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Datos disponibles en https://github.com/fmsabatini/sPlotOpen_Code, EU H2020 project BACI, Grant No. 640176 (...), Sabatini, F.M., Lenoir, J., Hattab, T., Arnst, E.A., Chytrý, M., Dengler, J., De Ruffray, P., Hennekens, S.M., Jandt, U., Jansen, F., Jiménez-Alfaro, B., Kattge, J., Levesley, A., Pillar, V.D., Purschke, O., Sandel, B., Sultana, F., Aavik, T., Aćić, S., Acosta, A.T.R., Agrillo, E., Alvarez, M., Apostolova, I., Arfin Khan, M.A.S., Arroyo, L., Attorre, F., Aubin, I., Banerjee, A., Bauters, M., Bergeron, Y., Bergmeier, E., Biurrun, I., Bjorkman, A.D., Bonari, G., Bondareva, V., Brunet, J., Čarni, A., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Ćušterevska, R., De Bie, E., de Gasper, A.L., De Sanctis, M., Dimopoulos, P., Dolezal, J., Dziuba, T., El-Sheikh, M.A.E.-R.M., Enquist, B., Ewald, J., Fazayeli, F., Field, R., Finckh, M., Gachet, S., Galán-de-Mera, A., Garbolino, E., Gholizadeh, H., Giorgis, M., Golub, V., Alsos, I.G., Grytnes, J.-A., Guerin, G.R., Gutiérrez, A.G., Haider, S., Hatim, M.Z., Hérault, B., Hinojos Mendoza, G., Hölzel, N., Homeier, J., Hubau, W., Indreica, A., Janssen, J.A.M., Jedrzejek, B., Jentsch, A., Jürgens, N., Kącki, Z., Kapfer, J., Karger, D.N., Kavgacı, A., Kearsley, E., Kessler, M., Khanina, L., Killeen, T., Korolyuk, A., Kreft, H., Kühl, H.S., Kuzemko, A., Landucci, F., Lengyel, A., Lens, F., Lingner, D.V., Liu, H., Lysenko, T., Mahecha, M.D., Marcenò, C., Martynenko, V., Moeslund, J.E., Monteagudo Mendoza, A., Mucina, L., Müller, J.V., Munzinger, J., Naqinezhad, A., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G.E., Pärtel, M., Pauchard, A., Peet, R.K., Peñuelas, J., Pérez-Haase, A., Peterka, T., Petřík, P., Peyre, G., Phillips, O.L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rivas-Torres, G., Rodwell, J.S., Ruprecht, E., Rūsiņa, S., Samimi, C., Schmidt, M., Schrodt, F., Shan, H., Shirokikh, P., Šibík, J., Šilc, U., Sklenář, P., Škvorc, Ž., Sparrow, B., Sperandii, M.G., Stančić, Z., Svenning, J.-C., Tang, Z., Tang, C.Q., Tsiripidis, I., Vanselow, K.A., Vásquez Martínez, R., Vassilev, K., Vélez-Martin, E., Venanzoni, R., Vibrans, A.C., Violle, C., Virtanen, R., von Wehrden, H., Wagner, V., Walker, D.A., Waller, D.M., Wang, H.-F., Wesche, K., Whitfeld, T.J.S., Willner, W., Wiser, S.K., Wohlgemuth, T., Yamalov, S., Zobel, M., Bruelheide, H.

Aquatic habitats are vulnerable to the invasion of alien species, so early warning protocols are necessary for eradication. The presence in Italy of two alien duckweeds in freshwaters has been documented: Lemna minuta, that showed high invasivity, and L. valdiviana, still confined to south Lazio. These two species may be mistaken for each other and for the domestic L. minor and L. gibba due to morphological variation. Here, we assess the applicability of DNA barcoding as a complement to morphological analysis for monitoring the spread of alien Lemna. We chose two chloroplast genome sequences for their ability to discriminate all Lemna species: the 5’ intron of the trnK gene and the matK gene. Among 48 samples of Lemna collected at 20 sites in Central Italy, 20 were identified as L. minor, 19 as L. minuta, five as L. trisulca and four as L. gibba. L. minuta was present at most sampling sites; in particular, at six locations of Lake Trasimeno, eight L. minuta samples were found. We demonstrate that DNA sequence analyses with cost-effective barcoding techniques can effectively support expert efforts in species determination for an early alert system of invasive Lemna species.

