EPA 600/R-04/050

February 2005

Wildlife Research Strategy

U. S. Environmental Protection Agency

Office of Research and Development

National Health and Environmental Effects Research Laboratory

Research Triangle Park, NC 27711
Disclaimer

The information in this document has been subjected to review by the National Health and

Environmental Effects Research Laboratory and has been approved for publication. Approval

does not signify that the contents reflect the views of the Agency, nor does mention of trade

names or commercial products constitute endorsement or recommendation for use.

ii
Foreword

The United States Environmental Protection Agency’s Office of Research and

Development (ORD) describes its research directions through a series of Multi-year Research

Plans. These multi-year plans identify the principal issues facing environmental decision

makers, the critical scientific uncertainties behind these issues needing resolution through

research, the expected research outputs, and a projected time line for conducting this research. 

The plans are organized around topical areas that deal with specific program areas (e.g.,

particulate matter, drinking water, safe food) or more general broad-based areas such as

ecosystem protection or human health research. 

An issue that cuts across several of ORD’s Multi-year Research Plans is the need to

better understand and predict the effects of human activities on wildlife populations. EPA has

historically relied upon laboratory-derived, species-specific toxicity data to protect aquatic and

terrestrial species. As we face more complex environmental problems, we realize that we must

integrate responses of organisms to multiple stressors as well as incorporate changing habitat

conditions. To empower State, local and Tribal decision makers, we also must provide tools that

can explicitly be applied to specific geographic locations. This is a challenging research

endeavor that calls for an organized approach to bring together a variety of disciplines and tools.

 To help meet this need, ORD’s National Health and Environmental Effects Research

Laboratory developed the Wildlife Research Strategy. The Strategy lays out a conceptual

framework that when realized will provide the scientific basis for spatially explicit population

level environmental risk assessments for application to a wide variety of environmental stressors. 

We recognize that solving this issue is a long-term commitment that will require the knowledge,

expertise, and research far beyond the capacity of EPA alone. We welcome your insights and

contributions as we undertake this challenge.

Lawrence Reiter, Ph.D.

Director

National Health and Environmental

 Effects Research Laboratory

iii
Abstract

This document describes a strategy for conducting wildlife effects research within the

U.S. Environmental Protection Agency’s (EPA) National Health and Environmental Effects

Research Laboratory (NHEERL). The Strategy is designed to address critical research areas and

produce methods, models, and findings that the EPA Program and Regional Offices, the States,

and Tribes can use to conduct wildlife population risk assessments and to develop associated

criteria. 

Consistent with the EPA's ecological risk assessment guidelines, the Strategy is designed

to improve problem formulation, effect characterization, and risk characterization steps. Within

this context, the Strategy supports a tiered approach to wildlife risk assessment and criteria

development by arraying a series of assessments from most general and broadly based (screening

level) to most realistic, accurate, and situation-specific (definitive level). While the

sustainability of wildlife populations remains the assessment endpoint of concern throughout the

tiered approach, increasingly accurate and realistic models and data are needed in higher tier risk

assessments to narrow the band of uncertainty around the estimate of risk. The Strategy

proposes development of a suite of methods and models with increasing realism and accuracy 

that will first concentrate on lower-tier risk assessment and criteria needs then on research that

provides techniques and approaches for higher-tier applications. Specifically, the Strategy is

focused on three major research objectives:

1. Develop mechanistically based approaches for extrapolating toxicological data

across wildlife species, media, and individual-level response endpoints.

2. Develop approaches for predicting population-level responses to stressors. Identify

the responses at the individual level that have the greatest influence on populationlevel responses.

3. Develop approaches for evaluating the relative risks from chemical and nonchemical stressors on spatially structured wildlife populations across large areas or

regions.

The proposed research across these three objectives will provide increasingly sophisticated methods and models for avian, amphibian, and mammalian wildlife risk assessments and

criteria development. Further development of this Strategy, fulfilment of its goals, and its

ultimate implementation require interaction among the Agency’s Program Offices and Regions

and collaboration within the Office of Research and Development and with other federal

partners. 

iv
Table of Contents 

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i v

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v i

Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

Acknowledgments ............................................................ i x

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

Introduction ..................................................................1

Programmatic Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Major Research Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Research Scope, Objectives, and Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Research Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Conceptual Approach to Wildlife Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1

Considerations about Ecological Models ....................................1 1

Tiered Risk Assessment Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2

Methodological Requirements of the Tiered Protocol ...........................1 5

Mechanistically Based Approaches for Interspecies Extrapolation of Toxicological Information

(Objective 1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 9

Statement of the Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 9

Background ...........................................................1 9

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1

Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2

Modeling Approaches for Predicting Responses of Wildlife Populations to Anthropogenic

Stressors From Individual-Based Information (Objective 2) . . . . . . . . . . . . . . . . . . . . . 2 5

Statement of the Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 5

Background ...........................................................2 5

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 6

Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 6

Spatially Explicit Modeling to Assess Relative Risks From Chemical and Non-Chemical

Stressors (Objective 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 9

Statement of the Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 9

Background ...........................................................2 9

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 0

Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 0

Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 4

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 6

v
Tables

Table 1. Research and data collection activities ................................... . 8

Table 2. Example attributes of screening and definitive tiers of wildlife risk assessments . . .14

Table 3. Types of extrapolation required by the conceptual approach of wildlife risk

assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7

vi
Figures

 

Figure 1. Ecological risk assessment framework highlighting the effects components to which

NHEERL research will contribute most directly ............................. 1

Figure 2. Conceptual approach to wildlife risk assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 3. Ordination scheme for ecological models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1

Figure 4. Tiered risk assessment approach for wildlife, moving from screening levels to

definitive levels .....................................................1 3

Figure 5. Conceptualization of uncertainty in a tiered risk assessment . . . . . . . . . . . . . . . . . . 1 4

Figure 6. Decision points in a tired risk assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5

Figure 7. Example life cycle diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 6

Figure 8. Schematic representation of how a dose-response function could be used to modify a

matrix model parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 8

 

 

vii
Abbreviations

DDT Dichlorodiphenyltrichloroethane

ECOFRAM Ecological Committee on FIFRA Risk Assessment Methods 

EPA Environmental Protection Agency

FIFRA Federal Insecticide, Fungicide, and Rodenticide Act 

FWS Fish and Wildlife Service 

GIS Geographical Information Systems

LC50 Lethal Concentration 50

NCEA National Center for Environmental Assessment 

NERL National Exposure Research Laboratory

NHEERL National Health and Environmental Effects Research Laboratory

NPL National Priorities List

ORD Office of Research and Development

p Probability 

PATCH Program to Assist in Tracking Critical Habitat

PBTK Physiologically Based Toxicokinetic

PCB Polychlorinated Biphenyls

RCRA Resource Conservation and Recovery Act 

SAB Science Advisory Board

TCDD 2,3,7,8-Tetrachlorodibenzo-o-Dioxin

TSCA Toxic Substances Control Act

USGS U.S. Geological Survey 

viii
Acknowledgments

The Wildlife Research Strategy was prepared by scientists from the following

organizations: 

Timothy Gleason National Health and Environmental Effects Research Laboratory, Atlantic

Ecology Division, Narragansett, Rhode Island

Wayne Munns National Health and Environmental Effects Research Laboratory, Atlantic

Ecology Division, Narragansett, Rhode Island

Steve Bradbury Formerly of the National Health and Environmental Effects Research

Laboratory, Mid-Continent Ecology Division, Duluth, Minnesota

Tala Henry National Health and Environmental Effects Research Laboratory, MidContinent Ecology Division, Duluth, Minnesota

John Nichols National Health and Environmental Effects Research Laboratory, MidContinent Ecology Division, Duluth, Minnesota

Anett Trebitz National Health and Environmental Effects Research Laboratory, MidContinent Ecology Division, Duluth, Minnesota

Joan Baker National Health and Environmental Effects Research Laboratory, Western

Ecology Division, Corvallis, Oregon

Selena Heppell National Health and Environmental Effects Research Laboratory, Western

Ecology Division, Corvallis, Oregon

Nathan Schumaker National Health and Environmental Effects Research Laboratory, Western

Ecology Division, Corvallis, Oregon

Development of the Strategy included an initial retreat that provided valuable input from EPA

Program Offices including:

Office of Air Quality Planning and Standards, Office of Air and Radiation;

Office of Emergency and Remedial Response, Office of Solid Waste and Emergency Response;

Office of Pesticide Programs, Office of Prevention, Pesticides, and Toxic Substances; and the

Office of Science and Technology, Office of Water.

ix
Executive Summary

This document describes a strategy for conducting wildlife effects research within the

U.S. Environmental Protection Agency’s (EPA) National Health and Environmental Effects

Research Laboratory (NHEERL). The goal of NHEERL’s wildlife research is to develop

scientifically valid approaches for assessing risks to wildlife populations from multiple stressors. 

