  The home range of a specific animal describes the geographic area where this
individual spends most of the time while carrying out its usual activities
(eating, resting, reproduction, ...). Although a well-established definition of
this concept is lacking, there is a variety of home range estimators. The first
objective of this work is to review and categorize the statistical
methodologies proposed in the literature to approximate the home range of an
animal, based on a sample of observed locations. The second aim is to address
the open question of choosing the "best" home range from a collection of them
based on the same sample. We introduce a numerical index, based on a
penalization criterion, to rank the estimated home ranges. The key idea is to
balance the excess area covered by the estimator (with respect to the original
sample) and a shape descriptor measuring the over-adjustment of the home range
to the data. To our knowledge, apart from computing the home range area, our
ranking procedure is the first one which is both applicable to real data and to
any type of home range estimator. Further, the optimization of the selection
index provides in fact a way to select the smoothing parameter for the kernel
home range estimator. For clarity of exposition, we have applied all the
estimation procedures and our selection proposal to a set of real locations of
a Mongolian wolf using R as the statistical software. As a byproduct, this
review contains a thorough revision of the implementation of home range
estimators in the R language.
