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Genomic variation from an extinct species is retained in the extant radiation following speciation reversal


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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Fig. 1: Partial loss of genetic differentiation between Lake Constance whitefish species during eutrophication-induced speciation reversal.
Fig. 2: Directionality of introgression during speciation reversal.
Fig. 3: Genomic distribution and characterization of introgression derived from extinct C. gutturosus.

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Data availability

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 ( 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

Code availability

Scripts used for data analysis are available on GitHub (


  1. Vamosi, J. C., Magallon, S., Mayrose, I., Otto, S. P. & Sauquet, H. Macroevolutionary patterns of flowering plant speciation and extinction. Annu. Rev. Plant Biol. 69, 685–706 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Rhymer, J. M. & Simberloff, D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).

    Article  Google Scholar 

  3. Vonlanthen, P. et al. Eutrophication causes speciation reversal in whitefish adaptive radiations. Nature 482, 357–362 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. Seehausen, O. Conservation: losing biodiversity by reverse speciation. Curr. Biol. 16, R334–R337 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Seehausen, O., Takimoto, G., Roy, D. & Jokela, J. Speciation reversal and biodiversity dynamics with hybridization in changing environments. Mol. Ecol. 17, 30–44 (2008).

    Article  PubMed  Google Scholar 

  6. Kearns, A. M. et al. Genomic evidence of speciation reversal in ravens. Nat. Commun. 9, 906 (2018).

  7. Meier, J. I. et al. Ancient hybridization fuels rapid cichlid fish adaptive radiations. Nat. Commun. 8, 11 (2017).

    Article  Google Scholar 

  8. Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Barlow, A. et al. Partial genomic survival of cave bears in living brown bears. Nat. Ecol. Evol. 2, 1563–1570 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kuhlwilm, M., Han, S., Sousa, V. C., Excoffier, L. & Marques-Bonet, T. Ancient admixture from an extinct ape lineage into bonobos. Nat. Ecol. Evol. 3, 957–965 (2019).

    Article  PubMed  Google Scholar 

  11. Palkopoulou, E. et al. A comprehensive genomic history of extinct and living elephants. Proc. Natl. Acad. Sci. USA 115, E2566–E2574 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ottenburghs, J. Ghost introgression: spooky gene flow in the distant past. BioEssays 42, 2000012 (2020).

    Article  Google Scholar 

  13. Schluter, D. The Ecology of Adaptive Radiation (Oxford Univ. Press, 2000).

  14. Rundle, H. D. & Nosil, P. Ecological speciation. Ecol. Lett. 8, 336–352 (2005).

    Article  Google Scholar 

  15. Schluter, D. Evidence for ecological speciation and its alternative. Science 323, 737–741 (2009).

    Article  CAS  PubMed  Google Scholar 

  16. Nosil, P. Ecological Speciation (Oxford Univ. Press, 2012).

  17. Yeaman, S. & Whitlock, M. C. The genetic architecture of adaptation under migration–selection balance. Evolution 65, 1897–1911 (2011).

    Article  PubMed  Google Scholar 

  18. Ghosh, S. M. & Joshi, A. Evolution of reproductive isolation as a by-product of divergent life-history evolution in laboratory populations of Drosophila melanogaster. Ecol. Evol. 2, 3214–3226 (2012).

    Article  Google Scholar 

  19. Seehausen, O., van Alphen, J. J. M. & Witte, F. Cichlid fish diversity threatened by eutrophication that curbs sexual selection. Science 277, 1808–1811 (1997).

    Article  CAS  Google Scholar 

  20. Taylor, E. B. et al. Speciation in reverse: morphological and genetic evidence of the collapse of a three-spined stickleback (Gasterosteus aculeatus) species pair. Mol. Ecol. 15, 343–355 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Grant, P. R. & Grant, B. R. Hybridization increases population variation during adaptive radiation. Proc. Natl. Acad. Sci. USA 116, 23216–23224 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kagawa, K. & Takimoto, G. Hybridization can promote adaptive radiation by means of transgressive segregation. Ecol. Lett. 21, 264–274 (2018).

    Article  PubMed  Google Scholar 

  23. Feller, A. F. et al. Rapid generation of ecologically relevant behavioral novelty in experimental cichlid hybrids. Ecol. Evol. 10, 7445–7462 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Pfennig, K. S., Kelly, A. L. & Pierce, A. A. Hybridization as a facilitator of species range expansion. Proc. R. Soc. B (2016).

  25. Lamichhaney, S. et al. Rapid hybrid speciation in Darwin’s finches. Science 359, 224–227 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Hudson, A. G., Vonlanthen, P., Bezault, E. & Seehausen, O. Genomic signatures of relaxed disruptive selection associated with speciation reversal in whitefish. BMC Evol. Biol. 13, 17 (2013).

