DOI: 10.5061/dryad.fxpnvx12s
title: Data from: The article Euclimatch: An R package for climate matching with Euclidean distance metrics

description: Climate matching, a tool for predicting non-native species survival in
 target (recipient) regions, is commonly used in invasive species
 frameworks such as horizon scanning and screening-level risk assessment
 protocols. Screening-level risk assessments often require the analysis of
 many species with limited resources, and climate matching can be
 advantageous to identify a reduced number of species for more detailed
 analyses. Additionally, risk screening may require examination of
 non-native species’ source pools where species occurrence records are not
 used in model training data. In these instances, climate matching is an
 effective method for assessing the survival of non-native species or their
 source pools in a target region and has practical advantages over species
 distribution models. We introduce the R package Euclimatch for
 quantitative climate matching with the Euclidean distance algorithm
 Climatch. The package provides tools for creating a streamlined
 data-agnostic climate-matching workflow. First, climate data are extracted
 for species occurrence records or regions. Second, climate match is
 modelled between two regions as a similarity score per grid cell or
 summarized across a target region. Third, visualizations of the climate
 match model outputs are created. We demonstrate the use of the Euclimatch
 package with the climate match of two popular aquarium trade species and a
 region-to-region analysis. We also demonstrate differences in results
 between Euclidean distance metric standardization methods when
 incorporating climate-change projections. The scale of each example is
 global, under historical and projected climates. Euclimatch provides a
 scripting interface for Euclidean climate matching for the screening
 assessment of non-native species or regions under any climatic conditions.
 Euclimatch can be downloaded from the comprehensive R archive network
 (CRAN).
description: Raw data were collected from sources cited in the article and in
 the code. Climate matching analyses were carried out as described in the
 article. 
description: # Data from: The article Euclimatch: An R package for climate matching
 with Euclidean distance metrics Raw data were accessed from public
 repositories Climate data was accessed using the 'geodata' R
 package in the code but is also accessible from: Historical period -
 [https://worldclim.org/data/worldclim21.html](https://worldclim.org/data/worldclim21.html) Future projections - [https://worldclim.org/data/cmip6/cmip6climate.html](https://worldclim.org/data/cmip6/cmip6climate.html) The Freshwater Ecoregions of the World data is available from: [https://www.feow.org/download](https://www.feow.org/download) Global terrestrial surface polygon download from: [https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-land/](https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-land/) Species occurrences were accessed via GBIF and the code will download the data directly: Oscar - GBIF.org. 2023a. Astronotus ocellatus (Oscar) Occurrence Download. [https://doi.org/10.15468/dl.yzjcv7](https://doi.org/10.15468/dl.yzjcv7) Goldfish - GBIF.org. 2023b. Carassius auratus Occurrence Download. [https://doi.org/10.15468/dl.nmpxbj](https://doi.org/10.15468/dl.nmpxbj) All code used in the publication was written with R version 4.4.1 (2024-06-14 ucrt) ## R files: 1. Euclimatch Workflow.Rproj -- RStudio project file. Useful for reading in and exporting data. Place all files in a single folder and open the R project file, the script will then use the 'here' package to load the data and the script without setting a working directory.  2. Euclimatch_workflow_code.R -- Description: Downloads and wrangles climate data and species data. Loads freshwater ecoregion data. Runs the climate matching and produces the figures. Descriptions of loaded packages are provided in the script. ## Data Files: Data files for Figure 1. These data are converted/projected to a SpatRastes with the climatch_plot function. 1. climatch_hist_gf_5.Rda -- A vector of the historical climate match of Goldfish to each grid cell globally 2. climatch_fut_gf_CMCC_5.Rda -- A vector of the projected (CMCC-ESM2) climate match of Goldfish to each grid cell globally 3. climatch_fut_gf_MRI_5.Rda -- A vector of the projected (MRI-ESM2-0) climate match of Goldfish to each grid cell globally 4. climatch_hist_os_5.Rda -- A vector of the historical climate match of Oscar to each grid cell globally 5. climatch_fut_os_CMCC_5.Rda -- A vector of the projected (CMCC-ESM2) climate match of Oscar to each grid cell globally 6. climatch_fut_os_MRI_5.Rda -- A vector of the projected (MRI-ESM2-0) climate match of Oscar to each grid cell globally Data files for Figure 2. These files are added to the Freshwater ecoregions SpatVector and plotted. 1. ac_hist_cmh_a.Rda -- Figure 2. Scenario 1 - VARhist - top left panel 2. ac_both_cmh_b.Rda -- Figure 2. Scenario 2 - VARhist - middle left panel 3. ac_fut_cmh_c.Rda -- Figure 2. Scenario 3 - VARhist - bottom left panel 4. ac_hist_cmb_a.Rda -- Figure 2. Scenario 1 - VARcomb - top right panel 5. ac_both_cmb_b.Rda -- Figure 2. Scenario 2 - VARcomb - middle right panel 6. ac_fut_cmb_c.Rda -- Figure 2. Scenario 3 - VARcomb - bottom right panel