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Rapid sex-specific evolution of age at maturity is shaped by genetic architecture in Atlantic salmon

Nature Ecology & Evolution volume 2, pages 1800–1807 (2018)Cite this article

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Abstract

Understanding the mechanisms by which populations adapt to their environments is a fundamental aim in biology. However, it remains challenging to identify the genetic basis of traits, provide evidence of genetic changes and quantify phenotypic responses. Age at maturity in Atlantic salmon represents an ideal trait to study contemporary adaptive evolution as it has been associated with a single locus in the vgll3 region and has also strongly changed in recent decades. Here, we provide an empirical example of contemporary adaptive evolution of a large-effect locus driving contrasting sex-specific evolutionary responses at the phenotypic level. We identified an 18% decrease in the vgll3 allele associated with late maturity in a large and diverse salmon population over 36 years, induced by sex-specific selection during sea migration. Those genetic changes resulted in a significant evolutionary response only in males, due to sex-specific dominance patterns and vgll3 allelic effects. The vgll3 allelic and dominance effects differed greatly in a second population and were likely to generate different selection and evolutionary patterns. Our study highlights the importance of knowledge of genetic architecture to better understand fitness trait evolution and phenotypic diversity. It also emphasizes the potential role of adaptive evolution in the trend towards earlier maturation observed in numerous Atlantic salmon populations worldwide.

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Fig. 1: Change in mean age at maturity.
Fig. 2: Mean age at maturity as a function of the vgll3 genotype.
Fig. 3: Temporal changes in vgll3*L allele frequency associated with late maturation.
Fig. 4: Model-predicted mean vgll3*L allele frequency as a function of the sex and reproductive status in Tenojoki and Inarijoki populations.

Data availability

The data supporting the findings of this study are available in the Dryad Digital Repository with the identifier doi:10.5061/dryad.7hm4708.

References

  1. Losos, J. B. Ecological character displacement and the study of adaptation. Proc. Natl Acad. Sci. USA 97, 5693–5695 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Andrew, R. L. et al. A road map for molecular ecology. Mol. Ecol. 22, 2605–2626 (2013).

    Article  PubMed  Google Scholar 

  3. Sharpe, D. M. T. & Hendry, A. P. Life history change in commercially exploited fish stocks: an analysis of trends across studies. Evol. Appl. 2, 260–275 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: is there any evidence? Evol. Appl. 7, 156–168 (2014).

    Article  PubMed  Google Scholar 

  5. 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  PubMed  Google Scholar 

  6. Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Merilä, J. & Hoffmann, A. A. in Oxford Research Encyclopedia of Environmental Science (ed. Shugart, H.) https://doi.org/10.1093/acrefore/9780199389414.013.136 (Oxford Univ. Press, New York, 2016).

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

    Article  CAS  PubMed  Google Scholar 

  9. Crnokrak, P. & Roff, D. A. Dominance variance: associations with selection and fitness. Heredity 75, 530–540 (1995).

    Article  Google Scholar 

  10. Barson, N. J. et al. Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon. Nature 528, 405–408 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Liang, Y. et al. A gene network regulated by the transcription factor VGLL3 as a promoter of sex-biased autoimmune diseases. Nat. Immunol. 18, 152–160 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Fleming, I. A. Reproductive strategies of Atlantic salmon: ecology and evolution. Rev. Fish Biol. Fish. 6, 349–416 (1996).

    Article  Google Scholar 

  13. Mank, J. E. Population genetics of sexual conflict in the genomic era. Nat. Rev. Genet. 18, 721–730 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Chaput, G. Overview of the status of Atlantic salmon (Salmo salar) in the North Atlantic and trends in marine mortality. ICES J. Mar. Sci. 69, 1538–1548 (2012).

    Article  Google Scholar 

  15. Erkinaro, J. et al. Life history variation across four decades in a diverse population complex of Atlantic salmon in a large subarctic river. Can. J. Fish. Aquatic Sci. https://doi.org/10.1139/cjfas-2017-0343 (2018).

