全 文 ：第 34卷 第 2期 生 态 科 学 34(2): 136−147
2015 年 3 月 Ecological Science Mar. 2015

收稿日期: 2014-07-01; 修订日期: 2014-09-15
基金项目: 淮河流域水生态功能三级、四级分区研究 2012ZX07501002-003
作者简介: 朱为菊(1980—), 女, 山东泰安人, 博士研究生, 从事浮游植物生态学研究, E-mail: gdzxzwj@163.com
*通信作者: 王幼芳, 女, 教授, E-mail: yfwang@bio.ecnu.edu.cn; 王全喜, 男, 教授, E-mail: wangqx@shnu.edu.cn

朱为菊, 尤庆敏, 庞婉婷, 等. 中国大型人口密集型流域(淮河流域)浮游植物群落的分布格局[J]. 生态科学, 2015, 34(2):
136−147.
ZHU Weiju, YOU Qingmin, PANG Wanting, et al. Phytoplankton community distribution patterns in a densely populated river basin,
China[J]. Ecological Science, 2015, 34(2): 136−147.

中国大型人口密集型流域(淮河流域)浮游植物群落的
分布格局
朱为菊 1,2, 尤庆敏 2, 庞婉婷 2, 潘仰东 3, 王幼芳 1, 王全喜 2,*
1. 华东师范大学生命科学学院, 上海 200062
2. 上海师范大学生命与环境科学学院, 上海 200234
3. 波特兰州立大学, 波特兰 97207

【摘要】 近年来, 淮河流域(流域面积为 2.7×105km2)水体污染严重并导致水质的恶化。为了分析淮河流域浮游植物群
落的分布格局及其与环境因子之间的关系, 于 2013 年 5 月对淮河流域 217 个样点进行浮游植物样品的采集, 并测定水
体的环境指标。结果显示, 淮河流域浮游植物群落主要由甲藻(占总生物量的 26.1%)、硅藻(23.4%)和隐藻(19.5%)组成。
聚类分析显示, 浮游植物群落可以分为四组: 组 1 主要以腰鞭毛飞燕角甲藻(Ceratium hirundinella)为优势种, 组 2 主要
以硅藻门的梅尼小环藻(Cyclotella meneghiniana)为主。组 1 和组 2 的采样点大多位于林地区域。组 3 主要以隐藻类
(Cryptomonas erosa)为主, 采样点位于人为干扰较大的区域, 如农业污染、采砂和农业养殖区。组 4 主要以金藻门的色
金藻(Chromulina sp.)为主。浮游植物的群落结构主要受自然条件(如海拔和河流的级别)和人类活动(如总悬浮固体物和
总氮)的影响。我们的结果显示, 浮游植物可以作为大型流域生物评价中的指示类群, 同时在水质监测中也应该考虑自
然条件的变化。

关键词：淮河流域; 浮游植物; 人为活动; 水质生物学评价; 非度量多维尺度分析
doi:10.3969/j.issn. 1008-8873.2015.02.021 中图分类号：Q145 文献标识码：A 文章编号：1008-8873(2015)02-136-12
Phytoplankton community distribution patterns in a densely populated river
basin, China
ZHU Weiju1,2, YOU Qingmin2, PANG Wanting2, PAN Yangdong3, WANG Youfang1*, WANG Quanxi 2,*
1. School of Life Sciences, East China Normal University, Shanghai 200062, China
2. College of Life and Environmental Sciences, Shanghai Normal University, Shanghai 200234, China
3. Environmental Science and Management, Portland State University, Portland, OR 97207, USA
Abstract: The Huaihe River Basin (HRB, 270,000 km2) has experienced substantial nutrient enrichment, which has resulted
in water quality degradation. To characterize algal distribution patterns with relation to environmental conditions in the HRB,
both algae and environmental data were collected at 217 sites in May 2013. The phytoplankton community was relatively
diverse (144 species) and dominated by dinoflagellates (26.1% of total biomass), diatoms (23.4%), and cryptophytes (19.5%).
