内蒙古西辽河流域渔业评估与可持续发展研究
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摘要
内蒙古西辽河流域所处地理位置主要在内蒙古东部的赤峰市和通辽市,行政区划包括赤峰市12个旗、县、区,通辽市的8个旗、县、区。水系绝大部分位于内蒙古自治区东北部,除河源部分在河北省外,上游在内蒙古自治区的赤峰市境内,下游在内蒙古自治区通辽市境内。上游为山区,下游则是冲积平原。西辽河流域现有大型水库7座,总库容33.52×108m3,兴利库容7.9×108m3。中型水库23座,总库容4.37×108m3,兴利库容2.42×108m3。小型水库84座,总库容1.56×108m3,兴利库容0.94×108m3。
对西辽河流域两个地区的淡水渔业进行评估,可以客观反映该流域渔业的发展潜力和可持续利用能力,以为该流域地区的渔业管理、保护和持续利用提供科学的依据,这也是该篇论文选题的意义所在。
灰色系统理论是我国学者邓聚龙教授于1982年提出的处理不完全信息的一种理论。其基本理论是把一切随机量看作是在一定范围内变化的灰色量并加以处理,将具有随机性的原始数据列转化为一个具有较强规律性的数据列,从而可以建立连续微分方程模型,弱化不确定性,强化规律性。灰色系统在较少量数据及在灰色信息的情况下较为适用。与传统方法比较,灰色建模不存在误差积累问题,可用于长期高精度的预测,弥补了常规统计方法的不足。渔业系统是个多因素、多层次、多目标的大系统,由诸多错综复杂的关系组成,因此渔业系统是典型的灰色系统,适用于灰色系统理论。然而灰色模型在淡水渔业上的应用尚不多见。因此,本论文应用灰色系统预测模型对通辽市、赤峰市2009-2013淡水养殖产量和养殖面积进行了预测。
本论文利用灰色模型对通辽市、赤峰市及其相关水库的渔业产量和渔业面积进行预测。对16组数据分析的结果中,用5年数据进行预测,共有7组数据预测精度达到一级,7组数据预测精度达到二级,1组数据预测精度达到三级,1组数据预测精度达到四级,二级以上精度占87.5%。用10年数据进行预测,有4组数据预测精度达到一级,有5组数据预测精度达到二级,有4组数据预测精度达到三级,有3组数据预测精度较差,不符合预测要求,二级以上精度占56.3%。用15年数据进行预测(其中通辽市渔业产量和养殖水面是12年的数据),有2组数据预测精度达到一级,有5组数据预测精度达到二级,有6组数据预测精度达到三级,有1组数据预测精度达到四级,有2组数据预测精度较差,不符合预测要求,二级以上精度占43.7%。用20年数据进行预测,有5组数据预测精度达到二级,有3组数据预测精度达到三级,有2组数据预测精度达到四级,没有一级精度。用23年数据预测赤峰市渔业产量,达到二级精度。可见,使用短期资料进行预测精度相对比较高,用中长期资料进行预测精度相对比较低,甚至不符合预测精度要求。利用GM(1,1)灰色预测模型分段建模进行预测,其精度变化也表明渔业生产系统的稳定性。利用GM(1,1)灰色预测模型进行预测的过程中并不能排除极端气候或政策干预对渔业生产系统的影响,因此,预测过程中依赖于系统的稳定性。如利用通辽市1998-2001年的数据为依据,模型预测值回代的相对误差也极小,但是并不能准确预测2002年渔业产量的暴跌。因此,渔业系统的稳定性是预测准确的关键。而如何判断判断系统是否稳定,将是研究的重点。在目前,可以结合统计的方法,在明确灰色系统外界某些干扰因素的情况下,可以使用分段预测的方法,进行预测,这种方法也是基于一个基本的假设即短时间内系统不会发生剧烈的突变。因此可以认为灰色预测模型能够对生产相对稳定的短期预测是比较准确的,不宜做长期的外推应用。
由于西辽河流域常年少雨干旱,灰色模型的预测结果表明,赤峰地区的渔业养殖面积仍有发展的空间,而通辽地区的渔业养殖面积在自然环境持续恶劣的情况下,不会有太大的起色。虽然两个地区渔业产量还呈逐年上升趋势,但提升的潜力非常有限。
在渔业管理中,实现最大可持续产量仍旧是其一个主要的目标。剩余产量模型是预测最大可持续产量(MSY)的经典方法,也是现代渔业资源评估和管理的主要理论模型之一。它是把种群或资源群体作为一个研究分析的单位,研究一个资源群体的持续产量、最佳捕捞努力量和资源群体大小之间的平衡关系。其所要求的资料仅仅是多年的渔获量和捕捞努力量或单位捕捞努力量渔获量的渔业统计资料,不需要资源群体本身的生物学资料。剩余产量模型因其简单和所需数据较少的特点为渔业资源评估所广泛采用。该模型是以种群增长S型曲线一般模型为理论基础,把资源群体的补充、生长、和自然死亡综合起来作为资源群体大小的
一个单变量函数进行分析,然后推导出所需的剩余产量模型的数学式。在我国,
自1973年开始应用Schaefer (1957)模型评估广东珠江口蓝园鲹资源以来,这个
模型已成为我国有关渔业部门最熟悉的数学模型之一,并被广泛应用于我国海洋
和内陆大水面水域渔业资源评估的分析研究。本论文应用了二个经典剩余产量模
型,四个非平衡剩余产量模型,和二个由欧美科学家开发的软件对赤峰市渔业的
最大持续产量和与最大持续产量相对应的捕捞努力量(渔船马力数)进行预测。对于赤峰市渔业统计数据,此文的结果显示不同的评估模型产生不同的参数
估计。结合这些结果和原始数据分析,我倾向于建议赤峰市渔业的最大持续产量
(MSY)大致在8000t左右。在此文应用到的产量模型中,线性非平衡产量模型
(Schnute, W-H, D-Fox, I-Fox)的表现较好,其中W-H和D-Fox模型的平均相
对估计误差(REE)值小于10%。对于Schaefer和Fox型的模拟数据,Schaefer
和Fox型的产量模型对模拟数据的类型并不敏感,均可以获得较好的参数估计值。
此文的结果还显示移动平均和白色噪音并不能显著地影响结果。根据剩余产量模
型的预测结果可以得知,赤峰市渔业生产单位的渔业资源已基本上得到充分利用,
如若拓展空间,任务十分艰巨。实施流域渔业生产可持续发展战略,对促进流域经济的繁荣,社会的发展具
有非常重大意义。本论文的建议如下：一是加强流域生态治理,提高水资源的再
生能力。二是加强流域开发管理,提高水资源的利用能力。三是加强流域用水管
理,提高水资源的节约能力。四是加强流域环保管理,提高水资源的防污能力。
五是调整渔业产业结构,提高流域渔业生产能力。六是加大科技投入力度,确保
渔业生产稳步增长。七是加强基础工程建设,提高渔业生产保障能力。八是搞好
渔业资源评估,为生产提供科学的依据。九是加强渔业执法管理,提高渔业生产
管理水平。
XiLiao River of Inner Mongolia is the geographical position is mainly in Tongliao city and Chifeng city of the eastern Inner Mongolia, there are12banners, counties and districts in Chifeng city, there are8banners, counties and districts in Tongliao city. Most of the water is in Inner Mongolia autonomous region, except for parts in the northeastern of Hebei province. Upstream is in mountains in Chifeng City, and the downstream is in plain in Tongliao City. In the XiLiao River, there are7large reservoirs with a total capacity of3352million m3, irrigation capacity790million m3; there are23medium-sized reservoirs, with a total capacity of437million m3, irrigation capacity of242million m3; there84small reservoirs with a total capacity of156million m3, irrigation capacity of94billion m3.
The stock assessment of fresh-water fishery resources of the two cities of XiLiao River, can indicate the development potential and sustainable utilization capacity of the fishery resources in the region. This will provide some scientific evidences for the management, protection and sustainable utilization of the fishery resource in this region. This is also the purpose of this thesis.
The gray system theory is put forward by the Chinese scholar professor Deng Julong in1982, it deals with the incomplete information. The basic theory is that all the random variables are regarded as gray variables which changed within a certain range. It transforms the original random data vector into a data vector which has relatively strong orders. Thus we could build continuous differential equation model, weaken uncertainties, strength the orderliness. Grey system may be used for few data and in the grey information condition. In comparison with the traditional methods, grey modeling has no error accumulation problem, and can be used for the long-term and high precision prediction, make up for the deficiency of conventional statistic methods. Fishery system is a multi-factor, multi-layer and multi-object system, which is consisted of many intricate relationships, thus fishery system is a typical grey system and the grey system theory can be used it it. However, there is few reports of the application of grey model in fresh-water fisheries. Therefore, the thesis applied the grey forecasting model to predict the fresh-water culture production and aquaculture area in ChiFeng city and Tongliao city for2009-2013.
Using gray prediction model, this thesis predicted fishery production and fishery area in TongLiao city, ChiFeng city and the related reservoirs. In the analysis results of the16data sets, when using5year data for prediction, there are7data sets with prediction accuracy of grade1, there are7data sets with prediction accuracy of grade2, there is1data set with prediction accuracy of grade3, there is1data set with prediction accuracy of grade4, the percentage of accuracy above grade2is87.5%. When using10year data for prediction, there are4data sets with prediction accuracy of grade1, there are5data sets with prediction accuracy of grade2, there are4data sets with prediction accuracy of grade3, there are3data sets with poor prediction accuracy which do not meet the prediction requirement, the percentage of accuracy above grade2is56.3%. When using15year data for prediction (there are12year data in TongLiao city), there are2data sets with prediction accuracy of grade1, there are5data sets with prediction accuracy of grade2, there are6data sets with prediction accuracy of grade3, there is1data set with prediction accuracy of grade4, there are2data sets with poor prediction accuracy which do not meet the prediction requirement, the percentage of accuracy above grade2is43.7%. When using20year data for prediction, there are5data sets with prediction accuracy of grade2, there are3data sets with prediction accuracy of grade3, there are2data sets with prediction accuracy of grade4, there is no data set with prediction accuracy of grade1. When using23year data to predict the fishery production of ChiFeng city, the prediction accuracy is grade2. Therefore, when using short time series data sets the prediction accuracy was high, the long time series data sets have low prediction accuracy, and even do not meet the prediction accuracy requirements. When using of GM(1,1) Gray prediction model to predict the sub-model, the accuracy of change suggested the stability of fish production systems. However, GM(1,1) Gray prediction model can not rule out the affect of extreme weather or policy impact on fishery production system, therefore, this prediction depends on the stability of system. If we use the data based on1998-2001, even the model prediction values back to the generation of relative error which is also very small, but it can not predict the crash of fishery production accurately in2002. Therefore, the key to accurate prediction in fishery system is stability. To make sure how to determine the stability of this system is the focus of study. At present, we can use sub prediction method combining statistical to predict, when we clear some outside interfering factors of gray system, and this method is based on an underlying assumption that the system dose not exist severe mutation in a short time. So we can think that it is accurate for the gray prediction model predicting the production of relative stable short-term, it is not appropriate for a long term extrapolation application. Because of the Xiliao He River Basin drought in a dry environment perennial, the gray prediction model showed that there is still room for the development of fish farming area in Chifeng, and Tongliao fish farming area could not improve under the adverse conditions of natural environment. Even though both of this regional fisheries production has increased year by year, the upgrade potential is really limited. Although sometimes the prediction accuracy of using long and short time series data sets were the same, but the prediction results are far from those in actual production. Then it may be concluded that the grey prediction model is able to make accurate short-term prediction, but may not be used for long-term extrapolation.
Because the fishery area in XiLiao River generally declined due to frequent droughts, the fishery production has been limited by the environment carrying capacity. Although the gray model predicted that the fishery production is still increasing year by year, but the potential of increase is very limited.
In fisheries management, maximum sustainable yield (MSY) is still one of the management goal. Surplus production models are the classic methods to predict MSY, and are among the main theories and models in modern fish stock assessment and management. It treat fish population or resource stock as a study unit, analyze the relationship between sustainable yield, optimum fishing effort and population size. The required data are a time series of fishery statistics of yield and fishing effort or catch per unit effort, it does not require the biological information of the fish population. Because of simplicity and less data demanding, the surplus production model have been widely used in fish stock assessment. This model is based on the theory of S style population growth, it combines recruitment, growth, and natural mortality together as an one variable function, then derives the appropriate math equations for the surplus production model. In China in1973the Schaefer (1973) model was used to assess the round scad resource in the Pearl river estuary in Guangdong. Since then this model has become one of the familiar models in the fisheries department in China, and had been widely used in the fish stock assessment of marine and large inland fresh water fisheries in China. This thesis applied two classical surplus production models, four non-equilibrium surplus production model, and two software packages developed by European and US scientists, to estimate the maximum sustainable yield (MSY) and fishing effort at MSY (power of fishing boats) of the fisheries of ChiFeng city
For the fisheries statistics of ChiFeng city, the results of this dissertation showed that different assessment models produced different parameter estimates. On the basis of the results and the original data, I tend to suggest that the MSY of ChiFeng city fisheries may be about8000t. Among the production models used in this dissertation, the non-equilibrium surplus production model (Schnute, W-H, D-Fox, I-Fox) had better performances, in particular the relative estimation errors (REE) of W-H and D-Fox were less than10%. For the Schaefer and Fox type simulated data, the Schaefer and Fox type production models were not sensitive to the simulated data, they achieved relatively good parameter estimates. The results also showed that the moving average and white noises could not significantly influence the results. On the basis of the surplus production model results, the fisheries resource of ChiFeng city have been basically fully utilized, it can be a difficult task if further expansion of the fishery is sought.
The implementation of sustainable development strategy of fisheries in the region has very important significance for the economic prosperity and social development. This thesis has put forward the following suggestions:1, to strengthen the regional ecological management and to increase the renewable ability of water resources;2, to strengthen the regional exploitation management to increase the utilization ability of water resources;3, to strengthen the regional water consumption management to increase the saving ability of water resources;4, to strengthen the regional environment protection management to increase anti-pollution ability of water resources;5, to adjust the fisheries industry structure to increase the fishery production ability of the region;6, to increasing the investment in science and technology to ensure steady growth of fishery production;7, to strengthen the construction of infra-structure to safe-guard the fishery production;8, to improve fish stock assessment to provide scientific basis for the fishery production;9, to strengthen the fishery law enforcement management to improve the fishery production management level.
对西辽河流域两个地区的淡水渔业进行评估,可以客观反映该流域渔业的发展潜力和可持续利用能力,以为该流域地区的渔业管理、保护和持续利用提供科学的依据,这也是该篇论文选题的意义所在。
灰色系统理论是我国学者邓聚龙教授于1982年提出的处理不完全信息的一种理论。其基本理论是把一切随机量看作是在一定范围内变化的灰色量并加以处理,将具有随机性的原始数据列转化为一个具有较强规律性的数据列,从而可以建立连续微分方程模型,弱化不确定性,强化规律性。灰色系统在较少量数据及在灰色信息的情况下较为适用。与传统方法比较,灰色建模不存在误差积累问题,可用于长期高精度的预测,弥补了常规统计方法的不足。渔业系统是个多因素、多层次、多目标的大系统,由诸多错综复杂的关系组成,因此渔业系统是典型的灰色系统,适用于灰色系统理论。然而灰色模型在淡水渔业上的应用尚不多见。因此,本论文应用灰色系统预测模型对通辽市、赤峰市2009-2013淡水养殖产量和养殖面积进行了预测。
本论文利用灰色模型对通辽市、赤峰市及其相关水库的渔业产量和渔业面积进行预测。对16组数据分析的结果中,用5年数据进行预测,共有7组数据预测精度达到一级,7组数据预测精度达到二级,1组数据预测精度达到三级,1组数据预测精度达到四级,二级以上精度占87.5%。用10年数据进行预测,有4组数据预测精度达到一级,有5组数据预测精度达到二级,有4组数据预测精度达到三级,有3组数据预测精度较差,不符合预测要求,二级以上精度占56.3%。用15年数据进行预测(其中通辽市渔业产量和养殖水面是12年的数据),有2组数据预测精度达到一级,有5组数据预测精度达到二级,有6组数据预测精度达到三级,有1组数据预测精度达到四级,有2组数据预测精度较差,不符合预测要求,二级以上精度占43.7%。用20年数据进行预测,有5组数据预测精度达到二级,有3组数据预测精度达到三级,有2组数据预测精度达到四级,没有一级精度。用23年数据预测赤峰市渔业产量,达到二级精度。可见,使用短期资料进行预测精度相对比较高,用中长期资料进行预测精度相对比较低,甚至不符合预测精度要求。利用GM(1,1)灰色预测模型分段建模进行预测,其精度变化也表明渔业生产系统的稳定性。利用GM(1,1)灰色预测模型进行预测的过程中并不能排除极端气候或政策干预对渔业生产系统的影响,因此,预测过程中依赖于系统的稳定性。如利用通辽市1998-2001年的数据为依据,模型预测值回代的相对误差也极小,但是并不能准确预测2002年渔业产量的暴跌。因此,渔业系统的稳定性是预测准确的关键。而如何判断判断系统是否稳定,将是研究的重点。在目前,可以结合统计的方法,在明确灰色系统外界某些干扰因素的情况下,可以使用分段预测的方法,进行预测,这种方法也是基于一个基本的假设即短时间内系统不会发生剧烈的突变。因此可以认为灰色预测模型能够对生产相对稳定的短期预测是比较准确的,不宜做长期的外推应用。
由于西辽河流域常年少雨干旱,灰色模型的预测结果表明,赤峰地区的渔业养殖面积仍有发展的空间,而通辽地区的渔业养殖面积在自然环境持续恶劣的情况下,不会有太大的起色。虽然两个地区渔业产量还呈逐年上升趋势,但提升的潜力非常有限。
在渔业管理中,实现最大可持续产量仍旧是其一个主要的目标。剩余产量模型是预测最大可持续产量(MSY)的经典方法,也是现代渔业资源评估和管理的主要理论模型之一。它是把种群或资源群体作为一个研究分析的单位,研究一个资源群体的持续产量、最佳捕捞努力量和资源群体大小之间的平衡关系。其所要求的资料仅仅是多年的渔获量和捕捞努力量或单位捕捞努力量渔获量的渔业统计资料,不需要资源群体本身的生物学资料。剩余产量模型因其简单和所需数据较少的特点为渔业资源评估所广泛采用。该模型是以种群增长S型曲线一般模型为理论基础,把资源群体的补充、生长、和自然死亡综合起来作为资源群体大小的
一个单变量函数进行分析,然后推导出所需的剩余产量模型的数学式。在我国,
自1973年开始应用Schaefer (1957)模型评估广东珠江口蓝园鲹资源以来,这个
模型已成为我国有关渔业部门最熟悉的数学模型之一,并被广泛应用于我国海洋
和内陆大水面水域渔业资源评估的分析研究。本论文应用了二个经典剩余产量模
型,四个非平衡剩余产量模型,和二个由欧美科学家开发的软件对赤峰市渔业的
最大持续产量和与最大持续产量相对应的捕捞努力量(渔船马力数)进行预测。对于赤峰市渔业统计数据,此文的结果显示不同的评估模型产生不同的参数
估计。结合这些结果和原始数据分析,我倾向于建议赤峰市渔业的最大持续产量
(MSY)大致在8000t左右。在此文应用到的产量模型中,线性非平衡产量模型
(Schnute, W-H, D-Fox, I-Fox)的表现较好,其中W-H和D-Fox模型的平均相
对估计误差(REE)值小于10%。对于Schaefer和Fox型的模拟数据,Schaefer
和Fox型的产量模型对模拟数据的类型并不敏感,均可以获得较好的参数估计值。
此文的结果还显示移动平均和白色噪音并不能显著地影响结果。根据剩余产量模
型的预测结果可以得知,赤峰市渔业生产单位的渔业资源已基本上得到充分利用,
如若拓展空间,任务十分艰巨。实施流域渔业生产可持续发展战略,对促进流域经济的繁荣,社会的发展具
有非常重大意义。本论文的建议如下：一是加强流域生态治理,提高水资源的再
生能力。二是加强流域开发管理,提高水资源的利用能力。三是加强流域用水管
理,提高水资源的节约能力。四是加强流域环保管理,提高水资源的防污能力。
五是调整渔业产业结构,提高流域渔业生产能力。六是加大科技投入力度,确保
渔业生产稳步增长。七是加强基础工程建设,提高渔业生产保障能力。八是搞好
渔业资源评估,为生产提供科学的依据。九是加强渔业执法管理,提高渔业生产
管理水平。
XiLiao River of Inner Mongolia is the geographical position is mainly in Tongliao city and Chifeng city of the eastern Inner Mongolia, there are12banners, counties and districts in Chifeng city, there are8banners, counties and districts in Tongliao city. Most of the water is in Inner Mongolia autonomous region, except for parts in the northeastern of Hebei province. Upstream is in mountains in Chifeng City, and the downstream is in plain in Tongliao City. In the XiLiao River, there are7large reservoirs with a total capacity of3352million m3, irrigation capacity790million m3; there are23medium-sized reservoirs, with a total capacity of437million m3, irrigation capacity of242million m3; there84small reservoirs with a total capacity of156million m3, irrigation capacity of94billion m3.
The stock assessment of fresh-water fishery resources of the two cities of XiLiao River, can indicate the development potential and sustainable utilization capacity of the fishery resources in the region. This will provide some scientific evidences for the management, protection and sustainable utilization of the fishery resource in this region. This is also the purpose of this thesis.
The gray system theory is put forward by the Chinese scholar professor Deng Julong in1982, it deals with the incomplete information. The basic theory is that all the random variables are regarded as gray variables which changed within a certain range. It transforms the original random data vector into a data vector which has relatively strong orders. Thus we could build continuous differential equation model, weaken uncertainties, strength the orderliness. Grey system may be used for few data and in the grey information condition. In comparison with the traditional methods, grey modeling has no error accumulation problem, and can be used for the long-term and high precision prediction, make up for the deficiency of conventional statistic methods. Fishery system is a multi-factor, multi-layer and multi-object system, which is consisted of many intricate relationships, thus fishery system is a typical grey system and the grey system theory can be used it it. However, there is few reports of the application of grey model in fresh-water fisheries. Therefore, the thesis applied the grey forecasting model to predict the fresh-water culture production and aquaculture area in ChiFeng city and Tongliao city for2009-2013.
Using gray prediction model, this thesis predicted fishery production and fishery area in TongLiao city, ChiFeng city and the related reservoirs. In the analysis results of the16data sets, when using5year data for prediction, there are7data sets with prediction accuracy of grade1, there are7data sets with prediction accuracy of grade2, there is1data set with prediction accuracy of grade3, there is1data set with prediction accuracy of grade4, the percentage of accuracy above grade2is87.5%. When using10year data for prediction, there are4data sets with prediction accuracy of grade1, there are5data sets with prediction accuracy of grade2, there are4data sets with prediction accuracy of grade3, there are3data sets with poor prediction accuracy which do not meet the prediction requirement, the percentage of accuracy above grade2is56.3%. When using15year data for prediction (there are12year data in TongLiao city), there are2data sets with prediction accuracy of grade1, there are5data sets with prediction accuracy of grade2, there are6data sets with prediction accuracy of grade3, there is1data set with prediction accuracy of grade4, there are2data sets with poor prediction accuracy which do not meet the prediction requirement, the percentage of accuracy above grade2is43.7%. When using20year data for prediction, there are5data sets with prediction accuracy of grade2, there are3data sets with prediction accuracy of grade3, there are2data sets with prediction accuracy of grade4, there is no data set with prediction accuracy of grade1. When using23year data to predict the fishery production of ChiFeng city, the prediction accuracy is grade2. Therefore, when using short time series data sets the prediction accuracy was high, the long time series data sets have low prediction accuracy, and even do not meet the prediction accuracy requirements. When using of GM(1,1) Gray prediction model to predict the sub-model, the accuracy of change suggested the stability of fish production systems. However, GM(1,1) Gray prediction model can not rule out the affect of extreme weather or policy impact on fishery production system, therefore, this prediction depends on the stability of system. If we use the data based on1998-2001, even the model prediction values back to the generation of relative error which is also very small, but it can not predict the crash of fishery production accurately in2002. Therefore, the key to accurate prediction in fishery system is stability. To make sure how to determine the stability of this system is the focus of study. At present, we can use sub prediction method combining statistical to predict, when we clear some outside interfering factors of gray system, and this method is based on an underlying assumption that the system dose not exist severe mutation in a short time. So we can think that it is accurate for the gray prediction model predicting the production of relative stable short-term, it is not appropriate for a long term extrapolation application. Because of the Xiliao He River Basin drought in a dry environment perennial, the gray prediction model showed that there is still room for the development of fish farming area in Chifeng, and Tongliao fish farming area could not improve under the adverse conditions of natural environment. Even though both of this regional fisheries production has increased year by year, the upgrade potential is really limited. Although sometimes the prediction accuracy of using long and short time series data sets were the same, but the prediction results are far from those in actual production. Then it may be concluded that the grey prediction model is able to make accurate short-term prediction, but may not be used for long-term extrapolation.
Because the fishery area in XiLiao River generally declined due to frequent droughts, the fishery production has been limited by the environment carrying capacity. Although the gray model predicted that the fishery production is still increasing year by year, but the potential of increase is very limited.
In fisheries management, maximum sustainable yield (MSY) is still one of the management goal. Surplus production models are the classic methods to predict MSY, and are among the main theories and models in modern fish stock assessment and management. It treat fish population or resource stock as a study unit, analyze the relationship between sustainable yield, optimum fishing effort and population size. The required data are a time series of fishery statistics of yield and fishing effort or catch per unit effort, it does not require the biological information of the fish population. Because of simplicity and less data demanding, the surplus production model have been widely used in fish stock assessment. This model is based on the theory of S style population growth, it combines recruitment, growth, and natural mortality together as an one variable function, then derives the appropriate math equations for the surplus production model. In China in1973the Schaefer (1973) model was used to assess the round scad resource in the Pearl river estuary in Guangdong. Since then this model has become one of the familiar models in the fisheries department in China, and had been widely used in the fish stock assessment of marine and large inland fresh water fisheries in China. This thesis applied two classical surplus production models, four non-equilibrium surplus production model, and two software packages developed by European and US scientists, to estimate the maximum sustainable yield (MSY) and fishing effort at MSY (power of fishing boats) of the fisheries of ChiFeng city
For the fisheries statistics of ChiFeng city, the results of this dissertation showed that different assessment models produced different parameter estimates. On the basis of the results and the original data, I tend to suggest that the MSY of ChiFeng city fisheries may be about8000t. Among the production models used in this dissertation, the non-equilibrium surplus production model (Schnute, W-H, D-Fox, I-Fox) had better performances, in particular the relative estimation errors (REE) of W-H and D-Fox were less than10%. For the Schaefer and Fox type simulated data, the Schaefer and Fox type production models were not sensitive to the simulated data, they achieved relatively good parameter estimates. The results also showed that the moving average and white noises could not significantly influence the results. On the basis of the surplus production model results, the fisheries resource of ChiFeng city have been basically fully utilized, it can be a difficult task if further expansion of the fishery is sought.
The implementation of sustainable development strategy of fisheries in the region has very important significance for the economic prosperity and social development. This thesis has put forward the following suggestions:1, to strengthen the regional ecological management and to increase the renewable ability of water resources;2, to strengthen the regional exploitation management to increase the utilization ability of water resources;3, to strengthen the regional water consumption management to increase the saving ability of water resources;4, to strengthen the regional environment protection management to increase anti-pollution ability of water resources;5, to adjust the fisheries industry structure to increase the fishery production ability of the region;6, to increasing the investment in science and technology to ensure steady growth of fishery production;7, to strengthen the construction of infra-structure to safe-guard the fishery production;8, to improve fish stock assessment to provide scientific basis for the fishery production;9, to strengthen the fishery law enforcement management to improve the fishery production management level.
引文
蔡为武.西辽河流域开发的建议[J].东北水利水电,1994,1：17-24.
曹华.做好渔业环保工作促进渔业持续发展[J].科学养鱼,2003,11：6.
