Comparison of Logbook Reports of Incidental Blue Shark Catch Rates by Hawaii-based Longline Vessels to ®shery Observer Data by Application of a Generalized Additive Model

A generalized additive model (GAM) of blue shark, Prionace glauca, catch rates (catch per set) was ®tted to data gathered by National Marine Fisheries Service (NMFS) observers stationed aboard Hawaii-based commercial longline vessels (N ˆ 2010 longline sets) from March 1994 to December 1997. Its coef®cients were then applied to the values of predictor variables, which were also contained in logbook records that described the remainder of ®shery-wide effort during the study period (N ˆ 41 319 longline sets). The objective was to determine whether predictions generated by such a GAM could serve in lieu of observers on the large fraction of longline trips that do not carry an observer (approximately 95%). After deleting data considered false or inaccurate, much of which was associated with a small number of vessels, the relationship between catch rates as reported in logbooks and GAM predictions was expressed by log e …Y ‡ 1† ˆ 0:7952 log e …X ‡ 1† À 0:0586 where Y is the catch rate (i.e., the number of blue shark caught per set) and X the GAM predictions …R 2 ˆ 0:307; N ˆ 40 243†. Patterns of correspondence between logbook trends and GAM predictions were further re®ned by plotting the trends according to the type of ®shing effort (e.g., tuna-or sword®sh-directed). The highest mean catch rates reported in logbooks, the highest mean GAM predictions, and the greatest differences between the two occurred consistently in midyear on sword®sh trips. In contrast, mean values from logbooks and mean GAM predictions were closest for tuna-directed effort, but this re¯ected an order of magnitude reduction in the scale of catch rates rather than closely similar trends. A bootstrapping algorithm developed for the GAM yielded an estimate of 23.9% under-reporting for the study period, with approximate 95% prediction limits of 15.4±28.9%. We conclude that prediction with a GAM ®tted to ®shery observer data is a useful monitoring technique for the Hawaii-based commercial longline ®shery. It allowed us: to gain insight into ¯eet-wide and individual logbook reporting practices, to estimate the relationship between logbook data and predicted values, to characterize bias in this relationship, and to identify patterns speci®c to each major sector of the ®shery.