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Climate driven spatiotemporal variations in seabird bycatch hotspots and implications for seabird bycatch mitigation

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dc.contributor.authorBi, Rujiaen
dc.contributor.authorJiao, Yanen
dc.contributor.authorBrowder, Joan A.en
dc.date.accessioned2022-02-28T04:49:41Zen
dc.date.available2022-02-28T04:49:41Zen
dc.date.issued2021-10-19en
dc.date.updated2022-02-28T04:49:36Zen
dc.description.abstractBycatch in fisheries is a major threat to many seabird species. Understanding and predicting spatiotemporal changes in seabird bycatch from fisheries might be the key to mitigation. Inter-annual spatiotemporal patterns are evident in seabird bycatch of the U.S. Atlantic pelagic longline fishery monitored by the National Marine Fisheries Service Pelagic Observer Program (POP) since 1992. A newly developed fast computing Bayesian approximation method provided the opportunity to use POP data to understand spatiotemporal patterns, including temporal changes in location of seabird bycatch hotspots. A Bayesian model was developed to capture the inherent spatiotemporal structure in seabird bycatch and reduce the bias caused by physical barriers such as coastlines. The model was applied to the logbook data to estimate seabird bycatch for each longline set, and the mid-Atlantic bight and northeast coast were the fishing areas with the highest fleet bycatch estimate. Inter-annual changes in predicted bycatch hotspots were correlated with Gulf Stream meanders, suggesting that predictable patterns in Gulf Stream meanders could enable advanced planning of fishing fleet schedules and areas of operation. The greater the Gulf Stream North Wall index, the more northerly the seabird bycatch hotspot two years later. A simulation study suggested that switching fishing fleets from the hindcasted actual bycatch hotspot to neighboring areas and/or different periods could be an efficient strategy to decrease seabird bycatch while largely maintaining fishers’ benefit.en
dc.description.versionPublished versionen
dc.format.extentPages 20704en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 20704 (Article number)en
dc.identifier.doihttps://doi.org/10.1038/s41598-021-00078-zen
dc.identifier.eissn2045-2322en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.orcidJiao, Yan [0000-0002-8804-5005]en
dc.identifier.other10.1038/s41598-021-00078-z (PII)en
dc.identifier.pmid34667197en
dc.identifier.urihttp://hdl.handle.net/10919/108911en
dc.identifier.volume11en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.relation.urihttps://www.nature.com/articles/s41598-021-00078-zen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectConservation biologyen
dc.subjectStatisticsen
dc.subjectPELAGIC LONGLINE FISHERYen
dc.subjectGAUSSIAN MODELSen
dc.subjectMANAGEMENTen
dc.subjectMORTALITYen
dc.subjectINFERENCEen
dc.subjectDISCARDSen
dc.subjectTRAWLen
dc.subject.meshAnimalsen
dc.subject.meshBirdsen
dc.subject.meshBayes Theoremen
dc.subject.meshConservation of Natural Resourcesen
dc.subject.meshEcosystemen
dc.subject.meshClimateen
dc.subject.meshFisheriesen
dc.subject.meshArchitectural Accessibilityen
dc.subject.meshComputer Simulationen
dc.subject.meshHuntingen
dc.titleClimate driven spatiotemporal variations in seabird bycatch hotspots and implications for seabird bycatch mitigationen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dcterms.dateAccepted2021-10-05en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Natural Resources & Environmenten
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/Fish and Wildlife Conservationen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/CNRE T&R Facultyen

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