Revisiting soil microbial biomass: Considering changes in composition with growth rate

Abstract

Soil microbial biomass is assumed to have stable chemical composition. Various components of the biomass such as DNA, ATP, or chloroform-labile organic carbon are measured in soil and converted into total microbial biomass using experimentally derived conversion factors, which are also assumed to be constant. However, several observations suggest the opposite. The composition of soil microbial biomass is likely changing with specific growth rate as observed in pure cultures of single microbial species. In this study, we define a “sub-Microbial” model that explicitly represents changes in composition of soil microbial biomass associated with changes in specific growth rate. We calibrate the model with published data and compare its performance with the simpler Monod and Pirt models, which consider microbial biomass as a single pool with invariant chemical composition. The model explains well the variability in chloroform-labile content of microbial biomass following organic substrate additions as well as variability in ratios of different components of microbial biomass. Changes in composition of soil microbial biomass are quantitatively significant and occur over hours and days resulting in our sub-Microbial model outperforming both the Monod and Pirt models. Our results further indicate that the composition of soil microbial biomass changes consistently with growth rate across various soils. Here, we provide a methodological recommendation how to determine total soil microbial biomass and its physiological characteristics such as growth rate, turnover rate and substrate use efficiency as accurately as possible. In light of the presented results, we would like to initiate a discussion about the methodological issues associated with measurement of soil microbial biomass as these measurements are expected to inform a new generation of microbially-explicit soil biogeochemical models predicting development of terrestrial ecosystems under various scenarios.