JOURNAL OF INTERNATIONAL ACADEMIC RESEARCH FOR ...The fluorescence activated cell sorting (FACS)...

JOURNAL OF INTERNATIONAL ACADEMIC RESEARCH FOR MULTIDISCIPLINARY Impact Factor 3.114, ISSN: 2320-5083, Volume 4, Issue 11, December 2016

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PHYTOPLANKTON COMMUNITY STUDY FROM EUTROPHIC WETLAND EMPLOYING FLUORESCENCE ACTIVATED CELL SORTING METHOD

ANINDITA SINGHA ROY1

DEBAJIT BHOWMICK2 RUMA PAL1

1Phycology laboratory, Department of Botany, University of Calcutta, Kolkata, West Bengal, India

2Centre for Research in Nanoscience and Nanotechnology, Acharya Prafulla Chandra Roy Sikhsha Prangan, University of Calcutta, Kolkata, West Bengal, India

Abstract

The fluorescence activated cell sorting (FACS) method was used for a rapid

screening, sorting and counting of phytoplankton populations from a eutrophic wetland. The

abundance of phytoplankton population was estimated to be 8.34 ×10^4 cells/µL. Around 25

different phytoplankton genera belonging to different groups like chlorophytes,

cyanobacteria, bacillariophytes and euglenophytes were sorted and identified. Identified

genera included Cosmarium sp., Chlorococcum sp., Tetraedron spp., Tetrastrum sp.,

Scenedesmus spp., Chlorella sp., Kirchneriella spp., Crucigenia spp., Chlamydomonas sp.,

Ankistrodesmus spp., Centritactus sp., Selenastrum sp., Pediastrum spp., Lepocinclis spp.,

Trachelomonas spp., Phacus spp., Spirulina spp., Synechococcus sp., Synechocystis sp.,

Anabaenopsis spp. Rhabdoderma sp., Chroococcus sp., Planktolyngbya spp., Merismopedia

spp., Pseudoanabaena sp., Cyanarcus sp. Some planktonic groups were also sorted and

designated as unknown species (US). Also Shannon diversity index (H’) was calculated

which helped inferring the diversity of the selected wetland to be moderately high. Overall

the FACS process was found to be an alternative, fast, accurate and efficient method for

phytoplankton counting and diversity determination.

Keywords: Autofluorescence, Fluorescence Activated Cell Sorting (FACS), Flow Cytometry

(FCM), Wetland, Photopigments, Phytoplankton.

Introduction

Phytoplankton community is a dynamic functioning component of an aquatic

ecosystem having an important role to play. Their abundance and diversity pattern are

essential in water quality assessment, including the influence of environmental variables

(Palmer 1956; Sladacek 1973; Bahura 2001; Naselli-Flores et al. 2013). The species

composition of phytoplankton also provides information about nutrient status like

eutrophication or pollution level of that particular aquatic ecosystem (Willen 1976; Naselli-


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Flores et al. 2013). In order to obtain a reliable information on the phytoplankton abundance,

different methods like light microscopic cell counting method (Willen 1976), chlorophyll-a

estimation (Arnon 1949; Strickland and Parsons 1972), sedimentation technique (Utermohl

1925, 1931, 1958), etc can be applied. Chlorophyll-a estimation describes the total biomass

of the phytoplankton community, but cannot decipher the species diversity which is a big

demerit for statistical analysis of biotic and abiotic variables of aquatic ecosystem. Light

microscopic field study is superlative regarding total count, species diversity index

determination and identification also. Nevertheless, there may always be a chance for

misinterpretation of the water quality assessment due to erroneous manual counting,

especially in ecosystem with very high plankton count.

With the development of Florescence Activated Cell Sorting (FACS), an

alternative and attractive plankton counting method has emerged recently as compared to

other methods cited above. FACS grabs a greater advantage over light microscopy due to

rapid cell counting along with the physical separation of the cells from a mixed assemblage

(Trask et al. 1982; Phinney and Cucci 1989; Veldhuis and Kraay 1990; Partensky et al.

