Rapid niche expansion by selection on functional genomic variation after ecosystem recovery

Abstract

It is well recognized that environmental degradation caused by human activities can result in dramatic losses of species and diversity. However, comparatively little is known about the ability of biodiversity to re-emerge following ecosystem recovery. Here, we show that a European whitefish subspecies, the gangfisch Coregonus lavaretus macrophthalmus, rapidly increased its ecologically functional diversity following the restoration of Lake Constance after anthropogenic eutrophication. In fewer than ten generations, gangfisch evolved a greater range of gill raker numbers (GRNs) to utilize a broader ecological niche. A sparse genetic architecture underlies this variation in GRN. Several co-expressed gene modules and genes showing signals of positive selection were associated with GRN and body shape. These were enriched for biological pathways related to trophic niche expansion in fishes. Our findings demonstrate the potential of functional diversity to expand following habitat restoration, given a fortuitous combination of genetic architecture, genetic diversity and selection.

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Fig. 1: Phenotypic and ecological diversity.
Fig. 2: Rate of change in GRN and GRN ranges.
Fig. 3: Evolutionary history of introgression.
Fig. 4: Genotype–phenotype associations and signatures of selection.
Fig. 5: Functional gene expression variation in gangfisch.

Data availability

The sequence datasets have been deposited in the National Center for Biotechnology Information Sequence Read Archive with the BioProject accession code PRJNA497182 (corresponding to BioSample accessions SAMN10250325 to SAMN10250521). Phenotype and ecological data are available at the ‘Enlighten: Research Data’ repository of the University of Glasgow: https://doi.org/10.5525/gla.researchdata.680.

References

  1. 1.

    Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).

    CAS  PubMed  Google Scholar 

  2. 2.

    Hendry, A. P., Gotanda, K. M. & Svensson, E. I. Human influences on evolution, and the ecological and societal consequences. Philos. Trans. R. Soc. B 372, 20160028 (2017).

    Google Scholar 

  3. 3.

    Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).

    PubMed  Google Scholar 

  4. 4.

    Vonlanthen, P. et al. Eutrophication causes speciation reversal in whitefish adaptive radiations. Nature 482, 357–362 (2012).

    CAS  PubMed  Google Scholar 

  5. 5.

    Taylor, E. B. et al. Speciation in reverse: morphological and genetic evidence of the collapse of a three‐spined stickleback (Gasterosteus aculeatus) species pair. Mol. Ecol. 15, 343–355 (2006).

    CAS  PubMed  Google Scholar 

  6. 6.

    Hendry, A. P., Farrugia, T. H. O. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29 (2008).

    PubMed  Google Scholar 

  7. 7.

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

    CAS  PubMed  Google Scholar 

  8. 8.

    Bullock, J. M., Aronson, J., Newton, A. C., Pywell, R. F. & Rey-Benayas, J. M. Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends Ecol. Evol. 26, 541–549 (2011).

    PubMed  Google Scholar 

  9. 9.

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

  10. 10.

    Gilman, R. T. & Behm, J. E. Hybridization, species collapse, and species reemergence after disturbance to premating mechanisms of reproductive isolation. Evolution 65, 2592–2605 (2011).

    PubMed  Google Scholar 

  11. 11.

    Yeaman, S. & Whitlock, M. C. The genetic architecture of adaptation under migration–selection balance. Evolution 65, 1897–1911 (2011).

    PubMed  Google Scholar 

  12. 12.

    Schluter, D. Evidence for ecological speciation and its alternative. Science 323, 737–741 (2009).

    CAS  PubMed  Google Scholar 

  13. 13.

    Maan, M. E. & Seehausen, O. Ecology, sexual selection and speciation. Ecol. Lett. 14, 591–602 (2011).

    PubMed  Google Scholar 

  14. 14.

    Nümann, W. Attempt of a classification of the coregonines in the Lake of Constance with regard to the so-called blaufelchen by combination of several characteristics. Arch. Hydriobiol. 82, 500–521 (1978).

    Google Scholar 

  15. 15.

