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

Abstract

Generalized additive model (GAM) and regression tree analyses were conducted 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 methodology to relate catch rates to a tractable suite of readily measured or computed variables. Because the predictor variables are also either provided in or easily computed from the logbooks that commercial vessels submit upon landing fish for sale, it is likely that a model or models fitted to accurate observer data could then be applied on a fleet-wide basis to serve as a standard of comparison for the logbooks. The GAM included nine spatio-temporal, environmental, and operational variables and explained 72.1% of the deviance of blue shark catch rates. Latitude exerted the strongest effects of any individual variable; longitude was the most influential variable when adjusted for the effects of all other factors. Relatively cold sea surface temperatures were associated with high catch rates. The initial regression tree included 68 terminal nodes and 11 predictors. It was refined to a final tree with 42 terminal nodes, which reduced the root mean deviance by 65.3%. The tree was partitioned first on latitude 26.6°N, 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 characterized by higher and more variable blue shark catch rates. Predictions from the two analyses were highly correlated ( r=0.903, P⪡0.001). Moreover, use of these methods in combination aided greatly in the interpretation of results. We conclude that GAM and regression tree analyses can be usefully employed in the assessment of blue shark catch rates in this fishery. We suggest that either or both of these models could serve as comparison standards for commercial logbooks.