The goal of habicon is to calculate habitat patch and corridor
priority in terms of their degree of importance to ecological
connectivity. It is designed to integrate habitat suitability data
(e.g., outputs of species distribution models) and connectivity/corridor
data (e.g., outputs from connectivity models such as Circuitscape)
You can install the development version of habicon on GitHub with:
devtools::install_github("ccmothes/habicon")This is a basic example using simulated habitat suitability and connectivity maps demonstrating the functions available so far.
NOTE: This package is still in the early stages of development and more functions with increased user flexibility are coming soon!
library(habicon)First let’s visualize the simulated raster maps included as example
data. These are SpatRaster objects that first need to be unpacked
using the unwrap() helper function from terra.
These include suit where values represent habitat suitability and range from 0-1 (1 being high suitability):
suit <- terra::unwrap(suit)
terra::plot(suit)
And second corr where values represent “current” (e.g. Circuitscape outputs) or similar, where larger values reflect more movement:
corr <- terra::unwrap(corr)
terra::plot(corr)
Other inputs needed for the priority calculations are binary maps, which
can be created using the bin_map function.
Users can test and view binary maps over a range of values:
suit_bin <- bin_map(suit, threshold = c(0.4, 0.6, 0.8))
terra::plot(suit_bin)
Often species distribution models calculate potential threshold values that users can choose to use. Or, say you want to identify patches of the highest habitat suitability, say the top 20% of suitability values:
suit_bin <- bin_map(suit, threshold = quantile(terra::values(suit), 0.8))
terra::plot(suit_bin)
For this example, let’s stick with the top 20% of suitability and conductance values to make the binary maps that identify the individual habitat patches and corridors.
corr_bin <- bin_map(corr, threshold = quantile(terra::values(corr), 0.8))
terra::plot(corr_bin)
This function ranks individual patches based on multiple connectivity metrics, including quality-weighted area, weighted betweenness centrality, and dEC (see Saura et al. 2011). Higher values indicate higher importance for all metrics. This function returns a raster layer for each metric, with a connectivity importance value assigned to each patch. It also returns a sumamry table of all metric scores for each patch. For this example I kept the default minimum area (removes patches that are just one pixel) and set the medium dispersal distance (d) to 100. Note that this function takes distances in meters. The minimum area argument works on vectors of two numbers (since working with pixels), so if applying your own minimum area you must enter, for example, min_area = c(10,10) or min_area = 2*res(suit), since ‘res’ returns a vector of two numbers.
patch <- patch_priority(suit = suit, suit_bin = suit_bin, corr_bin = corr_bin,
resist = terra::unwrap(resist), d = 100)
Now using the same surfaces as in the patch_priority function, we can
calculate corridor priority with the corr_priority function. This
function identifies and weights possible paths within identified
corridors by mapping all the shortest paths from all possible points of
origin between each pair of connected patches and weights each path
based on the area, quality, and weighted betweenness centrality with a
negative effect of path distance.The final raster output then sums, for
each cell, all path values that cross that cell, so cells with more,
higher valued paths crossing through them are given higher priority.
NOTE: This function may take a while depending on raster size and number
of connected patches. The rasters in this example consist of 22,500
cells and this function finished in a couple minutes.
corr_prior <- corr_priority(suit = suit, suit_bin = suit_bin, corr = corr_rescale, corr_bin = corr_matrix)