Bloom-Forming Toxic Cyanobacterium Microcystis: Quantification And

Water Research 183 (2020) 116091

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Bloom-forming toxic cyanobacterium Microcystis: Quantiﬁcation and monitoring with a high-frequency echosounder

* Ilia Ostrovsky a, 1, Sha Wu a, b, c, 1, Lin Li b, , Lirong Song b a The Yigal Allon Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research, Migdal, 14950, Israel b Key Laboratory of Algal Biology, Institute of Hydrobiology, The Chinese Academy of Sciences, Wuhan, 430072, China c University of Chinese Academy of Sciences, Beijing, 100049, China article info abstract

Article history: Harmful cyanobacterial blooms pose a serious environmental threat to freshwater lakes and reservoirs. Received 1 November 2019 Investigating the dynamics of toxic bloom-forming cyanobacterial genus Microcystis is a challenging task Received in revised form due to its huge spatiotemporal heterogeneity. The hydroacoustic technology allows for rapid scanning of 17 June 2020 the water column synoptically and has a significant potential for rapid, non-invasive in situ quantification Accepted 19 June 2020 of aquatic organisms. The aim of this work is to develop a reliable cost-effective method for the accurate Available online 22 June 2020 quantification of the biomass (B) of gas-bearing cyanobacterium Microcystis in water bodies using a high- frequency scientific echosounder. First, we showed that gas-bearing Microcystis colonies are much Keywords: Backscattering signal stronger backscatterers than gas-free phytoplanktonic algae. Then, in the tank experiments, we found a fi Gas-bearing cyanobacterium strong logarithmic relationship between the volume backscattering coef cient (sv) and Microcystis B Gas vesicle proxies, such as Microcystis-bound chlorophyll a (Chl aMicro) and particle volume concentration. The sv/B Remote quantification ratio remained unchanged over a wide range of B concentrations when the same source of Microcystis Vertical distribution material was used. Our measurements in Lake Dianchi (China) also revealed strong logarithmic rela- Surface scum tionship between sv and Chl aMicro. The biomass-calibrated echosounder was used to study the diurnal variability of Microcystis B in the lake. We found a sharp increase in the cyanobacterium B and sv/Chl aMicro ratio near the water surface during the daytime and more uniform distribution of these parameters during the nighttime. We argue that the variations in B and sv/Chl aMicro ratio could be associated with temporal changes in thermal stratification and turbulent mixing. Our data suggest that the sv/Chl aMicro ratio positively correlates with (i) the percentage of larger colonies in population and/or (ii) the content of free gas in cells. The last properties allow Microcystis colonies to attain rapid floating, which enables them to concentrate at the water surface at conducive ambient conditions. The sv/Chl aMicro ratio can be a new important variable reflecting the ability of Microcystis colonies to migrate vertically. Monitoring of this ratio may help to determine the early warning threshold for Microcystis scum formation. The proposed acoustic technology for in situ quantification of Microcystis biomass can be a powerful tool for accurate monitoring and assessment of this cyanobacterium at high spatiotemporal resolution in water bodies. © 2020 Elsevier Ltd. All rights reserved.

1. Introduction (Chorus and Bartram, 1999; Paerl and Otten, 2013; Qin et al., 2010; Srivastava et al., 2013). We lack a comprehensive understanding of Cyanobacteria blooms have major environmental, social, and the role of different ecological factors in the development of economic impacts. In particular, Microcystis blooms and the asso- Microcystis blooms, and as a result, the control of such blooms is a ciated algal toxin microcystin have been implicated in human and challenging task. The development of suitable methods for moni- animal illnesses and are causing increasing worldwide concern toring cyanobacterial blooms for purposes of research and management became an important issue in recent decades (Anderson, 2009). Various physical and chemical approaches are used to

