世界南方蓝鳍金枪鱼渔业的资源状况与管理

金枪鱼类属于鲭科，为高度洄游性鱼类，遍及温带到热带的沿岸及大洋，几乎世界所有海洋都有金枪鱼类分布。国际社会大规模地开发利用金枪鱼类是第二次世界大战后[１]。由于金枪鱼渔业具有较高的经济效益，且直接关系着一个国家和地区所享有的公海权益，因而世界许多远洋渔业国家和地区都非常重视发展金枪鱼渔业。南方蓝鳍金枪鱼又称为南方黑鲔（Southernbluefintuna），学名为马苏金枪鱼（Thunnusmaccoyii），为金枪鱼类的一种，其肉质鲜美，非常适合制作高品质的生鱼片和寿司，具有很高的经济价值。由于长期的过度开发，２０世纪８０年代…     

DNA identification of Pacific bluefin tuna (Thunnus orientalis) in the New Zealand fishery

Muscle samples were collected from 69 specimens identified as Pacific bluefin tuna (Thunnus orientalis) (Temminck and Schlegel, 1844) in the New Zealand Exclusive Economic Zone (EEZ) between 1990 and 2000. Identifications before 1996 were based on body size and colour of the caudal keel; later identifications were mostly based on the shape of abdominal cavity. The tissue samples were tested with a diagnostic mitochondrial DNA marker that distinguishes southern bluefin Thunnus maccoyii (Castelnau, 1872) and Pacific bluefin tuna T. orientalis; 59 specimens were confirmed as T. orientalis and 10 as T. maccoyii. Specimens recorded as Pacific bluefin tuna by the shape of the abdominal cavity were correctly identified as T. orientalis, and this character can be used to identify large specimens landed on tuna vessels. Some specimens recorded as Pacific bluefin tuna on the basis of colour and size were T. maccoyii; and early records of T. orientalis in New Zealand waters, based on these characters, are unreliable. Unusual colour patterns were reported in some specimens of T. orientalis but not T. maccoyii. The Pacific bluefin tuna T. orientalis accounted for less than 0.3% of the bluefin tuna catch in the New Zealand EEZ during the 1990s.     

自然死亡和亲体补充关系对黄鳍金枪鱼资源评估的影响

黄鳍金枪鱼（Thunnus albacares）是全球经济和生态价值最重要的鱼类之一,其资源养护和管理受到各方的高度关注。本文依据年龄结构产量模型研究了印度洋黄鳍金枪鱼的资源状态,着重探讨了其生活史特征的不确定性对资源评估结果的影响。研究结果显示,1960−1985年间印度洋黄鳍金枪鱼资源量保持相对稳定,之后开始逐渐下降,相应的捕捞死亡系数也在2010年之后迅速增加,目前其种群可能存在过度捕捞（F₂₀₂₀/F_MSY>1,SSB₂₀₂₀/SSB_MSY<1）。印度洋黄鳍金枪鱼的资源评估结果对自然死亡系数（M）和亲体−补充量关系陡度参数（h）的改变较为敏感。当h增大时,SSB_MSY和初始SSB（即SSB₀）的变化较大,分别减少了约25.53万t和34.04万t;F₂₀₂₀/F_MSY减小了1.15。当M增大时,F₂₀₂₀/F_MSY、SSB_MSY、SSB₀均减小。综上所述,今后应重视印度洋黄鳍金枪鱼资源的开发程度,重视其资源养护管理,同时充分了解黄鳍金枪鱼的生活史特征,提高自然死亡系数和陡度参数估算的准确性,以期为印度洋黄鳍金枪鱼的资源评估和渔业管理提供更准确的信息,实现该渔业的长期可持续发展。     

Habitat overlap between southern bluefin tuna and yellowfin tuna in the east coast longline fishery - implications for present and future spatial management

