Gut-associated functions are favored during microbiome assembly across a major part of C. elegans life.

The microbiome expresses a variety of functions that influence host biology. The range of functions depends on the microbiome's composition, which can change during the host's lifetime due to neutral assembly processes, host-mediated selection, and environmental conditions. To date, the exact dynamics of microbiome assembly, the underlying determinants, and the effects on host-associated functions remain poorly understood. Here, we used the nematode Caenorhabditis elegans and a defined community of fully sequenced, naturally associated bacteria to study microbiome dynamics and functions across a major part of the worm's lifetime of hosts under controlled experimental conditions. Bacterial community composition initially shows strongly declining levels of stochasticity, which increases during later time points, suggesting selective effects in younger animals as opposed to more random processes in older animals. The adult microbiome is enriched in genera Ochrobactrum and Enterobacter compared to the direct substrate and a host-free control environment. Using pathway analysis, metabolic, and ecological modeling, we further find that the lifetime assembly dynamics increase competitive strategies and gut-associated functions in the host-associated microbiome, indicating that the colonizing bacteria benefit the worm. Overall, our study introduces a framework for studying microbiome assembly dynamics based on stochastic, ecological, and metabolic models, yielding new insights into the processes that determine host-associated microbiome composition and function. IMPORTANCE: The microbiome plays a crucial role in host biology. Its functions depend on the microbiome composition that can change during a host's lifetime. To date, the dynamics of microbiome assembly and the resulting functions still need to be better understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modeling both the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time-series data obtained for the microbiome of the nematode Caenorhabditis elegans across the major part of its lifetime. Stochastic processes played a minor role, whereas beneficial bacteria as well as gut-associated functions enriched in hosts. This indicates that the host might actively shape the composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.

Chaotic turnover of rare and abundant species in a strongly interacting model community.

The composition of ecological communities varies not only between different locations but also in time. Understanding the fundamental processes that drive species toward rarity or abundance is crucial to assessing ecosystem resilience and adaptation to changing environmental conditions. In plankton communities in particular, large temporal fluctuations in species abundances have been associated with chaotic dynamics. On the other hand, microbial diversity is overwhelmingly sustained by a "rare biosphere" of species with very low abundances. We consider here the possibility that interactions within a species-rich community can relate both phenomena. We use a Lotka-Volterra model with weak immigration and strong, disordered, and mostly competitive interactions between hundreds of species to bridge single-species temporal fluctuations and abundance distribution patterns. We highlight a generic chaotic regime where a few species at a time achieve dominance but are continuously overturned by the invasion of formerly rare species. We derive a focal-species model that captures the intermittent boom-and-bust dynamics that every species undergoes. Although species cannot be treated as effectively uncorrelated in their abundances, the community's effect on a focal species can nonetheless be described by a time-correlated noise characterized by a few effective parameters that can be estimated from time series. The model predicts a nonunitary exponent of the power-law abundance decay, which varies weakly with ecological parameters, consistent with observation in marine protist communities. The chaotic turnover regime is thus poised to capture relevant ecological features of species-rich microbial communities.

Understanding and leveraging phenotypic plasticity during metastasis formation.

Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis at the cell population level, and its treatment consequences.

Dynamics in a behavioral-epidemiological model for individual adherence to a nonpharmaceutical intervention.

The SARS-CoV-2 pandemic has highlighted the importance of behavioral drivers in epidemic dynamics. With the relaxation of mandated nonpharmaceutical interventions (NPIs) formerly in place to decrease transmission, such as mask-wearing or social distancing, adherence to an NPI is now the result of individual decision-making. To study these coupled dynamics, we embed a game-theoretic model for individual NPI adherence within an epidemiological model. When the disease is endemic, we find that our model has multiple (but none concurrently stable) equilibria: one each with zero, complete, or partial NPI adherence. Surprisingly, for the equilibrium with partial NPI adherence, the number of infections is independent of the transmission rate. Therefore, in that regime, a change in the rate of pathogen transmission, e.g., due to another (mandated) NPI or a new variant, has no effect on endemic infection levels. On the other hand, we show that vaccination successfully decreases endemic infection levels, and, unexpectedly, also reduces the number of susceptibles at equilibrium when there is partial adherence. From a game-theoretic perspective, we find that highly effective NPIs lead at most to partial adherence. As this effectiveness decreases, partially effective NPIs initially lead to increases in population-level adherence, especially if the risk is high enough. However, a completely ineffective NPI results in no adherence. Furthermore, we identify parameter regions where the individual incentives may not align with those of society as a whole. Overall, our findings illustrate complexities that can arise due to behavioral-epidemiological feedback and suggest appropriate measures to avoid more pessimistic population-level outcomes.

