Estimation of Home Range by Clustering
Description
clusthr
allows the estimation of the home range by clustering
(see details).
plot.clusthr
plots the results.
clusthr.area
computes the homerange size for given percents of
relocations included in the home range, and plots the results.
getverticesclusthr
computes the coordinates of the vertices of
the home ranges (kver.rast
and kver2shapefile
can be
used to export the results toward GIS, see the help page of these
functions).
Usage
1 2 3 4 5 6 7 8 9 10 11  clusthr(xy, id = NULL)
## S3 method for class 'clusthr'
print(x, ...)
## S3 method for class 'clusthr'
plot(x, whi = names(x), pch = 21,
bgpts = "white", colpts = "black", cex = 0.7,
plotit = TRUE, colpol = "grey", ...)
clusthr.area(x, percent = seq(20, 100, by = 5),
unin = c("m", "km"), unout = c("ha", "km2", "m2"),
plotit = TRUE)
getverticesclusthr(x, whi = names(x), lev=95)

Arguments
xy 
a data frame with two columns containing the coordinates of the relocation of the monitored animals 
id 
a factor giving the identity of the animal for each relocation 
x 
an object of class 
whi 
a vector of character indicating the animals to be plotted 
pch 
either an integer specifying a symbol or a single character
to be used as the default in plotting points. See 
bgpts 
background ("fill") color for the open plot symbols given by

colpts 
character. The color of the points 
cex 
The size of the points (see 
plotit 
logical. Whether the plot should be drawn. 
colpol 
a character string indicating the colors to be used. Can
be set to 
percent,lev 
100 minus the proportion of outliers to be excluded from the home range 
unin 
the units of the relocations coordinates. Either

unout 
the units of the output areas. Either 
... 
additional arguments to be passed to the functions

Details
This method estimates home range using a modification of
singlelinkage cluster analysis developped by Kenward et
al. (2001). The clustering process is described hereafter: the three
locations with the minimum mean of nearestneighbour joining distances
(NNJD) form the first cluster. At each step, two distances are
computed: (i) the minimum mean NNJD between three locations (which
corresponds to the next potential cluster) and (ii) the minimum of the
NNJD between a cluster "c" and the closest location. If (i) is smaller
that (ii), another cluster is defined with these three locations.
If (ii) is smaller than (i), the cluster "c" gains a new location. If
this new location belong to another cluster, the two cluster
fuses. The process stop when all relocations are assigned to the same
cluster.
At each step of the clustering process, the proportion of all relocations which are assigned to a cluster is computed (so that the home range can be defined to enclose a given proportion of the relocations at hand, i.e. to an uncomplete process). At a given step, the home range is defined as the set of minimum convex polygon enclosing the relocations in the clusters.
Value
clusthr
returns a list of class clusthr
. This list has one
component per animal (named as the levels of argument id
). Each
component is itself a list, with the following subcomponents:
xy 
the animals' relocations 
results 
a data.frame with three columns: 
plot.clusthr
returns an invisible list (see invisible
),
with one component per animal. Each component is itself a list with
one component per step of the clustering process. At each step, an
object of class area
describes the home range of the animal
clusthr.area
returns a data.frame of class hrsize
, which
can be plotted using the generic function plot
.
getverticesclusthr
returns a list of class kver
, where each
element is an object of class area
.
Author(s)
Clement Calenge clement.calenge@oncfs.gouv.fr
References
Kenwward R.E., Clarke R.T., Hodder K.H. and Walls S.S. (2001) Density and linkage estimators of homre range: nearest neighbor clustering defines multinuclear cores. Ecology, 82, 19051920.
See Also
kver
for further information on objects
of class kver
.
Examples
1 2 3 4 5 6 7 8 9 10 11 