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Successful management of wildlife populations requires accurate estimates ofabundance. Abundance estimates can be confounded by imperfect detection duringwildlife surveys. N-mixture models enable quantification of detectionprobability and, under appropriate conditions, produce abundance estimates thatare less biased. Here, we demonstrate use of the R-INLA package for R toanalyze N-mixture models and compare performance of R-INLA to two other commonapproaches: JAGS (via the runjags package for R), which uses Markov chain MonteCarlo and allows Bayesian inference, and the unmarked package for R, which usesmaximum likelihood and allows frequentist inference. We show that R-INLA is anattractive option for analyzing N-mixture models when (i) fast computing timesare necessary (R-INLA is 10 times faster than unmarked and 500 times fasterthan JAGS), (ii) familiar model syntax and data format (relative to other Rpackages) is desired, (iii) survey-level covariates of detection are notessential, and (iv) Bayesian inference is preferred.

Estimating animal abundance with N-mixture models using the R-INLA package for R