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dc.contributor.authorAndrén, Henrik
dc.contributor.authorHobbs, N. Thompson
dc.contributor.authorAronsson, Malin
dc.contributor.authorBrøseth, Henrik
dc.contributor.authorChapron, Guillaume
dc.contributor.authorLinnell, John Durrus
dc.contributor.authorOdden, John
dc.contributor.authorPersson, Jens
dc.contributor.authorNilssen, Erlend B.
dc.coverage.spatialNoreg, Norge, Norway, Sverige, Swedennb_NO
dc.date.accessioned2020-01-02T13:01:32Z
dc.date.available2020-01-02T13:01:32Z
dc.date.issued2019
dc.identifier.issn1051-0761
dc.identifier.urihttp://hdl.handle.net/11250/2634618
dc.description.abstractHarvesting large carnivores can be a management tool for meeting politically set goals for their desired abundance. However, harvesting carnivores creates its own set of conflicts in both society and among conservation professionals, where one consequence is a need to demonstrate that management is sustainable, evidence‐based and guided by science. Furthermore, because large carnivores often also have high degrees of legal protection, harvest quotas have to be carefully justified and constantly adjusted to avoid damaging their conservation status. We developed a Bayesian state‐space model to support adaptive management of Eurasian lynx harvesting in Scandinavia. The model uses data from the annual monitoring of lynx abundance and results from long‐term field research on lynx biology, which has provided detailed estimates of key demographic parameters. We used the model to predict the probability that the forecasted population size will be below or above the management objectives when subjected to different harvest quotas. The model presented here informs decision makers about the policy risks of alternative harvest levels. Earlier versions of the model have been available for wildlife managers in both Sweden and Norway to guide lynx harvest quotas and the model predictions showed good agreement with observations. We combined monitoring data with data on vital rates and were able to estimate unobserved additional mortality rates, which are most probably due to poaching. In both countries, the past quota setting strategy suggests that there has been a de facto threshold strategy with increasing proportion, which means that there is no harvest below a certain population size, but above this threshold there is an increasing proportion of the population harvested as the population size increases. The annual assessment of the monitoring results, the use of forecasting models, and a threshold harvest approach to quota setting will all reduce the risk of lynx population sizes moving outside the desired goals. The approach we illustrate could be adapted to other populations of mammals worldwide.nb_NO
dc.language.isoengnb_NO
dc.subjectadaptive managementnb_NO
dc.subjectBayesian state-Space modelnb_NO
dc.subjectcarnivorenb_NO
dc.subjectEurasian Lynxnb_NO
dc.subjectforecastingnb_NO
dc.subjecthuntingnb_NO
dc.subjectharvestnb_NO
dc.subjectNorwaynb_NO
dc.subjectpoachingnb_NO
dc.subjectquotanb_NO
dc.subjectSwedennb_NO
dc.titleHarvest models of small populations of a large carnivore using Bayesian forecastingnb_NO
dc.typePeer reviewednb_NO
dc.typeJournal article
dc.description.versionacceptedVersionnb_NO
dc.rights.holder© 2019 by the Ecological Society of Americanb_NO
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480nb_NO
dc.source.journalEcological Applicationsnb_NO
dc.identifier.doi10.1002/eap.2063
dc.identifier.cristin1765243


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