threshold: Produce a minimum planar graph (MPG) at multiple scales

Description

Perform a scalar analysis of a minimum planar graph (MPG) by building the graph at a series of link thresholds. As the threshold value increases more nodes in the graph become connected, forming increasingly fewer components, until the graph becomes connected (e.g., Brooks, 2003). N.B. Grains of connectivity (GOC) done by GOC is also a scalar analysis using Voronoi tessellations rather than patches (see Galpern et al., 2012).

Usage

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

Arguments

A mpg object produced by MPG.

...

Additional arguments (not used).

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. "eucPerimWeight" use the Euclidean distance from the perimeters of patches as the link weight.

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 link{threshold} to identify thresholds of interest. Provide either nThresh or doThresh not both.

Value

A list object with the following elements:

summary: summarizes the thresholded graphs generated and their properties;
th: a list of length nThresh or length(doThresh) giving the thresholded graph (class igraph) at each threshold.

References

Brooks, C.P. (2003) A scalar analysis of landscape connectivity. Oikos 102:433-439.

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.

Examples

Run this code

# NOT RUN {
## 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)

## Threshold this graph at a representative subset of 10 thresholds
tinyThresh <- threshold(tinyPatchMPG, nThresh = 10)

## Examine the properties of one of these threshold graphs
print(tinyThresh$th[[7]], vertex = TRUE, edge = TRUE)
# }