Fixation probability and time in subdivided populations

Stochastic viability in an island model with partial dispersal: Approximation by a diffusion process in the limit of a large number of islands

In this paper, we investigate a finite population undergoing evolution through an island model with partial dispersal and without mutation, where generations are discrete and non-overlapping. The population is structured into D demes, each containing N individuals of two possible types, A and B, whose viability coefficients, sA and sB, respectively, vary randomly from one generation to the next. We assume that the means, variances and covariance of the viability coefficients are inversely proportional to the number of demes D, while higher-order moments are negligible in comparison to 1/D. We use a discrete-time Markov chain with two timescales to model the evolutionary process, and we demonstrate that as the number of demes D approaches infinity, the accelerated Markov chain converges to a diffusion process for any deme size N≥2. This diffusion process allows us to evaluate the fixation probability of type A following its introduction as a single mutant in a population that was fixed for type B. We explore the impact of increasing the variability in the viability coefficients on this fixation probability. At least when N is large enough, it is shown that increasing this variability for type B or decreasing it for type A leads to an increase in the fixation probability of a single A. The effect of the population-scaled variances, σA2 and σB2, can even cancel the effects of the population-scaled means, μA and μB. We also show that the fixation probability of a single A increases as the deme-scaled migration rate increases. Moreover, this probability is higher for type A than for type B if the population-scaled geometric mean viability coefficient is higher for type A than for type B, which means that μA-σA2/2>μB-σB2/2.

Male-mediated early maturation unlikely to evolve via adaptive evolution

The Vandenbergh effect, or male-mediated maturation, occurs when females reach sexual maturation upon exposure to a novel male. Male-mediated maturation is found across mammals, including in geladas, Theropithecus gelada, where it may be an adaptive counterstrategy to infanticide that follows the immigration of a new male; maturing after male immigration maximizes a female's chances of weaning her first offspring before the next infanticidal male immigrates (the 'optimal timing hypothesis'). Alternatively, the nonadaptive 'Bruce effect by-product hypothesis' posits that male-mediated maturation in geladas (and possibly other mammals) is triggered by the same physiological changes that, in pregnant females, produce spontaneous abortion (the Bruce effect). We test both hypotheses using theory and observational data. We show that neither male-mediated maturation nor its associated hormonal changes occur in baboons (Papio cynocephalus × P. anubis), a primate without the Bruce effect. An individual-based model suggests that male-mediated maturation should not evolve via adaptive evolution in either geladas or baboons. Finally, we derive the selection coefficient for male-mediated maturation and show it is likely to be very small because male-mediated maturation yields only marginal potential benefits unless the system is extremely fine-tuned. We conclude that male-mediated maturation in geladas is a by-product of the Bruce effect and more broadly that the Vandenbergh effect may be nonadaptive.

Fixation times on directed graphs

Computing the rate of evolution in spatially structured populations is difficult. A key quantity is the fixation time of a single mutant with relative reproduction rate r which invades a population of residents. We say that the fixation time is "fast" if it is at most a polynomial function in terms of the population size N. Here we study fixation times of advantageous mutants (r > 1) and neutral mutants (r = 1) on directed graphs, which are those graphs that have at least some one-way connections. We obtain three main results. First, we prove that for any directed graph the fixation time is fast, provided that r is sufficiently large. Second, we construct an efficient algorithm that gives an upper bound for the fixation time for any graph and any r ≥ 1. Third, we identify a broad class of directed graphs with fast fixation times for any r ≥ 1. This class includes previously studied amplifiers of selection, such as Superstars and Metafunnels. We also show that on some graphs the fixation time is not a monotonically declining function of r; in particular, neutral fixation can occur faster than fixation for small selective advantages.

Environmental gradients mediate dispersal evolution during biological invasions

Rapid evolution of increased dispersal at the edge of a range expansion can accelerate invasions. However, populations expanding across environmental gradients often face challenging environments that reduce fitness of dispersing individuals. We used an eco-evolutionary model to explore how environmental gradients influence dispersal evolution and, in turn, modulate the speed and predictability of invasion. Environmental gradients opposed evolution of increased dispersal during invasion, even leading to evolution of reduced dispersal along steeper gradients. Counterintuitively, reduced dispersal could allow for faster expansion by minimizing maladaptive gene flow and facilitating adaptation. While dispersal evolution across homogenous landscapes increased both the mean and variance of expansion speed, these increases were greatly dampened by environmental gradients. We illustrate our model's potential application to prediction and management of invasions by parameterizing it with data from a recent invertebrate range expansion. Overall, we find that environmental gradients strongly modulate the effect of dispersal evolution on invasion trajectories.

Fixation times of de novo and standing beneficial variants in subdivided populations

The rate at which beneficial alleles fix in a population depends on the probability of and time to fixation of such alleles. Both of these quantities can be significantly impacted by population subdivision and limited gene flow. Here, we investigate how limited dispersal influences the rate of fixation of beneficial de novo mutations, as well as fixation time from standing genetic variation. We investigate this for a population structured according to the island model of dispersal allowing us to use the diffusion approximation, which we complement with simulations. We find that fixation may take on average fewer generations under limited dispersal than under panmixia when selection is moderate. This is especially the case if adaptation occurs from de novo recessive mutations, and dispersal is not too limited (such that approximately FST<0.2). The reason is that mildly limited dispersal leads to only a moderate increase in effective population size (which slows down fixation), but is sufficient to cause a relative excess of homozygosity due to inbreeding, thereby exposing rare recessive alleles to selection (which accelerates fixation). We also explore the effect of metapopulation dynamics through local extinction followed by recolonization, finding that such dynamics always accelerate fixation from standing genetic variation, while de novo mutations show faster fixation interspersed with longer waiting times. Finally, we discuss the implications of our results for the detection of sweeps, suggesting that limited dispersal mitigates the expected differences between the genetic signatures of sweeps involving recessive and dominant alleles.

