Data from: Climatic resilience after extreme drought in Mediterranean shrubland plant communities
Data files
Jun 30, 2025 version files 1.08 MB
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climatic_resilience_components.csv
4.39 KB
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community_CD_CIC_climatic_resilience_components.R
5.99 KB
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community_composition.csv
910.55 KB
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disequilibrium.csv
15.93 KB
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interannual_climatic_niche_modelling.R
6.90 KB
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lineal_mixed_effect_models.R
7.08 KB
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niche_required_functions.R
15.62 KB
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README.md
6.52 KB
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species_centroid.csv
1.90 KB
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work_ext.zip
104.08 KB
Abstract
Extreme climatic events are increasing with climate change, producing changes in communities climatic characterization. So, mismatches between climatic conditions inferred from species requirements (Community Inferred Climate, CIC) and macroclimate (climatic disequilibrium, CD) may change with extreme climatic events. Climatic resilience is defined as the ability to maintain or recover community climatic characteristics, regardless of species identity, after disturbance or stress. We evaluated the dynamics of plant community climatic characterization in Mediterranean shrublands that experienced a drought event, considering CIC and CD. CIC was calculated by averaging species’ climatic niche centroids, weighted by species' relative abundances, in the multivariate environmental space obtained from the climate of the species' geographical occurrence. CD was estimated as Euclidean distance in this space between the observed macroclimate and CIC. Climatic resistance was inferred by the distance between pre-drought and drought CIC, climatic resilience by the distance between pre-drought and post-drought CIC, and relative climatic resilience by the interaction between both distances. We found a significant reduction in community CD after drought, with CIC becoming more arid, likely due to environmental filtering of those species with wetter distributions. Communities with less pre-drought CD showed higher climatic resistance, but pre-drought CD did not explain climatic resilience. Communities with more arid CIC exhibited high climatic resilience regardless of drought impact (high relative climatic resilience), except for those with the most arid CIC, which suffered delayed die-off. Communities with less arid CIC showed low relative climatic resilience, as their resilience was associated with high resistance. The study highlights community impacts by extreme droughts through filtering of species distributed in more humid climates when their populations grow close to the arid edge. This produces changes in CD, whose resilience is determined by CIC, pre-drought CD, and drought impact in terms of CIC change.
“work_ext.zip” contains the vector boundary (work_extension.shp and auxiliary files dbf, .shx, .prj) used to define the study region for climatic niche modeling. The spatial mask delimits the Mediterranean Basin and adjacent regions (Longitude: -18.64°W to 37.29°E; Latitude: 26.70°N to 56.34°N), optimizing computational efficiency by restricting analyses to the area where the study species were distributed.
“species_centroids.csv” contains the centroids’ coordinates in the common climatic space (PCA1 and PCA2) for all the studied species.
- “species”: valid scientific name of the studied species
- “centroid_pca1”: axis 1 coordinate in the common climatic space for each studied species
- “centroid_pca2”: axis 2 coordinate in the common climatic space for each studied species
“Interannual_observed_climate.csv” contains the 12 biovariable values for the study plot positions for the reference period.
- “plot”: identifier of each plot
- “lat”: UTM latitude coordinate
- “long”: UTM longitude coordinate
- “bio_1”: Annual Mean Temperature (°C)
- “bio_2”: Mean Diurnal Range (°C) (Mean of monthly (max temp - min temp))
- “bio_3”: Isothermality (%) (BIO2/BIO7)(×100)
- “bio_4”: Temperature Seasonality (°C) (standard deviation ×100)
- “bio_5”: Max Temperature of Warmest Month (°C)
- “bio_6”: Min Temperature of Coldest Month (°C)
- “bio_7”: Temperature Annual Range (BIO5-BIO6)
- “bio_8”: Mean Temperature of Wettest Quarter (°C)
- “bio_9”: Mean Temperature of Driest Quarter (°C)
- “bio_10”: Mean Temperature of Warmest Quarter (°C)
- “bio_11”: Mean Temperature of Coldest Quarter (°C)
- “bio_12”: Annual Precipitation (mm)
- “bio_13”: Precipitation of Wettest Month (mm)
- “bio_14”: Precipitation of Driest Month (mm)
- “bio_15”: Precipitation Seasonality (Coefficient of Variation)
- “bio_16”: Precipitation of Wettest Quarter (mm)
- “bio_17”: Precipitation of Driest Quarter (mm)
- “bio_18”: Precipitation of Warmest Quarter (mm)
- “bio_19”: Precipitation of Coldest Quarter (mm)
“Community_composition.csv” list of individuals registered in the study communities during the three study periods (pre-drought, drought, post-drought)
- “year”: year of the field survey. “2013” corresponds to the pre-drought period, “2016” corresponds to the drought period, and “2022” corresponds to the post-drought period.
