Estimating the total allowable catch and management of Threadfin porgy(Evynnis cardinalis)fisheries in the northern South China Sea based on sampling surveys conducted at fishing ports

In order to develop a method that apply sampling survey data randomly obtained at fishing ports to fish stock assessments,based on fish landing surveys conducted at fishing ports in the northern South China Sea(SCS),19 Threadfin porgy(Evynnis cardinalis)catch per unit effort(CPUE)datasets were collected for fishing vessels with different gear types and engine powers and incorporated into surplus production models.Considering only the fitting performance,the Schaefer model had the best overall goodness of fit,followed by the Fox,CYP,W-H,and Schnute models.Among fishing vessels with different gears and engine powers,the data were best fitted for single-trawl vessels powered by 301-400-kW engines and for gillnet vessels powered by>200-kW engines.Eight model expressions were superior and selected for subsequent analyses based on their goodness of fit and relative residuals.The Kobe plot analysis results showed an optimistic fish stock status when using the four model expressions,required more caution when using three model expressions and output pessimistic estimations using one model expression.Considering the incomplete information acquired,a compromising decision-making method was used to derive a 2017 northern SCS E.cardinalis total allowed catch(TAC)of 44,691.21 t.The different conclusions drawn from estimations using CPUEs reflect variable exploitation and utilization fish stock statuses among fishing vessels with different gears and engine powers.Hence,the fishing operations were grouped according to their CPUE relationship,and recommendations regarding optimum fishing efforts were assigned to the groups following a fundamental principle:to improve fishery TAC management,fishing efforts should be reduced if the fish stock assessment is pessimistic and maintained if the assessment is optimistic.This study providing a feasible technical method for the TAC management of China’s offshore fisheries.

Catch per unit effort,maximum sustainable yield and exploitation of demersal fish of Omani artisanal fishery

Objective:To evaluate exploitation status of the stocks of demersal fishes in Omani artisanal fisheries.Methods:Time-series data between 2005 and 2014 on catches and effort represented by the number of fishing boats were used to estimate catch per unit effort and maximum sustainable yields applying Schaefer surplus production model.Regression analyses were made online using GraphPad software.Results:The study revealed that increasing the number of boats on the fishery caused a decrease of catch per unit effort of some species.Maximum sustainable yields and exploitation status were estimated for these species applying.Conclusions:Some demersal fish species were found to be caught in quantities exceeding maximum sustainable yields during some fishing seasons indicating overexploitation of their stocks.

Modeling Monthly Spatial Distribution of Ommastrephes bartramii CPUE in the Northwest Pacific and Its Spatially Nonstationary Relationships with the Marine Environment

There are substantial spatial variations in the relationships between catch-per-unit-effort(CPUE) and oceanographic conditions with respect to pelagic species. This study examines the monthly spatiotemporal distribution of CPUE of the neon flying squid, Ommastrephes bartramii, in the Northwest Pacific from July to November during 2004–2013, and analyzes the relationships with oceanographic conditions using a generalized additive model(GAM) and geographically weighted regression(GWR) model. The results show that most of the squids were harvested in waters with sea surface temperature(SST) between 7.6 and 24.6℃, chlorophyll-a(Chl-a) concentration below 1.0 mgm^(-3), sea surface salinity(SSS) between 32.7 and 34.6, and sea surface height(SSH) between-12.8 and 28.4 cm. The monthly spatial distribution patterns of O. bartramii predicted using GAM and GWR models are similar to observed patterns for all months. There are notable variations in the local coefficients of GWR, indicating the presence of spatial non-stationarity in the relationship between O. bartramii CPUE and oceanographic conditions. The statistical results show that there were nearly equal positive and negative coefficients for Chl-a, more positive than negative coefficients for SST, and more negative than positive coefficients for SSS and SSH. The overall accuracies of the hot spots predicted by GWR exceed 60%(except for October), indicating a good performance of this model and its improvement over GAM. Our study provides a better understanding of the ecological dynamics of O. bartramii CPUE and makes it possible to use GWR to study the spatially nonstationary characteristics of other pelagic species.

