Temporal shifts in cyanobacterial communities at different sites on the Nakdong River in Korea

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 6 9 7 3 e6 9 8 2

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Temporal shifts in cyanobacterial communities at different sites on the Nakdong River in Korea Moonsuk Hur a, Injung Lee a, Bo-Mi Tak a, Hae Jin Lee a, Jae Jeong Yu a, Se Uk Cheon a, Bong-Soo Kim b,* a

Nakdong River Environment Research Center, National Institute of Environmental Research, Goryeong-gum, Gyeongsangbuk-do 717-873, Republic of Korea b Chunlab, Inc., Seoul National University, Daehak-dong, Gwanak-gu, Seoul 151-742, Republic of Korea

article info

abstract

Article history:

The studies of cyanobacterial blooms resulting from eutrophication or climate change and

Received 26 March 2013

investigation of changes in the cyanobacterial community in freshwater environments are

Received in revised form

critical for the management of drinking water. Therefore, we investigated the cyano-

8 July 2013

bacterial communities at 6 sites along the Nakdong River in South Korea from May 2012 to

Accepted 24 September 2013

October 2012 by using high-throughput sequencing techniques and studied their rela-

Available online 20 October 2013

tionship with various geochemical factors at sampling sites. Diverse genera (total of 175 genera) were detected within the cyanobacteria, and changes in their compositions were

Keywords:

analyzed. The genus Prochlorococcus predominated in the May samples, especially in those

Cyanobacteria

obtained from the upstream part of the river, whereas the relative abundance of Microcystis

Pyrosequencing

and Anabaena increased with increase in water temperature. The relationship between the

16S rRNA gene

cyanobacterial community and environmental factors was analyzed by canonical corre-

Korean river

lation analysis, and the correlation between harmful cyanobacteria and chemical factors was analyzed by nonmetric multidimensional scaling ordination. Various environmental factors such as dissolved oxygen, pH, electric conductivity, temperature were found to affect the cyanobacterial communities in the river. The results of this study could help in the management of freshwater environments and in maintenance of drinking water quality. Crown Copyright ª 2013 Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Climate changes such as global warming affect various ecosystems and may be related to the development of cyanobacterial blooms in water environments (Huber et al., 2012; Paerl and Huisman, 2008; Paerl and Paul, 2012). The rising water temperature provides optimal conditions for the growth of cyanobacteria and gives them a competitive advantage over other algae (Butterwick et al., 2005; Johnk et al., 2008). The pH

and input of nutrients such as phosphorus and nitrogen have also been reported to increase cyanobacterial blooms in water bodies (Johnk et al., 2008; Paerl and Huisman, 2008). Eutrophication (nutrient overenrichment) has been promoted by human activities related to urban, agricultural, and industrial development over the past few centuries. Cyanobacterial blooms have drawn worldwide attention from scientists because of the release of toxic materials from secondary metabolites produced by cyanobacteria (Briand et al., 2003;

* Corresponding author. Tel.: þ82 2 875 2501; fax: þ82 2 875 7250. E-mail address: [email protected] (B.-S. Kim). 0043-1354/$ e see front matter Crown Copyright ª 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.watres.2013.09.058