The need to advance wildlife risk assessment methods and knowledge bases is recognized across

the Agency’s air, water, pesticide, toxic substances, and hazardous waste programs. Through a

series of EPA's Science Advisory Board reviews and consultations, as well as other EPA peerreviews, four key areas of research have been identified where advances in the science would be

instrumental in improving wildlife risk assessment techniques and criteria methodology. These

areas include the following:

1. Extrapolation research that improves the basis for predicting toxicological responses

among wildlife species and exposure scenarios of concern.

2. Coordinated wildlife population biology and wildlife toxicology research to improve

predictions of population dynamics in spatially explicit habitats.

3. Research to advance techniques for assessing the relative risk of chemical and nonchemical stressors on wildlife populations.

4. Research to define appropriate geographical regions/spatial scales for wildlife risk

assessments.

The Strategy is designed to address these critical research areas and produce methods,

models, and findings that will provide scientifically credible approaches for the EPA Program

and Regional Offices, the States, and Tribes to conduct wildlife population risk assessments and

to develop associated criteria. 

Consistent with the EPA's ecological risk assessment guidelines, the Strategy is designed

to improve problem formulation, effect characterization, and risk characterization steps. Within

this context, the Strategy employs a tiered approach to wildlife risk assessment and criteria

E-1
development by arraying a series of assessments from most general and broadly based (screening

level) to most realistic, accurate and situation-specific (definitive level). While the sustainability

of wildlife populations remains the assessment endpoint of concern throughout the tiered

approach, increasingly accurate and realistic models and data are needed in higher-tier risk

assessments to narrow the band of uncertainty around the estimate of risk. The appropriate final

tier for a specific wildlife risk assessment is based on a risk management analysis that weighs

risk assessment uncertainties against the context of the management decision and the costs

associated with a “wrong” decision compared to the costs of gathering and employing

increasingly realistic and accurate data and models. The Strategy proposes development of a

suite of methods and models with increasing realism and accuracy that will first concentrate on

lower-tier risk assessment and criteria needs followed by research that provides techniques and

approaches for higher-tier applications.

In reviewing wildlife risk assessments and criteria development, four steps were

identified that are critical to completing effects characterizations. The first step involves the

spatial and temporal characterization of stressors; e.g., contaminant exposure, habitat suitability,

and introduced species that may adversely affect the wildlife population of concern. Based on

the results of Step 1, quantitative chemical dose-response relationships and habitat-response

relationships at the individual level are developed in Step 2 (e.g., relationships to fecundity and

life-stage specific probability of survival). In Step 3, these demographic rates are used in

population models to generate outputs describing population growth rates or other appropriate

population-level endpoints. Finally, in Step 4, these relationships are “inserted” back into the

landscape to determine cumulative population dynamics across the landscape and assess effects

due to chemical exposure as well as other forms of habitat disturbance. As discussed previously,

the level of accuracy and realism required for these four steps varies with risk assessment and

management needs.

Based on the four steps required to assess effects in wildlife populations, the Strategy is

focused on three major research objectives:

1. Develop mechanistically based approaches for extrapolating toxicological data

across wildlife species, media, and individual-level response endpoints.

E-2
2. Develop approaches for predicting population-level responses to stressors. Identify

the responses at the individual level that have the greatest influence on populationlevel responses.

3. Develop approaches for evaluating the relative risks from chemical and nonchemical stressors on spatially structured wildlife populations across large areas or

regions.

These research objectives are consistent with recommendations provided by the Science

Advisory Board (SAB) and other peer-review panels and correspond to the wildlife effect

characterization steps described above. Objective 1 deals with development of individual-level

exposure-response relationships. The research proposed under Objective 1 will emphasize

approaches for developing and extrapolating toxicity data to a broader array of species, environmental media, and response endpoints (in particular, the endpoints required as input to population response models). Objective 2 deals with extrapolating from individual-level responses up

to the population. The primary approach and organizing structure of research conducted under

Objective 2 will be the development, application, and evaluation of population response models. 

Analyses will also be conducted to identify responses at the individual level that have the

greatest influence on population-level responses to help prioritize future research under

Objective 1. Research addressing Objective 3 introduces issues associated with the spatial and

temporal heterogeneity of populations and stressors and extends the analyses under Objectives 1

and 2 to applications in real landscapes with multiple stressors. 

The proposed research across these three objectives will provide increasingly sophisticated methods and models for avian, amphibian, and mammalian wildlife risk assessments and

criteria development. The projected outputs will advance techniques and knowledge bases

needed to support early-tier risk assessments and a national methodology for wildlife criteria. 

Through the selection of prototypical compounds and landscapes of concern, the research effort

will also develop methods and models that can support increasingly situation-specific risk

assessments and site- and species-specific wildlife criteria.

Further development of this research strategy, fulfillment of its goals, and its ultimate

implementation require interaction among the Agency’s Program Offices and Regions and

E-3
collaboration within the Office of Research and Development (ORD) and with other federal

partners. While development of the wildlife strategy was based in part on discussions with

Program Office and Regional representatives and a review of recent wildlife risk assessment and

criteria activities, this initial dialogue must be expanded to ensure that the regulatory challenges

have been properly understood and that the proposed research approach addresses the associated

needs. Collaboration with the National Exposure Research Laboratory (NERL) and the National

Center for Environmental Assessment (NCEA) is required to ensure that approaches developed

to assess effects on wildlife populations are compatible with approaches for exposure and risk

characterization. Collaboration with other federal research organizations will also be essential. 

For example, it is not envisioned that NHEERL would conduct new mammalian and avian

toxicity and physiology experiments in-house because such work can be achieved by

collaborating with existing federal facilities better suited for such efforts, such as the U.S. 

Geological Survey (USGS) Patuxent Wildlife Research Center. In a related manner, fulfillment

of research goals associated with population modeling and spatially explicit applications would

benefit from collaboration with Department of Interior scientists. Finally, it will be essential to

integrate research undertaken within ORD and other federal facilities with any future EPA grant

initiatives to ensure ORD-sponsored research undertaken by academia complements in-house

efforts.

E-4
Ecological Risk Assessment

PROBLEM FORMULATION

RISK CHARACTERIZATION

A

N

A

L

Y

S

I

S

Planning

(Risk

Assessor/

Risk Manager

Dialogue)

Communicating Results

to the Risk Manager

Risk Management

Characterization

of Exposure

Characterization

of

Ecological

Effects

Figure 1. Ecological risk assessment framework

highlighting the effects components to which NHEERL

research will contribute most directly.

 Introduction

This document describes a strategy for conducting wildlife effects research within the

U.S. Environmental Protection Agency’s (EPA) National Health and Environmental Effects

Research Laboratory (NHEERL). The goal of NHEERL’s wildlife research is to develop

scientifically valid approaches for assessing risks to wildlife populations from multiple

stressors. Consistent with the mission of NHEERL, the emphasis is on improved approaches for

effects components of the risk assessment process (Figure 1). NHEERL research will be

conducted in consultation with the EPA National Exposure Research Laboratory (NERL) and

National Center for Environmental Assessment (NCEA) as well as other organizations to ensure

that approaches developed to assess effects on wildlife populations are compatible with

approaches for exposure and risk

characterization.

The purpose of this document is to

describe NHEERL’s strategy for wildlife

research. It defines a conceptual framework

for wildlife risk assessments and identifies

high priority areas of research to be

undertaken within NHEERL, linkages among

these components, and the general types of

products that could be expected over the next

six years. It does not describe specific

research approaches or projects. Rather, the

strategy provides the foundation from which

these more-detailed research plans will be

developed, implemented, and integrated. It

also provides a starting point for discussions

with other organizations regarding uncertainties in risk assessment techniques and

potential collaborative research and

interactions.

1
Programmatic Needs. Several EPA programs deal with issues associated with effects on wildlife. The need to advance wildlife risk assessment knowledge bases and methods is recognized

across EPA. Immediate needs primarily concern the potential adverse effects of chemical

contaminants; however, the importance of considering effects associated with multiple types of

stressors is recognized.

Within EPA’s toxic substances and pesticide programs, the goal of advancing probabilistic risk assessments for wildlife has been articulated in risk assessment reviews, workgroups,

and advisory bodies. For example, in 1996 the Environmental Fate and Effects Division within

the Office of Pesticide Programs presented two ecological risk assessment case studies for

review to the Scientific Advisory Panel for the Federal Insecticide, Fungicide, and Rodenticide

Act (FIFRA). While reaffirming the utility of the ecological assessment process, the Panel also

offered a number of suggestions for improvement. Foremost, the Panel recommended moving

beyond the current single-point deterministic assessment approach and to developing the tools

and methods necessary for a probabilistic assessment of risk. As a follow-up to this and other

reviews and to develop specific recommendations for revising the assessment process, the Office

of Pesticide Programs formed the Ecological Committee on FIFRA Risk Assessment Methods

(ECOFRAM).

