基于灰色系统西南大西洋阿根廷滑柔鱼资源丰度预测模型的构建

阿根廷滑柔鱼（Illex argentinus）是西南大西洋鱿钓渔业的主要作业鱼种，对资源丰度进行准确的预测可指导企业合理安排渔业生产。因此，本研究根据2000-2016年我国西南大西洋阿根廷滑柔鱼的生产数据，以单位捕捞努力量的渔获量（Catch per unit effort， CPUE）为阿根廷滑柔鱼资源丰度的指标，利用灰色绝对关联分析和灰色预测建模的方法（GM（0， N）），计算2001-2015年CPUE的时间序列值与产卵期（6-8月）产卵场海表面温度（Sea surface temperature， SST）时间序列值的灰色绝对关联度，选取产卵场海域中灰色绝对关联度大于0.90的海区SST建立资源丰度预测模型，并用2016年实际CPUE进行验证。灰色绝对关联分析表明，6-8月，30°~40°S，45°~60°W海域内存在若干海区的SST与次年对数CPUE时间序列呈现较强的关联度，可作为预报因子。GM（0， N）模型结果表明，以6-8月产卵场SST作为环境因子建立的模型4能较好地拟合出阿根廷滑柔鱼资源丰度变动趋势，与2016年真实值相比，相对误差为7%，该模型可较好地作为阿根廷滑柔鱼资源丰度的预测模型。相反，包含6月和7月SST的模型1效果优于不包含6月SST的模型2或不包含7月SST的模型3，拟合得到的2016年的数据与真实值相比，相对误差分别为128%和289%，这说明6月和7月是西南大西洋阿根廷滑柔鱼的主要产卵月份。     

利用基于环境因子的动态产量模型评估西南大西洋阿根廷滑柔鱼

西南大西洋阿根廷滑柔鱼，Illex argentinus，巴塔哥尼亚南部群体是重要的经济种类。海洋环境因子在柔鱼资源分布中起着重要的作用。本研究利用基于环境因子的动态产量模型评估2000-2010年的滑柔鱼的资源量。假设海洋环境因子（滑柔鱼产卵场最适宜海表温度占比）影响动态产量模型的参数K，DIC值表明在正态分布和均匀分布下均是基于环境因子的评估模型优于基本的动态产量模型。阿根廷滑柔鱼的最大可持续产量（MSY）在351600吨到685 100吨之间，资源生物量在1322400吨到1 803 000吨之间，其捕捞死亡系数均小于F_0.1和F_MSY，资源处在良好状态，没有遭受过度捕捞。本研究为应用环境因子在柔鱼类的资源评估与管理提中供了科学的参考方法。     

基于GAM模型西北印度洋鸢乌贼CPUE标准化

单位捕捞努力量渔获量(Catch Per Unit Effort,CPUE)是资源评估的前提和基础,为了更好地评估西北印度洋鸢乌贼资源,采用广义加性模型(generalized additive model,GAM)对2016~2020年西北印度洋鸢乌贼的CPUE进行了标准化。结果显示,月份、海表温度(sea surface temperature,SST)、海面高度(sea surface height,SSH)、经度和纬度对CPUE呈显著性影响,通过对不同GAM模型的AIC(Akaike information criterion)值比较,由月份、SST、SSH、经度和纬度5个因子构成的GAM模型为最优CPUE标准化模型,对CPUE偏差的解释率为40.3%。研究表明,西北印度洋鸢乌贼高CPUE主要出现在9月至翌年3月,海域范围为16°~19°N、60°~65°E,SST为25~28℃、SSH为0.2~0.4m的海域内。整体而言,标准化CPUE低于名义CPUE,但二者的变化趋势基本一致。     

利用BP神经网络标准化东南太平洋茎柔鱼(Dosidicus gigas)资源丰度

东南太平洋茎柔鱼(Dosidicus gigas)是短生命周期大洋性经济鱼类,其资源量受环境因素变化的影响较大。根据我国鱿钓船队2013~2017年在东南太平洋的生产统计数据,结合海洋环境数据包括海表面温度(SST)、海表面盐度(SSS)、叶绿素a浓度(chl a),运用BP神经网络(back propagation network)模型来标准化单位捕捞努力量渔获量(catch per unit effort, CPUE,也称名义CPUE)。以均方误差(mean square errors, MSE)和平均相对变动值(average relative variances, ARV)为最优模型判断依据,比较隐含层节点3-10的神经网络模型,发现6-9-1结构为最优模型。用Garson算法解释模型结果,发现各输入层因子对东南太平洋茎柔鱼资源丰度影响重要度排序为chl a、SST、经度(Lon)、SSS、纬度(Lat)、月份(Month)。并作名义CPUE和标准化CPUE资源丰度对比分布图,结果显示CPUE与标准化CPUE总体分布状况基本一致,但局部区域存在明显差异, 80°~85°W及10°~20°S海域适宜鱿钓生产,表明BP神经网络模型可以适用于东南太平洋茎柔鱼的CPUE标准化,从而为鱿钓渔业生产提供一定参考依据。     