Cluster analysis identified four major phytoplankton groups. Group 1 was characterized by mainly dinoflagellates including
Ceratium hirundinella. The most characteristic taxa in group 2 were diatoms including Cyclotella meneghiniana. Most sites
2 期 ZHU Weiju, et al. Phytoplankton community distribution patterns in a densely populated river basin, China 137
in group 1 and 2 located near the forest land. Group 3 was characterized by cryptophytes (e.g., Cryptomonas erosa). Most
sites in group 3 were near the human-affected location (e.g., agricultural land, sand mining, poultry culture zone). The most
characteristic taxa in group 4 were the flagellate algae (e.g., Chromulina sp.). The phytoplankton community composition
were strongly correlated with both natural (e.g., elevation and stream size) and anthropogenic (e.g., total suspended solids
and nutrients) factors. Our results revealed that phytoplankton should be considered indicators in the basin-wide
bio-assessment and natural variations should also be accounted for future monitoring of water quality conditions in the basin.
Key words: Huaihe River Basin; phytoplankton; anthropogenic; bioassessment; NMDS

1 Introduction
The application of phytoplankton indicators to
rivers is increasing[1−2], as they are generally more
sensitive to nutrients variation[3] and they integrate the
effects of natural variation and anthropogenic stressors.
Furthermore, phytoplankton have short generation
times for ecological indicators, allowing species assem-
blages compositions to quickly respond to ecosystem
changes. Several studies suggested that the biomass of
river phytoplankton was determined by nutrients[4−5]
and by physical factors like catchment area, mean depth
and flushing rate[6−7]. Most of these river plankton
studies focused on small or large rivers. Basin-wide
systematic studies of phytoplankton in rivers with
numerous small impoundments have been limited.
Streams and rivers throughout the world have
been extensively impacted by human activities[8−10].
The management of lotic systems for societal and
economic benefits has led to extensive flow and
channel modifications, the creation of dams and
impoundments, and changes in floodplain habitat
quality and connectivity[11−13]. In the Huaihe River
Basin (HRB) in China, approximately 11, 000 dams
and sluices had been built by the year 2000 primarily
for flood controls. The number of such structures in
this river basin accounts for approximately half of
those in China and a quarter of those in the world[14].
Dams and sluices in the basin have resulted hydrologic
‘‘fragmentation’’, which changes both flow and habitat
characteristics, and consequently ecosystem processes
such as nutrient cycling and phytoplankton growth[15−17].
In addition, over the last half century, the HRB has
been subject to nutrient enrichment, with the main
pollutant sources from industrial and municipal point
sources and agricultural nonpoint sources[18]. Hydro-
logical regime alteration and nutrient enrichment have
resulted in degraded water quality and greatly impaired
aquatic ecosystems. The water quality in the basin is
the worst among the nation’s seven major river basins[19].
Water quality in 83% of rivers in the basin didn’t meet
the national criteria (GB3838—2002). In 2008 the
Chinese Ministry of Environmental Protection laun-
ched a Major Science and Technology Program for
Water Pollution Control and Management (MSTPWPCM).
According to the National Guidelines on Medium and
Long-Term Program for Science and Technology
Development (2006—2020), the Huaihe River Basin
has been designated as the river pollution control
demonstration area.
This study is part of the MSTPWPCM program.
Basin-wide characterization of phytoplankton comm-
unity and their association with environmental condi-
tions will assist monitoring efforts. The HRB remains
largely outside the focus of most scientific investi-
gations on the phytoplankton community composition.
Previous work on phytoplankton in the HRB has
been limited to species records or ecological studies
restricted to specific localities[20−21]. Systematic basin-
wide sampling and detailed analysis of the phyto-
plankton has not been conducted in the HRB. In this
study our objects are to (1) describe the phytoplankton
community distribution patterns in the HRB; (2) assess
which environmental factors predominantly structure
riverine phytoplankton community; and (3) identify
phytoplankton community can be used as indicators in
the basin-wide bio-assessment.
