Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Timothy D. Meehan, Nicole L. Michel, Håvard Rue
Title: Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R
Abstract: 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.

Page views:: 926. Submitted: 2017-04-26. Published: 2020-10-07.
Paper: Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R     Download PDF (Downloads: 307)
Supplements:
INLA_20.03.17.tar.gz: R source package Download (Downloads: 11; 319MB)
v95i02.R: R replication code Download (Downloads: 26; 31KB)

DOI: 10.18637/jss.v095.i02

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.