Anindita Singha Roya,
Prakash Chandra Goraina,
Ishita Paulb,
Sarban Senguptaa,
Pronoy Kanti Mondalc and
Ruma Pal*a
aPhycology Laboratory, Department of Botany, University of Calcutta, 35, Ballygunge Circular Road, Kolkata – 700019, West Bengal, India. E-mail: rpalcu@rediffmail.com; Tel: +91-9433116320
bAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur – 721 302, India
cHuman Genetics Unit, Indian Statistical Institute, Kolkata – 700108, West Bengal, India
First published on 5th March 2018
Phytoplankton diversity, their abundance based on flow cytometric (FCM) analysis and seasonal nutrient dynamics were investigated from a waste water fed wetland of Eastern India (88° 24.641′E and 22° 33.115′N). The primary objective of the study was to correlate the seasonal fluctuations in phytoplankton abundance to the environmental variables. Total chlorophyll content and FCM based cell counts were used to characterize and quantify the phytoplankton population. Multivariate statistical methods were employed in predicting the possible relationships between biotic and abiotic variables. Distinct seasonal variations characterized by high abundance during the pre-summer period compared to other seasons were detected. The results indicated that environmental factors like water temperature and nutrients, such as various forms of nitrogen and phosphate, influenced the seasonal phytoplankton accumulation. Cluster analysis and non-metric multidimensional scaling helped analyze the seasonal distribution of phytoplankton based on their composition. The dominant genera among the entire phytoplankton community were Scenedesmus spp. of Chlorophyta, followed by Merismopedia spp. of Cyanoprokaryota. Around 3.7 × 105 phytoplankton mL−1 were recorded during the study period. Due to the very high count of individual species in the community, FCM based counting was applied for determination of Species Diversity Index. The entire population was divided into 13 subpopulations based on the cell sorting method and the seasonal abundance in each sub-population was illustrated.
Various methods like microscopic cell counting, chlorophyll estimation, biomass estimation, etc. have been exploited for the quantification and characterization of phytoplankton communities as an index of water quality. During the past few decades, flow cytometry (FCM) has been recognized as a potent tool for the study of phytoplankton ecology, especially for studying spatial and seasonal trends.10,19–21 Due to the auto-fluorescent properties of the phytoplankton, mixed aquatic populations can be discriminated with the help of FCM.22 Generally, allometric and taxonomic analyses of FCM data contribute to characterization of plankton assemblages.23,24
Besides open oceans, documentation of standing crop of phytoplankton from different wetlands and their ecological factors have also been carried out throughout the world by various authors in North American Great Plains,25 southern coastal areas of North America,26 Eastern England,14 Eastern Europe,27 Southern Africa28 etc. Wetlands are ideal habitats for phytoplankton, which act as nutrient sinks, flood control buffers and breeding grounds for aquatic fauna.29 Some noteworthy works on the nutrient dynamics study related to phytoplankton productivity from fresh water wetlands are available.11,13,30
In India wetlands are economically important and are mainly used for fish cultivation; moreover, they have distinct architecture, resulting in extensive purification of waste waters.31 Pradhan et al.,32 suggested that phytoplankton growth could be an important factor for greater fish production and could also act as a biomonitor for water quality assessment in the wetland ecosystems. The wetlands of eastern India represent one of the world's largest integrated resource recovery practice based on a combination of aquaculture, agriculture and horticulture practices.
The wetland currently under study is a wastewater fed aquaculture pond of East Kolkata Wetlands (EKW) of Eastern India – a Ramsar site. Here phytoplankton-nutrient dynamics have direct role in fish production together with natural carbon sequestration.32 Out of 26 wetlands in India, this wetland is one of the world's largest and oldest integrated resource recovery system based on aquaculture production.31 There have been some sporadic reports regarding waste water management at the EKW.31–35 The phytoplankton diversity of the EKW has already been reported by some of the present authors.36–38 In the present investigation an attempt has been taken to explain the seasonal variations in phytoplankton population in response to changes in environmental variables of EKW with special emphasis on Flow Cytometry based cell sorting methods.
