Results and Discussion

Variations in Physico-chemical Parameters of Lakes

The lakes monitored in this study varied in their physico-chemical characteristics as these lakes receive untreated sewage or pollutants. An alteration of the physical, chemical, and biological properties of water happens due to pollution with the sustained inflow of untreated or partially treated wastewater from domestic and industrial sources (Udhayakumar et al., 2016). The changes in water quality can alter phytoplankton community structure and distribution in lakes. The discussions on physico-chemical parameters analyzed are elaborated below:

Water Temperature and pH

Temperature strongly influences the chemical composition of cells, uptake of nutrients, uptake of carbon-dioxide, and growth rate of phytoplankton (Singh and Singh, 2015; Brönmark and Hansson, 2002). Water temperature varied between

Biodiversity Challenges: A Way Forward

23.9 oC to 29.7 oC in monitored lakes during the current study (Figure 10.2). pH indicates hydrogen ion concentration in lake water (Kumar and Mahajan, 2020) and is generally governed by the equilibrium between carbon-dioxide, carbonate, and bicarbonate ions. Here, alkaline conditions prevailed in lakes, which ranged from 7.01 – 8.5. This range of pH is suitable for phytoplankton growth. During photosynthetic activity by phytoplankton, pH increases due to the consumption of carbon-dioxide.

Total Dissolved Solids (TDS) and Electrical Conductivity (EC)

The total dissolved solids (TDS) include inorganic salts and small amounts of organic matter in water. The concentration of total dissolved solids ranged from

152.5 mg/L to 883.42 mg/L in lakes (Figure 10.3). The variations in TDS occur naturally from rocks and soil as well as from anthropogenic sources through sewage, industrial effluent discharges, and urban run-off (Ntengwe, 2006). The conductivity is a measure of the capacity of water to conduct an electric current. The value of electrical conductivity ranged from 306.67 µS/cm to 1303.67 µS/ cm (Figure 10.3). EC depends on the type, quantity, and ionic state of dissolved substances (electrolytes), thus, indicates the mineral content of water (Le et al., 2017). Mallathahalli lake had the highest level of TDS and EC, hence shows ionic pollution.

Total Hardness, Calcium, and Magnesium

Hard water is not suitable for domestic and industrial purposes. The cations that impart total hardness to water are calcium, magnesium, iron, strontium, and manganese. The total hardness, calcium, and magnesium varied among sampled lakes and ranged from 78.67 mg/L- 463.5 mg/L, 23.51 mg/L - 117.9 mg/L, and 4.85 mg/L - 41.09 mg/L,respectively (Figure 10.4). Mallathahalli lake had the highest levels of total hardness, calcium, and magnesium, which indicates ionic pollution.

Total Alkalinity and Chloride

Total alkalinity is determined by the concentration of hydroxide, carbonate, and bicarbonate in water. Thus, total alkalinity influences the composition and abundance of phytoplankton (Bose et al., 2019).

Mallathahalli lake receives a lot of untreated domestic and industrial sewage, so profuse growth of phytoplankton was observed that increased the alkalinity of lake water. In addition, the discharge of domestic and industrial wastes increases chloride levels in lake water. The concentration of total alkalinity and chloride in lakes ranged from 81.33 mg/L - 684.33mg/L and 26.74 mg/L - 305.89 mg/L, respectively (Figure 10.5).

Turbidity and Dissolved Oxygen (DO)

Turbidity in water occurs mainly due to particles of clay, silt, organic particles, inorganic matter, micro-organisms, and algae bloom. The range of turbidity in monitored lakes was found as 5.77 NTU - 103.51 NTU (Figure 10.6). Under

Biodiversity Challenges: A Way Forward

Biodiversity Challenges: A Way Forward

turbid conditions, only less light can penetrate through the water column to the lake bottom. This results in inhibition of the growth of phytoplankton and lower photosynthetic activity (Yang et al., 2012). This may, in turn, decrease the dissolved oxygen level in lake water. The dissolved oxygen levels in lakes ranged from 0.81 mg/L - 9.84 mg/L (Figure 10.6). Dissolved oxygen affects the various physico- chemical attributes and biological processes in lake water (Ramachandra et al., 2014).

Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD)

Biochemical oxygen demand (BOD) determines the amount of oxygen required by aerobic micro-organisms to stabilize the organic matter (Avvannavar and Shrihari, 2008). High COD concentrations indicate pollution related to untreated domestic sewage and industrial effluents. The BOD and COD of monitored lakes ranged between 12.2 mg/L - 60.97 mg/L and 21.33 mg/L - 128 mg/L, respectively (Figure 10.7). The higher levels of BOD and COD show the level of pollution, which will increase the oxygen demand in lakes. Among the sampled lakes, Kempambudhi lake had more elevated BOD and COD, indicating organic pollution.

Nitrate and Ortho-phosphate

Nutrients such as nitrogen and phosphorus are the limiting factors for phytoplankton growth. In the present study, nitrate and ortho-phosphate ranged between 0.01 mg/L - 3.1 mg/L and 0.077 mg/L - 3.701 mg/L, respectively (Figure 10.8). The excessive amount of nitrogen and phosphorus can stimulate eutrophic conditions (He et al., 2011). The discharge of phosphorus into aquatic ecosystems results in eutrophication, whichhas enhanced the overgrowth of algae and macrophytes (Çelekli and Sahin, 2021). Kempambudhi lake was found to be rich in nitrate and ortho-phosphate (OP), which indicates nutrient pollution.

The study reveals that (i) Mallathahalli lake had higher levels of TDS, EC, total hardness, calcium, magnesium, chloride, total alkalinity, (ii) Kempambudhi lake with high pH, BOD, COD, and OP show organic and nutrient pollution, and (iii)Yediyur lake had less ionic contents such as TDS, total hardness, calcium, magnesium, and COD. Thus, the monitored lakes are polluted as they receive untreated domestic waste, agricultural residues, and industrial effluents. This may threaten biodiversity, reduce the aesthetic and recreational value of lakes.

Phytoplankton Community in Lakes

The present study covers 15 lakes in Vrishabhavathi valley of Bangalore, which recorded a total of 58 genera of phytoplankton belonging to 5 phyla, 9 class, 24 orders, and 41 families were identified (Figure 10.9). The fifty-eight species of phytoplankton were represented by five major groups, namely, Chlorophyta (25 species), Bacillariophyta (12 species), Cyanobacteria (12 species), Charophyta (5 species), and Euglenozoa (4 species).

Biodiversity Challenges: A Way Forward

Biodiversity Challenges: A Way Forward

Diversity of Phytoplankton in Lakes of Bangalore, Karnataka, India

Cyanobacteria, commonly known as blue-green algae, are photosynthetic prokaryotes that form algal blooms in water (Elliott, 2012). Cyanobacteria are represented by species such as Anabaena sp., Aphanocapsa sp., Chroococcus sp., Coelosphaerium sp., Cylindrospermopsis sp., Gloeocapsa sp., Merismopedia sp., Microcystis sp., Oscillatoria sp., Phormidium sp., Planktothrix sp. and Spirulina sp. Cyanobacteria, mainly Microcystis sp.was dominant and formed surface blooms in Doraikere, Uttarahalli, ISRO layout, and Yediyur lakes in Vrishabhavathi valley, Greater Bangalore, Karnataka (Figure 10.10). A total of 25 species of Chlorophyta was recorded in this study, such as Actinastrum sp., Ankistrodesmus sp., Asterococcus sp., Botryococcus sp., Chlamydomonas sp., Chlorella sp., Chlorococcum sp., Coelastrum sp., Crucigenia sp., Desmodesmus sp., Dictyosphaerium sp., Golenkinia sp., Kirchneriella sp., Micractinium sp., Monoraphidium sp., Oocystis sp., Pandorina sp., Pediastrum sp., Scenedesmus spp., Schroederia sp., Stichococcus sp., Stigeoclonium sp., Tetraedron sp. and Tetrastrum sp. Both pennate and centric forms of Bacillariophyta (or diatoms) are present lakes inVrishabhavathi valley, Bangalore.Achnanthes sp., Amphora sp., Cyclotella sp., Fragilaria sp., Gomphonema sp., Melosira sp., Navicula sp., Nitzschia sp., Pinnularia sp., Stauroneis sp., Surirella sp. and Synedra sp. belonging to Bacillariophyta was recorded.