Evolutionary graph theory beyond single mutation dynamics: on how network-structured populations cross fitness landscapes

Spatially resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network-structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially resolved datasets of cellular organization.

Multiscale selection in spatially structured populations

The spatial structure of populations is key to many (eco-)evolutionary processes. In such cases, the strength and sign of selection on a trait may depend on the spatial scale considered. An example is the evolution of altruism: selection in local environments often favours cheaters over altruists, but this can be outweighed by selection at larger scales, favouring clusters of altruists over clusters of cheaters. For populations subdivided into distinct groups, this effect is described formally by multilevel selection theory. However, many populations do not consist of non-overlapping groups but rather (self-)organize into other ecological patterns. We therefore present a mathematical framework for multiscale selection. This framework decomposes natural selection into two parts: local selection, acting within environments of a certain size, and interlocal selection, acting among them. Varying the size of the local environments subsequently allows one to measure the contribution to selection of each spatial scale. To illustrate the use of this framework, we apply it to models of the evolution of altruism and pathogen transmissibility. The analysis identifies how and to what extent ecological processes at different spatial scales contribute to selection and compete, thus providing a rigorous underpinning to eco-evolutionary intuitions.

Two sympatric lineages of Australian Cnestus solidus share Ambrosiella symbionts but not Wolbachia

Sympatric lineages of inbreeding species provide an excellent opportunity to investigate species divergence patterns and processes. Many ambrosia beetle lineages (Curculionidae: Scolytinae) reproduce by predominant inbreeding through sib mating in nests excavated in woody plant parts wherein they cultivate symbiotic ambrosia fungi as their sole source of nutrition. The Xyleborini ambrosia beetle species Cnestus solidus and Cnestus pseudosolidus are sympatrically distributed across eastern Australia and have overlapping morphological variation. Using multilocus sequencing analysis of individuals collected from 19 sites spanning their sympatric distribution, we assessed their phylogenetic relationships, taxonomic status and microbial symbionts. We found no genetic differentiation between individuals morphologically identified as C. solidus and C. pseudosolidus confirming previous suggestions that C. pseudosolidus is synonymous to C. solidus. However, within C. solidus we unexpectedly discovered the sympatric coexistence of two morphologically indistinguishable but genetically distinct lineages with small nuclear yet large mitochondrial divergence. At all sites except one, individuals of both lineages carried the same primary fungal symbiont, a new Ambrosiella species, indicating that fungal symbiont differentiation may not be involved in lineage divergence. One strain of the maternally inherited bacterial endosymbiont Wolbachia was found at high prevalence in individuals of the more common lineage but not in the other, suggesting that it may influence host fitness. Our data suggest that the two Australian Cnestus lineages diverged allopatrically, and one lineage then acquired Wolbachia. Predominant inbreeding and Wolbachia infection may have reinforced reproductive barriers between these two lineages after their secondary contact contributing to their current sympatric distribution.

Widespread distribution of rodenticide resistance-conferring mutations in the Vkorc1 gene among house mouse populations in Portuguese Macaronesian islands and Iberian Atlantic areas

Growing evidence of widespread resistance to anticoagulant rodenticides (ARs) in house mice pose significant challenges to pest control efforts. First-generation ARs were introduced in the early 1950s but resistance to these emerged later that decade. Second-generation rodenticides were then developed, with resistance being reported in the late 1970s. Research has linked resistance to ARs with mutations in the Vkorc1 gene, leading to the use of more toxic and environmentally harmful compounds. In this study, 243 tail tips of house mice from mainland Portugal and Southern Spain, the Azores and Madeira archipelagos were analysed for all 3 exons of the Vkorc1 gene. Mutations L128S, Y139C, along with the so-called spretus genotype Vkorc1spr are considered responsible for reduced susceptibility of house mice to ARs. All these sequence variants were broadly detected throughout the sampling regions. Vkorc1spr was the most often recorded among mainland populations, whereas Y139C was nearly ubiquitous among the insular populations. In contrast, L128S was only detected in mainland Portugal and four islands of the Azores archipelago. All first generation ARs such as warfarin and coumatetralyl are deemed ineffective against all Vkorc1 variants identified in this study. Second-generation bromadiolone and difenacoum should also be discarded to control populations carrying Vkorc1spr, Y139C or L128S mutations. Inadequate use of ARs in regions where resistant animals have been found in large proportions will result in the spreading of rodenticide resistance among rodent populations through the positive selection of non-susceptible individuals. Consequently, ineffectiveness of rodent control will increase and potentiate environmental contamination, hazarding non-target wildlife through secondary poisoning. We highlight the need for Vkorc1 screening as a crucial tool in rodent management, aiding in the selection of the most appropriate control/eradication method in order to prevent misuse of these toxic biocides and the spread of rodenticide resistance among house mouse populations.

Mutant fixation in the presence of a natural enemy

The literature about mutant invasion and fixation typically assumes populations to exist in isolation from their ecosystem. Yet, populations are part of ecological communities, and enemy-victim (e.g. predator-prey or pathogen-host) interactions are particularly common. We use spatially explicit, computational pathogen-host models (with wild-type and mutant hosts) to re-visit the established theory about mutant fixation, where the pathogen equally attacks both wild-type and mutant individuals. Mutant fitness is assumed to be unrelated to infection. We find that pathogen presence substantially weakens selection, increasing the fixation probability of disadvantageous mutants and decreasing it for advantageous mutants. The magnitude of the effect rises with the infection rate. This occurs because infection induces spatial structures, where mutant and wild-type individuals are mostly spatially separated. Thus, instead of mutant and wild-type individuals competing with each other, it is mutant and wild-type "patches" that compete, resulting in smaller fitness differences and weakened selection. This implies that the deleterious mutant burden in natural populations might be higher than expected from traditional theory.