The water temperature (temp.) and transparency (transp.) were recorded in situ using a Celsius thermometer and a secchi disc respectively. In the laboratory, pH was measured using an electronic pH meter. Different chemical parameters including nitrate (NO3−) (phenol disulphonic acid method), nitrite (NO2−) (diazotization method), dissolved inorganic phosphate (DIP) (ammonium molybdate method), dissolved inorganic silicate (DSi) (molybdosilicate method), ammonium nitrogen (NH4+) (phenate method) and hardness (EDTA titration method) were measured spectrophotometrically following the standard protocols of APHA.40
Dissolved oxygen (DO) was measured using Winkler iodometric titration method41 using the formula:
The gross primary productivity (GPP), net primary productivity (NPP) and community respiration rate (CRR) were determined following light and dark bottle method after 3 hours incubation. Productivity rates were determined by converting DO to carbon equivalence using photosynthetic quotient of 1.2 and respiration quotient of 1.0. Productivity values were determined from the following formulae:
The chlorophyll (chl)concentration was measured spectrophotometrically after extraction in 90% acetone.42
Fig. 2 Bivariate scatter plots analyzed using FACSort flow cytometry, showing gating of phytoplankton population based on pigment auto-fluorescence and their cell size. |
The optical filters other than FSC and SSC transmitted autofluorescence wavelengths emitted by different major photo-pigments excited by lasers. For instance, chlorophyll-a on excitation at 488 nm, emits red fluorescence at around 685 nm which can be transmitted through PE-Texas Red (Filter-616/23) filter. Again, phycoerythrin on excitation at 496 nm, emits orange fluorescence at 560–585 nm transmitted through PE (Filter-585/42) filter.43,44 Similarly, another important photo-pigment, phycocyanin, emits blue-green fluorescence at around 670 nm on being excited at 650 nm, transmitted through APC (Filter-660/20) or PE-Texas Red (Filter-616/23) filters.45 Red autofluorescence (at 695 nm) of peridinine–chlorophyll–protein complex within photosynthetic apparatus excited by 482 nm radiation was transmitted through Per CP-CY5-5-A (695/40) filters.46 Each P1 was gated and sorted into smaller entities (P4, P5, P6 and P7) based on two-colour pigment fluorescence (Fig. 2).21 Sorted entities (P4–P7) were subdivided on the basis of two-colour pigment fluorescence using a different set of filters, so that 13 distinct and consistent sub-populations (P8, P9, P11–P18, P25–P27) were obtained. These sub-populations were studied for a 24 month period (October 2013–September 2015) to find out seasonal variation patterns among the phytoplankton assemblages.
The gating of the entire population into subpopulations was followed by cell sorting of those gated populations in 4 way sorting precision. After cell sorting each sub-population (P8, P9, P11–P18, P25–P27) was collected on a microplate. The microplate with the sorted samples was identified under light microscope (BD Pathway 855), showing the different phytoplankton taxa obtained. Flow cytometric determination of abundance of each phytoplankton sub-population in each sample was estimated using the formula: N = (n × 1000) (q × t)−1, where q is the flow rate (μL min−1), t is the duration of analysis, n is the number of events counted by the FACS, and N is the number of cells per milliliter.
The diversity index of the phytoplankton community study was determined using Shannon Wiener's Index (H′), species richness and species evenness (e).47
Fig. 3 Variations in monthly average values of physical parameters (a) pH, (b) water temperature and (c) water transparency of the study area. |
The dissolved inorganic nitrogen (DIN) concentration of the habitat water (Fig. 4a) varied from 441.80 to 2112.03 μM. The DIN was found to occur in natural waters in various forms, including NO3−, NO2− and NH4+ with NO3− as the most common form. The values of NO3−, NH4+ and NO2− contents of the sample water ranged from 411.45 to 2076.49 μM, 1.37 to 23.67 μM and 6.52 to 101.74 μM respectively, with maximum concentrations during the summer season (Fig. 4b–d). The DIP concentration obtained was maximum in May 2012 (4.47 μM) and minimum during the winters (0.45 μM) (Fig. 4e). The concentrations of DIN and DIP validated the eutrophic status of the water body.49 It was apparent from Fig. 4f that the DSi concentration ranged from 29.42 to 91.18 μM showing maxima in summer season. The sample water was hard, with hardness values ranging between 69.00 to 210.00 mg CaCO3 L−1 (Fig. 4g).