A total of four species under Charophyta were recorded, such as Closterium sp., Cosmarium sp., Euastrum sp., Spirogyra sp. and Staurastrum sp. in the present investigation. Euglenozoa is represented by species such as Euglena spp., Lepocinclis sp., Phacus sp. and Trachelomonas sp. Euglena sp. is mixotrophic algae, which can grow faster in wastewater because they can utilize organic matter as an energy source (Mahapatra et al., 2013).

Diversity Indices

Various diversity measures were used to estimate the phytoplankton diversity of lakes in Vrishabhavathi valley, Bangalore. Shannon-Wiener index ranged from

0.16 -2.54. Shannon and Wiener index value of 0.0-1.0 indicates heavy pollution, 1.0-2.0 indicates moderate pollution, 2.0-3.0 indicates light pollution, and 3.0-

4.5 indicates slight pollution. The species diversity indices decrease in polluted waters (Shanthala et al., 2009). Thus, Doraikere (2.54) is heavily polluted with less species diversity. Simpson index ranged from 0.045 (Doraikere) -0.882 (Sompura, Mallathahalli). Pielou evenness index reveals an even distribution of individuals among varied species (Miao et al., 2019). The evenness index ranges from near 0 (low evenness) to 1 (dominance). Evenness index ranged from 0.07 (Uttarahalli)

– 0.80 (Deepanjali Nagar, Srinivasapura). Menhinick index ranged from 0.26 (Doraikere) – 1.59 (Hemmigepura) in this study. Margalef species richness index measures the number of different species in each sample (Ray et al., 2021). Margalef index ranged from 0.72 – 4.77 as Dubasipalya lake has the highest taxa whereas Srinivasapura has the lowest taxa and individuals. A high value of richness index indicates healthy status, while a low value indicates the unhealthy and contaminated status (Hussien and Zaghloul, 2017). The Berger-Parker index gives the measure

of the most abundant species (Berger and Parker, 1970). Berger-Parker index in this study ranged from 0.21 (Deepanjali Nagar) -0.98 (Doraikere).

Correlation between Water Quality Parameters and Phytoplankton

Pearson correlation analysis is used to measure the interrelation and extent of associations among the water quality parameters and algal phyla. Bacillariophyta negatively correlated with BOD (r = -0.31) and COD (r = -0.38). Bacillariophyta responds rapidly to organic and nutrient contamination (Saxena et al., 2020). Charophyta positively correlated with water temperature (r = 0.41), electrical conductivity (r = 0.36), magnesium (r = 0.40), ortho-phosphate (r = 0.31) and Bacillariophyta (r = 0.57). Chlorophyta positively correlated with water temperature (r = 0.38), Charophyta (r = 0.68) and Bacillariophyta (r = 0.90). A positive correlation was found between temperature and phytoplankton such as Charophyta and Chlorophyta (Gogoi et al., 2019) as temperature plays an important role in growth and reproduction of phytoplankton. Cyanobacteria negatively correlated with pH (r = -0.45), total hardness (r = -0.44), calcium (r = -0.39), magnesium (r= -0.46), total alkalinity (r = -0.49) and positively correlated with nitrate (r = 0.63). A similar pattern of positive correlation recorded between Cyanophytes with nitrate, and inverse correlation with alkalinity, pH, total hardness, calcium and magnesium (Mishra et al., 2019). In Baiyangdian lake, Cyanophyta and Chlorophyta became dominant because of increased organic matter from industrial wastewater and domestic sewage (Wang et al., 2013). Euglenozoa positively correlated with TDS (r = 0.49), EC (r = 0.59), total hardness (r = 0.49), calcium (r = 0.36), magnesium (r = 0.61), chloride (r = 0.58), total alkalinity (r = 0.43), ortho-phosphate (r = 0.31), Bacillariophyta (r = 0.39), Charophyta (r = 0.79), Chlorophyta (r = 0.72) and negatively correlated with DO (r = -0.30). The dominance of Euglenophyta indicates organic pollution, water pollution, and eutrophic condition of water body (Shams and Shamsabadi, 2019).

