import_results: Import rasters resulting from projection functions

In kuenm2: Detailed Development of Ecological Niche Models

Import rasters resulting from projection functions

Description

This function facilitates the import of results that have been generated and written to disk by the project_selected(), projection_changes(), variability_projections(), and projection_mop() functions. Users can select specific periods (past/future), emission scenarios, General Circulation Models (GCMs), and result types for import.

Usage

import_results(projection,
               consensus = c("median", "range", "mean", "stdev"),
               present = TRUE, past_period = NULL, past_gcm = NULL,
               future_period = NULL, future_pscen = NULL, future_gcm = NULL,
               change_types = c("summary", "by_gcm", "by_change"),
               mop_types = c("distances", "simple", "basic",
                             "towards_high_combined",
                             "towards_low_combined",
                             "towards_high_end",
                             "towards_low_end"))

Arguments

projection	an object of class model_projections, changes_projections, variability_projections, or mop_projections. This object is the direct output from one of the projection functions listed in the description.
consensus	(character) consensus measures to import. Available options are: 'median', 'range', 'mean' and 'stdev' (standard deviation). Default is c("median", "range", "mean", "stdev"), which imports all options. Only applicable if projection is a model_projections object.
present	(logical) whether to import present-day projections. Default is TRUE. Not applicable if projection is a changes_projections object.
past_period	(character) names of specific past periods (e.g., 'LGM' or 'MID') to import. Default is NULL, meaning all available past periods will be imported.
past_gcm	(character) names of specific General Circulation Models (GCMs) from the past to import. Default is NULL, meaning all available past GCMs will be imported.
future_period	(character) names of specific future periods (e.g., '2041-2060' or '2081-2100') to import. Default is NULL, meaning all available future periods will be imported.
future_pscen	(character) names of specific future emission scenarios (e.g., 'ssp126' or 'ssp585') to import. Default is NULL, meaning all available future scenarios will be imported.
future_gcm	(character) names of specific General Circulation Models (GCMs) from the future to import. Default is NULL, meaning all available future GCMs will be imported.
change_types	(character) names of the type of computed changes to import. Available options are: 'summary', 'by_gcm', 'by_change' and 'binarized'. Default is c("summary", "by_gcm", "by_change"), importing all types. Only applicable if projection is a changes_projections object.
mop_types	(character) type(s) of MOP to import. Available options are: 'basic', 'simple', 'towards_high_combined', 'towards_low_combined', towards_high_end', and 'towards_low_end'. Default is NULL, meaning all available MOPs will be imported. Only applicable if projection is a mop_projections object.

Value

A SpatRaster or a list of SpatRasters, structured according to the input projection class:

• If projection is model_projections: A stacked SpatRaster containing all selected projections.

• If projection is changes_projections: A list of SpatRasters, organized by the selected change_types (e.g., 'summary', 'by_gcm', and/or 'by_change').

• If projection is mop_projections: A list of SpatRasters, organized by the selected mop_types (e.g., 'simple' and 'basic').

• If projection is variability_projections: A list of SpatRasters, containing the computed variability.

See Also

prepare_projection(), projection_changes(), projection_variability(), projection_mop()

Examples

# Load packages
library(terra)
# 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_raw2")
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_raw2")

## 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 = "2041-2060",
                         future_pscen = c("ssp126", "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")
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)

# Use import_results to import results:
raster_p <- import_results(projection = p, consensus = "mean")
plot(raster_p)

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