, to 12:00 CDT, August 30, 

2005. The grid spacing of these data as the storm passed 

through south Mississippi was approximately 0.054 degrees 

in latitude and longitude resulting in a linear distance of 

approximately 5.25 km in easting and 6 km in northing near 

the city of Bay St. Louis, Mississippi. These data included 

sustained surface windspeed (mph) and direction (azimuth 

degrees), both of which are believed to be influential in 

structural (Powell and Houston 1996) and forest damage 

(Ramsey and others 2001) over time. The third data type 

also came from the AOML and was another gridded surface 

wind product demonstrating the maximum sustained 

windspeeds (mph) inflicted by Katrina as it moved through 

the entire State. The fourth and final storm attribute data 

obtained for this project included a storm surge extent 

vector layer acquired from Federal Emergency Management Agency’s (FEMA’s) Katrina recovery GIS Web site 

(http://www.fema.gov/hazard/flood/recoverydata/katrina/

katrina_ms_gis.shtm).

Model Creation

Independent Variable Assignment—

Remotely Sensed Data

Using the two described band transforms, NDVI and 

NDMI, and the four original bands from both pre- and 

poststorm imagery, 18 remotely sensed variables were 

defined. These variables were created using bands one 

through four for both pre- and poststorm data sets as well as 

a delta variable whereby the six prestorm layers, comprising 

bands and transforms, were subtracted by their poststorm 

counterparts.

Land Cover and Type Thematic Data

The MIFI thematic data demonstrating statewide age and 

forest types were used as continuous variables and model 

strata, respectively. In this construct, age, which could at 

best be determined back to 1972, was used as a continuous 

variable with detected year of regeneration being reduced 

by 2, to account for nearly half the temporal resolution of 

the age-creation process, and then subtracted from 2005, 

the year of Katrina’s landfall. If the year of regeneration 

was not found for a forested area, we assumed it was older 

than the timeframe afforded by the Landsat program, and it 

was coded with age 40 (2005 minus 1965). This represented 

a reduction of one temporal resolution interval of 5 minus 

the interval midpoint correction of 2 years for 1972, the last 

year of detection. Our assumption was that within the three 

forest types beyond this age, stand-stocking levels and size 

were probably more alike than not. As a discriminant, the 

types could or could not be used as classes for stratifying 

three different models for softwood, mixed, and hardwood 

areas.

Ancillary Storm Data

Among the four data set types downloaded for use in this 

study as storm attributes, one was removed from consideration, one was left alone, and two were compressed into 

a series of time- and location-dependent storm variables. 

The surge variable was removed owing to low sampling 

intensity because the data set indicated that 21 hardwood, 

4 mixed, and 0 softwood plots were located within the area 

of mapped surge inundation. Unlike the 3-hour intervals of 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

windspeed and direction data, the maximum sustained wind 

variable required no manipulation for use in the model, but 

it did require interpolation. Using Imagine 8.7, these gridded data were surfaced to the same resolution and orientation as the MIFI base data by using a linear rubber sheeting 

method (ERDAS 2003).

The first step in transforming the direction and speed 

attributes from the gridded 3-hour-interval surface wind 

field data into more usable variables was to create a Microsoft Excel spreadsheet. This helped interpolate the wind 

data at each plot into more continuous values with regard to 

speed; direction, as azimuth drastically steps between 360° 

and 0°; and time, which, in this case, was interpreted into 

3-minute (0.05-hour) intervals. Azimuth values were converted to sine and cosine trigonometric function values, and 

the plot nearest a respective grid point at that point’s designated time was allowed to assign its sine, cosine, and windspeed values to that plot at that time. Time intervals located 

between fixed 3-hour periods were next assigned weighted 

x- and y-coordinate locations away from a weighted location 

of the storm’s eye. Weighting was defined so that proximity 

to the upper or lower 3-hour time bound for an interpolation 

time was used to create proportional weights, with the two 

weights summing to one, for calculating the location and 

variable weighted averages at an interpolation point. These 

weights were next used in weighted variable averaging 

by taking an interpolation time point’s weighted location, 

with respect to storm eye, and calculating windspeed and 

directional values from the corresponding above and below 

bounding time points at the same relative weighted location. 

In Figure 1, a graph of the resulting 3-minute windspeeds 

for a plot located near Katrina’s eye track, illustrates an 

example of this process’s result.

The resulting 3-minute windspeed, sine, and cosine 

values were next used to create wind duration and stability variables per the anticipated applicability (Powell and 

Houston 1996, Ramsey and others 2001) of these variables 

in hurricane damage modeling. The duration variables 

were calculated over given windspeed thresholds in 10 mph 

intervals from 30 to 100 mph. For example, the variable for 

Figure 1—This is a plot-level graph of the resulting 3-minute-interval continuous windspeeds over a 39-hour period from 21:00 CDT, August 28, 2005, to 12:00 CDT August 30, 

2005, for a plot located along Katrina’s eye track (note the drop to near zero in the graph’s 

middle), illustrating the converted data used in determining wind duration and stability 

variables.
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GENERAL TECHNICAL REPORT PNW-GTR-802

wind duration at or above 40 mph at a specific plot would 

count the number of 3-minute time intervals attributed to 

that plot that were at or above 40 mph and multiply that 

count by 0.05 hours to get duration in hours. The stability values were also calculated over given windspeed 

thresholds in 10 mph intervals from 30 to 100 mph. They 

were done such that the sine and cosine values, isolated for 

interpolated time points that met the windspeed threshold 

criteria, were used to calculate two variances, one each for 

sine and cosine, and then combined into a pooled variance.

Dependent Variable Assignment—

To replace the lack of field data due to ongoing storm 

damage field sampling, we performed interpretations of 

high-resolution imagery, pre- and poststorm, with the 

expectation that aerial-viewed canopy damages were highly 

correlated with field-measured forest damage. The sample 

area included Mississippi’s six southernmost counties, 

corresponding to that portion of south Mississippi from 31° 

N to the Gulf Coast. The USACE imagery was acquired 

and stratified into 54 interaction classes based on combined 

forest type, maximum sustained wind (max mph windspeed 

of >93.5, 93.5-76.5, and <76.5), and age (year constraints 

of >1993, 1993-88, 1987-83, 1982-78, 1977-70, and <1970). 

