亚热带河流型水库浮游植物群落研究——以分水江水库为例

为了解亚热带河流型水库-分水江水库蓄水后的水体富营养化状况与浮游植物群落结构特征,于2006年1月至2007年12月,对其进行了20次采样调查。结果表明,分水江水库总氮浓度较高,平均值大于2mg／L,氮污染严重,磷污染较轻,水质总体评价较差,为劣V水体。分水江水库浮游植物群落组成以硅藻、隐藻和绿藻为主,密度为0．7×10^4-168310×10。个／L,以富营养种类为主。浮游植物组成随季节变化而有所不同,春季以直链藻（Melosira）、实球藻（Pandorina）、盘星藻（Pediastrum）为优势类群;夏季以微囊藻（Microcystis）、鱼腥藻（Ana-baena）、脆杆藻（Fragilaria）为优势类群;秋冬季以小环藻（Cyclotella）、针杆藻（Synedra）、蓝隐藻（Chroomonas）为主。分水江水库浮游植物密度、叶绿素a与降雨量均呈显著正相关,浮游植物的生长受水文气象条件变化的影响较大,间接说明分水江水库受面源污染比较严重。浮游植物密度和叶绿素a与水体理化因子也有较强的相关性,特别是水温、pH、溶解氧、高锰酸盐指数和总磷均与浮游植物呈显著正相关。     

亚热带河流型水库浮游植物群落研究———以分水江水库为例

为了了解亚热带河流型水库蓄水后的水体富营养化状况与浮游植物群落结构特征,于2006年1月至2007年12月,对其进行了20次采样调查。结果表明,分水江水库总氮浓度较高平均值大于2mg/L,氮污染严重,磷污染较轻,水质总体评价较差。分水江水库浮游植物密度范围在0.07×105 ~16831×105ind./L,群落组成以硅藻、隐藻和绿藻为主,在丰度上水库浮游植物主要以富营养指示种为主。浮游植物组成随季节变化而有不同,春季以直链藻（Melosira）、实球藻（Pandorina）、盘星藻（Pediastrum）为优势类群,夏季以微囊藻（Microcystis）、鱼腥藻（Anabaena）、脆杆藻(Fragilaria)为优势类群,秋冬季以小环藻（Cyclotella）、针杆藻（Synedra）、蓝隐藻（Chroomonas）为主。分水江水库浮游植物密度、叶绿素a与降雨量均呈显著正相关,浮游植物的生长受水文特征变化的影响较大,这也间接说明分水江水库受面源污染比较严重。浮游植物与理化因子也有较强的相关性,特别是水温、pH、溶解氧、高锰酸盐指数和总磷均与浮游植物呈显著正相关,但是总氮浓度较高且与浮游植物不相关,说明氮元素不是水库藻类生长的限制因子。     

亚热带水库浮游植物群落结构季节演替及其春季水华分析——以浙江汤浦水库为例

汤浦水库位于浙江省曹娥江支流的小舜江流域,是绍虞平原区域性供水专用水源地。为了研究其浮游植物群落结构季节演替及其水质状况,于2010年5、8、11月及次年2月连续四个季节分别对其浮游植物、水文特征和理化因子进行调查分析。共鉴定到浮游植物7门58属105种,主要由绿藻、硅藻和蓝藻组成。浮游植物物种数季节变化为：夏>秋>冬>春。各样点物种数呈现出由库尾至取水口递减的变化趋势。浮游植物丰度变幅较大,在3.27×106~128.90×106 Cells /L之间,其中冬季丰度最低,春季最高。春季浮游植物主要由硅藻和蓝藻组成,夏季主要由蓝藻和绿藻组成,秋季为隐藻和硅藻,冬季主要由硅藻组成。浮游植物丰度及其多样性指数均表明春季水质最差,且暴发藻类水华,其中微型硅藻链状弯壳藻(Achnanthidium catenatum)为春季水华的绝对优势种。此次水华暴发可能受降水、水库调度、营养盐等因素的综合影响所致。     

