Catch rate standardization for yellowfin tuna (Thunnus albacares) in Taiwan's distant-water longline fishery in the Western and Central Pacific Ocean,with consideration of target change

Catch per unit of effort (CPUE) needs to be standardized to remove the effects of factors such as fishing time and location, before it can be used as an index of abundance in fish stock assessments. One of the most substantial effects arises from a change of target species. This is particularly important for the Taiwanese distant-water longline fishery, which has a long history of fishery data from two fleets that target various tuna species across three oceans. We review the development of the Taiwanese distant-water longline fishery and compare five designs for standardizing the catch rate of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean, using generalized linear models with lognormal and delta-lognormal error assumptions. Two approaches to address targeting effects were tested: separating fishing fleet data based on observer records, and including four target indicators calculated from catch data. Four statistical regions (relating to major fishing grounds) were treated as a single factor in the first three cases and were treated separately for the last two (one independent run for each region). The last case, which involved independent analyses for each fishing fleet for each region, and using the delta-lognormal approach, was considered to provide the most informative standardized CPUE trends for yellowfin tuna.     

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。     

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.     

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。     

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。     

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。     

金枪鱼延绳钓钓具的最适浸泡时间

根据2010年10月—2011年1月金枪鱼延绳钓海上调查数据,分两种起绳方式,建立每次作业每一根支绳的浸泡时间计算模型。将钓具的浸泡时间以1 h为间隔分别统计每个区间的支绳数量及大眼金枪鱼(Thunnus obesus)、黄鳍金枪鱼(Thunnus albacores)的渔获尾数,并计算其钓获率(CPUE)。结果表明:1)大眼金枪鱼和黄鳍金枪鱼的CPUE都随浸泡时间的增加呈现先增后减的趋势,这是由于饵料的诱引效果变化及渔获的丢失引起的;2)二次曲线可拟合浸泡时间与大眼金枪鱼和黄鳍金枪鱼CPUE的关系;3)大眼金枪鱼和黄鳍金枪鱼CPUE最高的浸泡时间分别为9.9 h和10.1 h。建议:1)今后在金枪鱼延绳钓作业中,保证每一根支绳在水中的浸泡时间为9.5~10.5 h,以提高捕捞效率并减少副渔获物;2)可把延绳钓钓具的浸泡时间作为有效捕捞努力量,并用于CPUE的标准化。研究结果可用于提高捕捞效率并减少副渔获物的技术方案制订,并为渔业生产和CPUE的标准化提供科学参考。     

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.     

Climatic oscillations and tuna catch rates in the Indian Ocean: a wavelet approach to time series analysis

We analysed the influence of climatic oscillations [based on the Indian Oscillation Index (IOI)] on monthly catch rates of two tropical tuna species in the equatorial Indian Ocean. We carried out wavelet analysis, an efficient method of time series analysis to study non‐stationary data. Catch per unit of effort (CPUE) of bigeye tuna was computed from Japanese longline statistics from 1955 to 2002 in the equatorial Indian Ocean and CPUE of yellowfin tuna was derived from industrial purse seine statistics from 1984 to 2003 in the Western Indian Ocean. Wavelet analyses allowed us to quantify both the pattern of variability in the time series and non‐stationary associations between tuna and climatic signals. Phase analyses were carried out to investigate dependency between the two signals. We reported strong associations between tuna and climate series for the 4‐ and 5‐yr periodic modes, i.e. the periodic band of the El Niño Southern Oscillation signal propagation in the Indian Ocean. These associations were non‐stationary, evidenced from 1970 to 1990 for bigeye, and from 1984 to 1991 and then from 1993 to 2001 for yellowfin. Warm episodes (low negative IOI values) matched increases of longline catch rates of bigeye during the 1970–1990 time frame, whereas the strong 1997–1998 warm event matched a decrease of purse seine catch rates of yellowfin. We discussed these results in terms of changes in catchability for purse seine and longline.     

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.     

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.     

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.     

