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Experimental evolution of adaptive divergence under varying degrees of gene flow

Abstract

Adaptive divergence is the key evolutionary process generating biodiversity by means of natural selection. Yet, the conditions under which it can arise in the presence of gene flow remain contentious. To address this question, we subjected 132 sexually reproducing fission yeast populations, sourced from two independent genetic backgrounds, to disruptive ecological selection and manipulated the level of migration between environments. Contrary to theoretical expectations, adaptive divergence was most pronounced when migration was either absent (allopatry) or maximal (sympatry), but was much reduced at intermediate rates (parapatry and local mating). This effect was apparent across central life-history components (survival, asexual growth and mating) but differed in magnitude between ancestral genetic backgrounds. The evolution of some fitness components was constrained by pervasive negative correlations (trade-off between asexual growth and mating), while others changed direction under the influence of migration (for example, survival and mating). In allopatry, adaptive divergence was mainly conferred by standing genetic variation and resulted in ecological specialization. In sympatry, divergence was mainly mediated by novel mutations enriched in a subset of genes and was characterized by the repeated emergence of two strategies: an ecological generalist and an asexual growth specialist. Multiple loci showed consistent evidence for antagonistic pleiotropy across migration treatments providing a conceptual link between adaptation and divergence. This evolve-and-resequence experiment shows that rapid ecological differentiation can arise even under high rates of gene flow. It further highlights that adaptive trajectories are governed by complex interactions of gene flow, ancestral variation and genetic correlations.

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Fig. 1: Schematic of the experiment.
Fig. 2: Fitness as a function of ecological selection and gene flow.
Fig. 3: Partitioning of genetic variation and its relationship to fitness.
Fig. 4: Candidate genetic variants under disruptive selection in α populations.

Data availability

All of the data generated for this study are archived in the Sequence Read Archive (under BioProject ID PRJNA604890) at the National Centre for Biotechnology Information (www.ncbi.nlm.nih.gov/sra).

Code availability

All of the code used for the analyses, fitness data and a list of genetic variants (in .vcf format) are available at https://github.com/EvoBioWolf/SchPom_Exp_AdaptDiv and Zenodo (https://doi.org/10.5281/zenodo.4133489)89.

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Acknowledgements

We thank S. L. Ament-Velásquez, R. Butlin, U. Knief, D. Metzler, C. Peart, R. Pereira, R. Stelkens, M. Weissensteiner and members of the Immler and Wolf laboratories for providing intellectual input on the various analyses, and comments on the manuscript. We are further indebted to T. Ahmad, G. Kumpfmüller, H. Lainer and N. Zajac for help with the laboratory work, D. Scofield for bioinformatics support, and B. Haller for support with running SLiM. We further acknowledge support for genomic data generation from the SNP&SEQ Technology Platform in the National Genomics Infrastructure, Uppsala, Sweden. Flow cytometry was performed at the Core Facility Flow Cytometry at LMU. The computational infrastructure was provided by the UPPMAX Next-Generation Sequencing Cluster and Storage (UPPNEX) project funded by the Knut and Alice Wallenberg Foundation and Swedish National Infrastructure for Computing. Funding was provided to J.B.W.W. by LMU Munich, the Science of Life Laboratories National Projects and Uppsala University.

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Contributions

S.T., B.P.S.N., S.I. and J.B.W.W. conceived of the study idea. S.T., B.P.S.N. and B.W. performed the experiments. S.T. and B.W. performed the phenotypic measurements. All analyses were performed by S.T., with contributions to the phenotypic analyses from B.P.S.N. S.T. and J.B.W.W. wrote the manuscript with input from B.P.S.N. and S.I.

Corresponding authors

Correspondence to Sergio Tusso or Jochen B. W. Wolf.

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The authors declare no competing interests.

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Peer review information Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Distance between relative fitness as a function of migration treatment.

a. PCA using z-score normalized fitness values per genetic background. Distribution of populations are given per treatment, with pairs of populations connected by a line. b. Boxplot of Euclidian distance between pairs of connected population pairs per treatment and genetic background. For allopatric populations (Allo*), the mean distance was calculated for a subset of 100 bootstrapped combinations of all top and bottom populations. In parapatric populations, the boxplot displays the distances between connected populations (Para) and bootstrapped combinations between independent top and bottom populations as in Allo* (Para*). Pairs of parapatric and local mating population are more similar in fitness than allopatric, randomized parapatric or sympatric populations. This is consistent with a homogenizing effect of gene flow for these treatments. Boxplots description: center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers.

Extended Data Fig. 2 Principle component analyses of z-score standardized log transformed relative fitness values across fitness components.

Each point represents one population or subpopulation subjected to the top (blue) or bottom (red) selection regime. Eigenvectors are indicated by arrows for each fitness component. Arrows pointing in the same direction reflect a positive relationship between components, arrows in opposite direction indicate a negative correlation or trade-off and orthogonal arrows suggest no relationship. The relative contribution to variance in PC1 and PC2 is symbolized by length of the arrow. Proportion of variance explained by each PC is shown in parenthesis for both axes. Results are shown for the α and β genetic background. Abbreviations of migration treatments as in the main manuscript.

Extended Data Fig. 3 Standing genetic variation in ancestral populations and total genetic variation per evolved population.

a. Venn diagram displaying the number of genetic variants per genetic background in the two ancestral populations at the beginning of the experiment. The left panel includes all variants, whereas the right panel is restricted to variants with allele frequency higher than 0.2. b. Boxplot with number of genetic variants per population, differentiating between standing genetic variation and new mutations emerging during the experiment. Panels include either all variants or only variants with allele frequency higher than 0.2, as well as between genetic backgrounds (α and β genetic background). The orange point shows the number of genetic variants in ancestral populations at the beginning of the experiment. At the end of the experiment we counted in total 1,472 and 1,318 genetic variants (α and β respectively) of which 1,217 and 1,061 arose de-novo during the experiment. Most variants were present at low frequencies (1,179 and 1,073 variants with a maximum frequency of 0.2), and most were limited to single populations (950 and 809 variants). At the end of the experiment, each of the 132 evolved populations had between 71 and 183 mutations. Boxplots as in Extended Data Fig. 1.

