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1.
Hiroshi Shono 《Fisheries Science》2008,74(4):712-717
Abstract: A procedure is suggested for estimation of the approximate confidence intervals of the extracted catch per unit effort (CPUE) year trend in the delta-type two-step model used for CPUE standardization with a lot of zero-catch data. This method is a simple way to combine the Taylor expansion and delta method and is suitable for practical use. This model was applied to the catch and effort data with more than 80% zero catch for silky shark in the North Pacific Ocean caught by Japanese training vessels. As a result, realistic values of the 95% confidence interval of CPUE year trend are obtained. A method for left–right unsymmetrical interval estimation based on the asymptotic normality of the natural logarithm of CPUE is also suggested. In the example of silky shark, both CPUE year trends obtained from these two methods are similar. 相似文献
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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 总被引：2，自引：0，他引：2
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。 相似文献
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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. 相似文献
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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。 相似文献
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Nan-Jay Su Su-Zan Yeh Chi-Lu Sun Andr E. Punt Yong Chen Sheng-Ping Wang 《Fisheries Research》2008,90(1-3):235-246
Catch per unit effort (CPUE) is often used as an index of relative abundance in fisheries stock assessments. However, the trends in nominal CPUE can be influenced by many factors in addition to stock abundance, including the choice of fishing location and target species, and environmental conditions. Consequently, catch and effort data are usually ‘standardized’ to remove the impact of such factors. Standardized CPUE for bigeye tuna, Thunnus obesus, caught by the Taiwanese distant-water longline fishery in the western and central Pacific Ocean (WCPO) for 1964–2004 were derived using three alternative approaches (GLM, GAM and the delta approach), and sensitivity was explored to whether catch-rates of yellowfin tuna and albacore tuna are included in the analyses. Year, latitude, and the catch-rate of yellowfin explained the most of the deviance (32–49%, depending on model configuration) and were identified consistently among methods, while trends in standardized catch-rate differed spatially. However, the trends in standardized catch-rates by area were found to be relatively insensitive to the approach used for standardization, including whether the catch-rates of yellowfin and albacore were included in the analyses. 相似文献
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金枪鱼延绳钓钓具的最适浸泡时间 总被引：1，自引：1，他引：1
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。 相似文献
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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. 相似文献
10.
基于GAM 的吉尔伯特群岛海域黄鳍金枪鱼栖息地综合指数 总被引：2，自引：1，他引：2
为了可持续利用黄鳍金枪鱼(Thunnus albacares)资源,本文利用2009年10月～12月吉尔伯特群岛海域海上实测的34个站点海洋环境垂直剖面数据,黄鳍金枪鱼渔获率数据,应用广义加性模型(generalized additive model,GAM) 进行建模,预测渔获率,并通过wilcoxon检验来判断预测渔获率与名义渔获率是否存在显著相关性。根据预测渔获率估算黄鳍金枪鱼的栖息地综合指数(IHI),通过对各水层IHI均值分析和Pearson相关系数,判断该方法的预测能力。使用2010年11月～2011年1月在吉尔伯特群岛海域实测的16个站点40～80m水层和0～240m水体的环境数据,验证模型。结果表明：(1)拟合的各水层的IHI值分布各不相同,各水层中影响黄鳍金枪鱼分布的因子各不相同,黄鳍金枪鱼主要栖息在40～120m水层;(2)2010年数据验证结果表明,GAM模型的预测能力较好;(3)GAM在筛选影响黄鳍金枪鱼分布的因子时比较有效,能反应黄鳍金枪鱼渔获率与环境因子之间的非线性关系;(4)可通过GAM建立IHI指数模型来分析大洋性鱼类栖息地的空间分布。 相似文献
11.
