Remote Sensing-Driven Pacific Oyster (Crassostrea gigas) Growth Modeling to Inform Offshore Aquaculture Site Selection
Aquaculture increasingly contributes to global seafood production, requiring new farm sites for continued growth. In France, oyster cultivation has conventionally taken place in the intertidal zone, where there is little or no further room for expansion. Despite interest in moving production further offshore, more information is needed regarding the biological potential for offshore oyster growth, including its spatial and temporal variability. This study shows the use of remotely-sensed chlorophyll-a and total suspended matter concentrations retrieved from the Medium Resolution Imaging Spectrometer (MERIS), and sea surface temperature from the Advanced Very High Resolution Radiometer (AVHRR), all validated using in situ matchup measurements, as input to run a Dynamic Energy Budget (DEB) Pacific oyster growth model for a study site along the French Atlantic coast (Bourgneuf Bay, France). Resulting oyster growth maps were calibrated and validated using in situ measurements of total oyster weight made throughout two growing seasons, from the intertidal zone, where cultivation currently takes place, and from experimental offshore sites, for both spat (R2 = 0.91; RMSE = 1.60 g) and adults (R2 = 0.95; RMSE = 4.34 g). Oyster growth time series are further digested into industry-relevant indicators, such as time to achieve market weight and quality index, elaborated in consultation with local producers and industry professionals, and which are also mapped. Offshore growth is found to be feasible and to be as much as two times faster than in the intertidal zone (p < 0.001). However, the potential for growth is also revealed to be highly variable across the investigated area. Mapping reveals a clear spatial gradient in production potential in the offshore environment, with the northeastern segment of the bay far better suited than the southwestern. Results also highlight the added value of spatiotemporal data, such as satellite image time series, to drive modeling in support of marine spatial planning. The current work demonstrates the feasibility and benefit of such a coupled remote sensing-modeling approach within a shellfish farming context, responding to real and current interests of oyster producers.