Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mosaic genome evolution in a recent and rapid avian radiation


Recent genomic analyses of evolutionary radiations suggest that ancestral or standing genetic variation may facilitate rapid diversification, particularly in cases involving convergence in ecological traits. Likewise, lateral transfer of alleles via hybridization may also facilitate adaptive convergence, but little is known about the role of ancestral variation in examples of explosive diversification that primarily involve the evolution of species recognition traits. Here, we show that genomic regions distinguishing sympatric species in an extraordinary radiation of small finches called munias (genus Lonchura) have phylogenetic histories that are discordant with each other, with the overall pattern of autosomal differentiation among species, and with sex-linked and mitochondrial components of the genome. Genome-wide data for 11 species sampled in Australia and Papua New Guinea indicate substantial autosomal introgression between sympatric species, but also identify a limited number of divergent autosomal regions, several of which overlap known colour genes (ASIPEDN3, IGSF11KITLG, MC1R and SOX10). Phylogenetic analysis of these outlier regions shows that different munia species have acquired unique combinations of alleles across a relatively small set of phenotypically relevant genes. Our results demonstrate that the recombination of ancestral genetic variation across multiple loci may be an important mechanism for generating phenotypic novelty and diversity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Geographic distributions and mtDNA phylogeny.
Fig. 2: Results of ddRAD-seq analyses.
Fig. 3: Co-ancestry matrix from fineRADstructure.
Fig. 4: Illustration of phylogenetic heterogeneity among genomic outlier regions.
Fig. 5: Mosaic distribution of alleles at outlier loci.


  1. 1.

    Via, S. & West, J. The genetic mosaic suggests a new role for hitchhiking in ecological speciation. Mol. Ecol. 17, 4334–4335 (2008).

    Article  PubMed  Google Scholar 

  2. 2.

    Nosil, P., Funk, D. J. & Ortiz-Barrientos, D. Divergent selection and heterogeneous genomic divergence. Mol. Ecol. 18, 375–402 (2009).

    Article  PubMed  Google Scholar 

  3. 3.

    Wu, C. I. The genic view of the process of speciation. J. Evol. Biol. 14, 851–865 (2001).

    Article  Google Scholar 

  4. 4.

    Turner, T. L., Hahn, M. W. & Nuzhdin, S. V. Genomic islands of speciation in Anopheles gambiae. PLoS Biol. 3, 1572–1578 (2005).

    CAS  Article  Google Scholar 

  5. 5.

    Toews, D. P. L. et al. Plumage genes and little else distinguish the genomes of hybridizing warblers. Curr. Biol. 26, 2313–2318 (2016).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Ellegren, H. et al. The genomic landscape of species divergence in Ficedula flycatchers. Nature 461, 756–760 (2012).

    Google Scholar 

  7. 7.

    Poelstra, J. W. et al. The genomic landscape underlying phenotypic integrity in the face of gene flow in crows. Science 344, 1410–1414 (2014).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Malinsky, M. et al. Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake. Science 350, 1493–1498 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Irwin, D. E., Alcaide, M., Delmore, K. E., Irwin, J. H. & Owens, G. L. Recurrent selection explains parallel evolution of genomic regions of high relative but low absolute differentiation in a ring species. Mol. Ecol. 25, 4488–4507 (2016).

    Article  PubMed  Google Scholar 

  10. 10.

    Givnish, T. J. Adaptive radiation versus ‘radiation’ and ‘explosive diversification’: why conceptual distinctions are fundamental to understanding evolution. New Phytol. 207, 297–303 (2015).

    Article  PubMed  Google Scholar 

  11. 11.

    Brawand, D. et al. The genomic substrate for adaptive radiation in African cichlid fish. Nature 513, 375–381 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Jones, F. C. et al. The genomic basis of adaptive evolution in threespine sticklebacks. Nature 484, 55–61 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lamichhaney, S. et al. Evolution of Darwin’s finches and their beaks revealed by genome sequencing. Nature 518, 371–375 (2015).

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    The Heliconius Genome Consortium. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487, 94–98 (2012).

