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dc.contributor.authorMeehan, Timothy D.
dc.contributor.authorMichel, Nicole L.
dc.contributor.authorRue, Haavard
dc.date.accessioned2017-12-28T07:32:13Z
dc.date.available2017-12-28T07:32:13Z
dc.date.issued2017-05-03
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 often produce abundance estimates that are less biased. The purpose of this study was to demonstrate the use of the R-INLA package to analyze N-mixture models and to compare performance of R-INLA to two other common approaches -- JAGS (via the runjags package), which uses Markov chain Monte Carlo and allows Bayesian inference, and unmarked, which uses Maximum Likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models when (1) familiar model syntax and data format (relative to other R packages) are desired, (2) survey level covariates of detection are not essential, (3) fast computing times are necessary (R-INLA is 10 times faster than unmarked, 300 times faster than JAGS), and (4) Bayesian inference is preferred.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1705.01581v1
dc.relation.urlhttp://arxiv.org/pdf/1705.01581v1
dc.rightsArchived with thanks to arXiv
dc.titleEstimating animal abundance with N-mixture models using the R-INLA package for R
dc.typePreprint
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionNational Audubon Society, Boulder, CO USA
dc.contributor.institutionNational Audubon Society, San Francisco, CA USA
dc.identifier.arxividarXiv:1705.01581
kaust.personRue, Haavard
refterms.dateFOA2018-06-14T05:31:11Z


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