The accuracy of Fastloc‐GPS locations and implications for animal tracking

Abstract

Summary1. Over recent years, a major breakthrough in marine animal tracking has occurred with the advent of Fastloc-GPS that provides highly accurate location data even for animals that only surface briefly such as sea turtles, marine mammals and penguins. 2. We assessed the accuracy of Fastloc-GPS locations using fixed trials of tags in which >45 000 locations were obtained. Procedures for determining the speed of travel and heading were developed by simulating tracks and then adding Fastloc-GPS location errors. The levels of detail achievable for speed and heading estimates were illustrated by using empirical Fastloc-GPS data for a green turtle (Chelonia mydas, Linnaeus, 1758) travelling over 3000 km across the Indian Ocean. 3. The accuracy of Fastloc-GPS locations varied as a function of the number of GPS satellites used in the location calculation. For example, when Fastloc-GPS locations were calculated using 4 GPS satellites, 50% of locations were within 36 m and 95% within 724 m of the true position. These values improved to 18 and 70 m, respectively, when 6 satellites were used. Simulations indicated that for animals travelling around 2Á5 km h À1 (e.g. turtles, penguins and seals) and depending on the number of satellites used in the location calculation, robust speed and heading estimates would usually be obtained for locations only 1-6 h apart. 4. Fastloc-GPS accuracy is several orders of magnitude better that conventional Argos tracking or light-based geolocation and consequently will allow new insights into small-scale movement patterns of marine animals.

Movements of the reef manta ray (Manta alfredi) in the Red Sea using satellite and acoustic telemetry

“…Secondary bathymetric correction was applied to both model outputs using an individual's maximum daily depth and the 'analyzepsat' library in R (Galuardi et al 2010). No track estimation was necessary for MK10-AF tags as the error of GPS locations is often <100 m (Dujon et al 2014). Sea surface temperature (SST) has been used to further optimize track estimates in many studies; however, this approach inhibited model convergence in the Red Sea due to the homogeneity of the SST field (Raitsos et al 2013) and was therefore not used in this study.…”

Habitat selection by green turtles in a spatially heterogeneous benthic landscape in Dry Tortugas National Park, Florida

“…The estimated accuracy is unknown for LCs A and B (A and B differ in number of received messages), and locations failing the Argos plausibility tests are tagged as class LC Z (CLS 2011). Following Eckert (2006), we applied the Douglas Argos-Filter for satellite data keeping the highest 3 location classes (LC 0−2), and using a speed filter of 3 km h −1 based on Dujon et al (2014), who assumed the average swim speed of green turtles to be 2.5 km h −1 . We used 15 for the angle parameter setting (about 26°), and a maximum radius of 2 km, and we also removed apparent erroneous locations (<1% of the data).…”

How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

“…The launch of the ARGOS (Advanced Research and Global observation satellite) satellite network in the late 1970s overcame this problem, as receivers were placed in earth-orbiting satellites and by the 1980's, animals were tracked with satellite transmitters for the first time (Schweinsburg and Lee, 1982) (Figure 1). In subsequent years, satellite telemetry has progressed rapidly with miniaturization of electronics, improved battery capacity and the integration of the Global Positioning System (GPS), allowing position estimates with much lower error (Dujon et al, 2014) and faster acquisition of satellite data. Today these satellite tags have been attached to a range of terrestrial and marine animals and have catalyzed discovery (Hussey et al, 2015;Kays et al, 2015).…”