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Highlights: • Modelled fish biomass was affected by interannual variability in the plankton food. • The effects were small compared with the high variability in observations. • Fish were highly affected by changes in the larval mortality of anchovy. Abstract: The Northern Humboldt Current System is the most productive eastern boundary upwelling system, generating about 10 % of the global fish production, mainly coming from small pelagic fish. It is bottom-up and top-down affected by environmental and anthropogenic variability, such as El-Niño Southern Oscillation and fishing pressure, respectively. The high variability of small pelagic fish in this system, as well as their economic importance, call for a careful management aided by the use of end-to-end models. This type of models represent the ecosystem as a whole, from the physics, through plankton up to fish dynamics. In this study, we utilised an end-to-end model consisting of a physical–biogeochemical model (CROCO-BioEBUS) coupled one-way with an individual-based fish model (OSMOSE). We investigated how time-variability in plankton food production affects fish populations in OSMOSE and contrasted it against the sensitivity of the model to two parameters with high uncertainty: the plankton accessibility to fish and fish larval mortality. Relative interannual variability in the modelled fish is similar to plankton variability. It is, however, small compared with the high variability seen in fish observations in this productive ecosystem. In contrast, changes in larval mortality have a strong effect on anchovies. In OSMOSE, it is a common practice to scale plankton food for fish, accounting for processes that may make part of the total plankton in the water column unavailable. We suggest that this scaling should be done constant across all plankton groups when previous knowledge on the different availabilities is lacking. In addition, end-to-end modelling systems should consider environmental impacts on other biological processes such as larval mortality in order to better capture the interactions between environmental processes, plankton and fish.