Infinitely many distinct trait values may arise in populations bearing quantitative traits, and modeling their population dynamics is thus a formidable task. While classical models assume fixed or infinite population size, models in which the total population size fluctuates due to demographic noise in births and deaths can behave qualitatively differently from constant or infinite population models due to density-dependent dynamics. In this paper, I present a stochastic field theory for the eco-evolutionary dynamics of finite populations bearing one-dimensional quantitative traits. I derive stochastic field equations that describe the evolution of population densities, trait frequencies, and the mean value of any trait in the population. These equations recover well-known results such as the replicator-mutator equation, Price equation, and gradient dynamics in the infinite population limit. For finite populations, the equations describe the intricate interplay between natural selection, noise-induced selection, eco-evolutionary feedback, and neutral genetic drift in determining evolutionary trajectories. My work uses ideas from statistical physics, calculus of variations, and SPDEs, providing alternative methods that complement the measure-theoretic martingale approach that is more common in the literature.

This paper considers Lotka-Volterra competitive systems characterizing laboratory experiment by Hu et al. (Science, 378:85-89, 2022). Using dynamical systems theory and projection method, we give theoretical analysis and numerical simulation on the model with four species by demonstrating equilibrium stability, periodic oscillation and chaotic fluctuation in the systems. It is shown that varying one competition strength could lead to emergent phases and phase transitions between stable full coexistence, stable partial coexistence, stable persistence of a unique species, persistent periodic oscillation, and persistent chaotic fluctuation in a smooth fashion. Here, the stronger the competition is, the less the number of stable coexisting species, or the higher the amplitude of periodic oscillation, or the more irregular the fluctuation. Our results are consistent with experimental observation and provide new insight. This work is important in understanding effect of competition on emergent phases and phase transitions in competitive systems.

Mutation accumulation (MA) experiments play an important role in understanding evolution. For microbial populations, such experiments often involve periods of population growth, such that a single individual can make a visible colony, followed by severe bottlenecks. Previous work has quantified the effect of positive and negative selection on MA experiments, demonstrating for example that with 20 generations of growth between bottlenecks, big-benefit mutations can be over-represented by a factor of five or more (Wahl and Agashe, 2022). This previous work assumed a deterministic model for population growth. We now develop a fully stochastic model, including realistic offspring distributions that incorporate genetic drift and allow for the loss of rare lineages. We demonstrate that when stochastic offspring distributions are considered, selection bias is even stronger than previously predicted. We describe several analytical and numerical methods that offer an accurate correction for the effects of selection on the observed distribution of fitness effects, describe the practical considerations in implementing each method, and demonstrate the use of this correction on simulated MA data.

We model natural selection for or against an anti-parasite (or anti-predator) defense allele in a host (or prey) population that is structured into many demes. The defense behavior has a fitness cost for the actor compared to non defenders ("cheaters") in the same deme and locally reduces parasite growth rates. Hutzenthaler et al. (2022) have analytically derived a criterion for fixation or extinction of defenders in the limit of large populations, many demes, weak selection and slow migration. Here, we use both individual-based and diffusion-based simulation approaches to analyze related models. We find that the criterion still leads to accurate predictions for settings with finitely many demes and with various migration patterns. A key mechanism of providing a benefit of the defense trait is genetic drift due to randomness of reproduction and death events leading to between-deme differences in defense allele frequencies and host population sizes. We discuss an inclusive-fitness interpretation of this mechanism and present in-silico evidence that under these conditions a defense trait can be altruistic and still spread in a structured population.

Phages use bacterial host resources to replicate, intrinsically linking phage and host survival. To understand phage dynamics, it is essential to understand phage-host ecology. A key step in this ecology is infection of bacterial hosts. Previous work has explored single and multiple, sequential infections. Here we focus on the theory of simultaneous infections, where multiple phages simultaneously attach to and infect one bacterial host cell. Simultaneous infections are a relevant infection dynamic to consider, especially at high phage densities when many phages attach to a single host cell in a short time window. For high bacterial growth rates, simultaneous infection can result in bi-stability: depending on initial conditions phages go extinct or co-exist with hosts, either at stable densities or through periodic oscillations of a stable limit cycle. This bears important consequences for phage applications such as phage therapy: phages can persist even though they cannot invade. Consequently, through spikes in phage densities it is possible to infect a bacterial population even when the phage basic reproductive number is less than one. In the regime of stable limit cycles, if timed right, only small densities of phage may be necessary.

