Component processes of detection probability in camera-trap studies: understanding the occurrence of false-negatives

Abstract

AbstractCamera-trap studies in the wild record true-positive data, but data loss from false-negatives (i.e. an animal is present but not recorded) is likely to vary and widely impact data quality. Detection probability is defined as the probability of recording an animal if present in the study area. We propose a framework of sequential processes within detection – a pass, trigger, image registration, and images being of sufficient quality. Using closed-circuit television (CCTV) combined with camera-trap arrays we quantified variation in, and drivers of, these processes for three medium-sized mammal species. We also compared trigger success of wet and dry otter Lutra lutra, as an example of a semiaquatic species. Data loss from failed trigger, failed registration and poor capture quality varied between species, camera-trap model and settings, and were affected by different environmental and animal variables. Distance had a negative effect on trigger probability and a positive effect on registration probability. Faster animals had both reduced trigger and registration probabilities. Close passes (1 m) frequently did not generate triggers, resulting in over 20% data loss for all species. Our results, linked to the framework describing processes, can inform study design to minimize or account for data loss during analysis and interpretation.