淡水浮游植物功能类群划分方法及其生态学应用研究进展(综述)

浮游植物功能类群分类方法是一种以浮游植物个体生态学特征为依据的生态分类法,弥补了传统分类方法在生态学应用上存在的不足,是开展淡水生态研究的重要工具。文中概述浮游植物功能类群的理论基础、分类依据及其生态学研究中的优势,并介绍功能类群分类法在国内外淡水浮游植物生态学研究中的应用进展和现状,最后探讨浮游植物功能类群研究中存在的问题及未来发展方向。     

亚热带水库浮游植物群落的季节变化及其与环境的关系:两种功能群分类法的比较

浮游植物是水库生态系统最重要的生物组成,因其对水体环境的变化敏感而常被用于指示水体生态系统状态.为了解功能群(FG)和形态功能群(MBFG)两种浮游植物功能群分类方法在指示水库环境变化中的适用性和差异性,本研究以青狮潭水库为例,于2016年3月—2017年2月对该水库浮游植物和环境因子进行逐月采样调查.应用两种浮游植物功能群分类方法分析了青狮潭水库浮游植物群落结构、季节演替及其影响因子,并比较了两种功能群分类方法在指示水库环境状态中的应用及差异.结果表明:青狮潭水库共检出浮游植物7门109种,可划分为17个FG功能群和5个MBFG功能群.两种功能群分类法的结果都表明青狮潭水库为中-富营养水体;在一年周期的浮游植物群落季节演替中,FG优势功能类群指示大坝和库中群落分别经过8、9个演替阶段,而MBFG优势功能类群指示大坝和库中群落均经过8个演替阶段.冗余分析表明,在相同环境梯度上,FG功能类群的响应比MBFG功能类群更灵敏,尤其是对pH值和硝态氮浓度的响应.在探究亚热带水库浮游植物群落演替过程及其与环境的关系上,采用FG功能群分类方法更为合适.     

澜沧江小湾库区2017-2019夏季浮游植物功能群特征及其环境驱动因子

为探究澜沧江小湾库区夏季浮游植物功能群的特征及其关键驱动因子,于2017-2019年8月进行采样调查.采用浮游植物功能群(FG)、冗余分析以及Pearson相关分析对数据进行分析.结果 表明:调查共鉴定出浮游植物7门71种,其中绿藻门种类最多(34种,占比47.89％);依据FG分类法将鉴定出的71种藻类归纳分为23个...     

简议我国古代植物分类和命名

在参阅大量古籍文献的基础上,对我国古代植物的分类和命名进行了探讨。我国很早就用功能属性分类法和自然属性分类法对植物进行分类,有些类群的划分与国际上科学分类法的观点非常相似;运用植物的取象名素创立的“类部“和”种部”的植物命名方法类似于国际上植物命名的双名法。对我国古代植物分类学与国外分类学的异同,以及我国古代植物分类学发展不足的原因也作了分析。     

基于功能类群分析呼兰河口湿地浮游植物群落结构特征

浮游植物对水环境条件变化敏感,其群落演替特征被广泛应用于指示水环境变化。基于3种浮游植物功能类群方法,对呼兰河口湿地浮游植物群落结构进行研究。于2018年春季（4、5月）、夏季（6、7、8月）和秋季（9、10月）三季,在呼兰河口湿地共设置11个采样点,通过ASNOSIM和SIMPER分析探索FG、MFG和MBFG功能类群的演替特征,基于RDA分析阐述功能类群对环境因子的响应模式。研究期间共鉴定浮游植物243个分类单位,隶属于7门9纲18目32科75属;其种类组成主要以绿藻门（46.09%）、裸藻门（19.34%）和硅藻门（18.52%）为主。共划分FG功能类群25个,MFG功能类群20个,MBFG功能类群6个;ANOSIM分析表明不同季节之间FG和MFG功能类群结构差异显著;SIMPER分析表明S1/Lo/W1/P/J/Y、9b/5a/11b/6a/2c/2d/1c和Ⅲ/Ⅰ类群是影响不同季节之间功能类群变化的主要类群;RDA分析表明,FG、MFG和MBFG功能类群的演替受多种环境因素共同影响,其中pH、BOD₅、Tur.和COD_Mn与功能类群演替关系密切。相较于MBFG功能类群,FG和MFG功能类群能更好的响应呼兰河口湿地水环境的空间异质性。     

