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dc.contributor.authorTurek, Daniel
dc.contributor.authorMilleret, Cyril Pierre
dc.contributor.authorErgon, Torbjørn
dc.contributor.authorBrøseth, Henrik
dc.contributor.authorDupont, Pierre
dc.contributor.authorBischof, Richard
dc.contributor.authorde Valpine, Perry
dc.date.accessioned2021-11-30T13:36:32Z
dc.date.available2021-11-30T13:36:32Z
dc.date.created2021-02-18T15:14:06Z
dc.date.issued2021
dc.identifier.citationEcosphere. 2021. 12( 2):e03385.en_US
dc.identifier.issn2150-8925
dc.identifier.urihttps://hdl.handle.net/11250/2832139
dc.description.abstractCapture–recapture methods are a common tool in ecological statistics, which have beenextended to spatial capture–recapture models for data accompanied by location information. However,standard formulations of these models can be unwieldy and computationally intractable for large spatialscales, many individuals, and/or activity center movement. We provide a cumulative series of methodsthat yield dramatic improvements in Markov chain Monte Carlo (MCMC) estimation for two examples.These include removing unnecessary computations, integrating out latent states, vectorizing declarations,and restricting calculations to the locality of individuals. Our approaches leverage the exibility providedby the nimble R package. In our rst example, we demonstrate an improvement in MCMC efciency (therate of generating effectively independent posterior samples) by a factor of 100. In our second example, wereduce the computing time required to generate 10,000 posterior samples from 4.5 h down to ve minutes,and realize an increase in MCMC efciency by a factor of 25. These approaches can also be applied gener-ally to other spatially indexed hierarchical models. We provide R code for all examples, an executable web-appendix, and generalized versions of these techniques are made available in the nimbleSCR R package. Markov chain Monte Carlo; Mark–recapture; nimble; sampling efficiency; spatial capture–recapture
dc.language.isoengen_US
dc.titleEfficient estimation of large-scale spatial capture–recapture modelsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersion
dc.subject.nsiVDP::Basale biofag: 470
dc.subject.nsiVDP::Basic biosciences: 470
dc.source.volume12en_US
dc.source.journalEcosphereen_US
dc.source.issue2en_US
dc.identifier.doi10.1002/ecs2.3385
dc.identifier.cristin1891440
dc.relation.projectNorges forskningsråd: 286886
dc.relation.projectAndre: NorwegianEnvironment Agency (Miljødirektoratet)
dc.relation.projectAndre: Swedish Protection Agency
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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