Run the Feature Climate-Smart Approach

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

Usage

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

Arguments

features

An sf object of conservation features.

metric

An sf object with a column named metric.

targets

A data.frame with columns feature and target.

direction

1 or -1.

percentile

Numeric (0-100). Defaults to 35.

refugiaTarget

Numeric (0-1). Defaults to 0.3.

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 original features plus climate_layer.

• Targets: data.frame with adjusted targets including climate_layer.

Details

This function orchestrates two steps:

1. Preprocessing via splnr_climate_feature_preprocess().

2. Target Assignment via splnr_climate_feature_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)

Feature_result <- splnr_climate_featureApproach(
  features      = dat_species_bin,
  metric        = dat_clim,
  targets       = initial_targets,
  direction     = 1,
  percentile    = 35,
  refugiaTarget = 0.3
)
out_sf_feature <- Feature_result$Features
targets_feature <- Feature_result$Targets
} # }