Individual-Based Modeling of Collective Dynamics

Abstract

Collective dynamics play an important role in facilitating group movement, decision-making, and other large-scale behaviors in a wide variety of biological systems. In recent years, technological advances have made it possible to probe deeper into the microscopic factors underlying these macroscopic phenomena using computer-assisted mathematical modeling and data analysis. In this thesis, I describe and validate a mathematical individual-based model of collective motion developed by Couzin et al. (2005). I demonstrate how diffusion mapping, a relatively new data-mining technique, can be used to systematically analyze simulation data generated by the Couzin model to identify microscopic influences that can cause a coherent group to break apart. I find that group breakups occur when the orientation of the group deviates from its coherent direction by approximately 90°, and that changes in the orientation of only a few members of the group may play a disproportionate role in initiating an irreversible change in the orientation of the group as a whole. I suggest that an understanding of the breakup mechanism could be used to inform improved methods of controlling harmful locust swarms, illustrating this potential application with two case studies: the 1986-1989 outbreak of desert locusts (Schistocerca gregaria) in the Sahel region of northern Africa, and the 2010-2011 outbreak of Australian plague locusts (Chortoicetes terminifera) in southeastern Australia.