A New Statistical Dynamic Analysis of Ecosystem Patterns

While phenomenological investigations of ecosystem patterns often fail to reveal underlying dynamic mechanisms, we highlight a universal principle for pattern formation in ecosystems. We consider ecosystems to be typical complex adaptive systems that seek an optimal process to obtain maximized flux under given constraints. An analysis of the optimal process reveals underlying microscopic dynamic mechanisms that induce complex patterns in ecosystems. We emulate ecosystem patterns using a Self-Organization Feature Map: an artificial neural network theoretical model by which evolution processes, structural classifications, and the fractal growth of ecosystem patterns can be simulated. The results help us analyze the formation and dynamics of ecosystem patterns, with attending implications for the classification, protection, and optimization of ecosystems.