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The ecological and genomic basis of explosive adaptive radiation

Nature volume 586, pages 75–79 (2020)Cite this article

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Abstract

Speciation rates vary considerably among lineages, and our understanding of what drives the rapid succession of speciation events within young adaptive radiations remains incomplete1,2,3,4,5,6,7,8,9,10,11. The cichlid fish family provides a notable example of such variation, with many slowly speciating lineages as well as several exceptionally large and rapid radiations12. Here, by reconstructing a large phylogeny of all currently described cichlid species, we show that explosive speciation is solely concentrated in species flocks of several large young lakes. Increases in the speciation rate are associated with the absence of top predators; however, this does not sufficiently explain explosive speciation. Across lake radiations, we observe a positive relationship between the speciation rate and enrichment of large insertion or deletion polymorphisms. Assembly of 100 cichlid genomes within the most rapidly speciating cichlid radiation, which is found in Lake Victoria, reveals exceptional ‘genomic potential’—hundreds of ancient haplotypes bear insertion or deletion polymorphisms, many of which are associated with specific ecologies and shared with ecologically similar species from other older radiations elsewhere in Africa. Network analysis reveals fundamentally non-treelike evolution through recombining old haplotypes, and the origins of ecological guilds are concentrated early in the radiation. Our results suggest that the combination of ecological opportunity, sexual selection and exceptional genomic potential is the key to understanding explosive adaptive radiation.

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Fig. 1: Cichlid speciation rates and their predictors on macroevolutionary timescales.
Fig. 2: Large indels are associated with speciation and adaptation across cichlid radiations.
Fig. 3: Networks of IBD blocks reveal the evolutionary history of the Lake Victoria cichlid radiation.

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Data availability

Genomic data are available at NCBI BioProject (PRJNA626405) other data are available from Dryad (https://doi.org/10.5061/dryad.fn2z34tr0), including trees and networks labelled by species.

Code availability

Scripts are available from Dryad (https://doi.org/10.5061/dryad.fn2z34tr0).

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Acknowledgements

We thank R. Bireley and the Sacramento Aquarium Society, P. George and the New England Cichlid Association, M. Welss and the IgV, R. Borstein of the Greater Chicago Cichlid Association and the American Cichlid Association for advice and access to specimens for photography in Fig. 1, K. Pedersen and J. Øgaard for Uganda fieldwork assistance along with NaFiRRI staff, including J. Balirwa, as well as cooperation from Kyoga region village elders, TAFIRI for hosting O.S. and team members during fieldwork, M. Kayeba, M. Haluna and H. Mrosso for assistance with Tanzanian fieldwork and K. Wagner, J. van Rijssel, F. Moser, O. Selz and L. Kaufman for discussions. This research was funded by Swiss National Science Foundation grant 31003A_163338 to O.S., L.E. and R.B., an American Cichlid Association Guy Jordan Fellowship to S.R.B. and Australian Research Council DE180101558 to M.D.M.

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Author notes

  1. These authors contributed equally: Matthew D. McGee, Samuel R. Borstein.

Authors and Affiliations

  1. Aquatic Ecology and Evolution, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland

    Matthew D. McGee, Joana I. Meier, David A. Marques & Ole Seehausen

  2. Department of Fish Ecology and Evolution, Centre for Ecology, Evolution, and Biogeochemistry, Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Kastanienbaum, Switzerland

    Matthew D. McGee, Joana I. Meier, David A. Marques, Salome Mwaiko & Ole Seehausen

  3. School of Biological Sciences, Monash University, Melbourne, Victoria, Australia

    Matthew D. McGee

  4. Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA

    Samuel R. Borstein

  5. Department of Zoology, University of Cambridge, Cambridge, UK

    Joana I. Meier

  6. National Fisheries Resources Research Institute, Jinja, Uganda

    Anthony Taabu

  7. Tanzania Fisheries Research Institute, Dar es Salaam, Tanzania

    Mary A. Kishe

  8. Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA

    Brian O’Meara

  9. Interfaculty Bioinformatics Unit, University of Bern, Bern, Switzerland

    Rémy Bruggmann

  10. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Rémy Bruggmann & Laurent Excoffier

  11. Computational and Molecular Population Genetics (CMPG), Institute of Ecology and Evolution, University of Bern, Bern, Switzerland

    Laurent Excoffier

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Contributions

M.D.M. and S.R.B. designed the phylogenetic analyses with assistance from O.S. and B.O’M. Trait data were gathered by S.R.B. and O.S. with assistance from M.D.M. GIS data were gathered by M.D.M. Genomic data was gathered by S.M., J.I.M. and M.D.M. M.D.M. and O.S. designed the genomic analyses with assistance from J.I.M. and advice from D.A.M., L.E. and R.B. M.D.M. performed fieldwork in Uganda with assistance from A.T. and coordination by O.S. O.S. and S.M. performed fieldwork in Tanzania with assistance from M.A.K. M.D.M. and O.S. wrote the manuscript with assistance from S.R.B., D.A.M. and J.I.M., with comments from and final version approval by all authors.

Corresponding authors

Correspondence to Matthew D. McGee or Ole Seehausen.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 D-statistics across the Victoria radiation.

a, Multispecies coalescent tree generated by ASTRAL for Lake Victoria cichlids (n = 100 species). Species diets and habitats are coded by colour and shape, respectively. b, The same arrangement of taxa as the coalescent tree, but highlighting the highest D-statistic associated with each genome. c, The 95% credible intervals of the ecology and sexual selection-based predictors of high introgression (Bonferroni-corrected P < 0.05 jackknife of the D-statistic) are shown, taking into account the non-independence of comparisons between the same genomes. Effect sizes (odds ratio = eeffect size) indicate whether introgression is more likely when the ingroup genomes (A + B) have a similar diet, habitat or male nuptial coloration, or whether introgression is more likely when the potentially introgressing genomes (B + C) do. Photographs were produced by the authors (O.S. and M.D.M.).

Supplementary information

Supplementary Information

This file contains Supplementary Methods and additional discussion, including additional information on phylogeny construction, comparative models, STAN output, genotyping, indels, and IBD models. It includes 10 Supplementary Tables and 3 Supplementary Figs.

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McGee, M.D., Borstein, S.R., Meier, J.I. et al. The ecological and genomic basis of explosive adaptive radiation. Nature 586, 75–79 (2020). https://doi.org/10.1038/s41586-020-2652-7

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