There is growing interest in using mechanistic ecosystem models for ecosystem-based management, as they have the advantage of capturing both bottom-up and top-down processes as well as system interactions from food web structure, spatial constraints, and human activities. However, they have the disadvantage of requiring many parameters, many of which are unknown and must be estimated or calibrated to available data. Sensitivity analysis (SA) is an important part of simulation model development in order to understand model uncertainty and which parameters are more or less influential, but has been relatively neglected with Atlantis models due to the large number of parameters and long simulation run time. The Atlantis Eastern English Channel (Atlantis-EEC) model has been applied to investigate ecosystem dynamics and processes as well as fishery management scenarios. Here we present the results of a SA of growth, mortality, and recruitment parameters, which are parameters particularly difficult to measure and thus commonly tuned through model calibration. To manage the large number of parameters in the model, we used a Morris screening approach. This method can efficiently provide information on parameter main effects and interactions/non-linear effects with relatively few simulations. We performed an initial SA including all groups on 90 parameters, where we found that the most important drivers of system dynamics and biomass across groups were: (1) plankton growth and mortality rates and (2) top predator's fixed recruitment and juvenile mortality rates. We then performed a follow-up SA on a subset of 61 parameters, excluding top predators and plankton groups from the analysis. We found that all parameters were important for system stability, while individual groups’ biomass were generally most influenced by their own parameters and a subset of benthic invertebrates. Nonlinear/interaction effects were widespread, demonstrating the prevalence of feedback loops in the trophic structure, and the importance of bottom-up effects and, to a lesser extent, top-down effects. The information gained from this SA provided a better understanding of the model structure. It also allowed us to make recommendations on the general Atlantis model calibration process as well as suggesting which parameters may be most important for propagation of uncertainty in model scenarios.