Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 1 of 44
1
Dissolved organic carbon and relationship with bacterioplankton community
2
composition in three lake regions of Lake Taihu, China
3
Xinghong Pang, Hong Shen, Yuan Niu, Xiaoxue Sun, Jun Chen, and Ping Xie
4 5
X. H. Pang. Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of
6
Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese
7
Academy of Sciences, Wuhan, Hubei, PR China; Institute of Huai river water resources
8
protection, Huaihe River Water Resources Protection Bureau, Bengbu, Anhui, PR China.
9 10
E-mail: [email protected] Y. Niu. Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of
11
Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese
12
Academy of Sciences, Wuhan, Hubei, PR China; Research Center For Lake Ecology and
13
Environment, Chinese Research Academy of Environmental Sciences，Beijing, China.
14
E-mail: [email protected]
15
X. X. Sun. College of Fishery, Huazhong Agricultural University, Wuhan, Hubei, PR
16
China.
17
E-mail: [email protected]
18
J. Chen. Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of
19
Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese
20
Academy of Sciences, Wuhan, Hubei, PR China.
21
E-mail: [email protected]
22
Corresponding authors: Hong Shen (Donghu Experimental Station of Lake Ecosystems,
23
State Key Laboratory of Freshwater Ecology and Biotechnology of China, Institute of
24
Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, PR China.e-mail:
25
[email protected]),
26 27
Ping Xie (Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese 1
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 2 of 44
28
Academy of Sciences, Wuhan, Hubei, PR China. Tel./fax: +86 27 68780622. E-mail:
29
[email protected])
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
2
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 3 of 44
51
Abstract: In order to clarify the relationships between dissolved organic carbon (DOC) and
52
bacterioplankton community composition (BCC), a one-year survey (June, 2009 - May, 2010)
53
was conducted in three regions of Lake Taihu (Meiliang Bay, Lake Center and Eastern Taihu),
54
China. Polymerase Chain Reaction-denaturing gradient gel electrophoresis (PCR-DGGE) was
55
used to analyze the composition and heterogeneity of the bacterioplankton community.
56
Canonical correspondence analysis (CCA) was used to explore the relationships between
57
DOC concentration and BCC. We found a significant negative correlation between DOC
58
concentration and bacterioplankton community diversity (measured as the Shannon-Wiener
59
index (H')). The results show that spatial variation in the bacterioplankton population was
60
stronger than the seasonal variation and that DOC concentration influences BCC in Lake
61
Taihu. DOC concentration, followed by macrophytes biomass, water turbidity and
62
phytoplankton biomass were the most influential factors which account for BCC changes in
63
Lake Taihu. More detailed studies on the relationship between DOC concentration and BCC
64
should focus on differences in DOC concentrations and quality among these lake regions.
65
DOC had a significant impact on BCC in Meiliang Bay. The relationship between DOC and
66
BCC in two other regions studied (Lake Center and Eastern Bay) was weaker. The results of
67
this study add to our understanding of the BCC in eutrophic lakes, especially regarding the
68
role of the microbial loop in lake ecosystems.
69 70
Key words: dissolved organic carbon (DOC), bacterioplankton community composition
71
(BCC), denaturing gradient gel electrophoresis (DGGE), canonical correspondence analysis
72
(CCA), Lake Taihu
73
3
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 4 of 44
74 75
Introduction In freshwater ecosystems, bacterioplankton plays a significant role in nutrient cycling
76
and decomposition of organic matter and acts as a food source for higher trophic levels (Cole
77
et al. 1988; Caron 1994; Cotner and Biddanda 2002). Thus, a more comprehensive
78
understanding of bacterioplankton genetic diversity and the relationships with different
79
environmental factors is needed (Kolmonen et al. 2011). Previous researches have
80
investigated the relationships between environmental factors and bacterioplankton community
81
composition (BCC) in various aquatic ecosystems (Wu et al. 2006; Van der Gucht et al. 2007)
82
and experimental systems (Lindström et al. 2005; Li et al. 2011). Water temperature (Kan et al.
83
2007; Shade et al. 2007), pH (Lindström and Leskinen 2002; Yannarell and Triplett 2005),
84
protozoan predation (Langenheder and Jürgens 2001), phytoplankton (Höfle et al. 1999;
85
Rooney-Varga et al. 2005), nutrient availability (Berdjeb et al. 2010), vegetation cover
86
(Declerck et al. 2005), salinity (Laque et al. 2010) and DOC (Kritzberg et al. 2005) have been
87
identified as factors governing BCC. The assimilation of DOC by bacterioplankton is a key
88
process in the flow of energy (carbon) through bacteria and bacterioplankton known as the
89
“microbial loop” (Azam et al. 1983; Tranvik 1992). However, the effects of DOC on BCC in
90
freshwater ecosystems are not fully understood (Crump 2003; Jones 2009).
91
Previous studies (Haukka et al. 2005; Nelson 2009; Li et al. 2011a) investigated linkages
92
between DOC and BCC and found that the effects of DOC on BCC depend on trophic
93
condition. In oligotrophic and ultraoligotrophic lakes, BCC is significantly influenced by
94
DOC (Crump et al. 2003; Kritzberg et al. 2005). In oligotrophic lakes, high DOC
95
concentrations are associated with low taxon richness of bacterioplankton communities (Fujii 4
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 5 of 44
96
et al. 2011). However, in eutrophic lakes, there is a divergence in the relationship between
97
DOC and BCC. Fujii et al. (2011) showed that there were no links between DOC and BCC in
98
eutrophic lakes. Eiler et al. (2003) found that both quantity and quality of DOC can control
99
BCC in eutrophic lakes. An enclosure experiment in Lake Taihu found a positive correlation
100
between DOC concentration and heterotrophic bacterioplankton abundance (Li et al. 2011a).
