Home range

Home range is a concept that can be traced back to a publication in 1943 by W. H. Burt, [Burt, W. H. 1943. Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24:346–352.] who constructed maps delineating the spatial extent or outside boundary of an animal's movement during the course of its everyday activities. Associated with the concept of a home range is the concept of a utilization distribution, [Jennrich, R. I. and Turner F. B. 1969. Measurement of non-circular home range. J. Theoretical Biology 22:227-237.] [Ford, R. G. and Krumme D. W. 1979. The analysis of space use patterns. - J. Theoretical Biology 76:125-157.] which takes the form of a two dimensional probability density function that represents the probability of finding an animal in a defined area within its home range. The home range of an individual animal is typically constructed from a set of location points that have been collected over a period of time identifying the position in space of an individual at many points in time. Such data are now collected automatically using collars placed on individuals that transmit through satellites or using mobile cellphone technology the position of the animal, using global positioning systems (GPS) technology, at regular intervals .

The simplest way to draw the boundaries of a home range from a set of location data is to construct the smallest possible convex polygon around the data. This approach is referred to as the minimum convex polygon (MCP) method which is still widely employed, [Baker, J. 2001. Population density and home range estimates for the Eastern Bristlebird at Jervis Bay, south-eastern Australia. – Corella 25:62-67.] [Creel, S. and Creel N. M. 2002. The African Wild Dog: Behavior, Ecology, and Conservation. - Princeton University Press, Princeton, New Jersey, 341 p.] [Meulman, E. P. and Klomp N. I. 1999. Is the home range of the heath mouse Pseudomys shortridgei an anomaly in the Pseudomys genus? - Victorian Naturalist. 116:196-201.] [Rurik, L. and Macdonald D. W. 2003. Home range and habitat use of the kit fox (Vulpes macrotis) in a prairie dog (Cynomys ludovicianus) complex. - J. Zoology, 259:1-5.] but has many drawbacks including often overestimating the size of home ranges. [Burgman, M. A. and Fox J. C. 2003. Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. -Animal Conservation 6:19-28.]

The best known methods for constructing utilization distributions are the so-called bivariate Gaussian or normal distribution kernel methods. [Silverman, B. W. 1986. Density estimation for statistics and data analysis. - Chapman and Hall, London, UK] [Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. - Ecology 70:164–168.] [Seaman, D. E. and Powell R. A. 1996. An evaluation of the accuracy of kernel density estimators for home range analysis. - Ecology 77:2075–2085.] This group of methods is part of a more general group of parametric kernel methods that employ distributions other than the normal distribution as the kernel elements associated with each point in the set of location data.

Recently, the kernel approach to constructing utilization distributions was extended to include a number of nonparametric methods such as the Burgman and Fox's alpha-hull method. [Burgman, M. A. and Fox J. C. 2003. Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. -Animal Conservation 6:19-28.] and Getz and Wilmers local convex hull (LoCoh) method [Getz, W. M. and C. C. Wilmers, 2004. A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27:489-505. [http://www.cnr.berkeley.edu/%7Egetz/Reprints04/Getz&WilmersEcoG_SF_04.pdf View PDF] ] This latter method has now been extended from a purely fixed-point LoCoH method to fixed radius and adaptive point/radius LoCoH methods. [Getz, W.M, S. Fortmann-Roe, P. C. Cross, A. J. Lyonsa, S. J. Ryan, C.C. Wilmers, in review. LoCoH: nonparametric kernel methods for constructing home ranges and utilization distributions. [http://www.cnr.berkeley.edu/%7Egetz/Reprints06/GetzEtAlPLoSLoCoH07.pdf View PDF] ]

Although, currently, more software is available to implement parametric than nonparametric methods (because the latter approach is newer), the cited papers by Getz et al. demonstrate that LoCoH methods generally provide more accurate estimates of home range sizes and have better convergence properties as sample size increases than parametric kernel methods.

Computer packages for implementing parametric and nonparametric kernel methods are available online. [ [http://locoh.cnr.berkeley.edu/ LoCoH: Powerful algorithms for finding home ranges ] ] [ [http://www.faunalia.it/animov/ AniMove - Animal movement methods ] ] .

ee also

*Territoriality
*Dear enemy recognition

References