Physicochemical parameters and zooplankton
The two extreme quality of the waterbodies of Bankura district are shown in Fig. 4. Variations of the water quality parameters of studied water bodies have been summarized in Table 1. One-way ANOVA of physicochemical parameters and zooplankton shows significant difference (p < 0.05) for pH, DO, TH, EC, Phosphate-P, Nitrite-N, Cladocera, Rotifera and Protozoa (Table 2). With Pearson’s bivariate correlation analysis, it has been found that there was a strong positive correlation between total hardness (TH) and pH (r = 0.778, p < 0.01) (Fig. 5). This suggests that higher total hardness corresponds to elevated pH levels in the waterbodies. There was a strong positive correlation between TH and bicarbonate (r = 0.835; p < 0.01), indicating that waterbodies with higher total hardness also have increased bicarbonate concentrations. Phosphate concentration demonstrated significant correlations, including negative associations with pH (r = -0.857; p < 0.01), DO (r = -0.587; p < 0.05), TH (r = -0.636; p < 0.05), and EC (r = -0.624; p < 0.05). Moreover, phosphate exhibited a positive correlation with nitrite (r = 0.871; p < 0.01). Nitrite concentration displayed significant negative correlations with pH (r = -0.744; p < 0.01), DO (r = -0.682; p < 0.05), and EC (r = -0.706; p < 0.05). Additionally, nitrite shows positively correlation with phosphate (r = 0.871; p < 0.01). Boxplots of the nutrient parameters are shown in Fig. 6. Cladocera abundance showed positive correlations with pH (r = 0.778; p < 0.01) and bicarbonate (r = 0.835; p < 0.01), while displaying a negative correlation with phosphate (r = -0.636; p < 0.05). Rotifera abundance correlated positively with nitrite concentration (r = 0.683; p < 0.05). Protozoa exhibited significant correlations with pH (r = -0.792; p < 0.01), TH (r = -0.673; p < 0.05), chloride (r = -0.621; p < 0.05), EC (r = − 0.727; p < 0.01), phosphate (r = 0.736; p < 0.01), and nitrite (r = 0.613; p < 0.05) (Fig. 4). Physicochemical parameters play a significant role to define the status of the waterbodies. DO, pH, FC play crucial roles in aquatic life maintenance. Carbon dioxide sources include respiration of aquatic organisms and atmospheric mixing with pond water. The statistical analysis using one-way ANOVA revealed noteworthy variations (p < 0.05) among physicochemical parameters and zooplankton populations. The parameters that exhibited significant differences in different study sites include pH (F = 38.00), DO (F = 11.218), TH (F = 5.518), EC (F = 34.771), Phosphate-P (F = 29.074), Nitrite-N (F = 26.249), Cladocera (F = 5.518), Rotifera (F = 4.701), and Protozoa (F = 24.701). These findings highlight the diverse responses within these parameters across the studied water bodies. The results align with previous studies conducted on different water bodies, such as (Rai and Gary, 1980; Shardendu and Ambashth, 1988; Sinha, 1995; Zang et al., 2011; and Liu et al., 2020). In few cases it is observed that standard deviations of the parameters are greater than the mean value, it may be due to the large variance of the data.
Table 2
Variation of the studied parameters in different study sites
Parameters | Mean ± SD | 95% of CI | F value | P (< 0.05) |
Water Temperature [ºC] | 20.89 ± 3.83 | 18.46–23.32 | 1.076 | 0.324 |
pH | 7.42 ± 0.60 | 7.03–7.79 | 38.000 | 0.000 |
Dissolved Oxygen [mg/l] | 160.84 ± 60.99 | 122.08-199.59 | 0.018 | 0.897 |
Free Carbon dioxide [mg/l] | 4.96 ± 1.79 | 3.81–6.09 | 11.218 | 0.007 |
Chloride Ions (Cl−) [mg/l] | 19.66 ± 6.11 | 15.77–23.53 | 0.423 | 0.530 |
Total Alkalinity [mg/l] | 126.11 ± 61.35 | 87.13-165.09 | 0.562 | 0.471 |
Total Hardness [mg/l] | 128.19 ± 30.54 | 108.78-147.59 | 5.518 | 0.041 |
Bi Carbonate (HCO3−) [mg/l] | 290.25 ± 147.51 | 196.52-383.97 | 1.363 | 0.270 |
Phosphate-P (PO4 − 3) [mg/l] | 610 ± 252.84 | 449.35-770.64 | 34.771 | 0.000 |
Ammonium-N (NH4−) [mg/l] | 1.46 ± 1.22 | 0.68–2.24 | 29.074 | 0.000 |
Nitrite-N (NO2−) [mg/l] | 0.08 ± 0.13 | 0.00-0.16 | 0.414 | 0.534 |
Conductivity [µS/cm] | 0.26 ± 0.27 | 0.08–0.43 | 26.249 | 0.000 |
Cladocera [Nos/l] | 125.19 ± 30.54 | 108.78-147.59 | 5.518 | 0.041 |
Copepoda [Nos/l] | 226.67 ± 260 | 61.46-391.86 | 0.192 | 0.671 |
Rotifera [Nos/l] | 235.83 ± 287.83 | 52.96–418.70 | 4.701 | 0.055 |
Ostracoda [Nos/l] | 115 ± 88.91 | 58.51-171.48 | 2.408 | 0.152 |
Protozoa [Nos/l] | 1401.25 ± 1355.93 | 539.73-2226.76 | 17.177 | 0.002 |
*Significant (p < 0.05) F values are represented in bold.