    Hirsch, P. E., Eckmann, R., Oppelt, C. & Behrmann-Godel, J. Phenotypic and genetic divergence within a single whitefish form—detecting the potential for future divergence. Evol. Appl. 6, 1119–1132 (2013).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Jochimsen, M. C., Kümmerlin, R. & Straile, D. Compensatory dynamics and the stability of phytoplankton biomass during four decades of eutrophication and oligotrophication. Ecol. Lett. 16, 81–89 (2013).

    PubMed  Google Scholar 

  17. 17.

    Thomas, G., Quoss, H., Hartmann, J. & Eckmann, R. Human‐induced changes in the reproductive traits of Lake Constance common whitefish (Coregonus lavaretus). J. Evol. Biol. 22, 88–96 (2009).

    CAS  PubMed  Google Scholar 

  18. 18.

    Lundsgaard-Hansen, B., Matthews, B., Vonlanthen, P., Taverna, A. & Seehausen, O. Adaptive plasticity and genetic divergence in feeding efficiency during parallel adaptive radiation of whitefish (Coregonus spp.). J. Evol. Biol. 26, 483–498 (2013).

    CAS  PubMed  Google Scholar 

  19. 19.

    Harrod, C., Mallela, J. & Kahilainen, K. K. Phenotype–environment correlations in a putative whitefish adaptive radiation. J. Anim. Ecol. 79, 1057–1068 (2010).

    PubMed  Google Scholar 

  20. 20.

    Østbye, K., Bernatchez, L., Naesje, T. F., Himberg, K.-J. M. & Hindar, K. Evolutionary history of the European whitefish Coregonus lavaretus (L.) species complex as inferred from mtDNA phylogeography and gill-raker numbers. Mol. Ecol. 14, 4371–4387 (2005).

    PubMed  Google Scholar 

  21. 21.

    Quevedo, M., Svanbäck, R. & Eklöv, P. Intrapopulation niche partitioning in a generalist predator limits food web connectivity. Ecology 90, 2263–2274 (2009).

    PubMed  Google Scholar 

  22. 22.

    Behrmann-Godel, J. Parasite identification, succession and infection pathways in perch fry (Perca fluviatilis): new insights through a combined morphological and genetic approach. Parasitology 140, 509–520 (2013).

    CAS  PubMed  Google Scholar 

  23. 23.

    Vonlanthen, P. et al. Divergence along a steep ecological gradient in lake whitefish (Coregonus sp.). J. Evol. Biol. 22, 498–514 (2009).

    CAS  PubMed  Google Scholar 

  24. 24.

    Luczynski, M., Rösch, R., Vuorinen, J. A. & Brzuzan, P. Biochemical genetic study of sympatric Lake Constance whitefish (Coregonus lavaretus) populations: blaufelchen and gangfisch. Aquat. Sci. 57, 136–143 (1995).

    Google Scholar 

  25. 25.

    Gingerich, P. D. Quantification and comparison of evolutionary rates. Am. J. Sci. 293, 453–478 (1993).

    Google Scholar 

  26. 26.

    Hamilton, J. A. & Miller, J. M. Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conserv. Biol. 30, 33–41 (2016).

    PubMed  Google Scholar 

  27. 27.

    Shafer, A. B. A. & Wolf, J. B. W. Widespread evidence for incipient ecological speciation: a meta-analysis of isolation-by-ecology. Ecol. Lett. 16, 940–950 (2013).

    PubMed  Google Scholar 

  28. 28.

    Laporte, M. et al. RAD-QTL mapping reveals both genome-level parallelism and different genetic architecture underlying the evolution of body shape in lake whitefish (Coregonus clupeaformis) species pairs. G3 5, 1481–1491 (2015).

    PubMed  Google Scholar 

  29. 29.

    Chaves, J. A. et al. Genomic variation at the tips of the adaptive radiation of Darwin’s finches. Mol. Ecol. 25, 5282–5295 (2016).

    CAS  PubMed  Google Scholar 

  30. 30.

    Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).

    CAS  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).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Burri, R. Interpreting differentiation landscapes in the light of long‐term linked selection. Evol. Lett. 1, 118–131 (2017).

    Google Scholar 

  33. 33.

