Chinese Journal of Oceanology and Limnology
Vol. 28 No. 2, P. 295-303, 2010
DOI: 10.1007/s00343-010-9257-1
Temporal dynamics of phytoplankton communities in a
semi-enclosed mariculture pond and their responses to
environmental factors*
XU Henglong (许恒龙)†,††,**, MIN Gi-Sik††, CHOI Joong-Ki†††, AL-RASHEID Khaled A. S. ††††,
LIN Xiaofeng (林晓凤)†††††, ZHU Mingzhuang (朱明壮)†
†
Laboratory of Protozoology, KLM, Ocean University of China, Qingdao 266003, China
††
Department of Biological Sciences, Inha University, Incheon 402-751, Republic of Korea
†††
Department of Oceanography, Inha University, Incheon 402-751, Republic Korea
††††
Zoology Department, King Saud University, Riyadh 11451, Saudi Arabia
†††††
Laboratory of Protozoology, Key Laboratory of Ecology and Environmental Science in Guangdong Higher Education,
South China Normal University, Guangzhou 510631, China
Received Nov. 26, 2008; revision accepted Mar. 17, 2009
© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag Berlin Heidelberg 2010
Abstract
Variations in physical-chemical factors, species composition, abundance and biomass of
nano- and micro-phytoplankton assemblages, as well as their responses to environmental factors, were
investigated over a complete cycle (6 months) in a semi-enclosed shrimp-farming pond near Qingdao,
northern China. The aim was to establish the temporal patterns of phytoplankton communities and to
evaluate protists as suitable bioindicators to water quality in mariculture systems. A total of 34 taxa with
nine dominant species were identified, belonging to six taxonomic groups (dinoflagellates, diatoms,
cryptophyceans, chlorophyceans, euglenophyceans and chrysophyceans). A single peak of protist
abundance occurred in October, mainly due to chlorophyceans, diatoms and chrysophyceans. Two
biomass peaks in July and October were primarily due to dinoflagellates and diatoms. Temporal patterns
of the phytoplankton communities significantly correlated with the changes in nutrients, temperature and
pH, especially phosphate, either alone or in combination with NO3-N and NH3-N. Species diversity,
evenness and richness indices were clearly correlated with water temperature and/or salinity, whereas the
biomass/abundance ratio showed a significant correlation with NO3-N. The results suggest that
phytoplankton are potentially useful bioindicators to water quality in semi-enclosed mariculture systems.
Keyword: bioindicator; phytoplankton; environmental stress; microbial ecology; shrimp-farming;
temporal variations
1 INTRODUCTION
In aquatic ecosystems, phytoplanktonic protists
are the main component of the microplankton
community and play an important role as primary
producers in the microbial food web (Finlay et al.,
1998; Polat et al., 2007). With their rapid growth
rates, these protists may respond more quickly to
environmental changes than other eukaryotic
organisms and, thus, may serve as bioindicators to
water quality (Xu et al., 2008). Furthermore, some
bloom-forming protists are harmful to other aquatic
species and, thus, the cause of heavy economic losses
in coastal communities dependent on mariculture
(Tada et al., 2001; Madoni et al., 2007; Peštová et al., 2008).
Semi-enclosed mariculture water bodies are
usually small-sized, with poor water exchange and
high nutrient and/or contaminant inputs, which can
result in eutrophic or hypertrophic environments.
Furthermore, environmental conditions in this
specific ecosystem (e.g., water temperature, salinity,
pH, and nutrients) are often subject to high
variability on short temporal scales resulting in
*
Supported by the National Natural Science Foundation of China (Nos.
40976075, 30700069), a post-doctoral fellowship awarded to XU
Henglong by Inha University, a grant from the Center of Excellence in
Biodiversity Research, King Saud University and the 111 Project of China
(No. B08049)
*
* Corresponding author: xuhl@ouc.edu.cn
296
CHIN. J. OCEANOL. LIMNOL., 28(2), 2010
significant changes in the abundance, biomass,
diversity and community structure of microplanktons
(Nuccio et al., 2003).
