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Abstract Relationships between plant biodiversity and productivity are highly variable across studies in managed grasslands, partly because of the challenge of accounting for confounding's and reciprocal relationships between biodiversity and productivity in observational data collected at a single point in time. Identifying causal effects in the presence of these challenges requires new analytical approaches and repeated observations to determine the temporal ordering of effects.Though rarely available, data collected at multiple time points within a growing season can help to disentangle the effects of biodiversity on productivity and vice versa. Here we advance this understanding using seasonal grassland surveys from 150 managed grassland sites repeated over 2 years, along with statistical methods that are relatively new in ecology, that aim to infer causal relationships from observational data. We compare our approach to common methods used in ecology, that is, mixed‐effect models, and to analyses that use observations from only one point in time within the growing seasons.We find that mixed models overestimated the effect of biodiversity on productivity by two standard errors as compared to our main models, which find no evidence for a strong positive effect. For the effect of productivity on biodiversity we found a negative effect using mixed models which was highly sensitive to the time at which the data was collected within the growing season. In contrast, our main models found no evidence for an effect. Conventional models overestimated the effects between biodiversity and productivity, likely due to confounding variables.Synthesis. Understanding the biodiversity‐productivity relationships is a focal topic in ecology, but unravelling their reciprocal nature remains challenging. We demonstrate that higher‐resolution longitudinal data along with methods to control for a broader suite of confounding variables can be used to resolve reciprocal relationships. We highlight future data needs and methods that can help us to resolve biodiversity‐productivity relationships, crucial for reconciling a long‐running debate in ecology and ultimately, to understand how biodiversity and ecosystem functioning respond to global change. 

Biodiversity-ecosystem functioning (BEF) research grew rapidly following concerns that biodiversity loss would negatively affect ecosystem functions and the ecosystem services they underpin. However, despite evidence that biodiversity strongly affects ecosystem functioning, the influence of BEF research upon policy and the management of ‘real-world’ ecosystems, i.e., semi-natural habitats and agroecosystems, has been limited. Here, we address this issue by classifying BEF research into three clusters based on the degree of human control over species composition and the spatial scale, in terms of grain, of the study, and discussing how the research of each cluster is best suited to inform particular fields of ecosystem management. Research in the first cluster, small-grain highly controlled studies, is best able to provide general insights into mechanisms and to inform the management of species-poor and highly managed systems such as croplands, plantations, and the restoration of heavily degraded ecosystems. Research from the second cluster, small-grain observational studies, and species removal and addition studies, may allow for direct predictions of the impacts of species loss in specific semi-natural ecosystems. Research in the third cluster, large-grain uncontrolled studies, may best inform landscape-scale management and national-scale policy. We discuss barriers to transfer within each cluster and suggest how new research and knowledge exchange mechanisms may overcome these challenges. To meet the potential for BEF research to address global challenges, we recommend transdisciplinary research that goes beyond these current clusters and considers the social-ecological context of the ecosystems in which BEF knowledge is generated. This requires recognizing the social and economic value of biodiversity for ecosystem services at scales, and in units, that matter to land managers and policy makers. 

A large body of research shows that biodiversity loss can reduce ecosystem functioning. However, much of the evidence for this relationship is drawn from biodiversity–ecosystem functioning experiments in which biodiversity loss is simulated by randomly assembling communities of varying species diversity, and ecosystem functions are measured. This random assembly has led some ecologists to question the relevance of biodiversity experiments to real-world ecosystems, where community assembly or disassembly may be non-random and influenced by external drivers, such as climate, soil conditions or land use. Here, we compare data from real-world grassland plant communities with data from two of the largest and longest-running grassland biodiversity experiments (the Jena Experiment in Germany and BioDIV in the United States) in terms of their taxonomic, functional and phylogenetic diversity and functional-trait composition. We found that plant communities of biodiversity experiments cover almost all of the multivariate variation of the real-world communities, while also containing community types that are not currently observed in the real world. Moreover, they have greater variance in their compositional features than their real-world counterparts. We then re-analysed a subset of experimental data that included only ecologically realistic communities (that is, those comparable to real-world communities). For 10 out of 12 biodiversity–ecosystem functioning relationships, biodiversity effects did not differ significantly between the full dataset of biodiversity experiments and the ecologically realistic subset of experimental communities. Although we do not provide direct evidence for strong or consistent biodiversity–ecosystem functioning relationships in real-world communities, our results demonstrate that the results of biodiversity experiments are largely insensitive to the exclusion of unrealistic communities and that the conclusions drawn from biodiversity experiments are generally robust. 

Abstract 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.