基于不同权重的栖息地指数模型预报阿根廷滑柔鱼中心渔场

本文根据2003-2009年1-5月和2011年1-5月西南大西洋海域阿根廷滑柔鱼(Illex argentinus)的生产数据,结合遥感获得的海表面温度(SST)和海表面高度(SSH)数据,利用不同权重的栖息地指数模型来预报阿根廷滑柔鱼的中心渔场。采用外包络法,利用作业次数与SST、SSH建立适应性指数(SI)模型,依据作业次数比重和产量比重来比较不同权重的算术加权模型(AWM),从而筛选出最佳模型,并对最佳模型进行验证。结果显示,确定AWM(a=0.3,SST权重为0.3,SSH的权重为0.7)为最佳模型,当栖息地适应性指数(HSI)大于0.6时,作业次数的比重为93.23%,产量比重为89.28%,当HSI小于0.4时,作业次数的比重为2.12%,产量比重为3.35%。利用2011年1-5月的生产数据和环境数据对AWM(a=0.3)进行验证,结果显示,在HSI大于0.6的海域,各月作业次数比重均在91%以上,产量比重均在95%以上。研究表明,在阿根廷滑柔鱼渔场形成中SSH比SST更为重要,基于SST和SSH的AWM(a=0.3)能够较好地预测西南大西洋阿根廷滑柔鱼的中心渔场。     

基于产卵场环境因子的阿根廷滑柔鱼资源补充量预报模型研究

西南大西洋阿根廷滑柔鱼Illex argentinus是短生命周期种类,其资源量极易受到海洋环境变化的影响。根据2003—2011年我国鱿钓船队在西南大西洋的生产统计数据,以及产卵场海洋表面温度(SST)、海表温度距平值(SSTA),计算分析了阿根廷滑柔鱼在产卵期产卵场各月最适表层水温范围占总面积的比例(用PS表示)以及表征海流强度的SST、SSTA等多种环境变量因子与单位捕捞量渔获量(CPUE)的相关性,建立多种基于主要环境因子的资源补充量预报模型,同时分析比较预报模型的优劣。相关性分析表明:6月份有3片连续区域的SST与CPUE之间存在强相关性,分别为38°~39°S、54°~55°W,40.5°~41.5°S、51°~52°W,39.9°~40.4°S、42.6°~43.1°W。利用6月份此3片连续区域SST与次年CPUE建立的三元线性模型,模型符合统计检验,偏差解释率为82.4%。在此基础上加入7月份PS影响因子建立3种方案下的误差反向传播(EBP)神经网络模型。结果认为,包含了福克兰寒流与巴西暖流表温信息的方案3模型优于其他两种模型,其准确率可以达到90%以上。     

基于产卵场海表温度的阿根廷滑柔鱼资源丰度预测模型研究

阿根廷滑柔鱼(Illex argentinus)为短生命周期种，其资源丰度易受海洋环境变化的影响，尤其是在产卵场的早期生活史阶段。根据2003?2016年我国鱿钓船队在西南大西洋的生产统计数据，以及产卵场海洋表面温度(SST)卫星遥感数据，用相关性分析方法筛选出阿根廷滑柔鱼产卵旺季期间(6月份)表征产卵场SST变化的特征海域；基于阿根廷滑柔鱼产卵场最适SST范围占总面积之比(Ps)与资源丰度单位捕捞努力渔获量(CPUE，t/船)呈正相关性的假设，回溯阿根廷滑柔鱼最适的产卵场及水温环境条件，并据此建立多种基于表征产卵场SST环境因子的资源丰度多元线性预测模型。相关性分析结果表明:6月份有两片连续海域(Area 1、Area 2)的SST与CPUE之间存在显著相关性，分别为42.5°～44°S、57.5°～59°W(Area 1)和39°～39.5°S、45°～46°W(Area 2)；回溯的阿根廷滑柔鱼产卵场范围为37.5°～44°S、41.5°～51.5°W，产卵场最适SST范围为16～17.5℃。利用2个特征海域(Area 1、Area 2)SST以及回溯的产卵场Ps建立4种的多元线性资源丰度指数(ICPUE)预测模型，结果表明，包含表征寒暖流的特征海域和回溯产卵场Ps的方案4模型优于其他3种模型，其资源丰度指数预测模型为ICPUE=1.390 4×Ps+0.261 9×SST_{Area 1}+0.096 2×SST_{Area 2}?3.248 0。     

