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We developed delta generalised additive models (GAMs) to predict the spatial distribution of different size classes of South African hakes, Merluccius capensis and M. paradoxus, using demersal trawl survey data and geographical (latitude and longitude) and environmental features (depth, temperature, bottom dissolved oxygen and sediment type). Our approach consists of fitting, for each hake size class, two independent models, a binomial GAM and a quasi-Poisson GAM, whose predictions are then combined using the delta method. Delta GAMs were validated using an iterative cross-validation procedure, and their predictions were then employed to produce distribution maps for the southern Benguela. Delta GAM predictions confirmed existing knowledge about the spatial distribution patterns of South African hakes, and brought new insights into the factors influencing the presence/absence and abundance of these species. Our GAM approach can be used to produce distribution maps for spatially explicit ecosystem models of the southern Benguela in a rigorous and objective way. Ecosystem models are critical features of the ecosystem approach to fisheries, and distribution maps constructed using our GAM approach will enable a reliable allocation of species biomasses in spatially explicit ecosystem models, which will increase trust in the spatial overlaps and, therefore, the trophic interactions predicted by these models.Keywords: distribution maps, Merluccius capensis, Merluccius paradoxus, South Africa, spatial distributions

A dataset of linefish catch, effort and fish size distribution records has been assembled from archives to cover three short periods over the 100 years from 1897 to 1998 in four regions of the former Cape Colony, South Africa. Linefish catch and effort have increased several-fold over the period. Aggregate catch per unit effort (cpue) declined by more than 80&#37 from values in the 1890s, but the cpue of several species within that aggregate have declined much more. Analysis of historical mean size and modern length frequency data shows that in seven of 12 species considered, the mean length of fish declined substantially along with the increased fishing pressure. Multivariate analysis of cpue shows that the years 1897–1906 cluster quite close to the years 1927–1931, but a major change by the years 1986–1998, revealing a large change in abundances of linefish between the 1930s and the 1990s, which is also the period when fishing effort increased most. A related dataset was used to calculate the combined distribution of fish sizes of the 12 species in logarithmic size-classes in the same years. The negative slope of that size spectrum indicates the decline in numbers of large size-classes compared with small ones; the more negative the slope, the greater the relative decline in numbers of large fish. Slopes become significantly more negative in the modern period, showing that the modern linefish catch has fewer large fish and relatively more small ones than previously. Changes in linefish assemblages, implied by changes in catch composition, are different in the four regions studied. The cool-temperate upwelling regions differ from the warm-temperate ones, particularly with regard to the influence of the fast-growing, nomadic, pelagic snoek Thyrsites atun. Inclusion of snoek gives the size spectrum of the cool-temperate regions a shallower slope than the warm-temperate ones. A new method of plotting the size spectrum is believed to free the intercept (height) from dependence on the slope and simplifies interpretation of the relative values of height, which reflect overall fish abundance. Dominance curves reflect the distribution of biomass among species. The cool waters of the Western Cape show a trend towards increasing dominance with increased effort, whereas the warm-temperate regions show decreased dominance with increased fishing pressure. These findings have important consequences for fisheries management, because not only are several stocks badly overfished, but the linefish considered are predators at different trophic levels that influence the tropho-dynamic functioning of whole ecosystems.Afr. J. mar. Sci. 26: 161–177