流式细胞术在富营养淡水湖泊微型浮游植物细胞中的应用

应用流式细胞术(FCM)对一个富营养化淡水湖泊表、底层微型浮游植物细胞进行了初步研究。研究结果表明：流式细胞术可快速、多参数区分3种不同类群微型浮游植物。微型浮游植物细胞在表、底层占50μm以下微型颗粒物数量比例分别为21．08％、17．87％,在不同水层,微型浮游植物的优势类群及数量也不同。流式细胞术大大提高了淡水微型浮游生物研究监测水平。     

长江口赤潮高发区浮游植物与水动力环境因子的分布特征

报道了2002年春季长江口海域(30°50′～31°50′N,121°50′～123°00′E)的22个大面观察站和一个昼夜连续观察站的水样和网样浮游植物的种类组成、丰度分布与水动力环境因子、营养盐的关系．结果表明,长江口区共有浮游植物5门45属110种．主要赤潮生物优势种为中肋骨条藻(Skeletonema castaturn)和具齿原甲藻(Prorocentrum dentatum)等．浮游植物丰度的昼夜变化白天大于夜间,垂直分布不明显．浮游植物主要生态类型可划分为：沿岸河口低盐半咸水类群、沿岸偏低盐广布性类群、外海高盐暖水性类群等．长江口区浮游植物丰度在1．6×10^3～75．2×10^3个.dm-3．浮游植物的种类组成和丰度分布与长江冲淡水密切相关．在该区域存在三股不同性质的水,即长江河口水、长江冲淡水及外海水(台湾暖流)影响着浮游植物的分布．     

浅析淡水浮游动物的种类组成及其生态功能作用

浮游动物是浮游生物中的一大生物类群,终生营浮游性生活.淡水浮游动物主要包括原生动物、轮虫、枝角类和桡足类四大类群.这四类浮游动物在水域生态学研究和经济水产中均占有重要地位.它们通过摄食控制浮游植物的数量,来调节水体生态平衡,因此可以通过浮游动物的群落结构变化进行水质监测;同时它们又是许多经济水产动物的饵料,因而其数量变化可以直接影响鱼类养殖.现在,对于浮游动物的研究已不止限于宏观,随着科研技术的进步、分子技术的发展,其研究方法已经实现了宏观与微观的结合,促进了浮游动物生态学研究的进步.     

景观生态分类的研究现状及其发展趋势

景观生态分类是景观生态学研究的一项重要内容 ,是开展景观评价、规划与管理等工作的基础 ,对实现景观的可持续发展具有重要的意义。本文对国内外在景观生态分类的理论和方法方面的研究情况进行了论述、分析和讨论 ,总结了目前研究存在的一些问题和不足 ,并进一步指出了今后景观生态分类研究的重点和发展趋势     

群落排表分类的两种数学方法

本文描述两种执行群落排表分类的数学方法:X²分类法和信息熵分类法,并以德国西北部草地数据为例进行了应用和分析。结果表明这两个方法都是有效的群落排表分类方法,它们所排的群落表可直接地反映群落类型和种类组成之间的关系,体现了Braun-Blanquet传统排表法的特点。     

Comparing biological classifications of freshwater phytoplankton: a case study from South China

The use of ecological classification systems is becoming more and more widely used when studying phytoplankton. Grouping phytoplankton species into ecologically coherent groups allow to reduce redundancy and in this way, to handle a minor number of biological variables when investigating the ecological status of aquatic ecosystems. Three ecological classifications are mostly used when freshwater phytoplankton is studied: functional groups or coda, morpho-functional groups (MFGs) and morphology-based functional groups (MBFGs). In this study, these three ecological classifications were comparatively used along with two taxonomic classifications based on species and genera to analyse phytoplankton response to environmental variability in three sub-tropical Chinese reservoirs. Canonical correspondence analysis was performed to compare the five mentioned biological classifications. When ecological classifications were used, the percentage of variance explained in the biological groups?Cenvironmental variables was higher than that explained by the taxonomic classifications. Coda and MFGs showed a very high degree of overlapping, but since coda are associated to very detailed environmental templates, this method was more helpful in explaining phytoplankton variability in relation to environmental factors.     

