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  • Print publication year: 2010
  • Online publication date: July 2011

11 - Artificial neural networks in models of specialisation, guild evolution and sympatric speciation

from Part III - Artificial neural networks as models of perceptual processing in ecology and evolutionary biology


11.1 Introduction

The existence of sympatric speciation has been a contentious issue because empirical support was scarce and the underlying theoretical mechanisms were not as fully understood as we might like (e.g. Futuyma & Mayer, 1980; Rundle & Nosil, 2005). The view on sympatric speciation is currently changing, however. Recent theories demonstrate how ecological adaptations can drive speciation (Dieckmann et al., 2004; Doebeli et al., 2005). In concert with theoretical development, empirical evidence corroborating this view is accumulating (Barluenga et al., 2006; Panova et al., 2006; Savolainen et al., 2006). An obstacle for sympatric speciation is the exchange of alleles between lineages and the homogenising effect of recombination in sexual reproduction (Felsenstein, 1981; Rice & Salt, 1988). The current view on sympatric speciation is therefore that disruptive selection for evolutionary divergence has to be correlated with assortative mating and reproductive isolation (Felsenstein, 1981; Rundle & Nosil, 2005). This can be through linkage between ecological genes and mating genes, or a pleiotropic effect of ecological genes on mating behaviour. Orr & Smith (1998) make the distinction between extrinsic and intrinsic barriers to gene flow. Extrinsic factors are physical barriers in the environment that prevent encounters between individuals. Intrinsic factors are genetic traits that increase pre- or post-zygotic reproductive isolation. They define sympatric speciation as ‘the evolution of intrinsic barriers to gene flow in the absence of extrinsic barriers’.

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