    Delmore, K. E. et al. Comparative analysis examining patterns of genomic differentiation across multiple episodes of population divergence in birds. Evol. Lett. 2, 76–87 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Gagnaire, P.-A., Normandeau, E., Pavey, S. A. & Bernatchez, L. Mapping phenotypic, expression and transmission ratio distortion QTL using RAD markers in the lake whitefish (Coregonus clupeaformis). Mol. Ecol. 22, 3036–3048 (2013).

    CAS  PubMed  Google Scholar 

  35. 35.

    Jacobs, A., Womack, R., Chen, M., Gharbi, K. & Elmer, K. Significant synteny and co-localization of ecologically relevant quantitative trait loci within and across species of salmonid fishes. Genetics 207, 741–754 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Ranz, J. M. & Machado, C. A. Uncovering evolutionary patterns of gene expression using microarrays. Trends Ecol. Evol. 21, 29–37 (2006).

    PubMed  Google Scholar 

  37. 37.

    Gibson, G. The environmental contribution to gene expression profiles. Nat. Rev. Genet. 9, 575–581 (2008).

    CAS  PubMed  Google Scholar 

  38. 38.

    Jeukens, J., Renaut, S., St-Cyr, J., Nolte, A. W. & Bernatchez, L. The transcriptomics of sympatric dwarf and normal lake whitefish (Coregonus clupeaformis spp., Salmonidae) divergence as revealed by next-generation sequencing. Mol. Ecol. 19, 5389–5403 (2010).

    PubMed  Google Scholar 

  39. 39.

    Park, P. J. & Bell, M. A. Variation of telencephalon morphology of the threespine stickleback (Gasterosteus aculeatus) in relation to inferred ecology. J. Evol. Biol. 23, 1261–1277 (2010).

    PubMed  Google Scholar 

  40. 40.

    Conejeros, P. et al. Differentiation of sympatric Arctic char morphotypes using major histocompatibility class II genes. Trans. Am. Fish. Soc. 143, 586–594 (2014).

    CAS  Google Scholar 

  41. 41.

    Jacobs, A. et al. Convergence in form and function overcomes non-parallel evolutionary histories in Arctic charr. Preprint at https://doi.org/10.1101/265272 (2018).

  42. 42.

    Ahi, E. Signalling pathways in trophic skeletal development and morphogenesis: insights from studies on teleost fish. Dev. Biol. 420, 11–31 (2016).

    CAS  PubMed  Google Scholar 

  43. 43.

    Yohannes, E., Grimm, C., Rothhaupt, K.-O. & Behrmann-Godel, J. The effect of parasite infection on stable isotope turnover rates of δ15N, δ13C and δ34S in multiple tissues of Eurasian perch Perca fluviatilis. PLoS ONE 12, e0169058 (2017).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Siwertsson, A., Knudsen, R., Adams, C. E., Præbel, K. & Amundsen, P. A. Parallel and non-parallel morphological divergence among foraging specialists in European whitefish (Coregonus lavaretus). Ecol. Evol. 3, 1590–1602 (2013).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Rohlf, F. J. TpsDig (Department of Ecology and Evolution, State University of New York, Stony Brook, 2004); http://life.bio.sunysb.edu/morph/

  46. 46.

    Klingenberg, C. P. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).

    PubMed  Google Scholar 

  47. 47.

    Klingenberg, C. P. & McIntyre, G. S. Geometric morphometrics of developmental instability: analyzing patterns of fluctuating asymmetry with Procrustes methods. Evolution 52, 1363–1375 (1998).

    PubMed  Google Scholar 

  48. 48.

    Benjamini, Y., Hochberg, Y., Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  49. 49.

    Haldane, J. B. S. Suggestions as to quantitative measurement of rates of evolution. Evolution 3, 51–56 (1949).

    CAS  PubMed  Google Scholar 

  50. 50.

    Recknagel, H., Jacobs, A., Herzyk, P. & Elmer, K. R. Double-digest RAD sequencing using Ion Proton semiconductor platform (ddRADseq-ion) with nonmodel organisms. Mol. Ecol. Resour. 15, 1316–1329 (2015).

    CAS  PubMed  Google Scholar 

  51. 51.

    Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (2010); http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  52. 52.

    Catchen, J. M. et al. Stacks: building and genotyping loci de novo from short-read sequences. G3 1, 171–182 (2011).

