Package lconnect: patch connectivity metrics and patch prioritization

March 20, 2019

(This article was first published on R Code – Geekcologist , and kindly contributed to R-bloggers)

Today we are presenting a new package, lconnect. This package is intended to be a very simple approach to derive landscape connectivity metrics. Many of these metrics come from the interpretation of landscape as graphs.

Additionally, it also provides a function to prioritize landscape patches based on their contribution to the overall landscape connectivity. For now this function works only with the Integral Index of connectivity, by Pascual-Hortal & Saura (2006).

For now we only have a development version in GitHub, but a more definitive version should be uploaded to CRAN in the coming days.

Here’s a brief tutorial!

First install the package:

#load package from GitHub
#remove.packages("lconnect", lib="~/R/win-library/3.5")

Then, upload the landscape shapefile …

#Load data
vec_path <- system.file("extdata/vec_projected.shp", package = "lconnect")

…and create a ‘lconnect’ class object:

#upload landscape
land <- upload_land(vec_path, habitat = 1, max_dist = 500)
## [1] "lconnect"

And now, let’s plot it:

plot(land, main="Landscape clusters")


If we wish we can derive patch importance (the contribution of each individual patch to the overall connectivity):

land1 <- patch_imp(land, metric="IIC")
##  [1]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.1039501
##  [7]  0.1039501  0.0000000  0.1039501  0.0000000  0.0000000  0.1039501
## [13]  0.3118503 21.9334719  0.0000000 15.5925156  2.5987526  0.1039501
## [19]  0.1039501  0.2079002  0.0000000  0.0000000  0.0000000  0.0000000
## [25]  0.9355509  0.0000000 14.2411642  2.9106029  0.2079002 12.9937630
## [31]  0.3118503  0.7276507  0.0000000  7.5883576  0.5197505 70.2702703

Which produces an object of the class ‘pimp’:

## [1] "pimp"

And, finally, we can also plot the relative contribution of each patch to the landscape connectivity:

plot(land1, main="Patch prioritization (%)")


And that’s it!

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