Empirical forecast models for guiding decision-making in complex landscapes with competing wildlife management goals
Scott, Jason Adam
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Wildlife managers can improve their effectiveness by understanding the expected response of target and non-target species to management actions prior to implementation. Empirical forecast modeling that uses quantitatively-derived habitat relationships to describe the change in distribution for a species under potential future habitat conditions offers managers a robust tool for assessing management actions. The need for quantitative tools to assess management outcomes is compounded in landscapes with competing management objectives where a poorly informed decision could have profound impacts on a locally sensitive species. Currently, there is a distinct void of practical case studies that apply forecasting procedures to evaluate habitat management actions leaving a framework for structuring the modeling process largely undescribed for wildlife managers. In an attempt to help fill that void, I demonstrate the application and utility of results produced from single- and multi-species empirical forecast models in a politically tense management environment commonplace on military installations. I sought to balance the conflicting management goals of single-species conservation promoted by the Endangered Species Act, and multi-species management promoted by the Sikes Act on military bases. Using Fort Bragg, North Carolina as a case study, I used logistic regression and hierarchical occupancy models to empirically describe habitat relationships for 5 species including the endangered red-cockaded woodpecker (Picoides borealis), northern bobwhite (Colinus virginianus), white-tailed deer (Odocoileus virginianus), eastern fox squirrel (Sciurus niger niger), and eastern wild turkey (Meleagris gallopavo silvestris). I tested how species distributions would change under proposed alterations to the current habitat management strategy. My results identified how habitat management actions should be prioritized both structurally (e.g., reduction in small-diameter pine density, and avoidance of reduction in large-diameter pine density), and spatially (targeting areas of largest net benefit) that would balance the needs for the endangered species with those of other sympatric species. In addition, my methods are data-driven (empirical), making decisions derived from them justifiable and defendable. In times when wildlife managers are increasingly asked to do more with less, empirical forecast models offer a means to streamline effort and cost while balancing the habitat needs of multiple species.