Continental-scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature

Authors

Erin E. Gorsich, Colorado State University & University of WarwickFollow
Colleen T. Webb, Colorado State UniversityFollow
Andrew A. Merton, Colorado State University
Jennifer A. Hoeting, Colorado State University
Ryan S. Miller, USDA APHISFollow
Matthew Farnsworth, USDA, APHIS, Veterinary ServicesFollow
Seth R. Swafford, USDA, APHIS, Department of Fisheries and Wildlife, Mississippi State University
Thomas J. DeLiberto, U.S. Department of AgricultureFollow
Kerri Pedersen, APHISFollow
Alan B. Franklin, USDA-APHIS-WS National Wildlife Research CenterFollow
Robert G. McLean, WS National Wildlife Research CenterFollow
Kenneth R. Wilson, Colorado State University - Fort CollinsFollow
Paul Doherty, Colorado State University

ORCID IDs

ERIN E. GORSICH https://orcid.org/0000-0002-3017-0540

RYAN S. MILLER https://orcid.org/0000-0003-3892-0251

Date of this Version

2021

Citation

Gorsich, E. E., C. T. Webb, A. A. Merton, J. A. Hoeting, R. S. Miller, M. L. Farnsworth, S. R. Swafford, T. J. DeLiberto, K. Pedersen, A. B. Franklin, R. G. McLean, K. R. Wilson, and P. F. Doherty Jr.. 2021. Continental-scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature. Ecological Applications 31(00):e02245.

doi:10.1002/eap.2245

Comments

This is an open access article under the terms of the Creative Commons Attribution License

Abstract

Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long-distance migration). Here, we combined a large, continental-scale data set on low pathogenic, Type A AIV in the United States with a novel network-based application of bird banding/recovery data to investigate the migration-based drivers of AIV and their relative importance compared to well-characterized local drivers (e.g., demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out-ofsample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterizing local epidemic dynamics were strongly supported, with age, the age-specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network-based features of the migration processes, such as clustering or between-cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV.