Larry Feinstein
Project: Computational Modeling of Ecological Community Assembly and Structure.
Objective: Describe and statistically quantify emergent properties of individual agents in a Complex Adaptive System.
Background and Justification: In the temperate forest floor microbial decomposer community, gene expression of extracellular enzymes is the mechanism for metabolism of substrate (leaves). Because different leaf species possess different biochemical composition (which is what microbes live off of), substrate that changes during decomposition opens up a multitude of variable niches for species to inhabit. Gene expression is the primary physiological mechanism that leads to changing microbial community composition, and is part of an intricate cascade of feedback loops between microbial physiology, the environment, and biological interactions such as competition for resources.
Ecosystems are complex adaptive self-organizing systems where individual agent behavior (gene expression) leads to emergent properties (increasingly complex community structure which increases system resilience and exergy).
There are conflicting perspectives of ecological theory that have been developed to attempt to understand community assembly and structure. Is community assembly random or directed? Is community composition a result of a founder effect (first come, first served) or due to ability of some species to out-compete others? What role do environmental filters play in determining in community composition? There are many important questions that science does not have definitive answers for. Microbial communities are especially difficult to study due to their inherent characteristics (size, similarity in morphology, inability to culture and observe most species). Computer simulation offers a powerful means to test ecological theory in the context of Complex Adaptive System agent behavior. Parameters may be set to simulate alternate theory (or no theory at all) and emergent community structure may be quantified. Cellular automata can be used to visualize changing community structure which can be quantified with multivariate statistical analysis.