基于栖息地指数的东太平洋黄鳍金枪鱼渔场预报

黄鳍金枪鱼是东太平洋海域重要的金枪鱼种类之一,也是我国金枪鱼延绳钓的主要捕捞对象之一。根据2011年东太平洋海域(20°N—35°S、85°W—155°W)延绳钓生产统计数据,结合表温(SST)和海面高度(SSH)的遥感数据,采用频次分析法获得黄鳍金枪鱼分布的SST和SSH适宜范围;运用一元非线性回归方法,以渔获量为适应性指数,按季度分别建立了基于SST和SSH的长鳍金枪鱼栖息地适应性指数,采用算术平均法获得基于SST和SSH环境因子的栖息地指数综合模型,并用2012年各月实际作业渔场进行验证。结果显示,黄鳍金枪鱼渔场多分布在SST为24—29℃、SSH为0.3—0.7 m的海域。采用一元非线性回归建立的各因子适应性指数模型在统计上均为显著(P0.05)。经与2012年实际生产情况比较,作业渔场预报准确性达66%以上。研究获得栖息地指数模型可为金枪鱼延绳钓渔船寻找中心渔场提供参考。     

热带大西洋黄鳍金枪鱼垂直分布空间分析

为了解热带大西洋黄鳍金枪鱼(Thunnus albacares)延绳钓适宜渔获水温的等温线时空分布,分析黄鳍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大西洋13℃和距海洋表层水温8℃(Δ8℃)的月平均等温线场,网格化计算了13℃和Δ8℃等温线深度值和温跃层下界深度差,并结合大西洋金枪鱼会委员(International Commission for the Conservation of Atlantic Tunas ICCAT)的黄鳍金枪鱼延绳钓渔业数据,绘制了13℃和Δ8℃等温线深度与月平均CPUE的空间叠加图,用于分析热带大西洋黄鳍金枪鱼中心渔场单位捕捞努力渔获量(Catch per unit effort CPUE)时空分布和次表层环境季节性变化关系。结果表明,13℃等温线,在高值CPUE出现的海域深度值大多小于250 m,主要在170—249 m,深度值超过250 m的海域CPUE普遍较小。5°S—9°N区域,Δ8℃等温线高值CPUE出现的海域深度值大多小于150 m,主要在50—139 m;7—10月份在南半球的非洲西海岸,在Δ8℃等温线深度值为150—350 m的海域也会出现中心渔场。全年在低纬度区域,高渔获率的垂直分布深度更加集中。13℃等温线影响热带大西洋黄鳍金枪鱼的空间分布,温跃层下界温度影响黄鳍金枪鱼的垂直分布。采用频次分析和经验累积分布函数计算其适宜次表层环境因子分布,13℃等温线180—240 m;Δ8℃等温线50—139 m;与下界深度差:13℃等温线-70—29 m;海表以下8℃等温线30—149 m。文章初步得出热带大西洋黄鳍金枪鱼适宜的水平、垂直深度分布区间。结果可以辅助渔情预报,为热带大西洋黄鳍金枪鱼实际生产作业和资源管理提供参考依据。     

南太平洋长鳍金枪鱼渔场CPUE时空分布及其与关键海洋环境因子的关系

根据2009—2012年南太平洋长鳍金枪鱼(Thunnus alalunga)延绳钓生产统计数据及遥感获取的海表温度(sea surface temperature,SST)、叶绿素a浓度(chlorophyll a concentration,Chl-a)和海面高度距平(sea surface height anomaly,SSHA)等环境数据,分析了长鳍金枪鱼单位捕捞努力量渔获量(catch per unit of fishing effort,CPUE)的时空分布及其与环境因子的相关性。结果表明:长鳍金枪鱼作业渔场主要集中在4°S—28°S、158°E—176°E附近海域;长鳍金枪鱼渔场CPUE呈明显的季节性变化,1—3月CPUE值较低(12.5尾·千钩-1),随后逐渐增加,至7月达到最大值为18.1尾·千钩-1,而8—12月基本呈逐渐降低趋势;1月渔场重心位于16°S、168°E附近海域,2—3月向西北偏移,而在3—7月逐渐向东南方向转移,8月以后开始逐渐回撤至西北方向,在9—12月渔场重心变化幅度相对较小,主要位于15°S—16°S、168°E—169°E海域;总体来说,长鳍金枪鱼中心渔场最适SST为27.0~30.5℃,次适SST为20~24℃;最适叶绿素a浓度为0.02~0.08mg·m-3,最适海面高度距平为3~23 cm。     