As a part of our investigations on the dynamics of
protist communities, a 6-month baseline survey of
phytoplankton communities was also carried out in a
shrimp-farming pond near the coast of Qingdao,
northern China. It should be noted, however, that the
present study was restricted to nano- and
micro-planktonic protists; thus, no data are available
for picoplanktonic forms (0.2–2 μm) or cyanobacteria,
for example. This study mainly focused on: (1)
composition of the phytoplankton communities; (2)
temporal dynamics of the eukaryotic microalgae with
special regard to species number, abundance and
biomass; (3) correlations with environmental
variables, with the aim of documenting the
community dynamics of phytoplanktonic protists in a
semi-enclosed mariculture ecosystem.
2 MATERIALS AND METHODS
2.1 Study site
The shrimp-farming pond was located on the coast
of Laoshan Bay near Qingdao, northern China. The
shallow marine pond, in maximum depth of ~1.2 m
and a mud/sand bottom, covered an area of ~800 m2
connecting the sea via a long narrow canal that could
be closed by a sluice-gate. Shrimp juveniles were
introduced on June 15, 2002 and fed with an artificial
granular diet after 2 weeks. During the study period
(May–October), the water depth in the pond was
maintained at ~1.2 m by adding seawater via the
sluice-gate approximately every 2 weeks.
2.2 Sampling, identification, measurements and
analysis
Fifteen samples (designated 22-May, 3-June, etc.)
were collected every 10 days throughout the study
period (Xu et al., 2008). Sampling, identification,
enumeration and biovolume estimates of protists, as
well as the recording of environmental parameters,
were carried out following the scheme of Xu et al.
(2008) (Steidinger et al., 1997; Song et al., 2003; Xu
et al., 2008).
Temporal variations in and structural parameters
of phytoplankton communities were analyzed using
the PRIMER (Plymouth Routines in Multivariate
Ecological Research) package (Clarke et al., 1993).
A Bray–Curtis similarity coefficient matrix was
calculated on square-root-transformed data. Separate
clusters for both phytoplankton species and samples
were assigned by clustering (CLUSTER) analysis,
Vol.28
while temporal variations in community structures
were analyzed using multidimensional scaling (MDS)
ordination on Bray–Curtis similarities. Differences
between groups of samples were tested by the
ANOSIM submodule. The contribution of each
species to the average Bray–Curtis dissimilarity
between groups of samples and to similarity within a
group was examined by the SIMPER submodule.
Species diversity (H′), evenness (J′) and species
richness (D) of samples were calculated using the
diversity submodule of the PRIMER package (Xu et
al., 2008). Temporal variations in diversities were
analyzed using percentage of cumulative dominance
of species by plotting k-dominance curves with the
dominance plot submodule (Clarke et al., 1993).
For analyses of the relationship between biotic
data and environmental variables, the multivariate
biota–environment (BIOENV) submodule of the
PRIMER package and the univariate correlation
procedure of the SPSS statistical program (Ver. 11)
were used (Clarke et al., 1993; Xu et al., 2008).
Significant biota–environment correlations were
tested using the RELATE submodule of the PRIMER
package (Clarke et al., 1993).
3 RESULTS AND DISCUSSION
3.1 Environmental variables
The values for seven environmental variables in
each of the 15 samples are shown in Table 1. Water
temperature ranged from 19.5 to 31°C; leveling off
between May and June, increasing slowly, but then
dropping sharply after peaking in late August.
Salinity averaged 28.7, maintaining high levels
between May and the middle of July, dropping
sharply to the lowest level (8.1) at the end of July
(22-Jul sample) due to heavy rainfall, and reverting
to its original levels from the end of July to the
beginning of September. pH values remained
relatively stable, ranging 7.24–8.05. The average
dissolved inorganic nitrogen (DIN) value over the
sampling period was 5.5 mg L-1. There was an initial
decline in DIN between May and mid-June, followed
by an increase after the introduction of shrimp
juveniles. NH3-N (mean 3.10 mg L-1) represented
56% of total DIN and exhibited an increasing trend,
whereas NO3-N (mean 0.89 mg L-1) and NO2-N
(mean 1.31 mg L-1) leveled off steadily at lower
values. The concentrations of soluble reactive
phosphate (SRP) ranged from 0.01 to 6.24 mg L-1,
being much higher in the period after the introduction
of shrimp juveniles (13-June) than beforehand.
