Oysters (Crassostrea virginica) are an ecologically and economically important species that have experienced a sharp decline in recent years due to changes in their habitat. In the Mississippi Sound, these shifts in the environment have largely been attributed to more frequent openings of the Bonnet Carre Spillway (BCS) due to the sudden and prolonged decreases in salinity and large influxes of nutrients that lead to hypoxic conditions. In 2019 the spillway opened twice causing some oyster reefs to experience up to 100% mortality as conditions dropped out of the suitability range for an extended period. These dramatic shifts in the environment have created concerns around long-term habitat suitability for oysters. Maxent is a machine learning algorithm that uses presence only and environmental background data to model species distributions. For oyster distribution in the Mississippi Sound, Maxent will be used to compile and compare multiple environmental variables, such as salinity, temperature, dissolved oxygen, and substrate then model areas that have favorable conditions in relation to oyster presence. Additionally, drifters will be released in a hydrodynamic model to simulate oyster larval movement. This model will be run under normal environmental conditions, previous operational conditions of the BCS, and alternative operational regimes to determine its effect on oysters and larval dispersal. Further, by testing alternate operational regimes of the BCS, an operational regime that prevents flooding and has a lower impact on oysters could be identified. Finally, assessing both larval movement and species distribution may identify more optimal sites away from the BCS for potential reefs and help guide reef restoration efforts.