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

Volumes 2

 

Previous
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 2 

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 percieve 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 Phytophthora 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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FIRE

Syntheses
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249

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

Jay O’Laughlin

Jay O’Laughlin, professor, University of Idaho, College of 

Natural Resources, Moscow, ID 83844-1134.

Abstract

Risk is a combined statement of the probability that something of value will be damaged and some measure of the 

damage’s adverse effect. Wildfires burning in the uncharacteristic fuel conditions now typical throughout the Western 

United States can damage ecosystems and adversely 

affect environmental conditions. Wildfire behavior can be 

modified by prefire fuel treatments, thereby reducing risks 

to firefighters, structures, and ecosystems, but such projects pose their own environmental risks. To support fuels 

treatment decisions, environmental analysis of alternatives 

is generally required, including taking no action. How 

can managers determine whether risks of actively treating 

fuels are greater than risks posed by no action? The riskreduction benefits of fuel treatment are often overlooked 

in decision processes for comparing wildfire effects with 

and without fuel treatment. To fill the void, a comparative 

ecological risk assessment conceptual model is presented. 

Both prefire fuels treatment and postfire events produce 

sediment that can adversely affect water quality and aquatic 

organisms. Similarly, both prescribed fire and wildfire can 

adversely affect air quality. The model’s tradeoff diagram 

tests a risk management hypothesis: The benefits of restoring natural (historical) fire regimes and native vegetation in 

a particular location, plus the benefits of reducing the severity of wildfire effects, balance favorably against any adverse 

effect, either short- or long-term, from fuels treatment. 

Managers may believe this hypothesis, but policies require 

environmental analysis to support it. A tradeoff diagram 

illustrates the conceptual model and graphically replies 

to the question: Which produces more sediment, wildfire 

burning under untreated conditions, wildfire burning after 

fuels are reduced, or the treatments designed to reduce 

wildfire risks? Similarly: Which situation would produce 

more fine particulate matter (PM2.5) air pollution? Tradeoff 

diagrams of such situations may contribute to sustainable 

resource management decisions by improving communications between risk assessors, public agency managers, and 

interested nongovernmental parties. 

Keywords: Comparative ecological risk assessment, 

conceptual model, hazardous fuel reduction, policy, risk 

management, wildfire.

Introduction

Compared to not taking any action, fuel treatments may 

or may not reduce adverse ecological and environmental 

effects that accompany wildfires. Risk management is 

central to many human enterprises and is the foundation for 

all fire management activities (USDA-FS/USDI and others 

2001). The U.S. Department of Agriculture, Forest Service 

(USDA-FS), and the U.S. Department of the Interior, 

Bureau of Land Management (BLM), have been advised to 

adopt a systematic risk-based approach to target fuel reduction projects across landscapes and to make fully informed 

decisions about fuels treatment project alternatives and their 

effects (GAO 2004, USDA-OIG 2006).

Risk is usually defined as having two components: 

a measure of adverse effects, and the probability of the 

adverse effects’ occurrence. Many people have difficulty 

comprehending risk, and its quantification has challenged 

and confused lay persons and professionals (Haimes 2004). 

A simplifying approach for fire-adapted ecosystems is to 

use the fire return interval as the time horizon for analyzing environmental effects. By definition, this ensures that 

a wildfire will occur during the analytical period, and 

different conditions that affect fire behavior can be compared more readily by eliminating the need for knowing the 

probability distribution of fire occurrence. The risk management question then becomes: What is the desired condition 

for a particular ecosystem and location when the inevitable 

lightning bolt strikes?

Wildfire risk management is a synthesis of scientific 

and nonscientific concerns. Information from ecological, 

social, managerial, and policy sciences is integrated into 

a decision process framework that incorporates social 

values and concerns. These include democratic process and 

Ecological Risk Assessment to Support Fuels Treatment 

Project Decisions
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GENERAL TECHNICAL REPORT PNW-GTR-802

institutions for governing collective decisions in our society. 

Risk management depends on effective communication of 

information between risk assessors in regulatory agencies, 

risk managers in land management agencies, and interested 

members of the public who may be directly or indirectly 

affected by wildfire.

Risk is related to each of the three components of 

the general sustainable forestry model—risk of losing 

ecosystem components or damaging environmental values, 

economic investment risk, and social risk in communities 

facing forest-based change (O’Laughlin 2004, 2006). This 

synthesis takes a problem-oriented approach (see Clark 

2002) to wildfire risk management and deals primarily 

with the ecological and environmental aspects of wildland 

fire management embedded in decisionmaking and social 

contexts and reflected in various institutions. Whether fuels 

treatment should take place on public lands is a collective 

decision. 

Managers may lack the tools and information to 

demonstrate the beneficial effect that fuel treatment projects 

could have on environmental quality. To fill the void, a 

conceptual model of decision tradeoffs has been developed 

(O’Laughlin 2005a, 2005d). Herein, it is applied to the 

multiobjective fire/fish risk management problem in order 

to compare postfire sedimentation with and without fuel 

treatment. This same approach can be used to assess other 

environmental effects including fine particulate matter 

(PM2.5) air pollution from either wildfire or prescribed 

fire smoke. The model is based on the framework of the 

Environmental Protection Agency’s Guidelines for Ecological Risk Assessment (EPA 1998). In a comparative risk 

assessment framework, the integration of sediment, smoke 

particles, and other environmental risks with land management objectives is accomplished not by science, but via 

social process, primarily the public involvement processes 

required by environmental and land use planning laws. 

Risk assessors and risk managers interact with stakeholders to determine which risks are most important. Risks to 

firefighters, structures, scenery and aesthetics, vegetation 

species and age classes, wildlife habitat, and air and water 

quality should be considered. Using cause-and-effect modeling results developed by risk assessors, risk managers can 

demonstrate for each risk whether prefire fuels reduction 

could potentially reduce postwildfire adverse effects. 

This section is concerned with all aspects of risk 

analysis, including risk communication as well as risk 

assessment and management. First, appropriate terminology is presented. Then the wildfire situation in the Western 

United States is defined as a fuels management problem 

and placed within its decision process and social contexts. 

Complications from spatial and temporal dimensions, as 

well as risk governance, arise from policy requirements 

and social perceptions of risk that may require institutional 

changes to produce sustainable improvements in forest 

ecosystem conditions. This synthesis rationalizes the choice 

of parameters selected to adapt the EPA’s ecological risk 

assessment framework for the purpose of supporting fuels 

treatment decisions by comparing wildfire risk management 

alternatives using a with-or-without framing consistent 

with National Environmental Policy Act (NEPA 1969) 

requirements. A conceptual model diagram is the core of 

ecological risk assessment and the principal product of this 

synthesis, where it is used to compare wildfire effects on 

sediment with and without fuel reduction.

Risk Analysis Terminology

Risk terms can be a barrier to effective communications if 

not properly defined. Risk is a combined statement of the 

probability that something of value will be damaged and 

some measure of the damage’s adverse effect. Risk simply 

gives meaning to the things, forces, or circumstances that 

pose danger to people or what they value (NRC 1996). Risk 

can mean both the probability of loss and the hazard or 

threat that might cause that loss (Harwood 2000). A hazard

or threat is something that poses danger or can cause an 

adverse effect. Stressor, an EPA term, seems to be synonymous with hazard or threat and is any physical, chemical, 

or biological entity that can induce an adverse response in 

an ecological risk assessment endpoint. The endpoint is an 

explicit expression of the environmental value that is to be 

protected, operationally defined by an ecological entity and 

its attributes (EPA 1998). Ecological risk assessment is a 

process that evaluates the likelihood that adverse ecological 

effects may occur or are occurring as a result of exposure to 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

one or more stressors (EPA 1998). Ecological risk assessments are developed within a risk management context to 

evaluate human-induced changes that are considered undesirable and are used to support many types of management 

actions, including the management of watersheds or other 

ecosystems affected by multiple nonchemical and chemical 

stressors (EPA 1998). 

Adverse effects and their potential damages and consequences are real components of risk (Haimes 2004). The 

meaning of risk has always been inherently controversial 

and political because it depends on value-based (i.e., nonscientific) judgments about adverse effects (Slovic 1999). 

Changes are considered undesirable because they alter 

valued structural or functional characteristics of ecosystems or their components. An evaluation of adversity may 

consider the type, intensity, and scale of the effect as well as 

the potential for recovery from the risk-inducing event (EPA 

1998). The probability component of risk is an imagined, 

mathematical human construct (Haimes 2004). Although 

most people understand probability in its simplest form—

the likelihood of outcomes from tossing a coin or rolling 

dice—conditional and joint probabilities can be perplexing. 

Risk analysis is usually considered to be the process 

of assessing, characterizing, communicating, and managing 

risk (e.g., Haimes 2004, NRC 1996). Effective risk analysis 

is integrated into decisionmaking processes, not treated as 

a gratuitous add-on task (Haimes 2004). Risk assessment 

asks: What can go wrong, and what are the consequences? 

Human and organizational failures are sources of risk and 

may be caused by environmental and institutional elements. 

Risk management asks what can be done, and what are the 

impacts on future options? Risks cannot be managed until 

they have been assessed, and some form of model is necessary for that (Haimes 2004).

Fuels Problem and Context

The accumulation of fuels over time can lead to uncharacteristic wildfires and associated problems. Alteration of 

historical fire regimes often causes serious changes in forest 

ecosystem processes, resulting in unusually intense, large 

fires. Current forest conditions and wildfire regimes pose 

risks to many environmental and socioeconomic values and 

threaten human communities (USDA-FS 2004). Wildfires 

near human communities can have devastating effects, as 

can postfire floods (May 2008). Wildfire poses immediate 

threats of damage or loss to nearby structures, and endangerment of public safety is an even more important concern 

during a wildfire incident and for many years thereafter. 

Wildfires also pose secondary threats to ecological and 

environmental resources (Summerfelt 2003). 

The acreage affected by wildfire nationwide has 

steadily increased over the past four and a half decades, 

with a trend towards uncharacteristically severe and uncontrollable fire behavior (NIFC 2006b). Trends of increasing 

fire size and severity have emerged over the past 20 years 

(USDA-FS 2004) as wildfires in the Western States have 

increased. The trend is influenced by changes in climate, 

extreme droughts, and, in some forests, overabundant fuels 

(Westerling and others 2006). 

The combined effects of increased fuel accumulations, 

lengthened fire seasons, and intensified burning conditions 

are expected to contribute to larger and more extensive 

wildfires in the near future (Covington and others 1994), 

with increases of 74 to 118 percent in wildfire burn areas 

expected over the next century (Running 2006). These 

expectations underscore the urgency of fuels management 

to reduce wildfire hazards to human communities as well 

as actions to mitigate wildfire impacts in forests that have 

undergone substantial alterations from past land uses 

(Covington and others 1994, Westerling and others 2006).

An understanding of the decision processes (“Decision 

Process”) and social context (“Social Context”) for wildland fire management is necessary if risk analysis is to be 

integrated into land and resource management decisionmaking. Decision processes are defined by public policies 

and laws, which are a function of the social environment 

within which wildland fire management occurs. Although 

various agencies and organizations may perceive wildfire 

risks differently, management decision processes are 

affected by the same evolving institutional framework of 

laws and policies. By engaging collaboratively with stakeholders in formal decision process forums, managers can 

integrate different perspectives regarding environmental 
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GENERAL TECHNICAL REPORT PNW-GTR-802

risks and thereby arrive at socially acceptable decisions 

regarding wildland fire management and fuels reduction.

Decision Process

The ultimate utility of decision analysis is not necessarily 

articulating the best policy option, but avoiding extreme 

events (Haimes 2004), such as large-scale, uncharacteristically severe wildfires. Risk analysis traditionally has been 

used for other purposes, but it can address forest management issues in a transparent way and disclose risk tradeoffs 

that are often not accounted for in other decision analysis 

techniques (Hollenstein 2001). Land and resource management decisions always involve risk, including the decision 

not to take action (Thomas and Dombeck 1996).

The Federal Wildfire Policy (USDA-FS/USDI and 

others 2001) recognizes that sound risk management is 

a foundation for all fire management activities. Together 

with the National Fire Plan (NFP), this policy provides 

the institutional framework for Federal agencies, States, 

Native American tribes, local governments, and communities to manage wildfire risk, improve land conditions, and 

reduce impacts to communities while ensuring sufficient 

firefighting capacity for the future. The NFP has established 

a long-term hazardous fuels reduction program in which 

treatments are designed to reduce wildfire risks to people, 

communities, and natural resources while restoring forest 

and rangeland ecosystems to closely match their historical 

structure, function, diversity, and dynamics (USDA-FS/

USDI 2006a). The NFP is implemented through a collaborative framework agreed upon by Federal and State agencies, 

tribes, and other parties (WGA 2006). The collaborative 

framework is a way to address fire and fuel management 

problems within their social context.

The National Environmental Policy Act (NEPA 1969) 

is the cornerstone of our environmental laws. It requires 

that Federal agencies analyze the short- and long-term 

adverse environmental consequences of a range of proposed 

management alternatives, including no action. The result 

of the NEPA process is some type of an environmental 

impact assessment document. According to an EPA scientist 

(Fairbrother and Turnley 2005), the NEPA process has 

various shortcomings that have hampered decisionmaking 

and significantly reduced public acceptance of fuels treatment. Risk assessment integrated into the NEPA process 

could result in more meaningful environmental impact 

analyses, thereby providing a more technically sound and 

robust means for assessing and comparing potential adverse 

outcomes of proposed management alternatives (Fairbrother 

and Turnley 2005). 

The healthy forests policy—composed of the President’s Healthy Forests Initiative and the Healthy Forests 

Restoration Act (HFRA 2003)—has led to modification or 

streamlining of decision processes (see O’Laughlin 2005b). 

The HFRA requires that before a court can issue an injunction, there must be a weighing of the environmental effects 

of doing fuels treatment against not doing fuels treatment. 

A legacy of HFRA therefore may be the stimulation, if not 

institutionalization, of comparative ecological risk assessment. Courts will look to land management agencies for 

such analysis. At this writing, it is unclear what the analysis 

might look like. One option is adapting the EPA (1998) 

Guidelines (“EPA’s Risk Assessment Framework”) to a 

wildfire risk management problem (“Fire/Fish Risk Management Application”).

Social Context 

Risk is a social construction, combining science and judgment with psychological, social, cultural, and political factors (Slovic 1997). What risks are taken into account, how 

they are framed, and what constitutes a solution to a risk 

problem are all matters that go beyond scientific inquiry 

(De Marchi 2003). Environmental risk involves value judgments that reflect much more than just the probability and 

consequences of the occurrence of an event (Kunreuther 

and Slovic 1996). Because risk assessment brings specific 

values into consideration, it can help reveal which values are 

at greater risk (Molak 1997). Interested parties can contribute knowledge that risk assessors would otherwise overlook 

(NRC 1996). Serious attention to citizen participation and 

process issues may eventually lead to more satisfying and 

successful ways to manage risks (Slovic 1997). If done 

systematically and transparently, risk assessment can help 

build trust (Slovic 1993) primarily by adding transparency 

to forest decisionmaking processes (Hollenstein 2001). 
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Social interaction is the phase of the risk analysis 

process during which integration of different risk assessment endpoints can be addressed. To enable management 

actions to improve the fire and fuel problem on Federal 

lands, managers should focus on the things people care 

about. These include:

1. The condition of forests relative to forest values 

associated with management objectives (i.e., 

forest health).

2. Forest values at risk of damage from nature’s

forces as well as human actions.

3. The environmental and socioeconomic effects 

of wildland fire management policy that protects 

some values while putting others at greater risk 

(O’Laughlin 2006).

For example, trying to protect fish habitat in fireadapted ecosystems by not allowing a riparian vegetation 

management project simply because it will produce a small 

amount of sediment may be counterproductive in the long 

term. Fire is inevitable, and the magnitude of adverse 

effects is more important than when the effects occur 

(O’Laughlin and others 1998).

When interacting with stakeholders, risk assessors and 

managers should use qualitative and quantitative approaches 

as appropriate, while disclosing assumptions and potential 

for errors. Risk assessment can help risk managers compare 

the environmental effects of management alternatives. Clear 

objectives are needed, consistent with a long-term vision 

of what the land should look like. Effective communication 

of short- and long-term risks and risk-reduction benefits 

can help build trust with stakeholders (O’Laughlin 2005c). 

The thought process that goes into evaluating a particular 

hazard is more important than the application of some 

sophisticated mathematical technique or formula (Molak 

1997). More emphasis on the risk management aspects of 

risk analysis would mean greater stakeholder involvement 

and de-emphasis on quantitative characterization of risk and 

uncertainty (Power and McCarty 1998). Fuels management 

projects on several California national forests emphasize 

the importance of stakeholder collaboration and modeling 

tools to help facilitate communications (see “Strategic Fuel 

Treatment”). 

Improving the Fuels Problem

The goal for improving the wildland fire and fuels problem 

is to reduce the long-term risk wildfire poses to human and 

ecological communities. Managers need to weigh the shortterm risks posed by active management against the longterm risks posed by continued inaction, and to communicate 

these risks in a meaningful way to the public (Bosworth, 

Dale. 2003. Risk assessment for decisionmaking related to 

uncharacteristic wildfire. Unpublished keynote address to 

conference in Portland, OR, November 17. On file with Jay 

O’Laughlin, College of Natural Resources, University of 

Idaho, P.O. Box 441134, Moscow, ID 83844-1134). Managers cannot change weather or topography, but fuels can be 

modified to change the burning and value-loss characteristics at specific locations as well as across large landscapes. 

This not only reduces the negative impacts on those forests 

but the wildfire itself may also provide benefits (Finney 

2005). Benefits include environmental risks prevented by 

management actions (Davies 1996). For example, before 

enjoining “an agency action under an authorized hazardous 

fuel reduction project, the court reviewing the project shall 

balance the impact to the ecosystem likely affected by the 

project of (1) the short- and long-term effects of undertaking the agency action; against (2) the short- and long-term 

effects of not undertaking the agency action” (HFRA 2003, 

title 1, section 106).

Fire hazard in a given area is partly a function of 

the combustible materials located on site. Thinning and 

prescribed burning are the primary fuel management 

activities and repeatedly have shown reduced fire intensities 

and increased survival of some forest types (Finney 2005). 

Considering anticipated changes in interior West forests, 

including climate, Covington and others (1994) concluded 

that the undesirable consequences of inaction far exceed 

those of action. Without active management of fuels, many 

forests will continue to be subject to uncharacteristically 

severe fires, and the costs of firefighting will continue to 

increase (Stephens and Ruth 2005). Active management can 

mitigate wildfire risks to watersheds in some situations, but, 

in others, forest management may not be effective (Bisson 

and others 2003, Schoennagel and others 2004, Westerling 

and others 2006).
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Benefits from prefire management are most likely to 

come from prioritizing treatment areas (Dunham and others 

2003). Priorities can be based on ecological value, evolutionary significance, and the risk of loss (Bisson and others 

2003). The scale of the problem, however, is enormous. 

High-priority treatment areas cover 397 million acres of 

forests and grasslands across all ownerships, public and 

private, an area three times the size of France. Some 73 

million acres of forests in the low- and mixed-severity 

regimes are far denser than they ought to be, increasing 

their vulnerability to stand-replacing fires. These have 

been identified as high-priority treatment areas (USDA-FS 

2006a). 

Successful projects for reducing fire hazard depend on 

taking many factors into account and developing protocols 

for deciding which stands should be thinned and by how 

much, with each situation evaluated on its own merit and 

operations planned carefully to ensure that the cure is not 

worse than the disease (NRC 2000). In risk management, 

avoiding actions in which the cure is worse than the disease 

means avoiding extreme events, i.e., the worst and the most 

disastrous situations (Haimes 2004). 

Problem fires are today’s parlance for extreme fire 

events (NIFC 2006b). Of all ignitions, 2 to 3 percent escape 

initial attack and become the problem fires that damage 

resources, threaten communities, and cost millions of 

dollars in suppression efforts. Whereas not all wildland 

fires grow to such proportions, problem fires are those 

events that are large, destructive, dangerous, and costly to 

manage. Problem fires are the symptoms of a larger forest 

health issue, where ecological realities conflict with social 

expectations and economic limitations (NIFC 2006b). 

Spatial fire behavior models used in collaborative settings 

(“Strategic Fuel Treatment”) offer some promise in dealing 

with social issues at various spatial and temporal scales 

(“Spatial Scale Issues” and “Temporal Scale Issues”). Especially on National Forest System lands (“National Forest 

System Issues”), institutional improvements (“Institutional 

Improvement”) may be necessary to help put such technologies in place.

Spatial Scale Issues

Wildland fire risk reduction is a national goal that depends 

on landscape-level planning and project-level actions 

(Barbour and others 2005). The EPA (1998) guidelines 

have been used to compare risks at a regional scale (Landis 

2005). To date, such efforts have not included forest threat 

assessment in general or wildfire in particular. Although 

sustainable forest management issues involve multiple 

scales, achieving the national goals of sustainability rest, 

in large part, on actions that are carried out at the local or 

forest management unit scale (USDA-FS 2004). A variety 

of modeling approaches are now available to meet the 

landscape-level planning needs. However, there is a lack of 

explicit guidance about how to connect perceptions of risk 

across vast spatial expanses of geographic or ecological 

regions to the outcomes of specific management activities 

at the project scale of a few to tens of thousands of acres 

(Barbour and others 2005). The guidance document for 

Federal healthy forests project implementation (USDAFS/USDI 2004) recognizes the importance of scale and 

assumes assessments at scales larger than individual 

projects have been done before fuels treatment projects 

are initiated. 

Tying small area project-level analyses to larger scales 

helps managers think about the importance of different 

resources through space and time (Barbour and others 

2005). Refining analyses at the midscale helps to understand 

how different resources interact. Considering resource conditions and management objectives at very broad scales can 

help managers understand where they might concentrate 

efforts (Barbour and others 2005). Broad-scale assessments should set priorities for reducing the risk to social 

and ecological values caused by uncharacteristically dense 

vegetation. To reduce risk, the assessments should evaluate 

the potential for vegetation treatments, such as mechanical 

treatments and prescribed fire. A tactical schedule of priority vegetation-treatment projects should result from strategic assessments of the need for fuel treatments conducted at 

appropriate landscape scales (USDA-FS/USDI 2004). 

Spatial data and risk-based methods are available to 

analyze 2-million-acre watersheds in order to prioritize 

treatments at the subwatershed scale of 20,000 acres 
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(Hessburg and others, this volume). At the subwatershed 

scale, it is not necessary to treat an entire landscape for 

effective risk reduction (Ager and Finney 2007). Instead, 

strategic placement of fuel treatments (SPOTS) can be used 

to attain the desirable outcome of modifying fire behavior 

(McDaniel 2006).

Strategic Fuel Treatment

Wildland-urban interface (WUI) areas are generally recognized as high priorities for fuels treatment (Pyne 2004). 

However, treating only WUI areas alone will not achieve 

the wide range of human and natural resource benefits 

forests provide (Summerfelt 2003). The large-scale fires 

of 2002—Hayman (Colorado), Rodeo-Chediski (Arizona), 

and Biscuit (Oregon-California)—caused considerable 

damage and disruption in WUI areas. These fires began 

miles beyond the WUI where excessive fuel loadings had 

accumulated (USDA-FS/USDI 2006b). In addition, at higher 

elevations outside the WUI, wildfire can damage riparian 

areas and associated watershed benefits and values (Dreesen 

2003, Obedzinski and others 2001).

Firesheds are large (thousands of acres) landscapes, 

delineated based on fire regime, condition class, fire history, 

fire hazard and risk, and potential wildland fire behavior. 

Fireshed assessment refers to an interdisciplinary and collaborative process for designing and scheduling site-specific 

projects (NIFC 2006b). The purpose of fireshed assessments 

is designing the most effective fuel treatment program 

with the resources available for reducing the likelihood of 

a large, severe problem fire (McDaniel 2006). 

SPOTS is an interagency, interdisciplinary, collaborative landscape-scale GIS-based tool that has emerged from 

fireshed assessment efforts on several national forests in 

California. The strength of the process lies in purposeful 

dialogue between interested parties (McDaniel 2006). The 

important thing is to focus on exploring everyone’s ideas 

and not trying to find one right answer. Project leaders 

stress this collaborative approach and active learning by 

participants as the key strength. Although most people 

have a perception of the fireshed assessments as a set of 

modeling tools, project managers indicate that the models 

really are used to promote dialogue (McDaniel 2006). 

The SPOTS concept contributes to an overall understanding of the spatial dynamics of fuel and related fire 

behavior by employing fire modeling tools that describe 

fire potential on a specific landscape. The SPOTS approach 

considers tradeoffs between multiple treatment options by 

gaming fire scenarios with fire behavior and spread modeling software. The SPOTS framework meets the need identified by the U.S. Government Accountability Office (e.g., 

GAO 2004) to establish a consistent way to define risk and 

test potential solutions. SPOTS was developed in California, 

and, in 2005, the Forest Service and BLM tested it in eight 

pilot areas across the country, including central Oregon. 

SPOTS analysis approaches should dovetail with 

the Fire Program Analysis (FPA) system, which is a new 

interagency planning and budgeting tool for evaluating 

the effectiveness of alternative fire management strategies 

(NIFC 2006a). The SPOTS approach also may allow managers time to implement long-term management strategies 

to restore ecosystems, perhaps including effective decision 

support for wildland fire use (WFU) (Gercke and Stewart 

2006). WFU allows lightning-ignited fires to burn in order 

to attain planned resource management objectives. 

Temporal Scale Issues

There is a lack of explicit guidance about how to consider 

changes in conditions that occur over the decades or even 

centuries required for ecological processes to play out on 

the landscape (Barbour and others 2005). Proposed projects 

that could produce benefits by reducing risks over the long 

term are sometimes considered unacceptable because they 

pose a small amount of risk in the short term. For example, 

in some situations prefire hazardous fuel reduction treatments can reduce sedimentation and smoke in the long 

term, but, in the short term, such treatments produce additional quantities of sediment and smoke that some people 

may consider unacceptable, no matter how small. 

Compliance with the NEPA (1969) requirement for 

short- and long-term effects analysis raises the issue of 

appropriate time horizon selection. Comparison of environmental risks should be done within the same time period, 

and risks prevented by management programs should be 

included in the comparison (Davies 1996). Extinction 
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should be viewed over hundreds of years so that short-term 

considerations do not create long-term problems (NRC 

1995). For risks to native fish, 100 years is a minimum (Rieman and others 2003a). By selecting the fire return interval 

as the minimum time horizon, the probability of a fire on 

the landscape is assured, and risk analysis can proceed to 

focus on reducing adverse effects.

National Forest System Issues

The buildup of forest fuel and changes in vegetation 

composition are particularly problematic on National Forest 

System lands (O’Laughlin 2006, O’Laughlin and Cook 

2003). According to former USDA Forest Service Chief 

Dale Bosworth (2003), the situation on National Forest System lands is not sustainable—ecologically, economically, or 

socially. Active management of Federal lands that maintains 

forest cover and structure within a range consistent with 

long-term disturbance processes can reduce the potential for 

severe fire behavior, maintain and enhance long-term ecological integrity, and provide the mix of goods and services 

people want from ecosystems (Quigley and others 1998). 

To improve forest health conditions on Federal lands, 

managers generally must support project-level decisions 

with NEPA (1969) analysis documents and demonstrate 

the short- and long-term effects on environmental values 

other than woody vegetation. These project-level analyses 

allow managers to consider protecting or enhancing specific 

resources (Barbour and others 2005). For example, wildfire 

risk and northern spotted owl (Strix occidentalis caurina)

habitat suitability are complex issues requiring site-specific 

assessment and management (Lee and Irwin 2005). Managers need tools at the project level to help them work through 

the project approval process (USDA-FS/USDI 2004). In 

addition, projects need to be prioritized so that scarce 

resources can be used effectively (Bisson and others 2003). 

The effect of NEPA—in combination with the Clean 

Water Act, Clean Air Act, Endangered Species Act, Federal 

Land Policy and Management Act, and the National Forest 

Management Act—has been to create increasingly difficult decisionmaking on Federal lands. The USDA Forest 

Service process predicament report presents an argument 

that because of policy-driven delays, the agency is hindered 

from producing on-the-ground results, including improving 

forest health (USDA-FS 2002). Breaking decisionmaking 

gridlock is one reason for applying formal risk assessment 

(Lackey 1994). 

Institutional Improvement

Managing ecological risks depends on an integrated 

approach because risks arise from many sources—hydrologic, forest, rangeland, and aquatic as well as economic 

and social—and reducing risks from one source may 

increase risk to another ecological component (Quigley 

and others 1998). The integration will come through social 

process. One such approach is illustrated by the fire/fish 

risk management problem (“Fire/Fish Risk Management 

Application”). 

Ecologists generally recognize that barriers to improving ecological conditions may be more social or institutional 

than scientific (Szaro and others 1998). Improving the 

wildfire problem will be a complex, lengthy, expensive, and 

risky process, not only because of the ecological legacy on 

the land, but also the institutional legacy (Busenberg 2004). 

The framework for implementing the National Fire Plan 

is dependent on effective collaboration between Federal 

agencies, other levels of government, and interested parties 

or stakeholders (WGA 2006). Finding ways to meaningfully incorporate risk analysis—especially cooperative or 

collaborative risk assessment and risk management—into 

decisionmaking processes seems to be the most direct 

institutional path to on-the-ground improvements in 

ecosystem conditions. 

Risk can be thought of as a game in which the rules 

must be socially negotiated within the context of a specific 

problem. This contextual approach highlights the need 

for interested parties to define and play the game, and 

emphasizes the importance of institutional, procedural, 

and societal processes in risk management decisions 

(Kunreuther and Slovic 1996). Risk assessment methods, 

assumptions, and conclusions differ dramatically across 

the Federal government (Cantor 1996). Standardization 

of policies and procedures among Federal agencies is an 

ongoing objective in wildland fire management (USDA-FS/

USDI and others 2001). Nevertheless, different agencies can 
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be expected to have different perceptions of risk based upon 

their agency missions and policies. Unless there are appropriate forums for reconciling differences in risk perceptions 

among all interested parties, information developed in risk 

assessments is unlikely to change the way land and resource 

management decisions are made. 

Comparative ecological risk assessment can play a role 

by facilitating communications between risk managers, 

risk assessors, and interested parties. Two things need to 

be accomplished with stakeholders: (a) identify the things 

they care about, i.e., risk assessment endpoints; and (b) 

communicate what is known and unknown about the causeeffect relationships of factors (“stressors”; i.e., threats or 

hazards) affecting those endpoints. Slovic’s (1999) advice to 

wildland fire managers is to forgo attempting to determine 

what stakeholders think may be an acceptable level of risk, 

and, instead, focus on demonstrating the benefits from risk 

management actions.

Participative governance for managing risks requires a 

shift of mentality, broad changes in professional and institutional practices, and the design and implementation of new 

instruments and procedures (De Marchi 2003). These are 

difficult things to change. One opportunity to incorporate 

societal concerns in the governance of risks is to encourage 

public participation from the beginning of decisionmaking 

processes. The twofold challenge in risk governance is first 

providing the forums where citizens present and debate 

their interests and ideas about public matters and then 

making such deliberations a meaningful part of democratic 

decisionmaking (De Marchi 2003). The design of the EPA 

(1998) Guidelines explicitly addresses both challenges. 

EPA’s Risk Assessment Framework 

Spurred by considerable political interest in the 1990s, a 

substantial body of literature exists on environmental and 

ecological risk analysis (Molak 1997). A review of laws 

and policies supports the conclusion that Federal land and 

resource management agencies, and agencies responsible 

for environmental protection, must use some form of risk 

assessment in their decisionmaking processes (O’Laughlin 

2005b). Neither laws nor policies prescribe how agencies 

should do risk analysis or what the end result should look 

like. The EPA’s (1998) Guidelines for Ecological Risk 

Assessment provides a useful starting point. 

The EPA risk assessment process estimates the likelihood of the occurrence of an unwanted adverse effect 

(Fairbrother and Turnley 2005). At least nine Federal 

agencies, including the USDA Forest Service, have used 

the EPA (1998) “guidelines and agreed that they provide a 

common basis for analyzing risks” (CENR 1999). The EPA 

framework (Figure 1) recognizes that the interface among 

risk assessors, risk managers, and interested parties at the 

beginning (during planning) and end of the risk assessment 

process (during risk communications) is crucial for ensuring 

that the results of the risk assessment can be used to support 

a management decision (EPA 1998).

The first step in the EPA framework is a well-defined 

problem formulation built on the involvement of stakeholders as well as scientific information about the magnitude 

of wildfire effects. Next, risk characterization makes a 

comprehensive statement about risk, including assertions 

about uncertainty, and clearly communicates results to 

resource managers and interested stakeholders (Fairbrother 

and Turnley 2005).

Decision analysis and other structured problem-solving 

methods emphasize the need for clearly articulated objectives, along with criteria to evaluate how well various 

alternatives might meet those objectives (NRC 1995). Sustainable resource management depends on clear objectives 

describing desired future conditions. Objectives provide 

managers with targets and others with benchmarks for holding managers accountable for their actions. For risk analysis 

objectives, called assessment endpoints, EPA “Guidelines 

recommend specific ecological entities and their attributes, 

and caution against the use of vague ideas such as sustainability and integrity” (EPA 1998). 

Fire/Fish Risk Management Application

To enable risk-reducing fuels treatment projects, managers 

need to take a problem-oriented approach to reducing fuels 

without causing irreparable harm to fish populations. I call 

this integrated multiple-objective situation the fire/fish risk 

management problem (O’Laughlin 2005a, 2005d). 
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Risk assessment can be used to support many sustainable forest management decisions, including comparing 

wildfire risks to various environmental values with and 

without fuels treatment. Herein, only the problem formulation phase of the EPA framework is covered in detail. 

Problem formulation (“Problem Formulation”) involves 

understanding the situation well enough to develop a 

conceptual model (“Conceptual Model”), which consists 

of a risk management hypothesis and a conceptual model 

diagram. Both of these model components clearly document the risk assessor’s thought process regarding cause 

and effect relationships. This approach facilitates risk 

characterization and communication with interested parties. 

Fish are selected as the risk assessment endpoint, and the 

stressor adversely affecting them is sediment from logging 

or wildfire burning or both under different conditions that 

vary according to fuel loadings. A quantitative example is 

provided (“Quantitative Application”), and uncertainties are 

explicitly addressed (“Uncertainty”).

Problem Formulation

The first phase of ecological risk assessment is problem 

formulation, and a conceptual model is an essential part of 

the process (Figure 1). The inability of management and 

regulatory agencies and the public to articulate common 

goals and conceptual approaches to land management is 

Figure 1— Framework for ecological risk assessment. 

Source: Redrawn from EPA (1998) 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

part of the problem, and until there is improved coordination and recognition of a common conceptual framework for 

management actions, conflicts are likely to continue (Bisson 

and others 2003). 

The underlying structures of belief, perception, and 

appreciation people have toward situations are called frames 

(Schön and Rein 1994). Framing resource management 

problems as questions is a clarifying exercise. Lackey 

(1997) asked: If ecological risk assessment is the answer, 

what is the question? In the fire/fish context, Rieman and 

others (2003b) replied: Which is worse, new fires that may 

result from past management, or new management intended 

to mitigate those fires? These are good questions. Providing 

answers to the wrong questions and missing the relevant 

aspects of a problem because of inaccurate framing of a risk 

issue should be avoided (De Marchi 2003). 

To consider the “which is worse” question in a fire/

fish decision model, the relevant parameters are the adverse 

environmental effects of fire with and without fuel treatments and the beneficial effects of treatments. Two risk 

analysis experts suggest focusing on risk management 

benefits. Haimes (2004) cautioned that suboptimal decisions 

are likely unless the beneficial as well as adverse effects 

of current decisions on future options are assessed and 

evaluated to the extent possible. Slovic (1999) recommended 

focusing on the benefits of managing wildland fire, instead 

of trying to determine the acceptable level of risk from 

adverse effects. 

Many factors adversely affect fish populations. Wildfire 

can cause fish mortality directly and indirectly by modifying habitat quality (Rieman and Clayton 1997). By affecting 

vegetation, wildfire can accelerate soil erosion rates and 

sediment delivery to streams (Wondzell and King 2003). 

Although closer integration of terrestrial and aquatic management is necessary, the lack of a common understanding 

or conceptual foundation is a fundamental challenge to 

progress (Rieman and others 2003a). 

Conceptual Model

Risk cannot be managed unless it has been properly 

assessed, and some form of model provides the best assessment process (Haimes 2004). The EPA framework relies on 

a conceptual model, and it has two principal components: 

(a) a risk hypothesis describing predicted relationships 

among stressor, exposure, and assessment endpoint 

response, along with rationales for their selection; and (b) 

diagrams illustrating these relationships (Figure 1).

By highlighting what we know and do not know about a 

system, a conceptual model provides an opportunity for 

others to evaluate explicit expressions of the assumptions 

underlying decisions. Conceptual models can represent 

many relationships, including exposure scenarios qualitatively linking land-use activities to stressors (EPA 1998). 

A conceptual model for the fire/fish risk problem compares 

short-term effects of fuel treatment project implementation to long-term effects with and without fuel treatment, 

including project benefits from reducing post-wildfire 

environmental damage. Sediment production is the environmental effect analyzed. The idea that active management 

can improve conditions is a testable risk hypothesis that 

can be visualized and communicated in a conceptual model 

diagram. 

Cause-and-Effect Hypothesis—

In the problem formulation phase of the EPA framework 

(Figure 1) the objective of the analytical phase of the 

assessment is called the endpoint. The stressor’s ecological 

effects on the endpoint are described in stressor-response 

profiles. The EPA (1998) guidelines illustrate these concepts 

using salmon reproduction and age class structure as a risk 

assessment endpoint and logging sediment as a stressor. A 

key assumption in the fire/fish risk management conceptual 

model is that hazardous fuel reduction treatments will 

reduce wildfire intensity and subsequent severity of environmental effects by reducing postfire sediment delivery. 

The relationship of a sediment-causing disturbance and 

a fish population is illustrated in a conceptual model diagram (Figure 2) describing the relationship of quantities of 

sediment delivered to the stream and fish biomass (Rieman 

2003). This model diagram serves as the stressor-response 

profile called for in the EPA’s approach to risk analysis. In 

the diagram, fish biomass is reduced almost immediately in 

response to a disturbance event, which could be either wildfire or logging. Sediment rapidly returns to the pre-event 

level, but it may take decades for fish biomass to return 
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to the pre-event level. Over the long term, fish biomass 

becomes higher than before the event and remains there for 

centuries (Figure 2). In other words, sediment produced by 

a disturbance will have short-term adverse effects on fish, 

offset by long-term benefits.

A risk hypothesis is a fundamental component of an 

ecological risk assessment model (EPA 1998). Hypothesis: 

the benefits of restoring natural (historical) fire regimes 

and native vegetation on a particular site, plus the benefits 

of reducing the severity of effects from stand-replacing 

wildfires, balance favorably against any adverse effect, 

either short or long term, from hazardous fuel reduction 

treatments. The hypothesis is derived from language in a 

memorandum from the directors (Williams and Hogarth 

2002) of the two Federal agencies charged with implementing the Endangered Species Act (ESA 1973). The memo 

provides guidance for ESA regulatory personnel engaging 

in interagency consultation with land management agencies. 

It is consistent with NEPA (1969) requirements that Federal agencies analyze and document short- and long-term 

environmental effects of proposed major actions, including 

the no-action alternative.

Formulating the problem as a temporal comparison of 

adverse effects, however, often results in decisions to reject 

fuels treatment projects near imperiled species habitat. 

Adverse effects from fuels treatment are certain in the 

short term, whereas wildfire occurrence in the short term is 

uncertain. 

An alternative problem formulation focuses on the relative magnitude of adverse and beneficial effects from wildfire burning under different fuel conditions. By selecting 

a long-term planning horizon corresponding to fire return 

interval, wildfire becomes a certainty. The magnitude of 

postfire effects remains an uncertainty, but such effects are 

Figure 2—Conceptual model for sediment and fish relationship.

Source: Redrawn from Rieman 2003. 
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certain to occur at some level. Instead of trying to confront 

the landscape-level uncertainties of if, when, and where an 

uncharacteristically severe wildfire will occur, the environmental analysis question in the project area simply becomes 

“Which prefire condition produces the more desirable postfire effect—fuel treatment or no fuel treatment?” Managers 

may accept the fuels treatment hypothesis, but they need to 

present evidence in NEPA documents to convince others 

who may be skeptical.

Conceptual Model Diagram—

The objective of fuel treatment is modification of fire behavior (Stephens and Ruth 2005). One way to do that is to move 

from a higher fire regime condition class (FRCC) (Hann 

and others 2003) to a lower one (see, e.g., USDA-FS/USDI 

2006b). In 2000, approximately 151 million acres of Federal 

forest land was in FRCC 2 or 3 (USDA-FS 2001). Some of 

these lands could be improved by restoring FRCC 1 conditions through fuels reduction. On Federal multiple-objective 

lands, fuels management projects must meet a variety of 

objectives, including water and air quality standards. How 

does moving vegetation from a higher to lower FRCC 

affect other values? In the fire/fish example, sediment that 

adversely affects fish habitat is portrayed as environmental 

risk on the vertical axis of the tradeoff diagram (Figure 3). 

Other effects such as increased stream temperature from 

reduced shade, or PM2.5 from different smoke regimes, 

could be analyzed similarly.

Line (a) is the initial environmental risk of sediment 

produced by a wildfire burning under uncharacteristic fuel 

Figure 3—Conceptual model for comparing short-term fuel treatment implementation risk with long-term environmental risk reduction.

Source: Redrawn from O’Laughlin (2005a); modified from U.S. Dept. of Energy (2002) 
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conditions (FRCC 3). Point R2 is at the origin of line (a) 

and represents the current risk of postfire sediment; as fuels 

continue to accumulate over time, the postfire sediment 

load on line (a) increases without fuel treatment. In NEPA 

(1969) terminology, line (a) is the postfire effect of the “noaction” alternative. Line (b), a constant at R1, is residual 

environmental risk, which is postfire sediment associated 

with the management target fuel reduction objective (FRCC 

1). When a wildfire occurs at any future time, the environmental risk from the condition represented by line (a) is 

considerably greater than that of line (b). This reflects the 

difference in prefire forest conditions and the severity of 

postfire effects as measured by sediment production from 

the different FRCCs.

The project described by line (c) results in postfire 

environmental risk reduction. Line (c) traces over time the 

effect of implementing a fuels treatment project. Shortly 

after project initiation, the implementation risk of additional 

postfire sediment from logging rises above and exceeds 

that of the initial environmental risk on line (a). At time T1, 

implementation risk is maximized at R3, and then it begins 

to decline. At T2, environmental risk reduction commences 

as the benefit of reduced postfire sediment from the fuel 

treatment project on line (c) drops below the amount of 

postfire sediment on line (a) that would occur without fuel 

treatment. At T3, project benefits continue to increase, but 

implementation risk still exceeds environmental risk reduction (A1 > A2). Over time, environmental risk reduction 

continues, and, at T4, project benefits exceed implementation risk (A1 < A2 + A3). Sediment from the project results 

from management actions to change an ecosystem from the 

condition represented by line (a), or initial environmental 

risk, to that of line (b), or residual environmental risk. 

The decision whether to undertake the management 

project conceptualized in Figure 3 depends on the decisionmaker’s time horizon, the decision rule, and the relationship 

of lines (a), (b), and (c). For this discussion, the contours of 

the lines are similar to those in the source document (U.S. 

Department of Energy 2002) from which the diagram and 

terminology are derived. The lines may be expected to take 

on different configurations for specific forest types, fire 

regime conditions, and sediment production relationships. 

For example, there is no particular reason to expect that line 

(a) would be linear. 

Based on this conceptual diagram, it would be difficult 

to argue that a decision using only information at time T3

or earlier would be more sustainable than a decision using 

information at time T4 or later. Sustainability is about many 

things, but first among them is the consideration of intergenerational equity. Fairness of current decisions for future 

generations of fish or people cannot be determined with a 

short-term outlook.

Quantitative Application

Sediment production can be quantitatively modeled using 

Forest Service Watershed Erosion Prediction Project 

(WEPP) tools. An Internet interface for WEPP Fuel 

Management Erosion (FuME) is capable of providing the 

necessary sediment estimates for the conceptual model 

described in “Conceptual Model.” The sediment prediction 

model estimates fuel treatment sediment and simulates 

postfire precipitation with or without treatment, averaging the outcomes. WEPP FuME also has a road sediment 

feature. The WEPP FuME user interface is currently under 

revision (see USDA-FS 2006b). 

To estimate sediment in Western United States ecoregions from fuels treatment opportunities, researchers used 

WEPP tools to compare effects of thinning and prescribed 

burning to those of wildfire in several different representative forest ecosystems. Based on their results, the relationships in Figure 3 seem reasonable. The average of predicted 

results, on a per unit area affected basis, was that wildfire 

would yield 70 times as much sediment as thinnings 

employed during hazardous fuels reduction efforts (USDAFS 2005). In lieu of empirical data on sediment production 

from different FRCCs on a particular forest site, consider 

a hypothetical example based on this 70:1 relationship. In 

Figure 3, R2 = 70 units of postfire sediment under current 

conditions in a project area. A thinning project would add 

one unit of sediment, i.e., R3 = 71. By visual inspection, the 

target fuel reduction goal [line (b)] to produce the depicted 

relationship would be a very modest 2-percent postfire sediment reduction, i.e., R1 = 68.6. 
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The BlueSky project tools could be used to similarly 

estimate PM2.5 air pollution. BlueSky is a short-term planning tool to aid land managers using fire on the landscape in 

making go/no-go/go-slow smoke management decisions. It 

is a Web-based modeling framework for predicting cumulative impacts of smoke from forest, agricultural, and range 

fires, including prescribed fire and wildfire. By combining 

data and models for fuels, fire, smoke, and weather, BlueSky 

makes emission, dispersion, and weather prediction model 

outputs easily accessible to the operational fire and air quality management communities. It provides hourly predictions 

of PM2.5 concentrations based on information available 

from multiagency tracking systems, wildfire reports, and, 

in some cases, from manually entered burn data (USDA-FS/

EPA 2006).

Uncertainty

Any approach to integrating fire, fuels, and aquatic ecosystem management has inherent risks and uncertainties 

(Bisson and others 2003). It is not safe to ignore uncertainty 

because it may be important to our decisions (Morgan 

and Henrion 1990). However, many events that affect 

ecosystems (e.g., disease outbreaks, fire patterns, weather) 

and human systems (e.g., innovation, changes in preferences, political change) cannot be predicted in advance 

(NRC 2004). Owing in part to uncertainty, and in part to 

inadequacies in risk assessment techniques, risk analyses 

often have failed to meet expectations that they can improve 

decisionmaking (NRC 1996). 

Managing risks on public lands necessitates communicating the results of risk assessment with interested parties. 

Risk characterization is an intermediate step (Figure 1), but 

one of paramount importance in risk analysis (NRC 1996). 

Ultimately, the condition of land and resources is more 

important than the terms used to describe various situations, alternatives, and outcomes. However, to avoid adding 

another source of uncertainty, risk assessors and managers 

should choose their terminology carefully. Ambiguous 

terms like forest health and sustainability are useful to draw 

people into discussions, but when deliberation about management alternatives commences, clarity is more important 

than ambiguity. 

In the fire/fish risk management problem, nuances of 

definitions are less important than the risk management 

question—Which effect on fish is worse: (1) wildfires 

burning uncharacteristically under high fuel load conditions, e.g., FRCC 3; or (2) wildfires following management 

designed to reduce wildfire intensity to a level corresponding with FRCC 1? As discussed earlier, the question can be 

converted during the problem formulation phase of ecological risk assessment to a risk management hypothesis and 

visualized in a diagram (Figure 3). The diagram presumes 

that in fire-adapted forests typical of the Western United 

States, fire is inevitable. If the analytical time horizon is 

far enough in the future, fire is a certainty, and its environmental effects are realized. The magnitude of the effect, 

however, is uncertain and affected by many variables, 

including fuels (Finney 2005).

If risk can be quantified, or at least qualitatively 

ranked, ecosystems under greatest threat can be identified 

and efforts to improve these situations prioritized; few ecological risks, however, can be measured accurately (Lackey 

1994). The probability of wildfire occurrence in fire-adapted 

forests is an exception. Fire is certain to occur within the 

fire return interval period, but we do not know precisely 

when or what the magnitude of the effects will be. Molak 

(1997) cautions that if uncertainty is not clearly spelled out, 

the numbers derived by risk analysis can be misleading. 

Haimes (2004) concludes with a risk analysis paradox: “To 

the extent that risk assessment is precise, it is not real. To 

the extent that risk assessment is real, it is not precise.” 

Summary: Risk Assessment for Managing 

Wildland Fire Effects on Ecosystems

Table 1 lists some ideas risk assessors and risk managers 

should consider when adapting the EPA (1998) framework 

(Figure 1) and Guidelines for Ecological Risk Assessment

for wildland fire risk management. This approach could 

be adapted to fit situations other than the fire/fish risk 

problem, such as PM2.5 emissions in prescribed fire and 

wildfire smoke. The conceptual model diagram (Figure 3) 

can enhance communications between risk managers, risk 

assessors, and stakeholders by graphically demonstrating 

whether the reduction in environmental risk following a 
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GENERAL TECHNICAL REPORT PNW-GTR-802

wildfire, represented by a change in sediment production, 

would exceed the implementation risk of pre-emptive fuel 

treatment. Fire management programs require repeated 

treatments (Franklin and Agee 2003). The model can be 

modified to include a series of fuel treatments to maintain 

desired conditions over the long term.

All the points in Table 1 have been covered in previous sections, except 10 (discounting) and 11 (quantitative 

and qualitative analysis). The caution on discounting is 

self-explanatory. Quantitative models appropriate for a 

given risk management situation should be used along 

with whatever data may be available. Deferring decisions 

until quantitative models and data are available, however, 

Table 1—A risk assessment approach for managing wildland fire effects on ecosystems

1. Keep it simple. Resource managers need project-level decision support models. Start with the essentials and add 

 complexity only as necessary to fit the management situation.

2. Determine desired future forest conditions. The management objective is attaining a specific forest condition that will 

 reduce wildfire risks, expressed clearly in terms the manager can be held accountable for. Many areas in the Western 

 United States have accumulated levels of fuels that represent uncharacteristically hazardous conditions that can be 

 categorized by fire regime condition classes (FRCC) (Hann and others 2003). An appropriate objective would be 

 historical species composition and stand structure prior to the time fire suppression policy was implemented.

3. Select risk assessment endpoints consistent with management objectives. An appropriate endpoint for assessing logging 

 effects is viable salmonid fish populations and appropriate quality of spawning and rearing habitat conditions (EPA 

 1998). The EPA’s guidelines caution against using vague concepts like “sustainability” and “integrity.” 

4. Develop a stressor-response profile. One effect of either wildfire or management on population viability is additional 

 sediment, a stressor from either logging management options or wildfire disturbance events. The response is an effect 

 on fish biomass (Figure 2). A similar approach could be taken with reduced riparian vegetation and shade effects on 

 stream temperature, or vegetation change and wildlife habitat effects, or fine particulate matter (PM2.5) from pre-

 scribed fire and wildfire smoke.

5. Use an appropriately long time horizon to compare postwildfire sediment production with and without fuel treatment. If 

 the fire/fish risk management situation involves imperiled species, anything less than 100 years is inappropriate (NRC 

 1995, Rieman and others 2003a).

6. Wildfire is certain to occur in fire-adapted forest ecosystems. This can be assured in risk analysis by selecting an 

 appropriate long-term time horizon, such as the fire return interval of natural (historical) or characteristic fire 

 regimes. This deterministic approach avoids the difficulty of assessing and communicating a probability distribution 

 of fire risk potential, but probabilistic refinements such as the relationship of precipitation and sediment production 

 could be used to add stochastic elements if appropriate data are available.

7. Compare the magnitude of adverse environmental effects (e.g., sediment production or PM2.5 emissions) on aquatic 

 habitat quality from wildfire with and without fuel treatment.

8. Include the benefits of fuel treatment in the analysis. Fire regime condition class (FRCC) categorizations are useful for 

 this. The effects of wildfire with fuel treatment (e.g., restoring to FRCC 1 or historical fuel load levels) should reflect 

 the reduction of adverse postfire environmental effects, such as sediment or PM2.5, that can be attributed to fuel treat-

 ment. The effect of wildfire without fuel treatment is the no-action alternative represented by current FRCCs 2 or 3. 

 The benefit is obtained by management actions that change forests from a higher to a lower FRCC. 

9. Focus on the benefits of preemptive or prefire management of forests instead of trying to determine safe or accept-

 able levels of risk. The problem of determining the level of risk society is willing to accept is avoided 

 altogether, and analysis focuses on comparing two options, one against the other, rather than against a nonexistent or 

 elusive, value-laden, socially determined standard of acceptable risk.

10. Avoid the difficulties involved in discounting future ecological effects to the present time by not discounting (Davies 

 1996). If economic or social considerations are added, discounting may be appropriate. To reduce bias against future 

 generations, use a very low discount rate (Solow 1994). 

11. Use quantitative data when they exist. Qualitative assessments and comparisons of ecological risks can provide 

 useful insights for environmental decisionmaking, even if the scientific understanding of them is poor (NRC 1996).

12. Display relationships in a conceptual model decision diagram, e.g., Figure 3 that compares adverse and beneficial 

 effects over time.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

may create additional risk from inaction, and qualitative 

approaches may be necessary.

Risk management decisions rely on a mix of science 

and policy; the most important role for science is providing 

information to be used in environmental decisionmaking 

(Power and McCarty 1997). Ecological risk assessment 

parameters can be represented quantitatively with existing 

data or qualitatively with expert opinion. Scientific quantification exists to aid judgment, not to supplant decisions 

(Clark 2002). Qualitative assessments of relative ecological 

risks can provide useful insights for environmental decisionmaking (NRC 1996). None of the scientific difficulties 

of estimation negate the importance for policy decisions of 

considering ecological outcomes. Interested and affected 

parties may want to take account of ecological effects even 

if the scientific understanding of them is poor (NRC 1996), 

as in the fire/fish risk management problem.

In conclusion, simple conceptual models used in 

decision analysis frameworks can be powerful communication tools (EPA 1998). The tradeoff diagram in Figure 

3 is capable of demonstrating to the public, regulatory 

agencies, and the courts the long-term net benefits of active 

forest management designed to modify fire behavior. The 

transparency and clarity of such models can help people 

think through the questions of if, where, and when hazardous fuels reduction projects should be undertaken. Further 

development and use of conceptual models may help guide 

us along the path to sustainable resource management.

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

Jeffrey P. Prestemon and David T. Butry

Jeffrey P. Prestemon, research forester, Forestry Sciences 

Laboratory, USDA Forest Service, Southern Research 

Station, Research Triangle Park, NC 27709; and David 

T. Butry, economist, National Institute of Standards and 

Technology, Gaithersburg, MD 20899-8603 (formerly with 

the USDA Forest Service, Southern Research Station).

Abstract

Wildland arson makes up the majority of fire starts in some 

parts of the United States and is the second leading cause 

of fires on Eastern United States Federal forests. Individual 

arson fires can cause damages to resources and communities totaling over a hundred million dollars. Recent research 

has uncovered the temporal and spatial patterns of arson 

fires and their long- and short-term drivers. In statistical 

analyses, explanatory variables include those associated with 

general economic conditions and law enforcement. Research 

findings indicate that wildland arson ignitions are consistent 

with other kinds of crimes, in terms of their relationships 

to hypothesized factors. Arson is predictable in short and 

long timespans, as its rate is heavily influenced by weather, 

climate, fuels, and recent information on other nearby and 

recent arson fires. These results could be used to enhance the 

effectiveness of law enforcement and wildfire management 

resources. 

Keywords: Arson, autocorrelation, crime, spatiotemporal, wildfire prevention.

Introduction

Over 0.5 million fires are set by arsonists each year in the 

United States, resulting in over $3 billion in damages (TriData Corporation 1997). Arson is a leading cause of wildfire 

in several heavily populated States, including California and 

Florida. Furthermore, arson fires are concentrated in urban 

interface areas (Butry and others 2002), where values at risk 

are likely to be high. Several recent large wildfires were 

intentionally set, including the Hayman Fire near Denver in 

2002, which caused damages exceeding $100 million (Kent 

and others 2003). In the Eastern United States, wildland fire 

is primarily a human-initiated phenomenon. Data provided 

by Schmidt and others (2002) show that for 92 percent of the 

area burned 1986-1996 in 18 Eastern U.S. States, the causes 

were attributed to human-related ignition sources. During 

the same period, 74 percent of area burned on all Eastern 

Federal forests was from human-related ignition sources (18 

percent by arsonists). Wildland arson ignitions in Florida 

compose a quarter of all fire starts. Arson fires are set for 

a variety of reasons, but a primary feature of these fires is 

that they are ignited close to high values at risk, and, hence, 

also threaten human safety. In spite of the potentially large 

economic losses associated with such events, wildland arson 

has received scant attention in the refereed literature, with 

some exceptions (e.g., Donoghue and Main 1985, Prestemon 

and Butry 2005). The vast majority of research into wildland fire management and policy in the United States has 

been concerned with wildfire suppression, fuel treatments, 

fire physics, and overall economic efficiency questions.

Worldwide, wildland fire setting has been a common 

practice of rural residents for centuries (Gamst 1974), and at 

least in the 20th century in parts of the United States South 

(Doolittle and Lightsey 1979, Kuhlken 1999). In early parts 

of the European settlement of America, fires were often set 

intentionally, for prescriptive purposes. Fires were set to 

shape vegetation communities, enhance forage for grazing animals, reduce pests, and clear land for agriculture 

(Doolittle and Lightsey 1979). However, some fires were set 

in the same context as many are today—for revenge against 

a landowner, as an act of protest, as an attempt to cover up 

another crime, or as vandalism. It is unclear whether the 

kind of relatively innocuous, managerial-type fire setting 

persists today in the United States. However, as we explain 

in this article, current wildland arson ignition patterns 

appear to closely align with certain behavioral patterns 

found with other criminal activities. 

Long-term studies of intentionally set fires are nonexistent. Instead, the analyst has to resort to statistical 

tests based on short time series to establish the validity of 

proposed theories. Here, we attempt to provide an overview 

of what is known about wildland arson today, as it has 

manifested itself since 1970, based on such research. We 

Wildland Arson: A Research Assessment
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GENERAL TECHNICAL REPORT PNW-GTR-802

focus on wildland arson within the context of crime, generally describing how it follows patterns similar to violent and 

nonviolent crimes. In this, we exclude consideration of fires 

ignited by children, which we consider accidental (although 

this carries with it several assumptions that we leave for 

other analysts for the moment).

Wildland Arson Background

Spatial and Temporal Scope of the Wildland 

Arson Problem at the National and State Levels in 

the United States

Wildland arson fires on national forests have exhibited conflicting trends. Arson ignitions on national forests clearly 

trended downward from the mid-1980s to 2004 (Figure 

1), but no identifiable trend exists on the area burned by 

wildland arson (Figure 2). The ignition share of arson (proportion of all ignitions attributed to arson) appears to be less 

correlated with the ignition shares of lightning and other 

fire sources than those sources are to each other (Figure 

3). There is, however, no research that provides statistical 

evidence that the apparent decline in arson ignitions has 

led to an overall decline in wildfire activity. Prestemon and 

others (2002) showed how wildfire area burned in Florida 

by wildland arson responds to the same kinds of factors as 

do fires started by other means. But Mercer and Prestemon 

(2005) indicate that wildfire ignitions by arsonists respond 

differently to influential factors—especially socioeconomic 

variables—compared to other fire causes. The overall 

decline in the trend of arson fire ignitions and nontrending 

area burned, however, translates into a relative decline in 

arson area burned compared to other fire sources (Figure 

4), as total area burned by all causes has apparently trended 

upward.

Information from the State of Florida, which includes 

fires on both public and private lands, also demonstrates 

that arson appears to be on a negative time trend. Prestemon 

and Butry (2005) suggested that rising policing levels, 

rising wage rates, and falling poverty rates explain some 

of the negative trend. However, more research is needed to 

establish the general validity of such findings.

Wildland Arson in the Context of Crime

Crime and Criminology

Wildland arson is classified as one subset of arson, a serious 

crime that is tracked by State and national authorities. Icove 

and Estepp (1987) reported that wildland arson is the third 

most common type of arson behind arson in residential and 

educational structures. These crime categories are each 

summarized at local, State, and national levels into an index 

of the number of crimes of each type per 100,000 individuals in the reporting location. Indices reported across 

States (and smaller geographical and political jurisdictions) 

include the violent crimes of murder, rape, robbery, and 

aggravated assault and the property crimes or nonviolent 

crimes of burglary, larceny, motor vehicle theft, and arson. 

Historically, arson has not been consistently tracked across 

States, so a long-term nationwide picture of arson is not 

available. However, a nationwide picture is available for all 

index crimes besides arson. 

National Crime Trends

Between 1972 and 2004, nationwide index crimes have 

undergone a rise and then a fall (Figure 5). Crime trends 

have been used to empirically test economic theories of 

crime and to uncover the primary drivers of the observed, 

aggregate time trends, and the reasons for differences in 

rates across space (e.g., Burdett and others 2003; Corman 

and Mocan 2000; Gould and others 2002; Grogger 1995, 

1998). Beginning from foundational work on the economics 

of crime by Becker (1968), research has shown that aggregate crime rates follow socioeconomic and law enforcement 

variables, which are linked to the opportunity costs experienced by criminals. Thus, crime-rate fluctuations are related 

to changing wage rates, unemployment rates, intensities 

of law enforcement, length of prison sentences, and the 

proportion of the population incarcerated. 

Criminology Theories

Many theories have been used to explain levels of crime 

and their variations, and an extensive review of these is not 

possible here. Instead, we mention Becker’s (1968) crime 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 1—Wildland arson and other wildland fire ignitions on all national forests, 1970-2004. (Source: USDA 

Forest Service 2007)

Figure 2—Wildland arson and other wildland fire areas burned on national forests, 1970-2004. (Source: USDA 

Forest Service 2007)
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 3—Share of wildland arson, lightning, and other wildfire ignitions on national forests, 1970-2004. 

(Source: USDA Forest Service 2007)

Figure 4—Share of wildfire area burned attributed to arson on national forests, 1970-2004. (Source: USDA 

Forest Service 2007)
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

function and what Cohen and Felson (1979) labeled “Routine Activity Theory.” Becker’s seminal work conceptualized the decision to commit a crime as:

(1) Oi = Oi (πi , fi , ui )￼

where Oi

 is the number of offenses committed, πi is the 

probability of being caught and convicted, f

i

 is the wealth 

loss experienced by the criminal if caught and convicted, 

and ui measures other factors influencing the decision to 

attempt and success in completion of the crime. From this, 

Becker (1968) expressed crime commission as generating 

positive utility (welfare), through either wealth generation or 

some enhancement of psychic pleasure for the perpetrator. 

Think of this as, for example, the expected utility generated 

from successfully igniting an arson wildfire:

(2)     E[Ui (Oi)] = πi ,Ui(gi – ci – f

i (Wi

, wi )) + (1 – πi)Ui (gi – ci)

where E is a mathematical expectations operator, Ui is the 

criminal’s utility function, gi is the criminal’s psychic and 

income benefits from the illegal activity (e.g., fire setting), 

ci is the production cost for the activity, f

i

(Wi,wi) is the loss 

from being caught and convicted of the crime (a positive 

function of income while employed), Wi

 is the employment 

status, and wi is the wage rate. This theory has been tested 

by economists and criminologists in analyses of many kinds 

of crimes, and it has generally held up well. Prestemon and 

Butry (2005) were the first to successfully test the theory in 

the case of wildland arson in Florida.

In routine activity theory, crime is committed when a 

set of necessary conditions coincide: an offender, a target, 

and a lack of capable guardians (e.g., police or neighborhood 

watch groups). The routine activities approach explains that 

crime varies across space and over time according to how 

everyday human activities vary in response to seasonal or 

economic differences across space and time. Variations in 

the crime rate across space and time in the United States, 

then, can be explained by variations in the convergence 

or the availability of all necessary ingredients. It would 

be possible to express wildland arson as deriving from 

routine activity theory by recognizing that the “target” is 

wildland (or property owned by a target); it would have to 

Figure 5—Nationwide index crimes (left-hand scale) and a national forest arson ignition index (right-hand scale), 

1972-2004. (Sources: Federal Bureau of Investigation 2005, USDA Forest Service 2007)
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GENERAL TECHNICAL REPORT PNW-GTR-802

be augmented, however, to accommodate weather and fuel 

conditions.

A challenge of all theories of crime is applying the 

results of the research to decisionmaking. One way that 

criminologists have done this is by developing computerbased tools that are derived from mathematical models of 

criminal activity. The mathematical models are loosely 

based on statistical representations of routine activity 

theory and the economics of crime in the context of Becker 

(1968). These models can identify crime hotspots, or crime 

intensity maps, in space and time. Hotspotting crime 

research is concerned with developing mapping tools for 

law enforcement and other authorities that can be used to 

identify crime hotspots and therefore aid in crime control 

(Bowers and Johnson 2004, Johnson and Bowers 2004). 

The idea of crime hotspots in space and time is not new. 

Lottier (1938) pointed out how crime is concentrated in 

locations across space, which is useful for targeting law 

enforcement. Boggs (1966) evaluated urban crime patterns 

and how they tend to be concentrated in space-time dimensions. It seems likely that law enforcement has recognized 

this kind of clustering for as long as crime has existed. For 

wildland arson, few efforts have been made to understand 

the spatial and temporal extent of clustering. However, 

Butry and Prestemon (2005) developed a preliminary 

statistical tool for hotspotting of wildland arson, specified at 

the census tract level and on daily arson ignitions.

Spatio-Temporal Crime Processes—

Crime as a Spatio-Temporal Process

Butry and Prestemon (2005) and Prestemon and Butry 

(2005) found that wildland arson is a crime that may be 

particularly suited to hotspotting. Wildland arson, like other 

crimes, is concentrated in space and in time, due to concentrations of criminals and fuel quantities in space and the 

concentration of amenable fire-setting weather and dry fuel 

in time. Genton and others (2006) showed how this clustering in space can last many years, whereas Prestemon and 

Butry (2005) showed concentrations at the daily time scale, 

and Butry and Prestemon (2005) showed concentrations at 

the daily time scale in relatively limited geographical areas.

Wildland Arson as a Spatio-Temporal Process

Prestemon and Butry (2005) found that, for small county 

aggregates in Florida, wildland arson is concentrated in 

time, with an elevated risk for more such ignitions for up 

to 11 days after an initial ignition. This kind of temporal 

clustering is consistent with models of serial and copycat 

criminal activity, patterns, and behaviors observed for 

other crimes (Brandt and Williams 2001, DiTella and 

Schargrodsky 2004, Surrette 2002). Also important are 

weather, as measured by the Keetch-Byram Drought Index, 

which indexes fuel conditions on fine temporal scales and 

captures the effect of fuel moisture on ignition success rates 

or the cost of igniting a wildfire; historical wildfires and 

prescribed fire in the location, which reduce fuels and usually reduce arson risk by making fire setting more difficult 

or costly; and intra-annual patterns of weather, as measured 

by month dummy variables, which are probably also related 

to fuel conditions and therefore fire setting cost or success. 

Other explanatory variables, which fit an economic model 

of crime, include police per capita, the retail wage rate, and 

poverty. Daily variations were also found to matter, with 

arson more common on Saturdays and sometimes holidays.

Butry and Prestemon (2005) evaluated wildland arson 

patterns in Florida, where ignitions were geolocated to the 

census tract. This analysis related wildland arson ignitions 

in a single day to, in addition to the same set of variables 

used in Prestemon and Butry (2005) at the county level, 

wildland arson ignitions in previous days in the same and 

neighboring census tracts in six high-arson tracts in the 

State. Their analysis found that wildland arson ignitions 

in the census tract are related positively to ignitions in 

the same tract in the previous several days and in local 

(immediately surrounding) and regional (an outer shell of) 

neighboring tracts in the previous 11 days. They found that 

a current day’s count of ignitions could be explained by 

local neighbors for up to 11 days, regional neighbors for 

up to 4 days, and ignitions in the same tract for up to 10 

days. Thus, it appears that short-term arson ignition process 

propagates like a contagion (although the exact pattern in 

space-time was not identifiable).

In research similar to that reported by Prestemon and 

Butry (2005), we report here an analysis of arson ignitions 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

on national forests in California (Figure 6). We estimate 

daily and annual models of arson fires at the national forest 

level. Models use wildfire data from the USDA Forest 

Service National Interagency Fire Management Integrated 

Database (NIFMID) (USDA Forest Service 2004), unemployment and population data from the California Department of Finance (2005), law enforcement data from the 

California Department of Justice (2005), and climate and 

weather data from the National Oceanic and Atmospheric 

Administration (2005) and the National Climatic Data 

Center (2004). The models are generally specified as in 

Prestemon and Butry (2005), capturing temporal autocorrelation and not directly quantifying spatio-temporal patterns, 

but omit information on wages and poverty. Also, unlike in 

Prestemon and Butry (2005), the number of sworn full-time 

equivalent police officers, a measure of law enforcement 

effort, is lagged one year to avoid issues of simultaneous 

determination with crime and to allow for lagged perceptions among potential arsonists on arrest probability (Lochner 2007). As in Prestemon and Butry (2005), a Poisson 

autoregressive model of order p (Brandt and Williams 2001) 

is specified for the daily ignitions for the San Bernardino, 

Sierra, Cleveland, and Angeles National Forests, 1994-2002 

(Table 1). An annual panel fixed-effects Poisson model 

(Greene 2003) is specified using data from all 18 national 

forests in the State, 1993-2002 (Table 2).

In the daily model, significant variables (at five percent) 

influencing arson ignitions on the two national forests 

studied include up to five ignition lags (positively); alternative models that drop these lags (i.e., non-autoregressive 

Figure 6—National forests and wildland arson on national forests in California, 1970-2003.
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GENERAL TECHNICAL REPORT PNW-GTR-802

alternative versions) explain significantly less variation 

in daily wildfire at high significance (last row of Table 1). 

This result is consistent with serial or copycat fire setting 

activity. The model also found that many month dummy 

variables are significant, which indicates that arson, like 

other fire causes, has a seasonal pattern that may be related 

to seasonal weather and fuel moisture variations that 

affect the difficulty or costs of successfully igniting arson 

fires. Also, for these national forests, the coefficient on the 

Saturday dummy is significant and positive in two cases 

(Sierra N.F. and Cleveland N.F.) and the coefficient on the 

holiday dummy variable is significant and positive in one 

Table 1—Poisson autoregressive models of order p of daily wildland arson fire 

occurrences for selected high-arson California National Forests, 1994-2002

San Bernardino Sierra Cleveland Angeles

N.F. N.F. N.F. N.F.

Intercept -1.49 26.97 -1.73 34.65***

Average annual Palmer drought 0.93 3.11 -0.65 0.24

 severity index, t

Weekday -0.66*** -0.35 0.16 -0.12

Saturday -0.74** 1.22** 0.66* 0.40

Sunday -0.47 -0.54 0.76** 0.15

Holiday -0.36 1.57** 0.35 0.17

January -0.20 n.p. -0.73 0.37

February 1.07 n.p. -1.18 0.87

March -0.72 1.97 -0.36 0.20

April -0.62 1.62 0.22 1.15**

May 1.46* 0.55 0.66 1.55***

June 1.72** 0.96 1.22* 1.57***

July 1.62* 1.72 1.32* 2.04***

August 1.96** 0.96 1.36** 2.16***

September 2.02** -0.25 1.72*** 1.75***

October 1.84** -0.30 0.87 1.37**

November 0.82 1.26 0.46 0.81

Law Enforcement Officers Per -1.90 13.67 * -17.09* -1.75

 Capita, t-1

Population, t n.p. -0.088 5.6549 -0.0028*

Unemployment, t 4.76* -6.37** 10.39*** -3.71*

Wildfire, t-1 to t-3 0.02 -1.12 -0.37** -0.08*

Wildfire, t-4 to t-6 0.01 0.59 -0.14 -0.11

Wildfire, t-6 to t-9 0.06 0.77 0.03 -1.02**

Wildfire, t-10 to t-12 -0.23 0.06 0.02 -1.09**

Wildfire, t-13 to t-15 -0.05 0.75 -0.27** -0.04

AR(1) 0.29*** 0.49** 0.37*** 0.26***

AR(2) 0.38*** n.p. 0.60*** 0.16***

AR(3) 0.24** n.p. n.p. 0.17***

AR(4) n.p. n.p. n.p. 0.21***

AR(5) n.p. n.p. n.p. 0.16***

n 3,089 3,090 3,091 3,103

Likelihood ratio statistic, model 

 all AR terms are zero (df = p) 38.37*** 7.47*** 70.77*** 142.32***

Log likelihood, model -651.60 -257.29 -566.56 -896.62

Note: ***indicates significance at 1 percent, **at 5 percent, and *at 15 percent; “n.p.” indicates that models with this 

variable included would not converge in maximum likelihood estimation.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

case (Sierra N.F.), both results indicating higher arson fire 

probability, which, in the context of an economic model of 

crime, is consistent with lower opportunity costs of time 

faced by arsonists those days. In contrast, dummy variables 

for weekdays are not significant, indicating that those days 

of the week have the same arson probabilities as Mondays. 

We also find that the average annual Palmer Drought 

Severity Index is not significantly related to fires. The 

unemployment rate is negatively related to arson ignitions 

in two cases (Sierra N.F. and Angeles N.F.), which conflicts with our expectation, but positive in two cases (San 

Bernardino N.F. and Cleveland N.F.), which does fit with 

our expectation. A similar conflicting result occurs for law 

enforcement: higher law enforcement levels are significantly and negatively related to arson rates in one case (the 

Cleveland N.F.) but are positively related to arson in another 

(Sierra N.F.). The latter finding is not expected and could be 

a consequence of omitted variables related to wages or other 

justice-related expenditures (e.g., sanction levels). But, the 

former finding for the Sierra N.F. would be expected based 

on the opportunity costs of being caught and convicted of 

setting an arson wildfire. Finally, lagged wildfire area (the 

running total of the area burned by wildfire in the national 

forest in previous years) is negatively related to arson 

wildfire in all cases where statistical significance is found. 

This is consistent with our expectations, as wildfires can 

reduce landscape fuels, providing the prospective arsonists 

with greater difficulty or higher costs of successful arson 

fire setting. In summary, the majority of these findings are 

consistent with those of Prestemon and Butry (2005), 

Table 2—Fixed-effects panel Poisson model of wildland arson fire occurrences, 

18 California National Forests, 1993–2002

Variable Coefficient Standard Error

Population, t -7.89E-07 3.11E-07**

Unemployment percent, t 4.81E-02 3.05E-02*

Per capita sworn law enforcement officers, t-1 -655 195***

Pacific decadal oscillation, t 0.50 0.09***

Palmer drought severity index, January-March, t -0.21 0.05***

Palmer drought severity index, April-June, t 0.13 0.04***

Palmer drought severity index, July-September, t 9.1E-02 2.8E-02***

Palmer drought severity index, October-December, t -9.8E-02 2.5E-02***

Nino-3 SST anomaly, t -0.43 0.07***

Wildfire area, t-1 -2.14E-06 1.35E-06*

Wildfire area, t-2 4.78E-06 1.10E-06***

Wildfire area, t-3 3.47E-06 1.27E-06***

Wildfire area, t-4 -7.21E-06 2.37E-06***

Wildfire area, t-5 -1.03E-06 2.44E-06

Wildfire area, t-6 -2.07E-06 1.49E-06

Wildfire area, t-7 6.16E-07 1.58E-06

Wildfire area, t-8 -3.57E-06 1.66E-06**

Wildfire area, t-9 -9.96E-07 1.46E-06

Wildfire area, t-10 -2.44E-06 1.66E-06*

Wildfire area, t-11 2.58E-06 1.23E-06**

Wildfire area, t-12 4.72E-06 1.42E-06***

Observations 180

Number of cross-sections (18 national forests) 8

Number of periods (10 years, 1993-2002) 10

Log-likelihood model -521.96

Likelihood ratio statistic, model versus intercept 1,559.56***

 only (21 d.f.)

Note: ***indicates significance at 1 percent, **at 5 percent, and *at 15 percent. 
280

GENERAL TECHNICAL REPORT PNW-GTR-802

supporting an economic model of crime and one that 

confirms the temporal clustering of arson ignitions on these 

forests. However, some conflicting results lead us to conclude that further research into the daily arson fire setting 

process for California National Forests is necessary.

In contrast with our uncertainty about the role of law 

enforcement and socioeconomic factors in wildland arson, 

the annual models provide results consistent with expectations (Table 2). Here, law enforcement officers per capita are 

significantly and negatively related and unemployment is 

significant and positively related to wildland arson ignitions. 

The finding of the deterrent (negative) effect of policing 

on wildland arson is entirely consistent with crime theory, 

indicating that either (i) arsonists perceive higher opportunity costs of being caught, or (ii) more arsonists are caught 

and convicted and, hence, are removed from the arson fire 

setting population. 

Biophysical variables also explain significant variation 

in annual levels of wildland arson ignitions on national 

forests: lagged wildfire (positively for 2-, 3-, 11-, and 

12-year lags and negatively for 1-, 4-, 8-, and 10-year lags); 

the Pacific decadal oscillation (positively); the Palmer 

Drought Severity Index (positively for 2- and 3-quarter lags 

and negatively for 1- and 4-quarter lags); and the Niño-3 

SST anomaly (negatively) (Table 2). Population is negatively 

related to wildland arson risk, a finding that we cannot fully 

explain, except in the context of omitted variable bias. Nor 

can we explain the unusual statistical correlations between 

arson ignitions and longer lags of wildfire activity, so we 

leave this to future research. 

What these results for the California National Forests 

show is broad consistency with the results found for Florida: 

wildland arson ignitions are clustered in time (5 days in two 

high-arson national forests in California, up to 11 days in 

Florida); law enforcement is generally negatively related to 

arson rates; climate and weather variables matter, in a more 

complex intra-annual pattern in California than in Florida; 

and fuel levels matter, although in a more complicated way 

in California than in Florida. Left uninvestigated are the 

influences of other labor market variables, poverty, and 

other measures of criminal sanctions. 

Summary and Conclusions

Law Enforcement Lessons and Programs

Beginning with initial studies by Donoghue and Main 

(1985) through studies by the authors reported here, it seems 

clear that law enforcement deployment and other efforts 

to apprehend and incarcerate arsonists work to reduce 

wildland arson in the long run in high-arson locations in 

the United States. As found by Prestemon and Butry (2005) 

and Butry and Prestemon (2005), wildland arson appears to 

be clustered in time and space. Law enforcement personnel 

could use these results to advance hotspotting models for 

wildland arson or develop tactical responses to reducing 

the number of such ignitions in such outbreaks or both. 

Although it may not be clear that reducing wildland arson 

ignitions results in large-scale and long-run reductions in 

the amount of area burned on an annual basis, reducing 

such ignitions could have significant benefits for society, 

especially in places where arsonists tend to set fires: closer 

to built-up areas with greater values at risk (Butry and 

others 2002, Genton and others 2006). The results of more 

recent research indicate that it might be worthwhile to 

redirect law enforcement efforts to certain locations during 

periods of weak labor markets and even higher poverty 

rates. In this case, however, we caution that careful analysis 

is needed that would quantify the tradeoffs of redirection 

away from other policing activities. During certain months 

of the year and also during droughts, arsonists are more 

active, so law enforcement could also pay special attention to weather and fire season variations. As well, from a 

strategic standpoint, authorities could also monitor trends 

in climate variables or their predictions (Ji and others 1998) 

as indicators of broad trends in climatic factors that create 

conditions favorable for fire setting. Special attention to 

weather and climate is important, as conditions favorable 

to ignitions may also favor large and intense fires once they 

are successfully ignited. 

Wildland Manager Lessons

Wildland managers can use the same lessons as indicated 

for law enforcement. There is a distinct degree of seasonality in arson wildfire ignitions, and firefighting resource 

allocations can conform to this seasonality. Arson ignitions 
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are correlated with dry weather in ways similar to other 

ignition types, so wildland managers should be ready for 

these kinds of fires, even in times when fires are banned 

(perhaps reducing accidental ignitions) or when lightning 

storms are not occurring. In time scales longer than days 

or weeks, managers can also pay attention to forecasts of 

ocean temperatures (e.g., Ji and others 1998), which might 

foretell upcoming drought situations that would raise future 

arson risks and therefore plan firefighting resource allocations accordingly. Finally, wildland managers might be able 

to reduce landscape fuel levels in ways that reduce wildland 

arson opportunities. 

Future Research and Development Needs 

Modern studies of the spatial and temporal patterns of wildland arson are few, and much remains to be investigated. In 

our view, key needs include understanding how wildland 

arson fits within the larger picture of structural arson and 

other crime. The research results reported in this paper are 

only suggestive of similarities between wildland arson and 

other crimes, but real connections have not been identified 

and quantified. Many criminals commit multiple crimes of 

different types, and it is possible that wildland arsonists do 

the same. Hence, it may be possible to include information 

on structural arson or other crimes to improve the accuracy 

of statistical models of wildland arson. 

Another need is to develop hotspotting models of 

wildland arson that would be applicable in different locations and useful for wildland managers and law enforcement. Before operational models are developed, more work 

needs to be done to understand the spatio-temporal patterns 

of wildland arson. To date, these patterns fit with spatiotemporal crime functions found by other criminologists and 

modelers. A key challenge in development of hotspotting 

tools is operational usefulness. The hotspotting tools envisioned may require real-time data updating that may not be 

possible for law enforcement agencies or land management 

organizations with tight budgets. However, if such tools 

bring long-term savings for fire and police organizations 

through reduced firefighting and fire investigation activities 

and lower property losses, investments would lead to net 

societal and agency budgetary gains.

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

D.A. Weinstein and P.B. Woodbury

D.A. Weinstein, senior research associate, Department of 

Natural Resources, Cornell University, Ithaca, NY 14853; 

and P.B. Woodbury, senior research associate, Department 

of Soil and Agriculture, Cornell University, Ithaca, NY 

14853. 

Abstract

We describe methodologies currently in use or those under 

development containing features for estimating fire occurrence risk assessment. We describe two major categories 

of fire risk assessment tools: those that predict fire under 

current conditions, assuming that vegetation, climate, and 

the interactions between them and fire remain relatively 

similar to their condition during recent history, and those 

that anticipate changes in fire risk as climate and vegetation 

communities change through time. Three types of models 

have proven useful for predicting fire under current conditions: (1) biophysical models that predict fire from vegetation type, fuel load, and climate; (2) statistical models; and 

(3) fire behavior models. Programs such as LANDFIRE 

have great promise for using biophysical properties to 

estimate risk. Statistical models that use historical data 

to predict fire probabilities if landscape-fire relationships 

continue to remain relatively unchanged, are gaining interest as more data become available. Fire behavior models are 

producing accurate predictions of the ways individual fires 

will move across the landscape. For longer periods, fire risk 

needs to be evaluated by models that predict the ways vegetation communities will change over time because these 

changes will alter fire probabilities. We identified models 

capable of being used to track changes in vegetation and the 

resulting effect on changes in fire frequency. Risk systems 

need to be designed to track changes in fire susceptibility as 

the climate changes, using models such as MAPSS. 

Prediction of fire occurrence is just the first part of a 

complete analysis of risks associated with fire. Fire occurrence risk needs to be combined with models that determine 

the risk of the effects of fire. Models that predict mortality, 

fuel consumption, smoke production, and soil heating 

caused by prescribed fire or wildfire should be used, as 

well as those capable of evaluating second order effects, 

such as changes in site productivity, animal use, insects, 

and disease. Fire must be looked at in the context of other 

stresses, such as invasive insects and pathogens, encroaching urbanization, and loss of critical habitat. There are 

interactions among stresses that play a role in affecting the 

frequency and intensity of fire, and fire, in turn, can affect 

the probability of those stresses. Consequently, risk evaluation systems need to be created that can simultaneously 

estimate the probability of other major stresses influencing 

ecosystem development. 

Keywords: Fire prediction, fire susceptibility, modeling, risk assessment, wildfire.

Introduction

Methodologies are described here that may be useful for 

estimating fire occurrence risk assessment, including the 

probability of ignition and the spatial spread and intensity 

of the fire during its lifetime. Two types of risk need to be 

assessed: (1) fire risk occurrence (hereafter referred to as 

fire risk), and (2) risk to the ecosystem as a result of fire 

(hereafter ecosystem risk) (Finney 2005). For our purposes, fire risk includes the probability of ignition and the 

spatial spread and intensity of the fire during its lifetime. 

Ecosystem risk includes all of the consequences to plant 

and animal populations and to the soil during the recovery 

period once the fire has concluded. There is significant 

understanding of the factors that influence fire risk, with 

many studies analyzing the long-term consequences to 

ecosystems (Fairbrother and Turnley 2005). Tools to predict 

the likely distributional frequency of fire risk under combinations of various conditions are the focus of this review. 

The two major categories of fire risk assessment tools 

are (1) those that predict fire under current conditions, 

assuming that vegetation, climate, and the interactions 

among them and fire remain relatively similar to their 

condition during recent history; and (2) those that anticipate 

changes in fire risk as climate and vegetation communities 

change through time. 

Review of Methods for Developing Probabilistic Risk 

Assessments. Part 1: Modeling Fire
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Fire Risk under Current Conditions

Many models are available to evaluate fire risk under 

current conditions, although the majority of these were 

designed for analysis in specific locations or under specific 

sets of conditions. One of the initial attempts to implement 

a more generic assessment of fire risk was the National Fire 

Danger Rating System (Burgan 1988, Deeming and others 

1972). This system relied on expert judgment to evaluate 

the risk from a set of explanatory variables, principally 

fuels, topography, and weather. It allowed land managers to 

estimate fire danger for today or tomorrow for a given rating 

area. It characterized fire danger by evaluating the approximate probable upper limit of fire behavior in a fire danger 

rating area during a 24-hour period. A relative rating of the 

potential growth and behavior of any wildfire was based on 

a loose correlation between the date of fire discovery and 

the eventual size of a fire. Attempts to improve upon the 

National Fire Danger Rating System fall into one of three 

categories:

1. Biophysical models that predict fire from 

vegetation type, fuel load, and climate.

2. Statistical models that rely on relationships extracted from historical data.

3. Fire behavior models that emphasize the role 

of spatial distributions of wind, topography, 

and fuel in determining what portions of a 

landscape will burn.

As an example, the first and third of these can be combined in a fire risk calculation (shown in Figure 1) in which 

fire is predicted from the biophysical influences on fire and 

from the spread of fire once it gets established. 

Figure 1—Calculation of fire risk. There are three aspects to predicting fire. Fire is predicted from (1) the probability of 

ignition; (2) the biophysical influences on fire, such as fuel load, moisture content, flammability of the vegetation, and 

topography; and (3) the spread of fire once it gets established. Effects of fire can then be calculated to form a complete 

estimation of the risks related to fire.
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Biophysical models combine local weather patterns 

(temperature, humidity, wind), vegetation (fuel type, 

moisture level), and topography (elevation, slope) to arrive 

at an estimate of fire risk. Biophysical models range from 

deterministic models in which a given set of input variables will always yield the same prediction, to systems 

that include simulation. Some of these models combine 

quantitative and qualitative criteria to arrive at a fire danger 

index. Biophysical models designed to estimate fire risk 

across large regions tend to base their predictions on fewer 

variables because the number of variables that have been 

quantified across large regions is limited. 

Statistical models use regression models developed 

from historical data to estimate probabilities of fire occurrence under various local environmental conditions. 

Statistical models are valuable for understanding general 

historical trends. They are used to provide predictions for 

such factors as the expected number of fires in an area from 

explanatory variables such as vegetation patterns, fuel moisture conditions, meteorological variables, number of people 

visiting a forest, and past history of fires. We describe these 

types of models only briefly because there are currently 

few published examples of statistical fire models available. 

However, the increasing availability of large historical data 

sets on fire frequency will undoubtedly cause a great expansion of studies using this method in the near future.

Fire behavior models attempt to characterize the 

propagation and spread of fires under various environmental conditions. Fire behavior probabilities are dependent 

on ignitions occurring off-site and the fuels, topography, 

weather, and relative fire direction allowing each fire to 

reach that location. Because these models require input 

variables that may not be available over large regions such 

as entire national forests, they tend to be applied to specific 

watersheds in which these properties have been mapped or 

quantified. Because there are many possible interactions 

of weather with spatial landscape features, fire behavior 

predictions require the use of spatial fire spread simulations. 

To provide a sense of how the different types of 

approaches are applied and which resources each might 

require, we discuss in detail examples from each category. 

Biophysical models are first discussed, with particular 

attention to two such systems, LANDFIRE and WALTER. 

Because LANDFIRE is the newest, and, perhaps, most 

complex approach being pursued, we discuss this approach 

in depth. WALTER is described as an example of an 

approach that makes extensive use of expert opinion. An 

example of the statistical modeling approach relying on 

large historical data sets is described with a discussion of 

a probability-based model. Finally, we discuss a suite of 

fire behavior models—FARSITE, BehavePlus, FlamMap, 

NEXUS, and Visualized Fire Simulation (VFS). 

Fire Risk under Changing Vegetation or Climate

Many models have been constructed with capabilities of 

projecting changes in vegetation composition over time 

and the way these changes alter fire risk (Keane and others 

2004). Other models are capable of simulating how changes 

in climate will change biological processes and interactions 

in ecosystems. Slow changes in vegetation or climate or 

both can be incorporated into the fire risk calculation using 

these models. Recent reviews (Keane and others 2003) 

have concluded that simulation modeling produces the best 

predictive ability under changing climate and vegetation 

conditions compared to prediction based on either biophysical properties or statistical prediction from historical data. 

Over longer time periods, climate and vegetation change is 

very likely to occur. 

We discuss the advantages of these models, building on 

the extensive evaluations of succession fire models (Keane 

and others 2004). Further, we attempt to place the models 

they considered in the context of a wider range of tools. 

We discuss the four most widely used succession models 

that contain processes that link vegetation change to fire 

prediction: SIMPPLLE, MAGIS, VDDT, and TELSA. Many 

other available models perform well for specific locations 

for which they were designed. We focused on these four 

because they have the potential to be easily applied to many 

different areas in the Western United States. 

A final category of simulators includes those that 

utilize broad concepts of biological processes to project the 

ways forests are likely to change and where various forest 

types are likely to be found under future climate conditions. Although this type of model has had a long history 
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of development in forest ecology, it is only recently that 

several models have had fire intimately incorporated into 

the model structure. We describe the MAPSS model as an 

example of this category.

Biophysical Fire Risk Systems

Biophysical fire risk models traditionally use regional 

characteristics of weather patterns (temperature, humidity, 

wind), vegetation (fuel type, moisture level), and topography (elevation, slope) to produce a prediction index of fire 

risk based on historical correlations among these variables 

and fire. More recently, models are being developed that 

substitute spatial simulations for some of these correlations.

LANDFIRE 

In response to a need for a national evaluation of the 

spatial distribution of fire risk, the USDA Forest Service 

developed a partnership with four other agencies to develop 

LANDFIRE (http://www.landfire.gov/index.html). The goal 

of LANDFIRE is to identify areas at risk because of the 

accumulation of hazardous fuel for the purpose of prioritizing hazardous fuel reduction projects and improving 

hazardous fuel treatment coordination between agencies. 

The program is designed to produce landscape-scale maps 

and data describing vegetation, fire, and fuel characteristics 

across the United States (at a 30-m grid resolution) (Keane 

and others 2004, Rollins and others 2002, 2004; Schmidt 

and others 2002). LANDFIRE is providing many of the raw 

materials that will be necessary to produce an estimate of 

fire risk. Although it is not yet in general use or publicly 

available, it is important to discuss it here because of the 

key role LANDFIRE will play in fire risk assessment over 

the next decade.

The spatial distribution of potential vegetation, existing vegetation, canopy height, and canopy cover is mapped 

using gradient-based field inventories coupled with gradient modeling, remote sensing, ecosystem simulation, and 

statistical analyses. Biophysical gradient maps have been 

created containing 38 geographical information system 

(GIS) layers describing the direct and indirect conditions 

affecting the distribution of vegetation and fire regimes 

(Figure 2). The vegetation of the continental United States is 

divided into approximately 500 biophysical units, based on 

plant composition and the Ecological Systems categorization (http://www.natureserve.org/). Each successional stage 

of each biophysical type is separately tracked. Fuel models 

are assigned to each mapping region, producing fuel maps 

for fire behavior models, canopy fuel projections, and fuel 

characterization classes. Crown bulk density and height to 

crown base are calculated at the plot level from tree lists. 

Inputs consist of coarse-scale, 1-km2 resolution, spatial 

data layers. These include potential natural vegetation 

type, current cover type, site characteristics (such as soils, 

climate, and topography), historical natural fire regime (fire 

frequency and severity), and Fire Regime Current Condition 

Class (layer depicting the degree of departure from historical fire regimes). There are three additional databases that 

are used as input:

1. National Fire Occurrence—Federal and nonFederal fire occurrences from 1986 to 1996.

2. Potential Fire Characteristics—the number of 

days of high or extreme fire danger calculated 

from 8 years of historical National Fire Danger 

Rating System (NFDRS) data.

3. Wildland Fire Risk to Flammable Structures—

the potential risk of wildland fire burning flammable structures based on an integration of 

population density, fuel, and weather spatial data.

Landscape characteristics that are used to determine 

fire occurrence and behavior include height to crown base, 

crown bulk density, fuel loadings, cover type, percentage 

cover, height for each of the forest, shrub, and herbaceous 

layers, and an estimate of the departure from reference 

normal fire regime condition class. The interaction between 

these characteristics and fire probability is estimated by 

using a suite of computer models, WXFIRE, BIOME-BGC, 

LANDSUMv4, FARSITE, and HRVStat, discussed in the 

next section.

LANDFIRE produces three fire regime maps:

1. Simulated historical fire frequency and severity.

2. Fire regime condition class (FRCC).

3. Indices of departure from reference conditions.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

These tools are used for determining the degree to 

which current landscape conditions have departed from historical reference condition vegetation, fuel, and disturbance 

regimes. Figure 3 shows an example map of fire regime 

condition classes produced by LANDFIRE. An example 

map of reference fire regimes is shown in Figure 4.

The data layers being produced by LANDFIRE will 

provide basic information from which a risk assessment can 

be calculated. However, there are drawbacks to this system. 

LANDFIRE'S classifications of fire conditions may be too 

coarse. The calculation of probabilities may require data on 

a continuous scale. Further, it is not clear whether this data 

is sufficient to predict the average likelihood of a fire at a 

location or the distribution probabilities of fires of different sizes and intensities. The probability of a worst-case 

scenario would be difficult to estimate from LANDFIRE’S 

products.

Because the past condition and fire susceptibility 

had much to do with past climate, it is unclear whether 

LANDFIRE will correctly predict the relationship between 

vegetation, fuel loadings, and fire that will be shaped by 

future climates. LANDFIRE places less emphasis on the 

importance of the heterogeneity of types of fire and the 

key differences among these types, such as crown fires 

vs. ground fires. LANDFIRE focuses on classes that are 

relevant to management and current vegetation classifications. There may be advantages to having the flexibility to 

Figure 2—A diagram of the Landscape Ecosystem Inventory System used by LANDFIRE. Boxes in the 

center represent the steps used to create the ECODATA database, the LEIS GIS, and landscape planning 

maps. Initial and derived GIS layers (left side) are used as independent variables to predict maps of 

landscape and ecosystem characteristics (right side) for landscape assessments and ecosystem management (Keane and others 2002).
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make changes in these classifications. Natural variability in 

landscape and fire characteristics, and their influence on fire 

(and the uncertainty with which predictions can be based 

on these characteristics) is not treated implicitly within the 

system.

LANDFIRE Models—

WXFIRE (Keane and Holsinger 2006) computes spatially 

explicit, climate-based biophysical variables at any landscape scale or resolution using daily weather data, topography, and soils parameters, and a diverse set of integrated 

environmental functions. WXFIRE computes over 50 

biophysical attributes such as potential evapo-transpiration 

for each simulation unit. The user must estimate all input 

parameters for each simulation unit to create an input file 

to WXFIRE. WXFIRE then calculates, record by record, 

all biophysical attributes by accessing the DAYMET spatial 

weather database (Thornton and others 1997) (http://

www.daymet.org) and using the daily weather to compute 

important climate and ecosystem biophysical variables. The 

DAYMET database consists of 18 years of daily temperature, humidity, radiation, and precipitation estimates at a 

1-km spatial resolution for the contiguous United States. 

Output from WXFIRE can be used to digitally map those 

ecosystem characteristics needed by land management 

including fire regime, fuel load, and vegetation cover type. 

BIOME-BGC (Thornton and others 2002) is used 

to calculate expected forest productivity in response to a 

given set of environmental conditions. BIOME-BGC is 

an ecosystem process model that simulates carbon, water, 

energy, and nitrogen budgets for both vegetation and soil. 

Figure 3—LANDFIRE National Fire Regime Condition Class Map, 2002, Missoula Fire Sciences Laboratory (Hann and others 2004, 

Hann and Bunnell 2001, Schmidt and others 2002). Conditions 1 (low risk, forest regimes are within their historical range, shown in 

green), 2 (moderate risk, forest regimes are moderately altered from their historical range, shown in yellow), and 3 (high risk, forest 

regimes. are significantly altered from their historical range, shown in red).
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BIOME-BGC is inherently non-spatial and can be run on 

an area of any size: a single point, a watershed, a continent. 

BIOME-BGC computes a set of carbon and water budget 

metrics on a daily time step driven by daily weather data 

such as gross primary production, net primary production, 

evapo-transpiration, and runoff. The model requires three 

types of information as input: site physical characteristics, 

plant physiological characteristics, and daily weather data. 

A set of generic plant functional type (PFT) ecophysiological parameter files has been developed from the literature 

for use with BIOME-BGC (White and others 2000). The 

PFTs include evergreen needle leaf forest, deciduous broadleaf forest, evergreen broadleaf forest, deciduous needle leaf 

forest, evergreen shrub, c3grass, and c4grass. 

The range and variation of historical landscape dynamics are estimated with LANDSUMv4 (Landscape Succession Model version 4.0) and HRVStat (Historical Range and 

Variation Statistics) of landscape characteristics (Keane and 

Holsinger 2006, Keane and others 2002). LANDSUMv4 

simulates fire and succession on fine-scale landscapes for 

land management applications. Species composition and 

stand structure are assumed to change at a predefined rate, 

although disturbance initiation is modeled stochastically, 

and disturbance effects are based on the current vegetation 

conditions. 

The model FARSITE projects the spread of fire across 

landscapes, using slope relationships and wind vectors (discussed separately under the fire behavior model heading). 

FIREHARM (http://ams.confex.com/ams/pdfpapers/66069.

pdf) is used by LANDFIRE to produce probabilities of 

a given fire event from long-term weather. It identifies 

areas of highest risk based on fuel consumption, smoke 

production, tree mortality, soil heating, crown fire index, 

and proximity to urban areas. FIREHARM will produce 

a probability of fire for each category of weather conditions (i.e., the number of days the fire potential is above a 

certain level). FIREHARM will calculate four fire behavior 

variables (fireline intensity, spread rate, flame length, crown 

fire potential), five fire danger variables (spread component, 

burning index, energy release component, Keetch-Byram 

drought index, ignition component), and five fire effects 

variables (smoke, fuel consumption, soil heating, tree mortality, scorch height) for every day in the DAYMET 18-year 

record for each spatial location. The program will simulate 

Figure 4—LANDFIRE rapid assessment reference fire regimes. The figure identifies the locations with fire regimes with a 0 to 35-, 35 

to 200-, and greater than 200-year frequency and replacement severities ranging from low and mixed to replacement. Also identified are 

locations of alpine, bare rock, wetland, snow/ice, water, and unclassified landforms. (http://www.landfire.gov/RA2Image.html; Updated 

map http://www.landfire.gov/RA3Image.html).
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moisture conditions for each dead fuel component (e.g., 

duff, litter, downed woody, logs) and live fuel component 

(e.g., shrubs, herbs, trees) using a complex set of biophysical 

equations. 

Wildfire Alternatives (WALTER) (FCS model)

The Wildfire Alternatives (WALTER; http://walter.arizona.

edu/index.asp) system for estimating fire risk is being developed at the University of Arizona. It is an interdisciplinary 

research initiative aimed at improving our understanding 

of the processes and consequences of interactions among 

wildfire, climate, and society. WALTER seeks to capitalize 

on advances in geospatial, analytical, and Web delivery 

technology to provide access to scientific research activities 

and findings and educational materials using the decision 

support tool, Fire-Climate-Society Strategic Fire Model 

(FCS-1).

FCS-1 is an online, spatially explicit strategic wildfire 

planning model with an embedded multi-criteria decision 

process that facilitates the construction of user-designed fire 

risk assessment maps for different climates (Figure 5). The 

resulting maps show spatially explicit information about the 

geographical distribution of fire probability and values at 

risk for the selected study area.

FCS-1 was developed for the varying vegetation, 

climate, and topography as well as the unique human 

dimensions of wildfire found in southeastern Arizona and 

northern New Mexico. The model currently is made up of 

five fire probability and four values-at-risk model components. FCS-1 can be run under differing climate and corresponding fuel moisture conditions. Through an analytical 

hierarchy process, FCS-1 allows users to assign weights 

to individual model components. The online application 

Figure 5—Components of the Fire-Climate-Society Model in Wildfire Alternatives (WALTER), showing 

the principal processes integrated into the full model analysis. (Adapted from http://java.arid.arizona.edu/

ahp/).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

can be used to help understand differing views and build 

consensus.

Like many expert systems, WALTER has the framework for considering an array of disparate factors in 

evaluating the ways they may influence fire. However, 

expert opinion can have uncertain predictive ability. The 

main value of this type of system is to identify interrelationships that may require management to control fire frequency 

and pattern.

Expected Net Value Change

A model has been developed for calculating the expected 

net value change (ENVC) as the product of the probability 

of a fire at a specific location and the resulting change in 

financial or ecological value (Finney and others 2007). The 

model calculates the sum of the product of the probability of 

the i-th fire behavior at a specific location over N fires multiplied by the benefits and losses afforded for the j-th value of 

M values received from the i-th fire behavior. The expected 

net value change (ENVC) can include financial, ecological, 

or other values at present day or future discounted values. 

Assumptions about the effect of wildfire suppression on 

wildfire probability and value change can also be incorporated into the (ENVC) equation. The estimation of wildfire 

probabilities for a specific future period is derived from a 

calculation of the minimum travel time a hypothetical fire 

would take moving across the landscape, using algorithms 

from the FARSITE fire behavior model (discussed under 

the "Fire Behavior Modeling" heading). These models are 

run in parallel using a set of networked computers and an 

estimate of the spatial pattern of forest vegetation and fuels.

This model has been applied to a 16 000-ha wildlandurban interface in eastern Oregon simulating 12 fuel 

management scenarios and four land value schemes. Burn 

probabilities were estimated by simulating 200 randomly 

ignited wildfires, and then the average net value change for 

each fire and pixel on the landscape was estimated. The 

results indicate that fuel reduction on a relatively minor 

percentage of the landscape (20 percent) resulted in a 

20-percent to 50-percent positive change in ENVC for most 

of the scenarios simulated.

Probability-Based Fire Statistical Models

Probability-based fire risk models (Brillinger and others 

2006) defined risk using three probabilities:

1. The probability of fire occurrence.

2. The conditional probability of a large fire 

assuming an ignition occurs.

3. The unconditional probability of a large fire.

An illustration of the general approach shared by these 

types of models is the probability-based fire risk model 

(Preisler and others 2004), which estimates fire probability by fitting a nonparametric logistic regression to data 

grouped in cells of 1 km2

 with a temporal resolution of 1 

day. The input used by this model is:

1. The probability of fire occurrence (historical 

data).

2. The conditional probability of a large fire given 

ignition (daily values of precipitation, lightning, 

temperature, windspeed, and humidity).

3. The unconditional probability of a large fire 

(10-hour lag fuel moisture).

It produces maps of predicted probabilities and estimates of the total number of expected fires in a given region 

and time period. This method is particularly useful for 

assessing the utility of explanatory variables, such as fire, 

weather, and danger indices for predicting fire risk. It has 

the advantage of basing fire predictions on rigorous use of 

probability statistics.

Fire Behavior Modeling 

Fire behavior models predict the propagation of fire by 

assuming that the landscape is subdivided into cells, and 

each cell has a probability of burning that depends on 

conditions in the cell and in surrounding cells (e.g., Beer 

1991). Many of these models use a deterministic version 

of the elliptical growth model (Green and others 1983, 

Richards 2000) to simulate spread of forest fires. The input 

data for these models include a base vegetation map, which 

can be generated with a vegetation simulator, such as Forest 

Vegetation Simulator (FVS) (Dixon 2002) and a fuel load 

simulator, such as Fuels and Fire Extension (http://www.

essa.com/downloads/prognosis/ffe.pdf). Fire behavior 
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predictors were initially built for local-scale projections, 

where initial ignitions and spatial distribution of landscape 

properties could be precisely specified.

Among the more widely applied fire behavior models 

is FARSITE, which predicts fire spread and intensity across 

a specific landscape as a continuous fire over multiple 

time steps at a user-specified resolution, commonly 30 m. 

The focus of FARSITE is to simulate fire growth and the 

changes that occur over time for a specific fire. It uses spatial information on topography and fuels along with weather 

and wind files to simulate surface fire, crown fire, spotting, 

postfrontal combustion, and fire acceleration. FARSITE 

requires spatial landscape information from a GIS to run, 

including slope, aspect, surface fuel, canopy cover, crown 

base height, crown bulk density, and stand height. The 

outputs include a prediction of fire perimeter length and 

location, spread rate, intensity, flame length, and heat per 

unit area. Although the focus is on a single fire, a regional 

prediction could be made by combining output from all fires 

each year. Fire spread predictions are based on a snapshot 

of current vegetation structure. In order to simulate fire 

behavior under future conditions, the changes that will 

occur in the vegetation structure must be known.

BehavePlus (http://fire.org/) predicts fire behavior 

based on fuels, weather, topography, and wildfire situations. BehavePlus uses a minimum amount of site-specific 

input data to predict fire behavior for a point in time and 

space (i.e., spatially explicit data layers are not used). The 

present version of BehavePlus simulates only surface fire 

spread, but later versions may include crown fire simulation 

capability.

FlamMap (http://fire.org/) is a fire behavior mapping 

and analysis program, based in a geographic information 

system (GIS) that computes potential fire behavior characteristics (spread rate, flame length, fireline intensity, etc.) 

over an entire FARSITE landscape for constant weather 

and fuel moisture conditions. FlamMap uses spatial information on topography and fuels to calculate fire behavior 

characteristics at one point in time, assuming that all cells 

in the landscape function independently of one another. 

A map is produced for an area of any modeled value, such 

as fuel moisture, fireline intensity, scorch height, or fuel 

consumption. Comparisons can be made between locations, 

or the effect of fuel treatment can be examined. 

NEXUS (http://fire.org/) is an Excel spreadsheet linking surface and crown fire prediction models to compute 

indices of relative crown fire potential. NEXUS is useful 

for evaluating alternative treatments for reducing crown 

fire risk and assessing the potential for crown fire activity. 

NEXUS includes several visual tools useful in understanding how surface and crown fire models interact.

VFS (http://fire.org/) is a graphical user interface- 

(GUI-) based computer program to simulate and animate 

fire on heterogeneous landscapes. VFS captures fire spread 

behaviors based on fuel configuration, wind regime and 

topographical effects using percolation algorithms such as 

static percolation, depth first search recursive algorithm, 

and dynamic percolation with fire front. Users can compare 

the simulation capability of each method (e.g., burned pattern maps). Furthermore, output from VFS can be linked to 

GIS and used to cross-validate other fire simulation models. 

To evaluate the sensitivity of the input parameters of each 

method, users can specify a range for each parameter in 

VFS and test the influence of changes in parameters on 

model predictions. VFS can be used as a parameterization 

tool for the forest landscape models that incorporate fire 

spread simulation. 

Models to Estimate Effect of Vegetation 

Change on Fire Risk 

Fire risk over long periods cannot be adequately evaluated 

without projecting the ways that the structure and composition of forest vegetation and fuels will change over time. 

Fire is very sensitive to vegetation structure and composition (Clark 1993, Swetnam 1997, Swetnam and Baisan 

1996). Fire will, in turn, affect the rate and direction of 

vegetation change (Lenihan and others 1998). 

Keane and colleagues classified 44 linked fire succession models, based on their complexity, principal 

mechanism, and stochasticity in their simulation design 

(Keane and others 2004). Most of these models fall into 

the category of fire behavior models because they attempt 

to simulate, at a fine scale, the dynamics that cause fire to 
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spread across a landscape. These authors provide a handy 

table for deciding which models to use based on (1) whether 

the purpose is management or research, (2) the importance 

of predicting fire patterns, (3) whether information is 

Table 1—Model selection key for selecting the most appropriate linked fire succession models for fire 

management and research applications

Criteria Models

Management application

Limited computer resources, modeling expertise, and/or input data available

Fire pattern important

Support and documentation available TELSA

Not as above LANDSUM

Fire pattern NOT important

Support and documentation available FFE-FVS

Not as above SIMPPLLE, FETM

Abundant computer resources, modeling expertise, and/or input data available

Individual tree or species processes important

Support and documentation available None

Not as above LANDIS, QLAND, FIN-LANDIS

Only stand level characteristics important

Support and documentation available LANDMINE, SELES

Not as above BFOLDS, CAFE, DISPATCH, 

 EMBYR, INTELAND, LADS, 

 LANDSIM, RMLANDS, SAFE-

 FOREST, SEM-LAND

Research application

Explore climate, vegetation, and fire dynamics

Coarse-scale applications BFOLDS, BIOME-BGC, CENTURY, 

 MC-FIRE, GLOB-FIR

Landscape-scale applications

Individual tree or species-level processes important

Fire pattern important FIRE-BGC, LAMOS, 

 SIERRA

Not as above DRYADES, ZELIG-L, ZELIG-B

Only stand-level characteristics important

Fire pattern important MAQUIS, FIRESCAPE

Not as above REG-FIRM 

Explore fire and vegetation dynamics

Coarse-scale applications ALFRESCO, FIREPAT

Landscape-scale applications

Individual tree-level processes important

Fire pattern important FIN-LANDIS, LANDIS 

Not as above None

Only stand level characteristics important

Fire pattern important ANTON, CAF ́ E, DISPATCH, 

 EMBYR, INTELAND, LANDSIM, 

 MAQUIS, MOSAIC, QTIP, RATZ, 

 RMLANDS, SELES, SEM-

 LAND, SUFF2

Not as above BANKSIA, FLAP-X, ON-FIRE, 

 SUFF1, SUFF2, VASL

Source: Keane and others 2004.
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needed on specific species or just on stand characteristics, 

and (4) what spatial scale is of interest (Table 1).

Complex models such as Fire-BGC (Keane and others 

1989) and LANDIS (Mladenoff 2004, Mladenoff and others 

1996) simulate vegetation change as a complex function of 

either the development of nutrient cycling conditions or the 

driving force of individual life history characteristics. The 

most complex of these are the gap models such as ZELIGSP (Miller and Urban 1999) that predict successional 

development by simulating the dynamics of each individual 

tree on representative plots in the forest. Simpler models, 

such as SIMPPLLE (Chew and others 2004), represent 

vegetation changes as a predictable succession of stages 

following a resetting disturbance. We have selected a few of 

these models that are in common use for further discussion 

below. 

SIMPPLLE and MAGIS

SIMPPLLE (SIMulating Vegetative Patterns and Processes 

at Landscape scaLEs) is a stochastic non-spatial simulation model for projecting vegetative change over time in 

the presence of natural processes, either with or without 

management treatments. It models interaction of various 

natural processes on a landscape. Because it is stochastic, 

multiple simulations are run to generate a record of the 

frequency of natural disturbances for each polygon in the 

landscape. These frequencies represent an estimate of the 

risk of these natural processes occurring over a given period 

of time and are used to develop a risk index for each section 

of the landscape. 

MAGIS (Multiple-resource Analysis and Geographic 

Information System (http://www.fs.fed.us/rm/econ/magis/) 

is a spatial decision-support system for using the risk index 

to find optimal management practices spatially and temporally for a landscape. MAGIS uses optimization to select the 

spatial arrangement and timing of treatments that fit userdetermined objectives and constraints. A variety of resource 

effects, management targets, and economic costs or benefits 

can be used to specify the objective and constraints for 

scheduling both vegetation treatments and road activities. 

With these two programs, the user can evaluate a variety of management alternatives. However, their projections 

are dependent on the estimate of current fuel distributions 

across the landscape. When projecting into the future, they 

will be increasingly in error as these fuel loads change.

Vegetation Disturbance Dynamics Tool

Vegetation Disturbance Dynamics Tool (VDDT) (http://

www.essa.com/downloads/vddt/) was developed to support the Interior Columbia River Basin Assessment. This 

nonspatial tool uses a state-and-transition matrix approach 

to predict changes in vegetative composition and structure 

using disturbance probabilities and successional pathways, 

including infrequent large-scale disturbances such as standreplacement fires.

VDDT models typically apply to potential vegetation types. For each of these types, succession classes are 

defined according to the cover type and structural stage. In 

the absence of disturbance, vegetation community assemblage changes from one succession class to the next. Both 

natural and man-caused disturbances that affect vegetation 

can be examined. In VDDT, disturbances are defined for 

each succession class according to type (e.g., wildland fire, 

harvest, etc.), succession class destination, probability of 

occurrence, and the relative ages for which each probability 

applies. For each year of the simulation, VDDT determines 

whether each landscape unit is subjected to a disturbance.

Tool for Exploratory Landscape Scenario 

Analyses 

Tool for Exploratory Landscape Scenario Analyses 

(TELSA) (http://www.essa.com/downloads/telsa/index.

htm) is a spatially explicit extension to VDDT that simulates forest succession, natural disturbances, and forest

management activities. It is designed to simulate up to 

250 000-ha landscape units. TELSA can be used to simulate 

multiple scenarios, each characterized by different assumptions about management actions and natural disturbances. 

Because wildfires and other natural disturbance events 

that affect vegetation dynamics are inherently unpredictable, the model can use multiple stochastic simulations of 

each scenario to provide estimates of the mean, range, and 

variability of the selected performance indicators. Unlike 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

many other strategic planning models of landscape dynamics, TELSA takes into account natural disturbances so users 

can explore how their proposed management strategies will 

interact with vegetation succession and disturbances to alter 

landscape composition and structure. This model has been 

used to define the transition times between various succession classes (combinations of species composition and structural stage), the probabilities and impacts of disturbance by 

insects, fire or other agents, and the impacts of landscape 

management actions on structure and composition.

Models to Estimate the Effect of Climate 

Change on Fire Risk

Biophysical process models can be used to estimate the 

effect of vegetation change on fire risk. The representation 

of basic processes in biophysical process models allows 

them to project the consequences of known relationships 

under future conditions. These models are capable of examining the effects of long-term changes in conditions such as 

climate change. An example of these model types, MAPSS 

(http://www.fs.fed.us/pnw/corvallis/mdr/mapss/) predicts 

vegetation distributions from either the availability of water 

in relation to transpirational demands or the availability of 

energy for growth (Neilson and Wullstein 1983, Neilson 

and others 1989, Stephenson 1990, Woodward and Williams 

1987). 

MAPPS and its recent derivative, MAPSS-CENTURY1 

(MC1), simulate life-form mixtures and vegetation types, 

fire disturbance, and ecosystem fluxes of carbon, nitrogen, 

and water (Lenihan and others 2003). MC1 is routinely 

implemented on spatial data grids of varying resolution (i.e., 

grid cell sizes ranging from 900 m2 to 2500 km2) where 

the model is run separately for each grid cell (i.e., there 

is no exchange of information across cells) (Bachelet and 

others 2000, 2001; Daly and others 2000). MAPSS has been 

implemented at a 10-km resolution over the continental 

United States and at a 0.5-degree resolution globally (Neilson 1993, 1995; Neilson and Marks 1994). It has also been 

implemented at the watershed scale (MAPSS-W, 200-m 

resolution) through integration with a distributed catchment 

hydrology model (Daly and others 1994, Wigmosta and 

others 1994).

Using climate data at a monthly time step, the model 

calculates the leaf area index (LAI) of three generic life 

form groups—trees, shrubs, and grasses—in competition 

for both light and water given a site water balance consistent 

with observed runoff (Neilson 1995). Water in the surface 

soil layer is apportioned to the two life forms in relation to 

their relative LAIs and stomatal conductance. 

MAPSS is used to develop midterm forecasts of fire 

risk by using spatially distributed, high-resolution climate 

data and potential future climate forecasts from climate 

models. An example of these predictions is given in Figure 

6. The PRISM model (http://www.ocs.orst.edu/prism) is 

used to produce high-resolution data grids of observed 

fire weather extending back to 1895 and interpolations of 

weather station data that are sensitive to topography. Fire 

risk forecasts, including fire occurrence, area burned, and 

fire behavior are generated from the historical and forecast 

weather data (Bachelet and others 2000, Lenihan and others 

2003) (http://www.fs.fed.us/pnw/corvallis/mdr/mapss/pubs.

html). A fire event is triggered in any given cell on any day 

if one of three thresholds is exceeded. A threshold of the 

12-month standardized precipitation index (SPI) is used 

as an indicator of moderate to severe drought to control 

the inter-annual timing of fire events. A threshold of the 

1,000-hour fuel moisture content of dead fuels is used as an 

indicator of extreme fire potential to control the seasonal 

timing of fire events. A threshold of fine fuel flammability 

is used as an indicator of the sustainability of fire starts to 

control the timing of fire events at the daily time step. There 

is no constraint on fire occurrence by the availability of an 

ignition source, such as lightning or human-caused ignition. 

Once a fire event is triggered, the MC1 fire module determines the fraction of each cell burned, which is dependent 

on the current vegetation type, the current drought condition, and the number of years since fire. 

Potential fire behavior is also influenced by estimates 

of the mass, vertical structure, and moisture content of 

several live and dead fuel size-classes. The consumption 

of aboveground biomass, carbon, and nitrogen stocks are 

simulated as a function of the moisture content of each live 

and dead fuel size-class and the vertical structure of the 

canopy. The more rapidly growing grasses are assumed to 
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gain an advantage over woody life forms in the competition for water and nutrients, promoting even greater grass 

production which, in turn, produces a more flammable fuelbed and more frequent fire. MAPSS is capable of producing 

predictions of future vegetation change and fire risk over a 

large geographical area.

Conclusions Concerning the Use of Fire 

Modeling Systems

Much effort has gone into creating a capability of predicting fires throughout the region, both in their likely location 

and frequency. We described two major categories of fire 

risk assessment tools: those that predict fire under current conditions, assuming that vegetation, climate, and 

the interactions between them and fire remain relatively 

similar to their condition during recent history; and those 

that anticipate changes in fire risk as climate and vegetation 

communities change through time. Three types of models 

have proven useful for predicting fire under current conditions:

1. Biophysical models that predict fire from 

vegetation type, fuel load, and climate.

2. Statistical models that use historical data to 

predict fire probabilities if landscape-fire 

relationships continue to remain relatively 

unchanged.

3. Fire behavior models that produce predictions of 

the ways individual fires will move across the 

landscape.

Programs such as LANDFIRE have great promise for 

using biophysical properties to estimate risk. The LANDFIRE program is creating base data sets of fuel loadings, 

biophysical variables, and vegetation composition. Risk 

assessments conducted with LANDFIRE tools will depend 

heavily on well-tested models such as FARSITE. Since the 

intention is to implement the LANDFIRE project nationally 

at a fine-scale resolution, its data sets could provide the 

framework around which to build other risk evaluations. 

However, the data sets produced by LANDFIRE could be 

made even more valuable if they contained information on 

Figure 6—MAPSS simulation of fire probability MC1 DGVM fire risk consensus forecast, May 

to October 2006. The map shows the locations where from one (blue) to five (red) weather forecasts resulted in a fire occurrence (http://www.fs.fed.us/pnw/corvallis/mdr/mapss/GRAPHICS/

CONSENSUS).
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the probabilities of fires of different sizes, intensities, and 

heterogeneity of fire types at any given location. 

For longer periods, fire risk must be evaluated by 

models that predict the ways vegetation communities will 

change over time because these changes will alter fire probabilities. Risk systems must be designed to track changes 

in fire susceptibility as climate changes, using models such 

as MAPSS. LANDFIRE is not currently designed to track 

these changes, so it is unclear whether it will correctly 

predict the relationship between vegetation, fuel loadings, 

and fire that will be shaped by future climates. MAPSS 

would have a much higher likelihood of being able to track 

these changes in relationships. 

Prediction of fire occurrence is just the first part of a 

complete analysis of fire risk. Fire occurrence risk must be 

combined with models that determine the risk of the effects 

of fire. For example, one such effects model is FOFEM 

(a First Order Fire Effects Model) (http://fire.org/), which 

predicts mortality, fuel consumption, smoke production, and 

soil heating caused by prescribed fire or wildfire. It uses an 

algorithm key that selects different functions for different 

geographic areas and cover types. It can be used for setting 

acceptable upper and lower fuel moistures for conducting 

prescribed burns, determining the number of acres that may 

be burned on a given day without exceeding particulate 

emission limits, assessing effects of wildfire, developing 

timber salvage guidelines following wildfire, and comparing expected outcomes of alternative actions. There are 

second-order effects, such as changes in site productivity, 

animal use, insects, and disease that need to be evaluated by 

other models. 

Fire must be looked at in the context of other stresses, 

such as invasive insects and pathogens, encroaching 

urbanization, and loss of critical habitat. There are interactions among stresses that play a large role in affecting 

the frequency and intensity of fire, and fire, in turn, can 

affect the probability of those stresses. Fire probability can 

increase in stands that have experienced large amounts of 

tree mortality caused by native pest infestations. Because 

these precursor stresses have received less attention than 

fire, the uncertainty for predicting their probability is much 

higher. The effects of fire are dramatic, but its role in shaping the future of forest systems may be equaled or exceeded 

by other stresses. Consequently, it is vitally important that 

risk evaluation systems be created that can simultaneously 

estimate the probability of all the major stresses influencing 

forest and grassland development. 

In the development of fire modeling systems, data sets 

containing fine-scale grids of key data on the landscape 

have been established, with most containing detailed 

information on variables such as fuel loads, vegetation, 

and climate trends. These variables are likely to be useful 

in evaluations of many other kinds of risks. LANDFIRE'S 

classifications of fire conditions may be too coarse and 

inflexible to be useable to assess risks across a number of 

stresses at a given location. Vegetation mapping systems 

being developed (Kerns and Ohmann 2004, Ohmann and 

Gregory 2002, Wimberly and Ohmann 2004) may provide a 

much more flexible solution to this problem. Although there 

is not yet a movement toward a standard set of spatial data 

where models require identical inputs, such standardization 

could certainly aid risk comparisons. However, in order 

to compare fire risks to those from other threats, a flexible 

classification of forest types may be needed that is not just 

centered on fire risk characteristics. 

The great proliferation of fire modeling systems in different portions of the regions suggests that each has specific 

strengths in simulating fires in the area for which the model 

was originally designed. However, maintaining so many 

different types of models in the fire modeling toolbox will 

inevitably prove unwieldy and confusing to potential users. 

Consequently, an effort toward consolidation (or choice of 

the best models) is likely to occur. The ultimate question 

is whether any of the tools discussed above could provide 

an initial framework on which evaluations of risks to other 

threats could be added. The best models would be those that 

facilitate comparison between fire risk and its associated 

ecosystem risk and the risk from other threats. It was not 

possible in this manuscript to evaluate which fire models 

offer a better route for considering multiple interacting 

stresses. This question must remain a primary one to 

consider in the choice and use of models.
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Fire

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

D. Buckley, and A. Ralowicz

James Brenner, fire management administrator, Florida 

Department of Agriculture, Division of Forestry, 

Tallahassee, FL 32399; Don Carlton, President, Fire Program Solutions, Sandy, OR 97055; Sue McLellan, systems 

project administrator, Tallahassee, FL 32399; Allan Dozier, 

Chief, Forest Fire Control, Georgia Forestry Commission, 

Dry Branch, GA 31020; Thomas Spencer, Predictive 

Services Department Head, Texas Forest Service, College 

Station, TX 77840; David Buckley, senior consultant, 

Sanborn Map Company, Aurora, ON, L4G6A1; Andrew E. 

Ralowicz, principal geospatial scientist, Environmental and 

GIS Services, MDA Federal Inc., Rockville, MD 20852.

Abstract

Florida’s Wildland Fire Risk Assessment (FRA), which 

was completed in 2002, is a statewide effort to develop a 

comprehensive suite of standardized spatial data layers 

developed to support implementation of a statewide fuels 

management strategy. By maintaining focus on fire and 

fuel dynamics for use with scientifically credible local to 

statewide applications, the FRA builds on a statewide surface fuels map, fire history data from many agencies, and 

weather data collected over a period of 20 years. Change 

detection is currently being utilized to update the statewide 

surface fuels layer. The process used in the FRA builds on a 

process first applied in the Lake Tahoe Basin Land Management Unit. Subsequently, the methods used in the FRA have 

recently been applied to 13 Southern States in the Southern 

Wildfire Risk Assessment.

Introduction

Florida possesses a unique set of characteristics that make 

much of the state highly susceptible to wildfire. The state is 

blessed with an abundance of wildlands. The state has also 

experienced an influx of new residents into these wildlands, 

creating an intermingling of urban settlement within wildlands and increasing need for wildfire protection services. 

Florida’s weather is conducive to starting and spreading numerous and sometimes large wildfires. Florida’s rate 

of lightning strikes is unequaled in the Nation. Lightning, 

coupled with extended periods of drought, sets the stage for 

catastrophic fire episodes. Whereas lightning accounts for 

a large proportion of wildfire ignitions, human-caused fires 

are increasing as the population rises.

Florida’s wildland vegetation evolved in a fire ecosystem. The vegetation is adapted to burn periodically. Fine 

fuels, which are easily ignited and spread fire rapidly, are 

abundant throughout Florida. The lack of managed fire in 

much of the wildlands has promoted an accumulation of 

these fuels that can burn with high intensity and be difficult 

to control.

To reduce the risk to life and property loss from wildfire, communities and fire management organizations are 

encouraged to actively manage wildland fire risk. Managing 

wildland fire risk can be challenging as fuels frequently 

change across the landscape and through time. Fire behavior 

can be affected by changes in land development policies, 

fuels, weather conditions, and topography. In addition, 

many social, technical, and institutional barriers to proactive fire-risk management and planning frequently exist.

Florida’s fire managers face a complex problem of managing wildfire risk that is compounded by increasing fire 

intensities owing to accumulation of vegetative materials, 

continued residential growth into wildland fire-prone areas, 

and increasing firefighting costs. To address this problem, 

the Florida Division of Forestry (FDOF) initiated a process 

to assess fire risk and the values to be protected. The process developed (the Florida Fire Risk Assessment, or FRA) 

provides managers with a strategic view of the state to 

improve public safety and protect them from property losses 

like those experienced in 1985, 1989, 1998, and 1999.

The purpose of the FRA is to identify the potential for 

wildfires within the State of Florida and prioritize areas 

where mitigation options may be desirable. The FRA can 

also be used to locate areas within the state where interagency planning may be of value to effectively manage 

wildland fire risk. The results can be used to complete a 

more detailed analysis at the local level and communicate 

wildland fire management issues to the public.

The objective of this case study is to present the risk 

assessment methodology and results from the Southern 

Managing Wildland Fire Risk in Florida
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Wildfire Risk Assessment (SWRA) (Buckley and others 

2006) for the State of Florida. We start by describing the 

methods involved in defining our index of wildland fire 

risk, which we call Level of Concern, based on its major 

components, wildland fire susceptibility and fire effects. We 

then describe the construction of a fire response accessibility index. Finally, we discuss how, in Florida, the fire risk 

and fire response accessibility indices are being used and 

applied to improve fire protection decisions.

Wildland Fire Risk

Webster’s dictionary defines risk as “the possibility of 

suffering harm or loss.” As one can see, there needs to be 

both a likelihood and effect of an action or event before one 

can incur a risk. Two primary indices were assigned to each 

30- by 30-m cell in all 13 Southern States including Florida. 

These are the Level of Concern (LOC) Index and the Fire 

Response Accessibility (FRA) Index (Figure 1).

