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Results for “Evolutionary Biology”

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bioRxiv2018

On the feasibility of saltational evolution

One of the key tenets of Darwins theory that was inherited by the Modern Synthesis of evolutionary biology is gradualism, that is, the notion that evolution proceeds gradually, via accumulation of \"infinitesimally small\" heritable changes 1,2. However, some of the most consequential evolutionary changes, such as, for example, the emergence of major taxa, seem to occur abruptly rather than gradually, as captured in the concepts of punctuated equilibrium 3,4 and evolutionary transitions 5,6. We examine a mathematical model of an evolutionary process on a rugged fitness landscape 7,8 and obtain analytic solutions for the probability of multi-mutational leaps, that is, several mutations occurring simultaneously, within a single generation in one genome, and being fixed all together in the evolving population. The results indicate that, for typical, empirically observed combinations of the parameters of the evolutionary process, namely, effective population size, mutation rate, and distribution of selection coefficients of mutations, the probability of a multi-mutational leap is low, and accordingly, their contribution to the evolutionary process is minor at best. However, such leaps could become an important factor of evolution in situations of population bottlenecks and elevated mutation rates, such as stress-induced mutagenesis in microbes or tumor progression, as well as major evolutionary transitions and evolution of primordial replicators.

evolutionary biology↗

bioRxiv2016

Accelerated simulation of evolutionary trajectories in origin-fixation models

We present an accelerated algorithm to forward-simulate origin--fixation models. Our algorithm requires on average only about two fitness evaluations per fixed mutation, whereas traditional algorithms require, per one fixed mutation, a number of fitness evaluations on the order of the effective population size Ne. Our accelerated algorithm yields the exact same steady state as the original algorithm but produces a different order of fixed mutations. By comparing several relevant evolutionary metrics, such as the distribution of fixed selection coefficients and the probability of reversion, we find that the two algorithms behave equivalently in many respects. However, the accelerated algorithm yields less variance in fixed selection coefficients. Notably, we are able to recover the expected amount of variance by rescaling population size, and we find a linear relationship between the rescaled population size and the population size used by the original algorithm. Considering the widespread usage of origin--fixation simulations across many areas of evolutionary biology, we introduce our accelerated algorithm as a useful tool for increasing the computational complexity of fitness functions without sacrificing much in terms of accuracy of the evolutionary simulation.

evolutionary biology↗

bioRxiv2018

Evolutionary adaptability linked to length variation in short genic tandem repeats

Intrinsically disordered protein regions are of high importance for temperature sensing and immune responses in plants. Tracts of identical amino acids accumulate in these regions and can vary in length over generations due to expansions and retractions of short tandem repeats at the genomic level. However, little attention has been paid to what extent length variation is shaped by natural selection. By environmental association analysis on 2,514 length variable tracts in 770 whole-genome sequenced wild Arabidopsis thaliana we show that length variation in glutamine and asparagine amino acid homopolymers, as well as in interaction hotspots, correlate with local bio-climatic habitat. We determined experimentally that the promoter activity of a light-stress gene depended on polyglutamine length variants in a disordered transcription factor. Our results show that length variations impact protein function and are substantially shaped by natural selection. Length variants modulating protein function at a global genomic scale has implications for understanding protein evolution and eco-evolutionary biology.

evolutionary biology↗

bioRxiv2016

Information theory and the phenotypic complexity of evolutionary adaptations and innovations

Two main lines of research link information theory to evolutionary biology. The first focuses on organismal phenotypes, and on the information that organisms acquire about their environment. The second connects information-theoretic concepts to genotypic change. The genotypic and phenotypic level can be linked by experimental high-throughput genotyping and computational models of genotype-phenotype relationships. I here use a simple information-theoretic framework to compute a phenotypes information content (its phenotypic complexity), and the information gain or change that comes with a new phenotype. I apply this framework to experimental data on DNA-binding phenotypes of multiple transcription factors. Low phenotypic complexity is associated with a biological systems ability to discover novel phenotypes in evolution. I show that DNA duplications lower phenotypic complexity, which illustrates how information theory can help explain why gene duplications accelerate evolutionary adaptation. I also demonstrate that with the right experimental design, sequencing data can be used to infer the information gain associated with novel evolutionary adaptations, for example in laboratory evolution experiments. Information theory can help quantify the evolutionary progress embodied in the discovery of novel adaptive phenotypes.

