印度洋大眼金枪鱼延绳钓适宜渔获环境的初步研究

数值分析得出大眼金枪鱼的渔获适宜环境参数值范围：温度14～17℃,盐度34．5～35．4．溶解氧浓度1．5～4．5mg／L,温跃层深度80～160m,营养盐氮、磷、硅浓度分别大于12-μg／L,09μg／L,14μg／L。聚类分析表明,钓获率与营养盐关系最为密切。另外,通过因子分析,深化了对钓获率和渔获环境因子两者间关系的理解。     

Population structure of bigeye tuna (Thunnus obesus) in the Indian Ocean inferred from mitochondrial DNA

Population structure of bigeye tuna (Thunnus obesus) in the Indian Ocean, Western Pacific Ocean and Eastern Atlantic Ocean were investigated using mitochondrial (mt) DNA sequence data. A total of 380 specimens were sampled from four regions in the Indian Ocean (Cocos Islands, Southeastern Indian Ocean, Southwestern Indian Ocean and Seychelles), and one region each from the Atlantic (Guinea) and the Western Pacific Oceans, respectively. The reconstructed neighbor-joining phylogeny based on the first hypervariable region (HVR-1) of the mitochondrial control region sequence data showed that haplotypes from the Indian and the Western Pacific Oceans could be grouped into two clades (Clades I and III), whereas in the Atlantic Ocean, two divergent clades (Clades I and II) coexisted. A single stock of bigeye tuna in the Indian Ocean was supported by hierarchical AMOVA tests and pairwise Φ_ST analyses. Clade I was the dominant population in the Indian and the Western Pacific Oceans which consisted of more than 96% of the specimens and Clade II was a specific group exclusively restricted to the Atlantic Ocean which made up 77% of its specimens. A new minor Clade, Clade III was discovered in the Indian and the Western Pacific Ocean. Overall, these analyses indicated that bigeye tuna of the Indian Ocean constituted a single panmictic population.     

Standardizing catch and effort data: a review of recent approaches

The primary indices of abundance for many of the world's most valuable species (e.g. tunas) and vulnerable species (e.g. sharks) are based on catch and effort data collected from commercial and recreational fishers. These indices can, however, be misleading because changes over time in catch rates can occur because of factors other than changes in abundance. Catch-effort standardization is used to attempt to remove the impact of these factors. This paper reviews the current state of the art in the methods for standardizing catch and effort data. It outlines the major estimation approaches being applied, the methods for dealing with zero observations, how to identify and select appropriate explanatory variables, and how standardized catch rate data can be used when conducting stock assessments.     

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.     

基于GAM模型分析水温垂直结构对热带大西洋大眼金枪鱼渔获率的影响

通过模型分析环境变量对延绳钓大眼金枪鱼渔获率的影响,评估适宜垂直活动空间对大西洋大眼金枪鱼延绳钓渔获率的作用。首先采用回归分析检验环境变量对延绳钓渔获率(由单位捕捞努力渔获量(catch per unit fishing effort,CPUE)表示)的影响显著性,结合时空变量,采用GAM(generalized additive model)模型分析各变量对大眼金枪鱼CPUE非线性作用。模型结果表明,环境因子和时空变量对热带大西洋延绳钓大眼金枪鱼渔获率空间分布影响明显。大西洋大眼金枪鱼延绳钓的高渔获率月份出现在夏季和冬季,空间上在赤道以北和30?~50?W。12℃等温线深度对大眼金枪鱼延绳钓渔获率的影响表现为抛物线形状,高渔获率出现在深度较浅的250 m水层,随着12℃等温线深度的增加,大眼金枪鱼延绳钓渔获率降低。温跃层下界深度和深度差对大眼金枪鱼延绳钓渔获率的影响都是穹顶状。随着温跃层下界深度值和深度差由小变大至200 m,延绳钓渔获率递增;温跃层下界深度和深度差超过200 m后,延绳钓渔获率变小。温跃层下界深度和深度差对大眼金枪鱼延绳钓渔获率影响显著的水层分别是200 m和50 m。研究结果显示,12℃等温线深度和温跃层对热带大西洋延绳钓大眼金枪鱼渔获率影响是交叉的,在大眼金枪鱼适宜垂直活动水层受限到和延绳钓作业深度相同时,延绳钓渔获率最高;在适宜垂直活动空间过深或者过浅时,延绳钓渔获率都变小,但可以通过改变作业方式提高渔获率。采用延绳钓CPUE进行渔场和资源评估要考虑金枪鱼适宜垂直活动空间。     

