Evaluation of Argos Telemetry Accuracy in the High-Arctic and Implications for the Estimation of Home-Range Size

Abstract

Animal tracking through Argos satellite telemetry has enormous potential to test hypotheses in animal behavior, evolutionary ecology, or conservation biology. Yet the applicability of this technique cannot be fully assessed because no clear picture exists as to the conditions influencing the accuracy of Argos locations. Latitude, type of environment, and transmitter movement are among the main candidate factors affecting accuracy. A posteriori data filtering can remove “bad” locations, but again testing is still needed to refine filters. First, we evaluate experimentally the accuracy of Argos locations in a polar terrestrial environment (Nunavut, Canada), with both static and mobile transmitters transported by humans and coupled to GPS transmitters. We report static errors among the lowest published. However, the 68th error percentiles of mobile transmitters were 1.7 to 3.8 times greater than those of static transmitters. Second, we test how different filtering methods influence the quality of Argos location datasets. Accuracy of location datasets was best improved when filtering in locations of the best classes (LC3 and 2), while the Douglas Argos filter and a homemade speed filter yielded similar performance while retaining more locations. All filters effectively reduced the 68th error percentiles. Finally, we assess how location error impacted, at six spatial scales, two common estimators of home-range size (a proxy of animal space use behavior synthetizing movements), the minimum convex polygon and the fixed kernel estimator. Location error led to a sometimes dramatic overestimation of home-range size, especially at very local scales. We conclude that Argos telemetry is appropriate to study medium-size terrestrial animals in polar environments, but recommend that location errors are always measured and evaluated against research hypotheses, and that data are always filtered before analysis. How movement speed of transmitters affects location error needs additional research.

Movement tactics of a mobile predator in a meta‐ecosystem with fluctuating resources: the arctic fox in the High Arctic

“…The PTT transmitted daily or every two days for a 3‐ to 4‐h period (13:00‒17:00 UTC, corresponding to 07:00‒11:00 local time) with a repetition rate of 60 s. Duty cycles of the PTT varied slightly between years and collars (details in Supplementary material Appendix 2 Table A1). Argos locations were filtered using a speed filter (see S1 File in Christin et al ) implemented in R 3.1.0. (< http://www.r-project.org >).…”

“…Locations were then projected in the Universal Transverse Mercator, North American Datum 83 system, and any location requiring unrealistic speed values from the previous one (> 7 km h –1 cruising speed, with possible 12‐min acceleration bouts of 10 km h –1 ) was removed. We set speed values from data obtained from GPS collars in the same fox population (Christin et al ). We kept for analyses one location per transmission period, based on the smallest location error, in order to reduce spatial autocorrelation in further analyses.…”

Peer Review #1 of "Empirical analysis and modeling of Argos Doppler location errors in Romania (v0.2)"

“…This suggests that locations from fragmented areas may be highly imprecise and can lead to biased conclusions about animal movement and location if not adequately filtered (Lopez et al 2015). The variance explained by the random part of the linear mixed effects model suggests that the satellite detecting the location of the PTT had a minimal impact on accuracy, while the dominant source of positional errors were probably due to other random factors, such as poor line of sight to the satellite as a result of local topography, the presence of obstructing vegetation or the relative orientation of the respective PTT with respect to the sky (Christin et al 2015;Doherty et al 2017;Dubinin et al 2010;Soutullo et al 2007).…”

“…CLS pre-processes these locations using one of Argos's nominal filters such as the Least Squares algorithm or the Kalman filter (Lopez et al 2015). In practice, location errors of 10 to 100 kilometers often occur due to communication conditions driven by the environment or animal behavior (e.g., animal speed, terrain fragmentation, rain, cloud cover, temperature) (Christin et al 2015;Costa et al 2010;Douglas et al 2012;Dubinin et al 2010;Sauder et al 2012;Witt et al 2010). Thus, filtering the data to exclude implausible Argos locations before employing movement analysis has become a standard approach for researchers (Hooten et al 2017).…”

Adaptations of the rare endemic Grey Falcon Falco hypoleucos that enable its permanent residence in the arid zone of Australia

“…In summary, the accuracy of the location estimates corresponds with the accuracy that the testing of the research hypothesis requires (as recommended by Christin et al (2015)).…”