101
A shift in quality of organic matter might cause shifts in BCC, especially in dominant
102
bacterioplankton (Eiler and Bertilsson 2004), which might relate to the specific function of
103
some heterotrophic bacterioplankton. For example, -Proteobacteria (Rhodobacteraceae) may
104
play an important role in decomposing vascular plant-derived DOC (van Hannen et al. 1999);
105
The Cytophagaceae Bacteroidetes were found to dominate during diatom blooms (Riemann et
106
al. 2000). β -Proteobacteria can decompose many aromatic substrates under anaerobic
107
conditions (Li et al. 2011b).
108
Our knowledge about factors regulating bacterioplankton composition is far from
109
comprehensive. In this study, we investigated how DOC concentration affects BCC in large
110
shallow lakes such as Lake Taihu which contain lake regions with varying trophic conditions.
111
We hypothesized that BCC could vary along with DOC concentration in Lake Taihu and that
112
DOC might play different roles in controlling BCC in various lake districts. Lake Taihu is a
113
eutrophic shallow subtropical lake located in the southeast Yangtze River Delta Region. Lake
114
Taihu is the third largest freshwater lake in China, with a surface area of 2338 km2 (maximum
115
length, 68.5 km, maximum width 56 km, maximum depth, 2.6 m; Qin et al. 2004). Lake Taihu
116
experiences cyanobacteria blooms every year in certain regions, which are one of the most
117
important problems related to water quality in Lake Taihu. Because microbial communities 5
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 6 of 44
118
play an important role in energy transfer, improved knowledge of microbial diversity and
119
function in freshwater lakes is needed (Hanh 2006).
120
Since first publication by Muyzer et al. (1993), the PCR- DGGE fingerprinting has been
121
intensively used in environmental microbiology and recognized to give an acceptable view on
122
differences and similarities of the populations of microbial communities (Muyzer and Smalla
123
1998; Lindström and Leskinen 2002; Lindström and Bergström 2005; Niu et al. 2011).
124
Likewise, Canonical correspondence analysis (CCA) has been frequently used to evaluate the
125
effects of environmental variables on the BCC and explained successfully the dynamics of
126
BCC (Simek et al. 2001; TerBraak and Smilauer 2002; Wu et al. 2007; Berdjeb et al. 2010).
127
In the current study, to test our hypothesis, we used PCR-DGGE and CCA to investigate the
128
temporal and spatial similarities and differences of BCC and the influential factors of BCC in
129
Lake Taihu.
130 131 132 133 134 135 136 137 138 139
6
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 7 of 44
140
Materials and methods
141
Study sites and sampling design
142
Based on differences in physical-chemical parameters and macrophyte and plankton
143
community structure, Lake Taihu can be divided into several distinct ecological regions.
144
Meiliang Bay, a hypertrophic basin in northern Lake Taihu, has a surface area of ca. 100 km2
145
and a mean depth of 1.89 m. Hypereutrophication of Meiliang Bay has resulted from
146
discharge of domestic and industrial wastewater from the Liangxi and Zhihu rivers. The
147
central region of Lake Taihu is characterized by low transparency due to wind-driven
148
sediment resuspension (Chen et al. 2003; Zhang et al. 2003). Sediment resuspension
149
facilitates exchange of organic matter and nutrients among water, sediment pore water and
150
sediment (Xing and Kong 2007). The eastern region of Lake Taihu is covered by various
151
submersed macrophytes. Thus, the eastern region is characterized by clear water, a diverse
152
fish population, and relatively low phytoplankton biomass (Scheffer et al. 1993; Qin et al.
153
2004; Wu et al. 2007). Thus the three sampling sites in this study were located at Meiliang
154
Bay (site M, 31º28＇07" N, 120º10＇51" E), Lake Center (site L, 31º14＇44" N, 120º13＇
155
54" E) and Eastern Taihu (site E, 31º03＇44" N, 120º29＇13" E; Fig.1). Water samples were
156
collected monthly from June 2009 to May 2010.
157
Sampling and field factor analyses
158
Water samples were collected at the surface (0-0.5 m) using a 5 litre Schindler sampler.
159
Water temperature (Temp), dissolved oxygen (DO), conductivity (Cond), total dissolved solid
160
(TDS), water turbidity (Turb), pH, and Secchi depth were measured in situ with a YSI 6600
161
Multi-Parameter Water Quality Sonde. Macrophyte coverage was estimated at the same time. 7
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 8 of 44
162
For determination of total bacterial abundance, water samples (50 ml) were fixed with
163
glutaraldehyde to a 2% final concentration. A 1 litre water sample was preserved by addition
164
of 1% Lugol’s iodine solution for identification and counting of phytoplankton. Zooplankton
165
were concentrated by screening 10 litre water samples through a 64 µm plankton net and
166
fixed with formalin (37%). For bacterioplankton composition analysis, bacterioplankton
167
samples (200-300 ml of water) were filtered on 0.2µm-pore-size filters after filtering through
168
5.0 µm-pore-size filters (diameter 47 mm; Whatmann, UK). All filters were preserved at
169
-80°C until analysis.
170
Chemical analyses
171
Total nitrogen (TN), total dissolved nitrogen (TDN), nitrate (NO3--N), ammonium
172
(NH4+-N), nitrite (NO2--N), total phosphorus (TP), phosphate (PO43--P), total dissolved
173
phosphorus (TDP) and Chlorophyll a (Chl a) were analyzed according to standard methods
174
(Jin and Tu, 1990). Dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC)
175
were measured with a TOC analyzer (Model 1010, USA).
