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。     

Catch per unit effort standardization of the eastern Bering Sea walleye pollock (Theragra chalcogramma) fleet

A general linear model (GLM) was used to standardize catch per unit effort (CPUE) data for Alaska walleye pollock (Theragra chalcogramma) from the Bering Sea fleet for the years 1995–1999. Data were stratified temporally by year and season and spatially by area using either Alaska Department of Fish and Game (ADF&G) or National Marine Fisheries Service (NMFS) reporting areas. Four factors were used: vessel identification (ID) number, vessel speed, percentage of pollock by weight in the haul (a measure of targeting), and whether most of the haul took place before or after sunset. At least 29 combinations of main effects, quadratic covariates, and interactions were tested for each year/area/season stratum. GLM models explained from 31 to 48% of the total sums of squares. Vessel identification number was included in all models and explained the most variability. Of the remaining factors, the square of the percentage of pollock in the haul was included in most models, following an F-test to determine parsimony. Analysis of the vessel identification number coefficients indicated that larger vessels tended to have higher CPUEs; and that this relationship differed between dedicated catcher vessels and offshore catcher processors. Coefficient estimates and response surfaces generally indicated increased CPUEs with the percentage of pollock in the haul and showed mixed results with vessel speed. The vessel identification number incorporated most vessel characteristics, leaving vessel speed primarily as a fitting variable with less biological meaning. The year/area/season stratification procedure was found to be necessary due to the unbalanced design, which otherwise would have factor levels with no data in a large combined model. In addition, the stratification procedure reduced the variability in CPUE substantially.     

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.     

2012/2013渔季CAMLR 48区南极磷虾(Euphausia superba)资源时空分布

根据2013年1–9月辽宁远洋渔业有限公司“福荣海”轮南极磷虾拖网调查数据,以3n mile/h拖曳获得的产量作为CPUE指标,对南极磷虾资源时空分布进行了分析。结果显示,1–6月的月均CPUE值相对稳定,7–9月逐月下降。各渔区中平均CPUE值以48.1区最高,为(25.12±31.04) t/h;48.3区最低,为(11.49±12.06) t/h;CPUE值的波动幅度48.1区大于48.2和48.3区。48.1区的南极磷虾群主要分布于0 –100 m水层,CPUE值以25–50 m水层为最高;48.2区虾群主要分布于50–150 m水层,CPUE值以100–150 m水层最高;48.3区虾群主要分布于100–250 m水层,CPUE值以200– 250 m水层最高。海底深度<500 m的近岸海域是磷虾主要集群分布区和商业捕捞渔场,以水深<250 m的浅水区渔场虾群密度最大,平均CPUE值为(17.54±35.26) t/h,水深250–1500 m的深水区渔场平均CPUE值变化较小,在12.0–14.0 t/h之间波动,但水深>1500 m时,平均CPUE值降到(9.62±9.54) t/h。作业渔场的表温SST主要集中在－1–2℃,当SST为－1–0℃时,平均CPUE值最高。探捕调查发现了5个主要的磷虾集群,集群时间可达30 d以上,但集群密度随时间发生变化。调查结果可为研究南极磷虾渔场形成机制和渔业管理提供基础数据,并为商业捕捞提供参考。     

CPUE标准化方法与模型选择的回顾与展望

 单位捕捞努力量渔获量(Catch Per Unit Effort, 简称CPUE)常被假设与渔业资源量成正比关系而广泛应用于渔业资源评估与管理中, 但大量研究表明, CPUE与资源量间的正比关系常因受众多因素的影响而难以成立。为能有效利用CPUE数据, 渔业工作者常使用各种统计模型对CPUE进行标准化, 以重新构建该正比关系。因此, 在渔业资源评估与管理中, CPUE标准化是一项极为重要的基础性工作。本文对CPUE标准化的基本概念、构建过程、统计模型和模型选择方法进行了全面回顾, 并强调了模型选择的不确定性, 介绍了基于模型平均的多模型推断方法。同时, 对CPUE标准化所面临的问题及处理方法进行介绍与讨论, 对其未来研究工作进行展望, 以期为CPUE标准化研究提供理论参考。

     

Effects of intensive fishing on the perch population in a large oligotrophic lake in eastern Finland

The perch population in Lake Höytiäinen was intensively fished to reduce the density of the population and hence also the predation pressure by perch on vendace larvae. A hypothesis suggests that this predation can prevent recovery of commercially important vendace stock from a state of low-density. In the 1990s the density of the perch population in Lake Höytiäinen has increased, while the vendace stock has been sparse. Intensive fishing was conducted by professional fishermen with a paired bottom trawl, seine net, hoop net and small fish traps and by recreational fishermen with small fish traps. The size of perch population during the intensive fishing period was studied by test fishing with multi-mesh gillnets and the Leslie method in which trawl YPUE was regressed on the cumulative yield. The results suggest that the population size clearly diminished in the area where the fishing pressure was strongest. YPUE of test fishing decreased ca. 30% whereas the Leslie method gave almost a decrease twice as large as that of the former. Mean weight of perch increased in the trawl and test fishing catches during the intensive fishing period. The most effective fishing gear was trawl (62% of the total yield), but importance of trap net fishing by recreational fishermen was also high as they caught 22% of the total yield.     

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.     

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.