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.     

印度洋西北海域秋季鸢乌贼渔场分布与浮游动物的关系

根据2004年10~11月我国鱿钓船在印度洋西北海域进行鸢乌贼资源调查所获得的资料,对表层浮游动物与鸢乌贼渔场分布之间的关系进行了探讨。结果表明:桡足类、箭虫类和糠虾类在调查海域的出现率在86%以上。浮游动物总生物量平均值为39.51±114.06mg/m3。鸢乌贼的平均日产量为4.6 t,平均CPUE为3.90 ind/线/h。在浮游动物中,生物量最高的种类为尖尾海萤,平均值为24.30mg/m3,但空间分布差异极大,并且与中心渔场分布无关。其次为箭虫类、桡足类和糠虾类,生物量平均值分别为9.18mg/m3、2.32mg/m3和1.38mg/m3,与中心渔场分布关系显著,并可作为渔场分布的指示种类。     

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.     

Inherent bias in using aggregate CPUE to characterize abundance of fish species assemblages

We have analyzed the practice of assessing an assemblage of fish species in a multispecies fishery on the basis of aggregate catch per unit effort (CPUE), which is the summed catch of all species per unit of effort. We show that at the onset of fishing or of a large positive or negative change in fishing effort, aggregate CPUE will be hyper-responsive, that is, relative change of aggregate CPUE will be greater than that of aggregate abundance. We also show that as the fishery reaches equilibrium, the aggregate CPUE in most circumstances will continue to be hyper-responsive, with a greater relative change from its value at the start than the aggregate abundance. However, there are less likely circumstances in which the aggregate CPUE will be hyper-stable compared to aggregate abundance. The circumstances leading to hyper-responsiveness or hyper-stability depend on the distribution of productivity and fishery vulnerability parameters among the species in the aggregation.     

Considerations of stock structure of yellowfin tuna (Thunnus albacares) in the Indian Ocean based on fishery data

Yellowfin stock structure in the Indian Ocean was studied by using industrial tuna longline fishery data. Three types of test variables were used to detect stock structure, i.e., CPUE, age-specific CPUE, and coefficient of variation for size. Time-series data of test variables were compiled for six sub-areas that were arranged by dividing the whole region systematically along longitude lines every 20 degrees. Then time-series data were smoothed by moving averages, and regressed by simple models. Patterns of time-series trends were graphically and statistically compared to classify homogeneous sub-area groups. Two assumptions were (a) that homogeneous stocks exist longitudinally and overlap in adjacent waters, and (b) that test variables within homogeneous sub-area groups are equally affected, and hence patterns of the time-series trends are similar. After graphical screening for significant sub-area groups, analysis of covariance was applied to test homogeneity of regression parameters representing patterns of the time-series trends. By classifying homogeneous sub-area groups, stock structures were determined at the P <0.05 and P <0.50 levels. The P<0.50 level was recognized as a useful criterion for ‘weak’ test variables since masked or vague structures at the P <0.05 level were likely cleared at this level in many cases. Results of this study and past stock structure studies were reviewed and compared. It was concluded that there are two major and two minor stocks of yellowfin tuna. The two major stocks (the western and the eastern) are located at 40^o-90^oE and 70^o-130^oE respectively. The minor stocks are the far western and the far eastern stocks (the latter possibly being a part of the Pacific stock), which are located westward of 40^oE and eastward of 110^oE respectively. Neighboring stocks are intermingled in adjacent waters.     

Relationships among long'term fisheries abundances, hydrographic variables, and gross pollution indicators in northeastern U.S. estuaries

Categorical time series regression was applied to 55 fish stocks in the Potomac, Hudson, Narragansett, Delaware, and Connecticut estuaries for the period 1929–1975. Interannual variability in catch per unit effort (CPUE) was related to CPUE, hydrographic variables, and pollution variables, lagged back in time to represent the conditions contributing to the multiple ages comprising each fishery. Hydrographic variables included water temperature and flow in the estuary– and, for offshore spawning stocks, wind direction and magnitude–during the months of spawning and early life stage development. Pollution variables included measures of dissolved oxygen conditions in the estuaries, volume of material dredged, and sewage loading (or human population). Lagged CPUE, hydrographic variables, and pollution variables all played important roles in explaining historical variability in CPUE. Lagged CPUE was significant in 45 of 55 stocks generally accounting for 5–35% of the variability. Lagged hydrographic variables were significant in 53 of 55 stocks, explaining an additional 5–40% of the variability unaccounted for by lagged CPUE. Lagged pollution variables were significant in 35 of 55 stocks, generally accounting for an additional 5–30% of the variability not explained by lagged CPUE and hydrographic variables. Results did not exhibit expected patterns of consistency in the importance of lagged CPUE for a species across estuaries or consistency in the importance of pollution variables across estuaries. Results did exhibit the expected north-to-south longitudinal pattern in the importance of timing of the hydrographic variables, the months of importance being one or two months later in more northerly estuaries. Higher-order interaction effects were important in almost all stocks that were well-modeled by categorical time series regression. Of the 30 stocks with final regression models having R² > 0.55, 26 stocks involved significant interaction effects, five had only significant interaction effects (no significant main effects), and 20 stocks had significant interactions involving variables not significant as main effects. The difficulties involved in analyzing long-term trends in fish populations and partitioning variability between natural and anthropogenic sources are discussed.     

