Projecting distributions of Argentine shortfin squid(Illex argentinus) in the Southwest Atlantic using a complex integrated model

We developed an approach that integrates generalized additive model(GAM) and neural network model(NNM)for projecting the distribution of Argentine shortfin squid(Illex argentinus). The data for this paper was based on commercial fishery data and relevant remote sensing environmental data including sea surface temperature(SST), sea surface height(SSH) and chlorophyll a(Chl a) from January to June during 2003 to 2011. The GAM was used to identify the significant oceanographic variables and establish their relationships with the fishery catch per unit effort(CPUE). The NNM with the GAM identified significant variables as input vectors was used for predicting spatial distribution of CPUE. The GAM was found to explain 53.8% variances for CPUE. The spatial variables(longitude and latitude) and environmental variables(SST, SSH and Chl a) were significant. The CPUE had nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to be effective and robust in the projection with low mean square errors(MSE) and average relative variances(ARV).The integrated approach can predict the spatial distribution and explain the migration pattern of Illex argentinus in the Southwest Atlantic Ocean.     

基于复合模型的西南大西洋阿根廷滑柔鱼（Illex argentinus）时空分布研究

本文利用2003-2011年西南大西洋阿根廷滑柔鱼渔业数据和海洋环境数据,包括海表温度（sea surface temperature, SST）,海面高度（sea surface height, SSH）和叶绿素浓度（chlorophyll a, Chl a）,开发基于广义加性模型（GAM）和神经网络模型（NNM）的复合模型研究滑柔鱼资源时空分布。GAM用于选择关键影响因子,并分析与单位捕捞努力量渔获量（catch per unit effort, CPUE）的关系,NNM用于建立关键影响因子与CPUE之间的预报模型。结果表明：GAM选择的影响因子的偏差解释率为53.8%,空间变量（经度和纬度）,环境变量（SST、SSH、Chl a）均匀CPUE之间存在显著相关性。CPUE与SST和SSH之间为非线性关系,与Chl a之间为线性关系。NNM模型的MSE和ARV较低,其精度高且稳定。此复合模型也能够解释解释西南大西洋阿根廷滑柔鱼时空变化趋势和迁徙模式。     

基于深度卷积嵌入式聚类（DCEC）的海洋环境特征提取对渔情预报模型的改进研究

In order to improve the forecasting ability of the fishery forecast model for the longline bigeye tuna (Thunnus obesus), we proposed a marine environment feature extraction method based on deep convolutional embedded clustering (DCEC), combined with generalized additive model (GAM) for forecasting the longline bigeye tuna fishing grounds in the Southwest Indian Ocean. We used the MODIS-Aqua and MODIS-Terra sea surface temperature (SST) three-level inversion image data (in days) from January to December in 2018 at 0.041 6°×0.041 6° to construct a DCEC model, determined the optimal number of clusters based on the Davies-Bouldi index (DBI), and extracted the category feature value (F_M) of each month’s sea surface temperature (SST); we used monthly 1°×1° bigeye tuna longline fishery data from January to December in 2018 generated from the Indian Ocean Tuna Commission (IOTC), and calculated the catch per unit effort (CPUE); we matched the monthly category feature value F_M and the monthly average value of Chl a concentration with the CPUE data to construct an improved GAM; we matched the monthly average SST, the monthly average Chl a concentration and CPUE data to build a basic GAM; we used the joint hypothesis test (F test) to verify the influence of model explanatory variables; we used akaike information criterion (AIC), mean square error (MSE), and draw the frequency distribution diagrams and box diagrams of measured and predicted values, etc., to analysis the improvement effect of the improved GAM compared to the basic GAM. The results showed that: (1) the category feature value (F_M) extracted based on the DCEC model could better reflect the temporal and spatial dynamic characteristics of SST in the Southwest Indian Ocean, and was related with the climatic conditions, monsoon conditions, and hydrological characteristics in the Southwest Indian Ocean; (2) the factor interpretation of F_M was higher than that of the monthly average SST in GAM, which means F_M had more significant impact on the CPUE of bigeye tuna. The high catch rate was concentrated in the areas where the F_M category was 2, 10, 24 with intersections between the warm and cold currents; (3) the AIC of the improved GAM was reduced by 9.17% than that of the basic GAM and MSE of the improved GAM was reduced by 26.7% than that of the basic GAM; the frequency distribution of the CPUE logarithmic value predicted by the improved GAM was closer to the normal distribution, and the high frequency distribution interval was closer to that of the measured value; the scatter plot showed that the CPUE predicted by the improved GAM had a significant correlation with the measured CPUE, with r equaled to 0.60. This study proves the effectiveness of the DCEC model in extracting marine environmental features, and can provide a reference for the further study on the bigeye tuna fishery forecast.     

