Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Historical contingency shapes adaptive radiation in Antarctic fishes

Nature Ecology & Evolution volume 3, pages 1102–1109 (2019)Cite this article

Subjects

An Author Correction to this article was published on 10 March 2020

This article has been updated

Abstract

Adaptive radiation illustrates links between ecological opportunity, natural selection and the generation of biodiversity. Central to adaptive radiation is the association between a diversifying lineage and the evolution of phenotypic variation that facilitates the use of new environments or resources. However, is not clear whether adaptive evolution or historical contingency is more important for the origin of key phenotypic traits in adaptive radiation. Here we use targeted sequencing of >250,000 loci across 46 species to examine hypotheses concerning the origin and diversification of key traits in the adaptive radiation of Antarctic notothenioid fishes. Contrary to expectations of adaptive evolution, we show that notothenioids experienced a punctuated burst of genomic diversification and evolved key skeletal modifications before the onset of polar conditions in the Southern Ocean. We show that diversifying selection in pathways associated with human skeletal dysplasias facilitates ecologically important variation in buoyancy among Antarctic notothenioid species, and demonstrate the sufficiency of altered trip11, col1a2 and col1a1a function in zebrafish (Danio rerio) to phenocopy skeletal reduction in Antarctic notothenioids. Rather than adaptation being driven by the cooling of the Antarctic, our results highlight the role of historical contingency in shaping the adaptive radiation of notothenioids. Understanding the historical and environmental context for the origin of key traits in adaptive radiations extends beyond reconstructing events that result in evolutionary innovation, as it also provides a context in forecasting the effects of climate change on the stability and evolvability of natural populations.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Punctuated elevation in genomic diversification before ecological change and adaptive radiation.
Fig. 2: Skeletal reduction occurs before the cryonotothenioid radiation.
Fig. 3: Skeletal genes under diversifying selection uncover genetic mechanisms regulating bone density.

Similar content being viewed by others

Data availability

The sequencing data have been deposited in the NCBI database as Bioproject PRJNA531677. Assembled contig data have been deposited in the Zenodo repository (10.5281/zenodo.2628936).

Change history

  • 10 March 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

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

  2. Chan, Y. F. et al. Adaptive evolution of pelvic reduction of a Pitx1 enhancer. Science 327, 302–306 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Santos, M. E. et al. The evolution of cichlid fish egg-spots is linked with a cis-regulatory change. Nat. Commun. 5, 5149 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Rabosky, D. L. Phylogenetic tests for evolutionary innovation: the problematic link between key innovations and exceptional diversification. Phil. Trans. R. Soc. B 372, 20160417 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016).

    Article  Google Scholar 

  6. Losos, J. B. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (Univ. of California Press, 2009).

  7. Gould, S. J. Wonderful Life: The Burgess Shale and the Nature of History (W. W. Norton & Co., 1989).

  8. Gould, S. J. The Structure of Evolutionary Theory (Harvard Univ. Press, 2002).

  9. Jablonski, D. Approaches to macroevolution: 1. General concepts and origin of variation. Evol. Biol. 44, 427–450 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Near, T. J. et al. Ancient climate change, antifreeze, and the evolutionary diversification of Antarctic fishes. Proc. Natl Acad. Sci. USA 109, 3434–3439 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Dornburg, A., Federman, S., Lamb, A. D., Jones, C. D. & Near, T. J. Cradles and museums of Antarctic teleost biodiversity. Nat. Ecol. Evol. 1, 1379–1384 (2017).

    Article  PubMed  Google Scholar 

  12. Eastman, J. T. Antarctic Fish Biology: Evolution in a Unique Environment (Academic Press, Inc., 1993).

  13. DeVries, A. L. & Eastman, J. T. Lipid sacs as a buoyancy adaptation in an Antarctic fish. Nature 271, 352–353 (1978).

    Article  Google Scholar 

  14. Eastman, J. T., Witmer, L. M., Ridgely, R. C. & Kuhn, K. L. Divergence in skeletal mass and bone morphology in Antarctic notothenioid fishes. J. Morphol. 275, 841–861 (2014).

