U.S. Geological Survey Science Strategy for Highly 

Pathogenic Avian Influenza in Wildlife and the 

Environment (2016–2020)

Open-File Report 2016–1121

U.S. Department of the Interior

U.S. Geological Survey
Cover. Male Northern Pintail flying above Seedskadee National Wildlife Refuge in Wyoming. 

Photograph by Tom Koerner, U.S. Fish and Wildlife Service. Licensed under Creative Commons 

Attribution 2.0 Generic license.
U.S. Geological Survey Science Strategy 

for Highly Pathogenic Avian Influenza in 

Wildlife and the Environment (2016–2020)

By M. Camille Harris, John M. Pearce, Diann J. Prosser, C. LeAnn White, A. Keith 

Miles, Jonathan M. Sleeman, Christopher J. Brand*, James P. Cronin, Susan De 

La Cruz, Christine L. Densmore, Thomas W. Doyle, Robert J. Dusek, Joseph P. 

Fleskes, Paul L. Flint, Gerald F. Guala, Jeffrey S. Hall, Laura E. Hubbard, Randall J. 

Hunt, Hon S. Ip, Rachel A. Katz, Kevin W. Laurent, Mark P. Miller, Mark D. Munn, 

Andrew M. Ramey, Kevin D. Richards, Robin E. Russell, Joel P. Stokdyk, John Y. 

Takekawa, and Daniel P. Walsh 

[*authors listed from here forward are in alphabetical order]

Open-File Report 2016–1121

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

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Suggested citation:

Harris, M.C., Pearce, J.M., Prosser, D.J., White, C.L., Miles, A.K., Sleeman, J.M., Brand, C.J., Cronin, J.P., 

De La Cruz, S., Densmore, C.L., Doyle, T.W., Dusek, R.J., Fleskes, J.P., Flint, P.L., Guala, G.F., Hall, J.S., Hubbard, L.E., 

Hunt, R.J., Ip, H.S., Katz, R.A., Laurent, K.W., Miller, M.P., Munn, M.D., Ramey, A.M., Richards, K.D., Russell, R.E., 

Stokdyk, J.P., Takekawa, J.Y., and Walsh, D.P., 2016, U.S. Geological Survey science strategy for highly pathogenic 

avian influenza in wildlife and the environment (2016–2020): U.S. Geological Survey Open-File Report 2016–1121, 

38 p., http://dx.doi.org/10.3133/ofr20161121.

ISSN 2331-1258 (online)

Photograph on preceding page: Mallard duck, courtesy of public-domain-image.com.
iii

Contents

Executive Summary.......................................................................................................................................1

Vision .............................................................................................................................................................2

Introduction.....................................................................................................................................................2

Science Outcomes of the U.S. Geological Survey Science Strategy on HPAI in Wildlife 

and the Environment........................................................................................................................8

U.S. Geological Survey HPAI Science Goals ............................................................................................8

Science Goal 1: Augment National HPAI Surveillance Plan ........................................................8

Objective 1. Develop tools and strategies for early HPAI detection in wildlife 

and the environment....................................................................................................10

Objective 2. Conduct HPAI surveillance in wild birds and peridomestic wildlife ..........10

Science Goal 2: Determine Mechanisms of HPAI Disease Spread in Wildlife and the 

Environment............................................................................................................................11

Objective 1. Determine mechanisms of HPAI spread and persistence in wildlife ........11

Objective 2. Determine mechanisms of HPAI spread and persistence in the 

environment ..................................................................................................................11

Objective 3. Determine mechanisms of HPAI spread and persistence in 

peridomestic wildlife found near poultry operations .............................................12

Objective 4. Determine implications of LPAI and HPAI exposure for wildlife 

populations....................................................................................................................14

Science Goal 3: Characterize HPAI Viruses Circulating in Wildlife ..........................................14

Objective 1. Characterize AI viruses circulating in wild birds ..........................................15

Objective 2. Characterize AI viruses circulating in wild mammals..................................16

Objective 3. Characterize HPAI viruses circulating outside North America 

and on the continental fringes with potential for introduction to 

North America ..............................................................................................................16

Science Goal 4: Understand Implications of Avian Ecology on HPAI Spread.........................17

Objective 1. Map spatial and temporal distributions of wild and domestic 

avian populations for risk model development .......................................................17

Objective 2. Address AI-relevant information gaps in avian migratory 

connectivity data..........................................................................................................17

Objective 3. Investigate HPAI transmission in avian communities and the 

impact of HPAI on avian populations........................................................................19

Science Goal 5: Develop HPAI Forecasting and Decision-Making Tools ................................19

Objective 1. Provide decision support to management agencies....................................20

Objective 2. Develop HPAI risk assessment and predictive models................................22

Objective 3. Develop online HPAI data visualization tools ................................................23

Acknowledgments.......................................................................................................................................23

References Cited..........................................................................................................................................23

Appendix 1. Overview of U.S. Geological Survey Capabilities for Avian Influenza Research.......34

Integrating HPAI Science Across U.S. Geological Survey Mission Areas ...............................34

Appendix 2. Examples of U.S. Geological Survey Capabilities for Avian Influenza Research.......36

Genetic and Stable Isotope Analysis of Wildlife Populations.....................................................36

Telemetry Movement Studies of Wildlife Populations..................................................................36

Disease Risk Modeling.......................................................................................................................36

....
iv

Statistical and Geospatial Modeling ...............................................................................................37

Epidemiological Modeling.................................................................................................................37

Laboratory and Experimental Animal Studies on Diseases.........................................................37

Decision Science and Data Visualization Tools.............................................................................38

Virus Detection in the Aquatic Environment ..................................................................................38

Figures

1. Conceptual model of the process of introduction and wild bird dispersal of AI and

subsequent infection of commercial poultry holdings ...........................................................3

2. Diagram showing the U.S. Geological Survey Science Strategy for HPAI in Wildlife

and the Environment is based on the relationship between scientific discovery and

the application of new knowledge.............................................................................................5

3. Illustration showing the U.S. Geological Survey Science Strategy for HPAI in Wildlife 

and the Environment (2016–2020) is focused on five long-term science goals..................6

A1–1. Diagram showing the U.S. Geological Survey has considerable scientific

capabilities nationwide to study avian influenza ..................................................................35

Highlights

1: What is the Role of the USGS in the Federal Response to HPAI?..................................................4

2: The Importance of Long-Term Research for Disease Surveillance ..............................................9

3: AI in the Environment...........................................................................................................................13

4: Genetics of AI Viruses .........................................................................................................................15

5: Merging Wild Bird Migration and Disease Ecology.......................................................................18

6: Risk Assessment Models....................................................................................................................21
v

Abbreviations

AI avian influenza

APHIS USDA Animal and Plant Health Inspection Service

ASC USGS Alaska Science Center

CDC Centers for Disease Control and Prevention

CIDA USGS Office of Water Information Center for Integrated Data

CRU USGS Cooperative Research Unit

DOI U.S. Department of the Interior

FRESC USGS Forest and Rangeland Ecosystem Science Center

GHSA Global Health Security Agenda

GPS global positioning system

HA hemagglutinin

HEDDS USGS Highly Pathogenic Avian Influenza Early Detection Data System

HPAI highly pathogenic avian influenza

icA highly pathogenic avian influenza intercontinental group A viruses

LIDE USGS-USDA Laboratory for Infectious Diseases in the Environment

LPAI low pathogenic avian influenza

NA neuraminidase

NOROCK Northern Rocky Mountain Science Center

NWHC USGS National Wildlife Health Center

OIE Office International des Epizooties (World Organization for Animal Health)

PWRC USGS Patuxent Wildlife Research Center

qPCR quantitative polymerase chain reaction

UMESC USGS Upper Midwest Environmental Science Center 

U.S. United States

USDA U.S. Department of Agriculture

USFWS U.S. Fish and Wildlife Service

USGS U.S. Geological Survey

WARC USGS Wetland and Aquatic Research Center

WERC USGS Western Ecological Research Center

WHISPers USGS Wildlife Health Information Sharing Partnership—event reporting system
A pair of Northern Pintails, courtesy of public-domain-image.com.
U.S. Geological Survey Science Strategy for Highly 

Pathogenic Avian Influenza in Wildlife and the 

Environment (2016–2020)

By M. Camille Harris, John M. Pearce, Diann J. Prosser, C. LeAnn White, A. Keith Miles, Jonathan M. 

Sleeman, Christopher J. Brand*, James P. Cronin, Susan De La Cruz, Christine L. Densmore, Thomas W. 

Doyle, Robert J. Dusek, Joseph P. Fleskes, Paul L. Flint, Gerald F. Guala, Jeffrey S. Hall, Laura E. Hubbard, 

Randall J. Hunt, Hon S. Ip, Rachel A. Katz, Kevin W. Laurent, Mark P. Miller, Mark D. Munn, Andrew M. 

Ramey, Kevin D. Richards, Robin E. Russell, Joel P. Stokdyk, John Y. Takekawa, and Daniel P. Walsh 

[*authors listed from here forward are in alphabetical order]

Executive Summary

An outbreak of highly pathogenic avian influenza (HPAI) 

occurred in North American poultry and wildlife across 

21 States (2014–2015). The Congressional Research Service 

estimated economy-wide losses of 3.3 billion dollars and the 

U.S. Department of Agriculture (USDA) has reported that 

over 50 million chickens and turkeys were dead or culled as 

a result of the HPAI outbreak. Because this disease is at the 

interface of wildlife, poultry, and humans, the complexity 

of resource management and policymaking decisions ranges 

from the local to national level. Avian influenza (AI) must be 

considered in an ecological context because its transmission 

and persistence is impacted by wildlife and environmental 

conditions. Predictive modeling of these viruses is challenging 

because of the diversity of avian host species, their migratory 

patterns, trophic interactions, and behavior. Climate, hydrological, and landscape changes, as well as mammalian bridge 

vectors and invasive species, have the potential to further 

enhance the complexity of AI dynamics in North America. 

The U.S. Geological Survey (USGS) has established longterm research on infectious diseases, birds, watersheds, and 

environmental conditions. USGS science has and will continue 

to significantly contribute to understanding, predictions, and 

decisions made by resource managers and policymakers about 

AI. This document provides a strategic direction for continued 

USGS HPAI research over the next five years (2016‒2020) 

that builds upon that foundation. Implementation of novel 

objectives and proposed research actions with international, 

Federal, State, tribal, and academic partnerships will serve as 

the basis for developing robust decision-making frameworks 

to aid resource managers and policymakers faced with the new 

biological threat of HPAI. The strategy encompasses five longterm research science goals:

• Science Goal 1. Augment National HPAI Surveillance 

Plan. In 2015, the USDA led an interagency effort to 

revise a national surveillance plan for HPAI in wild 

migratory birds in the United States. This goal supplements that plan to ensure that research and surveillance efforts fill data gaps rather than collect redundant 

information.

• Science Goal 2. Determine mechanisms of HPAI 

disease spread in wildlife and the environment. The 

USGS has the unique capabilities to understand the 

spread and persistence of HPAI in wildlife, water, and 

sediments. As environmental conditions change, the 

USGS can predict and then work with resource managers to assess how these changes affect prevalence of AI 

viruses and population outcomes for wildlife, poultry, 

and public health.

• Science Goal 3. Characterize HPAI viruses circulating 

in wildlife. The USGS capabilities in genomics and 

bioinformatics allow for characterization of AI viruses 

circulating in wildlife and the environment. This information will contribute to spatiotemporal assessment of 

risk to poultry production, wildlife, and public health.

• Science Goal 4. Understand implications of avian ecology on HPAI spread. Migratory birds are the reservoirs 

of AI. The USGS is a leader in avian ecology research 

and has the capabilities to elucidate how intercontinental migration and avian community interaction influence HPAI spread.

• Science Goal 5. Develop HPAI forecasting and 

decision-making tools. The USGS has the expertise to 

provide formal HPAI decision consulting (for example, 

structured decision making) to management agencies. 

Based on management needs, the USGS will conduct 
2 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

decision analyses and develop risk and decision 

models. Scientific findings from the first four goals 

(and prior USGS research) can be integrated into risk 

assessment and decision models to improve AI detection and improve outbreak prevention strategies in 

poultry and wildlife management outcomes. 

Identification of key decision points and critical information gaps (Science Goal 5) will guide prioritization of research 

in Science Goals 1–4 and serve as the basis for the development of models and data visualization tools. This science 

strategy will be reviewed annually to ensure that USGS HPAI 

research will address each goal area and refine scientific and 

decision-support products in order to provide timely information for key decisions being made by resource managers and 

policymakers.

Vision

Through the Science Strategy for HPAI in Wildlife and 

the Environment, the USGS will assess AI dynamics in an 

ecological context to inform decisions made by resource 

managers and policymakers from the local to national level. 

Through collection of unbiased scientific information on the 

ecology of AI viruses and wildlife hosts in a changing world, 

the USGS will enhance the development of AI forecasting 

tools and ensure this information is integrated with a quality 

decision process for managing HPAI.

Introduction

Historically, AI viruses that circulated in migratory birds 

were generally not highly pathogenic (capable of causing 

significant disease in poultry). Low pathogenic AI (LPAI) 

viruses that naturally circulate in aquatic birds (shorebirds, 

ducks, geese, and swans) rarely cause disease in poultry. 