基于产卵场环境因子的阿根廷滑柔鱼资源补充量预报模型研究

西南大西洋阿根廷滑柔鱼Illex argentinus是短生命周期种类,其资源量极易受到海洋环境变化的影响。根据2003—2011年我国鱿钓船队在西南大西洋的生产统计数据,以及产卵场海洋表面温度(SST)、海表温度距平值(SSTA),计算分析了阿根廷滑柔鱼在产卵期产卵场各月最适表层水温范围占总面积的比例(用PS表示)以及表征海流强度的SST、SSTA等多种环境变量因子与单位捕捞量渔获量(CPUE)的相关性,建立多种基于主要环境因子的资源补充量预报模型,同时分析比较预报模型的优劣。相关性分析表明:6月份有3片连续区域的SST与CPUE之间存在强相关性,分别为38°~39°S、54°~55°W,40.5°~41.5°S、51°~52°W,39.9°~40.4°S、42.6°~43.1°W。利用6月份此3片连续区域SST与次年CPUE建立的三元线性模型,模型符合统计检验,偏差解释率为82.4%。在此基础上加入7月份PS影响因子建立3种方案下的误差反向传播(EBP)神经网络模型。结果认为,包含了福克兰寒流与巴西暖流表温信息的方案3模型优于其他两种模型,其准确率可以达到90%以上。     

Spatial pattern,harvesting and management of the artisanal fishery for purple clam (Amiantis purpurata) in Patagonia (Argentina)

The purple clam Amiantis purpurata supports a small-scale fishery in Patagonia by two modalities: (i) hand gathering along the beach (intertidal zone); (ii) diving fishery in the subtidal zone. In the first case, the influence of the fishermen's behavior (serial depletion of patches, degree of organization, economic threshold) on the catch per unit effort (CPUE) was analyzed. Harvesting processes in subtidal portions of populations were described matching the spatial distribution of abundance, derived from pre- and post-harvest surveys (1995 and 2005), and from temporal patterns of CPUE. Concentration profiles were used to establish an economic threshold; only 16.4% of the clams were dispersed in unprofitable areas. Reduction of density is in accordance with absence of recruitment during two decades. A management scheme was proposed with operative short-term management actions and strategic long-term components to avoid recruitment overfishing.     

阿拉伯海鲐鱼渔场时空分布及其与海洋环境的关系

采用2016—2017年中国印度洋围拖网生产数据和同期的海表温度、叶绿素、表层海流和海面高度数据, 绘制了阿拉伯海鲐鱼Scomber australasicus围网月平均单位捕捞努力量渔获量(CPUE)和环境因子空间叠加图, 分析鲐鱼渔场与海洋环境因子之间关系, 采用频次分析和经验累积分布函数计算鲐鱼渔场最适宜的海洋环境区间。结果表明, 该海域月平均CPUE呈现先减少后增加的趋势; 围网渔场渔汛主要在东北季风期间, 从10月到翌年3月; 作业渔场重心分布在59°—62°E、13°—17°N, 具有明显的月变化, 基本呈现西南移动趋势。空间上, CPUE 分布在西边界流速较大的海域右侧, 在海流最大值和最低值中间区域。在印度洋东北季风期间, 阿拉伯海围网鲐鱼渔场适宜海表温度在25~28℃; 叶绿素浓度在0.2~0.5mg·m ^-3; 表层海流在0.05~0.25m·s ^-1; 海表高度0.2~0.35m。     

Indicators of abundance and spatial distribution of lobsters (Homarus americanus) from standard traps

Indicators of abundance for American lobster (Homarus americanus) based on 8 years of trap catch rates (catch‐per‐unit‐effort, CPUE) were evaluated. Volunteer harvesters recorded count, sex and size of lobsters captured in standard traps on a daily basis during the fishing season in coastal Nova Scotia, Canada. We examined the extent to which standardised CPUEs of prerecruits predict the future catches of legal sizes and explored spatial patterns in the abundance of lobsters of different size and reproductive status. The standardised CPUE of prerecruits was correlated with legal size catches in only one of five areas examined. This area had a strong signal of incoming recruitment. Improving the capacity of prerecruit CPUE for predicting legal size catches several years later most likely lies with model incorporation of variables associated with catchability. The spatial distribution of catch rates showed that the area with the highest historical landings per unit area also had the highest relative abundance of prerecruits. The spatial distribution data point to further areas of research related to recruitment processes in lobster in coastal Nova Scotia.     

南海外海鸢乌贼渔场范围与海洋环境的关系

为研究南海外海鸢乌贼渔场范围与海洋环境因子之间的联系,本文根据2013-2018年广西壮族自治区北海市灯光罩网渔船在南海外海的鸢乌贼生产数据和海洋环境遥感数据,对鸢乌贼渔场范围的时空分布与海表面温度(SST)、海表面高度(SSH)和海表面叶绿素a(Chl a)浓度的关系进行研究.结果表明:在5°～20°N,108°～1...     

利用BP神经网络标准化东南太平洋茎柔鱼(Dosidicus gigas)资源丰度

东南太平洋茎柔鱼(Dosidicus gigas)是短生命周期大洋性经济鱼类,其资源量受环境因素变化的影响较大。根据我国鱿钓船队2013~2017年在东南太平洋的生产统计数据,结合海洋环境数据包括海表面温度(SST)、海表面盐度(SSS)、叶绿素a浓度(chl a),运用BP神经网络(back propagation network)模型来标准化单位捕捞努力量渔获量(catch per unit effort, CPUE,也称名义CPUE)。以均方误差(mean square errors, MSE)和平均相对变动值(average relative variances, ARV)为最优模型判断依据,比较隐含层节点3-10的神经网络模型,发现6-9-1结构为最优模型。用Garson算法解释模型结果,发现各输入层因子对东南太平洋茎柔鱼资源丰度影响重要度排序为chl a、SST、经度(Lon)、SSS、纬度(Lat)、月份(Month)。并作名义CPUE和标准化CPUE资源丰度对比分布图,结果显示CPUE与标准化CPUE总体分布状况基本一致,但局部区域存在明显差异, 80°~85°W及10°~20°S海域适宜鱿钓生产,表明BP神经网络模型可以适用于东南太平洋茎柔鱼的CPUE标准化,从而为鱿钓渔业生产提供一定参考依据。     

利用渔场环境因子标准化西北太平洋柔鱼CPUE的研究

本文利用1998-2016年西北太平洋柔鱼渔业数据及其渔场（35°~45°N,140°~165°E）的海洋遥感环境数据,包括海表温度、海面高度异常和叶绿素浓度,采用基于渔场环境的方法标准化西北太平洋柔鱼单位捕捞努力量渔获量（catch per unit effort,CPUE）。结果表明:柔鱼高频次作业的海表温度范围为10.2~22.2℃（96.05%）,海面高度异常范围为-15.9~28.2 cm（97.91%）,叶绿素浓度范围为0.0~1.0 mg/m3（96.69%）。名义CPUE和基于环境因子的标准化CPUE年际变化趋势基本一致。但由于柔鱼作业方式高度集中,有效捕捞努力量远低于名义捕捞努力量,以及考虑环境因子影响效应,名义CPUE均低于标准化CPUE。在深入理解鱿钓渔业和其生物学特性的基础上,基于渔场环境因子准化后的CPUE更具代表性,建议在以后的柔鱼资源评估与管理中使用基于渔场环境因子的标准化CPUE。     

基于产卵场海表温度的阿根廷滑柔鱼资源丰度预测模型研究

阿根廷滑柔鱼(Illex argentinus)为短生命周期种,其资源丰度易受海洋环境变化的影响,尤其是在产卵场的早期生活史阶段。根据2003?2016年我国鱿钓船队在西南大西洋的生产统计数据,以及产卵场海洋表面温度(SST)卫星遥感数据,用相关性分析方法筛选出阿根廷滑柔鱼产卵旺季期间(6月份)表征产卵场SST变化的特征海域;基于阿根廷滑柔鱼产卵场最适SST范围占总面积之比(Ps)与资源丰度单位捕捞努力渔获量(CPUE,t/船)呈正相关性的假设,回溯阿根廷滑柔鱼最适的产卵场及水温环境条件,并据此建立多种基于表征产卵场SST环境因子的资源丰度多元线性预测模型。相关性分析结果表明:6月份有两片连续海域(Area 1、Area 2)的SST与CPUE之间存在显著相关性,分别为42.5°～44°S、57.5°～59°W(Area 1)和39°～39.5°S、45°～46°W(Area 2);回溯的阿根廷滑柔鱼产卵场范围为37.5°～44°S、41.5°～51.5°W,产卵场最适SST范围为16～17.5℃。利用2个特征海域(Area 1、Area 2)SST以及回溯的产卵场Ps建立4种的多元线性资源丰度指数(ICPUE)预测模型,结果表明,包含表征寒暖流的特征海域和回溯产卵场Ps的方案4模型优于其他3种模型,其资源丰度指数预测模型为ICPUE=1.390 4×Ps+0.261 9×SST_{Area 1}+0.096 2×SST_{Area 2}?3.248 0。     

不同气候模态下西北太平洋秋刀鱼资源丰度预测模型建立

秋刀鱼（Cololabis saira）资源对海洋环境因素极为敏感,不同气候模态可能对秋刀鱼资源丰度产生不同的影响。根据1990-2014年西北太平洋日本的秋刀鱼渔业中单位捕捞努力量渔获量（CPUE,以此作为资源丰度）,以及相应产卵场、索饵场的海表温（SST）遥感数据,探讨太平洋年际震荡（PDO）指数冷、暖年下,秋刀鱼资源丰度CPUE变化与产卵场、索饵场SST的关系,并分别建立资源丰度的预测模型。研究表明,PDO冷年索饵场4月SST与年CPUE显著相关（P<0.05）,PDO暖年索饵场11月的SST与年标准化CPUE显著相关（P<0.05）。PDO冷、暖年的秋刀鱼资源丰度的预测模型中,CPUE均与索饵场11月的SST、索饵场4月SST呈现正相关的关系,统计学上为显著相关（P<0.05）。PDO冷年（2012年）和PDO暖年（2014年）的CPUE预测值与实际值相对误差分别为14.03%、-16.26%,具有较好的拟合效果。研究认为,不同气候模态下,可用于秋刀鱼资源丰度预测的环境因子不同,上述建立资源丰度模型可用于业务化运行。     

Seasonally and spatially referenced estimates of recreational shore-angling effort,catch composition,catch rates and total catch in the Goukamma Marine Protected Area,South Africa