Phytoplankton biomass was estimated in terms of chl (chlorophyll) content. The chl content of sampled water ranged from 0.095 to 0.501 mg L−1 (Fig. 5a) with values higher during the pre-summer and lower during the post-monsoon for both years. In addition to the estimation of the phytoplankton abundance, their photosynthetic activity was also determined in terms of DO. The DO level of the sample water varied from 3.24 mg L−1 to 8.14 mg L−1 following similar trend in seasonal variation as that of chl content (Fig. 5b), and showing maximum values in winter. The BOD value ranged from 1.44 to 6.0 mg L−1 (Fig. 5b). As evident from Fig. 5c, the values of GPP, NPP and CRR ranged from 283.61 to 2147.71 mgC m−3 h−1, from 114.11 to 1471.86 mgC m−3 h−1 and from 26.00 to 976.67 mgC m−3 h−1 respectively. The GPP values were higher than NPP, thereby establishing a positively productive ecosystem (Fig. 5c). Maximum productivity (GPP) was recorded in the month of March 2014 and minimum productivity was recorded in August 2015.
Fig. 5 Variations in monthly average values of (a) chlorophyll, (b) DO and BOD, (c) GPP, NPP, CRR of the experimental site. |
Correlation matrix (Table 1) based on Pearsonian r values (N = 48) revealed that chl had significant negative correlation with temp (r = −0.644) and hardness (r = −0.359); while very weak to moderately negative correlations with pH (r = −0.018), NO3− (r = −0.148), DIN (r = −0.115). However, significant positive correlations were obtained between chl and NH4+ (r = 0.489), NO2− (r = 0.627), Dsi (r = 0.511), while with DIP (r = 0.029) a weak positive correlation was obtained.
Chl | pH | Temp | Transp | DO | BOD | GPP | NPP | CRR | NO3− | NO2− | NH4+ | DIN | Dsi | DIP | Hardness | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a Correlation is significant at the 0.05 level (2-tailed), r= Pearson correlation, N = 48. | |||||||||||||||||
Chl | 1 | −0.018 | −0.644a | −0.237 | 0.713a | 0.623a | 0.474a | 0.536a | 0.419a | −0.148 | 0.627 | 0.489a | −0.115 | 0.511a | 0.029 | −0.359a | |
pH | −0.018 | 1 | 0.415a | 0.356a | 0.019 | −0.055 | 0.102 | −0.054 | −0.196 | 0.780a | −0.092 | 0.354a | 0.786a | 0.501a | 0.499a | −0.022 | |
Temp | −0.644a | 0.415a | 1 | 0.357a | −0.605a | −0.522a | −0.243 | −0.352a | −0.237 | 0.625a | −0.707a | 0.106 | 0.595a | 0.098 | 0.519a | 0.187 | |
Transp | −0.237 | 0.356a | 0.357a | 1 | −0.278 | 0.138 | −0.316a | −0.389a | 0.094 | 0.448a | −0.134 | −0.182 | 0.445a | −0.095 | −0.040 | −0.304a | |
DO | 0.713a | 0.019 | −0.605a | −0.278 | 1 | 0.791a | 0.514a | 0.711a | 0.299a | −0.180 | 0.567a | 0.279 | −0.151 | 0.199 | −0.158 | −0.171 | |
