Estimating animal abundance and effort–precision relationship with camera trap distance sampling

Noémie Cappelle,Christophe Boesch,Hjalmar S. Kühl,Eric J. Howe

doi:10.1002/ecs2.3299

Abstract

AbstractEffective monitoring methods are needed for assessing the state of biodiversity and detecting population trends. The popularity of camera trapping in wildlife surveys continues to increase as they are able to detect species in remote and difficult‐to‐access areas. As a result, several statistical estimators of the abundance of unmarked animal populations have been developed, but none have been widely tested. Even where the potential for accurate estimation has been demonstrated, whether these methods estimators can yield estimates of sufficient precision to detect trends and inform conservation action remains questionable. Here, we assess the effort–precision relationship of camera trap distance sampling (CTDS) in order to help researchers design efficient surveys. A total of 200 cameras were deployed for 10 months across 200 km2 in the Taï National Park, Côte d'Ivoire. We estimated abundance of Maxwell's duikers, western chimpanzees, leopards, and forest elephants that are challenging to enumerate due to rarity or semi‐arboreality. To test the effects of spatial and temporal survey effort on the precision of CTDS estimates, we calculated coefficient of variation (CV) of the encounter rate from subsets of our complete data sets. Estimated abundance of leopard and Maxwell's duiker density (20% < CV < 30% and CV = 11%, respectively) were similar to prior estimates from the same area. Abundances of chimpanzees (20% < CV < 30%) were underestimated, but the quality of inference was similar to that reported after labor‐intensive line transect surveys to nests. Estimates for the rare forest elephants were potentially unreliable since they were too imprecise (60% < CV < 200%). Generalized linear models coefficients indicated that for relatively common, ground‐dwelling species, CVs between 10% and 20% are achievable from a variety of survey designs, including long‐term (6+ months) surveys at few locations (50), or short term (2‐week to 2‐month) surveys at 100–150 locations. We conclude that CTDS can efficiently provide estimates of abundance of multiple species of sufficient quality and precision to inform conservation decisions. However, estimates for the rarest species will be imprecise even from ambitious surveys and may be biased for species that exhibit strong reactions to cameras.

Highlights

Camera traps (CT) that detect wildlife using heat or motion sensors can detect a wide variety of wildlife including rare, nocturnal, and elusive species
The random encounter model (REM), the model proposed by Campos-Candela et al (2018), and the random encounter staying time (REST) model (Nakashima et al 2018) are all based on ideal gas models predicting collision rates; an estimator for animal density can be derived from the rates of contact between animals and camera traps (Hutchinson and Waser 2007, Rowcliffe et al 2008)
The REST is an extension of the REM that substitutes staying time for the speed of movement

Summary

Introduction

Camera traps (CT) that detect wildlife using heat or motion sensors can detect a wide variety of wildlife including rare, nocturnal, and elusive species They are useful for monitoring biodiversity over large areas, and in habitats with poor visibility and limited access The REM, the model proposed by Campos-Candela et al (2018), and the random encounter staying time (REST) model (Nakashima et al 2018) are all based on ideal gas models predicting collision rates; an estimator for animal density can be derived from the rates of contact between animals and camera traps (Hutchinson and Waser 2007, Rowcliffe et al 2008). Campos-Candela et al.’s (2018) model replaces speed movement with home range size based on the principle of association, defined as the number of ongoing occurrences within a given area and at a given instant (Hutchinson and Waser 2007)

Methods

Results

Conclusion