A new spatial framework incorporating uncertain stock and fleet dynamics for estimating fish abundance

Relative abundance indices based on catch and effort data can become biased unless consideration is given to the spatial dynamics of the fishery such as changes in either the spatial distribution of fishing effort or the range of the stock over time. The construction of such indices therefore needs to take into account features of the fishery itself. In this paper, a general framework is presented for developing more appropriate abundance indices based on fishery catch and effort data. In developing this framework, it adopts the approach of (i) developing a range of hypotheses which encompass the uncertainties in the spatial–temporal dynamics of the stock and the fishing effort, (ii) identifying the hypotheses underlying the different CPUE series, and (iii) evaluating the available information relative to these hypotheses as the basis for evaluating CPUE indices. Observations from the fishery for southern bluefin tuna (Thunnus maccoyii) are used to illustrate various hypotheses about the nature of the fishery which can be used to construct indices of stock abundance while a simple simulation framework is used to explore the implications of some of these hypotheses on the accuracy of such indices.     

Modeling environmental influence on Atlantic bluefin tuna bycatch by Mexican longliners in the Gulf of Mexico

Atlantic bluefin tuna (ABFT) stocks have been considered overfished over the last decades, especially the western stock, whose main spawning grounds are in the Gulf of Mexico (GoM). Despite the current measures implemented, spawner bycatch by the longline fleet targeting yellowfin tuna (YFT) may explain the lack of recovery of local stocks. This situation demands the implementation of appropriate spatiotemporal management strategies to minimize bluefin bycatch in the GoM, which involves knowledge in depth of its distribution and environmental forcing. Using catch and effort data from the Mexican commercial longline fleet with 100% scientific observer coverage from 1999 to 2012 and satellite derived environmental data, this study investigated the influence of environmental conditions on catch per unit effort (CPUE) of ABFT and YFT. General additive models (GAMs) were fitted using a negative binomial distribution and applying Akaike information criterion (AIC) to select the best model. Bluefin CPUE exhibited a marked seasonality, reaching higher values in February and March while YFT catches occurred throughout the year. Two main locations were identified with higher ABFT bycatch rates, Campeche Bay and the western‐central area of the GoM. Higher ABFT CPUE was significantly associated with areas with negative sea level anomalies and low sea surface temperatures, characteristic of cyclonic eddies. Instead, YFT CPUE showed a lesser environmental influence in its distribution. To our knowledge, the patterns shown in this study provide the first in‐depth approach to understand ABFT bycatch in Mexican waters, which will help in further development of adequate management strategies.     

Evidence for fine geographical scale heterogeneity in gene frequencies in yellowfin tuna (Thunnus albacares) from the north Indian Ocean around Sri Lanka

Yellowfin tuna are currently considered by the member nations of the Indian Ocean Tuna Commission to constitute a single stock in the Indian Ocean due to a lack of knowledge about yellowfin tuna population structure in this region. Previous studies of Indian Ocean yellowfin tuna based on morphology and fisheries data have hinted at the presence of multiple stocks in the region, and further, that stocks may mix in the north western Indian Ocean around Sri Lanka. To better understand the genetic stock structure of yellowfin tuna in the north western Indian Ocean, we examined genetic variation in 285 yellowfin individuals collected over a period of 4 years from six fishing grounds around Sri Lanka and a single fishing ground in the Maldive Islands. We screened variation in both the mitochondrial ATPase 6 and 8 region (498 bp) and three microsatellite loci. Significant genetic differentiation was detected among sites for mitochondrial DNA (Φ_ST = 0.1285, P < 0.001) and at two microsatellite loci (F_ST = 0.0164, P < 0.001 and F_ST = 0.0064, P < 0.001), while spatial analysis of molecular variance of mtDNA data identified three genetically heterogenous groups namely; western, south eastern and all remaining sites. These results suggest the possibility that genetically discrete yellowfin tuna populations may be present in the north western Indian Ocean.     