Then we randomly allocated 5 plots into each interaction 

class, yielding 270 total plots.

Crown closure interpretations and resulting pre- and 

poststorm differences began with the creation of GIS-generated 0.084-ha rectangular plots (29 by 29 m). Interpretation 

of these plots involved use of GIS-generated plot boundaries and regular dot grids to employ a systematic method 

for determining green canopy coverage. The grids were 

created in a 5 by 5 construct, allowing each dot to represent 

4 percent of plot canopy, with outer rows and columns 

being spaced 2.9 m from their immediate plot bounds and 

inner rows being spaced 5.8 m in sequence from each other 

(Figure 2). Using this grid, interpretation was reduced from 

estimating plot-level green canopy percentages to counting 

the dots in each plot that fell on interpreted green canopy 

pixels. The purpose in creating this data set was to develop 

a bank of prestorm, poststorm, delta (i.e., pre- minus poststorm), or all, canopy data for use in model construction.

Resulting interpretation data were next edited to 

remove plots that fell within the cloud-classified areas in 

the AWiFS-derived cloud mask. This masking reduced the 

plot count from 270 to 252 with an additional 7 plots being 

removed in the photointerpretation phase owing to lack of 

forest canopy (this either indicated error in the MIFI forest 

type layer or canopy removal since acquisition of the 2003 

imagery used to generate the MIFI product). The resulting 

245 plots were situated across the mapped softwood, mixed, 

and hardwood forest types in counts of 82, 83, and 80, 

respectively.

Upon reviewing these edited prestorm, poststorm, and 

delta canopy measures, the intuitive dependent variable 

choice appeared to be the delta canopy variable (defined 

as pre- minus poststorm green canopy cover percentage). 

However, because these data were largely dependent on 

prestorm canopy data, this variable was added to the list 

of independent variables and incorporated into the list of 

prestorm variables. Also, we believe that prestorm canopy 

can be created for the study area with moderate to good 

success (Cohen and others 2001).

Model Definition and Fitting—

The model creation stage of this study was focused on 

creating multiple linear regression models using ordinary 

least squares. The sought-after final models were hoped to 

be parsimonious with optimistic fit values, which, in this 

case, were high adjusted coefficient of determination or 

R2

adj values and low RMSE values, for the variable types 

of interest in the study’s objectives. Initial attempts to use 

the 37 previously described variables (Table 1) in single 

linear terms produced unsatisfactory fit values. Immediate 

improvements, however, were noted with the introduction of 

interaction and quadratic terms (Rawlings and others 1998). 

The result of creating all these new variables from the above 

single variables increased the number of possible variables 

from 37 to 740.

With 740 possible variables available for use in this 

modeling exercise, a two-step process was organized to 

reduce the possible number of variables to 40 or fewer, 

which represented about half (or fewer) the number of 

plots in each forest type. The first reduction took place 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 2—A plot-level view of a photointerpretation plot bound and grid. This image illustrates a highly storm-damaged plot from Pearl 

River County, Mississippi (note the southeast to northwest oriented downed stems). In the inset, the 5- by 5-point grid used in determining green canopy percentages along with the plot’s boundary can be seen.

using stepwise regression methods in SAS’s PROC REG 

procedure (SAS 1999). Using this method, eight model 

types were reduced using some or all available variables: an 

overall (regardless of forest type) pre- (including ancillary 

storm variables) and poststorm data model; an overall 

prestorm data model; three (for each forest type) prestorm, 

poststorm, and MIFI variable (forest age) models; and three 

prestorm and MIFI variable models. In order to regulate 

that the output stepwise models identify 40 or fewer independent variables for phase two reduction, the significance 

entry level (SLENTRY in SAS) was set at 0.5, whereas the 

significance stay level (SLSTAY in SAS) was accordingly 

and incrementally adjusted up or down from an initial 

setting of 0.25 in 0.01 increments until 40 or fewer variables 

were isolated in the final step.

After isolating eight reduced but still cumbersome 

models, the leaps-and-bounds algorithm (Furnival and 

Wilson 1974) in PROC REG (SAS 1999) was employed to 

hone in on the eight best remaining variables from the field 

of 40 or fewer with respect to R2

adj and RMSE. The use of 

R2

adj was deemed advantageous at this point because this 

value tends to be more comparable than R2

 over models 
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GENERAL TECHNICAL REPORT PNW-GTR-802

involving different numbers of parameters (Rawlings 

and others 1998). This best-eight rule followed the rule of 

thumb to have 10 observations for each variable while being 

mindful that the hardwood strata had only 80 observations. 

To make a more theoretically sound set of model decisions, 

we also used Hocking’s (1976) prediction criteria of using 

the smallest model, with regard to variable count, that had a 

Mallow’s Cp (Rawlings and others 1998) value of less than 

or equal to 1 plus the particular model’s variable count.

Results

In Table 2, the best eight variable models illustrate moderate fits with RMSE values below 0.15 in only one model 

and R2

adj values above 0.55 in two models. Between the 

no MIFI variables pre- and poststorm models, there was 

a dramatic increase in R2

adj from 0.176 in the prestorm 

model to 0.439 in the pre- and poststorm situation along 

with a matching magnitude reduction in RMSE from 0.219 

to 0.181, respectively. In order to compare these no MIFI 

models to MIFI models, which were created in multiples 

of three matching the three MIFI forest type designations, 

a set of pooled RMSE and R2

adj values were created. This 

creation occurred by combining the three models’ error sum 

of squares, in the case of RMSE values, and by combining 

the three error and total corrected mean sum of squares 

considering all three models’ degrees of freedom, in the 

case of the R2

adj values. Comparing these fit values for 

the eight variable models again demonstrated the drastic 

improvement in model fit. With the use of poststorm 

imagery, we observed R2

adj values increasing from 0.381 

to 0.506 and RMSE decreasing from 0.190 to 0.170. Additional gains were made in using the MIFI data in models 