2 Materials and methods
2.1 Study area
The Huaihe River Basin (HRB)(30°55’-36°36’ N,
138 生 态 科 学 34 卷
111°55’ -121°25’ E) with a drainage area of 270,000
km2, is located between the Yangtze River and the
Yellow River in China[22]. It forms a geographical
divide between north and south of China. The south of
the Huaihe River is subtropical monsoon region, while
the north of the Huaihe River lies in the temperate
monsoon region. The annual precipitation was about
880 mm, with about 50%-80% of the total annual
precipitation during the summer months (June-Sep-
tember), while the winter (December-February) was
dry. The annual temperature was 11℃-16℃ with four
distinct seasons.
The Huaihe River originates in Tongbai Mountain
in Henan province, flows into the Yangtze River after
1,000 km. It is the most densely inhabited river basin
and the main grain-producing area in China. In 2005,
the total population and grain yield accounted for
13.1% and 16.1%, respectively, of the national total[23].
The northeast of the HRB is the central and
southern mountainous region of the Shandong province,
an alluvial plain districts of the Yellow River and the
Huaihe River are in the middle of the basin, hilly and
mountainous region are in the west and south of the
basin. Major vegetation cover is coniferous broad-
leaved mixed forest at the upstream and the midstream.
Major land uses in the basin are agriculture (nearly
50%), with the principal row crop of wheat, corn, rice
and soybeans.
2.2 Sampling and measurement procedures
Two hundred and seventeen sampling stations
were selected randomly (Fig. 1). In each site, three
cross-section transects were established. Sampling was
conducted during low-water period in May 2013. We
measured in situ environmental variables including
water temperature (WT), pH, conductivity (Cond),
turbidity and total suspend solids (TSS) using a
portable HACHCDC40105. We also measured Secchi
depth (SD), water depth, stream width, water velocity
and elevation. Spectrophotometer (DR5000) was used
to measure total phosphorus (TP), total nitrogen (TN),
and chemical oxygen demand (CODMn) according to
standard methods[24].
For phytoplankton, a 1000 mL sample from the
0.5 m depth below surface was collected and preserved
with 1% Lugol’s iodine solution immediately in the
field and concentrated to 50 mL after sedimentation for

Fig. 1 The map of China (at the top of right) and the HRB showing all sampling stations of phytoplankton
2 期 ZHU Weiju, et al. Phytoplankton community distribution patterns in a densely populated river basin, China 139
48 h. After complete mixing, 0.1 mL of the concen-
trated sample was counted directly in a 0.1 mL
counting chamber under a microscope at 40× magnifi-
cation. Phytoplankton was identified according to the
reference book by Hu and Wei (2006)[25]. Phyto-
plankton biomass was expressed as wet biomass and
was estimated for individual species by assigning a
geometric shape similar to the shape of each phyto-
plankton species[26].
2.3 Data analysis
We excluded ‘rare’ taxa from analyses. The rare
taxa were defined as those with average relative
biomass (RB)<0.5% and the occurrence<10. Phyto-
plankton relative biomass (RB) was square-root
transformed to dampen the impacts of dominant
species on both cluster and ordination analysis. A
combination of hierarchical agglomerative cluster
method with an average link and Partitioning around
medoids (PAM) method was used to identify relatively
homogeneous groups using the Bray-Curtis dissi-
milarity index. Phytoplankton communities in each
group were further characterized using indicator
species analysis[27] . Indicator species were defined as
the taxa with P values<0.05. P values of indicator
values for all taxa were determined using Monte Carlo
permutation tests (1000 times). Variation of phytoplan-
kton communities within each group and among groups
were summarized using non-metric multidimensional
scaling (NMDS), a multivariate ordination technique
commonly used in ecological community analysis[28].
Bray-Curtis dissimilarity indices were calculated
among the sites. NMDS projects each site into a
species-defined ordination space with two or more
dimensions based on their ranked dissimilarity. The
goodness-of-fit for the NMDS projections was
measured as a stress value which quantified the
deviation from a monotonic relationship between the
distance among sites in the original Bray-Curtis
dissimilarity matrix and the distance among sites in the
ordination plot. The NMDS was run 20 times each
with a random starting configuration. The final NMDS
dimension was selected based on the lowest stress
value among the best solutions.