热带印度洋黄鳍金枪鱼渔场时空分布与温跃层的关系

根据Argo浮标剖面温度数据重构热带印度洋各月月平均温跃层特征参数,并结合印度洋金枪鱼委员会(IOTC)黄鳍金枪鱼延绳钓数据,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黄鳍金枪鱼渔场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,以及黄鳍金枪鱼中心渔场分布都具有明显的季节性变化特征,黄鳍金枪鱼中心渔场分布和温跃层季节性变化有关。在东北季风期间,高值CPUE渔区的温跃层上界深度的范围为30-40m,超过70m的渔区CPUE值普遍偏低;在西南季风期间温跃层上界最深达到120m。在东北季风期间,高值CPUE渔区温跃层下界深度不超过200m,在西南季风期间,深度会超过300m。在东北季风期间,高值CPUE渔区对应的温跃层上界温度都超过25℃,温度小于24℃的渔区CPUE值普遍较低;在西南季风期间,高值CUPE区域对应的温跃层上界温度范围变大,温跃层上界温度延伸到22℃,在22℃以下渔区CPUE值都很低。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,得出黄鳍金枪鱼最适的温跃层上、下界温度范围分别是25-29℃和13-16℃;其上、下界深度范围分别为30-70m和140-200m。K-S检验结果表明,上述结论可靠。     

大西洋中部延绳钓黄鳍金枪鱼渔场时空分布与温跃层的关系

为了解大西洋延绳钓黄鳍金枪鱼(Thunnus albacares)渔场适宜的温跃层参数分布区间,采用Argo浮标水温信息和大西洋金枪鱼会委员(International Commission for the Conservation of Atlantic Tunas ICCAT)的黄鳍金枪鱼延绳钓渔获数据,绘制了大西洋中部月平均温跃层特征参数和月平均单位捕捞努力量渔获量(Catch per unit effort CPUE)的空间叠加图,用于分析大西洋中部延绳钓黄鳍金枪鱼中心渔场时空分布和温跃层特征参数关系。分析结果表明:大西洋中部温跃层上界深度、温度具有明显的季节性变化,而温跃层下界深度、温度没有明显的季节变化特征。空间叠加图显示,1-6月份在赤道地区中心渔场主要分布在温跃层上界深度为20-60 m之间。7-9月份在60-80 m,同期在纳米比亚外海,中心渔场区域温跃层上界深度超过100 m。10-12月份,中心渔场区域温跃层上界深度下降到60 m左右。全年在赤道区域,中心渔场CPUE主要分布在温跃层上界温度26-29 ℃,低于24℃区域渔获率很低;温跃层下界深度在160-250 m,集中在230 m;温跃层下界温度在12-14 ℃之间,在此区间外CPUE值都比较低。7-11月份,在纳米比亚外海的中心渔场区域上界温度会低至20 ℃,下界深度分布在140-160 m,下界温度在14-15 ℃左右。数值计算得出大西洋中部黄鳍金枪鱼适宜的温跃层上界温度是26-28.9 ℃;适宜的温跃层下界温度和深度分别是12-14.9 ℃和150-249 m,而上界深度和中心渔场CPUE关系不明显。研究得出大西洋延绳钓黄鳍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,为延绳钓黄鳍金枪鱼实际生产作业和资源管理提供理论参考。     

中东太平洋金枪鱼延绳钓中心渔场的时空变化

根据2009年6月—2012年1月上海金优远洋渔业有限公司在中东太平洋作业的5艘金枪鱼延绳钓渔船的渔捞日志资料,结合卫星遥感反演的海表温度数据,分析了中东太平洋金枪鱼延绳钓渔场的年际和季节变化规律以及渔获量与海表温度的关系。结果表明:在厄尔尼诺年(2009年),金枪鱼延绳钓渔场作业重心会向偏东方向移动,且金枪鱼钓获率较高;而拉尼娜年(2010、2011年)则向偏西方向移动,且钓获率下降;研究海域作业渔场的最适宜海表温度范围为25~30℃;2009年最适海表温度略高于2010年和2011年;平均月渔获量与月平均海表温度呈显著正相关(P0.05)。     

