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1.
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。 相似文献
2.
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. 相似文献
3.
长江口刀鲚汛期特征及其资源状况的年际变化分析 总被引：3，自引：2，他引：1
刀鲚是长江水域重要的洄游性经济鱼类。根据2008-2011年捕捞汛期长江口刀鲚的观测数据,对刀鲚汛期特征及汛期体长、体重以及渔获量的年变化进行研究。研究结果表明:2008-2011年各年调查汛期内长江口刀鲚的体长分布均呈显著差异,2008年的优势体长组为26~38 cm,2009年的优势体长组为22~32 cm,2010、2011年的优势体长组为24~34 cm,2008年优势叉长组较其他年份大,2011年的体长均值小于其他年份;调查的汛期样本中,150 g以上的大规格刀鲚在群体中所占比例逐年下降,而50 g以下的小规格刀鲚比例逐年增加,刀鲚个体小型化趋势明显;汛期内长江口刀鲚单船每网的渔获量(CPUE)不断减少,2011年最低,相较2010年,其单船每网渔获量下降了95.5%;2008-2011年各年刀鲚的汛期特征基本表现一致,3月下旬至4月中旬进入刀鲚的旺汛期,捕捞产量较大。 相似文献
4.
基于渔捞日志的长江靖江段刀鲚渔获量的时空特征分析 总被引：1，自引：1，他引：0
江苏靖江段位于近长江口段,是长江刀鲚渔汛最集中的水域。为了弄清靖江段刀鲚的渔汛特征,本文于2008-2009年和2012-2013年对16艘持刀鲚捕捞许可证的渔船作了渔获量监测,分析了渔获量的时空变化及其环境影响。结果表明,靖江段单船日渔获数量NB和重量WB分别为(21±38)ind./d和(2.0±4.1)kg/d,单船全汛总渔获尾数Nt和重量Wt为(890±929)ind.和(92.3±91.1)kg。NB和Nt具有一致性的年变化趋势,以2013年的最大,其他年份比较接近。但WB和Wt的年变化趋势与渔获数量的年变化趋势有所不同,表现为2008-2012年间持续下滑,2013年显著增长。NB和WB在2-3月间均极低,但4月增至(23.0±31.3)ind./d和(2.4±3.5)kg/d,5月达(78.0±81.0)ind./d和(7.7±9.1)kg/d。ANOVA分析显示,西水域的年NB和WB分别为东水域的2.5和2.7倍。研究亦显示,靖江段刀鲚的WB与同日水温和最高潮位均呈极显著的正相关(P﹤0.05)。长江口外的水温提升,可能是刀鲚开始生殖洄游的重要环境诱导因子。而高潮期在靖江段出现最高渔汛,可能是因为所采用的固定刺网过滤了更多的江水所致。 相似文献
5.
TSUTOMU NISHIDA 《Fisheries Oceanography》1992,1(2):143-152
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 40o-90oE and 70o-130oE 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 40oE and eastward of 110oE respectively. Neighboring stocks are intermingled in adjacent waters. 相似文献
6.
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. 相似文献
7.
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. 相似文献
8.
智利外海渔场竹筴鱼资源分布特征 总被引：13，自引：0，他引：13
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。 相似文献
9.
东南太平洋智利竹■鱼渔场分布及其与海表温关系的研究 总被引：1，自引：0，他引：1
根据2005年3月至2006年1月我国大型拖网加工渔船在东南太平洋的生产资料,结合海表温数据,按经纬度1°×1°的空间单位进行分析,利用Marine Explorer 4.0软件作图,研究作业渔场CPUE分布与海表温的关系。结果表明,适宜作业海表温为12~15℃,月平均CPUE呈正态分布:8月最高,为11.34 t/h;6~9月均超过7.00 t/h,密集鱼群区域分布在34°~40°S,79°~92°W,其海表温范围为13~15℃;10月平均CPUE为6.08 t/h,其表温范围为14~17℃;其余各月平均CPUE均不超过4.30 t/h。CPUE与适宜海表温关系通过K-S的检验。 相似文献
10.
基于海表面温度SST的剩余产量模型评估太平洋褶柔鱼秋生群资源 总被引：5，自引：4，他引：1
选择5—9月的平均海表面温度(sea surface temperature,SST)作为环境因子,采用Schaefer模型和Fox模型对太平洋褶柔鱼秋生群渔获量进行评价。假设ΔU(观测和预测单位捕捞努力量渔获量残差)是由SST引起的,从而将SST引入太平洋褶柔鱼秋生群的评估模型中。根据1960年以来太平洋褶柔鱼秋生群渔业整体发展情况,以1993和2003年为界对1985—2014年的总渔获量进行分段分析,分别为:1985—1993年、1994—2002年和2003—2014年。根据是否引入SST和引入SST后是否分段,分别构建了3个Schaefer模型和3个Fox模型。结果显示,分段Schaefer model-SST的拟合效果最好,ΔU与SST显著负线性相关(P0.05),渔获量在18～23℃会随温度升高而降低。建议:模型建立过程中应根据不同时间段的情况不同而进行分段分析,这样可以提高拟合效果;用分段Schaefer model-SST对未来渔获量进行评估,以期对相关资源管理起到一定的借鉴意义。 相似文献