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dc.contributor.authorBraun, Camrin D.  Concept link
dc.contributor.authorGaluardi, Benjamin  Concept link
dc.contributor.authorThorrold, Simon R.  Concept link
dc.date.accessioned2018-05-30T15:55:51Z
dc.date.available2018-05-30T15:55:51Z
dc.date.issued2017-11
dc.identifier.urihttps://hdl.handle.net/1912/10390
dc.descriptionAuthor Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Methods in Ecology and Evolution 9 (2018): 1212-1220, doi:10.1111/2041-210X.12959.en_US
dc.description.abstractElectronic tagging of marine fishes is commonly achieved with archival tags that rely on light levels and sea surface temperatures to retrospectively estimate movements. However, methodological issues associated with light-level geolocation have constrained meaningful inference to species where it is possible to accurately estimate time of sunrise and sunset. Most studies have largely ignored the oceanographic profiles collected by the tag as a potential way to refine light-level geolocation estimates. Open-source oceanographic measurements and outputs from high-resolution models are increasingly available and accessible. Temperature and depth profiles recorded by electronic tags can be integrated with these empirical data and model outputs to construct likelihoods and improve geolocation estimates. The R package HMMoce leverages available tag and oceanographic data to improve position estimates derived from electronic tags using a hidden Markov approach. We illustrate the use of the model and test its performance using example blue and mako shark archival tag data. Model results were validated using independent, known tracks and compared to results from other geolocation approaches. HMMoce exhibited as much as 6-fold improvement in pointwise error as compared to traditional light-level geolocation approaches. The results demonstrated the general applicability of HMMoce to marine animals, particularly those that do not frequent surface waters during crepuscular periods.en_US
dc.description.sponsorshipThis work was funded by awards to C. Braun from the Martin Family Society of Fellows for Sustainability Fellowship at the Massachusetts Institute of Technology, the Grassle Fellowship and Ocean Venture Fund at the Woods Hole Oceanographic Institution, and the NASA Earth and Space Science Fellowship.en_US
dc.language.isoen_USen_US
dc.relation.urihttps://doi.org/10.1111/2041-210X.12959
dc.subjectSatellite telemetryen_US
dc.subjectMovement ecologyen_US
dc.subjectOceanographyen_US
dc.subjectState-space modelen_US
dc.subjectBehavioral stateen_US
dc.titleHMMoce : an R package for improved geolocation of archival-tagged fishes using a hidden Markov methoden_US
dc.typePreprinten_US


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