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

Citation

Jonsen, ID and Patterson, TA and Costa, DP and Doherty, PD and Godley, BJ and Grecian, WJ and Guinet, C and Hoenner, X and Kienle, SS and Robinson, PW and Votier, SC and Whiting, S and Witt, MJ and Hindell, MA and Harcourt, RG and McMahon, CR, A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags, Movement Ecology, 8, (1) Article 31. ISSN 2051-3933 (2020) [Refereed Article]


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Copyright Statement

Copyright 2020 the authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1186/s40462-020-00217-7

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.

Item Details

Item Type:Refereed Article
Keywords:Southern Ocean, tracking, management, animal-borne sensors, bio-telemetry, foieGras R package, global positioning system, seabird, pinniped, sea turtle, template model builder
Research Division:Earth Sciences
Research Group:Oceanography
Research Field:Biological oceanography
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Assessment and management of terrestrial ecosystems
UTAS Author:Hindell, MA (Professor Mark Hindell)
UTAS Author:McMahon, CR (Dr Clive McMahon)
ID Code:140604
Year Published:2020
Funding Support:Australian Research Council (DP180101667)
Web of Science® Times Cited:8
Deposited By:Ecology and Biodiversity
Deposited On:2020-08-31
Last Modified:2021-02-11
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