Turbulence increases the risk of microcystin exposure in a eutrophic lake (Lake Taihu) during cyanobacterial bloom periods

Turbulence increases the risk of microcystin exposure in a eutrophic lake (Lake Taihu) during cyanobacterial bloom periods

Harmful Algae 55 (2016) 213–220 Contents lists available at ScienceDirect Harmful Algae journal homepage: www.elsevier.com/locate/hal Turbulence in...

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Harmful Algae 55 (2016) 213–220

Contents lists available at ScienceDirect

Harmful Algae journal homepage: www.elsevier.com/locate/hal

Turbulence increases the risk of microcystin exposure in a eutrophic lake (Lake Taihu) during cyanobacterial bloom periods Jian Zhou a,b, Boqiang Qin a,*, Xiaoxia Han a, Lin Zhu a a

Taihu Lake Laboratory Ecosystem Station, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, PR China b University of Chinese Academy of Sciences, Beijing 100049, PR China



Article history: Received 3 September 2015 Received in revised form 12 March 2016 Accepted 15 March 2016 Available online

Toxic cyanobacterial harmful algal blooms (CyanoHABs) have posed serious water use and public health threats because of the toxins they produce, such as the microcystins (MCs). The direct physical effects of turbulence on MCs, however, have not yet been addressed and is still poorly elucidated. In this study, a 6day mesocosm experiment was carried out to evaluate the effects of wind wave turbulence on the competition of toxic Microcystis and MCs production in highly eutrophicated and turbulent Lake Taihu, China. Under turbulent conditions, MCs concentrations (both total and extracellular) significantly increased and reached a maximum level 3.4 times higher than in calm water. Specifically, short term (3 days) turbulence favored the growth of toxic Microcystis species, allowing for the accumulation of biomass which also triggered the increase in MCs toxicity. Moreover, intense turbulence raises the shear stress and could cause cell mechanical damage or cellular lysis resulting in cell breakage and leakage of intracellular materials including the toxins. The results indicate that short term (3 days) turbulence is beneficial for MCs production and release, which increase the potential exposure of aquatic organisms and humans. This study suggests that the importance of water turbulence in the competition of toxic Microcystis and MCs production, and provides new perspectives for control of toxin in CyanoHABsinfested lakes. ß 2016 Published by Elsevier B.V.

Keywords: Microcystin Turbulence Toxic Microcystis CyanoHABs Eutrophic lake

1. Introduction Global climate change and eutrophication have been suggested to significantly increase the frequency and global distribution of toxic cyanobacterial harmful algal blooms (CyanoHABs) in lake systems worldwide (Paerl and Otten, 2013), and their incidence and severity are predicted to increase (Wilhelm et al., 2011; Cheung et al., 2013). CyanoHABs have become one of the most widespread environmental and social problems, posing major challenges to water management, public health and local economies (Otten et al., 2012). A critical aspect of CyanoHABs ecology is the production of toxins by CyanoHABs species during blooms. CyanoHABs are widely known to produce a variety of toxins which are serious causes for concern (Paerl and Otten, 2013). Microcystins (MCs) are the most common and potent cyanotoxins in freshwater systems worldwide and are predominantly produced by Microcystis spp. (Otten et al., 2012), with more than 90 variants identified so far (Pantelic et al.,

* Corresponding author. Tel.: +86 025 86882080. E-mail address: [email protected] (B. Qin). http://dx.doi.org/10.1016/j.hal.2016.03.016 1568-9883/ß 2016 Published by Elsevier B.V.