The Office of Water developed prototypical methodologies to assess risks of bioaccumulative chemicals to wildlife in 1995 through the Great Lakes Water Quality Initiative (U.S. EPA

1995). The wildlife criteria methodology, as well as numeric criteria for four specific pollutants

(DDT; 2,3,7,8-TCDD; PCB, and mercury), were developed collaboratively by federal and state

scientists and risk assessors. A draft Memorandum of Agreement among the Office of Water,

U.S. Fish and Wildlife Service (FWS), and National Marine Fisheries Service (Federal

Register, January 7, 1999) calls for development of improved approaches for wildlife criteria

derivation and requires EPA to explicitly address protection of threatened and endangered

species in implementation of the Clean Water Act. The draft Jeopardy Opinion for the

California Toxics Rules also requires the Office of Water to derive new wildlife criteria,

specifically for mercury and selenium.

The 1990 Clean Air Act Amendments require the Office of Air to consider possible

effects on wildlife from airborne deposition of hazardous substances. Adverse effects from

2
airborne deposition of mercury are of special interest. In the Agency’s Mercury Report to

Congress (U.S. EPA 1997), the approaches used in the Great Lakes Water Quality Initiative

(U.S. EPA 1995) were adapted to assess risks to piscivorous birds and mammals; and limitations

in current techniques and databases were noted. The Office of Air is currently developing

approaches to assess the significance of effects from other hazardous air pollutants on wildlife

and related ecosystem components.

Finally, EPA’s hazardous waste (Superfund) program is attempting to establish consistency in wildlife risk assessment approaches for organic compounds and metals, with an

emphasis on terrestrial ecosystems. In the absence of a cohesive approach to wildlife risk

assessment, risk assessors render decisions affecting wildlife based on variable assumptions

regarding exposure and effects. Currently, an Office of Solid Waste and Emergency Response

sponsored multi-stakeholder workgroup is attempting to develop scientifically sound screening

levels for chemicals in soils that would be protective of mammalian and avian wildlife

populations. The mammalian and avian benchmarks will be based on a hazard quotient method

derived from toxicity data for higher vertebrates and a generic food chain model. In addition to

generating discrete benchmark values, the workgroup is considering using methods to estimate

the likelihood of wildlife effects based upon probabilistic distributions of toxicity and exposure

data for some chemicals.

Major Research Needs. Underlying each of these Program Office efforts is the need for

improved techniques to extrapolate effects across species, levels of biological organization, and

landscapes. In consultation with external peer-review panels including EPA’s Science SAB

(U.S. EPA 1992, 1994, 1998), four key areas of research have been identified where advances in

the science would improve wildlife risk assessments:

1. Research that improves the basis for predicting and extrapolating toxicological

responses across wildlife species and exposure scenarios of concern. In general,

toxicological data are not available for species of concern and instead must be

extrapolated from a limited number of studies with “model laboratory organisms.” In

addition, chemical exposure data derived from monitoring studies or fate and transport

models are not always reported in a medium most useful for assessing effects. 

Consequently, there is a need to improve experimental approaches and predictive models

3
to advance techniques for extrapolating toxicological responses across wildlife species

using translators appropriate for estimating exposure from different media. This

extrapolation requires consideration of the separate but related issues of differences

among species in their exposure and differences among species in their sensitivity and

nature of toxicological response.

2. Coordinated wildlife population biology and toxicology research to improve predictions

of population dynamics in spatially explicit habitats. To establish a scientifically

credible approach to assess the risk of direct chemical effects on populations, research is

needed to better define those toxicological responses at the individual level that are most

critical in perturbing population dynamics. In addition, there is a need to predict

population responses to spatially explicit scenarios of chemical exposure.

3. Research to advance techniques for assessing the relative risk of chemical and nonchemical stressors on wildlife populations. Landscape characterization studies combined

with experimental approaches are required to better quantify the relative effects of

chemical stressors, habitat alterations, and the introduction of exotic species on wildlife

populations. Associated with this effort is the need to develop and integrate predictive

models so that the outcome of different management scenarios can be quantified based

on chemical loading, habitat alterations, exotic species control, and other management

options.

4. Research to define appropriate geographical regions / spatial scales for wildlife risk

assessments. A significant effort is needed to define scientifically credible spatial scales

for wildlife risk assessments. Habitat requirements for wildlife species associated with

aquatic and terrestrial ecosystems must be established and referenced to regulatory

jurisdictions to ensure coordinated implementation of risk-based decisions. A consensus

on current or potential habitat ranges is needed to identify wildlife species of concern and

to evaluate approaches in risk assessments that consider spatial population structure.

4
Research Scope, Conceptual Framework and Objectives

This research strategy is designed to address the critical research needs identified by the

SAB and other peer-review bodies and to produce models, approaches, and data that would assist

the Program Offices and other risk managers in conducting wildlife risk assessments.

Research Scope. The focus of the research strategy is on the effects on wildlife, in particular,

amphibians, birds, and mammals. Historically, EPA ecological research has dealt predominately

with aquatic biota and, to a lesser degree, terrestrial vegetation, as the basis for defining water

and air quality criteria authorized under the Clean Water and Clean Air Acts. The present focus

on wildlife is designed to provide techniques to help rectify this imbalance and will build on

approaches developed previously for aquatic biota. Tests of new models and hypotheses will

incorporate both wildlife and aquatic biota to ensure that the approaches developed are robust

across taxa and ecosystem types.

The assessment endpoint of concern is effects on populations; that is, the abundance

(numbers, biomass) and long-term viability of a given species within a defined geographic area. 

It is recognized that other assessment endpoints (e.g., at the individual or community level) may

be appropriate for some wildlife risk assessments. NHEERL’s research will focus on

population-level effects, however, because populations represent ecologically and legislatively

important endpoints of concern as expressed, for example, in the Great Lakes Water Quality

Initiative (U.S. EPA 1995) and the Endangered Species Act. While mortality or injury of

individuals (e.g., malformations) may cause concern, a more important question is whether these

individual losses affect population growth and viability. Community-level effects, such as

declines in species diversity or disruption of food webs, are important endpoints as well but are

far less tangible from both a scientific and management perspective. Furthermore, information

on population-level responses represents an important stepping stone towards an improved

understanding of community-level responses.

EPA regulatory authorities deal most directly with limiting the release of contaminants

into the environment. Wildlife populations also are affected, however, by many other stressors

resulting directly or indirectly from human activities (e.g., habitat loss and alterations,

introduced species, hunting pressure). The sensitivity of a population to a given contaminant, as

5
well as the ecological significance of a contaminant effect, is influenced by these other stressors. 

Thus, an important aspect of this research strategy is to develop approaches for assessing risks of

contaminants within this broader context. Consistent with recommendations of the SAB

(Section I), this Strategy deals explicitly with the combined effects of contaminants, habitat loss

and alteration, and introduced species on wildlife populations. Other types of stressors could

also be readily incorporated into the framework and models but will not be the subject of

focused attention in the time frame encompassed by this Strategy.

Neither stressors nor wildlife populations are distributed uniformly within the environment. The interplay between spatial and temporal heterogeneity in wildlife population structure

and spatial and temporal patterns of stressors is a major factor controlling the severity of effects

on wildlife populations (e.g., Kareiva 1990; Turner et al., 1995; Hanski 1998). Thus, a critical

feature of this Strategy is development of probabilistic models that deal explicitly with the

spatial distribution of population and stressors over time. These models will be designed for

application to real landscapes by interfacing with geographical information systems (GIS).

NHEERL’s approach to wildlife risk assessment links wildlife toxicology, population

biology, and landscape ecology. It is impractical for NHEERL to undertake an extensive empirical testing program for all species, contaminants, and habitats; therefore, an extrapolation

approach to fill data gaps will be applied. Consequently, the Research Strategy reflects an

integration of strategic laboratory and field-based studies with predictive modeling.

Conceptual Framework. Figure 2 outlines the NHEERL conceptual framework for wildlife

risk assessments, focusing on the effects component of the assessment process. Step 1 involves

spatial and temporal characterization of stressors, in particular contaminant exposure, habitat

suitability, and introduced species, that may adversely affect the population of concern. Much of

this information, especially data on contaminant exposures, would be derived from studies

conducted by others (e.g., NERL exposure modeling). Results from Step 1 provide the input

into Step 2, quantification of the exposure-response and habitat-response relationships at the

individual level. The specific response variables estimated in Step 2 are spatially explicit demographic rates (fecundity and life-stage-specific probability of survival) of individuals within the

6
HABITAT

UNIT B

HABITAT

UNIT A

HABITAT

UNIT C

nt = Mnt-1

FECUNDITY

HABITAT SUITABILITY

SURVIVAL PCB CONCENTRATION

Conceptual Approach

STEP 1 STEP 2 STEP 3 STEP 4

Figure 2. Conceptual approach to wildlife risk assessment. Steps 1-4 show landscape

characterization, development of exposure and habitat response relationships, estimating

population responses, and spatial modeling.

population. These demographic rates in turn drive population models in Step 3, generating

outputs describing population growth rate or other appropriate population-level endpoints (e.g.,

extinction probabilities). Finally, these population dynamics are inserted back into 

7
the landscape in Step 4 to determine habitat-specific population sources and sinks using spatially

explicit modeling platforms. Analysis of the cumulative population dynamics across the landscape provides the estimates of wildlife risks from chemical exposure, habitat changes, introduced species, and other forms of disturbance in the landscape. Example research and data

collection activities for each step in this process are listed in Table 1.