176
Bacterioplankton abundance
177
2 millilitre pre-fixed water samples were filtered onto 0.2 µm pore size black
178
polycarbonate filters (25 mm, Whatman) after staining with 4', 6'-diamidino-2-phenolindole
179
(DAPI, Sigma) at 1 µg/ml final concentration for 15 minutes (Poter and Feig, 1980). Total
180
bacterioplankton cell numbers were counted using a Zeiss Axioshop 20 epifluorescence
181
microscope. For each sample, a minimum of 10 fields of view and 1000 cells were counted.
182
In addition, the data were transformed to Log10.
183
8
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 9 of 44
184
Identification and counting of phytoplankton and zooplankton After sedimentation for 48 h, phytoplankton samples were concentrated to 50 ml. Then
185 186
0.1 millilitre concentrated samples were counted under a microscope (Zeiss, Germany).
187
Colonial Microcystis spp. cells were separated by ultrasonication and then counted.
188
Phytoplankton species were identified according to Hu and Wei (2006). Phytoplankton
189
biomass (mg/L) was calculated from cell numbers and size measurements, assuming that
190
1mm3 of phytoplantkton volume is equal to 1 milligram of fresh weight biomass.
191
Zooplankton (Copepods and Cladocerans) were identified according to Sheng (1979) and
192
Chiang and Du (1979) and counted under a microscope (Zeiss, Germany) with a
193
magnification of 100×.To estimate changes in phytoplankton community, we used the
194
Shannon Wiener Index (H'p, bits) s
H'p =-
195
∑
ni n
ln nin
i =1
196
where ni is biomass of the ith genus, n is the total biomass of all genera, and s is the total
197
number of genera.
198
DNA extraction and PCR amplification
199
Bacterioplankton genomic DNA was extracted using a bacterial DNA Kit (Omega, USA)
200
following standard protocols, and then purified using QIAamp DNA Kit (Qiagen, Valencia,
201
CA, USA). The purified DNA was used as template for PCR amplification of the V3 region of
202
16S
203
(5'-CCTACGGGAGGCAGCAG-3') with a 40 bp GC-clamp attached to its 5'end, and the
204
universal primer 518R (5'-ATTACCGCGGCTGCTGG-3') (Muyzer et al.1993). PCR mixtures
205
of 50 µl contained 1 × PCR buffer, 1.5 mM MgCl2, 200 µM of each dNTP, 0.2 µM of each 9
rRNA
gene
fragment
by
using
the
bacteria-specific
primer
357F
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 10 of 44
206
primer, 2.5 U of Taq DNA polymerase (Takara) and 50 ng of template DNA. In the procedure,
207
5 minutes of initial denaturation at 94°C was followed by a thermal cycling program as
208
follows: 1 minute denaturation at 94°C; 1 minute annealing at an initial 65°C, decreasing 1°C
209
every cycle to a final temperature of 55°C; 1 minute extension at 72°C. 30 cycles were run
210
followed by a final 10 minute extension at 72°C. A negative control, in which the template
211
was replaced by an equivalent volume of sterile deionized water, was included. PCR products
212
were confirmed by 1.5% agarose electrophoresis.
213
Denaturing gradient gel electrophoresis (DGGE)
214
DGGE was performed with a Dcode system (Bio-Rad Laboratories, USA) using 8%
215
(w/v) polyacrylamide gel (acrylamide: bisacrylamide 37.5: 1) with a denaturing gradient
216
ranging from 40 to 55%, where 100% denaturant is defined as 40% deionized formamide and
217
7M urea. The same amount of PCR products (about 800 ng) for each sample was loaded, and
218
the gel was run at 60°C for 7 h at 150V using 1 × TAE running buffer (20 mMTris, 10 mM
219
acetic acid, 0.5 mM EDTA, pH 8.0). The gel was stained with GelRed nucleic acid staining
220
solution (1:10000 dilution, Biotium, USA) for 30 minutes and photographed with a Bio Image
221
System (Gene Com.) under UV light. DGGE were performed separately for sites M, L and E.
222
However for samples from site E, the M-NOV and L-MAY were loaded on the ends as
223
references to site M and L, respectively.
224
Cluster analysis of DGGE profiles and Statistics analysis
225
We analyzed all the bands of the samples from 3 sampling sites by using band references
226
(M-NOV and L-MAY). CCA was performed separately for the three sites. To assess the
227
bacterioplankton community in the different samples, the DGGE data were used to generate 10
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 11 of 44
228
several binary matrixes according to assigning scores for the presence (1) or absence (0) of
229
bands with Quantity One software (BioRad). Pairwise similarities between gel banding
230
patterns were quantified using the Dice coefficient as: SD = (2NAB)/(NA + NB), where NAB is
231
the number of bands common to the samples A and B, and NA and NB are the number of
232
bands in samples A and B, respectively. Then the similarity coefficients were used to
233
construct a dendrogram by using the unweighted pair group method with arithmetic average
234
(UPGMA) with the sequential, hierarchical, agglomerative, and nested clustering routine of
235
the NTSYS program (version 2.10e, Exeter software, Setauket, NY, USA).
