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.

Recent years have seen an explosion in the availability of biodiversity data describing the distribution, function, and evolutionary history of life on earth. Integrating these heterogeneous data remains a challenge due to large variations in observational scales, collection purposes, and terminologies. Here, we conceptualize widely used biodiversity data types according to their domain (what aspect of biodiversity is described?) and informational resolution (how specific is the description?). Applying this framework to major data providers in biodiversity research reveals a strong focus on the disaggregated end of the data spectrum, whereas aggregated data types remain largely underutilized. We discuss the implications of this imbalance for the scope and representativeness of current macroecological research and highlight the synergies arising from a tighter integration of biodiversity data across domains and resolutions. We lay out effective strategies for data collection, mobilization, imputation, and sharing and summarize existing frameworks for scalable and integrative biodiversity research. Finally, we use two case studies to demonstrate how the explicit consideration of data domain and resolution helps to identify biases and gaps in global data sets and achieve unprecedented taxonomic and geographical data coverage in macroecological analyses.

Understanding how species diversity is related to sampling area and spatial scale is central to ecology and biogeography. Small islands and small sampling units support fewer species than larger ones. However, the factors influencing species richness may not be consistent across scales. Richness at local scales is primarily affected by small‐scale environmental factors, stochasticity and the richness at the island scale. Richness at whole‐island scale, however, is usually strongly related to island area, isolation and habitat diversity. Despite these contrasting drivers at local and island scales, island species–area relationships (SARs) are often constructed based on richness sampled at the local scale. Whether local scale samples adequately predict richness at the island scale and how local scale samples influence the island SAR remains poorly understood. We investigated the effects of different sampling scales on the SAR of trees on 60 small islands in the Raja Ampat archipelago (Indonesia) using standardised transects and a hierarchically nested sampling design. We compared species richness at different grain sizes ranging from single (sub)transects to whole islands and tested whether the shape of the SAR changed with sampling scale. We then determined the importance of island area, isolation, shape and habitat quality at each scale on species richness. We found strong support for scale dependency of the SAR. The SAR changed from exponential shape at local sampling scales to sigmoidal shape at the island scale indicating variation of species richness independent of area for small islands and hence the presence of a small‐island effect. Island area was the most important variable explaining species richness at all scales, but habitat quality was also important at local scales. We conclude that the SAR and drivers of species richness are influenced by sampling scale, and that the sampling design for assessing the island SARs therefore requires careful consideration.

According to Thompson's principle of similarity, the area of an object should be proportional to its length squared. However, leaf area-length data of some plants have been demonstrated not to follow the principle of similarity. We explore the reasons why the leaf area-length allometry deviates from the principle of similarity and also examine whether there is a general model describing the relationship among leaf area, width and length. More than 11,800 leaves from the six classes of woody and herbaceous plants were sampled to check the leaf area-length allometry. Six mathematical models were compared based on root-mean-square error as the measure of goodness-of-fit. The best supported model described a proportional relationship between leaf area and the product of leaf width and length (i.e., the Montgomery model). We found that the extent to which the leaf area-length allometry deviates from the principle of similarity depends upon the variation of the ratio of leaf width to length. Estimates of the parameter of the Montgomery model ranged between 1/2 and π/4 for the six classes of plants. This is a narrower range than imposed by the limits 1/2 (for a triangular leaf with leaf length as its height and leaf width as its base) to π/4 (for an elliptical leaf with leaf length as its major axis and leaf width as its minor axis). The narrow range in practice implies an evolutionary stability for the leaf area of large-leaved plants despite the fact that leaf shapes of these plants are rather different.

Recent years have seen an explosion in the availability of biodiversity data describing the distribution, function, and evolutionary history of life on earth. Integrating these heterogeneous data remains a challenge due to large variations in observational scales, collection purposes, and terminologies. Here, we conceptualize widely used biodiversity data types according to their domain (what aspect of biodiversity is described?) and informational resolution (how specific is the description?)(Fig. 1). Applying this framework to major data providers in biodiversity research reveals a strong focus on the disaggregated end of the data spectrum, e.g. point occurrence data or trait measurements, whereas aggregated data types, e.g. floras or taxonomic monographs, remain largely under-utilized. We discuss the implications of this imbalance for the scope and representativeness of current macroecological research and lay out how an explicit consideration of domain and resolution can inform data collection, mobilization, imputation, and integration. We use two case studies to substantiate our points. In the first case study, we show how coarse-grained regional plant checklists can be used to predict global growth form spectra at high spatial resolutions. Our predictions are highly consistent with fine-grained empirical data based on point occurrence records and vegetation plots. While such dis-aggregated data produce reliable results only for a limited set of well-covered regions, aggregated data types can provide critical information for the extrapolation of biodiversity patterns into less well-sampled regions. In the second case study, we revisit the latitudinal gradient in seed mass, which has been reported to exhibit a 320-fold decrease between 0 and 60 degrees latitude as well as a sharp, 7-fold drop at the edge of the tropics. Re-assessing this relationship using an independent dataset revealed a much more moderate, 11-fold decrease of seed mass towards the poles with little evidence for any abrupt changes. We show that the original results, which were based on relatively sparse, disaggregated data, are confounded by substantial biases in the representation of biomes and growth forms. This, we argue, amplified the magnitude and distorted the shape of the latitudinal gradient in seed mass. In summary, this talk aims to provide both theoretical and practical arguments for a more integrated biodiversity data landscape, which utilizes the strengths and weaknesses of multiple data types across multiple domains and resolutions in order to produce more robust, comprehensive and informative macroecological inferences.