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Haider et al., 2003). The presence of these cyanotoxins in freshwater bodies (lakes, rivers, and reservoirs) could be a potential risk to human health through exposure via water ingestion, aerosol inhalation, and fish consumption (Carmichael, 2001; Codd et al., 2005; Dietrich et al., 2008). Addressing the issues of cyanobacterial blooms and toxin production is critical for the effective management of the water environment and the minimization of health risks associated with cyanotoxins. Therefore, cyanobacterial blooms have been studied with respect to the factors that affect their occurrence and the related public health issues (Codd et al., 2005; de Figueiredo et al., 2004; Dolman et al., 2012; Xie et al., 2012). Microcystin is a cyanotoxin (hepatotoxin) that may cause acute poisoning and is a potential carcinogenic agent, which makes it a serious public health issue (de Figueiredo et al., 2004; Zhou et al., 2002). This toxin is mainly produced by Microcystis, Anabaena, Oscillatoria, and Aphanizomenon (de Figueiredo et al., 2004; Kaebernick and Neilan, 2001). Investigation of cyanobacterial communities and the identification of predominant cyanobacterial species are important for the prediction and prevention of economical and health problems related to the presence of microcystin in drinking water. To date, morphological classification using light microscopy has been used to identify the algae present in water (Soares et al., 2011). However, assessment of the toxicity due to cyanobacterial blooms by morphological identification or bioassays using animals is associated with ethical issues, low sensitivity, and high cost (Lawton et al., 1999; Pearson and Neilan, 2008). The toxicity of cyanobacteria relies on the genotypic diversity of different strains and on the various growth and toxic characteristics of each strain (de Figueiredo et al., 2004). Molecular approaches can be used to address these problems, identify closely related algal species, and investigate uncultured or minor species in various environments (Komarek, 2010; Nu¨bel et al., 1997; Tringe and Hugenholtz, 2008; Vinten et al., 2011). Molecular studies using genes related to toxin synthesis, such as mcyA, mcyB, mcyD, and mcyE, have been used to monitor toxin-producing cyanobacteria and have increased the sensitivity and reliability of detection; however these studies were designed for specific cyanobacterial species of Microcystis, Anabaena, or Aphanizomenon (Fortin et al., 2010; Mankiewicz-Boczek et al., 2006). Analysis of various cyanobacteria is required to further our understanding of cyanobacterial blooms and cyanotoxins because the ecosystem of water bodies consists of diverse cyanobacteria. Only a few studies have investigated total cyanobacterial communities in water environments (Nu¨bel et al., 1997; Treusch et al., 2012). Recently, the advances provided by high-throughput sequencing have enabled the analysis of thousands of sequences per sample and comprehensive study of bacterial communities at the species level (Hur et al., 2011; Khodakovskaya et al., 2013; Steven et al., 2012). Application of this technique in studying cyanobacteria can provide extended information regarding blooms and their interaction with environmental factors. In this study, we investigated shifts in cyanobacterial communities from May to October at 6 different sites along the Nakdong River in Korea by using 16S rRNA gene

pyrosequencing. In addition, we analyzed the relationships between the total cyanobacterial community and environmental factors such as the following: concentration of microcystin; levels of chlorophyll-a (Chl_a), nitrogen, and phosphorus; and river water pH. These findings may provide (1) an understanding of the cyanobacterial community in rivers with changing environmental factors and (2) information for monitoring harmful cyanobacteria with respect to the management of drinking water.

2.

Materials and methods

2.1.

Sampling sites and sample collections

The Nakdong River is the longest river in Korea, with a length of 521.5 km and a surface area of 23,817 km2. Its water is used for residential, agricultural, and industrial purposes in the southeastern part of Korea. The four rivers project has been carried out in the Nakdong River since 2008 by the Korean government and 8 weirs were constructed along the Nakdong River in 2012. The longest stagnant section of the river water is 500 m upstream of the weirs. This stagnancy could influence the growth of microbes, including cyanobacteria. The river passes through a major city and is used for drinking water for most of the population of southeastern Korea. Therefore, analysis of the Nakdong River (particularly at site upstream of weirs) is crucial to the management of drinking water and to public health. Surface water samples (4 L each) were collected twice per month (biweekly) at 6 sampling sites (5 sites located 500 m upstream constructed weirs and 1 downstream area) along the Nakdong River from May to October (sampled once in October) 2012 (Fig. 1). After the samples were collected, they were immediately placed on ice and transported to the laboratory. In total, 66 samples were collected and used for analyzing cyanobacterial communities.

2.2.

Environmental measurements

Data for biological oxygen demand (BOD), chemical oxygen demand (COD), and suspended solids (SS) were obtained according to the Korea National Standard Methods for Water Quality Analysis (Ministry of Environment, 2011). Water temperature (Tm), pH, electric conductivity, and dissolved oxygen (DO) were analyzed using a Multiparameter YSI 556 MPS instrument (Yellow Spring Instruments, Yellow Springs, OH, USA). Decomposition of total organic carbon (TOC) was performed using a TOC analyzer (Sievers 5310C; General Electric Co., Boulder, CO, USA). Total nitrogen (TN) and total phosphorus (TP) levels were analyzed using an Integral Futura Autoanalyzer (Alliance Instruments, Fre´pillon, France). Nitrate (NO3-N) was analyzed using ion chromatography (850 Professional IC; Metrohm, Herisau, Switzerland). Ammonium (NH4-N) and phosphate (PO4-P) were analyzed on an ion analyzer (Smartchem 140, Alliance Instruments, Fre´pillon, France). Chlorophyll-a (Chl-a) was measured with a Lambda 45 UV/VIS spectrophotometer after 90% acetone extraction from filtered water using glass fiber GF-F (Whatman, Florham Park, NJ, USA). Turbidity was analyzed using a 2100 N turbidimeter (Hach Co., Loveland, Co, USA). Information regarding

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Fig. 1 e The map indicates sampling sites in the Nakdong River, Republic of Korea.

monthly rainfall at each site was obtained from the website of the water management information system (http://www. wamis.go.kr/wkw/rf_dubrfobs.aspx). The concentration of microcystin was measured by an immunoassay using a commercial Microcystin Tube kit (Beacon Analytical System Inc., Portland, ME, USA). Environmental factor analyses for the samples were determined with 3 replications at the National Institute of Environmental Research in Korea. The average values of the replicated environmental parameters (except Tm, pH, and DO) have been provided in Table S1.

