Abstract

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.

Highlights

  • Across the globe, large carnivores face threats from habitat loss and fragmentation, prey depletion, and poaching [1]

  • We address these issues through a simulation study and using a remote camera-trapping data set for the common leopard (Panthera pardus) from the lower, subtropical foothills of Royal Manas National Park (RMNP), Bhutan

  • For all sampling interval lengths, the Distance model estimated a lower density of animals than any of the models with sex-specific parameters, such that the Distance model median did not fall within the 95% credibility intervals (CI) of the estimates from the other models

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Summary

Introduction

Large carnivores face threats from habitat loss and fragmentation, prey depletion, and poaching [1]. To address the imperfect detectability of large carnivores, researchers have adopted noninvasive, remote sampling techniques, such as automatically triggered camera traps, hair snares or scat surveys that likely reduce animal avoidance of target sites. For many species, these data can identify unique individuals based on genetics or unique markings, facilitating a capturemark-recapture (CMR) analysis that explicitly incorporates detection probability into the estimate of abundance and/or density. SCR models can integrate ecological and behavioral factors into density estimates using individual, trap or spatial covariates, which can further improve the accuracy of estimates [12, 20,21,22]

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