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ClimaRep: Estimating climate representativeness

Overview

The ClimaRep package offers tools to analyze the climate representativeness of defined areas, assessing current conditions and evaluating how they are projected to change under future climate change scenarios. Using spatial data, including climate raster layers, the input area polygons, and a polygon of the study area, the package quantifies this representativeness and analyzes its transformation.

Key features include: * Filtering raster climate variables to reduce multicollinearity (vif_filter). * Estimating current climate representativeness (mh_rep). * Estimating changes in climate representativeness under future climate projections (mh_rep_ch). * Estimating climate representativeness overlay (rep_overlay).

Installation

You can install the development version of ClimaRep from CRAN with:

install.packages("ClimaRep")

Alternatively, you can install the development version from GitHub:

install.packages("devtools")
library(devtools)
devtools::install_github("MarioMingarro/ClimaRep")

Dependencies:

This package relies on other R packages, such as:

terra for efficient handling of raster data (SpatRaster objects).

sf for robust handling of vector data (sf objects).

stats for statistical analysis.

ggplot2 and tidyterra for visualization tasks.

These dependencies will be installed automatically when you install ClimaRep.

Getting Started

This section provides a brief example demonstrating the core workflow of the package.

First, load the package:

library(ClimaRep)
library(terra)
library(sf)

Next, prepare the essential input data:

1. Climate variables as an SpatRaster objects with consistent extent, resolution, and Coordinate Reference System (CRS).

2. Polygon as an sf object containing one or more polygons, with a column identifying each distinct area (e.g., a ‘name’ or ‘ID’ column).

3. Study area as a single sf object, representing the overall geographical region for analysis and thus the climate space being worked on.

Here is a practical example using simulated data: The example simulates a pair of defined input areas and assesses their climate representativeness and transformation within a defined climate space. This involves creating a simulated climate space within which the analysis is performed.

Generate simulated climate raster layers:

set.seed(2458)
n_cells <- 100 * 100
r_clim_present <- terra::rast(ncols = 100, nrows = 100, nlyrs = 7)
values(r_clim_present) <- c(
  (rowFromCell(r_clim_present, 1:n_cells) * 0.2 + rnorm(n_cells, 0, 3)),
  (rowFromCell(r_clim_present, 1:n_cells) * 0.9 + rnorm(n_cells, 0, 0.2)),
  (colFromCell(r_clim_present, 1:n_cells) * 0.15 + rnorm(n_cells, 0, 2.5)),
  (colFromCell(r_clim_present, 1:n_cells) + 
     rowFromCell(r_clim_present, 1:n_cells) * 0.1 + rnorm(n_cells, 0, 4)),
  (colFromCell(r_clim_present, 1:n_cells) /
     rowFromCell(r_clim_present, 1:n_cells) * 0.1 + rnorm(n_cells, 0, 4)),
  (colFromCell(r_clim_present, 1:n_cells) *
     (rowFromCell(r_clim_present, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4))),
  (colFromCell(r_clim_present, 1:n_cells) *
     (colFromCell(r_clim_present, 1:n_cells) + 0.1 + rnorm(n_cells, 0, 4))))
names(r_clim_present) <- c("varA", "varB", "varC", "varD", "varE", "varF", "varG")
terra::crs(r_clim_present) <- "EPSG:4326"
terra::plot(r_clim_present)

Figure 1: Example of simulated climate raster layers (r_clim_present).

1. Filter Climate Variables

A crucial first step in processing the climate variables is often to address multicollinearity. Multicollinearity among climate variables can affect multivariate analyses.

To handle this, the vif_filter function can be used to iteratively remove variables with a Variance Inflation Factor (VIF) above a specified threshold (e.g., th = 5).

The output of vif_filter returns a list object with a filtered SpatRaster object and a statistics summary.

The SpatRaster object, containing only the variables that were kept, also provides a comprehensive summary printed to the console.

The summary list including: - The lists of variables that were kept and those that were excluded. - The original Pearson’s correlation matrix between all initial variables. - The final VIF values for the variables that were retained after the filtering process.

vif_result <- vif_filter(r_clim_present, th = 5)
print(vif_result$summary)

Kept layers: varA, varC, varE, varF, varG 
Excluded layers: varD, varB 

Pearson correlation matrix of original data:
        varA    varB   varC   varD    varE    varF   varG
varA  1.0000  0.8870 0.0087 0.0938 -0.0745  0.5816 0.0028
varB  0.8870  1.0000 0.0043 0.0997 -0.0769  0.6513 0.0003
varC  0.0087  0.0043 1.0000 0.8523  0.0517  0.5676 0.8341
varD  0.0938  0.0997 0.8523 1.0000  0.0576  0.7077 0.9529
varE -0.0745 -0.0769 0.0517 0.0576  1.0000 -0.0256 0.0653
varF  0.5816  0.6513 0.5676 0.7077 -0.0256  1.0000 0.6305
varG  0.0028  0.0003 0.8341 0.9529  0.0653  0.6305 1.0000

