KANG Zhenjun, YANG Bin, *, LAI Junxiang, NING Yi, ZHONG Qiuping,LU Dongliang, LIAO Riquan, WANG Pei, Solomon Felix Dan, SHE Zhicai, JIA Zhen, LAO Yanling,and LI Nan

Bloom Monitoring: Based on.Induced Seawater Viscosity Modification Adjacent to a Nuclear Power Plant in Qinzhou Bay, China

KANG Zhenjun1), 2), YANG Bin1), 2), *, LAI Junxiang3), NING Yi4), ZHONG Qiuping1),LU Dongliang1), LIAO Riquan2), WANG Pei1), Solomon Felix Dan1), SHE Zhicai1), JIA Zhen1), LAO Yanling1),and LI Nan5), *

1) Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China 2) Key Laboratory of Coastal Science and Engineering, Beibu Gulf, Guangxi, Beibu Gulf University, Qinzhou 535011, China 3) Guangxi Key Laboratory of Marine Environmental Science, Guangxi Beibu Gulf Marine Research Center, Guangxi Academy of Sciences, Nanning 530007, China 4) Scientific and Technical Information Institution of Qinzhou City, Qinzhou 535011, China 5) Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China

seawater viscosity;; red tide; monitoring

1 Introduction

Rapid urbanization and industrial development, agriculture, mariculture, inshore eutrophication has become increasingly apparent (Kaiser., 2013; Lai., 2014), and the scale and duration of red tide have also significantly increased in the Beibu Gulf (Xu., 2019) in recent decades. Over the past eight years, a large scale ofbloom occurred annually off Guangxi coastal waters in the Beibu Gulf (Gong., 2018; Yu., 2018; Xu., 2019). The most striking feature ofis that it forms gel-like colonies ranging from several millimeters to centimeters in diameter (Verity., 2003), and produces viscous, slimy, and springy brown jelly layers, thus modifying the rheological properties of seawater. During the thermal test of the Fangchenggang Nuclear Power Plant (FCGNPP) from December 2014 to January 2015, there was an extensive outbreak ofbloom in Guangxi coastal waters, with a large number of high-density, high-colloid colonies blocking the cooling water filter system (Fig.1) (Xu., 2019; Yu., 2018), posing a threat to the operational safety of the FCGNPP. This was the first case where the red tide organism affected the operational safety of a nuclear power plant around the world, and became surprising to the local government and the scientists because the Guangxi coastal waters is always regarded as the last ‘clean ocean’ and one of the most abundant fishing grounds in China (Chen., 2011; Xu., 2019).

Under normal circumstances, seawater could be described as a Newtonian fluid, in which its viscosity is mainly affected by temperature and salinity (Miyake., 1948). On the other hand, seawater may become a non- Newtonian fluid (Jenkinson., 1986) when it is under the influence of organic exopolymeric substances, including mucilage, which is derived mostly from phytoplankton (Jenkinson., 1995, 2015; Seuront., 2006, 2007, 2008,2010).The presence of tiny particles and polymers in the marine environment can make seawater a more viscous and elastic medium. Wide biologically induced modifications of seawater viscosity duringbloom were observed in the Mediterranean in 1986 and 1987 (Jenkinson., 1993), German Bight and North Sea in 1988 (Jenkinson., 1995), eastern English Channel in 2002, 2003 and 2004 (Kesaulya., 2008; Seuront., 2006, 2007 2008), and East Antarctica (30-80?E) in 2006 (Seuront., 2010).

Fig.1 The cooling water entry ditch of nuclear power plant (Top left); The cooling water entry filter (Top right); The Phaeocystis globosa colony (Bottom left); The Phaeocystis globosa non-flagellate cells (Bottom right). Photos: Junxiang Lai.