Although extensively studied, the maintenance of biodiversity remains a highly debated and investigated topic of contemporary research in ecology. Several studies have quantified the contributions of various coexistence mechanisms to biodiversity. However, often stochastic individual-level interactions are abstracted away, or mechanisms are studied in isolation. The intertwined nature and reciprocal influences between mechanisms, as they arise from individual-level interactions, are therefore rarely considered. We propose a novel mechanistic simulation model grounded in neutral theory to capture and quantify emergent effects arising from such mechanism interactions. Three coexistence mechanisms are supported: storage effect, intransitivity, and resource partitioning. We show that basic neutral dynamics and related models of isolated mechanisms can be replicated. Beyond that, we observe difficult to predict, yet significant emergent effects for mechanism combinations. In some cases, coexistence times could be extended more than tenfold compared to the individual mechanisms' performances. Our findings suggest that studies of individual coexistence mechanisms might be insufficient and indeed misleading for quantifying their overall impact on biodiversity. The particular combination of mechanisms and their interactions appear to be of vital importance.

A main challenge in molecular evolution is to find computationally efficient mutation models with flexible assumptions that properly reflect genetic variation. The infinite sites model assumes that each mutation event occurs at a site never previously mutant, i.e. it does not allow recurrent mutations. This is reasonable for low mutation rates and makes statistical inference much more tractable. However, recurrent mutations are common enough to be observable from genetic variation data, even in species with low per-site mutation rates such as humans. The finite sites model on the other hand allows for recurrent mutations but is computationally unfeasible to work with in most cases. In this work, we bridge these two approaches by developing a novel molecular evolution model, the almost infinite sites model, that both admits recurrent mutations and is tractable. We provide a recursive characterization of the likelihood of our proposed model under complete linkage and outline a parsimonious approximation scheme for computing it.

Tree-killing bark beetle infestations are a cause of massive coniferous forest mortality impacting forest ecosystems and the ecosystem services they provide. Models predicting bark beetle outbreaks are crucial for forest management and conservation, necessitating studies of the effect of epidemiological traits on the probability and severity of outbreaks. Due to the aggregation behaviour of beetles and host tree defence, this epidemiological interaction is highly non-linear and outbreak behaviour remains poorly understood, motivating questions about when an outbreak can occur, what determines outbreak severity, and how aggregation behaviour modulates these quantities. Here, we apply the principle of distributed delays to create a novel and mathematically tractable model for beetle aggregation in an epidemiological framework. We derive the critical outbreak threshold for the beetle emergence rate, which is a quantity analogous to the basic reproductive ratio, R, for epidemics. Beetle aggregation qualitatively impacts outbreak potential from depending on the emergence rate alone in the absence of aggregation to depending on both emergence rate and initial beetle density when aggregation is required. Finally, we use a stochastic model to confirm that our deterministic model predictions are robust in finite populations.

Ordinary differential equation models such as the classical SIR model are widely used in epidemiology to study and predict infectious disease dynamics. However, these models typically assume that populations are homogeneously mixed, ignoring possible variations in disease prevalence due to spatial heterogeneity. To address this issue, reaction-diffusion models have been proposed as an alternative approach to modeling spatially continuous populations in which individuals move in a diffusive manner. In this study, we explore the conditions under which such spatial structure must be explicitly considered to accurately predict disease spread, and when the assumption of homogeneous mixing remains adequate. In particular, we derive a critical threshold for the diffusion coefficient below which disease transmission dynamics exhibit spatial heterogeneity. We validate our analytical results with individual-based simulations of disease transmission across a two-dimensional continuous landscape. Using this framework, we further explore how key epidemiological parameters such as the probability of disease establishment, its maximum incidence, and its final epidemic size are affected by incorporating spatial structure into SI, SIS, and SIR models. We discuss the implications of our findings for epidemiological modeling and identify design considerations and limitations for spatial simulation models of disease dynamics.