Within the risk assessment, the Level of Concern is the 

best measure of wildland fire risk. The Level of Concern 

Index is calculated from the likelihood of an acre burning, 

called the Wildland Fire Susceptibility Index (WFSI) and 

the expected effects of the fire (Fire Effects Index, or FEI). 

The FRA Index is a measure of the initial attack response 

time to a cell from existing initial dispatch locations for 

fire protection resources. Taken as a pair, these two indices 

(LOC and FRA) define a cell’s accessibility and its vulnerability to wildland fire occurrence and effects.

Wildland Fire Susceptibility Index

As used in the Florida and Southern Wildfire Risk Assessment, the Wildland Fire Susceptibility Index is a value 

developed to represent an index related to the probability 

of an acre burning. The determination of an acre burning 

integrates the probability of an ignition and expected final 

Figure 1—Southern Wildlife Risk Assessment (SWRA) model is shown above.
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fire size, the latter being affected by rates of fire spread in 

four weather categories and fire suppression effectiveness.

Fire Occurrence (Fire Occurrence Areas)—

The first task to determine the WFSI is to determine the 

probability of an acre igniting. A fire occurrence area (FOA) 

is an area where the probability of each acre igniting is the 

same. The historical fire ignition locations for a defined 

period of time are used. Pictorially, if one were to locate the 

point location for historic ignitions on a map of an FOA, 

the points would appear with randomly dispersed densities 

different from adjacent FOAs.

A grid illustrating the probability of a wildfire igniting 

was developed using ArcMap by analyzing the location of 

historic ignitions from 1997 to 2003. Fire occurrence rates 

were described as the number of fire ignitions per 1,000 

acres per year. A surface grid with fires per 1,000 acres 

per year was generated using a spatial filtering calculation 

available in ArcMap. The FOAs were developed to identify 

areas where the probability of a fire igniting was similar. 

Hence, within an FOA, the probability of each acre igniting 

is the same.

An example of a FOA map for Flagler County in 

Florida is shown in Figure 2.

Weather Influence Zones—

To determine an estimate of fire spread upon fire ignition 

using a fire behavior model, environmental conditions 

are needed so that fuel moisture and windspeed values 

can be used in the fire behavior models. To determine 

these environmental conditions, areas of uniform weather 

conditions were defined and the weather conditions within 

each area determined. A weather influence zone (WIZ) is 

an area where the weather on any given day is uniform. A 

fire weather meteorologist developed 20 weather influence 

zones in Florida, and these are displayed in Figure 3.

Development of Percentile Weather Values—

Within each WIZ, daily weather data are gathered for a 

defined period of time. These data were gathered from 

land management agency weather stations (National Fire 

Danger Rating System [NFDRS]) and from National 

Oceanographic and Atmospheric Administration (NOAA)-

maintained weather stations. A computer program 

developed by research meteorologist Dr. Scott Goodrick 

(Forestry Sciences Laboratory, 320 Green Street, Athens, 

GA 30602-2044) was used to change weather observations 

from NOAA stations to NFDRS standards. Another program developed by Dr. Goodrick was used to georeference 

the weather observations from the weather stations within 

a WIZ to the geographical center of the WIZ. Hence, one 

weather data set was developed with a weather observation 

for each day during the defined period for each WIZ. From 

this weather data set, percentile weather was developed for 

each WIZ.

The weather observation data set was checked for 

errors and then imported into the USDA Forest Service’s 

FireFamilyPlus program. The NFDRS index spread component (SC) was calculated for each day. The fire season was 

set for each WIZ and the SC calculated using the NFDRS 

fuel model G. Fuel model G is used, as it contains fuel 

loading in all of the dead (1-h, 10-h, 100-h and 1,000-h) and 

live (herbaceous and woody) fuel categories. This allows for 

the influence in the spread component calculation of the fuel 

moisture values from all of the fuel categories. In addition, 

climate class 3 (subhumid/humid) and slope class most 

applicable to the WIZ were used.

The spread component was then divided into four commutative percentile categories: low (0-15 percent), moderate 

(16-90 percent), high (91-97 percent), and extreme (98-100 

percent). The median SC was determined for each category. 

The environmental values for 1-h, 10-h, 100-h timelag fuel 

moisture, live herbaceous fuel moisture, live woody fuel 

moisture, and the 20-foot, 10-minute average windspeed 

were determined as the average of the respective values on 

days when the SC was equal to the median SC. This allows 

for the determination of four percentile weather categories 

with the percentage of occurrence of each category and 

with environmental values to define the weather conditions 

within each category.

Probability of a Fire Occurrence Within Each FOA by 

Percentile Weather Category—

We allow for the possibility that the higher percentile 

weather categories may be relatively more conducive to 

generating fire ignitions from ignition-initiating sources. 

That is, if 15 percent of the days during the fire season are 
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in the low percentile weather category, one cannot assume 

that 15 percent of the fires during the fire season will occur 

on the days in this percentile weather category. Four percentile weather categories were developed: low, moderate, 

high, and extreme. The percentage of days within each is 15 

percent, 75 percent, 7 percent, and 3 percent, respectively.

Each fire within the fire occurrence database for all 

agencies within a weather influence zone has a fire start 

date. Each historical fire was assigned a spread component 

based on the fire’s start date from the results of the 

FireFamilyPlus runs. The four percentile weather categories 

were also developed using the same assumptions for SC, 

and the four categories have SC ranges. Hence, a correlation is made assigning each historical fire to one of the four 

percentile weather categories. From these assignments, the 

proportion of fires that occurred in each percentile weather 

category by WIZ was determined. For Florida, 14.2 percent, 

74.1 percent, 8.1 percent, and 3.5 percent of the fires started 

within the low, moderate, high, and extreme categories, 

respectively. 

Figure 2—Flagler County, fire occurrence areas are detailed above.
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The probability of a fire within an FOA for each 

percentile weather category is the product of the total fire 

occurrence rate in the FOA by the proportion of fires within 

each percentile weather category.

Fire Behavior Prediction Inputs

Predicting fire behavior requires knowledge of fuels, 

weather, and topography. The previous section provides 

information on how the environmental conditions (weather) 

can be determined. The topographic conditions required are 

knowledge of slope steepness, aspect, and elevation. Aerial 

and surface fuel data required include canopy cover and the 

surface fuel model. If aerial fuel attributes are provided, 

then the occurrence and behavior of a crown (canopy) fire 

can be modeled. The aerial attributes needed are canopy 

base height (CBH), canopy bulk density, and stand height.

Data layers for the state were developed for slope, 

aspect, and elevation for U.S. Geological Survey Digital 

Elevation Model information. Fuel models for Florida 

were developed in 2002 for the FRA using a process where 

actual satellite imagery was correlated with the surface fuel 

model. These fuel models were also used in the SWRA, 

and a statewide fuel model map is displayed in Figure 4. An 

SWRA and FRA design requirement was to classify each 

acre of burnable land using the fuel models (Anderson 1982) 

in the Fire Behavior Prediction System (FBPS). A data layer 

defining the percentage canopy cover was developed using 

satellite imagery for Florida. For the FRA and SWRA, none 

of the canopy fuel layers (Canopy Ceiling Height, Canopy 

Base Height, and Canopy Bulk Density) were developed or 

used. All fire behavior predictions were based on surface 

fuel models.

Figure 3—Weather influence zones are illustrated in Florida.
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Fire Behavior Outputs

Fire behavior outputs are a key component of the model 

used to estimate the WFSI. Potential fire behavior can be 

evaluated using a fire behavior prediction program, much 

like FARSITE (Fire Area Simulator) (Finney 1998) and 

FlamMap (Finney 2006). For the FRA, the FlamMap 

program was used.

The fire behavior program uses topographic information, fuel characteristics, and weather to calculate rate of 

spread, flame length, fire type, and other characteristics 

of fire behavior. Fire behavior prediction can also be done 

using a fire behavior dynamic link language (dll) program, 

which provides a more flexible and customizable method 

of calculating the required fire behavior outputs needed for 

the risk assessment model. The fb3.dll used in the SFRA 

has the advantage of providing tight integration capabilities with geographic information systems (GIS) and other 

programs.

The main fire behavior variable calculated by the fire 

behavior prediction programs such as the fb3.dll for the 

calculation of the WFSI is fire spread rate. This variable 

was developed because it can be used to estimate a fire’s 

expected size.

For further analysis and display, it is worthy to note 

that additional fire behavior outputs such as fire intensities and flame length are available outputs of the fb3.dll 

program. The FlamMap program and the fb3.dll calculate 

the behavior of a fire occurring in each 30- by 30-m cell 

under defined weather conditions. Fire behavior is described 

independently for each individual cell.

Fire Suppression Effectiveness—Rate of Spread 

vs. Final Fire Size Relationships

For a cell, the FOA designation provides an estimate of the 

cell igniting. To calculate the WFSI, the expected size of 

a fire needs to be determined. To do this, it is necessary 

Figure 4—Surface fuel models are displayed in Florida.
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to develop relationships between fire spread rates and the 

expected final fire size. The inputs to this relationship are 

the expected fire behavior, which depends on fuels, weather, 

and topography and a measure of suppression effectiveness 

of fire protection forces.

For each weather influence zone, a relationship 

between the rate of spread and final fire size is developed 

using historical fire report data. This relationship can also 

be determined from the outputs of preparedness staffing 

modeling. Development using historical fire reports data 

requires the creation of several fire size classes where the 

time from fire start to fire containment can be estimated 

using fire report data. For all weather influence zones, the 

time from fire start to fire containment for the benchmark 

fire sizes of 0.5, 2, 10, 50, 100, 500, and 1,000 acres was 

determined. Additional fire sizes greater than 1,000 acres 

are used when fires of these sizes occurred historically 

within a WIZ.

The average fire rate of spread for each benchmark fire 

size is estimated by using the double ellipse area model 

developed by Fons (1946) as documented by Anderson 

(1983). The model calculates fire size (Area) as: Area = K 

× D2

 where K is a constant dependent solely on midflame 

wind speed, and D is the distance the fire has traveled from 

its point of origin (D = rate of spread times containment 

time). A relationship between the fire size and average rate 

of spread values for the benchmark fire sizes is developed 

using multivariate regression using a power series equation 

form (Y = A + BXC + DXE

 where X = rate of spread, Y is 

the expected fire size and A through E are the regression 

coefficients). In some cases, a fourth-order polynomial 

equation form was utilized. In some WIZes, the constant 

term A was changed so that a 0.5-acre fire was expected 

when the rate of spread was 1 chain per hour (1.1 feet per 

minute). In addition, for each WIZ a maximum fire size was 

assigned.

Example of the Calculation of the Cellular Value 

for the Probability of an Acre Burning

The cellular value for the probability of an acre burning 

(CPAB) is calculated for each percentile weather category 

for each 30- by 30-m cell on burnable acres within the State 

of Florida. The four values from the four percentile weather 

categories are summed to obtain the total cellular value for 

the probability of an acre burning for a cell. The calculation is done for cells within an FOA and WIZ intersection. 

When the calculation is done for a cell, it is assumed that all 

cells in the FOA and WIZ intersection have the attributes 

of the cell. In essence, one is asking, “What would be the 

expected probability of an acre burning if all cells in the 

FOA and WIZ intersection were the same as the selected 

cell?”

To assist in the understanding of the calculation, an 

example is presented. Assume that the calculation is being 

done for a cell in FOA 1, WIZ 1 (Figure 5). The data flow is 

shown via the example in Table 1.

For the example, assume that the fire occurrence rate 

in FOA 1 is 0.1 fire per 1,000 acres per year and assume 

there are 1,000,000 acres in the FOA 1, WIZ 1 intersection 

(Figure 5).

Note there are 100 fires per year. Row 1 of Table 1 gives 

the percentage of fires that have historically occurred within 

each of the percentile weather categories. Multiplying the 

proportion of fires in each percentile weather category by 

the total number of fires in the FOA 1 / WIZ 1 intersection 

(100 fires) allows for determination of the number of fires in 

Figure 5—Example WIZ and FOA are detailed.
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each percentile weather category, Row 2 of Table 1.

The fire program (FlamMap for the FRA and the fb3.dll for 

the SFRA) has calculated a rate of spread for each percentile weather category (Row 3, Table 1) and a rate of spread 

versus expected final fire size relationship (Row 4, Table 1) 

has been determined. This allows for the determination of 

the expected final fire size within each percentile weather 

category.

Multiplying the number of fires per year in each 

percentile weather category by the expected final fire size 

yields the annual expected acres burned for each percentile 

weather category (Row 5, Table 1). Dividing the annual 

expected acres burned for each percentile weather category 

by the total acres within the FOA 1, WIZ 1 intersection 

(1,000,000 acres) yields the CPAB within each percentile 

weather category (Row 6, Table 1). The CPAB for the cell 

is the sum of the four percentile weather category CPAB 

values (Figure 6).

To consider the flammability of cells in the area of a 

given cell, a roving window (eight cells in radius) is drawn 

around each cell, and the average WFSI for all of the cells 

within that roving window is determined resulting in the 

roving window probability of an acre burning value (Figure 

7). This allows for integration of the nearby CPAB values to 

reflect the flammability of the cells around a given cell.

Fire Effects 

The Fire Effects Index comprises two input ratings: values 

impacted and suppression difficulty. The purpose of the 

index is to identify those areas that have important values at 

risk to wildland fire or are costly to suppress, or both.

Values Impacted

Several important values potentially impacted by wildfire 

were combined into an index for inclusion in the SWRA. 

These values were also used in the FRA and include:

• Transportation and infrastructure areas

• Urban interface

• Plantations (natural and planted)

The transportation infrastructure effects were created 

from calculating a 300-m buffer around level 1, 2, and 3 

roads and a 500-m buffer around elementary and secondary 

schools, airports, and hospitals.

The wildland urban interface (WUI) was downloaded 

from the SILVIS Lab at the University of Wisconsin - 

Madison (USDA FS 2001). The WUI is composed of both 

interface and intermix communities. In both interface and 

intermix communities, housing must meet or exceed a minimum density of 1 structure per 40 acres (16 ha). Intermix 

communities are places where housing and vegetation intermingle. Intermix areas have continuous wildland vegetation 

cover of more than 50 percent and more than 1 house per 

16 ha. Interface communities are areas with housing in the 

vicinity of contiguous vegetation. Interface areas have more 

than 1 house per 40 acres, have less than 50 percent vegetation, and are within 1.5 miles of an area (made up of one or 

more contiguous census blocks) over 1,325 acres (500 ha) 

that is more than 75 percent vegetated. The minimum size 

limit ensures that areas surrounding small urban parks are 

not classified as interface WUI.

The plantation data were obtained from each individual 

state. This information was supplemented with a crosswalk 

from Gap Analysis Program data where available.

Table 1—Example calculation of the cellular Wildland Fire Susceptility Index

Percentile weather

Row Item Low Moderate High Extreme Total

1 Percentage of fires 10% 80% 8% 2% 100%

2 Number of fires 10 80 8 2 100

3 Rate of spread (chains/hr) 2 5 12 24 N/A

4 Final fire size (acres) 1 6 98 900 N/A

5 Annual acres burned 10 480 784 1800 3074

6 Cellular probability of an 0.00001 0.00048 0.000784 0.00180 0.003074

 acre burning
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Figure 6—Flagler County, Cellular Wildland Fire Susceptibility Index. 

Each value-impacted input was assigned an impact 

score by state fire managers using a matrix to assign a value 

of 1 to 4 (1 being low effect, 4 for high) for each flame 

length vs. fire size scenario. To arrive at a score for a value 

impacted, the individual values in the matrix were summed, 

i.e., 33. The Values Impacted Rating was determined 

by summing the values impacted scores for a cell × 100 

and dividing that total by the maximum possible score to 

normalize the result to a value between 1 and 100.

Suppression Difficulty Elements

The suppression difficulty elements are fuel type, topography, and soil type. A fuels layer was used to assign each 

cell in the state a fuel type of grass, shrub, timber litter, or 

slash. A topography multiplier was assigned to each of the 

following slope classes by state fire managers: slope class 

1 is 0 to 25 percent; slope class 2 is 26 to 40 percent; slope 

class 3 is 41 to 55 percent; slope class 4 is 56 to 75 percent, 

and slope class 5 is 76+ percent. Organic/peat (muck) soils 
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GENERAL TECHNICAL REPORT PNW-GTR-802

were extracted from SURGO data by the states. These soils 

constitute areas of concern for firefighting efforts, as fires 

within these areas tend to be expensive and difficult to 

extinguish.

In arriving at the Suppression Difficulty Rating, suppression costs are evaluated by fuel type and topography. 

Each burnable cell in the state was assigned a suppression 

score using a matrix process similar to the one used for the 

values impacted score. The grass, shrub, timber litter, and 

slash fuel type scores were based on professional judgment of the state fire managers. The increased difficulty of 

suppression based on slope was also made. The suppression 

difficulty score for organic/peat soils was assigned to be 60, 

which is 1.25 times the maximum score of 48. This higher 

value is to reflect the increased suppression difficulty in this 

situation. The Suppression Difficulty Rating for each cell 

was calculated by multiplying the fuel type score and the 

topography multiplier by the product of the maximum fuel 

type score and the maximum slope multiplier.

Figure 7—Flagler County, roving window Wildland Fire Susceptibility Index.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 8—Flagler County, Level of Concern (LOC).

Fire Effects Index

The Fire Effects Index was calculated as the sum of the 

Values Impacted Rating times 0.68 and the Suppression 

Difficulty Rating times 0.32. The final Fire Effects Index 

can range from 0 to 100.

Level of Concern Index

The Level of Concern Index is calculated as the Wildland 

Fire Susceptibility Index times the Fire Effects Index. The 

WFSI is a value between 0 and 1. The Fire Effects Index 

is a value between 0 and 100. Hence, the LOC is a value 

between 0 and 100.

The output values were assigned to 10 LOC categories 

ranging from low concern to high concern. The LOC output 

can be used to prioritize areas for further analysis. An 

example LOC map for Flagler County in Florida is shown 

in Figure 8.
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GENERAL TECHNICAL REPORT PNW-GTR-802

The LOC results can be used to complete a more 

detailed analysis at the local level and communicate 

wildland fire management concerns. The LOC results can 

be used to:

• Identify areas where mitigation options may be 

of value.

• Allow agencies to work together and better 

define priorities.

• Develop a refined analysis of a complex landscape and fire situations using GIS.

• Increase communication with local residents 

to address community priorities and needs.

Fire Response Accessibility Index

The Fire Response Accessibility Index (FRAI) is a relative 

measure of how long it would take initial attack resources 

to drive from their resource location to each cell. The Fire 

Response Accessibility Index is calculated based on the 

distance from resource locations. The speed traveled on 

roads was estimated based on the class of road. Travel off of 

roads was assumed to be at 5 mph. Water was coded as “NO 

DATA,” meaning that travel across water could not be done 

unless there was a road crossing. A cost distance analysis 

was run allowing Arc/Info to assign an approximate time 

to reach each cell.

The Fire Response Accessibility Index allows users 

to identify areas of low accessibility from their resources. 

Coupled with the Levels of Concern data, this information 

will highlight areas where accessibility is low and the level 

of concern is high, providing valuable information for those 

concerned with the impacts of wildland fire. An example 

FRAI map for Flagler County in Florida is shown in 

Figure 9. 

Uses and Application in Florida

During the initial development phase of the Florida Risk 

Assessment, the development team outlined the following 

project objectives:

• Rapidly identify areas that may require additional 

tactical planning.

• Allow agencies to work together to better define 

priorities and improve emergency response.

• Develop refined analysis of a complex landscape 

and fire situations using GIS.

• Increase communication with local residents to 

address community priorities and needs.

• Plan for fire protection resource needs.

• Identify fire protection resource allocation based 

on potentially severe fire problems.

Although it is generally believed that the goals and 

objectives were met, one point concerning the assessment 

should be emphasized. The FRA has many parts, and some 

of these parts have been used to support other state agency 

critical applications such as the Fire Management Information System. The success of the FRA extended beyond the 

original goals and objectives.

The Division of Forestry has produced two products to 

convey the Wildland Fire Risk in Florida. The first is the 

stand-alone desktop application that requires the following 

software: ArcView 3.x, Spatial Analyst, and FlamMap (Version 1) as well as the data for the specific areas of concern. 

This application permits the user to view the published data 

and make changes to both the fire occurrence and fuels 

to alter the relative risk in the area. The purpose of the 

modifications to fire occurrence or to fuels is to determine 

the effect a changing prevention effort or fuels management 

effort might have on the overall wildfire risk in a particular 

area. 

The second application is Web based and can be found 

at: http://www.fl-dof.com/wildfire/wf_fras.html. This tool 

allows anyone with Web access to view the four primary 

published results data layers for Florida. These include the 

Wildland Fire Susceptibility Index, which is an indicator of 

the potential for wildfire in that area; the Fire Occurrence 

Areas, which is a map of the probability of an acre igniting 

based on the fire history in an area; the Surface Fuel Model 

Layer, which maps the surface fuels across the state; and the 

Level of Concern, which is a combination of all of the above 

as well as the suppression costs and environmental effects 

to give the user the general associated risk from wildland 

fire for a particular area. This tool has been very popular 

with homeowners and the media.

As an example of the interest in the FRA by the media, 

the following example from Brevard County is provided. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 9—Flagler County, Fire Response Accessibility Index.

In May 2006, the Florida Today newspaper published an 

article about the Florida Wildland Fire Risk Assessment and 

highlighted some particular points the paper felt the general 

public should know. The paper detailed information about 

certain areas in Brevard County, which was impacted by the 

2006 fire season. The following is a list of bullets that were 

included in the article: 

• Thousands of homes in Brevard County lie within 

zones state foresters deem the most dangerous 

places for wildfires but often the least practical to 

burn or clear.

• Many single-family houses (5,000+) in Brevard were 

built on land considered at highest risk for wildfires.

• Thousands more mobile homes and other structures 

fall within the same danger zones.

• In Brevard County, 78,669 acres are in the highrisk zone, or about 12 percent of the county. Most 

of it, 49,545 acres, is in unincorporated areas, such 
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GENERAL TECHNICAL REPORT PNW-GTR-802

as those surrounding Lake Washington, west of 

Melbourne, and Lake Poinsett, west of Cocoa.

• West Melbourne, Melbourne, Melbourne Village, 

and Rockledge had the highest percentage of highrisk land, West Melbourne being the worst with 67 

percent of its 3,648 acres within the highest risk 

area.

• Trees and brush border most homes, making few 

Brevardians immune from the wildfire threat.

• State fire managers focus most of their preseason 

prevention where forest hugs neighborhoods and 

important infrastructure. So they hope people such 

as Dyan Hilton, who lives in Poinsett Trailer Park—

west of Cocoa and across from a huge wildfire 

danger zone to the south—take steps to keep the 

flames away.

In addition to the media, county and municipal governments are using the FRA as part of the county or local 

Comprehensive Planning Program that requires that all risk 

be considered as part of the planning for new development.

Fire departments and county planners are closely 

monitoring how communities structure access as well as do 

vegetation/fuel management in the initial phases of community development. It is emphasized that developers and 

homeowners should accept each party’s responsibility in the 

protection of the property. When drought conditions occur 

and the fire weather gets to the point that fires begin to 

impact neighborhoods, firefighters have difficulty providing 

structure protection at every location within a subdivision. 

Planning for the future can prevent many of the problems 

experienced in recent years.

Florida has staffed four fuels mitigation teams that 

exclusively work in urban-interface areas. The FRA is 

the primary designator as to where these teams plan their 

efforts. The FRA paints a bull’s-eye across the Florida landscape for everyone managing land and fire today, placing 

the priorities where they need to be based on fuels, climate, 

and historical fire activity.

References

Anderson, H.E. 1982. Aids to determining fuel models 

for estimating fire behavior. Gen. Tech. Rep. INT-122. 

Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment 

Station. 20 p.

Anderson, H.E. 1983. Predicting wind-driven wild 

land fire size and shape. Res. Pap. INT-305. Ogden, 

UT: U.S. Department of Agriculture, Forest Service, 

Intermountain Forest and Range Experiment Station. 

26 p.

Buckley, D.; Carlton, D.; Krieter, D.; Sabourin, K. 2006. 

Southern wildfire risk assessment project. Final Rep. 

Prepared for Texas Forest Service and the Southern 

Group of State Foresters. 123 p.

Finney, M.A. 1998. FARSITE: Fire area simulator—model 

development and evaluation. Res. Pap. RMRS-4. Fort 

Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 45 p.

Finney, M.A. 2006. Cumulative effects of fuel management 

on landscape-scale fire behavior and effects. Final Rep. 

to the Joint Fire Sciences Committee. JFS Project 01-1-2-

21. 17 p.

Fons, W.T. 1946. Analysis of fire spread in light forest 

fuels. Journal of Agricultural Research. 72(3): 93–121.

USDA Forest Service. 2001. The 2000 wildland-urban 

interface in the U.S. Madison, WS: SILVIS Laboratory, 

Forest Ecology & Management, University of Wisconsin. 

[Not paged]. http://www.silvis.forest.wisc.edu/projects/

WUI_Main.asp). [Date accessed unknown].
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

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

N.E. Grulke, research plant physiologist, USDA Forest 

Service, Pacific Southwest Research Station, Riverside, CA 

92507; T. Paine, professor, Department of Entomology, 

and R.A. Minnich, professor, Department of Geography, 

University of California, Riverside, CA 92521; and P. Riggan, research soil scientist, USDA Forest Service, Pacific 

Southwest Research Station, Riverside, CA 92507.

Abstract

Many factors increase susceptibility of forests to wildfire. 

Among them are increases in human population, changes 

in land use, fire suppression, and frequent droughts. These 

factors have been exacerbating forest susceptibility to 

wildfires over the last century in southern California. Here 

we report on the significant role that air pollution has on 

increasing forest susceptibility to wildfires, as unfolded 

in the San Bernardino Mountains from 1999 to 2003. Air 

pollution, specifically ozone (O3), and wet and dry deposition of nitrogenous compounds from fossil fuel combustion, 

has significantly increased since industrialization of the 

region after WWII. Ozone and elevated nitrogen deposition 

cause specific changes in forest tree carbon, nitrogen, and 

water balance that enhance individual tree susceptibility to 

drought and bark beetle attack, and these changes contribute 

to whole ecosystem susceptibility to wildfire. For example, 

elevated O3 and N deposition increase leaf turnover rates 

and leaf and branch litter, and decrease decomposability 

of litter. Uncharacteristically, deep litter layers develop in 

mixed conifer forests affected by air pollutants. Elevated 

O3 and N deposition decrease the proportion of whole tree 

biomass in foliage and roots, the latter effect increasing tree 

susceptibility to drought and beetle attack. Because both 

foliar and root masses are compromised, carbohydrates 

are stored in the bole over winter. Elevated O3 increases 

drought stress by significantly reducing plant control of 

water loss. The resulting increase in canopy transpiration, 

combined with [O3 + N deposition]-induced decreases 

in root mass significantly increase tree susceptibility to 

drought stress, and when additionally combined with 

increased bole carbohydrates, perhaps all contribute to 

success of bark beetle attack. Phenomenological and 

experimental evidence is presented to support the role of 

these factors contributing to the susceptibility of forests to 

wildfire in southern California.

Keywords: Bark beetle, fire suppression, forest densification, N deposition, O3 exposure.

Introduction

Many factors combine to increase forest susceptibility to 

wildfire in southern California, and most of these were 

set in motion decades ago. These factors include a rapid 

increase in human population and resource use; a shift 

from timber production to recreational forest use; fire 

suppression with subsequent forest densification; periodic, 

extreme drought; and bark beetle outbreaks. The contribution of air pollution to forest susceptibility to wildfire has 

not been studied extensively. In this paper, we will link air 

pollution to increasing forest densification, litter build up, 

drought stress, tree susceptibility to successful bark beetle 

attack, tree mortality, and increased forest susceptibility to 

wildfire (Figure 1). A case study will be presented for the 

San Bernardino Mountain Range in the Transverse Range 

north and east of Los Angeles, California. We will focus on 

pollutant effects on ponderosa pine, which dominates the 

mixed conifer forest in the western part of the range.

In the late 19th century, gold and other valuable minerals were discovered in the San Bernardino Mountains, and 

the population rapidly increased (Minnich 1988). The forest 

was logged for buildings, mine shaft support, and for fuel. 

In 1899, a severe drought occurred, water was limiting, 

and a premium was placed on reservoir development (Lake 

Gregory, Arrowhead, Big Bear). As the reservoirs were 

established, they became magnets for recreation use in the 

1920s. With the shift from resource utilization to recreation, 

incursions of fire from the chaparral into the forest were 

suppressed, and forest density increased through the 1940s. 

Air Pollution Increases Forest Susceptibility to Wildfires: A Case 

Study for the San Bernardino Mountains in Southern California
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GENERAL TECHNICAL REPORT PNW-GTR-802

In the 1950s, the Forest Service made an attempt to thin the 

forests, but, for aesthetic reasons, the mountain communities strongly opposed both branch trimming and stand thinning. As a consequence, the forest continued to increase in 

density, and trees grew increasingly closer to structures. In 

the 1980s, the community councils drew up “Forest Plans” 

that included branch trimming and thinning of trees within 

30 m of valued structures (Asher and Forrest 1982). However, these recommendations were not followed or enforced. 

The region was, and is, highly susceptible to wildfire.

Air Pollution Effects: O3 and N Deposition

The primary source of air pollution is fossil fuel combustion from trucks, cars, trains, ships, and industry (South 

Coast Air Quality Management District 1997). Fossil fuel 

combustion emits nitrogen oxides, which are converted to 

other nitrogen oxides and ozone (O3) in the presence of high 

energy UV light. Both nitrogen oxides and O3 are strong 

oxidizing agents and cause damage to cells. Ozone is transported long distances. Nitrogen oxides are not transported 

as far as O3, but at moderate O3 levels, dry and wet deposition of nitrogen to plant communities is significant (6 to 9 

kg/ha per year) (Fenn and others 1996) and accumulates 

through time. The effects of O3, N deposition, and periodic 

drought are evaluated here as contributing factors to forest 

susceptibility to wildfires.

Ozone is primarily deposited on surfaces (such as 

surfaces of leaves, branches, bark, soil, and litter), and 

there decomposes. Bauer and others (2000) estimated that 

approximately one-third of the O3 is taken into the plant 

Figure 1—Many factors contributing to forest susceptibility to wildfire were set in motion decades before ignition. However, increased 

O3 exposure and nitrogen (N) deposition significantly contribute to forest susceptibility to wildfire by increasing tree drought stress (with 

subsequent increase in successful bark beetle attack) and promoting formation of deeper, more recalcitrant litter layers.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

via stomata. When plants take up CO2, they also take up 

O3. Once O3 enters the leaf, it may be decomposed in a 

thin film of water (apoplastic water) that surrounds cells 

in the substomatal chamber, or it may pass across the cell 

membrane to the chloroplast where it degrades photosynthetic pigments. The decomposition of O3 in the apoplastic 

water requires the regeneration of oxidized ascorbate with 

glutathione in the cytosol and energy (De Kok and Tausz 

2001). As it passes across membranes, the acidity changes, 

and membrane permeability is altered such that ions that 

should be retained by the cell now leak out (K+), others 

influx along a chemical gradient (Zhang and others 2001), 

and other mechanisms of ion transport are blocked (e.g., 

Ca++ channeling) (McAinsh and others 2002). When strong 

oxides degrade photosynthetic pigments in the chloroplast, 

the pigments must be reconstructed into functional arrays, 

which requires energy.

The first measurable effect of O3 on plants is a decrease 

in the efficiency of photosynthesis or the carbon-capturing 

mechanism. Because O3 damages tissue, there is a metabolic cost in energy and constituent building materials 

that increases respiration and decreases the total carbon 

stored by the plant. Carbon-carbon links store energy in 

plants for later use. Lower total carbon stored and greater 

requirements for N (to build more photosynthetic pigments) 

result in retranslocation of materials out of older needles. 

Older branches are excised in O3-exposed trees because the 

net carbon balance is lower. Presumably, when net carbon 

balance drops below zero, the branch is excised (Sprugel 

and others 1991).

At the whole plant (tree) level, O3 exposure results 

in premature senescence of needles (within-whorl loss of 

needles, fewer needle-age classes) in ponderosa pine (Pinus 

ponderosa Dougl. ex Laws) (Grulke and Balduman 1999) 

and lower branch abscission (Miller and others 1996b). 

Greater O3 exposure and N deposition cause more needle 

and branch loss. The western end of the San Bernardino 

Mountains, the part of the mountain range closest to Los 

Angeles, has the highest N deposition and O3 exposure 

(30 to 40 kg N/ha per year, 80 parts per billion (ppb) O3

per hour, averaged over 24 hours for the 6-month growing 

season). Premature needle senescence is so extreme that 95 

percent of the total canopy biomass in whole-tree harvests is 

in current year foliage (Grulke and Balduman 1999). At an 

atmospherically clean site (low N deposition, and 38 ppb O3

per hour, averaged over 24 hours for the 6-month growing 

season) near Lassen Volcanic National Park, canopy foliar 

biomass was evenly distributed across four to five needleage classes. In response to pollutant deposition, increased 

needle and branch loss significantly contribute to increased 

litter inputs to the mixed conifer ecosystem. Needles 

produced in high O3 exposure environments have higher 

lignin content. Greater lignin content reduces decomposability (Fenn and Dunn 1989), further exacerbating litter 

layer buildup. For example, in the western end of the San 

Bernardino Mountain Range, where trees are most affected 

by transported air pollutants, litter depth averaged 25 cm. In 

the eastern end of the range with significantly lower longterm pollutant exposure, litter depth averaged 0 to 3 cm 

(N.E. Grulke, field observations). Significant litter buildup 

in mixed conifer forest was predicted by the simulation 

model BGC for both high N deposition and O3 exposure 

(Arbaugh and others 1999) based on field leaf turnover rates 

(Miller and others 1996a).

Perhaps because of the increased repair costs for 

aboveground tissues, less biomass is retained in roots. From 

the western to the central San Bernardino Mountains, both 

higher O3 concentration and greater N deposition contributed to significantly lower fine and medium root mass at 

10-, 30-, and 50-cm depths. Root mass at a cleaner site was 

6 to 14 times as great as that at a moderate pollution site 

(6 to 9 kg N/ha per year, and 62 ppb O3 per hour, averaged 

over 24 hours for the 6-month growing season) (Grulke and 

others 1998).

Ponderosa pine had lower leaf mass, lower root mass, 

and lower carbohydrate content in both leaf and root tissue 

in areas with higher pollution exposure. In general, over the 

winter, carbohydrates for spring growth are stored in the 

roots. However, because of the lower leaf and root mass, 

overwintering carbohydrate is stored in tree boles (Grulke 

and others 2001). Although the theory is untested, increased 

carbohydrate storage in tree boles may enhance fecundity of 

bark beetles.
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At the scale of the stand, both N deposition and 

moderately high to high O3 concentrations increased 

stand density (Arbaugh and others 1999, Miller and others 

1989), especially on north-facing slopes and in microsites 

with more water availability or lower evapotranspiration 

(topographic lows). Individual tree growth further increased 

after O3 concentrations had been decreased in the early 

1990s because of heightened regulatory controls (see Tingey 

and others 2004). Across seven sites varying in both O3

exposure and N deposition, three of the four sites with the 

highest pollutant load (Cedar Pines Park, Dogwood Campground, and Camp Angeles) had the highest tree mortality 

rate (primarily ponderosa pine) (Arbaugh and others 1999).

Effects of Periodic Drought

Although there is an increase in evapotranspiration from 

west to east, weather that results in precipitation in the San 

Bernardino Mountains is generally a regional phenomenon. 

We have used the longest record of precipitation for the 

range, collected over the last 120 years at Big Bear Dam 

(San Bernardino Water Management District), to identify 

the level of drought stress experienced by ponderosa pine 

from year to year. Moderate drought stress is defined 

physiologically as reduced cell turgor that generally results 

in reduced stomatal conductance (reduced water loss from 

the leaf), and lower cellular water potential, which allows 

the tissue to hold onto the water that is in the leaf more 

tenaciously (Levitt 1980). In 1994, a year of 80 percent of 

the average precipitation (preceded by an above-average 

precipitation year), ponderosa pine experienced moderate drought stress from mid-July through the end of the 

growing season (Grulke 1999). Severe drought stress is 

also accompanied by reduced cell turgor, reduced stomatal 

conductance, and reduced cell-water potential. The water 

potential is lowered sufficiently that cell solutes are concentrated enough to disrupt enzymatic function, and cell turgor 

is reduced enough and for a long enough duration that cell 

elongation growth is limited. Needles produced in years 

of severe drought stress are shorter. In 1996, a year of 60 

percent of the average precipitation (preceded by an aboveaverage precipitation year), ponderosa pine experienced 

Figure 2—Long-term record of precipitation at Big Bear Dam, California. Total annual precipitation 

(y-axis in cm) from October 1 to September 30 was accumulated and plotted. The overall average is 

denoted by a solid line (96 cm), with the 80 percent of average (below which moderate drought stress is 

incurred) and the 60 percent of average (below which severe drought stress is incurred) denoted by dashed 

and dotted lines respectively.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

severe drought stress from the end of June through the end 

of the growing season (Grulke 1999). Over the period of the 

long-term precipitation record, roughly 15 percent of the 

years had low enough total annual precipitation to result in 

moderate drought stress; 30 percent of the years had low 

enough total annual precipitation to result in severe drought 

stress. Using this rough index of the level of physiological 

stress, ponderosa pine experienced drought stress 45 percent 

of the years since 1883 when precipitation records were 

initiated (Figure 2).

Where O3 exposure and nitrogen deposition reduce root 

biomass, trees are predisposed to drought stress. In general, 

low to moderate O3 exposures (<60 ppb hourly O3, averaged over 24 hours for the 6-month growing season) reduce 

water loss from trees. Ozone reduces photosynthetic rates, 

less CO2 is required, and stomatal apertures are reduced 

to conserve water. However, under concentrations that are 

moderately high or higher, O3 exposure modifies stomatal 

behavior in ways that increase drought stress.

For example, sugar maple (Acer saccharum Marsh.)

was exposed to O3 concentrations of 70 ppb during daylight 

hours (Tjoelker and others 1995). Early in the growing 

season and experiment, neither the net photosynthetic rate 

nor stomatal conductance was affected by the treatment. 

By midseason, there was a significant decrease in water-use 

efficiency—at the same level of carbon gain, seedlings 

growing in chronic O3 exposure had twice the level of water 

use as had control seedlings grown in charcoal-filtered 

air. By late season, both net photosynthesis and stomatal 

conductance were suppressed in plants grown in chronic O3

exposure. In a field study of sensitive and tolerant genotypes 

of Jeffrey pine (Pinus jeffreyi; Grev. & Balf.) exposed to 

the same ambient O3 levels (~68 ppb O3 averaged over 24 

hours, for the 6-month growing season in Sequoia National 

Park), sensitive genotypes had lower water loss under 

moist, favorable conditions and higher water loss under 

dry, unfavorable conditions (Patterson and Rundel 1989). 

Under favorable conditions, Jeffrey pine had less water loss, 

but because the stomatal apertures were smaller, there was 

also less photosynthetic carbon gain. Under unfavorable 

conditions (most of the day in the Sierra Nevada), sensitive 

Jeffrey pine had higher water loss, which would result in 

greater desiccation.

Although physiologists often report plant response 

under steady state (stable) conditions, the light environment 

in the forest is often dynamic. Understanding stomatal 

responses under rapidly changing environmental conditions 

with concurrent O3 exposure can perhaps better explain 

why trees exposed to moderately high and higher concentrations of O3 lose more water. In typical forest environments, 

foliage on a primary branch on the southern aspect of an 

open-grown tree receives flecky light two-thirds of the time 

(Grulke 2000). For example, the cutleaf coneflower (Rudbeckia laciniata L. var. digitata Mill. Fiori) is one of the 

most sensitive native plants to ambient O3 concentrations in 

Great Smoky Mountains National Park. It persists in forest 

gaps and on forest-meadow margins, both with flecky light 

environments. Tolerant plants of cutleaf coneflower had 

normal responses to experimentally manipulated change in 

light from low to high and back down to low levels. However, O3-sensitive plants had either no stomatal response 

or a muted stomatal response to changes in light level. The 

level of water loss from leaves with no or muted response 

to changing light level was high—they did not conserve 

water when light was low, and this failure to conserve 

water would contribute to desiccation. When humidity was 

lowered slowly, O3-sensitive plants closed their stomata at 

much lower relative humidities than did O3-tolerant plants, 

and this also contributed to greater desiccation (Grulke 

and others 2007a). In a similar experiment with California 

black oak saplings (Quercus kelloggii Newb.) exposed to 

anthropogenic high O3 in a natural stand, stomatal closure 

in response to abruptly reduced light level was slower in 

plants without additional N amendment, and N amendment partially mitigated the desiccating effects of high O3

exposure (Grulke and others 2005).

Moderate to high O3 exposure can also cause stomata 

to remain partially open at night. In experimental O3

exposures, this was first observed in Norway spruce (Picea 

abies (L.) Karst), (Weiser and Havranek 1993) and in 

birch (Betula pendula Roth), (Matyssek and others 1995). 

Nighttime water loss rates were 25 percent as great as 
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GENERAL TECHNICAL REPORT PNW-GTR-802

full daytime rates for Norway spruce, and 50 percent as 

great for birch. This was corroborated in ponderosa pine 

across the San Bernardino Mountain pollution gradient, 

with both higher O3 and NO2 and HNO3 exposure. In the 

San Bernardino Mountains, nighttime water losses were 

10 percent of full daytime rates (Grulke and others 2005). 

Because these studies were largely phenomenological, a 

new gas exchange system was designed and built to directly 

test known O3 concentrations on single leaves. Chronic, 

moderate O3 exposure (70 ppb O3 for 8 hours per day for 1 

month) significantly increased nighttime foliar water loss 

in California black oak and blue oak (Quercus douglasii 

Hook. & Arn.) (Grulke and others 2007b). Nighttime water 

losses were attributable directly to O3 exposure and were 

30 percent and 20 percent, respectively, of daytime rates 

in these species. Moderately high (or higher) O3 exposure 

increases foliar water loss and increases tree susceptibility 

to drought stress.

Susceptibility to Successful Bark Beetle 

Attack

Air pollution exposure (O3 and N deposition) increases tree 

susceptibility to drought stress, and drought stress increases 

tree susceptibility to successful beetle attack. Dunning 

(1928) was one of the first to report a relationship between 

drought conditions and increased levels of tree mortality 

caused by western bark beetles. In the west, bark beetles 

reach epidemic proportions after 2 to 3 years of drought. 

The correlation between beetle attacks and climate can be 

diffuse because bark beetles may delay or prolong the exact 

time of tree mortality. However, mortality tends to increase 

in multiyear droughts, particularly in highly dense stands or 

those with pre-existing damage or stress. Figure 2 indicates 

that sequences of 2 to 3 years of drought have occurred nine 

times (Table 1) in the last 120 years. We can document five 

bark beetle epidemics associated with those sequences.

Beetles are opportunists that attack trees in a weakened 

state. With only a few exceptions, either the host tree is 

killed by the colonizing bark beetles or the host resistance 

of the tree kills the attacking adults. To kill a tree, large 

numbers of bark beetles must successfully colonize it in 

a relatively short period of time (Paine and others 1984, 

1997). However, fewer beetles may be sufficient to kill 

a compromised tree (Paine and others 1984). The bark 

beetles most commonly responsible for tree mortality in 

the western San Bernardino Mountains are western pine 

beetle (Dendroctonus brevicomis) and mountain pine beetle 

(D. ponderosae). Western pine beetle can produce up to 

four generations in a year in southern California, where 

the Mediterranean climate is conducive to rapid population 

expansion when an abundance of susceptible host material 

is available for colonization.

Eggs are laid in the inner bark and the larvae excavate 

galleries. Pupation occurs in either the inner or outer 

bark, depending on the species of beetle. Adults emerge 

from the larval host tree and search for susceptible hosts. 

Healthy pines and firs respond by exuding pitch, which 

either pitches out the adults or blocks their progress. Resin 

production impedes bark beetle attack both physically and 

chemically. Oleoresin pressure, caused by turgidity of cells 

lining the resin ducts, forces preformed resin to the site of 

injury or invasion and results in a flushing action. The cell 

turgidity is derived from the transpirational stream, so if the 

tree is under moisture stress, the cells become increasingly 

flaccid, the resin pressure is reduced, and the effectiveness 

Table 1—Historical record of drought years and bark 

beetle epidemics in the San Bernardino Mountains

Aver. Beetle

Drought years percent ppt epidemic?

1898-1900 50 Not known

1923-1925 66 Not known

1927-1930 70 Not known

1948-1951 79 Yes1

1959-1961 63 Yes1

1970-1972 59 no 

1976-1977 61 Yes1

1988-1990 45 Yes1

1999-2002 45 Yes2

Note: Shown are the years of moderate or severe drought, the average 

percentage of total average annual precipitation (based on the 120-year 

record), and whether a bark beetle epidemic occurred after the sequence 

of drought years.

1 California Pest Reports, 1949 to present.

2 N. Grulke, personal observation.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

of the preformed resistance is compromised (Vite 1961). In 

weak trees with reduced resin pressure, the adults are able 

to initiate colonization and produce specific pheromones 

that attract other colonizing adults. Pheromone production 

ceases when the host tree ceases resin flow (i.e., when the 

tree dies) (Raffa and Berryman 1983).

Severe drought and other stresses also reduce the 

photosynthetic capacity of trees and the levels of carbohydrates used for growth, defense, and tissue repair. This can 

have significant impact on the ability of the tree to induce 

an effective response to invasion (Paine and Stephen 1987a, 

1987b). Drought-stressed trees are also known to have 

elevated levels of free, translocatable proteins (Lei and 

others 2006), which are produced to generally increase cell 

osmoticum. We conjecture that increased bole carbohydrate 

content as a result of O3 exposure + N deposition + drought 

and elevation of protein levels in response to drought 

enhance beetle fecundity. Pollutant-exposed trees may thus 

be primed for successful bark beetle attack.

The forest had been recently thinned early in the 

late 19th century by commercial logging, so we would 

not expect to observe an epidemic beetle infestation in 

immature, low-density stands despite the drought stress 

experienced in the late 1920s (Minnich and others 1995). 

Human population in the Los Angeles Basin significantly 

increased after World War II, but air pollution levels were 

not quantified (or reconstructable) until 1963. From 1963 

through 1980, peak 1-hour O3 concentrations averaged 

250 to 425 ppb (Lee and others 2003). From 1980 on, peak 

1-hour O3 concentrations were still high (>250 ppb), but 

cumulative O3 exposures over the growing season began to 

decline. Through strong regulatory controls, O3 concentrations declined further to tens of occurrences to only isolated 

occurrences of hourly concentrations exceeding 95 ppb 

from the mid-1990s to present in the mountains. Throughout this time, N deposition continued to accumulate, and 

drought stress was exacerbated by chronic, if not acute, O3

exposure.

The most extreme drought recorded in 250 years was 

experienced in the hydrologic year 2002 (10/1/01 through 

09/30/02) after 3 years of chronic drought. Results of 3 

years of chronic drought (1999–2001) and extreme drought 

(2002) are shown in a sequence of imagery taken at 5 km 

above the forest canopy at the most polluted site in the 

western San Bernardino Mountains (Figure 3). After the 

chronic drought, bark beetles attacked, and there were clusters of tree mortality. However, the average stand tree mortality rate was near 0 percent. After the chronic and severe 

drought, tree mortality (primarily ponderosa pine) from 

both bark beetle and drought increased to approximately 5 

percent at the stand level. In the spring following the wet 

winter, bark beetle populations reached epidemic proportions, and 42 percent of the stand had died (ponderosa pine, 

white fir [Abies concolor (Gord. & Glend.) ex Hildebr.], and 

sugar pine). The stand was at high risk for an intense fire 

with high litter layers, high numbers of standing dead trees, 

and exacerbated drought stress. In autumn of that year, the 

Old Fire swept through the stand. Interestingly, not all of the 

red trees (standing dead trees with needles retained) were 

consumed in a crown fire because the highly dense understory was not in contact with the lower branches of the 100+ 

year-old trees—the effects of O3 exposure, N deposition, 

and drought had promoted lower branch abscission (Miller 

and others 1996b), so that the lowest branches were attached 

at heights of 60 ft or greater. Trees are still dying from bark 

beetle at this site (as of 7/06), but the rate of change is now 

statistically undetectable.

Conclusions

The role of air pollutants in increasing tree susceptibility 

to drought, successful bark beetle attack, tree mortality, 

and the susceptibility of forests to wildfire have not been 

formally supported. Air pollutants, specifically strong 

oxidants and nitrogen deposition, contribute to increased 

litter accumulation and increased tree susceptibility to 

drought stress. It is well known that drought-stressed trees 

are more susceptible to bark beetle attack. The combination of chronic drought in 1999-2001 and acute drought in 

2002 resulted in a bark beetle epidemic in the western San 

Bernardino Mountains. We contend that the severity of tree 

mortality in the western San Bernardino Mountains was 

significantly exacerbated by the higher air pollutant deposition in this region.
326

GENERAL TECHNICAL REPORT PNW-GTR-802

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

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

Kevin M. James, and R. Brion Salter

Paul F. Hessburg, research ecologist, and Keith M. 

Reynolds, research forester, USDA Forest Service, Pacific 

Northwest Research Station, Corvallis, OR 97331; Robert 

E. Keane, research ecologist, USDA Forest Service, Rocky 

Mountain Research Station, Missoula, MT 59808; Kevin 

M. James, botanist, and R. Brion Salter, geographer, 

USDA Forest Service, Pacific Northwest Research Station, 

Wenatchee, WA 98801.

Abstract

We present a prototype decision support system for evaluating wildland fire danger and prioritizing subwatersheds 

for vegetation and fuels treatment. We demonstrate the use 

of the system with an example from the Rocky Mountain 

region in the State of Utah, which represents a planning 

area of about 4.8 million ha and encompasses 575 complete 

subwatersheds. In a logic model, we evaluate fire danger 

as a function of three primary topics: fire hazard, fire 

behavior, and ignition risk. Each primary topic has secondary topics under which data are evaluated. The logic model 

shows the state of each evaluated landscape with respect 

to fire danger. In a decision model, we place summarized 

fire danger conditions of each evaluated landscape in the 

context of the amount of associated wildland-urban interface (WUI). The logic and decision models are executed in 

EMDS, a decision-support system that operates in ArcGIS. 

We show that a decision criterion such as relationship to 

WUI can significantly influence the outcome of a decision 

to determine treatment priorities. For example, we show that 

subwatersheds that were in relatively poor condition with 

respect to fire hazard, behavior, and ignition risk may not 

be the best candidates for treatment. Additional strategic 

or logistical factors such as proximity to population centers, 

presence of endangered species, slope steepness, and 

road access all might be taken into account in selection 

of specific watersheds within a management area for treatment. Thus, the ecological status of each ecosystem can be 

placed in one or more social and human values contexts to 

further inform decisionmaking. The application introduced 

here can be readily expanded to support strategic planning 

at national and regional scales and tactical planning at local 

scales.

Keywords: Decision support, EMDS, fire behavior, fire 

danger, fire hazard, forest restoration, ignition risk, landscape evaluation, monitoring, NetWeaver, wildland-urban 

interface.

Introduction

Wildland fuels have accumulated in many western forests of 

the United States for at least the past 70 years owing to 20th

century settlement and management activities (Agee 1998, 

Hessburg and Agee 2003), and, to some extent, changing 

climatic conditions (Burkett and others 2005, Schoennagel 

and others 2004). As demonstrated by recent wildland fires, 

added fuels are fostering more intense wildfires that are 

more difficult to contain and control. Consequently, valuable property and natural resources have been destroyed, 

costs of fire management have escalated, fire-dependent 

forest ecosystems have deteriorated, and risks to human life 

and property continue to escalate (U.S. GAO 2002, 2003, 

2004).

Historically, fires of varying size, frequency, and intensity maintained spatial patterns of forest vegetation, as well 

as temporal variation in those patterns (Agee 2003, Hessburg and others 2005, Schoennagel and others 2004, Turner 

1989). In fact, many agents interacted to shape vegetation 

patterns and their spatio-temporal variation, including forest 

insect outbreaks, forest diseases, fires, weather and climatic 

events, and intentional aboriginal burning (Hessburg and 

others 2005, Whitlock and Knox 2002). Their interactions 

resulted in characteristic landscape patterns and caused 

variation in forest structural attributes, species composition, 

and habitats that resonated with the dominant disturbance 

Evaluating Wildland Fire Danger and Prioritizing Vegetation 

and Fuels Treatments1

1

This document also appears in 2007 Forest Ecology and Management. 247(1–3): 1–17.
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GENERAL TECHNICAL REPORT PNW-GTR-802

processes. Patterns of forest vegetation were directly 

linked with the processes that created and maintained them 

(Hessburg and others 2005, Pickett and White 1985, Turner 

and others 2001).

Circumstances are quite different today. Patterns and 

processes are still tightly linked, but not as before. Human 

influences have created anomalous vegetation patterns, and 

these patterns support fire, insect, and disease processes 

that display uncharacteristic duration, spatial extent, and 

intensity (Ferry and others 1995, Hessburg and others 

2005, Kolb and others 1998). For example, 20th century fire 

suppression and prevention programs significantly reduced 

fire frequency in many dry mixed coniferous forests. 

Contemporary wildland fires are now larger and more 

intense on average than those of the prior 2 or 3 centuries 

Healthy Forests Restoration Act 2003; (U.S. GAO 2002, 

2003, 2004, and references therein). In short, settlement 

and management activities have altered spatial patterns 

of forest structure, composition, snags, and down wood at 

patch to province scales. As a result, significant changes 

in fire frequency, severity, and spatial extent are linked to 

changes in forest vegetation patterns at patch to province 

scales (Agee 1998, 2003, Ferry and others 1995, Hessburg 

and Agee 2003).

Here, we present a decision-support system for evaluating wildland fire danger and prioritizing subwatersheds for 

vegetation and fuels treatment. In our descriptions, we 

adopt the nomenclature of the National Wildfire Coordinating Group (NWCG 1996, 2005) and Hardy (2005). The 

decision-support system consists of a logic model and a 

decision model. In the logic model, we evaluate danger as a 

function of three primary topics: fire hazard, fire behavior, 

and risk of ignition. Each primary topic has secondary 

topics under which data are evaluated. The logic model 

shows the state of each evaluated landscape with respect to 

fire danger. In the decision model, we place the fire danger 

summary conditions of each evaluated landscape in the 

context of the amount of associated wildland-urban interface (WUI). The logic and decision models are executed 

in EMDS (Reynolds and others 2003), a decision-support 

system that operates in ArcGIS. We show that a decision 

criterion such as relationship to WUI can significantly 

influence the outcome of a decision to determine treatment priorities. We demonstrate use of the system with 

an example from the Rocky Mountain region in the State 

of Utah, which represents a planning area of about 4.8 

million ha and encompasses 575 complete subwatersheds. 

We discuss considerations for extending the application to 

support strategic planning at national and regional scales 

and tactical planning at local scales.

This decision-support system is comparable in 

some aspects to the National Fire Danger Rating System 

(NFDRS) (Burgan 1988, Deeming and others 1977), but 

there are important differences and advances, too. For 

example, the NFDRS summarizes fire danger information 

pertaining to fire hazard, fire behavior, and ignition risk, 

the primary topics of fire danger, at a regional scale using 

annual weather and forest conditions information. The 

fire danger variables computed by FIREHARM and used 

in this application reflect a broader set, are computed at a 

stand or patch scale, and summarized to subwatersheds, and 

the variables are computed as probabilities of exceeding a 

severe fire threshold using 18 years rather than a single year 

of data.

Methods

Study Area

We selected one map zone as a proving ground for our modeling approach, but these methods could be applied to any 

and all United States map zones. Map zones were developed 

in the United States by the Earth Resources Observation 

and Science (EROS) Data Center (http://www.nationalmap.

gov). They are broad biophysical land units represented 

by similar surface landforms, land cover conditions, and 

natural resources; there are 66 in the continental United 

States (Figure 1). Map zone 16 falls almost entirely within 

the State of Utah. Within this study area, we evaluated wildland fire danger for the 575 subwatersheds that were entirely 

contained within map zone 16 (Figure 2). The average size 

of subwatersheds was 8,300 ha, and size ranged from 2,800 

to 18,000 ha. For reference, a subwatershed represents the 

6th level in the watershed hierarchy of the U.S. Geological 

Survey (Seaber and others 1987).
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Data Sources

Most spatial data used in this study came from the LANDFIRE prototype project mapping effort. The LANDFIRE 

project created spatial data layers of topography, biophysical 

environments, vegetation, and fuels at 30-m resolution for 

two map zones in the Rocky Mountains (map zones 16 and 

19). All layers were available at the http://www.landfire.gov 

Web site.

The fuels layers used in this study included two surface 

fuel classifications: (1) the 13 fire behavior fuel models 

(FBFM) of Albini (1976), defined by Anderson (1982), and 

mapped using methods described by Keane and others 

(1998, 2000, 2007); and (2) the default fuel characterization 

classes defined in the Fuel Characterization Classification 

System (FCCS) described by Sandberg and others (2001) 

(http://www.fs.fed.us/pnw/fera) and mapped using methods 

described by Keane and others (2007). The FBFMs, which 

do not represent actual surface fuels, provided an indication 

of the expected surface fire behavior whereas the FCCS 

classes indicated the characteristics of the actual surface 

fuelbed, information useful for fire effects simulation (Ottmar and others 2004). In the next update of our fire danger 

model, we will incorporate the expanded set of 40 recently 

derived fire behavior fuel models of Scott and Burgan 

(2005). Note that when we refer to “fire behavior” we are 

referring to the physical characteristics of the combustion 

process (Rothermal 1972). When we refer to "fire effects" 

we are referring to the direct and indirect consequences of 

the combustion process (DeBano and others 1998).

The canopy fuels layers used were the LANDFIRE 

canopy bulk density and canopy base-height layers. Canopy 

bulk density (CBD) represents the mass of available 

canopy fuel per unit volume of canopy in a stand (Scott 

and Reinhardt 2002), and it is defined as the dry weight 

of available canopy fuel per unit volume of the canopy 

including the spaces between the tree crowns (Scott and 

Figure 1—Map zones of the United States from the Earth Surface Resources and Science (EROS) Data Center. There are 66 map zones 

in the continental United States. The study area is map zone 16. 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Reinhardt 2001). Canopy base height (CBH) represents 

the level above the ground at which there is enough aerial 

fuel to carry the fire into the canopy, and it is defined as 

the height from the ground to the bottom of the live canopy 

(Scott and Reinhardt 2001) but may also include dense, dead 

crown material that can carry a fire. These two map layers 

were developed for the forested lands of map zone 16 using 

a predictive landscape modeling approach that integrated 

remotely sensed data, biophysical gradients, and field 

reference data (Keane and others 2007). The canopy fuel 

characteristics were calculated for numerous plots distributed throughout the map zone using the FUELCALC model 

(Scott and Reinhardt 2001), and each plot was described 

from a set of predictor variables computed and mapped

specifically for the LANDFIRE project. The predictor 

variables were related to CBD and CBH using a classification and regression tree (CART) approach.

Fire behavior was simulated with these surface and 

canopy fuel layers assuming 90th percentile weather 

conditions using the FIREHARM (Keane and others 2004) 

program to estimate surface fire spread rate, flame length, 

and fireline intensity based on the Rothermel (1972) fire 

spread model and crown fire intensity and spread based 

on the Rothermel (1991) and the Scott (1999) crown fire 

algorithms. FIREHARM is a computer program that calculates four fire behavior variables (fireline intensity, spread 

rate, flame length, crown fire potential), five fire danger 

variables (spread component, burning index, energy release 

component, Keetch-Byram drought index [Burgan 1993], 

ignition component), and five fire effects variables (smoke 

emissions, fuel consumption, soil heating, tree mortality, 

scorch height) for each day across an 18-year climate record 

(6,574 days), and for every polygon in a user-specified 

landscape. Daily values across the 18-year period can be 

used to estimate probabilities that fire behavior, fire danger, 

or fire effects variables may exceed important thresholds. 

These probabilities can be mapped onto the landscape in a 

geographic information system (GIS), and maps can be used 

to prioritize, plan, and implement fuel or fire treatments.

In addition, LANDFIRE provided a fire regime condition class (FRCC) digital map created by simulating historical landscape conditions and comparing these simulations 

with current vegetation conditions derived from satellite 

images. FRCC is an ordinal index with three categories that 

describe how far the current landscape has departed from 

presettlement-era conditions (Hann 2004) (see http://www.

frcc.gov for complete details).

Several other data layers were used to derive ignition risk. Relative plant greenness was estimated from an 

AVHRR image from June 1, 2004 (Burgan and Hartford 

1993). These data were obtained from the USDA Forest 

Service, Rocky Mountain Research Station, Missoula Fire 

Sciences Laboratory. The effects of long-term drought 

were estimated from Palmer Drought Severity Index data 

obtained from the National Climate Data Center. Available 

PDSI data represented a span of 20 years (1971-1990), and 

Figure 2—Subwatersheds of map zone 16 in State of Utah, U.S.A. 

The average size of subwatersheds was about 8,274 ha (min 5,000 

ha, max 10,000 ha). A subwatershed represents the 6th level in the 

established U.S. Geological Survey watershed hierarchy (Seaber 

and others 1987).
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data were derived from a 2.5-degree continental scale grid 

of PDSI reconstructed by Cook and others (2004). Lightning strike data were obtained from the National Lightning 

Detection Network (Vaisala 2010). 

Broad Outline

We evaluate relative fire danger in individual subwatersheds 

of an entire map zone. We show how evidence for fire danger can be modeled as a logic-based discourse in a decisionsupport system to support national, regional, and local 

landscape analysis and planning. Results of evaluations are 

expressed in terms of evidence for low wildfire danger in 

each subwatershed. This information is used subsequently 

in a decision model to prioritize subwatersheds for treatment, considering additional logistical information.

Implementation Steps

Under the fire hazard topic (Table 1), we estimated for each 

elementary topic (lowest level in the model where data are 

evaluated) the percentage area and degree of aggregation 

of observations exceeding a specified threshold value using 

spatial data layers provided by the LANDFIRE project and 

a spatial analysis program (FRAGSTATS, McGarigal and 

others 2002, Table 2). For each elementary topic under fire 

behavior and ignition risk, we estimated the probability that 

conditions within a given watershed exceeded a specified 

threshold value based on spatial layers of fire spread rate 

and intensity generated by the FIREHARM model using 

the Rothermel (1972) spread model. We constructed a logic 

model within the EMDS modeling system to show how all 

elementary topics contributed to an evaluation of fire danger. We evaluated evidence for low wildfire danger within 

watersheds of a map zone to provide an ecological basis 

for determining treatment priority. A decision analysis was 

then run in a separate but related decision model to incorporate ecological and logistical considerations for planning 

fuels treatment across the study area.

Logic Model Design

We graphically designed the logic model for evaluating the 

relative danger of wildland fire (hereafter, fire danger) with 

the NetWeaver Developer (Rules of Thumb, Inc., North 

East, PA) modeling system. Note that 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. We present the formal logic specification both as a topic outline for readability 

and compactness (Table 1) and as a dendrogram (Figure 

3). Each topic in a NetWeaver model represents a topic for 

which a premise or proposition is evaluated. For example, 

the overall fire danger topic, representing the top level in the 

model, evaluates the proposition that wildland fire danger is 

low (Table 1, Figure 3). All other propositions in the model 

similarly take the null form; i.e., the test for all topics is 

always for a low condition.

The complete evaluation of fire danger depends on 

three primary topics—fire hazard, fire behavior, and ignition risk—each of which incrementally contribute to the 

evaluation of fire danger, as indicated by the union operator 

(Table 1). Moreover, because the union operator specifies 

that premises incrementally contribute to the proposition 

of their parent topic, low strength of evidence for one 

topic can be compensated by strong evidence from others. 

Notice that if the fire danger topic is thought of as testing 

a conclusion, then the three topics on which it depends can 

be thought of as its logical premises. Similarly, each of the 

three topics under fire danger has its own logic specification 

that includes a set of secondary topics or premises. The full 

logic structure (Table 1), considered in its entirety, constitutes what we referred to earlier as the logical discourse. 

Note that this logic model represents one of many possible 

logical configurations, and the current configuration is 

readily adapted. Any of the primary and secondary topics 

may be modified, and topics may be added or removed with 

relative ease. Likewise, thresholds of elementary topics (discussed below) can be modified to fit customized or evolving 

evaluations as a function of adaptation and learning.

Primary Topic—Fire Hazard—

Evaluation of fire hazard (Table 1, Figure 3) depends on 

the union of topics addressing surface fuels, canopy fuels, 

and fire regime condition class, each of which depends on 

two additional elementary topics that directly evaluate data 

(Tables 1, 2). Evaluation of each elementary topic under 
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GENERAL TECHNICAL REPORT PNW-GTR-802 Table 1—Logic outline for evaluation of wildfire dangera Model Primary Secondary Elementary Propositionc topic topicb topic topic (stated in the null form) Data inputsd Fire danger Danger of severe wildfire is low (unione

)

Fire hazard Fuel conditions do not support severe wildfie

 (union)

Surface fuels Condition of surface fuels is not conducive to (union) severe wildfire

Fire behavior Expected fire behavior is not severe FBFMarea, 

 fuel model FBFMaggregation

Fuel characteri- Observed fuel load classes are not conducive to FCCarea, 

 zation class severe wildfire FCCaggregation 

Canopy fuels Condition of canopy fuels is not conducive to (union) severe wildfire

Canopy bulk Canopy bulk density is not conducive to severe CBDarea,

 density wildfire CBDaggregation

Canopy base height Canopy base height is not conducive to severe wildfire CBHarea,

CBHaggregation 

Fire regime Fire regime condition class is not conducive to FRCCarea,  condition severe wildfire FRCCaggregtion class

Fire behavior Expected fire behavior associated with wildfire

 (union) is relatively benign or low impact

Spread rate Likelihood of high spread rate of surface fire is low Spread rate

Flame length Likelihood of high flame length is low Flame length

Fireline intensity Likelihood of high fire line intensity is low Fireline intensity

Crown fire potential Likelihood of high crown fire spread potential is low Crownfire potential

Ignition risk Likelihood of wildfire ignition is low

 (union)

Palmer drought Likelihood of long-term drought is low Palmer index

 severity index 

Keetch-Byram Likelihood of short-term drought is low Keetch-Byram 

 drought index index

AVHRR NDVIf Relative plant greenness for the subwatershed is high AVHRR-NDVI

Lightning strike Relative lightning strikes in the subwatershed are low Lightning strike

a The logic outline specifies how data related to wildfire danger (Table 2) are interpreted in NetWeaver®, a logic modeling system. b The level of each primary, secondary, and elementary topic in the outline is indicated. The overall topic of the model is wildfire danger. Evaluation of overall fire danger depends directly 

on the evaluation of the primary topics—fire hazard, fire behavior, and ignition risk. Terms in parentheses following a topic indicate the logic operator used to evaluate the propositions under a topic. For example, fire danger is evaluated as the union of hazard, behavior, and ignition risk. Subtopics shown under “Elementary topics evaluated” indicate the elementary topics occurring at lowest level in the logic model where data are evaluated (Table 2).

c Each proposition evaluates a set of premises (see footnote b) or data relative to a specific landscape unit. For this analysis, subwatersheds were the landscape units.d Definitions of data items are presented in Table 2.e The union operator treats the premises of a proposition as factors that incrementally contribute to the proposition.f Advanced very high resolution radiometer (AVHRR) is used in producing normalized difference vegetation index (NDVI).
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Table 2—Definition of data inputs evaluated by elementary topic, data source, and reference conditions for each datuma

 Reference conditionsb

Full

Datum Definition Data source No evidence evidence

AVHRR-NDVI AVHRR-NDVId relative greenness value on June 1, 2004 Missoula Fire Labe 0.00 1.00

CBDaggregationf Aggregation index for canopy bulk density > 0.15 kg/m3 LANDFIREg (derived) 93.02 75.97

CBDarea Likelihoodc of canopy bulk density > 0.15 kg/m3 LANDFIRE 0.79 0.29

CBHaggregation Aggregation index for canopy base height < 3.1 m LANDFIRE (derived) 72.99 36.92

CBHarea Likelihood of canopy base height < 3.1 m LANDFIRE 0.38 0.04

crownfirePotential Likelihood of index for crown fire potential > 7 FIREHARMe (derived) 1.00 0.89

FBFMaggregation Aggregation index for fire behavior fuel model > 9h LANDFIRE (derived) 35.83 3.05

FBFMarea Likelihood of value for fire behavior fuel model > 9 LANDFIRE 1.00 0.02

FirelineIntensity Likelihood of fireline intensity > 400 kW/m FIREHARM 0.97 0.59

FlameLength Likelihood of flame length > 1.2 m FIREHARM 0.92 0.09

FCCaggregation Aggregation index for fuel loading > 56 Mg/hai  FCCSi (derived) 89.73 33.00

FCCarea Likelihood of fuel loading > 56 Mg/ha FCCS 0.80 0.03

FRCCaggregation Aggregation index for fire regime condition class j FIREHARM (derived) 99.50 97.76

FRCCarea Likelihood of fire regime condition class > 2 FIREHARM 0.28 0.01

Keetch-byramIndex Likelihood of a Keetch-Byram drought index value > 400 FIREHARM 0.84 0.46

LightningStrike Probability of cloud-to-ground lightning strike indexed by the NLDN

m 1.00 0.00

 maximum valuel

PalmerIndex Likelihood of summer Palmer drought severity index value < -2 NCDCo 37.00 0.00

SpreadRate Likelihood of a wildfire spread rate > 8.0 kph FIREHARM 1.00 0.89

a Data items in this table correspond to data listed for elementary topics evaluated in Table 1. Each datum represents an observation for a subwatershed, the unit of analysis in this study. b Reference conditions for no evidence and full evidence define critical values for which the fuzzy membership function of the associated elementary topic (Table 1) indicate no support and 

full support, respectively, for the proposition. The range of the reference conditions is the median 80-percent range of data for the variable of interest. An observed value for the associated 

datum that falls in the open interval defined by the two reference conditions maps to partial support for the proposition based on linear interpolation. Data with the suffix, Area, are not 

evaluated with respect to reference conditions; however, they are compared to minimum and maximum conditions within conditional tests to determine the logic for evaluation of elementary 

topics (see text for additional explanation).

c Each likelihood was estimated as the proportion of raster grid cells in the subwatershed area that exceeded the specified threshold for the attribute. All likelihoods were estimated from 

30-m resolution data, except those for LightningStrike, and PalmerIndex, which were estimated from available 1-km resolution data.

d The normalized difference vegetation index (NDVI), obtained from NOAA-11, AVHRR satellite image, represents relative greenness, and, in this usage, the effect of apparent moisture 

level on vegetation drying or curing. For further details, see Burgan and Hartford 1993, White and others 1997, and http://www.fs.fed.us/land/wfas/wfas11.html.

e Obtained from the USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Laboratory, Missoula, MT, rbartlette@fs.fed.us.

f An aggregation index was computed with FRAGSTATS (McGarigal and others 1995) for each attribute of hazard (see also Table 1) by reclassifying data in the 30-m resolution raster grid 

for the attribute to 0 (attribute ≤ threshold) or 1 (attribute > threshold).

g LANDFIRE (http://www.landfire.gov/) is a multipartner wildland fire, ecosystem, and fuel mapping project, one of whose partners is the Missoula Fire Sciences Laboratory of the USDA 

Forest Service, Rocky Mountain Research Station, Missoula, MT, who provided the data. Data sources labeled “LANDFIRE” indicate base data layers provided by the LANDFIRE project. 

Data sources labeled “FIREHARM” indicate data derived from base LANDFIRE layers by the FIREHARM model (Keane and others 2004) of the LANDFIRE project. With the exception 

of the data source for crownfirePotential, data sources labeled (derived) indicate an aggregation statistic that we derived from the LANDFIRE base layers with the FRAGSTATS (McGarigal 

and others 1995) spatial analysis package. In the case of crownfirePotential, (derived) indicates a composite index that we developed from FIREHARM crown fire ignition and crown fire 

spread outputs.

h Fire behavior fuel models represent 13 distinct distributions of fuel loadings found among surface fuel components (live and dead), fuel size classes, and fuel types. The fuel 

models are described by the most common fire carrying fuel type (grass, brush, timber litter, or slash), fuel loading, and surface area-to-volume ratio by size class and component, fuelbed 

depth, and moisture of extinction. Further detail about the original fire behavior fuel models can be found in Albini 1976, Anderson 1982, and Rothermel 1972 and 1983.
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GENERAL TECHNICAL REPORT PNW-GTR-802 Table 2—Definition of data inputs evaluated by elementary topic, data source, and reference conditions for each datuma (continued) i Fuel Characteristic Class System (Sandberg and others 2001, http://www.fs.fed.us/pnw/fera/nfp/haze/FCCS-lower48.zip j Fire regime condition class is a qualitative measure of departure from historical vegetation and fire regime conditions (Schmidt and others 2002).

k In contrast to the Palmer drought severity index, the Keetch-Byram drought index represents the short-term effects of precipitation and temperature on duff, litter, and soil drying in the 

top 20 cm. An index value of 400 corresponds to a deficit of 10 cm of water in the top 20 cm.

l The lightning strike probability is based on actual strikes triangulated and recorded over 15 years (1990 to 2004, Schmidt and others 2002).m Data were obtained from the National Lightning Detection Network (NLDN, http://ghrc.msfc.nasa.gov/).n The Palmer drought severity index is used to characterize effects of long-term drought. An index value of −2 corresponds to moderate drought conditions. Continuous maps of PDSI for the continental United States were interpolated by Cook and others (2004) based on their reconstructions of drought at grid points on a 2.5-degree grid of the continent.o Web site for the National Climate Data Center (NCDC), NOAA. (http://www.ncdc.noaa.gov/paleo/newpdsi.html).

hazard involved two class metrics computed by the FRAGSTATS program: (1) the proportion of subwatershed area 

exceeding a specified threshold value, and (2) an index that 

shows the degree of spatial aggregation of observed values 

exceeding the threshold value. Threshold values were based 

on the fire literature, and, where literature values were 

lacking, were based on our judgment. Use of the metrics 

to evaluate the elementary topic for canopy bulk density 

(CBD) is presented below as an example; methods for evaluation of each of the other elementary topics under hazard 

are analogous.

Within the elementary topic for CBD, the logic first 

tests the value of CBDarea; the percentage of the subwatershed area with CBD exceeding a threshold value of 0.15 

kg/m3

 (Table 2):

• If CBDarea is < 0.29, (i.e., < 29 percent of the subwatershed area exhibits CBD values > 0.15 kg/m3

), 

then evidence for low CBD is fully satisfied, else

• If CBDarea is > 0.79, (i.e., > 79 percent of the subwatershed area exhibits CBD values > 0.15 kg/m3

), 

then there is no evidence for low CBD, else

• Evidence for low CBD is evaluated as a function 

of CBDaggregation.

The value 0.29 represents the lower bound of the 

median 80-percent range for the set of all CBDarea data in 

map zone 16. The value 0.79 represents the upper bound of 

the median 80-percent range (Table 2). If the last condition 

above was satisfied, then we tested the observed value for 

CBDaggregation against a fuzzy membership function 

(Figure 4). This was done to determine the strength of evidence for a low degree of aggregation of high CBD values 

(i.e., values of CBD exceeding the threshold value of 0.15 

kg/m3

) relative to a set of reference conditions that defined 

the median 80-percent range of the CBDaggregation data 

from the set of all subwatersheds (Table 2). Notice that each 

elementary topic (Table 2) is similarly evaluated against the 

median 80-percent range of its associated datum, hence our 

characterization of fire danger as relative.

• If CBDaggregation is ≤ 76, (i.e., ≤ 76 percent of 

the maximum value of aggregation), then evidence 

for low aggregation of high CBD values is fully 

satisfied, else
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Figure 3—Dendrogram showing how the overall fire hazard topic is organized and evaluated. The complete evaluation of fire hazard is 

made up of three parts—evaluation of fire hazard, fire behavior, and ignition risk, which are primary topics. Under each of these three 

primary topics are secondary and elementary topics. Under fire hazard are the topics surface fuels, canopy fuels, and fire regime. Under 

behavior are the elementary topics spread rate, flame length, fireline intensity, and crown fire potential. Under ignition risk are the 

secondary topics fire weather and ignition potential.

• If CBDaggregation is ≥ 93, (i.e., ≥ 93percent of the 

maximum value of aggregation), then there is no 

evidence for low aggregation of high CBD values, 

else

• Observed values of CBDaggregation fall within the 

open interval (76, 93), and evaluate to partial support for the proposition, based on a linear interpolation between 76 and 93. The open interval (76, 93) 

represents the median 80-percent range of the data.

Primary Topic—Fire Behavior—

Evaluation of fire behavior depends on the union of topics 

addressing spread rate, flame length, fireline intensity, and 

crown fire potential (Table 1), each of which is an elementary topic that directly evaluates data (Tables 1, 2). The 

spread rate topic evaluates the proposition that likelihood 

of spread rate of surface fire > 8.0 kph within the subwatershed is low. The flame length topic evaluates the proposition 

that likelihood of flame length > 1.2 m within the watershed 

is low. The fireline intensity topic evaluates the proposition 

that likelihood of fireline intensity > 400 kW/m within the 

watershed is low. The crown fire potential topic evaluates 

the proposition that likelihood of crown fire spread potential > 7 within the watershed is low. This last metric is an 

index based on crown fire ignition and crown fire spread 

potentials (Keane and others 2004) and represents the ratio 

of crown fire behavior to surface fire behavior based on 

Rothermel (1972, 1991) surface and crown fire algorithms.

None of the fire behavior elementary topics are entirely 

independent of the other topics; rather, one or more of these 

topics is used in the calculation of the others. For example, 

flame length influences the spread rate calculation, and 

fireline intensity influences flame length. In fact, fireline 

intensity is double weighted in our model because of the 
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GENERAL TECHNICAL REPORT PNW-GTR-802

equivalence of flame length and fireline intensity (Chandler 

and others 1983). We used both in the model because intensity relates best to fire effects, and flame length is easily 

observed and often asked for. Each selected elementary 

topic is used here to provide a more comprehensive picture 

of expected fire behavior. Whereas complete independence 

among the topics would be desirable, there is no set of fire 

behavior attributes with such independence, and there is 

also no independent set that provides a comprehensive 

picture of expected fire behavior.

Primary Topic—Ignition Risk—

Evaluation of ignition risk depends on the union of four 

elementary topics—Palmer drought severity index (Palmer 

1965), the Keetch-Byram drought index (Keane and others 

2004), the advanced very high resolution radiometer 

normalized difference vegetation index (AVHRR-NDVI) 

relative greenness index (Keane and others 2004), and 

lightning strike probability (Tables 1, 2). First, the probability of a summer Palmer drought severity index value 

< −2 is evaluated. A value of −2 corresponds to moderate 

drought in the Palmer rating system. This elementary topic 

is included because it allows consideration of the effects 

of long-term drought on vegetation and fuels. Second, 

the probability of a Keetch-Byram drought index (KBDI)

value > 400 is evaluated. The topic considers the short-term 

effects of precipitation and temperature on duff, litter, 

and soil moisture in the top 20 cm. An index value of 400 

corresponds to a deficit of 10 cm of water in the top 20 cm; 

Burgan (1993) suggested that severe fire behavior often 

occurs when the KBDI exceeds this value.

The AVHRR-NDVI relative greenness value on Julian 

day 152 (June 1, 2004) is then considered as a topic that 

indirectly represents fuel condition by incorporating vegetation drying or curing in a measure of relative greenness. 

June 1 is used to represent the height of the growing season 

in the study area; the greenest values indicate lesser chance 

for fire ignition. Future versions of this modeling system 

would include dates to capture the span of the fire season of 

each unique map zone.

Finally, lightning strike probability is evaluated, which 

we base on actual strikes triangulated and recorded over 15 

years (1990 to 2004). The probability of human-caused ignitions is also important but omitted in this implementation. 

We constructed a logic module for evaluating the likelihood 

of human-caused ignitions, but it is not implemented in 

this version because wall-to-wall human ignition density 

data were unavailable for map zone 16. In a future version, 

we will incorporate a direct evaluation based on recorded 

Figure 4—The fuzzy membership function used to evaluate strength of evidence 

for the proposition of low canopy bulk density. The proposition is fully satisfied 

when the observed value of CBDaggregation ≤ 76, and there is no evidence for the 

proposition if CBDaggregation ≥ 93 (Table 2). Observed values of CBDaggregation that fall within the open interval (76, 93) evaluate to partial support for the 

proposition, based on linear interpolation between 76 and 93.
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human-ignition densities, or an indirect measure of likelihood involving road density maps and maps of human 

congregation sites.

Priorities for Fuels Treatment

A decision model for determining priorities of subwatersheds for fuels treatment was graphically designed with Criterium DecisionPlus (InfoHarvest, Inc., Seattle, WA), which 

uses both the analytic hierarchy process (AHP, Saaty 1992) 

and the Simple Multi-Attribute Rating Technique (SMART, 

Kamenetsky 1982) to support planning activities such as 

priority setting, alternative selection, and resource allocation. We used a decision model structure that was nearly 

identical to that of the logic model (Figure 3). In the context 

of decision models based on the AHP, the concept of topics 

is replaced by criteria. Thus, in the decision model for fuels 

treatment, the first level of the model contained the three 

criteria, fire hazard, wildfire behavior, and ignition risk. 

However, for purposes of setting treatment priorities for 

subwatersheds, we also added a fourth criterion, percentage 

of subwatershed area classified as wildland-urban interface 

(WUI), to illustrate expanding the scope of analysis to 

include additional logistical factors that can influence decisions about priorities. Note that numerous other criteria and 

subcriteria could be included to account for other logistical 

considerations that might influence decisions about treatment priorities.

Weights for each criterion at the first level of the 

decision model were derived from the standard pair-wise 

comparison procedure of the AHP (Saaty 1992) in which 

a decisionmaker is asked to judge the relative importance 

of one criterion versus each of the others. We provided the 

judgments on relative importance for our example application. Weights for sets of subcriteria under each criterion 

(the second level of the decision model) were derived in 

the same manner. For purposes of subsequent discussion, 

criteria at the lowest level of an AHP model are commonly 

referred to as attributes of a decision alternative, and these 

attributes correspond to the elementary topics of the logic 

model (Table 1).

A SMART utility function was specified for each 

attribute of a subwatershed, and this function represented 

the mirror image of the fuzzy membership function of its 

corresponding elementary topic; i.e., the fuzzy parameters 

defining no support and full support (Table 2) were now 

used to define utility values of 1 (full utility) and 0 (no 

utility), respectively, on the SMART utility scale of [0, 1]. 

Note, however, that the WUI criterion is both a primary 

(first level) criterion of the decision model and an attribute 

of a subwatershed for which there is no corresponding 

elementary topic in the logic model. In this case, the critical 

values corresponding to full and no utility were separately 

specified as 67 and 0 percent, respectively, and represent the 

maximum and minimum of observed WUI percentages.

Analysis

Fire danger evaluation (Table 1) for all subwatersheds in the 

study area was performed with the NetWeaver logic engine 

(Miller and Saunders 2002) in EMDS (Reynolds and others 

2003). Continuous data related to recent burns in map zone 

16 were not available and were not implemented in this 

version of the fire danger model. This component should be 

added as data become available. Priority setting for fuels 

treatments among subwatersheds was performed with Priority Analyst, an engine for running Criterium DecisionPlus 

models in EMDS.

Results

We describe results in terms of the strength of evidence in 

support of the overarching proposition of low fire danger or 

of subordinate propositions under fire danger. Recall that all 

propositions take the null form; for example, low strength 

of evidence based on the underlying evaluation implies that 

the proposition of low fire danger has poor support.

Fire Danger

There were pronounced differences in fire danger between 

subwatersheds in the northern and southern portions of the 

study area (Figure 5). Support for the proposition of low 

fire danger was generally moderate in the north and low in 

the south, which also contained small pockets of very low 

support. Dangerous wildfire conditions were largely driven 

by conditions conducive to severe fire behavior. Figure 6 

shows the partial products of the entire evaluation process; 
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GENERAL TECHNICAL REPORT PNW-GTR-802

from viewing this composite, it is possible to see the various 

contributions to overall fire danger. We summarize the 

results of the partial products immediately below.

Fire Hazard—

Throughout much of the northern half of map zone 16, evaluation of fire hazard showed moderate to full support for the 

proposition of low fire hazard. The outstanding exception 

was the northern peninsula of subwatersheds extending 

to the east, where most of the subwatersheds showed low 

support for the proposition (Figure 6). Likewise, in much of 

the northern half of the map zone, evaluation of fire regime 

condition class showed moderate to full support for the 

proposition of low departure of vegetation and fuel conditions from historical ranges. The southern half was mixed in 

its support but with a considerable number of subwatersheds 

showing low, very low, and no support.

The canopy fuels evaluation was composed of the 

partial evaluations of canopy bulk density and canopy base 

height. In general, the canopy fuels evaluation showed 

subwatersheds displaying conditions favorable to severe 

wildfire in both the northern and southern portions of the 

map zone. Evaluation of canopy base height showed conditions conducive to severe wildfire in the northern peninsula 

of subwatersheds extending to the east and especially in the 

southern subwatersheds. Evaluation of canopy bulk density 

showed conditions favorable to severe wildfire throughout 

the map zone, but most especially in the northern peninsula 

of subwatersheds extending to the east.

The surface fuels evaluation was composed of the 

partial evaluations of fire behavior fuel model and fuel 

loading. In general, the surface fuels evaluation showed 

subwatersheds displaying conditions favoring severe 

wildfire in both the northern and southern portions of the 

map zone, but most especially in the northern peninsula of 

subwatersheds extending to the east (Figure 6). Here, fuels 

were dominated by shrub types with grassland-savanna fuel 

types also common. Evaluation of fire behavior fuel model 

showed that with the exception of the northernmost peninsula of subwatersheds extending to the east, the northern 

half of the map zone showed moderate to full support for 

the proposition that expected fire behavior would be low. 

In the subwatersheds of the southeastern portion of the 

map zone, the evaluation suggested that expected wildfire 

behavior would be severe. The evaluation of fuel characterization class showed highly mixed results throughout the 

map zone, with the exception of the northernmost peninsula 

of subwatersheds extending to the east where surface fuels 

were conducive to severe wildfire.

Fire Behavior–

The fire behavior evaluation consisted of the partial product 

evaluations of fire spread rate, flame length, fireline intensity, and crown fire potential (Table 1, Figure 6). Throughout the map zone, there was low to very low support for the 

proposition that expected wildfire behavior would be low.

The evaluation of wildfire spread rate showed that 

expected spread rate of surface fires would be high under 

90th percentile conditions especially in the central and 

northern sectors. In the flame length evaluation, the 

likelihood of high flame length was high in the southern 

half of the map zone and in the southernmost peninsula of 

subwatersheds extending to the east in the northern sector. The evaluation of fireline intensity produced results 

similar to those of the flame length evaluation, and crown 

fire potential results were similar to those of the spread rate 

evaluation (Figure 6).

Ignition Risk—

The ignition risk evaluation consisted of the partial product evaluations of the Palmer drought severity index, the 

Keetch-Byram drought index, NDVI-relative greenness, 

and the relative number of cloud-to-ground lightning 

strikes. Throughout the southern half of the map zone, there 

was low support for the proposition that likelihood of wildfire ignition is low. In general, higher overall ignition risk 

was driven by the tendency for more severe annual summer 

drought and lower relative greenness in the southern portion 

of map zone 16, and moderate to full support for relatively 

fewer lightning strikes in the northern and central sectors of 

the map zone.

Priorities for Fuels Treatment

The map for fuels treatment priorities (Figure 7a) took into 

account most of the same factors as used to produce the 

map for fire danger and its components (Figure 6) but with 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 5—Evaluation of fire danger for map zone 16. Full support denotes complete support for the proposition of low fire danger.

weighting of criteria and subcriteria by a fire ecologist and 

also considering the influence of wildland-urban interface 

(Figure 7b). Ideally, when developing operational decision 

models for management, derivation of weights would be 

performed by a panel of managers and scientists. Indeed, 

we emphasize the importance of such collaborative development in our conclusions. Here, for illustration purposes, and 

considering a simple decision model in which three of the 

four decision criteria are more technical in nature, development of weights by a fire ecologist seemed appropriate.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 6—Composite of all partial product evaluations leading to the full evaluation of fire danger (Figure 5) for map zone 16. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

The majority of subwatersheds with a priority rating 

of high or very high occurred in the southern two-thirds of 

the map zone (Figure 7a). The map of treatment priorities 

(Figure 7a) was strongly conditioned by the presence of 

wildland-urban interface in a subwatershed because of the 

emphasis placed on this criterion in the decision model. 

Normalized weights on primary criteria, derived from 

the pair-wise comparison process, were: wildland-urban 

interface, 0.50; fire behavior, 0.27; fire hazard, 0.15; and 

ignition risk, 0.08. A more detailed view of a small region 

in Figure 7 (Figure 8) shows the correspondence between 

wildland-urban interface and decision scores for fuels 

treatment for subwatersheds. Indicated subwatersheds with 

wildland-urban interface ≥ 10 percent (Figure 8b) were classified as very high priority (Figure 8a). Model output from 

the Priority Analyst (Figure 9) shows how the four primary 

decision criteria contribute to the overall decision score for 

a sampling of 10 subwatersheds. The three highest ranked 

subwatersheds (Figure 9) are also labeled in Figure 8b. 

Notice that the three highest ranked cases could be distinguished from the next seven cases by the level of influence 

of the wildland-urban interface. Furthermore, although the 

relative contribution of fire behavior was fairly consistent 

across the top 10 cases, the contributions of fire hazard and 

ignition risk were relatively low among the top three.

Discussion

The relative nature of our evaluation of fire danger has 

at least three important implications. First, the observed 

data value for each elementary topic in the logic model 

and for each attribute in the decision model was evaluated 

against reference conditions that were defined by the data 

themselves (Table 2). As a result, basic evaluations at the 

lowest level of each model were relatively objective. A 

second consequence of defining reference conditions in this 

manner was that the models were maximally sensitive to 

the data, thus assuring a high level of discrimination among 

outcomes over the set of subwatersheds in map zone 16. 

Finally, this method of deriving reference conditions means 

that the values used depended on the spatial extent of the 

assessment area. For example, reference conditions appropriate to an assessment of the entire Southwestern United 

States would be at least somewhat broader than those for 

map zone 16 alone. 

Evaluation outcomes and their underlying premises 

are affected by the scale of input data, whether they are at a 

relatively fine (e.g., 30- to 90-m pixels) patch scale or, in the 

case of the PDSI data used here, the continental scale. For 

map zone 16, evaluating the likelihood that a subwatershed 

experienced drought in the past 20 years was derived from 

a 2.5-degree continental-scale grid of reconstructed PDSI 

(Figure 10). Although there was wide variation in the probability of experiencing a long-term drought (PDSI < -2) for 

the continental United States (0 to 37 percent, Figure 10), 

map zone 16 exhibited a relatively narrow range of probabilities from 14 to 23 percent; or about 25 percent of the 

continental-scale variation. Thus, one might be concerned 

that the contribution of long-term drought to the evaluation 

of ignition risk at the scale of a map zone may be neutral, as 

if adding a constant. This was not the case. Figures 11a and 

11b illustrate the influence of including continental-scale 

drought data in the map zone evaluation of fire danger. 

Differences can be seen among subwatersheds within evaluations of fire danger (Figure 11a) and ignition risk (Figure 

11b) when comparing the same evaluations with and without 

PDSI. For map zone 16, PDSI does provide information on 

long-term drought that is beneficial to managers.

In addition to considering the scale of input data, the 

contributions of topics at each level to overall fire danger 

should be considered when interpreting an evaluation. For 

example, 10 subwatersheds that share a similar overall 

result for evaluation of fire danger (i.e., moderate support, 

0.56, for the proposition of low fire danger) are shown in 

Figure 12, but they differed by evaluation result at the primary topic and lower levels. Use of the union operator in the 

design of the knowledge base made it possible for relatively 

high fire hazard within a subwatershed to be offset by relatively low predicted fire behavior in the event of a wildfire 

(e.g., see subwatershed 224, Figure 12). Similarly, subwatershed 339 (Figure 12) displayed evidence for low fire 

behavior but high ignition risk. An important strength of the 

logic model is that the full range of variability is expressed 

among subwatersheds at the level of an elementary topic, 

and each elementary topic contributes to evaluations of 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 7—Priorities for fuels treatment in subwatersheds of map zone 16. (A) Priorities of subwatersheds. This map, which reflects the 

influence of both weighting decision criteria and consideration of proximity to the wildland-urban interface, should be compared with 

Figure 5. (B) Percentage of wildland-urban interface in each subwatershed. Both maps are symbolized using a natural breaks algorithm 

in ArcMap to define the classes in the legend. Bounding boxes in A and B indicate corresponding detailed views in Figure 8.

Figure 8—Detailed views of example subregions from bounding box in Figure 7, (A) priorities and (B) percentage of wildland-urban 

interface. Both maps are symbolized using a natural breaks algorithm in ArcMap to define the classes in the legend.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 9—Contributions of primary decision criteria to decision scores for priority of fuels treatment in subwatersheds of 

map zone 16.

secondary and primary topics within a subwatershed and 

among subwatersheds. Thus, it is important to keep in mind 

that variability of support for a subwatershed at the elementary topic level in the hierarchy should be considered when 

interpreting a primary or secondary topic level evaluation 

result for any subwatershed and among subwatersheds.

The present study illustrates application of EMDS for 

evaluating wildland fire danger and prioritizing vegetation 

and forest fuels treatments at the spatial extent of a U.S. 

Geological Survey (USGS) map zone. With the national 

LANDFIRE mapping effort (http://www.landfire.gov) 

complete for the continental United States (CONUS), it is 

technically feasible to conduct an analysis of fire danger 

for all subwatersheds in the CONUS in the same manner as 

we have illustrated here. Moreover, it is a relatively simple 

matter, given such a base analysis, to summarize such 

watershed-scale evaluations to various intermediate broader 

scales such as States, geographic regions, forest boundaries, 

or forest planning zones as a basic input to broad-scale planning and resource allocation.

At the other extreme, the present study provides a starting point for finer scale planning. We have examined the 

evidence for fire danger in subwatersheds of map zone 16, 

but this information, by itself, is not necessarily sufficient 

for fuels treatment planning. As shown above, subwatersheds that exhibit a similar moderate level of fire danger 

do not necessarily share the same evaluation results for 

primary topics (Figure 12). Thus, variability of support for 

propositions within a subwatershed at the level in the logic 

model where data are evaluated should be considered when 

interpreting an evaluation result among subwatersheds at 

the level of the primary or secondary topics.

To that end, subwatersheds in the worst condition with 

respect to fuels may not be the best candidates for fuels 

treatment. In particular, additional strategic or logistical 

factors such as proximity to population centers, presence 

of endangered species, slope steepness, and road access all 

might be taken into account in selection of specific watersheds within a management area for fuel treatment. Such an 

approach was illustrated by Reynolds and Hessburg (2005) 

using the Priority Analyst component of EMDS, which 

uses a decision engine for such purposes. In that study, they 

considered the compositional and structural integrity of 

forests along with contemporary fire risks, and the technical 

and economic feasibility of restoration. Carefully designed 

decision models can not only assist with a more circumspect 

approach to selection of individual treatment units, but can 

also show which of several treatment options may be most 
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GENERAL TECHNICAL REPORT PNW-GTR-802

suitable in a given unit, thus also providing support for the 

tactical level of planning.

Similarly, evaluation of treatment priorities related 

to fire danger is not necessarily limited to fuel and fire 

characteristics; it can also incorporate human impacts and 

social or economic, or other value considerations. One such 

consideration, when evaluating the context of fire danger, 

may be the pattern of wildland-urban interface in the study 

area (Figure 7b). Readers might fairly ask, “Given that the 

structures of the logic model for danger evaluation and the 

decision model for treatment priorities are so similar in 

this example, why bother with two separate models?” First, 

and perhaps most obviously, the two models produce very 

different interpretations of the data (compare fire danger 

in Figure 5 with treatment priority in Figure 7a). The logic 

model is a relatively objective interpretation of fire danger, 

given that parameters used to interpret observations (Table 

2) were derived from field data, and given that the logic is 

presented in a relatively pure form insofar as all topics (with 

the exception of fireline intensity and flame length) are 

equally weighted. Although weights can easily be applied 

to topics in a logic model, they also add an additional level 

of subjectivity that is more effectively managed within the 

context of decision models, such as those based on the analytic hierarchy process, for example, that are more specifically designed to deal with such issues (Reynolds and others 

2003). Logic models also offer the opportunity to synthesize 

and summarize potentially complex information, thus simplifying the structure of a decision model. In this study, for 

example, the decision model used summarized information 

about the topics under fire hazard that would otherwise have 

been difficult to adequately represent in an intrinsically 

Figure 10—Grid points of the Palmer Drought Severity Index (PDSI) and drought map for the average of 20 years (1971-1990).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 11—Comparison of (A) overall fire danger and (B) ignition risk evaluations with and without 

the Palmer Drought Severity Index (PDSI) elementary topic evaluation.

linear decision model (see, for example, the description of 

the CBD topic in “Primary Topic—Fire Hazard”).

Finally, the two types of models are very complementary in the sense that the logic model focuses on the 

question, “What have I got?”, whereas the decision model 

focuses on the question, “Now that I know what I have, 

what should I do about it?” Notice that logistical issues are 

not pertinent to the first question, but they may be extremely 

important for the second. An important consequence of 

separating the overall modeling problem into these two 

complementary phases is that each phase is rendered 

conceptually simpler. The logic model evaluates and keeps 

separate the status of the components of each ecological 

system under evaluation; in this case, the components of 
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wildland fire danger of each subwatershed in the map zone. 

The decision model takes the ecological status of each 

ecosystem and places it in one or more social contexts that 

are designed to further inform decisionmaking. The decisions will be based only partially on the ecological status 

information. They will also be based on social context and 

human values, in this case, proximity to and amount of 

wildland-urban interface, which captures a measure of the 

potential risk of fire damage to people and their structures. 

After priorities have been derived by the decision model 

concerning what to do about the existing fire danger conditions, the decisionmaker can look back at the decision and 

see the relative contributions of the ecological states and 

their social context(s) to the overall decision. This transparent model design and structure aids in decision explanation, 

and it allows decisionmakers to consider, in the sense of 

scenario planning, the effects of alternative weightings of 

important decision criteria.

As George Box (1979) noted, “All models are wrong; 

some are useful.” Thus, as with any model intended to 

support significant management decisions, our model of fire 

danger requires both verification and validation because all 

models are necessarily simplifications of reality. The present 

model has, in fact, been substantially verified in the sense 

that it performs as expected based on our own analyses 

and has been vetted in several meetings over the past year 

involving substantial numbers of prominent fire managers 

and fire scientists who agree that the representation of fire 

danger is reasonable. In contrast to verification, validation 

is a more rigorous process in which model accuracy is 

objectively evaluated by comparing predicted and actual 

outcomes, ideally with statistical procedures. Readers unfamiliar with logic-based models may wonder if validation is 

even possible. However, models based on logic are no better 

or worse in this respect than their probabilistic counterparts. 

Although a detailed discussion of this assertion is beyond 

the scope of this report, it may be sufficient to note that metrics expressing strength of evidence have commonly been 

treated as subjective probabilities (Zadeh 1968). Finally, 

model validation was not feasible within the temporal scope 

of our study. Realistically, even a preliminary validation in 

this context would require 5 to 10 years. If the model for 

fire danger were to be adopted as a tool to support strategic 

planning for fuels treatment, then we certainly recommend 

Figure 12—Comparison of 10 subwatersheds in map zone 16, each of which displayed moderate support 

(strength of evidence=0.56 in the interval [0,1]) for the proposition of low fire danger. Note that level of 

support varies considerably by primary topic (fire vulnerability, fire behavior, ignition risk).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

that explicit provisions for validation be an integral part of 

any ongoing assessment process designed to support it.

Conclusions

Given the widespread increase in danger of wildland fire 

throughout the Western United States over the past 70 years 

or more, the sustainability of western forest ecosystems is 

clearly at stake. Decision-support systems such as EMDS 

can play a role in assisting with restoration to improve or 

maintain their sustainability. Issues surrounding decisions 

about fuels management are complex and often require 

abstraction, but logic and decision models are well suited 

to representing the inherent complexities and abstractness 

of the problem, thus rendering the analytical problem more 

manageable. This particular application of EMDS also is 

an example of how decision-support systems can not only 

be used as tools for technical specialists and decisionmakers, but as tools for communicating clearly and effectively 

with the general public who understandably have a strong 

interest in the topic of wildfire and want to understand and 

be involved in any proposed solution. Both logic and decision models are good at explaining themselves in relatively 

intuitive terms, and thus provide a basis for an effective 

public dialog.

Finally, there is an important interdependency 

between science, policy, and decision-support systems 

such as EMDS. Although logic models are sometimes used 

for prediction, they are fundamentally concerned with 

interpretation (Reynolds and others 2003). In other words, 

what does the information mean? Meaning can be highly 

normative or highly subjective, and usually falls somewhere 

in between the two extremes. As a result, virtually all 

interpretation embeds some degree of subjectivity; that is, 

to some degree, values and policy are inextricable aspects of 

logic and decision models. The practical implication is that 

successful application of most decision-support systems to 

real-world situations ultimately depends on a close collaboration between the scientific community that brings its 

facts to the table and the policymakers that need to reach 

decisions based on that information and additional social 

and economic considerations. Decision-support systems 

provide a conspicuous advantage in this context—detailed 

documentation of a decisionmaking process. With ongoing 

monitoring and evaluation, lessons learned can be readily 

incorporated into decision models providing increasing 

effectiveness to decisionmaking and an explicit vehicle for 

adapting management.

Acknowledgments

This research was supported in part by the Managing 

Disturbances Program of the Pacific Northwest Research 

Station (RWU-4577) and by the National LANDFIRE 

Prototype Project, USDA Forest Service. Mark Twery, Rich 

Lasko, Norm Johnson, and three anonymous reviewers 

provided helpful reviews of an earlier draft.

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Digital Aerial Sketchmapping and Downlink Communications: 

A New Tool for Fire Managers

Everett Hinkley, Tom Zajkowski, and Charlie SchraderPatton

Everett Hinkley, program manager, and Tom Zajkowski, 

instructor/remote sensing analyst, USDA Forest Service, 

Remote Sensing Applications Center, Salt Lake City, UT 

84119; and Charlie Schrader-Patton, remote sensing 

analyst, USDA Forest Service, Remote Sensing Applications Center, Bend, OR 97701.

Abstract

Aerial sketchmapping is the geolocating of features that are 

seen on the ground below an aircraft and the subsequent 

recording of those features. Traditional aerial sketchmapping methods required hand-sketching on hardcopy maps 

or photos and the translation of that information to a digital 

file. In 1996, the U.S. Department of Agriculture (USDA) 

Forest Service embarked on a project to develop a digital 

aerial sketchmapping system (D-ASM) to replace or 

augment the traditional (manual) methodology. Advances 

in microprocessor speed and Personal Computer (PC) 

system performance made possible the use of PCs to aid in 

aerial sketchmapping. The USDA Forest Service Remote 

Sensing Applications Center (RSAC) and the Forest Health 

Technology Enterprise Team worked with a software vendor 

to develop a product that would meet the needs of aerial 

surveyors. Several hardware and software options were 

investigated.

The Remote Internet Protocol Communications 

System (RIPCom) culminated from a successful 2-year 

collaboration between National Aeronautics and Space 

Administration (NASA)-Goddard and RSAC to develop a 

cost-effective, multitask communications solution for the 

Forest Service based on NASA expertise. The RIPCom 

consists of commercial off-the-shelf components that 

were each chosen because of their desirable performance 

characteristics. The 2003-2004 field tests demonstrated that 

the RIPCom would deliver the required data throughput 

(1 Mbps) up to a range of 20 mi. A contractor constructed 

the second generation RIPCom system incorporating the 

lessons learned from flight testing and operational deployments.

The planned integration of the D-ASM and RIPCom 

systems will enable firefighting experts to collect information about an incident (fire perimeter and hotspot locations) 

on the D-ASM and rapidly disseminate this information, via 

wireless devices, to the incident’s geographic information 

system (GIS) technician or directly to firefighters on the 

ground. Potential system users within the wildfire community include Air Attack, Helitack, Situation Unit Leaders, 

Operations Chiefs, and Incident Commanders.

Keywords: GIS, global positioning system, pen-tablet, 

Remote Internet Protocol Communications System (RIPCom), wildland fire monitoring and suppression.

Introduction 

Aerial Sketchmapping

Aerial sketchmapping for Forest Health Protection has been 

conducted since the 1940s. Typically, sketchmapping surveyors fly the area to be surveyed in a high-winged, highperformance aircraft at elevations of 1,000 to 3,000 ft above 

the ground. The surveyor tracks the plane’s location on a 

hardcopy map (typically several maps that are trimmed, 

edge-joined, and taped together) or aerial photographs, then 

sketches areas of the forest that have been damaged during 

the past year (Figure 1). The sketched features (points and 

polygons) are then registered and digitized into a geographic information system (GIS). Finally, the resulting GIS 

data are used to create maps and reports at forest, regional 

and national levels.

Although skilled surveyors have successfully employed 

manual sketchmapping techniques for years, there is great 

potential for error in the process. Some examples of the 

sources of error are:

• Not knowing the aircraft’s location on the map. 

• Reinking map data upon completion of 

the day’s flying.

• Inaccurate registration, line following, and 

attribute capture when digitizing data into the 

GIS.
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In addition to the potential for error, some of the 

processes used in manual sketchmapping require additional 

time to complete. Examples of those processes that can be 

eliminated with a digital system are:

• The reinking of map data upon completion of the 

day’s flying.

• The management of numerous taped and folded 

maps in the cockpit.

• The taping and folding of maps prior to the mission, 

the cutting apart of those maps prior to digitizing, 

and the retaping of the maps after digitizing, but 

prior to proof-checking the digitizing results.

• The digitizing process itself.

The positional accuracy of sketched features is largely 

determined by the ability of the sketchmapper to keep 

track of the aircraft’s position on the map and to correctly 

relate features seen on the ground to the map. This can 

be especially difficult in unfamiliar or flat terrain or both. 

At typical airspeeds of 100 to 140 miles per hour (mph), a 

sketchmapper has approximately 15 sec/mi2

 to accurately 

locate, sketch, and attribute features (assuming a visual 

mapping distance of 2 mi from the aircraft). Accuracyare 

a relative term, and the end use of the data must be considered. If the data collected during a sketchmap mission 

are for general monitoring purposes, then more error can 

be tolerated than if field crews need to locate the affected 

stands using the sketchmapped data.

To be favorably compared against manual sketchmapping, a competent digital aerial sketchmapping system 

should address the problems listed above and should meet 

the following list of requirements, which were compiled for 

early system development:

1. The map display must be linked to a global 

positioning system (GPS) receiver so that an 

icon on the display represents the current 

position of the aircraft on the movable digital 

map.

2. The map display must update quickly at ground 

speeds of 130 mph and reorient when turning, 

if desired by the surveyor, based on the GPS 

position and heading of the aircraft.

3. The screen must be viewable under a variety of 

lighting conditions including full sunlight and 

must also display, at minimum, full (256) color.

4. The viewable screen size must be at least 10 

inches measured diagonally.

5. The software must have the capability to: 

• Digitize points, lines, and polygons 

– including nested and overlapping 

polygons.

• Attribute digitized features (points, lines, 

and polygons) quickly and easily. 

• Allow editing of feature attributes quickly 

and easily.

Figure 1—The principle of aerial sketchmapping—observing ground features and annotating those features on a paper or digital map—

is displayed in the aerial photograph and accompanying map.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

• Allow the user to predefine the feature 

attributes to be collected during that day’s 

mission.

• Collect the GPS log file of the flight.

• Allow the user to zoom quickly to different 

map scales.

• Retrieve and display common raster and 

vector data types as background maps 

quickly and seamlessly (including the 

U.S. Geological Survey 1:100,000-scale 

digital roster graphics DRG aerial 

surveyors currently use in hardcopy form).

• Export files in a format that is easily 

imported into ESRI Arc/Info software, thus 

eliminating the intermediate digitizing step

• Save data automatically to the computer’s 

disk. 

6. The hardware should be operational in moderately harsh conditions (32 to 120 °F, high 

humidity, dusty conditions).

7. The primary input device must be a touch

screen with stylus.

After a great deal of testing and evaluation, the Forest 

Service successfully met these requirements, and two 

systems emerged and are in common use today; a twoscreen system and a pen-tablet system. Advantages of the 

Digital Aerial Sketchmapping System (D-ASM) include 

automatic tracking of the aircraft’s position on a map base 

through a link to a GPS receiver and a significant reduction in the time spent moving data into a GIS. The D-ASM 

greatly speeds data collection and processing and improves 

the positional accuracy of the data being collected. Now 

a mature system, the D-ASM has been widely embraced 

by the sketchmapping community with over 90 systems in 

service with Federal and State forest health agencies.

Following initial system development, a D-ASM was 

shown to wildfire air attack crews who liked the concept, 

but wanted the system to be smaller and for it to include 

a means to get the information down to the incident command in real time. Two technological developments solved 

these requirements: commercially available touchscreen 

pen-tablet computers and the National Aeronautics and 

Space Administration (NASA) developed long-range 

high-speed data link named RIPCom. or Remote Internet 

Protocol Communication.

The Forest Service is now working to further expand 

the utility of the D-ASM by adding live downlink capabilities to the existing system.

D-ASM Mapping Software

For the D-ASM, the Forest Service selected the consumer 

off-the-shelf (COTS) mapping software that most closely 

met the system requirements, and the software provider 

agreed to make necessary modifications to their product to 

meet our sketchmapping needs.

With this software, feature type keys (point, line, 

and polygon) are used to display the aircraft’s position on 

the on-screen map, and a user-defined keypad is used to 

attribute features. The observer selects the type of feature 

to be sketched, sketches the feature on the screen, and then 

assigns attributes to the feature using the keypad. The 

screen remains frozen while the feature is being sketched 

and is updated after the enter key is pressed. When the 

aircraft icon advances to the edge of the window, the map 

display updates and moves the aircraft icon to the center 

of the window. Upon completion of the sketchmapping 

mission, a translation step converts the sketched features 

into ESRI shapefiles. These shapefiles are then copied to an 

external disk for transfer to a GIS.

Aerial surveyors often want to display large shapefiles 

over their raster map data; these shapefiles may be the 

previous seasons’ survey data, administration boundaries, 

or section lines. Fire managers may want to see the previous 

day’s fire perimeters shown on top of raster aerial imagery 

or digitized raster maps. The software can display many 

different types of geospatial data as the background map 

and thus can accommodate a variety of user preferences.

The software provider continues to enhance the 

software by developing modifications such as editing of 

polygons, multiple user-defined keypad sets for attributing 

features, display of feature attributes, and editing of feature 

attributes.
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GENERAL TECHNICAL REPORT PNW-GTR-802

D-ASM Mapping Hardware

Two computing options have been tested and are in use in 

Forest Service sketchmapping systems: a two-screen laptopbased system with a separate touchscreen, and a pen-tablet 

sketchmapping table (Figure 2). Because of the potential for 

bright sunlight on the screen, the visibility of the map image 

on the touchscreen display is critical. We evaluated several 

touchscreens, which varied in screen size, brightness, and 

touchscreen technology. Resistive touchscreens have a 

membrane layer installed over a fine wire mesh grid in the 

screen; when this membrane is pressed on to the grid, a 

signal is sent to the computer, and the cursor jumps to that 

spot on the screen. Virtually any object can be used on 

the screen—a finger, an eraser, stylus, etc. This can be a 

disadvantage for sketchmapping, because any inadvertent 

touch to the screen may result in undesired actions. Many 

touchscreens have a rather small screen size (10.4 diagonal), 

which limits the amount of map area visible on the screen.

A supplier manufactured several screens to Forest 

Service specifications for early testing. These screens have 

800 by 600 resolution, 12.1-in diagonal screen size, 1,500 

nits brightness, and capacitive touchscreens. This type 

of touchscreen is activated by completion of an electrical 

circuit using a tethered stylus. There is no inadvertent activation of the resistive touchscreen because the screen can be 

activated only with the tethered pen. It should be noted that 

this system is quickly becoming obsolete and will no longer 

be supported by the Forest Health Technology Enterprise 

Team (FHTET).

Pen-tablet Personal Computers (PCs) were also tested 

as an alternative platform for the D-ASM. The advantages 

of the pen tablet for use in sketchmapping include a smaller, 

simpler hardware profile and fewer cables. The disadvantages include a smaller screen area and decreased screen 

visibility in full sunlight.

We have tested many GPS receivers, and it seems that 

any receiver capable of producing an National Marine Electronics Association 0813 output string will be compatible 

with the software. Expensive, survey-grade receivers are 

not necessary; various recreation-grade units are adequate 

for the task. The GPS signal enters the PC via a COM port 

Universal Serial Bus ([USB] or Serial) connection. Newer 

USB GPS units have the advantage of drawing power from 

the laptop PC. Wireless GPS receivers provide an attractive 

alternative, and testing of such receivers is ongoing.

RIPCom Background

The RSAC has been working with NASA Goddard Space 

Flight Center (GSFC) on adapting technology NASA has 

developed for unmanned aerial vehicles (UAV) to Forest 

Service applications. The RIPCom system that is being 

developed by NASA-GSFC is of particular interest to the 

Forest Service because it can be used to download fire 

imagery during flight, which will save the Forest Service 

Figure 2—Forest Service digital sketchmapping systems are displayed. At left is the two-screen system with 

external global positioning system (GPS) receiver. At right is the pen-tablet sketchmapping table with built-in GPS 

receiver (the windshield-mounted antenna is shown in the lower right).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 3—RIPCom concept is shown in detail.

time and money while enhancing safety. RIPCom is a 

wireless communication system that makes any RIPComequipped aircraft look like a network node in the sky. 

RIPCom-provides an Ethernet to Radio Frequency (RF) 

connection solution for real-time data transmission, and its 

design allows the end points of the communication system 

to become nodes on a network with assigned IP address. 

RIPCom’s versatility makes it valuable for any system that 

requires a high-speed, digital wireless network.

The NASA-GSFC has modified the original RIPCom 

design so that it meets Forest Service requirements. These 

include the ability to dump data at much faster rates and at 

greater ranges.

Specific Forest Service requirements include:

• Providing a wireless network connection at 

distance of at least 10 mi.

• Supplying a data rate of at least 1 Mbps.

• Providing a long-distance wireless network 

that can also maintain a connection with a

moving target.

• Design for aircraft that is 10,000 ft aboveground level. 

• Data acquired at the ground station from an aircraft operating at 300 mph.

The RIPCom consists of COTS components from different vendors chosen because of their unique performance 

characteristics to meet Forest Service goals. Numerous 

wireless network technologies currently exist that are candidates for RIPCom, and many factors such as frequency, RF 

power output, size, and cost were considered for the Forest 

Service system design. The aircraft and ground station 

nodes were designed individually owing to the unique 

conditions of each environment. Figures 3 and 4 illustrate 

the major components of the RIPCom concept and details of 

the system’s ground station and aircraft components.

The major consideration for the airborne components 

is the antenna, which must be very aerodynamic while 

still providing a workable radiation pattern for this implementation. In addition, the antenna must be placed so that 

shadowing by the wings or other aircraft components is 

minimized. A 2.4 GHz, 5dB gain blade antenna (Figure 

5) has been installed on the aircraft’s fuselage just aft of 

the forward landing gear bay, which gives the antenna an 

excellent field of view.

RIPCom’s ground station is designed so that it is able 

to provide bi-directional communication with a lower gain 

antenna 10 mi away. The ground station accomplishes 

this by utilizing a three-sector, omni-directional antenna. 

The resulting system is able to provide a point-to-point 

connection for high-speed data transfer at an affordable 

cost ($7,500 for the ground station, less tripod, and $3,200 

for aircraft station, less installation). Because RIPCom is 

platform independent, (it does not matter which operating 

system is used), the only requirement is that ground station 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 4— Detailed diagram of Forest Service RIPCom solution is shown.

and aircraft have a Transmission Control Protocol/Internet 

Protocol connection.

Flight Tests

Initial flight-testing of the RIPCom system took place near 

Boise, Idaho, the week of April 14-19, 2003. Tests were 

conducted for 6-, 8-, 9-, 10-, 11-, 12-, and 15-mi orbits. During the flight test, several issues were evaluated, checked, 

and recorded:

• Determine the strongest channel (One is able 

to choose among 11 channels.).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 6— (Left) Case 1, (Center) Case 1 with computer tray removed, (Right) Case 2.

Figure 5— View of the aircraft gain blade antenna that is attached 

to the aircraft’s fuselage. 

• Ping each network device for each throughput 

setting (11, 5.5, 2 Mbps), and transmit a 36 Mb 

and 77 Mb file from the plane to the ground 

station.

• Determine the time it took to transmit the data.

• Characterize the location and duration of any 

data drops during data transmission.

Other tests included having the pilots fly elliptical 

orbits, flying directly over or to the sides of the ground 

station to characterize the radiation pattern of the ground 

station and to determine if there was any Doppler interference in the data transmissions.

The RIPCom exceeded expectations during flight-testing as it had with the ground tests. Performance was very 

good. Data rates of 2 to 3 Mbps were achieved at a range 

of 30 mi. The system was robust; File Transfer Protocol 

transfers could be completed without errors even if the 

transmission was interrupted (up to 45 sec). We also found 

that the jet could fly directly to and from the ground station 

at 300 mph without the data transmissions being affected by 

the Doppler Effect.

RIPCom 2

The April 2003 flight tests were an unqualified success 

based on the National Infrared Operations requirement 

for long-range broadband real-time transmission of highresolution imagery acceptable by the Infrared Interpreter. 

Upon later assessment, it was decided that the current state 

of technology could not fulfill both the performance and 

updated portability (single suitcase as carry-on luggage) 

requirements.

The NASA/GFSC and the Forest Service went back to 

work to develop a self-contained unit that could be easily 

transported by one person. It was a conscious decision to 

sacrifice both range and data bandwidth in order to meet 

the portability requirement. This collaborative effort 

resulted in a second generation RIPCom unit that utilizes 

a 36-in omni-directional antenna and a single radio. The 

portable approach yields a much lighter package that can 

be transported in two pelican cases. One case contains the 

modem, radio, and laptop computer. The second contains 

the antenna, mast, and cables (Figure 6). The bottom line 

for the end-user is that the portable RIPCom ground station 

is capable of 1 Mbps data rate at a range of 8 to 10 miles.

Summary

By integrating two successful technologies, the Forest 

Service is working to create a new tool that will allow fire 

managers to relay critical fire information to dispersed field 

units in real time. The Remote Sensing Applications Center 

will be working with NASA GSFC and wildland fire personnel to integrate the RIPCom sketchmapping system into 

wildland-fire operations during the 2007 fire season. The 

ultimate goal is to increase firefighter safety and to improve 

overall operational effectiveness in wildland fire monitoring 

and suppression.
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361

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

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

Steven P. Norman and Danny C. Lee, research ecologists, 

USDA Forest Service, Southern Research Station, Eastern 

Forest Environmental Threat Assessment Center, Asheville, 

NC 28804; Sandra Jacobson, wildlife biologist, and 

Christine Damiani, research ecologist, USDA Forest Service, Pacific Southwest Research Station, Arcata, CA 95521.

Abstract

The tradeoffs that surround forest management are inherently complex, often involving multiple temporal and 

spatial scales. For example, conflicts may result when fuel 

treatments are designed to mediate long-term fuel hazards, 

but activities could impair sensitive aquatic habitat or 

degrade wildlife habitat in the short term. This complexity 

makes it hard for managers to describe and communicate 

the conditional nature of risk and to justify planned activities to stakeholders. In addition, our understanding of how 

proposed activities will affect resources of concern is often 

limited owing to informational shortcomings and imprecise 

models. To be robust and transparent, a risk assessment 

framework needs to reveal these limitations while quantifying the probable outcomes of project effects to multiple 

resources of concern. In this analysis, we describe the 

effects of fuel treatments using such a planning framework 

called CRAFT (Comparative Risk Assessment Framework 

and Tools). CRAFT provides a platform from which diverse 

ancillary models and other relevant information can be 

transparently integrated and evaluated.

We conducted our case study in the southwestern 

Klamath Mountains of California. As is typical of most 

montane forests of California, this area has experienced 

decades of fire suppression, and severe effects from wildfire 

are a concern. Working with managers, we identified a 

range of measurable objectives involving the wildland 

urban interface, fire behavior, fire effects, and sensitive 

wildlife. We then developed a conceptual model describing 

how components of the system interrelate. From this, we 

developed a probabilistic framework, using Bayesian belief 

networks, in which we employed existing fire models to 

address how expected fire behavior varies across different 

burning scenarios. Our framework provides decisionmakers 

and stakeholders with insights into the condition probability 

that management alternatives will be successful.

Keywords: Bayesian belief network, comparative risk 

analysis, CRAFT, fire effects, objectives, tradeoffs.

Introduction

Forest management decisions are often difficult because 

ecosystems are inherently complex, and the system’s 

response to management is uncertain. Management tradeoffs typically involve very different objectives that can be 

difficult to compare or model across spatial and temporal 

scales. In addition, future conditions are often dependent 

on stochastic variation in the system that can be difficult 

to predict. One way for managers to address this complexity is to consider outcomes as conditioned on a range of 

influential factors that are assessed in terms of how likely 

they are to occur. This approach is more comprehensive 

than the single-scenario analyses that are commonly used 

in forest planning today, and it provides key information for 

decisionmakers and stakeholders.

Traditional risk assessment approaches were developed 

to reduce the likelihood of catastrophe such as engineering 

failures, insurance-related loss, or environmental contamination. These approaches may provide a poor model for 

forest risk assessments because management activities and 

nonactivities may influence a wide array of values across 

space and time. When something could happen that is 

unambiguously bad, such as a nuclear plant meltdown, the 

failure of a critical aircraft part, or a toxic spill, prevention 

is a clear and high-priority objective. In forest management, disturbances may be both a threat to the system and 

a critical requirement for the long-term viability of the 

system. For example, frequent fires may reduce surface 
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GENERAL TECHNICAL REPORT PNW-GTR-802

fuels and increase the resilience of old trees, yet they may 

also spread invasive species, reduce air quality, and threaten 

homes in the surrounding wildland-urban interface. Rather 

than decide how to prevent fire, forest managers increasingly must decide where, when, and how to conduct fire 

and fuel management to balance competing tradeoffs and 

diverse stakeholder values. Unlike risks associated with an 

unambiguous catastrophe, the risks and tradeoffs associated 

with forest management are better viewed comparatively. In 

this analysis, we introduce a comparative risk assessments 

framework and tools called CRAFT that was developed to 

address the tradeoffs associated with forest management 

decisions. A more detailed discussion of CRAFT can be 

found at: http://www.fs.fed.us/psw/topics/fire_science/craft/. 

[Date accessed unknown].

Study Area

We selected a 3000-km2 area located in the Klamath 

Mountains of northwestern California centered on the town 

of Hayfork. The majority of the landscape is under Federal 

management and includes portions of the Shasta-Trinity and 

Six Rivers National Forests. Private inholdings are common 

and contribute to wildland-urban interface management 

problems that are characteristic of the West. These include 

issues related to wildland fire, air quality, and biodiversity 

management. Both national forests fall under the Northwest 

Forest Plan, a 1994 regional plan designed to maintain 

sustainable forest products and to sustain species. As part of 

that plan, areas were allocated for different purposes. The 

primary land allocations present in the study area include 

late successional reserves that were set aside for old-growth 

species, including the northern spotted owl, and the Hayfork 

Adaptive Management Area set aside for experimentation 

and learning.

The vegetation of the study area consists of a diverse 

array of conifers, hardwoods, shrublands, and grassy meadows. Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) 

forests dominate a large portion of the area, but forests of 

ponderosa pine (Pinus ponderosa Dougl. ex Laws.) occur 

on dry sites, and Jeffrey pine (Pinus jeffreyi Gred. & Baif.) 

and incense-cedar (Calocedrus decurrens Torr.) dominate 

forests on ultramafic soils. Mixed-conifer forests are found 

where pine, Douglas fir, white fir (Abies concolor (Gord. 

& Glend.) Lindl. ex Hildebr.), and incense-cedar co-occur 

and typically include various species of hardwoods in 

Figure 1—Overview of the Comparative Risk Assessment Framework and Tools process showing how increasingly specific objectives (in green) from left to right tier to risk assessment endpoints. The effects of different 

management alternatives (in blue) and scenarios are modeled using a probabilistic belief network (in yellow).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 2—A partial objectives hierarchy for the Hayfork study area. Upper level objectives (left) suggest general goals, whereas 

lower level objectives (right) can be formulated into specific risk assessment endpoints, monitoring endpoints, or specific management activities.

the canopy or understory. The most common hardwoods 

include tanoak (Lithocarpus densiflorus (Hook. & Arn. 

Rehd.), canyon live oak (Quercus chrysolepis Liebm.), black 

oak (Quercus kelloggii (Newb.) and chinquapin (Chrysolepis chrysophylla (Hook.) Hjelmqvist). Surface and live 

fuel composition and structure have been altered from their 

historical condition by 20th-century fire suppression and 

logging. Wildfires occur on a regular basis, but extensive, 

long-burning wildfires have only occurred in 1987 and 

1999.

The CRAFT Process

The CRAFT planning process leads managers through four 

stages involving (1) objective setting and problem conceptualization, (2) alternatives design, (3) probabilistic modeling 

of effects, and (4) synthesis. In this analysis, we emphasize 

steps 1 and 3 to show how objectives can be incorporated 

within a single modeling framework and then considered in 

terms of their uncertainties. Figure 1 provides a graphical 

overview of the CRAFT process.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Specifying and Structuring Objectives

A critical first step in CRAFT is to fully understand the 

problems at hand. It is easy to sidestep this step by jumping 

to alternatives or even modeling, but a thoughtful, interdisciplinary exploration of the objectives and the problems 

is needed before specific modeling tools and data sets are 

considered. Existing information and familiar models may 

be ill suited for the specific problems at hand.

In CRAFT, problem formulation is addressed using 

two planning tools: (1) an objectives hierarchy that helps 

planners and stakeholders focus on specific measurable 

objectives rather than on overarching goals, and (2) by a 

cause-and-effect model that provides a transparent documentation of how the system is thought to operate. In addition to documenting values and beliefs, these tools serve to 

identify potential tradeoffs for the comparative assessment 

of risk.

Objectives hierarchies have been used for a variety of 

planning purposes, but they are rarely used by public agencies in the United States (Clemen and Reilly 2001, Keeney 

1992). An objectives hierarchy simply separates intangible 

objectives (the upper levels) from detailed, measurable 

objectives (the lower levels) and structures them within a 

hierarchy. Specific lower level objectives suggest how upper 

level objectives might be achieved. This structure ensures 

that planners have formulated explicit lower level objectives that will be translated into risk assessment endpoints. 

Detailed objectives may also serve as appropriate monitoring endpoints as part of a broader forest management 

strategy. We show a partial Objectives Hierarchy for our 

study area in Figure 2.

From an agency perspective, an objectives hierarchy 

simplifies risk assessments by providing focus, context, and 

clarity. Upper level objectives often correspond to general 

forest mandates, whereas lower level objectives may relate 

to the extension of statutes to specific instances or locations. For example, although maintaining biodiversity is 

mandated by a number of statutes, the specific requirements 

for maintaining viable habitat and populations for individual 

species often reflect regulatory policy.

From a stakeholder perspective, objectives hierarchies 

can be powerful tools for involvement. Stakeholder values 

differ in terms of their specificity, and, by using an objectives hierarchy, stakeholders are more likely to see how their 

values fit with those of the agency. Comprehensive objectives hierarchies can also suggest opportunities for stakeholder-agency collaboration. As examples, the protection 

of homes from wildfire and invasive species control both 

require agency and stakeholder involvement to ultimately be 

successful. Perhaps, most valuably, an objectives hierarchy 

can provide a common vision for what a managed ecosystem could look like. Stakeholders are more likely to remain 

engaged when they can imagine desired future conditions. 

Once future objectives are clarified, specific management 

choices and tradeoffs can be considered through a separate 

process.