GLM-Based Analysis on Seasonal Variation of Fishery Resources in Dapeng Bay, China

Dapeng Bay is a typical offshore fisheries area in the South China Sea (SCS). In order to understand the fishery resources, seasonal variations of species composition, dominant species composition, standardized catch per unit effort (SCPUE) and community diversity of fishery resources in Dapeng Bay, China were analyzed based on trawl survey data in spring (March) and summer (May) of 2013 as well as autumn (August) and winter (December) of 2012. Results demonstrated that there are 113 fishery species, which belong to 78 categories, 50 families, 14 catalogues, 3 classes. There are the most species in summer (61 species) and about 56 species in other seasons. In all four seasons, fish and crustacea are dominant species, and there are the fewest cephalopoda. High values of SCPUE occur in spring and autumn, reaching 5.65 and 5.33, respectively. SCPUE is generally low in summer, ranging between 0.52 - 0.96. Fish biodiversity is the highest in summer and the lowest in winter. Among biodiversity, Shannon-Wiener diversity index, Margalef diversity index and Pielou evenness index vary in the same trend. They are decreasing from summer, spring, autumn to winter successively. Therefore, there are violent seasonal variation of species composition, quantity and structure of fishery resources in Dapeng Bay. The community structure and function are highly stable in summer, but they have poor stability in autumn and winter. This research can provide scientific references for protection and sustainable use of fishery resources in typical offshore fisheries area in the SCS.

Application of a Delay-Difference Model for the Stock Assessment of Southern Atlantic Albacore(Thunnus alalunga)

Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In this study, a delay-difference model was applied to fit catch and catch per unit effort(CPUE) data(1975–2011) of the southern Atlantic albacore(Thunnus alalunga) stock. The proposed delay-difference model captures annual fluctuations in predicted CPUE data better than Fox model. In a Monte Carlo simulation, white noises(CVs) were superimposed on the observed CPUE data at four levels. Relative estimate error was then calculated to compare the estimated results with the true values of parameters α and β in Ricker stock-recruitment model and the catchability coefficient q. α is more sensitive to CV than β and q. We also calculated an 80% percentile confidence interval of the maximum sustainable yield(MSY, 21756 t to 23408 t; median 22490 t) with the delay-difference model. The yield of the southern Atlantic albacore stock in 2011 was 24122 t, and the estimated ratios of catch against MSY for the past seven years were approximately 1.0. We suggest that care should be taken to protect the albacore fishery in the southern Atlantic Ocean. The proposed delay-difference model provides a good fit to the data of southern Atlantic albacore stock and may be a useful choice for the assessment of regional albacore stock.

Effects of spatio-temporal and environmental factors on distribution and abundance of wintering anchovy Engraulis japonicus in central and southern Yellow Sea

We investigated the spatio-temporal and environmental factors that affect the distribution and abundance of wintering anchovy and quantifi es the infl uences of these factors. Generalized additive models(GAMs) were developed to examine the variation in species distribution and abundance with a set of spatiotemporal and oceanographic factors, using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea during 2000–2011. The fi nal model accounted for 28.21% and 41.03% of the variance in anchovy distribution and abundance, respectively. The results of a two-step GAM showed that hour, longitude, latitude, temperature gradient(TGR), and chlorophyll a(Chl- a) concentration best explained the anchovy distribution(presence/absence) and that a model including year, longitude, latitude, depth, sea surface temperature(SST), and TGR best described anchovy abundance(given presence). Longitude and latitude were the most important factors affecting both distribution and abundance, but the area of high abundance tended to be east and south of the area where anchovy were most likely to be present. Hour had a signifi cant effect on distribution, but year was more important for anchovy abundance, indicating that the anchovy catch ratio varied across the day but abundance had an apparent interannual variation. With respect to environmental factors, TGR and Chl- a concentration had effects on distribution, while depth, SST, and TGR affected abundance. Changes in SST between two successive years or between any year and the 2000–2011 mean were not associated with changes in anchovy distribution or abundance. This fi nding indicated that short- and long-term water temperature changes during 2000–2011 were not of suffi cient magnitude to give rise to variation in wintering anchovy distribution or abundance in the study area. The results of this study have important implications for fi sheries management.