* Corresponding author. quantify Microcystis biomass, including microscopic counting E-mail address: [email protected] (L. Li). (Geider et al., 1997; Srivastava et al., 2013), measurements of 1 Co-first authors, both authors contributed to the work equally. https://doi.org/10.1016/j.watres.2020.116091 0043-1354/© 2020 Elsevier Ltd. All rights reserved. 2 I. Ostrovsky et al. / Water Research 183 (2020) 116091 chlorophyll a (Chl a) concentrations (Cullen, 1982; Iriarte et al., Medwin, 1977). Thus, a scattering cross-section of a small GV- 2007; Yentsch and Menzel, 1963), laser particle analysers (Karp- containing cell, cell-containing colony, and closely spaced popula- Boss et al., 2007; Li et al., 2014), the flow-cytometer method tion of colonies do not equal the sum of cross-sections of their (Wang et al., 2014; Wert et al., 2013), and satellite remote sensing respective components. While much effort has been made to study (Kahru, 1997; Wu et al., 2015). Despite this, there is no satisfactory the physical characteristics of Microcystis colonies, their backscat- method for fast and accurate monitoring of the spatiotemporal tering acoustic properties are still unknown. organization of cyanobacterial blooms. For instance, microscopic Echosounder and ADCP measurements made during an intense counting is a widely used method for identifying phytoplankton Microcystis sp. bloom in Lake Kinneret (Israel) suggested that the species composition; however, microscopic observations are time- presence of GVs in cells make this cyanobacterium acoustically consuming and only allow for analysis of a small number of water visible, which also allows for observing its spatiotemporal vari- samples. Other commonly used methods, such as Chl a measure- ability and vertical migrations (Ostrovsky et al., 2017, 2018). To ment, laser particle analysers, and flow-cytometry measure proxies thoroughly examine the possibility to quantify the cyanobacterial of Microcystis biomass in limited volumes may not be able to biomass with an echosounder, we carried out intensive measure- distinguish Microcystis from other phytoplankton organisms. These ments of acoustic backscattering signal at different concentrations methods are labour and time-intensive, thus hampering the rapid of the cyanobacteria in an outdoor tank and a lake. monitoring of the Microcystis biomass in a dynamic and spatially The main aims of this study were: (1) to investigate the rela- heterogeneous environment. In contrast, satellite remote sensing tionship between the acoustic backscattering signal from Micro- may provide detailed spatial information on phytoplankton distri- cystis and its biomass under controlled experimental conditions bution over large aquatic areas, but it only detects phytopigment and in the field; (2) to elaborate a simple cost-effective acoustic densities in the near-surface water layer at relatively large time method for high resolution quantitative in situ monitoring and intervals (Bok et al., 2010; Kim et al., 2010) and is highly dependent accurate investigations of the spatiotemporal variability of Micro- on weather conditions (Kim et al., 2018). Therefore, more accurate cystis in water bodies; (3) to demonstrate the advantages of the methodologies are urgently needed to monitor Microcystis vari- acoustic technology based on 24-h observations on Lake Dianchi. ability at a high spatiotemporal resolution, in the entire water column, over large aquatic basins. 2. Materials and methods Contrary to the above-mentioned methods, acoustic remote- sensing methods allow quasi-synoptic surveys of large volumes of 2.1. Measurements design and instrumentation water. Scientific echosounders provide high-resolution data on organism abundance in both space and time, and have been widely Acoustic data were collected with a Simrad EY60 scientific used to quantify fish, zooplankton (Huber et al., 2011; Simmonds echosounder equipped with a 200-kHz narrow-beam (7) trans- and MacLennan, 2005; Stanton et al., 1994), and gas bubbles ducer ES200-7. The system was used to measure the acoustic (Ostrovsky, 2003). However, acoustic methods cannot typically backscattering signal from Microcystis colonies in an outdoor classify individual targets taxonomically. Small gas-bearing fish experimental tank (the Guanqiao field station, Wuhan, China) and larvae and zooplankton are strong sound scatterers and thus can be Lake Dianchi (Kunming, China). The main purposes of the tank detected with scientific echosounders and Acoustic Doppler Cur- experiments were: (1) to evaluate the contribution of GVs to a rent Profilers (ADCP) (Lorke et al., 2004; Potiris et al., 2018). Recent Microcystis colony backscattering signal and (2) to quantify the studies showed that spatial and temporal variability of gas- relationships between the acoustic backscattering signal and containing cyanobacteria could be observed with acoustic devices Microcystis sp. abundance over a wide range of biomasses. We used at ultrasonic frequencies (Godlewska et al., 2018; Hofmann and Chl a concentration and particle volume concentration (PVC) as Peeters, 2013). To date, no reliable methods have been developed common biomass proxies. for in situ acoustic quantification of bloom-forming cyanobacteria First, we compared the acoustic backscattering signal from and monitoring their populations. intact Microcystis colonies versus that of colonies with collapsed in- Microcystis, similar to some other cyanobacteria, contain gas cell GVs (after exposure to critical collapse pressure) in the exper- vesicles (GVs) to regulate buoyancy and manage their position in imental tank at a given biomass concentration. The measurements the water column. The GVs have a hollow structure and are usually were carried out on Microcystis of Chl a concentrations of 53 mgL 1 shaped like rods, with a diameter of approximately 100 nm. The GV and 55 mgL1 for cells with intact and with collapsed GVs, shell walls are composed of 2 nm thick protein molecules that make respectively, from the same source. To collapse the GVs in the cells, the structure rigid and enable them to sustain pressure collapse of the Microcystis collected from the pond were placed in a steel up to 0.5 MPa (Walsby, 1994). A recent study of frequency- chamber and exposed for 1 min under high pressure of 0.7 MPa that dependent backscattering of rod-shaped GVs from cyanobacte- exceeds the critical collapse pressure of GVs. This method has been rium Anabaena flos-aquae revealed two resonance peaks (120 and used to study the mechanical properties of Microcystis (Walsby, 85 MHz) which demonstrate that their acoustic behaviour is 1991). The GV collapse was verified using a transmission electron different from those of microbubbles (Yang et al., 2017). The GVs are microscope HT-770 (Hitachi, Japan). Next, we investigated the aggregated in gas vacuoles of irregular outline within the cell relationship between the backscattering signal of Microcystis con- (Walsby, 1994), while cells form colonies of different sizes and often taining intact GVs and biomass concentration in the tank experi- complex shapes. The presence of gas-containing structures signif- ments. We used the Microcystis material collected from the same icantly increases the backscattering properties of planktonic or- pond in these tests. Further, in Lake Dianchi, we studied the in situ ganisms (Lavery et al., 2007; Martin et al., 1996; Stanton et al., relationship between the backscattering signal and the biomass of 1994). The acoustic properties of the Microcystis colony are hard phytoplankton, with Microcystis sp. being the predominant species. to infer from the properties of its components. The concurrent acoustic and biological samplings were carried out When scatterers are tightly packed together, their scattered in the field over 24 h. fields interact at particle separations less than the wavelength, e.g. for 200-kHz echosounder at distances <0.75 cm. For randomly 2.2. Acoustic considerations, measurements, and data processing packed units, the face of the cluster behaves as a pressure release surface, and sound reflection takes place at this interface (Clay and Small aquatic organisms (e.g. plankton) aggregate at specific I. Ostrovsky et al. / Water Research 183 (2020) 116091 3 strata and the reflected sound from these organisms is observed as calculated as pulse length multiplied by sound velocity and divided a scattering layer on the echogram of an echosounder (Iida et al., by 2 (Ona and Mitson, 1996), was ~0.1 m. The data were collected at 1996). The volume density, N (ind. m 3) of echo reflecting objects sampling rates of 10 pings s 1. The lower threshold for data (e.g. fish, gas bubbles, plankton) can be estimated using the echo collection was set to 100 dB. A minimum scattering strength integration method (Simmonds and MacLennan, 2005; Ostrovsky of 35 dB was chosen for boundary detection (bottom or water et al., 2008; Macaulay et al., 2018). surface). Prior to the start of each sampling campaign, the echosounder was calibrated with a 13.7 mm diameter standard N ¼ sv /sbs, (1) copper sphere (Foote, 1982). The acoustic data were collected concurrently with water samples taken by siphons for ground 2 3 where sv is the volume backscattering coefficient (m m ) and sbs truthing and Microcystis biomass assessment. is the mean backscattering cross-section (m2). Taking the mean All collected acoustic data were stored on a computer and later weight (biovolume) of individuals (W) into account, one can relate processed using the Echoview software (version 8.0, Myriax Pty Ltd, the biomass concentration (B ¼ NW) of these organisms to sv: Hobart, Tasmania, Australia), which automatically compensates the received echo depending on the range (R, m) by the time-varied B ¼ sv /(sbs/W) (2) gain (TVG). A 20 log10 (R) TVG is applied during the process of echo integration. The acoustic data were collected over 5e6 min The denominator sbs/W ¼ b is a scaling factor for sv translation intervals adjacent to or coinciding with the time of water column into the biomass/biovolume. If the mean sbs and W are constants at sampling for Microcystis ground truthing, as detailed below. given conditions, the denominator in (2) is also constant. Then, the biomass of planktonic organisms is directly proportional to sv (Iida 2.3. Outdoor tank experiment et al., 1996) and the sv/B ratio is the constant b. With the volume backscattering strength, Sv (dB re 1 m) ¼ 10 log The experiments were conducted at the Guanqiao field station sv, the relationship between Sv and B takes the following form: (Wuhan, China) in an outdoor plastic cylindrical tank with a 2 m diameter and 2 m height (Fig. 1)on18th May 2017. The tank was Sv ¼ 10 log (B) þ 10 log (b) (3) filled with tap water a day before the measurements to allow the chlorine to dissipate. The water temperatures during the experi- In practice, the linear regression between Sv and log (B) can be ment were 21e24 C. calculated, assuming that Sv ¼ a log (B) þ c, where a and c are the A downward oriented transducer was positioned a couple of parameters. If a is equal (or close) to 10, then the sv/B ratio is the centimetres below the water surface at the tank centre. First, we constant. If the correlation between these variables is strong and a performed acoustic measurements in the tank in pure tap water to significantly differs from 10, the sv/B ratio varies with B, and the determine the background noise caused mainly by the reverbera- power relationship can be used to approximate the relationship tion of the emitted echo signal prior to the introduction of the a/10 (a/10 e 1) between sv and B: sv ¼ b B . In the last case, sv/B ~ B .In Microcystis colonies. We experimentally selected two depths of this study, the biomass concentration (B) is represented by Chl a 1.2 ± 0.1 m and 1.6 ± 0.1 m, where the noise level was minimal. concentration or PVC. Note that the scaling factor b (¼sbs/W) de- Measurements in these layers allowed us to minimize the acoustic pends on the echo-reflecting properties (sbs) of the specific target threshold for Microcystis detection in the tank. The estimated noise (e.g. Microcystis colony) and its weight equivalent (e.g. biovolume, level (see below) was subtracted from the measured backscattering Chl a concentration). In turn, the sbs may also depend on the signals obtained for various Microcystis concentrations. Further, we amount of GVs per colony biovolume; while Chl a concentration measured Sv of six Microcystis concentrations produced in the tank. may vary with species and ambient conditions (e.g. light, nutrient Microcystis used for the experiments were collected with a availability). Thus, the actual relationship between sv and B may bolting-silk plankton net (mesh size 64 mm) from a pond located vary with location and time. close to the tank. Coarse debris of organic particles was removed by The presence of gas in small planktonic organisms makes them filtering through a 300 mm mesh. Microscopic examination showed acoustically contrast with a much stronger backscattering signal that Microcystis colonies contributed to more than 98% of the total than gas-free organisms (Hofmann and Peeters, 2013). Therefore, phytoplankton biovolume in our experiments. one can expect that sv of gas-bearing Microcystis colonies in the Six different concentrations of Microcystis colonies were pro- water column should be correlated to their biomass, or one of the duced by adding the mesh-concentrated material in the tank. Each biomass proxies, e.g. Chl a or biovolume concentration (Ostrovsky increasing concentration of Microcystis was approximately double et al., 2018). the preceding one. After each addition of the concentrated Micro- For a 200 kHz transducer used in this study, the wavelength is cystis material, the water in the tank was mixed to homogeneously 7.5 mm, which is much larger than the typical diameter of Micro- disperse the colonies throughout the water column. After each cystis colonies (40e500 mm) in our study (see below, Fig. 2). This mixing procedure, the downward oriented transducer was centred causes Rayleigh (or resonant) scattering by colonies (Urick, 1967). so that the acoustic beam was directed along the central tank axis. The sbs for a particle in the Rayleigh regime is proportional to the To avoid the possible effect of the enhanced turbulence induced sixth power of the particle diameter and is inversely proportional to during the water mixing on the backscattering signal (Orr et al., the fourth power of wavelength. The latter means that larger par- 2000), the acoustic data collection was started ~10 min after mix- ticles have much greater sbs than smaller ones and that ing. Acoustic data were collected over 6 min (3600 pings). The data echosounders at higher acoustic frequency have superior sensi- were binned into 100-ping segments, resulting in 36 data points tivity to smaller particles. The higher the frequency of the per sampling depth, for each cyanobacterium concentration. echosounder, the shorter the obtainable range due to increased The Chl a concentrations and PVC in the tank water serve as the signal attenuation (Ostrovsky et al., 2008). The nearfield distance, ground truth for acoustic measurements. At each Microcystis con- i.e. the distance between the transducer and the closest measurable centration, water samples were siphoned via 8 mm silicone tubes range (Ona and Mitson,1996), was 0.59 m for a 200-kHz transducer. positioned along the inner tank wall as far as possible from the At a maximum range of 3 m and pulse duration of 0.128 ms used, acoustic beam, thus minimizing possible interference. The samples the near-boundary “dead zone”, where sv cannot be assessed, was were collected into plastic jars after removal of three volumes of 4 I. Ostrovsky et al. / Water Research 183 (2020) 116091

Fig. 1. Experimental outdoor tank schematics.

