Ecosystem degradation and biodiversity loss are major global challenges. When reproductive isolation between species is contingent on the interaction of intrinsic lineage traits with features of the environment, environmental change can weaken reproductive isolation and result in extinction through hybridization. By this process called speciation reversal, extinct species can leave traces in genomes of extant species through introgressive hybridization. Using historical and contemporary samples, we sequenced all four species of an Alpine whitefish radiation before and after anthropogenic lake eutrophication and the associated loss of one species through speciation reversal. Despite the extinction of this taxon, substantial fractions of its genome, including regions shaped by positive selection before eutrophication, persist within surviving species as a consequence of introgressive hybridization during eutrophication. Given the prevalence of environmental change, studying speciation reversal and its genomic consequences provides fundamental insights into evolutionary processes and informs biodiversity conservation.
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Genomic architecture of adaptive radiation and hybridization in Alpine whitefish
Nature Communications Open Access 02 August 2022
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The raw sequencing files are accessible on SRA (PRJEB43605). Additional supporting data (genotype and genotype-likelihood files, morphological raw data, data underlying Fig. 3, full output table of GO enrichment analysis) are deposited on the eawag research data institutional collections (https://doi.org/10.25678/0005AP)78. The Alpine whitefish reference genome56 used was downloaded from ENA and is accessible with accession GCA_902810595.1. The S. salar outgroup sample66 used was downloaded from SRA and is accessible with accession SRR3669756. Gene ontology (GO) terms were downloaded from https://datadryad.org/stash/dataset/doi:10.5061/dryad.xd2547ddf.
Scripts used for data analysis are available on GitHub (https://github.com/freidavid/Genomic-Consequences-of-Speciation-Reversal).
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We thank all professional fisherman for providing specimens, D. Bittner for compiling the historical whitefish scale collection, M. Kugler from the Amt für Natur, Jagd und Fischerei, St.Gallen and the Institute of Seenforschung and Fischereiwesen Langenargen for providing historical whitefish scales from Lake Constance and IGKB for providing the yearly averaged total phosphorus data. We thank the NGS facility of the University of Bern for sequencing support and the Genetic Diversity Centre at ETH Zurich for bioinformatic support. Further, we are very thankful to the Fish Ecology and Evolution Department of Eawag, especially to C. Dönz, B. Matthews, D. Marques, K. Kagawa, J. Meier and J.T. Brink for feedback, comments and ideas on this manuscript. Also, thanks to O. Osborne for advice on assessing GO term enrichment. This work received financial support from Eawag (including Eawag Discretionary Funds 2018–2022) and the Swiss Federal Office for the Environment. The work was further supported by the grant ‘SeeWandel: Life in Lake Constance—the past, present and future’ within the framework of the Interreg V programme ‘Alpenrhein-Bodensee-Hochrhein (Germany/Austria/Switzerland/Liechtenstein)’ which funds are provided by the European Regional Development Fund as well as the Swiss Confederation and cantons (to P.F. and O.S.). The funders had no role in study design, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Extended Data Fig. 1 Maximum likelihood phylogeny of all historical and contemporary samples.
Maximum likelihood phylogeny of all pre- (crosses) and post-eutrophication (points) individuals of the four Lake Constance whitefish species based on 58’831 SNPs. Colours correspond to species (see Fig. 1). Support values from 100 bootstrap replicates are shown on each node. Note that the branch length for the S. salar outgroup is biased due to the ascertainment towards SNPs segregating within Lake Constance whitefish (see Methods section).
Extended Data Fig. 2 Log-likelihood and frobenius error for different K’s of the admixture analysis.
Log-likelihood values (a) and frobenius error (b) for different K’s of the PCAngsd admixture analysis shown in Fig. 1c. K = 4 turned out to represent the data best, also corresponding to the number of species included.
Extended Data Fig. 3 Comparison of the change in differentiation across the eutrophication period.
Global FST values and sample sizes for pre-eutrophication and post-eutrophication populations of all species of Lake Constance whitefish by Vonlanthen et al. 20123 based on 10 microsatellite markers, compared to the genetic differentiation estimates and sample sizes for the same populations based on our whole-genome sequencing approach and 477’981 SNPs. Values in brackets include samples of the now extinct C. gutturosus.
Extended Data Fig. 4 D-statistic results for all tests for introgression shown in Fig. 2.
