Bivalve aquaculture can profoundly affect coastal ecosystems by controlling phytoplankton through suspension-feeding activities and removing nutrients via the harvest of cultivated biomass. As a result, bivalve aquaculture has been advocated as a cost-effective tool to improve nearshore water quality, although quantitative impact evaluation of large-scale aquaculture remains limited. This study aims to assess oyster farming's impact on phytoplankton biomass and nutrient contents in a eutrophic bay, the Deep Bay in Southern China. The Bay has sizable oyster floating farms, occupying over a third of the bay area, and five long-term water quality monitoring sites monthly measuring essential ecosystem variables. Making use of the water quality data and dynamic energy budget (DEB) theory, we develop and calibrate an individual-based DEB model to simulate the growth and reproduction of oysters. The model is then upscaled to estimate the bay-scale oyster filtration rate of particulate organic matter (including phytoplankton) and net removal of nitrogen and phosphorus. These estimates are further compared with the primary production rates derived from the observed chlorophyll variations and sediment nutrient fluxes to gain more insights into oyster farming’s top-down control impact and nutrient removal efficiency. Lastly, critical steps toward a more comprehensive impact assessment of large-scale oyster aquaculture are discussed.
 

bivalve aquaculture, eutrophication mitigation, water quality, ecosystem modeling