However, influenza A viruses have genetic characteristics that 

promote constant change and unpredictable consequences. 

AI is a collection of viruses that are identified by two broad 

types of antigens: hemagglutinin (HA) and neuraminidase 

(NA). There are 144 possible combinations of subtypes (HA 

(H1-H16), NA (N1-N9)). AI viruses, primarily H5 and H7 

subtypes, are classified as HPAI if they are capable of causing significant disease in poultry (Dugan and others, 2008). 

In the fall of 2014, HPAI was discovered in commercial 

poultry in southern British Columbia, Canada. In December 

2014, strains of HPAI with genetic origins in East Asia were 

first detected in migratory birds in the United States (Ip and 

others, 2015). By the end of the 2014–2015 HPAI outbreak, 

over 50 million poultry across 15 States were dead or culled 

(U.S. Department of Agriculture, 2016). Mortality also 

occurred in 9 wild bird species (primarily raptors), although 

in limited numbers. Detections of HPAI in wildlife provided 

poultry producers and public health officials with an early 

warning system for this disease. Understanding HPAI dynamics in wildlife can also provide insight into the spread of other 

influenza viruses. For example, the 2009 swine flu pandemic 

was a new strain of H1N1 that resulted from a triple reassortment of bird, swine, and human influenza viruses (Smith 

and others, 2009). The Obama Administration’s National 

Security Strategy (https://www.whitehouse.gov/sites/default/

files/docs/2015_national_security_strategy.pdf) highlights the 

need to strengthen our capacity to counter biological threats. 

Specifically, the security strategy highlights the need for the 

U.S. to strengthen “our ability to prevent outbreaks and ensure 

sufficient capacity to respond rapidly and manage biological 

incidents.” The U.S. will accelerate its work with international 

partners through the Global Health Security Agenda (GHSA) 

to pursue “a world that is safer and more secure from infectious diseases.” As public health officials, poultry producers, 

and wildlife managers respond to HPAI viruses in the U.S., the 

USGS is uniquely positioned to provide scientific knowledge 

(from long-term and new research) on HPAI in wild birds and 

the environment, and provide decision support tools (see highlight 1: What is the Role of the USGS in the Federal Response 

to HPAI?). The USGS has extensive capabilities that include 

(1) characterizing molecular attributes of LPAI and HPAI 

viruses, (2) assessing the role of wildlife and the environment 

in disease spread and persistence, and (3) conducting traceconcentration monitoring of AI in the physical environment. 

The USGS is collaborating with the USDA to investigate the 

AI wildlife-poultry interface so that research results can promote the enhancement of biosecurity and improve domestic 

and wild avian population outcomes.

The overall goal of this USGS Science Strategy for HPAI 

in Wildlife and the Environment goes beyond just documenting the occurrence and distribution of AI viruses in wild birds. 

The USGS aims to understand the epidemiological processes 

and environmental factors that influence HPAI distribution 

and describe the mechanisms of transmission between wild 

birds and poultry. USGS scientists (Flint and others, 2015) 

developed a conceptual model describing the process linking 

HPAI dispersal in wild waterfowl to the outbreaks in poultry (fig. 1). This strategy focuses on five long-term science 

goals. These goals will help define and describe the processes 

outlined in the conceptual model with the ultimate goal of 

facilitating biosecurity and minimizing transfer of diseases 

across the wildlife-poultry interface. The first four science 

goals are focused on scientific discovery and the fifth goal 

is application-based (fig. 2). Decision analyses in the fifth 

goal will guide prioritization of proposed actions in the first 

four goals.
Introduction 3

• Science Goal 1. Augment National HPAI Surveillance 

Plan. The U.S. Department of Agriculture (USDA) led 

an interagency effort to revise national surveillance 

and strategic plans for HPAI in wild migratory birds 

in the United States (U.S. Department of Agriculture 

2015a,b). The USGS will support and supplement 

those plans to ensure that USGS research and surveillance efforts help to understand the distribution and 

occurrence of HPAI viruses at the continental scale. 

The USGS will focus on filling data gaps in the USDA 

plans rather than collect redundant information.

• Science Goal 2. Determine mechanisms of HPAI 

disease spread in wildlife and the environment. The 

USGS has the unique capabilities to understand the 

spread and persistence of HPAI in wildlife, water, and 

sediments. As environmental conditions change, the 

USGS can predict and then work with resource managers to assess how these changes affect prevalence of AI 

viruses and population outcomes for wildlife, poultry, 

and public health.

• Science Goal 3. Characterize HPAI viruses circulating 

in wildlife. The USGS capabilities in genomics and 

bioinformatics allow for characterization of AI viruses 

circulating in wildlife and the environment. The USGS 

will analyze the genetics of AI viruses detected in 

wildlife and the environment and assess the risk of 

introduction of novel strains. This information will be 

shared with our partners for poultry, susceptible wildlife, and public health AI risk assessments.

• Science Goal 4. Understand implications of avian ecology on HPAI spread. Migratory birds are the reservoirs 

of AI. The USGS is a leader in avian ecology research 

and has the capabilities to elucidate how intercontinental migration and avian community interaction 

influence HPAI spread. The USGS will work with the 

USDA to integrate avian movement and community 

ecology data with AI surveillance and poultry distribution data to develop poultry risk models.

• Science Goal 5. Develop HPAI forecasting and 

decision-making tools. The USGS has the expertise to 

provide formal HPAI decision consulting (for example, 

structured decision making) to management agencies. 

Based on the needs of resource managers, the USGS 

will conduct decision analyses and develop risk and 

decision models. Scientific findings from the first four 

goals (and prior USGS research) will be integrated into 

risk assessment and decision models to refine AI detection and outbreak prevention strategies for poultry and 

wildlife management. 

Timing of

outbreak 

Species 

infected 

Patterns of 

habitat use

Weather

Migratory

behavior 

Biosecurity

Environmental 

persistence

1

2

3

Avian influenza transmitted to 

a specific area by wild 

waterfowl movements

Interspecific transfer of AI from 

waterfowl to a vector that 

frequents poultry operations 

(transfer to bridge species) 

Transfer from bridge species

to commercial poultry 

Figure 1. Conceptual model of the process of introduction and wild bird dispersal of AI and subsequent infection 

of commercial poultry holdings. The rectangular boxes (1–3) represent probabilities, and the circles represent 

external factors that influence the probabilities. It is unlikely that direct transfer between waterfowl and poultry 

occurs. It has been proposed that “bridge species” that interact or share habitats with wild waterfowl (and occur 

on poultry farms) facilitate the transfer. Potential bridge species may be birds, rodents, and (or) invertebrates. 

Figure modified from Flint and others (2015). Abbreviation: AI, avian influenza.
4 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Highlight 1: What is the Role of the USGS in the Federal Response to HPAI?

The USGS is the science organization of the U.S. Department of the Interior, with a mission to provide reliable 

scientific information to describe and understand the Earth 

and minimize loss of life and property from natural disasters 

and hazards including wildlife disease. Since 2006, the USGS 

has been part of the Federal interagency team for the detection 

and response to HPAI viruses in North America. In particular, 

the USDA and the DOI actively coordinate with other State 

and Federal wildlife, agricultural, and human health agencies to understand AI distribution and dynamics with a goal 

of aiding natural resource managers, public health officials, 

and the poultry industry in making informed decisions. In 

December 2014, the USGS in collaboration with the USDA 

was instrumental in first detecting HPAI viruses in wild birds 

of North America.

The USGS conducts research on AI at multiple science centers across the nation and has amassed substantial 

internationally-recognized expertise investigating this disease. 

USGS scientists are (1) defining the role of migratory birds 

in viral spread, (2) characterizing genetics of viruses in wild 

birds, (3) determining environmental conditions affecting 

survival of the viruses in wetlands, (4) examining the potential 

role of non-waterfowl hosts, and (5) investigating the potential 

ecological consequences of infection in migratory birds. The 

USGS also has capabilities in AI diagnostics, surveillance, 

and mathematical modeling to map potential risk factors for 

disease spread between wild and domestic bird species. Additionally, USGS analytics expertise supports decision makers in 

management of diseases and natural resources.

The USGS has contributed substantial resources to 

enhance surveillance for HPAI across North America and to 

provide scientific information about the type, distribution, and 

wildlife species that harbor HPAI through timely public alerts; 

see Wildlife Health Bulletins at http://www.nwhc.usgs.gov/

publications/wildlife_health_bulletins/index.jsp, the online 

Wildlife Health Information Sharing Partnership event reporting system (WHISPers) at https://www.nwhc.usgs.gov/whispers/, and other USGS scientific publications.

U.S. Geological Survey (USGS) geneticist Andy Ramey releases a Northern Pintail during research on avian influenza in 

Japan. USGS scientists are investigating the intercontinental movement of migratory birds and avian influenza (Dusek and 

others, 2014; Prosser and others, 2014; Ramey and others, 2014; Ramey and others, 2015). Photograph by the USGS.
Introduction 5

Figure 2. Diagram showing the U.S. Geological Survey (USGS) Science Strategy for HPAI in Wildlife 

and the Environment is based on the relationship between scientific discovery and the application of new 

knowledge. A, Discovery of new HPAI knowledge through surveillance and research (Science Goals 1–4) 

will lead to application of that knowledge (tool development and management strategies, Science Goal 5). 

Applications (Science Goal 5), in turn, will highlight new needs (Science Goals 1–4) that lead to additional 

discovery. B, The application of knowledge can spark discovery of new knowledge. Figure modified from 

Williams and others (2013). Abbreviation: HPAI, highly pathogenic avian influenza.

Discovery

(Science Goals 1–4)

Surveillance for HPAI viruses

Mechanisms of HPAI disease spread

Characterization of HPAI viruses

Implication of avian ecology on HPAI 

spread

Application

(Science Goal 5)

Development of HPAI forecasting and 

decision-making tools

Implementation of management 

strategies based on scientific discoveries

Discovery

(Science Goals 1–4)

Application

(Science Goal 5)

Discovery

through

applications

New knowledge

New knowledge

New needs

New needs

A

B

Identification of key decision points and critical information gaps (Science Goal 5) will guide prioritization of 

research in Science Goals 1–4 and serve as the basis for the 

development of models and data visualization tools. Each of 

the five science goals are elaborated in detail below and are 

further characterized into objectives and proposed actions 

(fig. 3). This strategy is congruent with the Department of the 

Interior (DOI) strategic plan for fiscal years 2014–2018 (see 

DOI’s Goal #3 of Mission Area 6: “Provide scientific data to 

protect, instruct, and inform communities” at https://www.

doi.gov/sites/doi.gov/files/migrated/pmb/ppp/upload/DOIStrategic-Plan-for-FY-2014-2018-POSTED-ON-WEBSITE-4.

pdf). Implementation of the 5 science goals will employ 

the strategic approaches outlined in the USGS Ecosystems 

Science Strategy (http://pubs.usgs.gov/circ/1383c/): 

• Assess Information Needs for Ecosystem Science 

Through Enhanced Partnerships

• Promote the Use of Interdisciplinary Ecosystem 

Science

• Enhance Modeling and Forecasting

• Support Decision-Making
6 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Figure 3. Illustration showing the U.S. Geological Survey 

(USGS) Science Strategy for HPAI in Wildlife and the Environment 

(2016–2020) is focused on five long-term science goals. Science 

goals are characterized into objectives (light blue) and proposed 

actions (tan). Ongoing proposed actions are shown with an 

asterisk (*). Abbreviations: AI, avian influenza; DOI, Department 

of the Interior; HPAI, highly pathogenic avian influenza; LPAI, low 

pathogenic avian influenza; USDA, U.S. Department of Agriculture.

