TRY Database Research Articles

Leaf mass per area: An investigation into the application of the ubiquitous functional trait from a paleobotanical perspective.

Leaf mass per area (LMA) is a widely used functional trait in both neobotanical and paleobotanical research that provides a window into how plants interact with their environment. Paleobotanists have used site-level measures of LMA as a proxy for climate, biome, deciduousness, and community-scale plant strategy, yet many of these relationships have not been grounded in modern data. In this study, we evaluated LMA from the paleobotanical perspective, seeking to add modern context to paleobotanical interpretations and discover what a combined modern and fossil data set can tell us about how LMA can be best applied toward interpreting plant communities. We built a modern data set by pulling plant trait data from the TRY database, and a fossil data set by compiling data from studies that have used the petiole-width proxy for LMA. We then investigated the relationships of species-mean, site-mean, and site-distribution LMA with different climatic, phylogenetic, and physiognomic variables. We found that LMA distributions are correlated with climate, site taxonomic composition, and deciduousness. However, the relative contributions of these factors are not distinctive, and ultimately, LMA distributions cannot accurately reconstruct the biome or climate of an individual site. The correlations that make up the leaf economics spectrum are stronger than the correlations between LMA and climate, phylogeny, morphospace, or depositional environment. Fossil LMA should be understood as the culmination of the influences of these variables rather than as a predictor.

Understory plant biodiversity is inversely related to carbon storage in a high carbon ecosystem.

Given that terrestrial ecosystems globally are facing the loss of biodiversity from land use conversion, invasive species, and climate change, effective management requires a better understanding of the drivers and correlates of biodiversity. Increasingly, biodiversity is co-managed with aboveground carbon storage because high biodiversity in animal species is observed to correlate with high aboveground carbon storage. Most previous investigations into the relationship of biodiversity and carbon co-management do not focus on the biodiversity of the species rich plant kingdom, which may have tradeoffs with carbon storage. To examine the relationships of plant species richness with aboveground tree biomass carbon storage, we used a series of generalized linear models with understory plant species richness and diversity data from the USDA Forest Service Forest Inventory and Analysis dataset and high-resolution modeled carbon maps for the Tongass National Forest. Functional trait data from the TRY database was used to understand the potential mechanisms that drive the response of understory plants. Understory species richness and community weighted mean leaf dry matter content decreased along an increasing gradient of tree biomass carbon storage, but understory diversity, community weighted mean specific leaf area, and plant height at maturity did not. Leaf dry matter content had little variance at the community level. The decline of understory plant species richness but not diversity to increases in aboveground biomass carbon storage suggests that rare species are excluded in aboveground biomass carbon dense areas. These decreases in understory species richness reflect a tradeoff between the understory plant community and aboveground carbon storage. The mechanisms that are associated with observed plant communities along a gradient of biomass carbon storage in this forest suggest that slower-growing plant strategies are less effective in the presence of high biomass carbon dense trees in the overstory.

No general support for functional diversity enhancing resilience across terrestrial plant communities

AbstractAimUnderstanding the mechanisms promoting resilience in plant communities is crucial in times of increasing disturbance and global environmental change. Here, we present the first meta‐analysis evaluating the relationship between functional diversity and resilience of plant communities. Specifically, we tested whether the resilience of plant communities is positively correlated with interspecific trait variation (following the niche complementarity hypothesis) and the dominance of acquisitive and small‐size species (following the mass ratio hypothesis), and for the context‐dependent effects of ecological and methodological differences across studies.LocationGlobal.Time Period2004–2021.Major Taxa StudiedVascular plants.MethodsWe compiled a dataset of 69 independent sites from 26 studies that have quantified resilience. For each site, we calculated functional diversity indices based on the floristic composition and functional traits of the plant community (obtained from the TRY database) which we correlated with resilience of biomass and floristic composition. After transforming correlation coefficients to Fisher's Z‐scores, we conducted a hierarchical meta‐analysis, using a multilevel random‐effects model that accounted for the non‐independence of multiple effect sizes and the effects of ecological and methodological moderators.ResultsIn general, we found no positive functional diversity–resilience relationships of grand mean effect sizes. In contrast to our expectations, we encountered a negative relationship between resilience and trait variety, especially in woody ecosystems, whereas there was a positive relationship between resilience and the dominance of acquisitive species in herbaceous ecosystems. Finally, the functional diversity–resilience relationships were strongly affected by both ecological (biome and disturbance properties) and methodological (temporal scale, study design and resilience metric) characteristics.Main ConclusionsWe rejected our hypothesis of a general positive functional diversity–resilience relationship. In addition to strong context dependency, we propose that idiosyncratic effects of single resident species present in the communities before the disturbances and biological legacies could play major roles in the resilience of terrestrial plant communities.

