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1.
中国大陆阿根廷滑柔鱼鱿钓渔业CPUE标准化   总被引:8,自引:0,他引:8       下载免费PDF全文
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.  相似文献   

2.
我国东、黄海鲐鱼灯光围网渔业CPUE标准化研究   总被引:8,自引:1,他引:7       下载免费PDF全文
李纲  陈新军  田思泉 《水产学报》2009,33(6):1050-1059
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。  相似文献   

3.
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。  相似文献   

4.
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。  相似文献   

5.
阿根廷滑柔鱼(Illex argentinus)是西南大西洋重要的经济性头足类,也是中国大陆鱿钓渔业重要的捕捞对象,掌握其渔场时空分布特性及其与海洋环境之间的关系是合理开发、有效管理资源的基础。本文根据20002010年中国大陆鱿钓渔业在西南大西洋39°2010年中国大陆鱿钓渔业在西南大西洋39°51°S、57°51°S、57°67°W海域的生产统计数据,对西南大西洋阿根廷滑柔鱼中心渔场时空分布及与海洋表层温度(sea surface temperature,SST)的关系进行了研究。结果表明,167°W海域的生产统计数据,对西南大西洋阿根廷滑柔鱼中心渔场时空分布及与海洋表层温度(sea surface temperature,SST)的关系进行了研究。结果表明,15月间,阿根廷滑柔鱼产量及作业次数在纬度方向由南向北逐渐递增,经度方向上由西向东逐渐递增。不同年份间,阿根廷滑柔鱼渔场重心纬度方向上存在显著性差异,而经度方向分布则不存在显著性差异。相关性分析表明,15月间,阿根廷滑柔鱼产量及作业次数在纬度方向由南向北逐渐递增,经度方向上由西向东逐渐递增。不同年份间,阿根廷滑柔鱼渔场重心纬度方向上存在显著性差异,而经度方向分布则不存在显著性差异。相关性分析表明,15月阿根廷滑柔鱼渔场重心纬度、经度的变化与SST之间存在显著相关性,并且渔场重心均随着SST的升高呈现向南和向西移动的趋势。  相似文献   

6.
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以及对应区域的环境数据,构建了20种误差反向传播人工神经网络(error backpropagation network, EBP)模型以标准化CPUE。模型以月份(month)、经度(Lon)、纬度(Lat)、海表面温度(SST)、95 m深层水温(PT95)、叶绿素a浓度(Chl-a)、海表面盐度(SSS)为输入因子,隐含层结点数从1~20个逐步增加,输出层为CPUE,以决定系数(R2)、最小均方误差(MSE)和平均相对方差(ARV)作为模型评价标准。结果显示,7-18-1结构模型为最优模型,输入层因子权重从大到小依次为SST、SSS、Month、PT95、Lon、Lat和Chl-a。研究表明,最优BP神经网络模型能较好地预测CPUE时空变化趋势,可以尝试用来作为阿根廷滑柔鱼CPUE标准化的新方法。  相似文献   

7.
根据 2012—2018 年山东省海洋捕捞渔业信息船的生产统计数据, 分析了黄渤海蓝点马鲛(Scomberomorus niphonius)双船拖网渔业名义单位捕捞努力量渔获量(CPUE)和渔场重心的时空变化, 结合表层水温等海洋环境因子, 应用广义可加模型(GAM)分析了蓝点马鲛双船拖网渔业名义 CPUE 与时空及环境因子的关系, 并对 CPUE 进行标准化。结果表明, 蓝点马鲛名义 CPUE 和渔场重心存在明显的年际和月变化; 名义 CPUE 在 2012—2018 年间呈波动下降趋势, 每年在 9—11 月出现最大值。渔场重心的月变化呈现从东南到西北再转向东南的趋势, 这与蓝点马鲛的洄游规律相一致。渔场重心呈现一定的年变化, 其经度变化与水温变化趋势相反, 纬度变化与水温变化趋势相同。GAM 模型分析表明, 年份、表层水温、经度和纬度对名义 CPUE 有极显著影响, 其中年份对名义 CPUE 影响最显著, 名义 CPUE 随着表层水温升高呈上升趋势。标准化 CPUE 与名义 CPUE 具有相似变化趋势, 但标准化 CPUE 波动幅度较小, 且其值均小于名义 CPUE。  相似文献   

