Introduction

Wetlands are essential element in biological diversity and ecosystem function. This utility performance varied by the hydrological and ecosystem functions (Banner and MacKenzie 2000). This is a most productive system which carried out critical regulatory functions of hydrological process within the watershed (Banner et al. 1988). In natural condition, wetlands provide essential habitat for many wildlife such as fish species, animals, birds, amphibians and mammals, plant species (macrophytes, plankton diversity) and focal ecosystem for many other (Hernandez and Mitsch 2007; Palit and Mukherjee 2012). Aquatic plants can act as measurable indicators of the ecological conditions of surface waters. Notably, the submerged species strongly dependent on water quality have proved to be vulnerable to changes in the aquatic environment (Robach et al. 1996; Dawson et al. 1999). The light transmission decreased by an increase in water turbidity, which leads to changes in community structure and reduction in vegetation diversity and depth (Middleboe and Markager 1997; Duarte et al. 1986; Chambers and Kalff 1985). Thus, submerged macrophytes are considered to be suitable eutrophication indicators and are sensitive to local environmental conditions (Dennison et al. 1993; Lacoul and Freedman 2006; Sondergaard et al. 2010). Aquatic vegetation can also influence the abiotic conditions (Flessa 1994; Moore et al. 1994; Barko et al. 1991) and influence wetland biota across multiple trophic levels (Norlin et al. 2005) by providing both habitat and food. The hydrodynamics of the ecosystem varied with soils, topography, climate, hydrology, water chemistry, vegetation and other factors including human disturbance. The fast development of industrial, urban and agricultural activities contributes to the significant alteration and destruction of wetland (Banner and MacKenzie 2000). Present day, the importance of wetlands has focused considerably due to rich potential production, untapped resources and productive nourishment resources for many other organisms (Williams 1990; Secmen and Leblebici 1996). However, the aquatic macrophytes also influence the water quality changes and being used as bio-indicator of pollution (Tripathi and Shukla 1991). The present study aims to investigate the status of the physiochemical parameters and its influences of aquatic macrophytes in seasonal wetland ecosystem in the southeast coast of India.

Materials and methods

Description of the study area

Tharavai wetland is one of the essential and largest freshwater seasonal wetland ecosystems in Ramanathapuram district of Tamil Nadu, southeast coast of India (Fig. 1), which receives water mainly from precipitation. Water is available throughout the year and is mainly used for bathing, washing clothes, sheep grazing, irrigation and fishing by local folks. This wetland originates from Regunathapuram village near to Ramanathapuram town, and it has a length of 35–40 km and breadth of 100–200 m up to Pirappanvalasai village. Then it flows in the small channel (30–40 m breadth) and finally ends in Palk Bay coast. The total area of wetland ecosystem is 2425.6 ha (245.5 sq. km)—a total stretch of 15 km of Tharavai wetland located along the coastal villages like Thamaraikulam, Ammapattinam, Pudumadam, Nariyurani, Katukkavalasai, Notchiyurani and Manankudi. Of this, three sites were selected for sampling, namely Thamaraikulam, Pudumadam and Manankudi.

Fig. 1
figure 1

Sampling stations in Tharavai wetland, Ramanathapuram district of India

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Station 1 is the Thamaraikulam site located 2.5 km away from Pudumadam village. The breadth is 100–200 m, and the depth is 5–10 feet. The northern part of this station is occupied by agricultural lands mainly paddy field, cereals and coconut cultivation. Station 2 is Manankudi village is situated in the eastern side of Pudumadam village and ending in Pirappanvalasai village. The length of this station is 4 km long, and breadth is 100–200 m. The depth varies between 5 and 10 feet. However, the northern part of the Manankudi is also covered by agricultural lands, mostly paddy fields. Station 3 is situated in Pudumadam: The breadth varies from 100 to 200 m, and the depth varies between 5 and 10 feet. This station is situated in the southern part of wetland, and the agricultural lands cover the northern part. The bank of this wetland is maximum covered by Acacia sp. and Cyperus sp., and some other varieties of grasses (Poaceae family members) were also present.

