Data from: The article Euclimatch: An R package for climate matching with Euclidean distance metrics
Data files
Dec 03, 2024 version files 75.08 MB
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ac_both_cmb_b.Rda
401 B
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ac_both_cmh_b.Rda
405 B
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ac_fut_cmb_c.Rda
225 B
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ac_fut_cmh_c.Rda
259 B
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ac_hist_cmb_a.Rda
586 B
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ac_hist_cmh_a.Rda
586 B
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climatch_fut_gf_CMCC_5.Rda
14.14 MB
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climatch_fut_gf_MRI_5.Rda
14 MB
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climatch_fut_os_CMCC_5.Rda
10.61 MB
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climatch_fut_os_MRI_5.Rda
10.07 MB
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climatch_hist_gf_5.Rda
15.94 MB
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climatch_hist_os_5.Rda
10.31 MB
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README.md
3.21 KB
Abstract
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).
README: 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
Future projections - https://worldclim.org/data/cmip6/cmip6climate.html
The Freshwater Ecoregions of the World data is available from: https://www.feow.org/download
Global terrestrial surface polygon download from: 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
Goldfish - GBIF.org. 2023b. Carassius auratus Occurrence Download. 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:
- 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.
- 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.
- climatch_hist_gf_5.Rda -- A vector of the historical climate match of Goldfish to each grid cell globally
- climatch_fut_gf_CMCC_5.Rda -- A vector of the projected (CMCC-ESM2) climate match of Goldfish to each grid cell globally
- climatch_fut_gf_MRI_5.Rda -- A vector of the projected (MRI-ESM2-0) climate match of Goldfish to each grid cell globally
- climatch_hist_os_5.Rda -- A vector of the historical climate match of Oscar to each grid cell globally
- climatch_fut_os_CMCC_5.Rda -- A vector of the projected (CMCC-ESM2) climate match of Oscar to each grid cell globally
- 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.
- ac_hist_cmh_a.Rda -- Figure 2. Scenario 1 - VARhist - top left panel
- ac_both_cmh_b.Rda -- Figure 2. Scenario 2 - VARhist - middle left panel
- ac_fut_cmh_c.Rda -- Figure 2. Scenario 3 - VARhist - bottom left panel
- ac_hist_cmb_a.Rda -- Figure 2. Scenario 1 - VARcomb - top right panel
- ac_both_cmb_b.Rda -- Figure 2. Scenario 2 - VARcomb - middle right panel
- ac_fut_cmb_c.Rda -- Figure 2. Scenario 3 - VARcomb - bottom right panel
Methods
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.