亚热带水库浮游植物群落结构季节演替及其春季水华成因分析——以浙江汤浦水库为例

为了研究汤浦水库浮游植物群落结构季节演替及水质状况,于2010年5、8、11月及2011年2月连续4个季节分别对其浮游植物、水文特征和理化因子进行调查分析.结果表明,浮游植物有7门、58属、105种,主要由绿藻、硅藻和蓝藻组成,其物种数季节变化为:夏＞秋＞冬＞春.各样点物种数呈现出由河流区向湖泊区递减的变化趋势.浮游植物丰度变幅较大,在3.27×106～128.90×106个/L,其中冬季丰度最低,春季最高.春季浮游植物主要由硅藻和蓝藻组成,夏季主要由蓝藻和绿藻组成,秋季为隐藻和硅藻,冬季主要由硅藻组成.浮游植物丰度及其多样性指数均表明春季水质最差,且暴发藻类水华,其中微型硅藻链状弯壳藻(Achnanthidium catenatum)为春季水华的绝对优势种.水华暴发可能受降水、水库调度、营养盐等因素的综合影响所致.     

信阳市大型水库开发利用中的生态环境问题及其成因分析--以南湾水库为例

信阳市的5座大型水库在区域国民经济和社会发展中发挥着日益重要的作用.在简要概括信阳市大型水库基本特征的基础上,以南湾水库为例,从库区及其周边区域两方面分析了水库经济开发活动对水库产生的生态破坏和环境污染问题,指出历史与政策因素、管理体制、领导干部政绩观念、资源占用与利益分配差异等是造成这些问题的深层次因素,提出理顺管理体制、统筹规划库区及其周边区域的发展战略、建立适当的生态补偿机制、多项措施并举解决乡镇环境污染和牛态破坏问题等措施.     

淡水水体气泡的声学信号特征与识别研究 ——以乐昌峡水库为例

淡水湖泊和水库底部沉积物产生的甲烷(CH_4)常以气泡方式从水中逸出,由于气泡目标强度(target strength, TS)分布范围与鱼类目标强度高度重合,导致信号误判而影响数据处理。2017年7月,使用分裂波束渔探仪Simrad EY60(120 kHz, 200 W)在北江水系乐昌峡水库进行声学探测,研究不同航速下的信号类别,为水下气体跟踪、温室气体释放通量、水体底质演变及渔业资源评估提供参考依据。结果显示,气泡(多数)和鱼类(少数)为乐昌峡水库主要声学散射体,与鱼类信号的随机性相比,气泡具有一定的规律性,一直上升且水平位移较小,气泡与鱼类在相对游泳速度、垂直方向变化、目标轨迹的垂直变化等变量存在显著差异(P0.01)。气泡在水中的平均上升速度为23.95 cm/s, 95%置信区间为23.17～24.74 cm/s;平均气泡目标强度为-60.09 dB,分布范围为-73.82～-33.29 dB,半径范围0.20～21.65 mm;气泡在上升过程中速度逐渐减慢,半径逐渐减小,与水深呈显著相关关系(r=0.99,P0.01)。船速3 km/h,回波图能清晰分辨气泡和鱼体信号,可作为目标运动轨迹识别依据。     

多目标资源调查站位优化设计——以渤海为例

本研究以渤海为研究区域,通过数值模拟,分析了种类出现率(将基于渤海调查的17个主要渔业种类分为3类:Ⅰ类出现率≥70%,Ⅱ类出现率50%～70%,Ⅲ类出现率50%)和栖息水层对单种类资源量相对误差(Relative error, REE)的影响,不同调查站位数量(48和60)对定点采样与分层随机采样分析结果的影响,并优化了渤海多目标渔业资源调查的设计方案。结果显示,Ⅰ类中5种资源量REE在20%以内,Ⅱ类中3种资源量REE在30%以内,Ⅲ类中6种资源量REE在35%以内,即单种资源量评估值随种类出现率下降,相对误差变大;种类的栖息水层对种类资源量REE无明显影响。定点采样评估值随站位数量减少,精度下降(鱼类、虾类、蟹类、头足类的资源量指数和Margalef丰富度指数的REE分别增加了1.1%、2.5%、8.4%、4.4%和3.3%);分层随机采样可弥补站位数减少带来的精度下降,如站位数为48的分层随机采样获得的鱼类资源量指数评估精度(REE为4.6%)高于站位数为60的定点采样的精度(REE为7.7%),有助于减少调查成本和保护资源量低的种类。然而,每种采样方法并不能完全满足多目标最优,不同站位分配方案影响分层随机采样的精度,按照抽样费用最优准则设置站位,可获得精确度较高的鱼类、虾类、蟹类、头足类及黄鲫(Setipinnataty)、口虾蛄(Oratosquillaoratoria)、日本枪乌贼(Loligojaponica)、鳀鱼(Engraulis japonicus)、叫姑鱼(Johniusgrypotus)、泥脚隆背蟹(Carcinoplaxvestita)、中国对虾(Fenneropenaeus chinensis)等主要种类资源量评估结果,可作为渤海多目标种类资源调查的站位设计方案。     

