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20 results for “European vegetation”
Current and future European potential vegetation types
<p>This dataset contains Potential Natural Vegetation (PNV) estimates for the European continent at 1km grain size. Estimates are made for six different vegetation types following the MAES Ecosystem classification at level 1. The predictions have been made through an ensemble of Bayesian Habitat distribution models available through the <em>ibis.iSDM</em> package <a href="https://doi.org/10.1016/j.ecoinf.2023.102127" target="_blank" rel="noopener">(Jung 2023)</a>. For more information on the methodology, original data and used covariates, please see the accompanying preprint (<a href="https://doi.org/10.31223/X59H71">Jung 2024</a>).<br><br><strong>Uploaded are:</strong></p> <ul> <li>The most likely current PNV transition (see screenshot) as categorical raster (and screenshot, see png)<br>(Classes: 1=Woodland.and.forest | 2=Heathland.and.shrub | 3=Grassland | 4=Sparsely.vegetated.areas | 5=Wetlands | 6=Marine.inlets.and.transitional.waters)</li> <li>Current PNV estimates as cloud-optimized geoTIFF ("COG") files (.tif)</li> <li>Future PNV estimates (zipped) for each considered SSP - GCM combination as geoTIFF (.tif).</li> </ul> <p><strong>Variable naming scheme:</strong><br>Current: "pnv_XX_laea_1km.tif"<br>where XX represents the vegetation type<br>Future: Here the hierachical organization scheme of Essential Biodiversity Variables (EBV) is followed where files are separated in folders by<br>Scenario | metric | entity | time, so for example "SSP126-GFDL-ESM4/suitability_mean/grassland/"<br>Filenames are labelled by the date (e.g. "2040.tif").<br><br><strong>Metrics and layers names and their interpretation:</strong><br>For current:<br>"mean" = Average Ensemble posterior prediction<br>"sd" = Standard deviation of posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction<br>"mode" = Most commonly encountered value of posterior prediction<br>"cv" = Coefficient of variation of posterior prediction<br><br>For future:<br>"mean" = Average Ensemble posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction</p> <p>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, <p>IPSL-CM6A-LR, </p> <p>MPI-ESM1-2-HR,</p> <p>MRI-ESM2-0,</p> <p>UKESM1-0-LL</p> </td> </tr> <tr> <td>Spatial grain</td> <td>1 km²</td> </tr> <tr> <td>Geographic projection</td> <td>LAEA</td> </tr> <tr> <td>Temporal grain</td> <td>30 year climatologies</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1990 to 2020 (Current), 2020 - 2100 (Future)</td> </tr> <tr> <td>Number of variables/entities</td> <td>7</td> </tr> </tbody> </table> <p>All files are provided as is and the author takes no responsibility for errors or misuse and misinterpretation. </p>
EUNIS-ESy: Expert system for automatic classification of European vegetation plots to EUNIS habitats
<p><strong>EUNIS-ESy</strong> is an expert system for automatic classification of European vegetation plots to habitat types of the EUNIS Habitat Classification. The EUNIS classification and the principles of the expert system are described by <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. The classification of a set of vegetation plots can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>), TURBOVEG 3 program (Hennekens 2015) and an R script (<a href="https://doi.org/10.1111/avsc.12562">Bruelheide et al. 2021</a>).</p> <p>This dataset contains two parts: (1) the expert system and related files necessary for running it; (2) characterization of EUNIS habitats based on the results of the expert system classification.</p> <p><strong>1. Expert system and related files necessary to run it</strong></p> <p>1.1. <strong>EUNIS-ESy-2025-10-03.txt </strong>– a file containing the script for the classification of vegetation plots by EUNIS-ESy. This version contains tested definitions for the revised EUNIS classification of vegetated Marine (MA), Coastal (N), Wetland (Q), Grassland (R), Shrubland (S), Forest (T), Inland sparsely vegetated (U) and Man-made (V). It also contains tested definitions of Aquatic plant communities (P3) and Springs (P2N). This file is different from the analogous file in the previous versions.