Speciation: more likely through a genetic or through a learned habitat preference?

Abstract

A problem in understanding sympatric speciation is establishing how reproductive isolation can arise when there is disruptive selection on an ecological trait. One of the solutions that has been proposed is that a habitat preference evolves, and that mates are chosen within the preferred habitat. We present a model where the habitat preference can evolve either by means of a genetic mechanism or by means of learning. Employing an adaptive-dynamical analysis, we show that evolution proceeds either to a single population of specialists with a genetic preference for their optimal habitat, or to a population of generalists without a habitat preference. The generalist population subsequently experiences disruptive selection. Learning promotes speciation because it increases the intensity of disruptive selection. An individual-based version of the model shows that, when loci are completely unlinked and learning confers little cost, the presence of disruptive selection most probably leads to speciation via the simultaneous evolution of a learned habitat preference. For high costs of learning, speciation is most likely to occur via the evolution of a genetic habitat preference. However, the latter only happens when the effect of mutations is large, or when there is linkage between genes coding for the different traits.

Figure 1

Examples of trajectories of evolution of the specialization coefficient a, the genetic habitat preference g and the learning ability l. Note that although the trait space is drawn as a cube, in reality the biologically meaningful trait space is only limited in the g and l directions, but not in the a direction. The outcome of directional evolution depends on σ², which determines the trade-off between viabilities in the two habitats: (a) at low σ², evolution leads to a population of specialists with a genetic preference for their optimal habitat; (b) at intermediate σ², it depends on the initial conditions whether evolution proceeds to specialists with a genetic preference for their optimal habitat, or to generalists without habitat preference; (c) at high σ², evolution ends with a population of generalists without habitat preference. Parameters: E=10, K=10 000, c=0.10, a_A=0, a_B=1. The elements on the main diagonal of the mutational matrix used for the calculation of the trajectories are 4x(1−x)—where x represents the traits a, g and l—while the off-diagonal elements are all zero.

Figure 2

Examples of the possible end results of the individual-based simulations of evolution of the specialization coefficient a, the genetic habitat preference g and the learning ability l. The darkness of the squares indicates the number of individuals with different values of a, g and l at a particular time (a white square means that no individuals of that type are present). (a) Evolution to specialists preferring habitat A (k_c=10, σ²=0.20, c=0.04); (b) evolution to generalists without habitat preference (k_c=10, σ²=0.25, c=0.08); (c) speciation through a genetic habitat preference (k_c=5, σ²=0.18, c=0.05); (d) speciation through a learned habitat preference (k_c=10, σ²=0.18, c=0); (e) speciation through a combination of genetic and learned habitat preference (k_c=10, σ²=0.21, c=0.04); (f) polymorphism in a and g (k_c=10, σ²=0.21, c=0.07). Other parameters: K=400, E=10, ϕ=1, k_a=16, k_g=16, k_l=16. The linkage scheme used in these examples was ‘predetermined linkage’ (explanation in model description).

Figure 3

The outcome of individual-based simulations of evolution of the specialization coefficient a, the genetic habitat preference g and the learning ability l for free recombination (a), and when all loci are distributed over k_c chromosomes (b). The evolutionary end result depends on σ², which determines the trade-off between viabilities in the two habitats, and on c, the cost of learning. The evolutionary outcome for each parameter combination is based on a single run. Parameters: K=400, E=10, ϕ=1 and in (b) k_a=16, k_g=16, k_l=16.