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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.

The European Vegetation Archive (EVA) has been developed since 2012 by the IAVS Working Group European Vegetation Survey as a centralized database of European vegetation plots. It stores copies of national and regional vegetation-plot databases on a single software platform. Data storage in EVA does not affect the ongoing independent development of the contributing databases, which remain the property of the data contributors. A prototype of the database management software TURBOVEG 3 has been developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their matches to a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA, contributing in total 1 024 236 vegetation plots from 57 countries, 82% of them with geographical coordinates. EVA provides a unique data source for large-scale analyses of European vegetation diversity both in fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database.

Question: How many vegetation plot observations (relevés) are available in electronic databases, how are they geographically distributed, what are their properties and how might they be discovered and located for research and application? Location: Global. Methods: We compiled the Global Index of Vegetation-Plot Databases (GIVD; http://www.givd.info), an Internet resource aimed at registering metadata on existing vegetation databases. For inclusion, databases need to (i) contain temporally and spatially explicit species co-occurrence data and (ii) be accessible to the scientific public. This paper summarizes structure and data quality of databases registered in GIVD as of 30 December 2010. Results: On the given date, 132 databases containing more than 2.4 million non-overlapping plots had been registered in GIVD. The majority of these data were in European databases (83 databases, 1.6 million plots), whereas other continents were represented by substantially less (North America 15, Asia 13, Africa nine, South America seven, Australasia two, multi-continental three). The oldest plot observation was 1864, but most plots were recorded after 1970. Most plots reported vegetation on areas of 1 to 1000 m2; some also stored time-series and nested-plot data. Apart from geographic reference (required for inclusion), most frequent information was on altitude (71%), slope aspect and inclination (58%) and land use (38%), but rarely soil properties (<7%). Conclusions: The vegetation plot data in GIVD constitute a major resource for biodiversity research, both through the large number of species occurrence records and storage of species co-occurrence information at a small scale, combined with structural and plot-based environmental data. We identify shortcomings in available data that need to be addressed through sampling under-represented geographic regions, providing better incentives for data collection and sharing, developing user-friendly database exchange standards, as well as tools to analyse and remove confounding effects of sampling biases. The increased availability of data sets conferred by registration in GIVD offers significant opportunities for large-scale studies in community ecology, macroecology and global change research.

. The program JUICE was designed as a Microsoft® WINDOWS® application for editing, classification and analysis of large phytosociological tables and databases. This software, with a current maximum capacity of 30 000 relevés in one table, includes many functions for easy manipulation of table and header data. Various options include classification using COCKTAIL and TWINSPAN methods, calculation of interspecific associations, fidelity measures, average Ellenberg indicator values, preparation of synoptic tables, automatic sorting of relevé tables, and export of table data into other applications (word processors, spreadsheet programs or mapping packages). JUICE is optimized for use in association with TURBOVEG which is the most widespread database program for storing phytosociological data in Europe.

AIMS: Most vegetation classification systems developed for large areas include various inconsistencies. Therefore, we (1) propose a new consistent Cocktail‐based approach to redefine the traditional phytosociological classification of species‐poor vegetation; (2) apply it to create a classification protocol for aquatic vegetation; (3) implement this protocol in a computer expert system; and (4) test it with a data set previously classified using an older version of the Cocktail method. METHODS: The new approach uses formal logic to provide formal definitions of vegetation units. In the classification protocol for aquatic vegetation we defined consistent criteria for delimitation of associations according to the concepts that are predominantly used in phytosociology, based on species cover, dominance patterns and functional species groups. We applied these criteria in a computer expert system running in the JUICE 7.0 program, and applied them to a test data set of 12 171 vegetation plots from the Czech Republic containing at least one aquatic species. The new classification was compared with (1) the previous national Cocktail classification based on species cover values and in few cases on sociological species groups, and (2) a non‐formalized expert‐based classification. RESULTS: Thirteen functional species groups were created to build logical formulas of 64 aquatic associations and 5297 (44% of the total data set) vegetation plots were assigned to these associations, i.e. by 4% and 12% more than in the previous Cocktail and expert‐based classifications, respectively. There was 94% and 83% classification agreement with the previous Cocktail and expert‐based classification. CONCLUSIONS: The new approach produces a formal, consistent and unequivocal classification of species‐poor vegetation with several advantages over similar approaches. It provides not only a set of formal definitions of vegetation units, but also a set of rules for building such definitions. All associations with common characteristics are defined by structurally identical formulas, ensuring consistency of the classification. While similar approaches for species‐rich vegetation use sociological species groups, which are not applicable to species‐poor vegetation, the new approach introduces the use of functional species groups, which reflect vegetation physiognomy and spatial structure and, in combination with species dominance, enable the classification of species‐poor vegetation in a similar manner as in traditional phytosociology.

