Detecting autocorrelation problems from GPS collar data in livestock studies
Publication Type: |
Journal Article |
Year of Publication: |
2012 |
Authors: |
H.L. Perotto-Baldivieso, S.M. Cooper, A.F. Cibils, M. Figueroa-Pagán, K. Udaeta, C.M. Black-Rubio |
Publication/Journal: |
Applied Animal Behaviour Science |
Keywords: |
animal tracking, distance statistics, livestock, sampling intervals |
ISBN: |
01681591 |
Abstract:
Uneven use of grasslands and savannas by livestock has a significant impact on ecosystem productivity, biodiversity, and function. In studies of livestock distribution, global positioning systems (GPS) collars are frequently used and the rapid rate of technological improvement has brought new opportunities to collect extremely large amounts of very accurate spatial information. However, these advances also pose statistical hallenges associated with the analysis of large, temporally correlated, datasets. Our main goal was to find the optimal sampling time intervals for GPS collar schedules when studying livestock distribution in semi-arid ecosystems. The schedule must provide maximum spatio-temporal information while avoiding problems of autocorrelation of sequential locations to provide a methodology that is both practical and statistically valid. We used GPS collar data collected in the Southwestern region of the United States. In each study cattle were tracked and data were recorded every 5 min. Location information from the 5-min GPS fixes were subsampled into 10, 20, 30, 60, 90, 120, 150, 180, 240, 300, 360, and 420-min regular intervals. We calculated the Euclidean distance between pairs of successive locations then conducted correlation analyses to determine the degree of similarity between successive traveled distances. We then selected two correlated and two non-correlated time-interval datasets to
compare estimates of kernel home range and minimum convex polygon areas. Successive Euclidean distances between GPS locations were significantly correlated when time intervals were <120 min. The calculated distance traveled was significantly reduced as time intervals between successive locations increased. Kernel home range values were smaller in correlated than in non-correlated datasets yet minimum convex polygon values were greater in correlated data than in non-correlated data sets. Our study shows the importance of considering different livestock sampling time intervals using GPS to achieve accurate and meaningful results on animal distributions especially in semi-arid ecosystems. Circumstances in which researchers may elect to use short-time interval autocorrelated data sets are also discussed.