环境因子对东南太平洋智利竹䇲鱼渔场时空分布异质性影响

基于2012—2018年4—8月我国东南太平洋智利竹?鱼(Trachurus murphyi)渔捞日志数据,应用地理权重回归模型(GWR)探究智利竹?鱼渔场资源分布与环境因子的空间异质性关系。结果表明,环境因子海面温度基于GWR模型回归的拟合优度为0.54,校正的拟合优度为0.34,赤池信息准则(Akaike Information Criterion,AIC)值为1022.08;叶绿素a浓度基于GWR模型回归的拟合优度为0.48,校正的拟合优度为0.36,AIC值为2321.95;海面温度异常值的拟合优度为0.74,校正的拟合优度为0.58,AIC值为2268.07;海面高度异常值的拟合优度为0.72,校正的拟合优度为0.59,AIC值为2201.93;作业水深的拟合优度为0.46,校正的拟合优度为0.42,AIC值为2675.07;海面温度异常对东南太平洋智利竹?鱼渔场时空分布影响最大。GWR模型便于发现资源分布的“热点”海域,可为我国智利竹筴鱼渔船生产提供科学依据。     

西南大西洋阿根廷滑柔鱼渔业中国大陆鱿钓船CPUE标准化

阿根廷滑柔鱼是我国重要的头足类渔业之一,对其单位捕捞努力量渔获量( CPUE)进行标准化是对其资源评估的重要内容。本研究根据2000～2010年我国在西南大西洋的产量统计数据和卫星遥感获得的海洋环境数据（表温,表温水平梯度,海面高度,叶绿素浓度）,利用广义线性模型（GLM,general linear model）和广义加性模型（GAM,generalized additive model）对其CPUE进行标准化。GLM模型结果表明,年、纬度、表温以及交互项年与纬度对CPUE影响最大。GAM模型研究结果表明,年、月、经度、纬度、表温、海面高度以及交互项年与纬度、年与经度对CPUE影响较大。根据AIC数值,包含上述8个显著变量的GAM模型为最佳模型,对CPUE的解释率为49.20%。高CPUE出现在夏季表温为12～16°C、海面高为-20~20cm、46.5°～48.5°S海域。研究表明,GAM模型较GLM模型更适合用于西南大西洋阿根廷滑柔鱼CPUE标准化。     

1999—2011年东、黄海鲐资源丰度年间变化分析

根据1999—2011年我国鲐大型灯光围网渔业数据,使用广义线性模型(generalized linear model,GLM)和广义加性模型(generalized additive model,GAM)估算了影响CPUE的时间(年、月)、空间(经度、纬度)、捕捞性能和环境效应[海表面温度(sea surface temperature,SST)、海表面高度、海表面叶绿素浓度],并以年效应作为资源丰度指数,分析了东、黄海鲐资源丰度的年间变化,东、黄海鲐资源丰度指数的年间变化与产卵场海表面温度以及捕捞强度间的关系。GAM结果表明,时间、空间、捕捞和环境变量对CPUE偏差的解释率为11.69%,其中变量年的解释率最大,占总解释率的38%。结果显示,1999—2011年东、黄海鲐鱼资源丰度指数(abundance index,AI)总体上呈下降趋势,2008年以来更是持续下降,丰度指数由2008年的1.22降至2011年的0.82。东、黄海鲐资源丰度指数年间与产卵场呈正相关,关系式为AI=-3.51+0.23SST(P0.05),这表明较高的产卵场SST对鲐资源量增加有利。过高的渔获量以及我国群众围网渔业渔船数量的快速增长是导致近年来鲐鱼资源下降的重要原因。     

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.     

基于不同模型研究环境因子对中西太平洋鲣资源丰度的影响

根据1998—2013年中西太平洋鲣(Katsuwonus pelamis)生产数据,选取时空因子(年、月、经纬度)和环境因子[海表面温度(SST)、海表面高度(SSH)、尼诺指数(ONI)和叶绿素a浓度]Chl-a)],通过两种不同的模型(广义加性模型GAM和提升回归树模型BRT)研究各因子对鲣资源丰度(以CPUE表示)的影响。研究结果认为,GAM模型中,经度对CPUE的影响最大,累计解释偏差超过50%,其次为纬度、年和月;在环境因子中,SSH最为重要,其次为ONI,而SST和Chl-a的影响相对较低。BRT模型分析结果与GAM分析结果类似,时空因子相对占据了重要的地位,其中经度的影响最大,其次为年、纬度和月;而在环境因子中,ONI的重要性相对更高,其次为SSH,SST和Chl-a同样影响较低。研究认为,两种模型均能较好地反映出因子对CPUE的影响。由于厄尔尼诺/拉尼娜现象引起的海洋环境变化会使鲣资源分布产生差异,因此在后续的渔情预报研究中,应该更多地考虑将ONI因子纳入渔情预报模型中,以提高预测精度。     

