Evolution of functional specialization and division of labor

Abstract

Division of labor among functionally specialized modules occurs at all levels of biological organization in both animals and plants. Well-known examples include the evolution of specialized enzymes after gene duplication, the evolution of specialized cell types, limb diversification in arthropods, and the evolution of specialized colony members in many taxa of marine invertebrates and social insects. Here, we identify conditions favoring the evolution of division of labor by means of a general mathematical model. Our starting point is the assumption that modules contribute to two different biological tasks and that the potential of modules to contribute to these tasks is traded off. Our results are phrased in terms of properties of performance functions that map the phenotype of modules to measures of performance. We show that division of labor is favored by three factors: positional effects that predispose modules for one of the tasks, accelerating performance functions, and synergistic interactions between modules. If modules can be lost or damaged, selection for robustness can counteract selection for functional specialization. To illustrate our theory we apply it to the evolution of specialized enzymes coded by duplicated genes.

Fig. 1.

Convex (A) and concave (B) performance landscape for the first task. Performance , plotted on the z axis, is a monotonically increasing function of the trait values of the two modules, θ₁ and θ₂, plotted on the x and y axes, respectively. Shaded lines show performance along the constrained trait space defined by . Thick solid lines show performance along a straight line orthogonal to the constrained trait space. Another possibility to visualize the curvature of performance landscapes is by means of contour lines or iso-performance curves. An iso-performance curve consists of all combinations that result in the same level of performance . Iso-performance curves are shown as thin solid lines and are useful in Fig. 2 and Fig. S1. Importantly, for F₁ iso-performance curves are concave if and only if the thick solid line is convex (A) and convex if and only if the thick solid line is concave (B). For F₂ this relationship is reversed. Note that with nonequivalent modules it is also possible that a performance landscape is convex along one axis and concave along the other.

Fig. 2.

Fitness landscape for the case of two equivalent modules (A–C) and two nonequivalent modules (D–F). Contours of the fitness landscape are indicated by shading with lighter shades indicating higher fitness. Solid circles indicate the location of fitness maxima in the constrained trait. The expected direction of the evolutionary dynamics is indicated by arrows. The solid and dashed curves depict iso-performance curves of the underlying performance functions F₁ and F₂, respectively, as introduced in the Fig. 1 legend. (A) Iso-performance curves are convex for F₁ and concave for F₂, indicating that functional differentiation decreases performance in both tasks. Thus, the point is a fitness maximum. (B) Iso-performance curves are concave for F₁ and convex for F₂, indicating that functional differentiation increases performance in both tasks. Thus, the point is a saddle point of the fitness landscape. (C) Iso-performance curves are concave for both F₁ and F₂, indicating that functional differentiation increases performance for task 1 and decreases performance for task 2. In this particular example the increase in performance in task 1 is sufficiently large to outweigh the decrease in performance in task 2 such that the point is still a saddle point of the fitness landscape. For plots D, E, and F it is assumed that module 1 has an intrinsic advantage in contributing to task 2 whereas module 2 has an intrinsic advantage in contributing to task 1. Each plot in the lower row is a perturbation of the corresponding plot in the upper row. In D nonequivalence of modules moves the fitness maximum above the diagonal whereas in E and F nonequivalence moves the saddle point below the diagonal. In both cases, selection favors specialization of module 1 for task 1 and of module 2 for task 2. Note that extrema of the fitness landscape correspond to points where iso-performance curves for F₁ and F₂ are tangent to each other. Plots show the function with F₁ and F₂ defined in Eq. S2 in SI Text A with and .

Fig. P1.

Hypothetical fitness landscapes for two modules, each described by a quantitative trait. Along the thick, black line, modules are identical; i.e., no functional differentiation exists. For undifferentiated modules, the fitness landscape has a maximum at an intermediate value of the quantitative trait, indicated by the black circle. The red line corresponds to phenotypes in which modules are specialized for alternative tasks. Functional specialization is favored if an undifferentiated phenotype is located at a saddle point of the fitness landscape (A) and disfavored if it is located at a maximum of the fitness landscape (B).