Bi, RujiaJiao, YanBrowder, Joan A.2022-02-282022-02-282021-10-192045-232210.1038/s41598-021-00078-z (PII)http://hdl.handle.net/10919/108911Bycatch 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.Pages 20704application/pdfenCreative Commons Attribution 4.0 InternationalConservation biologyStatisticsPELAGIC LONGLINE FISHERYGAUSSIAN MODELSMANAGEMENTMORTALITYINFERENCEDISCARDSTRAWLAnimalsBirdsBayes TheoremConservation of Natural ResourcesEcosystemClimateFisheriesArchitectural AccessibilityComputer SimulationHuntingArticle - Refereed2022-02-28https://doi.org/10.1038/s41598-021-00078-z111Jiao, Yan [0000-0002-8804-5005]346671972045-2322