基于GAM模型的阿拉伯海鲐鱼渔场分布与环境关系

基于2016-2017年中国印度洋围拖网生产数据以及同期的海表温度、叶绿素、表层海流和海面高度数据,采用广义加性模型(Generalized Additive Model,GAM)建立了围网单位捕捞努力量渔获量(catch per unit effort,CPUE)对海洋环境的非线性响应模型,分析了阿拉伯海鲐鱼(Scomber australasicus)渔场分布与海洋环境关系.结果表明:空间因子和环境因子对阿拉伯海鲐鱼渔场有显著影响,GAM模型的方差解释率为30.1%;印度洋季风对鲐鱼CPUE影响大,鲐鱼CPUE在印度洋东北季风高,在夏季季风低;全年阿拉伯海鲐鱼围网渔场主要分布在60°E、13°N-15°N斜向椭圆区域;模型表明,鲐鱼渔场适宜海表温度为26~28℃,叶绿素浓度0.2~0.5 mg·m^-3,海面高度0.2~ 0.4 m;影响鲐鱼渔场的因子按重要性依次为海面高度、经纬度、海表温度和叶绿素浓度.     

影响长江口毗邻海域低氧区多种时间尺度变化的水文因素

对2006年6月、8月和10月与1999年8月在长江口向东至125°E、27°30′—33°30′N之间海域(统称长江口毗邻海域)开展的4次多学科综合海洋调查资料进行分析,了解长江口毗邻海域低氧现象的季节变化、年间变化及其与水团变化的关系。研究再次发现该海域存在长江口和浙江近海2处低氧水体、且2处低氧水体具有不同的季节演替和年际变化特征:长江口附近海域低氧水体的溶解氧浓度低、覆盖的面积大,低氧持续时间相对较短、溶解氧浓度的季节变化较大;浙江沿海低氧水体面积较小、溶解氧浓度的季节变化较小、但持续时间较长(6—10月份);2处水体低氧现象的年际变化均很显著。长江口毗邻海域的多种水动力因素及其相互作用导致了水团消长的季节和年际变化,并与该海域低氧现象的季节和年际变化具有较好的关联。季节性跃层的成长是近底层低氧形成的必要条件,而水团迁移和消长过程及其季节和年际变动是导致低氧区不同时间尺度变化的重要物理因素。针对2006年与1999年夏季长江口低氧区的显著变化给出观测证据,提出该时期内长江口的水团结构发生了变化,是导致低氧核心区的位置偏北的主要动力原因。2006年和1999年夏季长江口附近低氧水体的年际变化与同时期叶绿素高浓度区的位置变动是一致的,也为此期间水团消长情况提供了证据。     

应用年龄结构产量模型评估印度洋黄鳍金枪鱼资源

利用年龄结构产量模型(Age structured production model,ASPM)评估了印度洋黄鳍金枪鱼资源状况,同时结合亲体量-补充量曲线陡度系数和年龄组自然死亡系数的敏感性分析,描述了黄鳍金枪鱼资源的发展趋势、判断了开发状况。研究认为,陡度系数设在0.6-0.8才可能使亲体量产生出最大可持续产量(Maximum sustainable yield,MSY)的水平。采用美洲热带金枪鱼委员会推荐的自然死亡系数值时,评估结果最接近渔业现状。研究发现,随着捕捞努力量的增加,总资源量和亲体量呈逐年下降趋势,但总资源量自1990年后趋向稳定,维持在195.9-263.2万t,平均为221万t;亲体量在1994年后下降到100万t以下,1997年以后处在维持MSY所需亲体量的水平之下,目前仍呈下降趋势。补充量在渔业初期呈现大幅度波动,1978年后趋于稳定,并维持在3258.36-6583.35×106尾,平均为4687.66×106尾。未成熟鱼的数量总体较为稳定,但成熟鱼的数量出现剧减,从渔业初期的246.51×106尾减少到2005年的19.02×106尾。模型估计的总捕捞死亡系数从渔业初期开始逐渐上升,1991年后出现大幅度上升,处于0.334-0.456间,2003年时超过FMSY,捕捞产量也于2003年超过MSY。分析认为,2003年以来印度洋黄鳍金枪鱼的持续高产量被认为是不可持续,根据ASPM估算,2003-2006年均产量46.4万t,超过了MSY(36.4万t);S/SMSY为0.76;Fall/FMSY为1.39,由此判断现阶段印度洋黄鳍金枪鱼正处于过度捕捞状态。     