Aims: Expert systems are increasingly popular tools for supervised classification of large datasets of vegetation-plot records, but their classification accuracy depends on the selection of proper species and species groups that can effectively discriminate vegetation types. Here, we present a new semi-automatic machine-learning method called GRIMP (GRoup IMProvement) to optimize groups of species used for discriminating among vegetation types in expert systems. We test its performance using a large set of vegetation-plot records. - Methods: We defined discriminating species groups as the groups that are unique to each vegetation type and provide optimal discrimination of this type against other types. The group of discriminating species of each vegetation type considerably overlaps with the group of diagnostic species of this type, but these two groups are not identical because not all diagnostic species have sufficient discriminating power. We developed the GRIMP iterative algorithm, which optimizes the groups of discriminating species to provide the most accurate vegetation classification, using a training set of a priori classified plot records. We tested this method by comparing classification accuracy before and after the GRIMP optimization of species groups using vegetation-plot records from the Czech Republic a priori classified to 39 phytosociological classes, and three initial sets of candidate discriminating species from different sources. - Results: The GRIMP algorithm improved the classification accuracy at the class level from 65% correctly classified plots in the test dataset before group optimization to 88% thereafter. The other plots were misclassified or unclassified, but misclassifications were reduced by adding further expert-based criteria considering dominant growth forms. - Conclusions: GRIMP-optimized groups of discriminating species are very useful for semi-automatic construction of expert systems for vegetation classification. Such expert systems can be developed from an a priori unsupervised or expert-based classification of at least some vegetation plots.

Aims: Humans have deeply eroded biogeographic barriers, causing a rapid spread of alien species across biomes. The Mediterranean Basin is a biodiversity hotspot but is also known as a hub of alien plant invasions, particularly in its European part. Yet, a comprehensive inventory of alien species in the area is missing and understanding of the drivers of Mediterranean invasions is poor. Here, we aim to identify the main alien plant species in the European part of the Mediterranean Basin and quantify their invasion success in order to understand the plant species flows from other biomes of the world. Location: The Mediterranean region of Europe, Anatolia and Cyprus. Methods: We analyzed 130,000 georeferenced vegetation plots from the European Vegetation Archive (EVA) and identified 299 extra-European alien plant species. We identified their biomes of origin and quantified the mean geographic distance, trade exchange and climatic similarity from each biome to the study area. After estimating the invasion success of each species in the study area, we tested which biomes have donated more alien species than expected by chance and which drivers best explain these non-random patterns. Results: We found that other Mediterranean climatic regions, as well as temperate and xeric biomes of the world, are the main donors of successful alien species to Mediterranean Europe, beyond what would be expected by chance. Our results suggest that climatic matching, rather than geographic proximity or trade, has been the most important driver of invasion. However, climatic pre-adaptation alone also does not appear to predict the invasion success of established species in the study area. Conclusions: Our results highlight the need to pay special attention to alien plant species from the same or climatically similar biomes, but also suggest that further research is needed for early screening of the most problematic alien species.

