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

Serum Metabolites Associated with Muscle Hypertrophy after 8 Weeks of High- and Low-Load Resistance Training

The mechanisms responsible for the similar muscle growth attained with high- and low-load resistance training (RT) have not yet been fully elucidated. One mechanism is related to the mechanical stimulus and the level of motor unit recruitment; another mechanism is related to the metabolic response. We investigated the electromyographic signal amplitude (sEMG) and the general metabolic response to high-load RT (HL) and low-load resistance training (LL). We measured muscle thickness by ultrasound, sEMG amplitude by electromyography, and analysis of metabolites expressed through metabolomics. No differences were observed between the HL and LL groups for metabolic response and muscle thickness. A greater amplitude of sEMG was observed in the HL group. In addition, a correlation was observed between changes in muscle thickness of the vastus lateralis muscle in the HL group and levels of the metabolites carnitine, creatine, 3-hydroxyisovalerate, phenylalanine, asparagine, creatine phosphate, and methionine. In the LL group, a correlation was observed between changes in muscle thickness of the vastus lateralis muscle and levels of the metabolites acetoacetate, creatine phosphate, and oxypurinol. These correlations seem to be related to the characteristics of activated muscle fibers, the metabolic demand of the training protocols used, and the process of protein synthesis

Early metabolic response after resistance exercise with blood flow restriction in well-trained men: a metabolomics approach

The present study aimed to compare the early metabolic response between high-load resistance exercise (HL-RE) and low-load resistance exercise with blood flow restriction (LL-BFR). Nine young well-trained men participated in a randomized crossover design in which each subject completed LL-BFR, HL-RE or condition control (no exercise) with a one-week interval between them. Blood samples were taken immediately before and five minutes after the exercise sessions. Nuclear magnetic resonance (NMR) spectroscopy identified and quantified 48 metabolites, six of which presented significant changes among the exercise protocols. The HL-RE promoted a higher increase in pyruvate, lactate and alanine compared to the LL-BFR and the control. HL-RE and LL-BFR promoted a higher increase in succinate compared to the control, however, there was no difference between HL-RE and LL-BFR. Also, while there was no difference in acetoacetate between HL-RE and LL-BFR, a greater decrease was observed in both compared to the control. Finally, LL-BFR promoted a greater decrease in choline compared to the control. In conclusion, this study provides by metabolomics a new insight in metabolic response between LL-BFR and HL-RE by demonstrating a distinct response to some metabolites that are not commonly analyzed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

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