Aim: To analyse the biogeographic patterns of Temperate Deciduous Forests (TDFs) in Western Eurasia based on different life‐forms and forests layers and explore their relationships with the current climate, Last Glacial Maximum (LGM) climate and topography.Location: Western Eurasia.Taxon: Vascular plants.Methods: We delimited nine regions encompassing the variability of TDFs in Western Eurasia and collected 1000 vegetation plots from each. We deconstructed the plant communities into three layers, tree, shrub and floor. We used (i) generalized linear mixed models (GLMM) to analyse the influence of current climate, historical climate and topography on species richness by accounting for regional effects and (ii) redundancy analysis (RDA) with variance partitioning to describe the variation in life forms along abiotic gradients. The three forest layers were analysed jointly and separately.Results: The Balkans, Alps and Carpathians appeared to be the richest in plant species, whereas the British Isles and the Hyrcanian region were the poorest. Annual temperature range and annual mean temperature were the best predictors of species richness for the whole dataset and for the shrub layer. The tree layer richness was mainly explained by the annual temperature range and by elevation, whereas the forest floor richness was more related to the annual temperature range and the annual mean temperature differences between the LGM and current climate. The current climate was the main predictor of the composition of the whole community, the tree layer and the floor layer, while the shrub layer was also influenced by historical climate.Main conclusions: Our overview of the diversity of temperate deciduous forests in Western Eurasia demonstrates different patterns and drivers across life‐forms and forest layers. While the diversity of trees is mainly linked to current climatic conditions, the shrub layer is also driven by postglacial‐glacial climatic stability, suggesting a different origin from forest trees.

Protecting global biodiversity is one of the most urgent tasks for the coming decades. Area-based conservation is a pillar for preserving ecosystems and species. Strictly protected areas specifically preserve biodiversity and ecosystem processes. The “EU Biodiversity Strategy for 2030” targets strict protection for 10% of land area. Here we performed the first analysis of strictly protected areas (as IUCN type Ia, Ib, and II) across Europe, by investigating their area coverage at the level of biogeographical regions, countries and elevation gradients. We show that, with few exceptions, the amount of strictly protected area is very limited and the spatial distribution of such protected areas is biased towards higher elevation sites, as in the case of other protected areas. Then, we suggest that potential areas should be identified to expand strictly protected areas with low economic and social costs including, for instance, areas with high biodiversity value, low population, and low productive land use. Finally, we propose that a coordinated effort and a strategic plan to achieve continental-scale conservation are fundamental, and at least half of this land under strict conservation (i.e. 5%) should be under the protection categories Ia and Ib.

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 plots as samples of vascular plant diversity 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.

The importance of collection, storage and exchange of georeferenced vegetation plot-based data has significantly grown in the recent decades, because of the new potentialities offered by ecoinformatics. In this article we introduce the Alma Mater Studiorum – University of Bologna vegetation database (AMS-VegBank; GIVD code EU-IT-021) compiling 17,505 georeferenced vegetation-plot observations within a time span of 90 years. This database includes 337,799 occurrence data of vascular plant species, belonging to many different habitat types. The historical relevance of the presented database is highlighted by the presence of some of the most ancient vegetation-plot observations in Europe (years 1930–1938). The geographic coverage of the database is mostly for Italian territory but it includes also data from other countries. The thematic focuses represented in the database are various, such as small Mediterranean islands, the Dolomite Mountains and the Italian National Parks. The large amount of historical plots available for the country not previously included in existing databases, combined with the constant action to improve the georeferencing of existing data and the addition of new data, highlight the uniqueness of this database. AMS-VegBank represents thus an important tool for studying plant biodiversity within the context of continental and global vegetation plot databases. Taxonomic reference: All plant names reported in this article follow the nomenclature by Pignatti et al. (2017–2019). Abbreviations: EVA = European Vegetation Archive; GIVD = Global Index of Vegetation-Plot Databases.