IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v221y2010i2p338-352.html
   My bibliography  Save this article

Fish recruitment prediction, using robust supervised classification methods

Author

Listed:
  • Fernandes, Jose A.
  • Irigoien, Xabier
  • Goikoetxea, Nerea
  • Lozano, Jose A.
  • Inza, Iñaki
  • Pérez, Aritz
  • Bode, Antonio

Abstract

Improving our ability to predict recruitment is a key element in fisheries management. However, the interactions between population dynamics and different environmental factors are complex and often non-linear, making it difficult to produce robust predictions. ‘Machine-learning’ techniques (in particular, supervised classification methods) have been proposed as useful tools, to overcome such difficulties. In this study, a methodology is proposed to build a robust classifier for fish recruitment prediction with sparse and noisy data. The methodology consists of 4 steps: (1) a semi-automated recruitment discretization method; (2) supervised discretization of predictors; (3) multivariate and non-redundant predictors selection; (4) learning a probabilistic classifier. In terms of fisheries management, the classifier estimated performance has important consequences and, to be useful, the manager needs to know the risk that is being taken when using this number. Probabilistic classifiers such as ‘naive Bayes’, have the advantage that, in addition to the predictions, estimate also the probability of each possible outcome. Anchovy (Engraulis encrasicolus) and hake (Merluccius merluccius) recruitments are used as application examples. ‘Two-intervals’ recruitment discretization accomplishes 70% accuracies and Brier scores of around 0.10, for both anchovy and hake recruitment. In comparison, ‘three-intervals’ recruitment discretization accomplishes 50% accuracies; and Brier scores of around 0.25 for anchovy and 0.30 for hake recruitment. These statistics are the result of validating not only the classifier, but also the previous steps, as a whole methodology.

Suggested Citation

  • Fernandes, Jose A. & Irigoien, Xabier & Goikoetxea, Nerea & Lozano, Jose A. & Inza, Iñaki & Pérez, Aritz & Bode, Antonio, 2010. "Fish recruitment prediction, using robust supervised classification methods," Ecological Modelling, Elsevier, vol. 221(2), pages 338-352.
  • Handle: RePEc:eee:ecomod:v:221:y:2010:i:2:p:338-352
    DOI: 10.1016/j.ecolmodel.2009.09.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009006437
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.09.020?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. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    2. Dreyfus-León, Michel & Chen, D.G., 2007. "Recruitment prediction with genetic algorithms with application to the Pacific Herring fishery," Ecological Modelling, Elsevier, vol. 203(1), pages 141-146.
    3. Sebastiani, Paola & Ramoni, Marco, 2005. "Normative selection of Bayesian networks," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 340-357, April.
    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. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    2. Fernandes, Jose A. & Santos, Lionel & Vance, Thomas & Fileman, Tim & Smith, David & Bishop, John D.D. & Viard, Frédérique & Queirós, Ana M. & Merino, Gorka & Buisman, Erik & Austen, Melanie C., 2016. "Costs and benefits to European shipping of ballast-water and hull-fouling treatment: Impacts of native and non-indigenous species," Marine Policy, Elsevier, vol. 64(C), pages 148-155.
    3. Ropero, R.F. & Aguilera, P.A. & Rumí, R., 2015. "Analysis of the socioecological structure and dynamics of the territory using a hybrid Bayesian network classifier," Ecological Modelling, Elsevier, vol. 311(C), pages 73-87.

    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. Di Zhang & Xinping Yan & Zaili Yang & Jin Wang, 2014. "An accident data–based approach for congestion risk assessment of inland waterways: A Yangtze River case," Journal of Risk and Reliability, , vol. 228(2), pages 176-188, April.
    2. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    3. Jim Lewis & Kerrie Mengersen & Laurie Buys & Desley Vine & John Bell & Peter Morris & Gerard Ledwich, 2015. "Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-21, July.
    4. Nicholson, Ann E. & Flores, M. Julia, 2011. "Combining state and transition models with dynamic Bayesian networks," Ecological Modelling, Elsevier, vol. 222(3), pages 555-566.
    5. Moe, S. Jannicke & Haande, Sigrid & Couture, Raoul-Marie, 2016. "Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach," Ecological Modelling, Elsevier, vol. 337(C), pages 330-347.
    6. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    7. Mostafa Shaaban & Carmen Schwartz & Joseph Macpherson & Annette Piorr, 2021. "A Conceptual Model Framework for Mapping, Analyzing and Managing Supply–Demand Mismatches of Ecosystem Services in Agricultural Landscapes," Land, MDPI, vol. 10(2), pages 1-19, January.
    8. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    9. Dayong Li & Zengchuan Dong & Liyao Shi & Jintao Liu & Zhenye Zhu & Wei Xu, 2019. "Risk Probability Assessment of Sudden Water Pollution in the Plain River Network Based on Random Discharge from Multiple Risk Sources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4051-4065, September.
    10. Tiller, Rachel Gjelsvik & Hansen, Lillian & Richards, Russell & Strand, Hillevi, 2015. "Work segmentation in the Norwegian salmon industry: The application of segmented labor market theory to work migrants on the island community of Frøya, Norway," Marine Policy, Elsevier, vol. 51(C), pages 563-572.
    11. Leonel Lara-Estrada & Livia Rasche & L. Enrique Sucar & Uwe A. Schneider, 2018. "Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks," Land, MDPI, vol. 7(1), pages 1-13, January.
    12. Jinjia Zhang & Yiping Zeng & Genserik Reniers & Jie Liu, 2022. "Analysis of the Interaction Mechanism of the Risk Factors of Gas Explosions in Chinese Underground Coal Mines," IJERPH, MDPI, vol. 19(2), pages 1-18, January.
    13. Nguyen, Minh-Hoang, 2023. "Investigating urban residents' involvement in biodiversity conservation in protected areas: Empirical evidence from Vietnam," Thesis Commons z2hjv, Center for Open Science.
    14. Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
    15. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    16. Meyer, Spencer R. & Johnson, Michelle L. & Lilieholm, Robert J. & Cronan, Christopher S., 2014. "Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA," Ecological Modelling, Elsevier, vol. 291(C), pages 42-57.
    17. Mastrangelo, Matias Enrique & Sun, Zhanli & Seghezzo, Lucas & Müller, Daniel, 2019. "Survey-based modeling of land-use intensity in agricultural frontiers of the Argentine dry Chaco," Land Use Policy, Elsevier, vol. 88(C).
    18. Anna Sperotto & Josè Luis Molina & Silvia Torresan & Andrea Critto & Manuel Pulido-Velazquez & Antonio Marcomini, 2019. "Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks," Sustainability, MDPI, vol. 11(17), pages 1-34, August.
    19. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
    20. Renken, Henk & Mumby, Peter J., 2009. "Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach," Ecological Modelling, Elsevier, vol. 220(9), pages 1305-1314.

    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:221:y:2010:i:2:p:338-352. 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.