Carbon Dynamics in Epibenthic Hypersaline Microbial Mats from Patagonia (Argentina)

Description

Climate change mitigation strategies focus primarily on reducing greenhouse gas emissions, a crucial but often resisted approach. An alternative strategy, carbon (C) sequestration through land and ocean ecosystems, is gaining momentum in climate policy. This study explores C sequestration by microbial mats in two semiarid coastal tidal flats in Buenos Aires Province, Argentina: Puerto Cuatreros (PC) and Paso Seco (PS). Interannual CO2 fluxes and C storage were measured using an optic CO2 sensor and sediment core analyses. Microbial mats in PS exhibited larger CO2 fluxes, releasing more CO2 into the atmosphere compared to PC. Seasonal trends showed higher CO2 release during spring and summer at both sites, with microbial mats in PS consistently releasing more CO2. Sediment core analyses indicate that PC had higher organic C content, whereas PS had significant inorganic C accumulation, attributed to periodic CaCO3 precipitation in the evaporitic environment. Microbial mats in these semiarid regions contribute to C sequestration, but their net effect varies with environmental conditions and seasonal changes. These results underscore the need for further research on microbial mats in semiarid regions to fully understand their role in global C cycling and their potential as natural climate solutions. Finally, the presence of buried CaCO3 in PS highlights the importance of considering both organic and inorganic C in C stock calculations for an accurate global C budgeting.

Steps to reproduce

Data was generated with a sensair K30 CO2 sensor. The following equation from Harmon et al. (2015) was used to calculate the flux: F=PV/RTA × dC/dt where F is the efflux (mol m-2 s-1), P is the barometric pressure (Pa), V is the chamber volume (m3), R is the gas constant (8.31446 J mol-1 K-1), T is the temperature (K), A is the area of the chamber in touch with the sediment surface, and dC/dt is the slope of the CO2 in time (mol m-3 s-1). To estimate the slope, a linear regression of the CO2 variation measured in the chamber was used.

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