Although belowground ecosystems have been studied extensively and soil biota play integral roles in biogeochemical processes, surprisingly we have a limited understanding of global patterns in belowground biomass and community structure. To address this critical gap, we conducted a meta‐analysis of published data (> 1300 datapoints) to compare belowground plant, microbial and faunal biomass across seven of the major biomes on Earth. We also assembled data to assess biome‐level patterns in belowground microbial community composition. Our analysis suggests that variation in microbial biomass is predictable across biomes, with microbial biomass carbon representing 0.6–1.1% of soil organic carbon (r² = 0.91) and 1–20% of total plant biomass carbon (r² = 0.42). Approximately 50% of total animal biomass can be found belowground and soil faunal biomass represents < 4% of microbial biomass across all biomes. The structure of belowground microbial communities is also predictable: bacterial community composition and fungal : bacterial gene ratios can be predicted reasonably well from soil pH and soil C : N ratios respectively. Together these results identify robust patterns in the structure of belowground microbial and faunal communities at broad scales which may be explained by universal mechanisms that regulate belowground biota across biomes.

A critical assumption underlying terrestrial ecosystem models is that soil microbial communities, when placed in a common environment, will function in an identical manner regardless of the composition of that community. Given high species diversity in microbial communities and the ability of microbes to adapt rapidly to new conditions, this assumption of functional redundancy seems plausible. We test the assumption by comparing litter decomposition rates in experimental microcosms inoculated with distinct microbial communities. We find that rates of carbon dioxide production from litter decomposition were dependent upon the microbial inoculum, with differences in the microbial community alone accounting for substantial (∼20%) variation in total carbon mineralized. Communities that shared a common history with a given foliar litter exhibited higher decomposition rates when compared to communities foreign to that habitat. Our results suggest that the implicit assumption in ecosystem models (i.e., microbial communities in the same environment are functionally equivalent) is incorrect. To predict accurately how biogeochemical processes will respond to global change may require consideration of the community composition and/or adaptation of microbial communities to past resource environments.

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.