2013). Microcystins are considered a serious health hazard due to its potent liver toxicity and carcinogenic potential (Zilliges et al., 2011). They are also extremely stable and resistant to heat, hydrolysis, and oxidation, thus posing a threat to drinking water supplies and aquatic food products (Mohamed et al., 2015). In fact, episodes of human poisoning and incidences of primary liver cancer from the exposure to MCs have been highly reported worldwide and are still increasing (Backer et al., 2013; Cheung et al., 2013; Mohamed et al., 2015). Therefore, understanding the dynamics of MCs production will enhance capacities to control and mitigate potential impacts to lives and properties. It has long been acknowledged that ‘‘there is no life without water, and there is no life in water without turbulence in water’’ (Margalef, 1997). Turbulence is an intrinsic and ubiquitous characteristic of aquatic environments, which in turn affect their physico-chemico-biological profiles (Visser et al., 2009). Recent studies have also shown the possible link between climate change and the observed increasing frequency and intensity of tropical cyclones (Knutson et al., 2010; Sriver, 2010). These physical disturbances strongly affect the stability of water column and turbulent state. Specifically, microbial communities in the surface


J. Zhou et al. / Harmful Algae 55 (2016) 213–220

waters are strongly influenced by wind waves and this has been implicated in the formation and persistence of cyanobacterial blooms (Moreno-Ostos et al., 2009; Wu et al., 2010; Wu et al., 2013). Although a number of studies have already investigated the impacts of turbulence and mixing on the growth of some cyanobacteria, to date, the direct physical effects of turbulence on MCs production have not been addressed, nor elucidated. Additionally, the impact of environmental stimuli on the production of MCs is still in question (Meissner et al., 2013), despite reports linking MCs production to biotic (growth rate, competition, and grazer) and abiotic (nutrients, light, temperature, and pH) factors (Horst et al., 2014). Large shallow lakes are strongly influenced by wind-driven wave turbulence (G.-To´th et al., 2011). Meanwhile, most of the developed and urbanized lakeside communities are near these shallow lakes making them prone to eutrophication (e.g., Lake Taihu and Lake Chaohu in China, and Lake Erie and Lake Okeechobee in the USA) (Qin et al., 2010; Paerl et al., 2011). Lake Taihu (Taihu), China’s third largest freshwater lake, is a shallow eutrophic lake with an average depth of 1.9 m, a maximum depth of 2.6 m and a total surface area of 2230 km2 (Qin et al., 2007). It is a major source of drinking water, livelihood and food supply for the surrounding communities of more than 8 million people (Wilhelm et al., 2011). However, since the mid1980s, severe blooms of toxin-producing Microcystis have appeared in the lake from late spring through autumn annually (Qin et al., 2010). In 2007, a severe algae bloom occurred over most of Lake Taihu, which led to a drinking water crisis in Wuxi City that interrupted the drinking water supply for approximately two million inhabitants for at least a week (Qin et al., 2010). Previous surveys showed that the lake is heavily contaminated with MCs during summer months (Du et al., 2013). Moreover, Taihu experiences tropical cyclones originating from the Western Pacific Ocean several times each summer, which were found to stimulate blooms of Microcystis spp. (Zhu et al., 2014a). This study presents how toxic Microcystis and MCs production may be affected by wind-driven turbulence, using a mesocosm experiment. Specifically, this study aims to deliver deeper insights into the competition of toxic Microcystis and MCs regulation, and understand the coupling of MCs production and turbulence. It will be important in understanding the response of toxic Microcystis to environmental changes and leading to more effective, processbased management and remediation strategies in the future. 2. Materials and methods 2.1. Experimental design The duration of the experiments was limited to 6 days (from the 7th to 13th of July 2014) at the Taihu Laboratory for Lake Ecosystem Research (TLLER) on the shores of Taihu (318410 83500 N, 1208220 04400 E). Totally 12 tanks and each customized tank (60 cm  30 cm  70 cm, described in a previous study (Zhou et al., 2015), Fig. S1 of the Supporting Information) was filled with 96 L of lake water pumped from 0.2 m subsurface of the water column in Meiliang Bay. These tanks were floated and fixed in an outside artificial pond (10 m  10 m  2 m) which was filled with lake water. 2.2. Turbulence generation The submerged wave-maker pumps described earlier, fixed under surface water by strong magnets, were used to generate turbulence simulate to the ones induced by natural wind waves as demonstrated in previous studies (Pekcan-Hekim et al., 2013; Harkonen et al., 2014; Zhou et al., 2015). The pump frequency was set to 1 Hz and the turbulence generated was monitored and measured by an acoustic Doppler

