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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,<ref>{{cite journal |last=Baker |first=J. |year=2001 |title=Population density and home range estimates for the Eastern Bristlebird at Jervis Bay, south-eastern Australia |journal=Corella |volume=25 |issue= |pages=62–67 |doi= }}</ref><ref>{{cite book |last=Creel |first=S. |last2=Creel |first2=N. M. |year=2002 |title=The African Wild Dog: Behavior, Ecology, and Conservation |publisher=Princeton University Press |location=Princeton, New Jersey |isbn=0691016550 }}</ref><ref>{{cite journal |last=Meulman |first=E. P. |last2=Klomp |first2=N. I. |year=1999 |title=Is the home range of the heath mouse Pseudomys shortridgei an anomaly in the Pseudomys genus? |journal=Victorian Naturalist |volume=116 |issue= |pages=196–201 |doi= }}</ref><ref>{{cite journal |last=Rurik |first=L. |last2=Macdonald |first2=D. W. |year=2003 |title=Home range and habitat use of the kit fox (''Vulpes macrotis'') in a prairie dog (''Cynomys ludovicianus'') complex |journal=J. Zoology |volume=259 |issue=1 |pages=1–5 |doi=10.1017/S0952836902002959 }}</ref> but has many drawbacks including often overestimating the size of home ranges.<ref>{{cite journal |last=Burgman |first=M. A. |last2=Fox |first2=J. C. |year=2003 |title=Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning |journal=Animal Conservation |volume=6 |issue=1 |pages=19–28 |doi=10.1017/S1367943003003044 }}</ref>
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,<ref>{{cite journal |last=Baker |first=J. |year=2001 |title=Population density and home range estimates for the Eastern Bristlebird at Jervis Bay, south-eastern Australia |journal=Corella |volume=25 |issue= |pages=62–67 |doi= }}</ref><ref>{{cite book |last=Creel |first=S. |last2=Creel |first2=N. M. |year=2002 |title=The African Wild Dog: Behavior, Ecology, and Conservation |publisher=Princeton University Press |location=Princeton, New Jersey |isbn=0691016550 }}</ref><ref>{{cite journal |last=Meulman |first=E. P. |last2=Klomp |first2=N. I. |year=1999 |title=Is the home range of the heath mouse Pseudomys shortridgei an anomaly in the Pseudomys genus? |journal=Victorian Naturalist |volume=116 |issue= |pages=196–201 |doi= }}</ref><ref>{{cite journal |last=Rurik |first=L. |last2=Macdonald |first2=D. W. |year=2003 |title=Home range and habitat use of the kit fox (''Vulpes macrotis'') in a prairie dog (''Cynomys ludovicianus'') complex |journal=J. Zoology |volume=259 |issue=1 |pages=1–5 |doi=10.1017/S0952836902002959 }}</ref> but has many drawbacks including often overestimating the size of home ranges.<ref>{{cite journal |last=Burgman |first=M. A. |last2=Fox |first2=J. C. |year=2003 |title=Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning |journal=Animal Conservation |volume=6 |issue=1 |pages=19–28 |doi=10.1017/S1367943003003044 }}</ref>