- “plot”: identifier of each plot
- “cod”: identifier of each plot during each period
- “species”: valid scientific name of the studied species
- “n”: number of recruits of each species belonging to each categorical group
“disequilibrium.csv” contains the community inferred climate and climatic disequilibrium of communities during each period.
- “cod”: identifier of each plot during each period
- “plot”: identifier of each plot
- “year”: year of the field survey. “2013” corresponds to the pre-drought period, “2016” corresponds to the drought period, and “2022” corresponds to the post-drought period.
- “oc_x”: axis 1 coordinate in the common climatic space for each plot and period, therefore, experienced by communities (observed climate)
- “oc_y”: axis 2 coordinate in the common climatic space for each plot and period, therefore, experienced by communities (observed climate)
- “cic_x”: community inferred climate for axis x represented as the axis 1 coordinate in the common climatic space for each community and period
- “cic_y”: community inferred climate for axis y represented as the axis 2 coordinate in the common climatic space for each community and period
- “cd”: climatic disequilibrium of each community during each period, calculated as the difference between observed climate and community inferred climate.
“climatic_resilience_components.csv” contains climatic resistance and climatic resilience of each plot.
- “plot”: identifier of each plot
- “cic_x_pred”: axis 1 coordinate in the common climatic space for each community during the pre-drought period, calculated through the species centroid axis 1 coordinate (community inferred climate)
- “cd_pred”: climatic disequilibrium of each community during the pre-drought period, calculated as the difference between observed climate and pre-drought community inferred climate.
- “cd_postd”: climatic disequilibrium of each community during the post-drought period, calculated as the difference between observed climate and post-drought community inferred climate.
- “climatic_resistance”: climatic resistance of each community, calculated as distance between pre-drought CIC(cic_x_pred, cic_y_pred) and drought CIC(cic_x_d, cic_y_d)
- “climatic_resilience”: climatic resilience of each community, calculated as distance between pre-drought CIC (cic_x_pred, cic_y_pred) and post-drought CIC (cic_x_postd, cic_y_postd)
KEY INFORMATION SOURCES/ CODE
species niches and climatic disequilibrium
“interannual_climatic_niche_modelling.R” contains the code for modeling biovariables and obtaining each species’ niche centroid in the multivariate climatic space.
The analysis requires (1) downloading and filtering species occurrence records from GBIF (DOIs provided in the Supplementary Material) and (2) extracting climatic variables for each occurrence location from WorldClim 2.0 (Historical monthly weather data — WorldClim 1 documentation) at 2.5 minutes resolution, using the spatial delineations provided in “work_ext.zip”. Some required functions are contained in “niche_required_functions.R”.
“community_CD_CIC_climatic_resilience_components.R” allows to estimate each community inferred climate (CIC) and climatic disequilibrium (CD) basing on multivariates species niche centroids obtained from “Interannual_climatic_niche_modelling.R”, species relative abundances in each community, contained in table “Community_composition.csv” and historic observed climate of each plot contained in “Interannual_observed_climate.csv. Some required functions are contained in “niche_required_functions.R”. This R code consists of a collection of functions used in niche modeling, spatial data analysis, and data visualization, particularly for ecological and biogeographic studies.
RESULTS
To perform the statistical analyses specified in the manuscripts, use the “linear_mixed_effect_models.R” R script. The “disequilibrium.csv” and “climatic_resilience_components.csv” tables are required.