Conceptualizing Cause and Effect

Following the identification of measurable objectives in 

CRAFT, cause-and-effect models are developed that identify the factors that are known to influence relevant lower 

level objectives of concern. A conceptual model consists of 

a bubble-arrow diagram and supportive text that explains 

the relationship in detail (U.S. Environmental Protection 

Agency 1998). Conceptual models help managers identify 

the key reasons why a particular management objective may 

or may not be met. Causes and effects can be established 

across spatial and temporal scales so that the interconnectedness of the ecosystem is apparent. This step is designed 

to be conceptually inclusive and unrestrained by the 

availability of local data or existing supportive models that 

have been used in the past. Later on, when a quantitative 

effects model is being developed, ancillary models, data, 

and expert opinion are more likely to be used appropriately 

when a clear conceptual model exists. Cause-and-effect 

relationships may be based on existing research, ancillary 

models, or expert opinion.

From an agency perspective, this task transparently 

documents the factors that were considered beyond those 

that are carried forward in the formal risk modeling step 

that follows. Conceptual models also provide a transparent 

venue for group involvement by describing the problem in a 

way that both interdisciplinary team members and stakeholders can dispute and ultimately come to agreement.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

At some point, the cause-and-effect model will likely 

suggest specific management activities. In CRAFT, as with 

National Enviromental Policy Act, these activities are then 

combined into formal management alternatives.

Effects Modeling

A formal effects model results from careful restructuring of 

lower level objectives and the conceptual model. Two key 

decisions are (1) the selection of suitable risk assessment 

endpoints, and (2) how cause and effect will be analyzed 

and integrated.

Risk assessment endpoints tier to lower level objectives, 

and they must be place and time specific. For example, a 

well-focused wildland-urban interface endpoint may be to 

prevent any wildfire from entering within 1.0 mi of the town 

of Hayfork indefinitely. This specificity allows management 

alternatives to be tailored to real places. Further, spatial and 

temporal needs will influence the structural type of effects 

model used.

The effects modeling platform can incorporate an 

assortment of ancillary models, available data, and expert 

opinion. In CRAFT, that integrative platform is a Bayesian 

belief network that reveals assumptions, uncertainties, and 

likelihoods. Belief networks have been used for a range of 

natural resource planning issues (e.g., Lee and Irwin 2005, 

Marcot and others 2001). Belief networks have strict structural limitations that require a careful translation from the 

conceptual model. Relationships between parent and child 

nodes in the network can be calculated using multiple runs 

of deterministic ancillary models, analyses of existing data 

sets, or expert opinion. Each set of relationships provides 

a scenario that is defined by the model assumptions. The 

relative importance of each scenario in the model is then 

conditioned on the specified probability distributions of 

the nodes that have no parent nodes (i.e., the driver nodes). 

Importantly, the model can provide information even if 

no distributions are specified. Decisionmakers can readily 

explore the sensitivity of outcomes to different assumptions 

of the driver nodes.

The Hayfork Effects Model—

During the conceptual modeling phase, wildfire behavior 

was identified as a key node that was critical for wide 

ranging objectives of concern. To illustrate how variability 

in fire behavior can be implemented in a belief network, we 

defined a number of vegetation-related variables and varied 

two others—fire weather and wind direction.

Fire behavior can be modeled by a wide array of 

models, but relatively few are useful for characterizing 

how fire behavior is likely to vary across the landscape. 

Landscape modeling is especially important in mountainous 

terrain such as our Klamath study area because patterns of 

vegetation and fuel, fire behavior, and winds are strongly 

influenced by topography. We used FlamMap (Finney 2006) 

to describe spatial patterns of fire behavior. FlamMap is 

not a dynamic fire spread model, and we did not calculate 

fire behavior using real-time fire weather or results that 

are conditional on spread from discrete ignition points. We 

essentially burned the entire landscape under fixed scenarios to limit the number of permutations and to transparently 

represent the assumptions used in our belief network.

The spatial information used in FlamMap includes 

slope, aspect, canopy cover, and surface fuel model. To 

model crown fire, we also used stand height, lower canopy 

height, and canopy bulk density. We derived topographic 

parameters (i.e., elevation, slope, and aspect) from a 30-m 

digital elevation model. Canopy cover was based on a 

30-m, satellite-derived geographic information system layer 

from 2001 (http://www.seamless.usgs.gov [Date accessed 

unknown]). Other vegetation attributes were directly or 

indirectly derived from a U.S. Forest Service Region 5 

existing vegetation layer from the mid-1990s that provided 

compositional and coarse age structural data in polygons 

of variable size. Stand height was calculated by using the 

dominant tree species in the classified polygons and height 

equations used by the Forest Vegetation Simulator (FVS, 

Dixon 2002). Canopy bulk density was calculated using 

published information for individual trees and was then 

multiplied by percent canopy cover. Canopy base height 

includes ladder fuels that are typically low across the study 

area owing to fire suppression (Taylor and Skinner 2003). 

Canopy base heights were assigned a value of 0.5 m in 

mature conifer stands and 0.2 m in young conifer plantations with a 0.8 live crown ratio for hardwoods. Fuel models 

were assigned by recasting the original 13 fuel models used 
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in the Shasta-Trinity National Forest fire management plan 

for different vegetation types based on the new 40 fuel models (Scott and Burgan 2005). Although there are substantial 

uncertainties associated with most of these vegetationrelated factors across the landscape, we fixed all of the 

foregoing parameters to focus on fire weather and wind.

Fire weather was modeled with four fuel moisture 

scenarios (i.e., 99th, 95th, 90th, and 80th percentiles) using 

two decades of fire weather collected at the Hayfork Ranger 

Station. Percentile conditions reflect the percentage of 

the time that weather conditions are likely to occur based 

on historical patterns. Data were manipulated using the 

software Fire Family Plus (Bradshaw and McCormick 

2000). In FlamMap, fuel moistures were conditioned 

across topographic positions and aspects for 7 days using 

a daily range of temperature and humidity typical of the 

percentile conditions. Gridded windspeeds were paired with 

fuel moisture scenarios based on the speeds modeled on 

Hayfork Bally, the highest peak north of the weather station. 

Four fire weather scenarios were defined based on the 

fuel moisture conditions that characterized extreme, high, 

moderate, and low fire weather during the fire season (i.e., 

the 99th, 95th, 90th, and 80th percentile conditions). Gridded 

wind maps were assigned to these moisture conditions using 

mountain-top windspeeds of 32, 24, 12, and 6 miles per 

hour (mph), respectively.

Wind is an important driver of fire behavior in 

mountainous  terrain,  and  our  study  area is no exception. 

Extensive fire runs have been observed during east-wind 

events that are typically associated with frontal systems. 

Topography can greatly reduce wind speed and direction, 

but locally, this effect is often contingent on the regional 

wind direction. To address the effect of regional wind 

direction, we generated estimates of local winds using a 

beta version of WindWizard (developed by the U.S. Forest 

Service, Rocky Mountain Research Station, by B. Butler 

and J. Forthorfer). Whereas downbursts, fire-generated 

winds, and winds related to land use and gravity are ignored 

in this model, gridded wind reduces windspeeds according to topographic position consistent with observations. 

Gridded wind from four synoptic directions (i.e., northwest 

[NE], southeast [SE], southwest [SW], and northwest [NW]) 

was used in FlamMap to provide a better estimate of local 

wind direction and speed than simply modeling a uniform 

speed and direction across all topographic positions.

Every node in a belief network has a probability 

table associated with it, but values are derived differently, 

depending on where the node sits in the network. Nodes that 

Figure 3—The probabilistic effects model used to characterize fire behavior (flamelength). For simplicity, only fire 

weather and wind direction were varied in this example. The probabilities for states of these two nodes were derived 

from retrospective analyses of the area burned during long-duration fires in northwest California, whereas the 

values in the node flamelength represent iterative calculations of the ancillary model, FlamMap.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Table 1—Conditional probabilities of low (< 1.2 m), moderate (1.2 to 

3.1 m), and high (> 3.1 m) across combinations of four fire weather 

and four wind direction scenarios for the study area 

Fire weather Wind direction High Moderate Low

Extreme NE 0.397 0.320 0.281

Extreme NW 0.381 0.328 0.291

Extreme SE 0.384 0.354 0.262

Extreme SW 0.362 0.368 0.270

High NE 0.243 0.403 0.353

High NW 0.224 0.413 0.363

High SE 0.242 0.421 0.337

High SW 0.226 0.424 0.349

Moderate NE 0.135 0.444 0.421

Moderate NW 0.127 0.449 0.422

Moderate SE 0.135 0.448 0.417

Moderate SW 0.129 0.443 0.428

Low NE 0.090 0.418 0.492

Low NW 0.089 0.411 0.498

Low SE 0.089 0.416 0.495

Low SW 0.088 0.410 0.502

Joint probabilities 0.207 0.412 0.381

Joint probabilities reflect the relative likelihoods of these different scenarios occurring based 

on the priors shown in Figure 3.

are influenced by other nodes have conditional probability 

tables that show how probabilities change in response 

to changes in the parent nodes. These relationships are 

often calculated using ancillary models. Nodes that vary 

independently of other nodes must have their probabilities 

(or priors) specified. In our belief network, the probability 

distributions for fire weather and wind direction were based 

on analyses of daily fire progression maps of two recent 

and nearby long-duration fires—the Biscuit Fire and the 

Big Bar Complex. Fuel moistures calculated in Fire Family 

Plus were associated with daily area burned using midday 

fire weather data, and the fire spread direction of a stratified 

sample of points was used to approximate wind direction 

in terms of area burned.

Results of Effects Modeling—

Retrospective analysis of fire weather during the Big 

Bar long-duration Fire indicates that nearly half of the 

area burned when Fire weather was high (defined as fuel 

moistures ranging from the 93th to 96th percentile conditions during the May 1 through October 31 fire season). 

Roughly 22 percent of the area burned when conditions 

were moderate (86th to 92th percentile), 16 percent burned 

when conditions were low (less than 86th percentile), and 

only 13 percent of the area burned when fuel moistures 

were extreme (97th to 100th percentile). These values were 

used as priors in the fire weather node of the belief network 

(Figure 3).

Analysis of fire spread during the Big Bar and Biscuit 

Fires showed that sites burned under a range of wind 

conditions, but winds from the NE and NW were associated 

with a greater area burned than were winds from the SE. An 

average of these two long-duration fires was used to define 

priors in the wind direction node of the belief network 

(Figure 3).

We calculated the conditional probabilities of fire 

behavior (i.e., flamelength) with multiple runs of FlamMap 

using different scenarios of fire weather and wind direction. 

For each scenario, we determined the area with modeled 

flamelengths less than 1.2 m (4 ft), 1.2 to 3.1 m (4 to 10 ft), 

and greater than 3.1 m (10 ft). These conditional probabilities are shown in Table 1. Wind direction has no appreciable 

effect on the overall pattern of expected flamelength as it 

varies by only a few percentages across wind scenarios. 
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In contrast, fire weather has a substantive effect on flamelength (Figure 4). When fire weather is low, half the area is 

expected to have flamelengths that are low. These flamelengths are likely to result in minimal mortality to mature 

trees while reducing surface fuels and fuel ladders. During 

extreme  conditions,  high  flamelengths  dominate  the 

landscape, and fires are likely to cause widespread mortality. Based on the joint probability distribution (Table 1), 

low- or moderate-severity flamelengths are likely to occur, 

presuming that future fire weather and winds are consistent with those of prior long-duration fire events. About 

21 percent of the landscape can be expected to burn with 

flamelengths greater than 3.1 m during low to moderate 

fire weather compared to 38 percent that is likely during 

extreme fire weather.

We then compared the effects of fire weather and 

wind on the flamelengths of different portions of the study 

area. For example, the expected flamelengths in 43 northern 

Figure 4—Maps showing conditional fire behavior (flamelength) according to four 

fire weather scenarios for an 18- by 23-km portion of the study area. Flamelengths 

were classified as low (shown in grey; less than 1.2 m), moderate (in yellow; 1.2 to 

3.1 m), and high (in red; greater than 3.1 m). The values shown on each map were 

calculated with a weighted average of the four wind directions shown in Figure 2.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 5—Change in fire risk for northern spotted owl habitat areas across fire weather and wind direction 

scenarios. High flamelengths shown in red are much more likely to be lethal to nesting trees than are low 

flamelengths. Note that under any modeled scenario, the habitat is likely to experience a range of flamelengths, 

as is typical of the region’s mixed-severity fire regime.

spotted owl (Strix occidentalis) core areas is shown in 

Figure 5. Each core area is 60 ha (100 ac). Consistent with 

the overall pattern, the area expected to burn under different fire weather scenarios increases with the severity of fire 

weather, but flamelengths show little variability across the 

four modeled wind directions.

Summary

The simple-effects model shown here demonstrates how an 

important driver of forest outcomes—namely fire behavior—can be modeled in terms of conditional probability. 

This process transparently shows how assumptions of 

expected results may or may not matter. By varying vegetation and fuel inputs with proposed fuel treatment options, 

management outcomes can be considered within this same 

framework. Moreover, the uncertainties associated with the 

effects of different fire behaviors can be addressed with an 

expanded belief network.

The range of fire behavior that occurs during a single 

scenario in our model shows that the project area has a 

mixed-severity fire regime. This is consistent with retrospective analyses of past fires (Odion and others 2004, 

Taylor and Skinner 2003). Large fires burn under a range of 

fire weather and wind scenarios, and this leads to further 

variation in fire behavior. Owing to the combined influence 

of these spatial and temporal patterns, the fire regimes of 

the Klamath Mountains are truly complex. This complexity 

is likely to have contributed to the high biodiversity that 

characterizes the Klamaths, but it makes multiresource 

management decisions more challenging.

To be meaningful, comparative risk assessments must 

be capable of accommodating this ecological complexity. The CRAFT process allows managers to incorporate 

diverse objectives and sophisticated effects modeling to deal 

with these complexities and their associated uncertainties.

Literature Cited

Bradshaw, L.; McCormick, E. 2000. Fire family plus 

users guide. Gen. Tech. Rep. RMRS-67. Fort Collins, 

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

Rocky Mountain Research Station. [Pages unknown].

Clemen, R.T.; Reilly, T. 2001. Making hard decisions. 

Pacific Grove, CA: Duxbury Thomson Learning. [Pages 

unknown].
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GENERAL TECHNICAL REPORT PNW-GTR-802

Dixon, G.E. 2002. Essential FVS: A user’s guide to the 

forest vegetation simulator. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Forest 

Service Forest Management Service Center. [Page nos. 

unknown].

Finney, M.A. 2006. An overview of FlamMap fire 

modeling capabilities. In: Fuels management—how to 

measure success: Conference Proceedings. RMRS-P-41. 

Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 213–220.

Keeney R. 1992. Value focused thinking. Cambridge, MA: 

Harvard University Press. 432 p.

Lee, D.C.; Irwin, L.L. 2005. Assessing risks to spotted 

owls from forest thinning in fire adapted forests of the 

western United States. Forest Ecology and Management. 

211: 191–209.

Marcot, B.G.; Holthausen, R.S.; Raphael, M.G. [and 

others]. 2001. Using Bayesian belief networks to 

evaluate fish and wildlife population viability under land 

management alternatives from an environmental impact 

statement. Forest Ecology and Management. 153: 29–42.

Odion, D.C.; Frost, E.J.; Stritthold, J.R. [and others]. 

2004. Patterns of fire severity and forest conditions in the 

western Klamath Mountains, California. Conservation 

Biology. 18: 927–936.

Scott, J.; Burgan, R.E. 2005. Standard fire behavior fuel 

models: a comprehensive set for use with Rothermel’s 

surface fire spread model. Gen. Tech. Rep. RMRS-153. 

Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 72 p.

Taylor, A.H.; Skinner, C.N. 2003. Spatial and temporal 

patterns of historic fire regimes and forest structure 

as a reference for restoration of fire in the Klamath 

Mountains. Ecological Applications. 13: 704–719.

U.S. Environmental Protection Agency. 1998. Guidelines 

for ecological risk assessment. Notice, May 14, 1998. 

Federal Register. (63)93: 26845–26924.
371

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

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

Haiganoush K. Preisler, research statistician, USDA Forest 

Service, Pacific Southwest Research Station, Albany, CA 

94710; Alan A. Ager, operations research analyst, and 

Jane L. Hayes, research biological scientist, USDA Forest 

Service, Pacific Northwest Research Station, La Grande, 

OR 97850. 

Abstract

Building probabilistic risk models for highly random forest 

disturbances like wildfire and forest insect outbreaks is 

a challenging. Modeling the interactions among natural 

disturbances is even more difficult. In the case of wildfire 

and forest insects, we looked at the probability of a large 

fire given an insect outbreak and also the incidence of insect 

outbreaks following wildfire. We developed and used a 

probabilistic model framework for estimating (1) the probability that a wildfire, at a given location and time, reaches a 

given size class under the conditions at the site—including 

history of insect outbreaks; and (2) the probability of an 

insect infestation at a given location and year under the 

conditions at the site—including history of fire occurrence 

and size. The study used historical data (1980 through 2004) 

on fire occurrence and forest insect outbreaks collected 

in Oregon and Washington. Spatial data on insect activity 

was obtained from aerial sketch maps created by the Forest 

Service Forest Health Protection program. Federal wildfire 

data obtained from the Desert Research Institute included 

information on the date, location, and size of the fire. Average monthly temperature and Palmer Drought Severity Indices were obtained from the National Climatic Data Center’s 

climate division data set Web page. The methods employed 

provide an objective tool for modeling complex hybrid 

processes and estimating associated probability maps.

Keywords: Forest threats, multinomial regression, multiple stressors, nonparametric regression, spatial regression, 

spline functions.

Probabilistic Risk Models for Multiple Disturbances: 

An Example of Forest Insects and Wildfires

Introduction

Wildfire and insect infestations are two major disturbances 

of forest lands in the United States. Historically, insect 

infestations and wildfires have had a dominant influence 

on successional processes in forests of the Western United 

States (Agee 2003). Fire suppression over the past 100 

years has resulted in larger, more severe wildfires and 

insect outbreaks (Hessburg and others 1994). In 2005, over 

0.14 million hectares (0.34 million ac) of Federal lands in 

Oregon and Washington were affected by wildfires (http://

www.nifc.gov) and approximately 0.8 million hectares (2.1 

million acres) sustained damage from insects such as bark 

beetles and defoliators (http://www.fs.fed.us/r6/nr/fid). In 

response to concerns over the size and severity of wildfires 

and insect outbreaks, Federal land management agencies 

have adopted forest management strategies that call for 

restoration activities that include reintroduction of natural 

and prescribed fire over wide areas of the Western United 

States. These activities have demonstrated beneficial effects 

in terms of moderating wildfire, but the potential effects on 

insect dynamics and insect-caused tree mortality are less 

clear. 

A reciprocal and synergistic association has frequently 

been described between fire and insects. For example, tree 

mortality resulting from insect outbreaks has been seen 

as setting the stage for subsequent wildfires (Geiszler and 

others 1980, Parker and Stipe 1993). Conversely, wounding 

and mortality from fire can create focus trees, which act as 

magnets for bark beetles (McCullough and others 1998). In 

separate studies, McHugh and others (2003) and Cunningham and others (2005) followed tree mortality and beetle 

attacks for 3 years after wildfire and found bark beetles 

more likely to attack trees with fire injury. Similar results 

were found following prescribed fire (Bradley and Tueller 

2001, Wallin and others 2003). However, others have found 

that beetles did not preferentially attack trees with fireinjured boles, but attack success was higher in injured trees 

when beetle population levels were low (Elkin and Reid 

2004). Additionally, Sanchez-Martinez and Wagner (2002) 
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We obtained average monthly temperature and Palmer 

Drought Severity Indices (PDSI) from U.S. Climate Divisions (NCDC 1994). The values at the climate-division 

level were projected onto a 1-km2 grid to provide a monthly 

climate record for each grid cell. Variables at the climate 

division level may not be good estimates for local weather 

conditions, but they were used here to demonstrate how 

weather variables can be included in the model.

Spatial data on insect activity was obtained from aerial 

sketch maps created by the Forest Service Forest Health 

Protection program (http://www.fs.fed.us/r6/nr/fid). In 

particular, we obtained data on (1) total number of trees 

(lodgepole pine, Pinus contorta (Dougl. ex Loud.), and ponderosa pine, P. ponderosa (Dougl. ex Laws.) killed by two 

bark beetle species (mountain pine beetle, Dendroctonus 

ponderosae Hopkins, and western pine beetle, D. brevicomis LeConte) per year (1980 through 2004) in each square 

milometer grid of Forest Service lands (Region 6); and (2) 

total area defoliated by western spruce budworm (Choristoneura occidentalis Freeman) in each square kilometer of 

Forest Service land per year.

In this study we did not use information on fire boundary. Consequently, the models described below are used for 

estimating associations between histories of fire sizes and 

insect infestations within 1 km of each other. Additional 

predictors (e.g., distance to nearest infestation or fire) may 

improve estimation results. In this study, we are mainly 

concerned with describing the statistical framework given a 

set of predictors.

Probability Framework

We are interested in obtaining estimates of disturbance 

probabilities in the presence of multiple stressors. For 

example, we would like to quantify:

Prob {damage from disturbance 1 given the size of 

damage from disturbance 2 and values of other predictors}

Two specific disturbances, bark beetle infestation and 

fire size, were analyzed in the present case study. Fires of all 

sizes were used in the analysis.

found low population levels of bark beetles regardless of 

stand treatment history, including stand replacement by 

wildfire. These results confirm that the association between 

insects and fire is a complex one, particularly when evaluated over time and at a large scale. 

Numerous studies have begun to examine functional 

and numerical interactions between insects and fire at the 

tree and stand level, but few quantitative studies have been 

carried out that consider the spatiotemporal dynamics of 

wildfire and insect outbreaks at the landscape scale (Barclay 

and others 2005, Bebi and others 2003, Fleming and others 

2002, Kulakowski and others 2003, Lynch and Moorcroft 

2004, Veblen and others 1991). Existing analytical techniques for spatiotemporal analysis of multiple interacting 

disturbances are not well developed, and large-scale data 

sets suitable for studying interactions are few (Lynch and 

Moorcroft 2004).

The present study describes a framework for estimating probabilities, at a landscape level, of various forest 

disturbances as a tool for quantifying associations between 

multiple interacting disturbances. The framework builds on 

related work by the authors and collaborators on estimating 

wildfire probabilities in relation to weather and fire danger 

indices and spatial location (Brillinger and others 2003, 

Preisler and Westerling 2007, Preisler and others 2004).

Methods

Data

Our study area was Oregon and Washington national forest 

lands. We obtained historical Federal wildfire occurrence 

data (1980 through 2004) from Desert Research Institute 

(DRI), http://www.dri.edu. The DRI fire occurrence data 

are based on the National Interagency Fire Management 

Integrated Database (NIFMID) at the USDA Forest Service 

National Information Technology Center in Kansas City, 

Missouri. The data included information on the date, location, and size of the fire. The DRI version of the data had 

been subjected to a quality-control procedure in which each 

fire-occurrence record was flagged as usable or otherwise 

(Brown and others 2002). Fire locations are the latitude and 

longitude of the fire when first discovered.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Bark Beetle Infestation—

Let Y be a random variable such that Yi = 1 if an infestation 

is present in a square kilometer grid at location (utmxi, 

utmyi) and year yri and Yi = 0 otherwise. The random variable Y is assumed to follow a Bernoulli distribution with 

probability of response

 pi

 = Pr[Yi = 1 | Xi ] = e

q (xi)

 /[1 + e

q (xi) ] [1]

where q (Xi

) = bo + g(utmxi, utmyi ) + Sgk (Xik )

and where the matrix X of predictors includes history 

of fire and insect infestation in previous years and within 

1 km of infestation. The functions gk(Xk

) in equation [1] are 

transformations of the variable Xk using linear-basis splines 

(Hastie and others 2001). For the spatial component, g(utmx, 

utmy), we utilized the two-dimensional version of the basis 

function, specifically, the thin plate spline function. All 

estimations were done within the statistical package R (R 

Development Core Team 2004). The required modules for 

fitting thin plate splines within R were downloaded from 

the Web (Geophysical Statistics Project 2002). By including 

variables such as location and history of insect infestation in 

the model, we were able to study the effect of fire (occurrence and size), on the probability of an infestation, in the 

presence of confounding factors (e.g., presence of infestations in previous years). The spatial component in the model 

can be viewed as a surrogate for other predictors (e.g., 

elevation, fuel type, and other variables) that do not change 

substantially over time and are not part of the model for a 

variety of reasons (Hobert and others 1997). Because the 

number of cells with no insect damage was very large (2.4 

million), only a random sample of insect-free locations was 

included in the analysis. The final probabilities were then 

adjusted accordingly (Maddala 1992, Preisler and others 

2004).

Results of fitting equation [1] were expressed as maps 

of estimated probabilities (and estimated standard errors) 

that are spatially explicit on 1-km2

 grid cells. The maps 

may be used to forecast probabilities of occurrence of an 

insect infestation for a succeeding year, given the history 

of fire and insect infestation up to that year. Outputs can 

be appraised directly by comparing observed frequencies 

of occurrences with predicted probabilities using crossvalidation.

Using this model, we were able to quantify the interactions between different disturbances by studying the significance and shape of the relationships between the probability 

of an infestation and the occurrence and size of fire in 

previous years. In particular, we produced estimates of the 

odds of an infestation as a function of fire size relative to 

the odds when no fire is present. Odds were defined as 

p /(1 – p , where p is the probability of a response of interest, in this case, bark beetle attack.

Fire Size

To study the association between insect disturbances and 

the risk of wildfire becoming large at a given location, we 

divided fires into three size classes. Consider the ordinal 

random variable Y where Yi = 1 if a fire at location (utmxi, 

utmyi) and time t

i

 burns an area less than or equal to 1 ha; 

Yi = 2 if area burned is 1100 ha; and Yi = 3 if area burned is 

greater than 100 ha. Next, assume that the random variable, 

Y, follows a multinomial distribution with probability of 

response at level k (k = 1, 2, 3), given by pik = Pr[Yi = k | X] 

and pi1 + pi2 + pi3 = 1. The list of predictors, X, included 

average monthly temperature and PDSI, size of bark beetle 

infestation (trees killed per square kilometer), and size of 

spruce budworm infestation (area defoliated per square 

kilometer) in the previous 6 years. We obtained estimates 

for pik by fitting a complementary log-log function to the 

conditional probabilities of response. Specifically, we used 

the model

 pik = Pr[Yi = k |Yi >k–1, Xi] = 1 –exp[– exp[bo + g(utmxi, utmyi )

 +Sgj (Xij )]] [2]

   j

where the functions g(utmxi, utmyi) and g(X) are as defined 

in (“Bark Beetle Infestation”). We obtained estimates of the 

multinomial probabilities from the conditional probabilities 

using the relationships pi1 = pi1; pi2 = (1 – pi1)pi2; and pi3

= (1- pi1 – pi2). As in section (“Bark Beetle Infestation”) 

the model goodness-of-fit was appraised by comparing 

observed and predicted frequencies of fires in the three 

different size classes.

We produced maps of estimated probabilities displaying the spatial patterns of fires of different size classes. We 

also studied association between insect infestations and fire 

size by producing graphs of estimated odds of a fire of a 
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Bark Beetle Infestation

The probability of beetle infestation was significantly 

influenced by spatial location, size of infestation in previous year, and size of 1- year-old fire within 1 km (P-value 

<< 0.05). The estimated probabilities of bark beetle outbreaks, evaluated after controlling for all other significant 

predictors, demonstrated a spatial pattern with increasing 

probabilities of outbreak as one moves away from the 

coastal areas and into the eastern regions of Washington 

and Oregon (Figure 2). This pattern of declining forest 

health in the dry coniferous forests of eastern Oregon and 

Washington has been noted by others in recent assessments 

given size class—plotted against infestation size—relative 

to the odds when no infestation is present.

Results and Discussion

The models in equations [1] and [2] appeared to give reasonable fits to 1982 to 2004 data on bark beetle attacks 

and wildfire sizes on Forest Service lands in Region 6 

(Figure 1). For example, consider all cells over the years 

when the model in equation [1] predicted a 40-percent probability of a beetle attack (bottom right panel, Figure 1), the 

actual percentage of time an attack was observed was 

37 percent, which is well within the 95-percent limits (36 to 

40 percent) of the predicted probability.

Figure 1—Observed versus predicted multinomial probabilities of a fire reaching size class (a) (0 to 1] ha, (b) (1 to 100] ha, 

(c) >100 ha. The value n in each panel is the total number of fires in the corresponding size class during the study period. 

(d) Observed versus predicted probability of a bark beetle occurrence in a given 1-km2

 grid cell.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

and syntheses (Gast and others 1991, Hayes and others 2001, 

Hessberg and others 1994, Jaindl and Quigley 1996).

One of the most important predictors when evaluating a 

bark beetle outbreak appears to be size of the beetle infestation in the previous year. The odds of a beetle outbreak 

seem to increase as the number of trees killed per square 

kilometer by bark beetles in the previous year increases 

(Figure 3). Although this outcome is probably expected, 

given the outbreak dynamics of insect populations in 

general (e.g., Barbosa and Schultz 1987), it is important to 

know that it was included in the model and, consequently, is 

accounted for in a reasonable fashion. The other significant 

predictor was the size of fire in the previous year. The odds 

of an insect outbreak appeared to increase as the size of a 

fire increases from 0 (no fire) to ~750 ha, after which the 

odds appear to decrease. The standard errors are large, 

especially for large fire sizes. This result is consistent with 

the expectation that as fire increases in size, the number of 

trees with significant crown damage and bole or root scorch 

increases, and, therefore, susceptibility to bark beetle attack 

may increase. However, very large fires may completely 

eliminate susceptible hosts in the area, resulting in less 

observable beetle damage when large fires are nearby.

Figure 2—Estimated spatial pattern of beetle attacks on 

national forests in Washington and Oregon.

Figure 3—Estimated odds ratio (solid curves) and 95-percent 

confidence bounds (dashed curves) describing the increase in odds 

of a beetle attack (a) as the number of trees killed by beetles in the 

previous year increases from 0 to 10,000 trees/km2

 (left panel) 

and (b) as the size of a fire in the previous year, and within 1 km 

of the infestation, increases from 0 to > 2500 ha (right panel). The 

hatched marks in the bottom of the right panel indicate distribution of observed fire sizes. Note that only four fires of size > 1000 

ha were observed in the sampled data. 

Trees per km2

Fire Size

The following predictors were found to have significant 

effects (P-value << 0.05) on the multinomial probabilities 

of a fire reaching one of three size classes: spatial location; 

size of beetle infestations in previous 3 years; size of spruce 

budworm infestations 4 to 6 year ago; average monthly 

temperature; and average monthly PDSI.

The estimated spatial pattern of fire sizes (Figure 4) 

seems to indicate northern Washington and eastern Oregon 

as some of the regions with the highest probabilities of a 

fire getting large after controlling for all other predictors 

in the model. Again, these results appear to be consistent 

with observed changes over the last 60 to 100 years in fire 

frequency and size in interior forests of the Pacific Northwest (Hessburg and Agee 2003).

According to the present data, beetle infestations that 

are 3 to 6 years old do not seem to be significantly associated with fire size; however, the odds of a fire getting large 

seem to increase significantly when the total size of beetle 

infestation in the last 3 years is greater than 5,000 trees 
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GENERAL TECHNICAL REPORT PNW-GTR-802

5, bottom panels). Lack of foliage following defoliation 4 

to 6 years prior may actually reduce the risk of crown fire. 

Although the decrease in odds appears to continue as the 

size of defoliated area increases past 300 ha, the standard 

error for this range of defoliation is too large to warrant a 

precise conclusion. 

Conclusions

Multiple disturbances, such as wildfire-insect outbreak 

interactions, are not well understood at provincial scales. 

Lack of large-scale data sets and easily available analytical 

techniques to address multiple disturbances have contributed to a limited number of quantitative studies. These 

studies have had confounding results. Here, we have 

killed per square kilometer (Figure 5, top panels). Canopies 

with residual dead needles and limbs in recently killed 

trees may contribute to crown conditions that significantly 

influence fire behavior. Following a simulated outbreak, 

Ager and others (in press) found that beetle-caused tree 

mortality increased surface fuel loadings and the potential 

for stands to sustain  crown  fire  in  the  simulation.  A  different  association was seen between fire size and size of 

spruce budworm infestation. No significant association was 

apparent between recent defoliations (area defoliated in 

previous 3 years) and sizes of fires nearby. However, a significant decrease was seen in the odds of a fire getting large 

as the size of older defoliations (total area defoliated 3 to 6 

years ago) increased from a total of 100 to 300 ha (Figure 

Figure 4—Probability of fire in one of three size classes on national forests in Washington and Oregon, 1970-2004.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

described a probabilistic model framework that was used in 

a case study of spatiotemporal interactions between wildfire 

and insect outbreaks in the Pacific Northwest. To create 

the model, we used historical spatial data (1980 to 2004) 

on fire occurrence and insect outbreaks on forested lands 

of Oregon and Washington along with average monthly 

temperature and PDSI.

Results of our analyses seem to imply an increasing 

risk of new bark beetle infestations with increasing size of 

nearby fires (within 1 km) up to fires of ~750 ha. The latter 

was indicated by the estimated odds relative to locations 

with no fire damage. Also, there appears to be a decrease in 

Figure 5—Estimated odds ratio (solid lines) and 95-percent confidence bounds (dashed lines) of fire reaching 

size class 2 or 3 plotted against values of two predictors (bark beetle and spruce budworm). The odds ratio is 

relative to when there is no insect infestation. The red dashed line indicates an odds ratio of one. When the 

red line is within the 95-percent bounds, the odds are not significantly different from the odds when no insect 

infestation is present. 

risk of a fire getting large at locations with 3- to 6-year-old 

spruce budworm damage of size greater than 100 ha.

The methods we employed provide an objective tool 

for modeling complex processes with time lags in the 

predictors and estimating associated probability maps for 

competing risks (multiple disturbances). The framework 

is especially amenable to quantifying uncertainties from 

multiple sources (stochastic disturbance, sampling and measurement errors, approximate models, and missing predictor 

variables), which are essential for model appraisal purposes. 

The analyses can be expanded to other areas using the rich, 

spatially explicit, historical data set available for wildfire 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Brown, T.J.; Hall, B.L.; Mohrle, C.R.; Reinbold, 

H.J. 2002. Coarse assessment of forest wildland fire 

occurrence data. CEFA Rep. 02-04. [Available at: http://

www.cefa.dri.edu/Publications/publications_index.

htm]. Reno, NV: Program for Climate, Ecosystem and 

Fire Applications, Desert Research Institute. 31 p. [Date 

accessed unknown].

Cunningham, C.A.; Jenkins, M.J.; Roberts, D.W. 2005. 

Attack and brood production by the Douglas-fir beetle 

(Coleoptera: Scolytidae) in Douglas-fir Pseudotsuga 

menziesii var. Glauca (Pinacaea), following fire. Western 

North American Naturalist. 65: 70–79.

Elkin, C.M.; Reid, M.L. 2004. Attack and reproductive 

success of mountain pine beetles (Coleoptera: Scolytidae) 

in fire-damaged lodgepole pine. Environmental 

Entomology. 33: 1070–1080.

Fleming, R.; Candau, J.; McAlpine, R. 2002. Landscapescale analysis of interactions between insect defoliation 

and forest fire in central Canada. Climatic Change. 

55: 251–272.

Gast, W.R., Jr.; Scott, D.W.; Schmitt, C. [and others]. 

1991. Blue Mountain forest health report: new 

perspectives in forest health. U.S. Department of 

Agriculture, Forest Service, Malheur, Umatilla, and 

Wallowa-Whitman National Forests. [Unconventional 

pagination].

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1980. Fire, fungi, and beetle influences on a lodgepole 

pine ecosystem of south-central Oregon. Oecologia. 

46: 239–243.

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elements of statistical learning: data mining, inference, 

and prediction. New York: Springer. 533 p.

Hayes, J.L.; Quigley, T.M.; Starr, G.L., eds. 2001. 

Special issue on forest health and productivity in eastern 

Oregon and Washington. Northwest Science. 75: 1–251.

occurrence across all of the United States (NIFMID). It also 

has application for forest insects and diseases through Forest Health Protection annual aerial survey data. Although 

we only examined three insect species (mountain pine 

beetle, western pine beetle, and western spruce budworm), 

other stressors could be considered, including nonnative 

invasive species and stress level owing to pollution. Risk 

assessment maps can be produced by multiplying the 

estimated probabilities by a loss function.

Acknowledgments

We would like to thank Julie Johnson, Pacific Northwest 

Research Station, and Charles McHugh, Rocky Mountain 

Research Station, for assistance with data preparation.

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381

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

Brian Schwind, Ken Brewer, Brad Quayle, 

and Jeffery C. Eidenshink

Brian Schwind, operations program leader, Ken Brewer, 

Inventory Analysis and Accuracy Assessment Program 

leader, and Brad Quayle, Geographic information system/

remote sensing analyst, U.S. Department of Agriculture, 

Forest Service, Remote Sensing Applications Center, Salt 

Lake City, UT 84119; and Jeffery C. Eidenshink, Remote 

sensing/fire scientist, U.S. Geological Survey, National 

Center for Earth Resources Observation and Science. Data 

Center, Sioux Falls, SD 57198.

Abstract

There is a need to provide agency leaders, elected officials, 

and the general public with summary information regarding the effects of large wildfires. Recently, the Wildland 

Fire Leadership Council (WFLC), which implements and 

coordinates National Fire Plan (NFP) and Federal Wildland 

Fire Management Policies adopted a strategy to monitor 

the effectiveness and effects of the National Fire Plan and 

the Healthy Forests Restoration Act. One component of 

this strategy is to assess the environmental impacts of large 

wildland fires and identify the trends of burn severity on all 

lands across the United States.

To that end, WFLC has sponsored a 6-year project, 

Monitoring Trends in Burn Severity (MTBS), which 

requires the U.S. Department of Agriculture, Forest Service 

(USDA-FS) and the U.S. Geological Survey (USGS) to map 

and assess the burn severity for all large current and historical fires. Using Landsat data and the differenced Normalized Burn Ratio (dNBR) algorithm, the USGS/EROS Data 

Center and USDA-FS/ Remote Sensing Applications Center 

will map burn severity of all fires occurring from 1984 to 

2010. Only fires that are greater than 500 ac in the East, 

and 1,000 ac in the West will be included. We anticipate 

mapping a total of more than 9,000 historical fires and fires 

that occur during the course of the study.

The MTBS project will generate burn-severity data, 

maps, and reports, which will be available for use at local, 

State, and national levels to evaluate trends in burn severity 

and help develop and assess the effectiveness of land management decisions. Additionally, the information developed 

will provide a baseline from which to monitor the recovery 

and health of fire-affected landscapes over time. Spatial and 

tabular data quantifying burn severity will augment existing 

information used to estimate risk associated with a range of 

current and future resource threats. For example, fire severity data along with associated biophysical characteristics 

provide an analytical basis for assessing risk from invasive 

species as well as native insects and pathogens. All data and 

results will be distributed to the public via a Web interface.

Keywords: Burn severity, Landsat, monitoring, spatial, 

temporal, trends, wildland fire.

Introduction

Consistent geospatial information characterizing effects 

of large wildland fires does not exist for lands within the 

United States. Changing trends in fire frequency, severity, and size have resulted in the need to acquire data and 

develop information that can establish a baseline for trend 

analysis and begin to look at recent historical shifts in these 

fire characteristics. Furthermore, there is a need to understand the impacts of fire and resource management policies 

on fire occurrence and severity (Stephens and Ruth 2005). 

These needs are recognized across agencies and at various 

levels within land management organizations. Moreover, the 

general public is increasingly exposed to information suggesting that increases in uncharacteristic fire are due in part 

to past land management practices. It can be assumed that 

public interest in current and future fire policy will increase.

The Wildland Fire Leadership Council (WFLC), a 

national-level interagency body with responsibility for 

implementing and coordinating the National Fire Plan 

(NFP) and Federal Wildland Fire Management Policies 

(http://www.forestsandrangelands.gov/), has adopted a 

strategy to monitor the effectiveness and effects of the NFP 

Establishing a Nationwide Baseline of Historical Burn-Severity 

Data to Support Monitoring of Trends in Wildfire Effects and 

National Fire Policies
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GENERAL TECHNICAL REPORT PNW-GTR-802

and the Healthy Forests Restoration Act. One component of 

this strategy is to assess the environmental impacts of large 

wildland fires and identify the trends of fire severity on all 

lands across the United States (WFLC 2004 Monitoring 

Proposal, Module 2.1). In 2004, the Government Accountability Office recommended that the Forest Service and 

Bureau of Land Management develop and implement comprehensive assessments of fire severity to provide consistent 

summary information characterizing the environmental 

effects of wildland fires and to meet the requirements of 

WFLC.

Beyond the needs of WFLC, it is widely recognized 

that nationally consistent and current data are necessary 

to address issues of ecosystem health and sustainability. 

The National Report on Sustainable Forests (USDA Forest 

Service 2004) describes criteria and indicators that are an 

important framework for sustainable management. Whereas 

the report does not specifically address the need for fire 

occurrence and effects data, it is easy to see both the direct 

and indirect relationships that exist between these criteria 

and indicators and the understanding of spatial pattern, 

magnitude, and frequency of fire in ecosystems across the 

United States. Specifically, the criterion addressing the 

maintenance of forest ecosystem health and vitality and the 

criterion addressing maintenance of forest contribution to 

global carbon cycles are sensitive to the spatial and temporal variability of fire effects and the gradient of change in 

forest ecosystems.

Project Background

In 2006, WFLC sponsored a 6-year project to map the 

fire severity and perimeters on large fires (>500 ac in the 

East and 1,000 ac in the West) in the United States across 

all ownerships for the period of 1984 through 2010. The 

project is referred to as the Monitoring Trends in Burn 

Severity (MTBS) project and is implemented jointly by 

the U.S. Geological Survey (USGS), National Center for 

Earth Resources Observation and Science (EROS), and the 

USDA Forest Service, Remote Sensing Applications Center 

(RSAC). This work is an extension of the existing cooperation between these two national centers that has provided 

rapid-response burn-severity mapping products to Forest 

Service and Department of the Interior Burn Area Emergency Response teams.

The primary objective of this project is to provide for 

a national analysis of trends in fire severity for the NFP. 

Due to severe periodic droughts, increased fuel loads, and 

a higher frequency of uncharacteristic fires in recent years 

(since 2000), it is essential for the trend analysis to span a 

significant period of time to better account for variability 

in factors potentially affecting fire severity (e.g., climate). 

Secondary objectives include providing geographic and 

fire-specific data for use at regional and subregional scales 

to support resource and risk assessments, resource management planning, project planning and implementation, 

monitoring, and research activities. Sufficiently fine spatial 

and thematic resolution is necessary to support the wide 

range of operational and research-related information needs 

at larger scales.

This project will serve four primary user groups with 

one set of data and information:

• National policymakers such as WFLC, that 

require information about long-term trends in 

burn severity and recent burn severity within 

vegetation types, fuel models, condition classes, 

and treatment accomplishments.

• Field managers that benefit from geographic 

information system ready maps and data for 

informing and supporting prefire and postfire 

management decisions and monitoring.

• Project managers for existing databases such 

as LANDFIRE and the National Land Cover 

Data set that benefit from burn-severity data 

produced at comparable spatial scales and resolution for validation and updating of geospatial 

data sets.

• Academic and agency research entities interested in fire-severity data over significant geographic and temporal extents.

Burn Severity Definition

Terminology commonly used when discussing fire behavior 

and fire effects is often inconsistently and interchangeably applied. Inconsistent definitions associated with risk, 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

hazard, and severity confound our ability to characterize 

and communicate postfire effects and their implications 

to resource and cultural values (Hardy 2005). Data and 

information developed by this project are intended to 

primarily characterize fire effects in aboveground biomass. 

Despite significant variation in published definitions of burn 

severity and fire severity (Lentile and others 2006), postfire 

effects in aboveground biomass have been described using 

both terms. The term burn severity as it applies to this 

project is best represented by the definition for fire severity in the National Wildfire Coordination Group Glossary 

of Wildland Fire Terminology, stated as the “Degree to 

which a site has been altered or disrupted by fire; loosely, a 

product of fire intensity and residence time” (NWCG 2005). 

The following additional statements have been adopted to 

further clarify the nature of the products developed by this 

project:

• Burn severity is a composite of first and secondorder fire effects on biomass.

• Occurs on a gradient or scale (ordinal).

• Occurs within a fire perimeter.

• Occurs within landcover strata.

• Longer term effects are complicated by variables that this project is not characterizing.

• Severity is mapable.

• Remote sensing provides a measurement 

framework.

Project Scope

The project has been divided into geographic mapping 

zones representing broadly similar ecological conditions. 

The mapping zones illustrated in Figure 1 were created 

from aggregations of National Land Cover Data set (NLCD) 

mapping zones originally derived from Bailey’s ecological 

sections (Homer and others 2004). The primary purpose of 

the mapping zones is to provide ecologically meaningful 

processing areas that are also efficient production units. 

Secondary consideration was given to significant administrative boundaries where they correlated closely with 

ecological unit edges. We recognize that application of the 

products will occur at a variety of ecological and administrative extents, and the analysis and summarization of data 

for the primary sponsors may have limited utility at larger 

scales. However, use at larger scales is both appropriate 

and technically feasible owing to the spatial and thematic 

resolution inherent in the products. Furthermore, the 

spatially aggregate nature of fires and the ability to easily 

identify discreet events within the product sets allow for 

analysis ranging in extent from a single fire to multiple fires 

spanning space or time, or both. This is not to say that all 

analysis scales will be supported by these data. Indeed, as 

with all geospatial data, there are limits to effective application, and users will be encouraged to consider their analysis 

objectives and information needs relative to the spatial and 

thematic characteristics of the products.

Burn-severity mapping is being conducted in two time 

phases. Fires occurring in 2004 through 2010 are considered 

current and will be mapped and reported annually for the 

entire project extent. Historical fires occurring from 1984 

through 2003 will be mapped, analyzed, and reported by 

mapping zone through the duration of the project. Mapping 

zones have been prioritized based on fire frequency, acres 

affected, and data availability. Figure 1 also illustrates 

processing schedules for historical fires by mapping zone.

The historical range of this project (1984 through 2010) 

was determined on the basis of availability of remotely 

sensed data necessary to consistently characterize the extent 

and severity of individual fires. A longer historical period 

would afford the ability to more precisely analyze environmental and policy-based influences on wildland fire that are 

significant over decades and centuries. Recent land and fire 

management activities may be the most directly relatable 

influences on fire effects as depicted by these products. 

Beyond the scope of this project, but a valuable extension, 

would be the use of other, historically extensive sources of 

remotely sensed data to extend the fire effects record generated by MTBS, albeit with compromises in consistency.

Products

Products for the MTBS project fall into three categories: 

unclassified (input) data, geospatial layers and maps (raster 

and vector), and summary analysis.

Unclassified or input data are comprised of Landsat 

Thematic Mapper (TM) and Enhanced Thematic Mapper 
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GENERAL TECHNICAL REPORT PNW-GTR-802

(ETM) images that form the basis for measuring spectral 

response of individual fires. Methods used to process and 

classify these data will be discussed in more detail under 

section 2. These data have been processed by USGS EROS 

through the National Landsat Archive Production System 

(NLAPS) (Huang and others 2002) and are representative of 

the level and format of Landsat data typically delivered to 

the scientific and operational communities.

A series of geospatial layers make up the intermediate 

and final products characterizing postfire spectral response, 

burn severity, and fire perimeters. The following are the 

principal outputs:

• 30-m resolution Normalized Burn Ratio (NBR) 

calculated from Landsat (prefire and postfire);

• 30 m-resolution, continuous differenced NBR 

(relative and absolute).

• 30-m resolution, arithmetic partitions of dNBR.

• Vector format fire perimeter based on dNBR.

• Metadata for geospatial data.

Analysis outputs are necessarily limited in scope to 

achieve the primary objectives of the project. In depth trend 

analysis, correlations to other factors, including climate 

change and management practices, and implications for 

other resources fall outside the scope of this project. Formats and resolution of the geospatial products are designed 

to allow flexibility for application to a wide range of 

analysis objectives that pertain to burn severity. Indeed, it is 

expected (and desired) that these data will be used in broadand moderate-scale research and management activities 

Figure 1—Monitoring Trends in Burn Severity mapping zones. Historical fires are scheduled to be mapped during the fiscal years 

illustrated within each zone.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

where a consistent data record of postfire effects is valuable. 

Primary analysis products delivered by this project include:

• Tabular data summarizing acres burned by 

severity class.

• Tabular data summarizing acres burned by 

severity classes and vegetation cover types 

(where available).

• Tabular data summarizing acres burned by 

severity classes and presence or absence of fuel 

treatments (where available).

Methods

Methods selection for this project was fundamentally driven 

by two requirements: (1) The need to develop consistent 

information across all lands within the project extent, and 

(2) the need to develop consistent information spanning 

a significant historical period. Based on these requirements, remotely sensed data were considered to be the only 

cost-effective and spatially resolved source to consistently 

delineate and measure the response of thousands of individual fires across a continental extent and multidecade 

timeframe. A significant body of literature exists evaluating the effectiveness of various scales of remotely sensed 

data to characterize fire severity (Brewer and others 2005, 

Chuvieco and Congalton 1988, Diaz-Delgado and others 

2003, Fernandez and others 1997, Justice and others 1993, 

Kasischke and French 1995, Key 2005, Milne 1986, Patterson and Yool 1998, Pereira 1999, Roy and Landmann 

2005, Sa and others 2003, Smith and others 2005, Sunar 

and Ozkan 2001, Wagtendonk and others 2004, White and 

others 1996). Scientific and operational precedent exists for 

the use of a remote sensing-based approach.

The Landsat TM and ETM data provide the longest 

record of relatively high spatial and spectral resolution 

data for mapping fire severity. Not only does this enable 

the mapping of historical fire severity, it also facilitates the 

use of multitemporal approaches for characterizing postfire 

effects. Landsat data have been shown to be responsive to 

relative changes in aboveground biomass as a result of fire 

(Epting and others 2005, Kushla and others 1998, LopezGarcia and Caselles 1991, Miller and Yool 2002). More 

specifically, multitemporal change detection approaches 

based on prefire and postfire Landsat data have proven to be 

a cost-effective and relatively accurate means of mapping 

fire severity (Brewer and others 2005). Furthermore, the 

availability and low cost of Landsat data were additional 

factors supporting their use for a project of this geographic 

and temporal extent.

Multitemporal approaches applying image ratios and 

image differencing techniques to Landsat data have been 

developed for a variety of assessment objectives. Imagery 

is commonly transformed mathematically into indices 

by ratioing a spectral component(s) or band with another 

spectral component(s) for each pixel. The transformation 

of Landsat data into vegetation indices (e.g., Normalized 

Difference Vegetation Index) to strengthen the relationship 

between spectral response and vegetation characteristics 

has been widely used, and a number of indices exist 

(Lyon and others 1998). Lopez-Garcia and Caselles (1991) 

published the first index specifically derived to enhance 

the relationship between Landsat spectral response and 

burned vegetation. This Normalized Difference index was 

subsequently adapted and operationally implemented by 

Key and Benson (2002) and was used to develop historical 

fire-severity data and atlases on numerous national parks. 

The approach has been named the Normalized Burn Ratio 

(NBR) and, combined with multitemporal differencing, has 

been utilized in fire-severity mapping efforts by the USGS 

and the USDA Forest Service since 2002. The NBR is 

calculated as (TM4 - TM7)/(TM4 + TM7) where TM4 represents the near infrared spectral range and TM7 represents 

the short wave infrared spectral range. A differenced NBR 

image (dNBR) is created by subtracting the prefire NBR 

image from a postfire NBR image.

The dNBR data have been used operationally for both 

rapid response and longer term assessment and monitoring 

(Bobbe and others 2003, Gmellin and Brewer 2002, Key 

and Benson 2002). Rapid response needs require the use of 

immediate postfire imagery to show first order fire effects 

on vegetation and soils and to prioritize rehabilitation 

resources. Longer term assessments have relied on image 

data typically acquired during the growing season following the fire in order to include delayed first order effects 

(e.g., delayed tree mortality) and dominant second order 
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GENERAL TECHNICAL REPORT PNW-GTR-802

effects that are ecologically significant (e.g., initial site 

response and early secondary mortality agents). Extended 

assessments are intended to provide a more comprehensive 

ecological indication of fire severity. Both initial and 

extended assessments have uncertainty associated with the 

dNBR-based fire-severity characterizations. Prefire vegetation conditions and postfire management activity influence 

the nature and magnitude of this uncertainty. The sensitivity of a given set of analysis objectives to the uncertainties 

associated with initial and extended assessment dNBR data 

should be considered when using these data.

Based on the scientific foundation in the literature and 

operational precedent, the dNBR approach was selected to 

characterize fire severity and delineate fire perimeters for 

this project. Extended assessments will be conducted on 

forest and shrub ecosystems, and initial assessments will 

be conducted on grasslands and other specific vegetation 

communities known to recover from fire within a single 

growing season. A simple production model was developed 

around this approach to ensure timely and consistent products. We recognize that application of a simple production 

model across the ecological extent and variation covered 

by the project may itself be a source of uncertainty. Indeed, 

limitations associated with this approach to characterizing 

postfire effects are not yet known in many ecosystems, and 

we expect the MTBS project to expand our understanding 

of how consistently and precisely Landsat data can map 

burn severity. The MTBS product suite is a reflection of the 

need for a range of data to suit specific analysis objectives. 

The following steps outline the process of identifying fire 

locations through summarization of the results:

• Fire history database compilation

○ Data acquisition

○ Data standardization and aggregation

• Image data selection and preprocessing

○ Scene selection

○ Preprocessing

○ Delivery and archiving

• Fire-severity interpretation and perimeter 

delineation

○ Normalized Burn Ratio calculation and 

 differencing

○ Interpretation and thresholding into 

 severity classes

○ dNBR partitioning

○ dNBR fire perimeter delineation

• Stratification and summarization of severity 

information.

Fire History Database Compilation

Existing fire history and location databases were compiled 

into a single, standardized project database that formed the 

basis for image scene selection. Fire history sources were 

from two general origins—Federal agency databases and 

State databases. In some cases, State and Federal agencies 

have collaborated in developing and maintaining a single 

database for State and Federal incidents. Federal agency 

data are aggregated into the Incident Command System 

database known as the ICS 209 and are maintained by the 

Table 1—Brief description of database elements contained within the Monitoring Trends in Burn Severity 

(MTBS) fire history database

ID Fire name Agency Year Start date

Unique MTBS ID Incident name Reporting agency Year occurred Incident start

 that includes source from the source from the source from the source day/month/year from

 ID database database database the source database

Reported

acres Long Lat Path Row Disposition

Incident acres Longitude Latitude Landsat path Landsat row Description of

 from the source issues relative to a fire’s 

 database visibility or spatial 

 accuracy on the 

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

National Interagency Fire Center (NIFC) in Boise, Idaho 

(http://www.nifc.gov/). ICS 209 data make up the bulk 

records in the MTBS project database. States were solicited 

for State-maintained data where there was uncertainty of 

their inclusion in the ICS 209 data.

Whereas there is some level of standardization within 

ICS 209, Federal land management agencies have varying or no standards for content, geospatial accuracy, and 

nomenclature. Duplicate records and name changes are 

common because the same incidents are often reported by 

multiple agencies. Further standardization and correction, 

where possible, were performed as part of the compilation 

of an MTBS project database. For the purposes of this 

project, standardization was accomplished by selecting data 

elements common to the source databases and not through 

record editing or manipulation of the source data except 

in the case of geospatial coordinates. Where a record was 

grossly and obviously incorrect, and a correction could be 

made confidently, coordinates were adjusted. Table 1 lists 

the elements that comprise the MTBS fire history database. 

Records for these elements were harvested from the ICS 

209, and State data and source links were included to ensure 

that data could be traced to their databases of origin. Figure 

2 depicts the spatial distribution and relative frequency of 

fire occurrences across the project extent. Likely omissions 

and coarse spatial precision are noticeable, particularly 

in the Central and Eastern portions of the conterminous 

United States.

Image Scene Selection and Data Preprocessing

Scene selection is driven by the MTBS fire history database. 

Scenes are selected using the Global Visualization Image 

Figure 2—Wildland fire locations across the project extent. Patterns of fire frequency are clearly visible. Suspected omissions of fire 

occurrences as well as coarse spatial precision are also visible in portions of the project, particularly the Midwestern, Southern, and 

Northeastern United States. MTBS refers to “Monitoring Trends in Burn Severity.”
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GENERAL TECHNICAL REPORT PNW-GTR-802

Selection (GLOVIS) browser developed by USGS-EROS 

(http://glovis.usgs.gov/). Enhancements were made to 

GLOVIS specifically to facilitate the magnitude of sceneselection effort required for this project. These enhancements, available to all GLOVIS users, include the ability 

to incorporate ESRI ARCGIS shape files in the viewer to 

aid scene selection and scene-specific Advanced Very High 

Resolution Radiometer greenness graphs for determining 

peak periods of photosynthetic activity, or peak-of-green 

periods. The fire history shapefile, specific to an MTBS 

mapping zone, is loaded into the viewer, and analysts use 

fire locations to guide scene selection for each fire. Prefire 

and postfire images are selected for each incident. Scenes 

selected for fires that will be processed as an extended 

assessment are based on peak-of-green condition or as 

close as cloud-free data are obtainable. Limitations in data 

availability due to atmospheric conditions will naturally 

compromise selections for fires in areas prone to summer 

and fall cloud and smoke obscurity. Northern latitudes will 

also be subject to a shorter period of optimal scene selection 

owing to undesirable sun angles in the fall.

Selected scenes are ordered and processed according 

to existing USGS-EROS protocols. Image data are geometrically (including terrain correction) and radiometrically 

corrected through the NLAPS process. Image data are 

delivered to EROS and RSAC analysts to be processed into 

fire-severity information. Landsat image data acquired for 

this project will become part of the national image archive 

and will be available at NLCD archive costs. Current estimates expect an increase in available archive data of more 

than 7,000 scenes. The existing USGS Multi-Resolution 

Land Characteristics image archive (http://www.mrlc.

gov/), available through several existing Web portals (e.g., 

GLOVIS), will be the primary repository for these data.

Fire Severity and Perimeter Mapping

The NBR index is calculated for prefire and postfire images 

as described in “Methods.” Prefire and postfire images are 

inspected for coregistration accuracy and corrected if spatial differences are excessive and extensive (> 30 m). NBR 

images are differenced for each fire-scene pair to generate 

the dNBR. A relativized dNBR (RdNBR) is also calculated 

based on the work of Miller and Thode (2007) to evaluate 

potential limitations of dNBR to characterize fire severity 

on low-biomass sites and potentially enhance interfire comparability of the results at larger scales. The RdNBR data 

have been shown to have stronger correlations to Composite 

Burn Index (CBI) plot data in some low-biomass western 

ecosystems (Miller and Thode 2007, Thode 2005). Figure 3 

illustrates the sequence of data layers generated.

Ecological Severity Thresholding—

Processing the Landsat image data to dNBR is a straightforward series of objective calculations requiring limited 

analyst interaction and relying principally on automated 

production sequences. Subsequent to dNBR derivation, 

the process of developing fire severity and perimeter maps 

becomes much more dependent on analyst interpretation. 

The dNBR data are calculated as signed 16-bit with a 

maximum digital number (DN) range of -32,767 to +32,767. 

However, the practical range of DN values representing 

fire-related change and no change is typically within -2,000 

to +2,000. Values further away from zero represent greater 

change as a result of both first and second-order fire effects 

(within the fire perimeter). Negative values indicate a 

positive vegetation response (growth) and positive values 

indicate a negative vegetation response (mortality). Figure 4 

illustrates a dNBR image for the Cerro Grande Fire (2003), 

and Figure 5 depicts the associated data range. The analyst 

evaluates the dNBR data range and determines where significant thresholds exist in the data to discriminate between 

severity classes. Interpretations are conducted on the dNBR 

data aided by raw prefire and postfire imagery, plot data, 

and analyst experience with fire behavior and effects in a 

given ecological setting. The CBI data (Key and Benson 

2006) have been the most commonly collected groundbased data to estimate postfire effects. Correlations between 

CBI and dNBR have been used to demonstrate the sensitivity of dNBR to postfire effects and to establish numerical thresholds in dNBR data that discriminate severity 

categories (Cocke and others 2005, Key 2005). Where CBI 

and similar plot data have been collected, and plot-dNBR 

relationships published, analysts will guide their interpretations based on these relationships. Limited extrapolation of 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

plot-based thresholds beyond their geographic bounds but 

within ecologically similar conditions will be examined.

Thresholding dNBR data into thematic class values 

results in an intuitive map depicting a manageable number 

of ecologically significant classes (typically 4 to 7 class 

values). Within this project, the thematic raster data will 

characterize severity in five discreet classes—unburned/

unchanged, low severity, moderate severity, high severity, 

and increased postfire response. These classes will serve 

as a means to easily summarize severity acres across broad 

scales and provide a coarse look at effect gradients within 

fires. However, there are uncertainties in this approach 

stemming from analyst subjectivity and limited or no plot 

data to guide threshold selection. Large-scale analysis may 

best be conducted on the continuous dNBR data, which provide the greatest range of data quantifying postfire change. 

Although not a direct measure of fire severity, dNBR data 

have been shown to correlate to field-based estimations of 

fire severity (Hudak 2006, Key 2005).

Ecological significance is issue dependent, and one set 

of thresholds cannot be expected to apply equally well to all 

analysis objectives and management issues. Other severity 

classifications such as described by Stephens and Ruth 

(2005) may be used as the basis for thresholding but must 

be considered for the appropriateness of their application 

to dNBR data. Fire-severity classifications that are based 

on fire effects not readily discernible on Landsat data (e.g., 

subsurface biomass combustion or soil chemistry changes) 

should not be applied to these data.

dNBR Partitioning—

In addition to ecological thresholds as a means of discriminating severity classes, dNBR will be arithmetically 

partitioned into discreet classes to facilitate objective and 

flexible pattern-and-trend analysis. Arithmetic partitioning 

is not intended to provide information on the ecological 

severity of fires at large spatial scales or limited temporal 

extents. Methods for partitioning dNBR have yet to be 

Figure 3—The sequence of layers produced between Landsat images and a fire perimeter. NBR = Normalized Burn Ratio. dNBR = 

differenced Normalized Burn Ratio.
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GENERAL TECHNICAL REPORT PNW-GTR-802

determined, and the algorithm(s) and subsequent grain 

of partitioning will depend on a given technique’s ability 

to reveal meaningful patterns in fire severity over time. 

Gmellin and Brewer (2002) used a simple equal-interval 

calculation to establish objective burn severity classes 

between observed unburned and high-severity conditions 

in the Northern region of the Forest Service. Brewer and 

others (2005) used the same approach in a methods comparison study that concluded dNBR to be the most effective 

approach of those evaluated for mapping fire severity. The 

relative ease and quickness of arithmetically partitioning 

dNBR data will allow for rapid evaluation of meaningful 

Figure 4—A dNBR image for the Cerro Grande Fire (2003) where dNBR refers to differenced Normalized Burn Ratio. 

Lighter areas represent higher positive values corresponding to greater degrees of change (i.e., higher fire severity).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

spatial and temporal scales in the context of fire severity 

trends. Moreover, dNBR data can be efficiently analyzed 

and classified to suit the fire severity information needs of a 

specific management issue.

Perimeter Delineation—

Fire perimeters will be generated by on-screen interpretation and delineation of dNBR images. Analysts will digitize 

perimeters around dNBR values reflecting fire-induced 

change. To ensure consistency and high spatial precision, 

digitization will be performed at on-screen display scales 

between 1:24,000 and 1:50,000. Incident perimeters, where 

available, will be used in an ancillary fashion to inform 

the analyst. This can be particularly useful in identifying 

unburned islands within a perimeter or isolated, disjunct 

spots outside the main perimeter. Owing to limited and 

variable availability, as well as inconsistent spatial precision, incident perimeters were not considered appropriate as 

a source for MTBS project perimeters.

Data Summarization

Tabular data will be generated from statistical summaries 

of the fire severity class layers. Reporting units will vary 

in extent depending on the needs of WFLC and other 

multiagency user groups but, at a minimum, summary 

data will be produced for each State as well as at a national 

extent. Three sets of tabular data are currently specified in 

the MTBS product suite and are listed in “Products.” Total 

acres burned by severity class are the most directly extractable information summary from the spatial data.

Summarizing acres burned by severity class and vegetation cover types requires consistent geospatial vegetation 

data of similar resolution to provide the most meaningful 

stratifications. Existing vegetation types currently being 

mapped by the LANDFIRE program (http://www.landfire.

gov/index.php) will offer the most spatially extensive and 

nationally consistent data by which severity classes can be 

summarized. Since Landsat imagery is the spatial basis for 

both MTBS and LANDFIRE data, uncertainties that may 

Figure 5—A graphical depiction of the dNBR data range associated with Cerro Grande Fire where dNBR refers to differenced Normalized Burn Ratio. Positive values represent a gradient of increasing fire severity whereas negative values represent increased spectral 

response usually associated with higher photosynthetic activity in postfire vegetation over prefire vegetation. Values near zero represent 

little or no change. 
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GENERAL TECHNICAL REPORT PNW-GTR-802

result from summarizing severity classes within vegetation 

cover types mapped at a significantly different spatial scale 

should be minimized. In conjunction with the information 

needs of WFLC, the accuracy of LANDFIRE data will need 

to be evaluated to determine the most appropriate thematic 

scale for reporting.

Summarizing acres burned by severity class in relation to fuel treatment activities will be dependent on the 

availability of spatial data depicting the location and extent 

of individual treatments. Currently, the National Fire Plan 

Operations Reporting System (NFPORS) database is the 

primary standardized Federal database containing such 

data in digital format. As described on the Web site (http://

www.nfpors.gov/), “NFPORS is an interagency system 

designed to assist field personnel in managing and reporting 

accomplishments for work conducted under the National 

Fire Plan.” It is expected that non-Federal fuel treatment 

data will not be available, and spatial data on Federal lands 

will be sporadic. Tabular data generated under these criteria 

will only have applicability to specific administrative and 

geographic extents.

It is recognized that these tabular data may have limited 

utility at finer spatial scales and for addressing research 

and management information needs not considered within 

the scope of this project. A composite database containing additional ecological and administrative spatial units, 

including 4th-field hydrologic units (Seaber and others 1987) 

and Federal ownership, will be available to enable userspecified summarization of MTBS data at larger scales. The 

production and distribution of both continuous and thematic 

spatial data sets described in “Fire Severity and Perimeter 

Mapping” are considered the primary geospatial data legacy 

available to scientific and operational interests outside this 

project.

Data Distribution

All spatial and tabular data will be distributed through 

Web-based interfaces. Data portals developed and maintained by USGS and the USDA Forest Service will be 

primary access points for the data and associated reports as 

they are completed and become available. Access to MTBS 

data-distribution nodes will be through the project Web 

site (http://www.mtbs.gov). Additional distribution nodes 

may be developed in partnership with other Federal and 

academic institutions.

A technology transfer phase of the project will be 

initiated subsequent to completion of the first historical data 

sets. The intent of this effort will not only be to educate 

potential users about the structure and content of the data 

but also to explore applications of the data at multiple 

scales. It is expected that independent studies that utilize 

MTBS data will reveal utility and limitations that will 

be important to guiding operational use. The technology 

transfer phase will make efforts to synthesize internal and 

external assessments of data utility and provide an efficient 

means to access this information. Web-based and workshop 

formats will be used to engage potential users.

Applications for Fire-Severity Data

Central to the missions of both the Western Wildland 

Environmental Threat Assessment Center and the Eastern 

Forest Environmental Threat Assessment Center is the 

early detection, identification, and assessment of multiple 

environmental threats such as insects, disease, invasive species, fire, loss or degradation of forests, and weather-related 

risks. The MTBS project will contribute to successfully 

accomplishing this mission by informing and supporting a 

variety of fire severity-related analysis applications.

It is beyond the scope of this paper for us to describe, 

in detail, specific examples of fire-severity data use. The 

few examples that follow suggest that burn severity or fire 

severity, or both, have been reported in a variety of research 

applications including surface runoff and sediment yields 

(Robichaud and Waldrop 1994), burned area relationships 

to natural reforestation (Lopez-Garcia and Caselles 1991), 

forest stand conversion and regeneration establishment 

(Blackwell and others 1992, 1995), restoration of natural 

fire regimes with prescribed fire programs (Brown and 

others 1995, Keifer and Stanzler 1995), and wildlife habitat 

components (Hutto 1995).

Although explicit analytical uses of the MTBS data are 

not provided in this paper, a general discussion on scale of 

application is warranted. Multiscale, integrated analysis to 

support planning at both strategic and tactical levels has 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

been presented as an effective way to accomplish management objectives within the context of ecological function 

(Hann and Bunnell 2001). As described in previous sections, MTBS data provide a practical basis for multiscale 

analysis. Barbour and others (2005) emphasized the need to 

look across scales in order to understand potential differences in perception of wildfire risk between planning scales 

and between management objectives. Using the analysis 

scales described by Barbour and others (2005), a simple 

conceptual step-down analysis demonstrates how MTBS 

data can generally be applied at broad, mid, and fine planning and monitoring scales.

At the broad scale, general spatial and temporal 

patterns of fire can be juxtaposed against generalized 

depictions of biophysical setting, current vegetation, and 

historical vegetation conditions to identify landscapes 

that are experiencing fire frequency and behavior outside 

estimated historical ranges of variability. This information 

may serve the purpose of streamlining resource allocation 

decisions at the national level and provide risk strata for 

larger scale analysis. Landscapes with potential for higher 

risk can be assessed at the midlevel for specific patterns 

of fire occurrence and magnitude that threaten resource 

and social values (e.g., sensitive species habitat, hydrologic 

function, rural communities, and recreational opportunities) 

identified in regional and forest plans. The spatial resolution 

of MTBS products align closely with resource data layers 

commonly found at the regional and forest unit levels of 

the Forest Service (Brewer and others 2003, Franklin and 

others 2000, Mellin and others 2004). The scalable nature 

of dNBR values also allows for severity characterizations 

specific to analysis needs. Management activity planning 

and design intended to specifically address issues identified in midscale planning efforts are based principally on 

fine-scale analyses. Management activities designed to 

mitigate potential future threats, including severe fire and 

insect and disease outbreaks, require precise information 

about landscape condition and disturbance history. At sitespecific scales, MTBS data reveal important information 

about severity patterns within fires, which are necessary to 

understand current condition and to relate past management 

activities.

It is our belief that the geographic and temporal extents, 

along with the consistency of MTBS data products, will 

provide a rich data legacy from the project. These data will 

provide the analytical basis for research that would have 

been logistically impossible without the MTBS project. 

These data provide the opportunity to stratify by appropriate biophysical environment settings and generate efficient 

and effective sampling strategies for agents such as insects, 

pathogens, and invasive plant species. They also provide the 

basis to substitute space for time to evaluate factors such 

as climatic effects or site water balance characteristics on 

timing and duration of water yield. Whereas these data do 

not address all environmental threat assessment needs, they 

do provide high-quality, consistent data and a contextual 

framework for the keystone disturbance agent for many 

wildland ecosystems.

Conclusions

The MTBS project will develop the data and information 

necessary to meet the strategic analysis objectives of WFLC 

and other policymaking and monitoring bodies. In addition 

to meeting the primary objective of providing the information necessary to support an assessment of recent trends 

in fire severity, a valuable data legacy will be available to 

support a broad range of research and operational uses at 

multiple scales. High-resolution fire-severity data generated at the individual fire scale, yet assembled at broad 

geographic extents, offers the potential to support analysis 

ranging in scope from event-specific to continental. Future 

applications will explore the utility of these data to support threat assessments across a wide range of ecological 

systems and provide a spatial and temporal framework to 

better understand the immediate and longer term interrelationships of wildland disturbance agents and risk factors 

in postfire settings.

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397

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Patricia L. Winter and Heidi Bigler-Cole

Patricia L. Winter, research social scientist, USDA Forest 

Service, Pacific Southwest Research Station, Riverside, 

CA 92507; and Heidi Bigler-Cole, social scientist, USDA 

Forest Service, Pacific Northwest Research Station, Forestry 

and Range Sciences Laboratory, La Grande, OR 97850.

Abstract

Making complex risk-related decisions involves a degree of 

uncertainty. How that uncertainty is addressed or presented 

in reports or data tables can be tailored to meet information 

users’ needs and preferences. Involving the recipients of 

risk-related information in the design of information to be 

delivered (including the types of information delivered, 

format, and approach to risk) follows Fischoff’s (1995) 

recommendation for involving the recipients of risk 

information in the crafting of the risk message. Here, we 

describe a study conducted with people who use risk-related 

information. We contacted users and potential users of 

National Predictive Services, an information clearinghouse for people who work with prescribed and wildland 

fire. Specifically, this service supplies information to fire 

managers and associated personnel that will help them 

make short-term (30-day) decisions regarding fire personnel 

and resource deployment. Each potential user is a member 

of the fire-management community, including fire management officers, meteorologists, information officers or public 

affairs personnel, and members of various fire-management 

teams. The information users focused on in this study were 

employed within Federal agencies. Respondents completed 

a self-administered survey via a Web-based service. Findings examine the types of information users need, preferred 

formats for information delivery, and the likelihood of 

applying information from Predictive Services in decisions 

made about fire. Of interest to this paper’s risk management/risk communication focus are the reported views on 

acceptability of risk and tolerance for errors, implications of 

risk, and trust and confidence in the information delivered 

through Predictive Services. Barriers and facilitators to 

utilization of Predictive Services are illuminated.

Keywords: Information needs, National Predictive 

Services, reliance, trust, user-needs assessment.

Introduction

A Needs-Assessment Approach to Evaluation

A framework for program evaluation is found in needs 

assessment. According to Rossi and others (1999), in a 

needs assessment, a program is assessed in light of the 

presenting conditions that make the program necessary. 

Current and prospective service recipients may be surveyed 

to explore such pertinent issues as target audience for the 

services or program, service utilization, services desired, 

shortcomings of existing services, and barriers to service 

utilization. Additional items of interest in a user-needs 

assessment are (1) a detailed examination of the characteristics of the target audience (e.g., gender or time in position 

of employment); (2) need for specific products and services; 

(3) program design, including preferred delivery systems 

(e.g., the Internet, in-person briefings) and delivery styles 

(e.g., maps and graphs); and (4) program operations (i.e., 

whether potential Predictive Services clients are actually 

using the products and services, and if not, why not). A 

careful examination from the perspective of key informants 

(users and potential users) facilitates a formative evaluation process. This process includes adjustment of existing 

products and services to better meet user needs and development of new products identified as necessary but not 

currently offered. The evaluation can result in a negative 

appraisal of a program, causing some consternation among 

program sponsors and enthusiasts. However, findings of a 

careful needs assessment that result in subsequent adjustment can help increase program value and effectiveness. 

Such a process might be taken in stride as part and parcel 

to setting up a program designed to meet a specific need 

or set of needs, and growing pains in adjusting and further 

developing a program through its life. Undertaking a userneeds assessment represents an openness and a commitment 

to service. Evaluation should be a part of every serious risk 

communication effort (Slovic and others 1990).

Information Needs, Acceptability of Risk, Trust, and Reliance: 

The Case of National Predictive Services Customers
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GENERAL TECHNICAL REPORT PNW-GTR-802

Crafting of Risk Messages to Meet Users’ Needs—

Access to information is crucial to effective management 

of risk. Information available prior to a risk-related event 

can assist in advance planning, including allocating and 

distributing resources. Information available during a 

risk-related event could help determine how severe the event 

might become, adequacy of management resources, and 

the type and degree of intervention that is needed to protect 

human, structural, and natural resources. Information 

following a risk-related event might be used to aid recovery 

efforts and conduct analysis directed toward future riskrelated responses. Wildfire is one example of a risk-related 

event; fires represent threat and potential harm to natural, 

structural, and human resources.

Factors That Affect Acceptability and Use of Risk 

Information—

Not all audience members targeted for risk-related information will have the same information needs or interests; thus, 

variability in message content is an important consideration 

in crafting risk-related messages. Multiple factors have been 

demonstrated to influence perceptions of risk and riskrelated decisions, including gender (Finucane and others 

2000, Satterfield and others 2004, Siegrist 2000), age (Otani 

and others 1992), time in decisionmaking role, and degree 

of experience with risk situation (Payne and others 1992, 

Reyna 2004), educational level (Vaughan and Nordenstam 

1991), expertise in the topic area (including expert vs. 

layperson views) (Fischoff and others 1984, Plough and 

Krimsky 1987, Slovic 2000), and individual worldviews 

(e.g., culture, attitudes, and values) (Slovic and Peters 1998, 

Vaughan and Nordenstam 1991, Weber and others 1998). 

Contextual and situational factors further influence risk 

perceptions and decisionmaking (see, for example, Kneeshaw and others 2004).

In addition, trust in an information source and confidence in the information received have been repeatedly 

demonstrated as essential to how information will be perceived, responded to, and accepted (Borrie and others 2002, 

Cvetkovich and Winter 2003, Siegrist 2000, Siegrist and 

others 2000) and has direct applications to fire-management 

issues (Cvetkovich and Winter 2004; Shindler and others 

2004; Winter and others 2002, 2004). Trust in the information source tends to foster greater acceptance and belief in 

the risk-related message.

The Present Study

This analysis presents findings from a study initiated in 

2005 through request from the National Predictive Services 

Group (NPSG), a group chartered under the National Fire 

and Aviation Executive Board (NFAEB) that provides 

oversight, leadership, and strategic direction to the Predictive Services program. The NPSG identified a user-needs 

assessment as one of its program-related goals, and they 

have played a central role in execution of the assessment. 

The assessment examines the Predictive Services program, 

which offers products and services through Web sites, 

briefings, and emails administered through the National 

Interagency Fire Center (NIFC) and the Geographic Area 

Coordination Centers (GACCS). The main purpose of this 

service is to integrate climate, weather, situation, resources 

status, and fuels information into products that will enhance 

the ability of managers to make sound short- and long-term 

strategic planning and resource allocation decisions and to 

ensure the safety of firefighting and emergency personnel. 

Predictive Services is a multiagency effort, with support 

from the USDA Forest Service, the USDI Bureau of Land 

Management, National Park Service, Fish and Wildlife 

Service, Bureau of Indian Affairs, and the National Weather 

Service. Products are aimed at the fire management community in each of these agencies as well as State and county 

agencies.

The NPSG requested assistance from the authors 

to conduct a user-needs assessment; to gain information 

toward improvement of current products and services, if 

necessary; and to identify additional products and services 

that might be offered. Evaluation results are more likely to 

be utilized if they directly address the information needs of 

decisionmakers (Patton 1986). Thus, the survey and sample 

were constructed in close collaboration with the NPSG 

in order to ensure the assessment met their needs. Respondents included fire management officers, fire weather 

meteorologists, suppression personnel, dispatchers, fire 

behavior analysts, fuels specialists, fire researchers, incident 
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management team members, fire use team members, multiagency coordinators, aviation personnel, and public affairs 

personnel. Study findings may interest other agencies and 

individuals examining dimensions of risk management, 

including fire and threat information needs.

Method

Respondents—

A sample of email addresses representing users and 

potential users of Predictive Services products and services 

was compiled using key contact and snowball approaches. 

Sources of addresses included the NPSG, a list of incident 

information officers, the National Wildland Fire Management Directory, contacts at various Federal agencies, and 

online directories. We compiled a national list of 2,999 

Federal contacts. This initial list was composed of Federal 

sector fire management personnel within the selected 

agencies (see respondent description below) with a focus 

on assuring that fire management officers, fire behavior 

analysts, incident meteorologists, GACC managers, regional 

coordinators, public affairs and information officers, 

dispatchers, incident management team members, fire use 

personnel, and aviation personnel were included. A census 

listing was not available through any of the agencies, so a 

compilation from email lists containing fire management 

types, a training record, and other preestablished lists 

and directories was used. Because the Predictive Services 

group wanted as many respondents as possible, we used 

all members in our list whose email addresses could be 

obtained. The sample was intended to be as comprehensive 

as possible given the lack of a census listing.

Beyond those included in the sample, an additional 

number of individuals responded as volunteers. Two circumstances prompted volunteering. The first occurred when 

initial contacts forwarded the survey link to others after 

completing it themselves. In some cases, initial contacts felt 

they were not the best person to complete the survey and 

forwarded it to another contact within their agency.

The respondents included 1,078 individuals (including 

63 volunteers or 5.8 percent of the sample). The majority 

(69.1 percent) were male, employed with the USDA Forest 

Service (53.3 percent), National Oceanic and Atmospheric 

Administration and the National Weather Service (14.3 

percent), Bureau of Land Management (12.6 percent), the 

National Park Service (10.0 percent), U.S. Fish and Wildlife 

Service (4.7 percent), and Bureau of Indian Affairs (3.5 

percent). The remainder were employed within a Federal 

interagency group (0.6 percent) and various other Federal 

agencies (0.9 percent). Respondents had typically been 

in their current job for 3 years (median response). We 

had a final response rate of 36.5 percent with less than 1 

percent of the sample refusing to participate (12 individuals). A random sample of nonrespondents was contacted 

by telephone and asked to complete a brief phone survey 

covering reasons for nonresponse, use of various GACC 

Web sites, and familiarity with products and services. The 

main reasons for nonresponse were lack of familiarity 

with the program and lack of time during the study period. 

Nonrespondents were similar to respondents in geographic 

location and agency of employment.

A comparison of the sample respondents and volunteers 

revealed that the volunteers were twice as likely to be 

employed within the Bureau of Indian Affairs (7.9 versus 

3.3 percent of each sample) and Bureau of Land Management (22.2 versus 12 percent), and were less likely to be 

from the USDA Forest Service (31.7 versus 54.7 percent). 

The average length of employment was significantly 

different, with volunteers reporting fewer years (2.9 years 

for volunteers and 6.2 years for the original sample, t = 

3.326, p = 0.001). Gender distribution was similar for the 

two groups. The volunteers and original sample members 

are combined for the purposes of this paper because further 

analyses showed that there were few differences between 

these two groups.

The Survey—

Topics addressed in the survey included sociodemographics (e.g., employing agency, years in current position, and 

gender), who the Predictive Services audience should be, 

preferred information formats, preferred products and services, acceptability of risk and tolerance for errors, implications of risk in making decisions, trust and confidence in the 

products, reliance on Predictive Services products, reliance 

on other information, and facilitators and barriers to using 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Predictive Services information. The survey included 

closed-ended (including semantic differentials, checklists, 

and other formats) and open-ended questions. Some survey 

items were modeled after recent studies conducted by 

another Federal agency to allow for comparison, whereas 

others were developed specifically for this study’s purposes. 

A draft instrument was submitted through peer review and 

review of the Predictive Services group commissioning 

the study. The instrument was pretested with a random 

sample of respondents, and adjustments were made to 

items that seemed unclear or were described as confusing 

by pretest participants. The survey was posted on the Web 

service Question Pro (http://www.questionpro.com). A 

Web-based survey was desirable because of the significant 

cost reduction achieved by eliminating printing and mailing 

costs, greater availability of email addresses than mailing 

addresses for the sample members, and increased familiarity of Web-based instruments among Federal personnel. 

Failed addresses were typically bounced back within 

minutes rather than days, allowing for attempted correction 

and remailing, or, when appropriate, elimination from 

the sample.

Procedure—

Respondents were emailed an invitation and brief letter 

describing the study, along with a link to the survey site. 

Three reminders were sent over the course of the data collection period, with a total of 42 days allowed for response. 

The first reminder was sent 10 days after the initial mailing, 

the second was sent 14 days after the first reminder, and 

the last was sent 10 days after the second mailing, 1 week 

prior to the close of the survey site. Each of the reminders 

contained a brief message and the link to the survey site. 

Reminders were sent to sample members who had not 

completed the survey as well as those who had not been 

removed from the sample owing to email failures.

Results

Identified Audiences—

Respondents were asked whom Predictive Services should 

include as the primary audience for their products. The 

primary audiences selected by the majority were local and 

district fire managers (75.8 percent), regional and State 

fire managers (75.3 percent), and national fire managers 

(65.5 percent). Nonfire land managers were listed as a 

primary audience by about one-third (33.5 percent), and the 

public was listed by about one-fourth of the respondents 

(27 percent; note that percents do not sum to 100 because 

respondents could provide multiple answers on this item).

Information Used and Utility of Information—

Preferred Formats

Respondents were asked to indicate how useful each of 11 

styles and formats of presenting information was to them. 

The average ratings of all items except one fell above 3 (the 

neutral point on the scale). From greatest to least, the most 

useful formats were regional or national maps (χ = 3.91, sd 

= 1.03, n = 879), satellite maps (χ = 3.76, sd = 1.15, n = 870), 

brief executive summaries of data (χ = 3.75, sd = 1.08, n = 

858), brief annotations that accompany data (χ = 3.56, sd 

= 1.02, n = 850), radar maps (χ = 3.53, sd = 1.19, n = 857), 

data in table form (χ = 3.53, sd = 1.05, n = 863), bar charts 

or figures that summarize data (χ = 3.37, sd = 1.09, n = 856), 

data in text form (χ = 3.33, sd = 1.03, n = 849), Web-based 

ArcIMS maps with user-defined layers and scales (χ = 3.31, 

sd = 1.23, n = 832), and data in spreadsheet form (χ = 3.21, 

sd = 1.10, n = 853). Least useful to respondents was nonWeb-based Geo database files (χ = 2.62, sd = 1.12, n = 793).

Preferred Products and Services

Thirty-eight products and services were listed in the survey. 

Some of these products are available elsewhere as well and 

are provided as a courtesy to the Web site users. For each 

item, respondents were to indicate if they had not used 

the product, and, if they had, to rate the usefulness of that 

product. Several of the products stood out because at least 

70 percent of the respondents had used the products and 

rated them as useful or very useful. They included daily 

fire weather forecasts, red flag warnings (this term is used 

by fire weather forecasters to alert users to an ongoing or 

imminent critical fire weather pattern, http://www.nwcg.

gov/pms/pubs/glossary/index.htm), Incident Management 

Situation reports, drought information, and Interagency 

Situation reports. A number of products were used by a 

majority (at least 50 percent), although ratings of usefulness 

varied. A few items offered through Predictive Services had 
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been used by a minority of respondents and were not rated 

as very useful by those who had used them. Among the 

products in this category were regional monsoon updates, 

upper air soundings, Predictive Services forms, and stateof-the-fuels reports.

Suggestions for Improved Formats and Products

Respondents gave several suggestions for improvement in 

response to open-ended questions. In terms of format, there 

were several suggestions for improving Web site performance, including making sure that GACC and Predictive 

Services employees could direct people to the right location, streamlining information searches by allowing users 

to bookmark relevant information, having a professional 

Web designer improve the sites’ navigability, and removing information that is no longer accurate. In terms of 

expanding products and services, there is a desire for more 

location-specific products and more two-way conversations 

between Predictive Services and people who work on the 

local level. This communication would support local decisionmaking and possibly increase the relevance and quality 

of information provided by Predictive Services. People 

working in off-season or prescribed burning capacities, 

or both, suggested more year-round coverage. Additional 

topics for Web site content were offered including information on smoke management, fuel moisture, safety, real-time 

information, and current fire behavior.

Acceptability of Risk and Issues of Accuracy—

Respondents were asked to choose the statement that best 

fit their preference regarding error in predicting risk. The 

majority (67.3 percent) chose “Statements of danger or risk 

be issued with a greater margin of error allowing for an 

early response, knowing that this may lead to unnecessary 

alarms and response (better safe than sorry)” over “Statements of danger or risk should only be given with certainty, 

knowing that this may allow a few dangerous events to 

emerge that were not anticipated (don’t cry wolf)” (chosen 

by 23.9 percent of respondents). In other words, the majority 

preferred erring on the side of caution when reporting on 

fire danger and high fire potential.

Open-ended responses pointed to concerns surrounding 

information accuracy. Among the topics of concern were 

the need for clear statements of the limitations of the data 

and known degrees of accuracy (for example, some would 

like to see confidence intervals reported along with data). 

There was also an interest in sources and assumptions used 

in creating the products offered.

Implications of Risk in Decisionmaking—

The perceived impacts of inaccurate information were 

examined. To address this concept, two items were used. 

The first was “Inaccurate Predictive Services information 

would decrease my ability to predict fire behavior” rated 

on a scale from 1 to 5 (1 = strongly disagree, 5 = strongly 

agree). The majority leaned toward slight agreement with 

this statement (χ = 3.36, sd = 1.16, n = 712) with 12 percent 

indicating strong agreement and another 20 percent indicating agreement. Another 18.2 percent neither agreed nor 

disagreed with this statement.

The second item was “Inaccurate Predictive Services 

used in my decisionmaking may adversely impact firefighter 

or public safety” again rated on the 1 to 5 scale. The majority leaned toward slight agreement on this statement as well 

(χ = 3.48, sd = 1.18, n = 744), with 20.3 percent indicating 

agreement and 16.0 percent indicating strong agreement; 

17.8 percent neither agreed nor disagreed with this statement.

Written comments pointed to concerns surrounding 

accuracy in data gathered to make predictions and communication issues. These comments revealed a disconnection between Predictive Services and local field units. 

Comments indicated that Predictive Services might benefit 

from a better awareness of local weather and fire problems. 

Communication-related comments addressed concerns over 

the need for consistency in content, streamlining of information, and concentration on materials directly relevant to 

fire-use decisions.

Trust, Confidence, and Reliance—

Trust and confidence in the information provided by 

Predictive Services were assessed in a general item “How 

much trust and confidence do you have in the information 

provided by Predictive Services?” rated on a scale from 1 

to 5 (1 = none at all, 5 = a great deal). Very few respondents 

selected 1 (none at all, 8.8 percent) or 2 (5.3 percent) on this 
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GENERAL TECHNICAL REPORT PNW-GTR-802

item. About one-fourth (25.7 percent) indicated some trust 

and confidence, whereas almost half selected either 4 (35.4 

percent) or 5 (12.8 percent), indicating a majority of respondents had trust and confidence in the information provided.

In addition, when asked about three specific trustrelated issues that might be barriers to using Predictive 

Services, very few indicated that trust was an issue. Only 

3.5 percent indicated that they did not trust the products and 

services, 1.4 percent indicated a lack of trust in advice about 

using the products, and less than one percent indicated a 

lack of trust in information produced by multiple agencies. 

These specific items suggest that most had trust in the 

information provided.

Comments specific to trust and confidence included the 

desire among respondents to have a working relationship 

with the people who provide the information, as exemplified 

by this quote: “The local weather service offices continue 

to provide one-on-one support for weather products. The 

level of trust in a forecast product is directly related to the 

personal conversations I have had with the forecasters.”

In spite of some trust, to a great deal of trust expressed by 

the majority of respondents, the majority do not rely on 

Predictive Services in decisionmaking. About 10 percent 

(9.6) relied on Predictive Services a great deal (a rating 

of 5 on a 1 to 5 scale, 1 = none at all, 5 = a great deal). 

About one-fifth (21.2 percent) provided a rating of 4, and 

about one-third indicated little to no reliance on Predictive 

Services information (12.5 percent gave a rating of 2; 21.5 

percent a rating of 1).

When asked how true the statement “I rely on other 

sources more heavily than the products and services 

provided by Predictive Services,” the majority indicated 

that this statement was somewhat to very true (51.1 percent), 

whereas 16.8 percent indicated the statement was not at all 

true. The likelihood of taking action based on Predictive 

Services information received or gathered from a Web site 

suggested respondents were somewhat likely to take action 

(χ = 2.96, sd = 1.23, n = 979, on a 1 to 5 scale, 1 = not at all 

likely, 5 = very likely).

Facilitators and Barriers to Utilization—

Two facilitators to utilization were queried based on 

accessibility and utility. The first of these was “I can access 

and apply Predictive Services information as part of my 

job duties.” Almost half (46.3 percent) agreed or strongly 

agreed with this statement. Approximately another fifth 

were neutral (18.9 percent), and almost one-third did not 

supply a response (27.7 percent). The second item related 

to utilization facilitators was “Predictive Services information helps me perform my job with greater precision,” with 

which 13.7 percent agreed, whereas almost one-third (31.4 

percent) disagreed or strongly disagreed. About one-third 

(32.5 percent) did not respond to this item.

Barriers to utilization were explored through a general 

item related to uniqueness of the information “I think there 

is overlap in the type of information that I can obtain from 

Predictive Services and other sources.” More than half (56.5 

percent) indicated that this statement was somewhat to very 

true. Respondents noted overlap between the products and 

services offered by Predictive Services and other sources, 

particularly the National Weather Service. Some suggested 

a closer coordination between the two providers in order to 

reduce or eliminate redundancies.

Specific barriers to utilization not related to trust of the 

information (already presented above) were examined. The 

most frequent barrier selected was “I never thought about 

it,” (indicated by 26.9 percent). Other barriers selected by 

at least one-tenth of the respondents included “My current 

management practices don’t require the types of information provided by Predictive Services,” (14.7 percent); “I 

don’t know how to use these products,” (14.1 percent); and 

“I need information that is site specific” (13.5 percent). 

Some respondents also mentioned a lack of resources as a 

barrier (lack of time mentioned by 9.3 percent; lack of technology by 4.0 percent; and lack of money at 1.4 percent).

Open-ended responses offer additional insights into 

barriers in using Predictive Services including levels 

of awareness and access. Some respondents were either 

unaware of the products and services or indicated a limited 

knowledge of the array of available information and its 

potential uses. Respondents made several suggestions that 

would address this situation, including advertising to targeted markets, annual notices of new and existing products 

and services, and developing Web-based orientation or 

training, or both, to familiarize potential users with the 
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products. Respondents also suggested presenting information in lay terms, including a glossary of acronyms to 

further enhance understanding, and creating a Web feature 

that allows users to earmark their most relevant Web links. 

Respondents suggested that improved graphics might ease 

information utilization.

Predicting Reliance and Use of Predictive Services 

Information—

The ability to predict reliance on Predictive Services information, and the likelihood of taking action were examined 

through simultaneous multiple regression. Approximately 

50 percent of the variance (R2 adj. = 0.50, F 4, 927 = 234.16, 

p < 0.001) in “How much do you rely on the information 

provided by Predictive Services to assist in decisionmaking” was predicted by trust and confidence in the information provided, gender, years in current position, and 

educational level (Table 1).

Male respondents were significantly more likely to rely 

on Predictive Services information (t = 6.36, df = 483.68, p 

< 0.001, males χ = 3.00, females χ = 2.42). Reliance had an 

inverse relationship with years in position in job (r = –0.086, 

p = 0.006, n = 1,003); federal employees with longer tenure 

were less likely to rely on Predictive Services information. 

Those expressing greater trust and confidence in Predictive 

Services were far more likely to rely on the information in 

decisionmaking (r = 0.704, p < 0.001, n = 944). There was 

not a significant linear relationship between reliance and 

education.

Approximately 48 percent of the variance (R2 adj. = 

0.481, F 4, 922 = 215.71, p < 0.001) in “How likely are you to 

take action based on Predictive Services information that 

you gather or receive from a Web site” was predicted by 

trust and confidence in the information provided, gender, 

years in current position, and educational level (Table 2).

Male respondents were significantly more likely to take 

action based on Predictive Services information (t = 4.57, df 

= 429.25, p < 0.001, males χ = 3.08, females χ = 2.66). Taking action had an inverse relationship with years in position 

(r = –0.120, p < 0.001, n = 979). Those expressing greater 

trust and confidence in Predictive Services were far more 

likely to take action based on the information (r = 0.688, 

p < 0.001, n = 939). There was not a significant linear 

relationship between reliance and education. 

Conclusions and Discussion

Current and Desired Services and Format: Where 

Are the Gaps?

In keeping with Rossi and others (1999) recommendations 

for a user-needs assessment, we explored the issue of target 

audience. According to our key informants, users and 

potential users of Predictive Services, fire managers at the 

local, district, regional, State, and national levels should 

be the primary target audience for products and services. 

Although the public was listed by about one-fourth of 

respondents as an audience, serving this target audience 

presents challenges. There is ample evidence that the public 

differ in their degree of knowledge about risk and, in particular, fire (see, for example, Winter and Cvetkovich 2003). 

Whereas experts might want technically relevant and appropriate information, the public may want culturally relevant 

Table 1—Regression results for predicting reliance 

on Predictive Services information

Independent variable β t sr

2 1

Trust and confidence 0.687 29.266 0.460

Years in current position -.021 -.903 <.001

Gender -.099 -4.264 <.001

Education -.031 -1.351 <.001 1

 Squared semipartial correlation is a measure of the unique contribution 

of the independent variable to the amount of variance explained within 

that set of independent variables. According to the numbers shown, trust 

and confidence is the only variable contributing a substantial unique 

variance beyond the other independent variables (Tabachnik and Fidell 

2001)

Table 2—Regression results for taking action based 

on Predictive Services information

Independent variable β t sr

2 1

Trust and confidence 0.677 28.303 0.449

Years in current position -.042 -1.767 <.001

Gender -.079 -3.329 <.001

Education -.020 -.841 <.001

1

 Squared semipartial correlation is a measure of the unique contribution 

of the independent variable to the amount of variance explained within 

that set of independent variables. According to the numbers shown, trust 

and confidence is the only variable contributing a substantial unique 

variance beyond the other independent variables (Tabachnik and Fidell 

2001)
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GENERAL TECHNICAL REPORT PNW-GTR-802

and value-based information (Fischoff and others 1984, 

Plough and Krimsky 1987). Serving the layperson through 

the products and services would have different implications 

than serving the fire management community. For example, 

executive summaries or annotations attached to data presentations may be of greater interest to lay people. Some of 

our key informants from the fire-management community 

expressed an interest in this type of summary information.

The overlap between data offered through Predictive 

Services and other resources (such as National Weather 

Service) may need to be re-examined by Predictive Services. Overlap was sometimes viewed as a redundancy and, 

perhaps, a misallocation of resources. The daily fire weather 

forecasts, red flag warnings, Incident Management Situation 

reports, drought information, and interagency reports all 

seemed to be on target as products currently offered that 

are of high utility to our respondents. To augment these 

services, the Web sites might be adjusted to streamline 

information searches and to have more real-time updates 

(including removal of information that is no longer accurate 

or timely). Products that seemed of little interest and might 

be deleted included regional monsoon updates, upper air 

soundings, Predictive Services forms, and state-of-the-fuels 

reports. However, before these are dropped entirely, it might 

be helpful for the NPSG to explore with some key contacts 

what the intended purpose and barriers to use are. It may be 

that the information is of interest and would be useful in a 

different form. Some products are offered regionally rather 

than nationally, so consideration of availability is essential 

to interpreting low levels of use among these nationally 

distributed respondents.

A number of respondents seemed to have little awareness of the products and services offered and expressed 

a desire for more information on Predictive Services. 

Communication aimed at the fire management community 

to gain increased awareness of Predictive Services seems 

in order. In addition, respondents expressed a desire for a 

Web-based orientation or training, and a glossary to assist 

the user. These comments suggest that respondents who 

are currently aware of Predictive Services see benefit in 

imbedding more user support into the products to facilitate 

utilization of the products as well as to ease comprehension 

of the products.

Lessons From Acceptability of Risk

Our respondents expressed a clear desire for accurate and 

timely information. When uncertainty was characteristic of 

the data, some indicated a desire to have a clear disclosure 

of the information’s limitations and constraints. When 

respondents were asked to choose between erring on the 

side of caution when uncertainty might be involved in 

reporting, the majority leaned toward a cautious approach. 

However, we did not present detailed narratives or scenarios 

with tangible situations in our questions (see, for example, 

Kneeshaw and others 2004), which may have yielded 

a more complex picture of risk-reporting preferences. 

Specifically, if one had asked the respondent to contrast 

uncertainty in risk estimates for fires involving human life 

versus those burning in uninhabited areas, the results might 

have been different.

The Role of Trust and Confidence in Reliance

Most respondents expressed trust and confidence in the 

information provided by Predictive Services. Few respondents selected trust-related barriers as impediments to utilization. A few comments suggested that a personal working 

relationship with Predictive Services personnel would be 

essential to building and maintaining trust. In spite of this 

expressed trust, respondents tended toward not relying on 

Predictive Services in decisionmaking. A majority indicated 

they rely more on other sources than Predictive Services 

and were somewhat likely to take action based on Predictive 

Services. The regressions predicting reliance and taking 

action suggest that trust and confidence is the significant 

predictor in both cases (with gender, years in position, and 

level of education contributing to the overall equation, 

but not as individual significant contributors). This finding affirms the importance of trust and confidence in the 

delivery of Predictive Services products and services. The 

inverse relationship between years in current position and 

reliance and taking action support Reyna’s (2004) finding 

that more experienced decisionmakers tend to capture the 

gist of factors leading up to a decision, rather than relying 
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heavily on technical details. Expertise leads to a different 

way of information processing, most likely reflected 

here. This also falls in line with the work of Siegrist and 

colleagues (2000) that shows an inverse relationship 

between the level of knowledge about a topic and the 

importance of trust. According to this line of research, those 

who know more about an issue tend to rely less on trust in 

making determinations about issues than do those with less 

knowledge.

Lessons for Risk Communications and 

Information in Threat Management

Our findings offer some lessons for risk communications 

and information in threat management. The importance of 

marketing the products and services to potential users was 

reflected in a desire among many respondents to know more 

about what Predictive Services offers. Additionally, risk 

information could be of greater value if Predictive Services 

provided tools to facilitate comprehension (e.g., appendices, 

glossaries, executive summaries). Stating constraints or 

assumptions used to gather, analyze, and report data could 

facilitate utilization further. This information would allow 

the user to better understand and make informed decisions 

about using the information presented. Although Reyna 

(2004) found a tendency to use the gist of information in 

arriving at decisions, she also found that decisionmakers 

prefer to distill information and arrive at this gist or fuzzy 

information on their own. Therefore, we do not recommend 

that a distilled approach be the only method of information 

presentation. The perception that there is an undesirable 

overlap between resources available from Predictive 

Services and other sources was an interesting revelation. 

Whereas overlap is intentional in this case, the driving 

force for it was user convenience. It may be that establishing Predictive Services as a unique niche for information 

would be preferred. Then links to other reliable sources for 

distinctly different information could be presented. This 

would eliminate overlap, but point the user to where they 

could find other information of interest.

Opportunities to Enhance Risk Communication

Respondents indicated that building relationships, dialogue with those providing the data, and assistance with 

interpretation and underlying assumptions might facilitate their use of Predictive Services. Because trust and 

confidence influence both users’ reliance on products and 

services and their likelihood of taking action, more of a 

direct connection to the fire-management community might 

be desired. This could be facilitated through the Geographic 

Coordination Centers and might be addressed through a 

hosted chat link or a hotline that users could call for assistance. Face-to-face briefings might also facilitate familiarity 

and relationships.

Tools to Move Us Forward

This user-needs assessment highlighted products used by 

fire personnel and factors that might be facilitators and 

barriers to Predictive Services usage. It helped clarify 

informant views on reliance on the products and services 

and the role of trust in that reliance. Ideas for refinement 

were offered that might help improve the existing products. These include considering the target audience when 

developing communication strategies, addressing overlap 

in available products and services, providing accurate and 

timely information that discloses assumptions and limitations, and developing Web-based tools that facilitate use. 

Study results also demonstrated the importance of trust in 

respondents’ decisions to use Predictive Services information. Fostering relationships with users by involving them in 

the development and maintenance of products and services 

might increase user trust and usage of Predictive Services.

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

Patricia L. Winter and George T. Cvetkovich

Patricia L. Winter, research social scientist, USDA Forest 

Service, Pacific Southwest Research Station, Riverside, CA 

92507; and George T. Cvetkovich, professor of psychology 

and interim director, Center for Cross-Cultural Research, 

Department of Psychology, Western Washington University, 

Bellingham, WA 98229-9089.

Abstract

The risk and impact of fires have been significant on the San 

Bernardino National Forest. It is important to understand 

how residents of areas surrounded by the forest perceive 

the impact of fires. If fire management agencies understand 

these perceptions, fire management agencies will be better 

equipped to communicate with publics about risk-reduction 

efforts that agencies, community residents, and property 

owners need to take. Issues of interest include residents’ 

responses to fire risk, beliefs about personal and agency 

responsibility for addressing risk, personal experiences with 

fire, and stressors associated with living in a fire-prone area. 

These issues are examined in light of values perceived as 

being shared with the Forest Service and other community 

residents, as well as trust.

A series of studies of natural resource management 

issues surrounding risk to habitat, nonhuman species, and 

humans has informed our understanding of the role of perceived similar salient values and trust. Trust continues to be 

highlighted as an essential element of fire management and 

communication, and risk management and communication 

in general. However, the functions of salient values similarity and trust have not been explored in the context of the 

experience of residing in a community in a fire-prone area.

The authors arranged for residents of fire-prone communities surrounding an urban national forest to participate 

in focus-group discussions and complete self-administered 

surveys. It was found that most study participants had 

multiple fire-related experiences, and that many regarded 

the risk of fire as part of living in the mountains. Although 

participants considered the Forest Service and the California Department of Forestry to be primarily responsible for 

reduction of fire risk, they also rated personal and community responsibility highly. When participants saw their 

own values and those of the Forest Service as similar with 

respect to fire management, they seemed to consider the 

consistency of agency actions with those values an important basis for making judgments to trust the agency. Public 

meetings with the Forest Service were supported, although 

some participants stipulated that the meetings needed to 

involve dialogue. Other means of communication were also 

supported. Implications for communication and collaboration, education, and management actions are discussed in 

light of the role of salient values similarity and trust in a 

risk environment.

Keywords: Beliefs, fire-prone communities, risk 

communication, risk management, salient values, San 

Bernardino National Forest, stresses.

Introduction

Trust and Risk Management/Risk Communication

Trust has been identified as an important component in 

examinations of public response in risk situations (Siegrist 

2000, Siegrist and others 2000). Examinations of trust of 

publics in fire-management agencies have also been applied 

to fire-management issues (Cvetkovich and Winter 2004, 

Liljeblad and Borrie 2006, Schindler and others 2004, 

Winter and others 2002, 2004). General trends towards 

trust (Winter 2003) and distrust (Liljeblad and Borrie 2006) 

have been presented. Trust seems to be target specific and 

situation specific and involves degree of risk and perceived 

impacts (Kneeshaw and others 2004, Langer 2002, Winter 

and others 2004). Trust has been documented as an essential 

component of effective communication surrounding risk 

management (Covello and others 1986, Freudenberg and 

Rursch 1994, Johnson 2004, Slovic 2000). Those who trust 

the source of a communication about risk are more likely 

to believe the communicated message and more likely to 

accept initiatives designed to address the risk, including 

actions they must take themselves.

Shared Values and Trust: The Experience of Community 

Residents in a Fire-Prone Ecosystem
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GENERAL TECHNICAL REPORT PNW-GTR-802

Past Studies on Values, Trust, and Natural Resource 

Management—

A series of studies examining the interactions between 

salient values similarity and trust has been conducted. 

Across these studies, salient values similarity has been a 

significant predictor of public trust in the Forest Service to 

address a number of natural resource management issues 

including a proposed program of research (Cvetkovich 

and others 1995), a recreation fee demonstration program 

(Winter and others 1999), and acceptance of approaches 

to management of threatened and endangered species 

(Cvetkovich and Winter 2003, Winter and Cvetkovich 2000, 

Winter and Knap 2001). Other significant influences that 

have been explored in conjunction with this line of inquiry 

include community of interest and place, ethnicity, gender, 

concern, and knowledge about the target topic. In one study 

(Cvetkovich and Winter 2003), participants repeatedly 

raised the issue of consistency between Forest Service 

actions and similar salient values. From this we built a pair 

of items and tested them with publics regarding issues of 

endangered species management (Winter and Cvetkovich 

2008) and fire management (Winter and Cvetkovich 2007). 

We confirmed that consistency and validity of inconsistency 

are instrumental in further understanding patterns of trust 

and distrust among publics. These findings are outlined 

in greater detail elsewhere (Cvetkovich and Winter 2004). 

However, the study of attitudes towards fire and fire management (Winter and Cvetkovich 2007) involved random 

samples of residents residing in four Southwestern States, 

including those with little direct experience with fire.

Values and Trust in a Fire-Prone Community—

Variation in degree of experience with fire is undeniably 

an important consideration. It may be that direct experience represents an opportunity to develop greater personal 

knowledge about fire, which, based on past work, would 

then be expected to reduce the reliance on trust in making 

judgments about fire management issues (Siegrist and 

others 2000). Another explanation may be that trust has 

been blurred with issues centered on direct experience 

and reflects confidence rather than assessments of trust 

(Cvetkovich and Winter 2007, Earle and others 2001, 

Siegrist and others 2003).

The Present Study

The present study examined trust and salient values 

similarity among residents in fire-prone communities 

surrounded by a southern California forest. These residents 

were assumed to have direct personal experiences with fire, 

based on the fire regime and recent fire history of this forest.

Method

Participants—

Residents and homeowners (n = 89) in fire-prone communities on the San Bernardino National Forest participated 

in this study. Participants were invited through fire-safe 

councils, local announcements in newspapers and on radio, 

a forest district email tree focused on partnerships, and personal phone calls from the investigators. The majority (57.3 

percent) of participants were male, white (92.1 percent), 

55 years of age or older (68.6 percent), with at least some 

college education (85.3 percent, with 30.3 percent reporting 

some graduate study). Participants’ total annual household 

income was between $50,000 and $74,999 (13.5 percent), or 

mostly greater (42.7 percent).

Survey Instrument—

A self-administered survey was created for the purposes 

of this study and included a number of Likert-type items 

focused on concern about fire and fire management (concern held by respondents and respondents’ judgments of 

concern held by residents in general), knowledge about 

fire management (self, residents, and Forest Service), 

salient values similarity and trust, consistency and validity 

of inconsistency, personal impact of fire, and perceived 

effectiveness of fire-risk-reduction efforts. There were also 

items that examined personal experiences with fire (a series 

of yes/no items), actions taken (a series of yes/no items), and 

stress-related experiences related to fire risk (a series of yes/

no items, adapted from the Impact of Event Scale-Revised, 

cited in Weiss and Marmar 1996). To assess perceived 

responsibility of various agencies, political representatives, 

scientists, visitors and tourists, and members of the community, participants were provided a list of 10 parties and 

were asked to assign points to each, where the sum was to 

be 100. An “other” option was provided so that respondents 
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could add parties to the list. Respondents could leave point 

assignments blank or enter 0 in case of no responsibility 

for reduction of fire risk. Paired with that responsibility 

was a followup question, wherein respondents were asked 

to assign a grade in the range A through F to any party 

they had assigned points to (Table 1). The grade was to 

reflect how the party had performed in the past 12 months 

in reducing the risk of wildland fires in the San Bernardino 

Mountains.

Focus Group Protocol—

Participants were led through a series of discussion items 

regarding fire and fire management on the San Bernardino 

National Forest. These items included objectives for fire 

management, concerns in fire management, alternatives to 

accomplish fire management objectives, and shared values 

and trust in Forest Service fire management.

Procedure—

Each session lasted one and one-half hours and started with 

a statement of purpose of the study, the voluntary nature 

of responses, importance of respect of other views in the 

discussion, and ability to opt out of any items that made the 

participant uncomfortable. Participants completed the selfadministered questionnaire and then were led through the 

discussion topics. Each discussion was audiotaped; a note 

taker recorded key comments and concepts to help anchor 

the transcription of audio records. A total of 10 sessions 

were conducted over a 3-week period.

Results

Resident Experiences With Fire and Fire Risk—

Participants reported a number of personal experiences 

with fire during their lifetimes. The vast majority had 

experienced seeing a wildland fire (91 percent), smoke from 

a wildland fire (89.9 percent), or road closure owing to a 

fire (87.6 percent). Additional experiences shared by the 

majority included evacuation from their homes on account 

of fire (69.7 percent), went without power, which was shut 

off to reduce fire risk (65.2 percent), and a prescribed burn 

near their homes (62.9 percent). Less common were having 

a family, friend, or close neighbor who suffered loss or 

damage to personal property (44.9 percent); loss or damage to personal property (15.7 percent); health problems or 

discomfort (14.6 percent); personal injury (5.6 percent); and 

a family, friend, or neighbor injured by wildland fire (5.6 

percent). Reported health problems were primarily smoke 

related. Of the 11 potential personal experiences, an average 

of six were reported. Participants judged the direct, personal 

impact of fire on the San Bernardino National Forest. A 

majority (61.8 percent) selected a 6, 7, or 8 on the 8-point 

impact scale (1 = no impact, 8 = extensive impact), although 

almost one-tenth (9 percent) selected no impact (a rating 

of 1).

Stresses Experienced—

A list of 22 possible difficulties was presented, and respondents indicated which, if any, they had experienced in 

Table 1—Perceived responsibility and performance of selected parties

Range of 

responsibility Average Grade point

Party Number points responsibility average

1

USDA Forest Service 85 5 - 80 χ = 18.68, SD = 11.89 2.94

California Department of Forestry 79 2 - 50 χ = 14.25, SD = 10.02 2.95

Local fire departments 76 5 - 40 χ = 11.51, SD = 8.32 3.30

Federal legislators and representatives 65 1 - 40 χ = 8.80, SD = 8.60 1.78

State legislators and representatives 61 1 - 25 χ = 6.70, SD = 6.23 1.67

Local community 76 3 - 50 χ = 10.79, SD = 9.06 2.37

Visitors and tourists 60 1 - 30 χ = 8.10, SD = 6.35 1.27

Local business owners 52 1 - 12.5 χ = 6.55, SD = 2.98 1.98

Scientists and researchers 52 1 - 20 χ = 7.06, SD = 3.38 2.40

Me and the people who live with me 76 1 - 80 χ = 11.81, SD = 12.39 3.10 1

Represents the average of grades assigned by respondents for each party, ranging from 1 = F to 5 = A.

1

 Represents the average of grades assigned by respondents for each party, ranging from 1 = F to 5 = A.
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GENERAL TECHNICAL REPORT PNW-GTR-802

the past 7 days with respect to wildland-fire risk. Almost 

one-third of our participants had not experienced any of the 

items listed (the modal response was 1). Slightly more than 

one-third (38.2 percent) indicated that “I avoided letting 

myself get upset when I thought about it or was reminded 

of it,” and almost one-third (29.2 percent) reported “any 

reminder brought back feelings about it,” as well as “I felt 

watchful or on guard” (29.2 percent). About one-fourth 

(25.8 percent) reported that “other things kept making me 

think about it,” and “pictures about it popped into my mind” 

(24.7 percent). About one-fifth (18.0 percent) thought about 

it when they didn’t mean to. Approximately one-tenth of 

our respondents reported “I had waves of strong feelings 

about it” (13.5 percent), “I tried not to think about it” (11.2 

percent), “I felt irritable and angry” (9.0 percent), and feeling like they were back in a time when there was no fire (9.0 

percent). Reporting of physical symptoms (sweating, trouble 

breathing, or nausea) was rare (only 3.4 percent). However, 

more than one-third (41.0 percent) indicated that more than 

one difficulty was experienced within the past 7 days.

Beliefs—

Beliefs about responsibility and performance in meeting 

that responsibility are reported in terms of responsibility 

points assigned to each and overall grade point average (or 

GPA) representing the average of the letter grades assigned 

to each responsible party (Table 1).

About Agency Responsibility

The three agencies listed were among the parties assigned 

the most responsibility. The USDA Forest Service received 

the highest average responsibility points. This means that 

respondents felt that the Forest Service had the greatest amount of responsibility for fire management in San 

Bernardino (Table 1). The agency received a B grade on 

performance. California Department of Forestry received 

the second highest average number of responsibility points 

and was also given a B grade on performance. Local fire 

departments had somewhat lower responsibility ratings—

about equal to the ratings the respondents gave themselves 

and those who lived with them. Local fire departments were 

given a somewhat higher average grade for performance 

(about B+). A few respondents listed city and county planning agencies under the “other” option. When they did so, 

they tended to assign or give the agencies high responsibility ratings and below-average performance grades. Planning 

regulations and codes were also listed as having responsibility, and these were assigned below-average or failing 

performance grades.

About Responsibility of Federal and State Representatives

Responsibility of Federal and State legislators and representatives was also rated and was scored much lower than that 

of most other parties. Among the 10 parties listed, Federal 

legislators and representatives were ranked about sixth in 

responsibility for fire management. Although they were perceived as having less responsibility than agency-affiliated 

parties, Federal representatives were given a performance 

grade of C- (1.78 GPA). State legislators and representatives 

were seen as having even less responsibility (last among the 

parties listed), but were also given a grade of C- (1.67 GPA).

About the Responsibility of Other Parties

Other parties listed that did not fit the agency or representative categories included the local community, visitors 

and tourists, local business owners, and scientists and 

researchers. The local community was fifth in average 

responsibility, at the middle of the parties rated. The 

average performance grade for the local community was a 

C+ (2.37 GPA). Whereas visitors and tourists ranked only 

seventh in responsibility, and were given a below-average 

performance grade of D+ (1.27 GPA). This was the lowest grade assigned to any of the parties and open-ended 

comments reflected significant concern about the role of 

recreationists and tourists in fire. Local business owners 

were assigned the least average responsibility and were 

given a passing grade for performance (GPA of 1.98, or a 

C). Scientists and researchers were also assigned relatively 

little responsibility compared to other parties (eighth out of 

the 10 parties rated), and were also given a passing grade 

for performance (GPA of 2.40, or a C+). A few respondents 

mentioned environmentalists and environmental groups. In 

such instances, poor grades were given.

About Personal Responsibility

Most respondents (76) assigned at least some responsibility to themselves and the people who live with them. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Respondents and the people who live with them ranked, on 

average, as the third most responsible group out of the 10 

rated. They received a B average (GPA of 3.10) for performance.

A number of actions that could be effective in reducing 

fire risk were reported. The vast majority of respondents 

had read about home protection from wildland fires (97.8 

percent), implemented defensible space around their 

property (94.4 percent), and attended a public meeting about 

wildland fire (93.3 percent). A majority had also reduced 

flammable vegetation on their property (75.3 percent), 

worked with a community effort focused on fire protection 

(75.3 percent), made inquiries of the local fire safety council 

or volunteers on how to reduce fire risk (73.0 percent), made 

inquiries of the local fire department on how to reduce fire 

risk (64.0 percent), or made inquiries of the local forest 

ranger (56.2 percent). About a third had changed the structure of their home to reduce risk (38.2 percent) or worked on 

a wildland-fire-suppression effort (38.2 percent). Others had 

volunteered through various efforts or had worked through 

a fire-safe council.

An overall judgment of the effectiveness of those 

actions in reducing the risk of losing one’s home during 

a wildland fire was requested. Effectiveness was rated on 

a scale from 1 to 8 (1 = not at all effective, 8 = extremely 

effective), and was rated positively (χ = 6.01, SD = 1.55, 

median=6).

Participants were queried about barriers to effective 

reduction of fire risk, with one-half of the participants (50.6 

percent) selecting “my neighbors have not done their part,” 

Other entities that had not done their part according to 

respondents included public agencies (29.2 percent selected 

this barrier) and the Forest Service (22.5 percent selected 

this barrier). About one-fifth of respondents indicated 

various barriers to reduction of risk including: “I don’t 

have adequate financial resources” (21.3 percent), “My own 

physical limitations” (21.3 percent), “I don’t want to change 

the landscape” (21.3 percent), “I don’t want to change my 

roof or other built structures” (20.2 percent), and “I’m not 

worried about fire risk” (19.1 percent). A few indicated, “I 

am not sure what will really work” (13.5 percent), or “I don’t 

know who to call/hire to help” (3.4 percent). Other barriers 

added by respondents were focused on land use policies, 

growth and housing, community restrictions on removal of 

trees and vegetation, a lack of coordination between agencies, and environmentalists.

Trust and Shared Values and Other Evaluations—

Using the salient values similarity model of trust, we 

examined dimensions of trust and shared values regarding 

community and the Forest Service.

Community

Participants were asked to rate how concerned San Bernardino National Forest community residents are regarding 

fire and the risk of fire. Using an eight-point scale (1 = 

not at all concerned, 8 = very concerned), residents were 

rated as concerned about fire (χ = 6.71, SD = 1.38, median 

= 7, n = 87). This was only slightly below the average 

level of concern of respondents, who rated their own 

concern about fire at 7.38 (SD = 1.00, n = 88). Participants 

believed that San Bernardino National Forest community 

residents were somewhat knowledgeable about effective 

fire management (χ = 3.92, SD = 1.47, median = 4, n = 88; 

using an eight-point scale, 1 = not very knowledgeable, 8 

= very knowledgeable). However, they saw themselves as 

more knowledgeable (χ = 6.13, SD = 1.60, median = 6, n 

= 88). When asked the extent to which fellow community 

residents share participants’ values about fire management, 

the average response was above the midpoint on the scale, 

indicating moderately shared values (χ = 5.58, SD = 1.55, 

median = 6, n = 81).

Significant positive relationships were found between 

rating of one’s own concern about fire and rating of concern 

of community residents (r = 0.305, p < 0.01, n = 86), rating 

of one’s own knowledge about fire and rating of knowledge 

of community residents (r = 0.355, p = 0.001, n = 88), and 

concern of community residents and perceived shared values with community residents (r = 0.424, p < 0.001, n = 80). 

Participants who believed that neighbors had not done their 

part and that this was a barrier to effective fire management 

did not rate others’ concern, others’ knowledge about fire, 

or perceived shared values differently (t-tests, p > 0.05) than 

did participants who did not believe this.
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Forest Service

We were also interested in how knowledgeable participants 

believed the Forest Service to be regarding effective fire 

management on the San Bernardino National Forest. Participants’ Forest Service knowledge ratings averaged 6.86 

(SD = 1.32, median = 7, n = 88), indicating they believed the 

agency to be fairly knowledgeable.

Participants’ ratings of the salient values similarity 

items indicated a perception of shared values (values: χ = 

6.61, SD = 1.53, median = 7, n = 85; goals: χ = 6.37, SD = 

1.75, median = 7, n = 84; views: χ = 6.31, SD = 1.56, median 

= 6, n = 81). Less than 6 percent of the participants provided 

ratings below the midrange on each of these items, indicating dissimilar values. Participants were also asked to what 

extent they trust the Forest Service in their fire management 

efforts. Based on an 8-point scale (1 = I completely distrust 

the Forest Service, 8 = I completely trust the FS), responses 

leaned toward trust (χ = 5.85, SD = 1.68, median = 6, n = 

86), with the majority (64 percent) providing a rating of 6, 7, 

or 8 on the trust item.

We asked participants to indicate how often the Forest 

Service makes decisions and takes actions consistent with 

their values, goals, and views. A small portion selected 

never (1.1 percent) or rarely (5.6 percent), and about onefourth (25.8 percent) selected sometimes. About one-third 

(33.7 percent) indicated Forest Service actions were usually 

consistent with their values, another fourth (24.7 percent) 

chose almost always, and a few (2.2 percent) said Forest 

Service actions were always consistent with their values. 

Participants were then asked to respond to “If or when the 

Forest Service makes decisions or takes actions inconsistent 

with my values, goals, and views, the reasons for doing 

so are valid.” A few disagreed with the statement (3.4 

percent completely disagreed, and another 15.7 percent 

disagreed). Almost one-third (31.5 percent) neither agreed 

nor disagreed. Almost half agreed that inconsistency 

between values and Forest Service actions was valid when 

it occurred (39.3 percent agreed, 4.5 percent completely 

agreed). One participant expressed this balance between 

trust and the perception that there are valid reasons why the 

agency might not get things done as follows: “I would trust 

one of them with my life. The only problem is red tape and 

money constraints.” Another participant pointed to policyrelated constraints: “What I am thinking is that the people 

in the Forest Service have the rulebook and are playing by 

the rulebook and the negligence comes with the change in 

policy. Maybe we need to have a more flexible policy. I trust 

the Forest Service people, but they are stuck with the policy 

and they need to figure a way to change policies.”

Relationship Between Trust and Salient Values

A significant portion of the variance in trust of the 

Forest Service was accounted for by similarity ratings for 

values, goals, and views (R2adj. = 0.468, F (3, 76) = 2 4.129, 

p < 0.001). When consistency and validity of inconsistency 

were added to the trust prediction model that was based 

on salient value similarity for values, goals, and views, the 

resulting model accounted for an increased proportion of 

the variance in trust (R2adj. = 0.582, F (5, 70) = 21.893, p < 

0.001). The most influential predictors in this equation were 

consistency of action with values (t = 3.870, p < 0.001) and 

shared values (t = 2.546, p = 0.013).

Relationship Between Trust and Other Fire-Related Items

The relationship between trust in the Forest Service and 

personal actions taken to reduce fire risk and number of 

perceived barriers to reduction of fire risk was examined. 

Neither actions nor barriers had a significant relationship 

with trust. Participants who had experienced more firerelated impacts (such as smoke or knowing someone who 

lost property) were less likely to trust the Forest Service 

(r = -0.293, p < 0.01, n = 83). Likewise, participants who 

reported more difficulties (such as having waves of strong 

feelings or feeling watchful and on guard) tended to trust 

the Forest Service less (r = -0.366, p < 0.01, n = 80).

Finally, grades assigned to the Forest Service in fire 

risk reduction efforts were related to trust (r = 0.648, p < 

0.001, n = 81). An ANOVA to examine average trust ratings 

by grade was completed (F (4, 76) = 17.850, p < 0.001). 

Participants who had assigned an F to the Forest Service 

had a mean trust rating of 2.50, the D group had a mean of 

3.17, C’s had an average of 5.67, B’s had an average of 5.71, 

and A’s had an average rating of 7.00.
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Reasons for Reliance on the Forest Service

We asked participants to indicate whether or not a series 

of items were reasons to rely on the Forest Service’s fire 

management on the San Bernardino. A majority felt that the 

following were not reasons to rely on the Forest Service: 

media coverage of Forest Service fire management (60.7 

percent said this was not a reason), and Congress holds the 

Forest Service accountable for its fire management (52.8 

percent said this was not a reason). The majority agreed or 

strongly agreed that the following were reasons they relied 

on the Forest Service: procedures that ensure the Forest Service uses effective fire management (67.4 percent), personal 

relationships I have with Forest Service personnel (59.6 

percent), the Forest Service’s past record of fire management 

(58.4 percent), and the laws controlling the Forest Service’s 

fire management (52.8 percent). Participants were almost 

equally divided on ”opportunities that I have to voice my 

views about fire management” (38.2 percent said this was 

not a reason, 46.1 percent said it was a reason).

Communication and Education

Participants had many relevant views on approaches to 

communication, collaboration, and education. The most 

preferred sources of information were public meetings the 

Forest Service leads so the community can ask questions 

(88.8 percent) and community meetings (84.3 percent). 

Other preferred information sources included a Web site 

(79.8 percent), brochures and pamphlets available on request 

(77.5 percent), articles in the local paper (77.5 percent), 

an email tree sent by a Forest Service representative and 

forwarded by fire-safe council volunteers (75.3 percent), 

local television/radio spots put on by a local Forest Service 

ranger (64.0 percent), and information and displays at Forest 

Service visitor center (60.7 percent). Additional suggestions 

included emails directly from the Forest Service, signs, 

a hotline or number residents could call to speak directly 

with someone knowledgeable, and messages on community 

bulletin boards. Flyers and newsletters left on residence 

doors were also brought up as a means of getting the word 

out. It should be noted that the strong support for community meetings and direct engagement with the Forest 

Service was expressed by participants who themselves had 

come to participate in a meeting. There were many residents 

who did not attend. From many we heard about scheduling 

conflicts, burnout from so many meetings, or the need to 

have a direct, tangible outcome from the meeting before 

they would commit to participation.

Some residents did not participate because of road 

closures or weather-related concerns (an unusual series of 

late-season snowstorms and fog occurred during the study 

period). However, others told us they felt there was not 

adequate notice about our meetings. This was in spite of the 

radio and newspaper announcements, including media Web 

sites, as well as email notices and telephone calls from the 

researchers or through fire-safe councils. Identifying the 

most effective communication networks, including those 

that are community based, was an important part of our 

research effort, and we only had partial success. On one forest district, many of our contacts came through an email list 

derived from various partnership and collaborative efforts. 

This proved an invaluable resource to us, and the direct 

contact with a Forest Service employee who was known 

to residents helped pave the way. In sum, we found that a 

number of routes and contacts were necessary. These routes 

varied greatly and, in some ways, reflected the unique 

nature of the communities we tried to reach.

