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.     

我国东、黄海鲐鱼灯光围网渔业CPUE标准化研究

日本鲐是我国近海重要的中上层鱼类资源之一,评估其资源量需要对单位捕捞努力量渔获量（CPUE）进行标准化。影响CPUE标准化的因素很多，包括季节、区域和海洋环境等。本文利用广义线型模型（GLM）和广义加性模型（GAM），结合时空、捕捞船、表温等因子，对1998-2006年东、黄海大型灯光围网渔业鲐鱼CPUE进行标准化，并评价各因子对CPUE的影响。首先应用GLM模型评价时间、空间、环境以及捕捞渔船参数对CPUE的影响，并确定显著性变量。其次，将显著性变量逐一加入GAM模型，根据Akaike信息法则（AIC），选择最优的GAM模型。最后，利用最优的GAM模型对CPUE标准化，并定量分析时间、空间、环境以及捕捞渔船参数对鲐鱼CPUE的影响。GLM模型结果表明：8个变量对CPUE有重要影响，依次为年、船队、船队与年的交互效应、月、船队与月份的交换效应、经度、纬度和海表温。根据AIC，包含上述8个显著性变量的GAM模型为最优模型，对CPUE偏差的解释为27.78%。GAM模型结果表明：高CPUE分别出现在夏季海表温为28～31 ℃的东海中部和冬季海表温为12～16 ℃的黄海；1998-2006年，标准化后的CPUE呈逐年下降趋势，与持续增长的捕捞努力量有关。     

西南大西洋阿根廷滑柔鱼渔业中国大陆鱿钓船CPUE标准化

阿根廷滑柔鱼是我国重要的头足类渔业之一，对其单位捕捞努力量渔获量( CPUE)进行标准化是对其资源评估的重要内容。本研究根据2000～2010年我国在西南大西洋的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（GLM，general linear model）和广义加性模型（GAM，generalized additive model）对其CPUE进行标准化。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数值，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°C、海面高为-20~20cm、46.5°～48.5°S海域。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CPUE标准化。     

西北太平洋秋刀鱼CPUE标准化研究

秋刀鱼(Cololabis saira)是西北太平洋海域重要的渔业种类之一，其资源评估工作已成为热点问题，单位捕捞努力量渔获量(CPUE)标准化可以为开展有效的资源评估研究提供科学依据。为此，本研究利用2003~2017年中国大陆西北太平洋秋刀鱼渔业生产统计资料，结合卫星遥感获得的海洋环境数据，如海表面温度、海表温度梯度、海表面高度等，基于广义线性模型(General linear model, GLM)和广义可加模型(Generalized additive model, GAM)对中国大陆西北太平洋秋刀鱼渔业进行CPUE标准化。结果显示，根据BIC准则，在GLM模型结果中，年份、月份、经度、纬度、海表面温度、海表面高度、海表温度梯度及年份与月份对CPUE具有显著影响，并组成了GLM模型的最佳模型，对CPUE偏差的解释率为52.47%；在GAM模型结果中，除上述8个影响变量外，交互项月份与经度和月份与纬度也对CPUE影响较大，GAM的最佳模型对CPUE偏差的解释率为61.9%。通过5-fold交叉验证分析发现，GAM模型标准化结果较优于GLM模型，更适合于西北太平洋秋刀鱼渔业CPUE标准化。     

利用气候因子对Fox模型计算东海总经济鱼类CPUE的优化

气候因子是影响海洋鱼类资源的重要因素之一.本研究采用1951-1984年东海总渔获量和捕捞努力量数据,尝试将海表温度(SST)、冬夏季风、台风、长江径流4个气候因子引入Fox剩余产量模型中来对单位捕捞努力渔获量(CPUE)进行优化.在线性和指数回归模型中,运用AIC准则将气候因子对模型参数α进行逐步回归.结果均筛选出SST、夏季风2个主要气候因子作为模型的补充变量,经AIC准则判断,线性模型为最适模型.气候因子和参数α的线性回归模型回归系数R2为0.495,模型中各参数的P值都小于0.01,95%置信区间均不包含0.经气候因子优化后的模型对CPUE的拟合效果比优化前显著增强,说明SST、夏季风对东海渔业资源量的影响最为显著.     

鱼类栖息地模拟的比较研究—以东海鲐鱼为例

利用东海海域1997年10月、1998年3月、1999年7月和2000年1月鲐鱼渔获率及表层温度、盐度和初级生产力等环境因子数据,构建东海鲐鱼资源空间分布与环境因子间的一般线性模型(GLM)和广义加性模型(GAM),并将GLM和GAM模拟值的均值分别与实测值进行独立样本t检验及其误差分析.分析结果表明,GLM与GAM模型的独立样本t检验P值均大于0.05,因此两者都可模拟东海鲐鱼栖息地的空间分布.其中,GLM模型对鲐鱼渔获率空间分布趋势的模拟更为准确;而GAM模拟值对鲐鱼栖息地环境因子的变化比较敏感,能够解释更多空间数据的变化,因而GAM在分析鱼类栖息地与环境因子间关联程度或资源评估方面具有优势.     

