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. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.

Summary 1.Competitor, stress-tolerator, ruderal (CSR) theory is a prominent plant functional strategy scheme previously applied to local floras. Globally, the wide geographic and phylogenetic coverage of available values of leaf area (LA), leaf dry matter content (LDMC) and specific leaf area (SLA) (representing, respectively, interspecific variation in plant size and conservative vs. acquisitive resource economics) promises the general application of CSR strategies across biomes, including the tropical forests hosting a large proportion of Earth's diversity. 2. We used trait variation for 3068 tracheophytes (representing 198 families, six continents and 14 biomes) to create a globally calibrated CSR strategy calculator tool and investigate strategy-environment relationships across biomes world-wide. 3. Due to disparity in trait availability globally, co-inertia analysis was used to check correspondence between a 'wide geographic coverage, few traits' data set and a 'restricted coverage, many traits' subset of 371 species for which 14 whole-plant, flowering, seed and leaf traits (including leaf nitrogen content) were available. CSR strategy/environment relationships within biomes were investigated using fourth-corner and RLQ analyses to determine strategy/climate specializations. 4. Strong, significant concordance (RV = 0Á597; P < 0Á0001) was evident between the 14 trait multivariate space and when only LA, LDMC and SLA were used. 5. Biomes such as tropical moist broadleaf forests exhibited strategy convergence (i.e. clustered around a CS/CSR median; C:S:R = 43:42:15%), with CS-selection associated with warm, stable situations (lesser temperature seasonality), with greater annual precipitation and potential evapotranspiration. Other biomes were characterized by strategy divergence: for example, deserts varied between xeromorphic perennials such as Larrea divaricata, classified as S-selected (C:S:R = 1:99:0%) and broadly R-selected annual herbs (e.g. Claytonia perfoliata; R/CR-selected; C:S:R = 21:0:79%). Strategy convergence was evident for several growth habits (e.g. trees) but not others (forbs). 6. The CSR strategies of vascular plants can now be compared quantitatively within and between biomes at the global scale. Through known linkages between underlying leaf traits and growth rates, herbivory and decomposition rates, this method and the strategy-environment relationships it elucidates will help to predict which kinds of species may assemble in response to changes in biogeochemical cycles, climate and land use.

Abbreviations LA = leaf area; SLA = specific leaf area; LDMC = leaf dry matter content; LNC = leaf nitrogen content; carbon:nitrogen ratio = C:N; d 13 C = 13 C isotope content; d 15 N = 15 N isotope content; H max = plant maximal height; A max = carbon assimilation; CWM = communityweighted mean; PCA = principal components analysis; RDA = redundancy analysis. AbstractQuestions: (1) How do community-weighted mean (CWM) trait values of 23 functional traits measured on 34 plant species vary along a gradient of aridity under grazed and ungrazed conditions in an arid steppe? (2) How does variation in our CWM trait values differ from those of more mesic grasslands?Location: Eastern Morocco. Methods:We measured relative abundance and functional traits along a short aridity gradient over two consecutive years at five heavily grazed sites, each with an exclosure preventing grazing. We analysed the relationship between aridity, grazing, and the expression of CWM trait values using ordination methods and a fourth-corner analysis.Results: Unconstrained and constrained ordinations identified three distinct suites of temporally consistent functional traits that co-varied with aridity and grazing, and the fourth-corner analysis identified a number of significant but weak trait-environment associations. Grazing selected for short, fast-growing annual species with high SLA, high pastoral value and low seed mass, while aridity selected for species possessing succulent leaves with high d 13 C leaf content, spines, low LDMC and short stature, although the relative importance of precipitation and grazing changed between years. Conclusions:Although distinct from more mesic grasslands, our study sites exhibited patterns of trait correlations that were similar to the worldwide leaf economics spectrum. These correlation patterns represented three groups that were reminiscent of Grime's C-S-R model. Direct ordinations supported this interpretation. Temporal variation in our results was due in part to precipitation fluctuations. Our results also indicated selection for a grazing avoidance strategy under heavy grazing. Integrating plant functional traits in conservation and management of arid ecosystems represents a novel and challenging task to ensure more sustainable use of these lands.