中西太平洋鲣鱼围网渔业资源的热点分析和空间异质性

中西太平洋是世界鲣鱼围网主要作业水域。基于我国渔船2005—2009年的中西太平洋鲣鱼围网生产数据,运用空间统计方法对该水域鲣鱼资源的空间自相关性和空间异质性特征进行分析,并结合海洋环境特征分析资源分布的热点区域。(1)通过常规统计学计算获得鲣鱼资源的偏态Sk、峰态数Ku、变异值Cv、s2/m和全局空间自相关Geary c系数,发现中西太平洋鲣鱼资源总体上是以低密度区域为主,高密度区域较少;鱼类资源密度值差异较大,资源表现出强烈集聚分布,总体的空间自相关性中等偏弱。(2)通过局部空间自相关的热点分析方法计算,发现局部空间自相关性较强,存在多个在统计学上通过显著性检验的资源热点和冷点。(3)通过地统计方法研究鲣鱼资源的空间变异性特征和方向变异时,空间自相关类型上最优模型是球形模型,鲣鱼资源密度各向同性,最大相关距离1000km左右。发现空间自相关引起的差异占整个差异的50%左右,为中等强度变异;在方向性变异上,主要体现在南北向上,其该向上结构性误差占67%,而东西向结构性误差占49%。这一结果和海洋环境的南北向上结构性远好于东西向结构性有关;从各方向的分维数看,数值介于1.876—1.9之间,数值较大,空间自相关较弱。(4)以资源热点区域作为区域性渔场,结合海洋温度和叶绿素场海洋环境特征,将中西太平洋鲣鱼资源分为3个不同的局部渔场,即2个暖池渔场,1个冷舌渔场。冷舌渔场由中东太平洋赤道上升流引起,在锋面地带提供了较为丰富的初级生产力,便于鱼类获得丰富的食物;暖池渔场靠近岛屿和陆地区域,近岸上升流系统提供了丰富的初级生产力。(5)将热点分析和渔场重心方法及栖息地指数的优缺点做了对比,建议以后采用空间残差模型深入研究空间自相关问题。     

Estimation of overall fishing intensity of tuna longline fishery-yellowfin tuna (Thunnus albacares) fishery in the indian ocean as a case study

According to the FAO catch statistics, the total catch of yellowfin tuna (Thunnus albacares) from the Indian Ocean is characterised by decline in the longline fishery and rapid increase in the surface fishery. In the present communication, an attempt has been made to estimate the overall effective fishing intensity of longline fishery for yellowfin tuna by the Japanese longliners during the years 1973–1975. The results on areas and seasons of effective effort expended are presented, along with estimates of tuna availability, effective fishing intensity and the relative gear efficiency.     

Mitochondrial DNA analysis reveals three stocks of yellowfin tuna Thunnus albacares (Bonnaterre, 1788) in Indian waters

Yellowfin tuna (Thunnus albacares) is an epipelagic, oceanic species of family Scombridae found in tropical and subtropical region of Pacific, Indian and Atlantic Ocean. It is commercially important fish and accounts for 19 % of total tuna catches in Indian waters. In present study, population structure of yellowfin tuna was examined using sequence analysis of mitochondrial DNA from seven geographically distinct locations along the Indian coast. A 500 bp segment of D-loop region was sequenced and analysed for 321 yellowfin samples. Hierarchical analysis of molecular variance showed significant genetic differentiation among three groups (VE); (AG); (KO, TU, PO, VI, PB) analyzed (Φ _ST  = 0.03844, P ≤ 0.001). In addition, spatial analysis of molecular variance identified three genetically heterogeneous groups of yellowfin tuna in Indian waters. Results were further corroborated by significant value of nearest neighbour statistic (S _nn = 0.261, P ≤ 0.001). Thus finding of this study rejects the null hypothesis of single panmictic population of yellowfin tuna in Indian waters.     

