IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v364y2017icp113-123.html
   My bibliography  Save this article

Bayesian semi-individual based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns populations on the Great Barrier Reef

Author

Listed:
  • Chen, C.C.-M.
  • Drovandi, C.C.
  • Keith, J.M.
  • Anthony, K.
  • Caley, M.J.
  • Mengersen, K.L.

Abstract

Outbreaks of Crown-of-Thorns Starfish (CoTS), Acanthaster planci, are a major cause of coral decline on the Great Barrier Reef, second only to cyclones. Although various models have been developed in the past to assist management decision making, most of these models were cohort-based deterministic descriptions with little inclusion of parameter and individual uncertainties, or they were structured around a generic ecological modelling framework, or they were not calibrated with observational data.

Suggested Citation

  • Chen, C.C.-M. & Drovandi, C.C. & Keith, J.M. & Anthony, K. & Caley, M.J. & Mengersen, K.L., 2017. "Bayesian semi-individual based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns populations on the Great Barrier Reef," Ecological Modelling, Elsevier, vol. 364(C), pages 113-123.
    • Handle: RePEc:eee:ecomod:v:364:y:2017:i:c:p:113-123
    DOI: 10.1016/j.ecolmodel.2017.09.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380017301291
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2017.09.006?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. C. C. Drovandi & A. N. Pettitt, 2011. "Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation," Biometrics, The International Biometric Society, vol. 67(1), pages 225-233, March.
    2. van der Vaart, Elske & Beaumont, Mark A. & Johnston, Alice S.A. & Sibly, Richard M., 2015. "Calibration and evaluation of individual-based models using Approximate Bayesian Computation," Ecological Modelling, Elsevier, vol. 312(C), pages 182-190.
    3. Maxime Lenormand & Franck Jabot & Guillaume Deffuant, 2013. "Adaptive approximate Bayesian computation for complex models," Computational Statistics, Springer, vol. 28(6), pages 2777-2796, December.
    4. Brenda N Vo & Christopher C Drovandi & Anthony N Pettitt & Graeme J Pettet, 2015. "Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-22, December.
    5. repec:dau:papers:123456789/5724 is not listed on IDEAS
    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. Henri Pesonen & Umberto Simola & Alvaro Köhn‐Luque & Henri Vuollekoski & Xiaoran Lai & Arnoldo Frigessi & Samuel Kaski & David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Jukka Corander, 2023. "ABC of the future," International Statistical Review, International Statistical Institute, vol. 91(2), pages 243-268, August.
    2. Zhang, Jingjing & Dennis, Todd E. & Landers, Todd J. & Bell, Elizabeth & Perry, George L.W., 2017. "Linking individual-based and statistical inferential models in movement ecology: A case study with black petrels (Procellaria parkinsoni)," Ecological Modelling, Elsevier, vol. 360(C), pages 425-436.
    3. Brenda N Vo & Christopher C Drovandi & Anthony N Pettitt & Graeme J Pettet, 2015. "Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-22, December.
    4. Lorenzo Pacchiardi & Pierre Künzli & Marcel Schöngens & Bastien Chopard & Ritabrata Dutta, 2021. "Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 288-317, May.
    5. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    6. Li, J. & Nott, D.J. & Fan, Y. & Sisson, S.A., 2017. "Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 77-89.
    7. Watson, Joseph W & Boyd, Robin & Dutta, Ritabrata & Vasdekis, Georgios & Walker, Nicola D. & Roy, Shovonlal & Everitt, Richard & Hyder, Kieran & Sibly, Richard M, 2022. "Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass✰," Ecological Modelling, Elsevier, vol. 466(C).
    8. George Karabatsos, 2023. "Approximate Bayesian computation using asymptotically normal point estimates," Computational Statistics, Springer, vol. 38(2), pages 531-568, June.
    9. Grimm, Volker & Berger, Uta, 2016. "Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue," Ecological Modelling, Elsevier, vol. 326(C), pages 177-187.
    10. Boult, Victoria L. & Quaife, Tristan & Fishlock, Vicki & Moss, Cynthia J. & Lee, Phyllis C. & Sibly, Richard M., 2018. "Individual-based modelling of elephant population dynamics using remote sensing to estimate food availability," Ecological Modelling, Elsevier, vol. 387(C), pages 187-195.
    11. Walker, Nicola D. & Boyd, Robin & Watson, Joseph & Kotz, Max & Radford, Zachary & Readdy, Lisa & Sibly, Richard & Roy, Shovonlal & Hyder, Kieran, 2020. "A spatially explicit individual-based model to support management of commercial and recreational fisheries for European sea bass Dicentrarchus labrax," Ecological Modelling, Elsevier, vol. 431(C).
    12. David B. Stern & Nathan W. Anderson & Juanita A. Diaz & Carol Eunmi Lee, 2022. "Genome-wide signatures of synergistic epistasis during parallel adaptation in a Baltic Sea copepod," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    13. Singer, Alexander & Johst, Karin & Banitz, Thomas & Fowler, Mike S. & Groeneveld, Jürgen & Gutiérrez, Alvaro G. & Hartig, Florian & Krug, Rainer M. & Liess, Matthias & Matlack, Glenn & Meyer, Katrin M, 2016. "Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?," Ecological Modelling, Elsevier, vol. 326(C), pages 63-74.
    14. Aushev, Alexander & Pesonen, Henri & Heinonen, Markus & Corander, Jukka & Kaski, Samuel, 2022. "Likelihood-free inference with deep Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    15. Muchmore Patrick & Marjoram Paul, 2015. "Exact likelihood-free Markov chain Monte Carlo for elliptically contoured distributions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 317-332, August.
    16. Maxime Lenormand & Franck Jabot & Guillaume Deffuant, 2013. "Adaptive approximate Bayesian computation for complex models," Computational Statistics, Springer, vol. 28(6), pages 2777-2796, December.
    17. R Zachariah Aandahl & Josephine F Reyes & Scott A Sisson & Mark M Tanaka, 2012. "A Model-Based Bayesian Estimation of the Rate of Evolution of VNTR Loci in Mycobacterium tuberculosis," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-9, June.
    18. Boyd, Robin & Roy, Shovonlal & Sibly, Richard & Thorpe, Robert & Hyder, Kieran, 2018. "A general approach to incorporating spatial and temporal variation in individual-based models of fish populations with application to Atlantic mackerel," Ecological Modelling, Elsevier, vol. 382(C), pages 9-17.
    19. Baey, Charlotte & Smith, Henrik G. & Rundlöf, Maj & Olsson, Ola & Clough, Yann & Sahlin, Ullrika, 2023. "Calibration of a bumble bee foraging model using Approximate Bayesian Computation," Ecological Modelling, Elsevier, vol. 477(C).
    20. Troost, Christian & Huber, Robert & Bell, Andrew R. & van Delden, Hedwig & Filatova, Tatiana & Le, Quang Bao & Lippe, Melvin & Niamir, Leila & Polhill, J. Gareth & Sun, Zhanli & Berger, Thomas, 2023. "How to keep it adequate: A protocol for ensuring validity in agent-based simulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 159, pages 1-21.

    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:364:y:2017:i:c:p:113-123. 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.