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.

Personal and transgenerational cues are nonadditive at the phenotypic and molecular level

Abstract

Organisms can gain information about their environment from their ancestors, their parents or their own personal experience. ‘Cue integration’ models often start with the simplifying assumption that information from different sources is additive. Here, we test key assumptions and predictions of cue integration theory at both the phenotypic and molecular level in threespined sticklebacks (Gasterosteus aculeatus). We show that regardless of whether cues about predation risk were provided by their father or acquired through personal experience, sticklebacks produced the same set of predator-adapted phenotypes. Moreover, there were nonadditive effects of personal and paternal experience: animals that received cues from both sources resembled animals that received cues from a single source. A similar pattern was detected at the molecular level: there was a core set of genes that were differentially expressed in the brains of offspring regardless of whether risk was experienced by their father, themselves or both. These results provide strong support for cue integration theory because they show that cues provided by parents and personal experience are comparable at both the phenotypic and molecular level, and draw attention to the importance of nonadditive responses to multiple cues.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Experimental design.
Fig. 2: The effect of personal and paternal experience with predation risk on offspring phenotypes was nonadditive.
Fig. 3: Brain gene expression responses to personal experience with risk and paternal experience with risk.

References

  1. 1.

    Dall, S. R., McNamara, J. M. & Leimar, O. Genes as cues: phenotypic integration of genetic and epigenetic information from a Darwinian perspective. Trends Ecol. Evol. 30, 327–333 (2015).

    Article  Google Scholar 

  2. 2.

    Leimar, O. The evolution of phenotypic polymorphism: randomized strategies versus evolutionary branching. Am. Nat. 165, 669–681 (2005).

    Article  Google Scholar 

  3. 3.

    Leimar, O. & McNamara, J. M. The evolution of transgenerational integration of information in heterogeneous environments. Am. Nat. 185, E55–E69 (2015).

    Article  Google Scholar 

  4. 4.

    Stamps, J. A. & Frankenhuis, W. E. Bayesian models of development. Trends Ecol. Evol. 31, 260–268 (2016).

    Article  Google Scholar 

  5. 5.

    Stamps, J. A. & Krishnan, V. V. Combining information from ancestors and personal experiences to predict individual differences in developmental trajectories. Am. Nat. 184, 647–657 (2014).

    Article  Google Scholar 

  6. 6.

    English, S., Pen, I., Shea, N. & Uller, T. The information value of non-genetic inheritance in plants and animals. PLoS ONE 10, e0116996 (2015).

    Article  Google Scholar 

  7. 7.

    McNamara, J. M., Dall, S. R. X., Hammerstein, P., Leimar, O. & Coulson, T. Detection vs selection: integration of genetic, epigenetic and evnironmental cues in fluctuating environments. Ecol. Lett. 19, 1267–1276 (2016).

    Article  Google Scholar 

  8. 8.

    Rivoire, O. & Leibler, S. A model for the generation and transmission of variations in evolution. Proc. Natl Acad. Sci. USA 111, E1940–E1949 (2014).

    CAS  Article  Google Scholar 

  9. 9.

    West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford Univ. Press, Oxford, 2003).

  10. 10.

    Frankenhuis, W. E. & Panchanathan, K. Balancing sampling and specialization: an adaptationist model of incremental development. Proc. R. Soc. B 278, 3558–3565 (2011).

    Article  Google Scholar 

  11. 11.

    McCollum, S. A. & Van Buskirk, J. Costs and benefits of predator-induced polyphenism in the gray treefrog Hyla chrysoscelis. Evolution 50, 583–593 (1996).

    Article  Google Scholar 

  12. 12.

    Buoro, M., Gimenez, G. & Prévost, E. Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the latent environmental threshold model. Evolution 66, 996–1009 (2012).

    Article  Google Scholar 

  13. 13.

    Sih, A. Prey uncertainty and the balancing of antipredator and feeding needs. Am. Nat. 139, 1052–1069 (1992).

    Article  Google Scholar 

  14. 14.