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XU et al.: Temporal dynamics of phytoplankton communities and their responses to environmental factors
297
Table 1 Environmental parameters in mariculture pond water samples from May to October 2002
Samples
T (°C)
S
pH
NH3-N(mg L-1)
NO2-N (mg L-1)
NO3-N (mg L-1)
SRP (mg L-1)
22-May
03-Jun
13-Jun
26-Jun
08-Jul
18-Jul
26-Jul
05-Aug
15-Aug
25-Aug
04-Sep
14-Sep
24-Sep
04-Oct
14-Oct
24.00
26.80
24.80
25.50
27.70
27.30
27.70
27.70
29.10
31.00
27.70
21.00
23.00
23.00
19.50
26.80
32.50
32.30
31.50
34.30
32.70
8.30
21.70
25.10
28.50
31.30
29.20
32.50
32.30
32.10
7.97
7.67
7.53
7.54
7.74
7.79
7.87
8.05
7.58
7.87
7.84
7.59
7.75
8.01
7.95
1.30
2.50
0.70
1.20
2.00
2.50
3.60
3.50
1.80
2.60
5.50
1.60
3.90
5.90
4.30
4.40
1.10
1.10
0.80
1.00
0.90
0.90
1.30
0.90
0.80
1.30
1.10
1.80
2.40
2.10
0.70
1.50
0.90
0.70
1.10
0.60
0.60
1.20
1.00
1.20
1.20
0.90
1.20
1.10
1.30
0.01
0.07
0.08
1.16
6.48
4.34
4.80
5.92
1.86
5.10
5.72
4.56
6.24
5.60
5.20
S = salinity; SRP = soluble reactive phosphate; T = temperature
3.2 Species composition and temporal distribution
Investigations on other semi-enclosed marine
water bodies, for example, various Mediterranean
lagoons, such as the Varano Lagoon near the Adriatic
Sea, and the Center-Western Sardinia lagoon,
revealed that protist abundance shows distinct peaks
for a limited number of species in summer and,
occasionally, in winter, mainly diatoms and
flagellates (e.g. chlorophyceans, cryptophyceans,
and euglenophyceans) (Gilabert, 2001; Nuccio et al.,
2003). To the best of our knowledge, there are no
previous studies on protist communities in
semi-enclosed shrimp-farming ponds with which to
compare our data; nevertheless, some points of
interest can be noted.
The present study confirmed some characteristics
of the phytoplankton communities. A total of 34
phytoplankton taxa were identified during the
6-month survey, comprising six taxonomic
assemblages (cryptophyceans, chlorophyceans,
dinoflagellates, euglenophyceans, chrysophyceans
and diatoms) (Table 2). Dinoflagellates, diatoms and
cryptophyceans were the most common forms,
accounting for 29.41%, 29.41%, 14.71% and 11.76%,
respectively, of the species number recorded; the
other two groups were represented by comparatively
few species (Fig.1). These findings are similar to
other reports for marine lagoons (Gilabert, 2001;
Nuccio et al., 2003).
A dendrogram, showing species-abundance data
of phytoplankton communities in the 15 samples,
was plotted using group-average clustering on
Bray–Curtis similarities from square root
transformed abundance data (Fig.2A). Cluster
analysis resulted in the 34 species falling into six
groups at a 20% similarity level: group I comprised
15 common/dominant species with a high occurrence
frequency and abundance, representing the primary
contributors to protist community succession
(Fig.2A: I); the other five groups included those species
with comparative low abundance and/or occurrence
(Fig.2A: II–VI). This finding suggests that, in this
particular shrimp-farming cycle, protist communities
can be divided into several assemblages in terms of
temporal species distribution during succession, each
comprising species with similar occurrence and
abundance.
There were nine dominant species, the individual
abundance of which exceeded 40% of the total at
some point during the sampling period: Hillea
fusiformis, Hillea marina, Pseudoscourfieldia marina,
Peridinium sp. 4, Peridinium sp. 2, Thalassiosira sp. 1,
Chromulina sp. Mamiella sp. and Teleaulax acuta.
The abundance of five of these species (Hillea
fusiformis, H. marina, Pseudoscourfieldia marina,
Teleaulax acuta and Peridinium sp. 4) had a single
high peak and, at least, one other smaller peak,
whereas the other four species (Peridinium sp. 2,
Thalassiosira sp. 1, Chromulina sp. and Mamiella sp.)
occurred in significant numbers on only one occasion
(Fig.2B). These nine dominant species showed a
clear succession from May to October (Fig.2B).
