Year of Award

2020

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, Mark A. Hurley, Angela D. Luis, Alexander L. Metcalf, Michael S. Mitchell

Keywords

classification error, data weighting, Integrated population model, mule deer (Odocoileus hemionus), optimal resource use, population monitoring

Abstract

Accurate knowledge regarding trends in the abundance of wildlife populations provides a foundation for the understanding of wildlife ecology and effective wildlife management. Abundance estimates enable managers and researchers to track the status of wildlife populations, supply information on which to base wildlife management decisions, and provide a metric to assess the outcome of specific management actions. Consequently, accurate estimates of abundance provide essential knowledge for managing wildlife populations effectively. However, the amount of resources available for monitoring wildlife populations is limited and often fluctuates in response to changes in annual budgets. Therefore, agencies responsible for the management of wildlife populations would benefit from knowing the most efficient manner in which to allocate their limited monitoring resources.

In order to determine the most efficient manner for allocating monitoring resources, I investigated several methods for improving upon and expanding current wildlife monitoring techniques. I used data collected by the Idaho Department of Fish and Game on mule deer (Odocoileus hemionus) populations throughout the state of Idaho. I compared survival rate estimates generated from global positioning system and very high frequency radio collars to determine if there are significant differences in estimates from the two data collection technologies. I developed a method for incorporating classification error into age and sex ratio estimates from data collected via aerial surveys. I assessed which types of commonly collected data about mule deer populations have the greatest influence on estimates of abundance generated using integrated population models. Finally, I developed a method to quantify the amount of information that can be gained about the abundance of a wildlife population under various levels of monitoring resource availability. The predicted gains in information are then used to suggest data collection scenarios that result in the optimal allocation of wildlife monitoring resources for mule deer. While all methods are developed in relation to the mule deer population of Idaho they can be extended to other species in a variety of geographical settings.

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© Copyright 2020 Charles Raymond Henderson Jr.