Simple ecological models have been used by ecologists to understand how interactions such as density-dependence affect population dynamics. These relatively elementary models ignore the spatial aspect of populations. This research will contribute to the understanding of how spatial processes influence population dynamics. Cellular automata models of populations that assume that each individual exists in a unique location and that competition is occurring over some predefined local scale will be developed. How local competitive interactions translate into spatial and temporal dynamics and what general class of patterns (stable, cyclical, or chaotic) are expected for competing populations distributed in space will be determined. Conditions under which dispersal and competition can lead to coexistence in a spatially distributed system will be examined, as will the problem of how differences in competitive scale and dispersal scale influence the patterns. Simple models have already yielded important insights into how intra- and interspecific competition can produce chaotic spatial dynamics. These models for single species and two species will be generalized to models for multiple species. %%% Overall, results from this work ought to enhance our knowledge of how local competitive interactions translate into spatial and temporal dynamics. It will ascertain what general class of patterns can be expected for competing populations that are distributed in space. In addition, it will determine the conditions under which dispersal and competition can lead to coexistence in a spatially distributed system and will examine how differences in scale impacts patterns within populations and ecological communities. Such information will be useful in learning how often we should expect chaotic spatial dynamics in natural systems. By intensively studying two important variables (competition and dispersal), it will be possible to discover what patterns are most likely in spatially distributed populations.
