Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries(declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging(MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error(REE) of maximum sustainable yield(MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model(low REE) in all cases when the white noise level was low(coefficient of variation(CV) = 0.02). However, when the white noise level increased(CV= 0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.
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