Kernel Home Range Estimation for ArcGIS, using VBA - Fish and ...

By using MCPs in the asymptote analysis, the user will be able to pick out dispersal events or
significant sallies if the data are added sequentially (Figure 5.2.1.a.). A spike in the graph shows
a significant increase in area. Finding the dates of such events and the associated age for each
individual may be a way of estimating the dispersal age in a population. Estimating time gaps
between spikes may indicate tenure times where territories are held. If the points are added
randomly, this will not be possible (Figure 5.2.1.b.). Since kernel density estimates should be
more robust towards outliers in the dataset, the detail required for analyzing individual behaviors
is lost. The kernel asymptote analysis will however provide a better estimate for home range
asymptotes using either consecutive locations (Figure 5.2.2.a.) or randomly added locations
(Figure 5.2.2.b.).
a
b
Figure 5.2.1. MCP asymptote analyses for one female cheetah using consecutively (a) and
randomly added locations (b). Spikes in the graph indicate potential range shifts or
exploratory sallies or perhaps dispersal.
a
b
Figure 5.2.2. Kernel density asymptote analyses for one female cheetah using consecutively (a)
and randomly added locations (b). Kernel estimation is relatively insensitive to range shifts,
exploratory sallies or dispersal, and probably provides a better asymptote estimate if enough
data are available.
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