3.3 Temporal variation in species number,
abundance and biomass
The species numbers in the 15 samples varied
considerably with respect to the shrimp-farming
cycle. The temporal variation in species number had
a unimodal distribution during the 6-month period,
CHIN. J. OCEANOL. LIMNOL., 28(2), 2010
298
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Table 2 Phytoplankton species identified in 15 samples,
including abundance, biomass, and occurrence
Species
Abundance Biomass Occurrence (%)
Chrysophyceans
Chromulina sp.
++++
+++
53
Ochromonas sp.
++
+
7
Euglenophyceans
Euglena sp.1
+
+
7
Euglena sp.2
++
++
40
Eutreptia sp.
+
+
7
20
Cryptophyceans
Chroomonas marina
+
+
Hemiselmis sp.
+
+
7
Hillea fusiformis
++
++
60
Hillea marina
++
+
20
Teleaulax acuta
++
++
73
Dinoflagellates
Gymnodium sp.
++
+
13
Gyrodinium spirale
++
+++
60
Peridinium sp.1
+
++
53
Peridinium sp.2
++
+
20
Peridinium sp.3
++
++
47
Peridinium sp.4
++
++++
60
Prorocentrum minimum
++
++
73
Prorocentrum rostratum
++
++
33
Prorocentrum sp.1
+
+
7
Prorocentrum sp.2
+
+
13
Chlorophyceans
Chlorella sp.
+
+
60
Dictyocha sp.
+
+
53
Mamiella sp.
++++
++
20
++
+
47
Achnanthes sp.
+
++
27
Chaetoceros sp.1
++
+
7
Chaetoceros sp.2
++
+
20
Ephenera sp.
++++
+++
13
Gyrosigma sp.
+
++
20
Manguinea sp.
+
+
13
Minidiscus sp.
+
+
13
Navicula sp.1
+
++
20
Navicula sp.2
+
+
60
+++
+
7
Pseudoscourfieldia marina
Diatoms
Thalassiosira sp.
Abundance (ind. ml-1): += 0–50; ++ = 50–500; +++= 500–5 000; ++++= >5 000.
Biomass (μg L-1): + = 0–10; ++ = 10–100; +++ = 100–1 000; ++++ ≥1 000
with a peak in August (Fig.3A). Dinoflagellates and
diatoms were primarily responsible for this peak
(Fig.3A). The lowest species number (three species)
was found in the 26-Jun sample, i.e., the first sample
after the introduction of shrimp juveniles (Fig.3A).
The temporal variation in phytoplankton
abundance exhibited a unimodal distribution
(Fig.3B). Abundance values were relatively low
from May to September, followed by a peak in
October. Chlorophyceans (Mamiella sp., etc.), chryso
phyceans (Chromullina sp., etc.) and diatoms
(Ephenera sp., etc.) were primarily responsible for
Fig.1 Common taxonomic assemblages (%) in
phytoplankton communities between May and October 2002
the October peak (Fig.3B). Of total protist abundance
for the 6-month period, chlorophyceans accounted
for 48.861%, chrysophyceans 26.04%, and diatoms
18.54% compare to dinoflagellates (3.21%),
cryptophyceans (3.00%) euglenophyceans (0.36%)
(Fig.4A).
The temporal variation in biomass showed a clear
bimodal distribution, with one peak in July and
another in October, but only the latter corresponded
to the abundance peak (Fig.3C). Dinoflagellates (e.g.,
Peridinium sp.4) were responsible for the July peak,
while diatoms, dinoflagellates and chrysophyceans
were the major contributors to the October peak
(Fig.3C). Dinoflagellates and diatoms accounted for
71.53 and 18.97%, respectively, of the total biomass,
compared
to
chrysophyceans
(4.26%),
cryptophyceans (3.53%), euglenophyceans (1.09%)
and chlorophyceans (0.62%) (Fig.4B).