Impact of Climatic Factors on Albacore Tuna <i>Thunnus alalunga</i>in the South Pacific Ocean

Over the years there has been growing interest regarding the effects of climatic variations on marine biodiversity. The exclusive economic zones of South Pacific Islands and territories are home to major international exploitable stocks of albacore tuna (Thunnus alalunga);however the impact of climatic variations on these stocks is not fully understood. This study was aimed at determining the climatic variables which have impact on the time series stock fluctuation pattern of albacore tuna stock in the Eastern and Western South Pacific Ocean which was divided into three zones. The relationship of the climatic variables for the global mean land and ocean temperature index (LOTI), the Pacific warm pool index (PWI) and the Pacific decadal oscillation (PDO) was investigated against the albacore tuna catch per unit effort (CPUE) time series in Zone 1, Zone 2 and Zone 3 of the South Pacific Ocean from 1957 to 2008. From the results it was observed that LOTI, PWI and PDO at different lag periods exhibited significant correlation with albacore tuna CPUE for all three areas. LOTI, PWI and PDO were used as independent variables to develop suitable stock reproduction models for the trajectory of albacore tuna CPUE in Zone 1, Zone 2 and Zone 3. Model selection was based on Akaike Information Criterion (AIC), R2 values and significant parameter estimates at p < 0.05. The final models for albacore tuna CPUE in all three zones incorporated all three independent variables of LOTI, PWI and PDO. From the findings it can be said that the climatic conditions of LOTI, PWI and PDO play significant roles in structuring the stock dynamics of the albacore tuna in the Eastern and Western South Pacific Ocean. It is imperative to take these factors into account when making management decisions for albacore tuna in these areas.

Identification and Mapping of Essential Fish Habitats Using Remote Sensing and GIS on Lake Victoria, Kenya

Fisheries in Lake Victoria have been threatened by declining fish stocks and diversity, environmental degradation due to increased input of pollutants, industrial and municipal waste, overfishing and use of unapproved fishing methods, infestation by aquatic weeds especially water hyacinth, de-oxygenation and a reduction in the quantity and quality of water. Remote sensing and GIS are essential tools in detection of fishing grounds which is important in providing fish sustainability for human beings and allows fishing grounds detection at minimal cost and optimizes effort. This research tends to identify the most favorable both environmentally and ecologically satisfactory factors which favor fish breeding and growth. The main aim of the study was to identify habitat variables that promote fish breeding and growth to maturity including the extraction of environmental variables from Landsat 8 images for the study period and using suitability index derived from fishery data. The study concentrated on establishing suitability ratings in different parts of Lake Victoria using lake surface temperature and chlorophyll-a levels. The study was conducted for months;January, May and December 2019 on Lake Victoria (limited by the availability of recent data). The factors were analysed and the favorable regions mapped satisfying the conditions for fish breeding. The output obtained illustrated the availability of suitable and habitable zones within the lake using satellite imagery and the suitability index. The fish catch data and satellite derived variables were used to determine habitat suitability indices for fish during January, May and December 2019. More than 90% of the total catch was found to come from the areas with sea surface temperature of 23.0˚C - 28.3˚C and chlorophyll-a concentration between 0.72 - 1.31 mg/m3. The catch data was used to validate the images. This study indicated the capability of High Satellite Resolution Imageries (HSI) as a tool to map the potential fishing grounds of fish species in Lake Victoria. The variables were affected by climatic change factors like rainfall and temperature of the lake basin and other human activities around the lake and also the species ecosystem like competition or predation.

