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The genetics of monarch butterfly migration and warning colouration

Nature volume 514, pages 317–321 (2014)Cite this article

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Abstract

The monarch butterfly, Danaus plexippus, is famous for its spectacular annual migration across North America, recent worldwide dispersal, and orange warning colouration. Despite decades of study and broad public interest, we know little about the genetic basis of these hallmark traits. Here we uncover the history of the monarch’s evolutionary origin and global dispersal, characterize the genes and pathways associated with migratory behaviour, and identify the discrete genetic basis of warning colouration by sequencing 101 Danaus genomes from around the globe. The results rewrite our understanding of this classic system, showing that D. plexippus was ancestrally migratory and dispersed out of North America to occupy its broad distribution. We find the strongest signatures of selection associated with migration centre on flight muscle function, resulting in greater flight efficiency among migratory monarchs, and that variation in monarch warning colouration is controlled by a single myosin gene not previously implicated in insect pigmentation.

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Figure 1: Global dispersal of the monarch butterfly.
Figure 2: A selective sweep associated with migration.
Figure 3: Divergent selection on collagen IV α-1.
Figure 4: The genetic basis of warning colouration.

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Accession codes

Primary accessions

Sequence Read Archive

Data deposits

Sequence data are deposited in the NCBI Short Read Archive (SRA) database (accession numbers SRP045457 and SRP045468).

References

  1. Dingle, H., Zalucki, M. P., Rochester, W. A. & Armijo-Prewitt, T. Distribution of the monarch butterfly, Danaus plexippus (L.) (Lepidoptera: Nymphalidae), in western North America. Biol. J. Linn. Soc. 85, 491–500 (2005)

    Article  Google Scholar 

  2. Lyons, J. I. et al. Lack of genetic differentiation between monarch butterflies with divergent migration destinations. Mol. Ecol. 21, 3433–3444 (2012)

    Article  PubMed  Google Scholar 

  3. Malcolm, S. B. & Zalucki, M. P. Biology and Conservation of the Monarch Butterfly (Natural History Museum of LA County, 1993)

    Google Scholar 

  4. Oberhauser, K. S. & Solensky, M. J. The Monarch Butterfly: Biology and Conservation (Cornell Univ. Press, 2004)

    Google Scholar 

  5. Urquhart, F. A. Found at last; the monarch’s winter home. Natl Geogr. Mag. 150, 161–173 (1976)

    Google Scholar 

  6. Urquhart, F. A. & Urquhart, N. R. Autumnal migration routes of the eastern population of the monarch butterfly (Danaus p. plexippus L.; Danaidae; Lepidoptera) in North America to the overwintering site in the Neovolcanic Plateau of Mexico. Can. J. Zool. 56, 1759–1764 (1978)

    Article  Google Scholar 

  7. Wassenaar, L. I. & Hobson, K. A. Natal origins of migratory monarch butterflies at wintering colonies in Mexico: new isotopic evidence. Proc. Natl Acad. Sci. USA 95, 15436–15439 (1998)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Froy, O., Gotter, A. L., Casselman, A. L. & Reppert, S. M. Illuminating the circadian clock in monarch butterfly migration. Science 300, 1303–1305 (2003)

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Heinze, S. & Reppert, S. M. Sun compass integration of skylight cues in migratory monarch butterflies. Neuron 69, 345–358 (2011)

    Article  CAS  PubMed  Google Scholar 

  10. Merlin, C., Gegear, R. J. & Reppert, S. M. Antennal circadian clocks coordinate sun compass orientation in migratory monarch butterflies. Science 325, 1700–1704 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Reppert, S. M., Gegear, R. J. & Merlin, C. Navigational mechanisms of migrating monarch butterflies. Trends Neurosci. 33, 399–406 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sauman, I. et al. Connecting the navigational clock to sun compass input in monarch butterfly brain. Neuron 46, 457–467 (2005)

