Comprehensive analysis of parameter and driver sensitivity is key to establishing the credibility of models
representing complex systems. This is especially so for models of natural systems where experimental
manipulation of the real-world to provide controlled validation data is not possible. End-to-end
ecosystem models (nutrients to birds and mammals) of marine ecosystems fall into this category with
applications for evaluating the effects of climate change and fishing on nutrient fluxes and the abundances
of flora and fauna. Here we present results of both ‘one-at-a-time’ (OAT) and variance based
global sensitivity analyses (GSA) of the fish and fishery aspects of StrathE2E, an end-to-end ecosystem
model of the North Sea. The sensitivity of the model was examined with respect to internal biological
parameters, and external drivers related to climate and human activity. The OAT Morris method was first
used to screen for factors most influential on model outputs. The Sobol GSA method was then used to
calculate quantitative sensitivity indices. The results indicated that the fish and shellfish components of
the model (demersal and pelagic fish, filter/deposit and scavenge/carnivore feeding benthos) were influenced
by different sets of factors. Harvesting rates were highly influential on demersal and pelagic fish
biomasses. Suspension/deposit feeding benthos were directly sensitive to changes in temperature, while
the temperature acted indirectly on pelagic fish through the connectivity between model components
of the food web. Biomass conversion efficiency was the most important factor for scavenge/carnivorous
feeding benthos. The results indicate the primacy of fishing as the most important process affecting total
fish biomass, together with varying responses to environmental factors which may be relevant in the
context of climate change. The non-linear responses and parameter interactions identified by the analysis
also highlight the necessity to use global rather than local methods for the sensitivity analysis of
ecosystem models.