Temporal and spatial distribution of Microcystis biomass and genotype in bloom areas of Lake Taihu

Temporal and spatial distribution of Microcystis biomass and genotype in bloom areas of Lake Taihu

Chemosphere 209 (2018) 730e738 Contents lists available at ScienceDirect Chemosphere journal homepage: www.elsevier.com/locate/chemosphere Temporal...

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Chemosphere 209 (2018) 730e738

Contents lists available at ScienceDirect

Chemosphere journal homepage: www.elsevier.com/locate/chemosphere

Temporal and spatial distribution of Microcystis biomass and genotype in bloom areas of Lake Taihu Dong-Xing Guan a, c, 1, Xingyu Wang a, b, 1, Huacheng Xu d, Li Chen e, Pengfu Li b, *, Lena Q. Ma a, f a

State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, China d State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China e Provincial Key Laboratory of Plateau Geographical Processes and Environmental Change, School of Tourism and Geography, Yunnan Normal University, Kunming, 650500, China f Soil and Water Science Department, University of Florida, Gainesville, FL, 32611, USA b c

h i g h l i g h t s

g r a p h i c a l a b s t r a c t

 The horizontal distribution of toxic Microcystis in Lake Taihu was investigated.  The spatial variations in chl-a and mcyJ/cpcBA ratio correlated with N and P.  The temporal variation in cpcBA genotype was significantly correlated with N and P.  Horizontal transport determined spatial distribution of potentially toxic Microcystis.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 February 2018 Received in revised form 21 June 2018 Accepted 22 June 2018 Available online 26 June 2018

Cyanobacterial blooms as a global environmental issue are of public health concern. In this study, we investigated the spatial (10 sites) and temporal (June, August and October) variations in: 1) their biomass based on chlorophyll-a (chl-a) concentration, 2) their toxic genotype based on gene copy ratio of mcyJ to cpcBA, and 3) their cpcBA genotype composition of Microcystis during cyanobacterial bloom in Lake Taihu. While spatial-temporal variations were found in chl-a and mcyJ/cpcBA ratio, only spatial variation was observed in cpcBA genotype composition. Samples from northwestern part had a higher chl-a, but mcyJ/ cpcBA ratio didn't vary among the sites. High chl-a was observed in August, while mcyJ/cpcBA ratio and genotypic richness increased with time. The spatial variations in chl-a and mcyJ/cpcBA ratio and temporal variation in cpcBA genotype were correlated negatively with dissolved N and positively with dissolved P. Spatial distribution of Microcystis biomass was positively correlated with nitrite and P excluding October, but no correlation was found for spatial distribution of mcyJ/cpcBA ratio and cpcBA genotype. Spatial distribution of toxic and cpcBA genotypes may result from horizontal transport of Microcystis colonies, while spatial variation in Microcystis biomass was probably controlled by both nutrient-mediated growth and horizontal transport of Microcystis. The temporal variation in Microcystis biomass, toxic genotype and

Handling Editor: Tsair-Fuh Keywords: Toxic Microcystis genotype Horizontal transport Nutrient level mcyJ/cpcBA ratio cpcBA genotype Chlorophyll-a

* Corresponding author. E-mail address: [email protected] (P. Li). 1 They contributed equally to the work. https://doi.org/10.1016/j.chemosphere.2018.06.141 0045-6535/© 2018 Elsevier Ltd. All rights reserved.

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cpcBA genotype composition were related to nutrient levels, but cause-and-effect relationships require further study. © 2018 Elsevier Ltd. All rights reserved.

