Ellenberg-type indicator values for European vascular plant species

Aims: Ellenberg-type indicator values are expert-based rankings of plant species according to their ecological optima on main environmental gradients. Here we extend the indicator-value system proposed by Heinz Ellenberg and co-authors for Central Europe by incorporating other systems of Ellenberg-type indicator values (i.e., those using scales compatible with Ellenberg values) developed for other European regions. Our aim is to create a harmonized data set of Ellenberg-type indicator values applicable at the European scale. Methods: We collected European data sets of indicator values for vascular plants and selected 13 data sets that used the nine-, ten- or twelve-degree scales defined by Ellenberg for light, temperature, moisture, reaction, nutrients and salinity. We compared these values with the original Ellenberg values and used those that showed consistent trends in regression slope and coefficient of determination. We calculated the average value for each combination of species and indicator values from these data sets. Based on species’ co-occurrences in European vegetation plots, we also calculated new values for species that were not assigned an indicator value. Results: We provide a new data set of Ellenberg-type indicator values for 8908 European vascular plant species (8168 for light, 7400 for temperature, 8030 for moisture, 7282 for reaction, 7193 for nutrients, and 7507 for salinity), of which 398 species have been newly assigned to at least one indicator value. Conclusions: The newly introduced indicator values are compatible with the original Ellenberg values. They can be used for large-scale studies of the European flora and vegetation or for gap-filling in regional data sets. The European indicator values and the original and taxonomically harmonized regional data sets of Ellenberg-type indicator values are available in the Supporting Information and the Zenodo repository

EUNIS-ESy: Expert system for automatic classification of European vegetation plots to EUNIS habitats

EUNIS-ESy 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 Chytrý et al. (2020). The classification of a set of vegetation plots can be run using the JUICE program (Tichý 2002; https://www.sci.muni.cz/botany/juice/), TURBOVEG 3 program (Hennekens 2015) and an R script (Bruelheide et al. 2021). 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. 1. Expert system and related files necessary to run it 1.1. EUNIS-ESy-2021-06-01.txt – a file containing the script for the classification of vegetation plots by EUNIS-ESy. This version contains tested definitions for the revised classification vegetated Marine (coastal saltmarshes), Coastal, Wetland, Grassland, Shrubland, Forest, Inland sparsely vegetated and Man-made habitats, and preliminary non-tested definitions of the older classification of Marine, Aquatic and Inland sparsely vegetated habitats. 1.2. Nomenclature-translation-from-Turboveg-2-databases.zip – an archive containing the scripts for automatic translation of taxon concepts and names used in individual European Turboveg 2 databases (Hennekens & Schaminée 2001; https://www.synbiosys.alterra.nl/turboveg/) to the nomenclature that can be used as an input for EUNIS-ESy. 1.3. EUNIS-ESy-User-Guide.pdf – 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. 2. Characterization of the EUNIS habitats based on the results of the EUNIS-ESy classification 2.1. EUNIS-habitats-2021-06-01.xlsx – the current list of EUNIS habitats. 2.2. Habitat-factsheets-EUNIS-habitats-2021-06-01.pdf – a summary of data on EUNIS vegetated Marine (coastal saltmarshes), Coastal, Wetland, Grassland, Shrubland, Forest, Inland sparsely vegetated and Man-made habitats. These data were extracted from vegetation plots from the European Vegetation Archive (EVA; Chytrý et al. 2016; http://euroveg.org/eva-database) and other databases classified by EUNIS-ESy v2021-06-01. Each habitat is described in a factsheet that includes a brief habitat description, distribution map, corresponding alliances of EuroVegChecklist (Mucina et al. 2016; https://www.synbiosys.alterra.nl/evc/) and characteristic species combination divided into diagnostic, constant and dominant species. 2.3. Characteristic-species-combinations-EUNIS-habitats-2021-06-01.xlsx – a database of habitats' characteristic species combinations in a spreadsheet format. 2.4. Data-sources-EUNIS-classification-2021-06-01.pdf – a list of data sources used to produce the distribution maps and characteristic species combinations. Differences from the previous version (2020-06-08) Vegetated Marine (coastal saltmarshes) and Inland sparsely vegetation habitats were added to the expert system. New vegetation-plot records added to the EVA database by 7 April 2021 were used to characterize habitat types

