Moderating <scp>A</scp>rgos location errors in animal tracking data

TLDR

Argos satellite tracking is widely used for free‑ranging animals, yet low‑quality locations (classes A, 0, B, Z) dominate datasets and can have errors ranging from tens of metres to hundreds of kilometres. The Douglas Argos‑filter algorithm flags implausible positions using user‑defined thresholds that can be tuned to species movement and study goals, and was evaluated on 21,044 GPS‑paired Argos points from 13 species and 314 individuals in Movebank. Applying the filter reduced the 95th‑percentile error of class 0, A, B, Z locations from 35.8, 59.6, 163.2, 220.2 km to 17.2, 15.0, 20.9, 18.6 km, improving overall accuracy by 50–90 % and demonstrating that more conservative thresholds further increase precision at the cost of fewer retained points.

Abstract

Summary The A rgos S ystem is used worldwide to satellite‐track free‐ranging animals, but location errors can range from tens of metres to hundreds of kilometres. Low‐quality locations ( A rgos classes A , 0, B and Z ) dominate animal tracking data. Standard‐quality animal tracking locations ( A rgos classes 3, 2 and 1) have larger errors than those reported in A rgos manuals. The D ouglas A rgos‐filter ( DAF ) algorithm flags implausible locations based on user‐defined thresholds that allow the algorithm's performance to be tuned to species' movement behaviours and study objectives. The algorithm is available in M ovebank – a free online infrastructure for storing, managing, sharing and analysing animal movement data. We compared 21,044 temporally paired global positioning system ( GPS ) locations with A rgos location estimates collected from A rgos transmitters on free‐ranging waterfowl and condors (13 species, 314 individuals, 54,895 animal‐tracking days). The 95th error percentiles for unfiltered A rgos locations 0, A , B and Z were within 35·8, 59·6, 163·2 and 220·2 km of the true location, respectively. After applying DAF with liberal thresholds, roughly 20% of the class 0 and A locations and 45% of the class B and Z locations were excluded, and the 95th error percentiles were reduced to 17·2, 15·0, 20·9 and 18·6 km for classes 0, A , B and Z , respectively. As thresholds were applied more conservatively, fewer locations were retained, but they possessed higher overall accuracy. Douglas A rgos‐filter can improve data accuracy by 50–90% and is an effective and flexible tool for preparing Argos data for direct biological interpretation or subsequent modelling.