1. Introduction Both climate change and eutrophication contribute to cyanobacterial blooms in freshwater ecosystems (Reichwaldt and Ghadouani, 2012; Pace et al., 2017). The outbreak of cyanobacterial blooms is a global environmental issue, resulting in water quality deterioration, loss of habitat and natural resources, and oxygen depletion (Paerl et al., 2001; Xu et al., 2016). The toxins and taste-and-odor compounds synthesized by cyanobacteria are released into the water phase, causing health risks to humans and limiting the utilization of recreational and drinking water (Briand et al., 2003; Graham et al., 2010; Xu et al., 2013). Among the bloom-forming species, Microcystis spp. are the most common species responsible for toxic cyanobacterial bloom worldwide (Fastner et al., 2001; Wang et al., 2012). In natural water bodies, Microcystis blooms contain multiple genotypes, including toxic and non-toxic (Otten and Paerl, 2011; Otten et al., 2012; Berry et al., 2017). Toxic Microcystis genotypes contain a gene cluster for biosynthesis of hepatotoxic microcystins spanning 55 kb, composed of 10 bidirectionally transcribed open reading frames arranged in two putative operons, i.e., mcy(microcystin synthetase)AeC and mcyDeJ (Tillett et al., 2000). Microcystins synthesized by these genes are a family of more than 100 structurally similar hepatotoxins, inhibiting serine/threonine protein phosphatase 1 and 2A (Qi et al., 2015). The presence of mcy genes is necessary to synthesize microcystins, therefore many of the mcy genes have been used for detection and quantitative analysis of microcystin-producing toxic cyanobacteria genotypes (Rinta-Kanto et al., 2005; Kim et al., 2010; Otten et al., 2017). The genetic structure of Microcystis population is also important for Microcystis to survive in the changing environment. A study showed that mean colony size, toxicity and growth rate of M. aeruginosa vary intra-specifically (Wilson et al., 2006). High genetic variability in photosynthetic pigment concentration and chemical quenching are also found among M. aeruginosa strains ~ ares-Espan ~ a et al., 2007). The heterogeneity observed in (Ban quantum yield, respiration, toxin production, and cell size of  pez-Rodas Microcystis is mainly attributed to genetic factors (Lo et al., 2006; Rico et al., 2006). All these results demonstrate that both interspecific and intraspecific differences in physiobiochemical and morphological properties exist in the genus of Microcystis, which are largely determined by genetic factors. Therefore, the genetic structure of Microcystis populations is important for the growth and toxicity of Microcystis blooms. Lake Taihu is the third largest freshwater lake in China, with an area of 2250 square kilometers and mean depth of 1.9 m (Qin et al., 2007). Unfortunately, because of pollution and resultant eutrophication, toxic Microcystis blooms in Lake Taihu have caused massive problems for industry, recreation, tourism and local drinking water supplies. Seasonal dynamics of toxic Microcystis blooms was observed in the northern part (Otten et al., 2012) or Meiliang Bay (Li et al., 2017) of Lake Taihu. But the temporal distribution of Microcystis biomass and genotype during cyanobacterial blooms in the whole bloom areas of the lake requires further research. A recent study revealed that the exchange of Microcystis blooms occurs between hypereutrophic Meiliang Bay and the adjacent open water of the lake (Wu et al., 2010). Therefore, we

proposed that during cyanobacterial blooms (June to October) the exchange of Microcystis cells between adjacent water areas impact the distribution of Microcystis biomass and genotype in the entire bloom area of Lake Taihu. To confirm this hypothesis, a spatialtemporal investigation was conducted in northern, western and central parts of Lake Taihu during Microcystis blooms in 2011. The specific objectives of this study were to investigate the spatial (10 sites) and temporal (June, August and October) variations in: 1) their biomass based on chlorophyll-a (chl-a) concentration, 2) their toxic genotype based on gene copy ratio of mcyJ (microcystin synthetase J) to cpcBA (intergenic spacer of c-phycocyanin genes cpcB and cpcA) (do Carmo Bittencourt-Oliveira et al., 2001), and 3) cpcBA genotype composition of Microcystis in cyanobacterial bloom areas of Lake Taihu. Statistical analysis was further conducted to assess the relationship between Microcystis variables (chl-a mcyJ/ cpcBA, and cpcBA genotype composition) and hydrochemical parameters (total and dissolved N and P, and chemical oxygen demand-COD). This study will not only provide valuable/historical data about the spatial-temporal distribution of Microcystis biomass and genotype during cyanobacterial blooms in the whole bloom areas of a large shallow lake, but also contribute to the overall understanding of the relationship between dynamics of toxic Microcystis blooms and environmental factors in Lake Taihu.

2. Material and methods 2.1. Water sampling Water samples were collected at 10 sites (Fig. 1) on three dates: June 9 (early summer when cyanobacterial blooms start to occur) and August 22 (summer) and October 15 (autumn) in 2011 during cyanobacterial bloom. Samples (S01, S04, S05, S08, S10, S13, S16, S17, S19 and S20) are distributed in northern, western and central areas of Lake Taihu, where cyanobacterial blooms take place frequently. In each site, 10 L water sample was collected from the water column using a 2-m-long sampler, which was transported to the lab within several hours. Subsamples (1 L) were filtered through 0.45 mm filters for physiochemical parameter analysis (Xu and Guo, 2017). The remaining water samples were filtered through 1.2 mm GF/C filters (Whatman, UK), with the resulting filters being freeze-dried and stored at 20  C before Microcystis DNA extraction and chl-a determination.

