Journal
MOVEMENT ECOLOGY
Volume 8, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s40462-020-00217-7
Keywords
Animal-borne sensors; Bio-telemetry; foieGras R package; Global Positioning System; Seabird; Pinniped; Sea turtle; Template Model Builder
Categories
Funding
- Macquarie University
- Office of Naval Research
- Integrated Marine Observing System - Animal Tracking Facility
- Ocean Tracking Network
- Taronga Conservation Society
- Birds Canada
- Innovasea/Vemco
- CSIRO Oceans AMP
- Atmosphere internal research funding scheme
- National Science Foundation Office of Polar Projects research grant
- National Oceanographic Partnership Program
- Gordon and Betty Moore Foundation
- David and Lucille Packard Foundation
- Sloan Foundation
- California Sea Grant Program
- Australian Government under the Caring for Country Initiative
- Anindilyakwa Land Council
- Northern Territory Government
- Charles Darwin University
- ANZ Trustees Foundation - Holsworth Wildlife Research Endowment
- Defra Darwin [17-005]
- Peninsula Institute for Marine Renewable Energy
- Harvest Natural Resources
- IPEV program 109
- IPEV program 1201
- CNES-TOSCA program Elephants de Mer Oceanographe
- SNO-MEMO
- NERC [NE/G001014/1]
- Peninsula Research Institute for Marine Renewable Energy
- EU INTERREG Project CHARM III
- Vaalco Energy Inc.
- NERC [NE/P00623X/1] Funding Source: UKRI
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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.
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