热带中西太平洋海域黄鳍金枪鱼的摄食生物学特性

为探究气候因子对黄鳍金枪鱼渔获量的影响,根据1960—2021年的南方涛动指数 (SOI)、太平洋年代际涛动 (PDO)、北大西洋涛动 (NAO)、北太平洋指数 (NPI)、全球海气温度异常指标 (dT) 以及厄尔尼诺相关指标 (Niño1+2、Niño3、Niño4以及Niño3.4) 等9种气候因子数据和全球黄鳍金枪鱼渔获量数据,采用相关性分析、BP神经网络、长短期记忆网络 (LSTM) 模型、双向长短期记忆网络 (BiLSTM) 模型和卷积神经网络结合双向长短期记忆网络 (CNN-BiLSTM) 模型对低频气候因子与黄鳍金枪鱼渔获量的关系进行了研究。结果表明,气候变化表征因子对黄鳍金枪鱼渔获量的重要性依次为dT>SOI>Niño1+2>PDO>NPI>NAO,其对应的最佳滞后年限分别为0、11、6、5、15、0年。CNN-BiLSTM模型的预测效果最优,其后依次为BiLSTM模型、LSTM模型、BP神经网络模型。最优预测模型显示预测值与实际值的拟合优度为0.887,平均绝对误差为0.125,均方根误差为0.154,预测值与实际值变化趋势基本一致,模型拟合效果良好。

     

基于集成学习的大西洋热带水域大眼金枪鱼渔情预报

为提高大西洋大眼金枪鱼渔场预报模型的准确率,实验利用13艘中国延绳钓渔船2013—2019年的渔捞日志数据和对应的海洋环境数据(海表面风速、叶绿素a浓度、涡动能、混合层深度和0～500 m水层的垂直温度、盐度和溶解氧等),以天为时间分辨率、2°×2°为空间分辨率、以数据集的75%为训练数据建立了K最近邻(KNN)、逻辑斯蒂回归(LR)、分类与回归树(CART)、支持向量机(SVM)、人工神经网络(ANN)、随机森林(RF)、梯度提升决策树(GBDT)和Stacking集成(STK)渔情预报模型,以25%的测试数据进行模型性能测试、比较。结果显示,(1) STK (由KNN、RF、GBDT模型集成)模型的大眼金枪鱼渔场预报性能较KNN、LR、CART、SVM、ANN、RF和GBDT模型有所提高且相对稳定,上述模型对应的准确率和ROC曲线下面积(AUC)依次分别为81.62%、0.781,79.44%、0.778,72.81%、0.685,74.84%、0.717,73.67%、0.702,67.70%、0.500,80.96%、0.780和78.13%、0.747;(2) STK模型预测...     

Acoustic telemetry versus monitored longline fishing for studying the vertical distribution of pelagic fish: bigeye tuna (Thunnus obesus) in French Polynesia

This study compares detailed, nearly continuous, observations on bigeye tuna, Thunnus obesus equipped with electronic tags, with discrete observations on a larger number of individuals from fishing experiments in order to validate the use of instrumented longlines to study the vertical distribution of fish. We show that the depth distributions obtained from the two different observation techniques regarding different environmental variables (temperature, dissolved oxygen (DO), prey distribution) are similar. Bigeye tuna do not seem to be attracted by baits in the vertical dimension (no modification of their vertical distribution by the fishing gear), which allows the use of instrumented longlines to study the vertical behaviour of pelagic species. This technique, when used with appropriate deployment strategy, could therefore represent an alternative to electronic tags (acoustic or archival tags) when there is a need to determine the vertical distribution of fish species by size or sex, in different environments for the study of fishery interactions.     

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     

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.     

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.     