Extended Data Fig. 4 Genetic differentiation between populations.

PCA including all populations per genetic background. The analysis was conducted including all genetic variants (upper graphs) or using only variants present at the beginning of the experiment (standing genetic variation; lower graphs). Decomposition of genetic variation across all populations using principal component analyses shows a similar distribution of populations for the genetic backgrounds across the different levels of gene flow for the first two principal components. Using all genetic variants, the two major axes of variation explained in total 17.3 % and 20.1 % of the genetic variation for the α and β background, respectively. This increased to 27.7 and 25.9% when using only standing genetic variation, however, the distribution of populations remained similar.

Extended Data Fig. 5 Dxy between populations.

Boxplot of distribution of Dxy between pairs of populations. Each point represents a Dxy value for a single pair of populations. Dxy values were calculated using all genetic variants (left) or only standing genetic variation (right). Distribution of values were divided per genetic background (α and β). For each treatment, comparisons were done between populations with the same or opposite ecological selection regime (Top - T and Bottom – B). For parapatric populations, Dxy was additionally calculated between connected population pairs (Para_Pairs). Boxplots as in Extended Data Fig. 1.

Extended Data Fig. 6 Fst between populations.

Boxplot of distribution of Fst between pairs of populations. Each point represents an Fst value for a single pair of populations. Fst values were calculated using all genetic variants (left) or only standing genetic variation (right). Distribution of values were divided per genetic background (α and β). For each treatment, comparisons were done between populations with the same or opposite ecological selection regime (Top - T and Bottom – B). For parapatric populations, Fst was additionally calculated between connected population pairs (Para_Pairs). Boxplots as in Extended Data Fig. 1.

Extended Data Fig. 7 Genetic divergence between Top and Bottom ecotypes.

Boxplot of genetic divergence, measured as Da (Difference between Dxy and mean π), between the top and bottom ecotypes per population. Each point represents the comparison between top and bottom ecotypes within each population. The plot is divided by treatment (Local mating and Sympatry) and by genetic background. In the α genetic background genetic divergence was higher for Sympatric populations compared with Local Mating populations, which is consistent with phenotypic data. This divergence was not observed for the β background, however the absolute phenotypic variation was lower in this genetic background (Fig. 2a). Boxplots as in Extended Data Fig. 1.

Extended Data Fig. 8 Summary statistics of allele frequency change between subpopulations.

Data are shown for the local mating (LM) and sympatry (SYM) treatment and are displayed for both ancestral genetic backgrounds (α and β). For each of the 11 populations per treatment the allele frequencies of segregating variants were compared between subpopulations including the following comparisons. Left panel (Top-Bottom): Comparison between the top and bottom ecotypic fractions; comparison between the population pool (prior to ecological selection) with the top (Middle panel: Pool-Top) or bottom ecotypic fraction (Right panel: Pool -Bottom). For each population the proportion of genetic variants with an allele frequency shift of > 0.2 were calculated. The resulting proportion of genetic variants changing in either direction as displayed for each population in Supplementary Fig. 4 are here summarized to show the general trend. Following the colour scheme from Supplementary Fig. 4 boxplots are coloured by the fraction showing an increase in frequency, viz. blue for the top ecotype (diff_Top), red for the bottom ecotype (diff_Bottom) or orange for the pool (diff_Pool). In the α background sympatric populations showed higher similarity between the whole pool sample and the top ecotype fraction (higher proportion of variants increased in top fraction, lower proportion of variants differing between top and the whole pool sample, and higher proportion of variants differing between bottom and whole pool sample). In local mating populations, the pattern was reversed showing higher similarity with the bottom ecotype fraction. In the β background, showed a similar trend, but the difference between treatments was lower, which is consistent with no observed divergence in the phenotypic data. Significance of the difference between groups was tested using a quasibinomial model in a nested generalised lineal model with treatment and fraction as fixed variables. Significant difference between treatments and group are shown with blue and black asterisks respectively. Boxplots as in Extended Data Fig. 1.

Extended Data Fig. 9 Distribution of effect sizes for all genetic variants.

Boxplot of distribution of maximum allele frequencies grouping variants by the predicted functional effect level. Each point represents the maximum allele frequency observed in all populations per variant. Upper and lower panels are differentiated by genetic background (α and β populations) and include all variants (left) or only variants already present in the respective ancestral population (standing genetic variation, right). Effect levels as described in method section. P-value < 0.001 (***), < 0.01 (**), or < 0.05 (*). For further details see methods. Boxplots as in Extended Data Fig. 1.

Extended Data Fig. 10 Candidate genetic variants under disruptive selection per genetic background.

Boxplot of allele frequency distribution. Only variants with significant difference between top and bottom allopatric population following from logistic regression are shown. Genetic variants are labelled with chromosome, position and alternative allele relative to the reference genome. Red stars indicate variants with a significant difference between top and bottom populations, using a quasibinomial model accounting for over dispersion in the data. Allele frequencies for ancestral populations in each genetic variant is shown with orange points. Boxplots as in Extended Data Fig. 1.

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Tusso, S., Nieuwenhuis, B.P.S., Weissensteiner, B. et al. Experimental evolution of adaptive divergence under varying degrees of gene flow. Nat Ecol Evol 5, 338–349 (2021). https://doi.org/10.1038/s41559-020-01363-2

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