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Aims: Oak–hornbeam forests and related vegetation types (phytosociological order Carpinetalia betuli) are widespread in temperate western Eurasia. However, their national classification systems are poorly compatible, and a broad‐scale classification based on numerical analyses is lacking. Therefore, we aimed to establish a unified formalized classification system based on a large data set of vegetation plots covering the entire range of these forests. Location: Europe, Anatolia, Caucasus and northern Iran. Methods: We compiled a data set of 15,817 vegetation plots from the European Vegetation Archive and the Hyrcanian Forest Vegetation Database, using the formal definition of the EUNIS habitat type T1E Carpinus and Quercus mesic deciduous forest. We classified the data set using TWINSPAN. Biogeographically and ecologically similar plot clusters were merged into oak–hornbeam forest types, which were interpreted as alliances. We also developed expert systems for automatically classifying vegetation at the alliance level for both the EuroVegChecklist (EVC) system and the revised classification. In addition, we calculated ordinations to show the major gradients in the species composition of the data set. Results: We present a revised classification system of the order Carpinetalia betuli with nine alliances, including basic descriptions of their species composition, distribution, ecology and syntaxonomy. The analyses largely supported the biogeographic concept of classification, analogous to EVC. Compared to EVC, we recognized an additional alliance Physospermo verticillati‐Quercion cerridis (southern Italy) but found no support for the alliances Astrantio‐Carpinion, Erythronio‐Carpinion and Scillo‐Quercion. The greatest difference in species composition was found between the southern and northern‐northeastern Carpinetalia types. Expert systems for the revised classification system (~89% of plots classified) and the EVC system (~72%) are also included. Conclusions: We provide the first comprehensive overview of alliances of the order Carpinetalia betuli across its whole distribution range. The associated expert systems allow consistent application of the classification of these forests in nature conservation, habitat monitoring, and biodiversity and ecological research.We provide the first formal classification for oak–hornbeam forests and related vegetation types (phytosociological order Carpinetalia betuli) across Europe and adjacent areas. It includes both traditional and revised classifications of this vegetation. Furthermore, we developed expert systems for vegetation‐plot assignment to vegetation types, which can be applied in research and biodiversity conservation, monitoring and management. [ABSTRACT FROM AUTHOR]

Aims: Vegetation‐plot sampling usually involves estimating species cover. For classifying plots to vegetation types, covers are often transformed to decrease the effect of dominant species. However, it remains unclear which transformation is optimal. We suggest that for vegetation classification, optimal is such transformation that contributes to creating clusters of plots in an unsupervised classification that are most similar to the widely accepted vegetation types, e.g., phytosociological associations. Here our aim is to find and recommend such optimal transformation by testing a range of transformation options against the national vegetation classifications of three European countries. Location: Czech Republic, The Netherlands, Great Britain. Methods: Three national datasets of vegetation plots with species cover information, classified to associations or community types of the respective national vegetation classification systems, were analysed. From each dataset, multiple subsets of plots were selected randomly, each subset representing a vegetation‐plot table containing several similar associations/community types. Species cover values in these subsets were subjected to various transformations (power transformation, logarithmic transformation and pseudo‐species cut levels). Then each subset was classified by an agglomerative classification method (beta‐flexible clustering with different beta values), and the classification was compared with the units of the national vegetation classification using the adjusted Rand index. Results: Power transformations of percentage covers with an exponent between 0.3 and 0.6 produced the best match between the unsupervised classifications and the national vegetation classifications. This result did not depend on the classification method used. A similar degree of matching was achieved with some cut levels of pseudo‐species and with logarithmic transformation of percentage cover. Conclusions: If an unsupervised classification of vegetation plots aims at defining vegetation types that are close to the phytosociological associations accepted in national vegetation classifications, the best transformation is close to the square‐root of percentage cover (i.e., power transformation with exponent 0.5). [ABSTRACT FROM AUTHOR]

Questions: What are the main vegetation types of forest and shrubland vegetation in central and eastern Euxine Turkey and SW Georgia? What are the main environmental factors affecting their diversity? What is their syntaxonomic position? Can we integrate them into the European vegetation classification system? Location: Central and eastern Euxine Turkey and SW Georgia. Methods: We collected 3104 vegetation plots of forest and shrubland vegetation in the study region and performed Two‐Way Indicator Species Analysis (TWINSPAN) classification. We described vegetation types based on the classification results, expert knowledge and information from literature sources. We defined diagnostic species and prepared distribution maps for each vegetation type. To determine the most significant environmental variables on floristic differentiation, we used canonical correspondence analysis. Detrended correspondence analysis with passive projection of most significant environmental variables was run to interpret the environmental variation of vegetation types. Results: The studied vegetation was divided into 29 vegetation types related to seven main vegetation groups: relict Mediterranean forests and shrubland (mainly along the coastline, beside some inland localities), lowland to submontane forests, central Euxine mountain forests, eastern Euxine (Colchic) mountain forests, subeuxine forests, azonal riparian forests and subalpine and alpine shrubland. Elevation is the most important factor causing the differentiation in vegetation. It is followed by longitude and latitude. Among climatic variables, temperature seasonality, annual precipitation and precipitation of the wettest quarter are the most significant factors for vegetation differentiation. These factors correlate with the reduction of maritime climate and geomorphological features. Conclusions: Vegetation types mostly correspond to the syntaxa accepted in the EuroVegChecklist. However, some of them do not appear in the EuroVegChecklist since they appear only beyond Europe. We described three syntaxa as new: Abietion equi‐trojani, Querco cerridis‐Carpinion orientalis and Piceo orientalis‐Fagenion orientalis. The study revealed high vegetation diversity of the region that should be taken into consideration in ecosystem management and used as a reference in restoration and mitigation of the effects of global changes. [ABSTRACT FROM AUTHOR]

Aim: The first comprehensive checklist of European phytosociological alliances, orders and classes (EuroVegChecklist) was published by Mucina et al. (2016, Applied Vegetation Science, 19 (Suppl. 1), 3–264). However, this checklist did not contain detailed information on the distribution of individual vegetation types. Here we provide the first maps of all alliances in Europe. Location: Europe, Greenland, Canary Islands, Madeira, Azores, Cyprus and the Caucasus countries. Methods: We collected data on the occurrence of phytosociological alliances in European countries and regions from literature and vegetation‐plot databases. We interpreted and complemented these data using the expert knowledge of an international team of vegetation scientists and matched all the previously reported alliance names and concepts with those of the EuroVegChecklist. We then mapped the occurrence of the EuroVegChecklist alliances in 82 territorial units corresponding to countries, large islands, archipelagos and peninsulas. We subdivided the mainland parts of large or biogeographically heterogeneous countries based on the European biogeographical regions. Specialized alliances of coastal habitats were mapped only for the coastal section of each territorial unit. Results: Distribution maps were prepared for 1,105 alliances of vascular‐plant dominated vegetation reported in the EuroVegChecklist. For each territorial unit, three levels of occurrence probability were plotted on the maps: (a) verified occurrence; (b) uncertain occurrence; and (c) absence. The maps of individual alliances were complemented by summary maps of the number of alliances and the alliance–area relationship. Distribution data are also provided in a spreadsheet. Conclusions: The new map series represents the first attempt to characterize the distribution of all vegetation types at the alliance level across Europe. There are still many knowledge gaps, partly due to a lack of data for some regions and partly due to uncertainties in the definition of some alliances. The maps presented here provide a basis for future research aimed at filling these gaps. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]