USING HIGH-RESOLUTION REMOTE SENSING TO CHARACTERIZE SUSPENDED PARTICULATE ORGANIC MATTER AS BIVALVE FOOD FOR AQUACULTURE SITE SELECTION

Characterizing relative temporal and spatial variations in both the living and detrital components of bivalve food is required to predict bivalve growth across environments with contrasting seston compositions. The present article describes how remote sensing can be applied for such characterization, both over large spatial scales and fine spatial resolutions (i.e., farm scale; 10's of meters), thereby providing key information for bivalve aquaculture operations and site selection, including the restoration of native species. Using natural seawater samples collected from contrasting culture sites in North America and Europe, a simple model was developed to predict the total particulate organic matter (POM) available as food to bivalves from high-resolution remote-sensing images of coastal embayments which estimate chlorophyll (CHL) and turbidity, in which CHL acts as a proxy for living organics and turbidity as a measure of total suspended particulate matter (SPM). The resulting POM derived from satellite images, along with temperature and CHL, are then used as inputs to the bivalve bioenergetic model, ShellSIM, to predict the growth of Mytilus edulis, Crassostrea virginica, and Ostrea edulis along the coast of Maine, one of the most convoluted coasts in the United States, for aquaculture site selection.

Automated summary

The article discusses how remote sensing can be used to characterize the temporal and spatial variations in both living and detrital components of bivalve food, and develop a model to predict the total particulate organic matter available to bivalves from high-resolution remote-sensing images. The results from satellite images, along with temperature and chlorophyll, are used as inputs to predict the growth of various bivalve species along the coast of Maine for aquaculture site selection.