Generalized additive model and regression tree analyses of blue shark (Prionace glauca) catch rates by the Hawaii-based commercial longline fishery

Generalized additive model (GAM) and regression tree analyses were conducte d with blue shark, Prionace glauca, catch rates (catch per set) as reported by National Marine Fisheries Service observers serving aboard Hawaii-based commercial longline vessels from March 1994 through December 1997 (N=2010 longline sets). The objective was to use GAM and regression tree methodolog y to relate catch rates to a tractable suite of readily measured or compute d variables. Because the predictor variables are also either provided in or easily computed from the logbooks that commercial vessels submit upon land ing fish for sale, it is likely that a model or models fitted to accurate o bserver data could then be applied on a fleet-wide basis to serve as a stan dard of comparison for the logbooks. The GAM included nine spatio-temporal, environmental, and operational variables and explained 72.1% of the devian ce of blue shark catch rates. Latitude exerted the strongest effects of any individual variable; longitude was the most influential variable when adju sted for the effects of all other factors. Relatively cold sea surface temp eratures were associated with high catch rates. The initial regression tree included 68 terminal nodes and I I predictors. It was refined to a final t ree with 42 terminal nodes, which reduced the root mean deviance by 65.3%. The tree was partitioned first on latitude 26.6 degreesN, and then branched out to reach terminal nodes after 2-8 additional partitionings. Sets south of this latitude were characterized by lower catch rates and partitionings on a greater number and variety of predictors. Northerly sets were charact erized by higher and more variable blue shark catch rates. Predictions from the two analyses were highly correlated (r=0.903. P much less than0.001). Moreover, use of these methods in combination aided greatly in the interpre tation of results. We conclude that GAM and regression tree analyses can be usefully employed in the assessment of blue shark catch rates in this fish ery. We suggest that either or both of these models could serve as comparis on standards for commercial logbooks. Published by Elsevier Science B.V.