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。     

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.     

Living on the continental shelf edge: habitat use of juvenile shortfin makos Isurus oxyrinchus in the Great Australian Bight,southern Australia

We used satellite telemetry data to investigate the movement patterns and habitat use of juvenile shortfin makos Isurus oxyrinchus (Lamnidae) tagged in the Great Australian Bight, southern Australia. Tracking durations ranged from 49–672 days and six deployments were > 1 year. During winter and spring, some shortfin makos migrated to the tropical NE Indian Ocean and Coral Sea, and the Subtropical Front region. One shortfin mako undertook an extended migration of 25 550 km across the Indian Ocean. Areas characterized by sea‐mounts in the NE Indian Ocean, the oceanic Subtropical Front region, and the continental shelf edge (200‐m depth) and slope canyons were visited by several sharks. Juvenile shortfin makos used the outer continental shelf, the shelf edge, the slope and oceanic waters during migrations and mostly exhibited fidelity in the mid‐outer shelf, the shelf edge and slope habitats characterized by high bathymetric relief and oceanographic frontal gradients. Our findings highlighted that the continental shelf and slope and associated submarine canyons of the Great Australian Bight represent ecologically important habitats for juvenile shortfin makos. The findings of this study will be pertinent during future management processes for this highly migratory species in this Southern Hemisphere region.     

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.     

Relationships between CPUE fluctuation of southern bluefin tuna and ocean temperature variability in the Central Indian Ocean

In this study, catch and effort data of southern bluefin tuna (SBT) from Taiwan longliners operating in the Central Indian Ocean (CIO) during 1982 to 2003 were compiled and their catch per unit effort (CPUE) was standardized using the generalized linear model (GLM). The GLM includes factors such as year, season, by-catch, latitude, sea surface temperature (SST) and the interactive effects among factors. The standardized CPUE and its relationship with SST fluctuation were then analyzed to understand the effects of fishing ground SST variations on CPUE of SBT, as well as their connection to El Niño-Southern Oscillation (ENSO) events. The standardized CPUE in the CIO seemed to oscillate with the sea surface temperature anomalies (SSTA) between 30 and 50°S where SSTA fluctuations were prolonged and slower than the ENSO cycle. It is then very likely that fishing conditions at the CIO fishing ground were influenced by the expansion of the cold water mass from the Southern Ocean, and the colder SST is beneficial to increasing SBT catch rate.     

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 of albacore (Thunnus alalunga) in the Indian Ocean and its relation to environmental factors

The distribution pattern of albacore, Thunnus alalunga, in the Indian Ocean was analyzed based on catch data from the Taiwanese tuna longline fishery during the period 1979–85. The Taiwanese tuna fishery began operating in the Indian Ocean in 1967. We used a geographic information system to compile a fishery and environmental database and statistically explored the catch per unit effort (CPUE) distribution of albacore. Our results indicated that immature albacore were mainly distributed in areas south of 30°S although some displayed a north–south seasonal migration. Mature albacore, which were mainly concentrated between 10°S and 25°S, also showed a north–south migration. Within 10°S and 30°S, the separation of mature, spawning, and immature albacore life history stages roughly coincided with the boundaries of the three oceanic current systems in the Indian Ocean. The optimal environmental variables for CPUE prediction by stepwise discriminant analysis differed among life history stages. For immature albacore, the sea surface variables sea surface temperature (SST), chlorophyll concentration and surface salinity were significant. For mature albacore, SST was significant, while for spawning albacore, the sub‐surface variables temperature at 100 m and oxygen at 200 m were significant. Spawning albacore evidently prefer deep oceanographic conditions. Our results on the oceanographic conditions preferred by different developmental stages of albacore in the Indian Ocean were compatible with previous studies found in the Pacific Ocean.     

Multi‐timescale interactions between pink and chum salmon catch per unit effort in the Bering Sea

Based on generalized linear models, interspecific interactions were identified between chum and pink salmon. In addition, the effects of sea surface temperature and location on the variability of catch per unit effort (CPUE) of chum salmon from gill‐net surveys carried out between 1972 and 2010 were investigated. In the optimal model, interspecific interactions between CPUEs of chum and pink salmon on a year scale were positive for approximately half of all years in the central Bering Sea. In addition, interspecific interactions on a multi‐year scale were positive in even‐numbered years. The effects of location on the CPUE of chum salmon were significant variables in the optimal model. The CPUEs of chum salmon located near the continental shelf in the Bering Sea were higher than those of other locations. This study provides new evidence of positive interspecific interactions between the CPUEs of chum and pink salmon. The results also suggest that the standardized CPUE of chum salmon from the gill‐net surveys reflects relative chum salmon abundance in the North Pacific Ocean in the following year.     

Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna (Thunnus albacares) longline CPUE data

Catch-per-unit-effort (CPUE) data have often been used to obtain a relative index of the abundance of a fish stock by standardizing nominal CPUE using various statistical methods. The theory underlying most of these methods assumes the independence of the observed CPUEs. This assumption is invalid for a fish population because of their spatial autocorrelation. To overcome this problem, we incorporated spatial autocorrelation into the standard general linear model (GLM). We also incorporated into it a habitat-based model (HBM), to reflect, more effectively, the vertical distributions of tuna. As a case study, we fitted both the standard-GLM and spatial-GLM (with or without HBM) to the yellowfin tuna CPUE data of the Japanese longline fisheries in the Indian Ocean. Four distance models (Gaussian, exponential, linear and spherical) were examined for spatial autocorrelation. We found that the spatial-GLMs always produced the best goodness-of-fit to the data and gave more realistic estimates of the variances of the parameters, and that HBM-based GLMs always produced better goodness-of-fit to the data than those without. Of the four distance models, the Gaussian model performed the best. The point estimates of the relative indices of the abundance of yellowfin tuna differed slightly between standard and spatial GLMs, while their 95% confidence intervals from the spatial-GLMs were larger than those from the standard-GLM. Therefore, spatial-GLMs yield more robust estimates of the relative indices of the abundance of yellowfin tuna, especially when the nominal CPUEs are strongly spatially autocorrelated.     

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。     

应用贝叶斯生物量动态模型评估印度洋黄鳍金枪鱼资源

利用贝叶斯生物量动态模型对印度洋黄鳍金枪鱼(Thunnus albacares)资源进行了评估,并分析了不同标准化单位捕捞努力渔获量(catch per unit effort,CPUE)、内禀增长率(r)先验分布对评估结果的影响。结果表明:(1)模型能较好拟合日本延绳钓渔业的标准化CPUE,但对中国台湾延绳钓渔业的标准化CPUE拟合较差;当模型单独使用日本标准化CPUE时,评估结果显示印度洋黄鳍金枪鱼被过度捕捞;若模型单独使用中国台湾标准化CPUE,则结果相反,显示印度洋黄鳍金枪鱼未被过度捕捞;而当同时使用两个标准化CPUE时,日本标准化CPUE数据获得更大估计权重,因此,评估结果与单独使用日本标准化CPUE的结果类似。(2)当r采用无信息先验时,r估计偏小,而环境容纳量(K)估计则偏大,参数估计不合理;当r采用信息先验时,r与K的后验分布估计相对合理;由于r与K存在显著的负相关关系,生物量动态模型难于同时有效估计这两个参数,特别是在数据质量较差情况下,因而采用信息先验能提高生物量动态模型参数估计的质量。(3)本研究利用偏差信息准则(Deviance Information Criterion,DIC)与均方误差(Mean Square Error,MSE)统计量对模型进行了比较,并选择模型S8用于评价印度洋黄鳍金枪鱼的资源状态。评估结果认为印度洋黄鳍金枪鱼被过度捕捞,既存在捕捞型过度捕捞,也存在资源型过度捕捞,这与资源合成(Stock synthesis version 3,SS3)等模型的评价结果一致。     

A study on the variability of albacore (Thunnus alalunga) longline catch rates in the southwest Pacific Ocean

Relationships between albacore tuna (Thunnus alalunga) longline catch per unit effort (CPUE) and environmental variables from model outputs in New Caledonia’s Exclusive Economic Zone (EEZ) were examined through generalized linear models at a 1° spatial resolution and 10‐day temporal resolution. At a regional (EEZ) scale, the study demonstrated that a large part of albacore CPUE variability can be explained by seasonal, interannual and spatial variation of the habitat. Results of the generalized linear models indicated that catch rates are higher than average in the northwestern part of the EEZ at the beginning of the year (January) and during the second half of the year (July–December). In the northwestern region of the EEZ, high CPUEs are associated with waters <20.5° in the intermediate layer and with moderate values of primary production. Longline CPUE also appeared to be dependent on prey densities, as predicted from a micronekton model. Albacore CPUE was highest at moderate densities of prey in the epipelagic layer during the night and for relatively low prey densities in the mesopelagic layer during the day. We also demonstrated that the highest CPUEs were recorded from 1986 to 1998, which corresponds to a period with frequent El Niño events.     

