Marine Protected Areas (MPAs) are successful place-based management tools in protecting Essential Fish Habitat (EFH) from commercial and recreational fishing pressures. In the southeast Atlantic, the morphometric environment of the seafloor has been found to be a control on Essential Fish Habitat (EFH) (Sedberry and Van Dolah 1984). To this end, modern methods of acoustic data acquisition and morphometric analysis of the seascape are promising oceanographic techniques for identifying and delineating EFH. In July, 2013, the NOAA Ship Pisces collected bathymetric, backscatter and water column data for potential habitat sites along the U.S. Southeast Atlantic continental shelf. A total of 205 km2 of seafloor were mapped between Mayport, FL and Wilmington, NC, using the SIMRAD ME70 multibeam echosounder system. In addition, a total of n = 7410 fish presences were recorded within the water column, using the SIMRAD EK 60 split-beam echosounder system. These data were processed in CARIS HIPS, QPS Fledermaus, MATLAB and Echoview. This study provides a morphometric characterization and quantitative assessment of fish present within each survey site and identifies features of the bathymetry that help explain the presence of demersal fish. A total of 106 unique maps were created, illustrating seafloor morphometrics and fish distributions across the seascape. In ArcGIS, 14 morphometrics were generated as candidate explanatory variables for fish abundances in small (5-12 cm), medium (12-29 cm) and large (>29 cm) size classes. We explored fish-seascape interactions at two spatial scales in the GIS using a site-wide and 50 x 50 m grid scale. At the site- wide scale, X¯ Slope (R2 = 0.97), X¯ Slope of Slope (R2 = 0.90) and σ Depth (R2 = 0.87) provided the strongest explanatory power in a bivariate analysis and may be used to help identify EFH at a coarse scale. At a 50 x 50 m grid scale, X¯ Slope, X¯ Slope of Slope and X¯ Backscatter emerged as the strongest contributing variables, when combined in a multivariate analysis. Overall, multivariate model R2 values were low and not predictive, but allow for the identification of variables contributing to the characterization of fish-seascape interactions at a finer scale.
|Advisor:||Levine, Norman S.|
|Commitee:||Kracker, Laura M., Nowlin, Matthew C., Sautter, Leslie R.|
|School:||College of Charleston|
|School Location:||United States -- South Carolina|
|Source:||MAI 54/04M(E), Masters Abstracts International|
|Subjects:||Biological oceanography, Geomorphology, Aquatic sciences|
|Keywords:||Acoustics, Biogeography, Data visualization, Ecosystem modeling, Geospatial analysis, Seafloor mapping|
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