Table 1. Research and data collection activities for each step in the Conceptual Approach to

Wildlife Risk Assessment.

Step Research or Data Collection Activity

1. Landscape

characterization

Spatial / temporal aspects of contaminant exposure

Spatial / temporal aspects of habitat and habitat quality

Spatial / temporal aspects of introduced species

2. Exposure / habitat

response

relationships 

Evaluation of chemical effects on vital rates

Taxonomic extrapolation of dose-response effects on vital rates

Extrapolation of toxic response across media and endpoints

Effects of interactions of multiple stressors on individuals

Exposure-response model development

Habitat-response model development

3. Population responses

and modeling

Population model development

Identification of species response classes

Effects of interactions of multiple stressors on populations

Evaluation of model outputs, parameters, and assumptions

4. Spatial modeling Spatial model development

Identification of risk classes

Identification of appropriate spatial scales for assessment

Evaluation of relative risks from multiple stressors and effectiveness

of alternative management strategies

8
Research Objectives. The conceptual framework outlined above organizes the research

strategy and defines three major research objectives:

1. Develop mechanistically based approaches for extrapolating toxicological data across

wildlife species, media, and individual-level response endpoints. 

2. Develop approaches for predicting population-level responses to stressors. Identify the

responses at the individual level that have the greatest influence on population-level

responses for a wide range of life-history types.

3. Develop approaches for evaluating relative risks from chemical and non-chemical

stressors on spatially structured wildlife populations across large areas or regions.

Objectives 1-3 correspond to Steps 2-4, respectively, in the conceptual framework

presented in Figure 2. Objective 1 deals with development of individual-level exposureresponse relationships. We are not proposing to conduct substantial new mammalian and avian

toxicity testing, in part because such work can be achieved by collaborating with existing

facilities better suited for such efforts (e.g., USGS Patuxent Research Center). Rather, NHEERL

research will emphasize approaches for extrapolating toxicity data to a broader array of species,

environmental media, and response endpoints (in particular, the endpoints required as input to

population response models). This approach is consistent with the recommendations of the

SAB.

Objective 2 deals with extrapolating from individual-level responses up to the

population. Development and application of population response models will be the primary

approach and organizing structure. Modeling will be supplemented with targeted field and

laboratory studies designed to evaluate model outputs, key assumptions, model parameters, and

potential population-level compensatory mechanisms. Analyses will also be conducted to

identify responses at the individual level that have the greatest influence on population-level

responses to help prioritize future research under Objective 1.

Objective 3 introduces issues associated with the spatial and temporal heterogeneity of

populations and stressors and extends the analyses under Objectives 1 and 2 to applications in

9
real landscapes. Because different stressors tend to be distributed heterogeneously in the

landscape, it is under Objective 3 that we can most completely address the interactive effects of

contaminants, habitat alteration, and introduced species on wildlife populations. Models and

analyses under Objective 3 will both assess risks from multiple stressors and evaluate the

relative effectiveness of alternative management strategies.

It is important to note that research supporting these objectives will produce results and

models of value both independently and jointly. For example, screening-level wildlife risk

assessments could be undertaken without applying the quantitative population and spatial models

described under Objectives 2-3.

The remainder of this document is organized around the three objectives that define

NHEERL’s wildlife research strategy. Section III reviews concepts of ecological modeling and

a tiered approach to risk assessment. Sections IV-VI describe our strategic approach to

Objectives 1-3. Each Objectives section includes: (1) a brief summary of the state of the

science and research needs specific to that objective; (2) a list of more detailed research

questions and proposed research activities; and (3) an overview of the proposed research

approach in the context of existing research and expertise within NHEERL. Section VII

describes the role of and criteria for selecting case studies to be conducted jointly with others

(e.g., Program Offices, Regions, NERL, NCEA, other organizations) to provide an opportunity

to evaluate and demonstrate the approaches developed.

10
1

Use of the terms accuracy and precision in this plan follows their connotations in the field of inferential

statistics. Accuracy refers to how well an estimate matches the true value of a particular parameter or value being

estimated (in this case, ecological risk), and typically is quantified using some measure of bias. Precision refers to

the amount of variation among multiple estimates made of the parameter and usually is expressed using some

measure of scatter. 

GENERALITY GENERALITY

 REALISM REALISM ACCURACY ACCURACY

• Applicable in most situations

• Don’t predict specific outcomes well

• Typically “simple” 

• Good for exploring relationships 

Applied Models

“Physics”

 Models

Theoretical

Models

• Account for known relationships

• May be complex

• Accurately describe specific 

 situations

• May have limited use in other

 situations

• Typically detailed

Figure 3. Ordination scheme for ecological models (modified

from Levins 1968).

 Conceptual Approach to Wildlife Risk Assessment

To understand the NHEERL strategic approach to wildlife research, it is important to

consider features of ecological models and how they can be used to address different aspects of

wildlife risk assessment. These features suggest an approach for optimizing the effort and costs

of individual assessments based on the needs of environmental managers. The following

discussion provides background concerning the methodological requirements and research

needed to develop and implement a wildlife risk assessment protocol.

Considerations About Ecological Models. In his broad discussion of ecological theory and

models, Levins (1968) describes a triangular scheme for ordinating ecological models that has

the attributes of generality, realism, and accuracy (originally precision1

) as its apices (Figure 3). 

In this context, general models are those that tend to be simple,apply to a broad range of

situations, and therefore are appropriate for exploring relationships among model parameters and

outputs. Realistic models attempt to account for known relationships and processes in ecological

systems and, as a result, can be relatively complex. Accurate models are constructed with the

objective of minimizing numerical differences between model outputs and actual ecological

system dynamics. Their often case-specific nature limits their use in broader applications. 

Levins (1968) pointed out that

any two of these attributes could be

maximized at one time but that it is not

possible to maximize all three with a

single model. Typically, models

developed for use in applied situations

(e.g., conservation biology) are

intended to give realistic, accurate

answers; parameterization depends

upon the actual conditions of the

11
situation being modeled (for example, the specific life history and demographic characteristics of

the species of interest). Increased generality can be achieved by expanding the range of values

assumed for particular model coefficients or by assuming broad functional relationships among

parameters, but such actions necessarily reduce the accuracy achievable in any particular

application. Model accuracy can be enhanced by increasing the specificity of model

parameterization relative to a particular species or environmental situation.

With this ordination scheme in mind, ecological models and, specifically, population

models, can be used in wildlife risk assessments for at least three, arguably different purposes. 

The first is to detect (and perhaps diagnose) previous or ongoing adverse effects on wildlife

population dynamics. Such a use typically requires sufficient high quality data to be able to

detect statistical changes in population abundance and to relate those changes to variation in

chemical exposure, habitat, or other forms of disturbance. The second purpose is to project the

consequences of a given set of environmental conditions (or changes in conditions) to the

dynamics of a population. Here the intent may be to evaluate the ramifications of particular

environmental management decisions as determined by trends in population numbers or changes

in extinction probabilities. The final purpose is to forecast or predict the future behavior of the

population based on a understanding of environmental variability and the dynamic interactions

of density and biological processes. [The distinction between projection and forecasting used

here follows that given by Caswell (1986).] This last use of population models may produce the

most accurate results although the generality of the analysis will suffer. While certain model

formulations are more appropriately used for one of these purposes or another, all three purposes

have value in the context of wildlife risk assessment.

Tiered Risk Assessment Protocol. Wildlife risk assessments are conducted with a variety of

objectives in mind, including evaluation of the general consequences of environmental management actions, evaluation of the susceptibility of individual species to particular stressors, identification of classes of species that respond to stress similarly, and analysis of risks to specific

populations resulting from particular combinations of stress. The quality and quantity of data

available as input to risk assessment varies across assessment situations. Differences also exist

in the costs (ecological, monetary, and/or societal) associated with making wrong decisions. 

Considering the potential range of management objectives, data requirements, and management

implications, we propose development of a tiered approach to wildlife risk assessment. Just as is

12
PROBLEM FORMULATION

RISK CHARACTERIZATION

Ecological Risk Assessment

Characterization

of

Ecological

Effects

Characterization

of

Exposure

ANALYSIS

PROBLEM FORMULATION

RISK CHARACTERIZATION

Ecological Risk Assessment

Characterization

of

Ecological

Effects

Characterization

of

Exposure

ANALYSIS

PROBLEM FORMULATION

RISK CHARACTERIZATION

Ecological Risk Assessment

Characterization

of

Ecological

Effects

Characterization

of

Exposure

ANALYSIS

Figure 4. Tiered risk assessment approach for wildlife,

moving from screening levels to definitive levels.

used in other risk-based programs (e.g., TSCA, FIFRA), a tiered protocol for wildlife risk

assessment will permit optimization of analysis effort and cost based on the objectives of the

assessment.

