Abstract

The simultaneous development of technology (e.g. camera traps) and statistical methods, particularly spatially capture–recapture (SCR), has improved monitoring of large mammals in recent years. SCR estimates are known to be sensitive to sampling design, yet existing recommendations about trap spacing and coverage are often not achieved, particularly for sampling wide-ranging and rare species in landscapes that allow for limited accessibility. Consequently, most camera trap studies on large wide-ranging carnivores relies on convenience or judgmental sampling, and often yields compromised results. This study attempts to highlight the importance of carefully considered sampling design for large carnivores that, because of low densities and elusive behavior, are challenging to monitor. As a motivating example, we use two years of snow leopard camera trapping data from the same areas in the high mountains of Pakistan but with vastly different camera configurations, to demonstrate that estimates of density and space use are indeed sensitive to the trapping array. A compact design, one in which cameras were placed much closer together than generally recommended and therefore have lower spatial coverage, resulted in fewer individuals observed, but more recaptures, and estimates of density and space use were inconsistent with expectations for the region. In contrast, a diffuse design, one with larger spacing and spatial coverage and more consistent with general recommendations, detected more individuals, had fewer recaptures, but generated estimates of density and space use that were in line with expectations. Researchers often opt for compact camera configurations while monitoring wide-ranging and rare species, in an attempt to maximize the encounter probabilities. We empirically demonstrate the potential for biases when sampling a small area approximately the size of a single home range—this arises from exposing fewer individuals than deemed sufficient for estimation. The smaller trapping array may also underestimate density by significantly inflating sigma. On the other hand, larger trapping array with fewer detectors and poor design induces uncertainties in the estimates. We conclude that existing design recommendations have limited utility on practical grounds for devising feasible sampling designs for large ranging species, and more research on SCR designs is required that allows for integrating biological and habitat traits of large carnivores in sampling framework. We also suggest that caution should be exercised when there is a reliance on convenience sampling.

Highlights

  • From endangered to ­vulnerable[6], there is little doubt that the species is data deficient throughout large parts of its range, meaning that any population status assessment is incomplete at ­best[6,7]

  • The approach is constrained by sample size requirements and study design challenges, as large carnivores often yield sparse data owing to their biological traits

  • While sparse and imperfect data is an inevitable outcome in studies of large carnivores, it is critical that the gap in understanding feasible spatial capture–recapture (SCR) study design for challenging landscapes is addressed

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Summary

Introduction

From endangered to ­vulnerable[6], there is little doubt that the species is data deficient throughout large parts of its range, meaning that any population status assessment is incomplete at ­best[6,7]. The study design is often ignored in large carnivores’ studies that implement SCR, due to limited familiarity in field biologists about this relatively new method and deficiency of guidelines for adopting this framework for rugged terrains and elusive species. This is surprising given explicit link between the number and configuration of traps and the quality of the data, and the reliability of the inferences ­made[20,21]. Rules-of-thumb for SCR study design exists about trap spacing and spatial c­ overage[30,31,32], their generality has yet to be tested, and they represent situations that are unlikely to be attainable in real landscapes, especially for rare and large ranging species (e.g., uniform grids, contiguous habitats, and ignoring terrain limitations, see Dupont et al (2020)

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