Create a dataframe for the relative and absolute targets for features

This function creates a dataframe of targets for each of the features to be used in the prioritization

Usage

features_targets(
  targets,
  features,
  pre_patches,
  locked_out = NULL,
  locked_in = NULL
)

Arguments

targets: a vector of targets for protection (range between 0 and 1); must be the same length as the number of features + the pre-patches variable
features: a raster or sf object that includes all relevant features to be used in the prioritization; each layer of the raster or each column of the sf object identifies the location of each feature
pre_patches: a raster or sf object that includes the feature that is to be split into patches (not the layer that is already split into patches)
locked_out: a raster or sf object for areas to be locked out (absolutely not protected) in the prioritization
locked_in: a raster or sf object for areas to be locked in (absolutely protected) in the prioritization

Value

A data frame to be used to specify targets for features. Will need to be plugged into constraint_targets() before being implemented into prioritizr

Examples

# Start with a little housekeeping to get the data from oceandatr
# Choose area of interest (Bermuda EEZ)
area <- oceandatr::get_area(area_name = "Bermuda",  mregions_column = "territory1")
projection <-'+proj=laea +lon_0=-64.8108333 +lat_0=32.3571917 +datum=WGS84 +units=m +no_defs'
# Create a planning grid
planning_raster <- spatialgridr::get_grid(area, projection = projection)
# Grab all relevant data
features_raster <- oceandatr::get_features(spatial_grid = planning_raster)
#> Getting depth zones...
#> This may take seconds to minutes, depending on grid size
#> Getting seamount data...
#> Spherical geometry (s2) switched off
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Spherical geometry (s2) switched on
#> Getting knoll data...
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
#> Getting geomorphology data...
#> Getting coral data...
#> |-- Coral data found for antipatharia coral, cold coral, octocoral
#> Getting environmental regions data... This could take several minutes
# Separate seamount data - we want to protect entire patches
seamounts_raster <- features_raster[["seamounts"]]
features_raster <- features_raster[[names(features_raster)[names(features_raster) != "seamounts"]]]
# Create a "cost" to protecting a cell - just a uniform cost for this example
cost_raster <- stats::setNames(planning_raster, "cost")
# Create patches from layer
patches_raster <- create_patches(seamounts_raster)
# Create patch dataframe
patches_raster_df <- create_patch_df(spatial_grid = planning_raster, features = features_raster,
  patches = patches_raster, costs = cost_raster)
#> [1] "Processing patch 1 of 7"
#> [1] "Processing patch 2 of 7"
#> [1] "Processing patch 3 of 7"
#> [1] "Processing patch 4 of 7"
#> [1] "Processing patch 5 of 7"
#> [1] "Processing patch 6 of 7"
#> [1] "Processing patch 7 of 7"
# Create boundary matrix for prioritizr
boundary_matrix <- create_boundary_matrix(spatial_grid = planning_raster, patches = patches_raster,
  patch_df = patches_raster_df)
# Create target features - using just 20% for every feature
features_targets <- features_targets(targets = rep(0.2, (terra::nlyr(features_raster)) + 1),
  features = features_raster, pre_patches = seamounts_raster)