Conceptually, the tiered approach

consists of series of complete risk assessments (Figure 4) arrayed from most

general and broadly based (screening

level) to most realistic, accurate, and

situation-specific (definitive level). Tiers

in the array vary from one another in the

types of models used, quality and quantity

of data needed, nature of risk conclusions

developed, and the degree to which results

can be extrapolated to other situations

(Table 2); but the assessment endpoint

(sustainability of the specific wildlife

population) remains constant. To

illustrate, screening-level assessments

would employ general models with low

data requirements and would produce

conclusions that can be extrapolated to a

wide range of scenarios. Conversely,

definitive-level assessments would use realistic models that are accurate for particular situations

(combinations of species and environmental settings) and that therefore require potentially large

amounts of high quality data describing the life history and demography of the species, as well

as information about the regional landscape. The risk estimates drawn from definitive

assessments would be very specific to the situation evaluated and consequentially could be

extrapolated to a limited number of other scenarios. The number of tiers needed for any

particular risk assessment would not be prescribed; rather, assessors would cycle through

increasingly more realistic and accurate assessment tiers until either the confidence associated

with risk conclusions became acceptable (relative to the costs of making a wrong decision) or the

availability of data and resources prevented moving to the next tier. Alternately, the process

13
ESTIMATE OF RISK

TIER OF RISK ASSESSMENT

Figure 5. Conceptualization of uncertainty in a tiered

risk assessment. Solid line is estimate of risk; dashed

lines are uncertainty bounds. 

could be entered and completed at any single tier based on the requirements of the assessment

and nature of available information. 

Table 2. Example attributes of screening and definitive tiers of wildlife risk assessments.

Tier Modeling Tools Data Needs Applicability Extrapolatability

screening general low robust high

definitive realistic, accurate high specific low

Reflective of this trend from general, screening-level tiers to realistic and accurate

definitive-level tiers is an expected increase in the confidence associated with the estimates of

risk to the assessment population. As illustrated conceptually in Figure 5, the use of data and

models that are increasingly specific to the population and assessment situation should lead to a

narrowing of the bounds of uncertainty around the estimates of risk. Decisions to move from

one tier to the next will be in part based on the level of confidence/uncertainty acceptable to the

14
RISK

DECISION

SCREENING

RA

DEFINITIVE

RA

INTERMEDIATE

RA(s)

UNCERTAINTIES

ACCEPTABLE?

UNCERTAINTIES

ACCEPTABLE?

RISK

DECISION

yes

no

RISK

DECISION

yes

no

Figure 6. Decision points in a tiered risk assessment,

illustrating movement to higher tiers if uncertainties

and available data warrant.

risk manager. Figure 6 illustrates the decision

process relative to assessment tiers; with the

communication of risk estimated at each tier, the

risk manager will decide whether the uncertainties associated with that tier are acceptable in the

context of the decision to be made and the costs

associated with a wrong decision or if next tier

assessment is warranted. In practice, estimates of

risk that suggest either extremely low risk or

extremely high risk to the wildlife population

should permit immediate decisions regarding that

risk and how to manage it. Estimates of risk that

are ambiguous and are associated with sufficiently high levels of uncertainty should invoke

further assessment at the next higher tier. Working through the risk assessment tiers permits

decisions to be made as early as possible in the process.

Methodological Requirements of the Tiered Protocol. Full implementation of the proposed

tiered protocol will require development and verification of a suite of methods, models, and data

appropriate for each tier. Although the actual tools employed may vary across tiers from the

general to the more realistic and/or accurate, the basic process for assessing risk is more or less

constant across tiers (Figure 2). Each tier requires information regarding the habitat and

stressors therein (Step 1), methods to translate habitat suitability and stressor levels to the

demographic rates of survivorship and fecundity (Step 2), models to characterize populationlevel effects (Step 3), and methods for interpreting the consequences of spatial and temporal

variation in the patterns of stressors and wildlife population structure (Step 4). In some

instances, the tools to accomplish these steps may not change between tiers, but rather their use

and data requirements will vary. In other instances, the tools themselves will change. Research

undertaken to meet Objectives 1-3 will address the range of methodological requirements

dictated by the tiered protocol.

Information and data specific to the wildlife species of concern often will be limited and

will vary across tiers. New approaches will be needed to develop those data and may include

15
tools for habitat quantification and generation of biological response data (i.e., assays of stressorresponse). In particular, issues of extrapolation (and interpolation) are likely to be critically

important within any given tier and will play a role in each step of the risk assessment process

(Table 3). In Step 2 of the assessment, for example, data available to describe the demographic

consequences of chemical exposure to the assessment species (e.g., river otter) may be unavailable; and reproductive and survivorship rates may need to be extrapolated from those of a more

commonly tested and related species (e.g., mink). Similarly, Step 3 requires extrapolation of

population-level responses from changes in demographic rates. The types of extrapolation

needed include taxonomic (among species), hierarchical (across levels of biological organization), spatial, and temporal. Several approaches can be employed to meet these needs, ranging

from empirically-based statistical models to process-based mechanistic models; the considerations described above for ecological models also are cogent here. Much of the research

described in this strategy focuses on development and verification of extrapolation

methodologies. 

16
Table 3. Types of extrapolation required by the conceptual approach to wildlife risk assessment.

Step Type of

Extrapolation

Example Use Methodological

Requirement

1. Landscape

characterization

spatial quantify habitat suitability GIS methods

temporal develop chemical exposure profile exposure models

2. Exposure /

habitat

response

relationships 

interspecific estimate toxicological response of

one species from data on another

empirical models

mechanistic models

intraspecific estimate variability in

demographic response within a

population

empirical models

developmental estimate adult response from

juvenile response

empirical models

mechanistic models

endpoint to

endpoint

estimate reproductive response

from survival response

empirical models

exposure -

response

estimate response at differing

levels of exposure

empirical models

lab to field estimate response of a species in

the field from laboratory data

empirical models

hierarchical estimate demographic response

from physiological response

empirical models

mechanistic models

3. Population

responses

and modeling

hierarchical estimate population response from

demographic response

mechanistic models

4. Spatial

modeling

spatial describe population distribution mechanistic models

temporal describe population dynamics mechanistic models

By its very nature, the tiered assessment protocol requires the exposure, ecological effects,

and risk characterization methods necessary and sufficient to conduct a complete ecological risk

assessment. Although this Strategy focuses primarily on critical effects research that will be

17
conducted by NHEERL, much of the methodological and data needs associated with wildlife risk

assessment are more appropriate to the missions of NERL, NCEA, and other research partners;

consequently, interaction with and the active participation of these groups will be sought.

18
Objective 1: Mechanistically Based Approaches for Interspecies

Extrapolation of Toxicological Information

Statement of the Objective. Develop mechanistically based approaches for extrapolating

individual-based toxicity testing information to population-level effects across wildlife species,

media, and individual response endpoints. Mechanistically based approaches provide a credible

basis for better understanding observed differences among species with respect to exposureeffects relationships. The focus of this research is on toxicity endpoints that can be directly

related to adverse effects on populations thereby providing a linkage between Objectives 1 and

2. Critical components of this Objective include identifying and developing the capability for

predicting toxicokinetic and toxicodynamic differences among species.

Background. Predictions about the potential for toxic effects on wildlife occurring from exposure to chemicals are generally made using individual-based toxicity data. Studies conducted in a

controlled setting using relatively few species of common laboratory animals (e.g., rats, mice,

dogs, chickens) constitute the majority of toxicity data. For many chemicals, the endpoints

measured in these studies were chosen to address human health concerns (e.g., cancer) as

opposed to ecological concerns. While the capability exists to test new chemicals and species

when there is a need for more accurate assessments, resources available to conduct these studies

will never be sufficient to test all chemicals and species of concern. A need exists, therefore, to

develop approaches that can be used to extrapolate existing toxicity data from a relatively few

tested species to wildlife species of concern. Additionally, there is a need to move beyond

unbounded expressions of effect (e.g., no-observable-adverse-effects levels) and to better characterize the uncertainty in predicted effects.

A variety of extrapolations are commonly used to “convert” individual-based toxicity

information from a particular study to information that can be used to predict effects in a species

of concern. Extrapolations are performed to adjust or account for (1) taxonomic differences

(interspecies extrapolation); (2) differences in dose-response relationships among different

endpoints (endpoint extrapolation); (3) toxicological effects levels (e.g., lowest-observable to noobservable-adverse-effects extrapolation); (4) differences in exposure duration between the test

and the actual exposure (subchronic-to-chronic extrapolation); (5) differences in exposure route

between the test situation and the actual exposure (route-to-route extrapolation); (6) sensitivity

differences among individuals in a population (intraspecies extrapolation); and (7) differences

19
between laboratory and field conditions (lab-to-field extrapolation). This list of extrapolations is

not exhaustive, but it demonstrates the range of extrapolations necessary to perform effects

characterizations for untested species. There are currently major uncertainties associated with

the necessity, validity, precision, and accuracy of performing such toxicological extrapolations.