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。     

基于空间相关性的西北太平洋柔鱼CPUE标准化研究

CPUE标准化方法通常都假设名义CPUE之间是相互独立且没有相关性,然而鱼类集群分布通常存在着空间相关性,为此本研究以西北太平洋柔鱼的CPUE标准化为例,采用1999-2012年6-11月中国鱿钓生产数据以及对应的海表面温度和叶绿素浓度的环境数据,将空间相关性加入广义线性模型(general linear model,GLM)中.在空间GLM模型中运用4个距离模型(指数模型、球面模型、线性模型和高斯模型),进行标准GLM模型和4种空间GLM模型的CPUE标准化结果比较.结果发现,4种空间GLM模型均比标准GLM模型的最小信息准则(akaike information criterion,AIC)更小,标准化结果更准确.同时,在4个距离模型中,指数模型的AIC值最小,其CPUE标准化结果最佳.研究表明,在CPUE标准化中,鉴于鱼类集群与分布特性,应该充分考虑空间相关性这一因素.     

我国东、黄海鲐鱼灯光围网渔业CPUE标准化研究

日本鲐是我国近海重要的中上层鱼类资源之一,评估其资源量需要对单位捕捞努力量渔获量（CPUE）进行标准化。影响CPUE标准化的因素很多,包括季节、区域和海洋环境等。本文利用广义线型模型（GLM）和广义加性模型（GAM）,结合时空、捕捞船、表温等因子,对1998-2006年东、黄海大型灯光围网渔业鲐鱼CPUE进行标准化,并评价各因子对CPUE的影响。首先应用GLM模型评价时间、空间、环境以及捕捞渔船参数对CPUE的影响,并确定显著性变量。其次,将显著性变量逐一加入GAM模型,根据Akaike信息法则（AIC）,选择最优的GAM模型。最后,利用最优的GAM模型对CPUE标准化,并定量分析时间、空间、环境以及捕捞渔船参数对鲐鱼CPUE的影响。GLM模型结果表明：8个变量对CPUE有重要影响,依次为年、船队、船队与年的交互效应、月、船队与月份的交换效应、经度、纬度和海表温。根据AIC,包含上述8个显著性变量的GAM模型为最优模型,对CPUE偏差的解释为27.78%。GAM模型结果表明：高CPUE分别出现在夏季海表温为28～31 ℃的东海中部和冬季海表温为12～16 ℃的黄海;1998-2006年,标准化后的CPUE呈逐年下降趋势,与持续增长的捕捞努力量有关。     

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。     

Distribution,body length,and abundance of blue shark and shortfin mako offshore of northeastern Japan,as determined from observed pelagic longline data, 2000–2014

Longline surveys have been conducted in the Northwest Pacific Ocean from 2000 to 2014 using chartered commercial longline vessels. Each year, two cruises were conducted offshore of northeastern Japan from mid‐April to mid‐June. For each longline set during the surveys, onboard scientists collected detailed biological information about the species caught, such as the size and sex, and recorded the catch numbers for all species. Blue shark (Prionace glauca) and shortfin mako (Isurus oxyrinchus) have eurythermal distributions, but the application of a generalized additive model (GAM) showed that the sea surface temperatures (SSTs) at catch sites positive for shortfin mako were warmer than those for blue shark. On the basis of the GAM, the probabilities of occurrence of both sharks differed by size category: small sharks had a narrower SST range than that of large sharks. Most catches of both sharks were juveniles, and the nominal catch rate of blue shark was more than 10 times that of shortfin mako. The standardized catch per unit effort (CPUE) for both species was calculated using a generalized linear model (GLM) with negative binomial errors, or a delta‐lognormal GLM. The standardized CPUE for blue shark in the second quarter of the year peaked in the mid‐2000s and then decreased, but it has been increasing since 2012. The CPUE for shortfin mako in the second quarter generally increased, with fluctuations.     