Discussion and Conclusion

Experiences in These Fire-Prone Communities

The majority of participants reported multiple fire-related 

experiences, though a minority had suffered personal injury 

or personal property loss. However, almost half knew 

others who had suffered loss or damage. Comments about 

fire risk revealed that many took the risk of fire in stride 

as part of living in the mountains. The one exception to 

this surrounded discussions about prescribed fire where 

participants mentioned the risk of fires getting out of control 

and the concern surrounding that management technique. A 

majority indicated that fire had an impact on them directly, 

but the reporting of stress-related experiences within the 

past 7 days reflected the time elapsed from the last fire event 

to the study period. We expect that this timing had something to do with the reports of stress-related events being 

somewhat low. Another factor may have been the active 
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GENERAL TECHNICAL REPORT PNW-GTR-802

role participants have taken in direct actions to reduce fire 

risk and to educate themselves about fire. This would be an 

interesting area for further research.

Responsibility and Performance

Participants were most likely to view agencies as having 

a majority of responsibility for reduction of fire risk, with 

personal and community responsibility following closely. 

Agencies, including the Forest Service, personal households, and community were viewed as doing fairly well, 

although respondents suggested that the Forest Service and 

neighbors might not always have done their part in reducing 

fire risk. Although assigned little responsibility overall, 

tourists and visitors were viewed as doing poorly in reducing fire risk. Comments indicated support for limitations 

on tourists and visitors, including more limits on access or 

more limits on forms of use (such as no fires at yellow post 

sites).

The Interplay Between Values, Trust, Risk, and 

Response

Whereas perceived salient values and trust were significantly related to each other, consistency between perceived 

shared values and actions taken by the Forest Service 

seemed to be more influential in determinations of trust 

than were the shared values alone. This may have been 

due to the relatively high average rating of perceived value 

similarity, paired with low variability. Direct personal experiences with fire and stressful impacts were both negatively 

associated with trust. Trust was significantly related to 

perceived effectiveness of the Forest Service in reducing fire 

risk. Given the role of trust in acceptance of agency actions 

and communications, we expected to find a relationship 

between direct actions and trust. However, the number of 

actions taken had no relationship to trust. This has interesting implications for study of the relationship between trust 

and public response. Perhaps only those actions directly 

advocated through the Forest Service might be expected to 

be influenced by trust and perceived similar salient values. 

Reliance on procedures and personal relationships seemed 

to be a factor in deciding to rely on the Forest Service’s fire 

management efforts. The past record of fire management 

seemed a bit less important but was still held by a majority 

to be a reason for reliance.

Implications for Communication and Education

A majority of the participants supported public meetings 

with the Forest Service, although comments made clear the 

need to have an open forum where they could ask questions and receive answers from a knowledgeable source. 

Most of the methods of communication listed are already 

employed in these communities to some extent, although 

some residents expressed the feeling that it had been a while 

since they had met with the Forest Service and that they 

were starting to feel out of touch with what was going on. 

Others who did not attend the study sessions expressed a 

sense of overload on meetings. Clearly, various kinds of 

contacts need to be used on an ongoing basis, and the use of 

community organizations and networks, including the firesafe councils, seems to be an effective vehicle to include. 

Although media were included in the means of contact, the 

local newspaper received more support than television or 

radio spots. A Web site for current and community-based 

information seemed to receive strong support. One community declined participation because they were waiting for 

the agency to act on commitments made in prior meetings. 

This demonstrates the importance of following up with 

community members after meetings and keeping them 

informed on an ongoing basis. It would probably be helpful 

even to report efforts to meet commitments. If barriers were 

met, those could also be reported, as it seemed participants 

understood that funding, policies, and other challenges 

could prevent the Forest Service from taking action.

Gaps and Where We Go From Here

Participants were fairly similar and not representative of 

the overall populations within these forest communities. 

Although we made a concentrated effort to recruit seasonal 

residents, only a few actually participated. Whereas a 

past study sheds light on differences between seasonal 

and year-round residents of the San Bernardino mountain 

communities (Vogt 2008), some participants suggested 

that seasonal residents and those leasing or renting their 

properties were less concerned and less similar in values 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

than were the year-round community members. Additional 

studies of the differing perceptions of seasonal and yearround residents, including how the Forest Service and other 

fire management agencies view these two groups, would 

be of interest. The lack of relationship between personal 

actions taken and trust levels was somewhat surprising, 

although the relatively small sample size and little variance 

in trust may have suppressed any relationship between these 

two variables. The interest in meetings and information 

from the Forest Service, and an interest in maintaining an 

ongoing dialogue, were made clear. The need to report on 

actions taken, progress made, and barriers experienced 

by the Forest Service in its fire management efforts was 

affirmed. These steps would assist the agency in continuing 

to develop trust and a positive basis for interaction in these 

communities who sometimes view themselves as very alone 

in their efforts to reduce risk.

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Pests/Biota

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

Frank H. Koch and William D. Smith

Frank H. Koch, research assistant professor, Department 

of Forestry and Environmental Resources, North Carolina 

State University, Research Triangle Park, NC 27709; and 

William D. Smith, quantitative research ecologist, USDA, 

Forest Service, Southern Research Station, Research 

Triangle Park, NC 27709.

Abstract

Historically, U.S. forests have been invaded by a variety of 

nonindigenous insects and pathogens. Some of these pests 

have catastrophically impacted important species over a 

relatively short timeframe. To curtail future changes of this 

magnitude, agencies such as the U.S. Department of Agriculture Forest Service have devoted substantial resources 

to assessing the risks associated with recent or potential 

forest invaders. These assessments of risk typically include 

a mapping component; among other things, this presents 

a useful way to organize early-detection/rapid-response 

procedures. However, forest pest risk mapping is often 

limited to readily available and manageable data sets, which 

results in representations of risk that heavily favor climatic 

factors or estimates of host species distribution. Detailed 

examinations of human-mediated pathways of spread are 

often neglected in forest pest risk analyses owing to a lack 

of spatial data or uncertainty about a pest’s predictive model 

parameters.

Humans are the most important facilitator of forest pest 

introduction and spread. With expanding global trade and 

interstate commerce, the number of potential forest invaders 

is likely to rise, making the analysis of human-mediated 

pathways particularly timely. In this synthesis, we present a 

number of spatial data sources, collected by Federal agencies and private companies for a range of purposes, which 

can be utilized to represent these human-mediated pathways. Although general in nature, queries can often be used 

to tailor these data sets to address specific pests. Perhaps, 

most importantly, the source data can usually be acquired 

for free or at negligible cost.

Using the sudden oak death pathogen (Phytophthora 

ramorum) and other pests as examples, we illustrate how 

some of these data sources can be employed for mapping 

risks associated with human-mediated pathways. First, we 

demonstrate the use of foreign import cargo statistics—

marine, airborne, and transborder—to assess the risk of 

introduction of new species at United States ports of entry. 

Second, we examine the utility of inland waterway cargo 

statistics, freight analysis networks, and other databases on 

domestic commodity traffic for mapping regional and local 

spread of forest pests. Third, we explain the diverse applications of business databases, not only to identify clusters of 

high-risk businesses, but also to rank these businesses using 

a suite of socioeconomic factors. Finally, we discuss the 

limited availability of up-to-date land use/landcover data, 

and present alternative data sources for representing highrisk areas of current urbanization as well as the forest-urban 

interface.

Whereas many of these data sets are imperfect depictions of human-mediated pathways, integration of several 

can add significant depth to early-detection/rapid-response 

projects. To facilitate further applications, we discuss user 

considerations, future information needs, and potential 

sources of additional data regarding human-mediated 

pathways.

Keywords: Commodities, dispersal, forest pests, 

freight, human-mediated, risk, WUI.

Introduction

In 2000, annual forest losses and control costs in the United 

States due to nonindigenous forest insects and pathogens 

were estimated at $4.3 billion (Pimentel and others 2000), 

and that figure is likely to rise due to the increasing transport of species beyond their native habitats (Brockerhoff 

and others 2006b). In ecological terms, these pests alter 

forest composition, structure, and productivity; in some 

cases, pests basically remove host tree species from forest 

ecosystems (Brockerhoff and others 2006b, Levine and 

D’Antonio 2003). A case in point is the once-dominant 

American chestnut (Castanea dentata (Marsh.) Borkh.), 

Representing Human-Mediated Pathways in Forest 

Pest Risk Mapping
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GENERAL TECHNICAL REPORT PNW-GTR-802

which was virtually eliminated from eastern forests by 

chestnut blight (Cryphonectria parasitica) within 50 years 

of the pathogen’s introduction from Asia (Liebhold and 

others 1995). Over a similar timeframe, the hemlock woolly 

adelgid (Adelges tsugae Amnand) has caused extensive 

mortality throughout the Northeastern United States 

and has recently spread into the southern Appalachian 

Mountains, raising fears that it will decimate both eastern 

hemlock (Tsuga canadensis (L.) Carriere) and Carolina 

hemlock (T. caroliniana Engelm.) populations in the region. 

Notably, the adelgid was considered a mere nuisance pest of 

ornamental hemlocks for a few decades after its accidental 

introduction around 1953, until it began to spread to natural 

forest stands during the mid-1980s (Souto and others 1996). 

A related pest, the balsam woolly adelgid (A. piceae Rutzeburg), was also accidentally introduced in the latter part of 

the 20th century, and has removed more than 90 percent of 

Fraser fir (Abies fraseri (Pursh) Poir.) from already sparsely 

distributed spruce-fir communities in the southern Appalachians (Pimentel and others 2000). In a recent example, 

the emerald ash borer (Agrilus plannipennis Fairmaire) now 

infests an estimated 40 000 km2 in the Great Lakes region 

and Ontario, where it has killed at least 15 million ash 

(Fraxinus spp.) trees and has regularly jumped quarantine 

boundaries. It is likely that the insect was introduced to the 

United States 10 or more years before it was first recognized in 2002, during which time populations were able to 

establish and spread undeterred (US-GAO 2006).

Forest Pest Risk Assessments and Risk Maps

Forest pest risk assessments are intended to provide forest 

managers with information to prepare for such situations, 

and in turn, reduce the number of invasive species that 

become harmful pests (Byers and others 2002). A risk 

assessment for a particular pest categorizes or quantifies its 

risk of introduction, establishment, spread if established, 

and potential economic and environmental impacts (UNFAO 1995). The spatial representation of such an assessment 

is a map depicting the varying level of risk of introduction 

or establishment of a pest throughout a geographic region of 

interest (Andersen and others 2004b). Risk maps facilitate 

early-detection/rapid-response procedures by providing a 

template for the design of regulatory programs and detection surveys or, if a pest has already been established in 

one part of the geographic area of interest, through control 

tactics that prioritize areas for which the risk of pest spread 

is the highest (Andersen and others 2004a).

Importance of Human-Mediated Pathways

Pest risk maps are typically assembled using spatial data 

from three principal subject areas: host species distribution, environmental factors affecting pest persistence (e.g., 

climate), and pathways of pest movement (Bartell and Nair 

2004). Risk maps often focus on climatic factors or host 

species distribution or both (e.g., Meentemeyer and others 

2004), perhaps because of a lack of information regarding the relevant pathways for a given pest. Nonetheless, 

human activities—particularly in the area of commercial 

trade—have received much attention for enabling the 

spread of invasive pests. Human-mediated pest transport 

is largely unintentional (Jenkins 1996): forest pests may 

travel undetected on a variety of materials including wood 

products, airline baggage, nursery plant stock, solid wood 

packing materials, hikers’ clothing, and passenger vehicles 

(Campbell 2001, Liebhold and others 2006, McCullough 

and others 2006, Tkacz 2002, Work and others 2005).

In 2004, a joint workshop by the Society for Risk 

Analysis Ecological Risk Assessment Specialty Group and 

the Ecological Society of America Theoretical Ecology 

Section focused on the standardization of methods for risk 

assessment as a component of a National Invasive Species 

Management Plan. Workshop participants noted a critical need for data to represent pathways of introduction of 

nonindigenous pests that are related to human trade and 

transport (Andersen and others 2004b). This information 

need has been echoed by policymakers focused specifically 

on forest pests (Chornesky and others 2005). For forest pest 

risk mapping, the challenge is threefold. First, the amount of 

spatial data specifically collected for forest pests is limited. 

Second, it can be a challenge to apply available information 

on pest biology and other fundamental characteristics to the 

tasks of spatial data mining and interpretation, especially 

since that fundamental information may be spread across 

a wide range of journals, government documents, and 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

other literature (Ricciardi and others 2000). Finally, many 

human-mediated pathways operate on broad (i.e., landscape, 

regional, national, or even continental) spatial scales, 

impeding their analysis.

Goals of This Synthesis

During the past couple of years, we have collaborated 

with scientists from the U.S. Department of Agriculture 

Forest Service Forest Health Technology Enterprise Team 

(FHTET), the U.S. Department of Agriculture Animal 

and Plant Health Inspection Service (APHIS), and other 

organizations to develop national-scale risk maps for a suite 

of forest pests. In the process, we have gathered an assortment of data sets for analysis and representation of humanmediated pathways. These data are maintained and updated 

by Federal agencies, academic institutions, nongovernmental organizations, and, in some cases, private companies 

for a range of purposes. Many of the data sets are statistical 

and not explicitly spatial but can be used in combination 

with more readily available spatial data. Queries can often 

be used to tailor these data sets to depict spatial patterns or 

temporal trends, or both, that are relevant for specific forest 

pests. Perhaps most importantly, the source data can usually 

be acquired for free or at negligible cost, typically through 

Internet download. It should be noted that because they are 

generally not collected with forest pest risk assessment in 

mind, some of the data sets’ characteristics (format, classification scheme, and resolution) limit their straightforward 

application. Based on our knowledge of human-mediated 

pathways, we have sometimes adopted simplifying assumptions in order to apply the data sets. Nevertheless, it is our 

belief that the data significantly improve the forest pest risk 

assessment process, despite their limitations.

Whereas it is infeasible to create an exhaustive list, in 

this paper we present an overview of data sets from four 

categories generally pertaining to human-mediated pathways of forest pest movement:

• Foreign cargo statistical data

• Domestic commodity movement data

• Business data

• Land use/landcover data

For each of these categories, we discuss conceptual 

linkages between the available data and the depiction of 

forest pest introduction or spread risk. We describe some 

specific data sets from each category, highlighting characteristics that should be considered by the potential user. We 

then illustrate their application using a series of examples 

related to several current forest pest threats. Finally, to 

facilitate further applications, we highlight some future 

information needs and note where researchers may look for 

additional data regarding human-mediated pathways.

Foreign Cargo Statistical Data

International trade comprised 12 percent of the country’s 

freight tonnage in 1998, and that percentage is expected 

to double by 2020 (USDOT-FHA 2002b). Many nonnative 

species are transported to the United States in import cargo 

shipments. A fairly conservative projection suggests that 

international trade will result in the establishment of 120 

nonnative insects and plant pathogens in the United States 

between 2000 and 2020 (Levine and D’Antonio 2003). 

For forest insects, the largest introduction risk seems to 

be associated with solid wood packing materials (Haack 

2003, 2006). Some of these materials may sit unattended in 

distribution facilities near ports of entry for weeks or more 

(Campbell 2001). Several nonnative insects now detected 

in parts of the United States have been regularly associated 

with solid wood packing materials, including the Mediterranean pine engraver (Orthotomicus erosus (Wollaston)) and 

the sirex woodwasp (Sirex noctilio Fabricius) (Haack 2006, 

Hoebeke and others 2005, Lee and others 2005). Joint risk 

assessments by APHIS and the Forest Service suggest that 

the importation of unmanufactured wood, especially in raw 

log form, is a high-risk pathway of introduction for a diverse 

range of insects and diseases (Tkacz 2002). The commercial 

trade of nursery stock and plant materials is another likely 

pathway of introduction; evidence suggests that the sudden 

oak death pathogen (Phytophtnora ramorum) was likely 

introduced via the commercial plant trade (Ivors and others 

2006).

The Port Interception Network (PIN) database, maintained by APHIS since 1984, provides information on plant 

pests intercepted at U.S. ports of entry (Work and others 
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GENERAL TECHNICAL REPORT PNW-GTR-802

2005). A number of analyses have made use of the PIN 

database, along with other data sources such as the APHIS 

Agricultural Quarantine Inspection Monitoring (AQIM) 

protocol, to analyze trends in the places of origin and 

commodities most likely to carry nonnative pests, primarily 

insects (Brockerhoff and others 2006a; Haack 2003, 2006, 

McCullough and others 2006; Work and others 2005). 

Although informative, such analyses are constrained by the 

fact that only 2 percent of cargo is inspected (NRC 2002), 

and, thus, many pest introductions may go undetected. As 

an alternative, publicly available statistical data on international trade offer a means to rank the Nation’s ports of entry 

according to forest pest risk. In particular, filtering such 

data for countries or commodity types of interest or both 

may reveal ports where the likelihood of intercepting a specific pest is greatest. In addition, certain data sets provide 

information on eventual inland destinations, facilitating 

simple but nationwide risk analyses.

A number of government agencies keep databases on 

some segment of foreign imports to the United States. The 

associated data are used to generate national reports on 

current import patterns, as well as changes in those patterns 

through time. The databases are similar in format; typically, 

each database record contains fields listing a particular port 

of entry, a country or region of origin, an import tonnage, 

and (sometimes) the commodity type. Records can usually 

be queried and summarized through time to determine the 

amounts of high-risk cargo arriving from high-risk locations of origin at individual U.S. ports of entry. Readily 

available coordinate information for these ports can be used 

to develop maps depicting relative risk of introduction for 

forest pest(s) of interest.

Foreign Marine Cargo Statistics

In 2001, more than 78 percent of the total U.S. import tonnage was transported on cargo ships (USDOT-BTS 2003). 

The U.S. Army Corps of Engineers, Navigation Data Center, Waterborne Commerce Statistics Center issues annual 

summary reports on inbound and outbound foreign marine 

cargo. The agency also provides public Internet access to 

statistical data used to generate the reports (USACE-NDC 

2006a). Annually compiled data tables are available for 

1997 through 2004. Each annual table, available in ASCII 

or DBASE format, has greater than 100,000 records listing 

the total weight tonnage of cargo shipments for a specific 

foreign port (and country) of origin, U.S. destination port, 

and commodity type. Commodities are classified into one 

of 41 categories based on the Navigation Data Center’s 

Lock Performance Monitoring System. The U.S. destination 

ports listed in the tables may be mapped using geographic 

information systems (GIS) layers provided by the Navigation Data Center or with ports data included in the National 

Transportation Atlas Databases (USDOT-BTS 2005).

Foreign Air Cargo Statistics

The U.S. Department of Transportation, Bureau of Transportation Statistics publishes monthly commercial air 

carrier survey information as part of its T-100 databank 

(USDOT-BTS 2006c). A portion of the T-100 databank 

focuses specifically on inbound and outbound international 

flights, listing foreign airport and country, passenger 

counts, and total pounds of enplaned mail and freight. 

Currently, ASCII data tables may be downloaded for 1990 

through 2005. Unfortunately, freight commodity categories 

are unspecified in the T-100 databank.

As a possible alternative, the U.S. Department of 

Transportation, Federal Highway Administration, Office of 

Freight Management and Operations publishes the Freight 

Analysis Framework (FAF) database in Microsoft Access 

format. The FAF compiles data from several sources, 

including international and domestic waterborne commerce data from the U.S. Army Corps of Engineers as 

well as the T-100 air cargo data. The most recent version, 

the FAF2

 database, compiles data from 2002 on freight 

movement by all transportation modes. Notably, although 

FAF2 is intended to supercede the previous FAF1 database 

(constructed using 1998 data), a supplementary data table in 

FAF1 focuses specifically on international air freight shipments and contains projections of likely freight totals for 

2010 and 2020 (USDOT-FHA 2002a). In this supplementary 

data table, commodities are classified by a two-digit code 

based on the Standard Transportation Commodity Code 

system (resulting in 40 categories). The geographic precision of this data table is limited, with shipment destinations 
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reported as States (rather than individual airports) and shipment origins designated only by trade region: Asia, Europe, 

Latin America, Canada, Mexico, and the rest of the world.

Trans-Border Cargo Statistics

The Bureau of Transportation Statistics publishes land 

border crossing data collected by the U.S. Department 

of Homeland Security, Customs and Border Detection 

Division (USDOT-BTS 2006a). The data detail the total 

number of loaded and empty truck and rail containers 

passing through specific United States-Canada and United 

States-Mexico land border and international ferry crossings. 

ASCII data tables are available for 1995 through 2004, but 

as with the T-100 air carrier databank, commodity categories are unspecified. The aforementioned FAF2

 database is 

perhaps a more useful source, presenting import and export 

land border crossing data from 2002 in a dedicated table of 

greater than 200,000 records (USDOT-FHA 2006a). Each 

record lists a unique combination of origin, destination, 

border crossing location (sometimes reported as a specific 

city, but often just a State), the cargo transport mode, the 

two-digit Standard Classification of Transported Goods 

commodity category (43 categories), and the total tonnage 

of that commodity. Origin and destination are reported as 

Canada, Mexico, or 1 of 114 U.S. metropolitan areas or restof-State statistical regions (USDOT-FHA 2006a).

Commodity Coding Systems

A consideration when using the above-described data sets is 

that any commodity information provided may be of limited 

specificity. The two FAF databases and the Navigation Data 

Center’s marine cargo statistics all use two-digit commodity codes. A category that might be of specific interest for 

a forest pest, such as nursery stock, is likely subsumed by 

a much broader category (e.g., noncereal-grain agricultural 

products). This may mean that to use the data requires some 

assumptions, or instead, the use of other data to estimate the 

breakdown of the broader category. In addition, all of these 

databases use different commodity coding systems. For 

example, the Lock Performance Monitoring System, used 

with the foreign marine cargo data, has a category labeled 

41 – forest products, lumber, logs, woodchips. In contrast, 

the Standard Transportation Commodity Codes, used with 

the FAF1 database, include category 24 – lumber and wood 

products, and the Standard Classification of Transported 

Goods, used with FAF2, has two corresponding categories: 

25 – logs and other wood in the rough and 26 – wood 

products. There appears to be movement towards use of the 

Standard Classification of Transported Goods, which would 

be beneficial for more sophisticated, multimodal analyses. 

Documentation for the FAF2 database includes cross-walk 

tables for the three commodity coding systems mentioned 

here (USDOT-FHA 2006b).

Domestic Commodity Movement Data

Human activity enhances the spread of many invasive pests 

by enabling long-distance dispersal beyond their natural 

abilities. For instance, insect eggs, larvae, and adults may 

be accidentally carried on cars or other vehicles, as has 

been the case with the range expansion of the gypsy moth 

(Lymantria dispar (L.)) in the Eastern United States (Marshall 1981). Perhaps more significantly, domestic shipments 

of commodities may carry undetected insects and pathogens 

from ports of entry to previously uninfested locations with 

favorable conditions. For example, research suggests that 

the normal spread of the emerald ash borer through local 

flights has been greatly exacerbated in the Great Lakes 

region by human transport of infested saplings or firewood 

(Muirhead and others 2006).

As with the international cargo data, a number of 

government agencies have developed data sets that track the 

flow of commercial freight via various transportation modes 

across the country. These databases may be categorized as 

either networked or non-networked in structure, depending 

on whether their focus is on the flow of commodity types 

or on an arc-node pattern for modeling a particular mode 

of transport (e.g., freight trucks). The user must consider 

attribute resolution (i.e., the level to which commodities 

are specified) and what this allows in terms of filtering and 

querying the data for application in forest pest risk maps.

Non-Networked Commodity Data

The U.S. Census Bureau and the Bureau of Transportation 

Statistics jointly issue Commodity Flow Survey reports and 
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GENERAL TECHNICAL REPORT PNW-GTR-802

an accompanying database as part of a national Economic 

Census. The most recent iteration of this joint effort was 

completed using 2002 data (USCB and USDOT-BTS 2005). 

The Commodity Flow Survey database is a key source of 

geographically summarized data on the country’s domestic 

commodity movement. The database is delivered with 

a customized interface that allows data to be filtered for 

commodity types, transportation modes, distance traveled, 

and origin and destination regions of interest. Moreover, 

previous versions (using 1993 and 1997 data) may be 

downloaded from the Bureau of Transportation Statistics’ 

TranStats data warehouse (USDOT-BTS 2006b), permitting 

analysis of change through time.

The Commodity Flow Survey 2002 database comprises 

summary tables for several levels of geographic detail: 

national, regional; State; and metropolitan area. Significantly, the level of specificity for commodity information 

reported in a table is tied to the level of geographic detail. 

Far more specific commodity codes are available for 

broad-scale (e.g., region-to-region) flows than fine scale 

(e.g., metro-to-metro) flows. This can be an important issue 

to consider for pest risk mapping. For instance, P. ramorum

has been spread to nurseries throughout the United States 

by infected nursery stock. In the Commodity Flow Survey 

database, the commodity category “nursery crops and live 

plants” is only available at a regional scale; finer scale tables 

use two-digit commodity codes, meaning nursery stock 

is lumped into an “other agricultural products” category. 

In such cases, it may be necessary to use ancillary data 

or combine tables from different spatial detail levels to 

adjust tonnages accordingly. Moreover, although the list of 

industries covered by the survey has expanded each time 

it has been completed, certain industries are generally 

excluded, including transportation, construction, and some 

retail industries (USCB and USDOT-BTS 2005). Farms 

and fisheries are also excluded, meaning that the survey’s 

agricultural commodities data only illustrate shipment from 

market area to market area, rather than directly capturing 

areas of commodity production (USCB and USDOT-BTS 

2005).

The FAF is a practical alternative to the Commodity 

Flow Survey database. The FAF2

 database includes a single 

large table on domestic commodity flow (at the metropolitan 

area level) that incorporates Commodity Flow Survey data 

as well as other data sources (e.g., U.S. carload waybill for 

rail traffic, inland waterborne commerce statistics). The 

FAF2 database incorporates these additional sources using 

a modeling approach; approximately 40 percent of the 

total commodity tonnage depicted in the FAF2 domestic 

commodity table is outside the scope of the Commodity 

Flow Survey and is thus supplied by the modeling effort 

(USDOT-FHA 2006c). This adjustment is intended to create 

consistent nationwide coverage. Nonetheless, it is important 

to recognize that many of the reported tonnages are modelbased estimates, and the degree of error in these estimates 

is not reported. Commodities are categorized in one of 43 

categories (from the Standard Classification of Transported 

Goods), which is comparable to the level of detail for metroto-metro flows in the Commodity Flow Survey.

Spatially Explicit Transportation Networks

Spatially explicit networks are regularly used for transportation planning and summary analysis. For example, the 

Federal Highway Administration’s FAF1 database includes a 

Highway Truck Volume and Capacity component based on 

1998 data, with a similar component for the FAF2 database 

due in late 2006 (USDOT-FHA 2002a). The Highway Truck 

Volume and Capacity component consists of a database 

table of freight truck traffic flows along major U.S. roadways, which can be linked to corresponding features in a 

vector GIS road layer. Daily estimates of the number of 

truck trips along more than 96,000 road segments are calculated programmatically from domestic commodity flow tonnage data (Tang 2006). This permits ranking of road routes 

most likely to carry trucked freight and the identification of 

major arteries of interstate freight movement. The network 

does not include commodity categories, nor have nodes in 

the network been linked to corresponding populated places 

or other meaningful locations, although this can generally 

be addressed using ancillary data sets.

The National Transportation Atlas Databases (NTAD) 

are a collection of GIS data sets compiled and edited by the 

Bureau of Transportation Statistics. The most recent version 

of the NTAD collection was issued in 2005 (USDOT-BTS 
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2005) and contains two arc-node networked data sets: the 

National Highway Planning Network and the National 

Rail Network. The National Highway Planning Network 

contains greater than 176,000 arcs and 139,000 nodes, 

whereas the National Rail Network contains greater than 

167,000 arcs and 130,000 nodes. Although neither data set 

contains explicit information on commodity flow along arcs 

in the network, the nodes are linked to populated places 

and other meaningful locations (e.g., airports, railyards). In 

short, these networked data sets depict routes at a fine scale 

but may need to be linked to commodity information from 

some other data source to make them useful for assessing 

broad-scale patterns of forest pest risk.

The U.S. Army Corps of Engineers Navigation Data 

Center publishes annual Waterborne Commerce Statistics of 

the U.S. data sets (USACE-NDC 2006b). The annual data 

sets, spanning the years 1993 to 2004, are broken into four 

reporting regions: Atlantic, Pacific, Great Lakes, and Mississippi Valley/gulf coast. This is probably the most complete of the networked data sets, providing information on 

the tonnages of commodities moving along all major inland 

rivers. Each record in an annual data set has codes for commodity type, direction of traffic (e.g., inbound receiving or 

outbound shipping), and the specific waterway, which can 

then be linked to corresponding GIS data. The 146-category 

commodity coding system is the most elaborate of any of 

the domestic or international commodity data sets (e.g., 

five categories for wood products, including fuel wood, 

wood chips, wood in the rough, lumber, and forest products 

not elsewhere classified). Nevertheless, it is important to 

consider that the vast majority of domestic commodity 

movement is by truck (with rail a distant second), so the 

networked waterway data may have limited utility for 

analyzing potential forest pest pathways.

Business Data

Business data sets relevant to the topic of human-mediated 

pathways fall into two general categories: those that 

describe general geographic patterns of business activity 

and those that can be used to pinpoint and analyze individual businesses. The former can be used to describe regional 

trends in business activities that may increase or decrease 

forest pest risk. For example, it may be possible to identify 

geographic areas in the United States with high levels of 

retail nursery sales, which may be relevant to the risk of 

introducing a forest pathogen. Unfortunately, such data 

sets cannot identify specific businesses that may represent 

key nodes in pest dispersal pathways. It may be useful to 

identify those key nodes as well as elucidate meaningful 

spatial patterns in terms of certain of their characteristics 

(e.g., business location size or sales volume). For example, 

a large number of retail nurseries in the Eastern United 

States received P. ramorum-infected plants from west coast 

nurseries during the last few years. Because natural forests 

around infected nurseries face an elevated risk of potential 

infection, as do the residential landscapes served by those 

nurseries, the mapping of specific nursery locations may 

help determine the best placement of detection survey plots. 

This is possible using commercially available business 

location databases.

Industry/Business Classification Systems

There are two primary industry/business classification systems. The North American Industrial Classification System 

(NAICS) has become the accepted system for the United 

States, Canada, and Mexico, replacing the older Standard 

Industrial Classification (SIC) codes (USCB 2006c). Most of 

the business data sources discussed in subsequent sections 

use NAICS codes (or both), and cross-walk tables between 

SIC and NAICS codes are available (USCB 2006c). The 

NAICS is a hierarchical classification system built upon 

Table 1—Example of the North American Industrial 

Classification System 

NAICS 2002 code and brief description

11 Agriculture, forestry, fishing, and hunting

111 Crop production

1114 Greenhouse, nursery, and floriculture production 

11141 Food crops grown under cover

111411 Mushroom production

111419 Other food crops grown under cover

11142 Nursery and floriculture production

111421 Nursery and tree production

111422 Floriculture production

An example from the 2002 NAICS coding system, showing the hierarchy 

of sub-classes related to nursery production within the primary business 

sector Agriculture, Forestry, Fishing and Hunting (NAICS Code 11).
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20 primary business sectors (USCB 2006c). The system 

is regularly updated on the same time step as the U.S. 

Economic Census (described below). The 1997 and 2002 

versions of the NAICS have been released, and the 2007 

version is in development. A 1,400-page manual describing 

the classes in the 2002 system is available from the NAICS 

Web site. Table 1 shows a sample of the hierarchy related 

to nursery production. Notably, the finest level of detail 

separates nursery and tree production from floriculture 

production. Whereas this is more specific than the commodity coding systems described previously, it is still broad in 

some regards (e.g., not separating nurseries that specialize 

in trees from those that specialize in shrubs).

General Business Data

The U.S. Census Bureau is a good source of general 

business patterns data. In particular, the bureau releases 

an economic census every 5 years, with the most recent 

version compiled using 2002 data (USCB 2006a). The data 

are summarized for geographic units as fine-scale as ZIP 

code areas. Types of information that are available include 

per-area dollar values of sales, receipts, or shipments for a 

particular industry class, as well as per-area counts of the 

number of businesses within a particular industry class. 

By linking the economic census data to GIS data layers, 

it is possible to map region-to-region patterns in business 

activities, and—using information from a previous economic census—to highlight short-term growth or decline 

in business sectors of interest. Economic census data sets 

may be downloaded via the Census Bureau’s American 

Fact Finder file transfer protocol interface (USCB 2006b) or 

are available on DVD-ROMs purchasable by subscription 

(USCB 2006a).

With respect to the agricultural business sector, the 

U.S. Department of Agriculture, National Agricultural 

Statistics Service (NASS) issues summary reports on 

production of a wide variety of crops, including nursery 

plants (see the NASS Web site for access to a wide range of 

reports, http://www.nass.usda.gov/index.asp). Data tables 

in these summary reports are typically available as ASCII 

files. Similar to the Commodity Flow Survey, the attribute 

resolution for a given data table depends on the degree of 

geographic detail. For example, regarding P. ramorum, one 

might be interested in nursery stock production coming out 

of California, Oregon, and Washington. A national-scale 

NASS report on nursery crops lists the total number of 

producers and sales quantities for 18 States (including the 

above three States), broken down into specific nursery stock 

type (e.g., broadleaf evergreens, a category that includes 

the most significant hosts of P. ramorum) (USDA-NASS 

2004b). However, the report only details operations with 

greater than $10,000 in annual sales. In contrast, regularly 

issued State-level Census of Agriculture reports list the 

number of all farms that normally have greater than $1,000 

in sales, as well as the total greenhouse and open-air 

acreages per county for broader crop categories such as 

all nursery stock (e.g., USDA-NASS 2004a). Notably, if a 

county has just a few operations in a given crop category, 

greenhouse and open acreages are withheld to protect 

farmowner privacy.

Business Location Databases

ReferenceUSA is a subscription-based online business database (InfoUSA 2006). Users have access to more than 13 

million detailed records on business locations in the United 

States and Canada. The data were compiled from phone 

books, business registries, and phone call verifications of 

individual businesses. Records include fields describing a 

business’s primary industry classification, as well as other 

industry classes with which it might be associated. Each 

record has been assigned geographic coordinates. Attribute 

fields that might be used to sort records include size (square 

footage), estimated sales volume range, and number of 

employees. The database may be searched and filtered by 

location (city, State, ZIP code, and others) or industrial 

classification codes. Selected records may be downloaded as 

ASCII files and compiled in statistical software or mapped 

in GIS software.

Other companies (e.g., Dun & Bradstreet) have 

developed similar packages, but none are free. Many 

libraries and government agencies have subscriptions to 

ReferenceUSA, so at least limited access is available to 

many users. A related issue that might be of concern to a 

user is the limitation on the number of records that may 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

be downloaded at one time (the size limitation depends on 

the paid subscription price). A user must also remember 

the sometimes-broad scope of industry classifications, and 

that an individual business may define itself by a number of 

industry classifications.

Land Use/Landcover Data Sets

The last data category has a loose definition because it 

involves a wide assortment of data formats and sources. 

It is beyond the scope of this article to describe all land 

use/landcover information that may depict some aspect of 

human activity relevant to pest spread. Instead, we will 

highlight what we see as two main uses of these data. The 

first purpose is to map areas that have appropriate conditions to sustain forest pests and that, ultimately, may permit 

them to spread into naturally forested landscapes. These 

data sets depict areas where a pest is likely to invade a 

region and have access to at least a few host individuals. A 

chief example is spatial data depicting the extent and nature 

of the forest-urban interface in a given region. The second 

purpose is to find areas of land use/landcover change owing 

to human development. The fragmentation or disturbance 

of natural forests owing to urbanization leads to more 

chances for forest pest introduction or establishment or both 

(Chornesky and others 2005). For managing pest risk, it 

would be preferable to pinpoint areas currently undergoing 

such changes or facing such changes in the near future. This 

is a significant challenge, as we discuss below, owing to the 

timeframe at which most land use/landcover data sets are 

currently collected and published.

Forest-Developed Interface Data

Landcover maps derived from satellite imagery—such as 

the U.S. National Land Cover Data (NLCD) (Vogelmann 

and others 2001)—may be analyzed using moving-window 

functions (e.g., Riitters and others 2002) to highlight edges 

between forest and developed landcover types. This may 

be a straightforward way of identifying interface areas 

with a high level of pest risk, but it may be even easier to 

take existing data products and apply them in a forest pest 

context. For instance, the SILVIS Lab at the University of 

Wisconsin has created GIS coverages of wildland-urban 

interface (WUI) for the conterminous United States. The 

coverages, composed of U.S. Census blocks, distinguish 

between WUI (where a block contains less than 50 percent 

wildland vegetation but is within 2.4 km of an area at least 

5 km2 in size with greater than 75 percent wildland vegetation) and intermix (where wildland vegetation occupies 

greater than 50 percent of a block). SILVIS Lab researchers 

combined housing density information, landcover percentages derived from 1992 NLCD, and forest proximity 

measurements to classify blocks into 1 of 13 classes of 

WUI or intermix. For each block, they calculated the degree 

of interface/intermix based on both 1990 and 2000 housing 

density estimates, allowing spatial analyses of changes 

through time (UW-SILVIS 2006). Although created with 

a primary focus on fire risk, the WUI data are easily 

translatable to forest pest risk (Radeloff and others 2005). 

High levels of intermix may be of particular interest; such 

intermingling between wildland and residential land may 

significantly reduce the functional distance between a forest 

pest and its host species.

Contemporary Land Use/Landcover Change

The wall-to-wall land use/landcover classification provided 

by the NLCD is sufficient for many analyses. However, 

remote sensing-derived landcover data sets present some 

difficulties. In particular, the most recently available complete NLCD coverage dates from 1992, although the 2001 

NLCD version is approaching completion. This is because 

the processing of satellite imagery into classified maps, and 

subsequent accuracy assessment, is a costly, labor-intensive 

process. Unfortunately, this means that there will typically 

be a long time step between subsequent wall-to-wall land 

use/landcover data sets. This fails to address short-term or 

recent land use/landcover changes, both of which may be of 

great interest when assessing forest pest pathways.

As an alternative, it may be possible to use vector GIS 

data sets that are more regularly updated to depict more 

recent land use/landcover changes. For instance, many GIS 

users have access to fine-scale (1:100,000-scale or greater 

detail) roads data through the Enviromental Systems 

Resourch Institute (ESRI) Streetmap extension. These 

roads data are supplemented versions of U.S. Census Tiger 
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data (ESRI 2005), with new versions of the data issued 

almost annually. By creating a nationwide grid of cells and 

intersecting a roads data set with this grid, it is possible to 

derive an estimate of road density (i.e., the total road length 

per grid cell). This process can be easily repeated for data 

sets of any given year, enabling analysis of road density 

changes. Road density change can serve as an indirect surrogate for areas of general development (i.e., new roads lead 

to nearby residential and/or commercial construction). This 

may be an efficient method to highlight areas of increased 

disturbance owing to construction and areas of potential 

interaction between pests and hosts.

Application Examples

In the following sections, we detail ways that data for representing human-mediated pathways of forest pest spread 

have been or may be used for analytical purposes. All of 

these examples describe ongoing projects, and we would 

point out that validation or other assessment procedures 

have not been completed. Although that is an ultimate goal, 

for now we wish to simply illustrate some of the capabilities of the described data sets and, thus, encourage users to 

integrate them in additional forest pest risk mapping efforts.

Sirex Woodwasp and Foreign Marine Cargo 

Statistics

The sirex woodwasp primarily attacks Pinus spp. but, 

occasionally, will attack other conifers in the Pinaceae 

family. It is a native of Europe, Asia, and northern Africa, 

where it is rarely a pest. However, it has been accidentally 

introduced to many parts of the world, resulting in outbreaks in pine plantation forests in Australia, New Zealand, 

South Africa, and parts of South America (Hoebeke and 

others 2005). Sirex noctilio was intercepted at U.S. ports of 

entry more than 100 times between 1985 and 2000 (Hoebeke and others 2005). In early 2005, the Forest Service’s 

FHTET organized a team to map the risk of S. noctilio

introduction and establishment in the United States. An 

important component of the S. noctilio introduction risk 

map was a ranking of U.S. port locations based on their 

relative likelihood of receiving cargo carrying the pest. 

This ranking was constructed using the U.S. Army Corps 

of Engineers foreign marine cargo statistics database. The 

database was queried to select records (from all years available at the time, 1997 to 2003) associated with countries 

with a known or accepted S. noctilio presence as well as 

with commodities that might harbor stowaways of this pest. 

The commodity categories were selected by consulting 

APHIS PIN data to identify commodities typically linked 

to the pest; commonly, these were goods that are associated 

with wood packaging materials. The resulting map of 151 

marine ports (Figure 1) suggests that numerous ports in the 

Eastern United States face a substantial risk of accidental S. 

noctilio introduction. In particular, several high-risk points 

of entry in the Southeastern United States are close to large 

amounts of potential host forest. Notably, a single specimen 

of S. noctilio was found in a trap a short distance from the 

port of Fulton, New York, about the time this project was 

undertaken. Subsequent delimiting surveys during summer 

2005 detected the pest more than 30 mi inland from the 

port, and 2006 surveys have detected the pest as far south 

as Pennsylvania. Surveys for S. noctilio in other parts of the 

United States are ongoing, with the survey design driven by 

the risk maps developed for this project.

Wood Product Flows From Canada and Mexico 

Into the United States

Cargo statistics data can also be used to explore risks 

associated with general pathways rather than specific pests. 

As an example, in 1998 the Forest Service issued a pest risk 

assessment regarding the importation of unprocessed Pinus

and Abies logs from Mexico (Tkacz and others 1998). The 

assessment noted substantial risks of forest pest introduction 

through wood flows from Mexico. This issue was addressed 

in recent Federal regulations, which now require treatment 

of raw wood products from Mexico (USDA-APHIS 2004a). 

Currently, wood may travel from Canada under a general 

permit, but with recent restrictions imposed on pine shoot 

beetle host material (USDA-APHIS 2004b). We used FAF2

trans-border cargo statistics (from 2002) to map the regional 

flow of wood products into the United States via land transport from Mexico and Canada (Figure 2). Notably, although 

“wood products” and “logs and other wood in the rough” 

are separate commodity categories in the FAF2 database, 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 1—Potential introduction risk of Sirex noctilio at U.S. marine ports of entry. Map of U.S. marine ports 

handling commodities from countries where S. noctilio is known or believed to be established. Port locations 

are scaled in size according to the total tonnage (U.S. short tons) of high-risk commodities received from 1997 

to 2003. The background is the distribution of susceptible forest (primarily Pinus spp.) in the conterminous 

United States, generated through spatial interpolation of Forest Inventory and Analysis phase 2 plot data.

they are not distinguished in the border crossing data table. 

In this simple analysis, Mexico accounts for a very small 

percentage of total trans-border wood product shipments; 

however, most States, particularly Texas and California, 

do receive some volume of Mexican wood products. Most 

of the Canadian wood products are destined for States just 

across the border, but some southern metropolitan areas 

such as Atlanta, Los Angeles, and Houston also receive a 

large quantity of wood products from Canada.

Truck Traffic Data and Risk of S. noctilio Spread 

Beyond Points of Entry

The example in “Sirex Woodwasp and Foreign Marine 

Cargo Statistics” described a process for ranking U.S. 

marine ports of entry according to their relative risk of 

importing cargo infested by S. noctilio. This example builds 

on that by using spatially explicit transportation network 

data to predict likely locations of S. noctilio spread beyond 

those initial ports of entry (Figure 3). We employed the 

FAF1

 Highway Truck Volume and Capacity data to classify U.S. roadway line segments into five classes based 

on daily truck traffic (roughly 8,000 of the > 96,000 line 

segments in the network reported no measurable freight 

traffic). We then defined a set of 96 cities (i.e., urban area 

polygons defined by the U.S. Census) as ports by tracing 

the highest-traffic route leaving each marine port of entry 

point and identifying the first census-designated city along 

that route. If there were two equally high-traffic routes 

leaving the port, we selected the first cities encountered 

along both routes. Naturally, some marine points of entry 

were located within major urban areas, but a number were 

geographically distinct, making the tracing of high-traffic 

routes particularly important. To create a set of potential 

markets, we intersected the Highway Truck Volume and 

Capacity data with a coverage depicting census-delineated 

populated areas. If a polygon in this coverage intersected a 
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line segment with a measurable level of truck traffic, then it 

was incorporated in the final markets layer, which included 

2,921 urbanized areas. The port and market layers generated 

by these analyses, as well as the marine ports data described 

in “Sirex Woodwasp and Foreign Marine Cargo Statistics,” 

became the primary pieces of the national-scale risk map 

for S. noctilio introduction. As was noted earlier, this map is 

being used to guide S. noctilio detection surveys throughout 

the country.

Network Model of Nursery Stock Flows

First recognized in the mid-1990s, P. ramorum has infected 

live and red oaks in coastal forests in California and 

Oregon, with mortality rates of approximately 40 percent 

(Garbelotto and others 2001, Rizzo and Garbelotto 2003). 

More critically, the pathogen affects dozens of shrub host 

species. Many of these shrubs persist after infection and 

yield large quantities of aerially dispersed spores (Davidson 

and others 2002, Tooley and others 2004). A number of 

these shrubs (e.g., rhododendrons, azaleas, camellias) are 

popular landscaping plants that are often sold as nursery 

stock (Garbelotto and others 2001, Tooley and others 2004). 

In the past few years, wholesale nurseries in California, 

Oregon, and Washington unknowingly shipped hundreds 

of infected plants to retail outlets in 39 States (Stokstad 

2004). Although surveys have not detected P. ramorum in 

natural forests outside the west coast, there is some concern 

that this pathway may lead to spread of the pathogen from 

infected nursery stock planted in home landscapes to susceptible forest, of which there is a substantial acreage in the 

Eastern United States. As already noted, nursery stock is 

not well represented in the Commodity Flow Survey, so we 

have adopted an integrated modeling approach to examine 

the issue of potential P. ramorum dispersal via nursery 

stock. By combining two networked data sets (the FAF1

Highway Truck Volume and Capacity data and the National 

Highway Planning Network), we created a nationwide, arcnode GIS infrastructure for dynamic modeling of nursery 

stock flows (see Figure 4 for regional example). We labeled 

Figure 2—Trans-border wood product flows to United States from Mexico and Canada. Map of 114 metropolitan 

areas or rest-of-State statistical regions in the conterminous United States. The bar graphs show 2002 wood product 

imports from Mexico and Canada to each region, reported in thousands of tons.
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nodes as entry, destination, or transit depending on their 

roles in the model: nodes in proximity to U.S. wholesale or 

production nurseries are designated as entry nodes, destination nodes correspond to populated places in the United 

States, and transit nodes are unweighted link points in the 

network. We have adopted a Bayesian approach to model 

probabilities of transmission of P. ramorum from entry to 

destination nodes within the network. For destination nodes, 

probability of infection is based on the number of plants 

received from entry nodes as well as the probability that 

some of the received plants are infected with P. ramorum. 

The number of plants shipped from any entry node is based 

Figure 3—Port and market cities at risk for spread of Sirex noctilio. Figure 3a is a map of U.S. 

urban areas that have a high risk of S. noctilio spread. These areas are either port cities or likely 

markets for imports associated with the pests. Figure 3b is a closeup view of the Washington, 

DC-New York City corridor, showing roadway routes leaving marine ports of entry, ranked by 

their volume of truck traffic.
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on the number of nurseries in proximity and the likely 

volume of outgoing nursery stock, calculated from the U.S. 

Commodity Flow Survey, the FAF2 database, and NASS 

reports on county-level nursery production (described in 

more detail in “General Business Data”). The probability 

that some plants received at a given destination node are 

infected with P. ramorum is calculated from infection rates 

observed in trace forward data (i.e., inspections of plants 

shipped from known infected production nurseries to retail 

outlets), and trace back data (i.e., determination of the 

source of plants found infected at retail outlets) collected by 

APHIS. Probabilities are also modified by factors affecting 

how much nursery stock is sold and planted in a given area 

(e.g., U.S. Census population). We believe the network modeling framework, when completed, will be general enough 

that it should be applicable for a variety of commodity flow 

analyses relevant to forest pest risk.

P. ramorum and Production Nurseries

Production nurseries present a higher risk of infecting 

nearby forests than retail nursery outlets due to several factors: large quantities of stock grown onsite; artificially high 

moisture levels conducive to the pathogen; and disproportionately large amounts of interface with natural forests. 

We queried the Reference USA database for all businesses 

with nursery and tree production as their primary industry 

classification. This query yielded more than 6,500 business 

locations nationwide. We ranked the selected businesses 

into four classes based on their total square footage (from 

the Reference USA database) and mapped them in relation 

to overstory host species distribution, represented in the 

example by a surface depicting the forest percentage of red 

oak, live oak, or both calculated from Forest Inventory and 

Analysis data. Figure 5 shows a subset of the nationwide 

coverage that includes the States of North Carolina and 

Figure 4—Linear network modeling of Phytophthora ramorum spread via nursery stock flows. Regional closeup 

(California and the Southwestern United States) of a national arc-node network model for predicting potential longdistance dispersal of the P. ramorum pathogen on infected nursery stock. The model links wholesale and production 

nurseries on the west coast to cities and towns throughout the region and the rest of the country. Input nodes are 

ranked and color-coded by the number of wholesale or production nurseries or both in close proximity. Destination 

nodes are ranked and color-coded by U.S. Census population.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

South Carolina. Larger production nurseries seem to be 

associated with areas of moderate oak distribution. Whereas 

this may just be a geographic anomaly, it indicates the 

kinds of analyses that are possible using specific business 

location data from ReferenceUSA or similar resource. The 

production nurseries data generated from ReferenceUSA 

will be incorporated in a refined version of a preliminary 

P. ramorum risk map developed by the U.S. Department of 

Agriculture Forest Service (USDA-FS 2004).

Wildland-Urban Interface Data and Areas at Risk 

for P. ramorum Introduction

We used WUI coverages developed by the SILVIS Lab to 

examine spatial patterns of potential residential-to-forest 

landscape spread risk for P. ramorum. We focused our 

analysis on an 11-State region encompassing the areas in 

the Eastern United States that were identified as high risk 

for P. ramorum introduction or establishment or both in the 

Forest Service’s preliminary risk map (USDA-FS 2004). 

We combined the individual State coverages into a single 

map and converted this map to a grid format (1-km2

 cells). 

We then reclassified the 13 original WUI coverage classes 

into 5 classes: little or no intermix, low intermix, moderate 

intermix, high intermix, and very high intermix. (Areas 

labeled as “interface” in the original WUI coverages were 

classified as somewhat lower risk than intermix areas—see 

“Forest-Developed Interface Data”). We examined changes 

in the intermix pattern between 1990 and 2000. Our reclassified maps (Figure 6) depict an expansion of low intermix 

into new areas at the rural fringes of metropolitan areas, 

as well as a subtle, but significant, increase in intermix in 

many suburban areas, particularly along the interstate highway corridor from Atlanta to northern Virginia. Whereas 

all areas with intermix face some P. ramorum risk, areas 

with greater than a one-class increase between 1990 and 

2000 may be of most immediate concern; such areas appear 

to be widely scattered across the entire 11-State region. As 

with the production nurseries data, some form of the WUI 

Figure 5—Geographic relationship between production nurseries and Phytophthora ramorum host distribution in 

North Carolina and South Carolina. Map of production nurseries in North Carolina and South Carolina ranked by 

size class (in square footage). Backdrop map shows overstory red oak or live oak distribution or both.
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GENERAL TECHNICAL REPORT PNW-GTR-802

data will be utilized for the refined version of the Forest 

Service’s national P. ramorum risk map (USDA-FS 2004).

Road Density Change and Increased 

P. ramorum Risk

A major concern for the refined P. ramorum risk map is 

identification of areas that may receive large quantities 

of nursery plant stock that are potentially infected by the 

pathogen. As noted previously, areas of new residential or 

commercial development or both are strong candidates as 

potential introduction sites for P. ramorum. As an indirect 

measure of areas recently or currently undergoing such 

development, we calculated road densities (total length of all 

road segments within a cell) for a nationwide grid of 4-km2

cells using ESRI roads data for 1995, 2003, and 2004. We 

then calculated road density changes by subtracting the 

1995 and 2003 road densities from the 2004 road density 

for each cell and mapped these changes in relationship to 

U.S. urban areas. Figure 7 shows a subset of the national 

maps, focusing on the Research Triangle region of North 

Carolina. For this subset, the areas with the greatest road 

density change between 1995 and 2004 do correspond, at 

least anecdotally, with areas that underwent substantial 

urbanization during this time period. The 2003 through 

2004 difference image also seems to accurately depict areas 

of recent construction, although the image may highlight 

major roadwork and not necessarily current residential or 

commercial expansion, both of which are likely to occur in 

these areas in the future.

Final Considerations

One of our goals with this synthesis was to provide 

examples that would stimulate discussion and the interest of 

other researchers in looking for ways to represent humanmediated pathways in forest pest risk mapping efforts. 

Although there is widespread awareness that humans 

contribute to the spread of nonnative pests, there has not 

been much attention paid to this issue in a spatial analysis 

Figure 6—Wildland-urban intermix in an 11-State region, 1990 and 2000. Raster maps (1 km2 resolution) for 1990 (Figure 6a) 

and 2000 (Figure 6b), depicting the distribution of five wildland-urban intermix levels (from little to very high intermix) across 

11 States in the Southeastern and mid-Atlantic regions.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

context. We hope this will change, but we would also like 

to note some considerations for researchers who decide to 

perform similar investigations.

Utility of Above-Described Data Sets

There are basically three overriding issues with respect 

to these data sets: attribute resolution, spatial scale, and 

imperfect representation of the pathway-related variable 

of interest. Attribute resolution refers to the level at which 

a variable of interest is aggregated. As noted, commodity 

classifications in the foreign and domestic cargo data are 

usually quite broad, so it is difficult to track many specific 

commodities of interest (e.g., nursery plants). Rather, users 

must do one of two things. One way is to work with the 

tonnage values of the broader commodity category and 

assume that the spatial flow pattern of this broader category 

mirrors the pattern of the specific commodity of interest,

so the relative ranking of different locations remains 

consistent. The alternative is to use ancillary data or some 

other means to estimate what percentage of the tonnage 

of the broader category belongs to the specific commodity 

of interest and how this percentage varies spatially across 

the area of analysis. The industry classification in the 

business location databases such as ReferenceUSA is, at 

times, similarly broad (e.g., we can identify nurseries, but 

not exactly which plants they grow or sell). This is a caveat 

that business location databases may be good for examining 

spatial patterns and trends, but should be used sparingly, if 

at all, for analyzing individual locations.

Related to the issue of attribute resolution is spatial 

scale, or the size of the geographic units at which the data 

are summarized. We have noted how the Commodity 

Flow Survey summarizes domestic commodity flow data 

at multiple scales, but the finest scale is at the level of 

Figure 7—Use of road density change data to identify areas at risk for Phytophthora ramorum introduction. Changes 

in road density (total length of all road segments) for the Research Triangle region of North Carolina between 1995 

and 2004 and between 2003 and 2004. Minimum resolution of individual cells is 4 km2

. Cells are color-coded 

according to the degree of change (cells with minimal change are omitted). This information is overlaid on a percentage tree cover map developed from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery.
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GENERAL TECHNICAL REPORT PNW-GTR-802

metropolitan area. The Freight Analysis Framework 

databases are similarly scaled at the metropolitan area level. 

This is not necessarily problematic, although it should be 

considered when choosing a working scale for a risk map 

that incorporates multiple pathway data layers as well as 

climate and host distribution information. In cases where 

the resolution of attribute information is tied to the output 

spatial scale (e.g., the Commodity Flow Survey database), 

the user should evaluate whether more specific attribute 

information is worth the tradeoff in geographic specificity. 

Naturally, this may depend on the pest of interest.

Finally, many of the data sets are imperfect, surrogate 

representations of a particular risk of interest. This is especially true of the land use/landcover data. As an example, 

change in road density may generally suggest where there 

are areas of increased residential or commercial development, and this, in turn, may suggest where nursery plants 

infected with a pathogen or infested by an insect are likely 

to be planted. These may be reasonable assumptions for 

portraying the variability of relative risk across a landscape. 

However, it is important to consider the potential uncertainty that comes with using indirect measures.

Availability and Accessibility

Many of the data sets described here are free or can be 

purchased for a small fee. Most may be downloaded directly 

through the Internet, although some must be ordered on disk 

(e.g., the Commodity Flow Survey and the National Transportation Atlas Databases, both of which will be shipped 

to the requesting individual at the agency’s expense). Some 

of the statistical data sets are already in database format 

(e.g., the FAF databases, which are in Microsoft Access), 

although most are comma-delimited data that may be 

assembled into a database by the user. In terms of data 

accessibility, there are two notable exceptions. First, the 

business location databases typically require a subscription 

(see “Business Location Databases”). Also, the roads data 

use to assess contemporary land use/landcover change are 

only available to ESRI ArcGIS software users with a license 

for the optional StreetMap extension.

The timespan of the available data varies widely owing 

to both when the data are collected, and how often their 

parent agencies analyze them and report the results. The 

current pattern suggests that many of the commodity flow 

data sets will be updated roughly every 5 years, although 

this likely depends on how much analysis time is required. 

For data sets that have previous versions, there have often 

been changes in how the data were processed or summarized between versions. We would advise potential users 

to carefully note these differences if combining multiple 

time steps of one of these data sets. More generally, it is 

important to consider differences in temporal scale, as well 

as spatial scale, if working with multiple data sources.

If initiating an analysis similar to what has been 

described in this synthesis, a few Internet sites offer a good 

place to start when searching for additional data sets:

• U.S. Army Corps of Engineers, Navigation Data 

Center: http://www.iwr.usace.army.mil/ndc/

index.htm

• U.S. DOT Bureau of Transportation Statistics, 

TranStats Data Warehouse: http://www.transtats.bts.gov/

• University of Wisconsin-Madison, SILVIS 

Laboratory: http://www.silvis.forest.wisc.edu/

• U.S. Census Bureau, Business and Industry 

page: http://www.census.gov/econ/www/index.

html

• U.S. Census Bureau, Geography page: http://

www.census.gov/geo/www/index.html

Areas of Future Research

We will continue to search for additional data sets to represent human-mediated pathways in forest pest risk maps, 

but such a large data mining task would clearly benefit from 

the contributions of additional researchers and analysts. 

Some areas of significant data need include urban forest 

spatial distribution and condition, as well as recreational or 

informal pathways (e.g., movement of firewood) by which 

humans may spread forest pests. In addition, some of the 

data sets we have identified in our pest-focused research 

may be useful for examining other forest threats. For 

instance, we have only discussed insect and pathogen forest 

pests, but many of the same pathways are likely applicable 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

for invasive plants. There are strong possibilities for 

researching the cross-applicability of these data sets.

A major challenge for forest pest risk mapping is the 

integration of multiple data sources with different attribute, 

spatial, and temporal scales in a single model for a given 

pest. This includes the combination of disparate data on 

host species, environmental factors, and pathways of 

spread. Basically, forest pest risk mapping has many of the 

same problems as other analyses that involve ranking or 

classification with large, complex data sets. First, it can be 

difficult to select the most significant variables out of a large 

suite of model inputs. Second, even if the most important 

variables can be selected, it is also difficult to define appropriate threshold values for ranking risk. It is our experience 

that these thresholds are often determined ad hoc or through 

expert opinion. Although this can be an adequate approach, 

additional research on quantitative techniques for building 

robust, parsimonious risk models would be fruitful (e.g., 

Downing and others, this volume). Moreover, there may 

be opportunities to combine quantitative risk assessment 

with quantitative analysis of different management options, 

revealing how they might affect the distribution of risk 

(Woodbury and Weinstein, this volume).

Spatial and thematic accuracy, the propagation of error, 

and the characterization of uncertainty in spatial data sets—

as well as how these characteristics may influence the forest 

pest risk mapping process–are often downplayed in the need 

to quickly derive a useful risk map product for forest planners. The issues have really never been considered for the 

human-mediated pathways data described in this synthesis, 

which have only recently been adapted to a risk mapping 

context. Research opportunities abound in terms of developing quantitative techniques to address some of these issues 

in the forest pest risk-assessment process.

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

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

Homans

Shefali V. Mehta, University of Minnesota, Department 

of Applied Economics, Saint Paul, MN 55108; Robert G. 

Haight, research forester, USDA Forest Service, Northern 

Research Station, Saint Paul, MN 55108; Frances R. 

Homans, professor, University of Minnesota, Department 

of Applied Economics, Saint Paul, MN 55108.

Abstract

Invasive species management is closely entwined with 

the assessment and management of risk that arises from 

the inherently random nature of the invasion process. The 

theory and application of risk management for invasive 

species with an economic perspective is reviewed in this 

synthesis. Invasive species management can be delineated 

into three general categories: exclusion, detection, and 

control. Key ideas and approaches in current literature 

and potential applications of existing theory are presented 

in this synthesis. Economic literature tends to emphasize 

either individual management strategies, such as preventing 

invasive species from entering an ecosystem or controlling 

extant populations. There is also a growing focus on the 

optimal allocation between multiple activities for the same 

species such as between prevention and control. The key 

biological and economic relationships included vary across 

frameworks and objectives. 

The synthesis is organized into sections, which 

cover the salient aspects of invasive species management 

by separating the major veins of economic literature on 

decisionmaking. Section 1: invasive species management 

is discussed and a brief overview of risk management and 

economic theory is provided. Section 2: an overview of the 

key factors causing invasions is presented. Section 3: exclusion activities to prevent introductions is the focus. Section 

4: control activities after the species has been successfully 

introduced are emphasized. Section 5: the tradeoffs between 

multiple management strategies are addressed. Section 6: a 

discussion concludes the synthesis.

Keywords: Decisionmaking, economics, invasive 

species, non-native, risk management.

Introduction: the Role of Risk Management 

in Invasive Species Management

An overview of decisionmaking under risk in invasive 

species management, with an emphasis on the economic 

literature, is provided in this synthesis, and the following topics are discussed: an overview of invasive species 

management; the key factors causing invasions; the exclusion activities aimed at preventing introductions; postintroduction control activities; and tradeoffs between multiple 

management strategies. Scientists have studied the impacts 

of invasive species on nonnative ecosystems for many years. 

Recently, the public has become more aware of the problems associated with invasive species, due largely to greater 

levels of media coverage and public information campaigns 

regarding the issue. Government agencies deal explicitly 

with risk in their ongoing invasive species management 

programs; thus, risk management plays a crucial role in 

these programs. 

The synthesis is organized into sections, which 

cover the salient aspects of invasive species management 

by separating the major veins of economic literature on 

decisionmaking. In section 1, invasive species management 

is discussed, and a brief overview of risk management and 

economic theory is provided. In section 2, an overview of 

the key factors causing invasions is offered. The focus is 

on exclusion activities to prevent introductions in section 

3. Control activities after the species has been successfully introduced are presented in section 4. The trade-offs 

between multiple management strategies are addressed 

in section 5. The synthesis concludes with a discussion in 

section 6.

Owing to the breadth of this risk management, and 

discrepancies among discipline-specific terms, the sections 

below clarify the usage of terminology in this synthesis.

Defining Risk

The word risk has three common definitions: (1) an 

[adverse] event (as in “non-native invasive species are a 

DecisionMaking Under Risk in Invasive Species Management: 

Risk Management Theory and Applications
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GENERAL TECHNICAL REPORT PNW-GTR-802

risk to ecosystems”); (2) the probability that the event will 

occur (as in “the goal of management is to reduce the risk 

of invasive species introductions to new ecosystems”); 

and (3) the probability that an event will occur weighted 

by the consequences of the event (as in “the possibility 

that invasive species will harm oak forests is a substantial 

risk”). In economics, the term is most often used to describe 

situations in which the results of a decision follow some sort 

of probability distribution. The probability distribution may 

be objectively determined, either through a priori reasoning 

(such as the probability that a fair die will show the number 

6) or through repeated experiments. Probabilities may also 

be subjectively determined without clear experimental 

evidence. These are known as beliefs. The term ambiguity 

applies when probabilities are not known with certainty. 

When probabilities are involved, it is possible to define (and 

maximize) objective functions that are weighted by these 

probabilities. We adopt the economics usage of the term 

risk, referring to a situation in which management decisions 

affect outcomes and their probabilities.

There may be cases when it is not possible to assign 

probabilities to outcomes. One example is the case of 

global climate change as discussed by Woodward and 

Bishop (1997). Although it is possible to assemble a panel 

of experts to glean their beliefs about possible dangers, 

and it is possible to delineate the range of possible options, 

Woodward and Bishop argue that it is not reasonable to 

assign probabilities to these options based on the number of 

experts sharing a particular belief. Woodward and Bishop 

call this pure uncertainty, following the distinction as 

defined by Frank Knight in 1921. Others term this ignorance 

(Arrow 1972 and Hurwicz). In these cases, it is not possible 

to define a function that is weighted by probabilities. 

However, the term uncertainty has a number of 

different definitions, including simply: uncertainty arises 

whenever a decision can lead to more than one possible 

consequence (Hammond 1998). This definition includes 

situations such as lotteries where probabilities are wellestablished. In the risk assessment literature, uncertainty 

arises due to the lack of precise knowledge about parameters, models, or scenarios. It can also come from differences among modelers (Linkov and Burmistrov 2003). 

We adopt the usage in the risk analysis literature, where 

uncertainty refers to this lack of precision, and use the term 

pure uncertainty to refer to the case where it is not possible 

to assign probabilities. 

Risk Assessment and Risk Management

Risk analysis consists of risk assessment and risk management activities. Risk assessment is defined as “the systematic, scientific characterization of potential adverse effects 

of human or ecological exposures to hazardous agents or 

activities” (The Presidential Congressional Commission on 

Risk Assessment and Risk Management 1997). Risk assessment informs risk management decisions by supplying this 

characterization of potential outcomes and probabilities. In 

invasive species management, risk assessment informs two 

specific areas: the risk surrounding introductions of new 

species, including vectors, species, and potential damages; 

and the risk associated with existing invasives, including 

the potential spread and damages caused by established 

species (Andersen and others 2004). For example, the 

date of the introduction of a pest, like the Siberian moth 

(Dendrolimus superans sibiricus Tschetverikov) may not be 

known. However, numerous factors can be used to construct 

probabilities to characterize the chances of the Siberian 

moth being introduced at any particular date. These factors 

include the number of pathways that it has, the level of 

interaction between the United States and its native territory, and the introduction success of similar species. 

The frameworks for assessing invasive species risk vary 

substantially (Woodbury and Weinstein, this volume). 

Risk management, as the Presidential Commission 

(1997) states, is “the process of identifying, evaluating, 

selecting, and implementing actions to reduce risk to human 

health and to ecosystems.” Risk management relies heavily 

on underlying risk assessments to establish the potential for 

adverse events occurring as a consequence of a particular 

action. In addition, risk management must account for 

resource, social, ethical, political, and legal constraints. In 

this synthesis, with our focus on the economic literature, we 

pay particular attention to how resource constraints guide 

risk management decisions. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

A (Brief) Primer in Economic Theory

Some common economic concepts and terms used 

throughout the review are briefly covered in this section. 

Welfare economics focuses on the implications of alternative resource allocation methods on social welfare, both 

in market and nonmarket settings. One criterion used to 

judge whether an outcome is efficient is Pareto optimality; 

a Pareto optimal allocation is one in which no one can be 

made better off without making someone else worse off. A 

competitive equilibrium will be Pareto optimal unless there 

are market failures. One example of a market failure is an 

externality, where a consumer or producer generates costs 

that they do not bear themselves. Invasive species represent 

an externality in that accidental introductions of invasives 

impose a cost to society and can occur as a consequence of 

consumption or production activities. Therefore, one role 

of management is to align individual incentives with social 

goals—to internalize external costs—so that a Pareto-optimal allocation will result. One possible tool that can be used 

to align incentives is a tax on the externality, calculated as 

the difference between private cost and social cost at the 

optimum. This is known as a Pigovian tax. Another tool is a 

system of tradable permits in which an agency sets the total 

allowable level of the externality, but the allocation of that 

level emerges out of a permit market. Both of these have 

been suggested as ways to handle the invasive species that 

are introduced as a byproduct of economic activity.

When situations are risky, it may not be possible to 

guarantee a particular outcome, but it may be possible 

to choose among alternative probability distributions by 

choosing the level of conditioning variables. Economists 

have developed theories of rational choice that are appropriate in risky situations, including the Expected Utility theory 

of von Neumann and Morgenstern (1944) for objective 

probabilities and the Subjective Expected Utility Theory 

of Savage (1954) for subjective probabilities. For example, 

it may be possible to reduce the probability of species 

introductions by altering trade levels. Expected utility 

theory is one framework that can guide these choices. The 

objective function consists of the expected level of utility, 

where utility represents an individual’s satisfaction derived 

from their preferences for consuming or experiencing goods 

and services. Utility functions, or numerical representations of individual preferences, can account for costs and 

benefits that accrue under unknown future events. The 

vonNeumann-Morgenstern utility function offers one characterization of an individual’s preferences over all potential 

outcomes. Expected utility theory essentially states that 

such a characterization exists if an individual's preferences 

conform to certain axioms. Such representations allow for 

identification of optimal behavior that maximizes expected 

utility. They also provide a basis to compare different 

risk preferences, or attitudes toward risky situations. For 

example, an individual may be risk-averse (i.e., he or she 

prefers a situation with little or no risk to a more risky 

situation even if the expected outcome in the risky situation 

is higher). 

When probabilities cannot be assigned to possible 

outcomes—situations of Knightian pure uncertainty—it 

is possible to use other criteria, such as maxi-min (choose 

the course of action that leads to the best case when the 

worst state of nature occurs) or mini max-regret (choose 

the course of action that leads to the lowest possible regret, 

Loomes and Sugden 1982). Ciriacy-Wantrup (1964) promoted the Safe Minimum Standard criterion, which would 

suggest that irreversible losses should be avoided. This is 

echoed in the Precautionary Principle of Perrings (1991). 

These criteria can be justified as rational in situations where 

possible future outcomes are knowable, but the probabilities 

of those outcomes are not.

The theme of optimization over time is prominent in 

the area of natural resource economics, and the balance of 

the productivity of natural assets with that of other assets 

typically characterizes an optimal solution. In the context 

of renewable resources, the optimal harvest rate is one 

that equates the returns from the stock of the resource to 

the returns one could achieve in an alternative investment. 

Extinction can be an optimal outcome, particularly if there 

are no nonmarket benefits associated with the resource. 

This theme is echoed in the literature on invasive species, 

where both corner solutions (eradication) and interior solutions (control at some positive population level), are possible. The probability of random catastrophic events has 

been shown to increase the appropriate discount rate (risk 
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adjusted discounting) and accelerate harvest (Reed 1984). 

It is also possible to factor in the stochasticity associated 

with population growth (Pindyck 1984), and this can either 

introduce caution or intensify harvest pressure. An alternative approach is to specify some probability of extinction 

and then set management levers so that this probability is 

not exceeded; for example, one possible criterion is that 

the probability of extinction should never be higher than 

1 percent (Haight 1995, Montgomery and others 1994). 

Analogous criteria can be used in invasive species management.

Invasive Species Management

Invasive species management spans a variety of activities 

that can be grouped into three areas: exclusion, detection, 

and control (Figure 1). The management activities concentrate on different parts of the invasion process, which 

comprises three main stages: introduction, establishment, 

and spread. Even though agencies engage in additional 

activities, these categories capture the majority of decisions 

facing managers. Thus, the synthesis is arranged according to the general management categories. Although the 

theory behind the risk management models is discussed, the 

emphasis is on the potential outcomes.

Various species may be in different stages of the 

invasion process so that management agencies engage in 

exclusion, detection, and control activities simultaneously. 

Additionally, the dynamic nature of the invasion process 

implies that it is optimal to make management decisions in 

a forward-looking manner by accounting for future stages 

Figure 1—Management activities and corresponding invasion process stages. This diagram depicts the major management activities—exclusion (prevention), detection (search), 

or control (removal), which correspond to the stages of the invasion process—introduction, establishment, and spread—as used in this synthesis. Whereas many of these activities occur concurrently, the focus in the synthesis is largely on the individual management 

activities (sections 3 and 4) with a lesser emphasis on the relationships between multiple 

activities (section 5). To illustrate the simplest example, the finite-horizon timeline 

represents a situation where an agency is managing a single species and activities are 

undertaken in isolation. Thus, the manager first engages in preventing introductions and, 

if that fails, switches to detection (at time period τ1) and then potentially to controlling the 

species if it establishes and spreads (at time period τ2)
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

in current actions. Existing literature often analyzes such 

aspects of the relationships between the management activities. Many papers in the economic literature, for example, 

consider the optimal allocation between exclusion and 

management activities for the same species.

Factors Fueling the Invasion Process

Key factors are focused on in this section that are thought 

to contribute to the risk of invasions, including factors that 

can be controlled for the purposes of risk management. 

One commonly held view is the disturbance hypothesis 

(Dalmazzone 2000), which asserts human activities and 

their accompanying disturbances to the environment 

primarily cause both species’ introductions and their subsequent invasions of new ecosystems. In addition, human 

and commodity movement provide the major vectors for 

species to enter new ecosystems. Risk management requires 

the knowledge of which vectors pose the greatest chance 

for new introductions of invasives, and this translates to a 

reliance on comprehensive risk assessment (e.g., cargo data 

in “Factors Fueling the Invasion Process;” Koch and Smith, 

this volume). Current risk assessments typically do not use 

an economic framework and economic data to understand 

the relationship between trade and invasion risk. Costello 

and others (2007) provided one of the few examples of such 

a framework when they parameterize a model based on 

invasive species introductions data to find that the threat of 

new invasions depends on the past trade level with a region 

and the past exposure to invasive species. Using invasion 

data from the San Francisco Bay over a period of 138 years, 

they identify trade partners from the Atlantic/Mediterranean and West Pacific regions as posing the greatest risk 

of introductions to the San Francisco Bay Area. Explicitly 

incorporating economic aspects can potentially alter the 

results of a risk assessment and, in turn, change suggested 

approaches to risk management. In this case, risk management can potentially reduce risks through targeted trade 

restrictions and economic mechanisms. However, such 

restrictions can produce unintended negative consequences 

if inappropriately targeted or implemented.

Economic activities can produce other externalities 

such as land disturbance and the loss of biodiversity. 

Scientists have long argued that biodiversity loss increases 

an ecosystem’s susceptibility to invasion. This idea stems 

from a theory postulated by Elton (1958) that diverse 

habitats can better fend off invasions. One major reason 

for biodiversity loss to affect invasions arises from the 

interactions between native and non-native species. Many 

experts believe that interactions between native and exotic 

species are generally detrimental, often stemming from 

competition over limited space or food (e.g., Shigesada and 

Kawasaki 1997; Tilman 1982, 2004). However, studies have 

shown that the relationship between native and non-native 

species is quite complex, and some species may actually 

benefit due to mutualism (Bruno and others 2003). Hence, 

a simple classification based on the number of species in a 

geographic area can not necessarily predict the susceptibility to new invasions; instead, the effects of the interaction 

often depend on the spatial scale (Fridley and others 2004, 

Meiners and others 2004). 

Apart from the externalities from economic activities, each species’ unique biological traits largely affect 

their impacts in a new ecosystem. Thus, risk assessment 

emphasizes biological traits of potential invasive species 

as indicators of potential risk (Downing and others, this 

volume; Iverson and others, this volume; Pontius and others, 

this volume). However, given the numerous possible factors 

and characteristics that contribute to a species’ invasiveness, the selection of appropriate characteristics poses much 

difficulty. For example, Rejmanek (1999) posited that all 

salient biological characteristics provide some information 

on the potential risk posed by a species. Besides the species 

specific biological traits, landscape characteristics of the 

potential habitat also provide important indicators (PageDumroese and others, this volume; Royo and Carson, this 

volume; Shore and others, this volume).

Other researchers, such as Williamson (1996), believe 

that very few characteristics provided suitable predictors 

for invasions. Williamson (1996) argued that most invasion 

patterns elude generalization over wide taxonomic ranges 

due to the specificity of success factors. Still, propagule 

pressure, habitat suitability, and prior invasion success 

serve as rough indicators of invasion success. Of these, 

propagule pressure, or the number of organisms in an area, 
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is the variable that can be most directly altered through 

risk management. As the propagule pressure increases, the 

chances of survival increase, whereas the effects of predators, stochasticity, and the Allee effect (“Eradication as an 

Optimal Strategy”) diminish. Unsurprisingly, a positive 

correlation exists between propagule pressure and disturbed 

land. Disturbed land tends to have greater economic activity, which translates to increased exposure to vectors for 

exotic species. 

In addition to establishing general predictive factors, Williamson (1996) formulated the Tens’ Rule, which 

postulates that of the exotic species introduced to an area, 

10 percent will become established and 10 percent of those 

will spread (approximately 1 percent of introduced species). 

Although this is a very general rule-of-thumb, assigning 

probabilities with educated guesses for the underlying 

probability distributions can produce an approximation 

of the actual invasion process (e.g., Lockwood and others 

2001). Using such approximations, a decisionmaker can 

perform a more structured analysis, which potentially 

widens the possible management options available (Perrings 

2005, “Understanding Risk Mitigation Versus Adaptation”). 

However, whereas general rules, such as the Tens’ Rule, 

serve as useful benchmarks to provide a sense of the magnitude of potential invasions at the aggregate level, they do 

not predict outcomes at a micro-level scale. The pine shoot 

beetle (Tomicus piniperda) is an example of how aggregate 

level predictions can fail at the regional level. Pine-shoot 

beetle sightings were met with strict quarantines due to 

its classification as a high-risk pest causing potentially 

high economic damages. The pine-shoot beetle actually 

produced relatively low damages, but the quarantine 

measures resulted in significant losses to the pine industry 

in the affected areas (Haack and Poland 2001). As the 

authors state, “... it is difficult for agencies like APHIS to 

change course once they have enacted a federal quarantine 

given that the concerns of the uninfested states have been 

heightened by the initial establishment of the quarantine.” 

Thus, whereas risk assessment can inform risk management, it could potentially exclude key impacts such as in the 

economic consequences of quarantines on industry or the 

irreversibility of certain actions.

Risk management frameworks incorporate these 

relationships into the decisionmaking problem facing the 

agency manager along with the spatial, temporal and stochastic dimensions. However, there is no clear rule dictating 

which relationships must be included or how they should be 

included. Inclusion of specific relationships depends largely 

on the agency manager’s objective, and the choice 

of relationships will impact the model’s outcome.

Exclusion Strategies and Risk 

Management

Exclusion strategies occupy much of the limelight in risk 

management for invasive species because the majority of 

species introductions have been attributed to human activities (“Factors Fueling the Invasion Process”). Reducing the 

risk of species introduction involves managing the potential 

pathways that species use to enter a new ecosystem. Marketbased mechanisms, such as tariffs or permits, can regulate 

trade behavior and produce socially optimal outcomes 

(“Policy and Market-Based Mechanisms to Manage the 

Risk of Introductions”). The optimal strategy necessary to 

prevent a species introduction may be substantially affected 

by whether or not you know the probability distribution of 

the invasion process.

Policy and Market-Based Mechanisms to Manage 

the Risk of Introductions

The varying policies and market-based mechanisms aimed 

at reducing introductions produce substantially different outcomes and can lead to unintended effects such as 

economic losses as illustrated by the papers in this section. 

Increased trade volume creates greater opportunities for 

species to engage in ecosystem hopping, leading some 

to argue for tighter trade restrictions to reduce this risk 

(Jenkins 1999). However, the sheer volume of trade coupled 

with strong political resistance and social welfare losses 

requires a targeted approach. The first step is identifying 

the high risks within trade (“Factors Fueling the Invasion 

Process”). The next step involves evaluating mechanisms, 

which reduce this risk. Market-based mechanisms can 

ensure socially optimal outcomes, but their implementation 

is often confounded by political issues and the inability to 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

properly capture the full implications of these mechanisms. 

Optimal tariff policy is discussed in a majority of these 

papers, but the role of politics should not be overlooked. 

Several policies, such as phytosanitary measures (https://

www.ippc.int/IPP/En/default.jsp), aim to reduce the potential damages of pests that arrive through trade. However, 

it can be argued that extant tariffs and trade policies often 

stem more from political motives than the conscious desire 

to mitigate environmental damages produced by trade. As 

Margolis and others (2005) illustrated, delineating protectionist tariffs from those designed to mitigate invasion 

risk requires knowledge regarding the social costs inflicted 

by invasive species and the value the government places 

on societal welfare. Without this information, it is hard 

to gauge the consequences of tariffs. Achieving socially 

optimal outcomes involves two steps: (1) identifying the 

mechanisms that induce behavior to produce the socially 

optimal outcome, as demonstrated in the papers in the 

following sections, and (2) implementing these mechanisms. 

The first step pertains to this synthesis; however, the 

obstacles that arise during implementation must also be 

kept in mind while evaluating these mechanisms. 

Trade Policy— 

Models that utilize taxes to manage invasive species 

introductions are discussed in this section. Although the 

economic focus on invasive species is relatively new, frameworks from the environmental economics literature can 

inform decisionmaking models. For example, Costello and 

McAusland 2003, Knowler and Barbier 2005, and McAusland and Costello 2004 draw from the pollution literature 

by treating invasives as a negative externality comparable 

to pollution. Knowler and Barbier (2005) evaluated the 

extent to which market-based mechanisms, such as taxes, 

can produce a socially optimal level of exotic plant imports. 

Private industry and agriculture rely heavily on exotic 

species for a range of purposes (McNeely 1999). To address 

the unintended consequences of intentional introductions, 

Knowler and Barbier assessed the effects of Pigovian taxes 

for the private nursery industry. Pigovian taxes impose a 

penalty on the loss associated with an agent’s actions; in the 

pollution literature, Pigovian taxes are levied against firms 

based on the amount that they pollute (“A [Brief] Primer 

in Economic Theory”). Setting optimal levels of Pigovian 

taxes requires perfect information on firm practices, and, 

more importantly, the impacts of those actions. Assessing 

the contribution of individual firms on overall species’ 

introductions is difficult. However, from a social welfare 

perspective, optimal Pigovian taxes provide a better alternative than total prohibition of exotic imports. As shown in 

the pollution literature, market-based mechanisms such as 

Pigovian taxes can internalize the externalities of private 

actions to result in a socially optimal outcome.

To assess the impacts of a Pigovian tax, Knowler and 

Barbier developed a model to analyze the decision facing 

a policymaker regulating a private industry. Specifically, 

they studied the commercial nursery industry, where 

importing, breeding and selling behaviors often occur 

without consideration of the potential loss to society from 

unintended spread following the sale of the exotic species. 

The social benefits of plant imports are represented by the 

discounted aggregate profits of the private nursery industry. 

The expected losses depend on the quantity of land invaded 

by the species once the invasion occurs. The overall net 

benefits are the total profits until the time when the invasion 

occurs minus the discounted losses following the invasion. 

A hazard function characterizes the probability that the 

species will arrive by a particular date. The hazard function in their model incorporates the salient factors driving 

invasions, such as the invasiveness of the species and the 

number of firms in the industry. Solving the dynamic 

optimization problem yields a time path of the optimal 

number of firms in the industry. The application of this 

model requires empirical analysis. However, the uncertainty 

in several relationships, such as potential damages, potential 

invasiveness, and the time of the invasions, necessitate 

assumptions based on educated guesses and a priori beliefs. 

To facilitate the model’s implementation, Knowler and Barbier made several simplifying assumptions in their analysis 

of the saltcedar (Tamarisk spp.), an ornamental shrub, which 

became invasive. The hazard function is estimated from 

a survey of decisionmakers on their perceived risk of 

invasiveness. Based on USDA data for the horticultural 

industry, the industry’s profit function is fitted at the state 
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level using a second-order polynomial equation. The analysis of four model specifications for different treatments of 

the relationship between the number of firms and the hazard 

function, coupled with varying specifications of the hazard 

level (low and high hazard) and four levels of profitability 

illustrate two key points: (1) the optimal number of firms is 

always lower than the optimal long-run equilibrium without 

invasion risk and (2) the optimal number of firms and level 

of Pigovian taxes is sensitive to the hazard level. Under 

some conditions, the optimal number of firms is zero (i.e., it 

is optimal to prohibit all imports of the exotic species).

Conventional wisdom states that tariffs, or any protectionism, will reduce invasion risk as a consequence 

of a reduction in trade; Costello and McAusland (2003) 

demonstrated that this may not be the case. Protectionist 

policies can achieve a reduction in overall invasive species 

introductions. However, the failure to account for the role 

of agricultural damages skews the interpretation of the 

true efficacy of protectionist policies, which may actually 

increase invasion risk. This result is an example of the 

aforementioned disturbance hypothesis (“Factors Fueling 

the Invasion Process”), and, in this case, human disturbance 

is often believed to increase the chance of invasion. Higher 

tariffs on agriculture will reduce agricultural imports, so 

domestic producers will increase production. This, in turn, 

increases land disturbance as more land is converted to 

agricultural production. This increase in land disturbances 

facilitates invasions by extant invasives as well as new 

ones, thus, any reductions in invasion risk from the tariffs 

are offset by increased risk from land disturbance. A 

complex theoretical framework presented in their paper 

incorporates the potential damages from different trade 

levels and commodities, the supply and demand elasticities 

for various commodities, and the level of protectionism on 

these commodities. Though their model is theoretical and 

difficult to parameterize, the analysis illustrates that setting 

policy based on conventional beliefs may lead to suboptimal 

results. As Costello and McAusland stated, “...the rate of 

introduction causing crop damages provides minimal (if not 

outright misleading) information about the rate of ecologically damaging invasions. This has important implications 

for the use of existing estimates of invasion-related damage; 

while existing estimates are staggering, they omit invasionrelated costs to biodiversity and other non-monetized 

assets.”

Whereas Costello and McAusland’s model does not 

incorporate averting behavior by farmers or the role of 

invasive species management activities, several salient 

observations are provided in their paper such as the usefulness of current introduction estimates and the impact of 

using incorrect empirical models. Also illustrated is the 

value of expanding the breadth of the analysis to better 

inform decisionmaking by including both direct and indirect consequences of incentive mechanisms, in addition to 

the underlying stochastic relationships. The crucial component is acknowledging and incorporating the economic 

aspects, such as price distortions and demand and supply 

responses to import changes. An often overlooked aspect 

of invasive species policies is addressed—the potential 

behavioral responses by the import-competing industries 

who respond to the supply changes resulting from the tariffs 

impact on the importing firms. Tariffs may produce positive 

effects for some firms while concurrently altering production choices by other firms, which can lead to other changes 

such as an increase in domestic agriculture intensity. The 

need for analysis to encompass the full extent of changes 

resulting from a policy is illustrated in this paper.

In a subsequent paper, McAusland and Costello (2004) 

analyze the combination of tariffs and monitoring practices 

to achieve the socially optimal level of prevention activities. 

The assumption that all components are known is shown 

in their model. Whereas tariffs and cargo inspections 

reduce the introductions of invasive species, the omission 

of explicit stochastic elements excludes this model from 

frameworks that can be implemented directly for risk 

management. The results of the analysis, though, are worth 

mentioning as they can provide insight for risk management 

practices:

1. It is always optimal to have a positive tariff 

although it may be optimal not to have inspections in some situations (i.e., if the level of 

infection of the partner is so high that it is 

optimal to not inspect but instead to set a high 

tariff).
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2. Higher infection rates necessitate higher tariffs 

but not necessarily greater inspections.

3. Extending the time horizon results in greater 

inspections but not necessarily higher tariffs.

They draw an analogy between this situation and the 

one involving pollution emissions, which requires monitoring to determine the pollution levels to levy the correct 

taxes. Here, the monitoring entails inspections that sort the 

uninfected goods from the infected ones. The findings from 

the inspections determine the amount of monitoring needed 

in the future and the level of taxes that should be set. At 

sufficiently high levels of infection, the optimal strategy is 

to discontinue monitoring and rely solely on taxes to repay 

the costs of invasives.

Permit Trading Models—

Horan and Lupi (2005) explore a tradable permit program 

as an alternative to current regulation of ballast water to 

reduce the number of invasive species entering the Great 

Lakes. Permits allow commercial vessels to release ballast water, which carries species. However, releases are 

unobservable; thus, they must be estimated as a function 

of vessel characteristics and management practices. The 

authors find that although permit trading produces the 

most efficient outcomes, appropriately targeted technology 

regulations can lead to similar results. Emission permits 

are considered more efficient than regulation because they 

provide a performance-based mechanism, which is why 

permits are preferred for pollution control. However, unlike 

pollution emissions, the existence of invasive species in 

ballast water is unknown beforehand, and, even after the 

species have been released, the species introductions are not 

observed due to a lack of appropriate monitoring technology. Also, a potentially lengthy lag between introductions 

and spread (Crooks and Soulé 1999) further obfuscates the 

ability to pinpoint the individual polluter. This means that 

a specific vessel cannot be connected to a specific species 

introduction, because the outcome of a vessel’s actions is 

not directly observable. To overcome this lack of information, tradable permits can act as a proxy for the potential 

risk posed by each vessel via a performance measure that 

incorporates vessel-specific characteristics and firm actions 

aimed at reducing introduction risk.

Horan and Lupi’s permit trading model (2005) relates 

to a previous one introduced in Horan and others (2002) 

regarding prevention strategies in risky and uncertain 

scenarios (“Pure Uncertainty Versus Risk in Assessing Prevention Strategies,” below). In this recent model, each firm 

introduces a range of species, measured by their biomass, 

through their vessels. The potential biosecurity actions that 

a firm can employ to reduce the risk of transporting species 

are considered inputs. There are two stochastic relationships: (1) the size of the biomass, which depends upon the 

biosecurity inputs and the firm’s characteristics, and (2) the 

post-introduction probability of establishment and spread, 

which depends on the given control strategy, the biomass, 

and the biosecurity inputs. Combining these two separate 

stochastic relationships characterizes the probability of a 

firm introducing an invasive species. The authors characterize the total probability of an individual species introduction as the sum of the separate probabilities of introduction 

over all firms. This total invasion probability drives the 

expected damages resulting from a species invasion. The 

policymaker’s objective is to minimize the social costs, 

which are represented by the costs of biosecurity inputs for 

all firms plus the expected damages resulting from successful invasions. Focusing on ballast water released by vessels 

in the Great Lakes, Horan and Lupi illustrate the model 

using data and estimates for probability of introduction and 

successful invasion, firm characteristics, biosecurity inputs, 

and costs. 

Optimally, the marginal cost of an action (choice of 

biosecurity input) for each firm equals the expected marginal benefits of that action, or the decrease in expected 

damages. Permit trading requires much information 

including vessel- and firm-specific characteristics and 

actions, all potential invaders and expected damages, and 

probabilities for introductions and successful invasions 

as they relate to new habitats and firm behavior. Creating 

permits based on the specific characteristics of each vessel 

and firm and for each specific species would be the first-best 

option. However, with this scheme, the multiple permits for 

each species and each vessel would result in a cumbersome 

system. A second-best permit trading scheme, related to the 

first-best option, produces a desirable outcome but with only 
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one permit for all species, instead of several different ones 

targeting different species. Whereas less efficient than the 

first-best approach, a single permit reduces the information 

requirements because the policymaker does not require 

detailed information for each specific firm and vessel. 

This approach finds that the first-best scheme provides 

the least costly option followed by the second-best trading 

scheme when simulating three risk scenarios for different 

mechanisms: (1) the first-best trading scheme with varying 

permits, (2) the second-best scheme with one permit, and 

(3) various technology regulations. This result holds for 

relatively moderate permitted levels of invasive species 

introductions; the difference between the regulation mechanisms fades with stricter permitted levels. Interestingly, 

their outcomes suggest that technology regulations can 

inexpensively mitigate risks if suitable technology is chosen 

and appropriately regulated. Although direct implementation requires several assumptions, this model offers a useful 

tool to analyze potential regulatory policies while accounting for the major stochastic relationships. Also, it takes the 

Knowler and Barbier (2005) analysis (“Trade Policy”) one 

step further by demonstrating the potential loss incurred by 

simplifying assumptions to address information gaps.

By characterizing the unknown elements of any situation, managers can employ explicit frameworks to evaluate 

the potential outcomes of various policy mechanisms as 

illustrated in these papers. The authors were forced to make 

several simplifying assumptions to deal with a lack of data 

or to address the stochastic elements, but they still provide 

valuable analysis. They also illustrate the importance of 

evaluating policies in an explicit economic framework 

to capture the full extent of the repercussions such as the 

social welfare loss from posing industry-wide taxes or 

implementing tariffs without accounting for the changes 

in industrial behavior.

Pure Uncertainty Versus Risk in Assessing 

Prevention Strategies

Whereas it is assumed in most papers that decisionmakers have some information that can help characterize 

risk in invasive species management, there may be cases 

(Knightian pure uncertainty) where it is misleading to 

assign probabilities, and information is lost when the true 

lack of knowledge is overlooked. Horan and others (2002) 

tackle this issue using an aggregate model to capture 

preinvasion decisions by firms whose actions can introduce 

invasive species. Horan and others argue that invasions 

cannot be analyzed using standard economic theory, which 

assigns probabilities to all situations regardless of the 

level of uncertainty. Traditional risk-management models 

function similarly by characterizing all risk, irrespective 

of the level of uncertainty, with probability distributions. 

The authors argue that standard expected utility theory (or 

traditional risk-management) does not apply to low probability events, especially when the events are catastrophic, 

as they could be in the case of invasive species. Non-native 

species invasions can be considered catastrophic since 

irreversibility of invasions poses potentially very high costs 

and irrevocable damage to native ecosystems. To illustrate 

the effects of incorporating differing levels of uncertainty 

into the decisionmaking framework, the authors identify 

optimal prevention strategies by firms under the traditional 

risk-management framework (with assumed probabilities) 

versus an ignorance model (full uncertainty, which is not 

characterized by probabilities), which appropriately reflects 

the circumstances before the invasion occurs. 

In the traditional risk management model, the probability of introducing a species depends on the firm’s characteristics and the control strategies chosen by each individual 

firm. A species’ successful invasion depends on the biomass 

of the introduced species, the characteristics of the environment, and the firm’s characteristics. From the perspective of 

a policymaker regulating firms in an industry, the concept 

presented in the paper by Horan and others (2002) creates 

a framework where the stochastic elements are the species 

introductions and the success of the invasion. The framework is fairly theoretical and the information necessary to 

implement this model directly may not be available. General 

aggregate-level decisions are focused on in their paper. The 

probability of an invasion follows a Bernoulli distribution 

that depends on the actions of all firms in the industry. As 

the number of firms increases, the probability of an invasion 

approaches one. The present value of damages facing 

society depends on expected damages, expected costs, and 
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the possible set of all species that may be introduced. The 

risk management problem is static meaning the state-ofthe-world remains the same for the single planning period. 

The firm minimizes expected damages caused by the 

species plus the control, or abatement, costs that lessen the 

probability of a species introduction. The major distinction 

between the traditional framework and the ignorance model 

is the potential set of invading species; in the traditional 

framework, all species that can invade are known, whereas 

under the ignorance model, the set of potential species 

contains a subset of species that is completely unknown. 

This approach gives rise to the idea that events are associated with different levels of potential surprise.

According to the traditional risk-management framework (i.e., the expected value approach), the policymaker 

has two potential optimal strategies: (1) all firms should 

be unregulated or (2) all firms should undertake expensive 

measures so that the probability of an invasion is driven 

down to zero. Also, with a large number of firms, abatement 

is not optimal because the chances of invasion are high 

regardless of the control strategies pursued by individual 

firms. In both frameworks, the optimal strategy is to set 

marginal costs equal to expected marginal benefits, or the 

negative of damages. Under ignorance, though, firms will 

evaluate the marginal impacts of the events and subsequent 

potential outcomes quite differently. The difference in 

valuations of the marginal costs and damages leads to 

different outcomes for the two approaches. In the expected 

value scenario, low abatement is an optimum strategy for 

all firms, whereas that is not the case for full uncertainty 

because the firms’ values are significantly different. Subsequently, policies using the uncertainty framework establish 

uniform performance limits for all firms as opposed to the 

risk management framework where limits vary for each 

firm. Straightforward application of this framework is 

unlikely; however, the theoretical model, which illustrates 

the importance of considering alternative decision frameworks when elements of the model are unknown is the 

greater contribution of this paper. Due to the importance of 

uncertainty in the invasion process, continued reliance on 

the traditional approach for characterizing risk could lead 

to a severely restricted view of the true situation facing us. 

This does not mean that the expected value approach is not 

valuable, but it is crucial to be aware of other characterizations and unspoken caveats of these models.

Control Strategies

After the species successfully establishes, the decisionmaker may employ several control strategies: eradicate 

the population, slow the spread of the population through 

spatial control strategies, or take no action. As in the other 

stages of the invasion process, a species’ spreading success 

relates to its biological characteristics and the interaction 

with its surrounding habitat and species. Unlike previous 

stages, there may be more available information on the 

species’ characteristics at this stage, which can inform 

decisionmaking. From an ecological perspective, eradication may yield the most desirable outcome. However, it may 

be costly to achieve under conditions such as larger spatial 

scales or substantial population sizes. Consequently, eradication attempts often fail to reach their objectives. Section 

4.1 focuses on the spatial aspects of control. “Eradication as 

an Optimal Strategy” highlights the efficacy of eradication 

as a control strategy.

Spatial Control Strategies

Invasive species management is inherently about the management of land, or space. Ecological literature provides the 

theoretical framework to capture the spatial aspects (e.g., 

Shigesada and Kawasaki 1997); however, the majority of the 

economic literature fails to explicitly incorporate the spatial 

aspect. Discussion of the spatial effects on management is 

only found in the literature pertaining to control strategies 

following successful establishment. This literature is quite 

limited and does not include any stochastic aspects. 

Barrier zones reduce the spread of species either 

through eradication of small populations or quarantining 

a population. Using a dynamic framework, Sharov and 

Liebhold (1998a) assess the management of barrier zones for 

gypsy moths in the United States. To assess the efficacy of 

barrier zones, the authors construct a model of pest dispersal, which factors in the monetary damages and benefits 

of control. Model application requires information about 

the specific population: the length of the population front, 
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the shape of the population, the spread rate, the cost of the 

barrier zone, the damages caused by the species, and the 

discount rate. The conceptual framework (based on the spatial, economic, and biological components) is evaluated for 

three different spatial population spread scenarios: a strip 

with a constant width, a rectangular area, and a circular 

area. Parameterizing this framework with gypsy moth data 

and information from the Slow-the-Spread program (http://

www.gmsts.org/operations/), the authors demonstrate the 

benefits produced by containment and eradication strategies 

over disparate geographic areas. 

The authors indicate that eradication is viable for a 

species with a limited range, whereas slowing the species spread can be optimal in several scenarios. Using 

gypsy moth data as a case study, the model analysis shows 

eradication is optimal for certain small or isolated populations or both, whereas slowing the spread is better for 

larger, more established populations. Slowing the spread, 

as a control strategy, can yield substantial reductions in 

population spread (Sharov and Liebhold 1998b). The merger 

of economic and ecological relationships into a spatial 

model is demonstrated for one of the first times in this 

paper. Hof (1998) constructed a spatial model to illustrate 

how the effectiveness of barrier zones is reduced by the 

dynamics of the managed population. As the population 

grows, it can extend the size of the barrier zone or splinter, 

thus reducing the viability of barrier zones as an optimal 

management tool. However, an important caveat is pointed 

out in these two papers that, as with most papers reviewed 

in this synthesis, implementing such a framework has 

certain limitations. The choice of functional forms, model 

structure, and the data greatly influence the outcomes. 

Altering assumptions on these functional forms or other 

relationships included in the model can lead to varying 

outcomes. However, Sharov and Liebhold’s model provides 

a spatial framework with explicit economic aspects that 

can be expanded to incorporate several scenarios and could 

potentially be extended to analyze decisions before the 

species begins spreading.

Building upon the framework set forth by Sharov and 

Liebhold (1998a), Cacho and others (2004) analyzed the 

critical points that govern the optimal control strategy: 

eradication, containment, or doing nothing. Their model 

focuses on plants and includes several parameters such 

as maximum rate of spread, seed longevity, and costs of 

control. The authors represented the unknown length of 

seed longevity in differing environments by using a range 

of values in the biological parameters. They determined 

the switching points at which eradication and control are 

no longer optimal strategies by employing Scotch broom 

(Cytisus scoparius, L.) data and estimates. Based on this 

analysis, the salient characteristics are seed longevity and 

the spread rate. As the spread rate increases, the two switching points move closer together indicating that management 

should emphasize eradication.

Useful frameworks for incorporating the spatial dimensions into risk management strategies are proposed in these 

papers. Sharov and Liebhold (1998a) provided a caution in 

their paper, which is applicable to all models: “Control of 

natural resources may depend considerably on social factors; thus the model...cannot automatically generate decisions.” Further work to understand and incorporate societal 

and other factors will increase the viability of these frameworks. Overall, very little literature explicitly analyzes the 

spatial aspects, and future work should definitely focus 

on the spatial dimension as it is one of the most crucial 

components in the invasive species management problem. 

Perhaps researchers can learn from areas with substantial 

existing spatial research such as wildfire prevention or land 

conservation.

Eradication as an Optimal Strategy

Eradication as a control strategy yields the most desirable 

outcome—total elimination of the invasive species from 

the habitat—but this strategy often fails due to numerous 

obstacles that impede complete removal, leading many to 

question the circumstances when eradication is feasible and 

optimal. Myers and others (2000) cited several successful 

eradication cases noting that success relies upon certain 

key conditions. Simberloff (2001) argues that eradication in 

itself is not impossible, but is idiosyncratic and contingent 

upon several criteria:

1. Sufficient resources to successfully complete

the project.
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2. Clear and identifiable authority to oversee the 

project.

3. Fairly good information regarding the biological 

characteristics of the species; i.e., the same basic 

criteria needed for successfully implementing 

any activity involved in invasive species 

management.

He mentions resource constraints but without explicitly 

employing economic frameworks to assess the management 

options in the control stage. Several authors have addressed 

this gap by identifying the economic conditions under 

which eradication is optimal (e.g., Eiswerth and van Kooten 

2002, Olson and Roy 2002, Regan and others 2006, Taylor 

and Hastings 2004).

Olson and Roy (2002) focused on the costs of control 

and damages of species currently under management 

(i.e., they captured the decision of a manager who must 

choose future strategies for an existing population). The 

policymaker minimizes the expected discounted control 

costs plus the damages caused by the remaining population 

conditional upon the species’ growth function. The growth 

function incorporates environmental disturbances as a 

random process. As these disturbances increase, so do the 

chances of the population growing. Using this framework, 

they develop a rough guide of conditions favoring eradication. For small populations with marginal damages greater 

than marginal control costs, eradication is always optimal. 

When marginal damages are less than marginal costs, 

eradication is still optimal if the growth rate is sufficiently 

high. Irrespective of population size, eradication is optimal 

if the damages significantly outweigh the control costs in 

the worst possible scenario of environmental disturbances. 

Whereas this stylized framework is fairly general and 

cannot be directly implemented, it provides an approximate 

rule-of-thumb to ascertain the optimality of eradication as a 

management strategy. The one drawback is the information 

requirements; the marginal costs relative to the marginal 

damages must be known fairly well to determine the 

optimal management strategy.

Eradication not only depends on the relative costs and 

damages of controlling the population, but also upon the 

tenuous relationships between the population and its habitat. 

Environmental and demographic stochasticity and the Allee 

effect can drive low-density populations towards extinction 

(Liebhold and Bascompte 2003). The Allee effect works 

similarly to the critical depensation point or a threshold 

under which a population cannot survive. The Allee effect 

has been observed for low-density populations, but could 

apply to other populations as well. This effect contributes 

to an extinction threshold; if a species’ population is low 

enough, extinction will automatically occur. All species 

exhibit this effect, except asexual organisms like some 

plants. In general, management methods should be aimed at 

increasing the probability of extinction. Extinction is highly 

likely if an adequate number of the population is removed, 

although achieving 100-percent eradication is difficult. 

Taylor and Hastings (2004) utilized Spartina alterniflora (a 

non-native grass in Washington that exhibits a weak Allee 

effect) to test this theory while accounting for economic 

aspects. Their analysis of the Spartina alterniflora data 

indicates that, in the absence of an Allee effect, the optimal 

strategy involves the removal of isolated, high-growth, lowdensity species. The model analysis establishes a relationship between budget and optimal strategy: lower budgets 

necessitated the removal of low-density plants, and the 

optimal strategy with larger budgets is to focus on eradicating high-density areas. For this particular species, the Allee 

effect does not lead to cheaper eradication. Hence, the Allee 

effect plays a role in determining eradication strategies, 

but it must be considered on a species-specific basis and in 

conjunction with budget constraints.

Regan and others (2006) constructed a theory to 

analyze the optimal time needed to survey an area before 

declaring that an eradication attempt has been successful. 

Evaluating the efficacy of eradication strategies depends on 

the reliability of survey strategies, which, in turn, depends 

on the amount of time and resources devoted to detection. 

These authors postulated that managers facing budget 

constraints may prematurely cease surveying, which could 

result in a new eruption of the pest if the species was not 

fully eradicated. The authors develop a simple rule of thumb 

for the optimal number of consecutive zero surveys by 
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minimizing the sum of survey costs and expected damages. 

They compare this rule of thumb with the results of a fully 

optimal forward-looking solution derived using stochastic 

dynamic programming. The key difference between the 

two approaches is that stochastic dynamic programming 

incorporates all the possibilities that can occur in the future, 

including the possibility that the plant will re-emerge, and 

a new attempt at eradication will have to be undertaken and 

then finds the best decision. The authors parameterize these 

two seemingly different approaches—the rule-of-thumb 

and the stochastic dynamic problem—using bitterweed 

(Helenium amarum) data. The authors state that this ruleof-thumb can reduce variability in decision strategies while 

increasing evaluating the sensitivity of their decisions to 

various parameters in the eradication programs.

Eiswerth and van Kooten (2002) argued that the 

categorization of risk in subjective terms necessitates the 

use of fuzzy membership functions, which differ from the 

traditional expected value approaches (similar to Horan 

and others in “Pure Uncertainty Versus Risk in Assessing Prevention Strategies”). Subjective risk assessments 

can produce widely varying outcomes depending on the 

scientists or experts administering the assessment (e.g., 

Woodward and Bishop 1997). A stochastic dynamic model 

maximizing the agricultural producers’ discounted present 

value of net returns is presented in the paper. The objective 

function consists of the agricultural production, which 

depends on the size of the invasion and the choice of control 

technology. The objective function is conditional upon the 

species growth function, which includes a stochastic term. 

As part of this research, the authors surveyed land managers 

to gauge their management choices under risk. The authors 

parameterize this model using results of this survey and 

extant data for the yellow starthistle (Centaurea solstitialis). 

The analysis illustrates that land managers tend to aggressively control a species even when the economic criteria do 

not warrant such a stringent control regime. The optimal 

control strategy involves managing the spread of yellow 

starthistle instead of full eradication, even though this weed 

has significantly impacted agriculture. 

Allocating Resources Among Multiple 

Strategies

Management activities in one stage have direct consequences on other stages although specific stages of the 

invasion process are the focus of several papers. For 

example, scarce resources necessitate allocation between 

several activities. Decisionmakers determine these allocations concurrently, thus the framework should incorporate 

the relationships between these stages. Economic literature 

often focuses on the introduction and postestablishment 

stages of the invasion process to identify the optimal strategies between exclusion and control activities. The allocation 

between control and other activities, such as postintroduction detection, is the focus of some papers. The interaction 

between mitigation and adaptation activities is discussed 

in “Understanding Risk Mitigation Versus Adaptation.” 

Optimal resource allocation strategies amongst differing 

activities are addressed in the other sections.

Understanding Risk Mitigation Versus Adaptation 

Risk analysis often treats mitigation and adaptation separately, but invasive species risk analysis needs to account 

for both of these actions for effective management practices. 

Shogren (2000) discussed the distinction between mitigation—actions where people actively reduce the probability 

of a bad state, and adaptation—actions which reduce the 

magnitude of a bad state if it happens (as with insurance). 

He proposed the need to account for both of these actions 

simultaneously owing to the fact that an action to reduce 

risk affects the consequences if the species does invade. 

His model is based on endogenous risk theory to analyze 

risk-benefit tradeoffs for explosive invaders, and it depicts 

the problem facing a representative policymaker allocating 

scarce resources. These ideas stem from economic theory 

on decisionmaking under risk and uncertainty as addressed 

in “Defining Risk” and “A (Brief) Primer in Economic 

Theory” (de Finetti 1974, Drèze 1987, Savage 1954, Von 

Neumann and Morgenstern 1944). 

Perrings (2005) built upon Shogren’s framework and 

extended it to examine decisionmaking practices aimed 

at allocating resources between these two strategies. He 

classified management strategies addressing risk into the 
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same categories: mitigation and adaptation. Mitigation 

refers to actions that alter the chances of an event occurring. 

In invasive species literature, mitigation activities reduce 

the likelihood of invasions. Adaptation refers to actions 

that alter the value of the outcome. These activities would 

reduce the impact cost of invasions without changing the 

probability of the invasions themselves. Decisions regarding 

mitigation and adaptation activities often occur simultaneously. The chosen strategy relates to where the species is in 

the invasion process (i.e., whether the species has just been 

introduced or whether it has already established). The manager must also assess whether the situation is observable or 

controllable. Perrings pointed out that there are two schools 

of thought regarding the predictability of invasions (“Factors Fueling the Invasion Process”). The first school, including Williamson (1996), argues that invasions can rarely 

be predicted beyond a few indicators such as propagule 

pressure and the past invasion history of the species. Others, 

such as Rejmánek (1999), believe that the invasiveness of a 

species and the susceptibility of the land can be predicted 

by analyzing a wider range of salient characteristics.

Using probability transition matrices that follow a 

Markov Chain, Perrings evaluated four possible outcomes 

once a species has been introduced:

1. It may not establish.

2. It may establish irrespective of management 

activities.

3. It may establish, and its population will depend 

on the state of nature (including management 

activities).

4. It may establish and have an unstable 

population in the long run.

The probabilities in the transition matrices represent 

the overall resilience of the land against invasion. If these 

probabilities are known, a model of the system’s dynamics 

and the value function (both dependent on the probability 

transition matrix) can guide the optimal choice of strategies. 

In addition to the probabilities, the model requires knowledge of the expected net benefits and costs of different 

control regimes, and a feedback matrix that links control 

choices to the probability transition matrix. If this information is known, the outcomes of control measures (e.g., those 

that only reduce population size, can be assessed for their 

long-run effectiveness).

Mitigation is an appropriate option if the expected 

outcomes of management activities can be assigned some 

probabilities. In situations where probabilities for the 

connections between actions and outcomes are unknown, 

mitigation cannot occur, and managers are left with adaptation as the only possible strategy. Perrings’ main objective 

was to draw attention to the need to quantify unknown 

aspects as he stated at the end of his paper, “In an environment in which decisionmaking is increasingly dominated by 

non-probabilistic ‘scenarios’ which drive decisionmakers to 

focus on adaptation, it is important to remind ourselves that 

this may be both inefficient and inequitable.” This argument 

arises from the idea that any structured analysis based on 

some quantitative information is better than the alternative 

because conventional wisdom does not necessarily lead 

to optimal strategies, such as the case of tariffs to reduce 

invasion risk (“Policy and Market-Based Mechanisms to 

Manage the Risk of Introductions”). 

Maximizing Welfare Through Invasive Species 

Management Activities 

Unlike the previous papers in this synthesis, the focus in 

this section is on the tradeoffs between management strategies and their social benefits and costs. Welfare functions 

allow the analyst to capture the overall benefits and losses 

of a management decision. The use of welfare functions is 

employed in several papers in their objective functions to 

assess optimal resource allocation strategies (e.g., Finnoff 

and Tschirhart 2005, Finnoff and others 2005, Leung and 

others 2002). 

Leung and others (2002) showed that prevention 

is more cost effective than control. Stochastic dynamic 

programming captures the situation facing a policymaker 

allocating resources between prevention and control activities on an aggregate level. Welfare consists of the profit 

function minus the costs of invasive species management 

activities. The invasive species grows according to a logistic 

function plus some stochastic term representing uncertainty 

in species growth patterns in the new environment. The 

planner’s maximization problem optimizes welfare over 
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a probability transition matrix that reflects the probability 

of moving across States (i.e., different invasion outcomes) 

given various allocations between exclusion and control 

strategies. 

Implementing the Leung and others (2002) model 

requires the following: data on a species’ growth function, 

the costs of controlling that particular invasive species, 

the efficacy of control, the total inputs and costs for the 

industry, the total outputs and prices for the industry, the 

monetary loss caused by the invasive species, and the probability of invasion. Data on zebra mussels and powerplants, 

coupled with estimates of certain biological characteristics 

and the probability of invasion, are used to simulate three 

possible scenarios for lakes: uninvaded over a 25-year 

time horizon, invaded over 25 years and uninvaded for 5 

years. The simulations determine the optimal allocation for 

prevention strategies given two control options (do nothing 

or do something) for 10 years. The optimal expenditures for 

prevention activities yield the greatest welfare. However, 

the difference in cumulative welfare resulting from optimal 

expenditures, suboptimal expenditures, and taking no 

action is relatively small. Engaging in optimal prevention 

activities is ideal over the longer time horizon (25 years), 

whereas the optimal strategy with the shorter time horizon 

(5 years) is to not take any action. As in several other 

papers, Leung and others (2002) employ data from a highly 

invasive species with high growth rates and high damages 

(in this case, the zebra mussel). Using such a species illustrates the worst case scenario. Applying this model to less 

insidious invasive species may produce different outcomes. 

The advantage of this model is the explicit linkage between 

private industry and management activities. Whereas actual 

data may not exist for all components of the model, estimates can be used to analyze the potential scenarios facing 

the policymaker for diverse industries and invasive species.

Leung and others (2005) follow up their previous work 

with an attempt to bridge the gap between theory and application by proffering a framework to identify general rulesof-thumb for resource allocation over various invasive species management activities. Extending the concepts in their 

earlier paper, the authors establish the relationships underlying optimal choice of exclusion and control strategies. The 

policymaker endeavors to maximize cumulative social 

welfare conditional on several factors: (1) the welfare in 

an invaded state, (2) the welfare in an uninvaded state, and 

(3) the probability of invasion dependent on the prevention 

strategy, invasion parameters, and the efficacy of prevention. Based on the model analysis, optimal control expenditure increases with the system’s value and decreases with 

uncontrollable damages (amongst other rules). The optimal 

prevention expenditure is closely tied to the preventability 

of invasions. Several rules characterize the optimal expenditure including one stating expenditures decrease as the 

probability of unpreventable invasions increase. The authors 

provide a detailed list of data required to implement the 

model as well as a thorough comparative statics analysis 

of the interaction between the various parameters and 

variables. This model’s strength lies in its application using 

available data. However, the simplified framework comes 

at a cost—several strong assumptions (e.g., the specific 

functional forms, the relationships included or excluded in 

the framework, the availability of data necessary to implement the framework, etc.) underlie the model. The loss of 

specificity translates to a gain in the ease of implementation 

and a decrease in the time needed to reach general management rules.

Building upon the underlying tradeoff between prevention and control, Finnoff and others (2007) evaluated the 

effect of manager’s risk preferences on the optimal investment in management activities. Risk preferences dictate 

the valuation and incorporation of risk into decisionmaking 

frameworks. The authors postulated that, based on their 

endogenous risk model (an extension of Shogren 2000, 

“Understanding Risk Mitigation Versus Adaptation”), 

risk-averse models tend to over-invest in control while 

under-investing in prevention. As a manager’s risk aversion increases, so does the propensity to implement control 

activities. This behavior results in increased invasions 

as indicated in their paper. This paper was based upon 

an earlier one (Finnoff and others 2005) where a similar 

endogenous risk framework analyzed the role of feedback 

between decisionmakers (i.e., the firms or the manager) and 

biological and economic aspects associated with invasions. 
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Here, feedback refers to the ability of the decisionmakers to 

update beliefs based on changes in the situation. If decisionmakers neglect to respond to these changes, the results 

could range from minor efficiency loss to severe biological 

and economic consequences as a result of invasions.

Determining Optimal Allocations Based on InterSpecies Relationships

Like humans, plants can be thought of as welfare maximizing organisms whose survival success depends on 

certain biological traits, which can predict outcomes from 

interaction with other plants, humans, and their environment (Finnoff and Tschirhart 2005). Contrary to previous 

papers on species management, the focus in this paper is 

on the species (i.e., the plant) as an optimizing agent, which 

aims to maximize its biomass conditional on specified 

parameters and the presence of competitors in the habitat. 

Finnoff and Tschirhart explain the uniqueness of this model 

compared to previous ones: “In the plant community model 

herein, the theory starts prior to population updates by first 

assuming the individual plant behaves as if it is choosing its 

optimum biomass. Optimization is done given the plant’s 

parameters and the presence of other competing plants in its 

own and other species.” Using this model, the authors evaluate individual plant behavior and species interactions as 

they result from plant-specific traits, environmental factors 

such as temperature, and human interaction. Each scenario 

analysis offers a rough guideline for plant behavior given 

certain conditions. 

The authors classify invasive species as redundant 

or successful. Redundant species fail to invade successfully but remain in the habitat as biological insurance until 

environmental conditions become favorable for them. 

Successful species effectively invade the new habitat from 

the start. These two categories are mutually exclusive, but 

species can switch groups over time as the environmental 

circumstances change. The plant’s efficient energy usage 

dictates its growth function, which updates the model. An 

individual plant’s optimization problem—maximizing 

net energy—includes the leaf size, the flow of solar radiation, the biomass, and the energy expended for the plant’s 

functioning. Additionally, as a member of a plant community, the population size and growth vis-à-vis the available 

land capacity combine to enter as a space constraint that 

also influences the individual plant’s optimization. The 

model of plant behavior is then incorporated to a policymaker’s welfare maximization problem because there is 

feedback between human decisions such as agricultural 

management and species success. Through this framework, 

the authors capture the interconnection between ecological 

and economic relationships in a situation with multiple 

species. The policymaker chooses prevention and control 

efforts to manage an invasive species. The probability of 

invasion depends solely on prevention efforts. Accounting 

for human effects on species population, the plant’s growth 

function has altered to now include population reductions 

through harvest and control measures. 

Based on the relationships and factors in just the plant 

relationships, the analysis determines that the optimum 

plant biomass in the steady state depends largely on plantspecific traits, namely those related to respiration activity. 

Expanding this result to multiple species provides criteria 

to predict species success in steady-state scenarios. Factors 

beyond the plant-specific parameters, such as temperature, 

also drive the optimization behavior. After augmenting 

the aforementioned plant relationships by temperature, the 

authors analyze the optimization behavior to find that any 

number of species can co-exist regardless of the resource 

constraints in this model. This outcome deviates from 

previous papers in that the number of resources dictates 

the maximum number of coexisting species populations. 

The authors construct a conceptual framework encompassing the major economic and ecological factors that impact 

plant success. Although the authors do not apply empirical 

data to the model, this can be done using data for current 

species and estimates for potential species. The majority 

of the model is deterministic except for the probability of 

invasions, thus the information necessary to implement 

the model should be available. By explicitly incorporating 

complex species interactions, a creative, albeit unorthodox, 

approach for evaluating the ecological consequences of 

human actions is proffered in this paper. 
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Focusing on the Cost Versus Damage Tradeoff to 

Identify Optimal Strategies

Optimal strategies for multiple activities can be found 

by focusing on the tradeoffs between the management 

costs and the species’ damages deterred by engaging in 

the particular management activity. The optimal resource 

allocation between prevention and control activities with a 

stochastic initial population size depends on the marginal 

damage function of the invading species (Olson and Roy 

2005). Whereas this model cannot be directly implemented 

due to the theoretical nature of the framework, their 

analysis produces general rule-of-thumb principles for 

optimal resource allocation between prevention and control 

activities. The framework represents a situation where an 

invasive species has been controlled, and the decisionmaker 

must allocate resources for potential management strategies for the same species. As an example, the gypsy moth 

(Lymantria dispar) presents such circumstances; it has been 

controlled in certain areas and requires continuous management. The management options can differ from exclusionary activities for preventing new introductions of the gypsy 

moth, to control strategies for managing remaining gypsy 

moth populations. 

The policymaker chooses the level of prevention and 

control activities. The costs of control and prevention are 

assumed to be known, but the damages from the resulting 

invasion are driven by a stochastic relationship representing 

the risk of an unknown invasion. The policymaker selects 

the prevention and control strategies simultaneously prior to 

the invasion, which reflects the decisionmaking process in 

risk management. However, the established population size 

is known indicating that the invasion had already occurred 

and these management decisions focus on potential invasions going forward from either the same species or other 

species. 

The role of risk on optimal resource allocations is 

highlighted in this paper. An increase in risk is represented 

by an increase in the variability associated with the chance 

of an invasion. The optimal choice between prevention and 

control following such an increase in risk depends mainly 

on the shape of the marginal damage function. Thus, the 

species’ damage function must be known to apply this 

framework. Data on past damages from the species can 

be used to estimate the damage function, which can then 

determine the optimal management strategy for the species 

in an uninvaded area or a reoccurrence of the species in the 

same area.

Whereas the focus is on the introduction and spread 

stages in many papers, there are few where the detection 

stage between the introduction of the species and the subsequent establishment and spread are explicitly addressed. 

The unclear relationship between species that are intercepted or discovered during the introduction stage and the 

established species being found in ecosystems is due to the 

fact that successful introductions do not often translate to 

successful invasions (Williamson 1996). Even those species 

that successfully establish often begin to spread after long 

lag periods (Crooks and Soulé 1999). Lags occur for many 

reasons such as natural lags in population dynamics or 

changes in the environment and the genetic composition of 

extant species. Additionally, past experiences with species 

do not provide good indicators of their future invasiveness 

due to an ever-changing environment and the response to 

and by other species. Also, species introduced many years 

ago may now have populations that are sizeable enough to 

detect (Costello and Solow 2003). These factors contribute 

to the uncertainty surrounding the establishment stage of 

the invasion process.

If populations are detected early in the invasion 

process, either before they fully establish or as they are 

establishing, control strategies can commence sooner 

and, possibly, at a lower cost. Some species, such as the 

black-striped mussel (Mytilopsis sp.) in Australia, have 

been eradicated due to detection activities, which included 

constant surveying followed by quick mobilization upon 

detection (Myers and others 2000). Mehta and others (2007) 

captured the stochastic and dynamic aspects of this tradeoff 

between detection and control activities. The model focuses 

on a decisionmaker minimizing costs and expected damages for a single invasive species by choosing a constant 

optimal search level at the detection stage. The time of 

detection is stochastic and depends on the effort devoted 

to search activities and how easy it is to detect the species. 
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Based on simulations representing four types of species, the 

model analysis indicates that it is often optimal to devote 

significant resources to detection efforts for species causing 

high damages, even if the species is difficult to detect. The 

optimal strategy for species that do not have a high-damage 

potential involves undertaking no action if the population 

is sufficiently small, if the detection is quite difficult, or if 

post-detection control activities are costly. Even if a species 

causes a high level of damage, it may not be optimal to 

invest in detection when the post-detection control strategy 

is relatively costly (i.e., the control costs are near or greater 

than the damages produced by the species). The simulations 

show that the biological parameters are more influential 

than the economic parameters. This may be an artifact 

of the specific model, but it is a point worthy of further 

exploration. It is demonstrated in the paper that the optimal 

detection strategy relies greatly on the detectability of the 

species, similar to findings from Cacho and others (2006) 

who applied search theory to a spatial model aimed at analyzing detection and control strategies. Whereas the Cacho 

and others model does not include any economic aspects, 

it does incorporate the risk underlying these activities and 

the role of detection on subsequent eradication strategies 

to illustrate the importance of detectability in the optimal 

detection strategies for weeds.

Some characterizations of the tradeoff between the 

costs of managing invasive species and the damages 

inflicted by them are provided in these papers. The variety 

of potential methods of addressing resource allocation 

amongst several activities is also touched upon. General 

guidelines for resource allocation are established as well. 

However, direct application of these models is fairly difficult. Specific models, or examples, of these strategies in 

practice would be quite instructive and useful for pragmatic 

application.

Discussion

An overview of some of the existing frameworks for 

evaluating risk management from an economic perspective 

is provided in this synthesis, as the field of invasive species 

management literature continues to evolve and expand. New 

collaborations and new knowledge have spawned, and will 

continue to create, a wide range of methodologies aimed at 

identifying optimal strategies and mechanisms for diverse 

management cases and objectives. The individual sections 

illustrate that several creative and insightful decisionmaking frameworks have already been explored. Nonetheless, 

there are numerous potential research areas that need to be 

investigated. 

Space and invasive species are closely intertwined. 

Models, which explicitly incorporate the spatial and economic aspects are crucial to the invasive species management problem, yet very few currently exist. Also, current 

economic models focus on only three major management 

activities. Other management activities, such as restoration and public outreach, offer high returns for invasive 

species management and ought to be considered in the 

risk management framework as they occupy a place in the 

decisionmaking framework for agency managers. The set of 

activities included in risk management framework should 

be expanded, as well as the number of activities included in 

resource allocation frameworks. Realistically, management 

activities are undertaken concurrently and the theoretical 

frameworks should reflect this. 

Only a few models incorporate multiple species, so this 

should be expanded to understand the interaction between 

species as well as optimal resource allocations across species. Approaches that transcend the traditional risk management, or expected values and framework are employed in 

some papers; they highlight crucial issues involving the 

levels of risk facing managers. Increasing an awareness of 

different methodologies for incorporating stochastic elements will help agency managers and expand the number of 

tools available for characterizing management risk. Overall, 

these models tend to be general. Whereas that is important 

for establishing overall frameworks and guidelines, future 

work should focus on specific species to emphasize the 

link between theory and application. Also, the focus tends 

to be solely on insidious species in some papers, whereas 

agencies face a wide gamut of invasive species. These 

frameworks should be applied to a variety of different types 

of species, and the first step towards this has been taken 

through the range of simulations used in these papers.
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The interdisciplinary body of literature in this field is 

constantly growing. As such, certain key papers have been 

focused on in this synthesis while acknowledging that other 

recent or related papers may have been omitted. The purpose of the synthesis is to provide a basic overview of the 

existing state of invasive species risk management literature 

from an economic perspective. Hopefully, this review will 

encourage readers to continue to push the boundaries of 

this research by engaging across the disciplines to discover 

novel and exciting approaches for decisionmaking tools for 

invasive species.

Acknowledgments

The authors conducted this research under Research Joint 

Venture Agreement 05-JV-11231300-005 between the 

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

University of Minnesota. The authors would like to thank 

the anonymous peer reviewers and the participants of the 

USDA Forest Threats Risk Assessment Conference for their 

insightful comments. 

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521–538. Vol. 2. 

Woodward, R.T.; Bishop, R.C. 1997. How to decide when 

experts disagree: uncertainty based choice rules in 

environmental policy. Land Economics. 73(4): 492–507.
469

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

Alejandro A. Royo and Walter P. Carson

Alejandro A. Royo, research ecologist, Forestry Sciences 

Laboratory, USDA Forest Service, Northern Research 

Station, Irvine, PA 16329; and Walter P. Carson, associate 

professor, Department of Biological Sciences, University of 

Pittsburgh, Pittsburgh, PA 15260.

Abstract

Alterations to natural herbivore and disturbance regimes 

often allow a select suite of forest understory plant species 

to dramatically spread and form persistent, mono-dominant 

thickets. Following their expansion, this newly established 

understory canopy can alter tree seedling recruitment rates 

and exert considerable control over the rate and direction 

of secondary forest succession. No matter where these 

native plant invasions occur, they are characterized by one 

or more of the following: (1) the understory layer typically 

has greater vegetation cover and lower diversity than was 

common in forest understories in the past; (2) this layer can 

delay stand renewal and alter species composition by inhibiting tree regeneration; and (3) once this layer is formed, it 

can resist displacement by other species and remain intact 

for decades. In this paper, we evaluate the processes that 

trigger the expansion of several plant species native to 

forests and review their ecological characteristics to provide 

general guidelines in assessing native invasion risk in forest 

stands. 

We argue that major anthropogenic changes to disturbance and browsing regimes bring about the monopolization of the forest understory by native plants. In all cases 

reviewed, aggressive understory plant expansion followed 

alterations in overstory disturbance regimes. Although these 

disruptions included predictable and manageable impacts 

such as tree harvesting, other less predictable overstory 

disturbance agents including catastrophic fires, insect outbreaks, and pathogens were involved. Assessing and managing risk from these alternative threats is challenging as their 

occurrence is often erratic, hard to control, and not limited 

by land ownership and administrative boundaries. In many 

cases, the risk to forest understories was particularly acute 

if the effects of multiple stressors occurred in a stand, either 

in tandem or within a short period of time. Specifically, the 

synergy between overstory disturbance and uncharacteristic 

fire regimes or increased herbivore strongly controls species 

richness and leads to depauperate understories dominated 

by one or a few species.

