Given a series of GOC models built at different scales, visualize the corridor (or shortest path) between two points using one of the tessellations (i.e., scales) in these models.

corridor(x, ...) # S4 method for goc corridor(x, whichThresh, coords, weight = "meanWeight", ...)

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

whichThresh | Integer giving the index of the threshold to visualize. |

coords | A two column matrix or a |

weight | The GOC graph link weight to use in calculating the distance.
Please see details in |

An object of class `corridor`

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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.

library(raster)#>#> #>#>#> #>## Load raster landscape tiny <- raster(system.file("extdata/tiny.asc", package = "grainscape")) ## Create a resistance surface from a raster using an is-becomes reclassification tinyCost <- 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) ## Quick visualization of a corridor corridorStartEnd <- rbind(c(10,10), c(90,90)) tinyPatchCorridor <- corridor(tinyPatchGOC, whichThresh = 3, coords = corridorStartEnd) plot(tinyPatchCorridor)#>## More control over a corridor visualization plot(tinyPatchCorridor@voronoi, col = "lightgrey", lwd = 2)mtext(paste("Corridor shortest path length:", round(tinyPatchCorridor@corridorLength, 2), "resistance units"), side = 1)