Publication Date:
2022-05-26
Description:
© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS One 9 (2014): e92277, doi:10.1371/journal.pone.0092277.
Description:
Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were 〈5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.
Description:
This research was primarily funded by Fundação para a Ciência e a Tecnologia (FCT), Fundo Regional da Ciência, Tecnologia (FRCT), through research projects TRACE-PTDC/MAR/74071/2006 and MAPCET-M2.1.2/F/012/2011 [FEDER], the Competitiveness Factors Operational (COMPETE), QREN European Social Fund, and Proconvergencia Açores/EU Program]. We acknowledge funds provided by FCT to LARSyS Associated Laboratory and IMAR-University of the Azores/the Thematic Area D & E of the Strategic Project PEst-OE/EEI/LA0009/2011–1012 and 2013–2014 (OE & Compete) and by the FRCT - Government of the Azores pluriannual funding. MAS was supported by an FCT postdoctoral grant (SFRH/BPD/29841/2006) and is currently supported by POPH, QREN European Social Fund and the Portuguese Ministry for Science and Education through an FCT Investigator grant. RP was supported by an FCT doctoral grant (SFRH/BD/41192/2007) and by the research grant from the Azores Regional Fund for Science and Technology (M3.1.5/F/115/2012). IJ was supported by the Natural Sciences and Engineering Research Council (NSERC) and the Canada Foundation for Innovation (CFI) through their support of the Ocean Tracking Network. DJFR is funded by the United Kingdom Department of Energy and Climate Change as part of their Offshore Energy Strategic Environmental Assessment program. DT is funded by Natural Environment Research Council and Marine Scotland.
Repository Name:
Woods Hole Open Access Server
Type:
Article
Format:
application/pdf
Format:
application/msword
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