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.

Ecology Letters (2011) 14: 939–947 Tropical rain forests play a dominant role in global biosphere‐atmosphere CO₂ exchange. Although climate and nutrient availability regulate net primary production (NPP) and decomposition in all terrestrial ecosystems, the nature and extent of such controls in tropical forests remain poorly resolved. We conducted a meta‐analysis of carbon‐nutrient‐climate relationships in 113 sites across the tropical forest biome. Our analyses showed that mean annual temperature was the strongest predictor of aboveground NPP (ANPP) across all tropical forests, but this relationship was driven by distinct temperature differences between upland and lowland forests. Within lowland forests (< 1000 m), a regression tree analysis revealed that foliar and soil‐based measurements of phosphorus (P) were the only variables that explained a significant proportion of the variation in ANPP, although the relationships were weak. However, foliar P, foliar nitrogen (N), litter decomposition rate (k), soil N and soil respiration were all directly related with total surface (0–10 cm) soil P concentrations. Our analysis provides some evidence that P availability regulates NPP and other ecosystem processes in lowland tropical forests, but more importantly, underscores the need for a series of large‐scale nutrient manipulations – especially in lowland forests – to elucidate the most important nutrient interactions and controls.

The sensitivity of soil organic matter decomposition to global environmental change is a topic of prominent relevance for the global carbon cycle. Decomposition depends on multiple factors that are being altered simultaneously as a result of global environmental change; therefore, it is important to study the sensitivity of the rates of soil organic matter decomposition with respect to multiple and interacting drivers. In this manuscript, we present an analysis of the potential response of decomposition rates to simultaneous changes in temperature and moisture. To address this problem, we first present a theoretical framework to study the sensitivity of soil organic matter decomposition when multiple driving factors change simultaneously. We then apply this framework to models and data at different levels of abstraction: (1) to a mechanistic model that addresses the limitation of enzyme activity by simultaneous effects of temperature and soil water content, the latter controlling substrate supply and oxygen concentration for microbial activity; (2) to different mathematical functions used to represent temperature and moisture effects on decomposition in biogeochemical models. To contrast model predictions at these two levels of organization, we compiled different data sets of observed responses in field and laboratory studies. Then we applied our conceptual framework to: (3) observations of heterotrophic respiration at the ecosystem level; (4) laboratory experiments looking at the response of heterotrophic respiration to independent changes in moisture and temperature; and (5) ecosystem‐level experiments manipulating soil temperature and water content simultaneously.