    Article  Google Scholar 

  15. 15.

    Berner, D. & Salzberger, W. The genomics of organismal diversification illuminated by adaptive radiations. Trends Genet. 31, 491–499 (2015).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Weir, J. T. & Price, T. D. Limits to speciation inferred from times to secondary sympatry and ages of hybridizing species along a latitudinal gradient. Am. Nat. 177, 462–469 (2011).

    Article  PubMed  Google Scholar 

  17. 17.

    Campagna, L. et al. Rapid phenotypic evolution during incipient speciation in a continental avian radiation. Proc. R. Soc. B 279, 1847–1856 (2012).

    Article  Google Scholar 

  18. 18.

    Campagna, L. et al. Repeated divergent selection on pigmentation genes in a rapid finch radiation. Sci. Adv. 3, e1602404 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Haldane, J. B. S. Sex ratio and unisexual sterility in hybrid animals. J. Genet. 12, 101–109 (1922).

    Article  Google Scholar 

  20. 20.

    Immelmann, K. Besiedlungsgeschichte und Bastardierung von Lonchura castaneothorax und Lonchura flaviprymna in Nordaustralien. J. Ornithol. 103, 352–357 (1962).

    Article  Google Scholar 

  21. 21.

    Restall, R. Munias and Mannikins (Pica Press, Crowborough, 1996).

    Google Scholar 

  22. 22.

    Servedio, M. R. & Noor, M. A. F. The role of reinforcement in speciation: theory and data. Annu. Rev. Ecol. Syst. 34, 339–364 (2003).

    Article  Google Scholar 

  23. 23.

    Kimura, M. & Ohta, T. The age of a neutral mutant persisting in a finite population. Genetics 75, 199–212 (1973).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Slatkin, M. Rare alleles as indicators of gene flow. Evolution 39, 53–65 (1985).

    Article  PubMed  Google Scholar 

  25. 25.

    Malinsky, M., Trucchi E., Lawson, D. & Falush D. RADpainter and fineRADstructure: population inference from RADseq data. Preprint at (2016).

  26. 26.

    Warren, W. C. et al. The genome of a songbird. Nature 464, 757–762 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Domyan, E. T. et al. Epistatic and combinatorial effects of pigmentary gene mutations in the domestic pigeon. Curr. Biol. 24, 459–464 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Lamichhaney, S. et al. A beak size locus in Darwin’s finches facilitated character displacement during a drought. Science 352, 470–474 (2016).

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Nadeau, N. J. et al. Genomic islands of divergence in hybridizing Heliconius butterflies identified by large-scale targeted sequencing. Phil. Trans. R. Soc. B 367, 343–353 (2012).

    CAS  Article  Google Scholar 

  30. 30.

    Vijay, N. et al. Evolution of heterogeneous genome differentiation across multiple contact zones in a crow species complex. Nat. Commun. 7, 13195 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Cruickshank, T. E. & Hahn, M. W. Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol. Ecol. 23, 3133–3157 (2014).

    Article  PubMed  Google Scholar 

  32. 32.

    Burri, R. et al. Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers. Genome Res. 25, 1656–1665 (2915).

    Article  Google Scholar 

  33. 33.

    Cutter, A. D. & Payseur, B. A. Genomic signatures of selection at linked sites: unifying the disparity among species. Nat. Rev. Genet. 14, 262–274 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Vijay, N. et al. Genome-wide signatures of genetic variation within and between populations—a comparative perspective. Preprint at (2017).

  35. 35.

    Wolf, J. B. W. & Ellegren, H. Making sense of genomic islands of differentiation in light of speciation. Nat. Rev. Genet. 18, 87–100 (2017).

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Hill, G. E. & Johnson, J. D. The mitonuclear compatibility hypothesis of sexual selection. Proc. R. Soc. B 280, 20131314 (2013).

    Article  Google Scholar 

  37. 37.

    Petit, R. J. & Excoffier, L. Gene flow and species delimitation. Trends Ecol. Evol. 24, 386–393 (2009).