BOD | 0.623a | −0.055 | −0.522a | 0.138 | 0.791a | 1 | 0.333a | 0.456a | 0.489a | −0.176 | 0.453a | 0.170 | −0.154 | 0.104 | −0.208 | −0.538a | |
GPP | 0.474a | 0.102 | −0.243 | −0.316a | 0.514a | 0.333a | 1 | 0.402a | 0.088 | −0.022 | 0.252 | 0.372a | −0.008 | 0.172 | 0.166 | −0.037 | |
NPP | 0.536a | −0.054 | −0.352a | −0.389a | 0.711a | 0.456a | 0.402a | 1 | 0.052 | −0.147 | 0.273 | 0.351a | −0.133 | 0.077 | −0.160 | −0.035 | |
CRR | 0.419a | −0.196 | −0.237 | 0.094 | 0.299a | 0.489a | 0.088 | 0.052 | 1 | −0.190 | 0.200 | 0.115 | −0.181 | 0.138 | 0.071 | −0.383a | |
NO3- | −0.148 | 0.780a | 0.625a | 0.448a | −0.180 | −0.176 | −0.022 | −0.147 | −0.190 | 1 | −0.287a | 0.418a | 0.999a | 0.445a | 0.541a | −0.058 | |
NO2- | 0.627a | −0.092 | −0.707a | −0.134 | 0.567a | 0.453a | 0.252 | 0.273 | 0.200 | −0.287a | 1 | 0.004 | −0.239 | 0.137 | −0.327a | −0.036 | |
NH4+ | 0.489a | 0.354a | 0.106 | −0.182 | 0.279 | 0.170 | 0.372a | 0.351a | 0.115 | 0.418a | 0.004 | 1 | 0.428a | 0.724a | 0.597a | −0.075 | |
DIN | −0.115 | 0.786a | 0.595a | 0.445a | −0.151 | −0.154 | −0.008 | −0.133 | −0.181 | 0.999a | −0.239 | 0.428a | 1 | 0.461a | 0.533a | −0.061 | |
Dsi | 0.511a | 0.501a | 0.098 | −0.095 | 0.199 | 0.104 | 0.172 | 0.077 | 0.138 | 0.445a | 0.137 | 0.724a | 0.461a | 1 | 0.691a | −0.083 | |
DIP | 0.029 | 0.499a | 0.519a | −0.040 | −0.158 | −0.208 | 0.166 | −0.160 | 0.071 | 0.541a | -0.327a | 0.597a | 0.533a | 0.691a | 1 | −0.004 | |
Hardness | −0.359a | −0.022 | 0.187 | −0.304a | −0.171 | −0.538a | −0.037 | −0.035 | −0.383a | −0.058 | −0.036 | −0.075 | −0.061 | −0.083 | −0.004 | 1 |
The DO and photosynthetic productivity (GPP and NPP) showed significant positive correlations with chl (Table 1). A significant positive correlation of DO with GPP (r = 0.514) suggested an increased oxygen concentration with higher photosynthetic activity. However, increased BOD levels were observed with increasing values of chl, DO and GPP. The DO values also showed negative correlation with temp. (r = −0.605), probably indicating inverse relationship between the solubility of oxygen in water and temperature.
Different nutrient parameters like NO3−, NH4+, DIN, DIP, Dsi and hardness of the habitat water showed significant positive correlations with the pH of the water (Table 1), thereby indicating their contribution towards the alkaline nature of the habitat water. Again, NO3−, DIN and DIP were significantly correlated with temp., suggesting a probable higher nutrient concentration during the warm season.