2.2. Hydrochemical parameters and chlorophyll a analyses   Ammonia (NHþ 4 -N), nitrate (NO3 -N), nitrite (NO2 -N), total dissolved N (TDN), total N (TN), dissolved inorganic P (DIP), total dissolved P (TDP), total P (TP), COD and chl-a were determined (Huang et al., 1999). Analyses were performed in Taihu Laboratory for Lake Ecosystem Research at the northeast bank of Lake Taihu. According to Sun et al. (2012), Microcystis constituted over 90% of the phytoplankton in the heavy bloom areas from May to December, which was confirmed in our study. Recently, chl-a was identified as a robust and useful metric for predicting microcystin variance and Microcystis cell equivalents (Otten et al., 2012). Therefore, chl-a was used to represent Microcystis biomass.

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Fig. 1. Ten sampling sites in bloom areas of Lake Taihu.

2.3. Real-time PCR and DGGE analyses GF/C filters were cut into pieces to extract Microcystis DNA (Tillett and Neilan, 2000). The toxic genotype of Microcystis population was determined by real-time PCR using primer sets cpc57Fecpc356R and mcyJMFemcyJMR, TaqMan probes 50 -AGCTACTTCGACCGCGCCG-30 for cpcBA and 50 -TCGAGTTTTG0 CAGCCCGGTG-3 for mcyJ (Kim et al., 2010). The primer sets cpc57Fecpc356R and mcyJMFemcyJMR amplified the conserved sequences of cpcBA gene and mcyJ gene among Microcystis genus. Each probe was attached with a fluorescent reporter dye FAM at the 50 ends and an Eclipse Quencher (quenching range 390e625 nm) at the 30 ends. The real-time PCR was performed in a 25 mL system containing 10 mL of a Premix Ex TaqTM (Perfect Real Time kit, TaKaRa, Japan), 0.2 mM of each primer, 0.4 mM of a TaqMan probe, 0.4 mL of ROX Reference Dye and 2 mL of template DNA on a ABI 7300 Real-Time PCR System (Applied Biosystems, USA). The realtime PCR programs for both cpcBA and mcyJ consisted of 30 s at 95  C, followed by 40 cycles of 5 s at 95  C and 31 s at 60  C. The standard curves for the cpcBA and mcyJ genotypes were determined using the genomic DNA of M. aeruginosa PCC 7820 as standard (Kim et al., 2010). The toxic genotype of Microcystis population was determined as the ratio of mcyJ to cpcBA genotype abundance (mcyJ/cpcBA). The genetic structure of Microcystis community was analyzed by using denaturing gradient gel electrophoresis (DGGE). Primer set cpc57Fecpc356R was used to amplify the conserved sequence of

cpcBA gene, with the forward primers cpc57F being attached with a GC clamp (50 -CGCCCGCCGCGCCCCGCGCCCGGCCCGCC 0 GCCCCCGCCCG-3 ) at the 50 end. DGGE was performed with a DGGE-2001 system (CBS Scientific, USA) (Wang et al., 2012). Samples on different DGGE gels were aligned by standards, with bands with a relative intensity of <0.2% being discarded. This procedure was semi-automated by using Quantity One. For each sample, PCR amplification and DGGE analysis were performed at least twice, and they were both reproducible. Therefore, one DGGE gel of samples was used for analysis. 2.4. Statistical analysis Relationships between mcyJ/cpcBA ratio and chl-a concentration of Microcystis population to hydrochemical parameters were analyzed by Spearman's rank correlation coefficient using PASW Statistics (Version 18.0, SPSS). Multivariate analysis provides appropriate statistical tools for studying variation in ecological communities (Muylaert et al., 2002; Kisand and Noges, 2004), thus, it was adapted to analyze the variation in genetic structure of Microcystis population using CANOCO 4.5 for windows (Microcomputer Power, USA). Genetic structure of Microcystis population was represented by DGGE matrix with the relative intensity of each band (Muylaert et al., 2002). Before analyses, all data were log(xþ1) transformed. Detrended correspondence analyses of DGGE matrices indicated that the longest axes were below 3, therefore principal component analysis (PCA) and redundancy analysis (RDA)

