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A polar bear paleogenome reveals extensive ancient gene flow from polar bears into brown bears

Abstract

Polar bears (Ursus maritimus) and brown bears (Ursus arctos) are sister species possessing distinct physiological and behavioural adaptations that evolved over the last 500,000 years. However, comparative and population genomics analyses have revealed that several extant and extinct brown bear populations have relatively recent polar bear ancestry, probably as the result of geographically localized instances of gene flow from polar bears into brown bears. Here, we generate and analyse an approximate 20X paleogenome from an approximately 100,000-year-old polar bear that reveals a massive prehistoric admixture event, which is evident in the genomes of all living brown bears. This ancient admixture event was not visible from genomic data derived from living polar bears. Like more recent events, this massive admixture event mainly involved unidirectional gene flow from polar bears into brown bears and occurred as climate changes caused overlap in the ranges of the two species. These findings highlight the complex reticulate paths that evolution can take within a regime of radically shifting climate.

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Fig. 1: Sample location and genetic affinity of bears.
Fig. 2: PSMC plot of effective population size over time for bears.
Fig. 3: Admixture between polar and brown bears estimated using DFOIL.
Fig. 4: MSMC-IM analysis of gene flow between brown and polar bears over time.

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Data availability

Raw reads generated from Bruno are available from the NCBI SRA BioProject accession no. PRJNA720153. Other data used are listed in Supplementary Tables 2 and 3.

Code availability

Scripts and codes for genome analysis can be accessed at https://github.com/PopGenomics-WMS/Bruno_aDNA_analysis.

References

  1. Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Muhlfeld, C. C. et al. Invasive hybridization in a threatened species is accelerated by climate change. Nat. Clim. Change 4, 620–624 (2014).

    Article  Google Scholar 

  3. Taylor, S. A. et al. Climate-mediated movement of an avian hybrid zone. Curr. Biol. 24, 671–676 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Cahill, J. A. et al. Genomic evidence of widespread admixture from polar bears into brown bears during the last ice age. Mol. Biol. Evol. 35, 1120–1129 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Mao, Y., Economo, E. P. & Satoh, N. The roles of introgression and climate change in the rise to dominance of Acropora corals. Curr. Biol. 28, 3373–3382.e5 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Vianna, J. A. et al. Genome-wide analyses reveal drivers of penguin diversification. Proc. Natl Acad. Sci. USA 117, 22303–22310 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Racimo, F., Sankararaman, S., Nielsen, R. & Huerta-Sánchez, E. Evidence for archaic adaptive introgression in humans. Nat. Rev. Genet. 16, 359–371 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. McKelvey, K. S. et al. Patterns of hybridization among cutthroat trout and rainbow trout in northern Rocky Mountain streams. Ecol. Evol. 6, 688–706 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kim, B. Y., Huber, C. D. & Lohmueller, K. E. Deleterious variation shapes the genomic landscape of introgression. PLoS Genet. 14, e1007741 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wu, D.-D. et al. Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nat. Ecol. Evol. 2, 1139–1145 (2018).

    Article  PubMed  Google Scholar 

  11. Wang, M.-S. et al. Ancient hybridization with an unknown population facilitated high-altitude adaptation of canids. Mol. Biol. Evol. 37, 2616–2629 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Meier, J. I. et al. Ancient hybridization fuels rapid cichlid fish adaptive radiations. Nat. Commun. 8, 14363 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Haig, S. M., Mullins, T. D., Forsman, E. D., Trail, P. W. & Wennerberg, L. I. V. Genetic identification of spotted owls, barred owls, and their hybrids: legal implications of hybrid identity. Conserv. Biol. 18, 1347–1357 (2004).

    Article  Google Scholar 

  14. vonHoldt, B. M. et al. Whole-genome sequence analysis shows that two endemic species of North American wolf are admixtures of the coyote and gray wolf. Sci. Adv. 2, e1501714 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Liu, S. et al. Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears. Cell 157, 785–794 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kumar, V. et al. The evolutionary history of bears is characterized by gene flow across species. Sci. Rep. 7, 46487 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Preuß, A., Gansloßer, U., Purschke, G. & Magiera, U. Bear-hybrids: behaviour and phenotype. Zool. Gart. 78, 204–220 (2009).

    Article  Google Scholar 

  18. Cahill, J. A. et al. Genomic evidence for island population conversion resolves conflicting theories of polar bear evolution. PLoS Genet. 9, e1003345 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cahill, J. A. et al. Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears. Mol. Ecol. 24, 1205–1217 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Pongracz, J. D., Paetkau, D., Branigan, M. & Richardson, E. Recent hybridization between a polar bear and grizzly bears in the Canadian Arctic. Arctic 70, 151–160 (2017).