8.
1999—2011年东、黄海鲐资源丰度年间变化分析   总被引:4,自引:1,他引:3  
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。  相似文献   

9.
蓝圆鲹(Decapterus maruadsi)是中国东南近海重要的经济鱼类之一。本研究根据2012—2018年南海西北部捕捞产量数据和海洋环境遥感数据,分析了该海域蓝圆鲹季节平均单位捕捞努力量渔获量(catch per unit effort, CPUE)的时空分布特征,并运用广义可加模型(generalized additive model, GAM)探究了CPUE与环境因子的关系。结果显示,蓝圆鲹的CPUE具有明显的季节性:夏季最高,CPUE达0.848 kg/(kW?d);冬季最低,CPUE为0.087 kg/(kW?d)。2016年CPUE的异常增加可能是受到2015—2016年超强厄尔尼诺事件的影响。GAM分析显示,该海域蓝圆鲹CPUE与经度、海表温度(sea surface temperature, SST)、叶绿素a (chlorophyll a, Chl-a) 浓度、海水深度、海表盐度(sea surface salinity, SSS)、涌浪波向、风浪波向及其周期显著相关。相对较高CPUE海域范围为110.5°~114°E,SST为26~30℃,Chl-a为0.2~1.0 mg/m3,海水深度<120 m,SSS为33.4~33.8,涌浪波向为75°~120°、150°~175°,风浪波向为50°~75°、120°~135°、175°~190°,风浪周期为3.0~4.5 s;其中,风浪波向对CPUE贡献最高,涌浪波向其次,然后是SST。南海西北部蓝圆鲹的资源丰度变化和其洄游特性与季风变化等引起的环境因子的变动有关。  相似文献   

10.
根据 2 0 0 2年 1~ 6月中国大陆地区鱿钓渔船在西南大西洋的生产统计及其海表水温数据 ,按 1°× 1°的格式进行统计分析 ,并用MarineExplore 4 .0软件进行作图 ,对阿根廷滑柔鱼渔场、CPUE分布及其与表温的关系进行了分析。结果表明 ,1~ 3月份中心渔场集中在 4 5°S、6 0°W一带 ,4~ 6月份作业渔场集中在 4 2°S、5 7°W附近。各月产量和CPUE有很大变动 ,其中 1月份产量最高 ,3月份开始下降 ,6月份达到渔汛的末期。全年产量较高海区的表温为 12~ 15℃ ,各月份的最适渔获表温不同 ,1月为 14~ 15℃ ,2月为 13~ 15℃ ,3月为 12~ 14℃ ,4月为 9~ 13℃ ,5月为 8~ 10℃ ,6月为 7~ 9℃ ,并且每月的作业水温逐渐降低 ,平均每月下降约 1℃。经K S检验 ,结果表明 ,各月表温和CPUE的差异均不显著。  相似文献   

