IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v222y2011i23p3823-3837.html
   My bibliography  Save this article

Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters

Author

Listed:
  • Clark, James R.
  • Daines, Stuart J.
  • Lenton, Timothy M.
  • Watson, Andrew J.
  • Williams, Hywel T.P.

Abstract

Recent advances in genomics have led to a dramatic upwards revision of marine microbial diversity and a greater appreciation of the important role evolutionary dynamics play in structuring microbial communities. This has presented a significant challenge to marine ecosystem models, which are traditionally diversity poor, and often do not include adaptive/evolutionary processes. Here we explore the use of evolutionary individual-based models (IBMs) as a means of addressing some of these issues. In the IBM, we associate a digital ‘genome’ with each agent, which codes for the phenotypic traits of simulated organisms. Random mutations at the point of reproduction then allow adaptation in response to changing environmental conditions. Trade-offs between different physiological parameters result in different growth strategies emerging under different forcing scenarios. As an idealised test-case we consider resource competition in a chemostat environment, and compare the individual-based approach to a more traditional population-level model. When run in a non-evolutionary context using a clonal population of agents, the IBM reproduces the results of the population-level model. With evolutionary processes enabled, optimally adapted agents are observed to rise to prominence within the agent population. Their physiological trait values are shown to compare well with theoretical optimal trait combinations derived using resource competition theory. In more variable environments, the model is also shown to capture adaptation in response to changed environmental conditions. We conclude that IBMs represent a useful framework for building detailed models linking (sub-)individual-level processes to emergent ecosystem-level behaviour in simplified 0- or 1-D representations of the environment, which should complement global marine ecosystem models of high spatial complexity but necessarily simple process representation.

Suggested Citation

  • Clark, James R. & Daines, Stuart J. & Lenton, Timothy M. & Watson, Andrew J. & Williams, Hywel T.P., 2011. "Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters," Ecological Modelling, Elsevier, vol. 222(23), pages 3823-3837.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:23:p:3823-3837
    DOI: 10.1016/j.ecolmodel.2011.10.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380011004868
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2011.10.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Richard E. Lenski & Charles Ofria & Travis C. Collier & Christoph Adami, 1999. "Genome complexity, robustness and genetic interactions in digital organisms," Nature, Nature, vol. 400(6745), pages 661-664, August.
    2. Hellweger, Ferdi L. & Bucci, Vanni, 2009. "A bunch of tiny individuals—Individual-based modeling for microbes," Ecological Modelling, Elsevier, vol. 220(1), pages 8-22.
    3. Christopher A. Klausmeier & Elena Litchman & Tanguy Daufresne & Simon A. Levin, 2004. "Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton," Nature, Nature, vol. 429(6988), pages 171-174, May.
    4. Adrian Marchetti & Micaela S. Parker & Lauren P. Moccia & Ellen O. Lin & Angele L. Arrieta & Francois Ribalet & Michael E. P. Murphy & Maria T. Maldonado & E. Virginia Armbrust, 2009. "Ferritin is used for iron storage in bloom-forming marine pennate diatoms," Nature, Nature, vol. 457(7228), pages 467-470, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Castellani, Marco & Rosland, Rune & Urtizberea, Agurtzane & Fiksen, Øyvind, 2013. "A mass-balanced pelagic ecosystem model with size-structured behaviourally adaptive zooplankton and fish," Ecological Modelling, Elsevier, vol. 251(C), pages 54-63.
    2. Hense, Inga & Beckmann, Aike, 2015. "A theoretical investigation of the diatom cell size reduction–restitution cycle," Ecological Modelling, Elsevier, vol. 317(C), pages 66-82.

