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Mathematical simulation of the diel O, S, and C biogeochemistry of a hypersaline microbial mat
Authors:Decker K L M  Potter C S  Bebout B M  Marais D J Des  Carpenter S  Discipulo M  Hoehler T M  Miller S R  Thamdrup B  Turk K A  Visscher P T
Institution:Division of Science and Environmental Policy, California State University, Monterey Bay, Mail Stop 242-4, Moffett Field, CA 94035, USA. kdecker@mail.arc.nasa.gov
Abstract:The creation of a mathematical simulation model of photosynthetic microbial mats is important to our understanding of key biogeochemical cycles that may have altered the atmospheres and lithospheres of early Earth. A model is presented here as a tool to integrate empirical results from research on hypersaline mats from Baja California Sur (BCS), Mexico into a computational system that can be used to simulate biospheric inputs of trace gases to the atmosphere. The first version of our model, presented here, calculates fluxes and cycling of O(2), sulfide, and dissolved inorganic carbon (DIC) via abiotic components and via four major microbial guilds: cyanobacteria (CYA), sulfate reducing bacteria (SRB), purple sulfur bacteria (PSB) and colorless sulfur bacteria (CSB). We used generalized Monod-type equations that incorporate substrate and energy limits upon maximum rates of metabolic processes such as photosynthesis and sulfate reduction. We ran a simulation using temperature and irradiance inputs from data collected from a microbial mat in Guerrero Negro in BCS (Mexico). Model O(2), sulfide, and DIC concentration profiles and fluxes compared well with data collected in the field mats. There were some model-predicted features of biogeochemical cycling not observed in our actual measurements. For instance, large influxes and effluxes of DIC across the MBGC mat boundary may reveal previously unrecognized, but real, in situ limits on rates of biogeochemical processes. Some of the short-term variation in field-collected mat O(2) was not predicted by MBGC. This suggests a need both for more model sensitivity to small environmental fluctuations for the incorporation of a photorespiration function into the model.
Keywords:Microbial mat  Biogeochemistry  Modeling  Oxygen  Sulfur  Carbon Microcoleus chthonoplastes  Cyanobacteria
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