Empirical comparison of density estimators for large carnivores

Abstract

Summary1. Population density is a critical ecological parameter informing effective wildlife management and conservation decisions. Density is often estimated by dividing capture-recapture (C-R) estimates of abundance (N) by size of the study area, but this relies on the assumption of geographic closure -a situation rarely achieved in studies of large carnivores. For geographically open populationsN is overestimated relative to the size of the study area because animals with only part of their home range on the study area are available for capture. This bias ('edge effect') is more severe when animals such as large carnivores range widely. To compensate for edge effect, a boundary strip around the trap array is commonly included when estimating the effective trap area (Â). Various methods for estimating the width of the boundary strip are proposed, butN ⁄ estimates of large carnivore density are generally mistrusted unless concurrent telemetry data are available to defineÂ. Remote sampling by cameras or hair snags may reduce study costs and duration, yet without telemetry data inflated density estimates remain problematic. 2. We evaluated recently developed spatially explicit capture-recapture (SECR) models using data from a common large carnivore, the American black bear Ursus americanus, obtained by remote sampling of 11 geographically open populations. These models permit direct estimation of population density from C-R data without assuming geographic closure. We compared estimates derived using this approach to those derived using conventional approaches that estimate density asN ⁄Â. 3. Spatially explicit C-R estimates were 20-200% lower than densities estimated asN ⁄Â. AIC c supported individual heterogeneity in capture probabilities and home range sizes. Variable home range size could not be accounted for when estimating density asN ⁄Â. 4. Synthesis and applications. We conclude that the higher densities estimated asN ⁄ compared to estimates from SECR models are consistent with positive bias due to edge effects in the former. Inflated density estimates could lead to management decisions placing threatened or endangered large carnivores at greater risk. Such decisions could be avoided by estimating density by SECR when bias due to geographic closure violation cannot be minimized by study design.

Threatened predator on the equator: multi-point abundance estimates of the tiger Panthera tigris in central Sumatra

“…Using spatially explicit capture-recapture models our estimates of tiger density were almost 50% lower compared to densities estimated with traditional capture-recapture models, a finding noted in other studies (Tredick & Vaughan, 2009;Obbard et al, 2010;Sharma et al, 2010;Gerber et al, 2012).…”

Coexistence between wildlife and humans at fine spatial scales

“…The model integrates individual animal capture histories and spatial locations of camera traps using a statistical point process model. This approach avoids having to use an ad hoc effective sample area (e.g., minimum convex polygon), which often inflates density estimates (54), and instead, it calculates density as the number of animal activity centers that fall within some region encompassing the trap array. We ran three capture-recapture models for each year using data from different groups of camera traps: (i) inside the park, (ii) outside the park, and (iii) entire study site.…”

Effect of prey mass and selection on predator carrying capacity estimates

“…½ MMDM methods; Obbard et al 2010, Noss et al 2012). Boundary strip methods using ½ MMDM seem to overestimate density because they underestimate the effective trap area which subsequently results in an overestimation of density (Soisolo & Cavalcanti 2006, Obbard et al 2010.…”