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Integr Comp Biol. 2010 Dec;50(6):1167-84. doi: 10.1093/icb/icq117. Epub 2010 Aug 18.

Predicting evolution with generalized models of divergent selection: a case study with poeciliid fish.

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Biological Station and Department of Zoology, University of Oklahoma, Norman, OK 73019, USA.


Over the past century and half since the process of natural selection was first described, one enduring question has captivated many, "how predictable is evolution?" Because natural selection comprises deterministic components, the course of evolution may exhibit some level of predictability across organismal groups. Here, I provide an early appraisal of the utility of one particular approach to understanding the predictability of evolution: generalized models of divergent selection (GMDS). The GMDS approach is meant to provide a unifying framework for the science of evolutionary prediction, offering a means of better understanding the causes and consequences of phenotypic and genetic evolution. I describe and test a GMDS centered on the evolution of body shape, size of the gonopodium (sperm-transfer organ), steady-swimming abilities, fast-start swimming performance, and reproductive isolation between populations in Gambusia fishes (Family Poeciliidae). The GMDS produced some accurate evolutionary predictions in Gambusia, identifying variation in intensity of predation by piscivorous fish as a major factor driving repeatable and predictable phenotypic divergence, and apparently playing a key role in promoting ecological speciation. Moreover, the model's applicability seems quite general, as patterns of differentiation in body shape between predator regimes in many disparate fishes match the model's predictions. The fact that such a simple model could yield accurate evolutionary predictions in distantly related fishes inhabiting different geographic regions and types of habitat, and experiencing different predator species, suggests that the model pinpointed a causal factor underlying major, shared patterns of diversification. The GMDS approach appears to represent a promising method of addressing the predictability of evolution and identifying environmental factors responsible for driving major patterns of replicated evolution.

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