The table includes the ordering of the populations on the four-taxon topology used for the ABBA BABA test, as well as the resulting D values, Z-scores and p-values of the block-jackknife approach in 5 Mb blocks. All sequenced individuals per population have been used for each single test (see Extended Data Fig. 10).
Extended Data Fig. 5 Rarefaction analysis of the C. gutturosus genome maintained in extant whitefish species.
Rarefaction curves for all species combined showing the estimated number of introgressed windows in contemporary populations of Lake Constance whitefish (a) and for each extant species of the Lake Constance whitefish radiation (b–d). The x axis shows the estimated total number of introgressed 50 kb windows (whole genome corresponds to 31’476 windows) for a given number of sampled individuals. The dashed lines show the sample size-based extrapolation curves and the grey areas around the curves indicate the 95% confidence intervals.
Extended Data Fig. 6 C. gutturosus admixture proportions in post-eutrophication populations of Lake Constance whitefish.
Table shows mean admixture proportions averaged across all individuals for the PCAngsd approach (see Fig. 1), proportions estimated with the rarefaction analysis for windows showing signals of C. gutturosus introgression in the TWISST analysis (Fig. 3 and Extended Data Fig. 5) and genome-wide means of admixture proportions estimated with fd (see Methods section).
Extended Data Fig. 7 Morphological differentiation of contemporary Lake Constance whitefish.
a) Shape PCA of the first two principal components based on body characters (PELVFB, PELVFS, PELVF, PECFB, PECF1, PECF2, DFB, DFAe, DFAd, DFPe, AFB, AFAe, AdFB, CF, CD, CL, PAdC, DHL, PreP, PreA, SL, TL, PreD, BD, PostD; see Table 1 in Selz et al.30. Morphological characters were measured and analysed following Selz et al.30. b) The plot shows shape PC1 of panel a against the total gill raker count of the individuals. Individuals used for genomic analysis are indicated with filled circles, additional individuals of the contemporary species are indicated with crossed circles. Colours correspond to species (orange C. arenicolus, green C. macrophthalmus and blue C. wartmanni).
Extended Data Fig. 8 Tajima’s D based on genotype likelihoods for windows identified to have been under selection in C. gutturosus using nSL.
Violin plots of Tajima’s D in C. gutturosus (n = 11) calculated in 50 kb windows comparing the 315 windows identified to be in the top 1 percentile of the nSL analysis to all other windows of the genome. We found a significant difference in Tajima’s D between selected and non-selected windows identified by nSL (two-sided Wilcoxon rank sum test, W = 8352543, p < 2.2e-16, indicated with bars above the plot ‘***’). Plots show the estimated kernel densities, black boxes show the interquantile range, white dots correspond to medians and spikes are extending to the upper and lower adjacent values.
Extended Data Fig. 9 Comparison of gene density in introgressed and non-introgressed windows.
a) Comparison of gene density between windows identified to be introgressed and those that did not show evidence for introgression (non-introgressed) from C. gutturosus (n = 11) across all three extant species (n = 14). There was no significant difference between introgressed and non-introgressed windows (two-sided Wilcoxon rank sum test, W = 84559580; p = 0.5458) and thus the test is not represented in the figure. b) Comparison of exon density between windows identified to be introgressed and those that did not show evidence for introgression (non-introgressed) from C. gutturosus (n = 11) across all three extant species (n = 14). There was no significant difference between introgressed and non-introgressed windows (two-sided Wilcoxon rank sum test, W = 85267215; p = 0.0906) and thus the test is not represented in the figure. Plots show the estimated kernel densities, black boxes show the interquantile range, white dots correspond to medians and spikes are extending to the upper and lower adjacent values.
Extended Data Fig. 10 Overview over all sequenced samples.
Year of sampling, sequencing platform used, total yield of reads, mean fragment length of library, lab identification code and mean coverage at polymorphic sites for each individual sequenced. Samples collected before 1950 are scale samples, while samples from 2015 are fin-clip samples.
Supplementary Table 1.
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Frei, D., De-Kayne, R., Selz, O.M. et al. Genomic variation from an extinct species is retained in the extant radiation following speciation reversal. Nat Ecol Evol 6, 461–468 (2022). https://doi.org/10.1038/s41559-022-01665-7
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