The GPS is now commonly employed in range management research, to monitor and analyze the use of areas by livestock, either solely [14,15] or in combination with wildlife [16]. Animal-borne GPS devices provide continuous and accurate thereby records of animal location over time. However, location alone does not represent a Inhibitors,Modulators,Libraries complete picture with regard to estimation of the spatial distribution of grazing pressure, because animals do not graze actively all the time; they divide their time among several activities, such as resting, traveling (without Inhibitors,Modulators,Libraries grazing), and active grazing. It would be a great enhancement of GPS data use if we could also infer the activity timeline of an animal.The inference of behavior from data provided by sensors mounted in the head region, such as a tri-axial accelerometer for goats [17] and a pitch and roll tilt sensor for sheep [18], has been explored.

Some GPS devices incorporate sensors that can give an indication of activity. Lotek GPS collars (Lotek Wireless Inc., Inhibitors,Modulators,Libraries Newmarket, ON, Canada) include sensors of motion along two axes, and these store the numbers of movements they register during each GPS-fix interval. These data, in conjunction with the distances between consecutive GPS locations, have been used in a study of calibration of statistical models for inferring animal activity, but the rate of misclassification in that study was found to be significant [19]. One might expect the distance between GPS locations in itself to be an adequate indicator of activity at three levels�Clow, medium, and high��which would correspond to resting, grazing, and walking, respectively.

However, whereas this works fairly well for walking, GPS error and the fact that the distance between consecutive GPS location Inhibitors,Modulators,Libraries readings of a stationary device is not zero, blurs the distinction between Entinostat resting and grazing [20]. The addition of information from motion sensors does improve matters, but not as much as might be expected: first, resting animals move their heads; second, the precise fit of the GPS collar around the neck may have a significant influence on motion sensor responses and counts [21]; and third, we suspect that device sensitivity differs among factory batches. Visual calibration of individual cow-collar combinations or even of individual collars is not a practicable option, because visual observations are extremely sellectchem time consuming.We hypothesized that leg movements might correspond to activity more directly and mechanistically than head and neck movements. Combining data from a pedometer with those from a GPS collar might, therefore, enable reduction in the rates of misclassification of animal activity. This would, however, require a pedometer of exceptional temporal resolution.