Search

Despite sophisticated mathematical models, the theory of microevolution is mostly treated as a qualitative rather than a quantitative tool. Numerical measures of selection, constraints, and evolutionary potential are often too loosely connected to theory to provide operational predictions of the response to selection. In this paper, we study the ability of a set of operational measures of evolvability and constraint to predict short‐term selection responses generated by individual‐based simulations. We focus on the effects of selective constraints under which the response in one trait is impeded by stabilizing selection on other traits. The conditional evolvability is a measure of evolutionary potential explicitly developed for this situation. We show that the conditional evolvability successfully predicts rates of evolution in an equilibrium situation, and further that these equilibria are reached with characteristic times that are inversely proportional to the fitness load generated by the constraining characters. Overall, we find that evolvabilities and conditional evolvabilities bracket responses to selection, and that they together can be used to quantify evolutionary potential on time scales where the G‐matrix remains relatively constant. [ABSTRACT FROM AUTHOR]

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can 'store' and 'recall' multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and 'generalize' (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time. [ABSTRACT FROM AUTHOR]