Extract a grain of connectivity (GOC) tessellation at a given scale

Extract a grain (i.e. a scaled version of a Voronoi tessellation) from a GOC model.

Usage

grain(x, ...)

# S4 method for goc
grain(x, whichThresh, ...)

Arguments

x: A goc object created by GOC().
...: Additional arguments (not used).
whichThresh: Integer giving the grain threshold to extract. This is the index of the threshold extracted by GOC().

Value

A list object containing the following elements:

summary: gives the properties of the specified scale/grain whichThresh of the GOC model;
voronoi: a RasterLayer giving the Voronoi tessellation the specified scale/grain whichThresh of the GOC model;
centroids: a SpatialPoints objects giving the centroids of the polygons in the Voronoi tessellation at the specified scale/grain whichThresh;
th: a igraph object giving the graph describing the relationship among the polygons at the specified scale/grain whichThresh

References

Fall, A., M.-J. Fortin, M. Manseau, D. O'Brien. (2007) Spatial graphs: Principles and applications for habitat connectivity. Ecosystems 10:448:461.

Galpern, P., M. Manseau. (2013a) Finding the functional grain: comparing methods for scaling resistance surfaces. Landscape Ecology 28:1269-1291.

Galpern, P., M. Manseau. (2013b) Modelling the influence of landscape connectivity on animal distribution: a functional grain approach. Ecography 36:1004-1016.

Galpern, P., M. Manseau, A. Fall. (2011) Patch-based graphs of landscape connectivity: a guide to construction, analysis, and application for conservation. Biological Conservation 144:44-55.

Galpern, P., M. Manseau, P.J. Wilson. (2012) Grains of connectivity: analysis at multiple spatial scales in landscape genetics. Molecular Ecology 21:3996-4009.

Author

Paul Galpern and Alex Chubaty

Examples

## Load raster landscape
tiny <- raster::raster(system.file("extdata/tiny.asc", package = "grainscape"))

## Create a resistance surface from a raster using an is-becomes reclassification
tinyCost <- raster::reclassify(tiny, rcl = cbind(c(1, 2, 3, 4), c(1, 5, 10, 12)))
## Produce a patch-based MPG where patches are resistance features=1
tinyPatchMPG <- MPG(cost = tinyCost, patch = tinyCost == 1)
## Extract a representative subset of 5 grains of connectivity
tinyPatchGOC <- GOC(tinyPatchMPG, nThresh = 5)
## Very quick visualization at the finest scale/grain/threshold
tinyPatchGOCgrain <- grain(tinyPatchGOC, whichThresh = 1)
if (interactive())
  plot(tinyPatchGOCgrain, col = topo.colors(10))

## Visualize the model at the finest scale/grain/threshold
## Manual control of plotting
if (interactive()) {
  plot(grain(tinyPatchGOC, whichThresh = 1)@voronoi,
       col = sample(rainbow(100)), legend = FALSE, main = "Threshold 1")
}