基于栖息地指数的东太平洋长鳍金枪鱼渔场分析

长鳍金枪鱼(Thunnus alalunga)是东太平洋海域重要的金枪鱼种类之一,也是我国金枪鱼延绳钓的主要捕捞对象之一。本文根据2009~2011年美洲间热带金枪鱼委员会(IATTC)在东太平洋海域(20°N~30°S、85°W~150°W)长鳍金枪鱼延绳钓生产统计数据,结合海洋遥感获得的表温(SST)和海面高度(SSH)的数据,运用一元非线性回归方法,以渔获产量、单位捕捞努力量CPUE为适应性指数,按季度分别建立了基于SST和SSH的长鳍金枪鱼栖息地适应性指数,采用算术平均法获得基于SST和SSH环境因子的栖息地指数综合模型,并用2012年各月实际作业渔场进行验证。研究结果显示,在东太平洋长鳍金枪鱼的栖息地预测中,以CPUE为适应性指数的栖息地指数模型比以渔获量为适应性指数的栖息地指数模型预测更为准确。2012年中心渔场的预报准确性达75%以上,具较高预报准确度,可为金枪鱼延绳钓渔船寻找中心渔场提供指导。     

基于贝叶斯概率的印度洋大眼金枪鱼渔场预报

本文采用贝叶斯概率为模型基础框架,利用来自印度洋金枪鱼管理委员会(IOTC)的大眼金枪鱼延绳钓历史渔获统计数据和美国国家海洋大气管理局(NOAA)的海温最优插值再分析数据,进行适用于印度洋金枪鱼延绳钓渔场的模型参数估算与预报模型构建。模型回报精度验证结果表明,印度洋大眼金枪鱼延绳钓渔场综合预报的准确率达到了65.96%。模型预报结果用概率百分比来表示,符合渔业资源分布的客观特点。利用中分辨率成像光谱仪MODIS提供的SST产品进行业务化运行的渔场预报,利用模型结果每周生成印度洋大眼金枪鱼延绳钓渔场概率预报图,用不同大小的圆形来表示渔场概率的高低,可以为印度洋区域的远洋渔业生产提供信息支持。     

ENSO与中西太平洋金枪鱼围网资源丰度及其渔场变动的关系

中西太平洋是世界金枪鱼围网的重要作业渔场之一,主要捕捞鲣鱼（Katsuwonus pelamis）、黄鳍金枪鱼（Thunnus albacares）和大眼金枪鱼（Thunnus obesus）等.     

基于LightGBM的南太平洋长鳍金枪鱼渔场预报模型研究

长鳍金枪鱼以高经济效益、资源丰富等优点成为世界金枪鱼渔业主要捕捞目标之一,进行长鳍金枪鱼渔场预报研究,可以有效提高渔获产量,对渔业生产具有重要意义。传统的线性模型在面对复杂多变的海洋环境数据时无法准确分析其关键因子。本研究选取2000—2015年南太平洋长鳍金枪鱼的延绳钓生产数据,结合海表温度、叶绿素a质量浓度和海面高度等海洋环境因子以及月份和经、纬度等时空数据,采用集成学习模型—轻度量化梯度促进机(LightGBM)模型进行长鳍金枪鱼渔场预报,并与朴素贝叶斯、XGBoost和BP神经网络模型进行对比。同时采用网格搜索算法获取LightGBM模型的最优参数,利用交叉验证法验证模型的稳定性。试验结果表明,利用LightGBM模型对南太平洋长鳍金枪鱼渔场的最佳预报准确率可达72.6%,对比其他模型,LightGBM模型的准确率有了显著提高。研究表明,海面高度和海面温度为南太平洋长鳍金枪鱼渔场形成的关键影响因子。     

北太平洋长鳍金枪鱼延绳钓渔场分布及其与海水表层温度的关系

根据北太平洋长鳍金枪鱼渔获量、海水表层温度等数据,研究了长鳍金枪鱼渔获量的分布区及其海水表层温度(SST)的统计特征.结果表明,北太平洋长鳍金枪鱼渔场主要分布于25～40°N之间的海域.长鳍金枪鱼渔场区平均SST为23.6℃,中位数为24.5℃,多数渔场区位于暖温带海域,其平均SST多数为16～28℃,产量数据分布为正偏.海水表层温度为16～23℃的海域,长鳍金枪鱼的平均产量和平均CPUE变化趋势类似,且表层温度为18～20℃的海域,长鳍金枪鱼的平均产量最高.渔获量分布于表层温度为16～23℃和24～27℃海域,但主要集中于16～23℃的范围.交叉相关分析表明长鳍金枪鱼CPUE同太平洋年际振荡指数具有相关性.     