    Article  PubMed  Google Scholar 

  38. 38.

    Stern, D. L. The genetic causes of convergent evolution. Nat. Rev. Genet. 14, 751–764 (2013).

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Colosimo, P. F. et al. Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles. Science 307, 1928–1933 (2005).

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Norris, L. C. et al. Adaptive introgression in an African malaria mosquito coincident with the increased usage of insecticide-treated bed nets. Proc. Natl Acad. Sci. USA 112, 815–820 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Seutin, G. B., White, N. & Boag, P. T. Preservation of avian blood and tissue samples for DNA analyses. Can. J. Zool. 69, 82–90 (1991).

    CAS  Article  Google Scholar 

  42. 42.

    DaCosta, J. M. & Sorenson, M. D. Amplification biases and consistent recovery of loci in a double-digest RAD-seq protocol. PLoS ONE 9, e106713 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Novembre, J. & Stephens, M. Interpreting principal components analyses of spatial population genetic variation. Nat. Genet. 40, 646–649 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Nei, M. & Li, W.-H. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl Acad. Sci. USA 76, 5269–5273 (1979).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2008).

    Google Scholar 

  48. 48.

    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).

    Article  Google Scholar 

  50. 50.

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

    Article  Google Scholar 

  51. 51.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  52. 52.

    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Langmeade, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  54. 54.

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

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    DePristo, M. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genetics 43, 491–498 (2011).

    CAS  Article  PubMed  Google Scholar 

  56. 56.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Van der Auwera, G. A. et al. From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

    Google Scholar 

  58. 58.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Bhatia, G., Patterson, N., Sankararaman, S. & Price, A. L. Estimating and interpreting F ST: the impact of rare variants. Genome Res. 23, 1514–1521 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Reich, D., Thangaraj, K., Patterson, N., Price, A. L. & Singh, L. Reconstructing Indian population history. Nature 461, 489–494 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Willing, E.-M., Dreyer, C. & van Oosterhout, C. Estimates of genetic differentiation measured by F ST do not necessarily require large sample sizes when using many SNP markers. PLoS ONE 7, e42649 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

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

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Kuhner, M. K. & Felsenstein, J. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol. 11, 459–468 (1994).

    CAS  PubMed  Google Scholar 

  64. 64.

    Cunningham, F. et al. Ensembl 2015. Nucleic Acids Res. 43, D662–D669 (2015).

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    Knief, U. & Forstmeier, W. Mapping centromeres of microchromosomes in the zebra finch (Taeniopygia guttata) using half-tetrad analysis. Chromosoma 125, 757–768 (2016).

    Article  PubMed  Google Scholar 

  66. 66.

    Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v1.6 (2014);

  68. 68.

    Gibb, G. C. et al. New Zealand passerines help clarify the diversification of major songbird lineages during the Oligocene. Genome Biol. Evol. 7, 2983–2995 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Pratt, R. C. et al. Toward resolving deep neoaves phylogeny: data, signal enhancement, and priors. Mol. Biol. Evol. 26, 313–326 (2009).

    CAS  Article  PubMed  Google Scholar 

  70. 70.

    Claramunt, S. & Cracraft, J. A new time tree reveals Earth history’s imprint on the evolution of modern birds. Sci. Adv. 1, e1501005 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Jarvis, E. D. et al. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320–1331 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015).

    CAS  Article  PubMed  Google Scholar 

  73. 73.

    Eom, D. S. et al. Melanophore migration and survival during zebrafish adult pigment stripe development requires the immunoglobulin superfamily adhesion molecule Igsf11. PLoS Genet. 8, e1002899 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Fariello, M.-I. et al. Selection signatures in worldwide sheep populations. PLoS ONE 9, e103813 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Guenther, C. A., Tasic, B., Luo, L., Bedell, M. A. & Kingsley, D. M. A molecular basis for classic blond hair color in Europeans. Nat. Genet. 46, 748–752 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Miller, C. T. et al. Cis-regulatory changes in Kit ligand expression and parallel evolution of pigmentation in sticklebacks and humans. Cell 131, 1179–1189 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Sulem, P. et al. Genetic determinants of hair, eye and skin pigmentation in Europeans. Nat. Genet. 39, 1443–1452 (2007).

