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

Author:

Bouch Paul1ORCID,Minto Cóilín2,Reid Dave G1

Affiliation:

1. Fisheries Ecosystems Advisory Services, Marine Institute, Rinville, Co. Galway, Oranmore, Ireland

2. Marine and Freshwater Research Centre, Galway-Mayo Institute of Technology (GMIT), Dublin Road, Galway, Ireland

Abstract

Abstract 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/FMSY of B/BMSY, 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.

Funder

Department of Agriculture, Food and the Marine's Competitive Research Funding Programmes

FishKOSM

Publisher

Oxford University Press (OUP)

Subject

Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography

Reference70 articles.

1. Improving estimates of population status and trend with superensemble models;Anderson;Fish and Fisheries,2017

2. Robustness in the Strategy of Scientific Model Building

3. Ecosystem-based fishery management: what is it and how can we do it?;Brodziak;Bulletin of Marine Science,2002

4. Evaluating methods for setting catch limits in data-limited fisheries;Carruthers;Fisheries Research,2014

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