Native microbiome dominates over host factors in shaping the probiotic genetic evolution in the gut

Probiotics often acquire potentially adaptive mutations in vivo, gaining new functional traits through gut selection. While both the host and microbiome can contribute to probiotics' genetic evolution, separating the microbiome and the host's contribution to such selective pressures remains challenging. Here, we introduced germ-free (GF) and specific pathogen-free (SPF) mouse models to track how probiotic strains, i.e., Lactiplantibacillus plantarum HNU082 (Lp082) and Bifidobacterium animalis subsp. lactis V9 (BV9), genetically evolved under selection pressures derived from host factors alone and both host and microbial ecological factors. Notably, compared to the genome of a probiotic strain before consumption, the host only elicited <15 probiotic mutations in probiotic genomes that emerged in the luminal environment of GF mice, while a total of 840 mutations in Lp082 mutants and 21,579 mutations in BV9 were found in SPF mice, <0.25% of those derived from both factors that were never captured by other experimental evolution studies, indicating that keen microbial competitions exhibited the predominant evolutionary force in shaping probiotic genetic composition (>99.75%). For a given probiotic, functional genes occurring in potentially adaptive mutations induced by hosts (GF mice) were all shared with those found in mutants of SPF mice. Collectively, the native microbiome consistently drove a more rapid and divergent genetic evolution of probiotic strains in seven days of colonization than host factors did. Our study further laid a theoretical foundation for genetically engineering probiotics for better gut adaptation through in vitro artificial gut ecosystems without the selection pressures derived from host factors.

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 role of migration in mutant dynamics in fragmented populations

Mutant dynamics in fragmented populations have been studied extensively in evolutionary biology. Yet, open questions remain, both experimentally and theoretically. Some of the fundamental properties predicted by models still need to be addressed experimentally. We contribute to this by using a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, while the mean frequency of mutants is not influenced by migration, the probability distribution is. To address this empirically, we performed in vitro experiments, where mixtures of GFP-labelled ("mutant") and non-labelled ("wid-type") murine cells were grown in wells (demes), and migration was mimicked via cell transfer from well to well. In the presence of migration, we observed a change in the skewedness of the distribution of the mutant frequencies in the wells, consistent with previous and our own model predictions. In the presence of de novo mutant production, we used modelling to investigate the level at which disadvantageous mutants are predicted to exist, which has implications for the adaptive potential of the population in case of an environmental change. In panmictic populations, disadvantageous mutants can persist around a steady state, determined by the rate of mutant production and the selective disadvantage (selection-mutation balance). In a fragmented system that consists of demes connected by migration, a steady-state persistence of disadvantageous mutants is also observed, which, however, is fundamentally different from the mutation-selection balance and characterized by higher mutant levels. The increase in mutant frequencies above the selection-mutation balance can be maintained in small ( N < N c ) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection-mutation balance decays exponentially with N / N c . The observed increase in the mutant numbers is not explained by the change in the effective population size. Implications for evolutionary processes in diseases are discussed, where the pre-existence of disadvantageous drug-resistant mutant cells or pathogens drives the response of the disease to treatments.

Genetic Drift Shapes the Evolution of a Highly Dynamic Metapopulation

The dynamics of extinction and (re)colonization in habitat patches are characterizing features of dynamic metapopulations, causing them to evolve differently than large, stable populations. The propagule model, which assumes genetic bottlenecks during colonization, posits that newly founded subpopulations have low genetic diversity and are genetically highly differentiated from each other. Immigration may then increase diversity and decrease differentiation between subpopulations. Thus, older and/or less isolated subpopulations are expected to have higher genetic diversity and less genetic differentiation. We tested this theory using whole-genome pool-sequencing to characterize nucleotide diversity and differentiation in 60 subpopulations of a natural metapopulation of the cyclical parthenogen Daphnia magna. For comparison, we characterized diversity in a single, large, and stable D. magna population. We found reduced (synonymous) genomic diversity, a proxy for effective population size, weak purifying selection, and low rates of adaptive evolution in the metapopulation compared with the large, stable population. These differences suggest that genetic bottlenecks during colonization reduce effective population sizes, which leads to strong genetic drift and reduced selection efficacy in the metapopulation. Consistent with the propagule model, we found lower diversity and increased differentiation in younger and also in more isolated subpopulations. Our study sheds light on the genomic consequences of extinction-(re)colonization dynamics to an unprecedented degree, giving strong support for the propagule model. We demonstrate that the metapopulation evolves differently from a large, stable population and that evolution is largely driven by genetic drift.

Globally Relaxed Selection and Local Adaptation in Boechera stricta

The strength of selection varies among populations and across the genome, but the determinants of efficacy of selection remain unclear. In this study, we used whole-genome sequencing data from 467 Boechera stricta accessions to quantify the strength of selection and characterize the pattern of local adaptation. We found low genetic diversity on 0-fold degenerate sites and conserved non-coding sites, indicating functional constraints on these regions. The estimated distribution of fitness effects and the proportion of fixed substitutions suggest relaxed negative and positive selection in B. stricta. Among the four population groups, the NOR and WES groups have smaller effective population size (Ne), higher proportions of effectively neutral sites, and lower rates of adaptive evolution compared with UTA and COL groups, reflecting the effect of Ne on the efficacy of natural selection. We also found weaker selection on GC-biased sites compared with GC-conservative (unbiased) sites, suggested that GC-biased gene conversion has affected the strength of selection in B. stricta. We found mixed evidence for the role of the recombination rate on the efficacy of selection. The positive and negative selection was stronger in high-recombination regions compared with low-recombination regions in COL but not in other groups. By scanning the genome, we found different subsets of selected genes suggesting differential adaptation among B. stricta groups. These results show that differences in effective population size, nucleotide composition, and recombination rate are important determinants of the efficacy of selection. This study enriches our understanding of the roles of natural selection and local adaptation in shaping genomic variation.

