Current World Environment

Introduction 

Among different ecosystems, wetlands constitute one of the most important ecosystems for man offering numerous regulating services. Water quality assessment of small water bodies of the wetlands are of immense importance in the management of fisheries, water supply, and irrigation. Pollution status of water bodies are usually expressed as biological and physico-chemical parameters.¹ Several authors have extensively documented the responses of macro-invertebrates to organic and inorganic pollution.^2,3 Chatla floodplain (24⁰42^/697^// N and 92⁰46^/264^//E) situated in the south of Silchar town, Barak Valley, Assam has 1500 fishery ponds and 12 seasonal lakes. (Fig.1). Although the wetland is resourceful with variety of macrophytes, trees and fishes it is in a derelict or near derelict state due to high rates of siltation, infestation of weeds, unscientific fishing activities, and  use of pesticides in the surrounding tea gardens and  agricultural fields ⁴ which led to a loss of  73% wetland area of Chatla floodplain.⁵  All these factors can affect the communities of aquatic organisms leading to loss of diversity and species extinction.⁶ Since, fluctuations in aquatic insect community can give quick information of their surrounding water quality and are commonly used as tools for marking an integrated assessment of water quality, investigation on water quality of two fishery ponds of Chatla wetland with special reference to aquatic insects was carried out.

Material and Methods

The topography of the Chatla floodplain is fenland type with small hillocks strewn among large stretches of lowland.  Pond 1 is a fish pond and is relatively undisturbed. Pond 2 is a fishery cum community pond. Water and insect samples were collected in replicates from both the sites during 2009-2010. Physico-chemical parameters such as Air temperature (AT), Water temperature (WT), Transparency, pH, Electrical Conductivity (EC), Dissolved oxygen (DO), Free CO_{2 ,} Total alkalinity (TA), Nitrate (NO₃^-) and Phosphate (PO₄ ^3-), Nitrite (NO₂^-), and Ammonium (NH₄ ⁺) content of water were analyzed by standard methods.^7,8 The aquatic insects were collected by kick method whereby the vegetation was disturbed and the circular net (mesh size 60µm) was dragged around the vegetation for one minute.^9,10 They were immediately sorted, preserved in 70% ethyl alcohol and were later identified using Dewinter advanced stereo zoom microscope with the help of standard keys.^{11,12,13,14,15,16,17} A number of identified insects were confirmed in the entomological laboratory of Zoological Survey of India. Statistical analyses were done by MS EXCEL 2007; SPSS 15.0 for Windows, Shannon Wiener Index of Diversity (H^/ ), Evenness Index (J^/) and Berger–Parker Index of  Dominance (d)  were calculated by Biodiversity professional version 2 for windows.

Result and Discussion

Different physico-chemical parameters (AT, WT, Transparency, pH, EC, DO, Free CO₂,TA, PO₄ ^3-_, NO₃ ^-_, NO₂ ^-_, NH₄ ⁺)  in pond 1 and 2 during Post monsoon 2009 to Monsoon 2010  and their mean concentrations are shown in Table 1. Table 2 showed the distribution of aquatic insects in pond 1 and 2. The significant correlations that exist among environmental variables, diversity and density of insect are shown in Table 3. Fig.2 showed the relative abundance of aquatic insect orders recorded from pond 1 and pond 2 during the study period. Relative abundance of aquatic insect families and aquatic insect species in pond 1 and 2 are shown in the Fig.3 and Fig.4 respectively.  Pattern of variation in the levels of Shannon –Weiner Diversity index (H') and Evenness index (J') and Berger-Parker index of Dominance (d) are shown in the Fig.5.The study revealed that in pond 1 and 2 both air and water temperature did not show much variation. In pond 1 DO, EC, NH₄ and NO₃ ^-concentration were slightly higher than that of pond 2 while other parameters such as Transparency, Free CO₂, TA, pH, and PO₄ ^3-concentration were recorded to be higher in pond 2. The solubility and availability of nutrients is affected by oxygen content of water and therefore the productivity of aquatic ecosystems.¹⁸ The range of DO recorded in the present study is similar to the DO concentration reported in a previous study in the same area.¹⁹ In pond 1 correlation coefficient analyses revealed a significant negative relationship of WT with pH and DO. Classical negative relationship of WT with DO was also recorded in a previous study on Chatla floodplain²⁰ which is attributed to the fact that in lower temperature oxygen carrying capacity of water increases.²¹ Negative relationship of DO with Rainfall might be an indication that surface runoff transported sewage, fertilizer etc. into the pond which have lowered DO value by bacterial respiration.²² EC was found to be higher in pond 1 (4.59ms/ppt ± 2.93) compared to pond 2 ( 3.24ms/ppt ± 0.20) where it showed significant positive correlation with TA and NO₃^-. The range  of NO₃^-. between 0.1 - 3.0   mgl^-1 is considered favorable for fish productivity.²³ In both the ponds, NO₃^-. concentration was found within the said range indicating their suitability for fish production.  In pond 2 EC showed significant positive correlation with TA, Free CO_2, and DO. Higher free CO₂ accompanied by higher TA and higher pH in pond 2 could be due to external application of lime. It is known that addition of lime increases fish production in soft (low total hardness) waters by stabilizing the pH of bottom mud and increasing the availability of Phosphorus and Carbon dioxide for photosynthesis. The overall effect of liming is to increase phytoplankton production which results in increased fish production.²⁴ Another reason might be that in heavily stocked fish ponds, Carbon dioxide (CO₂) concentration can become high as a result of respiration. High CO₂ concentrations are almost always accompanied by low DO concentrations (high respiration). Acidity of rain water has impact on the pH of natural water bodies. As rain falls to the earth, each droplet becomes saturated with CO₂ and pH is lowered.²⁵ This explained the negative relationship of Rainfall with pH in pond 1. However in pond 2 no such relationship could be found due to application of lime. In pond 1 DO has shown significant positive correlation with pH , such type of positive correlation in between DO and pH have been recorded from the study of Asa lake llorin , Nigeria²⁶ where DO distribution followed a similar annual cycle with the pH. In pond 2 DO has shown a positive significant correlation with TA. These alkalinity relationships are extremely important in water chemistry, since the most prominent water problems are deposits and corrosion, and these are closely related to the instability of each  specific water caused by the tendency of CaCO₃ to dissolve in or precipitate from it.²⁷ Range of PO₄^3- (0.86 ± 0.27  in pond 1 and 1.25 ± 0.72 in pond 2) recorded in the present study is  supported by the previous study in a marsh of the same floodplain.²⁸ Relatively high concentration of PO₄^3- in pond 2 might be due to its use as community pond where PO₄^3- is contributed by household activities such as washing, bathing etc. In pond 1 PO₄^3- has shown significant positive correlation with NO₃ ^-. Actually large concentration of PO₄^3- and NO₃ ^- reported together from a water body indicate  that water is eutrophic in nature ²⁹ but  as the water of this pond showed low concentration of both ,it indicates the water is not eutrophicated. Rainfall showed significant negative relationship with NO₃ ^- and positive correlation with NO₂^- in pond 2. A previous study conducted in the same study area also reported relatively high concentration of NO₃ ^- during dry months.³⁰ A positive correlation between NO₂^- and NH₄⁺ is supported by the fact that the most possible way of Nitrate entry in an aquatic system is through oxidation of Ammonia form of Nitrogen to NO₂^-   and to NO₃ ^- consequently.³¹
 

