Circumventing kinetics in biogeochemical modeling

Abstract

Significance Predicting biochemical processes driven by microbes in the environment remains challenging, because the “kinetic” parameters conventionally used to predict reaction rates are usually poorly known. Here we mathematically show that in poorly mixed systems, such as stagnant waters, bulk biochemical reaction rates can become limited by the slow transport of substrates across space and essentially independent of kinetic parameters. We demonstrate our arguments for a large and heavily studied ocean basin, where we accurately predict the microbially driven fluxes of various substances, across a depth range of hundreds of meters. Our work opens up avenues for predicting ecosystem processes without knowledge of kinetic parameters and without laborious chemical profile measurements. Microbial metabolism drives biogeochemical fluxes in virtually every ecosystem. Modeling these fluxes is challenged by the incredible diversity of microorganisms, whose kinetic parameters are largely unknown. In poorly mixed systems, such as stagnant water columns or sediments, however, long-term bulk microbial metabolism may become limited by physical transport rates of substrates across space. Here we mathematically show that under these conditions, biogeochemical fluxes are largely predictable based on the system’s transport properties, chemical boundary conditions, and the stoichiometry of metabolic pathways, regardless of the precise kinetics of the resident microorganisms. We formalize these considerations into a predictive modeling framework and demonstrate its use for the Cariaco Basin subeuphotic zone, one of the largest anoxic marine basins worldwide. Using chemical concentration data solely from the upper boundary (depth 180 m) and lower boundary (depth 900 m), but without a priori knowledge of metabolite fluxes, chemical depth profiles, kinetic parameters, or microbial species composition, we predict the concentrations and vertical fluxes of biologically important substances, including oxygen, nitrate, hydrogen sulfide, and ammonium, across the entire considered depth range (180–900 m). Our predictions largely agree with concentration measurements over a period of 14 years (R2 = 0.78–0.92) and become particularly accurate during a period where the system was near biogeochemical steady state (years 2007–2009, R2 = 0.86–0.95). Our work enables geobiological predictions for a large class of ecosystems without knowledge of kinetic parameters or geochemical depth profiles. Conceptually, our work provides a possible explanation for the decoupling between microbial species composition and bulk metabolic function, observed in various ecosystems.