‘rtry’: An R package to support plant trait data preprocessing

AbstractPlant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the ‘rtry’ R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, ‘rtry’ avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. ‘rtry’ is therefore easy to use even for beginners in R. Notably, ‘rtry’ does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While ‘rtry’ primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The ‘rtry’ package is available on the Comprehensive R Archive Network (CRAN; https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows.

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research.

Animal trait data are scattered across several datasets, making it challenging to compile and compare trait information across different groups. For plants, the TRY database has been an unwavering success for those ecologists interested in addressing how plant traits influence a wide variety of processes and patterns, but the same is not true for most animal taxonomic groups. Here, we introduce ZooTraits, a Shiny app designed to help users explore and obtain animal trait data for research in ecology and evolution. ZooTraits was developed to tackle the challenge of finding in a single site information of multiple trait datasets and facilitating access to traits by providing an easy-to-use, open-source platform. This app combines datasets centralized in the Open Trait Network, raw data from the AnimalTraits database, and trait information for animals compiled by Gonçalves-Souza etal. (2023, Ecology and Evolution 13, e10016). Importantly, the ZooTraits app can be accessed freely and provides a user-friendly interface through three functionalities that will allow users to easily visualize, compare, download, and upload trait data across the animal tree of life-ExploreTrait, FeedTrait, and GetTrait. By using ExploreTrait and GetTrait, users can explore, compare, and extract 3954 trait records from 23,394 species centralized in the Open Traits Network, and trait data for ~2000 species from the AnimalTraits database. The app summarizes trait information for numerous taxonomic groups within the Animal Kingdom, encompassing data from diverse aquatic and terrestrial ecosystems and various geographic regions worldwide. Moreover, ZooTraits enables researchers to upload trait information, serving as a hub for a continually expanding global trait database. By promoting the centralization of trait datasets and offering a platform for data sharing, ZooTraits is facilitating advancements in trait-based ecological and evolutionary studies. We hope that other trait databases will evolve to mirror the approach we have outlined here.

Comparing the performance of machine learning methods in predicting soil seed bank persistence

Soil seed banks are pivotal for understanding plant population life histories and elucidating plant-environment relationships. Although experimental exploration of persistent seed banks yields valuable insights, it is often time-consming, labor-intensive, and costly. Consequently, the development of efficient computational methods like machine learning techniques is crucial to augment traditional fieldwork and enhance our capacity for effective seed bank data analysis and interpretation. In this study, we collated a dataset of 398 observations from the TRY database, encompassing measurements such as seed thickness, seed length, seed width, seed dry mass, and seed bank type. Notably, some species' seed bank types within this dataset are identified as having a soil seed bank, without distinguishing between persistent or transient seed banks. Our primary objective was to utilize this data to predict whether a species' soil seed bank is persistent or transient. To this end, we evaluated the performance of four classic machine learning methods—Adaptive Boosting (Adaboost), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—in predicting seed bank types. We gauged the performance of each model using five metrics: Accuracy, AUC (Area Under the Curve), kappa, sensitivity, and specificity. Among the four models examined, the ANN model proved to be the most effective in predicting the persistence of soil seed banks, suggesting its efficacy in predicting seed bank types based on a limited number of independent variables. This study underscores the potential of machine learning methods in soil seed bank research and paves the way for future investigations. It can potentially aid in crafting conservation strategies by enhancing our understanding of plant population dynamics.

Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016

The value of leaf photosynthetic capacity (Vcmax) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such Vcmax variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yr−1). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit Vcmax derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific Vcmax, the inclusion of the LCC-derived Vcmax improved the model estimates of GPP, with the coefficient of determination (R2) increased by 23% and the root mean square error (RMSE) decreased by 25%. Vcmax values are typically parameterized with PFT-specific Vcmax calibrated from flux tower observations or empirical Vcmax based on the TRY database (which includes 723 data points derived from Vcmax field measurements). On the regional scale, compared with GPP, using the LCC-derived Vcmax, the conventional method of fixing Vcmax using the calibrated Vcmax or TRY-based Vcmax overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yr−1 or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yr−2 or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yr−1). Our study highlighted the urgent need to develop spatially and temporally consistent Vcmax products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change.

