title: Spatial self-organization in a cyclic resource-species model.
abstract: Biological communities are remarkable in their ability to form cooperative ensembles that lead to coexistence through various types of niche partitioning, usually intimately tied to spatial structure. This is especially true in microbial settings where differential expression and regulation of genes allows members of a given species to alter their lifestyle so as to fill a functional role within the community. The resulting species interactions can involve feedback, as in the case of some bacterial consortia that participate in the cooperative degradation of a given resource in a succession of steps and in such a way that certain "later" species provide catalytic support for the primary degrader. We seek to capture the essential features of such spatially extended biological systems by introducing a lattice-based stochastic spatial model (interacting particle system) with cyclic local dynamics. Here, a given site progresses through a sequence of resource and species states in a prescribed order. Furthermore, this succession of states (at a site) is assumed to form a cyclic pattern due to a natural feedback mechanism. We explore conditions under which all the species are able to coexist and consider the extent to which this coexistence requires the development of spatio-temporal patterns, including spiral waves. This self-organization, if it occurs, results when synchronization of the dynamics at the microscopic level leads to macroscopic patterns. These patterns result in consumer-driven resource fluctuations that generate a form of spatio-temporal niche partitioning. As with most models of this complexity, we employ a mixture of mathematical analysis and simulations to develop an understanding of the resulting dynamics.
authors.affiliation: Department of Mathematics, University of Idaho, Moscow, ID 83844-1103, USA. krone@uidaho.edu
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Krone
authors.forename: Yongtao
authors.initials: Y
authors.lastname: Guan
mesh_terms: D000220:Adaptation, Biological; D001419:Bacteria / Q000378:metabolism*; D003198:Computer Simulation; D017753:Ecosystem; D004777:Environment; D005246:Feedback; D008954:Models, Biological; D015233:Models, Statistical