Compare measurements the connectivity of different scenarios — compare

We can measure the connectivity of a given habitat and barrier with habitat_connectivity(). We can also compare the connectivity, say for example if you have the same area habitat and barrier, but you want to understand what the change in connectedness is when you remove, or add some habitat, or some barrier(s), or both. This function help you do that.

Usage

compare_connectivity(connectivity, connectivity_baseline, ...)

# Default S3 method
compare_connectivity(
  connectivity,
  connectivity_baseline,
  interpatch_distance = 10,
  res = NA,
  species = "blue-tongued lizard",
  ...
)

Arguments

connectivity: Numeric vector. Area of a connected patch.
connectivity_baseline: Numeric vector. Baseline area of a connected patch.
...: extra arguments to pass through for default method
interpatch_distance: Numeric. The distance (in meters) where habitat patches are considered connected. E.g., if set to 500, patches 498m apart are connected, those 501m apart are not connected. This is passed internally to a spatial operation known as "buffering", where this distance is used as a radius from the edge of the habitat zone. This means the specified interpatch_distance is halved exactly. So an interpatch distance of 500 will be converted to 250.
res: pixel resolution - relevant to rasters only
species: name of species

Value

tibble with "scenario", "interpatch_distance", "species", "n_patches", "effective_mesh_ha", and "prob_connectedness".

Examples

# for demonstration purposes - let's imagine the area decreases by 20%
baseline_areas <- round(lizard_areas_connected$area)
new_areas <- baseline_areas[-1] * 0.8
compare_connectivity(
  connectivity = new_areas,
  connectivity_baseline = baseline_areas,
  interpatch_distance = 10,
  species = "blue-tongued lizard"
)
#> # A tibble: 3 × 7
#>   scenario   interpatch_distance res   species       n_patches effective_mesh_ha
#>   <chr>                    <dbl> <lgl> <chr>             <int>             <dbl>
#> 1 baseline                    10 NA    blue-tongued…        73              4.47
#> 2 new                         10 NA    blue-tongued…        72              2.86
#> 3 difference                  10 NA    blue-tongued…        -1             -1.61
#> # ℹ 1 more variable: prob_connectedness <dbl>