236
Gel images were analyzed with Gel-Pro Analyzer version 4.5. A suitable densitometric
237
curve was calculated for each lane manually and the bands of each lane were produced
238
automatically. Then, the relative intensities of all bands for each sample were obtained. The
239
Shannon-Wiener Index (H') was calculated to evaluate bacterioplankton community diversity: n
240
H' =-
∑ Pi ln Pi i =1
241 242
,
where n is the total number of bands in each lane and Pi is the relative intensity of each band. Multivariate statistics were used to reveal the relationships between BCC and the
243
explanatory variables (including physicochemical factors, zooplankton biomass and different
244
phytoplankton taxonomic groups). The software package CANOCO 4.5 (Microcomputer
245
Power, Ithaca, New York, USA) was used for all analyses. Redundancy analysis (RDA) was
246
carried out by assuming linear species-environment relationships when the length of the first
247
axis was < 2 with detrended correspondence analysis (DCA) running on a DGGE profile
248
matrix. Similarly, canonical correspondence analysis (CCA) was performed by assuming
249
unimodal species-environment relationships when the length of the first axis was > 2 11
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 12 of 44
250
(Lindström and Bergström 2005; terBraak 1987; terBraak and Verdonschot 1995). The
251
species data were the binary matrixes mentioned above according to Quantity One software.
252
The environmental factors significantly related to BCC were identified by forward selection
253
and 499 unrestricted Monte Carlo permulation tests. The different factors were compared
254
using one-way analysis of variance (ANOVA) except for total bacterial abundance which was
255
analyzed using nonparametric comparisons. Statistical analyses used SPSS software (version
256
14.0) for Windows. Differences were considered statistically significance at p < 0.05.
257
Square-root transformations were used to normalize the data. Bacterioplankton concentrations
258
were transformed to Log10
259 260 261 262 263 264 265 266 267 268 269 270 271
12
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 13 of 44
272
Results
273
Water chemistry
274
The results for water chemistry (TN, TDN, NH4+-N, NO3--N, NO2--N, TP, TDP, PO43--P,
275
TN:TP, DO, Turbidity, pH, Secchi depth, Water temperature, Conductivity, TDS, DOC and
276
DIC) and biological parameters (Chl a and phytoplankton biomass) are shown in Table 1, Fig.
277
2 and Fig. 3. TDN concentrations at site M and L greatly exceed TDN concentrations at site E
278
(p < 0.05). Chl a concentrations at site M were significantly higher (p < 0.05) than Chl a
279
concentrations at site L and E. The highest Chl a concentrations occurred in July (Microcystis
280
bloom), May (Microcystis bloom) and January (Cyclotella and Cryptomonas dominant) at
281
sites M, L and E, respectively (Fig. 2f). There were no significant differences (p > 0.05)
282
among the 3 sites in zooplankton abundance (personal communication, Xiaoxue Sun).
283
Dissolved organic carbon
284
Variations in DOC concentrations at the 3 sites are shown in Fig. 4. DOC concentrations
285
in site M were significantly higher (p < 0.05) than DOC concentrations in sites L and E. The
286
average DOC concentrations were 5.71±2.45, 4.78±1.45 and 4.64±1.21 mg C/L at site M, L
287
and E, respectively. Maximum DOC concentrations occurred with the appearance of
288
Microcysis blooms in September, October and August, respectively. Minimum DOC
289
concentrations occurred near the maxima in phytoplankton groups belonging to
290
Bacillariophyta and Cryptophyta (March, April and December). There was a strong positive
291
correlation between DOC concentration and Microcystis biomass (r = 0.627, p< 0.0001). We
292
also found a significant negative correlation between DOC concentration and Cryptomonas
293
biomass (r = -0.325, p = 0.031). 13
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 14 of 44
294 295
Diversity of bacterioplankton communities and total bacterial abundance The seasonal changes in total bacterioplankton abundance（Log10 transformed data）are
296
shown in Fig. 5a. There were no significant differences between the 3 sites (yearly average
297
values of site L, M and E 7.45±0.81, 7.16±0.27, 7.10±0.54, respectively; p > 0.05). Seasonal
298
variation in bacterioplankton diversity (Fig. 5b) did not vary among the 3 sites. The
299
correlation between DOC and H' (r = -0.653, p < 0.000) was statistically significant. Among
300
the 3 sites, variation in H' at site M was the highest, with the minimum diversity in
301
September and maximum diversity in May. The values of H'were significantly lower in site
302
M than the other two sites (p < 0.05). At sites L and E, the diversity gradually decreased until
303
October and reached the maximum in spring (February and March).
304
Phytoplankton community
305
The seasonal changes in phytoplankton diversity are displayed in Fig. 3a. There was no
306
significant correlation between H' for phytoplankton and H' for bacterioplankton (p > 0.05).
307
Changes in the phytoplankton species composition (from June, 2009 to May, 2010) are shown
308
in Fig. 6. Cyanophyta, Bacillariophyta and Cryptophyta were the main phyla at all the 3 sites,
309
but these groups dominated at different times. Cyanophyta were dominant from June to
310
November at sites M and L, and Bacillariophyta and Cryptophyta were dominant from
311
December to May. At site E, Bacillariophyta and Cryptophyta were dominant for the entire
312
year except during August. Microcystis, Cyclotella and Cryptomonas were the main genera in
313
the Cyanophyta, Bacillariophyta and Cryptophyta, respectively. The seasonal variation in
314
phytoplankton biomass is shown in Fig. 3b. Phytoplankton biomass at site M was higher (p
0.05).
317
Variations in bacterioplankton community composition
318
The temporal changes in bacterioplankton diversity showed a similar pattern (Fig. 5b).