    Article  Google Scholar 

  27. Hudson, A. G., Vonlanthen, P. & Seehausen, O. Rapid parallel adaptive radiations from a single hybridogenic ancestral population. Proc. R. Soc. B 278, 58–66 (2011).

    Article  PubMed  Google Scholar 

  28. Jacobs, A. et al. Rapid niche expansion by selection on functional genomic variation after ecosystem recovery. Nat. Ecol. Evol. 3, 77–86 (2019).

    Article  PubMed  Google Scholar 

  29. Steinmann, P. Monographie der schweizerischen Koregonen. Beitrag zum problem der entstehung neuer Arten. Schweiz. Z. Hydrol. 12, 340–491 (1950).

    Google Scholar 

  30. Selz, O. M., Donz, C. J., Vonlanthen, P. & Seehausen, O. A taxonomic revision of the whitefish of lakes Brienz and Thun, Switzerland, with descriptions of four new species (Teleostei, Coregonidae). Zookeys 989, 79–162 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hudson, A. G., Lundsgaard-Hansen, B., Lucek, K., Vonlanthen, P. & Seehausen, O. Managing cryptic biodiversity: fine-scale intralacustrine speciation along a benthic gradient in Alpine whitefish (Coregonus spp.). Evol. Appl. 10, 251–266 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Doenz, C. J., Bittner, D., Vonlanthen, P., Wagner, C. E. & Seehausen, O. Rapid buildup of sympatric species diversity in Alpine whitefish. Ecol. Evol. 8, 9398–9412 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Vonlanthen, P. et al. Divergence along a steep ecological gradient in lake whitefish (Coregonus sp.). J. Evol. Biol. 22, 498–514 (2009).

    Article  CAS  PubMed  Google Scholar 

  34. Feulner, P. G. D. & Seehausen, O. Genomic insights into the vulnerability of sympatric whitefish species flocks. Mol. Ecol. 28, 615–629 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Meisner, J. & Albrechtsen, A. Inferring population structure and admixture proportions in low-depth NGS data. Genetics 210, 719–731 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-statistic on low-coverage whole-genome data. Genes Genomes Genet. 8, 551–566 (2018).

    Google Scholar 

  37. Wahl, B. & Loffler, H. Influences on the natural reproduction of whitefish (Coregonus lavaretus) in Lake Constance. Can. J. Fish. Aquat. Sci. 66, 547–556 (2009).

    Article  Google Scholar 

  38. Nümann, W. The Bodensee: effects of exploitation and eutrophication on the Salmonid community. J. Fish. Res. Board Can. 29, 833–884 (1972).

    Article  Google Scholar 

  39. Deufel, J., Löffler, H. & Wagner, B. Auswirkungen der eutrophierung und anderer anthropogener einflüsse auf die laichplätze einiger Bodensee-Fischarten. Österr. Fisch.39, 325–336 (1986).

    Google Scholar 

  40. Straile, D. & Geller, W. Crustacean zooplankton in Lake Constance from 1920 to 1995: response to eutrophication and re-oligotrophication. Adv. Limnol. 58, 255–274 (1998).

    Google Scholar 

  41. Eby, L. A., Crowder, L. B., McClellan, C. M., Peterson, C. H. & Powers, M. J. Habitat degradation from intermittent hypoxia: impacts on demersal fishes. Mar. Ecol. Prog. Ser. 291, 249–261 (2005).

    Article  Google Scholar 

  42. Powers, S. P. et al. Effects of eutrophication on bottom habitat and prey resources of demersal fishes. Mar. Ecol. Prog. Ser. 302, 233–243 (2005).

    Article  Google Scholar 

  43. Caires, A. M., Chandra, S., Hayford, B. L. & Wittmann, M. E. Four decades of change: dramatic loss of zoobenthos in an oligotrophic lake exhibiting gradual eutrophication. Freshw. Sci. 32, 692–705 (2013).

    Article  Google Scholar 

  44. Martin, S. H. & Van Belleghem, S. M. Exploring evolutionary relationships across the genome using topology weighting. Genetics 206, 429–438 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).

    Article  Google Scholar 

  46. Ferrer-Admetlla, A., Liang, M., Korneliussen, T. & Nielsen, R. On detecting incomplete soft or hard selective sweeps using haplotype structure. Mol. Biol. Evol. 31, 1275–1291 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Korneliussen, T. S., Moltke, I., Albrechtsen, A. & Nielsen, R. Calculation of Tajima’s D and other neutrality test statistics from low depth next-generation sequencing data. BMC Bioinform. 14, 289 (2013).

    Article  Google Scholar 

  48. Doenz, C. J. & Seehausen, O. Rediscovery of a presumed extinct species, Salvelinus profundus, after re-oligotrophication. Ecology 101, e03065 (2020).

