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", ...)

Arguments

x

A goc object created by GOC.

...

Additional arguments (not used).

whichThresh

Integer giving the index of the threshold to visualize.

coords

A two column matrix or a SpatialPoints object giving coordinates at the end points of the corridor.

weight

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

Value

An object of class corridor.

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, visualize

Examples

library(raster)
#> Loading required package: sp
#> #> Attaching package: ‘raster’
#> The following object is masked from ‘package:grainscape’: #> #> distance
## 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)
#> Extracting Voronoi boundaries...
## More control over a corridor visualization plot(tinyPatchCorridor@voronoi, col = "lightgrey", lwd = 2)
plot(tinyPatchCorridor@linksSP, col = "darkred", lty = "dashed", add = TRUE)
plot(tinyPatchCorridor@nodesSP, col = "darkred", pch = 21, bg="white", add = TRUE)
plot(tinyPatchCorridor@shortestLinksSP, col = "darkred", lty = "solid", lwd = 2, add = TRUE)
plot(tinyPatchCorridor@shortestNodesSP, col = "darkred", pch = 21, bg = "darkred", add = TRUE)
mtext(paste("Corridor shortest path length:", round(tinyPatchCorridor@corridorLength, 2), "resistance units"), side = 1)