319
Species diversity was significantly higher at site L and E than species diversity at site M (p
Page 1 of 44
1
Dissolved organic carbon and relationship with bacterioplankton community
2
composition in three lake regions of Lake Taihu, China
3
Xinghong Pang, Hong Shen, Yuan Niu, Xiaoxue Sun, Jun Chen, and Ping Xie
4 5
X. H. Pang. Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of
6
Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese
7
Academy of Sciences, Wuhan, Hubei, PR China; Institute of Huai river water resources
8
protection, Huaihe River Water Resources Protection Bureau, Bengbu, Anhui, PR China.
9 10
E-mail: [email protected] Y. Niu. Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of
11
Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese
12
Academy of Sciences, Wuhan, Hubei, PR China; Research Center For Lake Ecology and
13
Environment, Chinese Research Academy of Environmental Sciences，Beijing, China.
14
E-mail: [email protected]
15
X. X. Sun. College of Fishery, Huazhong Agricultural University, Wuhan, Hubei, PR
16
China.
17
E-mail: [email protected]
18
J. Chen. Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of
19
Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese
20
Academy of Sciences, Wuhan, Hubei, PR China.
21
E-mail: [email protected]
22
Corresponding authors: Hong Shen (Donghu Experimental Station of Lake Ecosystems,
23
State Key Laboratory of Freshwater Ecology and Biotechnology of China, Institute of
24
Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, PR China.e-mail:
25
[email protected]),
26 27
Ping Xie (Donghu Experimental Station of Lake Ecosystems, State Key Laboratory of Freshwater Ecology and Biotechnology of China, Institute of Hydrobiology, Chinese 1
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 2 of 44
28
Academy of Sciences, Wuhan, Hubei, PR China. Tel./fax: +86 27 68780622. E-mail:
29
[email protected])
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
2
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 3 of 44
51
Abstract: In order to clarify the relationships between dissolved organic carbon (DOC) and
52
bacterioplankton community composition (BCC), a one-year survey (June, 2009 - May, 2010)
53
was conducted in three regions of Lake Taihu (Meiliang Bay, Lake Center and Eastern Taihu),
54
China. Polymerase Chain Reaction-denaturing gradient gel electrophoresis (PCR-DGGE) was
55
used to analyze the composition and heterogeneity of the bacterioplankton community.
56
Canonical correspondence analysis (CCA) was used to explore the relationships between
57
DOC concentration and BCC. We found a significant negative correlation between DOC
58
concentration and bacterioplankton community diversity (measured as the Shannon-Wiener
59
index (H')). The results show that spatial variation in the bacterioplankton population was
60
stronger than the seasonal variation and that DOC concentration influences BCC in Lake
61
Taihu. DOC concentration, followed by macrophytes biomass, water turbidity and
62
phytoplankton biomass were the most influential factors which account for BCC changes in
63
Lake Taihu. More detailed studies on the relationship between DOC concentration and BCC
64
should focus on differences in DOC concentrations and quality among these lake regions.
65
DOC had a significant impact on BCC in Meiliang Bay. The relationship between DOC and
66
BCC in two other regions studied (Lake Center and Eastern Bay) was weaker. The results of
67
this study add to our understanding of the BCC in eutrophic lakes, especially regarding the
68
role of the microbial loop in lake ecosystems.
69 70
Key words: dissolved organic carbon (DOC), bacterioplankton community composition
71
(BCC), denaturing gradient gel electrophoresis (DGGE), canonical correspondence analysis
72
(CCA), Lake Taihu
73
3
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 4 of 44
74 75
Introduction In freshwater ecosystems, bacterioplankton plays a significant role in nutrient cycling
76
and decomposition of organic matter and acts as a food source for higher trophic levels (Cole
77
et al. 1988; Caron 1994; Cotner and Biddanda 2002). Thus, a more comprehensive
78
understanding of bacterioplankton genetic diversity and the relationships with different
79
environmental factors is needed (Kolmonen et al. 2011). Previous researches have
80
investigated the relationships between environmental factors and bacterioplankton community
81
composition (BCC) in various aquatic ecosystems (Wu et al. 2006; Van der Gucht et al. 2007)
82
and experimental systems (Lindström et al. 2005; Li et al. 2011). Water temperature (Kan et al.
83
2007; Shade et al. 2007), pH (Lindström and Leskinen 2002; Yannarell and Triplett 2005),
84
protozoan predation (Langenheder and Jürgens 2001), phytoplankton (Höfle et al. 1999;
85
Rooney-Varga et al. 2005), nutrient availability (Berdjeb et al. 2010), vegetation cover
86
(Declerck et al. 2005), salinity (Laque et al. 2010) and DOC (Kritzberg et al. 2005) have been
87
identified as factors governing BCC. The assimilation of DOC by bacterioplankton is a key
88
process in the flow of energy (carbon) through bacteria and bacterioplankton known as the
89
“microbial loop” (Azam et al. 1983; Tranvik 1992). However, the effects of DOC on BCC in
90
freshwater ecosystems are not fully understood (Crump 2003; Jones 2009).
91
Previous studies (Haukka et al. 2005; Nelson 2009; Li et al. 2011a) investigated linkages
92
between DOC and BCC and found that the effects of DOC on BCC depend on trophic
93
condition. In oligotrophic and ultraoligotrophic lakes, BCC is significantly influenced by
94
DOC (Crump et al. 2003; Kritzberg et al. 2005). In oligotrophic lakes, high DOC
95
concentrations are associated with low taxon richness of bacterioplankton communities (Fujii 4
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 5 of 44
96
et al. 2011). However, in eutrophic lakes, there is a divergence in the relationship between
97
DOC and BCC. Fujii et al. (2011) showed that there were no links between DOC and BCC in
98
eutrophic lakes. Eiler et al. (2003) found that both quantity and quality of DOC can control
99
BCC in eutrophic lakes. An enclosure experiment in Lake Taihu found a positive correlation
100
between DOC concentration and heterotrophic bacterioplankton abundance (Li et al. 2011a).