1999). Applications of standard techniques of FACS in the field of maritime research are

numerous and expanding (Trask et al. 1982; Phinney and Cucci 1989; Lange et al. 1996),

mainly focusing on oceanographic work (Olson et al. 1985; Veldhuis and Kraay 1990;

Campbell et al. 1997; Marie et al. 1997; Chisholm et al. 1988; Partensky et al.1999; Sciandra

et al. 2000; Veldhuis and Kraay 2000; Jacquet et al. 2001; Shalapyonok et al. 2001;

Dandonneau et al. 2006). Generally, two inherent properties of the aquatic cells like the size

and photosynthetic pigment fluorescence are taken into consideration for addressing the

ecological questions. Allometric (size versus pigment fluorescence) and ataxonomic (two-

colour pigment fluorescence) analyses provide an estimate of particle concentration which is

helpful in terms of characterizing aquatic plankton assemblages (Trask et al. 1982; Phinney

and Cucci 1989). FACS requires small sample volumes, but allows more samples and less

sub-sampling to obtain good statistical significance (Dubelaar and Jonker 2000). Thus, along

with rapid analysis of large number of cells from small sample volumes, FACS permits more

significant statistical data analysis very easily, thereby minimizing errors associated with

manual calculation and increasing higher chances of accuracy in data accumulation

(Gisselson et al. 1999; Dubelaar and Jonker 2000).

From previous reports it is evident that FACS is a powerful tool for studying

phytoplankton ecology. Establishment of monocultures of eight algal species monitoring the


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optical properties of the samples such as Forward Light Scatter (FLS), Perpendicular Light

Scatter (PLS), chlorophyll fluorescence, etc. by Trask et al. (1982) brought into light a new

application of FACS. Combination of parameters made it evident that all the eight species

could be resolved separately. Phinney and Cucci (1989) also succeeded in studying

phytoplankton diversity by considering two inherent properties which is size and

phytoplankton pigment fluorescence. The discovery of one of the smallest known

phytoplankton Prochlorococcus marinus to be abundant in all major subtropical oceans in

terms of both biomass and productivity by Veldhius and Kraay (2000) was another milestone

of FACS application. The observations were based on the combination of parameters like

size, autofluorescence, DNA-specific fluorescent staining, etc. Li and Dickie (2001) also

observed the predominance of phytoplankton including Synechococcus and Cryptophytes in

Bedford Basin surface. The results were based on chlorophyll auto fluorescence and cell size.

Recently, Cellamare et al. (2009) was successful to screen the phytoplankton community

from four different freshwater aquatic ecosystems where forty-five phytoplankton taxa were

isolated, including green algae (29), cyanobacteria (8), diatoms (7), and cryptomonad (1) with

the help of FACS. However, FACS study of phytoplankton community especially in

freshwater environment is limited (Crosbie et al. 2003; Tijdens et al. 2008; Toepel et al.

2004, 2005; Cellamare et al. 2009).

In the present investigation, suitability of live-cell sorting flow cytometry was

assessed for quantitative analysis and diversity determination of phytoplankton community

from a freshwater eutrophic wetland of Ramsar site, India. The natural samples were directly

analyzed without any prior culture maintenance. Being eutrophic, this wetland harbors a

diverse and abundant phytoplankton assemblage. Thus, light microscopic counting becomes

time consuming and with high chance for manual error. The alternative method of FACS was

used for swift and accurate counting of the huge phytoplankton population along with

physical separation or sorting of cells using proper gating parameters based on the

fluorescence pattern and cell size. The number of events occurring and the duration of

analysis were recorded for estimating the cell concentration.

Materials and methods:

Study site:

The investigation was carried out in freshwater eutrophic wetland, commonly known

as ‘East Kolkata wetland’ (EKW), declared as the “Wetland of International Importance” by


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Government of India and “Ramsar Site no. 1203” in Ramsar Convention in 19th August 2002

(Ramsar Secretariate 2013). It harbors one of the oldest traditions of resource recovery from

the Kolkata Municipal waste. The geographic location of the studied site was 88° 24.641′ east

latitude and 22° 33.115’ north latitude, as obtained from GARMIN GPS map 76 CSx device.

Flow cytometric determination:

The sample water was collected in an amber bottle after filtering through

approximately 100 µm pore sized phytoplankton net. The filtered samples were brought to

the laboratory in cold condition. The light scattering and the fluorescent properties of the

unicellular organisms were determined in Fluorescence Activated Cell Sorter (FACS), BD

FACS Aria Tm III which could measure up to 70,000 particles/sec. The machine was equipped

with a 100µ nozzle, and the flow rate of the sample was maintained at 6µL/ min (lowest

possible). Sheath pressure was adjusted to 20 Psi. Available laser emissions of 488nm,

642nm and 375nm were set as light sources. Forward scatter (FSC) indicates the cell size and

shape while the side scatter (SSC) is indicative of cell granularity, size and refractive index.