基于深度卷积嵌入式聚类(DCEC)的海洋环境特征提取对渔情预报模型的改进研究——以西南印度洋大眼金枪鱼为例

为提高大眼金枪鱼(Thunnus obesus)延绳钓渔情预报模型的预测能力,本研究提出了一种基于深度卷积嵌入式聚类(DCEC)的海洋环境时空特征提取方法,结合广义可加模型(GAM)对西南印度洋大眼金枪鱼延绳钓渔场进行预报。采用2018年1-12月0.041 6°×0.041 6°的MODIS-Aqua和MODISTerra海表面温度三级反演图像数据(以日为单位)构建DCEC模型,基于Davies-Bouldi指数(DBI)确定最佳聚类数,在此基础上提取各月海表温度(SST)的类别特征值F M;采用美国国家海洋和大气管理局网站2018年1-12月1°×1°的Chl a浓度月平均值作为辅助环境特征因子;采用印度洋金枪鱼委员会2018年1-12月1°×1°的大眼金枪鱼延绳钓渔业数据(以月为单位),计算单位捕捞努力量渔获量(CPUE);将SST月类别特征值F M、Chl a浓度月平均值与CPUE数据进行时空匹配,构建改进GAM;采用SST月平均值、Chl a浓度月平均值与CPUE数据构建基础GAM;采用联合假设检验(F检验)验证模型解释变量对响应变量的影响;采用赤池信息准则(AIC)、均方误差(MSE)、绘制实测值和预测值的散点图并计算相关系数r,分析改进GAM相比于基础GAM的提升效果。实验结果表明:(1)基于DCEC模型提取的F M能够较好地反映西南印度洋海表温度的时空动态特征与规律,并与西南印度洋的气候条件、季风状况和水文特征等相互耦合;(2) F M相比SST平均值的因子解释率更高,对大眼金枪鱼CPUE影响更为显著,高渔获率集中在暖冷流交汇区域;(3)改进GAM相比基础GAM的AIC值降低了9.17%,MSE降低了26.7%,散点图显示改进GAM预测的CPUE对数值与实测CPUE对数值的相关性较显著,r为0.60。本研究证明了DCEC模型在海洋环境特征提取方面的有效性,可为后序大眼金枪鱼延绳钓渔情预报模型的改进研究提供参考。     

东太平洋赤道海域茎柔鱼(Dosidicus gigas)小型群体资源分布及其渔场环境特征

茎柔鱼(Dosidicus gigas)是我国远洋渔业的重要捕捞对象。当前针对茎柔鱼渔场分布及其与环境关系的研究多集中于秘鲁海域,而在赤道海域则研究较少。根据2019年12月至2020年2月在赤道海域获取的茎柔鱼生物学数据,以及同期的渔业生产和环境数据,运用胴长-体重关系拟合、Arcgis地统计插值、广义可加模型(GAM)探究其资源分布及渔场环境状况。结果表明:东太平洋赤道海域茎柔鱼胴长范围为136~407 mm,体重范围为117~1 557 g; 2019年12月~2020年4月各月渔获量呈先增加后减小趋势, 2月渔获量最高; CPUE (单位捕捞努力量渔获量)除2月增加外,总体呈下降趋势;渔场集中分布于0°~3°S、105°~114°W海域,不同月份渔场中心经向变化明显;渔场最适SST范围是24.5~25.5 ℃,最适chl a范围是0.16~0.20 mg/m³,月份是影响茎柔鱼CPUE的主要因子。进一步分析表明:该海域茎柔鱼渔获主要为小型群体;小型群体生长发育期(2~3月)对渔场分布有重要影响,生长发育期前茎柔鱼集群度高,生长发育期后逐渐分散活动;单一影响因子与茎柔鱼CPUE相关性不显著,综合考虑其他环境因素及其交互影响是今后的研究方向。     

