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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 68th 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.

Item Type:	Refereed Article
Keywords:	animal migration, animals, Australia, ecosystem, female, geographic information systems, geography, homing behavior, likelihood functions, satellite communications, turtles
Research Division:	Biological Sciences
Research Group:	Ecology
Research Field:	Marine and estuarine ecology (incl. marine ichthyology)
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the biological sciences
UTAS Author:	Hoenner, X (Mr Xavier Hoenner)
UTAS Author:	Hindell, MA (Professor Mark Hindell)
UTAS Author:	McMahon, CR (Dr Clive McMahon)
ID Code:	83792
Year Published:	2012
Web of Science® Times Cited:	46
Deposited By:	IMAS Research and Education Centre
Deposited On:	2013-03-22
Last Modified:	2017-11-01
Downloads:	288 View Download Statistics