101
A shift in quality of organic matter might cause shifts in BCC, especially in dominant
102
bacterioplankton (Eiler and Bertilsson 2004), which might relate to the specific function of
103
some heterotrophic bacterioplankton. For example, -Proteobacteria (Rhodobacteraceae) may
104
play an important role in decomposing vascular plant-derived DOC (van Hannen et al. 1999);
105
The Cytophagaceae Bacteroidetes were found to dominate during diatom blooms (Riemann et
106
al. 2000). β -Proteobacteria can decompose many aromatic substrates under anaerobic
107
conditions (Li et al. 2011b).
108
Our knowledge about factors regulating bacterioplankton composition is far from
109
comprehensive. In this study, we investigated how DOC concentration affects BCC in large
110
shallow lakes such as Lake Taihu which contain lake regions with varying trophic conditions.
111
We hypothesized that BCC could vary along with DOC concentration in Lake Taihu and that
112
DOC might play different roles in controlling BCC in various lake districts. Lake Taihu is a
113
eutrophic shallow subtropical lake located in the southeast Yangtze River Delta Region. Lake
114
Taihu is the third largest freshwater lake in China, with a surface area of 2338 km2 (maximum
115
length, 68.5 km, maximum width 56 km, maximum depth, 2.6 m; Qin et al. 2004). Lake Taihu
116
experiences cyanobacteria blooms every year in certain regions, which are one of the most
117
important problems related to water quality in Lake Taihu. Because microbial communities 5
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 6 of 44
118
play an important role in energy transfer, improved knowledge of microbial diversity and
119
function in freshwater lakes is needed (Hanh 2006).
120
Since first publication by Muyzer et al. (1993), the PCR- DGGE fingerprinting has been
121
intensively used in environmental microbiology and recognized to give an acceptable view on
122
differences and similarities of the populations of microbial communities (Muyzer and Smalla
123
1998; Lindström and Leskinen 2002; Lindström and Bergström 2005; Niu et al. 2011).
124
Likewise, Canonical correspondence analysis (CCA) has been frequently used to evaluate the
125
effects of environmental variables on the BCC and explained successfully the dynamics of
126
BCC (Simek et al. 2001; TerBraak and Smilauer 2002; Wu et al. 2007; Berdjeb et al. 2010).
127
In the current study, to test our hypothesis, we used PCR-DGGE and CCA to investigate the
128
temporal and spatial similarities and differences of BCC and the influential factors of BCC in
129
Lake Taihu.
130 131 132 133 134 135 136 137 138 139
6
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 7 of 44
140
Materials and methods
141
Study sites and sampling design
142
Based on differences in physical-chemical parameters and macrophyte and plankton
143
community structure, Lake Taihu can be divided into several distinct ecological regions.
144
Meiliang Bay, a hypertrophic basin in northern Lake Taihu, has a surface area of ca. 100 km2
145
and a mean depth of 1.89 m. Hypereutrophication of Meiliang Bay has resulted from
146
discharge of domestic and industrial wastewater from the Liangxi and Zhihu rivers. The
147
central region of Lake Taihu is characterized by low transparency due to wind-driven
148
sediment resuspension (Chen et al. 2003; Zhang et al. 2003). Sediment resuspension
149
facilitates exchange of organic matter and nutrients among water, sediment pore water and
150
sediment (Xing and Kong 2007). The eastern region of Lake Taihu is covered by various
151
submersed macrophytes. Thus, the eastern region is characterized by clear water, a diverse
152
fish population, and relatively low phytoplankton biomass (Scheffer et al. 1993; Qin et al.
153
2004; Wu et al. 2007). Thus the three sampling sites in this study were located at Meiliang
154
Bay (site M, 31º28＇07" N, 120º10＇51" E), Lake Center (site L, 31º14＇44" N, 120º13＇
155
54" E) and Eastern Taihu (site E, 31º03＇44" N, 120º29＇13" E; Fig.1). Water samples were
156
collected monthly from June 2009 to May 2010.
157
Sampling and field factor analyses
158
Water samples were collected at the surface (0-0.5 m) using a 5 litre Schindler sampler.
159
Water temperature (Temp), dissolved oxygen (DO), conductivity (Cond), total dissolved solid
160
(TDS), water turbidity (Turb), pH, and Secchi depth were measured in situ with a YSI 6600
161
Multi-Parameter Water Quality Sonde. Macrophyte coverage was estimated at the same time. 7
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 8 of 44
162
For determination of total bacterial abundance, water samples (50 ml) were fixed with
163
glutaraldehyde to a 2% final concentration. A 1 litre water sample was preserved by addition
164
of 1% Lugol’s iodine solution for identification and counting of phytoplankton. Zooplankton
165
were concentrated by screening 10 litre water samples through a 64 µm plankton net and
166
fixed with formalin (37%). For bacterioplankton composition analysis, bacterioplankton
167
samples (200-300 ml of water) were filtered on 0.2µm-pore-size filters after filtering through
168
5.0 µm-pore-size filters (diameter 47 mm; Whatmann, UK). All filters were preserved at
169
-80°C until analysis.
170
Chemical analyses
171
Total nitrogen (TN), total dissolved nitrogen (TDN), nitrate (NO3--N), ammonium
172
(NH4+-N), nitrite (NO2--N), total phosphorus (TP), phosphate (PO43--P), total dissolved
173
phosphorus (TDP) and Chlorophyll a (Chl a) were analyzed according to standard methods
174
(Jin and Tu, 1990). Dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC)
175
were measured with a TOC analyzer (Model 1010, USA).