I social network sono canali di comunicazione utilizzati per condividere enormi quantità di dati, che possono essere utilizzati per la ricerca scientifica, anche nel campo della biodiversità. Per sapere quanto i dati ricavati dai social network possono integrare quelli raccolti per scopi scientifici, è necessario individuarne i bias. Utilizzando i dati estratti da un gruppo Facebook specializzato nella flora vascolare siciliana, abbiamo analizzato quali sono i caratteri che aumentano la probabilità che una pianta spontanea venga fotografata e postata su un social network. A tal fine, abbiamo confrontato frequenze e attributi delle specie fotografate dai membri del gruppo Facebook con quelli delle specie registrate nel database EVA, relative a 6.366 rilevamenti vegetazionali della Sicilia. I nostri risultati suggeriscono che i dati provenienti dal gruppo Facebook analizzato sono affetti da bias diversi da quelli relativi a dati floristici raccolti per altri fini, ad esempio, per il rilevamento della vegetazione. I membri del gruppo Facebook hanno intercettato con maggior frequenza specie aliene e specie rare rispetto a quanto rilevato da chi studia la vegetazione, dimostrando che i dati contenuti nei social network offrono un importante complemento a quelli raccolti dagli scienziati per fini di ricerca. Sintesi e applicazioni. Malgrado Facebook non supporti la georeferenziazione dei dati, né una query per l'accesso e l'analisi degli stessi, esso rappresenta una potenziale fonte di dati sulla biodiversità, utili per integrare quelli raccolti da botanici professionisti. Il principale vantaggio dei dati provenienti dai social network è il loro elevato dinamismo, poiché registrano la presenza di una data specie quasi in tempo reale. Pertanto, i dati raccolti da gruppi Facebook moderati da esperti possono risultare utili (1) per monitorare le dinamiche di popolazione di specie minacciate ed esotiche; (2) come informazioni aggiuntive sulla presenza di specie rare, in particolare per quelle che maggiormente attraggono i botanici dilettanti, come le orchidee; (3) per individuare tempestivamente la presenza di nuove esotiche potenzialmente invasive; (4) per studi fenologici, in particolare per monitorare l’inizio della stagione di fioritura delle specie. Social networks offer communication channels through which people share huge amounts of primary data that can be used for scientific analyses, including biodiversity research. To understand to what extent data extracted from social networks could complement data collected for scientific purposes, it is necessary to quantify the bias of such data. We analysed which plant traits increased the probability of a wild‐growing plant species to be photographed and posted to a social network based on the data from an unstructured citizen science tool; a Facebook group focused on the vascular flora of Sicily (Italy). Then, we compared botanical data collected by this Facebook group members with data collected by scientists in 6,366 vegetation plots sampled across Sicily, stored in the EVA database. Our results suggested that data proceeding from the analysed Facebook group were affected by various sampling biases, which differed from the biases inherent to other types of biodiversity data such as those from vegetation plots. Facebook users recorded a higher proportion of red‐listed and alien species than vegetation scientists. Therefore, social networks can provide a valuable complement to the data collected by scientists for research purposes. Synthesis and applications. Despite Facebook does not support geotagging and interface for data access and analysis, it is an invaluable source of biodiversity data that could complement those collected by professional researchers. The main advantage of data from social networks is their high dynamism, as they report large amounts of species occurrences in almost real time. Therefore, citizen science data from a Facebook group where the records are curated by expert volunteers can be used (a) for monitoring population dynamics of threatened and alien species; (b) as a source of additional data on rare species occurrences, particularly for plants that are attractive for amateur botanists, such as orchids; (c) for early warning systems of potential new invasions; and (4) for phenological studies, especially at the beginning of the flowering season.

Aims River gravel bars are endangered habitats in Europe. However, classification schemes of their vegetation and habitat types differ among European countries, and they are even ignored in some national schemes. This causes problems in conservation planning, monitoring and management. Hence we aimed at building the first unified vegetation classification for river gravel-bar habitats across European mountain systems. Location Europe. Methods In total 4,769 vegetation plot records of river gravel-bar plant communities were collected from national, regional or private databases, digitized from the literature and newly collected in the field. A hierarchical classification expert system with formal definitions of vegetation types was created. The definitions combined the criteria of presence or cover of groups of species with similar ecology or single species narrowly specialized to a particular gravel-bar habitat. The TWINSPAN classification was applied to early-successional vegetation types to check whether the classification based on formal definitions was supported by the results of unsupervised classification. Similarity patterns among vegetation types were visualized using the detrended correspondence analysis (DCA) ordination. Results Early-successional and scrub gravel-bar vegetation types were respectively classified into two classes: Thlaspietea rotundifolii and Salicetea purpureae. Eleven associations and four alliances (Calamagrostion pseudophragmitae, Epilobion fleischeri, Salicion cantabricae and Salicion eleagno-daphnoidis) were defined formally. Based on a critical revision, some associations or alliances defined in the previous literature were merged or discarded. The main gradient in variability within the gravel-bar vegetation is connected with the altitudinal gradient, biogeographical variation, local hydromorphological processes and various successional changes. Conclusions The first unified and formalized classification system of the European mountain river gravel-bar vegetation was created, and species composition, ecology and distribution of these types were characterized. The syntaxonomical nomenclature of these types was checked and revised. This study provides a base for conservation planning of these threatened and rapidly disappearing habitats.

