1. Destructive subsampling or restrictive sampling are often standard proce
dures to obtain independence of spatial observations in home range analyses
. We examined whether home range estimators based upon kernel densities req
uire 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 tra
velled 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 da
ta set improved at shorter time intervals despite the increase in autocorre
lation among the observations.
4. Subsampling did not reduce autocorrelation among locational observations
of snapping turtles or antler flies, and home range size, time partitionin
g and total distance travelled were better represented by autocorrelated ob
servations.
5. We found that kernel densities do not require serial independence of obs
ervations when estimating home range, and we recommend that researchers max
imize the number of observations using constant time intervals to increase
the accuracy and precision of their estimates.