Chlorophyta (51%) | Cyanoprokaryota (18%) | Bacillariophyta (17%) | Euglenophyta (14%) | |
---|---|---|---|---|
Chloroccum humicola | Desmodesmus bicaudatus | Merismopedia minima | Aulacosiera granulata | Euglena viridis |
C. echinozygotum | D. pleiomorphus | M. punctata | Navicula phyllepta | E. polymorpha |
Chlamydomonas mucicola | D. itascaensis | M. trolleri | N. cryptocephala | E. tuberculata |
C. globosa | D. quadricauda | M. glauca | N. tripunctata | E. gracilis |
Stauridium tetras | D. armatus var. bicaudatus | Planktolyngbya contorta | N. peregrine | E. deses |
S. tetras var. apiculatum | D. abundans | Anabaenopsis circularis | N. pupula | E. acus |
Pediastrum privum | D. opoliensis | A. tanganyikae | Cocconeis pediculus | Euglenaformis proxima |
P. boryanumvar. brevicorne | D. quadricauda var. longispinum | A. arnoldii | C. costata | Lepocinclis globulus |
P. duplex var. clathratum | Tetrastrum triangulare | A. raciborskii | Cymbella lanceolata | L. ovum |
P. boryanum var. perforatum | T. heteracanthum | Chroococcus limneticus | Cyclotella striata | L. salina |
P. duplex var. duplex | T. staurogeniaeforme | C. dispersus | C. meneghiniana | L. salina var. vallicauda |
P. subgranulatum | Treubaria setigera | C. dispersus var. minor | Pseudonitzschia sp. | Monomorphina pseudonordstedii |
P. sarmae | Schroederia judayi | C. turgidus | Craticula halophila | Trachelomonas volvonica |
P. duplex var. genuinum | Eutetramorus tetrasporus | Synechococcus elongatus | C. cuspidata | T. volzii var. intermedia |
Lacunastrum sp. | Monoraphidium minutum | Synechocystis aquatilis | Nitzschia acicularis | T. intermedia |
Pseudopediastrum boryanum | M. contortum | Spirulina subsalsa | N. palea | Cryptoglena skujae |
Tetraedron minimum | Crucigenia quadrata | S. subtilissima | N. frustulum | Peranemopsis trichophora |
T. muticum | C. tetrapedia | S. laxissima | N. fruticosa | Phacus tortus |
T. caudatum | Chlorella vulgaris | S. nordstedtii | Pleurosigma angulatum | P. acuminatus |
T. caudatum var. longispinum | C. ellipsoidea | Oscillatoria acutissima | Amphora coffaeformis | P. caudatus |
T. trigonum | Crucigeniella crucifera | O. rubescens | Thalassiosira weissflogii | P. curvicauda |
T. trigonum var. gracile | C. apiculata | Rhabdoderma irregulare | Leptocylindricus danicus | P. anacoleus var. undulatus |
T. regulare | C. rectangularis | R. lineare | Acnanthes sp. | P. glaber |
T. pusillum | C. irregularis | Coelosphaerium palladium | Synedra ulna | P. chloroplastes var. incisa |
Scenedesmus dimorphus | Ankistrodesmus gracilis | Gomphosphaeria aponina | P. sesquitortus | |
S. denticulatus | A. falcatus | Rhabdogloea fascicularis | P. longicauda | |
S. bernardii | A. falcatus var. acicularis | R. raphidioides | Rhabdomonas costata | |
S. acuminatus | A. falcatus var. tumidus | Pseudoanabaena catenata | Euglenaria sp. | |
S. ecornis | A. falcatus var. stipitatus | P. galeata | ||
S. acutus | A. convolutus | Microcystis aeruginosa | ||
S. bijuga | Selenastrum bibraianum | |||
S. pleiomorphus | S. gracile | |||
S. disciformis | S. westii | |||
S. pseudoopliensis | Actinastrum gracillum | |||
Coelastrum microporum | Mucidosphaerium sphagnale | |||
C. reticulatum | Mucidosphaerium sp. | |||
C. proboscidium | Desmococcus olivaceum | |||
C. pseudomicroporum | Carteria cerasiformis | |||
Kirchneriella lunaris | Hematococcus lacustris | |||
K. contorta | Deasonia granata | |||
K. obese | Oocystidium ovale | |||
K. elongata | Oocystis borgei |
Seasonal distribution of the phytoplankton community composition was revealed from the FACS study. Populations of cells (P4–P7) sorted on the basis of two-color pigment fluorescence intensity at two FACS filters were further sorted based on fluorescence intensity at two other filters (Fig. 2). The phytoplankton abundance in terms of cell count was mapped based on these sorted sub-populations (P8, P9, P11– P18, P25– P27) representing mixed assemblages of taxa with similar pigment profiles (Table 3), and plotted on cytograms for different seasons (pre-summer, summer, monsoon, post-monsoon and winter) (Fig. 7). Each of these subpopulations was tagged by a specific colour (Table 3). The change in colour intensity of the subpopulations during different seasons indicated their variations in abundance at different seasons in terms of abundance. Colour intensity of any one sub-population was directly proportional to number of cells in the corresponding assemblage (Fig. 7). In general, comparatively higher cell counts were obtained for all assemblages during the pre-summer, while lower counts occurred in post-monsoon (for P11, P12, P8 and P9), monsoon (for P15–P18) and winter (for P13, P14, P25–P27). Along with the phytoplankton abundance, types of phytoplankton recorded from each population were also studied through microscopic identification (Table 3). Most of the sub-populations obtained by FACS consisted of mixtures of microplanktonic phyla, although a few (P14, P17, P25 and P26) contained members of single phyla only.
