Linking larval transport and fisheries demographic models to study the influence of environmental variability and larval behavior on juvenile recruitment to oyster populations
Abstract Annual variations in freshwater flow and wind may influence the dispersal of oyster larvae (by affecting circulation patterns) and the survival of adults (by influencing salinity-dependent disease mortality) in Chesapeake Bay, a region whose oyster fishery has greatly declined. These observations suggest that environmental variability has important implications for rehabilitation efforts that involve enhancing native oyster broodstock. We tested this hypothesis for the eastern oyster Crassostrea virginica by linking larval transport and fisheries demographic models. Physical conditions in both models were based on five years with differing wind and freshwater flow patterns (1995)(1996)(1997)(1998)(1999). The larval transport model predicted spatial settlement patterns by using a particle tracking model parameterized with larval behaviors discerned in preliminary analysis of ongoing laboratory studies. The demographic model incorporated estimates of stockrecruitment relationships, growth, natural mortality, disease mortality, fishing mortality, and the effect of extreme events such as freshets. A validation analysis of the linked models compared predictions of juvenile oyster settlement success with field observations from 1995 to1999. Larval transport model results indicated that settlement success of C. virginica larvae was influenced by freshwater flow conditions, with larval settlement higher in low freshwater flow years. The validation analysis indicated that including spawning stock abundance and mortality estimates in the linked larvaltransport-demographic model may have improved predictions of juvenile recruitment. Our linked model could be used to assess risks associated with introducing a new species and guide oyster management activities such as locating brood stock sanctuaries and managing harvest in response to climate variability