Relationship between Phytoplankton Community and Physico- chemical Factors

Canonical correspondence analysis (CCA) was computed with phytoplankton composition data (58 genera), 15 physico-chemical parameters across 15 lakes during the study period to evaluate the role of environmental variables in structuring phytoplankton communities. For CCA, 15 physico-chemical parameters, namely: water temperature, pH, conductivity, total alkalinity, dissolved oxygen, nitrate, ortho-phosphate, turbidity, TDS, BOD, COD, chloride, calcium, magnesium, and total hardness,were selected. Different species of phytoplankton showed varied responses to physico-chemical parameters. The length of the arrow in CCA ordination indicates the relative importance of environmental variables. Eigenvalues of the first two axes are 0.58 and 0.48,which explain 34.97 per cent of the variance of phytoplankton and environmental data. Axis 1 has strong positive loadings of

turbidity. Thus, Spirulina sp., Actinastrum sp., Asterococcus sp., Micractinium sp. and Phacus sp. are positively correlated with the turbidity. Turbidity of freshwater lakes increases due to the influx of urban run-off, silt, sand, and organic matter (Nandigam et al., 2016). This decreases the phytoplankton diversity and increases the dominance of Spirulina sp. as in Konasandra lake.

Axis 2 has strong positive loadings of total hardness, calcium, magnesium, and total alkalinity. Total hardness, calcium, magnesium, and total alkalinity are found in lakes such as Deepanjali Nagar, Hemmigepura, Sompura, Kempambudhi, Srinivasapura, Ullal, Doraikere, Dubasipalya, Hosakerehalli, and Uttarahalli. Therefore, the growth of species such as Anabaena sp., Chroococcus sp., Cymbella sp., Fragilaria sp., Gomphonema sp., Lepocinclis sp., Monoraphidium sp., Navicula sp., Oscillatoria sp., Pandorina sp., Phormidium sp., Pinnularia sp., Spirogyra sp., Stauroneis sp., Stichococcus sp. and Surirella sp. are positively dependent on the parameters like total hardness, calcium, magnesium, and total alkalinity. Magnesium is crucial for chlorophyll synthesis and serves as a limiting factor for phytoplankton growth and reproduction (Narchonai et al., 2019). The remaining species did not show a relation with the selected variables. Phytoplankton species such as Amphora sp., Aphanocapsa sp., Botryococcus sp., Chlorococcum sp., Desmodesmus sp., Dictyosphaerium sp., Euastrum sp., Golenkinia sp., Kirchneriella sp., Melosira sp., Microcystis sp., Pediastrum sp., Planktothrix sp., Scenedesmus spp., Schroederia sp., Staurastrum sp., Stigeoclonium sp., Tetraedron sp., Tetrastrum sp. and Trachelomonas sp. found nearer to the center of CCA ordination has a broad range of tolerance to the variations in physico-chemical parameters in lakes. Anabaena sp., Merismopedia sp., Microcystis sp. and Oscillatoria sp. indicate eutrophic lake conditions (Yilmaz et al., 2018).

Palmer Index

The phytoplankton community composition alters quickly due to changes in the physico-chemical characteristics of water. For example, the presence of varied species of phytoplankton can indicate the quality of water. For example, Oscillatoria and Euglena are indicators of dirty water, while Pediastrum indicates clean water (Kantachote et al., 2009). Thus,phytoplankton communities serve as indicators of pollution in an aquatic ecosystem. The Palmer index is a rapid, efficient, and cost- effective tool that reveals the water quality status or water pollution in freshwater bodies (Salem et al., 2017). Palmer prepared a list of algal genera and species that are tolerant to varying levels of organic pollution (Vishal and Meeta, 2020). Palmer index accounts for the abundant pollution tolerant phytoplankton taxa and categorizes water bodies based on the pollution status. The palmer index value of 20 or more confirms high organic pollution. A value of 15-20 indicates probable organic pollution. The values between 10-15 and 0-10 show moderate pollution and lack of organic pollution, respectively (Jose and Kumar, 2011).