  16. Otero, J. et al. Contemporary ocean warming and freshwater conditions are related to later sea age at maturity in Atlantic salmon spawning in Norwegian rivers. Ecol. Evol. 2, 2192–2203 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Crozier, L. G. & Hutchings, J. A. Plastic and evolutionary responses to climate change in fish. Evol. Appl. 7, 68–87 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Vähä, J.-P., Erkinaro, J., Niemelä, E. & Primmer, C. R. Temporally stable genetic structure and low migration in an Atlantic salmon population complex: implications for conservation and management. Evol. Appl. 1, 137–154 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Heinimaa, S. & Heinimaa, P. Effect of the female size on egg quality and fecundity of the wild Atlantic salmon in the sub-Arctic River Teno. Boreal Environ. Res. 9, 55–62 (2004).

    Google Scholar 

  20. Jonsson, B., Jonsson, N. & Albretsen, J. Environmental change influences the life history of salmon Salmo salar in the North Atlantic Ocean. J. Fish Biol. 88, 618–637 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Ohlberger, J., Ward, E. J., Schindler, D. E. & Lewis, B. Demographic changes in Chinook salmon across the Northeast Pacific Ocean. Fish Fish. 19, 533–546 (2018).

    Article  Google Scholar 

  22. Friedland, K. D. et al. The recruitment of Atlantic salmon in Europe. ICES J. Mar. Sci. 66, 289–304 (2009).

    Article  Google Scholar 

  23. Frainer, A. et al. Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc. Natl Acad. Sci. USA 114, 12202–12207 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kortsch, S. et al. Climate change alters the structure of Arctic marine food webs due to poleward shifts of boreal generalists. Proc. R. Soc. B 282, 20151546 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jensen, A. J. et al. Cessation of the Norwegian drift net fishery: changes observed in Norwegian and Russian populations of Atlantic salmon. ICES J. Mar. Sci. 56, 84–95 (1999).

    Article  Google Scholar 

  26. Kuparinen, A. & Hutchings, J. A. Genetic architecture of age at maturity can generate either directional or divergent and disruptive harvest-induced evolution. Phil. Trans. R. Soc. B 372, 20160035 (2016).

    Article  Google Scholar 

  27. Hjermann, D. Ø., Ottersen, G. & Stenseth, N. C. Competition among fishermen and fish causes the collapse of Barents Sea capelin. Proc. Natl Acad. Sci. USA 101, 11679–11684 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).

    Article  Google Scholar 

  29. Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–612 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Vähä, J.-P., Erkinaro, J., Niemelä, E. & Primmer, C. R. Life-history and habitat features influence the within-river genetic structure of Atlantic salmon. Mol. Ecol. 16, 2638–2654 (2007).

    Article  PubMed  Google Scholar 

  31. Vähä, J.-P., Erkinaro, J., Falkegård, M., Orell, P. & Niemelä, E. Genetic stock identification of Atlantic salmon and its evaluation in a large population complex. Can. J. Fish. Aquat. Sci. 74, 327–338 (2016).

    Article  Google Scholar 

  32. Report of the Working Group on North Atlantic Salmon (WGNAS) (International Council for the Exploration of the Sea, 2013).

  33. Pritchard, V. L. et al. Genomic signatures of fine-scale local selection in Atlantic salmon suggest involvement of sexual maturation, energy homeostasis, and immune defence-related genes. Mol. Ecol. 27, 2560–2575 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Report of the Workshop on Age Determination of Salmon (WKADS) (International Council for the Exploration of the Sea, 2011).

  35. Niemelä, E. et al. Temporal variation in abundance, return rate and life histories of previously spawned Atlantic salmon in a large subarctic river. J. Fish Biol. 68, 1222–1240 (2006).

    Article  Google Scholar 

  36. Niemelä, E. et al. Previously spawned Atlantic salmon ascend a large subarctic river earlier than their maiden counterparts. J. Fish Biol. 69, 1151–1163 (2006).

    Article  Google Scholar 

  37. Aykanat, T., Pritchard, V. L., Lindqvist, M. & Primmer, C. R. From population genomics to conservation and management: a workflow for targeted analysis of markers identified using genome-wide approaches in Atlantic salmon. J. Fish Biol. 89, 2658–2679 (2016).