Physicochemical parameters analysis

To estimate the physicochemical water quality, the samples were collected from the selected study area (north and south direction) fortnightly from February 2010 to January 2011. Temperature, pH and salinity were observed in situ using a mercury-in-glass thermometer, portable pH meter (Eutech, Japan) and refractometer (ATAGO, Japan), respectively. The dissolved oxygen (DO) was determined by using Winkler’s method (APHA 1998). Turbidity (NTU) and conductivity were measured using a TN-100 turbid meter by Eutech Instruments, Singapore, and conductivity meter, model 1601, respectively. Total dissolved solid was described using the method by Goel and Trivedy (1987).

Identification of wetland flora

The aquatic plants were collected from the study area; it was preserved by using formalin in polythene bags. All the aquatic hydrophytes were identified using standard protocols published in books and literature (Nair and Henry 1983; Henry et al. 1987, 1989; Matthew 1991; Saini et al. 2010).

Statistical analysis

The statistical tools such as ANOVA, correlation and water quality index (WQI) of physicochemical parameters were used. Pearson correlation (r) test was carried out to identify the association between pairs of variables for sampling stations and the number of species. The water quality index is a fundamental mathematical tool for calculating a single value from multiple test results. The results of an index represent the level of water quality in a given water basin such as lake, river, stream and ponds too (Akkaraboyina and Raju 2012). WQI attempts to provide a mechanism for presenting a cumulatively derived, numerical expression defining a certain level of water quality (Miller et al. 1986). In this study, seven parameters were chosen to calculate the water quality index, using the standards of drinking water quality recommended by BIS (1993) and ICMR (1975).

Water quality index of wetland water is calculated by adopting Chaterjee and Razuddin (2002) method, and the formula is given below.

$${\text{WQI}} = {{\mathop \sum \limits_{n = 1}^{n} q_{n} w_{n} } \mathord{\left/ {\vphantom {{\mathop \sum \limits_{n = 1}^{n} q_{n} w_{n} } {\mathop \sum \limits_{n = 1}^{n} w_{n} }}} \right. \kern-0pt} {\mathop \sum \limits_{n = 1}^{n} w_{n} }}$$

where qn = 100 [Vn − Vio]/(Sn − V10)], qn quality rating for the ith water quality parameter; Vn estimated value of the ith parameter at a given sampling station; Sn standard permissible value of ith parameter; Vio ideal value of ith parameter in pure water (pH 7, DO = 14 mg/l and for other parameter = 0); Wn unit weight for ith parameter; Sn standard value for ith parameter; K constant for proportionality.

