Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth...Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.展开更多
剩余产量模型是最简单和应用最广泛的渔业资源评估模型之一。CEDA(catch-effort data analysis)和ASPIC(a surplus-production model incorporating covariates)是使用非平衡剩余产量模型对渔业产量和捕捞努力量数据进行分析的计算机软...剩余产量模型是最简单和应用最广泛的渔业资源评估模型之一。CEDA(catch-effort data analysis)和ASPIC(a surplus-production model incorporating covariates)是使用非平衡剩余产量模型对渔业产量和捕捞努力量数据进行分析的计算机软件。根据中国台湾延绳钓渔业的单位捕捞努力量渔获量(CPUE)数据,利用CDEA和ASPIC软件对南大西洋长鳍金枪鱼(Thunnus alalunga)渔业进行研究。结果显示,CEDA中使用对数正态误差假设的Fox模型产生了最大的R2值以及最接近ASPIC分析结果的种群参数值,但是CEDA得到的R2值低于ASPIC。CEDA对不同初始B1/K值的反应比ASPIC敏感。ASPIC中Logistic产量模型对不同初始B1/K值的反应比Fox模型更加灵敏。CEDA和ASPIC得出的最大可持续产量基本一致。展开更多
长鳍金枪鱼(Thunnus alalung)是南太平洋金枪鱼渔业的主要捕捞种类之一,具有很高的经济价值,掌握其渔场分布与主要海洋环境因子关系是高效开发利用的基础。根据2011年我国南太平洋渔业生产数据和遥感环境数据,按周分析了长鳍金枪鱼单位...长鳍金枪鱼(Thunnus alalung)是南太平洋金枪鱼渔业的主要捕捞种类之一,具有很高的经济价值,掌握其渔场分布与主要海洋环境因子关系是高效开发利用的基础。根据2011年我国南太平洋渔业生产数据和遥感环境数据,按周分析了长鳍金枪鱼单位捕捞努力量渔获量(Catch per unit of fishing effort,CPUE)的时空变化及与主要环境因子之间的关系。结果表明,南太平洋长鳍金枪鱼渔场重心主要集中在13°S^23°S,164°E^174°E,总体走势为先向东南方向迁移,后又沿西北方向推移。长鳍金枪鱼全年均可作业,其中5月至7月渔获量最高。作业渔场最适温度(Sea Surface Temperature,SST)范围为26~29℃,相应区域内最适叶绿素a浓度(Chlorophyll a concentration,Chl-a)为0.02~0.05 mg/m3,适宜海面高度距平(Sea surface height anomaly,SSHA)范围为4~24 cm。展开更多
Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with diffe...Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The re展开更多
Global change determines the environmental condition and leads to decide the carrying capacity. While carrying capacity determines the extinction of the species, it is an important issue to estimate the extinction poi...Global change determines the environmental condition and leads to decide the carrying capacity. While carrying capacity determines the extinction of the species, it is an important issue to estimate the extinction point of the species, the minimal carrying capacity, or the tolerant limitation of the species. If it is possible to estimate the tolerant limitation of the species, it will be possible to control the global change. Applied the above idea to the albacore stocks, it revealed that extinction point was about 0.0018% of the present status. From these results, it implies that this method may also suitable to other species for estimating their carrying capacities.展开更多
Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international...Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international exploitable stocks of albacore tuna (Thunnus alalunga);however the impact of climatic variations on these stocks is not fully understood. This study was aimed at determining the climatic variables which have impact on the time series stock fluctuation pattern of albacore tuna stock in the Eastern and Western South Pacific Ocean which was divided into three zones. The relationship of the climatic variables for the global mean land and ocean temperature index (LOTI), the Pacific warm pool index (PWI) and the Pacific decadal oscillation (PDO) was investigated against the albacore tuna catch per unit effort (CPUE) time series in Zone 1, Zone 2 and Zone 3 of the South Pacific Ocean from 1957 to 2008. From the results it was observed that LOTI, PWI and PDO at different lag periods exhibited significant correlation with albacore tuna CPUE for all three areas. LOTI, PWI and PDO were used as independent variables to develop suitable stock reproduction models for the trajectory of albacore tuna CPUE in Zone 1, Zone 2 and Zone 3. Model selection was based on Akaike Information Criterion (AIC), R2 values and significant parameter estimates at p < 0.05. The final models for albacore tuna CPUE in all three zones incorporated all three independent variables of LOTI, PWI and PDO. From the findings it can be said that the climatic conditions of LOTI, PWI and PDO play significant roles in structuring the stock dynamics of the albacore tuna in the Eastern and Western South Pacific Ocean. It is imperative to take these factors into account when making management decisions for albacore tuna in these areas.展开更多
单位捕捞努力量渔获量(catch per unit effort, CPUE)权重问题对于渔业资源评估而言至关重要。本研究使用印度洋长鳍金枪鱼(Thunnus alalunga)的渔业独立和非独立数据,构建了年龄结构资源评估模型(ASAP)。利用评估模型估算得出的参数,...单位捕捞努力量渔获量(catch per unit effort, CPUE)权重问题对于渔业资源评估而言至关重要。本研究使用印度洋长鳍金枪鱼(Thunnus alalunga)的渔业独立和非独立数据,构建了年龄结构资源评估模型(ASAP)。利用评估模型估算得出的参数,使用年龄结构种群模拟器(age based population simulator, PopSim)模拟“真实”的资源种群动态以及相应的捕捞动态。针对不同序列的CPUE数据赋予不同的权重因子,同时考虑种群关键参数(自然死亡系数M和陡度h)的错误设置,进行敏感性分析,阐述CPUE权重的错误设置对评估结果的影响。结果表明,当估算模型中的M和h被正确指定或被低估时,若给具有较高准确性或较长时间序列的CPUE分配更多的权重,模型估算的捕捞死亡系数F和产卵亲体生物量B具有较小的相对误差(RE)和相对均方根误差(RMSE),即估算更为准确。同时,对不确定性较高的CPUE赋予更大的权重会使F_(last)/F_(start)的估计值过高,而B_(last)/B_(start)的估计值准确性较低。因此,当使用多组CPUE数据时,对具有较高准确性或较长时间序列的CPUE分配更高的权重,或可提高资源状态指标估算的准确性。同时,在CPUE权重的分配中应考虑重要生物学参数(例如M和h)的准确性,至少应进行敏感性分析,以涵盖潜在的模型或参数的错误设置对CPUE权重的影响。展开更多
Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In...Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In this study, a delay-differ- ence model was applied to fit catch and catch per unit effort （CPUE） data （1975-2011） of the southern Atlantic albacore （Thunnus alalunga） stock. The proposed delay-difference model captures annual fluctuations in predicted CPUE data better than Fox model. In a Monte Carlo simulation, white noises （CVs） were superimposed on the observed CPUE data at four levels. Relative estimate error was then calculated to compare the estimated results with the true values of parameters a and fl in Ricker stock-recruitment model and the catchability coefficient q. a is more sensitive to CV than fl and q. We also calculated an 80% percentile confidence interval of the maximum sustainable yield （MSY, 21756 t to 23408 t; median 22490 t） with the delay-difference model. The yield of the southern Atlantic albacore stock in 2011 was 24122t, and the estimated ratios of catch against MSY for the past seven years were approxi- mately 1.0. We suggest that care should be taken to protect the albacore fishery in the southern Atlantic Ocean. The proposed de- lay-difference model provides a good fit to the data of southern Atlantic albacore stock and may be a useful choice for the assessment of regional albacore stock.展开更多
根据2009—2012年南太平洋长鳍金枪鱼（Thunnus alalunga）延绳钓生产统计数据及遥感获取的海表温度（sea surface temperature,SST）、叶绿素a浓度（chlorophyll a concentration,Chl-a）和海面高度距平（sea surface height anomaly,S...根据2009—2012年南太平洋长鳍金枪鱼（Thunnus alalunga）延绳钓生产统计数据及遥感获取的海表温度（sea surface temperature,SST）、叶绿素a浓度（chlorophyll a concentration,Chl-a）和海面高度距平（sea surface height anomaly,SSHA）等环境数据,分析了长鳍金枪鱼单位捕捞努力量渔获量（catch per unit of fishing effort,CPUE）的时空分布及其与环境因子的相关性。结果表明：长鳍金枪鱼作业渔场主要集中在4°S—28°S、158°E—176°E附近海域;长鳍金枪鱼渔场CPUE呈明显的季节性变化,1—3月CPUE值较低（〈12.5尾·千钩-1）,随后逐渐增加,至7月达到最大值为18.1尾·千钩-1,而8—12月基本呈逐渐降低趋势;1月渔场重心位于16°S、168°E附近海域,2—3月向西北偏移,而在3—7月逐渐向东南方向转移,8月以后开始逐渐回撤至西北方向,在9—12月渔场重心变化幅度相对较小,主要位于15°S—16°S、168°E—169°E海域;总体来说,长鳍金枪鱼中心渔场最适SST为27.0～30.5℃,次适SST为20～24℃;最适叶绿素a浓度为0.02～0.08mg·m-3,最适海面高度距平为3～23 cm。展开更多
南太平洋长鳍金枪鱼是我国远洋渔业的重点捕捞对象,对南太平洋长鳍金枪鱼进行准确的渔场预报,可以提高捕捞效率,提高渔业的生产能力。本研究根据1993-2010年南太平洋长鳍金枪鱼的延绳钓生产数据以及海洋卫星遥感数据(海水表面温度,SST;...南太平洋长鳍金枪鱼是我国远洋渔业的重点捕捞对象,对南太平洋长鳍金枪鱼进行准确的渔场预报,可以提高捕捞效率,提高渔业的生产能力。本研究根据1993-2010年南太平洋长鳍金枪鱼的延绳钓生产数据以及海洋卫星遥感数据(海水表面温度,SST;海面高度,SSH)和ENSO(ElNinoSouthern Oscillation)指标,采用DPS(data processing system)数据处理系统中的BP人工神经网络模型,以渔获产量(单位时间的渔获尾数)和单位捕捞努力量渔获量(CPUE,Catch per unit of effort)分别作为中心渔场的表征因子,并作为BP模型的输出因子,以月、经度、纬度、SST、SSH和ENSO指标等作为输入因子,分别构建4-3-1,5-4-1,5-3-1,6-5-1,6-4-1,6-3-1等BP模型结构,比较渔场预报模型优劣。研究结果表明,以CPUE作为输出因子的BP人工神经网络结构总体上较优,其中以6-4-1模型结构为最优,相对误差只有0.006 41。研究认为,以CPUE为输出因子的6-4-1结构的人工神经网络模型,能够准确预报南太平洋长鳍金枪鱼的渔场位置。展开更多
文摘Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.
文摘剩余产量模型是最简单和应用最广泛的渔业资源评估模型之一。CEDA(catch-effort data analysis)和ASPIC(a surplus-production model incorporating covariates)是使用非平衡剩余产量模型对渔业产量和捕捞努力量数据进行分析的计算机软件。根据中国台湾延绳钓渔业的单位捕捞努力量渔获量(CPUE)数据,利用CDEA和ASPIC软件对南大西洋长鳍金枪鱼(Thunnus alalunga)渔业进行研究。结果显示,CEDA中使用对数正态误差假设的Fox模型产生了最大的R2值以及最接近ASPIC分析结果的种群参数值,但是CEDA得到的R2值低于ASPIC。CEDA对不同初始B1/K值的反应比ASPIC敏感。ASPIC中Logistic产量模型对不同初始B1/K值的反应比Fox模型更加灵敏。CEDA和ASPIC得出的最大可持续产量基本一致。
文摘长鳍金枪鱼(Thunnus alalung)是南太平洋金枪鱼渔业的主要捕捞种类之一,具有很高的经济价值,掌握其渔场分布与主要海洋环境因子关系是高效开发利用的基础。根据2011年我国南太平洋渔业生产数据和遥感环境数据,按周分析了长鳍金枪鱼单位捕捞努力量渔获量(Catch per unit of fishing effort,CPUE)的时空变化及与主要环境因子之间的关系。结果表明,南太平洋长鳍金枪鱼渔场重心主要集中在13°S^23°S,164°E^174°E,总体走势为先向东南方向迁移,后又沿西北方向推移。长鳍金枪鱼全年均可作业,其中5月至7月渔获量最高。作业渔场最适温度(Sea Surface Temperature,SST)范围为26~29℃,相应区域内最适叶绿素a浓度(Chlorophyll a concentration,Chl-a)为0.02~0.05 mg/m3,适宜海面高度距平(Sea surface height anomaly,SSHA)范围为4~24 cm。
