TY - JOUR

T1 - Estimating animal abundance with n-mixture models using the r-inla package for r

AU - Meehan, Timothy D.

AU - Michel, Nicole L.

AU - Rue, Haavard

N1 - KAUST Repository Item: Exported on 2020-10-30

PY - 2020/10/4

Y1 - 2020/10/4

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/626491

UR - http://www.jstatsoft.org/v95/i02/

UR - http://www.scopus.com/inward/record.url?scp=85092484842&partnerID=8YFLogxK

U2 - 10.18637/jss.v095.i02

DO - 10.18637/jss.v095.i02

M3 - Article

SN - 1548-7660

VL - 95

SP - 1

EP - 26

JO - Journal of Statistical Software

JF - Journal of Statistical Software

IS - 2

ER -