曹少鹏,刘群.把不确定性引入生物学参考点F0.1和Fmax的估计以评估东海带鱼渔业资源[J].南方水产,2007,3(2)：42-48.
陈海明.基于可持续发展的渔业资源管理研究[D].华南理工大学,2009,12-06.
陈文河,邵青.拉格朗日插值法在渔业资源评估上的应用[J].湛江海洋大学学报,2003,23(6)：35-38.
陈文河,梁振林.广东省海洋捕捞产量灰色预测[J].海洋水产研究,2006,27(5)：74-78.
陈卫忠,李长松,俞连福.用剩余产量模型专家系统(CLIMPROD)评估东海鲐参鱼类最大持续产量[J].水产学报,1997,21(4)：404-408.
陈卫忠,李长松,胡芬等.渔业资源评估专家系统设计及实践[J].水产学报,1999,23(4)：343-349.
陈卫忠,李长松,胡芬.实际种群分析法在绿鳍马面纯资源评估中的应用和改进[J].水产学报,2000,24(6)：522-526.
陈新军.海洋渔业资源可持续利用评价[D].南京农业大学,2001,09-01.
陈新军.渔业资源经济学[M].北京,中国农业出版社,2004,138-177.
陈新军,周应祺.论渔业资源的可持续利用[J].资源科学,2001,23(2)：70-74.
陈新军,周应祺.渔业资源可持续利用系统化评价方法的应用研究[J].上海水产大学学报,2002,12(1)：29-34.
陈新军,周应棋.渔业资源可持续利用的灰色相对关联评价[J].水产学报,2002,26(4)：331-336.
陈新军,周应祺.中国渔业产量结构的灰色关联分析[J].中国渔业经济,2002,2：30-33.
陈新军,周应祺.基于BP模型的渔业资源可持续利用预警系统评价[J].中国渔业经济,2003,3：23-25.
陈新军,钱卫国,刘必林等.利用衰减模型评估柔鱼西部冬春生群体资源[J].海洋湖沼通报,2008,2：130-140.
陈新军.灰色系统理论在渔业科学中的应用[M].北京：中国农业出版社,2003,1-3,94-95.
赤峰市人民政府地方志办公室.赤峰市年鉴2009[M].呼和浩特,内蒙古科学技术出版社,2009.51-74.
赤峰市统计局.赤峰市统计年鉴2008.2008.
赤峰市统计局.赤峰市2009国民经济和社会发展统计公报.2010.
赤峰市水产站.赤峰市2008年渔业工作总结及2009年工作安排[内部资料].2008.
赤峰市水产站.赤峰市渔业“十一五”规划[内部资料],2005.
赤峰市水产技术工作站.1986-2008年赤峰市渔业统计年报汇总表.
崔鹤,刘群,王艳君.连续形Fox产量模型在模拟和实际渔业评估中的应用[J].南方水产,2008,4(2)：34-42.
达里诺尔自然保护区管理委员会,赤峰市环境保护局.内蒙古达里诺尔自然保护区综合考察资料汇编[内部资料].1996,
党耀国,刘思峰,王正新等.灰色预测与决策模型研究[M].北京：科学出版社,2009,1.
邓景耀,叶昌臣.渔业资源学[M].重庆：重庆出版社,2000.
邓聚龙.灰色系统(社会·经济)[M].北京：国防工业出版社,1985,1-5.
冯波,陈新军,西田勤.应用年龄结构产量模型评估印度洋黄鳍金枪鱼资源[J].生态学报2010,30(13)：3375—3384.
高红建,蔡锦忠,潘焕祥.灰色理论在城市总体规划中的应用研究[J].基建优化,2005,26(5)：106-1081.
高清廉,邱天霞,宋协法等.山东省海洋渔业结构调整研究[J].青岛海洋大学学报,1999,29(2)：215-223.
郭明.渤海对虾产量的灰色预测[J].水产科学,1992,11(3)：10-14.
郭玉春,刘艳红,张福泉等.莫力庙水库的管理工作[J].内蒙古水利,2007,4：174-175.
郭玉春,刘艳红,张福泉等.浅谈通辽市沙漠水库的经济效益[J].内蒙古水利,2007,3：48-49.
韩一平.达里湖渔业资源现状调查分析[J].内蒙古水利,2007,1：45-46.
黄良敏,于浩.我国黄鳍马面鲀的资源评估与可持续利用初探[J].福建水产,2003,2：10-14.
黄样棋.中国内陆渔业在世界上的地位[J].内陆水产,1998,3：27-28.
黄样棋.我国水产品产量在世界上的地位[J].中国渔业经济研究,1998,2：36-27.
何福林.永州市渔业资源开发利用研究[D],2006,12-01.
侯庆国.吉林省西部半干旱地区生态环境特征及其水资源合理开发利用[J].农业现代化研究,2006,27(10)：32-34.
贾敬德.21世纪我国淡水渔业展望[J].淡水渔业,2000,30(1)：3-6.
贾敬德.我国西部地区的渔业现状及发展前景[J].淡水渔业,2001,31(1)：3-5.
贾敬德.淡水渔业环境现状及保护对策[J].淡水渔业,2004,34(5)：59-61.
贾敬德.保护环境是渔业持续发展的当务之急[J].淡水渔业,1997,27(4)：26-29.
贾敬德.长江渔业生态环境变化的影响因素[J].中国水产科学,1999,6(2)：112-114.
贾海明.我国渔业发展的问题、原因及对策研究[D].中国海洋大学,2008,5.
景倩.插值法在渔业资源评估上的应用[J].商场现代化,2008,7：229.
贾玉鹏.通辽市西辽河流域生态保护建设存在的问题和建议[J].内蒙古环境保护,2004,16(1)：38-40.
贾晓平,李纯厚,邱永松等.广东海洋渔业资源调查评估与可持续利用对策[M].北京：海洋出版社,2005.
姜东锴,包玉风,宝顺等.关于西辽河通辽站断面以下河段断流问题的探讨[J].内蒙古科技与经济,2002,3：23-24.
焦念志.山东省渔业产量的灰色预测[J].齐鲁渔业,1993,3：33-37.
黎旦光,李桂珍.西辽河防洪问题[J].内蒙古水利,1994,3：10-15.
李岗,熊猛.浅析灰色系统的发展及其展望[J].四川建筑科学研究,2004,30(1)：124-126.
李凤民,徐进章.黄土高原半干旱地区集水型生态农业分析[J].中国生态农业学报2002,10(1)：102-103.
李继龙,吴万夫.我国水产品生产与对食物安全的贡献[J].中国渔业经济研究,1997,6：19-21.
李孜军.1992-2001年我国灰色系统理论应用研究进展[J].系统工程,2003,21(5)：8-12.
李树国.内陆水产养殖的水域污染及其防治对策[J].水产科学,2005,24(3)：34-35.
李壮,刘群.应用渔业体长分析方法ELEFAN和SLCA估算鱼类生长参数的研究[J].海洋湖沼通报,2007,3：81-87.
林彦红,彭本荣.基于种群层次的渔业资源评估模型研究进展[J].福建水产,2008,26(4)：79-84.
刘军.用灰色理论预测青海湖裸鲤的年产量[J].大连水产学院学报,2006,21(4)：390-393.
柳富荣.21世纪我国内陆渔业作用与发展战略[J].河南水产,1998,4：4-6.
柳富荣.发展内陆渔业的战略作用[J].中国渔业经济研究,1999,3：23.
刘群,任一平,沈海学等.渔业资源评估在渔业管理中的作用[J].海洋湖沼通报,2003(1)：72-76.
刘群,王艳君,尤凯.渔业产量和资源生物量数值模型及相关因子的初步研究[J].中国海洋大学学报,2005,35(3)：375-380.
刘群,任一平,王艳君等.应用非平衡产量模型对卡塔尔渔业的分析[J].青岛海洋大学学报,2003,33(2)：206-210.
刘群,任一平.Fish population behavior and their relation to the environment variations [J].青岛海洋大学学报,2000,30(3)：472-481.
刘群,任一平,王艳君等.大眼金枪鱼的资源现状[J].海洋湖沼通报,2003,2：74-78.
刘群,徐宾铎,任一平.灰色预测模型在青岛市淡水养殖产量预测中的应用[J].南方水产,2009,5(5)：38-43.
刘思峰,谢乃明.灰色系统理论及其应用[M].4版.北京：科学出版社,2008,2.
刘思峰,谢乃明等.灰色系统理论及其应用[M].3版.北京：科学出版社,2008,1.
刘思峰.灰色系统理论的产生、发展及前沿动态[J].浙江万里学院学报,2003,16(4)：14-17.
刘祥,孙文丽.西辽河水系变迁[J].内蒙古水利,2001,4：69-71.
刘祥,侯兰英,李晓丽.通辽地区环境退化与水资源的关系[J].内蒙古地质,2000,1：5-40.
刘祥,关德武.完善红山水库除险加固工程促进西辽河流域经济社会发展[J].中国水利,2000,12：50-51.
刘贤赵,刘德林,宋孝玉.西北干旱区水资源开发利用现状及对策[J].水资源与水工程学报2005,16(2)：1-6.
南梅,邱莲,南英等.论库区水土保持生态建设在红山水库今后发展中的地位[J].内蒙古科技与经济,2002,4：98-99.
内蒙古统计厅.内蒙古统计年鉴2008[公开资料].2008.
内蒙古自治区红山水库管理委员会.红山水库志[内部资料].2001.