Self-loops in evolutionary graph theory: Friends or foes?

Evolutionary dynamics in spatially structured populations has been studied for a long time. More recently, the focus has been to construct structures that amplify selection by fixing beneficial mutations with higher probability than the well-mixed population and lower probability of fixation for deleterious mutations. It has been shown that for a structure to substantially amplify selection, self-loops are necessary when mutants appear predominately in nodes that change often. As a result, for low mutation rates, self-looped amplifiers attain higher steady-state average fitness in the mutation-selection balance than well-mixed populations. But what happens when the mutation rate increases such that fixation probabilities alone no longer describe the dynamics? We show that self-loops effects are detrimental outside the low mutation rate regime. In the intermediate and high mutation rate regime, amplifiers of selection attain lower steady-state average fitness than the complete graph and suppressors of selection. We also provide an estimate of the mutation rate beyond which the mutation-selection dynamics on a graph deviates from the weak mutation rate approximation. It involves computing average fixation time scaling with respect to the population sizes for several graphs.

2023 BMC Ecology and Evolution image competition: the winning images.

In 2023, researchers from around the world entered the BMC Ecology and Evolution photography competition. As a result, we received a spectacular collection of photographs that capture the wonder of nature, those looking to understand it and glimpses into long lost worlds. This editorial celebrates the winning images selected by the Editor of BMC Ecology and Evolution and senior members of the journal's editorial board.

Bacterial c-di-GMP has a key role in establishing host-microbe symbiosis.

Most microbes evolve faster than their hosts and should therefore drive evolution of host-microbe interactions. However, relatively little is known about the characteristics that define the adaptive path of microbes to host association. Here we identified microbial traits that mediate adaptation to hosts by experimentally evolving the free-living bacterium Pseudomonas lurida with the nematode Caenorhabditis elegans as its host. After ten passages, we repeatedly observed the evolution of beneficial host-specialist bacteria, with improved persistence in the nematode being associated with increased biofilm formation. Whole-genome sequencing revealed mutations that uniformly upregulate the bacterial second messenger, cyclic diguanylate (c-di-GMP). We subsequently generated mutants with upregulated c-di-GMP in different Pseudomonas strains and species, which consistently increased host association. Comparison of pseudomonad genomes from various environments revealed that c-di-GMP underlies adaptation to a variety of hosts, from plants to humans. This study indicates that c-di-GMP is fundamental for establishing host association.

Individual costs and societal benefits of interventions during the COVID-19 pandemic.

Individual and societal reactions to an ongoing pandemic can lead to social dilemmas: In some cases, each individual is tempted to not follow an intervention, but for the whole society, it would be best if they did. Now that in most countries, the extent of regulations to reduce SARS-CoV-2 transmission is very small, interventions are driven by individual decision-making. Assuming that individuals act in their best own interest, we propose a framework in which this situation can be quantified, depending on the protection the intervention provides to a user and to others, the risk of getting infected, and the costs of the intervention. We discuss when a tension between individual and societal benefits arises and which parameter comparisons are important to distinguish between different regimes of intervention use.

Promoting extinction or minimizing growth? The impact of treatment on trait trajectories in evolving populations.

When cancers or bacterial infections establish, small populations of cells have to free themselves from homoeostatic regulations that prevent their expansion. Trait evolution allows these populations to evade this regulation, escape stochastic extinction and climb up the fitness landscape. In this study, we analyze this complex process and investigate the fate of a cell population that underlies the basic processes of birth, death, and mutation. We find that the shape of the fitness landscape dictates a circular adaptation trajectory in the trait space spanned by birth and death rates. We show that successful adaptation is less likely for parental populations with higher turnover (higher birth and death rates). Including density- or trait-affecting treatment we find that these treatment types change the adaptation dynamics in agreement with a geometrical analysis of fitness gradients. Treatment strategies that simultaneously target birth and death rates are most effective, but also increase evolvability. By mapping physiological adaptation pathways and molecular drug mechanisms to traits and treatments with clear eco-evolutionary consequences, we can achieve a much better understanding of the adaptation dynamics and the eco-evolutionary mechanisms at play in the dynamics of cancer and bacterial infections.

Categorizing update mechanisms for graph-structured metapopulations.

