Find the shortest network distance between pairs of points using the GOC graph. This can be used as an effective distance for landscape connectivity assessments.

distance(x, y, ...)

# S4 method for goc,SpatialPoints
distance(x, y, weight = "meanWeight", ...)

# S4 method for goc,matrix
distance(x, y, weight = "meanWeight", ...)

# S4 method for goc,numeric
distance(x, y, weight = "meanWeight", ...)

Arguments

x

A goc object produced by GOC.

y

A two column matrix or a SpatialPoints object giving the coordinates of points of interest.

...

Additional arguments (not used).

weight

The GOC graph link weight to use in calculating the distance. Please see Details for explanation.

Value

A list object giving a distance matrix for each threshold in the GOC object. Distance matrices give the pairwise grains of connectivity network distances between sampling locations. Matrix indices correspond to rows in the coords matrix (y).

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, 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 reclassifification 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) ## Three sets of coordinates in the study area loc <- cbind(c(30, 60, 90), c(30, 60, 90)) ## Find the GOC network distance matrices between these points ## for each of the 5 grains of connectivity tinyDist <- grainscape::distance(tinyPatchGOC, loc)