Scattered light (a sizing parameter) was measured at FSC (Filter-488/10), SSC (Filter-488/15)

channels. Emitted fluorescence was detected and distributed among the filters viz. FITC

(Filter-530/30), PE (Filter-585/42), PE-Texas Red (Filter-616/23), Per CP-Cy 5-S (Filter-

780/60) and APC (Filter- 660/20). The following emission filters represent wavelengths of

different major photo pigments’ auto-fluorescence range. For instance, chlorophyll-a on

excitation at 488nm, emits fluorescence at around 685nm (Red fluorescence) (Trask et al.

1982; Phinney and Cucci 1989). This wavelength corresponds to PE-Texas Red (Filter-

616/23) of the instrument. Again phycoerythrin on excitation at 488nm or 550 nm, emits

orange fluorescence at 560-585nm (Trask et al. 1982; Phinney and Cucci 1989) which

corresponds to PE (Filter-585/42) of the instrument. Similarly, another important

photopigment phycocyanin has blue-green fluorescent emission at around 670nm on being

excited at 640nm (Dubelaar and Jonker 2000). This emission resembles that of APC (Filter-

660/20) or PE-Texas Red (Filter-616/23) of the instrument. Orange to yellowish orange

fluorescence of carotenoid pigments have a resemblance to that of FITC (Filter-530/30) of

the instrument used (Phinney and Cucci 1989). All parameters were adjusted to logarithmic

scale. The duration of analysis and number of events occurring were recorded for estimating

the cell concentration. After analyzing, different types or sub-communities were gated based

on florescence patterns and size and then desired gates were sorted (Fig.1). Cell sorting was


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done following 4 way sorting precision and the total sub communities obtained are

represented in Fig.1. The choice of gating parameters like FSC-A versus FSC-H or APC-A

versus PE-Texas Red-A was absolutely a trial and error method to distinguish the clusters of

sub-communities. Afterwards, light microscopic observations were carried out for

identification of individual genus from the sorted sub-communities. Each community was

represented by different colour in order to distinguish them from one another. Total number

of 1,300,000 events or cells (Fig.1a or black box of Fig.1j) represented the entire community.

Of the total events, P1 community (Fig.1a) was selected as the mother community. The P1

community was plotted separately (Fig.1b,c) and was further divided downstream into

community P2 and P3 in one cytograms based on PE-Texas Red (Filter-616/23) and APC

(Filter- 660/20) (Fig.1b) and into P4, P5, P6 and P7 taking PE (Filter-585/42) and Per CP-

Cy5-S (Filter- 780/60) into consideration in another cytogram as parameters for subdivision

(Fig.1c). Numbers of cells/events in P2, P3, P4, P5, P6 and P7 have been displayed in Fig.1j.

Communities P2 and P3 were found to occur as distinct small clusters and thus were not

classified further. However, communities P4, P5, P6 and P7 were further separately plotted in

different graphs based on different parameters as shown in Fig. 1(d to g respectively and j).

For instance, P4 community was plotted (Fig.1d,j) taking PE-Texas Red and APC into

consideration and was classified into distinct colonies of P13, P14, P25, P26 and P27. The

number of cells occurring in each sub-communities of P4 is represented in Fig.1j, along with

the percentage of occurrence. Similar attempts were taken for communities P5, P6 and P7

using sets of parameter for downstream subdivision as depicted in Fig.1(e to j).The P5

community showed to be further classified into sub-communities like,P8 and P9

(Fig.1e,j),whereas P6 community showed divisions of P15, P16, P17 and P18 sub-

communities (Fig.1 f, j) and lastly P7 was subdivided into P11 and P12 sub-communities

(Fig.1g,j). It was found that communities P8 and P12 could be further subdivided for better

analysis. But P8 (Fig.1h,j) was classified downstream into P19, P20 and P21. Sub-community

P12 (Fig.1i,j) also showed divisions of P22, P23 and P24 sub-communities as separate

distinct clusters. Thus, based on the above mentioned gating parameters, the cells were gated

and sorted into 19 sub-communities. After cell sorting they were collected on a multiwell

(99-welled) microplate. The microplate with the sorted samples was checked under light

microscope (BD Pathway 855), where 3x3 montage captured images were taken for the final

19 subpopulations to study the diversity and the number of the phytoplanktons (Fig.2,

representing a sorted sub-community P9). Further, microscopic identification was done


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employing proper phytoplankton monographs (Prescott 1982; Desikachary 1959; Komarek

and Anagnostidis 1999, 2005, 2013; Anand 1998; Jaiswal and Tiwari 2003; Rath and

Adhikari 2005) and confirmed from Algaebase (Guiry and Guiry 2015). Fig. 3 illustrates the

different phytoplanktons that were gated and sorted under BD FACS Aria Tm III, viewed and

identified under low, high and oil immersion objective of Carl-Zeiss Axiostar microscope.

Microphotographs of these samples were taken using a digital camera (Canon T2-T2 1,6x

SLR 426115). In the present communication only generic identification was included.

Phytoplankton community study:

Diversity index was determined for the phytoplankton community study by using

Shannon Wiener Index (H’). The basic formula is:

H' = -sΣi=1 pi log pi,

Where; the pi was estimated from ni/N as the proportion of the total population of

individuals (N) belonging to the ith species (ni), s equals the total number of species in the

sample (Shannon and Weaver 1948; Magurran 2004).

Results:

From the phytoplankton community of the studied eutrophic wetland, the algal and

cyanobacterial genera were sorted based on pigment fluorescence and size variation and

finally identified upto genus level by light microscopy (Table1). A total of 26 phytoplankton

genera have been identified. Most of them belonged to Chlorophytes (13 genera), followed

by Cyanobacteria with 9 genera and Euglenophytes (3 genera). The Bacillariophytic members

were recognised as a single group Bacillariophyta. Some unknown taxa were also recognized

as unknown species (US). Among the Chlorophytes, the dominant algal genus obtained was

Scenedesmus sp. (13.45% of the total phytoplankton population) followed by Chlorella sp.

(9.8% of the total). This entire process of sorting and identification of different phytoplankton

genera was based on the different auto-fluorescent properties of the chromophores. The

community P1 (Fig. 1a,j) was the main mother population taken into consideration for

analysis, from which 6 communities P2 to P7 were derived separately on the basis of

different parameters as mentioned above. These communities were further clustered into

distinct sub-communities P8 to P27 (Fig.1a to j). The genera sorted from each community are

listed in Table 1. For instance, in community P9, 12 different microplanktonic genera were

recorded viz. Chlorococcum sp. (9.30%), Tetrastrum spp. (4.65%), Scenedesmus spp.


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(20.93%), Chlorella sp. (9.30%), Kirchneriella sp. (6.97%), Ankistrodesmus spp. (2.33%),

Spirulina spp. (23.25%), Synechococcus sp. (2.33%), Synechocystis sp. (2.33%), US 2

(2.33%), US 3 (2.33%) and Diatom (18.6%). This community (P9) comprised a mixture of

phytoplankton belonging to phylum Chlorophyta, Cyanoprokaryota and Bacillariophyta.

However, community P13 was again purely picoplanktonic community (US4). Another

community P25 comprised of two taxa, Spirulina spp. (16.67%) and Synechococcus sp.

(83.33%), both of which belong to the same Cyanoprokaryotic phylum. The band-pass filters

for gating used were APC-A (660nm) versus PE- Texas Red-A (616nm). Both the filters

belonged within the range of fluorescence emission of the photopigment phycocyanin, one of

the major pigment constituent of Cyanobacteria. In other words, P25 comprised of

Cyanobacteria.

A total number of 200,000 cells were recorded in 24 seconds and the flow rate of the

sample used was 6µL/ min (lowest possible). The total phytoplankton concentration of 8.34

×10^4 cells/µL was obtained and the Shannon diversity index (H’) was calculated as 2.57,

indicating moderately high abundance of species assemblages.

Discussion:

The efficiency, accuracy and rapidity in counting phytoplankton communities makes

FACS an alternative method and many fold advantageous over other methods of ecological

analysis (Li 1995; Dubelaar and Gerritzen 2000). Waters with varying degree of pollution or

eutrophication contain phytoplankton communities with varied species composition in

different level of abundance. The expression of species diversity derived from Shannon's

formula (H’) typically range between 1.5 and 3.5 (Lloyd and Ghelardi 1964). An increase in

the number of species and a tendency towards a more equal distribution of individuals among

species can result in higher values of H' (Shannon and Weaver 1948; Sager and Hasler 1969).

The Shannon index obtained from the present study thus interprets the eutrophic freshwater

habitat to harbor a diverse and an uneven number of species compositions.

In the present investigation the natural heterogenous phytoplankton community was

exploited based on their size and autofluorescence property of the algal pigments. A diverse

array of pigments do occur among different algal groups such as chlorophyll (a, b, c and d),

carotenoids, phycobilliprotiens (phycocyanin, phycoerythrin and allophycocyanin),

xanthophylls, luciferin and many more. The algal phyla are characterized by particular

pigment composition. For example, Cyanoprokaryota comprises chlorophyll a as major


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pigment, together with carotenoids and phycobilliproteins (Rowan 1989; Lee 2008). Different

phyla of eukaryotic algae contain different pigment compositions having one or few pigments

as major. The phylum Chlorophyta is characterized by chlorophyll a and chlorophyll b along

with carotenoids, Rhodophyta - phycoerythrin and chlorophyll d, Bacillariophyta –

fucoxanthin, whereas Euglenophyta lack chlorophyll c, but contains chlorophyll a, b,

neoxanthin and diadeoxanthin (Rowan 1989; Lee 2008). Every pigment has a particular

fluorescent excitation and emission spectra. The instrument BD FACS Aria Tm III is usually

equipped with different optical filters like FITC (530/30), PE (585/42), PE-Texas Red

(616/23), Per CP-Cy 5/7 (780/60), APC (660/20), thereby detecting the fluorescent

properties. Using these various optical filters, a total of about 19 subpopulations had been

gated into distinct clusters. Some communities obtained were found to be pure up to generic

level (P13), some up to phylum level (P11, P25), while some with a mixture of members

belonging to different phyla (P3, P9, P14-P17, P19-P24 and P26). The concentration of

phytoplankton community obtained from the above observation was 8.34x 10^4 cells/µL. The

present wetland (EKW) under study showed the dominance of Chlorophytic members

followed by the members of Cyanobacteria, Bacillariophyta and Euglenophyta. The genus

found to be predominating was Scenedesmus sp. The phytoplankton diversity recorded was a

useful indicator for water quality assessment which was in conjunction to the reports of

Pradhan et al. (2008) Further; the putative role of the identified genera in bioremediation

from previous studies of Pradhan et al. (2008) helped this wetland to be worth for sewage

reclamation of the Metropolitan city.

Future application of FCM include more fine modification of gating principles and

further subdivisions which could ultimately help in sorting fully pure algal genera or species

also. This could be of immense help in grounds of pure strain isolation by FACS from

phytoplankton population of natural habitat, including further molecular studies and

molecular taxonomic classification.

Conclusions:

In the present investigation, the trial for accurate and rapid counting of phytoplankton

including their diversity index study was successful. This helped in indicating the actual

phytoplankton abundance of particular ecosystem with their diversification. Therefore, this

approach based on a combination of FACS followed by microscopy would be an alternative


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approach for phytoplankton counting and diversity study of any natural habitat very acutely

within a short time.

Acknowledgements:

We would like to thank University Grant Commission (UGC), New Delhi, India for

their financial assistance. We would also like to thank Department of Botany and Center for

Research in Nanoscience and Nanotechnology (CRNN) of University of Calcutta for

instrumental facilities.

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Figure legends:

Figure 1: Bivariate scatter plots analyzed using FACSort flow cytometry, showing splitting

of the main population (P1) into its subpopulations (P2-P24) (a-i) based on auto-fluorescence

and their cell size and their percent of occurrence of the total recorded population (j).

Figure 2: A 3x3 montage capture using light microscope (BD Pathway 855) showing the

sorted population P15 (comprising different algal genera belonging to single phylum-

Cyanoprokaryota).

Figure 3: Microphotographs of phytoplankton sorted and identified from a eutrophic wetland

(10μm scale): 1. Cosmarium sp., 2. Chlorococcum humicola, 3. Tetrastrum triangulare, 4.

Chlorella sp., 5. Kirchneriella sp., 6.Tetraedron minimum, 7. T. muticum, 8. T. trigonum var.

gracile, 9. T. trigonum, 10. T. caudatm var. longispinum, 11. Centritactus dubius , 12.