Zooplankton diversity and bloom formation
The abundance of different zooplankton has been studied and result shows abundance of Rotifer, Protozoa, Ostracoda in the bloom forming waterbodies, whereas Cladocera was mainly found in the normal waterbodies (Fig. 8). The various plankton diversity indices were calculated for different sampling sites (R1 to R6 and S1 to S6). These indices provide insights into the ecological diversity and evenness of zooplankton communities in the studied water bodies. The Dominance index (D) measures the abundance of the most dominant species relative to the total abundance of all species. Higher values of D indicate a dominance of few species, while lower values suggest a more evenly distributed community. Our results reveal varying levels of dominance across sites, ranging from 0.488 to 0. 903.Simpson’s Diversity Index (1-D) quantifies the probability that two randomly selected individuals from the community belong to different species. Higher values of 1-D indicate greater diversity and evenness in species distribution. Our calculations show that Simpson's Diversity Index ranges from 0.726 to 0.949 across the sampled sites. The Shannon-Wiener Diversity Index (H) combines both species richness and evenness in its calculation. It provides a comprehensive view of species diversity within a community. Our analysis reveals Shannon-Wiener Diversity Index values ranging from 1.47 to 1.854 across the studied sites. Menhinick's Index is a measure of species richness relative to the square root of total abundance. It provides insights into the species richness per individual. Our results indicate Menhinick's Index values ranging from 0.261 to 0. 373.Equitability (J) is a measure of how evenly individuals are distributed among species within a community. Higher values of J indicate a more equitable distribution of species. Our findings display Equitability Index values ranging from 0.293 to 0.453. The Berger-Parker Index reflects the proportion of the most dominant species in the community. It provides insights into the dominance of a few species relative to the total abundance. Our results show Berger-Parker Index values varying from 0.515 to 0. 903.Evenness (eH/S) considers both the Shannon- Wiener Diversity Index (H) and taxa richness (S) to provide a measure of community evenness. Higher values of evenness indicate a more balanced distribution of species. Our analysis indicates evenness values ranging from 0.294 to 0.482. All the diversity indices reveal varying degrees of species richness, dominance, diversity, and evenness across the sampled sites (Fig. 7). These indices collectively contribute to our understanding of the ecological composition and dynamics of the zooplankton communities in the studied water bodies. The study indicates that plankton is essential for fisheries and aquaculture research, as it directly impacts fish production. Zooplankton, particularly Cladocera, Copepoda, Rotifers, Ostracods, and Protozoans, play pivotal roles in freshwater ecosystems. Among these, Cladocera dominate, contributing to fish nutrition and acting as pollution indicators (Majumder, 2020; Sakhare and Chalak, 2020). Copepoda, the third most abundant zooplankton group, supports freshwater aquaculture. Abundance of copepods are found in normal waterbodies which are highly preferable for aquaculture (Majumder et al., 2019b; Majumder, 2020). Rotifers, possessing a short life cycle, are important indicators of aquatic pollution. They were observed in both types with higher abundance in normal waterbodies compared to bloom forming waterbodies (Majumder, 2020). Ostracods were observed in both types of waterbodies but their decreased abundance in Type II ponds may affect fish yield (Majumder, 2020). Protozoa, particularly Euglena sp., displayed notable populations in bloom forming waterbodies and a minor presence in normal waterbodies. Euglena sp. capable of toxic impacts and overgrowth under specific conditions, can adversely affect aquatic environments (Majumder, 2020; Zimba et al., 2004; Zohdi, 2019). Nutrients like Phosphate-P, Nitrite-N and Ammonium-N has also a leading role in the water ecosystem. Comparing the standard values (BIS, 2003) of these nutrients we have found that bloom forming waterbodies are nutrient rich waterbodies with high value of Phosphate-P. This is called Cultural Eutrophication which may be caused by anthropogenic activities (Welch and Lindell, 1980).