Biodiversity Challenges: A Way Forward

(Abbreviations: a1: Achnanthes sp.; a2: Actinastrum sp.; a3: Amphora sp.; a4: Anabaena sp.; a5: Ankistrodesmus sp.; a6: Aphanocapsa sp.; a7: Asterococcus sp.; b: Botryococcus sp.; c1: Chlamydomonas sp.; c2: Chlorella sp.; c3: Chlorococcum sp.; c4: Chroococcus sp.; c5: Closterium sp.; c6: Coelastrum sp.; c7: Coelosphaerium sp.; c8: Cosmarium sp.; c9: Crucigenia sp.; c10: Cyclotella sp.; c11: Cylindrospermopsis sp.; c12: Cymbella sp.; d1: Desmodesmus sp.; d2: Dictyosphaerium sp.; e1: Euastrum sp.; e2: Euglena spp.; f- Fragilaria sp.; g1: Gloeocapsa sp.; g2: Golenkinia sp.; g3: Gomphonema sp.; k: Kirchneriella sp.; l: Lepocinclis sp.; m1: Melosira sp.; m2: Merismopedia sp.; m3: Micractinium sp.; m4: Microcystis sp.; m5: Monoraphidium sp.; n1: Navicula sp.; n2: Nitzschia sp.; o1: Oocystis sp.; o2: Oscillatoria sp.;p1: Pandorina sp.; p2: Pediastrum sp.; p3: Phacus sp.; p4: Phormidium sp.; p5: Pinnularia sp.; p6: Planktothrix sp.; s1: Scenedesmus spp.; s2: Schroederia sp.; s3: Spirogyra sp.; s4: Spirulina sp.; s5: Staurastrum sp.; s6: Stauroneis sp.; s7: Stichococcussp.; s8: Stigeoclonium sp.; s9: Surirella sp.; s10- Synedra sp.; t1: Tetraedron sp.; t2: Tetrastrum sp. and t3: Trachelomonas sp.).

According to Palmer index (Table 10.1), Doraikere, Deepanjali Nagar, Srinivasapura, Hosakerahalli lakes had moderate pollution as the Palmer index for these lakes was 10 -15.On the other hand, lakes such as ISRO Layout, Uttarahalli, Sankey, Yediyur, Sompura, Ullal, Hemmigepura, Konasandra, Dubasipalya, Mallathahalli, and Kempambudhi had high organic pollution (with Palmer index of 20 or more) in the present study. The dominance of species such as Chlorella, Oscillatoria, Pediastrum, Scenedesmus, Nitzschia, Melosira, Gomphonema, Navicula, Euglena, etc., indicates of organic pollution in water bodies (Kshirsagar, 2013; Parvez et al., 2019). The Palmer algal genera pollution index was in the range of 15 -24 in Vaigai river in Madurai (Noel and Rajan, 2015), while 5 - 28.6 in Chulband River in Maharashtra,which indicate organic pollution (Shahare, 2017). Palmer pollution index revealed that Sidi Abderrahmane is an oligotrophic or mesotrophic reservoir (Belokda et al., 2019). Any alteration in the water quality of the aquatic ecosystem will alter the phytoplankton community structure. Only pollution tolerant species can thrive well in polluted waters, whereas sensitive species disappear, as they cannot survive in polluted water. Thus, phytoplankton serves as a bioindicator for assessing the extent of pollution of water bodies (Abdulwahid, 2016).

Table 10.1: Palmer Pollution Index of Lakes Based on algal genus

The physico-chemical parameters are the major factors controlling the structure and composition of phytoplankton in freshwater ecosystems. Phytoplanktons are an important component of an aquatic ecosystem and are responsible for primary productivity. The development of phytoplankton was favored by higher concentrations of nutrients and organic matter (Devercelli and