    CAS  Article  PubMed  Google Scholar 

  78. 78.

    Dutton, K. A. et al. Zebrafish colourless encodes sox10 and specifies non-ectomesenchymal neural crest fates. Development 128, 4113–4125 (2001).

    CAS  PubMed  Google Scholar 

  79. 79.

    Gunnarson, U. et al. The dark brown plumage color in chickens is caused by an 8.3-kb deletion upstream of SOX10. Pigment Cell Melanoma Res. 24, 268–274 (2011).

    Article  Google Scholar 

  80. 80.

    Hubbard, J. K., Uy, J. A. C., Hauber, M. E., Hoekstra, H. E. & Safran, R. J. Vertebrate pigmentation: from underlying genes to adaptive function. Trends Genet. 26, 231–239 (2010).

    CAS  Article  PubMed  Google Scholar 

  81. 81.

    Baião, P. C., Schreiber, E. A. & Parker, P. G. The genetic basis of plumage polymorphism in red-footed boobies (Sula sula): a melanocortin-1 receptor (MC1R) analysis. J. Hered. 98, 287–292 (2007).

    Article  PubMed  Google Scholar 

  82. 82.

    Mundy, N. I. et al. Conserved genetic basis of a quantitative plumage trait involved in mate choice. Science 303, 1870–1873 (2004).

    CAS  Article  PubMed  Google Scholar 

  83. 83.

    Nachman, M. W., Hoekstra, H. E. & D’Agostino, S. L. The genetic basis of adaptive melanism in pocket mice. Proc. Natl Acad. Sci. USA 100, 5268–5273 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Nadeau, N. J., Minvielle, F. & Mundy, N. I. Association of a Glu92Lys substitution in MC1R with extended brown in Japanese quail (Coturnix japonica). Anim. Genet. 37, 287–289 (2006).

    CAS  Article  PubMed  Google Scholar 

  85. 85.

    Theron, E., Hawkins, K., Bermingham, E., Ricklefs, R. E. & Mundy, N. I. The molecular basis of an avian plumage polymorphism in the wild: a melanocortin-1-receptor point mutation is perfectly associated with the melanic plumage morph of the bananaquit, Coereba flaveola. Curr. Biol. 11, 550–557 (2001).

    CAS  Article  PubMed  Google Scholar 

  86. 86.

    Uy, J. A., Moyle, R. G., Filardi, C. E. & Cheviron, Z. A. Difference in plumage color used in species recognition between incipient species is linked to a single amino acid substitution in the melanocortin-1 receptor. Am. Nat. 174, 244–254 (2009).

    Article  PubMed  Google Scholar 

  87. 87.

    Hiragaki, T. et al. Recessive black is allelic to the yellow plumage locus in Japanese quail and associated with a frameshift deletion in the ASIP gene. Genetics 178, 771–775 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Nadeau, N. J. et al. Characterization of Japanese quail yellow as a genomic deletion upstream of avian homolog of the mammalian ASIP (agouti) gene. Genetics 178, 777–786 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Oribe, E. et al. Conserved distal promoter of the agouti signaling protein (ASIP) gene controls sexual dichromatism in chickens. Gen. Comp. Endocrinol. 177, 231–237 (2012).

    CAS  Article  PubMed  Google Scholar 

  90. 90.

    Berryere, T. G., Kerns, J. A., Barsh, G. S. & Schmutz, S. M. Association of an agouti allele with fawn or sable coat color in domestic dogs. Mamm. Genome 16, 262–272 (2005).

    CAS  Article  PubMed  Google Scholar 

  91. 91.

    Bonilla, C. et al. The 8818 G allele of the agouti signalling protein (ASIP) gene is ancestral and associated with darker skin color in African Americans. Hum. Genet. 116, 402–406 (2005).

    CAS  Article  PubMed  Google Scholar 

  92. 92.

