binarize_changes: Binarize changes based on the agreement among GCMs

In kuenm2: Detailed Development of Ecological Niche Models

Binarize changes based on the agreement among GCMs

Description

Highlights areas where a specified number of Global Climate Models (GCMs) agree on a given outcome in the projected scenario. The function can identify areas classified as suitable, unsuitable, stable-suitable, stable-unsuitable, gained, or lost.

Usage

binarize_changes(changes_projections, outcome = "suitable", n_gcms)

Arguments

changes_projections	an object of class changes_projections, generated by projection_changes() or imported using import_projections(), containing the ⁠$Summary_changes⁠ element.
outcome	(character) The outcome to binarize. Available options are "suitable", "unsuitable", "stable-suitable", "stable-unsuitable", "gain" or "loss". Default is "suitable". See details.
n_gcms	(numeric) The minimum number of GCMs that must agree on the specified outcome for a cell to be included in that category.

Details

The interpretation of the outcomes depends on the temporal direction of the projection. When projecting to future scenarios:

• suitable: Areas that remain suitable (stable-suitable) or become suitable (gain) in the future.

• unsuitable: Areas that remain unsuitable (stable-unsuitable) or become unsuitable (loss) in the future.

• gain: Areas that are currently unsuitable become suitable in the future.

• loss: Areas that are currently suitable become unsuitable in the future.

• stable-suitable or stable-unsuitable: Areas that retain their current classification in the future, whether suitable or unsuitable.

When projecting to past scenarios:

• suitable: Areas that remain suitable (stable-suitable) or become unsuitable (loss) in the present.

• unsuitable: Areas that remain unsuitable (stable-unsuitable) or become suitable (gain) in the present

• gain: Areas that were unsuitable in the past are now suitable in the present.

• loss: Areas that were suitable in the past are now unsuitable in the present.

• stable-suitable or stable-unsuitable: Areas that retain their current classification in the future, whether suitable or unsuitable.

Value

A SpatRaster or a list of SpatRaster objects (one per scenario) with the binarized outcomes. For example, if outcome = "suitable" and n_gcms = 3, cells with a value of 1 indicate areas where three or more GCMs agree that the area is suitable for the species in that scenario.

Examples

# Step 1: Organize variables for current projection
## Import current variables (used to fit models)
var <- terra::rast(system.file("extdata", "Current_variables.tif",
                               package = "kuenm2"))

## Create a folder in a temporary directory to copy the variables
out_dir_current <- file.path(tempdir(), "Current_raw_bin")
dir.create(out_dir_current, recursive = TRUE)

## Save current variables in temporary directory
terra::writeRaster(var, file.path(out_dir_current, "Variables.tif"))


# Step 2: Organize future climate variables (example with WorldClim)
## Directory containing the downloaded future climate variables (example)
in_dir <- system.file("extdata", package = "kuenm2")

## Create a folder in a temporary directory to copy the future variables
out_dir_future <- file.path(tempdir(), "Future_raw_bin")

## Organize and rename the future climate data (structured by year and GCM)
### 'SoilType' will be appended as a static variable in each scenario
organize_future_worldclim(input_dir = in_dir, output_dir = out_dir_future,
                          name_format = "bio_", static_variables = var$SoilType)

# Step 3: Prepare data to run multiple projections
## An example with maxnet models
## Import example of fitted_models (output of fit_selected())
data(fitted_model_maxnet, package = "kuenm2")

## Prepare projection data using fitted models to check variables
pr <- prepare_projection(models = fitted_model_maxnet,
                         present_dir = out_dir_current,
                         future_dir = out_dir_future,
                         future_period = c("2081-2100"),
                         future_pscen = c("ssp585"),
                         future_gcm = c("ACCESS-CM2", "MIROC6"),
                         raster_pattern = ".tif*")

# Step 4: Run multiple model projections
## A folder to save projection results
out_dir <- file.path(tempdir(), "Projection_results/maxnet_bin")
dir.create(out_dir, recursive = TRUE)

## Project selected models to multiple scenarios
p <- project_selected(models = fitted_model_maxnet, projection_data = pr,
                      out_dir = out_dir)

# Step 5: Identify areas of change in projections
## Contraction, expansion and stability
changes <- projection_changes(model_projections = p, write_results = FALSE,
                              return_raster = TRUE)

# Step 6: Binarize changes
future_suitable <- binarize_changes(changes_projections = changes,
                                    outcome = "suitable",
                                    n_gcms = 1)
terra::plot(future_suitable)

kuenm2 documentation built on April 21, 2026, 1:07 a.m.