Modeled CO2 emissions from coastal wetland transitions to other land uses: Tidal marshes, mangrove forests, and seagrass beds

Abstract

The sediments of coastal wetlands contain large stores of carbon which are vulnerable to oxidation once disturbed, resulting in high levels of CO2 emissions that may be avoided if coastal ecosystems are conserved or restored. We used a simple model to estimate CO2 emissions from mangrove forests, seagrass beds, and tidal marshes based on known decomposition rates for organic matter in these ecosystems under either oxic or anoxic conditions combined with assumptions of the proportion of sediment carbon being deposited in either oxic or anoxic environments following a disturbance of the habitat. Our model found that over 40 years after disturbance the cumulative CO2 emitted from tidal marshes, mangrove forests, and seagrass beds were ~70-80% of the initial carbon stocks in the top meter of the sediment. Comparison of our estimates of CO2 emissions with empirical studies suggests that (1) assuming 50% of organic material moves to an oxic environment after disturbance gives rise to estimates that are similar to CO2 emissions reported for tidal marshes; (2) field measurements of CO2 emissions in disturbed mangrove forests were generally higher than our modeled emissions that assumed 50% of organic matter was deposited in oxic conditions, suggesting higher proportions of organic matter may be exposed to oxic conditions after disturbance in mangrove ecosystems; and (3) the generally low observed rates of CO2 emissions from disturbed seagrasses compared to our estimates, assuming removal of 50% of the organic matter to oxic environments, suggests that lower proportions may be exposed to oxic conditions in seagrass ecosystems. There are significant gaps in our knowledge of the fate of wetland sediment carbon in the marine environment after disturbance. Greater knowledge of the distribution, form, decomposition, and emission rates of wetland sediment carbon after disturbance would help to improve models.