Scenedesmus dimorphus, 13. S. quadricauda, 14. S. armatus var. bicaudatus, 16. S.

denticulatus, 17. S. itascaensis, 18. Crucigenia crucifera, 19. C. tetrapedia, 20. C. apiculata,

21. C. quadrata, 22. Pediastrum tetras var. apiculatum, 23. P. boryanum, 24. P. tetras, 25.

Selenastrum bibraianum, 26. Ankistrodesmus falcatus, 27. A. tumidus, 28. A. falcatus var.

stipitatus, 29. A. convolutes, 30. Lepocinclis globulus, 31. Trachelomonas volvocina, 32.

Phacus tortus, 33. P. Nordstedii, 34. Spirullina nordstedii, 35. S. subtilissima, 36.

Merismopedia minima, 37. M..glauca, 38. Planktolyngbya contorta, 39. Chroococcus

dispersus, 40. Synechocystis aquatilis, 41. Synechococcus elongatus, 42. Rhabdoderma

irregulare, 43. Pseudoanabaena catenata, 44. Microcystis sp., 45. Coelosphaerium pallidum,

46. Cyanarcus hamiformis.


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Table 1: List of phytoplankton diversity observed in different sorted populations:-

Sorted Population

Diversity observed from microscope

P3 Cosmarium (8.9%); Cyanophytic population (4.16%); US 1(87.5%) P9 Chloroccum (9.30%); Tetrastrum (4.65%); Scenedesmus (20.93%); Chlorella

(9.30%; Kirchneriella (6.97%); Ankistrodesmus (2.33%); Spirulina (23.25%); Synechococcus (2.33%); Synechocystis (2.33%); US 2 (2.33%); US 3 (2.33%); Diatom (18.6%)

P11 US 2 (16.67%); Trachelomonas (83.33%) P13 US 4 (100%) P14 Tetrastrum (2.99%); Scenedesmus (4.47%); Tetraedron (2.99%); Crucigenia

(2.99%); US 5 (15.40%); Synechococcus (61.19%); Rhabdoderma (2.99%); Chroococcus (2.99%)

P15 Kirchneriella (1.81%); Crucigenia (7.88%); Chlamydomonas (0.61%); Spirulina (1.2%); Synechococcus (3.03%); Synechocystis (8.48%); Chroococcus (12.12%); Planktolyngbya (1.81%); Merismopedia (7.27%); Pseudoanabaena (0.61%); Cyanophytic population (41.25%)

P16 Crucigenia (17.65%); Ankistrodesmus (2.94%); Selenastrum (8.82%); Spirulina (5.88%); Synechococcus (11.76%); Planktolyngbya (8.82%); Merismopedia (26.47%); US 2 (11.76%); Cyanophytic population (35.29%)

P17 Tetrastrum (3.57%); Chlorella (75%); Ankistrodesmus (3.57%); Selenestrum (10.71%); Diatom population (7.14%)

P19 Tetrastrum (1.47%); Scenedesmus (33.82%); Chlorella (36.76%); Tetraedron (5.88%); Spirulina (1.47%); Synechocystis (13.24%); Rhabdoderma (1.47%); Merismopedia (1.47%); Diatom (4.41%)

P20 Chlorococcum (21.51%); Tetrastrum (2.15%); Scenedesmus (24.73%); Chlorella (11.83%); Kirchneriella (1.08%); Ankistrodesmus (1.08%); Centritactus (1.08%); Selenastrum (3.23%); Rhabdoderma (2.15%); Lepocinclis (3.23%); Diatom population (27.96%)

P21 Cosmarium (2.45%); Chlorococcum (7.35%); Tetrastrum (1.22%); Scenedesmus (13.88%); Chlorella (4.08%); Kirchneriella (2.45%); Tetraedron (0.41%); Crucigenia (1.63%); Selenastrum (1.22%); Pediastrum (2.04%); Diatom (63.67%)

P22 Scenedesmus (33.33%); Spirulina (8.3%); Merismopedia (8.3%); Trachelomonas (16.67%); Diatom (33.33%)

P23 Scenedesmus (33.33%); Trachelomonas (11.11%); Diatom (33.33%) P24 US 1 (70%); Rhabdoderma (15%); Diatom (15%) P25 Spirulina (16.67%); Synechococcus (83.33%) P26 Ankistrodesmus (16.98%); Synechococcus (20.75%); Merismopedia (35.85%);

US 2 (13.21%); Cyanophytic population (8.49%)


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Figure 1:


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Figure 2


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