Science Goal 1 Science Goal 2 Science Goal 3 Science Goal 4 Science Goal 5

Augment National HPAI 

Surveillance Plan

Determine mechanisms 

of HPAI disease 

spread in wildlife and 

the environment

Characterize HPAI 

viruses circulating in 

wildlife

Understand implications 

of avian ecology on 

HPAI spread

Develop HPAI forecasting and decision-making tools

Objective 1:

Develop tools and strategies for early HPAI 

detection in wildlife 

and the environment

Objective 1:

Determine mechanisms 

of HPAI spread and 

persistence in wildlife

Objective 1:

Characterize AI viruses 

circulating in wild birds

Objective 1:

Map spatial and temporal 

distributions of wild 

and domestic avian 

populations for risk 

model development

Objective 1:

Provide decision support 

to management agencies

Proposed Action 1:

Develop new detection 

strategies for HPAI in 

environmental samples

*Proposed Action 1: 

Experimental studies 

to understand the role 

of sea ducks in HPAI 

movement, transmission, and evolution

*Proposed Action 1:

Characterize HPAI 

viruses in wild birds 

across North America

Proposed Action 1:

Develop wild and 

peridomestic bird 

distribution models as 

inputs for disease risk 

assessments

Proposed Action 1:

Work with management 

agencies to develop a 

quality HPAI decisionmaking framework

*Proposed Action 2:

Detect potential HPAI 

introductions into the 

United States

*Proposed Action 2: 

Examine the role of 

filter-feeding organisms 

in HPAI persistence, 

transmission, or inactivation 

*Proposed Action 2:

Characterize LPAI 

viruses in wild birds 

across North America

Proposed Action 2:

Develop high resolution models of poultry 

densities as inputs for 

disease risk assessments

Proposed Action 2: 

Synthesize HPAI 

decision analyses from 

different management 

agencies to identify 

common information 

gaps and critical uncertainties

Objective 2: 

Conduct HPAI surveillance in wild birds and 

peridomestic wildlife

Objective 2: 

Determine mechanisms 

of HPAI spread and 

persistence in the environment

Proposed Action 3: 

Characterize LPAI and 

HPAI strains in peridomestic wild birds near 

poultry operations

Proposed Action 3: 

Examine the influence 

of climate change on 

bird migration and its 

implications on AI 

dynamics

Proposed Action 3:

Refine HPAI research 

priorities based on 

management partner 

needs

*Proposed Action 1:

Implement the National 

Interagency Surveillance Plan for HPAI in 

waterfowl

Proposed Action 1:

Determine hydrologic 

characteristics that 

facilitate persistence 

of HPAI in fresh and 

brackish waters 

Objective 2:

Characterize AI viruses 

circulating in wild 

mammals

Objective 2: 

Address AI-relevant information gaps in avian 

migratory connectivity 

data 

Objective 2:

Develop HPAI risk assessment and predictive 

models

*Proposed Action 2:

Collaborate with 

USDA to conduct 

HPAI surveillance of 

potential hosts near 

poultry facilities

Proposed Action 2:

Examine persistence of 

AI viruses in sediment

Proposed Action 1:

Characterize AI viruses 

circulating in peridomestic mammals near 

poultry operations

Proposed Action 1: 

Develop quantitative 

flyway maps of AI 

reservoir species

Proposed Action 1:

Develop dynamic 

occupancy models to 

estimate probabilities 

of AI occurrence

Proposed Action 3:

Conduct meta-analysis 

of HPAI surveillance 

strategies

*Proposed Action 3: 

Characterize AI transport in groundwater

Proposed Action 2:

Characterize AI viruses 

circulating in feral 

swine

Proposed Action 2:

Determine population connectivity of 

avian AI reservoir 

species through genetic 

analyses

Proposed Action 2: 

Conduct a metaanalysis of existing AI 

mechanistic models
Introduction 7

Science Goal 1 Science Goal 2 Science Goal 3 Science Goal 4 Science Goal 5

Augment National HPAI 

Surveillance Plan

Determine mechanisms 

of HPAI disease 

spread in wildlife and 

the environment

Characterize HPAI 

viruses circulating in 

wildlife

Understand implications 

of avian ecology on 

HPAI spread

Develop HPAI forecasting and decision-making tools

Proposed Action 4:

Investigate climate 

variability and change 

as a driver of AI persistence and spread

Objective 3: 

Characterize HPAI 

viruses circulating 

outside North America 

and on the continental 

fringes with potential 

of intruduction

Proposed Action 3:

Characterize landscape 

connectivity between 

avian AI reservoirs and 

wild birds susceptible 

to HPAI disease

Proposed Action 3:

Develop geospatial 

models of HPAI 

transmission risk at the 

wild-to-domestic bird 

interface

Objective 3: 

Determine mechanisms of 

HPAI spread and persistence in peridomestic wildlife found near 

poultry operations

*Proposed Action 1: 

Characterize AI viruses 

circulating in wildlife outside of North 

America

Objective 3: 

Investigate HPAI 

transmission in avian 

communities and the 

impact of HPAI on 

avian populations

Proposed Action 4:

Develop Landsat-based 

HPAI risk maps

Proposed Action 1:

Interagency workshop on the wildlifedomestic animal HPAI 

interface

*Proposed Action 2:

Characterize AI viruses 

on continental fringes 

of North America

Proposed Action 1:

Determine HPAI 

transmission pathways 

within ecological communities

Proposed Action 5: 

Develop hydrologic 

HPAI risk maps

*Proposed Action 2:

Field research to examine the role of wild 

birds in HPAI movement

Proposed Action 2: 

Assess impact of HPAI 

on North American 

avian populations

Proposed Action 6:

Develop models of 

HPAI transmission risk 

for avian communities

*Proposed Action 3:

Experimental studies 

to understand the role 

of wild birds in HPAI 

movement, transmission, and evolution 

Objective 3: 

Develop online HPAI data 

visualization tools

*Proposed Action 4: 

Conduct research to 

examine the role of 

non-avian species in 

HPAI movement and 

transmission

Proposed Action 1: 

Improve existing collaborative databases 

to store historical and 

future HPAI data

Objective 4: 

Determine implications of 

LPAI and HPAI exposure for wildlife

Proposed Action 2:

Develop an HPAI data 

visualization tool

Proposed Action 1: 

Investigate population 

level impacts of HPAI 

exposure on DOI trust 

species

Proposed Action 2:

Determine the genetic 

factors associated with 

HPAI resistance and 

susceptibility 

Proposed Action 3: 

Compare wildlife HPAI 

immune responses for 

targeted HPAI surveillance and management

Figure 3. Illustration showing the U.S. Geological Survey (USGS) 

Science Strategy for HPAI in Wildlife and the Environment (2016–

2020) is focused on five long-term science goals.—Continued.

Geese flying over a frozen lake. 

Photograph courtesy of 

photos-public-domain.com.
8 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Science Outcomes of the 

U.S. Geological Survey Science 

Strategy on HPAI in Wildlife and the 

Environment

The U.S. now has to contend with potentially endemic 

HPAI viruses in poultry and wildlife. HPAI has negatively 

impacted the poultry industry and may impact populations 

of susceptible wildlife species and public health. Because 

migratory birds and environmental factors are important in 

the spread and persistence of AI viruses, the USGS is wellpositioned to support decision-makers through the development of surveillance, risk assessment, and forecasting tools 

(see appendix 2). In collaboration with international, Federal, 

State, tribal, and non-governmental collaborators, the USGS 

will accomplish the following outcomes in accordance with 

the five science goals of the Science Strategy on HPAI (fig. 3):

Science Goal 1: Augment National HPAI Surveillance Plan.

• New, validated sampling and diagnostic HPAI strategies.

• New and strengthened international partnerships for 

early HPAI detection.

• Completed meta-analysis of HPAI surveillance strategies. 

Science Goal 2: Determine mechanisms of HPAI disease 

spread in wildlife and the environment.

• Description of mechanisms of HPAI maintenance and 

spread (transmission and movement to new areas) in 

avian and non-avian wildlife and the environment. 

Science Goal 3: Characterize HPAI viruses circulating in 

wildlife.

• Genetic characterization of viruses circulating in avian 

and non-avian wildlife.

Science Goal 4: Understand implications of avian ecology on 

HPAI spread.

• Description of distribution, migratory connectivity, 

and aspects of avian ecology that may influence HPAI 

persistence and spread. 

Science Goal 5: Develop HPAI forecasting and decision making tools.

• Guidance for the development of disease management 

strategies and identification of key uncertainties that 

need to be addressed through future research.

• HPAI risk assessment and predictive modeling tools 

to identify high risk areas and populations for HPAI 

transmission.

• Rapid response decision model that forecasts how to 

get in front of potential and unknown future outbreaks. 

The output from the model will be designed to facilitate “proactive prevention.”

• Online data visualization tools for situational awareness, education, and access to developed predictive and 

decision models. 

U.S. Geological Survey HPAI Science 

Goals

Science Goal 1: Augment National HPAI 

Surveillance Plan

One Health is the recognition that public health and 

domestic animal health is linked to wildlife health and the 

environment (Zinsstag and others, 2011). Approximately 

60 percent of emerging human infectious diseases are zoonotic. Over 70 percent of these zoonoses originate in wildlife 

(Jones and others, 2008). Many of these diseases also affect 

domestic animals, resulting in economic consequences. 

Through sustained research and response to changes in wild 

populations and the environment, these pathogens of concern 

may be detected before they reach agricultural or human populations. Indeed it was through continued passive monitoring 

of wild birds for avian disease mortality events that HPAI was 

first detected in the U.S. by the USDA and USGS in December 

2014. The HPAI intercontinental group A (icA) H5 viruses 

have since been detected in wild birds (waterfowl, birds of 

prey, and a songbird), captive wild birds (birds of prey) and 

domestic fowl (backyard flocks and commercial poultry 

operations) in the United States (U.S. Department of Agriculture 2015c). HPAI outbreaks in U.S. commercial poultry 

operations (primarily laying hens and turkeys) have resulted 

in an estimated 3 billion dollar economic impact to the United 

States (Greene, 2015).

National scale surveillance for HPAI was outlined 

in a Surveillance Plan for HPAI in Waterfowl in the U.S. 

(U.S. Department of Agriculture, 2015a) developed by the 

USDA, USGS, U.S. Fish and Wildlife Service (USFWS), and 

the National Flyway Council. Samples collected through this 

Surveillance Plan will provide information on the distribution 

of HPAI in the U.S. through the sampling of dabbling ducks 

in priority U.S. watersheds. The USGS is collaborating with 

the USDA to augment its current contributions to this multiagency plan by developing advanced tools for environmental 

and wildlife HPAI surveillance, conducting HPAI wildlife 

surveillance (see highlight 2: The Importance of Long-Term 

Research for Disease Surveillance), and analyzing surveillance 

strategies to ensure sampling designs are efficient.
U.S. Geological Survey HPAI Science Goals 9

Highlight 2: The Importance of Long-Term Research for Disease Surveillance 

Informed surveillance and research plans rely upon 

scientific baselines established through prior work and expert 

knowledge. The USGS long-term research on wildlife, hazards, hydrology, and data modeling and visualization provide 

an essential foundation for current and future disease surveillance strategies (see appendix 2). These long-term research 

platforms provide a base of knowledge to compare with novel 

and emerging conditions and to promote national preparedness. Some examples of long-term USGS science that will 

inform current and future HPAI surveillance strategies include 

the National Wildlife Health Center mortality and morbidity investigations, decision science, and quantitative ecology 

research (see appendix 2), as well as ongoing research examining the intercontinental dispersal and introduction of influenza 

A viruses into the United States (for example, Koehler and 

others, 2008; Reeves and others, 2013; Dusek and others, 

2014; Ramey, Walther, and others, 2014; Ramey, Reeves, 

Sonsthagen, and others, 2015).

Alaska Department of Fish and Game biologist Jason Schamber releases Lesser Snow Geese from a capture pen in northern Alaska. 

U.S. Geological Survey (USGS) scientists are investigating the dynamics of avian influenza in lesser snow geese (Reeves and others, 

2013; Samuel and others, 2015). Photograph by the USGS.
10 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Objective 1. Develop tools and strategies 

for early HPAI detection in wildlife and the 

environment.

Surface water contaminated by infected waterfowl is 

considered a primary source for the transmission of AI in wild 

bird populations. The persistence of AI viruses in the environment (for example, in water and sediment) may influence the 

transmission and distribution of HPAI. Distribution is also 

influenced by the movement of AI viruses by wild birds across 

continents.

Proposed Action 1. Develop new detection strategies for 

HPAI in environmental samples.

In collaboration with the USDA, the USGS will refine 

and validate methods to detect low concentrations of HPAI 

viruses in environmental samples and determine molecular 

subtype associations in samples. Portable, rapid molecular 

detection kits are being validated by the USGS for invasive 

species detection (Merkes and others, 2016). This technology 

has been used to shorten the field diagnostic time for Ebola 

viruses (Kurosaki and others, 2016) and has the potential to 

provide new avenues for increased sensitivity and decreased 

time to HPAI detection. Based on partner needs, the USGS 

novel environmental detection strategies can support international and national HPAI surveillance. Rapid detection is 

essential for applying effective management actions (Flint and 

others, 2015).

Proposed Action 2. Detect potential HPAI introductions 

into the United States

Through an international network, the USGS has found 

genetic evidence of intercontinental transport of AI viruses 

by wild birds (Koehler and others, 2008; Dusek and others, 

2014; Lee and others, 2015; Ramey, Reeves, Sonsthagen, and 

others, 2015). The USGS will develop an international sampling network to detect and monitor possible areas of HPAI 

introductions into the U.S. This network will build on the 

Global Health Security Agenda (GHSA), an effort in which 

the United States has committed to support 30 developing 

countries in their capabilities to prevent, detect, and respond 

to infectious disease outbreaks (https://www.whitehouse.gov/

the-press-office/2015/11/16/fact-sheet-us-commitment-globalhealth-security-agenda). This surveillance network could also 

promote an early warning system for HPAI viruses that could 

enter the United States.

Objective 2. Conduct HPAI surveillance in wild 

birds and peridomestic wildlife.

The Interagency Wild Bird HPAI Surveillance Plan 

developed by the USDA, the USGS, and other agencies 

focuses on dabbling ducks in watersheds that have been 

prioritized based on AI risk (U.S. Department of Agriculture, 

2015a). To better elucidate HPAI dynamics in North America, 

the USGS will also conduct surveillance of potential alternative hosts (peridomestic wildlife). Field surveillance integrated 

into long-term avian ecology research will also support the 

interagency plan. Integrated conclusions from these efforts 

about the distribution of AI viruses will be used to develop 

predictive models that will be communicated to resource 

managers.

