Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

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 fourth edition of the International Code of Phytosociological Nomenclature (ICPN) was prepared by the Steering Committee of the IAVS Working Group for Phytosociological Nomenclature (GPN). The edition consists of 14 Definitions, 7 Principles, 53 Articles, and 7 Appendices. When compared with the previous edition, the main amendments are: (a) the acceptance of electronic publications (Art. 1); (b) the introduction of binding decisions (Definition XIV, Principle II, Articles 1, 2b, 3c, 29b, 40, 42, 44, Appendices 6 and 7); (c) the mandatory use of the English or Latin terminology for syntaxonomic novelties (Definition II, Principle II, Articles 3d and 3i); (d) the introduction of autonyms for the main ranks when the corresponding secondary ranks are created (Articles 13b and 24); (e) the automatic correction of the taxon names (name‐giving taxa) used in the names of syntaxa in accordance with the International Code of Nomenclature for algae, fungi, and plants (ICN) (Article 44); (f) the possibility to mutate the name of a syntaxon in using other correct, alternative names for the name‐giving taxa (Article 45); (g) the introduction of inadequate names, a new category of rejected names (Definition V, Articles 43 through 45); and (h) the introduction of a conserved type (Definition XIII, Article 53). The fourth edition of ICPN was approved by the GPN on 25 May 2019 and becomes effectively binding on 1 January 2021.

Biome is and remains a key community ecological and biogeographical concept and as such profited from overall progress of community ecology which was punctuated by two major innovations: shifting focus from pure pattern description to understanding functionality, changing approach from observational to explanatory and, most importantly, from descriptive to predictive. The functional focus enabled development of mechanistic and function‐focused predictive and retrodictive modelling; it also shaped the current understanding of the concept of biome as a dynamic biological entity having many faces, deep roots in evolutionary past, and undergoing changes. The evolution of the biome concept was punctuated by three synthetic steps: The First Synthesis formulated a solid body of theory explaining ecological and biogeographical meaning of zonality and collated our knowledge on drivers of vegetation patterns at large spatial scales. The Second Synthesis translated this knowledge into effective mechanistic modelling tools, developing further linking ecosystem functionality and biogeography. The Third Synthesis (still in progress) is seeking common grounds between large‐scale ecological and biogeographic phenomena, using tools of macroecology and macroevolutionary research.