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961.
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综述了近年来微颗粒型鱼苗开口饵料在配方、制备工艺和应用方面所取得的进展,进行了对比分析,指出某些不足之处,重点讨论了微颗粒型鱼苗开口饵料所碰到的营养物渗漏问题,并对今后的发展提出建议。 相似文献
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Errors in fitting models to data are usually assumed to follow a normal (or log normal) distribution in fisheries. This assumption is usually used in formulating likelihood functions often required in frequentist and Bayesian stock assessment modelling. Fisheries data are commonly subject to atypical errors, resulting in outliers in stock assessment modelling. Because most stock assessment models are nonlinear and contain multiple variables, it is difficult, if not impossible, to identify outliers by plotting fisheries data alone. Commonly used normal distribution‐based frequentist and Bayesian stock assessment methods are sensitive to outliers, resulting in biased estimates of model parameters that are vital in defining the dynamics of fish stocks and evaluating alternative strategies for fisheries management. Because of the high likelihood of having outliers in fisheries data, frequentist or Bayesian methods robust to outliers are more desirable in fisheries stock assessment. This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods. Using simulated fisheries as examples, we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in fisheries data and compare the robust approaches with the commonly used normal distribution‐based approach. The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods. 相似文献
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The agarophyte Gracilaria birdiae Plastino & Oliveira was cultivated under field conditions in an estuary over a 6-month period. Biomass production varied significantly (p<0.01) and ranged from 900.0 to 3537.0 g m−2 with a mean of 2124.4±1004.36 g m−2 over the cultivation period. The epiphyte-biomass varied from 64.7 to 313 g m−2, with maximum values observed in July. Relative growth rate (RGR) of G. birdiae varying from −0.59 to 4.67% day−1, with a maximum mean observed in July. Correlation analysis showed that RGR was positively correlated with epiphyte-biomass (R-Pearson=0.69; p< 0.01) and negatively correlated with salinity (R-Pearson=−0.41; p<0.05). These correlations were used in a mathematical regression model to estimate the growth of Gracilaria in an estuary. The regression model explained 61% of RGR variability demonstrating that growth rates of Gracilaria were directly related to the variables salinity and epiphytes. The model was developed to help to predict production at a cultivation site in order to evaluate its suitability for Gracilaria cultivation. 相似文献
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