Table 1: Physico-chemical properties of water of Pond 1 and Pond 2 Click here to View table

 

Table 2: Distribution of aquatic insect species in Ponds 1 and 2 of Chatla floodplain during study period Click here to View table

 

Table 3: Significant Correlations among environmental variables, diversity and density of aquatic insects for pond 1 and pond 2 Click here to View table

 

Figure 1: Location of the two Ponds , 1 and 2 in the floodplain of Chatla Wetland Click here to View figure

 

Figure 2: Relative abundance of insect orders in Pond 1 and Pond 2 Click here to View figure

 

Figure 3: Relative abundance of aquatic insect families in Pond 1 and Pond 2 Click here to View figure

Aquatic insect community of pond 1 was represented by two orders- Hemiptera, Odonata; four families- Gerridae, Notonectidae, Mesoveliidae (Hemiptera), Coenagrionidae (Odonata) and seven species. Pond 2 was represented by three orders Hemiptera, Odonata, Diptera; five families- Notonectidae, Gerridae, Mesoveliidae (Hemiptera), Coenagrionidae (Odonata); Culicidae (Diptera) and seven species (Table 2). In both the ponds order Hemiptera was the most prominent order, having 98% relative abundance in pond 1 and 94% in pond 2. The most abundant family in Pond 1 is Notonectidae (64%), followed by Gerridae (32%), Mesoveliidae (2%), and Coenagrionidae (2%). In Pond 2 the relative abundance of Gerridae was highest (76%) followed by Notonectidae (16%), Coenagrionidae (5%), Mesoveliidae (2%) and Culicidae (1%) (Fig.2 and 3). The aquatic insect species found common in both the ponds were Gerris lepcha Distant, Limnogonus nitidus Mayr, Enithares fusca Brooks, Mesovelia vittigera Horvath, Enallagma sp. and Anisops barbata Brooks. In addition to these species Neogerris parvula Stål was recorded in pond 1 and Culex sp. in pond 2 (Fig.4).  Values of Shannon –Weiner Diversity index (H') and Evenness index  (J')  were found higher in pond 1 than that of Pond 2  while  Berger-Parker index of Dominance (d)  value was  found higher in pond 2 (Fig. 5).  However the H’ values were found to be less than 1 in both the ponds indicating polluted nature of water ³² . In pond 1 insect density has shown negative correlations with PO₄^3- and NO₂^-. This might be due to the reason that increased pollution level with high concentration of PO₄^3- and NO₂^-.might have disturbed the colonization as many species of aquatic insects are very susceptible to pollution or alteration of their habitat ³³.  In pond 2 density of aquatic insects showed positive correlation with Free CO₂ which might be due to increased respiration by more number of insects. Rainfall ³⁴ has shown no significant relationship with diversity or density of aquatic insects in both the ponds. The diversity of aquatic insects showed positive correlation with Transparency. Such kind of positive correlation was reported from lake Victoria ³⁵. From the study, it can be said that different physico chemical parameters of water quality are inter related and these factors influence diversity, density and distribution of aquatic insects in a particular water body.
 

Figure 4: Relative abundance of aquatic insect species in Pond 1 and Pond 2 Click here to View figure

 

Figure 5: Pattern of variation in the levels of Shannon diversity index, Evenness index and Berger-Parker dominance of different insect species in both the Ponds Click here to View figure

Acknowledgements

The authors are thankful to University Grants Commission, New Delhi, India for financial support. Authors are also thankful to the Head, Department of Ecology and Environmental Science, Assam University, Silchar, Assam for providing laboratory facilities.

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