176
Bacterioplankton abundance
177
2 millilitre pre-fixed water samples were filtered onto 0.2 µm pore size black
178
polycarbonate filters (25 mm, Whatman) after staining with 4', 6'-diamidino-2-phenolindole
179
(DAPI, Sigma) at 1 µg/ml final concentration for 15 minutes (Poter and Feig, 1980). Total
180
bacterioplankton cell numbers were counted using a Zeiss Axioshop 20 epifluorescence
181
microscope. For each sample, a minimum of 10 fields of view and 1000 cells were counted.
182
In addition, the data were transformed to Log10.
183
8
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 9 of 44
184
Identification and counting of phytoplankton and zooplankton After sedimentation for 48 h, phytoplankton samples were concentrated to 50 ml. Then
185 186
0.1 millilitre concentrated samples were counted under a microscope (Zeiss, Germany).
187
Colonial Microcystis spp. cells were separated by ultrasonication and then counted.
188
Phytoplankton species were identified according to Hu and Wei (2006). Phytoplankton
189
biomass (mg/L) was calculated from cell numbers and size measurements, assuming that
190
1mm3 of phytoplantkton volume is equal to 1 milligram of fresh weight biomass.
191
Zooplankton (Copepods and Cladocerans) were identified according to Sheng (1979) and
192
Chiang and Du (1979) and counted under a microscope (Zeiss, Germany) with a
193
magnification of 100×.To estimate changes in phytoplankton community, we used the
194
Shannon Wiener Index (H'p, bits) s
H'p =-
195
∑
ni n
ln nin
i =1
196
where ni is biomass of the ith genus, n is the total biomass of all genera, and s is the total
197
number of genera.
198
DNA extraction and PCR amplification
199
Bacterioplankton genomic DNA was extracted using a bacterial DNA Kit (Omega, USA)
200
following standard protocols, and then purified using QIAamp DNA Kit (Qiagen, Valencia,
201
CA, USA). The purified DNA was used as template for PCR amplification of the V3 region of
202
16S
203
(5'-CCTACGGGAGGCAGCAG-3') with a 40 bp GC-clamp attached to its 5'end, and the
204
universal primer 518R (5'-ATTACCGCGGCTGCTGG-3') (Muyzer et al.1993). PCR mixtures
205
of 50 µl contained 1 × PCR buffer, 1.5 mM MgCl2, 200 µM of each dNTP, 0.2 µM of each 9
rRNA
gene
fragment
by
using
the
bacteria-specific
primer
357F
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 10 of 44
206
primer, 2.5 U of Taq DNA polymerase (Takara) and 50 ng of template DNA. In the procedure,
207
5 minutes of initial denaturation at 94°C was followed by a thermal cycling program as
208
follows: 1 minute denaturation at 94°C; 1 minute annealing at an initial 65°C, decreasing 1°C
209
every cycle to a final temperature of 55°C; 1 minute extension at 72°C. 30 cycles were run
210
followed by a final 10 minute extension at 72°C. A negative control, in which the template
211
was replaced by an equivalent volume of sterile deionized water, was included. PCR products
212
were confirmed by 1.5% agarose electrophoresis.
213
Denaturing gradient gel electrophoresis (DGGE)
214
DGGE was performed with a Dcode system (Bio-Rad Laboratories, USA) using 8%
215
(w/v) polyacrylamide gel (acrylamide: bisacrylamide 37.5: 1) with a denaturing gradient
216
ranging from 40 to 55%, where 100% denaturant is defined as 40% deionized formamide and
217
7M urea. The same amount of PCR products (about 800 ng) for each sample was loaded, and
218
the gel was run at 60°C for 7 h at 150V using 1 × TAE running buffer (20 mMTris, 10 mM
219
acetic acid, 0.5 mM EDTA, pH 8.0). The gel was stained with GelRed nucleic acid staining
220
solution (1:10000 dilution, Biotium, USA) for 30 minutes and photographed with a Bio Image
221
System (Gene Com.) under UV light. DGGE were performed separately for sites M, L and E.
222
However for samples from site E, the M-NOV and L-MAY were loaded on the ends as
223
references to site M and L, respectively.
224
Cluster analysis of DGGE profiles and Statistics analysis
225
We analyzed all the bands of the samples from 3 sampling sites by using band references
226
(M-NOV and L-MAY). CCA was performed separately for the three sites. To assess the
227
bacterioplankton community in the different samples, the DGGE data were used to generate 10
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 11 of 44
228
several binary matrixes according to assigning scores for the presence (1) or absence (0) of
229
bands with Quantity One software (BioRad). Pairwise similarities between gel banding
230
patterns were quantified using the Dice coefficient as: SD = (2NAB)/(NA + NB), where NAB is
231
the number of bands common to the samples A and B, and NA and NB are the number of
232
bands in samples A and B, respectively. Then the similarity coefficients were used to
233
construct a dendrogram by using the unweighted pair group method with arithmetic average
234
(UPGMA) with the sequential, hierarchical, agglomerative, and nested clustering routine of
235
the NTSYS program (version 2.10e, Exeter software, Setauket, NY, USA).