基金the National Natural Science Foundation of China(No.32273185)the National Key R&D Program of China(No.2020YFD0901205)the Marine Fishery Resources Investigation and Exploration Program of the Ministry of Agriculture and Rural Affairs of China in 2021(No.D-8006-21-0215)。
文摘Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The re
文摘Global change determines the environmental condition and leads to decide the carrying capacity. While carrying capacity determines the extinction of the species, it is an important issue to estimate the extinction point of the species, the minimal carrying capacity, or the tolerant limitation of the species. If it is possible to estimate the tolerant limitation of the species, it will be possible to control the global change. Applied the above idea to the albacore stocks, it revealed that extinction point was about 0.0018% of the present status. From these results, it implies that this method may also suitable to other species for estimating their carrying capacities.
文摘Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international exploitable stocks of albacore tuna (Thunnus alalunga);however the impact of climatic variations on these stocks is not fully understood. This study was aimed at determining the climatic variables which have impact on the time series stock fluctuation pattern of albacore tuna stock in the Eastern and Western South Pacific Ocean which was divided into three zones. The relationship of the climatic variables for the global mean land and ocean temperature index (LOTI), the Pacific warm pool index (PWI) and the Pacific decadal oscillation (PDO) was investigated against the albacore tuna catch per unit effort (CPUE) time series in Zone 1, Zone 2 and Zone 3 of the South Pacific Ocean from 1957 to 2008. From the results it was observed that LOTI, PWI and PDO at different lag periods exhibited significant correlation with albacore tuna CPUE for all three areas. LOTI, PWI and PDO were used as independent variables to develop suitable stock reproduction models for the trajectory of albacore tuna CPUE in Zone 1, Zone 2 and Zone 3. Model selection was based on Akaike Information Criterion (AIC), R2 values and significant parameter estimates at p < 0.05. The final models for albacore tuna CPUE in all three zones incorporated all three independent variables of LOTI, PWI and PDO. From the findings it can be said that the climatic conditions of LOTI, PWI and PDO play significant roles in structuring the stock dynamics of the albacore tuna in the Eastern and Western South Pacific Ocean. It is imperative to take these factors into account when making management decisions for albacore tuna in these areas.
