Abstract

Microbial iron and sulfate reduction are the primary drivers of coastal acid sulfate soil (CASS) passive bioremediation schemes. Microbial sulfate reduction is the limiting step for pyrite formation, a desirable endpoint for CASS remediation. Little is known, however, about the impacts of microbial activity or species interaction on long-term iron and sulfur cycling in CASS ecosystems. Using a combination of molecular biology, geochemical speciation and artificial intelligence-powered computational modelling, we deduced from microbial activity patterns (RNA-based) and geochemical measurements a best-fit equation for predicting biogeochemical pyrite formation in a model CASS ecosystem. In addition to the time-dependent activities of key sulfate-reducing prokaryotic taxa (e.g. Desulfobacteraceae), this equation required methylotrophs (Methylobacteriaceae) and bacterial predators (Bacteriovorax) for best-fitting, suggesting that specific microbial interactions exert meaningful influences on CASS bioremediation efficiency. Our findings confirmed that CASS microorganisms act as an assemblage in response to rewetting by tidewater. Accurate predictions of long-term CASS bioremediation efficiency require modelling of complex and interdependent relationships between geochemical speciation and microbial activity.

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