At present, red tide monitoring mainly depends on themeasurements of chemical and biological properties of water samples from a research vessel, and/or remote sensing from aircraft and satellites (Anderson., 2001, 2017;Stumpf., 2003; Gohin., 2008). However, there exist some limitations of the above methods in the monitoring ofbloom. Moreover, the temporal and spatial changes of algal blooms can not be reflected in real-time due to the fact that sample handling and analytical processes will take at least 2–3d. Additionally, the flagellated cells ofare very difficult to recognize due to their characteristic small sizes (3–8μm) under the light microscope, and various fixatives (such as Lugol’s Solution, aldehydes, or saline ethanol) that are used for preservation may damage the cells, rendering their enumeration imprecise (Houliez., 2011). Although the distribution of red tide organisms could be monitored through remote sensing over larger spatial scale, the high-cost, cloud cover and the need for high-resolution imagery obviates the use of remote sensing for localizedbloom studies (Anderson., 2001, 2012).

Since the 1990s, the automatic monitoring technology of marine water quality has developed rapidly, and the technology for HABs monitoring using the ocean buoys has gradually emerged (Cullen., 1997; Kim., 2004; Kim., 2006; Shibata., 2015; Smith., 2005). Although automatic water quality monitoring from a buoy can reflect real-time water quality variation in the monitoring area, some experts have shown that the automatic water quality monitoring buoy is not suitable for the monitoring of thebloom (Li., 2015). Comparing the monitoring data ofbloom in February 2012 withbloom in February 2014 obtained by automatic monitoring buoy in the Beibu Gulf in Guangxi, Li (2015)found that an automatic monitoring buoy could play a good role in the monitoring of red tide of a high Chl-containing phytoplankton, such as diatom. However, the use of automatic monitoring buoys in the monitoring of pico-phytoplankton bloom, mixed and heterotrophic nutrition species, such asbloom in the Beibu Gulf is scarce and should be studied.

Microalgal blooms are known to clog the entrance of the cooling water filtration system of many industrial plants (.., by spring diatom blooms in coastal and fresh waters), and have caused the closure of up to 18 desalination plants in the Arabian Gulf (by dinoflagellate blooms of Cochlodinium) (Richlen., 2010; Berktay., 2011; Villacorte., 2015a; Villacorte., 2015b; Anderson., 2017). This study shows that the haptophyte flagellateis causing problems at the entrance of a cooling water filtration system of a nuclear plant in China. The daily cooling water consumption of the FCGNPP is 10.37 millionm3(Equivalent to 120m3s-1water inflow), which is a large amount of seawater through the cooling water filter. An outbreak ofbloom will block the filter, thus threatening the safe operation of the FCGNPP. From this perspective, there is a clear demand for methods that can simplify the dynamic monitoring ofbloom adjacent to the FCGNPP. The aim of this work was to explore the effects of abiotic factors (temperature, salinity and dissolved oxygen), and biological factors (Chl, phytoplankton community,colony and transparent exopolymer particles) on the seawater viscosity, and to test the possibility of using seawater viscosity to monitor thebloom.

2 Materials and Methods

2.1 Study Area and Sampling

Qinzhou Bay is located in the south of Guangxi province, China, and situated at the northern Beibu Gulf.The climate of the region is an example of a subtropical marine monsoon climate. The average annual water temperature and rainfall is 22.4℃ and about 2100mm, respectively (Meng., 2016). Its water area is 380km2(Gu., 2015). Generally, the depth of the bay is between 2m and 18m, and the maximum water depth is 29m (Huang., 2019). The Bay is dominated by diurnal tide, with an average tidal range of 2.5m and a maximum tidal range of 5.5m (Meng., 2016). The FCGNPP is located in the coastal zone of the northwest edge of the Qinzhou Bay basin.

As shown in Fig.2, the blue star represents the location of the FCGNPP. Qinzhou Port is oppositely located to the FCGNPP, and is the nearest bonded port between China and the Asean countries. Sampling sites are shown in Fig.2 (orange triangles). Site P1 is located upstream of the FCGNPP, representing the environmental condition of the inner Qinzhou Bay. Site P2 is located near the water inlet of the FCGNPP, which represents the environmental condition unaffected by the FCGNPP. Sites P3 and P5 are located on either side of the water outlet of the FCGNPP, representing the influence of heated water on the environment. Site P4 is located downstream of the FCGNPP and represents the environmental condition outside of Qinzhou Bay. Sites P6, P7, and P8 are located near the Qinzhou Port, representing the environmental condition of the Qinzhou Port. At each site, both surface (about 0.5 m depth) and near-bottom (about 1 m above the sediment) water samples were collected with a 5L plexiglass water sampler. The CTD (conductivity, temperature, pressure (depth) sensors, RBRconcerto, Canada) was used to measure water temperature, salinity and DO during the sampling campaign. Sampling was conducted during the high tide, before (25 October 2017, 11 November 2017, and 28 November 2017), during (28 December 2017 and 15 January 2018) and after (1 February 2018) thebloom adjacent to the FCGNPP, respectively.