Hydrodynamic flow and concentration gradients in the gut enhance neutral bacterial diversity

The gut microbiota features important genetic diversity, and the specific spatial features of the gut may shape evolution within this environment. We investigate the fixation probability of neutral bacterial mutants within a minimal model of the gut that includes hydrodynamic flow and resulting gradients of food and bacterial concentrations. We find that this fixation probability is substantially increased, compared with an equivalent well-mixed system, in the regime where the profiles of food and bacterial concentration are strongly spatially dependent. Fixation probability then becomes independent of total population size. We show that our results can be rationalized by introducing an active population, which consists of those bacteria that are actively consuming food and dividing. The active population size yields an effective population size for neutral mutant fixation probability in the gut.

Evolution of polygenic traits under global vs local adaptation

Observations about the number, frequency, effect size, and genomic distribution of alleles associated with complex traits must be interpreted in light of evolutionary process. These characteristics, which constitute a trait's genetic architecture, can dramatically affect evolutionary outcomes in applications from agriculture to medicine, and can provide a window into how evolution works. Here, I review theoretical predictions about the evolution of genetic architecture under spatially homogeneous, global adaptation as compared with spatially heterogeneous, local adaptation. Due to the tension between divergent selection and migration, local adaptation can favor "concentrated" genetic architectures that are enriched for alleles of larger effect, clustered in a smaller number of genomic regions, relative to expectations under global adaptation. However, the evolution of such architectures may be limited by many factors, including the genotypic redundancy of the trait, mutation rate, and temporal variability of environment. I review the circumstances in which predictions differ for global vs local adaptation and discuss where progress can be made in testing hypotheses using data from natural populations and lab experiments. As the field of comparative population genomics expands in scope, differences in architecture among traits and species will provide insights into how evolution works, and such differences must be interpreted in light of which kind of selection has been operating.

Toward a Universal Model for Spatially Structured Populations

A key question in evolution is how likely a mutant is to take over. This depends on natural selection and on stochastic fluctuations. Population spatial structure can impact mutant fixation probabilities. We introduce a model for structured populations on graphs that generalizes previous ones by making migrations independent of birth and death. We demonstrate that by tuning migration asymmetry, the star graph transitions from amplifying to suppressing natural selection. The results from our model are universal in the sense that they do not hinge on a modeling choice of microscopic dynamics or update rules. Instead, they depend on migration asymmetry, which can be experimentally tuned and measured.

Fixation probabilities in network structured meta-populations

The effect of population structure on evolutionary dynamics is a long-lasting research topic in evolutionary ecology and population genetics. Evolutionary graph theory is a popular approach to this problem, where individuals are located on the nodes of a network and can replace each other via the links. We study the effect of complex network structure on the fixation probability, but instead of networks of individuals, we model a network of sub-populations with a probability of migration between them. We ask how the structure of such a meta-population and the rate of migration affect the fixation probability. Many of the known results for networks of individuals carry over to meta-populations, in particular for regular networks or low symmetric migration probabilities. However, when patch sizes differ we find interesting deviations between structured meta-populations and networks of individuals. For example, a two patch structure with unequal population size suppresses selection for low migration probabilities.

Chromosome number is key to longevity of polyploid lineages

Polyploidy is ubiquitous and often recursive in plant lineages, most frequently resulting in extinction but occasionally associated with great evolutionary success. However, instead of chromosome numbers exponentially increasing due to recurrent polyploidy, most angiosperm species have fewer than 14 chromosome pairs. Following genome duplication, diploidisation can render one copy of essential genes nonfunctional without fitness cost. In isolated subpopulations, alternate (homoeologous) gene copies can be lost, creating incompatibilities that reduce fitness of hybrids between subpopulations, constraining exchange of favourable genetic changes and reducing species fitness. When multiple sets of incompatible genes are genetically linked, their deleterious effects are not independent. The effective number of independently acting sets of incompatible loci in hybrids is limited by chromosome number and recombination. Therefore, species with many chromosomes are subject to a higher fitness penalty during diploidisation. Karyotypic changes, especially fusions, that reduce gene flow are normally fitness disadvantages, but during the diploidisation process, can increase fitness by reducing mixing of differentially diploidised alleles. Fitness penalties caused by diploidisation favour accelerated karyotypic change, with each change increasing barriers to gene flow, contributing to speciation. Lower chromosome numbers and increased chromosome fusions confer advantages to surviving the diploidisation process following polyploid formation, by independent mechanisms.

Fast and strong amplifiers of natural selection

Selection and random drift determine the probability that novel mutations fixate in a population. Population structure is known to affect the dynamics of the evolutionary process. Amplifiers of selection are population structures that increase the fixation probability of beneficial mutants compared to well-mixed populations. Over the past 15 years, extensive research has produced remarkable structures called strong amplifiers which guarantee that every beneficial mutation fixates with high probability. But strong amplification has come at the cost of considerably delaying the fixation event, which can slow down the overall rate of evolution. However, the precise relationship between fixation probability and time has remained elusive. Here we characterize the slowdown effect of strong amplification. First, we prove that all strong amplifiers must delay the fixation event at least to some extent. Second, we construct strong amplifiers that delay the fixation event only marginally as compared to the well-mixed populations. Our results thus establish a tight relationship between fixation probability and time: Strong amplification always comes at a cost of a slowdown, but more than a marginal slowdown is not needed.

Hidden by the name: A new fluorescent pumpkin toadlet from the Brachycephalus ephippium group (Anura: Brachycephalidae)

Species of Brachycephalus has been having taxonomical issues due its morphological similarity and genetic conservatism. Herein, we describe a new species of Brachycephalus from the south Mantiqueira mountain range and semidecidual forests in the municipalities of Mogi das Cruzes, Campinas and Jundiaí, state of São Paulo, Brazil, based on an integrative approach. It can be distinguished from all species of the B. ephippium species group based on morphological characters (especially osteology and head shape), advertisement call and divergence in partial mitochondrial DNA gene sequences (16S). The new species is genetically similar to B. margaritatus and morphologically similar to B. ephippium. It can be differentiated from B. ephippium by the presence of dark faded spots on skull and post-cranial plates, presence of black connective tissue connective tissue scattered over dorsal musculature, parotic plate morphology, smaller snout-vent length (adult SVL: males 13.46-15.92 mm; females 16.04-17.69 mm) and 3% genetic distance. We also present natural history data and discuss the robustness of the integrative approach, geographic distribution, genetic data, behaviour, fluorescence in ontogeny, and conservation status.