基于圆形统计和时间序列的月相对两种拖网渔业CPUE的影响

为了解和掌握月相对渔业产量或单位捕捞努力量渔获量(catch per unit effort,CPUE)的影响规律,利用2016—2019年(每年3月2日—11月29日)马达加斯加西海岸底拖网独角新对虾(Metapenaeus monoceros)和2017—2020年(每年6月15日—10月9日)西白令海中层拖网狭鳕(Theragra chalcogramma)的渔业生产数据,结合基于圆形统计的广义线性模型(GLM)和基于时间序列的广义加性模型(GAM)2种不同的月相量化和统计的方法,分析月相对拖网渔业CPUE的影响。结果表明:月相对独角新对虾的CPUE具有显著性影响(P<0.05),2种方法得出的影响趋势较为一致,较高CPUE出现在上弦月;基于圆形统计的GLM显示,月相对狭鳕CPUE具有显著性影响(P<0.05),较高CPUE出现在新月期,而基于时间序列的GAM显示,月相对狭鳕CPUE的影响不显著(P>0.05);交叉验证显示,基于圆形统计的GLM平均绝对误差(E_(MA))和均方根误差(E_(RMS))均小于基于时间序列的GAM,而GLM分析的决定系数R~2则大于GAM,表明前者的拟合具有更好的准确性、稳定性和拟合优度。研究表明,当周期性循环变量(月份、月相和小时等)具有较弱的显著性时,使用基于圆形统计的GLM更能反映月相对拖网渔业CPUE的影响。

东海带鱼渔获量对捕捞压力和气候变动的响应

为了解捕捞压力和气候变动对东海带鱼渔业产量的共同影响,对1956—2006年东海带鱼渔获量进行了分析。东海带鱼渔获量时间序列可划分成变化趋势和年间变动。变化趋势主要归因于捕捞努力量的单调增长,Fox模型拟合结果,东海带鱼渔获量与捕捞努力量关系显著(P<0.01)。移除趋势后,其年间变动与陆地降水、季风风速、海表水温和热带气旋影响指数相关(P<0.05)。结果表明,气候变动影响到东海带鱼渔获量年间变动:陆地降雨和径流携带大量营养盐进入沿岸生态系统;而季风则驱动营养盐的扩散和循环,影响着营养盐的利用效率;水温的升高不仅有利于带鱼的性腺发育与成熟,还能增加带鱼的饵料供应;热带气旋所形成的水团流动、风生混合、上升流等能促进营养盐供应并增加水域的生物量。从捕捞努力量和气候变量拟合东海带鱼渔获量的结果来看,与实际渔获量显著相关(P<0.01),并且能够很好地反映出实际带鱼渔获量的变化趋势和年间变动。这说明渔获量的变化受到捕捞效应和气候变动的双重影响,未来气候变化将有利于东海带鱼渔业产量的增加,且渔获量年间变动幅度将会比以往更大。

长江口水域春季鱼卵仔鱼分布及其与温度盐度的关系

利用2003年春季在长江口水域进行调查的数据,对该水域的鱼卵仔鱼的种类组成和数量分布、表层水温、盐度的分布特征以及它们之间的关系进行了研究。结果表明:共捕获鱼卵仔鱼13种,出现率为81.25%。鱼卵出现站位资源密度(CPUE)为1～760粒/网,平均每网采获鱼卵63.6粒;仔稚鱼出现站位CPUE为1～174尾/网,平均每网捕获仔稚鱼18.75尾。按照表层温度、盐度的变化,该水域可分为高温低盐、低温高盐、低温低盐三个区。鱼卵出现水域主要集中在高温低盐区;而仔稚鱼在长江口水域广有分布,但主要出现水域是高温低盐和低温低盐区控制的近岸水域,在远离海岸的低温高盐区仅有少量的分布。

环境因子对东海区帆式张网主要渔获物渔获量影响

帆式张网渔业是东海区重要的渔业捕捞方式之一 .利用广义加性模型 (GAMs)定量研究了海表水温、渔场水深、渔场位置和潮汐 (农历日期 )等环境因子对东海区帆式张网主要渔获物渔获量变动的影响 .结果表明 ,海表水温和渔场水深是影响带鱼、小黄鱼和鲳鱼单位努力渔获量 (CPUE)的主要因子 ,且各因子的影响程度不尽相同 ,多呈非线性相关关系 .从模型的拟合结果来看 ,海表水温对带鱼的影响最大 ,其次是水深 ,渔场位置和潮汐的影响很小 .渔场水深对小黄鱼的影响最大 ,海表水温、渔场位置和潮汐的影响比较接近 .渔场水深和海表水温是影响鲳鱼单位努力渔获量的主要环境因子 ,影响程度比较接近 .