Euglena sp. a protist prevalent in freshwater environments worldwide, possesses a retractable flagellum that typically remains within the cell body, even when fully extended. Additionally, these organisms are characterized by granular eye spots of various types and have been observed to reproduce asexually through mitosis. Being photosynthetic algae, they function as autotrophs containing chlorophyll and an accessory pigment called astaxanthin, which serves as a carotenoid. This accessory pigment plays a crucial role in preventing the cell's chloroplast from becoming overwhelmed by excessive light conditions. Consequently, cells exhibit a red hue when utilizing this pigment and a green color when the pigments retract into vesicles. An intriguing aspect of Euglena sp. is the production of a compound known as euglenophycin, which exhibits ichthyotoxic, herbicidal, and anticancer properties at low concentrations, ranging from parts per million (ppm) to parts per billion (ppb) dosages (Paul et al., 2010). Researchers have hypothesized that this toxin functions as a neurotoxin due to its impact on behavioural changes. Notably, juvenile catfish exposed to cultures of the algal isolates experienced mortality within 2 hours of exposure (Zimba et al., 2004). Some species of euglenophytes have been observed to possess a red coloration or the ability to turn red, which is attributed to Euglena sp. This microalga, commonly inhabiting eutrophic lentic freshwater habitats globally, was originally described from a red-colored water sample from Silesia. It is worth noting that the cells initially appear green and later turn red (Laza-Martínez et al., 2018). The present study further unveiled the robust growth of Euglena sp. in shallow-water ponds during the transition from winter to summer.
Principal Component Analysis (PCA)
The results of the Principal Component Analysis (PCA) demonstrated significant correlations among water temperature, dissolved oxygen, and pH concerning zooplankton density. These findings strongly underscore the substantial role these parameters play in facilitating the heightened growth and production of zooplankton. The significant correlation observed through PCA signifies the potential utility of these interrelated physicochemical and biological factors in effectively promoting zooplankton growth. This further underscores the efficacy of PCA as a valuable tool for pattern recognition, aiming to elucidate the variance within an extensive dataset of interdependent variables through a more concise set of independent variables (Simeonov et al., 2003). The first five principal components (PC1 to PC5) capture the majority of the variance in the dataset. PC1, which has the highest eigenvalue, explains 38.44% of the variance (Table 3). As we move down the list of principal components, each subsequent PC explains progressively less variance. These five principal components collectively explain 88.16% of the total variance in the dataset. The cumulative variance indicates the total amount of variance explained by the included principal components. In this analysis, the cumulative variance reaches 92.19% with just six components, indicating that a substantial amount of information from the original dataset can be captured using a smaller number of components. PCA results suggest that a significant portion of the variability in the dataset can be summarized by a smaller number of principal components. These components likely represent the underlying patterns and relationships between the physicochemical parameters and plankton diversity across the sampled sites. Researchers can use these principal components to identify key factors driving the observed variation and potentially uncover hidden trends in the data. Graphical representation of Principal Component Analysis provides a compact exposure of the complex relationships between physicochemical parameters and plankton diversity, offering insights into the primary sources of variability within the studied water bodies (Fig. 9).
