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.

1 An international group of scientists has built an open internet data base of life-history traits of the Northwest European flora (the LEDA-Traitbase) that can be used as a data source for fundamental research on plant biodiversity and coexistence, macro-ecological patterns and plant functional responses. 2 The species-trait matrix comprises referenced information under the control of an editorial board, for ca. 3000 species of the Northwest European flora, combining existing information and additional measurements. The data base currently contains data on 26 plant traits that describe three key features of plant dynamics: persistence, regeneration and dispersal. The LEDA-Traitbase is freely available at http://www.leda-traitbase.org. 3 We present the structure of the data base and an overview of the trait information available. 4 Synthesis. The LEDA Traitbase is useful for large-scale analyses of functional responses of communities to environmental change, effects of community trait composition on ecosystem properties and patterns of rarity and invasiveness, as well as linkages between traits as expressions of fundamental trade-offs in plants.

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.

Question: How do multivariate methods perform in relating species‐ and community‐level trait responses to the environment?Location: (1) Field data from grazed semi‐natural grasslands, NE Germany; (2) artificial data.Methods: Research questions associated with trait–environment relationships were briefly reviewed and seven available methods evaluated. The main distinction between research questions is whether trait–environment relationships should be addressed at community or species level. A redundancy analysis (RDA) of mean trait values of species in a plot weighted by their abundances (CWM‐RDA) is exclusively suitable for the community level. The other six methods address the species level. A double inertia analysis of two arrays (RLQ) and double canonical correspondence analysis (doubleCCA) use combinations of ordinations to simultaneously analyse species and trait responses to the environment. A combination of the outlying mean index with generalized additive models (OMI‐GAM) predicts the response of species to environmental variables on trait gradients.RDA‐RegTree first analyses species responses to the environment withRDAand then uses a regression tree to classify trait expressions according to scores of species responses on the ordination axes. Cluster regression uses cluster analyses and logistic regression to search for trait combinations with the best response to the environmental variables. This method models the distribution of functional groups on environmental gradients. All methods and data are available as R scripts.Results: All methods consistently revealed the main trait responses to environment in the field data set, namely that life history was associated with available phosphorus while grazing intensity was related to leafC:Nratio and canopy height. At community level,CWM‐RDAgave a good overview of trait–environment relationships, as also provided by the species‐based methodsRLQand doubleCCA.OMI‐GAMrevealed non‐linear relationships in the field data set. Field and artificial data gave that the number and stability of functional groups produced byCluster regression andRDA‐RegTree varied more strongly thanRLQ, doubleCCAandOMI‐GAM.Conclusions: Each method addresses particular ecological concepts and research questions. If a user asks for the response of average trait expressions of communities to environmental gradients,CWM‐RDAmay be the first choice. However, species‐based methods should be applied to address questions regarding co‐existence of different life histories or to assess how groups of species respond to environmental changes. The artificial data set revealed that the methods differed in sensitivity to gradient lengths and random data.

Correlations among plant traits often reflect important trade‐offs or allometric relationships in biological functions like carbon gain, support, water uptake, and reproduction that are associated with different plant organs. Whether trait correlations can be aggregated to “spectra” or “leading dimensions,” whether these dimensions are consistent across plant organs, spatial scale, and growth forms are still open questions. To illustrate the current state of knowledge, we constructed a network of published trait correlations associated with the “leaf economics spectrum,” “biomass allocation dimension,” “seed dimension,” and carbon and nitrogen concentrations. This literature‐based network was compared to a network based on a dataset of 23 traits from 2,530 individuals of 126 plant species from 381 plots in Northwest Europe. The observed network comprised more significant correlations than the literature‐based network. Network centrality measures showed that size traits such as the mass of leaf, stem, below‐ground, and reproductive tissues and plant height were the most central traits in the network, confirming the importance of allometric relationships in herbaceous plants. Stem mass and stem‐specific length were “hub” traits correlated with most traits. Environmental selection of hub traits may affect the whole phenotype. In contrast to the literature‐based network, SLA and leaf N were of minor importance. Based on cluster analysis and subsequent PCAs of the resulting trait clusters, we found a “size” module, a “seed” module, two modules representing C and N concentrations in plant organs, and a “partitioning” module representing organ mass fractions. A module representing the plant economics spectrum did not emerge. Synthesis. Although we found support for several trait dimensions, the observed trait network deviated significantly from current knowledge, suggesting that previous studies have overlooked trait coordination at the whole‐plant level. Furthermore, network analysis suggests that stem traits have a stronger regulatory role in herbaceous plants than leaf traits.