IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i11p6101-d564479.html
   My bibliography  Save this article

Assessing the Potential of Catch-Only Models to Inform on the State of Global Fisheries and the UN’s SDGs

Author

Listed:
  • Rishi Sharma

    (FAO Viale Del Terme de Caracella, 00153 Rome, Italy)

  • Henning Winker

    (Joint Research Centre, European Commission, 21027 Ispra, Italy)

  • Polina Levontin

    (Centre for Environmental Policy, Imperial College London, London SW7 2BX, UK)

  • Laurence Kell

    (Centre for Environmental Policy, Imperial College London, London SW7 2BX, UK)

  • Dan Ovando

    (SAFS, University of Washington, Seattle, WA 98195, USA)

  • Maria L. D. Palomares

    (Sea Around Us, University of British Columbia, Vancouver, BC V6T 1Z4, Canada)

  • Cecilia Pinto

    (Joint Research Centre, European Commission, 21027 Ispra, Italy)

  • Yimin Ye

    (FAO Viale Del Terme de Caracella, 00153 Rome, Italy)

Abstract

Catch-only models (COMs) have been the focus of ongoing research into data-poor stock assessment methods. Two of the most recent models that are especially promising are (i) CMSY+, the latest refined version of CMSY that has progressed from Catch-MSY, and (ii) SRA+ (Stock Reduction Analysis Plus), one of the latest developments in the field. Comparing COMs and evaluating their relative performance is essential for determining the state of regional and global fisheries that may be lacking necessary data that would be required to run traditional assessment models. In this paper we interrogate how performance of COMs can be improved by incorporating additional sources of information. We evaluate the performance of COMs on a dataset of 48 data-rich ICES (International Council for the Exploration of Seas) stock assessments. As one measure of performance, we consider the ability of the model to correctly classify stock status using FAO’s 3-tier classification that is also used for reporting on sustainable development goals to the UN. Both COMs showed notable bias when run with their inbuilt default heuristics, but as the quality of prior information increased, classification rates for the terminal year improved substantially. We conclude that although further COM refinements show some potential, most promising is the ongoing research into developing biomass or fishing effort priors for COMs in order to be able to reliably track stock status for the majority of the world’s fisheries currently lacking stock assessments.

Suggested Citation

  • Rishi Sharma & Henning Winker & Polina Levontin & Laurence Kell & Dan Ovando & Maria L. D. Palomares & Cecilia Pinto & Yimin Ye, 2021. "Assessing the Potential of Catch-Only Models to Inform on the State of Global Fisheries and the UN’s SDGs," Sustainability, MDPI, vol. 13(11), pages 1-14, May.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6101-:d:564479
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/11/6101/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/11/6101/
    Download Restriction: no
    ---><---

    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:gam:jsusta:v:13:y:2021:i:11:p:6101-:d:564479. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.