Multivariate bathymetry-derived generalized linear model accurately predicts rockfish distribution on Cordell Bank, California, USA

Accurate efficient estimation of actual and potential species distribution is a critical requirement for effective ecosystem-based management and marine protected area design. In this study we tested the applicability of a terrestrial landscape modeling technique in a marine environment for predicting the distribution of ecologically and economically important groundfish, using 3 species of rockfish at Cordell Bank National Marine Sanctuary (CBNMS) as a model system. Autoclassification of multibeam bathymetry along with georeferenced submersible video transect data of the seafloor and demersal fishes were used to model the abundance and distribution of rockfish. Generalized linear models (GLMs) were created using habitat classification analyses of high-resolution (3 m) digital elevation models combined with fish presence/absence observations. Model accuracy was assessed using a reserved subset of the observation data. The resulting probability of occurrence models generated at 3 m resolution for the entire 120 km2 study area proved reliable in predicting the distribution of all the species. The accuracies of the models for Sebastes rosaceus, S. flavidus and S. elongatus were 96, 92 and 92%, respectively. The probability of occurrence of S. flavidus and S. rosaceus was highest in the high relief rocky areas and lowest in the low relief, soft sediment areas. The model for S. elongatus had an opposite pattern, with the highest predicted probability of occurrence taking place in the low relief, soft sediment areas and a lower probability of occurrence in the rocky areas. These results indicate that site-specific and species-specific algorithmic habitat classification applied to high-resolution bathymetry data can be used to accurately extrapolate the results from in situ video surveys of demersal fishes across broad areas of habitat. © Inter-Research 2010.