Final VIF values for kept variables:
        VIF
varA 2.2832
varC 3.3473
varE 1.0121
varF 3.8325
varG 4.3545

r_clim_present_filtered <- vif_result$filtered_raster
terra::plot(r_clim_present_filtered)

Figure 2: Example of filtered climate dataset, showing remaining variables (r_clim_present_filtered) after vif_filter() function.

2. Estimate Climate Representativeness.

Create example input area polygon (sf) and a study_area polygon (sf) to define the region and climate space for analysis:

hex_grid <- sf::st_sf(
  sf::st_make_grid(
    sf::st_as_sf(
      terra::as.polygons(
        terra::ext(r_clim_present))), 
  square = FALSE))
sf::st_crs(hex_grid) <- "EPSG:4326"
polygons <- hex_grid[sample(nrow(hex_grid), 2), ]
polygons$name <- c("Pol_1", "Pol_2")
sf::st_crs(polygons) <- sf::st_crs(hex_grid)
study_area_polygon <- sf::st_as_sf(as.polygons(terra::ext(r_clim_present)))
sf::st_crs(study_area_polygon) <- "EPSG:4326"
terra::plot(r_clim_present[[1]])
terra::plot(polygons, add = TRUE, color= "transparent", lwd = 3)
terra::plot(study_area_polygon, add = TRUE, col = "transparent", lwd = 3, border = "red")

Figure 3: Example of input polygons (black outline) and study area (red outline) overlaid on a climate raster layer.

Use mh_rep to estimate Climate Representativeness for each input polygon.

The function calculates the Mahalanobis distance from the multivariate centroid of climate conditions within each polygon to all cells in the study_area.

Cells within a certain percentile threshold (th) of distances found within the input polygon are considered representative.

mh_rep(
  polygon = polygons,
  col_name = "name",
  climate_variables = r_clim_present_filtered,
  study_area = study_area_polygon,
  th = 0.95,
  dir_output = tempdir(),
  save_raw = TRUE)
  
----------------------------
Validating and adjusting Coordinate Reference Systems (CRS)
Starting per-polygon processing:

Processing polygon: pol_1 (1 of 2)

Processing polygon: pol_1 (2 of 2)

All processes were completed

Output files in: C:\Users\AppData\Local\Temp\RtmpY1rKKD
----------------------------

This process generates 3 subfolders within the directory specified by dir_output (e.g., tempdir()).

list.files(tempdir())
 [1] "Charts"             "Mh_Raw"             "Representativeness"

1. The Charts subfolder contains the binary representativeness image files (.jpeg) for each input polygon.

list.files(file.path(tempdir(), "Charts"))
[1] "pol_1_rep.jpeg" "pol_2_rep.jpeg"

Figure 4: Example of binary representativeness map for Pol_1 (pol_1_rep.jpeg).

2. The Mh_raw subfolder contains the continuous Mahalanobis distance rasters (.tif) for each input polygon. Lower values indicate climates more similar to the polygon’s centroid.

mh_rep_raw <- terra::rast(list.files(file.path(tempdir(), "Mh_Raw"),  pattern = "\\.tif$", full.names = TRUE))
terra::plot(mh_rep_raw[[1]])
terra::plot(polygons[1,], add = TRUE, color= "transparent", lwd = 3)

Figure 5: Example of continuous Mahalanobis distance raster for Pol_1. Darker shades indicate cells with climate conditions more similar to Pol_1.

3. The Representativeness subfolder contains the binary representativeness rasters (.tif) for each input polygon, based on the threshold (th) applied to the raw Mahalanobis distance.

Cells are coded 1 for representative and 0 for not representetive.

mh_rep_result <- terra::rast(list.files(file.path(tempdir(), "Representativeness"),  pattern = "\\.tif$", full.names = TRUE))
terra::plot(mh_rep_result[[1]])
terra::plot(polygons[1,], add = TRUE, color= "transparent", lwd = 3)

Figure 6: Example of binary representativeness raster for Pol_1, showing cells classified as representetive (value 1).

3. Estimate change in climate representativeness.

To estimate how Representativeness Changes, a future climate scenario is required.