Fig.2 Study area (red dashed box) and location of the sampling sites (orange triangles) adjacent to the Fangchenggang Nuclear Power Plant (blue star) in Qinzhou Bay, northern Beibu Gulf, China.

2.2 Chlorophyll a Analysis

Chlconcentrations were measured from 500mL sea water sample following Suzuki (1990). Samples were filtered through 0.45μm Whatman GF/F glass fiber filters. Chlwas extracted by direct immersion of the filters in 5 mL of N, N-dimethylformamide at 5℃ in the dark for 24 h, and the extractions were analyzed with fluorescence spectrophotometer (F-4500, Hitachi Co., Japan) with excitation wavelength at 450nm and emission wavelength at 685nm after centrifugation, and the concentrations were calculated using equations proposed by (Standar- dization Administration, 2007).

2.3 Phytoplankton Community Analysis

The collected algal samples were preserved with Lugol’s solution. Phytoplankton enumeration was carried out with an inverted biological microscope (Olympus CKX53, Japan). Phytoplankton net samples were also taken and used to assist identification. Phytoplankton Shannon-Weiner diversity index () (Shannon-Wiener, 1949), Simpson’s species evenness index () (Gleason, 1922), Pielou’s species evenness index (sw) (Pielou, 1966), and Margalef species richness index (MG) were calculated using the respective formulae.

2.4 Seawater Viscosity Measurement

After the ship arrived at the designated site, the surface sea water (below 0.5m of the water surface) and the bottom sea water (over 1m of the seafloor) were collected with a 5L plexiglass water collector, and then 1L of sea water was stored in a polyethylene bottle to avoid light and temperature. Seawater viscosity was measured immediately using ViscoLab4000 viscometer (Cambridge Applied Systems Inc., Boston) in the laboratory following the procedure detailed in Seuront Laurent (2006). Before determining each sample, the temperature control system equipped with the viscometer was used to control the sample measurement temperature to thetemperature (the temperature value derived from the CTD instrument). First, a 5mL seawater sample placed in the sampling bottle in the dark was taken with a pipette; the impurities and zooplankton were removed by filteration through 200μm filter, and then the visibleglobosa colonies were removed with a pipette under a dissecting microscope (100′magnification, Olympus SZX7, Japan). Secondly, a 3mL treated sample was injected into the measuring chamber with a pipette to measure the viscosity. There is a low mass stainless steel piston in the viscometer-measuring chamber. After the viscosity measurement begins, the piston moves back and forth in the chamber under the action of a magnetic field. The smaller the viscosity of the liquid measured, the shorter the time it takes for the piston to reach equilibrium in the liquid, and vice versa. Finally, the viscosity of the measured liquid can be known according to the readings on the viscometer screen. Each sample was measured three times, and then the average value was taken. Between each sample measurement, the viscometer chamber was carefully rinsed with deionized water in order to avoid any potential dilution of the next sample.

2.5 Transparent Exopolymer Particles (TEP) Measurement

The concentrations of TEP in the seawater sample was determined by Alcian blue staining spectrophotometry following the method of Passow (1995). Briefly, a 500 mL water sample was filtered through a 0.4μm polycarbonate filter membrane to collect the particulate matter. During filtration, the negative pressure was maintained below 0.02MPa to avoid the breakage of large particles and the penetration of small particles. After that, deionized water was used to rinse the particles three times, and 0.5mL of 0.02% Alcian blue (8GX, Sigma-Aldrich) with a pH of 2.5 was added for staining for 2min. The filter membrane containing the stained particles was washed with deionized water and transferred to a beaker. 6 mL 80% H2SO4solution was added to the beaker and the particles were soaked for 2h. The supernatant was collected by centrifugation; a small amount of the supernatant was taken and placed in a 1mL colorimetric dish, and the absorbance of the solution was measured at wavelength of 787nm with Ultraviolet-visible spectrophotometer (Unico UV-4802, China). The TEP concentration was calculated through the standard curve. In the experiment, xanthan was used as the standard material to make the standard curve, and the TEP concentration was expressed as xanthan equivalent (Xeq), with the unit of μgL?1(Xeq).