by increasing prestorm no MIFI versus pooled R2

adj values 

from 0.176 to 0.381 and reducing RMSE from 0.219 to 

Table 1—Individual (main effects) model variables and their types 

Pre- Post- Pre- PostVariable MIFI storm storm Variable MIFI storm storm

Forest age X Delta AWiFS NDVI X

Prestorm green canopy X Delta AWiFS NDMI X

Max sustained wind X Wind duration > 30 mph X

Prestorm AWiFS band 1 X Wind duration > 40 mph X

Prestorm AWiFS band 2 X Wind duration > 50 mph X

Prestorm AWiFS band 3 X Wind duration > 60 mph X

Prestorm AWiFS band 4 X Wind duration > 70 mph X

Prestorm AWiFS NDVI X Wind duration > 80 mph X

Prestorm AWiFS NDMI X Wind duration > 90 mph X

Poststorm AWiFS band 1 X Wind duration > 100 mph X

Poststorm AWiFS band 2 X Wind stability > 30 mph X

Poststorm AWiFS band 3 X Wind stability > 40 mph X

Poststorm AWiFS band 4 X Wind stability > 50 mph X

Poststorm AWiFS NDVI X Wind stability > 60 mph X

Poststorm AWiFS NDMI X Wind stability > 70 mph X

Delta AWiFS band 1 X Wind stability > 80 mph X

Delta AWiFS band 2 X Wind stability > 90 mph X

Delta AWiFS band 3 X Wind stability > 100 mph X

Delta AWiFS band 4 X

Note: A listing of all 37 single variable or main effects used in the study’s modeling exercise along with pertinent variable type classifications used 

in model comparisons. Even though MIFI forest type data were used, they were employed defining strata, not as a model variable.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Table 2—Model fit results by variable types and variable selection criteria 

Best eight 

variable Hocking’s Other models

models criteria models of interest

Variables Forest

type(s) used type(s) R2

adj RMSE No. of var. R2adj RMSE No. of var. R2adj RMSE

Prestorm, post- All 0.4392 0.1805 23 0.4923 0.1717

 storma

Prestorm, post- Softwood 0.6796 0.1168 24 0.7893 0.0947

 storm, MIFI

Prestorm, post- Mixed 0.5701 0.1800 25 0.8588 0.0860

 storm, MIFI

Prestorm, post- Hardwood 0.4897 0.2009 15 0.5881 0.1805

 storm, MIFI

Prestorm, post- Pooled 0.5058 0.1694 0.7081 0.1302

 storm, MIFIa

Prestorma All 0.1758 0.2188 21 0.2765 0.2050

Prestorm, MIFI Softwood 0.3366 0.1680 26 0.6405 0.1237

Prestorm, MIFI Mixed 0.4300 0.1727 37 0.7735 0.1088 25 0.7142 0.1223

Prestorm, MIFI Hardwood 0.3647 0.2242 37 0.7542 0.1394 15 0.5258 0.1937

Prestorm, MIFIa Pooled 0.3812 0.1896 0.7345 0.1242 0.5994 0.1525

Note: The modeling exercise results indicating variable types used and MIFI forest type usage along with fit values (adjusted R2 or R2

adj and RMSE) and 

number of variables used, when not fixed by the variable selection method.

a

 All types or pooled model rows used in numerical data type comparisons.

0.190. Similarly, by incorporating MIFI thematic data, 

poststorm models were affected with an R2

adj increase from 

0.439 to 0.506 and an RMSE reduction from 0.181 to 0.170.

The models derived using Hocking’s (1976) method 

(Table 2) illustrate the same general trend as the eight 

variable models. Again, improvements with the addition 

of MIFI and poststorm data were noted in overall (with no 

MIFI data) and pooled (with MIFI data) R2

adj and RMSE 

values, except in cases where the application of Hocking’s 

method yielded models with very large variable counts 

(2 instances used 37 variables). In an attempt to rectify 

this problem, the other models in Table 2 were created for 

models that still used a large number of variables. These 

further reduced models corresponded to the Hocking’s 

identified MIFI, prestorm, and poststorm variable models in 

the number of employed variables for mixed and hardwood, 

MIFI, and prestorm models. Examination of these modified 

models indicates one difference from the eight variable 

comparisons. The pooled fit values for the prestorm and 

MIFI models (R2

adj = 0.599 and RMSE = 0.153), in tandem 

with the prestorm, poststorm, and MIFI models (R2

adj = 

0.708 and RMSE = 0.130), when compared to the pre- and 

poststorm model (R2

adj = 0.492 and RMSE = 0.172) and 

prestorm model (R2

adj = 0.277 and RMSE = 0.205) indicate 

an increased advantage from the eight variable models with 

regard to using MIFI data as opposed to poststorm image 

data.

Discussion

Model and Variable Characteristics

The prediction-minded evaluation of data/variable types 

reported in the previous section states the obvious—that 

more independent variables tend to improve model predictability of dependent variables. This situation says nothing 

about the direct applicability of all the work involved with 

this study and the possible creation of predicted damage 

values across or outside of the study area, or both, with the 

various models produced. What is of importance in this 

work, however, is where, with respect to the variables and 

variable types used, the gains in model fit occur, although 
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GENERAL TECHNICAL REPORT PNW-GTR-802

no statistical inference can be associated with these gains.

We expect that similar models can yield predicted canopy 

changes with RMSE values at or near 0.13 (13 percent) 

for situations like the passage of a strong hurricane over 

a mostly undisturbed Southern forested area, such as 

south Mississippi before Katrina. These estimates can be 

improved from the forest industry perspective. Industry is 

often focused on the softwood resource in the South, which 

is where the best of the stratified models we developed 

demonstrated a RMSE 0.09 (9 percent). Model fit was 

comparable in mixed and softwood stands but was poor in 

hardwoods. This could be the result of a variety of issues 

from some unknown data bias that was unintentionally 

introduced into the modeling process or some natural 

occurrence unknown and unaccounted for in these analyses. 

These poor results could also illustrate the inherent difficulty and complexity in modeling conditions in hardwood 

areas.

Potential model flexibility to create comparable predictive models, regardless of the use of poststorm imagery, 

was a much sought-after finding in this study with mixed 

results. The reason for this exploration was to display the 

applicability of modeling anticipated storm damage prior to 

a weather event. This focus was best explored in comparing the other models (or adjusted) pooled fit values for the 

MIFI and prestorm variables models and Hocking method 

pooled MIFI, prestorm, and poststorm variables models 

where RMSE values were 0.153 versus 0.130, respectively. 

