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dc.contributor.authorSilva, Monica A.
dc.contributor.authorJonsen, Ian
dc.contributor.authorRussell, Deborah Jill Fraser
dc.contributor.authorPrieto, Rui
dc.contributor.authorThompson, David
dc.contributor.authorBaumgartner, Mark F.
dc.date.accessioned2014-03-26T09:31:01Z
dc.date.available2014-03-26T09:31:01Z
dc.date.issued2014-03-20
dc.identifier.citationSilva , M A , Jonsen , I , Russell , D J F , Prieto , R , Thompson , D & Baumgartner , M F 2014 , ' Assessing performance of Bayesian state-space models fit to Argos Satellite telemetry locations processed with Kalman Filtering ' , PLoS One , vol. 9 , no. 3 , e92277 . https://doi.org/10.1371/journal.pone.0092277en
dc.identifier.issn1932-6203
dc.identifier.otherPURE: 105771498
dc.identifier.otherPURE UUID: 4e6f9198-df80-47c2-806c-958514377707
dc.identifier.otherScopus: 84924584925
dc.identifier.otherORCID: /0000-0002-1969-102X/work/49052026
dc.identifier.otherORCID: /0000-0003-1546-2876/work/56862218
dc.identifier.otherWOS: 000333352800092
dc.identifier.urihttp://hdl.handle.net/10023/4538
dc.description.abstractArgos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.
dc.language.isoeng
dc.relation.ispartofPLoS Oneen
dc.rightsCopyright: © 2014 Silva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectAlgorithmsen
dc.subjectSatellite-tracked animalsen
dc.subjectKalman filter (KF)en
dc.subjectLeast Squares (LS) algorithmen
dc.subjectBayesian state-space models (SSMs)en
dc.subjectHarbour seal (Phoca vitulina)en
dc.subjectARGOS satellite transmitteren
dc.subjectFin whales (Balaenoptera physalus)en
dc.subjectSwitching state-space models (SSSM)en
dc.subjectGE Environmental Sciencesen
dc.subjectGC Oceanographyen
dc.subject.lccGEen
dc.subject.lccGCen
dc.titleAssessing performance of Bayesian state-space models fit to Argos Satellite telemetry locations processed with Kalman Filteringen
dc.typeJournal articleen
dc.contributor.sponsorNERCen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0092277
dc.description.statusPeer revieweden
dc.identifier.grantnumberAgreement R8-H12-86en


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