A phase transition induces chaos in a predator-prey ecosystem with a dynamic fitness landscape

The role of bacteriophages in shaping bacterial composition and diversity in the human gut

The microbiota of the gut has continued to co-evolve alongside their human hosts conferring considerable health benefits including the production of nutrients, drug metabolism, modulation of the immune system, and playing an antagonistic role against pathogen invasion of the gastrointestinal tract (GIT). The gut is said to provide a habitat for diverse groups of microorganisms where they all co-habit and interact with one another and with the immune system of humans. Phages are bacterial parasites that require the host metabolic system to replicate via the lytic or lysogenic cycle. The phage and bacterial populations are regarded as the most dominant in the gut ecosystem. As such, among the various microbial interactions, the phage-bacteria interactions, although complex, have been demonstrated to co-evolve over time using different mechanisms such as predation, lysogenic conversion, and phage induction, alongside counterdefense by the bacterial population. With the help of models and dynamics of phage-bacteria interactions, the complexity behind their survival in the gut ecosystem was demystified, and their roles in maintaining gut homeostasis and promoting the overall health of humans were elucidated. Although the treatment of various gastrointestinal infections has been demonstrated to be successful against multidrug-resistant causative agents, concerns about this technique are still very much alive among researchers owing to the potential for phages to evolve. Since a dearth of knowledge exists regarding the use of phages for therapeutic purposes, more studies involving experimental models and clinical trials are needed to widen the understanding of bacteria-phage interactions and their association with immunological responses in the gut of humans.

Limited predictability of body length in a fish population

Recent theoretical studies have identified chaotic dynamics in eco-evolutionary models. Yet, empirical evidence for eco-evolutionary chaos in natural ecosystems is lacking. In this study, we combine analyses of empirical data and an eco-evolutionary model to uncover chaotic dynamics of body length in a fish population (northeast Arctic cod: Gadus morhua). Consistent with chaotic attractors, the largest Lyapunov exponent (LE) of empirical data is positive, and approximately matches the LE of the model calculation, thus suggesting the potential for chaotic dynamics in this fish population. We also find that the autocorrelation function (ACF) of both empirical data and eco-evolutionary model shows a similar lag of approximately 7 years. Our combined analyses of natural time series and mathematical models suggest that chaotic dynamics of a phenotypic trait may be driven by trait evolution. This finding supports a growing theory that eco-evolutionary feedbacks can produce chaotic dynamics.

Rethinking the Prevalence and Relevance of Chaos in Ecology

Chaos was proposed in the 1970s as an alternative explanation for apparently noisy fluctuations in population size. Although readily demonstrated in models, the search for chaos in nature proved challenging and led many to conclude that chaos is either rare or nigh impossible to detect. However, in the intervening half-century, it has become clear that ecosystems are replete with the enabling conditions for chaos. Chaos has been repeatedly demonstrated under laboratory conditions and has been found in field data using updated detection methods. Together, these developments indicate that the apparent rarity of chaos was an artifact of data limitations and overreliance on low-dimensional population models. We invite readers to reevaluate the relevance of chaos in ecology, and we suggest that chaos is not as rare or undetectable as previously believed.

Predator-Prey Evolution from an Eco-evolutionary Trade-off Model: The Role of Trait Differentiation

We develop a novel eco-evolutionary modelling framework and demonstrate its efficacy by simulating the evolution of trait distributions in predator and prey populations. The eco-evolutionary modelling framework assumes that population traits have beta distributions and defines canonical equations for the dynamics of each total population size, the population's average trait value, and a measure of the population's trait differentiation. The trait differentiation is included in the modelling framework as a phenotype analogue, Q, of Wright's fixation index [Formula: see text], which is inversely related to the sum of the beta distribution shape parameters. The canonical equations may be used as templates to describe the evolution of population trait distributions in many ecosystems that are subject to stabilising selection. The solutions of the "population model" are compared with those of a "phenotype model" that simulates the growth of each phenotype as it interacts with every other phenotype under the same trade-offs. The models assume no sources of new phenotypic variance, such as mutation or gene flow. We examine a predator-prey system in which each population trades off growth against mortality: the prey optimises devoting resources to growth or defence against predation; and the predator trades off increasing its attack rate against increased mortality. Computer solutions with stabilising selection reveal very close agreement between the phenotype and population model results, which both predict that evolution operates to stabilise an initially oscillatory system. The population model reduces the number of equations required to simulate the eco-evolutionary system by several orders of magnitude, without losing verisimilitude for the overarching population properties. The population model also allows insights into the properties of the system that are not available from the equivalent phenotype model.

