Show simple item record

dc.contributor.authorMeehan, Timothy D.
dc.contributor.authorMichel, Nicole L.
dc.contributor.authorRue, Haavard
dc.date.accessioned2020-10-29T12:31:58Z
dc.date.available2017-12-28T07:32:13Z
dc.date.available2020-10-29T12:31:58Z
dc.date.issued2020
dc.date.submitted2017-04-26
dc.identifier.citationMeehan, T. D., Michel, N. L., & Rue, H. (2020). Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R. Journal of Statistical Software, 95(2). doi:10.18637/jss.v095.i02
dc.identifier.issn1548-7660
dc.identifier.doi10.18637/jss.v095.i02
dc.identifier.urihttp://hdl.handle.net/10754/626491
dc.description.abstractSuccessful 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.
dc.publisherFoundation for Open Access Statistic
dc.relation.urlhttp://www.jstatsoft.org/v95/i02/
dc.rightsThis work is licensed under the licenses.
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.titleEstimating animal abundance with n-mixture models using the r-inla package for r
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalJournal of Statistical Software
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionNational Audubon Society, Boulder, Colorado, United States of America
dc.contributor.institutionNational Audubon Society, San Francisco, California, United States of America
dc.identifier.volume95
dc.identifier.issue2
dc.identifier.pages1-26
dc.identifier.arxividarXiv:1705.01581
kaust.personRue, Haavard
dc.date.accepted2018-10-29
dc.identifier.eid2-s2.0-85092484842
refterms.dateFOA2018-06-14T05:31:11Z
dc.date.posted2017-05-03


Files in this item

Thumbnail
Name:
estimating.pdf
Size:
764.2Kb
Format:
PDF
Description:
Published version

This item appears in the following Collection(s)

Show simple item record

This work is licensed under the licenses.
Except where otherwise noted, this item's license is described as This work is licensed under the licenses.
VersionItemEditorDateSummary

*Selected version