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.

Background A subset of patients with COVID-19 develops a hyperinflammatory syndrome that has similarities with other hyperinflammatory disorders. However, clinical criteria specifically to define COVID-19-associated hyperinflammatory syndrome (cHIS) have not been established. We aimed to develop and validate diagnostic criteria for cHIS in a cohort of inpatients with COVID-19. Methods We searched for clinical research articles published between Jan 1, 1990, and Aug 20, 2020, on features and diagnostic criteria for secondary haemophagocytic lymphohistiocytosis, macrophage activation syndrome, macrophage activation-like syndrome of sepsis, cytokine release syndrome, and COVID-19. We compared published clinical data for COVID-19 with clinical features of other hyperinflammatory or cytokine storm syndromes. Based on a framework of conserved clinical characteristics, we developed a six-criterion additive scale for cHIS: fever, macrophage activation (hyperferritinaemia), haematological dysfunction (neutrophil to lymphocyte ratio), hepatic injury (lactate dehydrogenase or asparate aminotransferase), coagulopathy (D-dimer), and cytokinaemia (C-reactive protein, interleukin-6, or triglycerides). We then validated the association of the cHIS scale with in-hospital mortality and need for mechanical ventilation in consecutive patients in the Intermountain Prospective Observational COVID-19 (IPOC) registry who were admitted to hospital with PCR-confirmed COVID-19. We used a multistate model to estimate the temporal implications of cHIS. Findings We included 299 patients admitted to hospital with COVID-19 between March 13 and May 5, 2020, in analyses. Unadjusted discrimination of the maximum daily cHIS score was 0·81 (95% CI 0·74–0·88) for in-hospital mortality and 0·92 (0·88–0·96) for mechanical ventilation; these results remained significant in multivariable analysis (odds ratio 1·6 [95% CI 1·2–2·1], p=0·0020, for mortality and 4·3 [3·0–6·0], p<0·0001, for mechanical ventilation). 161 (54%) of 299 patients met two or more cHIS criteria during their hospital admission; these patients had higher risk of mortality than patients with a score of less than 2 (24 [15%] of 138 vs one [1%] of 161) and for mechanical ventilation (73 [45%] vs three [2%]). In the multistate model, using daily cHIS score as a time-dependent variable, the cHIS hazard ratio for worsening from low to moderate oxygen requirement was 1·4 (95% CI 1·2–1·6), from moderate oxygen to high-flow oxygen 2·2 (1·1–4·4), and to mechanical ventilation 4·0 (1·9–8·2). Interpretation We proposed and validated criteria for hyperinflammation in COVID-19. This hyperinflammatory state, cHIS, is commonly associated with progression to mechanical ventilation and death. External validation is needed. The cHIS scale might be helpful in defining target populations for trials and immunomodulatory therapies. Fundi...

Considering intraspecific trait variability (ITV) in ecological studies has improved our understanding of species persistence and coexistence. These advances are based on the growing number of leaf ITV studies over local gradients, but logistical constraints have prevented a solid examination of ITV in root traits or at scales reflecting species’ geographic ranges. We compared the magnitude of ITV in above‐ and below‐ground plant organs across three spatial scales (biophysical region, locality and plot). We focused on six understorey species (four herbs and two shrubs) that occur both in disturbed and undisturbed habitats across boreal and temperate Canadian forests. We aimed to document ITV structure over broad ecological and geographical scales by asking: (a) What is the breadth of ITV across species range‐scale? (b) What proportion of ITV is captured at different spatial scales, particularly when local scale disturbances are considered? and (c) Is the variance structure consistent between analogous leaf and root traits, and between morphological and chemical traits? Following standardized methods, we sampled 818 populations across 79 forest plots simultaneously, including disturbed and undisturbed stands, spanning four biophysical regions (~5,200 km). Traits measured included specific leaf area (SLA), specific root length (SRL) and leaf and root nutrient concentrations (N, P, K, Mg, Ca). We used variance decomposition techniques to characterize ITV structure across scales. Our results show that an important proportion of ITV occurred at the local scale when sampling included contrasting environmental conditions resulting from local disturbance. A certain proportion of the variability in both leaf and root traits remained unaccounted for by the three sampling scales included in the design (36% on average), with the largest amount for SRL (54%). Substantial differences in magnitude of ITV were found among the six species, and between analogous traits, suggesting that trait distribution was influenced by species strategy and reflects the extent of understorey environment heterogeneity. Even for species with broad geographical distributions, a large proportion of within‐species trait variability can be captured by sampling locally across ecological gradients. This has practical implications for sampling design and trait selection for both local studies and continental‐scale modelling. A free Plain Language Summary can be found within the Supporting Information of this article.

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