Landscape Genetics of Bobcats (Lynx rufus) Across Western Texas

Various environmental, ecological, and anthropogenic factors impact the genetic structure of natural populations. I compared the heterozygosity of a highly mobile, generalist species, the bobcat (Lynx rufus), to environmental variables throughout western Texas to determine whether landscape-level changes impact population structure. Tissue samples from 102 bobcats were collected from 5 diverse ecoregions throughout western Texas. Bobcats were genotyped for 9 microsatellite loci to estimate genetic variation and population structure. I observed high genetic variation with little population structure (K=2) throughout western Texas. Bobcats were significant for isolation by distance (P = 0.009), but also exhibited significant differences in allele frequencies throughout the sample set and in comparisons AMONG ecoregions. Bobcats also demonstrated a relatively high inbreeding coefficient (FIS = 0.1002), possibly as a result of philopatry and sensitivity to habitat fragmentation. Factorial detrended correspondence analysis indicated weak affinity for ecoregion locations within a single cluster, which is likely the result of habitat affinities and local adaptations to a highly variable landscape. To examine specific environmental variables, I performed local and regional scale ordination analyses. Canonical correspondence analyses indicated that ~25% of the variation in heterogeneity was explainable by the nine environmental variables used. At the local level, mesquite-lotebush plant associations most strongly impacted bobcat heterozygosity, while mesquite-lotebush and urbanization were the variables most strongly correlated to increased heterozygosity at the regional scale. Mean patch size, mean patch edge, and Shannon’s diversity index of patch size were also selected as meaningful variables for both scales, suggesting the relevance of spatial heterogeneity to bobcat gene flow. Bobcat genetic structure in western Texas appears to be partially impacted by habitat variation, vegetation composition, and spatial heterogeneity, though more information is needed to determine if other direct or indirect environmental gradients influence gene flow. There was very little variation in the genetic data, however, which reduced the meaningfulness of any correlations. My study suggested that local variables can impact population structure even when species are highly mobile and occupy variable ranges. As a result, managers should consider a myriad of landscape factors instead of individual barriers before making assumptions about the adaptations and flexibility of ecological generalists. Identifying landscape variables relevant to population structure has important management and conservation implications for maintaining genetic variation in highly mobile populations.