Results and discussion

Physicochemical parameters are considered as one of the most important factors that are capable of influencing the aquatic environment and have shown wide temporal and spatial differences. The statistical variations in the water quality parameters for the seasonal wetlands are presented in Table 1. Temperature is one of the controlling factors, which alter the functions of the aquatic ecosystem, and it influences the growth and distribution of flora and fauna (Dwivedi and Pandey 2002; Singh and Mathur 2005; Jalal and Sanalkumar 2012; Tank and Chippa 2013). Temperature has been determined using the pattern of distribution of macrophytes, thereby influencing the productivity and species composition, and this is varied with depth, season and geographical location. The temperatures were consistent between the wetland to wetland; it was ranged from 30.78 ± 0.45 to 31.57 ± 0.32 °C with this minimum and maximum being recorded in station 2 and station 1, respectively. It is not easy to discriminate the difference between the effects of environmental variables on the distribution of aquatic macrophytes. Human effect on the environment and natural activities switches the structure of aquatic macrophytes. Eutrophication affects the concentration of distribution, diversity and density and productivity of macrophytes. The pH, which regulates the acidic or basic characteristics, is a vital property of any aquatic ecosystem since all the biochemical functions and retention of physicochemical attributes of the water are greatly depending on pH of the surrounding environments (Jalal and Sanalkumar 2013). Most of the similar studies recommended that water samples are slightly alkaline due to the presence of carbonates and bicarbonates (Tank and Chippa 2013; Gopalkrushna 2011; Verma et al. 2012). This can be toxic when it is more than the desirable limit and can influence the ammonia, hydrogen sulfide and heavy metals (Klontz 1993). The higher level of pH can affect the aquatic life at a certain level; however, an optimum level of 7–8.5 was recommended by BIS (2003). In the present study, the pH was ranged from 7.32 ± 0.11 to 7.43 ± 0.07 with lowest and highest being recorded in station 1 and station 2, respectively. This finding was supported by Bala and Mukherjee (2010) who observed the pH 5.34–8.67 in Nadia wetlands of West Bengal. The minimum concentration of salinity 0.82 ± 0.02‰ was recorded in station 2, and the maximum of 1.13 ± 0.15‰ was recorded in station 1. The salinity is the main physical parameter that can be attributed to the biological diversity, which acts as a limiting factor and influences the distribution of aquatic organisms (Kouwenberg 1994; Neelam and Nair 1997; Chandramohan and Sreenivas 1998; Balasubramanian and Kannan 2005; Sridhar et al. 2006). Electrical conductivity is the measure of the ability of an aqueous solution to transmit an electric current in the aquatic environments (Lodh et al. 2014). It was found to be lowest of 355.75 ± 4.4 ms/cm in station 2 and highest of 435.33 ± 43.88 ms/cm in station 1. Bala and Mukherjee (2010) supported this study results. Total dissolved solids (TDS) are the materials dissolved in water like bicarbonate, sulfate, phosphate, nitrate, calcium, magnesium, sodium and organic ions. In the present study, TDS ranged from 230 ± 7.56 to 275.42 ± 12.77 ms/cm with the minimum in station 2 and maximum recorded in station 1, which is influenced mainly by the urbanization, fertilization runoff (agricultural) and domestic effluents. Present findings correlated with the earlier reports (Bala and Mukherjee 2010). Dissolve oxygen (DO) regulates that health of the ecosystems refers to the volume of oxygen present in the water body. It is an important water quality parameter to maintain because of its significant biological and physicochemical property of surrounding water. Oxygen enters into the water by aerial diffusion and as a photosynthetic by-product of aquatic plants (Kotadiya Nikesh and Acharya 2014). The DO depends upon the temperature, salinity and pressure of the water. The DO level indicates the degree of pollution in the water bodies (Gopalkrushna 2011). A minimum DO of 5 mg/l is recommended (BIS/ICMR). In the present study, a minimum of 7.88 ± 0.1 mg/l was recorded in station 3, whereas a maximum of 8.05 ± 0.1 mg/l was recorded at station 1. However, in the present study, the dissolved oxygen was found to be more than the required limit and has negative influences toward these wetland ecosystems. Turbidity is the expression of optical property by which light is scattered by the colloidal particles present in the water. Phytoplankton, microscopic organisms, clay and other organic matter make a lake turbid (Das and Shrivastva 2003). In the present study, the minimum concentration of 4.57 ± 0.4 NTU was recorded in station 2 and the maximum of 5.38 ± 0.53 NTU was recorded in station 1, which was supported by Bala and Mukherjee (2010).

Table 1 Physicochemical characteristics of Tharavai wetland at selected sampling stations
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The correlation matrix of mean water quality parameters was computed to assess the relationship between the stations. The salinity was strongly positively correlated with electrical conductivity (r = 0.987), total dissolved solids (r = 0.987) and turbidity (r = 0.975) at p < 0.01 level. The conductivity was strongly positively correlated with TDS (r = 0.989) and turbidity (r = 0.984) with a statistical significance level of 0.01, whereas electrical conductivity was negatively correlated with the distribution of species (r = − 0.877) at p < 0.05 level. Total dissolved solids were strongly correlated with turbidity (r = 0.956) at p < 0.01 level and negatively correlated with the distribution of species numbers (r = − 0.824) with a statistical significance level of 0.05 level. Turbidity was also negatively correlated with the number of species (r = − 0.858) with statistically significant at the 0.05 level (Table 2). One-way analysis of variance (ANOVA) was conducted to explore the relationship of water quality in the different study areas. The statistically significant difference at a p < 0.05 level was noted in temperature, conductivity and total dissolved solids for the three stations, whereas no significant difference at the p > 0.05 level was observed for the parameters such as pH, salinity, dissolved solids and turbidity at all stations (Table 1).