    CAS  PubMed  Google Scholar 

  53. 53.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 1–16 (2015).

    Google Scholar 

  55. 55.

    Lischer, H. E. L. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2012).

    CAS  PubMed  Google Scholar 

  56. 56.

    Alexander, D. H. & Novembre, J. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).

    CAS  Google Scholar 

  58. 58.

    Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Meirmans, P. G. & Tienderen, P. H. genotype and genodive: two programs for the analysis of genetic diversity of asexual organisms. Mol. Ecol. Notes 4, 792–794 (2004).

    Google Scholar 

  60. 60.

    Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    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).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Kautt, A. F., Machado-Schiaffino, G. & Meyer, A. Multispecies outcomes of sympatric speciation after admixture with the source population in two radiations of Nicaraguan crater lake cichlids. PLoS Genet. 12, e1006157 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Rougeux, C., Bernatchez, L. & Gagnaire, P.-A. A. Modeling the multiple facets of speciation-with-gene-flow toward inferring the divergence history of lake whitefish species pairs (Coregonus clupeaformis). Genome Biol. Evol. 9, 2057–2074 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Egger, B., Rösti, M., Böhne, A., Roth, O. & Salzburger, W. Demography and genome divergence of lake and stream populations of an East African cichlid fish. Mol. Ecol. 26, 5016–5030 (2017).

    CAS  PubMed  Google Scholar 

  67. 67.

    Sutherland, B. J. G. et al. Salmonid chromosome evolution as revealed by a novel method for comparing RADseq linkage maps. Genome Biol. Evol. 8, 3600–3617 (2016).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Moore, J. et al. Genomics and telemetry suggest a role for migration harshness in determining overwintering habitat choice, but not gene flow, in anadromous Arctic char. Mol. Ecol. 26, 6784–6800 (2017).

    CAS  PubMed  Google Scholar 

  69. 69.

    Lien, S. et al. The Atlantic salmon genome provides insights into rediploidization. Nature 533, 200–205 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Luu, K., Bazin, E. & Blum, M. G. pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17, 67–77 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Frichot, E., Schoville, S. D., Bouchard, G. & François, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30, 1687–1699 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Carruthers, M. et al. De novo transcriptome assembly, annotation and comparison of four ecological and evolutionary model salmonid fish species. BMC Genomics 19, 32 (2018).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Roberts, A. & Pachter, L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat. Methods 10, 71–73 (2013).

    CAS  PubMed  Google Scholar 

  79. 79.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, 17 (2005).

    Google Scholar 

  82. 82.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 1–13 (2008).

    Google Scholar 

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Acknowledgements

We thank H. Thiele, M. Schmid, W. Kornberger and A. Sulger for assistance with specimen and data collection, D. Straile for providing background data, and P. Hirsch, H. Recknagel and A. Yurchencko for comments and advice. This work was funded by a Marie Curie Action Career Integration Grant (321999) to K.R.E., a BBSRC WestBio Doctoral Training Partnership studentship to M.C., C.E.A. and K.R.E. (BB/J013854/1), ERASMUS+ (to J.B.-G. and K.R.E.), a Fisheries Society of the British Isles Research Grant (to K.R.E. and A.J.), and AFF funding from the University of Konstanz to E.Y. and J.B.-G.

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J.B.-G. and K.R.E. designed the experiment. A.J., M.C., K.R.E. and J.B.-G. collected the data. M.C. generated and analysed the eco-morphological and transcriptomic data. A.J. generated and analysed the genomic data, and analysed the eco-morphological and stable isotope data. R.E. analysed the life history data. E.Y. generated the stable isotope data. C.E.A., J.B.-G. and K.R.E. supervised the project. A.J. wrote the paper, along with M.C., J.B.-G. and K.R.E. All authors commented on and approved the final manuscript.

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Correspondence to Jasminca Behrmann-Godel or Kathryn R. Elmer.

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Jacobs, A., Carruthers, M., Eckmann, R. et al. Rapid niche expansion by selection on functional genomic variation after ecosystem recovery. Nat Ecol Evol 3, 77–86 (2019). https://doi.org/10.1038/s41559-018-0742-9

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