We suggest that aggressive expansion by native 

understory plant species can be explained by considering 

their ecological requirements in addition to their environmental context. Some plant species are particularly invasive 

by virtue of having life-history attributes that match one or 

more of the opportunities afforded by multiple disturbances. 

Increased overstory disturbance selects for shade-intolerant 

species with rapid rates of vegetative spread over slower 

growing, shade-tolerant herbs and shrubs. Altered fire 

regimes select for only those species that can survive the 

fire or resprout thereafter. Finally, overbrowsing selects 

for only those species that are well defended or tolerant to 

browsing. Ultimately, these processes create novel conditions that favor only a small subset of species that possess 

some combination of the following life-history characteristics: rapid vegetative growth, relatively shade intolerant, fire 

tolerant, and herbivore tolerant. The result is a low diversity 

but dense understory that can persist for long periods of 

time even if the canopy closes.

The framework advanced by this review could aid land 

managers in implementing informed management policies 

and practices that both limit the spread of these plants and 

target control and remediation treatments directed at the 

precise mechanism of interference. We suggest vigilant 

monitoring of stand conditions to ensure that alterations to 

the overstory and understory disturbance regimes do not 

operate concurrently, particularly when control over these 

factors falls under the purview of different management 

agencies (e.g., wild game vs. forestry management agencies). Finally, we caution that decisions regarding partial 

or complete overstory removals should consider a site’s 

The Formation of Dense Understory Layers in Forests 

Worldwide: Consequences and Implications for Forest 

Dynamics, Biodiversity, and Succession
470

GENERAL TECHNICAL REPORT PNW-GTR-802

understory conditions including inadequate advance regeneration, presence of clonal understory plants, fire history, 

and high herbivore impact.

Keywords: Competition, interference, invasive, recalcitrant understory layer, regeneration.

Introduction

Major anthropogenic changes in the frequency and severity 

of natural disturbance regimes can radically alter understory species composition and threaten the long-term sustainability and biodiversity of plant ecosystems (Alpert and 

others 2000, Roberts 2004, Rooney and others 2004). These 

changed disturbance regimes often trigger rapid expansion 

of native plant species that previously occupied a relatively 

minor portion of the understory flora (de la Cretaz and 

Kelty 1999, Mallik 2003, Vandermast and Van Lear 2002). 

Following their release, these herbs, shrubs, trees, and 

vines aggressively colonize and overtake disturbed patches 

forming persistent, nearly monospecific, and seemingly 

impenetrable thickets. This layer, identified in the literature 

as competing vegetation, interfering plants, low canopy, 

non-crop vegetation, native invasives, recalcitrant understory layer, or weeds, creates conditions below its canopy 

that reduce tree seedling establishment and survival, inhibit 

seedling growth into the sapling-size class, and alter species 

composition (Bashant and others 2005, Horsley 1993a, 

Messier and others 1989, Nilsen and others 2001, Tappeiner 

and others 1991). The impacts of this interfering layer alter 

the rate, direction, and composition of tree regeneration so 

profoundly that forest recovery following disturbance may 

contrast sharply with the predicted patterns of vegetation 

development for a particular forest type. Thus, these dominant understory layers often are the crucial factor determining success or failure of tree regeneration following harvest, 

thus threatening sustainable forest management (Ehrenfeld 

1980, Gill and Marks 1991, Huenneke 1983). 

In this paper, we first review the processes that cause 

the formation of recalcitrant understory layers. Second, 

we describe how these layers alter the rate and direction 

of forest succession. Third, we review published work to 

identify how these layers control tree recruitment, growth, 

and survivorship and, thus, patterns of tree regeneration and 

succession. Fourth, we identify the most prominent causal 

mechanisms for the formation of these layers and outline the 

consequences of their formation on successional dynamics 

and forest regeneration. Finally, we discuss how recalcitrant 

understory layers may reduce floristic diversity, we argue 

for their incorporation into forest successional models, 

and we explore management options for mitigation of their 

impacts.

On the Development of Recalcitrant Understory 

Layers Worldwide

Recent changes in disturbance and browsing regimes have 

strongly impacted species composition in forest understories worldwide (Coomes and others 2003, de la Cretaz and 

Kelty 1999, Mallik 2003, Vandermast and Van Lear 2002). 

Typically, these changes have led to large increases in the 

density and cover of a small number of native understory 

plant species (e.g., Mallik 2003). In many cases, these species expand to form persistent, monodominant layers that, 

in some cases, are nearly impenetrable (Figure 1, Tables 1 

and 2). We term these dense strata recalcitrant understory 

layers. No matter where they occur worldwide, they are 

characterized by sharing one or more of the following 

attributes: (1) the understory layer is often more dense with 

greater vegetation cover and lower diversity than was common in forest understories in the past; (2) this layer can alter 

successional trajectories and slow the rate of succession by 

creating conditions in the understory near ground level that 

are inimical to seeds and seedlings of many tree species 

(e.g., very low light at the soil surface); or (3) once this layer 

is formed, it can resist displacement by other species and 

remain intact for decades, even beneath closed-canopy forests. These layers and species have been termed low canopies (Schnitzer and others 2000) and native invasives (de la 

Cretaz and Kelty 1999), respectively. We prefer recalcitrant 

understory layer because it emphasizes that the effect of this 

layer occurs in the understory and is resistant to displacement. Additionally, the term native invasive suggests these 

species, similar to exotic invasives (e.g., exotic Japanese 

barberry, Amur honeysuckle; reviewed by Richburg and 

others 2001), are invading novel habitat when in fact the 

species that formed these layers were present throughout the 
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Figure 1—Diagrammatic representation of the conversion from (A) forests 

containing a diverse and structured advanced regeneration layer with sparse 

understory plant abundance (shown as grey inverted triangles), to (B) forests 

where a native understory species expands and monopolizes the understory. The 

dense herbaceous or shrubby cover represents a new vegetation layer that exerts 

direct and indirect interference effects and prevents seedling (shown as small 

green disks) recruitment into the sapling class. (C) Photographic example with 

hay-scented fern in northwestern Pennsylvania forests (Photograph by Alejandro Royo, USDA Forest Service)

habitat at varying degrees of abundance. Overall, we argue 

that models and theories of forest succession must now consider that many forests have a strong understory filter that 

determines which tree species are present to take advantage 

of a newly formed gap. In many cases, these recalcitrant 

understory layers are dramatically altering forestwide species diversity and patterns of succession.

Processes Causing the Formation of 

Recalcitrant Understory Layers

In this section, we discuss how natural processes, including 

such stressors as (1) overstory disturbance, (2) elevated 

herbivore regimes, and (3) altered fire regimes may be 

treated either as threats or benefits to forest communities. 

Overstory disturbances reinitiate stand development (Oliver 

and Larson 1996), and characteristic fire and herbivore 

regimes often promote species coexistence (reviewed in 

Bond and Keeley 2005, Huntly 1991). What constitutes a 

threat or a risk often is intrinsically linked (and, thus, often 

critiqued) to a subjective value of what constitutes a loss 

in biological or ecological diversity, function, or service 

(see Power and Adams 1997 for a vigorous debate). To 

be workable, we narrowly categorize an uncharacteristic 

disturbance regime as a threat or risk if its occurrence 

results in a persistent negative impact on the ability of the 

disturbed stand to regenerate its predisturbance tree species 

composition. Utilizing that definition, it is clear that alterations to disturbance regimes can constitute a threat to forest 
472

GENERAL TECHNICAL REPORT PNW-GTR-802 Table 1—Interfering species examplesa Rapid Areas vegetative Overstory Species Family affected growth Herbivory Fire disturbance Mechanism References Dennstaedtia Dennstaedtiaceae NE US √ √ (T) - √ 1,2,3,5 [4] Cody and others 1977; Anderson and Egler punctilobula 1988; Drew 1988; McWilliams and others (Michx.) T. Moore 1995; de la Cretaz and Kelty 1999, 2002; George and Bazazz 1999 a,b; Horsley and Marquis 1983; Horsley 1977, 1993 a,b; Horsley and others 2003; Hill 1996; Hill and Silander 2001; Brach and others 1993 Pteridium Dennstaedtiaceae Worldwide √ - √ √ 1,2,3,5 [4] Gliessman 1978; Gleissman and Muller 1972, aquilinum (T) 1978; Dolling 1996, 1999; Dolling and (L.) Kuhn others 1994; Ferguson and Adams 1994; Den Ouden 2000 Thelypteris Thelypteridaceae NE US √ √ (T) - √ [1] Hill and Silander 2001

 noveboracensis

(L.) Nieuwl.

Blechnum spp. Blechnaceae New √ √ (T) - √ - Coomes and others 2003,

 Zealand Cunningham 1979

Cyathea spp. Cyatheaceae New √ √ (T) - √ - Jane and Pracy 1974, Rogers and 

 Zealand Leathwick 1997

Dicranopteris Gleicheniaceae Sri Lanka, √ - √ √ - Maheswaran and Gunatilleke 1988,

 linearis Hawaii Russell and others 1998Dicranopteris Gleicheniaceae Puerto √ - √ √ [1] Guariguata 1990, Walker 1994, 

 pectinata Rico Walker and Boneta 1995, Russell 

 (Burm.) Underw. and others 1998, May 2000Gleichenia Gleicheniaceae Puerto √ - √ √ - Guariguata 1990, Walker and Boneta 1995

 bifida Rico

 (Willd.) ChingGleichenia Gleicheniaceae Malasia √ - √ - Kochummen and Ng 1977

 linearis

 (Burm. f.) C.B. 

 ClarkeCalamagrostis Poaceae N. America √ - √ (T) √ 1,5 [4] Cater and Chapin 2000, Hogg and Lieffers 

 1991, Lieffers and others 993

 canadense Boreal

 (Michx.) P. Forests 

Beauv.

Cortaderia spp. Poaceae New √ √ - √ - Jane and Pracy 1974, Rogers and Leathwick

 Zealand 1997

Deschampsia Poaceae European √ √ (T) - √ - Berquist and others 1999

 flexuosa Boreal (L.) Trin. ex Forests
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Rapid

Areas vegetative Overstory

Species Family affected growth Herbivory Fire disturbance Mechanism References

Astrogyne Arecaceae Costa - - - - 1,2,[6] Denslow and others 1991

 martiana Rica

 (H. Wendl.  ex Drude)Geonoma Arecaceae Costa - - - - 1,2,[6] Denslow and others 1991

 cuneata Rica

 H. Wendl.  ex spruceOenocarpus Arecaceae Costa √ - - - 6,[1] Farris-Lopez and others 2004

 mapora Rica

H. Karst.

Asplundia Cyclanthaceae Costa - - - 1,2,[6] Denslow and others 1991 uncinata Rica

 HarlingAechmea Bromeliaceae Panama √ - - √ [1] Brokaw 1983, Pfitsch and Smith 1988

 magdalenae (André) André ex BakerSinarundinaria Poaceae China √ - - - - Taylor and Zisheng 1988

 gangianaChusquea spp. Poaceae W. South √ - - √ [1,4] Lusk 2001, Veblen 1982, 

 America Gonzalez and others 2002

Guadua Poaceae S. America √ - √ √ [1,6] Griscom and Ashton 2003

 sarcocarpa Londono &  Peterson, sp. nov.Fargesia Poaceae China √ - - - - Taylor and others 1995

 denudataSasa spp. Poaceae Japan √ - - √ [1,4] Abe and others 2002, Iida 2004, Wada 1993; 

Yushania Poaceae Bhutan √ - - - [1] Gratzer and others 1999

 microphylla

Munro

Calluna vulgans Ericaceae W. Europe √ - √ (T) √ [3] Mallik 1995

Kalmia Ericaceae E. Canada √ - √ (T) √ [2,3] Hall and others 1973, Meades 1983, Mallik 

angustifolia & United 1995, Inderjit and Mallik 1996, Mallik and

 (L.) States Inderjit 2001, Bradley and others 1997,

 Yamasaki and others 1998

Kalmia Ericaceae E. United √ - √ (T) √ [1] Woods and Shanks 1959, Monk and others 

latifolia States 1985, Lipscomb and Nilsen 1990, Mallik 

 (L.) 1995
474

GENERAL TECHNICAL REPORT PNW-GTR-802 Rapid Areas vegetative Overstory Species Family affected growth Herbivory Fire disturbance Mechanism References Rhododendron Ericaceae E. United √ - √ √ (T) 1,[2,3,4,5] Monk and others 1985; Lipscomb and Nilsen maximum States 1990; Clinton and others 1994, Clinton and Vose 1996, Walker and others 1999; Nilsen and others 1999, 2001; Beckage and others 2000; Lei and others 2002; Beckage and Clark 2003, Christy 1986;, Hille Ris Lambers and Clark 2003; Vandermast and Van Lear 2002 aylussaccia Ericaceae E. United √ - - √ 1,2 Beckage and Clark 2003 baccata States (Wang.) K. Koch Gaultheria Ericaceae W. Canada √ - √ (T) √ 3 Price and others 1986; Messier and shallon Pacific Kimmins 1990, 1991; Messier 1992, 1993; Pursh Northwest Chang and Preston 2000, Chang and others 1996a,b; Bunnell 1990

Vaccinium Ericaceae European √ - √ (T) √ 1,2,3 Maubon and others 1995, Jäderlund and others 1997, 

myrtillus boreal Moola and Mallik 1998, Frak and Ponge 

 (L.) forests 2002

Empetrum Ericaceae European √ - - √ 1,2,3 Michelsen and others 1995, Mallik 2003, 

hermaphroditum boreal Nilsson 1994, Wardle and Nilsson 1997, 

 (Lange ex forests Zackrisson and others 1997, Nilsson and 

 Hagerup) Böcher others 2000

Pseudowintera Winteraceae New - √ (T) - √ - Godley and Smith 1981, Allen and others 

colorata Zealand 1984, Coomes and others 2003, Husheer 

 and others 2003

Rubus spp. Rosaceae Pacific NW √ - - √ [1,4,5] Franklin and Pechanec 1967, Tappeiner and 

 & Europe others 1991, Schreiner and others 2000

Rhus glabra Anacardiaceae E. United √ - - √ 1,2 Putz and Canham 1992

 States

Lianas (various - Tropical √ - √ √ 1,2,[6] Smith 1984, Dillenberg and others 1993, 

 genera) Forests & Perez-Salicrup and Barker 2000, Schnitzer 

 E. United and others 2000, Schnitzer and Bongers 

 States 2002, Gerwing 2001, Grauel and Putz 2004

Cornus spp. Cornaceae NE United √ - - √ 1,2 Ehrenfeld 1980, Huenneke 1983, Putz and 

 States Canham 1992

Corylus Betulaceae Lake States √ - - √ 1,2 Sabhasri and Ferrell 1960; Batzer and 

cornuta United Popp 1985, Tappeiner 1971, Tappeiner and 

 Marsh. States John 1973, Tappeiner and others 1991, 

 2001; Kurmis and Sucoff 1989

Acer Aceraceae SE Canada, √ - - √ [1] Aubin and others 2000

spicatum NE United

 Lam. States

a Occurrence of dense, monodominant understory species. Information in this table summarizes whether the species possess rapid vegetative growth; if their increase in abundance is linked to alterations in the herbivory, fire, or overstory disturbance regimes; and whether they are fire or browse tolerant (Ts). “Mechanism” indicates the specific interference mechanisms exerted by a species (1 = aboveground competition, 2 = belowground competition, 3 = allelopathy, 4 = seed/seedling predation, 5 = litter, and 6 = mechanical damage). Mechanisms were tested using manipulative field experiments, unless they are in brackets, in which case they are speculative.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

regeneration if they result in a degraded understory plant 

community composition monopolized by a select species 

that interferes with tree regeneration. 

We found that major anthropogenic changes to disturbance and browsing regimes underlie the development of 

most recalcitrant understory layers (see Hobbs and Huenneke 1992 for their similar conclusion regarding exotic 

invasives). Indeed, overbrowsing, altered fire regimes, and 

increased overstory disturbance were implicated in 18, 34, 

and 82 percent, respectively, of the cases in Table 1. More 

importantly, our review suggests that the formation of a 

dense understory canopy layer arises approximately 53 

percent of the time in the cases when overstory disturbances 

and altered understory fire and browsing regimes occur in 

tandem (Table 1). Additionally, these understory layers are 

depauperate because repeated canopy disturbances combined with other processes (i.e., fire and browsing) strongly 

favor a small subset of species.

Increased Overstory Disturbance

Direct and indirect human-induced disturbances (including logging, fires, insect outbreaks, and pathogens) have 

increased the extent and particularly the frequency of 

overstory disturbance over the past century (Carson and 

others 2004, Seymour and others 2002, Sharitz and others 

1992, Youngblood and Titus 1996). These disturbances 

typically increase resource availability (e.g., light) in the 

understory both in the short and long-term. There is little 

doubt that these disturbances increase the establishment 

and growth of seedlings and saplings of canopy trees at 

least in the short term (Canham 1989, Canham and others 

1994, Denslow 1987, Finzi and Canham 2000, Hartshorn 

1978, Runkle 1982). However, these extensive and repeated 

overstory disturbances may be most beneficial to a few 

understory species that possess high rates of growth and 

vegetative expansion when exposed to high light (Ehrenfeld 

1980, Huenneke 1983, Schnitzer and others 2000) (Table 

1). These species are typically shade intolerant, yet highly 

plastic, so that they can persist at low-light levels following 

canopy closure by utilizing sunflecks or clonal integration 

(e.g., Brach and others 1993, Lipscomb and Nilsen 1990, 

Messier 1992, Moola and Mallik 1998).

There are numerous examples worldwide whereby 

canopy disturbances lead to the formation of recalcitrant 

understory layers (Table 1). Tappeiner and others (1991) 

found that logging increased the formation of salmonberry 

(Rubus spectabilis) tangles by nearly 300 percent over uncut 

stands. Throughout the Tropics, large-scale disturbances 

can create bamboo and fern thickets that persist for decades 

(Griscom and Ashton 2003, Guariguata 1990, Russell and 

others 1998, Walker 1994). In temperate and boreal forests, 

both native and exotic insect outbreaks open up vast areas 

of forest canopies (e.g., Gypsy moth (Lymantria dispar L.) 

and Spruce budworm (Choristoneura fumiferana Clemens.) 

often leading to an increase in the density and dominance 

of a few shrub species (Aubin and others 2000, Batzer 

and Popp 1985, Ehrenfeld 1980, Ghent and others 1957, 

Hix and others 1991, Muzika and Twery 1995). Fungal 

pathogens have opened up canopies in central New York 

(Dutch elm disease, Ophiostoma ulmi) causing the formation of widespread and dense patches of Alnus, Cornus, and 

Viburnum spp. (Huenneke 1983). Both Huenneke (1983) and 

Ehrenfeld (1980) argued that these dense shrub layers would 

delay canopy formation and alter its composition. Likewise, 

Chestnut blight (Cryphonectria parasitica) apparently led to 

the aggressive expansion of Rhododendron maximum in the 

Southern Appalachians (Vandermast and Van Lear 2002). 

In general, any process (whether anthropogenic or not) 

that increases light availability in the understory has the 

potential to lead to the formation of recalcitrant understory 

layers. Nonetheless, it appears that several processes must 

be altered in combination before these recalcitrant layers 

can form. 

The Interaction of Elevated Herbivore and Canopy 

Disturbance

This section describes how extended periods of elevated 

browsing by either introduced or native mammalian 

herbivores (e.g., white-tailed deer in the Eastern United 

States; reviewed by Côte and others 2004, McShea and 

others 1997, Russell and others 2001) often coincide with 

large-scale canopy disturbances leading to the development 

of dense interfering layers. Frelich (2002) characterized 

chronic overbrowsing as a low-intensity disturbance, which, 
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GENERAL TECHNICAL REPORT PNW-GTR-802 Table 2—Estimates of spatial coverage by understory native plant invasions in forested areas at both local and regional scales Estimated Proportion of forest forested area Species Location Type (ha) affected References Dennstaedtia Pennsylvania Temperate 2.1 million 0.33 McWilliams and others 1995 punctilobula hardwoods Dennstaedtia Allegheny Temperate 241,000 - Approx. 0.47 - 0.59 Royo, unpublished data punctilobula National hardwoods 303,000 Forest, 

 Pennsylvania

Rhododendron Southern Temperate 2.5 million - Monk and others 1985

maximum Appalachian hardwoods

 Region, U.S.

Lianas (various Barro Colorado, Tropical forests - Approx. 0.22 Schnitzer and others 2000

genera) Rep. of of all gaps

Panama

Pteridium Fontainebleau Old-growth - 0.02 - 0.17 Koop and Hilgen 1987 aquilinum Forest, France Beech forestPteridium The Netherlands Temperate 288,500 Approx. 0.7 den Ouden 2000 aquilinum hardwood of entire

and conifer country

Kalmia Newfoundland, Black spruce - 0.55 English and Hackett 1994 angustifolia CanadaGaultheria British Columbia Cedar/hemlock 100,000 - Weetman and others 1990 shallon forestGuadua Amazonia Tropical forests 18 million - Nelson 1994 sarcocarpaCalamagrostis Western Boreal forests - 0.25 - 0.5 Lieffers, pers comm.

canadensis Canadian of all cutover

 Provinces forest

Note: dash (-) in “Estimated area affected” and “Proportion of forested area affected” means that no information was available for this entry.a Coverage data convey either the total forested land area (in hectares) or the proportion of forested area dominated by a particular species within a region. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

over time, can lead to depauperate understories composed 

almost entirely of highly browse tolerant or unpalatable 

species (Banta and others 2005, Frelich and Lorimer 1985, 

Horsley and others 2003, Rooney and Dress 1997, Waller 

and Alverson 1997). If these browse-tolerant or unpalatable species happen to be clonal shrubs or herbs, then any 

canopy disturbance that suddenly elevates understory light 

levels can cause the rapid expansion of these species. One 

of the best examples of the interplay between long periods 

of overbrowsing and canopy disturbance is hay-scented 

fern (Dennstaedtia punctilobula). This species historically 

occupied <3 percent of the understory in Pennsylvania 

(Lutz 1930) but currently forms a recalcitrant understory 

layer covering more than a third of the forested area in that 

State (Table 2) and is abundant throughout much of the 

Northeastern United States (de la Cretaz and Kelty 1999). 

Essentially, years of overbrowsing created a depauperate 

forest understory and suppressed woody establishment in 

the advance regeneration layer. When light levels increased, 

continued overbrowsing prevented successful seedling 

establishment and growth while the unpalatable hay-scented 

fern rapidly spread into this sparsely occupied habitat-forming dense monospecific stands (Figure 1). Other examples 

include Sweden, where clearcutting and overbrowsing 

convert forests to unpalatable grass-dominated communities (e.g., Deschampsia flexuosa; Bergquist and others 1999) 

and in New Zealand where arboreal herbivory by marsupials opens up the canopy, and, in combination with deer 

overbrowsing, leads to stands of unpalatable plant species 

(Allen and others 1984, Coomes and others 2003, Jane and 

Pracy 1974, Rogers and Leathwick 1997, Wardle and others 

2001). In parts of New Zealand, forest area cover by shrubs, 

ferns, and grasses has increased from < 1 percent to nearly 

30 percent in just 30 years (Batcheler 1983).

The Interaction of Altered Fire Regimes and 

Canopy Disturbance

This section discusses how human alterations to the 

frequency or severity of fire in various ecosystems (Attiwill 1994, Mallik 2003, May 2000) are often linked to 

the increase in interfering species. Frequent understory 

fires thin the understory by reducing seedling and sapling 

density, thereby increasing light availability, and favoring species that can survive the fire or resprout thereafter 

(Abrams 1992, Collins and Carson 2003, Donlan and Parker 

2004). When canopy disturbances and surface fires occur 

in tandem or within a relatively short period, the increase 

in light can contribute to the development of a recalcitrant 

understory layer (Mallik 2003, Payette and Delwaide 2003). 

For example, in boreal forests, Payette and Delwaide (2003) 

found that a “synergy” existed between fires and overstory 

disturbance, which created shrub-dominated heathlands. 

These heathlands became dominated by shrub species, 

mainly Calluna, Kalmia, and Vaccinium spp., which can 

rapidly resprout and spread clonally following severe fires 

(Mallik 1995, Meades 1983). Similarly, in tropical forests, 

various shade-intolerant ferns (Dicranopteris, Gleichenia, 

or Pteridium spp.) or bamboo (Guadua) that also spread 

clonally can rapidly colonize and monopolize areas following catastrophic fires (Dolling 1999, Finegan 1996, Gliessman 1978, May 2000, Nelson 1994).

Alternatively, canopy disturbances that coincide with 

a decrease in fire frequency can lead to the development of 

recalcitrant understory layers. Mallik (2003) hypothesized 

that long-term fire suppression in logged or defoliated 

stands led to forest “conversion” to Kalmia, Calluna, and 

Gaultheria heathlands. In temperate forest systems, fire 

suppression and canopy disturbances contribute to the 

spread of rhododendron and mountain laurel (Kalmia latifolia). These species now form recalcitrant understory layers 

that cover an estimated 2.5 million hectares in the Southeastern United States alone (Table 2; Monk and others 1985, 

Vandermast and Van Lear 2002). Furthermore, studies from 

the Coweeta Basin in North Carolina confirm the expansion 

continues with a doubling of rhododendron cover in only 17 

years (Nilsen and others 1999).

The separate and combined effects of disturbances and 

browsing act as strong filters on species richness creating 

depauperate understories dominated by one or a few species. The degree of control or release of specific species will 

depend on the degree to which disturbance and browsing 

regimes are altered as well as the life-history characteristics 
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GENERAL TECHNICAL REPORT PNW-GTR-802

of the understory plant species (Roberts 2004). Overbrowsing selects for only those species that are well defended or 

tolerant to browsing (Banta and others 2005, Horsley and 

others 2003). Frequent fires select for only those species that 

can survive the fire or resprout thereafter (Gliessman 1978, 

Mallik 2003, Payette and Delwaide 2003). Finally, increased 

overstory disturbance selects for shade-intolerant species 

with rapid rates of vegetative spread vs. slower growing 

shade tolerant herbs and shrubs (Ehrenfeld 1980, Moola and 

Mallik 1998, Schnitzer and others 2000). Ultimately, these 

processes create novel conditions that favor only a small 

subset of species that possess some combination of the following life-history characteristics: rapid vegetative growth, 

relatively shade intolerant, and herbivore tolerant (Table 

1; see also Roberts 2004). The result is a low diversity but 

dense understory that can persist for long periods of time 

even if the canopy closes.

Recalcitrant Understory Layers Arrest, 

Delay, and Alter Forest Succession

This section describes different ways that a recalcitrant 

understory layer can influence forest regeneration and stand 

development following a disturbance event. In the following sections we briefly review the literature to evaluate the 

evidence for three different successional pathways (Figure 

2). These pathways include (1) indefinite suppression of 

subsequent tree regeneration (arrested succession), (2) a protracted period of stand establishment (delayed succession), 

and (3) a differential reduction of tree seedling recruitment 

that constricts species composition in the ensuing forest 

stand (altered succession). 

Arrested Succession

In a small number of documented cases, recalcitrant 

understory layers appear to exclude tree regeneration for 

extended periods of time. This pathway is described by a 

Figure 2—Conceptual model illustrating factors precipitating change from historical gap-phase regeneration into low-canopy dominance. The model also reveals various interference mechanisms and illustrates the ensuing successional pathways. The size and boldness 

of the arrows denote the relative importance of each transition as revealed by our review. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

variety of terms including self-perpetuating climax community (Horsley and Marquis 1983), alternate stable state 

(Schmitz and Sinclair 1997, Stromayer and Warren 1997), 

polyclimax (Tansley 1935), or arrested succession (Niering 

and Goodwin 1974). Although the long-term stability of 

these systems is difficult to confirm (Connell and Sousa 

1983; Peterson 1984; Sutherland 1974, 1990), there are compelling examples where shrubs and ferns have persisted for 

decades in stands formerly dominated by trees (Den Ouden 

2000, Horsley 1985, Koop and Hilgen 1987, Latham 2003, 

Mallik 2003, Niering and Egler 1955, Petraitis and Latham 

1999, Raich and Christensen 1989). It is unclear whether 

these layers are self-sustaining (e.g., via strong interference; 

Stromayer and Warren 1997) or if continued browsing or 

frequent fire is required to perpetuate them and retard the 

reestablishment of trees (Hill 1996, Mallik 2003).

Delayed Succession

A recalcitrant understory layer can slow the growth rate of 

tree species, thereby slowing the rate of successional change 

without altering the eventual tree species composition. 

For example, in boreal forests, the grass Calamagrostis

canadensis suppresses the regeneration of dominant tree 

species, including white spruce (Picea glauca). This delays 

stand development by 20 to 30 years until saplings eventually emerge through the C. canadensis canopy, and the 

stands revert to forest (reviewed by Lieffers and others 

1993). Delayed successions also occur in other boreal forests 

where a dense ericaceous shrub layer suppresses the-growth 

and emergence of tree species including western redcedar 

(Thuja plicata), Sitka spruce (Picea sitchensis), and Norway 

spruce (Picea abies) (Mallik 1995, Maubon and others 1995, 

Messier and Kimmins 1991, Messier and others 1989).

Additionally, a recalcitrant understory layer may reduce 

tree species survivorship sufficiently to delay succession. 

For example, in tropical forests, gaps promote the expansion of resident understory lianas (Schnitzer and others 

2000). These understory lianas can become so dense after 

gap creation that they inhibit the subsequent growth and 

survival of both pioneers and shade-tolerant trees, thus 

stalling succession for decades (Schnitzer and others 2000). 

This dynamic of delayed gap-phase regeneration occurs 

in tropical and temperate forests where lianas, fern, and 

bamboo thickets effectively clog gaps (Abe and others 2002, 

Guariguata 1990, Kochummen and Ng 1977, Schnitzer and 

others 2000, Taylor and Zisheng 1988, Walker 1994). In 

time, trees emerge from this layer and reach the canopy, 

apparently with little impact on species composition or the 

ensuing successional trajectories (Abe and others 2002).

Altered Forest Succession

A recalcitrant understory layer may differentially reduce 

establishment among co-occurring tree species, thereby 

controlling the composition of the advance regeneration 

layer (George and Bazzaz 1999a, 1999b). Dense understories create conditions near the soil surface that are inimical 

to tree germination and early growth and survivorship. For 

example, understory layers that generate a thick litter layer 

may inhibit germination of small-seeded species (FarrisLopez and others 2004, George and Bazzaz 1999a), whereas 

those that strongly preempt light can preclude the establishment of many shade-intolerant and intermediately tolerant 

species (de la Cretaz and Kelty 2002, Gonzalez and others 

2002, Horsley 1993a). These dense layers may substantially 

suppress tree recruitment by a combination of at least six 

different types of interference mechanisms (Table 1). Consequently, only a few tree species may possess the necessary traits to persist under, and eventually emerge through, 

this understory layer to constitute the advance regeneration 

layer (Connell 1990, Runkle 1990). If so, then the species 

composition of the advance regeneration layer and subsequent successional dynamics will contrast sharply in forests 

with a recalcitrant understory layer vs. one without.

Mechanisms of Interference Over Tree 

Establishment, Survival, and Growth

This section describes different ways that a dense understory canopy can suppress regeneration. Because most 

studies fail to distinguish among these mechanisms, 

Muller (1969) proposed the term interference to describe 

the suppression of one species or layer on another species. 

In the following sections, we briefly review the literature 

to evaluate the evidence for six different mechanisms of 

interference between the understory layer and co-occurring 
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tree species. These mechanisms include (1) resource competition, (2) allelopathy, (3) physically impeding seedling 

germination and growth, (4) through modifications of interspecific interactions (Figure 2). We suggest that the most 

efficient and cost-effective remediation of the deleterious 

effects of these recalcitrant understory layers will require a 

greater understanding of how these layers alter patterns of 

forest regeneration and succession.

Resource Competition

In forested systems, perhaps the most prominent interference mechanism exerted by a recalcitrant understory layer 

would be direct competition for above- and belowground 

resources. In closed-canopy forests, dense understories 

exacerbate the degree of light attenuation caused by the 

midstory and canopy (Beckage and others 2000, de la 

Cretaz and Kelty 2002, Messier and others 1998, Nilsen 

and others 2001). Photosynthetically active radiation (PAR) 

levels can drop well below 5 percent of full sun beneath 

these layers (Aubin and others 2000, Clinton and Vose 

1996, George and Bazzaz 1999a, Hill 1996, Horsley 1993a, 

Kelly and Canham 1992, Lei and others 2002, Lusk 2001, 

Nakashizuka 1987, Wada 1993, Walker 1994). Additionally, 

these dense, low canopies can reduce light quality (e.g., 

red: far-red wavelengths), thereby preventing germination, 

altering internode elongation, and inhibiting flowering 

(Horsley 1993a, Mancinelli 1994, Messier and others 1989). 

Furthermore, dense, low canopies decrease the availability 

of sunflecks particularly for seedlings (Denslow and others 

1991, Lei and others 2002, Nilsen and others 2001). Finally, 

if canopy gaps do form, they may not operate as gaps at 

all if seedlings remain trapped beneath a dense understory 

layer (Beckage and others 2000, Lusk 2001, Webb and 

Scanga 2001). Under this scenario, regeneration may be 

limited to only a few individuals of those few species that 

are highly shade tolerant.

Dense understories may also exacerbate belowground 

competition (Dillenburgh and others 1993, Messier 1993, 

Putz and Canham 1992). Some studies infer resource limitation by detecting increased growth or survival of target 

plants following fertilization or measuring lower nutrient 

and water concentrations in soil beneath dense understory 

cover vs. more open areas (e.g., Inderjit and Mallik 1996, 

Messier 1993, Nilsen and others 2001, Yamasaki and others 

1998). Similarly, vine-covered saplings often have lower 

foliar nitrogen levels, reduced preleaf water potential, and 

decreased diameter growth when compared to vine-free 

saplings (Dillenburgh and others 1993, Perez-Salicrup and 

Barker 2000). The above studies are suggestive of resource 

limitation though they typically do not distinguish between 

competition for water vs. soil nutrients. Because nutrient 

and water availability covary, decoupling these two factors is difficult (Casper and Jackson 1997, Nambiar and 

Sands 1993). Additionally, few experiments use factorial 

manipulations to disentangle a dense understory layer’s 

aboveground vs. belowground effects and their interactions 

(McPhee and Aarssen 2001).

Horsley (1993a) experimentally tested the influence 

of aboveground vs. belowground competition. He tied 

back hay-scented fern fronds while leaving their roots and 

rhizomes intact, thereby reducing light competition and 

isolated seedlings within PVC tubes, thereby reducing root 

competition. He found that light attenuation, not belowground competition, was the mechanism of interference 

(Horsley 1977, 1993a, 1993b). Putz and Canham (1992) 

conducted similar aboveground and belowground manipulations. They found that a dense shrubby understory layer 

reduced tree regeneration primarily because of belowground 

competition (see also Christy 1986), although this varied 

with soil fertility. Belowground competition was more 

important in infertile sites, whereas aboveground competition was more important in fertile sites. Clearly wellreplicated factorial experiments are required to ascertain 

the relative importance of aboveground vs. belowground 

competition, although other processes may confound the 

results of these experiments (e.g., allelopathy; see section on 

allelopathy).

Allelopathy

This section discusses the potential effects of the phenomenon called allelopathy: i.e., the inhibition of growth or 

survivorship of one plant species by chemicals produced 

by another species. Direct field evidence for allelopathy 

remains equivocal and elusive. In forests that have dense 
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understories dominated by ericaceous shrubs, the phenolics 

and other phytochemical compounds produced by these 

shrubs can disrupt nitrogen mineralization and inhibit 

ectomycorrhizal fungi; this significantly reduces conifer 

growth and survivorship (Walker and others 1999; reviewed 

by Mallik 1995, 2003 and Wardle and others 1998). In these 

systems, Nilsson (1994) used factorial manipulations of 

aboveground and belowground competition and allelopathy 

to identify how the boreal shrub Empetrum hermaphroditum suppressed tree regeneration. She found that both 

belowground competition and allelopathy were important, 

but that belowground competition played the primary role. 

Similarly, Jäderlund and others (1997) found that Vaccinium 

myrtillus interfered with Norway Spruce (Picea abies) primarily through belowground competition. In forests where 

ferns form dense understories, bioassays and greenhouse 

studies have suggested the potential for strong allelopathic 

effects on tree regeneration (Gliessman and Muller 1972, 

1978; Horsley 1977); however, further field experimentation 

failed to find strong allelopathic effects (Den Ouden 2000, 

Dolling 1996, Horsley 1993b, Nilsen and others 1999). 

Despite these results, too few studies have tried to experimentally disentangle resource competition from allelopathy 

via field experiments. Future research must move beyond 

merely documenting the existence of phytotoxic exudates 

in greenhouse and laboratory studies (Fuerst and Putnam 

1983, Inderjit and Callaway 2003, Weidenhamer 1996, 

Williamson 1990).

Seed Predation

This section discusses how a dense understory layer can 

increase the activity of small mammals, thereby increasing 

the rate and impact of seed and seedling predation (Den 

Ouden 2000, George and Bazzaz 1999a, Gliessman 1978, 

Schreiner and others 2000, Wada 1993). This can create a 

situation where it appears that low seedling densities are 

caused by resource competition (e.g., light attenuation) 

when, in fact, they are caused by seed and seedling predation (Connell 1990; Holt 1977, 1984). Connell (1990) defined 

this as a type of apparent competition (sensu Holt 1977, 

1984). Experiments that use canopy removals confound 

the direct competitive release caused by the removal of the 

understory layer with the indirect benefits of removing this 

layer, particularly the decrease in seed and seed predation 

by small mammals (Reader 1993). Even though small 

mammals are abundant, forage preferentially beneath dense 

vegetative cover, and consume copious quantities of seeds, 

few experiments have attempted to evaluate the role of seed 

or seedling predators vs. resource competition. Nonetheless, 

long-term studies in other plant systems have documented 

that selective seed and seedling predation can lead to rapid 

changes in plant community composition (e.g., Brown and 

Heske 1990, Gill and Marks 1991, Howe and Brown 2001, 

Ostfeld and Canham 1993).

Litter Accumulation

A thick litter layer typically reduces plant species diversity 

and density through a wide variety of direct and indirect 

mechanisms (see Facelli and Pickett 1991). For example, 

George and Bazzaz (1999a) found that a thick fern litter 

layer directly limited the establishment of small-seeded 

tree species (see also Beckage and others 2000, FarrisLopez and others 2004, Lei and others 2002, Veblen 1982). 

Alternatively, in boreal forests, the insulative properties of 

a dense grass litter layer results in decreased soil nitrogen 

mineralization, water uptake, and seedling photosynthetic 

rates, thus indirectly diminishing conifer growth and 

survival (Cater and Chapin 2000, Hogg and Lieffers 1991, 

Lieffers and others 1993). Aside from these examples, there 

are few experimental tests that unravel the many facets of 

litter interference or evaluate its importance relative to other 

mechanisms (e.g., resource competition). However, in forests characterized by a recalcitrant understory litter layer, it 

is clear that this alternative remains a viable and potentially 

important mechanism.

Mechanical Interference

A dense understory layer can reduce tree seedling regeneration via non-competitive, physical interference. Clark 

and Clark (1991) demonstrated that the passive shedding 

of branches and leaves of subcanopy palms smothered 

seedlings present in the understory. Similarly, collapsing 

Guadua bamboo culms can reduce tree seedling growth 

and survival (Griscom and Ashton 2003). Additionally, 
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GENERAL TECHNICAL REPORT PNW-GTR-802

the physical weight of a large liana load may suppress tree 

seedling and sapling growth (Gerwing 2001, Putz 1991, 

Schnitzer and others 2004). If tree species respond differentially to these physical stresses, then this mechanism alone 

can potentially alter understory tree species composition 

and modify future successional trajectories (e.g., Gillman 

and others 2003, Guariguata 1998).

The Relationship Between Mechanisms of 

Interference and Phenology

This section discusses how the intensity and duration of any 

particular interference mechanism can vary temporally as a 

result of the species’ life history, whether evergreen, deciduous, or monocarpic. In fact, this trait may provide clues to 

understand both the strength and type of interference. For 

example, evergreen species may pose a greater impediment 

to tree regeneration as their effects are exerted throughout 

the year on all tree seedling life-history transitions (Givnish 

2002). In contrast, herbaceous perennials that senesce in 

the fall or deciduous shrubby species only exert competitive effects during the growing season (e.g., de la Cretaz 

and Kelty 2002, Nilsen and others 2001). This delayed 

expansion of the recalcitrant understory layer provides a 

brief window of opportunity for evergreen tree species, 

species with early germination (e.g., Acer rubrum L.), or 

species with early leaf expansion (e.g., Betula lenta L.) to 

overcome the understory stratum’s deleterious effects on 

early establishment. This temporal advantage can provide 

sufficient photosynthetic and growth opportunity to enable 

trees to survive and eventually grow through a fern layer 

(de la Cretaz and Kelty 2002). Additionally, if the intensity 

of seed and seedling predation decreases with senescence of 

the low canopy, then the impact of pervasive seed predation 

may decrease in the fall. This timing of senescence may 

generate increased predation on early seed dispersers (e.g., 

Quercus spp.) relative to later dispersers (e.g., A. saccharum 

Marsh., Fagus grandifolia Ehrh.).

Causes and Consequences of a 

Recalcitrant Understory Layer

In this section, we discuss our contention that the expansion and monopolization of the understory by a narrow set 

of plant species is often an inadvertent outcome of policies and management decisions that deviate from natural 

forest overstory disturbance, fire, and herbivory regimes. 

We propose a general conceptual model through which 

alterations in the dynamics of the overstory, understory, 

or both generate increases in a select few understory plant 

species (Figure 1). These alterations involve changes in the 

frequency and scale of overstory disturbance, increased or 

decreased fire frequency, or increased herbivory that release 

a restricted set of understory species from prior competitive 

constraints. Once released, these species increase dramatically in abundance and cover over large portions of the 

forested landscape (Table 1). Following its establishment, 

this recalcitrant understory layer interferes with tree regeneration through a variety of direct and indirect mechanisms 

including above- and belowground competition, allelopathy, 

microhabitat-mediated seed/seedling predation, litter, and 

mechanical damage. Consequently, this recalcitrant layer 

itself inhibits tree regeneration and strongly influences 

which tree species establish and survive beneath its canopy 

(e.g., Cater and Chapin 2000, Clinton and others 1994, Dolling 1996, Veblen 1982). The strength and selectivity of this 

filter can retard succession, alter the tree species participating in the successional sere, or potentially arrest succession.

We found only 25 percent of the published studies reviewed the reported results of manipulative field 

experiments designed to identify the existence of one 

or more particular interference mechanism(s) (Table 1). 

Above- and belowground competition and allelopathy 

were the predominant mechanisms tested (37, 32, and 13 

percent, respectively; Table 1). Apart from competition 

and allelopathy, various interference mechanisms were 

speculated on in many papers, but few, if any, were tested 

experimentally. Given the paucity of information for most 

systems, we lack the information needed to clearly establish 

by which mechanism a recalcitrant understory layer inhibits 

tree regeneration (see Levine and others 2003 for a similar 

conclusion on exotic invasives).

We argue that a move towards a more mechanistic 

understanding of the “interference” phenomenon could 

begin by considering the most limiting resource(s) within 

a given system. For example, on a coarse scale, forested 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

ecosystems differ in the identity of the most limiting 

resource(s) (e.g., light, soil nutrients, and water), and these 

differences could provide insight into the most plausible 

interference mechanism. Boreal and cool-temperate forests 

are typically nutrient poor (primarily N) and less light 

limited relative to their temperate and tropical counterparts 

(Attiwill and Adams 1993, Kimmins 1996, Krause and 

others 1978, Reich and others 1997) (reviewed by Coomes 

and Grubb 2000 and Ricard and others 2003). We found that 

dense low canopies in these forest types suppress regeneration directly via belowground competition and indirectly via 

allelopathic interactions that mediate resource availability 

and uptake (Table 1; Christy 1986, Jäderlund and others 

1997, Nilsson 1994). In contrast, temperate deciduous and 

tropical rain forests tend to be more light limited (Finzi and 

Canham 2000, Pacala and others 1994, Ricard and others 

2003). In these systems, we found that other mechanisms 

including aboveground competition and seed predation 

were generally more important than belowground competition (Table 1; Den Ouden 2000, Denslow and others 1991, 

Horsley 1993a). Ideally, the best tests would link a series of 

carefully controlled laboratory or greenhouse studies with 

field experimentation in order to identify which mechanisms merit further investigation. Furthermore, we strongly 

argue that manipulative field experiments remain among 

the best tools to test the relative importance of each factor 

independently as well as any interactions.

Floristic Diversity and Forest Succession

The increasingly common development of recalcitrant 

understory layers worldwide plays a strong, yet vastly 

under-appreciated role in determining future successional 

patterns, forest composition, and diversity because of their 

tendency to selectively suppress tree regeneration. Indeed, 

studies examining the regeneration success of a variety of 

tree species demonstrate that a majority of tree species suffer decreased seedling densities and limited height growth 

underneath recalcitrant understory canopies (e.g., de la Cretaz and Kelty 2002; George and Bazzaz 1999a, 1999b; Hille 

Ris Lambers and Clark 2003; Horsley and Marquis 1983). 

The presence of this additional filter on floristic diversity in 

forest understories together with increased herbivory and 

altered fire regimes strongly restricts the number of species 

that can successfully regenerate. The potential consequences of these ecological filters (sensu George and Bazzaz 1999a, 1999b) on species composition remains poorly 

understood. Nevertheless, we suggest that floristic diversity 

in such areas is so severely constricted that succession may 

move steadily toward monodominance or complete regeneration failure. These extreme cases include the fern-and 

grass-covered orchard stands in Pennsylvania where 50- to 

80-year-old failed clearcuts remain devoid of tree regeneration (Horsley 1985) or bracken-covered tropical regions of 

Central America that have persisted for centuries following 

forest removal (Den Ouden 2000).

Forest Dynamics Models

Computer-based forest successional models (e.g., JABOWAFORET [Shugart and West 1977, Smith and Urban 1988] 

and SORTIE [Pacala and others 1994]) remain the best tool 

to explore long-term successional outcomes; however, forest 

dynamics models typically fail to include a dense understory layer’s impact on early seedling survival and growth. 

For example, in the original SORTIE calibrations, the 

growth and mortality parameters derived from saplings (15 

to 750 cm in height) are applied to small seedlings as well 

(Kobe and others 1995, Pacala and others 1994). Additionally, the authors acknowledge their recruitment parameter 

estimate is potentially unreliable as the survival of individuals < 5 years old is highly variable, and mortality is often 

intense (Pacala and others 1994. Indeed, researchers have 

documented that density dependent (e.g., Packer and Clay 

2000) and density independent mortality can dramatically 

alter initial seedling distribution patterns, particularly under 

a dense understory layer (Hille Ris Lambers and Clark 

2003, Schnurr and others 2004). By constraining the model 

and its parameters to the 5-or more-year-old age class, 

SORTIE assumes away part of the early dynamics that may 

occur low to the ground underneath a recalcitrant understory layer and help shape the composition sapling class.

As originally calibrated (Pacala and others 1994), 

SORTIE did not include the effects of a recalcitrant understory layer into its resource (light) submodel. More recent 

developments note that SORTIE can underestimate light 
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GENERAL TECHNICAL REPORT PNW-GTR-802

attenuation (Beaudet and others 2002) and the long-term 

development of shade-intolerant tree species following 

major disturbance (Tremblay and others 2005). It is suggested in both papers that this may be due to the lack of an 

understory layer component in the model, and it is stressed 

that this goal is an ongoing research focus (see also Aubin 

and others 2000 and Beaudet and others 2004). We know of 

only one effort that has integrated a recalcitrant understory 

layer into SORTIE. Hill (1996) incorporated hay-scented 

fern abundance as a function of light as well as hay-scented 

fern’s impact on light availability as a function of frond 

density. With the increased light limitation imposed by fern 

cover, successional projections indicated faster reductions in 

shade-intolerant species abundance and an accelerated shift 

towards dominance by shade-tolerant species (Hill 1996). 

Nevertheless, none of the simulations containing a dense 

fern layer reflected the pattern of complete regeneration 

failure documented in the field (Hill 1996). We concur with 

Hill that the inconsistencies between model projections and 

observable field patterns likely result from overestimates in 

seedling growth and underestimates in seedling mortality 

inherent in SORTIE. We argue these inconsistencies are 

due to (1) ignoring the early (fewer than 5 years) seedling 

dynamics, and (2) failure to incorporate additional interference mechanisms causing seedling mortality (e.g., seed and 

seedling predation) beyond light competition.

Forest Management

Understanding the autoecological characteristics of interfering plant species may allow land managers to preemptively 

limit the aggressive spread of these species as well as 

provide alternative options for their control. We found that 

alterations in forest canopy disturbance, fire, and herbivory 

regimes may lead to the establishment of recalcitrant 

understory layers, particularly when alterations to the overstory and understory disturbance regimes occur in tandem 

(e.g., Payette and Delwaide 2003). We suggest managers 

monitor overstory and understory conditions to ensure 

that modifications to either of these strata do not operate 

concurrently in an effort to mitigate invasion risk. Furthermore, if control over overstory and understory factors falls 

under the purview of different agencies (e.g., wild game vs. 

forestry management agencies), then communication and 

coordination between them is essential in order to minimize 

the chance of concurrent or overlapping disturbance events. 

We caution that decisions regarding partial or complete 

overstory removals should consider the site’s understory 

conditions including inadequate advance regeneration, presence of clonal understory plants, and high herbivore impact 

(e.g., Marquis and others 1990). We further suggest the 

implementation of management practices that more closely 

resemble natural disturbance levels.

Knowledge of a species life-history traits and interference mechanisms may also provide managers with 

alternative treatments to promote tree regeneration when 

conventional treatments like herbicides are not desired 

or permitted (Berkowitz and others 1995). For example, 

mowing or cutting of ferns, grasses, and shrubby interfering 

vegetation may successfully ameliorate their aboveground 

competitive effects and enhance regeneration (Biring 

and others 2003, Davies 1985, Marrs and others 1998). 

Alternatively, if belowground competition is the major 

interference mechanism, fertilizer application may mitigate 

the competitive effects of interfering plants and promote 

tree regeneration (Haywood and others 2003, Prescott and 

others 1993). Additional remediation techniques tailored 

to other interference mechanisms could include direct 

seeding of propagules coated with small mammal repellent 

(Campbell 1981, Nolte and Barnett 2000), soil scarification 

or controlled burning to reduce litter interference (Nyland 

2002), and activated carbon as a treatment to mitigate 

allelopathic interference (Jäderlund and others 1997). A 

basic understanding of possible successional outcomes 

following the establishment of a low canopy may further aid 

land managers. In areas where the low canopy simply stalls 

succession, successful regeneration will ultimately occur 

without any silvicultural techniques. Finally, where the 

recalcitrant understory layer filters tree species composition 

or arrests succession, managers could manipulate the rate 

and direction of regeneration by underplanting tree species 

relatively unaffected by the interfering layer (e.g., shadetolerant species) in order to attain a desirable and diverse 

mixture of regeneration species (Löf 2000). 
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Acknowledgments

This contribution represents a modified version of a paper 

by the co-authors that originally appeared in the Canadian

Journal of Forest Research, Volume 36, pages 1345–1362. 

Authors have made minor changes in content and style 

to facilitate online publication in the Forest Encyclopedia 

(http://www.forestencyclopedia.net). Ideas expressed in this 

contribution and published online are intended for supplementation as further information is developed. 

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

T.L. Shore, research scientist, Natural Resources Canada, 

Canadian Forest Service, Victoria, BC, Canada V8Z 1M5; 

A. Fall, President, Gowlland Technologies Ltd., Victoria, 

BC, Canada V9E 2A3, and Resource and Environmental 

Management, Simon Fraser University; W.G. Riel, research 

scientist, Natural Resource Canada, Canadian Forest 

Service, Victoria, BC, Canada V8Z 1M5; J. Hughes, Ph.D. 

candidate, Department of Zoology, University of Toronto, 

Toronto, ON, Canada M5S-3G5; and M. Eng, manager, 

Special Investigations, British Columbia Forest Practices 

Board, Stn Prov Govt, Victoria, BC, Canada V8W-9R1.

Abstract

The objective of our paper is to provide practitioners with 

suggestions on how to select appropriate methods for risk 

assessment of bark beetle infestations at the landscape scale 

in order to support their particular management decisions 

and to motivate researchers to refine novel risk assessment 

methods. Methods developed to assist and inform management decisions for risk assessment of bark beetle infestations at the landscape scale have been diverse, ranging from 

simple empirical correlations to complex systems models. 

These approaches have examined different bark beetle species, forest types and systems, and management questions, 

and they differ in spatial and temporal precision, the types 

of processes included, and the form of output. Bark beetle 

risk assessment methods, however, share a common theme: 

they aim to quantify expected levels of attack and loss due 

to beetles. By focusing on this commonality, we present a 

gradient in which methods can be classified and ranked, 

ranging from more structural, pattern-oriented methods to 

more functional, process-oriented methods. Our objective 

is to describe a framework for comparing methods in terms 

of how risk is represented and in terms of the complexity of 

application. To illustrate how diverse methods can be cast 

within a common frame of reference, we describe and provide brief examples of four types of methods that we have 

used in British Columbia, Canada, to examine landscapescale risk of mountain pine beetle attack in lodgepole pine 

forests. We then provide some guidance on how to select an 

appropriate method for a given system and set of questions. 

The most appropriate method is the simplest one that can 

address the questions, minimize uncertainty, and inform the 

decision process in the required timeframe. It is important 

that researchers and practitioners can view bark beetle 

risk-assessment methods as a toolkit and select appropriate 

tools for a given task, as no single method is best for all 

situations.

Introduction

A variety of risk assessment tools have been designed to 

help managers quantify expected losses from bark beetles, 

losses which can be quite severe. We present a framework 

to help managers choose the tools that are most appropriate 

to their needs.

Several bark beetle species, mostly in the family 

Curculionidae, subfamily Scolytinae, have the potential 

for dramatic population increases under favorable forest 

and climate conditions, which can result in landscape-scale 

mortality to the host tree species (e.g., Wood and Unger 

1996). For example, the mountain pine beetle (MPB, 

Dendroctonus ponderosae Hopk.) has killed much of the 

mature lodgepole pine (Pinus contorta Dougl. ex. Loud.) 

over an area of approximately 9 million hectares in British 

Columbia in recent years (Westfall 2005), and the spruce 

beetle (D. rufipennis) has infested several hundred thousand 

hectares of spruce forest in southwestern Yukon, Canada 

(R. Garbutt, pers. comm.). These events have widespread 

implications for current and future forest management, 

ranging from effects on timber supply and operations, 

wildfire-urban interface, wildlife habitat, and aesthetics. 

Landscape-scale risk assessment of bark beetle infestation aims to quantify the spatial and temporal likelihood 

of attack extent and severity. Methods to assess risk can 

range from structural risk (i.e., strictly assessing patterns) 

to functional risk (i.e., assessing interactions and feedbacks 

between pattern and process). Susceptibility and risk rating 

systems (Bentz and others 1993) generally classify stands 

Methods to Assess Landscape-Scale Risk of Bark Beetle 

Infestation to Support Forest Management Decisions
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without a temporal dimension in a relatively simply spatial 

context and are useful for a quick overview of landscape 

state and general patterns. Landscape connectivity methods 

(O’Brien and others 2006) can join stands of higher susceptibility into a network that can help give an integrated 

landscape perspective but are still temporally static. Connectivity assessments have been useful in areas with limited 

current attack to provide an assessment of the spatial 

pattern of hosts and likely pathways of attack. Empirical 

projection models explicitly model outbreak dynamics 

based on historical temporal patterns and have been useful 

to assess very broad-scale dynamics and potential interactions with management (Eng and others 2005). Population 

models capture system dynamics and feedback between 

host patterns and beetle demographics in detail (Dunning 

and others 1995) and can help to gain insight into likely 

trends of outbreak development and to explore management 

alternatives in relatively fine detail.

These methods, and others in the literature, share a 

common theme: they aim to quantify expected levels of 

attack and loss due to beetles. Although the way in which 

risk is quantified may differ among methods, risk can 

always be cast in terms of a probability distribution that 

represents likelihood of losses or impacts. This provides 

a common frame of reference with which methods can 

be compared on a gradient from simpler, structure-based 

methods to more complex, process-based methods, allowing 

methods to be assessed in terms of tradeoffs between data 

required, difficulty of application, and precision of results. 

To demonstrate this framework, we examine a diverse 

suite of tools useful to assess risk of bark beetle attack at 

broad spatial scales. For each, we provide an overview of 

the method as applied to bark beetle infestation risk, with 

a focus on data requirements and outputs. We highlight 

the pros and cons and outline the types of management 

questions that can be addressed and present an example of 

management application.

Management Implications of Bark Beetle 

Infestations

There are three main management strategies for major bark 

beetles in forestry: prevention, direct control, and salvage 

(Shore and others 2006b). Preventive management is used 

when beetles are at, or below, endemic levels, and managers have the opportunity to be proactive in making trees, 

stands, and landscapes less susceptible to large infestations 

(Whitehead and others 2006). Direct control is used in the 

situation when an infestation is underway and management 

efforts are reactive and primarily directed at killing beetles 

in order to reduce population size and spread (Carroll and 

others 2006). Salvage occurs either during or following 

those outbreaks that are too large for effective control (Eng 

and others 2005). 

Bark beetle management requires decision-support 

tools to provide information on which to base decisions, 

including identification of infested trees and susceptible 

stands. Many decisions are involved in resource allocation 

including budgets, risk to surrounding stands, access to the 

infested trees, other resource constraints, allowable harvest 

levels, and treatment efficacy to name a few. Landscapescale risk assessment can provide information to support 

some of these decisions.

What Is Risk Assessment in This Context?

Risk can be defined as the likelihood of an undesirable 

outcome combined with the magnitude of impact. Events 

with low likelihood of occurrence (e.g., meteorite impact) 

are not classified as high risk, nor are events with minor 

negative impacts (e.g., attack by secondary bark beetles on 

weaker, smaller trees in a stand). We define landscape-scale 

risk of bark beetle infestation as the probability of a given 

magnitude of loss of standing timber due to attack and 

concurrent management response. That is, conceptually, 

it is a probability distribution that quantifies the potential 

of attack at broad spatial scales (Figure 1). The specific 

shape and expected values of this distribution will depend 

on the landscape under study (e.g., the configuration and 

composition of hosts and beetles), and the method used 

to assess risk (e.g., the quantity reflected by the method, 

such as proportion of stand volume at risk or proportion of 

stands that may be attacked within a given timeframe). In 

general, this distribution cannot be fully mapped owing to 

uncertainties in future events (e.g., weather) and data (e.g., 

infestation locations and severity). In addition, management 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

decisions influence risk, so each management option results 

in a different risk probability distribution. Often, however, 

risk focuses mostly on the mean, or expected, value of 

the distribution (Fall and others 2004). Hence, a high-risk 

scenario is one with a high probability of large levels of 

loss (e.g., example distribution p3 in Figure 1). A mediumrisk scenario may be due to a high probability of medium 

levels of loss (e.g., example distribution p2 in Figure 1), or 

a medium probability of high levels of loss (e.g., example 

distribution p1 in Figure 1) (Shore and Safranyik 1992).

Why Landscape-Scale Risk Assessment Is 

Needed

Forest management decisions in the context of potential or 

existing bark beetle infestations require practical information in a timely manner (Maclauchlan and Books 1994, 

Safranyik and others 1974). Forest managers want to know 

the most likely outcomes of a range of alternative choices, 

both to help select an option and to communicate rationale. 

When a landscape has no current infestation, managers 

want to know the likelihood of an infestation starting (e.g., 

if one is imminent or a more remote possibility), and which 

stands are most likely to be affected first. In landscapes 

with a current infestation, managers want to know the 

impact of different types and levels of management effort 

(e.g., harvest levels, fell and burn treatment levels, global 

positioning system [GPS] surveys), on the timing, location, 

and magnitude of loss (Shore and others 2006b). In essence, 

the choices involve appropriate resource allocation and how 

to deal with uncertainty in order to assess tradeoffs and 

costs/benefits. Landscape-scale risk assessment provides 

key information to support this decision process.

A key role of researchers providing decision support 

is to help decisionmakers frame their specific questions in 

terms that can be addressed using risk assessment methods. 

Getting at the fundamental question helps to identify the 

best methods to apply that can balance information desires 

with the limitations of data availability, system knowledge, 

Figure 1—Conceptual diagram of landscape-scale risk of bark beetle infestation. Landscape-scale 

risk of bark beetle infestation can be conceptualized as a probability distribution of loss of standing 

timber owing to attack and management response (where probability of loss within a given range 

is the area under the probability density function curve, p, within the specified range). Loss may 

be quantified as area attacked, volume killed, or volume lost owing to decay. Variance about the 

mean may be due to the source data (e.g., if the distribution was formed from frequency information) or uncertainty in future processes (e.g., weather). Three example risk distributions are shown. 

The first two (p1 and p2) have lower overall risk relative to the third (p3). Based on a 67 percent 

confidence interval, p1 has an expected loss of between 3 and 24 percent (mean of 20 percent), p2 

has an expected loss of between 16 and 24 percent (mean of 20 percent), and p3 has an expected 

loss of between 45 and 70 percent (mean of 59 percent).
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and timeframe. Researchers also need to recast risk assessment results back into a language and format that can be 

communicated to decisionmakers in a comprehensible and 

useful manner (Gustafson and others 2005).

Risk-Assessment Methods

This section provides information and example applications 

for categorizing risk methods, susceptibility/risk rating 

systems, graph-based connectivity assessments, empirical outbreak projections, population modeling, and other 

risk-assessment methods. The examples provided in this 

section illustrate how we categorize risk methods. These 

examples are based on a range of tools we have used to 

assess potential impacts of MPB at scales from stands to the 

entire province of British Columbia. Although the specific 

details of the methods differ substantially, they are essentially just different approaches to assessing risk. They differ 

fundamentally in terms of the degree to which ecological 

and management processes are taken into account and 

can be viewed along a gradient (Figure 2). Structural

approaches to risk focus mostly on landscape patterns and 

correlations between past outbreak behaviour and stand 

structure, whereas functional approaches focus on underlying processes and interactions in the system (cf. distinction 

between structural and functional habitat connectivity, 

Taylor and others 1993).

Susceptibility/Risk-Rating Systems

Susceptibility and risk rating systems classify each stand 

or location in a landscape according to local characteristics 

(e.g., forest age, distance to nearest attack). We define 

susceptibility in terms of conditions inherent to a stand (i.e., 

how suitable is the stand for the beetle species) and risk rating as a function of susceptibility and beetle pressure (Shore 

and Safranyik 1992, Shore and others 2006a). Stand susceptibility rating systems provide the forest manager with 

a tool that identifies the likelihood of damage to a stand 

should a beetle infestation occur in it. When implemented 

on a map, these tools form the starting point for setting 

management priorities by identifying stands and landscapes 

that are more susceptible than others. When combined with 

maps of beetle locations a manager can look at the risk of 

loss. Highly susceptible stands in closer proximity to large 

numbers of beetles can be given management priority.

Data Requirements—

To apply an existing rating system requires basic digital 

spatial forest cover data on attributes such as stand age, 

basal area, percentage of host and other tree species. Data 

on the location of each stand (elevation, latitude, longitude) 

and infested tree locations are also required. To develop 

a rating system, however, requires substantial fieldwork 

to identify correlations between beetle biology and stand 

characteristics (e.g., Perkins and Roberts 2003).

Output—

The main output is a spatial map of relative or absolute 

susceptibility rating, defined as the likely proportion of susceptible volume that would be killed if beetles arrived in the 

stand. Structural risk rating systems output a spatial map of 

Figure 2—Gradient of landscape-scale methods for assessing risk of bark beetle attack. Landscape-scale methods for assessing risk 

of bark beetles can be placed on a gradient from structural to functional risk. Structural risk methods focus on patterns, such as 

stand susceptibility or connectivity networks. Functional risk methods focus on process, such as demographics and movement.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

the estimated loss and likelihood of attack based on proximity to existing attack. These maps can be cast as frequency 

distributions, creating a form consistent with Figure 1.

Pros and Cons—

This method has the distinct advantage of simplicity of 

application and minimal data requirements. The mountain 

pine beetle susceptibility and risk rating system (Shore 

and Safranyik 1992; Shore and others 2000, 2006a) 

remains one of the most widely used MPB  landscape risk 

assessment tools. On the other hand, these approaches are 

temporally static. Likely pathways cannot be identified with 

this method, and there is a limited ability to incorporate 

management. Susceptibility and risk rating have a limited 

capacity to use spatial information of known outbreak 

locations. In general, distances are simply Euclidean (i.e., 

do not account for direction or intervening land types), and 

the magnitude of pressure from nearby outbreaks is difficult 

to incorporate.

Management Application—

Susceptibility rating systems are best used to help prioritize harvest in landscapes without current or imminent 

attack (preventative management). Risk-rating systems are 

designed for landscapes with some attack but are best used 

in the early stages of an outbreak and are of limited use 

during epidemics (Shore and others 2006a).

Example Application—

We contrasted two landscapes of approximately equal size 

(Figure 3): Nadina Forest District in west-central British 

Columbia is approximately 3.0 million ha in size, and 

Dawson Creek timber supply area (along with a portion 

of Tree Farm License 48) is approximately 2.6 million ha. 

The general pattern of susceptibility differs substantially 

in these two landscapes (Figure 4). Nadina has larger 

areas of high susceptibility and more continuous cover, 

whereas Dawson Creek has overall lower susceptibility, 

but with a substantial amount of moderate susceptibility 

in the southeast. In terms of the present outbreak, Nadina 

has experienced very high levels of impact and mortality, 

whereas Dawson Creek, being east of the Rocky Mountains, 

is more isolated from the predominate outbreak populations 

in British Columbia, and population buildup is more recent 

(Eng and others 2005). We can quantify these differences 

as a frequency distribution of susceptibility (Figure 5). The 

percentage of forested area with very low susceptibility is 

higher in Dawson Creek than Nadina, whereas Nadina has 

more area with higher susceptibility rating for most cases 

above 25.

Graph-Based Connectivity Assessment

Examining the network of inter-connections between 

susceptible host patches can provide a broad perspective of 

landscape patterns. A variety of methods are available for 

assessing habitat connectivity (Gustafson 1998, Schumaker 

1996, With and others 1997), which is defined as the degree 

to which a landscape facilitates or impedes movement of 

organisms among habitat patches (Taylor and others 1993, 

Tischendorf and Fahrig 2000). Graph-based methods are 

emerging as an effective approach that supports multiscale 

analysis and that provides a good balance between fieldintensive studies that aim to directly measure functional 

connectivity, but are limited to small areas and less vagile 

species (Tischendorf and Fahrig 2000) and pattern analysis 

methods that focus on structural connectivity (Calabrese 

and Fagan 2004, Urban and Keitt 2001). We have developed 

an extension to graph theory (Harary 1972) that we call 

spatial graphs, which captures features relevant to geospatial ecological analysis (Fall and others, in press; O’Brien 

and others 2006). Unlike conservation situations, management of bark beetles generally aims to reduce connectivity 

of host habitat. Hence, a key objective of connectivity 

analysis is to help identify where opportunities exist in a 

landscape to increase the level of fragmentation. This can 

be done by analyzing the spatial scales at which patches of 

susceptible hosts are well-connected, in particular, connected to areas with existing attack.

Data Requirements—

Digital spatial forest cover data and information on beetle 

movement and current infestation locations are required. 

Spatial graph connectivity assessment requires two spatial 

inputs: a patch layer (e.g., areas of high susceptibility) and a 

cost surface (e.g., relative difficulty or speed of movement 

or spread through different cover types). These require a 
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susceptibility rating to define habitat patches and information on the permeability of different cover types in the 

matrix between patches (O’Brien and others 2006). Current 

infestation locations can be used to analyze the resulting 

connected network of patches in terms of effective distances 

from susceptible hosts to current attack.

Output—

The main output of graph-based connectivity analysis 

is a spatial graph that shows the location of connections 

between host patches and the cost of those links, derived 

from the patch map and cost surface. Analysis of this 

graph can identify scales (effective distances) at which 

large increases in host connectivity occur—these can be 

examined spatially using the graph to draw connections 

at those scales. Given infestation locations, the graph can 

be reoriented to identify scales at which hosts become 

connected to current attack. The results can then be cast 

as a distribution of high susceptibility stands according 

Figure 3—British Columbia, Canada, showing the Nadina Forest District (Lakes and Morice timber supply areas [TSA]) and 

Dawson Creek TSA.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 4—Susceptibility of stand loss owing to mountain pine beetle attack in the Nadina and Dawson Creek study areas (based on 

Shore and Safranyik 1992). The Nadina forest district (right) has high overall susceptibility and large contiguous areas of moderate to 

high susceptibility. Susceptibility in the Dawson Creek study area (left) is more moderate and fragmented.

Nadina Forest District

Dawson Creek Study Area
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GENERAL TECHNICAL REPORT PNW-GTR-802

to distance from current attack. One key benefit of using 

spatial graphs is the ability to translate from a graph back to 

the landscape, which is critical for effective communication 

and decision support.

Pros and Cons—

These methods required a low to moderate effort to apply, 

and data requirements are modest. The required information on movement cost/impedance can be challenging to 

parameterize with statistical confidence (O’Brien and others 

2006), but cost surfaces derived using simpler methods, 

such as expert opinion, can be used for some less precise 

analysis. Although still static in nature, likely pathways 

can be identified as well-connected corridors, especially 

between areas with current attack and areas with clusters 

of high susceptibility. In addition, large areas can still be 

processed efficiently. However, it provides a static perspective on a dynamic problem and has a limited ability to 

incorporate management. 

Management Application—

Graph-based connectivity assessment is best used in situations with low or no attack. In a sense, this approach can be 

viewed as increasing the spatial dimension of susceptibility and risk-rating systems. The results can help to focus 

management effort on stands to reduce connectivity within 

management limits. The utility is based on the premise that 

the most important stands to treat in a landscape are not 

necessarily just the most susceptible, but the susceptible 

stands that are most connected. This is especially important 

in landscapes for which the area of susceptible stands 

greatly exceeds management capacity.

Figure 5—Frequency distribution of stand susceptibility rating for mountain pine beetle in Nadina and Dawson Creek 

study areas as percentage of forested area. Susceptibility was defined on a range from 0 to 100 as an index of potential 

basal area mortality according to Shore and Safranyik (1992) and Shore and others (2000). The Nadina distribution has 

a mean of 23.3 and a standard deviation of 28.8. The Dawson Creek distribution has a mean susceptibility of 12.9 and a 

standard deviation of 20.0.
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Example Application—

We used spatial graphs to assess the potential of the current 

mountain pine beetle outbreak in British Columbia to 

spread into the boreal forest of northern Alberta. The study 

area consists of about 11.2 million ha, with Dawson Creek 

(Figure 3) on the western side and extending east to Slave 

Lake in central Alberta, north of Edmonton. Susceptibility 

was defined on a range from 0 to 100 according to Shore 

and Safranyik (1992). High susceptibility patches were 

defined as contiguous areas of susceptibility greater than, 

or equal to, 65 (Figure 6). The cost surface was produced 

using the reverse of susceptibility (i.e., increasing cost 

with decreasing susceptibility), based on the assumption 

that beetles will spread more effectively through higher 

susceptibility stands, and that this increases linearly with 

susceptibility. The base graph extracted joined host patches 

into a network (Figure 7). This graph was reoriented, so 

that costs,(which can be interpreted as effective distance), 

through the graph correlated with distance to the nearest 

infested patch.

This resulting graph was analyzed by thresholding: at 

each threshold from 0 to 600 km in units of 100 m (effective distance in cost units), all connections longer than 

the threshold were discarded, and only patches connected 

to current attack in Dawson Creek and western Alberta 

(mapped by heli GPS, M. Duthie-Holt, pers. comm.) were 

Figure 6—Patches with high susceptibility to mountain pine beetle attack and loss used for connectivity analysis. High susceptibility patches (those with greater than or equal to 65 on the Shore and Safranyik [1992] susceptibility rating system) 

for the Dawson Creek and west-central Alberta study area for which connectivity analysis was performed (white areas in 

the Figure). The area east of Dawson Creek and north of the Alberta portion of the study area is farmland. The southwest 

boundary is the Rocky Mountain divide. The different shades of grey in the underlying map denote different management 

units (national parks, provincial parks, timber management areas called forest management units in Alberta and timber 

supply areas or tree farm licenses in British Columbia).

Dawson Creek

Jasper National Park
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Figure 7—Isolines (of the results of graph thresholding. Mapping the results of graph thresholding in 

Dawson Creek/west Alberta study area by drawing isolines lines of equal effective distance from current 

attack) at the critical scales identified in Figure 8. Effective distance is the cost through the graph from 

infested patches to other patches.

retained (Figure 8), (Fall and others, in press). Hence, at a 

threshold of 0, only patches containing current attack were 

retained. As thresholds increase, the area of host joined 

to current attack increases until all host area is included. 

The pattern of these increases provides insight into pattern 

across spatial scale. Steep areas indicate scales with rapid 

increases in connectivity to current attack. These critical scales can then be cast back onto the original map of 

susceptible patches using isolines (Figure 7). Areas corresponding to more gradual increases in connectivity likely 

represent areas with higher likelihood that management can 

reduce connectivity, and, hence, risk in a timely manner. In 

this example, the areas corresponding to thresholds 65 to 

150 km and 250 to 350 km appear to provide the best opportunity to reduce the risk of spread across this landscape.

Empirical Outbreak Projection

The development of methods for modeling and analyzing spatially and temporally autocorrelated data such as 

the historical spread of a bark beetle outbreak across a 

heterogeneous landscape is a current and active area of 

research (e.g., Augustin and others 2007, Wikle 2003, Zhu 

and others 2005). However, appropriate statistical modeling techniques are not yet sufficiently well developed or 

disseminated for timely and practical application in many 

situations. Semi-Markov models are a standard method of 

projecting vegetation dynamics (Acevedo and others 1995, 

Baker 1989). Where data are abundant, the statistical challenge of modeling transition probabilities may be avoided 

by categorizing observations and using observed transition 

probabilities directly. Until robust statistical methods are 

available, this direct approach is reasonably well suited 

for practical and timely decision support. The approach 

can be extended to include multiple predictive factors in a 

probabilistic state-transition table. A time series of infestation progression is collected and categorized into infestation 

intensity classes. That time series is combined with spatial 

data about the physical environment. All of the data are 
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cast in a grid cell (raster) environment. Two kinds of factors 

determine the state of a cell:

1. Factors based on the state of the infestation 

itself such as the history of the infestation in a 

particular cell, and some measure of the 

influence of infestations in other parts of the 

landscape. Simple neighborhood rules (e.g., 

number of infested cells within some distance) 

are a common method of characterizing spatial 

effects, but we have found a more biologically 

informed model of dispersal pressure to be 

more useful. 

2. Factors based on the nature of the physical 

environment such as forest age, species com--

position, and elevation.

Transition probabilities, from one state to another, are 

calculated directly from the observed transitions. Care must 

be taken not to over-specify the model (ensure adequate 

sample sizes for probability calculations). The model may 

be refined somewhat by introducing hierarchy into the 

transition table. For example, the factors that best predict 

the probability of infestations starting in cells with no previous infestation history may be different than the factors 

that predict how infestations proceed once they have arisen. 

The state transition table is used to project an infestation 

through time. 

Data Requirements—

Spatially explicit information about infestation history is 

required, categorized into states that represent the level of 

damage caused. In addition, information about the physical 

environment relevant to the progression of the infestation, 

such as forest cover mapping, must be available in digital 

form.

Figure 8—Results of graph thresholding in Dawson Creek/west Alberta study area. The total area of 

high susceptibility to mountain pine beetle attack and loss (greater than, or equal to 65, according to 

the Shore and Safranyik [1992] rating system) habitat is just over 700 000 ha (yellow line). The amount 

of high-susceptibility habitat connected to current attack in Dawson Creek through the graph increases 

as cost thresholds increase (blue line). At a threshold of 0, only host patches containing current attack 

are connected. As the threshold increases, more patches are joined at distances less than or equal to 

the threshold. At an effective distance of just under 600 km, all habitat is connected in a single cluster. 

Arrows indicate scales over which there are rapid increases in connectivity indicated by changes in 

slope.
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Output—

The principal output is a spatially explicit projection of the 

effect of an infestation on the forest resource. The spatial 

resolution of the output will be the same as the resolution 

of the input layers, whereas the precision (e.g., the level of 

the effect) will depend on the resolution of the historical 

outbreak information. Output is in the form of the state of 

the infestation (in terms of severity classes) in a given year 

and grid cell, based on the states represented by the input 

infestation maps. Numerous other outputs can be derived 

from the projection of the state; for example, spatially 

explicit projections of the volume of timber that is killed 

and tabular summaries of the area affected. As such models 

are stochastic, each scenario can be used to generate an 

expected distribution of attack, such as the form illustrated 

by Figure 1. However, mean values are generally used to 

communicate spatial and temporal dynamics and compare 

alternative scenarios.

Pros and Cons—

This approach is relatively data intensive, and a significant 

amount of analysis is required to develop the state transition 

tables. The approach will not provide new insights into the 

behavior of an infestation. As a strictly empirical approach, 

a key assumption is that the future behavior of the outbreak 

will resemble the behavior in the past. This approach, 

however, is one of the simplest ways to provide a spatially 

and temporally explicit dynamic projection. Although the 

data requirements are reasonably onerous, there is only a 

limited requirement for understanding the processes that 

govern the progression of the outbreak. A key advantage 

of the approach is that the spatially and temporally explicit 

projection can be integrated with other management or 

planning models to explore interactions between the effect 

of the infestation and the management response.

Management Application—

This approach could be applied over an area of any size. 

It is only applicable in situations where there is enough 

historical data on all outbreak phases to develop a useful 

state transition table. Its primary use is to project potential infestation trajectories, which can be used to clarify 

management options and consequences and guide strategic 

policy development. 

Example Application—

British Columbia is currently in the midst of the biggest 

MPB outbreak in recorded history. We used an empirical 

outbreak projection to forecast the possible impact of the 

outbreak over the entire province for the next 20 years (Eng 

and others 2005, Figure 3). We obtained 7 years of infestation history collected through the Provincial Aerial Overview of Forest Health (Ebata 2004, Figure 9). Forest cover 

information and a host of other data regarding the physical 

environment and management regime were collected 

primarily from the Province of British Columbia’s Land 

and Resource Data Warehouse (http://lrdw.ca). An outbreak 

projection model along with a forest management response 

model was implemented using the SELES (Spatially Explicit 

Landscape Event Simulator) spatio-temporal modeling tool 

(Fall and Fall 2001). SELES combines a spatial database 

for a landscape with a high-level, declarative modeling 

language used to specify key processes and a discrete-event 

simulation engine that interprets and executes such models. 

Based on the most recent infestation mapping, we 

estimate that approximately 25 percent of the merchantable 

pine volume in the province was observed to be dead (red 

or grey crowns) during the summer of 2005 (Figure 10). 

Because trees killed during the summer cannot be detected 

through aerial surveys (their crowns are still green), we rely 

on the projection model to estimate that an additional 10 

percent of the pine volume was killed during that summer. 

The difference between the two projections is due to the 

dramatic increase in infestation in 2005. We show both 

to illustrate how empirical projection models are driven 

by observation, but focus on the one driven with a more 

complete data set (i.e., including 2005 data). This difference, however, does not alter the primary conclusions. We 

project that by 2010 over 60 percent of the merchantable 

pine volume in the province will be observed as dead, and 

that about 80 percent will be killed (Figures 10 and 11) by 

2013 when the infestation will have largely run its course. 

Further maps of the input data and the projections can be 

found at http://www.for.gov.bc.ca/hre/bcmpb.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 9—Mountain pine beetle attack from 2005 observation and projected at 2009. Comparison of 

patterns of mountain pine beetle attack from 2005 aerial overview surveys (top) and projected at 2009 

using an empirical model (bottom) (from Eng and others, unpublished, found at http://www.for.gov.

bc.ca/hre/bcmpb).
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The results of the projection have been used for a 

variety of purposes. Notably, they have been widely cited 

in the press and have been extensively communicated to 

natural resource managers in an effort to increase awareness about the severity of the problem. The results of the 

interactions between the outbreak projection model and 

our forest management model have been used to help direct 

funding for control efforts, examine the impacts of the 

forest management response on the transportation system, 

and to investigate the possibility of developing a bioenergy 

plant in the most severely affected area. The results of the 

infestation projection itself have been incorporated into 

detailed modeling for timber supply analyses. 

Population Modeling

Population models capture outbreak dynamics by explicitly 

modeling demographic changes with processes of mortality, birth, dispersal, etc. (Caswell 1989). We designed a 

landscape-scale MPB population model to assess impacts 

at scale of ~1 000 000 ha to explore likely trajectory and 

broad spatial patterns of an outbreak, to evaluate a range of 

management options, and to estimate likelihood of different 

outcomes (Fall and others 2004). The general concept is to 

project an infestation forward using a landscape model that 

combines a spatially explicit MPB population model (Dunning and others 1995) with a spatial management model for 

timber supply, strategic forest management, and fell and 

burn treatments, so that interactions between management 

and beetles can be assessed. 

The MPB population model scales results from a more 

detailed stand-level MPB population model, MPBSIM, 

which projects expected development of a beetle outbreak 

in a stand of up to several hectares (Riel and others 2004, 

Safranyik and others 1999). Our approach is to conceptually run MPBSIM in each cell of the landscape. Because 

it is not feasible or desirable to do this directly, we first 

run MPBSIM under a wide range of conditions to produce 

a table linking conditions to consequences. Conditions 

include stand attributes (e.g., age, percentage of pine), 

Figure 10—Observed and projected annual kill based on British Columbia mountain pine beetle model runs 

in the timber harvesting land base (THLB) over the entire province of British Columbia. Based on the 2004 

and 2005 Provincial Aerial Overviews (Eng and others, 2005, Eng and others unpublished found at http://

www.for.gov.bc.ca/hre/bcmpb/BCMPB.v3.BeetleProjection.Update.pdf).
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outbreak status (e.g., number of attacking beetles), etc. 

(Riel and others 2004). Consequences refer to the effect 

of 1 year of attack under those conditions (e.g., number of 

dispersers and number of trees killed). The landscape-level 

model uses this table to project MPB dynamics in each 1-ha 

cell containing beetles. The stand table includes stochastic 

variation in number of emerging beetles, and we control this 

to capture synchronous annual variation and above-average 

weather conditions. 

Dispersal between cells provides the spatial context for 

an outbreak, leading to an increased beetle population in 

cells within a current outbreak, or starting an outbreak in 

a currently uninfested cell, expanding an existing spot, or 

starting a new spot. The flight period, including beetle local 

and long-distance dispersal and pheromone production and 

diffusion, is modeled as a spatial process. Long-distance 

dispersal is largely governed by wind speed and direction 

used to select distance locations for MPB spread, whereas 

local dispersal is influenced by wind, susceptibility, pheromones, and distance. During attack, beetles kill pine trees, 

resulting in standing dead volume that may be salvaged by 

the logging sub-model.

Data Requirements—

Data requirements include detailed digital spatial forest 

cover data, infestation locations/intensity, beetle population 

estimates, and demographic parameters. In addition, availability of a stand-level population model to support scaling, 

Figure 11—Projection of pine volume from British Columbia mountain pine beetle (MPB) model. This graph indicates the projected 

proportion of merchantable pine volume on the timber harvest land base in the 20 management units in British Columbia with the highest 

level of susceptible pine (from Eng and others, unpublished, found at http://www.for.gov.bc.ca/hre/bcmpb/BCMPB.v3.ReferenceScenario.

Update.pdf). Left diagonal hatching indicates volume logged during the scenario. Right hatching indicates volume not available for 

harvest owing to policy constraints (e.g., visual areas) or economic limits (road access, minimum volume). No hatching indicates volume 

available for harvest (standing live and dead merchantable wood). NRL is nonrecovered loss (volume lost owing to decay). Chips are for 

products with lower requirements for wood quality (e.g., oriented strand board), whereas saw logs are for products with higher requirements for wood quality (e.g., structural lumber). All pine killed by MPB are covered by the NRL, chips, and dead saw logs category. Pine 

volume not killed by MPB is in the green pine category.
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or the information required to develop process sub-models 

is required. This poses substantial effort and a long-term 

program to obtain the ecological information for parameterization (Fall and others 2004). 

Output—

Nonspatial indicators summarize information across space 

as time-series output that includes:

1. The MPB outbreak indicators such as volume 

killed. 

number of trees killed, and area attacked

2. Growing stock inventory: cubic meters of live 

forest.

3. Management indicators such as annual volume 

and area harvested, volume of nonrecovered 

loss, volume salvaged, and amount of available 

salvageable wood.

Because the approach is stochastic, multiple replicates 

of each scenario are run. We designed several spatial indicators that summarize information across time and replicate:

1. The number of runs in which each 1-ha cell was 

attacked at least once, which can be roughly 

thought of as the probability that a cell will be 

attacked at some point in the 10-year horizon.

2. The cumulative volume killed, which shows 

areas likely to have the highest timber impacts.

3. The cumulative percentage of pine killed, 

which shows areas likely to have the higher 

ecological impacts.

Pros and Cons—

This approach requires substantial effort to develop and has 

fairly high data requirements, in particular the need for a 

reasonable understanding of beetle biology and interactions 

with hosts at relatively fine scales. The main advantage is 

that a population model takes a process-oriented approach 

to dynamic projections. This provides a closer match 

with the ecological process and greater ability to assess 

interactions with management. These methods can be 

used to identify likely trends over time and can integrate 

with management models. The process-based perspective 

enables emergent (bottom-up) properties not possible in a 

more strictly empirical (top-down) approach (Korzukhin 

and others 1996). That is, a population can respond to future 

landscape conditions that haven’t been encountered in the 

historical record. As with the empirical projection method, 

each scenario could potentially produce risk information in 

the form illustrated by Figure 1, but generally mean values 

are used to facilitate comparison of scenarios and to communicate spatial or temporal dimensions or both.

Management Application—

This method is applicable at landscape scales where cell 

resolution can be fairly fine (1 ha) and is designed for situations with an existing outbreak. Its strength is the ability 

to explore dynamic interactions between management 

alternatives and beetle populations, an approach that we 

have applied in a number of landscapes across BC (Fall and 

others 2004, in press). Information on the relative effects of 

beetle management strategies on area infested and volume 

killed can be used to assess impacts directly or to serve as 

input for further analysis of economic, social or ecological 

cost/benefits. 

Example Application—

The MPB attack was first confirmed in the Dawson Creek 

area in 2002 (Figure 3). The main outbreak in British 

Columbia was expanding rapidly, and it appeared that there 

was some long-distance dispersal through the Rocky Mountains. Spots recently detected in Dawson Creek most likely 

originated from the main outbreak, and were transported 

over long distance via wind and through mountain passes 

(A. Carroll, pers. comm.). Growth rates, as indicated by 

green: red attack ratios have been relatively low (M. DuthieHolt, pers. comm.), but nonetheless showed potential for 

population growth. This suggested that recent weather was 

sufficiently warm to support an outbreak, whereas historical climate likely precluded outbreaks (Carroll and others 

2004). There has been substantial effort in Dawson Creek 

focused on dealing with detection and treatment of spots, 

with cooperation among licensees, the Forest Service, and 

parks. A landscape-scale projection of outbreak potential 

was deemed to be useful to inform this process and to help 

clarify some tradeoffs between options available (Fall and 

others, in press).
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The main purpose of this study was to apply a population-based model methodology to evaluate the effectiveness 

of bark beetle management activities in reducing losses to 

the MPB and to analyze the potential spread, likely trajectory, and impacts of the beetle across the study area. To 

achieve this goal, we started with the current conditions and 

projected likely outcomes under various management scenarios representing alternative beetle management regimes 

derived from workshops held in Dawson Creek with 

government and industry. We projected system dynamics 

for 10 years, with 10 replicates per scenario. A wide range 

of experimental scenarios was also assessed for calibration 

and sensitivity analysis.

Our results generally showed that this area still has 

the potential for beetle management to have an impact 

on population levels, in particular current local practices 

(Figure 12). In addition to general information to help with 

strategic planning, this modeling approach can provide 

spatial outputs to visualize and quantify patterns of the 

expected outbreak trajectory under different management 

and weather conditions (Figure 13). This information is 

important for communicating risk potential in a landscape 

and can help with tactical planning of areas that may need 

management focus.

Other Risk-Assessment Methods

It may be possible to interpret other methods for assessing landscape-scale risk of bark beetle outbreaks in the 

framework presented (Figure 2), such as spatial temporal 

statistical methods (Augustin and others 2007, Wikle 2003, 

Zhu and others 2005) and field experiments. An alternative 

approach is employed in the Westwide Pine Beetle Model 

in which contagion forms the basis for a spatially explicit 

spread model (Beukema and others 1997). Another processbased approach to spatio-temporal modeling of MPB 

dynamics has been taken in the MPBpde model (Powell and 

others 2000), in which the MPB-pine interaction is represented by a system of partial differential equations that can 

Figure 12—Projected cumulative volume killed in Dawson Creek mountain pine beetle population model over 10 years for the base beetle management scenarios and no harvesting. Initialized using 2004 or 2005 survey information (averages of 10 replicates). These scenarios include 

estimated changes in climatic suitability (Carroll and others 2004) and immigration from the 

main outbreak to the west. NoMgmt is no harvesting and no fell and burn. SalvageOnly only 

allows salvage of dead merchantable timber (i.e., focus is on recovery of dead wood, not population reduction). BeetleMgmt is general beetle management, aimed at reducing populations 

where possible. CurrentPractices approximates better current management in Dawson Creek, 

which aims at population reduction as well as reducing risk of spread eastward into Alberta. 

Three variations were assessed, differing primarily in budget limits on treatment levels.
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be explored numerically (e.g., Logan and others 1998) and 

analytically (e.g., Powell and others 2000). Partial differential equation methods can be applied over broad scales, but 

are challenging to combine with other landscape processes 

that are more discrete in time and space (e.g., timber 

harvesting). Hughes (2007) modeled beetles individually 

at a similar spatial and temporal scale. These models allow 

detailed exploration of how beetles interact functionally 

with a landscape. However, individual-based approaches 

are generally prohibitive at the landscape scale because 

such models are computationally demanding and because 

we lack the detailed land cover and beetle data required to 

parameterize them.

Discussion

In this section, we provide information for use in selecting an appropriate method of risk assessment, provide a 

discussion on model verification and validation, and discuss 

future research needs in this area. 

Selecting an Appropriate Method

Some key aspects of a given problem can help guide the 

most appropriate choice of risk assessment method. In 

general, the best method is the simplest one (Occam’s 

razor) that addresses the desired management questions in 

the required timeframe, using data available for the study 

area. The possible methods for a given problem are in the 

intersection of these issues. That is, these issues can help 

filter infeasible approaches. Generally, management needs 

emphasize more detailed and precise (more functional) risk 

assessment methods, whereas timeframe and data/knowledge availability emphasize simpler and coarser (more 

structural) methods. There is always a tension between the 

goals of maximizing information and minimizing uncertainty.

The following are some key considerations to help 

narrow the range of potential methods:

• Identifying management questions: Often, 

decisionmakers want to “know what will 

Figure 13—Spatial presentations of landscape risk for the no management scenario in the Dawson Creek study area. Left: 

projected likelihood of attack (proportion of 10-year runs in which each cell is attacked in projection). Right: projected 

intensity of attack represented as the percentage of pine killed (average cumulative percentage killed). These include 

estimated changes in climatic suitability (Carroll and others 2004) and immigration from the main outbreak to the west.
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happen” in the future. However, it is essential to clarify in precise terms the nature of the 

decision problem. What decisions need to be 

made? What level of information would be sufficient (as opposed to desirable)? It is important 

to maintain a transparent and collaborative relationship (e.g., Fall and others 2001) to ensure 

that results are useful. It is also important to 

communicate the uncertainties associated with 

different options and different questions to 

increase confidence in the chosen method.

• Decision timeframe: The timing of decisions 

plays a key role. For ongoing or long-term decisions, there may be time to collect new field 

data and develop more complex models. Short 

timeframes will require the use of currently 

available information and methods that can be 

supported by available data. Articulating the 

timeframe required for different options can 

help foster a shared understanding of the constraints imposed upon the choices available.

• Defining area of interest: Often, the spatial 

scale of a decision will determine the study 

area (e.g., harvest levels and strategies are 

often specified at the scale of a timber supply 

area). Sometimes the ecological scale of a process influences this decision. For example, a 

very broad-scale output may indicate that a 

larger study area is needed to ensure that adequate context is captured. It is critical to avoid 

scale mismatch problems (Cumming and others 

2006).

• Data availability: Data availability can impose 

a significant constraint on which risk-assessment method can be employed. Lack of detailed 

knowledge about beetle demographics and 

movement may prohibit a population- or indi

vidual-based approach. Lack of readily available 

spatial information on historical outbreak patterns may prohibit an empirical approach. If 

the available data are not adequate to support a 

given method, this should lead to serious consideration of its applicability. The apparent 

precision provided by applying a more detailed 

method and using inadequate data may be a 

false benefit compared to the more accurate 

but less precise results that would be achieved 

using a coarser method. Additionally, the 

data requirements to develop a new method 

or model are often different (and in many 

cases more onerous) than the data required 

to adapt an existing method developed elsewhere.

The most important decision is selection of a good team 

with a broad range of skills. At the outset, the primary focus 

should be on the questions or issues to address, and then 

the team should work backwards towards the tools. That 

is, clarify the issues and constraints of data and knowledge 

and timing raised in the preceding subsections, while 

developing and formalizing conceptual models. Flexibility 

and often an iterative approach are required to shift course 

as information (or lack thereof) becomes apparent. The 

simplest method that meets the needs of the study should be 

chosen. If temporal dynamics or outputs are not required, 

a static approach can be used. If spatial interactions or outputs are not needed, a non-spatial approach is appropriate. 

We presented the example methods in order of increasing 

complexity. Other methods can be fit into this framework at 

different points. A suite of methods that may be applicable 

should be identified, filtering out methods that are not 

adequate to meet the requirements. The remaining methods 

should then be contrasted to pick the simplest one because 

higher complexity and data requirements imply higher 

uncertainty as well as longer timeframes for application.

Model Verification and Validation

We define verification as an assurance that the model is 

implemented as specified, and validation as an assurance of 

the appropriateness of the model for its intended use (Rykiel 

1996). That is, validation relates to the level of certainty one 

can place in model outputs; (i.e., the degree to which model 

results differ from expectations). Verification is an essential 

step and must be considered in model selection and application. 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Validation is often defined as the degree to which 

model output matches an independent data set (Rykiel 

1996). More structural approaches to risk assessment 

facilitate validation more easily than more process-oriented 

approaches (e.g., Cameron and others 1990, Dodds and 

others 2004). Static methods such as susceptibility rating 

can be statistically tested in areas with field data on past 

and current attack (Dymond and Wulder 2006). Empirical 

and connectivity approaches are driven by observation, 

and predictions can be compared with actual outcomes 

as an outbreak proceeds (and such data can then be used 

to improve the model parameters). Empirical, or data, 

validation for a spatio-temporal model is only possible in 

cases with short time lags in system response or for which 

suitable replicates exist (e.g., for chronosequence-type 

comparisons). The exact conditions encountered within 

large landscape systems cannot be found outside the system 

(Levin 1992). In addition, observational data isn’t available for assessing hypothetical management alternatives. 

In relatively process-oriented approaches, it may be more 

appropriate to rely on conceptual and logical validation 

(Rykiel 1996), where we view the model as a hypothesis and 

model output as a consequence of the hypothesis. That is, 

the purpose of such models are to make a clear link between 

the initial conditions, parameter values, and process 

behavior, and the consequences of those assumptions, 

which are projected via simulation (which in this sense is 

akin to theorem proving), and not to predict the real state 

of the future forest (i.e., projection not prediction). Logical 

validation inherently relies on the adequacy of the input 

information regarding initial conditions, model processes, 

and appropriate parameter values. Refinement of these can 

occur over time as ecological knowledge is refined.

Future Research Needs

New methods will be developed, and existing methods will 

be improved in the area of risk assessment. We suggest 

that using the proposed framework for comparing tools 

will assist tool selection for a given situation and improve 

understanding of the differences between tools in terms of 

precision, uncertainty, and resources required. In addition 

to ensuring that the set of tools forms a cohesive toolkit, it 

will also be important to improve the application of tools. 

That is, evaluating the applicability of a tool in a given 

situation needs to be easy, and usage of the method should 

be as straightforward and transparent as possible.

Although the examples we present focus on MPB in 

lodgepole pine forests, the concepts underlying the riskassessment methods and the classification gradient apply to 

other bark beetle species and forest systems. Susceptibility 

rating systems have been developed for MPB in ponderosa 

pine, Pinus ponderosa Dougl. ex Laws. (Chojnacky and 

others 2000, Negron and Popp 2004) and whitebark pine, 

P. albicaulis Engelm. (Perkins and Roberts 2003). Dodds 

and others (2004) and Negron (1998) examined risk rating for Douglas-fir beetle (Dendroctonus pseudotsugae

Hopk.). Susceptibility rating systems have been developed 

for spruce beetle (D. rufipennis Kby.) in Alaska (Reynolds 

and Holsten 1996). Connectivity analysis for risk assessment is not very common at present. We have ongoing 

work to explore risk of spruce beetle (across a large area of 

southwestern Yukon, Canada, using connectivity methods. 

In addition to susceptibility rating, statistical methods to 

examine spatial and temporal autocorrelation of environmental factors (Gumpertz and others 2000) and simulationbased approaches (Mawby and Gold 1984) have been 

applied to the southern pine beetle (D. frontalis Zimm.).

Applying methods in new systems presents a number of 

challenges and high levels of uncertainty. The MPBs have 

been expanding the northeastern limit of their range and are 

approaching boreal jack pine (P. banksiana Lamb.) forests 

in Alberta, Canada (H. Ono, pers. comm.). These changes 

increase uncertainty owing both to the dynamic character 

of the changes and because little information is known 

on MPB—host interactions in these forests. Nonetheless, 

managers of these systems are faced with challenging decisions, and risk-rating systems can provide some insights. 

In conjunction with climatic suitability work (Taylor and 

others 2006), we have ongoing work to adapt and apply 

susceptibility and connectivity methods in the boreal forest 

of British Columbia and Alberta, Canada. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Conclusion

We presented a common framework within which methods 

to assess landscape-scale risk of bark beetle infestations 

can be classified. This framework has two elements. First, 

conceptualizing landscape-scale risk as a probability 

distribution of potential loss provides a common basis to 

compare methods and allows varying degrees of precision, uncertainty, and stochasticity to be included. Second, 

viewing methods along a gradient from structural (patternoriented) to functional (process-oriented) approaches to 

risk assessment helps to clarify tradeoffs between precision, uncertainty, data requirements and timeframes for 

application. A key message is that no single tool or method 

can address all needs. Viewing methods along a gradient 

of complexity helps provide a system to classify methods, 

which, in turn, facilitates comparison and selection for a 

given set of questions.

Acknowledgments

We would like to acknowledge support for this project from 

the Mountain Pine Beetle Initiative of the Canadian Forest 

Service (CFS). We also thank Les Safranyik (CFS emeritus), Adrian Walton (British Columbia Forest Service), and 

Peter Hall (BC Forest Service). We would like to thank all 

the people in various regions of BC and elsewhere who have 

collaborated on projects and methods presented.

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

P.B. Woodbury and D.A. Weinstein

P.B. Woodbury, senior research associate, Department of 

Crop and Soil Sciences, Cornell University, Ithaca, NY 

14853; D.A. Weinstein, senior research associate, Department of Natural Resources, Cornell University, Ithaca, NY 

14853.

Abstract

We reviewed probabilistic regional risk assessment methodologies to identify the methods that are currently in use 

and are capable of estimating threats to ecosystems from 

fire and fuels, invasive species, and their interactions with 

stressors. In a companion chapter, we highlight methods 

useful for evaluating risks from fire. In this chapter, we 

highlight methods useful for evaluating risks from invasive 

species.

The issue of invasive species is large and complex 

because there are thousands of potential invasive species 

and constant movement of new and established plants, 

plant material, pests, and pathogens. Adequate data are not 

always available to support rigorous quantitative modeling of the different stages of invasion. However, even a 

semiquantitative rule-based approach can help to identify 

locations that contain host species susceptible to specific 

pathogens or insect pests, and where propagules are more 

likely to enter based on the current locations of the invasive 

species, ports of entry, and methods of spread. Predicting 

long-distance movement is much more difficult, as such 

events are rare, often poorly understood, and are often influenced by human behavior. Even so, published methods to 

make probabilistic predictions of pest establishment could 

be expanded to provide quantitative estimates of spread 

beyond an initial port of entry. Many invasive species are 

transported along roads, and so road networks provide some 

information about the likelihood of introduction into a new 

region. 

Models based on fundamental biological and physical 

processes, such as population demographics and movement 

of organisms, can be more robust than purely statistical 

approaches. Process-based models may better support 

extrapolation beyond the range of available or historical 

data because they use predictor variables that represent 

physical and biological processes. However, even simple 

correlative approaches may be useful to quantify the 

overlap in spatial distribution of stressors and ecological 

receptors as a screening-level analysis. Furthermore, if 

predictors are chosen carefully, they may represent important processes. For example, data on nonindigenous species 

may be quite useful for predicting the occurrence of much 

rarer invasive species because the correlation is based on 

the key processes of human-influenced transport, establishment, reproduction, and dispersal of propagules. Ecological 

niche-modeling approaches are useful because they can use 

data from museum collections in other countries to make 

estimates of potential new range areas in the United States. 

Other spatial data such as road networks may also be useful 

to predict the number of nonindigenous species or presence 

of a particular species. Such relationships may also support 

extrapolation to future conditions if there will be more 

roads or a higher traffic volume.

As for any regional stressor, the use of multiple models 

and a weight-of-evidence approach would help to increase 

confidence in predictions of ecological risks from invasive 

species. Two approaches to predicting the risk of Asian 

longhorned beetle (Anoplophora glabripennis Motschulsky) 

throughout U.S. forests make quite different predictions 

because they focus on different stages in the process of 

establishment and spread, thus combining such approaches 

should result in more robust predictions. Invasive species 

management should be addressed at multiple spatial scales, 

including reducing importation of new species at border 

crossings and ports, national and regional mapping of locations of invasive species, methods to reduce long-distance 

transport, and methods to reduce local movement.

Keywords: Ecological risk assessment, invasive species, probabilistic risk assessment, regional risk assessment, 

risk analysis.

Review of Methods for Developing Regional Probabilistic 

Risk Assessments, Part 2: Modeling Invasive Plant, Insect, 

and Pathogen Species
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Introduction

This review provides an overview of issues in probabilistic 

risk modeling at the regional scale and suggestions for productive directions for future risk assessments and research. 

Invasive nonindigenous species are a serious and increasing threat to many ecosystems throughout the United 

States (NRC 2002, Pimentel 2005). For example, invasive 

species are implicated as threats for more than half of all 

endangered species in the United States (Wilcove and 

others 1998). Invasive species are also altering fire regimes, 

hydrology, nutrient cycling, and productivity of ecosystems 

in the Western United States, particularly rangelands and 

riparian areas (Dukes and Mooney 2004). Plant species 

such as yellow star-thistle (Centaurea solstitialis L.), other 

Centaurea species, and cheatgrass (Bromus tectorum L.) 

have overtaken large areas of native ecosystems in the 

Western United States (LeJeune and Seastedt 2001). Leafy 

spurge (Euphorbia esula L.), knapweeds (Centaurea sp.), 

tamarisk (also known as salt cedar, Tamarix ramosissima

Ledeb.), nonnative thistles, purple loosestrife (Lythrum 

salicaria L.), and cheatgrass are some of the most severe 

problems on national forest lands. For example, the number 

of counties in Washington, Oregon, Montana, and Wyoming 

where yellow star-thistle has been found has been increasing exponentially during the last 100 years (D’Antonio 

and others 2004). Furthermore, the number of new exotic 

species has increased roughly linearly over this time period, 

reaching a total of nearly 800 by 1997 (D’Antonio and others 2004). Annual costs of selected nonindigenous species 

in the United States have been estimated at $120 million 

(Pimentel and others 2005). However, this estimate does not 

account for all effects of invasive species on rangelands and 

forests (Dukes and Mooney 2004), and it is clear that such 

costs are substantial. Despite the difficulty in quantifying 

economic damage, there is substantial evidence suggesting that invasive species have many deleterious effects in 

ecosystems in the Western United States, and that improved 

management of invasive species in wildlands is crucial 

(D’Antonio and others 2004). For example, tamarisk alone 

has been estimated to cost $133 to $285 million per year 

(in 1998 U.S. dollars) for lost ecosystem services including 

irrigation water, municipal water, hydropower, and flood 

control (Zavaleta 2000).

Various aspects of invasive species biology and ecology, as well as policy and management issues (NRC 2002), 

are addressed in many published reviews. We will review 

briefly some key issues, but the focus in this piece is on 

modeling methods suitable for spatially explicit probabilistic risk assessments for invasive species. This chapter, and a 

companion chapter addressing fire (Weinstein and Woodbury, this volume), present results of a project sponsored by 

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

Western Wildland Environmental Threat Assessment 

Center during its development; but these results should not 

be construed to represent the views of the center nor its 

personnel. The overall goal of our project was to identify 

promising methods for analyzing ecological risks to forest, 

rangeland, and wildland ecosystems from multiple stressors. The results of such risk analyses are intended to provide 

information useful for strategic planning and management 

of wildlands including national forests. The specific goal of 

this chapter is to identify modeling approaches suitable for 

making spatially explicit, probabilistic estimates of ecological risks from invasive plant, insect, and pathogen species 

throughout large regions such as the Western United States. 

Such modeling approaches ideally should be capable of:

1. Calculating risk of a detrimental environmental 

effect. 

2. Using spatially heterogeneous environmental 

data to drive calculation of risk at different 

points throughout a region. Spatial scales of 

interest include landscape, sub-State region, 

State, region of the United States, or the entire 

conterminous United States.

3. Relying primarily on available regional (in 

United States, state or multi-State) or national 

data.

4. Being useful for many species, not just a single 

invasive species.

5. Modeling effects of interaction among multiple 

stressors. 

6. Modeling effects of changes in environmental 

conditions in the future.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

We review selected modeling approaches relevant to the 

goals listed above, and more detailed analyses of specific 

aspects of invasive species assessment and management are 

provided by other chapters within this broader work. 

Stages of Invasion and Risk Assessment 

Frameworks

This section provides an overview of the stages of the 

invasion process, key factors that affect these stages, and 

different frameworks that can be used to assess risks due 

to invasive species. The process by which a nonindigenous 

species becomes an invasive species can be divided into the 

following five stages:

1. Uptake/entry into transport system

2. Survival and transport to the United States 

via land, air, or water, with or without vectors

3. Initial establishment—survival and 

reproduction

4. Local dispersion

5. Widespread dispersion

Three classes of key factors influence the likelihood 

that a potential invader will pass through each stage: (A) 

propagule pressure, (B) physicochemical requirements 

of the potential invader, and (C) community interactions 

(Colautti and MacIsaac 2004). However, even successful 

modeling of all stages of the invasion process still does not 

address the likelihood or degree of damage caused by the 

invasive species. For this purpose, an ecological risk assessment approach is required. 

The topic of invasive species has begun to be addressed 

by practitioners of ecological risk assessment (Andersen 

and others 2004a, 2004b; Stohlgren and Schnase 2006). 

Andersen and others (2004a, 2004b) reviewed the regulatory framework for invasive species in the United States and 

some of the issues in extending the approach to ecological 

risk assessment (originally developed for contaminants) 

in order to address biological stressors such as invasive 

species. They also provide information about a series of 

articles of the journal “Risk Analysis” that report the results 

of a joint workshop between the Society for Risk Analysis 

Ecological Risk Assessment Specialty Group and the 

Ecological Society of America Theoretical Ecology Section. 

In addition, they identify research needs for this field. 

Of relevance to this chapter, they suggest that “Spatially 

explicit, multiscale decision-support systems will contribute 

to better decisionmaking through enhanced credibility, an 

explicit and direct relationship with managing for sustainability, and explicit illustration of trade-offs and the cost of 

inaction.” Presented in one of the articles in this series is a 

model of establishment risks for Asian long-horned beetle 

(Anoplophora glabripennis Motschulsky) introduction via 

solid wood packing materials (Bartell and Nair 2004). This 

approach estimates both the probability of establishment 

at the port of entry and the probability of spread based on 

environmental factors, host availability, and traits of the 

invasive species. Uncertainty in key parameters is investigated by means of Monte Carlo analysis. Additionally, there 

is investigation of the efficacy of different management 

techniques. Integration of quantitative risk analysis and 

quantitative analysis of management options within a single 

analytical framework is much too rare and should be applied 

more widely. Another article in this series describes how the 

conceptual model in the relative risk model can be applied 

to predict the effects of invasive species (Landis 2003). 

This approach is promising in that it is capable of addressing multiple stressors simultaneously at the regional scale 

by means of a ranking procedure. Although complete risk 

assessments are not reported in this article, it illustrates how 

invasive species risk can be analyzed at the regional scale 

in the context of multiple stressors and multiple endpoints. 

A case study of this approach has been implemented for a 

European green crab (Carcinus maenas L.) for a region of 

Washington State (Colnar and Landis 2007).

Transport to the United States and Within 

U.S. Regions

Most exotic plant species have been introduced to the 

United States intentionally, whereas most insects and 

pathogens have entered the United States unintentionally 

(Mack and Erneberg 2002). Global travel and trade have 

increased the amount of plant material, wood, and wood 

products moving into U.S. ports, increasing the likelihood 

of introduction of invasive plants, insects, and pathogens. 

By 2020, it has been predicted that more than 100 new 
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insect species and 5 new plant pathogens will become established (Levine and D’Antonio 2003). A particularly highrisk pathway for forest insects and pathogens is importation 

of raw logs (Tkacz 2002). As an example for the Pacific 

Northwest, surveys of ports, port areas, mills and businesses known to have received or handled imported wood 

or wood products from 1996 to 1998 found seven species 

of wood-boring beetles from Asia, Europe, and the Eastern 

United States (Mudge and others 2001). For the United 

States as a whole, inspections of all types of products in 

four cargo pathways at ports and border crossings found the 

highest rate of insect introductions in refrigerated maritime 

cargo, with 1 new insect species found in every 54 inspections (Work and others 2005). It was estimated in this study 

that fewer than half of such new species are detected, and 

42 insect species may have become established from 1997 

to 2001. These species do not necessarily pose a high risk of 

widespread infestation or damage, but they do indicate that 

exotic species are entering the United States at an alarming 

rate. Many of the issues of invasive species transport and 

establishment from other countries to the United States 

also apply to establishment of new populations owing to 

long-distance transport of invasive species among regions 

in the United States. Gypsy moth (Lymantria dispar L.) is 

an example species known to cause severe infestation and 

damage in Eastern U.S. forests (Liebhold and Tobin 2006). 

Gypsy moth has been long established in the Eastern United 

States but has been prevented from establishing, to date, in 

the Pacific Northwest owing to surveillance and eradication 

efforts (Hayes and Ragenovich 2001).

To manage invasions and reduce risks, it is vastly more 

cost-effective to prevent establishment, or eradicate an 

invasive species as soon after entry as possible (Simberloff 

2003, Stocker 2004). However, most invasive species are 

difficult to locate and may not appear to present any significant risk to ecosystems until they have become well established, often many decades after introduction. Thus, most 

management and control efforts focus on severe known 

problems rather than preventing future severe problems. 

Also unfortunately, it is difficult to predict which nonindigenous species will become invasive, and which invasive 

species will become severe problems (Smith and others 

1999). A number of initiatives have been undertaken in the 

United States to address various aspects of invasive species 

monitoring, risk assessment, and management owing to the 

severity of problems caused by invasive species. 

Existing National Invasive Management 

Programs

A number of international, national, and regional efforts are 

underway to attempt to reduce the risks posed by invasive 

species. Some of these efforts for the United States are 

discussed briefly below, with a focus on programs related to 

forest and rangeland ecosystems. It is beyond the scope of 

this review to discuss all international programs that may 

provide valuable information for invasive species in the 

United States. However, some sources of global information 

are mentioned in the subsequent section on invasive species 

databases.

The National Invasive Species Council (NISC) consists 

of eight Federal departments and was formed in 1999 by 

Executive Order 13112. The NISC 2001 National Management Plan called for development of a risk analysis system 

for nonnative species by 2003. The NISC is intended to provide a gateway to information, programs, organizations, and 

services about invasive species. Their Web site (http://www.

invasivespecies.gov) provides information about the impacts 

of invasive species and the Federal government’s response, 

as well as select species profiles and links to agencies and 

organizations dealing with invasive species issues.

The USDA Animal and Plant Health Inspection Service 

(APHIS) protects not only agricultural but also forest, 

rangeland, and wetland ecosystems. APHIS works closely 

with the USDA Forest Service and the U.S. Department of 

the Interior's Bureau of Land Management, National Park 

Service, and Fish and Wildlife Service. APHIS conducts 

risk assessments with a dual mission to promote international trade and prevent invasive species that may cause 

serious harm from entering the United States. Some APHIS 

activities focus on protecting and managing endangered 

species as well as migratory bird populations. APHIS 

maintains the Port Information Database, and there is 

great potential to strengthen and make broader use of this 
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database for understanding the pathways taken by invasive 

species entering the United States (NRC 2002). 

The USDA Forest Service, working in conjunction with 

Federal, State, tribal, and private partners, has developed 

the Early Warning System (EWS) to detect and respond 

to environmental threats to forest lands in the United 

States. The EWS comprises many existing programs, 

along with new initiatives such as the Western Wildland 

Environmental Threat Assessment Center and the Eastern 

Forest Environmental Threat Assessment Center. The EWS 

addresses potential catastrophic threats such as insects, 

diseases, invasive species, fire, weather-related risks, and 

other episodic events. The system is intended to:

1. Improve understanding of the crucial elements 

involved in early detection and response to 

environmental threats.

2. Help identify and remedy weaknesses in the 

current system of early detection and response.

3. Aid for strategic planning and resource 

allocation.

There are many groups both within and outside the 

Forest Service that participate in the process of detecting 

and responding to threats to forests. Further information 

about some component groups that conduct regional risk 

analyses is presented in other chapters in this volume. Further information about the EWS is available at the following 

Web site: <http://www.fs.fed.us/foresthealth/programs/

early_warning_system.shtml>.

The National Aeronautic and Space Administration 

(NASA) and the U.S. Geological Service (USGS) are 

developing a National Invasive Species Forecasting System 

(ISFS) for the management and control of invasive species 

on Department of Interior and adjacent lands. The system 

provides a framework for using USGS’s early-detection 

and monitoring protocols and predictive models to process 

remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Enhanced Thematic 

Mapper, and the Advanced Spaceborne Thermal Emission 

and Reflection Radiometer as well as commercial remote 

sensing data. The goal is to create on-demand, regionalscale assessments of invasive species patterns and vulnerable habitats. Additional information can be found at the 

following Web site: http://bp.gsfc.nasa.gov/. This approach 

has recently been used to predict the relative suitability of 

all areas in the conterminous United States for tamarisk, 

an invasive woody shrub (Morisette and others 2006). This 

analysis is reviewed below under the heading of USGS and 

NASA Invasive Species MODIS-Regression methodology.

Within the USDA Forest Service, the establishment 

of the two Threat Assessment Centers is a key part of 

the strategy for improving the management of invasive 

species. These efforts build upon ongoing programs and 

projects such as the Forest Inventory and Analysis Program 

(including Forest Health Monitoring) and Forest Health 

Protection. Further information about the strategies of these 

agencies for invasive species management is provided at 

the following Web site: http://www.off-road.com/land/invasive_species_strategy.html. Recommendations for control 

of invasives in rangelands are provided at the following 

Web site: http://www.fs.fed.us/rangelands/ecology/invasives.

shtml

The USDA Forest Service’s Forest Health Technology Enterprise Team (FHTET) is using an expert opinion 

approach to model risks of invasive pests and tree pathogens at the national scale for national strategic planning 

purposes. Potential tree mortality risk is modeled based 

on expert opinion, forest inventory data, and other GIS 

(geographic information system) data (Marsden and others 

2005), also see the following URL: http://www.fs.fed.us/

foresthealth/technology/products.shtml. Further discussion 

of this approach is presented below under the heading of 

“FHTET national risk map.”

Availability of Spatial Data 

Many kinds of regional data may be useful for developing 

regional probabilistic risk assessments, including land cover 

and land use data, transportation networks (e.g., roads and 

trails), hydrography, climate, digital elevation models, etc. 

Many such databases are available in GIS format from the 

National Atlas, which also includes data on selected invasive species (http://www.nationalatlas.gov/atlasftp.html). 

Data on land use is available from the National Land Cover 

Characterization database that is being compiled across 
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States as a cooperative mapping effort of the Multi-Resolution Land Characteristics Consortium. Landcover databases 

are being developed by bioregion based on remotely sensed 

imagery acquired from 1999 to 2003 and are complete or 

nearly complete for most portions of the United States, 

including the West Coast and much of the Southeast (http://

www.mrlc.gov/mrlc2k_nlcd.asp). It is beyond the scope of 

this review to discuss all of these types of data, or even all 

types of databases specifically on invasive species, but a 

brief overview of invasive species survey data is presented 

below.

At the global scale, the Global Invasive Species Information Network is developing an online registry of data sets 

related to nonnative species (Simpson 2004), and ongoing 

efforts are being made to develop linkages among national 

and multicountry invasive species databases (Simpson and 

others 2006). The Global Invasive Species Programme 

(Mooney 1999) provides an online list of invasive species 

databases, including those covering the conterminous 

United States, Alaska, and Hawaii (http://www.gisp.org/

links/index.asp). In the United States, a survey was undertaken recently to identify data sets of nonnative species at 

county, State, region, national, and global scales (Crall and 

others 2006). Based on a literature survey, Internet search, 

and responses from surveys sent to 1,500 experts, a total of 

319 data sets were identified, and metadata were collected 

for most data sets (79 percent). Of the total, 57 percent are 

available online (see the following Web site for further 

information: http://www.niiss.org). Categories of data sets 

for which metadata are available consist of the following: 

77 percent cover vegetation, 38 percent cover vertebrates, 

77 percent cover invertebrates, 14 percent cover pathogens, 

and 9 percent cover fungi. Note that these percentages sum 

to greater than 100 percent because some data sets cover 

multiple taxa or categories. The scale of data sets for which 

metadata are available are as follows: 33 percent are at the 

county scale, 20 percent at the State scale, 17 percent at 

the multi-State regional scale, 15 percent are national, and 

14 percent are global. Although this number of data sets 

is encouraging, the authors note that only 55 percent of 

the data sets have a quality assurance and quality control 

procedure, suggesting that the accuracy of many data sets 

may be questionable or undetermined. 

Other sources of data useful for regional assessments 

of invasive species are databases developed by the Forest 

Inventory and Analysis (FIA) Program of the USDA Forest 

Service (http://fia.fs.fed.us/). The FIA Program collects data 

for all land meeting a specific definition of forest land in 

three phases. Historically, Phase 1 has been based on aerial 

photography, but now satellite remote sensing imagery is 

being used. Phase 1 points are used to identify forested and 

nonforested locations. Phase 2 includes ground measurements such as tree species, height, diameter, disturbance, 

and stand age on more than 100,000 stratified sampling 

plots across the country. Historically, the focus was on 

timber resources that are available for potential harvest, but 

during recent decades there has been increased emphasis 

on a broader suite of forest characteristics including forest 

health and invasive species. In particular, Phase 3 sampling 

is done on a subset of plots to determine the species, abundance, and spatial arrangement of all trees, shrubs, herbs, 

grasses, ferns, and fern allies (horsetails and club mosses). 

This Phase 3 sampling was begun as a separate program 

called Forest Health Monitoring but is now administered 

through the FIA Program. As an example, a pilot study collecting Phase three data on plots throughout Oregon found 

at least 1 nonnative species on 70 percent of all forested 

plots, and 20 percent of plant cover was nonnative in one of 

10 forested plots (http://earthscape.org/r1/ES16479/pnrs_science%20update.pdf; note: membership is required to access 

this Web site, but free trial membership is available). In 

addition to data specifically on invasive species, the Phase 

2 FIA data are a valuable source of vegetation data because 

they have been collected in statistically designed surveys for 

decades. Information on forest type, stand age, and disturbance history are available and can be used in conjunction 

with data on invasive species to predict vulnerability of 

forest stands to invasion. Such an approach is underway in 

the Southern United States (Ridley and others 2006). Phase 

2 FIA data are also being used in conjunction with other 

data to develop regional and national vegetation databases 

in other research programs including LANDFIRE. See the 
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topic “Conclusions Concerning the Use of Fire Modeling 

Systems” in Weinstein and Woodbury (this volume).

Review of Selected Methodologies

In this section, we review selected modeling approaches relevant to the goals listed above in the “Introduction” section. 

The focus is on invasive species of concern for the Western 

United States, particularly forest and rangeland ecosystems. 

Examples were selected to cover a range of analytical 

techniques with an emphasis on the State or regional scale. 

In addition, we selected examples of two different methods 

applied to an invasive pathogen that is the causal agent of 

sudden oak death disease (Phytophthora ramorum Werres, 

de Cock & In’t Veld) and two methods applied to an 

invasive insect: the Asian long-horned beetle.

Climatic and Ecological Niche Models

The most common and readily applied approaches to 

predicting the risk of invasive species occupying sites 

across a large region rely on biogeographical distribution 

models. These models are based on information about the 

biophysical factors that limit where a species can survive. 

Such models are known as bioclimatic envelope models, 

biogeographical distribution models, and (ecological) 

niche models. Such models are generally correlative and 

may be either statistically based or rule based. As applied 

to invasive species, such approaches typically attempt to 

map which parts of a region are suitable for the invading 

species, and suitability is typically based on habitat requirements. For pests and pathogens, the simplest approach is to 

map the presence or absence of suitable hosts. Such maps 

are typically developed from available regional data sets, 

which often provide relevant but not necessarily ideal data 

for a particular invasive species. Such maps may be useful 

for strategic planning at the regional scale, but may be of 

limited use for managing specific areas presuming that 

the managers of those areas already know where different 

species occur.

Niche models typically identify habitats for invasive 

species based on records of their presence at known locations. Such records can be obtained from museum collections such as herbaria, but currently, only 5 to 10 percent 

of such data are available in electronic form worldwide 

(Graham and others 2004). To define the niche or bioclimatic envelope, biophysical data for each such location are 

often extracted from regional databases, usually in a GIS. 

The most important distinction among such approaches 

is whether they use absence data in addition to presence 

data. In other words, whether locations where the invasive 

species does not occur (absence) are used to define biophysical conditions that are outside of the niche. Either approach 

is problematic for invasive species because, typically, they 

have not yet occupied all possible sites. Thus, sites where 

the species doesn’t occur may not necessarily provide information about the species niche or requirements; instead, 

those may be sites that the invasive species haven’t yet 

reached. Presence and absence data can be obtained from 

the native region of the invasive species, but the species may 

have a different niche in the part of the world it is invading, 

as compared with its native region. However, use of data 

from the native region may be the only reasonable choice 

for species that have not become widely established in the 

United States. Even so, there may be substantial uncertainty 

in such predictions until a species becomes widely established. For example, an analysis of purple loosestrife (a 

common invasive species in Eastern United States wetland 

areas) determined that a reliable prediction of the current 

nonnative distribution in North America was only possible 

150 years after initial establishment (Welk 2004).

Many variations of the niche approach are used to 

predict the niche of the invasive species including:

1. Simple ranges for factors based on mean 

climatic variables such as the widely used 

BIOCLIM and DOMAIN models.

2. Fuzzy rather than crisp calculations of the 

niche (Robertson and others 2004).

3. The use of spatial statistical techniques and 

newer computational approaches, such as 

genetic algorithms and support vector 

machines.

We have evaluated a few examples of such approaches 

below, with a focus on the Western United States. For each 

of these examples, we discuss how they meet the criteria 

listed above.
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GARP Niche Modeling Approach

In this family of approaches implemented in a software tool, 

the potential range of invasive species is predicted based on 

point data from the species native home range and spatial 

data including mean annual temperature, rainfall and elevation (Anderson and others 2003, Costa and others 2002, 

Godown and Peterson 2000, Peterson 2001, Peterson and 

Cohoon 1999, Peterson and Kluza 2003, Peterson and others 

2003b, Stockwell and Peterson 2002, Underwood and others 

2004; also see http://www.lifemapper.org/desktopgarp/). 

This approach shares many features with other approaches 

to predict ecological niches based on bioclimatic data, 

including climate envelope modeling and other methods 

for niche modeling. All of these approaches assume that 

bioclimatic predictor variables (for example, mean annual 

temperature and precipitation) control the native distribution of an invasive species, and these factors will also 

control the potential distribution in the United States This 

technique differs from others because it uses a machine 

learning method (also known as artificial intelligence) 

named Genetic Algorithm for Rule-Set Prediction (GARP). 

Based on only 15 to 20 records of locations of a species 

from its native home range (species input data), the method 

can predict the potential distribution (home range, or niche) 

of a species. This approach has been used by its developers 

to model the niche of both invasive species and noninvasive 

species. The user needs to provide species input data of 

known points where the species has been found in its native 

region. These data should be well distributed throughout 

the species native range and need to be georeferenced. The 

user also needs to provide environmental data covering 

the entire area for which predictions are desired, including mean annual temperature and precipitation (modeled 

surfaces). Potentially, many other input data could be used 

such as remote sensing images, but they might need to be 

available for both native region and the analysis region. 

The software used is desktopGARP, which can be 

downloaded from the following Web site: http://nhm.

ku.edu/desktopgarp/. The user selects a type of inferential 

tool, such as logistic regression, or bioclimatic rules. The 

input data are then divided into training data and validation 

data. The software generates pseudodata via resampling, 

and then iteratively tries a large number of rule sets, 

continuing either until there is no further improvement in 

the predictions, or 1,000 iterations. The output from the 

model is a map of species niche as presence/absence, with 

some confidence values. Modeling may be done for either 

counties or for grid cells (pixels). The primary prediction 

is whether a county or a pixel is contained in the species 

potential (fundamental) niche. A measure of likelihood is 

generated by using multiple models, and assigning higher 

likelihoods to counties or pixels predicted to be included in 

the niche by multiple models (Peterson and others 2004).

In the following citations, only one predicted value is 

made per county, although the approach could be extended 

for finer grain analyses if input data are available at finer 

scales. The methodology (Peterson 2003) and its use to 

predict the distribution of four alien plant species in North 

America for a single point in time (the fundamental niche) 

are described in the references reviewed herein. Invasive 

plant species analyzed to date include Hydrilla (Hydrilla

L.C. Rich.), Russian olive (Elaeagnus angustifolia L.), 

sericea lespedeza (Lespedeza cuneata (Dum.-Cours.) G. 

Don), and garlic mustard (Alliaria petiolata (Bieb.) Cavara 

& Grande) (Peterson and others 2003a). To predict the 

spread of Asian long-horned beetle, the GARP approach 

has been combined with a spatial model of spread originally 

developed for forest fire (Peterson and others 2004). 

The GARP approach has several strengths for the regional 

risk analysis of invasive species, which are as follows:

1. It has been applied to a number of taxa of invasive 

and noninvasive species in the United States and 

elsewhere.

2. A freely available software tool has been 

developed that implements this approach.

3. Data requirements for this approach are 

modest. 

Most weaknesses of the GARP approach are shared by 

all niche modeling approaches, which include:

1. Not all of the stages of the invasion process 

are modeled.

2. Only presence or absence of a species is 

predicted, not effects of invasive species.

3. Results may be biased, depending on the 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

source of data and the use of pseudo-absence 

data (Graham and others 2004).

Other approaches such as support vector machines and 

generalized additive model (GAM) approaches may be less 

biased and provide more optimal statistical solutions (Elith 

and others 2006, Stockwell 2005), but see also Anderson 

and others (2003) for improving on model selection 

methods.

FHTET National Risk Mapping Approach

This approach is also a family of related approaches to 

predict tree mortality risk owing to an invasive insect or 

pathogen based on expert opinion, forest inventory data, 

and other GIS data (Marsden and others 2005), and also 

consult FHTET products Web site: http://www.fs.fed.us/

foresthealth/technology/products.shtml). Specifically, predictions are made of the potential basal area loss of susceptible tree species owing to an invasive insect or pathogen. 

The location of suitable host species is interpolated using 

inverse-distance weighting based on forest inventory data. 

