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 n‐dimensional hypervolumes are commonly applied in ecology and evolutionary studies to describe and compare niches, trait spaces characterizing phenotypes or the functional composition of communities. Classical ecological surveys, modern analytical tools and the establishment of online data bases will produce large multivariate data sets, which demands robust statistical tools to analyse and interpret hypervolumes. Existing approaches often have weaknesses; for example, they rely on multivariate normally or elliptically distributed data, perform poorly in higher dimensions, or their outputs vary arbitrarily with parameter choice. Here we introduce dynamic range boxes as a robust nonparametric approach to quantify size and overlap of n‐dimensional hypervolumes. Dynamic range boxes (implemented in the r package dynRB) improve the concept of multivariate range boxes by accounting for the distribution of the data within their range, while still no assumptions on the underlying distributions are needed. In addition to calculating the whole n‐dimensional hypervolume, the package dynRB also provides functions for a coordinate‐wise analysis and interpretation of the data. The concept of dynamic range boxes reliably computes sizes and overlaps of n‐dimensional hypervolumes, which makes dynamic range boxes readily applicable for a broad range of data sets to answer questions related to various disciplines.

Chemical communication is ubiquitous. The identification of conserved structural elements in visual and acoustic communication is well established, but comparable information on chemical communication displays (CCDs) is lacking. We assessed the phenotypic integration of CCDs in a meta-analysis to characterize patterns of covariation in CCDs and identified functional or biosynthetically constrained modules. Poorly integrated plant CCDs (i.e. low covariation between scent compounds) support the notion that plants often utilize one or few key compounds to repel antagonists or to attract pollinators and enemies of herbivores. Animal CCDs (mostly insect pheromones) were usually more integrated than those of plants (i.e. stronger covariation), suggesting that animals communicate via fixed proportions among compounds. Both plant and animal CCDs were composed of modules, which are groups of strongly covarying compounds. Biosynthetic similarity of compounds revealed biosynthetic constraints in the covariation patterns of plant CCDs. We provide a novel perspective on chemical communication and a basis for future investigations on structural properties of CCDs. This will facilitate identifying modules and biosynthetic constraints that may affect the outcome of selection and thus provide a predictive framework for evolutionary trajectories of CCDs in plants and animals.

SummaryThe basic units of ecological and evolutionary processes are individuals. Network studies aiming to infer mechanisms from complex systems, however, usually focus on interactions between species, not individuals. Accordingly, the structure and underlying mechanisms of individual-based interaction networks remain largely unknown.In a common garden, we recorded all interactions on flowers and leaves of 97 Sinapis arvensis individuals from seedling stage to fruit set and related interindividual differences in interactions to the plant individuals' phenotypes.The plant individuals significantly differed in their quantitative and qualitative interactions with arthropods on flowers and leaves. These differences remained stable over the entire season and thus were time-invariant. Variation in interacting arthropod communities could be explained by a pronounced intraspecific variability in flowering phenology, morphology and flower scent, and translated into variation in reproductive success. Interestingly, plant individuals with a similar composition of flower visitors were also visited by a similar assemblage of interaction partners at leaves.Our results show that the nonuniformity of plant species has pronounced effects in community ecology, potentially with implications for the persistence of communities and populations, and their ability to withstand environmental fluctuations.