Large-scale assessment of genetic structure to assess risk of populations of a large herbivore to disease.

Chronic wasting disease (CWD) can spread among cervids by direct and indirect transmission, the former being more likely in emerging areas. Identifying subpopulations allows the delineation of focal areas to target for intervention. We aimed to assess the population structure of white-tailed deer (Odocoileus virginianus) in the northeastern United States at a regional scale to inform managers regarding gene flow throughout the region. We genotyped 10 microsatellites in 5701 wild deer samples from Maryland, New York, Ohio, Pennsylvania, and Virginia. We evaluated the distribution of genetic variability through spatial principal component analysis and inferred genetic structure using non-spatial and spatial Bayesian clustering algorithms (BCAs). We simulated populations representing each inferred wild cluster, wild deer in each state and each physiographic province, total wild population, and a captive population. We conducted genetic assignment tests using these potential sources, calculating the probability of samples being correctly assigned to their origin. Non-spatial BCA identified two clusters across the region, while spatial BCA suggested a maximum of nine clusters. Assignment tests correctly placed deer into captive or wild origin in most cases (94%), as previously reported, but performance varied when assigning wild deer to more specific origins. Assignments to clusters inferred via non-spatial BCA performed well, but efficiency was greatly reduced when assigning samples to clusters inferred via spatial BCA. Differences between spatial BCA clusters are not strong enough to make assignment tests a reliable method for inferring the geographic origin of deer using 10 microsatellites. However, the genetic distinction between clusters may indicate natural and anthropogenic barriers of interest for management.

Factors impacting bat activity and species richness in protected parks in the oak openings region of Northwest Ohio.

Protected areas are important for wildlife, especially in heavily developed areas. Bats are one group utilizing protected areas, but it is unclear what makes an ideal place for bats to live in parks, especially since preferences vary between open and forest foraging species and at different scales. The main objective of this study was to determine the landscape and vegetation factors at multiple scales most associated with higher bat activity and species richness in protected parks. Total bat activity, species richness, and activity for open and forested foraging species were compared to small-scale data vegetation structure collected in the field and larger-scale landscape data calculated in ArcGIS and FRAGSTATS. Bat activity and species richness increased with higher percentages of dry and open land cover types such as sand barrens, savanna, cropland, and upland prairie and decreased with higher percentages of forest and wet prairies. Patch richness, understory height, and clutter at the 3-6.5 m level were negatively associated with total bat activity. The most important variables for bats differed depending on spatial scale measured and if species were open or forest adapted. When managing for bats in parks, it would be advantageous to restore open land cover types such as savanna and mid-level clutter, and excessive fragmentation. Whether species are open or forest adapted and scale-specific differences should also be considered.