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Plant functional traits may explain the positive relationship between species richness and ecosystem functioning, but species‐level trait variation in response to growth conditions is often ignored in trait‐based predictions of community performance. In a large grassland biodiversity experiment (Jena Experiment), we measured traits on plants grown as solitary individuals, in monocultures or in mixtures. We calculated two measures of community‐level trait composition, i.e., community‐weighted mean traits (CWM) and trait diversity (Rao's quadratic entropy; FD) based on different contexts in which traits were measured (trait origins). CWM and FD values of the different measurement origins were then compared regarding their power to predict community biomass production and biodiversity effects quantified with the additive partitioning method. Irrespective of trait origin, models combining CWM and FD values as predictors best explained community biomass and biodiversity effects. CWM values based on monoculture, mixture‐mean or community‐specific trait data were similarly powerful predictors, but predictions became worse when trait values originated from solitary‐grown individuals. FD values based on monoculture traits were the best predictors of community biomass and net biodiversity effects, while FD values based on community‐specific traits were the best predictors for complementarity and selection effects. Traits chosen as best CWM predictors were not strongly affected by trait origin but traits chosen as best FD predictors varied strongly dependent on trait origin and altered the predictability of community performance. We conclude that by adjusting their functional traits to species richness and even specific community compositions, plants can change community‐level trait compositions, thereby also changing community biomass production and biodiversity effects. Incorporation of these plastic trait adjustments of plants in trait‐based ecology can improve its predicti

Several multi-year biodiversity experiments have shown positive species richness–productivity relationships which strengthen over time, but the mechanisms which control productivity are not well understood. We used experimental grasslands (Jena Experiment) with mixtures containing different numbers of species (4, 8, 16 and 60) and plant functional groups (1–4; grasses, legumes, small herbs, tall herbs) to explore patterns of variation in functional trait composition as well as climatic variables as predictors for community biomass production across several years (from 2003 to 2009). Over this time span, high community mean trait values shifted from the dominance of trait values associated with fast growth to trait values suggesting a conservation of growth-related resources and successful reproduction. Increasing between-community convergence in means of several productivity-related traits indicated that environmental filtering and exclusion of competitively weaker species played a role during community assembly. A general trend for increasing functional trait diversity within and convergence among communities suggested niche differentiation through limiting similarity in the longer term and that similar mechanisms operated in communities sown with different diversity. Community biomass production was primarily explained by a few key mean traits (tall growth, large seed mass and leaf nitrogen concentration) and to a smaller extent by functional diversity in nitrogen acquisition strategies, functional richness in multiple traits and functional evenness in light-acquisition traits. Increasing species richness, presence of an exceptionally productive legume species (Onobrychis viciifolia) and climatic variables explained an additional proportion of variation in community biomass. In general, community biomass production decreased through time, but communities with higher functional richness in multiple traits had high productivities over several years. Our results sugg

Background: The different hypotheses proposed to explain positive species richness–productivity relationships, i.e. selection effect and complementarity effect, imply that plant functional characteristics are at the core of a mechanistic understanding of biodiversity effects. Methodology/Principal Findings: We used two community-wide measures of plant functional composition, (1) community- weighted means of trait values (CWM) and (2) functional trait diversity based on Rao’s quadratic diversity (FDQ) to predict biomass production and measures of biodiversity effects in experimental grasslands (Jena Experiment) with different species richness (2, 4, 8, 16 and 60) and different functional group number and composition (1 to 4; legumes, grasses, small herbs, tall herbs) four years after establishment. Functional trait composition had a larger predictive power for community biomass and measures of biodiversitity effects (40–82% of explained variation) than species richness per se (,1–13% of explained variation). CWM explained a larger amount of variation in community biomass (80%) and net biodiversity effects (70%) than FDQ (36 and 38% of explained variation respectively). FDQ explained similar proportions of variation in complementarity effects (24%, positive relationship) and selection effects (28%, negative relationship) as CWM (27% of explained variation for both complementarity and selection effects), but for all response variables the combination of CWM and FDQ led to significant model improvement compared to a separate consideration of different components of functional trait composition. Effects of FDQ were mainly attributable to diversity in nutrient acquisition and life-history strategies. The large spectrum of traits contributing to positive effects of CWM on biomass production and net biodiversity effects indicated that effects of dominant species were associated with different trait combinations. Conclusions/Significance: Our results suggest that the identif

Background and Aims Functional trait differences and trait adjustment in response to influences of the biotic environment could reflect niche partitioning among species. In this study, we tested how variation in above-ground plant traits, chosen as indicators for light and nitrogen acquisition and use, differs among taxonomically closely related species (Poaceae) to assess their potential for niche segregation at increasing plant diversity. Methods Traits of 12 grass species were measured in experimental grasslands (Jena Experiment) of varying species richness (from 1 to 60) and presence of particular functional groups (grasses, legumes, tall herbs and small herbs). Key Results Grass species increased shoot and leaf length, investment into supporting tissue (stem mass fraction) and specific leaf area as well as reduced foliar δ13C values with increasing species richness, indicating higher efforts for light acquisition. These species-richness effects could in part be explained by a higher probability of legume presence in more diverse communities. Leaf nitrogen concentrations increased and biomas s : N ratios in shoots decreased when grasses grew with legumes, indicating an improved nitrogen nutrition. Foliar δ15N values of grasses decreased when growing with legumes suggesting the use of depleted legume-derived N, while decreasing δ15N values with increasing species richness indicated a shift in the uptake of different N sources. However, efforts to optimize light and nitrogen acquisition by plastic adjustment of traits in response to species richness and legume presence, varied significantly among grass species. It was possible to show further that trait adjustment of grass species increased niche segregation in more diverse plant communities but that complementarity through niche separation may differ between light and nutrient acquisition. Conclusions The results suggest that even among closely related species such as grasses different strategies are used to cope