This editorial of Vegetation Classification and Survey (VCS) focusses on how to prepare good synoptic tables. While synoptic tables are a core element for the standardised documentation of classification schemes, we regularly find that authors are challenged with preparing them in a concise and informative manner. Thus, we explain the key aspects of synoptic tables and how we handle them in VCS. The central information should always be the percentage constancy, not a constancy class (loss of information) nor the phi-value or any other measure of fidelity (because these depend on which other columns are included in the table). While in the main text there is usually only space for a shortened synoptic table (which should include the most frequent companion species), it is crucial that a full version is provided in the supplementary material, and the criteria for shortening and sorting should be documented transparently. On a more general note, VCS is generally doing well with a first Journal Impact Factor of 3.0 being published and the CiteScore constantly increasing. Finally, we present the four Editors’ Choice articles of the year, among which Joelson et al. (2025; Vegetation Classification and Survey 6: 37–56) won the Editors’ Award 2025. Abbreviations : VCS = Vegetation Classification and Survey.

Vegetation classification is a useful tool for basic and applied research as well as for environmental management. As classification of vegetation serves many different purposes, there is no single approach to defining vegetation types. Establishing formalized standard procedures is desirable, however, because the purposes and uses of vegetation classifications are similar in different countries and regions. With the aim of promoting methodological standardization in classification across countries and vegetation scientists, this manuscript is centered on two ideas: (1 ) the need to explicitly distinguish between the conceptual activities involved in the definition of vegetation types (membership determination, characterization, validation and naming); and (2) the need to perform assignments of new vegetation observations to previously defined vegetation types in accordance with how these types were originally defined, a concept that we refer to as consistency in assignment. We demonstrate that our conceptual framework provides a useful tool to better understand what classification methods do. In order to manage and use classifications in a better way, vegetation scientists should produce, store and report the rules that provide consistent assignments to vegetation types.

Questions: Which are the main vegetation types of lowland hay meadows and pastures in Western and Central Europe? What are the main environmental gradients that drive patterns of species composition? Is it possible to classify these grasslands to phytosociological alliances that reflect management practices? Location: Western and Central Europe (excluding the Alps and Carpathians). Methods: A database of 21 400 vegetation plots of mesic grasslands across Western and Central Europe was compiled. After geographically stratified resampling, semi-supervised classification based on the K-means algorithm was applied to assign a subset of plots into 32 a priori association-level vegetation types and to search for new types within the subset of non-assigned plots. The vegetation plots assigned into the final vegetation types were submitted to another K-means classification to show the grouping into higher-level vegetation types. Results: A total of 36 associations were distinguished in the resampled subset of 8277 vegetation plots and were grouped into four large groups: (1) eutrophic and intensively managed hay meadows and permanent pastures; (2) nutrient-rich grasslands developed from recently abandoned fields or managed under irregular practices of mowing and manuring; (3) non-eutrophic lowland and submontane hay meadows; (4) extensively managed pastures and Atlantic grazed hay meadows. A PCoA of the associations of these four groups showed that extensively managed pastures were floristically more similar to non-eutrophic hay meadows than to permanent intensively managed pastures, which was more obvious in the Atlantic region than in Central Europe. Species composition of the lowland hay meadows was clearly differentiated according to biogeographic sectors. Other floristic differences were related to climate, altitude, soil base status and topography. Conclusions: This analysis challenges the traditional concept of mesic grassland alliances separating hay meadows from pastures. New classification should be based mainly on the differences in management intensity rather than in management practice. Consequently, nutrient-poor extensive pastures, which currently are not considered in the European Habitats Directive, should receive the same conservation attention as low-intensive hay meadows, because both types of vegetation can be equally species-rich and do not differ substantially in floristic composition from each other.

