As the sick bird flies: Combining movement data with mechanistic models to understand relationships among movement, environmental change, and infectious disease in wildlife

Background/Question/Methods

Animal movement patterns can drive the transmission and spread of infectious diseases at multiple spatial scales. For animals that move long distances, traveling across broad landscapes can provide escape from infection, or alternatively, can increase their exposure to pathogens found in diverse habitats. Long distance migration can also rapidly spread pathogens across habitats and continents. As animal movement patterns change in response to land-use and climate change, animals’ roles in dispersing and transmitting infectious diseases might also change. Understanding these interactions requires simultaneously gathering information on animal movement patterns, responses to environmental change, and infection patterns, which can be difficult due to logistical and time constraints. One potential solution to this challenge is to use mechanistic models to estimate missing information about movement, landscape change, pathogen exposure, and/or infection. Here, we analyze telemetry data from a large, multi-species study of waterfowl in the Pacific Flyway that incorporates sampling for avian influenza viruses. We identify temporal and infection-related patterns in migration phenology, then use these results to inform a model of avian influenza dynamics in a population of migrating waterfowl.

Results/Conclusions

Migration phenology varied across species and time. Geese showing evidence of prior influenza infection made shorter stopovers than previously uninfected geese on spring migration, possibly linked to a later departure from the wintering grounds, but migration in other species was unaffected by prior influenza infection. When interannual and interspecific variation in migration phenology was included in models of avian influenza transmission, migration timing affected the geographic spread and persistence of influenza viruses: when migration began earlier and was more synchronized, influenza viruses reached higher prevalence and were more likely to persist for multiple years. Climate change is affecting migration phenology by altering the spatial and temporal variation in resource availability for wildlife. The results from this empirical study and model suggest that even small changes in movement behavior can affect infection spread and pathogen prevalence, one element of community composition. Understanding these mechanistic links between climate change and infectious disease transmission (e.g., changes in animal movement) will be crucial for managing wildlife, livestock, and human health in a changing world.