Everything one connectivity analysis produces

Runs the connectivity pipeline once per distance and keeps both the summary and the spatial layers the maps need. habitat_connectivity() returns the summary alone; this also keeps the buffered habitat and the patch-ID raster for each distance, which is what the downloadable assets and the report are built from.

Usage

connectivity_report_data(
  habitat,
  barrier,
  species,
  interpatch_distance,
  scenario = NULL,
  scenario_kind = NULL,
  scenario_name = NULL,
  verbose = TRUE
)

Arguments

habitat

Terra SpatRaster. The habitat layer.

barrier

Terra SpatRaster. The barrier layer.

species

Species name. E.g., "Superb Fairy Wren".

interpatch_distance

Numeric. The distance (in metres) at which habitat patches are considered connected. May be a scalar or a vector; a vector runs the pipeline once per distance.

scenario

Optional layer to compare against the baseline: an sf, SpatVector or SpatRaster standing in for one of habitat and barrier. It is put on the baseline's own grid with onto_grid(), so it does not have to arrive on one. Supplying it runs the pipeline a second time, on the scenario landscape, and fills the scenario_* fields and comparison below.

scenario_kind

Which layer scenario replaces, "habitat" or "barrier". Required with scenario. Only one layer may change at a time, so that the difference in connectivity is attributable to it.

scenario_name

Optional name for the scenario, carried in the comparison so several can be stacked.

verbose

Logical. Display progress messages (default: TRUE).

Value

A connectivity_report_data object: a list of

• connectivity, a connectivity summary with one row per distance

• habitat and barrier, the layers as supplied

• buffered_habitat and patch_id_raster, lists of SpatRaster, one per distance and named by it

• species and interpatch_distance

and, when a scenario was given, the same view of the scenario landscape: scenario_kind, scenario_habitat, scenario_barrier, scenario_buffered_habitat, and comparison. Without one these are all NULL. scenario_layer() returns whichever layer the scenario changed.

See also

habitat_connectivity() when only the summary is wanted, and habitat_connectivity_comparison() to compare two landscapes you have already assembled yourself.

Examples

# \donttest{
report_data <- connectivity_report_data(
  habitat = example_wren_habitat(),
  barrier = example_wren_barrier(),
  species = "Superb Fairy Wren",
  interpatch_distance = 200,
  verbose = FALSE
)

report_data$connectivity
#> # A tibble: 1 × 9
#>   species     interpatch_distance n_patches effective_mesh_ha prob_connectedness
#>   <chr>                     <dbl>     <int>             <dbl>              <dbl>
#> 1 Superb Fai…                 200       282              334.          0.0000225
#> # ℹ 4 more variables: patch_area_mean <dbl>, patch_area_total_ha <dbl>,
#> #   data_resolution <chr>, patch_size <list>
names(report_data$patch_id_raster)
#> [1] "200"

# with a scenario, the baseline and the scenario are each run once and
# the comparison is built from the two
with_scenario <- connectivity_report_data(
  habitat = example_wren_habitat(),
  barrier = example_wren_barrier(),
  species = "Superb Fairy Wren",
  interpatch_distance = 200,
  scenario = example_wren_barrier_scenario(),
  scenario_kind = "barrier",
  verbose = FALSE
)

with_scenario$comparison
#> # Connectivity comparison: baseline / scenario / change / pct_change
#> # A tibble: 4 × 10
#>   scenario_name measure    species           interpatch_distance n_patches
#>   <chr>         <chr>      <chr>                           <dbl>     <dbl>
#> 1 NA            baseline   Superb Fairy Wren                 200   282    
#> 2 NA            scenario   Superb Fairy Wren                 200   283    
#> 3 NA            change     Superb Fairy Wren                 200     1    
#> 4 NA            pct_change Superb Fairy Wren                 200     0.355
#>   effective_mesh_ha prob_connectedness patch_area_mean patch_area_total_ha
#>               <dbl>              <dbl>           <dbl>               <dbl>
#> 1           334.          0.0000225           52556.               1482.  
#> 2           333.          0.0000224           51828.               1467.  
#> 3            -0.965      -0.0000000651         -728.                -15.3 
#> 4            -0.289      -0.289                  -1.38               -1.04
#>   data_resolution 
#>   <chr>           
#> 1 9.99673x10.00151
#> 2 9.99673x10.00151
#> 3 9.99673x10.00151
#> 4 9.99673x10.00151
#> # change = scenario - baseline (positive = scenario is higher)
#> # pct_change = 100 * change / baseline
# }