智利外海渔场竹筴鱼资源分布特征

根据在智利 2 0 0海里专属经济区外海的渔场周年探捕调查 ,对智利竹鱼 (Trachurusmur phyi)单位努力量渔获量 (CPUE)的构成和季节变化及其资源分布特征进行了初步探讨。结果显示 ,竹鱼在智利外海分布广 ,30°～ 43°S ,78°～ 87°W海区均可形成拖网作业渔场。南半球冬季竹鱼密集分布区较偏南 (38°～43°S) ,8月密集分布区向北偏移至 35°～ 40°S,春季鱼群继续向北洄游至 30°～35°S ,并开始分散索饵 ,集群性较差 ,到翌年秋季再集群向南洄游 ,在 38°～ 43°S ,78°～ 85°W形成越冬场。CPUE以冬季最高 ,春、秋季次之 ,夏季最低。冬季以 6月份平均CPUE最高 ,达 1 5 .1 8t/h ,夏季以 3月份平均CPUE最低 ,仅 1 .1 2t/h。     

基于BP神经网络的阿根廷滑柔鱼资源CPUE标准化研究

西南大西洋阿根廷滑柔鱼(Illex angentinus)是世界上重要的经济柔鱼类,也是我国远洋鱿钓的主要捕捞对象之一.单位努力量渔获量(CPUE)是渔业中广泛使用的表达种群丰度的指标,但CPUE易受到其他因素的影响,需对其进行标准化.本研究利用2012-2017年1-4月中国大陆西南大西洋阿根廷滑柔鱼鱿钓生产统计数据...     

Application of the Tweedie distribution to zero-catch data in CPUE analysis

We focus on the zero-catch problem of CPUE (catch per unit effort) standardization. Because the traditional CPUE model with a log-normal error structure cannot be applied in this case, three methods have often been utilized as follows:

(1) Ad hoc method adds a small constant value to all response variables.

(2) Catch model with a Poisson or negative-binomial (NB) error structure.

(3) Delta-type two-step method such as the delta-normal model (after estimating the ratio of zero-catch using a logit or probit model, a model such as CPUE log-normal or Catch-Poisson is applied to CPUE without zero-data).

However, there are some statistical problems with each of these methods.In this paper, we carried out the CPUE standardization mainly using the Tweedie distribution model based on the actual by-catch data (silky shark, Carcharhimus falciformis, in the North Pacific Ocean caught by Japanese training vessels) including many observations with zero-catch (>2/3rd) and tuna fishery data as a target (yellowfin tuna, Thunnus albacares, in the Indian Ocean caught by Japanese commercial vessels) where the ratio of zero-catch is not so high (<1/3rd). The Tweedie model is an extension of compound Poisson model derived from the stochastic process where the weight of the counted objects (i.e., number of fish) has a gamma distribution and has an advantage of handling the zero-catch data in a unified way.We also compared four candidate models, the Catch-NB model, ad hoc method, Delta-lognormal model (delta-type two-step method) and Tweedie distribution, through CPUE analyses of actual fishery data in terms of the statistical performance. Square error and Pearson's correlation coefficient were calculated based on the observed CPUE and the corresponding predicted CPUE using the n-fold cross-validation.As a result, the differences in the trend of CPUE between years and model performance between the ad hoc method and Tweedie model were found to be not so large in the example of yellowfin tuna (target species). However, the statistical performance of Tweedie distribution is rather better than Delta-lognormal model, the Catch-NB distribution and ad hoc method in the example of silky shark (by-catch species). Standardized CPUE year trend of ad hoc method was found to be quite different from that of the Tweedie distribution and other two models. Model performance of the Tweedie distribution is good judging from the 5-fold cross-validation using the fishery data if including many zero-catch data such as by-catch species.     

广东东莞松木山水库鱼类群落结构及其与环境因子的关系

为探讨广东东莞松木山水库鱼类群落结构特征及其与环境因子的关系,分析其结构特征在沉浮网间的差异性,在该水库设置3个采样点采用多网目刺网对鱼类进行了调查。结果显示,共采集到鱼类17种,隶属4目、6科,物种数以鲤形目为主（占58.82%）。相对重要性指数（IRI）显示,优势种为海南似鱎（IRI占比29.66%）、?（18.98%）、尼罗非鲫（18.46%）、鲢（14.85%）和莫桑比克非鲫（11.36%）,其中单位努力捕捞数量（NPUE）以海南似鱎（45.72%）占优、单位努力捕捞重量（BPUE）以尼罗非鲫（34.60%）为主。聚类分析表明,鱼类物种组成在季节间无显著差异,物种数、NPUE和BPUE亦无季节变化（P>0.05）。鱼类群落物种数、NPUE和BPUE沉浮网间无显著差异（P>0.05）,但鱼类数量组成沉浮网间存在显著差异（P<0.001）,与海南似鱎有关,其NPUE浮网显著高于沉网（P<0.05）,其他5种主要鱼类沉浮网间无显著差异（P>0.05）。透明度、pH和总磷是影响鱼类物种数量时空分布的关键环境因子。研究表明,松木山水库鱼类多样性低,可能与水面积较小、连通性低、外来种入侵及入库河道以人工排渠为主有关,为科学合理评估鱼类数量组成建议水库鱼类调查需要同时使用沉浮网。