Ben D. Golas | Geology Reports

USGS2025

Density dependence and weather drive dabbling duck spatiotemporal distributions and intercontinental migration

Understanding migratory waterfowl spatiotemporal distributions is important because, in addition to their economic and cultural value, wild waterfowl can be infectious reservoirs of highly pathogenic avian influenza virus (HPAIV). Waterfowl migration has been implicated in regional and intercontinental HPAIV dispersal, and predictive capabilities of where and when HPAIV may be introduced to susceptible spillover hosts would facilitate biosecurity and mitigation efforts. To develop forecasts for HPAIV dispersal, an improved understanding of how individual birds interact with their environment and move on a landscape scale is required. Using an agent-based modeling approach, we integrated individual-scale energetics, species-specific morphology and behavior, and landscape-scale weather and habitat data in a mechanistic stochastic framework to simulate Mallard ( Anas platyrhynchos ) and Northern Pintail ( Anas acuta ) annual migration across the Northern Hemisphere. Our model recreated biologically realistic migratory patterns using a first principles approach to waterfowl ecology, behavior, and physiology. Conducting a limited structural sensitivity analysis comparing reduced models to eBird Status and Trends in reference to the full model, we identified density dependence as the main factor influencing spring migration and breeding distributions, and wind as the main factor influencing fall migration and overwintering distributions. We show evidence of weather patterns in Northeast Asia causing significant intercontinental pintail migration to North America. By linking individual energetics to landscape-scale processes, we identify key drivers of waterfowl migration while developing a predictive model responsive to daily weather patterns. This model paves the way for future waterfowl migration research predicting HPAIV transmission, climate change impacts, and oil spill effects.

Avian Research↗

USGS2017

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Ecology Letters↗