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. 2004 Jul 27;101(30):11019-24.
doi: 10.1073/pnas.0305059101. Epub 2004 Jul 15.

The Evolution of Reproductive Restraint Through Social Communication

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Free PMC article

The Evolution of Reproductive Restraint Through Social Communication

Justin Werfel et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

The evolution of altruistic behavior through group selection is generally viewed as possible in theory but unlikely in reality, because individual selection favoring selfish strategies should act more rapidly than group selection favoring cooperation. Here we demonstrate the evolution of altruism, in the form of conditional reproductive restraint based on an explicitly social mechanism, modulated by intrapopulation communication comprising signal and evolved response, in a spatially distributed predatory/parasitic/pathogenic model system. The predatory species consistently comes to exploit a signal implying overcrowding, individuals constraining their reproduction in response, with a corresponding increase in equilibrium reproduction rate in the absence of signal. This signaled restraint arises in a robust way for a range of model spatial systems; it outcompetes non-signal-based restraint and is not vulnerable to subversion by noncooperating variants. In these systems, communication is used to evaluate population density and regulate reproduction accordingly, consistent with central ideas of Wynne-Edwards [Wynne-Edwards, V. C. (1962) Animal Dispersion in Relation to Social Behavior (Hafner, New York)], whose claims about the evolutionary importance of group selection helped ignite decades of controversy. This quantitative simulation model shows how the key evolutionary transition from solitary living to sociality can occur. The process described here of cooperation evolving through communication may also help to explain other major evolutionary transitions such as intercellular communication leading to multicellular organisms.

Figures

Fig. 1.
Fig. 1.
Snapshots of a small area (20 × 20 cells) of the lattice (250 × 250) at successive time steps (left to right and then top to bottom), showing the spread and death of hosts and consumers (g = 0.1 and v = 0.2) and the initiation and diffusion of signal. Black cells are empty, green cells are hosts alone, red cells are hosts in the presence of consumers, and signal is shown as a striped overlay.
Fig. 3.
Fig. 3.
Shown are mean transmissibility τ (A), response to signal δ (B), and fraction of consumers in the presence of signal (C) for various values of host growth rate (g) and consumer virulence (v). The dashed lines mark runs without response (δ fixed at 0), and dotted lines mark runs where consumers do not signal but modify their transmissibility by δ at random with the probability given in C. In most cases with communication-based modulation of transmissibility, δ evolves to be significantly negative (more so in runs where signal is more infrequently present), and mean τ is increased accordingly compared with runs without response (A, dashed lines). When modulation occurs at random rather than in response to signal, mean δ is not significantly different from 0 (B, dotted lines). Means were taken over the consumer population, over time between steps 100,000 and 150,000 (or 50,000 and 100,000 for modulation-free runs, which converge very rapidly), and over 10 independent runs; error bars give the expected error in the final average over runs. Fluctuations over time of the population average within individual runs with communication-based modulation had standard deviations in both τ and δ 1/2 to 1 order of magnitude larger than the error bars shown.
Fig. 2.
Fig. 2.
Transmissibility τ (Upper) and response to signal δ (Lower) during a typical single run with g = 0.1 and v = 0.2. The solid line shows the mean value across the population, and the dashed lines show maximum and minimum values.

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