Shape matters: lifecycle of cooperative patches promotes cooperation in bulky populations

Digital Evolution for Ecology Research: A Review

In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research.

In silico bacteria evolve robust cooperaion via complex quorum-sensing strategies

Many species of bacteria collectively sense and respond to their social and physical environment via 'quorum sensing' (QS), a communication system controlling extracellular cooperative traits. Despite detailed understanding of the mechanisms of signal production and response, there remains considerable debate over the functional role(s) of QS: in short, what is it for? Experimental studies have found support for diverse functional roles: density sensing, mass-transfer sensing, genotype sensing, etc. While consistent with theory, these results cannot separate whether these functions were drivers of QS adaption, or simply artifacts or 'spandrels' of systems shaped by distinct ecological pressures. The challenge of separating spandrels from drivers of adaptation is particularly hard to address using extant bacterial species with poorly understood current ecologies (let alone their ecological histories). To understand the relationship between defined ecological challenges and trajectories of QS evolution, we used an agent-based simulation modeling approach. Given genetic mixing, our simulations produce behaviors that recapitulate features of diverse microbial QS systems, including coercive (high signal/low response) and generalized reciprocity (signal auto-regulation) strategists - that separately and in combination contribute to QS-dependent resilience of QS-controlled cooperation in the face of diverse cheats. We contrast our in silico results given defined ecological challenges with bacterial QS architectures that have evolved under largely unknown ecological contexts, highlighting the critical role of genetic constraints in shaping the shorter term (experimental evolution) dynamics of QS. More broadly, we see experimental evolution of digital organisms as a complementary tool in the search to understand the emergence of complex QS architectures and functions.

Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure

Background

Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental change. However in stable conditions, they suffer from the increased mutational load, hence being overcome by non-mutators. However, these results don’t take into account the fact that an elevated mutation rate can impact the genetic structure, hence changing the sensitivity of the population to mutations. Here we used Aevol, an in silico experimental evolution platform in which genomic structures are free to evolve, in order to study the fate of mutator populations evolving for a long time in constant conditions.

Results

Starting from wild-types that were pre-evolved for 300,000 generations, we let 100 mutator populations (point mutation rate ×100) evolve for 100,000 further generations in constant conditions. As expected all populations initially undergo a fitness loss. However, after that the mutator populations started to recover. Most populations ultimately recovered their ancestors fitness, and a significant fraction became even fitter than the non-mutator control clones that evolved in parallel. By analyzing the genomes of the mutators, we show that the fitness recovery is due to two mechanisms: i. an increase in robustness through compaction of the coding part of the mutator genomes, ii. an increase of the selection coefficient that decreases the mean-fitness of the population. Strikingly the latter is due to the accumulation of non-coding sequences in the mutators genomes.

Conclusion

Our results show that the mutational burden that is classically thought to be associated with mutator phenotype is escapable. On the long run mutators adapted their genomes and reshaped the distribution of mutation effects. Therewith the lineage is able to recover fitness even though the population still suffers the elevated mutation rate. Overall these results change our view of mutator dynamics: by being able to reduce the deleterious effect of the elevated mutation rate, mutator populations may be able to last for a very long time; A situation commonly observed in nature.

Pleiotropy, cooperation, and the social evolution of genetic architecture

Pleiotropy has been suggested as a novel mechanism for stabilising cooperation in bacteria and other microbes. The hypothesis is that linking cooperation with a trait that provides a personal (private) benefit can outweigh the cost of cooperation in situations when cooperation would not be favoured by mechanisms such as kin selection. We analysed the theoretical plausibility of this hypothesis, with analytical models and individual-based simulations. We found that (1) pleiotropy does not stabilise cooperation, unless the cooperative and private traits are linked via a genetic architecture that cannot evolve (mutational constraint); (2) if the genetic architecture is constrained in this way, then pleiotropy favours any type of trait and not especially cooperation; (3) if the genetic architecture can evolve, then pleiotropy does not favour cooperation; and (4) there are several alternative explanations for why traits may be linked, and causality can even be predicted in the opposite direction, with cooperation favouring pleiotropy. Our results suggest that pleiotropy could only explain cooperation under restrictive conditions and instead show how social evolution can shape the genetic architecture.

Second-order cooperation: Cooperative offspring as a living public good arising from second-order selection on non-cooperative individuals

Switching rate between cooperating and non-cooperating genotypes is a crucial social evolution factor, often neglected by game theory-inspired theoretical and experimental frameworks. We show that the evolution of alleles increasing the mutation or phenotypic switching rates toward cooperation is in itself a social dilemma. Although cooperative offspring are often unlikely to reproduce, due to high cost of cooperation, they can be seen both as a living public good and a part of the extended parental phenotype. The competition between individuals that generate cooperators and ones that do not is often more relevant than the competition between cooperators and non-cooperators. The dilemma of second-order cooperation we describe relates directly to eusociality, but can be also interpreted as a division of labor or a soma-germline distinction. The results of our simulations shine a new light on what Darwin had already termed a "special difficulty" of evolutionary theory and describe a novel type of cooperation dynamics.