4.6 Article

Comparative performance of data-poor CMSY and data-moderate SPiCT stock assessment methods when applied to data-rich, real-world stocks

期刊

ICES JOURNAL OF MARINE SCIENCE
卷 78, 期 1, 页码 264-276

出版社

OXFORD UNIV PRESS
DOI: 10.1093/icesjms/fsaa220

关键词

benchmarking; data-limited stock assessment; management advice; model bias; surplus production models

资金

  1. Department of Agriculture, Food and the Marine's Competitive Research Funding Programmes (DAFM) [15/S/744]

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A study comparing CMSY and SPiCT methods with ICES age-based assessments for 17 data-rich fish stocks found that both methods often differed considerably from the ICES assessment. CMSY tended to overestimate relative fishing mortality and underestimate relative stock biomass, while SPiCT showed the opposite tendency.
All fish stocks should be managed sustainably, yet for the majority of stocks, data are often limited and different stock assessment methods are required. Two popular and widely used methods are Catch-MSY (CMSY) and Surplus Production Model in Continuous Time (SPiCT). We apply these methods to 17 data-rich stocks and compare the status estimates to the accepted International Council for the Exploration of the Sea (ICES) age-based assessments. Comparison statistics and receiver operator analysis showed that both methods often differed considerably from the ICES assessment, with CMSY showing a tendency to overestimate relative fishing mortality and underestimate relative stock biomass, whilst SPiCT showed the opposite. CMSY assessments were poor when the default depletion prior ranges differed from the ICES assessments, particularly towards the end of the time series, where some stocks showed signs of recovery. SPiCT assessments showed better correlation with the ICES assessment but often failed to correctly estimate the scale of either F/F-MSY of B/B-MSY, with the indices lacking the contrast to be informative about catchability and either the intrinsic growth rate or carrying capacity. Results highlight the importance of understanding model tendencies relative to data-rich approaches and warrant caution when adopting these models.

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