The global spectrum of plant form and function: enhanced species-level trait dataset

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.

Analysis of Plant Trait Data of Host Plants of Lycorma delicatula in the US Suggests Evidence for Ecological Fitting

Plant traits, used by the invasive insect herbivores to find and select suitable hosts, can play an important role in insect host range expansion. With regard to invasive Lycorma delicatula, it is not well explored, however, how the plant origin affects insect host selection, and whether native and introduced host plants differ in their morphology, lifespan, as well as environmental requirements for growth. We addressed this issue in our study through the comprehensive assessment of 25 relevant plant traits (a total of 27,601 records retrieved from the TRY database), as well as the origin and phylogenetic relationships of 37 host plants of L. delicatula in the U.S. Our results showed that only leaf area, leaf chlorophyll content, and canopy size were significantly greater in the introduced hosts than that in native plants. We did not detect a significant effect of the plant origin on other characteristics. Additionally, no significant differences between native and introduced hosts of L. delicatula in genetic distances from introduced Ailanthus altissima (the most preferred host) were detected. These results, for the first time, suggest strong evidence for ecological fitting which might drive the host plant selection of L. delicatula and its rapid spread in the U.S.

FunAndes – A functional trait database of Andean plants

We introduce the FunAndes database, a compilation of functional trait data for the Andean flora spanning six countries. FunAndes contains data on 24 traits across 2,694 taxa, for a total of 105,466 entries. The database features plant-morphological attributes including growth form, and leaf, stem, and wood traits measured at the species or individual level, together with geographic metadata (i.e., coordinates and elevation). FunAndes follows the field names, trait descriptions and units of measurement of the TRY database. It is currently available in open access in the FIGSHARE data repository, and will be part of TRY’s next release. Open access trait data from Andean plants will contribute to ecological research in the region, the most species rich terrestrial biodiversity hotspot.

Building trait datasets: effect of methodological choice on a study of invasion

Trait-based approaches are commonly used to understand ecological phenomena and processes. Trait data are typically gathered by measuring local specimens, retrieving published records, or a combination of the two. Implications of methodological choices in trait-based ecological studies—including source of data, imputation technique, and species selection criteria—are poorly understood. We ask: do different approaches for dataset-building lead to meaningful differences in trait datasets? If so, do these differences influence findings of a trait-based examination of plant invasiveness, measured as abundance and spread rate? We collected on-site (Victoria, Australia) and off-site (TRY database) height and specific leaf area records for as many species as possible out of 157 exotic herbaceous plants. For each trait, we built six datasets of species-level means using records collected on-site, off-site, on-site and off-site combined, and off-site supplemented via imputation based on phylogeny and/or trait correlations. For both traits, the six datasets were weakly correlated (ρ = 0.31–0.95 for height; ρ = 0.14–0.88 for SLA), reflecting differences in species’ trait values from the various estimations. Inconsistencies in species’ trait means across datasets did not translate into large differences in trait-invasion relationships. Although we did not find that methodological choices for building trait datasets greatly affected ecological inference about local invasion processes, we nevertheless recommend: (1) using on-site records to answer local-scale ecological questions whenever possible, and (2) transparency around methodological decisions related to selection of study species and estimation of missing trait values.

How to measure flower ultraviolet reflectance using digital photography

AbstractAimsFlower ultraviolet (UV) reflectance strongly influences insects’ ability to detect flowers and locate pollen/nectar sources. Although included in the TRY database, the lack of a cost‐effective standardised method of measurement hampers the availability of information on this key floral trait. Digital photography and image processing allow for a novel approach to flower UV reflectance measurement that is both accessible and accurate.LocationEuropean semi‐natural grasslands.MethodsWe used a UV‐LED light and a mirrorless camera with a filter, which represents a low‐cost equipment for narrow emission/reflection photography (350–380 nm). Flowers were photographed with two standards of known reflection, and UV reflectance values were obtained using open‐source image processing software. We measured UV reflectance for 57 plant species typical of European semi‐natural grasslands.ResultsOur values substantially matched the categorical classes obtained by analogue photography available in TRY and showed a highly significant relationship with spectrophotometric measures.ConclusionsThe method proposed here overcomes the one based on analogue photography and subjective visual estimates, and represents an easy and low‐cost alternative to spectrophotometry. It may promote the standardised measurement of flower UV reflectance and broaden the information of this trait globally, meeting the needs of functional ecology and trait‐based community assembly studies.

