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dc.contributor.authorLowther, A.D.
dc.contributor.authorLydersen, C.
dc.contributor.authorFedak, M.A.
dc.contributor.authorLovell, P.
dc.contributor.authorKovacs, K.M.
dc.date.accessioned2018-09-04T20:22:42Z
dc.date.available2018-09-04T20:22:42Z
dc.date.issued2015
dc.identifier.citationLowther, A.D.; Lydersen, C.; Fedak, M.A.; Lovell, P. and Kovacs, K.M. (2015) The Argos-CLS Kalman Filter: Error Structures and State-Space Modelling Relative to Fastloc GPS Data. PLoS ONE 10(4): e0124754. DOI:10.1371/journal.pone.0124754en_US
dc.identifier.urihttp://hdl.handle.net/11329/508
dc.identifier.urihttp://dx.doi.org/10.25607/OBP-90
dc.description.abstractUnderstanding how an animal utilises its surroundings requires its movements through space to be described accurately. Satellite telemetry is the only means of acquiring movement data for many species however data are prone to varying amounts of spatial error; the recent application of state-space models (SSMs) to the location estimation problem have provided a means to incorporate spatial errors when characterising animal movements. The predominant platform for collecting satellite telemetry data on free-ranging animals, Service Argos, recently provided an alternative Doppler location estimation algorithm that is purported to be more accurate and generate a greater number of locations that its predecessor. We provide a comprehensive assessment of this new estimation process performance on data from free-ranging animals relative to concurrently collected Fastloc GPS data. Additionally, we test the efficacy of three readily-available SSM in predicting the movement of two focal animals. Raw Argos location estimates generated by the new algorithm were greatly improved compared to the old system. Approximately twice as many Argos locations were derived compared to GPS on the devices used. Root Mean Square Errors (RMSE) for each optimal SSM were less than 4.25km with some producing RMSE of less than 2.50km. Differences in the biological plausibility of the tracks between the two focal animals used to investigate the utility of SSM highlights the importance of considering animal behaviour in movement studies. The ability to reprocess Argos data collected since 2008 with the new algorithm should permit questions of animal movement to be revisited at a finer resolution.en_US
dc.language.isoenen_US
dc.rightsAttribution 4.0*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo/*
dc.subject.otherArgosen_US
dc.subject.otherAnimal borne sensorsen_US
dc.subject.otherState-space model (SSM)en_US
dc.subject.otherAnimal movementen_US
dc.titleThe Argos-CLS Kalman Filter: Error Structures and State-Space Modelling Relative to Fastloc GPS Data.en_US
dc.typeJournal Contributionen_US
dc.description.refereedRefereeden_US
dc.format.pagerangee012475 [16pp.]en_US
dc.identifier.doi10.1371/journal.pone.01247
dc.subject.parameterDisciplineParameter Discipline::Physical oceanography::Other physical oceanographic measurementsen_US
dc.subject.dmProcessesData Management Practices::Data acquisitionen_US
dc.bibliographicCitation.titlePLoS ONEen_US
dc.bibliographicCitation.volume10en_US
dc.bibliographicCitation.issue4en_US
dc.description.sdgSDG14en_US
dc.description.maturitylevelTRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space)en_US
dc.description.bptypeManualen_US
dc.description.bptypeGuideen_US
obps.contact.contactemailandrew.lowther@npolar.no
obps.resourceurl.publisherhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0124754en_US


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Attribution 4.0
Except where otherwise noted, this item's license is described as Attribution 4.0