    Nesse, R. M. The smoke detector principle. Natural selection and the regulation of defensive responses. Ann. NY Acad. Sci. 935, 75–85 (2001).

    CAS  Article  Google Scholar 

  15. 15.

    Tulley, J. J. & Huntingford, F. A. Paternal care and the development of adaptive variation in anti-predator responses in sticklebacks. Anim. Behav. 35, 1570–1572 (1987).

    Article  Google Scholar 

  16. 16.

    McGhee, K. E. & Bell, A. M. Paternal care in a fish: epigenetics and fitness enhancing effects on offspring anxiety. Proc. R. Soc. B 281, 20141146 (2014).

    Article  Google Scholar 

  17. 17.

    Stein, L. R. & Bell, A. M. Paternal programming in sticklebacks. Anim. Behav. 95, 165–171 (2014).

    Article  Google Scholar 

  18. 18.

    Stein, L. R. & Bell, A. M. Consistent individual differences in paternal behavior in threespined sticklebacks. Curr. Zool. 58, 45–52 (2012).

    Article  Google Scholar 

  19. 19.

    Pressley, P. H. Parental effort and the evolution of nest-guarding tactics in the threespined stickleback, Gasterosteus aculeatus. Evolution 35, 282–295 (1981).

    Article  Google Scholar 

  20. 20.

    Stein, L. R. & Bell, A. M. Consistent individual differences in paternal behavior: a field study of three-spined stickleback. Behav. Ecol. Sociobiol. 69, 227–236 (2015).

    Article  Google Scholar 

  21. 21.

    Meaney, M. J. Maternal care, gene expression, and the transmission of individual differences in stress reactivity across generations. Annu. Rev. Neurosci. 24, 1161–1192 (2001).

    CAS  Article  Google Scholar 

  22. 22.

    Endler, J. A. Multiple-trait coevolution and environmental gradients in guppies. Trends Ecol. Evol. 10, 22–29 (1995).

    CAS  Article  Google Scholar 

  23. 23.

    Vervust, B., Grbac, I. & Van Dame, R. Differences in morphology, performance and behaviour between recently diverged populations of Podarcis sicula mirror differences in predation pressure. Oikos 116, 1343–1352 (2007).

    Article  Google Scholar 

  24. 24.

    Bell, A. M., Dingemanse, N. J., Hankison, S. J., Langenhof, M. B. W. & Rollins, K. Early exposure to nonlethal predation risk by size-selective predators increases somatic growth and decreases size at adulthood in threespined sticklebacks. J. Evol. Biol. 24, 943–953 (2011).

    CAS  Article  Google Scholar 

  25. 25.

    Frommen, J. G. et al. Costly plastic morphological responses to predator specific odour cues in three-spined sticklebacks (Gasterosteus aculeatus). Evol. Ecol. 25, 641–656 (2011).

    Article  Google Scholar 

  26. 26.

    Reimchen, T. E. in The Evolutionary Biology of the Threespined Stickleback (eds Bell, M. A. & Foster, S. A.) 240–273 (Oxford Univ. Press, Oxford, 1994).

  27. 27.

    Donelan, S. C. & Trussell, G. C. Synergistic effects of parental and embryonic exposure to predation risk on prey offspring size at emergence. Ecology 99, 68–78 (2018).

    Article  Google Scholar 

  28. 28.

    Harney, E., Paterson, S. & Plaistow, S. J. Offspring development and life-history variation in a water flea depends upon clone-specific integration of genetic, non-genetic and environmental cues. Funct. Ecol. 31, 1996–2007 (2017).

    Article  Google Scholar 

  29. 29.

    Beaty, L. E. et al. Shaped by the past, acting in the present: transgenerational plasticity of anti-predatory traits. Oikos 125, 1570–1576 (2016).

    Article  Google Scholar 

  30. 30.

    Luquet, E. & Tariel, J. Offspring reaction norms shaped by parental environment: interaction between within- and trans-generational plasticity of inducible defences. BMC Evol. Biol. 16, 209 (2016).