velocimeter (ADV, 10 MHz ADVField; Sontek/YSI, San Diego, California, USA). The turbulence intensity was measured from the middle of the tank with a 25 Hz measurement for a period of 2 min. Measurements were performed after the turbulent motion in the tank had reached a steady state (after 10 min). To define the characteristic speed of the turbulence, the root mean square velocity (U, cm s1) was calculated by using the following formula: U¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m2RMSx þ m2RMSy þ m2RMSz




sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2 P mx ð mx Þ2 =n ¼ n1


is the fluctuation of the flow for Cartesian vector x (which is similarly calculated for the y and z vectors) and n is the number of samples per measurement. The U were expressed as averages for the whole tank. The energy dissipation rate (e, m2 s3), which describes the rate at which the turbulent energy decays over time, was deduced from the U (m s1) following the formula described by Sanford (1997):

e ¼ A1

U3 l


where A1 is an dimensional constant of order 1 (Kundu and Cohen, 2010), and l is the water depth (m) describing the size of the largest vortices. The Reynolds number (Re, the ratio of inertial forces to viscous forces) for the given turbulence levels was calculated following (Peters and Redondo, 1997): Re ¼




where l is the water depth (m) and v is the kinematic viscosity for water (8.5  107 m2 s1). In shallow lakes, because turbulence has little space in which to dissipate, the turbulent kinetic-energy content is rather high (G.To´th et al., 2011). The different levels of turbulence intensities used in this study were based on actual conditions previously observed in Lake Taihu during summer 2013. In Taihu, the corresponding energy dissipation rates (e) significantly varied ranging from 6.01  108 to 2.39  104 m2 s3 (Table 1), which corresponded to the range of values (from 1.07  107 to 6.67  103 m2 s3) previously measured in the large shallow Lake Balaton in Hungary (G.-To´th et al., 2011). Based on the e values in Taihu, the U tested in this study (treatments) were 0.85 (low), 2.53 (medium), and 4.33 cm s1 (high). These velocities had corresponding e values that ranged from 1.12  106 to 1.48  104 m2 s3 and Reynolds numbers (Re) between 5500 and 92,620 (Table 1). A control treatment (calm), which was without any hydrodynamic turbulence, was also set-up. All treatments were conducted in triplicate. Table 1 Summary of the root mean square velocities (U), energy dissipation rates (e), and Reynolds numbers (Re) of the four levels of turbulence (treatments) used in the experiments and in Lake Taihu. These include the (1) control (calm water), (2) low, (3) medium, and (4) high turbulence intensities. Turbulence level

U (cm s1)

e (m2 s3)


