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Lion, S.

Publications and source records attributed to Lion, S..

11 recordsLinked to original sources

Persistence and near persistence via trait evolution: pathways to coexistence

Modern coexistence theory relies on the invasion criterion: each species must be able to increase when rare in a resident community at ecological equilibrium. However, when considering trait evolution, for example of competing species, this concept must be refined to account for evolution potentially feeding back on population dynamics. We analyze a model of two competing species whose quantitative traits evolve under stabilizing selection, assuming fixed but potentially unequal intraspecific trait variances. Using persistence theory, we derive conditions under which both species persist for all initial trait values, and we introduce the concept of near persistence, in which coexistence holds for a specific, biologically relevant, subset of initial trait configurations. Our analysis reveals that equal trait variances between species, which is commonly assumed in previous work, yield highly specific outcomes and obscure a broader set of coexistence scenarios that emerge under asymmetric trait variation. We identify fifteen distinct qualitative dynamical regimes as a function of initial conditions and trait variances. Moreover, we show that invasion analysis must often be performed at multiple eco-evolutionary equilibria, as invader dynamics may converge to different trait values depending on the ecological and evolutionary context. We discuss the biological implications of these results and perspectives for future work.

ecology↗

The ambivalent effect of spatial structure on the spread of cooperative anti-CRISPR phages

Some phages have evolved the ability to cooperate to evade the immunity triggered by their bacterial host. A first exposure to the phage may weaken the host defences and allow later infections to be successful. Because this cooperation requires sequential infections, the phage can invade the host population only if its initial density is sufficiently high in a well-mixed environment. However, most natural bacterial populations are spatially structured. Could spatial structure create more favourable conditions for viral spread? Here we study the effect of spatial structure on the dynamics of cooperative anti-CRISPR (Acr) phages spreading in a population of CRISPR-Cas resistant Pseudomonas aeruginosa bacteria. We show experimentally that spatial structure does not always promote the spread of Acr-phages. In particular, the effect of spatial structure is modulated by the efficacy of the bacterial hosts CRISPR-Cas resistance and by the efficacy of the phage Acr protein. These results are discussed in the light of a mathematical model we developed to describe the spread of the phage. The model allows us to understand the ambivalent effects of spatial structure via its effects on the reproduction and on the persistence of the phage. More generally, we find that spatial structure can have opposite effects on the epidemiological dynamics of the phage, depending on the properties of the Acr protein encoded by the phage. This joint experimental and theoretical work yields a deeper understanding of the spatial dynamics of cooperative strategies in phages.

microbiology↗

DoubleTrouble: Multiple infections and the coevolution of virulence-resistance in structured host-parasite populations.

Parasite competition is a key factor driving the epidemiology and evolution of parasites, and is expected to modulate the selective pressures acting upon the host, and to alter its response to infection. Using a nested co-evolutionary model where epidemiological traits (transmission, virulence and recovery) are derived from within-host interactions between parasite strains, we analyse using adaptive dynamics methodology the joint evolution of parasite virulence and host resistance. We compare the predicted coevolutionarily stable states under different competition regimes, including single infections (preemption), superinfection (strain dominance) and coinfections (transient strain coexistence). We find that coinfections select for lower virulence compared to superinfection, while the opposite trend is observed for host resistance. The local coexistence of parasites in coinfected hosts creates a kin selection effect that reduces both parasite virulence and host response. We show that the magnitude of this effect depends on the ecological context, and notably the degree of coupling between hosts. Our work contributes to building the gap between metapopulation ecology and epidemiology.

evolutionary biology↗

Effect of unequal vaccination coverage and migration on long-term pathogen evolution in a metapopulation

Worldwide inequalities in vaccine availability are expected to affect the spread and spatial distribution of infectious diseases. It is unclear, however, how spatial variation in vaccination coverage can affect the long-term evolution of pathogens. Here we use an analytical model and numerical simulations to analyse the influence of different imperfect vaccines on the potential evolution of pathogen virulence in a two-population model where vaccination coverage varies between populations. We focus on four vaccines, with different modes of action on the life-cycle of a pathogen infecting two host populations coupled by migration. We show that, for vaccines that reduce infection risk or transmissibility, spatial heterogeneity has little effect on pathogen prevalence and host mortality, and no effect on the evolution of pathogen virulence. In contrast, vaccines that reduce pathogen virulence can select for more virulent pathogens and may lead to the coexistence of different pathogen strains, depending on the degree of spatial heterogeneity in the metapopulation. This heterogeneity is driven by two parameters: pathogen migration and the difference in the vaccination rate between the two populations. We show that vaccines that only reduce pathogen virulence select mainly for a single pathogen strategy in the long term while vaccines that reduce both transmission and virulence can favor the coexistence of two pathogen genotypes. We discuss the implications and potential extensions of our analysis.