Compared to the southern Ross Sea,
phytoplankton abundance and biomass in this
specific semi-enclosed pond are very high. The
abundance of phytoplankton in our sampling pond,
for example, ranged between 4.40×105 to
1.23×108 ind. L-1, whereas in the southern Ross Sea,
the abundances only ranged 0.20×105–7.07×106 ind. L-1
(Dennett et al., 2001). In comparison with a coastal
lagoon, the abundance of phytoplankton in the
mariculture pond were also high; for example, the
maximum number of phytoplankton only reached
10×106 ind. L-1 in the Orbetello lagoon (Nuccio et al.,
2003). However, species diversity is generally lower
in a semi-enclosed ecosystem than in other sites. For
example, the diversity of dinoflagellates, diatoms,
chlorophyceans cryptophyceans and chrysophyceans
in the pond were low, but their respective abundance
was very high; therefore, they are likely to make a
significant contribution to microplankton dynamics
(Gilabert, 2001; Nuccio et al., 2003).
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XU et al.: Temporal dynamics of phytoplankton communities and their responses to environmental factors
299
Fig.2 Cluster analysis of 34 phytoplankton species in 15 samples, using group-average clustering on Bray–Curtis similarities
from square root transformed abundance (A) and temporal succession of the nine dominant phytoplankton species (B)
(I–VI = group I–VI)
Studies from other Mediterranean lagoons, e.g.,
the Orbetello lagoon, a central western Sardinia
lagoon or the Spanish Mar Menor, demonstrated that
phytoplankton abundance represent a sudden
increase in a limited number of species, with the
highest values in summer and/or a peak in winter.
Mainly flagellates belonging to different classes
(chlorophyceans, cryptophyceans and euglenophyceans)
and summer diatom blooms characterize the
composition of phytoplankton (Gilabert, 2001;
Nuccio et al., 2003).
Our study also reveals that the abundance,
community
succession
and
diversity
of
phytoplankton communities changed with the
seasonal cycle, despite the short-time period. The
6-month cycle of phytoplankton diversity showed a
sharp seasonality (high in summer/low in autumn);
the temporal pattern well reflecting variations in
community structure due to the changes in
environmental conditions. These findings are similar
to previous reports (Nuccio et al., 2003).
3.4 Temporal patterns in community structure
Fig.3 Temporal variations in phytoplankton species number
(A), abundance (B) and biomass (C) between May and
October 2002
Although six taxonomic assemblages appeared in
almost all samples, the patterns of communities in the
15 samples represented a clear temporal succession
300
CHIN. J. OCEANOL. LIMNOL., 28(2), 2010
Vol.28
Fig.4 Common taxonomic assemblages (%) contributing to average abundance (A) and biomass (B) of phytoplankton
communities between May and October 2002
(Fig.5A,B,C). In terms of relative abundance, protist
communities are distinguished by five temporal
patterns: (1) cryptophyceans dominated the protist
communities from May to June, followed by a bloom
of chlorophyceans in the 26-Jun sample; (2)
dinoflagellates dominated between June and August;
(3) diatoms in August; (4) chrysophyceans in
September; (5) chlorophyceans in October (Fig.5A).
Although dinoflagellates were the most important
contributors to biomass, temporal variation also
showed a similar scale to relative abundance
(Fig.5B). These findings are consistent with previous
findings, e.g., the increased number of
cryptophyceans at cooler temperatures, the high
abundance of certain flagellate groups, such as the
chlorophyceans and cryptophyceans, associated with
low abundance of diatoms, and the co-dominance of
diatoms and dinoflagellates during the summer
period (Dennett et al., 2001; Nuccio et al., 2003).
A dendrogram was plotted using group-average
clustering on Bray–Curtis similarities from square
root transformed species-abundance values (Fig. 5D).
Cluster analysis assigned the 15 assemblages to five
groups (I–V) at a 25% similarity level. Group I was
composed of the first three samples, which
represented the temporal pattern of phytoplankton
communities before the introduction of juvenile
shrimp, while the other groups represented the
community structure, with high frequency changes,
during the shrimp-farming cycle. MDS ordination
showed the temporal pattern of 15 communities with
five groups appearing at separated locations on the
plot, which is in agreement with the dendrogram (Fig.
5E). These findings suggest that during the entire
farming cycle the temporal dynamics of
phytoplankton communities represented a successive
variation, undergoing different temporal scales with
changes in environmental conditions.
Fig.6 showed the diversity of protists using
k-dominance curves plotted by the PRIMER package.