</p> <p>1.2. <strong>Nomenclature-translation-from-Turboveg-2-databases.zip </strong>– an archive containing the scripts for automatic translation of taxon concepts and names used in individual European Turboveg 2 databases (<a href="https://doi.org/10.2307/3237010">Hennekens & Schaminée 2001</a>; <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) to the nomenclature that can be used as an input for EUNIS-ESy. This file is the same as in the previous versions.</p> <p>1.3. <strong>EUNIS-ESy-User-Guide.pdf </strong>– a brief user guide to the classification of vegetation plots by EUNIS-ESy using the JUICE program. Please read this guide carefully before running the expert system to avoid misclassifications. This file is the same as in the previous versions.</p> <p><strong>2. Characterization of the EUNIS habitats based on the results of the EUNIS-ESy classification</strong></p> <p>2.1. <strong>EUNIS-habitats-2025-10-03.xlsx </strong>– the current list of EUNIS habitats. This file is different from the analogous file in the previous versions.</p> <p>2.2. <strong>EUNIS-EuroVegChecklist-crosswalk-2025-10-03.xlsx</strong> – a crosswalk between the EUNIS habitat classification and phytosociological alliances of EuroVegChecklist (<a href="http://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>; <a href="https://floraveg.eu/vegetation/">https://floraveg.eu/vegetation/</a>).</p> <p>2.3. <strong>EUNIS-habitats-Characteristic-species-combintation-2025-10-03.xlsx </strong>– a database of habitats' characteristic species combinations in a spreadsheet format. These species combinations are based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03. Analytical methods are described in <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. This file is different from the analogous file in the previous versions.</p> <p>2.4. <strong>EUNIS-habitats-Distribution-maps-2025-10-03.xlsx </strong>– a set of distribution maps in the TIFF format based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03.</p> <p>2.5. <strong>Data-sources-EUNIS-classification-2025-10-03.pdf </strong>– a list of data sources used to produce the distribution maps and characteristic species combinations.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p><strong>Differences from the previous version (2021-06-01)</strong></p> <p>Aquatic plant communities (P3), spring (P2N), some wetland (Q61-Q63) and some inland sparsely vegetated (U71-U72) habitats were added to the EUNIS-ESy expert system. Plant taxon concepts and nomenclature were extensively revised. Some previously included habitat definitions were slightly refined. New vegetation-plot records added to the EVA database by 8 August 2025 were used to characterize habitat types. Unlike in the previous version, this version does not provide Habitat factsheets because summarized information about each habitat is now available in the FloraVeg.EU database at <a href="https://floraveg.eu/habitat/">https://floraveg.eu/habitat/</a>.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p> </p> <p><strong>Recommended citation of this version of the EUNIS-ESy expert system</strong></p> <p>Chytrý et al. (2020), version 2025-10-03</p> <p>Chytrý M., Tichý L., Hennekens S.M., Knollová I., Janssen J.A.M., Rodwell J.S., Peterka T., Marcenò C., Landucci F., Danihelka J., Hájek M., Dengler J., Novák P., Zukal D., Jiménez-Alfaro B., Mucina L., Abdulhak S., Aćić S., Agrillo E., Attorre F., Bergmeier E., Biurrun I., Boch S., Bölöni J., Bonari G., Braslavskaya T., Bruelheide H., Campos J.A., Čarni A., Casella L., Ćuk M., Ćušterevska R., De Bie E., Delbosc P., Demina O., Didukh Y., Dítě D., Dziuba T., Ewald J., Gavilán R.G., Gégout J.-C., Giusso del Galdo G.P., Golub V., Goncharova N., Goral F., Graf U., Indreica A., Isermann M., Jandt U., Jansen F., Jansen J., Jašková A., Jiroušek M., Kącki Z., Kalníková V., Kavgacı A., Khanina L., Korolyuk A.Yu., Kozhevnikova M., Kuzemko A., Küzmič F., Kuznetsov O.L., Laiviņš M., Lavrinenko I., Lavrinenko O., Lebedeva M., Lososová Z., Lysenko T., Maciejewski L., Mardari C., Marinšek A., Napreenko M.G., Onyshchenko V., Pérez-Haase A., Pielech R., Prokhorov V., Rašomavičius V., Rodríguez Rojo M.P., Rūsiņa S., Schrautzer J., Šibík J., Šilc U., Škvorc Ž., Smagin V.A., Stančić Z., Stanisci A., Tikhonova E., Tonteri T., Uogintas D., Valachovič M., Vassilev K., Vynokurov D., Willner W., Yamalov S., Evans D., Palitzsch Lund M., Spyropoulou R., Tryfon E., Schaminée J.H.J. (2020) EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science, 23, 648–675. https://doi.org/10.1111/avsc.12519</p>
Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"
<p>Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science</p> <p>See file ReadMe.txt for a description of the content and the original publication for further explanations.</p> <p>You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication.<br> For further questions, contact sabine.rumpf@unibas.ch</p>
Structural, ecological and biogeographical attributes of European vegetation alliances
<p>This is a database of structural, ecological and biogeographical attributes of 1115 European phytosociological alliances. The original version was published by Preislerová et al. (2024). This article also contained definitions and descriptions of individual attributes.</p> <p>Version 2 of this dataset has been updated to match Version 3 of EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes <em>Ammophiletea arundinaceae</em>, <em>Helichryso-Crucianelletea maritimae</em> and <em>Honckenyo peploidis-Leymetea arenarii</em>), Mediterranean pine forests (order <em>Pinetalia halepensis</em>) and bogs (class <em>Oxycocco-Sphagnetea</em>) approved by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jiroušek et al. (2022), respectively.</p> <p>The data are provided in two files with identical contents, one in the XLSX format, and the other in the TXT format with columns separated by tabs.</p> <p><strong>References</strong></p> <div> <div> <div> <ul> <li>Bonari G., Fernández‐González F., Çoban S., Monteiro‐Henriques T., Bergmeier E., Didukh Ya. P. … Chytrý, M. (2021). Classification of the Mediterranean lowland to submontane pine forest vegetation. <em>Applied Vegetation Science</em>, 24, e12544. <a href="https://doi.org/10.1111/avsc.12544">https://doi.org/10.1111/avsc.12544</a></li> <li>Jiroušek, M., Peterka, T., Chytrý, M., Jiménez-Alfaro, B., Kuznetsov, O.L., Pérez-Haase, A. … Hájek, M. (2022). Classification of European bog vegetation of the Oxycocco-Sphagnetea class. <em>Applied Vegetation Science</em>, 25, e12646. <a href="https://doi.org/10.1111/avsc.12646">https://doi.org/10.1111/avsc.12646</a></li> <li>Marcenò, C., Guarino, R., Loidi, J., Herrera, M., Isermann, M., Knollová, I. … Chytrý, M. (2018). Classification of European and Mediterranean coastal dune vegetation. <em>Applied Vegetation Science</em>, 21, 533–559. <a href="https://doi.org/10.1111/avsc.12379">https://doi.org/10.1111/avsc.12379</a></li> <li>Marcenò, C., Danihelka, J., Dziuba, T., Willner, W. & Chytrý, M. (2024). Nomenclatural revision of the syntaxa of European coastal dune vegetation. <em>Vegetation Classification and Survey</em>, 5, 27–37. <a href="https://doi.org/10.3897/VCS.108560">https://doi.org/10.3897/VCS.108560</a></li> <li>Mucina, L., Bültmann, H., Dierßen, K., Theurillat, J.-P., Raus, T., Čarni, A. … Tichý, L. (2016). Vegetation of Europe: Hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities. <em>Applied Vegetation Science</em>, 19(Suppl. 1.), 3–264. <a href="https://doi.org/10.1111/avsc.12257">https://doi.org/10.1111/avsc.12257</a></li> <li>Preislerová Z., Marcenò C., Loidi J., Bonari G., Borovyk D., Gavilán R.G., Golub V., Terzi M., Theurillat J.-P., Argagnon O., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ćušterevska R., Dengler J., Didukh Ya., Dítě D., Fanelli G., Fernández-González F., Guarino R., Hájek O., Iakushenko D., Iemelianova S., Jansen F., Jašková A., Jiroušek M., Kalníková V., Kavgacı A., Kuzemko A., Landucci F., Lososová Z., Milanović Đ., Molina J.A., Monteiro-Henriques T., Mucina L., Novák P., Nowak A., Pätsch R., Perrin G., Peterka T., Rašomavičius V., Reczyńska K., Rūsiņa S., Sánchez Mata D., Santos Guerra A., Šibík J., Škvorc Ž., Stešević D., Stupar V., Świerkosz K., Tzonev R., Vassilev K., Vynokurov D., Willner W. & Chytrý M. (2024) Structural, ecological and biogeographical attributes of European vegetation alliances. <em>Applied Vegetation Science</em>, 27, e12766. <a href="https://doi.org/10.1111/avsc.12766">https://doi.org/10.1111/avsc.12766</a></li> </ul> </div> </div> </div>
Top-down and bottom-up vegetal and animal product metabolic profiles for 29 european countries (EU27 + United Kingdom + Norway).
