title: Evolutionary entropy predicts the outcome of selection: Competition for resources that vary in abundance and diversity.
abstract: Competition between individuals for resources which are limited and diverse in composition is the ultimate driving force of evolution. Classical studies of this event contend that the outcome is a deterministic process predicted by the growth rate of the competing types-a tenet called the Malthusian selection principle. Recent studies of competition indicate that the dynamics of selection is a stochastic process, regulated by the population size, the abundance and diversity of the resource, and predicted by evolutionary entropy-a statistical parameter which characterizes the rate at which the population returns to the steady state condition after a random endogenous or exogenous perturbation. This tenet, which we will call the entropic selection principle entails the following relations:  This article delineates the analytic, computational and empirical support for this tenet. We show moreover that the Malthusian selection principle, a cornerstone of classical evolutionary genetics, is the limit, as population size and resource abundance tends to infinity of the entropic selection principle. The Malthusian tenet is an approximation to the entropic selection principle-an approximation whose validity increases with increasing population size and increasing resource abundance. Evolutionary entropy is a generic concept that characterizes the interaction dynamics of metabolic entities at several levels of biological organization: cellular, organismic and ecological. Accordingly, the entropic selection principle represents a general rule for explaining the processes of adaptation and evolution at each of these levels.
authors.affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA. ldemetr@oeb.harvard.edu
authors.forename: Lloyd
authors.initials: L
authors.lastname: Demetrius
authors.forename: Stéphane
authors.initials: S
authors.lastname: Legendre
mesh_terms: D005075:Biological Evolution; D008962:Models, Theoretical; D012641:Selection, Genetic