2.3.

Pyrosequencing

The water samples (500 mL each) collected were filtered using a 0.22-mm filter (Millipore, Bedford, MA, USA), and the filters were then sliced and subjected to total DNA extraction using a

DNeasy Plant Mini Kit (Qiagen, Valencia, CA, USA). The extracted DNA was amplified using the modified cyanobacterial primers CYA359F and CYA781R (Nu¨bel et al., 1997). Unique barcodes for each sample were attached to the reverse primers followed by a 2-base linker for fusion primers (Hur et al., 2011). Amplification was performed using a C1000 Touch thermal cycler (BioRad, Hercules, CA, USA) in a final volume of 50 mL with 10 Taq buffer, dNTP mixture (Takara, Shiga, Japan), each barcoded fusion primer at a concentration of 10 mM (http://oklbb.ezbiocloud.net/content/1001), and 2 U of Taq polymerase (Ex Taq; Takara). The amplification conditions were as follows: initial denaturation at 94  C for 5 min; followed by 30 cycles of denaturation (30 s, 94  C), primer annealing (30 s, 55  C), and extension (30 s, 72  C); with a final extension step of 7 min at 72  C. A QIAquick PCR Purification Kit (Qiagen) was used to purify the amplified products, and a

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PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA) was then used to quantify the concentrations of purified products. Equimolar concentrations of each amplified product were mixed and purified again using an AMPure Bead Kit (Agencourt Bioscience, Beverly, MA, USA). The beads recovered after emulsion PCR were deposited on a 454 picotiter plate, and sequencing was performed using a Roche/454 GS Junior system according to the manufacturer’s instructions.

2.4.

Sequence analysis and statistical analysis

Pyrosequences were analyzed according to methods in previous descriptions (Hur et al., 2011; Jeon et al., 2013). Briefly, sequence reads from each sample were separated by unique barcodes, and low-quality reads (average quality score < 25 or read length < 300 bp) were excluded from further analysis. The primer sequences were removed by pairwise sequence alignment, and sequences were clustered to correct sequencing errors. The representative sequences in each cluster were selected for taxonomic assignment. A BLAST search for each representative sequence was conducted using the EzTaxon-e database (http://eztaxon-e.ezbiocloud.net) (Kim et al., 2012), and the taxonomic positions of each read were determined according to the highest pairwise similarity among the top 5 BLAST hits. Chimeric sequences were detected and removed by UCHIME (Edgar et al., 2011). Cyanobacterial profiles were extracted from the whole community result of each sample for further analysis. Canonical correlation analysis (CCA) and nonmetric multidimensional scaling (NMDS) ordination were performed to analyze the relationships between chemical factors and the cyanobacterial community using the R software (ver 2.15.2). All the species in each sample were used as community matrix for CCA, whereas target species in each sample were used as the abundance matrix for NMDS. CCA was based on Chisquared distance, and the significance of correlation was assessed by 1000 permutation test using mock ANOVA. The BrayeCurtis similarity coefficient was used as a distance measure for ordination in NMDS, and the linear fitting function (“env_fit”) was used to explain significant environmental components in relation to the cyanobacterial genus (Oksanen et al., 2009). A stress value of 10 was considered as an indicator of good ordination and the significance of each vector was assessed with a goodness-of-fit statistic (r2) using 1000 permutations. The significantly correlated environmental factors (p value < 0.05) were selected and presented in plots. All pyrosequencing reads obtained from this study are available in the EMBL SRA database under study number ERP002342 (http://www.ebi.ac.uk/ena/data/view/ERP002342).

3.

Results and discussion

3.1.

Environmental measurements at each sampling site

Environmental factors at each sampling site were analyzed for the sampling periods (Table S1). The temperature of water at all the sites increased from May to June (mean temperature: 21.9  1.4  C in May and 24.9  0.8  C in June) and decreased from September (mean temperature: 23.0  1.4  C). The

highest water temperature was observed in late July and early August (mean temperature: 28.3  1.3  C). The highest values of DO, pH, BOD, and Chl-a were detected in May, whereas the electronic conductivity and microcystin level were higher in June than at the other sampling times. The highest values of turbidity, SS, phosphorus, PO4-P, and rainfall were detected in September. The concentration of DO was lowest in August (mean value, 7.94  1.1 ppm), and increased again to 9.67  1.8 ppm in October. The concentration of TOC increased from May (3.4  0.5 ppm) to August (4.18  0.5 ppm), and then decreased to 3.7  0.6 ppm in October. Most of the values for the environmental factors changed significantly during the summer months (from June to August) and returned to the previous (May) values in October. The changes of environmental parameters could be affected by interactions of geochemical factors or pollutants, and these could be altered by the interaction between members of organisms in the summer. In turn, these changes could affect other organisms and ecosystems in the river.

3.2. Profiles of cyanobacterial communities during the sampling periods at each site In total, 62,384 sequences were identified as belonging to cyanobacteria by using the EzTaxon-e database. The average compositions of cyanobacterial communities at 6 sites were compared according to sampling time (Fig. 2). Chroobacteria was the most abundant class throughout the sampling periods; the highest proportions was in May (93.3% of sequence reads) and the lowest was in October (55.7%), followed by the class Hormogoneae, which constituted 4.6% of the total cyanobacterial sequences in May and 41.2% in October. The relative abundance of the Prochlorococcus genus was highest in May (59.2%), decreased from June to September (ranging from 3.7% to 14.8%), and increased again to 24.9% in October. The relative abundance of Microcystis increased from June to August (ranging from 29.4% to 38.8%) and then decreased to 14.8% in September. The cyanobacterial composition differed at each sampling time, and the maximum number of genera was observed in the September sample. The highest proportion of Aphanizomenon was detected in June (22.9%), and the proportion of Anabaena was highest in October (36.8%). The relative abundance of Limnothrix was similar across sampling periods, whereas that of Merismopedia increased from July. The composition of class changed from June to October, whereas that of genera changed dynamically throughout the sampling periods. Changes in the cyanobacterial communities at each site during the sampling periods were analyzed at the class and genus levels (Fig. S1). Although Chroobacteria was the predominant class in most of the samples, Hormogoneae class was also dominant in some samples. At the genus level, Prochlorococcus was dominant at most sites, while the Limnothrix, Cyanobium, and uncultured Planktothrix (similar to AY168749) genera were predominant in May samples. Compared to the results for the May samples, the proportions of the Microcystis and Aphanizomenon genera were higher in samples collected on June 11 from the downstream site, while the proportions of Merismopedia and Anabaena were higher in samples collected on June 25 from a relatively upstream area. Microcystis was

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Fig. 2 e Double pie charts of typical cyanobacterial communities in samples from each month were compared. The inner pie indicates the class composition and the outer pie indicates the genus composition of the cyanobacterial communities. The names for each color appear below the figure. The nomenclature for each phylotype is based on the EzTaxon-e database (Kim et al., 2012; http://eztaxon-e.ezbiocloud.net/).

dominant in the downstream region from June to August. Although Prochlorococcus became the dominant genus again from September, more diverse genera were detected in the September samples than in the October and May samples. These changes may be the result of seasonal changes in the river, including water temperature and sunlight time. The optimal growth temperature of Prochlorococcus has been reported below 25  C, and the growth rate of Prochlorococcus has been found to be more positively related to lower light intensity than those of other cyanobacteria such as Synechococcus (Johnson et al., 2006; Moore et al., 1995). These properties of the genus Prochlorococcus likely influenced the composition of the cyanobacterial communities during the sampling periods of this study. The detailed cyanobacterial profiles for each site during the sampling periods presented in Fig. S1. Chroobacteria was the most abundant class in May in samples from all sites. The highest proportion of Chroobacteria was found at site 1 (over 98.9% of total reads), whereas the relative abundance of Hormogoneae was higher at sites 3 (10.53%) and 4 (10.65%) in the

samples collected on May 7 and at sites 2 (12.4%) and 3 (12.62%) in the samples collected on May 21. Prochlorococcus was the predominant genus in most of the May samples (over 30.8% of total reads), whereas uncultured Planktothrix (AY16849) was the most abundant genus at site 6 (32.1%) on May 21. The relative abundance of Prochlorococcus on May 7 was similar for relatively close sites (sites 1/2, 3/4, and 5/6). The proportion of Cyanobium was higher on May 7 than on May 21 at all sites, and the relative abundance of Limnothrix was high in samples from sites 3 (16.99%) and 4 (32.84%) on May 7. The genera were more diverse in samples collected on May 21 than on May 7. The relative abundance of class and genus were similar for sequential sites, which suggests that regional distance contributes to differences in community composition. The proportion of Hormogoneae was higher in June samples than in May samples (Fig. S1). Hormogoneae was the most dominant class in the samples from sites 5 and 6 on June 11 and in those from site 3 on June 25. Although Chroobacteria was the most dominant class at site 1 in the June samples, the

