Geospatial analysis of eastern oyster habitat and disease in the Chesapeake Bay

Crassostrea virginica, common name eastern oyster, in the Chesapeake Bay is currently at 1% of its peak annual landings in 1884 (600,000 metric tons). This decline is in spite of being considered a resilient species. Causes of the decline include overharvesting, disease, and habitat loss. While efforts have been made to combat each cause, the key element to recovering the population is coordinated habitat restoration. This study aims to develop a GIS-based habitat model for the eastern oyster in the Chesapeake Bay. The first goal of this study was to determine the water quality parameters necessary for successful oyster population restoration and to locate where those conditions exist concurrently. To identify these areas that are most suitable for restoration efforts, a habitat analysis was performed using GIS data of water quality parameters in the bay consisting of water temperature, salinity, dissolved oxygen, pH, and bathymetry. Data obtained from monitoring stations throughout the bay were used to estimate the water quality parameters. Those parameters were interpolated using inverse distance weighting to create continuous rasters of seasonal averages for each parameter. Then rasters were combined and analyzed using geospatial processing to determine the areas that contain the most favorable conditions for oyster growth at both larval and adult life stages. Additionally, the conditions for proliferation of oyster diseases MSX and Dermo were assessed geospatially. The second goal of this study was to determine the potential changes to suitable oyster habitat area and Dermo proliferation following the climate change experts’ prediction of a 2℃ water temperature increase. Resulting models showed a decrease in total area of high quality habitat for larval and adult models with the 2℃ increase, as compared to the current condition models. Disease proliferation of Dermo also exhibited increases in higher risk areas in the 2℃ increase model when compared to the current condition model