LoCoH {adehabitatHR}  R Documentation 
The functions computes the home range of one or several animals using the LoCoH family of methods.
The functions LoCoH.k
, LoCoH.r
, and LoCoH.a
implement the kLoCoH, rLoCoH, and aLoCoH respectively (Getz et
al. 2007).
The functions LoCoH.k.area
, LoCoH.r.area
, and
LoCoH.a.area
compute the curve showing the relationships
between the homerange size (computed to a specified percent) and the
k, r or a parameters respectively.
LoCoH.k(xy, k=5, unin = c("m", "km"), unout = c("ha", "m2", "km2"), duplicates=c("random","remove"), amount = NULL) LoCoH.r(xy, r, unin = c("m", "km"), unout = c("ha", "m2", "km2"), duplicates=c("random","remove"), amount = NULL) LoCoH.a(xy, a, unin = c("m", "km"), unout = c("ha", "m2", "km2"), duplicates=c("random","remove"), amount = NULL) LoCoH.k.area(xy, krange, percent=100, unin = c("m", "km"), unout = c("ha", "m2", "km2"), duplicates=c("random","remove"), amount = NULL) LoCoH.r.area(xy, rrange, percent=100, unin = c("m", "km"), unout = c("ha", "m2", "km2"), duplicates=c("random","remove"), amount = NULL) LoCoH.a.area(xy, arange, percent=100, unin = c("m", "km"), unout = c("ha", "m2", "km2"), duplicates=c("random","remove"), amount = NULL)
xy 
An object inheriting the class 
k 
numeric. The number of nearest neighbors minus one out of which to create convex hulls 
r 
numeric. The convex hulls are created out of all points within r distance from the root points 
a 
numeric. Create convex hulls from the maximum number of nearest neighbors such that the sum of their distances is less than or equal to this parameter 
unin 
the units of the relocations coordinates. Either

unout 
the units of the output areas. Either 
duplicates 
a setting to determine how duplicated points are
handled. If " 
amount 
if 
krange 
a vector containing the values of k for which the home range size is to be estimated. 
arange 
a vector containing the values of k for which the home range size is to be estimated. 
rrange 
a vector containing the values of k for which the home range size is to be estimated. 
percent 
the percentage level of the home range. For the
function 
The functions LoCoH.*
return either objects of class
SpatialPolygonsDataFrame
(if the relocations of only one
animals are passed as the xy
argument) or a list of
SpatialPolygonsDataFrame
(if the relocations of several
animals are passed as the xy
argument).
The functions LoCoH.*.area
return invisibly either a vector (if
the relocations of only one animals are passed as the xy
argument) or a data frame containing the homerange sizes for various
values of k, r (rows) for the different animals (columns).
These functions rely on the packages rgeos, gpclib, and maptools.
The LoCoH family of methods for locating Utilization Distributions consists of three algorithms: Fixed k LoCoH, Fixed r LoCoH, and Adaptive LoCoH. All the algorithms work by constructing a small convex hull for each relocation, and then incrementally merging the hulls together from smallest to largest into isopleths. The 10% isopleth contains 10% of the points and represents a higher utilization than the 100% isopleth that contains all the points.
Fixed k LoCoH: Also known as kNNCH, Fixed k LoCoH is described in Getz and Willmers (2004). The convex hull for each point is constructed from the (k1) nearest neighbors to that point. Hulls are merged together from smallest to largest based on the area of the hull.
Fixed r LoCoH: In this case, hulls are created from all points
within r
distance of the root point. When merging hulls, the
hulls are primarily sorted by the value of k generated for each hull
(the number of points contained in the hull), and secondly by the area
of the hull.
Adaptive LoCoH: Here, hulls are created out of the maximum nearest neighbors such that the sum of the distances from the nearest neighbors is less than or equal to d. Use the same hull sorting as Fixed r LoCoH.
Fixed r LoCoH and Adaptive LoCoH are discussed in Getz et al (2007).
All of these algorithms can take a significant amount of time. Time taken increases exponentially with the size of the data set.
Clement Calenge clement.calenge@ofb.gouv.fr
with contributions from Scott FortmannRoe scottfr@gmail.com
Getz, W.M. & Wilmers, C.C. (2004). A local nearestneighbor convexhull construction of home ranges and utilization distributions. Ecography, 27, 489–505.
Getz, W.M., FortmannRoe, S.B, Lyons, A., Ryan, S., Cross, P. (2007). LoCoH methods for the construction of home ranges and utilization distributions. PLoS ONE, 2: 1–11.
## Not run: ## Load the data data(puechabonsp) ## The relocations: locs < puechabonsp$relocs locsdf < as.data.frame(locs) head(locsdf) ## Shows the relocations plot(locs, col=as.numeric(locsdf[,1])) ## Examinates the changes in homerange size for various values of k ## Be patient! the algorithm can be very long ar < LoCoH.k.area(locs[,1], k=c(8:13)) ## 12 points seems to be a good choice (rough asymptote for all animals) ## the kLoCoH method: nn < LoCoH.k(locs[,1], k=12) ## Graphical display of the results plot(nn, border=NA) ## the object nn is a list of objects of class ## SpatialPolygonsDataFrame length(nn) names(nn) class(nn[[1]]) ## shows the content of the object for the first animal as.data.frame(nn[[1]]) ## The 95% home range is the smallest area for which the ## proportion of relocations included is larger or equal ## to 95% In this case, it is the 22th row of the ## SpatialPolygonsDataFrame. ## The area covered by the home range is for this first animal ## equal to 22.87 ha. ## shows this area: plot(nn[[1]][11,]) ## rasterization of the home ranges: ## use the map of the area: image(puechabonsp$map) ras < MCHu.rast(nn, puechabonsp$map, percent=100) opar < par(mfrow=c(2,2)) lapply(1:4, function(i) { image(ras,i); box()}) par(opar) ## rLoCoH and aLoCoH can be applied similarly ## End(Not run)