Phylogenetic structure of European forest vegetation

Aims: (a) To determine the contribution of current macro-environmental factors in explaining the phylogenetic structure of European forest vegetation, (b) to map and describe spatial patterns in their phylogenetic structure and (c) to examine which lineages are the most important contributors to phylogenetic clustering and whether their contribution varies across forest types and regions. Location: Europe. Taxon: Angiosperms. Methods: We analysed the phylogenetic structure of 61,816 georeferenced forest vegetation plots across Europe considering alternative metrics either sensitive to basal (ancient evolutionary dynamics) or terminal (recent dynamics) branching in the phylogeny. We used boosted regression trees to model metrics of the phylogenetic structure as a function of current macro-environmental factors. We also identified clades encompassing significantly more taxa than under random expectation in phylogenetically clustered plots. Results: Phylogenetic clustering was driven by climatic stress and instability and was strong in the areas glaciated during the Pleistocene, likely reflecting limited postglacial migration, and to a lower extent in areas of northern-central Europe and in summer-dry Mediterranean regions. Phylogenetic overdispersion was frequent in the hemiboreal zone in Russia, in some areas around the Mediterranean Basin, and along the Atlantic seaboard of the Iberian Peninsula. The families Ericaceae, Poaceae and Fagaceae were overrepresented in clustered plots in different regions of Europe. Main conclusions: We provide the first maps and analyses on the phylogenetic structure of European forest vegetation at the plot level. Our results highlight the role of environmental filtering, postglacial dispersal limitation and spatial transitions between major biomes in determining the distribution of plant lineages in Europe

Classification of the Mediterranean lowland to submontane pine forest vegetation

Aim: Vegetation types of Mediterranean thermophilous pine forests dominated by Pinus brutia, Pinus halepensis, Pinus pinaster and Pinus pinea were studied in various areas. However, a comprehensive formal vegetation classification of these forests based on a detailed data analysis has never been developed. Our aim is to provide the first broad-scale classification of these pine forests based on a large data set of vegetation plots. Location: Southern Europe, North Africa, Levant, Anatolia, Crimea and the Caucasus. Methods: We prepared a data set of European and Mediterranean pine forest vegetation plots. We selected 7,277 plots dominated by the cold-sensitive Mediterranean pine species Pinus brutia, Pinus halepensis, Pinus pinaster and Pinus pinea. We classified these plots using TWINSPAN, interpreted the ecologically and biogeographically homogeneous TWINSPAN clusters as alliances, and developed an expert system for automatic vegetation classification at the class, order and alliance levels. Results: We described Pinetea halepensis as a new class for the Mediterranean lowland to submontane pine forests, included in the existing Pinetalia halepensis order, and distinguished 12 alliances of native thermophilous pine forests, including four newly described and three informal groups merging supposedly native stands and old-established plantations. The main gradients in species composition reflect elevational vegetation belts and the west–east, and partly north–south, biogeographical differences. Both temperature and precipitation seasonality co-vary with these gradients. Conclusions: We provide the first formal classification at the order and alliance levels for all the Mediterranean thermophilous pine forests based on vegetation-plot data. This classification includes traditional syntaxa, which have been critically revised, and a new class and four new alliances. We also outline a methodological workflow that might be useful for other vegetation classification syntheses. The expert system, which is jointly based on pine dominance and species composition, is a tool for applying this classification in research and nature conservation survey, monitoring and management