236
Gel images were analyzed with Gel-Pro Analyzer version 4.5. A suitable densitometric
237
curve was calculated for each lane manually and the bands of each lane were produced
238
automatically. Then, the relative intensities of all bands for each sample were obtained. The
239
Shannon-Wiener Index (H') was calculated to evaluate bacterioplankton community diversity: n
240
H' =-
∑ Pi ln Pi i =1
241 242
,
where n is the total number of bands in each lane and Pi is the relative intensity of each band. Multivariate statistics were used to reveal the relationships between BCC and the
243
explanatory variables (including physicochemical factors, zooplankton biomass and different
244
phytoplankton taxonomic groups). The software package CANOCO 4.5 (Microcomputer
245
Power, Ithaca, New York, USA) was used for all analyses. Redundancy analysis (RDA) was
246
carried out by assuming linear species-environment relationships when the length of the first
247
axis was < 2 with detrended correspondence analysis (DCA) running on a DGGE profile
248
matrix. Similarly, canonical correspondence analysis (CCA) was performed by assuming
249
unimodal species-environment relationships when the length of the first axis was > 2 11
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 12 of 44
250
(Lindström and Bergström 2005; terBraak 1987; terBraak and Verdonschot 1995). The
251
species data were the binary matrixes mentioned above according to Quantity One software.
252
The environmental factors significantly related to BCC were identified by forward selection
253
and 499 unrestricted Monte Carlo permulation tests. The different factors were compared
254
using one-way analysis of variance (ANOVA) except for total bacterial abundance which was
255
analyzed using nonparametric comparisons. Statistical analyses used SPSS software (version
256
14.0) for Windows. Differences were considered statistically significance at p < 0.05.
257
Square-root transformations were used to normalize the data. Bacterioplankton concentrations
258
were transformed to Log10
259 260 261 262 263 264 265 266 267 268 269 270 271
12
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 13 of 44
272
Results
273
Water chemistry
274
The results for water chemistry (TN, TDN, NH4+-N, NO3--N, NO2--N, TP, TDP, PO43--P,
275
TN:TP, DO, Turbidity, pH, Secchi depth, Water temperature, Conductivity, TDS, DOC and
276
DIC) and biological parameters (Chl a and phytoplankton biomass) are shown in Table 1, Fig.
277
2 and Fig. 3. TDN concentrations at site M and L greatly exceed TDN concentrations at site E
278
(p < 0.05). Chl a concentrations at site M were significantly higher (p < 0.05) than Chl a
279
concentrations at site L and E. The highest Chl a concentrations occurred in July (Microcystis
280
bloom), May (Microcystis bloom) and January (Cyclotella and Cryptomonas dominant) at
281
sites M, L and E, respectively (Fig. 2f). There were no significant differences (p > 0.05)
282
among the 3 sites in zooplankton abundance (personal communication, Xiaoxue Sun).
283
Dissolved organic carbon
284
Variations in DOC concentrations at the 3 sites are shown in Fig. 4. DOC concentrations
285
in site M were significantly higher (p < 0.05) than DOC concentrations in sites L and E. The
286
average DOC concentrations were 5.71±2.45, 4.78±1.45 and 4.64±1.21 mg C/L at site M, L
287
and E, respectively. Maximum DOC concentrations occurred with the appearance of
288
Microcysis blooms in September, October and August, respectively. Minimum DOC
289
concentrations occurred near the maxima in phytoplankton groups belonging to
290
Bacillariophyta and Cryptophyta (March, April and December). There was a strong positive
291
correlation between DOC concentration and Microcystis biomass (r = 0.627, p< 0.0001). We
292
also found a significant negative correlation between DOC concentration and Cryptomonas
293
biomass (r = -0.325, p = 0.031). 13
Can. J. Microbiol. Downloaded from www.nrcresearchpress.com by San Diego (UCSD) on 09/15/14 For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record.
Page 14 of 44
294 295
Diversity of bacterioplankton communities and total bacterial abundance The seasonal changes in total bacterioplankton abundance（Log10 transformed data）are
296
shown in Fig. 5a. There were no significant differences between the 3 sites (yearly average
297
values of site L, M and E 7.45±0.81, 7.16±0.27, 7.10±0.54, respectively; p > 0.05). Seasonal
298
variation in bacterioplankton diversity (Fig. 5b) did not vary among the 3 sites. The
299
correlation between DOC and H' (r = -0.653, p < 0.000) was statistically significant. Among
300
the 3 sites, variation in H' at site M was the highest, with the minimum diversity in
301
September and maximum diversity in May. The values of H'were significantly lower in site
302
M than the other two sites (p < 0.05). At sites L and E, the diversity gradually decreased until
303
October and reached the maximum in spring (February and March).
304
Phytoplankton community
305
The seasonal changes in phytoplankton diversity are displayed in Fig. 3a. There was no
306
significant correlation between H' for phytoplankton and H' for bacterioplankton (p > 0.05).
307
Changes in the phytoplankton species composition (from June, 2009 to May, 2010) are shown
308
in Fig. 6. Cyanophyta, Bacillariophyta and Cryptophyta were the main phyla at all the 3 sites,
309
but these groups dominated at different times. Cyanophyta were dominant from June to
310
November at sites M and L, and Bacillariophyta and Cryptophyta were dominant from
311
December to May. At site E, Bacillariophyta and Cryptophyta were dominant for the entire
312
year except during August. Microcystis, Cyclotella and Cryptomonas were the main genera in
313
the Cyanophyta, Bacillariophyta and Cryptophyta, respectively. The seasonal variation in
314
phytoplankton biomass is shown in Fig. 3b. Phytoplankton biomass at site M was higher (p
0.05).
317
Variations in bacterioplankton community composition
318
The temporal changes in bacterioplankton diversity showed a similar pattern (Fig. 5b).
319
Species diversity was significantly higher at site L and E than species diversity at site M (p