    Article  PubMed  Google Scholar 

  15. Daane, J. M., Rohner, N., Konstantinidis, P., Djuranovic, S. & Harris, M. P. Parallelism and epistasis in skeletal evolution identified through use of phylogenomic mapping strategies. Mol. Biol. Evol. 33, 162–173 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Near, T. J., Parker, S. K. & Detrich, H. W. A genomic fossil reveals key steps in hemoglobin loss by the Antarctic icefishes. Mol. Biol. Evol. 23, 2008–2016 (2006).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9, e89543 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Gistelinck, C. et al. Zebrafish type I collagen mutants faithfully recapitulate human type I collagenopathies. Proc. Natl Acad. Sci. USA 115, E8037–E8046 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Van Dijk, F. S. & Sillence, D. O. Osteogenesis imperfecta: clinical diagnosis, nomenclature and severity assessment. Am. J. Med. Genet. A 164, 1470–1481 (2014).

    Article  PubMed Central  Google Scholar 

  21. Albertson, R. C. et al. Molecular pedomorphism underlies craniofacial skeletal evolution in Antarctic notothenioid fishes. BMC Evol. Biol. 10, 4 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Witkos, T. M. & Lowe, M. The golgin family of coiled-coil tethering proteins. Front. Cell Dev. Biol. 3, 86 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Smits, P. et al. Lethal skeletal dysplasia in mice and humans lacking the golgin GMAP-210. N. Engl. J. Med. 362, 206–216 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Eastman, J. T. & McCune, A. R. Fishes on the Antarctic continental shelf: evolution of a marine species flock? J. Fish Biol. 57, 84–102 (2000).

    Google Scholar 

  25. Chen, L., DeVries, A. & Cheng, C. Evolution of antifreeze glycoprotein gene from a trypsinogen gene in Antarctic notothenioid fish. Proc. Natl Acad. Sci. USA 94, 3811–3816 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chen, Z. et al. Transcriptomic and genomic evolution under constant cold in Antarctic notothenioid fish. Proc. Natl Acad. Sci. USA 105, 12944–12949 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chown, S. L. et al. The changing form of Antarctic biodiversity. Nature 522, 431–438 (2015).

    Article  CAS  PubMed  Google Scholar 

  28. Chown, S. L. et al. Antarctica and the strategic plan for biodiversity. PLoS Biol. 15, e2001656 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Bilyk, K. T., Vargas-Chacoff, L. & Cheng, C. H. C. Evolution in chronic cold: varied loss of cellular response to heat in Antarctic notothenioid fish. BMC Evol. Biol. 18, 143 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Shin, S. C. et al. The g enome sequence of the Antarctic bullhead notothen reveals evolutionary adaptations to a cold environment. Genome Biol. 15, 468 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Tine, M. et al. European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation. Nat. Commun. 5, 5770 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Herrero, J. et al. Ensembl comparative genomics resources. Database 2016, bav096 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Kozomara, A. & Griffiths-Jones, S. MiRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39, D152–D157 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Dimitrieva, S. & Bucher, P. UCNEbase - a database of ultraconserved non-coding elements and genomic regulatory blocks. Nucleic Acids Res. 41, 101–109 (2013).

    Article  CAS  Google Scholar 

  35. 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 

  36. 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 

  37. Altschul, S., Gish, W. & Miller, W. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  38. Huang, X. CAP3: a DNA sequence assembly program. Genome Res. 9, 868–877 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sedlazeck, F. J., Rescheneder, P. & Von Haeseler, A. NextGenMap: fast and accurate read mapping in highly polymorphic genomes. Bioinformatics 29, 2790–2791 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Katoh, K., Kuma, K., Toh, H. & Miyata, T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 33, 511–518 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Robinson, J. T. et al. Integrative genome viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Henke, K. et al. Genetic screen for post-embryonic development in the zebrafish (Danio rerio): dominant mutations affecting adult form. Genetics 207, 609–623 (2017).

  44. Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Chen, K., Durand, D. & Farach-Colton, M. NOTUNG: a program for dating gene duplications and optimizing gene family trees. J. Comput. Biol. 7, 429–447 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Ranwez, V., Harispe, S., Delsuc, F. & Douzery, E. J. P. MACSE: multiple alignment of coding SEquences accounting for frameshifts and stop codons. PLoS ONE 6, e22594 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sela, I., Ashkenazy, H., Katoh, K. & Pupko, T. GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters. Nucleic Acids Res. 43, W7–W14 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Kubatko, L. S. & Degnan, J. H. Inconsistency of phylogenetic estimates from concatenated data under coalescence. Syst. Biol. 56, 17–24 (2007).

    Article  CAS  PubMed  Google Scholar 

  51. Roch, S. & Steel, M. Likelihood-based tree reconstruction on a concatenation of aligned sequence data sets can be statistically inconsistent. Theor. Popul. Biol. 100, 56–62 (2015).