Studying small purse‐seine vessel fishing behavior with tuna catch data: Implications for eastern Pacific Ocean dolphin conservation

Despite achievements in dolphin conservation for the tuna purse‐seine fishery of the eastern Pacific Ocean, debate continues about the magnitude and importance of dolphin mortality caused by small (unobserved) vessels. In‐port sampling of tuna catch size composition is a potentially cost‐effective means of identifying unobserved vessels that may be catching tunas associated with dolphins because yellowfin tuna caught in association with dolphins are larger, on average, than those caught in other types of purse‐seine sets. A classification algorithm to predict purse‐seine set type (“dolphin” vs. “nondolphin”) was built from port‐sampling data on yellowfin tuna length‐frequencies and the date and location of fishing of large (observed) vessels. This classification algorithm was used to screen the port‐sampling data of small vessels collected during 2006‐2009, assuming the fishing practices of the two groups resulted in similar catch characteristics. From these results, hypothetical time series of dolphin mortality for small vessels were constructed and incorporated into a population dynamics model, along with mortalities of large vessels. Results suggest that any dolphin mortality of small vessels is unlikely to be substantially affecting trends in dolphin abundance. These results underscore the importance of in‐port sampling, in combination with at‐sea observation and fishery‐independent surveys, to effective management.     

Modelling the Spatial Behaviour of a Tropical Tuna Purse Seine Fleet

Industrial tuna fisheries operate in the Indian, Atlantic and Pacific Oceans, but concerns over sustainability and environmental impacts of these fisheries have resulted in increased scrutiny of how they are managed. An important but often overlooked factor in the success or failure of tuna fisheries management is the behaviour of fishers and fishing fleets. Uncertainty in how a fishing fleet will respond to management or other influences can be reduced by anticipating fleet behaviour, although to date there has been little research directed at understanding and anticipating the human dimension of tuna fisheries. The aim of this study was to address gaps in knowledge of the behaviour of tuna fleets, using the Indian Ocean tropical tuna purse seine fishery as a case study. We use statistical modelling to examine the factors that influence the spatial behaviour of the purse seine fleet at broad spatiotemporal scales. This analysis reveals very high consistency between years in the use of seasonal fishing grounds by the fleet, as well as a forcing influence of biophysical ocean conditions on the distribution of fishing effort. These findings suggest strong inertia in the spatial behaviour of the fleet, which has important implications for predicting the response of the fleet to natural events or management measures (e.g., spatial closures).     

Spatial and seasonal distribution of the tuna catch on floating objects in the eastern pacific ocean during 1992–1993

An analysis of the catch associated with floating objects by the Mexican tuna purse‐seine fleet in the eastern Pacific Ocean during 1992–1993 was made to determine the spatial and seasonal distribution. The information used was generated by observers of the Programa Nacional de Aprovechamiento del Atun y Protección a los Delfines (PNAAPD). There was no clear seasonal and spatial distribution of floating objects examined in this study, however there were areas where floating objects were more common; the mouth of the Gulf of California, waters offshore Peru, and in oceanic waters. The largest catch of yellowfin tuna was offshore of Peru in winter. Two areas with largest (length) yellowfin tuna were the mouth of the Gulf of California and offshore Peru. For skipjack tuna, the largest catch was offshore Peru in winter, but the largest skipjack were caught between 120° and 130°W along 10°N in spring. The largest yellowfin tuna were captured by sets on bamboo, fish aggregating devices (FADs), planks and boards, and logs (trees or parts). The largest skipjack were captured by sets on dead whales, kelp paddies, planks and boards, and pallets and crates. Most of the sets were made during the early hours of the day but an important number of log sets were made in the early afternoon. For the period analyzed, floating objects were more frequent during fall and winter with the area offshore of Peru the most important.     

