Date of Award:

12-2022

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Biology

Committee Chair(s)

Kimberly Sullivan

Committee

Kimberly Sullivan

Committee

Karen Kapheim

Committee

William Pearse

Committee

Clark Rushing

Committee

Douglas Ramsey

Abstract

Both state and federal wildlife agencies strive to conserve and protect wildlife and their habitats as an important public resource. Applied management decisions often rely on being able to obtain data that can efficiently and effectively enhance the understanding of these systems for informing management actions. Wildlife managers often focus efforts on a small subset of species from an ecosystem, typically called focal species, who can serve as surrogates for understanding the health and function of the system. Models that consider how these focal species interact with the ecosystem are often used to better understand important aspects of their life history, ecology, and conservation needs.

Birds are ideal candidates for use as focal species as they often are sensitive to disturbance, tied to a narrow subset of habitat characteristics for different parts of their life cycle success, and are often easy to monitor and study. The recent advent of advanced GPS and spatial technology allows managers the chance to consider birds and their relationship with their habitat on a deeper level by considering interactions at finer spatial scales. However, GPS and spatial technology as well as the methods to analyze the spatially explicit data have only recently been available for many avian species.

In this study, the Utah State University partners with the U.S. Forest Service in Utah, U.S. Forest Service Rocky Mountain Research Station, and the Nevada Department of Wildlife to analyze spatial data collected for northern goshawks (Accipiter gentilis) and white-headed woodpeckers (Dryobates albolarvatus). While the spatial data for this project was previously collected as part of other management objectives, the collaborations for this project make it possible to analyze this data with some of the latest methods in spatial and movement ecology. We used methods such as predictive modeling with the Forest Vegetation Simulator, resource selection analysis, and integrated step selection analysis to examine each of these species’ relationships with their habitat on a finer scale than previously considered and to help create management recommendations based on our findings.

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