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     

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.     

热带大西洋金枪鱼延绳钓兼捕鲨鱼种类组成和渔获率及其与表温的关系

根据2007年12月~2008年3月采集的热带大西洋(05°37′~12°01′N、29°00′~36°51′W)金枪鱼延绳钓渔获物数据,分析了金枪鱼延绳钓兼捕鲨鱼的种类组成、渔获量、渔获率及其与表温的关系。本次调查共捕获鲨鱼8种,隶属3目7科7属,总渔获尾数为633 ind,总渔获量达26 837.4 kg,其中大青鲨为主要兼捕种类。各种鲨鱼渔获率平均值在0.003~1.524 ind/1 000 hooks之间,其中大青鲨最高,其值为1.524 ind/1 000 hooks,大眼砂锥齿鲨最低,其值为0.003 ind/1 000 hooks。各种鲨鱼渔获率月变化不明显(ANOVA,P=0.901)。鲨鱼总渔获率和大青鲨渔获率与表温都呈显著性负相关。大青鲨主要出现渔场的表温范围为24.6~25.8℃。     

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.     

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.     

大西洋海域大眼金枪鱼年龄与生长的初步研究

根据2001年6~10月在大西洋海域金枪鱼延绳钓渔业中采集的89 ind大眼金枪鱼样本,对其叉长、体重进行测定,并以脊椎骨作为年龄鉴定材料。结果表明,叉长组成为85~186 cm,体重组成为11.5~132.5kg,年龄为2~6龄。体重与叉长关系式为W=4.5026×10-5×FL2.8200。利用一般Von Bertalanffy生长方程来拟合,叉长和体重生长方程为:FL=257.90×(1-e-0.1960(t+3.7919))2.5933,Wt=284.28×[(1-e-0.1960(t+3.7919))2.5933]2.8200。叉长和体重的生长拐点分别为1.07龄和5.75龄。     

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...     

热带大西洋西部水域大眼金枪鱼延绳钓渔场分布及其与表温之间的关系

根据2004年9月至2005年3月农业部渔业观察员项目在热带大西洋西部水域调查所获得的渔业数据和现场表温数据,结合哥伦比亚大学网站下载的1°×1°表温资料,利用地理信息系统(GIS)软件Marineexplore 4.0图示了各月钓获率及钓获率与表温关系的分布并进行分析。结果表明,2004年11月的产量和CPUE均达到最高,分别为24.539 t和0.332 t/v.d。调查期间,各月钓获率大部分(80.3%)分布在表温高于26.0℃。     

Tuna and swordfish catch in the U.S. northwest Atlantic longline fishery in relation to mesoscale eddies

To analyze the effects of mesoscale eddies, sea surface temperature (SST), and gear configuration on the catch of Atlantic bluefin (Thunnus thynnus), yellowfin (Thunnus albacares), and bigeye tuna (Thunnus obesus) and swordfish (Xiphias gladius) in the U.S. northwest Atlantic longline fishery, we constructed multivariate statistical models relating these variables to the catch of the four species in 62 121 longline hauls made between 1993 and 2005. During the same 13‐year period, 103 anticyclonic eddies and 269 cyclonic eddies were detected by our algorithm in the region 30–55°N, 30–80°W. Our results show that tuna and swordfish catches were associated with different eddy structures. Bluefin tuna catch was highest in anticyclonic eddies whereas yellowfin and bigeye tuna catches were highest in cyclonic eddies. Swordfish catch was found preferentially in regions outside of eddies. Our study confirms that the common practice of targeting tuna with day sets and swordfish with night sets is effective. In addition, bluefin tuna and swordfish catches responded to most of the variables we tested in the opposite directions. Bluefin tuna catch was negatively correlated with longitude and the number of light sticks used whereas swordfish catch was positively correlated with these two variables. We argue that overfishing of bluefin tuna can be alleviated and that swordfish can be targeted more efficiently by avoiding fishing in anticyclonic eddies and in near‐shore waters and using more light sticks and fishing at night in our study area, although further studies are needed to propose a solid oceanography‐based management plan for catch selection.     

Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data

The physical environment directly influences the distribution, abundance, physiology and phenology of marine species. Relating species presence to physical ocean characteristics to determine habitat associations is fundamental to the management of marine species. However, direct observation of highly mobile animals in the open ocean, such as tunas and billfish, is challenging and expensive. As a result, detailed data on habitat preferences using electronic tags have only been collected for the large iconic, valuable or endangered species. An alternative is to use commercial fishery catch data matched with historical ocean data to infer habitat associations. Using catch information from an Australian longline fishery and Bayesian hierarchical models, we investigate the influence of environmental variables on the catch distribution of yellowfin tuna (Thunnus albacares). The focus was to understand the relative importance of space, time and ocean conditions on the catch of this pelagic predator. We found that pelagic regions with elevated eddy kinetic energy, a shallow surface mixed layer and relatively high concentrations of chlorophyll a are all associated with high yellowfin tuna catch in the Tasman Sea. The time and space information incorporated in the analysis, while important, were less informative than oceanic variables in explaining catch. An inspection of model prediction errors identified clumping of errors at margins of ocean features, such as eddies and frontal features, which indicate that these models could be improved by including representations of dynamic ocean processes which affect the catch of yellowfin tuna.     

Environmental influences on Atlantic bluefin tuna (Thunnus thynnus) catch per unit effort in the southern Gulf of St. Lawrence

The Atlantic bluefin tuna (Thunnus thynnus) population in the western Atlantic supports substantial commercial and recreational fisheries. Despite quota establishment and management under the auspices of the International Commission for the Conservation of Atlantic Tunas, only small increases in population growth have been estimated. In contrast to other western bluefin tuna fisheries indices, contemporary estimates of catch per unit effort (CPUE) in the southern Gulf of St. Lawrence have increased rapidly and are at record highs. This area is characterized by the Cold Intermediate Layer (CIL) that is defined by waters <3°C and located at depths of 30–40 m in September. We investigated the influence of several in situ environmental variables on the bluefin tuna fishery CPUE using delta‐lognormal modelling and relatively extensive and consistent oceanographic survey data, as well as dockside monitoring and mandatory logbook data associated with the fishery. Although there is considerable spatial and temporal variation of water mass characteristics, the amount of available habitat in the southern Gulf of St. Lawrence (assuming a > 3°C thermal ambit) for bluefin tuna has been increasing. The percentage of the water column occupied by the CIL was a significant environmental variable in the standardization of CPUE estimates. There was also a negative relationship between the spatial extents of the CIL and the fishery. Properties of the CIL account for variation in the bluefin tuna CPUE and may be a factor in determining the amount of available feeding habitat for bluefin tuna in the southern Gulf of St. Lawrence.     

Comparison of the behavior of skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares) and bigeye (T. obesus) tuna associated with drifting FADs in the equatorial central Pacific Ocean

We evaluated the behavior of skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares) and bigeye tuna (T. obesus) associated with drifting fish aggregating devices (FADs) in the equatorial central Pacific Ocean. A total of 30 skipjack [34.5–65.0 cm in fork length (FL)], 43 yellowfin (31.6–93.5 cm FL) and 32 bigeye tuna (33.5–85.5 cm FL) were tagged with coded transmitters and released near two drifting FADs. At one of the two FADs, we successfully monitored the behavior of all three species simultaneously. Several individuals remained around the same FAD for 10 or more days. Occasional excursions from the FAD were observed for all three species, some of which occurred concurrently for multiple individuals. The detection rate was higher during the daytime than the nighttime for all the species, and the detection rate for bigeye tuna was higher than for yellowfin or skipjack tuna. The swimming depth was deeper during the daytime than nighttime for all species. The fish usually remained shallower than 100 m, but occasionally dived to around 150 m or deeper, most often for bigeye and yellowfin tuna during the daytime. The swimming depth for skipjack tuna was shallower than that for bigeye and yellowfin tuna, although the difference was not large, and is probably not sufficient to allow the selective harvest of skipjack and yellowfin tuna by the purse seine fishery. From the detection rate of the signals, bigeye tuna is considered to be more vulnerable to the FAD sets than yellowfin and skipjack tuna.     

