利用贝叶斯动态产量模型评估印度洋长鳍金枪鱼资源状态

印度洋长鳍金枪鱼(Thunnusalalunga)的生物学信息相对较少,渔业数据存在较多问题,致使其资源评估结果仍存在较大的不确定性,从而影响了渔业管理的科学性。为此,本研究基于印度洋长鳍金枪鱼的渔业捕捞、标准化CPUE (catch per unit effort)数据及相关种群假设,利用贝叶斯动态产量模型对该种群进行了资源评估研究,结果显示:(1)渔获量的观测误差对模型参数估计、资源状态的判断及渔业管理具有重要影响,渔获量观测误差的增大使模型评估的过度捕捞概率上升,导致总可捕量(total allowable catch, TAC)减少;(2)动态产量模型形状参数、r的先验分布和资源丰度指数的选择均会影响资源评估的质量,本研究显示, Fox模型的资源评估结果比Schaefer模型的评估结果更合理,r先验分布范围的增大使模型评估的资源状态变好,使用西南海域标准化CPUE时的评估结果相对较好;(3)设置某些年份的资源量比例(φ、P2017)范围有助于提高数据缺乏下渔业资源评估的质量;(4)评估结果表明印度洋长鳍金枪鱼发生资源型与捕捞型过度捕捞的概率分别为34%、50%,两种过度捕捞同时...     

渔获量不确定性对印度洋大眼金枪鱼资源评估的影响

大眼金枪鱼(Thunnus obesus)是最具经济价值的热带金枪鱼类,其资源状况一直是区域性金枪鱼渔业管理组织关注的重点。由于多种渔业作业、捕捞船队构成复杂,印度洋大眼金枪鱼的历史渔获量统计存在一定的偏差(Bias),但国际上近些年开展资源评估时都忽略了这一偏差。本研究根据1979~2015年的年渔获量、年龄结构渔获量及相对丰度指数数据,运用年龄结构资源评估模型(ASAP)对印度洋大眼金枪鱼资源进行评估,重点考查渔获量的不确定性(观测误差和统计偏差)对资源评估结果的影响。结果显示,印度洋大眼金枪鱼当前资源总体没有过度捕捞,但2015年初显示轻微的过度捕捞,通过对比基础模型与8个灵敏度分析模型的评估结果发现,渔获量观测误差(CV)的预设对资源开发状态的判断有一定的影响。当渔获量统计偏差调整量为15%时(即历史渔获量被低估了),评估结果与基础模型基本一致;统计偏差调整量为20%时,评估结果有过度捕捞的趋势。本研究结果表明,资源评估模型中渔获量观测误差的设定和历史渔获量统计偏差均会对评估结果产生影响,后者更为明显,因此,二者均不能忽略。     

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。     

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。     

印度洋大眼金枪鱼Thunnus obesus(Lowe) 的渔业生产和资源状况概述

印度洋是世界上第二大金枪鱼类产区．其产量占全球金枪鱼类总产量的三分之一，也是我国近年重要的捕捞渔场。本文主要概述印度洋大眼金枪鱼的生物学和生态特性、渔业生产、资源状况及管理对策。     

印度洋大眼金枪鱼生物学特性初步分析

根据 2003年 1~6月大洋性延绳钓作业中测定的数据，对印度洋大眼金枪鱼的生物学特性进行了初步分析。印度洋大眼金枪鱼的饵料种类较杂，鱼的优势叉长为 121~180cm，优势体重在 41~90kg， 雌雄比例为 1∶1．3。原条鱼重量与加工后重量之间的关系为 y=1．1345x+1．4879，相关系数 R= 0．9969；原条鱼重量与叉长的关系为 y=6×10^-5 x^2．7781 ，相关系数 R=0．9829。     

