Methods in Ecology and Evolution (Dec 2023)
Reducing bias in density estimates for unmarked populations that exhibit reactive behaviour towards camera traps
Abstract
Abstract Density estimates guide wildlife management, and camera traps are commonly used to estimate sizes of unmarked populations. Unfortunately, animals often alter their natural behaviour in the presence of camera traps, which may bias subsequent density estimates. We simulated 100 populations of known density to test several new and existing methods that aimed to reduce bias in density estimates from camera trap distance sampling (CTDS) and the random encounter model (REM). Within our simulated populations, we introduced different behavioural reactions including attraction towards cameras, freezing when near cameras and fleeing from cameras. CTDS and REM provided density estimates with decent coverage of confidence intervals (CTDS = 94%, REM = 87%), mean coefficient of variation (CTDS = 0.121, REM = 0.071) and minimal bias (root‐mean squared error: CTDS = 1.336, REM = 0.913) for simulated populations with no reactive behaviour. However, failure to implement a method to account for reactive behaviour resulted in low coverage, large bias and potentially imprecise density estimates when 30% of the simulated population potentially reacted by attraction to or fleeing from camera traps. We identified a corrective strategy that enhanced confidence interval coverage, increased precision and reduced bias for every behavioural reaction except when individuals potentially fled from cameras. Synthesis and applications. We provide empirically tested methods for reducing bias of density estimates. Wildlife managers requiring population estimates of animals that exhibit reactive behaviour can use our methods to reduce inaccuracy. We encourage future studies to quantify behavioural responses to camera traps and to implement, test and possibly extend our methods to reduce bias through simulation.
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