Control Low Medium High Lake Taihu

0 0.85 2.53 4.33 0.49–7.69

0 1.12  106 2.95  105 1.48  104 6.01  108 to 2.39  104

0 5500 16,371 92,620 2882–180,941

J. Zhou et al. / Harmful Algae 55 (2016) 213–220

2.3. Environmental characteristics and chlorophyll a Physico-chemical parameters were measured every day from day 0 to day 6 between 7:00 and 8:00 in the morning, and the observations of total and toxic Microcystis spp. start at day 0 until day 6 with 3 days in between each sampling. Water temperature (WT), dissolved oxygen (DO) and pH were first determined using a 6600 multi-sensor sonde (Yellow Springs Instruments, San Diego, California, USA). Then, samples were collected by sampling 0.75 L of vertically integrated water using a tube sampler. Nutrients were also analyzed including total nitrogen (TN), total dissolved nitrogen (TDN), ammonium (NH4+-N), nitrate (NO3-N), nitrite (NO2-N), total phosphorus (TP), total dissolved phosphorus (TDP), soluble reactive phosphorus (SRP), and suspended solids (SS), following the methods described in Zhu et al. (2014a). The particulate fractions of nitrogen (PN) and phosphate (PP) were obtained by subtracting the TDN/P from the TN/P. The chlorophyll a (Chl a) concentrations were measured by the spectrophotometric method (Papista et al., 2002). Samples were first filtered through GF/F filters, frozen at 20 8C and pigments were extracted with 90% hot acetone. 2.4. Microcystins analysis Microcystins were measured everyday using an enzyme-linked immunosorbent assays (ELISA). Toxin levels (concentration) were monitored using the commercial microplate kits for MCs (detection limit 0.01 mg L1, Beacon Analytical Systems Inc., Portland, ME, USA). The extracellular MCs concentrations were determined by directly measuring the filtrate after passing through Whatman GF/C filter. For the total MCs, water samples were thawed and refrozen seven times with ultrasonic pulse to release the toxins from the cells before being filtered through a GF/C filter. The intracellular MCs was obtained by subtracting extracellular MCs from total MCs. Measurements followed the manufacturer’s recommendations and were all done in triplicates (Hur et al., 2013). Plates were then read on an ELISA plate reader (Multiskan GO, Thermo Scientific, USA) at 450 nm. 2.5. DNA extraction Between 100 and 150 mL of water was filtered onto a 0.2 mm polycarbonate membrane filter (47 mm diameter; Millipore, Cork, Ireland) and stored at 80 8C in preparation for DNA extractions.


Table 2 Oligonucleotides used as primers for qPCR. DNA target


Sequence (50 –30 )


Microcystis 16S rRNA

184F 431R


Neilan et al. (1997)


F2 R2


Kaebernick et al. (2000)

DNA was extracted from the filters using a FastDNA Power-Max Soil DNA Isolation Kit (MP Biomedical, USA) according to the manufacturer’s protocol and served as templates for qPCR amplification. 2.6. Standard curve preparation To quantify the abundance of total Microcystis spp. and toxic Microcystis spp., a qPCR assay was performed using primers based on 16S rRNA genes and mcyD genes according to previously described methods (Pan et al., 2002; Ha et al., 2009). Microcystis aeruginosa PCC7806 was used as the standard strain for the quantification of both total Microcystis spp. and toxic Microcystis spp. The standard curve was perpetrated according by Zhu et al. (2014b). Ten milliliters of the PCC7806 strain containing 1.71  107 cells mL1 (determined by a direct microscopic count) was filtered through 0.2 mm pore-size filters (Track-Etched Membranes, Whatman1NucleporeTM). A series of 10-fold dilutions (ranging from 1.71  106 cells to 1.71 cells) of the PCC7806 DNA template standard solution was used as the external standard for the qPCR. CT calculations were completed automatically for each qPCR assay using CFX Manager (Bio-Rad, Hercules, CA) and the maximum correlation coefficient approach. In this approach, the threshold is automatically determined to obtain the highest possible correlation coefficient (r2) for the standard curve. 2.7. qPCR conditions All reactions were completed in a total volume of 20 mL comprising 0.5 mM each primer, 10 mL SYBR green PCR master mix (TOYOBO, Japan), 0.8 mL bovine serum albumin (3 mg mL1; Sigma), double-distilled H2O, and template DNA. The primers used for amplification of total Microcystis spp. and toxic Microcystis spp. are listed in Table 2. Two separate assays were performed to

Table 3 Mean and range of the physical, chemical and biological measurements with control and three turbulence treatments (low, medium, and high) during the experiments. These include water temperature (WT), dissolved oxygen (DO), suspended solids (SS), total nitrogen (TN), total dissolved nitrogen (TDN), particulate nitrogen (PN), ammonium (NH4+-N), nitrate (NO3-N), nitrite (NO2-N), total phosphorus (TP), total dissolved phosphorus (TDP), particulate phosphorus (PP), soluble active phosphorus (SRP), chlorophyll a (Chl a). Parameter




