Inference of Animal Activity From GPS Collar Data on Free-Ranging Cattle
Abstract
Global positioning systems (GPSs) enable continuous and automatic tracking of an animal’s position. The value of such spatial–temporal information can be improved if the corresponding activity of the animal is known. We evaluated the potential of LotekGPS collars to predict activity of beef cattle on extensive rangeland in 2 contrasting foraging environments. Collars wereconfigured to record animal location at intervals of 20 minutes (United States) or 5 minutes (Israel), together with counts from2 motion sensors. Synchronized field observations of collared cows were conducted in 1999 (United States) and in 2002 and2003 (Israel). Grazing, traveling (without grazing), and resting activities were recorded as minutes out of 20 for each category(United States), or as a single category (Israel). For the US data, stepwise regression models of grazing, traveling, and rest-ing time accounted for 74%–84% of the variation, on the basis of the motion sensor counts for the left–right axis and the distancesbetween GPS fixes. Regression tree analysis of grazing time yielded a simple model (4 splits) that accounted for 85% of the vari-ation. For the Israeli data, the misclassification rates obtained by discriminant analysis and classification tree analysis of animalactivity were 14% and 12%, respectively. In both analyses, almost all grazing observations were correctly classified, but otheractivities were sometimes misclassified as grazing. Distance alone is a poor indicator of animal activity, but grazing, traveling,and resting activities of free-ranging cattle can be inferred with reasonable accuracy from data provided by Lotek GPS collars.