Award: OCE-1756592

Salinity is a major environmental factor that influences the population dynamics of fish and shellfish along coasts and estuaries, yet methods for hindcasting salinity at specific sampling stations are not widely available. The specific aim of this research was to predict the salinity experienced by juvenile and adult oysters (Crassostrea virginica) collected at sampling stations in Delaware Bay. To do so, mathematical models were created to predict salinity at five oyster bed stations using data from an observing system (a monitoring system that collects salinity data year-round). The mathematical models were then applied to reconstruct the salinity that oysters experienced over their lifetime at these stations.

Three independent salinity data sources were used along with the observing system data to construct and validate the mathematical models. Results demonstrated that data from an observing system near the head of Delaware Bay could be used to predict salinity within +/- 2 psu at oyster bed stations as far down-estuary as 39 km. When these models were applied to estimate low salinity exposure of 2-year-old oysters, they showed that there could be as much as a 42-day difference in low salinity exposure for oysters at stations just 31 km apart.

This research has broad impacts by developing an accurate and lost-cost technique for predicting salinity at specific locations in estuaries based on observing system data, a method that is widely applicable and accessible to other researchers who study physical processes, geochemistry, and organisms that are influenced by salinity in other estuaries. In addition, these models provide valuable predictive information for industries that rely on waters with certain salinity requirements (e.g., power plants that need no or low salinity waters, shellfish aquaculture farms that need salinities > 5 psu). A webpage was created that allows users to hindcast salinity at Delaware Bay stations, thereby supporting better understanding and prediction of oyster population dynamics, disease mortality and production.

This project supported one graduate student and, in doing so, helped to expand participation of women and minorities within the STEM workforce.

Last Modified: 12/16/2024
Modified by: Elizabeth W North