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

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 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.

Motes enhance data recovery from satellite-relayed biologgers and can facilitate collaborative research into marine habitat utilization

“…7 could be construed to depths of less than 200 m (and temperatures higher than 17 °C, not shown), whereas Mote data showed that the shark dove to 300 m (and temperatures of 9 °C). Spatially, Motes logged ~96% of the GPS locations recorded by tags, which means that not only did the additional locations provide a much more complete picture of where, when and how sharks use their environment, but as GPS locations are more accurate than Argos locations, the uncertainty around animal's movements was also reduced [12,24]. Consequently, Motes have the potential to improve our knowledge of whales and sharks' behaviors both quantitatively and qualitatively.…”

Bahamas connection: residence areas selected by breeding female loggerheads tagged in Dry Tortugas National Park, USA

“…The model was described in 2005 [41] and has previously been applied to the movement of marine animals including turtles [1,3,4,42,43,[46][47][48][49][50]56,80]. Location data obtained through satellite transmitters are often received at irregular time intervals and sometimes involve large gaps and positional errors.…”

“…Ad hoc filtering of location data based on location quality is not sufficient to remove erroneous locations and also results in loss of information [42]. Switching SSM estimates location and behavioral mode at regular time intervals, accounting for satellite positional errors and dynamics of the animal movement pattern [41] and is recommended as the best analytical technique for Argos tracking data once post processed by removing land points and adding back in good Argos locations [56].…”

“…When combined with MCP (simple polygon created with home-range locations [51,52]) or KDE (a nonparametric method used to identify one or more areas of disproportionately heavy use [i.e. core areas] within a home-range boundary, see [53][54][55]), SSM has been suggested as a way to enhance analysis of Argos tracking data sets [56]. SSM was recently used to delineate foraging zones for post-nesting Kemp's ridleys in the GoM [4], define the migrations and foraging areas for leatherback turtles nesting in the Pacific Ocean [47,57], determine foraging areas for juvenile green turtles in the SW Atlantic [58], and describe residence or foraging areas in the GoM for female loggerheads from three separate subpopulations [3].…”

Foraging areas, migratory movements and winter destinations of blue whales from the western North Atlantic

“…Determining the scale of seasonal movements and distribution, and identifying frequently used habitat, their periods of use, and features rendering habitats suitable for vital functions such as breeding or foraging, are central to species conservation, and to the mitigation of impacts from global development (Hoenner et al 2012). Advances in telemetry technologies exploiting the Argos satellites and, more recently, GPS have improved the remote tracking of wildlife over extensive distances and in hard-to-access places (Hussey et al 2015).…”