Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

Summary Trait variability in space and time allows plants to adjust to changing environmental conditions. However, we know little about how this variability is distributed and coordinated at different organizational levels. For six dominant tree species in northeastern Spain (three Fagaceae and three Pinaceae) we quantified the inter‐ and intraspecific variability of a set of traits along a water availability gradient. We measured leaf mass per area (LMA), leaf nitrogen (N) concentration, carbon isotope composition in leaves (δ13C), stem wood density, the Huber value (Hv, the ratio of cross‐sectional sapwood area to leaf area), sapwood‐specific and leaf‐specific stem hydraulic conductivity, vulnerability to xylem embolism (P50) and the turgor loss point (Ptlp). Differences between families explained the largest amount of variability for most traits, although intraspecific variability was also relevant. Species occupying wetter sites showed higher N, P50 and Ptlp, and lower LMA, δ13C and Hv. However, when trait relationships with water availability were assessed within species they held only for Hv and Ptlp. Overall, our results indicate that intraspecific adjustments along the water availability gradient relied primarily on changes in resource allocation between sapwood and leaf area and in leaf water relations.

Tree stems exchange CO 2 , CH 4 and N 2 O with the atmosphere but the magnitudes, patterns and drivers of these greenhouse gas (GHG) fluxes remain poorly understood. Our understanding mainly comes from static-manual measurements, which provide limited information on the temporal variability and magnitude of these fluxes. We measured hourly CO 2 , CH 4 and N 2 O fluxes at two stem heights and adjacent soils within an upland temperate forest. We analyzed diurnal and seasonal variability of fluxes and biophysical drivers (i.e., temperature, soil moisture, sap flux). Tree stems were a net source of CO 2 (3.80 ± 0.18 µmol m −2 s −1 ; mean ± 95% CI) and CH 4 (0.37 ± 0.18 nmol m −2 s −1 ), but a sink for N 2 O (−0.016 ± 0.008 nmol m −2 s −1 ). Time series analysis showed diurnal temporal correlations between these gases with temperature or sap flux for certain days. CO 2 and CH 4 showed a clear seasonal pattern explained by temperature, soil water content and sap flux. Relationships between stem, soil fluxes and their drivers suggest that CH 4 for stem emissions could be partially produced belowground. High-frequency measurements demonstrate that: a) tree stems exchange GHGs with the atmosphere at multiple time scales; and b) are needed to better estimate fluxes magnitudes and understand underlying mechanisms of GHG stem emissions.

Summary Tree stems from wetland, floodplain and upland forests can produce and emit methane (CH4). Tree CH4 stem emissions have high spatial and temporal variability, but there is no consensus on the biophysical mechanisms that drive stem CH4 production and emissions. Here, we summarize up to 30 opportunities and challenges for stem CH4 emissions research, which, when addressed, will improve estimates of the magnitudes, patterns and drivers of CH4 emissions and trace their potential origin. We identified the need: (1) for both long‐term, high‐frequency measurements of stem CH4 emissions to understand the fine‐scale processes, alongside rapid large‐scale measurements designed to understand the variability across individuals, species and ecosystems; (2) to identify microorganisms and biogeochemical pathways associated with CH4 production; and (3) to develop a mechanistic model including passive and active transport of CH4 from the soil–tree–atmosphere continuum. Addressing these challenges will help to constrain the magnitudes and patterns of CH4 emissions, and allow for the integration of pathways and mechanisms of CH4 production and emissions into process‐based models. These advances will facilitate the upscaling of stem CH4 emissions to the ecosystem level and quantify the role of stem CH4 emissions for the local to global CH4 budget.

R soil is a fraction of R eco and theoretically must be lower than R eco R eco was not consistently higher than R soil from daily to annual scales We discuss issues with current practices influencing under or overestimation of R eco and R soil Flux networks need a better integration of spatial and temporal variability of R eco and R soil