Approaches used to perform extrapolations across species include the application of

uncertainty factors, allometric scaling, and correlative statistical models. Each of these methods

assumes that the chemical’s mode-of-action is similar in both the tested species and the species

of concern and that a similar chemical dose at a site of action will produce a similar response. 

Furthermore, it is assumed that toxicokinetic differences between the two species (e.g., differences in metabolic biotransformation) do not result in substantial interspecies differences in the

relationship between delivered dose and dose at the target tissue. Not surprisingly, the reliability

of these methods tends to vary directly with the taxonomic relatedness of the two species (Suter

1993). While none of these approaches is inherently right or wrong, each has its limitations. 

 

Uncertainty factors are applied to toxic effects data to account for uncertainties about the

relationship between the test conditions and a “real world” situation. These factors are often

applied in orders of magnitude (i.e., a factor of 10); when uncertainty exists in more than one

area, the uncertainty factors are usually multiplied. Uncertainty factors may be derived from

expert judgment, ratios of observed values (e.g., acute-to-chronic LC50 values), or differences

between observed values (e.g., difference between most sensitive and least sensitive species). 

Thus, they are by no means “standardized” and may or may not incorporate a mechanistic understanding of toxicity (Chapman et al., 1998). Allometric scaling functions, usually based on body

weight or surface area, are used to convert the toxic dose from one animal to a toxic dose in

another. This approach assumes that the toxic dose is related to kinetic factors (e.g., absorption

and elimination routes/rates) which are themselves functions of body weight or surface area. 

The use of this approach does not require knowledge of these kinetic factors; it is assumed,

however, that similar factors determine the toxic dose in both species (Travis and White 1988). 

Regression models are based on the assumption that two classes of toxicity data (e.g., lethal dose

for two mammals) are correlated and can therefore be used predict one another. Typically,

regression models for toxic effects extrapolations are statistical expressions of an observed

relationship and do not consider the mechanism that underlies the relationship (Suter 1993).

Less often employed, but potentially more useful, are physiologically based toxicokinetic

(PBTK) models. PBTK models provide estimates of internal (target tissue) dose in both a tested

20
species and the species of interest. These estimates are based on mathematical descriptions of

absorption, distribution, metabolism, and elimination and require anatomical, physiological, and

biochemical information for both species. Dosimetry estimates are then combined with doseresponse data from the tested species to predict toxicity in the untested animal. PBTK models

have several advantages, including (1) accurate estimation of target dose, (2) integration of

temporal dosing dynamics, and (3) simulation of variance in target tissue dose within and across

species. These models have been used extensively in human health risk assessment to extrapolate data from laboratory animals to humans and are commonly linked to biologically based

models of chemical effect (Reitz et al., 1996). The greatest impediment to the development and

application of PBTK models in wildlife risk assessment is the lack of kinetic data (i.e., the

chemical time-course in specific tissues) for relevant/representative chemicals and species.

Toxicity extrapolations are more tenuous when the chemical mode-of-action varies

among species. Under these circumstances there is a need to understand the cause of the

observed differences in toxic response. It may then be possible to characterize the circumstances

under which dose-based extrapolations are still possible while also identifying those cases for

which additional information is needed. This information could conceivably include toxicity

testing data for species of interest or studies of physiological and biochemical differences among

species. 

Research Questions. In the context of individual-level effects, it is possible to identify three

major areas of uncertainty in wildlife risk assessments for toxic contaminants: (1) quality and

quantity of existing data; (2) extrapolation of existing data to untested species and chemicals;

and (3) prediction of field responses from laboratory data. From these three major areas of

uncertainty come the following research questions: 

a. To what extent and for what purposes can existing toxicity testing data be used in

wildlife risk assessments? This question includes the use of toxicity data for wildlife

species as well as that collected using common laboratory test animals.

b. In efforts to fill existing data gaps, what types of toxicity testing (chemicals, species,

duration, dosing route, etc.) should receive priority?

c. How well do current extrapolation techniques serve the needs of wildlife risk

assessors and under what circumstances do they fail?

21
d. How can current extrapolation techniques be extended and improved?

As indicated previously, there is a need to link individual-level effects to population-level

responses. This need suggests the following additional research questions:

e. What types of toxicity test data (endpoints and expressions of effect) are most useful

in relating individual-level effects to effects on populations?

f. What are the relationships between common toxicity test endpoints (e.g., lethality,

growth reduction) and endpoints used for population modeling (e.g., vital rates)? 

Research Approach. Critical evaluation of existing toxicity data: Current methods for extrapolating toxicological data among species generally assume that if a chemical operates in two

species via the same mode-of-action, similar doses at the site of action will produce similar

effects. If this is true to some reasonable approximation, it is prudent to focus on techniques

such as PBTK modeling as a basis for extrapolating dose (and by extension, effect) across

species. Alternatively, physiological and biochemical differences among species may result in

substantially different effects at the same tissue dose. In this case, elucidation of the

fundamental basis for the observed difference in effect must precede any detailed consideration

of chemical kinetics. To this end, existing toxicity testing data for wildlife species should be

systematically evaluated to elucidate exposure-residue-response relationships for individual

chemicals and chemical classes. Evaluation of these relationships along with toxicokinetic

information will provide insight into whether toxicodynamic or toxicokinetic differences

underlie apparent differences in sensitivity. When sensitivity differences are largely due to

kinetic differences, PBTK models will provide the appropriate extrapolation tool. The utility of

this effort for cases where the dose-response equivalence assumption is not met will be the

identification of gaps in our current understanding of chemical mode-of-action and, thus, a future

research need. 

The focus of these efforts should be placed on species for which exposure factors have

previously been determined (U.S. EPA 1993). In a manner similar to that employed in human

health risk assessment, existing data should be used to derive reference doses for selected compounds and taxa. These data should also be used to evaluate techniques currently used for interspecies extrapolation (e.g., allometric scaling of toxic dose). There is a need to compile data on

22
chemicals’ mode-of-action at the cellular, tissue, and whole animal levels to provide a basis for

interspecies extrapolation and as a means of dealing with chemical mixtures. There is also a

need to identify fundamental differences among taxa (e.g., oviparous versus placental reproduction) that result in large differences in toxic response. Finally, an effort should be undertaken to

evaluate the suitability of data from toxicity tests with common laboratory species for use in

wildlife risk assessments. Such research could coordinate with and build upon current NHEERL

efforts to collect wildlife toxicity data in the TERRETOX database. 

 

Targeted collection of toxicity testing data: Toxicity tests with selected wildlife species should

be performed to complement existing information. Data gaps identified in a review of existing

information will aid in planning these efforts. These studies should be designed to yield

exposure-residue-response relationships for high priority chemicals under environmentally

relevant exposure conditions (e.g., dosing route and duration). Chemical selection should be

guided by the need to characterize adverse effects of specific chemical classes representing

known or suspected modes of action. Candidates for this activity include persistent bioaccumulative toxicants, high-use pesticides, and compounds with known or suspected endocrinedisrupting activity. Throughout these efforts there is a need to evaluate effects endpoints

relevant to population modeling efforts. 

Extrapolation of existing data to new chemicals and species and from lab-to-field: The utility

of laboratory-derived toxicity-testing data in effects assessments for wildlife should be critically

examined. Existing techniques for extrapolating this information should be reviewed to identify

strengths and limitations and to provide guidance for their use. Special emphasis should be

placed on identification of factors that could limit the application of laboratory testing data to

chemical exposures that take place in the field. A partial listing of these factors includes the

chemical route of exposure, fluctuating exposures, and site-specific differences in chemical

bioavailability. Where possible, characteristics of field exposures should be identified to guide

future laboratory testing efforts.

Development of PBTK models for selected species: Improved techniques for interspecies

extrapolation should be developed and the utility of these methods evaluated through application

to specific wildlife risk assessment problems. An emphasis should be placed on the development of mechanistic models for predicting chemical disposition as a means of relating applied

dose rates to chemical residues in critical target tissues. Emphasis should also be placed on the

development of PBTK models for selected species. The development and use of PBTK models

23
for interspecies extrapolation is a philosophy as much as it is a definitive tool. Understanding

the toxicokinetic and toxicodynamic processes that underlie chemical toxicity will improve all

approaches currently used for interspecies extrapolation. For example, knowledge of the mechanism of action of a chemical and physiological processes critical for manifestation of toxicity

will add credibility to the professional judgement used in applying uncertainty factors. An

understanding of chemical toxicokinetics, combined with knowledge of physiological and

metabolic parameters, will also improve the interpretation of correlative statistical models and

allometric scaling efforts.

The development of PBTK models requires that necessary anatomical, physiological, and

biochemical information be obtained (measured or estimated) for each new species. There is

also a need to estimate equilibrium tissue partitioning coefficients for compounds of interest. 

Controlled exposures should be conducted to evaluate model predictions. Additional research

may also be required to obtain key parameter estimates such as rates of metabolic

biotransformation. It is suggested that initial efforts be devoted to the development of a PBTK

model for a commonly tested species of bird. Candidate species include the mallard duck,

Japanese quail, pheasant, and chicken. Additional models for amphibians and mammalian

wildlife species would be developed as needed to address specific research and risk assessment

needs. 