2.6 Statistical Analyses

3 Results

3.1 Environmental Conditions

The temporal and spatial variations of seawater viscosity andhave been studied in the waters adjacent to the Fangchenggang nuclear power plant. There were three phases ofbloom in the research area: the pre-bloom stage (B0) was between October 25 and November 28, the bloom stage (B1) was between November 29and January 15, and the post-bloom stage (B2) was between January 16and February 1. No vertical stratification phenomenon was found in the temperature, salinity and DO profiles, indicating a well-mixed water column over the course of the survey. The average surface temperature fluctuated from 24.59℃ on October 25 to 12.86℃ on February 1, exhibiting a clear monthly change (Table 1). In contrast, the average surface salinity did not exhibit any characteristic pattern but fluctuated from 27.18 on October 25to 27.48 on February 1(Table 1). DO was relatively lower in the early B0stage with a value of 6.95mgL?1, increased continuously in the B1stage, reached 9.13mgL?1at the peak of the bloom on January 15, and at the same time decreased gradually in B2stage (Table 1).

Table 1 Time course of seawater temperature (℃), salinity, DO(mg L-1), (cP), (cP), and (%). The lowercase letters in each column represents the significant difference at the 0.05 level

3.2 Chlorophyll aConcentration Variation

Overall, Chlin surface water exhibited a decreasing trend in the B0stage, a slightly increasing trend in the B1stage and a declined moderately in the B2stage (Fig.3). The first peakoccurred on October 25,with an average value of 3.98μgL?1(caused by diatoms), while the second peak occurred on January 15,with average value 1.75 μgL?1(caused by). Chlin the surface and bottom water layer had a similar variation tendency but the concentration was distinctly lower in the bottom layer than the surface layer on October 25, and higher in the bottom layer on January 15. This indicates that diatoms, the dominant species in the B0stage were mainly located in the surface layer, while,the dominant species during the B1stage were mainly located in the bottom layer.

In terms of spatial distribution(Fig.4), Chlconcentrationwas relatively higher on October 25 than that in other dates and the high-value zones were located at sites P1, P5, P6, P7 and P8 with values ranging from 4.0 to 5.3 μgL?1. In the B1stage, Chlincreased slightly and reached a secondary high value on January 15at site P3 with 3.1μgL?1. In the B2stage, Chlconcentration decreased slightly with high-value zones located at sites P3 and P8, with an average value of 1.6μgL?1.

Fig.3 Time course of Chl-a concentration (μgL?1) and seawater viscosity (cP). The light blue line represents the Chl-a in surface water, and the pink line represents the Chl-a in bottom water (the top graph); the red line represents the(cP), the blue line represents the(cP), the green line represents the(cP), and the black column represents the(cP) (the bottom graph). B0 is the pre- bloom period, B1 is the P. globosa bloom period, and B2 is the post-bloom period.

Fig.4 Spatial and temporal distribution of Chl-a concentration (μgL?1) in surface water in the study area.

3.3 Seawater Viscosity Variation

Fig.5 Spatial and temporal distribution of the(%) in surface water in the study area.

Fig.6 Phytoplankton abundance (cellsL?1) in surface water in the study area during (a) the B0 stage; (b) the B1 stage; and (c) the B2 stage.

3.4 Phytoplankton Variation (except P. globosa)

In the B0stage, the number of phytoplankton species was at the highest level (11 species), and the dominant species wereand.was found at every site, and the maximum value (about 3.56×104cellsL?1) occurred at site P7.was found near the outer bay, and the maximum value (about1.99×104cellsL?1) occurred at site P4 (Fig.6a). The highest values of species richness index,MG, were at sites P2 (0.56) and P4 (0.58), respectively, and the lowest values were at sites P3 (0.45) and P8 (0.44), respectively. This indicated that the most abundant phytoplankton species existed at sites P2 and P4, while relatively lower phytoplankton species existed at sites P3 and P8. From the perspective of species evenness index,SWand, the values were relatively higher at sites P1 and P5, but were relatively lower at sites P7 and P8. This indicated that phytoplankton species at sites P1 and P5 were evenly distributed, while those at sites P7 and P8 were dominated by some species.

