Capture history of Asiatic black bear from Himachal Pradesh, India

<p><span>Robust population estimation of rare or elusive threatened species lacking distinct identifiable features poses a challenge in the field of conservation and management. The Asiatic black bear (<em>Ursus thibetanus</em>) is one such species. Methodological frameworks—such as radiotelemetry, genetic sampling, and camera-trapping—though crucial and advantageous, sometimes require additional information through invasive methods for individual identification</span><span>. </span><span>In this study, w</span><span>e estimated the population density of Asiatic black bear in 2 protected areas in the Indian Himalayan Region without information on individual identification. We conducted the study through a spatial capture–recapture framework using camera traps</span><span> in the summer during May–July 2018 in Daranghati Wildlife Sanctuary (WLS) and May–July 2019 in Rupi Bhaba WLS</span><span>. </span><span>Using the recently developed </span><span>Spatial Presence–Absence model, we estimated g0 (detection probability), σ (scale or movement parameter related to home range of the species), and N (population size) of </span><span>Asiatic black bears</span><span> from the camera-trap data using a Bayesian framework. We estimated a population density of 2.5 individuals/100 km2 (95% Credible Interval = 1.42–9.63 individuals/100 km2) from Daranghati WLS and 0.3 individuals/100 km2 (95% Credible Interval = 0.2–0.7 individuals/100 km2) from Rupi Bhaba WLS. Abundance estimates produced by extrapolating these densities were 11 </span><span>Asiatic black bear</span><span> individuals (95% Credible Interval = 4–27) from Daranghati WLS and 2 </span><span>Asiatic black bear</span><span> individuals (95% Credible Interval = 1–3) from Rupi Bhaba WLS. This is the first population estimate of</span><span> Asiatic black bear</span><span> from the Indian Himalaya without individual identification. </span><span>We recommend that this method, which provides minimal sampling bias and ease of sampling, can be replicated in other mountainous landscapes for a robust density estimation of this species.</span></p>