Hindcasting Estuarine Bottom Salinity Using Observing Systems Data and Nonlinear Regression, as Applied to Oysters in Delaware Bay

Salinity is a major environmental factor that influences the population dynamics of fish and shellfish along coasts and estuaries, yet empirical 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, empirical relationships were created to predict salinity at five oyster bed stations using observing systems data. These relationships were then applied to construct indices of salinity exposure over an oyster’s lifetime. Three independent salinity data sources were used in conjunction with observing systems data to construct and validate the predictive relationships. The root mean square error (RMSE) of the models ranged from 0.5 to 1.6 psu when model predictions were compared with the three independent data sets. 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 via the metric of consecutive days below 5 psu, the indices suggested that there could be as much as a 42-day difference in low salinity exposure for oysters at stations just 31 km apart. The approach of using observing systems data to hindcast salinity could be applied to advance understanding of salt distribution and the effect of low salinity exposure on organisms in other estuaries, especially bottom-associated species.