Eliminating autocorrelation reduces biological relevance of home range estimates

1. Destructive subsampling or restrictive sampling are often standard procedures to obtain independence of spatial observations in home range analyses. We examined whether home range estimators based upon kernel densities require serial independence of observations, by using a Monte Carlo simulation, antler flies and snapping turtles as models. 2. Home range size, time partitioning and total straight line distances travelled were tested to determine if subsampling improved kernel performance and estimation of home range parameters. 3. The accuracy and precision of home range estimates from the simulated data set improved at shorter time intervals despite the increase in autocorrelation among the observations. 4. Subsampling did not reduce autocorrelation among locational observations of snapping turtles or antler flies, and home range size, time partitioning and total distance travelled were better represented by autocorrelated observations. 5. We found that kernel densities do not require serial independence of observations when estimating home range, and we recommend that researchers maximize the number of observations using constant time intervals to increase the accuracy and precision of their estimates.