    Article  CAS  PubMed  Google Scholar 

  13. Mouritsen, H. & Frost, B. J. Virtual migration in tethered flying monarch butterflies reveals their orientation mechanisms. Proc. Natl Acad. Sci. USA 99, 10162–10166 (2002)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ackery, P. R. & Vane-Wright, R. I. Milkweed Butterflies: Their Cladistics and Biology (British Museum, 1984)

    Google Scholar 

  15. Altizer, S. & Davis, A. K. Populations of monarch butterflies with different migratory behaviors show divergence in wing morphology. Evolution 64, 1018–1028 (2010)

    Article  PubMed  Google Scholar 

  16. Dockx, C. Directional and stabilizing selection on wing size and shape in migrant and resident monarch butterflies, Danaus plexippus (L.), in Cuba. Biol. J. Linn. Soc. 92, 605–616 (2007)

    Article  Google Scholar 

  17. Vane-Wright, R. I. in Biology and Conservation of the Monarch Butterfly (eds Malcolm, S. B. & Zalucki, M. P. ) 179–187 (Natural History Museum of LA County, 1993)

    Google Scholar 

  18. Haeger, J. F. & Jordano, D. The Monarch butterfly Danaus plexippus (Linnaeus, 1758) in the Strait of Gibraltar (Lepidoptera: Danaidae). SHILAP Rev. Lepidopterol. 37, 421–438 (2009)

    Google Scholar 

  19. Zhu, H., Casselman, A. & Reppert, S. M. Chasing migration genes: a brain expressed sequence tag resource for summer and migratory monarch butterflies (Danaus plexippus). PLoS ONE 3, e1345 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhan, S., Merlin, C., Boore, J. L. & Reppert, S. M. The monarch butterfly genome yields insights into long-distance migration. Cell 147, 1171–1185 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kitching, I. J., Ackery, P. R. & Vane-Wright, R. I. in Biology and Conservation of the Monarch Butterfly (eds Malcolm, S. B. & Zalucki, M. P. ) 11–16 (Natural History Museum of LA County, 1993)

    Google Scholar 

  22. Gauthreaux, S. A. in Avian Biology Vol. 4 (eds Farner, D. S., King, J. R. & Parkes, K. C. ) Ch. 2 93–168 (Elsevier, 1982)

    Book  Google Scholar 

  23. Zalucki, M. P. & Clarke, A. R. Monarchs across the Pacific: the Columbus hypothesis revisited. Biol. J. Linn. Soc. 82, 111–121 (2004)

    Article  Google Scholar 

  24. Brower, L. P., Oberhauser, K. S., Boppré, M., Brower, A. V. Z. & Vane-Wright, R. I. Monarch sex: ancient rites, or recent wrongs? Antenna 31, 12–18 (2007)

    Google Scholar 

  25. Peter, B. M. & Slatkin, M. Detecting range expansions from genetic data. Evolution 67, 3274–3289 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  26. 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 

  27. Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Schnorrer, F. et al. Systematic genetic analysis of muscle morphogenesis and function in Drosophila. Nature 464, 287–291 (2010)

    Article  ADS  CAS  PubMed  Google Scholar 

  31. Kelemen-Valkony, I. et al. Drosophila basement membrane collagen col4a1 mutations cause severe myopathy. Matrix Biol. 31, 29–37 (2012)

    Article  CAS  PubMed  Google Scholar 

  32. Plaisier, E. et al. COL4A1 mutations and hereditary angiopathy, nephropathy, aneurysms, and muscle cramps. N. Engl. J. Med. 357, 2687–2695 (2007)

    Article  CAS  PubMed  Google Scholar 

  33. Hakeda, S., Endo, S. & Saigo, K. Requirements of Kettin, a giant muscle protein highly conserved in overall structure in evolution, for normal muscle function, viability, and flight activity of Drosophila. J. Cell Biol. 148, 101–114 (2000)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gibo, D. L. & Pallett, M. J. Soaring flight of monarch butterflies, Danaus plexippus (Lepidoptera: Danaidae), during the late summer migration in southern Ontario. Can. J. Zool. 57, 1393–1401 (1979)

