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A broad-scale, small-fish survey was carried out in northern New Zealand inshore waters using beach seines. The survey covered 30 estuaries spanning ca 1000 km of coastline and three degrees of latitude. Correspondence analysis and cluster analysis were used to identify assemblages, and Generalized Additive Models (GAMs) were used to model the abundance and occurrence of individual species. We aimed to assess the utility of these models for making predictions. The results were mixed. Descriptive models of fish abundance performed well for four out of 12 species; for most other species, and species richness, the models described the data well but performed poorly to moderately under cross validation. Predictive models of fish abundance usually performed worse than descriptive models, but appeared reasonable for four species. Presence–absence models performed better overall than abundance models: descriptive models showed good performance for all 12 species, and predictive models performed well for eight species. For an independent data set, the models successfully predicted occurrence for five species. Water clarity, salinity and the amount of freshwater inflow were important predictor variables. Despite the limitations of our GAMs, they should be useful for planning intensive process-based research, and for guiding the management of human activities that impinge on coastal marine environments.