Calculate habitat connectivity using terra

This performs the entire connectivity workflow, returning a dataframe output. The steps are:

• create_barrier_mask(): Creating barrier mask.

• drop_habitat_under_barrier(): Removes Habitat underneath barrier.

• habitat_buffer(): Buffers the habitat layer by the interpatch distance (m).

• fragment_habitat(): Fragments habitat layer along barrier intersection.

• assign_patches_to_fragments(): Assign patch ID to fragments.

• aggregate_connected_patches(): Summarise area in each patch.

Usage

habitat_connectivity(
  habitat,
  barrier,
  species,
  interpatch_distance,
  verbose = TRUE
)

Arguments

habitat

Terra SpatRaster. Habitat raster.

barrier

Terra SpatRaster. Barrier raster.

species

Species name. E.g., "Blue-tongued Lizard".

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. For the buffer to be representable on the raster, keep resolution <= interpatch_distance / 2; below that the buffer is a no-op and a warning is raised. See vignette("interpatch-distance-and-resolution").

verbose

Logical. Display progress messages (default: TRUE).

Value

A connectivity object (one row): a tibble of landscape-level connectivity metrics (patch count, effective mesh size, probability of connectedness, mean and total patch area) for the species and interpatch distance supplied. The per-patch areas the summary is built from are stored in a list column, "patch_size" – retrieve them with patch_sizes().

See also

vignette("interpatch-distance-and-resolution") for the relationship between interpatch distance, buffer radius, and resolution.

Examples

lizard_habitat <- example_habitat()
lizard_barrier <- example_barrier()
connectivity <- habitat_connectivity(
    habitat = lizard_habitat,
    barrier = lizard_barrier,
    species = "Blue-tongued Lizard",
    interpatch_distance = 12
  )
#> ℹ Creating barrier mask
#> ✔ Creating barrier mask [18ms]
#> 
#> ℹ Removing habitat underneath barrier
#> ✔ Removing habitat underneath barrier [15ms]
#> 
#> ℹ Buffering habitat for an interpatch distance of 12m
#> ✔ Buffering habitat for an interpatch distance of 12m [128ms]
#> 
#> ℹ Fragmenting habitat layer along barrier intersection
#> ✔ Fragmenting habitat layer along barrier intersection [13ms]
#> 
#> ℹ Assigning patches ID to fragments
#> ✔ Assigning patches ID to fragments [1.6s]
#> 
#> ℹ Summarising area in each patch
#> ✔ Summarising area in each patch [282ms]
#> 
connectivity
#> # A tibble: 1 × 9
#>   species     interpatch_distance n_patches effective_mesh_ha prob_connectedness
#>   <chr>                     <dbl>     <int>             <dbl>              <dbl>
#> 1 Blue-tongu…                  12       399              4.21          0.0000160
#> # ℹ 4 more variables: patch_area_mean <dbl>, patch_area_total_ha <dbl>,
#> #   data_resolution <chr>, patch_size <list>

# get the patch size:
patch_sizes(connectivity)[[1]]
#> # patch_size_tbl:      data.frame
#> # Species:             Blue-tongued Lizard
#> # Patches:             399
#> # Resolution:          2x2
#> # Interpatch Distance: 12 m
#>   patch_id   area
#>      <dbl>  <dbl>
#> 1        1 95326.
#> 2        3   364.
#> 3        6  2344.
#> 4       13  1220.
#> 5       48  1112.
#> # ℹ 394 more rows