<p>This repository contains the data needed to reproduce the results in:</p> <p>Cadillo-Benalcazar, J.; Renner, A. Giampietro, M. (2020). A multiscale integrated analysis of the factors characterizing the sustainability of food systems in Europe. Journal of Environmental Management. Volume 271, 1 October 2020, 110944. <a href="https://doi.org/10.1016/j.jenvman.2020.110944">https://doi.org/10.1016/j.jenvman.2020.110944</a></p> <p>Renner, A.; Cadillo-Benalcazar, J.; Benini, L., Giampietro, M. (2020). Environmental pressure of the European agricultural system: Anticipating the biophysical consequences of internalization. Ecosystem services. Special Issue: Agro-futures. Volume 46, December 2020, 101195. <a href="https://doi.org/10.1016/j.ecoser.2020.101195">https://doi.org/10.1016/j.ecoser.2020.101195</a></p>
Dataset for "Phylogenetic structure of European forest vegetation" - Journal of Biogeography (DOI: 10.1111/jbi.14046)
<p>This dataset contains the list of plant occurrences and geographical and environmental attributes of the vegetation-plots analyzed in the paper titled “Phylogenetic structure of European forest vegetation” by Padullés Cubino et al. (2021; Journal of Biogeography; DOI: 10.1111/jbi.14046). </p> <p>The dataset contains 3 tables:</p> <ol> <li>“Table_taxa.csv”: It includes the list of angiosperm plant taxa in selected vegetation plots.</li> <li>“Table_sites.csv”: It includes data on the environmental variables of plots, their classification into different forest types, their location in 1<sup>o</sup> × 1<sup>o</sup> grid cells, and the reference to the original datasets archived in the European Vegetation Archive (EVA; http://euroveg.org/eva-database-participating-databases).</li> <li>“Metadata.csv”: It includes a description of the fields found in the two previous tables.</li> </ol>
Maps of the diversity and distribution of Raunkiær's life forms in European vegetation
<p>This repository contains raster files (TIF format) with a 50 km × 50 km resolution (over UTM grid EPSG:32633), showcasing the diversity and distribution of Raunkiær’s life forms in European vegetation. The maps are based on two key metrics: (i) the proportion (%) of species within each life form and (ii) the diversity of life forms, including richness and evenness.</p> <p>To generate these maps, we averaged plot-level metric values across a comprehensive dataset comprising 546,501 vegetation plots sourced from the European Vegetation Archive (EVA; Project 163; <a href="https://euroveg.org" target="_new">https://euroveg.org</a>). These plots cover diverse habitats, including 173,190 forests, 260,884 grasslands, 52,517 scrubs, and 59,910 wetlands.</p> <p>The maps encompass the entire dataset, offering a visualization of the geographical distribution patterns of life forms across Europe. Additionally, we created habitat-specific maps by subsetting the dataset to explore unique patterns within each habitat type (forest, grassland, scrub, and wetland).</p> <p>Furthermore, we generated additional maps based on standardised effect sizes (SES) of diversity metrics. Through 500 species identity shuffles without replacement, specific to each habitat type, we examined the deviations from random expectations. SES values outside the range of -1.96 to 1.96 indicate significantly lower or higher metric values than expected at random, respectively. </p> <p> </p> <table> <tbody> <tr> <td><strong>Folder name</strong></td> <td><strong>Description of TIF raster values</strong></td> </tr> <tr> <td>full.div</td> <td>Mean richness and evenness of life forms across all habitat types</td> </tr> <tr> <td>full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types</td> </tr> <tr> <td>habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland)</td> </tr> <tr> <td>SES.full.div</td> <td>Mean richness and evenness of life forms across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.full.mean.rel.prop</td> <td>Mean proportion of each life form across all habitat types measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.div</td> <td>Mean richness and evenness of life forms