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3.3. Analyses of correlations between environmental factors and cyanobacterial community compositions The relationships between geochemical factors and cyanobacterial communities at all sites during the sampling periods were analyzed by CCA with the R software. The correlation between environmental factors and cyanobacterial communities obtained in all sampling periods are present in Fig. 3. Overall, the first canonical axis represented 48.0% of variance

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CCA2 (32.1%)

1

2

September

946 +

Turbidity 944 Rain ++ + ++ + + + + ++ + PO P SS +844 + + + ++ 941 + TP ++++ 942+ + + + +++ 923 + 945+++++++843 + + COD 821+++1024 +1022 October + + TN NO N + + 922 + ++ ++ + 1021 1026 + + + 822 +744 ++++ 1025 + + TOC + + + + + +721++ + + 741++ 543 526 + + 846 + + + 743 + + 742 + ++ + + + 525 + 724 August 746 + + 545 823 July +++ ++ + + + 541 + 621+ + 723 ++ + 641 ++ + + ++ + + BOD 622 824 + + DO + + + ++ NH N 523 + + 642 + + + 544 + + ++ 542 ++ + Tm 826 825 + Chl_a+ + + + 643 + 646 524 ++ +

645 625

522 521

May

644 Microcystin + + + 726 623 June 624

-2

compositions of genera within Chroobacteria were different between samples of June 11 and 25. At site 1, Prochlorococcus was the most abundant genus on June 11 (85.9% of total reads), whereas Merismopedia was the most abundant on June 25 (48.4%). The compositions of genera within Chroobacteria differed significantly between sites in June. Microcystis was enriched from site 2e5 on June 11 and at site 4e6 on June 25, Aphanizomenon was enriched at sites 5 and 6 on June 11, and Anabaena was enriched at site 2e4 on June 25. These results show that Aphanizomenon was enriched at relatively downstream regions, Microcystis was enriched in the middle-todownstream regions, and Anabaena was enriched in the middle-to-upstream regions. At most sites, the relative abundance of Chroobacteria was higher in July than in June. However, compared to the results for the May and June samples, the proportion of Chroobacteria at site 1 decreased in the July 9 samples, and the genera at site 1 diversified. Hormogoneae was the second most dominant class in all samples, and Vampirovibrio was enriched on July 23. The high abundance of Microcystis was maintained at sites 5 and 6, whereas the abundance of Merismopedia increased at sites 2e4 on July 9. The proportion of Prochlorococcus was higher in samples from every site on July 23 than on July 9. The proportion of Chroobacteria increased at sites 3e6 in the first half of August. Within Chroobacteria, Microcystis dominated in terms of community composition at sites 3 (48.5%) and 6 (85.4%), while Oscillatoria dominated at site 2 (41.1%) (Fig. S1). The relative abundance of Hormogoneae increased on Aug 27 compared to Aug 13; within Hormogoneae, the Anabaena and Aphanizomenon genera were dominant. Dynamic changes in the cyanobacterial communities in the river were observed every 2 weeks in this study. Large shifts in community composition over relatively short time periods, particularly over the course of algal bloom development, have also been reported in eutrophic lakes (Steven et al., 2012). The relative abundance of Microcystis was higher downstream than upstream during summer (from June to August). However, Microcystis was more abundant at sites 1 and 2 (upstream sites) on September 10 than in the summer months, and similar proportions of Microcystis were observed upstream and downstream on that date. Most sites had various genera in their cyanobacterial communities on September 24, with a total of 109 genera being identified. Chroobacteria was dominant in the October samples in the upstream and downstream regions, and Prochlorococcus showed increased proportions at those sites. However, within Hormogoneae, Anabaena dominated at sites 3 and 4 in the middle region of the river. These changes may be related to seasonal changes and variations in chemical factors at each site during the sampling periods.