A morphological classification capturing functional variation in phytoplankton

1. A logical way of distinguishing functional groups of phytoplankton is to cluster species according to their functional traits, such as growth rate and nutrient assimilation constants. However, data for such an approach are lacking for the vast majority of the species. 2. In this study, we show that a classification based on simple morphological traits may capture much of the variability in functional properties among the phytoplankton. We used information on more than 700 freshwater species, from more than 200 lakes situated in climate zones ranging from subpolar to tropical. 3. Morphological characteristics correlated well with functional properties, such as growth rate and sinking rate, and also with the population size and biomass attained in the field. This suggests that morphology is a good predictor of the functional characteristics of species. 4. Cluster analysis was used to define seven species groups based on morphology. Although some of the clusters are taxonomically homogeneous, others include species of several separate divisions. Functional traits (not used for the classification) differed significantly among the clusters, suggesting that the clusters may indeed represent meaningful functional groups. 5. Advantages of our morphological approach to classification include its objectivity, its independence from taxonomic affiliations, and the relative ease of its application to the majority of species for which physiological traits are unknown and are not readily determined.     

The habitat template of phytoplankton morphology-based functional groups

The identification of the main factors driving phytoplankton community structure is essential to understand and adequately manage freshwater ecosystems. We hypothesize that differences in morphological traits reflect phytoplankton functional properties that will be selected under particular environmental conditions, namely their habitat template. We apply a morphology-based functional groups (MBFG) approach to classify phytoplankton organisms and define each group template. We use machine learning techniques to classify a large number of phytoplankton communities and environmental variables from different climate zones and continents. Random forest analysis explained well the distribution of most groups’ biovolume and the selected variables reflected ecological preferences according to morphology. By means of a classification tree it was also possible to identify thresholds of the environmental variables promoting groups dominance in different lakes. For example group III (filaments with aerotopes and high surface/volume including potentially toxic species) was dominant when light attenuation coefficient was >3.9 m⁻¹ and total nitrogen was >2,800 μg l⁻¹. We demonstrate that morphology captures ecological preferences of phytoplankton groups and provides empirical values to describe their habitat template.     

Unveiling Distribution Patterns of Freshwater Phytoplankton by a Next Generation Sequencing Based Approach

The recognition and discrimination of phytoplankton species is one of the foundations of freshwater biodiversity research and environmental monitoring. This step is frequently a bottleneck in the analytical chain from sampling to data analysis and subsequent environmental status evaluation. Here we present phytoplankton diversity data from 49 lakes including three seasonal surveys assessed by next generation sequencing (NGS) of 16S ribosomal RNA chloroplast and cyanobacterial gene amplicons and also compare part of these datasets with identification based on morphology. Direct comparison of NGS to microscopic data from three time-series showed that NGS was able to capture the seasonality in phytoplankton succession as observed by microscopy. Still, the PCR-based approach was only semi-quantitative, and detailed NGS and microscopy taxa lists had only low taxonomic correspondence. This is probably due to, both, methodological constraints and current discrepancies in taxonomic frameworks. Discrepancies included Euglenophyta and Heterokonta that were scarce in the NGS but frequently detected by microscopy and Cyanobacteria that were in general more abundant and classified with high resolution by NGS. A deep-branching taxonomically unclassified cluster was frequently detected by NGS but could not be linked to any group identified by microscopy. NGS derived phytoplankton composition differed significantly among lakes with different trophic status, showing that our approach can resolve phytoplankton communities at a level relevant for ecosystem management. The high reproducibility and potential for standardization and parallelization makes our NGS approach an excellent candidate for simultaneous monitoring of prokaryotic and eukaryotic phytoplankton in inland waters.     