Proposed Action 1. Implement the National Interagency 

Surveillance Plan for HPAI in waterfowl.

The USGS will assist in implementation of the National 

Surveillance Plan for HPAI (U.S. Department of Agriculture, 

2015a). The USGS National Wildlife Health Center, part of 

the USDA’s National Animal Health Laboratory Network, 

will continue to conduct diagnostic HPAI testing of samples 

from live wild birds, from hunter-harvested waterfowl, and 

from dead wild birds as part of morbidity/mortality event 

investigations. Moreover, USGS long-term wild bird surveillance efforts will include sampling outside of the priority 

watersheds.

Proposed Action 2. Collaborate with the USDA to 

conduct HPAI surveillance of potential alternative hosts in 

the immediate vicinity of infected poultry facilities.

The USDA has speculated that rodents and small wild 

birds are involved in the movement of HPAI within the United 

States (U.S. Department of Agriculture, 2015d). The USGS 

will continue a 2015 pilot study of influenza surveillance in 

peridomestic wildlife (such as rodents, raccoons, and songbirds) and will work with the USDA-APHIS1

 National Feral 

Swine Damage Management Program to examine feral swine 

and other invasive species. These data will support interagency 

HPAI surveillance efforts and provide data for decision-support tools.

1

Animal and Plant Health Inspection Service

Photograph courtesy of photos-public-domain.com
U.S. Geological Survey HPAI Science Goals 11

Proposed Action 3. Conduct meta-analysis of HPAI 

surveillance strategies.

Meta-analyses of HPAI surveillance sample strategies 

(such as temporal, geographic, species, and sample type) will 

be conducted to assess the importance of various factors in 

surveillance design. Through interagency collaboration and 

an adaptive management approach, surveillance strategies 

for HPAI will be refined to improve efficiency based on these 

results.

Science Goal 2: Determine Mechanisms 

of HPAI Disease Spread in Wildlife and the 

Environment

Since the initial detection of icA H5 HPAI in the U.S., the 

spread of highly pathogenic viruses across the Pacific, Central, 

and Mississippi Flyways has been unpredictable. Phylogenetic 

analyses by the USDA and the USGS suggest that migratory 

birds introduced HPAI H5N8 to the U.S. from Eurasia (Lee 

and others, 2015). However, it is unclear if the spread of icA 

H5 viruses across poultry facilities is consistent with wild bird 

movements or if viruses are spread in other ways. The USDA 

has speculated about rodents, passerine birds, and environmental factors in the dissemination of HPAI within the United 

States (U.S. Department of Agriculture, 2015d). In order to 

forecast future HPAI outbreaks, we need to better understand 

the mechanisms of spread and persistence in wildlife and the 

environment (see highlight 3: AI in the Environment).

Objective 1. Determine mechanisms of HPAI 

spread and persistence in wildlife.

While dabbling ducks are known reservoirs of AI (Keawcharoen and others, 2008), the role of other wild bird species 

and non-avian wildlife in HPAI spread and persistence has 

not been fully elucidated. Research suggests that filter-feeding 

organisms may play a role in AI dynamics (Huyvaert and 

others, 2012; Meixell and others, 2013). The USGS continues 

to assess the significance of non-avian wildlife in HPAI spread 

and persistence. Additionally, the USGS has established captive wild bird research colonies and long-term avian ecology 

studies, which can serve as important resources for future AI 

research in wild birds.

Proposed Action 1. Conduct experimental HPAI studies 

to understand the role of sea ducks in the movement, 

transmission, and evolution of these viruses.

Additional information is needed on sea duck responses 

to HPAI viruses. These species are long distance migrants 

that breed in far northern regions of the globe and congregate 

in wintering areas along coasts. The few studies available 

suggest they are often exposed to AI viruses, but it is unclear 

where and when active infection and shedding take place (Hall 

and others, 2015; Ramey, Reeves, Poulson, and others, 2015). 

Using established sea duck colonies, the USGS has begun 

collaborating with the USDA to evaluate the pathogenesis, 

minimum infectious dose, asymptomatic period, routes of 

shedding, and transmissibility of HPAI in sea duck species. 

This information will be integrated into risk models (Science 

Goal 5, Objective 2).

Proposed Action 2. Examine the role of filter-feeding 

organisms in HPAI persistence, transmission, or 

inactivation.

Experimental research has shown that AI viruses can 

bioaccumulate in filter-feeding organisms like common water 

fleas (Meixell and others, 2013), clams (Huyvaert and others, 

2012) and zebra mussels (Stumpf and others, 2010). USGS 

field surveillance has also detected AI in a sentinel oyster 

(Densmore and others, 2016). Because bivalves are an important prey species for diving ducks (Richman and Lovvorn, 

2004), it is also important to investigate the possibility of AI 

transmission from invertebrate prey to water bird species.

Objective 2. Determine mechanisms of HPAI 

spread and persistence in the environment.

Aquatic birds (waterfowl and shorebirds) are the primary 

biotic reservoirs of LPAI viruses, often showing no signs of 

illness. Viruses replicate in the intestinal tract, leading to high 

concentrations being shed with excrement into the environment (Webster and others, 1978). LPAI viruses can persist 

in surface waters under certain environmental conditions. 

Experimentally, Eurasian H5N1 viruses are similar to LPAI 

in regards to survivability in water (Brown and others, 2014). 

Additional field and laboratory research is needed to examine HPAI spread and persistence in the environment (surface 

water, groundwater, and sediment). Geospatial data from this 

environmental surveillance can be used for novel Landsatbased identification of similar HPAI high-risk habitats for the 

development of HPAI forecasting and risk assessment tools 

(Science Goal 5, Objective 2).

Proposed Action 1. Determine hydrologic characteristics 

and flows that facilitate persistence of HPAI in fresh and 

brackish waters.

The National Surveillance Plan for HPAI (U.S. Department of Agriculture, 2015a) identifies high priority watersheds 

for surveillance based on historic hot spots of LPAI virus 

detections and known congregation of dabbling ducks. In 

order to understand how HPAI is spread in a watershed, it is 

crucial to examine its persistence in water (groundwater and 

surface water). Experimental work has shown that low temperatures, a neutral-basic pH, and low salt and ammonia concentrations are associated with LPAI persistence (Keeler and 

others, 2014). Under certain conditions, HPAI infectivity has 

been documented in brackish water (Domanska-Blicharz and 
12 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

others, 2010). While hydrologic transport has been considered 

in models of water-borne parasites (Gurarie and Seto, 2009), 

this aquatic connectivity has not been examined for AI in 

watersheds. Through field and laboratory research, the USGS 

will determine the water conditions and flows that allow HPAI 

to remain infectious and persist in watersheds. These data will 

be integrated into risk assessment models (Science Goal 5, 

Objective 2).

Proposed Action 2. Examine persistence of AI viruses in 

sediment.

Low pathogenic avian influenza viruses have been 

detected in pond sediments suggesting sediment may be 

important for the maintenance of AI viruses (Lang and others, 

2008). It is unknown how long HPAI remains infectious in 

similar settings and what factors allow for viral persistence. 

While the fate and transport of water-borne parasites in sediment has been considered (Ferguson and others, 2003), the 

particle size with which AI viruses are transported and its sedimentation in watersheds has not been fully examined. Field 

and experimental research will be conducted to understand the 

transport and persistence of HPAI in sediment.

Proposed Action 3. Characterizing AI transport to and 

from groundwater.

In addition to ponds, waste water lagoons are known 

surface water features that birds frequent. Further examining 

AI occurrence in waste water lagoons could lead to identification of another transport mechanism as lagoons are frequently 

emptied and spray irrigated on croplands. A USDA and USGS 

proof-of-concept study provided the first known documentation of influenza A (H5) RNA in groundwater underlying a 

poultry operation that was infected with HPAI during the 

spring 2015 outbreak (Hubbard and others, 2016). Previous 

research has documented that groundwater concentrations 

of human enteric viruses are highly transient and variable 

because of virus inactivation, dilution by well pumping, 

changes in source strength, and changes in virus transport 

driven by factors such as precipitation (Borchardt and others, 

2012; Bradbury and others, 2013). The USGS will continue 

this collaborative research with the USDA, expanding the 

geographic coverage of infected poultry facilities and creating a spatiotemporal dataset of groundwater and poultry waste 

water lagoon samples for HPAI. This groundwater data will 

be integrated into HPAI risk assessment and predictive models 

(Science Goal 5, Objective 2).

Proposed Action 4. Investigate climate variability and 

change as a driver of AI persistence and spread.

Variability in patterns of air and water temperatures 

and precipitation is known to impact the dynamics of waterborne diseases (Rose and others, 2001; Bradbury and others 

2013; Morand and others, 2013). Yet the influence of climate 

variability on AI transport, spread, and persistence in North 

American watersheds is unknown. The USGS will evaluate 

AI virus detections (Science Goal 2, Objective 2, Proposed 

Actions 1‒3) in light of present and past climatic conditions 

to determine if climate variability influences AI dynamics. 

The results will be communicated to resource managers and 

incorporated into HPAI risk assessment and predictive models 

(Science Goal 5, Objective 2).

Objective 3. Determine mechanisms of HPAI 

spread and persistence in peridomestic wildlife 

found near poultry operations.

Research is needed to understand the role of peridomestic 

wild birds (such as resident Canada geese, European starlings, 

sparrows, and doves) and non-avian wildlife (such as small 

mammals) in HPAI spread (Hall and others, 2008; Tejeda and 

others, 2015). In 2015, HPAI was detected in resident Canada 

geese in Michigan and a songbird (black-capped chickadee) in Minnesota (U.S. Department of Agriculture, 2015c). 

Similar to birds of prey (Hall and others, 2009), these species 

displayed signs of neurological disease and died. It has been 

suggested that peridomestic wild birds may serve as bridge 

species for HPAI spread onto poultry operations (Flint and 

others, 2015), and there is experimental evidence that songbirds can spread HPAI to poultry through shared water (Jones 

and others, 2015). Likewise, experimental evidence suggests a 

role for small mammals in the movement and transmission of 

HPAI (Hall and others, 2008; Tejeda and others, 2015). USGS 

scientists will enhance partnerships with USDA scientists to 

determine the role of peridomestic wildlife in HPAI spread and 

persistence.

Proposed Action 1. Conduct an interagency workshop on 

wildlife-domestic animal HPAI interface.

An interagency (USGS, USDA) workshop will be 

conducted to bring together scientists for the coordination 

of studies that examine the HPAI interface between wildlife 

and domestic animals. Complementary capabilities from the 

participating agencies will be leveraged to ensure future interagency studies will meet the needs of decision-makers and 

managers coordinating AI control efforts.

Proposed Action 2. Conduct field research to examine 

the role of peridomestic wild birds in the movement and 

transmission of HPAI.

The USGS will collaborate with the USDA to conduct AI 

research on peridomestic wild birds in the vicinity of poultry 

facilities in regions where HPAI is detected. The resulting 

information will be used to refine models through reductions 

in uncertainty and identification of alternative biosecurity 

actions to dampen the economic impact of HPAI on poultry 

operations. 
U.S. Geological Survey HPAI Science Goals 13

Highlight 3: AI in the Environment

Environmental features (water and soil) have been 

identified as playing potentially important roles in the maintenance of AI viruses. Additionally, multi-year assessments of 

AI viral communities suggest that genetic diversity is likely 

maintained in the environment as well as in wildlife. Changes 

in aquatic ecosystems related to climate warming or other 

environmental stressors could have impacts on the dynamics 

of the host-pathogen-environment relationship. Therefore, 

an understanding of the factors allowing persistence and 

transmission of pathogens from water and soil is needed to 

determine their significance to wildlife, domestic animals, 

and humans. The USGS demonstrated that water and filter 

feeding organisms can play a role in the maintenance of AI 

viruses and has tested methods for detecting and determining 

the survival of viruses in the environment (Dalton and others, 

2009; Millen and others, 2012; Francy and others, 2013; 

Meixell and others, 2013; see appendix 2). The Laboratory for 

Infectious Disease in the Environment (LIDE), a joint facility 

of the USGS and USDA Agricultural Research Service, has 

been able to detect very low levels of AI virus in groundwater 

and surface-water samples. The LIDE collected and analyzed 

samples from groundwater and surface water related to the 

2015 HPAI outbreak, and scientists from the LIDE are trained 

in USDA highly pathogenic AI responder methods. The LIDE 

uses molecular and culture techniques to quantify AI viruses 

in environmental samples and determine the infectivity of AI 

viruses. LIDE scientists identified recent AI gene sequence 

mutations associated with the 2015 outbreak that allowed them 

to more reliably detect and identify the AI virus.

U.S. Geological Survey (USGS) hydrologist conducts research in Alaska. USGS scientists are investigating levels of avian 

influenza virus in groundwater and surface water (Haack and others, 2015; Hunt and others, 2014; Meixell and others, 2013). 

Photograph by the USGS.
14 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Proposed Action 3. Conduct experimental HPAI studies 

to understand the role of peridomestic wild birds in the 

movement, transmission, and evolution of these viruses.