基于灰色系统的西北太平洋柔鱼冬春生群资源丰度预测模型

柔鱼（Ommastrephes bartramii）是西北太平洋重要的经济头足类之一,科学预测柔鱼资源丰度有利于其合理的开发和利用。研究结合1998-2008年北太平洋柔鱼生产统计数据和产卵场环境及其气候因子,使用灰色关联分析和灰色预测建模的方法,对产卵期内（1-4月）影响柔鱼冬春生群体资源丰度（CPUE）的产卵场环境以及气候指标进行分析,并建立柔鱼冬春生群体资源丰度的预报模型。结果表明,产卵期内影响柔鱼冬春生群体资源丰度的因子依次是:3月份产卵场平均海表面温度SST（average sea surface temperature）、1月份太平洋年代际震荡指数PDO（Pacific Decadal Oscillatio index）,4月份Niño3.4指标和4月份平均叶绿素浓度Chl a（average chlorophyll a concentration）。灰色预报模型分析表明,基于3月份SST、1月份PDO和4月份Chl a的GM（1,4）模型有着较好的预测效果,其预测准确率在80%以上,可用于西北太平洋柔鱼冬春群体资源丰度的预测。     

Projecting distributions of Argentine shortfin squid(Illex argentinus) in the Southwest Atlantic using a complex integrated model

We developed an approach that integrates generalized additive model(GAM) and neural network model(NNM)for projecting the distribution of Argentine shortfin squid(Illex argentinus). The data for this paper was based on commercial fishery data and relevant remote sensing environmental data including sea surface temperature(SST), sea surface height(SSH) and chlorophyll a(Chl a) from January to June during 2003 to 2011. The GAM was used to identify the significant oceanographic variables and establish their relationships with the fishery catch per unit effort(CPUE). The NNM with the GAM identified significant variables as input vectors was used for predicting spatial distribution of CPUE. The GAM was found to explain 53.8% variances for CPUE. The spatial variables(longitude and latitude) and environmental variables(SST, SSH and Chl a) were significant. The CPUE had nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to be effective and robust in the projection with low mean square errors(MSE) and average relative variances(ARV).The integrated approach can predict the spatial distribution and explain the migration pattern of Illex argentinus in the Southwest Atlantic Ocean.     

太平洋褶柔鱼冬生群资源丰度变动对ENSO事件的响应研究

太平洋褶柔鱼为大洋性经济鱼种，具有一年生命周期，其资源变动受气候和海洋环境条件的显著影响。本研究根据日本提供的2003-2012年太平洋褶柔鱼冬生群体的渔业统计数据，结合产卵场环境数据以及尼诺指数ONI（定义为Niño 3.4区海表温度距平值），分析不同气候条件下（厄尔尼诺和拉尼娜）太平洋褶柔鱼冬生群体产卵场海表温度（SST）、叶绿素a（Chl-a）浓度以及适宜产卵面积（SSA）的变动情况及对其资源丰度（CPUE）的影响。结果表明，太平洋褶柔鱼冬生群体产卵场SST、Chl-a浓度和SSA具有明显的季节性变化。相关分析表明，各年CPUE与Chl-a浓度以及SSA具有显著的正相关关系（p<0.05），但与SST相关性不显著（p>0.05）。此外，厄尔尼诺和拉尼娜事件通过驱动太平洋褶柔鱼冬生群体产卵场SSA和关键海域（25°-29°N，122.5°-130.5°E）内的Chl-a空间分布和大小变化，从而改变其资源丰度，但影响作用随各异常事件的强度不同而变化，具体表现为：发生弱强度厄尔尼诺事件时，产卵场SSA较高，Chl-a浓度处于较低水平，导致资源补充量处于较低水平，CPUE降低；发生中等强度厄尔尼诺事件时，产卵场SSA较低，但Chl-a浓度处于较高水平，导致资源补充量增加，CPUE处于上升水平；发生中等强度拉尼娜事件时，产卵场SSA和Chl-a浓度均处于较高水平，资源补充量显著增加，CPUE显著升高。研究表明，厄尔尼诺和拉尼娜事件对太平洋褶柔鱼冬生群体产卵场摄食孵化环境和资源丰度变动具有显著影响。     