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.     

不同气候模态下西北太平洋柔鱼渔场环境特征分析

柔鱼冬春生群体广泛分布于西北太平洋,其种群分布与大小受气候变化和环境因子调控。本实验根据中国鱿钓组提供的1995—2011年渔业捕捞数据和海洋环境数据包括海表温度(SSTA)、海表面高度(SSHA)和混合层深度(MLDA)的距平值,分析不同气候模态下(PDO暖期和PDO冷期)柔鱼渔场环境的变化。结果显示,PDO暖期时,柔鱼CPUE高;PDO冷期时,CPUE变低。柔鱼渔场SSTA、SSHA和MLDA年间变化显著,各环境变量的时间变化与PDO冷暖相位对应。SSTA和SSHA与PDO指数负相关,滞后时间分别为–9~10月和–20~17月,且均在0月时相关系数最大;而MLDA与PDO指数呈正相关,滞后时间为–6~5月,在–1月相关系数最大。利用经验正交函数分析了SSTA、SSHA和MLDA时空变化的主要模态,前5个模态特征向量分别反映了西北太平洋柔鱼渔场SSTA、SSHA和MLDA分布场78.73%、32.82%和64.57%的信息。研究表明,气候模态变化驱动柔鱼渔场环境的变化,进而对西北太平洋柔鱼资源丰度产生显著影响。     

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。     

南极海冰变动对西南大西洋阿根廷滑柔鱼资源丰度及空间分布的影响

南极海冰变化会是重要的大尺度环境变化,会对海洋环境及多种生物产生直接或间接影响。南极海冰覆盖范围(Sea Ice Extent)可以作为指示南极海冰变化的指数,本文探究其对西南大西洋阿根廷滑柔鱼(Illex argentinus)渔场的影响。根据2013-2018年1-6月中国鱿钓科学技术组提供的西南大西洋公海渔场阿根廷滑柔鱼捕捞数据、南极海冰覆盖范围数据以及作业渔场5m、55m、95m、195m海水温度数据,以单位捕捞努力量渔获量(CPUE)表征资源丰度,本文探究了南极海冰覆盖范围、不同水深海水温度对阿根廷滑柔鱼渔场变动的影响。结果发现,阿根廷滑柔鱼的捕捞量、CPUE、南极海冰覆盖范围与不同深度海水温度均有明显的年间及月间变化。相关分析法表明,南极海冰覆盖范围与CPUE在年间与月间变化上均为正相关;海冰覆盖范围与5m海水温度在年间与月间变化上均为负相关,而与95m海水温度在月间变化上呈正相关。依据频率分布法估算了不同海水深度滑柔鱼各月适宜和最适温度范围,不同深度的各月最适温度范围占渔场总面积比例与海冰覆盖范围呈正相关关系,推测海冰覆盖范围会影响不同深度的栖息地适宜比例,并进一步影响阿根廷滑柔鱼资源丰度。研究表明,南极海冰覆盖范围变化会显著影响阿根廷滑柔鱼渔场内不同水层的水温,进而影响其渔场的分布及资源丰度。     

不同气候模态下西北太平洋柔鱼冬春生群资源时空分布变化研究

柔鱼(Ommastrephes bartramii)是大洋性短生命周期物种,其生活史和生物量受环境和气候因子影响明显。基于2004—2015年西北太平洋柔鱼渔捞日志、海表面温度(Sea surface temperature,SST)、叶绿素(Chlorophyll a,Chl a)浓度及太平洋年代际震荡(Pacific decadal oscillation,PDO)数据,利用空间自相关统计方法、热点分析和小波分析法研究PDO冷期与暖期两种气候模态下西北太平洋柔鱼渔场的时空变化。结果表明,PDO指数(Pacific Decadal Oscillation Index,PDOI)与柔鱼单位捕捞努力量渔获量(Catch per unit effort,CPUE)呈正相关,且CPUE滞后PDO 8个月(R2=0.548,P<0.05),CPUE与PDO指数的共轭周期为2~4个月。不同气候模态下的渔场热点分布特征为,暖期时渔场重心向高纬方向分布较为明显,空间上的集聚性强;冷期时向低纬分布较为明显,空间上的集聚性相对较差。PDO冷暖时期对西北柔鱼时空分布影响显著,该研究对柔鱼资源的可持续开发具有一定的科学意义。     

