A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags
Résumé
Abstract
Background: 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.