ASSORTATIVE MATE CHOICE AND DOMINANCE MODIFICATION: ALTERNATIVE WAYS OF REMOVING HETEROZYGOTE DISADVANTAGE

In genetic polymorphisms of two alleles, heterozygous individuals may contribute to the next generation on average more or fewer descendants than the homozygotes. Two different evolutionary responses that remove a disadvantageous heterozygote phenotype from the population are the evolution of strictly assortative mate choice, and that of a modifier making one of the two alleles completely dominant. We derive invasion fitness of mutants introducing dominance or assortative mate choice in a randomly mating population with a genetic polymorphism for an ecological trait. Mutations with small effects as well as mutants introducing complete dominance or perfect assorting are considered. Using adaptive dynamics techniques, we are able to calculate the ratio of fitness gradients for the effects of a dominance modifier and a mate choice locus, near evolutionary branching points. With equal resident allele frequencies, selection for mate choice is always stronger. Dominance is more strongly selected than assortative mating when the resident (common) alleles have very unequal frequencies at equilibrium. With female mate choice the difference in frequencies where dominance is more strongly selected is smaller than when mutants of both sexes can choose without costs. A symmetric resource-competition model illustrates the results.     

When maladaptive gene flow does not increase selection

Populations receiving high maladaptive gene flow are expected to experience strong directional selection—because gene flow pulls mean phenotypes away from local fitness peaks. We tested this prediction by means of a large and replicated mark‐recapture study of threespine stickleback (Gasterosteus aculeatus) in two stream populations. One of the populations (outlet) experiences high gene flow from the lake population and its morphology is correspondingly poorly adapted. The other population (inlet) experiences very low gene flow from the lake population and its morphology is correspondingly well adapted. Contrary to the above prediction, selection was not stronger in the outlet than in the inlet, a result that forced us to consider potential reasons for why maladaptive gene flow might not increase selection. Of particular interest, we show by means of a simple population genetic model that maladaptive gene flow can—under reasonable conditions—decrease the strength of directional selection. This outcome occurs when immigrants decrease mean fitness in the resident population, which decreases the strength of selection against maladapted phenotypes. We argue that this previously unrecognized effect of gene flow deserves further attention in theoretical and empirical studies.     

INTERGENERATIONAL PHENOTYPIC MIXING IN VIRAL EVOLUTION

Viral particles (virions) are made of genomic material packaged with proteins, drawn from the pool of proteins in the parent cell. It is well known that when virion concentrations are high, cells can be coinfected with multiple viral strains that can complement each other. Viral genomes can then interact with proteins derived from different strains, in a phenomenon known as phenotypic mixing. But phenotypic mixing is actually far more common: viruses mutate very often, and each time a mutation occurs, the parent cell contains different types of viral genomes. Due to phenotypic mixing, changes in viral phenotypes can be shifted by a generation from the mutations that cause them. In the regime of evolutionary invasion and escape, when mutations are crucial for the virus to survive, this timing can have a large influence on the probability of emergence of an adapted strain. Modeling the dynamics of viral evolution in these contexts thus requires attention to the mutational mechanism and the determinants of fitness.     

The contribution of the mitochondrial genome to sex‐specific fitness variance

Maternal inheritance of mitochondrial DNA (mtDNA) facilitates the evolutionary accumulation of mutations with sex‐biased fitness effects. Whereas maternal inheritance closely aligns mtDNA evolution with natural selection in females, it makes it indifferent to evolutionary changes that exclusively benefit males. The constrained response of mtDNA to selection in males can lead to asymmetries in the relative contributions of mitochondrial genes to female versus male fitness variation. Here, we examine the impact of genetic drift and the distribution of fitness effects (DFE) among mutations—including the correlation of mutant fitness effects between the sexes—on mitochondrial genetic variation for fitness. We show how drift, genetic correlations, and skewness of the DFE determine the relative contributions of mitochondrial genes to male versus female fitness variance. When mutant fitness effects are weakly correlated between the sexes, and the effective population size is large, mitochondrial genes should contribute much more to male than to female fitness variance. In contrast, high fitness correlations and small population sizes tend to equalize the contributions of mitochondrial genes to female versus male variance. We discuss implications of these results for the evolution of mitochondrial genome diversity and the genetic architecture of female and male fitness.     

