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.

Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. Here, we perform a global, plot-level analysis of trait–environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions.

Significance: The stability of ecological communities under ongoing climate and land-use change is fundamental to the sustainable management of natural resources through its effect on critical ecosystem services. Biodiversity is hypothesized to enhance stability through compensatory effects (decreased synchrony between species). However, the relative importance and interplay between different biotic and abiotic drivers of stability remain controversial. By analyzing long-term data from natural and seminatural ecosystems across the globe, we found that the degree of synchrony among dominant species was the main driver of stability, rather than species richness per se. These biotic effects overrode environmental drivers, which influenced the stability of communities by modulating the effects of richness and synchrony.

1 By comparing plant species under the same experimental field conditions, the direct effects of plant traits on ecosystem processes can be studied. We have analyzed the role of leaf traits (leaf lamina dry matter content, LDMC; leaf lamina N content, LNC and specific leaf lamina area, SLA) for the annual above-ground primary productivity (ANPP) and quality (pepsin-cellulase digestibility, crude protein content) for herbivores of 13 perennial C₃ pasture grass species. 2 These relationships were investigated over 2 years with monocultures grown in a fully factorial block design crossing the plant species, the cutting frequency and the N supply factors. 3 The within species variation in leaf traits, ANPP, digestibility and protein content was less than between species variation. Species ranks for leaf traits were conserved among N supply and cutting frequency levels. Highly significant (P < 0·001) between species allometric relationships were found for LNC × SLA and SLA × LDMC, with common slopes but differences in intercept and shifts among factor levels. 4 The between species variation in ANPP was strongly (P < 0·001) and negatively correlated with the fresh-matter based leaf N content (i.e. LDMC × LNC) and was not affected by SLA, apparently because of a trade-off between SLA and leaf lamina fraction. Digestibility increased with SLA and declined with LDMC. Protein content increased with both fresh and dry-matter based LNC. 5 N supply increased LNC and SLA but reduced LDMC. Cutting frequency increased LDMC and reduced LNC. In response to cutting frequency, changes in digestibility and in fresh-matter based LNC were positively correlated. 6 We conclude that the between species variation in the annual production of digestible energy and of proteins by pasture grasses is controlled in an additive way by two leaf traits: LNC and LDMC.