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", ...)

x | A |
---|---|

y | A two column matrix or a |

... | Additional arguments (not used). |

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

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`

).

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.

## 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)