WT (8C) DO (mg L1) pH SS (mg L1)

27.8 9.1 8.9 20.5

27.8 7.7 8.8 27.7


TN (mg L1) TDN (mg L1) PN (mg L1) NH4+-N (mg L1) NO3-N (mg L1) NO2-N (mg L1) TP (mg L1) TDP (mg L1) PP (mg L1) SRP (mg L1)

1.7 (1.5–2.0) 1.2 (1.0–1.4) 0.49 (0.35–0.58) 0.18 (0.12–0.35) 0.58 (0.46–068) 0.03 (0.03–0.04) 59.4 (33.3–91.5) 13.0 (4.2–26.0) 43.6 (17.8–65.5) 2.1 (0.9–7.1)

1.8 (1.6–2.0) 1.1 (0.9–1.4) 0.64 (0.51–0.72) 0.22 (0.14–0.35) 0.54 (0.33–0.67) 0.03 (0.03–0.04) 64.7 (36.4–91.5) 14.1 (6.3–26.0) 50.6 (28.7–69.6) 2.2 (1.0–7.1)

1.9 (1.8–2.0) 1.1 (0.9–1.4) 0.75 (0.51–0.84) 0.26 (0.20–0.35) 0.48 (0.23–0.70) 0.03 (0.02–0.04) 68.5 (46.9–91.5) 12.7 (3.8–26.0) 55.8 (37.9–73.9) 2.2 (0.5–7.1)

1.9 (1.7–2.0) 1.2 (1.1–1.4) 0.69 (0.51–0.77) 0.25 (0.18–0.35) 0.57 (0.44–0.68) 0.04 (0.03–0.04) 68.7 (40.8–91.5) 15.2 (6.6–26.0) 53.6 (29.8.6–71.2) 2.1 (0.4–7.1)


Chl a (mg L1)

22.4 (14.0–37.7)

31.2 (17.7–41.7)

34.3 (17.7–45.7)

33.76 (17.7–47.0)

(25.8–30.0) (8.4–10.7) (8.5–9.4) (7.8–38.0)

(25.7–30.1) (7.2–8.8) (8.5–9.2) (15.3–38.0)



27.8 7.8 8.7 34.2

27.7 7.8 8.6 30.7

(25.7–30.2) (7.3–8.8) (8.4–9.0) (24.3–38.1)

Bold values indicate that there is a significant difference between the control and the treatments, which was determined by ANOVA.

(25.6–30.2) (7.2–8.8) (8.4–8.9) (13.1–42.6)


