User Guide for CMSY++.

Froese, Rainer , Demirel, Nazli, Coro, Gianpaolo and Winker, Henning (2021) User Guide for CMSY++. Open Access . GEOMAR, Kiel, Germany, 17 pp.

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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.

Document Type: Report (Manual)
Additional Information: This is an accompanying document for Froese, R., Winker, H., Coro, G., Palomares, MLD., Tsikliras, A.C., Dimarchopoulou, D., Touloumis, K., Demirel, N., Scarcella, G., de Souza Vianna, G.M., Liang, C. and Pauly, D. 2021. Catch time series as the basis for fish stock assessments: CMSY++ and its applications, submitted in March 2021
Research affiliation: OceanRep > GEOMAR > FB3 Marine Ecology > FB3-EV Marine Evolutionary Ecology
Corresponding Author:
Corresponding Author Name
Froese, Rainer
Froese, Rainer
FB3-EV Marine Evolutionary Ecology
Publisher: GEOMAR
Date Deposited: 24 Mar 2021 12:09
Last Modified: 04 Nov 2021 08:56

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