倪建峰,刘群.剩余产量模型在不同渔业中的应用[J].海洋湖沼通报,2004.1：60-67.
潘澎.灰色预测模型在山东渔业产量预测中的应用研究[J].齐鲁渔业,1997,14(3)：1-2.
彭本初.内蒙古自治区大中水面资源利用现状及今后渔业发展思路[J].华北农学报,2004,19(S1)：134-136.
饶群,薛联青,芮孝芳.流域开发的可持续性研究[J].西北水资源与水工程,2001,12(3)6-12.
任一平,刘群,李庆怀等.青岛近海小型鳀鲱鱼类渔业生物学特性的研究[J].海洋湖沼通报,2002,1：69-74.
申玉春,杨景峰,祁宝霞等.孟家段水库渔业资源调查报告[内部资料]2000.
双辽市统计局.双辽市统计年鉴2008[公开资料].2008.
水利部东北勘测设计研究院.西辽河干流防洪规划报告[内部资料].2001.
孙利东,刘群.连续Schaefer产量模型在三种模拟渔业评估中的应用[J].海洋湖沼通报,2004,4：68-74.
通辽市统计局.通辽市2009年国民经济和社会发展统计公报.2010
通辽市水产站.内蒙古通辽市“十五”期间渔业发展概况[内部资料].2006.
通辽市水产站.通辽市2008年渔业工作总结及2009年工作计划[内部资料].2008.
通辽市水产站.通辽市“十一五’渔业发展规划[内部资料].2005.
通辽市水资源可持续利用规划报告[内部资料]
通辽市水产站.通辽市水生生物资源调查被告[内部资料].2007.
通辽市水产站.1997-2008年通辽市渔业统计年报汇总表.
王海华,徐厚民,黄江峰.我国水产养殖业现状与发展对策[J].江西水产科技,2003,1：9-12.
王学锋,冯波,侯刚.基于EPR模型的北部湾二长棘鲷渔业管理研究[J].农业系统科学与综合研究,2011,27(4),444-448.
王阳.西辽河流域赤峰段水资源现状及保护对策[J].内蒙古环境保护,2004,16(4)：35-38
王迎宾,刘群,王征等.一种基于渔获量和资源量指数求算鱼类自然死亡系数方法的研究[J].浙江海洋学院学报(自然科学版),2009,28(1)：5-11.
王迎宾,刘群.不同误差结构对运用股分析(CA)模型求算鱼自然死亡系数影响的初步研究[J].南方水产,2006,2(3)：7-15.
吴传钧.20世纪中国学术大典[M].福州：福建教育出版社,2002,148-150.
吴莉芳,刘春力,张东鸣等.中国湖泊和水库渔业资源发展利用存在的问题及其出路[J].吉林农业大学学报,2000,22(4)：100-104.
武坤.有关预测精度的几个问题[J].预测,1992,1：55-57.
夏章英,颜云榕.渔业管理[M].北京,海洋出版社,2008,58.
许民,任晓辉.赤峰市水资源现状分析及可持续利用措施[J].赤峰学院学报,2006,22(6)：41-42.
杨立敏,刘群.利用博弈模型分析和评价日本渔业管理[J].中国海洋大学学报,2007,37(3)：372-376.
叶元土,刘方贵.西部地区渔业现状与发展对策分析[J].中国渔业经济,2001,3：20-22.
殷瑞,刘群.应用体重结构的实际种群分析模型估算捕捞死亡系数和资源的初步研究[J].南方水产,2007,3(2)：36-41.
于长剑,刘廷玺,朝伦巴根等.通辽市宏观经济水资源优化配置研究[J].水利水电技术,2004,12：5-9.
乐佩琦.中国濒危淡水鱼类致危成因分析[J].湖泊科学,1995,773：271-276.
曾代球,赵毓秀,黄庆莲.辽河各河段的径流特性[J].水文,1997,3：54-56.
曾晓起,朴成华,姜伟等.胶州湾及其邻近水域渔业生物多样性的调查研究[J].中国海洋大学学报2004,34(6)：977-982.
詹秉义.渔业资源评估学[M].北京：中国农业出版社,1995,1-7,167-194.
张铭羽,吴万夫.关于西部内陆渔业的分析研究[J].中国渔业经济,2005,5：3-5.
张月霞,苗振清.渔业资源的评估方法和模型研究进展[J].浙江海洋学院学报(自然科学版),2006,25(3)：305—311.
赵文武.中国水产养殖业的发展前景[J].中国渔业经济研究,1998,1：12-14.
赵淑贞,任伯平.淡水资源可持续开发模式的思考[J].技术经济究,1996,6：42-43.
赵学山,石华.西辽河流域水资源开发利用现状及其发展方向[J].内蒙古利,2004,4：19-20.
赵中奇,吴瑞明,王浣尘.渔业资源最优经营管理问题[J].系统工程学报,2004,19(4)：423-426.
中国大百科全书总编辑委员会《地理学》编辑委员会.中国大百科全书地理学[M].北京：中国大百科全书出版社,1992,149-150.
中国渔业统计年鉴2008[M].北京,中国农业出版社,2009.
中共通辽市委史志办公室.通辽年鉴2008.
郑波,陈新军,李纲.GLM和GAM模型研究东黄海鲐资源渔场与环境因子的关系[J].水产 学报,2008,32(3)：379-386.
郑芳,刘群,王艳君.环境因子对黄海鳀鱼亲体-补充量关系影响的初步研[J].南方水产,2008,4(2)：15-20.
郑延学,李宏友.西辽河流域上游赤峰地区的水文特征[J].内蒙古水利,2000,4：57-58.
周元建,关则良.海洋渔业产量的灰色预测与分析[J].湛江水产学院学报,1994,14(1)：53-55.
朱家帅,张凯.从干旱区的水利生态观看中国21世纪水资源安全对策[J].湖北水利水电职业技术学院学报,2009,5(2)：37-40.
朱立新,李丽芳,梁振林.不确定性对北部湾二长棘鲷渔业资源评估影响的模拟研究[J].广东海洋大学学报,2009,29(1)：36-43.
Allen, K.R. A personal retrospect of the history of fisheries modeling. In population dynamics for fisheries management. Handbook, D.A. [ed.] North Beach, W.A. Australian society for fish biology.1994.21-28.
Barber, W. E. Maximum sustainable yield lives on. N. Am. J. [J]. Fish. Manage.1988,8:153-157.
Butterworth, D.S., P.A. Andrew. Dynamic catch-effort models for the hake stocks in ICSEAF Divisions 1.3 to 2.2. Coll. Sci. Pap. Int. Comm. SE Atl. Fish.,1984,11(1):29-58
Caddy, J.F., McGarvey, R. Targets or limits for management of fisheries? North American J. of Fisheries Management,1996,16:479-487
David, K.W., J.L. Deng. Contrasting grey system theory to probability and fuzzy. ACM SIGICE Bulletin.1995,20(3):3-9
Dounis, A.I., Tiropanis P., Tseles D., et al. A comparison of grey model and fuzzy predictive model for time series [J]. Int J Comput. Intel,2006,2 (3):176-181.
Fournier, D.A., Warburton, A.R. Evaluating fisheries management models by simulated adaptive control-introducing the composite model. Can. J. Fish. Aquat. Sci.,1989,46:1002-1012
Fox, W.W. An exponential yield model for equations from fishery science and a new formulation[J]. Trans Am Fish soc,1970,99:80-88.
Fox, W.W. Fitting the generalized stock production model by least-squares and equilibrium approximation[J]. Fish. Bull.1975,73:23-37.
Francis, R.I.C.C. Population modeling in New Zealand's quota management system. In population dynamics for fisheries management. Hancock, D.A. [ed.] North Beach, W.A. Australia, Australian society for fish Biology.1994.1-15
Gordon, G.N.G., Andrew, N.L., Montgomery, S.S. Deterministic compartmental model for the eastern king prawn fishery in New South Wales. Mar. Freshwater Res.,1995,46:793-807
Graham, M. Modern theory of exploiting a fishery and application to North Sea trawling[J]. J Cons Int Explor Mer,1935,37:199-204.
Gulland, J.A. Fishing and stocks of Iceland. Fish. Invest. Minist. Agric. Fish. Food,1961,4:1-52
Gulland, J.A. Science and fishery management. J. Cons. Int. Explor. Mer,1971,33:471-477
Gulland, J.A. Fish stock assessment, A Manual of Basic Methods[M]. Chichester:John Wiley and Sons.1983.83-146.
Gulland, J.A. Fish population dynamics (2nd edition). Chichester:John Wiley and Sons.1988
Hart, P.J.B., Reynolds, J.D. Fish biology and Fisheries, volume 2. Cornwall:Black Well Publishing, 2002.105-125
Hilborn, R. Comparison of fisheries control systems that utilize catch and effort data. J. Fish. Res. Bd. Can.,1979,36:1477-1489
Hilborn, R., C.J. Walters Quantitative fisheries stock assessment:choice, dynamics & uncertainty[M]. New York:Chapman & Hall,1992:9-13.
Hoggarth, D.D., S. Abeyasekera, R.I. Arthur, et al, Stock assessment for fishery management -A framework guide to the stock assessment tools of the Fisheries Management Science Program (FMSP),2006, FAO Fisheries Technical Paper,487, Rome, FAO, pp261.