    Article  Google Scholar 

  35. Niitepõld, K. et al. Flight metabolic rate and Pgi genotype influence butterfly dispersal rate in the field. Ecology 90, 2223–2232 (2009)

    Article  PubMed  Google Scholar 

  36. Mitikka, V. & Hanski, I. Pgi genotype influences flight metabolism at the expanding range margin of the European map butterfly. Ann. Zool. Fenn. 47, 1–14 (2010)

    Article  Google Scholar 

  37. Niitepõld, K., Mattila, A. L. K., Harrison, P. J. & Hanski, I. Flight metabolic rate has contrasting effects on dispersal in the two sexes of the Glanville fritillary butterfly. Oecologia 165, 847–854 (2011)

    Article  ADS  PubMed  Google Scholar 

  38. Reichstein, T., von Euw, J., Parsons, J. A. & Rothschild, M. Heart poisons in the monarch butterfly. Science 161, 861–866 (1968)

    Article  ADS  CAS  PubMed  Google Scholar 

  39. Ritland, D. B. & Brower, L. P. The viceroy butterfly is not a batesian mimic. Nature 350, 497–498 (1991)

    Article  ADS  Google Scholar 

  40. Stimson, J. & Kasuya, M. Decline in the frequency of the white morph of the monarch butterfly (Danaus plexippus plexippus L. Nymphalidae) on Oahu, Hawaii. J. Lepid. Soc. 54, 29–32 (2000)

    Google Scholar 

  41. Stimson, J. S. & Meyers, L. Inheritance and frequency of a color polymorphism in Danaus plexippus (Lepidoptera: Danaidae) on Oahu, Hawaii. J. Res. Lepid. 23, 153–160 (1984)

    Google Scholar 

  42. Nijhout, H. F. The Development and Evolution of Butterfly Wing Patterns (Smithsonian Press, 1991)

    Google Scholar 

  43. Mercer, J. A., Seperack, P. K., Strobel, M. C., Copeland, N. G. & Jenkins, N. A. Novel myosin heavy chain encoded by murine dilute coat colour locus. Nature 349, 709–713 (1991)

    Article  ADS  CAS  PubMed  Google Scholar 

  44. Fukuda, M. & Kuroda, T. S. Missense mutations in the globular tail of myosin-Va in dilute mice partially impair binding of Slac2-a/melanophilin. J. Cell Sci. 117, 583–591 (2004)

    Article  CAS  PubMed  Google Scholar 

  45. Rendón-Salinas, E. & Tavera-Alonso, G. Forest Surface Occupied by Monarch Butterfly Hibernation Colonies in December 2013 (World Wildlife Fund-México, 2014)

    Google Scholar 

  46. Haber, W. A. in Biology and Conservation of the Monarch Butterfly (eds Malcolm, S. B. & Zalucki, M. P. ) 201–207 (Natural History Museum of LA County, 1993)

    Google Scholar 

  47. James, D. G. in Biology and Conservation of the Monarch Butterfly (eds Malcolm, S. B. & Zalucki, M. P. ) 189–200 (Natural History Museum of LA County, 1993)

    Google Scholar 

  48. Smith, D. A. S. & Owen, D. F. Colour genes as markers for migratory activity: The butterfly Danaus chrysippus in Africa. Oikos 78, 127–135 (1997)

    Article  Google Scholar 

  49. Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 21, 936–939 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  51. Zhan, S. & Reppert, S. M. MonarchBase: the monarch butterfly genome database. Nucleic Acids Res. 41, D758–D763 (2013)

    Article  CAS  PubMed  Google Scholar 

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

  53. International Silkworm Genome Consortium. The genome of a lepidopteran model insect, the silkworm Bombyx mori. Insect Biochem. Mol. Biol. 38, 1036–1045 (2008)

