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Continental‐scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature
Author(s) -
Gorsich Erin E.,
Webb Colleen T.,
Merton Andrew A.,
Hoeting Jennifer A.,
Miller Ryan S.,
Farnsworth Matthew L.,
Swafford Seth R.,
DeLiberto Thomas J.,
Pedersen Kerri,
Franklin Alan B.,
McLean Robert G.,
Wilson Kenneth R.,
Doherty Paul F.
Publication year - 2021
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.2245
Subject(s) - waterfowl , wildlife , ecology , geography , biology , scale (ratio) , demography , habitat , cartography , sociology
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‐of‐sample 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.