This comparison is somewhat indecisive as MIFI, prestorm, 

poststorm variables models outperformed the MIFI and 

prestorm variables models but only by a small amount (difference in RMSE of < 0.03). Similarly, in the corresponding 

eight variable models, there was a difference of 0.02 with 

respect to RMSE. This difference is of a smaller magnitude, 

however, than the lack of MIFI data comparisons of overall 

pre- and poststorm variables (R2

adj = 0.492 and RMSE 

= 0.172) versus prestorm only variables (R2

adj = 0.277 

and RMSE = 0.205). In comparing the Hocking pre- and 

poststorm model (RMSE = 0.172) versus the adjusted 

pooled MIFI and prestorm model (RMSE = 0.153) and the 

corresponding eight variable models (RMSE = 0.181 versus 

RMSE = 0.190), it does appear that use of the MIFI data in 

model development at least offsets, maybe even improves, 

model performance in using prestorm data with the absence 

of poststorm data.

Possible Model/Variable Improvements

Actual field damage values are being collected in MIFI’s 

Southeast region, which includes Jefferson Davis, Covington, Jones, Wayne, Marion, Lamar, Forrest, Perry, Greene, 

Pearl River, Stone, George, Hancock, Harrison, and Jackson 

Counties. These data are the direct metric of interest in this 

series of work, as opposed to the photointerpreted canopy 

metric used here. Incorporation of these data is expected to 

improve development, although model fits may worsen, of 

any hurricane damage assessment model subsequently created owing to the dependent variable’s added meaning. The 

data could also help address a noted problem of hardwood 

defoliation versus damage. Poststorm high-resolution 

imagery indicated that many hardwood areas, particularly 

in the Pearl River bottom, were defoliated with only minor 

damage to tree crowns and boles. Differences in this defoliation versus damage aspect of hardwood areas may also be 

more sensitive to individual hardwood species, which is one 

of the field metrics, as opposed to the whole hardwood type.

Along with the analysis of field data, future work will 

also incorporate statistically inferential results, such as variable significance, as opposed to the simple fit comparisons 

made in this work. These analyses will provide more meaningful results with potential adaptations for collinearity and 

validation of model assumptions. Model development for 

repeated application may also be achieved in order to create 

a more robust and possibly automated product.

Conclusions

Clutter and others (1983) defined risk in the statement: “the 

inability to estimate future cash flows with certainty is the 

basic cause of risk in an investment.” At play in this analysis 

are other issues that effect the probability of acquiring an 

expected return. Examples include rotation lengths (with 

which MIFI type information may be of further use), 

intermediate weather conditions (i.e., droughts, floods, etc.), 

and market fluctuations. Whereas this study is not a risk 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

assessment in totality, it does place a foundation, albeit not a 

large one, for development in this direction.

The implications developed in this work with regard 

to variable creation and the data types utilized are promising for future meaningful region-level continuous damage 

assessment model creation. The thrust will continue to 

locate additional ancillary data that may serve to further 

supplant the advantages of poststorm imagery incorporation 

in the development of these models. Field data will soon 

replace the photointerpreted data so heavily relied upon 

here, and with it a new set of obstacles are expected. In 

all, however, it does appear possible to create a meaningful 

damage model that will aid in both economic recovery 

and assessment of risk associated with storms similar to 

Hurricane Katrina, possibly before said storms occur.

Acknowledgments

Prestorm aerial photography, which was acquired through 

the USDA’s National Agriculture Inventory Program, was 

provided by MARIS. Poststorm aerial photography, which 

was acquired for the USACE as well as other remotely 

sensed data, were provided via an USGS disaster relief Web 

site. Storm attribute-related data sets were obtained from 

NOAA’s HRD AOML (i.e., 3-hour-interval and overall max 

sustained surface wind data) and FEMA’s Katrina recovery 

GIS Web site (i.e., storm surge extent). The primary author 

greatly appreciates the quantitative support provided by Dr. 

Thomas Matney.

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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Thomas E. Perry and Jeremy S. Wilson

Thomas E. Perry, research professional and Jeremy S. 

Wilson, associate professor of forest management, University of Maine, Orono, ME 04469.

Abstract

Numerous factors, some of which cannot be controlled, are 

continually interacting with the forest resource, introducing 

risk to management, and making consistent predictable 

management outcomes uncertain. Included in these factors 

are threats or hazards such as windstorms and wildfire. 

Factors influencing the probability (risk) of windthrow or 

windsnap occurring can be grouped into four broad categories: regional climate, topographic exposure, soil properties, 

and stand characteristics. Of these categories, stand characteristics are most commonly and easily modified through 

forest management. To augment our understanding of the 

interaction between forest management and wind damage 

vulnerability in Maine, we developed a wind damage 

model that reflects site and stand characteristics. Model 

calibration used information from published literature and 

experiences of regional managers. The model was evaluated 

using spatially explicit wind damage records from a 

40 800-ha managed forest area in northern Maine. A 

comparison of means analysis identified significant differences in vulnerability index values between categorical 

populations of stands that have either recorded blowdown or 

no recorded blowdown during the last 15 years.

Keywords: GIS, natural disturbance, vulnerability 

assessment, wind modeling, windthrow risk.

Introduction

“Windthrow is a complex process resulting from interactions between natural and anthropogenic factors” (Ruel 

1995). Understanding the interactions between these factors 

and the inherent risk to stands from this disturbance has 

numerous benefits to natural resource managers. Windthrow 

is affected by several factors; climate, topography, physical 

and biological stand attributes, soil characteristics, and 

silviculture all play a role in the dynamics of wind disturbance (Ruel 1995).

Evaluating the Vulnerability of Maine Forests to Wind Damage

Understanding windthrow risk throughout the landscape can provide insights into natural vegetation patterns 

and habitat types. Risk evaluation can also be used to help 

predict how current forests may change without harvesting 

and subsequent impacts to forest health. Risk evaluation 

can help managers (1) evaluate where to locate plantations, 

(2) decide which regeneration strategies are appropriate, 

(3) determine where thinning and other partial harvests are 

acceptable, and (4) determine what species composition or 

rotation length is desirable for individual sites. Predicting 

damage, or potential for damage, provides the opportunity 

for impacts to be considered during prescription development, allowing for revision of management objectives or 

incorporation of mitigative actions into management plans 

(Mitchell 1998).