Aim The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation-plot records to the habitats of the EUNIS system, use it to classify a European vegetation-plot database, and compile statistically-derived characteristic species combinations and distribution maps for these habitats. Location Europe. Methods We developed the classification expert system EUNIS-ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set-theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species-to-habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man-made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS-ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment. The previous versions of the expert system and related reports were produced within a contract from the European Environment Agency to Wageningen Environmental Research and Masaryk University. The opinions expressed are those of the contractor and do not represent the Agency's official position. EVA data management and preparation of this paper were supported by the Czech Science Foundation (project no. 19-28491X to MC, LT, IK, TP, CM, JDa, MH, PN, DZ, GB, AJ, AKu, ZL and DV). IB and JAC were supported by the Basque Government (project no. T936-16). TB, ET, and LK were supported by the Ministry of Science and Higher Education of the Russian Federation (TB and ET project no. AAAA-A18-118052590019-7; LK project no. AAAA-A19-119012490096-2).

Aims: To create a comprehensive, consistent and unequivocal phytosociological classification of European marsh vegetation of the class Phragmito-Magnocaricetea. Location: Europe. Methods: We applied the Cocktail method to a European data set of 249, 800 vegetation plots. We identified the main purposes and attributes on which to base the classification, defined assignment rules for vegetation plots, and prepared formal definitions for all the associations, alliances and orders of the class Phragmito-Magnocaricetea using formal logic. Each formula consists of the combination of “functional species groups”, cover values of individual species, and in the case of high-rank syntaxa also of “discriminating species groups” created using the Group Improvement (GRIMP) method. Results: The European Phragmito-Magnocaricetea vegetation was classified into 92 associations grouped in 11 alliances and six orders. New syntaxa (previously invalidly published according to the International Code of Phytosociological Nomenclature) were introduced: Bolboschoeno maritimi-Schoenoplection tabernaemontani, Glycerio maximae-Sietum latifolii, Glycerio notatae-Veronicetum beccabungae, Schoenoplectetum corymbosi and Thelypterido palustris-Caricetum elongatae. Based on a critical revision, some other syntaxa were rejected or excluded from the class Phragmito-Magnocaricetea. Conclusions: This work provides the first consistent classification of the class Phragmito-Magnocaricetea at the European scale, which is an important tool for nature conservation. Our classification largely respects previously existing concepts of syntaxa, but it also proposes modifications to the recently published EuroVegChecklist. This work also provides a protocol that can be used for extending the current classification to new syntaxa and geographical regions.

While classification of vegetation can be conducted in many ways, international homogenization of procedures and typologies is desirable for human societies that are highly connected in terms of sharing biodiversity information. This Special Issue of Phytocoenologia includes 12 papers that document several of the plot-based classification approaches currently used throughout the world. The issue includes approaches from five continents, but noticeable gaps are South America, middle-eastern countries, northern Africa and southeastern Asia. We include in this editorial a brief synthesis of the papers included in the Special Issue, with respect to (1) the amount of vegetation-plot data and characteristics of the classification systems developed in different areas and (2) the concepts and procedures of classification approaches. One of the most important common attributes among the classification approaches is the need to define vegetation units at a low level of abstraction. ‘Association’ (and perhaps ‘alliance’ too) may be a classification level for which international homogenization of procedures would be most easy to achieve, perhaps establishing different consistent classification sections depending on ecological conditions. Several papers in this issue demonstrate that multiple approaches may coexist for higher levels, as long as they abstract vegetation from the same low level units by focusing on a specific set of concepts and defined from the perspective of applications.