evolutionary biology↗

Extending eco-evolutionary theory with oligomorphic dynamics

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. "Oligomorphic dynamics" can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feed-backs, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.

evolutionary biology↗

Multi-morph eco-evolutionary dynamics in structured populations

Our understanding of the evolution of quantitative traits in nature is still limited by the challenge of including realistic trait distributions in the context of frequency-dependent selection and ecological feedbacks. We extend to class-structured populations a recently introduced "oligomorphic approximation" which bridges the gap between adaptive dynamics and quantitative genetics approaches and allows for the joint description of the dynamics of ecological variables and of the moments of multimodal trait distributions. Our theoretical framework allows us to analyse the dynamics of populations composed of several morphs and structured into distinct classes (e.g. age, size, habitats, infection status, species...). We also introduce a new approximation to simplify the eco-evolutionary dynamics using reproductive values. We illustrate the effectiveness of this approach by applying it to the important conceptual case of two-habitat migration-selection models. In particular, we show that our approach allows us to predict both the long-term evolutionary endpoints and the short-term transient dynamics of the eco-evolutionary process, including fast evolution regimes. We discuss the theoretical and practical implications of our results and sketch perspectives for future work.

evolutionary biology↗

Life-history evolution of class-structured populations in fluctuating environments

What is the influence of periodic environmental fluctuations on life-history evolution? We present a general theoretical framework to understand and predict the long-term evolution of lifehistory traits under a broad range of ecological scenarios. Specifically, we investigate how periodic fluctuations affect selection when the population is also structured in distinct classes. This analysis yields time-varying selection gradients that clarify the influence of the fluctuations of the environment on the competitive ability of a specific life-history mutation. We use this framework to analyse the evolution of key life-history traits of pathogens. We examine three different epidemiological scenarios and we show how periodic fluctuations of the environment can affect the evolution of virulence and transmission as well as the preference for different hosts. These examples yield new and testable predictions on pathogen evolution, and illustrate how our approach can provide a better understanding of the evolutionary consequences of time-varying environmental fluctuations in a broad range of scenarios.

evolutionary biology↗

The impact of antigenic escape on the evolution of virulence

Understanding the evolutionary drivers determining the transmission rate and virulence of pathogens remains an important challenge for evolutionary theory with clear implications to the control of human, agricultural and wildlife infectious disease. Although disease is often very dynamic, classical theory examines the long-term outcome of evolution at equilibrium and, in simple models, typically predicts that R0 is maximized. For example, immune escape may lead to complex disease dynamics including repeated epidemics, fluctuating selection and diversification. Here we model the impact of antigenic drift and escape on the evolution of virulence and show analytically that these non-equilibrium dynamics select for more acute pathogens with higher virulence. Specifically, under antigenic drift and when partial cross immunity leads to antigenic escape, our analysis predicts the long-term maximization of the intrinsic growth rate of the parasite resulting in more acute and virulent pathogens than those predicted by classic R0 maximization. Furthermore, it follows that these pathogens will have a lower R0 leading to implications for epidemic, endemic behavior and control. Our analysis predicts both the timings and outcomes of antigenic shifts leading to repeated epidemics and predicts the increase in variation in both antigenicity and virulence before antigenic escape. There is considerable variation in the degree of antigenic escape that occurs across pathogens and our results may help to explain the difference in virulence between related pathogens most clearly seen in the human A, B and C influenzas. More generally our results show the importance of examining the evolutionary consequences of non-equilibrium dynamics.

evolutionary biology↗

Evolution of pathogen traits in response to quantitative host resistance in heterogeneous environments

