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dc.contributor.authorKindsvater, Holly K.-
dc.contributor.authorDulvy, Nicholas K.-
dc.contributor.authorHorswill, Cat-
dc.contributor.authorJuan-Jorda, Maria Jose-
dc.contributor.authorMangel, Marc; Matthiopoulos, Jason-
dc.date.accessioned2019-06-18T11:48:58Z-
dc.date.available2019-06-18T11:48:58Z-
dc.date.issued2018-
dc.identifierISI:000442598900006-
dc.identifier.citationTRENDS IN ECOLOGY \& EVOLUTION, 2018, 33, 676-688-
dc.identifier.issn0169-5347-
dc.identifier.urihttp://dspace.azti.es/handle/24689/791-
dc.description.abstractHow can we track population trends when monitoring data are sparse? Population declines can go undetected, despite ongoing threats. For example, only one of every 200 harvested species are monitored. This gapleads to uncertaintyabout the seriousness of declines and hampers effective conservation. Collecting more data is important, but we can also make better use of existing information. Prior knowledge of physiology, life history, and community ecology can be used to inform population models. Additionally, in multispecies models, information can be shared among taxa based on phylogenetic, spatial, or temporal proximity. By exploiting generalities across species that share evolutionary or ecological characteristics within Bayesian hierarchical models, we can fill crucial gaps in the assessment of species' status with unparalleled quantitative rigor.-
dc.language.isoeng-
dc.publisherELSEVIER SCIENCE LONDON-
dc.subjectSIZE SPECTRUM-
dc.subjectDYNAMICS-
dc.subjectPREDATION-
dc.subjectHISTORY-
dc.subjectMODELS-
dc.subjectLIFE-
dc.subjectMETAANALYSIS-
dc.subjectUNCERTAINTY-
dc.subjectPREDICTIONS-
dc.subjectEXTINCTION-
dc.titleOvercoming the Data Crisis in Biodiversity Conservation-
dc.typeReview-
dc.identifier.journalTRENDS IN ECOLOGY \& EVOLUTION-
dc.format.page676-688-
dc.format.volume33-
dc.contributor.funderUS National Science Foundation [DEB-1556779, DEB-1555729]-
dc.contributor.funderNatural Environment Research Council [NE/P004180/1]-
dc.contributor.funderMarine Alliance for Science and Technology Scotland [SG411]-
dc.identifier.e-issn1872-8383-
dc.identifier.doi10.1016/j.tree.2018.06.004-
Aparece en las tipos de publicación: Artículos científicos



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