J. Zhou et al. / Harmful Algae 55 (2016) 213–220

quantify the Microcystis 16S rRNA gene and mcyD for all samples. The qPCR program for Microcystis 16S rRNA and mcyD (toxic Microcystis/total Microcystis) was as follows: 95 8C for 2 min, followed by 40 cycles of 95 8C for 30 s and 55 8C for 1 min. All PCRs were run in triplicate on 96-well plates (Bio-Rad) sealed with optical-quality sealing tape (Bio-Rad). Three negative controls without DNA were included for each PCR run. 2.8. Statistical analysis Test for significant differences among and between the various treatments, one-way analysis of variance (ANOVA) was employed. Post hoc multiple comparisons of treatment means were performed by Tukey’s least significant difference procedure and the standard deviation in the variations of the triplicates was calculated. All statistical calculations were performed in Statistical Product and Service Solutions (SPSS 22.0) statistical package for personal computers, and the level of significance used was P < 0.05 for all tests (Yockey, 2010). 3. Results 3.1. Environmental characteristics Water temperature generally ranged from 25.6 to 30.2 8C. DO and pH were higher in the control (calm) than in the treatments (low, medium, and high turbulence) during the experiment (Table 3). The concentration of TN and TP ranged from 1.5 to 2.0 mg L1 and from 33.3 to 91.5 mg L1, respectively. Also, the average concentrations of the total and particulate nutrients (TN, PN, TP, and PP) varied between the treatments. For example, the average TN/P and PN/P were higher in the treatments than in the control (Table 3). The same was also true for the concentrations of different nitrogen (TDN, NH4+-N, NO3-N, and NO2-N) and phosphorus (TDP and SRP) species (Table 3). Moreover, the 6day average of pigment concentrations and SS in the control was lower compared to the treatments, and highest in medium (Table 3). These characteristics had no significant differences among the various treatments (P > 0.05), except the DO and PN (Table 3). 3.2. Impacts of turbulence on the abundance of total and toxic Microcystis spp. The abundance of total and toxic Microcystis spp. in all tanks were generally increased during the experiments, which were both higher in the turbulent treatments than in the control and highest in the medium (P > 0.05, Fig. 1a, b). Interestingly, the abundance of total and toxic Microcystis spp. in the treatments mainly increased faster before 3 days compare to the last 3 days (except toxic Microcystis spp. in the low), which was contrary in the control (Fig. 1a, b). Moreover, the proportions of toxic Microcystis spp. increased in the control (40.0–64.4%), low (40.0–60.8%) and high (40.0–65.5%) during the experiments (Fig. 1c). In the medium, the proportions of toxic Microcystis spp. rapidly increased before 3 days and then decreased (40.0–65.9%, Fig. 1c). Notably, similar to the variations of the abundance of total and toxic Microcystis spp., the proportions of toxic Microcystis spp. increased faster before 3 days in the treatments compare to the control (Fig. 1c). 3.3. Impacts of turbulence on MCs concentration Under the turbulent conditions, the total MCs concentrations significantly increased before day 3 and persisted until day 6 (Fig. 2a), which exceeded the WHO provisional guideline value of 1 mg L1 for MCs in drinking water (WHO, 2003). Specifically, the

Fig. 1. Variations in the abundance of (a) total and (b) toxic Microcystis spp. and the proportion of (c) toxic Microcystis spp. in the control and three turbulence treatments (low, medium, high) during the experiment. The toxic proportion was determined by dividing the relative number of copies of the mcyD gene by the total number of copies in Microcystis determined with the Microcystis 16S rRNA primer set. Control (square), low (circle), medium (triangle) and high (inverted triangle).

highest concentration was detected in medium treatments (2.2 mg L1), followed by low at 1.7 mg L1 and the lowest (1.4 mg L1) in the most turbulent condition (P > 0.05, Fig. 2a). In the control, the total MCs concentrations were observed to have increased gradually peaking at 1.1 mg L1 but were still generally lower than the treatments (P > 0.05, Fig. 2a). Similar to total MCs concentrations, the intracellular MCs mainly increased until day 3 and then persisted under the turbulent conditions (except in high turbulence treatments at day 1, Fig. 2b). Also, in the control, the intracellular MCs gradually increased but were still lower than the treatments (except in high before day 3, P > 0.05, Fig. 2b). Throughout the entire experiment,

J. Zhou et al. / Harmful Algae 55 (2016) 213–220


Fig. 2. Variations in the concentration of (a) total microcystins, (b) intracellular microcystins, and (c) extracellular microcystins as well as boxplot results of one-way analysis of variance (ANOVA) in the control and three turbulence treatments (low, medium, high) during the experiment. Control (square), low (circle), medium (triangle) and high (inverted triangle).

the MCs were mainly composed by intracellular MCs (81.04– 94.73%), while extracellular MCs accounted for only a very small proportion of the total MCs (Fig. 2). Notably, at day 1 in high turbulence treatment, the extracellular MCs concentration increased drastically and dominated (86%) the total MCs fraction coupled with the decrease of intracellular MCs (Fig. 2). Also, the extracellular MCs increased and persisted until day 3 in intense turbulence (medium and high) treatments (Fig. 2c). In the control and low, the extracellular MCs, however, kept stable for the entire duration of the experiments and were significantly lower than the medium and high (P < 0.05, Fig. 2c).