Evolutionary Biology↗

bioRxiv2018

Inferring Species Trees Using Integrative Models of Species Evolution

Evolutionary models account for either population- or species-level processes, but usually not both. We introduce a new model, the FBD-MSC, which makes it possible for the first time to integrate both the genealogical and fossilization phenomena, by means of the multispecies coalescent (MSC) and the fossilized birth-death (FBD) processes. Using this model, we reconstruct the phylogeny representing all extant and many fossil Caninae, recovering both the relative and absolute time of speciation events. We quantify known inaccuracy issues with divergence time estimates using the popular strategy of concatenating molecular alignments, and show that the FBD-MSC solves them. Our new integrative method and empirical results advance the paradigm and practice of probabilistic total evidence analyses in evolutionary biology.

evolutionary biology↗

bioRxiv2017

Reconstruction of body mass evolution in the Cetartiodactyla and mammals using phylogenomic data.

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100042).\n\nReconstructing ancestral characters on a phylogeny is an arduous task because the observed states at the tips of the tree correspond to a single realization of the underlying evolutionary process. Recently, it was proposed that ancestral traits can be indirectly estimated with the help of molecular data, based on the fact that life history traits influence substitution rates. Here we challenge these new approaches in the Cetartiodactyla, a clade of large mammals which, according to paleontology, derive from small ancestors. Analysing transcriptome data in 41 species, of which 22 were newly sequenced, we provide a dated phylogeny of the Cetartiodactyla and report a significant effect of body mass on the overall substitution rate, the synonymous vs. non-synonymous substitution rate and the dynamics of GC-content. Our molecular comparative analysis points toward relatively small Cetartiodactyla ancestors, in agreement with the fossil record, even though our data set almost exclusively consists of large species. This analysis demonstrates the potential of phylogenomic methods for ancestral trait reconstruction and gives credit to recent suggestions that the ancestor to placental mammals was a relatively large and long-lived animal.

evolutionary biology↗

bioRxiv2018

Convergence in form and function overcomes non-parallel evolutionary histories in Arctic charr

Understanding the extent to which evolution is predictable under multifarious selection is a longstanding question in evolutionary biology. However, the interplay of stochastic and contingent factors influencing the extent of parallelism in nature is not well understood. To test the predictability of evolution, we studied a natural experiment on different organismal levels across lakes and evolutionary lineages of a freshwater salmonid fish, Arctic charr (Salvelinus alpinus). We identified significant phenotypic parallelism between Arctic charr ecotype pairs within a continuum of parallel evolution and highly parallel adaptive morphological traits. Variability in phenotypic predictability was explained by complex demographic histories, differing genomic backgrounds and genomic responses to selection, variable genetic associations with ecotype, and environmental variation. Remarkably, gene expression was highly similar across ecotype replicates, and explained the observed parallelism continuum. Our findings suggest that parallel evolution by non-parallel evolutionary routes is possible when the regulatory molecular phenotype compensates for divergent histories.

evolutionary biology↗

bioRxiv2018

Evolution of correlated complexity in the radically different courtship signals of birds-of-paradise