A multi-criteria risk ranking model is developed based 

on expert opinion about the factors that influence pest or 

pathogen establishment, spread, and tree mortality. An 

iterative process is used to develop risk maps, so the experts 

and analysts can alter the weighting of difference factors 

to adjust the maps to match expert opinion. This approach 

has been used to predict the potential effect of oak wilt in 

the North Central States and of wood wasp (Sirex noctilio

Fabricius) throughout the conterminous United States: 

(http://www.fs.fed.us/foresthealth/technology/invasives_

sirexnoctilio_riskmaps.shtml).

The following are the key required input data and their 

sources:

1. Principal U.S. ports - Army Corps of Engineers, 

Waterborne Commerce, Foreign Cargo 

Statistics.

2. Markets for wood products - Federal High-way 

Administration, Freight Management and 

Operations, Freight Analysis Framework, 

Highway Truck Volume and Environmental 

Systems Research Institute’s (ESRI) polygon 

data.

3. Distribution centers - National Transportation Atlas Database.

4. Species occurrence and basal area of 

individual tree species – USDA Forest 

Service, Forest Inventory and Analysis (FIA), 

National and New York State Christmas 

Tree Association Web sites.

5. Road density and distance to road – U.S. 

Geological Survey, Heterogeneous 

Distribution Indicator.

Use of this approach requires one or more experts 

on the pest or pathogen, expertise in the use of FIA data, 

and expertise in GIS software. The spatial scope is the 

conterminous Unites States for a single time period. 

Required software includes ArcView 3.x, Spatial Analyst 

ModelBuilder (ESRI, Inc.), and IDRISI 32 (a raster GIS 

software package). Model output includes maps of predicted 

occurrence based on (1) hosts known to be susceptible and 

(2) hosts suspected to be susceptible. 

For regional and national risk analysis, the approach of 

mapping factors that influence a stressor and then combining these factors with weightings derived from expert 

opinion are intuitively appealing and fairly common. This 

flexible, iterative expert opinion-based approach can be 

used for virtually any pest or pathogen, and a risk map can 

be generated fairly quickly because the system is already 

in place. Other strengths of this approach include the use of 

national FIA data and the quantification of potential damage 

in terms of tree mortality. However, the flexible expert 

opinion-based approach is also a weakness because it is so 

open-ended, subjective, and difficult to validate. To date, it 

does not appear that an attempt has been made to determine 

which environmental factors were actually associated with 

pest presence, or to quantify uncertainties in GIS layers 

or predictions. In contrast, a statistical inference approach 

that made quantitative predictions of pest occurrence would 

be more useful because it could be better tested against 

validation data. 

Meentemeyer Sudden Oak Death Approach

Meentemeyer and others (2004) used a rule-based function 

to predict spread of sudden oak death pathogen distribtions 
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GENERAL TECHNICAL REPORT PNW-GTR-802

in grid cells (30 by 30 m) throughout California. A prediction was made of the likelihood of presence of the disease 

based on rules derived from expert opinion and published 

data on plant species susceptibility, pathogen reproduction, 

and host climate. This method is focused on evaluating a 

single risk, the probability of oaks on a given site being 

infected by P. ramorum. More specifically, the method 

begins with mapping five predictor variables in a GIS and 

then using a set of rules to determine the risk of infection based on these predictor variables. The predictor 

variables are host species index, precipitation, maximum 

temperature, minimum temperature, and relative humidity. 

Host species index is weighted three times as strongly as 

precipitation and maximum temperature, which in turn are 

weighted twice as strongly as relative humidity and minimum temperature. Each variable is classified on a relative 

index, with host scored on a scale from 0 to 10, precipitation, maximum temperature, and humidity scored from 0 to 

5, and minimum temperature scored from 0 to 1. The model 

was tested against 323 field observations in California. The 

model generally predicted higher risk for sites where P. 

ramorum is currently present and lower risk for sites where 

it is currently absent. However, it appears that approximately 20 percent of low-risk sites were infected. 

Input data for the model include host susceptibility, pathogen reproduction, and host climate suitability. 

Like many modeling approaches, this approach requires 

expertise in GIS and database analysis. The model output 

is in the form of a map with estimated risk of occurrence 

of the pathogen at a single time period – movement of the 

pathogen is not modeled. The spatial scope includes all of 

California, and the map unit is landscape cell (30 by 30 m). 

The approach uses the CALVEG database (USDA Forest 

Service RSL 2003) for vegetation alliance and presence of 

P. ramorum and the Parameter-elevation Regressions on 

Independent Slopes Model (PRISM) for elevation-based 

regression extrapolations from base weather stations for climate data, which are available for the conterminous United 

States (http://www.wcc.nrcs.usda.gov/climate/prism.html).

The method meets the criterion of calculating the 

risk of detrimental environmental effect by mapping the 

probability of pathogen occurrence in each forest grid-cell 

and could be extended to predict the presence of pathogens 

in smaller regions or pixels. But the focus is assessment of 

effects over a region, specifically bioregions, rather than at 

all points within a region. The method meets the criterion 

of using spatially heterogeneous environmental data to 

drive calculation of risk at different points throughout 

the Western United States. Potentially, it could be used to 

evaluate the risk from a number of stressors, but relationships between habitat conditions and probability of stressor 

occurrence would have to be developed. Potentially, the 

method could be extended to consider effects of interaction 

among multiple stressors, but interaction terms would need 

to be identified and parameterized in a regression model. 

The approach does not currently consider the effect of 

changes in environmental conditions over time.

Unfortunately, no attempt was made to determine 

which environmental factors were actually associated with 

disease presence. A statistical inference approach that made 

quantitative predictions of pathogen occurrence would be 

more useful because it could be better tested against validation data when they become available. The finding that 21 

percent of sites predicted to be low risk, yet were found to 

be infected, suggests that the model has limited predictive 

power. This limited power is likely due to data limitations 

as well as lack of precision in rules and weights applied to 

them. The investigators do state that they plan to use FIA 

data to improve the predictions. This study was evaluated 

because it addressed an important risk factor in Western 

and potentially Eastern U.S. forests, but use of a method 

that makes more quantitative predictions would be useful in 

the future.

Nowak Host Range Approach

This approach predicts potential home range of an (invasive) 

insect or pathogen of trees by modeling the location of suitable host species based on forest inventory data (Nowak and 

others 2001, http://www.fs.fed.us/ne/syracuse/Data/Nation/

InsectPoten.htm). A model of urban forests (UFORE) is 

used to predict urban forest composition based on data from 

a limited number of cities in the United States. Predictions 

are also made of the amount of tree cover that could be lost 

owing to tree death and the costs of replacing killed trees. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

A simple model of spread (moving outward at a constant 

rate from one location) was used to predict the length of 

time required for invasion to occur in each major city. This 

approach has been used to predict the potential effect of 

Asian long-horned beetle throughout all urban areas in the 

United States (Nowak and others 2001), and preliminary 

predictions have been made for nonurban areas (http://www.

fs.fed.us/ne/syracuse/Data/Nation/InsectPoten.htm). Preliminary predictions have also been made for the emerald ash 

borer (Agrilus planipennis Fairmaire) (http://www.fs.fed.us/

ne/syracuse/Data/Nation/InsectPoten.htm). The main type 

of required input is appropriate forest inventory data. Model 

output is in the form of maps of predicted occurrence based 

on (1) hosts known to be susceptible and (2) hosts suspected 

to be susceptible. The model has been used at the scale of 

the conterminous United States for a single point in time. 

One strength of this approach is the use of FIA data in 

conjunction with a model that has been used for many years. 

Another strength of this approach is the quantification of 

damage in terms of economic losses of urban trees. For 

urban trees, such economic losses are quite high, though for 

wildlands they will be much lower for an individual tree and 

much harder to estimate for a forested region. A limitation 

for regional risk assessment and management is that the 

focus of the model is urban areas. Another limitation, typical of most niche modeling efforts, is that not all steps in the 

process of invasive dispersion and reproduction are modeled, and that predictions are primarily of the potential host 

range of the pathogen, not of effects of the pathogen other 

than economic losses owing to the death of urban trees.

USGS and NASA Invasive Species MODISRegression

In this approach, a logistic regression is developed to 

predict the suitability of each 1-km pixel as habitat for 

tamarisk throughout the conterminous United States 

(Morisette and others 2006). Various ground surveys of 

tamarisk occurrence were integrated into a single database 

as presence or absence of tamarisk. Land cover, normalized 

difference vegetation index (NDVI), and enhanced vegetation index (EVI) were derived from MODIS data products. 

A discrete Fourier transform was used to model a constant 

amplitude yearly sine wave to each pixel, and the mean, 

amplitude, and phase of both NDVI and EVI were used as 

potential predictor variables along with a fitted parameter 

for each land cover class in a logistic regression model to 

predict the likelihood of habitat suitable for tamarisk. The 

ground data were split into a training set to fit the model 

(67 percent of data) and a validation set (33 percent of data). 

The best model included land cover, and seasonal variability 

in NDVI and EVI. The proportion of correctly predicted 

observations using a threshold of 0.5 was 0.90. The main 

model inputs are MODIS data and surveys of tamarisk 

presence. Because it is a regression procedure, many other 

input data could be used, such as human population density, 

trail networks, air temperature, etc. The main model output 

is a relative ranking of the likelihood of suitable habitat for 

an invasive species.

This general approach would be useful for regional 

assessments because it uses remotely sensed data that cover 

the entire conterminous United States. However, for each 

invasive species, a large database of ground survey data 

is required. If FIA or other systematic survey data could 

be used for this purpose, that would make the approach 

useful for many more invasive species. A limitation of 

this approach is that it uses statistical correlation to make 

predictions, thus it cannot readily predict the effect of 

future environmental conditions such as changes owing to 

development, changes in hydrology, or changes in regional 

or global climate. Other examples of logistic regression to 

analyze invasive species include multiple species in South 

Africa (Higgins 1999) and Russian knapweed (Acroptilon 

repens (L.) DC.) in Colorado (Goslee and others 2003).

Dark Invasive Species Spatial Autoregressive 

Approach

This approach uses spatial statistical analysis to predict 

the distribution of invasive and noninvasive alien plants 

throughout all bioregions in California (Dark 2004). Spatial 

autoregressive (SAR) models were used to assess the relationship between alien plant species distribution and native 

plant species richness, road density, population density, 

elevation, area of sample unit, and precipitation. Three 

predictors were found to be statistically significant for both 
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GENERAL TECHNICAL REPORT PNW-GTR-802

invasive and noninvasive plants: elevation, road density, 

and native plant species richness. The best model (with all 

predictors) explained about 80 percent of the variance in 

the number of alien species in each bioregion. Additionally, 

there was significant spatial correlation for both invasive 

and noninvasive alien plants. Both invasive and noninvasive 

alien plants are found in regions with low elevation, high 

road density, and high native-plant species richness. Spatial 

data input requirements include a digital elevation model, 

precipitation (a modeled surface), road networks, native 

species richness, and occurrence of alien species. Because 

it is a regression procedure, many other input data could be 

used, such as population density, trail networks, air temperature, traffic volume, etc. The model has been applied to all 

of California for a single time, with bioregions as the map 

unit. Model outputs include maps of the number of invasive 

and noninvasive alien species by bioregion. The method 

could be extended to predict the presence of invasive species in smaller regions or pixels.

This general approach would be useful for regional 

probabilistic risk assessments because it uses widely 

available data in conjunction with a flexible spatial statistical approach. Additionally, it predicts the total number of 

nonindigenous (alien) species within a region. This technique could be feasibly extended to predict the probability 

of occurrence of invasive species based on the occurrence 

of noninvasive alien species. This would be very useful 

because noninvasive species were found to be roughly 

tenfold more common than invasive species for the bioregions. This would be a useful first step for regional risk 

assessment for large regions such as the Western United 

States in order to identify areas with higher overall risk for 

invasive species. The approach could be improved by using 

more detailed data on vegetation types rather than bioregions. A limitation of this approach is that it uses statistical 

correlation to make predictions, thus it cannot readily 

predict the effect of future environmental conditions, such 

as changes owing to development, changes in hydrology, or 

changes in regional or global climate. However, it might be 

feasible to develop statistically based extrapolations from 

existing data. For example, if the number of nonindigenous 

species in a region can be predicted based on some measure 

of the transportation network, or other environmental factor, 

one could extrapolate to future conditions with more roads 

or a higher traffic volume. A future scenario of new road 

development or greater traffic or both on existing transportation networks could be developed based on planned State 

and Federal transportation projects. This scenario could be 

used to predict the subsequent increase in occurrence of 

nonindigenous species and invasive species.

Guo Support Vector Machine Approach

This method uses a type of machine learning algorithm 

called support vector machine (SVM) in a niche modeling 

approach to predict risk of occurrence of sudden oak death 

throughout California (Guo and others 2005). A useful comparison is made of presence-only (one class SVM) versus 

presence with pseudo-absence data (2-class SVM). Based 

on their results, the use of pseudo-absence data does not 

appear to be a good choice for modeling invasive species—

they inherently lead to bias because they conflate environmentally determined absence with absence on account of 

infestation not having occurred yet in a particular location. 

Input data include 14 environmental variables including 

mean annual temperature, mean annual precipitation, 

distance to roads, distance to patches of hosts, and presence 

of susceptible species. The use of this approach currently 

requires an analyst with not only GIS skills, but also substantial programming skill. Also, assistance may be needed 

from algorithm developers to modify code. Model output is 

a map of the potential location of the invasive species. The 

spatial scope includes all of California, and the map unit is 

a 1-km grid cell for a single time. Two regional databases 

are used as input data: California GAP and climate surfaces 

from the DAYMET model (http://www.daymet.org/). The 

software used is LIBSVM, which is a library of generic 

support vector machine functions developed by Chang and 

Lin 2001, as cited by Guo and others 2005. In this approach, 

risk is calculated only as potential presence of the disease. 

There are some probabilistic components, but many sources 

of uncertainty are not quantified.

This approach would be useful for regional probabilistic risk assessments because it is a generic machine learning 

technique applied to niche modeling. Thus, it could be used 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

for invasive plants, insects, diseases, and possibly other 

stressors. One-class SVMs appear particularly attractive 

because they are statistically based and unbiased and 

theoretically optimum, unlike some other machine learning 

methods and don’t require a lot of model tuning. A weakness of the approach, at least for many potential users, is 

dependence on a library of computer code functions rather 

than a more mature and user-friendly software package, 

and assistance may be required from the library developers 

to apply the functions in an analysis. This approach also 

does not account for time, nor does it incorporate spatial 

processes such as dispersion. It may be difficult to specify 

weights for each variable. Like all niche models, it is 

dependent on data quality, and there will likely be issues of 

spatial support and spatial scaling. 

Discussion

The issue of invasive species is large and complex because 

there are thousands of potential invasive species and constant movement of plants, plant material, pests and pathogens, in addition to established invasive species. It seems 

clear that the most cost-effective approach is to control 

invasive species very early in the process of transport from 

the native range and entry to the United States. This issue 

has received national recognition as an important threat 

and should be addressed at the national scale (NRC 2002). 

Increased international trade is exacerbating the problem, 

and despite this increase, the budget for APHIS, the first 

line of defense, has been decreasing in recent decades as a 

function of the volume of imported material (D’Antonio and 

others 2004). 

Despite the lack of complete data sets and complete 

information about the biology and ecology of invasive 

species, it is feasible to develop risk analyses of invasive 

species at the regional scale that should provide information useful for land managers. Even a semiquantitative 

rule-based approach can help to identify locations that 

contain susceptible host species for specific pathogens or 

insect pests and where propagules are more likely to enter, 

based on the current locations of the invasive species and 

methods of spread (for example, Meentemeyer and others 

2004, Nowak and others 2001). As discussed above, the use 

of regional forest inventory data and detailed vegetation 

mapping based on these and other data provide an important 

starting point for regional risk assessments of invasive 

species. 

A broad range of niche modeling approaches are useful 

because they can use data from museum collections in other 

countries to make estimates of potential new range areas in 

the United States. Such data provide information about the 

fundamental niche of the organism, although this information must be evaluated critically by scientists skilled in 

taxonomy and biogeography and applied with care (Graham 

and others 2004). The GARP approach would be useful for 

regional assessments because a software package is available specifically to apply this method to niche modeling. 

However, other approaches such as support vector machines 

and GAM approaches may be less biased and provide more 

optimal solutions (Elith 2006, Stockwell 2005). 

As compared to predicting the fundamental ecological 

niche of a species, predicting the rate of long distance movement is much more difficult because such events are rare, 

may not be well understood, and may be affected by human 

behavior. The approach demonstrated recently by Bartell 

and Nair (2004) to examine pest establishment and spread 

could be expanded and adapted to provide quantitative 

estimates of spread beyond an initial port of entry. There 

is a large body of work in the spatial ecology literature 

addressing various aspects of the spread of populations and, 

more generally, the role of space in structuring populations 

and metapopulations (Tilman and Kareiva 1997). In recent 

years, there has been an increase in the number of publications using empirical data in conjunction with modeling 

approaches to predict the spread of invasive plant species. 

This process is complex because of the rare, but crucial 

events of long-distance transport, including movement 

from the native range to the United States. Whereas simple 

diffusion models may be useful in some instances, the 

issue of long distance transport by human vectors needs 

to be addressed (Hastings and others 2005). Some of the 

methods discussed above included estimates of spread. One 

such analysis to assess the risk posed by Asian long-horned 

beetle combined the GARP niche modeling approach with a 

simple model of spread from likely ports of entry (Peterson 
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and others 2004). This approach makes predictions quite 

different from those based on analysis of species host range, 

as discussed above (see “Nowak Host Range”). 

Models based on fundamental biological and physical 

processes, such as population demographics and movement of organisms, generally are preferable to correlative 

statistical approaches. This does not mean that correlative 

approaches are not valuable for probabilistic regional risk 

assessments. They may be useful first steps for regional 

analysis (for example, to quantify the overlap in spatial 

distribution of stressors and ecological receptors throughout 

the Western United States). Correlative models such as 

that of Dark (2004) may be extended with some confidence 

beyond the range of available data because they use predictor variables that represent physical and biological processes. For example, the distribution of nonindigenous and 

invasive species was found to be similar, because both must 

pass through the same environmental filters or stages. The 

approach of using data on locations of all nonindigenous 

species to predict the occurrence of much rarer probleminvasive species may be quite useful because the correlation is based on the key processes of human-influenced 

transport, establishment, reproduction, and dispersal of 

propagules. In such cases, statistically based extrapolations 

from existing data should be quite credible and useful. In 

addition to extrapolating from all nonindigenous species to 

only invasive species, future environmental scenarios might 

be developed to predict future risks. For example, one could 

extrapolate to future conditions with more roads or a higher 

traffic volume, if the number of nonindigenous species in 

a region can be predicted based on some measure of the 

transportation network (Larson 2003, McKinney 2002) or 

other environmental factor. A future scenario of new road 

development or greater traffic or both on existing transportation networks could be developed based on planned State 

and Federal transportation projects. This scenario could be 

used to predict the subsequent increase in occurrence of 

nonindigenous species and invasive species.

Risk assessment for invasive species will be most 

useful if it helps provide information about the degree 

of potential harm, or damage. For certain invasive plant 

species, especially serious and common weeds of crop and 

rangelands, damage can be quantified in economic terms. 

However, it can be difficult to quantify the ecological 

effects of many invasive species, especially for effects on 

wildlands. For example, it has been assumed that purple 

loosestrife is a serious threat to wetlands in the Northeastern United States, and considerable effort has been made 

to eradicate it. However, an analysis of ecological effects 

found little evidence for damage to wetlands (Hager and 

McCoy 1998), although one recent publication did find some 

evidence that it can reduce native plant diversity (Schooler 

and others 2006). The lack of evidence of severe ecological 

effects in wildland ecosystems does not mean that such 

effects don’t exist. Rather, such a lack of evidence may 

indicate a lack of research on wildland ecosystem effects 

and the difficulty in quantifying such effects in wildland 

ecosystems as compared to highly managed ecosystems 

such as agricultural row crops. This difficulty in assessing 

economic damage of invasive species has been recognized 

as a key challenge for research (Andersen and others 

2004a). Despite the challenge, such efforts may be useful, 

as they may provide evidence that even large expenditures 

required for removal of invasive species may provide a 

valuable economic return. For example, it has been estimated that the costs of eradication of tamarisk throughout 

the Western United States would be fully recouped within 

17 years with continued ongoing benefits beyond that time 

(Zavaleta 2000).

As for any regional stressor, the use of multiple models 

and a weight of evidence approach would help to increase 

confidence in predictions of ecological risks from invasive 

species. As discussed above, two approaches to predicting 

the risk of Asian long-horned beetle throughout U.S. forests 

make quite different predictions because they focus on 

different stages in the process of establishment and spread. 

All models have some level of uncertainty both in the 

data used to drive the model and in the calculations made 

within the model. A focus on uncertainty as an important 

type of information is crucial for meaningful assessments 

of invasive species risk. There is strong evidence of the 

potential for invasion and damage to occur for certain 

species such as those already on lists of noxious weeds. The 

strongest predictor for a species is if that species is already 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

an invasive species causing substantial damage in another 

part of the world. For these species, there is generally quite 

a bit of information about aspects of their life history that 

are important for predicting risk, such as host range, reproductive potential, and phenotypic plasticity. However, for 

other species there is little or no information. For example, 

the causal agent of sudden oak death in California was only 

discovered because of unusual mortality and morbidity in 

California live oaks. Investigation revealed a new species; 

thus, there was virtually no information about the ecology 

of the species such as host range, climatic requirements, 

and reproductive potential. Until such information began 

to be gathered, it was not possible to make any meaningful 

prediction of invasiveness or ecological risk. 

Finally, risk assessments will not be useful unless they 

provide guidance for management. Land managers could 

benefit in particular from regional risk assessments that 

provide information about potential future risks. Invasive 

species management should be addressed at multiple spatial 

scales such as:

1. Reducing importation of new species at border

crossings and ports.

2. Conducting national and regional mapping of 

locations of invasive species.

3. Developing procedures to reduce long-distance 

transport if possible.

4. Developing local procedures to reduce 

movement of invasive species.

Because many invasive species become established 

along roadways and trails, it may be easier to locate and 

eradicate them before they spread. However, costs of 

eradication can be very high, and the most cost-effective 

approaches will be at the national and regional scale, rather 

than the scale of a single national forest. Quantitative 

approaches to estimate the costs and benefits of management options are needed. The feasibility of estimating 

such costs has been demonstrated (Bartell and Nair 2004, 

Zavaleta 2000), but much more work is required. Developing such estimates by bringing together risk assessors and 

land managers should be considered in developing regional 

risk assessments that will help focus on key issues for 

management.

In summary, we offer the following suggestions to 

be considered when selecting modeling approaches for 

probabilistic risk assessment for invasive species at the 

regional scale:

1. Define management options and formulate the 

risk problem definition at the same time so that 

predictions will be useful for making management decisions. 

2. Ecosystems are spatially explicit, so use 

spatially explicit data, such as vegetation type, 

topography, stream networks, and elevation.

3. Use both socioeconomic and ecological information. 

4. Do not assume that the initial conditions of a 

landscape can all be captured by a few regionalized variables because of the large role that site 

history often plays in shaping future dynamics.

5. Whenever possible, make quantitative predictions of risks rather than using ranks (such as 

low, medium, and high). Ranked values can 

lead to erroneous interpretations because it 

may not be clear what is meant by a high risk 

and also because of uncertainty about what 

happens at the boundaries of the rank categories. 

6. Quantify important spatial and nonspatial 

sources of data uncertainty and address these 

uncertainties in the analysis. 

7. Quantify important sources of uncertainty in 

model equations, including aggregation and 

scaling issues, and address these uncertainties 

in the analysis.

8. Whenever feasible, use multiple models based 

on different approaches and data.

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Wilcove, D.S.; Rothstein, D.; Dubow, J. [and others]. 

1998. Quantifying threats to imperiled species in the 

United States. Bioscience. 48: 607–615.

Work, T.T.; McCullough, D.G.; Cavey, J.F.; Komsa, 

R. 2005. Arrival rate of nonindigenous insect species 

into the United States through foreign trade. Biological 

Invasions. 7: 323–332.

Zavaleta, E. 2000. The economic value of controlling an 

invasive shrub. Ambio. 29: 462–467.
Pests/Biota

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

Ronald Billings, L. Allen Smith, Jin Zhu, Shailu Verma, 

Nick Kouchoukos, and Joon Heo

Ronald Billings, manager, Forest Pest Management, Texas 

Forest Service, College Station, TX 77840; L. Allen Smith, 

staff forester II, Texas Forest Service, Longview, TX 75604; 

Jin Zhu, geographic information system specialist III, 

Texas Forest Service, College Station, TX 77840; Shailu 

Verma, vice president, and Nick Kouchoukos, director of 

information systems, Lanworth, Inc., Itasca, IL 60143; and 

Joon Heo, assistant professor, Yonsei University, Seoul 

120-749, Korea. 

Abstract

The objective of this research project is to develop and 

validate a method for using satellite images and digital 

geospatial data to map the distribution of southern pine 

beetle (SPB) habitats across the pinelands of east Texas. Our 

approach builds on a work that used photo interpretation 

and discriminant analysis to identify and evaluate environmental conditions suitable for SPB infestation. Because 

current implementations of Billings and Bryant’s method by 

the Texas Forest Service (TFS) use manual photo interpretation, they are relatively costly, labor intensive, and require 

sampling. Satellite imagery and geographic information 

system (GIS) technology present potential means to reduce 

operational costs and improve accuracy. Here we report the 

principal results of our work in a pilot area of east Texas, 

specifically: (1) development and integration of satellite 

and digital inputs into the Billings and Bryant model, (2) 

accuracy assessment of model inputs, (3) validation of the 

model adaptation through comparison of satellite-derived 

SPB hazard maps to operational maps produced by TFS, 

and (4) revalidation of the model through comparison of 

satellite-derived SPB hazard maps to known locations 

of SPB infestations. Collectively, the results point to the 

considerable potential of satellite imagery and automated 

analysis techniques to produce timely, accurate, and costeffective maps of SPB hazard at the landscape level.

Keywords: Dendroctonus frontalis, GIS, hazard rating, 

remote sensing, risk assessment, satellite data, Texas.

Introduction and Background

The southern pine beetle (SPB), Dendroctonus frontalis

(Coleoptera: Curculionidae: Scolytidae), is one of the most 

destructive insect pests of pine forests in the Southern 

United States, Mexico, and Central America (Thatcher and 

others 1980). The beetle’s range extends from New Jersey 

to Texas and from New Mexico and Arizona to Nicaragua. Because populations build rapidly during periodic 

outbreaks and large numbers of trees are killed, this insect 

generates more concern among managers of southern pine 

forests than any other insect pest. In the Southern United 

States, average annual losses may exceed 100 million board 

feet of sawtimber and 20 million cubic feet of growing stock 

(Price and others 1998).

Southern pine beetle outbreaks have increased in frequency, severity, and distribution during the past 30 years. 

Preventive silvicultural practices offer the most promising 

and long-lasting means of reversing this trend (Belanger and 

others 1993, Nebeker and others 1985). If pine stands are 

weakened by drought, flooding, lightning strikes, careless 

logging, or overcrowding, they become more susceptible 

to attack by the beetle (Blanche and others 1983, Hicks 

and others 1980). Mature trees in pure, dense stands have 

long been considered most susceptible to SPB attack, but, 

in recent years, unthinned pine plantations have increasingly supported SPB infestations (Cameron and Billings 

1988). Trees less than 5 years of age or less than 4 inches in 

diameter are seldom attacked. Dense pine stands also are 

more likely to suffer extensive losses from the expansion of 

established SPB infestations in the absence of direct control 

(Hedden and Billings 1979).

The most practical approach to minimizing timber 

losses and avoiding costly short-term suppression projects 

is to maintain forests in a vigorous, healthy condition 

(Belanger 1980, Hedden 1978). To manage SPB populations 

Developing and Validating a Method for Monitoring and 

Tracking Changes in Southern Pine Beetle Hazard at the 

Landscape Level
542

GENERAL TECHNICAL REPORT PNW-GTR-802

more effectively, foresters need reliable means of predicting where infestations are most likely to occur. Once this 

capability is developed, areas where beetle-caused timber 

losses are likely can be identified and managed through 

long-range plans (Peterson 1984), silvicultural manipulations (Belanger and Malac 1980, Belanger and others 1993) 

or more responsive direct control tactics (Swain and Remion 

1981), or all. Several practical stand hazard rating systems 

have been developed that utilize easily measured stand and 

site factors (basal area, tree age or height, growth rate in 

the last 5 years, land form, etc.) to ascertain susceptibility 

to SPB at the stand level (Hicks and Mason 1982, Hicks 

and others 1980, Lorio and others 1982, Mason and others 

1985). Identification of SPB hazard at the landscape level, 

however, has received much less attention.

A system for mapping SPB hazard at the landscape 

level using aerial photography has been developed and 

implemented by the Texas Forest Service (TFS) on an 

18,000-acre grid (Billings and Bryant 1983, Billings and 

others 1985). The rating system uses conventional photointerpretive methods to describe host presence, coverage, 

density, and site conditions within 30-acre photo plots. 

Figure 1—Location of the project area in east Texas. Boundaries of counties and 18,000-acre grid blocks are indicated in white.
543

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

Twenty circular photo plots, equally spaced in five rows and 

four columns, provide a 3-percent systematic sample of host 

conditions within each grid block.

An initial hazard map for east Texas based on 1981-83 

aerial photography covered 656 grid blocks (11,808,000 

acres) and was validated using subsequent SPB detection 

records (Billings and others 1985). In 2003-04, as part of 

the ongoing SPB prevention project, the east Texas hazard 

map was updated using 1996 color infrared photography. 

The updated map identified 16 grid blocks (2 percent) as 

extreme hazard, 92 (12 percent) as high hazard, 291 (37 percent) as moderate hazard, 280 (35 percent) as low hazard, 

and 117 (15 percent) as very low hazard.

Although the east Texas hazard maps have been 

valuable for predicting where SPB outbreaks are most 

likely to occur and for targeting prevention programs, their 

production process has three limitations that have prevented 

widespread adoption of this protocol. These limitations are:

• Expense: Creating a SPB hazard map across 

east Texas (14.3 million acres) requires high 

resolution color infrared imagery and procedures to digitize and orthorectify the imagery. Furthermore, photo interpretation must be 

performed by trained technicians. All three 

requirements are very costly.

• Accuracy: Though the aerial imagery is analyzed systematically, the manual interpretation 

process requires sampling and frequent judgment calls, both of which introduce inaccuracy.

• Frequency: Presently, the time and expense of 

aerial photo collection and interpretation limit 

the extent and update frequency of SPB hazard 

maps. If the process were relatively inexpensive

and automated, hazard maps could be generated 

frequently over larger areas.

To address these limitations, the Texas Forest Service 

(TFS) and Forest One (now Lanworth, Inc.), with the support of the USDA Forest Service Southern Research Station, 

began a project to investigate the potential of satellite 

imagery and digital image processing methods to lower the 

costs and improve the accuracy of the operational east Texas 

SPB hazard maps. Here we report the principal results of 

this investigation in a pilot area of east Texas (Figure 1), 

specifically:

1. Development and integration of satellite and 

digital inputs into the Billings and Bryant 

model.

2. Accuracy assessment of the model inputs.

3. Validation of the model adaptation through 

comparison of satellite-derived SPB hazard 

maps to operational maps produced by TFS. 

4. Revalidation of the model through comparison 

satellite-derived SPB hazard maps to known 

locations of SPB infestation.

Methodology

The Billings and Bryant model of SPB hazard takes the 

form of the discriminant function:

Table 1—Independent variables used in the Billings and Bryant (1983) hazard model

Variable Description

A Nonhost (open land, hardwoods, water)

B Young pine (less than 15 years of age)

C Pine host, < 70 percent coverage, < 80 percent crown closure, other terrain

D Pine host, < 70 percent coverage, < 80 percent crown closure, bottomland

E Pine host, < 70 percent coverage, ≥ 80 percent crown closure, other terrain

F Pine host, < 70 percent coverage, ≥ 80 percent crown closure, bottomland

G Pine host, ≥ 70 percent coverage, < 80 percent crown closure, other terrain

H Pine host, ≥ 70 percent coverage, < 80 percent crown closure, bottomland

I Pine host, ≥ 70 percent coverage, ≥ 80 percent crown closure, other terrain

J Pine host, ≥ 70 percent coverage, ≥ 80 percent crown closure, bottomland
544

GENERAL TECHNICAL REPORT PNW-GTR-802

DS = -1.35 - 0.108(A) + 0.135(D) + 0.330(E) + 0.404(F) 

+ 0.305(I) + 0.271(J)

where the values of the discriminating variables A, D, E, F, 

I, and J are the numbers of photo plots in a grid block that 

have the combination of site/stand factors listed in Table 1. 

Thus, if five photo plots in a given grid block fall on water 

or agricultural land (factor combination A) and seven others 

fall on dense, old pine (factor combination I), the values of 

variables A and I are 5 and 7, respectively. Because only 20 

photo plots are analyzed per grid block, the possible value 

of each variable ranges from 0 to 20. Note, however, that the 

values of the discriminating variables will not necessarily 

sum to 20 as not all site/stand factor combinations appear 

in the discriminant function.

The challenge of applying satellite data to the determination of SPB hazard at the grid block scale lies in 

replicating the process of photo interpretation used by TFS 

without violating the assumptions and conditions of the 

discriminant function.

The foundation of our approach to SPB hazard mapping is Forest One’s Forest Age Map product, a raster map 

based in Landsat imagery in which each 28-m (30.6 yd) cell 

(pixel) is classified into one of four forest types (softwood, 

hardwood, mixed, nonforest) and in which all softwood 

Figure 2—Distribution of hazard factor combinations (habitat types) across the project area (December 2004).
545

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

pixels are further classified into 3-year age classes (e.g., 0 to 

3 years, 7 to 10 years, etc.). To develop a SPB host map for 

the year 2004, Forest One recoded its Forest Age Map, classifying all hardwood, nonforest, and softwood younger than 

15 years as nonhost and all softwood older than 15 years as 

host. To compare satellite-derived SPB hazard to existing 

TFS hazard maps from 1996 and SPB spot data from 1989 

to 1993, Forest One also prepared Forest Age Maps and host 

maps for 1994 and 1990.

Because the TFS hazard rating protocol considers the 

percentage of host pine within a 30-acre (12.1 ha) photo 

plot rather than the predominant host type in a 28-m (30.6 

yd) (12.1 ha) pixel, the host maps must be transformed to 

represent varying percentage of host pine across the study 

area. This was accomplished by recoding the host maps 

so that pixels classified as nonhost and young pine have a 

value of 0, and pixels classified as pine host have a value of 

1. A 13 x 13 average filter was then passed over the recoded 

maps, replacing each pixel by the arithmetic mean of its 

neighborhood. A 13 x 13 matrix of 28-m (30.6 yd) pixels has 

an effective area of 32.7 acres (13.2 ha), and the resulting 

pixel value will therefore estimate the proportion of host 

pine in a photo plot-sized area centered on each pixel.

Figure 3—Grid block hazard ratings for 1994 derived from satellite and digital geospatial data.
546

GENERAL TECHNICAL REPORT PNW-GTR-802

As a further qualification of site/stand conditions, the 

TFS hazard rating protocol measures host density as a function of canopy closure in areas of each plot containing host 

pine. As a proxy for host density, we selected a vegetation 

index computed from the red, near-infrared, and midinfrared bands of Landsat TM imagery acquired in 1990, 1996, 

and 2004. The vegetation index, called NDVIc, uses the 

distinctively high reflectance of green vegetation in the 

near-infrared wavelengths relative to the red and middleinfrared wavelengths to map the relative distribution of 

green biomass across the project area. Furthermore, because 

soils tend to reflect strongly in the middle-infrared wavelengths, middle-infrared reflectance is negatively correlated 

with canopy closure. NDVIc is a unitless quantity that varies between -1 and 1. To restrict this measurement to areas 

of host pine, the host maps were used to assign null values 

to areas of young pine and nonhost. A 13 x 13 average filter 

was then passed over the NDVIc maps to estimate for each 

pixel the average density of host pine in a photo plot-sized 

area centered on that pixel.

Current TFS protocol distinguishes between pine 

stands with less than 80 percent canopy closure and those 

with 80 percentage of closure or greater. To determine 

the actual relationship between NDVIc and percentage of 

canopy closure, we partitioned several 1-m color-infrared 

digital orthophoto quarter quadrangles from 1995 into 

canopy and noncanopy pixels. We then computed the 

proportion of 1-m (39.4 in) canopy pixels present within 

the area covered by each 28-m (30.6 yd) pixel from a 1994 

Landsat image. The average NDVIc value of all Landsat 

pixels containing more than 80 percent crown elements 

(NDVIc = 0.425) was then selected as the threshold for 80 

percent canopy closure.

The final site/stand factor considered under the TFS 

protocol is landform, expressed as bottomland or other terrain. To determine the landform most characteristic of the 

photo plot-sized area centered on each 28-m pixel within the 

Table 2—Comparison of satellite (Forest One 1994) and photo-interpreted (TFS 1996) 

hazard class predictions. Agreement within 1 hazard class is 89 percent

 F1 hazard class

Extreme High Moderate Low Very low Total

Extreme

High

Moderate

Low

Very low

Total

N Percent

73 38 percent

58 31 percent

38 20 percent

21 11 percent

Texas Forest Service hazard class

 Agreement

 F1 underestimate (1 class)

 F1 overestimate (1 class)

 Disagreement

4

5

5

1

0

 1

11

13

 5

 0

 0

9

16

13

 0

 4

5

27

41

 7

 0

0

 5

21

 1

 5

30

66

81

 8

15 30 38 80 27 190
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

study area, the National Elevation Data set digital terrain 

model was resampled to match the grid resolution of the 

Forest Age Map. Percentage of slope was then computed for 

each grid cell, and a 13 x 13 average filter was passed over 

both the slope and elevation maps. Based on conversations 

with photo interpreters at TFS, bottomland was operationally defined as those areas having an average elevation 

less than 90 m (98.4 yd) and an average slope less than 3 

percent.

Once all relevant site/site-stand factors were estimated 

using remotely sensed inputs, a series of rules was used 

to assign each pixel to 1 of the 10 factor combinations 

expected by the Billings and Bryant model (Figure 2). Once 

a site/stand factor combination was assigned to each 28-m 

grid cell, the percentage coverage of each factor combination was computed for each of the 190 grid blocks in the 

study area. This percentage value was then multiplied by 

0.2 to scale percentage cover to the 0 to 20 range expected 

by the discriminant function. The discriminant score was 

then computed for each grid block for the years 1990, 

1994, and 2004, and scores were assigned to hazard classes 

based on breakpoints used in the 2003-04 TFS update. The 

satellite-derived hazard map for 1994 is shown as Figure 3.

Accuracy of Model Inputs

To determine the accuracy of the satellite-derived inputs, 

we compared our maps of host type and coverage to reference data on managed pine stands of known age. The reference data were distributed widely across the project area 

and constitute a 5-percent sample by area. Analysis shows 

that Forest One’s Forest Age Map classifies pine with at 

least 80 percent accuracy and can distinguish between pine 

greater than 15 years and pine younger than 15 years with 

82 percent accuracy. We also compared our measurements 

of crown closure and landform to operational photo-interpreted measurements made by TFS technicians and found 

similar levels of agreement. Our conclusion, therefore, is 

that the satellite-derived inputs into the model are sound.

Comparison of Satellite-Derived 

and TFS Hazard Maps

The principal result of our work so far has been SPB hazard 

maps for 1990, 1994, and 2004. The accuracy of the 2004 

map is currently being assessed through comparison to 

operational measurements of hazard factors by TFS. The 

accuracy of the 1994 map has been assessed through comparison to the 1996 hazard map produced by TFS (Table 2).

The comparison shows that the 1994 map correctly 

predicted the hazard rating of 38 percent of the grid blocks. 

The maps predicted a further 51 percent of the grid blocks 

within 1 hazard rating class. Given some uncertainty about 

the accuracy of the TFS reference map, we propose to treat 

all agreements within 1 hazard class as correct predictions 

and offer 89 percent as the final accuracy of the satellitederived hazard map. In general, the satellite-derived map 

tends to underpredict hazard ratings slightly. Forest One 

and TFS are working to explain and improve the correspondence between the two hazard rating systems.

Comparison of Satellite-Derived Maps 

and Historic SPB Infestation Data

As a further check on the accuracy of the satellite-derived 

hazard map and the validity of the underlying discriminant 

function, TFS supplied data on the location of SPB infestations within the study area during the years 1989-93. These 

data were organized as SPB infestation counts for each 

15-arc second grid block within the study area. To compare 

these data to the 1990 satellite-derived hazard map, we 

selected all 15-arc second grid blocks that were infested in 

1991, 1992, or 1993 but not in 1989 or 1990. This allowed us 

to create a map of new infestations since 1990. These infestations were presumably related to landscape conditions 

Table 3—Observed rates of SPBa infestation (1991-93) 

by satellite-predicted hazard class (1990)

Infestation rate

Hazard New spots N Grid (spots/class/

class (1991-93) blocks grid block)

Extreme 159 15 10.60

High 409 28 14.61

Moderate 652 58 11.24

Low 583 81 7.20

Very low 21 8 2.63

a

 SPB = Southern pine beetle.
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GENERAL TECHNICAL REPORT PNW-GTR-802

represented by the 1990 satellite map rather than to lingering or renewed infestations from prior years.

To allow comparison of new infestations to satellitepredicted hazard class in 1990, we aggregated all new infestations to the 18,000-acre grid block level and summarized 

these counts by hazard class (Table 3). Because the area 

(number of grid blocks) of each hazard class is not constant 

over the study area, we divided the count of new infestations by the number of grid blocks in each hazard class that 

were either newly infested in 1991-93 or were not infested at 

all. This normalized measure was interpreted as the average 

infestation rate for each hazard class.

Our results reveal a qualitatively strong, positive correlation between the observed infestation rate and the model 

-predicted hazard class. The mean rate of SPB infestation 

declined from nearly 15 infestations/grid block in the high 

hazard class to fewer than 3 infestations per grid block 

in the very low hazard class. The only observed anomaly 

is that the infestation rate is slightly lower than expected 

for the extreme hazard class, perhaps indicating limited 

resolving power of either the satellite data or the underlying 

model. This anomaly is currently being investigated by 

Forest One and TFS.

Conclusions

From the investigations and analyses reported here, we 

conclude that satellite imagery, together with ancillary digital geospatial data and automated processing techniques, 

presents a powerful and cost-effective tool for operational 

mapping of SPB infestation hazard at the landscape scale.

Literature Cited

Belanger, R.P. 1980. Silvicultural guidelines for reducing 

losses to the southern pine beetle. In: Thatcher, R.C.; 

Searcy, J.L.; Coster, J.E.; Hertel, G.D., eds. The southern 

pine beetle. Tech. Bull. 1631. Washington, DC: U.S. 

Department of Agriculture, Forest Service, Science and 

Education Administration: 165–177.

Belanger, R.P.; Hedden, R.L.; Lorio, P.L. 1993.

Management strategies to reduce losses from the 

southern pine beetle. Southern Journal of Applied 

Forestry. 17(3): 150–154.

Belanger, R.P.; Malac, B.F. 1980. Silviculture can reduce 

losses from the southern pine beetle. Agric. Handb. 576. 

Washington, DC: U.S. Department of Agriculture, Forest 

Service. 17 p.

Billings, R.F.; Bryant, C.M. 1983. Developing a system 

for mapping the abundance and distribution of southern 

pine beetle habitats in east Texas. Zeitschrift für 

Angewandte Entomologie. 96: 208–216.

Billings, R.F.; Bryant, C.M.; Wilson, K.H. 1985. 

Development, implementation, and validation of a large 

area hazard- and risk-rating system for southern pine 

beetle. In: Branham, S.J.; Thatcher, R.C., eds. Integrated 

pest management symposium proceedings. New Orleans, 

LA: U.S. Department of Agriculture, Forest Service, 

Southern Forest Experiment Station: 226–232.

Blanche, C.A.; Hodges, J.D.; Nebeker, T.E.; Moehing, 

D.M. 1983. Southern pine beetle: the host dimension. 

Bull. 197. Mississippi State, MS: Mississippi Agricultural 

and Forestry Experiment Station, Mississippi State 

University. 29 p.

Cameron, R.S.; Billings, R.F. 1988. Southern pine beetle: 

factors associated with spot occurrence and spread in 

young plantations. Southern Journal of Applied Forestry. 

12: 208–214.

Hedden, R.L. 1978. The need for intensive management 

to reduce southern pine beetle activity in east Texas. 

Southern Journal of Applied Forestry. 2: 19–22.

Hedden, R.L.; Billings, R.F. 1979. Southern pine beetle: 

factors influencing the growth and decline of summer 

infestations in east Texas. Forest Science. 25: 547–556.

Hicks, R.R.; Howard, J.E.; Watterson, K.G.; Coster, 

J.E. 1980. Rating forest stand susceptibility to 

southern pine beetle in east Texas. Forest Ecology and 

Management. 2: 269–283.

Hicks, R.R.; Mason, G.N. 1982. Southern pine beetle 

hazard rating works in east Texas. Southwestern 

Entomologist. 7(3): 174–180.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Lorio, P.L., Jr.; Mason, G.N.; Autry, G.L. 1982. Stand 

risk rating for southern pine beetle: integrating pest 

management with forest management. Journal of 

Forestry. 80: 212–214.

Mason, G.N.; Lorio, P.L., Jr.; Belanger, R.P.; Nettleton, 

W.A. 1985. Rating the susceptibility of stands to 

southern pine beetle attack. Agric. Handb. 645. 

Washington, DC: U.S. Department of Agriculture, Forest 

Service. 31 p.

Nebeker, T.E.; Hodges, J.D.; Karr, B.K.; Moehring, 

D.M. 1985. Thinning practices in southern pines with 

pest management recommendations. Tech. Bull. 1703. 

New Orleans, LA: U.S. Department of Agriculture, 

Forest Service, Southern Forest Experiment Station. 

 36 p.

Peterson, R.M. 1984. The role of integrated pest 

management in forest planning and management. In: 

Branham, S.J.; Hertel, G.D., eds. Proceedings: integrated 

forest pest management symposium. Athens, GA: 

University of Georgia: 2–7.

.

Price, T.S.; Doggett, C.; Pye, J.M.; Smith, B. 1998. 

A history of southern pine beetle outbreaks in the 

Southeastern United States by the Southeastern Forest 

Insect Working Group. Macon, GA: Georgia Forestry 

Commission. 72 p.

Swain, K.M.; Remion, M.C. 1981. Direct control of the 

southern pine beetle. Agric. Handb. 575. Washington, 

DC: U.S. Department of Agriculture. 15 p.

Thatcher, R.C.; Searcy, J.L.; Coster, J.E.; Hertel, G.D., 

eds. 1980. The southern pine beetle. Tech. Bull. 1631. 

Washington, DC: U.S. Department of Agriculture, Forest 

Service, Science and Education Administration. 266 p.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Stephen Cook, Karen Humes, Ryan Hruska, 

Christopher Williams, and Grant Fraley

Stephen Cook, associate professor, Department of Forest 

Resources, University of Idaho, Moscow, ID 83844-1133; 

Karen Humes, associate professor, Geography Department, University of Idaho, Moscow, ID 83844-3021; Ryan

Hruska, senior scientist, Idaho National Laboratory, P.O. 

Box 1625, Idaho Falls, ID 83415-2213; Christopher Williams, professor, Division of Statistics, University of Idaho, 

Moscow, ID 83844-1104; Grant Fraley, research assistant, 

Geography Department, University of Idaho, Moscow, ID 

83844-3021.

Abstract

The balsam woolly adelgid, Adelges piceae, and hemlock 

woolly adelgid, A. tsugae (Homoptera: Adelgidae), are 

invasive pests of coniferous forests in both the Eastern and 

Western United States. Balsam woolly adelgid is capable of 

attacking and killing native North American firs, with Fraser fir (Abies fraseri (Pursh) Poir.) in the East and subalpine 

fir (A. lasiocarpa (Hook.) Nutt.) in the West being particularly susceptible to infestation. Hemlock woolly adelgid is 

capable of infesting native hemlocks and is a serious pest 

in forests of the Eastern United States where it is causing 

significant mortality to both eastern (Tsuga canadensis

(L.) Carr.) and Carolina hemlock (T. caroliniana Engelm.). 

Infestations by either of these insects may take several years 

to kill the host tree. Damage by hemlock woolly adelgid 

frequently causes needles to discolor from deep green to 

grayish green. Discoloration of needles is also one of the 

symptoms used to diagnose infestations of balsam woolly 

adelgid. Traditional methods for assessing damage by these 

adelgid species include field surveys and aerial detection 

surveys. However, because infestations frequently occur 

in remote locations and can take years to build up, stand 

damage may accrue prior to visual detection of the infestations. Branch-level, spectral data of the foliage from trees 

were collected for several categories of infestation. In the 

Western United States, data were collected from subalpine fir infested with balsam woolly adelgid in northern 

Idaho. In the Eastern United States, data were collected 

from eastern hemlock in western North Carolina. Trees 

were sampled using a hand-held spectroradiometer. The 

measured radiance spectra were converted to percentage of 

reflectance and comparisons made between the infestation 

categories. Separation of the infestation levels occurred in 

a progressive pattern moving from noninfested to newly (or 

lightly) infested to heavily infested trees. Results suggest 

that previsual detection of this group of invasive insects 

may be possible with appropriate spatial and spectral sensor 

resolution.

Keywords: Host resistance, hyperspectral, insect detection, multispectral, remote sensing.

Invasive Adelgids in North American 

Conifers

Adelgids (Homoptera: Adelgidae) are small insects with 

piercing and sucking mouth parts. They have a white woolly 

covering that is secreted over the body. There are several 

native adelgid species within North America such as the 

Cooley spruce gall adelgid (Adelges cooleyi), and some of 

these can cause growth loss in trees or cause trees to reach 

economic injury levels under some conditions. However, 

the two adelgid species that are currently causing the most 

economic and ecological impacts within North America 

are the introduced balsam woolly adelgid (A. piceae) and 

the hemlock woolly adelgid (A. tsugae), both of which are 

established in both the Eastern and Western United States.

Balsam Woolly Adelgid: Hosts and Biology

Balsam woolly adelgid is native to the fir forests of central 

Europe and was introduced into the United States around 

1900. The life cycle of the balsam woolly adelgid consists of 

the egg, three larval instars, and the adult (see Hain 1988 for 

a more thorough description). The only life stage capable of 

movement is the first instar larva (termed the crawler) that, 

upon locating a suitable feeding site, inserts its stylet into 

the bark and transforms (without molting) into a nonmobile 

phase, after which the insect is permanently attached to 

the host tree. As the female feeds, she secretes a dense 

woolly covering that ultimately covers the entire insect. 

Previsual Detection of Two Conifer-Infesting Adelgid Species 

in North American Forests
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GENERAL TECHNICAL REPORT PNW-GTR-802

The crawler stage does not have wings, and between-tree 

dispersal is a passive process. The adult female produces as 

many as 248 eggs. These are oviposited within the woolly 

mass, which acts to protect all of the life stages except the 

crawler.

All of the true firs (Abies) that are native to North 

America show some degree of susceptibility to the balsam 

woolly adelgid (Mitchell 1966). The susceptibility ranges 

from slight for noble fir (A. procera Rehd.) and white fir 

(A. concolor (Gord. & Glend.) Lindl. ex Hildebr) to moderate for grand fir (A. grandis Dougl. ex D. Don) Lindl., corkbark fir (A. lasiocarpa var. arizonica (Merriam) Lemm.), 

and Shasta red fir (A. magnifica var. shastensis Lemm.) to 

severe for subalpine fir (A. lasiocarpa (Hook.) Nutt.), Fraser 

fir (A. fraseri (Pursh) Poir.), balsam fir (A. balsamea (L.) 

Mill.), and Pacific silver fir (A. amabilis Dougl. ex Forbes). 

The insect is established on susceptible hosts in the Eastern and Western United States where it is responsible for 

significant levels of mortality in some stands. Prior studies 

suggest that there may be some connection between host 

monoterpenes and attack success by balsam woolly adelgid 

(Arthur and Hain 1987).

Hemlock Woolly Adelgid: Hosts and Biology

Hemlock woolly adelgid is native to Asia and was first 

reported in the Pacific Northwest in the 1920s. The adelgid 

was reported in Eastern North America in the 1950s and 

Connecticut in the 1980s. The insect is now present in 

many of the hemlock forests of the Eastern United States, 

where infestations frequently result in significant mortality 

to native hemlocks (Souto and others 1995). The hemlock 

woolly adelgid is a serious pest of Eastern hemlocks and 

represents a significant threat to the sustainability of native 

hemlocks (Tsuga canadensis (L.) Carr. and T. caroliniana 

Engelm) in the Eastern United States (McClure 1992). 

Whereas the adelgid is also established in the Western 

States, it does not appear to be a threat to the western hemlock species (T. heterophylla (Raf.) Sarg. and T. mertensiana

(Bong.) Carr.) at the present time.

Hemlock woolly adelgid has two generations per year 

in much of its range in the Eastern United States. Only 

females are present, and the spring generation lays between 

100 and 300 eggs. Upon hatching, the crawlers search for 

suitable feeding sites, insert their stylets and begin to feed. 

As with balsam woolly adelgid, crawlers become immobile 

once they settle and begin to feed. When the crawlers reach 

maturity, two types of adults can form. One type of adult 

has wings and dies as it searches for the alternate spruce 

host, which is not present in North America. The other is 

wingless and capable of laying eggs to produce the next 

generation.

Conifer Resistance to Insect Attack

There are several hypotheses regarding plant resistance to 

insect attack that involve the production and allocation of 

resources within the plant as they relate to the plant’s resistance mechanisms. The carbon: nutrient balance hypothesis 

correlates the production of plant secondary metabolites that 

are important in determining the relative resistance/susceptibility of the plant with the ratio of carbon to other nutrients 

within the plant (see Herms and Mattson 1992). The growth 

differentiation balance hypothesis also views changes in the 

production and maintenance of plant secondary metabolites 

as a tradeoff owing to environmental constraints on growth 

and secondary metabolism (i.e., differentiation) (see Herms 

and Mattson 1992). The growth differentiation balance 

hypothesis predicts that under moderate stress, plant growth 

will be limited, and the production of secondary metabolites 

such as those important in insect resistance will increase.

Conifer Resistance to Insect Attack: Generalized 

Response Sequence

Conifer resistance to stem-invading insects has received 

much attention and involves a generalized, three-step 

sequence of wound cleansing, infection containment, and 

wound healing (Berryman 1972, Hain and others 1983). The 

first step of this response, wound cleansing, is characterized 

by the production and flow of constitutive resins. The second step of the resistance sequence, infection containment, 

can be described as a rapid necrosis of cells surrounding 

the infection site that is accompanied by the development 

of traumatic resin ducts and an increased concentration of 

monoterpenes and phenolics in the reaction zone (Cook 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

and Hain 1986, Raffa and Berryman 1982, Reid and others 

1967). The accumulation of terpenes and phenolics in the 

reaction zone is also accompanied by a decrease in the 

level of soluble sugars in that zone (Cook and Hain 1986, 

Wong and Berryman 1977). Wound healing, or formation of wound periderm, is the final step of the resistance 

sequence. This isolates the wound from the rest of the tree. 

Wound periderm is located adjacent to the necrotic tissue 

and protects living tissue from the adverse effects of the 

dead cells in the necrotic zone surrounding the attack site(s) 

(Mullick 1977). The three-step resistance sequence requires 

an expenditure of energy by the tree, and there is typically a 

resulting change of color (fading) within the tree’s foliage.

Fir Response to Stem Attack by the Balsam Woolly 

Adelgid—

The impact of balsam woolly adelgid infestation on North 

American firs has been studied extensively over the past 

several decades. Infestation by the adelgid results in anatomical and structural changes within host tissues that may 

be the result of salivary excretions from the insect’s stylet 

during feeding. Physically, the xylem tissue of infested 

trees has higher concentrations of ray tissue (Mitchell 1967, 

Smith 1967), thickened cell walls, and shorter tracheids 

(Doerksen and Mitchell 1965). The tracheids have encrusted 

pit membranes that more closely resemble the pit membranes associated with heartwood (Puritch and Johnson 

1971). There is a corresponding reduction in waterflow in 

infested trees (Mitchell 1967) that puts the tree into a state 

of physiological drought; this, in turn, reduces photosynthesis and respiration (Puritch 1973) and can ultimately result 

in tree death.

The damage to the host tree is related to both the size of 

the tree and the intensity of the infestation. Balsam woolly 

adelgid infestations in the crown of a tree usually result in 

gouting of the outer branches (characterized by node or bud 

swelling or both with a decrease in new growth of the stem 

and foliage) (Mitchell 1966). Over time, the crown thins, 

and the foliage fades in color. Balsam woolly adelgid infestations also occur on the stems of trees. In North America, 

these stem infestations usually kill native firs within 6 years 

(Hain 1988).

Hemlock Resistance to Attack by Hemlock Woolly 

Adelgid—

Once hemlock woolly adelgid settles onto a twig, the tree 

usually suffers needle loss and bud mortality, followed by 

branch and whole tree mortality (usually within 6 years) 

(McClure 1991, Shields and others 1995). Foliar chemistry 

appears to play some role in host susceptibility/resistance 

to hemlock woolly adelgid, with resistance being related 

to foliar levels of calcium, potassium (K), nitrogen (N), 

and phosphorous (Pontius and others, 2006). These authors 

suggest that higher levels of N and K in the foliage enhance 

host palatability and, thus, result in increases in the population levels of hemlock woolly adelgid. In addition, soil 

and foliar chemistry along with landscape position can be 

used to model hemlock susceptibility to Hemlock Woolly 

Adelgid (Pontius and others 2009). These hypothesized 

relationships between foliar chemistry and infestation could 

be important for early detection of hemlock woolly adelgid 

infestations because some foliar constituents such as chlorophyll, N, cellulose, and sugar can be accurately estimated 

using spectral data (Curran and others 2001).

As with other conifers, monoterpenes are major constituents of tree chemistry of hemlocks (i.e., Li and others 

2001). These compounds may function in several ways to 

mediate the interaction between trees and herbivores, but 

one impact is that they are frequently toxic to attacking 

insects such as bark beetles (i.e., Cook and Hain 1988) or 

other arthropods such as spider mites (i.e., Cook 1992). It 

has been suggested that the monoterpene content of western 

hemlocks may function as a deterrent to hemlock woolly 

adelgid (Lagalante and Montgomery 2003). The authors 

suggest that elevated levels a-pinene, b-caryophyllene, and 

a-humulene may act as feeding deterrents against hemlock 

woolly adelgid, and that elevated levels of isobornyl acetate 

may attract the adelgid.

Importance of Previsual Detection

Minimizing the elapsed time between when a tree becomes 

infested with an insect and when that infestation is detected 

can increase the treatment options available to forest 

managers. Detection of an infestation prior to when the 

foliage begins to visibly fade should give managers more 
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time to respond. Active resistance mechanisms by a host 

tree to insect attack can be energy intensive to maintain 

and utilize. The decline that occurs within a host following 

infestation by adelgids may be categorized into various 

levels as characterized for hemlock infested with hemlock 

woolly adelgid (Pontius and others 2005) or balsam fir 

infested with balsam woolly adelgid (Luther and Carroll 

1999). Changes in foliar chemistry that are related to tree 

stress can be manifested in measurable spectral changes 

within the foliage. Much of the literature with regard to 

another tree-killing insect, mountain pine beetle (Dendroctonus ponderosae), is reviewed by Wulder and others 

(2006). The review suggests that remotely sensed data is 

useful for detecting infestations of mountain pine beetle 

damage and that future experimental work be conducted at 

several spatial scales.

Spectral Data

Both the spatial resolution (i.e., pixel size) and spectral 

resolution (the width of the individual spectral wavebands 

over which plant response is measured) of spectral data, 

as well as the overall wavelength range examined (some 

sensors operate through the middle infrared region, some 

do not), can influence the ability to detect infested trees. 

Multispectral remotely sensed data types tend to have 

fewer, wider spectral wavebands and are operationally 

available from satellite platforms over a wide range of 

spatial resolution (< 1 to 30 m). Landscape-scale hyperspectral data are less widely available and have a large number 

of very narrow wavebands. Because most available data 

sets are acquired from aircraft platforms, these data tend to 

have spatial resolution on the order of 6 to 20 m. Handheld 

spectroradiometers with wavelength widths and numbers 

similar to hyperspectral sensors are often employed in the 

field and laboratory to study spectral response of canopy 

components.

Prior Attempts to Use Spectral Imagery to Detect 

or Delineate Adelgid Infestations

There have been several prior studies related to the detection and classification of trees infested with invasive 

adelgids. Luther and Carroll (1999) examined several 

foliar indices for assessing stress in balsam fir using 

spectral reflectance data and reported that foliar reflectance 

decreased consistently with vigor. These authors conducted 

their work in the laboratory using a fixed-position spectoradiometer. Adelgid infestation was not specifically investigated, but infestation of fir with balsam woolly adelgid 

does result in tree stress (see Hain 1988). At the landscape 

scale, hemlock stands were similarly assessed and analyzed 

for health status using multispectral Landsat Thematic 

Mapper data (Bonneau and others 1999). The best overall 

accuracy for classifying stand health based on hemlock 

woolly adelgid infestation was obtained using the Modified 

Soil Adjusted Vegetation Index-2. Pontius and others (2005) 

used hyperspectral data to examine the abundance and early 

decline of hemlock infested with hemlock woolly adelgid. 

These authors suggest that wavelengths in the low end of 

the spectral range may be useful in assessing early stages of 

decline of hemlock infested with hemlock woolly adelgid. 

One purpose of our ongoing research is to determine if 

host decline resulting from infestation by invasive adelgids 

in multiple tree genera can be evaluated by using similar 

spectra among the host genera.

Comparison of Hyperspectral Data for the 

Previsual Detection of Balsam Woolly Adelgid and 

Hemlock Woolly Adelgid Infestations

Our studies have used hyperspectral data collected at the 

branch level. Spectral data were collected using a Geophysical Environmental Research Corp. 2600 handheld 

spectroradiometer with a spectral resolution of 1.5 nm from 

350 nm to 1050 nm and a resolution of 11.5 nm from 1050 

nm to 2500 nm. In the case of balsam woolly adelgid, we 

have concentrated on subalpine fir, the primary host of this 

insect in Idaho. Our studies of hemlock woolly adelgid have 

concentrated on its primary host in western North Carolina, 

Tsuga canadensis. For both insect-tree pairs, spectral data 

were collected from trees in various stages of infestation. 

Five branches were cut from each tree that was examined. 

Branches were cut from various heights and orientations 

throughout the canopy of the trees. The branches and foliage 

were placed on a flat black surface with negligible amounts 

of measurable radiation, and five measurements per tree 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

were obtained in an iterative manner, with the foliage being 

rearranged between each measurement. The radiometer was 

placed at a height of approximately 50 cm above the branch 

samples, and measurements were made when the sun angle 

was within 10° of solar noon. The spectra for these five 

replicates of branch measurements were averaged to obtain 

a measure of each tree’s reflectance properties. The data for 

each tree were smoothed using a weighted moving filter, 

and comparisons were made of the spectral response among 

infestation classes.

In Idaho, subalpine firs in three infestation categories 

were sampled. The categories included trees that had no 

current infestation with balsam woolly adelgid, trees that 

were infested with balsam woolly adelgid but had no apparent crown fading, and trees that were infested with balsam 

woolly adelgid and had visible signs of this infestation. 

Using Analysis of Variance procedures and the SAS statistical analysis package, significant differences were found 

among the three infestation categories for some wavelength 

regions. Our results demonstrated a consistent response in 

the normalized spectral reflectance curve of subalpine fir, 

stressed by infestation of balsam woolly adelaid, across 

the reflectance spectrum shown in Figure 1. More specifically, there is an increased reflectance in the visible region 

of the reflectance curve (< 700 nm), decreased reflectance 

in the Near Infra Red plateau (centered around 1000 nm), 

and increased reflectance in the shortwave infrared region 

(beginning around 1450 nm) as visual decline becomes 

apparent. The overall changes in spectra are similar to 

those reported for other stresses in balsam fir (Luther and 

Carroll 1999). Multispectral aerial imagery (Landsat and 

SPOT data) was also collected for areas with active balsam 

woolly adelaid infestations. Because of the relatively 

Figure 1—Spectral measurements of subalpine firs, Abies lasiocarpa, in Idaho with three levels of infestation with balsam woolly 

adelgid, Adelges piceae. The infestation categories are not infested = blue, infested but with no visible symptoms = red, and 

infested with visible symptoms = black.
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GENERAL TECHNICAL REPORT PNW-GTR-802

narrow canopy architecture of subalpine fir and the patchy 

distribution of the species in the areas of data collection, no 

conclusive results were obtained.

In North Carolina, hemlock trees that were recently 

infested (within the past year) or that had been infested 

for multiple years were sampled as was eastern white pine 

(Pinus strobus L.) (the only other conifer present within the 

stands that we sampled) during June of 2005. No uninfested 

stands of hemlock were found within the study areas. There 

were visible differences in the overall spectral measurements between the hemlocks that were recently infested 

with hemlock woolly adelgid and those that had been 

infested for a longer period of time (Figure 2). The spectral 

signature of eastern white pine, the only other conifer 

present within the stands that could be confused with the 

hemlocks, differed significantly from both categories of 

infested hemlock within the stands (Figure 2). The pattern 

of decreased spectral values with increasing stress is similar 

to the decreases measured in subalpine fir infested with 

balsam woolly adelgid in the Near Infa Red plateau and 

increases again in the shortwave Infa Red region (Figure 1). 

The ability to distinguish declining hemlock at the branch 

level also supports the prior landscape-level investigations 

of Pontius and others (2005), but larger data sets from a 

variety of geographic locations are still needed.

Implications for Detection and Delineation 

of Forest Insect Infestations

The branch-level spectral data for both tree species infested 

with their specific invasive adelgids were both consistent 

and in general agreement with the shoot-level spectral 

changes of balsam fir under various stresses that were 

measured under laboratory conditions (Luther and Carroll 

1999). The measurements were also in general agreement 

with the results of Pontius and others (2005) who examined 

Figure 2—Spectral measurements of eastern white pine, Pinus strobus, and eastern hemlocks, Tsuga canadensis, in North 

Carolina with two levels of infestation with hemlock woolly adelgid, Adelges tsugae. The spectral categories are eastern white pine 

= green, recently infested but with no visible symptoms = blue, and infested with visible symptoms = red.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

hemlock woolly adelgid at the landscape level. Therefore, 

the spectral changes that occur with stress are measurable 

at several scales. The combined results of these studies 

suggest that spectral data may aide in developing a tool 

for previsual detection and monitoring of forest decline 

associated with these adelgid species. However, limitations 

do exist. One of the primary limitations may be the ability 

to separate different stressing agents or factors.

Acknowledgments

The work was supported in part by the University of Idaho 

and by grants from the NSF-EPSCoR program and the 

USDA Forest Service Special Technology Development 

program. Ladd Livingston (Idaho Department of Lands, 

retired), Rusty Rhea (USDA Forest Service, Asheville, 

Northern Carolina (NC), and Fred Hain (NC State University) were consulted on site selection. We also thank 

Stephani Sandoval, Kendra Schotzko, and Emily Heward 

for assistance with the collection of field data.

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

Marla C. Downing, Thomas Jung, Vernon Thomas, 

Markus Blaschke, Michael F. Tuffly, and Robin Reich

Marla C. Downing, program manager, Invasive Species 

Information, USDA Forest Service, Forest Health Protection, Forest Health Technology Enterprise Team, Fort 

Collins, CO 80526; Thomas Jung, independent scientist 

and consultant for tree diseases, pathologist, Brannenburg, 

Germany; Vernon Thomas, spatial analyst, Information 

Technology Experts, Inc., Fort Collins, CO 80525; Markus 

Blaschke, Bavarian State Institute of Forestry (LWF), Section Forest Ecology and Forest Protection, Freising, Germany; Michael F. Tuffly, ERIA Consultants, LLC, spatial 

analyst, Boulder, CO 80305; and Robin Reich, Department 

of Forest, Rangeland and Watershed Stewardship, professor, 

Colorado State University, Fort Collins, CO 80525.

Abstract

Phytophthora alni subspecies alni Brasier and S.A. Kirk is 

a recently hybridized soil and waterborne pathogen causing 

root and collar rot of species of the genus Alnus spp. (alder). 

It has quickly spread throughout Europe via planting of 

infested nursery stock and irrigating fields with infested 

river water. Once introduced, the pathogen spreads naturally by means of streams, floods, and other drainage water. 

Phytophthora alni can also be passively transported with 

the bare-root nursery stock, as it is able to adhere to and 

infect fine roots of visually symptomless plants of alder and 

other tree species exposed to the pathogen.

We used a classification tree on 434 infested and 

healthy sample points to determine the required conditions 

for P. alni to successfully infest a nonflooded forest site. 

Sample points had been collected from 2003 through 2006, 

and a potential distribution surface was created for forested 

areas in Bavaria. A tenfold cross-validation accuracy of 78 

percent was attained. To understand the potential hazard 

posed by P. alni elsewhere in the world, the rules from the 

Bavarian classification tree were applied along with additional expert knowledge in a multicriteria model to create a 

global susceptibility surface for P. alni.

Keywords: Alder, Alnus, classification tree, hazard, 

pathogen, Phytophthora alni, risk. 

Introduction

Phytophthora alni Brasier and S.A. Kirk is a host-specific, 

highly aggressive soil and waterborne pathogen that causes 

root and collar rot of Alnus (alder) spp. All European alder 

species (i.e., black alder [A. glutinosa (L.) Gaertn.], gray 

alder [A. incana (L.) Moench], Italian alder [A. cordata 

(Loisel.) Duby], and green alder [A. viridis] (Chaix) DC.) 

and the North American red alder (A. rubra Bong. ) are 

highly susceptible (Jung and Blaschke 2006, Gibbs and 

others 2003). The susceptibility of other North and South 

American and Asian alder species is currently unknown. 

Phytophthora alni was shown to be a recent interspecific 

hybrid between P. cambivora (Petri) Buisman and another 

species closely related to P. fragariae Hickman (Brasier 

and others 1995, 1999, 2004). There are three subspecies of 

Phytophthora alni, with markedly different aggressiveness 

to common alder (Brasier and others 2004, Brasier and Kirk 

2001). The disease was first detected in 1993 in Southern 

Britain (Gibbs 1995) and has since been confirmed in 12 

other European countries and across the United Kingdom 

(Figure 1) (Brasier and Jung 2003, 2006; Gibbs and others 

1999, 2003; Jung and Blaschke 2004, 2006; Schumacher 

and others 2005; Streito and others 2002) (Orlikowski, 

L. Pers. comm., 2006. Pathologist, Research Institute of 

Pomology and Floriculture, Pomologiczna 18, 96-100 

Skierniewice, Poland). Moreover, P. alni is likely present 

in Czech Republic, Spain, and Switzerland because typical 

symptoms and mortality of alders are reported from these 

countries. The disease occurs mainly along riverbanks, but 

also in orchard shelterbelts and forest plantations (Gibbs 

1995; Gibbs and others 1999, 2003; Jung and Blaschke 

2004; Streito and others 2002). Disease symptoms include 

abnormally small, sparse, and often yellowish foliage and 

crown dieback (Figure 2). Other symptoms are early and 

often excessive fructification and tongue-shaped necroses 

of the inner bark and cambium. Necroses can extend up to 

3 m from the stem base and are marked by tarry or rusty 

spots on the surface of the outer bark (Figure 3) (Gibbs and 

Estimating the Susceptibility to Phytophthora alni Globally 

Using Both Statistical Analyses and Expert Knowledge
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GENERAL TECHNICAL REPORT PNW-GTR-802

others 1999, 2003; Jung and Blaschke 2001, 2004) (http://

www.baumkrankheiten.com/gallery/docs-en/alder_dieback/

index.html). On riparian sites, P. alni has caused mortality 

as high as 70 percent in some locations. Disease incidences 

of 50 percent and high mortality rates were common (Gibbs 

and others 2003, Jung and Blaschke 2004, Streito and others 

2002). The pathogen was shown to be widespread in alder 

nursery fields (Jung and Blaschke 2004, Schumacher and 

others 2005). Infected plants seldom showed symptoms, 

which made control efforts difficult.

A thorough investigation of disease pathways in 

Bavaria demonstrated that in most infested river systems, 

P. alni was introduced via infested young alder plantations 

established on the riverbanks or on forest sites that drain 

into the rivers (Jung and Blaschke 2004). Once introduced, 

P. alni spreads downhill with water runoff and downstream 

with streams and floods. Many infected alders were planted 

in afforestations of former agricultural land and also on wet 

sites in woodlands to stabilize steep slopes and banks of 

white water rivers (Jung and Blaschke 2004). These plantings increased the risk of infestation of riparian sites with 

the increased length of the river and upstream tributaries.

The rapid proliferation of P. alni throughout Europe 

probably resulted from the increased importance of alders 

in afforestation activities on wet sites and on former agricultural lands. The combined anthropogenic impacts of

Figure 1—Current distribution of Phytophthora alni (yellow).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

outplanting infected nursery stock and utilizing contaminated river water to irrigate nursery fields were contributing 

factors (Brasier and Jung 2003, Gibbs and others 2003, 

Jung and Blaschke 2004). Phytophthora alni may also be 

passively transported on bare-root nursery stock because 

it is able to adhere to and infect the fine roots of alders as 

well as adhere to other nonhost tree species exposed to the 

pathogen.

As short-time control measures, coppicing of infected 

alder trees and stools is recommended along water courses 

(Gibbs 2003), but not in infested forest plantations (Jung 

and Blaschke 2006). Some survivors in highly infested 

common alder stands were shown to be less susceptible 

p Figure 2—Mature riparian stand of common alder 

(Alnus glutinosa) with sparse, chlorotic and small foliage 

and crown dieback owing to Phytophthora alni root and 

collar rot.

t Figure 3—Mature grey alder (Alnus incana), with collar rot 

caused by Phytophthora alni; typical tarry spots at the outer bark 

and tongue-shaped orange-brown necrosis of the inner bark.
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GENERAL TECHNICAL REPORT PNW-GTR-802

to P. alni than declining trees, and in the long term, a 

resistance screening program may help to sustain alders as 

major components of riparian and swamp forests (Jung and 

Blaschke 2006).

The Exotic Forest Pest Web site, which is sponsored 

by the North American Forest Commission (NAFC 2006) 

lists P. alni as a high risk pest to North American forests 

for its potential to (1) adversely affect the economic trade 

of alder trees and (2) affect the environment; specifically by 

changing forest composition, reducing wildlife food and 

habitat, increasing soil erosion, and changing soil composition owing to alder’s nitrogen-fixing capabilities (Cree 

1999).

An investigation of the conditions present at 434 

sample locations in forested areas in Bavaria (Figure 4) 

was conducted using classification tree analyses and a 

tenfold cross validation to estimate the error. Classification 

and regression trees are a nonparametric iterative approach 

to compare all possible splits among the independent 

Figure 4—Study area of forested land cover (green) in Bavaria with Phytophthora alni infested (pink) and healthy 

(yellow) sample locations overlain.

Sample Points

Healthy

P. aini infected

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

variables using a partitioning algorithm that maximizes the 

dissimilarities among groups. Classification trees are best 

used with binary data and regression trees with continuous 

data. Advantages of using decision trees such as classification and regression trees include the nonparametric nature 

of the model, ease of interpretation, and the robustness of 

the test (De’ath and Fabricius 2000). Decision trees have 

been successfully developed recently for (1) modeling 

landscape dynamics of the spread of P. ramorum (Kelly and 

Meentemeyer 2002), (2) mapping hemlocks via tree-based 

classification of satellite imagery and environmental data 

(Koch and others 2005), (3) predicting the presence and 

absence of lichen and past fires in Jalisco, Mexico (Reich 

and others 2005), (4) developing a spatial model for estimating fuel loads in the Black Hills, South Dakota (Reich and 

others 2004), and 6) developing a methodology to predict 

oak wilt distribution in Minnesota and Texas (Downing and 

others 2007).

In this study, rules from the classification tree were 

used to create the potential distribution surface for Bavaria. 

Another potential distribution model, a Multicriteria model 

(Eastman 2001, Eastman and others 1995), was created for 

the globe using both the rules from the Bavarian classification tree, and additional parameters established with 

expert knowledge. Global susceptibility surfaces, such as 

the P. alni surface, may be used to illustrate the need for 

a pathway approach to regulate nursery stock and for host 

species resistance testing. Multicriteria models have been 

helpful to produce pest-risk maps for forested land in the 

United States (Krist and others, this volume). 

Figure 5—Classification tree model for presence and absence of Phytophthora alni in forested areas in Bavaria.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Methods

Between the spring of 2003 and the winter of 2006, a total 

of 307 P. alni infested and 127 healthy/noninfested alder 

tree locations were sampled in forested areas in Bavaria 

(Figure 4). Among the 307 infested sites, there were 232 

points where alder trees had been planted, and 75 points 

where alders were naturally occurring. Of the 127 healthy 

sample points, 38 were planted and 89 had natural alder 

growth. A geographic information system sample point 

theme of the dependent variable was created containing all 

434 sample point locations for analysis in the classification 

tree.

Thirteen independent variable raster data sets were 

used in the Bavarian classification tree analysis (Table 1). 

Specifically, these were twelve 93-m physiographic data sets 

including nine soil texture components (minimum, mean, 

and maximum percentage values for sand, silt, and clay 

polygons), aspect, slope, and landform an index of concavity and convexity. The 13th data set was the Normalized 

Difference Vegetation Index (NDVI) calculated from the 

Moderate Resolution Imaging Spectroradiometer (MODIS) 

satellite imagery at 250 m. Numerical values were extracted 

from each of the independent variable grids at each of 

the sample locations from the sample point theme. These 

values were then used to compose the spatial information 

database that was exported to S-PLUS© statistical software 

(S-PLUS©, Statistical Sciences 2000) for analysis. The 

independent variables selected by the classification tree 

were silt, sand, slope, aspects that were Southeast, South, 

Southwest, and West, and the landform index.

The default S-PLUS© validation technique, tenfold 

cross validation, was used to prune the tree to avoid overfitting the classification tree model to the Spatial Information 

Database. The tenfold cross validation was used, as it does 

not rely on an independent data set and can identify the 

optimum tree size for minimizing prediction errors.

Table 1—Bavarian independent variables used to produce a potential distribution for Phytopthora alni in 

Bavarian forested areas

A. Aspect: was derived via the DEM surface. (DEM: Digital Elevation Model) surface. The DEM surface was used 

to create ancillary data: slope, aspect, and landform. The DEM was created from the Shuttle Radar Topography Mission 

(SRTM) in February 2000. SRTM was a joint project between the National Geospatial-Intelligence Agency and the National 

Aeronautics and Space Administration. The DEM surface was obtained and distributed by the U.S. Geological Survey Earth 

Resources Observation and Science (EROS) Seamless Data Distribution System (http://seamless.usgs.gov)]. The cell values 

of zero slope (flat) are assigned an aspect of -1. Therefore, the continuous aspect grid was reclassed to eight basic cardinal 

directions. Plus another class for zero slope. 

B. Soil Fraction/Texture Percent: Soil fraction data were obtained as a soil polygon shapefile from the German Federal 

Institute for Geosciences and Natural Resources. Soil fraction percentage pertains to the volume amount by soil type 

(sand, silt, and clay) found in the soil sample. The percentage of all three soil fractions sums to 100 percent. To capture the 

variance of each soil type, three categories of soil fraction percentage were created for each soil type: (1) the minimum soil 

fraction value for each soil type, (2) the mean soil fraction value, and (3) the maximum soil fraction value. With three data 

sets included for each of the three soil types, there were a total of nine soil data sets used in the analysis.

C. Landform Index: Landform was derived via the DEM. Landform is independent of slope and created using a custom 

ArcView Avenue application. The application uses an irregular 3 by 3 kernel, where positive landform values indicate 

concavity, negative values indicate convexity, and a zero value indicates flat terrain (McNab 1989).

D. Normalized Difference Vegetation Index (NDVI): The NDVI was created from 250-m Moderate Resolution Imaging 

Spectroradiometer (MODIS) satellite data. The MODIS data was obtained from the Remote Sensing Applications Center 

(RSAC) in Salt Lake City, Utah. Personnel at RSAC downloaded an archived Bavarian MODIS image and performed the 

NDVI model. The values of NDVI relate to the relative greenness of the vegetation.

E. Slope: Slope was created using the “Derive Slope” function in the ArcView 3.3 Spatial Analyst extension coupled with 

the DEM surface. Slope units were degrees.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Based on the results of the classification tree analysis 

(Figure 5), conditional statements (CON statements; 

ESRI ArcView, 2000) were used to create a binary P. alni

potential distribution (i.e., presence and absence) surface for 

the forested areas in Bavaria. The significant independent 

variables selected by the classification tree, as well as the 

decision tree rules (e.g., threshold values taken at the tree 

nodes), were the input for the CON statements.

Only three of the independent variables that were 

selected by the Bavarian classification tree were available 

globally. These were slope, aspect, and the landform index. 

To see how the rules would change given only the three 

independent variables, a second classification tree was 

developed for Bavaria using only those three data sets. This 

second model had limited utility as it overpredicted the 

presence of P. alni, predicting more than 90 percent of the 

study area to have P. alni present. Still, the rules from the 

second model did provide some additional insight regarding 

the broader range of conditions within which P. alni might 

be present. Therefore, the rules from both the original as 

well as from the second model were combined, along with 

additional expert knowledge, in a final multicriteria model 

to create a global susceptibility surface for P. alni.

To develop the multicriteria susceptibility model for 

the globe (Figure 6), the unique numerical values from each 

criterion had to be standardized. Therefore, each data set 

was reclassified using a hazard ranking of 0 to 10. The decision rules from both classification trees as well as additional 

expert knowledge were used as a guide in setting the hazard 

rankings.

Areas where alder and P. alni could not grow were 

eliminated from the global analysis by creating masks from 

climate and landcover data. To determine temperature 

thresholds for the climate mask, an investigation of Alnus

species was conducted. It was determined from frost hardiness and heat/drought hardiness zones for all alder species 

that alder does not survive temperatures +/- 40 degrees 

Celsius.

In addition, lab results performed by Dr. Jung indicated 

that soil temperatures greater than 32 degrees Celsius 

prevent the survival of P. alni. Although soil temperature 

data is not available, a regression formula (Temperature 

MAX threshold value = (Soil Temperature MAX threshold 

value - intercept estimate)/Regression Coefficient Estimate), 

was applied to determine that 32 degrees Celsius equates to 

air temperatures of 34 degrees Celsius.

Figure 6—Phytophthora alni multicriteria model for the globe.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Consequently, areas with temperatures less than -40 

degrees Celsius and greater than +34 degrees Celsius, as 

well as areas that could not support alder such as tundra, 

bare ground, and bodies of water, were removed from 

further analysis. The binary climate and landcover masks 

were combined by multiplying both surfaces together to 

create a combined binary temperature and landcover mask. 

The resulting mask was combined again in a weighted 

overlay with the reclassified criteria to produce a potential 

global distribution.

Because slope predicted most of the variability in the 

classification tree, it was weighted at 50 percent; aspect and 

the landform index were both weighted at 25 percent.

To produce the final global susceptibility surface, areas 

that were identified in the global distribution as having a 

potential for a P. alni infestation were classified according to hazard. Biome and stream data were combined in 

an equal weighted overlay to assign a hazard ranking. 

The hazard ranking was based on each pixel’s occurrence 

within ecological biomes similar to the biomes where P. 

alni presently occurs, as well as its proximity to streams. 

Thus, pixels within the selected biomes were assigned 

hazard rankings based on their proximity to streams. A set 

of three global stream buffers at distances of 1km were used 

to assign the hazard rankings. Pixels that had the potential 

for infestation were given a high hazard ranking if they fell 

within 1 km of the stream. Those pixels between 1 and 2 

km were assigned a medium potential hazard, and pixels 

between 2 and 3 km from the stream were assigned a low 

potential hazard. Pixels that were found greater than 3 km 

Figure 7—Potential distribution of Phytophthora alni (presence and absence) for all forested areas in Bavaria.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

from a stream or outside the selected biomes were given a 

hazard ranking of little or no potential hazard. 

Results

For the original Bavarian model: of the 19 620 forested km2

assessed in Bavaria, approximately 14 015 km2 (71.43 percent) were modeled to have a high potential for P. alni root 

and collar rot, and 5604 km2 (28.56 percent were modeled 

to have a high potential to remain healthy (Figure 7). Seven 

terminal end nodes were used and accounted for 78.34 percent of the variability. The tenfold cross validation gave a 

higher accuracy for predicting the 307 P. alni infested sites 

at 86 percent, and showed 63 percent accuracy in predicting 

the 127 healthy sites. The independent variables important 

in predicting the presence or absence of P. alni were 

minimum silt fraction values less than 20 percent (range = 0 

to 80 percent), mean sand fraction values less than 5 percent 

(range = 0 to 93 percent), slope less than 2.97 degrees (range 

= 0 to 30.74 degrees), aspects that were Southeast, South, 

Southwest, and West, and the landform index less than 6.6 

(range = - 15.20 to + 21.60; < 0 = concave; 0 = totally flat; > 

0 = convex) (Figure 5). 

For the second Bavarian model, with only the three 

independent variable data sets that are available globally 

being used, 14 terminal end nodes accounted for 77.19 

percent of the variability. The tenfold cross validation still 

Figure 8—The final global susceptibility surface had 27, 835, 766 km2 of suitable area where alder and P. alni could survive.
568

GENERAL TECHNICAL REPORT PNW-GTR-802

showed a higher accuracy for predicting the 307 infested 

sites at 92.51 percent, but only 40.16 percent accuracy in 

predicting the 127 healthy sites. As expected, all three of the 

independent variables were used to predict the presence or 

absence of P. alni, but, in this model, slopes less than 19.57 

degrees had a higher probability for infestation, and the East 

aspect was selected in addition to the four aspects that had 

originally been selected. Also, landform indexes less than 

6.2 had a higher probability of infestation.

The final global susceptibility surface had 27,835,766 

km2 of suitable area where alder and P. alni could survive 

(Figure 8). Of that area 1,482,487 km2 (5.33 percent) were 

highly susceptible to P. alni; 3,930,660 km2 (14.12 percent) 

had a medium susceptibility; 5,721,467 km2 (20.55 percent) 

had a low susceptibility; and 16,701,152 km2 (60.00 percent) 

had little or no susceptibility.

Discussion

The original Bavarian classification tree identified five 

ecological factors important in the distribution of P. alni. 

Where these factors occur together in the environment, the 

likelihood of infection is increased. Specifically, where silt 

minimum values are less than 20 percent, and sand mean 

values are less than 5 percent, the probability of a P. alni

infection is high. When silt minimum values are less than 

20 percent, and sand means are greater than 5 percent, the 

site is more likely to have P. alni infections if slopes are 

less than 2.97 degrees and have warmer aspects. Sites with 

a landform index measure of less than 6.6 (concave, flat or 

slightly convex) also have an increased probability of a P. 

alni infestation. These results make biological sense. Areas 

with poor drainage and warmer aspects provide an optimal 

environment for the pathogen to flourish, as will sites with 

fairly flat or concave physical structure. Conversely, areas 

with less clay and more silt or sand will drain better, as will 

sites with steeper slopes and convex landform. These types 

of sites will not provide a wet environment for this waterborne pathogen to form sporangia and release zoospores 

that are essential for the spread and infection of P. alni.

Not all of the five ecological factors identified as being 

important by the first Bavarian model for predicting the 

distribution of P. alni were available for the global model. 

A second model for Bavaria, which utilized only the three 

data sets that were available globally, demonstrated the 

limitations of modeling invasive species at a global scale 

without appropriate data. The limitation most notable was 

the soil texture data because it was selected by the first 

Bavarian model as the most important variable for predicting the presence and absence of the soil-borne pathogen P.

alni. In addition, data was not available for (1) forest species 

type (i.e., distribution of the individual alder species), and 

(2) susceptibility of North and South American and Asian 

alder species to P. alni. We addressed forest species type 

by keeping our analysis near and around streams and flood 

plains where most alders tend to grow. We also looked at the 

temperature range, eliminating areas with temperatures that 

were too cold or hot for alder and P. alni survival. Although 

we made compromises to work within the data limitations, 

this work emphasizes the need for quality spatial environmental data at the global scale.

Since planting infected nursery stock is one of the 

primary pathways by which P. alni has been spread, we 

were careful to consider the social or cultural habits in 

association with outplanting alder trees. Of particular concern was the outplanting of infected alder trees in respect 

to elevation. At higher elevations, alder trees are planted 

much less frequently than at lower elevations. Yet, it has 

been observed by the Jung that where P. alni-infected alder 

was planted at higher elevations, those sites have become 

infested and further contribute to infections downhill and 

downstream. Because alder was rarely planted at higher 

elevations, P. alni was much less prevalent on higher elevation sites. We therefore assumed that the model would be 

biased toward selecting elevation as an important variable 

for predicting presence and absence. Consequently, elevation was not used in the model.

A higher accuracy was attained for predicting the P. 

alni-infested sites than for predicting healthy sites. This is 

likely an outcome of having three times more infested than 

healthy sample locations. Had we sampled a greater number 

of locations for the healthy condition, it is likely that the 

accuracy for predicting healthy sites would improve.

Of the 127 healthy alder tree locations collected 

between 2003 and 2006, some sites may have changed in 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

status. Some of the sites that were not infested by 2006 

may become infested in the future. These are problems 

one would expect in attempting to model a species that is 

unlikely to have been in existence before the 1980s (Gibbs 

and others 2003, Jung and Blaschke 2004) and has not 

yet completely expanded into its potential range. With no 

complete range map for P. alni, the Bavarian model provides managers worldwide with useful decision rules and 

a data mining tool for estimating the susceptibility of their 

resources to P. alni.

Because all of the applicable variables from the first 

Bavarian model are available in data sets for the United 

States, the extrapolation of the Bavarian model to forests 

in the United States should demonstrate the specific 

improvement that can be gained by applying the appropriate 

data sets identified by the Bavarian model. 

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Randy Hamilton, Kevin Megown, James Ellenwood, 

Henry Lachowski, and Paul Maus

Randy Hamilton, sponsored projects group leader, 

entomologist and remote sensing specialist, and Kevin 

Megown, program leader, Resource Mapping, Inventory, 

and Monitoring, USDA Forest Service Remote Sensing 

Applications Center, Salt Lake City, UT 84119; James 

Ellenwood, remote sensing program manager, USDA Forest 

Health Technology Enterprise Team, Fort Collins, CO 

80526; Henry Lachowski, retired, formerly program leader, 

Integration of Remote Sensing and Paul Maus, remote 

sensing specialist, USDA Forest Service, Remote Sensing 

Applications Center, Salt Lake City, UT 84119.

Abstract

In recent years, unprecedented tree mortality has occurred 

throughout the national forests owing to insect infestations and disease outbreaks. The magnitude and extent 

of mortality, coupled with the lack of routine monitoring 

in some areas, has made it difficult to assess the damage, 

associated ecological impact, and fire hazard in a timely and 

cost-effective manner. To aid forest managers in assessing 

the damage, a cost-effective multistage sampling method, 

using high-resolution digital aerial photography, was 

developed to estimate overall mortality across large areas. 

The method was tested within a 332,000-acre piñon/juniper 

woodland west of Flagstaff, Arizona, within the Kaibab 

National Forest. Piñon pine mortality caused by piñon ips 

bark beetles (Ips confusus (LeConte)) was assessed from 

high-resolution digital aerial imagery within percent-cover 

strata with the use of a digital dot grid. The sample revealed 

that dead trees covered 7.0 ± 0.3 percent of the study area. 

As a percentage of total tree cover, 20.0 ± 0.8-percent 

mortality had occurred. The cost to obtain this estimate was 

approximately $0.04 per acre.

Keywords: Dot grid, imagery, Ips confusus, pinyon, 

remote sensing, sample.

Introduction

Over the past century, stand density and fuel loading have 

increased in forests and rangelands throughout the United 

States, leading to a general decline in ecosystem health. The 

weakened condition of the forests and rangelands, coupled 

with drought stress in the Western United States, now place 

nearly 200 million acres of Federal forest and rangeland 

in the contiguous United States at risk of epidemic insect 

and disease outbreaks and catastrophic wildfires (USDA 

and USCI 2004). In many areas, devastating wildfires and 

unprecedented insect and disease outbreaks have already 

occurred.

To address the threat and impact of fire, insects, and 

diseases to the Nation’s forests and rangelands, the U.S. 

Federal Government launched the Healthy Forests Initiative 

(HFI) in 2002 and enacted the Healthy Forests Restoration 

Act (HFRA) in 2003. Primary goals of the HFI and HFRA 

are to facilitate, expedite, and provide national guidance 

on hazardous-fuel reduction and ecosystem restoration. 

In areas where insects and diseases have already caused 

extensive tree mortality, the HFRA calls for accelerated 

information gathering on the impact of these mortality 

agents (USDA and USDI 2004). However, collecting information on the extent and severity of mortality in a timely 

and cost-effective manner can be very difficult because of 

the vast acreages that are affected, coupled with the lack of 

routine monitoring in some areas. In some cases, the extent 

of mortality is so great that even traditional assessment 

methods such as aerial sketch-mapping become impractical 

in terms of time and cost. An efficient and cost-effective 

alternative assessment method is needed.

One possible alternative is to sample rather than map 

mortality. Unlike aerial sketch-mapping, which produces a 

map, but no quantitative measure of mortality, a sample provides a quantitative measure of mortality, but no map. Not 

all sample designs are efficient or cost-effective, but some 

have the potential to be less costly and time consuming 

than traditional methods of assessing mortality. A multistage sample, for example, is one type of sample design 

Assessing Insect-Induced Tree Mortality Across Large 

Areas With High-Resolution Aerial Photography in a Multistage 

Sample
572

GENERAL TECHNICAL REPORT PNW-GTR-802

that attempts to optimize sampling efficiency. Sampling 

efficiency is increased by constraining final sampling units 

to fall within only certain subsections or regions of a study 

area rather than distributing them across the entire area. 

Assessing groups or clusters of sample locations is generally easier and less time consuming than assessing widely 

dispersed sample locations.

In a multistage sample design, a study area is initially 

partitioned into coarse subunits, called primary sampling 

units (PSUs). A subset of PSUs is selected and subdivided 

into secondary sampling units (SSUs). This process of 

selecting and further subdividing sampling units may be 

repeated as often as necessary, but a three-stage design 

is common (Figure 1). In the final stage of the sample, a 

subset of the previous stage’s sampling units are selected 

and sampled or assessed. From these samples, the variable 

of interest can be estimated for the entire study area (Ciesla 

2000, Schreuder and others 2004).

The dimensions and layout of sampling units can be 

arbitrary; however, it is generally advantageous to use strata 

correlated with the variable of interest as sampling units, 

particularly in the early stages of the design. Sampling 

within wisely chosen strata allows a multistage sample to 

take advantage of known sources of variability in the population and can greatly increase the precision of the estimate. 

When assessing tree mortality, possible strata might include 

cover type, stand density, elevation, slope, aspect, soil type, 

proximity to water, and others.

Multistage sample designs have been used previously 

to inventory timber, estimate tree mortality and volume 

loss caused by insects in conifer forests, and estimate tree 

mortality caused by diseases (Ciesla 2000, Langley 1971, 

Munson and others 1985, White and others 1983). Ciesla 

(2000) reviewed an assortment of aerial photography-based 

multistage forest inventories. Typically, these inventories 

used vegetation type or tree mortality or both as initialstage strata. Mortality strata were obtained by aerial sketchmapping or from complete coverage aerial photography. 

Subsequent stages in these surveys involved collecting 

aerial photography over plots within the PSU strata. In most 

of the surveys, the third stage consisted of further subdividing the SSUs into tertiary sampling units (TSUs). A subset 

of the TSUs was then ground sampled.

Although samples are less costly and time consuming 

than censuses, multistage samples based on aerial photography and manual photo interpretation can still be expensive 

and time consuming. Replacing traditional aerial photography in multistage samples with digital aerial and satellite 

imagery can potentially reduce survey time and related 

costs. The cost of digital imagery continues to decrease, and 

economical, yet powerful computers and image processing 

software can now automate some of the tasks traditionally 

done by hand. In addition, some field assessments can be 

greatly reduced by analyzing very high-resolution imagery 

acquired over the final sampling units.

Figure 1—A common multistage sample design defines final 

sampling units in three consecutive stages: primary sampling units 

(PSUs), secondary sampling units (SSUs), and tertiary sampling 

units (TSUs).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

To aid forest managers in assessing the severity and 

extent of widespread tree mortality, the USDA Forest 

Service Remote Sensing Steering Committee sponsored a 

pilot study to develop a cost-effective, rapid, and statistically rigorous multistage sample design incorporating 

digital remotely sensed imagery to quantify tree mortality 

across large geographic areas. The method was evaluated in 

a piñon pine/juniper woodland.

Case Study—Assessing Piñon Pine 

Mortality

Beginning around 2002, extensive piñon pine (especially 

Pinus edulis Engelm. and P. monophylla Torr. & Frem.) 

mortality appeared throughout the Western United States. 

Dense stocking and sustained drought weakened the 

defenses of the pines, making them highly susceptible to 

attack by piñon ips bark beetles (Ips confusus (LeConte)) 

and other insects, as well as infection by black stain root 

disease (Leptographium wageneri (Kendrick) Wingfield) 

and other disease agents. Insect feeding and diseases 

destroy and clog the conductive tissues of the trees, causing them to die. The piñon ips beetle, the most important 

mortality-causing agent of the recent mortality event, is 

a native species that typically plays an important role in 

maintaining healthy forests by removing stressed or injured 

trees. This process thins the forest and reduces competition 

for water, nutrients, and light. Healthy trees are generally unaffected by the beetles. However, the abundance 

of drought-weakened trees allowed beetle populations to 

explode, causing extensive mortality in stressed and healthy 

trees throughout the piñon/juniper range (Figure 2) (Keyes 

and Hebertson 2003, Negron and Wilson 2003, Shaw and 

others 2005).

Because piñon/juniper woodlands are not routinely 

monitored, assessing the ecological impact of the mortality and the fire hazard it presented became very difficult. 

Therefore, a rapid and cost-effective multistage sample 

design incorporating digital aerial imagery was developed 

to assess tree mortality.

Methods

A multistage sample design was developed and evaluated 

in a study area located west of Flagstaff, Arizona, in and 

around the Williams Ranger District of the Kaibab National 

Forest. The footprint of a SPOT 5 satellite image (60 by 

60 km) provided the approximate geographical boundary 

for the study (Figure 3). In this region, a mixture of piñon 

pine and juniper (Juniperus sp.) dominates lower elevations 

whereas the upper-elevation forests are predominantly 

ponderosa pine (Pinus ponderosa Dougl. ex Laws.) interspersed with aspens (Populus tremuloides Michx.) and 

higher-altitude conifers.

Stage 1—Piñon/Juniper Woodland Cover Type (PSUs)—

The first stage of the multistage sample design consisted of 

locating the piñon/juniper cover type within the study area 

to eliminate non-piñon/juniper areas from further analysis. 

Existing cover-type maps (i.e., from the Gap Analysis Program, the Rocky Mountain Resource Information System, 

the National Landover Data set, and other sources) were 

evaluated for this purpose. These data sets were deemed 

unsatisfactory for this study because changes in cover type 

from vegetation management activities were sometimes not 

Figure 2—Piñon pine mortality caused by piñon ips beetles (see 

inset). (Photograph courtesy of William M. Ciesla, Forest Health 

Management International, Bugwood.org)
574

GENERAL TECHNICAL REPORT PNW-GTR-802

reflected in these maps and some obvious fine-scale errors 

were also present. Therefore, a three-class vegetation map 

(piñon/juniper, ponderosa pine and other conifers, and 

meadow/bare ground/other) was developed for the study 

area using image segmentation and regression tree classification (Figure 4) (Hamilton and others 2004). This map 

formed the PSUs. For this study, the entire 332,000-acre 

piñon/juniper cover type was advanced to the second stage 

of the sample.

Stage 2—Percent-Cover Strata (SSUs)—

Negron and Wilson (2003) reported that the likelihood 

of piñon ips infestation increased with stand density in 

Arizona. To reduce sample variance, this known source 

of variability was incorporated into the sample design by 

stratifying the piñon/juniper vegetation class by percent 

cover. The percent-cover strata became the SSUs. Breaks 

for the percent-cover strata were based on the broad-level 

vegetation cover categories established by the USDA Forest 

Service Existing Vegetation Classification and Mapping 

Figure 3—The pilot study area was located west of Flagstaff, Arizona, within the Williams Ranger District of the 

Kaibab National Forest. A SPOT 5 satellite imagery scene (acquired September 16, 2003)—displayed in false color 

infrared with topographic enhancement—defined the extent of the study area. Green and blue/green areas are piñon/

juniper woodlands, whereas red areas are primarily ponderosa pine forests. Flight lines where airborne imagery was 

collected are shown as yellow lines.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Technical Guide (Brohman and Bryant 2005) (Table 1). 

The first vegetation-cover category (0 to 29.9 percent), was 

further subdivided into 0 to 9.9-percent and 10 to 29.9-percent categories to refine the SSUs.

Because no existing percent-cover maps were available 

for the study area, a map was created from 1992 digital 

orthophoto quadrangles (DOQs). The DOQs were not 

current, and we anticipated that the mapped percent cover 

would generally be lower than the actual percent cover. 

However, as the objective of this exercise was to stratify 

the study area (not to measure percent cover), relative 

differences in actual percent cover compared to mapped 

percent cover would still allow the map to serve its purpose 

of stratifying the study area. The relative differences in 

percent cover are inconsequential so long as percent cover 

and, consequently, mortality, is more homogeneous within 

than between strata. To create the percent-cover map, a 

two-class classification (tree and nontree) was created 

from the DOQs in ERDAS Imagine. Then, for each image 

segmentation polygon (average size ≈ 2.5 acre) used in the 

vegetation classification, the percentage of the polygon 

occupied by mapped trees was calculated (Figure 5). No 

accuracy assessment was conducted for the percent-cover 

map. However, final sample results verified that percent 

Figure 4—A vegetation-cover map, used to restrict the multistage sample to piñon/juniper woodlands, was created 

using regression tree classification. This is the first stage of the multistage sample.
576

GENERAL TECHNICAL REPORT PNW-GTR-802

cover and mortality were more homogeneous within than 

between strata.

Stage 3—Digital Aerial Imagery Plots (TSUs)—

Tertiary sampling units for this study consisted of 60-by 

60-m digital aerial imagery plots. Plots were sampled for 

mortality using a digital dot grid. On March 30 and April 

24, 2004, high-resolution digital camera (Kodak Pro Back 

645C digital back attached to a Contax 645 medium format 

camera) imagery was acquired in 18 flight lines at various 

locations across the study area (Figure 3). The imagery was 

acquired with an 80 mm lens at an altitude of approximately 

1375 m, producing 16-cm spatial-resolution imagery with 

a swath width of approximately 640 m. The imagery was 

orthorectified using OrthoBASE in ERDAS Imagine. Thirty 

TSU plots were randomly located within each percent-cover 

stratum (for a total of 120 plots) and the geographic boundaries of the aerial imagery. Although the percent-cover 

strata were not equal in size (Table 1), they were sampled 

equally to ensure a minimum number of samples in each 

stratum.

The plot size and dot density were chosen to optimize 

the accuracy and precision of the sample while minimizing the total number of dots per sample (i.e., minimizing 

costs). This was accomplished by sampling several areas 

from each percent-cover stratum using multiple dot grids, 

ranging in size from 30 by 30 m to 240 by 240 m with dot 

densities of 364, 648, or 1,012 dots per acre. A plot size of 

60 by 60 m with dot density of 364 dots per acre (18 by 18 

dots per plot) was considered optimal as the precision of the 

estimate increased only slightly with increased plot size or 

dot density or both. The dot grid was created from graphic 

elements in ArcGIS 8.3, allowing it to be moved easily from 

one plot to the next by dragging and dropping. Although 

this graphic dot grid worked well for this study, a new tool 

for ArcGIS 8.3 and 9.x, Digital Mylar—Image Sampler, was 

recently developed by the USDA Forest Service Remote 

Sensing Applications Center and now provides a more 

automated and user-friendly approach to dot grid sampling 

(USDA Forest Service 2005).

At each sample location, the dot grid was placed over 

the high-resolution imagery (Figure 6). The total number 

of dead-tree, live-tree, and ground hits from the dots was 

counted. First-year and later mortality were all grouped 

into the dead-tree category for this study. In this sampling 

procedure, piñon pines were not distinguished from junipers 

owing to the difficulty of distinguishing the two species on 

this imagery by photo interpretation. The proportion of each 

plot covered by dead trees, live trees, total tree cover (i.e., 

live plus dead trees), and other (typically bare ground) was 

calculated from the dot grids by tallying the hits of the individual variables and dividing by the total number of dots in 

the sample. Subsequently, mean values of these variables 

were calculated for each percent-cover stratum. The mean 

proportion of the entire piñon/juniper region (i.e., across all 

strata) covered by dead trees, live trees, total tree cover, and 

other was calculated from the estimates of each percentcover stratum. These estimates were made using a standard 

weighted disproportionate-strata estimation equation, 

with the strata areas from Table 1 as weights (Equation 1) 

(Scheaffer and others 1990). Because relative proportions of 

strata areas for the entire piñon/juniper region were similar 

to those falling within the boundaries of the aerial imagery 

(Table 1), no adjustments were needed to extrapolate the 

estimate from the extent of the aerial imagery to the entire 

study area.

 1 p̂

st = — S (Ni pi ) , (1) N i

where pˆ

st is the mean across-strata proportion of land area 

covered by dead trees, live trees, total tree cover, or other; 

ˆ

ˆ

Table 1—Percent-cover ranges of SSU strata and 

strata acreages within the entire piñon/juniper region 

and the region of aerial imagery coverage are shown, 

including proportions by stratum of total study area 

and aerial imagery coverage for the two regions

Acreage and (proportion 

Strata of total area)

(percent-cover Piñon/juniper Aerial imagery

ranges) region coverage

0-9.9 percent 36,224 (0.11) 739 (0.10)

10-29.9 percent 108,633 (0.33) 2,376 (0.32)

30-59.9 percent 140,162 (0.43) 3,582 (0.49)

60-100 percent 39,430 (0.12) 642 (0.09)

 Total 324,449 7,339

SSU = Secondary Sampling Units.
577

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

pˆ

i

 is the mean proportion of land area within each percentcover stratum, indexed by i, covered by dead trees, live 

trees, total tree cover, or other; N is the total possible number of individual dot samples within the entire study area; 

and Ni is the weight of the i

th percent-cover stratum (i.e., the 

proportion of possible dot samples within the piñon/juniper 

forests of the i

th percent-cover stratum to the total possible 

number of dot samples within the entire piñon/juniper 

forested area).

Standard errors of the across-strata mean proportion 

estimates were also calculated (Equation 2). From these 

Figure 5—Percent-cover strata for the study area, derived from digital orthophoto quadrangles, were developed for 

the second stage of the multistage sample and formed the secondary sampling units.

results, the mean proportions of total tree cover that had 

died were calculated for each percent-cover stratum as well 

as across strata for the entire piñon/juniper population.

where ˆE(pˆ

st) is the error of the mean across strata proportion of land area covered by dead trees, live trees, total tree 

cover, or other; ni

 is the number of dot samples taken from 

each percent-cover stratum; and Ni , N, p̂

i

 are as described 

in equation 1.















 (2) 1 n Ni

 – ni pˆi (1 – p̂

i

) ˆ

E(pˆ

st) = 2 S Ni Ni ni N – 1, 2 2

i
578

GENERAL TECHNICAL REPORT PNW-GTR-802

Results and Discussion

Estimates calculated for the entire piñon/juniper region of 

the study area (across strata) showed that dead tree canopies 

occupied 7 ± 0.3 percent of the total area as viewed from 

aerial imagery (Table 2). As a percentage of total tree cover, 

20 ± 0.8-percent mortality had occurred (Table 3).

Actual percentage of cover derived from the dot-grid 

samples often differed from the predefined percent-cover 

ranges of the SSUs. However, the stratification still served 

its purpose of reducing sample variance—percent cover and 

mortality were more homogeneous within than between 

strata. The results of this study generally support Negron 

and Wilson’s (2003) conclusion that mortality increases 

with percent cover (Table 3).

Costs to conduct this multistage sample included $5,050 

for materials and approximately 30 days of labor (Table 4). 

If labor were priced at $300 per day, the cost per acre for the 

332,000-acre study area would be slightly more than $0.04. 

These cost estimates do not include the cost of creating a 

vegetation-cover map to narrow the study region to only 

piñon/juniper woodlands. It is assumed that sufficiently 

accurate digital maps of vegetation cover are or will soon be 

available for most of the Nation.

The cost incurred in this study can serve as a reference 

point for similar studies in other areas. However, costs can 

vary greatly depending on location, imagery resolution, size 

of the study area, and other factors. The cost-per-unit area, 

for example, is scale dependent—decreasing as the size of 

the study area increases.

Conclusions

Forests and rangelands throughout the United States are at 

risk of severe insect and disease outbreaks and catastrophic 

wildfires. Epidemic insect infestations have already caused 

Figure 6—Natural-color, digital aerial imagery with dot-grid sample collected March 30, 2004, over an area of piñon mortality. The 

inset shows an example of a dot-grid sample of mortality within a 60 by 60 m (approximately 1 acre) tertiary sampling unit (TSU). 

Observations of dead trees, live trees, and other are color-coded and tallied for the site.
579

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

extensive mortality in many of the Nation’s forests and 

rangelands. The severity and large extent of mortality, coupled with the lack of routine monitoring in some areas, has 

made it difficult to assess the damage, ecological impact, 

and fire hazard in a timely and cost-effective manner.

The multistage sampling method used in this study 

offers a statistically rigorous approach to estimate tree 

mortality across large geographic areas, increase sampling 

efficiency, and provide a confidence interval about the 

mean. By using a digital dot-grid to assess mortality from 

high-resolution aerial imagery, the need for time consuming and costly field assessments was eliminated. The use 

of imagery also allowed samples to be drawn from remote 

areas that would have been prohibitively time consuming to 

sample in the field. This method was tested over a 332,000-

acre piñon/juniper woodland west of Flagstaff, Arizona, 

but the technique is applicable to many other forest types 

impacted by insects and diseases. The multistage sample 

estimated that, as of spring 2004, dead-tree canopies occupied approximately 7 percent of the area within the piñon/

juniper woodlands of the Williams Ranger District. Relative 

to the total tree cover, approximately 20-percent mortality 

was sustained. For the piñon/juniper study area, the cost 

to estimate mortality was approximately $0.04 per acre. In 

other areas, the cost per unit area to conduct a similar study 

could vary greatly depending on the location, size of the 

study area, and imagery requirements.

Literature Cited

Brohman, R.; Bryant, L., eds. 2005. Existing vegetation 

classification and mapping technical guide. Gen. Tech. 

Rep. WO–67. Washington, DC: U.S. Department of 

Agriculture, Forest Service, Ecosystem Management 

Coordination Staff. 305 p.

Table 4—Approximate material costs and labor for 

an experienced analyst to conduct a multistage 

sample to estimate piñon mortality in the Williams 

Ranger District

Expense item Labor (days) Cost

Materials $5,050

Orthorectification of aerial 9

 imagery 

Percent-cover stratification 9.5

Dot-grid sampling 10.5

Calculating results 1

 Total 30 $5,050

Table 3—For each percent-cover stratum, the mean 

proportion (±SE) of total tree cover that has died, 

and mean weighted proportion (±SE) across all 

strata are provided

Stratum 

(percent-cover Mean proportion of total

range) tree cover that is dead

0-9.9 0.125 ± 0.031

10-29.9 0.186 ± 0.033

30-59.9 0.221 ± 0.033

60-100 0.197 ± 0.033

 All strata 0.196 ± 0.008

Table 2—Mean proportions of land area comprised of various cover types for 

each stratum. For each stratum, the mean proportions (±SE) of area (as viewed 

from aerial imagery) comprised of dead, live, and total tree cover, and ground 

are showna

Stratum 

(percent-cover Mean proportion of stratum land area, by type of cover 

range) Dead Live Total tree cover Ground

0-9.9 0.021 ± 0.006 0.150 ± 0.018 0.171 ± 0.020 0.829 ± 0.020

10-29.9 0.059 ± 0.015 0.256 ± 0.014 0.315 ± 0.018 0.685 ± 0.018

30-59.9 0.090 ± 0.016 0.319 ± 0.018 0.409 ± 0.016 0.591 ± 0.016

60-100 0.082 ± 0.017 0.336 ± 0.021 0.418 ± 0.018 0.582 ± 0.018

 All strata 0.071 ± 0.003 0.281 ± 0.005 0.352 ± 0.006 0.648 ± 0.006 a Weighted mean proportions (±SE) for the entire piñon/juniper study area (i.e., across strata) are also provided.
580

GENERAL TECHNICAL REPORT PNW-GTR-802

Ciesla, W.M. 2000. Remote sensing in forest health 

protection. Rep. No. 00-03. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Forest Health 

Technology Enterprise Team. 266 p.

Hamilton, R.; Megown, K.; Ellenwood, J. [and others]. 

2004. Mapping the extent and severity of piñon mortality 

in the Colorado plateau. Rep. No. RSAC-0055-RPT2. 

Salt Lake City, UT: U.S. Department of Agriculture, 

Forest Service, Remote Sensing Application Center. 14 p.

Keyes, C.; Hebertson, L. 2003. Engraver beetle attacks 

pinyon in Utah. Utah Forest News. Logan, UT: Utah 

State University Extension. 7(1). http://extension.usu.edu/

files/publications/newsletter/Volume_7_Number_1.pdf. 

(September 30, 2008).

Langley, P.G. 1971. Multistage sampling of earth resources 

with aerial and space photography. In: Colwell, R.N., ed. 

Monitoring Earth resources from aircraft and spacecraft. 

SP-275. Washington, DC: National Aeronautics and 

Space Administration. 170 p.

Munson, A.S.; White, W.B.; Myhre, R.J.; Hoskins, 

W.H. 1985. Evaluation of three survey methods for 

determining spruce-fir mortality caused by eastern 

spruce budworm. Rep. No. 85–2. Ft. Collins, CO: U.S. 

Department of Agriculture, Forest Service, Forest Pest 

Management, Methods Application Group. 18 p.

Negron, J.F.; Wilson, J.L. 2003. Attributes associated with 

probability of infestation by the piñon ips, Ips confusus

(Coleoptera: Scolytidae), in piñon pine, Pinus edulis. 

Western North American Naturalist. 63: 440–451.

Scheaffer, R.L.; Mendenhall, W.; Ott, L. 1990. 

Elementary survey sampling. 4th ed. Boston, MA: PWSKENT Publishing Company. 390 p.

Schreuder, H.T.; Ernst, R.; Ramirez-Maldonado, H. 

2004. Statistical techniques for sampling and monitoring 

natural resources. Gen. Tech. Rep. RMRS-126. Fort 

Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 111 p.

Shaw, J.D.; Steed, B.E.; DeBlander, L.T. 2005. Forest 

inventory and analysis (FIA) annual inventory answers 

the question: What is happening to piñyon-juniper 

woodlands? Journal of Forestry. 103: 280–285.

U.S. Department of Agriculture, Forest Service. 2005. 

Digital Mylar—Image Sampler. Washington, DC: U.S. 

Department of Agriculture, Forest Service. http://fsweb.

geotraining.fs.fed.us/tutorials/digmylar/ (September 30, 

2008).

U.S. Department of Agriculture, Forest Service; 

U.S. Department of the Interior, Bureau of Land 

Management [USDA and USDI]. 2004. The healthy 

forests initiative and healthy forests restoration act: 

interim field guide. FS-799. Washington, DC: U.S. 

Department of Agriculture, Forest Service. 58 p.

White, W.B.; Bousfield, W.E.; Young, R.W. 1983. A 

survey procedure to inventory ponderosa and lodgepole 

pine mortality caused by the mountain pine beetle. Insect 

and Disease Management Survey Methods Manual. 3.1.2. 

U.S. Department of Agriculture, Forest Service. 27 p.
581

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

Louis R. Iverson, Anantha Prasad, Jonathan Bossenbroek, 

Davis Sydnor, and Mark W. Schwartz

Louis R. Iverson, research landscape ecologist, and 

Anantha Prasad, ecologist, USDA Forest Service, Northern Research Station, Delaware, OH 45015; Jonathan 

Bossenbroek, assistant professor, University of Toledo, 

Department of Earth, Ecological and Environmental 

Sciences, Lake Erie Center, Oregon, OH 43618; Davis 

Sydnor, professor, Ohio State University, School of Natural 

Resources, Columbus, OH 43210; and Mark W. Schwartz, 

professor University of California-Davis, Department of 

Environmental Science and Policy, Davis, CA 95616-8573.

Abstract

The emerald ash borer (EAB, Agrilus planipennis Fairmaire) is threatening to decimate native ashes (Fraxinus

spp.) across North America and, so far, has devastated ash 

populations across sections of Michigan, Ohio, Indiana, and 

Ontario. We are attempting to develop a computer model 

that will predict EAB future movement by adapting a model 

developed for the potential movement of tree species over 

a century of climate change. We have two model variants, 

an insect-flight model and an insect-ride model to assess 

potential movement.

The models require spatial estimates of EAB abundance and ash abundance. The EAB abundance map shows 

a zone of initial infestation in the western suburbs of 

Detroit, with ash trees first dying about 1998. The fine-scale 

(270-m cells) ash basal area maps show highly variable 

values, but woodlots often have very high levels of ash. At 

the coarse scale (20-km cells) for the Eastern United States, 

available ash is high throughout the northern part of the 

country.

With the flight model, probability of movement is 

dependent on EAB abundance in the source cells, the 

quantity of ash in the target cells, and the distances between 

them. With the insect-ride model, we used geographic information system data to weight factors related to potential 

human-assisted movements of EAB-infested ash wood or 

just hitchhiking insects. We are developing a gravity model 

that considers traffic volumes and routes between EAB 

source areas and various distances to campgrounds.

Preliminary results from a test strip through northern 

Ohio show (1) the insect-flight model creates a relative 

probability of colonization that decreases quickly from the 

EAB range boundary edge; and (2) the insect-ride model 

provides occasions for long-distance transport via humanaided dispersals.

Keywords: Ash, dispersal, emerald ash borer, invasive, 

Ohio.

Introduction

The emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), poses a serious threat to 

all ash trees in North America. The larvae feed on phloem, 

producing galleries that eventually kill large trees in 3 to 

4 years and small trees in as little as 1 year (Poland and 

McCullough 2006). A native of Northeastern China, Korea, 

Japan, Mongolia, Taiwan, and Eastern Russia, the species 

was first identified in the United States near Detroit, Michigan, in July 2002 (Haack 2006). The borer was probably 

imported into Michigan in the early 1990s via infested ash 

crating or pallets (Herms and others 2004).

The impact of EAB may be enormous. An estimated 

8 billion ash trees exist in the United States, comprising 

roughly 7.5 percent of the volume of hardwood sawtimber, 

14 percent of the urban leaf area (as estimated across eight 

U.S. cities), and with a value exceeding $300 billion (Poland 

and McCullough 2006).

Research into the spatial distribution of the host ash 

species helps us better understand the resource at risk and 

the potential for EAB spread. The USDA Forest Service’s 

Forest Inventory and Analysis (FIA) units continually 

conduct inventories across more than 100,000 plots in the 

Eastern United States (Miles and others 2001). This invaluable data source provides the information critical to the 

assessment of the ash resource, including the work reported 

here. For a detailed look, we rely on 30-m Landsat data that 

have been classified into forest types with associated ground 

samples to calculate ash content.

Modeling Potential Movements of the Emerald Ash Borer: 

the Model Framework
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GENERAL TECHNICAL REPORT PNW-GTR-802

There are a variety of approaches for using computer 

models to predict the risk and spread of invasive insects 

(e.g., Rykiel and others 1988, Sharov and Liebhold 1998, 

Sharov and others 1997, Sturtevant and others 2004, 

Turchin 2003). To summarize, modeling insect movement 

is a complicated venture, especially in heterogeneous 

landscapes. BenDor and Metcalf (2006) and BenDor and 

others (2006) have initiated a dynamic modeling approach 

to learn more about EAB spread and possible mechanisms 

to retard it. These authors also call for high resolution data 

on the ash resource and human-assisted components such as 

campgrounds to move this work forward. In this paper, we 

present a slightly different approach using higher resolution 

databases.

The objectives of this work are to (1) evaluate the ash 

quantity across the Eastern United States at a coarse level 

and in Ohio at a fine scale; (2) assess EAB spread and rate 

of spread through the region so far; and (3) begin to model 

future spread through the two modeling approaches of 

insects flying and insects riding with humans.

Methods

Distribution of Ash

Fraxinus (ash) is the only genus that EAB has attacked 

in North America. There is no observed host expansion. 

Consequently, we mapped ash availability as a resource for 

EAB spread, at a coarse resolution for the Eastern United 

States (20- by 20-km cell) and at a fine resolution for Ohio 

(30- by 30-m cell size).

Coarse-Level Analysis of Ash for the Eastern United 

States—

We used FIA data (Miles and others 2001) to determine ash 

distribution and abundance for 9,782 cells over the Eastern 

United States. We created these data sets for a climate 

change atlas (Iverson and others 1999) and have made these 

data available (Prasad and Iverson 2003). We summed the 

data for four species of ash that comprise the vast majority 

of ash in the Eastern United States: Fraxinus americana L. 

(white ash), F. pennsylvanica Marsh. (green ash), F. nigra

Marsh. (black ash), and F. quadrangulata Michx. (blue ash). 

This effort produced maps of relative availability of rural 

ash (FIA does not sample urban areas well) to the EAB. The 

methodology used data from the 100,000+ forested plots to 

calculate the basal area (BA) of ash per plot, then calculated 

average BA of ash per unit area based on all the plots within 

the 20- by 20-km cell. One drawback of this methodology 

is that in some cells with small quantities of forest, no FIA 

plots were established so that the average ash BA is calculated as zero for that cell. In reality (as seen in the fine-scale 

analysis), every cell in the Midwest has at least some forest 

(and, most likely, some ash). Another drawback is that the 

urban forest resource is underrepresented in the FIA data. 

(There is an ongoing effort within our group to conduct 

surveys to better understand the forest resource in certain 

urban areas.)

The next step was to create a map of percentage of forest cover by 20- by 20-km-grid cell. We acquired classified 

30 m Landsat TM-interpreted data from Riitters and others 

(2002). The data were reclassified into forest or nonforest and tallied by 20- by 20-km cell to yield an estimated 

percentage of forest cover for each cell.

Data from the average BA map was multiplied by the 

percentage of forest cover per 20- by 20-km cell, providing 

an estimate of the total availability of the ash resource to the 

invasive species, in thousands of square feet of BA per 20 

by 20 km.

Fine-Scale Analysis of Ash in Ohio—

For a detailed estimate of ash resource availability in 

Ohio, we combined estimates of ash BA per FIA plot with 

a Landsat TM-based classification of forest types. We 

acquired the landcover data from the Ohio Gap Analysis 

Program (GAP) (Ramirez and others 2005). This data set 

contains vegetation classes based on leaf-on and leaf-off 

imagery from 1999 to 2002 (Ramirez and others 2005), 

at 30-m resolution. Vegetation classes conform to the 

NatureServe’s Ecological Classification system (Comer and 

others 2003). We attempted to establish ash percentages 

for 28 classes, including the following classes important 

in northwestern Ohio: row crop, open water, low density 

urban, high density urban, urban forested, grassland, 

evergreen forest, North-Central Interior Dry Oak Forest and Woodland (CES202.047), North-Central Interior 

Floodplain (CES202.694), North-Central Interior Wet 
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Flatwoods (CES202.700), and North-Central Oak Barrens 

(CES202.727).

To estimate ash BA in each landcover type, 2,298 FIA 

plots were overlaid on the Ohio GAP data set by Elizabeth 

LaPoint, FIA geographic information system (GIS) Service 

Center. Due to the restriction on releasing FIA plot coordinates, Ms. LaPoint performed the overlay and reported the 

average ash BA per class. However, only a portion of the 

plot coordinates (ca 40 percent) were global positioning system (GPS) corrected so that locational error is present in the 

overlay (which caused, for example, some plots to appear in 

open water). For certain classes, including the urban classes, 

row crop, and oak barrens, we believed, based on number 

of FIA plots available and ancillary information, the data 

reported for southern Michigan by McFarlane and others 

(2005) better represented the quantity of ash present in the 

types. So, the McFarlane and others data were substituted 

for the FIA estimates.

Finally, an EAB habitat availability map was prepared 

by applying the estimated BA of ash per class. To prepare a 

smoother map, which was needed for the modeling effort, 

we calculated the mean ash BA per 270-m pixel (a 9 by 

9 cell average). This product was used for mapping and 

modeling, which requires estimates of ash quantities per 

cell (see “Modeling Spread in Ohio”).

Mapping Estimated EAB Spread, 1998–2005

To model potential future spread and assess observed 

spread rates, a preliminary map of historical spread was 

created. For this, multiple data sets and GIS manipulations 

were used, and for the most part, represent the spread of 

visible damage to ash trees rather than the initial infestation 

of EAB. The Michigan Department of Natural Resources 

(DNR) mapped pest outbreaks, which estimated the range 

of EAB-damaged ash in 2002 and 2003. These were 

accepted as accurate. We also used data from interstate 

highway exit surveys for 2003 and 2004, with 10 ash trees 

per exit tallied for death/life (irrespective of what killed the 

ash) by Smitley (2005) and Smitley and others (2005). These 

data were useful to show where ash death first occurred in 

the region. A Michigan ash damage survey from September 

2004 also was used (Michigan State University, 2006) along 

with the actual locations and density of extent of known 

EAB locations as of December 2005, obtained from the 

Cooperative Emerald Ash Borer Project (2006). In addition, 

multiple dates of these national EAB positive maps were 

acquired to detect additional finds temporally. Finally, our 

own field work on ash tree assessment in northern Ohio 

and southern Michigan during the summers of 2004 and 

2005 yielded additional spatial information, particularly on 

ash not yet visually affected by EAB. Again, most of the 

data were based on visual observations of damage, which 

can be delayed for several years after EAB colonization in 

otherwise healthy, larger ash trees. It should also be noted 

that girdled detection trees were used in Michigan in 2004 

and 2005, which increased detection capability over that 

available from visual inspection alone. Thus, this new 

survey method may have artificially expanded the estimated 

front in 2004 and 2005 over what would have been detected 

with only observable symptoms. It definitely expanded the 

number of outliers detected outside the main front.

Known EAB locations were inputted into ArcGIS 

(GRID - focalsum commands), and four EAB abundance 

classes were derived based on the number of EAB positives 

recorded within three spheres of influence around each 

point: 1, 2.5, and 5 km. Each 270-m cell within the study 

area was assigned EAB abundance class 0 (no EAB positives within 5 km), 1 (1 to 5 positives within 5 km), 2 (1 to 3 

positives within 2.5 km or 6 to 10 positives within 5 km), or 

3 (1 to 38 positives within 1 km, or 4 to 100 positives within 

2.5 km, or 11 to 110 positives within 5 km). The resulting 

map presented the best summary of EAB concentration 

areas as of December 2005.

Armed with all the above data sets overlaid in ArcGIS, 

we manually drew estimated boundaries of the EAB-damaged ash front for 2005, 2004, 2001, 2000, 1999, and 1998. 

Lines were drawn to encompass the higher EAB density 

zones, the mortality estimates, and the nearby new finds 

for each year. For 2002 and 2003, we used the pest maps 

from Michigan DNR mentioned above. Although EAB 

probably entered the United States prior to 1998 and was 

likely present in these trees prior to that date, we started 

with 1998 as the first year to estimate the visual damage 

front based primarily on the Smitley (2005) data. These 
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data indicated that ash tree mortality was already quite high 

in that zone by 2003, which coincided with our assumption 

that it takes about 6 years for a cell to reach peak infestation 

(see “Gravity Model Scenarios”). Subsequent studies of tree 

rings in the initial zone of infestation have indicated that 

initial death of ash trees occurred in 1997 (Nathan Siegert. 

Personal communication, 2006. Department of Forestry, 

Michigan State University, East Lansing, MI 48824). The 

final map shows estimated limits of the front by year for 

1998–2005.