大眼金枪鱼渔场与环境关系的研究进展

大眼金枪鱼是金枪鱼远洋渔业的主要捕捞对象。本文从大眼金枪鱼适宜环境因子、大眼金枪鱼渔场变动、资源丰度及其与环境因子间关系的研究方法等几方面总结了大眼金枪鱼渔场与环境关系的研究进展。大眼金枪鱼种群资源丰度的指标主要是CPUE和标准化后的CPUE,CPUE标准化的方法主要是GLM模型和GLM/HBM模型;目前,分析大眼金枪鱼资源变化与环境间关系的研究方法主要有聚类分析法、G IS软件定性分析法和栖息地指数模型。其中,聚类分析适用于研究大眼金枪鱼的渔场变动,包括系统聚类分析法、动态聚类分析法和灰色星座分析法,利用G IS软件定性分析适用于分析单个环境因子对渔场产生的影响;而栖息地指数模型能综合多个环境因子,分析它们共同对渔场产生的影响。     

Alternative error distribution models for standardization of catch rates of non-target species from a pelagic longline fishery: billfish species in the Venezuelan tuna longline fishery

Alternative error distributions were evaluated for calculating indices of relative abundance for non-target species using catch and effort data from commercial fisheries. A general procedure is presented for testing the underlying assumptions of different error distributions. Catch rates, from an observer program, of billfish caught mainly as bycatch in a pelagic tuna longline fishery in the Western Central Atlantic were standardized. Although catches of billfishes are not common in pelagic tuna longline fisheries, these fisheries are one of the main sources of fishing mortality for these stocks in the central Atlantic due to the magnitude and spatial extent of longline fishing effort. Billfish CPUE data are highly skewed with a large proportion of zero observations. Delta distribution models can accommodate this type of data, and involve modeling the probability of a non-zero observation and the catch rate given that the catch is non-zero separately. Three different Delta models were compared against other error distributions, including the lognormal, log-gamma, and Poisson. Diagnostic checks and deviance table analyses were performed to identify the best error distribution and the set of factors and interactions that most adequately explained the observed variability. The results indicated that the Delta-lognormal model (a binomial error distribution for the probability of a non-zero catch and lognormal error for the positive catch rates) complied best with the underlying characteristics of the data set. Analyses of catch rates for blue marlin, white marlin and sailfish confirmed the spatio-temporal nature of their distribution in the central Atlantic and Caribbean Sea. Also, the analyses indicated that catch rates of billfish differed among fishing vessel types; larger vessels had a higher probability of catching blue marlin, the more oceanic-oriented species, and lower probabilities of catching the more coastal-oriented species white marlin and sailfish. Standardized catch rates indicated in general a lower relative abundance for blue and white marlin in the most recent years, although estimated confidence intervals overlap through the years especially for white marlin.     

The effects of mesoscale oceanographic structures and ambient conditions on the catch of albacore tuna in the South Pacific longline fishery

Albacore tuna (Thunnus alalunga) exhibit patchy concentrations associated with biological process at a wide range of spatial scales, resulting in variations in their catchability by fishing gears. Here, we investigated the association of catch variation for pelagic longlines in the South Pacific Ocean with oceanographic mesoscale structures (in horizontal dimension) and ambient conditions (in vertical dimension). The distribution of albacore tuna as indicated by catch per unit effort (CPUE) of longlines was significantly related to the presence of mesoscale structures, with higher CPUE found at locations closer to thermal fronts and with greater gradient magnitudes, as well as areas marked by peripheral contour line of the anticyclone indicated by Sea Surface Height Anomalies ~0.05 m. Surface mesoscale current velocity had the negative effect on the catch, probably as a result of decreased catchability by shoaling the hook depth. Vertical distribution of albacore in the survey region of South Pacific Ocean was hardly restricted by ambient temperature and oxygen concentration, though effect of ambient temperature was relevant and showed a negatively linear correlation with CPUE at the range of 20–24°C. On the contrary, albacore distribution was evidently dominated by the water depth and showed strong preference on water depth of 200 m, which was likely a representative feeding layer. The presence of prey resources and their accessibility by albacore revealed by mesoscale structures in the biological and physical processes, and catchability determined by the location of the baited hooks comprehensively contribute to the variability of catch.     

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.     

基于多因子栖息地指数模型的南太平洋长鳍金枪鱼渔场预报

文章利用2008—2015年南太平洋长鳍金枪鱼 (Thunnus alalunga) 延绳钓渔业数据,结合11个环境指标 (海表温度、叶绿素a (Chl-a)浓度、海表温度距平、叶绿素距平、海表温度梯度、叶绿素梯度、海平面异常以及渔区格网对应的前后各1个月海表温度和叶绿素值) 和3个时空指标 (月、经度和纬度),并基于6种集成学习模型,以月为时间分辨率、0.5°×0.5°为空间分辨率,开展了南太平洋长鳍金枪鱼渔场模型构建和预报研究。模型通过10折交叉验证和网格搜索思想确定最佳参数,采用的随机森林、Bagging决策树、C5.0决策树、梯度提升决策树、AdaBoost、Stacking集成模型分别取得了75.52%、73.87%、72.99%、71.14%、71.33%、75.84%的分类准确率。经对比,Stacking集成模型准确率最高。利用2015年环境数据进行预报精度检验,预报总体准确率为63.86%~82.14%,平均70.99%;高单位捕捞努力量渔获量 (Catch per unit effort, CPUE) 渔区预报准确率为62.71%~97.85%,平均78.76%。结果表明Stacking集成模型对南太平洋长鳍金枪鱼渔场的预报具有较好的效果及可行性。

     

中国大陆阿根廷滑柔鱼鱿钓渔业CPUE标准化

阿根廷滑柔鱼是我国重要的头足类渔业之一,单位捕捞努力量渔获量(CPUE)标准化是对其资源进行评估的重要内容.研究根据2000-2010年中国大陆在西南大西洋的鱿钓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(general linear model,GLM)和广义加性模型(generalized additive model,GAM)对中国大陆西南大西洋阿根廷滑柔鱼渔业CPUE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模型研究结果则表明年、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准则,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℃、海面高度为-20 ～20 cm和46.5° ～48.5°S范围内.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渔业CPUE标准化.     

南太平洋长鳍金枪鱼延绳钓渔获率与环境因子的关系研究

为掌握不同水层的环境因子对长鳍金枪鱼(Thunnus alalunga)延绳钓渔获率的影响,根据2015-2017年中国大陆在该海域的长鳍金枪鱼延绳钓渔捞日志资料,结合同期海洋环境数据,采用广义可加模型(Generalized additive model,GAM)对渔获率与各因子的关系进行研究。通过相关分析获取各环境因子相关系数,对相关性较大的环境因子分组建模。结果表明:1)海表面温度与120 m水深温度、海表面温度与海表面高度、120 m水深温度与海表面高度、300 m水深温度与300 m水深盐度为高度相关因子,海表面盐度、叶绿素a浓度、海表风场南北分量与其他环境因子之间的相关性均较小;2)模型的总解释偏差介于30%~40%,各环境因子重要性依次为120 m水深温度、海表温度、300 m水深温度、120 m水深盐度、海表面高度、300 m水深盐度、海表盐度、混合层深度、海面风场南北分量、海面风场东西分量、叶绿素a浓度;3)120 m水深温度与单位捕捞努力渔获量(CPUE)在15~30℃呈负相关。海表温度整体趋势与120 m水深温度类似,其中在25~28℃呈正相关。300 m水深温度与CPUE在10~18℃呈现明显的正效应关系。     

西北印度洋延绳钓大眼金枪鱼渔获率的时间序列小波特征

基于西北印度洋日本延绳钓金枪鱼1965-2006年间大眼金枪鱼(Thunnus obesus)每月渔获统计资料,本研究运用小波方法对大眼金枪鱼渔获率变化、印度洋涛动指数(IOI)及两者之间的响应关系进行了分析.研究发现,大眼金枪鱼渔获率在时间序列上存在48～60个月的小波振荡,IOI振荡周期主要为36月和60月,2种尺度振幅具有跷跷板效应.小波交叉分析结果表明,1994年前在36月和60月的时间尺度上大眼金枪鱼渔获率与IOI之间的响应关系是明显的,之后这种关系并不显著.在典型的小波振荡周期为4年的尺度卜,IOI和大眼金枪鱼渔获率的振荡间隔在1～3年之间变化,平均为1.5年左右.IOI振荡周期在3～6年间变化,时快时慢,而大眼金枪鱼渔获率信号振荡较平稳.南于捕捞因素影响,近年来渔获率的振荡幅度不断减弱.研究认为可排除由于较大的捕捞努力量和渔业资源量时空变动导致的捕获率信号变异,从捕捞因素和环境因素上讨论了捕获率信号小波特征及其与环境变动的响应关系.     

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.