Ornaments used in courtship often vary wildly among species, reflecting the evolutionary interplay between mate preference functions and the constraints imposed by natural selection. Consequently, understanding the evolutionary dynamics responsible for ornament diversification has been a longstanding challenge in evolutionary biology. However, comparing radically different ornaments across species, as well as different classes of ornaments within species, is a profound challenge to understanding diversification of sexual signals. Using novel methods and a unique natural history dataset, we explore evolutionary patterns of ornament evolution in a group - the birds-of-paradise - exhibiting dramatic phenotypic diversification widely assumed to be driven by sexual selection. Rather than the tradeoff between ornament types originally envisioned by Darwin and Wallace, we found positive correlations among cross-modal (visual/acoustic) signals indicating functional integration of ornamental traits into a composite unit - the courtship phenotype. Furthermore, given the broad theoretical and empirical support for the idea that systemic robustness - functional overlap and interdependency - promotes evolutionary innovation, we posit that birds-of-paradise have radiated extensively through ornamental phenotype space as a consequence of the robustness in the courtship phenotype that we document at a phylogenetic scale. We suggest that the degree of robustness in courtship phenotypes among taxa can provide new insights into the relative influence of sexual and natural selection on phenotypic radiations.\n\nAuthor SummaryAnimals frequently vary widely in ornamentation, even among closely related species. Understanding the patterns that underlie this variation is a significant challenge, requiring comparisons among drastically different traits - like comparing apples to oranges. Here, we use novel analytical approaches to quantify variation in ornamental diversity and richness across the wildly divergent birds-of-paradise, a textbook example of how sexual selection can profoundly shape organismal phenotypes. We find that color and acoustic complexity, along with behavior and acoustic complexity, are positively correlated across evolutionary time-scales. Positive covariation among ornament classes suggests that selection is acting on correlated suites of traits - a composite courtship phenotype - and that this integration may be partially responsible for the extreme variation we see in birds-of-paradise.

evolutionary biology↗

bioRxiv2016

Host-selected mutations converging on a global regulator drive an adaptive leap by bacteria to symbiosis

Even though eukaryote health relies on beneficial symbionts, host defenses targeting pathogens create substantial obstacles for the establishment of these essential partnerships. To reveal mechanisms of symbiotic adaptation, we experimentally evolved ecologically distinct bioluminescent Vibrio fischeri through Euprymna scolopes squid light organs. Serial passaging of V. fischeri populations through squid hosts produced eight distinct mutations in the binK sensor kinase gene that conferred an exceptional selective advantage demonstrated through both empirical and theoretical analysis. Squid-adaptive binK alleles promoted colonization and immune evasion behavior which was mediated by symbiotic polysaccharide (Syp). binK variation also produced metabolic convergence with native symbionts, and altered quorum sensing and luminescence. Preexisting coordination of symbiosis traits facilitated an efficient solution where altered function of a regulator was the key to unlock multiple colonization barriers. These results identify a genetic basis for microbial adaptability and underscore the importance of hosts as selective agents that shape emergent symbiont populations.\n\nImpact statementSquid selection on non-native Vibrio fischeri drove rapid adaptation through convergent mutations of large effect, unmasking preexisting coordinate regulation of symbiosis.\n\nMajor subject areas, keywords, and research organism(s)\n\nMajor subject area: Genomics and evolutionary biology

Evolutionary Biology↗

bioRxiv2017

Extensive phenotypic plasticity in a seasonal butterfly limits potential for evolutionary responses to environmental change

Understanding how populations adapt to changing environments is a major goal in evolutionary biology and ecology, and particularly urgent for predicting resilience to climate change. Phenotypic plasticity, the ability to express multiple phenotypes from the same genome, is a widespread adaptation to short-term environmental fluctuations, but whether it facilitates adaptation to environmental change on evolutionary timescales, such as those under climate change, remains contentious. Here, we investigate plasticity and adaptive potential in an African savannah butterfly displaying extensive plasticity as adaptation to predictable dry-wet seasonality. We assess the transcriptional architecture of seasonal plasticity and find pervasive gene expression differences between the seasonal phenotypes, reflecting a genome-wide plasticity programme. Strikingly, intra-population genetic variation for this response is highly depleted, possibly reflecting strong purifying selection in its savannah habitat where an environmental cue (temperature) reliably predicts seasonal transitions and the cost of a mismatched phenotype is high. Under climate change the accuracy of such cues may deteriorate, rendering dominant reactions norm maladaptive. Therefore, depleted variation for plasticity as reported here may crucially limit evolutionary potential when conditions change, and seasonally plastic species may in fact be especially vulnerable to climate change.

