Vascular plants are the main entry point for energy and matter into the Earth's terrestrial ecosystems. Their Darwinian struggle for growth, survival and reproduction in very different arenas has resulted in an extremely wide variety of form and function, both across and within habitats. Yet it has long been thought 1-8 that there is a pattern to be found in this remarkable evolutionary radiation-that some trait constellations are viable and successful whereas others are not.Empirical support for a strongly limited set of viable trait combinations has accumulated for traits associated with single plant organs, such as leaves 7,9-12 , stems 13,14 and seeds [15][16][17] . Evidence across plant organs has been rarer, restricted geographically or taxonomically, and often contradictory [18][19][20][21][22][23][24][25][26][27][28][29] . How tightly whole-plant form and function are restricted at the global scale remains unresolved.Here we present the first global quantitative picture of essential functional diversity of extant vascular plants. We quantify the volume, shape and boundaries of this functional space via joint consideration of six traits that together capture the essence of plant form and function: adult plant height, stem specific density, leaf size expressed as leaf area, leaf mass per area, leaf nitrogen content per unit mass, and diaspore mass. Our dataset, based on a recently updated communal plant trait database 30 , covers 46,085 vascular plant species from 423 families and to our knowledge spans the widest range of growth-forms and geographical locations to date in published trait analyses, including some of the most extreme plant trait values ever measured in the field (Table 1, Extended Data Fig. 1). On this basis we reveal that the trait space actually occupied is strongly restricted as compared to four alternative null hypotheses. We demonstrate that plant species largely occupy a plane in the six-dimensional trait space. Two key trait dimensions within this plane are the size of whole plants and organs on the one hand, and the construction costs for photosynthetic leaf area, on the other. We subsequently show which sections of the plane are occupied, and how densely, by different growth-forms and major taxonomic groups. The design opportunities and limits indicated by today's global spectrum of plant form and function provide a foundation to achieve a better understanding of the evolutionary trajectory of vascular plants and help frame and test hypotheses as to where and Earth is home to a remarkable diversity of plant forms and life histories, yet comparatively few essential trait combinations have proved evolutionarily viable in today's terrestrial biosphere. By analysing worldwide variation in six major traits critical to growth, survival and reproduction within the largest sample of vascular plant species ever compiled, we found that occupancy of six-dimensional trait space is strongly concentrated, indicating coordination and trade-offs. Threequarters of trait variation is captured in a t...

Aim Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait-trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales.Innovation For this purpose we introduce BHPMF, a hierarchical Bayesian extension of probabilistic matrix factorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation from point measurements to larger spatial scales. We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF.Main conclusions Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal -also for extremely sparse trait matrices -and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait-based research in macroecology and functional biogeography.

1. Forest dieback caused by drought-induced tree mortality has been observed worldwide. Forecasting which trees in which locations are vulnerable to drought-induced mortality is important to predict the consequences of drought on forest structure, biodiversity and ecosystem function. 2.In this paper, our central aim was to compile a synthesis of tree traits and associated abiotic variables that can be used to predict drought-induced mortality. 3.We reviewed the literature that specifically links drought mortality to functional traits and site conditions (i.e. edaphic variables and biotic conditions), targeting studies that show clear use of tree traits in drought analysis. We separated the review into five climatic-zones to determine global versus regionally restricted relationships between traits and mortality. 4.Our synthesis identifies a number of traits that have clear relationships with drought-induced mortality (e.g. wood density at the species level and tree size and growth at the individual level). However, the lack of direct relationships between most traits and drought-induced mortality highlights areas where future research should focus to broaden our understanding. 5.Synthesis and applications. Our synthesis highlights established relationships between traits and drought-induced mortality, presents knowledge-gaps for future research focus and suggests monitoring and research avenues for improving our understanding of drought-induced mortality. It is intended to assist ecologists and natural resource managers choose appropriate and measurable parameters for predicting local and regional scale tree mortality risk in different climatic-zones within constraints of time and funding availability

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles.

No abstract

Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.

<p>Data on plant traits are increasingly used to understand relationships between biodiversity and ecosystem processes. Large trait databases are sparse because they are compiled from many smaller and usually more local databases. This sparsity severely limits the potential for both multivariate and global data analyses, and so "gap-filling" (imputation) approaches are commonly used to predict missing trait data prior to analysis. Data imputation can result in large biases and circularity; yet, no best practice has evolved for the appropriate use of gap-filled data. Here, we use the TRY database, the largest global database of plant traits, in combination with the commonly used gap-filling algorithm, BayesianHierarchical Probabilistic Matrix Factorization (BHPMF), to address opportunities and problems introduced by gap-filling. BHPMF is the gap-filling method of choice for both TRY, and the large and widely used database sPLOT. It predicts missing trait data using the taxonomic hierarchy and observed patterns of trait variance and trait-trait correlations. We use three metrics: root mean square error estimates, coefficient of variation to assess univariate deviation, and silhouette indices to assess multivariate deviation and clustering strength. We show that gap-filling results in deviation of these metrics calculated for groupings at lower taxonomic levels (intra-specific and intra-genera), but less so at higher taxonomic levels (family) and for functional groups. Trait-trait correlations are preserved at all levels. The strength of deviations depends both on the percentage of gaps, and on data characteristics, e.g. intra-taxa variability. Gap-filling with dataset-external trait data generally ameliorates prediction error, but the deviations of intra-taxonomic variation measures depend on the content of the added data. We conclude that BHPMF gap-filling introduces little bias if specifically used for analyses of traits within functional groups, including growth forms and plant functional types (PFTs), as well as trait-trait correlations. However, we generally discourage their use for analyses of taxonomic groupings at or below the family level. In summary, our study supports decisions on when and how to integrate BHPMF gap-filled trait data in future studies. We conclude with selected best practices when using sparse databases.</p>

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, 'gap-filling' approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage.Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), ( 2) correlations (bi-and multivariate) and (3) taxonomic and functional clustering (valuewise, uni-and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set-external trait data had little effect on induced taxonomic clustering and stabilized trait-trait correlations.Main Conclusions: Our study extends the criteria for the evaluation of gap-filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation.