Optimizing functional groups in ecosystem models: Case study of the Great Barrier Reef

Uncertainty is inherent in ecosystem modelling, however its effects on modelling results are often poorly understood or ignored. This study addresses the issue of structural uncertainty or, more specifically, model resolution and its impact on the analysis of ecosystem vulnerability to threats. While guidelines for node assignments exist, they are not always underlined with quantitative analysis. Different resolutions of a coral reef network are investigated by comparing the simulated network dynamics over time in various threat scenarios. We demonstrate that the error between a higher-resolution and a lower-resolution models increases, first slowly then rapidly with increased degree of node aggregation. This informs the choice of an optimal model resolution whereby a finer level of a food web representation yields only minimal additional accuracy, while increasing computational cost substantially. Furthermore, our analysis shows that species biomass ratio and the consumption ratio are important parameters to guide node aggregation to minimize the error.