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Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs - determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation - but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models. [ABSTRACT FROM AUTHOR]

• We propose a novel framework to generate Plant Functional Groups (PFGs) for ecological modelling. • We devised a workflow that ecompasses data preparation, clustering procedures and validation. • We successfully apply the framework to classify global plant data and retrieve 465 discrete groups. • These PFGs can aid understanding ecological relationships and ecosystem functioning. • These PFGs can be used to characterise plant communities, becoming indicators of ecological rarity. An effective way to reduce complexity in ecological modelling is by grouping species that share similar characteristics into functional groups or types. Often, the creation of plant functional groups (PFGs) is carried out for each case study in an ad-hoc way using a small set of traits. This limits the transferability of these PFGs to other geographical areas or study systems. We propose a novel generic framework to generate PFGs that considers the most important ecological dimensions, is applicable to case studies globally, and that emerges from patterns of functional redundancy across species. Based on most relevant and measured plant characteristics, we designed a multi-step process that includes: i) data harmonisation and missing values imputation; ii) species clustering based on multiple characteristics encompassing the main ecological dimensions featured in plant community ecological models (i.e., dispersal, competition, and demography) and iii) the combination of ecological dimension-specific groups into comprehensive PFGs. We demonstrate this framework by applying it to a global dataset of plant characteristics including a functional traits dataset and a plant-soil co-occurrence dataset for 19,102 species. Lastly, to test the ability of generated PFGs to summarise species' functional variation within plant communities, we correlate taxonomical and functional diversity indices calculated at the species and at the PFGs level across a global dataset of plant communities (sPlotOpen). Our framework generated 465 global, robust data-driven PFGs with non-overlapping combinations of traits for each ecological dimension divided by growth form. The validation returned positive correlation values between PFGs and species-level diversity metrics, supporting the ability of the obtained PFGs to capture functional and taxonomic diversity patterns across a variety of plant communities worldwide. The framework allows for the easy integration of newly available species characteristics data. The obtained global PFGs, covering all main known ecological processes and environmental conditions at small resolution, can increase the predictive power and accuracy of process-based models and help furthering varying-scale ecological studies. [ABSTRACT FROM AUTHOR]

Abstract: The most widely used method to quantify gross N transformation rates in soils is based on 15N dilution and enrichment principles. To identify rate parameters, 15N-tracing experiments are analysed by models that are linked to algorithms that try to minimize the misfit between modelled and observed data. In currently available 15N-tracing models optimization algorithms are based on the Levenberg–Marquardt method that is suitable for the determination of small number of parameters. Therefore, these models are restricted to a few processes. Methods based on Monte Carlo sampling have the potential to overcome restrictions on parameter numbers but have not been tested for application in 15N-tracing models. Here, for the first time, we use a Markov chain Monte Carlo (MCMC) method with a tracing model to simultaneously determine the probability density functions (PDFs) of the whole set of parameters for a previously published data set [Müller, C., Stevens, R.J., Laughlin, R.J., 2004. A 15N tracing model to analyse N transformations in old grassland soil. Soil Biology & Biochemistry 36, 619–632]. We show that the MCMC method can simultaneously determine PDFs of more than 8 parameters and demonstrate for the first time that it is possible to optimize models where transformations are described by Michaelis–Menten kinetics. Setting the NH4 + oxidation rate to Michaelis–Menten kinetics reduced the misfit by 19%. Together with monitoring diagnostics for parameter convergence, the MCMC method is a very efficient and robust technique to determine PDFs for parameters in 15N-tracing models that contain large number of N transformations and complex process descriptions. [Copyright &y& Elsevier]

Aim: Plant functional groups are widely used in community ecology and earth system modelling to describe trait variation within and across plant communities. However, this approach rests on the assumption that functional groups explain a large proportion of trait variation among species. We test whether four commonly used plant functional groups represent variation in six ecologically important plant traits. Location: Tundra biome. Time period: Data collected between 1964 and 2016. Major taxa studied: 295 tundra vascular plant species. Methods: We compiled a database of six plant traits (plant height, leaf area, specific leaf area, leaf dry matter content, leaf nitrogen, seed mass) for tundra species. We examined the variation in species‐level trait expression explained by four traditional functional groups (evergreen shrubs, deciduous shrubs, graminoids, forbs), and whether variation explained was dependent upon the traits included in analysis. We further compared the explanatory power and species composition of functional groups to alternative classifications generated using post hoc clustering of species‐level traits. Results: Traditional functional groups explained significant differences in trait expression, particularly amongst traits associated with resource economics, which were consistent across sites and at the biome scale. However, functional groups explained 19% of overall trait variation and poorly represented differences in traits associated with plant size. Post hoc classification of species did not correspond well with traditional functional groups, and explained twice as much variation in species‐level trait expression. Main conclusions: Traditional functional groups only coarsely represent variation in well‐measured traits within tundra plant communities, and better explain resource economic traits than size‐related traits. We recommend caution when using functional group approaches to predict tundra vegetation change, or ecosystem functions relating to plant size, such as albedo or carbon storage. We argue that alternative classifications or direct use of specific plant traits could provide new insights for ecological prediction and modelling. [ABSTRACT FROM AUTHOR]