The higher the percentage of cumulative dominance
in the communities, the lower the diversity. Thus,
changes in diversity are clearly revealed by the
k-dominance curves. It was clearly shown that the
k-dominance curves of the samples in each group
were more or less crossed, which meant there were
similarities between samples within the same group
(Fig.6A–E). For example, group II (Fig.6F) had the
lowest diversity of the five groups; similar diversities
were exhibited among groups I, III and V (Fig.6F:
1–3, 5–7 and 12–15); group IV (Fig.6F: 5–7) had the
highest diversity. These results suggest that
k-dominance curves can reveal the variations in
diversity and structure of microbial communities
associated with the changes in environmental
conditions, and can, thus, be used as an effective
graphic parameter in monitoring water quality.
3.5 Interaction between biota and environmental
variables
Species diversity, evenness and richness indices
are commonly employed in community-level
investigations and are amenable to simple statistical
analyses (Ismael et al., 2003). In our case, however,
univariate correlation analysis showed that these
ecological parameters correlated at a high level of
significance with water temperature and salinity only,
but failed to show significant correlation with
nutrients (Table 3). All three indices sharply
increased at the end of June and exhibited a peak in
the middle of August. This might have been due to
low salinity and high water temperature during this
period.
Correlations
(Spearman,
SPSS)
between
environmental variables and abundance, biomass,
biomass/abundance ratio (B/A) in 15 protist samples
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301
Fig.5 Temporal succession of relative abundance (A), relative biomass (B) and relative species number (C) of phytoplankton
during the 6-month period, and cluster analysis (D) and MDS ordination (E) for the 15 samples on Bray-Curtis similarity
from square root transformed species-abundance data
1 = 22-May; 2 = 02-Jun; 3 = 13-Jun; 4 = 26-Jun; 5 = 08-Jul; 6 = 18-Jul; 7 = 26-Jul; 8 = 05-Aug; 9 = 15-Aug; 10 = 25-Aug; 11 = 04-Sep; 12 = 14-Sep; 13 = 24-Sep;
14 = 04-Oct; 15 = 14-Oct; I-V = group I to V
Fig.6 Variations in k-dominance curves for 15 samples (For symbols, see Fig.5)
CHIN. J. OCEANOL. LIMNOL., 28(2), 2010
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Table 3 Correlations between environmental variables and species diversity (H′), species evenness (J′), species richness (d),
abundance, biomass and biomass/abundance ratio (B/A) for the protist communities
Abundance
Biomass
B/A
H′
J′
D
T
-0.630**
-0.267
0.265
0.509*
0.504*
0.406
S
0.410
0.236
0.106
*
-0.389
-0.338
-0.585*
0.247
pH
0.438
0.480
-0.002
0.074
0.027
NH3-N
0.591*
0.595**
-0.010
-0.013
-0.066
0.130
NO2-N
0.566*
0.335
-0.296
-0.237
-0.229
-0.194
NO3-N
0.345
0.048
-0.446*
0.007
-0.071
0.218
*
0.246
0.103
0.041
0.160
DIN
0.294
0.504
SRP
0.693**
0.574*
-0.211
-0.103
-0.140
0.048
DIN+SRP
0.581*
0.766**
0.111
0.038
-0.010
0.163
*
P < 0.05; ** P < 0.01; DIN = dissolved inorganic nitrogen; see Table 1 for other abbreviations
are shown in Table 3. Both protist abundance and
biomass basically exhibited significant positive
correlations with nutrients (Table 3). However, the
biomass/abundance ratio (B/A) had a significant
negative correlation with the nutrient NO3-N
(Table 3). Related analysis via PRIMER revealed
that the temporal variations in phytoplankton
communities was significantly correlated with
environmental variables in terms of species
abundances (r=0.27; P=0.018). BIOENV analysis
showed that the highest correlation occurred with
either the combination of five variables (water
temperature, NH3-N, NO2-N, NO3-N, SRP) in
abundance or the combination of two variables (pH
and SRP) in biomass. It was also found that the
combination of water temperature and NH3-N was
the only combination that showed the highest
correlation with biota in abundance, while SRP is
was the only variable that exhibited the highest
correlation with biota in biomass.
These results suggested that phytoplankton might
be useful bioindicators of water quality, especially
with respect to eutrophication. In addition, it should
be noted that the biomass/abundance (B/A) ratio of
the assemblages, i.e., the mean body-size of species
in sample, showed a strong negative correlation with
NO3-N, i.e., the higher NO3-N, the more small-sized
species were present.
