Determining The Influence Of Seascape Structure On Coral Reef Fishes In Hawaii Using A Geospatial Approach

摘要

We assessed the utility of several LIDAR-derived seascape metrics (e.g. depth, rugosity, slope, variance in depth) to determine which measures of the seascape demonstrated important relationships with fish assemblage structure and would ultimately serve as the best ecological criteria to advance predictive modeling of fish assemblages using remote sensing and GIS analysis. Variance in depth (within a 75 m radius) was the seascape metric that had the strongest relationships with most fish assemblage metrics, followed by depth and slope. Our results demonstrate the potential for using remotely sensed measures of the seascape to support predictive mapping and modeling offish assemblages.