中西太平洋金枪鱼围网自由鱼群渔场重心变动及其与南方涛动指数的关系

文章根据2013—2017年中国中西太平洋金枪鱼围网船队捕捞日志,利用捕捞自由鱼群作业位置、作业时间和渔获量等数据信息,分析了自由鱼群渔场重心月间变化、年际变化与南方涛动指数(South Oscillation Index,SOI)的关系。结果显示,渔获量较高的海域海表温度(Sea surface temperature,SST)高于29℃;自由鱼群的渔场重心主要分布介于160°E—175°W;2013—2015年渔场重心有逐年向东偏移的趋势,但无明显的月间变化规律;SOI为正值时,中西太平洋“暖池”较正常年份向西偏移,自由鱼群渔场重心亦明显向西偏移;反之,自由鱼群渔场重心较正常年份向东偏移。相关性分析显示SOI和月间渔场重心的经度之间呈负相关(相关系数为?0.27,P<0.05),表明金枪鱼围网渔场变动和异常气候的发生存在密切联系。研究结果对于掌握中西太平洋金枪鱼围网渔场变动规律具有一定参考价值。     

太平洋金枪鱼延绳钓渔业

本文主要介绍了最近几年太平洋金枪鱼延绳钓渔业的发展情况和太平洋沿岸国家金枪鱼延绳钓生产现状,重点叙说了太平洋金枪鱼延绳钓捕捞的几个主要种类的渔场分布、产量变化动态和发展趋势,为我国远洋渔业单位进行金枪鱼延绳钓开发提供参考。     

西北印度洋大眼金枪鱼渔场预报模型建立与模块开发

根据1990—2003年印度洋大眼金枪鱼延绳钓渔业数据和美国国家海洋和大气管理局提供的海表温度、叶绿素-a历史环境数据,应用环境因子叠加方法,构建了西印度洋大眼金枪鱼渔场预报模型,用于金枪鱼渔场预报。分析得出各月适宜海表温度、叶绿素-a浓度范围和历史高产区空间位置;导入实时海表温度、叶绿素-a等遥感栅格数据,分别提取适宜海表温度、适宜叶绿素-a浓度和历史高产区的空间栅格数据集,最后在空间上对3种栅格数据进行空间叠加并取交集。交集所指空间区域即为大眼金枪鱼潜在渔场位置。通过精度检验,表明该模型渔场预报精度为60.5%。并以VC++6.0工具为开发平台,对此模型进行了设计开发,实现了模块预报西北印度洋大眼金枪鱼渔场。     

2015年超强厄尔尼诺事件对西北太平洋柔鱼渔场变动的影响

为研究超强厄尔尼诺事件对西北太平洋海域柔鱼（Ommastrephes bartarmii）资源量变动的影响,并分析柔鱼栖息地在极端气候条件下的变化规律,根据上海海洋大学鱿钓科学技术组提供的中国柔鱼生产捕捞数据,比较2008年正常气候年份与2015年超强厄尔尼诺年份的单位捕捞努力量渔获量（CPUE）、产量、捕捞努力量以及渔场纬度重心（LATG）的变化;利用栖息地适宜性指数模型对西北太平洋柔鱼栖息地的海表温度（SST）、光合有效辐射范围（PAR）和海表面高度距平（SSHA）3个关键环境因子进行分析。渔业数据时间为2008年和2015年9—11月,数据覆盖范围为36°N~48°N、150°E~170°E。结果发现,相对于2008年正常年份,2015年超强厄尔尼诺事件下的CPUE明显降低,且LATG向南偏移;此外,2015年适宜的SST和PAR范围均显著降低,导致适宜的栖息地面积与正常年份相比大幅减少;最适宜的SST和PAR等值线向南偏移,导致有利的栖息地纬度位置向南移动。研究认为,2015年超强厄尔尼诺事件发生时,柔鱼渔场环境不适于柔鱼生长,适宜栖息地面积减少且向南移动,导致该年份柔鱼资源丰度骤减,渔场向南偏移。     

南海及临近海域黄鳍金枪鱼渔场时空分布与海表温度的关系

为得到南海及临近海域黄鳍金枪鱼(Thunnus albacores)渔场最适宜栖息海表温度(SST)范围,基于美国国家海洋大气局(NOAA)气候预测中心月平均海表温度(SST)资料,结合中西太平洋渔业委员会(WCPFC)发布的南海及临近海域金枪鱼延绳钓渔业数据,绘制了月平均SST和月平均单位捕捞努力量渔获量(CPUE)的空间叠加图,用于分析南海及临近海域黄鳍金枪鱼渔场CPUE时空分布和SST的关系。结果表明,南海及临近海域黄鳍金枪鱼CPUE在16℃~31℃均有分布。在春季和夏季(3~8月),位于10°~20°N的大部分渔区CPUE较高,其南北侧CPUE较低;而到了秋季和冬季(9月到次年2月),高产渔场区域会向南拓宽。CPUE在各SST区间的散点图呈现出明显的负偏态分布,高CPUE主要集中在26℃~30℃,最高值出现在29℃附近;在22℃~26℃范围内CPUE散点分布较为零散,但在这个范围也会出现相当数量的高CPUE;在22℃以下的CPUE几乎属于低CPUE和零CPUE;零CPUE的平均SST为26.7℃(±3.2℃),低CPUE的平均SST为27.8℃(±2.1℃),高CPUE的平均SST为28.4℃(±1.5℃),高CPUE在各SST区间的分布要比零CPUE和低CPUE更为集中。采用频次分析和经验累积分布函数计算其最适SST范围,得到南海及临近海域黄鳍金枪鱼最适SST为26.9℃~29.4℃。本研究初步得到南海及临近海域黄鳍金枪鱼中心渔场时空分布特征及SST适宜分布区间,可为开展南海及临近海域金枪鱼渔情预报工作提供理论依据和参考。     

Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data

Satellite‐based oceanographic data of sea surface temperature (SST), sea surface chlorophyll‐a concentration (SSC), and sea surface height anomaly (SSHA) together with catch data were used to investigate the relationship between albacore fishing ground and oceanographic conditions and also to predict potential habitats for albacore in the western North Pacific Ocean. Empirical cumulative distribution function and high catch data analyses were used to calculate preferred ranges of the three oceanographic conditions. Results indicate that highest catch per unit efforts (CPUEs) corresponded with areas of SST 18.5–21.5°C, SSC 0.2–0.4 mg m^?3, and SSHA ?5.0 to 32.2 cm during the winter in the period 1998–2000. We used these ranges to generate a simple prediction map for detecting potential fishing grounds. Statistically, to predict spatial patterns of potential albacore habitats, we applied a combined generalized additive model (GAM) / generalized linear model (GLM). To build our model, we first constructed a GAM as an exploratory tool to identify the functional relationships between the environmental variables and CPUE; we then made parameters out of these relationships using the GLM to generate a robust prediction tool. The areas of highest CPUEs predicted by the models were consistent with the potential habitats on the simple prediction map and observation data, suggesting that the dynamics of ocean eddies (November 1998 and 2000) and fronts (November 1999) may account for the spatial patterns of highest albacore catch rates predicted in the study area. The results also suggest that multispectrum satellite data can provide useful information to characterize and predict potential tuna habitats.     

Association between the interannual variation in the oceanic environment and catch rates of bigeye tuna (Thunnus obesus) in the Atlantic Ocean

The environmental processes associated with variability in the catch rates of bigeye tuna in the Atlantic Ocean are largely unexplored. This study used generalized additive models (GAMs) fitted to Taiwanese longline fishery data from 1990 to 2009 and investigated the association between environmental variables and catch rates to identify the processes influencing bigeye tuna distribution in the Atlantic Ocean. The present findings reveal that the year (temporal factor), latitude and longitude (spatial factors), and major regular longline target species of albacore catches are significant for the standardization of bigeye tuna catch rates in the Atlantic Ocean. The standardized catch rates and distribution of bigeye tuna were found to be related to environmental and climatic variation. The model selection processes showed that the selected GAMs explained 70% of the cumulative deviance in the entire Atlantic Ocean. Regarding environmental factors, the depth of the 20 degree isotherm (D20) substantially contributed to the explained deviance; other important factors were sea surface temperature (SST) and sea surface height deviation (SSHD). The potential fishing grounds were observed with SSTs of 22–28°C, a D20 shallower than 150 m and negative SSHDs in the Atlantic Ocean. The higher predicted catch rates were increased in the positive northern tropical Atlantic and negative North Atlantic Oscillation events with a higher SST and shallow D20, suggesting that climatic oscillations affect the population abundance and distribution of bigeye tuna.     