11.
The selection of spatial scales is of particular importance in modeling relationships between fishery abundance and its influencing factors, because these relationships are significantly affected by spatial scale. Here, we explore the spatial scale effects of catch per unit effort (CPUE)–factor relationships for Ommastrephes bartramii in the northwest Pacific. The original commercial fishery data and oceanographic factors were tessellated to 12 spatial scales from 5′ to 60′ with an interval of 5′. Under the original scale and 12 tessellated scales, we constructed the generalized additive models (GAMs) to model the relationships between the O. bartramii CPUE and the influencing factors, including Year, Month, Latitude (Lat), Longitude (Lon), sea surface salinity (SSS), sea surface temperature (SST), sea surface chlorophyll‐a (Chl‐a) concentration, and sea surface height (SSH). Our multi‐scale analysis showed that the relationships are sensitive to spatial scales. Among the factors, Year, Month, and SSS share quadratic polynomial scaling relations; Lat, SST, and Chl‐a illustrate power law scaling relations; Lon has a linear scaling relation; and SSH presents an exponential scaling relation. Considering the scale sensitivity of the factor sort‐order and the accumulation of explained residual deviance in GAM, we suggest 30′45′ as the optimal range of spatial scales for analyzing the CPUE–factor relationships for O. bartramii. Our research improves understanding of the impacts of changing scales in fisheries and provides a potential method for the selection of a suitable spatial scale for fisheries analysis and resource surveying.  相似文献   

12.
根据1998—2013年中西太平洋鲣(Katsuwonus pelamis)生产数据,选取时空因子(年、月、经纬度)和环境因子[海表面温度(SST)、海表面高度(SSH)、尼诺指数(ONI)和叶绿素a浓度]Chl-a)],通过两种不同的模型(广义加性模型GAM和提升回归树模型BRT)研究各因子对鲣资源丰度(以CPUE表示)的影响。研究结果认为,GAM模型中,经度对CPUE的影响最大,累计解释偏差超过50%,其次为纬度、年和月;在环境因子中,SSH最为重要,其次为ONI,而SST和Chl-a的影响相对较低。BRT模型分析结果与GAM分析结果类似,时空因子相对占据了重要的地位,其中经度的影响最大,其次为年、纬度和月;而在环境因子中,ONI的重要性相对更高,其次为SSH,SST和Chl-a同样影响较低。研究认为,两种模型均能较好地反映出因子对CPUE的影响。由于厄尔尼诺/拉尼娜现象引起的海洋环境变化会使鲣资源分布产生差异,因此在后续的渔情预报研究中,应该更多地考虑将ONI因子纳入渔情预报模型中,以提高预测精度。  相似文献   

13.
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.  相似文献   

14.
基于空间相关性的西北太平洋柔鱼CPUE标准化研究   总被引:6,自引:1,他引:5  
徐洁  官文江  陈新军 《水产学报》2015,39(5):754-760
CPUE标准化方法通常都假设名义CPUE之间是相互独立且没有相关性,然而鱼类集群分布通常存在着空间相关性,为此本研究以西北太平洋柔鱼的CPUE标准化为例,采用1999-2012年6-11月中国鱿钓生产数据以及对应的海表面温度和叶绿素浓度的环境数据,将空间相关性加入广义线性模型(general linear model,GLM)中.在空间GLM模型中运用4个距离模型(指数模型、球面模型、线性模型和高斯模型),进行标准GLM模型和4种空间GLM模型的CPUE标准化结果比较.结果发现,4种空间GLM模型均比标准GLM模型的最小信息准则(akaike information criterion,AIC)更小,标准化结果更准确.同时,在4个距离模型中,指数模型的AIC值最小,其CPUE标准化结果最佳.研究表明,在CPUE标准化中,鉴于鱼类集群与分布特性,应该充分考虑空间相关性这一因素.  相似文献   

15.
Longline surveys have been conducted in the Northwest Pacific Ocean from 2000 to 2014 using chartered commercial longline vessels. Each year, two cruises were conducted offshore of northeastern Japan from mid‐April to mid‐June. For each longline set during the surveys, onboard scientists collected detailed biological information about the species caught, such as the size and sex, and recorded the catch numbers for all species. Blue shark (Prionace glauca) and shortfin mako (Isurus oxyrinchus) have eurythermal distributions, but the application of a generalized additive model (GAM) showed that the sea surface temperatures (SSTs) at catch sites positive for shortfin mako were warmer than those for blue shark. On the basis of the GAM, the probabilities of occurrence of both sharks differed by size category: small sharks had a narrower SST range than that of large sharks. Most catches of both sharks were juveniles, and the nominal catch rate of blue shark was more than 10 times that of shortfin mako. The standardized catch per unit effort (CPUE) for both species was calculated using a generalized linear model (GLM) with negative binomial errors, or a delta‐lognormal GLM. The standardized CPUE for blue shark in the second quarter of the year peaked in the mid‐2000s and then decreased, but it has been increasing since 2012. The CPUE for shortfin mako in the second quarter generally increased, with fluctuations.  相似文献   

