Undergraduate Honors Thesis


Quantitative Conservation of the Gray Wolf (Canis lupus): Implications of Monitoring and Modeling the Yellowstone Wolves Public Deposited

  • The 1995 reintroduction of wolves to the Greater Yellowstone ecosystem had lasting effects on our understanding of reintroduction biology as a whole. However, continued study of a system as complex and intricately interwoven as this should be done with minimal human influence on the ecosystem. The states comprising the tri-state area surrounding Yellowstone National Park—Wyoming, Idaho, and Montana—have recently delisted wolves from the endangered species list. Here, I assess the greater implications in conservation that can be deduced from quantitative analysis of the Yellowstone wolves, and question whether the species is, in fact, stable enough for delisting in this area. I used multiple regressions on population growth as a function of population size to test the claim that the carrying capacity for wolves in Yellowstone is approximately 170 individuals. After finding that the estimate of 170 is much larger than my calculated values of carrying capacity, I move forward to design six distinct models that each enforce a predesignated carrying capacity on 100 different simulated wolf populations over the next 100 years. All of these simulations experienced zero probability of extinction. However, I discuss general trends that may still be extracted from the models, potential flaws in my modeling for this particular system, and a variety of different approaches to designing simulations that may more effectively predict the fate of the Yellowstone wolf population. I offer observations of how population dynamics change with respect to carrying capacities once the carrying capacity has been attained, and address differences between population dynamics in a pre-established population versus and those in a reintroduced population. Overall, I conclude by noting that the fate of the wolves is not certain, and should therefore be monitored and maintained with a more adaptive management strategy.
Date Awarded
  • 2018-01-01
Academic Affiliation
Committee Member
Granting Institution
Last Modified
  • 2019-12-02
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