3.5 Phaeocystis Globosa Colonies Variation

According to the spatial distribution variation ofcolonies over time (Figs.7, 8), there were sporadic colonies in Qinzhou Port on October 25, with an average of 6 coloniesm?3, and the size of these colonies was 0–1mm. No colonies were found in the outer sea area during this time, indicating thatoriginated in the eutrophic waters around the Qinzhou Port. On November 11, the number of colonies near the Qinzhou port continued to increase to hundreds, reaching 246 colonies m?3at site P1, and the colonies’ size was still dominated by 0–1mm, with the colonies of 1–5mm being formed in the Qinzhou port. On November 28, the number of colonies near the Qinzhou port had reached thousands, and many colonies had gathered at the entrance area of the nuclear power plant, with about 1768 coloniesm?3at site P3. The colonies of 0~1mm at sites P1–P4 still had an absolute advantage over others. The size of colonies was mainly 1–5mm at site P5, and 5–10mm at sites P6, P7, and P8, which were nearer to the industrial park. During thebloom period on December 28 and January 15, the number of colonies was maintained at thousands at the study area, with an average of 4000coloniesm?3. The colony size was mainly 1–5mm, accounting for 60%; 5–10mm accounted for 20%, 10–15mm accounted for about 3%, and 15–20mm only occurred at some sites. On February 1, during the decline period ofbloom, the number of colonies in the inlet and outlet area of the nuclear power plant was hundreds, with an average of 400 coloniesm?3, and it was thousands in the outer sea, reaching about 1378 coloniesm?3at site P4, with the colonies size ranging from 0–1mm and 1–5mm.

Fig.7 The horizontal distribution of P. globosa colony abundance (coloniesm?3) in the adjacent waters of the FCGNPP.

Fig.8 The relative proportions of P. globosa colonies with different diameters during the different bloom stages in the study area.

3.6 Transparent Exopolymer Particles (TEP) Variation

The mechanism behind Chlconcentration and seawater viscosity variation is also consistent with the dynamics of TEP produced by. In the B0stage, TEP was relatively low, with an average of 165μgL?1in the surface layer and 118μgL?1in the bottom layer. TEP was significantly higher at sites P1, P2 and P3 than in other sites (Fig.9a). In the B1stage, TEP increased abruptly at all the investigated sites, with an average of 576μgL?1in the surface layer, 248% higher than that in the B0stage, and 422μgL?1in the bottom layer, 256% higher than that in the B0stage. Sites P1, P3, P7, and P8 were the high-value areas of TEP (Fig.9b). In the B2stage, the value of TEP was lower than that in the previous stage. The average TEP value was 206μgL?1in the surface layerand 293μgL?1in the bottom layer. The TEP value was significantly higher in the bottom layer than that in the surface layer at sites P1 and P3 (Fig.9c).

Fig.9 Variation of Transparent exopolymer particles (TEP) concentrations (μgL?1) in the study area during (a) the B0 stage; (b) the B1 stage; and (c) the B2 stage.

3.7 The Correlation Between Seawater Viscosity and Physicochemical Parameters

Table 2 Pearson correlation matrix for the seawater viscosity and physicochemical parameters in the study area

Fig.10 The relationship between excess seawater viscosity(%) and Chl-a (μgL?1) during different bloom stages in the study area.

