grainscape (version 0.5.0)

GOC: Produce a grains of connectivity model at multiple scales (patch-based or lattice GOC)

Description

Produce a grains of connectivity (GOC) model at multiple scales (resistance thresholds) by scalar analysis. Patch-based or lattice GOC modelling can be done with this function.

Usage

GOC(x, ...)
# S4 method for mpg
GOC(
  x,
  nThresh = NULL,
  doThresh = NULL,
  weight = "lcpPerimWeight",
  verbose = 0,
  ...
)

Value

A goc() object.

Arguments

x

A mpg object produced by MPG(). For lattice GOC MPG must be run with patch set as an integer value.

...

Additional arguments (not used).

nThresh

Optional. An integer giving the number of thresholds (or scales) at which to create GOC models. Thresholds are selected to produce a maximum number of unique grains (i.e., models). nThresh thresholds are also approximately evenly spread between 0 and the threshold at which all patches or focal points on the landscape are connected. This is a simple way to get a representative subset of all possible GOC models. Provide either nThresh or doThresh not both.

doThresh

Optional. A vector giving the link thresholds at which to create GOC models. Use threshold() to identify thresholds of interest. Provide either nThresh or doThresh not both.

weight

A string giving the link weight or attribute to use for threshold. "lcpPerimWeight" uses the accumulated resistance or least-cost path distance from the perimeters of patches as the link weight.

verbose

Set verbose=0 for no progress information to console.

Author

Paul Galpern

Details

Grain or scalar analysis of connectivity may be appropriate for a variety of purposes, not limited to visualization and improving connectivity estimates for highly-mobile organisms. See Galpern et al. (2012), Galpern & Manseau (2013a, 2013b) for applications and review of these capabilities.

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

MPG(), grain(), distance(), point()

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)
## Examine the properties of the GOC graph of grain 3 of 5
graphdf(grain(tinyPatchGOC, whichThresh = 3))

## Extract grains of connectivity
## representation of the finest grain and three others
## by giving thresholds in link weights (doThresh)
tinyPatchGOC <- GOC(tinyPatchMPG, doThresh = c(0, 20, 40))