南极磷虾中层拖网捕捞参数对CPUE变动的影响

根据中国在南极海洋生物资源养护委员会(CCAMLR)48.1亚区和48.2亚区内2010—2014年南极磷虾拖网渔船的捕捞数据,采用广义可加模型(generalized additive model, GAM)分析各拖网捕捞参数(因子)对南极磷虾汛期单位捕捞努力量渔获量(catch per unit effort, CPUE )的影响。结果表明:2010—2014年我国南极磷虾作业渔场主要位于南极半岛等岛屿的周边海域,CPUE 值月变化明显,2—5月为捕捞高峰期。 CPUE 最高值为2014年,最低值为2012年,年平均CPUE呈略上升趋势。从月变化来看,除6月外,标准化 CPUE 均大于名义 CPUE;且最大值出现在4月或5月,最小值均出现在10月。GAM分析表明各捕捞参数对 CPUE 的影响由大到小依次为:拖网速度、网口高度、网口水平扩张、拖曳水深、曳纲长度;各因子的偏差解释率分别为22.8%、14.5%、12.7%、6.0%、1.6%,所有因子对 CPUE 的总体偏差解释率为57.6%。研究认为,我国南极磷虾捕捞作业中最适拖网速度范围2.6~3.1 kn;最适网口高度范围22~30 m;最适网口水平扩张范围20~25 m;最适曳纲长度范围100~200 m;最适拖曳水深范围0~40 m。

2012年秋冬季利文斯顿岛南极磷虾渔业CPUE指标变动及其影响因素

基于南极磷虾渔业科学观察员收集的南极磷虾渔业数据,对2012年秋冬季利文斯顿岛南极磷虾渔业单位捕捞努力量产量(CPUE)的两个指标[单位作业小时产量(CH)和单位扫海面积产量(CPUA)]的变化及其影响因素进行了研究。结果表明:该水域南极磷虾渔业CH和CPUA分别为0～84.0 t/h和0～15.0×103t/km2,平均值分别为10.0 t/h和4.7×103t/km2;作业水域主要集中在利文斯顿岛西部水域;南极磷虾丰度较高的时间在8:00～16:00,而其他时间段则并非该渔业的最佳作业时段;46.2%的作业区域表温为-0.4～-0.2℃,43.4%的作业拖曳深度为40～60 m,78.3%的作业区域均在100 m以浅的水域。

基于广义可加模型分析时空和环境因子对2013年南极磷虾资源分布的影响

根据2013年1—9月南极磷虾拖网调查数据,以单位捕捞努力量渔获量作为资源密度指标,分析南极磷虾资源空间分布特征和月间变化。结果表明,1—6月的月均单位捕捞努力量渔获量值相对稳定,7—9月逐月下降。渔区的平均单位捕捞努力量渔获量值以48.1小区最高,为(25.12±31.04)t/h,48.3小区最低,为(11.49±12.06)t/h;单位捕捞努力量渔获量值的波动幅度48.1小区大于48.2小区和48.3小区。广义可加模型筛选出的解释变量对南极磷虾空间分布的影响由大到小依次为:月份、经度、拖网深度、水深、纬度和表层水温。各因子的偏差解释率分别为16.82%、11.17%、3.98%、1.75%、1.54%和0.88%,所有因子对单位捕捞努力量渔获量的总偏差解释率为36.14%。本次调查的结果可为研究南极磷虾渔场形成机制和渔业管理提供参考。

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(P<0.05),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。