海湾鱼类群落物种共存机制——以湄洲湾为例

海湾鱼类群落共存机制仍不明确,分析群落物种生态隔离及生境过滤的相对重要性是探讨物种共存模式的重要途径。以湄洲湾鱼类群落为例,根据2012年9月及2013年4月单拖网调查数据,运用零模型对其鱼类共存机制展开研究。结果表明,基于物种出现/不出现的C-score、成对物种棋盘格数目、SES指数的实测值与预测值差异均不显著(P0.05),基于丰度数据的Pianka及Czechanowski生态重叠指数的实测值与预测值之间差异也不显著(P0.05),说明无论是物种还是个体之间均呈随机分布格局,显示湄洲湾鱼类群落是一种受竞争排斥与生境过滤双重影响的群落。研究结果对湄洲湾生态系统生物多样性保护具有重要意义。     

引黄灌溉的管理制度及其绩效分析——以山东省为例

以山东省为例考察引黄灌溉的管理制度和绩效,并提出改进政策.调查表明,现有制度在农户层面上的管理绩效不佳:农户缺乏建设、维护和管理灌溉工程设施的积极性,缺乏节水激励措施,灌溉效率低下.以改善灌溉管理绩效、提高灌溉管理效率为目标,提出相关政策建议如下:对原有灌溉工程设施的产权制度进行多元化改造,积极推进灌溉供水计量制度和水费征收制度改革,推行用水户参与灌溉管理,赋予农户稳定安全的水权,加快水管单位管理制度与运行机制的改革,培育和健全灌溉水权交易市场.     

引黄灌溉的管理制度及其绩效分析——以山东省为例

以山东省为例考察引黄灌溉的管理制度和绩效,并提出改进政策。调查表明,现有制度在农户层面上的管理绩效不佳：农户缺乏建设、维护和管理灌溉工程设施的积极性,缺乏节水激励措施,灌溉效率低下。以改善灌溉管理绩效、提高灌溉管理效率为目标,提出相关政策建议如下：对原有灌溉工程设施的产权制度进行多元化改造,积极推进灌溉供水计量制度和水费征收制度改革,推行用水户参与灌溉管理,赋予农户稳定安全的水权,加快水管单位管理制度与运行机制的改革,培育和健全灌溉水权交易市场。     

方斑东风螺的耗氧率及饲养水中溶氧变化的初步研究

为对方斑东风螺养殖生产提供参考依据,试验测定了饥饿和饱食状态下方斑东风螺幼螺在不同水温(25℃、28℃、30℃)时的耗氧率,测定了幼螺摄食后水中溶解氧浓度的变化。结果表明,饱食后幼螺的生理代谢活动加强,耗氧率增加;随着水温的升高,耗氧率也增加。幼螺摄食后水中的溶解氧浓度随着时间的延长不断下降。试验观察发现,在方斑东风螺养殖的正常水温范围内(25℃~30℃),DO高于3mg/L时,幼螺生活正常;DO在2.40mg/L~2.35mg/L时,幼螺表现为缺氧状态;DO在1.5~1.0mg/L时,幼螺表现为严重缺氧状态。     

Research of dissolved oxygen prediction in recirculating aquaculture systems based on deep belief network

Recirculating aquaculture has received more and more attention because of its high efficiency of treatment and recycling of aquaculture wastewater. The content of dissolved oxygen is an important indicator of control in recirculating aquaculture, its content and dynamic changes have great impact on the healthy growth of fish. However, changes of dissolved oxygen content are affected by many factors, and there is an obvious time lag between control regulation and effects of dissolved oxygen. To ensure the aquaculture production safety, it is necessary to predict the dissolved oxygen content in advance. The prediction model based on deep belief network has been proposed in this paper to realize the dissolved oxygen content prediction. A variational mode decomposition (VMD) data processing method has been adopted to evaluate the original data space, it takes the data which has been decomposed by the VMD as the input of deep belief network (DBN) to realize the prediction. The VMD method can effectively separate and denoise the raw data, highlight the relations among data features, and effectively improve the quality of the neural network input. The proposed model can quickly and accurately predict the dissolved oxygen content in time series, and the prediction performance meets the needs of actual production. When compared with bagging, AdaBoost, decision tree and convolutional neural network, the VMD-DBN model produces higher prediction accuracy and stability.     