A roving creel survey of the recreational shore fishery along the 16.4-km coastline in the Goukamma Marine Protected Area on the south coast of South Africa was conducted from 2009 to 2011. Some 838 patrols were stratified equally among months, areas and years, but intentionally biased towards weekends. Angler densities at Buffalo Bay and Groenvlei were 0.59 and 0.28 anglers km?1, respectively. Weekend densities were double to quadruple weekday densities and fishing during winter was more popular than during summer. Area, habitat and distance to access points explained variation in angler densities. Shannon–Wiener diversity in catches declined from 2.18 in an earlier (1993–2002) survey to 1.79. Although the order of species abundance in the catches remained largely unchanged, blacktail Diplodus capensis dominance increased to 57.3% by number, at the expense of galjoen Dichistius capensis. Habitat explained 27% of the variance in catch composition. The catch per unit effort (CPUE) for the top nine species ranged from 0.19 to 6.35 fish 100-h?1. The CPUE of all species, except spotted grunter Pomadasys commersonnii, declined. Blacktail and galjoen CPUE declined by 17% and 77%, respectively. The total catch estimate was 2 986 fish y?1. Transgressions of size limits were common. The results suggest that the fishery is overexploited and that catch rates are declining.     

渤海海温与叶绿素季节空间变化特征分析

以2003年MODIS数据为数据源，在图像处理、空间插值的基础上作海温与叶绿素浓度的空间相关分析。结果表明，整个海域的叶绿素浓度和海温的分布具有明显的区域和季节变化特征。基本规律是叶绿素浓度从近岸向渤海中央递减；温度则随季节发生变化，随着温度升高，近海叶绿素浓度增高，而渤海中央区域叶绿素浓度降低。渤海叶绿素浓度的分布与河口径流、季节等因素有关。从空间关系看，海温与叶绿素浓度不存在很明显的空间分布相关性，但不同季节有不同的相关性。上述研究可用于估算海洋初级生产力。     

西北太平洋海域太平洋褶柔鱼秋生群产卵场指数动态评估

为探究太平洋褶柔鱼(Todarodes pacificus)秋生群资源丰度波动的原因,本研究利用适宜产卵的海表温度(SST)和水深数据,构建了太平洋褶柔鱼秋生群1979—2018年40年潜在产卵场模型,在此基础上开发了适宜海表温度均值(MVSS)、适宜性海表温度加权面积(SSWA)和等温线经向位置(MP)3种产卵场指数...     

渤、黄、东海海表面温度年际变化特征分析

将渤、黄、东海海表面温度作为一个整体场,研究其年际变化特征,并进一步探讨其与东亚季风场年际变化特征的关系.利用美国NOAA极轨卫星中的高级甚高分辨率辐射计(AVHRR)反演的海表面温度资料,采用EOF方法分冬夏两季对渤、黄、东海SST的年际变化做了初步分析,发现渤、黄、东海SST存在显著的年际变化周期,冬季存在5 a的显著变化周期,夏季存在4 a的显著变化周期,并研究了东亚季风场的年际变化对SST变化产生的影响.发现冬季日Nin0年东亚寒潮活动弱于La Nina年,El Nino年SST较La Nina年偏高;夏季El Nino.年东亚夏季风活动弱于La Nina年,El Nino年SST较La Nina年偏低,但是趋势不如冬季明显.     

