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. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.

Optimization models of stomatal conductance (g ) attempt to explain observed stomatal behaviour in terms of cost--benefit tradeoffs. While the benefit of stomatal opening through increased CO uptake is clear, currently the nature of the associated cost(s) remains unclear. We explored the hypothesis that g maximizes leaf photosynthesis, where the cost of stomatal opening arises from nonstomatal reductions in photosynthesis induced by leaf water stress. We analytically solved two cases, CAP and MES, in which reduced leaf water potential leads to reductions in carboxylation capacity (CAP) and mesophyll conductance (g ) (MES). Both CAP and MES predict the same one-parameter relationship between the intercellular : atmospheric CO concentration ratio (c /c ) and vapour pressure deficit (VPD, D), viz. c /c ≈ ξ/(ξ + √D), as that obtained from previous optimization models, with the novel feature that the parameter ξ is determined unambiguously as a function of a small number of photosynthetic and hydraulic variables. These include soil-to-leaf hydraulic conductance, implying a stomatal closure response to drought. MES also predicts that g /g is closely related to c /c and is similarly conservative. These results are consistent with observations, give rise to new testable predictions, and offer new insights into the covariation of stomatal, mesophyll and hydraulic conductances.

The timing of the commencement of photosynthesis (P*) in spring is an important determinant of growing‐season length and thus of the productivity of boreal forests. Although controlled experiments have shed light on environmental mechanisms triggering release from photoinhibition after winter, quantitative research for trees growing naturally in the field is scarce. In this study, we investigated the environmental cues initiating the spring recovery of boreal coniferous forest ecosystems under field conditions. We used meteorological data and above‐canopy eddy covariance measurements of the net ecosystem CO2 exchange (NEE) from five field stations located in northern and southern Finland, northern and southern Sweden, and central Siberia. The within‐ and intersite variability for P* was large, 30–60 days. Of the different climate variables examined, air temperature emerged as the best predictor for P* in spring. We also found that ‘soil thaw’, defined as the time when near‐surface soil temperature rapidly increases above 0°C, is not a useful criterion for P*. In one case, photosynthesis commenced 1.5 months before soil temperatures increased significantly above 0°C. At most sites, we were able to determine a threshold for air‐temperature‐related variables, the exceeding of which was required for P*. A 5‐day running‐average temperature (T5) produced the best predictions, but a developmental‐stage model (S) utilizing a modified temperature sum concept also worked well. But for both T5 and S, the threshold values varied from site to site, perhaps reflecting genetic differences among the stands or climate‐induced differences in the physiological state of trees in late winter/early spring. Only at the warmest site, in southern Sweden, could we obtain no threshold values for T5 or S that could predict P* reliably. This suggests that although air temperature appears to be a good predictor for P* at high latitudes, there may be no unifying ecophysiological relationship applicable across the entire boreal zone.

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This paper develops a statistical model for daily gross primary production (GPP) in boreal and temperate coniferous forests. The model applies the light use efficiency (LUE) approach, which estimates the conversion efficiency of daily absorbed photosynthetically active radiation (APAR) into daily GPP as a product of potential LUE and modifying factors. The latter were derived from daily total APAR and daily mean temperature, vapour pressure deficit (VPD) and soil water content (SWC). Modelling data came from five European eddy covariance measurement towers over 2-8 years. The model was tested against independent data from two AmeriFlux stations. The model with the APAR, temperature and VPD modifiers worked well in almost all the site-year combinations, but the SWC modifier only improved the fit in few cases. Geographical variation was found in the modifiers and potential LUE in site-specific models. When a model was fitted to pooled data, differences between sites could be explained by potential LUE, leaf area and environmental conditions. The test against the AmeriFlux data corroborated this finding. The potential LUE varied from 1.9 to 3.1 g C MJ À1 , and a weak correlation was found between foliar nitrogen concentration and potential LUE. Some year-to-year variation remained which could be captured by neither the pooled nor the site-specific models.