In this example, a simple virtual future climate conditions (SpatRaster) are created by adding a constant value to the r_clim_present_filtered data:

r_clim_future <- r_clim_present_filtered + 2 
names(r_clim_future) <- names(r_clim_present_filtered)
terra::crs(r_clim_future) <- terra::crs(r_clim_present_filtered)
terra::plot(r_clim_future)

Figure 7: Example of simulated future climate variables.

Use mh_rep_ch to compare representativeness between the present_climate_variables and future_climate_variables within the defined study_area.

This function calculates representativeness for each input polygon in both scenarios and determines cells where conditions:

• Retained - Climate conditions that exist in both the current and future scenarios.
• Lost - Climate conditions that exist now but are no longer present in the future.
• Novel - Climate conditions that are new and only appear in the future scenario.

mh_rep_ch(
polygon = polygons,
col_name = "name",
present_climate_variables = r_clim_present_filtered,
future_climate_variables = r_clim_future,
study_area = study_area_polygon,
th = 0.95,
model = "MODEL",
year = "2070",
dir_output = tempdir(),
save_raw = TRUE)

Validating and adjusting Coordinate Reference Systems (CRS).
Starting per-polygon processing:

Processing polygon: pol_1 (1 of 2)

Processing polygon: pol_2 (2 of 2)

All processes were completed

Output files in:  C:\Users\AppData\Local\Temp\RtmpY1rKKD

This process generates several subfolders within the directory specified by dir_output.

list.files(tempdir())
 [1] "Change"             "Charts"             "Mh_Raw_Pre"             "Mh_Raw_Fut"

The Change subfolder contains binary rasters (.tif) for each input polygon, indicating the category of change.

• 0 - Non representetive
• 1 - Retained
• 2 - Lost
• 3 - Novel

change_result <- terra::rast(list.files(file.path(tempdir(), "Change"),  pattern = "\\.tif$", full.names = TRUE))
terra::plot(change_result[[2]])
terra::plot(polygons[2,], add = TRUE, color= "transparent", lwd = 3)

Figure 8: Example of change in representativeness for Pol_2, showing areas Non representetive (0), Retained (1), Lost (2), Novel (3).

The Charts subfolder is updated or regenerated and contains summary map files (.jpeg) visualizing the change analysis results for each input polygon.

list.files(file.path(tempdir(), "Charts"))
[1] "pol_1_rep_change.jpeg" "pol_2_rep_change.jpeg"

Figure 9: Example of summary maps illustrating climate representativeness change for Pol_2 (pol_2_rep_change.jpeg).

The Mh_Raw_Pre subfolder contains the continuous Mahalanobis distance rasters (.tif) for the present scenario, calculated within the study_area extent relative to the climate conditions within each input polygon.

Mh_raw_Pre_result <- terra::rast(list.files(file.path(tempdir(), "Mh_Raw_Pre"),  pattern = "\\.tif$", full.names = TRUE))
terra::plot(Mh_raw_Pre_result[[2]])
terra::plot(polygons[2,], add = TRUE, color= "transparent", lwd = 3)

Figure 10: Example continuous present-day Mahalanobis distance raster (within study area) for Pol_2.

The Mh_Raw_Fut subfolder contains the continuous raw Mahalanobis distance rasters (.tif) for the future scenario, calculated within the study_area extent relative to the climate conditions within each input polygon.

Mh_raw_Fut_result <- terra::rast(list.files(file.path(tempdir(), "Mh_Raw_Fut"),  pattern = "\\.tif$", full.names = TRUE))
terra::plot(Mh_raw_Fut_result[[2]])
terra::plot(polygons[2,], add = TRUE, color= "transparent", lwd = 3)

Figure 11: Example continuous future Mahalanobis distance raster for Pol_2.

4. Estimate Environmental Representativeness Overlay (rep_overlay)

After obtaining the representativeness (mh_rep), or change (mh_rep_ch), rasters for multiple polygons, you can combine them to visualize where different change types (representetive / Retained, Lost, Novel) accumulate.

The rep_overlay function counting, for each cell, how many of the input rasters had a specific category value at that location.

ClimaRep_overlay <- rep_overlay(folder_path = file.path(tempdir(), "Change"),
                               output_dir = file.path(tempdir(), "ClimaRep_overlay"))

Processing 2 classification rasters from C:\Users\AppData\Local\Temp\RtmpY1rKKD/Change
Calculating counts for category: Lost (value = 2) 
Calculating counts for category: Retained (value = 1) 
Calculating counts for category: Novel (value = 3) 
All processes were completed
Output files saved in:  C:\Users\AppData\Local\Temp\RtmpY1rKKD/ClimaRep_overlay

terra::plotRGB(ClimaRep_overlay, stretch = "lin")

Figure 12: Visualisation of accumulated Lost (R), Retained (G) and Novel (B) cells.

terra::plot(ClimaRep_overlay)

Figure 13: Example of accumulate Lost (1), Retained (2) or Novel (3) cells.