The USGS will collaborate with the USDA to determine 

the role of peridomestic wild birds in spread, transmission, 

and maintenance of AI through experimental studies. When 

field research suggests a role for peridomestic birds, experimental research will be conducted in a high biocontainment 

laboratory to assess levels of HPAI virus shedding, morbidity, 

mortality, and transmission potential of these wild birds. The 

resulting information will be used to refine HPAI models by 

decreasing uncertainty and identifying alternative management 

strategies to reduce the economic impact of HPAI on poultry 

operations. The USGS recently published results from a study 

investigating the role of European starlings, invasive peridomestic birds, in HPAI dynamics (Hall and others, 2016).

Proposed Action 4. Conduct research to examine the role 

of non-avian peridomestic species in the movement and 

transmission of HPAI.

The USGS has begun pilot research with the USDA to 

coordinate HPAI research on non-avian peridomestic wildlife 

(raccoons and rodents) in the vicinity of affected poultry facilities. While there has been experimental evidence to suggest 

a role for small mammals in the movement and transmission 

of HPAI (Hall and others, 2008; Tejeda and others, 2015), 

additional species and pathways have yet to be investigated. 

Results of this research will be incorporated into management 

decisions and HPAI risk assessments (Science Goal 5).

Objective 4. Determine implications of LPAI and 

HPAI exposure for wildlife populations.

HPAI is primarily a disease of poultry, rarely impacting 

wild birds to a significant degree. However, there are a few 

wild bird species, such as raptors, that may develop neurological disease and die from HPAI. Enhanced knowledge of the 

avian immune response to these viruses and the underlying 

genetics will be used for risk assessments of population-level 

impacts on species of management and (or) conservation 

concern.

Proposed Action 1. Investigate population level impacts 

of HPAI exposure on Department of the Interior trust 

species.

The USGS will conduct a review of threatened and 

endangered species with known or likely susceptibility and 

(or) exposure to HPAI. In conjunction with experimental and 

field data, these analyses can be used to quantify possible 

population-level impacts of HPAI. This information will be 

shared with the USFWS for incorporation into species management plans.

Proposed Action 2. Determine the genetic factors 

associated with HPAI resistance and susceptibility.

The USGS will conduct comparative immunogenetic 

studies of wild birds that are resistant to HPAI (waterfowl) and 

those with known or suspected susceptibility to HPAI disease 

(raptors). Analyses of genomes and transcriptomes of uninfected and infected wild birds are known to provide insight 

into the immune response relative to HPAI (Huang and others, 

2013). The USGS has conducted transcriptome research in 

wildlife species of management concern (Bowen and others, 

2015a,b). Identification of biomarkers (immune gene transcription patterns), a non-invasive tool, will help natural 

resource managers identify species and populations that may 

be susceptible or resistant to HPAI disease when experimental 

studies are not feasible.

Proposed Action 3. Compare wildlife HPAI immune 

responses for targeted HPAI surveillance and 

management.

While many waterfowl infected by HPAI viruses may 

not show signs of disease, some wild birds show clear signs of 

morbidity and mortality. The neurological disease and resulting death observed in Canada geese (Pasick and others, 2007) 

and raptors (Hall and others, 2009; Kim and others, 2015) suggest there may be a different immune response to some HPAI 

viruses in these species. The USGS will collect baseline avian 

immunology data and conduct experimental research to determine the underlying immunological susceptibility of sensitive 

species to certain strains of HPAI. Understanding speciesspecific susceptibility and resistance is essential to efficiently 

design surveillance activities (Science Goal 1, Objective 2, 

Proposed Action 3) and management strategies for susceptible 

species of conservation concern (Science Goal 5, Objective 1).

Science Goal 3: Characterize HPAI Viruses 

Circulating in Wildlife

Research by the USGS and its partners has used AI 

genetic data to demonstrate the role of wild birds and their 

international migratory flyways in the dispersal of both LPAI 

and HPAI viruses (see highlight 4: Genetics of AI Viruses). 

In addition to host and environmental characteristics (Science 

Goal 2), HPAI dispersal potential depends upon the ecology and evolution of AI viruses (antigenic structure (HA and 

NA subtypes)), genetic diversity, virulence, host adaptation, 

reassortment rate, and dominant circulating genotype) (Dugan 

and others, 2008; Lee and others, 2015). Characterization of 

established and emerging strains of HPAI and co-occurring 

LPAI viruses will help determine how different HPAI strains 

persist and spread. Science Goal 3 data will be integrated 

into HPAI risk maps and models (Science Goal 5) to support 

AI management.
U.S. Geological Survey HPAI Science Goals 15

Highlight 4: Genetics of AI Viruses

The USGS conducts genetic analyses of AI viruses each 

year to determine the role of migratory birds in the dispersal of 

viruses and identify how viral communities are changing over 

time. This information is used to refine future surveillance 

efforts for North America and to inform biosecurity measures 

by U.S. poultry facilities. Viruses characterized in Alaska, Iceland, and along the U.S. Gulf Coast demonstrate that migratory bird flyways facilitate the movement of foreign-origin 

viruses into North America from Asia, Europe, and South 

America. Genetic analysis has determined pathways, timing, 

and mechanisms of transmission between species and regions, 

contributing to the design of HPAI surveillance strategies 

(Koehler and others, 2008; Pearce and others, 2009; Dusek 

and others, 2014; Ramey, Walther, and others, 2014; Lee and 

others, 2015; Ramey, Reeves, Sonsthagen, and others, 2015).

U.S. Geological Survey (USGS) biologist Lisa Pajot tests samples 

in a laboratory. As the lead Federal agency for wildlife disease 

surveillance, the USGS maintains specialized laboratories across 

the United States for research, diagnosis, and surveillance of 

wildlife pathogens. Photograph by the USGS.

Objective 1. Characterize AI viruses circulating 

in wild birds.

AI viruses are designated as either LPAI or HPAI, 

depending upon virulence in poultry. These viruses are genetically diverse with numerous subtypes and molecular characteristics common to continental landmasses (North America, 

South America, Asia, and Europe). Genetic sequencing can be 

used to distinguish LPAI versus HPAI, Eurasian versus North 

American genetic components, and phylogenetic relationships. 

Thus, genetic data can be useful for understanding geographic 

origins, timing of emergence, persistence of genotypes, likely 

pathways of geographic dispersal, and viral exchange across 

the wild bird-poultry interface.

Proposed Action 1. Characterize HPAI viruses in wild 

birds across North America.

Through mutations and reassortment events, HPAI 

viruses will continue to change over time. The USGS will 

continue to analyze the genetic characteristics and changes 

in viral communities over time (Lee and others, 2015). Data 

on the ecology and evolution of these viruses will be used to 

determine pathogenicity in wild and domestic birds, dispersal 

potential, persistence, and similarities to HPAI viruses circulating in poultry.

Proposed Action 2. Characterize LPAI viruses in wild 

birds across North America.

The USGS will continue to describe the genetic diversity 

of LPAI viruses (Reeves and others, 2013; Ramey, Walther, 

and others, 2014). Genetics of LPAI viruses co-circulating 

with HPAI and their contribution to novel reassortant HPAI 

strains will be incorporated into models (Science Goal 5) that 

can contribute to Federal, State, and tribal AI management 

decisions.

Proposed Action 3. Characterize LPAI and HPAI strains in 

peridomestic wild birds near poultry operations.

While identification of circulating HPAI strains is necessary for poultry risk assessments, characterization of LPAI 

strains is important for public health risk assessments. Human 

influenza pandemic viruses have had genes from LPAI strains 

(for example, 1918 Spanish Flu H1N1, 1957 H2N2, and 1968 

H3N2) (Horimoto and Kawaoka, 2001). The USGS will work 

with the USDA to assess the genetics of LPAI and HPAI 

strains circulating in peridomestic wild birds (such as starlings 

and finches) near poultry operations. This data will be integrated into risk assessments (Science Goal 5) and communicated to resource managers who make AI-relevant decisions.
16 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Objective 2. Characterize AI viruses circulating 

in wild mammals.

Determination of persistence and evolution of HPAI in 

North America may require sampling and viral characterization of non-traditional species. Such investigations will inform 

biosecurity and human health concerns. The 2009 swine flu 

pandemic was a new strain of H1N1 that resulted from a triple reassortment of bird, swine, and human flu viruses (Smith 

and others, 2009). Small mammal species are common in and 

around areas frequented by wild birds and domestic flocks 

and may serve as important bridge species between wildlife 

and poultry. Field and laboratory research can determine the 

role of non-avian wildlife (such as rodents and feral swine) in 

persistence or transmission of HPAI viruses in North America.

Proposed Action 1. Characterize AI viruses circulating in 

peridomestic mammals near poultry operations.

The USGS will collaborate with the USDA to analyze the 

genetics of AI isolates from peridomestic wildlife (raccoons, 

rodents) to understand their potential role as bridge vectors 

(Flint and others, 2015). Research has documented a potential 

role for raccoons (Hall and others, 2008) and rodents (Tejeda 

and others, 2015) in HPAI dynamics. Genomic analyses 

will be integrated into HPAI risk assessments and models 

(Science Goal 5).

Proposed Action 2. Characterize AI viruses circulating in 

feral swine.

The USGS will work with the USDA-APHIS National 

Feral Swine Damage Management Program to examine the 

role of feral swine in the generation and maintenance of 

novel influenza viruses. The genetics of virus isolates will be 

analyzed and compared with wild bird, poultry, and human 

isolates to assess the role of feral swine in HPAI reassortment. 

Feral swine share habitat with waterfowl, prey on and scavenge dead birds, are highly mobile, and come into contact with 

humans. Swine can harbor mixed influenza infections and promote genetic reassortment between avian, human, and swine 

influenza (Smith and others, 2009). Feral swine populations in 

the U.S. are expanding in both numbers and range (Bevins and 

others, 2014), and they are increasingly coming into contact 

with waterfowl, humans, and agricultural operations. While 

they are known to spread domestic swine and human influenza 

viruses (Feng and others, 2014; Sun and others, 2015), their 

role in HPAI dynamics is unclear. Results of these genomic 

analyses will be incorporated into risk assessments and models 

(Science Goal 5).

Objective 3. Characterize HPAI viruses 

circulating outside North America and on the 

continental fringes with potential for introduction 

to North America.

East Asia has recently been the epicenter of most HPAI 

H5 viral strains. HPAI H5N1 first emerged in 1996 and 

remained within Asia until 2005 when it began to spread globally, though never detected in North America. HPAI H5N8 

spread much faster; after first emerging in Asia in January 

2014, viruses spread to Europe and North America in the 

fall and early winter of the same year (Ip and others, 2015; 

Lee and others, 2015). In North America, H7 LPAI viruses 

emerge each spring in waterfowl that migrate north into the 

U.S. from central and South America (Ramey, Poulson, and 

others, 2014). Highly pathogenic H7 viruses that evolved from 

precursor low pathogenic H7 viruses have caused outbreaks of 

disease in poultry in Mexico (Kapczynski and others, 2013). 

Novel HPAI and LPAI strains will likely continue to emerge 

globally, each with different levels of impact to poultry and 

humans and different abilities to spread.

Proposed Action 1. Characterize AI viruses circulating in 

wildlife outside of North America.

The USGS will conduct research and surveillance with 

international collaborators in Asia, Europe, and South America 

to identify emergent LPAI and HPAI viruses with potential to 

spread to U.S. poultry and wildlife. This research will benefit 

the U.S. and our international partners (Global Health Security 

Agenda (GHSA) infectious disease detection). The USGS 

currently partners with scientists in Russia, China, Japan, and 

Iceland on AI research (Yamaguchi and others, 2010; Prosser, 

Cui, and others, 2011; Dusek and others, 2014; Samuel and 

others, 2015). Genetic data from this research network will be 

used to refine surveillance activities (Science Goal 1) and provide information for models (Science Goal 5) that can be used 

to improve biosecurity measures for the poultry industry.

Proposed Action 2. Characterize AI viruses on 

continental fringes of North America.

The USGS will determine genetic diversity of AI viruses 

within avian communities on the margins of North America 

(Alaska, U.S. Gulf Coast, and Atlantic Coast) that are identified as likely areas of introduction for foreign-origin viruses. 

Genetic analyses from these locations will test hypotheses 

regarding which avian species are more likely to bring 

foreign-origin viruses to the U.S., and the results will be integrated into risk assessments for AI management and control 

efforts (Science Goal 5).
U.S. Geological Survey HPAI Science Goals 17

Science Goal 4: Understand Implications of 

Avian Ecology on HPAI Spread

USGS research on the long distance, seasonal migratory 

behavior of wild birds and their unique ecologies has demonstrated its importance in HPAI spread within and across 

continents (see appendix 2 and highlight 5: Merging Wild 

Bird Migration and Disease Ecology). Outbreaks of HPAI 

H5N8 in Korea have corresponded with migratory bird movements, where the disease has not occurred in summer when 

birds were on northern breeding areas, but returned during 

the fall migration in 2014 and 2015 (Jeong and others, 2014). 

