The coevolution of cooperation and dispersal in social groups and its implications for the emergence of multicellularity

Abstract

Background: Recent work on the complexity of life highlights the roles played by evolutionary forces at different levels of individuality. One of the central puzzles in explaining transitions in individuality for entities ranging from complex cells, to multicellular organisms and societies, is how different autonomous units relinquish control over their functions to others in the group. In addition to the necessity of reducing conflict over effecting specialized tasks, differentiating groups must control the exploitation of the commons, or else be out-competed by more fit groups.

Results: We propose that two forms of conflict - access to resources within groups and representation in germ line - may be resolved in tandem through individual and group-level selective effects. Specifically, we employ an optimization model to show the conditions under which different within-group social behaviors (cooperators producing a public good or cheaters exploiting the public good) may be selected to disperse, thereby not affecting the commons and functioning as germ line. We find that partial or complete dispersal specialization of cheaters is a general outcome. The propensity for cheaters to disperse is highest with intermediate benefit:cost ratios of cooperative acts and with high relatedness. An examination of a range of real biological systems tends to support our theory, although additional study is required to provide robust tests.

Conclusion: We suggest that trait linkage between dispersal and cheating should be operative regardless of whether groups ever achieve higher levels of individuality, because individual selection will always tend to increase exploitation, and stronger group structure will tend to increase overall cooperation through kin selected benefits. Cheater specialization as dispersers offers simultaneous solutions to the evolution of cooperation in social groups and the origin of specialization of germ and soma in multicellular organisms.

Figure 1

Globally optimal associations in dispersal and exploitation strategy for Model 1. Axes: P measures the impact of the public good on individual fitness, and s is the individual cost to cooperators in contributing to the public good. σ * = y*/(z* + y*) indexes the tendency of cooperators to disperse (σ* > 0.5) or cheats to disperse (σ* < 0.5). Thick curves demarcate areas of parameter space yielding different levels of σ, whereas thin lines show areas in with either y* = 1 or z* = 1. Caption a: k = 1.2, n = 0.1; caption b: k = 10, n = 0.1; caption c: k = 1.2, n = 0.9; caption d: k = 10, n = 0.9. Note that for legibility, very thin areas parallel to thick lines are omitted, in which 0.5 < σ* < 1 for caption c, and 0 < σ* < 1 for caption d. Unless otherwise noted, dispersal rates are greater than zero and less than unity. Other parameters: c = e = 0.2, Q = 0.2. See main text for numerical methods.

Figure 2

Effects of parameters on optimal dispersal levels for Model 1. Effects of public good production (P), frequency of cooperators (n) and effective group size (k). Caption a: overall dispersal d*; Caption b: investment in cooperator dispersal y*; Caption c investment in cheater dispersal z*. Thin line: k = 1.2, n = 0.1; dashed line: k = 10, n = 0.1; thick line: k = 1.2, n = 0.9; thick dashed line: k = 10, n = 0.9. Other parameters: c = e = 0.2, Q = 0.2, s = 0.6.

Figure 3

The fraction of simulations in Model 2 leading to different local optima. Results based on 100 simulations in which initial levels of n, y, and z are each set to a random number between zero and one, inclusive. These simulations produced one of three equilibria: n* = 0, 0 <n* < 1 or n* = 1. Caption a effect of the cost of cooperator dispersal (c) with P = Q = 0.3, s = 0.5, k = 2, e = 0.2; caption b effect of effective group size (k) with P = Q = 0.2, s = 0.6, e = 0.2, c = 0.3.

Figure 4

Locally optimal associations between dispersal and exploitation strategy. The frequency of dispersal in cooperators (y) and cheaters (z) evolves, and the frequency of cooperators (n) and cheaters (1-n) evolves. Initial frequencies in numerical studies: y = z = n = 0.5. As for Figure 1 except caption a: k = 1.2, c = 0.1; caption b: k = 10, c = 0.1; caption c: k = 1.2, c = 0.3; caption d: k = 10, c = 0.3.

Figure 5

Relatedness, r*, associated with simulations in Figure 4.

Figure 6

Overall cooperation, Φ = n *(1 - y*)+(1-n*)z*, associated with simulations in Figure 4.