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dc.contributor.authorNicolau, Pedro Guilherme
dc.contributor.authorSørbye, Sigrunn Holbek
dc.contributor.authorYoccoz, Nigel
dc.date.accessioned2020-11-29T09:29:53Z
dc.date.available2020-11-29T09:29:53Z
dc.date.issued2020-08-31
dc.description.abstractPopulation dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state‐space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture–recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state‐space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state‐space models for density‐dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz–Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R‐package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray‐sided voles <i>Myodes rufocanus</i> from Northern Norway. We found that density‐dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.en_US
dc.identifier.citationNicolau PG, Sørbye SH, Yoccoz NG. Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture–recapture data. Ecology and Evolution. 2020en_US
dc.identifier.cristinIDFRIDAID 1846363
dc.identifier.doi10.1002/ece3.6642
dc.identifier.issn2045-7758
dc.identifier.urihttps://hdl.handle.net/10037/19928
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.ispartofNicolau, P.G. (2022). Boreal rodents fluctuating in space and time: Tying the observation process to the modeling of seasonal population dynamics. (Doctoral thesis). <a href=https://hdl.handle.net/10037/25284>https://hdl.handle.net/10037/25284</a>.
dc.relation.journalEcology and Evolution
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Zoology and botany: 480::Ecology: 488en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488en_US
dc.titleIncorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture–recapture dataen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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