Table 3
Eigen value (Value of the root character) of physicochemical parameters and zooplankton of different PCs
PC | Eigen Value | Variance (%) | Cumulative Variance (%) |
PC1 | 6.53453 | 38.44% | 38.44% |
PC2 | 3.29814 | 19.40% | 57.84% |
PC3 | 2.33013 | 13.71% | 71.55% |
PC4 | 1.58942 | 9.35% | 80.90% |
PC5 | 1.23493 | 7.26% | 88.16% |
PC6 | 0.68512 | 4.03% | 92.19% |
PC7 | 0.47 | 2.76% | 94.95% |
PC8 | 0.40226 | 2.37% | 97.32% |
PC9 | 0.2817 | 1.66% | 98.98% |
PC10 | 0.10073 | 0.59% | 99.57% |
PC11 | 0.07304 | 0.43% | 100.00% |
PC12 | 0 | 0.00% | 100.00% |
Canonical Correspondence Analysis (CCA)
Canonical Correspondence Analysis (CCA) technique was employed to establish relationships between physicochemical variables and phytoplankton (Palmer, 1993). Serving as a direct gradient analysis method, CCA extracts synthetic gradients from both biotic and environmental matrices (Braak et al., 1995). CCA shows the correlation between the environmental parameters with the biological parameters. CCA results show the relationships between biological variables and environmental variables in two-dimensional space (Axis 1 and Axis 2) (Figure-10). The loadings of species and environmental variables on these axes provide insights into the significant associations between species composition and environmental conditions. Axis 1 represents the primary source of variability in the dataset. The species and environmental variables that have strong loadings on this axis contribute significantly to explaining the observed variation. Positive loadings of Copepoda, Rotifera, Ostrocoda, EC, FC, TH, Cl−, pH, BC, Protozoa and Negative loadings of NO2−, PO4 − 3, NH4+, DO, TA, WT and Cladocera is found in Axis 1. These loadings suggest that Axis 1 represents a gradient related to specific species abundances (Copepoda, Rotifera, Ostrocoda, Protozoa) and environmental variables (EC, FC, TH, Cl−, pH, BC) that influence species distribution. Conversely, other species and variables may be negatively associated with this gradient. Axis 2 represents the secondary source of variability. The loadings on this axis contribute to explaining additional variation beyond Axis 1. FC, Cl−, TH, pH, Copepoda, EC have positive loadings and NO2−, PO4 − 3, DO, Cladocera, NH4+, WT, Ostrocoda, Protozoa have negative loadings with Axis 2. It may represent another set of species-environment associations. Positive loadings indicate species and variables that align with this axis, while negative loadings indicate those that are negatively aligned.
The water quality index
In this present study, the water quality index (WQI) (Table 4) has played a major role to evaluate the status of the waterbodies. In sample R1 (Hati bari-1) WQI value was 100.9 (unsuitable). In sample R3 (Sima bandh) WQI value was 80.34 (very poor). In sample R4 (Hati bari-3) and R6 (Rakshit pukur) WQI value were 52.09 (poor) and 70.01 (poor) respectively. These waterbodies are major sources of red bloom formation. WQI value of sample R2 (Hati bari-2), R5 (Had gora pukur), S2 (Tambli band pond) and S4 (Shit pukur lane area pond 2) were 40.72 (good), 34.48 (good), 32.06 (good), 28.07 (good) respectively. Initial stage of bloom formation has been found in sample site R2 and R5. Excellent class of water has been noted in sample S1 (Police lane area pond), S3 (Shit pukur lane area pond 1), S5 (Rath tala area pond) and S6 (Dol mandir area pond). In these waterbodies, no bloom has been found and they are ideal for aqua culture.
Table 4
Water quality index (WQI) of different study sites
Sample No. | Place | Latitude | Longitude | WQI Value | Class |
R1 | Hati bari-1 | 23°05'34.7"N | 87°15'53.4"E | 100.09 | Unsuitable |
R2 | Hati bari-2 | 23°05'36.8"N | 87°15'52.3"E | 40.72 | Good |
R3 | Sima bandh | 23°05'42.8"N | 87°15'31.0"E | 80.34 | Very Poor |
R4 | Hati bari-3 | 23°05'37.9"N | 87°15'29.7"E | 52.09 | Poor |
R5 | Had gora pukur | 23°05'51.2"N | 87°16'50.5"E | 34.48 | Good |
R6 | Rakshit pukur | 23°05'55.6"N | 87°16'46.4"E | 70.91 | Poor |
S1 | Police line area pond | 23°14′03.6″N | 87°03′26.5″E | 13.68 | Excellent |
S2 | Tambli band pond | 23°14′14.1″N | 87°03′35.2″E | 32.06 | Good |
S3 | Shit pukur lane area pond 1 | 23°13'51.9"N | 87°04'12.2"E | 16.38 | Excellent |
S4 | Shit pukur lane area pond 2 | 23°13'51.8"N | 87°04'15.1"E | 28.07 | Good |
S5 | Rath tala area pond | 23°14′18.1″N | 87°04′20.8″E | 7.15 | Excellent |
S6 | Dol mandir area pond | 23°14'14.1"N | 87°04'22.3"E | 4.85 | Excellent |