The comparison between the seasonal variations of total cell count and chl content of the recorded planktonic algal phyla evidenced that there were almost similar seasonal fluctuations in total chl content and cell count of individual groups (Fig. 8a). A positive relation between total cell count and total chl content was established (Fig. 8b), indicating the phytoplankton's contribution to the chlorophyll concentration of the present habitat water.
Fig. 9 Seasonal variations in Shannon–Wiener's Index (H′), species richness (R) and species evenness (e). |
Fig. 10 Principal component analysis (PCA) plots of PC1 vs. PC2. (a) Loadings plot for environmental variables. (b) Scores plot for sampled months. |
Among the variables, temperature has the largest but most negative loading for PC1 and thus its variability is explained almost totally by PC1, which accounted for 33% of the total variance (Fig. 10a). The variable chl has equivalent but positive loadings for PC1. This confirmed that chlorophyll content was strongly correlated (negatively) to water temperature. Almost similar length of the vectors for other variables like DO, productivity (GPP, NPP), BOD and nutrients, like NO2–, in the first quadrant along PC1 showed significant positive correlation with chl and thus in turn negative correlation with water temperature, which was already confirmed from the correlation study among environmental variables (Table 1). High loading values of ammonium nitrogen (NH4+), Dsi, and moderate values for nitrate (NO3−), total dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphate (DIP) occurred along PC2 that represented 27.4% of the variance. Thus, these components in the loading plot largely corresponded to high nutrient condition, a possible indication towards eutrophic nature of the habitat. DO and BOD showed high factor loadings along PC1 and intermediate loadings along PC2, which not only indicated similar patterns of variance but established the interdependence between them. The hardness of the water appeared to negatively relate with chl as well. Again, length and direction of the arrows of NO3−, DIN, DIP and pH suggest their similar pattern of seasonal variation. This was already confirmed from correlation study as well. The scores for the 24 sample months pointed towards the variables driving phytoplankton abundance in each season (Fig. 10b). While the winter and presummer months (Nov-Mar) showed high scores along PC1, summer (Apr-Jun) showed high scores along PC2 and monsoon (Jul-Oct) showed low scores along both axes. Comparing with the loadings of different variables, it was inferred that high phytoplankton abundance in winter and presummer was encouraged by low water temperature and led to high GPP, low nutrient (DIN and DIP) status, high BOD, high DO and low transparency. In summer, high temperature led to waning of phytoplankton bloom and accumulation of nutrients (eutrophication) as indicated by lower GPP, chl, DO and BOD but higher DIN and DIP. Arrival of monsoon diluted the nutrient concentrations, raised transparency but hindered phytoplankton bloom.
