IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v49y2000i3p327-342.html
   My bibliography  Save this article

Non‐linear state space modelling of fisheries biomass dynamics by using Metropolis‐Hastings within‐Gibbs sampling

Author

Listed:
  • Russell B. Millar
  • Renate Meyer

Abstract

State space modelling and Bayesian analysis are both active areas of applied research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state space models that may be non‐linear and have non‐normal errors, and hence it is particularly useful for modelling fisheries dynamics. Here, this approach is demonstrated by fitting a non‐linear surplus production model to data on South Atlantic albacore tuna (Thunnus alalunga). The state space approach allows for random variability in both the data (the measurement of relative biomass) and in annual biomass dynamics of the tuna stock. Sampling from the joint posterior distribution of the unobservables was achieved by using Metropolis‐Hastings within‐Gibbs sampling.

Suggested Citation

  • Russell B. Millar & Renate Meyer, 2000. "Non‐linear state space modelling of fisheries biomass dynamics by using Metropolis‐Hastings within‐Gibbs sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 327-342.
    • Handle: RePEc:bla:jorssc:v:49:y:2000:i:3:p:327-342
    DOI: 10.1111/1467-9876.00195
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9876.00195
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9876.00195?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
    ---><---

    Citations

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


    Cited by:

    1. Dunham, Kylee & Grand, James B., 2016. "Effects of model complexity and priors on estimation using sequential importance sampling/resampling for species conservation," Ecological Modelling, Elsevier, vol. 340(C), pages 28-36.
    2. Gimenez, Olivier & Rossi, Vivien & Choquet, Rémi & Dehais, Camille & Doris, Blaise & Varella, Hubert & Vila, Jean-Pierre & Pradel, Roger, 2007. "State-space modelling of data on marked individuals," Ecological Modelling, Elsevier, vol. 206(3), pages 431-438.
    3. Ruiz, Javier & Prieto, Laura & Astorga, Diana, 2012. "A model for temperature control of jellyfish (Cotylorhiza tuberculata) outbreaks: A causal analysis in a Mediterranean coastal lagoon," Ecological Modelling, Elsevier, vol. 233(C), pages 59-69.
    4. Min-Je Choi & Do-Hoon Kim, 2020. "Assessment and Management of Small Yellow Croaker ( Larimichthys polyactis ) Stocks in South Korea," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    5. Axel Finke & Ruth King & Alexandros Beskos & Petros Dellaportas, 2019. "Efficient Sequential Monte Carlo Algorithms for Integrated Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 204-224, June.
    6. Roa-Ureta, Ruben H. & Santos, Miguel N. & Leitão, Francisco, 2019. "Modelling long-term fisheries data to resolve the attraction versus production dilemma of artificial reefs," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
    7. Russell B. Millar, 2004. "Sensitivity of Bayes Estimators to Hyper-Parameters with an Application to Maximum Yield from Fisheries," Biometrics, The International Biometric Society, vol. 60(2), pages 536-542, June.
    8. Chaloupka, Milani & Balazs, George, 2007. "Using Bayesian state-space modelling to assess the recovery and harvest potential of the Hawaiian green sea turtle stock," Ecological Modelling, Elsevier, vol. 205(1), pages 93-109.
    9. Christoph Berninger & Almond Stocker & David Rugamer, 2020. "A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction," Papers 2006.05750, arXiv.org, revised Feb 2021.
    10. Ruth King & Stephen P. Brooks & Chiara Mazzetta & Stephen N. Freeman & Byron J. T. Morgan, 2008. "Identifying and diagnosing population declines: a Bayesian assessment of lapwings in the UK," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 609-632, December.
    11. Ji-Hoon Choi & Jae-Bong Lee & Sang-Chul Yoon & Do-Hoon Kim, 2021. "A Bioeconomic Analysis of the Sandfish ( Arctoscopus japonicus ) Management Policies of the Eastern Sea Danish Fishery in Korea," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    12. Su, Zhenming & Peterman, Randall M., 2012. "Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error," Ecological Modelling, Elsevier, vol. 224(1), pages 76-89.
    13. I. B. J. Goudie & M. Goudie, 2007. "Who captures the marks for the Petersen estimator?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 825-839, July.

    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:bla:jorssc:v:49:y:2000:i:3:p:327-342. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.