Direct and indirect anthropogenic factors have resulted in an increase in incidence and severity of pathogens among wild populations. Understanding how populations are distributed as a result of their environment can be useful knowledge for understanding pathogen spread. By merging the fields of population genetics and landscape ecology we can identify what environmental factors influence species distributions, which can include identifying barriers to movement, and key habitat for species persistence. Using two very different systems I will demonstrate the broad utility of landscape genetics to understanding pathogen spread. My first example examines the use of landscape genetics on white-tail, and mule deer in Canadian populations affected by chronic wasting disease to identify whether the landscape poses any barriers to deer dispersal, which would help limit the spread of disease. In my second example I will identify key environmental factors that predict species distributions in a forest tree system affected by mountain pine beetle to help identify the distribution of vulnerable populations.
Results/Conclusions
To understand chronic wasting disease spread, I used genetic profiles from >5000 white-tail, and mule deer, and examined whether there was evidence of landscape barriers to movement. The results differed slightly for the two species; for mule deer, there was no evidence of landscape effects on movement suggesting the disease will continue to extend its range at a steady rate relative to mule deer dispersal. For white-tailed deer, mountains pose a barrier to dispersal at a very broad-scale, but there was little genetic structure aside from that, suggesting the potential for large dispersals to create new disease foci beyond the primary range. For the mountain pine beetle system, I used microsatellite profiles from lodgepole pine, jack pine, and their hybrids to predict their distribution across the expanding range of mountain pine beetle. Jack pine is a naïve host to this destructive forest pest and is likely more susceptible to attack. Through landscape modelling of genetic ancestry I was able to develop a predictive distribution map that can be used to highlight populations at greater risk to mountain pine beetle for management to target control measures. Through these diverse examples I highlight how landscape genetics can be used to better understand pathogen spread by examining the host. Impacts of anthropogenic factors and climate change will continue to have global consequences, by incorporating landscape genetics in our management toolbox we can better mitigate negative economic and ecological outcomes.