Currently, little is known about the extent of both 

catastrophic and endemic wind damage in Maine. As a 

result, this project’s aim was to explore the nature of wind 

disturbance throughout the State’s large forest ownerships. 

To augment our understanding of the interaction between 

forest management and wind damage vulnerability, this 

project developed a generalized wind damage model that 

reflects topographic exposure (distance-limited Topex) 

(Ruel and others 1997), soil conditions (rooting depth), and 

stand characteristics (density, edge, height, species composition, and treatment history). Results from similar modeling projects in British Columbia suggest that these risk 

factors are consistent in varied locations, and this indicates 

that general models may be applicable to landscapes other 

than the ones for which they were developed (LanquayeOpoku and Mitchell 2005). This model was calibrated using 

information from published literature and experiences of 

regional managers. We then evaluated it using spatially 

explicit wind damage records from a 40 800-ha area of 

managed forest in northern Maine.

Methods

Windthrow risk in many parts of the world has been 

modeled and assessed. Empirical models are best suited 

for areas with complex, heterogeneous stand structure and 

composition (Lanquaye-Opoku and Mitchell 2005, Mitchell 
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Figure 1—Flow chart of cumulative risk grid development shows the grouping and integration 

of individual variables into composite risk variables. Composite stand and site risk variables are 

then combined into a cumulative windthrow risk variable. All variables are indexed between 0 

and 1 (high to low risk) and are combined additively.

and others 2001) like the forests of Maine. This empirical approach typically utilizes regression models relating 

wind damage to physical stand components. Generally, 

the models produce a probability value rating or index of 

damage potential based on the stand’s suite of environmental conditions. Index modeling of spatial phenomena 

is enhanced with GIS, which allows for the integration of 

spatially explicit model parameters.

Logistic regression is commonly used for evaluating 

these models and isolating highly correlated component 

variables (Lanquaye-Opoku and Mitchell 2005, Mitchell 

and others 2001). Rather than using logistic regression, this 

project produced a generalized model, retaining variables 

that would not be statistically significant in a logistic regression analysis. This approach is unique because it attempts to 

create a model that may be applicable regionally and not be 

limited to the landscape where it was developed.

Eight environmental parameters (elevation, soil rooting 

depth, topographic exposure, stand species type, stand 

height, stand density, stand history, and exposed edges) 

were used to generate a spatially explicit vulnerability index 

value. Mitchell (1995, 1998) advocated grouping the factors into three broad categories—exposure, soils, and stand 

characteristics—to form a windthrow triangle, a conceptual 

model of the relationships among these interacting factors. 

For this model, factors were broken down further into site 

and stand parameters and combined to generate the cumulative windthrow risk index (Figure 1). Data for model variables, including a spatially referenced database of windthrow 

history, was acquired from various sources and covered an 

area of private landholdings in the northern portion of the 

State.

Site: Exposure

Topographic exposure is a critical variable in assessing stand 

vulnerability. Several indices have been created to describe 

relative topographic exposure or topographic protection. 

Topex wind exposure index has been used for some time to 

assess windthrow risk in Great Britain (Miller 1985). The 

importance of topographic exposure in modeling windthrow 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

risk has been demonstrated in other areas with forest-based 

economies. According to Ruel and others (2002), this variable accounts for over 77 percent of the British wind hazard 

rating system’s total score. Distance-limited Topex was 

chosen for this project because of its relatively easy calculation and strong correlation to wind tunnel simulation 

(Ruel and others 1997). Topographic exposure rasters were 

generated using a model developed and provided by The 

Windthrow Research Group, University of British Columbia, Vancouver, Canada. The scripts calculate an index of 

exposure that is the summation of the maximum and minimum angles to the skyline within a user-specified distance. 

The index can be calculated in the eight cardinal directions 

and weighted according to user preferences. Ten exposure 

grids were produced for this project, simulating unweighted 

exposure at two limiting distances (1000 m and 1500 m) and 

exposure in the eight cardinal directions (limiting distance 

of 1000 m).

Site: Soils

Forest soils are also a major component in understanding susceptibility of a forest stand to wind damage. Soil 

aeration, ease penetration by roots (rooting depth), and 

moisture-holding capacity all affect the pattern of root 

development. Generally, loose dry soils facilitate deeper 

rooting and spreading than do shallow clayey soils (Mergen 

1954). Shallow soils, which limit rooting depth and saturate 

easily like those commonly found in the spruce flat forest 

type, are increasingly prone to windthrow when saturated. 

The mass of soil that roots adhere to for anchorage becomes 

so wet it no longer adheres to itself, and the tree loses a 

substantial portion of its basal mass, crucial for resistance 

to windthrow (Day 1950). To compound the problem on wet 

soils, the rocking of the root plate can pump mud out from 

under the tree, further reducing the tree’s stability (Maccurach 1991).

Depth to groundwater was consistently cited by Maine 

forest-land managers as crucial to predicting the likelihood of blowdown in stands. The University of Maine’s 

Cooperative Forestry Research Unit provided a continuous 

depth-to-water-table raster for this project. This variable 

was combined with coarse-scale soil depth data to create 

a raster selecting the minimum depth of the two available 

data sources and indexed between 0 and 1.

Site: Elevation

Elevation was also incorporated into the site component of 

the model. Elevation values from a 30-m resolution digital 

elevation model (DEM) of the study were recalculated 

into an index between 0 and 1. Elevation had statistically 

significant correlation with wind damage in cut-block edge 

vulnerability modeling by Mitchell and others (2001). 

Studies by Worrall and Harrington (1988) in Crawford 

Notch, New Hampshire, found that gap size from chronic 

wind stress and windsnap or windthrow increased strongly 

with elevation from over 60 percent of the gap area at 764 

m (2,521 feet) to almost 85 percent of the gap area at 1130 

m (3,729 feet). Gap formation led to subsequent mortality 

from chronic wind stress and windthrow in gap edge trees. 

This trend was confirmed by Perkins and others (1992) 

on Camel’s Hump in Vermont. These trends are driven by 

surface friction acting counter to the force of the wind. 

Windspeed will increase locally with elevation because 

surface friction will decrease (Bair 1992).

Stand: Composition and Characteristics

Variables describing stand composition and characteristics 

were extracted from the forest landowner GIS database, 

which contains stand-level information to a minimum size 

of 1 acre. Stand composition is recorded under a threevariable scheme (Table 1). The database also reports stand 

history from the present to the mid-1980s including stand 

damage by wind storms and previous harvest entries. The 

history records include the year of the event and the event 

type or silvicultural prescription. An iterative network of 

Microsoft AccessTM queries was developed and used to 

isolate prior stand entry and wind damage events and to 

create vulnerability indices for individual stands based on 

stand type and the presence or absence of balsam fir, stand 

height, and stand density.

The species risk index assigns ranks for the four 

potential forest types (H = 0.3; S = 0.7; HS = 0.45; SH = 

0.55). The forest type rank is combined additively with 

an adjustment factor for the presence of balsam fir (Abies
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GENERAL TECHNICAL REPORT PNW-GTR-802

balsamea (L.) Mill.) in the overstory because high rates of 

root rot predispose fir to wind damage (Whitney 1989). The 

balsam fir adjustment considers the relative abundance of 

balsam fir in the overstory as the primary, secondary, or 

tertiary overstory species.

The stand height risk grid was built directly from 

the landowner database. The stand type height code was 

rasterized, creating a raster with five potential data values 

(0-nonforest; and 1 through 4 representing the height 

classes found in Table 1). The values were divided by 4, the 

maximum value, to create the desired index range between 

0 and 1.

The density grid captures the overstory density of 

the stands in the study area. Access queries determined 

the height and density of the most dominant or two most 

dominant species in each stand, if more than one species 

were present. Queries assigned values corresponding to the 

original alphabetical density codes of the most dominant 

overstory species. Density of the primary overstory species 

was collected and modified if the secondary species was 

also in the same canopy strata.

Stand: Thinning

Stands are more vulnerable to windthrow following thinning for two reasons. First, the increase in spacing resulting 

from thinning creates more canopy roughness, which, in 

turn, increases turbulence of the wind at the canopy level. 

Increased turbulence and wind penetration result in reduced 

tree stability (Blackburn and others 1988, Maccurach 1991). 

Second, high initial stand density produces unfavorable 

height-to-diameter (H:D) ratios (Wilson and Oliver 2000). 

This is less of a problem if stand density remains high; 

however, thinning removes the support of neighboring 

stems, dramatically increasing stand vulnerability. This 

period of vulnerability may last from a few years to over a 

decade.

Owing to the prominence of partial removal harvests in 

the State (McWilliams and others 2005), it was determined 

that a binary variable would best capture risk associated 

with stand entry from thinning and partial harvesting. A 

binary index raster of stand treatments was created from the 

records of stand entry in the landowner database. All prior 

entries that involved incomplete removal of the overstory 

and occurred in the decade preceding the most recent wind 

event (2001) were classified as thinned. Clearcuts and uncut 

stands were classified as unthinned. Thinned stands were 

assigned a value of 1, and unthinned stands a value of 0.

Stand: Edge

The edge raster variable represents the percentage of the 

stand classified as edge. For this project, edge is defined as 

a two height-class difference between stands. Production 

of this data layer was a multistep procedure. First a raster 

of stand height was produced. Topex scripts were run on 

this raster with a limiting distance of 30 m, or 1 pixel. 

This identified all height-class differences between stands 

across the landscape. Positive values indicated edges of 

shorter stands, and negative values indicated edges of taller 

stands. The original height grid was also analyzed with 

Arc9’s zonal range statistics tool. Range statistics defined 

all edges classified by the height differences between the 

two adjacent stands. This raster was recoded to display 

only edges greater than two height-class differences. The 

Topex-generated edge raster and range statistics raster were 

combined to identify the edges of the taller stands (negative 

Topex scores) when the height difference between adjacent 

Table 1—Three-variable stand type scheme displays 64 unique stand 

type combinations possible 

Species type code Height code Density code

H: > 75% hardwoods 1: seedlings A: 100-75% crown closure

S: > 75% softwoods 2: saplings B: 75-50% crown closure

HS: > 50% hardwoods 3: pole-size timber C: 50-25% crown closure

SH: > 50% softwoods 4: sawlog timber D: 25-0% crown closure

Each stand is assigned only one code per category; for example, SH2B, representing a softwooddominated mixed-wood stand of saplings with crown closure between 75 and 50 percent. All 

stands in the GIS database are characterized by this three-variable code.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

stands was greater than two height classes (range statistic 

greater than 2). Stands delineated in the GIS database were 

used as zones to calculate the percentage of edge within 

the individual stands, and the index was generated directly 

from these values.

Cumulative Risk

The cumulative risk grid is composed in two stages. In 

the first stage, the three individual site and five individual 

stand components are combined additively to form separate 

composite site and composite stand grids. The second stage 

combines the composite site risk grid and composite stand 

risk grid additively to form a cumulative windthrow risk 

grid. Figure 2 displays the cumulative windthrow risk grid. 

Ten separate cumulative windthrow risk grids were produced, one for each unique exposure input grid (described 

in “Site: Exposure”). All grid combinations were performed 

Figure 2—The cumulative wind damage risk grid represents the integration of the two composite 

model variables representing site and stand risk, generated from eight unique variables. Risk 

values are displayed along a color ramp from red to blue. Red represents the highest risk and blue 

the lowest. Actual areas of recorded wind damage are shown in black.
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GENERAL TECHNICAL REPORT PNW-GTR-802

with the single output map algebra tool in Arc9’s spatial 

analyst toolbox.

Model Evaluation

To avoid problems associated with spatial autocorrelation, 

the wind damage vulnerability model was analyzed with a 

comparison of means from a random sample of polygons 

within the study area. Hawth’s analysis tools (Beyer 2006) 

were used to maintain a minimum distance between 

sampled points. To ensure consistent results, 10 separate 

random samples of polygons were drawn from the study 

area. These 10 samples are analyzed individually and the 

results pooled to measure consistency between the samples. 

