Simulation‐Based Spatially Explicit Close‐Kin Mark–Recapture

Estimating the size of wild populations is a critical priority for ecologists and conservation biologists, but tools to do so are often labour intensive and expensive. A promising set of newer approaches are based on genetic data, which can be cheaper to obtain and less invasive than information from more direct observation. One of these approaches is close-kin mark-recapture (CKMR), a type of method that uses genetic data to identify kin pairs and estimates population size from these pairs. Although CKMR methods are promising, one limitation to using them more broadly is a lack of CKMR models that can deal with spatially structured populations and spatial heterogeneity in sampling. In this paper, we introduce a spatially explicit approach to CKMR that uses individual-based simulation in concert with a deep convolutional neural network to estimate population sizes. Using simulations, we show that our method, CKMRnn, is highly accurate, even in the face of spatial heterogeneity in sampling and spatial population structure, and demonstrate that it can account for potential confounders such as unknown population histories. Finally, to demonstrate the accuracy of our method in an empirical system, we apply CKMRnn to data from a Ugandan elephant population, and show that point estimates from our method recapitulate those from traditional estimators but that the confidence interval on our estimator is approximately 30% narrower.