After extraction, the absorbance was measured at 630, 663, 750, and 645 nm. The total Chl a was determined using the method described by Yentsch and Menzel (1963). Sample signals were calibrated with a Chl a standard (Sigma Aldrich, C6144). All measurements were carried out in duplicate. Laser In situ Scattering and Transmissometry (LISST-200X), manufactured by Sequoia Scientific, Inc., was used for measuring PVCs. To calibrate the instrument, the background scattering measurements were acquired using deionized water, and the resulting plot was compared with the manufacturer’s calibration. For PVC measurements in the water samples, we followed the protocol suggested by Karp-Boss et al. (2007). The data were recorded at 1 Hz sampling rate in water subsamples for 2 min (120 measurements) without stirring, to avoid damaging the Microcystis colonies, and to minimize their possible upward migration. The raw data were further analysed at non-spherical mode using the LISST- SOP-200X program and presented as mean PVC (mLL 1). The instrument provides PVC in 36 logarithmically increasing size bins ranging from 1 mmto500mm. The colony size distribution measured with the LISST-200X showed that the diameters of pre- vailing colonies, which contribute the most to the total volume concentration, were in the range between 40 mm and 150 mm Fig. 2. The size-frequency distribution of Microcystis colonies (a) in the tank experiments and (b) in the near-surface water of Lake Dianchi at 10:00 a.m. on 16th July 2017. (Fig. 2a). The X-axis is the particle size (equivalent spherical diameter of the colony). Y-axis is the size-specific Particle Volume Concentration (PVC) in bins of logarithmically increasing size (bin width). Y-coordinate of the ith bin is calculated as the ratio of the 2.4. Field measurements ith bin PVC (%) to its bin width in mm. The dots show the centres of the bins. The sum of PVC of all bins (the areas of the rectangles) is 100%. The absolute PVCs (mLL 1) were The field data were collected in the shallow eutrophic Lake measured in water samples with the LISST-200X. Dianchi (Kunming), where Microcystis sp. was the dominant phytoplankton species at the time of sampling (16e17 July 2017). Water depth at the sampling site was 3.2 m. Acoustic data were tube water from each sampling depth. The collected samples were collected continuously for 24 h at a rate of 10 pings per second with kept on ice, brought to the laboratory, and analysed within 2 h after an upward oriented transducer positioned at the bottom of the sampling. lake. The level of passive noise (without sound transmission) was The water samples collected from the tank were analysed for Chl measured and then subtracted from the backscattering signal to a concentration using an ultravioletevisible spectrophotometer reduce ambient noise. To obtain accurate S values from Micro- (Shimadzu, UV-1800). The 500 mL water subsamples were filtered v cystiserelated echoes, the signals from large objects (fish and gas through Whatman ® glass microfiber filters, Grade GF/C (1.2 mm). bubbles rising from the bottom) were automatically removed using Chl a was extracted with 5 mL of 90% acetone in darkness at 4 C. the object detection algorithm in the Echoview software (Fig. 3). After 24 h, the extracts were centrifuged for 10 min at 4000 rpm. The remaining data were averaged and used to compute the mean I. Ostrovsky et al. / Water Research 183 (2020) 116091 5

Fig. 3. The echogram illustrates (a) the auto-detected tracks of the acoustic objects (fish and gas bubbles) in the water column and (b) their auto-removal (black strips containing the tracks) by the Echoview software. The horizontal tracks represent fish, while the inclined lines represent tracks of rising bubbles (Ostrovsky et al., 2008; Ostrovsky, 2009). The nearfield of the upward oriented transducer is marked by a vertical two-side black arrow at the bottom of the figures. Colour bar shows the Sv values (dB). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Sv and standard deviation (SD) of particulate material (mainly gas- measurement. The PhytoWin V2.13 software was then used to bearing Microcystis colonies). The acoustic data were binned into translate the recorded fluorescence responses of the various 100-ping time segments and 0.1 m depth segments, to match the phytoplankton groups into the relative amount of each group in 1 centres of specific acoustic bins to the water sampling locations. terms of Chl a equivalent (mg Chl a L ). The total Chl a (Chl aTot) The 24-h continuous acoustic sampling in Lake Dianchi was concentration was used as a proxy for phytoplankton biomass. The accompanied by the ground truth measurements that included Chl a concentration belonging to the “blue” group (Chl aMicro)was water sampling of Chl a for laboratory analysis. The water sampling used as a proxy for Microcystis biomass. was started at 10:00 a.m. on July 16, 2017 and continued at 3-h An example of particle size distributions measured with the intervals. The samples were collected from water depths ranging LISST-200X in the near-surface water of Lake Dianchi on 16th July from 0.2 m to 2.0 m at 0.2 m intervals resulting in ten sampling 2017 is presented in Fig. 2b. Note that the LISST measurements depths per time. The water samples were kept on ice in darkness for could overestimate the volume concentration of Microcystis col- ~4 h prior to measuring the Chl a concentration. To compare the onies as ~20% of Chl a was attributed to non-Microcystis phyto- acoustic measurements with Chl a concentration at each sampling plankton (see below). Since Microcystis colonies are notably larger time, a 5-min (3000 pings) fragment of the acoustic record was than other algae, only smaller size bins would be affected. Fig. 2 selected and processed by the Echoview. shows that in Lake Dianchi the contribution of large (>150 mm) The Chl a concentration in lake samples was measured using a colonies to the total volume concentration of all colonies/particles Multiple Excitation Wavelength Phytoplankton & Photosynthesis (31.6%), were ~21 times higher than in the tank experiments (1.5%). Analyzer (Phyto-PAM-II Walz, Heinz Walz GmbH, Effeltrich, Ger- Because the proportion of smaller Microcystis colonies in the Lake many). The instrument distinguishes four algal groups (cyanobac- Dianchi water sample could be overestimated, the real contribution teria, green algae, diatoms/dinoflagellates, and phycoerythrin- of the larger colonies could be even higher. containing algae) according to their characteristic absorbance spectra for their different antenna pigments at five excitation wavelengths (440, 480, 540, 590, and 625 nm) (Beecraft et al., 3. Results 2017). Reference spectra measured for Chlorella vulgaris and Cyclotella sp., as representatives of the “green group” (chlor- 3.1. Tank experiment results ophytes) and “brown group” (diatoms, dinoflagellates, chryso- phytes, cryptophytes) signals, respectively, were used throughout. First, we compared the backscattering signals from Microcystis For the “blue” group (most cyanobacteria) we used the spectrum of colonies containing intact GVs, and colonies exposed to high the microscopically defined dominant cyanobacterium Microcystis pressure, which collapses their GVs. The GV collapse was verified by aeruginosa. Subsamples of 3 mL each (in duplicate) were trans- examination under an electron microscope (Fig. 4 a, b). Comparison ferred to a dark incubator for in vivo Chl a fluorescence of the acoustic backscattering signal from Microcystis with intact and with collapsed GVs showed a dramatic difference in 6 I. Ostrovsky et al. / Water Research 183 (2020) 116091

Fig. 4. Electron microscope images of Microcystis cells (a) with intact gas vesicles (GVs) and (b) with collapsed GVs used in the outdoor tank experiments and respective raw 1 echograms showing acoustic backscattering signals (Sv)ofMicrocystis colonies (c) with intact GVs and (d) with collapsed GVs at similar concentrations of 53 mg Chl a L and 55 mg Chl a L 1, respectively. backscattering signals (Fig. 4 c, d). The Microcystis cells containing logarithmic relationship between these variables. 1 intact GVs had a mean Sv of 63.9 ± 0.7 dB. The mean Sv for cells For Chl a concentrations ranging from 6 mg Chl a L to 107 mg 1 with collapsed GVs was much weaker (75.1 ± 0.2 dB) and was Chl a L , the empirical relationship between Sv and log (Chl a) can very similar to the mean background noise level in the tank be described by the following equation (r2 ¼ 0.973, p < 0.001): of 76.5 ± 0.2 dB, (Fig. 5), which hindered the precise estimation of Sv for Microcystis with collapsed GVs. Our results show that GVs are Sv ¼ (9.86 ± 0.50) log(Chl a) e (80.39 ± 0.78) (4) the main source of acoustic backscattering signal of gas-bearing Microcystis. In addition, we measured the backscattering signals or from green algae and diatoms with similar Chl a concentration of 1 9 0.986±0.050 ~50 mg Chl a L under the same experimental conditions. The sv ¼ 9.15 10 Chl a (4a) estimated Sv values of these algae, which do not contain free gas in their cells, were similar to the background level, i.e. the actual Hereafter, the regression coefﬁcients are shown as the backscattering signal was undetectable under these experimental mean ± standard error. conditions. For PVC ranging from 9 mLL 1 to 280 mLL 1, the empirical Further, we investigated the relationship between the Sv of relationship between Sv and PVC can be described by the following Microcystis colonies with intact GVs and Microcystis biomass for six equation (r2 ¼ 0.940, p < 0.001): concentrations. This allowed us to compute regressions between Sv and two widely used cyanobacteria biomass proxies: Chl a con- Sv ¼ (8.82 ± 0.52) log (PVC) - (81.51 ± 0.97) (5) centration and PVC. The determined background noise level Sv and selected 5 dB signal-to-noise ratio limited the low range of or measured Microcystis concentrations to 6 mg Chl a L 1 and PVC to 1 9 0.882±0.050 9 mLL for the tank measurements. The data above these limits are sv ¼ 7.06 10 PVC (5a) shown in Fig. 6. The results demonstrate a highly correlated The power indices in eqn. (4a) and eqn. (5a) are ~1. This suggests I. Ostrovsky et al. / Water Research 183 (2020) 116091 7

th Fig. 5. Examples of raw echograms obtained in the outdoor tank experiments on 18 May 2017. The echograms show acoustic backscattering strength (Sv) in water with different 1 1 concentrations of Microcystis. Above each panel, the concentration of Microcystis is given in mg Chl a L and the corresponding mean Sv is in dB. Ping rate is 10 pings s . The nearﬁeld region and near-boundary dead zone are not shown.