Modeling Spread in Ohio

Most of the data collected in the preceding sections was a 

prerequisite for efforts to model the spread of the EAB. We 

have worked some years on a SHIFT model, designed to 

estimate the potential migration of trees under the northward climatic pressure (Iverson and others 1999, 2004a, 

2004b; Schwartz and others 2001). This model was adapted 

to work for the spread of EAB. The fundamental basis of 

this model is a spread model that is driven by existing local 

density of infestations, ash BA, and the distance of habitat 

patches to known and modeled infestations. This basic 

model required modification based on the idea that EAB 

spread is facilitated through human activities (insect-ride 

model).

The formula SHIFT uses to calculate the probability 

of an unoccupied cell becoming colonized during each 

generation is:

Pcolonization, i = HQi ( S HQj x Fj x (C/Di,j

x

))

where Pcolonization, i is the probability of unoccupied cell 

i being colonized by at least one individual and surviving 

into reproductive status; HQi and HQj are habitat quality 

scalars for unoccupied cell i and occupied cell j, respectively, that are based on the basal area of ash in each 270-m 

cell; Fj, an abundance scalar (0 to 1), is related to the current 

estimated abundance of EAB in the occupied cell j; and Di,j

is the distance between unoccupied cell i and an occupied 

cell j.

The colonization probability for each unoccupied cell, a 

value between 0 and 1, is summed across all occupied cells 

at each generation. Thus, an unoccupied cell very close to 

numerous occupied cells may end up with a colonization 

probability greater than 1.0. These cells are modeled as 

colonized. For cells with summed colonization probabilities 

less than one, a random number less than 1.0 is chosen, and 

all cells with a probability of colonization that exceeds the 

random number are colonized in that model step. Those 

“newly colonized” cells then contribute to the colonization 

probability of unoccupied cells in the next model time step. 

The value of C, a rate constant, is derived independently 

through trial runs to achieve a migration rate of approximately 20 km per year under high ash BA and moderate 

EAB abundance. The value of X, or dispersal exponent, 

determines the rate at which dispersal declines with distance. Being in the denominator, this decreases colonization 

with distance as an inverse power function. Further discussion on the dispersal function can be found in Schwartz and 

others (2001).

Insect-Flight Model—

With the insect-flight model, we use the modified SHIFT 

model to advance the front based on the current front location, the abundance of EAB behind the front, and the quantity of ash ahead of the front. The model runs at a 270-m 

cell size, and based on the known progression of EAB 

densities and ash mortality in outlier zones, we assume an 

11-year cycle for EAB initial infestation to death of all ash 

trees in the cell. EAB abundance in the cell was assumed to 

form a modified bell-shaped curve, with maximum abundance (multiplier = 1) in years 6, 7, and 8; a 0.6 multiplier in 

years 5 and 9; a 0.14 multiplier in year 4; a 0.011 multiplier 

in year 3; a 0.0003 multiplier in years 2 and 10; and a 0.0001 

multiplier in years 1 and 11. The assumptions for this curve 

include a slow EAB population increase for the first few 

years after colonization, followed by peak infestation for 

3 years starting with year 6, followed by a rapid decline 

as all the ash trees in the cell die off in years 9 to 11. The 

fine-scale ash BA for Ohio was normalized to 0 to 100 and 

also used as a multiplier. The 11-year cycle may be a liberal 

assumption on how fast the EAB infestations can grow, as 

there is some evidence that it may take as long as 10 years 

for populations to peak (rather than the 6 we assumed). For 

each cell, the program calculates the probability of new 

colonization, based on a small probability that the insect 
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will fly from an occupied cell to an unoccupied cell, for all 

surrounding cells within a specified search window (40 km 

in this case). Once selected for colonization, the cell starts 

the 11-year cycle of EAB increasing and then decreasing as 

ash dies out.

Insect-Ride Model—

To develop the insect-ride model, we used GIS data to 

weight factors related to potential human-assisted movements of EAB-infested ash wood or just hitchhiking insects: 

roads, urban areas, various wood products industries, population density, and campgrounds. Each of these five factors 

was converted into weighting layers that became multipliers 

for the ash BA component of the insect-ride model. That is, 

the increase in probability of EAB infestation by the insectride factors is made manifest by increasing the amount of 

ash available in those cells. Thus, if no ash exists in the 

cell, it matters not whether there is an escaped EAB from 

one of the human-assisted vectors, but if there is a large ash 

component, an escaped EAB could quickly find a place to 

colonize.

To register the increased probability of insects riding 

on windshields, radiators, or otherwise attached to vehicles 

moving down the road, we assigned weights to two widths 

of major road corridors. We used the U.S. Geological 

Survey major roads data and created buffers of 1 and 2 

km, with a scoring of 10 for 0 to 1 km and 5 for 1 to 2 km 

distance from the roads.

For urban areas, where there is much more vehicular 

density and opportunity for EAB transport, we assigned 

values of 7 if the urban center size was less than the median 

size and 10 if greater than the median. We therefore assume 

larger cities will have greater chance of EAB infestation via 

human movement. Data were acquired for the State of Ohio 

urban centers from the Department of Transportation Office 

of Technical Services (Ohio Department of Transportation 

2006).

Related to the urban areas, weighting is the population 

density scoring by zip code. This factor creates a wall-towall scoring and distinguishes rural from more urbanized 

areas. Data were acquired from the U.S. Census Bureau, 

which included population estimates for 2001 by zip code 

area. Population densities were divided into six classes with 

scoring as follows: 1 = 1 to 100 people/km2

; 2 = 101 to 200; 

4 = 201 to 800; 6 = 801 to 2,000; 8 = 2,001 to 4,000; 10 = 

4,001 to 16,582.

Wood products industries also have been responsible 

for some EAB movement, so a scheme was developed to 

weight buffers around individual businesses dealing in 

wood products. We performed an analysis of potential 

industries carrying wood products, based on the listing 

of SIC codes from Dunn and Bradstreet. We scored each 

industry for likelihood of EAB getting to the site and 

emerging based on our estimate of the amount and status of 

ash used in the industry: 0 = none; 2 = small likelihood; 4 = 

somewhat likely; 6 = higher likelihood. For example, forest 

nurseries and wood pallet industries scored a 6, whereas 

manufacturers of decorative woodwork or wooden desks 

scored a 4 (mostly used kiln-dried wood), and manufacturers of pressed logs of sawdust or woodchips scored a 2. 

Movement of material from nurseries historically has been 

a source for several infestations, which are not accounted 

for in this model. Presumably, this source has been slowed 

recently via quarantine regulation. Next, buffer distances 

around the businesses were created based on the number of 

employees (surrogate for size or volume of wood) working 

at the facility. For 1 to 10 employees, the buffer of 0 to 1 

km scored 8, and the 1 to 2 km buffer scored 3; for 11 to 50 

employees, the buffer of 0 to 1.5 km scored 9, and the 1.5 

to 3 km buffer scored 4; and if the facility had more than 

50 employees, the 0 to 2 km buffer scored 10, and the 2 to 4 

km buffer scored 5. Because facilities could be within each 

other’s buffer space, scores were added, and the maximum 

score over the study area was 22.

Finally, campgrounds were considered likely destinations of human-assisted EAB transport, primarily through 

the (mostly illegal) movement of firewood. The general 

public is the primary vector, so it is much more difficult 

(relative to industry vectors) to achieve education, regulation, and enforcement goals related to stopping EAB 

spread. Campgrounds were treated in two ways: through 

the weighting scheme described here and the gravity 

model described in the next section. Campground locations 

were acquired from Dunn & Bradstreet (unpublished data 
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purchased by Iverson) and the AAA Travel and Insurance 

Company (unpublished data provided to Bossenbroek). 

Similar to that described for wood products industries, 

we base the weighting on both distance (from the camp 

headquarters) and number of campsites. For campgrounds 

with less than 50 campsites, the buffer of 0 to 0.5 km scored 

10, and the buffer of 0.5 to 1 km scored 5; for 51 to 200 

campsites, the equivalent buffers were 0 to 1 (10 points) and 

1 to 2 km (5 points); for 201 to 400 campsites, buffers were 

0 to 1.5 and 1.5 to 3 km; for 401 to 600 campsites, buffers were 0 to 2 km and 2 to 4 km; and for more than 600 

campsites, buffers were 0 to 2.5 km and 2.5 to 5 km.

Gravity Model Scenarios—

In the second approach used with campgrounds, we are 

developing a gravity model (Bossenbroek and others 

2001) that considers traffic volumes and routes between 

EAB source areas and various distances to campgrounds 

(Muirhead and others 2006). Muirhead and others (2006) 

presented an initial model predicting human-mediated dispersal of the EAB through the movement of campfire wood. 

Given the rapid spread of the EAB and a need for a quick 

response, simple models based on simple assumptions, such 

as developed by Muirhead and others (2006), are an essential step. One of the goals of this project is to incorporate 

Figure 1—Ash basal area (square feet per acre for 20- by 20-km cells) across the Eastern United States, from Forest Inventory 

and Analysis data for white, green, black, and blue ash.
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more detail into the models of long-distance dispersal of the 

EAB. Empirical data on the use of campgrounds, i.e., reservation data, is only available for public campgrounds; thus 

to incorporate private campgrounds, a modeling framework 

is necessary. Here we develop a gravity model for Ohio to 

predict the relative number of campers traveling from EAB 

infested areas to the campgrounds of Ohio.

Gravity models calculate the number of individuals, 

(e.g., campers) who travel from location i to destination j, 

(e.g., a campground), Tij, as estimated as

Tij = Ai

OiWicy (1)

where, Ai is a scalar for location i (see below), Oi

 is the 

number of people at location i, Wj is the attractiveness of 

location j, cij is the distance from location i to location j, 

and α is a distance coefficient, or distance-decay parameter, which defines how much of a deterrent distance is to 

interaction. Ai is estimated via

N

Ai = 1 / S —j=1Wicy , (2)

where N represents the total number of destinations, and j

represents each destination in the study region. A production-constrained gravity model of the movement of firewood thus requires information on the number of campers, 

Figure 2—Percentage of forest cover per 20- by 20-km cell, based on the forest/nonforest classification of all the classified 30-m 

pixels within each cell.
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the residency of the campers, the location of potential 

destinations (i.e., campgrounds), the attractiveness of those 

destinations, and the distribution of the EAB (i.e., source 

locations). The spatial resolution of our gravity model is 

based on ZIP code regions for the residency of campers and 

the point locations of campgrounds. 

Based on data from Dunn & Bradstreet and the AAA 

Travel and Insurance Company, we identified the location of 

241 public and private campgrounds in Ohio. For a measure 

of attractiveness for each campground (w) we initially are 

using the number of camp sites at each location. Other 

factors, such as proximity to boating, fishing, and hiking, 

are likely to influence the attractiveness of individual 

campgrounds, but these data are unavailable on a regional 

and consistent basis. The distance between a ZIP code and a 

campground (c) was calculated as the road network distance between these locations. For simplification, the road 

network is based on all roads with either a State or Federal 

designation and excludes local roads. The point of origin for 

each ZIP code was determined as the road location nearest the centroid of the ZIP code region. Likewise, for each 

campground, the point location was determined as the point 

on the nearest road to the campground. The result of the 

gravity model is a prediction of the number of campers that 

Figure 3—Amount of ash available (square foot basal area of ash per 20- by 20-km cell) to the emerald ash borer. It is the product 

of Figures 1 (basal area of ash) and 2 (percent forest).
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travel from an area of EAB infestation to each particular 

campground.

To estimate the distance coefficient (α), we compared 

our gravity model with reservation data obtained from the 

Ohio Division of Parks and Recreation for 58 state parks. 

These records contained the number of reservations for 

each campground summed by ZIP code of the camper’s 

residence. We used sum of squares to measure goodnessof-fit between model predictions and the observed data. To 

identify the best-fit model, the value of α was systematically 

assessed over a range from 0.1 to 10. By fitting the model 

to the reservation data for Ohio state parks, we assume that 

campers using private and public campgrounds behave in 

the same manner, i.e., distance and attraction affect their 

travel decisions in the same manner.

Once the gravity model was parameterized, we used 

the estimated distance coefficient value to determine the 

expected number of campers that would travel to all 241 

campgrounds within Ohio. We reported the percentage of 

campers coming from EAB-infested ZIP codes (as of 2003) 

traveling to each campground in Ohio to give a relative 

estimate of risk.

Results and Discussion

This project is a work in progress, and consequently, results 

presented in sections 3.1 and 3.2 could change pending new 

Figure 4—Proportion of various genera of trees, based on basal area as calculated from data depicted in Figure 3. The proportions of ash are highest in the Northern States.
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data or analysis or both. Results reported in “Modeling 

Spread in Ohio” are very preliminary.

Distribution of Ash

Analysis of the distribution of ash at two scales showed

two facts: there is a lot of ash available to the insect, and 

it is distributed throughout the Eastern United States. 

Consequently, the EAB threat is real for most communities 

and rural locations throughout the region.

Coarse-Level Analysis of Ash for Eastern United 

States—

The map of ash BA (including white, green, black, and blue 

ash) per unit area of forest shows there is a great deal of ash 

in the woodlots and small forests common within the current range of the EAB (southern Michigan, northern Ohio, 

northeastern Indiana) (Figure 1). However, the amount of 

forest in that zone is limited (Figure 2), so the total available 

ash is less compared to the more forested regions (Figure 3). 

Of major concern is the large amount of ash available just 

south of Lake Erie (northeast Ohio, northwest Pennsylvania) 

and Lake Huron (western New York). The western edge of 

this zone is just now being reached by the EAB.

These maps show a high level of ash availability in the 

zones surrounding the borer’s current range, indicating a 

difficult control task ahead.

Figure 4 shows a map with the proportions of various 

genera of trees in each State of the Eastern United States. 

Ash comprises a significant proportion of basal area across 

the Northern States, but is less prevalent in the Southeastern 

States.

Figure 5—Estimated distribution of ash for a portion of northwest Ohio, calculated at a resolution of 270 m (but not resolved that finely 

for this map). Data are based on classified 30-m Landsat imagery and Forest Inventory and Analysis plot data for the entire State.
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Figure 6—Estimated emerald ash borer front spread by year, 1998-2005, as estimated from a variety of data.
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Fine-Scale Analysis of Ash for Ohio—

The fine-scale analysis for Ohio, using 30-m data and plot 

information, shows an estimate of the urban and riparian 

zones with levels of ash (BA) (Figure 5). Most of the area 

shown in Figure 5 is agricultural land, but ash is maintained 

in the landscape even in these croplands along roadsides, 

ditches, and small wetlands. There are also numerous 

woodlots, many of which contain high proportions of ash.

Mapping Estimated EAB Spread, 

1998–2005

The map of estimated EAB front locations was required 

for two reasons: (1) to create a baseline from which our 

spread modeling will commence; and (2) to estimate the 

average historical rate of spread that will help calibrate the 

model. The resulting map (Figure 6) shows expansion from 

a core area in western Detroit, with substantial concentric 

movement each year. Using these data, and assuming a 

start date of 1998, we calculated an average spread rate of 

approximately 20 km/yr for the years 1998 through 2005. 

This expansion rate is much faster than the field and laboratory dispersal (flight) studies that have been presented thus 

far of 1 km/yr (McCullough and others 2005) to 4.8 km/

yr (Taylor and others 2005), respectively. Clearly, much of 

the historical movement of the front, as we detected it here, 

is hastened by shorter human-assisted movements, and the 

two mechanisms (flight vs. ride) cannot be clearly distinguished from each other in the real world.

Modeling Spread in Ohio

We present a modeling framework that considers both the 

insect- and the human-controlled dispersal mechanisms 

(Figure 7). Though we have not completed this work, we 

have some preliminary results, which are presented here.

Figure 7—Modeling framework showing two major components–the insect flight and insect ride models. The insect 

ride model considers both a geographic information system weighting scheme and the gravity model of attractiveness 

of campgrounds for receiving emerald ash borer (EAB) from the core zone in southern Michigan. The insect flight 

model considers the observed rate of spread of 20 km/yr, the emeral ash borer (EAB) abundance, and the abundance 

of food for EAB.
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Insect-Ride Model—

When we include the five factors of human-assisted 

dispersal, all of the land is affected to some degree (Figure 

8). These factors together modify the environment for 

susceptibility for EAB invasion in our model by supplying a 

multiplier to the quantity of ash available to the EAB. In our 

example section of Ohio, we see that the largest multipliers 

will be in the densely populated centers, especially where 

there are wood products industries and roads nearby. We 

have yet to experiment with various weighting schemes 

among the five factors. For example, we plan to incorporate 

relatively more influence of campgrounds, probably via the 

gravity model.

Gravity Model—

In evaluating Ohio campgrounds, we demonstrate the 

influence of proximity to the core area of EAB presence. 

Figure 9 shows that the higher scores (larger symbols) are at 

campgrounds with more campsites (=more attractiveness), 

with more traffic, and that are closer to the core area of 

EAB infestation in southern Michigan. The areas around 

these larger symbols are potential areas that should be 

monitored with detection trees and visual inspections, as 

new outliers may emerge near these zones.

Insect-Flight Model—

The EAB Shift model produces an estimate of relative 

probability of colonization away from the already occupied 

zones. Figure 10 shows the preliminary results of a test strip 

from Toledo to Columbus, Ohio (same strip as shown in 

Figure 8). The relative probability of colonization decreases 

quickly from the EAB range boundary edge (Figure 10, top 

strip). When we add the influence of single factor weights 

(e.g., roads, campgrounds, population density, and wood 

products industries), there are some minor variations that 

align with the weights in the preliminary output (Figure 10). 

We emphasize that this example is only to show the kinds of 

outputs we are pursuing and that the testing and calibration 

Figure 8—Insect-ride components. Small map strips of Ohio stretching south from Toledo to Columbus, 

showing more detail of relative weights of the area for basal area of ash, roads, campgrounds, population 

density, and wood products industries.
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is still in progress. We also have begun to incorporate an 

outlier seed generator, which depends partly on a random 

generator and partly on the weighting scheme of the insectride components.

Conclusions

The results on assessment of the ash resource, estimates 

of past spread of EAB, and preliminary efforts to create a 

model of spread leave us with the following conclusions:

• There is a great deal of ash resource in the 

Eastern United States, especially in the northern 

half of the region. For many States, ash makes 

up a sizeable portion of the total BA.

• As of spring 2006, the front border of the 

current EAB infestation is just now reaching the 

areas with the largest amount of available ash, 

e.g., in northeast Ohio, northwest Pennsylvania, 

and western New York.

• Although much of the current expanding range 

of EAB in northwest Ohio and northeast 

Indiana is dominated by agriculture, our highresolution analysis shows plenty of ash exists 

for EAB expansion in this zone in small woodlots, riparian woods, small wetlands, and miscellaneous parcels bordering the agricultural 

fields.

• The map of our estimate of the expansion of 

the front from 1998 to 2005 shows a fairly consistent pattern of roughly 20 km/yr. This rate 

Figure 9—Ohio campground scores with gravity model. Larger marks represent increasingly higher scores of relative potential 

for emerald ash borer (EAB) invasion owing to higher attractiveness or travel or both from the EAB-infested core area in 

southern Michigan.
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of expansion would necessarily have to include 

both the biological dispersal capacity of the 

insect and some short-distance movement 

assisted by humans (e.g., on or in vehicles, 

plant material, wood material, etc.).

• The components of the insect-ride model (roads, 

campgrounds, wood products industries, population density, and urban centers) have been 

acquired and processed to create a weighting 

scheme based on various factors, including 

buffer distances and number of people involved 

in the endeavor. When combined, every 270-m 

pixel in the study area has been scored for its 

likelihood of enhancing EAB spread.

• The gravity model yielded a relative scoring of 

potential EAB invasion among campgrounds 

based on traffic from the core EAB zone and 

attractiveness of the campgrounds.

• Preliminary test results of movement of the 

front from the EAB shift model shows the 

probability of colonization diminishes quickly 

away from the front, and that the insect-ride 

components modify those results through the 

multiplier effects.

Figure 10—Combined model outputs using single-factor weights. These maps show preliminary results on the Toledo (left side) to 

Columbus (right side) test strip of the emerald ash borer Shift base run, and then with single-factor multipliers for roads, campgrounds, 

population density, and wood products industries. Numbers correspond to relative colonization probabilities after 11 years of runs. It 

does not include random or directed outlier generation that may occur through the insect-ride or gravity modeling components.
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GENERAL TECHNICAL REPORT PNW-GTR-802

• We hope to use these data along with GIS and 

modeling tools to better understand the potential rate of spread, which could inform management decisions that will hopefully slow the 

spread of this destructive pest.

Acknowledgments

Thanks to Doug Bopp of the Cooperative Emerald Ash 

Borer Project for providing data on EAB infestation 

locations. We are grateful to Elizabeth LaPoint from the 

FIA GIS Support Center for overlaying the Ohio FIA plots 

with the Ohio GAP data to estimate ash BA. We thank the 

Ohio Center for Mapping, especially Lawrence Spencer, 

for creating and providing the Ohio GAP data. We thank 

Matthew Swanson and Matthew Peters for their help in 

various aspects of the study. We thank Michael Kilpatrick 

of Ohio State University for his support with the EAB Web 

server. Also, we thank the reviewers of this manuscript for 

the improvements they have made.

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

Jennifer Juzwik, Jane Cummings-Carlson, and Kyoko 

Scanlon

Jennifer Juzwik, research plant pathologist, USDA Forest 

Service, Northern Research Station, St. Paul, MN 55108; 

Jane Cummings-Carlson, forest health coordinator, and 

Kyoko Scanlon, forest pathologist, Wisconsin Department 

of Natural Resources, Fitchburg, WI 53711.

Abstract

Oaks (Quercus spp.) are an important species group in 

the forests of Wisconsin. The State’s timberland typed as 

oak-hickory forest was estimated at 2.9 million acres in 

1996. Growing stock volume for red oak was estimated at 

2.4 billion cubic feet, whereas select white oak volume was 

estimated to be 927 million cubic feet. Oak wilt, the oak 

disease of greatest concern in Wisconsin, is widespread 

in the lower two-thirds of the State. Harvest activities in 

oak stands may result in introduction of the disease agent, 

Ceratocystis fagacearum (Bretz), into the stand or promote 

intensification of the disease within the stands or both. A 

risk-rating system based on scientific- and experience-based 

knowledge was used to develop a statewide system for oak 

wilt risk analysis. Guidelines for timber harvest activities 

in oak stands were then developed based on results of the 

risk analysis. The analysis and recommendations have been 

published (http://www.dnr.wi.gov/forestry/fh/oakWilt/

guidelines.asp [Date accessed: July 8, 2010]) in three different formats. The formats include a pdf version of decisiontrees with accompanying tables, a simple spreadsheet 

application allowing the user to obtain specific guidelines 

based on his/her response to five questions about the stand 

and timing under consideration, and an interactive online 

format derived from the spreadsheet version. The query 

page of the interactive formats is linked to a concealed table 

containing the risk analysis and recommendation matrix. 

The tool provides consistent, statewide guidelines for harvest activities that will, when applied, minimize spread and 

reduce the biological and economic impacts of oak wilt to 

Wisconsin’s oak timberlands. The rule-based, expert-driven 

system approach used to develop these guidelines could be 

used to assess risk and develop large-scale management 

guidelines for other established forest pathogens.

Keywords: Ceratocystis fagacearum, oak wilt, Quercus

spp., risk analysis, timber harvest guidelines.

Introduction

Oak Forests of Wisconsin

Oaks (Quercus spp.) are a dominant component of the 

extensive oak-hickory forests of the Central U.S.A. (Leopold and others 1998). In Wisconsin, timberland typed as 

oak-hickory forest was estimated at 2.9 million acres in 

1996 (Schmidt 1997). Growing stock volume for red oak 

(section Lobatae) was estimated at 2.4 billion cubic feet, 

whereas white oak (section Quercus) volume was estimated 

to be 927 million cubic feet (Schmidt 1997).

Oak Wilt – Primary Disease of Concern

Oak wilt, the oak disease of greatest concern in Wisconsin, 

occurs in 51 of the State’s 70 counties (http://www.na.fs.fed.

us/fhp/ow/maps/ow_dist_fs.shtm [Date accessed: July 8, 

2010]). Thousands of oaks in woodland and urban settings 

succumb to the disease every year. The causal fungus, 

Ceratocystis fagacearum (Bretz), is spread from diseased 

to healthy oaks belowground through functional root grafts 

or aboveground by insect vectors (Tainter and Baker 1996). 

Species of the sap beetle family (Coleoptera: Nitidulidae) 

are considered the primary vectors in Wisconsin. New 

disease centers are established when C. fagacearumcontaminated beetles visit fresh xylem-penetrating wounds 

(e.g., axe blazes, logging wounds, branch-pruning wounds) 

on healthy oaks and successfully inoculate them with propagules of the fungus (Gibbs et al. 1980, Juzwik and others 

2004). Stump surfaces created by tree felling and wounds to 

branches, stems, and roots by heavy equipment or adjacent 

falling trees are avenues for infection during timber stand 

improvement or harvesting activities. In a timber sale unit 

near Waube Lake, Wisconsin, many new infection centers 

occurred over a large area following a May 2001 timber 

harvest (M. Mielke 2006. Plant pathologist, Northeastern 

Area State and Private Forestry, USDA Forest Service). 

Risk Analysis and Guidelines for Harvest Activities in Wisconsin 

Oak Timberlands to Minimize Oak Wilt Threat
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GENERAL TECHNICAL REPORT PNW-GTR-802

Felling of diseased oaks adjacent to healthy oaks can lead to 

intensification of the disease within stands if root connections exist. Slow movement of the pathogen through grafted 

roots of healthy trees felled within 50 feet of a diseased tree 

explained the sporadic appearance of oak wilt in subsequent 

years at the edge of clear-felled areas (Yount 1955).

Need for Statewide Guidelines

The Wisconsin Department of Natural Resources (DNR) 

identified the need to develop consistent, statewide guidelines for timing harvest activities in oak timberland in order 

to minimize potential for oak wilt introduction or spread or 

both in existing and future stands where oak regeneration 

is the management objective. A committee of government, 

industrial, and consulting foresters was formed to develop 

such guidelines. Both scientific and experience-based 

knowledge of the oak wilt host – pathogen system were the 

basis of the guidelines. The approach used to (1) analyze the 

risk and the potential for introduction and spread of oak wilt 

in stands targeted for harvest, and (2) develop guidelines for 

timing harvest are described in this paper.

Approach

Risk Assessment

Risk refers to the chance of injury or loss defined as a 

measure of the probability and severity of an adverse effect 

to health, property, the environment, or other things of 

value (North American Forest Commission 2004). Our risk 

analysis includes (1) the assessment of risk posed by the oak 

wilt pathogen to oak timberland scheduled for harvest and 

regeneration to oak, and (2) recommendations for minimizing frequency of pathogen introduction to and spread within 

such stands. A rule-based, expert-driven model, such as 

that used for pest risk assessment in the Exotic Forest Pests 

(ExFor) system (North American Forest Commission 2004), 

was adapted for this analysis. This approach falls under 

the umbrella term of multicriteria decision analysis, which 

seeks to take multiple criteria into account when groups 

explore decisions that matter, e.g., natural resource management decisions (Mendoza and Martin 2006). Two criteria 

were evaluated within the risk assessment process.

Criterion 1: Risk of Ceratocystis fagacearum introduction to the stand [between-stand spread] or for initiation 

of new centers within the stand [within-stand spread] by 

insect vectors—

Statements were developed for this criterion that considered 

two factors: (1) time of year during which harvest activities 

would occur (resulting in fresh wounds suitable for infection), and (2) proximity of existing oak wilt centers in other 

locations to the stand in which harvest activities would 

occur. A risk rating, ranging from very low to very high, 

was then assigned to each of the possible combinations 

of time and proximity. The risk values were determined 

through a group consensus process after review of pertinent 

scientific literature and of each individual’s experience 

working with the disease.

Criterion 2: Risk of C. fagacearum belowground spread 

within an oak stand following pathogen establishment—

Statements for this criterion included three factors (i.e., 

stand conditions): (1) density of oaks, (2) general topographic relief, and (3) general soil type in the stand to be 

harvested. Each of these factors is known, either through 

scientific studies or experiential knowledge or both, to influence the frequency and the distance over which intraspecific 

root grafting occurs. Two or more levels were selected 

for each factor. Basal area (square feet per acre) levels for 

describing red oak species composition and density were 

less than 15, between 15 and 35, and greater than 35. The 

general levels for topographic relief were (a) flat to rolling 

terrain, and (b) steep hills with deep valleys terrain. Soil 

type was divided into light textured (sandy, loamy sand, and 

sandy loam) and heavier textured (all other types depicted 

in classic soil texture triangle). A risk rating, ranging from 

very low to very high was then assigned to each of all possible combinations of statements by factor. The risk values 

were determined by a group consensus process.

Overall risk: combined risk rating for the two criteria—

The ratings for each criterion were then used to generate 

the overall risk of oak wilt’s threat to the stand of interest 

following a timber harvesting event. The overall rating, 

ranging from very low to very high, was assigned to each 

stand scenario based on the combination of introduction and 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Within county?b Within stand? Proposed timing of harvest activities Risk ratinga

No No Spring to early summer M

No No Summer to early fall L

No No Mid-fall through winter VL

Yes No Spring to early summer VH

Yes No Summer to early fall M

Yes No Mid-fall through winter VL

Yes Yes Spring to early summer AP

Yes Yes Summer to early fall AP

Yes Yes Mid-fall through winter AP

a

 Explanation of ratings: VH (very high), M (moderate), L (low), VL (very low), AP (oak wilt already present in the stand).

b

 Includes stands occurring in oak-wilt-free counties, but within 6 miles of oak-wilt-affected counties.

root graft spread factors. As before, the risk values were 

determined through a group consensus process.

Timber Harvesting Guidelines

Timber harvest guidelines for minimizing the initiation 

of new infection centers and subsequent tree loss from 

spread within stands were developed based on results of the 

risk assessment. The risk rating for each stand condition 

scenario was considered and harvest recommendations 

determined through a group consensus process.

Display of Risk Analysis Results and Guidelines

Three methods were used to display results of the risk 

analysis. For the first method, graphical decision-trees 

were constructed, and associated tables were developed for 

harvest guidelines for three proximity levels (i.e., no oak 

wilt in county, oak wilt in county but not in stand, and oak 

wilt in the stand [not shown]). This output was used in the 

development of the electronic displays. Initially, the risk 

analysis and associated harvest guidelines were combined 

in a simple electronic spreadsheet. The spreadsheet features 

a front query page that allows the user to obtain risk ratings 

and recommendations for specific stand scenarios. The 

query page is linked to a concealed table containing the risk 

analysis and recommendation matrix. Later, an interactive, 

Web version of the spreadsheet product was developed for 

online use.

Results

Risk Analysis Results with Scientific Knowledge 

Basis

The combined risk ratings for Criterion 1 (“Criterion 1: 

Risk of Ceratocystis fagacearum Introduction to the Stand 

[Between-Stand Spread] or for Initiation of New Centers 

within the Stand [Within-Stand Spread] by Insect Vectors”) are shown in Table 1. The risk of overland pathogen 

transmission by sap beetles was considered to increase as 

proximity to an existing oak wilt center decreased. The 

existing centers would be the source from which inoculumladen beetles would originate, assuming oak wilt mats 

were formed on recently wilted red oaks in that originating 

center. Menges and Loucks (1984) and Shelstad and others 

(1991) found higher efficiencies of vector spread over short 

distances (e.g., ≤ 300 m); longer distance spread occurs very 

infrequently and on a random basis. Although the number 

of new centers occurring at greater distances is small, over 

time they can have a significant influence on distribution 

of oak wilt within the total forest area (Shelstad and others 

1991). Timber harvest activities would result in wounding of 

residual oaks in shelter wood cut situations or create stump 

surfaces of removed healthy oaks or both. Such xylemexposing cuts are attractive to dispersing sap beetles. The 

risk of pathogen transmission to such wounds by certain sap 

beetle species is high during the spring months, low from 

Table 1—Combined risk ratings for Criterion 1 factors - proximity of oak wilt centers 

to stand under consideration and proposed timing of harvest activities 

Proximity of oak wilt centers to 

stand of interest
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GENERAL TECHNICAL REPORT PNW-GTR-802

midsummer to early fall, and none during the late fall and 

winter (Ambourn and others 2005, French and Juzwik 1999, 

Juzwik and others 2006).

The combined risk ratings for Criterion 2 (“Criterion 

2: Risk of C. fagacearum Belowground Spread within an 

Oak Stand Following Pathogen Establishment”) are shown 

in Table 2. Frequencies of root graft spread increase with 

increasingly lighter textured soils, e.g., from silt loam to 

sands (Menges 1978). Furthermore, frequency of root graft 

transmission is highest for stands with > 60 percent red oak 

density (Menges and Loucks 1984). Lastly, oak wilt is very 

common in areas of low topographic relief in portions of 

Iowa, Michigan, Minnesota, and Wisconsin (e.g., Albers 

2001, Menges and Loucks 1984). In areas with obvious 

Table 3—Stand condition scenarios for which overall risk ratings were high (H) to very high 

(VH), where oak wilt is not yet present in the stand of interest but occurs elsewhere in the 

same county or in a second county that is less than 6 miles from the first

Timing Oak densitya Topographic Soil Overall

for harvest (ft2/acre) relief categoryb (texture) risk rating

Spring to early summer >35 Flat - rolling Light VH

Spring to early summer > 35 Hills & valleys Light H

Spring to early summer > 35 Flat - rolling Heavy H

Spring to early summer > 35 Hills & valleys Heavy H

Spring to early summer 15 – 35 Flat - rolling Light H

Spring to early summer 15 – 35 Hills & valleys Light H

Spring to early summer 15 – 35 Flat - rolling Heavy H

Spring to early summer 15 – 35 Hills & valleys Heavy H

a

 Density of oaks measured as basal area.

b

 Light texture includes sandy, loamy sand, sandy loam, sandy clay loam and loam; heavy texture includes sandy clay, clay, 

clay loam, silt, silt loam, silty clay loam, and clay loam. Based on classic soil texture triangle.

Table 2—Combined risk ratings for Criterion 2 factors – density of 

oaks, topographic relief, and general soil type 

Density of oaksa Soil categoryb Risk

(ft2/acre) Topographic relief (texture) ratingc

< 15 Flat – rolling Light L

15 – 35 Flat – rolling Light H

> 35 Flat – rolling Light VH

< 15 Flat – rolling Heavy L

15 – 35 Flat – rolling Heavy M

> 35 Flat – rolling Heavy H

< 15 Hills & valleys Light L

15 – 35 Hills & valleys Light H

> 35 Hills & valleys Light H

< 15 Hills & valleys Heavy VL

15 – 35 Hills & valleys Heavy M

> 35 Hills & valleys Heavy M a

 Density of oaks measured as basal area.

b

 Light texture includes sandy, loamy sand, sandy loam, sandy clay loam, and loam; Heavy 

texture includes sandy clay, clay, clay loam, silt, silt loam, silty clay loam, and clay loam. 

Based on classic soil texture triangle.

c

 Explanation of ratings: VH (very high), H (high), M (moderate), L (low), and VL (very low).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

topographic relief, oak wilt is most common on upper slopes 

and ridge tops (Anderson and Anderson 1963, Bowen and 

Merrill 1982, Cones and True 1967).

Each of the 108 stand condition scenarios described 

by combinations of the five factors was assessed for overall 

risk of oak wilt occurrence based on the individual criterion 

ratings. Overall risk ratings were high to very high for eight 

stand-condition scenarios where oak wilt was not known to 

be present in the stand (Table 3). Overall risk rating of very 

low, however, was often determined by a late fall–winter 

timing for the harvest.

Table 4—Summary of management guidelines for timing of timber harvest activities based on oak wilt risk 

analysis results

Guidelines by timing of timber harvest activities 

Stand proximity to

oak wilt centers Spring – early summer Summer – early fall Fall – winter

Not in county or No restrictions No restrictions No restrictions

within 6 miles of April 1 - July 15 (south)a July 16 - September 30. October 1 - March 31

county with oak wilt and April 15 - July 15 (12) (south) and April 14

and not in stand (north). (12)b (north). (12)

In county or within 6 May cut between April No restrictions No restrictions

miles of a county 1 - July 15 (south) and July 16 - September October 1 - March 31

with oak wilt, but not April 15 - July 15 (north) 30. (12) 15 (south) and April 14

in stand IF new stumps are (north). (12)

treated. (4)

Do not harvest or 

conduct activities that 

may wound oaks April 1 

- July 15 (south) and 

April 15 - July 15 (north). 

( 8 )

In county & in stand First consider owner First consider owner First consider owner

interest in oak wilt interest in oak wilt interest in oak wilt control;

control; otherwise, no control; otherwise, otherwise, no restrictions

restrictions April 1 no restrictions October 1 - March 31

- July 15 (south) and July 16 - September 30. (south) and April 14 (north).

April 15 - July 15 (north) (12) (12)

if new stumps are 

treated. (4)

First consider owner 

interest in oak wilt 

control; otherwise, do 

not harvest or 

conduct activities that 

may wound oaks April 1 

- July 15 (south) and 

April 15 - July 15 (north). 

(8)

a

 South denotes stands located south of the tension zone in Wisconsin; north denotes stands located north of the tension zone. Wisconsin’s tension zone

is a border between northern and southern floristic provinces (Curtis 1959). Data on average monthly temperatures and flight of oak wilt insect vectors 

support use of different risk dates for these portions of the State.

b

 Twelve scenarios are possible for each timing-proximity combination. Number of scenarios to which the particular guideline applies is stated in 

parentheses.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Timber Harvest Guidelines

Preventive measures developed for minimizing initiation of 

new oak wilt infection centers and the potential for future 

tree losses owing to oak wilt in regenerated stands were 

described in three brief statements: (1) Do not harvest or 

conduct activities that may wound oaks, (2) Harvesting 

may be conducted if stumps are treated, and (3) No restrictions. The first two measures largely apply to stand harvest 

activities being considered for spring and early summer. A 

summary of the stand/harvesting scenarios associated with 

each of the preventive recommendations when categorized 

by timing and proximity factors is presented in Table 4. 

For timber stands where oak wilt centers already exist 

and harvest of and regeneration to oak are planned, the 

guidelines include some further considerations. Specifically, foresters are advised to first consider the landowner’s 

tolerance for future tree losses to oak wilt in the regenerated 

stands. Disease control actions, such as stump extraction 

or soil trenching, could be valuable for greatly reducing the 

carryover of oak wilt into the future stand.

Risk Analysis and Guidelines Tool Formats

Three different formats of the risk analysis results and the 

harvest guidelines were developed for end users. A hardcopy, decision-tree format (filename: oakwiltguide031507.

Figure 1—Query section of user page of spreadsheet application for oak wilt risk rating and associated management guidelines. Shaded 

boxes are where user enters information.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

pdf) with accompanying tables is available from the 

Wisconsin DNR Web site (http://dnr.wi.gov/forestry/fh/

oakWilt/guidelines.asp). The electronic spreadsheet version 

of the results (filename: oakwiltguide031507.xls) is also 

available from the same site. The interactive, online format 

was adapted from the spreadsheet version. The front user 

page of the spreadsheet-based tool (Figure 1) and of the 

online tool requires the user to input conditions of the stand 

being considered for harvest. The questions asked of the 

user include (1) Is oak wilt present? (2) What time of the 

year do you propose cutting? (3) What is the basal area of 

red oak in the stand? (4) What is the general topography 

of the stand? and (5) What is the general soil texture of the 

stand? The user selects a response from the multiple-choice 

answers offered for each question. The spreadsheet application then selects and displays the appropriate ratings and 

recommendations for the conditions described by the user 

(Figure 2), as does the online version.

Discussion

The rule-based, expert-driven model used in an exotic pest 

risk analysis context (North American Forestry Commission 2004) was adapted for use in assessing risk of oak 

wilt introduction to and potential for subsequent spread 

within oak timberland based on spatial, temporal, and site 

factors. Such a system may be useful for analyzing spread 

and impact risks in the management of other significant 

forest diseases. The Wisconsin DNR plans to use the same 

approach to analyze risk and develop guidelines for reducing spread of Heterobasidion annosum in pine forests of the 

State. The model was also considered for use in modifying 

existing guidelines for managing oak wilt in urban and 

Figure 2—Response section of user page of spreadsheet application for oak wilt risk rating and associated management guidelines. 

Shaded boxes are where varying responses are displayed.
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GENERAL TECHNICAL REPORT PNW-GTR-802

periurban forests of Wisconsin. Existing guidelines were, 

however, considered sufficiently robust and did not warrant 

such an effort.

The success of our approach relied on a collaborative 

planning and decisionmaking environment. The participatory method sought and obtained the involvement of multiple experts, stakeholders, and end users. The committee 

responsible for developing the criteria, conducting the risk 

analysis, formulating guidelines appropriate to risk ratings, 

and reviewing the prerelease product met for 4 hours on 

each of 4 days. Solicitation of stakeholder and user response 

and suggestions to the proposed system occurred over a 

7-month time period through presentations and subsequent 

comment sessions held at numerous meetings, e.g., the 

Wisconsin Chapter of the Society of American Foresters’ 

annual meeting and the Wisconsin Woodland Owners’ 

Association annual meeting.

Several research questions were raised during the exercise of developing criteria, conducting the risk analyses, and 

developing guidelines appropriate for the assigned risks. 

The need for observed frequency or estimated probability 

for successful overland transmission of the oak wilt fungus 

between mid-July and early October is being addressed in a 

3-year study initiated in summer 2006. Questions were also 

raised about the ultimate quantitative impact of oak wilt 

introduced during shelter wood preparatory cuts or clearcutting on future oak stocking in stands regenerated on dry and 

dry-mesic sites. On the basis of results of a West Virginia 

study (Tyron and others 1983), we hypothesize that the 

impact would be low in areas where regeneration is mostly 

of seedling origin. However, where coppice or stump-sprout 

regeneration predominates, the ultimate impact of oak wilt 

on stand stocking would likely be higher. A long-term study 

is needed to address these questions. New knowledge or 

previously overlooked scientific knowledge pertinent to our 

risk assessment system will be considered in future revisions of the product.

Acknowledgments

Additional members of the Oak Wilt Risk Rating Committee are Rick Dailey, Clark County Forester; George Howlett, 

Consulting forest ecologist; Ron Jones, WI DNR Forester; 

John Morgan, Consulting forester; Juris Repsa, DomTar, 

Tim Tollefson, Stora Enso, and Scott Wessel, Grezenski 

Forest Products. The authors thank Paul Castillo and Megan 

Bowdish, USDA Forest Service, for technical assistance, 

and Joe O’Brien for guidance in criterion and statement 

development. The constructive comments and criticisms 

of two anonymous reviewers are also gratefully acknowledged.

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Portland, OR: Timber Press, Inc. 469 p.

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exotic forest pest information system for North America, 

participant’s guidelines. http://spfnic.fs.fed.us/exfor/docs/

guidelines.pdf. (December 18, 2006).

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of Agriculture, Forest Service, North Central Research 

Station. 150 p.

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Describing the spread of oak wilt using a geographic 

information system. Journal of Arboriculture. 

17: 192–199.

Tainter, F.H.; Baker, F.A. 1996. Oak wilt. In: Principles of 

forest pathology. New York: John Wiley: 671–682.

Tyron, E.H.; Martin, J.P.; MacDonald, W.L. 1983.

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and Management. 7: 149–155.

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oak roots as related to spread through root grafts. Plant 

Disease Reporter. 39: 256–275.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Frank H. Koch and John W. Coulston

Frank H. Koch, research assistant associate, Department 

of Forestry and Environmental Resources, North Carolina 

State University, Research Triangle Park, NC 27709; and 

John W. Coulston, supervisory research forester, USDA, 

Forest Service, Southern Research Station, Research 

Triangle Park, NC 37919.

Abstract

Current information on broad-scale climatic conditions is 

essential for assessing potential distribution of forest pests. 

At present, sophisticated spatial interpolation approaches 

such as the Parameter-elevation Regressions on Independent 

Slopes Model (PRISM) are used to create high-resolution 

climatic data sets. Unfortunately, these data sets are based 

on 30-year normals and rarely incorporate up-to-date data. 

Furthermore, because they are constructed on a monthly 

rather than a daily time step, they do not directly measure 

simultaneous occurrence of multiple climatic conditions 

(e.g., days in the past year with appropriate temperature and 

adequate precipitation). Yet, the actual number of days—

especially consecutive days—where multiple conditions 

are met could be significant for pest dispersal or establishment. For the sudden oak death pathogen (Phytophthora 

ramorum), we used National Oceanic and Atmospheric 

Administration daily weather station data to create current, 

national-scale grids depicting co-occurrence of multiple 

climatic conditions.

For each station, we constructed two count-based 

variables: the total number of days and the greatest number 

of consecutive days in a year where the station met several 

conditions (temperature, rain/fog, relative humidity). We 

then employed gradient plus inverse distance squared 

(GIDS) interpolation to generate grids (4-km2

 resolution) of 

these variables for 5 years (2000-2004). The GIDS technique weights standard inverse distance squared interpolation using coefficients based on geographic location (x, 

y) and a spatial covariate such as elevation. Using these 

variables, we determined the GIDS coefficients for each 

output grid cell via Poisson regression on the 30 closest 

stations. We also performed model selection to ensure only 

significant variables contributed to the GIDS coefficients.

We compared the GIDS approach to cokriging and 

detrended kriging using cross-validation and found similar 

accuracies among all three interpolation methods. We also 

compared the output grids to maps assembled from the 

PRISM data depicting the probability all conditions were 

met in a given year. As expected, we found differences in 

areas highlighted as suitable for P. ramorum establishment 

by the two methods. We suggest that using current weather 

data and calculating the variable of interest directly will 

provide more practical information for mapping forest pest 

risk.

Keywords: Climate, forest pests, GIDS, Phytophthora 

ramorum, risk, spatial interpolation.

Introduction

Forest pest risk assessments detail the nature and severity of 

threats posed to particular forest species and ecosystems by 

insects, pathogens, or other organisms (Andersen and others 

2004a). With respect to nonindigenous forest pests, risk 

can be categorized or quantified based on a combination 

of factors: the potential for the pest to become established, 

the potential for it to spread following introduction, the 

potential to cause economic damage, or the potential to 

cause environmental harm (NAFC 2004). A commonly 

desired product of such assessments is a map depicting the 

threat posed by introduction or establishment of a forest 

pest throughout a geographic area of interest (Andersen and 

others 2004a). These maps can facilitate early detection and 

response procedures, providing a template for the design 

of regulatory programs and detection surveys. If a pest 

has already been established in one part of the geographic 

area of interest, threat assessment maps are used to help set 

control priorities for other geographic areas that are at high 

risk of invasion (Andersen and others 2004b).

Importance and Availability of Climate Information

Forest pest risk maps are typically assembled by combining 

spatial data from three principal subject areas: host species 

Modeling Current Climate Conditions for Forest Pest Risk 

Assessment
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GENERAL TECHNICAL REPORT PNW-GTR-802

distribution, pathways of pest movement, and key environmental factors (Bartell and Nair 2004). Climatic attributes 

such as temperature and moisture strongly shape pest 

behavior, affecting survival, reproductive rate, and in many 

cases, the ability to spread at a continental scale. Thus, 

climatic data provide an important coarse filter for forest 

pest risk analyses. Regularly gridded climate maps covering 

the entire geographic area of interest are typically required 

for analytical purposes. Such maps may be constructed by 

spatial interpolation of weather station data. These data are 

readily available for much of the United States, dating back 

several decades, from the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data 

Center (NCDC).

Spatial Interpolation of Climatic Variables—

A wide array of spatial interpolation algorithms (e.g., geostatistical, regression, spline, inverse distance weighting) 

have been used to construct broad spatial-scale climatic 

data sets from weather station data (Daly 2006, Mardikis 

and others 2005, Nalder and Wein 1998, Price and others 

2000, Xia and others 2000). Most currently accepted 

methods acknowledge that terrain is a significant factor 

governing climate at all but the broadest scales, and they 

use elevation measurements to represent terrain and adjust 

climatic variable values accordingly (Daly 2006). One 

well- received interpolation approach is the Parameterelevation Regressions on Independent Slopes Model 

(PRISM). Initially developed to generate precipitation 

maps for the Pacific Northwest (Daly and others 1994), 

the approach has since been applied to create maps of 

temperature, relative humidity, snowfall, growing-degree 

days, and many other variables (Daly and others 2000). In 

particular, the PRISM approach was applied to generate 

most of the maps in the recent version of the Climate Atlas 

of the United States (Plantico and others 2002), as well as 

similar products for Canada and China (Daly and others 

2000). The PRISM approach is a knowledge-based system 

integrating a local climate-elevation regression with other 

algorithmic com-ponents: station weighting, topographic 

facets, coastal proximity, and a two-layer atmosphere (Daly 

and others 2002). When initially tested on precipitation in 

the Pacific Northwest, the PRISM approach outperformed 

other interpolation methods in comparative analyses (Daly 

and others 1994).

Limitations of Existing Interpolated Climatic Data 

Sets—

There are several limitations of PRISM-de rived or similar 

data sets with respect to their use for forest pest risk maps. 

First, most national-scale climatic data sets are calculated 

as normals, meaning an average of the variable of interest 

across a window of time, typically a 30-year period. For 

example, most data sets in the recent version of the Climate 

Atlas of the United States are based on inputs from 1961 

through1990 (Plantico and others 2002). Current weather 

data are not incorporated into the maps, so any pest risk 

map constructed from them will not include current 

events—and the accompanying variability—that may be 

relevant to an assessment of immediate risk.

Second, there are related issues of cost and data format. 

The Climate Atlas contains polygonal maps for a large 

number of potentially relevant climatic normals but does not 

include the regularly gridded data from which the maps are 

derived. These polygonal maps have limited attribute resolution, with the range of the original gridded data typically 

compressed into nine or fewer classes. Monthly gridded 

maps of a few variables—precipitation amount, mean 

minimum temperature, mean maximum temperature, and 

mean dewpoint—are available for public download from 

the PRISM group at Oregon State University (http://www.

ocs.orst.edu/prism/. [Date accessed unknown]). Notably, 

these maps are fairly current (finalized maps are available 

from 1997 through mid-2006), and the database is regularly 

updated, but it does not include many climatic variables that 

might be of interest for forest pest risk assessment (e.g., relative humidity, number of days above freezing, or number of 

days with measurable precipitation). Regularly gridded data 

of these and other (30-year normal) variables, derived using 

the PRISM method, are available, but at substantial cost 

(from the Climate Source: http://www.climatesource.com/. 

[Date accessed unknown]).

Third, most available climatic spatial data sets, whether 

derived using PRISM or other methods, are monthly or 

annual summaries depicting mean or extreme values over 

the time period. For some forest pests, the short-term, 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

even daily status of multiple weather conditions may be 

relevant to the pest’s growth, persistence, or invasiveness. Fungal pathogens are particularly affected by the 

interaction of temperature and moisture availability. For 

example, the pathogen that causes late blight of potato 

(Phytophthora infestans) develops best at cool temperatures 

during extended periods of wet weather, as do many other 

Phytophthora species (Davidson and others 2002, Harvell 

and others 2002, Marshall-Farrar and others 1998). The 

interaction of climatic variables can also be important for 

some insect pests (Harrington and others 2001, Peacock and 

others 2006). Nevertheless, although there has been some 

effort to create maps of daily precipitation and temperature 

at a broad scale (Hunter and Meentemeyer 2005), there has 

been little attention paid to the co-occurrence of multiple 

weather conditions favorable to pest persistence and spread. 

Daily weather data allow the counting of how often, and for 

how long, variables meet certain threshold values. Creation 

of broad-scale maps from data derived in this manner 

may require a different spatial interpolation approach than 

that used for continuously distributed variables (van de 

Kassteele and others 2005).

Objectives

Given the limitations of existing climatic data sets, we 

explored the use of NCDC daily weather station data for 

the United States as an alternate source for maps relevant to 

forest pest risk assessments. We had three basic objectives: 

(1) spatially interpolate annual counts of the number of days 

with co-occurrence of multiple climatic variables relevant to 

the growth and spread of a specific forest pest—the pathogen that causes sudden oak death (P. ramorum); (2) identify 

a spatial interpolation method appropriate for count-based 

data and compare it to some common geostatistical 

approaches; and (3) assess the utility of the derived maps for 

depicting risk.

Case Study Species: Phytophthora 

ramorum

Phytophthora ramorum was first recognized in the United 

States in 1994 and was likely introduced via international 

trade of commercial plants (Ivors and others 2006). Since its 

introduction, the pathogen has infected western live and red 

oaks in coastal forests of California and Oregon, sometimes 

causing mortality greater than 40 percent (Garbelotto and 

others 2001, 2003). In addition, P. ramorum infects dozens 

of commercial shrub host species that can yield large numbers of aerially dispersed spores (Davidson and Shaw 2003, 

Davidson and others 2002, Tooley and others 2004). Many 

of these shrubs (e.g., rhododendrons, azaleas, camellias) are 

sold as nursery stock (Garbelotto and others 2001, Tooley 

and others 2004). In the past few years, wholesale nurseries 

on the west coast have unknowingly shipped infected plants 

to retail and wholesale outlets in roughly 40 States (Stokstad 

2004), although surveys have not detected the pathogen in 

natural forests outside California and Oregon. 

A large portion of the Eastern United States is considered at high risk for establishment of P. ramorum if 

it is introduced into forested areas. Much of the concern 

has to do with climatic conditions believed to be favorable for the pathogen. Growth, sporulation, and infection 

are all affected by moisture and temperature. Optimal 

temperatures for P. ramorum growth, based on laboratory 

analysis, appear to be between 64.4 °F and 71.6 °F (Werres 

and others 2001), but some growth occurs across a wider 

temperature range (up to at least 80 °F). Peak sporangia formation appears to occur at 59 to 68 °F (Davidson and others 

2005). Persistent moisture on foliage is considered critical 

to spread. Laboratory inoculation trials on California bay 

laurel (Umbellularia californica (Hook. & Arn.) Nutt.), a 

major source of P. ramorum spores in California, suggest 9 

to 12 hours of free moisture on leaf surfaces under appropriate temperatures are necessary for significant leaf infection 

(Garbelotto and others 2003). Further studies suggest that at 

least 24 to 48 hours of generally wet conditions are necessary for sporulation, with infection requiring additional 

time (Davidson and Shaw 2003, Davidson and others 2002, 

Rizzo and Garbelotto 2003). Fog and high relative humidity 

may be important for spread of aerial Phytophthora species 

within forest stands (Werres 2003), as high air moisture 

can keep leaf surfaces wet and enable spore production. 

Nevertheless, despite regular summer fog in California, P. 

ramorum sporulation and infection seem to be restricted 

to the winter-spring rainy season (Rizzo and others 2005). 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Isolated rains during otherwise dry summer months do not 

appear to facilitate spore production or dispersal (Davidson and others 2002). Ultimately, it is unknown how the 

pathogen’s behavior on the west coast will translate to the 

Eastern United States, where warm season and cool season 

precipitation are similar (Akin 1991).

Methods

We downloaded 5 years (2000 to 2004) of daily surface 

data from the NCDC online climate data clearinghouse 

(http://cdo.ncdc.noaa.gov/CDO/dataproduct. [Date accessed 

unknown]). The downloaded data included dozens of 

climate variables recorded for more than 19,000 stations 

nationwide. We processed the data to extract four variables: 

total precipitation, minimum and maximum temperature, 

and relative humidity. For each station, we tallied (1) the 

total number of days and (2) the longest number of consecutive days in a given year that met the following conditions: 

maximum temperature greater than 60 °F, minimum 

temperature less than 80 °F, and at least a trace amount of 

precipitation or relative humidity of greater than 85 percent. 

These threshold values were selected to reflect current 

knowledge about the climatic conditions favorable for P. 

ramorum survival and spread.

We recorded the latitude, longitude, and elevation 

values for each weather station from an associated data set. 

We dropped any stations that fell outside the conterminous 

United States and any stations with more than 30 days of 

missing data for any variable in a given year. This filtering 

process reduced the number of usable stations (Table 1), 

but still yielded consistent national coverage. For stations 

missing 1 to 30 days of data, we normalized the total-day 

and consecutive-day count values by dividing them by the 

proportion of days in the year for which data were available 

and then rounding to the closest integer.

Gradient Plus Inverse Distance Squared 

Interpolation

We interpolated gridded maps of the conterminous United 

States for both the total-day and consecutive-day variables 

using a gradient plus inverse distance squared (GIDS) 

approach. This statistical method was first proposed as a 

way to interpolate climatic data on a broad spatial scale as 

input for plant growth models (Nalder and Wein 1998). The 

GIDS technique combines multiple linear regression with 

inverse distance weighting interpolation, and like other 

recently developed interpolation techniques, incorporates 

elevation as a covariate. For a given unmeasured location k 

and climatic variable Z, an ordinary least squares regression 

is performed using the N closest neighboring locations to 

calculate coefficients (Cx, Cy

, and Ce) representing x, y, and 

elevation gradients: Z = a + Cx X + Cy Y + Ce E + ε, where a

is the intercept and ε is error. Then, the basic GIDS formula 

is

 N Zi + Cx(Xk – Xi + Cy (Yk – Yi ) + Ce (Ek – Ei )

 S

 i=1 di 

 N 1

 Sd i=1 i 

where Zk = the predicted value at an unmeasured location k, 

Zi = the measured value at location i, X = the x-coordinate 

for the specified location, Y = the y-coordinate, Ei

 = the 

elevation value, and di = the distance from measured location i to Z (Nalder and Wein 1998). 

Nalder and Wein (1998) compared GIDS with several 

other methods for interpolating monthly normals of precipitation and temperature in the Canadian boreal forest region. 

The tested methods included inverse distance squared 

weighting, nearest neighbor interpolation, ordinary kriging, universal kriging, co-kriging, and detrended kriging. 

Based on cross-validation using a held-out subset of the 

data, the GIDS method resulted in the lowest mean absolute 

errors (MAE), which averaged 0.5 °C for temperature and 

3.6 mm, or 11 percent, for monthly precipitation. Price and 

Table 1—Number of NCDC weather 

stations used in interpolations

Year Number of stations

2000 4,310

2001 4,258

2002 4,302

2003 4,144

2004 3,926

NCDC = National Climatic Data Center.

2

2

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

others (2000) compared the GIDS method with thin-plate 

moving splines and noted that GIDS, as an inverse distance 

approach, may have greater occurrence of extreme errors. 

However, they also noted its transparency and ease of use.

Modification of GIDS for a Count-Based Variable—

The ordinary least squares regression implemented in the 

GIDS approach is intended for continuous, normally distributed variables. Because each of our variables of interest was 

a count, with large values being rare, we instead performed 

Poisson regression (Neter and others 1996). For each 

location of interest, we fitted a Poisson regression model, 

based on the 30 closest neighboring weather stations, using 

a maximum likelihood approach. We acknowledged that 

all three gradient variables (x, y, and elevation) could prove 

insignificant for a given prediction location and its closest 

measured neighbors. As a result, we evaluated a sequence 

of the full and all possible reduced models for statistical 

significance:

log(Z) = a + Cx X + Cy Y + Ce E + ε,

log(Z) = a + Cx X + Ce E + ε,

log(Z) = a + Cy Y + Ce E + ε,

log(Z) = a + Cx X + Cy Y + ε,

log(Z) = a + Cx X + ε,

log(Z) = a + Cy Y + ε,

log(Z) = a + Ce E + ε.

For each prediction location, we tested all seven 

regression models using the 30 closest stations and identified those models in which all variables were significant. In 

cases where more than one of the models had all significant 

variables, we identified the one that yielded the smallest 

value for Akaike’s Information Criterion (AIC). If the 

best-performing model was not the full Poisson regression model, then the coefficient(s) for any insignificant 

variable(s) were set to zero in the GIDS equation. If none of 

the tested models proved to have significant variables, then 

the GIDS interpolation reverted to inverse distance squared 

weighting (i.e., all variable coefficients were set to zero).

Interpolation Using GIDS—

We implemented the Poisson-based GIDS formulation in a 

script written for R statistical software (R Core Development Team 2006), which we then used to interpolate values 

for cells covering the conterminous United States. We 

created a regular grid (with x, y, and elevation values) for 

the country by resampling an 8100-m2 resolution digital 

elevation model (DEM) generated from U.S. Geological 

Survey data to 4-km2 cells using a nearest neighbor method. 

Notably, this is the same spatial resolution used in most 

of the data sets that are publicly downloadable from the 

PRISM Group as well as the data sets available for purchase 

from the Climate Source (see “Limitations of Existing 

Interpolated Climatic Data Sets”). For each 4-km2 cell, we 

determined the 30 closest NCDC weather stations using 

three-dimensional Euclidean distance measured from the 

cell’s centroid. We rounded the GIDS-predicted value for 

each grid cell to the nearest integer.

Evaluation

For comparison to the GIDS-derived total-day and 

consecutive-day count maps, we created gridded maps for 

2000 to 2004 using two spatial interpolation methods available through the ArcGIS Geostatistical Analyst extension 

(Johnston and others 2003). First, we performed cokriging 

on the count data using elevation as a covariate. Second, we 

performed detrended kriging, where we removed a secondorder trend from the data and then performed ordinary 

kriging on the residuals. For both methods, we fit a spherical semivariogram model to the input data, calculating the 

model parameters (nugget, range, and sill) using a weighted 

least squares approach (Cressie 1993). As with the GIDS 

maps, we generated a predicted value for each 4-km2 cell 

based on the 30 closest NCDC stations, and rounded the 

predicted value to the nearest integer.

We compared the accuracy of the three methods via 

station-by-station cross-validation. Using each interpolation 

method, we derived a predicted total-day and consecutiveday value for each station based on its 30 closest neighbors. 

We calculated errors by subtracting the actual observed 

counts for each station from the interpolated values. We 

then calculated three mean error measures: mean error 

(ME) indicates bias (positive = over-prediction, negative = 

underprediction); mean absolute error (MAE) indicates the 

magnitude of error regardless of sign; and root mean square 

error (RMSE) is sensitive to outliers and can be used to 
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assess the magnitude of extreme errors (Daly 2006, Nalder 

and Wein 1998).

To provide a basic visual reference, we used 30-year 

normal PRISM-derived data sets to construct U.S. maps 

depicting the total number of days and longest string of 

consecutive days when weather conditions are typically 

favorable for P. ramorum. We started with 12 monthly 

grids depicting the number of wet days (i.e., the number 

of days with precipitation) throughout the conterminous 

United States. For each monthly wet-days grid, we masked 

out any cells where temperatures did not fall within the 60 

to 80 °F range at some time during the month. Using map 

algebra, we added the 12 monthly grids together to develop 

a total-day count for each grid cell in our output map. 

The consecutive-day count map was, by necessity, more 

approximately constructed. First, we standardized values 

in each of the masked monthly grids by converting the 

number of wet days to a proportion (number of wet days / 

total number of days in the month) and then multiplying this 

proportion by 30. Then, using map algebra, we recorded the 

maximum standardized monthly value for each cell in our 

output map. This approximated the range of values in the 

GIDS-derived consecutive-day maps. Nonetheless, because 

we used monthly rather than daily data to build the PRISMderived maps, any comparison to the GIDS-derived maps 

must be done with care.

Results

In terms of cross-validation errors, the three spatial 

interpolation methods performed similarly for both the 

total-day and consecutive-day count variables (Tables 2 

and 3). The GIDS approach, as suggested by the ME values 

as well as the actual versus the predicted means, tended to 

over-predict slightly more than the other two techniques. 

The RMSE results indicate that, for some years, the GIDS 

approach yielded a few more extreme errors, although GIDS 

had a lower RMSE than cokriging for the total-day variable 

in 2002 and 2003, as well as a lower MAE in 2001, 2002, 

and 2003. In general, error differences among the three 

were not substantial, with MAE consistently holding at 

approximately 16 percent of the total-day mean value and 

25 percent of the consecutive-day mean value for all three 

techniques.

The GIDS-derived maps for the two count variables 

(Figures 1 and 2) most obviously show a great deal of 

annual variability. For the consecutive-day variable, the 

Table 2—Interpolation method comparison for total-day variablea

Interpolation

Method 2000 2001 2002 2003 2004

MeanObserved 62.94 61.77 58.77 64.43 70.32

GIDSb 63.44 62.21 59.23 64.93 70.86

Cokriging 62.97 61.81 58.89 64.64 70.36

Detrended kriging 62.98 61.79 58.79 64.41 70.37

RMSEGIDS 13.52 13.13 12.82 13.42 14.27

Cokriging 13.07 13.07 13.19 14.35 14.00

Detrended kriging 13.22 12.85 12.52 13.39 14.09

MAEGIDS 10.39 9.97 9.61 10.14 10.81

Cokriging 10.10 10.03 9.94 10.92 10.59

Detrended kriging 10.24 9.82 9.44 10.18 10.66

MEGIDS 0.508 0.438 0.464 0.500 0.532

Cokriging 0.033 0.037 0.114 0.216 0.029

Detrended kriging 0.046 0.018 0.025 -0.018 0.040

a

 Cross-validation results for each interpolation method based on five annual data sets. Errors calculated as 

observed values minus the predicted values; see text for interpretation of root mean square error (RMSE), 

mean absolute error (MAE), and mean error (ME).

b

 GID = gradient plus inverse distance squared.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Eastern United States generally tended to have higher 

values than the Western United States, with parts of the 

Appalachian Mountain region and States along the Gulf 

of Mexico typically exhibiting high values. However, the 

extent and spatial distribution of the highest-value area 

fluctuated substantially year to year. The total-day maps 

exhibited a similar spatial pattern, but more clearly highlighting some relatively high-value areas in the southern 

and central Rocky Mountains. Perhaps unsurprisingly, the 

patterns of the GIDS-derived maps were quite different than 

the patterns depicted in the PRISM-derived maps.

Discussion

Four main points of emphasis emerge from the results. 

First, for the tested data sets, the interpolation method did 

not significantly influence the resulting error. There are 

several possible explanations for this. Foremost, although 

the GIDS approach may be technically more appropriate 

than geostatistical approaches for count-based variables, the 

Poisson model may not have been a good fit for these data, 

or the data may have been approximately normal enough 

to remove any advantage of a Poisson-based process over 

geostatistical approaches. Furthermore, among weightedaverage interpolation approaches—a category that includes 

GIDS—kriging is often the best unbiased predictor for data 

that are not normally distributed (Johnston and others 2003). 

Another count-oriented approach—Poisson kriging—has 

recently emerged in health geography and ecological literature, and this may be a promising future direction for countbased spatial interpolation (Goovaerts 2005, Monestiez 

and others 2006). In the meantime, GIDS has a number of 

positive characteristics. It violates fewer assumptions than 

geostatistical approaches—in particular, the assumption 

of second-order stationarity (Cressie 1993). Furthermore, 

the GIDS approach is transparent and easily implemented. 

To use more complex approaches, particularly PRISM, 

requires estimation of numerous parameters, so a certain 

degree of subjectivity is involved. The GIDS approach can 

easily accommodate covariates besides elevation, and, in 

fact, could easily be adapted for multiple covariates in order 

to refine the results. Finally, the GIDS approach has been 

implemented in R code (R Core Development Team 2006), 

and as such is an open source resource that may be more 

readily available than GIS-based interpolation approaches.

Table 3—Interpolation method comparison for consecutive-day variablea

Interpolation

method 2000 2001 2002 2003 2004

MeanObserved 5.73 5.72 5.21 6.03 6.25

GIDS 5.78 5.77 5.25 6.08 6.30

Cokriging 5.74 5.73 5.21 6.04 6.25

Detrended kriging 5.73 5.74 5.20 6.03 6.25

RMSEGIDSb 2.14 1.98 1.89 2.15 2.35

Cokriging 2.10 1.97 1.83 2.14 2.30

Detrended kriging 2.09 1.98 1.85 2.15 2.30

MAEGIDS 1.46 1.41 1.28 1.51 1.62

Cokriging 1.44 1.41 1.24 1.51 1.59

Detrended kriging 1.45 1.42 1.25 1.51 1.60

MEGIDS 0.053 0.052 0.039 0.048 0.051

Cokriging 0.009 0.009 -0.001 0.006 -0.002

Detrended kriging 0.005 0.018 -0.007 0.003 -0.001

a

 Cross-validation results for each interpolation method based on five annual data sets. Errors calculated as 

observed values minus the predicted values; see text for interpretation of root mean square error (RMSE), 

mean absolute error (MAE), and mean error (ME).

b

 GID = gradient plus inverse distance squared.
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Second, the interpolations of the two-count variables 

appear to have an acceptable degree of error. The distribution of cross-validation errors for the GIDS interpolations 

are revealing in this regard. For the consecutive-day 

variable, across all 5 years, only 25 percent of values were 

exactly predicted, but nearly two-thirds of predicted values 

were within 1 day of the observed value. For the total-day 

variable, only 4 percent of values were exactly predicted, 

but nearly 50 percent were within 5 days and greater than 

75 percent were within 10 days. This should be adequate for 

Figure 1—Annual maps of the total number of days with weather conditions favorable for Phytophthora ramorum, interpolated using the 

gradient plus inverse distance squared method: (a) 2000, (b) 2001, (c) 2002, (d) 2003, and (e) 2004; (f) for visual comparison, a total-day 

map approximated using monthly Parometer-elevation Regressions Independent Slopes Model.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

broad-scale ranking of areas according to their relative risk 

based on climatic and weather conditions.

The third and perhaps more important point is that the 

information provided by the constructed annual count maps 

is substantially different from results that can be captured 

using monthly climatic data sets based on 30-year normals. 

For P. ramorum and other currently emerging threats, it 

may be advantageous to identify areas that have exhibited 

favorable conditions in a given year and determine whether, 

for example, the pathogen was positively detected at any 

Figure 2—Annual maps of the longest string of consecutive days with weather conditions favorable for Phytophthora ramorum, interpolated using the graident plus inverse distance squared method: (a) 2000, (b) 2001, (c) 2002, (d) 2003, and (e) 2004; (f) for visual comparison, a total-day map approximated using monthly Parometer-elevation Rrgressions Independent Slopes Model
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GENERAL TECHNICAL REPORT PNW-GTR-802

nurseries in those areas during that time period. In fact, 

this suggests a need for a regularly updated database, and 

the GIDS method may be one way to generate a regularly 

updated data set from the NCDC data. Recent annual maps 

can be used in conjunction with 30-year normal data to 

create a strong picture of current risk.

Fourth, if the count-based variables we calculated are 

reasonable representations of the level of favorable climatic 

conditions for P. ramorum, then this suggests that large 

portions of the Eastern United States—perhaps more than 

originally estimated—have periods during each year where 

they may be especially susceptible to infection. Because 

climate and weather may not be severely limiting factors, 

detailed analyses of potential pathways and potential host 

species distribution may be in order for much of the Eastern 

United States.

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

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

Frank J. Krist, Jr. and Frank J. Sapio, USDA Forest Service, Forest Health Technology Enterprise Team (FHTET), 

Fort Collins, CO 80525-1891; and Borys M. Tkacz, USDA 

Forest Service, Forest Health Monitoring RPC, Arlington, 

VA 22209.

Abstract

The construction of the 2006 National Insect and Disease 

Risk Map, compiled by the USDA Forest Service, State 

and Private Forestry Area, Forest Health Protection Unit, 

resulted in the development of a GIS-based, multicriteria 

approach for insect and disease risk mapping that can 

account for regional variations in forest health concerns 

and threats. This risk mapping framework, used by all nine 

Forest Service regions and 49 States, provides a consistent, 

repeatable, transparent process through which interactive 

spatial and temporal risk assessments can be conducted 

at various levels to aid in decisionmaking. The national 

framework was designed to be highly iterative, using input 

from a wide range of sources including subject area experts. 

The framework consists of a five-step process: (1) identify 

agents of concern (insects and diseases) and target-host species; (2) identify, rank, and weight criteria that determine 

the susceptibility (potential for introduction and establishment) and vulnerability (potential for tree mortality to occur 

if an agent is established) to each agent; (3) standardize 

criteria values, and combine the resultant maps using a 

series of weighted overlays; (4) convert modeled values for 

each agent to predicted basal area (BA) loss over a 15-year 

period; and (5) identify regions at risk of encountering a 

25-percent or greater loss of total basal area in the next 15 

years. This potentially interactive threshold was set by the 

National Risk Map Oversight team for the national risk map 

product.

The National Insect and Disease Risk Map resulted in 

the integration into a national map of 186 forest insect and 

disease models, individually run and assembled on a central 

server located at the Forest Health Technology Enterprise 

Team (FHTET) in Fort Collins, Colorado. The national 

framework also enables local knowledge and data to be 

entered into models, allowing for quick, large-scale assessments. The development of this national framework 

is described here.

Keywords: Forest health monitoring, GIS, 

insect and disease risk, multicriteria modeling, NIDRM, 

risk map.

Introduction

Ensuring the health of America’s forests requires the 

analysis, understanding, and management of complex and 

interrelated natural resources. Increasing human-use pressures, a continual threat from native and exotic insects and 

diseases (USDA FS 2005), and more complex management 

policies make natural resource management demanding. 

To accurately assess where and how forest resources are 

being impacted, resource managers and policymakers 

require information beyond tabular summaries. In turn, 

this requirement has created an increasing need for spatialbased, decision-support systems that can quickly summarize a wide range of tabular and geographic information. 

Such systems provide resource managers with the information they need to make clear, informed choices and efficiently allocate human and financial resources. Therefore, 

integrated and comprehensive approaches that use technologies, such as geographic information systems (GIS), with 

their ability to analyze a large number of spatial variables 

concurrently, are becoming increasingly important for the 

protection and management of our Nation’s forest resources 

(Ciesla 2000, McRoberts and others 2006, Mowrer 1992, 

Reynolds 1999, Stein and others 2005).

The primary goal in the development of the 2006 

National Insect and Disease Risk Map (NIDRM) is the 

creation of a national communications tool that will provide 

policymakers, USDA officials, and Federal and State land 

managers with a periodic, strategic assessment for risk of 

tree mortality from major insects and diseases. NIDRM is 

an integration of 186 individual risk models constructed 

within a common, consistent, GIS-based, multicriteria 

framework that accommodates regional variations in 

A Multicriteria Framework for Producing Local, Regional, and 

National Insect and Disease Risk Maps
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GENERAL TECHNICAL REPORT PNW-GTR-802

current and future forest health conditions, knowledge, and 

data availability. The 2006 NIDRM was created through 

a modeling process that is repeatable and transparent, 

and through which interactive spatial and temporal risk 

assessments can be conducted at various scales to aid in 

the allocation of resources for forest health management. 

This process is intended to increase the utilization of forest 

health risk maps within and outside the National Forest 

System.

The production of the 2006 risk map has been a 

highly collaborative process, coordinated by the 

USDA Forest Service, State and Private Forestry Area, 

Forest Health Monitoring Program (FHM). Entomologists 

and pathologists from all States and every FHM region 

were invited to take part in the process of developing the 

NIDRM. Teams were created with forest health and GIS 

specialists from the Forest Service, State agencies, and 

academia to oversee and assist in model development. Even 

though the goal of the authors is to describe in this paper the 

GIS framework developed for the construction of NIDRM 

and to briefly demonstrate how this process can be used to 

conduct assessments at multiple spatial scales, the authors 

want to emphasize the importance of a team approach that 

ensures participation from local resource managers.

The Assessment Framework

Defining Risk

The definitions for “risk” and “hazard” in forest pest 

management can be confusing and contradictory. Rather 

than reconcile the various definitions of risk and hazard we 

use the following construct.

When assessing risk as it relates to forest health, risk 

is often composed of two parts: the probability of a forest being attacked (susceptibility) and the probability of 

resulting tree mortality (vulnerability) (Mott 1963). Characterizing the spatially explicit probability of insect and 

disease activity requires spatially explicit quantitative data. 

However, because such data are often lacking at regional, 

national, and local levels, we define risk as the potential for 

harm owing to exposure to an agent(s). Also, we draw the 

distinction between susceptibility and vulnerability (Mott 

1963), but in the context of potential rather than probability.

Our threshold value for mapping risk is defined as the 

expectation that, without remediation, over the next 15 

years 25 percent or more of standing live basal area (BA) 

in trees greater than 1 inch in diameter will die owing 

to insects and diseases. The threshold value for mapping 

insect and disease risk is independent of the GIS framework 

discussed in the remainder of this paper. Therefore, the 

framework can support any threshold.

A Conceptual Overview of a National Risk 

Assessment Framework

Figure 1 provides a conceptual overview of the risk-assessment process discussed here. The modeler first indicates 

whether the forest pest under study is endemic or not. If a 

pest is established throughout a region, then the potential or 

source for actualized harm is assumed to be equal everywhere, and all host material is susceptible. In such cases, 

susceptibility assessments are not required. If a mechanism 

or data set exists that addresses varying pest densities 

in time and space, we can accommodate those densities 

within our framework. However, few national data sets 

for pest density exist, and we assume presence or absence 

in our modeling scenarios. A vulnerability model, which 

determines the likelihood and extent to which trees will 

be harmed by the pest of concern within the defined time 

frame of 15 years, is required to complete a risk assessment 

in a case where a pest is already established.

When considering forest pests, either nonnative 

exotics that have not been established or cyclic native pests 

whose outbreaks occur sporadically about the landscape, 

the modeler must first construct a model of pest potential 

or susceptibility. Susceptibility is based on the biological 

availability of a host and the potential for introduction and 

establishment of a forest pest within a predefined time 

frame (in this case, 15 years). With a susceptibility model 

constructed, the next step is to determine whether a forest 

pest will always kill its host.

Generally, once established, some risk agents, such as 

sudden oak death (Phytophthora ramorum) and chestnut 

blight (Cryphonectria parasitica), harm their hosts in the 

same manner throughout the landscape, regardless of existing site and stand conditions. This applies to exotics and
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

native species, alike, although some exotic pests, such as 

gypsy moth (Lymantria dispar), produce mortality rates 

that can differ greatly depending on site and stand 

conditions. If a vulnerability assessment is not required, 

i.e., pest effects are not site-dependent, then the susceptibility model can be used for the final risk assessment.

Figure 1—Risk assessment framework provides a conceptual overview of the multicriteria risk assessment framework used 

to construct the 2006 National Insect and Disease Risk Map (NIDRM).
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GENERAL TECHNICAL REPORT PNW-GTR-802

In some cases, as in the case of gypsy moth where 

both susceptibility and vulnerability models are run, the 

interaction of these models creates the risk assessment. The 

degree to which either vulnerability or susceptibility always 

results in harm determines how much influence each model 

has on the final outcome of the risk assessment. Think of 

the interaction between susceptibility and vulnerability as 

being on a continuum, whereupon agents such as gypsy 

moth are at or near the middle where both susceptibility and 

vulnerability receive equal influence, and risk agents such 

as sudden oak death and mountain pine beetle (Dendroctonus ponderosae) require only vulnerability assessments. All 

forest pests fall somewhere on this continuum. When a risk 

assessment is in hand, estimates of potential BA loss over 

the next 15 years can be derived.

A GIS-Based Multicriteria National Risk 

Assessment Framework: A Five-Step Process

The risk assessment framework used to construct NIDRM 

is best explained using a hypothetical example, particularly 

in steps 2 through 5. (A real world example is not used here, 

so the reader is free to focus on the process rather than 

the correctness of the example.) It should be noted that the 

modeling process presented is not limited to regional or 

national-level work; rather, it is designed to be usable at any 

scale. This is illustrated in the latter part of this paper. However, the accuracy of the model outputs depends on knowledge about forest pest behavior, the degree of informed 

personal judgment of the model developers, and the spatial 

accuracy and precision of the data driving the models.

Because of its availability from State and Federal 

agencies, ease of use, and relative stability, ESRI ArcView 

3.x Spatial Analyst 2.x ModelBuilder was selected as the 

software for the multicriteria framework. In addition, 

previous familiarity with ESRI Spatial Analyst 2.x among 

GIS specialists greatly reduced the rollout time of the risk 

assessment framework. Other commercial software that 

supports multicriteria modeling includes IDRISI and ESRI 

ArcGIS 9.x ModelBuilder. IDRISI (Eastman 2001, Eastman 

and others 1995) has a very comprehensive set of multicriteria modeling tools, but is not widely used in the Forest 

Service. In addition, the Ecosystem Management Decision 

Support (EMDS) (Reynolds 1999), an ESRI ArcGIS extension at version 3.0, was developed within the Forest Service 

to support local and regional decisionmaking and to provide 

a framework for conducting knowledge-based ecological 

assessments.

Step 1: Identify Risk Agents and Host Species—

Often, forest pest distributions are limited to specific 

climatic or biophysical regimes or both. In addition, 

pest behavior and population dynamics often differ by 

geographic area and must be modeled differently to accommodate local and regional conditions. It is possible within 

the NIDRIM framework to account for this variation by 

constructing multiple models for an individual forest pest. 

In order to better capture this natural variation and to 

prevent models from differing along political boundaries, 

models were constrained to the extents of Bailey’s (2004) 

ecoregions. Because ecoregions capture broad climatic and 

biophysical patterns, they provide a more realistic base map 

on which to delineate differences in forest-pest models.

For much of the remaining discussion, we will use 

the following hypothetical example: risk agent X is a 

nonendemic pest that attacks aspen in the central Rocky 

Mountains. The amount of aspen mortality occurring in 

infested trees varies according to site conditions. Because of 

this, risk assessments for agent X require the construction 

of both a susceptibility and vulnerability model (Figure 1).

Step 2: Identify, Rank, and Weight Criteria—

After risk agents and host species are identified, the criteria 

(factors and constraints) that determine both the potential 

for risk-agent establishment and host vulnerability for 

potential mortality must be identified. For the risk assessment framework presented here, we define:

• Susceptibility as the potential (rather than 

probability) for introduction and establishment, 

over a 15-year period, of a forest pest within the 

range of a tree species.

• Vulnerability as the potential (rather than 

probability) for mortality of a tree species at 

a maximum realizable mortality rate over a 

15-year period if a forest pest were to become 

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

Regions that are both very susceptible to a pest attack 

and highly vulnerable to its effects (as where many trees 

that are weakened or stressed are present) are the most 

likely to experience the maximum realizable mortality 

rate—an estimation of the largest likely mortality loss for a 

risk agent over a 15-year period. Regardless of how vulnerable trees are at any given location, they will not experience 

mortality from a risk agent if these regions are not susceptible to attack. In other words, under some circumstances, 

susceptibility can act as a constraint. Constraints are 

criteria that must be met for susceptibility and vulnerability 

potential to occur at any given location. For our hypothetical risk agent X, we have assigned the maximum realizable 

mortality rate of 100 percent, meaning that risk agent X is 

always lethal to aspen when all criteria for susceptibility 

and vulnerability are met. With risk agent X isolated, we 

can now identify a set of criteria for susceptibility and 

vulnerability.

Factors for susceptibility are:

1. Distance to known infestations

2. Average annual minimum extreme temperature 

3. Aspen host presence, with the latter criterion 

acting as a constraint.

Factors for vulnerability are:

1. Aspen BA

2. Aspen quadratic mean diameter (QMD)

3. Soil dryness/wetness

Although risk agent X requires both a susceptibility 

and vulnerability model for its risk assessment, recall that 

risk assessments for some pests require only one or the 

other.

Many forest pests invariably cause tree mortality 

whereas others only contribute to their demise. Stressors 

often work in concert to cause mortality. Modeling pest 

complexes that may work together to cause mortality 

requires a layer (or layers) representing the distribution 

and intensity of each insect and disease that contributes 

to the mortality in any given area. Depending on whether 

the additional agent(s) contributes to an increased risk of 

establishment or mortality or both, this layer is then used as 

a criterion in the susceptibility or vulnerability models of 

the primary risk agent. Owing to the lack of understanding 

of the interactive effects among multiple stressors, we treat 

interactions as additive. The exception to this is where we 

use one pest risk model to constrain the other. If the interactive effects of risk agents are understood, we can model 

them under this current framework.

The relative importance of each criterion, or rank, for 

determining whether an area, or pixel, has the potential 

to be susceptible and vulnerable to a risk agent is entered 

into a pairwise comparison matrix. A pairwise comparison 

matrix is a robust method for assessing the comparative 

importance of factors (Eastman 2001, Eastman and others 

1995, Saaty 1977). It is particularly useful when attempting to derive weight evaluations for multiple criteria under 

many considerations. Every possible pairing of factors 

must be identified and entered into the matrix, ordering 

the criteria most important to least important. The matrix 

is used to generate a set of weights representing the relative importance of each criterion (Table 1). The resultant 

weights, expressed as percentage influence, must sum to 

1 (or 100 percent) and are used to combine criteria values 

within a weighted overlay (see step 3). Separate matrices are 

generated for both susceptibility and vulnerability. A matrix 

is not needed if only a single criterion is present.

Prior to entering values into the pairwise matrix, comparisons must be made between criteria using a 10-point 

continuous rating scale (Table 2) modified by Krist (2001, 

2006) from the 9-point scale Eastman uses in the IDRISI 

software (Eastman 2001, Eastman and others 1995). Rankings in Table 2 represent the relative importance of each 

criterion. For example, QMD is moderately less important 

than BA for determining the vulnerability potential to risk 

agent X; therefore, QMD receives a value of one-third in the 

comparison matrix whereas BA receives a value of 1. Soil 

Table 1—Sample weights for vulnerability 

to risk agent X 

Criteria Weight

BA 65 percent

QMD 22 percent

Soil Dry/Wet 13 percent

Weights always sum to 100 percent and represent the 

relative importance of each criterion.
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GENERAL TECHNICAL REPORT PNW-GTR-802

dryness/wetness is of strongly less importance to BA and is 

assigned a value of one-fifth.

The 10-point rating system enables forest health specialists to select the most important factor(s) and compare 

the remaining criteria to it (them). All these criteria have a 

positive influence and contribute to potential. The negative 

impacts of a criterion can be accounted for by reversing 

the rankings for the criterion values, thus turning a negative relationship into a positive one (one that contributes to 

potential).

We simplified the workload of regional forest health 

specialists constructing models by enabling the spreadsheet 

(Figure 2) used to collect model information to simulate the 

comparison matrix developed by Saaty (1977) and to calculate weights automatically. Having the ability to calculate 

weights automatically enables the user to see changes in 

weights immediately as ranks are adjusted. The weights in 

the spreadsheet are calculated by first summing all the rank 

values and then dividing each rank by that sum.

Figure 2—The spreadsheet template allows staff to document models, rank criteria, and calculate weights. Values shown are for the risk 

agent X example.

Table 2—10-point continuous rating scale

Description Comparison rating

Most important 1

1/2

Moderately less 1/3

1/4

Strongly less 1/5

1/6

Very strongly less 1/7

1/8

Extremely less 1/9

1/10

Unsuitable N/A
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

In addition to weighting risk agent criteria, weights 

must be assigned to susceptibility and vulnerability, based 

on their importance in determining the potential for a tree 

species to experience the maximum realizable mortality 

rate from the pest of concern in the next 15 years. These 

weights are used to combine the resultant susceptibility and 

vulnerability models in the final risk assessment (see step 

3). In the case of risk agent X, equal weight (50 percent) was 

given to both susceptibility and vulnerability. Remember 

that susceptibility acts as a constraint in the final risk 

assessment; areas with no possibility of being susceptible in 

the next 15 years are not at risk.

Criteria, rankings, and weights for risk agent models 

can be selected in a number of ways. Ideally, if data on the 

distribution and intensity of a risk agent exist, statistical 

analyses may be performed in the hopes of identifying 

relationships between risk agent activity and forest and 

biophysical attributes, as represented in GIS layers. If such 

a relationship is found, the strength of the correlation can be 

used to determine weights. Unfortunately, this data-driven 

or literature/research-based approach is not always possible 

because, in many instances, data on risk agent distributions, 

intensity, and behavior are either inadequate or incomplete 

or both. In such instances, modelers must rely on informed 

professional judgment or expert opinion or both when 

selecting criteria and weights.

The information collected for steps 1 and 2, including 

the basis for a model or model certainty, appears in Figure 

2.

Step 3: Standardize Criteria Values and Combine the 

Resultant Maps—

With the risk agent criteria and their corresponding GIS 

layers identified and weights generated for susceptibility 

and vulnerability, the factor values must be standardized, 

based on a common evaluation scale. Standardization 

allows for the comparison of criteria with differing values, 

such as BA, with units of square feet per acre and QMD, 

with units in inches. An evaluation scale from 0 to 10, with 

0 representing little or no potential, and 10 representing the 

highest potential, was chosen. For example, in the case of 

risk agent X, higher stocked stands are more vulnerable; 

therefore, areas with stocking levels approaching 120 square 

feet or more are given a value of 10 (Table 3). Potential in 

areas with less than 20 square feet of aspen is very low and 

is assigned a 0. Assigning a 0 to a criterion does not eliminate the possibility for risk to occur. If other criteria have 

values greater than 0, potential is still possible at a location.

Regions without aspen are constrained. Constraints do 

not need to be standardized, but they do restrict modeled 

potential to specific areas, such as regions containing aspen 

or areas within a certain distance of current risk agent infestations. Because of the restrictions, the potential for risk can 

be precluded in some areas, regardless of the strength of the 

other criteria.

For simplicity, and due to the frequent lack of precise 

data layers or models or both at the national level, an integer 

scale from 0 to 10 was chosen. At a finer resolution and with 

more precise data, an extended standard scale may be of 

more use (e.g., an integer scale of 0 to 100 would capture 

a wider range of variation in the data).

A standard scale may be applied to a GIS data set 

through a manual recoding of the criterion values. This is 

particularly easy to do when data have sharp boundaries 

and discrete classes. However, this is not an easy task when 

the transition from criterion values with potential to values 

without potential is gradual. Consider the BA criterion in 

our example. Potential for vulnerability to pest X ends or 

Table 3—Rankings for BA (basal area) using linear 

and sigmoidal memberships 

BA Rank (linear) Rank (sigmoidal)

ft2

/ac

20-30 0 0

30-40 1 0

40-50 2 0.3

50-60 3 1.2

60-70 4 2.5

70-80 5 4.1

80-90 6 5.9

90-100 7 7.5

100-110 8 8.8

110-120 9 9.7

>120 10 10

Fuzzy memberships enable values to be reclassed or recoded in a variety 

of ways to capture relationships between insect and disease behavior and 

criteria. This example demonstrates the effects of two commonly used 

memberships on BA, with 10 representing the highest potential for risk.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 3—Available fuzzy memberships include: (a) sigmoidal functions, (b) J-functions, and (c) linear 

functions. Custom functions can be developed, based on the relationship of the data to potential risk.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

is very low when stocking levels are at or below 20 square 

feet and increases gradually until stocking levels reach 120 

square feet. Rather than manually break down this range 

into 10 discrete classes, a fuzzy membership set (Eastman 

2001, Eastman and others 1995) can be used to stretch or 

assign a continuous set of values, automatically. Another 

advantage to selecting a fuzzy membership is that the data 

can be assigned standardized values in differing ways, 

depending on how potential varies within a criterion. The 

most common method of assigning values based on fuzzy 

membership is a simple linear stretch in which potential 

increases linearly. However, if potential rises gradually and 

then accelerates and tapers off, a sigmoidal function may 

capture the natural variation more precisely.

Twelve fuzzy membership functions were used in 

the construction of NIDRM models, including linear, 

sigmoidal, and J-shaped functions (Figure 3). Table 3 shows 

the difference between the resultant values of a linear and 

sigmoidal stretch, the most common memberships used during the production of NIDRM. The letters (inflection points) 

on the graphs in Figure 3 represent where risk potential 

(a) begins, (b) peaks or reaches its highest, (c) begins to 

decrease (though this may or may not happen), and (d) 

ends or no longer changes (levels off). In the risk agent X 

example, a is set to 20 square feet of BA whereas b, c, and d

are set to 120 square feet. Notice the letters and descriptions 

on the column headers of the spreadsheet in Figure 2 and 

the Curve column, where a fuzzy membership (curve) is 

chosen for each criterion.

ESRI ArcView 3.x Spatial Analyst 2.x ModelBuilder 

does not contain a routine that will automatically recode or 

stretch criterion values according to a fuzzy membership, 

so a worksheet was developed to calculate break points that 

could then be manually entered into the RECLASS module 

of ESRI ModelBuilder (Figure 4), effectively dividing the 

values into 11 classes. Values for each risk agent’s criteria 

were manipulated in Model Builder in this way. Figure 5 

illustrates what a set of standardized GIS layers would look 

like for the central Rocky Mountains using risk agent X as 

an example.

With weights generated and values standardized for 

each criterion, all criteria can be combined in a series of 

weighted overlays representing susceptibility, vulnerability, 

and the final risk-agent-mortality assessment. Factors are 

combined within a weighted overlay or weighted linear 

combination by multiplying the factor weight by each 

criterion value, followed by a summation of the results 

(Saatty 1977):

P = ∑wixi

where:

P = potential for susceptibility, vulnerability, and risk

Wi = weight criterion i

Xi = criterion score of factor i

Figure 6 illustrates how sample values from risk agent 

X are combined in a series of three weighted overlays. The 

output from each weighted overlay has a value from 0 to 

10, the same as the standard evaluation scale used for each 

criterion (Figure 5). The higher the value, the greater the 

likelihood or potential for a tree species to be susceptible 

or vulnerable to a risk agent. The greater the value from the 

weighted overlay of the resultant susceptibility and vulnerability maps, the greater the likelihood or potential for a tree 

species to experience mortality over the next 15 years.

ESRI ModelBuilder provides a weighted overlay module in which criteria can be entered, weighted, and formally 

ranked. Figure 7 shows the Model Builder weighted overlay 

for the risk agent X vulnerability model.

Step 4: Convert Modeled Values to an Estimate of BA 

Loss—

Using a standardized scale from 0 to 10 allows for the easy 

conversion of risk potential to estimates of BA loss. Recall 

that when all criteria are met for susceptibility and vulnerability within a particular area or pixel, the host species 

within that area is likely to experience the maximum 

realizable mortality rate over the next 15 years. Based on 

this assumption, a pixel in our agent X risk assessment 

receiving a value of 10 would be assigned a 100-percent 

mortality rate, whereas a pixel with a value of 5 would 

receive a 50-percent mortality rate. Once pixels have been 

assigned their mortality percentages based on their mortality potential, this layer is multiplied by a surface representing host BA to produce loss estimates for a tree species. 

For example, a stand with 100 BA of aspen and a simulated 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 4—Criteria-ranking tool and RECLASSIFICTION module in ModelBuilder are shown. The 

criteria-ranking tool divides factor values into classes that can be entered into ModelBuilder using the 

RECLASSIFICTION module.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

mortality potential for risk agent X of 5 could lose 50 square 

feet of basal area (50 percent × 100 BA) in the next 15 years. 

Remember, model results do not guarantee that mortality 

will occur at any given location; rather they suggest the 

potential for loss. Figure 5 shows the map of BA loss for 

Risk agent X.

In cases where multiple pests are acting on a single 

forest species, the resultant BA losses cannot be added up to 

calculate total BA losses. For example, if one agent attacks a 

resource and has the potential to kill 75 percent of the trees 

and another agent attacks the same resource in the same 

area and may kill 75 percent of the trees, it is incorrect to 

say that up to 150 percent of the trees may be killed. Under 

the simplifying assumption that mortality agents act independently (a common assumption in the development of the 

NIDRM), mortality from multiple agents is calculated as:

D = 1 – (1-p1)(1-p2)(1-p3)... (1-pn)

where:

D = total proportionate mortality

p1 = proportionate mortality caused by agent 1

n = n

th agent

In the example above where two agents may each cause 

75-percent mortality, 94 percent of the total BA would be 

lost in that pixel when total losses are calculated using the 

simplifying assumption:

D = 1 – (1- p1)(1- p2)

D = 1 – (1- 0.75)(1- 0.75)

D = 0.9375 or 94 percent

We realize this mechanism does not address complex 

interactions between various pests. The body of literature 

Figure 5—Sample criteria maps and resultant model outputs are shown below. All criteria used in the risk agent X model are 

displayed with their corresponding weights. Notice how each layer affects the outcome. Arrows show the direction of flow during 

model construction.
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GENERAL TECHNICAL REPORT PNW-GTR-802

regarding pest interactions is limited, and cumulative 

impacts are not understood well enough so that complex 

interactions can be modeled with confidence.

Step 5: Identify Regions at Risk—

To calculate the percentage of total BA that might be lost in 

each pixel, estimates of potential 15-year BA loss compiled 

for all risk agents in step 4 were divided by a surface representing total BA. Pixels where the total loss exceeded or 

met 25 percent of the total BA were flagged for the national 

composite risk map (NIDRM). Because the original 

percentages are available, different threshold values for risk 

can be defined and mapped. Risk owing to individual pest 

species by host also were provided.

Modeling at Multiple Scales/Resolutions

Once a model has been constructed in ESRI ArcView 3.x 

Spatial Analyst 2.x ModelBuilder, models can be rerun 

at multiple scales. The GIS layers can be swapped in and 

out of ModelBuilder with little or no modification when 

standard measurement units exist across scales. As additional data become available at finer scales, supplementary 

criteria can be added to the model. Figure 8 shows the same 

southern pine beetle (Dendroctonus frontalis) (Thatcher and 

Figure 6—Weighted overlay for the risk agent X vulnerability model is displayed. Notice how much influence the relatively 

high weight for BA has on the final model outcome.

Barry 1982) model run at three different resolutions (1 km, 

250 m, and 30 m). The 30-m-resolution model includes an 

additional criterion depicting forest stand connectivity.

Discussion/Conclusions

The 2006 national risk assessment employed 186 risk-agent 

models representing over 50 risk agents acting on 61 tree 

species or species groups, with all models assembled into a 

national composite (NIDRM) (Figure 9). Given the nature 

of our assignment to construct a national, 15-year assessment of forest health risk from insects and diseases, we 

believe that NIDRM is successful because it is:

1. Based on an integrating technology. NIDRM 

represents the collection and integration of multiple 

risk models developed through an iterative, handson process by local forest health specialists. The 

risk assessment frame-work presented in this paper 

is able to integrate outputs from a wide range of 

models and is implemented through software that 

gives forest health specialists direct access to GIS

models.

2. Transparent and repeatable. The 2006 modeling 

framework provides a consistent, repeatable, 

transparent process to conduct risk assessments. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 7—The ModelBuilder weighted overlay module provides a user friendly interface in which criteria can be 

combined in a multicriteria model. This module also provides a means of documenting model information and can be 

used to rerun a model using various weights and ranks.
634

GENERAL TECHNICAL REPORT PNW-GTR-802

Within this framework, forest health specialists 

are able to determine why an area is at risk, what 

the source data are, and how the model(s) for that 

region were constructed, thus documenting any 

models composing NIDRM. This type of framework also enables shortcomings in data and models 

to be identified and can be used to prioritize future 

research and data development.

3. Interactive and scalable. The framework is

interactive enough to support sensitivity analysis 

while allowing risk assessments to be conducted at 

various spatial and temporal scales. Sensitivity 

analysis ensures that models can be adjusted 

according to local knowledge or as additional data 

and models become available or both. Scalability 

enables subject area experts to conduct local and 

regional assessments using an identical framework. 

This continuity ensures that national products do 

not conflict with local knowledge.

4. Efficacious. Efficiency, precision, accuracy, and 

usability must be considered when developing a 

framework. A national risk-map product with 

potentially hundreds of models behind it not only 

requires a highly efficient modeling process, it 

must be able to capture the information and 

variation within each individual model. With a 

wide range of audiences, including both subject 

area experts and private citizens, the risk map 

framework is able to produce detailed model 

documentation and results that are easy to interpret.

5. Comparable across geographic regions. The 2006 

modeling framework has resulted in a standard 

modeling process that provides a level playing field 

for every region being examined as part of NIDRM. 

This ensures that regional comparisons can be 

made. Without standardization, NIDRM would be 

little more than a federation of maps with little or 

Figure 8—Southern pine beetle risk models are shown in the following: 

each map depicts the potential for southern pine beetle mortality in a 

portion of east Texas. Red represents extreme risk, orange, high, yellow, 

medium, and blue, low.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

no consistency between them, making regional 

comparisons and national summaries impossible.

Although the framework described in this paper was 

developed around modeling potential risk of tree mortality from insects and diseases, the process can be used for 

a wide range of other applications including estimating 

potential for wildlife and forest habitat (Krist 2001, 2005).

Literature Cited

Bailey, R.G. 2004. Bailey’s ecoregions and subregions 

of the United States, Puerto Rico, and the U.S. Virgin 

Islands. (Digital Map). Reston, VA: U.S. Department of 

Agriculture, Forest Service.

Ciesla, W.M. 2000. Remote sensing in forest health 

protection. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Forest Health Technology 

Enterprise Team. [Page nos. unknown].

Eastman, J.R. 2001. IDRISI 32: Guide to GIS and image 

processing. Vol. 2. Software Manual. Worcester, MA: 

Clark Labs, Clark University. [Page nos. unknown].

Eastman, J.R.; Jin, W.; Kyem, P.A.K.; Toledano, J. 1995. 

Raster procedures for multi-criteria/multi-objective 

decisions. Photogrammetric Engineering and Remote 

Sensing. 61(5): 539–547.

Krist, F.J., Jr. 2001. A predictive model of paleo-Indian 

subsistence and settlement. East Lansing, MI: Michigan 

State University. Ph.D. dissertation. 

Krist, F.J., Jr. 2006. A tool for simulating hunter-gatherer 

decision making and behavior. In: Mehrer, M.W.; 

Wescott, K.L., eds. GIS and archaeological predictive 

modeling. Taylor & Francis: 335–353. 

Lewis, J.W. 2002. Mapping risk from forest insects and 

diseases. FS-754. Washington, DC: U.S. Department of 

Agriculture, Forest Service. 3 p.

McRoberts, R.E.; Barbour, R.J.; Gebert, K.M. [and 

others]. 2006. Using basic geographic information 

systems functionality to support sustainable forest 

management decision making and post-decision 

assessments. Journal of Sustainable Forestry. 23(4): 

13–34.

Figure 9—2006 National Insect and Disease Risk Map (NIDRM) for the United States characterizes the potential for significant losses 

(at or above 25 percent) of total forest basal area (BA), spatially.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Mott, D.G. 1963. The forest and the spruce budworm. 

In: Morris, R.F. ed. The dynamics of epidemic spruce 

budworm populations. Memoirs of the Entomological 

Society of Canada. 31: 189–202.

Mowrer, H.T. 1992. Decision support systems for 

ecosystem management: An evaluation of existing 

systems. Gen. Tech. Rep. RM-296. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station. 145 p.

Reynolds, K.M. 1999. EMDS users guide (Version 2.0): 

Knowledge-based decision support for ecological 

assessment. Gen. Tech. Rep. PNW-470. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Station. 63 p.

Saaty, T.L. 1977. A scaling method for priorities in 

hierarchical structures. Journal of Mathematical 

Psychology. 15: 234–281.

Stein, S.M.; McRoberts, R.E.; Alig, R.J.; [and others]. 

2005. Forests on the edge: housing development on 

America’s private forests. Gen. Tech. Rep. PNW-636. 

Portland, OR: U.S. Department of Agriculture, Forest 

Service. 16 p.

Thatcher, R.C.; Barry, P.J. 1982. Southern pine beetle. 

Forest Insect and Disease Leaflet 49. Reston, VA: U.S. 

Department of Agriculture, Forest Service. [Not paged].

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

FS]. 2005. Forest insect and disease conditions in the 

United States 2004. Fort Collins CO: Forest Health 

Technology Enterprise Team. [Not paged].
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

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

Crocker

Ronald E. McRoberts, mathematical statistician, and 

Mark A. Hatfield and Susan J. Crocker, foresters, USDA 

Forest Service, Northern Research Station, St. Paul, MN 

55108.

Abstract

Traditionally, natural resource managers have asked the 

question, “How much?” and have received sample-based 

estimates of resource totals or means. Increasingly, however, the same managers are now asking the additional 

question, “Where?” and are expecting spatially explicit 

answers in the form of maps. Recent development of natural 

resource databases, access to satellite imagery, development of image classification techniques, and availability of 

geographic information systems has facilitated construction 

and analysis of the required maps. Unfortunately, methods 

for estimating the uncertainty associated with map-based 

analyses are generally not known, particularly when the 

analyses require combining maps. A variety of uncertainty 

methods is illustrated for map-based analyses motivated 

by the threat of the emerald ash borer (Agrilus planipennis Fairmaire) to the ash tree (Fraxinus spp.) resource in 

southeastern Michigan. The analyses focus on estimating 

the uncertainty in forest/nonforest maps constructed 

using forest inventory data and satellite imagery, ash tree 

distribution maps constructed using forest inventory data, 

and estimates of the total number of ash trees for a selected 

region obtained by intersecting the two maps. A crucial 

conclusion of the study is that spatial correlation, an often 

ignored component of uncertainty analyses, made the greatest contribution to uncertainty in the estimate of the total 

number of ash trees.

Keywords: Emerald ash borer, forest classification, 

forest inventory, logistic model, Monte Carlo simulation, 

spatial correlation.

Introduction

The emerald ash borer (Agrilus planipennis Fairmaire, 

Coleoptera: Buprestidae) (EAB) is a wood-boring beetle 

native to Asia that was initially discovered in the United 

States in June 2002. It most likely entered the country in 

solid wood packing material such as crates and pallets and 

was transported to Detroit, Michigan, at least 10 years 

before it was discovered in 2002 (Cappaert and others 2005, 

Herms and others 2004). Ash trees are the only known host, 

and damage is the result of larval activity. Once eggs hatch, 

larvae bore into the cambium and begin to feed on and 

produce galleries in the phloem and outer sapwood. Larval 

feeding disrupts the translocation of water and nutrients and 

eventually girdles the tree. Tree mortality occurs within 1 

to 3 years, depending on severity of the infestation (Haack 

and others 2002, McCullough and Katovich 2004). All of 

Michigan’s native ash species (Fraxinus spp.) and planted 

cultivars are susceptible (Cappaert and others 2005). Since 

2002, southeastern Michigan has lost an estimated 15 million ash trees due to EAB (Cappaert and others 2005).

The natural rate of EAB dispersal is estimated to be 

less than 1 km per year in low-density sites. Natural dispersal has been enhanced by human transportation of infested 

firewood, ash logs, and nursery stock. This artificial spread 

of EAB has initiated the majority of outlier infestations 

(Cappaert and others 2005). Continued spread outside of 

the core zone increases the threat to ash across the United 

States.

The objective of the study was twofold: (1) to illustrate 

methods of uncertainty analysis, and (2) to investigate the 

contributions of spatial correlation to the uncertainty of 

areal estimates. Although the study was motivated by the 

need to predict the magnitude of potential loss of ash trees 

owing to the EAB, the biological context for this study was 

simply a medium to address the uncertainty objectives; 

no results from this study regarding the EAB or its effects 

should be construed as definitive.

Methods

The motivating problem for the study was to calculate an 

estimate, Atotal, of the total number of ash trees in a region 

of southeastern Michigan (Figure 1) that is susceptible to 

infestation by the EAB. The estimation approach entails 

intersecting two 30-m by 30-m-resolution spatial layers or 

maps, one depicting the spatial distribution of forest land 

Uncertainty Estimation for Map-Based Analyses
638

GENERAL TECHNICAL REPORT PNW-GTR-802

and the other depicting the spatial distribution of ash trees 

on forest land. The technical objective was to estimate 

the uncertainty of Atotal for a selected region. The layer 

depicting the distribution of forest land was derived from a 

forest probability layer constructed using forest inventory 

plot observations, Landsat Thematic Mapper (TM) satellite imagery, and a logistic regression model. The ash tree 

distribution layer was constructed using the same forest 

inventory plot observations and a spatial interpolation technique. Uncertainty in Atotal was estimated using an analytical technique for estimating the covariances of the logistic 

regression model parameters, a sample-based technique for 

estimating the uncertainty in interpolated ash tree counts 

per hectare, and Monte Carlo techniques for generating 

forest/nonforest maps, and for combining the components of 

uncertainty.

Data

The study area is wholly contained in Landsat scene path 

20, row 30 (Figure 1), for which three dates of Landsat TM/

ETM+ imagery were obtained: May 2002, July 2003, and 

October 2000. Preliminary analyses indicated that normalized difference vegetation index (NDVI) (Rouse and others 

1973) and the tassel cap (TC) transformations (brightness, 

greenness, and wetness) (Crist and Cicone 1984, Kauth and 

Thomas 1976) were superior to both the spectral band data 

and principal component transformations with respect to 

predicting forest attributes. Thus, 12 satellite image-based 

predictor variables were used—NDVI and the three TC 

transformations for each of the three image dates. Mapping 

units for all analyses consisted of the 30-m by 30-m TM 

pixels. 

The Forest Inventory and Analysis (FIA) program of 

the USDA Forest Service has established field plot centers 

in permanent locations using a sampling design that is 

assumed to produce a random, equal probability sample 

(Bechtold and Patterson 2005, McRoberts and others 2005). 

The plot array has been divided into five nonoverlapping, 

interpenetrating panels, and measurement of all plots in one 

panel is completed before measurement of plots in the next 

panel is initiated. Panels in the study area are selected on a 

5-year rotating basis. Over a complete measurement cycle, 

the Federal base sampling intensity is approximately one 

plot per 2400 ha. The State of Michigan provided additional 

funding to triple the sampling intensity to approximately 

one plot per 800 ha. In general, locations of forested or 

previously forested plots were determined using global positioning system receivers, whereas locations of nonforested 

plots were determined using aerial imagery and digitization 

methods. Each plot consists of four 7.31-m-radius circular 

subplots. The subplots are configured as a central subplot 

and three peripheral subplots with centers located at a 

distance of 36.58 m and azimuths of 0°, 120°, and 240° from 

the center of the central subplot. Data for 2,995 FIA plots or 

11,980 subplots with centers in the selected TM scene and 

measured between 2000 and 2004 were available for the 

study. For each subplot, the proportion of the subplot area 

that qualified as forest land was determined from field crew 

observations. The FIA program requirements for forest 

land are at least 0.4 ha, with at least 10-percent stocking, at 

least 36.58 m crown-to-crown width, and forest land use. 

For each subplot, the number of observed ash trees with 

diameters at breast height of at least 12.7 cm was scaled to 

a count-per-hectare basis. The ash tree count per hectare for 

the ith subplot is denoted Ai° where the superscript denotes 

a subplot observation and is distinguished from the ash tree 

count per hectare for the ith pixel, which is denoted Ai.

The spatial configuration of the FIA subplots with 

centers separated by 36.58 m and the 30-m by 30-m spatial 

resolution of the TM/ETM+ imagery permits individual 

subplots to be associated with individual image pixels. The 

subplot area of 167.87 m2

 is an approximately 19-percent 

sample of the 900 m2 pixel area.

Areal Estimation

The estimate, Atotal, for a region was calculated in three 

steps: (1) generate a 30-m by 30-m-resolution forest/nonforest map, (2) construct a 30-m by 30-m ash tree countper-hectare layer; for each pixel, estimate the number of 

ash trees as the product of the ash tree count per hecatare 

and the 0.09-ha pixel area, and (3) estimate Atotal as the 

sum over forest pixels from step 1 of pixel-level estimates 

of the number of ash trees from step 2. Thus, two layers are 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

required, a forest/non-forest layer and an ash tree countper-hectare layer. Both layers were constructed specifically 

for this study so their pixel-level uncertainties would be 

known. Two sets of analyses were conducted. First, forest/

nonforest, ash tree count per hectare, and the two associated 

uncertainty maps were constructed for a 30-km by 30-km 

study area in the selected TM scene (Figure 1). Second, 

uncertainty analyses for Atotal were conducted for a smaller 

2-km by 2-km portion of the larger study area (Figures 2 

and 3). The restriction of the latter analyses to the smaller 

area was due to technological constraints as is noted in a 

later section.

Forest/Nonforest Layer

Because satellite image pixels with different ground covers 

often have similar spectral signatures, assignment of classes 

to individual pixels is often probability based. A layer 

depicting the probability of forest was constructed using a 

logistic regression model (McRoberts 2006),

Figure 1—State of Michigan, U.S.A. with Landsat scene path 20, row 30 (large yellow box) and 

30-km by 30-km study area (small red box). (Base map courtesy of ESRI)

where pi is the probability of forest for the ith pixel, Xi is 

the vector of the 12 spectral transformations for the ith

pixel with xij being the jth element, the bs are parameters 

to be estimated, exp(.) is the exponential function, and E(.) 

denotes statistical expectation. When estimating the parameters of (1), only data for the 7,920 completely forested or 

completely nonforested subplots were used. The covariance 

matrix for the vector of parameter estimates was estimated 

analytically as,

   ̂ 

Vâr﴾β﴿ = ﴾Z′Vɛ

–1Z﴿–1 , (2)

where the elements of the matrix Z are defined as,

   ̂ ∂f﴾xi

; β﴿

Zij = , ∂βj

 12

 exp β0 + ∑βjx ij 

 

j=1

 E(pi) = f ﴾Xi;β﴿ = , (1)

 12

    1 + exp β0 + ∑βjx ij 

 

j=1
















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GENERAL TECHNICAL REPORT PNW-GTR-802

 1953 2

 Vâr﴾Âi﴿ = Σ ﴾Aj° – Âi﴿

2

, (4)

the elements of Vɛ

 are defined as,

 vij = √рˆi

﴾1 – pˆi﴿ √рˆj﴾1 – pˆj﴿ρˆ

ij ,

and ρˆ

ij is the spatial correlation among the standardized 

residuals estimated using a variogram (Gumpertz and others 

2000).

The most probable forest/nonforest classification of 

the imagery is constructed by comparing the probability, 

p̂, from (1) for each pixel to 0.5: if p̂ ≥ 0.5 , the pixel is 

classified as forest and assigned a numerical value of 1; if 

p̂ < 0.5, the pixel is classified as nonforest and assigned a 

numerical value of 0. However, because the assignment of 

forest or nonforest to pixels is based on probabilities, there 

is uncertainty as to whether this procedure correctly assigns 

forest or nonforest to individual pixels. However, forest/

nonforest realizations that reflect the uncertainty in the classification were obtained using a 4-step procedure designated 

procedure A:

A1. Using the procedure of Kennedy and Gentle 

(1980: 228-231), generate a vector of random

numbers from a multivariate Gaussian distribution

with mean 0 and covariance Vâr﴾β

ˆ

﴿ from (2);

 add these random numbers to the logistic

regression model parameter estimates to obtain

simulated parameter estimates.

A2. Using the simulated parameter estimates from step

A1 with (1), calculate a probability, p̂, of forest

for each pixel.

A3. For each pixel, generate a random number, r, 

from a uniform [0, 1] distribution; if r ≤ p̂ the 

pixel is designated forest with a numerical 

value of 1; if r > p̂ the pixel is designated nonforest with a numerical value of 0.

A4. Repeat steps A1-A3 a large number of times and

calculate the mean and variance of the numerical

values assigned to each pixel.

The variance of the forest/nonforest classifications for 

each pixel is a measure of the uncertainty of the pixel’s 

classification.

Ash Tree Distribution Layer

Because the ash tree distribution layer was for forest land, 

only data for the 1,953 FIA forest subplots were used in its 

construction. Using these plot data, an empirical variogram 

was constructed, and an exponential variogram model was 

fit to the data (Zhang and Goodchild 2002). An interpolated 

surface was constructed for which the ash tree count per 

hectare, Ai, for the ith pixel was estimated as,

vij is the predicted covariance from the variogram model 

corresponding to the distance, dij, between the ith pixel and 

the jth plot; λ̂ is the estimate of the variogram sill; and

 1953

 Wi = Σ wij , (3c) j=1

The variance of Âi was estimated as,

and is a measure of the uncertainty of the estimate, Âi

. This 

approach to constructing the ash tree count per hectare was 

selected to illustrate a sample-based method for estimating 

variances. Other approaches such as kriging could have 

been used to construct the ash tree count-per-hectare layer. 

Realizations of the ash tree count-per-hectare distribution 

were obtained by selecting for each pixel a random number 

from a normal distribution with mean 0 and variance, 

Vâr﴾Âi

﴿ from (4), and adding the number to Âi from (3a).

Uncertainty Estimation

Uncertainty in the areal estimate, Atotal, is due to contributions from four sources: (1) uncertainty in the logistic 

regression model parameter estimates, (2) uncertainty in 

the pixel-level forest/nonforest classifications, given a set 

of parameter estimates, (3) uncertainty in the interpolated 

pixel-level ash tree count-per-hectare estimates, Â, and (4) 

spatial correlation in forest/nonforest and ash tree observations not accommodated in the logistic regression model 

predictions and the ash tree count-per-hectare interpolated 

estimates.

 1 1 vij

 vij w λ λ ij = , (3b)

 0 if > 0.95

 if ≤ 0.95 1 2 3

̂ ̂

vij

λ̂

 1953 w ij

 Âi = Σ Aj (3a) j=1 Wi

°

where

wij

Wi j=1
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

The spatial correlation contribution to uncertainty in 

Atotal results from two phenomena. First, forest areas tend 

to be clustered rather than independently randomly distributed throughout the landscape. Thus, to mimic natural 

conditions, forest/nonforest realizations generated from 

the logistic regression model predictions of forest probability should exhibit clustering comparable to that observed 

among the FIA subplot observations of forest and nonforest. This feature requires that the random numbers used 

to generate the forest/nonforest realization in step A3 be 

drawn from a correlated uniform [0,1] distribution. Second, 

the errors obtained as the differences between Å and Â 

were expected to be spatially correlated; i.e., if an interpolation, Â, overestimates its true value, other interpolations in 

close spatial proximity would be expected to overestimate 

their true values also. However, for this investigation, the 

range of spatial correlation for the interpolation errors was 

only slightly more than the 30-m pixel width, was regarded 

as negligible, and was ignored.

To generate random numbers from an appropriately 

correlated uniform [0,1] distribution as required to accommodate spatial correlation, an 8-step procedure designated 

procedure B was used:

B1. Construct an empirical variogram,

γ̂ = 1

 ∑ (Fi – Fj)

2 2|n (d)|n(d)

where F is the numerical designation for forest or nonforest 

subplot observations, and n(d) denotes a collection of pairs, 

(Fi

,Fj), whose Euclidean distances in geographic space are 

within a given neighborhood of d.

B2. Fit an exponential variogram,

γ̂ (d) = αˆ 1

[1 – exp(αˆ 2 d)] , (5)

to the empirical variogram from step B1, where the estimate 

of the range of spatial correlation is

 1n(0.05) ;

αˆ 2

B3. Construct a spatial correlation matrix by assigning to 

each pixel pair, (i, j), a correlation, ρij, calculated as,

ρij = exp(γ dij) ,

where dij is the distance between the ith and jth pixel centers 

and, initially, γ = αˆ 2 from step B2.

B4. Generate a vector of random numbers, one for each 

pixel, from a multivariate Gaussian distribution with the 

correlation structure constructed in step B3 using the technique described by Kennedy and Gentle (1980: 228–231).

B5. Convert the Gaussian random numbers from step B4 to 

Gaussian cumulative frequencies, resulting in a correlated 

uniform [0, 1] distribution.

B6. Generate a forest/nonforest realization using procedure 

A with the correlated uniform [0, 1] distribution from step 

B5.

B7. Construct an empirical variogram of the forest/nonforest realization from step B5; fit an exponential variogram 

model; and estimate the range of spatial correlation as in 

step B2.

B8. Repeat steps B3 through B7, adjusting the g parameter 

in step B3 each iteration until the range of spatial correlation from step B7 is close to that obtained in step B2.

The exponential variogram model was used in step B2 

because of its simplicity and the adequacy of the fit to the 

data. Construction of the multivariate Gaussian distribution in step B4 requires the Cholesky decomposition of a 

covariance matrix corresponding to the correlation matrix 

constructed in step B3. For a square region, n pixels on 

a side, the correlation matrix will be n2

 by n2. Thus, the 

30-km by 30-km study area, which has 1,000 TM pixels on 

a side, would require decomposition of a 106 by 106 matrix. 

To accommodate personal computer space and processing 

limitations, analyses involving spatial correlations were 

constrained to a 2-km by 2-km region, which is approximately 67 pixels on a side, requires decomposition of a 

4,489 by 4,489 matrix, and comprises approximately 400 ha 

(Figures 2 and 3).

Uncertainty in Atotal for the 2-km by 2-km region was 

estimated using a 6-step Monte Carlo simulation procedure 

designated procedure C.

C1. Generate random numbers from a multivariate Gaussian distribution with mean 0 and variance matrix, Var(β), 

from (2); add these random numbers to the logistic regression model parameter estimates to obtain simulated parameter estimates; calculate the probability, p̂, of forest for each 

pixel using the simulated parameter estimates with (1).
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GENERAL TECHNICAL REPORT PNW-GTR-802

C6. Repeat steps C1 through C5 a large number of times; 

calculate the mean and variance of Ftotal and Atotal over all 

repetitions; estimate the uncertainties in Ftotal and Atotal as 

Vâr(Ftotal) and Vâr(Atotal), respectively.

In procedure C, the contribution of uncertainty due to 

the logistic regression model parameter estimates may be 

excluded by not adding uncertainty in step C1; the contribution of uncertainty in the classification, given the parameter 

estimates, may be excluded by skipping step C2 and comparing the probabilities generated in step C1 to 0.5 using 

procedure A; the contribution of uncertainty due to spatial 

correlation may be excluded by generating an uncorrelated 

uniform [0,1] distribution in step C2; and the contribution 

of uncertainty due to the interpolated ash tree counts per 

ha may be excluded by not adding uncertainty in step C4. 

The magnitude of the contributions of individual sources 

C2. For each pixel, generate a random number, r, from a 

correlated uniform [0,1] distribution using procedure B; if 

r ≤ p̂, designate the pixel as forest; if r > p̂, designate the 

pixel as nonforest.

C3. Calculate the total forest area, Ftotal, as the product of 

the number of forest pixels from step C2 and the 0.09-ha 

pixel area.

C4. For each pixel, generate a random number from a normal distribution with mean 0 and variance, Vâr(Âtotal) from 

(4); add the random number to the interpolated estimate of 

ash tree count per hectare, Â, to obtain a simulated ash tree 

count per hectare; multiply the simulated ash tree count per 

hectare and the 0.09-ha pixel area to obtain a simulated ash 

tree count for the pixel.

C5. Estimate Atotal as the sum of the simulated ash tree 

counts from step C4 for forest pixels from step C2.

Figure 2a—Forest/nonforest classification for 30-km by 30-km study area. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 2b—Standard errors of pixel forest/nonforest classifications 30-km by 30-km study area.

of uncertainty may be estimated by considering Vâr(Ftotal) 

and Vâr(Atotal) obtained by including contributions from all 

sources individually and in combinations.

Results

The analysis of Monte Carlo results indicated that all 

estimates stabilized to within less than 1 percent by 25,000 

simulations. Therefore, 25,000 simulations were used when 

applying procedure C to estimate the contributions of the 

various sources of uncertainty. 

The forest/nonforest maps constructed using logistic 

regression model predictions produced realistic spatial 

distributions, although no independent accuracy assessment 

was conducted (Figure 2a). However, considerable detail 

was revealed in the uncertainty map; e.g., the field structure, 

road networks, and highway on/off ramps (Figure 2b). 

Considerably less detail was revealed in the ash tree countper-hectare map, but this result was expected because of the 

fewer FIA plots available and the more continuous nature of 

the layer (Figure 3a). As expected, the greatest uncertainty 

in the latter map occurred in the same locations as the 

greatest estimated values (Figure 3b). This phenomenon is 

common to biological analyses.

The estimates obtained using procedure C revealed in 

dramatic fashion that the source of uncertainty making the 

greatest contribution to uncertainties in the estimates of 

both Ftotal and Atotal was spatial correlation in the realizations of the forest/nonforest maps (Table 1). The magnitude 

of this effect is highlighted by noting that when uncertainty 

from this source was included, 95-percent confidence 

intervals for both Ftotal and Atotal included or were close 

to including 0. The contribution of the uncertainty in the 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 3a—Ash tree count/ha interpolations for 30-km x 30-km study area.

Table 1—Monte Carlo simulation estimates from procedure C

Source of uncertainty Estimates

Model Ash tree

parameter Spatial count/ha Ftotal Atotal

estimates Classification correlation interpolation Mean SEa Mean SEa

--------ha-------- --------count---------

113.40 2860.11

X 110.93 25.37 2801.15 643.23

X 104.22 1.87 2637.11 47.61

X X 104.04 55.32 2541.56 1180.34

X X X 105.58 60.89 2633.00 1549.91

X 113.40 2860.55 47.57

X X X 105.53 18.97 2670.79 484.46

X X X X 106.25 61.92 2690.07 1576.47

a

 SE is standard error and is calculated as the square root of the variance of the mean.
645

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

Figure 3b—Standard error of pixel ash tree count-per-hectare interpolations for 30-km by 30-km 

study area.

underlying ash tree count-per-hectare layer to Vâr(Âtotal)

was much less than was the contribution from the uncertainty in the forest/nonforest layer.

Conclusions

Three conclusions may be drawn from this study. First, 

spatial correlation is a crucial contributor to uncertainty in 

map analyses that aggregate results over multiple mapping 

units. Ignoring this contribution inevitably leads to underestimates of variances and unwarranted statistical confidence 

in estimates. Unfortunately, the importance of this source 

of uncertainty is generally not known to those who conduct 

map-based analyses, and techniques for estimating its 

effects are generally unfamiliar. The second conclusion is 

that greater attention in the literature and academic environments should be given to uncertainty estimation. Third, 

estimation of uncertainty is not trivial, either conceptually 

or from a technical perspective. The necessity of decomposing very large matrices limits the size of regions that can be 

analyzed without high-speed computing facilities.

Literature Cited

Bechtold, W.A.; Patterson, P.L. eds. 2005. The enhanced 

Forest Inventory and Analysis Program: National 

sampling design and estimation procedures. Gen. 

Tech. Rep. SRS-80. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research Station. 

85 p.

Cappaert, D.; McCullough, D.G.; Poland, T.M.; 

Siegert, N.W. 2005. Emerald ash borer in North 

America: a research and regulatory challenge. American 

Entomologist. 51: 152–165.
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Crist, E.P.; Cicone, R.C. 1984. Application of the tasseled 

cap concept to simulated Thematic Mapper data. 

Photogrammetric Engineering and Remote Sensing. 

50: 343–352.

Foody, G. 2006. The valuation and comparison of thematic 

maps derived from remote sensing. Proceedings 

of accuracy 2006: 7th international symposium on 

spatial accuracy assessment in natural resources and 

environmental sciences. Lisbon: Instituto Geográfico 

Português: 18–31.

Gumpertz, M.L.; Wu, C.; Pye, J.M. 2000. Logistic 

regression for southern pine beetle outbreaks with spatial 

and temporal autocorrelation. Forest Science. 46: 95–107.

Haack, R.A.; Jendek, E.; Liu, H. [and others]. 2002. The 

emerald ash borer: a new exotic pest in North America. 

Newsletter of the Michigan Entomological Society. 

47: 1–5.

Herms, D.A.; McCullough, D.G.; Smitley, D.R. 2004.

Under attack. American Nurseryman. October 1, 2004. 

http://www.emeraldashborer.info/educational.cfm. (June 

2005).

Kauth, R.J.; Thomas, G.S. 1976. The tasseled cap: a 

graphic description of the spectral-temporal development 

of agricultural crops as seen by Landsat. In: Proceedings 

of the symposium on machine processing of remotely 

sensed data. West Lafayette, IN: Purdue University: 

41–51.

Kennedy, W.J., Jr.; Gentle, J.E. 1980. Statistical 

computing. New York: Marcel Dekker, Inc. 591 p.

McCullough, D.G.; Katovich, S.A. 2004. Emerald 

ash borer. NA-PR-02-04. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, State and 

Private Forestry Northeastern Area. 2 p.

McRoberts, R.E. 2006. A model-based approach to 

estimating forest area. Remote Sensing of Environment. 

103(1): 56–66.

McRoberts, R.E.; Bechtold, W.A.; Patterson, P.L. 

[and others]. 2005. The enhanced Forest Inventory 

and Analysis Program of the USDA Forest Service: 

historical perspective and announcement of statistical 

documentation. Journal of Forestry. 103(6): 304–308.

Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. 

1973. Monitoring vegetation systems in the Great Plains 

with ERTS. In: 3rd ERTS symposium. NASA SP-351, 

Volume 1. Washington, DC: NASA: 309–317.

van Oort, P.A.J.; Bregt, A.K.; de Bruin, S. 2006. Do 

users ignore spatial data quality? In: Proceedings 

of accuracy 2006: 7th international symposium on 

spatial accuracy assessment in natural resources and 

environmental sciences. Lisbon: Instituto Geográfico 

Português: 236–246.

