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Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras

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Remote cameras have become a promising, cost‐effective tool for monitoring wildlife populations. Yet, for species where individuals are indistinguishable, remote cameras’ ability to provide robust and precise density estimates has been limited without the use of invasive marking. Using the American black bear as a model species, we evaluated methods for estimating wildlife densities using remote camera detections of unmarked individuals against estimates from spatial capture–recapture (SCR) models using individual detections. We also tested the effect of incorporating varying proportions of marked individuals on model accuracy and precision. Spatial count (SC) models using unmarked individuals produced estimates of bear density within 0.6% of those from SCR. We extended SC models to incorporate variation in density as a function of land use/land cover, and identified identical relationships between variation in bear densities and housing density as obtained using SCR. Incorporating individual detection data from simultaneous non‐invasive genetic sampling lead to more precise, but biased estimates. Synthesis and applications. Our results identify contexts in which camera count data can be used as an alternative to spatial capture–recapture (SCR) when individual identification is prohibitive. Spatial count models provided an accurate, but less precise replication of spatial capture–recapture density estimates and may provide consistent insights into spatial variation in density. Mixed samples of camera counts and auxiliary individual detections are likely to be of limited use, but fitting spatial count models to populations with partial visual markings could improve their precision.
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Keywords: Bayesian; density; markÔÇôrecapture; monitoring; remote camera; spatial count model; spatially explicit; visual marking

Document Type: Research Article

Publication date: November 1, 2018

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