热带印度洋大眼金枪鱼渔场时空分布与温跃层关系

为了解印度洋大眼金枪鱼(Thunnus obesus)温跃层参数适宜分布区间及季节变化,采用Argo浮标剖面温度数据重构热带印度洋各月平均温跃层特征参数,并结合印度洋金枪鱼委员会(IOTC)大眼金枪鱼延绳钓渔业数据,本文绘制了月平均温跃层特征参数和月平均CPUE的空间叠加图,用于分析热带印度洋大眼金枪鱼渔场CPUE时空分布和温跃层特征参数的关系。结果表明,热带印度洋温跃层上界深度、温度和下界深度都具有明显的季节性变化,大眼金枪鱼中心渔场分布和温跃层季节性变化有关。夏季季风期间,高CPUE渔区温跃层上界深度在30~50 m,浅于冬季的50~70 m;温跃层上界温度范围为24~30℃。在冬季季风期间,高CPUE区域对应的温跃层上界温度范围为27~30℃;从马达加斯加岛北部沿非洲大陆至索马里附近海域,温跃层下界深度在170~200 m时的渔区CPUE普遍较高;当深度超过300 m时,CPUE值均非常低。采用频次分析和经验累积分布函数计算其最适温跃层特征参数分布,得出大眼金枪鱼最适温跃层的上界、下界温度范围分别是26~29℃和13~15℃;其上界、下界深度范围分别是30~60 m和140~170 m。文章初步得出印度洋大眼金枪鱼中心渔场温跃层各特征参数的适宜分布区间及季节变化特征,为金枪鱼实际生产作业和资源管理提供理论参考。     

中西太平洋金枪鱼围网黄鳍金枪鱼产量的时空分布及与表温的关系

黄鳍金枪鱼是中西太平洋金枪鱼围网渔业中的重要捕捞种类之一。本文根据2003年中西太平洋金枪鱼围网生产统计及其表温数据,利用频次统计分析和地理信息软件Marine Explorer 4.0对黄鳍金枪鱼产量和单位日产量(CPUE)的时空分布进行分析,探讨其与海水表温的关系。结果显示,产量和CPUE最高的是2月份,其次是9月份,5月份为最低。高产量的范围为140~160°E、0°~5°S;CPUE高值区分布在130°E、0°~15°S,140°~160°E、0°~15°S和175°W、0°~15°S;产量经纬度重心分别为150°30′E和3°48′S。产量主要分布在海表温为28~31℃的海域,产量比重高达95.45%,其中29~30℃产量为最高,占69.54%。     

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。     

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。     

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 (Aka...     

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。     

印度洋长鳍金枪鱼资源评估的影响因素分析

多个模型被用于印度洋长鳍金枪鱼(Thunnus alalunga)的资源评估,但这些模型的评估结果均存在较大的不确定性,为此,本文对影响印度洋长鳍金枪鱼资源评估的因素进行了分析。分析结果认为:(1)由于渔业数据存在不报、漏报或混报及采样样本数过低、采样协议出现变化等问题,造成印度洋长鳍金枪鱼渔业的渔获量、体长组成或年龄组成数据存在质量问题;(2)尽管对单位捕捞努力渔获量(catch per unit effort,CPUE)进行了标准化,但目标鱼种变化及捕捞努力量空间分布变化仍严重影响了标准化CPUE数据的质量;(3)印度洋长鳍金枪鱼的种群生态学及繁殖生物学研究仍比较薄弱,种群结构、繁殖、生长、自然死亡信息比较缺乏,在资源评估中,相关参数设置需借用其他洋区的研究结果;(4)海洋环境对印度洋长鳍金枪鱼的资源变动与空间分布具有显著影响,但评估模型较少考虑海洋环境的影响。由于上述问题的存在,导致当前评估结果存在较大不确定性。未来,应继续探索提高资源评估质量的方法,同时研究建立管理策略评价框架,以避免渔业资源评估结果的不确定性对该渔业可持续开发的影响。