Understanding how the disease is maintained and transmitted within wild bird communities relative to the ecology of 

wild birds is necessary (Yasué and others, 2006). Additionally, recent movement models developed by USGS scientists 

have identified associations between key avian staging areas, 

stopover sites, and migration corridors and AI spread in Asia 

(Tian and others, 2015). This quantitative movement data can 

be supplemented by research on the intercontinental genetic 

structure and gene flow of wild birds (Miller and others, 

2015). Elucidation of wild bird movements, population genetics, and trophic interactions in North America will inform risk 

assessment models for susceptible poultry and birds of prey 

(Science Goal 5).

Objective 1. Map spatial and temporal 

distributions of wild and domestic avian 

populations for risk model development.

Spatial and temporal distributional data on wild avian 

populations is available through existing bird surveys 

(USFWS, Canadian Wildlife Service) and USGS avian ecology international and national research (for example, Gaidet 

and others, 2010; Hill and others, 2012; Capelle and others, 

2014). Digitized data from these sources can be mapped to 

support risk assessment models and forecast avian community 

interactions that facilitate HPAI spread. Similarly, this wild 

bird data can be mapped with USDA and State poultry operations data to facilitate modeling the risk of HPAI spread from 

wild birds to poultry.

Proposed Action 1. Develop wild and peridomestic 

bird distribution models as inputs for disease risk 

assessments.

Using existing survey data (for example, USGS bird 

banding data, USFWS aerial survey information, and Christmas Bird Count), the USGS will develop nationwide gridded 

models for wild and peridomestic bird species as potential 

AI vectors. This effort will identify important informational 

gaps that should be addressed to understand the spread and 

persistence of AI between wild and peridomestic species. 

These models can serve as inputs for HPAI risk assessments 

(Science Goal 5).

Proposed Action 2. Develop high resolution models of 

poultry densities as inputs for disease risk assessments.

The Congressional Research Service estimated economy-wide losses of 3.3 billion dollars (Greene, 2015) and 

the USDA has reported that over 50 million chickens and 

turkeys were dead or culled as a result of the 2014–2015 

HPAI outbreak. Understanding where different poultry farms 

(species, meat versus egg-laying, biosecurity level, facility 

size, etc.) are spatially located in relation to wild bird densities is an important component of HPAI risk models. In 

collaboration with the USDA, the USGS will develop highresolution gridded models as inputs for HPAI risk assessments 

(Science Goal 5).

Proposed Action 3. Examine the influence of climate 

change on bird migration and its implications on AI 

dynamics.

Timing of bird migration is influenced by various factors 

such as temperature, plant phenology, and insect emergence. 

As climate varies, cascading effects may lead to changes in the 

predictable migration patterns of birds. This may impact reproductive success, survival, and AI forecasting. The USGS will 

examine the influence of climate change on migratory patterns 

of AI wild bird reservoirs for the development of predictive 

models (Science Goal 5).

Objective 2. Address AI-relevant information 

gaps in avian migratory connectivity data.

Existing survey and movement data that define avian 

migratory connectivity have information gaps that, once 

filled, could help elucidate AI virus spread and reassortment, 

leading to improved spatial allocation of HPAI management 

resources. The USGS will evaluate current databases and 

strategies for collecting avian distribution and movement data 

in order to address these gaps. The USGS will use a multifaceted approach to determine avian distribution, movement, 

and biological factors that correlate with AI distribution and 

reassortment.

Proposed Action 1. Develop quantitative flyway maps of 

avian AI reservoir species.

Building on current knowledge, the USGS will execute 

studies using satellite and Global Positioning System (GPS) 

telemetry to precisely identify movement and distribution 

patterns of avian species involved in AI spread between 

continents and its persistence in North America (see appendix 2). The USGS recently used dynamic Brownian bridge 

movement models to enhance traditional qualitative flyway 

maps with more quantitative data regarding sub-population 

stopover and staging sites along the Central Asian and East 

Asian-Australasian flyways (Palm and others, 2015). Use of 

these tools along North American flyways will enhance our 
18 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Highlight 5: Merging Wild Bird Migration and Disease Ecology

The USGS has determined that wild bird movements 

are important to the spread and distribution of AI viruses 

(Takekawa and others, 2010a; Lee and others, 2015). USGS 

research demonstrated that wild birds passed through an active 

outbreak area of Asian HPAI H5N1 and continued on with 

long-distance migrations to northeastern Russia (Yamaguchi 

and others, 2010). The USGS and collaborators found that this 

same migratory flyway was the probable source of the 2015 

HPAI H5N8 virus that impacted the United States (Lee and 

others, 2015). Overlapping migratory bird populations from 

different continents at stopover, breeding and wintering areas 

offer opportunities for viral exchange (Flint and others, 2009; 

Dusek and others, 2014), and subpopulations of different 

species can distribute AI viruses throughout a shared breeding 

area (Reeves and others, 2013). USGS-led research has also 

identified that infection rates of migratory birds differ between 

breeding, migration, and wintering areas, suggesting that AI 

viral exposure is occurring during several phases of the annual 

cycle (Samuel and others, 2015) and different AI viruses are 

carried by bird species at different times of the year (Ramey, 

Poulson, and others, 2014).

U.S. Geological Survey (USGS) biologist Craig Ely releases a marked Tundra Swan in western Alaska. USGS scientists and 

collaborators have investigated the dynamics of avian influenza exposure in Alaskan Tundra swan populations (Reeves and 

others, 2013; Wilson and others, 2013). Photograph by the USGS.
U.S. Geological Survey HPAI Science Goals 19

understanding of spatial relationships between migration 

corridors and HPAI outbreaks (Takekawa and others, 2010a). 

This migratory data will be incorporated into HPAI forecasting 

models (Science Goal 5).

Proposed Action 2. Determine population connectivity of 

avian AI reservoir species through genetic analyses.

Where needed, the USGS will use molecular genetic 

analyses to determine the population connectivity of migratory 

birds known to carry AI viruses (Flint and others, 2009). The 

USGS has used genetic data to identify long-term patterns of 

connectivity among populations (Miller and others, 2015; see 

appendix 2). Inferences regarding population connectivity can 

be related back to HPAI models (Science Goal 5) to determine 

relationships between wild bird populations and AI occurrence 

and reassortment. Avian movement patterns and distributions 

can also be compared to covariates (chronology, climatic 

conditions, landscape features, and non-avian AI detections) 

to refine understanding of links between avian population connectivity and the dynamics of viral transmission and spread.

Proposed Action 3. Characterize landscape connectivity 

between avian AI reservoirs and wild birds susceptible to 

HPAI disease.

Currently, HPAI-related mortality in the U.S. has been 

associated with a few species (such as bald eagles and 

peregrine falcons). Internationally, however, other species 

have died from HPAI (for example, bar-headed geese and 

black-necked cranes) (Takekawa and others, 2010b, 2013). 

The USGS will collaborate with USFWS to use bird survey, 

telemetry, and genetic data (see appendix 2) to characterize landscape connectivity of HPAI-susceptible birds and AI 

reservoir species at breeding and wintering sites in the United 

States. Models of this connectivity data (Science Goal 5) will 

be shared with natural resource managers (USFWS) for incorporation into species management plans.

Objective 3. Investigate HPAI transmission in 

avian communities and the impact of HPAI on 

avian populations.

Little is known about how ecological communities and 

food webs shape the transmission and spread of AI (Caron 

and others, 2012). Sampling of multiple food web vectors will 

establish the primary routes of transmission and identify the 

most susceptible species (avian and non-avian) along with the 

potential for transfer into related, weakly linked food webs. 

While AI viruses are known to affect waterfowl body weight 

(Kuiken, 2013), little is known of AI effects on avian fitness 

(reproductive success, survival) and populations.

Proposed Action 1. Determine HPAI transmission 

pathways within ecological communities.

The USGS will sample other birds in the avian community for AI simultaneously with targeted dabbling ducks. 

This action will complement wild bird surveillance efforts 

(Science Goal 1). Habitat loss and degradation as well as 

expansion of certain populations (for example, snow geese, 

Canada geese) have shifted trophic dynamics (such as the 

exploitation of agricultural crops) and community interactions 

(Jefferies, 2000). The impacts of these shifts on AI dynamics are unknown. Adopting a sampling design that includes 

different age groups and banded adults from the wintering 

and breeding grounds will further elucidate mechanisms that 

help spread disease within communities. The design of these 

AI community studies can be refined and standardized to best 

utilize life history and trophic dynamics data for risk models 

(Science Goal 5).

Proposed Action 2. Assess impact of HPAI on North 

American avian populations.

The USGS will collaborate with natural resource managers to assess the impact of HPAI on wild bird populations. 

AI avian sampling occurs with bird banding. Results of AI 

surveillance (Science Goal 1) will be combined with banding, survey, and telemetry data (see appendix 2) for statistical 

analyses of population and reproductive trends. This action 

will examine the potential trade-offs of HPAI infections on 

reproductive success and survival.

Science Goal 5: Develop HPAI Forecasting and 

Decision-Making Tools

The USGS has the expertise to provide formal decision 

analytical consulting to partner management agencies (see 

appendix 2). Decision analytic scientists can facilitate stakeholder identification of what scientific information is needed 

to help make better decisions about the response to HPAI. 

Identification of key decision points and critical information 

gaps will guide prioritization of research in Science Goals 

1–4 and serve as the basis for the development of models and 

data visualization tools (see appendix 2 and highlight 6: Risk 

Assessment Models). This process is most effective if established early in preparing for a disease response so that the 

science can be directed by the needs of management agencies. 

This iterative process allows the science to be driven directly 

by management needs and adapted as new information is 

obtained or as the situation evolves as is often the case with 

AI viruses.

Epidemiological modeling enhances our understanding of 

disease dynamics and influences the response to diseases (for 
20 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

example, measles, dengue, and Ebola). Rather than just developing models hypothesized to inform the disease response, 

HPAI models will be jointly developed with scientists and 

decision makers to ensure relevance and direct applicability to 

AI management and decision-making.

Building disease-specific models requires detailed information on key risk factors that can be included as model components. Over the past 16 years, a large body of knowledge 

has accumulated from the HPAI H5N1 experience in Eurasia 

and Africa, and North American AI surveillance. The USGS 

will harness knowledge from past and current HPAI research 

to provide decision support to management agencies, develop 

HPAI risk assessment and predictive models, and provide 

online data visualization tools.

Objective 1. Provide decision support to 

management agencies.

The field of decision analysis provides a wealth of tools 

to structure difficult decisions, elicit diverse management 

objectives, integrate relevant scientific knowledge, craft effective alternative strategies, and ultimately choose an optimal 

strategy. The interaction with decision makers also helps 

scientists identify key and common uncertainties to address in 

their research questions. The USGS has expertise in all aspects 

of decision analysis (Runge, 2011; Shea and others, 2014; 

Probert and others, 2015). Central to formal decision analysis 

is the understanding that a decision requires the integration 

of scientific assessment with policy and values judgment. An 

agency charged with management of disease has an enabling 

mandate that helps determine both the desired outcomes of 

management and the tools the agency has at its disposal. Additional input about the desired management outcomes can come 

from stakeholder analysis and consulting. In this context, the 

scientific task is to evaluate the available management actions 

in concert with the desired outcomes, and provide methods for 

identifying the most effective strategy. Taken from this applied 

science perspective, many aspects of scientific research may 

change their focus. For instance, decision analysts can use the 

value of information theory to assess whether the reduction 

of scientific uncertainty through additional research is valuable to a decision maker. The value of research depends on 

if the associated gain in knowledge would shift the decisionmaker’s course of action and expected outcomes. Likewise, 

to a decision maker, the purpose of surveillance or monitoring is to provide critical information that influences when 

and what action to take; there are well-established decision 

analytical tools for evaluating these types of questions that 

are useful to decision makers and researchers. With regard 

to HPAI, the USGS has an opportunity to offer expertise in 

decision analysis to partner management agencies, and use this 

partnership to optimize future surveillance design and refine 

future research agendas.

Proposed Action 1. Work with management agencies to 

develop a quality HPAI decision-making framework.

USGS decision analytic scientists will consult with 

Federal and State management agencies to understand their 

information needs and implement an adaptive management 

approach to HPAI where applicable. The USGS has expertise, 

and can develop greater capacity, to provide formal decision 

analytical consulting to partner management agencies involved 

in the management of AI. First, the USGS will identify Federal, State, and tribal management agencies that are, or could 

be, taking actions to reduce the spread or mitigate the impacts 

of HPAI. Second, the USGS would need to work with those 

agencies to understand the types of actions they can take, the 

regulatory or legal context under which they take them, and 

the information needs they have in making those decisions. 

Third, through individual consulting projects, the USGS could 

help partner management agencies identify the context of 

their decisions, articulate their management objectives and 

performance metrics, and craft the array of alternative strategies for consideration. This decision-framing sets the stage for 

robust assessment of the management strategies and methods 

to identify optimal strategies, taking account of uncertainty 

and the opportunity for adaptive management. This sort of 

decision analytical process can support interactions between 

management and science agencies, help management agencies 

choose an optimal strategy, and help communicate the decision process to the public.

Proposed Action 2. Synthesize HPAI decision analyses 

from different management agencies to identify common 

information gaps and critical uncertainties.