印度洋南部大眼金枪鱼年龄鉴定及其与生长的关系

为了对大眼金枪鱼(Thunnus obesus)种群结构和年龄生长做出判断,从而进行有效的资源评估和管理,需要一种简便、可行、精确的年龄鉴定方法。根据2008年9月至2009年5月在印度洋中南部执行专项调查时采集的275枚第一背鳍鳍条样本,确立了最适的切割位置和年龄鉴定方法,并通过年龄鉴定估算大眼金枪鱼的von Berta-lanffy生长方程。研究结果表明,大眼金枪鱼的叉长范围为570～1 820 mm,优势叉长组为910～1 500 mm,占总体的83.9%;年龄组成以3、4和5龄居多;第1、2条年轮轮径的平均值分别为(4.23±0.71)mm和(5.72±0.48)mm。根据鳍条的切割位置分为3个组别,线性回归关系为拟合大眼金枪鱼鳍条半径与叉长关系的最佳回归方程,3个组别在鉴定大眼金枪鱼年龄时不存在显著差异(P>0.05),但赤井信息量标准(AIC)分析结果表明,自骨突处起,全长(L)的10%处为最佳切割位置。大眼金枪鱼von Bertalanffy生长方程为Lt=250.5×[1 e 1.05(t+1.85)]。     

Comparing GLM,GLMM, and GEE modeling approaches for catch rates of bycatch species: A case study of blue shark fisheries in the South Atlantic

Modeling and understanding the catch rate dynamics of marine species is extremely important for fisheries management and conservation. For oceanic highly migratory species in particular, usually only fishery‐dependent data are available which have limitations in the assumption of independence and if often zero‐inflated and/or overdispersed. We tested different modeling approaches applied to the case study of blue shark in the South Atlantic, by using generalized linear models (GLMs), generalized linear mixed models (GLMMs), and generalized estimating equations (GEEs), as well as different error distributions to deal with the presence of zeros in the data. We used fractional polynomials to deal with non‐linearity in some of the explanatory variables. Operational (set level) data collected by onboard fishery observers, covering 762 longline sets (1,014,527 hooks) over a 9‐year period (2008–2016), were used. One of the most important variables affecting catch rates is leader material, with increasing catches when wire leaders are used. Spatial and seasonal variables are also important, with higher catch rates expected toward temperate southern waters and eastern longitudes, particularly between July and September. Environmental variables, especially SST, also affect catches. There were no major differences in the parameters estimated with GLMs, GLMMs, or GEEs; however, the use of GLMMs or GEEs should be more appropriate due to fishery dependence in the data. Comparing those two approaches, GLMMs seem to perform better in terms of goodness‐of‐fit and model validation.     

Changes to vertical thermoregulatory movements of juvenile bigeye tuna (Thunnus obesus) in the northwestern Pacific Ocean with time of day,seasonal ocean vertical thermal structure,and body size

Vertical movements related to the thermoregulation were investigated in 12 juvenile bigeye tuna (Thunnus obesus) in Japanese waters using archival tag data. Movements changed with time of day, season, and body size. During daytime, bigeye tuna descended to greater depths, presumably to feed in the deep scattering layer (DSL). Thereafter, they repeatedly ascended to shallower layers, suggesting attempts at behavioral thermoregulation, although the beginning of vertical thermoregulatory ascents might reflect a shift in DSL depth. By the end of such movement, the whole‐body heat‐transfer coefficient might decrease because, although the depth and ambient temperature of the upper layers did not change, the body temperature gradually decreased significantly just after ascent for thermoregulation. Seasonal patterns indicated that the vertical thermal structure of the ocean might influence this ascent behavior. For example, from January to May, bigeye tuna made fewer ascents to less shallow waters, suggesting that they respond to increasing depths of the mixed surface layer by reducing energy expenditure during vertical migration. In addition, as body size increased, fewer thermoregulatory ascents were required to maintain body temperature, and fish remained deeper for longer periods. Thus, vertical thermoregulatory movements might change with body size as bigeye tuna develop better endothermic and thermoregulatory abilities. We hypothesize that bigeye might also increase cold tolerance as they grow, possibly due to ontogenetic shifts in cardiac function.