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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. [ABSTRACT FROM AUTHOR]

Copyright of Basic & Applied Ecology is the property of Urban & Fischer Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Loss of biological diversity because of extinction is one of the most pronounced changes to the global environment. For several decades, researchers have tried to understand how changes in biodiversity might impact biomass production by examining how biomass correlates with a number of biodiversity metrics (especially the number of species and functional groups). This body of research has focused on species with the implicit assumption that they are independent entities. However, functional and ecological similarities are shaped by patterns of common ancestry, such that distantly related species might contribute more to production than close relatives, perhaps by increasing niche breadth. Here, we analyze 2 decades of experiments performed in grassland ecosystems throughout the world and examine whether the evolutionary relationships among the species comprising a community predict how biodiversity impacts plant biomass production. We show that the amount of phylogenetic diversity within communities explained significantly more variation in plant community biomass than other measures of diversity, such as the number of species or functional groups. Our results reveal how evolutionary history can provide critical information for understanding, predicting, and potentially ameliorating the effects of biodiversity loss and should serve as an impetus for new biodiversity experiments. [ABSTRACT FROM AUTHOR]

Aims: The potential for mixtures of plant species to produce more biomass than every one of their constituent species in monoculture is still controversially discussed in the literature. Here we tested how this so-called transgressive overyielding is affected by variation between and within species in monoculture yields in biodiversity experiments. [ABSTRACT FROM PUBLISHER]

Copyright of Basic & Applied Ecology is the property of Urban & Fischer Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This metadata set contains 761 effects of biodiversity on ecosystem, community, or population response variables reported in 137 publications from 1974 to June 2004. All data were obtained in experimental studies. Relationships between species richness or other biodiversity measures and response variables are described by their shape, direction, significance, and if available, the simple (regression) or multiple (analysis of variance) correlation coefficient. We classified response variables into groups according to the ecosystem function or service to which they were related, the trophic level at which they were measured, and several other classification schemes. Similarly, we classified studies into groups according to ecosystem type, experimental system (bottle/pot, greenhouse, field), range and trophic level of biodiversity treatments, and further design variables. Covariates include location of study, number of plots, and number of species used in the biodiversity treatments. Analyses of these metadata have shown, for example, that biodiversity effects on community (and ecosystem) variables are often positive, and those on population variables negative, that stocks are more responsive than rates to biodiversity manipulation, and that bottom-up biodiversity effects in multi-trophic studies are often negative. This metadata set gives a representative overview of the results of a first generation of biodiversity experiments and allows a quantitative assessment of the influence of a number of biological and design variables on the significance and shape of the relationship between biodiversity and response variables. [ABSTRACT FROM AUTHOR]