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Tuesday, February 7 • 4:00pm - 4:20pm
Technical Session. Evaluating the Sensitivity of Density Estimates in a Spatial Capture-Recapture Model of Black Bears in the Northern Lower Peninsula of Michigan

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AUTHORS: Jennifer Smith, Michigan State University; David Williams, Michigan State University; Mike Wegan, Michigan Department of Natural Resources, Dwayne Etter, Michigan Department of Natural Resources, William Porter, Michigan State University

ABSTRACT: Accurate and precise estimates of density are fundamental to the conservation of wildlife populations. Advances in non-invasive monitoring techniques and their application to traditional capture-recapture models have benefited efforts to reliably estimate abundance. However, traditional capture-recapture models do not directly assess effective sampling area, which prevents direct estimation of density. Spatial capture-recapture (SCR) models address this issue by modeling detection as a function of distance. Because detection probability depends on the distance of detectors from individual activity centers, the layout of detectors (i.e., spacing) influences our ability to estimate density. In 2003, efforts to estimate black bear (Ursus americanus) abundance in the northern Lower Peninsula of Michigan, were initiated by establishing an array of hair snares. This array was designed to achieve coverage of a large geographic area. Capture data for 2003 and three additional years (2005, 2009, 2013) was analyzed using non-spatial capture-recapture models. Recent management interests have resulted in a desire for spatially-explicit density estimates for bears using the existing data and snare array. We evaluated the robustness and sensitivity of the density estimate produced using SCR models across a range of plausible scenarios based on the current layout of hair snares. We summarize the results of 81 simulated scenarios that varied the parameters of density, the detection function (g0 and sigma), and number of sampling occasions. The simulations indicated estimates of density from the current trap array design are biased high. We discuss aspects of the current array that contribute to this bias and describe efforts to evaluate alternative designs. In order to effectively manage wildlife populations, it’s crucial to recognize and understand the limitations of our methodologies, particularly when we attempt to apply new tools to previously collected data.

Tuesday February 7, 2017 4:00pm - 4:20pm CST
Arbor I/II