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We examine the multiscale influence of environmental and hydrological features of the riverine landscape on spatial and temporal variation in fish assemblages in eastern Australia. Multiresponse artificial neural network models provided accurate predictions of fish assemblages in the Mary River based on species presence–absence data (mean Bray–Curtis similarity between predicted and observed composition = 84%) but were less accurate when based on species relative abundance or biomass (mean similarity = 62% and 59%, respectively). Landscape- and local-scale habitat variables (e.g., catchment area and riparian canopy cover) and characteristics of the long-term flow regime (e.g., variability and predictability of flows) were more important predictors of fish assemblages than variables describing the short-term history of hydrological events. The relative importance of these variables was broadly …