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.

Policies to mitigate climate change and biodiversity loss often assume that protecting carbon‐rich forests provides co‐benefits in terms of biodiversity, due to the spatial congruence of carbon stocks and biodiversity at biogeographic scales. However, it remains unclear whether this holds at the scales relevant for management, and particularly large knowledge gaps exist for temperate forests and for taxa other than trees. We built a comprehensive dataset of Central European temperate forest structure and multi‐taxonomic diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352 plots. We used Boosted Regression Trees (BRTs) to assess the relationship between above‐ground live carbon stocks and (a) taxon‐specific richness, (b) a unified multidiversity index. We used Threshold Indicator Taxa ANalysis to explore individual species’ responses to changing above‐ground carbon stocks and to detect change‐points in species composition along the carbon‐stock gradient. Our results reveal an overall weak and highly variable relationship between richness and carbon stock at the stand scale, both for individual taxonomic groups and for multidiversity. Similarly, the proportion of win‐win and trade‐off species (i.e., species favored or disadvantaged by increasing carbon stock, respectively) varied substantially across taxa. Win‐win species gradually replaced trade‐off species with increasing carbon, without clear thresholds along the above‐ground carbon gradient, suggesting that community‐level surrogates (e.g., richness) might fail to detect critical changes in biodiversity. Collectively, our analyses highlight that leveraging co‐benefits between carbon and biodiversity in temperate forest may require stand‐scale management that prioritizes either biodiversity or carbon in order to maximize co‐benefits at broader scales. Importantly, this contrasts with tropical forests, where climate and biodiversity objectives can be integrated at the stand scale, thus highlighting the need for context‐specificity when managing for multiple objectives. Accounting for critical change‐points of target taxa can help to deal with this specificity, by defining a safe operating space to manipulate carbon while avoiding biodiversity losses.