Recruitment abundance index of Pacific bluefin tuna using fisheries data on juveniles

ABSTRACT:   The recruitment abundance index of Pacific bluefin tuna Thunnus orientalis was estimated from 1980 to 2003 fishing year by using the troll fishery data in Nagasaki Prefecture, western Japan. It has been shown that the troll fishery in Nagasaki Prefecture operates with good time–area coverage of the species habitat, and that the fishing power slightly changed during the period analyzed, based on fisheries statistics, published information, and interviews with the fishers. Average catch per unit effort (CPUEs) were standardized by a generalized linear model (GLM) considering the effects of fishing year, season and landing area. Standardized CPUE of age-0 bluefin tuna showed larger fluctuations year by year than the nominal CPUE combined for all ages. High CPUEs in fishing years of 1981, 1994, 1996 and 1999 were observed. Data from these years agreed with the higher recruitments estimated by virtual population analysis (VPA) or higher catch of age-0 fish reported for the Pacific side. The age-specific standardized CPUE of age-0 bluefin tuna in this study was judged to be a useful indicator of recruitment.     

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.     

Application of support vector regression to CPUE analysis for southern bluefin tuna Thunnus maccoyii,and its comparison with conventional methods

This paper describes the catch per unit effort (CPUE) standardization using three models for data mining (support vector regression, neural network and tree regression model) and two conventional statistical methods (analysis of variance and generalized linear model) using the actual fishery data for southern bluefin tuna Thunnus maccoyii. Statistical performances of these five models were compared based on mean square error, mean absolute error and three correlation coefficients, which are measured by the difference between the observed and the corresponding predicted values. As a result, the performance of support vector regression is equivalent to (or better than) that of neural networks, and these two models are superior to the tree regression model, analysis of variance, and generalized linear model based on CPUE analyses of actual fishery data for southern bluefin tuna. We suggest a simple method for factorial analysis to extract the CPUE year trend based on the predicted values obtained from these data mining models. This method is expected to contribute markedly to reduce the difficulty of estimating the CPUE year trends by these models for data mining and should be applied to CPUE analyses because of its ease of use, general versatility and high performance .     

Goussia auxidis (Dogiel, 1948) (Apicomplexa: Calyptosporidae) from tuna (Pisces: Scombridae) in the South Pacific

Abstract. A coccidian identified as Goussia auxidis (Dogiel, 1948) is recorded for the first time from the liver and spleen of albacore, Thunnus alalunga; the liver of slender tuna, Allothunnus fallai , skipjack tuna, Katsuwonus pelamis and yellowfin tuna, T. albacares: and in the spleen of Scomber australasicus. All host fish were caught in the western and central South Pacific Ocean. Coccidian spores were not found in the liver of southern bluefin tuna, T. maccoyii , or butterfly tuna, Gasterochisma melampus.     

越南开发南海金枪鱼资源的历史和趋势

近十年来,越南将南海的金枪鱼资源作为其＂外向型＂渔业的重要支撑,不断增加捕捞强度,产量逐年升高。本文总结了越南发展南海金枪鱼渔业的过程,分析了南海金枪鱼资源的开发趋势。越南现代化的金枪鱼捕捞技术主要来自日本,使用的渔具主要有金枪鱼延绳钓、手钓、刺网和小型围网,捕捞的种类主要为鲣鱼、黄鳍金枪鱼和大眼金枪鱼,主要作业区域在西沙群岛南部海域和南沙群岛海域。越南2009年金枪鱼的产量已达到5.9×104t,计划2015年达到30×104t。根据越南海洋渔业研究所（RIMF）的评估,南海中西部的金枪鱼资源量为66~67×104t,可捕量23.3×104t,其中鲣鱼的可捕量21.6×104t,黄鳍金枪鱼和大眼金枪鱼的可捕量1.7×104t。随着全球金枪鱼捕捞配额的缩减和越南＂外向型＂渔业经济的发展,越南将继续加强对南海金枪鱼资源的开发。