Vegetation classification is a basic requirement of research and conservation. Numerous classifications have been established throughout the world during the 20th century, but steps toward international classification systems and standards are currently being sought. We introduce here a new working group of the International Association of Vegetation Science, the Vegetation Classification Working Group (VCWG), which will function as a scientific panel devoted to vegetation classification at any spatial or organizational scale, particularly the underlying methodologies and standards, ultimately allowing greater understanding and crosswalks among national classification systems. The VCWG currently has 166 members from 41 countries of six continents, a website for information, established subgroups for specific activities, and initiated a Special Issue on classification for Phytocoenologia. We summarize here the background of the VCWG, current and future activities, and contact information for those interested in becoming involved.

Aim: Formalized classifications synthesizing vegetation data at the continental scale are being attempted only now, although they are of key importance for nature conservation planning. Therefore, we aim to provide a vegetation classification and to describe the main biogeographical patterns of floodplain forests and alder carrs in Europe. Location: Europe. Methods: A database of more than 40 000 vegetation plots of floodplain forests and alder carrs across Europe was compiled. After geographic stratification, 16 392 plots were available for classification, which was performed using the supervised method Cocktail. We also searched for new associations using semi-supervised K-means classification. The main biogeographic patterns and climate-related gradients in species composition were determined using detrended correspondence analysis and cluster analysis. Results: Thirty associations of floodplain forests and alder carrs were distinguished, which belong to five alliances. The Alnion incanae includes riparian, seepage and hardwood floodplain forests in the nemoral and hemiboreal zones (dominated by Alnus glutinosa and Fraxinus excelsior) and in the boreal zone (dominated by A. incana). The Osmundo-Alnion represents oceanic vegetation dominated by Alnus glutinosa, Fraxinus angustifolia and F. excelsior distributed mostly on the Iberian Peninsula and composed of species with Atlantic distribution and Iberian endemics. The Populion albae comprises floodplain forests frequently dominated by Fraxinus angustifolia, Populus alba and P. nigra that are widespread in floodplains of large rivers under summer-dry climates in the Mediterranean region. The Platanion orientalis represents eastern Mediterranean floodplain forests dominated by Platanus orientalis. The Alnion glutinosae includes forest swamps dominated by Alnus glutinosa distributed mostly in the nemoral and hemiboreal zones. The main biogeographic patterns within European floodplain forests and alder carrs reflect the climatic contrasts between the Mediterranean, nemoral, boreal and mountain regions. Oceanic floodplain forests differ from those in the rest of Europe. The hydrological regime appears to be the most important factor influencing species composition within regions. Conclusions: This study is the first applying a formalized classification at the association level for a broad vegetation type at the continental scale. The proposed classification provides the scientific basis for the necessary improvement of the habitat classification systems used in European nature conservation.