The relative importance of lunar phase and environmental conditions on striped marlin (Tetrapturus audax) catches in sport fishing

We used generalized additive mixed models to analyze data on striped marlin (Tetrapturus audax) catches in Baja California sport fisheries (1987–1989) to investigate the relative importance of lunar phase, sea surface temperature (SST), El Niño/Southern Oscillation (ENSO) index, and vessel effects on catch per unit effort (CPUE). The results indicate that the moon phase has no or little (<1% of variance explained) influence on CPUE. In contrast, both SST (10%) and ENSO (6%) influenced CPUE positively. While most of the variation in CPUE remains unexplained (53%), the vessel identity explained small (ca. 1.5%) but significant amount of variance in CPUE. Hence, sport fishermen wishing to maximise their striped marlin catches would best plan their trips on the basis of weather conditions and boat/crew identities, rather than on the basis of lunar phase.     

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。     

Skipjack tuna fishery in relation to sea surface temperature off the southern Brazilian coast

Several oceanographic studies have associated tuna fisheries to sea surface temperature (SST) fields, although catch per unit of effort (CPUE) has not shown a clear relationship with SST. However, most results concerned species that occur deep in the water column. In this paper, we present a study on the relationship between SST and CPUE for the skipjack tuna fisheries off the southern Brazilian coast, which take place at the sea surface. We use historical data from the Japanese fleet, which operated in the area from July 1982 to June 1992. Fishing sets occurred only in areas where SST ranged from 17°C to 30°C. Frequency of occurrence vs. SST showed a Gaussian distribution, with highest CPUEs in waters of SST 22°-26.5°C. The relationship between CPUE (or fishing set occurrence) and SST varied seasonally. Largest CPUEs occurred in summer, independently of SST. Therefore, temperature alone could not be used as a determinant of CPUE, suggesting that seasonal variability of other environmental parameters has a stronger effect on the CPUE than does SST. However, when the seasonal cycle was excluded from the data sets, a relationship between the interannual variability of SST and CPUE became apparent. Cross-correlation analysis between CPUE and SST has shown that oscillations in CPUE anomalies precede oscillations in SST anomalies by a month, but the mechanism relating them in this way is unknown.     

Estimated distribution and movement of hairtail Trichiurus lepturus in the Aru Sea, based on the logbook records of trawlers

SUMMARY: The distribution and movement of the hairtail Trichiurus lepturus stock in the Aru Sea were investigated using logbook data from four Taiwanese commercial otter trawlers. Fluctuations in population size, population density index and the distribution of catch per unit effort (CPUE) and fishing effort are discussed. The CPUE data were standardized by using the generalized linear model (GLM) in which fishing month, fishing area and size category of the hairtail were taken into account. The fishing season could be divided into three episodes: aggregation between March and April, dispersion between May and June, and aggregation again between July and December. A seasonal two-peak distribution of the population size of hairtail reflects the aggregation–dispersion pattern. The first peak in May–June can be explained in terms of the hairtails' overwintering migration. The second peak in October–December is likely to be the result of spawning aggregation.     

Standardizing catch and effort data of the Taiwanese distant-water longline fishery in the western and central Pacific Ocean for bigeye tuna, Thunnus obesus

Catch per unit effort (CPUE) is often used as an index of relative abundance in fisheries stock assessments. However, the trends in nominal CPUE can be influenced by many factors in addition to stock abundance, including the choice of fishing location and target species, and environmental conditions. Consequently, catch and effort data are usually ‘standardized’ to remove the impact of such factors. Standardized CPUE for bigeye tuna, Thunnus obesus, caught by the Taiwanese distant-water longline fishery in the western and central Pacific Ocean (WCPO) for 1964–2004 were derived using three alternative approaches (GLM, GAM and the delta approach), and sensitivity was explored to whether catch-rates of yellowfin tuna and albacore tuna are included in the analyses. Year, latitude, and the catch-rate of yellowfin explained the most of the deviance (32–49%, depending on model configuration) and were identified consistently among methods, while trends in standardized catch-rate differed spatially. However, the trends in standardized catch-rates by area were found to be relatively insensitive to the approach used for standardization, including whether the catch-rates of yellowfin and albacore were included in the analyses.