4. Discussion Although many studies have already been done to examine the effects of different environmental factors on the biosynthesis of MCs (as reviewed by Neilan et al., 2013), the direct physical effects of turbulence on MCs production have not been addressed. This study firstly explored the direct relationship between turbulence and MCs production in lakes. Results showed that turbulence promoted the growth of total and toxic Microcystis spp., and increased the proportion of toxic Microcystis spp. before 3 days (Fig. 1). Meanwhile, turbulent conditions enhanced the production


J. Zhou et al. / Harmful Algae 55 (2016) 213–220

Fig. 3. Scatter diagrams showing the relationships between toxic Microcystis abundance and the concentration of (a) total microcystins, (b) intracellular microcystins, and (c) extracellular microcystins during the experiment.

of MCs fractions (total, intracellular, and extracellular), especially in the medium turbulence treatments (Fig. 2). Interestingly, the extracellular MCs concentrations significantly increased under intense turbulent conditions (medium and high) but kept a low level in the control and low turbulence (Fig. 2c). The results of this study indicate that turbulence may play an important role in the growth of toxic Microcystis and the regulation of MCs production in CyanoHABs-infested lakes. MCs production is thought to be influenced by a number of abiotic and biotic factors (Neilan et al., 2013; Horst et al., 2014). However, the role played by these factors on the proportion of toxic genotypes and MCs production is not clear as results are contradictory, and the specific effects of single or combined factors vary in different environments (WHO, 2003; Rinta-Kanto et al., 2009; El-Shehawy et al., 2012). Moreover, CyanoHABs are often comprised of toxic and non-toxic strains of the same or different species (El-Shehawy et al., 2012). However, toxic and non-toxic species of cyanobacteria and strain succession may respond differently to changes in environmental conditions (Sabart et al., 2010; Reichwaldt and Ghadouani, 2012; Cheung et al., 2013; Meissner et al., 2015). It has been shown before that MCs concentration depends on the biomass of toxic Microcystis, the toxin quota (MCs per cell), and MCs degrading processes (Reichwaldt and Ghadouani, 2012). Turbulence, which causes mixing and thus affects the physico-chemical properties of the water column, increases diffusion rate on cell surface and enhances uptake of nutrients (Bergstedt et al., 2004; Honzo and Wuest, 2008; Guasto et al., 2012; Prairie et al., 2012), which then promotes the growth of Microcystis spp. especially the toxic Microcystis spp. in short term (3 days, Fig. 1), suggesting that short term turbulence favored the competitive success and growth of toxic Microcystis species. Also, the resulting optimum condition for photosynthesis decreased dissolved nutrient concentrations in the turbulence treatments by converting them to particulate matter (Table 3). Moreover, MCs concentrations were positively correlated with the abundance of toxic Microcystis (P < 0.001, Fig. 3). Therefore, turbulence promoted the growth of toxic Microcystis spp. and increased the concentration of MCs. These results are similar to what has been reported by Neilan et al. (2013), where they suggested that the increase of MCs concentration is a direct function of toxin cell proliferation as promoted by turbulence. This is also supported by the general decrease in MCs concentration per Microcystis cell with time in all treatments and there is no significant difference among the treatments (P > 0.05, Fig. 4). Those results indicated that increases in cell abundance likely contributed more to the elevated toxin concentrations rather than increased toxin cellular quota. In this study, MCs was mainly composed of intracellular MCs, while extracellular MCs accounted for only a very small proportion of the