The best known methods for constructing utilization distributions are the so-called bivariate Gaussian or [[normal distribution]] [[Multivariate kernel density estimation|kernel density methods]].<ref>Silverman, B. W. 1986. Density estimation for statistics and data analysis. - Chapman and Hall, London, UK</ref><ref>Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. - Ecology 70:164–168.</ref><ref>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.</ref>
The best known methods for constructing utilization distributions are the so-called bivariate Gaussian or [[normal distribution]] [[Multivariate kernel density estimation|kernel density methods]].<ref>{{cite book |last=Silverman |first=B. W. |year=1986 |title=Density estimation for statistics and data analysis |publisher=Chapman and Hall |location=London |isbn=0412246201 }}</ref><ref>{{cite journal |last=Worton |first=B. J. |year=1989 |title=Kernel methods for estimating the utilization distribution in home-range studies |journal=[[Ecology (journal)|Ecology]] |volume=70 |issue=1 |pages=164–168 |doi=10.2307/1938423 }}</ref><ref>{{cite journal |last=Seaman |first=D. E. |last2=Powell |first2=R. A. |year=1996 |title=An evaluation of the accuracy of kernel density estimators for home range analysis |journal=Ecology |volume=77 |issue=7 |pages=2075–2085 |doi=10.2307/2265701 }}</ref> 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.
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.<ref>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.</ref> and Getz and Wilmers [[local convex hull]] (LoCoh) method<ref>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]</ref> This latter method has now been extended from a purely fixed-point LoCoH method to fixed radius and adaptive point/radius LoCoH methods.<ref>Getz, W.M, S. Fortmann-Roe, P. C. Cross, A. J. Lyonsa, S. J. Ryan, C.C. Wilmers, 2007. LoCoH: nonparametric kernel methods for constructing home ranges and utilization distributions. [http://www.cnr.berkeley.edu/%7Egetz/Reprints06/GetzEtAlPLoSLoCoH07.pdf View PDF]</ref>
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.<ref>{{cite journal |last=Burgman |first=M. A. |last2=Fox |first2=J. C. |year=2003 |title=Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning |journal=[[Animal Conservation]] |volume=6 |issue=1 |pages=19–28 |doi=10.1017/S1367943003003044 }}</ref> and Getz and Wilmers [[local convex hull]] (LoCoh) method.<ref>{{cite journal |last=Getz |first=W. M. |first2=C. C. |last2=Wilmers |year=2004 |title=A local nearest-neighbor convex-hull construction of home ranges and utilization distributions |journal=Ecography |volume=27 |issue=4 |pages=489–505 |doi=10.1111/j.0906-7590.2004.03835.x |url=http://www.cnr.berkeley.edu/%7Egetz/Reprints04/Getz&WilmersEcoG_SF_04.pdf }}</ref> This latter method has now been extended from a purely fixed-point LoCoH method to fixed radius and adaptive point/radius LoCoH methods.<ref>{{cite journal |last=Getz |first=W. M |first2=S. |last2=Fortmann-Roe |first3=P. C. |last3=Cross |first4=A. J. |last4=Lyonsa |first5=S. J. |last5=Ryan |first6=C. C. |last6=Wilmers |year=2007 |title=LoCoH: nonparametric kernel methods for constructing home ranges and utilization distributions |url=http://www.cnr.berkeley.edu/%7Egetz/Reprints06/GetzEtAlPLoSLoCoH07.pdf |journal=[[PLoS ONE]] |volume=2 |issue=2 |pages=e207 |doi=10.1371/journal.pone.0000207 }}</ref>


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.
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.


Home range estimation methods that have been developed since 2005 include:
Home range estimation methods that have been developed since 2005 include:
* LoCoH<ref>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]</ref>
* LoCoH<ref>{{cite journal |last=Getz |first=W. M. |first2=C. C. |last2=Wilmers |year=2004 |title=A local nearest-neighbor convex-hull construction of home ranges and utilization distributions |journal=Ecography |volume=27 |issue=4 |pages=489-505 |doi=10.1111/j.0906-7590.2004.03835.x |url=http://www.cnr.berkeley.edu/%7Egetz/Reprints04/Getz&WilmersEcoG_SF_04.pdf }}</ref>
* Brownian Bridge<ref>Horne, J. S., E. O. Garton, S. M. Krone and J. S. Lewis, 2007. Analyzing animal movements using Brownian Bridges. Ecology 88:2364-2363.</ref>
* Brownian Bridge<ref>{{cite journal |last=Horne |first=J. S. |first2=E. O. |last2=Garton |first3=S. M. |last3=Krone |first4=J. S. |last4=Lewis |year=2007 |title=Analyzing animal movements using Brownian Bridges |journal=Ecology |volume=88 |issue=9 |pages=2354-2363 |doi=10.1890/06-0957.1 }}</ref>
* Line-based Kernel<ref>Steiniger, S. and A.J.S. Hunter, in press. A scaled line-based kernel density estimator for the retrieval of utilization distributions and home ranges from GPS movement tracks. Ecological Informatics doi:10.1016/j.ecoinf.2012.10.002.</ref>
* Line-based Kernel<ref>{{cite journal |last=Steiniger |first=S. |first2=A. J. S. |last2=Hunter |title=A scaled line-based kernel density estimator for the retrieval of utilization distributions and home ranges from GPS movement tracks |journal=Ecological Informatics |volume= |year=2012 |issue= |doi=10.1016/j.ecoinf.2012.10.002 }}</ref>
* GeoEllipse<ref>Downs, J. A., M. W. Horner and A. D. Tucker, 2011. Time-geographic density estimation for home range analysis. Annals of GIS 17:163-171.</ref><ref>Long, J. A. and T. A. Nelson, 2012. Time geography and wildlife home range delineation. The Journal of Wildlife Management 76:407-413.</ref>
* GeoEllipse<ref>{{cite journal |last=Downs |first=J. A. |first2=M. W. |last2=Horner |first3=A. D. |last3=Tucker |year=2011 |title=Time-geographic density estimation for home range analysis |journal=Annals of GIS |volume=17 |issue=3 |pages=163–171 |doi=10.1080/19475683.2011.602023 }}</ref><ref>{{cite journal |last=Long |first=J. A. |first2=T. A. |last2=Nelson |year=2012 |title=Time geography and wildlife home range delineation |journal=[[Journal of Wildlife Management]] |volume=76 |issue=2 |pages=407–413 |doi=10.1002/jwmg.259 }}</ref>
* Line-Buffer<ref>Steiniger, S. and A.J.S. Hunter, 2012. OpenJUMP HoRAE – A free GIS and Toolbox for Home-Range Analysis. Wildlife Society Bulletin 36:600-608. (see also: [http://gisciencegroup.ucalgary.ca/wiki/OpenJUMP_HoRAE HoRAE - Home Range Analysis and Estimation Toolbox])</ref>
* Line-Buffer<ref>{{cite journal |last=Steiniger |first=S. |first2=A. J. S. |last2=Hunter |year=2012 |title=OpenJUMP HoRAE – A free GIS and Toolbox for Home-Range Analysis |journal=Wildlife Society Bulletin |volume=36 |issue=3 |pages=600-608 |doi=10.1002/wsb.168 }} (See also: [http://gisciencegroup.ucalgary.ca/wiki/OpenJUMP_HoRAE HoRAE - Home Range Analysis and Estimation Toolbox])</ref>