The USGS will conduct a meta-analysis of the HPAI 

decision frameworks developed in Proposed Action 1. As 

experience grows working with individual management agencies on particular decision contexts related to HPAI, a synthesis of those analyses would allow broader insights that could 

affect the direction of future USGS efforts. In particular, sensitivity analysis can be used to identify (1) which monitoring 

variables are critical for State-dependent decisions; (2) which 

uncertainties impede the choice of a preferred alternative and 

would be valuable to resolve through research or adaptive 

management (value-of-information analysis); and (3) which 

outcomes reflect common management goals and should be 

monitored to evaluate success. These analyses can be completed for individual projects, but the power of a meta-analysis 

would allow identification of those research and monitoring 

activities that would benefit multiple management partners.
U.S. Geological Survey HPAI Science Goals 21

Highlight 6: Risk Assessment Models

Understanding the spatial and temporal interface between browser. A zoom-able map has a sidebar showing the legend, 

wild and domestic bird populations, from which these viruses and the layers can be turned on and off. The map also has a 

emerge, is fundamental to taking action. The USGS has brush-enabled outbreak event timeline that is color-coded for 

led the development of visual representations of data from serotype, giving users the opportunity to follow the history of 

China that can demonstrate how the spread of AI viruses is the disease both spatially and temporally. Future models for 

influenced by environmental and human factors (Prosser and the U.S. (Science Goal 5, Objective 3, Proposed Action 2) will 

others, 2015; Prosser, Wu and others, 2011). This AI Transmis- show the potential risk of transmission of AI between wild and 

sion Risk Model web application depicts the intricate con- domestic birds, the presence and density of poultry production, 

nections among 16 layers of administrative, environmental, and the distribution of wild bird species in the area of poultry, 

and economic data in an application that runs inside a web all of which may influence outbreaks.

An example of a decision-making tool from Science Goal 5. In collaboration with the Food and Agriculture Organization, the U.S. Geological Survey has examined the risk of HPAI moving between wild birds and poultry in China. Similar risk models are being developed 

for the United States. Figure from http://www.pwrc.usgs.gov/ai/ (to view Web page use Google Chrome browser). 
22 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Proposed Action 3. Refine HPAI research priorities based 

on management partner needs.

Research on HPAI by the USGS will be aimed at supporting the needs of management agencies. Adaptive management 

(Proposed Action 1) may identify new HPAI information gaps. 

The outcomes of the synthesis in Proposed Action 2 can be 

used to further refine USGS research priorities and decisionsupport tools (models, data visualization tools) to meet the 

needs of management agencies.

Objective 2. Develop HPAI risk assessment and 

predictive models.

Predictive spatial modeling can be used to identify 

priority areas for pre-outbreak disease surveillance and postoutbreak response. This type of information can be valuable 

to partner agencies as they mount rapid response actions 

using limited resources for sampling wild birds and poultry. 

Additionally, identifying areas of increased transmission risk 

between wild and domestic populations will be important for 

managing wildlife populations (including threatened species) 

and implementing poultry biosecurity measures. In an adaptive 

manner, cursory models can be improved with the addition 

of data from mechanistic studies. Objective 2 actions will be 

prioritized based on partner-identified information gaps and 

critical uncertainties (Objective 1).

Proposed Action 1. Develop dynamic occupancy models 

to estimate probabilities of AI occurrence.

Dynamic models estimate rates of colonization (disease 

presence) and extinction (disease absence). Such models are 

valuable decision support tools for estimating the probability 

of AI occurrence in a spatial manner. Using established and 

new datasets (Science Goals 1‒4) on AI occurrence and spatial 

variables (proximity to and number of neighboring AI locations in wild birds and poultry, habitat, and season), the USGS 

will develop predictive models of AI movement by wild 

birds. Because not all States and poultry operations have been 

impacted by HPAI, these predictive models can support proactive AI management efforts.

Proposed Action 2. Conduct a meta-analysis of existing AI 

mechanistic models.

The USGS will conduct a meta-analysis of existing AI 

mechanistic models. There is a growing body of literature 

related to dynamic modeling of AI that can be drawn upon to 

develop a mechanistic disease framework for newly emerged 

HPAI. This work would entail collating existing information to 

parameterize a mechanistic model and applying the model to a 

Bayesian statistical framework. The model would enhance our 

understanding of the epidemiology of HPAI and support AI 

management decisions.

Proposed Action 3. Develop geospatial models of HPAI 

transmission risk at the wild-to-domestic bird interface.

The USGS will collaborate with the USDA to develop 

virus-specific (H5N8 or H5N2) spatial transmission risk models that focus on the interface between wild birds and poultry. 

Development of these models will be important to the poultry 

industry, wildlife managers, as well as decision-makers who 

are involved in preparedness and response planning for HPAI.

Proposed Action 4. Develop Landsat-based HPAI risk 

maps.

The USGS will collaborate with the USDA to develop 

habitat-based risk maps from geospatial HPAI occurrence 

data and Landsat, which provides continuously updated land 

imagery from space. Satellite imagery can play a key role in 

disease forecasting (Ford and others, 2009). Geospatial data 

on HPAI occurrence in wild birds and poultry will serve as the 

foundation for novel Landsat-based identification of similar 

habitats that are important for AI transmission. To understand 

future transmission risk, this data will be critical to project 

changes in transmission locations due to land use modifications and climate change. This information can help identify 

poultry facilities and wildlife habitats that have a high risk for 

HPAI outbreaks.

Proposed Action 5. Develop hydrologic HPAI risk maps.

The USGS will partner with the USDA to develop 

hydrologic HPAI risk maps from HPAI occurrence data and 

identification of locations with similar water characteristics 

and flows. Previous studies have shown that AI viruses can 

persist in wet environments under certain conditions (Brown 

and others, 2007), and that environmental transmission may be 

a key factor in the overall spread and persistence of AI viruses 

(Rohani and others, 2009). This action proposes to develop 

risk map layers specifically for the water environment based 

on environmental conditions and hydrologic flow regimes 

from potential source locations such as poultry farms or natural areas containing large congregations of wild birds.

Proposed Action 6. Develop models of HPAI transmission 

risk for avian communities.

Applying data from avian distribution and migratory connectivity research (Science Goal 4), the USGS will model the 

risk of HPAI movement to new areas via wild birds. Wintering 

grounds in California’s Central Valley have been identified 

as high risk areas for HPAI transmission (Hill and others, 

2012). These models will identify other high risk areas for AI 

transmission within and between resident and migratory wild 

populations in North America. This knowledge will be shared 

with partners to support HPAI management of poultry and 

HPAI-susceptible wild birds.
References Cited 23

Objective 3. Develop online HPAI data 

visualization tools.

Effective and efficient preparedness and response is aided 

greatly by having knowledge of what data already exists and 

having a platform for viewing these data in an integrated setting and decision context to know which information will be 

useful to managers. Effective cataloging of data and products 

as well as hierarchical dissemination through appropriate 

channels is key to reducing duplication of effort. This objective is focused on developing collaborative databases and 

online HPAI data visualization tools for resource managers. 

Input from management agencies (Objective 1) will guide 

the development of these tools as the USGS aims to meet 

partner needs.

Proposed Action 1. Improve existing collaborative 

databases to store historical and future HPAI data.

In support of the 2006 U.S. AI Interagency Strategic 

Plan, an online national database (Highly Pathogenic AI 

Early Detection Data System, or HEDDS) was developed 

to manage animal and specimen collection data on AI from 

multiple agencies. With USDA collaboration, recent advances 

in technology will be used to refine this system for increased 

ease of data entry, data export, security for users with sensitive 

data, and data analyses features. This improved database will 

provide a comprehensive view of national sampling efforts 

for both LPAI and HPAI from 2006 to present providing 

situational awareness and data availability for both decisionmaking agencies and researchers.

Proposed Action 2. Develop an HPAI data visualization 

tool.

The USGS will develop an online HPAI data visualization tool for partners making HPAI management decisions. 

In response to HPAI H5N1, the USGS developed a data 

visualization tool (http://www.pwrc.usgs.gov/ai/) to facilitate 

viewing of spatial datasets and modeling products pertinent to 

H5N1 in Asia (see highlight 6: Risk Assessment Models). This 

action will build upon the current tool and provide a platform 

for viewing data related to the current HPAI situation in the 

United States. The USGS will work with the USDA to include 

reported AI outbreaks in poultry, detections in wild birds, surveillance sampling, environmental risk factors, and developed 

models (for example, spatial risk models).

Acknowledgments

This report and writing team were supported by the 

U.S. Geological Survey Ecosystems Mission Area. The writing team thanks the Ecosystems Mission Area for sponsoring 

an essential writing team meeting on September 9‒10, 2015, 

in Reston, Va. The authors thank Cynthia S. Kolar, Evan H. 

Grant, and Mona Khalil for their reviews that significantly 

enhanced this report. We also thank Garth Herring, Collin A. 

Eagles-Smith, and Patricia Bright for contributing their time to 

the conference calls for the strategy developed in this report.

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bacteria from water using sodocalcic glass wool filters: 

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Appendixes

Photograph of tundra swans. Licensed under Creative Commons 3.0
34 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Appendix 1. Overview of U.S. Geological Survey Capabilities for Avian Influenza 

Research

The U.S. Geological Survey (USGS) employs over 

80 scientists at 9 strategically located Ecosystem Mission Area 

science centers and 26 Cooperative Research Units (CRUs) 

that are highly engaged in avian ecological studies. The 

capabilities of the research centers, located in Alaska, Oregon, 

California, Arizona, West Virginia, Wisconsin, Maryland, 

Florida, and Hawaii, are augmented by CRUs co-located at 

major universities across the contiguous United States. The 

collective expertise of the USGS is essential to understanding 

HPAI dynamics in North America (fig. A1–1).

USGS scientists conduct studies on the ecology and 

migration of multiple avian species across terrestrial and 

aquatic biomes, which help to determine likely areas of HPAI 

introduction and optimization of surveillance plans. USGS scientists are addressing questions about long-distance HPAI distribution by migratory birds with bird banding and telemetry 

(satellite and GPS) data. Subsequently, mathematical models 

are developed from that movement data to explain observed 

differences in the spread of the disease across international 

and U.S. flyways. Genetic and stable isotope techniques are 

used by USGS scientists to identify long-term population connectivity patterns in migratory birds (see appendix 2). Connectivity data can help elucidate major flight pathways used by 

sub-populations of individual species as well as interspecific 

interactions at staging, breeding, and wintering grounds that 

can influence HPAI spread.

USGS AI studies are conducted in computational, field 

and laboratory settings and include over two decades of 

collaborative research in Africa, Europe, Russia, Asia, and 

North America by scientists from ecosystem research centers 

across the United States. Unique laboratory investigations are 

possible because of USGS high biocontainment laboratories 

(Biosafety Level 3) and wild bird captive research colonies. 

As the lead Federal agency for wildlife disease surveillance, 

the USGS maintains the National Wildlife Health Center 

(NWHC), which supports USDA disease surveillance as part 

of the National Animal Health Laboratory Network. The 

NWHC is also an Office International des Epizooties (OIE, 

World Organization for Animal Health) collaborating center 

for research, diagnosis, and surveillance of wildlife pathogens.

Disease impacts on ecosystems and species (of management concern) remain a key focus of the USGS. The USGS 

has expertise in statistical and mathematical modeling of 

population dynamics, animal movements, resource selection, 

occupancy dynamics, as well as physical, geospatial, and 

temporal processes pertinent to HPAI. USGS scientists employ 

supercomputing and the latest advances in mathematical 

programming to model infectious disease epidemiology across 

ecosystems. These models are used to inform biosurveillance 

and response plans, as well as elucidate effects on host species 

and ecosystems. Such modeling is important to understand 

disease risks to wildlife, domestic animals, and humans. 

Additionally, USGS science can support managers through 

the use of formal decision-making frameworks (for example, 

structured decision-making and adaptive management) that 

can increase the probability of early detection, containment, 

and successful mitigation of HPAI.

The USGS has the expertise to investigate the distribution and persistence of HPAI viruses in the physical environment (for example, water and sediment) and forecast how 

changing environmental conditions can affect HPAI dynamics. 

Similarly, the USGS is well suited to provide the best predictive capability for determining the potential effects of climate 

change on HPAI dynamics and avian phenology in North 

America. Strategic coordination of the USGS effort at the 

national level is recognized as crucial to providing the most 

effective science useful to resource managers in effectively 

controlling the disease in wild and domestic environments. 

Successful implementation of this HPAI science strategy will 

require integration of HPAI science across USGS mission 

areas and science centers.

Integrating HPAI Science Across 

U.S. Geological Survey Mission Areas

Ecosystems—https://www.usgs.gov/science/mission-areas/

ecosystems

Core Science Systems—https://www.usgs.gov/science/

mission-areas/core-science-systems

Water—https://www.usgs.gov/science/mission-areas/water

Environmental Health—https://www.usgs.gov/science/

mission-areas/environmental-health

Climate and Land Use Change—https://www.usgs.gov/

science/mission-areas/climate-and-land-use-change

Natural Hazards—https://www.usgs.gov/science/

mission-areas/natural-hazards

Energy and Minerals—https://www.usgs.gov/science/

mission-areas/energy-and-minerals
Appendix 1 35

Figure A1–1. Diagram showing the U.S. Geological Survey (USGS) has considerable scientific capabilities nationwide to study avian 

influenza. Abbreviation: USDA, U.S. Department of Agriculture.

