The Modified Random Cluster algorithm of Saura and Martinez-Millan (2000) is used to generate a mask object representing patches of contiguous ‘habitat’ cells (pixels) within a ‘non-habitat’ matrix (‘non-habitat’ cells are optionally dropped). Spatial autocorrelation (fragmentation) of habitat patches is controlled via the parameter ‘p’. ‘A’ is the expected proportion of ‘habitat’ cells.
secr mask object to use as template
parameter to control fragmentation
parameter for expected proportion of habitat
integer code for adjacency (rook's move 4 or queen's move 8)
integer minimum size of patch
logical for whether to drop non-habitat cells
character name of covariate when
logical for whether intermediate stages should be plotted
Habitat is simulated within the region defined by the cells of mask. The region may be non-rectangular.
The algorithm comprises stages A-D:
A. Randomly select proportion
p of cells from the input mask
B. Cluster selected cells with any immediate neighbours as defined by
C. Assign clusters to ‘non-habitat’ (probability 1–A) and ‘habitat’ (probability A)
D. Cells not in any cluster from (B) receive the habitat class of the majority of the <=8 adjacent cells assigned in (C), if there are any; otherwise they are assigned at random (with probabilities 1–A, A).
Fragmentation declines, and cluster size increases, as p increases up to the ‘percolation threshold’ which is about 0.59 in the default case (Saura and Martinez-Millan 2000 p.664).
minpatch > 1 then habitat patches of less than
cells are converted to non-habitat, and vice versa. This is likely to
cause the proportion of habitat to deviate from
drop = FALSE a binary-valued (0/1) covariate with the
requested name is included in the output mask, which has the same extent
as the input. Otherwise, non-habitat cells are dropped and no covariate
An object of class ‘mask’. By default (
drop = TRUE) this
has fewer rows (points) than the input mask.
Single-linkage clustering and adjacency operations use functions
‘clump’ and ‘adjacency’ of the package raster; ‘clump’ also
requires package igraph0 (raster still uses this
decrecated version). Optional plotting
of intermediate stages (
plt = TRUE) uses the plot method for
rasterLayers in raster.
A non-rectangular input mask is padded out to a rectangular rasterLayer for operations in raster; cells added as padding are ultimately dropped.
The procedure of Saura and Martinez-Millan (2000) has been followed as far as possible, but this implementation may not match theirs in every detail.
This implementation allows only two habitat classes. The parameter A is the expected value of the habitat proportion; the realised habitat proportion may differ quite strongly from A, especially for large p (e.g., p > 0.5).
Anisotropy is not implemented; it would require skewed adjacency filters (i.e. other than rook- or queen-move filters) that are not available in raster.
Hijmans, R. J. and van Etten, J. (2011) raster: Geographic analysis and modeling with raster data. R package version 1.9-33. http://CRAN.R-project.org/package=raster.
Saura, S. and Martinez-Millan, J. (2000) Landscape patterns simulation with a modified random clusters method. Landscape Ecology, 15, 661–678.
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## Not run: tempmask <- make.mask(nx = 100, ny = 100, spacing = 20) mrcmask <- randomHabitat(tempmask, p = 0.4, A = 0.4) plot(mrcmask, dots = FALSE, col = "green") pop <- sim.popn(10, mrcmask, model2D = "IHP") plot(pop, add = TRUE) ## plot intermediate steps A, C, D par(mfrow = c(1,3)) mrcmask <- randomHabitat(tempmask, p = 0.4, A = 0.4, plt = TRUE) par(mfrow = c(1,1)) ## reset to default ## keep non-habitat cells mrcmask <- randomHabitat(tempmask, p = 0.4, A = 0.4, drop = FALSE) plot(mrcmask, covariate = "habitat", dots = FALSE, col = c("grey","green"), breaks = 2) ## effect of purging small patches opar <- par(mfrow=c(1,2)) mrcmask <- randomHabitat(tempmask, p = 0.4, A = 0.4, minpatch = 1) plot(mrcmask, dots = FALSE, col ="green") mrcmask <- randomHabitat(tempmask, p = 0.4, A = 0.4, minpatch = 5) plot(mrcmask, dots = FALSE, col ="green") par(opar) ## End(Not run)
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