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pH

Electronic conductivity

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-1

0

1 CCA1 (48.0%)

2

3

4

Fig. 3 e Canonical correspondence analysis (CCA) plots to determine the relationships between the abundance of cyanobacteria and the environmental features of sampling sites. The percentages on each axis represent the variation in the samples. The circles indicate the sample, and the red crosses indicate bacterial species. The number at the beginning of the sample name indicates the sampling month, and the second number in the sample name indicates the sampling week in each month. The last number in the sample name indicates the sampling site (Table S1). BOD: biological oxygen demand, COD: chemical oxygen demand, SS: suspended solids, Tm: water temperature, Rain: rainfall, DO: dissolved oxygen, TOC: total organic carbon, TN: total nitrogen, TP: total phosphorus, NO3-N: nitrate, NH4-N: ammonium, PO4-P: phosphate, and Chl-a: Chlorophyll-a. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

of cyanobacterial community and was correlated with DO, BOD, TOC, COD, Tm, and NH3-N. The second axis represented 32.1% of the variance and was correlated to TN, NO3-N, turbidity, rainfall, SS, PO4-P, TP, microcystin, and electric conductivity. The cyanobacterial communities were separated by sampling time. The cyanobacterial communities in May (sample ID starts with number 5) were distinguished from other months mainly by the first canonical axis, and they were correlated with DO, BOD, and pH in CCA bi-plot (p < 0.01 in the permutation test). The cyanobacteria in the June samples were separated from the May samples by the first axis and from the other months by the second axis. The microcystin concentration and electric conductivity (p < 0.01) were more correlated with the June samples, while the temperature (p < 0.01) and NH3-N (p < 0.5) factors were correlated to separate the June, July, and August samples from the other samples. The cyanobacteria in the August and September samples were separated from the June samples by the second canonical axis (representing 32.1% of variance), and TOC, COD, PO4-P, TP, SS, turbidity, and rainfall were correlated

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(p < 0.1) with these samples. The NO3-N and TN levels were related (p < 0.05) to the October samples. This analysis indicates that the changes in the cyanobacterial community were separated by sampling times and could be related to different environmental factors in each month. Detailed correlations between the shifted cyanobacterial communities and the environmental factors by sampling times were analyzed (Fig. S2). Electric conductivity (p < 0.01), TOC (p < 0.1), and temperature (p < 0.5) were correlated factors that distinguished the June samples from the May sample, representing 72.0% of the first canonical axis variance (Fig. S2a). NH3-N was correlated with cyanobacteria at site 3 and 4 in June (p < 0.5). DO, BOD, and pH were correlating factors (p < 0.5) with the May samples. Thus, the high pH and high DO concentration could be related to the dominance of Prochlorococcus and Cyanobium in the May samples (Fig. 2). The increased proportion of the class Hormogoneae, and the genera Microcystis, Aphanizomenon, and Anabaena in the June samples compared to that for the May samples could be related to temperature and electric conductivity (Fig. 2). In particular, temperature was correlating factor for the separation of the cyanobacterial communities of the June, July, and August samples from the other sampling times in the overall period analysis (Fig. 3). The predominance of Microcystis in June, July, and August (Fig. 2) could be related to the temperature. The electric conductivity and TOC were negatively correlated factors (p < 0.01) that distinguished the July samples from the June samples, whereas PO4-P (p < 0.1), temperature (p < 0.5), turbidity (p < 0.05), and TP (p < 0.1) correlated with the July samples and distinguished them from the June samples (Fig. S2b). These factors could be related to the increasing proportion of Merismopedia in the July samples. The correlation analysis between cyanobacterial communities and environmental factors for the other sampling times was also compared (Fig. S2cee). Electric conductivity, temperature, DO and TN were correlated factors (p < 0.01) that distinguished the July and August samples (Fig. S2c). TN, TP, NH3-N, pH, and DO were correlated factors (p < 0.01) that separated the August and September samples (Fig. S2d). TN, temperature, NO3-N, and pH were found to be possible separating factors for the September and October samples, which showed 38.5% and 27.2% of variances, respectively; however, there was no residual component in the permutation test of the CCA plots (Fig. S2e). TP, PO4-P, and TN were significant correlated factors (p < 0.01) that distinguished the cyanobacteria among the June, August, and September samples (Fig. S2f). This result is consistent with the previous overall period analysis (Fig. 3). TP (p < 0.01) and PO4-P (p < 0.1) were correlated with the August and September samples, which were separated from the July samples along TP and PO4-P arrow plots. TN (p < 0.01) and NO3-N (p < 0.05) could be related to the separation of the September and October samples from the July samples, and NH3-N might be related to the separation of the June and August samples from the July and October samples. Similar results were observed in the partition of the phosphorus and nitrogen parameters (Fig. S3). TP and PO4-P positively correlated with the August and September samples, whereas they were negative correlated to the May samples (p < 0.01). In contrast to the results for phosphorus, each nitrogen metabolite was correlated with the different sampling times

