基于浮游植物功能类群的广东省大型水库生态分区初探

2012年1月和7月通过对广东省境内10座供水水库进行水生态调查,采集48个站点的浮游植物样品,对其进行浮游植物功能类群的划分及聚类分析.调查区共鉴定出8门104属207种浮游植物,主要分属于30个功能类群.聚类分析将这10座水库聚合为4个类型区,第一生态类型区包括南水和新丰江水库,以适应低营养环境的功能类群为主;第二生态类型区包括飞来峡、白盆珠和新丰江水库,主要以中营养型和广谱型为优势功能类群;第三生态类型区和第四生态类型区包括高州、公平、大水桥、鹤地和汤溪水库,其中高州和公平水库主要以富营养型和广谱型为优势功能类群,其它水库则以适应富营养环境的功能类群为绝对优势类群.上述4个类型区浮游植物优势功能类群差异较大,且与水质营养状态有良好的响应关系.     

Phytoplankton dynamics and cyanobacterial dominance in Murchison Bay of Lake Victoria (Uganda) in relation to environmental conditions

Murchison Bay is a shallow embayment in the north-western part of Lake Victoria, strongly influenced by urban pollution from the Ugandan capital Kampala. Two stations, representing the semi-enclosed innermost part of the bay and the wider outer part of the bay, were sampled in the period from April 2003 to March 2004, in order to assess the phytoplankton community and the nutrient status in the bay. Murchison Bay was highly eutrophic with average concentrations (n=25) of total phosphorous >90 μg L⁻¹ and total nitrogen >1100 μg L⁻¹ in the inner part of the bay. The phytoplankton community was dominated by a variety of cyanobacterial species and diatoms. Cyanobacteria were dominant in the whole bay, whereas diatoms were more abundant in the outer part of the bay. Moreover, the proportion of N-fixing species like Anabaena sp. was higher in the outer part of the bay, whereas species like Microcystis sp. were more abundant in the inner part of the bay. The phytoplankton community, especially in the outer part of the bay, may be influenced by light limitation. Low NO₃-N concentrations in the bay may also indicate a possible N-limitation, thus favouring growth of N-fixing cyanobacteria. The open bay is, however, a complex system, and additional environmental factors and loss processes most likely affect the phytoplankton community.     

Type-specific and indicator taxa of phytoplankton as a quality criterion for assessing the ecological status of Finnish boreal lakes

Key issues in the implementation of the Water Framework Directive are classification of lakes using biological quality parameters and type-specific reference conditions. This work is one of three studies considering different metrics of phytoplankton in the classification of ecological status. Phytoplankton was studied in a total of 55 Finnish boreal lakes, including 32 reference lakes. We tested the suitability of taxonomic composition and abundance of phytoplankton groups for biological classification. We also preliminarily determined the type-specific taxa for the studied lakes. The type-specific taxa for reference conditions are coincidently the indicator species/taxa for high ecological status. Interestingly, some taxa type-specific for impacted oligo-humic lakes proved to be the type-specific taxa for humic reference lakes. The pressure of human impact was observed not only as increase of biomass but also as changes in the species composition. The phytoplankton composition indicated the ecological status of impacted lakes moderately well. There was some variation in the indications given by different algal groups, probably due to the preliminary class boundaries used. However, the preliminary combination of indicative parameters to estimate the ecological status of the studied impacted lakes was in general in accordance with earlier classification of water quality in Finnish lakes.     

城市湖泊春季绿藻水华特征及其影响因素——以宁波月湖为例

城市湖泊富营养化问题日趋严峻,以往对水华的研究多集中于大型自然淡水湖库,而对小型城市浅水湖泊的水华动态相对较少.以宁波月湖为研究对象,探讨水华暴发期间浮游植物变化特征及与影响因子之间的关系,以期判别影响城市湖泊水华的主控因子.月湖水华期间营养盐水平处于中富营养至极端富营养之间,此次共检出浮游植物8门61属,藻种组成以绿藻门（51.79%）和硅藻门（21.43%）为主,各点位浮游植物生长主要受水温、光照驱动,经历了隐藻门→硅藻门→绿藻门→蓝藻门的演替.水华种为雷氏衣藻（Chlamydomonas reinhardtii）,总藻密度最高达到1.55×10⁸ cells/L,水华暴发后各点位衣藻属比例升高（最高达到81.10%）,群落结构呈现单一化特征.通过Pearson相关性分析和RDA分析发现衣藻属生长与水温、pH、总磷浓度均呈显著正相关,春季气温回升、天气持续晴好,城市浅水湖泊高营养盐负荷、水体流动性差等特点为带鞭毛的衣藻属提供了适宜的生存条件,在环境条件均适宜的情况下拥有最大生长潜力的衣藻属在营养盐、光照等竞争中生长速率明显优于其他藻种,从而发生绿藻水华.     

浮游植物动力学模型及其在海域富营养化研究中的应用

浮游植物动力学模型用来研究特定海域浮游植物生物量的时空分布规律，定量确定各种物理，生物过程的贡献，对解决浮游植物生物量异常增加导致的富营养化问题具有至关重要的作用，综述了国内外海洋浮游植物动力学模型研究的发展过程和现状，介绍了几种不同时空尺度浮游植物动力学模型的特点和性能，此类模型在发达国家的海域富营养化研究和环境管理中已取得了相当的进展，而我国目前虽已开展了海洋生态模型的初步研究，但面临一些困难，其中不仅需要获取特定海域的过程参数，而且急需对海域的强迫过程和边界过程加强认识。     

大连湾春秋季浮游植物的调查研究

1999年5月和10月 ,对大连湾 (121°36′42″～121°46′53″N,38°28′39″～39°00′16″E)进行了浮游植物的调查研究 ,结果表明:大连湾浮游植物共53种。其中 ,硅藻门有26属42种 ,甲藻门有6属8种。浮游植物的平均数量为23.4×104 个/m3,其中 ,硅藻门的平均数量为23.7×104个/m3 ,占浮游植物总量的80.1% ,甲藻门的平均数量为25.9×104个/m3 ,占浮游植物总量的16.7 %。优势种中的丹麦细柱藻 (Leptocylindyusdanicus)、中肋骨条藻 (Skeletonemacostatum)、诺氏海链藻 (Thalassiosiranordenskioldii)均为赤潮生物 ,说明该区域有发生赤潮的危险。     

Study on optimization of parameters in a biological model

~lOWThere is a great progreSS tO apply inathernatics m~ in studying marine ~. Because of more Parameters than there that can be speified aCCOrding to the finite dsts, itS parameters had tO be ~hly evaluated in geneal. On the other side, the ~ ~ter fitting in a cendn area is USually unfit in another area for the complication Of ~ iodf.It was lucky that the pwhlerns, how to evalUate the Parameters as ~y as ~ie ina~ with the available dsta and how to judge to what a extent the ParameterS s…     

湛江港浮游植物与赤潮植物的初步研究

于1987年8月在湛江港设13个点采样。鉴定表明，共有浮游植物37属94种和变种；优势种类有中肋骨条藻、日本星杆藻、佛氏海毛藻等。湛江港浮游植物密度较高，港内外平均达1．3×104cell／L和7．3×105cell／L。经单项营养指数和营养状态结合指数分析表明，除两个点无机氮浓度略超标外，所有站位均未达到富营养化水平。调查期间，共发现赤潮生物31种，其中主要有中肋骨条藻、日本星杯藻、佛氏海毛藻、尖刺菱形藻和夜光藻等，中肋骨条漠和日本星杆藻曾形成水华。     

大嵛山岛海域浮游植物的生态

本文鉴定了1990年5、8、11月和1991年2月采自福建大存山岛海域的48价浮游植物样品，记录浮游植物115种，其中蓝藻2种、硅藻90种、甲藻22种和金藻1种。其月平均细胞总量为89．54×104个／m3。年高峰出现于春季5月，达236．65×104个／m3，次高峰为夏季8月，达113．19×104个／m3，最低谷出现在冬季2月，仅有3．75×104个／m3。     

Assemblages of phytoplankton pigments along a shipping line through the North Atlantic and tropical Pacific

A set of phytoplankton pigment measurements collected on eight quarterly transects from France to New Caledonia is analyzed in order to identify the main assemblages of phytoplankton and to relate their occurrence to oceanic conditions. Pigment concentrations are first divided by the sum [monovinyl chlorophyll a plus divinyl chlorophyll a] to remove the effect of biomass, and second are normalized to give an equal weight to all pigments. The resulting 17 pigments × 799 observations matrix is then classified into 10 clusters using neural methodology. Eight out of these 10 clusters have a well marked regional or seasonal character, thus evidencing adapted responses of the phytoplankton communities. The main gradient opposes two clusters with high fucoxanthin and chlorophyll c₁₊₂ in the North Atlantic in January, April and July, to three clusters in the South Pacific Subtropical Gyre with high divinyl chlorophyll a, zeaxanthin and phycoerythrin. One of the clusters in the South Pacific Subtropical Gyre has relatively high zeaxanthin and phycoerythrin contents and dominates in November and February (austral summer), while another with relatively high divinyl chlorophylls a and b dominates in May and August (austral winter). The third one in the South Pacific is characterized by high carotene concentration and its occurrence peaks in February and May. In the equatorial current system, one cluster, rich in chlorophylls b and c₁₊₂, is strictly located in a narrow zone centred at the equator, while another with relatively high violaxanthin concentration is restricted to the high nutrient - low chlorophyll waters in only the southern part of the South Equatorial Current. One cluster with relatively high prasinoxanthin content has a spatial distribution spanning the entire South Equatorial Current. Two clusters have a ubiquitous distribution: one in the equatorial Pacific, the Carribbean Sea and the North Atlantic during summer has pigment concentrations close to the average of the entire dataset, and the other in the South Pacific Subtropical Gyre, the Carribbean Sea and the North Atlantic during autumn clearly has an oligotrophic character. Many of the differences between clusters are caused by diagnostic pigments of nano- or picoflagellates. While the space and time characteristics of the clusters are well marked and might correspond to differences in physical and chemical forcing, knowledge of the ecological requirements of these flagellates is generally lacking to explain how the variability of the environment triggers these clusters.