一套具有更高时空分辨率的全球海洋叶绿素浓度遥感融合产品数据集

认识海洋在全球碳循环中的作用及其对环境变化的响应，需要高时空分辨率的观测数据。由于轨道宽度、云雨天气、太阳耀斑等的影响，单一的水色传感器的观测能力十分有限，将多源海洋水色卫星进行融合是提高水色数据时空覆盖的一种有效途径。SeaWiFS和MERIS分别于2010年12月11日和2012年5月9日停止运行，在很大程度上降低了水色融合产品时空覆盖的提升。我们在融合过程中加入了FY-3 MERSI数据，生成了全球海洋叶绿素浓度遥感融合产品数据集。数据源包括SeaWiFS、MERIS、MODIS-Aqua、VIIRS和MERSI。结果表明：加入MERSI后，融合产品的日平均有效空间覆盖提高了9%；采样频率（同一区域一年中获取有效数据的次数）由57天/年提高到109天/年。利用实测数据和国外同类融合产品（ESA GlobColour和NASA MEaSUREs）对新的数据集进行了质量评价。与实测数据相比，加入MERSI的融合产品精度与未加入MERSI的融合产品基本一致；与国外同类融合产品的偏差小于10%。新数据集的时间序列特性与未加入MERSI的融合产品以及单传感器的一致。     

Time series changes in sea-surface temperature,chlorophyll <Emphasis Type="Italic">a</Emphasis>, nutrients,and sea-wind in the East/Japan Sea on the left- and right-hand sides of typhoon shanshan’s track

Time series changes in sea surface temperature (SST), chlorophyll a (Chl a), nutrients (PO₄, NO₃), and sea winds, which correlated with the passage of Typhoon Shanshan in the East/Japan Sea (EJS), are illustrated using satellite data for Chl a, SST, sea winds, and in situ data for nutrients and water temperature. The sea-surface cooling (SSC) effect by the passage of the typhoon was higher at stations nearer to the center compared to stations further from the center. The SSC effect at stations in the colder water region (on the left side of the typhoon’s track) was higher than at stations in the Tsushima Warm Current region (on the right side of the typhoon). The SSC effect continued for approximately 10 days after the passage of the typhoon. The Chl a concentration at all stations increased after the passage of the typhoon. This increase continued for a period of approximately 10 days, but the duration period at each station varied with distance from the typhoon center. Changes in Chl concentrations at stations within a 2° distance on both sides from the typhoon’s center were higher than that at other stations. The changes in Chl a by the passage of the typhoon were measured at approximately 0.3–1.0 mg/m³ along the moving path of the typhoon. Phosphate and nitrate changes were inversely correlated with the water temperature changes; the nutrient concentration increased with the passage of the typhoon. Like the changes in SST, changes in nutrient concentrations on the left side of the typhoon’s track were higher compared to those at the center and the right side.     

水温增加对西北太平洋柔鱼潜在栖息地的影响

In the Northwest Pacific Ocean, the squid jigging fisheries from China, Japan and other countries and regions have targeted the west winter-spring cohort of neon flying squid(Ommastrephes bartramii) from August to November since the 1970 s. This squid is a short-lived ecological opportunist with a life-span of about one year,and its population is labile and recruitment variability is driven by the environment or climate change. This variability provides a challenge for ones to forecast the key habitats affected by climate change. The catch data of O. bartramii from Chinese squid jigging fishery and the satellite-derived sea surface temperature(SST) data are used in the Northwest Pacific Ocean from August to November of 1998 to 2004, the SST preferences of O.bartramii corresponding to high values of catch per fishing day(CPUE) are determined and monthly potential habitats are predicted using a histogram analysis of the SST data. The possible changes in the potential habitats of O. bartramii in the Northwest Pacific Ocean are estimated under four climate change scenarios based on the Fourth Assessment Report(AR4) of the Intergovernmental Panel on Climate Change, i.e., 0.5, 1, 2 and 4°C increases in the SST because of the climate change. The results reveal an obvious poleward shift of the potential habitats of O. bartramii in the Northwest Pacific Ocean.     