The integration of functional traits into vulnerability assessments is a promising approach to quantitatively capture differences in species sensitivity and adaptive capacity to climate change, allowing the refinement of tree species distribution models. In response to a clear need to identify traits that are responsive to climate change and applicable in a management context, we review the state of knowledge of the main mechanisms, and their associated traits, that underpin the ability of boreal and temperate tree species to persist and (or) shift their distribution in a changing climate. We aimed to determine whether current knowledge is sufficiently mature and available to be used effectively in vulnerability assessments. Marshalling recent conceptual advances and assessing data availability, our ultimate objective is to guide modellers and practitioners in finding and selecting sets of traits that can be used to capture differences in species' ability to persist and migrate. While the physiological mechanisms that determine sensitivity to climate change are relatively well understood (e.g., drought-induced cavitation), many associated traits have not been systematically documented for North American trees and differences in methodology preclude their widespread integration into vulnerability assessments (e.g., xylem recovery capacity). In contrast, traits traditionally associated with the ability to migrate and withstand fire are generally well documented, but new key traits are emerging in the context of climate change that have not been as well characterized (e.g., age of optimum seed production). More generally, lack of knowledge surrounding the extent and patterns in intraspecific trait variation, as well as co-variation and interaction among traits, limit our ability to use this approach to assess tree adaptive capacity. We conclude by outlining research needs and potential strategies for the development of trait-based knowledge applicable in large-scale modelling efforts, sketching out important aspects of trait data organization that should be part of a coordinated effort by the forest science community. [ABSTRACT FROM AUTHOR]

We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005-2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints. [ABSTRACT FROM AUTHOR]

Since 70% of global forests are managed and forests impact the global carbon cycle and the energy exchange with the overlying atmosphere, forest management has the potential to mitigate climate change. Yet, none of the land-surface models used in Earth system models, and therefore none of today's predictions of future climate, accounts for the interactions between climate and forest management. We addressed this gap in modelling capability by developing and parametrising a version of the ORCHIDEE land-surface model to simulate the biogeochemical and biophysical effects of forest management. The most significant changes between the new branch called ORCHIDEE-CAN (SVN r2290) and the trunk version of ORCHIDEE (SVN r2243) are the allometric-based allocation of carbon to leaf, root, wood, fruit and reserve pools; the transmittance, absorbance and reflectance of radiation within the canopy; and the vertical discretisation of the energy budget calculations. In addition, conceptual changes were introduced towards a better process representation for the interaction of radiation with snow, the hydraulic architecture of plants, the representation of forest management and a numerical solution for the photosynthesis formalism of Farquhar, von Caemmerer and Berry. For consistency reasons, these changes were extensively linked throughout the code. Parametrisation was revisited after introducing 12 new parameter sets that represent specific tree species or genera rather than a group of often distantly related or even unrelated species, as is the case in widely used plant functional types. Performance of the new model was compared against the trunk and validated against independent spatially explicit data for basal area, tree height, canopy structure, gross primary production (GPP), albedo and evapotranspiration over Europe. For all tested variables, ORCHIDEE-CAN outperformed the trunk regarding its ability to reproduce large-scale spatial patterns as well as their inter-annual variability over Europe. Depending on the data stream, ORCHIDEE-CAN had a 67 to 92% chance to reproduce the spatial and temporal variability of the validation data. [ABSTRACT FROM AUTHOR]

Since 70% of global forests are managed and forests impact the global carbon cycle and the energy exchange with the overlying atmosphere, forest management has the potential to mitigate climate change. Yet, none of the land-surface models used in Earth system models, and therefore none of today's predictions of future climate, accounts for the interactions between climate and forest management. We addressed this gap in modelling capability by developing and parametrising a version of the ORCHIDEE land-surface model to simulate the biogeochemical and biophysical effects of forest management. The most significant changes between the new branch called ORCHIDEE-CAN (SVN r2290) and the trunk version of ORCHIDEE (SVN r2243) are the allometric-based allocation of carbon to leaf, root, wood, fruit and reserve pools; the transmittance, absorbance and reflectance of radiation within the canopy; and the vertical discretisation of the energy budget calculations. In addition, conceptual changes were introduced towards a better process representation for the interaction of radiation with snow, the hydraulic architecture of plants, the representation of forest management and a numerical solution for the photosynthesis formalism of Farquhar, von Caemmerer and Berry. For consistency reasons, these changes were extensively linked throughout the code. Parametrisation was revisited after introducing 12 new parameter sets that represent specific tree species or genera rather than a group of often distantly related or even unrelated species, as is the case in widely used plant functional types. Performance of the new model was compared against the trunk and validated against independent spatially explicit data for basal area, tree height, canopy structure, gross primary production (GPP), albedo and evapotranspiration over Europe. For all tested variables, ORCHIDEE-CAN outperformed the trunk regarding its ability to reproduce large-scale spatial patterns as well as their inter-annual variability over Europe. Depending on the data stream, ORCHIDEE-CAN had a 67 to 92% chance to reproduce the spatial and temporal variability of the validation data. [ABSTRACT FROM AUTHOR]

