New developments in the analysis of catch time series as the basis for fish stock assessments: The CMSY++ method

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method, is presented. This new version, CMSY++ overcomes several of the deficiencies of CMSY, which itself improved upon the ‘Catch-MSY’ method published by S. Martel and R. Froese in 2013. Notably, CMSY++ now uses a Bayesian implementation of a modified Schaefer model which can predict stock status and exploitation from catch data alone. An Artificial Intelligence neural network assists in selecting appropriate priors for relative stock size based on patterns in catch time series. A number of applications of CMSY++ are presented, illustrating the ease with which it can consider data and information in addition to catch time series. Also, the equilibrium catch predictions of the modified Schaefer model are compared with those of alternative surplus-production and dynamic-pool models, demonstrating that the latter two are strongly biased towards underestimating the biomass required to sustain catches at depleted stock sizes, thus potentially contributing to the risks of overfishing. Overall, this suggests that CMSY++, which doesn’t suffer from this bias, should not be seen as a ‘catch-only’ method, but rather as a versatile integrative method that will be useful both in data-limited situations that are common in many parts of the world and in countries with advanced management and fisheries monitoring systems, as illustrated by a map showing the global distribution of the method’s applications.

FROESE Rainer;  WINKER Henning;  CORO Gianpaolo;  POLAMARES Deng;  TSIKLIRAS Athanassios C.;  DIMARCHOPOULOU Donna;  TOULOUMIS Konstantinos;  DEMIREL Nazli;  VIANNA Gabriel;  SCARCELLA Giuseppe;  SCHIJNS Rebeca;  LIANG Cui;  PAULY Daniel; 

2024-03-04

PENSOFT PUBLISHERS

JRC124125

0137-1592 (online),   

https://aiep.pensoft.net/article/105910/element/8/126030//,    https://publications.jrc.ec.europa.eu/repository/handle/JRC124125,   

10.3897/aiep.53.105910 (online),