What do we know about parasitic plants and the leaf economic spectrum?

AbstractThe leaf economic spectrum (LES) quantifies correlations between key leaf traits across vascular plants and distills much of the variation in these traits to a single axis. The LES, remarkable in its near universality, has been extensively researched across a variety of contexts. However, parasitic plants relationship to the LES framework remains relatively unexplored. Because the LES is, in part, driven by physiological tradeoffs in the acquisition of carbon (C), in theory heterotrophy in parasitic plants, which supplants some of the essential functions of leaves, could lead to departures from the LES. Using global leaf trait data from the TRY database, this work assessed the overall representation of parasitic plants in the TRY database, then compared the LES suite of leaf traits in parasitic plants to their non-parasitic counterparts. Despite their unique physiology, parasitic plants did not deviate dramatically from the LES, although there were examples of differences in position on the LES and relationships among traits. Perhaps more importantly, parasitic plants are not well represented in the TRY database, making any conclusions here premature.

Deep learning and citizen science enable automated plant trait predictions from photographs

Plant functional traits (‘traits’) are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth’s plant functional diversity.

Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries.

BackgroundMorphological leaf traits are frequently used to quantify, understand and predict plant and vegetation functional diversity and ecology, including environmental and climate change responses. Although morphological leaf traits are easy to measure, their coverage for characterising variation within species and across temporal scales is limited. At the same time, there are about 3100 herbaria worldwide, containing approximately 390 million plant specimens dating from the 16th to 21st century, which can potentially be used to extract morphological leaf traits. Globally, plant specimens are rapidly being digitised and images are made openly available via various biodiversity data platforms, such as iDigBio and GBIF. Based on a pilot study to identify the availability and appropriateness of herbarium specimen images for comprehensive trait data extraction, we developed a spatio-temporal dataset on intraspecific trait variability containing 128,036 morphological leaf trait measurements for seven selected species.New informationAfter scrutinising the metadata of digitised herbarium specimen images available from iDigBio and GBIF (21.9 million and 31.6 million images for Tracheophyta; accessed date December 2020), we identified approximately 10 million images potentially appropriate for our study. From the 10 million images, we selected seven species (Salix bebbiana Sarg., Alnus incana (L.) Moench, Viola canina L., Salix glauca L., Chenopodium album L., Impatiens capensis Meerb. and Solanum dulcamara L.) , which have a simple leaf shape, are well represented in space and time and have high availability of specimens per species. We downloaded 17,383 images. Out of these, we discarded 5779 images due to quality issues. We used the remaining 11,604 images to measure the area, length, width and perimeter on 32,009 individual leaf blades using the semi-automated tool TraitEx. The resulting dataset contains 128,036 trait records.We demonstrate its comparability to trait data measured in natural environments following standard protocols by comparing trait values from the TRY database. We conclude that the herbarium specimens provide valuable information on leaf sizes. The dataset created in our study, by extracting leaf traits from the digitised herbarium specimen images of seven selected species, is a promising opportunity to improve ecological knowledge about the adaptation of size-related leaf traits to environmental changes in space and time.

Scientific floras can be reliable sources for some trait data in a system with poor coverage in global trait databases

AbstractAimTrait‐based approaches are increasingly important in ecology and biogeography, but progress is often hampered by the availability of high‐quality quantitative trait data collected in the field. Alternative sources of trait information include scientific floras and taxonomic monographs. Here we test the reliability and usefulness of trait data acquired from scientific floras against trait values measured in the field, and those in TRY, the most comprehensive global plant trait database.LocationTenerife and La Palma, Canary Islands, Spain.MethodsWe measured leaf area and specific leaf area (SLA) in the field for 451 native vascular plant species and compared them with equivalent trait data digitised from the most recent and comprehensive guide of the Canarian flora, and data sourced from TRY. We regressed the field‐measured traits against their equivalents estimated from the literature and used the regression models from one island to predict the trait values on the other island.ResultsFor leaf area, linear models showed good agreement between values from the scientific flora and those measured in the field (r2 = 0.86). These models were spatially transferable across islands. In contrast, for SLA we found a weak relationship between field‐measured values and the best estimates from the scientific flora (r2 = 0.11). Insufficient data were available in the TRY database for our study area to calculate trait correlations with other data sources.ConclusionsScientific floras can act as useful data sources for quantitative plant trait data for some traits but not others, whilst the TRY database contains many traits, but is incomplete in species coverage for our study region, and oceanic islands in general.