基于深度卷积嵌入式聚类（DCEC）的海洋环境特征提取对渔情预报模型的改进研究

In order to improve the forecasting ability of the fishery forecast model for the longline bigeye tuna (Thunnus obesus), we proposed a marine environment feature extraction method based on deep convolutional embedded clustering (DCEC), combined with generalized additive model (GAM) for forecasting the longline bigeye tuna fishing grounds in the Southwest Indian Ocean. We used the MODIS-Aqua and MODIS-Terra sea surface temperature (SST) three-level inversion image data (in days) from January to December in 2018 at 0.041 6°×0.041 6° to construct a DCEC model, determined the optimal number of clusters based on the Davies-Bouldi index (DBI), and extracted the category feature value (F_M) of each month’s sea surface temperature (SST); we used monthly 1°×1° bigeye tuna longline fishery data from January to December in 2018 generated from the Indian Ocean Tuna Commission (IOTC), and calculated the catch per unit effort (CPUE); we matched the monthly category feature value F_M and the monthly average value of Chl a concentration with the CPUE data to construct an improved GAM; we matched the monthly average SST, the monthly average Chl a concentration and CPUE data to build a basic GAM; we used the joint hypothesis test (F test) to verify the influence of model explanatory variables; we used akaike information criterion (AIC), mean square error (MSE), and draw the frequency distribution diagrams and box diagrams of measured and predicted values, etc., to analysis the improvement effect of the improved GAM compared to the basic GAM. The results showed that: (1) the category feature value (F_M) extracted based on the DCEC model could better reflect the temporal and spatial dynamic characteristics of SST in the Southwest Indian Ocean, and was related with the climatic conditions, monsoon conditions, and hydrological characteristics in the Southwest Indian Ocean; (2) the factor interpretation of F_M was higher than that of the monthly average SST in GAM, which means F_M had more significant impact on the CPUE of bigeye tuna. The high catch rate was concentrated in the areas where the F_M category was 2, 10, 24 with intersections between the warm and cold currents; (3) the AIC of the improved GAM was reduced by 9.17% than that of the basic GAM and MSE of the improved GAM was reduced by 26.7% than that of the basic GAM; the frequency distribution of the CPUE logarithmic value predicted by the improved GAM was closer to the normal distribution, and the high frequency distribution interval was closer to that of the measured value; the scatter plot showed that the CPUE predicted by the improved GAM had a significant correlation with the measured CPUE, with r equaled to 0.60. This study proves the effectiveness of the DCEC model in extracting marine environmental features, and can provide a reference for the further study on the bigeye tuna fishery forecast.     

南海北部冬、夏季浮游群落呼吸率

Plankton respiration is an important part of the carbon cycle and significantly affects the balance of autotrophic assimilation and heterotrophic production in oceanic ecosystems. In the present study, respiration rates of the euphotic zone plankton community(CR_(eu)), size fractionated chlorophyll a concentration(Chl a), bacterial abundance(BAC), and dissolved oxygen concentration(DO) were investigated during winter and summer in the northern South China Sea(n SCS). The results show that there were obvious spatial and temporal variations in CR_(eu) in the n SCS(ranging from 0.03 to 1.10 μmol/(L·h)), CR_(eu) in winter((0.53±0.27) μmol/(L·h)) was two times higher than that in summer((0.26±0.20) μmol/(L·h)), and decreased gradually from the coastal zone to the open sea. The distribution of CR_(eu) was affected by coupled physical-chemical-biological processes, driven by monsoon events. The results also show that CR_(eu) was positively correlated with Chl a, BAC, and DO, and that BAC contributed the highest CR_(eu) variability. Furthermore, the results of the stepwise multiple linear regression suggest that bacteria and phytoplankton were the dominant factors in determining CR_(eu)(R~2 = 0.82, p0.05) in the n SCS. Based on this relationship, we estimated the integrated water column respiration rate(CRint) within 100 m of the investigated area, and found that the relationship between the biomass of the plankton community and respiration may be nonlinear in the water column.     

桑沟湾养殖生态系统浮游植物的粒径结构及浮游生物对栉孔扇贝碳需求的贡献

The biomass and size fraction of phytoplankton in terms of chlorophyll a(Chl a) was measured during four cruises conducted in April, July, October 2013 and January 2014 in mariculture area, the Sanggou Bay, China.Results show that total Chl a levels in the surface seawater of the Sanggou Bay generally range from 0.10 to 20.46μg/L, with an average value of 2.13 μg/L. Nano-phytoplankton was the most important size-fraction and accounted for about 65.1% of total Chl a. In order to evaluate the importance of the "protozoan trophic link" for energy transfer from the microbial loop to filter-feeding feeders, Zhikong scallop Chlamys farreri was then offered a natural planktonic community as potential prey. Results show that scallops obtained carbon source from natural plankton with the rate of 11 033.05 μg/(g·d). Protists(nanoflagellates and ciliates) were the dominant source of carbon retained by scallop(48.78%). The microbial loop provided 58.45% of the carbon source for farmed scallops. These results indicate that the microbial loop represent a valuable trophic resource in mariculture system of the Sanggou Bay.