across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> <tr> <td>SES.habitat.mean.rel.prop</td> <td>Mean proportion of each life form across separate habitat types (forest, grassland, scrub, and wetland) measured with standardized effect sizes (SES)</td> </tr> </tbody> </table> <p><br>Additional information is available in our publication:<br><br>Midolo, G., Axmanová, I., Divíšek, J., Dřevojan, P., Lososová, Z., Večeřa, M., Karger, D. N., Thuiller, W., Bruelheide, H., Aćić, S., Attorre, F., Biurrun, I., Boch, S., Bonari, G., Čarni, A., Chiarucci, A., Ćušterevska, R., Dengler, J., Dziuba, T., Garbolino, E., Jandt, U., Lenoir, J., Marcenò, C., Rūsiņa, S., Šibík, J., Škvorc, Ž., Stančić, Z., Stanišić-Vujačić, M., Svenning, J. C., Swacha, G., Vassilev, K., & Chytrý, M. (2024) Diversity and distribution of Raunkiær’s life forms in European vegetation.<em> Journal of Vegetation Science. </em>Accepted on the 10th of December 2023</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Figure 2 in Site selection of European ground squirrels (Spermophilus citellus) in Eastern Romania and how they are influenced by climate, relief, and vegetation
Figure 2. The European ground squirrel density (individuals/ha) variation against vegetation height measured in 872 surveyed squares (250 × 250 m grids) conducted during the 2013 season in Eastern Romania.
Figure 1 in Site selection of European ground squirrels (Spermophilus citellus) in Eastern Romania and how they are influenced by climate, relief, and vegetation
Figure 1. The study area location (in the small map marked with black) and the distribution of the surveyed squares (250 × 250 m grids) conducted during the 2013 season in Eastern Romania.
Data from: Cryogenic land surface processes shape vegetation biomass patterns in northern European tundra
<p>Tundra ecosystems have experienced changes in vegetation composition, distribution, and productivity over the past century due to climate warming. However, the increase in above-ground biomass (AGB) may be constrained by cryogenic land surface processes (LSP) that cause topsoil disturbance and variable microsite conditions. These effects have remained unaccounted for in tundra biomass models, although they can impact multiple opposing feedbacks between the biosphere and atmosphere, ecosystem functioning and biodiversity. Here, by using field-quantified data from northern Europe, remote sensing, and machine learning, we show that LSP substantially constrain AGB in tundra. The three surveyed LSP (cryoturbation, solifluction and nivation) collectively reduced AGB by an average of 123.0 g m<sup>-2 </sup>(-30.0%). This effect was significant over landscape positions and was especially pronounced in snowbed environments, where the mean reduction in AGB was 57.3%. Our results imply that LSP are pivotal in shaping future patterns of tundra biomass, as long as cryogenic ground activity is retained by climate warming.</p> <p>These are the key data and codes related from Aalto et al., (2021).</p> <p> </p>
Data from: Emergence patterns of novelty in European vegetation assemblages over the past 15,000 years
Plant communities are not stable over time and biological novelty is predicted to emerge due to climate change, the introduction of exotic species and land-use change. However, the rate at which this novelty may arise over longer time periods has so far received little attention. We reconstruct the emergence of novelty in Europe for a set of baseline conditions over the past 15 000 years to assess past rates of emergence and investigate underlying causes. The emergence of novelty is baseline specific and, during the early-Holocene, was mitigated by the rapid spread of plant taxa. Although novelty generally increases as a function of time, climate and human-induced landscape changes contributed to a non-linear post-glacial trajectory of novelty with jumps corresponding to periods of rapid changes. Emergence of novelty accelerated during the past 1000 years. Historical cultural landscapes experienced a faster novelty development due to the contribution from anthropogenic land-cover changes.