Ocean‐scale prediction of whale shark distribution

Aim Predicting distribution patterns of whale sharks (Rhincodon typus, Smith 1828) in the open ocean remains elusive owing to few pelagic records. We developed multivariate distribution models of seasonally variant whale shark distributions derived from tuna purse‐seine fishery data. We tested the hypotheses that whale sharks use a narrow temperature range, are more abundant in productive waters and select sites closer to continents than the open ocean. Location Indian Ocean. Methods We compared a 17‐year time series of observations of whale sharks associated with tuna purse‐seine sets with chlorophyll a concentration and sea surface temperature data extracted from satellite images. Different sets of pseudo‐absences based on random distributions, distance to shark locations and tuna catch were generated to account for spatiotemporal variation in sampling effort and probability of detection. We applied generalized linear, spatial mixed‐effects and Maximum Entropy models to predict seasonal variation in habitat suitability and produced maps of distribution. Results The saturated generalized linear models including bathymetric slope, depth, distance to shore, the quadratic of mean sea surface temperature, sea surface temperature variance and chlorophyll a had the highest relative statistical support, with the highest percent deviance explained when using random pseudo‐absences with fixed effect‐only models and the tuna pseudo‐absences with mixed‐effects models (e.g. 58% and 26% in autumn, respectively). Maximum Entropy results suggested that whale sharks responded mainly to variation in depth, chlorophyll a and temperature in all seasons. Bathymetric slope had only a minor influence on the presence. Main conclusions Whale shark habitat suitability in the Indian Ocean is mainly correlated with spatial variation in sea surface temperature. The relative influence of this predictor provides a basis for predicting habitat suitability in the open ocean, possibly giving insights into the migratory behaviour of the world’s largest fish. Our results also provide a baseline for temperature‐dependent predictions of distributional changes in the future.     

Discrimination of juvenile yellowfin (Thunnus albacares) and bigeye (T. obesus) Tunas using mitochondrial DNA control region and liver morphology

Yellowfin tuna, Thunnus albacares (Bonnaterre, 1788) and bigeye tuna, Thunnus obesus (Lowe, 1839) are two of the most economically important tuna species in the world. However, identification of their juveniles, especially at sizes less than 40 cm, is very difficult, often leading to misidentification and miscalculation of their catch estimates. Here, we applied the mitochondrial DNA control region D-loop, a recently validated genetic marker used for identifying tuna species (Genus Thunnus), to discriminate juvenile tunas caught by purse seine and ringnet sets around fish aggregating devices (FADs) off the Southern Iloilo Peninsula in Central Philippines. We checked individual identifications using the Neighbor-Joining Method and compared results with morphometric analyses and the liver phenotype. We tested 48 specimens ranging from 13 to 31 cm fork length. Morpho-meristic analyses suggested that 12 specimens (25%) were bigeye tuna and 36 specimens (75%) were yellowfin tuna. In contrast, the genetic and liver analyses both showed that 5 specimens (10%) were bigeye tuna and 43 (90%) yellowfin tuna. This suggests that misidentification can occur even with highly stringent morpho-meristic characters and that the mtDNA control region and liver phenotype are excellent markers to discriminate juveniles of yellowfin and bigeye tunas.     

Distinct Yellowfin Tuna (Thunnus albacares) Stocks Detected in Western and Central Pacific Ocean (WCPO) Using DNA Microsatellites

The yellowfin tuna, Thunnus albacares (Bonnaterre, 1788), covers majority of the Philippines’ tuna catch, one of the major fisheries commodities in the country. Due to its high economic importance sustainable management of these tunas has become an imperative measure to prevent stock depletion. Currently, the Philippine yellowfin tuna is believed to be part of a single stock of the greater WCPO though some reports suggest otherwise. This study therefore aims to establish the genetic stock structure of the said species in the Philippines as compared to Bismarck Sea, Papua New Guinea using nine (9) DNA microsatellite markers.DNA microsatellite data revealed significant genetic differentiation between the Philippine and Bismarck Sea, Papua New Guinea yellowfin tuna samples. (FST = 0.034, P = 0.016), which is further supported by multilocus distance matrix testing (PCoA) and model-based clustering (STRUCTURE 2.2).With these findings, this study posits that the yellowfin tuna population in the Philippines is a separate stock from the Bismarck Sea population. These findings add evidence to the alternative hypothesis of having at least 2 subpopulations of yellowfin tuna in the WCPO and calls for additional scientific studies using other parameters to investigate this. Accurate population information is necessary in formulating a more appropriate management strategy for the sustainability of the yellowfin tuna not only in the Philippines but also in the WCPO.