that the relationships between sv and Chl a as well as between sv when analysing the relationship between the backscattering signal and PVC can be also approximated by linear equations (see section from gas-bearing Microcystis and Chl aTot. The latter can be illus- 2.2.), indicating that the sv/Chl a and sv/PVC ratios do not change trated by two in situ relationships: Sv vs. Chl aTot and Sv vs. Chl aMicro signiﬁcantly with Microcystis concentration under experimental (Fig. 8). 1 conditions. For a broad range of Chl aTot concentrations (14 mg Chl a L to 1 179 mg Chl a L ), the relationship between Sv and Chl aTot can be 2 ¼ < 3.2. Field measurements described by the following equation (r 0.834, p 0.001):

¼ ± e ± In Lake Dianchi, the acoustic sampling was accompanied by the Sv (19.16 1.04) log(Chl aTot) (91.20 1.67) (7) quantification of Chl a concentration in water samples using a Phyto-PAM-II that allows us to distinguish between total Chl a (Chl or a ) and cyanobacteria-bound Chl a. At the time of our field mea- Tot ¼ 10 1.92±0.10 surements, Microcystis was the predominant cyanobacterium spe- sv 7.58 10 Chl aTot (7a) cies in the lake, therefore the last variable can be attributed to m 1 Microcystis-bound Chl a concentration (Chl a ). For a broad range of Chl aMicro concentrations (4 g Chl a L to Micro m 1 The Chl a varied vertically showing maximum values near the 167 g Chl a L ), the relationship between Sv and Chl aMicro can be Tot 2 ¼ < water surface and the lowest values near the bottom (Fig. 7a). The described by the following equation (r 0.872, p 0.001): percentage of Chl a relative to Chl a reached 80e93% in the Micro Tot ¼ ± ± upper 0.6 m stratum and decreased with depth down to 23e46% Sv (12.78 0.59) (log Chl aMicro) - (78.00 0.84) (8) close to the bottom (Fig. 7b). As a result, the Chl aMicro concentrations were 4e5 times higher in the upper water stratum than near or the bottom, which is characteristic of the vertical distribution of ¼ 8 1.28±0.06 buoyant cyanobacteria during low water turbulence (e.g. Hozumi sv 1.58 10 Chl aMicro (8a) et al., 2020). The correlation coefficients were significantly high for both re- The relationship between Chl aTot and Chl aMicro computed for the entire water column can be expressed by the following equa- gressions. However, taking into account that gas-bearing Micro- tion (r2 ¼ 0.991, p < 0.001): cystis alone could contribute to the acoustic backscattering strength (see above), only eqn. (8) should be viable. In contrast, eqn. (7) is biased due to the presence of acoustically transparent algae that Chl aTot ¼ (13.6 ± 0.6) þ (0.99 ± 0.01) Chl aMicro (6) contributed to Chl aTot. The large difference in the percentage of Chl aMicro in Chl aTot in various strata (see above) influenced the slope in The slope coefficient in eqn. (6) is ~1. Thus, Chl aTot can be eqn. (7a). Remarkably, the power index in eqn. (8a) is much closer presented as a simple sum of Chl aMicro and a constant of 13.6 mg Chl a L 1, which can be attributed to Chl a associated with other algae to one than that in eqn. (7a), so that the sv/Chl aMicro ratio in Lake (not cyanobacteria). This depicts the principal difference between Dianchi just slightly increased with Chl aMicro (i.e. toward the water the vertical distributions of Microcystis sp. and other phytoplankton surface). The low variability of this ratio concurs with the steadi- species in the water column, which should be taken into account ness of the sv/Chl a ratio in the tank experiments. 8 I. Ostrovsky et al. / Water Research 183 (2020) 116091

Examples of echograms and corresponding fragments of vertical distributions of Chl aMicro concentrations in Lake Dianchi during two 10-min sampling periods are presented in Fig. 9. The data show explicit vertical heterogeneity in Microcystis distribution in the water column with the highest concentrations in the upper 1-m stratum during both sampling periods. At 01:25, the Chl aMicro concentrations were noticeably lower than at 18:20 in the same strata. Thus, the data illustrates high spatiotemporal variability of Microcystis biomass, which can be caused by lateral transport of cyanobacterium patches with currents and/or vertical migrations of colonies. The diurnal dynamics of Sv in the water column recorded continuously over 24 h in Lake Dianchi is shown in Fig. 10a. The corresponding dynamics of Microcystis-bound Chl a concentrations calculated based on Sv using eqn. (8) are presented in Fig. 10b. The results show that below 0.6 m the Microcystis-bound Chl a concentration sharply decreased with depth during the daytime, such that concentrations of Chl aMicro near the water surface were 3e6 times higher than near the lake bottom. In contrast, the Microcystis colonies were more uniformly distributed in the water column in the nighttime. During the nighttime, the near-surface Chl aMicro was much lower compared to the daytime. Thus, our observations revealed large vertical variations and day/night differences in Microcystis biomass.

4. Discussion

4.1. Acoustic response of Microcystis sp.

The results of the tank experiments showed that the backscattering signal of gas-bearing Microcystis colonies was much stronger than the signal received from colonies with collapsed GVs. This indicates that the GVs are responsible for Microcystis’ strong backscattering response, which makes this cyanobacterium acoustically “visible” at ultrasonic frequencies. High acoustic “vis- ibility” of gas-bearing invertebrates to echosounders at ultrasound frequencies has been previously demonstrated, for instance, for some planktonic organisms, such as Siphonophora (Warren, 2001) and Chaoborus flavicans (Lorke et al., 2004). Wood and Gartner (2010) showed that the backscattering signal measured with ADCP at 614 kHz was primarily associated with the presence of the cyanobacterium Aphanizomenon flos-aquae, which has GV- Fig. 6. The relationships between volume backscattering strength (Sv) and the loga- containing cells. A high acoustic backscattering signal from Micro- rithm of Microcystis biomass proxies: (a) Chl a concentration and (b) PVC. The measurements were carried out in the outdoor tank experiment. Points in pairs correspond cystis was reported at Lake Kinneret (Ostrovsky et al., 2017, 2018). to measurements at two water depths (1.2 m and 1.6 m). Dotted lines show the Cherin et al. (2017) studied the acoustic properties of Halobacterium regression lines. Bars represent standard errors. salinarum containing GVs and characterized them as a “unique class of biologically derived ultrasound contrast agents”. To date, there are a limited number of publications where gas-bearing planktonic Thus, our measurements carried out in both tank experiments organisms were studied using acoustic techniques (Ostrovsky et al., and field surveys showed a high correlation between backscat- 2017, 2018; Godlewska et al., 2018). Our results show that the tering strength versus Microcystis-bound Chl a concentration. The presence of gas structures in Microcystis cells enables us to results obtained in experimental conditions and in situ suggest that instrumentally distinguish this cyanobacterium from gas-free the volume backscattering coefficient per unit of Microcystis phytoplankton and to quantify its biomass. biomass have little variance at given conditions. Still, the Sv values at specific Chl a concentrations in Lake Dianchi [eqn. (8)]were Micro 4.2. Relationship between Microcystis biomass and its 6e9 dB higher than those computed for the same concentrations of backscattering signal Chl a in tank experiments [eqn. (4)]. Such a notable difference indicates that acoustic backscattering response per unit of Microcystis The high correlation between Sv and log(B) obtained during the biomass can vary between locations and may be attributed to site- tank experiments and field measurements demonstrates that a specific or time-specific properties of species/strains, resulting backscattering signal can be used as a measure of Microcystis from adaptations to given ambient conditions. Likewise, the dif- biomass and enables spatiotemporal variability mapping of this ference in regression coefficients at various locations or conditions cyanobacterium. Implementation of acoustic techniques is also suggests that specific relationships should be estimated in each suggested for evaluating the spatial distribution of the buoyant case to allow for accurate quantification of the spatiotemporal cyanobacterium Planktothrix rubescens (Hofmann and Peeters, variability of Microcystis biomass based on acoustic measurements. 2013). I. Ostrovsky et al. / Water Research 183 (2020) 116091 9

Fig. 7. The mean vertical distributions of (a) total Chl a concentration, (b) percentage of Microcystis-bound Chl a, and (c) volume backscattering strength (Sv) in Lake Dianchi during 16e17 July 2017. Bars represent standard errors. Values averaged over 8 time intervals.

