Run the Climate Priority Area (CPA) Approach

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.

Usage

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

Arguments

features

An sf object of conservation features (binary presence/absence).

metric

An sf object with a column named metric.

targets

A data.frame with columns feature and target.

direction

1 (higher = more climate-smart) or -1 (lower = more climate-smart).

percentile

Numeric (0-100). Defaults to 5.

refugiaTarget

Numeric (0-1). Defaults to 1.

metric_col

A single character string giving the name of the column in metric that contains the climate metric values. Defaults to "metric" for backwards compatibility. Use this argument when your climate data column has a different name (e.g. "sst_trend").

Value

A list with:

• Features: sf object with _CS and _NCS columns per feature.

• Targets: data.frame with adjusted targets.

Details

This function orchestrates two steps:

1. Preprocessing via splnr_climate_priorityArea_preprocess().

2. Target Assignment via splnr_climate_priorityArea_assignTargets().

Examples

if (FALSE) { # \dontrun{
initial_targets <- dat_species_bin %>%
  sf::st_drop_geometry() %>%
  colnames() %>%
  data.frame() %>%
  setNames(c("feature")) %>%
  dplyr::mutate(target = 0.3)

CPA_result <- splnr_climate_priorityAreaApproach(
  features      = dat_species_bin,
  metric        = dat_clim,
  targets       = initial_targets,
  direction     = -1,
  percentile    = 5,
  refugiaTarget = 1
)
out_sf_cpa <- CPA_result$Features
targets_cpa <- CPA_result$Targets
} # }