Specialist species thriving under specific environmental conditions in narrow geographic ranges are widely recognized as heavily threatened by climate deregulation. Many might rely on both their potential to adapt and to disperse towards a refugium to avoid extinction. It is thus crucial to understand the influence of environmental conditions on the unfolding process of adaptation. Here, I study the eco-evolutionary dynamics of a sexually reproducing specialist species in a two-patch quantitative genetic model with moving optima. Thanks to a separation of ecological and evolutionary time scales and the phase-line study of the selection gradient, I derive the critical environmental speed for persistence, which reflects how the existence of a refugium impacts extinction patterns and how it relates to the cost of dispersal. Moreover, the analysis provides key insights about the dynamics that arise on the path towards this refugium. I show that after an initial increase of population size, there exists a critical environmental speed above which the species crosses a tipping point, resulting into an abrupt habitat switch. In addition, when selection for local adaptation is strong, this habitat switch passes through an evolutionary "death valley", leading to a phenomenon related to evolutionary rescue, which can promote extinction for lower environmental speeds than the critical one.

The evolution of microbe-microbe mutualistic symbiosis is considered to be promoted by repeated exchanges of fitness benefits, which can generate positive fitness feedbacks ('partner fidelity feedback') between species. However, previous evolutionary models for mutualism have not captured feedback dynamics or coupling of fitness between species. Here, a simple population model is developed to understand the evolution of mutualistic symbiosis in which two microbial species (host and symbiont) continuously grow and exchange fitness benefits to generate feedback dynamics but do not strictly control each other. The assumption that individual microbes provide constant amounts of resources, which are equally divided among interacting partner individual, enables us to reveal a simple rule for the evolution of costly mutualism with positive fitness feedbacks: the product of the benefit-to-cost ratios for each species exceeds one. When this condition holds, high cooperative investment levels are favored in both species regardless of the amount invested by each partner. The model is then extended to examine how symbiont mutation, immigration, or switching affects the spread of selfish or cooperative symbionts, which decrease and increase their investment levels, respectively. In particular, when a host associates with numerous symbionts without enforcement, neither mutation nor immigration but rather random switching would allow the spread of cooperative symbionts. Examples using symbiont switching for evolution would include large ciliates hosting numerous intracellular endosymbionts. The simple model and rules would provide a basis for understanding the evolution of microbe-microbe mutualistic symbiosis with positive fitness feedbacks and without enforcement mechanisms.

Settlement is a critical transition in the life history of reef fish, and the timing of this event can have a strong effect on fitness. Key factors that influence settlement timing are predictable lunar cyclic variation in tidal currents, moonlight, and nocturnal predation risk as larvae transition from pelagic to benthic environments. However, populations typically display wide variation in the arrival of settlers over the lunar cycle. This variation is often hypothesized to result from unpredictable conditions in the pelagic environment and bet-hedging by spawning adults. Here, we consider the hypothesis that the timing of spawning and settlement is a strategic response to post-settlement competition. We use a game theoretic model to predict spawning and settlement distributions when fish face a tradeoff between minimizing density-independent predation risk while crossing the reef crest vs. avoiding high competitor density on settlement habitat. In general, we expect competition to spread spawning over time such that settlement is distributed around the lunar phase with the lowest predation risk, similar to an ideal free distribution in which competition spreads competitors across space. We examine the effects of overcompensating density dependence, age-dependent competition, and competition among daily settler cohorts. Our model predicts that even in the absence of stochastic variation in the larval environment, competition can result in qualitative divergence between spawning and settlement distributions. Furthermore, we show that if competitive strength increases with settler age, competition results in covariation between settler age and settlement date, with older larvae settling when predation risk is minimal. We predict that competition between daily cohorts delays peak settlement, with priority effects potentially selecting for a multimodal settlement distribution.

