Compare several scenarios against one baseline, starting from layers

The layer-level counterpart to compare_scenarios(). Give it one baseline habitat/barrier pair and named lists of scenario layers, and it runs the connectivity pipeline on each and compares them all against that baseline.

Usage

habitat_connectivity_scenarios(
  habitat_baseline,
  barrier_baseline,
  species,
  habitat_scenarios = NULL,
  barrier_scenarios = NULL,
  interpatch_distance,
  verbose = TRUE
)

Arguments

habitat_baseline

Terra SpatRaster. The baseline habitat layer.

barrier_baseline

Terra SpatRaster. The baseline barrier layer.

species

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

habitat_scenarios

A named list of habitat layers, one per scenario, each compared against habitat_baseline with the barrier held at barrier_baseline. Names become the scenario_name column.

barrier_scenarios

A named list of barrier layers, the mirror of habitat_scenarios. Supply either list, or both; names must be unique across the two.

interpatch_distance

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

verbose

Logical. Display progress messages (default: TRUE).

Value

A compare_connectivity object with four rows per scenario per distance, labelled in the scenario_name column. Habitat scenarios come first, then barrier scenarios, in the order given.

Details

A scenario changes exactly one layer: habitat scenarios are run against the baseline barrier, and barrier scenarios against the baseline habitat. There's no way to write a scenario that changes both, which is what habitat_connectivity_comparison() has to check for.

The baseline is run once per distance and reused by every scenario, so comparing three scenarios at one distance runs the pipeline four times, not six.

See also

compare_scenarios() when you already have connectivity objects, and habitat_connectivity_comparison() for a single scenario.

Examples

# \donttest{
wren_habitat <- example_wren_habitat()
wren_barrier <- example_wren_barrier()

habitat_connectivity_scenarios(
  habitat_baseline = wren_habitat,
  barrier_baseline = wren_barrier,
  species = "Superb Fairy Wren",
  habitat_scenarios = list(
    "Bentley Project" = example_wren_habitat_scenario()
  ),
  barrier_scenarios = list(
    "Barrier upgrade" = example_wren_barrier_scenario()
  ),
  interpatch_distance = 200,
  verbose = FALSE
)
#> # Connectivity comparison: baseline / scenario / change / pct_change
#> # A tibble: 8 × 10
#>   scenario_name   measure    species           interpatch_distance n_patches
#>   <chr>           <chr>      <chr>                           <dbl>     <dbl>
#> 1 Bentley Project baseline   Superb Fairy Wren                 200   282    
#> 2 Bentley Project scenario   Superb Fairy Wren                 200   267    
#> 3 Bentley Project change     Superb Fairy Wren                 200   -15    
#> 4 Bentley Project pct_change Superb Fairy Wren                 200    -5.32 
#> 5 Barrier upgrade baseline   Superb Fairy Wren                 200   282    
#> 6 Barrier upgrade scenario   Superb Fairy Wren                 200   283    
#> 7 Barrier upgrade change     Superb Fairy Wren                 200     1    
#> 8 Barrier upgrade 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.               2.25e-5        52556.               1482.  
#> 2           280.               1.89e-5        45903.               1226.  
#> 3           -53.6             -3.62e-6        -6653.               -256.  
#> 4           -16.1             -1.61e+1          -12.7               -17.3 
#> 5           334.               2.25e-5        52556.               1482.  
#> 6           333.               2.24e-5        51828.               1467.  
#> 7            -0.965           -6.51e-8         -728.                -15.3 
#> 8            -0.289           -2.89e-1           -1.38               -1.04
#>   data_resolution 
#>   <chr>           
#> 1 9.99673x10.00151
#> 2 9.99673x10.00151
#> 3 9.99673x10.00151
#> 4 9.99673x10.00151
#> 5 9.99673x10.00151
#> 6 9.99673x10.00151
#> 7 9.99673x10.00151
#> 8 9.99673x10.00151
#> # change = scenario - baseline (positive = scenario is higher)
#> # pct_change = 100 * change / baseline
# }