Akademik Veri Yönetim Sistemi
ACTA ICHTHYOLOGICA ET PISCATORIA, cilt.53, ss.173-189, 2023 (SCI-Expanded)
Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limitedstock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of thedeficiencies of CMSY, which itself improved upon the “Catch-MSY” method published by S. Martell and R. Froese in 2013. Thecatch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fittingof 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 stockdepletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in thetraining of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limitsof the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are comparedwith those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towardsunderestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate howCMSY++ 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 evaluatedcase-by-case and ideally be replaced by independent prior knowledge.