24
Objective 2: Modeling Approaches for Predicting Responses of Wildlife Populations 

to Anthropogenic Stressors From Individual-Based Information

Statement of the Objective. Integrate wildlife population biology and wildlife toxicology

research to improve prediction of population responses. There are two primary goals in this

research area. The first goal is to develop approaches for integrating individual-level responses

in order to extrapolate and predict the population-level effects of anthropogenic stressors. The

second is to identify those responses at the individual level that have the greatest influence on

population-level responses. 

Background. Many Program and Regional Offices are now grappling with how to address the

effects of anthropogenic stressors on wildlife populations. Questions associated with extrapolation of effects across levels of biological organization are a recurring uncertainty associated

with wildlife risk assessments. From the perspective of ecotoxicology, Clements and Kiffney

(1994) concluded that “[the] two greatest challenges are interpreting the ecological significance

of [toxicological] effects and improving the predictive ability of [higher level] studies.” Anthropogenic stressors are not limited to chemicals, and our approach must be flexible enough to

encompass other forms of anthropogenic and natural stressors. In response to the recent declines

in amphibian species, Wake (1998) stressed the need for a basic understanding of population

biology, as well as the need to understand the roles of natural versus anthropogenic stressors. 

Population models have increasingly been used to evaluate conservation measures for threatened

and endangered species (Crouse et al., 1987; Doak et al., 1994; Caswell et al., 1999) and for

projecting population-level effects of chemicals (Caswell 1996; Munns et al., 1997). We

propose using population models to integrate individual measures of anthropogenic stress

(survival and reproduction) from Step 2 to project population-level effects that will support Step

4 (Figure 4).

There are many modeling approaches to choose from including bioenergetics models

(Hewett 1989), individual-based models (DeAngelis and Gross 1992), and matrix models

(Caswell 1989). In order to explicitly address the goals cited above, the selected model construct

must first provide population-specific models that can integrate the individual response data

from Step 2. Secondly, the selected model construct should be compatible with the spatially

explicit modeling framework identified in Step 4. Thirdly, the model construct must provide a

basis for understanding risk classes in demographic terms. Additional desirable features of a

25
Age-0 Age-1 Age-2 Age-3

P12 P23 P34

f2

f3

f4

Figure 7. Example life cycle diagram, where Pij represents the

probability of an individual surviving from life stage i to life stage j; and

fi

 represents the stage specific reproductive output. The life cycle

diagram is a schematic representation of a matrix model.

model type include the ability to utilize existing data (i.e., the model construct should not

require a great deal of new research except where very specific needs have been identified); 

applicability across multiple tiers of an ecological risk assessment (from general to highly

detailed and realistic [see Figure 4]); and ease of use for risk assessors to apply and interpret. 

Research Questions.

a. Which responses at the individual level have the greatest influence on

population-level responses?

b. How can individual-level responses to anthropogenic stressors be integrated with

and extrapolated to population-level effects? What is the most appropriate way to

extrapolate across levels of biological organization?

c. How effective are population models for addressing questions a and b above? Can

the veracity of model results be assessed? How sensitive are model results to model

parameters and assumptions?

d. What is the relative importance of compensatory mechanisms (e.g., density

dependence, natural selection, and adaptation) in terms of ultimate outcomes at the

population level? 

Research Approach. We have chosen to focus on developing demographic matrix models as

the modeling construct because they provide the best overall fit to the criteria identified in the

background section. Matrix

models utilize basic life history

data, including life stage-specific

survival and reproduction data

(Figure 7). This information has

been published for many species,

thus reducing the need to

conduct studies to collect

baseline data. Matrix models

provide a great deal of flexibility

and are appropriate for multiple

26
tiers in a risk assessment because they can be developed across a range of complexity from very

general to highly detailed. For example, simple density-independent matrix models using

literature-derived life-history data could be used for screening-level risk assessments. 

Alternatively, stochastic and/or density-dependent matrix models could be developed for

detailed site-specific risk assessments.

Matrix models can address both goals of this research objective. Matrix algebra provides

analytical solutions for a suite of population-level endpoints including population growth rate,

stable age distribution, reproductive value, and demographic sensitivity and elasticity (Caswell

1989). Changes in population growth rate in response to anthropogenic stress can be used as a

measure of population-level effect (Munns et al., 1997). Demographic elasticity is the relative

sensitivity of population growth rate to each of the matrix elements (life-history parameters) and

represents an approach for identifying responses at the individual level that have the greatest

influence on population-level response (Caswell et al., 1984, De Kroon et al., 1986). In addition

to analytical solutions, matrix models can also be used to conduct simulations to project stochastic population growth rates and to estimate extinction and quasi-extinction risks (Ginzburg et al.,

1982). If desired, it is also possible to embed other modeling attributes, such as densitydependent responses or migration between sites with different stressors, within the framework of

a matrix model. 

Matrix models use the same information generated by many common bioassay protocols

(i.e., life stage-specific survival and fecundity) making the incorporation of these endpoints a

relatively straightforward exercise (e.g., Gleason et al., 1999). Bioassay results can be used to

directly modify the matrix entries. Alternatively, life-stage-specific, dose-response functions

can be incorporated into the matrix entries (Figure 8). Matrix models are required for the

spatially explicit modeling construct proposed in the Step 4. Additionally, this modeling

construct takes advantage of existing NHEERL expertise and provides opportunities for

collaborative population-level research across the Ecology Divisions. Specifically, NHEERL

scientists have experience applying matrix models to habitat and conservation-related questions

and incorporating bioassay data into matrix models to assess population-level effects of

contaminants. 

27
Stressor Intensity

0

1

Survival

probability

multiplier

Figure 8. Schematic representation of how a dose-response function

could be used to modify a matrix model parameter. The survival

probability multiplier would then be used to modify the appropriate

survival probability (Pi

) based on stressor intensity.

The veracity of demographic model projections can be evaluated in several ways. One

approach is to evaluate the general utility of models by reviewing the success of published

models. Population model results could also be evaluated against empirical results from multigenerational laboratory life-table-response-experiments. Because laboratory results do not

necessarily accurately reflect the field, a third approach to model verification would be to

evaluate model results in specific field assessments (see Section VII). Model evaluation in the

field would be most effective for

populations that have long-term

demographic data sets. 

Research must address the

relative importance of compensatory

mechanisms (density dependence,

tolerance, local adaptation) because

they can potentially have a

significant influence on population

response. Density dependence, at a

minimum, requires an understanding

of the functional relations between density and population dynamics (often requiring multiple

years of population measurements). A potentially more intractable problem involves

determining the interactions between population density and stressor effects. The costs

associated with determining these interactions for specific cases may preclude such analyses for

all but the most critical risk assessments. As an example of the importance of considering

evolutionary processes, Nacci et al., (1999) have identified a population of a common estuarine

fish species that has a specific metabolic adaptation which allows it to persist in an extremely

contaminated environment. Research should address the generality of this phenomenon, as well

as other compensatory mechanisms such as life history shifts, the effects of anthropogenic stress

on genetic diversity, population genetics, and the potential ecological and evolutionary costs of

such adaptations.

28
Objective 3: Spatially Explicit Modeling to Assess Relative Risks from 

Chemical and Non-Chemical Stressors

Statement of Objective. Develop approaches for evaluating the relative risks from chemical

and non-chemical stressors to spatially structured wildlife populations across large areas or

regions. While Objective 2 addresses the potential effects of toxicants on homogeneous

populations, Objective 3 specifically addresses how spatial and temporal heterogeneity in both

stressor distribution and wildlife population structure influence wildlife population size, growth,

distribution, and persistence through time. Results from these analyses will also provide insight

into appropriate spatial scales for wildlife risk assessments.

Background. Numerous studies have documented that the spatial pattern and temporal

dynamics of habitats across large landscapes play important roles in the long-term viability of

populations (Doak et al.,1992; Kareiva and Wennergren 1995; Vitousek et al.,1997; Fahrig

1998). High-quality habitats serve as source areas for repopulating and sustaining populations in

lower quality habitats (population “sinks”) elsewhere in the landscape. With habitat loss and

degradation, these source areas become increasingly important. Contaminants or other stressors

can have markedly different effects on populations depending on whether the area affected is a

population source or sink. Likewise, because organisms move among habitat units within the

landscape, depressed reproductive or survival rates resulting from contaminant exposure could

cause population declines over fairly large areas even if the contaminant is confined to a relatively small area. Thus, assessments that ignore the influence of spatial structure and temporal

dynamics in landscapes and populations could seriously under- or overestimate the risks to

wildlife.