All methods described above were performed
using R software[29]. Specifically, we used ‘labdsv’
package for indicator species analysis, ‘MASS’ and
‘vegan’ packages for NMDS and cluster analysis.
3 Results
3.1 Phytoplankton community composition in the
Huaihe River Basin
The diversity of phytoplankton in the Huaihe
River Basin was relative diverse. Phytoplankton
community included 8 phyla, 84 genera, 143 species.
Taxa richness of both Chlorophyta (31 genus, 54
species) and Bacillariophyta (24 genus, 39 species)
were much higher than other groups (e.g.,
Euglenophyta: 5 genus, 21 species, Cyanophyta: 13
genus, 17 species). The species richness varied from 5
to 61 with an average of 25. Approximately 85%
biomass consists of unicellular taxa with 7.61% of
colonial taxa and 7. 44% of filamentous algae. More
than 50% of the taxa were flagellates. Euplankton
(62.11%) was common in the HRB and only 24.53%
was tychoplankton. The species with highest occur-
rences included Cryptomonas erosa (88.9%), Cyclotella
meneghiniana (83.9%), Scenedesmus quadricauda (78.8%),
Chroomonas acuta (74.7%), Navicula cryptocephala
(71%), Oscillatoria sp. (64%) and Nitzschia palea
(60.4%). The dinoflagellates were the most important
group, accounting for 26.1% of the total biomass.
Ceratium hirundinella (8.44%) was primarily respon-
sible for this contribution. Bacillariophyta contributed
with 23.36% of the total biomass, C. meneghiniana
(9.15%) and N. palea (4.42%) were primarily respon-
sible for this contribution. Cryptophyta accounted for
19.5% of the total biomass. The most representative
species were C. erosa (11.69%) and C. acuta (6.21%).
3.2 Phytoplankton and environmental conditions
cluster analysis
Cluster analysis divided the sites into four groups
according to phytoplankton community composition
(Table 1). Group 1 was characterized by mainly
140 生 态 科 学 34 卷
Tab. 1 List of the dominant taxa and indicator taxa proportional biomass (PB) recorded in the HRB
Taxa Mean PB Taxon Motility Cat Indicator value P value
Group 1(62 sites)
Ceratium hirundinella 0.25 Dino Y I 0.5 0.001
Cryptomonas erosa 0.095 Cry Y I
Cyclotella meneghiniana 0.076 Bac N I
Chromulina sp. 0.071 Chry Y I
Chroomonas acuta 0.053 Cry Y I
Phormidium sp. 0.049 Cyan N F 0.31 0.001
Pandorina morum 0.027 Chlo Y C 0.291 0.024
Euglena oxyuris 0.027 Eug Y I
Cryptomonas ovata 0.025 Cry Y I
Oscillatoria sp. 0.025 Cyan Y F
Mallomonas sp. 0.016 Chry Y I 0.26 0.042
Nitzschia sp1 0.012 Bac Y I 0.19 0.002
Group 2 (33 sites)
Cyclotella meneghiniana 0.092 Bac N I
Cymbella tropica 0.079 Bac N I 0.42 0.001
Cryptomonas erosa 0.065 Cry Y I
Ceratium hirundinella 0.05 Dino Y I
Navicula cryptocephala 0.048 Bac Y I
Nitzschia palea 0.048 Bac Y I 0.24 0.032
Chroomonas acuta 0.035 Cry Y I
Nitzschia amphibia 0.035 Bac Y I 0.32 0.001
Gomphonema sp. 0.034 Bac N I 0.43 0.001
Melosira varians 0.03 Bac N C 0.11 0.027
Navicula sp. 0.03 Bac Y I 0.31 0.001
Synedra sp. 0.028 Bac N I 0.51 0.001
Chromulina sp. 0.026 Chry Y I
Fragilaria sp. 0.026 Bac N C 0.42 0.001
Melosira granulata 0.026 Bac N C