    Article  Google Scholar 

  21. Pugach, I., Matveyev, R., Wollstein, A., Kayser, M. & Stoneking, M. Dating the age of admixture via wavelet transform analysis of genome-wide data. Genome Biol. 12, R19 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Farquharson, L. et al. Alaskan marine transgressions record out-of-phase Arctic Ocean glaciation during the last interglacial. Geology 46, 783–786 (2018).

    Article  Google Scholar 

  23. Kapp, J. D., Green, R. E. & Shapiro, B. A fast and efficient single-stranded genomic library preparation method optimized for ancient DNA. J. Hered. 112, 241–249 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl Acad. Sci. USA 104, 14616–14621 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fu, Q. et al. Genome sequence of a 45,000-year-old modern human from western Siberia. Nature 514, 445–449 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Schiffels, S. & Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46, 919–925 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Pease, J. B. & Hahn, M. W. Detection and polarization of introgression in a five-taxon phylogeny. Syst. Biol. 64, 651–662 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Barlow, A. et al. Middle Pleistocene genome calibrates a revised evolutionary history of extinct cave bears. Curr. Biol. 31, 1771–1779.e7 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. 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 

  31. Wang, K., Mathieson, I., O’Connell, J. & Schiffels, S. Tracking human population structure through time from whole genome sequences. PLoS Genet. 16, e1008552 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Polyak, L. et al. History of sea ice in the Arctic. Quat. Sci. Rev. 29, 1757–1778 (2010).

    Article  Google Scholar 

  33. Dutton, A. et al. Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science 349, aaa4019 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Salonen, J. S. et al. Abrupt high-latitude climate events and decoupled seasonal trends during the Eemian. Nat. Commun. 9, 2851 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Guarino, M.-V. et al. Sea-ice-free Arctic during the Last Interglacial supports fast future loss. Nat. Clim. Change 10, 928–932 (2020).

    Article  Google Scholar 

  36. Rode, K. D., Robbins, C. T., Nelson, L. & Amstrup, S. C. Can polar bears use terrestrial foods to offset lost ice-based hunting opportunities? Front. Ecol. Environ. 13, 138–145 (2015).

    Article  Google Scholar 

  37. Laidre, K. L., Stirling, I., Estes, J. A., Kochnev, A. & Roberts, J. Historical and potential future importance of large whales as food for polar bears. Front. Ecol. Environ. 16, 515–524 (2018).

    Article  Google Scholar 

  38. Miller, S., Wilder, J. & Wilson, R. R. Polar bear–grizzly bear interactions during the autumn open-water period in Alaska. J. Mammal. 96, 1317–1325 (2015).

    Article  Google Scholar 

  39. Steyaert, S. M. J. G., Endrestøl, A., Hackländer, K., Swenson, J. E. & Zedrosser, A. The mating system of the brown bear Ursus arctos. Mamm. Rev. 42, 12–34 (2012).

    Article  Google Scholar 

  40. Stirling, I., Spencer, C. & Andriashek, D. Behavior and activity budgets of wild breeding polar bears (Ursus maritimus). Mar. Mamm. Sci. 32, 13–37 (2016).

    Article  Google Scholar 

  41. Méheust, M., Stein, R., Fahl, K. & Gersonde, R. Sea-ice variability in the subarctic North Pacific and adjacent Bering Sea during the past 25 ka: new insights from IP25 and Uk′37 proxy records. Arktos 4, 1–19 (2018).

    Article  Google Scholar 

  42. Brigham-Grette, J. & Hopkins, D. M. Emergent marine record and paleoclimate of the last interglaciation along the northwest Alaskan coast. Quat. Res. 43, 159–173 (1995).

    Article  Google Scholar 

  43. Boessenkool, S. et al. Combining bleach and mild predigestion improves ancient DNA recovery from bones. Mol. Ecol. Resour. 17, 742–751 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, pdb.prot5448 (2010).

    Article  PubMed  Google Scholar 

  46. Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, e3 (2012).

    Article  CAS  PubMed  Google Scholar 

  47. Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Prüfer, K. snpAD: an ancient DNA genotype caller. Bioinformatics 34, 4165–4171 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Green, R. E. et al. A complete Neandertal mitochondrial genome sequence determined by high-throughput sequencing. Cell 134, 416–426 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA–MEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).

  55. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kumar, S., Stecher, G., Peterson, D. & Tamura, K. MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis. Bioinformatics 28, 2685–2686 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Vihtakari, M. PlotSvalbard: User Manual. Github https://mikkovihtakari.github.io/PlotSvalbard/articles/PlotSvalbard.html (2020).