0.92 and, in accordance with eqn. (7a),sv/Chl aTot ~ Chl aTot . Thus, a distinct increase in sv/Chl aTot ratio with Chl aTot occurred in the water column due to a concurrent increase in Chl aTot and the percentage of Microcystis in the total phytoplankton biomass (Chl aMicro/Chl aTot ratio) towards the water surface. Such a change can be easier to understand taking into account that both gas-bearing Microcystis and gas-free phytoplankton contribute to the Chl aTot, while the contribution of the second group to sv is negligible. This indicates that the observed variations of the sv/Chl aTot ratio in Lake Dianchi is biased. In contrast, the sv/Chl aMicro ratio altered in situ to a lesser extent and, in accordance with eqn. (8a),sv/Chl aMicro ~ Chl 0.28 aMicro. This ratio was less dependent on Microcystis biomass in Lake Dianchi and is in a better agreement with the results obtained in the tank experiments, where almost the same ratio was characteristic for a wide range of Microcystis concentrations. The sv/Chl aMicro ratio measured in the upper part of Lake Dia- nchi (7.6E-08) was 8.5 times higher than in the tank (8.9E-09) to which the colonies were delivered from a small pond. Such a large dissimilarity may be attributed to the difference in GV content across species/strains (Gao et al., 2016; Mlouka et al., 2004). The occurrence of GVs in cells strongly affects Microcystis’ acoustic properties, as demonstrated in our tank experiments carried out on Fig. 8. The relationships between volume backscattering strength (Sn) and concentrations of total Chl a (Chl a_Tot) and Microcystis-bound Chl a (Chl a_Micro) in Lake colonies with intact and with collapsed GVs. The concentration of Dianchi during 16the17th July 2017. The data includes all measurements in the water the GVs in cells depends on available light, nutrients, and other column collected between 0.2 m and 2.0 m depth at 0.2 m intervals. The highest Chl a ambient parameters (Deacon and Walsby, 1990; Oliver and Walsby, < m 1 values were detected at depths 0.6 m; the lowest concentrations ( 34 gL ) were 1984; Pfeifer, 2012) and, therefore, can vary in different water detected at depths 1.6 m. Each point on the graph represents the mean S at a v bodies. The larger proportion of GVs in colonies makes them more specific depth and time. The solid line shows the regression line for Chl aMicro and dotted line for Chl aTot. buoyant, allowing them to remain in the illuminated upper part of the water column even at a moderate level of turbulence (Wallace et al., 2000; Walsby, 1994; Wu and Kong, 2009). Moreover, the With a wide range of biomass concentrations from diluting the higher percentage of larger colonies in Lake Dianchi compared to concentrated Microcystis material, analysis of the parameters of the the tank/pond (Fig. 2) could be also responsible for greater scat- regression equations obtained during controlled tank experiments tering efficiency of the colonies and higher sv/Chl aMicro ratio. The fi e showed that the slope coef cient in the Sv log(B) regression was latter can be explained as, in the Rayleigh scattering regime (valid equal (or close) to 10, meaning that sv/B ratio was almost unvarying. for particles that are small enough compared to the wavelengths), The direct calculations of sv/Chl a and sv/PVC ratios showed very the scattering cross-section increases proportionally to the particle low variability (8.9E-09 ± 0.6E-09 and 4.5E-09 ± 0.4E-09, respec- radius to the sixth power (section 2.2). The presence of larger col- tively). This indicates that the acoustic properties of Microcystis onies in the relatively large and more turbulent lake should be colonies of the same origin (the material was taken from a nearby beneficial for the Microcystis population, as large colonies may pond) and size distribution were practically unvarying over a wide overcome the low and even moderate turbulence level more readily range of concentrations, and thus sv can be an accurate measure of than smaller ones and rise more rapidly to the water surface Microcystis-bound Chl a and volume concentration of colonies. (Hozumi et al., 2020; Wu et al., 2020), where the higher light in- Our analysis of in situ measurements in Lake Dianchi demon- tensity supports a higher photosynthetic rate. Thus, the difference fi strated that the sv/Chl aTot ratio varied signi cantly with Chl aTot in the sv/Chl aMicro ratios could reflect the adaptations of the 10 I. Ostrovsky et al. / Water Research 183 (2020) 116091

