After the games are over: life-history trade-offs drive dispersal attenuation following range expansion

Abstract

Increased dispersal propensity often evolves on expanding range edges due to the Olympic Village effect, which involves the fastest and fittest finding themselves together in the same place at the same time, mating, and giving rise to like individuals. But what happens after the range's leading edge has passed and the games are over? Although empirical studies indicate that dispersal propensity attenuates following range expansion, hypotheses about the mechanisms driving this attenuation have not been clearly articulated or tested. Here, we used a simple model of the spatiotemporal dynamics of two phenotypes, one fast and the other slow, to propose that dispersal attenuation beyond preexpansion levels is only possible in the presence of trade-offs between dispersal and life-history traits. The Olympic Village effect ensures that fast dispersers preempt locations far from the range's previous limits. When trade-offs are absent, this preemptive spatial advantage has a lasting impact, with highly dispersive individuals attaining equilibrium frequencies that are strictly higher than their introduction frequencies. When trade-offs are present, dispersal propensity decays rapidly at all locations. Our model's results about the postcolonization trajectory of dispersal evolution are clear and, in principle, should be observable in field studies. We conclude that empirical observations of postcolonization dispersal attenuation offer a novel way to detect the existence of otherwise elusive trade-offs between dispersal and life-history traits.

Keywords: Fitness; life‐history evolution; natural selection; theory; traveling wave.

Figure 1

Spatial spread of the slow type under different scenarios about density dependence and life‐history trade‐offs under the clonal model in equations (2a) and (2b). Each curve shows the invasion profile of the slow type at a given point in time, with time indicated by colors ranging from blue at time τ = 0 to red at time τ = 100 in the top row and τ = 400 in the bottom row.

Figure 2

Spatial spread of the slow type under different scenarios about density dependence and life‐history trade‐offs under the diploid model in equations (3), (4), (5), (6). Each curve shows the invasion profile of the slow type at a given point in time, with time indicated by colors ranging from blue at time τ = 0 to red at time τ = 100 in the top row and τ = 400 in the bottom two rows.

Figure 3

Frequency of the slow type across the entire spatial domain long after all locations have been invaded and equilibrium densities at each location have been attained (i.e., τ = 10³). These frequencies, which correspond to the frequency indicated by the red line in the bottom left panel of Figure 2, are shown here as a function of the length of the spatial domain (x‐axis), the initial frequency of the slow type (dashed lines, separate panels), and the relative dispersal ability of the fast type (square: D = 1.1, circle: D = 1.2, triangle: D = 1.3).