evolutionary biology↗

bioRxiv2015

Quantitative Genetics Meets Integral Projection Models: Unification of Widely Used Methods from Ecology and Evolution

O_LIMicro-evolutionary predictions are complicated by ecological feedbacks like density dependence, while ecological predictions can be complicated by evolutionary change. A widely used approach in micro-evolution, quantitative genetics, struggles to incorporate ecological processes into predictive models, while structured population modelling, a tool widely used in ecology, rarely incorporates evolution explicitly.\nC_LIO_LIIn this paper we develop a flexible, general framework that links quantitative genetics and structured population models. We use the quantitative genetic approach to write down the phenotype as an additive map. We then construct integral projection models for each component of the phenotype. The dynamics of the distribution of the phenotype are generated by combining distributions of each of its components. Population projection models can be formulated on per generation or on shorter time steps.\nC_LIO_LIWe introduce the framework before developing example models with parameters chosen to exhibit specific dynamics. These models reveal (i) how evolution of a phenotype can cause populations to move from one dynamical regime to another (e.g. from stationarity to cycles), (ii) how additive genetic variances and covariances (the G matrix) are expected to evolve over multiple generations, (iii) how changing heritability with age can maintain additive genetic variation in the face of selection and (iii) life history, population dynamics, phenotypic characters and parameters in ecological models will change as adaptation occurs.\nC_LIO_LIOur approach unifies population ecology and evolutionary biology providing a framework allowing a very wide range of questions to be addressed. The next step is to apply the approach to a variety of laboratory and field systems. Once this is done we will have a much deeper understanding of eco-evolutionary dynamics and feedbacks.\nC_LI

Evolutionary Biology↗

bioRxiv2018

Leaps and bounds: geographical and ecological distance constrained the colonisation of the Afrotemperate by Erica

AO_SCPCAPBSTRACTC_SCPCAPThe coincidence of long distance dispersal and biome shift is assumed to be the result of a multifaceted interplay between geographical distance and ecological suitability of source and sink areas. Here, we test the influence of these factors on the dispersal history of the flowering plant genus Erica (Ericaceae) across the Afrotemperate. We quantify similarity of Erica climate niches per biogeographic area using direct observations of species, and test various colonisation scenarios while estimating ancestral areas for the Erica clade using parametric biogeographic model testing. We infer that the overall dispersal history of Erica across the Afrotemperate is the result of infrequent colonisation limited by geographic proximity and niche similarity. However, the Drakensberg Mountains represent a colonisation sink, rather than acting as a \"stepping stone\" between more distant and ecologically dissimilar Cape and Tropical African regions. Strikingly, the most dramatic examples of species radiations in Erica were the result of single unique dispersals over longer distances between ecologically dissimilar areas, contradicting the rule of phylogenetic biome conservatism. These results highlight the importance of rare biome shifts, in which a unique dispersal event fuels evolutionary radiation.\n\nThis article has been peer-reviewed and recommended by: Peer Community in Evolutionary Biology (DOI: 10.24072/pci.evolbiol.100065)

evolutionary biology↗

bioRxiv2014

How the tortoise beats the hare: Slow and steady adaptation in structured populations suggests a rugged fitness landscape in bacteria

AbstractIn the context of Wrights adaptive landscape, genetic epistasis can yield a multipeaked or \"rugged\" topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to rapidly fix on one, which may not be the highest peak. Contrarily, beneficial mutations in a population with spatially restricted migration take longer to fix, allowing distant parts of the population to explore the landscape semi-independently. Such a population can simultaneous discover multiple peaks and the genotype at the highest discovered peak is expected to fix eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the Tortoise-Hare fable, the structured population (Tortoise) starts relatively slow, but eventually surpasses the unstructured population (Hare) in average fitness. In contrast, on single-peak landscapes (e.g., systems lacking epistasis), all uphill paths converge. Given such \"smooth\" topography, breadth of search is devalued, and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the Tortoise-Hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we then explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a Tortoise-Hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wrights Shifting Balance Process.\n\nSignificance StatementAdaptive landscapes are a way of describing how mutations interact with each other to produce fitness. If an adaptive landscape is rugged, organisms achieve higher fitness with more difficulty because the mutations to reach high fitness genotypes may not be always beneficial. By evolving populations of Escherichia coli with different degrees of spatial structure, we identified a Tortoise-Hare pattern, where structured populations were initially slower, but overtook less structured populations in mean fitness. These results, combined with genetic sequencing and computational simulation, indicate this bacterial adaptive landscape is rugged. Our findings address one of the most enduring questions in evolutionary biology, in addition to, providing insight into how evolution may influence medicine and engineering.

