The evolution of cooperation by the Hankshaw effect

Linked selection and the evolution of altruism in family-structured populations

Much research on the evolution of altruism via kin selection, group selection, and reciprocity focuses on the role of a single locus or quantitative trait. Very few studies have explored how linked selection, or selection at loci neighboring an altruism locus, impacts the evolution of altruism. While linked selection can decrease the efficacy of selection at neighboring loci, it might have other effects including promoting selection for altruism by increasing relatedness in regions of low recombination. Here, we used population genetic simulations to study how negative selection at linked loci, or background selection, affects the evolution of altruism. When altruism occurs between full siblings, we found that background selection interfered with selection on the altruistic allele, increasing its fixation probability when the altruistic allele was disfavored and reducing its fixation when the allele was favored. In other words, background selection has the same effect on altruistic genes in family-structured populations as it does on other, nonsocial, genes. This contrasts with prior research showing that linked selective sweeps can favor the evolution of cooperation, and we discuss possibilities for resolving these contrasting results.

Digest: Structuring interactions in Streptomyces

For bacteria growing in colonies, spatial structure can allow maintenance of costly traits such as the production of antibiotics. Using spatially structured environments, Westhoff et al. examined the benefits of streptomycin production for the bacterium Streptomyces griseus in competition with a streptomycin-susceptible strain. Streptomyces griseus outcompeted susceptible competitors, but the benefit of its antibiotic decreased as competitor resistance to streptomycin increased. Spatial structure also increased the ability of S. griseus to invade susceptible competitor populations from low starting densities. These results demonstrate that spatially structured environments can both provide and amplify benefits of antibiotics to antibiotic-producing bacteria on a microbial scale.

Computational Complexity as an Ultimate Constraint on Evolution

Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima-thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semismooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics-even ones that do not follow adaptive paths-can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question.

Resource abundance and the critical transition to cooperation

Cooperation is abundant in nature, occurring at all levels of biological complexity. Yet cooperation is continually threatened by subversion from noncooperating cheaters. Previous studies have shown that cooperation can nevertheless be maintained when the benefits that cooperation provides to relatives outweigh the associated costs. These fitness costs and benefits are not fixed properties, but can be affected by the environment in which populations reside. Here, we describe how one environmental factor, resource abundance, decisively affects the evolution of cooperative public goods production in two independent evolving systems. In the Avida digital evolution platform, populations evolved in environments with different levels of a required resource, whereas populations of Vibrio cholerae evolved in the presence of different nutrient concentrations. In both systems, cooperators and cheaters co-existed stably in resource-rich environments, whereas cheaters dominated in resource-poor environments. These two outcomes were separated by a sharp transition that occurred at a critical level of resource. These results offer new insights into how the environment affects the evolution of cooperation and highlight the challenges that populations of cooperators face when they experience environmental change.