Kimura, D.K. Variability in estimating catch in numbers at age and its impact on cohort analysis. Can. Spec. Pub. Fish. Aquat. Sci.,1989a,108:57-66
Kimura, D.K. Variability, tuning, and simulation for the Doubleday-Deriso catch at age model. Can. J. Fish. Aquat. Sci.,1989b,46:941-949
Kinas, P.G. Bayesian fishery stock assessment and decision making using adaptive importance sampling. Can. J. Fish. Aquat. Sci.,1996,53:414-423
Laloe, F. Should surplus production models be fishery description tools rather than biological models? Aquat. Living Resour.,1995,8:1-16
Larkin, P.A. An epitaph for the concept of maximum sustainable yield. Tan. Am. Fish. Soc.,1977,
Linhart, H., Zucchini, W. Model selection. New York:Wiley.1986
Ludwig, D., C.J. Walters, J. Cooke. Comparison of two models and two estimation methods for catch and effort data. Nat. Res. Modeling,1988,2:457-498
Ludwig, D. and C.J. Walters. A robust method for parameter estimation from catch and effort data[J]. Can. J. Fish. Aquat. Sci,1989,46:137-144.
Mace, P.M. A new role for MSY in single species and ecosystem approaches to fisheries stock assessment and management J]. Fish and Fisheries,2001(2):2-23.
Mao, M., Chirwa E.C. Combination of grey model GM (1,1) with three-point moving average for accurate vehicle fatality risk prediction [J]. Int J Crash,2005,10 (6):635-642.
Maunder, M.N. Is it time to discard the Schaefer model from the stock assessment scientist's toolbox?[J]. Fisheries Research,2003,61:145-149.
Polacheck, T., Hilborn, R., Punt, A.E. Fitting surplus production models:comparing methods and measuring uncertainty. Can. J. Fish. Aquat. Sci.,1993,50:2597-2607
Pell, J.J., Tomlinson, P.K. A generalized stock production model [J]. Bull. Inter-Am. Trop. Tuna Comm.1969,13:419-496.
Polacheck, T., Hilborn, R., Punt, A.E. Fitting surplus production models:comparing methods and measuring uncertainty. Can. J. Fish. Aquat. Sci.,1993,50:2597-2607
Prager, M.H. A suite of extensions to a non-equilibrium surplus production model [J]. Fishery Bulletin,1994,92:374-389.
Prager, M.H. Application of a surplus production model to a swordfish-like simulated stock with time-changing gear selectivity. Trans. Am. Fish. Soc.,1996,125:729-740
Prager, M.H. User's Manual for ASPIC:A Stock-Production Model Incorporating Covariates (ver.5) And Auxiliary Programs, National Marine Fisheries Service Miami Laboratory Document MIA-92/93-55 (revised) Beaufort Laboratory Document BL,2005,27pp.
Punt, A.E., and D.S. Butterworth. Results of first stage screening trials for a proposed whale stock management procedure. Rep. Int. Whal. Comm.,1989, Special Issue 11:191-197
Punt, A.E., P.A. Garratt, and A. Govender. On an approach for applying per-recruit methods to a protogynous hermaphrodite, with an illustration for the slinger Chrysoblephus puniceus. S. Afr. J. Mar. Sci.,1993,13:109-119
Schaefer, M.B. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries [J]. Bull Inter-Am Trop Tuna comm.1954,1(2):27-56.
Schaefer, M.B. A study of the dynamics of the fishery for yellow fin tuna in the eastern tropical Pacific Ocean [J]. Bull. Inter Am. Tuna Comm.1957,2:247-268.
Schnute, J. Improved estimates from the Schaefer production model:theoretical considerations[J]. Fish. Res. Board Can.,1977,34:583-103.
Su, S.F., Lin C.B., Hsu Y.T. A high precision global prediction approach based on local prediction approaches [J]. IEEE SMC-C,2002,32 (4):416-425.
Su, Z.M. and Liu, Q. An integral Fox-form of observation-error model (IFOXOEM)[J]. Fishery Research,1998,34:59-76.
Uhler, R.S. Least squares regression estimates of the Schaefer production model:Some Monte Carlo simulation result. Can. J. Fish. Aquat. Sci.,1979,37:1284-1294.
Walter, G.G. Delay-differential equation models for fisheries[J]. J. Fish. Res. Board Can.,1973, 30:939-945.
Walters, C.J., Hilborn, R., Adaptive control of fishing systems[J]. Fish. Res. Board Can.,1976, 33:145-159.
Walters, C.J., Hilborn, R., Exploratory assessment of historical recruitment patterns using relative abundance and catch data [J], Can. J. Fish. Aquat. Sci.,2005,62:1985-1990.
Walters, C.J., Hilborn, R., Christensen, R. Surplus production dynamics in declining and recovering fish populations[J], Can. J. Fish. Aquat. Sci.,2008,65:2536-2551.
Yoshimoto, S.S., Clarke, R.P. Comparing dynamic versions of the Schaefer and Fox production models and their application to lobster fisheries. Can. J. Fish. Aquat. Sci.,1993,50:181-189
曹华.做好渔业环保工作促进渔业持续发展[J].科学养鱼,2003,11：6.
曹少鹏,刘群.把不确定性引入生物学参考点F0.1和Fmax的估计以评估东海带鱼渔业资源[J].南方水产,2007,3(2)：42-48.
陈海明.基于可持续发展的渔业资源管理研究[D].华南理工大学,2009,12-06.
陈文河,邵青.拉格朗日插值法在渔业资源评估上的应用[J].湛江海洋大学学报,2003,23(6)：35-38.
陈文河,梁振林.广东省海洋捕捞产量灰色预测[J].海洋水产研究,2006,27(5)：74-78.
陈卫忠,李长松,俞连福.用剩余产量模型专家系统(CLIMPROD)评估东海鲐参鱼类最大持续产量[J].水产学报,1997,21(4)：404-408.
陈卫忠,李长松,胡芬等.渔业资源评估专家系统设计及实践[J].水产学报,1999,23(4)：343-349.
陈卫忠,李长松,胡芬.实际种群分析法在绿鳍马面纯资源评估中的应用和改进[J].水产学报,2000,24(6)：522-526.
陈新军.海洋渔业资源可持续利用评价[D].南京农业大学,2001,09-01.
陈新军.渔业资源经济学[M].北京,中国农业出版社,2004,138-177.
陈新军,周应祺.论渔业资源的可持续利用[J].资源科学,2001,23(2)：70-74.
陈新军,周应祺.渔业资源可持续利用系统化评价方法的应用研究[J].上海水产大学学报,2002,12(1)：29-34.
陈新军,周应棋.渔业资源可持续利用的灰色相对关联评价[J].水产学报,2002,26(4)：331-336.
陈新军,周应祺.中国渔业产量结构的灰色关联分析[J].中国渔业经济,2002,2：30-33.
陈新军,周应祺.基于BP模型的渔业资源可持续利用预警系统评价[J].中国渔业经济,2003,3：23-25.
陈新军,钱卫国,刘必林等.利用衰减模型评估柔鱼西部冬春生群体资源[J].海洋湖沼通报,2008,2：130-140.
陈新军.灰色系统理论在渔业科学中的应用[M].北京：中国农业出版社,2003,1-3,94-95.
赤峰市人民政府地方志办公室.赤峰市年鉴2009[M].呼和浩特,内蒙古科学技术出版社,2009.51-74.
赤峰市统计局.赤峰市统计年鉴2008.2008.
赤峰市统计局.赤峰市2009国民经济和社会发展统计公报.2010.
赤峰市水产站.赤峰市2008年渔业工作总结及2009年工作安排[内部资料].2008.
赤峰市水产站.赤峰市渔业“十一五”规划[内部资料],2005.
赤峰市水产技术工作站.1986-2008年赤峰市渔业统计年报汇总表.
崔鹤,刘群,王艳君.连续形Fox产量模型在模拟和实际渔业评估中的应用[J].南方水产,2008,4(2)：34-42.
达里诺尔自然保护区管理委员会,赤峰市环境保护局.内蒙古达里诺尔自然保护区综合考察资料汇编[内部资料].1996,
党耀国,刘思峰,王正新等.灰色预测与决策模型研究[M].北京：科学出版社,2009,1.
邓景耀,叶昌臣.渔业资源学[M].重庆：重庆出版社,2000.
邓聚龙.灰色系统(社会·经济)[M].北京：国防工业出版社,1985,1-5.
冯波,陈新军,西田勤.应用年龄结构产量模型评估印度洋黄鳍金枪鱼资源[J].生态学报2010,30(13)：3375—3384.
高红建,蔡锦忠,潘焕祥.灰色理论在城市总体规划中的应用研究[J].基建优化,2005,26(5)：106-1081.
高清廉,邱天霞,宋协法等.山东省海洋渔业结构调整研究[J].青岛海洋大学学报,1999,29(2)：215-223.
郭明.渤海对虾产量的灰色预测[J].水产科学,1992,11(3)：10-14.
郭玉春,刘艳红,张福泉等.莫力庙水库的管理工作[J].内蒙古水利,2007,4：174-175.
郭玉春,刘艳红,张福泉等.浅谈通辽市沙漠水库的经济效益[J].内蒙古水利,2007,3：48-49.
韩一平.达里湖渔业资源现状调查分析[J].内蒙古水利,2007,1：45-46.
黄良敏,于浩.我国黄鳍马面鲀的资源评估与可持续利用初探[J].福建水产,2003,2：10-14.
黄样棋.中国内陆渔业在世界上的地位[J].内陆水产,1998,3：27-28.
黄样棋.我国水产品产量在世界上的地位[J].中国渔业经济研究,1998,2：36-27.
何福林.永州市渔业资源开发利用研究[D],2006,12-01.
