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.

Summary Ecosystem services have received increasing attention in life sciences, but only a limited amount of quantitative data are available concerning the ability of weeds to provide these services. Following an expert focus group on this topic, a systematic search for articles displaying evidence of weeds providing regulating ecosystem services was performed, resulting in 129 articles. The most common service found was pest control and the prevailing mechanism was that weeds provide a suitable habitat for natural enemies. Other articles showed that weeds improved soil nutrient content, soil physical properties and crop pollinator abundance. Weeds were found to provide some important ecosystem services for agriculture, but only a small number of studies presented data on crop yield. Experimental approaches are proposed that can: (i) disentangle the benefits obtained from ecosystem services provisioning from the costs due to weed competition and (ii) quantify the contribution of diverse weed communities in reducing crop competition and in providing ecosystem services. Existing vegetation databases can be used to select weed species with functional traits facilitating ecosystem service provisioning while having a lower competitive capacity. However, for services such as pest control, there are hardly any specific plant traits that have been identified and more fundamental research is needed.

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Corn poppy is the most abundant broad-leaved weed in winter cereals of Mediterranean climate areas and causes important yield losses in wheat. Knowledge of the temporal pattern of emergence will contribute to optimize the timing of control measures, thus maximizing efficacy. The objectives of this research were to develop an emergence model on the basis of soil thermal time and validate it in several localities across Spain. To develop the model, monitoring of seedling emergence was performed weekly during the growing season in a cereal field located in northeastern Spain, during 3 yr. Cumulative thermal time from sowing date was used as the independent variable for predicting cumulative emergence. The Gompertz model was fitted to the data set of emergences. A base temperature of 1.0 C was estimated through iteration for maximum fit. The model accounted for 91% of the variation observed. Model validation in several localities and years showed general good performance in predicting corn poppy seedling emergence ( values ranging from 0.64 to 0.99 and root-mean-square error from 4.4 to 24.3). Ninety percent emergence was accurately predicted in most localities. Results showed that the model performs with greater reliability when significant rainfall (10 mm) occurs within 10 d after crop sowing. Complemented with in-field scouting, it may be a useful tool to better timing control measures in areas that are homogeneous enough regarding climate and crop management.

North African knapweed (Centaurea diluta Aiton) is an annual weed that is widespread in southern Spain and is of increasing concern in dryland cropping systems. Despite its expanding range in Spain, there is limited information on the emergence timing and pattern of this species, knowledge of which is critical for developing more timely and effective management strategies. Therefore, there is a need to develop simple and reliable models to predict the timing and emergence of this annual weed under dryland conditions. A multi-location field experiment was established across Spain in 2016 to 2017 to assess the emergence of C. diluta. At each of 11 locations, seeds were sown in the fall, and emergence was recorded. Overall emergence averaged 39% in the first year across all sites and 11% in the second year. In both years, the main emergence flush occurred at the beginning of the growing season. A three-parameter Weibull function best described seedling emergence of C. diluta. Emergence models were developed based on thermal time (TT) and hydrothermal time (HTT) and showed high predictability, as evidenced by root mean-square error prediction values of 10.8 and 10.7, respectively. Three cardinal points were established for TT and HHT at 0.5, 10, and 35 C for base, optimal, and ceiling temperatures, respectively, while base water potential was estimated at −0.5 MPa.