Spatial capture–recapture models allowing Markovian transience or dispersal
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Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.
KeywordsAnimal movement Density estimation Dispersal Spatial capture–recapture Spatially explicit capture–recapture Transience
Preliminary results of this research were presented by the authors at the 2014 Graybill/ENVR Conference: Modern Statistical Methods for Ecology, held September 7–10, 2014 at Colorado State University, Fort Collins, Colorado. We thank Michael Schaub and 2 anonymous referees for their thoughtful reviews of the manuscript. Part of this research was performed using the ATLAS HPC Cluster, a compute cluster with 672 cores, 4 Tesla M2090 GPU accelerators, supported by NSF grants (Award # 1059284 and 0832782).
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