Estimating The Effects Of Changes In Harvest Management On White-Tailed Deer (Odocoileous Virginianus) Populations

Open Access
- Author:
- Van Buskirk, Amanda
- Graduate Program:
- Ecology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- June 26, 2020
- Committee Members:
- Duane Diefenbach, Thesis Advisor/Co-Advisor
David Andrew Miller, Committee Member
Tyler Wagner, Committee Member
Jason Philip Kaye, Program Head/Chair - Keywords:
- white-tailed deer
rose-petal hypothesis
population estimation
resource selection function
distance sampling
agent-based model
population model
localized management
Odocoileous Virginianus - Abstract:
- White-tailed deer (Odocoileous Virginianus) are considered a dominant and abundant species in forested landscapes in the eastern United States, and deer management, which often involves reducing deer densities, is crucial to keep populations in balance with their habitat. In Pennsylvania, state agencies such as the Pennsylvania Game Commission and the Pennsylvania Department of Conservation and Natural Resources, Bureau of Forestry have implemented deer management programs that use hunter harvest to reduce deer densities in specific geographical areas in order to meet various land-use and human health objectives. We developed a spatially explicit, agent-based model to investigate the intensity of deer removal required to locally reduce deer density depending on the surrounding deer density, dispersal behavior, and size and shape of the area of localized reduction. Our model indicated that a localized reduction was successful for scenarios in which the surrounding deer density was lowest (30 deer/mi²), antlerless harvest rates were ≥ 30%, and the reduction area was 5 mi² or larger. Based on the results from our model simulations, we evaluated the effectiveness of a deer management program at reducing deer densities on four forested study areas that meet the criteria for a successful localized deer density reduction. We developed a method to estimate deer density that combines a distance sampling technique with a resource selection model of deer distribution to estimate deer density on the study areas and determine if deer density reductions occurred. In addition, we used data collected from distance sampling surveys and global positioning system (GPS) locations of collared deer on a disease management area (DMA) in Pennsylvania to assess two types of resource selection models: resource selection functions modeled as second-order selection and step-selection functions. We wanted to evaluate if each method resulted in different inferences regarding deer density estimates over four survey periods. Based on our estimates of deer density for the four forested study sites, deer densities are being reduced or maintained on all study areas. For data from deer on the DMA, both resource selection functions and step-selection functions predicted that deer distribution varied temporally. Overall, our analyses provide insight into the factors that contribute to a successful localized deer density reduction, offer a method for estimating deer density that suggests that the Pennsylvania Game Commission is effectively reducing deer densities on certain areas, and indicate that both resource selection functions and step selection functions may be appropriate for identifying temporal variations in deer distributions.