Evolutionary Biology↗

bioRxiv2018

An Empirical Demonstration of Unsupervised Machine Learning in Species Delimitation

One major challenge to delimiting species with genetic data is successfully differentiating species divergences from population structure, with some current methods biased towards overestimating species numbers. Many fields of science are now utilizing machine learning (ML) approaches, and in systematics and evolutionary biology, supervised ML algorithms have recently been incorporated to infer species boundaries. However, these methods require the creation of training data with associated labels. Unsupervised ML, on the other hand, uses the inherent structure in data and hence does not require any user-specified training labels, thus providing a more objective approach to species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three unsupervised ML approaches, specifically random forests, variational autoencoders, and t-distributed stochastic neighbor embedding, for species delimitation utilizing a short-range endemic harvestman taxon (Laniatores, Metanonychus). First, we combine mitochondrial data with examination of male genitalic morphology to identify a priori species hypotheses. Then we use single nucleotide polymorphism data derived from sequence capture of ultraconserved elements (UCEs) to test the efficacy of unsupervised ML algorithms in successfully identifying a priori species, comparing results to commonly used genetic approaches. Finally, we use two validation methods to assess a priori species hypotheses using UCE data. We find that unsupervised ML approaches successfully cluster samples according to species level divergences and not to high levels of population structure, while standard model-based validation methods over-split species, in some instances suggesting that all sampled individuals are distinct species. Moreover, unsupervised ML approaches offer the benefits of better data visualization in two-dimensional space and the ability to accommodate various data types. We argue that ML methods may be better suited for species delimitation relative to currently used model-based validation methods, and that species delimitation in a truly integrative framework provides more robust final species hypotheses relative to separating delimitation into distinct \"discovery\" and \"validation\" phases. Unsupervised ML is a powerful analytical approach that can be incorporated into many aspects of systematic biology, including species delimitation. Based on results of our empirical dataset, we make several taxonomic changes including description of a new species.

evolutionary biology↗

bioRxiv2016

Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms

A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large--but not intermediate-sized--populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.\n\nAuthor SummarySince the early days of theoretical population genetics. scientists have debated the role of population size in shaping evolutionary dynamics. Do large populations possess an evolutionary advantage towards complexity due to the strength of natural selection in these populations? Or do small populations have the advantage, as genetic drift allows for the exploration of fitness landscapes inaccessible to large populations? There are many theories that predict whether large or small populations-those with strong selection or those with strong drift-should evolve the greatest complexity. Here, we use digital experimental evolution to examine the interplay between population size and the evolution of complexity. We found that genetic drift could lead to increased genome size and phenotypic complexity in very small populations. However, large populations also evolved similar large genomes and complexity. Small populations evolved larger genomes through the fixation of slightly deleterious insertions, while large populations utilized rare beneficial insertions. Our results suggest that both strong drift and strong selection can allow populations to evolve similar complexity, but through different evolutionary trajectories.

