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Investigation of a Continuous Fox-form Production Model for Fishery Stock Assessment

Author: CuiHe
Tutor: LiuQun
School: Ocean University of China
Course: Fishery resources
Keywords: Fox form production model Monte Carlo simulation white noise
CLC: S932
Type: Master's thesis
Year: 2008
Downloads: 51
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Abstract

Surplus production models are the major theoretical models in modern fishery resources assessment and management. Surplus production models can be simply used in fish stock assessment because of their simplicity and relatively undemanding data needs, though fishery models are getting more and more complex. Surplus production models are among the classical methods to predict the MSY (maximum sustainable yield) for fisheries management, which is still one of the major fisheries management goals. Despite of the prevalence of age-structured population models, surplus production models which generally do not incorporate age structure remains useful for fish population dynamics. These models are of particular value when the catch can not be aged, or can not be aged precisely, and therefore age-structured models can not be applied. Surplus production models can be used as an alternative implementation to age-structured models, providing different views of the data and results.Continuous Fox form production model has not been systematically studied and applied since it was published in 1998. In this study, in order to find out the necessary conditions and data characteristics for the continuous Fox form production model to work best, it was analyzed using different simulated fisheries data and the North Atlantic swordfish fishery data.The Monte Carlo simulation study showed that: when the white noise was smaller than 10%, all the estimated parameters were close to the true values. When the white noise reached 30%, there were larger biases in the assessment results, the model performed poorly. Generally speaking, bias of the starting biomass (B1) and carrying capacity (K) is small but with large RIQR% values. The estimated r and q values were close to the true values. The estimated MSY and fMSY values were most accurate with the RIQR% values less than 75.1%. For the different fisheries, the RBM% values showed that the model performed best in fishery 1, and secondly in fishery 3. The average biases of all the 24 estimated parameters were: 2.6% for fishery 1, 4.6% for fishery 2; 4.3% for fishery 3. With regard to the RIQR% values, the model performed best in fishery 3, the average RIQR% values were: 46.6% for fishery 1, 55.1% for fishery 2, 31.6% for fishery 3.The smaller the white noise is, the closer the observation yields of the fisheries are to the estimated yields, the better the model performed. This shows that the accurate data is can produce better model assessment results. When the white noise is large, the estimated yields are less accurate, but the figures showed that the trends of the observation yields and estimated yields are similar. Although the model can not predict the yields accurately, but it can at least understand the trend of the yields. If additional information is available, the model assessment results may still be useful. When the white noise is larger (30%, 50%), fishery 3 had the smallest differences between the observation yield and estimated yield. This also showed that the model performed better in fishery 3 than in the other two fisheries.The assessment of the North Atlantic swordfish fishery showed that MSY is 15,678 tons, fMSY is 37×106 (hook, /year), For the same fishery, Prager (2002) reported MSY of 14,070 tons and 14,510 tons; SUN (2004) reported MSY of 12,493 tons and f MSY of 53×106 hook / year. The results of this study is close to the those findings, which indicated that the continuous Fox production model for the North Atlantic swordfish fishery assessment produced viable results. In the early 1980s, the fishing effort on the North Atlantic swordfish fishery was more than the optimum level, which resulted in an over-exploitation. When the international and national organizations adopted fisheries management measures to control the fishing effort, now it has been basically well controlled at the optimum level calculated in this paper. The data showed that the management measures play an important role in the recovery of the CPUE of the fishery.

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