Cooperative Fish and Wildlife Research Units Program: Wisconsin Wildlife
Education, Research and Technical Assistance for Managing Our Natural Resources

Recent improvements in molecular genetics in combination with powerful computers and new analytical tools (spatial analysis and Bayesian Markov Chain Monte Carlo methods) has lead to the development of the field of landscape genetics; an integration of population genetics and landscape ecology. The goal of landscape genetics is to understand and resolve the interaction between landscape features and genetics process (continuous gene flow, genetic barriers, habitat permeability, connectivity, and meta-populations) across different geographic scales. We will use methods based on genetic isolation by distance or matrix models and methods of spatial analysis to evaluate gene flow as a spatial predictor of disease prevalence. The goal of this research project is to further develop and apply the tools of landscape ecology and landscape genetics to the spread of wildlife diseases, in particular chronic wasting disease. . Genetics data collected on > 3,500 deer from the CWD affected area in south-central Wisconsin (approximately 120 m2) will provide the baseline genetics information in this research proposal for establishing gene flow patterns beyond this core affected area. This research will build upon these studies by applying landscape genetics to address broad geographic questions on gene flow and disease spread. This approach will evaluate gene flow patterns across the landscape, how landscape structure affects gene flow, and current disease patterns to evaluate and model the factors associated with the geographic distribution of CWD infection. These analyses will consider how distance for the disease focus, gene flow, and landscape features influence disease spread. Specific landscape features (riparian areas, river barriers, highway barriers, cities) and habitat types (forested areas, agricultural areas, urban areas) that result in landscape friction or permeability will also be evaluated as potential corridors and/or barriers to gene flow and thus to predicting disease spread.