A combined model of dissolved oxygen prediction in the pond based on multiple-factor analysis and multi-scale feature extraction

As dissolved oxygen (DO) is an important indicator of water quality in aquaculture, an accurate prediction for DO can effectively improve quantity and quality of product. Accordingly, a novel hybrid dissolved oxygen prediction model, which combines the multiple-factor analysis and the multi-scale feature extraction, is proposed. Firstly, considering that dissolved oxygen is affected by complex factors, water temperature and pH are chosen as the most relevant environmental factors for dissolved oxygen, using grey relational degree method. Secondly, the ensemble empirical mode decomposition (EEMD) is adopted to decompose the dissolved oxygen, water temperature and pH data into several sub-sequences, respectively. Then, the sample entropy (SE) algorithm reconstructs the sub-sequences to obtain the trend component, random component and detail component. Lastly, regularized extreme learning machine (RELM), a currently effective and stable artificial intelligent (AI) tool, is applied to predict three components independently. The prediction models of random component, detail component and trend component are RELM1, RELM2 and RELM3 respectively. The dissolved oxygen, water temperature and pH of the random component forms the input layer of RELM1, and predicted value of dissolved oxygen in the random component is the output layer of RELM1. The input and output of RELM2 and RELM3 are similar to that of RELM1. Final prediction results are obtained by superimposing three components predicted values. One of the main features of the proposed approach is that it integrates the multiple-factor analysis and the multi-scale feature extraction using grey correlation analysis and EEMD. Its performance is compared with several outstanding algorithms. Results for experiment show that the proposed model has satisfactory performance and high precision.     

Destratification in deep fish culture reservoirs in Israel: Practice and modelling

Thermal stratification negatively affects water quality, and thus is a threatto fish culture in deep water ponds and reservoirs. To prevent and controlit, a device based on an underwater propeller that vertically transportsoxygen rich warm water from the epilimnion down to the anoxic and colderhypolimnion was assembled. The empirical results so far obtained in Israelidual purpose reservoirs for field crop irrigation and fish culture aresummarized herein, and a mathematical model of the device's effect onreservoir thermal structure is presented.     

Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network

Dissolved oxygen in water is an important ecological factor in ensuring the healthy growth of aquatic products, as hypoxic stress is known to restrict the growth of aquatic products. The accurate monitoring and prediction of dissolved oxygen is the key to precise regulation and control of pond aquaculture water quality. The current dissolved oxygen prediction model has some limitations, such as a short prediction period and inadequate prediction accuracy for actual production demands. Therefore, a prediction model of dissolved oxygen in pond culture was proposed based on K-means clustering and Gated Recurrent Unit (GRU) neural network. Firstly, the key factors affecting the changes in dissolved oxygen were selected by principal component analysis (PCA). The dissolved oxygen time series was then subjected to K-means clustering, and the dissolved oxygen prediction model was constructed using GRU. To improve the clustering effect, we enhanced the similarity calculation for the time series based on the variation of dissolved oxygen. This process combined the Euclidean distance with the dynamic time-warping distance. The proposed method can predict the dissolved oxygen content of aquaculture water over different time intervals according to the demands of real-world scenarios. The average absolute error of the 30-min interval model was 0.264, and the mean absolute percentage error was 3.5 %. Experimental results indicated that the proposed method achieves higher prediction accuracy and flexibility than the conventional approach.     

