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

Human erythrocytes exposure to juglone leads to an increase of superoxide anion production associated with cytochrome b5 reductase uncoupling

Cytochrome b5 reductase is an enzyme with the ability to generate superoxide anion at the expenses of NADH consumption. Although this activity can be stimulated by cytochrome c and could participate in the bioenergetic failure accounting in apoptosis, very little is known about other molecules that may uncouple the function of the cytochrome b5 reductase. Naphthoquinones are redox active molecules with the ability to interact with electron transfer chains. In this work, we made an inhibitor screening against recombinant human cytochrome b5 reductase based on naphthoquinone properties. We found that 5-hydroxy-1,4-naphthoquinone (known as juglone), a natural naphthoquinone extracted from walnut trees and used historically in traditional medicine with ambiguous health and toxic outcomes, had the ability to uncouple the electron transfer from the reductase to cytochrome b5 and ferricyanide. Upon complex formation with cytochrome b5 reductase, juglone is able to act as an electron acceptor leading to a NADH consumption stimulation and an increase of superoxide anion production by the reductase. Our results suggest that cytochrome b5 reductase could contribute to the measured energetic failure in the erythrocyte apoptosis induced by juglone, that is concomitant with the reactive oxygen species produced by cytochrome b5 reductase.This work was supported by the Associate Laboratory for Green Chemistry-LAQV which is financed by national funds from FCT/MCTES (UID/QUI/50006/2019). Experimental work was also supported by funding from Ayuda a Grupos de la Junta de Extremadura (Group GR18118) co-financed by the European Funds for Structural Development (FEDER). The authors also thank FCT for funds to GHTM (UID/Multi/04413/2013) and AKSA acknowledges FCT/MCTES for their “Investigador Doutorado” contracts' funding and signed with FCT/UNL in accordance with DL 57/2016 e Lei 57/2017.Peer reviewe

Screen-printed electrodes testing for detection of potential stress biomarkers in sweat

Detection of stress biomarkers molecules, non-invasively, through (non-induced) sweat sampling is an important research field since sweat is a potential diagnostic fluid for early and continuous human health monitoring, not only for stress-related conditions directly but also for other pathologies (e.g., associated with chronic diseases). The simultaneous detection of multiple potential biomarkers in sweat samples, using simple and low-cost electrochemical methods (detecting patterns or “electrochemical fingerprints”) requiring no sample preparation and its correlation with physiological conditions, is an attractive methodology but not readily achievable. Several of these potential biomarkers’ electrochemical response presents very close oxidation potentials, among other factors that hamper the detection, such as pH dependence of the electrochemical response or partial adsorption on electrode surfaces. Disposable screen-printed electrode materials, with relatively low-cost, could be useful to overcome the difficulties. A set of selected potential stress-related (non-protein) biomarkers (tyrosine, phenylalanine, dopamine, serotonin, and hydrocortisone) was used in the current study for qualitative electrochemical detection on different screen-printed carbon-based electrodes. The detection was attained in simulated sweat solutions and real sweat samples. The goal was to evaluate the electrochemical response on the different surfaces and determine the most suitable carbon-based screen-printed electrodes that may be used in future sensing devices.This work was supported by the Associate Laboratory for Green Chemistry–LAQV which is fnanced by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020). Fundação para a Ciência e Tecnologia provided fnancial support through Project PTDC/SAU-SOC/28390/2017 (STRESSSENSE)

Global relationships in tree functional traits

Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.Understanding patterns in woody plant trait relationships and trade-offs is challenging. Here, by applying machine learning and data imputation methods to a global database of georeferenced trait measurements, the authors unravel key relationships in tree functional traits at the global scale

The global spectrum of plant form and function: enhanced species-level trait dataset.

Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date