The structure of a population strongly influences its evolutionary dynamics. In various settings ranging from biology to social systems, individuals tend to interact more often with those present in their proximity and rarely with those far away. A common approach to model the structure of a population is evolutionary graph theory. In this framework, each graph node is occupied by a reproducing individual. The links connect these individuals to their neighbours. The offspring can be placed on neighbouring nodes, replacing the neighbours-or the progeny of its neighbours can replace a node during the course of ongoing evolutionary dynamics. Extending this theory by replacing single individuals with subpopulations at nodes yields a graph-structured metapopulation. The dynamics between the different local subpopulations is set by an update mechanism. There are many such update mechanisms. Here, we classify update mechanisms for structured metapopulations, which allows to find commonalities between past work and illustrate directions for further research and current gaps of investigation.

The future of theoretical evolutionary game theory.

Evolutionary game theory is a truly interdisciplinary subject that goes well beyond the limits of biology. Mathematical minds get hooked up in simple models for evolution and often gradually move into other parts of evolutionary biology or ecology. Social scientists realize how much they can learn from evolutionary thinking and gradually transfer insight that was originally generated in biology. Computer scientists can use their algorithms to explore a new field where machines not only learn from the environment, but also from each other. The breadth of the field and the focus on a few very popular issues, such as cooperation, comes at a price: several insights are re-discovered in different fields under different labels with different heroes and modelling traditions. For example, reciprocity or spatial structure are treated differently. Will we continue to develop things in parallel? Or can we converge to a single set of ideas, a single tradition and eventually a single software repository? Or will these fields continue to cross-fertilize each other, learning from each other and engaging in a constructive exchange between fields? Ultimately, the popularity of evolutionary game theory rests not only on its explanatory power, but also on the intuitive character of its models. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'.

Generation Time in Stage-Structured Populations under Fluctuating Environments.

AbstractA common measure of generation time is the average distance between two recruitment events along a genetic lineage. In populations with stage structure that live in a constant environment, this generation time can be computed from the elasticities of stable population growth to fecundities, and it is equivalent to another common measure of generation time: the average parental age of reproductive-value-weighted offspring. Here, we show three things. First, when the environment fluctuates, the average distance between two recruitment events along a genetic lineage is computed from the elasticities of the stochastic growth rate to fecundities. Second, under environmental stochasticity, this measure of generation time remains equivalent to the average parental age of reproductive-value-weighted offspring. Third, the generation time of a population in a fluctuating environment may deviate from the generation time the population would have in the average environment.

Diversity enables the jump towards cooperation for the Traveler's Dilemma.

Social dilemmas are situations in which collective welfare is at odds with individual gain. One widely studied example, due to the conflict it poses between human behaviour and game theoretic reasoning, is the Traveler's Dilemma. The dilemma relies on the players' incentive to undercut their opponent at the expense of losing a collective high payoff. Such individual incentive leads players to a systematic mutual undercutting until the lowest possible payoff is reached, which is the game's unique Nash equilibrium. However, if players were satisfied with a high payoff -that is not necessarily higher than their opponent's- they would both be better off individually and collectively. Here, we explain how it is possible to converge to this cooperative high payoff equilibrium. Our analysis focuses on decomposing the dilemma into a local and a global game. We show that players need to escape the local maximisation and jump to the global game, in order to reach the cooperative equilibrium. Using a dynamic approach, based on evolutionary game theory and learning theory models, we find that diversity, understood as the presence of suboptimal strategies, is the general mechanism that enables the jump towards cooperation.

Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations.

Sensitivity analysis in ecology and evolution is a valuable guide to rank demographic parameters depending on their relevance to population growth. Here, we propose a method to make the sensitivity analysis of population growth for matrix models solely classified by stage more fine-grained by considering the effect of age-specific parameters. The method applies to stable population growth, the stochastic growth rate, and transient growth. The method yields expressions for the sensitivity of stable population growth to age-specific survival and fecundity from which general properties are derived about the pattern of age-specific selective forces molding senescence in stage-classified populations.

Suppressors of fixation can increase average fitness beyond amplifiers of selection.

Evolutionary dynamics on graphs has remarkable features: For example, it has been shown that amplifiers of selection exist that-compared to an unstructured population-increase the fixation probability of advantageous mutations, while they decrease the fixation probability of disadvantageous mutations. So far, the theoretical literature has focused on the case of a single mutant entering a graph-structured population, asking how the graph affects the probability that a mutant takes over a population and the time until this typically happens. For continuously evolving systems, the more relevant case is that mutants constantly arise in an evolving population. Typically, such mutations occur with a small probability during reproduction events. We thus focus on the low mutation rate limit. The probability distribution for the fitness in this process converges to a steady state at long times. Intuitively, amplifiers of selection are expected to increase the population's mean fitness in the steady state. Similarly, suppressors of selection are expected to decrease the population's mean fitness in the steady state. However, we show that another set of graphs, called suppressors of fixation, can attain the highest population mean fitness. The key reason behind this is their ability to efficiently reject deleterious mutants. This illustrates the importance of the deleterious mutant regime for the long-term evolutionary dynamics, something that seems to have been overlooked in the literature so far.