FISHER'S GEOMETRIC MODEL OF ADAPTATION MEETS THE FUNCTIONAL SYNTHESIS: DATA ON PAIRWISE EPISTASIS FOR FITNESS YIELDS INSIGHTS INTO THE SHAPE AND SIZE OF PHENOTYPE SPACE

The functional synthesis uses experimental methods from molecular biology, biochemistry and structural biology to decompose evolutionarily important mutations into their more proximal mechanistic determinants. However these methods are technically challenging and expensive. Noting strong formal parallels between R.A. Fisher's geometric model of adaptation and a recent model for the phenotypic basis of protein evolution, we sought to use the former to make inferences into the latter using data on pairwise fitness epistasis between mutations. We present an analytic framework for classifying pairs of mutations with respect to similarity of underlying mechanism on this basis, and also show that these data can yield an estimate of the number of mutationally labile phenotypes underlying fitness effects. We use computer simulations to explore the robustness of our approach to violations of analytic assumptions and analyze several recently published datasets. This work provides a theoretical complement to the functional synthesis as well as a novel test of Fisher's geometric model.     

Evolution in response to social selection: the importance of interactive effects of traits on fitness

Social interactions have a powerful effect on the evolutionary process. Recent attempts to synthesize models of social selection with equations for indirect genetic effects (McGlothlin et al. 2010) provide a broad theoretical base from which to study selection and evolutionary response in the context of social interactions. However, this framework concludes that social selection will lead to evolution only if the traits carried by social partners are nonrandomly associated. I suggest this conclusion is incomplete, and that traits that do not covary between social partners can nevertheless lead to evolution via interactive effects on fitness. Such effects occur when there are functional interactions between traits, and as an example I use the interplay in water striders (Gerridae) between grasping appendages carried by males and spines by females. Functional interactive effects between traits can be incorporated into both the equations for social selection and the general model of social evolution proposed by McGlothlin et al. These expanded equations would accommodate adaptive coevolution in social interactions, integrate the quantitative genetic approach to social evolution with game theoretical approaches, and stimulate some new questions about the process of social evolution.     

SELECTION ON VARIANCE‐CONTROLLING GENES: ADAPTABILITY OR STABILITY

Simulations on a model system where a variance‐controlling master locus scales the effects of a set of effector loci show that selection affects the variance‐controlling locus more strongly than the effector loci, and that the direction of selection is dependent on the frequency of environmental changes.     

LOCAL ADAPTATION IN A CHANGING WORLD: THE ROLES OF GENE‐FLOW,MUTATION, AND SEXUAL REPRODUCTION

In spatially heterogeneous environments, the processes of gene flow, mutation, and sexual reproduction generate local genetic variation and thus provide material for local adaptation. On the other hand, these processes interchange maladapted for adapted genes and so, in each case, the net influence may be to reduce local adaptation. Previous work has indicated that this is the case in stable populations, yet it is less clear how the factors play out during population growth, and in the face of temporal environmental stochasticity. We address this issue with a spatially explicit, stochastic model. We find that dispersal, mutation, and sexual reproduction can all accelerate local adaptation in growing populations, although their respective roles may depend on the genetic make‐up of the founding population. All three processes reduce local adaptation, however, in the long term, that is when population growth becomes balanced by density‐dependent competition. These relationships are qualitatively maintained, although quantitatively reduced, if the resources are locally ephemeral. Our results suggest that species with high levels of local adaptation within their ranges may not be the same species that harbor potential for rapid local adaptation during population expansion.     