Functions Reference

vif_filter()

This function iteratively filters layers from a SpatRaster object by removing the one with the highest Variance Inflation Factor (VIF) that exceeds a specified threshold (th).

vif_filter(x, th)

x: A SpatRaster object with climate layers.

th: The VIF threshold.

mh_rep()

This function calculates Mahalanobis-based Climate Representativeness for input polygon within a defined area.

Representativeness is assessed by comparing the multivariate climate conditions of each cell, of the reference climate space (climate_variables), with the climate conditions within each specific input polygon. The CRS of polygon will be used as the reference system.

mh_rep(polygon, col_name, climate_variables, th, dir_output, save_raw)

polygon: An sf object containing the input polygon(s) for which representativeness will be assessed.

col_name: The name of the column in polygon that contains unique identifiers for each input polygon.

climate_variables: A SpatRaster object with the climate layers (pre-filtered using vif_filter).

study_area: An sf object defining the overall study region.

th: The threshold for determining representativeness (e.g., 0.9 for the 90th percentile of distances within the input polygon).

dir_output: Path to the directory where output rasters and charts will be saved. The directory will be created if it doesn’t exist.

save_raw: Logical. If TRUE, saves the continuous Mahalanobis distance raster (Mh_raw) for each input polygon.

mh_rep_ch()

This function calculates Mahalanobis-based Climate Representativeness (or forward climate analogs) for input polygon across two time periods (present and future) within a defined area.

The function identifies areas of climate representativeness Retained, Lost, or Novel.

Representativeness is assessed by comparing the multivariate climate conditions of each cell, of the reference climate space (present_climate_variables and future_climate_variables), with the climate conditions within each specific input polygon. The CRS of polygon will be used as the reference system.

mh_rep_ch(polygon, col_name, present_climate_variables, future_climate_variables, study_area, th, model, year, dir_output, save_raw)

polygon: An sf object containing the input polygon/s for which representativeness change will be assessed.

col_name: The name of the column in polygon that contains unique identifiers for each input polygon.

present_climate_variables: A SpatRaster object with the present climate layers (pre-filtered using vif_filter).

future_climate_variables: A SpatRaster object with the future climate layers (usually same as present_climate_variables).

study_area: An sf object defining the overall study region.

th: The percentile threshold for determining representativeness in both scenarios (e.g., 0.9 for the 90th percentile of distances within the input polygon).

model: Character string identifying the climate model (e.g., “MIROC6”). This is used in output filenames for clear identification.

year: Character string identifying the future period (e.g., “2050”). This is used in output filenames for clear identification.

dir_output: Path to the directory where output rasters and charts will be saved. The directory will be created if it doesn’t exist.

save_raw: Logical. If TRUE, saves the continuous Mahalanobis distance rasters (Mh_raw) for both present and future scenarios within the study area extent.

rep_overlay()

Combines multiple single-layer rasters (tif), outputs from mh_rep or mh_rep_ch for different input polygons, into a multi-layered SpatRaster.

This function handles inputs from both mh_rep (which primarily contains representetive areas) and mh_rep_ch (which includes Retained, Lost, and Novel areas). The output layers consistently represent counts of each input.

rep_overlay(folder_path, dir_output)

folder_path: Character string. Path to the directory containing the input single-layer GeoTIFF classification rasters (outputs from mh_rep_ch() or mh_rep()). dir_output: Path to the directory where output rasters will be saved. The directory will be created if it doesn’t exist.

Citation

If the package itself is formally cited (e.g., on CRAN), please include the package citation as well:

Mingarro & Lobo (2021) Connecting protected areas in the Iberian peninsula to facilitate climate change tracking. Environmental Conservation, 48(3): 182-191. doi:10.1017/S037689292100014X

Mingarro, Aguilera-Benavente & Lobo (2020) A methodology to assess the future connectivity of protected areas by combining climatic representativeness and land-cover change simulations: the case of the Guadarrama National Park (Madrid, Spain). Environmental Planning and Management, 64(4): 734–753. doi.org/10.1080/09640568.2020.1782859

Mingarro & Lobo (2018) Environmental representativeness and the role of emitter and recipient areas in the future trajectory of a protected area under climate change. Animal Biodiversity and Conservation, 41(2): 333–344. doi.org/10.32800/abc.2018.41.0333

Farber & Kadmon (2003) Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling, 160: 115–130. doi:10.1016/S0304-3800(02)00327-7

Getting Help

If you encounter issues or have questions, please contact.

License

MIT, GPL-3

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.