The Domain algorithm (Carpenter et al. 1993) that has been extensively used for species distribution modeling. It is included here for that reason but please note that it generally does not perform very well in model comparison (Elith et al. 2006, Hijmans and Graham, 2006). The Domain algorithm computes the Gower distance between environmental variables at any location and those at any of the known locations of occurence ('training sites'). For each variable the minimum distance beteen a site and any of the training points is taken. To integrate over environmental variables, the maximum distance to any of the variables is used. This distance is substracted from one, and (in this R implementation) values below zero are truncated so that the scores are between 0 (low) and 1 (high).

domain(x, p, ...)
Raster* object or matrix
two column matrix or SpatialPoints* object
Additional arguments

  • An object of class 'Domain'


Carpenter G., A.N. Gillison and J. Winter, 1993. Domain: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity Conservation 2:667-680. Elith, J., C.H. Graham, R.P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R.J. Hijmans, F. Huettmann, J. Leathwick, A. Lehmann, J. Li, L.G. Lohmann, B. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. McC. Overton, A.T. Peterson, S. Phillips, K. Richardson, R. Scachetti-Pereira, R. Schapire, J. Soberon, S. Williams, M. Wisz and N. Zimmerman, 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151. Hijmans R.J., and C.H. Graham, 2006. Testing the ability of climate envelope models to predict the effect of climate change on species distributions. Global change biology 12: 2272-2281.

See Also

predict, maxent, bioclim, mahal

  • domain
  • domain,Raster,SpatialPoints-method
  • domain,Raster,matrix-method
  • domain,Raster,data.frame-method
  • domain,matrix,missing-method
  • domain,data.frame,missing-method
logo <- stack(system.file("external/rlogo.grd", package="raster"))
#presence data
pts <- matrix(c(48.243420, 48.243420, 47.985820, 52.880230, 49.531423, 46.182616, 54.168232, 69.624263, 83.792291, 85.337894, 74.261072, 83.792291, 95.126713, 84.565092, 66.275456, 41.803408, 25.832176, 3.936132, 18.876962, 17.331359,7.048974, 13.648543, 26.093446, 28.544714, 39.104026, 44.572240, 51.171810, 56.262906, 46.269272, 38.161230, 30.618865, 21.945145, 34.390047, 59.656971, 69.839163, 73.233228, 63.239594, 45.892154, 43.252326, 28.356155), ncol=2)
d <- domain(logo, pts)
p <- predict(d, logo)
Documentation reproduced from package dismo, version 0.5-1, License: GPL (>= 3)

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