a global database for metacommunity ecology, integrating species, traits, environment and space alienor Jeliazkov et al. #the use of functional information in the form of species traits plays an important role in explaining biodiversity patterns and responses to environmental changes. although relationships between species composition, their traits, and the environment have been extensively studied on a case-by-case basis, results are variable, and it remains unclear how generalizable these relationships are across ecosystems, taxa and spatial scales. to address this gap, we collated 80 datasets from trait-based studies into a global database for metaCommunity Ecology: Species, Traits, Environment and Space; "CEStES". Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the sampling sites. the CEStES database is a live database: it will be maintained and expanded in the future as new datasets become available. By its harmonized structure, and the diversity of ecosystem types, taxonomic groups, and spatial scales it covers, the CEStES database provides an important opportunity for synthetic trait-based research in community ecology. Background & SummaryA major challenge in ecology is to understand the processes underlying community assembly and biodiversity patterns across space 1,2 . Over the three last decades, trait-based research, by taking up this challenge, has drawn increasing interest 3 , in particular with the aim of predicting biodiversity response to environment. In community ecology, it has been equated to the 'Holy Grail' that would allow ecologists to approach the potential processes underlying metacommunity patterns 4-7 . In macroecology, it is common to study biodiversity variation through its taxonomic and functional facets along gradients of environmental drivers 8-10 . In biodiversity-ecosystem functioning research, trait-based diversity measures complement taxonomic ones to predict ecosystem functions 11 offering early-warning signs of ecosystem perturbation 12 .The topic of Trait-Environment Relationships (TER) has been extensively studied across the globe and across the tree of life. However, each study deals with a specific system, taxonomic group, and geographic region and uses different methods to assess the relationship between species traits and the environment. As a consequence, we do not know how generalizable apparent relationships are, nor how they vary across ecosystems, realms, and taxonomic groups. In addition, while there is an emerging synthesis about the role of traits for terrestrial plant communities 13,14 , we know much less about other groups and ecosystem types.To address these gaps, we introduce the CESTES database -a global database for metaCommunity Ecology: Species, Traits, Environment and Space. This database assembles 80 datasets from studies that analysed empirical multivariate trait-environment relationships between 1996 (the first...

Summary1. Global climate change is possibly one of the most important challenges for current and future human populations due to its wide-ranging effects on ecosystems. Global prediction models suggest that in some areas of the world (e.g. Northern Africa, Central America) an increase in aridity might strongly disturb agricultural production and affect food security. To counterbalance these negative effects, reliable predictive models are needed to anticipate ecosystem changes. 2. We tested the ability of the Community Assembly by Trait-based Selection (CATS) model that is based on the principle of maximum entropy and trait-based environmental filtering, to predict actual and future plant community composition in the arid steppes of eastern Morocco. Specifically, we asked whether this model was adequate for predicting actual community composition, based on predicted community-weighted mean (CWM) traits, and what would be the changes in community composition under various scenarios of climate change for the period 2080-2099. 3. The CATS model could predict > 90% of actual community composition if the actual CWM traits were known but only~40% if the CWM values were predicted from estimated aridity and grazing values. The predictions of community composition for 2080-2099 suggested that, regardless of the climate change scenario considered, the dominant group in grazed and ungrazed sites would shift from ruderal species to stress-tolerant sub-shrubs, which would constitute up over 80% of total community composition in some cases. 4. Synthesis. Our findings suggest that effects of climate change will strongly modify plant community structure in arid steppes, possibly accentuating the process of desertification, and reducing the pastoral value of the vegetation. Future research efforts should concentrate on the identification of strong trait-environment relationships to improve model predictions.