Landscape genetics and epidemiology of chronic wasting disease in Midwestern white-tailed deer

Chronic wasting disease (CWD), a fatal prion disease, is a major concern for the management of North American cervid populations. The detection of CWD in Wisconsin white-tailed deer precipitated rapid management efforts to contain the disease, along with economic losses in wildlife recreation and public unease. Understanding the impacts and dynamics of CWD in Wisconsin has become imperative to biologically-sound deer management. Molecular ecology of the host species provides an exciting new tool to understand epidemiological processes because individual genetic variation may affect disease resistance and population genetic structure may influence both the local transmission and broad-scale spatial spread of disease. To address the complex questions related to CWD in the Midwest, we employed a multi-disciplinary framework drawing on methods from landscape genetics, epidemiology, and statistical modeling.;We first investigated individual-level heterogeneity in CWD susceptibility. We found that common genetic polymorphisms led to differences in disease infection and mortality rates allowing resistant deer to achieve higher population growth and obtain a long-term fitness advantage. This provides a rare demonstration of disease-driven natural selection in a free-ranging wildlife population and has implications for long-term disease management.;We then examined broad-scale variation in disease patterns. To understand the linkage from ecological processes to host biology and then to disease dynamics we used landscape genetics and spatial analysis to investigate landscape influences on deer movement and population connectivity. Local genetic neighborhood structure was correlated with the amount and arrangement of forested habitat, and landscape-scale population connectivity was shaped by impediments to deer dispersal. These patterns of genetic connectivity were used to parameterize models of disease distribution across a region and predict CWD distribution across the landscape. We showed that risk of CWD infection declined with distance moving away from current disease foci, and was affected by landscape barriers identified through gene flow models. Our combination of population genetics and spatial epidemiological analysis led us to identify the landscape characteristics that shape animal movement and influence contact rates between populations. We were able to generate a predictive map of CWD risk that will provide a useful tool for disease management planning and public information.