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 were performed (Leps and Smilauer, 2003). To study the relations between spatial variations in mcyJ/cpcBA ratio, chl-a concentration and cpcBA genotype to hydrochemical parameters, samples collected on each date were analyzed separately, whereas for relations of temporal variations, samples collected on all dates were analyzed together. 3. Results and discussion 3.1. Variations in hydrochemical parameters Spatial-temporal variations in hydrochemical parameters are shown in Fig. 2. The spatial distribution patterns of N and P were similar, with the three samples in northwestern lake (S10, S16 and S17) generally having the higher N and P levels. For TN, the average concentrations of the three samples were 5.32 (June), 4.31 (August) and 3.61 mg L1 (October), with about 57%, 67%, 49% and 40% lower at sites in Meiliang Bay (S1, S4 and S5), Gonghu Bay (S13), central lake (S8 and S19) and southern lakeshore (S20), respectively. Similarly, for TP, the average concentrations at the three sites were 0.20 (June), 0.37 (August) and 0.18 mg L1 (October), compared to averagely 0.13, 0.06, 0.09 and 0.13 mg L1 at sites in Meiliang Bay, Gonghu Bay, central lake and southern lakeshore, respectively. The temporal variation patterns of N and P levels were similar among the 10 samples. For most samples, highest TN concentrations were detected in June (3.30 mg L1), which decreased to low levels in August and October (2.12e2.79 mg L1). The temporal variation in P levels was different from that of N, with the highest P being detected in August (0.14e0.22 mg L1 of TP). For most samples, TP concentrations peaked in August and were the lowest in June. The spatial variation in COD (Fig. 2i) was similar to N and P levels. COD concentrations in S10, S16 and S17 samples were higher than other sites in June and August, but not in October. The temporal variation in COD was similar to P level, with the lowest COD in June and the highest COD in August with some exceptions. According to a 7-year investigation from 2004 to 2010, cyanobacterial blooms took place frequently in northern and western parts of Lake Taihu (Zhao et al., 2011). In our study, the samples were distributed within these regions. S10, S16 and S17 usually had higher N, P and COD levels than other samples (Fig. 2). As shown in Fig. 1, S10 is close to the mouth of Chendonggang river, while S16 and 17 are in Zhushan Bay where Caoqiao river and Taige canal are located. The runoff with high nutrient loading is mostly from the west and northwest of Lake Taihu. Agricultural sources contribute substantial amounts of TN and TP to Lake Taihu, with non-point agricultural input coming mostly from the west (Qin et al., 2007). These data suggested that the eutrophication of western part of Lake Taihu was more severe than other areas. The high N, P and COD levels in our samples S10, S16 and S17 were consistent with the eutrophication level of this area. 3.2. Variations in Microcystis biomass based on chlorophyll-a concentration Microcystis biomass based on chl-a also showed spatialtemporal variation (Fig. 3), with chl-a increasing from June to August and decreasing from August to October (15.8, 116, and 33.3 mg L1). In June, the highest chl-a concentrations were observed in samples S10 (43.1 mg L1), S13 (17.6 mg L1) and S16 (27.1 mg L1). In August, chl-a concentrations at S10 (279 mg L1), S16 (193 mg L1), S17 (207 mg L1) and S20 (188 mg L1) were higher than that at other samples. In October, chl-a concentrations at S10 (27.5 mg L1), S16 (25.0 mg L1) and S17 (42.2 mg L1) were higher than that at other samples. These results showed that chl-a concentrations in samples S10, S16 and S17 were generally higher. In

