Accuracy of ARGOS locations of pinnipeds at-sea estimated using Fastloc GPS

Abstract

BACKGROUND: ARGOS satellite telemetry is one of the most widely used methods to track the movements of free-ranging marine and terrestrial animals and is fundamental to studies of foraging ecology, migratory behavior and habitat-use. ARGOS location estimates do not include complete error estimations, and for many marine organisms, the most commonly acquired locations (Location Class 0, A, B, or Z) are provided with no declared error estimate. METHODOLOGY/PRINCIPAL FINDINGS: We compared the accuracy of ARGOS locations to those obtained using Fastloc GPS from the same electronic tags on five species of pinnipeds: 9 California sea lions ( Zalophus californianus ), 4 Galapagos sea lions ( Zalophus wollebaeki ), 6 Cape fur seals ( Arctocephalus pusillus pusillus ), 3 Australian fur seals ( A. p. doriferus ) and 5 northern elephant seals ( Mirounga angustirostris ). These species encompass a range of marine habitats (highly pelagic vs coastal), diving behaviors (mean dive durations 2-21 min) and range of latitudes (equator to temperate). A total of 7,318 ARGOS positions and 27,046 GPS positions were collected. Of these, 1,105 ARGOS positions were obtained within five minutes of a GPS position and were used for comparison. The 68 th percentile ARGOS location errors as measured in this study were LC-3 0.49 km, LC-2 1.01 km, LC-1 1.20 km, LC-0 4.18 km, LC-A 6.19 km, LC-B 10.28 km. Conclusions/Significance The ARGOS errors measured here are greater than those provided by ARGOS, but within the range of other studies. The error was non-normally distributed with each LC highly right-skewed. Locations of species that make short duration dives and spend extended periods on the surface (sea lions and fur seals) had less error than species like elephant seals that spend more time underwater and have shorter surface intervals. Supplemental data ( S1 ) are provided allowing the creation of density distributions that can be used in a variety of filtering algorithms to improve the quality of ARGOS tracking data.