grainscape (version 0.5.0)

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

Description

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, ...)

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

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().

Author

Paul Galpern and Alex Chubaty

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.

See Also

GOC()

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")
}