Mapping species richness of plant families in European vegetation

none40siAims: Biodiversity is traditionally studied mostly at the species level, but biogeographical and macroecological studies at higher taxonomic levels can provide valuable insights into the evolutionary processes at large spatial scales. Our aim was to assess the representation of vascular plant families within different vegetation formations across Europe. Location: Europe. Methods: We used a data set of 816,005 vegetation plots from the European Vegetation Archive (EVA). For each plot, we calculated the relative species richness of each plant family as the number of species belonging to that family divided by the total number of species. We mapped the relative species richness, averaged across all plots in 50 km × 50 km grid cells, for each family and broad habitat groups: forests, grasslands, scrub and wetlands. We also calculated the absolute species richness and the Shannon diversity index for each family. Results: We produced 522 maps of mean relative species richness for a total of 152 vascular plant families occurring in forests, grasslands, scrub and wetlands. We found distinct spatial patterns for many combinations of families and habitat groups. The resulting series of 522 maps is freely available, both as images and GIS layers. Conclusions: The distinct spatial patterns revealed in the maps suggest that the relative species richness of plant families at the community level reflects the evolutionary history of individual families. We believe that the maps and associated data can inspire further biogeographical and macroecological studies and strengthen the ongoing integration of phylogenetic, functional and taxonomic diversity concepts.noneVecera M.; Axmanova I.; Padulles Cubino J.; Lososova Z.; Divisek J.; Knollova I.; Acic S.; Biurrun I.; Boch S.; Bonari G.; Campos J.A.; Carni A.; Carranza M.L.; Casella L.; Chiarucci A.; Custerevska R.; Delbosc P.; Dengler J.; Fernandez-Gonzalez F.; Gegout J.-C.; Jandt U.; Jansen F.; Jaskova A.; Jimenez-Alfaro B.; Kuzemko A.; Lebedeva M.; Lenoir J.; Lysenko T.; Moeslund J.E.; Pielech R.; Ruprecht E.; Sibik J.; Silc U.; Skvorc Z.; Swacha G.; Tatarenko I.; Vassilev K.; Wohlgemuth T.; Yamalov S.; Chytry M.Vecera M.; Axmanova I.; Padulles Cubino J.; Lososova Z.; Divisek J.; Knollova I.; Acic S.; Biurrun I.; Boch S.; Bonari G.; Campos J.A.; Carni A.; Carranza M.L.; Casella L.; Chiarucci A.; Custerevska R.; Delbosc P.; Dengler J.; Fernandez-Gonzalez F.; Gegout J.-C.; Jandt U.; Jansen F.; Jaskova A.; Jimenez-Alfaro B.; Kuzemko A.; Lebedeva M.; Lenoir J.; Lysenko T.; Moeslund J.E.; Pielech R.; Ruprecht E.; Sibik J.; Silc U.; Skvorc Z.; Swacha G.; Tatarenko I.; Vassilev K.; Wohlgemuth T.; Yamalov S.; Chytry M

Climate and socio-economic factors explain differences between observed and expected naturalization patterns of European plants around the world

Aim The number of naturalized (i.e. established) alien species has increased rapidly over recent centuries. Given the differences in environmental tolerances among species, little is known about what factors determine the extent to which the observed size of the naturalized range of a species and hence the extent to which the observed richness of naturalized species of a region approach their full potential. Here, we asked which region- and species-specific characteristics explain differences between observed and expected naturalizations. Location Global. Time period Present. Major taxa studied Vascular plants. Methods We determined the observed naturalized distribution outside Europe for 1,485 species endemic to Europe using the Global Naturalized Alien Flora (GloNAF) database and their expected distributions outside Europe using species distribution models. First, we investigated which of seven socio-economic factors related to introduction pathways, anthropogenic pressures and inventory effort best explained the differences between observed and expected naturalized European floras. Second, we examined whether distributional features, economic use and functional traits explain the extent to which species have filled their expected ranges outside Europe. Results In terms of suitable area, more than 95% of expected naturalizations of European plants were not yet observed. Species were naturalized in only 4.2% of their suitable regions outside of Europe (range filling) and in 0.4% of their unsuitable regions (range expansion). Anthropogenic habitat disturbance primarily explained the difference between observed and expected naturalized European floras, as did the number of treaties relevant to invasive species. Species of ornamental and economic value and with large specific leaf area performed better at filling and expanding beyond their expected range. Main conclusions The naturalization of alien plant species is explained by climate matching but also by the regional level of human development, the introduction pressure associated with the ornamental and economic values of the species and their adaptation to disturbed environments

Global trait:environment relationships of plant communities

Abstract Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. Here, we perform a global, plot-level analysis of trait–environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions

sPlot:a new tool for global vegetation analyses

Abstract Aims: Vegetation‐plot records provide information on the presence and cover or abundance of plants co‐occurring in the same community. Vegetation‐plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community‐weighted means and variances of traits using gap‐filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community‐weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale