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dc.contributor.authorJonsen, Ian D.
dc.contributor.authorPatterson, Toby A.
dc.contributor.authorCosta, Daniel P.
dc.contributor.authorDoherty, Philip D.
dc.contributor.authorGodley, Brendan J.
dc.contributor.authorGrecian, W. James
dc.contributor.authorGuinet, Christophe
dc.contributor.authorHoenner, Xavier
dc.contributor.authorKienle, Sarah S.
dc.contributor.authorRobinson, Patrick W.
dc.contributor.authorVotier, Stephen C.
dc.contributor.authorWhiting, Scott
dc.contributor.authorWitt, Matthew J.
dc.contributor.authorHindell, Mark A.
dc.contributor.authorHarcourt, Robert G.
dc.contributor.authorMcMahon, Clive R.
dc.identifier.citationJonsen , I D , Patterson , T A , Costa , D P , Doherty , P D , Godley , B J , Grecian , W J , Guinet , C , Hoenner , X , Kienle , S S , Robinson , P W , Votier , S C , Whiting , S , Witt , M J , Hindell , M A , Harcourt , R G & McMahon , C R 2020 , ' A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags ' , Movement Ecology , vol. 8 , 31 .
dc.identifier.otherRIS: urn:4294EC30BE25A3BD1A81D92B3F88E643
dc.identifier.otherRIS: Jonsen2020
dc.identifier.otherORCID: /0000-0002-6428-719X/work/77893814
dc.description.abstractBackground State-space models are important tools for quality control and analysis of error-prone animal movement data. The near real-time (within 24 h) capability of the Argos satellite system can aid dynamic ocean management of human activities by informing when animals enter wind farms, shipping lanes, and other intensive use zones. This capability also facilitates the use of ocean observations from animal-borne sensors in operational ocean forecasting models. Such near real-time data provision requires rapid, reliable quality control to deal with error-prone Argos locations. Methods We formulate a continuous-time state-space model to filter the three types of Argos location data (Least-Squares, Kalman filter, and Kalman smoother), accounting for irregular timing of observations. Our model is deliberately simple to ensure speed and reliability for automated, near real-time quality control of Argos location data. We validate the model by fitting to Argos locations collected from 61 individuals across 7 marine vertebrates and compare model-estimated locations to contemporaneous GPS locations. We then test assumptions that Argos Kalman filter/smoother error ellipses are unbiased, and that Argos Kalman smoother location accuracy cannot be improved by subsequent state-space modelling. Results Estimation accuracy varied among species with Root Mean Squared Errors usually <5 km and these decreased with increasing data sampling rate and precision of Argos locations. Including a model parameter to inflate Argos error ellipse sizes in the north - south direction resulted in more accurate location estimates. Finally, in some cases the model appreciably improved the accuracy of the Argos Kalman smoother locations, which should not be possible if the smoother is using all available information. Conclusions Our model provides quality-controlled locations from Argos Least-Squares or Kalman filter data with accuracy similar to or marginally better than Argos Kalman smoother data that are only available via fee-based reprocessing. Simplicity and ease of use make the model suitable both for automated quality control of near real-time Argos data and for manual use by researchers working with historical Argos data.
dc.relation.ispartofMovement Ecologyen
dc.subjectAnimal-borne sensorsen
dc.subjectfoieGras R packageen
dc.subjectGlobal Positioning Systemen
dc.subjectSea turtleen
dc.subjectTemplate model builderen
dc.subjectQH301 Biologyen
dc.subjectSDG 14 - Life Below Wateren
dc.titleA continuous-time state-space model for rapid quality control of argos locations from animal-borne tagsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.description.statusPeer revieweden

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