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Extensive_description_of_the_dataset
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The data contains contains plot-level aggregated data for 49,809 vegetation plots from the European vegetation archive on the location, proportion of reflected irradiation, evapotranspiration, net primary productivity, classification in EUNIS habitat types, community-weighted means and variances of 19 plant traits, as well as 28 bioclimatic variables.
Data preparation is extensively described in Kambach et al. (2024). Here, we present a brief summary of the variables in file "eva_data_merged_for_publication.csv" (also listed in "README_file.txt"):
1) Vegetation data
The compiled plots present a subset from 1,735,298 plots, collated and curated by the European Vegetation Archive (EVA, Chytrý et al. 2016) and accessed on May 12th, 2021 (EVA project number 123). Only plots recorded between 2001 and 2017 were selected. Each plot was assigned to a habitat type based on the expert system for the automatic classification of European vegetation plots to EUNIS habitats (European Nature Information System), which was updated on October 25th, 2021 (Chytrý et al. 2020; Chytrý et al. 2021). Here, selected and merged all plots into the following level2 habitat types: coniferous, deciduous, or broadleaved evergreen forests, alpine, heathland or temperate shrublands and alpine, dry, mesic, or wet grasslands.
2) Community-weighted trait values
We harmonized the species from the EVA using Taxonomic Name Resolution Service 5.0 (tnrs.biendata.org), merged subspecies and varieties at the species level (keeping higher level taxa). The resulting species list was matched with the taxonomic backbone 3.0 of the sPlot Global Vegetation Database (Bruelheide et al. 2019). Algae, bryophyte, fungi, and lichen species were omitted. Using the gap-filled TRY plant trait database, version 5 (Kattge et al. 2020), we matched 19 species-level averaged traits to 4,139 species and, when no species-level estimates were available, we matched genus-average values to an additional number of 385 species. Calculation of community-weighted trait means and variances followed Bruelheide et al. (2018).
3) Bioclimatic variables
For each plot, we extracted 19 bioclimatic variables and 9 BIOCLIM+ variables from the CHELSA Climatologies, (version 1.2, Karger et al. 2017; Karger et al. 2018, Brun et al. 2022).
4) Climate regulation variables
For each plot, we calculated the annual proportion of reflected irradiation, the annual evapotranspiration, and the annual net primary productivity, using MODIS and EUMESAT data recorded between 2001 and 2017 at a resolution of 500 m).
4.1) Monthly proportion of reflected irradiation was extracted from
MCD43A3 version 6 (Schaaf & Wang Wang 2015) and EUMESAT CM SAF product SARAH-2.1 (Pfeifroth et al. 2019).
4.2) Mean annual evapotranspiration was extracted from MOD16A3GF version 6.1 (Running et al. 2021).
4.3) Mean annual net primary productivity was extracted from
MOD17A3HGF version 6 (Running and Zhao 2019).
File "databases_included.txt" lists all databases included in the dataset and their code in the Global Index of Vegetation-Plot Databases (GIVD).
All files use UTF-8 character encoding.
References
- Kambach, S. et al. (2024). Climate regulation processes are linked to the functional composition of plant communities in European forests, shrublands, and grasslands. Global Change Biology.
- Bruelheide et. al (2018): Global trait-environment relationships of plant communities. Nat Ecol Evol 2 (12), 1906–1917. https://doi.org/ 10.1038/s41559-018-0699-8.
- Bruelheide et al.(2019): sPlot – A new tool for global vegetation analyses. J Veg Sci 30 (2), 161–186. https://doi.org/10.1111/jvs.12710.
- Brun et al. (2022): Global climate-related predictors at kilometre resolution for the past and future. Earth Sys Sci Data 24 (12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022.
- Chytrý et al. (2016): European Vegetation Archive (EVA): an integrated database of European vegetation plots. Appl Veg Sci 19 (1), 173–180. https://doi.org/10.1111/avsc.12191.
- Chytrý et al. (2020): EUNIS habitat classification: Expert system, characteristic species combinations and distribution maps of European habitats. Appl Veg Sci 23 (4), 648–675. https://doi.org/10.1111/avsc.12519.
- Chytrý et al. (2021): EUNIS-ESy, version 2021-06-01. https://doi.org/10.5281/zenodo.4812736.
- Karger et al. (2017): Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122. https://doi.org/10.1038/sdata.2017.122.
- Karger et al. (2018): Data from: Climatologies at high resolution for the earth’s land surface areas. EnviDat. https://doi.org/10.16904/envidat.228.v2.1
- Kattge et al. (2020): TRY plant trait database - enhanced coverage and open access. Glob Chang Biol 26 (1), 119–188. https://doi.org/10.1111/gcb.14904.
- Pfeifroth et al. (2019): Surface Radiation Data Set - Heliosat (SARAH) - Edition 2.1.
- Running et al. (2019): MOD17A3HGF MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid V006.
- Running et al. (2021): MODIS/Terra Net Evapotranspiration Gap-Filled Yearly L4 Global 500m SIN Grid V061.
- Schaaf & Wang (2015): MCD43A3 MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V006. |
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