池塘溶氧昼夜变化对鳜葡萄糖代谢和血糖含量的影响

为探究池塘溶氧昼夜变化对鳜(Siniperca chuatsi)葡萄糖代谢和血糖含量的影响,本研究分别在17:00 (T1)、23:00 (T2)、次日07:00 (T3)、11:00 (T4)、17:00 (T5)共5个连续时间点采样并测定分析,比较了鳜池塘常氧组(>5 mg/L)与昼夜溶氧变化组葡萄糖代谢相关指标的时序差异。结果显示,1 d中,池塘溶氧经历常氧与低氧的周期性变化,T3时,池塘溶氧最低,平均为0.93 mg/L;T5时,池塘溶氧最高,平均为10.58 mg/L。T3和T4时,血浆中葡萄糖显著降低,乳酸含量显著升高;肝脏中肝糖原含量下降,乳酸含量显著升高,ATP含量显著降低;肝脏有氧代谢柠檬酸合酶(CS)活性显著降低,无氧代谢酶乳酸脱氢酶(LDH)活性显著升高;糖酵解酶中己糖激酶(HK)、磷酸果糖激酶(PFK)、丙酮酸激酶(PK)活性显著升高,糖原分解酶糖原磷酸化酶(GP)活性无显著变化;hk、pfk、pk、ldh-a和gp基因表达量显著上升,cs表达量显著下降,葡萄糖转运蛋白glut2表达量无显著变化。研究表明,鳜养殖池塘中,溶氧存在昼夜周期性交替变化,溶氧水平...     

Simulation of future dissolved oxygen distribution in pond culture based on sliding window-temporal convolutional network and trend surface analysis

Dissolved oxygen (DO) is a key ecological factor to measure the quality of water in the aquaculture. As the pond water body is affected by the breeding environment, the spatial distribution of DO shows a certain law in the entire pond. Therefore, to simulate the distribution of DO in aquaculture waters and grasp the temporal and spatial variation of DO is the key to achieving precise regulation of DO. For this purpose, this paper proposed a method for simulating the temporal and spatial distribution of DO in pond culture based on a sliding window-temporal convolutional network together with trend surface analysis (SW-TCN-TSA). This paper first utilized SW to construct DO data sets with different prediction durations, and then used the improved TCN model to realize one-dimensional time series prediction for DO at single monitoring point. Based on the prediction results of DO, a TSA method was performed on the predicted values of DO at the extreme moments of all discrete monitoring points, so as to realize the simulation of the temporal and spatial distribution of DO in the pond. Experimental results show that the SW-TCN model has better prediction performance for one-dimensional time series prediction of DO. Compared with traditional deep networks, such as CNN, GRU, LSTM, CNN-GRU and CNN-LSTM, the values of evaluation indicators (MSE, MAE and RMSE) have been greatly improved. In the process of trend surface fitting, all fitting R² of DO at different water depths are higher than 0.9, indicating that the TSA can accurately reflect the temporal and spatial distribution of DO. This method can provide a basis for the prediction and early warning of DO in the three-dimensional space of the pond and has high practicability in aquaculture.     

福建省棉花滩水库富营养化状况评价及分析

以2012年1-5月份棉花滩水库调查资料为基础,利用综合营养状态指数对棉花滩水库的营养水平进行评价.结果表明,棉花滩水库大部分水体在Ⅳ～Ⅴ类水质标准范围,大部分水体呈中营养状态,个别水域存在富营养化趋势,主要的污染物是沿岸畜禽养殖业和生活污水污染.库区水体中鱼类种群结构失调,水体生态系统平衡状态濒临破坏.     

Estimating oxygen consumption of rainbow trout (Oncorhynchus mykiss) in a raceway: A Precision Fish Farming approach

The Precision Fish Farming (PFF) approach was applied to the estimation of fish oxygen consumption of rainbow trout in a raceway farm. A dynamic model, simulating the evolution of Dissolved Oxygen concentration, was identified: the daily oscillation of fish oxygen consumption rate was simulated by means of a sinusoidal function. The model was applied to the data set collected during a four-week field study, which was carried out in July 2019. Water temperature and Dissolved Oxygen concentration were measured with an hourly frequency in farm influent and effluent. Fish biomass was monitored on a daily basis by combining the data provided by a state-of-the art system for non-invasive estimation of fish weight distribution with mortality counting. The monitoring period was partitioned into two time-windows, as fish was not fed during the first two weeks. These windows were further partitioned into a calibration and validation set. Three model parameters, i.e. the average daily respiration rate, the amplitude of its daily oscillation, and its phase were estimated by fitting the model output to the time series of DO concentration in the effluent. The results of the calibration show that: 1) the daily average oxygen consumption rate is consistent with the literature; 2) the amplitude of the daily oscillation when fish is regularly fed is more than twice that estimated for fasting fish. The results of the validation suggest that the model could be used to implement a cost-effective automatic control of oxygen supply, based on the short-term prediction of oxygen demand.