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Research Studies

Estimating Deer Density Across Indiana

Zackary Delisle

A good estimate of the number of white-tailed deer in an area is critical for efforts by IDNR to manage the state’s deer herd. Our goal is to evaluate density estimation methods for reliability and cost-effectiveness in large-scale monitoring. To accomplish this, we will be estimating deer density using three different methods: fecal-pellet transects, trail cameras, and vertical-looking infrared (VLIR) aerial surveys from a small airplane. For all three of these methodologies, a “distance sampling” approach will be used to estimate deer density.

Relationship Between Deer and Habitat

Richard D. Sample

Over-browsing by deer can severely reduce habitat quality, so there is a need to accurately assess the impact deer have on vegetation communities. Deer densities and landscape context vary spatially across Indiana, thus techniques are needed to assess impacts of herbivory across a range of conditions. We have three overall objectives to help determine the effect deer have on their habitats. First, we seek to evaluate three different metrics of browse intensity. The use of all three methods will provide a more accurate estimation of the intensity of deer browsing across Indiana forests. In addition, a comparison of the efficiency and efficacy of the methods will identify the method that most reliably and efficiently estimates browse intensity across different regions of Indiana. Secondly, we will determine the relationship between deer densities, browsing intensity, and vegetation communities. Lastly, we will determine deer diet composition and how it relates to plant species availability.

The Human Dimensions of Deer Management in Indiana

Taylor Stinchcomb

Attaining the public trust ideal in deer management faces several challenges. First, effectively involving the public in management requires that managers and participants (or stakeholder groups) agree on deer population goals and understand who carries what responsibilities in the decision-making process. Coming to agreed-upon goals can be incredibly time consuming, and true consensus may never be reached due to conflicting values. Second, public interests in deer management to date have been limited to a select group of stakeholders, driven by mitigating deer-related impacts on property and livelihoods. This typically fails to account for emotional, cultural, and situational factors that can lead to human-human conflicts over deer management. Third, the informational gap between managers and the public is bi-directional: managers remain unaware of the degree to which public perceptions of deer vary, and the public is often unaware of the possibility and/or feasibility of different management approaches. Finally, deer-human interactions tend to depend on local contexts, demanding that management approaches adapt to changes in both social and ecological variables within a single state.  

Our study begins to integrate the social dimension into deer management in Indiana, aiming to address the above challenges using a combination of semi-structured interviews, surveys, and comparative analysis to understand the following questions:

  1. How do Indiana residents and natural resource management professionals currently perceive, value, and experience deer populations across the state? What outcomes do residents and managers desire from deer management?  
  2. What is the existing relationship between Indiana residents and deer management professionals? How can this relationship be shifted to more equitably incorporate stakeholder interests?  
  3. How can the social and ecological data be integrated effectively to inform deer decision-making in Indiana? 

Estimating Occupancy of Common White-tailed Deer Predators

Predators can have a major impact on fawn recruitment. In order to evaluate this impact, we will analyze noninvasively collected predator samples using DNA-based approaches to determine donor species, donor sex, donor-specific DNA fingerprint and determine the proportion of scat samples that contain deer DNA. These data can then be used in a broader modeling framework to estimate predator density, predator home ranges, and ultimately the potential impact of predators on deer populations in Indiana.