We model the evolutionary epidemiology of spore-producing plant pathogens in heterogeneous environments sown with several cultivars carrying quantitative resistances. The model explicitly tracks the infection-age structure and genetic composition of the pathogen population. Each strain is characterized by pathogenicity traits describing its infection efficiency and a time-varying sporulation curve taking into account lesion ageing. We first derive a general expression of the basic reproduction number [R]0 for fungal pathogens in heterogeneous environments. We show that evolutionary attractors of the model coincide with local maxima of [R]0 only if the infection efficiency is the same on all host types. We then study how three basic resistance characteristics (pathogenicity trait targeted, resistance effectiveness, and adaptation cost) in interaction with the deployment strategy (proportion of fields sown with a resistant cultivar) (i) lead to pathogen diversification at equilibrium and (ii) shape the transient dynamics from evolutionary and epidemiological perspectives. We show that quantitative resistance impacting only the sporulation curve will always lead to a monomorphic population, while dimorphism (i.e. pathogen diversification) can occur with resistance altering infection efficiency, notably with high adaptation cost and proportion of R cultivar. Accordingly, the choice of quantitative resistance genes operated by plant breeders is a driver of pathogen diversification. From an evolutionary perspective, the emergence time of the evolutionary attractor best adapted to the R cultivar tends to be shorter when the resistance impacts infection efficiency than when it impacts sporulation. In contrast, from an epidemiological perspective, the epidemiological control is always higher when the resistance impacts infection efficiency. This highlights the difficulty of defining deployment strategies of quantitative resistance maximising at the same time epidemiological and evolutionary outcomes.

evolutionary biology↗

Evolutionary emergence of infectious diseases in heterogeneous host populations

Emergence and re-emergence of pathogens are notoriously difficult to predict. The erratic nature of those events is reinforced by the stochastic nature of pathogen evolution during the early phase of an epidemic. For instance, mutations allowing pathogens to escape host resistance may boost pathogen spread and promote emergence. Yet, the ecological factors that govern such evolutionary emergence remain elusive both because of the lack of ecological realism of current theoretical frameworks and the difficulty of experimentally testing their predictions. Here we develop a theoretical model to explore the effects of the heterogeneity of the host population on the probability of pathogen emergence, with or without pathogen evolution. We show that evolutionary emergence and the spread of escape mutations in the pathogen population is more likely to occur when the host population contains an intermediate proportion of resistant hosts. We also show that lower pathogen inoculum size and higher diversity of host resistance decrease the probability of evolutionary emergence. Crucially, we present experimental confirmations of these predictions using lytic bacteriophages infecting their bacterial hosts containing diverse CRISPR-Cas immune defenses. We discuss the implications of these results for cross-species spillover and for the management of emerging infectious diseases.\n\nSignificance statementCan we predict the emergence of infectious diseases? The probability that an epidemic breaks out is highly dependent on the ability of the pathogen to acquire new adaptive mutations and to induce evolutionary emergence. Forecasting pathogen emergence thus requires a good understanding of the interplay between epidemiology and evolution taking place at the onset of an outbreak. Here, we provide a comprehensive theoretical framework to analyze the impact of host population heterogeneity on the probability of pathogen evolutionary emergence. We use this model to predict the impact of the fraction of susceptible hosts, the inoculum size of the pathogen and the diversity of host resistance on pathogen emergence. Our experiments using lytic bacteriophages and CRISPR-resistant bacteria support our theoretical predictions.

evolutionary biology↗

On the dynamics of reproductive values and phenotypic traits in class-structured populations

In natural populations, individuals of a given genotype may belong to different classes. Such classes can for instance represent different age groups, developmental stages, or habitats. Class structure has important evolutionary consequences because the fitness of individuals with the same genetic background may vary depending on their class. As a result, demographic transitions between classes can cause fluctuations that need to be removed when estimating selection on a trait. Intrinsic differences between classes are classically taken into account by weighting individuals by class-specific reproductive values, defined as the relative contribution of individuals in a given class to the future of the population. These reproductive values are generally constant weights calculated from a constant projection matrix. Here, I show, for large populations and clonal reproduction, that reproductive values can be defined as time-dependent weights satisfying dynamical demographic equations that only depend on the average between-class transition rates over all genotypes. Using these time-dependent demographic reproductive values yields a simple Price equation where the non-selective effects of between-class transitions are removed from the dynamics of the trait. This generalises previous theory to a large class of ecological scenarios, taking into account densitydependence, ecological feedbacks and arbitrary distributions of the trait. I discuss the role of reproductive values for prospective and retrospective analyses of the dynamics of phenotypic traits.\n\nNote on this versionCompared to the previous version of the manuscript, some changes have been made to improve the readability and structure of the text, and to clarify the connections with the existing literature. One reviewer pointed out inconsistencies in some of the numerical simulations, and in the mutation term in appendix A2, which have now been fixed. When checking the calculations, I have also identified a missing term in the equation for the dynamics of individual reproductive values. The new equation makes much more sense. Additional results are also presented, in particular for discrete-time models and populations with a continuous age structure, for which Fishers original definition of reproductive value can be recovered.

evolutionary biology↗