N-mixture model-based estimate of relative abundance of sloth bear (Melursus ursinus) in response to biotic and abiotic factors in a human-dominated landscape of central India

Abstract

Reliable estimation of abundance is a prerequisite for a species’ conservation planning in human-dominated landscapes, especially if the species is elusive and involved in conflicts. As a means of population estimation, the importance of camera traps has been recognized globally, although estimating the abundance of unmarked, cryptic species has always been a challenge to conservation biologists. This study explores the use of the N-mixture model with three probability distributions, i.e., Poisson, negative binomial (NB) and zero-inflated Poisson (ZIP), to estimate the relative abundance of sloth bears (Melursus ursinus) based on a camera trapping exercise in Sanjay Tiger Reserve, Madhya Pradesh from December 2016 to April 2017. We used environmental and anthropogenic covariates to model the variation in the abundance of sloth bears. We also compared null model estimates (mean site abundance) obtained from the N-mixture model to those of the Royle-Nichols abundance-induced heterogeneity model (RN model) to assess the application of similar site-structured models. Models with Poisson distributions produced ecologically realistic and more precise estimates of mean site abundance (λ = 2.60 ± 0.64) compared with other distributions, despite the relatively high Akaike Information Criterion value. Area of mixed and sal forest, the photographic capture rate of humans and distance to the nearest village predicted a higher relative abundance of sloth bears. Mean site abundance estimates of sloth bears obtained from the N-mixture model (Poisson distribution) and the RN model were comparable, indicating the overall utility of these models in this field. However, density estimates of sloth bears based on spatially explicit methods are essential for evaluating the efficacy of the relatively more cost-effective N-mixture model. Compared to commonly used index/encounter-based methods, the N-mixture model equipped with knowledge on governing biotic and abiotic factors provides better relative abundance estimates for a species like the sloth bear. In the absence of absolute abundance estimates, the present study could be insightful for the long-term conservation and management of sloth bears.

Factors affecting the occupancy of sloth bear and its detection probability in Parsa–Koshi Complex, Nepal

“…However, the lower occupancy is probably due to moderate vegetation density and variation within the landscape. Generally, they prefer higher forest cover and topographic heterogeneity (Chaudhuri et al, 2022 ; Puri et al, 2015 ; Srivathsa et al, 2018 ). The sloth bears are adapted to forest environments, relying on trees for shelter, nesting, and foraging (Chaudhuri et al, 2022 ; Srivathsa et al, 2018 ).…”

“…Generally, they prefer higher forest cover and topographic heterogeneity (Chaudhuri et al, 2022 ; Puri et al, 2015 ; Srivathsa et al, 2018 ). The sloth bears are adapted to forest environments, relying on trees for shelter, nesting, and foraging (Chaudhuri et al, 2022 ; Srivathsa et al, 2018 ). Higher canopy cover can provide suitable habitat conditions and protection for sloth bears, contributing to their increased occupancy in areas with dense tree cover.…”

N-mixture models for population estimation: Application in spotted lanternfly egg mass survey

“…N-mixture models have been widely used in wildlife surveys such as small vertebrates ( Ficetola et al, 2018 ), Italian wall lizard ( Podarcis siculus (Rafinesque) [Squamata: Lacertidae]) ( Sacchi et al, 2022 ), Nile crocodile ( Crocodylus niloticus (Laurenti) [Crocodilia: Crocodylidae]) ( Lyet et al, 2016 ), black rhinoceros ( Diceros bicornis L. [Perissodactyla: Rhnocerotidae]) ( Kidwai et al, 2019 ), African lion ( Panthera leo (L.) [Carnivora: Felidae]) ( Belant et al, 2016 ), sloth bear ( Melursus ursinus (Shaw) [Carnivora: Ursidae]) ( Chaudhuri et al, 2022 ), white-tailed deer ( Odocoileus virginianus (Zimmermann) [Artiodactyla: Cervidae]) ( Keever et al, 2017 ; Christensen et al, 2021 ), and marsh tit ( Poecile palustris (L) [Passeriformes: Paridae]) ( Neubauer et al, 2022 ). Its application in population survey in entomology, however, is relatively new to the best of our knowledge except that by Keller and Hoover (2023) .…”