CMSY++ user guide and code – published and ready for distribution

The “CMSY++ User guide and Code” of Froese et al. (Rainer Froese, Nazli Demirel, Gianpaolo Coro and Henning Winker) has been recently published and now ready for distribution. CMSY or the estimation of maximum sustainable yield from catch and resilience, is one of the novel stock assessment methods for data-poor fisheries developed by Froese et al. In May 2020, Dr Froese received the Ocean Award in Science for leading the development of these advance computer intensive methods which measures stability and sustainability of fish stocks.

One of the most recent applications of  these methods is the “Assessment of exploited fish species in the Lake Edward System, East Africa” study which produced several first-ever stock assessments. According to Dr Froese, “this goes to show that these  methods are applicable in the real world and have impact for better management of scarce resources and related food security. “It also shows that these ‘marine’ methods work fine in freshwater systems,” he added.

Please fell free to test it. forward it to others for testing, and send report of any errors, corrections and bugs encountered to Dr Rainer Froese and he will make sure they are fixed in the next version, which will be uploaded to the same URL.

Abstract: CMSY++ is an advanced state-space Bayesian method for stock assessment that estimates fisheries reference points (MSY, Fmsy, Bmsy) as well as status or relative stock size (B/Bmsy) and fishing pressure or exploitation (F/Fmsy) from catch and (optionally) abundance data, a prior for resilience or productivity (r), and broad priors for the ratio of biomass to unfished biomass (B/k) at the beginning, an intermediate year, and the end of the time series. For the purpose of this User Guide, the whole package is referred to as CMSY++ whereas the part of the method that deals with catch-only data is referred to as CMSY (catch MSY), and the part of the method that requires additional abundance data is referred to as BSM (Bayesian Schaefer Model). Both methods are based on a modified Schaefer surplus production model (see paper cited above for more details). The main advantage of BSM, compared to other implementations of surplus production models, is the focus on informative priors and the acceptance of short and incomplete (i.e., fragmented, with missing years) abundance data. This document provides a simple step-by-step guide for researchers who want to apply CMSY++ to their own data.

Joann Glorioso

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