title: Chaos and unpredictability in evolution.
abstract: The possibility of complicated dynamic behavior driven by nonlinear feedbacks in dynamical systems has revolutionized science in the latter part of the last century. Yet despite examples of complicated frequency dynamics, the possibility of long-term evolutionary chaos is rarely considered. The concept of "survival of the fittest" is central to much evolutionary thinking and embodies a perspective of evolution as a directional optimization process exhibiting simple, predictable dynamics. This perspective is adequate for simple scenarios, when frequency-independent selection acts on scalar phenotypes. However, in most organisms many phenotypic properties combine in complicated ways to determine ecological interactions, and hence frequency-dependent selection. Therefore, it is natural to consider models for evolutionary dynamics generated by frequency-dependent selection acting simultaneously on many different phenotypes. Here we show that complicated, chaotic dynamics of long-term evolutionary trajectories in phenotype space is very common in a large class of such models when the dimension of phenotype space is large, and when there are selective interactions between the phenotypic components. Our results suggest that the perspective of evolution as a process with simple, predictable dynamics covers only a small fragment of long-term evolution.
authors.affiliation: Department of Zoology, University of British Columbia, 6270 University Boulevard, Vancouver, B.C. V6T 1Z4, Canada; Department of Mathematics, University of British Columbia, 6270 University Boulevard, Vancouver, B.C. V6T 1Z4, Canada. doebeli@zoology.ubc.ca.
authors.forename: Michael
authors.initials: M
authors.lastname: Doebeli
authors.forename: Iaroslav
authors.initials: I
authors.lastname: Ispolatov
mesh_terms: D000818:Animals; D019143:Evolution, Molecular; D008957:Models, Genetic; D017711:Nonlinear Dynamics
keywords: Adaptive dynamics; chaos; complex dynamics; high-dimensional phenotype space; logistic competition models