IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0009264.html
   My bibliography  Save this article

Predictive Modeling of Coral Disease Distribution within a Reef System

Author

Listed:
  • Gareth J Williams
  • Greta S Aeby
  • Rebecca O M Cowie
  • Simon K Davy

Abstract

Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1) coral diseases show distinct associations with multiple environmental factors, 2) incorporating interactions (synergistic collinearities) among environmental variables is important when predicting coral disease spatial patterns, and 3) modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value) will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA), Porites tissue loss (PorTL), Porites trematodiasis (PorTrem), and Montipora white syndrome (MWS), and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT) within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response), led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to particular environmental conditions. Predictive statistical modeling can help to increase our understanding of coral disease ecology worldwide.

Suggested Citation

  • Gareth J Williams & Greta S Aeby & Rebecca O M Cowie & Simon K Davy, 2010. "Predictive Modeling of Coral Disease Distribution within a Reef System," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0009264
    DOI: 10.1371/journal.pone.0009264
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0009264
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0009264&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0009264?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. D. R. Bellwood & T. P. Hughes & C. Folke & M. Nyström, 2004. "Confronting the coral reef crisis," Nature, Nature, vol. 429(6994), pages 827-833, June.
    2. Thomas J. Kirn & Brooke A. Jude & Ronald K. Taylor, 2005. "A colonization factor links Vibrio cholerae environmental survival and human infection," Nature, Nature, vol. 438(7069), pages 863-866, December.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    4. John F Bruno & Elizabeth R Selig, 2007. "Regional Decline of Coral Cover in the Indo-Pacific: Timing, Extent, and Subregional Comparisons," PLOS ONE, Public Library of Science, vol. 2(8), pages 1-8, August.
    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. Conrad W Speed & Russ C Babcock & Kevin P Bancroft & Lynnath E Beckley & Lynda M Bellchambers & Martial Depczynski & Stuart N Field & Kim J Friedman & James P Gilmour & Jean-Paul A Hobbs & Halina T Ko, 2013. "Dynamic Stability of Coral Reefs on the West Australian Coast," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
    2. Brice X Semmens & Peter J Auster & Michelle J Paddack, 2010. "Using Ecological Null Models to Assess the Potential for Marine Protected Area Networks to Protect Biodiversity," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    3. Julie Vercelloni & M Julian Caley & Mohsen Kayal & Samantha Low-Choy & Kerrie Mengersen, 2014. "Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    4. Nicholas A J Graham & Tim R McClanahan & M Aaron MacNeil & Shaun K Wilson & Nicholas V C Polunin & Simon Jennings & Pascale Chabanet & Susan Clark & Mark D Spalding & Yves Letourneur & Lionel Bigot & , 2008. "Climate Warming, Marine Protected Areas and the Ocean-Scale Integrity of Coral Reef Ecosystems," PLOS ONE, Public Library of Science, vol. 3(8), pages 1-9, August.
    5. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    6. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    7. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    8. Teh, Louise S.L. & Teh, Lydia C.L. & Rashid Sumaila, U., 2014. "Time preference of small-scale fishers in open access and traditionally managed reef fisheries," Marine Policy, Elsevier, vol. 44(C), pages 222-231.
    9. Speers, Ann E. & Besedin, Elena Y. & Palardy, James E. & Moore, Chris, 2016. "Impacts of climate change and ocean acidification on coral reef fisheries: An integrated ecological–economic model," Ecological Economics, Elsevier, vol. 128(C), pages 33-43.
    10. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    11. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    12. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    13. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    14. Yu-Rong Cheng & Chi-Hsiang Chin & Ding-Fa Lin & Chao-Kang Wang, 2020. "The Probability of an Unrecoverable Coral Community in Dongsha Atoll Marine National Park Due to Recurrent Disturbances," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    15. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    16. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    17. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
    18. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    19. Phillip K Lowe & John F Bruno & Elizabeth R Selig & Matthew Spencer, 2011. "Empirical Models of Transitions between Coral Reef States: Effects of Region, Protection, and Environmental Change," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-15, November.
    20. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.

    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:pone00:0009264. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.