Evolutionary Biology↗

bioRxiv2016

Reconstructing the backbone of the Saccharomycotina yeast phylogeny using genome-scale data

Understanding the phylogenetic relationships among the yeasts of the subphylum Saccharomycotina is a prerequisite for understanding the evolution of their metabolisms and ecological lifestyles. In the last two decades, the use of rDNA and multi-locus data sets has greatly advanced our understanding of the yeast phylogeny, but many deep relationships remain unsupported. In contrast, phylogenomic analyses have involved relatively few taxa and lineages that were often selected with limited considerations for covering the breadth of yeast biodiversity. Here we used genome sequence data from 86 publicly available yeast genomes representing 9 of the 11 major lineages and 10 non-yeast fungal outgroups to generate a 1,233-gene, 96-taxon data matrix. Species phylogenies reconstructed using two different methods (concatenation and coalescence) and two data matrices (amino acids or the first two codon positions) yielded identical and highly supported relationships between the 9 major lineages. Aside from the lineage comprised by the family Pichiaceae, all other lineages were monophyletic. Most interrelationships among yeast species were robust across the two methods and data matrices. However, 8 of the 93 internodes conflicted between analyses or data sets, including the placements of: the clade defined by species that have reassigned the CUG codon to encode serine, instead of leucine; the clade defined by a whole genome duplication; and of Ascoidea rubescens. These phylogenomic analyses provide a robust roadmap for future comparative work across the yeast subphylum in the disciplines of taxonomy, molecular genetics, evolutionary biology, ecology, and biotechnology. To further this end, we have also provided a BLAST server to query the 86 Saccharomycotina genomes, which can be found at http://y1000plus.org/blast.

Evolutionary Biology↗

bioRxiv2017

Selection On Morphological Traits And Fluctuating Asymmetry By A Fungal Parasite In The Yellow Dung Fly

A Preprint reviewed and recommended by Peer Community Evolutionary Biology: http://dx.doi.org/10.24072/pci.evolbiol.100027\n\nEvidence for selective disadvantages of large body size remains scarce in general. Previous phenomenological studies of the yellow dung fly Scathophaga stercoraria have demonstrated strong positive sexual and fecundity selection on male and female size. Nevertheless, the body size of flies from a Swiss study population has declined by almost 10% from 1993 to 2009. Given substantial heritability of body size, this negative evolutionary response of an evidently positively selected trait suggests important selective factors being missed (e.g. size-selective predation or parasitism). A periodic epidemic outbreak of the fungus Entomophthora scatophagae allowedassessment of selection exerted by this parasite fatal to adult flies. Fungal infection varied over the season from ca. 50% in the cooler and more humid spring and autumn to almost 0% in summer. The probability of dying from fungal infection increased with adult body size. All infected females died before laying eggs, so there was no fungus impact on female fecundity beyond its impact on mortality. Large males showed the typical mating advantage in the field, but this pattern of positive sexual selection was nullified by fungal infection. Mean fluctuating asymmetry of paired appendages (legs, wings) did not affect the viability, fecundity or mating success of yellow dung flies in the field. This study demonstrates rare parasite-mediated disadvantages of large adult body size in the field. Reduced ability to combat parasites such as Entomophthora may be an immunity cost of large size in dung flies, although the hypothesized trade-off between fluctuating asymmetry, a presumed indicator of developmental instability and environmental stress, and immunocompetence was not found here.

evolutionary biology↗

bioRxiv2017

Sexual reproduction as bet-hedging

In evolutionary biology, bet-hedging refers to a strategy that reduces the variance of reproductive success at the cost of reduced mean reproductive success. In unpredictably fluctuating environments, bet-hedgers benefit from higher geometric mean fitness despite having lower arithmetic mean fitness than their specialist competitors. We examine the extent to which sexual reproduction can be considered a type of bet-hedging, by clarifying past arguments, examining parallels and differences to evolutionary games, and by presenting a simple model examining geometric and arithmetic mean payoffs of sexual and asexual reproduction. Sex typically has lower arithmetic mean fitness than asex, while the geometric mean fitness can be higher if sexually produced offspring are not identical. However, asexual individuals that are heterozygotes can gain conservative bet-hedging benefits of similar magnitude while avoiding the costs of sex. This highlights that bet-hedging always has to be specified relative to the payoff structure of relevant competitors. It also makes it unlikely that sex, at least when associated with significant male production, evolves solely based on bet-hedging in the context of frequently and repeatedly occupied environmental states. Future work could usefully consider bet-hedging in open-ended evolutionary scenarios with de novo mutations.

evolutionary biology↗