|dc.description.abstract||Golden eagles (Aquila chrysaetoes) are an iconic wide-ranging predator distributed across the Northern Hemisphere. In western North America, populations are considered to be stable, though there is a mounting concern that an anticipated increase in renewable wind energy development will threaten populations. Wind turbines are a known source of mortality for many avian species including golden eagles, thus there is a pressing need to offer land managers conservation planning guidance in light of future development. Working with several collaborators, I aimed to develop applied research in support of golden eagle conservation, while thoroughly testing the analytical rigor of methods we employed to address such questions. In chapter 1, I developed a stochastic population model for golden eagles with coauthors Zack Bowen, Brad Fedy, and Barry Noon. We sought to develop a model that faithfully captured the population dynamics of a non-migratory golden eagle population in western North America, while accounting for the demographic and environmental (process) variation inherent in vital rates. Using data from multiple long-term studies, we parameterized a stage-based matrix projection model and evaluated the contribution of vital rates to asymptotic population growth rates within a life-stage simulation analysis (LSA) framework. With a life history that is characterized by long-lived individuals with relatively low reproductive output, breeding adult survival dominates population dynamics for golden eagles. Thus it is unfortunate that breeding adult survival is the least-represented vital rate estimated in published literature. Simulating reduced survival across stage-classes revealed that a relatively minor (4%) reduction in survival resulted in a growing population to decline. Furthermore, targeting management at bolstering reproductive output is unlikely to compensate for reduced survival. Productivity rates (young fledged per pair) necessary to produce stable asymptotic growth rates (λ=1), when survival is reduced below 4% often falls above the range observed in field studies. Our findings combine to suggest that mitigating for eagle "take" (mortalities) due to anthropogenic sources including wind development should focus disproportionately on strategies that improve survival among breeding adults. Chapter 2 provides a spatially explicit framework for conservation planning and mitigation for golden eagles with respect to wind development. Co-advisor Brad Fedy and I fit resource selection functions (RSF) to golden eagle nest site data across two major ecoregions across Wyoming. Terrain indices, spatial surrogates for prey density, and landcover explained variation in nest-site locations compared to the available landscape. Overlaying predictive models of golden eagle nesting habitat with wind energy resource maps allowed us to highlight areas of potential conflict among eagle nesting habitat and wind development. Our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated, revealing opportunities for conservation practitioners and industry to collaborate on energy siting and mitigation strategies. While these models are useful for conservation planning during a critical life stage in which many eagles are tied to breeding territories, Chapter 3 provides a critical examination of the transient nature of range dynamics during a non-breeding season. Using golden eagle survey data from annual flights across the western US, coauthors Zack Bowen, Brad Fedy, Barry Noon, and I investigate how climate, anthropogenic disturbance, and ecosystem processes converge to influence late summer space use by golden eagles. We found that spatially invariant processes of Gross Primary Productivity and drought severity drive occurrence patterns, while human footprint and terrain ruggedness are more permanent features that explain variation in space use. Our predictive models are helpful for prioritizing conservation efforts for golden eagles, but underscore the large landscapes necessary for conservation for this wide-ranging species. Lastly, in Chapter 4 I worked with colleague Travis Gallo using simulation via " “virtual ecologist" framework to evaluate the potential for misleading inference when applying occupancy analyses to point count data, an increasing common trend particularly in avian research. We found that arbitrary decisions about the scale of sites (e.g. sample units) can lead to highly biased estimates with poor coverage across methodological approaches, especially for species with low detectability. Furthermore, varying patterns of detectability can obfuscate community inference –a common among avian point counts. We applied findings to an empirical dataset of songbird response of habitat-treatments targeted for mule deer (Odocoileus hemionus) in pinyon-juniper landscapes in northwestern Colorado.