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June and August, chl-a was positively correlated with N, P and COD levels (Table 1). In October, only COD was correlated with chl-a. During blooms in Lake Taihu, COD in water mainly controls phytoplankton degradation (Yin et al., 2011), consistent with the significant correlation between COD and chl-a concentration. The biomass of cyanobacterial blooms in certain water area can be controlled by in situ growth (Carstensen et al., 2007; Phlips et al., 2007) or horizontal migration of blooms (Webster and Hutchinson, 1994). In June and August, the biomass at each site was mainly determined by Microcystis growth, which was controlled by N and P inputs (Xu et al., 2010). Samples S10, S16 and S17 are located in the northern part of Lake Taihu, which is the most eutrophic part of Lake Taihu (Qin et al., 2007; Yan et al., 2011). In this study, the nutrient (N and P) concentrations in S10, S16 and S17 were high (Fig. 2), so the in situ growth of Microcystis was less restricted and they were able to maintain high chl-a levels during the blooms. In October, the biomass was mainly controlled by horizontal transport of cyanobacteria, which was strongly affected by wind in Lake Taihu. To explain this wind-driven horizontal transport in October, we need to see from summer to autumn (June to October): (1) how the wind changes in both direction and scale; and (2) how the cyanobacteria vary in size distribution, because different sizes mean different buoyancies, or wind transfer capacities. From summer to autumn, the wind direction in Lake Taihu turned from typically southwest to northeast (Tables S1-S4), causing the transport of cyanobacteria from the most eutrophic northern bay of the lake to the southern open waters. Meanwhile, the wind speed generally increased (Tables S1-S3 and S5), which fueled the concentration-driven transport of cyanobacteria. Synchronously, the proportion of large Microcystis colonies (size 50e200 mm) increased and dominated the population (Yue et al., 2014). Due to greater buoyancy of large Microcystis colonies (Wu and Kong, 2009; Wang et al., 2011; Xu et al., 2014), Microcystis population in the upper layer contains higher proportion of large colonies, and thus can be easily transported by wind. 3.3. Variations in toxic Microcystis genotype based on mcyJ/cpcBA ratio According to Fig. 3 and Fig. S1, during the cyanobacterial bloom, there was spatial variation in mcyJ/cpcBA ratio, but with no clear trend. Different from that of chl-a, the spatial distribution of mcyJ/ cpcBA ratio was not correlated with hydrochemical parameters (Table 1). The spatial variation in nutrients and cyanobacterial biomass in Lake Taihu were correlated, as the toxic Microcystis genotype in different areas seemed not affected by nutrient concentrations. In Lake Taihu, larger Microcystis colonies have higher proportion of toxic genotype than smaller colonies (Wang et al., 2013), so their horizontal distribution is strongly affected by wind conditions (Wu et al., 2010). Theoretically, larger and more toxic Microcystis colonies with greater buoyancy are more easily transported by wind than smaller and less toxic colonies. From summer to autumn (June to October), the wind in Lake Taihu generally varied in both direction and speed (Tables S1-S5). Therefore, in this study, the irregular distribution of toxic Microcystis genotype should be attributed to the wind-driven horizontal transportation of larger and more toxic Microcystis colonies. The mcyJ/cpcBA ratio also showed temporal variation during cyanobacterial bloom, which increased with time (0.10 in June, 0.39 in August and 0.67 in October) in all samples. A field investigation in Zhushan Bay of Lake Taihu showed that from June to October, the mcyJ/cpcBA ratio of Microcystis population kept increasing with some fluctuations (Wang et al., 2013). In this study, the seasonal increase in toxic Microcystis genotype occurred in all bloom areas. In sampling location-date combined analyses, mcyJ/cpcBA ratio had

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Fig. 2. Variations in (a) TN, (b) TDN, (c) ammonium, (d) nitrite, (e) nitrate, (f) TP, (g) TDP, (h) DIP and (i) COD in samples collected from 10 sites during the bloom period in Lake Taihu.

negative correlations with TDN and nitrate, and positive correlation with DIP. These results demonstrated that the seasonal increase in toxic Microcystis was correlated with seasonal variations in N and P levels. In Lake Taihu, large and more toxic Microcystis colonies tend to dominate the population during the Microcystis blooms (Wang et al., 2013; Yue et al., 2014), consistent with our results of larger Microcystis colonies containing more toxic Microcystis genotype. Meanwhile, the outburst of Microcystis blooms had feedback on

N and P levels, and might have large contribution to the seasonal decrease in N and seasonal increase in P. According to Ma et al. (2014), lower TN and TP concentrations at 7.75e13.9 and 0.41e0.74 mg L1 probably promoted the growth of large Microcystis colonies, but at higher concentrations, formation of single cells was promoted. In our study, the average TN concentration was 3.3 mg L1 in June, which decreased to 2.13e2.79 mg L1 in August and October. The average TP concentration was 0.09 mg L1 in June,

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Fig. 3. Variations in (a) mcyJ/cpcBA ratio and (b) chlorophyll-a (chl a) concentration in samples collected from 10 sites during the bloom period in Lake Taihu.

Table 1 Spearman's rank correlation coefficients of mcyJ/cpcBA ratio and chlorophyll-a concentration with hydrochemical parameters. mcyJ/cpcBA 9-Jun TN TDN Ammonium Nitrite Nitrate TP TDP DIP COD

0.04 0.12 0.47 0.11 0.05 0.17 0.05 0.30 0.56

chlorophyll-a 22-Aug 0.40 0.34 0.55 0.44 0.26 0.31 0.36 0.23 0.45

15-Oct 0.05 0.01 0.16 0.06 0.18 0.09 0.05 0.10 0.77**

Combineda 0.21 0.39* 0.24 0.27 0.50** 0.37* 0.33 0.40* 0.35

9-Jun 0.57 0.58 0.30 0.89** 0.62 0.86** 0.80** 0.62 0.49

22-Aug **

0.93 0.83** 0.56 0.85** 0.57 0.86** 0.86** 0.79** 0.96**

15-Oct

Combineda

0.03 0.18 0.30 0.30 0.35 0.15 0.33 0.15 0.91**

0.23 0.37* 0.13 0.02 0.48** 0.77** 0.44* 0.45* 0.891*

*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed). a Combined analysis of samples of samples collected on June 9, August 22 and October 15.

which increased to 0.22 mg L1 in August and decreased to 0.14 mg L1 in October. Although P level increased in summer and autumn, neither N nor P exceeded the thresholds favorable to forming single cells, so the water status of Lake Taihu turned from P-restricted in summer to both Ne and P-restricted in autumn (Xu et al., 2010). Therefore, the nutrient restriction in summer and autumn promoted the dominance of larger and more toxic Microcystis colonies. It is also reported that higher temperatures increased growth of 83% toxic Microcystis, but only 33% for non-toxic Microcystis based on in situ experiment (Davis et al., 2009). Moreover, the concurrent increases in temperature and nutrient (such as P) concentrations yield higher growth of toxic Microcystis cells (O'Neil et al., 2012; Paerl and Paul, 2012). In Lake Taihu, P concentrations and temperature in summer and autumn are higher than spring, which may increase growth of toxic Microcystis genotype. Blooms increase reactive oxygen species (ROS) directly via photosynthesis, and indirectly by augmenting the dissolved organic carbon pool in surface waters, which is in turn UV-photocatalyzed into ROS. This feedback selects for toxin-producing cyanobacterial strains (Paerl and Otten, 2013). In this study, the feedback of Microcystis blooms on nutrient concentrations in combination with elevated temperature was responsible for the seasonal increase in biomass and toxicity of Microcystis blooms in Lake Taihu. 3.4. Variation in Microcystis cpcBA genotype composition PCR-DGGE is a well-accepted technology to study the genetic structure of cyanobacterial community. DGGE profiles showed composition of Microcystis cpcBA genotype on three sampling dates

(Fig. 4). The number of bands on the DGGE fingerprint increased from 1.70 in June to 5.70 in August and 7.30 in October. The results indicated that the diversity of Microcystis cpcBA genotype increased from June to October. Analysis of DGGE profile of cpcBA fragments by an indirect PCA model demonstrated that samples collected on the same day gathered together on PCA ordination plot (Fig. 5). The first axis and second axis explained 48% and 19% variation in Microcystis cpcBA genotype composition. In the first axis of PCA ordination plot, a distinguish boundary was found between samples collected in June and those with August and October. While in the second axis, a relatively clear boundary was found between samples in August and October. However, this genotype composition at different sampling dates was different. In 2009, similar temporal variation in Microcystis cpcBA genotype composition was found in Meiliang Bay, Gonghu Bay and center of the lake (Wang et al., 2012). These results suggested that the succession in Microcystis cpcBA genotype composition happened in northern, western and central areas of Lake Taihu, where cyanobacterial blooms take place frequently. RDA model was used to assess the correlation between horizontal distribution of Microcystis cpcBA genotype and hydrochemical parameters in sampling location-date separate analyses (Table 2). In June, Microcystis cpcBA genotype composition was not correlated with hydrochemical parameters. In August, it was correlated with nitrite and DIP, while it had correlation with COD in October. These correlations were inconsistent through the blooms period, suggesting there was little relation between horizontal distribution of Microcystis cpcBA genotype and hydrochemical parameters. To some extent, this phenomenon further verified the previously-deduced conclusion that horizontal distribution of the

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Fig. 4. DGGE profiles of cpcBA gene fragments from samples collected from 10 sites during the bloom period in Lake Taihu.

