Year of Award

2022

Document Type

Dissertation

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Fish and Wildlife Biology

Department or School/College

W.A. Franke College of Forestry and Conservation

Committee Chair

Paul M. Lukacs

Commitee Members

Chad J. Bishop, Andrew Lindbloom, Winsor H. Lowe, Joshua J. Millspaugh, Elizabeth C. Metcalf

Keywords

habitat quality, mule deer, population dynamics, survival, time-series, white-tailed deer

Abstract

Habitat quality may be an underlying factor driving or exacerbating mule deer (Odocoileus hemionus) population declines across their range and concurrent white-tailed deer (Odocoileus virginianus) population increases. A clearer understanding of how the two species respond to habitat variables is needed to disentangle the drivers of mule deer decline and identify opportunities to change population trajectories through habitat management. Capitalizing on extensive monitoring data for sympatric populations of mule deer and white-tailed deer, this dissertation improves understanding of habitat quality by exploring and developing modeling approaches that connect habitat and demographics in these two species.

Using a resource selection function (RSF) in Chapter 1, I found a high degree of habitat overlap between mule deer and white-tailed deer and little support for niche differentiation between the two species during summer but not winter. Individual variation was strong and models were not predictive of future resource use. In contrast to the RSF, which assumes that resource selection corresponds with habitat quality, in Chapter 2 I used survival modeling to connect habitat to population performance. The survival model showed little connection between survival and winter severity, nutritional availability, or drought, suggesting that population-level survival of deer cannot be predicted by environmental conditions through these models. To overcome the limitations of current survival models, for Chapter 3 I developed a novel Survival and Habitat Quality model (SHQ) that directly estimates the effect of habitat on an individual’s unobservable survival probability. This autoregressive model allows inference to resources’ cumulative contribution to survival over an individual’s lifetime. Using the SHQ model in Chapter 4 for the first time, I estimated the long-term effects of habitat on survival. Unlike other survival models, the SHQ model was able to identify substantial differences between species and age classes in how environmental variables affected survival.

Together, these analyses build a more complete picture of habitat quality, selection, and use by sympatric ungulate species. The comparative investigation of methodologies can guide the selection of methodological approaches for species with comparable monitoring data. The improved approach developed in this dissertation will aid successful inference for conservation and management of many species.

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© Copyright 2022 Anna Katherine Moeller