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dc.contributor.authorAldrin, Magne Tommy
dc.contributor.authorHuseby, Ragnar Bang
dc.contributor.authorStien, Audun
dc.contributor.authorGrøntvedt, Randi Nygaard
dc.contributor.authorViljugrein, Hildegunn
dc.contributor.authorJansen, Peder A
dc.date.accessioned2017-08-30T12:50:29Z
dc.date.available2017-08-30T12:50:29Z
dc.date.created2017-07-12T10:28:00Z
dc.date.issued2017
dc.identifier.citationEcological Modelling. 2017, 359 333-348.nb_NO
dc.identifier.issn0304-3800
dc.identifier.urihttp://hdl.handle.net/11250/2452432
dc.description.abstractSalmon farming has become a prosperous international industry over the last decades. Along with growth in the production farmed salmon, however, an increasing threat by pathogens has emerged. Of special concern is the propagation and spread of the salmon louse, Lepeophtheirus salmonis. To gain insight into this parasite’s population dynamics in large scale salmon farming system, we present a fully mechanistic stage-structured population model for the salmon louse, also allowing for complexities involved in the hierarchical structure of full scale salmon farming. The model estimates parameters controlling a wide range of processes, including temperature dependent demographic rates, fish size and abundance effects on louse transmission rates, effect sizes of various salmon louse control measures, and distance based between farm transmission rates. Model parameters were estimated from data including 32 salmon farms, except the last production months for five farms, which were used to evaluate model predictions. We used a Bayesian estimation approach, combining the prior distributions and the data likelihood into a joint posterior distribution for all model parameters. The model generated expected values that fitted the observed infection levels of the chalimus, adult female and other mobile stages of salmon lice, reasonably well. Predictions for the periods not used for fitting the model were also consistent with the observational data. We argue that the present model for the population dynamics of the salmon louse in aquaculture farm systems may contribute to resolve the complexity of processes that drive this host-parasite relationship, and hence may improve strategies to control the parasite in this production system. Population model Aquaculture Stochastic model Sea lice countsnb_NO
dc.language.isoengnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectPopulation modelnb_NO
dc.subjectAquaculturenb_NO
dc.subjectStochastic modelnb_NO
dc.subjectSea lice countsnb_NO
dc.titleA stage-structured Bayesian hierarchical model for salmon lice populations at individual salmon farms – Estimated from multiple farm data setsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.subject.nsiVDP::Klinisk veterinærmedisinske fag: 950nb_NO
dc.subject.nsiVDP::Clinical veterinary sciences: 950nb_NO
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480nb_NO
dc.source.pagenumber333-348nb_NO
dc.source.volume359nb_NO
dc.source.journalEcological Modellingnb_NO
dc.identifier.doi10.1016/j.ecolmodel.2017.05.019
dc.identifier.cristin1481991
cristin.unitcode7511,4,0,0
cristin.unitnameTromsø
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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