title: Mountain goat genetic diversity and population connectivity in Washington and Southern British Columbia
Creators: name: Parks, Leslie Claire
Publisher: name: Western Washington University
Year: 2013

description: <p>Anthropogenic alterations to natural landscapes and the associated habitat fragmentation, habitat degradation, and climatic shifts threaten biodiversity from the local to the global scale. These perturbations disrupt historical patterns of gene flow causing reduced population connectivity, loss of genetic diversity, and increased risk of extinction. A landscape that is permeable to animal movement counteracts local population fluctuations, increases genetic diversity, increases adaptive potential, and provides corridors for range shifts in response to climate change. Maintaining population connectivity is critical for the conservation of small populations isolated by fragmented landscapes. This strategy requires an accurate understanding of the landscape's effect on gene flow and the processes, such as isolation by barrier (IBB), isolation by distance (IBD), or isolation by resistance (IBR), that are driving genetic isolation. Although the mountain goat (Oreamnos americanus) is not globally threatened, mountain goats in Washington have declined by more than 50% since the 1950s. While past unsustainable harvest is likely the cause of this decline, many populations have not recovered, and former historical habitat remains unoccupied despite nearly 20 years of drastically reduced hunting pressure. Mountain goats in Washington exhibit lower genetic diversity than populations from the core of the species' range, raising the possibility that genetic factors are limiting population recovery. Previous research revealed that transportation corridors impede mountain goat gene flow in Washington. In this study, I sought to understand the relationship between the mountain goat population in Washington and the much larger and more genetically diverse mountain goat populations in southern British Columbia. Anthropogenic activities in the Fraser lowlands and Okanagan Valley in British Columbia potentially diminish or sever historical linkages between the Washington population and the much larger populations in the Coast Range, Selkirk Mountains, and Purcell Mountains in British Columbia. To this end, I collected 261 genetic samples from scat, tissue, bone, and hair to generate indices of genetic diversity and an accurate model of population connectivity. In Chapter 1, I used methods based on both discrete and clinal population models to present alternative representations of genetic diversity. Discrete models identified four subpopulations separated by transportation corridors, urbanized areas, and agriculture. Genetic diversity was higher in British Columbia than Washington, illustrating the importance of maintaining gene flow from British Columbia into Washington. Clinal models of population structure found several regions of lower and higher diversity within the subpopulations identified by discrete models, refuting the assumption of IBB and panmixia within subpopulations. In Chapter 2, I examined the relative influence of IBB, IBD, and IBR on genetic isolation. I developed multiple hypotheses of IBR by systematically varying model parameters for four landscape features: distance to escape terrain, roads, landcover type, and elevation. I employed a causal modeling framework to create a multivariate model based on landscape features that met strict criteria for inclusion. This allows for a nonlinear relationship between landscape features and gene flow, accounts for interactions between variables, and minimizes the risk of spurious correlations. The optimized IBR model that I developed was highly correlated with genetic structure and better supported than the alternative models of genetic isolation, IBB and IBD. The best supported model of IBR indicated that urban landcover, agricultural landcover, and freeways present high resistance to mountain goat gene flow, while low elevation valleys resist gene flow to a lesser degree. I used this model of IBR to model gene flow across the study area and identify locations where population connectivity is compromised.</p>