The Argos-CLS Kalman filter : error structures and state-space modelling relative to Fastloc GPS data

• Published in: PLOS ONE
• Main Authors: Lowther, A.D., Lydersen, C., Fedak, M.A., Lovell, Philip, Kovacs, K.M.
• Other Authors: University of St Andrews. School of Biology, University of St Andrews. Sea Mammal Research Unit, University of St Andrews. Scottish Oceans Institute, University of St Andrews. Marine Alliance for Science & Technology Scotland
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2015
• Subjects: QH301 Biology; QH301; Norwegian Polar Institute
• Online Access: http://hdl.handle.net/10023/6651; https://doi.org/10.1371/journal.pone.0124754; http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0124754#sec014

Funding was provided by the Norwegian Polar Institute centre for Ice, Climate and Ecosystems (ICE). Understanding 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. Publisher PDF Peer reviewed