Synedra ulna 0.026 Bac N I 0.45 0.001
Cocconeis placentula 0.024 Bac N I 0.23 0.005
Euglena sp. 0.024 Eug Y I
Achnanthes sp. 0.023 Bac N I 0.48 0.001
Chlamydomonas sp. 0.022 Chlo Y I
Oscillatoria sp. 0.021 Cyan Y F
Group 3 (51 sites)
Cryptomonas erosa 0.23 Cry Y I 0.38 0.001
Cyclotella meneghiniana 0.12 Bac N I
Chroomonas acuta 0.09 Cry Y I
Nitzschia palea 0.063 Bac Y I
Cryptomonas ovata 0.049 Cry Y I
Ceratium hirundinella 0.03 Dino Y I
Nitzschia amphibia 0.029 Bac Y I
Chlamydomonas sp. 0.027 Chlo Y I
Melosira granulata 0.024 Bac N C
2 期 ZHU Weiju, et al. Phytoplankton community distribution patterns in a densely populated river basin, China 141
Continued
Taxa Mean PB Taxon Motility Cat Indicator value P value
Chromulina sp. 0.022 Chry Y I
Navicula cryptocephala 0.022 Bac Y I
Dinobryon sp. 0.021 Chry Y C
Nitzschia sp. 0.019 Bac Y I 0.36 0.001
Group 4 (71 sites)
Chromulina sp. 0.097 Chry Y I 0.35 0.001
Cyclotella meneghiniana 0.08 Bac N I
Cryptomonas erosa 0.078 Cry Y I
Chroomonas acuta 0.073 Cry Y I
Pseudanabaena sp. 0.058 Cyan N F 0.26 0.007
Euglena oxyuris 0.049 Eug Y I 0.24 0.022
Synedra sp. 0.046 Bac N I 0.45 0.001
Melosira granulata 0.045 Bac N C 0.2 0.041
Chlamydomonas sp. 0.043 Chlo Y I
Phormidium sp. 0.04 Cyan N F
Nitzschia palea 0.032 Bac Y I
Cryptomonas ovata 0.024 Cry Y I
Scenedesmus quadricauda 0.022 Chlo N C
Oocystis lacustris 0.015 Chlo N C 0.21 0.015
Synedra acus 0.012 Bac N I 0.44 0.038
Note: The following characteristics are specified in the table: GrTax, the taxonomic group (Cya, Cyanophyta; Din, Dinophyta; Cry, Cryptophyta;
Chry, Chrysophyta; Bac, Bacillariophyta; Chlo, Chlorophyta) ; Cat, the morphological category (i, individual cell; c, colonial; f, filamentous).
dinoflagellates including C. hirundinella. The most
characteristic taxa in group 2 were diatoms including C.
meneghiniana. Group 3 was characterized by
cryptophytes (C.erosa). The most characteristic taxa in
group 4 were the flagellate algae Chromulina sp.
Flagellate algae (C. hirundinella, C. erosa and
Chromulina sp.) were indicator taxa in group 1, 3 and
4, respectively. The indicator taxa in the group 2
included 11 diatom taxa, most of them are biraphids.
Several chain-forming small-size centric taxa and
araphid taxa dominated group 2. Nitzschia and
Navicula taxa with high or moderate motility in group
2 and 3 had the high relative biomass. Euglena oxyuris
was one of the dominant taxa in both group 1 and
group 4. Some filament blue-green algae were either
dominant or indicator taxa in group 1 and 4 (e.g.
Phormidium, Oscillatoria and Pseudanabaena).
The physical and chemical conditions in the
Huaihe River Basin were highly variable during the
period of this study (Table 2). The group 1 sites were
deeper and had higher TP, CODMn, conductivity and
pH. Conversely, group 4 sites had the lowest nutrient
concentrations and the highest SD but with the wider
river width. Sites in group 2 were small, fast-moving
streams in high elevation with lowest CODMn and
conductivity. Group 3 included the shallow streams/
rivers with highest TN, conductivity, and TSS.