The cluster analysis (CA) (Fig. 11) and NMDS (Fig. 12) ordinations provided a better insight into the seasonal pattern of species based upon their abundance data. The NMDS algorithm ranks distances between objects, and uses these ranks to map the objects nonlinearly onto a simplified, two-dimensional ordination space. From both CA and NMDS plots it was evident that the phytoplankton assemblages could be clustered into three different groups viz. Cluster I, II and III. Cluster I comprised of monsoon and post-monsoon dominating population. These included Cyanarcus sp. (Cya), Chroocococcus sp. (Ch), Synechocystis sp. (Sycs), Tetraedron sp. (Tetrad), etc. Cluster II represented populations which showed higher abundance in pre-summer followed by winter, summer, monsoon and the least in post-monsoon. Cluster III included the winter predominating phytoplankton groups. The Table 4 includes the genera grouped into Cluster I, II and III. Thus from the above two plots it was evident that Euglenophytes were abundant mostly in winters followed by presummer while the Cyanoprokaryotes dominated in the winter season. The Chlorophytes' abundance was maximum during the pre-summer. The maximum chlorophyll content was recorded previously during this season. This could in turn indicate that Chlorophytes accounted for maximum chlorophyll content and phytoplankton productivity.
Fig. 11 Cluster analysis (CA) of recorded genera using UPGA method (full forms of abbreviated names are listed above). |
Fig. 12 Non-metric multidimensional scaling (NMDS) of different recorded genera considering coordinates 1 and 2. (Inbox: table showing the list of abbreviated genera used for NMDS). |
Cluster | Cyanoprokaryota | Chlorophyta | Bacillariophyta | Euglenophyta |
---|---|---|---|---|
Cluster I | (Cyanarcus sp.) Cya., (Chroococcus sp.) Chr., (Synechocystis sp.) Sycs. | (Tetraedron sp.) Tetra., (Crucigenia sp.) Cru., (Tetrastrum sp.) Tetras., (Carteria sp.) Car. | — | — |
Cluster II | (Spirulina sp.) Spi., (Synechococcus sp.) Syn., (Rhabdomonas sp.) Rha. | (Haematococcus sp.) Hae., (Kirchneriella sp.) Kirch., (Selenastrum sp.) Sele., (Chlorococcum sp.) Chlo., (Scenedesmus sp.) Sce., (Chlorella sp.) Ch. | (Craticula sp.) Cra., (Synedra sp.) Syd., (Navicula sp.) Na., (Psuedonitzschia sp.) Psnit. | (Cryptoglena sp.) Cryp. |
Cluster III | (Aphanocapsa sp.) Aph., (Gleocystis sp.) Gleo., (Gomphospaeria sp.) Gom., (Merismopedia sp.) Mer., (Anabaena sp.) Ana., (Planktolyngbya sp.) Plan., (Cyanophytic population) Cyp. | (Ankistrodesmus sp.) Ank. | (Pleurosigma sp.) Pleu., (Cyclotella sp.) Cyc., (Amphora sp.) Am., (Thallasiosira sp.) Th. | (Lepocinclis sp.) Lepo., (Monomorphina sp.) Mono., (Phacus sp.) Ph., (Euglena sp.) Eu., (Euglenaria sp.) Eugl., (Trachelomonas sp.) Tra. |
The division Cyanoprokaryota and Euglenophyta mainly predominated during the winter seasons. The CA demonstrated progressive change of dominance through a warmer to cooler temperature gradient. The chlorophyll content and the phytoplankton count including FACS observations suggested a seasonal trend in phytoplankton assemblages with monsoon being least abundant due to seasonal precipitation, and presummer being the maximum. Similarities between variation patterns of total chlorophyll content and phytoplankton cell count suggested that the autotrophic productivity of the present ecosystem was primarily regulated by the phytoplankton biomass as indicated from GPP value also. It has already been observed that the chlorophyll values accorded with that of eutrophic ecosystem.57 The pH recorded was slightly alkaline which, from the above results, was evidently contributed by the different nutrients present therein. Besides, the higher pH values obtained validated the occurrence of eutrophication.58,59 An important ecological factor regulating phytoplankton growth is water temperature.32 Temperature seemed to be primarily responsible for the shifts in phytoplankton assemblages as significant negative correlation of temperature with chlorophyll, DO, GPP and NPP was obtained from the study. This inference was supported by PCA results, which indicated that water temperature and annual precipitation were major determinants of phytoplankton abundance, which was highest in winter and lowest in monsoon (Fig. 8 and 10). Although chlorophyll shows strong negative correlation with temperature, it is not winter (when temperature is minimum that productivity peaks. It is in presummer when the mix of high nutrient levels and optimum temperature causes maximium productivity. Both abundance and diversity of phytoplankton taxa diminished from presummer to summer despite eutrophic conditions in summer (Fig. 3–5, 8 and 9). Mesotrophic status was achieved in winter and presummer by assimilation of excess nutrients by phytoplankton, which also raised GPP and DO by oxygenic photosynthesis, and in turn encouraged high microbial abundance as indicated by high BOD and CRR.
