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Aim: Assessing the performances of different sampling approaches for documenting community diversity may help to identify optimal sampling efforts and strategies, and to enhance conservation and monitoring planning. Here, we used two data sets based on probabilistic and preferential sampling schemes of Italian forest vegetation to analyze the multifaceted performances of the two approaches across three major forest types at a large scale. Location: Italy. Methods: We pooled 804 probabilistic and 16,259 preferential forest plotsassamplesofvascularplantdiversity across the country. We balanced the two data sets in terms of sizes, plot size, geographical position, and vegetation types. For each of the two data sets, 1000 subsets of 201 random plots were compared by calculating the shared and exclusive indicator species, their overlap in the multivariate space, and the areas encompassed by spatially-constrained rarefaction curves. We then calculated an index of performance using the ratio between the additional and total information collected by each sampling approach. The performances were tested and evaluated across the three major forest types. Results: The probabilistic approach performed better in estimating species richness and diversity of species assemblages, but did not detect other components of the regional diversity, such as azonal forests. The preferential approach outperformed the probabilistic approach in detecting forest-specialist species and plant diversity hotspots. Conclusions: Using a novel workflow based on vegetation-plot exclusivities and commonalities, our study suggests probabilistic and preferential sampling approaches are to be used in combination for better conservation and monitor planning purposes to detect multiple aspects of plant community diversity. Our findings can assist the implementation of national conservation planning and large-scale monitoring of biodiversity.

High mountain ecosystems are hotspots of biodiversity that are highly vulnerable to climate warming and land use change. In Europe, high mountain habitats are included in the EC Directive 92/43/EEC (Habitats Directive) and the identification of practices facilitating effective monitoring is crucial for meeting HD goals. We analyzed the temporal changes in species composition and diversity on high mountain EU habitats and explored if the subgroup of diagnostic species was able to summarize the comprehensive information on plant community variations. We performed a re-visitation study, using a set of 30 georeferenced historical plots newly collected after 20 years on two EU habitats (Galium magellense community growing on screes (8120 EU) and Trifolium thalii community of snowbeds (6170 EU)) in the Maiella National Park (MNP), which is one of the most threatened Mediterranean mountains in Europe. The presence of several endangered species and the availability of a botanical garden, a seed bank, and a nursery, make the MNP an excellent training ground to explore in situ and ex situ conservation strategies. We compared overall and diagnostic species richness patterns over time by rarefaction curves and described the singular aspects of species diversity (e.g., richness, Shannon index, Simpson index, and Berger–Parker index), by Rènyi’s diversity profiles. Diversity values consistently varied over time and across EU habitat types, with increasing values on scree communities and decreasing values on snowbeds. These changes could be associated with both land use change, through the increase of grazing pressure of Apennine chamois (Rupicapra pyrenaica ornata), which determined a rise of nitrophilous species in the scree community, and an increase of grasses at the expense of forbs in snowbeds, and to climate change, which promoted a general expansion of thermophilous species. Despite the two opposite, ongoing processes on the two plant communities studied, our results evidenced that diagnostic species and overall species followed the same trend of variation, demonstrating the potential of diagnostics for EU habitat monitoring. Our observations suggested that the re-visitation of historical plots and the implementation of frequent monitoring campaigns on diagnostic species can provide important data on species abundance and distribution patterns in these vulnerable ecosystems, supporting optimized in situ and ex situ conservation actions.

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.

Multi-year temporal studies are invaluable tools for monitoring changes in biodiversity through time. However, their applications in coastal ecosystems are still scarce. We investigated temporal trends in coastal dunes analyzing a set of 858 randomly-sampled georeferenced relevés performed between 2002 and 2015 along Central Italy’s sandy coastlines. Specifically, we explored changes in species richness and cover of targeted sandy habitats, we investigated trends in the cover of selected psammophilous native species and we assessed patterns of invasion by means of regression techniques. We observed a significant decrease in species richness and cover of the dune grasslands habitat. The species-level analysis confirmed a negative trend for two characteristic species of dune grasslands, Cutandia maritima and Medicago littoralis, while revealing a similar decline for Crucianella maritima and for Ammophila arenaria subsp. australis, key species of mobile dunes. The most striking trends emerged analyzing patterns in the cover of an invasive alien species, Carpobrotus sp., which showed a concerning increase in shifting dunes. In conclusion, our analyses reveal concerning changes involving dune grasslands, and at the same time hint at “early warnings” of degradation processes traceable in shifting dunes.

Rendiconti Lincei has dedicated a Special Issue to present a number of case studies highlighting the role played by the European Vegetation Survey Working Group established by Sandro Pignatti and his colleagues in support to the implementation of the Habitats Directive. Studies include theoretical analyses as well as researches conducted at different spatial and temporal scales focused on the assessment and monitoring of the conservation status of habitats and target plant species, identification of new habitats and subtypes, and their diagnostic species.

The relationship between species' niche breadth (i.e. the range of environmental conditions under which a species can persist) and range size (i.e. the extent of its spatial distribution) has mostly been tested within geographically restricted areas but rarely at the global extent.[br/] Here, we not only tested the relationship between range size (derived from species' distribution data) and niche breadth (derived from species' distribution and co-occurrence data) of 1255 plant species at the regional extent of the European Alps, but also at the global extent and across both spatial scales for a subset of 180 species.[br/] Using correlation analyses, linear models and variation partitioning, we found that species' realized niche breadth estimated at the regional level is a weak predictor of species' global niche breadth and range size.[br/] Against our expectations, distribution-derived niche breadth was a better predictor for species' range size than the co-occurrence-based estimate, which should, theoretically, account for more than the climatically determined niche dimensions.[br/] Our findings highlight that studies focusing on the niche breadth vs range size relationship must explicitly consider spatial mismatches that might have confounded and diminished previously reported relationships.