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 
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下载免费PDF全文 Takayuki Matsumoto Keisuke Satoh Yasuko Semba Mikio Toyonaga 《Fisheries Oceanography》2016,25(6):565-581
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. 相似文献
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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. 相似文献
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基于不同模型研究环境因子对中西太平洋鲣资源丰度的影响 总被引：2，自引：1，他引：1
根据1998—2013年中西太平洋鲣(Katsuwonus pelamis)生产数据,选取时空因子(年、月、经纬度)和环境因子[海表面温度(SST)、海表面高度(SSH)、尼诺指数(ONI)和叶绿素a浓度]Chl-a)],通过两种不同的模型(广义加性模型GAM和提升回归树模型BRT)研究各因子对鲣资源丰度(以CPUE表示)的影响。研究结果认为,GAM模型中,经度对CPUE的影响最大,累计解释偏差超过50%,其次为纬度、年和月;在环境因子中,SSH最为重要,其次为ONI,而SST和Chl-a的影响相对较低。BRT模型分析结果与GAM分析结果类似,时空因子相对占据了重要的地位,其中经度的影响最大,其次为年、纬度和月;而在环境因子中,ONI的重要性相对更高,其次为SSH,SST和Chl-a同样影响较低。研究认为,两种模型均能较好地反映出因子对CPUE的影响。由于厄尔尼诺/拉尼娜现象引起的海洋环境变化会使鲣资源分布产生差异,因此在后续的渔情预报研究中,应该更多地考虑将ONI因子纳入渔情预报模型中,以提高预测精度。 相似文献
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Hiroshi Shono 《Fisheries Science》2014,80(5):879-886
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 . 相似文献
16.
Alberto Abad‐Uribarren Sofía Ortega‐García David March Antonio Medina 《Fisheries Oceanography》2019,28(6):672-685
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. 相似文献
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Angelia S. M. Vanderlaan Alex R. Hanke Joël Chassé John D. Neilson 《Fisheries Oceanography》2014,23(1):83-100
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. 相似文献
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探索渔业资源丰度与环境因子的关系, 并掌握种群分布对环境变化的响应机制, 是养护资源、实现渔业可持续发展的基础。然而, 渔业资源的变化受多个环境因素的综合影响, 这些因素之间存在复杂且相关的关系。目前的研究主要集中于环境因子对种群分布和资源丰度等直接影响, 而忽视了环境因素之间的相互作用。为了探索不同环境因子及其相互关系对毛里塔尼亚双拖鲣种群资源量的影响机制与路径, 本研究基于 2017—2019 年毛里塔尼亚海域双拖渔业鲣(Katsuwonus pelamis)单位捕捞努力量渔获量(CPUE), 采用结构方程模型(SEM)构建海表面温度 (SST)、海表面盐度(SSS)、海面高度异常(SLA)、溶解氧(DO)和叶绿素 a 浓度(Chl-a) 5 个环境因子对鲣 CPUE 直接和间接影响。结果表明: SEM 模型具有良好的拟合效果; SST、SSS、SLA、DO 和 Chl-a 对鲣 CPUE 均有直接影响, 其中 DO 和 SLA 对 CPUE 有着显著正相关影响, 而 SST、SSS 和 Chl-a 对 CPUE 有显著负相关影响; SST 等环境因子还会通过多种路径对鲣 CPUE 产生间接影响。研究揭示了毛塔海域 SST 通过直接影响或通过影响其他环境因子而间接影响鲣种群资源变动的潜在机制。 相似文献
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应用贝叶斯状态空间剩余产量模型框架评估印度洋大眼金枪鱼的资源状况 总被引：1，自引：0，他引：1
根据1950―2016年的渔获量数据及1955―2016年的单位捕捞努力量(Catch Per Unit Effort,CPUE)数据,采用贝叶斯状态空间剩余产量模型框架JABBA(Just Another Bayesian Biomass Assessment)对印度洋大眼金枪鱼(Thunnus obesus)的资源状况进行评估,分析了渔船效应、CPUE数据尺度对评估结果的影响。结果表明,模型拟合效果对于不同时间跨度下CPUE数据的选择比较敏感。当选用时间跨度为1979―2016年的CPUE数据且考虑渔船效应时,模型拟合效果最好。2016年大眼金枪鱼的资源量为812 kt,最大可持续产量(Maximum Sustainable Yield,MSY)为163 kt,远高于同年渔获量86.81 kt,其资源量具有82.50%的概率处于"健康"状态。当总允许可捕量为69.45～104.17 kt时(2016年渔获量的80%～120%),未来10年大眼金枪鱼的资源量仍高于B_(MSY)(达到MSY所需的生物量)。回顾性分析结果表明,该资源评估结果存在一定程度的回顾性问题,捕捞死亡率和资源量分别存在被低估和高估的现象。将来需要在模型结构设定、CPUE数据选择及模型参数的先验分布设置等方面进一步优化。 相似文献
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大眼金枪鱼渔场与环境关系的研究进展 总被引：2，自引：0，他引：2
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。 相似文献