The DO is essential to all forms of aquatic life, including those organisms responsible for the self-purification processes in natural waters.59 A regulatory network of DO along with photosynthetic activity and primary productivity therein equilibrates the ecological balance of the ecosystem. The present findings accorded with the previous reports of Hardy60 where DO shows a positive correlation with the phytoplankton biomass (Table 1). According to Ganf and Horne,61 if in a productive aquatic ecosystem respiration accounted for large proportion of GPP, it would be a measure of eutrophic nature. In general, decomposition of the sewage, dead plankton etc. along with respiration of the inhabitants are responsible for creating avenues for high CRR and ultimately BOD of the water column. The present investigation recorded higher BOD levels, which probably emphasized on the eutrophic status of the selected site. A positive correlation obtained between DO and BOD is indicative of higher heterotrophic microbial community along with planktonic autotrophs supported by higher CRR values and lower percentages of oxygen saturation. However, the obtained DO values ranged up to of 8.14 mg L−1 which according to WHO,60 supported the survival of biological communities including fish production. Higher values of GPP and its significant positive correlation with DO were useful in supporting the fact that phytoplankton contributes a natural method of biological purification for the sewage treatment in EKW. This was further supported by other studies from other EKW sites such as Dasgupta et al.62
During sewage treatment the microbial degradation of the sewage releases the nutrients stored in it, creating eutrophic conditions, which in turn support high rates of primary productivity.32,35 The excretion of nitrogenous compounds by fish is also a source of NO3−, NO2−, NH4+and other inorganic substances.63 Major nutrient like nitrogen occurs in natural waters in various forms, including NO3−, NO2− and NH4+. The NO3− is the essential nutrient for many photosynthetic autotrophs and has been identified as the growth limiting nutrient.59 However, in municipal and industrial waste-waters or effluents including biological treatment plants, NO3− concentrations are enhanced resulting in eutrophication.57 Similarly NO2− concentrations higher than 21.74 μM and NH4+ greater than 11.11 μM could be an indication of organic pollution.59 High availability of NO3− in EKW not only encourages phytoplankton abundance but also raises anaerobic metabolism, particularly under oxic conditions, while NO2− plays only a transient role in N cycling.64 Another major nutrient of aquatic systems for phytoplankton development is phosphate mostly in form of DIP which in general ranges from 0.053 to 0.21 μM in most natural surface waters.57 The present records also showed the positive role of NO3−, NH4+ and DIP on phytoplankton biomass growth.65 However, loadings of water parameters, particularly temperature, transparency, chlorophyll content, BOD and DO, obtained by PCA showed that biomass-induced opacity of the water column was mainly caused by photosynthetic microorganisms, indicating a productive ecosystem occurring in winter and presummer (Fig. 7–10). Since this productivity was seasonal, an effective way of sewage treatment would be periodic harvesting of the nutrient-containing photosynthetic biomass from the littoral zone throughout winter and presummer, leading to an oligotrophic and oxygenated habitat for pisciculture.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7ra12761h |
This journal is © The Royal Society of Chemistry 2018 |