Fig. 9. Examples of raw echograms (a and b) and corresponding Microcystis-bound Chl a concentrations (c and d) in Lake Dianchi at two time intervals: 01:25e01:35 (a and c) and 18:20e18:30 (b and d). Microcystis-bound Chl a concentrations in the water column were calculated using acoustic data (after removal of fish) and eqn. (8). The nearfield region and the near-boundary dead zone were excluded from calculations. Sv colour bar displays volume backscattering strength. Chl a colour bar displays Microcystis-bound Chl a concentration. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 10. Spatiotemporal dynamics of (a) volume backscattering strength (Sv) and (b) Microcystis-bound Chl a concentrations in Lake Dianchi. The volume backscattering strength (Sv) was continuously recorded with Simrad scientific echosounder EY60-200 kHz over a 24-h period starting from 10:00 a.m. on 16th July 2017. Microcystis-bound Chl a concentrations were calculated using the measured Sv and eqn. (8).Sv colour bar displays volume backscattering strength. Chl aeMicrocystis colour bar displays Microcystis-bound Chl a concentration. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) cyanobacterium populations to specific mixing conditions in increase in the proportion of large particles (Fig. 2) that are capable different water bodies. to rise in the water column. In the deeper stratum (>1.5 m) the low In shallow Lake Dianchi, the mean sv/Chl aMicro ratio in the up- the sv/Chl aMicro ratios (<4E-08) remained almost unchanged over per stratum (7.6E-08) was about twice higher than that in the near- the 24-h period. bottom stratum (3.8E-08). We also observed a distinct temporal Our results show that the accumulation of buoyant colonies variation in the ratio in the upper layer, where the high values of near the water surface can be monitored by measuring the sv/Chl the ratio (>6E-08) were detected in a sunny day at low wind aMicro ratio. We suggest that the upper limit of the ratio detected in disturbance, at which colonies could float up and concentrate in the the deeper stratum (~4E-08 in Lake Dianchi) can be taken as a upper 0.5-m layer (Fig. 10b). The daytime increase in the sv/Chl rough estimate of the early warning threshold for Microcystis sur- aMicro ratio near the water surface can mostly be explained by an face scum formation, i.e. the values exceeding this threshold may I. Ostrovsky et al. / Water Research 183 (2020) 116091 11 indicate accumulation of large buoyant colonies near the water quantified for each specific case in order to estimate the Microcystis surface with further development of surface scum at proper con- biomass accurately using the acoustic techniques. Such a “calibra- ditions. Since the sv/Chl aMicro ratio can be species/strain-specific tion” of sv versus Microcystis biomass is a simple and quick and depend on various factors, the threshold has to be deter- procedure. mined for each case. Thus, the sv/Chl aMicro ratio can be used to High-frequency ADCPs can also be used for the semi- predict the Microcystis surface scum formation, which is a harmful quantitative assessment of the distribution and dynamics of gas- phenomenon considered by the World Health Organization as a bearing planktonic organisms (Lorke et al., 2004). However, these high-risk factor (Chorus et al., 2000). instruments have comparative disadvantages to scientific echosounders, such as noisier acoustic signal, coarser vertical res- 4.3. Remarks on acoustic quantification of Microcystis biomass olution, Sv is provided in relative units, and the instruments require careful calibration (e.g. Deines, 1999). The time series measure- Direct high-resolution observations on dynamics and distribu- ments of Sv could greatly benefit from the concurrent acquisition of tion of cyanobacterial biomass in natural waters are limited other water column parameters (e.g. temperature, dissolved oxy- because of the lack of proper methods allowing remotely and gen, pH) and meteorological data (e.g. wind speed, solar radiation, rapidly quantify the biomass of these highly variable organisms. In surface temperature). Such information has allowed for the inves- contrast, modelling and experimental methods have been largely tigation of vertical migrations of various aquatic organisms in used to study the role of multiple factors and processes in the response to external forcing (Rubio-Rodríguez et al., 2018; formation of the cyanobacterial blooms (e.g. Visser et al., 1997; Ostrovsky et al., 2018; Lorke et al., 2004). Hajdu et al., 2007; Chien et al., 2013; Zhu et al., 2018). The hydro- Long-term acoustic sampling together with concurrent mea- acoustic methodology enables rapid high-resolution synoptic surement of abiotic and biotic components can be done at one or scanning of the entire water column and quantification of spatio- more fixed stations or during motile surveys from research vessels. temporal changes of Microcystis biomass. For instance, our hydro- The use of in situ automatic high-frequency monitoring systems acoustic observations on Microcystis dynamics in Lake Dianchi (the package of different monitoring instruments) in combination (Fig. 10b) show that high concentration of Microcystis near the with a scientific echosounder or ADCPs enables study of the dis- water surface occurred during the daytime at warm windless tribution and migration of aquatic organisms at a high vertical and conditions, at which the vertical stability of the water column in- temporal resolution as well as revealing the seasonal and basin- creases. Studies on Lake Kinneret have shown that the strength- scale changes (Marce et al., 2016). Deployment of such moni- ening of diurnal stratification leads to turbulence reduction in the toring systems at different lake locations can help to obtain detailed subsurface layers that, in turn, facilitate the upward migration of lake-wide information on harmful cyanobacterial bloom dynamics. buoyant Microcystis colonies (Hozumi et al., 2020; Ostrovsky et al., The motile acoustic surveys permit the study of the horizontal 2018). In contrast, more uniform vertical distribution of the cya- variability of cyanobacteria in spatially heterogeneous aquatic nobacteria during the nighttime in Lake Dianchi could be caused by ecosystems and the identification of micro- and mesoscale per- the enlarged turbulent mixing due to night cooling of the water turbations which influence phytoplankton patchiness. Due to its surface. These findings agree with an experimental study on up- simplicity and robustness, acoustic quantification of Microcystis ward migration of Microcystis colonies (Wu et al., 2020) and with biomass can be widely used for both monitoring and research direct in situ observations on vertical dynamics of the buoyant purposes. Thus, acoustic observations of cyanobacteria biomass colonies at various turbulent conditions in a large lake (Hozumi with high spatial and temporal resolution could prove valuable for et al., 2020). Remarkedly, the vertically integrated Microcystis better understanding the role of various factors and processes biomass measured at one sampling station on Lake Dianchi varied influencing the Microcystis bloom and surface scum formation in by more than one order of magnitude over a 24-h period. This inland water bodies. suggests the presence of Microcystis patches with varying densities and their horizontal transport in the lake. Thus, our acoustic ob- 5. Conclusions servations on Microcystis dynamics in Lake Dianchi illustrate the great potential of the acoustic technology in the study of the 5.1. The gas-bearing Microcystis colonies are strong back- spatiotemporal dynamics of gas-bearing cyanobacteria. scatterers at ultrasound frequencies. This property is related A common disadvantage of existing acoustic methods is the to the presence of gas vesicles in cells. inability to distinguish sound scattering objects. Therefore, parallel 5.2. In tank experiment, we found a strong logarithmic rela- measurements for verification of acoustic signals are needed to tionship between the volume backscattering coefficient (sv) obtain reliable information on objects of interest. For instance, and Microcystis biomass (B) proxies, such as Chl a concen- concurrent use of acoustic sampling with in situ multi-spectral tration and colony volume concentration. The sv/B ratio FluoroProbe vertical profiling allowed discrimination of gas- remained unchanged over a wide range of biomass concen- bearing cyanobacterium Planktothrix rubescens from other phyto- trations when the same source of Microcystis material was plankton species (Hofmann and Peeters, 2013). Measurements of used. excitation spectra of chlorophyll fluorescence, which can be used to 5.3. Our measurements in Lake Dianchi also reveal strong loga- differentiate between algal “spectral groups” (Beutler et al., 2002), rithmic relationships between sv and the Microcystis-bound and discriminate phytoplankton groups based on their size spectra Chl a concentration (Chl aMicro); however, the sv/Chl aMicro (Angles et al., 2008; Falkowski et al., 1998; Hood et al., 2006; Karp- ratio was not constant. Boss et al., 2007) can complement acoustic sampling. 5.4. The biomass-calibrated echosounder allowed us to monitor Quantification of the relationship between acoustic backscat- the diurnal spatiotemporal dynamics of Microcystis B with tering signals and biomass proxies should be done with caution. high vertical and temporal resolutions. The observed Our study showed that the relationship between sv and Chl aTot can changes in vertical distributions of Microcystis Bwere be biased if high volumes of acoustically transparent algae are apparently associated with temporal variations in thermal present in the water, as algae will contribute only to the Chl aTot but stratification and turbulence level. not to sv. The variability of the sv/Chl aMicro ratio in different water 5.5. In Lake Dianchi, the sv/Chl aMicro ratio varied temporally and bodies suggests that the sv e Chl aMicro relationship should be vertically, with higher values near the water surface. Our 12 I. Ostrovsky et al. / Water Research 183 (2020) 116091

