A spatially explicit capture recapture model for partially identified individuals when trap detection rate is less than one

TL;DR

This paper introduces a Bayesian spatially explicit capture-recapture model that accounts for imperfect detection and partially identified individuals, improving population estimates in ecological studies with less-than-perfect trap detection.

Contribution

The study develops a novel Bayesian SECR model that separates animal movement from detection process, enhancing accuracy over previous models that assume perfect detection.

Findings

Population size estimates improve with the new model.

Trap detection probability was estimated at 0.489 in a tiger survey.

Model performance was validated through simulations and real data.

Abstract

Spatially explicit capture recapture (SECR) models have gained enormous popularity to solve abundance estimation problems in ecology. In this study, we develop a novel Bayesian SECR model that disentangles the process of animal movement through a detector from the process of recording data by a detector in the face of imperfect detection. We integrate this complexity into an advanced version of a recent SECR model involving partially identified individuals (Royle, 2015). We assess the performance of our model over a range of realistic simulation scenarios and demonstrate that estimates of population size $N$ improve when we utilize the proposed model relative to the model that does not explicitly estimate trap detection probability (Royle, 2015). We confront and investigate the proposed model with a spatial capture-recapture data set from a camera trapping survey on tigers (\textit{Panthera tigris}) in Nagarahole, southern India. Trap detection probability is estimated at 0.489 and therefore justifies the necessity to utilize our model in field situations. We discuss possible extensions, future work and relevance of our model to other statistical applications beyond ecology.