Eco-evolutionary dynamics of clonal multicellular life cycles.

The evolution of multicellular life cycles is a central process in the course of the emergence of multicellularity. The simplest multicellular life cycle is comprised of the growth of the propagule into a colony and its fragmentation to give rise to new propagules. The majority of theoretical models assume selection among life cycles to be driven by internal properties of multicellular groups, resulting in growth competition. At the same time, the influence of interactions between groups on the evolution of life cycles is rarely even considered. Here, we present a model of colonial life cycle evolution taking into account group interactions. Our work shows that the outcome of evolution could be coexistence between multiple life cycles or that the outcome may depend on the initial state of the population - scenarios impossible without group interactions. At the same time, we found that some results of these simpler models remain relevant: evolutionary stable strategies in our model are restricted to binary fragmentation - the same class of life cycles that contains all evolutionarily optimal life cycles in the model without interactions. Our results demonstrate that while models neglecting interactions can capture short-term dynamics, they fall short in predicting the population-scale picture of evolution.

Sharp thresholds limit the benefit of defector avoidance in cooperation on networks.

Consider a cooperation game on a spatial network of habitat patches, where players can relocate between patches if they judge the local conditions to be unfavorable. In time, the relocation events may lead to a homogeneous state where all patches harbor the same relative densities of cooperators and defectors, or they may lead to self-organized patterns, where some patches become safe havens that maintain an elevated cooperator density. Here we analyze the transition between these states mathematically. We show that safe havens form once a certain threshold in connectivity is crossed. This threshold can be analytically linked to the structure of the patch network and specifically to certain network motifs. Surprisingly, a forgiving defector avoidance strategy may be most favorable for cooperators. Our results demonstrate that the analysis of cooperation games in ecological metacommunity models is mathematically tractable and has the potential to link topics such as macroecological patterns, behavioral evolution, and network topology.

2022 BMC Ecology and Evolution image competition: the winning images.

In 2022, researchers from around the world entered the BMC Ecology and Evolution photography competition. The contest produced a spectacular collection of photographs that capture the wonder of the natural world and the growing need to protect it as the human impact on the planet intensifies. This editorial celebrates the winning images selected by the Editor of BMC Ecology and Evolution and senior members of the journal's editorial board.

Vaccination strategies when vaccines are scarce: on conflicts between reducing the burden and avoiding the evolution of escape mutants.

When vaccine supply is limited but population immunization urgent, the allocation of the available doses needs to be carefully considered. One aspect of dose allocation is the time interval between the first and the second injections in two-dose vaccines. By stretching this interval, more individuals can be vaccinated with the first dose more quickly, which can be beneficial in reducing case numbers, provided a single dose is sufficiently effective. On the other hand, there has been concern that intermediate levels of immunity in partially vaccinated individuals may favour the evolution of vaccine escape mutants. In that case, a large fraction of half-vaccinated individuals would pose a risk-but only if they encounter the virus. This raises the question whether there is a conflict between reducing the burden and the risk of vaccine escape evolution or not. We develop an SIR-type model to assess the population-level effects of the timing of the second dose. Trade-offs can occur both if vaccine escape evolution is more likely or if it is less likely in half-vaccinated than in unvaccinated individuals. Their presence or absence depends on the efficacies for susceptibility and transmissibility elicited by a single dose.

On the effect of inheritance of microbes in commensal microbiomes.

BACKGROUND: Our current view of nature depicts a world where macroorganisms dwell in a landscape full of microbes. Some of these microbes not only transit but establish themselves in or on hosts. Although hosts might be occupied by microbes for most of their lives, a microbe-free stage during their prenatal development seems to be the rule for many hosts. The questions of who the first colonizers of a newborn host are and to what extent these are obtained from the parents follow naturally. RESULTS: We have developed a mathematical model to study the effect of the transfer of microbes from parents to offspring. Even without selection, we observe that microbial inheritance is particularly effective in modifying the microbiome of hosts with a short lifespan or limited colonization from the environment, for example by favouring the acquisition of rare microbes. CONCLUSION: By modelling the inheritance of commensal microbes to newborns, our results suggest that, in an eco-evolutionary context, the impact of microbial inheritance is of particular importance for some specific life histories.