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Enhancing the Use of Argos Satellite Data for Home Range and Long Distance Migration Studies of Marine Animals

Overview of attention for article published in PLOS ONE, July 2012
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Title
Enhancing the Use of Argos Satellite Data for Home Range and Long Distance Migration Studies of Marine Animals
Published in
PLOS ONE, July 2012
DOI 10.1371/journal.pone.0040713
Pubmed ID
Authors

Xavier Hoenner, Scott D. Whiting, Mark A. Hindell, Clive R. McMahon

Abstract

Accurately quantifying animals' spatial utilisation is critical for conservation, but has long remained an elusive goal due to technological impediments. The Argos telemetry system has been extensively used to remotely track marine animals, however location estimates are characterised by substantial spatial error. State-space models (SSM) constitute a robust statistical approach to refine Argos tracking data by accounting for observation errors and stochasticity in animal movement. Despite their wide use in ecology, few studies have thoroughly quantified the error associated with SSM predicted locations and no research has assessed their validity for describing animal movement behaviour. We compared home ranges and migratory pathways of seven hawksbill sea turtles (Eretmochelys imbricata) estimated from (a) highly accurate Fastloc GPS data and (b) locations computed using common Argos data analytical approaches. Argos 68(th) percentile error was <1 km for LC 1, 2, and 3 while markedly less accurate (>4 km) for LC ≤ 0. Argos error structure was highly longitudinally skewed and was, for all LC, adequately modelled by a Student's t distribution. Both habitat use and migration routes were best recreated using SSM locations post-processed by re-adding good Argos positions (LC 1, 2 and 3) and filtering terrestrial points (mean distance to migratory tracks ± SD = 2.2 ± 2.4 km; mean home range overlap and error ratio = 92.2% and 285.6 respectively). This parsimonious and objective statistical procedure however still markedly overestimated true home range sizes, especially for animals exhibiting restricted movements. Post-processing SSM locations nonetheless constitutes the best analytical technique for remotely sensed Argos tracking data and we therefore recommend using this approach to rework historical Argos datasets for better estimation of animal spatial utilisation for research and evidence-based conservation purposes.

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Geographical breakdown

Country Count As %
United States 4 2%
Chile 1 <1%
Italy 1 <1%
Brazil 1 <1%
Colombia 1 <1%
Iceland 1 <1%
Canada 1 <1%
Denmark 1 <1%
Argentina 1 <1%
Other 0 0%
Unknown 184 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 22%
Student > Ph. D. Student 34 17%
Student > Master 34 17%
Student > Bachelor 13 7%
Other 11 6%
Other 28 14%
Unknown 32 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 113 58%
Environmental Science 30 15%
Earth and Planetary Sciences 7 4%
Mathematics 2 1%
Medicine and Dentistry 2 1%
Other 9 5%
Unknown 33 17%