  54. She, R., Chu, J. S., Wang, K., Pei, J. & Chen, N. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 19, 143–149 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Birney, E., Clamp, M. & Durbin, R. GeneWise and Genomewise. Genome Res. 14, 988–995 (2004)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Talavera, G. & Castresana, J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 56, 564–577 (2007)

    Article  CAS  PubMed  Google Scholar 

  57. 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 

  58. Xia, Q. et al. Complete resequencing of 40 genomes reveals domestication events and genes in silkworm (Bombyx). Science 326, 433–436 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  59. PHYLIP. (phylogeny inference package) v. 3.6 http://evolution.genetics.washington.edu/phylip.html (Univ. Washington, 2005)

  60. Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Tang, H., Peng, J., Wang, P. & Risch, N. J. Estimation of individual admixture: analytical and study design considerations. Genet. Epidemiol. 28, 289–301 (2005)

    Article  PubMed  Google Scholar 

  62. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Knight, A. & Brower, L. P. The influence of eastern North American autumnal migrant monarch butterflies (Danaus plexippus L.) on continuously breeding resident monarch populations in southern Florida. J. Chem. Ecol. 35, 816–823 (2009)

    Article  CAS  PubMed  Google Scholar 

  64. Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005)

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Haag-Liautard, C. et al. Direct estimation of per nucleotide and genomic deleterious mutation rates in Drosophila. Nature 445, 82–85 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

  67. Keightley, P. D. et al. Analysis of the genome sequences of three Drosophila melanogaster spontaneous mutation accumulation lines. Genome Res. 19, 1195–1201 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zalucki, M. P. & Clarke, A. R. Monarchs across the Pacific: the Columbus hypothesis revisited. Biol. J. Linn. Soc. 82, 111–121 (2004)

    Article  Google Scholar 

  69. 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)

    Article  ADS  CAS  MATH  PubMed  PubMed Central  Google Scholar 

  70. Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989)

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57 (2009)

    Article  CAS  Google Scholar 

  73. Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010)

    Article  PubMed  Google Scholar 

  74. Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Librado, P. & Rozas, J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009)

    Article  CAS  PubMed  Google Scholar 

  76. Hudson, R. R., Kreitman, M. & Aguade, M. A test of neutral molecular evolution based on nucleotide data. Genetics 116, 153–159 (1987)

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Murrell, B. et al. FUBAR: a fast, unconstrained Bayesian approximation for inferring selection. Mol. Biol. Evol. 30, 1196–1205 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Murrell, B. et al. Detecting individual sites subject to episodic diversifying selection. PLoS Genet. 8, e1002764 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kosakovsky Pond, S. L. & Frost, S. D. W. Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol. Biol. Evol. 22, 1208–1222 (2005)

    Article  CAS  PubMed  Google Scholar 

  80. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnol. 28, 511–515 (2010)

    Article  CAS  Google Scholar 

  82. Bartholomew, G. A., Vleck, D. & Vleck, C. M. Instantaneous measurements of oxygen consumption during pre-flight warm-up and post-flight cooling in sphingid and saturniid moths. J. Exp. Biol. 90, 17–32 (1981)

    Google Scholar 

  83. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357–359 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank B. Ballister, S. Barribeau, R. Bartel, N. Chamberlain, R. Cook, A. Davis, D. Feary, D. Frey, M. Maudsley, G. Moreira, E. Osburn, R. Rarick, E. Rendon, D. Rodrigues, E. Sternberg and J. Stimson for assistance collecting or providing specimens. We also thank J. Jensen and D. Lohman for discussion. This work was supported by National Institutes of Health grant GM086794-02S1, National Science Foundation grants IOS-134367, DEB-0643831, DEB-1019746 and DEB-1316037, start-up funds from the Chinese Academy of Sciences and Shanghai Institutes for Biological Sciences, and Neubauer Funds from the University of Chicago.