4 Discussion

4.1 Effects of Abiotic Factors on Seawater Viscosity

Table 3 Comparisons of the range of ,,, andbetween different research areas. All values were for surface waters

4.2 Effects of Biological Factors on the Seawater Viscosity

Jenkinson first measured a 400-fold increase in viscosity in the phytoplankton cultures (Jenkinson., 1986). Since then, more and more researchers have found that organisms can induce the modification of seawater viscosity, especially the ubiquitous oceanic phytoplankton () can significantly change the seawater viscosity (Jenkinson., 1995, 2014, 2015;Kesaulya., 2008; Seuront., 2007, 2008, 2010, 2016.).

exists a complex polymorphic life cycle that alternates between solitary flagellated cells and gelatinous colonies (Rousseau., 1994). The morphological transformation offrom solitary cells into small colonies usually needs a solid substrate for attachment (Peperzak, 2000a; Riegman, 1996). A large number of(Peperzak., 2000b) appeared during the pre-bloom period, and many newly-formed colonies were found on the setae of, suggesting thatplays a key-role inbloom inception (Rousseau., 1994). In the present research, before the appearance ofcells in the waters, seawater viscosity variation seemed to be dependent on other phytoplankton. Considering that the dominant diatoms,andreached 3.6×104and 2.0×104cellsL?1respectively, seawater vis- cosity could thus be phytoplankton concentration dependent as previously suggested (Jenkinson., 1995). However, although the mucilage producerandwere often observed, the lower abundance compared tosuggested that their contribution to total mucus production was negligible.

Jenkinson (1995) measured viscosity and elasticity in seawater and found that viscosity was positively related to Chllevels. A positive correlation was reported between seawater viscosity and Chlconcentration duringblooms in the German Bight and the North Sea (Seuront., 2006). More specifically, there were positive and negative correlations between Chlconcentration and seawater viscosity before and after foam formation, respectively in the eastern English Channel (Seuront., 2006). This was because the mucopolysaccharide colonies ofaffected the seawater viscosity, while the flagellated cells affected the Chlconcentration, during the bloom decline; flagellated cells in the colony left their own mucus to swim in the zones relatively free of it, so there was a negative correlation between seawater viscosity and Chl.

The dynamics of seawater viscosity was also consistent with the TEP production. TEP mainly was exuded by phytoplankton, and derived from dissolved organic carbon (DOC) (Passow., 2002). Duringbloom, TEP originated from two distinct sources: one from coagulation of DOC during the growing phase ofbloom, and the other from the mucilaginous matrix ofcolonies released in the medium after disruption (Mari., 2005).

A positive correlation (Seuront., 2010) was also found between elevated seawater viscosity and bacteria abundance, which is consistent with the correlations between heterotrophic bacterial abundance and the amount of TEP in seawater (Corzo., 2005). As with phytoplankton, bacteria may modify the viscosity of seawater by producing polymeric materials. However, the relationship between bacteria and TEP is quite complex. TEP production or decomposition depends on environmental conditions (Grossart., 1999).

4.3 Application of Seawater Viscosity in P. globosa Bloom Monitoring

The coastal nuclear power plant needs to pump seawater into the cooling system to take away the excess energy released by the nuclear reactor, while the thick mucus layer ofblocks the water filter in the cooling system. This slows the heat removal process, which is very detrimental to the safe operation of the nuclear power plant. More importantly, the occurrence ofbloom increases the viscosity of seawater. The nuclear power plant pumps the seawater into the cooling system through a pipeline, and the pressure difference of the pumping pipe inlet increases when the seawater viscosity increases, and there is a risk of explosion of the cooling system due to the high pressure at any time. The following are the results of the calculation based on Poiseuille’s law of fluid:

According to the above calculation results, assuming that thebloom intensity in the waters adjacent to the FCGNPP was similar to the eastern English Channel, the pipe pressure of the cooling water inlet system will increase exponentially. Therefore, accurate and time- ly prediction ofbloom is very important to the safe operation of the nuclear power plant.