    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. Wan, Zhenwen & Bi, Hongsheng, 2014. "Comparing model scenarios of variable plankton N/P ratio versus the constant one for the application in the Baltic Sea," Ecological Modelling, Elsevier, vol. 272(C), pages 28-39.
    2. Fritsch, Coralie & Harmand, Jérôme & Campillo, Fabien, 2015. "A modeling approach of the chemostat," Ecological Modelling, Elsevier, vol. 299(C), pages 1-13.
    3. Li, Yu & Waite, Anya M. & Gal, Gideon & Hipsey, Matthew R., 2013. "An analysis of the relationship between phytoplankton internal stoichiometry and water column N:P ratios in a dynamic lake environment," Ecological Modelling, Elsevier, vol. 252(C), pages 196-213.
    4. Bouderbala, Ilhem & El Saadi, Nadjia & Bah, Alassane & Auger, Pierre, 2019. "A simulation study on how the resource competition and anti-predator cooperation impact the motile-phytoplankton groups’ formation under predation stress," Ecological Modelling, Elsevier, vol. 391(C), pages 16-28.
    5. Natalie R. Cohen & Arianna I. Krinos & Riss M. Kell & Rebecca J. Chmiel & Dawn M. Moran & Matthew R. McIlvin & Paloma Z. Lopez & Alexander J. Barth & Joshua P. Stone & Brianna A. Alanis & Eric W. Chan, 2024. "Microeukaryote metabolism across the western North Atlantic Ocean revealed through autonomous underwater profiling," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    6. Anna Y. Alekseeva & Anneloes E. Groenenboom & Eddy J. Smid & Sijmen E. Schoustra, 2021. "Eco-Evolutionary Dynamics in Microbial Communities from Spontaneous Fermented Foods," IJERPH, MDPI, vol. 18(19), pages 1-19, September.
    7. Dittrich, M. & Wehrli, B. & Reichert, P., 2009. "Lake sediments during the transient eutrophication period: Reactive-transport model and identifiability study," Ecological Modelling, Elsevier, vol. 220(20), pages 2751-2769.
    8. MacPherson, Brian & Gras, Robin, 2016. "Individual-based ecological models: Adjunctive tools or experimental systems?," Ecological Modelling, Elsevier, vol. 323(C), pages 106-114.
    9. Salama, El-Sayed & Kurade, Mayur B. & Abou-Shanab, Reda A.I. & El-Dalatony, Marwa M. & Yang, Il-Seung & Min, Booki & Jeon, Byong-Hun, 2017. "Recent progress in microalgal biomass production coupled with wastewater treatment for biofuel generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1189-1211.
    10. Jiancai Deng & Fang Chen & Weiping Hu & Xin Lu & Bin Xu & David P. Hamilton, 2019. "Variations in the Distribution of Chl- a and Simulation Using a Multiple Regression Model," IJERPH, MDPI, vol. 16(22), pages 1-16, November.
    11. Gras, Anna & Ginovart, Marta & Valls, Joaquim & Baveye, Philippe C., 2011. "Individual-based modelling of carbon and nitrogen dynamics in soils: Parameterization and sensitivity analysis of microbial components," Ecological Modelling, Elsevier, vol. 222(12), pages 1998-2010.
    12. Deygout, C. & Lesne, A. & Campillo, F. & Rapaport, A., 2013. "Homogenised model linking microscopic and macroscopic dynamics of a biofilm: Application to growth in a plug flow reactor," Ecological Modelling, Elsevier, vol. 250(C), pages 15-24.
    13. Haider Ali & Dongda Zhang & Jonathan L. Wagner & Cheol Woo Park, 2018. "Two-Phase Flow Modeling of Solid Dissolution in Liquid for Nutrient Mixing Improvement in Algal Raceway Ponds," Energies, MDPI, vol. 11(4), pages 1-21, April.
    14. Liberman, Uri & Feldman, Marcus, 2008. "On the evolution of epistasis III: The haploid case with mutation," Theoretical Population Biology, Elsevier, vol. 73(2), pages 307-316.
    15. Miguel A Fortuna & Luis Zaman & Charles Ofria & Andreas Wagner, 2017. "The genotype-phenotype map of an evolving digital organism," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-20, February.
    16. Han, Yue & Zhou, Yuntao, 2022. "Investigating biophysical control of marine phytoplankton dynamics via Bayesian mechanistic modeling," Ecological Modelling, Elsevier, vol. 474(C).
    17. Daniel Graeber & Mark J. McCarthy & Tom Shatwell & Dietrich Borchardt & Erik Jeppesen & Martin Søndergaard & Torben L. Lauridsen & Thomas A. Davidson, 2024. "Consistent stoichiometric long-term relationships between nutrients and chlorophyll-a across shallow lakes," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    18. Arhonditsis, George B. & Stow, Craig A. & Paerl, Hans W. & Valdes-Weaver, Lexia M. & Steinberg, Laura J. & Reckhow, Kenneth H., 2007. "Delineation of the role of nutrient dynamics and hydrologic forcing on phytoplankton patterns along a freshwater–marine continuum," Ecological Modelling, Elsevier, vol. 208(2), pages 230-246.
    19. Tsakalakis, Ioannis & Pahlow, Markus & Oschlies, Andreas & Blasius, Bernd & Ryabov, Alexey B., 2018. "Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity," Ecological Modelling, Elsevier, vol. 384(C), pages 241-248.
    20. Flynn, Kyle F. & Chapra, Steven C. & Suplee, Michael W., 2013. "Modeling the lateral variation of bottom-attached algae in rivers," Ecological Modelling, Elsevier, vol. 267(C), pages 11-25.

    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:eee:ecomod:v:222:y:2011:i:23:p:3823-3837. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.