文摘单位捕捞努力量渔获量(catch per unit effort, CPUE)权重问题对于渔业资源评估而言至关重要。本研究使用印度洋长鳍金枪鱼(Thunnus alalunga)的渔业独立和非独立数据,构建了年龄结构资源评估模型(ASAP)。利用评估模型估算得出的参数,使用年龄结构种群模拟器(age based population simulator, PopSim)模拟“真实”的资源种群动态以及相应的捕捞动态。针对不同序列的CPUE数据赋予不同的权重因子,同时考虑种群关键参数(自然死亡系数M和陡度h)的错误设置,进行敏感性分析,阐述CPUE权重的错误设置对评估结果的影响。结果表明,当估算模型中的M和h被正确指定或被低估时,若给具有较高准确性或较长时间序列的CPUE分配更多的权重,模型估算的捕捞死亡系数F和产卵亲体生物量B具有较小的相对误差(RE)和相对均方根误差(RMSE),即估算更为准确。同时,对不确定性较高的CPUE赋予更大的权重会使F_(last)/F_(start)的估计值过高,而B_(last)/B_(start)的估计值准确性较低。因此,当使用多组CPUE数据时,对具有较高准确性或较长时间序列的CPUE分配更高的权重,或可提高资源状态指标估算的准确性。同时,在CPUE权重的分配中应考虑重要生物学参数(例如M和h)的准确性,至少应进行敏感性分析,以涵盖潜在的模型或参数的错误设置对CPUE权重的影响。
基金supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 201022001)
文摘Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In this study, a delay-differ- ence model was applied to fit catch and catch per unit effort （CPUE） data （1975-2011） of the southern Atlantic albacore （Thunnus alalunga） stock. The proposed delay-difference model captures annual fluctuations in predicted CPUE data better than Fox model. In a Monte Carlo simulation, white noises （CVs） were superimposed on the observed CPUE data at four levels. Relative estimate error was then calculated to compare the estimated results with the true values of parameters a and fl in Ricker stock-recruitment model and the catchability coefficient q. a is more sensitive to CV than fl and q. We also calculated an 80% percentile confidence interval of the maximum sustainable yield （MSY, 21756 t to 23408 t; median 22490 t） with the delay-difference model. The yield of the southern Atlantic albacore stock in 2011 was 24122t, and the estimated ratios of catch against MSY for the past seven years were approxi- mately 1.0. We suggest that care should be taken to protect the albacore fishery in the southern Atlantic Ocean. The proposed de- lay-difference model provides a good fit to the data of southern Atlantic albacore stock and may be a useful choice for the assessment of regional albacore stock.
文摘根据2009—2012年南太平洋长鳍金枪鱼（Thunnus alalunga）延绳钓生产统计数据及遥感获取的海表温度（sea surface temperature,SST）、叶绿素a浓度（chlorophyll a concentration,Chl-a）和海面高度距平（sea surface height anomaly,SSHA）等环境数据,分析了长鳍金枪鱼单位捕捞努力量渔获量（catch per unit of fishing effort,CPUE）的时空分布及其与环境因子的相关性。结果表明：长鳍金枪鱼作业渔场主要集中在4°S—28°S、158°E—176°E附近海域;长鳍金枪鱼渔场CPUE呈明显的季节性变化,1—3月CPUE值较低（〈12.5尾·千钩-1）,随后逐渐增加,至7月达到最大值为18.1尾·千钩-1,而8—12月基本呈逐渐降低趋势;1月渔场重心位于16°S、168°E附近海域,2—3月向西北偏移,而在3—7月逐渐向东南方向转移,8月以后开始逐渐回撤至西北方向,在9—12月渔场重心变化幅度相对较小,主要位于15°S—16°S、168°E—169°E海域;总体来说,长鳍金枪鱼中心渔场最适SST为27.0～30.5℃,次适SST为20～24℃;最适叶绿素a浓度为0.02～0.08mg·m-3,最适海面高度距平为3～23 cm。
文摘南太平洋长鳍金枪鱼是我国远洋渔业的重点捕捞对象,对南太平洋长鳍金枪鱼进行准确的渔场预报,可以提高捕捞效率,提高渔业的生产能力。本研究根据1993-2010年南太平洋长鳍金枪鱼的延绳钓生产数据以及海洋卫星遥感数据(海水表面温度,SST;海面高度,SSH)和ENSO(ElNinoSouthern Oscillation)指标,采用DPS(data processing system)数据处理系统中的BP人工神经网络模型,以渔获产量(单位时间的渔获尾数)和单位捕捞努力量渔获量(CPUE,Catch per unit of effort)分别作为中心渔场的表征因子,并作为BP模型的输出因子,以月、经度、纬度、SST、SSH和ENSO指标等作为输入因子,分别构建4-3-1,5-4-1,5-3-1,6-5-1,6-4-1,6-3-1等BP模型结构,比较渔场预报模型优劣。研究结果表明,以CPUE作为输出因子的BP人工神经网络结构总体上较优,其中以6-4-1模型结构为最优,相对误差只有0.006 41。研究认为,以CPUE为输出因子的6-4-1结构的人工神经网络模型,能够准确预报南太平洋长鳍金枪鱼的渔场位置。