A freshwater ecoregion delineation approach based on freshwater macroinvertebrate community features and spatial environmental data in Taizi River Basin, northeastern China

The ecoregion is currently widely used as the basic geospatial unit in freshwater biodiversity conservation. The popularly used delineation is usually based on the assumption that attributes of aquatic ecosystems are influenced by landscape-scale environmental variables. However, few ecoregion delineations attempt to establish the local validity of this assumption prior to delineation, and few studies check the correspondence of the derived ecoregion boundaries with the distributions of attributes of aquatic biota. In this study, we established an approach to overcome these shortcomings. The notable features of the approach are: (1) the delineation variables were filtered through a series of analytical steps to select those that best represented the aquatic community traits, and which avoided redundancy in the data; (2) the method was quantitative and repeatable; and (3) the derived ecoregion boundaries were checked for consistency with the spatial attributes of aquatic biota. The approach was applied in the Taizi River Basin, northeast China. The procedure proposed here filtered out altitude and annual precipitation as the best variables to include in the freshwater ecoregion delineation. Then, using the quantitative ISODATA classification method, the basin was classified into three ecoregions. A test of accuracy indicated that freshwater ecoregions matched well with the spatial distribution pattern of macroinvertebrate community attributes. Statistical analysis showed that natural geographical attributes and river attributes were different in the three ecoregions, and indices representing macroinvertebrate community attributes are significantly different as a whole among the three ecoregions. The case study proved this approach effective on ecoregion delineation.     

Operational Effect Variables and Functional Ecosystem Classifications – a Review on Empirical Models for Aquatic Systems along a Salinity Gradient

This paper presents a comparative study based on a very comprehensive set of empirical data from many international data bases including fresh water systems, coastal brackish water areas and marine coastal areas. We present a general trophic level classification system (oligotrophic, mesotrophic, eutrophic and hypertrophic categories) for sites/areas characterised by a wide range of salinities. This classification system targets on the following operational effect variables (bioindicators), which are meant to reflect key structural and functional aspects of aquatic ecosystems and characteristic (median) values for entire defined areas (the ecosystem scale) for the growing season: Secchi depth (as a standard measure of water clarity), chlorophyll‐a concentrations (a measure of primary phytoplankton biomass), the oxygen saturation in the deep‐water zone (an indicator reflecting sedimentation, oxygen consumption, oxygen concentrations and the habitat conditions for zoobenthos, an important functional group) and the macrophyte cover (an important variable for the bioproduction potential, including fish production, and the “biological value” of aquatic systems). For a wide range of systems, these bioindicators can be predicted using practically useful models, i.e., models based on variables that can be accessed from standard monitoring programs and maps. These bioindicators are regulated by a set of abiotic factors, such as salinity, suspended particulate matter (SPM), nutrient concentrations (N and P), morphometry and water exchange. Empirical data ultimately form the basis for most ecological/environmental studies and this work uses maybe the most comprehensive data set ever related to trophic level conditions. It also gives compilations of empirically‐based (statistical) models quantifying how the variables are interrelated and how they reflect fundamental aspects of aquatic ecosystems. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

淡水湖泊浮游藻类对富营养化和气候变暖的响应

水体富营养化和气候变暖是淡水生态系统面临的两大威胁。文章分别阐述了富营养化和气候变暖对淡水湖泊浮游藻类直接和间接效应, 并总结气候变暖可能通过影响水体理化性质、水生植物组成、食物链结构从而直接或间接改变浮游藻类生物量或群落结构。作者重点分析了气候变暖下湖泊生态系统蓝藻水华暴发机制, 比较了不同湖泊蓝藻对气候变暖和富营养化响应的异同点, 发现气候变暖和富营养化对湖泊生态系统影响存在相似性, 表现在均促进湖泊由清水-浊水稳态转变、增加蓝藻水华发生频率和强度。然而二者对湖泊浮游藻类影响的相对重要性取决于分层型湖泊和混合型湖泊的差异性、不同营养型湖泊和不同类群蓝藻组成差异性。作者认为, 开展气候变暖和富营养化下, 湖泊浮游藻类功能群响应研究亟待进行。