The role of a mechanistic host in maintaining arctic rabies variant distributions: Assessment of functional genetic diversity in Alaskan red fox (Vulpes vulpes)

Populations are exposed to different types and strains of pathogens across heterogeneous landscapes, where local interactions between host and pathogen may present reciprocal selective forces leading to correlated patterns of spatial genetic structure. Understanding these coevolutionary patterns provides insight into mechanisms of disease spread and maintenance. Arctic rabies (AR) is a lethal disease with viral variants that occupy distinct geographic distributions across North America and Europe. Red fox (Vulpes vulpes) are a highly susceptible AR host, whose range overlaps both geographically distinct AR strains and regions where AR is absent. It is unclear if genetic structure exists among red fox populations relative to the presence/absence of AR or the spatial distribution of AR variants. Acquiring these data may enhance our understanding of the role of red fox in AR maintenance/spread and inform disease control strategies. Using a genotyping-by-sequencing assay targeting 116 genomic regions of immunogenetic relevance, we screened for sequence variation among red fox populations from Alaska and an outgroup from Ontario, including areas with different AR variants, and regions where the disease was absent. Presumed neutral SNP data from the assay found negligible levels of neutral genetic structure among Alaskan populations. The immunogenetically-associated data identified 30 outlier SNPs supporting weak to moderate genetic structure between regions with and without AR in Alaska. The outliers included SNPs with the potential to cause missense mutations within several toll-like receptor genes that have been associated with AR outcome. In contrast, there was a lack of genetic structure between regions with different AR variants. Combined, we interpret these data to suggest red fox populations respond differently to the presence of AR, but not AR variants. This research increases our understanding of AR dynamics in the Arctic, where host/disease patterns are undergoing flux in a rapidly changing Arctic landscape, including the continued northward expansion of red fox into regions previously predominated by the arctic fox (Vulpes lagopus).

Graph-structured populations and the Hill-Robertson effect

The Hill-Robertson effect describes how, in a finite panmictic diploid population, selection at one diallelic locus reduces the fixation probability of a selectively favoured allele at a second, linked diallelic locus. Here we investigate the influence of population structure on the Hill-Robertson effect in a population of size N. We model population structure as a network by assuming that individuals occupy nodes on a graph connected by edges that link members who can reproduce with each other. Three regular networks (fully connected, ring and torus), two forms of scale-free network and a star are examined. We find that (i) the effect of population structure on the probability of fixation of the favourable allele is invariant for regular structures, but on some scale-free networks and a star, this probability is greatly reduced; (ii) compared to a panmictic population, the mean time to fixation of the favoured allele is much greater on a ring, torus and linear scale-free network, but much less on power-2 scale-free and star networks; (iii) the likelihood with which each of the four possible haplotypes eventually fix is similar across regular networks, but scale-free populations and the star are consistently less likely and much faster to fix the optimal haplotype; (iv) increasing recombination increases the likelihood of fixing the favoured haplotype across all structures, whereas the time to fixation of that haplotype usually increased, and (v) star-like structures were overwhelmingly likely to fix the least fit haplotype and did so significantly more rapidly than other populations. Last, we find that small (N < 64) panmictic populations do not exhibit the scaling property expected from Hill & Robertson (1966 Genet. Res. 8, 269-294. (doi:10.1017/S0016672300010156)).

Compost spatial heterogeneity promotes evolutionary diversification of a bacterium

Spatial resource heterogeneity is expected to be a key driver for the evolution of diversity. However, direct empirical support for this prediction is limited to studies carried out in simplified laboratory environments. Here, we investigate how altering spatial heterogeneity of potting compost-by the addition of water and mixing-affects the evolutionary diversification of a bacterial species, Pseudomonas fluorescens, that is naturally found in the environment. There was a greater propensity of resource specialists to evolve in the unmanipulated compost, while more generalist phenotypes dominated the compost-water mix. Genomic data were consistent with these phenotypic findings. Competition experiments strongly suggest these results are due to diversifying selection as a result of resource heterogeneity, as opposed to other covariables. Overall, our findings corroborate theoretical and in vitro findings, but in semi-natural, more realistic conditions.

Limits on amplifiers of natural selection under death-Birth updating

The fixation probability of a single mutant invading a population of residents is among the most widely-studied quantities in evolutionary dynamics. Amplifiers of natural selection are population structures that increase the fixation probability of advantageous mutants, compared to well-mixed populations. Extensive studies have shown that many amplifiers exist for the Birth-death Moran process, some of them substantially increasing the fixation probability or even guaranteeing fixation in the limit of large population size. On the other hand, no amplifiers are known for the death-Birth Moran process, and computer-assisted exhaustive searches have failed to discover amplification. In this work we resolve this disparity, by showing that any amplification under death-Birth updating is necessarily bounded and transient. Our boundedness result states that even if a population structure does amplify selection, the resulting fixation probability is close to that of the well-mixed population. Our transience result states that for any population structure there exists a threshold r^⋆ such that the population structure ceases to amplify selection if the mutant fitness advantage r is larger than r^⋆. Finally, we also extend the above results to δ-death-Birth updating, which is a combination of Birth-death and death-Birth updating. On the positive side, we identify population structures that maintain amplification for a wide range of values r and δ. These results demonstrate that amplification of natural selection depends on the specific mechanisms of the evolutionary process.

Extensive literature exists on amplifiers of natural selection for the Birth-death Moran process, but no amplifiers are known for the death-Birth Moran process. Here we show that if amplifiers exist under death-Birth updating, they must be bounded and transient. Boundedness implies weak amplification, and transience implies amplification for only a limited range of the mutant fitness advantage. These results demonstrate that amplification depends on the specific mechanisms of the evolutionary process.

The opposing effects of genetic drift and Haldane's sieve on floral-morph frequencies in tristylous metapopulations

Tristyly is a genetic floral polymorphism in which three floral morphs are maintained at equal frequencies by negative frequency-dependent selection on alleles at two interacting loci. Because dominant alleles at these loci are maintained at a lower frequency than their recessive counterparts, they are more likely to be lost by founder events and genetic drift. Here we examine the hypothesis that dominant alleles under negative frequency-dependent selection should also be more likely to re-invade populations than recessive alleles, due to Haldane's Sieve, because recessive alleles not expressed in a heterozygote state cannot benefit from positive selection when rare. We used computer simulations of tristylous metapopulations to verify that Haldane's Sieve acting on migrants into occupied demes can indeed reverse the bias in allele frequencies expected for small single tristylous populations, particularly in situations of rapid population growth following colonisation. This effect is manifest both locally and at the metapopulation level. Our study illustrates the potential effect of Haldane's Sieve in the novel context of an iconic plant sexual-system polymorphism under the influence of metapopulation dynamics.

Evolutionary Dynamics in Structured Populations Under Strong Population Genetic Forces

In the long-term neutral equilibrium, high rates of migration between subpopulations result in little population differentiation. However, in the short-term, even very abundant migration may not be enough for subpopulations to equilibrate immediately. In this study, we investigate dynamical patterns of short-term population differentiation in adapting populations via stochastic and analytical modeling through time. We characterize a regime in which selection and migration interact to create non-monotonic patterns of population differentiation over time when migration is weaker than selection, but stronger than drift. We demonstrate how these patterns can be leveraged to estimate high migration rates using approximate Bayesian computation. We apply this approach to estimate fast migration in a rapidly adapting intra-host Simian-HIV population sampled from different anatomical locations. We find differences in estimated migration rates between different compartments, even though all are above [Formula: see text] = 1. This work demonstrates how studying demographic processes on the timescale of selective sweeps illuminates processes too fast to leave signatures on neutral timescales.

Role of host genetic diversity for susceptibility-to-infection in the evolution of virulence of a plant virus

Predicting viral emergence is difficult due to the stochastic nature of the underlying processes and the many factors that govern pathogen evolution. Environmental factors affecting the host, the pathogen and the interaction between both are key in emergence. In particular, infectious disease dynamics are affected by spatiotemporal heterogeneity in their environments. A broad knowledge of these factors will allow better estimating where and when viral emergence is more likely to occur. Here, we investigate how the population structure for susceptibility-to-infection genes of the plant Arabidopsis thaliana shapes the evolution of Turnip mosaic virus (TuMV). For doing so we have evolved TuMV lineages in two radically different host population structures: (1) a metapopulation subdivided into six demes (subpopulations); each one being composed of individuals from only one of six possible A. thaliana ecotypes and (2) a well-mixed population constituted by equal number of plants from the same six A. thaliana ecotypes. These two populations were evolved for twelve serial passages. At the end of the experimental evolution, we found faster adaptation of TuMV to each ecotype in the metapopulation than in the well-mixed heterogeneous host populations. However, viruses evolved in well-mixed populations were more pathogenic and infectious than viruses evolved in the metapopulation. Furthermore, the viruses evolved in the demes showed stronger signatures of local specialization than viruses evolved in the well-mixed populations. These results illustrate how the genetic diversity of hosts in an experimental ecosystem favors the evolution of virulence of a pathogen.

Population structure determines the tradeoff between fixation probability and fixation time

The rate of biological evolution depends on the fixation probability and on the fixation time of new mutants. Intensive research has focused on identifying population structures that augment the fixation probability of advantageous mutants. But these amplifiers of natural selection typically increase fixation time. Here we study population structures that achieve a tradeoff between fixation probability and time. First, we show that no amplifiers can have an asymptotically lower absorption time than the well-mixed population. Then we design population structures that substantially augment the fixation probability with just a minor increase in fixation time. Finally, we show that those structures enable higher effective rate of evolution than the well-mixed population provided that the rate of generating advantageous mutants is relatively low. Our work sheds light on how population structure affects the rate of evolution. Moreover, our structures could be useful for lab-based, medical, or industrial applications of evolutionary optimization.

Out of Africa by spontaneous migration waves

Hominin evolution is characterized by progressive regional differentiation, as well as migration waves, leading to anatomically modern humans that are assumed to have emerged in Africa and spread over the whole world. Why or whether Africa was the source region of modern humans and what caused their spread remains subject of ongoing debate. We present a spatially explicit, stochastic numerical model that includes ongoing mutations, demic diffusion, assortative mating and migration waves. Diffusion and assortative mating alone result in a structured population with relatively homogeneous regions bound by sharp clines. The addition of migration waves results in a power-law distribution of wave areas: for every large wave, many more small waves are expected to occur. This suggests that one or more out-of-Africa migrations would probably have been accompanied by numerous smaller migration waves across the world. The migration waves are considered "spontaneous", as the current model excludes environmental or other extrinsic factors. Large waves preferentially emanate from the central areas of large, compact inhabited areas. During the Pleistocene, Africa was the largest such area most of the time, making Africa the statistically most likely origin of anatomically modern humans, without a need to invoke additional environmental or ecological drivers.