侯庆国.吉林省西部半干旱地区生态环境特征及其水资源合理开发利用[J].农业现代化研究,2006,27(10)：32-34.
贾敬德.21世纪我国淡水渔业展望[J].淡水渔业,2000,30(1)：3-6.
贾敬德.我国西部地区的渔业现状及发展前景[J].淡水渔业,2001,31(1)：3-5.
贾敬德.淡水渔业环境现状及保护对策[J].淡水渔业,2004,34(5)：59-61.
贾敬德.保护环境是渔业持续发展的当务之急[J].淡水渔业,1997,27(4)：26-29.
贾敬德.长江渔业生态环境变化的影响因素[J].中国水产科学,1999,6(2)：112-114.
贾海明.我国渔业发展的问题、原因及对策研究[D].中国海洋大学,2008,5.
景倩.插值法在渔业资源评估上的应用[J].商场现代化,2008,7：229.
贾玉鹏.通辽市西辽河流域生态保护建设存在的问题和建议[J].内蒙古环境保护,2004,16(1)：38-40.
贾晓平,李纯厚,邱永松等.广东海洋渔业资源调查评估与可持续利用对策[M].北京：海洋出版社,2005.
姜东锴,包玉风,宝顺等.关于西辽河通辽站断面以下河段断流问题的探讨[J].内蒙古科技与经济,2002,3：23-24.
焦念志.山东省渔业产量的灰色预测[J].齐鲁渔业,1993,3：33-37.
黎旦光,李桂珍.西辽河防洪问题[J].内蒙古水利,1994,3：10-15.
李岗,熊猛.浅析灰色系统的发展及其展望[J].四川建筑科学研究,2004,30(1)：124-126.
李凤民,徐进章.黄土高原半干旱地区集水型生态农业分析[J].中国生态农业学报2002,10(1)：102-103.
李继龙,吴万夫.我国水产品生产与对食物安全的贡献[J].中国渔业经济研究,1997,6：19-21.
李孜军.1992-2001年我国灰色系统理论应用研究进展[J].系统工程,2003,21(5)：8-12.
李树国.内陆水产养殖的水域污染及其防治对策[J].水产科学,2005,24(3)：34-35.
李壮,刘群.应用渔业体长分析方法ELEFAN和SLCA估算鱼类生长参数的研究[J].海洋湖沼通报,2007,3：81-87.
林彦红,彭本荣.基于种群层次的渔业资源评估模型研究进展[J].福建水产,2008,26(4)：79-84.
刘军.用灰色理论预测青海湖裸鲤的年产量[J].大连水产学院学报,2006,21(4)：390-393.
柳富荣.21世纪我国内陆渔业作用与发展战略[J].河南水产,1998,4：4-6.
柳富荣.发展内陆渔业的战略作用[J].中国渔业经济研究,1999,3：23.
刘群,任一平,沈海学等.渔业资源评估在渔业管理中的作用[J].海洋湖沼通报,2003(1)：72-76.
刘群,王艳君,尤凯.渔业产量和资源生物量数值模型及相关因子的初步研究[J].中国海洋大学学报,2005,35(3)：375-380.
刘群,任一平,王艳君等.应用非平衡产量模型对卡塔尔渔业的分析[J].青岛海洋大学学报,2003,33(2)：206-210.
刘群,任一平.Fish population behavior and their relation to the environment variations [J].青岛海洋大学学报,2000,30(3)：472-481.
刘群,任一平,王艳君等.大眼金枪鱼的资源现状[J].海洋湖沼通报,2003,2：74-78.
刘群,徐宾铎,任一平.灰色预测模型在青岛市淡水养殖产量预测中的应用[J].南方水产,2009,5(5)：38-43.
刘思峰,谢乃明.灰色系统理论及其应用[M].4版.北京：科学出版社,2008,2.
刘思峰,谢乃明等.灰色系统理论及其应用[M].3版.北京：科学出版社,2008,1.
刘思峰.灰色系统理论的产生、发展及前沿动态[J].浙江万里学院学报,2003,16(4)：14-17.
刘祥,孙文丽.西辽河水系变迁[J].内蒙古水利,2001,4：69-71.
刘祥,侯兰英,李晓丽.通辽地区环境退化与水资源的关系[J].内蒙古地质,2000,1：5-40.
刘祥,关德武.完善红山水库除险加固工程促进西辽河流域经济社会发展[J].中国水利,2000,12：50-51.
刘贤赵,刘德林,宋孝玉.西北干旱区水资源开发利用现状及对策[J].水资源与水工程学报2005,16(2)：1-6.
南梅,邱莲,南英等.论库区水土保持生态建设在红山水库今后发展中的地位[J].内蒙古科技与经济,2002,4：98-99.
内蒙古统计厅.内蒙古统计年鉴2008[公开资料].2008.
内蒙古自治区红山水库管理委员会.红山水库志[内部资料].2001.
倪建峰,刘群.剩余产量模型在不同渔业中的应用[J].海洋湖沼通报,2004.1：60-67.
潘澎.灰色预测模型在山东渔业产量预测中的应用研究[J].齐鲁渔业,1997,14(3)：1-2.
彭本初.内蒙古自治区大中水面资源利用现状及今后渔业发展思路[J].华北农学报,2004,19(S1)：134-136.
饶群,薛联青,芮孝芳.流域开发的可持续性研究[J].西北水资源与水工程,2001,12(3)6-12.
任一平,刘群,李庆怀等.青岛近海小型鳀鲱鱼类渔业生物学特性的研究[J].海洋湖沼通报,2002,1：69-74.
申玉春,杨景峰,祁宝霞等.孟家段水库渔业资源调查报告[内部资料]2000.
双辽市统计局.双辽市统计年鉴2008[公开资料].2008.
水利部东北勘测设计研究院.西辽河干流防洪规划报告[内部资料].2001.
孙利东,刘群.连续Schaefer产量模型在三种模拟渔业评估中的应用[J].海洋湖沼通报,2004,4：68-74.
通辽市统计局.通辽市2009年国民经济和社会发展统计公报.2010
通辽市水产站.内蒙古通辽市“十五”期间渔业发展概况[内部资料].2006.
通辽市水产站.通辽市2008年渔业工作总结及2009年工作计划[内部资料].2008.
通辽市水产站.通辽市“十一五’渔业发展规划[内部资料].2005.
通辽市水资源可持续利用规划报告[内部资料]
通辽市水产站.通辽市水生生物资源调查被告[内部资料].2007.
通辽市水产站.1997-2008年通辽市渔业统计年报汇总表.
王海华,徐厚民,黄江峰.我国水产养殖业现状与发展对策[J].江西水产科技,2003,1：9-12.
王学锋,冯波,侯刚.基于EPR模型的北部湾二长棘鲷渔业管理研究[J].农业系统科学与综合研究,2011,27(4),444-448.
王阳.西辽河流域赤峰段水资源现状及保护对策[J].内蒙古环境保护,2004,16(4)：35-38
王迎宾,刘群,王征等.一种基于渔获量和资源量指数求算鱼类自然死亡系数方法的研究[J].浙江海洋学院学报(自然科学版),2009,28(1)：5-11.
王迎宾,刘群.不同误差结构对运用股分析(CA)模型求算鱼自然死亡系数影响的初步研究[J].南方水产,2006,2(3)：7-15.
吴传钧.20世纪中国学术大典[M].福州：福建教育出版社,2002,148-150.
吴莉芳,刘春力,张东鸣等.中国湖泊和水库渔业资源发展利用存在的问题及其出路[J].吉林农业大学学报,2000,22(4)：100-104.
武坤.有关预测精度的几个问题[J].预测,1992,1：55-57.
夏章英,颜云榕.渔业管理[M].北京,海洋出版社,2008,58.
许民,任晓辉.赤峰市水资源现状分析及可持续利用措施[J].赤峰学院学报,2006,22(6)：41-42.
杨立敏,刘群.利用博弈模型分析和评价日本渔业管理[J].中国海洋大学学报,2007,37(3)：372-376.
叶元土,刘方贵.西部地区渔业现状与发展对策分析[J].中国渔业经济,2001,3：20-22.
殷瑞,刘群.应用体重结构的实际种群分析模型估算捕捞死亡系数和资源的初步研究[J].南方水产,2007,3(2)：36-41.
于长剑,刘廷玺,朝伦巴根等.通辽市宏观经济水资源优化配置研究[J].水利水电技术,2004,12：5-9.
乐佩琦.中国濒危淡水鱼类致危成因分析[J].湖泊科学,1995,773：271-276.
曾代球,赵毓秀,黄庆莲.辽河各河段的径流特性[J].水文,1997,3：54-56.
曾晓起,朴成华,姜伟等.胶州湾及其邻近水域渔业生物多样性的调查研究[J].中国海洋大学学报2004,34(6)：977-982.
詹秉义.渔业资源评估学[M].北京：中国农业出版社,1995,1-7,167-194.
张铭羽,吴万夫.关于西部内陆渔业的分析研究[J].中国渔业经济,2005,5：3-5.
张月霞,苗振清.渔业资源的评估方法和模型研究进展[J].浙江海洋学院学报(自然科学版),2006,25(3)：305—311.
赵文武.中国水产养殖业的发展前景[J].中国渔业经济研究,1998,1：12-14.
赵淑贞,任伯平.淡水资源可持续开发模式的思考[J].技术经济究,1996,6：42-43.
赵学山,石华.西辽河流域水资源开发利用现状及其发展方向[J].内蒙古利,2004,4：19-20.
赵中奇,吴瑞明,王浣尘.渔业资源最优经营管理问题[J].系统工程学报,2004,19(4)：423-426.