  59. Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).

    Article  Google Scholar 

  60. Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinformatics 69, e96 (2020).

    Article  PubMed  Google Scholar 

  61. Yu, G., Lam, T. T., Zhu, H. & Guan, Y. Two methods for mapping and visualizing associated data on phylogeny using ggtree. Mol. Biol. Evol. 35, 3041–3043 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wang, L.-G. et al. Treeio: an R package for phylogenetic tree input and output with richly annotated and associated data. Mol. Biol. Evol. 37, 599–603 (2020).

    Article  CAS  PubMed  Google Scholar 

  63. Lindqvist, C. et al. Complete mitochondrial genome of a Pleistocene jawbone unveils the origin of polar bear. Proc. Natl Acad. Sci. USA 107, 5053–5057 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 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 

  66. Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kelleher, J., Etheridge, A. M. & McVean, G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Palkopoulou, E. et al. Complete genomes reveal signatures of demographic and genetic declines in the woolly mammoth. Curr. Biol. 25, 1395–1400 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Patterson, N. et al. Ancient admixture in human history. Genetics 192, 1065–1093 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Vershinina, A. O. et al. Ancient horse genomes reveal the timing and extent of dispersals across the Bering Land Bridge. Mol. Ecol. 30, 6144–6161 (2021).

    Article  PubMed  Google Scholar 

  72. Chen, L., Wolf, A. B., Fu, W., Li, L. & Akey, J. M. Identifying and interpreting apparent Neanderthal ancestry in African individuals. Cell 180, 677–687.e16 (2020).

    Article  CAS  PubMed  Google Scholar 

  73. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, PA1003 (2005).

    Google Scholar 

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Acknowledgements

We thank K. Wang and S. Schiffels for help with the MSMC-IM and MSMC analyses and E. Palkopoulou for assistance with the PSMC analysis. We thank B. Nelson for assistance with some of the Bruno extractions and the Science for Life Laboratory, Knut and Alice Wallenberg Foundation, National Genomics Infrastructure funded by the Swedish Research Council and the Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. L.D. acknowledges support from Formas (grant no. 2018-01640). Field support and initial funding for analysis was provided by the Arctic Field Office, Bureau of Land Management, Department of Interior, Fairbanks, Alaska, USA. This work was supported in part by National Science Foundation Division of Environmental Biology grant no. 1754451 to B.S.

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Authors and Affiliations

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Contributions

D.M., P.G. and M.K. discovered and excavated Bruno from the field site. B.S. and R.E.G. designed the research project and supervised the analysis. M.-S.W. and R.E.G. performed the analysis, with contributions from R.C.-D. and G.G.R.M. J.D.K. performed the extraction of ancient DNA and prepared the sequencing library. L.D. coordinated the genome sequencing and edited the manuscript. B.S., M.-S.W., G.G.R.M., I.S. and R.E.G. drafted the manuscript. A.O.V., M.A.S., S.E.C. and P.G. improved the figures and supplementary material. K.L.L. contributed the genomes for modern polar bears. All authors contributed to the final manuscript.

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Correspondence to Beth Shapiro.

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Extended data

Extended Data Fig. 1 Images of Bruno’s skull.

Images of Bruno’s skull. (a) ventral, (b) lateral, (c) dorsal views, and (d) shown along with a brown bear skull (left) and a mature polar bear skull (right). Photos by Pam Groves.

Extended Data Fig. 2 MapDamage plot for each of the 14 sequencing libraries prepared from DNA extracts from Bruno.

MapDamage plot for each of the 14 sequencing libraries prepared from DNA extracts from Bruno. Plots show the frequency of C>T and G>A substitutions. SE5145_BAN036 and SE5145_BAN037 are single-stranded DNA libraries and appear to have less damage than the other libraries.

Extended Data Fig. 3 An example of read length distributions for each of the 14 DNA sequencing libraries prepared from DNA extracts from Bruno.

An example of read length distributions for each of the 14 DNA sequencing libraries prepared from DNA extracts from Bruno. The observed ~10 bp periodicity of recovered fragment lengths is commonly observed in nuclear DNA recovered from old tissues and is most likely explained by nucleosomal protection of DNA molecules.

Extended Data Fig. 4 The derived allele frequencies of polar bears, brown bears, and Bruno at polar bear polymorphic sites.

