Using high fix rate GPS data to determine the relationships between fix rate, prediction errors and patch selection

The global positioning system (GPS) is increasingly being used to monitor animal movement and to develop more sustainable land management practices. Reliable and accurate GPS data are required to develop robust animal resource selection functions (RSFs). This study used Earth Centred Earth Fixed (ECEF) high fix rate (4 Hz) GPS data collected from Fleck™ 2 GPS collars fitted to six cows over a four-day period. The GPS data were used to derive speed and mean speed histograms. Maximum mean speed values were used to model the maximum distance travelled for a range of sample intervals. Maximum distance travelled was used to estimate the minimum area that could be predicted. To extend the methods further, we explored the use of probability density functions to describe the speed profiles; a Gamma probability density function (PDF) was shown to provide the best description. By modelling animal speeds using both the raw data and the PDF, it was possible to estimate the minimum area occupied for varying GPS sample frequencies and patch areas. Overall, the cows spent the greatest proportion (77%) of their time either stationary or moving very slowly (less than 0.2 m s−1). Despite the slow speeds, the prediction errors were around 90% for sample frequencies of greater than half an hour and a patch area of 100 m2. If study patch areas are between 10 and 100 m2 the GPS fix rate would need to be less than 10 s. When GPS fix intervals are recorded at 1 h intervals, the probability of accurately predicting selection within a 10 000 m2 patch is approximately 30%. Researchers and land managers that use GPS technology to monitor animal behaviour should be aware of the relationship between GPS fix rate and accuracy in predicting landscape use especially when attempting to develop resource selection functions. One approach to using low GPS fix rates would be to gather short duration, high fix rate GPS data to derive speed histograms for establishing probability density functions. These speed histograms could then be used in conjunction with the low fix rate data to establish the probability of an animal visiting a given patch within a landscape.