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Accueil du site → Doctorat → États-Unis → 2016 → Landscape-Level Approaches to Desert Bighorn Sheep (Ovis canadensis nelsoni) Conservation in a Changing Environment.

Oregon State University (2016)

Landscape-Level Approaches to Desert Bighorn Sheep (Ovis canadensis nelsoni) Conservation in a Changing Environment.

Creech, Tyler Graydon

Titre : Landscape-Level Approaches to Desert Bighorn Sheep (Ovis canadensis nelsoni) Conservation in a Changing Environment.

Auteur : Creech, Tyler Graydon

Université de soutenance : Oregon State University

Grade : Doctor of Philosophy (PhD) 2016

Résumé partiel
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

Mots Clés : NDVI diet quality landscape resistance bighorn sheep genetic simulation wildlife conservation adaptive capacity network theory national park genetic structure genetic diversity landscape ecology Mojave Desert climate change

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Page publiée le 9 janvier 2018