Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R

Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R

: abundance, detection, JAGS, N-mixture model, R, R-INLA, unmarked, wildlife

Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife
surveys. N-mixture models enable quantification of detection probability and, under appropriate conditions, produce abundance estimates that are less biased. Here, we demonstrate how to use the R-INLA package for R to analyze N-mixture models, and compare
performance of R-INLA to two other common approaches: JAGS (via the runjags package for R), which uses Markov chain Monte Carlo and allows Bayesian inference, and
the unmarked package for R, which uses maximum likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models
when (i) fast computing times are necessary (R-INLA is 10 times faster than unmarked
and 500 times faster than JAGS), (ii) familiar model syntax and data format (relative
to other R packages) is desired, (iii) survey-level covariates of detection are not essential,
and (iv) Bayesian inference is preferred

Timothy D. Meehan

https://www.jstatsoft.org/article/view/v095i02

National
Audubon Society

October 2020

Fajar bagus W

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