Data from: Climate change alters global invasion vulnerability among ecoregions
Data files
Sep 25, 2023 version files 39.97 MB
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climatch.ens.feow.245.90.Rda
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climatch.ens.feow.370.90.Rda
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climatch.ens.feow.585.90.Rda
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climatch.ens.teow.245.90.Rda
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climatch.ens.teow.370.90.Rda
1.75 MB
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climatch.ens.teow.585.90.Rda
1.81 MB
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climatch.feow.BC.245.90.Rda
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climatch.feow.BC.370.90.Rda
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climatch.feow.BC.585.90.Rda
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climatch.feow.CAN.245.90.Rda
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climatch.feow.CAN.370.90.Rda
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climatch.feow.CAN.585.90.Rda
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climatch.feow.CM6A.245.90.Rda
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climatch.feow.CM6A.370.90.Rda
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climatch.feow.CM6A.585.90.Rda
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climatch.feow.ESM2.245.90.Rda
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climatch.feow.ESM2.370.90.Rda
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climatch.feow.ESM2.585.90.Rda
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climatch.feow.hist.Rda
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climatch.feow.MIRES2.245.90.Rda
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climatch.feow.MIRES2.370.90.Rda
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climatch.feow.MIRES2.585.90.Rda
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climatch.feow.MRI.245.90.Rda
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climatch.feow.MRI.370.90.Rda
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climatch.feow.MRI.585.90.Rda
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climatch.teow.BC.245.90.Rda
1.28 MB
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climatch.teow.BC.370.90.Rda
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climatch.teow.BC.585.90.Rda
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climatch.teow.CAN.245.90.Rda
1.31 MB
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climatch.teow.CAN.370.90.Rda
1.34 MB
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climatch.teow.CAN.585.90.Rda
1.35 MB
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climatch.teow.CM6A.245.90.Rda
1.28 MB
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climatch.teow.CM6A.370.90.Rda
1.29 MB
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climatch.teow.CM6A.585.90.Rda
1.32 MB
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climatch.teow.ESM2.245.90.Rda
1.29 MB
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climatch.teow.ESM2.370.90.Rda
1.30 MB
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climatch.teow.ESM2.585.90.Rda
1.30 MB
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climatch.teow.hist.Rda
1.24 MB
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climatch.teow.MIRES2.245.90.Rda
1.28 MB
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climatch.teow.MIRES2.370.90.Rda
1.28 MB
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climatch.teow.MIRES2.585.90.Rda
1.29 MB
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climatch.teow.MRI.245.90.Rda
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climatch.teow.MRI.370.90.Rda
1.27 MB
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climatch.teow.MRI.585.90.Rda
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README.md
2.41 KB
Abstract
Aim: We assess climate similarity among global freshwater and terrestrial ecoregions under historical and future climate scenarios to determine where climate change will impact the climate filter of invasion process.
Location: Global.
Methods: We used the Climatch algorithm to conduct a climate-match analysis to quantify the climate similarity between freshwater and terrestrial ecoregions of the world. Climate match was modelled between all freshwater and terrestrial ecoregions. The analysis was conducted under historical climates and projected climates of 2090 under three shared-socioeconomic pathways SSP2-4.5, SSP3-7.0, SSP5-8.5. Climate matches of each ecoregion were presented as mean climate match to all other ecoregions. Friedman’s non-parametric rank sum two-way analysis of variance with repeated measures was used to examine differences in climate match between climate scenarios.
Description of data and code
Public data
Climate data is accessible from:
Historical period:https://worldclim.org/data/worldclim21.html
Future projections: https://worldclim.org/data/cmip6/cmip6climate.html
Ecoregion data is available from:
Freshwater: https://www.feow.org/download
Terrestrial: https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world
All scripts used in this publication were coded under R version 4.3.0 (2023-04-21 ucrt)
File "1_Climate_data.R" loads and wrangles climate data extracting climate data from each ecoregion and computing the global variance used in climate matching.
File "2_Climate_matching.R" loads extracted climate data and runs all the climate matches in parallel across 7 cores as written (runs in ~65 hours; Intel i7-9700K @ 3.6 GHz 8 core processor). Averages matches across GCMs for each shared-socioeconomic pathway. For climate matching a local package developed by article authors was used. See the "Euclimatch" R package published on CRAN after the acceptance of the related article for the same climate-matching functionality.
File "3_Statistical_analyses.R" Conducts statistical analysis and produces Figures 1 and 2 for and Supplemental Information Tables S1-4.
File "4_Maps.R" Produces Figures 3 and 4 for and Supplemental Information Figure S1.
File "5_Circular_plots.R" Produces Figure 5.
Naming convention to identify climate match datasets
Each data set is in an R format (.Rda) because they are so large. These can be loaded into R with the `load()` function.
Climatch data sets are dataframes of pairwise climatch scores (see methods in the article) between all possible pairs of ecoregions of each set (freshwater and terrestrial). Climatch scores were calculated for each ecoregion as a source and as a recipient.
Each dataset begins with "climatch" and contains the following:
GCM codes corresponding to each GCM run:
"ens" = Ensemble (average) of all GCMs of a given scenario (e.g. SSP5-8.5)
"BC" = BCC-CSM2-MR
"ESM2" = CNRM-ESM2-1
"CAN" = CanESM5
"CM6A" = IPSL-CM6A-LR
"MIRES2" = "MIROC-ES2L
"MRI" = MRI-ESM2-0
Climate scenarios, all projections under 2081-2100 (2090):
"hist" = The historical period (1981-2000)
"245" = SSP2-4.5
"370" = SSP3-7.0
"585" = SSP5-8.5
Ecoregion set:
"feow" = Freshwater ecoregions of the world
"teow" = Terrestrial ecoregions of the world
End
The original datasets were accessed from publicly available sources.
Biogeographic spatial units:
- Freshwater ecoregions of the world (Abell et al. 2008)
- Terrestrial ecoregions of the world (Olson et al. 2001)
Climate data:
Worldclim.org (Fick, S.E. and R.J. Hijmans, 2017) - Accessed December 2020.
Climate matching:
Climate match was computed between all ecoregions both as recipients and sources for each freshwater and terrestrial set, under historical and projected future climate conditions. The "Climatch" algorithm (Pheloung 1996; Crombie et al. 2008) was used.
All code is written in R.