A Minimal Spatial Cellular Automata for Hierarchical Predatory Prey Simulation of Food Chains.
Ken A. Hawick, Chris J. Scogings · Conference on Scientific Computing · 2010
Models of complex spatial environmental and ecological systems are usually constructed using partial differential equations (PDEs), but cellular automata (CA’s) can provide microscopically simple yet macroscopically rich alternatives. We develop a cellular automata model of a hierarchical predator-prey system and show that even a minimal automaton is able to capture the essential boom-bust and dynamical behaviour of real physical systems. A single probability rate of predator death is used to control predator behaviour. We describe the model in detail and explore the CA model for oneand two-predator food chains. We find a well delineated phase transition in the 2-predator system when the predator lifetime parameter is varied and present some system analysis and quantitative metrics. We discuss the CA model in comparison with PDE and more detailed event-driven agent-based models.