data suggest that the sv/Chl aMicro ratio positively correlates doi.org/10.1089/ees.2012.0318. with (i) the content of free gas in cells and (ii) the percentage Chorus, I., Bartram, J. (Eds.), 1999. Toxic Cyanobacteria in Water. A Guide to Their Public Health Consequences, Monitoring and Management. E&FN Spon, Lon- of larger colonies in population. don. https://doi.org/10.4324/9780203478073. 5.6. An increase in B and sv/Chl aMicro ratio at the water surface Chorus, I., Falconer, I.R., Salas, H.J., Bartram, J., 2000. Health risks caused by fresh- reflects the accumulation of large buoyant colonies that may water cyanobacteria in recreational waters. J. Toxicol. Environ. Health, Part B 3 (4), 323e347. https://doi.org/10.1080/109374000436364. forebode the formation of surface scum in conducive Clay, C.S., Medwin, H., 1977. Acoustical Oceanography: Principles and Applications. weather conditions. We argue that monitoring of this ratio New York. may help to determine the early warning threshold for Cullen, J.J., 1982. The deep chlorophyll maximum: comparing vertical profiles of chlorophyll a. Can. J. Fish. Aquat. Sci. 39 (5), 791e803. https://doi.org/10.1139/ Microcystis scum formation. f82-108. 5.7. The proposed acoustic technology for in situ quantification of Deacon, C., Walsby, A.E., 1990. Gas vesicle formation in the dark, and in light of Microcystis biomass can be a powerful tool for accurate different irradiances, by the cyanobacterium Microcystis sp. Br. Phycol. J. 25 (2), e monitoring and assessment of this cyanobacterium at high 133 139. https://doi.org/10.1080/00071619000650121. Deines, K.L., 1999. Backscatter estimation using Broadband acoustic Doppler current spatiotemporal resolution in water bodies. profilers. In: Proceedings of the IEEE Sixth Working Conference on Current Measurement (Cat. No.99CH36331), San Diego, CA, USA, 13e13 March, pp. 249e253. https://www.comm-tec.com/Library/Technical_Papers/RDI/ echopaper.pdf. Author contributions Falkowski, P.G., Barber, R.T., Smetacek, V., 1998. Biogeochemical controls and feedbacks on ocean primary production. Science 281 (6), 200e206. https:// Ilia Ostrovsky and Lirong Song conceived the research idea; Ilia doi.org/10.1126/science.281.5374.200. Foote, K.G., 1982. Optimizing copper spheres for precision calibration of hydro- Ostrovsky, Lin Li and Sha Wu designed and conducted the experi- acoustic equipment. J. Acoust. Soc. Am. 71 (3), 742e747. https://doi.org/10.1121/ ments, sampling and analysis, and prepared the manuscript. All 1.387497. authors contributed to manuscript revision. Gao, H., Zhu, T., Xu, M., Wang, S., Xu, X., Kong, R., 2016. pH-dependent gas vesicle formation in Microcystis. FEBS Lett. 590 (18), 3195e3201. https://doi.org/ 10.1002/1873-3468.12370. Declaration of competing interest Geider, R., MacIntyre, H., Kana, T., 1997. Dynamic model of phytoplankton growth and acclimation: responses of the balanced growth rate and the chlorophyll a: carbon ratio to light, nutrient-limitation and temperature. Mar. Ecol.: Prog. Ser. The authors declare that they have no known competing 148, 187e200. https://doi.org/10.3354/meps148187. financial interests or personal relationships that could have Godlewska, M., Balk, H., Kaczkowski, Z., Jurczak, T., Izydorczyk, K., Długoszewski, B., ˛ ł fi appeared to influence the work reported in this paper. Jaskulska, A., Gaga a-Borowska, I., Mankiewicz-Boczek, J., 2018. Night sh avoidance of Microcystis bloom revealed by simultaneous hydroacoustic measurements of both organisms. Fish. Res. 207, 74e84. https://doi.org/10.1016/ Acknowledgements j.fishres.2018.05.025. Hajdu, S., Hoglander,€ H., Larsson, U., 2007. Phytoplankton vertical distributions and composition in the Baltic Sea cyanobacterial blooms. Harmful Algae 6, 189e205. We thank Xingqiang Wu, Bowen Gu, Lili Hu, Cuiping Yang, https://doi.org/10.1016/j.hal.2006.07.006. Xiaojie Cai and Shiwei Yan for their efficient technical assistance Hofmann, H., Peeters, F., 2013. In-situ optical and acoustical measurements of the during field and laboratory experiments. We are grateful to Prof. buoyant cyanobacterium P. rubescens: spatial and temporal distribution patterns. PloS One 8 (11), e80913. https://doi.org/10.1371/journal.pone.0080913. Enhua Ni and Dr. Zhi Wang (Institute of Geodesy and Geophysics, Hood, R.R., Laws, E.A., Armstrong, R.A., Bates, N.R., Brown, C.W., Carlson, C.A., CAS) for providing LISST for our measurements. We thank the Chai, F., Doney, S.C., Falkowski, P.G., Feely, R.A., Friedrichs, M.A.M., Landry, M.R., anonymous reviewers and editor for their constructive comments Keith Moore, J., Nelson, D.M., Richardson, T.L., Salihoglu, B., Schartau, M., Toole, D.A., Wiggert, J.D., 2006. Pelagic functional group modeling: progress, and advices. This research was supported by the Joint NSFC-ISF challenges and prospects. Deep Sea Res., Part II 53 (5-7), 459e512. https:// Research Program (NSFC grant 41561144008, ISF grant 2033/15), doi.org/10.1016/j.dsr2.2006.01.025. the Israel Ministry of Science and Technology (MOST grant 3- Hozumi, A., Ostrovsky, I., Sukenik, A., Gildor, H., 2020. Turbulence regulation of 14952) and Major Science and Technology Program for Water Microcystis surface scum formation and dispersion during a cyanobacteria bloom event. Inland Waters 10 (1), 51e70. https://doi.org/10.1080/ Pollution Control and Treatment (2013ZX07102005, 20442041.2019.1637681. 2017ZX07203001). Huber, A.M.R., Peeters, F., Lorke, A., 2011. Active and passive vertical motion of zooplankton in a lake. Limnol. Oceanogr. 56 (2), 695e706. https://doi.org/ 10.4319/lo.2011.56.2.0695. References Iida, K., Mukai, T., Hwang, D., 1996. Relationship between acoustic backscattering strength and density of zooplankton in the sound-scattering layer. ICES J. Mar. Anderson, D.M., 2009. Approaches to monitoring, control and management of Sci. 53 (2), 507e512. https://doi.org/10.1006/jmsc.1996.0073. harmful algal blooms (HABs). Ocean Coast Manag. 52 (7), 342e347. https:// Iriarte, J.L., Gonzalez, H.E., Liu, K.K., Rivas, C., Valenzuela, C., 2007. Spatial and doi.org/10.1016/j.ocecoaman.2009.04.006. temporal variability of chlorophyll and primary productivity in surface waters Angles, S., Jordi, A., Garces, E., Maso, M., Basterretxea, G., 2008. High-resolution of southern Chile (41.5e43 S). Estuar., Coast. Shelf Sci. 74 (3), 471e480. https:// spatio-temporal distribution of a coastal phytoplankton bloom using laser in- doi.org/10.1016/j.ecss.2007.05.015. situ scattering and transmissometry (LISST). Harmful Algae 7 (6), 808e816. Kahru, M., 1997. Using satellites to monitor large-scale environmental change: a https://doi.org/10.1016/j.hal.2008.04.004. case study of cyanobacteria blooms in the Baltic Sea. In: Kahru, M., Brown, C.W. Beecraft, L., Watson, S.B., Smith, R.E.H., 2017. Multi-wavelength Pulse Amplitude (Eds.), Monitoring Algal Blooms: New Techniques for Detecting Large-Scale Modulated fluorometry (Phyto-PAM) reveals differential effects of ultraviolet Environmental Change, pp. 43e61. radiation on the photosynthetic physiology of phytoplankton pigment groups. Karp-Boss, L., Azevedo, L., Boss, E., 2007. LISST-100 measurements of phytoplankton Freshw. Biol. 62 (1), 72e86. https://doi.org/10.1111/fwb.12850. size distribution: evaluation of the effects of cell shape. Limnol Oceanogr. Beutler, M., Wiltshire, K.H., Meyer, B., Moldaenke, C., Lüring, C., Meyerhofer,€ M., Methods 5 (11), 396e406. https://doi.org/10.4319/lom.2007.5.396. Hansen, U.P., Dau, H., 2002. A fluorometric method for the differentiation of Kim, E., Lee, H., Na, J., Choi, J.W., Kang, D., 2010. 5-MHz acoustic-backscatter mea- algal populations in vivo and in situ. Photosynth. Res. 72, 39e53. https://doi.org/ surements of Cochlodinium polykrikoides blooms in Korean coastal waters. ICES 10.1023/A:1016026607048. J. Mar. Sci. 67 (8), 1759e1765. https://doi.org/10.1093/icesjms/fsq093. Bok, T.H., Paeng, D.G., Kim, E., Na, J., Kang, D., 2010. Ultrasound backscattered power Kim, H., Kang, D., Jung, S.W., 2018. Development and application of an acoustic from Cochlodinium polykrikoides, the main red tide species in the Southern Sea system for harmful algal blooms (HABs, red tide) detection using an ultrasonic of Korea. J. Plankton Res. 32 (4), 503e514. https://doi.org/10.1093/plankt/ digital sensor. Ocean Sci. J. 53 (1), 91e99. https://doi.org/10.1007/s12601-017- fbq001. 0061-0. Cherin, E., Melis, J.M., Bourdeau, R.W., Yin, M., Kochmann, D.M., Foster, F.S., Lavery, A.C., Wiebe, P.H., Stanton, T.K., Lawson, G.L., Benfield, M.C., Copley, N., 2007. Shapiro, M.G., 2017. Acoustic behavior of Halobacterium salinarum gas vesicles Determining dominant scatterers of sound in mixed zooplankton populations. in the high-frequency range: experiments and Modeling. Ultrasound Med. Biol. J. Acoust. Soc. Am. 22 (6), 3304e3326. https://doi.org/10.1121/1.2793613. 43 (5), 1016e1030. https://doi.org/10.1016/j.ultrasmedbio.2016.12.020. Li, M., Zhu, W., Gao, L., 2014. Analysis of cell concentration, volume concentration, Chien, Y.C., Wu, S.C., Chen, W.C., Chou, C.C., 2013. Model simulation of diurnal and colony size of Microcystis via laser particle analyzer. Environ. Manage. 53 vertical migration patterns of different-sized colonies of Microcystis employing (5), 947e958. https://doi.org/10.1007/s00267-014-0252-8. a particle trajectory approach. Environ. Eng. Sci. 30 (4), 179e186. https:// Lorke, A., McGinnis, D.F., Spaak, P., Wüest, A., 2004. Acoustic observations of I. Ostrovsky et al. / Water Research 183 (2020) 116091 13