    Article  CAS  PubMed  Google Scholar 

  38. Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).

    Article  CAS  PubMed  Google Scholar 

  40. Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).

    Article  Google Scholar 

  41. Jakobsson, M. & Rosenberg, N. A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Goudet, J., Raymond, M., De Meeüs, T. & Rousset, F. Testing differentiation in diploid populations. Genetics 144, 1933–1940 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).

    Article  Google Scholar 

  44. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  45. Venables, W. N. & Ripley, B. D. Modern Applied Statistics With S (Springer, New York, 2002).

  46. Fox, J. Effect displays in R for generalised linear models. J. Stat. Softw. 8, 1–27 (2003).

    Article  Google Scholar 

  47. Tataru, P., Simonsen, M., Bataillon, T. & Hobolth, A. Statistical inference in the Wright–Fisher model using allele frequency data. Syst. Biol. 66, e30–e46 (2017).

    PubMed  Google Scholar 

  48. Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon, Oxford, 1930).

  50. Brooks, S. P. B. & Gelman, A. G. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).

    Google Scholar 

  51. Lenth, R. V. Least-squares means: the R package lsmeans.J. Stat. Softw. 69, 1–33 (2016).

    Article  Google Scholar 

  52. Gompert, Z. Bayesian inference of selection in a heterogeneous environment from genetic time-series data. Mol. Ecol. 25, 121–134 (2016).

    Article  PubMed  Google Scholar 

  53. Foll, M., Shim, H. & Jensen, J. D. WFABC: a Wright–Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data. Mol. Ecol. Resour. 15, 87–98 (2015).

    Article  PubMed  Google Scholar 

  54. Waples, R. S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv. Genet. 7, 167–184 (2006).

    Article  Google Scholar 

  55. Do, C. et al. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Waples, R. S. & Yokota, M. Temporal estimates of effective population size in species with overlapping generations. Genetics 175, 219–233 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Belgorodski, N., Greiner, M., Tolksdorf, K. & Schueller, K. rriskDistributions: Fitting Distributions to Given Data or Known Quantiles R package version 2.0 (R Foundation for Statistical Computing, 2017).

  58. Allendorf, F. W. & Luikart, G. Conservation and the Genetics of Populations (Blackwell, Oxford, 2007).

  59. Plummer, M. JAGS Version 4.3.0 User Manual (2017); http://www.stat.yale.edu/~jtc5/238/materials/jags_4.3.0_manual_with_distributions.pdf

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Acknowledgements

We thank numerous fishers who participated in the collection of scales and phenotypic information over the 40-year study period, E. Niemelä for starting the programme and looking after contacts with fishers, J. Kuusela for organizing the collection of samples from the archive, and several scale readers—especially J. Haantie. This project received funding from the Academy of Finland (projects numbers 284941, 286334, 307593, 302873 and 318939) as well as the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 742312) and the University of Helsinki.

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Authors and Affiliations

  1. Department of Biology, University of Turku, Turku, Finland

    Yann Czorlich

  2. Natural Resources Institute Finland (Luke), Oulu, Finland

    Yann Czorlich, Jaakko Erkinaro & Panu Orell

  3. Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland

    Tutku Aykanat & Craig Robert Primmer

  4. Institute of Biotechnology, University of Helsinki, Helsinki, Finland

    Craig Robert Primmer

  5. Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, Finland

    Craig Robert Primmer

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  1. Yann Czorlich
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Contributions

J.E. and P.O. coordinated the collection of samples. C.R.P., Y.C., T.A. and J.E. designed the study. Y.C. analysed the data. Y.C., C.R.P. and T.A. wrote the manuscript. All authors contributed to revision of the manuscript.

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Correspondence to Craig Robert Primmer.

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Czorlich, Y., Aykanat, T., Erkinaro, J. et al. Rapid sex-specific evolution of age at maturity is shaped by genetic architecture in Atlantic salmon. Nat Ecol Evol 2, 1800–1807 (2018). https://doi.org/10.1038/s41559-018-0681-5

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