Stoichiometric models of microbial metabolic limitation in soil systems
Abstract
Abstract Aim Ecoenzymatic stoichiometry provides a promising avenue for deciphering resource constraints on soil microbial metabolism but is hampered by limitations in current modelling techniques. Innovation Herein we developed new models for quantifying microbial metabolic limitations based on the stoichiometric and metabolic theories of ecology, using an extensive database ( n = 2,667) that revealed relationships far from the widely recognized mean ratio of 1:1:1 for carbon : nitrogen : phosphorus (C : N : P) acquiring enzyme activities. We estimated the balance points of P and N acquisition ( x 0 , y 0 ) in the absence of resource constraints to redefine the boundary between P versus N limitation. We then calculated two alternative boundary conditions defining P versus N limitation by scaling the classic threshold element ratio (TER), generating two new models (TER EEA and TER L ). In addition, a new enzyme vector (V‐T) model was devised by correcting traditional vector calculations based on observed enzyme activities against these balance points. Main conclusions These three new models more consistently predicted microbial metabolic limitations than the traditional TER and vector models. They also predicted that microbial metabolism in high‐latitude grasslands and low‐latitude forests were predominantly limited by soil N and P, respectively, and that increases in soil organic C with ecosystem development could intensify these limitations. In contrast, fertilizers alleviated these limitations in agricultural ecosystems, suggesting that widespread anthropogenic effects could potentially alter microbial resource limitations even in natural ecosystems. In addition, C limitation to microbial metabolism identified by the new V‐T model showed a consistent negative correlation with microbial C use efficiency among ecosystems, confirming that resource constraints regulate microbial resource allocation. These new models provide more precise predictions of microbial metabolic limitations across a wide range of ecosystems and thus may be useful tools for the study of microbial macroecology.