|Abstract or Summary
- Landscape characteristics can strongly influence demographic and genetic processes in wildlife populations. Climate change and human land use are causing many landscapes to change rapidly, and the effects on wildlife populations must be understood to properly manage these threats and design effective conservation strategies. In this dissertation, I explored the implications of landscape heterogeneity for desert bighorn sheep (Ovis canadensis nelsoni), an ecologically and culturally important ungulate species in the southwestern United States, and demonstrated new approaches that can be applied to landscape-level conservation of many wildlife species in changing landscapes. This research focused on populations within and surrounding U.S. national parks, comprising a large portion of the desert bighorn sheep's geographic range, and utilized a genetic dataset including > 1,600 individuals that was developed during this and previous projects.
Landscape resistance models have been used extensively to predict potential linkages among fragmented wildlife populations, including desert bighorn sheep, but have rarely been used to guide systematic decision-making such as prioritizing conservation actions to maximize regional connectivity. In Chapter 1, I combined network theory and landscape resistance modeling to prioritize management for connectivity, including protection and restoration of dispersal corridors and habitat patches, in a desert bighorn sheep metapopulation in the Mojave Desert. I constructed network models of genetic connectivity (potential for gene flow) and demographicconnectivity (potential for colonization of empty habitat patches). I found that the type of connectivity and the network metric used to quantify had substantial effects on prioritization results; however, I was able to identify high-priority habitat patches and corridors that were highly ranked across all combinations of the above factors.
Potential diet quality varies across landscapes and through time for desert bighorn sheep and other ungulates, but is difficult to measure at fine spatial and temporal resolution using traditional field-based methods. The remotely sensed vegetation index NDVI can potentially overcome these limitations, but its relationship to diet quality has never been empirically validated for desert herbivores. In Chapter 2, I examined how strongly NDVI was associated with diet quality of desert bighorn sheep in the Mojave Desert using fecal nitrogen data from multiple years and populations, and considered the effects of temporal resolution, geographic variability, and NDVI spatial summary statistic. I found that NDVI was more reliably associated with diet quality over the entire growing season than with instantaneous diet quality for a population, and was positively associated with population genetic diversity (a proxy for long-term diet quality). Although NDVI was a useful diet quality indicator for Mojave Desert bighorn sheep, my analysis suggested that it may be unreliable if satellite data are too spatially coarse to detect microhabitats providing high-quality forage, or if diet is strongly influenced by forage items that are weakly correlated with landscape greenness.
Landscape genetic studies typically rely on neutral genetic markers to explore gene flow and genetic variation, but the potential for species to adapt to changing landscapes depends on how natural selection influences adaptive genetic variation. In Chapter 3, I optimized landscape resistance models for desert bighorn sheep in three regions with different landscape characteristics, and then used genetic simulations incorporating natural selection to determine how the spread of adaptive variation is influenced by differences among landscapes. Optimized landscape resistance models differed between regions but slope, presence of water barriers, and major roads had the greatest impacts on gene flow. Differences among landscapes strongly influenced the spread of adaptive genetic variation, with faster spread in landscapes with more continuously distributed habitat and when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation.
Climate change presents a substantial threat to desert bighorn sheep and wildlife worldwide, and adaptation may be required to persist in novel environmental conditions. Knowledge of how adaptive capacity - the potential to cope with climate change by persisting in situ or moving to more suitable ranges or microhabitats - varies across populations is needed to establish conservation priorities for minimizing climate change impacts to individual species. In Chapter 4, I explored variation in the evolutionary component of adaptive capacity for 62 desert bighorn sheep populations on and near U.S. national parks. I measured adaptive capacity of populations as a function of two factors that are strongly associated with the potential for evolutionary adaptation, genetic diversity and connectivity (estimated using a landscape resistance model from Chapter 3). Genetic diversity and connectivity were highly variable across regions and populations. I identified populations with high adaptive capacity that could serve as genetic refugia from climate change impacts (e.g., those in Death Valley and Grand Canyon National Parks), but also populations with low adaptive capacity that may require conservation actions to improve their potential for adaptation (e.g., those in eastern Utah and the southern Mojave Desert). Genetic structure analyses suggested that populations in eastern Utah were genetically distinct from the rest of the study area, likely resulting from restricted gene flow following regional population extinctions.
This dissertation highlighted the effects of landscape heterogeneity on genetic and demographic processes in desert bighorn sheep populations. Collectively, the information in these chapters should help guide management of desert bighorn sheep in the face of climate change and human land use. The landscape-level approaches demonstrated here may be useful for managing many other wildlife species.