Run the Percentile Climate-Smart Approach

splnr_climate_percentileApproach() implements the Percentile Approach to climate-smart conservation planning. This involves filtering features to their most climate-resilient areas and adjusting their conservation targets to account for this reduced feature distribution.

Usage

splnr_climate_percentileApproach(
  features,
  metric,
  targets,
  direction,
  percentile = 35
)

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 per feature to its distribution. Defaults to 35.

Value

A list with two components:

• Features: An sf object containing the binary information per Planning Unit for each feature, now filtered to include only its climate-smart occurrences. This is ready to be passed to prioritizr.

• Targets: A data.frame with the adjusted targets for the filtered features. This is also ready for prioritizr.

Details

This function orchestrates the steps for the Percentile Approach:

1. Preprocessing: It calls splnr_climate_percentile_preprocess() to identify, for each feature, its occurrences within the most climate-resilient percentile of its distribution based on a climate metric. This effectively "filters" the feature data to only include its climate-smart components.

2. Target Assignment: It then calls splnr_climate_percentile_assignTargets() to calculate and assign new targets for these filtered features. The targets are scaled up to ensure that the original conservation goals are still met, but specifically by selecting areas from the climate-smart portions of the features' distributions.

The output is a list containing the modified features (filtered to their climate-smart occurrences) 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 Percentile Approach where higher climate metric values mean
# more climate-smart areas.
Percentile_Approach_result <- splnr_climate_percentileApproach(
  features = dat_species_bin,
  metric = dat_clim,
  targets = initial_targets,
  direction = 1, # Example: higher metric values are more climate-smart
  percentile = 35
)

# Access the processed features and targets:
out_sf_percentile <- Percentile_Approach_result$Features
targets_percentile <- Percentile_Approach_result$Targets

print(head(out_sf_percentile))
print(head(targets_percentile))
} # }