The derived allele frequencies of polar bears, brown bears, and Bruno at polar bear polymorphic sites. We measured the derived allele frequency for all sites observed to be segregating polymorphism in the polar bears in the panel. For each 5% frequency bin, we measured the observed derived allele frequency within each polar bear (left panels) and each brown bear (right panels). The Bruno bear is in each panel (thick blue line). As expected, each polar bear carries the derived allele as often as would be expected given the derived allele frequency within polar bears (red, x = y line). Brown bears carry far fewer polar bear derived alleles. Genetically, Bruno is clearly not a modern polar bear nor a brown bear. An American black bear genome was used to polarize the ancestral state of alleles. Seven brown bears (SAMN07422261, SAMN07422264, SAMN07422265, SAMN07422266, SAMEA4762870, SAMN07422267, and SAMN07422270) were excluded from this analysis due to low sequencing coverage.

Extended Data Fig. 5 MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between Bruno and an extant brown bear and between extant polar bear and the same brown bear.

MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between Bruno and an extant brown bear and between extant polar bear and the same brown bear. Two independent analyses using different polar bears (IDs: SAMN02261853 and SAMN02261826) and brown bears (IDs: SAMN07422272 and SAMN07422262) are shown as A and B. For each calculation, we performed 30 bootstrap replicates, which are shown as thin red lines. Based on a 50% RCCR, we estimate the divergence time for modern polar bears and brown bears (T2) to be 481,049 ± 4500 (mean ± SD) years ago, which is consistent with previous estimates (Liu et al. 2014), and the divergence time for Bruno and brown bears (T1) to be 376.9 ± 3.3 kya (mean ± SD). The resulting estimate for the time difference between when Bruno and the modern polar bear diverged from the brown bear (a rough estimate of how long ago Bruno died) is: ∆T = T2-T1 = 104.4 ± 3.4 kya.

Extended Data Fig. 6 D-statistics analysis of gene flow between polar bears and brown bears.

D-statistics analysis of gene flow between polar bears and brown bears. (a) D-statistics in the form of D(brown bear-Europe; brown bear-N America; Bruno/Polar bear, Black bear). All combinations give significant negative values (−21.8 < Z-score < −21.3), suggesting admixture occurred between polar bears and North American brown bears, consistent with previous work. (b) D-statistics in the forms of D(PB-Greenland,PB-Alaska;BB-x,BLK), D(Bruno,PB-Alaska;BB-x,BLK) and D(Bruno,PB-Greenland; BB-x,BLK) are mostly non-significant (−3 < Z-score < 3). (c) Results of D-statistics in the form of D(All polar bears,Bruno;Brown bear,BLK).

Extended Data Fig. 7 Allele sharing among Bruno, all brown bears, and a polar bear.

Allele sharing among Bruno, all brown bears, and a polar bear. (a) and (b) show the shared derived alleles (transversions+transitions) and transversions, respectively. Replacing data from Bruno with data from an extant polar bear, KB06, results in fewer alleles shared uniquely with all brown bears to the exclusion of other polar bears. Statistical significance was measured by the wilcox.test (two-sided) using R version 4.10.

Extended Data Fig. 8 MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between brown bears.

MSMC estimates the relative cross coalescence rate (RCCR) and divergence time between brown bears. Three independent analyses were performed for each pair. Based on a 50% RCCR, divergence time for North American and European Brown bears is 23–65 kya, which is more recent than the death of Bruno. We note that polar bear ancestry in North American bear genomes may affect these MSMC estimates and therefore perform DFOIL in multiple configurations that assume both a more recent and more ancient divergence between North American and European brown bears than the age of Bruno.

Extended Data Fig. 9 IBDMix inferred the amount of the genome showing a signal of admixture between Bruno and brown bears.

IBDMix inferred the amount of the genome showing a signal of admixture between Bruno and brown bears. Blue, orange and gray lines show results when the minimum lengths of 30 kb, 40 kb and 50 kb were used as cut-off to recover admixed regions.

Extended Data Fig. 10 Analysis of simulated data shows that MSMC-IM is robust to the directionality of gene flow is robust.

Analysis of simulated data shows that MSMC-IM is robust to the directionality of gene flow is robust. We assumed eight scenarios in which two populations diverged 500 kya with asymmetrical and symmetrical migrations at 100 kya, as described in Methods. Labeling is as follows: ‘M01 = 5%’ indicates that pop0 contributed 5% ancestry to pop1, ‘M10 = 5%’ indicates that pop1 contributed 5% ancestry to pop0. Vertical red dashed lines show the time of migration at 100 kya. Models and magnitude of migration for each direction are shown within each plot.

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Wang, MS., Murray, G.G.R., Mann, D. et al. A polar bear paleogenome reveals extensive ancient gene flow from polar bears into brown bears. Nat Ecol Evol 6, 936–944 (2022). https://doi.org/10.1038/s41559-022-01753-8

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