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(Fig. S3b). The first canonical axis represented 51.5% of the variance and was correlated with NO3-N and NH4-N. The cyanobacteria in the May samples were distinguished from the June and July samples mainly by the first axis. Therefore, our result indicated that nitrogen and phosphorus were potential correlation factors for shifts in the cyanobacterial communities. The relationship of N and P with cyanobacteria has been reported in several studies (Biddanda et al., 2008; Dolman et al., 2012; Xie et al., 2012). Limnothrix was detected at a relatively similar abundance (2.2e6.5% of total cyanobacteria) across all sampling periods, which is consistent with the reported stability of this genus in environments with variable turbidity and nitrogen and phosphorus levels (Rucker et al., 1997; Xie et al., 2012). Although each environmental parameter could influence other factors (e.g., high temperature reduce DO), analysis of the CCA plots indicated that geochemical factors could be the cause of the cyanobacterial community shifts in the Nakdong River. Conversely, the environmental factors may also be influenced by the cyanobacteria.

3.4. NMDS analysis of correlations between environmental factors and the proportion of relatively harmful species in cyanobacterial communities The relationship between relatively harmful cyanobacteria (Anabaena, Aphanizomenon, and Microcystis) and environmental factors during the entire sampling period was analyzed by NMDS (Fig. 4). The relative abundance of Microcystis and significantly related environmental factors (p < 0.05) were analyzed for the samples collected from June to August (Fig. 4a). Among the environmental parameters tested, the proportion of Microcystis in the cyanobacterial communities was related to rainfall, temperature, electric conductivity, and DO (p < 0.05). The samples collected in August differed from the June and July samples with respect to temperature and rainfall. These factors were correlated with increased abundance of Microcystis at sites 5 and 6 on August 13 (825 and 826 samples, respectively). The environmental factors related to Aphanizomenon were the same as those related to Microcystis (p < 0.05; Fig. 4b). The relative abundance of Aphanizomenon was negatively correlated with temperature and rain. The proportion of Aphanizomenon in the cyanobacterial community was low when Microcystis was abundant, indicating that these 2 species may interact in the cyanobacterial community. The complex interactions among microbes in cyanobacterial communities can result in the ecological role of some genera, affecting other genera. For example, Microcystis blooms can induce phosphorus release from sediment, and phosphorus stimulates nitrogen-fixation activity (Chapin et al., 1991; Xie et al., 2003). This active could influence other cyanobacteria including Aphanizomenon. Compare to Microcystis and Aphanizomenon, the abundance of Anabaena was correlated with more various factors as Chl-a, pH, electric conductivity, turbidity, SS, PO4-P, phosphorus, and rainfall (Fig. 4c). The relatively high abundance of Anabaena in the June and October samples was negatively correlated with rainfall, chlorophylla, and pH. Electric conductivity and rainfall were also significant factors for both Microcystis and Aphanizomenon (Fig. 4). The relatively high abundance of Anabaena in the August and

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Fig. 4 e Non-metric multidimensional scaling ordination plots of Microcystis (a), Aphanizomenon (b), and Anabaena (c) with significant chemical characteristics (p < 0.05). Each circle represents the relative abundance of each genus in the samples, and the large circle corresponds to a higher abundance of each genus. The numbers in the plot represent sample names together with sampling month, sampling week in each month, and sampling sites (Table S1). For example, 621 indicates a sample obtained from site 1 in the second week (2) of June (6). DO: dissolved oxygen, Tm: water temperature, Rain: rainfall, TP: total phosphorus, PO4-P: phosphate, Tur: turbidity, SS: suspended solids, and Chl-a: Chlorophyll-a.

September samples was correlated with phosphorus, PO4-P, turbidity, and SS. The relative abundance of Microcystis and Aphanizomenon was closely related to same environmental factors (rainfall, temperature, electric conductivity, and DO), whereas the abundance of Anabaena was influenced by different factors. These NMDS results indicate that harmful cyanobacteria are influenced by environmental factors; these results are consistent with the association of geochemical factors and cyanobacteria community changes determined by CCA analyses (Fig 3 and S2).

3.5.