Fig. 5. PCA ordination plot of Microcystis cpcBA genotype composition of samples collected at 10 sampling sites of lake Taihu during blooms in 2011. Open circle (June 9), open square (August 22) and solid square (October 15) represent samples collected from different dates. Arrows represent correlation coefficients between significant variables (determined by Monte Carlo permutation test using RDA model) and the first two ordination axes.

genotype composition of Microcystis populations at all samples suggested that the exchange of Microcystis blooms happened in the blooms areas. In sampling location-date combined analysis, the relation between the succession in Microcystis cpcBA genotype and hydrochemical parameters is shown in Table 2. RDA result showed that nitrate, TDN, COD and TP were correlated with the variation in Microcystis cpcBA genotype composition. Microcystis cpcBA genotype composition had negative correlations with nitrate and TDN, but positive correlations with COD and TP. Our previous study confirmed that the succession in Microcystis cpcBA genotype composition, which happened in Meiliang Bay, Gonghu Bay and the center of Lake Taihu, is correlated with nitrate, pH, COD and TN (Wang et al., 2012). Among all parameters, nitrate explained most the variation in Microcystis cpcBA genotype composition. The cpcBA genotype compositions were different between Microcystis colonies with sizes of <50 mm, 50e100 mm, 100e270 mm and >270 mm (Wang et al., 2013). With the changes in colony size and proportion of toxic genotype, the succession in cpcBA genotype composition of Microcystis populations probably occurred corresponding to N and P levels. 4. Conclusions

Table 2 Correlation between variation in Microcystis cpcBA genotype composition and nutrient levels (%)a.

Nitrate TDN COD TP TN Ammonium DIP Nitrite TDP Total

9-Jun

22-Aug

15-Oct

Combinedb

12.2 21.5 3.70 14.2 19.9 42.9 10.9 23.4 10.5 0

20.1 26.1 20.2 24.8 23.0 11.0 31.4* 33.8* 25.3 33.8

21.6 10.8 25.5* 9.30 11.8 7.90 8.60 6.70 9.90 25.5

22.8** 15.1** 12.6** 11.7* 5.80 3.00 3.00 2.60 2.00 22.8

*and **Correlation is significant at the 0.05 and 0.01 level (2-tailed). a The bold values denote independent explanatory variables selected by forward selection. b Combined analysis of samples collected on June 9, August 22 and October 15.

blooms-forming Microcystis occurs in Lake Taihu and is strongly affected by wind conditions. In this study, the homogenous cpcBA

In this study, a systematic investigation was conducted on the spatial-temporal variations in Microcystis biomass, proportion of toxic Microcystis genotype and Microcystis cpcBA genotype composition within the cyanobacterial blooms area of Lake Taihu. Both spatial and temporal variations in the biomass of Microcystis blooms were observed, which were correlated with N and P. The spatial distribution of chl-a had positive correlations with nitrite, TP and TDP, but the correlations were insignificant in October. The temporal variation in chl-a also had positive correlation with TDP, but negative correlation with TDN. The toxic Microcystis genotype also had spatial and temporal variations. The spatial distribution of toxic Microcystis genotype didn't show a clear trend or correlation with hydrochemical parameters. However, the temporal variation in toxic genotype had negative correlation with TDN and nitrate and positive with DIP. The Microcystis cpcBA genotype composition of each sample was similar, but the succession in Microcystis cpcBA genotype composition was obvious, which showed negative correlation with TDN and positive with TP. Accordingly, we suggest that the spatial distributions of toxic and cpcBA genotypes were determined by horizontal transport of Microcystis colonies,