  49. Alexander, T. & Seehausen, O. Diversity, Distribution and Community Composition of Fish in Perialpine Lakes—“Projet Lac” Synthesis Report (Eawag, 2021).

  50. Gilman, R. T. & Behm, J. E. Hybridization, species collapse, and species reemergence after disturbance to premating mechanisms of reproductive isolation. Evolution 65, 2592–2605 (2011).

    Article  PubMed  Google Scholar 

  51. Nussle, S., Bornand, C. N. & Wedekind, C. Fishery-induced selection on an Alpine whitefish: quantifying genetic and environmental effects on individual growth rate. Evol. Appl. 2, 200–208 (2009).

    Article  PubMed  Google Scholar 

  52. Grabenstein, K. C. & Taylor, S. A. Breaking barriers: causes, consequences, and experimental utility of human-mediated hybridization. Trends Ecol. Evol. 33, 198–212 (2018).

    Article  PubMed  Google Scholar 

  53. Seehausen, O. Hybridization and adaptive radiation. Trends Ecol. Evol. 19, 198–207 (2004).

    Article  PubMed  Google Scholar 

  54. Wasko, A. P., Martins, C., Oliveira, C. & Foresti, F. Non-destructive genetic sampling in fish. An improved method for DNA extraction from fish fins and scales. Hereditas 138, 161–165 (2003).

    Article  PubMed  Google Scholar 

  55. Chen, S. F., Zhou, Y. Q., Chen, Y. R. & Gu, J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, 884–890 (2018).

    Article  Google Scholar 

  56. De-Kayne, R., Zoller, S. & Feulner, P. G. D. A de novo chromosome-level genome assembly of Coregonus sp. “Balchen”: one representative of the Swiss Alpine whitefish radiation. Mol. Ecol. Resour. 20, 1093–1109 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Kim, S. Y. et al. Estimation of allele frequency and association mapping using next-generation sequencing data. BMC Bioinform. 12, 231 (2011).

  60. Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Association testing for next-generation sequencing data using score statistics. Genet. Epidemiol. 36, 430–437 (2012).

    Article  PubMed  Google Scholar 

  61. Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinform. 15, 356 (2014).

  62. Duforet-Frebourg, N. & Slatkin, M. Isolation-by-distance-and-time in a stepping-stone model. Theor. Popul. Biol. 108, 24–35 (2016).

    Article  PubMed  Google Scholar 

  63. Meisner, J., Liu, S., Huang, M. & Albrechtsen, A. Large-scale inference of population structure in presence of missingness using PCA. Bioinformatics (2021).

  64. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).

    Article  Google Scholar 

  66. Kjaerner-Semb, E. et al. Atlantic salmon populations reveal adaptive divergence of immune related genes—a duplicated genome under selection. BMC Genom. 17, 610 (2016).

  67. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Bukowicki, M., Franssen, S. U. & Schlotterer, C. High rates of phasing errors in highly polymorphic species with low levels of linkage disequilibrium. Mol. Ecol. Resour. 16, 874–882 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Marburger, S. et al. Interspecific introgression mediates adaptation to whole genome duplication. Nat. Commun. 10, 5218 (2019).

  71. Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    Article  CAS  PubMed  Google Scholar 

  72. Meier, J. I., Marques, D. A., Wagner, C. E., Excoffier, L. & Seehausen, O. Genomics of parallel ecological speciation in Lake Victoria cichlids. Mol. Biol. Evol. 35, 1489–1506 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  74. Martin, S. H., Davey, J. W. & Jiggins, C. D. Evaluating the use of ABBA-BABA statistics to locate introgressed loci. Mol. Biol. Evol. 32, 244–257 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. Szpiech, Z. A. & Hernandez, R. D. Selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 31, 2824–2827 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1–11.12.34 (2014).

    Article  Google Scholar 

  77. Alexa, A. & Rahnenfuhrer, J. TopGO: enrichment analysis for gene ontology. R package version 2.42.0. (2020).

  78. Frei, D., De-Kayne, R., Selz, O. M., Seehausen, O. & Feulner, P. G. D. Data for: Genomic Variation From an Extinct Species is Retained in the Extant Radiation Following Speciation Reversal (Eawag, 2021);

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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.

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O.S. conceived of the study, D.F., O.S. and P.G.D.F. designed and conceptualized it. P.G.D.F. managed and supervised the study. O.M.S. collected contemporary specimens and collected and analysed morphological data. R.D.K. contributed to DNA extraction and genomic analysis. D.F. analysed genomic data and visualized the results. D.F. wrote the original manuscript draft with input from O.S. and P.G.D.F. All authors edited and reviewed the final manuscript.

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Correspondence to Philine G. D. Feulner.

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Extended data

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.

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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).

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