    Article  Google Scholar 

  52. Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19, 15–30 (2018).

    Article  CAS  Google Scholar 

  53. Sayyari, E. & Mirarab, S. Fast coalescent-based computation of local branch support from quartet frequencies. Mol. Biol. Evol. 33, 1654–1668 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Rabosky, D. L. et al. BAMMtools: an R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).

    Article  Google Scholar 

  55. Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17, 42 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Drummond, A. J. & Suchard, M. A. Bayesian random local clocks, or one rate to rule them all. BMC Biol. 8, 114 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Near, T. J. et al. Identification of the notothenioid sister lineage illuminates the biogeographic history of an Antarctic adaptive radiation. BMC Evol. Biol. 15, 109 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Smith, M. D. et al. Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kosakovsky Pond, S. L., Frost, S. D. W. & Muse, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics 21, 676–679 (2005).

    Article  CAS  Google Scholar 

  61. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinforma. 12, 41–51 (2011).

    Article  CAS  Google Scholar 

  63. Kinsella, R. J. et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database 2011, bar030 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Köhler, S. et al. The human phenotype ontology in 2017. Nucleic Acids Res. 45, D865–D876 (2017).

    Article  PubMed  CAS  Google Scholar 

  65. Daub, J. T., Moretti, S., Davydov, I. I. & Excoffier, L. Detection of pathways affected by positive selection in primate lineages ancestral to humans. Mol. Biol. Evol. 34, 1391–1402 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Nüsslein-Volhard, C. & Dahm, R. Zebrafish: A Practical Approach (Oxford Univ. Press, 2002).

Download references

Acknowledgements

The authors thank E. Snay and L. Oberg in the Department of Nuclear Medicine and Molecular Imaging at Boston Children’s Hospital for assistance in computed tomography of adult specimens. This work was supported in part by American Heart Association Postdoctoral Fellowship (No. 17POST33660801) to J.M.D., the National Institutes of Health (NIH) (No. U01DE024434), the John Simon Guggenheim Fellowship and William F. Milton Fund awarded to M.P.H., and the National Science Foundation (NSF) (No. PLR-1444167 to H.W.D. and No. IOS-1755242 to A.D.), the Bingham Oceanographic Fund from the Peabody Museum of Natural History, and Yale University, as well as the Children’s Orthopaedic Surgery Foundation at Boston Children’s Hospital. This is contribution No. 389 from the Marine Science Center at Northeastern University.

Author information

Author notes

  1. These authors contributed equally: H. William Detrich III, Matthew P. Harris.

Authors and Affiliations

  1. Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA, USA

    Jacob M. Daane & H. William Detrich III

  2. North Carolina Museum of Natural Sciences, Raleigh, NC, USA

    Alex Dornburg

  3. Orthopaedic Research Laboratories, Department of Orthopaedic Surgery, Boston Children’s Hospital, Boston, MA, USA

    Patrick Smits, M. Brent Hawkins & Matthew P. Harris

  4. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Daniel J. MacGuigan & Thomas J. Near

  5. Museum of Comparative Zoology, Harvard University, Cambridge, MA, USA

    M. Brent Hawkins

  6. Department of Genetics, Harvard Medical School, Boston, MA, USA

    M. Brent Hawkins & Matthew P. Harris

  7. Peabody Museum of Natural History, Yale University, New Haven, CT, USA

    Thomas J. Near

Authors
  1. Jacob M. Daane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex Dornburg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick Smits
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel J. MacGuigan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Brent Hawkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas J. Near
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. H. William Detrich III
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew P. Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.M.D., H.W.D. and M.P.H. conceived and designed the study. J.M.D., P.S. and M.B.H. performed the experiments. J.M.D., A.D., T.J.N., D.J.M., H.W.D. and M.P.H. analysed the data. J.M.D., A.D. and T.J.N. wrote the first drafts of the manuscript. All authors contributed to the writing of the final manuscript.

Corresponding authors

Correspondence to Jacob M. Daane, H. William Detrich III or Matthew P. Harris.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–13 and Supplementary Tables 1–11

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Daane, J.M., Dornburg, A., Smits, P. et al. Historical contingency shapes adaptive radiation in Antarctic fishes. Nat Ecol Evol 3, 1102–1109 (2019). https://doi.org/10.1038/s41559-019-0914-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-019-0914-2

This article is cited by

Search

Advanced search

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