基于GAM模型研究水温垂直结构对热带中西太平洋黄鳍金枪鱼渔获率的影响

黄鳍金枪鱼索饵水层影响延绳钓捕捞效率,而黄鳍金枪鱼索饵水层分布受水温垂直结构的影响,因此本文采用GAM模型分析次表层环境变量对延绳钓黄鳍金枪鱼渔获率的影响,评估黄鳍金枪鱼垂直水层分布对中西太平洋黄鳍金枪鱼延绳钓单位捕捞努力量渔获量(Catch Per Unite Effort, CPUE)的作用。模型结果表明,环境因子对热带中西太平洋延绳钓黄鳍金枪鱼渔获率空间分布影响明显。黄鳍金枪鱼延绳钓CPUE在2012年之后快速增多,高渔获率月份出现在北半球夏季,空间上在10°S,140°E附近区域。温跃层上界温度和深度、温跃层下界深度、18℃等温线深度、△8℃等温线深度及其和温跃层下界深度的深度差对延绳钓渔获率影响较大,是影响热带中西太平洋黄鳍金枪鱼延绳钓渔获率的关键环境因子。随着温跃层上界温度和深度值变大,延绳钓CPUE逐渐递增,对延绳钓CPUE影响密切的温度和深度分别为27～28℃和70～90 m。温跃层下界深度对延绳钓CPUE影响在250～280 m时最大;之后随着下界深度的变大,CPUE快速下降。18℃等温线深度对延绳钓CPUE影响呈现先震荡后递增的趋势,影响密切的区域在230 m深度上下。△8℃等温线深度与温跃层下界深度的差值对热带中西太平洋黄鳍金枪鱼延绳钓CPUE影响呈现先快速递减后缓慢增加的趋势,在深度差为70 m上下时影响最密切。研究结果揭示,在黄鳍金枪鱼活动水层受限或栖息水层和延绳钓作业深度相吻合时,延绳钓渔获率最高。依据黄鳍金枪鱼垂直活动水层调整延绳钓投钩,可以提高渔获率。因此,采用延绳钓CPUE进行渔场和资源评估时要考虑金枪鱼适宜垂直活动空间。     

西白令海狭鳕渔场与环境因子关系研究

根据2013~2018年白令海海域拖网作业的狭鳕( Theragra chalcogramma)渔获数据以及环境数据,利用GAM模型对CPUE进行了标准化,建立了三个基于不同环境因子的剩余产量模型:(1)基于SST因子的剩余产量模型;(2)基于SST和Chl-a因子的剩余产量模型;(3)基于SST、Chl-a和SSHA因子的剩余产量模型,分析了环境因子对西白令海狭鳕资源的影响。研究表明:基于SST和Ch-a因子的剩余产量模型拟合程度最好,表达式为Cm=0. 9343f-0. 0003 fm^2+0.155Tmfm +0.325 4cam fm,狭鳕资源量的变动受捕捞努力量、渔场SST以及Chl-a控制。分析认为:SST是导致西白令海狭鳕CPUE产生月间波动的最重要的环境因子,Chl-a对狭鳕CPUE也有一定的影响,而SSHA的影响则相对较小。建议将SST以及Chl-a作为狭鳕渔场分析与渔情预报研究的重要环境因子。     

基于灰色系统的西北太平洋柔鱼冬春生群资源丰度预测模型

柔鱼（Ommastrephes bartramii）是西北太平洋重要的经济头足类之一,科学预测柔鱼资源丰度有利于其合理的开发和利用。研究结合1998-2008年北太平洋柔鱼生产统计数据和产卵场环境及其气候因子,使用灰色关联分析和灰色预测建模的方法,对产卵期内（1-4月）影响柔鱼冬春生群体资源丰度（CPUE）的产卵场环境以及气候指标进行分析,并建立柔鱼冬春生群体资源丰度的预报模型。结果表明,产卵期内影响柔鱼冬春生群体资源丰度的因子依次是:3月份产卵场平均海表面温度SST（average sea surface temperature）、1月份太平洋年代际震荡指数PDO（Pacific Decadal Oscillatio index）,4月份Niño3.4指标和4月份平均叶绿素浓度Chl a（average chlorophyll a concentration）。灰色预报模型分析表明,基于3月份SST、1月份PDO和4月份Chl a的GM（1,4）模型有着较好的预测效果,其预测准确率在80%以上,可用于西北太平洋柔鱼冬春群体资源丰度的预测。     

集合同化方法在黄海海域海表面温度同化中的应用

The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.