Approximately 560 polygons are sampled in each iteration 

of random sampling, accounting for roughly 14 percent of 

the study area in each sample.

The analysis used either a two-sample t-test or a MannWhitney test to detect differences between the population 

means. Mann-Whitney was chosen as the default test 

because this nonparametric test is justified in all situations 

where the t-test is applicable and in situations where the 

assumptions of the two-sample t-test are not met (Zar 1984). 

The two-sample t-test was used for Mann-Whitney results 

indicating near statistical significance when variables met 

the assumptions of this test. Tests used an alpha of 0.05 to 

test the null hypothesis, which is that the two population 

means are equal: Ho: m1 = m2. Results from the analysis 

of the 10 samples were tested for consistency with a t-test. 

Significant results from the Mann-Whitney and two-sample 

t-tests were coded either 1, positive correlation with the 

model, or -1, negative correlation with the model. Nonsignificant results were coded as a 0.

The t-test procedure tested for statistically significant 

differences between the responses of the individual model 

variables across the 10 samples. A mean that was statistically not equal to 0 indicated consistent significance, 

reflecting either positive or negative correlation with the 

model. Tests used an alpha of 0.05 to test the null hypothesis 

that the means for the two populations are equal: 

Ho: m1 = m2.

Table 2—Results for individual component variables

Variable Minimum Maximum Direction Consistent

Density 0.141 0.945 NS —

Edge 0.001 0.969 Negative No

Height 0.000 0.137 Positive Yes

Species 0.060 0.960 NS —

Thinning 0.000 0.000 Positive Yes

Elevation 0.000 0.007 Positive Yes

Soil depth 0.000 0.101 Negative Yes

Expos_1000 0.050 0.929 Positive No

Expos_1500 0.060 0.873 NS —

Expos_north 0.002 0.805 Negative No

Expos_ne 0.040 0.922 Negative No

Expos_east 0.164 0.924 NS —

Expos_se 0.247 0.926 NS —

Expos_south 0.119 0.911 NS —

Expos_sw 0.014 0.791 Positive No

Expos_w 0.143 0.891 NS —

Expos_nw 0.001 0.647 Negative No

The table displays the minimum and maximum p-values obtained from the 10 samples in 

the comparison of means analysis. Exposure variables are listed by their corresponding 

directional weights. The column titled Direction indicates variable agreement with model 

assumptions, whereas NS indicates a nonsignificant response. The column titled Consistent 

indicates whether the variable’s response was consistently statistically significant.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Results and Discussion

Several model variables were found to have statistically significant differences between the two populations (blowdown 

and nonblowdown). However, not all statistically significant 

differences were in the direction expected. Positive differences indicate statistically significant population-to-population differences that are in the direction that is expected 

on the basis of preliminary model research. Negative 

differences indicate statistically significant population-topopulation differences that are in a direction that is opposite 

the direction expected on the basis of preliminary model 

research.

The consistency analysis utilized a one-sample t-test. 

Variables that displayed the same relationship in all 10 

iterations cannot be tested for consistency in this manner, 

but variables that displayed the same relationship in all 10 

iterations are considered inherently consistent. The thinning, elevation, composite stand, and all 10 cumulative 

risk variables displayed a positive difference between the 

population means through all 10 iterations and are considered statistically consistent. Results are displayed in Tables 

2 and 3.

For both tables, positive difference indicates that the 

mean risk value for the population of stands with recorded 

wind damage was higher than the mean risk value for 

the population of stands without recorded wind damage. 

Positive differences agree with the assumptions used during 

model construction. Negative difference indicates that the 

mean risk value for the population of stands with recorded 

wind damage was lower than the mean risk value for the 

population of stands without recorded wind damage. Negative differences do not agree with the assumptions used 

during model construction.

Table 3—Results for composite risk variables with minimum 

and maximum p-values obtained from the 10 samples in the 

comparison of means analysis 

Variable Minimum Maximum Direction Consistent

STAND 0.000 0.000 Positive Yes

SITE_1000 0.045 0.818 Positive Yes

SITE_1500 0.046 0.793 Positive Yes

SITE_north 0.214 0.968 NS —

SITE_ne 0.101 0.968 NS —

SITE_east 0.037 0.941 NS —

SITE_se 0.002 0.348 Positive Yes

SITE_south 0.127 0.190 NS —

SITE_sw 0.002 0.343 NS —

SITE_w 0.005 0.425 NS —

SITE_nw 0.089 0.919 NS —

CMLTV_1000 0.000 0.000 Positive Yes

CMLTV_1500 0.000 0.000 Positive Yes

CMLTV_north 0.000 0.000 Positive Yes

CMLTV_ne 0.000 0.000 Positive Yes

CMLTV_east 0.000 0.000 Positive Yes

CMLTV_se 0.000 0.000 Positive Yes

CMLTV_south 0.000 0.000 Positive Yes

CMLTV_sw 0.000 0.000 Positive Yes

CMLTV_w 0.000 0.000 Positive Yes

CMLTV_nw 0.000 0.000 Positive Yes

Site risk and cumulative risk variables are listed by their corresponding directional weights. 

The column titled Direction indicates variable agreement with model assumptions, 

whereas NS indicates a non-significant response. The column titled Consistent indicates 

whether the variable’s response was consistently statistically significant.
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GENERAL TECHNICAL REPORT PNW-GTR-802

The density variable (Table 2) did not produce significant differences between the population means in any of the 

iterations. The assumption based on the wind vulnerability 

literature (Gardiner and others 1997, Lohmander and Helles 

1987) was that the less dense stands would be more susceptible. This was thought to be the case in an area with a long 

management history of natural regeneration and frequent 

stand entry.

The edge variable (Table 2) had a negative difference 

between population means 30 percent of the time, not 

frequently enough to be considered statistically consistent. 

Most of the stands in the landscape being evaluated are 

in the two tallest height classes. This trend results in a 

landscape with very little edge in general. The edge that is 

present may be in areas at lower risk to wind or edge may 

not be a critical factor in this landscape.

