A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags

• Published in: Movement Ecology
• Main Authors: Jonsen, ID, Patterson, TA, Costa, DP, Doherty, PD, Godley, BJ, Grecian, WJ, Guinet, C, Hoenner, X, Kienle, SS, Robinson, PW, Votier, SC, Whiting, S, Witt, MJ, Hindell, MA, Harcourt, RG, McMahon, CR
• Format: Article in Journal/Newspaper
• Language: English
• Published: BioMed Central Ltd. 2020
• Subjects: Southern Ocean; tracking; management; animal-borne sensors; bio-telemetry; foieGras R package; global positioning system; seabird; pinniped; sea turtle; template model builder; Lanes
• Online Access: https://eprints.utas.edu.au/34523/; https://eprints.utas.edu.au/34523/1/140604%20-%20A%20continuous-time%20state-space%20model%20for%20rapid%20quality%20control%20of%20argos%20locations.pdf

BackgroundState-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.MethodsWe 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.ResultsEstimation accuracy varied among species with Root Mean Squared Errors usually ConclusionsOur 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.