Moderating Argos location errors in animal tracking data

• Published in: Methods in ecology and evolution Vol. 3; no. 6; pp. 999 - 1007
• Main Authors: Douglas, David C., Weinzierl, Rolf, C. Davidson, Sarah, Kays, Roland, Wikelski, Martin, Bohrer, Gil, Giuggioli, Luca
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.12.2012
• Subjects: accuracy; animal movement; Argos; Douglas Argos‐filter; Movebank; satellite telemetry
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/j.2041-210X.2012.00245.x

Summary The Argos System 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 (Argos classes A, 0, B and Z) dominate animal tracking data. Standard‐quality animal tracking locations (Argos classes 3, 2 and 1) have larger errors than those reported in Argos manuals. The Douglas Argos‐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 Movebank – 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 Argos location estimates collected from Argos transmitters on free‐ranging waterfowl and condors (13 species, 314 individuals, 54,895 animal‐tracking days). The 95th error percentiles for unfiltered Argos 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 Argos‐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. Video