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

Trait-based formal definition of plant functional types and functional communities in the multi-species and multi-traits context

The concepts of traits, plant functional types (PFT), and functional communities are effective tools for the study of complex phenomena such as plant community assembly. Here, we (1) suggest a procedure formalising the classification of response traits to construct a PFT system; (2) integrate the PFT, and species compositional data to formally define functional communities; and, (3) identify environmental drivers that underpin the functional-community patterns. A species–trait data set featuring species pooled from two study sites (Eneabba and Cooljarloo, Western Australia), both supporting kwongan vegetation (sclerophyllous scrub and woodland communities), was subjected to classification to define PFTs. Species of both study sites were replaced with the newly derived PFTs and projected cover abundance-weighted means calculated for every plot. Functional communities were defined by classifications of the abundance-weighted PFT data in the respective sites. Distance-based redundancy analysis (using the abundance-weighted community and environmental data) was used to infer drivers of the functional community patterns for each site. A classification based on trait data assisted in reducing trait-space complexity in the studied vegetation and revealed 26 PFTs shared across the study sites. In total, seven functional communities were identified. We demonstrate a putative functional-community pattern-driving effect of soil-texture (clay—sand) gradients at Eneabba (42% of the total inertia explained) and that of water repellence at Cooljarloo (36%). Synthesis. This paper presents a procedure formalising the classification of multiple response traits leading to the delineation of PFTs and functional communities. This step captures plant responses to stresses and disturbance characteristic of kwongan vegetation, including low nutrient status, water stress, and fire (a landscape-level disturbance factor). Our study is the first to introduce a formal procedure assisting their formal recognition. Our results support the role of short-term abiotic drivers structuring the formation of fine-scale functional community patterns in a complex, species-rich vegetation of Western Australia

Iron/iron oxide nanoparticles coated with silica as a nanoplatform for multimodal biomedical imaging

International audienc

Iron/iron oxide nanoparticles coated with silica as a nanoplatform for multimodal biomedical imaging

International audienc

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, 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