3.3 Linking the phytoplankton community to the
environmental variables
NMDS based on algae relative biomass data
revealed a distinct pattern, group 1, 2 and 4 were
separated, but high phytoplankton community overlap
in group 3 with other groups (Fig. 2). Forward
selection indicated that certain quality variables
dominated in their relationship to phytoplankton
community (Table 3). Phytoplankton spatial patterns
were associated with both natural (e.g., elevation) and
anthropogenic (e.g., total suspended solids and
nutrients) factors. C. erosa and C. hirundirella
decreased along the first NMDS axis (Fig. 3) while N.
palea, N. cryptocephala, C. meneghiniana and E.
oxyuris showed the opposite pattern (Fig. 3). The first
142 生 态 科 学 34 卷
Tab. 2 Comparison of water quality variables among four groups in the HRB (average (min-max))
Variables Group 1 Group 2 Group 3 Group 4
Elevation (m) 69.66 (–17-265) 85.91 (0-507) 71.51 (–6-297) 18.57 (-22-297)
TN (mg·L− 1) 4.58 (0.2-19.4) 3.9 (0.1-18.2) 5.63 (0.2-41.5) 2.96 (0.1-10.2)
TP (mg·L− 1) 1.35 (0.11-12.82) 1.25 (0.19-9.63) 1.24 (0.03-6.78) 0.87 (0.02-7.99)
NP Ratio 4.49 (0.29-27.37) 7.14 (0.24-62.76) 9.4 (0.36-76.67) 7.78 (0.18-62.86)
CODMn (mg·L− 1) 33.06 (4-125) 12.72 (1-56) 21.1 (2-98) 18.21 (3-62)
Depth(m) 2.5 8(0.17-17) 1.52 (0.15-20) 1.35 (0.18-12.75) 2.5 (0.15-10.3)
Width (m) 101.42 (1.38-600) 53.37 (6.71-241) 93.18 (7-976.67) 139.57 (1-442)
Velocity (m·S− 1) 0.07 (0-0.5) 0.17(0-0.66) 0.11(0-1.022) 0.05(0-0.54)
WT (℃) 23.93 (17.28-30) 23.77 (18.3-31.02) 22.77 (17.67-30.54) 23.85 (17.95-29.16)
Cond(ms·cm− 1) 0.99 (0.003-2.94) 0.66 (0.15-1.39) 0.6 (0-2.37) 0.86 (0-2.1)
pH 8.53 (7.79-9.68) 8.42 (7.68-9.3) 8.49 (4.4-9.78) 8.33 (6.29-10.52)
Turbidity(NTU) 24.94 (2.61-193.7) 38.53 (1.18-440) 61.27 (0.9-811) 33.21 (1.56-173)
TSS(mg·L− 1) 104.43 (13-774.5) 149.29 (4.5-950) 183.44 (1-686.5) 194.36 (21.5-824.5)
SD(cm) 56.51 (10-214) 44.64 (9-167) 52.7 (5-180) 72 (15-225)

Fig. 2 An ordination plot of sites based on phytoplankton community dissimilarity showing all four groups and the sampled
sites in each group. (1: Group 1, 2: Group 2, 3: Group 3, 4: Group 4) Vector length and direction indicate correlation with the
ordination and point to the direction of most rapid change in the environmental variable.
Tab. 3 The R2 and P value of key environmental variables
in Huaihe River Basin in 2013
Variable R2 P value
Elevation 0.2 0.001
NP 0.04 0.006
CODMn 0.05 0.005
Width 0.05 0.007
Velocity 0.04 0.016
TSS 0.15 0.001
SD 0.03 0.036

NMDS axis was correlated with anthropogenic factors
(CODMn, pH, TSS and N:P ratio) while the second
NMDS axis was most strongly associated with natural
factors (e.g., elevation, stream size, and water velocity)
(Table 3).
