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.

Summary: Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation. Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment‐related hypotheses of broad‐scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions. Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity. Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km². Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology.

Leaf size has considerable ecological relevance, making it desirable to obtain leaf size estimations for as many species worldwide as possible. Current global databases, such as TRY, contain leaf size data for ~30 000 species, which is only ~8% of known species worldwide. Yet, taxonomic descriptions exist for the large majority of the remainder. Here we propose a simple method to exploit information on leaf length, width and shape from species descriptions to robustly estimate leaf areas, thus closing this considerable knowledge gap for this important plant functional trait. Using a global dataset of all major leaf shapes measured on 3125 leaves from 780 taxa, we quantified scaling functions that estimate leaf size as a product of leaf length, width and a leaf shape-specific correction factor. We validated our method by comparing leaf size estimates with those obtained from image recognition software and compared our approach with the widely used correction factor of 2/3. Correction factors ranged from 0.39 for highly dissected, lobed leaves to 0.79 for oblate leaves. Leaf size estimation using leaf shape-specific correction factors was more accurate and precise than estimates obtained from the correction factor of 2/3. Our method presents a tractable solution to accurately estimate leaf size when only information on leaf length, width and shape is available or when labour and time constraints prevent usage of image recognition software. We see promise in applying our method to data from species descriptions (including from fossils), databases, field work and on herbarium vouchers, especially when non-destructive in situ measurements are needed.

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.

Island biogeography is the study of the spatio‐temporal distribution of species, communities, assemblages or ecosystems on islands and other isolated habitats. Island diversity is structured by five classes of process: dispersal, establishment, biotic interactions, extinction and evolution. Classical approaches in island biogeography focused on species richness as the deterministic outcome of these processes. This has proved fruitful, but species traits can potentially offer new biological insights into the processes by which island life assembles and why some species perform better at colonising and persisting on islands.Functional traits refer to morphological and phenological characteristics of an organism or species that can be linked to its ecological strategy and that scale up from individual plants to properties of communities and ecosystems. A baseline hypothesis is for traits and ecological strategies of island species to show similar patterns as a matched mainland environment. However, strong dispersal, environmental and biotic‐interaction filters as well as stochasticity associated with insularity modify this baseline. Clades that do colonise often embark on distinct ecological and evolutionary pathways, some because of distinctive evolutionary forces on islands, and some because of the opportunities offered by freedom from competitors or herbivores or the absence of mutualists. Functional traits are expected to be shaped by these processes.Here, we review and discuss the potential for integrating functional traits into island biogeography. While we focus on plants, the general considerations and concepts may be extended to other groups of organisms. We evaluate how functional traits on islands relate to core principles of species dispersal, establishment, extinction, reproduction, biotic interactions, evolution and conservation. We formulate existing knowledge as 33 working hypotheses. Some of these are grounded on firm empirical evidence, others provide opportunities for future research.We organise our hypotheses under five overarching sections. Section A focuses on plant functional traits enabling species dispersal to islands. Section B discusses how traits help to predict species establishment, successional trajectories and natural extinctions on islands. Section C reviews how traits indicate species biotic interactions and reproduction strategies and which traits promote intra‐island dispersal. Section D discusses how evolution on islands leads to predictable changes in trait values and which traits are most susceptible to change. Section E debates how functional ecology can be used to study multiple drivers of global change on islands and to formulate effective conservation measures.Islands have a justified reputation as research models. They illuminate the forces operating within mainland communities by showing what happens when those forces are released or changed. We believe that the lens of functional ecology can shed more light on these forces than research approaches that do not consider functional differences among species.

Assessing the distribution of geographically restricted and evolutionarily unique species and their underlying drivers is key to understanding biogeographical processes and critical for global conservation prioritization. Here, we quantified the geographic distribution and drivers of phylogenetic endemism for ~320,000 seed plants worldwide and identified centers and drivers of evolutionarily young (neoendemism) and evolutionarily old endemism (paleoendemism). Tropical and subtropical islands as well as tropical mountain regions displayed the world’s highest phylogenetic endemism. Most tropical rainforest regions emerged as centers of paleoendemism, while most Mediterranean-climate regions showed high neoendemism. Centers where high neo- and paleoendemism coincide emerged on some oceanic and continental fragment islands, in Mediterranean-climate regions and parts of the Irano-Turanian floristic region. Global variation in phylogenetic endemism was well explained by a combination of past and present environmental factors (79.8 to 87.7% of variance explained) and most strongly related to environmental heterogeneity. Also, warm and wet climates, geographic isolation, and long-term climatic stability emerged as key drivers of phylogenetic endemism. Neo- and paleoendemism were jointly explained by climatic and geological history. Long-term climatic stability promoted the persistence of paleoendemics, while the isolation of oceanic islands and their unique geological histories promoted neoendemism. Mountainous regions promoted both neo- and paleoendemism, reflecting both diversification and persistence over time. Our study provides insights into the evolutionary underpinnings of biogeographical patterns in seed plants and identifies the areas on Earth with the highest evolutionary and biogeographical uniqueness—key information for setting global conservation priorities.