adehabitatHR (version 0.4.22)

clusthr: Estimation of the Home Range by Single-Linkage Cluster Analysis

Description

clusthr allows the estimation of the home range by single-linkage cluster analysis (see details).

Usage

clusthr(xy, unin = c("m", "km"),
        unout = c("ha", "m2", "km2"),
        duplicates=c("random","remove"), amount = NULL)

Value

The function clusthr returns either objects of class

SpatialPolygonsDataFrame (if the relocations of only one animals are passed as the xy argument) or a list of

SpatialPolygonsDataFrame of class MCHu -- Multiple Convex Hull (if the relocations of several animals are passed as the

xy argument).

Arguments

xy

an object inheriting the class SpatialPoints containing the x and y relocations of the animal. If xy inherits the class SpatialPointsDataFrame, it should contain only one column (factor) corresponding to the identity of the animals for each relocation.

unin

the units of the relocations coordinates. Either "m" (default) for meters or "km" for kilometers

unout

the units of the output areas. Either "m2" for square meters, "km2" for square kilometers or "ha" for hectares (default)

duplicates

a setting to determine how duplicated points are handled. If "random" the duplicated points are slightly moved randomly. If "remove" the duplicated points are removed.

amount

if duplicates == random, this parameter controls the amount of noise added to the data (see the help page of jitter for additional information on this parameter).

Author

Clement Calenge

Details

This method estimates home range using the single-linkage cluster analysis modified by Kenward et al. (2001). The clustering process is described hereafter: the three locations with the minimum mean of nearest-neighbour 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.

Note that a given home-range contour can be extracted using the function getverticeshr.

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, 1905--1920.

See Also

MCHu for further information on the class MCHu, and SpatialPolygonsDataFrame-class for additional information on this class. See getverticeshr to extract a given home range contour.

Examples

data(puechabonsp)
lo<-puechabonsp$relocs[,1]

## Home Range Estimation
res <- clusthr(lo)

## Displays the home range
plot(res)

## Computes the home range size
MCHu2hrsize(res)

## get the 95 percent home range:
plot(getverticeshr(res, percent=95))