The fitness effect of mutations across environments: Fisher's geometrical model with multiple optima

When are mutations beneficial in one environment and deleterious in another? More generally, what is the relationship between mutation effects across environments? These questions are crucial to predict adaptation in heterogeneous conditions in a broad sense. Empirical evidence documents various patterns of fitness effects across environments but we still lack a framework to analyze these multivariate data. In this article, we extend Fisher's geometrical model to multiple environments determining distinct peaks. We derive the fitness distribution, in one environment, among mutants with a given fitness in another and the bivariate distribution of random mutants’ fitnesses across two or more environments. The geometry of the phenotype‐fitness landscape is naturally interpreted in terms of fitness trade‐offs between environments. These results may be used to fit/predict empirical distributions or to predict the pattern of adaptation across heterogeneous conditions. As an example, we derive the genomic rate of substitution and of adaptation in a metapopulation divided into two distinct habitats in a high migration regime and show that they depend critically on the geometry of the phenotype‐fitness landscape.     

FISHER'S GEOMETRIC MODEL WITH A MOVING OPTIMUM

Fisher's geometric model has been widely used to study the effects of pleiotropy and organismic complexity on phenotypic adaptation. Here, we study a version of Fisher's model in which a population adapts to a gradually moving optimum. Key parameters are the rate of environmental change, the dimensionality of phenotype space, and the patterns of mutational and selectional correlations. We focus on the distribution of adaptive substitutions, that is, the multivariate distribution of the phenotypic effects of fixed beneficial mutations. Our main results are based on an “adaptive‐walk approximation,” which is checked against individual‐based simulations. We find that (1) the distribution of adaptive substitutions is strongly affected by the ecological dynamics and largely depends on a single composite parameter γ, which scales the rate of environmental change by the “adaptive potential” of the population; (2) the distribution of adaptive substitution reflects the shape of the fitness landscape if the environment changes slowly, whereas it mirrors the distribution of new mutations if the environment changes fast; (3) in contrast to classical models of adaptation assuming a constant optimum, with a moving optimum, more complex organisms evolve via larger adaptive steps.     

SLOWLY SWITCHING BETWEEN ENVIRONMENTS FACILITATES REVERSE EVOLUTION IN SMALL POPULATIONS

Natural populations must constantly adapt to ever‐changing environmental conditions. A particularly interesting question is whether such adaptations can be reversed by returning the population to an ancestral environment. Such evolutionary reversals have been observed in both natural and laboratory populations. However, the factors that determine the reversibility of evolution are still under debate. The time scales of environmental change vary over a wide range, but little is known about how the rate of environmental change influences the reversibility of evolution. Here, we demonstrate computationally that slowly switching between environments increases the reversibility of evolution for small populations that are subject to only modest clonal interference. For small populations, slow switching reduces the mean number of mutations acquired in a new environment and also increases the probability of reverse evolution at each of these “genetic distances.” As the population size increases, slow switching no longer reduces the genetic distance, thus decreasing the evolutionary reversibility. We confirm this effect using both a phenomenological model of clonal interference and also a Wright–Fisher stochastic simulation that incorporates genetic diversity. Our results suggest that the rate of environmental change is a key determinant of the reversibility of evolution, and provides testable hypotheses for experimental evolution.     

Ploidy tug‐of‐war: Evolutionary and genetic environments influence the rate of ploidy drive in a human fungal pathogen

Variation in baseline ploidy is seen throughout the tree of life, yet the factors that determine why one ploidy level is maintained over another remain poorly understood. Experimental evolution studies using asexual fungal microbes with manipulated ploidy levels intriguingly reveals a propensity to return to the historical baseline ploidy, a phenomenon that we term “ploidy drive.” We evolved haploid, diploid, and polyploid strains of the human fungal pathogen Candida albicans under three different nutrient limitation environments to test whether these conditions, hypothesized to select for low ploidy levels, could counteract ploidy drive. Strains generally maintained or acquired smaller genome sizes (measured as total nuclear DNA through flow cytometry) in minimal medium and under phosphorus depletion compared to in a complete medium, while mostly maintained or acquired increased genome sizes under nitrogen depletion. Improvements in fitness often ran counter to changes in genome size; in a number of scenarios lines that maintained their original genome size often increased in fitness more than lines that converged toward diploidy (the baseline ploidy of C. albicans). Combined, this work demonstrates a role for both the environment and genotype in determination of the rate of ploidy drive, and highlights questions that remain about the force(s) that cause genome size variation.     

