Year of Award

2020

Document Type

Dissertation

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Wildlife Biology

Department or School/College

W.A. Franke College of Forestry and Conservation, Division of Biological Sciences, Montana Cooperative Wildlife Research Unit

Committee Chair

Michael S. Mitchell

Commitee Members

David E. Ausband, Mark Hebblewhite. Angela D. Luis, Paul M. Lukacs, James T. Peterson, Kevin M. Podruzny

Keywords

adaptive management, Bayesian, harvest management, integrated population model, population ecology, wolves

Publisher

University of Montana

Abstract

Regulated public harvest became an important management tool following recovery of gray wolves (Canis lupus) in the U.S. Northern Rocky Mountains. Decisions on harvest regulations, however, can be contentious due to conflicting stakeholder values, uncertainties in the effects of harvest on wolves, and difficulty in monitoring wolves. We addressed challenges associated with wolf management by 1) developing methods to estimate recruitment, 2) evaluating the role of hierarchical demography in wolf population dynamics, 3) developing competing population models to address uncertainty, and 4) developing an adaptive management framework to identify harvest regulations that best meet objectives for wolf management. We developed integrated population models (IPM) with and without social structure to evaluate the role of hierarchical demography in population dynamics of wolves. We tested and compared the IPMs on simulated populations with known demographic rates. We then used the IPM with hierarchical demography to estimate recruitment and population dynamics in wolves when productivity data were lacking. In addition, we developed a model to predict recruitment based on empirical data from Idaho and then tested the model in Montana. To better understand wolf population dynamics, we tested competing hypotheses of additive or compensatory harvest mortality and density dependent or density independent recruitment using population models and Bayesian model weight updating. Finally, we used stochastic dynamic programming and passive adaptive learning to find optimal season lengths and bag limits for wolf management in Montana. This framework accounted for uncertainty and included biological and societal objectives. We found that accounting for hierarchical demography improved estimation of demographic rates and population dynamics of wolves. Although regulated public harvest has appeared to decrease recruitment of pups and survival of adults, the population remained relatively stationary or only slightly declined. Using passive adaptive management, we found support for the hypothesis that net immigration into Montana was zero. Additionally, we found the optimal harvest strategy became more liberal as the wolf population grew. Following the optimal harvest strategy, we found that the wolf population was maintained around 650 wolves, which suggests that maintaining the population at this size best meets objectives.

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© Copyright 2020 Allison Christine Keever