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

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

Does soil pyrogenic carbon determine plant functional traits in Amazon Basin forests?

Amazon forests are fire-sensitive ecosystems and consequently fires affect forest structure and composition. For instance, the legacy of past fire regimes may persist through some species and traits that are found due to past fires. In this study, we tested for relationships between functional traits that are classically presented as the main components of plant ecological strategies and environmental filters related to climate and historical fires among permanent mature forest plots across the range of local and regional environmental gradients that occur in Amazonia. We used percentage surface soil pyrogenic carbon (PyC), a recalcitrant form of carbon that can persist for millennia in soils, as a novel indicator of historical fire in old-growth forests. Five out of the nine functional traits evaluated across all 378 species were correlated with some environmental variables. Although there is more PyC in Amazonian soils than previously reported, the percentage soil PyC indicated no detectable legacy effect of past fires on contemporary functional composition. More species with dry diaspores were found in drier and hotter environments. We also found higher wood density in trees from higher temperature sites. If Amazon forest past burnings were local and without distinguishable attributes of a widespread fire regime, then impacts on biodiversity would have been small and heterogeneous. Alternatively, sufficient time may have passed since the last fire to allow for species replacement. Regardless, as we failed to detect any impact of past fire on present forest functional composition, if our plots are representative then it suggests that mature Amazon forests lack a compositional legacy of past fire

Nomenclature instability in species culturomic assessments: Why synonyms matter

Culturomics is an emerging area of study that explores human culture through the quantitative analysis of large digital bodies of text. Culturomics shows great potential for the study of public perceptions and engagement with nature and biodiversity, and thus to contribute to the assessment and monitoring of major conservation goals (e.g. Aichi Target 1). In order to realize the full potential of culturomic approaches for conservation applications, researchers must develop solutions for existing methodological issues. For example, the use of scientific binomial names in species assessments has been recently proposed as a means to account for linguistic challenges associated with vernacular names, such as synonyms and homonyms. However, scientific names can also be affected by scientific synonyms arising from changes in species nomenclature. Here, we focus on a culturomic assessment of internet content and evaluate the importance of considering scientific name synonyms in such assessments. For this, we estimated how much omitting taxonomic synonyms affected webpage retrieval for bird species. Results indicate that failing to consider synonyms affected the number of webpages retrieved for over half of the species considered. In some cases, such omissions were severe (over 50% of total webpages omitted) and increased with the number of synonyms identified. We discuss the challenges posed by the dynamic nature of taxonomy in efforts to evaluate public interest in species using culturomic approaches and suggest that future studies should always strive to identify and account for any existing synonyms to minimize potential problems

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