Southern bluefin tuna (SBT) are presently a quota-managed species in the multi-species eastern Australian tuna and billfish longline fishery (ETBF). Capture of SBT is regulated by quota, as is access to regions likely to contain SBT. A habitat prediction model combining data from an ocean model and pop-up satellite archival tags is used to define habitat zones based on the probability of SBT occurrence. These habitat zones are used by fishery managers to restrict access by ETBF fishers to SBT habitat during a May-November management season. The zones display a distinct seasonal cycle driven by the seasonal southward expansion and northward contraction of the East Australia Current (EAC) and as a result access by fishers to particular ocean regions changes seasonally. This species also overlaps with the commercially valuable yellowfin tuna (YFT), thus, we modified the SBT model to generate YFT habitat predictions in order to investigate habitat overlap between SBT and YFT. There is seasonal variation in the overlap of the core habitat between these two species, with overlap early (May-Jul) in the management season and habitat separation occurring towards the end (Aug-Nov). The EAC is one of the fastest warming ocean regions in the southern hemisphere. To consider the future change in distribution of these two species compared to the present and to explore the potential impact on fishers and managers of the future, we use future ocean predictions from the CSIRO Bluelink ocean model for the year 2064 to generate habitat predictions. As the ocean warms on the east coast of Australia and the EAC extends southward, our model predicts the suitable habitat for SBT and YFT will move further south. There was an increase in the overlap of SBT and YFT habitat throughout the management season, due to regional variation of each species’ habitat. These results illustrate that a management tradeoff exists between restricting fisher access to SBT habitat and allowing access to YFT habitat. We suggest that some options to address this tradeoff are possible by identifying the seasonal variability of the overlap.     

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以西南印度洋大眼金枪鱼为例

为提高大眼金枪鱼(Thunnus obesus)延绳钓渔情预报模型的预测能力,本研究提出了一种基于深度卷积嵌入式聚类(DCEC)的海洋环境时空特征提取方法,结合广义可加模型(GAM)对西南印度洋大眼金枪鱼延绳钓渔场进行预报。采用2018年1−12月0.041 6°×0.041 6°的MODIS-Aqua和MODIS-Terra海表面温度三级反演图像数据(以日为单位)构建DCEC模型,基于Davies-Bouldi 指数(DBI)确定最佳聚类数,在此基础上提取各月海表温度(SST)的类别特征值$ {F}_{M} $;采用美国国家海洋和大气管理局网站2018年1−12月1°×1°的Chl a浓度月平均值作为辅助环境特征因子;采用印度洋金枪鱼委员会2018年1−12月1°×1°的大眼金枪鱼延绳钓渔业数据(以月为单位),计算单位捕捞努力量渔获量(CPUE);将SST月类别特征值$ {F}_{M} $、Chl a浓度月平均值与CPUE数据进行时空匹配,构建改进GAM;采用SST月平均值、Chl a浓度月平均值与CPUE数据构建基础GAM;采用联合假设检验($ F $检验)验证模型解释变量对响应变量的影响;采用赤池信息准则(AIC)、均方误差(MSE)、绘制实测值和预测值的散点图并计算相关系数r,分析改进GAM相比于基础GAM的提升效果。实验结果表明：(1)基于DCEC模型提取的$ {F}_{M} $能够较好地反映西南印度洋海表温度的时空动态特征与规律,并与西南印度洋的气候条件、季风状况和水文特征等相互耦合;(2) $ {F}_{M} $相比SST平均值的因子解释率更高,对大眼金枪鱼CPUE影响更为显著,高渔获率集中在暖冷流交汇区域;(3)改进GAM相比基础GAM的AIC值降低了9.17%,MSE降低了26.7%,散点图显示改进GAM预测的CPUE对数值与实测CPUE对数值的相关性较显著,r为0.60。本研究证明了DCEC模型在海洋环境特征提取方面的有效性,可为后序大眼金枪鱼延绳钓渔情预报模型的改进研究提供参考。     

我国金枪鱼围网渔业的初步研究

报道2002年6月至2003年5月两艘围网渔船在中西太平洋围捕金枪鱼及每月作业状况。结果显示,70%的网次围捕浮水鱼群,其空网率达52.5%,30%的网次围捕流木鱼群,其空网率为9.5%;渔获物中鲣鱼(Katsuwonuspelamis)占90%,优势体长为40～60cm,黄鳍金枪鱼(Thunnasalbacares)占10%左右,优势体长为100～150cm;作业渔场位于5°N～5°S,137°E～178°E;两艘船每月的空网率为9%～64%,每月渔获量在185～940t之间波动,平均有效网次产量不低于20t,最大网次产量为260t。     

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.     

The irony and the exclusivity of Atlantic bluefin tuna management

The eastern stock of Atlantic bluefin tuna (thunnus thynnus) was recently nominated for protection under the Convention on International Trade in Endangered Species of Flora and Fauna (CITES). That nomination failed, which leaves management of the heavily overfished stock in the hands of the International Commission for the Conservation of Atlantic Tunas (ICCAT). This paper reviews the history of ICCAT management of Atlantic bluefin tuna to show that (1) early elimination of competitive forces prevented agreement on management in keeping with scientific advice, resulting in the steep decline of the stock, (2) even just the threat of a CITES listing can provide temporary impetus for marked improvement in ICCAT management, and (3) long-term sustainable management by ICCAT will require continued economic and political pressure.