Choosy grazers: Influence of plant traits on forage selection by three cattle breeds

Abstract Forage selection by herbivores is a major driver of plant diversity in pasture vegetation. Yet, we know relatively little about how plant traits influence decisions of different herbivore species and breeds to select or avoid a certain plant species on semi‐natural pastures. We quantified the influence of the traits leaf N and P content, leaf dry matter content (LDMC), specific leaf area (SLA) and physical defence mechanisms on plant species selection for three cattle breeds: high‐yielding Angus × Holstein crossbreed, dual‐purpose Original Braunvieh and undemanding Highland Cattle. The cattle grazed a series of adjacent paddocks in different alpine pastures. Plant species selection was quantified by assessing the difference in biomass proportions of all plant species in 66 vegetation subplots per breed before and after grazing. Plant traits and indicator values were extracted from the TRY database. Data on 152 plant species were analysed using a local mixed‐effects model and a global multivariate hierarchical regression model. Plant traits had a clear impact on forage behaviour. Plants with high SLA, leaf N and P contents were significantly selected, whereas plants with high LDMC (e.g. woody plants) and defence mechanisms (e.g. thistles) were generally avoided. Species with high forage quality indicator values as defined by Briemle et al. (2002) were significantly preferred. More importantly, significant differences between forage behaviour of cattle breeds were detected. Selection by less‐productive Highland Cattle was much less influenced by plant traits than the selection by the two higher‐yielding breeds. Results indicate a clear impact of plant traits on forage selection and demonstrate breed‐specific influences. Highland Cattle (and possibly other robust breeds) graze less selectively and impose less selective exclusion on plants. Thereby, they likely influence plant species composition of pastures in a different way than high‐yielding breeds, thereby creating a distinct habitat. A free Plain Language Summary can be found within the Supporting Information of this article.

Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis.

The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types (PFTs). We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area [SLA], N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (dark respiration [Rd ], maximum RuBisCo carboxylation rate [Vc,max ], maximum electron transport rate [Jmax ]) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.

TRY plant trait database - enhanced coverage and open access.

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.

Inferring plant functional diversity from space: the potential of Sentinel-2

Plant functional diversity (FD) is an important component of biodiversity that characterizes the variability of functional traits within a community, landscape, or even large spatial scales. It can influence ecosystem processes and stability. Hence, it is important to understand how and why FD varies within and between ecosystems, along resources availability gradients and climate gradients, and across vegetation successional stages. Usually, FD is assessed through labor-intensive field measurements, while assessing FD from space may provide a way to monitor global FD changes in a consistent, time and resource efficient way. The potential of operational satellites for inferring FD, however, remains to be demonstrated. Here we studied the relationships between FD and spectral reflectance measurements taken by ESA's Sentinel-2 satellite over 117 field plots located in 6 European countries, with 46 plots having in-situ sampled leaf traits and the other 71 using traits from the TRY database. These field plots represent major European forest types, from boreal forests in Finland to Mediterranean mixed forests in Spain. Based on in-situ data collected in 2013 we computed functional dispersion (FDis), a measure of FD, using foliar and whole-plant traits of known ecological significance. These included five foliar traits: leaf nitrogen concentration (N%), leaf carbon concentration (%C), specific leaf area (SLA), leaf dry matter content (LDMC), leaf area (LA). In addition they included three whole-plant traits: tree height (H), crown cross-sectional area (CCSA), and diameter-at-breast-height (DBH). We applied partial least squares regression using Sentinel-2 surface reflectance measured in 2015 as predictive variables to model in-situ FDis measurements. We predicted, in cross-validation, 55% of the variation in the observed FDis. We also showed that the red-edge, near infrared and shortwave infrared regions of Sentinel-2 are more important than the visible region for predicting FDis. An initial 30-m resolution mapping of FDis revealed large local FDis variation within each forest type. The novelty of this study is the effective integration of spaceborne and in-situ measurements at a continental scale, and hence represents a key step towards achieving rapid global biodiversity monitoring schemes.