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Authors and Affiliations

  1. Key Laboratory of Insect Developmental and Evolutionary Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200032, China

    Shuai Zhan

  2. Department of Ecology & Evolution, University of Chicago, Chicago, 60637, Illinois, USA

    Shuai Zhan, Wei Zhang & Marcus R. Kronforst

  3. Department of Neurobiology, University of Massachusetts Medical School, Worcester, 01605, Massachusetts, USA

    Shuai Zhan & Steven M. Reppert

  4. Department of Biology, Stanford University, Stanford, 94305, California, USA

    Kristjan Niitepõld & Jeremy Hsu

  5. Department of Biosciences, University of Helsinki, FI-00014 Helsinki, Finland,

    Kristjan Niitepõld

  6. Departamento de Botánica, Ecología y Fisiología Vegetal, Universidad de Córdoba, 14071 Córdoba, Spain,

    Juan Fernández Haeger

  7. School of Biological Sciences, The University of Queensland, Brisbane, 4072, Queensland, Australia

    Myron P. Zalucki

  8. Odum School of Ecology, University of Georgia, Athens, 30602, Georgia, USA

    Sonia Altizer

  9. Department of Biology, Emory University, Atlanta, 30322, Georgia, USA

    Jacobus C. de Roode

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  1. Shuai Zhan
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Contributions

S.Z. designed and implemented analyses of dispersal and migration and co-wrote the manuscript. W.Z. performed wing colour analyses. K.N. performed respirometry experiments. J.H. helped design the project and collected and prepared samples for sequencing. J.F.H. and M.P.Z. provided samples and interpreted results. S.A., J.C.d.R. and S.M.R. helped design the project, provided samples, and interpreted results. M.R.K. conceived and directed the project, performed targeted population genetic analyses, and co-wrote the manuscript.

Corresponding authors

Correspondence to Shuai Zhan or Marcus R. Kronforst.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Relationships among monarch populations inferred using the maximum likelihood method implemented in Treemix.

Note, this is a fully resolved, bifurcating tree. The very short basal branches indicate little genetic drift in North American populations, not unresolved basal relationships. Colours correspond to those in Fig. 1. Treemix also inferred five migration events among populations: from Puerto Rico to Aruba, from Puerto Rico to Costa Rica, from New Caledonia to Fiji, from Belize or Costa Rica to Portugal, and from Belize to Puerto Rico.

Extended Data Figure 2 Demographic history of the monarch butterfly.

a, Patterns of linkage-disequilibrium decay across the genome in different geographic populations. b, Genome-wide distribution of minor allele frequencies. c, Heterozygosity across populations, estimated as the ratio of heterozygous SNPs to homozygous SNPs/individual. d, Demographic history inferred using PSMC. This analysis includes representative individuals of high sequencing depth for each geographic location. The period of the last glacial maximum (LGM; ∼20,000 years ago) is shaded in grey.

Extended Data Figure 3 ∂a∂i analysis parameter estimates.

a, Schematic of demographic scenario modelled in ∂a∂i labelled with parameters being estimated. Nu, effective population size (individuals); m, migration rate (individuals/year); T, time (years). b, Inferred parameter estimates. c, One-dimensional model-data comparison considering North America population only. In the left panel, the model is plotted in red and the data in blue. In the right panel, the residuals between model and data are plotted. d, Two-dimensional comparison for joint estimation of North America and dispersal populations (Central/South America, Pacific, Atlantic). The left two panels are marginal spectra for data and the maximum-likelihood model, respectively. The right two panels show the residuals.

Extended Data Table 1 Inferring the monarch range expansion
Full size table
Extended Data Table 2 Top 20 migration-associated genomic regions
Full size table

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Zhan, S., Zhang, W., Niitepõld, K. et al. The genetics of monarch butterfly migration and warning colouration. Nature 514, 317–321 (2014). https://doi.org/10.1038/nature13812

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