Computer packages for using parametric and nonparametric kernel methods are available online.<ref>[http://locoh.cnr.berkeley.edu/ LoCoH: Powerful algorithms for finding home ranges<!-- Bot generated title -->]</ref><ref>[http://www.faunalia.it/animov/ AniMove - Animal movement methods<!-- Bot generated title -->]</ref><ref>[http://gisciencegroup.ucalgary.ca/wiki/OpenJUMP_HoRAE HoRAE - Home Range Analysis and Estimation Toolbox (open source; methods: Point-Kernel, Line-Kernel, Brownian-Bridge, LoCoH, MCP, Line-Buffer)]</ref><ref>[http://cran.univ-lyon1.fr/web/packages/adehabitat/index.html adehabitat for R (open source; methods: Point-Kernel, Line-Kernel, Brownian-Bridge, LoCoH, MCP, GeoEllipse)]</ref>
Computer packages for using parametric and nonparametric kernel methods are available online.<ref>[http://locoh.cnr.berkeley.edu/ LoCoH: Powerful algorithms for finding home ranges<!-- Bot generated title -->]</ref><ref>[http://www.faunalia.it/animov/ AniMove - Animal movement methods<!-- Bot generated title -->]</ref><ref>[http://gisciencegroup.ucalgary.ca/wiki/OpenJUMP_HoRAE HoRAE - Home Range Analysis and Estimation Toolbox (open source; methods: Point-Kernel, Line-Kernel, Brownian-Bridge, LoCoH, MCP, Line-Buffer)]</ref><ref>[http://cran.univ-lyon1.fr/web/packages/adehabitat/index.html adehabitat for R (open source; methods: Point-Kernel, Line-Kernel, Brownian-Bridge, LoCoH, MCP, GeoEllipse)]</ref>

Revision as of 23:18, 26 October 2012

Home range is the area where an animal lives and travels in. It is closely related to, but not identical with, the concept of "territory".

The concept that can be traced back to a publication in 1943 by W. H. Burt,[1] 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,[2][3] 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 and 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,[4][5][6][7] but has many drawbacks including often overestimating the size of home ranges.[8]

The best known methods for constructing utilization distributions are the so-called bivariate Gaussian or normal distribution kernel density methods.[9][10][11] 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.[12] and Getz and Wilmers local convex hull (LoCoh) method.[13] This latter method has now been extended from a purely fixed-point LoCoH method to fixed radius and adaptive point/radius LoCoH methods.[14]

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.

