Modeling Delta Smelt Distribution for Hypothesized Swimming Behaviors

Abstract

Delta Smelt, Hypomesus transpacificus, is an endangered pelagic fish native to the San Francisco Estuary. The distribution of Delta Smelt in the estuary shifts landward from low-salinity habitat to freshwater habitat before spawning. This spawning migration often coincides with the first substantial freshwater inflow to the estuary during winter. To accomplish this landward shift in distribution, Delta Smelt are believed to use the tides by swimming to faster-moving currents during flood tides and then repositioning themselves to slower-moving currents to reduce seaward movement on ebb tides. Studies have hypothesized that the swimming behavior of Delta Smelt during this period is influenced by environmental conditions such as salinity and turbidity. The details of these swimming behaviors—including the extent to which flows, salinity, and turbidity affect behaviors and distributions—are uncertain. The spawning migration is of management interest because an increase in observed counts of Delta Smelt at the South Delta water-export facilities has coincided roughly with the spawning migration in many years. In this study, we investigated a range of hypothesized swimming behaviors using a three-dimensional particle-tracking model for water year 2002 during the spawning migration, and compared the predicted distributions of Delta Smelt to distributions inferred from catch data. Our goal was to improve understanding of the influence of Delta Smelt swimming on distribution, and, ultimately, to develop a modeling tool to help management agencies identify conditions associated with entrainment losses. Predictions of Delta Smelt distributions and entrainment varied greatly among behaviors. Without swimming, Delta Smelt would be rapidly transported seaward of Suisun Bay, while continuous tidal migration would move them deep into the interior Delta. These behaviors and a simple turbidity-driven behavior model predicted distributions inconsistent with observations, while more complex behavior rules allowed improved predictions.