Run the Feature Climate-Smart Approach

splnr_climate_featureApproach() implements the Feature Approach to climate-smart conservation planning. This involves defining a global "climate-smart" layer and adjusting conservation targets to ensure that a specified proportion of this layer is captured in the solution.

Usage

splnr_climate_featureApproach(
  features,
  metric,
  targets,
  direction,
  percentile = 35,
  refugiaTarget = 0.3
)

Arguments

features

An sf object representing conservation features (e.g., species distribution data).

metric

An sf object containing climate metric information. It must have a column named 'metric' with the climate metric values per Planning Unit.

targets

A data.frame with two columns: feature (character, listing the original feature names) and target (numeric, the initial conservation target for each feature as a proportion, e.g., 0.3).

direction

An integer specifying the direction of climate-smartness:

• 1: Higher metric values mean more climate-smart areas.

• -1: Lower metric values mean more climate-smart areas.

percentile

A numeric value (0-100) representing the cutoff threshold for determining whether an area is a climate priority area or not. This is applied globally to the metric data. Defaults to 35.

refugiaTarget

A numeric value (0-1) representing the target proportion assigned to the overall climate-smart layer. Defaults to 0.3 (30%).

Value

A list with two components:

• Features: An sf object containing the binary information per Planning Unit for each original feature, plus the new climate_layer feature. This is ready to be passed to prioritizr.

• Targets: A data.frame with the adjusted targets for all features, including the climate_layer. This is also ready for prioritizr.

Details

This function orchestrates the steps for the Feature Approach:

1. Preprocessing: It calls splnr_climate_feature_preprocess() to identify a region-wide climate-smart layer based on a percentile cutoff of the climate metric. This layer is then added as a new binary feature to your conservation data.

2. Target Assignment: It then calls splnr_climate_feature_assignTargets() to calculate and assign new targets. Crucially, a specific refugiaTarget is set for the newly created climate_layer feature, ensuring that a certain proportion of the most climate-resilient areas are included in the final conservation plan.

The output is a list containing the modified features (now including the climate_layer) and their corresponding adjusted targets, ready to be used in a prioritizr conservation problem.

Examples

if (FALSE) { # \dontrun{
# Assuming 'dat_species_bin' and 'dat_clim' are existing sf objects
# in your package.

# Define initial targets for species features.
initial_targets <- dat_species_bin %>%
  sf::st_drop_geometry() %>%
  colnames() %>%
  data.frame() %>%
  setNames(c("feature")) %>%
  dplyr::mutate(target = 0.3)

# Run the Feature Approach where higher climate metric values mean
# more climate-smart areas.
Feature_Approach_result <- splnr_climate_featureApproach(
  features = dat_species_bin,
  metric = dat_clim,
  targets = initial_targets,
  direction = 1, # Example: higher metric values are more climate-smart
  percentile = 35,
  refugiaTarget = 0.3
)

# Access the processed features and targets:
out_sf_feature <- Feature_Approach_result$Features
targets_feature <- Feature_Approach_result$Targets

print(head(out_sf_feature))
print(head(targets_feature))
} # }