O’Farrell, 2013). Water quality parameters such as temperature, pH, DO, EC, BOD, COD, phosphate, and nitrate influenced phytoplankton species composition and distribution (Cheraghpour et al., 2013). Phytoplankton grows well in nutrient- rich water and removes BOD, COD, and nutrients while providing oxygen to other aquatic organisms (Kiran et al., 2016). The abundance of Chlamydomonas sp., Cyclotella sp. and Euglenophyta indicates the excessive availability of N and P as these species are organic-tolerant (Ghobara and Salem, 2017). Domestic sewage rich in sodium triphosphate (a component in synthetic detergents) can stimulate the growth and proliferation of Chlorella vulgaris (Marchello et al., 2015). Cyclotella meneghinianais an indicator of organic pollution (Krupa et al., 2016). Euglena sp. is a biological indicator of high organic pollution (Mahapatra et al., 2011). A shift in phytoplankton composition towards bloom-forming cyanobacteria occurs due to excess phosphorus loading (Lv et al., 2011).

Cyanobacteria were found to be dominant in lakes of Vrishabhavathi valley, Bangalore. Microcystis sp. belonging to Cyanobacteria formed surface blooms in Doraikere, Uttarahalli, ISRO layout, and Yediyur lakes. Microcystis can tolerate high temperatures and hence survive well in warm, shallow, and eutrophic environments (Ke et al., 2008). They form blooms in water bodies due to the buoyant nature imparted by the gas vacuoles (Muthukumar et al., 2007). The surface blooms of Microcystis sp. will decrease the water transparency as well as light irradiation to bottom layers, which in turn inhibits the growth of benthic algae (Su et al., 2014; Teta et al., 2017). Harmful algal blooms (HABs) forms surface scum, induce hypoxia, alter the food web, and produce toxins thus, threatens ecosystem and human health (Fang et al., 2019; Lu et al., 2013; Catherine et al., 2013). Excessive phytoplankton growth eventually causes depletion of dissolved oxygen (DO) and kills fish as well as other aquatic life. Cyanobacteria produce toxic secondary metabolites called cyanotoxins, such as hepatotoxins, dermatotoxins, neurotoxins, and cytotoxins (Du et al., 2019; Beaver et al., 2018). These cyanotoxins differ in their chemical structure and toxicology (Merel et al., 2013). The hepatotoxic microcystins (MCs) produced by Microcystis sp. blooms at higher water temperature, pH, and dissolved oxygen (Wu et al., 2014). Generally, species under Cyanophyceae are resistant to inorganic pollution, organic pollution, untreated effluents, and anaerobiosis (Marchello et al., 2015). In Dongping lake, temperature and chemical oxygen demand (COD) are the main drivers of the cyanobacterial community composition (Lu et al., 2013). Cyanobacteria biomass increased with a rise in total phosphorus concentration (Yang et al., 2016). Microcystis aeruginosa, Spirulina sp., and Oscillatoria sp. are bioindicators of eutrophic conditions (Wijeyaratne and Nanayakkara, 2020). The cyanobacterial dominance was influenced by rainfall, flow, water temperature, EC, DO, pH, total nitrogen, nitrate, ammonia, total phosphorus, ortho-phosphate, chlorophyll–a, BOD, COD, total organic carbon, iron, and silicon dioxide content (Kim et al., 2019). Cyanophyta grows faster under conditions such as increased temperature, salinity, and light intensity with less water turbulence (Kouhanestani et al., 2019).

Water quality monitoring and management of lakes at regular time intervals are compulsory to control water pollution and deterioration of freshwater ecosystems. The integration of wastewater treatment plants with constructed wetlands and algal pond helps in efficient nutrient removal from urban lakes (Ramachandra et al., 2018). The periodic monitoring of lakes will also aid in bloom management. It is necessary to control the levels of nutrients, dissolved oxygen, and organic matter content of water bodies to prevent algal blooms (Chen et al., 2018). Aeration of water bodies will increase the dissolved oxygen levels and reduce bloom formation tendency (Ramachandra et al., 2015). A ban on the use of phosphate-containing detergents and improved wastewater treatment can reduce the phosphorus (P) loads in lakes. Reductions in nutrient (N and P) input are requisite for effective long- term control of cyanobacterial algal blooms (Paerl et al., 2011). The increasing water pollution and harmful cyanobacterial algal blooms due to nutrient enrichment in lakes pose a severe threat to freshwater resources and biodiversity.