Recent studies have shown that accounting for intraspecific trait variation (ITV) may better address major questions in community ecology. However, a general picture of the relative extent of ITV compared to interspecific trait variation in plant communities is still missing. Here, we conducted a meta-analysis of the relative extent of ITV within and among plant communities worldwide, using a data set encompassing 629 communities (plots) and 36 functional traits. Overall, ITV accounted for 25% of the total trait variation within communities and 32% of the total trait variation among communities on average. The relative extent of ITV tended to be greater for whole-plant (e.g. plant height) vs. organ-level traits and for leaf chemical (e.g. leaf N and P concentration) vs. leaf morphological (e.g. leaf area and thickness) traits. The relative amount of ITV decreased with increasing species richness and spatial extent, but did not vary with plant growth form or climate. These results highlight global patterns in the relative importance of ITV in plant communities, providing practical guidelines for when researchers should include ITV in trait-based community and ecosystem studies.

There are numerous ways in which plants can influence the composition of soil communities. However, it remains unclear whether information on plant community attributes, including taxonomic, phylogenetic, or trait-based composition, can be used to predict the structure of soil communities. We tested, in both monocultures and field-grown mixed temperate grassland communities, whether plant attributes predict soil communities including taxonomic groups from across the tree of life (fungi, bacteria, protists, and metazoa). The composition of all soil community groups was affected by plant species identity, both in monocultures and in mixed communities. Moreover, plant community composition predicted additional variation in soil community composition beyond what could be predicted from soil abiotic characteristics. In addition, analysis of the field aboveground plant community composition and the composition of plant roots suggests that plant community attributes are better predictors of soil communities than root distributions. However, neither plant phylogeny nor plant traits were strong predictors of soil communities in either experiment. Our results demonstrate that grassland plant species form specific associations with soil community members and that information on plant species distributions can improve predictions of soil community composition. These results indicate that specific associations between plant species and complex soil communities are key determinants of biodiversity patterns in grassland soils.

Many peatlands have been subjected to wildfire or prescribed burning but it is not 22 known how these fires influence near-surface hydrological processes. Macropores are 23 important flowpaths in the upper layers of blanket peat and were investigated through 24 the use of tension disk infiltrometers, which also provide data on saturated hydraulic 25 conductivity. Measurements were performed on unburnt peat (U), where prescribed 26 burning had taken place 2 years (B2), four years (B4) and >15 (B15+) years prior to 27 sampling, and where a wildfire (W) had taken place four months prior to sampling. 28Where there had been recent burning (B2, B4 and W), saturated hydraulic 29 conductivity was approximately three times lower than where there was no burning 30 (U) or where burning was last conducted > 15 years ago (B15+). Similarly, the 31 contribution of macropore flow to overall infiltration was significantly lower 32 (between 12 and 25 % less) in the recently burnt treatments compared to B15+ and U. 33There were no significant differences in saturated hydraulic conductivity or 34 macropore flow between peat which had been subject to recent wildfire (W) and those 35 which had undergone recent prescribed burning (B2 and B4). The results suggest fire 36 influences the near-surface hydrological functioning of peatlands but that recovery in 37 terms of saturated hydraulic conductivity and macropore flow may be possible within 38 two decades if there are no further fires.

The use of plant traits to predict ecosystem functions has been gaining growing attention. Above‐ground plant traits, such as leaf nitrogen (N) content and specific leaf area (SLA), have been shown to strongly relate to ecosystem productivity, respiration and nutrient cycling. Furthermore, increasing plant functional trait diversity has been suggested as a possible mechanism to increase ecosystem carbon (C) storage. However, it is uncertain whether below‐ground plant traits can be predicted by above‐ground traits, and if both above‐ and below‐ground traits can be used to predict soil properties and ecosystem‐level functions. Here, we used two adjacent field experiments in temperate grassland to investigate if above‐ and below‐ground plant traits are related, and whether relationships between plant traits, soil properties and ecosystem C fluxes (i.e. ecosystem respiration and net ecosystem exchange) measured in potted monocultures could be detected in mixed field communities. We found that certain shoot traits (e.g. shoot N and C, and leaf dry matter content) were related to root traits (e.g. root N, root C:N and root dry matter content) in monocultures, but such relationships were either weak or not detected in mixed communities. Some relationships between plant traits (i.e. shoot N, root N and/or shoot C:N) and soil properties (i.e. inorganic N availability and microbial community structure) were similar in monocultures and mixed communities, but they were more strongly linked to shoot traits in monocultures and root traits in mixed communities. Structural equation modelling showed that above‐ and below‐ground traits and soil properties improved predictions of ecosystem C fluxes in monocultures, but not in mixed communities on the basis of community‐weighted mean traits. Synthesis . Our results from a single grassland habitat detected relationships in monocultures between above‐ and below‐ground plant traits, and between plant traits, soil properties and ecosystem C fluxes. However, these relationships were generally weaker or different in mixed communities. Our results demonstrate that while plant traits can be used to predict certain soil properties and ecosystem functions in monocultures, they are less effective for predicting how changes in plant species composition influence ecosystem functions in mixed communities.

Process‐based models describing biogeochemical cycling are crucial tools to understanding long‐term nutrient dynamics, especially in the context of perturbations, such as climate and land‐use change. Such models must effectively synthesize ecological processes and properties. For example, in terrestrial ecosystems, plants are the primary source of bioavailable carbon, but turnover rates of essential nutrients are contingent on interactions between plants and soil biota. Yet, biogeochemical models have traditionally considered plant and soil communities in broad terms. The next generation of models must consider how shifts in their diversity and composition affect ecosystem processes. One promising approach to synthesize plant and soil biodiversity and their interactions into models is to consider their diversity from a functional trait perspective. Plant traits, which include heritable chemical, physical, morphological and phenological characteristics, are increasingly being used to predict ecosystem processes at a range of scales, and to interpret biodiversity–ecosystem functional relationships. There is also emerging evidence that the traits of soil microbial and faunal communities can be correlated with ecosystem functions such as decomposition, nutrient cycling, and greenhouse gas production. Here, we draw on recent advances in measuring and using traits of different biota to predict ecosystem processes, and provide a new perspective as to how biotic traits can be integrated into biogeochemical models. We first describe an explicit trait‐based model framework that operates at small scales and uses direct measurements of ecosystem properties; second, an integrated approach that operates at medium scales and includes interactions between biogeochemical cycling and soil food webs; and third, an implicit trait‐based model framework that associates soil microbial and faunal functional groups with plant functional groups, and operates at the Earth‐system level. In each of these models, we identify opportunities for inclusion of traits from all three groups to reduce model uncertainty and improve understanding of biogeochemical cycles. These model frameworks will generate improved predictive capacity of how changes in biodiversity regulate biogeochemical cycles in terrestrial ecosystems. Further, they will assist in developing a new generation of process‐based models that include plant, microbial, and faunal traits and facilitate dialogue between empirical researchers and modellers.