Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/41671
Title: Application of Bayesian surplus production model and traditional surplus production model on stock assessment of the southern Atlantic albacore (Thunnus alalunga)
Authors: Liao, Baochao
Zhang, Kui
Shan, Xiujuan
Chen, Xiao
Baset, Abdul
Memon, Khadim Hussain
Liu, Qun
Keywords: Bayesian model;Fox model;Thunnus alalunga;Southern Atlantic;Risk assessment
Issue Date: May-2017
Publisher: NISCAIR-CSIR, India
Abstract: Bayesian surplus Production model (BSP) and traditional surplus Production models (TSP) were used to evaluate the southern Atlantic albacore (Thunnusalalunga) stock. Population parameterswere estimated using CEDA (catch-effort data analysis) and ASPIC (a surplus-production model incorporate covariates) computer software packages. Performance of the BSP model and TSP model were compared by a Bayesian information criterion (BIC). Maximum sustainable yield (MSY) from the TSP model and BSP model were used to verify the MSY estimations by International Commission for the Conservation of Atlantic Tunas (ICCAT). Catch of 2011 (24122 t) was higher than the MSY from BSP (21756t, 23408t), and the relative fishing mortality ratio (F2011/FMSY) of the stock was higher than 1.0, which shows thatthis stock over-exploited. Different harvest strategies were set to assess the risk for this stock, and these estimates were used topredict the biomass and catch in 2025 (B2025, C2025) and other five indexes (B2025 /BMSY, B2025 /K, P (B2025> B2012), P (B2025> BMSY), P (B2025< BMSY/4)). Evaluated biological reference points (BRPs) from Bayesian model were compared with the results from traditional modeling method on the southern Atlantic albacore (T. alalunga) stock, and results showed that the measures should be taken for the sustainable utilization of this fish stock, and the harvest rate of 0.15 seemed tobe the best management measures.
Page(s): 922-928
URI: http://nopr.niscair.res.in/handle/123456789/41671
ISSN: 0975-1033 (Online); 0379-5136 (Print)
Appears in Collections:IJMS Vol.46(05) [May 2017]

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