Aims: Phytosociological classification of fen vegetation (Scheuchzerio palustris-Caricetea fuscae class) differs among European countries. Here we propose a unified vegetation classification of European fens at the alliance level, provide unequivocal assignment rules for individual vegetation plots, identify diagnostic species of fen alliances, and map their distribution. Location: Europe, western Siberia and SE Greenland. Methods: 29 049 vegetation-plot records of fens were selected from databases using a list of specialist fen species. Formal definitions of alliances were created using the presence, absence and abundance of Cocktail-based species groups and indicator species. DCA visualized the similarities among the alliances in an ordination space. The ISOPAM classification algorithm was applied to regional subsets with homogeneous plot size to check whether the classification based on formal definitions matches the results of unsupervised classifications. Results: The following alliances were defined: Caricion viridulo-trinervis (sub-halophytic Atlantic dune-slack fens), Caricion davallianae (temperate calcareous fens), Caricion atrofusco-saxatilis (arcto-alpine calcareous fens), Stygio-Caricion limosae (boreal topogenic brown-moss fens), Sphagno warnstorfii-Tomentypnion nitentis (Sphagnum-brown-moss rich fens), Saxifrago-Tomentypnion (continental to boreo-continental nitrogen-limited brown-moss rich fens), Narthecion scardici (alpine fens with Balkan endemics), Caricion stantis (arctic brown-moss rich fens), Anagallido tenellae-Juncion bulbosi (Ibero-Atlantic moderately rich fens), Drepanocladion exannulati (arcto-boreal-alpine non-calcareous fens), Caricion fuscae (temperate moderately rich fens), Sphagno-Caricion canescentis (poor fens) and Scheuchzerion palustris (dystrophic hollows). The main variation in the species composition of European fens reflected site chemistry (pH, mineral richness) and sorted the plots from calcareous and extremely rich fens, through rich and moderately rich fens, to poor fens and dystrophic hollows. ISOPAM classified regional subsets according to this gradient, supporting the ecological meaningfulness of this classification concept on both the regional and continental scale. Geographic/macroclimatic variation was reflected in the second most important gradient. Conclusions: The pan-European classification of fen vegetation was proposed and supported by the data for the first time. Formal definitions developed here allow consistent and unequivocal assignment of individual vegetation plots to fen alliances at the continental scale.

Questions: 1. Which habitats have the highest degree of invasion? 2. Do native species‐rich communities have also a high degree of invasion? 3. Do the patterns of association between native and alien species richness vary between habitats. Location: Catalonia region (NE Spain). Methods: We conducted a large regional analysis of 15655 phytosociological relevés to detect differences in the degree of invasion between European Nature Information System (EUNIS) habitats representative of temperate and Mediterranean European areas. Results: Alien species were present in less than 17 % of the relevés and represented less than 2% of the total number of species per habitat. The EUNIS habitats with the highest alien species richness were arable land and gardens followed by anthropogenic forb‐rich habitats, riverine and lakeshore scrubs, southern riparian galleries and thickets and trampled areas. In contrast, the following habitats had never any alien species: surface running waters, raised and blanket bogs, valley mires, poor fens and transition mires, base‐rich fens, alpine and sub‐alpine grasslands, sub‐alpine moist or wet tall‐herb and fern habitats, alpine and sub‐alpine scrub habitats and spiny Mediterranean heaths. There was a unimodal relationship between the mean native and mean alien species richness per EUNIS habitat with a high number of aliens in habitats with intermediate number of native species and a low number of aliens at both extremes of the native species gradient. Within EUNIS habitats, the relationship was positive, negative or non‐significant depending on the habitat type without any clear pattern related to the number of native species. Alien species richness was not related to plot size, neither between habitats nor within habitats. Conclusions: The analysis emphasised that the habitats with a higher degree of invasion were the most disturbed ones and that in general habitats rich in native species did not harbour less invaders than habitats poor in native species.