热带印度洋大眼金枪鱼垂直分布空间分析

为了解热带印度洋大眼金枪鱼(Thunnus obesus)适宜的垂直和水平空间分布范围,采用Argo浮标剖面温度数据重构热带印度洋10℃、12℃、13℃和16℃月平均等温线场,网格化计算了12℃、13℃等温线深度值和温跃层下界深度差,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,绘制了12℃、13℃等温线深度与月平均单位捕捞努力渔获量(CPUE)的空间叠加图,用于分析热带印度洋大眼金枪鱼中心渔场 CPUE 时空分布和高渔获率水温的等温线时空分布的关系.结果表明,从垂直分布来看,热带印度洋中心渔场延绳钓高渔获率区域垂直分布在温跃层下界以下,在表层以下150~400 m 深度区间.从水平分布来看,12℃等温线,高 CPUE 区域大多深度值<350 m,众数为225~350 m;深度值超过500 m的区域CPUE普遍较低.13℃等温线,高值CPUE出现的地方大多深度值<300 m,众数为190~275 m;深度值超过400 m的区域CPUE普遍较低.全年在15oS以北区域,高渔获率的垂直分布深度更加集中.采用频次分析和经验累积分布函数,计算其最适次表层环境因子分布,12℃等温线250~340 m;13℃等温线190~270 m;12℃深度差30~130 m;13℃深度差0~70 m.研究初步得出热带印度洋大眼金枪鱼中心渔场适宜的水平、垂直深度值分布区间,可以辅助寻找中心渔场位置,同时指导投钩深度,为热带印度洋金枪鱼实际生产作业和资源管理提供理论支持.     

大西洋大眼金枪鱼资源开发研究

统计分析了1995-2004年间大西洋大眼金枪鱼总渔获量及各渔区、各主要生产国的渔获量变化情况,结果表明：整个大西洋大眼金枪鱼渔获量呈下降趋势,其开发类型主要有延绳钓、饵钓和围网,其中以延绳钓的渔获量最大,1995-2004年期间以西北大西洋（21区）产量最高,为9.53×10^7t,东北大西洋与中西大西洋相对渔获量较少,各为2.47×10^4t,我国对大西洋大眼金枪鱼开发程度不高,占总渔获量的比例较小,针对我国的情况提出开发大西洋大眼金枪鱼相关建议。     

运用SS3评估东太平洋大眼金枪鱼(Thunnus obesus)资源——复杂模型和简化模型的比较

大眼金枪鱼(Thunnus obesus)是东太平洋最重要的商业性金枪鱼鱼种,其资源评估采用的是结构复杂的Stock Synthesis 3模型(SS3).模型简化是提高资源评估效率的必要手段,但对大眼金枪鱼简化模型的效果尚未开展研究.本研究尝试从渔业数据结构的角度,将SS3复杂模型的23个渔业简化为仅含围网和延绳钓2个渔业,从而比较简化模型的评估能力.结果显示,简化模型能较为准确地描述大眼金枪鱼补充量、亲体量、捕捞死亡系数等主要时间序列的历史动态变化,对传统生物学参考点FMSY的估计也较为准确,且受陡度和自然死亡系数的影响较小,但对其他参考点的估算误差较大.陡度参数对简化模型基于Kobe图判断资源状态的准确性有重要影响,陡度较低时,简化模型能较为准确地判断资源状态.研究表明,权衡模型的评估能力和降低模型结构的复杂性,是大眼金枪鱼资源评估今后需要重点研究的任务之一.此外,对模型简化的效果评价,与采用的生物学参考点和资源状况判断标准的选择有关.     

日本将对印度洋金枪鱼资源继续探捕调查

日本一直把印度洋作为远洋金枪鱼作业的后备渔场，因此，即使在远洋渔业最困难的时候，也没有放弃对印度洋金枪鱼资源的调查。2012年，日本派出的调查船仍将继续执行对印度洋大眼金枪鱼和黄鳍金枪鱼资源进行更细化的调查研究，具体的细化调查研究项目包括：     

基于混合效应模型的印度洋大眼金枪鱼生长特征异质性分析

大眼金枪鱼(Thunnus obesus)作为一种具有极高经济价值的公海金枪鱼捕捞对象,其资源状况和管理情况一直受到学者的高度关注,而对其生活史特征,尤其是生长特征的研究,是对大眼金枪鱼进行准确资源评估和合理养护管理的基础和关键部分。本研究基于中国科学观察员于2013―2018年收集的印度洋大眼金枪鱼生物学数据,通过体长-体重关系研究其生长特征,并运用线性混合效应模型分析其生长特征在不同年份、季度和海域间的差异。依据收集的8806尾大眼金枪鱼样本,求得其上颌叉长FL和加工重量GT (去掉鳃、尾鳍和内脏后的重量)之间的幂函数关系式,其中条件因子a的估计均值(95%置信区间)为1.07(0.99～1.14)×10~(-5),异速生长参数b的估计值(95%置信区间)为3.08 (3.07～3.10)。本研究构建了7个不同异质性组合的混合效应模型, AIC值和均方根误差值均表明同时考虑年份、季度和区域差异的模型拟合效果最佳。最佳模型的结果表明,印度洋15°S以南和以北海域的大眼金枪鱼个体生长特征差异极小,北部个体仅略重于南部个体;相比于第三和第四季度,相同体长的大眼金枪鱼在第一和第二季度具有更多的重量; 2015年和2016年采集的个体在同样体长时体重更重,而2014年和2017年的大眼金枪鱼体重比其他年份更轻。本研究结果旨在为大眼金枪鱼的资源评估及渔业管理提供基础资料,异质性的研究方法也可以应用于其他近海、远洋渔业种类的生活史特征、种群特征和资源评估研究。     