Wildlife populations are affected simultaneously by multiple stressors, both natural and

anthropogenic. By understanding the relative risks from different stressors, managers can design

effective strategies that will provide the greatest net benefit to wildlife populations. While EPA

regulatory authorities deal most directly with the release of contaminants into the environment,

the SAB and other reviewers (Kareiva 1990, Saunders et al., 1991) have identified habitat loss

and introduced species as major environmental risks. Thus, NHEERL research will focus on the

interactive effects of contaminants, habitat loss and alteration, and introduced species on wildlife

populations. Populations adversely affected by habitat loss or introduced species may be more

susceptible to additional stress from environmental contamination, but high-priced control

programs to reduce environmental contamination may have little measurable benefit if

29
populations continue to be depressed by widespread habitat loss and fragmentation.

Research Questions. Research under Objective 3 will address the following major research

questions:

a. Can GIS-based, time-varying models be developed that project wildlife population

responses to spatially distributed stressors with levels of precision and accuracy

suitable for wildlife risk assessments and evaluation of alternative management

scenarios?

b. Does consideration of spatial and temporal heterogeneity in landscapes and

population dynamics significantly alter estimates of wildlife population risks to

stressors? If so, under what circumstances is it most important to incorporate

spatially explicit, time-varying models into the assessment process?

c.. How do environmental contaminants interact with other landscape-level stressors, in

particular habitat loss and fragmentation and introduced species, to affect population

abundance and persistence? Under what circumstances are multiple stressors likely

to interact synergistically or antagonistically?

d. What life history characteristics, habitat requirements, and mobility/dispersal

characteristics have the greatest influence on the sensitivity of different wildlife

species to spatially distributed stressors? Can species be grouped into risk classes

based on these characteristics?

e. How does the spatial and temporal resolution and extent of analysis influence

predictions? What are the most appropriate spatial and temporal scales for wildlife

risk assessments?

Research Approach. Just as for Objective 2, modeling will provide the primary approach and

organizing structure for Objective 3 research. By necessity, research under Objective 3 will be

largely model-based because it will integrate issues associated with population dynamics and

landscape change at large spatial and temporal scales. Data and findings developed under

Objectives 1 and 2 will help parameterize and evaluate Objective 3 models. Additional field and

laboratory studies may be conducted to define and evaluate key model parameters, assumptions,

30
and outputs. For example, several ongoing NHEERL studies are evaluating amphibian population responses to multiple stressors in different regions of the U.S. (Heppell et al., 1999, U.S.

EPA 1999). The case studies described in Section VII may provide other opportunities for

model evaluation and demonstration. However, much of the data needed to parameterize and

evaluate the Objective 3 model(s) will be derived from non-NHEERL and non-EPA sources

(e.g., FWS research on habitat requirements and basic population biology). Thus, particularly

for Objective 3, we will seek out opportunities to collaborate with other organizations, especially

organizations with expertise and data relating to effects of habitat loss and introduced species on

wildlife populations.

One of the first tasks for Objective 3 will be development of GIS-based, time-varying

models that can project the population-level consequences of multiple, spatially distributed

stressors. To be suitable, a model should (1) be spatially explicit, preferably through the use of

digital landscape maps; (2) be probabilistic, providing a mechanism for including demographic

and environmental stochasticity; (3) incorporate species vital rates in the form of a population

projection matrix; (4) link species vital rates to habitat quality and the presence of contaminants;

(5) simulate the movement of organisms through a landscape; (6) alter movement behavior based

on habitat quality and the presence of contaminants; (7) permit habitat quality and chemical

contaminant severity and distribution to change with time; (8) be general enough to work with a

range of species, yet sophisticated enough to link species life histories to realistic landscape

patterns; and (9) balance simplicity against generality, keeping in mind the limitations of

existing and readily collected data.

The influence of landscape on populations will be mediated through the individual. The

behavior and contribution of each individual to the population will be affected by its survival

rate, reproductive rate, and ability to locate a suitable breeding site. Individual organisms will

respond to both landscape quality and pattern which may change through time. Organisms will

also respond to the presence of other individuals, which will allow the model to capture the

influence of invasive species via competitive and predator-prey relationships. The presence of

contaminants in portions of a landscape will likely be modeled as changes in habitat quality,

which in turn lower the fertility, survival, or dispersal ability of organisms trying to utilize the

affected areas. The exposure of individuals moving through contaminated areas could alter their

vital rates on a temporary or permanent basis. NHEERL models will not deal with contaminant

fate and transport, but could be linked with other models that do. Those models, by themselves,

could be applied to address “what if” scenarios regarding the spread and attenuation of contami31
nants on the landscape, altering habitat quality according to distance from source, time of year or

time since the contamination took place. However, it will be important to be aware of the

inherent limitations in the ability of such models to capture the effects of contaminants. For

instance, there are likely to be many consequences for wildlife of exposure to toxicants that

cannot be meaningfully collapsed into an effective change in habitat quality.

Because they require information about dispersal and the effects of habitat quality on

collections of individuals, spatially explicit models are more data intensive than site-specific,

homogeneous population models. To be well suited for the tiered assessment approach (Section

III), models selected for Objective 3 will have to accommodate a wide range of data uncertainty. 

The models will have to capture the generality necessary to incorporate real landscapes and

multiple species without becoming so parameter rich that verification is impossible. If movement or specific habitat data are lacking, the models should permit simulations to be conducted

with a simple landscape and movement rules to answer general questions about the effects of

landscape heterogeneity. If data are less limiting, the models should permit interactions between

species and their environments to become more complex. 

Spatially explicit models will be applied to the research questions outlined above dealing

with (a) stressor interactions, (b) the role of life history, habitat requirements, and dispersal/

mobility characteristics in determining species sensitivity to different types of stressors, (c)

identification of life-history-based risk classes, and (d) the influence of spatial and temporal

resolution and extent on model results. These sorts of analyses will be conducted using both real

and simulated landscapes. The use of actual landscapes will help keep the research applied and

relevant. Real landscapes, however, are typically complex so simple, fabricated landscapes will

be employed at times as an aid to the development of theory. An example of a theory that

Objective 3 will develop is the definition of species guilds by life history traits grouped by

anticipated responses to anthropogenic stressors. As noted earlier, the validity of such

theoretical investigations will be examined using the proposed case studies.

Our initial research efforts will focus on modifying an existing in-house GIS model that

already meets many of the above criteria. NHEERL researchers have completed a prototype

model, PATCH (a Program to Assist in Tracking Critical Habitat) (Schumaker 1998), designed

to track the effects of changes in habitat quality and pattern on populations territorial birds and

mammals having well-defined habitat requirements. Over the next six years, PATCH will be

adapted and/or new models developed that capture species interactions and address more taxa

32
and other stressors (in particular contaminants and introduced species). 

33
 Case Studies

As part of the NHEERL research effort, case studies will be used both to develop and

verify specific methods and tools associated with Objectives 1-3 and to verify and demonstrate

our overall approach for assessing risks to wildlife within a tiered assessment protocol. This

section focuses primarily on the second use of case studies and describes the objectives and

criteria for selection of candidate studies. Future selection of case studies and their implementation will depend on the opportunities available and on input from the Program Offices and other

research partners.

To be maximally useful to development of wildlife risk assessment methods, case studies

should help us accomplish several objectives. These include the following:

1. Evaluate the strengths and weakness of approaches to predict risks to wildlife

species.

2. Evaluate key hypotheses regarding risks to wildlife species.

3. Demonstrate the usefulness of research products to program offices and other

wildlife managers.

4. Provide information useful for solving existing wildlife problems.

5. Highlight additional research needs for wildlife risk assessment. 

In addition to meeting these objectives, case studies undertaken by NHEERL should

combine aspects of toxicology, population biology, and landscape ecology and make optimal use

of existing resources. 

The approach envisioned to accomplish these objectives involves conducting a series of

case studies. To maximize the immediate benefits of the research approaches and products, each

case study will focus on a real problem facing wildlife managers. Although somewhat

premature at this stage of research planning, some candidate case studies include the following:

1. Assessment of risks to a mammal or piscivorous bird species at a National Priorities

34
List (NPL) or Resource Conservation and Recovery Act (RCRA) site or pesticides

application site.

2. Assessment of risks to selected amphibian populations in National Parks or in

regions of known amphibian deformities and declines.

3. Development of a state-wide or site-specific wildlife criteria for a site or state.

4. Basin-wide assessment relating patterns of land use to population trends in wildlife,

fish, and aquatic invertebrate populations. 

To conduct these or other case studies, NHEERL will draw on its existing strengths and

involve Agency and external research partners to the extent feasible. Potential and desirable

collaboration and linkages with other groups include with NERL on exposure issues; with

NCEA on risk characterization methods; with the program offices and regions; and with

agencies such as FWS, NOAA, DOD, DOE to identify specific cases studies, to exchange data,

and to share expertise.

Potential products resulting from the case studies include the following:

1. Verified approaches for wildlife risk assessment.

2. Spatially explicit modeling systems for selected wildlife species.

3. Support for development of guidelines for wildlife risk assessment.

4. Support for other program office and research partner needs, including scientifically

sound methods for establishing wildlife chemical criteria and approaches for

examining the relative risks of multiple stressors to threatened and endangered

species.

35
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