zooplankton in lakes using a Doppler current profiler. Freshw. Biol. 49 (10), Rubio-Rodríguez, U., Villalobos, H., Nevarez-Martínez, M.O., 2018. Acoustic obser- 1280e1292. https://doi.org/10.1111/j.1365-2427.2004.01267.x. vations of the vertical distribution and latitudinal range of schools of small Macaulay, G.J., Scoulding, B., Ona, E., Fassler,€ S.M., Demer, H.e.D., 2018. Comparisons pelagic fish in the Midriff Islands Region, Gulf of California, Mexico. Lat. Am. J. of echo-integration performance from two multiplexed echosounders. ICES J. Aquat. Res. 46 (5) https://doi.org/10.3856/vol46-issue5-fulltext-12. Mar. Sci. 75 (6), 2276e2285. https://doi.org/10.1093/icesjms/fsy111. Simmonds, J., MacLennan, D., 2005. Fisheries Acoustics. Theory and Practice, second Marce, R., George, G., Buscarinu, P., Deidda, M., Dunalska, J., de Eyto, E., Flaim, G., ed. Blackwell Science, Oxford. Grossart, H.P., Istvanovics, V., Lenhardt, M., Morenso-Ostos, E., Obrador, B., Srivastava, A., Singh, S., Ahn, C.Y., Oh, H.M., Asthana, R.K., 2013. Monitoring ap- Ostrovsky, I., Pierson, D.C., Potuzak, J., Poikane, S., Rinke, K., Rodriguez- proaches for a toxic cyanobacterial bloom. Environ. Sci. Technol. 47 (16), Mozaz, S., Staehr, P.A., Sumberova, K., Waajen, G., Weyhenmeyer, G.A., 8999e9013. https://doi.org/10.1021/es401245k. Weathers, K.C., Zion, M., Ibelings, B.W., Jenning, E., 2016. Automatic high fre- Stanton, T.K., Wiebe, P.H., Chu, D., Benfield, M.C., Benfield, M.C., Scanlon, L., quency monitoring for improved lake and reservoir management. Environ. Sci. Martin, L., Eastwood, R.L., 1994. On acoustic estimates of zooplankton biomass. Technol. 50 (20), 10780e10794. https://doi.org/10.1021/acs.est.6b01604. ICES J. Mar. Sci. 51, 505e512. https://doi.org/10.1006/jmsc.1994.1051. Martin, L.V., Stanton, T.K., Wiebe, P.H., Lynch, J.F., 1996. Acoustic classification of Urick, R.J., 1967. Principles of Underwater Sound for Engineers. Tata McGraw-Hill zooplankton. ICES J. Mar. Sci. 53, 217e224. https://doi.org/10.1006/ Education. jmsc.1996.0025. Visser, P.M., Passarge, J., Mur, L.R., 1997. Modelling vertical migration of the Mlouka, A., Comte, K., Castets, A.M., Bouchier, C., Tandeau de Marsac, N., 2004. The cyanobacterium Microcystis. Hydrobiologia 349, 99e109. https://doi.org/ gas vesicle gene cluster from Microcystis aeruginosa and DNA rearrangements 10.1023/A:1003001713560. that lead to loss of cell buoyancy. J. Bacteriol. 186 (8), 2355e2365. https:// Wallace, B.B., Bailey, M.C., Hamilton, D.P., 2000. Simulation of vertical position of doi.org/10.1128/jb.186.8.2355-2365.2004. buoyancy regulating Microcystis aeruginosa in a shallow eutrophic lake. Aquat. Oliver, R.L., Walsby, A.E., 1984. Direct evidence for the role of light-mediated gas Sci. 62 (4), 320e333. https://doi.org/10.1007/pl00001338. vesicle collapse in the buoyancy regulation of Anabaena flos-aquae (cyanobac- Walsby, A.E., 1991. The mechanical properties of the Microcystis gas vesicle. J. Gen. teria). Limnol. Oceanogr. 29 (4), 879e886. https://doi.org/10.4319/ Microbiol. 137, 2401e2408. https://doi.org/10.1099/00221287-137-10-2401. lo.1984.29.4.0879. Walsby, A.E., 1994. Gas vesicle. Microbiol. Mol. Biol. Rev. 58 (1), 94e144. https:// Ona, E., Mitson, R., 1996. Acoustic sampling and signal processing near the seabed: doi.org/10.1128/mmbr.58.1.94-144.1994. the deadzone revisited. ICES J. Mar. Sci. 53 (4), 677e690. https://doi.org/ Wang, C., Wu, X., Tian, C., Li, Q., Tian, Y., Feng, B., Xiao, B., 2014. A quantitative 10.1006/jmsc.1996.0087. protocol for rapid analysis of cell density and size distribution of pelagic and Orr, M.H., Haury, L.R., Wiebe, P.H., Briscoe, M.G., 2000. Backscatter of high- benthic Microcystis colonies by FlowCAM. J. Appl. Phycol. 27 (2), 711e720. frequency (200 kHz) acoustic wavefields from ocean turbulence. J. Acoust. https://doi.org/10.1007/s10811-014-0352-0. Soc. Am. 108 (4), 1595e1601. https://doi.org/10.1121/1.1286883. Warren, J., 2001. In situ measurements of acoustic target strengths of gas-bearing Ostrovsky, I., 2003. Methane bubbles in Lake Kinneret: quantification and temporal siphonophores. ICES J. Mar. Sci. 58 (4), 740e749. https://doi.org/10.1006/ and spatial heterogeneity. Limnol. Oceanogr. 48 (3), 1030e1036. https://doi.org/ jmsc.2001.1047. 10.4319/lo.2003.48.3.1030. Wert, E.C., Dong, M.M., Rosario-Ortiz, F.L., 2013. Using digital flow cytometry to Ostrovsky, I., McGinnis, D.F., Lapidus, L., Eckert, W., 2008. Quantifying gas ebullition assess the degradation of three cyanobacteria species after oxidation processes. with echosounder: the role of methane transport by bubbles in a medium-sized Water Res. 47 (11), 3752e3761. https://doi.org/10.1016/j.watres.2013.04.038. lake. Limnol Oceanogr. Methods 6, 105e118. https://doi.org/10.4319/ Wood, T.M., Gartner, J.W., 2010. Use of Acoustic Backscatter and Vertical Velocity to lom.2008.6.105. Estimate Concentration and Dynamics of Suspended Solids in Upper Klamath Ostrovsky, I., 2009. Hydroacoustic assessment of fish abundance in the presence of Lake, South-Central Oregon: implications for Aphanizomenon flos-aquae: U.S. gas bubbles. Limnol Oceanogr. Methods 7, 309e318. https://doi.org/10.4319/ Geological Survey Scientific Investigations Report 2010e5203, p. 20. https:// lom.2009.7.309. doi.org/10.3133/sir20105203. Ostrovsky, I., Sukenik, A., Yacobi, Y.Z., Katsnelson, B., Uzhanskii, E., Moses, W.J., 2017. Wu, T., Qin, B., Brookes, J.D., Shi, K., Zhu, G., Zhu, M., Yan, W., Wang, Z., 2015. The Hydroacoustic assessment of spatiotemporal dynamics of toxic cyanobacterium influence of changes in wind patterns on the areal extension of surface cya- Microcystis: the role of physical factors in bloom formation. In: UACE2017 - 4th nobacterial blooms in a large shallow lake in China. Sci. Total Environ. 518e519, Underwater Acoustics Conference and Exhibition, pp. 81e86. http://www. 24e30. https://doi.org/10.1016/j.scitotenv.2015.02.090. uaconferences.org/docs/UACE2017_Papers/81_UACE2017.pdf. Wu, X., Kong, F., 2009. Effects of light and wind speed on the vertical distribution of Ostrovsky, I., Uzhanskii, E., Kaganovsky, S., Katsnelson, B., 2018. Implementation of Microcystis aeruginosa colonies of different sizes during a summer bloom. Int. acoustic methodology for investigation of the ecology of gas-containing toxic Rev. Hydrobiol. 3, 258e266. https://doi.org/10.1002/iroh.200811141. cyanobacterium Microcystis sp. In: Proceedings of 2018 Joint Conference Wu, X., Yang, T., Feng, S., Li, L., Xiao, B., Song, L., Sukenik, A., Ostrovsky, I., 2020. Acoustics, pp. 236e239. https://doi.org/10.1109/acoustics.2018.8502288. Recovery of Microcystis surface scum following a mixing event: insights from a Paerl, H.W., Otten, T.G., 2013. Harmful cyanobacterial blooms: causes, conse- tank experiment. Sci. Total Environ. 728 https://doi.org/10.1016/ quences, and controls. Microb. Ecol. 65 (4), 995e1010. https://doi.org/10.1007/ j.scitotenv.2020.138727. s00248-012-0159-y. Yang, Y., Qiu, Z., Hou, X., Sun, L., 2017. Ultrasonic characteristics and cellular prop- Pfeifer, F., 2012. Distribution, formation and regulation of gas vesicles. Nat. Rev. erties of Anabaena gas vesicles. Ultrasound Med. Biol. 43 (12), 2862e2870. Microbiol. 10, 705e715. https://doi.org/10.1038/nrmicro2834. https://doi.org/10.1016/j.ultrasmedbio.2017.08.004. Potiris, E., Frangoulis, C., Kalampokis, A., Ntoumas, M., Pettas, M., Petihakis, G., Yentsch, C.S., Menzel, D.W., 1963. A method for the determination of phytoplankton Zervakis, V., 2018. Acoustic Doppler current profiler observations of migration chlorophyll and phaeophytin by fluorescence. Deep-Sea Res. Oceanogr. Abstr. 10 patterns of zooplankton in the Cretan Sea. Ocean Sci. 14 (5), 783e800. https:// (3), 221e231. https://doi.org/10.1016/0011-7471(63)90358-9. Elsevier. doi.org/10.5194/os-14-783-2018. Zhu, W., Feng, G.Y., Chen, H.M., Wang, R.C., Tan, Y.Q., Zhao, H.R., 2018. Modelling the Qin, B., Zhu, G., Gao, G., Zhang, Y., Li, W., Paerl, H.W., Carmichael, W.W., 2010. vertical migration of different-sized Microcystis colonies: coupling turbulent A drinking water crisis in Lake Taihu, China: linkage to climatic variability and mixing and buoyancy regulation. Environ. Sci. Pollut. Res. 25, 30339e30347. lake management. Environ. Manage. 45 (1), 105e112. https://doi.org/10.1007/ https://doi.org/10.1007/s11356-018-3041-8. s00267-009-9393-6.