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。     

基于GAM的阿根廷滑柔鱼CPUE与环境因子关系分析

根据2013年渔季在阿根廷外海公海海域的渔业生产数据,结合时间、空间、表温、水深和流速等环境数据,建立广义可加模型(GAM),对2013年夏秋季阿根廷滑柔鱼(Illex argentinus)单位捕捞努力量渔获量(CPUE)与时空因素、环境因子的关系进行研究。结果表明,优化后的GAM模型对CPUE总偏差解释率为56.10%,其中作业日期、表温、水深和流速对CPUE影响较大。根据AIC准则,包含上述4个显著变量的广义可加模型为最佳模型,其pseduo系数PCf值为0.487,AIC值为660.688,表明其具有较好的拟合度。各环境因子(海水表温、水深和流速)中,水深与研究区域CPUE的关系最为密切,阿根廷滑柔鱼渔场(阿根廷外海公海)适宜水深为分别为100~120 m和250~500 m,适宜表温为8~14℃,最适表温为12~14℃。GAM模型分析结果表明,影响CPUE的因子按重要性依次为作业日期水深表温流速。     

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。     

1999—2011年东、黄海鲐资源丰度年间变化分析

根据1999—2011年我国鲐大型灯光围网渔业数据,使用广义线性模型(generalized linear model,GLM)和广义加性模型(generalized additive model,GAM)估算了影响CPUE的时间(年、月)、空间(经度、纬度)、捕捞性能和环境效应[海表面温度(sea surface temperature,SST)、海表面高度、海表面叶绿素浓度],并以年效应作为资源丰度指数,分析了东、黄海鲐资源丰度的年间变化,东、黄海鲐资源丰度指数的年间变化与产卵场海表面温度以及捕捞强度间的关系。GAM结果表明,时间、空间、捕捞和环境变量对CPUE偏差的解释率为11.69%,其中变量年的解释率最大,占总解释率的38%。结果显示,1999—2011年东、黄海鲐鱼资源丰度指数(abundance index,AI)总体上呈下降趋势,2008年以来更是持续下降,丰度指数由2008年的1.22降至2011年的0.82。东、黄海鲐资源丰度指数年间与产卵场呈正相关,关系式为AI=-3.51+0.23SST(P0.05),这表明较高的产卵场SST对鲐资源量增加有利。过高的渔获量以及我国群众围网渔业渔船数量的快速增长是导致近年来鲐鱼资源下降的重要原因。     

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.     

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.     

Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data

Satellite‐based oceanographic data of sea surface temperature (SST), sea surface chlorophyll‐a concentration (SSC), and sea surface height anomaly (SSHA) together with catch data were used to investigate the relationship between albacore fishing ground and oceanographic conditions and also to predict potential habitats for albacore in the western North Pacific Ocean. Empirical cumulative distribution function and high catch data analyses were used to calculate preferred ranges of the three oceanographic conditions. Results indicate that highest catch per unit efforts (CPUEs) corresponded with areas of SST 18.5–21.5°C, SSC 0.2–0.4 mg m^?3, and SSHA ?5.0 to 32.2 cm during the winter in the period 1998–2000. We used these ranges to generate a simple prediction map for detecting potential fishing grounds. Statistically, to predict spatial patterns of potential albacore habitats, we applied a combined generalized additive model (GAM) / generalized linear model (GLM). To build our model, we first constructed a GAM as an exploratory tool to identify the functional relationships between the environmental variables and CPUE; we then made parameters out of these relationships using the GLM to generate a robust prediction tool. The areas of highest CPUEs predicted by the models were consistent with the potential habitats on the simple prediction map and observation data, suggesting that the dynamics of ocean eddies (November 1998 and 2000) and fronts (November 1999) may account for the spatial patterns of highest albacore catch rates predicted in the study area. The results also suggest that multispectrum satellite data can provide useful information to characterize and predict potential tuna habitats.     

Standardizing catch and effort data of the Taiwanese distant-water longline fishery in the western and central Pacific Ocean for bigeye tuna, Thunnus obesus

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.     

Standardising the CPUE for the <Emphasis Type="Italic">Illex argentinus</Emphasis> fishery in the Southwest Atlantic

The catch per unit effort (CPUE) is a widely used index for assessing the abundance of exploited populations in fishery management. To obtain appropriate CPUE values, it is essential to standardise catch-effort data from fisheries. This task is particularly important for squid fisheries because squid generally have a short life-span and are vulnerable to environmental variability, and thus effective fishery management should take such factors into account. In this study, we analysed unit catches of paired vessels operating under similar fishing conditions to calculate their relative fishing power (RFP) in order to standardise the CPUE of the Taiwanese fleet jigging for Illex argentinus in the Southwest Atlantic. To evaluate the appropriateness of the method, we used a logbook dataset covering eleven years (1993–2003), in which 93.5% of the total catch during the period was included. The results indicate that 98.7% of the fishing effort can be standardised according to the estimated RFP. Compared to nominal CPUE, the standardised CPUE values projected an explainable temporal pattern, indicating an increasing trend in abundance from 1995 to 1999 and a subsequent sharp plunge from 1999 to 2003. However, the RFP was not related to apparent physical factors of the vessel, such as gross tonnage or vessel length. Our evaluations suggest that the RFP method is appropriate for standardising the CPUE, so that it can serve as an abundance index that reflects the annual recruitment size of the squid fishery, because the quality of the method can potentially take possible affecting factors into account in order to satisfy the general assumptions of standardisation criteria. However, the effects of varying the settings of parameters should be carefully examined prior to applying this standardisation method to other squid fisheries.     

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.