Analysis of microcystin and cyanobacteria

An understanding of toxin production in cyanobacterial communities is critical for drinking water management and public health; therefore, factors correlated with microcystin levels were evaluated in this study. The toxicity of microcystin in the Nakdong River during the sampling period was not serious because the concentration of microcystin detected was lower than 1 ppb. The microcystin concentration limit recommended by the World Health Organization for drinking water is 1 ppb (WHO, 2011). Relationships among the concentration of microcystin, the relative abundance of harmful cyanobacteria (Microcystis, Aphanizomenon, and Anabaena), and environmental factors were analyzed (Table S2). Samples that contained abundant Microcystis (over 30% of the total reads for each sample) were selected and compared. Relatively high concentrations of microcystin were detected in 4 samples collected from sites 4 and 5 in June and July (samples 624, 625, 644, and 745). The electric conductivity of 3 samples (624, 625, and 644) was 355e402 mmhos/cm, the pH range was 8.0e8.6, and temperatures range was 24.4e25.9  C. The concentration of microcystin was higher in sample 644 (0.944  0.001 ppb) than in samples 624 (0.359  0.001 ppb) and 625 (0.649  0.001 ppb). The relative abundance of Anabaena was higher in sample 644 (29.7% of the total cyanobacteria) than in the other 3 samples, indicating that the high concentration of microcystin in sample 644 may be influenced by

the relative abundance of Anabaena. The concentration of microcystin in sample 745 was also high (0.423  0.002 ppb); however, the values for the environmental factors and the relative abundance of Microcystis, Aphanizomenon, and Anabaena were lower than those for samples 624, 625, and 644. We suggest that the high concentration of microcystin in this sample might have been an abnormal product of stress caused by the high water temperature (30.6  C, Table S1). However, a similarly high temperature (30.2  C) was detected at site 4 on July 23 (742), where the proportion of Microcystis was lower than 10% of the total reads (Table S1). This finding suggests that Microcystis did not produce the microcystin in this sample because it was present at a low level. The dominant species among Microcystis was Microcystis aeruginosa (constituting more than 90% of the Microcystis genus organisms in each sample). Although the proportion of Microcystis was higher than 79.8% of the total reads at sites 4, 5, and 6 on August 13, the concentration of microcystin was relatively low (0.110e0.179 ppb), possibly because the growth of Microcystis occurred without production of microcystin at the optimal growth temperature (>28.4  C) in those samples (Martins et al., 2011). Taken together, these results indicate that the production of microcystin was influenced by interactions between diverse genera and various environmental factors. A previous study of the relationship between cyanobacterial toxicity and environmental factors in eutrophic lakes reported various correlations (Xie et al., 2012). Further studies and additional data accumulation are necessary to determine the role of environmental parameters and the cyanobacterial community in cyanobacterial toxin production.

4.

Conclusion

The effects of sampling periods and environmental factors on cyanobacterial communities at 6 sites in a Korean river were investigated. The major findings are as follows:

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 In total, 175 cyanobacterial genera were detected in the present study, indicating that diverse cyanobacteria are present in the river and that high-throughput sequencing using the cyanobacteria 16S rRNA gene can help detect various genera.  Prochlorococcus was predominant in May, whereas the abundance of Microcystis and Anabaena increased from June and the diversity of the cyanobacterial community increased until September. Similar compositions of cyanobacterial communities were observed between sites in close proximity; the communities were influenced by site location and environmental factors.  Cyanobacterial communities were affected by environmental factors from June to September, and similar communities were observed in May and October. Changes in environmental factors such as increased temperature, decreasing DO, phosphorus, and nitrogen parameters during sampling periods were related with changes in the cyanobacterial communities. The separation of the cyanobacterial community at each sampling time was related with TP, PO4-P, TN, and NO3-N. This indicates that the nitrogen and phosphorus are related to the shift of cyanobacterial community.  The presence of harmful cyanobacteria was related to environmental factors of the river. The abundance of Microcystis was significantly correlated with DO, electric conductivity, rainfall, and temperature (p < 0.05). The abundance of Aphanizomenon was correlated with these same factors, while the abundance of Anabaena was related to electrical conductivity, SS, turbidity, phosphorus, rainfall, and chlorophyll-a.  Environmental parameters could interact with each other, and these could affect the cyanobacterial community in river. In turn, the changes of cyanobacterial community could influence geochemical parameters in complex ecosystem.  Knowledge of the relationships between environmental factors and cyanobacterial communities will help in monitoring harmful cyanobacteria and the management of drinking water in the future.

Acknowledgments This study was supported by the Key Project Foundation from NIER, Ministry of Environment, Republic of Korea.

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.watres.2013.09.058.

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