USGS Avian Influenza

Research Capabilities

Disease Ecology

and Risk

Modeling

Wild Bird and

Environmental

Surveillance

Member of the USDA

National Animal Health

Laboratory Network

Diagnostic and

Research

Laboratories

Avian Ecology,

Migration, and

Disease Spread

International

Partners

Next Generation

Sequencing for Genomic

Analysis of Viruses

Decision

Support

Tools

Biological technician Sean Nashold of the U.S. Geological Survey (USGS) National Wildlife Health Center tests chicken eggs 

inoculated with a field sample from wild birds to detect the presence of avian influenza virus. Photograph by the USGS.
36 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Appendix 2. Examples of U.S. Geological Survey Capabilities for Avian Influenza 

Research

This appendix provides examples of U.S. Geological Survey (USGS) capabilities that will support implementation of 

the USGS Science Strategy for HPAI in Wildlife and the Environment. A brief description of each capability is followed by 

descriptions and citations of representative publications.

Genetic and Stable Isotope Analysis of Wildlife 

Populations

The USGS has substantial experience researching migratory connectivity patterns in avian populations using genetic 

markers and stable isotopes. These analyses provide insights 

about population connectivity and organismal movement, the 

basis for understanding disease transport dynamics that can 

facilitate development of more advanced forecasting models.

• ASC and FRESC.—The Alaska Science Center (ASC) 

and the Forest and Rangeland Ecosystem Science Center (FRESC) are fully equipped with all the instrumentation required to generate genetic and genomic data 

from avian samples. Laboratory personnel routinely 

provide blood and tissue sample kits to various groups, 

and can therefore provide resources to facilitate collection of samples for genetic analyses. The USGS facilities are also capable of long-term storage of blood and 

tissue samples to preserve valuable genetic samples for 

future analyses. Examples of representative publications by authors of the ASC and FRESC include Flint 

and others (2009) and Miller and others (2015).

Telemetry Movement Studies of Wildlife 

Populations

The USGS has extensive expertise conducting telemetry 

and movement studies.

• WERC, ASC, PWRC, and WARC.—The Western 

Ecological Research Center (WERC) and the Alaska 

Research Science Center (ASC) have focused on 

characterizing migratory movements and connectivity of species that are likely to introduce AI into North 

America, such as northern pintail ducks. Additionally, the WERC and the Patuxent Wildlife Research 

Center (PWRC) have developed a partnership with 

the United Nations Food and Agriculture Organization 

to study waterfowl movements globally in relation to 

HPAI H5N1 (>600 marked birds from 12 countries). 

These data have been paramount to increasing our 

understanding of how wild birds might be involved in 

the spread of HPAI in other regions of the world. The 

PWRC and the Wetland and Aquatic Research Center 

(WARC) also have a history of studying waterfowl 

population ecology and radar ornithology for tracking avian movement over broad spatial scales that 

would be applicable to understanding the spread of AI. 

Examples of representative publications include De La 

Cruz and others (2009), Newman and others (2009), 

Prosser and other (2009), Gaidet and others (2010), 

Takekawa and others (2010), Yamaguchi and others 

(2010), Gilbert and others (2011), Prosser, Cui, and 

others (2011), Takekawa and others (2013), Prosser 

and others (2014), and Palm and others (2015).

Disease Risk Modeling

The USGS has the capacity to model wildlife disease risk 

including AI transmission risk within wild and domestic bird 

populations and at this important interface.

• PWRC, WARC, NWHC, UMESC, and NOROCK.—The 

Patuxent Wildlife Research Center (PWRC) has been 

working on HPAI H5N1 AI transmission risk for wild 

and domestic populations since 2005. This research 

includes building spatial distributions for waterfowl 

and poultry host species, as well as formulating 

density-dependent and environmental transmission 

disease risk models. The PWRC has ties with the U.S. 

Fish and Wildlife Service (USFWS) Migratory Bird 

Office and the USDA Economic Research Service and 

is well-positioned to access and analyze national level 

waterfowl and poultry data. The Wetland and Aquatic 

Research Center (WARC) also has experience developing environmentally dependent transmission risk 

models for wildlife populations. The National Wildlife 

Health Center (NWHC), Upper Midwest Environmental Sciences Center (UMESC), and Northern Rocky 

Mountain Science Center (NOROCK) have developed 

risk models for a variety of wildlife diseases. Examples 

of representative publications include Almberg and 

others (2011), Martin and others (2011), Prosser, Wu, 

and others (2011), Van Boeckel and others (2011), 

Lorch and others (2013), Prosser and others (2013), 

Thogmartin and others (2013), and Cappelle and 

others (2014). 
Appendix 2 37

Statistical and Geospatial Modeling

The USGS has the capacity to conduct statistical modeling that can be leveraged for most data analysis endeavors.

• WARC, PWRC, NOROCK and NWHC.—The Wetland 

and Aquatic Research Center (WARC), Patuxent Wildlife Research Center (PWRC), Northern Rocky Mountain Science Center (NOROCK), and National Wildlife 

Health Center (NWHC) have expertise in statistical 

modeling of population dynamics, animal movements, resource selection, occupancy dynamics, and 

spatio-temporal processes. The PWRC has an adaptive 

management and structured decision-making program, 

which can be valuable in designing and evaluating 

epidemiological strategies. Each of these topics will be 

important to rigorously incorporate data into models 

for understanding disease dynamics while incorporating uncertainty into predictive results. The WARC and 

the PWRC also have strong expertise in developing 

geographic information support tools, which could 

support the spatial mapping of waterfowl movements 

in relation to zones of infection and mapping migratory connectivity patterns, as well as quantify uncertainty within the models and their output. Examples 

of representative publications include Hogerwerf and 

others (2010), Walsh and others (2011), Cross and 

others (2012), Russell and others (2012), Russell and 

others (2014), Walsh and others (2014), and Prosser 

and others (2015).

Epidemiological Modeling

The USGS has the capacity to model infectious disease 

processes including those associated with AI. These models 

can be used to inform biosurveillance and response plans, as 

well as elucidate important aspects of a pathogen, its effects 

on host species and the environment, and overall ecological 

impact. They are also important in understanding disease risk 

at the wildlife-livestock interface.

• NWHC, PWRC, NOROCK, and WARC.—The National 

Wildlife Health Center (NWHC), Patuxent Wildlife 

Research Center (PWRC), Northern Rocky Mountain Science Center (NOROCK), and Wetland and 

Aquatic Research Center (WARC) have exploited and 

developed new statistical techniques for conducting 

biosurveillance to increase the efficiency of surveillance activities and decrease the effort needed to 

ascertain disease status at a given statistical confidence 

level. Examples of representative publications include 

Heisey and others (2006), Farnsworth and Ward 

(2009), Heisey and others (2010a), Heisey and others 

(2010b), Henaux and others (2010), Walsh and Miller 

(2010), McClintock and others (2011), Walter and 

others (2011), Cronin and others (2012), Cronin and 

others (2014), Heisey and others (2014), and Shea and 

others (2014). 

Laboratory and Experimental Animal Studies on 

Diseases

The USGS has extensive expertise and capabilities to 

conduct laboratory wildlife disease research, diagnostics, and 

field investigations. The USGS has biocontainment facilities 

dedicated to wildlife disease research.

• NWHC.—The National Wildlife Health Center (NWHC) has Biosafety Level 3 (BSL–3) and 

Enhanced BSL–3 laboratory and experimental animal 

isolation facilities. These include personnel, laboratories, and isolation facilities that are certified under 

the Federal Select Agent Program to work with highly 

pathogenic AI viruses, which require Enhanced BSL–3 

certification from the USDA. Examples of representative publications include Hall and others (2009), Hall 

and others (2011), and Hall and others (2013). 

• PWRC.—The Patuxent Wildlife Research Center 

(PWRC) maintains captive colonies of sea ducks, 

kestrels, owls, cranes, and other species, which can 

be used in experimental studies to better understand 

the roles of various wildlife species and groups in 

the epidemiology of AI in wildlife, including species 

susceptibility, pathogenesis, immunity, viral shedding 

and transmission, and co-infection dynamics. These 

variables are important in informing epidemiological, 

ecological, and predictive and risk models. An on-site 

veterinarian, animal care staff, and veterinary hospital monitor the health and well-being of these avian 

colonies. Examples of representative publications 

include Ellis and others (2000) and Kozlowski and 

Hahn (2010). 
38 U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)

Decision Science and Data Visualization Tools

The USGS has the expertise and capabilities to support management agencies through formal decision analyses 

(structured-decision making and adaptive management). While 

capabilities have primarily been utilized for management of 

wildlife species, this capacity has been employed for disease 

management. The USGS has widespread experience with data 

visualization tools.

• CIDA and WiM.—The Office of Water Information 

Center for Integrated Data Analytics (CIDA, http://

cida.usgs.gov/) has extensive experience developing web applications and transactional information 

systems in support of the USGS and other Federal 

science initiatives. Wisconsin Internet Mapping (WiM) 

develops web-based visualization and exploration tools 

to support USGS science and other Federal initiatives. 

WiM projects range from full-featured encompassing 

database and exploration applications to targeted datavisualizations involving specific topic areas.

• NWHC.—The NWHC’s Wildlife Health Information 

Sharing Partnership event reporting system (WHISPers) is an online repository that contains basic information about historic and ongoing wildlife morbidity 

and mortality events that involve five or more wild 

animals (https://www.nwhc.usgs.gov/whispers/).

• USGS and CDC Disease Maps.—Dynamic online 

U.S. maps (http://diseasemaps.usgs.gov/mapviewer/) 

by the U.S. Geological Survey (USGS) and the Centers 

for Disease Control and Prevention (CDC) show the 

occurrence of vector-borne diseases in mosquitoes, 

birds, veterinary cases, sentinel animals, and humans. 

Vector-borne viruses visualized include West Nile 

virus, St. Louis encephalitis virus, eastern equine 

encephalitis virus, La Crosse encephalitis virus, Powassan virus, Dengue virus, and Chikungunya virus. 

Examples of representative publications include Lyons 

and others (2008), Runge (2011), Williams (2011), 

Mitchell and others (2013), Shea and others (2014), 

Probert and others (2015).

Virus Detection in the Aquatic Environment

The USGS scientists have experience sampling for 

extremely low concentrations of societally-relevant animal and 

human viruses in the environment. USGS scientists also have 

experience in virus source and transport studies, including the 

use of surrogate transport aspects. Specifically, in terms of AI, 

USGS capabilities include trace-concentration monitoring of 

the AI virus in the physical environment. 

• WWSC, IWSC and LIDE.—The USGS Water Science Center staff from Wisconsin (WWSC) and Iowa 

(IWSC) collect and analyze groundwater and surface water samples and are trained in USDA highly 

pathogenic AI responder methods. The Laboratory for 

Infectious Disease in the Environment (LIDE), a joint 

facility of the USGS and USDA Agricultural Research 

Service, is a Biosafety Level 2+ laboratory equipped to 

detect trace-concentration levels of AI virus in groundwater and surface water and equipped to identify AI 

subtypes, including strains responsible for outbreaks. 

The LIDE detects and quantifies AI virus using a suite 

of four quantitative polymerase chain reaction (qPCR) 

assay techniques, including two that were developed 

by the LIDE for increased sensitivity compared to previously established methods. Use of current AI genetic 

sequences from recent outbreak strains allows the most 

sensitive and accurate detection of the virus possible. 

The LIDE can also identify other common poultry and 

agricultural pathogens, zoonotic bacteria, and fecal 

indicators that provide insights needed to understand 

virus fate and transport. In addition to detection and 

quantification, the LIDE uses cell culture methods to 

determine AI virus infectivity.

The USGS’s capabilities and experience for the detection of viruses in the physical environment are located across 

Mission Areas and Regions, including the Midwest Region’s 

Water and Environmental Health Mission Areas in the 

Wisconsin and Iowa Water Science Centers, the Ecosystems 

Mission Area’s Alaska Science Center, and in partnership with 

the USDA through the USGS’s LIDE, which is part of the 

Wisconsin Water Science Center. Examples of representative 

publications include Hunt and others (2010), Bradford and 

others (2013), Meixell and others (2013), Abd-Elmaksoud 

and others (2014), Hunt and others (2014), Haack and others 

(2015), and Hubbard and others (2016). 
Publishing support provided by the 

Reston and Rolla Publishing Service Centers 

Edited by David A. Shields 

Layout by Suzanne C. Roberts

For more information concerning this report, contact: 

Anne Kinsinger 

USGS Associate Director for Ecosystems 

703-648-4050 

akinsinger@usgs.gov

M. Camille Harris 

USGS Wildlife Disease Coordinator 

703-648-4019 

mcharris@usgs.gov
Harris and others—U.S. Geological Survey Science Strategy for Highly Pathogenic Avian Influenza in Wildlife and the Environment (2016–2020)—OFR 2016–1121

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20161121