4 Discussion
4.1 Phytoplankton community composition in the
HRB
We identified four phytoplankton groups in the
HRB. Group 1 was characterized by mainly dinofla-
gellates including Ceratium hirundinella. C. hirundi-
nella is a common and wide spread freshwater
dinoflagellate. This large, slow-growing phytoplankton
2 期 ZHU Weiju, et al. Phytoplankton community distribution patterns in a densely populated river basin, China 143

Fig. 3 Bubble plot for the major phytoplankton species in HRB (From left to right: Cryptomonas erosa, Ceratium hirundirella,
Nitzschia palea, Euglena oxyuris, Navicula cryptocephala, Cyclotella meneghiniana). Bigger bubble size corresponds to higher
relative biomass.
is typically found during late summer in water bodies
with a warm, stable epilimnion and lower concen-
trations of nutrient[30]. The ability of C. hirundinella to
assimilate both organic and inorganic phosphorus and
vertically migrate in a water column gives the species
an advantage[31]. Padisák (1985)[32], suggested that C.
hirundinella is an indicator of clean waters, as it avoids
water that is rich inorganic compounds. However,
according to Rosén (1981)[33], C. hirundinella is found
in mesotrophic to eutrophic systems and appears in
great quantities when water blooms occur. The diatom
species Cyclotella meneghiniana was primarily
indicative for group 2. As a small centric planktonic
species C. meneghiniana has a high surface-to-volume
ratio, rapid growth rates, and low settling velocities[34].
This species has been frequently observed as a
dominant species in eutrophic water bodies[35−36],C.
meneghiniana can dominate over other diatoms in a
silica-rich river environment[37]. Reynolds et al.
(2002)[38] reported that C. meneghiniana dominated
eutrophic small- and medium-size lakes. In this study,
most sites in group 1 and 2 located near the forest land
type, Katsiapi (2012)[39] found that dinophytes and
diatoms were closely associated to forests land.
The assemblages in group 3 were dominated by
pollution-tolerant species Cryptomonas erosa. Crypto-
monads populations generally reach a maximum
following periods of moderate turbulence[40] and when
grazing is low[41]. The Cryptomonads as a group, are
present in all the groups examined in the HRB (Table
1). Consequently, a certain phytoplankton community
composition is typically the result of multiple causes
(drivers) which often interact and vary geographically.
In our case most sites in group 3 were near the
human-affected location, such as agricultural land,
sand mining, poultry culture zone. The common
species such as C. erosa, C.ovata and C.acuta, can live
under poor light conditions, tolerate hydraulic
disturbance of high frequency and maintain a rapid
reproduction rate[42].
High abundances of diatom taxa with motility
(i.e., Nitzschia and Navicula) in group 2 and 3
suggested high sedimentation and silty benthic habitats
in the HRB[43]. Moreover, Kawamura et al. (2004)[44]
demonstrated that Nitzschia species are affected by
grazing pressure of gastropods. Some Nitzschia taxa
including N. palea are classified as nitrogen
heterotrophs which demand organic forms of nitrogen
such as amino acids and often dominate habitats with
organic pollution[45]. Research reported that N. palea
144 生 态 科 学 34 卷
and C. meneghiniana are nitrogen heterotrophs and N.
palea is a very good indicator of pollution[36].
Mixotrophic Chrysophytes prevailed in group 4 at
a relative low nutrient concentration and higher stream
order. Their habitat template is shallow, well mixed
oligotrophic environments[38]. Olrik(1998)[46]observed
that the mixotrophic Chrysophytes increased with
increasing concentration of CODMn and suspended
matter. Our study supported the Chromulina sp.
increased with the TSS. Euglena oxyuris, the
biological indicators of organic pollution[47], was one
of the dominant taxa in group 4. Euglenophytes occasio-
nally appear as abundant members of microflora. For
example, these taxa were observed to be more abundant
in the Danube and in the Tisza River[48]. In both of those
studies, the authors suggested that the organisms had
arrived via the slow-flowing side arms of the rivers
and/or via the oxbows of the floodplain. The prevalence
of euglenoids may be a further indication of organic
contamination in the HRB.