Selective Sweeps

For almost 20 years, many inference methods have been developed to detect selective sweeps and localize the targets of directional selection in the genome. These methods are based on population genetic models that describe the effect of a beneficial allele (e.g., a new mutation) on linked neutral variation (driven by directional selection from a single copy to fixation). Here, I discuss these models, ranging from selective sweeps in a panmictic population of constant size to evolutionary traffic when simultaneous sweeps at multiple loci interfere, and emphasize the important role of demography and population structure in data analysis. In the past 10 years, soft sweeps that may arise after an environmental change from directional selection on standing variation have become a focus of population genetic research. In contrast to selective sweeps, they are caused by beneficial alleles that were neutrally segregating in a population before the environmental change or were present at a mutation-selection balance in appreciable frequency.

Evolutionary dynamics of bacteria in the gut microbiome within and across hosts

Gut microbiota are shaped by a combination of ecological and evolutionary forces. While the ecological dynamics have been extensively studied, much less is known about how species of gut bacteria evolve over time. Here, we introduce a model-based framework for quantifying evolutionary dynamics within and across hosts using a panel of metagenomic samples. We use this approach to study evolution in approximately 40 prevalent species in the human gut. Although the patterns of between-host diversity are consistent with quasi-sexual evolution and purifying selection on long timescales, we identify new genealogical signatures that challenge standard population genetic models of these processes. Within hosts, we find that genetic differences that accumulate over 6-month timescales are only rarely attributable to replacement by distantly related strains. Instead, the resident strains more commonly acquire a smaller number of putative evolutionary changes, in which nucleotide variants or gene gains or losses rapidly sweep to high frequency. By comparing these mutations with the typical between-host differences, we find evidence that some sweeps may be seeded by recombination, in addition to new mutations. However, comparisons of adult twins suggest that replacement eventually overwhelms evolution over multi-decade timescales, hinting at fundamental limits to the extent of local adaptation. Together, our results suggest that gut bacteria can evolve on human-relevant timescales, and they highlight the connections between these short-term evolutionary dynamics and longer-term evolution across hosts.

Fixation probabilities in weakly compressible fluid flows

Competition between biological species in marine environments is affected by the motion of the surrounding fluid. An effective 2D compressibility can arise, for example, from the convergence and divergence of water masses at the depth at which passively traveling photosynthetic organisms are restricted to live. In this report, we seek to quantitatively study genetics under flow. To this end, we couple an off-lattice agent-based simulation of two populations in 1D to a weakly compressible velocity field-first a sine wave and then a shell model of turbulence. We find for both cases that even in a regime where the overall population structure is approximately unaltered, the flow can significantly diminish the effect of a selective advantage on fixation probabilities. We understand this effect in terms of the enhanced survival of organisms born at sources in the flow and the influence of Fisher genetic waves.

Effective models and the search for quantitative principles in microbial evolution

Microbes evolve rapidly. Yet they do so in idiosyncratic ways, which depend on the specific mutations that are beneficial or deleterious in a given situation. At the same time, some population-level patterns of adaptation are strikingly similar across different microbial systems, suggesting that there may also be simple, quantitative principles that unite these diverse scenarios. We review the search for simple principles in microbial evolution, ranging from the biophysical level to emergent evolutionary dynamics. A key theme has been the use of effective models, which coarse-grain over molecular and cellular details to obtain a simpler description in terms of a few effective parameters. Collectively, these theoretical approaches provide a set of quantitative principles that facilitate understanding, prediction, and potentially control of evolutionary phenomena, though formidable challenges remain due to the ecological complexity of natural populations.

50-year anniversary of Lloyd's "mean crowding": Ideas on patchy distributions

This year marks the 50th anniversary of Monte B. Lloyd's "Mean Crowding" (1967) paper, in which he introduced a metric that accounts for an individual's experience of conspecific density. Mean crowding allows ecologists to measure the degree of spatial aggregation of individuals in a manner relevant to intraspecific competition for resources. We take the concept of mean crowding a step beyond its most common usage and that it has a mathematical relationship to many of the most important concepts in ecology and evolutionary biology. Mean crowding, a first-order approximation of the degree of nonrandomness in a distribution, can function as a powerful heuristic that can unify concepts across disciplines in a more general way that Lloyd originally envisioned.

Construction of arbitrarily strong amplifiers of natural selection using evolutionary graph theory

Because of the intrinsic randomness of the evolutionary process, a mutant with a fitness advantage has some chance to be selected but no certainty. Any experiment that searches for advantageous mutants will lose many of them due to random drift. It is therefore of great interest to find population structures that improve the odds of advantageous mutants. Such structures are called amplifiers of natural selection: they increase the probability that advantageous mutants are selected. Arbitrarily strong amplifiers guarantee the selection of advantageous mutants, even for very small fitness advantage. Despite intensive research over the past decade, arbitrarily strong amplifiers have remained rare. Here we show how to construct a large variety of them. Our amplifiers are so simple that they could be useful in biotechnology, when optimizing biological molecules, or as a diagnostic tool, when searching for faster dividing cells or viruses. They could also occur in natural population structures.

Fixation probability of a beneficial mutation conferring decreased generation time in changing environments

BACKGROUND

One central building block of population genetics is the fixation probability. It is a probabilistic understanding of the eventual fate of new mutations. Moreover, the fixation probability of new beneficial mutations plays an important effect on the adaptation of populations to environmental challenges. Great progress has been made in the study of the beneficial mutations that increases offspring number. However, the fixation probability of beneficial mutations with a shorter generation time under various genetic and ecological conditions has not been explored.

RESULTS

Here we extend the classical result of the fixation probability of beneficial mutations obtained by Haldane, and estimate the fixation probability of a beneficial mutation with a reduced generation time in a changing environment. Assuming that the selective advantage is very small, we concentrate all the changing factors of environment on a single quantity: effective selective advantage. Using a time-dependent branching process, we get the analytic approximation for the fixation probability of beneficial mutations that decrease the generation time. Then, we apply this approximation to four interesting biological cases.

CONCLUSIONS

In these instances, we show the comparison of the approximation with the accurate values. We find that they are consistent, demonstrating the effectiveness of our result for the fixation probability of beneficial mutations conferring a reduced replication time.