The height variable (Table 2) had a positive difference 

between population means 90 percent of the time. This statistically consistent difference likely reflects the increased 

vulnerability to wind damage with increased tree size 

(Lohmander and Helles 1987, Peltola and others 1997, Smith 

and others 1997). The species variable (Table 2) did not 

have significant differences between the population means 

in any of the iterations. This was surprising considering 

managers all cited softwoods and, most notably, balsam 

fir, as being the most sensitive to wind disturbance. It may 

indicate homogeneity within the landscape or an insensitivity of the index to differences in composition.

The thinning variable (Table 2) had statistically significant positive differences between the population means 

100 percent of the time. This agrees with conventional 

wisdom of the land managers—that windthrow is much 

more common in previously thinned stands. An evaluation 

of the landscape shows that 99.72 percent of the recorded 

blowdown occurred in thinned stands.

The composite stand grid, STAND, (Table 3) also had 

significant positive differences between the population 

means 100 percent of the time. This may be primarily 

driven by the combination of the height and thinning components that compose this composite variable. The thinning 

variable’s binary property makes it a relatively powerful 

component of the composite grid.

Differences between population means for the topographic exposure variables (Table 2) were never statistically 

consistent. This trend was noticed by Mitchell and others 

(2001) when an analysis of their model revealed a level of 

contribution from topographic variables to the model lower 

than expected. Simple terrain variables may not adequately 

describe airflow phenomena induced by complex terrain 

(Mitchell and others 2001).

The elevation variable (Table 2) had a positive difference between population means in the model 100 percent of 

the time. This agrees with the assumption that susceptibility 

increases in higher areas of the landscape, where exposure 

and windspeed are greater (Bair 1992).

The soil variable (Table 2) had a negative difference 

between population means. This trend was statistically 

consistent, occurring 70 percent of the time. This is counter 

to the original assumption of the model that forests growing 

in areas with more restricted rooting depths would be more 

vulnerable to wind disturbance (Day 1950, Mergen 1954). 

An analysis of the correlation between soil depth and elevation in this landscape yields a mean Pearson correlation of 

0.344 for the 10 iterations. This is substantially larger than 

the test statistic (0.088) for an alpha of 0.05 and a sample 

size greater than 100. This result indicates a statistically 

significant positive correlation between the two variables 

(Zar 1984); deeper soils are correlated with higher elevations in the landscape being evaluated.

Differences between population means for the composite site variables (Table 3) were either positive or nonsignificant. Differences between population means were 

statistically consistent for three exposure variants, both 

site grids with nondirectionally weighted exposure input 

variables (SITE_1500 and SITE_1000) and SITE_se, the 

site variant modeling topographic exposure to the southeast.

The positive difference between the population 

means of all site variables (with the exception of the north, 

northeast, and northwest exposure variants) suggests that 

topographic exposure is important, even though significant 

differences between the population means were not consistently detected for exposure as an individual variable.

Although the data within the GIS database are fairly 

coarse, the general spatial model we developed associates 
245

Advances in Threat Assessment and Their Application to Forest and Rangeland Management

moderate to high vulnerability ratings with reported wind 

damage in the landscape. All of the final risk assessment 

variables, CMLTV_direction (Table 3), have a positive 

difference between the population means. This validates 

the model’s ability to differentiate between vulnerability 

of damaged stands and vulnerability of undamaged stands. 

The difference between the population means in the 

cumulative risk variables is highly significant with p-values 

of 0.000 recorded in all 10 iterations for all 10 exposure 

variants.

Conclusion

Wind damage to forests in Maine is a continual consideration for forest managers across the region. The importance 

of wind damage in the State is likely to increase as large 

areas of forest that were regenerated during the spruce budworm outbreak of the 1970s and 80s continue to mature. In 

addition, the vast majority of harvesting in the State utilizes 

partial harvesting techniques that increase stand susceptibility to windthrow. If these trends in stand height and area 

thinned continue, managers will need tools and techniques 

to help them manage the growing wind damage threat. 

Spatial risk index modeling with GIS provides an alternative view of the landscape, allowing for threat assessment 

and more informed decisionmaking. The wind vulnerability 

model developed for this project can be used as a tool to 

assist in forest planning and provide insight into historical 

trends in forest dynamics and habitat associations. This 

tool should be portable to other regions because it contains 

variables that are frequently identified as critical in predicting windthrow vulnerability. The stand-level variables are 

general enough to adapt to similar forest typing schemes 

used by other managers in the State.

There are multiple complexities associated with 

modeling vulnerability to wind damage in forests. Foremost 

among these is modeling the interaction of rare regional 

wind events, chaotic local wind behavior, changing soil 

conditions (saturation and freezing), and dynamic stand 

characteristics (growth and manipulation). One approach 

for managing the uncertainty surrounding wind damage is 

to develop relatively simple models of vulnerability based 

on past observations of factors influencing damage. These 

more general models, like the one developed for this project, 

would not be expected to predict past wind damage as well 

as models developed directly from damage information 

collected after a particular storm or in a specific landscape. 

However, they may prove less biased towards particular 

sites, stand conditions, or individual wind events and, therefore, be more useful for guiding forest management across a 

large region or as stand conditions change through time.

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English Equivalents

When you know: Multiply by: To find:

Nanometers (nm) 3.94 x 10-8 Inches (in)

Millimeters (mm) 0.0394 Inches

Centimeters (cm) .394 Inches

Meters (m) 3.28 Feet (ft)

Cubic meters (m3) 35.3 Cubic feet (ft3)

Meter per second (m/s) 2.24 Miles per hour (mph)

Kilometers (km) .621 Miles (mi)

Kilometers per hour (kph) .621 Miles per hour

Hectares (ha) 2.47 Acres (ac)

Microliters (μL) .0000338 Ounces (fluid)

Milliliters (mL) .0338 Ounces (fluid)

Grams (g) .0352 Ounces

Kilograms (kg) 2.205 Pounds

Kilograms per cubic

meter (kg/m3) .0624 Pounds per cubic foot (lb/ft3)

Kilograms per hectare

(kg/ha) .893 Pounds per acre (lb/ac)

Megagrams per hectare

(Mg/ha .446 Tons per acre

Kiloequivalents per hectare

(keq/ha) .405 Kiloequivalents per acre

Trees per hectare .405 Trees per acre

Micrograms per gram (μg/g) 1 Parts per million

Degrees Celcius (C) 1.8C + 32 Fahrenheit (F)

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