Home range estimation methods that have been developed since 2005 include:

Computer packages for using parametric and nonparametric kernel methods are available online.[21][22][23][24]

See also

References

  1. ^ Burt, W. H. (1943). "Territoriality and home range concepts as applied to mammals". Journal of Mammalogy. 24 (3): 346–352. JSTOR 1374834.
  2. ^ Jennrich, R. I.; Turner, F. B. (1969). "Measurement of non-circular home range". J. Theoretical Biology. 22 (2): 227–237. doi:10.1016/0022-5193(69)90002-2.
  3. ^ Ford, R. G.; Krumme, D. W. (1979). "The analysis of space use patterns". J. Theoretical Biology. 76 (2): 125–157. doi:10.1016/0022-5193(79)90366-7.
  4. ^ Baker, J. (2001). "Population density and home range estimates for the Eastern Bristlebird at Jervis Bay, south-eastern Australia". Corella. 25: 62–67.
  5. ^ Creel, S.; Creel, N. M. (2002). The African Wild Dog: Behavior, Ecology, and Conservation. Princeton, New Jersey: Princeton University Press. ISBN 0691016550.
  6. ^ Meulman, E. P.; 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.
  7. ^ Rurik, L.; 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): 1–5. doi:10.1017/S0952836902002959.
  8. ^ Burgman, M. A.; Fox, J. C. (2003). "Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning". Animal Conservation. 6 (1): 19–28. doi:10.1017/S1367943003003044.
  9. ^ Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall. ISBN 0412246201.
  10. ^ Worton, B. J. (1989). "Kernel methods for estimating the utilization distribution in home-range studies". Ecology. 70 (1): 164–168. doi:10.2307/1938423.
  11. ^ Seaman, D. E.; Powell, R. A. (1996). "An evaluation of the accuracy of kernel density estimators for home range analysis". Ecology. 77 (7): 2075–2085. doi:10.2307/2265701.
  12. ^ Burgman, M. A.; Fox, J. C. (2003). "Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning". Animal Conservation. 6 (1): 19–28. doi:10.1017/S1367943003003044.
  13. ^ Getz, W. M.; Wilmers, C. C. (2004). "A local nearest-neighbor convex-hull construction of home ranges and utilization distributions" (PDF). Ecography. 27 (4): 489–505. doi:10.1111/j.0906-7590.2004.03835.x.
  14. ^ Getz, W. M; Fortmann-Roe, S.; Cross, P. C.; Lyonsa, A. J.; Ryan, S. J.; Wilmers, C. C. (2007). "LoCoH: nonparametric kernel methods for constructing home ranges and utilization distributions" (PDF). PLoS ONE. 2 (2): e207. doi:10.1371/journal.pone.0000207.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  15. ^ Getz, W. M.; Wilmers, C. C. (2004). "A local nearest-neighbor convex-hull construction of home ranges and utilization distributions" (PDF). Ecography. 27 (4): 489–505. doi:10.1111/j.0906-7590.2004.03835.x.
  16. ^ Horne, J. S.; Garton, E. O.; Krone, S. M.; Lewis, J. S. (2007). "Analyzing animal movements using Brownian Bridges". Ecology. 88 (9): 2354–2363. doi:10.1890/06-0957.1.
  17. ^ Steiniger, S.; Hunter, A. J. S. (2012). "A scaled line-based kernel density estimator for the retrieval of utilization distributions and home ranges from GPS movement tracks". Ecological Informatics. doi:10.1016/j.ecoinf.2012.10.002.
  18. ^ Downs, J. A.; Horner, M. W.; Tucker, A. D. (2011). "Time-geographic density estimation for home range analysis". Annals of GIS. 17 (3): 163–171. doi:10.1080/19475683.2011.602023.
  19. ^ Long, J. A.; Nelson, T. A. (2012). "Time geography and wildlife home range delineation". Journal of Wildlife Management. 76 (2): 407–413. doi:10.1002/jwmg.259.
  20. ^ Steiniger, S.; Hunter, A. J. S. (2012). "OpenJUMP HoRAE – A free GIS and Toolbox for Home-Range Analysis". Wildlife Society Bulletin. 36 (3): 600–608. doi:10.1002/wsb.168. (See also: HoRAE - Home Range Analysis and Estimation Toolbox)
  21. ^ LoCoH: Powerful algorithms for finding home ranges
  22. ^ AniMove - Animal movement methods
  23. ^ HoRAE - Home Range Analysis and Estimation Toolbox (open source; methods: Point-Kernel, Line-Kernel, Brownian-Bridge, LoCoH, MCP, Line-Buffer)
  24. ^ adehabitat for R (open source; methods: Point-Kernel, Line-Kernel, Brownian-Bridge, LoCoH, MCP, GeoEllipse)
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