total MCs (Fig. 2). Additionally, because MCs are extremely stable especially for the intracellular MCs in alive cells (Mohamed et al., 2015), losses due to degradation of the dissolved toxins can be ignored during this short-term experiment. Therefore, short term (3 days) turbulence significantly favored the competitive success of toxic Microcystis species, with increased biomass that was responsible for the increase MCs levels (Figs. 1 and 2). These results are consistent with previous observations in Taihu where strong wind events were found to stimulate significant algal toxicity and cyanobacterial growth leading to blooms in the lake on summer (Zhu et al., 2014a). Interestingly, the extracellular MCs concentrations were significantly higher under more turbulent conditions (i.e., medium and high) than in the less turbulent treatments such as in the control and low (P < 0.05, Fig. 2c). Specifically, in high, it was observed that the extracellular MCs fast increased with the decrease of intracellular MCs at day 1 (Fig. 2). Strong turbulence raises the shear stress in the algal surface and could cause cell mechanical damage resulting in cell breakage and leakage of intracellular materials including the toxins (Mitsuhashi et al., 1995). This then suggests that strong turbulence can promote the release of intracellular MCs and increase the risk of exposure to it by aquatic organism and humans. Meanwhile, strong turbulence

Fig. 4. Variations of intracellular microcystins per toxic Microcystis cell (106 mg L1) in the control and three turbulence treatments (low, medium, high) during the experiment. Control (square), low (circle), medium (triangle) and high (inverted triangle).

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(high) was disadvantage to the growth of phytoplankton (including toxic Microcystis; Zhou et al., 2016), which may be responsible for the smaller MCs levels comparing to other turbulence treatments (Fig. 2). This study only used ELISA and some RNAbased techniques to determine the MCs production which was limited to provide insights on the possible contributions of these different processes as they are affected by changes in turbulence. Therefore, in order to understand the mechanism underlying the role of turbulence in MCs, more detailed studies are needed, such as how these effects are regulated at a molecular level and how this translates to actual responses in the environment. Microcystis spp. was always the dominant species in all tanks (49  16% of biomass) during the experiment. It is widely known that changes in turbulent mixing of water are likely to shift the species composition of the phytoplankton (Huisman et al., 2004; Jager et al., 2008; Zhou et al., 2015). Although gas-vacuolated Microcystis usually dominates stable conditions and non-toxic strains (diatoms and green algae) are superior competitors under mixed conditions (Rabouille et al., 2003; Huisman et al., 2004; Kardinaal et al., 2007), short term (3 days) turbulence in this study extremely favored the growth of toxic Microcystis which led a higher toxin concentration in the water. The promotion of toxic Microcystis may be a defensive strategy under turbulent conditions. In addition, turbulent mixing induced by wind, also strongly affects the spatial distribution of the buoyant and potentially toxic Microcystis, which allowed the formation and expansion of cyanobacterial blooms in Lake Taihu (Wu et al., 2013). Therefore, after short-term occurrence and upon weakening of turbulence, the toxic Microcystis continuously floats upward, and favors bloom expansion. This study provides a new perspectives on CyanoHABs and MCs in response to water turbulence.

5. Conclusions This study based on 6-day mesocosm experiment in Lake Taihu demonstrated that turbulence promoted the growth of Microcystis spp. especially toxic Microcystis spp., and increased the proportion of toxic Microcystis spp. in short term (3 days) which favored the concentration of MCs fractions (total, intracellular, and extracellular). Moreover, strong turbulence enhanced the release of intracellular MCs, thus increasing potential exposure of aquatic organisms and humans to cyanobacterial toxins. The dramatically increase of MCs concentrations, under turbulent conditions, rose the importance of turbulence in the regulation of MCs production in CyanoHABs-infested lakes. This study indicated that turbulence, especially when combined with the presence of bacteria unable to degrade MCs compounds, will favor the development of toxic CyanoHABs and increase the exposure risk of MCs. Therefore, the control of CyanoHABs and MCs should take into consideration not only the chemical and biological factors, but also physical factors such as turbulence. Acknowledgements We acknowledge the National Natural Science Foundation of China (41230744). We wish to thank Lirong Song of Institute of Hydrobiology (Chinese Academy of Sciences) for help with qPCR and data analysis. We also thank Hans W. Paerl, Professor of the University of North Carolina at Chapel Hill, for providing valuable suggestions and manuscript revision.[CG] Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.hal.2016.03.016.


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