IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003550.html
   My bibliography  Save this article

The Evolution of Multivariate Maternal Effects

Author

Listed:
  • Bram Kuijper
  • Rufus A Johnstone
  • Stuart Townley

Abstract

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.Author Summary: In numerous organisms, mothers influence the phenotype of their offspring by transmitting hormones, antibodies and nutrients to the embryo. Evolutionary studies that make predictions about the evolution of these maternal effects typically focus, however, on single maternal characters only, in isolation of other traits. This contrasts with insights from quantitative genetics where reliable predictions about evolutionary change can only be made when measuring multiple traits simultaneously. The current study is therefore the first to make formal predictions about the evolutionary properties of multiple maternal effects. We show that maternal phenotypic characters generally give rise to developmental interactions in which one maternal character affects multiple offspring characters. In turn, such interactions can give rise to correlations between different traits in parent and offspring, which constrain evolutionary responses to sudden change. In addition, we find that the rate of environmental change directly affects some of the measurable properties of maternal effects: in rapidly changing environments, multivariate maternal effects are negative, so that offspring attain phenotypes that are different from their mothers, whereas positive maternal effects where offspring are more similar to their mothers occur in slowly changing environments. Hence, multivariate maternal effects provide a clear signature of the past selective environment experienced by organisms.

Suggested Citation

  • Bram Kuijper & Rufus A Johnstone & Stuart Townley, 2014. "The Evolution of Multivariate Maternal Effects," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-11, April.
  • Handle: RePEc:plo:pcbi00:1003550
    DOI: 10.1371/journal.pcbi.1003550
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003550
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003550&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003550?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Anurag A. Agrawal & Christian Laforsch & Ralph Tollrian, 1999. "Transgenerational induction of defences in animals and plants," Nature, Nature, vol. 401(6748), pages 60-63, September.
    2. Luis-Miguel Chevin & Russell Lande & Georgina M Mace, 2010. "Adaptation, Plasticity, and Extinction in a Changing Environment: Towards a Predictive Theory," PLOS Biology, Public Library of Science, vol. 8(4), pages 1-8, April.
    3. Luis-Miguel Chevin & Russell Lande & Georgina M Mace, 2010. "Adaptation, Plasticity, and Extinction in a Changing Environment: Towards a Predictive Theory," Working Papers id:2494, eSocialSciences.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wesley R. Brooks & Stephen C. Newbold, 2013. "Ecosystem damages in integrated assessment models of climate change," NCEE Working Paper Series 201302, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2013.
    2. Bruno R Ribeiro & Lilian P Sales & Paulo De Marco Jr. & Rafael Loyola, 2016. "Assessing Mammal Exposure to Climate Change in the Brazilian Amazon," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-13, November.
    3. Robert J. Knell & Stephen J. Thackeray, 2016. "Voltinism and resilience to climate-induced phenological mismatch," Climatic Change, Springer, vol. 137(3), pages 525-539, August.
    4. Ayllón, Daniel & Railsback, Steven F. & Vincenzi, Simone & Groeneveld, Jürgen & Almodóvar, Ana & Grimm, Volker, 2016. "InSTREAM-Gen: Modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change," Ecological Modelling, Elsevier, vol. 326(C), pages 36-53.
    5. Freitas, Osmar & Araujo, Sabrina B.L. & Campos, Paulo R.A., 2022. "Speciation in a metapopulation model upon environmental changes," Ecological Modelling, Elsevier, vol. 468(C).
    6. Harry R Harding & Timothy A C Gordon & Emma Eastcott & Stephen D Simpson & Andrew N Radford & Leigh Simmons, 2019. "Causes and consequences of intraspecific variation in animal responses to anthropogenic noise," Behavioral Ecology, International Society for Behavioral Ecology, vol. 30(6), pages 1501-1511.
    7. Greenspoon, Philip B. & Mideo, Nicole, 2017. "Evolutionary rescue of a parasite population by mutation rate evolution," Theoretical Population Biology, Elsevier, vol. 117(C), pages 64-75.
    8. Maldonado-Chaparro, Adriana A. & Read, Dwight W. & Blumstein, Daniel T., 2017. "Can individual variation in phenotypic plasticity enhance population viability?," Ecological Modelling, Elsevier, vol. 352(C), pages 19-30.
    9. Brooks, Wesley R. & Newbold, Stephen C., 2014. "An updated biodiversity nonuse value function for use in climate change integrated assessment models," Ecological Economics, Elsevier, vol. 105(C), pages 342-349.
    10. Michael J. Noonan & Chris Newman & Andrew Markham & Kirstin Bilham & Christina D. Buesching & David W. Macdonald, 2018. "In situ behavioral plasticity as compensation for weather variability: implications for future climate change," Climatic Change, Springer, vol. 149(3), pages 457-471, August.
    11. Anderson, James J. & Gurarie, Eliezer & Bracis, Chloe & Burke, Brian J. & Laidre, Kristin L., 2013. "Modeling climate change impacts on phenology and population dynamics of migratory marine species," Ecological Modelling, Elsevier, vol. 264(C), pages 83-97.
    12. Konstantinos Kougioumoutzis & Ioannis P. Kokkoris & Arne Strid & Thomas Raus & Panayotis Dimopoulos, 2021. "Climate-Change Impacts on the Southernmost Mediterranean Arctic-Alpine Plant Populations," Sustainability, MDPI, vol. 13(24), pages 1-23, December.
    13. Marie Rescan & Daphné Grulois & Enrique Ortega Aboud & Pierre de Villemereuil & Luis-Miguel Chevin, 2021. "Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment," PLOS Genetics, Public Library of Science, vol. 17(6), pages 1-23, June.
    14. Matt J. Michel & Huicheng Chien & Collin E. Beachum & Micah G. Bennett & Jason H. Knouft, 2017. "Climate change, hydrology, and fish morphology: predictions using phenotype-environment associations," Climatic Change, Springer, vol. 140(3), pages 563-576, February.
    15. Yahuza Lurwanu & Yan-Ping Wang & Waheed Abdul & Jiasui Zhan & Li-Na Yang, 2020. "Temperature-Mediated Plasticity Regulates the Adaptation of Phytophthora infestans to Azoxystrobin Fungicide," Sustainability, MDPI, vol. 12(3), pages 1-15, February.
    16. Davison, Raziel & Stadman, Marc & Jongejans, Eelke, 2019. "Stochastic effects contribute to population fitness differences," Ecological Modelling, Elsevier, vol. 408(C), pages 1-1.
    17. Reid S. Brennan & James A. deMayo & Hans G. Dam & Michael B. Finiguerra & Hannes Baumann & Melissa H. Pespeni, 2022. "Loss of transcriptional plasticity but sustained adaptive capacity after adaptation to global change conditions in a marine copepod," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    18. Karen B Strier & Anthony R Ives, 2012. "Unexpected Demography in the Recovery of an Endangered Primate Population," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-11, September.
    19. Minke B. W. Langenhof & Jan Komdeur, 2013. "Coping with Change: A Closer Look at the Underlying Attributes of Change and the Individual Response to Unstable Environments," Sustainability, MDPI, vol. 5(5), pages 1-25, April.
    20. Fatih Fazlioglu & Justin S. H. Wan, 2021. "Warming matters: alpine plant responses to experimental warming," Climatic Change, Springer, vol. 164(3), pages 1-17, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003550. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.