Eco-evolutionary dynamics may show an irreversible regime shift, illustrated by salmonids facing climate change

The enhanced or reduced growth of juvenile masu salmon (Oncorhynchus masou masou) may result from climate changes to their environment and thus impact on the eco-evolutionary dynamics of their life-history choices. Male juveniles with status, i.e., if their body size is larger than a threshold, stay in the stream and become resident males reproducing for multiple years, while those with smaller status, i.e., their body size is below the threshold, migrate to the ocean and return to the stream one year later to reproduce only once. Since juvenile growth is suppressed by the density of resident males, the fraction of resident males may stay in equilibrium or fluctuate wildly over a 2-year period. When the threshold value evolves, the convergence stable strategy may generate either an equilibrium or large fluctuations of male residents. If environmental changes occur faster than the rate of evolutionary adaptation, the eco-evolutionary dynamics exhibit a qualitative shift in the population dynamics. We also investigated the relative assessment models, in which individual life-history choices are made based on the individual’s relative status within the juvenile population. The eco-evolutionary dynamics are very different from the absolute assessment model, demonstrating the importance of understanding the mechanisms of life history choices when predicting the impacts of climate change.

Suppression gene drive in continuous space can result in unstable persistence of both drive and wild-type alleles

Rapid evolutionary processes can produce drastically different outcomes when studied in panmictic population models vs. spatial models. One such process is gene drive, which describes the spread of "selfish" genetic elements through a population. Engineered gene drives are being considered for the suppression of disease vectors or invasive species. While laboratory experiments and modelling in panmictic populations have shown that such drives can rapidly eliminate a population, it remains unclear if these results translate to natural environments where individuals inhabit a continuous landscape. Using spatially explicit simulations, we show that the release of a suppression drive can result in what we term "chasing" dynamics, in which wild-type individuals recolonize areas where the drive has locally eliminated the population. Despite the drive subsequently reconquering these areas, complete population suppression often fails to occur or is substantially delayed. This increases the likelihood that the drive is lost or that resistance evolves. We analyse how chasing dynamics are influenced by the type of drive, its efficiency, fitness costs, and ecological factors such as the maximal growth rate of the population and levels of dispersal and inbreeding. We find that chasing is more common for lower efficiency drives when dispersal is low and that some drive mechanisms are substantially more prone to chasing behaviour than others. Our results demonstrate that the population dynamics of suppression gene drives are determined by a complex interplay of genetic and ecological factors, highlighting the need for realistic spatial modelling to predict the outcome of drive releases in natural populations.

Cryptic selection forces and dynamic heritability in generalized phenotypic evolution

Individuals with different phenotypes can have widely-varying responses to natural selection, yet many classical approaches to evolutionary dynamics emphasize only how a population's average phenotype increases in fitness over time. However, recent experimental results have produced examples of populations that have multiple fitness peaks, or that experience frequency-dependence that affects the direction and strength of selection on certain individuals. Here, we extend classical fitness gradient formulations of natural selection in order to describe the dynamics of a phenotype distribution in terms of its moments-such as the mean, variance, and skewness. The number of governing equations in our model can be adjusted in order to capture different degrees of detail about the population. We compare our simplified model to direct Wright-Fisher simulations of evolution in several canonical fitness landscapes, and we find that our model provides a low-dimensional description of complex dynamics not typically explained by classical theory, such as cryptic selection forces due to selection on trait ranges, time-variation of the heritability, and nonlinear responses to stabilizing or disruptive selection due to asymmetric trait distributions. In addition to providing a framework for extending general understanding of common qualitative concepts in phenotypic evolution - such as fitness gradients, selection pressures, and heritability - our approach has practical importance for studying evolution in contexts in which genetic analysis is infeasible.