中国大百科全书总编辑委员会《地理学》编辑委员会.中国大百科全书地理学[M].北京：中国大百科全书出版社,1992,149-150.
中国渔业统计年鉴2008[M].北京,中国农业出版社,2009.
中共通辽市委史志办公室.通辽年鉴2008.
郑波,陈新军,李纲.GLM和GAM模型研究东黄海鲐资源渔场与环境因子的关系[J].水产 学报,2008,32(3)：379-386.
郑芳,刘群,王艳君.环境因子对黄海鳀鱼亲体-补充量关系影响的初步研[J].南方水产,2008,4(2)：15-20.
郑延学,李宏友.西辽河流域上游赤峰地区的水文特征[J].内蒙古水利,2000,4：57-58.
周元建,关则良.海洋渔业产量的灰色预测与分析[J].湛江水产学院学报,1994,14(1)：53-55.
朱家帅,张凯.从干旱区的水利生态观看中国21世纪水资源安全对策[J].湖北水利水电职业技术学院学报,2009,5(2)：37-40.
朱立新,李丽芳,梁振林.不确定性对北部湾二长棘鲷渔业资源评估影响的模拟研究[J].广东海洋大学学报,2009,29(1)：36-43.
Allen, K.R. A personal retrospect of the history of fisheries modeling. In population dynamics for fisheries management. Handbook, D.A. [ed.] North Beach, W.A. Australian society for fish biology.1994.21-28.
Barber, W. E. Maximum sustainable yield lives on. N. Am. J. [J]. Fish. Manage.1988,8:153-157.
Butterworth, D.S., P.A. Andrew. Dynamic catch-effort models for the hake stocks in ICSEAF Divisions 1.3 to 2.2. Coll. Sci. Pap. Int. Comm. SE Atl. Fish.,1984,11(1):29-58
Caddy, J.F., McGarvey, R. Targets or limits for management of fisheries? North American J. of Fisheries Management,1996,16:479-487
David, K.W., J.L. Deng. Contrasting grey system theory to probability and fuzzy. ACM SIGICE Bulletin.1995,20(3):3-9
Dounis, A.I., Tiropanis P., Tseles D., et al. A comparison of grey model and fuzzy predictive model for time series [J]. Int J Comput. Intel,2006,2 (3):176-181.
Fournier, D.A., Warburton, A.R. Evaluating fisheries management models by simulated adaptive control-introducing the composite model. Can. J. Fish. Aquat. Sci.,1989,46:1002-1012
Fox, W.W. An exponential yield model for equations from fishery science and a new formulation[J]. Trans Am Fish soc,1970,99:80-88.
Fox, W.W. Fitting the generalized stock production model by least-squares and equilibrium approximation[J]. Fish. Bull.1975,73:23-37.
Francis, R.I.C.C. Population modeling in New Zealand's quota management system. In population dynamics for fisheries management. Hancock, D.A. [ed.] North Beach, W.A. Australia, Australian society for fish Biology.1994.1-15
Gordon, G.N.G., Andrew, N.L., Montgomery, S.S. Deterministic compartmental model for the eastern king prawn fishery in New South Wales. Mar. Freshwater Res.,1995,46:793-807
Graham, M. Modern theory of exploiting a fishery and application to North Sea trawling[J]. J Cons Int Explor Mer,1935,37:199-204.
Gulland, J.A. Fishing and stocks of Iceland. Fish. Invest. Minist. Agric. Fish. Food,1961,4:1-52
Gulland, J.A. Science and fishery management. J. Cons. Int. Explor. Mer,1971,33:471-477
Gulland, J.A. Fish stock assessment, A Manual of Basic Methods[M]. Chichester:John Wiley and Sons.1983.83-146.
Gulland, J.A. Fish population dynamics (2nd edition). Chichester:John Wiley and Sons.1988
Hart, P.J.B., Reynolds, J.D. Fish biology and Fisheries, volume 2. Cornwall:Black Well Publishing, 2002.105-125
Hilborn, R. Comparison of fisheries control systems that utilize catch and effort data. J. Fish. Res. Bd. Can.,1979,36:1477-1489
Hilborn, R., C.J. Walters Quantitative fisheries stock assessment:choice, dynamics & uncertainty[M]. New York:Chapman & Hall,1992:9-13.
Hoggarth, D.D., S. Abeyasekera, R.I. Arthur, et al, Stock assessment for fishery management -A framework guide to the stock assessment tools of the Fisheries Management Science Program (FMSP),2006, FAO Fisheries Technical Paper,487, Rome, FAO, pp261.
Kimura, D.K. Variability in estimating catch in numbers at age and its impact on cohort analysis. Can. Spec. Pub. Fish. Aquat. Sci.,1989a,108:57-66
Kimura, D.K. Variability, tuning, and simulation for the Doubleday-Deriso catch at age model. Can. J. Fish. Aquat. Sci.,1989b,46:941-949
Kinas, P.G. Bayesian fishery stock assessment and decision making using adaptive importance sampling. Can. J. Fish. Aquat. Sci.,1996,53:414-423
Laloe, F. Should surplus production models be fishery description tools rather than biological models? Aquat. Living Resour.,1995,8:1-16
Larkin, P.A. An epitaph for the concept of maximum sustainable yield. Tan. Am. Fish. Soc.,1977,
Linhart, H., Zucchini, W. Model selection. New York:Wiley.1986
Ludwig, D., C.J. Walters, J. Cooke. Comparison of two models and two estimation methods for catch and effort data. Nat. Res. Modeling,1988,2:457-498
Ludwig, D. and C.J. Walters. A robust method for parameter estimation from catch and effort data[J]. Can. J. Fish. Aquat. Sci,1989,46:137-144.
Mace, P.M. A new role for MSY in single species and ecosystem approaches to fisheries stock assessment and management J]. Fish and Fisheries,2001(2):2-23.
Mao, M., Chirwa E.C. Combination of grey model GM (1,1) with three-point moving average for accurate vehicle fatality risk prediction [J]. Int J Crash,2005,10 (6):635-642.
Maunder, M.N. Is it time to discard the Schaefer model from the stock assessment scientist's toolbox?[J]. Fisheries Research,2003,61:145-149.
Polacheck, T., Hilborn, R., Punt, A.E. Fitting surplus production models:comparing methods and measuring uncertainty. Can. J. Fish. Aquat. Sci.,1993,50:2597-2607
Pell, J.J., Tomlinson, P.K. A generalized stock production model [J]. Bull. Inter-Am. Trop. Tuna Comm.1969,13:419-496.
Polacheck, T., Hilborn, R., Punt, A.E. Fitting surplus production models:comparing methods and measuring uncertainty. Can. J. Fish. Aquat. Sci.,1993,50:2597-2607
Prager, M.H. A suite of extensions to a non-equilibrium surplus production model [J]. Fishery Bulletin,1994,92:374-389.
Prager, M.H. Application of a surplus production model to a swordfish-like simulated stock with time-changing gear selectivity. Trans. Am. Fish. Soc.,1996,125:729-740
Prager, M.H. User's Manual for ASPIC:A Stock-Production Model Incorporating Covariates (ver.5) And Auxiliary Programs, National Marine Fisheries Service Miami Laboratory Document MIA-92/93-55 (revised) Beaufort Laboratory Document BL,2005,27pp.
Punt, A.E., and D.S. Butterworth. Results of first stage screening trials for a proposed whale stock management procedure. Rep. Int. Whal. Comm.,1989, Special Issue 11:191-197
Punt, A.E., P.A. Garratt, and A. Govender. On an approach for applying per-recruit methods to a protogynous hermaphrodite, with an illustration for the slinger Chrysoblephus puniceus. S. Afr. J. Mar. Sci.,1993,13:109-119
Schaefer, M.B. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries [J]. Bull Inter-Am Trop Tuna comm.1954,1(2):27-56.
Schaefer, M.B. A study of the dynamics of the fishery for yellow fin tuna in the eastern tropical Pacific Ocean [J]. Bull. Inter Am. Tuna Comm.1957,2:247-268.
Schnute, J. Improved estimates from the Schaefer production model:theoretical considerations[J]. Fish. Res. Board Can.,1977,34:583-103.
Su, S.F., Lin C.B., Hsu Y.T. A high precision global prediction approach based on local prediction approaches [J]. IEEE SMC-C,2002,32 (4):416-425.
Su, Z.M. and Liu, Q. An integral Fox-form of observation-error model (IFOXOEM)[J]. Fishery Research,1998,34:59-76.
Uhler, R.S. Least squares regression estimates of the Schaefer production model:Some Monte Carlo simulation result. Can. J. Fish. Aquat. Sci.,1979,37:1284-1294.
Walter, G.G. Delay-differential equation models for fisheries[J]. J. Fish. Res. Board Can.,1973, 30:939-945.
Walters, C.J., Hilborn, R., Adaptive control of fishing systems[J]. Fish. Res. Board Can.,1976, 33:145-159.
Walters, C.J., Hilborn, R., Exploratory assessment of historical recruitment patterns using relative abundance and catch data [J], Can. J. Fish. Aquat. Sci.,2005,62:1985-1990.
Walters, C.J., Hilborn, R., Christensen, R. Surplus production dynamics in declining and recovering fish populations[J], Can. J. Fish. Aquat. Sci.,2008,65:2536-2551.
Yoshimoto, S.S., Clarke, R.P. Comparing dynamic versions of the Schaefer and Fox production models and their application to lobster fisheries. Can. J. Fish. Aquat. Sci.,1993,50:181-189