Name: Interpolated predictions of substrate probability in White Zone

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Description: California has bathymetric, geologic, and habitat maps for a large portion of its nearshore marine habitats; these datasets were essential for the design of California’s marine protected area (MPA) network, and are currently informing ongoing management. However, the existing seafloor maps contain a critical gap—the very nearshore zone, where navigation hazards and technical limitations prevented ship-board mapping. This 50-500m wide band of unmapped seafloor, a data gap known as the “white zone”, extends from shore to 10-15m depth along the length of the California coast, encompassing much of the state’s kelp forests and essential habitat for commercially and recreationally important species. We used data available through the California’s Seafloor Mapping Program (CSMP) and the NOAA Environmental Sensitivity Index (ESI) shoreline habitat categorizations, to develop predictive maps of substrate characteristics in the white zone through interpolation. In order to determine an optimal method of interpolation, we used artificial white zones within the CSMP substrate maps, and tested ten ArcGIS interpolation techniques, crossed by five resampled pixel sizes, to test which combination of interpolation methods and pixel size generated the most accurate and precise prediction of rock versus sand substrate. We chose three of the methods with the highest precision and accuracy across multiple on-offshore white zone widths, and used them to generate predictive substrate maps of the white zone within the North Central Coast. This dataset represents the results of interpolation using Inverse Distance Weighting at a 30m cell size using a power of .5, a variable search radius, and 12 points.

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Copyright Text: University of California, Santa Cruz California Ocean Science Trust California Department of Fish and Wildlife, Marine Region

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