The distribution of fitness effects in an uncertain world

The distribution of fitness effects (DFE) among new mutations plays a critical role in adaptive evolution and the maintenance of genetic variation. Although fitness landscape models predict several key features of the DFE, most theory to date focuses on predictable environmental conditions, while ignoring stochastic environmental fluctuations that feature prominently in the ecology of many organisms. Here, we derive an extension of Fisher's geometric model that incorporates two common effects of environmental variation: (1) nonadaptive genotype‐by‐environment interactions (G × E), in which the phenotype of a given genotype varies across environmental contexts; and (2) random fluctuation of the fitness optimum, which generates fluctuating selection. We show that both factors cause a mismatch between the DFE within single generations and the distribution of geometric mean fitness effects (averaged over multiple generations) that governs long‐term evolutionary change. Such mismatches permit strong evolutionary constraints—despite an abundance of beneficial fitness variation within single environmental contexts—and to conflicting DFE estimates from direct versus indirect inference methods. Finally, our results suggest an intriguing parallel between the genetics and ecology of evolutionary constraints, with environmental fluctuations and pleiotropy placing qualitatively similar limits on the availability of adaptive genetic variation.     

Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Stochastic noise in gene expression causes variation in the development of phenotypes, making such noise a potential target of stabilizing selection. Here, we develop a new simulation model of gene networks to study the adaptive landscape underlying the evolution of robustness to noise. We find that epistatic interactions between the determinants of the expression of a gene and its downstream effect impose significant constraints on evolution, but these interactions do allow the gradual evolution of increased robustness. Despite strong sign epistasis, adaptation rarely proceeds via deleterious intermediate steps, but instead occurs primarily through small beneficial mutations. A simple mathematical model captures the relevant features of the single‐gene fitness landscape and explains counterintuitive patterns, such as a correlation between the mean and standard deviation of phenotypes. In more complex networks, mutations in regulatory regions provide evolutionary pathways to increased robustness. These results chart the constraints and possibilities of adaptation to reduce expression noise and demonstrate the potential of a novel modeling framework for gene networks.     

Detecting genetic variation in developmental instability by artificial selection on fluctuating asymmetry

Abstract Fluctuating asymmetry (FA) is frequently used as a measure of developmental instability (DI). Assuming a genetic basis to DI, many have argued that FA may be a good indicator of genetic quality to potential mates and to human managers of populations. Unfortunately FA is a poor indicator of DI, making it very difficult to verify this assertion. A recent review of the literature suggests that previous studies of the inheritance of FA and DI using half‐sib covariances and parent–offspring regression have been unable to put meaningful limits on the heritability of FA and DI because of the extremely low power of the experiments performed. In this study, we consider the power of artificial selection on FA as an alternative approach to studying the inheritance of FA and DI. Using simulations, we investigate the efficacy of selection for both increased and decreased FA for detecting genetic variation. We find that selection for increased FA has much more power to detect the presence of genetic variation than does selection for decreased FA. These results hold when realistic sample sizes are employed. Artificial selection for increased FA is currently the most powerful approach for the detection of genetic variation in DI.     