Although Chlis a key index for monitoring red tide, it is not accurate in thebloom monitoring. This is becausecontains many major pigments, including Chl c3, Chl c1+2, fucoxanthin, 19’-hexanoyloxy- fucoxanthin (hex-fuco) and Chl, with minor carotenoids such as 19’-butanoyloxyfucoxanthin(but-fuco), diadino- xanthin, and diatoxanthin (Riegman., 1996; Lai., 2014; Xu., 2019). The ratios Chl c1+2/Chl(between 10.22 and 12.44% w/w), fucoxanthin/Chl(between 57.4 and 72.4% w/w), and hex-fuco/Chlare very high and showed an increase in the late exponential growth phase and stationary phase (Buma., 1991). Since the 1980s, hex-fuco has been reported as a proxy to estimateglobosa abundance (Bj?rnland., 1988; Zapata., 2004). Antajan (2004) found that but-fuco and hex-fuco were undetectable in theglobosa strain isolated from field samples in 2001 in Belgian coastal waters. It was found that there was no significant correlation between hex-fuco concentration andglobosa biomass in the water column by comparying pigments and phytoplankton from field samples. Furthermore, Chlconcentration per cell varied duringbloom, which was 0.5 pgcell?1during the first period of the bloom, then increased towards the summer with the maximum value of 1.4pgcell?1, and finally declined to 0.8pgcell?1(Buma., 1991). The use of pigments as indicators forglobosa bloom monitoring should be carefully applied. For example, if hex-fuco should be used as a distinctive criterion for the identification ofspecies, the effect of temperature and growth phase on the presence and proportion of this pigment must be well defined (Buma., 1991).

In the present research, the concentration of Chlwas very high, but seawater viscosity was not high in the early stage ofbloom. The abundance ofandcontributes to the increase in the correlation between Chlconcentration and seawater viscosity. During the outbreak period of, the concentration of Chlwas not high, but the abundance ofwas very high, and the corresponding seawater viscosity was also very high. There was a good corresponding relationship betweenand seawater viscosity, which provided a good basis for the prediction ofbloom.

Seawater viscosity can be used as a new reference index forbloom monitoring. Strong supporting evidences (Jenkinson., 1993, 1995, 2015; Kesaulya., 2008; Seuront., 2006, 2007, 2008, 2010) including the following. 1) Under non-bloom conditions, seawater viscosity varies slightly with temperature. 2) Although other phytoplankton may change seawater viscosity, this could be ignored as compared with the effect ofon seawater viscosity. 3) During thebloom, seawater viscosity correlated positively with the colony numbers, especially significantly correlated with the TEP excreted by. To sum up, seawater viscosity has a certain advantage and potential value in the monitoring ofbloom. Due to the fragility ofcolonies, during the sampling process, and/or during the viscosity measurement with the viscometer, these processes should be carefully operated in practical application to avoid breaking colonies and releasing additional mucus into solution, which may lead to a biased increase in seawater viscosity.

5 Conclusions

In this study, the abiotic and biological factors affecting seawater viscosity were analyzed, and the deficiency of Chl(one of the traditional red tide monitoring indexes) and the feasibility and superiority of seawater viscosity inbloom monitoring was discussed, which provides a new method and idea for the monitoring and early warning ofbloom. 1) Chlvalue could only be used to reflect the variation of total phytoplankton and not suitable forbloom monitoring. 2) The changes of physical seawater viscosity have little effect on the changes of total seawater viscosity. 3) The increase in phytoplankton abundance, especially the outbreak ofand a large amount of mucus produced by the colonies can significantly increase seawater viscosity. 4) The biological seawater viscosity was completely consistent with the occurrence process ofbloom and could be used as a valuable index forbloom monitoring. However, the visible colonies need to be removed in the process of measuring seawater viscosity to avoid the fragmentation of the colonies, and increase the measurement deviation of seawater viscosity.

Acknowledgements

We are sincerely grateful to the undergraduates from the marine ecology laboratory for their help in the field sampling and Cole Rampy for checking the grammatical errors in the article. This work was supported by grants from the National Natural Science Foundation of China (Nos. 41966007, 41706083, 41966002), the Science and Technology Major Project of Guangxi (No. AA17202020), the Science and Technology Plan Projects of Guangxi Province (No. 2017AB43024), the Guangxi Natural Science Foundation (Nos. 2016GXNSFBA380108, 2017 GXNSFBA198135, 2018GXNSFDA281025, and 2018 GXNSFAA281295), the Guangxi ‘Marine Ecological Environment’ Academician Work Station Capacity Building (No. Gui Science AD17129046), the Distinguished Experts Programme of Guangxi Province, and the University’s Scientific Research Project (No. 2014XJKY-01A, 2016PY-GJ07).

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E-mail:binyang@bbgu.edu.cn

E-mail:nli@yic.ac.cn

February 11, 2020;

May 19, 2020;

June 7, 2020

(Edited by Ji Dechun)