Abstract
ContextData obtained from camera traps are increasingly used to inform various population-level models. Although acknowledged, imperfect detection probabilities within camera-trap detection zones are rarely taken into account when modelling animal densities.
AimsWe aimed to identify parameters influencing camera-trap detection probabilities, and quantify their relative impacts, as well as explore the downstream implications of imperfect detection probabilities on population-density modelling.
MethodsWe modelled the relationships between the detection probabilities of a standard camera-trap model (n=35) on a remotely operated animal-shaped soft toy and a series of parameters likely to influence it. These included the distance of animals from camera traps, animal speed, camera-trap deployment height, ambient temperature (as a proxy for background surface temperatures) and animal surface temperature. We then used this detection-probability model to quantify the likely influence of imperfect detection rates on subsequent population-level models, being, in this case, estimates from random encounter density models on a known density simulation.
Key resultsDetection probabilities mostly varied predictably in relation to measured parameters, and decreased with an increasing distance from the camera traps and speeds of movement, as well as heights of camera-trap deployments. Increased differences between ambient temperature and animal surface temperature were associated with increased detection probabilities. Importantly, our results showed substantial inter-camera (of the same model) variability in detection probabilities. Resulting model outputs suggested consistent and systematic underestimation of true population densities when not taking imperfect detection probabilities into account.
ConclusionsImperfect, and individually variable, detection probabilities inside the detection zones of camera traps can compromise resulting population-density estimates.
ImplicationsWe propose a simple calibration approach for individual camera traps before field deployment and encourage researchers to actively estimate individual camera-trap detection performance for inclusion in subsequent modelling approaches.
