Progress in marine ecosystem modeling has seen a proliferation of the number of state variables and processes represented, in order to realistically describe system dynamics and feedbacks associated with, for example, changing climate. Assigning realistic and robust values to the many associated model parameters has become increasingly difficult due to under determination through lack of data and sensitivity to chosen parameterizations. Complexity science is becoming ever more relevant in this regard, with novel approaches coming to the fore based on traits, trade-offs and the theory of complex adaptive systems. We describe one such approach in which a global ocean circulation model was seeded with many tens of plankton functional types (PFTs) whose physiological characteristics were assigned stochastically at the outset. After the simulation was set in motion, competition eliminated unfavorable PFTs, giving rise to a robust self-organizing model architecture as an emergent property of the system
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