In this paper we study invasion probabilities and invasion times of cooperative parasites spreading in spatially structured host populations. The spatial structure of the host population is given by a random geometric graph on [0,1], n∈N, with a Poisson(N)-distributed number of vertices and in which vertices are connected over an edge when they have a distance of at most r with r of order N for some 0<β<1. At a host infection many parasites are generated and parasites move along edges to neighbouring hosts. We assume that parasites have to cooperate to infect hosts, in the sense that at least two parasites need to attack a host simultaneously. We find lower and upper bounds on the invasion probability of the parasites in terms of survival probabilities of branching processes with cooperation. Furthermore, we characterize the asymptotic invasion time. An important ingredient of the proofs is a comparison with infection dynamics of cooperative parasites in host populations structured according to a complete graph, i.e. in well-mixed host populations. For these infection processes we can show that invasion probabilities are asymptotically equal to survival probabilities of branching processes with cooperation. Furthermore, we build on proof techniques developed in Brouard and Pokalyuk (2022), where an analogous invasion process has been studied for host populations structured according to a configuration model. We substantiate our results with simulations.

Multi-type birth-death processes underlie approaches for inferring evolutionary dynamics from phylogenetic trees across biological scales, ranging from deep-time species macroevolution to rapid viral evolution and somatic cellular proliferation. A limitation of current phylogenetic birth-death models is that they require restrictive linearity assumptions that yield tractable message-passing likelihoods, but that also preclude interactions between individuals. Many fundamental evolutionary processes - such as environmental carrying capacity or frequency-dependent selection - entail interactions, and may strongly influence the dynamics in some systems. Here, we introduce a multi-type birth-death process in mean-field interaction with an ensemble of replicas of the focal process. We prove that, under quite general conditions, the ensemble's stochastically evolving interaction field converges to a deterministic trajectory in the limit of an infinite ensemble. In this limit, the replicas effectively decouple, and self-consistent interactions appear as nonlinearities in the infinitesimal generator of the focal process. We investigate a special case that is rich enough to model both carrying capacity and frequency-dependent selection while yielding tractable message-passing likelihoods in the context of a phylogenetic birth-death model.

We introduce a multi-allele Wright-Fisher model with mutation and selection such that allele frequencies at a single locus are traced by the path of a hybrid jump-diffusion process. The state space of the process is given by the vertices and edges of a topological graph, i.e. edges are unit intervals. Vertices represent monomorphic population states and positions on the edges mark the biallelic proportions of ancestral and derived alleles during polymorphic segments. In this setting, mutations can only occur at monomorphic loci. We derive the stationary distribution in mutation-selection-drift equilibrium and obtain the expected allele frequency spectrum under large population size scaling. For the extended model with multiple independent loci we derive rigorous upper bounds for a wide class of associated measures of genetic variation. Within this framework we present mathematically precise arguments to conclude that the presence of directional selection reduces the magnitude of genetic variation, as constrained by the bounds for neutral evolution.

Leveraging the simplicity of nucleotide mismatch distributions, we provide an intuitive window into the evolution of the human influenza A 'nonstructural' (NS) gene segment. In an analysis suggested by the eminent Danish biologist Freddy B. Christiansen, we illustrate the existence of a continuous genetic "backbone" of influenza A NS sequences, steadily increasing in nucleotide distance to the 1918 root over more than a century. The 2009 influenza A/H1N1 pandemic represents a clear departure from this enduring genetic backbone. Utilizing nucleotide distance maps and phylogenetic analyses, we illustrate remaining uncertainties regarding the origin of the 2009 pandemic, highlighting the complexity of influenza evolution. The NS segment is interesting precisely because it experiences less pervasive positive selection, and departs less strongly from neutral evolution than e.g. the HA antigen. Consequently, sudden deviations from neutral diversification can indicate changes in other genes via the hitchhiking effect. Our approach employs two measures based on nucleotide mismatch counts to analyze the evolutionary dynamics of the NS gene segment. The rooted Hamming map of distances between a reference sequence and all other sequences over time, and the unrooted temporal Hamming distribution which captures the distribution of genotypic distances between simultaneously circulating viruses, thereby revealing patterns of nucleotide diversity and epi-evolutionary dynamics.