    Article  Google Scholar 

  31. 31.

    Seiter, M. & Schausberger, P. Maternal intraguild predation risk affects offspring anti-predator behavior and learning in mites. Sci. Rep. 5, 15046 (2015).

    CAS  Article  Google Scholar 

  32. 32.

    Stratmann, A. & Taborsky, B. Antipredator defences of young are independently determined by genetic inheritance, maternal effects and own early experience in mouthbrooding cichlids. Funct. Ecol. 28, 944–953 (2014).

    Article  Google Scholar 

  33. 33.

    Sultan, S. E., Barton, K. & Wilcek, A. M. Contrasting patterns of transgenerational plasticity in ecologically distinct congeners. Ecology 90, 1831–1839 (2009).

    Article  Google Scholar 

  34. 34.

    Agrawal, A. A., Laforsch, C. & Tollrian, R. Transgenerational induction of defences in animals and plants. Nature 401, 60–63 (1999).

    CAS  Article  Google Scholar 

  35. 35.

    Hales, N. R. et al. Contrasting gene expression programs correspond with predator-induced phenotypic plasticity within and across generations in Daphnia. Mol. Ecol. 26, 5003–5015 (2017).

    CAS  Article  Google Scholar 

  36. 36.

    Metzger, D. C. H. & Schulte, P. M. Maternal stress has divergent effects on gene expression patterns in the brains of male and female threespine stickleback. Proc. R. Soc. B 283, 20161734 (2016).

  37. 37.

    Swarup, H. States in the development of the stickleback Gasterosteus aculeatus. J. Embryol. Exp. Morphol. 6, 373–383 (1958).

    CAS  PubMed  Google Scholar 

  38. 38.

    Wootton, R. J. A Functional Biology of Sticklebacks (Univ. California Press, Berkeley, 1984).

  39. 39.

    Peichel, C. et al. The master sex-determination locus in threespined sticklebacks is on a nascent Y chromosome. Curr. Biol. 14, 1416–1424 (2004).

    CAS  Article  Google Scholar 

  40. 40.

    R v. 3.2.2 (R Core Team, Vienna, 2016).

  41. 41.

    Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  42. 42.

    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest: Tests for Random and Fixed Effects for Linear Mixed Effect Models R Package v. 2.0-6 (2014).

  43. 43.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  Google Scholar 

  44. 44.

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

    CAS  Article  Google Scholar 

  45. 45.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq: a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  46. 46.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to E. Murdoch for help with RNA extraction and building libraries for RNA-seq, and M. Bensky for the stickleback and sculpin illustrations. We also thank A. Sih, J. Stamps, G. Robinson, K. Hoke and S. English for valuable comments on the manuscript. This material is based upon work supported by the National Science Foundation under grant no. IOS 1210696 and a National Science Foundation Graduate Research Fellowship to L.R.S. Research reported in this publication was supported by National Institute of General Medical Sciences of the National Institutes of Health under award no. R01 GM082937. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Affiliations

Authors

Contributions

L.R.S. and A.M.B. conceived and designed the project. L.R.S. carried out the experiment and performed data collection. L.R.S., S.A.B. and A.M.B. analysed the data. L.R.S., S.A.B. and A.M.B. wrote the manuscript.

Corresponding author

Correspondence to Laura R. Stein.

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 Table 1; Supplementary Figures 1–3; Supplementary Reference

Reporting Summary

Supplementary Table 2

Alignment and percent reads mapped to stickleback genome

Supplementary Table 3

Differentially expressed gene lists for paternal experience, personal experience, and both cue treatments

Supplementary Table 4

GO and functional enrichments for all treatments

Supplementary Table 5

Phenotypic data of offspring

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Stein, L.R., Bukhari, S.A. & Bell, A.M. Personal and transgenerational cues are nonadditive at the phenotypic and molecular level. Nat Ecol Evol 2, 1306–1311 (2018). https://doi.org/10.1038/s41559-018-0605-4

Download citation

Further reading

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