Fully probabilistic deep models for forward and inverse problems in parametric PDEs

We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation leverages conventional PDE discretization techniques, deep neural networks, probabilistic modelling, and variational inference to assemble a fully probabilistic coherent framework. In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by deep neural networks. The PDE residual is assumed to be an observed random vector of value zero, hence we model it as a random vector with a zero mean and a user-prescribed covariance. The model is trained by maximizing the probability, that is the evidence or marginal likelihood, of observing a residual of zero by maximizing the evidence lower bound (ELBO). Consequently, the proposed methodology does not require any independent PDE solves and is physics-informed at training time, allowing the real-time solution of PDE forward and inverse problems after training. The proposed framework can be easily extended to seamlessly integrate observed data to solve inverse problems and to build generative models. We demonstrate the efficiency and robustness of our method on finite element discretized parametric PDE problems such as linear and nonlinear Poisson problems, elastic shells with complex 3D geometries, and time-dependent nonlinear and inhomogeneous PDEs using a physics-informed neural network (PINN) discretization. We achieve up to three orders of magnitude speed-up after training compared to traditional finite element method (FEM), while outputting coherent uncertainty estimates

BAAD: a Biomass And Allometry Database for woody plants

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01–100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub‐sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross‐section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world\u27s vegetation

Small lakes in big landscape : multi-scale drivers of littoral ecosystem in alpine lakes.

In low nutrient alpine lakes, the littoral zone is the most productive part of the ecosystem, and it is a biodiversity hotspot. It is not entirely clear how the scale and physical heterogeneity of surrounding catchment, its ecological composition, and larger landscape gradients work together to sustain littoral communities. A total of 113 alpine lakes from the central Pyrenees were surveyed to evaluate the functional connectivity between littoral zoobenthos and landscape physical and ecological elements at geographical, catchment and local scales, and to ascertain how they affect the formation of littoral communities. At each lake, the zoobenthic composition was assessed together with geolocation, catchment hydrodynamics, geomorphology and topography, riparian vegetation composition, the presence of trout and frogs, water pH and conductivity. Multidimensional fuzzy set models integrating benthic biota and environmental variables revealed that at geographical scale, longitude unexpectedly surpassed altitude and latitude in its effect on littoral ecosystem. This reflects a sharp transition between Atlantic and Mediterranean climates and suggests a potentially high horizontal vulnerability to climate change. Topography (controlling catchment type, snow coverage and lakes connectivity) was the most influential catchment-scale driver, followed by hydrodynamics (waterbody size, type and volume of inflow/outflow). Locally, riparian plant composition significantly related to littoral community structure, richness and diversity. These variables, directly and indirectly, create habitats for aquatic and terrestrial stages of invertebrates, and control nutrient and water cycles. Three benthic associations characterised distinct lakes. Vertebrate predation, water conductivity and pH had no major influence on littoral taxa. This work provides exhaustive information from relatively pristine sites, and unveils a strong connection between littoral ecosystem and catchment heterogeneity at scales beyond the local environment. This underpins the role of alpine lakes as sensors of local and large-scale environmental changes, which can be used in monitoring networks to evaluate further impacts

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

Distance travelled : Outcomes and evidence in flexible learning options

Flexible learning options (FLOs) provide individualised learning pathways for disengaged young people with strong emphasis on inclusivity and wellbeing support. Amidst a rapid expansion of Australia’s flexible learning sector, service providers are under increasing pressure to substantiate participant outcomes. This paper stems from a national study of the value of FLOs to young people and the broader Australian community. The study enumerates the outcomes valued by flexible learning practitioners, as well as the various evidence forms they cite to substantiate participant outcomes. Framing success as ‘distance travelled’ (i.e. an individual’s progress relative to his or her own starting point), practitioners demonstrate critical awareness of the social and structural mechanisms by which young people are marginalised from mainstream schooling. Holistic assessment practices also reveal practitioners’ efforts to expand the terms of reference by which educational outcomes may be validated in alternative education settings

A Functional Genomics Approach to Establish the Complement of Carbohydrate Transporters in Streptococcus pneumoniae

The aerotolerant anaerobe Streptococcus pneumoniae is part of the normal nasopharyngeal microbiota of humans and one of the most important invasive pathogens. A genomic survey allowed establishing the occurrence of twenty-one phosphotransferase systems, seven carbohydrate uptake ABC transporters, one sodium∶solute symporter and a permease, underlining an exceptionally high capacity for uptake of carbohydrate substrates. Despite high genomic variability, combined phenotypic and genomic analysis of twenty sequenced strains did assign the substrate specificity only to two uptake systems. Systematic analysis of mutants for most carbohydrate transporters enabled us to assign a phenotype and substrate specificity to twenty-three transport systems. For five putative transporters for galactose, pentoses, ribonucleosides and sulphated glycans activity was inferred, but not experimentally confirmed and only one transport system remains with an unknown substrate and lack of any functional annotation. Using a metabolic approach, 80% of the thirty-two fermentable carbon substrates were assigned to the corresponding transporter. The complexity and robustness of sugar uptake is underlined by the finding that many transporters have multiple substrates, and many sugars are transported by more than one system. The present work permits to draw a functional map of the complete arsenal of carbohydrate utilisation proteins of pneumococci, allows re-annotation of genomic data and might serve as a reference for related species. These data provide tools for specific investigation of the roles of the different carbon substrates on pneumococcal physiology in the host during carriage and invasive infection

The research on endothelial function in women and men at risk for cardiovascular disease (REWARD) study: methodology

Background Endothelial function has been shown to be a highly sensitive marker for the overall cardiovascular risk of an individual. Furthermore, there is evidence of important sex differences in endothelial function that may underlie the differential presentation of cardiovascular disease (CVD) in women relative to men. As such, measuring endothelial function may have sex-specific prognostic value for the prediction of CVD events, thus improving risk stratification for the overall prediction of CVD in both men and women. The primary objective of this study is to assess the clinical utility of the forearm hyperaemic reactivity (FHR) test (a proxy measure of endothelial function) for the prediction of CVD events in men vs. women using a novel, noninvasive nuclear medicine -based approach. It is hypothesised that: 1) endothelial dysfunction will be a significant predictor of 5-year CVD events independent of baseline stress test results, clinical, demographic, and psychological variables in both men and women; and 2) endothelial dysfunction will be a better predictor of 5-year CVD events in women compared to men. Methods/Design A total of 1972 patients (812 men and 1160 women) undergoing a dipyridamole stress testing were recruited. Medical history, CVD risk factors, health behaviours, psychological status, and gender identity were assessed via structured interview or self-report questionnaires at baseline. In addition, FHR was assessed, as well as levels of sex hormones via blood draw. Patients will be followed for 5 years to assess major CVD events (cardiac mortality, non-fatal MI, revascularization procedures, and cerebrovascular events). Discussion This is the first study to determine the extent and nature of any sex differences in the ability of endothelial function to predict CVD events. We believe the results of this study will provide data that will better inform the choice of diagnostic tests in men and women and bring the quality of risk stratification in women on par with that of men

BAAD: a Biomass And Allometry Database for woody plants

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01–100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub-sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross-section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world\u27s vegetation

Effects of Climate and Atmospheric Nitrogen Deposition on Early to Mid-Term Stage Litter Decomposition Across Biomes

open263siWe acknowledge support by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118), Scientific Grant Agency VEGA(GrantNo.2/0101/18), as well as by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement No. 677232)Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.openKwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y.Kwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant 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.Peer reviewe