基于GAM和GWR模型分析环境因子对南极磷虾资源分布的非线性和非静态性影响

分析南极磷虾分布与环境因子的非线性和空间非静态性关系,对南极磷虾的高效捕捞和管理具有重要意义。本研究基于“龙腾”船2015、2016年在南设得兰群岛捕捞作业的渔捞日志数据,应用广义加模型(Generalized additive model,GAM)和地理权重回归模型(Geographical weighted regression,GWR)探究南极磷虾(Euphausia superba)渔场分布与环境因子的非线性和空间非静态性关系,并比较这2种模型的模拟性能,为南极磷虾的渔场渔情预报、资源评估和渔业管理提供基础数据。GAM模型结果显示,2015、2016年单位捕捞努力量渔获量(Catch per unit effort,CPUE)与作业水深均呈显著负相关关系(P<0.01),表明在作业水深范围内,南极磷虾在较浅水域集群密度较高;2015年CPUE与表层水温呈显著正相关关系(P<0.01),但在2016年呈显著负相关关系(P<0.01),推测是由于2年调查作业位置不同所致;CPUE与离岸距离关系不显著(P≥0.05)。GWR模型结果显示,作业水深对CPUE的影响无显著的空间变化(P>0.05);海水表温和离岸距离对CPUE的影响具显著的空间变化(P<0.01),表明这2个因子对南极磷虾渔场分布的影响在空间上不连续,存在显著空间非静态性。GAM模型可用于研究资源分布与驱动因子的一般规律;GWR模型作为全局回归模型的有效补充,可用于探究一般规律不适合的特殊区域,便于发现资源分布的“热点”区域,未来在海洋生物资源分布研究中将有广阔的应用前景。

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。

向家坝和溪洛渡蓄水对圆口铜鱼不同年龄个体下行移动的影响

拦河筑坝所导致的物理阻隔和生态阻隔是影响鱼类迁徙的关键因素。圆口铜鱼Coreius guichenoti(Sauvage&Dabry de Thiersant,1874)作为长江上游重要的河流性洄游鱼类,其迁徙不可避免受到金沙江下游向家坝和溪洛渡大型水电站蓄水的影响。关于向家坝和溪洛渡蓄水是否会显著阻隔圆口铜鱼个体的下行以及不同年龄个体之间受阻隔的影响是否存在差异,迄今为止,还未有相关报道。本文拟通过2012—2014年对长江上游永善江段和宜宾江段圆口铜鱼年龄结构变动的研究,探讨向家坝和溪洛渡蓄水对圆口铜鱼不同年龄个体下行阻隔的影响程度。结果表明:1)向家坝和溪洛渡蓄水对圆口铜鱼下行产生了明显的阻隔效应;2)向家坝和溪洛渡蓄水对1龄个体的下行阻隔最明显且最严重;3)与单一的大型水库和大坝相比,多个梯级的大型水库和大坝会对圆口铜鱼的下行产生更为严重的阻隔效应。本文拟为金沙江下游圆口铜鱼的资源保护提供基础数据支持。

东海带鱼渔获量变动原因分析

利用1951～1984年东海带鱼年渔获量和捕捞努力量资料，以及降雨、风速和海表温度等的时间系列，分析了带鱼渔获量年际变化与捕捞努力量及环境因素的关系，并建立了渔获量对捕捞努力量和环境变量的回归模犁。带鱼渔获量随捕捞努力量的变化可用Fox模型拟合（R=0．89，P〈0．01），1951～1974年期间，渔获量随着捕捞努力量的增长而不断提高，但自1974年后，随着捕捞努力量的持续增长，渔获量开始下降。排除捕捞效应后的带鱼渔获量波动还与环境因素显著相关，分析结果表明，长江流域和东海沿岸地区年降水量、渤海海域年均风速、长江口年均风速、黄海和东海海表温度（2月）、东海中部年平均海表温度及南部冬季月平均海表温度等环境因子都与之显著相关。包含捕捞努力量和环境变量的渔获量模型的回归系数为0．97，其置信水平达到99％以上。运用1951～1984年的回归模型对1985和1986年的渔获量作出了预测，其预测值与实际渔获量的相对误差均小于5％，验证了其可靠性。研究的结果表明，带鱼渔获量变动不仅与捕捞作用有关，同时还受环境因素的影响，是两者综合作用的结果。