Supplementary digital data for "Widespread contamination of soils and vegetation with Current Use Pesticide residues along altitudinal gradients in a European Alpine valley"
<p>Supplementary digital data for the publication:</p> <p>Widespread contamination of soils and vegetation with Current Use Pesticide residues along altitudinal gradients in a European Alpine valley</p> <p>Data that was no publicly available but is used for Figure 1 - 3 in the manuscript is provided in this repository in addition to the raw data describing the occurrence in plant and soil matrices throughout the Vinschgau valley along the altitudinal transects.</p> <p> </p>
FIGURE 1 in The Integrated Plant Record (IPR) analysis: Methodological advances and new insights into the evolution of European Palaeogene/Neogene vegetation
FIGURE 1. Zelkova zelkovifolia is a representative of the broad-leaved deciduous component.
The European Vegetation Archive (EVA)
The European Vegetation Archive (EVA) is an initiative of European Vegetation Survey aimed at establishing and maintenance of a single data repository of vegetation-plot observations (i.e. records of plant taxon co-occurrence at particular sites, also called phytosociological relevés) from Europe and adjacent areas and to facilitate the use of these data for non-commercial purposes, mainly academic research and applications in nature conservation and ecological restoration. The initiative follows the EVA Data Property and Governance Rules. It closely cooperates with the Global Index of Vegetation-Plot Databases (GIVD), the Global Vegetation Database (sPlot) and the Plant Trait Database (TRY). <p></p>http://euroveg.org/eva-database<p></p>
Emergence patterns of novelty in European vegetation assemblages over the past 15 000 years
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Data from: The leaf economic and plant size spectra of European forest understory vegetation
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Data needed to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests"
<p>Data to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests".</p> <p>This includes:</p> <ol> <li>Tree ring data (meyer, bdn, principe, dittmar)</li> <li>LPJ-GUESS model output (frost_validation, frost_sensitivity, runs_22012024_revision)</li> <li>Data used for plotting</li> </ol>
Data from: General patterns of beavers' selective foraging: How to evaluate the effects of a re-emerging driver of vegetation change along Central European small watercourses
<p>This Excel table contains information about the woody plant supply and its utilization by the Eurasian beaver at 11 study sites along small watercourses located in Hungary. The following data are provided about each woody plant unit (trunk or branch): taxon group, genus, diameter class, and type of utilization. Two taxon groups were differentiated: softwood species (<em>Salix</em> and <em>Populus</em> spp.) and other species. The units were categorized into six diameter classes (d1-d6). Thin branches with a diameter of 0.8–4.9 cm were classified into the first one (d1), and five more diameter classes (d2–d6) were defined using Jenks natural breaks method: d2 – 5–9 cm, d3 – 10–18 cm, d4 – 19–32 cm, d5 – 33–54 cm, d6 – 55–146 cm. Three types of utilization were defined: <em>cutting</em>, <em>carving</em>, and <em>debarking</em>. We refer to trunks and branches not utilized by beavers as intact units. We also distinguished between fresh and old signs of utilization. In addition, a fresh dataset was defined, which consists of intact units, units with fresh signs of utilization, and formerly beaver-harmed but living trees.</p> <p> </p>
Supplementary material 1 from: Winiger N, Korner P, Arlettaz R, Jacot A (2018) Vegetation structure and decreased moth abundance limit the recolonisation of restored habitat by the European Nightjar. Rethinking Ecology 3: 25-39. https://doi.org/10.3897/rethinkingecology.3.29338
Site and moth data : Explanation note: Details about the study sites and moths.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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