For two Polish state spaces E and E, and an operator G, we obtain existence and uniqueness of a G-martingale problem provided there is a bounded continuous duality function H on E×E together with a dual process Y on E which is the unique solution of a G-martingale problem. For the corresponding solutions [Formula: see text] and [Formula: see text] , duality with respect to a function H in its simplest form means that the relation E[H(X,y)]=E[H(x,Y)] holds for all (x,y)∈E×E and t≥0. While duality is well-known to imply uniqueness of the G-martingale problem, we give here a set of conditions under which duality also implies existence without using approximating sequences of processes of a different kind (e.g. jump processes to approximate diffusions) which is a widespread strategy for proving existence of solutions of martingale problems. Given the process [Formula: see text] and a duality function H, to prove existence of [Formula: see text] one has to show that the r.h.s. of the duality relation defines for each y a measure on E, i.e. there are transition kernels [Formula: see text] from E to E such that E[H(x,Y)]=∫μ(x,dx)H(x,y) for all (x,y)∈E×E and all t≥0. As examples, we treat resampling and branching models, such as the Fleming-Viot measure-valued diffusion and its spatial counterparts (with both, discrete and continuum space), as well as branching systems, such as Feller's branching diffusion. While our main result as well as all examples come with (locally) compact state spaces, we discuss the strategy to lift our results to genealogy-valued processes or historical processes, leading to non-compact (discrete and continuum) state spaces. Such applications will be tackled in forthcoming work based on the present article.

This study describes a compact method for determining joint probabilities of identity-by-state (IBS) within and between loci in populations evolving under genetic drift, crossing-over, mutation, and regular inbreeding (partial self-fertilization). Analogues of classical indices of associations among loci arise as functions of these joint identities. This coalescence-based analysis indicates that multi-locus associations reflect simultaneous coalescence events across loci. Measures of association depend on genetic diversity rather than allelic frequencies, as do linkage disequilibrium and its relatives. Scaled indices designed to show monotonic dependence on rates of crossing-over, inbreeding, and mutation may prove useful for interpreting patterns of genome-scale variation.

Understanding the conditions that promote the evolution of migration is important in ecology and evolution. When environments are fixed and there is one most favorable site, migration to other sites lowers overall growth rate and is not favored. Here we ask, can environmental variability favor migration when there is one best site on average? Previous work suggests that the answer is yes, but a general and precise answer remained elusive. Here we establish new, rigorous inequalities to show (and use simulations to illustrate) how stochastic growth rate can increase with migration when fitness (dis)advantages fluctuate over time across sites. The effect of migration between sites on the overall stochastic growth rate depends on the difference in expected growth rates and the variance of the fluctuating difference in growth rates. When fluctuations (variance) are large, a population can benefit from bursts of higher growth in sites that are worse on average. Such bursts become more probable as the between-site variance increases. Our results apply to many (≥ 2) sites, and reveal an interplay between the length of paths between sites, the average differences in site-specific growth rates, and the size of fluctuations. Our findings have implications for evolutionary biology as they provide conditions for departure from the reduction principle, and for ecological dynamics: even when there are superior sites in a sea of poor habitats, variability and habitat quality across space determine the importance of migration.

The host microbiome can be considered an ecological community of microbes present inside a complex and dynamic host environment. The host is under selective pressure to ensure that its microbiome remains beneficial. The host can impose a range of ecological filters including the immune response that can influence the assembly and composition of the microbial community. How the host immune response interacts with the within-microbiome community dynamics to affect the assembly of the microbiome has been largely unexplored. We present here a mathematical framework to elucidate the role of host immune response and its interaction with the balance of ecological interactions types within the microbiome community. We find that highly mutualistic microbial communities characteristic of high community density are most susceptible to changes in immune control and become invasion prone as host immune control strength is increased. Whereas highly competitive communities remain relatively stable in resisting invasion to changing host immune control. Our model reveals that the host immune control can interact in unexpected ways with a microbial community depending on the prevalent ecological interactions types for that community. We stress the need to incorporate the role of host-control mechanisms to better understand microbiome community assembly and stability.