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whereas the spatial variations in Microcystis biomass were controlled both by nutrient-mediated in situ growth and horizontal transport of Microcystis. According to literature, the temporal variations in N and P levels are strongly affected by the feedback of cyanobacterial blooms, therefore the relations of Microcystis biomass with N and P levels reflected the feedback relation. The relations between the temporal variations in toxic genotype and cpcBA genotype composition with N and P levels were also based on the feedback of Microcystis blooms. Acknowledgements This work was supported by the National Natural Science Foundation of China (31270447 and 41461096), the National Basic Research Program of China (973Program, 2008CB418004) and the China Postdoctoral Science Foundation (2014M551554 and 2017T100350). Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.chemosphere.2018.06.141. References ~ ares-Espan ~ a, E., Lo  pez-Rodas, V., Costas, E., Salgado, C., Flores-Moya, A., 2007. Ban Genetic variability associated with photosynthetic pigment concentration, and photochemical and nonphotochemical quenching, in strains of the cyanobacterium Microcystis aeruginosa. FEMS Microbiol. Ecol. 60, 449e455. Berry, M.A., White, J.D., Davis, T.W., Jain, S., Johengen, T.H., Dick, G.J., Sarnelle, O., Denef, V.J., 2017. Are oligotypes meaningful ecological and phylogenetic units? A case study of Microcystis in freshwater lakes. Front. Microbiol. 8, 365. Briand, J.F., Jacquet, S., Bernard, C., Humbert, J.F., 2003. Health hazards for terrestrial vertebrates from toxic cyanobacteria in surface water ecosystems. Vet. Res. 34, 361e377. Carstensen, J., Henriksen, P., Heiskanen, A.S., 2007. Summer algal blooms in shallow estuaries: definition, mechanisms, and link to eutrophication. Limnol. Oceanogr. 52, 370e384. Davis, T.W., Berry, D.L., Boyer, G.L., Gobler, C.J., 2009. The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae 8, 715e725. do Carmo Bittencourt-Oliveira, M., de Oliveira, M.C., Bolch, C.J.S., 2001. Gentic variability of Brazilian strains of the Microcystis aeruginosa complex (cyanobacteria/cyanophyceae) using the phycocyanin intergenic spacer and flanking regions (cpcBA). J. Phycol. 37, 810e818. €hren, H., 2001. Determination of oligopeptide diversity Fastner, J., Erhard, M., von Do within a natural population of Microcystis spp. (cyanobacteria) by typing single colonies by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl. Environ. Microbiol. 67, 5069e5076. Graham, J.L., Loftin, K.A., Meyer, M.T., Ziegler, A.C., 2010. Cyanotoxin mixtures and taste-and-odor compounds in cyanobacterial blooms from the Midwestern United States. Environ. Sci. Technol. 44, 7361e7368. Huang, X.F., Chen, W.M., Cai, Q.M., 1999. Survey, Observation and Analysis of Lake ecology. Standard Methods for Observation and Analysis in Chinese Ecosystem Research Network, Series V. (In Chinese). Standards Press of China, Beijing. Kim, S.G., Joung, S.H., Ahn, C.Y., Ko, S.R., Boo, S.M., Oh, H.M., 2010. Annual variation of Microcystis genotypes and their potential toxicity in water and sediment from a eutrophic reservoir. FEMS Microbiol. Ecol. 74, 93e102. Kisand, V., Noges, T., 2004. Abiotic and biotic factors regulating dynamics of bacterioplankton in a large shallow lake. FEMS Microbiol. Ecol. 50, 51e62. pez-Rodas, V., Costas, E., Ban ~ ares, E., García-Villada, L., Altamirano, M., Rico, M., Lo Salgado, C., Flores-Moya, A., 2006. Analysis of polygenic traits of Microcystis aeruginosa (Cyanobacteria) strains by Restricted Maximum Likelihood (REML) procedures: 2. Microcystin net production, photosynthesis and respiration. Phycologia 45, 243e248.  Leps, J., Smilauer, P., 2003. Multivariate Analysis of Ecological Data Using CANOCO. Cambridge University Press, New York. Li, D.M., Zheng, H.Y., Pan, J.L., Zhang, T.Q., Tang, S.K., Lu, J.M., Zhong, L.Q., Liu, Y.S., Liu, X.W., 2017. Seasonal dynamics of photosynthetic activity, Microcystis genotypes and microcystin production in Lake Taihu, China. J. Great Lake. Res. 43, 710e716. Ma, J., Brookes, J.D., Qin, B., Paerl, H.W., Gao, G., Wu, P., Zhang, W., Deng, J., Zhu, G., Zhang, Y., Xu, H., Niu, H., 2014. Environmental factors controlling colony formation in blooms of the cyanobacteria Microcystis spp. in Lake Taihu, China. Harmful Algae 31, 136e142. Muylaert, K., Van der Gucht, K., Vloemans, N., De Meester, L., Gillis, M., Vyverman, W., 2002. Relationship between bacterial community composition

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