Operational oceanography applied to skipjack tuna (Katsuwonus pelamis) habitat monitoring and fishing in south‐western Atlantic

Skipjack tuna (Katsuwonus pelamis) ranks third among marine resources that sustain global fisheries. This study delimits the spatiotemporal habitat of the species in the south‐western Atlantic Ocean, based on operational oceanography. We used generalized additive models (GAMs) and catch data from six pole‐and‐line fishing vessels operating during 2014 and 2015 fishing seasons to assess the effect of environmental variables on catch. We also analysed Modis sensor images of sea surface temperature (SST) and surface chlorophyll‐α concentration (SCC) to describe fishing ground characteristics in time and space. Catch was positively related to thermocline depth (24–45 m), SST (22–24.5°C), SCC (0.08–0.14 mg/m³) and salinity (34.9–35.8). Through SST images, we identified that thermal fronts were the main surface feature associated with a higher probability to find skipjack. Also, we state that skipjack fishery is tightly related to shelf break because bottom topography drives the position of fronts in this area. Ocean colour fronts and plankton enrichment were important proxies, accessible through SCC, used to delineate skipjack fishing grounds. Catch per unit effort (CPUE) was higher towards summer (median 14 t/fishing day) due to the oceanographic characteristics of the southern region. High productivity in this sector of the Brazilian coast defines the main skipjack feeding areas and, as a consequence, the greatest abundance and availability for fishing.     

西北太平洋公海7~9月秋刀鱼渔场分布及其与水温的关系

根据2003~2005年7~9月西北太平洋公海秋刀鱼生产数据和水温遥感数据,对西北太平洋公海秋刀鱼作业渔场分布及其与表温和上层水温结构的关系进行了分析。结果认为,随着时间的推移,7~9月西北太平洋公海秋刀鱼渔场重心有从西南向东北变动的趋势;各月产量重心处水温结构有较大差异,9月混合层深度较7月和8月有所加深,渔场也较后者向北移动;各月高产渔区0~15m温度梯度都在0.25℃/m以下,0~40m温度梯度在0.1℃/m左右,40~60m温度梯度在0.25~0.42℃/m之间。灰色关联度分析表明,渔区月产量受到众多因素的影响,其中捕捞努力量、渔区平均日产量和表温是其主要影响因子,对渔区平均日产量影响较大的有表温、0~15m温度梯度、0~40m温度梯度和月份,其关联度都在0.80以上。     

西北太平洋柔鱼渔情速报系统的开发

The neon flying squid, Ommastrephes bartrami, is one of the most important jig fisheries in the northwest Pacific Ocean. In order to understand the movement of O. bartrami fishing-ground better and supply O. bartrami fishing-ground information for Chinese fishing boats in the northwest Pacific ocean, the fishing condition analysis and forecasting system of O. bartrami was developed successfully. The system was based on established comprehensive database, which included the catch data of O. bartrami (total yields, count of total fishing boats, fishing position etc. ) and oceanic environmental information (SST, SST gradient etc. ). Artificial intelligent technology about case-based reasoning was also combined with GIS component technology successfully in the system. The process and function of system establishment are composed of four parts: setting up of case database for central fishing-ground and its environmental factors, knowledge reasoning of fishery information, GIS visualization analyzing as well as trend forecasting of central fishing-ground and information production mapping. At last as an example of the results, an experimental central fishing-ground forecasting of O. bartrami from 9 to 15 in July 2002 in the northwest Pacific Ocean was given in the paper. The results showed that through three class similar searching forecasting central fishing-ground would move west, and indicating that forecasting of the system for O. bartrami central fishing-ground was correct by comparing to real fishing-ground from 16 to 22 in July 2002. Consequently, artificial intelligent expert system technology about case-based reasoning is a useful method for fishing condition and fishing-ground forecasting.     

Catch rate standardization for yellowfin tuna (Thunnus albacares) in Taiwan's distant-water longline fishery in the Western and Central Pacific Ocean,with consideration of target change

Catch per unit of effort (CPUE) needs to be standardized to remove the effects of factors such as fishing time and location, before it can be used as an index of abundance in fish stock assessments. One of the most substantial effects arises from a change of target species. This is particularly important for the Taiwanese distant-water longline fishery, which has a long history of fishery data from two fleets that target various tuna species across three oceans. We review the development of the Taiwanese distant-water longline fishery and compare five designs for standardizing the catch rate of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean, using generalized linear models with lognormal and delta-lognormal error assumptions. Two approaches to address targeting effects were tested: separating fishing fleet data based on observer records, and including four target indicators calculated from catch data. Four statistical regions (relating to major fishing grounds) were treated as a single factor in the first three cases and were treated separately for the last two (one independent run for each region). The last case, which involved independent analyses for each fishing fleet for each region, and using the delta-lognormal approach, was considered to provide the most informative standardized CPUE trends for yellowfin tuna.