Work

Using dynamic energy budget modeling and high-resolution satellite products to predict eastern oyster growth at a farm scale

Year: 2025

Type: article

Abstract: The expansion of marine aquaculture, particularly non-fed shellfish farming, necessitates advanced site selection methods that consider environmental variability for optimal growth. Here, we apply high-resolution remote sensing sea surface temperature (SST) products from Landsat 8 & 9 and either chlorophyll- a or particulate organic matter (POM) estimates from Sentinel 2 to model growth and predict the time to bring Crassostrea virginica to market in Maine's nearshore waters. We developed a regional dynamic energy budget model parameter set for C. virginica in the Northeast U.S. which was calibrated and validated using extensive growth data collected over multiple years and locations. The model had strong predictive accuracy with both in situ and satellite forcing data. Ten-year climatologies of SST and median chlorophyll-a and POM summaries allowed for model forcing despite the infrequent overpass time of high-resolution satellites. Our approach demonstrates that high-resolution satellite data can successfully predict cultured oyster growth, improving site selection by identifying optimal growing regions within complex coastal environments. This novel integration of high-resolution satellite data with physiological modeling provides a powerful tool for sustainable aquaculture development, enhancing industry resilience, and informing techno-economic analyses in coastal ecosystems worldwide.

Source: Aquaculture

DOI: 10.1016/j.aquaculture.2025.743133