| Summary: | 2015 Spring. Includes bibliographical references. Apex predators are ideal subjects for evaluating the effects of changing climatic conditions on the productivity of forested landscapes, because the quality of their breeding habitat depends primarily on the availability of resources at lower trophic levels. Identifying the environmental factors that influence the reproductive output of apex predators can, therefore, enhance our understanding of the ecological relationships that provide the foundation for effective forest management strategies in a variable environment. To identify the determinants of breeding-habitat quality for an apex predator in a forest food web, I investigated the relationships between site-specific environmental attributes and the reproductive probabilities of northern goshawks (Accipiter gentilis) on the Kaibab Plateau, Arizona during 1999-2004. I used dynamic multistate site occupancy models to quantify annual breeding probabilities (eggs laid) and successful reproduction probabilities (≥1 young fledged) relative to temporal and spatial variation in climatic conditions (precipitation and temperature), vegetation attributes (forest composition, structure, and productivity), and prey resources (abundances of 5 mammal and bird species). Climatic conditions during the study period varied extensively, and included extreme drought in 2003 and record-high precipitation in 2004. There was also substantial variation in the amount and distribution of 4 forest cover types among 102 goshawk territories within the 1,285-km² study area. The abundance of most prey species, especially mammals, also varied considerably among years, cover types, and goshawk territories. I identified the environmental components that best explained spatiotemporal variation in goshawk reproductive parameters using an information-theoretic approach to evaluate the relative weight of evidence for each model. Out of 62 potential models representing how the distribution of territories with breeding goshawks varied among ...
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