Since 70% of global forests are managed and forests impact the global carbon cycle and the energy exchange with the overlying atmosphere, forest management has the potential to mitigate climate change. Yet, none of the land-surface models used in Earth system models, and therefore none of today's predictions of future climate, accounts for the interactions between climate and forest management. We addressed this gap in modelling capability by developing and parametrising a version of the ORCHIDEE land-surface model to simulate the biogeochemical and biophysical effects of forest management. The most significant changes between the new branch called ORCHIDEE-CAN (SVN r2290) and the trunk version of ORCHIDEE (SVN r2243) are the allometric-based allocation of carbon to leaf, root, wood, fruit and reserve pools; the transmittance, absorbance and reflectance of radiation within the canopy; and the vertical discretisation of the energy budget calculations. In addition, conceptual changes were introduced towards a better process representation for the interaction of radiation with snow, the hydraulic architecture of plants, the representation of forest management and a numerical solution for the photosynthesis formalism of Farquhar, von Caemmerer and Berry. For consistency reasons, these changes were extensively linked throughout the code. Parametrisation was revisited after introducing 12 new parameter sets that represent specific tree species or genera rather than a group of often distantly related or even unrelated species, as is the case in widely used plant functional types. Performance of the new model was compared against the trunk and validated against independent spatially explicit data for basal area, tree height, canopy structure, gross primary production (GPP), albedo and evapotranspiration over Europe. For all tested variables, ORCHIDEE-CAN outperformed the trunk regarding its ability to reproduce large-scale spatial patterns as well as their inter-annual variability over Europe. Depending on the data stream, ORCHIDEE-CAN had a 67 to 92% chance to reproduce the spatial and temporal variability of the validation data. [ABSTRACT FROM AUTHOR]

Global warming caused by anthropogenic CO2 emissions is expected to reduce the capability of the ocean and the land biosphere to take up carbon. This will enlarge the fraction of the CO2 emissions remaining in the atmosphere, which in turn will reinforce future climate change. Recent model studies agree in the existence of such a positive climate–carbon cycle feedback, but the estimates of its amplitude differ by an order of magnitude, which considerably increases the uncertainty in future climate projections. Therefore we discuss, in how far a particular process or component of the carbon cycle can be identified, that potentially contributes most to the positive feedback. The discussion is based on simulations with a carbon cycle model, which is embedded in the atmosphere/ocean general circulation model ECHAM5/MPI-OM. Two simulations covering the period 1860–2100 are conducted to determine the impact of global warming on the carbon cycle. Forced by historical and future carbon dioxide emissions (following the scenario A2 of the Intergovernmental Panel on Climate Change), they reveal a noticeable positive climate–carbon cycle feedback, which is mainly driven by the tropical land biosphere. The oceans contribute much less to the positive feedback and the temperate/boreal terrestrial biosphere induces a minor negative feedback. The contrasting behavior of the tropical and temperate/boreal land biosphere is mostly attributed to opposite trends in their net primary productivity (NPP) under global warming conditions. As these findings depend on the model employed they are compared with results derived from other climate–carbon cycle models, which participated in the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP). [ABSTRACT FROM AUTHOR]

This dataset provides growth form classifications for 67,413 vascular plant species from North, Central, and South America. The data used to determine growth form were compiled from five major integrated sources and two original publications: the Botanical Information and Ecology Network ( BIEN), the Plant Trait Database ( TRY), the SALVIAS database, the USDA PLANTS database, Missouri Botanical Garden's Tropicos database, Wright (2010), and Boyle (1996). We defined nine plant growth forms based on woodiness (woody or non-woody), shoot structure (self-supporting or not self-supporting), and root traits (rooted in soil, not rooted in soil, parasitic or aquatic): Epiphyte, Liana, Vine, Herb, Shrub, Tree, Parasite, or Aquatic. Species with multiple growth form classifications were assigned the growth form classification agreed upon by the majority (>2/3) of sources. Species with ambiguous or otherwise not interpretable growth form assignments were excluded from the final dataset but are made available with the original data. Comparisons with independent estimates of species richness for the Western hemisphere suggest that our final dataset includes the majority of New World vascular plant species. Coverage is likely more complete for temperate than for tropical species. In addition, aquatic species are likely under-represented. Nonetheless, this dataset represents the largest compilation of plant growth forms published to date, and should contribute to new insights across a broad range of research in systematics, ecology, biogeography, conservation, and global change science. [ABSTRACT FROM AUTHOR]