Zhang, J.; Goodchild, M. 2002. Uncertainty in 

geographical information. London: Taylor and Francis. 

266 p.
647

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

An Aquatic Multiscale Assessment and Planning Framework 

Approach—Forest Plan Revision Case Study

Kerry Overton, Ann D. Carlson, and Cynthia Tait

Kerry Overton, fishery biologist/program manager, USDA 

Forest Service, Boise Aquatic Sciences Lab, Rocky Mountain Research Station, Boise, ID 83702; Ann D. Carlson, 

aquatic ecologist, USDA Forest Service, Northern Region, 

Missoula, Montana 59807; and Cynthia Tait, aquatic ecologist, USDA Forest Service, Intermountain Region, Ogden, 

Utah 84401-2310.

Abstract

The Aquatic Multiscale Assessment and Planning Framework is a Web-based decision-support tool developed to 

assist aquatic practitioners in managing fisheries and watershed information. This tool, or framework, was designed 

to assist resource assessments and planning efforts from 

the broad scale to the fine scale, to document procedures, 

and to link directly to relevant research. The framework is 

a hierarchical, hyperlinked template that is readily updateable. For aquatic resources in a planning area, such as 

those occupied by salmonid fishes, the framework produces 

tabular and spatial displays of (1) current habitat conditions 

and distributions, (2) desired future conditions, (3) risks and 

threats to the species concerned, (4) analysis approaches, (5) 

a conservation and restoration strategy, and (6) a monitoring, inventory, and research strategy. The framework also 

provides a logical system for developing, tracking, and 

documenting aquatic information at various spatial scales 

(subwatershed to basin) and can hyperlink management 

questions and data to best available science and procedures. 

For example, different analysis approaches (e.g., extinction 

risk matrices, influence diagrams, probabilistic networks) 

along with supporting science or case studies are directly 

linked and downloadable from the framework. The framework helps define and display information assumptions and 

gaps and is transparent and defensible.

Keywords: Aquatic population information, cutthroat 

trout, decision-support tool, multiscale assessments, multiscale planning, salmonid fish extinction risks, watershed 

information.

Introduction

The Aquatic Multiscale Assessment and Planning framework is a Web-based decision-support tool developed to 

facilitate conservation and restoration planning for aquatic 

species influenced by national forest management: (http://

www.fs.fed.us/rm/boise/research/techtrans/projects/multiscale_home.shtml). This tool, or framework, was designed 

to support resource assessments and planning efforts from 

the broad scale to the fine scale, to document procedures, 

and to hyperlink directly to relevant research. The framework was originally developed in collaboration between 

U.S. Forest Service Northern (R1) and Intermountain (R4) 

Regions and the Rocky Mountain Research Station (Boise, 

ID) to help aquatic biologists organize data and prioritize 

management actions for the restoration of native trout 

populations. Though not specifically designed for national 

forest plan revision, the framework lends itself well to this 

purpose, and forest planners have used all or parts of the 

framework to craft various planning components (e.g., 

species viability assessments, desired conditions, aquatic 

conservation strategies) required under the 1982 Planning 

Rule or the new 2005 Planning Rule (36CFR219, http://

www.fs.fed.us/emc/nfma/index.htm).

The framework provides a logical template for developing, tracking, and documenting aquatic population and 

watershed information. It summarizes data at various 

spatial scales, distinguishes quantitative from qualitative 

data, and is transparent and defensible. The framework 

hyperlinks data and management options to procedures, 

best available science, and case studies and spotlights 

assumptions made in the analysis process, as well as data 

gaps.

The framework template follows modern principles 

of conservation and restoration for aquatic ecosystems, 

addressing (1) ecological patterns and processes that 

contribute to persistence of aquatic ecosystems and species 

(Naiman and others 2000, Rieman and others 2006, Thurow 

and others 1997), (2) natural variability, ecosystem and 

species diversity, and population resilience and resistance 
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GENERAL TECHNICAL REPORT PNW-GTR-802

to disturbance (Angermeier 1997, Rieman and others 2006), 

(3) habitats needed for all life stages of aquatic species 

(McElhaney and others 2000); (4) genetic diversity of 

populations (Allendorf and Leary 1988, Likens and Graham 

1988, Rieman and others 1993, Soule 1987); and (5) habitat 

connectivity for dispersal and migration (Dunham and 

Rieman 1999, McElhany and others 2000, Rieman and 

Dunham 2000, Rieman and others 1993, Sheldon 1998).

The framework is designed to support U.S. Forest 

Service regional species status overviews, subbasin assessments, watershed analyses, cumulative effects assessments, 

National Enviromental Policy Act analyses, and consultation. Spatially explicit outputs are used to define and display 

risks and threats associated with fish, fish habitats, and 

watershed conditions. Broad-scale summaries provide 

context for fine-scale projects to help prioritize management 

actions for addressing risks and threats. The transparent 

design helps step down data and priorities for field unit 

verification and implementation.

The framework provides a hierarchical approach for 

summarizing available fisheries information at various 

spatial and watershed scales. All the geographic scales of 

a drainage system function together to create and maintain 

aquatic habitats (Wissmar 1997). The subwatershed (6th

field Hydrologic Unit Code [HUC], sensu Maxwell and 

others 1994) is often synonymous with local fish populations or their life stages, risks and threats, or project-level 

management action assessments or all. Subwatersheds that 

support self-sustaining populations, i.e., strongholds, act as 

sources for populations that bolster weaker populations or 

recolonize vacant habitats. Aquatic data used for national 

forest land management plans are usually summarized at 

the subwatershed scale.

In order to determine how habitat conditions are 

distributed across a larger geographic area, subwatershed 

information is aggregated up to the subbasin scale (4th field 

HUC). The subbasin is the primary broad-scale summary 

unit for addressing salmonid fish extinction risks. The 

subbasin acts as a terminal aquatic environment and is the 

spatial scale where metapopulations, or interacting groups 

of two or more local populations, operate as a hedge against 

extinction (Lee and others 1997, Rieman and others 1993). 

Thus, a multiscale approach allows for broader interpretations of current conditions in terms of salmonid population 

dynamics. In addition, aggregating data up to larger scales 

such as the basin (3rd field HUC) provides context for subbasin assessments and national forest plans.

The Six-Step Framework Template

The framework process is organized into six steps (Figure 

1). For aquatic resources in the planning area, following 

these steps produces tabular and spatial displays of (1) current conditions and distributions of populations or habitats, 

(2) desired future conditions, (3) risks and threats to the species concerned, (4) analysis approaches, (5) a conservation 

and restoration strategy, and (6) a monitoring, inventory, 

and research strategy.

The steps are in a logical order of priority for completing habitat and population assessments and providing base 

data for strategic planning. Framework steps 1 through 3 

can be worked on concurrently but should be completed 

prior to moving onto step 4. Information needed for each 

step is gleaned from numerous sources, including published 

research, broad-scale assessments, and conservation strategies (e.g., FEMAT 1993, Pacific Anadromous Fish Strategy 

[PACFISH] 1995, Inland Fish Strategy [INFISH] 1995, Lee 

and others 1997), and forest-level inventory and monitoring. 

Information used can be a combination of quantitative and 

qualitative data. For each step, the Web-based framework 

provides hyperlinks to data-collection protocols used, 

relevant research and case studies, assessment data, and 

planning products.

The following is a case study from the Bridger-Teton 

National Forest in Wyoming, which used the framework and 

its six steps to structure a conservation and restoration strategy for Bonneville cutthroat trout (Oncorhynchus clarki 

utah) as a part of the forest plan revision process. The forest 

used the framework to assemble data on the cutthroat, its 

habitat, stream-riparian ecosystems, and watersheds (Figure 

1), with a focus on the Central Bear River subbasin.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Step 1—Current Condition, Status, and 

Distribution of Native Trout Populations and 

Associated Stream-Riparian Habitats

Step 1 (Figure 1) provides the environmental baseline or 

current condition for cutthroat trout in the Central Bear 

River subbasin and is assumed to reflect natural disturbances and the effectiveness of the Forest’s current land use 

plan direction and past management actions.

Step 1(A) displays the current distribution of cutthroat 

trout populations across their assumed historical distribution in Central Bear River subbasin. This information was 

based on stream survey data collected by aquatic researchers as well as State, tribal, and forest biologists and compiled at the subwatershed level.

The status of the trout population in each subwatershed 

was coded as strong, depressed, or absent (includes extirpated) based on criteria developed for assessing interior 

Columbia River basin fish populations (Lee and others 

1997). For example, subwatersheds were coded as having 

strong populations if the subwatershed had all the following 

conditions:

• Fish presence had been verified within the last 

10 years using standard sampling methods.

• Major life history stages (e.g., resident, migratory) 

that historically occurred in the subwatershed were 

still present.

• Fish numbers were stable or increasing, and the 

population was no less than half of its historical 

size or density.

• The population or metapopulation was at least 

5,000 individuals or 500 adults; if the population 

size was based on a population that extended 

outside the subwatershed, the subwatershed was an 

important core area for this larger population.

In addition, current conditions of stream-riparian 

habitats were evaluated for each subwatershed. Qualitative 

approximations of subwatershed conditions were obtained 

from Inland West Water Initiative (IWWI) assessments (on 

file with Rocky Mountain Research Station, Boise Aquatic 

Sciences Lab, Idaho Water Center, Suite 401, 322 East 

Front St., Boise, Idaho 83702) and plotted in step 1(B). The 

Bridger-Teton National Forest chose to display water quality 

integrity, but other indicators of watershed condition could 

have been used.

Data for populations and subwatershed conditions were 

organized in standardized Excel format. The data were 

readily displayed and reviewed by creating geographic 

information system (GIS) spatial data layers both by subwatershed and subbasin and creating maps using ArcGIS©

tools.

In forest plan revisions, information developed in step 

1 could be incorporated into the comprehensive evaluation 

report (CER), a constituent of the planning set of documents, which provides current conditions and analyses for 

the Forest Plan.

Step 2—Desired Future Condition of Native Trout 

Populations and Associated Stream-Riparian 

Habitats

Desired future conditions for fish populations and watershed conditions were generated by forest biologists familiar 

with the subbasin. They used the Conservation Rules of 

Thumb summarized in earlier broad-scale aquatic assessments (FEMAT 1993, INFISH 1995, Lee and others 1997, 

PACFISH 1995):

• The greater the population size, the greater the 

chance of persistence.

• Population recovery potential is greater the closer 

you are to a source or strong population.

• Preserving genetic and phenotypic diversity 

requires maintaining populations throughout a wide 

geographic range in a variety of habitats.

• Project assessments and planning will have to 

address habitat disruption and population responses 

at both the local and regional scales.

In addition, the biologists considered key aquatic 

habitat characteristics, such as water quality/quantity, channel integrity, and riparian vegetation, and visualized those 

future states that would allow persistence and sustainability 

of aquatic populations. They recognized that ecologically 

healthy watersheds are maintained by natural disturbances 

(e.g., fire, landslides, and debris torrents, channel migration) 

that create spatial heterogeneity and temporal variability to 

the physical components of the system (Naiman 1992) and 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 1—The multiscale framework steps and example products. The schematic displays example outputs specific to the 

individual steps (six) within the framework tied to the document step-by-step description. 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management
652

GENERAL TECHNICAL REPORT PNW-GTR-802

that management consistent with natural variation should 

lead to more diverse, resilient, and productive biological 

systems (Rieman and others 2006).

Step 2 (B-D) displays desired future conditions over a 

period of time for cutthroat trout in the subbasin, showing 

fish population status and distribution at short-, mid- and 

long-term intervals. The long-term desired condition (Step 

2(D)) would be expected to represent a healthy, self-sustaining metapopulation occupying ≥ 50 percent of its historical 

range along with strong population characteristics and 

high-quality habitats (FEMAT 1993, Lee and others 1997).

Outputs from Step 2 would be consistent with the 

desired condition component of the plan in the forest plan 

revision process, which is to describe how management 

activities will cause desired natural resource conditions to 

be obtained. 

Step 3—Risks and Threats to Native Trout 

Populations and Associated Stream-Riparian 

Habitats

Extinction risks for salmonids are influenced by complex 

and interacting factors that are often difficult, if not 

impossible, to identify and measure. Despite this difficulty, 

understanding the nature of the extinction process and the 

characteristics of local fish populations and habitats can 

lead to management prescriptions that minimize risks. Relative extinction risks include deterministic, stochastic, and 

genetic factors at several spatial and temporal scales (Rieman and others 1993). The risk portion of step 3(A-C) was 

generated for cutthroat trout by agency biologists through 

application of an extinction risks matrix (Rieman and others 

1993) that matched population characteristics, such as size, 

isolation, and survival rates, with a variety of environmental 

and demographic parameters. The outcome was an estimate 

of relative risk of extinction (low to extreme) for trout in 

a given reach, subwatershed, or across a subbasin. For the 

Central Bear River subbasin, step 3(A) maps extinction 

risks in subwatersheds based on trout population size and 

isolation.

The persistence of trout populations in a subwatershed 

can be threatened by land use practices such as grazing, fire 

suppression, mining operations, recreational activities, road 

construction, dams and water diversions, and introduction 

of invasive species. Threats to fish populations or their habitat may be direct, as in the alteration of riparian and channel 

structure by overgrazing, or indirect, such as when high 

road density in the uplands impacts water quality and timing of flows. For the Central Bear River subbasin, biologists 

spatially displayed possible threats to cutthroat populations 

by mapping road densities (step 3(B)), and water quality 

integrity (step 3(C)). Aquatic specialists then evaluated the 

threats and identified those that Forest Service management 

or cooperative partnership actions could influence.

In the national forest plan revision process, risks and 

threats to native fish species on the subwatershed level 

would be included in the CER.

Step 4—Analysis and Interpretation of the 

Risks and Threats That Influence Native Trout 

Populations and Associated Stream-Riparian 

Habitats

In step 4, national forest biologists analyzed risks and 

threats to populations and their habitats at the subwatershed 

level, identifying those risks and threats most urgently to 

be considered and addressed by management. Influence 

diagrams, or cause-and-effect diagrams (step 4 (A, B)) were 

used to display the analysis thought process. These unambiguous diagrams helped biologists, managers, scientists, 

and interested publics visualize, discuss, and review all 

the factors and pathways that potentially impacted population and watershed condition. Forest biologists created an 

influence diagram for risks and threats to trout population 

persistence (step 4 (A)) and a separate influence diagram 

for risks and threats to habitat (step 4 (B)). The step 4 (A) 

diagram included elements such as migration barriers and 

presence of invasive species. To predict the future status of 

cutthroat trout populations in the subbasin, both influence 

diagrams were incorporated into a probabilistic network 

(in this case, a Bayesian belief network [Lee and Rieman 

1997, Rieman and others 2001]) and run under different 

management scenarios. For example, the biologists found 

that if riparian road densities were high in a subwatershed 

where invasive species (brook trout Salvelinus fontinalis) 

were present, the future population status for cutthroat 
653

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

was depressed. In this way, the forests could test different 

management alternatives and predict their impacts on trout 

population status at both the subwatershed and the subbasin 

scale (step 4(C)).

The framework facilitates risks and threats analysis by 

providing hyperlinks to potential analysis tools and current 

conservation literature. For example, links connect the user 

to the trout extinction risk matrix (Rieman and others 1993), 

effects of Federal land management alternatives on population trends (Rieman and others 2001), population viability 

assessment of salmonids using probabilistic networks (Lee 

and Rieman 1997), and conservation assessments for various fish species.

For forest plan revision, step 4 risks and threats analyses can be incorporated into the CER and thereafter provide 

background information for crafting the desired conditions 

and objective components of the plan. Step 4 is useful in the 

planning process in that it provides a transparent view of 

the spatial and temporal interaction of biological, physical, 

chemical, and social processes at work in the planning area. 

Implications of these interactions are used as the basis for 

development of the conservation and restoration strategies 

outlined in the next step of the framework, below.

Step 5—Conservation and Restoration Strategies 

to Address Risks and Threats to Native Fish 

Populations and Associated Stream-Riparian 

Habitats

In step 5, the forests used information gained in steps 1 

through 4 to design a conservation and restoration strategy that could maintain and restore aquatic ecosystems 

at subwatershed through subbasin scales. A network of 

subwatersheds (green polygons in step 5(A)) was selected to 

serve as strongholds to conserve cutthroat trout or highquality habitat or both. These conservation watersheds were 

intended to provide refugia across the landscape, areas 

where management focuses on protection and maintenance 

of quality aquatic habitat and strong native fish populations. 

Habitats that support the most productive, diverse, and 

otherwise critical populations provide the best opportunities 

for ensuring short-term persistence and provide an essential 

nucleus for rehabilitating more complete networks in the 

future (Rieman and others 2000).

In addition, restored watersheds, with assigned priorities of low to high, were essential to delineate because 

protection of fish strongholds alone would not be sufficient 

to sustain populations (Franklin 1993). By restoring 

watershed processes that create and maintain habitats across 

stream networks, the long-term productivity of aquatic and 

riparian ecosystems can be ensured (Rieman and others 

2000, Thurow and others 1997). Prioritizing the restoration 

in subwatersheds considers the extent of habitat degradation 

and how much natural diversity and ecological functioning still exists. For example, high-priority watersheds for 

restoration are those with limited loss of function and 

condition and that will likely serve as the next generation of 

strongholds for fish and water quality. Once the watersheds 

are prioritized for restoration, the type of restoration action 

implemented can be active (fence construction, bank stabilization, willow planting) or passive. Passive restoration 

relies on the implementation of guidelines, design criteria 

(i.e., Forest Service Manual and Handbook direction) and 

best management practices to maintain watershed processes 

and habitat conditions. Because passive restoration primarily maintains current conditions, active restoration is often 

needed to move a degraded system toward recovery.

Planning out the timing and sequence of restoration 

actions is aided by using displays generated by the framework. Step 5(B, C) illustrates the conservation and restoration strategy development process used by the Central Bear 

subbasin forests for a hypothetical watershed. This watershed has two subwatersheds with stronghold populations 

of cutthroat trout (step 5(A)). The primary risk to these two 

populations is isolation caused by threats such as degraded 

riparian areas and fish migration barriers. Through risk 

and threat analysis in step 4, the biologists determine the 

relative importance of the threats to population persistence 

and prioritize the timing and spatial sequence of restoration 

actions. The restoration strategy for the watershed can then 

be mapped (step 5(C)).

In the forest plan revision process, step 5 conservation 

and restoration strategies could be incorporated into various 

plan components. Conservation and restoration strategies 
654

GENERAL TECHNICAL REPORT PNW-GTR-802

could appear in “Guidelines and Special Areas” within the 

forest plan components and could be used as a foundation of 

the monitoring program in the plan set of documents.

Step 6—Inventory, Monitoring, and Research 

Strategies

Inventory and research strategies fill data gaps and validate 

assumptions. If information required for the framework 

(e.g., population status, habitat condition) is incomplete or 

outdated, inventories or research may be needed to obtain 

the data (step 6(A)). Monitoring strategies are used to track 

the progress of conservation and restoration actions. Three 

types of monitoring generally apply to species assessments and forest planning: implementation, effectiveness, 

and validation. Implementation or compliance monitoring ensures that management actions were implemented 

as planned, such as moving livestock before utilization 

exceeds a critical level. Effectiveness monitoring assesses 

the progress of management actions in attainment of desired 

conditions. Validation monitoring corroborates the assumptions made during evaluation and analysis and is important 

for determining if restoration activities result in desired 

population and habitat conditions.

Forest biologists in the Upper Salmon subbasin chose 

the PACFISH/INFISH Biological Opinion Effectiveness 

Monitoring Program (Kershner and others 2004) to measure 

and evaluate the effectiveness of their actions and best 

management practices in restoring and maintaining the 

structure and function of trout habitats at multiple landscape scales. In addition, periodic fish sampling surveys in 

specific subwatersheds monitored population responses to 

restoration activities, and results were aggregated across the 

subbasin to ascertain trends toward or away from desired 

condition for the planning area (step 6(B)). In our example, 

step 6(B) shows a movement away from the desired condition (Time 1) toward a less desired condition (Time 2). 

Research needs that emerged from the framework process 

included developing a decision-support strategy for barrier 

removal and brook trout control.

For national forest plan revision, the framework’s 

inventory, monitoring, and research strategies would slip 

seamlessly into the monitoring program portion of the planning set of documents.

Conclusions

The Aquatic Multiscale Assessment and Planning Framework, as a Web-based decision-support tool, will facilitate 

conservation and restoration planning for aquatic species 

and watersheds influenced by national forest management. 

Through a six-step process that organizes information into 

tabular and spatial (GIS) formats, the framework helps 

aquatic practitioners visualize and analyze complex fisheries and watershed data, from the site-specific project level 

up through national forest and regional spatial scales. The 

framework provides a one-stop shopping site for maintaining and updating data and analysis procedures

For aquatic resources, such as cutthroat trout populations and their habitat in a particular planning area, the 

framework produces tabular and spatial displays of (1) 

current status and distribution of populations or habitats, (2) 

desired future conditions, (3) risks and threats to the species 

concerned, (4) analysis approaches, (5) a conservation and 

restoration strategy, and (6) a monitoring, inventory, and 

research strategy. The framework also provides a logical 

system for developing, tracking, and documenting aquatic 

information. It summarizes available information at various 

spatial scales (subwatershed to basin) and can hyperlink 

the user to best available science resources and supporting 

information. For example, different analysis approaches 

(e.g., extinction risk matrices, influence diagrams, probabilistic networks) along with scientific papers or case studies 

are directly linked and downloadable from the framework. 

The framework helps define and display information 

assumptions and gaps and is transparent and defensible.

This transparent and defensible design will assist in 

clarifying decision outcomes and the elements and pathways used to support them. The ability to clearly track 

the background data and thought processes that go into 

controversial land use planning and decision documents 

may reduce misunderstandings and mistrust in stakeholders 

and enable timely and effective restorative actions that will 

benefit fishes and other aquatic organisms.
655

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

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Angermier, P.L. 1997. Conceptual roles of biological 

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Dombeck, M., eds. Watershed restoration: principles and 

practices. Bethesda, MD: American Fisheries Society: 

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Dunham, J.B.; Rieman, B.E. 1999. Metapopulation 

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INFISH. 1995. Inland native fish strategy: interim 

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in eastern Oregon and Washington, Idaho, western 

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others]. 2004. A plan to monitor the aquatic and 

riparian resources in the area of PACFISH/INFISH 

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steelhead. Gen. Tech. Rep. RMRS-121. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 104 p.

Lee, D.C.; Rieman, B. 1997. Population viability 

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Department of the Interior, Bureau of Land Management. 

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defining opportunities for managing fish and forest 

health. Environmental Management. 25(4): 425–444.

Rieman, B.E.; Peterson, J.T.; Clayton, J.L. [and others]. 

2001. Evaluation of potential effects of Federal land 

management alternatives on trends of salmonids and 

their habitats in the interior Columbia River basin. Forest 

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patterns of diversity, rarity, and risk. Conservation 

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M., ed. Viable populations for conservation. Cambridge, 

England: Cambridge University Press: 175–183.

Thurow, R.F.; Lee, D.C.; Rieman, B.E. 1997. Distribution 

and status of seven native salmonids in the interior 

Columbia River basin and portions of the Klamath and 

Great Basins. North American Journal of Fisheries 

Management. 17: 1094–1110.

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Fisheries Society: 66–79.
657

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

Jennifer Pontius, Richard Hallett, Mary Martin, 

and Lucie Plourde

Jennifer Pontius and Richard Hallett, research ecologists, 

USDA Forest Service, Northern Research Station, Durham, 

NH 03824; Mary Martin, research assistant professor, 

and Lucie Plourde, research scientist, Complex Systems 

Research Center, University of New Hampshire, Morse 

Hall, Durham, NH 03824. 

Abstract

The hemlock woolly adelgid (Adelges tsugae Annand) 

(HWA) is an invasive insect pest that is causing widespread 

mortality of eastern hemlock (Tsuga canadensis (L.) Carr.). 

However, some stands remain living more than a decade 

after infestation. The ability to target management efforts in 

locations where hemlock is most likely to tolerate prolonged 

HWA infestation is critical to successful integrated pestmanagement programs. Here, we build a landscape-scale 

hemlock risk model for the Catskills region of New York 

based on coverage like slope and aspect derived from a 

traditional Digital Elevation Model (DEM). We also show 

that additional data layers derived from hyperspectral 

sensors such as NASA’s Airborne Visible InfraRed Imaging 

Spectrometer (AVIRIS) can provide critical information for 

geographic information system (GIS) modeling. The initial 

landscape-only model was able to predict the rate of overall 

decline following HWA infestation for 21 plots across 

the Northeast with R2

 = 0.35, p = 0.027. Adding foliar N 

concentration to our model improved results to R2 = 0.79, 

p = 0.0009. An AVIRIS-derived hemlock abundance coverage was then used to define the hemlock resource and its 

relative vulnerability to rapid decline. These results indicate 

that the inclusion of both landscape and chemical variables 

is critical to predicting hemlock vulnerability to HWA, 

and that landscape-scale modeling in a GIS platform is 

possible with the addition of hyperspectral remote sensing 

coverages. Whereas the resulting risk map covers only the 

Catskills region of New York, the relationships established 

here should be applicable to HWA infestation across the 

A Landscape-Scale Remote Sensing/GIS Tool to Assess 

Eastern Hemlock Vulnerability to Hemlock Woolly AdelgidInduced Decline

range of eastern hemlock, providing a basis for forest land 

management agencies to make informed management 

decisions.

Keywords: AVIRIS, Adelges tsugae, forest health, 

invasive insect pest, susceptibility.

Introduction

Insect pests and pathogens represent the largest and most 

pervasive agents of natural disturbance in North American 

forests, with potentially significant economic, aesthetic, 

and ecological consequences for northern forest ecosystems 

(Ayres and Lombardero 2000). In order for land managers 

to make successful management decisions for mitigation 

and treatment activities, they must know the location and 

extent of the host resource as well as the anticipated risk of 

host mortality. Traditionally, land managers have relied on 

plot-based field sampling efforts to supply this information. 

Although this is useful, a comprehensive, landscape-scale, 

spatially continuous coverage of the resource and its 

vulnerability is needed to fully assess the potential impacts 

on the forest resource and to devise successful management 

strategies.

Recently, remote sensing technologies have greatly 

increased the amount and quality of information that is 

available for landscape-scale ecological risk modeling. 

This information includes abundance maps for individual 

tree species, detailed forest decline assessments (including 

previsual symptoms), and foliar chemical concentrations 

(Foody 2002; Martin and Aber 1997; Ollinger and others 

2002; Plourde and others 2007; Pontius and others 2005, 

2006; Smith and others 2002). Such remote-sensing-based 

products, combined in a geographic information system 

(GIS) platform with traditional topographic-based data 

layers, have expanded the tools available for risk modeling. 

They provide the potential to greatly enhance our ability 

to create spatially continuous, landscape-scale models of 

ecosystem function and response to disturbance. 

Hemlock woolly adelgid (Adelges tsugae Annand) is 

an invasive insect pest that is causing widespread mortality 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Although significant, these landscape variables typically explain only a small portion of the overall variation 

in hemlock decline. Adding foliar chemistry to site factors 

at the plot level, Pontius and others (2006) predicted an 

11-class decline rating with 98 percent one-class tolerance 

accuracy on an independent validation set, indicating that 

foliar chemistry may also play an important role in HWA 

dynamics and hemlock decline. Herbivory is often positively correlated with foliar nitrogen concentrations, with 

low nitrogen-limiting insect populations (McClure 1980, 

Schowalter and others 1986, White 1984). Nitrogen can 

be particularly limiting to insects because there is a large 

difference between the nitrogen concentration of plants 

(around 2 percent dry weight) and that of insects (approaching 7 percent) (Dale 1988). This link between nitrogen and 

aphid success has been documented for a variety of host 

species (Carrow and Betts 1973, Douglas 1993, Koritsas and 

Garsed 1985, McClure 1980). 

For relatively immobile insects such as HWA, the 

nutritive quality of forage becomes even more important. 

McClure (1991, 1992) found that N fertilization resulted in 

increased relative growth rate, survivorship, and fecundity 

of HWA, thus increasing hemlock vulnerability and reducing the effectiveness of implanted and injected pesticides. 

Regionally, Pontius and others (2006) also found that foliar 

N concentration was the strongest correlate with HWA 

infestation, with higher N consistently associated with 

higher HWA population levels. 

Although useful in identifying key factors in the 

hemlock decline complex, such plot-level and greenhousebased studies do little to assist land managers in making 

critical planning and treatment decisions for their forests. 

Here, we apply a plot-level empirical model from fieldbased observations of hemlock decline across the Northeast 

to a landscape-scale GIS model for hemlock woolly adelgid 

risk assessment. Because of the unique ecological niche 

occupied by hemlock stands, it is important to identify the 

stands that are most likely to tolerate HWA infestations so 

that hemlock can be preserved as a component of forest 

habitats in the region. At the same time, stands likely to 

suffer high rates of mortality can be evaluated for integrated 

pest management activities or conversion to other species.

of eastern hemlock (Tsuga canadensis (L.) Carr.). Current 

rates of spread into uninfested areas are estimated at 10 to 

15 miles per year, and all indications are that HWA will 

penetrate the entire range of eastern hemlock (McClure 

1995a). Because HWA may infest all hemlock stands eventually, susceptibility assessments (assessments of likelihood 

of infestation) are not necessarily informative for long-term, 

landscape-scale risk modeling. However, there is evidence 

of differing hemlock vulnerability (ability to tolerate 

prolonged HWA infestation). Many infested hemlock have 

shown minimal resistance to A. tsugae and little chance for 

recovery (McClure 1995b). However, some stands remain 

living more than a decade after infestation (Pontius and 

others 2006). Indeed, two adjacent hemlock stands can 

often respond very differently to attack, with differences 

commonly attributed to topographic characteristics such as 

landscape position, slope, and aspect (Bonneau and others 

1997, Hunter 1993, Orwig and others 2002, Royle and Lathrop 1999). Greenhouse studies have shown that the presence 

of HWA alone did not cause the death of hemlock seedlings, 

and that it is the combined stress of drought and infestation 

that ultimately leads to mortality (Sivaramakrishnan and 

Berlyn 1999). In the field, reduced growth rates were associated with infested trees on ridgetop and southwestern facing 

sites, but not with those on well-watered sites (Sivaramakrishnan and Berlyn 1999). Pontius and others (2006) found 

that several site factors could be used to predict hemlock 

decline across the Northeast. The severest hemlock decline 

was associated with markedly low growing-season precipitation levels, southern and western exposures, and ridgetop/

side-slope positions.

All of these landscape variables are in some way related 

to potential soil-moisture content, indicating that water 

availability may be an additional stressor, accelerating 

decline in the drought-sensitive eastern hemlock (Bonneau 

and others 1997, Orwig and Foster 1999, Orwig and others 

2002, Young and others 1999). Orwig and others (2002) 

concluded that although the duration of infestation primarily controls the intensity of hemlock decline and mortality, 

stands on xeric aspects succumb most rapidly.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Objectives

The goal of this work was to build a data-driven, empirical decline model for hemlock vulnerability to HWA that 

could be applied to a spatially continuous GIS model. Here 

we considered variables that were identified in previous 

research to be linked to HWA/hemlock decline and that 

were available in landscape-scale, wall-to-wall coverages. 

Using plot-level data from a regional set of infested hemlock 

stands, our specific objectives were to:

1. Develop and evaluate data-driven, quantitative 

linear models to predict the average rate of decline 

following HWA infestation based on (1) only 

landscape variables and (2) landscape variables plus 

foliar N concentrations.

Figure 1—The study area spanned the full range of hemlock health and infestation histories over 11 states in the Northeast. Of the 

48 total plots, 27 were concentrated in the Catskills region of New York, where hyperspectral coverages were available for GIS 

modeling.

2. Use the most effective model to incorporate key 

variables into a GIS model to map relative risk to 

hemlock on a landscape scale in the Catskills 

region of New York.

Methods

Plots in mature hemlock (where hemlock occupies >50 percent of the canopy) were established across a wide range of 

hemlock health, HWA infestation levels, site characteristics, 

and stand demographics. This included 48 sites characterizing the extremes of hemlock resistance and vulnerability to 

HWA from Pennsylvania to Maine (Figure 1). At each plot, 

a minimum of five hemlocks was sampled yearly between 

2001 and 2005. Whereas statistical models were based on 
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GENERAL TECHNICAL REPORT PNW-GTR-802

(Pontius and others 2002), percentage fine twig dieback, and 

live crown ratio (USDA Forest Service 1997). 

Because the goal was to calculate one summary variable to describe hemlock decline, we designed a method to 

normalize and then average all measured health variables 

into one value. Individual measurements for each variable 

were first normalized and rescaled to a 0 to 10 category 

value based on the quantile distribution cut-offs from a 

database of over 1,000 northeastern hemlock measurements 

(Table 1). These new normalized category values were then 

averaged to determine one summary decline rating that best 

described overall tree status (a continuous variable where 

0 = perfect health, 10 = dead). These summary values were 

averaged over all trees for each plot yearly for the duration 

of the study. 

The number of years since infestation was determined 

based on the first year that any HWA was witnessed on any 

hemlock within the plot. This is often first noticed at very 

low infestation levels (less than 5 percent infestation), typically well before the first year that HWA populations reach 

outbreak levels. In order to maximize accuracy of initial 

infestation dates, plots were selected from stands where 

monitoring and sampling have been conducted yearly by 

State, Federal, local, or private groups. We then calculated 

the change in overall plot-level decline from initial infestation to the current year to determine the dependent variable: 

plot-level data, development of a landscape-scale GIS risk 

coverage was limited to the Catskills region of New York 

where hyperspectral imagery was available.

Field and Laboratory Methods

Each plot was sampled and evaluated yearly for a suite of 

decline symptoms, foliar chemistry, and HWA infestation 

levels. Site, stand, climate, and soil physical and chemical 

characteristics by genetic horizon were added for all plots 

by the final year of the study. Here we considered only those 

variables that would be available as digital landscape-scale 

coverages. This included slope, aspect, and landscape 

position derived from a digital elevation model (DEM). 

Additional data layers of foliar N (Martin and Aber 1997, 

Smith and others 2003), and percentage hemlock basal area 

(Pontius and others 2005) derived from hyperspectral sensors were also considered for inclusion in the second model. 

Rather than rely on a nominal variable of vulnerable/

tolerant for model development, a continuous overall 

decline value was calculated for all trees. This overall 

decline value was assessed using methods specifically 

designed to quantify the various, sequential symptoms that 

follow A. tsugae infestation. This included the percentage of 

terminal branchlets with new growth, percentage transparency (quantified using a concave spherical densiometer 

Table 1—Decline ratings

Overall New Canopy Fine twig Live

Class decline status growth transparency dieback crown

-----------------------------Percent-----------------------------------

0 Perfect health > 98 0 to 3 0 > 97

1 Very healthy 96 to 98 2 to 5 - 91 to 96

2 Healthy 94 to 96 4 to 7 5 78 to 90

3 Pre-visual decline 86 to 94 6 to 9 64 to 77

4 Decline first visible 69 to 86 8 to 12 10 52 to 63

5 Early decline 48 to 69 11 to 18 15 42 to 51

6 Moderate decline 23 to 48 17 to 26 20 to 30 30 to 41

7 Severe decline 4 to 23 25 to 40 35 to 45 20 to 29

8 Extremely unhealthy 2 to 4 39 to 64 50 to 60 10 to 19

9 Death imminent 1 to 2 63 to 67 65 to 80 1 to 10

10 Dead 0 > 67 85 to 100 0

Overall summary decline ratings were calculated by averaging the class assignment for each measured variable according 

to Table 1. These class assignments were then averaged to summarize overall decline status.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

average yearly decline since infestation. This continuous 

output variable represents how rapidly health deteriorates 

and allows for flexibility in interpretation, depending on the 

needs of the user. 

In addition to decline variables, the five canopy dominant hemlocks on each plot were sampled yearly between 

2001 and 2005 for foliar chemical analyses. Needles were 

dried at 70 °C and ground to pass a 1-mm-mesh screen. A 

NIRSystems spectrophotometer was used to measure foliar 

nitrogen (N) concentrations (Bolster and others 1996). 

Dried and ground foliage was digested using a microwaveassisted, acid digestion procedure (US EPA 1995) and 

analyzed for calcium, potassium, magnesium, manganese, 

and phosphorus using an inductively coupled plasma 

spectrometer. Plot-level average chemistry was used from 

all years to compare to decline rates.

Slope and aspect were measured at plot center for 

each plot. Local physiography was assessed based on 

methods presented in Bailey and others (2004), where plots 

are assigned an ordinal classification based on landscape 

position as it relates to nutrient and moisture retention 

(streambeds and flats = 1, benches and toe slopes = 2, gentle 

midslopes = 3, moderate midslopes = 4, severe upper slopes 

= 5, and summit and shoulder positions = 6).

Predictive Model Calibration

Data from plots that are known to have been infested for at 

least 4 years were used to calibrate a linear, mixed stepwise 

model of average decline since infestation. In a mixed 

platform, forward and backward steps are enlisted. The 

most significant terms are entered first. Then any variables 

that become insignificant as the model becomes more 

complex are removed. It continues removing terms until the 

remaining terms are significant, when it changes back to the 

forward direction. Variables were retained under our mixed 

stepwise platform if the p-value was less than 0.1 and the 

variance inflation factor was below 2.0 (identifies potential 

autocorrelation between variables). The final model was 

then used to link key variables in a landscape-scale GIS 

model using ESRI® ArcGIS version 9.1. 

GIS Data Layers Used for Mapping Risk in the 

Catskills

Hemlock vulnerability to HWA is complex and linked to 

multiple site, climatic, stand, and chemical factors (Pontius 

and others 2006). Here, we were limited to those variables 

for which raster-GIS coverages are available for inclusion 

in a landscape-scale risk assessment of the Catskills. This 

included the following.

Topographic Features—

Topographic variables such as slope, aspect, and landscape 

position can easily be derived from DEMs using the 3D 

Analyst available in ArcToolbox (ESRI® ArcMap v.9.1). 

Here we used a digital raster DEM with 10-m resolution 

from the National Elevation Data set (NED) assembled by 

the U.S. Geological Survey (USGS). NED is designed to 

provide national elevation data in a seamless form with 

a consistent datum, elevation unit, and projection for the 

conterminous United States. These can be downloaded 

at no charge from http://seamless.usgs.gov. The selected 

GIS-available topographic variables included aspect (calculated as the degrees from southwest), slope (degrees), and 

physiographic landscape position (classes of 1 to 6 representing the least to most xeric landscape positions following 

Bailey and others [2004]).

Foliar Chemistry—

The researchers involved in this project have used National 

Aeronautic and Space Administration’s (NASA’s) AVIRIS 

instrument to predict foliar concentrations of N, lignin, and 

cellulose in forested areas of New England (Martin and 

Aber 1997) with a high degree of accuracy. Using these 

same methods, we have developed a map of foliar N from 

2001 AVIRIS imagery for the Catskills region of New York. 

Such coverages can be used to inform palatability or defensive chemical-based relationships related to risk assessment.

Hemlock Species Abundance Coverage—

Existing maps of hemlock abundance in the Catskills 

region were available from previous work (Pontius and 

others 2005). Using 2001 AVIRIS imagery, Mixture Tuned 

Matched Filtering in ENVI (v.4.2, ©Research Systems, Inc. 
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GENERAL TECHNICAL REPORT PNW-GTR-802

2005) was used to unmix spectra and quantify the hemlock 

signature contribution to each pixel. The availability of this 

distribution coverage allowed us to isolate only those areas 

dominated by hemlock (greater than 40 percent hemlock 

basal area) for the final risk coverage.

Risk Coverage for the Catskills

The results of this model highlight which environmental 

variables are significant in determining hemlock decline 

rates and the nature of those relationships. Whereas the 

ultimate goal is to apply the actual quantitative predictive 

model based on modeled parameter estimates for each 

variable, final published estimates of foliar nitrogen for the 

region are still being finalized. To account for this inability 

to use direct parameter estimates for this version of the 

risk coverage, coverages for each of the significant predictive variables were scaled to a continuous value from 0 to 

10 based on the nature of the modeled relationships. The 

Spatial Analyst available in ArcToolbox (ESRI® ArcMap 

v.9.1) was then used to add together the rescaled pixel values 

Figure 2—A stepwise linear regression model of landscape variables was only able to 

produce a predictive decline model that accounted for slightly more than a third of the overall 

variability witnessed in decline rates following infestation (R2

 = 0.35, RMSE = 0.19, 

p = 0.027). Variables included slope, aspect, and physiography, all discernable from digital 

elevation models in ArcToolbox.

from each significant variable, resulting in a risk map that 

highlights the cumulative effect of all significant variables 

in terms of relative hemlock vulnerability.

Results and Discussion

The final three-term predictive model included aspect and 

slope (Equation 1). This model accounted for a little over 

a third of the variability in decline rates for the 21-plot 

calibration set with a p = 0.02, R2 = 0.35, R2adjusted = 0.27

and RMSE = 0.19 (Figure 2). A PRESS statistic for jackknifed residuals of 1.04 indicates that, on average, if each 

plot was left out of the calibration and retained individually 

for validation, the average error would equal approximately 

0.23 for the 0 to 10 scale.

Decline_rate = -0.154 + (aspect × 0.002) – (slope × 0.118)

Equation 1. A landscape-variables-only model selected 

aspect (calculated as degrees from south) and slope 

(degrees) to predict the rate of decline expected following 

HWA infestation. This model accounted for 35 percent of 

the variability in the calibration set.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 3—A stepwise linear regression model that added nitrogen as an option to the 

landscape-variable-only model was able to produce a predictive decline model that accounted 

for over two-thirds of the overall variability witnessed in decline rates following infestation 

(R2

 = 0. 79, RMSE = 0.13, p = 0.0009). Nitrogen coverages were possible as additions to the 

GIS model from 2001 AVIRIS-derived hyperspectral coverages of the Catskills region. 

When we added foliar nitrogen concentrations to the 

mixed stepwise linear regression, model accuracy improved 

significantly. The resulting model based on slope, aspect, 

physiography, and foliar nitrogen concentration produced 

a p = 0.0009, R2 = 0.79, R2 adjusted = 0.69, and RMSE = 

0.13 (Figure 3). Jack-knifed residuals resulted in a PRESS

statistic of 0.55, or an average error of approximately 0.16 

on the 0 to 10 decline rating scale. 

Decline_rate = 0.643 – (aspect × 0.0003) – (slope × 0.158) +

(physiography x 0.049) ˗ (foliarN × 0.425) +

([aspect x slope] × 0.001) + ([aspect × foliarN] × 0.022)

Equation 2. The full GIS model again selected aspect 

(calculated as degrees from southwest) and slope (degrees) 

to predict the rate of decline expected following HWA 

infestation, with the addition of physiographic position and 

foliar nitrogen concentration. This model accounted for 79 

percent of the variability in the calibration set.

Using this final model based on both landscape 

variables and foliar N concentrations, we combined coverages of key variables to create a map of relative hemlock 

vulnerability to decline following HWA infestation for the 

Catskills region of New York (Figure 4). The availability 

of a hemlock distribution coverage from previous work 

(Pontius and others 2005) allows us to isolate only those 

areas dominated by hemlock (greater than 40 percent 

hemlock basal area) for the final risk coverage (Figure 5). 

The resulting coverage of hemlock and its relative vulnerability to infestation should aid land managers in targeting 

management activities in the region.

Although these quantitative models were statistically 

significant, we wanted to ensure that there was a theoretical basis for why these variables might exert influence on 

hemlock decline rates. The inclusion of landscape characteristics in our risk models has a strong theoretical basis in 

the literature. Similar to previous HWA research discussed 

in the introduction, we found that stands with a demonstrated resistance to long-term HWA infestation typically 

occupy lower physiographic positions, such as streambeds, 

flats, and toe-slopes (p = 0.0076, Figure 6). In addition to 

physiography, resistant stands were consistently found on 
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less steep terrain than susceptible stands across our calibration data set (p = 0.008, Figure 7). A weak yet significant 

correlation between aspect (in degrees from southwestern 

exposure) and the rate of decline (r = 0.24, p = 0.04) was 

also seen, with more rapid decline on southern facing 

exposures (p = 0.012, Figure 8). Significant interactions 

between aspect/slope (p = 0.06) and aspect/nitrogen (p = 

0.002) indicate that aspect may be more significant when 

other stressors (such as steeper slopes or higher nitrogen 

concentrations) are involved. 

Figure 4—A spatially continuous, comprehensive map of relative hemlock vulnerability to 

HWA-induced decline in the Catskills, New York, summarizes the cumulative effect of key 

variables identified in the statistical modeling.

The existing literature suggests that inherently low N 

concentration may limit HWA success, which, in turn, may 

impart some measure of resistance for host trees. Under low 

nitrogen conditions, concentrations may not be sufficient 

to maintain viable HWA populations. The data presented 

here support this “palatability-based” relationship between 

nitrogen and decline rates (Figure 9). The strongest correlate with hemlock decline rates across the region was the 

percentage infestation (r = -0.67, p = 0.008), with higher 

infestation levels associated with more rapid decline rates. 

In turn, the strongest correlate with HWA infestation 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 5—Masking nonhemlock pixels using an AVIRIS-derived coverage of hemlock basal 

area percentage produces a hemlock only risk map. This allows land managers to identify 

hemlock-dominated stands and their vulnerability to HWA to direct management efforts.

levels was foliar nitrogen concentration (r = 0.396, p = 

0.005), with higher infestation levels associated with higher 

nitrogen concentrations. This may explain the significant 

relationship between hemlock decline rates and foliar nitrogen concentrations (r = -0.46, p = 0.004). Higher nitrogen 

levels support a larger, more successful adelgid population, 

which is able to deplete hemlock of photosynthate more 

rapidly, leading to more rapid decline.

Whereas these statistics and jack-knifed residuals 

suggest that this final landscape and foliar nitrogen model 

is robust enough to apply to new input data, independent 

validation provides a better assessment of model accuracy. 

As a preliminary test, this model was applied to all regional 

plots, regardless of infestation history, resulting in an R2 = 

0.51 and RMSE = 0.142 (Figure 10). This reduction in model 

accuracy when newly infested plots are added is most likely 

due to a nonlinear decline response over the duration of 

infestation. Newly infested trees may decline only slightly 

in the first or second years because photosynthate reserves 

are available for injury response, defensive reaction, and 

continued productivity. Once these reserves have been 

reduced, decline becomes much more rapid. To validate 

this model, we will continue to track hemlock decline in the 

remaining plots over the next several years.
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 7—Susceptible stands were consistently found on steeper terrain than resistant stands 

(p = 0.008). Decline rating is a continuous scale from 0 (perfect health) to 10 (dead).

Figure 6—Resistant hemlock stands (R) were consistently found in lower landscape positions 

such as streambeds, flats, and toe-slopes. Susceptible stands (S) were consistently located on 

more xeric landscape positions (p = 0. 0076). Plots with average rates of decline (M) were 

generally located on midslopes. Physiographic landscape position (class 1 to 6 representing 

the least to most) is xeric landscape position following Bailey et al. (2004).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Conclusions

Creating an acceptable model of a complex, dynamic 

system is always challenging. Landscape-scale analyses 

increase the level of complexity by limiting the variables 

that are available for consideration. Here we applied 

knowledge- and data-driven approaches to model hemlock 

decline following HWA infestation. An initial review of 

existing literature identified potential variables for model 

inclusion and helped direct field measurement efforts. We 

used a mixed stepwise linear regression model based on 

plots that have been infested for at least 4 years to identify 

the set of landscape and chemical variables that could best 

predict the average rate of hemlock decline since HWA 

infestation. Using a continuous output variable instead 

of a simple tolerant/susceptible classification allows for 

flexibility in interpretation, depending on the needs of 

the user. For example, a research scientist with a limited 

number of HWA predator beetles to release may choose a 

conservative approach, selecting the stands with the lowest 

Figure 8—Southern-facing slopes experienced significantly faster decline following HWA infestation 

(p = 0.012). In addition, an interaction between aspect/slope (p = 0.06) and aspect/nitrogen (p = 0.002) 

indicates that the significance of aspect may come into play when other stressors (such as steeper slopes 

or higher nitrogen concentrations) are also a factor. Decline rating is a continuous scale from 0 (perfect 

health) to 10 (dead).

anticipated rate of decline and, therefore, highest probability 

of continued health in spite of HWA infestation. Conversely, 

a forester who wants to preserve a strong genetic pool of 

hemlock may decide not to cut any hemlock in stands that 

have even a marginal probability of sustaining long-term 

infestation with minimal health impacts.

Hemlock vulnerability to HWA is complex and likely 

results from a combination of landscape and chemical factors. Because the ability to map relative risk on a landscape 

scale could prove to be a useful tool for managers faced 

with HWA, we limited ourselves to variables available in 

digital, raster format for inclusion in a GIS model. A model 

based only on topographic variables derived from a 10-m 

DEM was able to account for almost one-third of the variability in hemlock decline rates from infested plots across 

the Northeast. This is consistent with previous studies that 

link variables related to soil-moisture availability with 

hemlock vulnerability. By adding foliar nitrogen concentrations to the model, over two-thirds of the variability in 

hemlock decline rate following infestation can be accounted 
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GENERAL TECHNICAL REPORT PNW-GTR-802

for. This is also consistent with previous fertilization and 

foliar chemistry studies, which identify a palatability-based 

relationship between foliar nitrogen and HWA population 

levels. 

The significant improvement in model accuracy with 

the inclusion of chemical data highlights the value of 

hyperspectral data-derived coverages in risk modeling. In 

addition to improved predictive accuracy, hyperspectral 

imagery can provide spatially continuous maps of host species abundance and detailed decline assessments for model 

validation. This will allow land managers to better locate 

the host resource, identify stands to target management 

activities, and monitor forest health. 

It is likely that the inclusion of other organic compounds, such as phenolics or other defensive chemicals 

would further improve this model (Bi and others 1997, 

Zucker and others 1992). However, the ability to use remote 

sensing platforms to assess secondary compound concentration has not been attempted to date. Other factors such 

as duration of infestation, climatic variables, and mineral 

nutrition likely interact, and these factors may exhibit 

different influence under different situations (Pontius and 

others 2006). In areas with available hyperspectral imagery, 

digital soil maps, and climate data, more complex models 

may soon be available to land managers. The addition of 

such data layers, which are not typically available for risk 

modeling, can be incorporated for more detailed and accurate risk maps. This type of spatially continuous information could be used by integrated pest management plans to 

help target specific areas on the ground where management 

efforts may be most effective.

This work will continue to be validated and improved 

in our future research efforts. By tracking infestation as it 

progresses through these stands and monitoring changes in 

hemlock health on newly infested plots, we will be better 

able to test the accuracy of this model. Finalized coverages 

of foliar nitrogen concentration will also be added to this 

model so that parameter estimates can be used to predict 

actual rates of decline instead of relative vulnerability to 

HWA.

Figure 9—A. Percentage infestation was the strongest correlate 

with hemlock decline rates across the region (r = -0.67, p = 0.008). 

B. In turn, foliar nitrogen was the strongest correlate with percentage infestation (r = 0.396, p = 0.005), indicating a palatabilitybased relationship between nitrogen and HWA population success. 

C. This may be why nitrogen was a significant factor (r = -0.436, 

p = 0.004) in determining hemlock decline rates in the final model. 

Decline rating is based on several normalized variables such as 

dieback, transparency, live crown ratio, and new growth where 0 = 

perfect health and 10 = dead.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Figure 10—Actual and predicted final model results for all plots, including those excluded 

from calibration due to insufficient infestation periods to determine decline rates, produced a 

p < 0.0001, r2

 = 0.51, and RMSE = 0.14.

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

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

Wisdom

Mary M. Rowland, wildlife biologist, and Michael J. 

Wisdom, research wildlife biologist, USDA Forest Service, 

Pacific Northwest Research Station, Forestry and Range 

Sciences Laboratory, La Grande, OR 97850; Lowell H. 

Suring, terrestrial wildlife ecologist, Northern Ecologic 

L.L.C., Suring, WI 54174. 

Abstract

The sagebrush (Artemisia spp.) ecosystem is one of the 

most imperiled in the United States. In the Great Basin 

ecoregion and elsewhere, catastrophic wildland fires are 

often followed by the invasion of cheatgrass (Bromus 

tectorum L.), eliminating or altering millions of hectares 

of sagebrush and other shrublands. Sagebrush in the Great 

Basin also is threatened by displacement from encroaching pinyon-juniper woodlands. Despite these changes, the 

ecoregion retains some of the Nation’s largest remaining 

expanses of sagebrush, most of which are federally managed. Because of these losses and degradation, sagebrushassociated species are declining. To address these issues, 

we conducted a regional assessment of habitat threats for 

40 sagebrush-associated vertebrates of concern in the Great 

Basin. Our goals were to (1) evaluate habitat conditions for 

species of concern for conservation planning and management, (2) demonstrate the application of new methods of 

regional threat assessment in shrubland communities, and 

(3) describe implications of results for management. Our 

analyses suggested that more than 55 percent (4.8 million 

ha) of sagebrush in the Great Basin is at moderate or high 

risk of being displaced by cheatgrass. Cheatgrass invasion 

also threatens other shrubland communities, particularly 

salt desert scrub (96 percent; 7.1 million ha at moderate or 

high risk). Substantial areas of sagebrush (41 percent; 2.0 

million ha) were predicted to be at moderate or high risk of 

displacement by pinyon-juniper in the eastern Great Basin. 

Although little sagebrush (less than 1 percent) was at high 

risk to both threats, more than one-third was at high risk 

Assessment of Habitat Threats to Shrublands in the Great 

Basin: A Case Study

to cheatgrass and low or moderate risk from woodlands. 

Habitat loss, including sagebrush and other native plant 

communities, to cheatgrass could exceed 65 percent (8 

million ha) for some of the 40 vertebrate species evaluated. 

Maintenance and restoration of native shrublands in the 

Great Basin will require both active and passive management to mitigate the formidable threats posed by cheatgrass 

and pinyon-juniper woodland expansion.

Keywords: Cheatgrass, Great Basin, models, pinyonjuniper woodlands, sagebrush, threats.

Setting the Stage: Shrublands at Risk

The vast shrublands of Western North America, including 

the sagebrush (Artemisia spp.) ecosystem, provide a wide 

range of resource values, including recreation, livestock 

grazing, mining, energy extraction, wildlife habitat, and 

wilderness. Increasingly, however, these arid and isolated 

lands have faced a wide range of threats, including wildland 

fires, invasive plants, roads, oil and gas development, and 

climate change (Connelly and others 2004, Knick 1999, 

Wisdom and others 2005a). (In this paper we define a threat 

as a potentially detrimental human activity or ecological 

process as it affects native species or their habitats [Wisdom 

and others 2005a] and risk as “the potential, or probability, 

of an adverse event” [Burgman and others 1993].) 

Despite the recognized values of shrublands and 

grasslands—collectively known as rangelands—such lands 

have seldom benefited from the long-term research and 

monitoring traditionally focused on forested ecosystems 

in the United States. This lack of a well-established body 

of research and monitoring presents special challenges in 

evaluating and predicting effects of disturbances, both natural and anthropogenic, on rangeland habitats and wildlife 

in the United States. The dearth of attention is especially 

noteworthy in that rangelands compose about 50 percent (39 

million ha) of the 77 million ha managed under the National 

Forest System. Nationwide, rangelands total 312 million ha, 

with 43 percent managed by the Federal Government (http://

www.fs.fed.us/rangelands/). 
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GENERAL TECHNICAL REPORT PNW-GTR-802

others 2004), and only a small fraction remains unaltered by 

anthropogenic disturbances (West 1999). 

A plethora of threats to sagebrush and other native 

shrubland communities has been identified (Connelly and 

others 2004, Wisdom and others 2005a). Among these are 

invasion of exotic vegetation, altered fire regimes, road 

development and use, mining, energy development, climate 

change, encroachment of pinyon (Pinus spp.) and juniper 

(Juniperus spp.) woodlands, intensive livestock grazing, and 

conversion to agriculture or urban areas. The cumulative 

effects of these stressors have resulted in the sagebrush 

ecosystem being regarded as one of the most endangered 

in the Nation (Noss and others 1995), and 20 percent of the 

plants and animals associated with this ecosystem may be 

at risk of extirpation (Center for Science, Economics, and 

Environment 2002). 

Several scientific assessments have addressed the 

effects of multiple threats in the sagebrush ecosystem (e.g., 

Boyle and Reeder 2005, Connelly and others 2004, West 

1999); however, despite the knowledge gained from these 

studies and others, efforts to abate the degradation and 

elimination of sagebrush have not been successful at large 

scales (Hemstrom and others 2002, West 1999).

Ecological Setting and Status of the Great Basin

The Great Basin ecoregion spans more than 29 million ha 

from the eastern Sierra Nevada Mountains in California to 

central Utah east of the Great Salt Lake (Figure 1). Federal 

lands dominate (80 percent) the landscape; thus, the role 

of Federal agencies is paramount in conservation planning 

and management in this area. Although classified as arid, 

with mean annual precipitation of 216 mm, precipitation is 

highly variable and falls primarily during winter and spring. 

Climate change has been pronounced during the past 150 

years, with warmer temperatures and increased carbon 

dioxide levels (Tausch and Nowak 2000, West 1999). 

Riparian systems of the Great Basin are particularly 

sensitive to effects of climate change (Chambers and Miller 

2004).

The Great Basin supports one of the largest extant 

concentrations of sagebrush in North America at more than 

To address the needs of land managers charged with 

conservation planning and management of shrublands and 

associated wildlife, Wisdom and others (2005a) described 

methods for conducting regional assessments in native 

shrubland ecosystems of Western North America. They 

applied those methods in a prototype assessment of habitat 

threats within the Great Basin ecoregion. Their goals were 

to (1) evaluate habitat conditions for species of concern for 

conservation planning and management, (2) demonstrate the 

application of newly developed methods of regional assessment of threats in shrubland communities, and (3) describe 

implications of results for management.

The following text draws heavily from their book. A 

requirement of the Great Basin prototype assessment as 

funded by the Bureau of Land Management (BLM) was that 

it be conducted with existing data (spatial and other) as a 

demonstration of products that could be readily delivered. 

Such pragmatism is often a necessity in natural resource 

management.

The reader is referred to the Wisdom and others (2005a) 

book for additional details of the Great Basin case study, 

especially background material and methods. Appendices 

with materials not published within the book can be found 

at http://www.fs.fed.us/pnw/pubs/sagebrush-appendices/. 

Status and Threats in the Sagebrush Ecosystem

The sagebrush ecosystem covers more than 43 million ha 

within the Western United States and Canada and constitutes one of the largest ecosystems in North America 

(Center for Science, Economics, and Environment 2002; 

Wisdom and others 2005a). More than two-thirds of the 

total area covered by sagebrush in the United States is on 

land publicly owned and managed by State or Federal agencies; the BLM alone manages 52 percent of the sagebrush 

in the United States (Knick and others 2003). Conservation 

and restoration of the sagebrush ecosystem are of special 

concern to State and Federal resource management agencies 

owing to extensive habitat degradation and loss (Knick 

1999, Knick and others 2003, Wisdom and others 2005a). 

Since European settlement, the area covered by sagebrush 

has been reduced more than 40 percent (Connelly and 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

8 million ha (29 percent of the landcover in the ecoregion). 

Indeed, among ecoregions of the sagebrush biome, the 

Great Basin ranks second in total extent of sagebrush, 

exceeded only by the Columbia Plateau (Wisdom and 

others 2005a). Although sagebrush loss in the ecoregion has 

occurred from a variety of causes, wildland fires have been 

especially devastating. More than 500 000 ha of sagebrush 

(6.4 percent of the sagebrush in the ecoregion) burned 

between 1994 and 2001 (Wisdom and others 2005a). Closely 

tied to losses from wildfire is the spread of cheatgrass 

(Bromus tectorum L.), introduced to the United States in the 

1800s and now a pervasive problem throughout much of the 

arid West (Billings 1994, Booth and others 2003, Peterson 

2006). Cheatgrass is estimated to currently occupy more 

than 2 million ha (7 percent) of the Great Basin (Bradley 

and Mustard 2006).

Figure 1—Estimated risk of cheatgrass displacement of sagebrush and other susceptible landcover types in the Great Basin 

ecoregion (from Wisdom and others 2005a).

Beyond its reputation as a stronghold for sagebrush, the 

Great Basin is recognized as an area of rich biodiversity, 

with its extremes of topography and climate contributing to 

a diverse assemblage of endemic plants and animals (Nachlinger and others 2001, Ricketts and others 1999). Among 

terrestrial ecoregions of North America, the Great Basin 

Shrub Steppe was among the most diverse in both number 

and endemism of vascular plants (Ricketts and others 1999).

Regional Assessment of Habitats

To address concerns about ongoing degradation of sagebrush habitats and associated species, Wisdom and others 

(2005a) identified steps for spatial analysis to be used in 

regional assessment. Those steps were subsequently applied 

in the Great Basin ecoregion in a case study approach. 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Describing Species-Habitat Relationships

Evaluation of the condition (e.g., risk levels) and spatial 

pattern of habitat for multiple species of concern provides 

essential context for the development of regionally based 

management and conservation strategies. A first step in 

habitat evaluation is defining the relationship between each 

species and environmental attributes, often vegetation 

cover types. For the Great Basin assessment, we identified 

vegetation associated with each species as habitat based 

on a recently completed landcover map (Comer and others 2002; [http://sagemap.wr.usgs.gov/]). This map was 

developed explicitly for regional assessment of sagebrush 

habitats across the Western United States and included the 

delineation of 8 sagebrush and 38 other landcover types in 

the Great Basin.

To define habitat relationships for Great Basin species of concern, we used the landcover types to construct 

a species-habitat association matrix, relying on literature 

sources and existing wildlife-habitat relationships databases. We then asked species experts to review the matrix, 

and we refined the habitat designations as necessary. This 

matrix was the basis for subsequent quantification of 

amount of habitat, and habitat at risk, within each species’ 

range in the Great Basin (“Quantifying Vegetation and 

Habitat in the Great Basin”).

Identifying Regional Threats

Wisdom and others (2005a) outlined a process for identifying threats to consider in regional assessment, using criteria 

such as spatial extent of the threat, available resources to 

address the threat, and cost-benefit analysis. The threats 

confronting sagebrush habitats in the Great Basin ecoregion 

typify those in most sagebrush-dominated ecoregions of 

Western North America. Two of these are pervasive and 

accelerating: the displacement of native vegetation by 

cheatgrass invasion and by encroaching pinyon-juniper 

woodlands.

Invasion by exotic species, particularly cheatgrass, 

is consistently cited as one of the major challenges to 

maintenance of healthy sagebrush communities (Connelly 

and others 2004, Knick 1999, Pyke 2000). In addition to its 

The following sections describe the methods and results 

of the Great Basin assessment, with primary emphasis on 

results and their interpretation for management. These 

methods involved selection of species for analysis, describing species-habitat relationships, identification of primary 

threats, quantification of vegetation and habitat in the study 

area, and development of predictive models of selected 

threats.

Selecting and Grouping Species of Concern

Using a variety of screening criteria (e.g., habitat association, estimated risk of extirpation), Wisdom and others 

(2005a) compiled a comprehensive list of more than 350 

sagebrush-associated species of concern. Species of concern 

were defined as those with populations or habitats that are 

rare or declining, or both. This list was dominated by vascular plants (68 percent), but also included terrestrial invertebrates (7 percent) and vertebrates (25 percent). For the Great 

Basin study, this list was narrowed by selecting species 

suitable for broad-scale assessment in the study area. The 

screening process yielded 40 species of concern, including 1 amphibian, 9 reptiles, 17 birds, and 13 mammals. 

Among those selected, greater sage-grouse (Centrocercus 

urophasianus) and pygmy rabbit (Brachylagus idahoensis) 

are of particular concern due to recent petitions to list these 

species under the U.S. Endangered Species Act (USDI Fish 

and Wildlife Service 2008, 2010).

The 40 species of concern were assigned to one of five 

groups according to similarities in habitat associations and 

amount of habitat in the ecoregion (see Table 1 for a list of 

species groups). The use of species groups, in combination 

with individual species, allows managers to:

1. Address either single- or multispecies needs.

2. Identify regional patterns of habitats, especially 

habitats at risk, that affect multiple species 

similarly.

3. Address the needs of many species efficiently 

and holistically.

4. Determine how well regional strategies for groups 

of species meet the needs of individual species.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

displacement of native understory species, the autecology 

of cheatgrass leads to an increased risk of catastrophic 

wildfires that eliminate the sagebrush overstory (Billings 

1994, Booth and others 2003). The presence of cheatgrass 

exacerbates fire hazard because of its high flammability. 

Cheatgrass can drastically shorten the fire recurrence 

interval in native sagebrush communities from 20 to 100 

years to 3 to 5 years in some sites.

Table 1—Percentage of watersheds in the Great Basin by combined classes of 

habitat abundance and risk that habitats will be displaced by cheatgrass, summarized for each of the five groups of species of conservation concern 

 Risk of habitat displacement by cheatgrass

Species Habitat Low- Moderate- All risk classes

group abundance None-low moderate high combined

 Percent

Sagebrush

(n = 519) Low 14 10 9 33

Moderate 7 21 18 46

High 2 14 7 23

 Totala 24 44 32 100

Salt desert

shrub Low 6 8 21 35

(n = 507) Moderate 4 4 23 31

High 5 3 26 34

 Total 14 15 71 100

Shrubland

(n = 521) Low 2 1 2 5

Moderate 3 4 10 17

High 7 21 51 79

 Total 11 26 63 100

Sagebrushwoodland Low 7 9 9 25

(n = 519) Moderate 3 18 8 29

High 5 32 8 45

 Total 15 59 26 100

Generalist

(n = 521) Low 1 <1 1 2

Moderate 3 1 5 9

High 14 37 37 88

 Total 18 39 43 100

Total number of watersheds for each species group is given below the group name. See “Selecting and Grouping 

Species of Concern” for methods used to group the species and “Characterizing Habitat Conditions” for 

characterization of habitat conditions for species groups.

a

 Total refers to all habitat abundance classes (low, moderate, and high) combined.

Source: Wisdom and others 2005a.

Pinyon-juniper woodlands have expanded greatly in 

the Great Basin when compared to their distribution more 

than 150 years ago (Miller and Tausch 2001). Tree density 

also has increased in established woodlands. These changes 

have been linked to a decrease in area burned by wildfire, 

a result of increased fire suppression and removal of fine 

fuels by livestock; climate change; historical patterns 

of livestock grazing; and increased atmospheric carbon 

dioxide (Polley and others 2002, Sakai and others 2001). 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Modeling of Displacement by Woodlands 

and Cheatgrass

We developed two independent, rule-based predictive models of displacement of sagebrush and other native vegetation 

by encroaching pinyon-juniper woodlands and cheatgrass, 

employing a variety of topographic and vegetation variables 

(Suring and others 2005). Simple rule-based models may 

be effective in displaying broad-scale patterns of risk in 

susceptible plant communities (Woodbury and Weinstein, 

this volume). The models were deterministic (i.e., the model 

assumptions completely determine the outcome of the 

model, with no element of randomness involved), and were 

intended to display broad-scale patterns of risk across the 

ecoregion, summarized at watershed (i.e., 5th hydrologic 

unit code) or larger scales. The models were applied first 

to vegetation, and then to habitats for individual species 

of concern (pinyon-juniper and cheatgrass models) and 

species groups (cheatgrass model only). The models were 

intended to display risk of displacement of native vegetation 

in 30 years by the two stressors; thus, areas mapped as high 

risk may or may not have been converted to cheatgrass or 

woodland-dominated sites at present.

We identified environmental variables thought to be 

most important for estimating the risk that sagebrush will 

be displaced by pinyon-juniper woodlands or cheatgrass 

by reviewing the literature, including studies of dispersal 

mechanisms for pine seeds and juniper berries, and consulting with species experts on the ecological relationships and 

traits of pinyon and juniper species and cheatgrass. The rule 

sets for both models used ecological provinces (described 

by Miller and others 1999b and West and others 1998) as a 

geographic basis for model development. The characterization of landscape conditions within the provinces provided a 

useful ecological context for model development.

Model Development

Pinyon-Juniper Woodland Model—

Variables selected for the pinyon-juniper risk model 

included landcover (i.e., sagebrush taxon), elevation, 

potential for seed dispersal (based on proximity of pinyonjuniper woodlands to sagebrush), precipitation, and landform. Variables were parameterized differently for groups 

Detrimental outcomes of pinyon-juniper woodland expansion include increased soil erosion, changes in soil fertility, 

losses in forage production, reductions in wildlife habitat 

for some species, and alteration of presettlement native plant 

communities (Miller and Tausch 2001). In reporting its 

findings for a petition to list greater sage-grouse under the 

Endangered Species Act, the U.S. Fish and Wildlife Service 

explicitly cited habitat degradation from invasive species, 

such as cheatgrass, and encroachment of pinyon-juniper 

woodlands as key threats to greater sage-grouse (USDI Fish 

and Wildlife Service 2010).

Although many other threats affect sagebrush habitats 

in the Great Basin such as overgrazing by domestic and 

wild ungulates (Nachlinger and others 2001), empirical 

data are often insufficient to model or estimate the risks 

posed by these threats at large scales. We chose these two 

threats—cheatgrass and pinyon-juniper woodlands—in the 

Great Basin assessment to exemplify the process of evaluating threats to sagebrush ecosystems at regional scales.

Quantifying Vegetation and Habitat in the Great 

Basin

To estimate vegetation at risk in the Great Basin, we first 

quantified the total area of each landcover type present 

in the study area using the landcover map described in 

“Describing Species-Habitat Relationships” (Comer and 

others 2002). The area burned in the Great Basin, especially 

sagebrush, increased dramatically beginning in 1994 (Connelly and others 2004). To accurately assess these landcover 

changes, areas in the Great Basin burned by large-scale fires 

since 1994 were reclassified as recently burned. We used 

fire data from 1994 to 2001 because the cover type map 

accounted for area burned prior to 1994.

The amount of habitat for each species was calculated 

by overlaying the landcover map—selecting only those 

cover types identified as habitat for the species (“Describing 

Species-Habitat Relationships”)—with the species’ geographic range within the Great Basin. These habitat maps 

were the basis for subsequent quantification and mapping 

of habitats at risk for each species (“Species’ Habitats at 

Risk”) and for species groups (“Characterizing Habitat 

Conditions”).
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

of ecological provinces within the Great Basin. Evaluation 

of the landcover map indicated that, within the study area, 

the spatial representation of pinyon-juniper woodlands 

was most accurate in three provinces in the eastern Great 

Basin. Consequently, our application of the risk model was 

restricted to those provinces (Figure 2).

Risk classes were defined as follows:

• Low—the probability that pinyon-juniper 

woodlands will displace existing sagebrush cover 

types within 30 years is minimal; currently, little or 

no pinyon-juniper is likely to be present in the overstory of these sage-brush stands.

• Moderate—the probability of displacement within 

30 years is likely, but less so than for sagebrush at 

Figure 2—Estimated risk of pinyon-juniper displacement of sagebrush in the Great Basin ecoregion. Levels of risk of sagebrush displacement are mapped in relation to all sagebrush cover types in the three ecological provinces in which the model 

was applied: Central High, High Calcareous, and Bonneville. Areas not considered to be at risk are landcover types other than 

sagebrush and are depicted in white (from Wisdom and others 2005a).

high risk; currently, pinyon-juniper is likely to be a 

minor to common component of the overstory of 

these stands. This class represents a transition 

phase in the conversion of sagebrush cover types to 

pinyon-juniper woodlands (Miller and others 1999a).

• High—the probability of displacement within 30 

years is high; currently, pinyon-juniper is likely to 

be a common to dominant component of the overstory of these stands.

Cheatgrass Model—

Variables selected for the cheatgrass model included aspect, 

slope, elevation, and landform by ecological province. 

Recently published research based on remotely sensed data 

from the Great Basin supports the use of several of these 
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GENERAL TECHNICAL REPORT PNW-GTR-802

distribution of pinyon-juniper woodlands throughout the 

three provinces (Figure 2). Areas of moderate and high risk 

tended to occur on the side slopes, with areas of low risk in 

the valley bottoms.

Vegetation at Risk of Displacement by Cheatgrass—

Nearly 80 percent of the landcover in the Great Basin was 

estimated to be susceptible to displacement by cheatgrass 

(i.e., low or greater risk; Figure 1). Of the susceptible area, 

26 percent was estimated to be at moderate risk and 40 

percent at high risk. Among vegetation types, salt desert 

scrub was most at risk, with 96 percent (7.1 million ha) 

estimated to be at moderate or high risk of displacement 

by cheatgrass. Sagebrush was also at risk from cheatgrass, 

with 38 percent at moderate risk and 20 percent at high risk, 

a total of 4.8 million ha. The overwhelming majority (88 

percent) of the sagebrush at high risk was Wyoming-basin 

big sagebrush.

Spatial patterns of risk of displacement by cheatgrass 

followed the typical north-south alignment of topographic 

features in the Great Basin. Areas of high risk generally 

occurred at lower elevations and on valley bottoms, whereas 

areas of low risk were typically in mountain ranges and 

higher elevation valleys.

Species’ Habitats at Risk—

For 35 of the 40 species of concern considered in our assessment, more than 30 percent of their sagebrush habitat was at 

high risk of displacement by pinyon-juniper woodlands in 

the three provinces in which this risk was modeled. Twelve 

species each had about 1.6 million ha of sagebrush habitats 

at high risk; these species used all eight sagebrush cover 

types as habitat and were widely distributed across the three 

provinces.

Within the Great Basin, the dominant category for 

species’ habitat at risk of displacement by cheatgrass was 

high risk (mean = 36 percent, n = 40). For 33 species (88 

percent), more than half of their habitat in the study area 

was at moderate or greater risk of displacement by cheatgrass. Ten species, including one amphibian, three reptiles, 

five raptors, and one small mammal, had more than 8 

million ha of habitat at high risk, an area equivalent to about 

variables (Bradley and Mustard 2006, Peterson 2006). 

The model was applied to all susceptible landcover types 

within the Great Basin ecoregion (Figure 1). Nonsusceptible 

landcover types (e.g., marsh/wetland) were assigned to the 

“none” risk level. The remaining risk classes were defined 

as follows:

• Low—the probability that cheatgrass will displace 

existing sagebrush or other susceptible cover types 

within 30 years is minimal; currently, native plants 

are likely to dominate the understory of these stands.

• Moderate—the probability of displacement 

within 30 years is moderate, but lower than for 

types at high risk; currently, either cheatgrass or 

native plants can dominate the understory.

• High—the probability of displacement within 

30 years is very likely; currently, cheatgrass is 

likely to dominate the understory.

Results of Model Application in the Great Basin

Outputs from both the pinyon-juniper and cheatgrass 

models were summarized by landcover type and by species 

(i.e., habitats at risk). Maps and associated spatial data in 

tabular form are in Wisdom and others (2005a); we present 

a brief summary of results below.

Vegetation at Risk of Displacement by Pinyon-Juniper—

Of the 4.8 million ha of sagebrush in the three provinces 

of the eastern Great Basin, 35 percent (1.7 million ha) was 

estimated to be at high risk of displacement by pinyonjuniper woodlands, whereas 60 percent (2.9 million ha) 

was at low risk. Very little sagebrush was in the moderate 

risk category (6 percent), which represents a transitional 

phase of encroachment. Mountain big sagebrush (Artemisia 

tridentata Nutt. vaseyana (Rydb.) Beetle) appeared to be the 

most susceptible sagebrush taxon (42 percent at high risk). 

However, Wyoming-basin big sagebrush (A. t. wyomingensis Beetle and Young-A. t. tridentata) was the dominant 

sagebrush cover type in the modeled area and made up

55 percent of all the high-risk sagebrush, exceeding 

930 000 ha.

The spatial pattern of areas estimated to be at high 

risk of displacement by pinyon-juniper closely followed the 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

one-fourth of the ecoregion. Relative to other species in 

our assessment, habitat for greater sage-grouse and pygmy 

rabbit was at lower risk of displacement by cheatgrass.

Application of Risk Models in Shrubland 

Management (Application to Management)

Federal agencies need information about habitat requirements and conditions for species and groups of species at 

spatial extents that are typically used in land management 

planning. To demonstrate the application of our threats 

modeling results (“Species’ Habitats at Risk”) to land 

management in the Great Basin, we used species groups 

(“Selecting and Grouping Species of Concern”) to generalize the spatial patterns of habitat at risk at the watershed 

scale in the Great Basin.

Characterizing Habitat Conditions

We used two habitat variables, habitat abundance and habitat at risk of displacement by cheatgrass, to characterize the 

composite habitat conditions for each species group at the 

watershed extent within the Great Basin. For each variable, 

watersheds were assigned to one of three classes, resulting 

in nine possible combinations of habitat abundance and risk 

(Table 1, Figure 3). 

The percentage of watersheds in each of the nine 

condition classes was relatively even for the sagebrush and 

salt desert shrub species groups, but one or two condition 

classes dominated the other three groups (Table 1, Figure 

3). The best condition class, that of high habitat abundance 

coupled with none to low risk, was consistently uncommon, with the exception of the generalist group of species 

(Table 1). Notably, the sagebrush group, which included 

both greater sage-grouse and pygmy rabbit, had the smallest 

percentage (2 percent) in this class (Table 1, Figure 3).

Prioritizing Restoration Activities

Different combinations of habitat abundance and habitat 

risk have different implications for conservation and 

restoration. Those characterizations can inform and 

guide restoration priorities. For example, watersheds with 

habitats of relatively higher abundance and lower risk may 

represent habitat strongholds (i.e., optimal habitat amount, 

quality, and spatial arrangement) for a group of species. 

Management objectives for these watersheds would likely 

be tailored to maintain current conditions through prudent 

management of human activities and potential threats 

that can alter the amount, quality, and spatial pattern of 

desired native habitats. In general, targeting management 

of native shrublands at moderate risk may be the most 

prudent approach, given (1) the relative security of lowrisk habitats and (2) the vast resources required to prevent 

high-risk areas from passing beyond ecological thresholds, 

after which restoration of native habitats may be difficult or 

impossible to achieve (Wisdom and others 2005b). Areas at 

high risk from these stressors may have already passed such 

thresholds.

By contrast, watersheds with limited habitat area and 

higher risk may be suboptimal relative to the requirements 

of a species group. Watersheds in this condition would 

likely have smaller patch sizes, lower habitat quality, or 

poor habitat connectivity in relation to the group’s needs. 

Management objectives for such watersheds would likely 

focus on retention of existing habitats, combined with 

substantial emphasis on restoration activities to mitigate 

past habitat losses and degradation. Identification of broadscale patterns of habitat risk for species of concern helps 

determine whether restoration activities need to be initiated 

(Wisdom and others 2005b).

Integration of Multiple Stressors

The combined risk of displacement by cheatgrass and by 

pinyon-juniper woodlands was evaluated for the 4.8 million 

ha of sagebrush in the three ecological provinces in which 

the pinyon-juniper model was applied. Of the five combined 

risk classes, the two dominant classes were high cheatgrass 

(high risk from cheatgrass and low or moderate from pinyon-juniper; 35 percent) and low to moderate (moderate risk 

from both stressors; 33 percent). Only a trace amount (less 

than 1 percent) of sagebrush in this region was at high risk 

to both stressors (see Wisdom and others 2005a for details). 

Other stressors not considered in our prototype assessment, 

such as climate change, may be critical in future modeling of risks in the Great Basin from both cheatgrass and 

pinyon-juniper expansion. Under one modeling approach, 
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GENERAL TECHNICAL REPORT PNW-GTR-802

restoration prescriptions needed to meet management goals 

for each group of species. Targeting areas for management 

attention through this process complements other strategies to identify threats, such as the conservation by design 

approach used by The Nature Conservancy (Nachlinger and 

others 2001).

Results of our regional assessment serve as a working 

example for analysis of species of concern, but also provide 

a sound basis for comprehensive land use planning in the 

ecoregion. Cheatgrass invasion and woodland encroachment are two of the highest priority issues for both research 

direct and indirect effects of global warming may result in 

the elimination of up to 80 percent of existing sagebrush in 

the United States (Neilson and others 2005).

Management Implications

Our analyses revealed that watersheds differ in spatial 

patterns of habitat abundance and risk, resulting in different 

implications for conservation and restoration of habitats. 

These differences in spatial patterns suggest that regional 

strategies could be developed for watersheds in each 

condition class to identify appropriate conservation and 

Figure 3—Habitat abundance (lower left), habitat risk (lower right), and composite conditions (top 

center) for the sagebrush group of species in watersheds of the Great Basin. For habitat abundance, 1 = 

low (<25 percent of the watershed is habitat), 2 = moderate (25 to 50 percent of the watershed is habitat), 

and 3 = high (>50 percent of the watershed is habitat). For the map of habitat risk, 1 = watersheds with 

habitats dominated by none to low risk of displacement by cheatgrass, 2 = watersheds with habitats 

dominated by low to moderate risk, and 3 = watersheds with habitats dominated by moderate to high 

risk. For the map of composite habitat conditions, the first number is for abundance, and the second 

number is for risk, using the definitions above for maps of habitat abundance and risk. For example, 1-1 

denotes watersheds with habitats of low abundance and none to low risk, whereas 3-3 denotes watersheds of high habitat abundance and moderate to high risk. Map colors on the composite map follow 

those for the map of habitat risk (blue = moderate to high risk, green = low-moderate risk, and brown = 

none-low risk), and intensity of colors follows those for the map of habitat abundance (darkest shade = 

high habitat abundance, intermediate = moderate, and lightest = low) (from Wisdom and others 2005a). 

Mean size of the 367 watersheds that occur entirely within the ecoregion boundaries is 66 000 ha.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

and management of native shrublands in the Great Basin. 

Because of the potential mismatch of administrative 

scales at which management occurs and the ecological 

scales at which these threats operate, regional assessment 

can provide key context for local management solutions. 

Integrated management of both threats is needed to conserve sagebrush habitats affected by both cheatgrass and 

pinyon-juniper expansion. For example, prescribed burning 

of some sagebrush habitats may curtail woodland encroachment but also may enhance expansion of cheatgrass on 

susceptible sites. Alternatively, combinations of mechanical 

treatments to reduce pinyon-juniper density, followed by 

chemical treatments to reduce cheatgrass abundance, may 

effectively reduce the combined risks posed by cheatgrass 

and woodlands. 

With any regional assessment encompassing a diverse 

set of species and habitats, uncertainty exists at several 

levels about the assumptions inherent in the analyses. For 

the Great Basin assessment, example areas of uncertainty 

include:

• The cover types associated with each species as 

habitat are assumed to contribute to population 

persistence, but additional factors beyond habitat 

abundance and configuration may influence 

populations. We have limited knowledge of how 

species respond to landscape-scale changes in 

native habitats, as brought about by displacement 

from cheatgrass and pinyon-juniper woodlands. 

Further studies of this topic are a high priority for 

research and management.

• Our predictions of risk of displacement of native 

shrublands are based on a prior knowledge of 

environmental conditions affecting distribution of 

cheatgrass and pinyon-juniper and require extensive 

field evaluation before results are widely applied in 

management. Such validation is currently underway 

(Rowland and others 2006) and will reduce the 

uncertainty associated with model predictions.

• The combined effects of multiple threats, especially 

the potential synergy among threats, are not well 

understood. New research is needed to improve 

knowledge about cumulative effects on shrubland 

habitats and species.

Restoration in sagebrush and other arid shrubland communities will require substantial inputs of resources owing 

to the lack of resiliency in these systems and the recent 

undesirable trends in vegetation dynamics and fire regimes 

affecting these communities (Hemstrom and others 2002). 

Concomitant with restoration, prevention and mitigation 

of threats that go beyond cheatgrass and woodland expansion, such as infrastructure associated with anthropogenic 

activities (e.g., powerlines, roads, energy development), are 

critical for maintaining functioning habitats for shrublandassociated species of concern in the Great Basin and 

surrounding areas.

Literature Cited

Billings, W.D. 1994. Ecological impacts of cheatgrass and 

resultant fire on ecosystems in the western Great Basin. 

In: Monsen, S.B.; Kitchen, S.G., comps. Proceedings: 

Ecology and management of annual rangelands. Gen. 

Tech. Rep. INT-313. Ogden, UT: U.S. Department of 

Agriculture, Forest Service: 22–30.

Booth, M.S.; Caldwell, M.M.; Stark, J.M. 2003.

Overlapping resource use in three Great Basin species: 

implications for community invasibility and vegetation 

dynamics. Journal of Ecology. 91: 36–48.

Boyle, S.A.; Reeder, D.R. 2005. Colorado sagebrush: a 

conservation assessment and strategy. Grand Junction, 

CO: Colorado Division of Wildlife. http://wildlife.state.

co.us/WildlifeSpecies/SagebrushConservation/. [Date 

accessed unknown].

Bradley, B.A.; Mustard, J.F. 2006. Characterizing the 

landscape dynamics of an invasive plant and risk of 

invasion using remote sensing. Ecological Applications. 

16: 1132–1147.

Burgman, M.A.; Ferson, S.; Akcakaya, H.R. 1993. Risk 

assessment in conservation biology. London: Chapman 

& Hall. 328 p.
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Miller, R.; Tausch, R.; Waichler, W. 1999b. Oldgrowth juniper and pinyon woodlands. In: Monsen, 

S.B.; Stevens, R., comps. Proceedings: Ecology and 

management of pinyon-juniper communities within 

the interior West. Proc. RMRS-P-9. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: 375–384.

Miller, R.F.; Tausch, R.J. 2001. The role of fire in pinyon 

and juniper woodlands: a descriptive analysis. In: Galley, 

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

John D. Waldron, Robert N. Coulson, David M. Cairns, 

Charles W. Lafon, Maria D. Tchakerian, Weimin Xi, 

Kier Klepzig, and Andrew Birt

John D. Waldron, Department of Environmental Studies, 

University of West Florida, Pensacola, FL 32514; Robert 

N. Coulson, Knowledge Engineering Laboratory, Texas 

A&M University, College Station, TX 77845; David M. 

Cairns and Charles W. Lafon, Department of Geography, 

Texas A&M University, College Station, TX 77845; Maria 

D. Tchakerian and Weimin Xi, Knowledge Engineering 

Laboratory, Texas A&M University, College Station, TX 

77845; Kier Klepzig, USDA Forest Service Southern 

Research Station, Forest Insect Research, Pine-ville, LA 

71360; and Andrew Birt, Knowledge Engineering Laboratory, Texas A&M University, College Station, TX 77845.

Abstract

The southern pine beetle, Dendroctonus frontalis (Zimmerman) (Coleoptera: Curculionidae: Scolytinae) (SPB), 

is an indigenous invasive species that infests and causes 

mortality to pines (Pinus spp.) throughout the Southern 

United States. The hemlock woolly adelgid, Adelges 

tsugae (Annand) (Homoptera: Adelgidae) (HWA), is a 

nonindigenous invasive species that infests and causes 

mortality to Eastern hemlock (Tsuga canadensis (L.) Carr.) 

and Carolina hemlock (T. caroliniana Engelm.) throughout 

their range in Eastern North America. Both of these insect 

species occur in the Southern Appalachians, and both have 

recently caused tree mortality exceeding historical records. 

Herbivory by both species is of concern to forest managers, 

but for different reasons. In the case of the SPB, emphasis 

centers on forest restoration strategies, and in the case of the 

HWA, the concern is on predicting the impact of removing 

hemlock from the forest environment. Both of these issues 

can be investigated using a landscape simulation modeling 

approach. LANDIS is a simulation modeling environment 

developed to predict forest landscape change over time. It 

is a spatially explicit, landscape-scale ecological simulation 

Evaluating the Impact of Invasive Species in Forest Landscapes: The Southern Pine Beetle and the Hemlock Woolly 

Adelgid

model that incorporates disturbance by fire, wind, biological disturbance (insects and pathogens) and harvesting. 

Herein, we present a case study using LANDIS to evaluate 

the impact of herbivory by the SPB and HWA on forest 

landscapes in the Southern Appalachians.

Keywords: Invasive species, LANDIS, modeling, 

Southern Appalachians.

Introduction

In 2003, five general areas were identified as concerns to 

healthy forests in the United States—wildfires, nonnative 

invasive insects and pathogens, invasive plant species, outbreaks of native insects, and changing ecological processes 

(USDA-FS 2003). Eastern forests in the United States have 

been subject to unprecedented threat due to invasion by 

forest pests (Brockerhoff and others 2006, Liebhold and 

others 1995, Lovett and others 2006) that threaten extinction of host species, engineer fragmented landscapes, and 

add to fuel loads, which increase risk of wildland fires. 

Disturbances exert a strong influence on forest structure, 

composition, and diversity (Connell 1978, Huston 1994, 

White 1979). However, different types of disturbance have 

different consequences for vegetation. Surface fires, for 

example, primarily kill small trees and spare the larger 

individuals (Abrams 2003, Frelich 2002), often slowing 

the rate of successional replacement. Canopy disturbances 

such as insect outbreaks primarily damage larger trees 

and may accelerate the process of succession (Abrams and 

Scott 1989, Frelich 2002, Lafon and Kutac 2003, Veblen and 

others 1989).

Stohlgran and Schnase (2006) suggested that risk 

analysis techniques, including simulation modeling, that 

are often used in the assessment of health risks and other 

hazards, are not only applicable to invasive species, but are 

needed. Forest managers have been increasingly integrating 

stand-level forecasting tools, such as the Forest Vegetation 

Simulator (FVS), in the forest decisionmaking process 

(Dixon 2002). More recently, landscape models that operate 
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GENERAL TECHNICAL REPORT PNW-GTR-802

opposite ends of the moisture gradient found in the Southern Appalachian Mountains (Figure 1), although they occasionally occur together at either end of their natural range. 

We chose these insects to illustrate the utility of LANDIS in 

investigating forest insect threats because they represent the 

extreme cases of an indigenous pest that has the potential to 

cause great damage (SPB) and an invasive pest that has the 

potential to remove an entire host plant species from eastern 

forests (HWA).

Southern Pine Beetle Case

In the Southern Appalachian Mountains, xeric slopes and 

ridges have historically been dominated by yellow pines 

(Pinus spp.). Because altered disturbance regimes have 

begun to change the appearance of the landscapes, understanding the dynamics of these systems is important to 

forest managers in implementing management strategies on 

public lands. On these landscapes, fire and SPB are the two 

most influential natural disturbance agents. SPB has caused 

extensive damage to pine forests throughout the Southeastern United States (Coulson 1980, Coulson and others 2004). 

On Southern Appalachian xeric ridges, SPB colonizes a 

variety of pine species including pitch pine (Pinus rigida 

Mill.), Virginia pine (Pinus virginiana P. Mill.), Table 

Mountain pine (Pinus pungens Lamb.), and occasionally 

eastern white pine (Pinus strobus L.) (Payne 1980). Interactions between available soil moisture and resin flow, the 

primary tree defense against SPB (Tisdale and others 2003, 

among others), have long been noted (Hodges and Lorio 

1975, Hodges and others 1979 ) and are likely affected by 

such landscape characteristics.

Fire and SPB are thought to drive the regeneration of 

yellow pine forests on xeric ridges in the Southern Appalachians (Harmon 1980, Harrod and others 1998, Williams 

1998). Williams (1998) conjectured that SPB and other nonfire disturbances in xeric pine-oak forests will lead toward 

hardwood domination in the absence of fire. It has further 

been hypothesized that these communities are maintained 

in a drought-beetle-fire cycle (Barden and Woods 1976, 

Smith 1991, White 1987, Williams 1998). Understanding 

the relationship between fire, SPB, and mesoscale forest 

at a scale of 100s to 1000s of km2 have begun to be evaluated for use in forest management (e.g., Shifley and others 

2000).

LANDIS (Mladenoff and He 1999) is a simulation 

modeling environment developed to predict forest landscape 

change over time. It is a spatially explicit landscape-scale 

ecological simulation model that incorporates both natural 

(fire, wind, and biological disturbance) and anthropogenic 

disturbance (harvesting). LANDIS has been adapted for use 

in a variety of forest management applications. Examples 

of applications relevant to this study include He and others 

(2002b) (forest harvesting and fire disturbance), Akcakaya 

(2001) (risk assessment and landscape habitat models), Schifley and others (2000), Mehtaa and others (2004) (landscape 

change and management practices), and Gustafson and 

others (2000) (forest succession and harvesting).

Landscape models offer the unique ability to assess 

forest process and pattern over broad spatial and temporal 

scales. Forest managers increasingly need to implement 

management strategies that incorporate forest sustainability, 

ecological restoration, wildlife habitat viability, recreational 

opportunities, and scenic value. Many of these concerns 

involve broad spatial and temporal scales. The objective 

of this study is to demonstrate the effectiveness of using 

LANDIS for forest threat assessment and restoration. To 

illustrate this, we examine one nonnative invasive insect, 

hemlock woolly adelgid (HWA) (Aldelges tsugae Annand 

[Homoptera: Adelgidae]) and one indigenous invasive 

insect, southern pine beetle (SPB) (Dendroctonus frontalis

Zimmermann [Coleoptera: Curculionidae]). Both insects 

currently threaten tree species within the Southern Appalachian Mountains of Eastern North America. In this analysis, 

we present initial results from our work with LANDIS 4.0 

and present a framework for using LANDIS II, which will 

help evaluate the potential impacts of existing and future 

multiple interacting forest threats in eastern forests.

Background

The SPB and the HWA are two very different forestdamaging insects that inhabit host tree species that exploit 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

dynamics can provide direction for forest planners and managers in maintaining and restoring this unique environment.

Hemlock Wooly Adelgid Case

Eastern hemlock (Tsuga canadensis (L.) Carr.) and Carolina 

hemlock (Tsuga caroliniana Engelm.) appear in mesic 

flats, draws, ravines, coves, and canyons of the Southern 

Appalachian Mountains (Whittaker 1956). Although once 

more abundant in the forest, hemlock populations declined 

dramatically approximately 5,500 years ago because of 

a climatic shift that resulted in summer droughts. These 

droughts weakened the hemlocks and left them vulnerable 

to a subsequent widespread insect outbreak (Allison and 

others 1986, Davis 1981, Haas and McAndrews 2000). 

In its northern range, canopy gaps were filled by Acer, 

Betula, Fagus, Pinus, Quercus, and Ulmus (Fuller 1998). 

Although hemlock did reestablish itself, its recovery may 

have taken up to 2,000 years and, in many sites, is still not 

as prominent as it was before the decline (Fuller 1998, Haas 

Figure 1—Density of yellow pine and hemlock in the Southern Appalachian Mountains. This illustration depicts the 

distribution of hemlock and pine density in the southern Appalachian Mountains. The highest densities of hemlocks are 

found in flats, draws, and ravines at an elevation of 3,000 to 4,500 feet, whereas the highest densities of yellow pines are 

found on ridges and peaks between elevations of 0 and 4,500 ft (Based on Whittaker 1956).

and McAndrews 2000). Now, hemlocks are at risk from the 

invasive exotic insect pest HWA.

In its native Japan, HWA populations are maintained 

at low densities on hemlocks (Tsuga diversifolia (Maxim.) 

Mast. and T. sieboldii Carr.) by a combination of host 

resistance and natural enemies (McClure 1992, 1995a, 

1995b; McClure and others 2000). The first report of HWA 

in North America was in the Pacific Northwest in the 1920s; 

however, western hemlocks were resistant to the adelgid. In 

the Eastern United States, the first reports of HWA were in 

1951 in Richmond, Virginia (Gouger 1971; McClure 1989, 

1991). With no natural resistance or natural predators, HWA 

slowly made its way northeast and has subsequently been 

moving southwest along the eastern side of the Appalachian 

Mountains. Little is known about stand-level characteristics 

that influence HWA susceptibility in the Southeastern 

United States. However, studies on HWA infestation levels 

in the northeastern range of this insect noted only latitudinal effects on infestation severity (Orwig and Foster 1998, 
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GENERAL TECHNICAL REPORT PNW-GTR-802

1999b; He and others 1996, 1999a, 1999b; Mladenoff and 

He 1999). Whereas LANDIS was originally developed to 

simulate disturbance and succession on glacial plains in the 

Upper Midwest (Mladenoff 2004), it has been successfully 

adapted for use in mountainous areas (He and others 2002a; 

Shifley and others 1998, 2000; Waldron and others 2007; Xu 

and others 2004).

LANDIS is raster-based, with tree species (max 30) 

simulated as the presence or absence of 10-year-age cohorts 

on each cell. At the site (cell) scale, LANDIS manages 

species life history data at 10-year time steps. Succession is 

individualistic and is based on dispersal, shade tolerance, 

and land type suitability. Disturbances that can be modeled include fire, wind, harvesting, and biological agents 

(insects, disease) (Sturtevant and others 2004a).

Fire in LANDIS is a hierarchical stochastic processes 

based on ignition, initiation, and spread (Yang and others 

2004). Mortality from fire is a bottom-up process whereby 

low-intensity fires kill young, fire-intolerant species, 

whereas fires of higher intensity can kill larger trees and 

more fire-tolerant species (He and Mladenoff 1999a).

Biological disturbances in LANDIS 4.0 are modeled 

using the Biological Disturbance Agent (BDA) module. 

Biological disturbances are probabilistic at the site (cell) 

level. Each site is assigned a Site Vulnerability (SV) probability value that is checked against a uniform random 

number to determine if that site has been infected. Site 

vulnerability can be directly equated with the Site Resource 

Dominance (SRD) value that ranges from 0 to 1 and is 

based on species and species age. This value can also be 

modified by three variables to determine the impact on a 

given site—Modified Site Resource Dominance (SRDm), 

Neighborhood Resource Dominance (NRD), and the 

temporal scale of outbreaks. The functioning of these 

variables and of the BDA in general is described in detail in 

Sturtevant and others (2004b).

Simulation Methods

We used LANDIS 4.0 to simulate forest dynamics on a 120-

ha idealized landscape. The landscape was a 100 by 120 cell 

grid with a cell size of 10 m by 10 m, the smallest cell size 

recommended for use with LANDIS. Using this small cell 

Orwig and others 2002). This would seem to suggest that 

all hemlock stands have the potential of being infested and 

killed, regardless of site and stand factors.

Methods

Study Area

This study uses a simulated landscape drawn from data 

approximating the communities and conditions within Great 

Smoky Mountains National Park. Great Smoky Mountains 

National Park is a 2110 km2 World Heritage Site and International Biosphere Reserve straddling the border between 

western North Carolina and eastern Tennessee. Great 

Smoky Mountains National Park serves as an ideal model 

for this study as most major ecosystems of the Southern 

Appalachians are represented, and the general topographic 

distribution of communities and tree species has previously 

been described (Whittaker 1956).

The Southern Appalachian Mountains, although not 

representative of all eastern forests, are unique because they 

represent one of the most biologically diverse regions of the 

world (SAMAB 1996). A complex system of physiography, 

environmental site conditions, adaptive life history characteristics, and disturbance history has created a distinctive 

vegetation structure (Elliott and others 1999). Due to this 

complexity, Southern Appalachian landscapes contain 

a variety of community types ranging from mesophytic 

hemlock-hardwood forests on moist valley floors to yellow 

pine woodlands on xeric ridges and from low-elevation 

temperate deciduous forests to high-elevation spruce-fir 

forests (Whittaker 1956, Stephenson and others 1993). Such 

high biodiversity areas have been thought by some to act as 

potential barriers to invasion because of increased competition and by others as at risk of invasion due to the higher 

potential for suitable habitat niches (Brown 2002, Brown 

and Peet 2003, Elton 1958, Kennedy and others 2002, 

Levine and D’Antonio 1999).

Model Description

LANDIS is a spatially explicit computer model designed 

to simulate forest succession and disturbance across broad 

spatial and temporal scales (He and Mladenoff 1999a 
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

size allowed us to operate at approximately the scale of the 

individual canopy tree, following the logic of gap models. 

The landscape was divided into 18 individual land types 

arranged according to the mosaic chart used by Whittaker 

(1956) to depict the elevation and moisture gradients on 

the Great Smoky Mountains landscape. The land types 

are arranged in three rows and six columns. The rows 

represent (from bottom to top) low (400–915 m)-, middle 

(916–1370 m)-, and high (1371–2025 m)-elevation zones. 

The columns represent different topographic moisture 

classes. Moisture availability decreases from left to right, as 

follows: (1) coves and canyons; (2) flats, draws, and ravines; 

(3) sheltered slopes; (4) east- to northwest-facing slopes; (5) 

southeast- to west-facing slopes; and (6) ridges and peaks. 

Elevation also influences moisture availability. For example, 

a low-elevation ridgetop would have drier conditions than a 

mid-elevation ridgetop. Although the simulated landscape 

incorporates the full range of environments in the Great 

Smoky Mountains, our interest in this paper is only on the 

successional patterns for those land types under the greatest 

threat by SPB and HWA (Figure 1). We present results for 

mid-elevation ridges and peaks (SPB) and mid-elevation 

flats, draws, and ravines (HWA) to illustrate the utility of 

the model in assessing insect threats.

Results and Discussion

SPB

Our first goal in this study was to investigate the role of 

fire and SPB in xeric Southern Appalachian landscapes. 

The modeling projections presented here suggest that the 

regime of multiple interacting disturbances has important 

implications for the successional dynamics and vegetation 

characteristics in yellow pine woodlands of the Southern 

Appalachian Mountains. When acting alone, fire was 

projected to create conditions favoring pine presence at 

levels higher than input, although SPB disturbance acting 

alone resulted in the removal of yellow pines. Additionally, 

our model projections suggest that a combination of fire and 

SPB disturbance creates sustainable yellow pine communities over the long term. This conclusion is consistent with 

the hypothesis that fire and SPB are part of a disturbance 

regime that maintains yellow pine woodlands (Harrod and 

others 1998, 2000; Lafon and Kutac 2003; White 1987; 

Williams 1998) (Figure 2).

The results of this study yield several conclusions 

that are important to forest managers when undertaking 

restoration efforts. First, our projections suggest that Table 

Mountain pine (Pinus pungens), more than any other 

species, thrives when in a disturbance regime combining 

SPB and fire on xeric sites. Because Table Mountain pine 

is a Southern Appalachian endemic, it is also important for 

biodiversity conservation (Zobel 1969). These factors suggest that Table Mountain pine could be a species of particular interest for restoration efforts on mid-elevation ridges 

and peaks in the Southern Appalachians. Our results apply 

to the restoration of such stands and suggest that periodic 

burning will be required to maintain the compositional and 

structural integrity of stands affected by SPB. This conclusion is substantiated by empirical analogue (e.g., Harrod and 

others 1998, 2000; Lafon and Kutac 2003).

Hemlock Wooly Adelgid

Our second goal was to investigate the impacts of HWA on 

species composition in the Southern Appalachian Mountains. The results from this study are preliminary, but do 

show a reduction in hemlock and subsequent replacement 

by hardwoods (Figure 3). In particular, we see replacement 

of hemlocks with basswood (Tilia spp.), sugar maple (Acer 

saccharum Marsh.), yellow buckeye (Aesculus octandra 

Marsh.), yellow birch (Betula alleghaniensis Britton) and 

northern red oak (Quercus rubra L.). These results may not 

be ecologically correct, as we would anticipate rhododendron (Rhododendron spp.) already present in the understory 

or several potential nonnative invasive species filling many 

of the gaps created by hemlock removal (Figure 4). Rhododendron and thick shrub cover, in general, have been shown 

to neutralize tree regeneration in canopy gaps (Beckage and 

others 2000). Riparian areas in the Southern Appalachians, 

where we find most hemlocks, have also been shown to 

contain high exotic species cover and diversity (Brown and 

Peet 2003). The discrepancies in the landscape approach 

can be corrected easily by incorporating finer resolution gap 

models.
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GENERAL TECHNICAL REPORT PNW-GTR-802

regeneration (Della-Bianca 1990). On the other hand, 

there are no immediate controls for HWA. Whereas the 

imminent destruction of hemlocks by HWA evokes parallels 

to the chestnut blight fungus (Cryphonectria parasitica), 

which has decimated American chestnuts, there is a major 

difference. Chestnut blight can survive quite well on the 

deadwood of a variety of species, and, hence, it will always 

be present in the environment—therefore constraining the 

ability of any viable new American chestnut populations. 

Hemlock woolly adelgid, on the other hand, requires the 

presence of either eastern or Carolina hemlock to survive in 

Eastern North America (McClure 1987). One management 

strategy would simply be to save nursery stocks of hemlocks in a controlled environment for replanting once the 

HWA has destroyed all the naturally occurring hemlocks 

and then, itself, perished due to a lack of viable hosts.

Discussion

Through this study, we have demonstrated that different 

forest pests in different ecological regions, within the same 

geographic bounds, require different management strategies. If the desired outcome were the maintenance of Table 

Mountain pine-pitch pine communities, managers would 

be warranted in using prescribed burning or allowing for 

natural fires to burn without suppression along with SPB 

chemical control measures on xeric mid-elevation ridges 

and peaks. If the maintenance of pitch pine were not considered important, then burning alone would be an acceptable 

strategy. Although other silvicultural practices could recreate these conditions in the short run, burning is necessary 

to maintain these conditions as Table Mountain pine needs 

stand and site disturbance, light, and heat for successful 

Figure 2—Successional trajectory of mid-elevation Ridges and Peaks. The following three graphs show the successional trajectory of 

mid-elevation ridges and peaks and represent percentage cover on the y-axis and model run year on the x-axis, which ends at year 500 

for mid-elevation ridges and peaks. The fire and SPB scenario (a) show a continued dominance of Table Mountain pine but a reduction in 

pitch pine. The SPB-only scenario (b) shows a replacement of both pine species with hardwoods. The fire-only scenario (c) demonstrates 

a continuation of Table Mountain pine and pitch pine. Graphs reflect the percentage of cells occupied on the land type by each species.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

We set out to describe a modeling framework for 

assessing the impacts of SPB and HWA herbivory on 

forests. The advantage of using LANDIS is that all of these 

processes and outputs are described and captured in a 

simple, tractable, and transparent modeling environment. In 

the context of periodically abundant pests or invasive species, this transparency and simplicity are important because 

there is often a need for reactive and immediate research 

into potential impacts. One of the great strengths of 

LANDIS is that because a well-described and proven model 

framework already exists, parameterization and hence, 

model outputs, can be achieved relatively easily by drawing 

on published literature, expert knowledge, and practical 

experience. The adoption of a proven model also allows 

a truly comparative study between the assessments of the 

impact of different pests. For this initial study, we chose to 

Figure 3—Successional trajectory of mid elevation Flats, Draws, and Ravines. The following three graphs represent percentage cover on 

the y-axis and model run year on the x-axis, which ends at year 500 for mid-elevation flats, draws, and ravines. In both the fire and HWA 

scenario (a) and the HWA-only scenario (b), eastern hemlock is replaced by hardwood species, particularly basswood, yellow buckeye, 

yellow birch, and sugar maple. In the fire-only scenario (c), hemlock maintains its dominance due to the very infrequent fire return on 

mesic sites. Graphs reflect the percentage of cells occupied on the land type by each species.

illustrate the temporal dynamics of vegetation in response 

to insect outbreaks within the most vulnerable land types 

in the Southern Appalachians. The spatial dynamics of 

vegetation change and insect outbreaks across land types is 

another key feature of this real-world problem that affects 

the distribution of pests and the damage they cause and the 

successional dynamics of impacted vegetation leading to 

restructuring of the forest. Although the broader spatial patterns and processes were not within the scope of this study, 

we are currently using LANDIS to explore these issues.

Directions for Future Research

Recently, LANDIS II has been released. In a major change 

from LANDIS, the life history parameters have been 

updated to include both minimum and maximum age of 

resprouting as well as a postfire resprout function, which 
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GENERAL TECHNICAL REPORT PNW-GTR-802

forests. This will allow us to determine the effects of 

changes in forest structure and composition on fire regimes, 

biodiversity, and wildlife habitat, to investigate strategies 

for restoring key ecosystems that may be significantly 

impacted by multiple-threat interactions, and to test 

contemporary ecological theory, such as the relationship 

between biodiversity and invasibility. Also, by incorporating Gap models, we will be able to address additional 

questions beyond those for which LANDIS II is suitable.

Gap models simulate the establishment, growth, and 

death of individual trees on small plots (Perry and Enright 

2006). Unlike LANDIS, they do not consider the influence 

of landscape structure on disturbance and succession. 

Their value lies in their ability to simulate interactions 

among individual plants in a detailed, mechanistic way. 

Such local-scale interactions between individual plants 

that vary in size, growth rate, shade-tolerance, moisture/

nutrient requirements, and other attributes are thought to 

govern successional processes, including exotic species 

invasions (Huston 2004, Shea and Chesson 2002). Gap 

models also are capable of representing the interactions 

between different plant functional types, e.g., trees and 

shrubs. Although most commonly applied to problems of 

forest succession, gap models have been used to investigate 

the dynamics of herbaceous vegetation as well (e.g., Peters 

2002). By employing a combined approach of gap and 

landscape modeling, we will be able to rectify the problems 

encountered in the HWA study and provide more detailed 

succession projections.

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

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

Tricia L. Wurtz, research ecologist, USDA Forest Service, 

Pacific Northwest Research Station, Fairbanks, AK 99775; 

Matt J. Macander, remote sensing and GIS Specialist, ABR Inc.—Environmental Research and Services, 

Fairbanks, AK 99708; and Blaine T. Spellman, graduate 

student, School of Natural Resources and Agricultural Sciences, University of Alaska, Fairbanks, AK 99775.

Abstract

Alaska has relatively few invasive plants, and most of them 

are found only along the state’s limited road system. One of 

the most widely distributed invasives in the state, Melilotus

alba Medik., or sweetclover, has been sown both as a forage 

crop and as a roadside stabilization species. Melilotus has 

recently been found to have moved from roadsides to the 

flood plains of at least three glacial rivers. This species has 

aggressively colonized the lower Stikine River flood plain 

and occurs there in dense, monospecific stands. It is at an 

earlier stage of colonization of the Matanuska River and 

Nenana River flood plains as well. We are developing a 

network model to examine the spatial relationships among 

roads, river crossings, and downstream public lands of 

high conservation significance in interior and south-central 

Alaska. In 2005 and 2006, we documented the distribution 

of Melilotus on roadsides and near river crossings in this 

part of the state; we present these data combined with other 

records of Melilotus distribution downloaded from the 

Alaska Exotic Plant Information Clearinghouse database. 

When considered together, the distribution data and the 

network model identify certain road-river interfaces as critical control points for preventing the movement of Melilotus

toward public land downstream. To illustrate how the model 

might be used by land managers, results are presented for 

major crossings upstream of the Kanuti National Wildlife 

Refuge. When complete, the network model will function 

as a generally applicable tool to identify the critical control 

points upstream from land under various ownerships and 

Spread of Invasive Plants From Roads to River Systems in 

Alaska: A Network Model

for any future invasive species that can disperse via roads 

and river networks in Alaska.

Keywords: Invasive plants, Alaska, modeling, networks, roads.

Introduction

Alaska has relatively few invasive plants. The state’s 

isolation, lack of development, and cold climate limit the 

introduction and success of many invasive species. Invasive 

species often disperse along road networks (Gelbard and 

Belnap 2003; Koch and Smith, this volume; Lugo and 

Gucinski 2000; Parendes and Jones 2000), and Alaska has 

only 0.02 miles of road per square mile of land area, compared to California’s 1.08 miles (Jeff Roach. 2002. Personal 

communication. Alaska Department of Transportation, 

2301 Peger Rd., MS-2550, Fairbanks, Alaska 99709).

Melilotus alba Medik., or sweetclover, has recently 

become a species of concern in Alaska. Melilotus has been 

grown as a crop in some parts of the state and used in a 

number of mining and industrial area reclamation efforts. 

Melilotus is widely distributed along roadsides around 

Alaska, a result of both intentional sowing as a roadside 

stabilization species, and unintentional transport of seed 

via contaminated soil and gravel. In recent years, Melilotus

appears to be spreading rapidly along these roadsides. 

Melilotus has been found on roadsides in Denali National 

Park and Preserve and near several other national parks in 

Alaska, lands of unquestionably high conservation value 

(Densmore and others 2001).

Melilotus has been found on several glacial flood 

plains. It has aggressively colonized the flood plain of the 

lower Stikine River in southeast Alaska, the lower Matanuska River in south-central Alaska, and portions of the 

middle and lower Nenana River in interior Alaska (Figure 

1). On all three flood plains, Melilotus is a major component 

of the vegetation on recently deposited silt and gravel bars. 

Although the sources of these populations aren’t known for 

certain, it’s likely that Melilotus spread onto the flood plains 

from roads, mines, and agricultural developments upstream. 

There are vast areas of glacial silt flood plain in interior 
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study in the application of the network model, focusing on 

major crossings upstream of the Kanuti National Wildlife 

Refuge as an example of how land managers might use 

model output to prevent the introduction of new invasive 

plants to the lands they manage.

Methods

Case Study Using Melilotus alba

In 2005 and 2006, we surveyed 1,780 miles of eight major 

highways in interior and south-central Alaska for Melilotus

alba. These surveys focused on two areas: roadsides and 

major river crossings. For the roadside surveys, we stopped 

every 10 miles at State of Alaska Highway milepost markers, and recorded whether or not Melilotus was visible on 

the roadside. We included both sides of the road and as 

far as we could see in either direction in these rapid 

Alaska. For example, about 40 percent of the entire Yukon 

Basin is drained by glacial melt-water dominated rivers 

(Brabets and others 2000; Paul Schuster. 2006. Personal 

communication. USGS, National Research Program, 3215 

Marine Street, Suite E-127, Boulder, CO 80303). Though 

many of Alaska’s most valuable public lands are located 

off the road system, they may be vulnerable to invasion by 

species that gain access to river flood plains from upstream 

roadside environments.

This project has two objectives. The first is to document 

the current distribution of Melilotus alba on roadsides and 

on river flood plains near bridges in interior and southcentral Alaska. The second is to develop a network model to 

examine the spatial relationships among roads, rivers, and 

public lands of high conservation value in the same part of 

the state. We use the Melilotus distribution data as a case 

Figure 1—The map of Alaska shows the flood plains of three rivers (the Stikine, the Matanuska, and the Nenana) known 

to have been colonized by Melilotus alba. The Nenana is a tributary of the Tanana, which, in turn, is the largest tributary 

of the Yukon.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

assessments. We acquired Melilotus distribution data for 

several additional highways from the Alaska Exotic Plant 

Information Clearinghouse database (Alaska Natural 

Heritage Program, n.d.) (Figure 2).

In addition, we surveyed for the presence or absence 

of Melilotus at 192 major crossings along these highways. 

We did not survey other types of road-river interfaces 

(see below). At each crossing, we recorded the presence or 

absence of Melilotus on the roadside immediately adjacent 

to the bridges and on natural flood-plain surfaces below and 

in the vicinity of the bridge. We considered both upstream 

and downstream flood-plain surfaces in making the floodplain assessments.

We characterized each downstream flood-plain surface 

in the vicinity of the bridge on its apparent vulnerability 

to invasion by Melilotus, based on our observations of the 

rivers shown in Figure 1. For example, densely vegetated 

surfaces were given a score of low, whereas bare silt or 

Figure 2—Alaska highways surveyed for Melilotus alba between 2004 and 2006. AKNHP = 

Alaska Natural Heritage Program, NPS = National Park Service, UAF/PNW = University of 

Alaska Fairbanks/USDA Forest Service, Pacific Northwest Research Station, SPF = USDA Forest 

Service, State and Private Forestry.

gravel flood plains were considered high in their vulnerability to invasion (Figure 3).

Network Model

The network model examines spatial relationships among 

roads, river networks, and public land in interior and 

south-central Alaska. Our analysis is supported by several 

geographic information system (GIS) data layers. The river 

network is modeled using the National Hydrography Data 

set (USGS 1999); the Alaska Department of Transportation 

global positionong system (GPS) centerline data set provides a detailed road network (Alaska DOT 2006); and the 

boundaries of federal and state lands were obtained from an 

Administrative Boundaries data set (Alaska DNR 2001).

National Hydrography Data Set (NHD)

The NHD is managed by the U.S. Geological Survey 

(USGS) and Environmental Protection Agency (EPA). It 
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GENERAL TECHNICAL REPORT PNW-GTR-802

Figure 3—Flood plain vulnerability to invasion is shown in these examples of flood plains characterized 

as having low vulnerability to invasion by Melilotus alba (top) and as being highly vulnerable to invasion 

by Melilotus (bottom). The top photo shows Fish Creek at the Parks Highway Crossing; the bottom photo 

shows Lower Miller Creek at the Richardson Highway Crossing. 

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

is a comprehensive set of digital spatial data that contains 

information about surface water features such as lakes, 

ponds, streams, rivers, springs, and wells. Within the NHD, 

surface water features are combined to form reaches, which 

provide the framework for linking water-related data to the 

NHD surface water drainage network. These linkages make 

it possible to analyze and display these water-related data in 

upstream and downstream order (USGS 1999). We started 

with the assumption that all migration of invasive plants on 

the river system would be downstream. We then used the 

NHD as our base layer for river networks because it covers 

the entire state at a fine scale (1:63360), and also incorporates flow network information allowing us to efficiently 

identify all river reaches downstream from individual road 

crossings.

Road Network

There is no comprehensive road network data set available 

for Alaska, but the Alaska Department of Transportation 

Figure 4—Land classification boundaries identified on the Administrative Large Parcel Boundaries data set from Alaska Department of Natural Resources. We selected parcel types to include in 

our model by reviewing the agency affiliations of land managers who have been active in Alaska’s 

Committee for Noxious and Invasive Plant Management (CNIPM) over the last few years.

(ADOT) provided a draft version of a new GPS road centerline network. It covers the contiguous highway system plus 

Kodiak and Cordova. Some roads are not included in this 

data set—for example, the Denali National Park road, State 

of Alaska-administered logging roads, and private roads—

but these road data are the best currently available.

Public Conservation Units

We began with the land classification boundaries identified 

on the Administrative Large Parcel Boundaries data set 

from December 20, 2001, produced by the Alaska Department of Natural Resources (Alaska DNR 2001) (Figure 

4). We selected parcel types to include in our model by 

reviewing the agency affiliations of land managers who 

have been active in Alaska’s Committee for Noxious and 

Invasive Plant Management (CNIPM) over the last few 

years. Managers who are already aware of and concerned 

about invasive plants on the public lands they manage may 

be most likely to benefit from the information provided by 
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minor crossings. To associate river reaches downstream 

from crossings with conservation units, the Administrative 

Large Parcel Boundary data set was intersected with river 

reaches downstream from crossings (after the river reaches 

were buffered by 100 m).

Example

Combining the network model with invasive species 

distribution data yields information that can help managers 

determine critical control points for particular conservation units. We provide model output related to the Kanuti 

National Wildlife Refuge (NWR) as an example of this 

application. Kanuti NWR is entirely located in NHD 

subregion 1904, and although it has no direct road access, 

there are 13 major and 112 minor crossings upstream of 

the refuge, all on the Dalton Highway. We surveyed only 

the southernmost 10 of the major crossings, because as we 

worked north, Melilotus had disappeared from the roadsides 

by the 10th major crossing.

this work. We refer to that list of parcel types in aggregate 

as conservation units. Land management objectives and 

agency attitudes and approaches toward invasive species 

differ widely among these conservation units.

Road-River Interfaces—

The interface between roads and rivers (or streams) can 

take several forms. Roads can cross, run parallel to, and 

dead-end at rivers. We defined crossings as intersections 

between the NHD-derived streams and rivers network 

and the ADOT road centerline network. At this phase, our 

analysis does not include the parallel or dead-end road-river 

interfaces, except for rare occasions where small spatial 

mismatches cause the river and road linework to intersect. 

The NHD subregion 1904 (the Alaska portion of the Yukon 

River drainage) yielded a total of 919 crossings. Where 

roads cross major rivers or streams, there is typically a 

bridge; we refer to these points on the landscape as major 

crossings. Small streams or ephemeral drainages typically 

pass beneath roads in culverts; we refer to these points as 

Figure 5—Distribution of Melilotus on roadsides, 2004–2006. Most of these data were collected at 

milepost markers spaced 10 miles apart.
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Results

Melilotus Distribution

We found Melilotus at many points along the highways that 

we surveyed (Figure 5), with roadsides in the Fairbanks 

area most consistently colonized. Notably, little or no Melilotus was found along several major highways, including 

the Taylor Highway, the Denali Highway, and the McCarthy 

Road. In addition, we found Melilotus on the roadside 

immediately adjacent to 64 major crossings (Figure 6).

Of the total of 192 major crossings surveyed, 17 had 

Melilotus growing on a natural flood-plain surface (Figure 

7). Most of these were light infestations, but four crossings 

had moderate to heavy infestations on the flood-plain surface. All 13 of these crossings also had Melilotus growing 

on the roadside immediately adjacent to the bridge.

Though we found Melilotus growing on flood-plain 

surfaces immediately upstream of several crossings, we 

do not interpret this to mean that this species is spreading 

Figure 6—Distribution of Melilotus on roadsides immediately adjacent to major crossings, 

2004–2006, are displayed. We defined crossings as intersections between the NHD-derived streams 

and rivers network and the ADOT road centerline network. Crossings with bridges are referred to 

as major crossings.

upstream. Instead, all cases where Melilotus was found 

upstream involved heavily colonized individual rivers with 

multiple road crossings. In these cases, Melilotus had spread 

all along the river from sources upstream of all the surveyed 

crossings.

Network Model

The network model is a work in progress. At this writing, 

we have compiled NHD subregion 1904 (the Alaska portion 

of the Yukon River watershed). Compilation of NHD 

subregion 1902 (the Matanuska River and Susitna River 

watersheds) is underway.

Example

Of the 10 major crossings we surveyed leading to Kanuti 

National Wildlife Refuge, none had Melilotus on a natural 

flood-plain surface either upstream or downstream of the 

crossing (Figure 8, Table 1). Six had Melilotus on the roadside immediately adjacent to the crossing. We characterized 
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Figure 8—Major crossings upstream of 

the Kanuti National Wildlife Refuge are 

outlined below. We defined crossings 

as intersections between the NHDderived streams and rivers network and 

the ADOT road centerline network. 

Crossings with bridges are referred to as 

major crossings; identification numbers 

shown here correspond to numbers in 

Table 1. There are 13 major and 112 

minor crossings upstream of the refuge, 

all on the Dalton Highway. We surveyed 

only the southernmost 10 of the major 

crossings because Melilotus had disappeared from the roadsides of the Dalton 

Highway by the crossing labeled here 

as 1.

Figure 7—Distribution of Melilotus on natural flood-plain surfaces at major crossings, 2004-2006, 

is shown below. We defined crossings as intersections between the NHD-derived streams and 

rivers network and the ADOT road centerline network. Crossings with bridges are referred to as 

major crossings.
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Advances in Threat Assessment and Their Application to Forest and Rangeland Management

Table 1—Melilotus survey results for major crossings upstream of Kanuti National Wildlife Refuge

Numberon DistanceFigure 8 Highway Milepost Crossing name to Kanuti Upstream Downstream Roadside Vulnerability 

km

1 Dalton 156 S_FORK_KOYUKUK_RIVER 80 None None Light High2 Dalton 144 JIM_RIVER_3 52 None None None High3 Dalton 142 DOUGLAS_CREEK 46 None None None Low4 Dalton 142 JIM_RIVER_2 45 None None None Mod5 Dalton 140 JIM_RIVER_1 42 None None None Low6 Dalton 135 PROSPECT_CREEK 36 None None Light Low7 Dalton 125 N_FORK_BONANZA_CREEK 50 None None Mod Low8 Dalton 124 S_FORK_BONANZA_CREEK 47 None None Light Mod9 Dalton 113 FISH_CREEK 27 None None Light Mod10 Dalton 105 KANUTI_RIVER 38 None None Light Low

Milepost refers to state highway milepost marker nearest the crossing. Distance to Kanuti gives the total river distance in kilometers from the Dalton Highway to the eastern boundary of the refuge. The last four columns give data collected in 2005 as part of our Melilotus survey. 

five of the crossings as moderately or highly vulnerable to 

invasion by Melilotus. Crossings of the Dalton Highway 

were from 27 river km to 80 km upstream of the eastern 

boundary of the refuge.

Discussion

The NHD and ADOT road network data sets are both works 

in progress, and some errors were identified in the NHD 

data. For example, several small drainages such as the 

Little Goldstream Creek were isolated from the rest of the 

hydrography network, and there were several interruptions 

in the Yukon River flow. These are the best data currently 

available on a statewide basis, however. We expect that in 

the future, work in other disciplines will be referenced to 

the NHD, allowing more detailed data to be incorporated 

if they become available. For example, additional data may 

become available regarding water chemistry and flood-plain 

substrate characteristics.

We are working to link the roads, crossings, reaches, 

roadsheds, and conservation units so that the data can readily be summarized based on any of these factors and so that 

an end user can identify a feature (for example, a national 

park or a national wildlife refuge) and return the crossings, 

upstream road segments, and river reaches that are associated with it.

In our example with the Kanuti National Wildlife 

Refuge, our work identified six major crossings where 

Melilotus was found on the roadside immediately adjacent 

to the bridge. One of these crossings was ranked as highly 

vulnerable to invasion at the crossing, and two were ranked 

as moderately vulnerable. Refuge managers could prioritize 

monitoring and control efforts based on these results. Taken 

together, the network model and the case study are an effective means of identifying certain river and stream crossings 

as critical control points for preventing the movement of 

Melilotus toward particular land ownerships downstream.

The general applicability of our network model is 

demonstrated by examining the structure of data columns 

in Table 1. The first five columns contain data describing the spatial relationship between roads, crossings, and 

bridges for the Kanuti National Wildlife Refuge. Other 

data, such as precise latitude and longitude coordinates and 
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GENERAL TECHNICAL REPORT PNW-GTR-802

digital photos, are also available for each crossing. The last 

four columns contain data from a single survey for a single 

invasive species. Any future surveys for Melilotus or other 

invasive plant species at these crossings can be directly 

incorporated using our network model framework and will 

provide up-to-date information to land managers in Kanuti.

Acknowledgments

We thank the USDA Forest Service, State and Private 

Forestry, for funding and assistance with the mapping 

components of this project. Cyndi Snyder and Tiphanie 

Henningsen provided valuable field assistance.

Literature Cited

Alaska Department of Natural Resources [Alaska 

DNR]. 2001. Administrative large parcel boundaries. 

Anchorage, AK. ADNR, LRIS. <ftp://ftp.dnr.state.ak.us/

asgdc/adnr/adminbnd.zip>. (June 15, 2006). 

Alaska Department of Transportation [Alaska DOT]. 

2006. Preliminary ADOT/Navstar Mapping Corporation 

centerline and feature inventory data. On file: Wurtz, 

T.L. USDA Forest Service, Pacific Northwest Research 

Station, 3700 Airport Way, Fairbanks, AK 99709.

Alaska Natural Heritage Program. [N.d.]. Alaska exotic 

plant information clearinghouse database. Anchorage: 

University of Alaska. http://akweeds.uaa.alaska.edu/. 

(November 6, 2006).

Brabets, T.P.; Wang, B.; Meade, R.H., Jr. 2000. 

Environmental and hydrologic overview of the Yukon 

River Basin, Alaska, and Canada. USGS WaterResources Investigations Rep. 99-4204. (200) WRi 99-

4204. 

Densmore, R.V.; McKee, P.C.; Roland, C. 2001. Exotic 

plants in Alaskan National Park units. Unpublished 

Report of the U.S. Geological Survey, Alaska Biological 

Science Center. On file: Wurtz, T.L. USDA Forest 

Service, Pacific Northwest Research Station, 3700 

Airport Way, Fairbanks, AK 99709.

Gelbard, J.L.; Belnap, J. 2003. Roads as conduits 

for exotic plant invasions in a semiarid landscape. 

Conservation Biology. 17: 420–432.

Koch, F.H.; Smith, W.D. 2010. Representing humanmediated pathways in forest pest risk mapping. In: Pye, 

J.M.; Rauscher, H.M.; Sands, Y.; Lee, D.C.; and Beatty, 

J.S., tech. eds. 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. 421–443 p. 

Vol. 2. 

Lugo, A.E.; Gucinski, H. 2000. Function, effects, and 

management of forest roads. Forest Ecology and 

Management. 133: 249–262.

Parendes, L.A.; Jones, J.A. 2000. Role of light availability 

and dispersal in exotic plant invasion along roads and 

streams in the H.J. Andrews Experimental Forest. 

Conservation Biology. 14: 64–75.

U.S. Geological Survey [USGS]. 1999. National 

hydrography data set—medium resolution. Reston, 

VA: U.S. Geological Survey and U.S. Environmental 

Protection Agency. http://nhd.usgs.gov. (June 15, 2006).
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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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