It should be noted, however, that the present study
was restricted to nano- and micro-planktonic protists.
Other methods, such as denaturing gradient gel
electrophoresis (DGGE) and real-time PCR, which
have previously been used to analyze prokaryote
communities in mariculture ponds, might usefully be
employed to expand the range of the eukaryotic
phytoplankton data to include, for example, the
picoplanktonic forms (Cytryn et al., 2003).
In summary, the results of this survey demonstrate
that: (1) phytoplankton were abundant and diverse in
the semi-enclosed mariculture pond near Qingdao,
northern China; (2) they were correlated with various
environmental parameters including nutrients, such
as nitrogen and phosphate; and (3) phytoplankton
might be useful bioindicators to eutrophication in
such systems. However, further research is needed on
other semi-enclosed mariculture water bodies and
over an extended time period to verify these
conclusions.
4 ACKNOWLEDGEMENTS
Especial thanks are due to Dr. A. Warren, Natural
History Museum, London, UK, and Professor Weibo
SONG, Laboratory of Protozoology, KLM, Ocean
University of China, China, for helpful discussions
and preparing the manuscript.
References
Clarke K R, Ainsworth M. 1993. A method of linking
multivariate community structure to environmental
variables. Mar. Ecol. Progr. Ser., 92: 205-219.
Cytryn E, Gelfand I, Barak Y, van Rijn J, Minz D. 2003.
Diversity of microbial communities correlated to
physiochemical parameters in a digestion basin of a
zero-discharge mariculture system. Environ. Microbiol.,
5: 55-63.
Dennett M R, Mathot S, Caron D A, Smith Jr. W O, Lonsdale
D J. 2001. Abundance and distribution of phototrophic
and heterotrophic nano- and microplankton in the
southern Ross Sea. Deep-Sea Res. II, 48: 4 019-4 037.
Finlay B J, Esteban G F. 1998. Freshwater protozoa:
Biodiversity and ecological function. Biol. Conserv., 7:
1 163-1 186.
Gilabert J. 2001. Seasonal plankton dynamics in a
Mediterranean hypersaline coastal lagoon: the Mar
Menor. J. Plankton Res., 23: 207-217.
Ismael A A, Dorgham M M. 2003. Ecological indices as a tool
for assessing pollution in El-Dekhaila Harbour
(Alexandria, Egypt). Oceanologia, 45: 121-131.
No.2
XU et al.: Temporal dynamics of phytoplankton communities and their responses to environmental factors
Madoni P, Braghiroli S. 2007. Changes in the ciliate
assemblage along a fluvial system related to physical,
chemical and geomorphological characteristics. Eur. J.
Protistol., 43: 67-75.
Nuccio C, Melillo C, Massi L, Innamorati M. 2003.
Phytoplankton abundance, community structure and
diversity in the eutrophicated Orbetello lagoon (Tuscany)
from 1995 to 2001. Oceanol. Acta, 26: 15-25.
Peštová D, Macek M, Pérez M E M. 2008. Ciliates and their
picophytoplankton-feeding activity in a high-altitude
warm-monomictic saline lake. Eur. J. Protistol., 44:
13-25.
Polat S, Koray T. 2007. Planktonic dinoflagellates of the
northern Levantine Basin, northeastern Mediterranean
303
Sea. Eur. J. Protistol., 43: 193-204.
Steidinger K, Tangen K. 1997. The planktonic Marine
Flagellates. In: Tomas C R ed. Identifying Marine
Phytoplankton. Academic Press, San Diego. p. 591-730.
Tada K, Morishita M, Hamada K, Montani S, Yamada M.
2001. Standing stock and production rate of
phytoplankton and a red tide outbreak in a heavily
eutrophic embayment, Dokai Bay, Japan. Mar. Pollut.
Bull., 42: 1 177-1 186.
Xu H, Song W, Warren A, Al-Rasheid K A S, Al-Farraj S A,
Gong J, Hu X. 2008. Planktonic protist communities in a
semi-enclosed mariculture pond: structural variation and
correlation with environmental conditions. J. Mar. Biol.
Assoc. UK, 88: 1 353-1 362.