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yielded the most accurate estimates of home range areas and had the smallest
variance. These estimates averaged 0.7 percent smaller than the true areas of
the simulated home ranges, whereas the adaptive kernel estimates averaged
about 25 percent larger than true. The harmonic mean estimator overesti-
mated true home range area by about 20 percent. The cross-validated, fixed
kernel estimator also estimated the shapes of the utility distributions the best
(figure 3.4). Figure 3.2 depicts the utility distribution isoclines for the home
range of an adult female black bear. In addition, for simple, simulated home
ranges, the fixed and adaptive kernel estimators generate consistent 95 percent
home range areas with as few as 20 location estimates (Noel 1993; Seaman et
al. 1999). However, the harmonic mean estimator requires 125 location esti-
mates or more.
The adaptive kernel estimators performed slightly worse than the fixed ker-
nel estimators in all of the tests, apparently through overestimation of periph-
eral use (Seaman 1993; Seaman et al. 1999; Seaman and Powell 1996). Adap-
tive kernel estimators also appear sensitive to autocorrelation within data sets.
The amount of kernel variation can be adjusted for adaptive kernel estimators,
but Seaman has found no consistent or predictable pattern of adjustment that
minimizes error for these estimators (Seaman et al. 1999). Consequently, the
best estimators at present are fixed kernel estimators with band width chosen
via least-squares cross-validation (Seaman 1993; Seaman et al. 1999; Seaman
and Powell 1996).
Kernel estimators share three shortcomings with most other home range
estimators. First, they ignore time sequence information available with most
data on animal locations (White and Garrott 1990). All estimators assume
that all location data points are independent and that time sequence informa-
tion is irrelevant. Future kernel estimators will incorporate brownian bridges
between consecutive location estimates, with the heights, widths, and shapes
of the bridges dependent on the time and distance between locations, as devel-
oped by Bullard (1999). Second, kernel estimators estimate the probability
that an animal will be in any part of its home range; therefore, they sometimes
produce 95 percent home range outlines that have convoluted shapes or dis-
junct islands of use. For example, figure 3.5 shows the 95 percent fixed kernel
home range for an adult female black bear, bear 61, whom I studied in
1983-1985. Bear 61's home range in 1983 nearly surrounds a large area not
designated as her home range. Surely, this bear was familiar with the sur-
rounded area and included it on her cognitive map; however, she chose not to
use that area regularly in 1983. In other years, she did use that area (figure 3.1).
The fixed kernel estimate of bear 61's home range accurately quantifies the
