Efficient estimation of large-scale spatial capture–recapture models

Abstract

Capture–recapture methods are a common tool in ecological statistics, which have beenextended to spatial capture–recapture models for data accompanied by location information. However,standard formulations of these models can be unwieldy and computationally intractable for large spatialscales, many individuals, and/or activity center movement. We provide a cumulative series of methodsthat yield dramatic improvements in Markov chain Monte Carlo (MCMC) estimation for two examples.These include removing unnecessary computations, integrating out latent states, vectorizing declarations,and restricting calculations to the locality of individuals. Our approaches leverage the exibility providedby the nimble R package. In our rst example, we demonstrate an improvement in MCMC efciency (therate of generating effectively independent posterior samples) by a factor of 100. In our second example, wereduce the computing time required to generate 10,000 posterior samples from 4.5 h down to ve minutes,and realize an increase in MCMC efciency by a factor of 25. These approaches can also be applied gener-ally to other spatially indexed hierarchical models. We provide R code for all examples, an executable web-appendix, and generalized versions of these techniques are made available in the nimbleSCR R package. Markov chain Monte Carlo; Mark–recapture; nimble; sampling efficiency; spatial capture–recapture