4.2 Environmental factors in HRB
The phytoplankton community composition
correlated with both natural (e.g., elevation) and
anthropogenic (e.g., total suspended solids and
nutrients) factors in the HRB. The elevation was found
to be the strongest factor (Table 2) grouping
phytoplankton community in the HRB. This factor
maybe a surrogate for land use effects. Total suspended
solids (TSS) was another significant variable affecting
the patterns of phytoplankton. In general, TSS
comprises organic and inorganic particles suspended in
the water such as silt, plankton and industrial wastes,
which can cause light-limiting conditions for phyto-
plankton growth[5]. But the motile algae are compara-
tively free of both resource limitation and disturbance
stress, because it has the physical capability of
selecting the most suitable habitat. The taxa migrate
diurnally in the water column which enables nutrient
retrieval from deeper nutrient-rich water layers[49]. In
our study, C. erosa and C. acuta were present in all the
groups, which agrees well with the finding that
cryptophytes seem to occur in different types of water
bodies forming pulses following declines of previously
dominating algae[50].
Among nutrients, our study showed that TN and
CODMn were the main factor controlling phytoplankton
growth in the HRB, especially impacted the growth of
flagellate algae (Fig. 2). C. erosa and C. acuta,
indicative of eutrophy, displayed positive relationships
with CODMn and TN (Fig. 2). Padisák et al. (2009)[51]
reported that Chroomonas belongs to Codon X2, a group
of algae in shallow, meso-eutrophic environments.
The important factors in shaping the structure of
phytoplankton assemblages in rivers were hydrological
regime parameters[52–53], but our results indicated the
relationship between phytoplankton community and
water velocity was weak (Table 2). Only some diatoms
showed positively with water flow, diatoms also have
heavy silicate shells that sink out of the water column
and require sufficient vertical mixing from high stream
flow to keep cells suspend in the water column[54]. The
centric diatoms C. meneghiniana grow well in low
light environments compared with other diatoms.
These diatoms are usually abundant in the spring when
vertical mixing is high and light is low[55].
4.3 Future work for phytoplankton community in
the HRB
Our results suggest that phytoplankton comm-
unity provide valuable information on environmental
conditions in the Huaihe River Basin. The relationships
between phytoplankton community and water quality
within each group may depend on the strength of the
water quality gradients, interactive effects of water
quality and habitat conditions, and phytoplankton
sampling design. Phytoplankton is abundant in rivers,
but attributing assemblages characteristics to specific
anthropogenic stressors or environmental factors is
challenging because the upriver origin and travel
distance of the phytoplankton are usually unknown.
Conventional phytoplankton sampling design, largely
developed for lakes, may not be effective to reflect
longitudinal patterns of phytoplankton assemblages
2 期 ZHU Weiju, et al. Phytoplankton community distribution patterns in a densely populated river basin, China 145
and their responses to environmental conditions within
a large river, especially when the longitudinal water
quality gradients are not very strong. Our study was
constrained in several ways in terms of sampling
design.
First, extensive construction of dams has greatly
dampened the seasonal and inter-annual stream-flow
variability of rivers, thereby altering phytoplankton
assemblages dynamics. A study specifically designed
to assess the effects of dams on water quality, habitat
structure, and phytoplankton assemblages could be
informative. Second, large river main-stems may be
substantially affected by tributaries. Phytoplankton
assemblages in large river likely are a mixture of local
resident taxa, washed-in other taxa from tributaries or
upstream sites, and euplankton. This study was not
specifically designed to assess the effects of tributaries
on phytoplankton assemblages. Our data showed a
large overlap in dominant taxa among the groups,
which suggests that some phytoplankton taxa maybe
transported from headwaters. Compared to lake and
wadeable streams, river phytoplankton-based assess-
ment poses several additional challenges in both
sampling design and data interpretation.

Acknowledgements The authors would like to
thank the staff of Nanjing University for the sampling
program. This project was supported by the National
Key Special Project of Sci-tech for Water Pollution
Control and Management (No.2012ZX07501002-003).
The authors gratefully acknowledge financial support
from China Scholarship Council. The authors also
thank the anonymous reviewers for improving the
quality of the manuscript.
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