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.

A survey and a formalized phytosociological classification of the marsh vegetation of the class Phragmito-Magno-Caricetea in central Italy is presented. Formal definitions of the majority of wetland associations recorded from the Italian territory were defined using the Cocktail method and applied to a large data set of vegetation plots extracted from the database VegItaly (hosted by the web database system "anArchive"). A total of 43 associations belonging to seven alliances and four orders were recognized. Detrended correspondence analysis (DCA), species indicator values and altitude were used to visualize ecological differences between the associations. Altitude, nutrient status and soil reaction were identified as the main environmental gradients responsible for diversification and distribution of the Phragmito-Magno-Caricetea communities in central Italy.

This study is aimed at collection of all available phytosociological data on vegetation of Sinai Peninsula, a region that is still poorly explored. An overview of the plant communities of Sinai is compiled based on additional field work and inventories. The database for Sinai vegetation will serve as a nucleus for the National Vegetation Databank of Egypt—‘VegEgypt’. Aspects like species richness, species turnover, conservation categories, and endemism were given special attention. The database was set up and analysed using the Turboveg and Juice programs. A matrix of 816 relevés and 496 species belonging to 69 families and 281 genera formed the basis for further analysis. Asteraceae, Poaceae and Fabaceae were the most represented families. The lists contain 52 threatened, 16 endemic and 20 sub-endemic species. The percentage of endemic species is highest in insular floras, peninsulas and mountain chains. The vegetation analysis resulted in 21 associations. The plant associations on sand habitats have the lowest species richness and species turnover because of water scarcity. On the other hand, plant communities or associations inhabiting rocky habitats have the highest species richness and species turnover.

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).

We introduce RanVegDunes (GIVD ID EU-IT-020), the first Italian database gathering standardized, randomly- sampled vegetation data in coastal dune environments. RanVegDunes currently consists of 979 original georeferenced relevés randomly collected over about 160 km of Holocenic dune systems of central Italy between 2002 and 2015. Plots were located in five Italian administrative regions (Lazio, Campania, Abruzzo, Molise and Puglia), where sandy vegetation was randomly recorded in standardized 4-m2plots across the whole coastal zonation, thus including upper beach, embryo dunes, shifting dunes, dune grasslands, wooded dunes and Mediterranean forests. Each plot was georeferenced with a high level of accuracy using a GPS unit. For each plot, a list of vascular plant species based on updated nomenclature was compiled, and an estimate of abundance was calculated for each species using a percentage cover scale ranging from 1 to 10 (1 = 1-10%; 2 = 10-20%; ...; 10 = 90-100%). Two habitat classification systems were used for the description of collected plots: specifically, each vegetation plot was classified according to the Interpretation Manual of the 92/43/EEC Habitats Directive and also to the hierarchical structure of the EUNIS habitat classification. Currently, RanVegDunes includes vegetation plots belonging to nine EU Habitats (1210, 2110, 2120, 2210, 2230, 2250, 2260, 2270, 9340), which in turn correspond to five EUNIS Habitats (B1.1, B1.3, B1.4, B1.6, B1.7). RanVegDunes is a powerful tool for a variety of studies addressing topics such as temporal trends in coastal vegetation, ecological modeling, re-visitation studies, spatio-temporal patterns of invasion and will therefore greatly benefit from further collection of data.

Question: While it is well known that species richness depends on plot size, it is not generally recognised that the same must be tme for constancy. Accordingly, many authors use varying plot sizes when classifying vegetation based on the comparison of constancies between groups of plots. We ask whether the constancy-area relationship follows a general mle, how strong the effect of plot sizes is on constancies, and if it is possible to correct constancies for area. Location: For empirical evaluation, we use data from plant communities in the Czech Republic, Sweden and Russia. Methods: To assess the potential influence of differences in plot size on constancies, we develop a mathematical model. Then, we use series of nested plot species richness data from a wide range of community types (herbaceous and forest) to determine the parameters of the derived function and to test how much the shape of the constancy-area relationship depends on taxa or vegetation types. Results: Generally, the constancy-area relationship can be described by C (A)= 1 - (1-C 0)(A/A0)̌d, with C being constancy, A area, C 0 known constancy on a specific area A0, and d a damping parameter accounting for spatial autocorrelation. As predicted by this function, constancies in plant communities always varied from values near 0% to near 100% if plot sizes were changed sufficiently. For the studied vegetation types, a two- to fourfold increase in plot size resulted in a change of conventional constancy classes, i.e. an increase of constancy by 20% or more. Conclusions: Vegetation classification, which largely relies on constancy values, irrespective of whether traditional or modem fidelity definitions are used, is strongly prone to distorting scale effects when relevés of different plot sizes are combined in studies. The constancy-area functions presented allow an approximate transformation of constancies to other plot sizes but are flawed by idiosyncrasies in taxa and vegetation types. Thus, we conclude that the best solution for future surveys is to apply uniform plot sizes within a few a priori delimited formations and to determine diagnostic species only within these formations. Finally, we suggest that more detailed analyses of constancy-area relationships can contribute to a better understanding of species-area relationships because the latter are the summation of the first for all species.