IS LIFE IMPOSSIBLE? INFORMATION,SEX, AND THE ORIGIN OF COMPLEX ORGANISMS

The earliest organisms are thought to have had high mutation rates. It has been asserted that these high mutation rates would have severely limited the information content of early genomes. This has led to a well‐known “paradox” because, in contemporary organisms, the mechanisms that suppress mutations are quite complex and a substantial amount of information is required to construct these mechanisms. The paradox arises because it is not clear how efficient error‐suppressing mechanisms could have evolved, and thus allowed the evolution of complex organisms, at a time when mutation rates were too high to permit the maintenance of very substantial amounts of information within genomes. Here, we use concepts from the formal theory of information to calculate the amount of genomic information that can be maintained. We identify conditions under which much higher levels of genomic information can be maintained than previously considered possible among origin‐of‐life researchers. In particular, we find that the highest levels of information are maintained when many genotypes produce identical phenotypes, and when reproduction occasionally involves recombination between multiple parental genomes. There is a good reason to believe that these conditions are relevant for very early organisms, and thus the results presented may provide a solution to a long‐standing logical problem associated with the early evolution of life.     

Hitchhiking in space: Ancestry in adapting,spatially extended populations*

Selective sweeps reduce neutral genetic diversity. In sexual populations, this “hitchhiking” effect is thought to be limited to the local genomic region of the sweeping allele. While this is true in panmictic populations, we find that in spatially extended populations the combined effects of many unlinked sweeps can affect patterns of ancestry (and therefore neutral genetic diversity) across the whole genome. Even low rates of sweeps can be enough to skew the spatial locations of ancestors such that neutral mutations that occur in an individual living outside a small region in the center of the range have virtually no chance of fixing in the population. The fact that nearly all ancestry rapidly traces back to a small spatial region also means that relatedness between individuals falls off very slowly as a function of the spatial distance between them.     

Implicit frequency dependence and kin selection in fluctuating environments

Summary I consider a general model of a fluctuating environment in which the environmental state each year is drawn at random from some given distribution. Each year organisms must choose what action to perform before the environmental state for that year is known. There is no interaction with kin. In this scenario, natural selection will tend to produce organisms which maximize their geometric mean fitness. In this paper I introduce the idea of the profile of a strategy. This function quantifies how the strategy peforms for each environmental state. I show that there is a unique profile such that a strategy is optimal if and only if it has this profile. I then give a characterization of the optimal profile which generalizes previous work by others in this area. The characterization of the optimal profile has a game theoretical interpretation. Motivated by this I introduce a game in which individuals play the field in a constant environment. This game may be interpreted as a cooperative game between kin. The key result of this paper shows that a strategy maximizes geometric mean fitness in the original fluctuating environment problem if and only if it is an evolutionarily stable strategy of the deterministic environment game. It is well known that an optimal strategy in a fluctuating environment may be mixed, involving adaptive coin-flipping. Others have previously noted that this may result in some individuals sacrificing individual reproductive success for the good of the genotype. My analysis shows that one may regain the concept of individual optimization if the quantity maximized is suitably defined. Under an optimal strategy every action taken maximizes the expected number of offspring produced, where this expectation is not calculated using the true distribution of environmental states, but a distribution modified to take account of the actions of kin.     

The evolution of cooperation by the Hankshaw effect

The evolution of cooperation—costly behavior that benefits others—faces one clear obstacle. Namely, cooperators are always at a competitive disadvantage relative to defectors, individuals that reap the benefits, but evade the cost of cooperation. One solution to this problem involves genetic hitchhiking, where the allele encoding cooperation becomes linked to a beneficial mutation, allowing cooperation to rise in abundance. Here, we explore hitchhiking in the context of adaptation to a stressful environment by cooperators and defectors with spatially limited dispersal. Under such conditions, clustered cooperators reach higher local densities, thereby experiencing more mutational opportunities than defectors. Thus, the allele encoding cooperation has a greater probability of hitchhiking with alleles conferring stress adaptation. We label this probabilistic enhancement the “Hankshaw effect” after the character Sissy Hankshaw, whose anomalously large thumbs made her a singularly effective hitchhiker. Using an agent‐based model, we reveal a broad set of conditions that allow the evolution of cooperation through this effect. Additionally, we show that spite, a costly behavior that harms others, can evolve by the Hankshaw effect. While in an unchanging environment these costly social behaviors have transient success, in a dynamic environment, cooperation and spite can persist indefinitely.