16.
In this study, catch and effort data of southern bluefin tuna (SBT) from Taiwan longliners operating in the Central Indian Ocean (CIO) during 1982 to 2003 were compiled and their catch per unit effort (CPUE) was standardized using the generalized linear model (GLM). The GLM includes factors such as year, season, by-catch, latitude, sea surface temperature (SST) and the interactive effects among factors. The standardized CPUE and its relationship with SST fluctuation were then analyzed to understand the effects of fishing ground SST variations on CPUE of SBT, as well as their connection to El Niño-Southern Oscillation (ENSO) events. The standardized CPUE in the CIO seemed to oscillate with the sea surface temperature anomalies (SSTA) between 30 and 50°S where SSTA fluctuations were prolonged and slower than the ENSO cycle. It is then very likely that fishing conditions at the CIO fishing ground were influenced by the expansion of the cold water mass from the Southern Ocean, and the colder SST is beneficial to increasing SBT catch rate.  相似文献   

17.
Generalized additive models (GAMs) were applied to examine the relative influence of various factors on fishery performance, defined as nominal catch- per-unit-effort (CPUE) of swordfish (Xiphias gladius) and blue shark (Prionace glauca) in the Hawaii-based swordfish fishery. Commercial fisheries data for the analysis consisted of a 5 year (1991–1995) time series of 27 901 longline sets. Mesoscale relationships were analysed for seven physical variables (latitude, longitude, SST, SST frontal energy, temporal changes in SST (ΔSST), SST frontal energy (ΔSST frontal energy) and bathymetry), all of which may affect the availability of swordfish and blue shark to the fishery, and three variables (number of lightsticks per hook, lunar index, and wind velocity) which may relate to the effectiveness of the fishing gear. Longline CPUE data were analysed in relation to SST data on three spatiotemporal scales (18 km weekly, 1°-weekly, 1°-monthly). Depending on the scale of SST data, GAM analysis accounted for 39–42% and 44–45% of the variance in nominal CPUE for swordfish and blue shark, respectively. Stepwise GAM building revealed the relative importance of the variables in explaining the variance in CPUE. For swordfish, by decreasing importance, the variables ranked: (1) latitude, (2) time, (3) longitude, (4) lunar index, (5) lightsticks per hook, (6) SST, (7) ΔSST frontal energy, (8) wind velocity, (9) SST frontal energy, (10) bathymetry, and (11) ΔSST. For blue shark, the variables ranked: (1) latitude, (2) longitude, (3) time, (4) SST, (5) lightsticks per hook, (6) ΔSST, (7) ΔSST frontal energy, (8) SST frontal energy, (9) wind velocity, (10) lunar index, and (11) bathymetry. Swordfish CPUE increased with latitude to peak at 35–40°N and increased in the vicinity of temperature fronts and during the full moon. Shark CPUE also increased with latitude up to 40°N, and increased westward, but declined abruptly at SSTs colder than 16°C. As a comparison with modelling fishery performance in relation to specific environmental and fishery operational effects, fishery performance was also modelled as a function of categorical time (month) and area (2° squares) variables using a generalized linear model (GLM) approach. The variance accounted for by the GLMs was ≈ 1–3% lower than the variance explained by the GAMs. Time series of swordfish and blue shark CPUE standardized for the environmental and operational variables quantified in the GAM and for the time-area effects in the GLM are presented. For swordfish, both nominal and standardized time series indicate a decline in CPUE, whereas the opposite trend was seen for blue shark.  相似文献   

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