IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v520y2026ics0304380026002000.html

CoralPOP: A morphology-based coral population model for transferable reef management projections

Author

Listed:
  • Browne, Nicola K.
  • Tadesse, Dagmawi
  • Ovsyanikova, Ekaterina
  • Button, Rio
  • Cuttler, Michael
  • Marrable, Dan
  • Favier, Foad
  • Wenger, Amelia

Abstract

Many coral reef models now capture complex demographic, trait-based or ecosystem dynamics, supporting detailed projections under climate stress. However, their application is often constrained by extensive site-specific data needs, specialised modelling expertise and reliance on proprietary software, limiting accessibility and practical transferability. Here we develop CoralPOP, a morphology-based, size-structured coral population model that uses commonly collected benthic data and a small set of calibrated parameters to simulate trajectories under bleaching and cyclone scenarios. The model advances coral community forecasting by: (1) morphology-based parameterisation that balances ecological realism with data accessibility, (2) global default parameters enabling deployment without extensive local datasets, (3) parsimonious calibration focused on three mechanistically important parameters and (4) demonstrated transferability, validated across four reef types and two regions (Great Barrier Reef and the Caribbean). Applications include exploring disturbance scenarios and initial community states to assess reef resilience and guide restoration and protection priorities.

Suggested Citation

  • Browne, Nicola K. & Tadesse, Dagmawi & Ovsyanikova, Ekaterina & Button, Rio & Cuttler, Michael & Marrable, Dan & Favier, Foad & Wenger, Amelia, 2026. "CoralPOP: A morphology-based coral population model for transferable reef management projections," Ecological Modelling, Elsevier, vol. 520(C).
  • Handle: RePEc:eee:ecomod:v:520:y:2026:i:c:s0304380026002000
    DOI: 10.1016/j.ecolmodel.2026.111672
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380026002000
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2026.111672?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    2. Rebecca Fisher & Pia Bessell-Browne & Ross Jones, 2019. "Synergistic and antagonistic impacts of suspended sediments and thermal stress on corals," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    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. Samuel Bandara & Johannes P Schlöder & Roland Eils & Hans Georg Bock & Tobias Meyer, 2009. "Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model," PLOS Computational Biology, Public Library of Science, vol. 5(11), pages 1-12, November.
    2. Dominic M Dunstan & Mark P Richardson & Eugenio Abela & Ozgur E Akman & Marc Goodfellow, 2023. "Global nonlinear approach for mapping parameters of neural mass models," PLOS Computational Biology, Public Library of Science, vol. 19(3), pages 1-28, March.
    3. repec:plo:pcbi00:1007279 is not listed on IDEAS
    4. Adel Dayarian & Madalena Chaves & Eduardo D Sontag & Anirvan M Sengupta, 2009. "Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-12, January.
    5. repec:plo:pgen00:1006132 is not listed on IDEAS
    6. Chen, Yu & Yu, Hui & Liu, Chengjie & Xie, Jin & Han, Jun & Dai, Houde, 2024. "Synergistic fusion of physical modeling and data-driven approaches for parameter inference to enzymatic biodiesel production system," Applied Energy, Elsevier, vol. 373(C).
    7. repec:plo:pcbi00:1003471 is not listed on IDEAS
    8. repec:plo:pone00:0024246 is not listed on IDEAS
    9. Amrita X Sarkar & Eric A Sobie, 2010. "Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-11, September.
    10. Hongwei Shao & Tao Peng & Zhiwei Ji & Jing Su & Xiaobo Zhou, 2013. "Systematically Studying Kinase Inhibitor Induced Signaling Network Signatures by Integrating Both Therapeutic and Side Effects," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-16, December.
    11. Alireza Yazdani & Lu Lu & Maziar Raissi & George Em Karniadakis, 2020. "Systems biology informed deep learning for inferring parameters and hidden dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-19, November.
    12. Fridtjof Brauns & Leila Iñigo de la Cruz & Werner K.-G. Daalman & Ilse Bruin & Jacob Halatek & Liedewij Laan & Erwin Frey, 2023. "Redundancy and the role of protein copy numbers in the cell polarization machinery of budding yeast," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Eberhard O Voit & Harald A Martens & Stig W Omholt, 2015. "150 Years of the Mass Action Law," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-7, January.
    14. Céline Christiansen-Jucht & Kamil Erguler & Chee Yan Shek & María-Gloria Basáñez & Paul E. Parham, 2015. "Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival," IJERPH, MDPI, vol. 12(6), pages 1-31, May.
    15. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
    16. Andrew White & Malachi Tolman & Howard D Thames & Hubert Rodney Withers & Kathy A Mason & Mark K Transtrum, 2016. "The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-26, December.
    17. Diego Fernández Slezak & Cecilia Suárez & Guillermo A Cecchi & Guillermo Marshall & Gustavo Stolovitzky, 2010. "When the Optimal Is Not the Best: Parameter Estimation in Complex Biological Models," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-10, October.
    18. Elba Raimúndez & Simone Keller & Gwen Zwingenberger & Karolin Ebert & Sabine Hug & Fabian J Theis & Dieter Maier & Birgit Luber & Jan Hasenauer, 2020. "Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-21, March.
    19. Joseph D Taylor & Samuel Winnall & Alain Nogaret, 2020. "Estimation of neuron parameters from imperfect observations," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-22, July.
    20. Sutulovic, Uros & Proverbio, Daniele & Katz, Rami & Giordano, Giulia, 2026. "gPC-based robustness analysis of neural systems through probabilistic recurrence metrics," Chaos, Solitons & Fractals, Elsevier, vol. 206(C).
    21. Xinxian Shao & Andrew Mugler & Justin Kim & Ha Jun Jeong & Bruce R Levin & Ilya Nemenman, 2017. "Growth of bacteria in 3-d colonies," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-19, July.
    22. repec:plo:pcbi00:1003705 is not listed on IDEAS
    23. Maksat Ashyraliyev & Ken Siggens & Hilde Janssens & Joke Blom & Michael Akam & Johannes Jaeger, 2009. "Gene Circuit Analysis of the Terminal Gap Gene huckebein," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-16, October.
    24. Agus Hartoyo & Peter J Cadusch & David T J Liley & Damien G Hicks, 2019. "Parameter estimation and identifiability in a neural population model for electro-cortical activity," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-27, May.
    25. Christian A Tiemann & Joep Vanlier & Maaike H Oosterveer & Albert K Groen & Peter A J Hilbers & Natal A W van Riel, 2013. "Parameter Trajectory Analysis to Identify Treatment Effects of Pharmacological Interventions," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-15, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:ecomod:v:520:y:2026:i:c:s0304380026002000. 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.