    Manceau, M., Domingues, V. S., Mallarino, R. & Hoekstra, H. E. The developmental role of agouti in color pattern evolution. Science 331, 1062–1065 (2011).

    CAS  Article  PubMed  Google Scholar 

  93. 93.

    Dorshorst, B. et al. A complex genomic rearrangement involving the endothelin-3 locus causes dermal hyperpigmentation in the chicken. PLoS Genetics 7, e1002412 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Shinomiya, A. et al. Gene duplication of endothelin 3 is closely correlated with the hyperpigmentation of the internal organs (fibromelanosis) in silky chickens. Genetics 190, 627–638 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Kaelin, C. B. et al. Specifying and sustaining pigmentation patterns in domestic and wild cats. Science 337, 1536–1541 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Xu, X. et al. The genetic basis of white tigers. Curr. Biol. 23, 1031–1035 (2013).

    CAS  Article  PubMed  Google Scholar 

  97. 97.

    Gunnarsson, U. et al. Mutations in SLC45A2 cause plumage color variation in chicken and Japanese quail. Genetics 175, 867–877 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Abzhanov, A., Protas, M., Grant, B. R., Grant, P. R. & Tabin, C. J. Bmp4 and morphological variation of beaks in Darwin’s finches. Science 305, 1462–1465 (2004).

    CAS  Article  PubMed  Google Scholar 

  99. 99.

    Wilkinson, L. et al. CRIM1 regulates the rate of processing and delivery of bone morphogenetic proteins to the cell surface. J. Biol. Chem. 278, 34181–34188 (2003).

    CAS  Article  PubMed  Google Scholar 

  100. 100.

    Miao, D. et al. Parathyroid hormone-related peptide is required for increased trabecular bone volume in parathyroid hormone-null mice. Endocrinology 145, 3554–3562 (2004).

    CAS  Article  PubMed  Google Scholar 

  101. 101.

    Fasquelle, C. et al. Balancing selection of a frame-shift mutation in the MRC2 gene accounts for the outbreak of the crooked tail syndrome in Belgian blue cattle. PLoS Genetics 5, e1000666 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Cardona, A. et al. Genome-wide analysis of cold adaptation in indigenous Siberian populations. PLoS ONE 9, e98076 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Raj, S. M., Pagani, L., Gallego Romero, I., Kivisikd, T. & Amos, W. A general linear model-based approach for inferring selection to climate. BMC Genet. 14, 87 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

Download references


This research was supported by the National Geographic Society (8933-11), the National Science Foundation (DEB 1210810 and DEB 1446085) and by small grants from the American Ornithologists’ Union, American Museum of Natural History, American Society of Naturalists, Society for the Study of Evolution, Society of Systematic Biologists, Systematics Association and Linnean Society. The Departments of Environment and Conservation in Papua New Guinea and Western Australia provided permits for the fieldwork. We thank the Western Australian Museum and Commonwealth Scientific and Industrial Research Organisation for tissue loans. J. Robins at the National Research Institute facilitated our applications for permits and visas in Papua New Guinea. This work was conducted under Boston University’s Institutional Animal Care and Use Committee protocol number 10-011. We thank J. Berv, S. Billy, C. Kieswetter, J. Lewis, R. McKay, P. Saguba, T. Stryjewski and many others for assistance with fieldwork, and C. Balakrishnan, J. DaCosta, D. Irwin and C. Schneider for comments on the paper.

Author information




M.D.S. conceived the study. K.F.S., with limited assistance from M.D.S., completed the fieldwork, collected and prepared the specimens and collected all of the genomic data. The authors worked together on analysing the data and writing and approving the paper.

Corresponding authors

Correspondence to Katherine Faust Stryjewski or Michael D. Sorenson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Supplementary Information

Supplementary Figures 1–36 and Supplementary tables 1–4

Life Sciences Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Stryjewski, K.F., Sorenson, M.D. Mosaic genome evolution in a recent and rapid avian radiation. Nat Ecol Evol 1, 1912–1922 (2017).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing