What genomic data can reveal about eco-evolutionary dynamics

Abstract

Recognition that evolution operates on the same timescale as ecological processes has motivated growing interest in eco-evolutionary dynamics. Nonetheless, generating sufficient data to test predictions about eco-evolutionary dynamics has proved challenging, particularly in natural contexts. Here we argue that genomic data can be integrated into the study of eco-evolutionary dynamics in ways that deepen our understanding of the interplay between ecology and evolution. Specifically, we outline five major questions in the study of eco-evolutionary dynamics for which genomic data may provide answers. Although genomic data alone will not be sufficient to resolve these challenges, integrating genomic data can provide a more mechanistic understanding of the causes of phenotypic change, help elucidate the mechanisms driving eco-evolutionary dynamics, and lead to more accurate evolutionary predictions of eco-evolutionary dynamics in nature.

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Fig. 1: Five major questions in eco-evolutionary dynamics.
Fig. 2: Using genomic tools to study a predator–prey eco-evolutionary dynamic.

References

  1. 1.

    Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306 (2003).

    Article  CAS  Google Scholar 

  2. 2.

    Hairston, N. G., Ellner, S. P., Geber, M. A., Yoshida, T. & Fox, J. A. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 8, 1114–1127 (2005).

    Article  Google Scholar 

  3. 3.

    Turcotte, M. M., Reznick, D. N. & Hare, J. D. The impact of rapid evolution on population dynamics in the wild: experimental test of eco-evolutionary dynamics. Ecol. Lett. 14, 1084–1092 (2011).

    Article  Google Scholar 

  4. 4.

    Becks, L., Ellner, S. P., Jones, L. E. & Hairston, N. G. The functional genomics of an eco-evolutionary feedback loop: linking gene expression, trait evolution, and community dynamics. Ecol. Lett. 15, 492–501 (2012).

    Article  Google Scholar 

  5. 5.

    Thompson, J. N. Rapid evolution as an ecological process. Trends Ecol. Evol. 13, 329–332 (1998).

    Article  CAS  Google Scholar 

  6. 6.

    Post, D. M. & Palkovacs, E. P. Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. Phil. Trans. R. Soc. B 364, 1629–1640 (2009).

    Article  Google Scholar 

  7. 7.

    Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Hendry, A. P. Eco-Evolutionary Dynamics (Princeton Univ. Press, Princeton, 2017).

  9. 9.

    Turcotte, M. M., Reznick, D. N. & Hare, J. D. Experimental test of an eco-evolutionary dynamic feedback loop between evolution and population density in the green peach aphid. Am. Nat. 181 (Suppl. 1), S46–S57 (2013).

    Article  Google Scholar 

  10. 10.

    Matthews, B., Aebischer, T., Sullam, K. E., Lundsgaard-Hansen, B. & Seehausen, O. Experimental evidence of an eco-evolutionary feedback during adaptive divergence. Curr. Biol. 26, 483–489 (2016).

    Article  CAS  Google Scholar 

  11. 11.

    Fussmann, G. F., Loreau, M. & Abrams, P. A. Eco-evolutionary dynamics of communities and ecosystems. Funct. Ecol. 21, 465–477 (2007).

    Article  Google Scholar 

  12. 12.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Savolainen, O., Lascoux, M. & Merilä, J. Ecological genomics of local adaptation. Nat. Rev. Genet. 14, 807–820 (2013).

    Article  CAS  Google Scholar 

  14. 14.

    Seehausen, O. et al. Genomics and the origin of species. Nat. Rev. Genet. 15, 176–192 (2014).

    Article  CAS  Google Scholar 

  15. 15.

    Thurman, T. J. & Barrett, R. D. H. The genetic consequences of selection in natural populations. Mol. Ecol. 25, 1429–1448 (2016).

    Article  Google Scholar 

  16. 16.

    Bergland, A. O., Behrman, E. L., O’Brien, K. R., Schmidt, P. S. & Petrov, D. A. Genomic evidence of rapid and stable adaptive oscillations over seasonal time scales in Drosophila. PLoS Genet. 10, e1004775 (2014).

    Article  CAS  Google Scholar 

  17. 17.

    Stapley, J. et al. Adaptation genomics: the next generation. Trends Ecol. Evol. 25, 705–712 (2010).

    Article  Google Scholar 

  18. 18.

    Hendry, A. P. Key questions in the genetics and genomics of eco-evolutionary dynamics. Heredity 111, 456–466 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Slobodkin, L. B. Growth and Regulation of Animal Populations (Holt, Rinehart, Winston, New York, 1961).

  20. 20.

    Hendry, A. P. & Kinnison, M. T. Perspective: The pace of modern life: measuring rates of contemporary microevolution. Evolution 53, 1637–1653 (1999).

    Article  Google Scholar 

  21. 21.

    Alberti, M. et al. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl Acad. Sci. USA 114, 8951–8956 (2017).

    Article  CAS  Google Scholar 

  22. 22.

    Post, D. M., Palkovacs, E. P., Schielke, E. G. & Dodson, S. I. Intraspecific variation in a predator affects community structure and cascading trophic interactions. Ecology 89, 2019–2032 (2008).

    Article  Google Scholar 

  23. 23.

    Harmon, L. J. et al. Evolutionary diversification in stickleback affects ecosystem functioning. Nature 458, 1167–1170 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bassar, R. D. et al. Local adaptation in Trinidadian guppies alters ecosystem processes. Proc. Natl Acad. Sci. USA 107, 3616–3621 (2010).

    Article  Google Scholar 

  25. 25.

    Rudman, S. M. & Schluter, D. Ecological impacts of reverse speciation in threespine stickleback. Curr. Biol. 26, 490–495 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Endler, J. A. Natural Selection in the Wild (Princeton Univ. Press, Princeton, 1986).

  27. 27.

    Reznick, D. N. & Bryga, H. Life-history evolution in guppies (Poecilia reticulata): 1. Phenotypic and genetic changes in an introduction experiment. Evolution 41, 1370–1385 (1987).

    PubMed  Google Scholar 

  28. 28.

    Gienapp, P., Teplitsky, C., Alho, J. S., Mills, J. A. & Merilä, J. Climate change and evolution: disentangling environmental and genetic responses. Mol. Ecol. 17, 167–178 (2008).

    Article  CAS  Google Scholar 

  29. 29.

    Vitti, J. J., Grossman, S. R. & Sabeti, P. C. Detecting natural selection in genomic data. Annu. Rev. Genet. 47, 97–120 (2013).

    Article  CAS  Google Scholar 

  30. 30.

    Quesada, H., Ramírez, U. E. M., Rozas, J. & Aguadé, M. Large-scale adaptive hitchhiking upon high recombination in Drosophila simulans. Genetics 165, 895–900 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Ohashi, J., Naka, I. & Tsuchiya, N. The impact of natural selection on an ABCC11 SNP determining earwax type. Mol. Biol. Evol. 28, 849–857 (2011).

    Article  CAS  Google Scholar 

  32. 32.

    Gompert, Z. et al. Experimental evidence for ecological selection on genome variation in the wild. Ecol. Lett. 17, 369–379 (2014).

    Article  Google Scholar 

  33. 33.

    Kinnison, M. T., Hairston, N. G. Jr & Hendry, A. P. Cryptic eco-evolutionary dynamics. Ann. NY Acad. Sci. 1360, 120–144 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Excoffier, L., Hofer, T. & Foll, M. Detecting loci under selection in a hierarchically structured population. Heredity 103, 285–298 (2009).

    Article  CAS  Google Scholar 

  35. 35.

    Bergland, A. O., Tobler, R., Gonzalez, J., Schmidt, P. & Petrov, D. Secondary contact and local adaptation contribute to genome-wide patterns of clinal variation in Drosophila melanogaster. Mol. Ecol. 25, 1157–1174 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Nuzhdin, S. V. & Turner, T. L. Promises and limitations of hitchhiking mapping. Curr. Opin. Genet. Dev. 23, 694–699 (2013).

    Article  CAS  Google Scholar 

  37. 37.

    Franssen, S. U., Nolte, V., Tobler, R. & Schlötterer, C. Patterns of linkage disequilibrium and long range hitchhiking in evolving experimental Drosophila melanogaster populations. Mol. Biol. Evol. 32, 495–509 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

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

    Article  CAS  Google Scholar 

  39. 39.

    Johnson, M. T. J. & Stinchcombe, J. R. An emerging synthesis between community ecology and evolutionary biology. Trends Ecol. Evol. 22, 250–257 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Rudman, S. M. et al. Adaptive genetic variation mediates bottom-up and top-down control in an aquatic ecosystem. Proc. R. Soc. B 282, 20151234 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Ellner, S. P., Geber, M. & Hairston, N. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol. Lett. 14, 603–614 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Govaert, L., Pantel, J. H. & De Meester, L. Eco-evolutionary partitioning metrics: assessing the importance of ecological and evolutionary contributions to population and community change. Ecol. Lett. 19, 839–853 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Stanton-Geddes, J., Yoder, J. B., Briskine, R., Young, N. D. & Tiffin, P. Estimating heritability using genomic data. Methods Ecol. Evol. 4, 1151–1158 (2013).

    Article  Google Scholar 

  44. 44.

    Bérénos, C., Ellis, P. A., Pilkington, J. G. & Pemberton, J. M. Estimating quantitative genetic parameters in wild populations: a comparison of pedigree and genomic approaches. Mol. Ecol. 23, 3434–3451 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Visscher, P. M. et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, e41 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Pelletier, F. et al. Eco-evolutionary dynamics in a contemporary human population. Nat. Commun. 8, 15947 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Becks, L., Ellner, S. P., Jones, L. E. & Hairston, N. G. Jr. Reduction of adaptive genetic diversity radically alters eco-evolutionary community dynamics. Ecol. Lett. 13, 989–997 (2010).

    PubMed  Google Scholar 

  48. 48.

    Agrawal, A. A., Hastings, A. P., Johnson, M. T. J., Maron, J. L. & Salminen, J.-P. Insect herbivores drive real-time ecological and evolutionary change in plant populations. Science 338, 113–116 (2012).

    Article  CAS  Google Scholar 

  49. 49.

    Agrawal, A. A., Johnson, M. T. J., Hastings, A. P. & Maron, J. L. A field experiment demonstrating plant life-history evolution and its eco-evolutionary feedback to seed predator populations. Am. Nat. 181 (Suppl. 1), S35–S45 (2013).

    Article  Google Scholar 

  50. 50.

    McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Drysdale, R. & FlyBase Consortium FlyBase: a database for the Drosophila research community. Methods Mol. Biol. 420, 45–59 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Berardini, T. Z. et al. The Arabidopsis information resource: making and mining the ‘gold standard’ annotated reference plant genome. Genesis 53, 474–485 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Shen, L., Courtois, B., McNally, K. L., Robin, S. & Li, Z. Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor. Appl. Genet. 103, 75–83 (2001).

    Article  CAS  Google Scholar 

  55. 55.

    Alonso, J. M. et al. Genome-wide insertional mutagenesis of Arabidopsis thaliana. Science 301, 653–657 (2003).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Giaever, G. & Nislow, C. The yeast deletion collection: a decade of functional genomics. Genetics 197, 451–465 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Arnegard, M. E. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    McKown, A. D. et al. Genome-wide association implicates numerous genes underlying ecological trait variation in natural populations of Populus trichocarpa. New Phytol. 203, 535–553 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356–369 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Whitham, T. G. et al. Community and ecosystem genetics: a consequence of the extended phenotype. Ecology 84, 559–573 (2003).

    Article  Google Scholar 

  61. 61.

    Bailey, J. K. et al. From genes to ecosystems: a synthesis of the effects of plant genetic factors across levels of organization. Phil. Trans. R. Soc. B 364, 1607–1616 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Schweitzer, J. et al. Genetically based trait in a dominant tree affects ecosystem processes. Ecol. Lett 7, 127–134 (2004).

    Article  Google Scholar 

  63. 63.

    Hanski, I. & Saccheri, I. Molecular-level variation affects population growth in a butterfly metapopulation. PLoS Biol. 4, e129 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Crutsinger, G. M. et al. Testing a ‘genes-to-ecosystems’ approach to understanding aquatic–terrestrial linkages. Mol. Ecol. 23, 5888–5903 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Mackay, T. F. C., Stone, E. A. & Ayroles, J. F. The genetics of quantitative traits: challenges and prospects. Nat. Rev. Genet. 10, 565–577 (2009).

    Article  CAS  Google Scholar 

  66. 66.

    Rockman, M. V. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution 66, 1–17 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Barbour, M. A. et al. Multiple plant traits shape the genetic basis of herbivore community assembly. Funct. Ecol. 29, 995–1006 (2015).

    Article  Google Scholar 

  68. 68.

    Farkas, T. E., Mononen, T., Comeault, A. A., Hanski, I. & Nosil, P. Evolution of camouflage drives rapid ecological change in an insect community. Curr. Biol. 23, 1835–1843 (2013).

    Article  CAS  Google Scholar 

  69. 69.

    Hiltunen, T. & Becks, L. Consumer co-evolution as an important component of the eco-evolutionary feedback. Nat. Commun. 5, 5226 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Alexander, T. J., Vonlanthen, P. & Seehausen, O. Does eutrophication-driven evolution change aquatic ecosystems? Phil. Trans. R. Soc. B 372, 20160041 (2017).

    Article  Google Scholar 

  71. 71.

    Tenaillon, O. et al. The molecular diversity of adaptive convergence. Science 335, 457–461 (2012).

    Article  CAS  Google Scholar 

  72. 72.

    Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Lind, P. A., Farr, A. D., Rainey, P. B. & Shou, W. Experimental evolution reveals hidden diversity in evolutionary pathways. eLife 4, e07074 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Long, A., Liti, G., Luptak, A. & Tenaillon, O. Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat. Rev. Genet. 16, 567–582 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Burke, M. K. et al. Genome-wide analysis of a long-term evolution experiment with Drosophila. Nature 467, 587–590 (2010).

    Article  CAS  Google Scholar 

  76. 76.

    Renaut, S., Owens, G. L. & Rieseberg, L. H. Shared selective pressure and local genomic landscape lead to repeatable patterns of genomic divergence in sunflowers. Mol. Ecol. 23, 311–324 (2014).

    Article  CAS  Google Scholar 

  77. 77.

    Telonis-Scott, M., Sgrò, C. M., Hoffmann, A. A. & Griffin, P. C. Cross-study comparison reveals common genomic, network, and functional signatures of desiccation resistance in Drosophila melanogaster. Mol. Biol. Evol. 33, 1053–1067 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    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 

  79. 79.

    Ralph, P. L. & Coop, G. The role of standing variation in geographic convergent adaptation. Am. Nat. 186 (Suppl. 1), S5–S23 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  80. 80.

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

    Article  CAS  Google Scholar 

  81. 81.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Kruuk, L. E. B. Estimating genetic parameters in natural populations using the ‘animal model’. Phil. Trans. R. Soc. B 359, 873–890 (2004).

    Article  Google Scholar 

  83. 83.

    Wang, J. Pedigrees or markers: which are better in estimating relatedness and inbreeding coefficient? Theor. Popul. Biol. 107, 4–13 (2016).

    Article  Google Scholar 

  84. 84.

    Gienapp, P. et al. Predicting demographically sustainable rates of adaptation: can great tit breeding time keep pace with climate change? Phil. Trans. R. Soc. B 368, 20120289 (2013).

    Article  Google Scholar 

  85. 85.

    Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, e3376 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Andolfatto, P. et al. Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Genome Res. 21, 610–617 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Bamshad, M. J. et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12, 745–755 (2011).

    Article  CAS  Google Scholar 

  89. 89.

    Dolezel, J. & Bartos, J. Plant DNA flow cytometry and estimation of nuclear genome size. Ann. Bot. 95, 99–110 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Li, R. et al. The sequence and de novo assembly of the giant panda genome. Nature 463, 311–317 (2010).

    Article  CAS  Google Scholar 

  91. 91.

    Mora-Márquez, F., García-Olivares, V., Emerson, B. C. & López de Heredia, U. ddradseqtools: a software package for in silico simulation and testing of double-digest RADseq experiments. Mol. Ecol. Resour. 17, 230–246 (2017).

    Article  CAS  Google Scholar 

  92. 92.

    Schlötterer, C., Tobler, R., Kofler, R. & Nolte, V. Sequencing pools of individuals — mining genome-wide polymorphism data without big funding. Nat. Rev. Genet. 15, 749–763 (2014).

    Article  CAS  Google Scholar 

  93. 93.

    Therkildsen, N. O. & Palumbi, S. R. Practical low-coverage genomewide sequencing of hundreds of individually barcoded samples for population and evolutionary genomics in nonmodel species. Mol. Ecol. Resour. 17, 194–208 (2017).

    Article  CAS  Google Scholar 

  94. 94.

    Alex Buerkle, C. & Gompert, Z. Population genomics based on low coverage sequencing: how low should we go? Mol. Ecol. 22, 3028–3035 (2013).

    Article  CAS  Google Scholar 

  95. 95.

    Lowry, D. B. et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17, 142–152 (2017).

    Article  CAS  Google Scholar 

  96. 96.

    Mostovoy, Y. et al. A hybrid approach for de novo human genome sequence assembly and phasing. Nat. Methods 13, 587–590 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Eaton, D. A. R. PyRAD: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics 30, 1844–1849 (2014).

    Article  CAS  Google Scholar 

  100. 100.

    Puritz, J. B., Hollenbeck, C. M. & Gold, J. R. dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ 2, e431 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The paper was conceived during a Monte Verita conference on ‘The Genomic Basis of Eco-Evolutionary Change’ organized by the Centre for Adaptation to a Changing Environment (ACE) at ETH Zürich. We thank the Congressi Stefano Franscini and ETH Zürich for funding and supporting the meeting.

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S.M.R. assembled the first draft of the manuscript based on contributions from all authors. All authors provided revisions.

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Correspondence to Seth M. Rudman.

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Rudman, S.M., Barbour, M.A., Csilléry, K. et al. What genomic data can reveal about eco-evolutionary dynamics. Nat Ecol Evol 2, 9–15 (2018). https://doi.org/10.1038/s41559-017-0385-2

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