Biurrun, Idoia/0000-0002-1454-0433; Rojo, Maria Pilar Rodriguez/0000-0001-5449-9386; Ermakov, Nikolai/0000-0001-7550-990X; De Sanctis, Michele/0000-0002-7280-6199; Svenning, Jens-Christian/0000-0002-3415-0862; Virtanen, Risto/0000-0002-8295-8217; Agrillo, Emiliano/0000-0003-2346-8346; Onyshchenko, Viktor/0000-0001-9079-7241; Marceno, Corrado/0000-0003-4361-5200; Willner, Wolfgang/0000-0003-1591-8386; Fernandez-Gonzalez, Federico/0000-0003-1234-4065; Jansen, Florian/0000-0002-0331-5185; Swacha, Grzegorz/0000-0002-6380-2954; Dengler, Jurgen/0000-0003-3221-660X; Guarino, Riccardo/0000-0003-0106-9416; Sopotlieva, Desislava/0000-0002-9281-7039; Venanzoni, Roberto/0000-0002-7768-0468; Chytry, Milan/0000-0002-8122-3075; Kuzemko, Anna/0000-0002-9425-2756; Danihelka, Jiri/0000-0002-2640-7867; Kuzemko, Anna/0000-0002-9425-2756; Venanzoni, Roberto/0000-0002-7768-0468; Gavilan, Rosario G./0000-0002-1022-445X; Jansen, Florian/0000-0002-0331-5185; Wohlgemuth, Thomas/0000-0002-4623-0894; Svenning, Jens-Christian/0000-0002-3415-0862; Sibik, Jozef/0000-0002-5949-862X; Casella, Laura/0000-0003-2550-3010; Lenoir, Jonathan/0000-0003-0638-9582; Attorre, Fabio/0000-0002-7744-2195; Kacki, Zygmunt/0000-0002-2241-1631; Jandt, Ute/0000-0002-3177-3669; Carni, Andraz/0000-0002-8909-4298; Jirousek, Martin/0000-0002-4293-478X; GARCIA-MIJANGOS, ITZIAR/0000-0002-6642-7782; Campos, Juan Antonio/0000-0001-5992-2753; Fanelli, Giuliano/0000-0002-3143-1212; Haveman, Rense/0000-0001-9127-4549; Acic, Svetlana/0000-0001-6553-3797; De Bie, Els/0000-0001-7679-743X; Font, Xavier/0000-0002-7253-8905; Moeslund, Jesper Erenskjold/0000-0001-8591-7149; Martynenko, Vasiliy/0000-0002-9071-3789; Jimenez-Alfaro, Borja/0000-0001-6601-9597; Ejrnaes, Rasmus/0000-0003-2538-8606; Carlon Ruiz, Luis/0000-0003-3442-8710; Angelini, Pierangela/0000-0002-5321-9757; Silc, Urban/0000-0002-3052-699X; Landucci, Flavia/0000-0002-6848-0384; Ewald, Jorg/0000-0002-2758-9324; Dziuba, Tetiana/0000-0001-8621-0890 WOS: 000368074600018 The European Vegetation Archive (EVA) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation-plot databases on a single software platform. Data storage in EVA does not affect on-going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 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 corresponding names on 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 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large-scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database. Czech Science Foundation (Centre of Excellence PLADIAS) [14-36079G] Our major thanks go to thousands of European vegetation scientists of several generations who collected the original vegetation-plot data in the field, published them or made their unpublished data available to others, and to those who spent myriad hours digitizing data and managing the contributing databases. EVA data management has been partly funded by the Czech Science Foundation (Centre of Excellence PLADIAS, 14-36079G).

Biodiversity informatics has experienced tremendous developments in the last 15 years. There are now comprehensive online checklists for plant taxa as well as many large plant-taxon related databases, including the vegetation-plot databases registered in the Global Index of Vegetation-Plot Databases (GIVD; http://www.givd.info). However, efficient maintenance, analysis, and integration of these databases are still much impeded by the failure of presently available electronic taxonomic reference lists of plants to fully meet the requirements of such applications. Here we outline the principal specifications of an electronic taxonomic reference list for Europe (“EuroSL” = European standard list of plant taxa) and identify features not met in current practice. EuroSL should cover all macroscopic taxa of vascular plants, bryophytes, lichens, and algae that occur in European vegetation in a uniform database, irrespective of their floristic status (e.g. native, archaeophyte, neophyte, casual). The adoption of informal aggregates is essential to cover deviating species concepts and to capture legacy data. EuroSL should not only assign names but also match taxonomic concepts. This task cannot be fully automated, as the same correctly applied taxon name can have different meanings depending on the taxonomic concept applied. In order to be a useful tool, EuroSL would need to be better documented than most existing electronic checklists and be released in fixed versions. Every subsequent version should contain an unambiguous connection linking each taxon to the corresponding unit in the previous version. We identify possible components of EuroSL, of which Euro+Med PlantBase, the recent European checklists of bryophytes, and the taxonomic crosswalks between various national Turboveg checklists collected for SynBioSys Europe, are the major ones. Concepts developed for GermanSL might be adopted for EuroSL, but implemented in a software framework that is yet to be developed from existing tools. Such a framework would allow documented editing of the content by specialists distributed across Europe. To become successful, EuroSL would require intensive collaboration between taxonomists, ecologists and biodiversity informaticians, as well as appropriate funding. Establishing EuroSL would dramatically enhance the usability and reliability of plant-taxon related databases in Europe for the purposes of pure and applied research and conservation legislation. Its development should therefore be of highest priority. peerReviewed