Evolutionary rescue theory, antibiotic resistance and the details of bacterial infection

When a population faces a novel (stressful) environment this may cause the population to decline. In such situations evolutionary rescue theoryaims to predict the probability that a population adapts to the new environment (rescue), instead of facing the otherwise inevitable extinction. Thus, evolutionary rescue theory has the potential to help us understand when to expect the evolution of antibiotic resistance in bacterial populations. Yet, current models of evolutionary rescue fail to account for the mechanisms deployed by bacteria to cope with stressful conditions (like the presence of antibiotics). Here we examine two such mechanisms using stochastic modelling. First we examine the effect of biofilm formation, which occurs in the majority of bacterial infections. Biofilms have an explicit spatial structure, whilst standard evolutionary rescue theory assumes well-mixed populations. Secondly we examine the influence of persistercells, these are dormant cells that tolerate antibiotics exposure, which are also not modeled in standard evolutionary rescue theory

Individual variation in parental care drives divergence of sex roles

In many animal species, parents provide care for their offspring, but the parental roles of the two sexes differ considerably between and within species. Here, we use an individual-based simulation approach to investigate the evolutionary emergence and stability of parental roles. Our conclusions are in striking contrast to the results of analytical models. In the absence of initial differences between the sexes, our simulations do not predict the evolution of egalitarian care, but either female-biased or male-biased care. When the sexes differ in their pre-mating investment, the sex with the highest investment tends to evolve a higher level of parental care; this outcome does not depend on non-random mating or uncertainty of paternity. If parental investment evolves jointly with sexual selection strategies, evolution results in either the combination of female-biased care and female choosiness or in male-biased care and the absence of female preferences. The simulations suggest that the parental care pattern drives sexual selection, and not vice versa. Finally, our model reveals that a population can rapidly switch from one type of equilibrium to another one, suggesting that parental sex roles are evolutionarily labile. By combining simulation results with fitness calculations, we argue that all these results are caused by the emergence of individual variation in parental care strategies, a factor that was hitherto largely neglected in sex-role evolution theory

Individual variation in parental care drives divergence of sex roles

In many animal species, parents provide care for their offspring, but the parental roles of the two sexes differ considerably between and within species. Here, we use an individual-based simulation approach to investigate the evolutionary emergence and stability of parental roles. Our conclusions are in striking contrast to the results of analytical models. In the absence of initial differences between the sexes, our simulations do not predict the evolution of egalitarian care, but either female-biased or male-biased care. When the sexes differ in their pre-mating investment, the sex with the highest investment tends to evolve a higher level of parental care; this outcome does not depend on non-random mating or uncertainty of paternity. If parental investment evolves jointly with sexual selection strategies, evolution results in either the combination of female-biased care and female choosiness or in male-biased care and the absence of female preferences. The simulations suggest that the parental care pattern drives sexual selection, and not vice versa. Finally, our model reveals that a population can rapidly switch from one type of equilibrium to another one, suggesting that parental sex roles are evolutionarily labile. By combining simulation results with fitness calculations, we argue that all these results are caused by the emergence of individual variation in parental care strategies, a factor that was hitherto largely neglected in sex-role evolution theory

Modelling the evolution of learning

The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. Here, we present a novel way of modeling learning using small neural networks and a simple, biology-inspired learning algorithm. We used this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning regularly evolved in our individual-based simulations. However, the evolution of learning was less likely in relatively constant environments(where learning is less important) or in case of short-lived agents (that cannot afford to spend much of their lifetime on exploration). Once learning did evolve, the characteristics of the learning strategy and the average performance after learning were surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, agent lifespan had a strong effect on the evolved learning strategy.Interestingly, a longer learning period did not always lead to a better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, our study shows that even a relatively simple learning mechanism can lead to efficient adaptation