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

Rishi Sharma, Henning Winker, Polina Levontin, Laurence Kell, Dan Ovando, Maria L. D. Palomares, Cecilia Pinto and Yimin Ye
Additional contact information
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

Sustainability, 2021, vol. 13, issue 11, 1-14

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.

Keywords: SDG 14.4.1; SOFIA; overfishing; sustainable; stock reduction; SRA+, CMSY+ (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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