| Abstract: |
Native oysters in Chesapeake Bay are nearing extinction due to a combination of disease, over-fishing, habitat loss, and declines in water quality. While numerous disparate datasets have been compiled over the years for various projects related to this species, little attempt has been made to compile these datasets and use them to develop a spatially-resolved model for oyster restoration. Here we present a spatially-explicit statistical model that relates known oyster densities with multiple key habitat characteristics to predict where oysters are most likely to occur in relatively high densities for two regions of the Bay. Geographically-weighted regression (GWR) was used in which the dependent variable was live oyster density from a recent patent tong survey, and the independent variables were bathymetry, water quality, and available oyster habitat. GWR is similar to weighted least squares regression (WLS) except that a set of weights which depends upon the location of a sample point relative to other sample points in the data set are applied to the parameter estimates. After construction, the model was validated using a validation dataset generated by overlaying a 500m X 500m grid over the rasters for each independent variable and extracting values for each variable to include in the model. The results of this model predict with a high degree of spatial resolution locations in the Chester and Choptank Rivers where oysters are likely to occur in relatively high densities and where resources should be concentrated to restore the native oyster. This modeling effort illustrates how existing large, long term datasets can be compiled and utilized to inform spatial planning and management for one of Chesapeake Bay’s most valued natural resources. |
