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 vegetationplot 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 largescale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/evadatabase.

Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.

Ecological theory and biodiversity conservation have traditionally relied on the number of species recorded at a site, but it is agreed that site richness represents only a portion of the species that can inhabit particular ecological conditions, that is, the habitat‐specific species pool. Knowledge of the species pool at different sites enables meaningful comparisons of biodiversity and provides insights into processes of biodiversity formation. Empirical studies, however, are limited due to conceptual and methodological difficulties in determining both the size and composition of the absent part of species pools, the so‐called dark diversity. We used >50,000 vegetation plots from 18 types of habitats throughout the Czech Republic, most of which served as a training dataset and 1083 as a subset of test sites. These data were used to compare predicted results from three quantitative methods with those of previously published expert estimates based on species habitat preferences: (1) species co‐occurrence based on Beals' smoothing approach; (2) species ecological requirements, with envelopes around community mean Ellenberg values; and (3) species distribution models, using species environmental niches modeled by Biomod software. Dark diversity estimates were compared at both plot and habitat levels, and each method was applied in different configurations. While there were some differences in the results obtained by different methods, particularly at the plot level, there was a clear convergence, especially at the habitat level. The better convergence at the habitat level reflects less variation in local environmental conditions, whereas variation at the plot level is an effect of each particular method. The co‐occurrence agreed closest the expert estimate, followed by the method based on species ecological requirements. We conclude that several analytical methods can estimate species pools of given habitats. However, the strengths and weaknesses of different methods need attention, especially when dark diversity is estimated at the plot level.

We studied how the landscape structure (percentage cover and diversity of surrounding habitats) affects different components of species diversity (alpha, beta and gamma) of vascular plants in semi-natural grasslands in the Slovak Republic. We analyzed all grasslands combined as well as four main types delimited according to their position along a moisture gradient (xerophilous, sub-xerophilous, mesophilous and wet grasslands). We used grassland records stored in the Slovak Vegetation Database. The geographically stratified dataset included 3795 plots with 1221 species of vascular plants. Along with the total number of species in the vegetation plots, we considered the numbers of target grassland species, forest species, archaeophytes, neophytes and species with high fidelity to non-natural habitats. The landscape parameters based on CORINE land cover maps and the National Grassland Inventory, were calculated for plot neighbourhoods of 4km2. Spatially constrained rarefaction curves were constructed to examine how different diversity components behave with changing structure of the surrounding landscape. Our study revealed that alpha diversity was affected by both percentage cover and diversity of different habitats in the plot neighbourhood. It increased with increasing proportion or diversity of different natural and semi-natural habitats and decreased with increasing proportion or diversity of non-natural habitats in the surrounding landscape. Beta and gamma diversities showed opposite pattern to that of alpha diversity for most analyzed factors. Alpha diversity in sub-xerophilous and mesophilous grasslands was more susceptible to changes in landscape structure than alpha diversity in xerophilous or wet grasslands. Regression trees and linear or quadratic regression models revealed that in xerophilous or wet grasslands, high alpha diversity was best predicted by a high proportion of ecologically valuable grasslands in the surroundings. In sub-xerophilous and mesophilous grasslands, the best predictor was proportion of natural and semi-natural habitats followed by the proportion of non-natural habitats. The detected pattern regarding alpha, beta and gamma diversity calculated for grassland target species did not differ from the pattern for the whole species assemblage. However, the surrounding landscape affected the number, proportion and cover of species typical of forest or non-natural habitats (including alien species) in the plots. We explain the results by the interplay of two main mechanisms: species pool and spatial mass effects. In our study, the effect of species pool on alpha diversity was stronger than the spatial mass effect. Based on differences indicated in the responses of various grassland types to the surrounding landscape structure, we suggest adoption of community type specific conservation measures

Some regions and habitats harbour high numbers of plant species at a fine scale. A remarkable example is the grasslands of the White Carpathian Mountains (Czech Republic), which holds world records in local species richness; however, the causes are still poorly understood. To explore the landscape context of this phenomenon and its relationships to diversity patterns at larger scales, we compared diversity patterns in grasslands and other vegetation types in the White Carpathians with those in nearby regions lacking extremely species-rich grasslands, using data from vegetation plots and flora grid mapping of entire landscapes. Although small-scale species richness of grasslands and ruderal/weed vegetation of the White Carpathians was higher than in the nearby regions, the number of grassland and ruderal/weed species in the regional flora of the White Carpathians was not. Diversity of forests was not higher in this region at any scale. Thus the remarkably high local species richness of the White Carpathian grasslands does not result from a larger grassland species pool in the region, but from the fine-scale co-occurrence of many grassland species in this landscape, which results in the formation of grassland communities that are locally rich but with similar species composition when comparing different sites (i.e. high alpha but low beta diversity). This pattern can be partly attributed to the large total area of these grasslands, which reduces random extinctions of rare species, low geological diversity, which enables many species to occur at many sites across the landscape, and high land-cover diversity, which supports mixing of species from different vegetation types