The emergence, maintenance, and demise of diversity in a spatially variable antibiotic regime

Antimicrobial resistance (AMR) is a growing global threat that, in the absence of new antibiotics, requires effective management of existing drugs. Here, we use experimental evolution of the opportunistic human pathogen Pseudomonas aeruginosa to explore how changing patterns of drug delivery modulates the spread of resistance in a population. Resistance evolves readily under both temporal and spatial variation in drug delivery and fixes rapidly under temporal, but not spatial, variation. Resistant and sensitive genotypes coexist in spatially varying conditions due to a resistance‐growth rate trade‐off which, when coupled to dispersal, generates negative frequency‐dependent selection and a quasi‐protected polymorphism. Coexistence is ultimately lost, however, because resistant types with improved growth rates in the absence of drug spread through the population. These results suggest that spatially variable drug prescriptions can delay but not prevent the spread of resistance and provide a striking example of how the emergence and eventual demise of biodiversity is underpinned by evolving fitness trade‐offs.

Protein evolution depends on multiple distinct population size parameters

That population size affects the fate of new mutations arising in genomes, modulating both how frequently they arise and how efficiently natural selection is able to filter them, is well established. It is therefore clear that these distinct roles for population size that characterize different processes should affect the evolution of proteins and need to be carefully defined. Empirical evidence is consistent with a role for demography in influencing protein evolution, supporting the idea that functional constraints alone do not determine the composition of coding sequences.

Given that the relationship between population size, mutant fitness and fixation probability has been well characterized, estimating fitness from observed substitutions is well within reach with well-formulated models. Molecular evolution research has, therefore, increasingly begun to leverage concepts from population genetics to quantify the selective effects associated with different classes of mutation. However, in order for this type of analysis to provide meaningful information about the intra- and inter-specific evolution of coding sequences, a clear definition of concepts of population size, what they influence, and how they are best parameterized is essential.

Here, we present an overview of the many distinct concepts that “population size” and “effective population size” may refer to, what they represent for studying proteins, and how this knowledge can be harnessed to produce better specified models of protein evolution.

Evolution of highly fecund haploid populations

We consider a model of viability selection in a highly fecund haploid population with sweepstakes reproduction. We use simulations to estimate the time until the allelic type with highest fitness has reached high frequency in a finite population. We compare the time between two reproduction modes of high and low fecundity. We also consider the probability that the allelic type with highest fitness is lost from the population before reaching high frequency. Our simulation results indicate that highly fecund populations can evolve faster (in some cases much faster) than populations of low fecundity. However, high fecundity and sweepstakes reproduction also confer much higher risk of losing the allelic type with highest fitness from the population by chance. The impact of selection on driving alleles to high frequency varies depending on the trait value conferring highest fitness; in some cases the effect of selection can hardly be detected.

Drift-Induced Selection Between Male and Female Heterogamety

Evolutionary transitions between male and female heterogamety are common in both vertebrates and invertebrates. Theoretical studies of these transitions have found that, when all genotypes are equally fit, continuous paths of intermediate equilibria link the two sex chromosome systems. This observation has led to a belief that neutral evolution along these paths can drive transitions, and that arbitrarily small fitness differences among sex chromosome genotypes can determine the system to which evolution leads. Here, we study stochastic evolutionary dynamics along these equilibrium paths. We find non-neutrality, both in transitions retaining the ancestral pair of sex chromosomes, and in those creating a new pair. In fact, substitution rates are biased in favor of dominant sex determining chromosomes, which fix with higher probabilities than mutations of no effect. Using diffusion approximations, we show that this non-neutrality is a result of "drift-induced selection" operating at every point along the equilibrium paths: stochastic jumps off the paths return with, on average, a directional bias in favor of the dominant segregating sex chromosome. Our results offer a novel explanation for the observed preponderance of dominant sex determining genes, and hint that drift-induced selection may be a common force in standard population genetic systems.

The Red Queen and King in finite populations

In antagonistic symbioses, such as host-parasite interactions, one population's success is the other's loss. In mutualistic symbioses, such as division of labor, both parties can gain, but they might have different preferences over the possible mutualistic arrangements. The rates of evolution of the two populations in a symbiosis are important determinants of which population will be more successful: Faster evolution is thought to be favored in antagonistic symbioses (the "Red Queen effect"), but disfavored in certain mutualistic symbioses (the "Red King effect"). However, it remains unclear which biological parameters drive these effects. Here, we analyze the effects of the various determinants of evolutionary rate: generation time, mutation rate, population size, and the intensity of natural selection. Our main results hold for the case where mutation is infrequent. Slower evolution causes a long-term advantage in an important class of mutualistic interactions. Surprisingly, less intense selection is the strongest driver of this Red King effect, whereas relative mutation rates and generation times have little effect. In antagonistic interactions, faster evolution by any means is beneficial. Our results provide insight into the demographic evolution of symbionts.

With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing

Background

Natural selection favors changes that lead to genotypes possessing high fitness. A conflict arises when several mutations are required for adaptation, but each mutation is separately deleterious. The process of a population evolving from a genotype encoding for a local fitness maximum to a higher fitness genotype is termed an adaptive peak shift.

Results

Here we suggest cooperative behavior as a factor that can facilitate adaptive peak shifts. We model cooperation in a public goods scenario, wherein each individual contributes resources that are later equally redistributed among all cooperating individuals. We use mathematical modeling and stochastic simulations to study the effect of cooperation on peak shifts in both panmictic and structured populations. Our results show that cooperation can substantially affect the rate of complex adaptation. Furthermore, we show that cooperation increases the population diversity throughout the peak shift process, thus increasing the robustness of the population to sudden environmental changes.

Conclusions

We provide a new explanation to adaptive valley crossing in natural populations and suggest that the long term evolution of a species depends on its social behavior.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-017-0983-2) contains supplementary material, which is available to authorized users.

Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep

Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.