Table 2 Pearson correlation coefficients matrix between the physicochemical parameters of study area
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Our results highlight that there is considerable variability in the aquatic habitat of seasonal wetlands. Physicochemical factors that alter the habitat structures differences among wetland classes are recognized, as are biogeographic factors significant that structuring wetland communities on broad spatial scales (Brazner and Beals 1997; Lougheed et al. 2001). The strong relations between common plant species and water properties potentially impact the changes in aquatic ecosystems (Naiman et al. 1993), and we made comparisons only among the water parameters and aquatic vegetations of the seasonal wetlands. In the present study, a total of 15 aquatic plant species were recorded; these species belong to 7 classes, 9 orders and 12 families. A total of 7 submerged macrophytes, namely Ceratophyllum demersum L., Egeria densa Planch., Lemna minor L., Marsilea quadrifolia L., Sagittaria guayanensis and Isoetes riparia; 6 rooted floating weeds, namely Potamogeton nodosus Poir., Nymphaea odorata Aiton., Nelumbo nucifera Gaertn., Myriophyllum spicatum L. and Hydrilla verticillata; 1 floating, namely Eichhornia crassipes Kunth; and 1 rooted macrophyte, namely Najas minor, were recorded in Tharavai wetland (Table 3). Adhishwar and Choudhary (2013) observed 137 macrophytes belong to 50 families in Gogabil lake wetland, Bihar, India. The submerged and free-floating hydrophytes have a higher capacity to remove large concentrations of nutrients (Sooknah and Wilkie 2004; Greenway 1997). Water hyacinth can be used to remove the higher amount of nitrogen concentration per square meter of growing area and is the fastest growing plant in the wastewater (Rakocy and Allison 1981). Free-floating macrophyte of Eichhornia crassipes Kunth was present in Thamaraikulam station. It has positive and negative impacts on the ecosystem and water quality. It affects the diminished flow of water, degrades the water quality by air water interface and greatly reduces the dissolved oxygen concentration in the water bodies (Penfound and Earle 1948). This also reduces the biological diversity, eliminating the native submerged species by obstructive sunlight and eliminating the underwater species like fishes (Gowanloch 1944). Water quality index is used to evaluate the status of characteristics of water table, the rage of the quality index and their status presented in Table 4. Water parameters regulates the distribution of aquatic plants; the temperature ranged from 30.5 to 32.7 °C; pH 7.0–7.7; salinity 0.7–1.6 ppt; conductivity 355–586 ms/cm; TDS 249–320 ms/cm; DO 7.8–8.4 mg/l; and turbidity 3.7–6.6 NTU with minimum of 7 species were recorded at station 1 due to the very poor water quality status and this evidenced from the results of WQI 86.25 (Table 5). This may be attributed due to increased suspended solids and algal blooms (Ruttner 1953; Wetzel 1966); this is conformed from the results of turbidity which was found to be higher in station 1 than that of other stations. This finding was also supported by Dennison et al. (1993) who studied the water quality by using aquatic submerged weeds in the Chesapeake Bay. Ten species were recorded in station 2, with the temperature ranged from 29.5 to 32.5 °C; pH 7.2–7.6; salinity 0.8–0.9 ppt; conductivity 348–372 ms/cm; TDS 214–254.5 ms/cm; DO 7.7–8.4 mg/l; and turbidity 3.7–6 NTU. However, 12 plant species were observed in station 3 due to irregular conditions with temperature ranged from 30 to 32 °C; pH 7.1–7.6; salinity 0.8–1.0 ppt; conductivity 350–378 (ms/cm); TDS 223–250 (ms/cm); DO 7.4–8.1 mg/l; and turbidity 3.9–6.0 NTU. The estimated WQI was around 78.10 and 77.72 for stations 2 and 3, respectively (Table 5), which means it has very poor water quality. This finding was conformed by other observation of Thakor et al. (2011) Jena et al. (2013) and Korgaonkar et al. (2014). Besides, WQI variation has been attained due to the presence of additional submerged vegetation which means it has 5 submerged aquatic vegetation out of 7 submerged vegetation in stations 2 and 3. Dennison et al. (1993) reported the submerged aquatic vegetation as an indicator of water clarity and nutrient level of the water.

Table 3 List of aquatic plants species that were recorded in the seasonal wetlands
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Table 4 Water quality index (WQI) and status of water quality status
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Table 5 Water quality index of the seasonal wetlands
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Conclusion

The water quality characteristics directly affect the aquatic macrovegetation, which was evidenced from the water quality index of the wetland. Also, the status of water quality varied due to usage and topography. Moreover, this study concludes that submerged aquatic vegetation is an indicator of the water quality parameter, despite the needed to take some adaptation measure to maintain the water quality for more extended domestic use.