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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. KEY WORDS: remote sensing, bivalve food, aquaculture site selection, particulate organic matter, turbidity, INTRODUCTION Modeling bivalve filter feeder growth in response to environmental variables in the coastal zone has applications to coastal zone management, and may include determination of estuary-wide carrying capacity, site [...]

ABSTRACT A field and modeling study of the food supply and demand of mussel (Mytilus edulis) rafts in Maine established the hydrodynamic and particle consumption characteristics of shellfish aquaculture structures. [...]