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Run the Climate Priority Area (CPA) Approach

Source: R/utils-climate.R
splnr_climate_priorityAreaApproach.Rd

splnr_climate_priorityAreaApproach() implements the Climate Priority Area approach by splitting conservation features into climate-smart (CS) and non-climate-smart (NCS) components and adjusting their targets accordingly. This allows conservation planning to prioritize areas with higher climate resilience.

Usage

splnr_climate_priorityAreaApproach(
  features,
  metric,
  targets,
  direction,
  percentile = 5,
  refugiaTarget = 1
)

Arguments

features

An sf object representing conservation features (e.g., species distribution data). Each column (excluding geometry) should typically be a binary representation of a feature's presence (1) or absence (0) in each Planning Unit.

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 climate-smart areas. For example, percentile = 5 means the most climate-smart 5% of areas (based on direction) are considered. This value represents the lower limit of the threshold. Defaults to 5.

refugiaTarget

A numeric value (0-1) representing the target proportion assigned specifically to climate-smart areas (refugia). Defaults to 1 (100%).

Value

A list with two components:

  • Features: An sf object containing the binary information per Planning Unit for each feature, now split into _CS (climate-smart) and _NCS (non-climate-smart) components. This is ready to be passed to prioritizr when creating a conservation problem.

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

Details

This function orchestrates the steps required for the CPA approach:

  1. Preprocessing: It calls splnr_climate_priorityArea_preprocess() to categorize each feature's occurrences into CS and NCS areas based on a climate metric and a specified percentile cutoff.

  2. Target Assignment: It then calls splnr_climate_priorityArea_assignTargets() to calculate and assign new targets for these CS and NCS feature components. This ensures that conservation goals reflect the desired emphasis on climate-smart areas (e.g., aiming for 100% representation of features in highly resilient areas).

The output of this function is a list containing the modified features (now split into CS/NCS components) 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 Climate Priority Area Approach where lower climate metric
# values mean more climate-smart areas.
CPA_Approach_result <- splnr_climate_priorityAreaApproach(
  features = dat_species_bin,
  metric = dat_clim,
  targets = initial_targets,
  direction = -1, # Example: lower metric values are more climate-smart
  percentile = 5,
  refugiaTarget = 1
)

# Access the processed features and targets:
out_sf_cpa <- CPA_Approach_result$Features
targets_cpa <- CPA_Approach_result$Targets

print(head(out_sf_cpa))
print(head(targets_cpa))
} # }