Vertical behavior of bigeye tuna (Thunnus obesus) in the northwestern Pacific Ocean based on archival tag data

The behavior of bigeye tuna (Thunnus obesus) in the northwestern Pacific Ocean was investigated using archival tag data for 28 fish [49–72 cm fork length (FL) at release, 3–503 days] released in Japanese waters around the Nansei Islands (24–29°N, 122–132°E) and east of central Honshu (Offshore central Honshu, 32–36°N, 142–148°E). Vertical behavior was classified into three types based on past studies: ‘characteristic’ (non‐associative), ‘associative’ (associated with floating objects) and ‘other’ (behavior not fitting into these two categories). The proportion of fish showing associative behavior decreased and that of characteristic behavior increased as fish grew, and this shift was pronounced at 60–70 cm FL. The fish usually stayed above the 20°C isotherm during the daytime and nighttime when showing associative behavior and below the 20°C isotherm during daytime for characteristic behavior. A higher proportion of characteristic behavior was seen between December and April around the Nansei Islands, and between September and December for offshore central Honshu. Seasonal changes in vertical position were also observed in conjunction with changes in water temperature. In this study, ‘other’ behavior was further classified into five types, of which ‘afternoon dive’ behavior, characterized by deep dives between around noon and evening, was the most frequent. The present study indicated that in the northwestern Pacific Ocean, the vertical behavior of bigeye tuna changes with size, as well as between seasons and regions.     

Application of a habitat-based model to estimate effective longline fishing effort and relative abundance of Pacific bigeye tuna (Thunnus obesus)

A new habitat‐based model is developed to improve estimates of relative abundance of Pacific bigeye tuna (Thunnus obesus). The model provides estimates of `effective' longline effort and therefore better estimates of catch‐per‐unit‐of‐effort (CPUE) by incorporating information on the variation in longline fishing depth and depth of bigeye tuna preferred habitat. The essential elements in the model are: (1) estimation of the depth distribution of the longline gear, using information on gear configuration and ocean currents; (2) estimation of the depth distribution of bigeye tuna, based on habitat preference and oceanographic data; (3) estimation of effective longline effort, using fine‐scale Japanese longline fishery data; and (4) aggregation of catch and effective effort over appropriate spatial zones to produce revised time series of CPUE. Model results indicate that effective effort has increased in both the western and central Pacific Ocean (WCPO) and eastern Pacific Ocean (EPO). In the WCPO, effective effort increased by 43% from the late 1960s to the late 1980s due primarily to the increased effectiveness of effort (deeper longline sets) rather than to increased nominal effort. Over the same period, effective effort increased 250% in the EPO due primarily to increased nominal effort. Nominal and standardized CPUE indices in the EPO show similar trends – a decline during the 1960s, a period of stability in the 1970s, high values during 1985–1986 and a decline thereafter. In the WCPO, nominal CPUE is stable over the time‐series; however, standardized CPUE has declined by ～50%. If estimates of standardized CPUE accurately reflect relative abundance, then we have documented substantial reductions of bigeye tuna abundance for some regions in the Pacific Ocean. A decline in standardized CPUE in the subtropical gyres concurrent with stability in equatorial areas may represent a contraction in the range of the population resulting from a decline in population abundance. The sensitivity of the results to the habitat (temperature and oxygen) assumptions was tested using Monte Carlo simulations.     

El Niño effects in the Palmyra Atoll region: oceanographic changes and bigeye tuna (Thunnus obesus) catch rate variability

A generalized additive model (GAM) was constructed to separate and quantify the effects of fishery‐based (operational) and oceanographic parameters on the bigeye tuna (Thunnus obesus) catch rates at Palmyra Atoll in the central Tropical Pacific. Bigeye catch, the number of hooks per set, and set location from 4884 longline sets spanning January 1994 to December 2003 were used with a temporally corresponding El Niño‐Southern Oscillation (ENSO) indicator built from sea surface height (SSH) data. Observations of environmental data combined with the results from the GAM indicated that there is an increase in bigeye catch rates corresponding to an increase in eastward advection during the winter months of El Niño events. A seasonal pattern with higher bigeye catch rates from December to April and a spatial pattern with higher rates to the northeast and northwest of the atoll were observed during this study period. It is hypothesized that the combination of the eastward advection of the warm pool coupled with vertical changes in temperature during the winter months of El Niño events increases the availability of bigeye tuna in this region. This increase in availability may be due to a change in exploitable population size, location, or both.     

太平洋大眼金枪鱼延绳钓渔获分布及渔场环境浅析

本文主要根据收集到的渔获量数据、海水表层温度数据和有关文献资料 ,应用GIS技术对太平洋大眼金枪鱼延绳钓渔业进行了定量或定性分析。结果表明 :太平洋大眼金枪鱼延绳钓渔场主要分布在 2 0°N～2 0°S之间的热带海域 ,具纬向分布特征。对渔获产量同海表温度的分月统计显示 :太平洋大眼金枪鱼渔场最适月平均表层水温约 2 8～ 2 9℃ ,渔场出现频次为偏态分布型。最后 ,结合有关文献综合讨论分析了海表温度、溶解氧含量、海流等环境因子与金枪鱼渔场分布和形成机制的关系     

Separation of the Taiwanese regular and deep tuna longliners in the Indian Ocean using bigeye tuna catch ratios

ABSTRACT:   Taiwanese longline (LL) fisheries operating in the Indian Ocean usually target albacore tuna (ALB), swordfish (SWO) and yellowfin tuna (YFT) using regular LL. Bigeye tuna (BET), however, is targeted using deep LL. Thus, these two types of LL are considered to be different gears as they target different tuna species. Regular or deep LL fishing is defined by number of hooks per basket (NHB): regular LL if 6 ≤ NHB ≤ 10 and deep LL if 11 ≤ NHB ≤ 20. However, NHB information was available in only some of the recent LL data (1995–1999). This situation had caused problems of biased results in stock analysis in the past. Thus, the objective of our study was to explore an effective method to separate the two types of LL fishing by considering species composition. Some intervals of BET catch ratios were found to be effective in separating the regular and deep LL catches, i.e. 0.0 ≤ BET/(BET + ALB + SWO) ≤ 0.4 and 0.8 ≤ BET/(BET + ALB) ≤ 1.0, respectively. Using these two separators, the LL known data set (1995–1999) (learning data set) was classified. Correct classification occurred in 67.7% of the data, while 23.1% of the data were unclassified (11.9% due to zero catches and 11.2% due to classification into both LL types), and 9.2% were misclassifications. Then, using the methods developed, the LL unknown data set in the historical data (1979–1999) was classified and nominal CPUE values were calculated for four species. The CPUE trends based on this study were likely to be more reliable than those of previous studies.     

大眼金枪鱼渔业现状和生物学研究进展

结合各海区大眼金枪鱼的产量,对大眼金枪鱼在各海区的渔业历史和现状,按照不同渔具进行了分析。并对其分布、运动模式、牛理特性、年龄和生长、繁殖、食性以及种群结构等生物学作了具体的阐述。     

Considerations of stock structure of yellowfin tuna (Thunnus albacares) in the Indian Ocean based on fishery data

Yellowfin stock structure in the Indian Ocean was studied by using industrial tuna longline fishery data. Three types of test variables were used to detect stock structure, i.e., CPUE, age-specific CPUE, and coefficient of variation for size. Time-series data of test variables were compiled for six sub-areas that were arranged by dividing the whole region systematically along longitude lines every 20 degrees. Then time-series data were smoothed by moving averages, and regressed by simple models. Patterns of time-series trends were graphically and statistically compared to classify homogeneous sub-area groups. Two assumptions were (a) that homogeneous stocks exist longitudinally and overlap in adjacent waters, and (b) that test variables within homogeneous sub-area groups are equally affected, and hence patterns of the time-series trends are similar. After graphical screening for significant sub-area groups, analysis of covariance was applied to test homogeneity of regression parameters representing patterns of the time-series trends. By classifying homogeneous sub-area groups, stock structures were determined at the P <0.05 and P <0.50 levels. The P<0.50 level was recognized as a useful criterion for ‘weak’ test variables since masked or vague structures at the P <0.05 level were likely cleared at this level in many cases. Results of this study and past stock structure studies were reviewed and compared. It was concluded that there are two major and two minor stocks of yellowfin tuna. The two major stocks (the western and the eastern) are located at 40^o-90^oE and 70^o-130^oE respectively. The minor stocks are the far western and the far eastern stocks (the latter possibly being a part of the Pacific stock), which are located westward of 40^oE and eastward of 110^oE respectively. Neighboring stocks are intermingled in adjacent waters.