Award: OCE-1633951

Overview This research developed a quantitative understanding of key factors that control carbon cycling in seagrass meadows, increasing our ability to quantify their potential as blue carbon sinks, or their ability to store carbon absorbed from the air. It utilized cutting-edge methods for evaluating oxygen and carbon exchange (gradient exchange and eddy covariance techniques) combined with biomass, sediment, and water column measurements to develop numerical models that can be scaled up to quantify carbon cycling and storage in seagrass meadows. This comparative analysis across latitudinal and geochemical gradients addressed the relative contributions of different species and geochemical processes to better constrain the role of seagrass carbon sequestration to global biogeochemical cycles. Specifically, this research quantified: (i) the relationship between C stocks and standing biomass for different species with different life histories and structural complexity, (ii) the influence of above- and below-ground metabolism on carbon exchange, and (iii) the influence of sediment type (siliciclastic vs. carbonate) on blue carbon storage. Sedimentation rates and isotopic composition of inorganic carbon, organic carbon, and iron sulfide precipitates, as well as porewater concentrations of dissolved sulfide, carbon dioxide, alkalinity and salinity enabled the development of a bio-optical-geochemical model that predicts the impact of seagrass metabolism on sediment geochemical processes that control carbon cycling in shallow waters. Model predictions were validated against direct measurements of carbon and oxygen exchange in seagrass meadows, enabling scaling of density-dependent processes to predict the impacts of seagrass distribution and density on carbon cycling and storage. Significance This research developed a quantitative understanding of the controls and drivers of carbon cycling in temperate and tropical seagrass meadows that increases our fundamental knowledge of carbon cycling in shallow coastal environments, generally, and increases our ability to quantify their potential as blue carbon sinks. This project advanced a new generation of bio-optical-geochemical models and tools (gradient exchange and eddy covariance techniques) that have the potential to transform our ability to measure and predict carbon dynamics in shallow water systems. To date, the results of this research have been disseminated through 7 peer-reviewed publications, 21 presentations at international meetings, numerous local meetings and presentations, and videos distributed through social media. Broader Impacts A major goal of this project was the training of young scientists. This work supported the research of an early career scientist, a postdoc, two Ph.D. students, and 6 undergraduate students, under the combined supervision of the PIs. Two of the undergraduate students were minority students and both have advanced to become PhD graduate students in the MIT-WHOI Joint Program. The postdoc has advanced to become faculty at WHOI. One undergraduate student presented their research at international meetings, in addition to other local meetings and events. Informational videos have been shared widely on the PIs webpage and social media, with one produced by a minority undergraduate student. This research provides key knowledge about submerged aquatic vegetation and its role in the global carbon cycle, and how these environments may be impacted by future climate change. These results have been disseminated to the general public and policy makers through a number of avenues and data products: 1. the model and data products provide a visual key for coastal managers to understand the impact of environmental controls on coastal seagrass ecosystems and their societal benefits, such as carbon storage, 2. videos and video abstracts have been shared and viewed widely (YouTube, Facebook, Instagram), 3. these results and products can now be implemented across a large spatial scale due to our analyses that surveyed across a tropical to temperate gradient, and can be generalized and extrapolated to inform policy and decision making processes. Last Modified: 01/13/2021 Submitted by: Matthew Long