Revisiting ecoenzymatic stoichiometry models for improved understanding of microbial resource limitations

Ecoenzyme stoichiometry (EEST) models are widely used to estimate microbial resource limitation, nutrient use efficiencies, threshold element ratios, and soil carbon sequestration potential, but uncertainties complicate their application and interpretation. For example, (1) the diagnostic enzyme activities only approximate target resource acquisition, with some enzymes involved in acquiring multiple nutrients; (2) evaluating resource availability is imprecise with neither dissolved nor bulk nutrient measurements fully representing availability to microbes; (3) microbial biomass stoichiometry may not consistently define maximum microbial growth because the stoichiometry of growing and non-growing microbes may differ; and (4) multiple environmental factors (e.g., temperature, pH, texture) simultaneously and differentially influence microbial metabolism, enzyme activities and resource availability. Based on a careful examination of the available evidence, we recommend the simultaneous evaluation of individual enzyme activities linked to the carbon (C)-, nitrogen (N)-, and phosphorus (P) cycles, together with enzyme C:N, C:P, and N:P activity ratios to better identify microbial resource limitation. Moreover, we anticipate that future research should unravel ecoenzyme induction mechanisms at the molecular level because environmental cues and resource availability strongly influence enzyme expression and thus the accuracy of EEST model predictions. To that end, parallel, high-resolution analyses of resource composition, corresponding enzyme activities, and key environmental drivers, using advanced multi-omics technologies such as metaproteomics, meta-transcriptomics, exometabolomics, and high-resolution mass spectrometry, are needed to more clearly link the mechanisms underlying process controls defining EEST models. Given the expanding use of EEST models, these considerations are timely to ensure their appropriate use and interpretation as well as maximize their potential contributions to understanding ecosystem functioning under changing environmental conditions.