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Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.

The size selectivity of trammel nets was investigated in the northern Aegean Sea using 10 different inner-panel mesh sizes ranging from 16 to 70 mm nominal mesh size (bar length). Selectivity estimates were made for the eight most abundant target and bycatch fish species, namely black scorpionfish, Scorpaena porcus Linnaeus, 1758; annular seabream, Diplodus annularis (Linnaeus, 1758); red mullet, Mullus barbatus Linnaeus, 1758; surmullet, Mullus surmuletus Linnaeus, 1758; round sardinella, Sardinella aurita Valenciennes, 1847; European hake, Merluccius merluccius (Linnaeus, 1758); greater weever, Trachinus draco Linnaeus, 1758; and blotched picarel, Spicara flexuosum Rafinesque, 1810, which accounted for 51.5% by number and 42.7% by weight of the fish caught with trammel nets in the sea trials. The SELECT method was used to estimate the selectivity parameters. Five different selectivity functions (i.e., normal scale, normal location, gamma, log-normal, and bi-normal) were applied with the bi-normal function providing the best fit as it had the lowest deviance value for all species and the lowest values for the dispersion parameter (D/df). The mesh size of 16 mm in most of the cases retained specimens below the size at first maturity (Lm). The mesh size of 19 mm seems more appropriate for red mullet, surmullet, and blotched picarel, the mesh size of 22 mm for annular seabream and round sardinella, while for European hake and black scorpionfish, the mesh size larger than 26 mm would be more appropriate.

Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the "Catch-MSY" method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge. [ABSTRACT FROM AUTHOR]