Physicochemical parameters
During the present study, the values for air temperature varied between 240 C -34.60 C and that of water temperature ranged between 220 C-32.90 C. The highest values for both air temperature and water temperature were recorded in summer at while the lowest in winter. In general, surface water temperature fluctuations appear to follow the atmospheric temperature (Golterman, 1975 and Hutchinson, 1976). Water current affects the invertebrate production in response to change in flow regime (Wetzel, 1983; APHA, 1998; Sharma et al., 2004). During the present investigation, water flow lies within the range of 1.4 cms-1 in summer at Station-6 to 13 cms-1 in monsoon at Station-4.
Turbidity of any water sample is the reduction of transparency due to the presence of particulate matter such as clay or silt, organic matter, plankton and other microscopic organisms (Trivedi and Goel, 1986; Dutta et al., 1988; Srivastava et al., 2011). During the present investigation, the value for turbidity ranged from 7 NTUs at Station-1 in winter to 16 NTUs at Station-5 in post monsoon due to the turbid surface run off as also observed by Venkatesharaju et al. (2010) in Cauvery River.
pH is an important parameter which evaluate the acid-base balance of water (Trivedi and Goel, 1986; Hall et al., 1987; Sharma et al., 2011). pH is significant in determining the suitability of water for various purposes, including toxicity to animals and plants (Venkatesharaju et al., 2010). During the present investigation, the range of pH varied from 6.1-8.8 representing slightly acidic to alkaline water in the Barna stream due to increased input of leached domestic wastes from several waste dumps, erosional and surface run off and other human activities within the watershed.
Total Dissolved Solids (TDS) gives an indication of the degree of dissolved substances and depends on various factors viz., amount of surface runoffs, geological character of catchment area and rainfall (Golterman, 1975; Singh et al., 2010a). During the present investigation, the range of TDS varied from 90-320 ppm at various stations. In the present study, the highest value was observed at Station-1 because of addition of domestic and other wastes through the catchment area (Kumar et al., 2011a and Singh et al., 2010b) while lowest value at Station-6 as the stream consist of soils and rocks which were not easily dissolved or of brief contact time with more easily dissolved rocks as explained by Neal et al. (2004) while preparing scientific investigation report of Indian River, Alaska.
Conductivity of water is a measure of capacity to conduct electrical current and depends on the concentration of ions and load of nutrients. The conductivity, thus also serves as a good and rapid measure of the total dissolved solids in water (Wetzel, 1983; Golterman, 1975; APHA, 1998; Srivastava et al, 2011). In the present investigation, the range of conductivity varied from 127 µScm-1 at Station-1 in summer 420 µScm-1 station-6 in monsoon. Some workers found the value for electrical conductivity within 189-1046 mhos in Ganga River, Ghazipur (Yadav and Srivastava, 2011); 340-734 mgl-1 in Yamuna River, Uttar Pradesh (Kumar et al., 2011a); 194.5-1030 µScm-1 in Krishna River, Western Maharashtra (Prasad and Patil, 2008).
During the present investigation, the value for total alkalinity was recorded 180 mgl-1 at Station- 6 in post monsoon and 390 mgl-1 at Station-5 in summer. Same findings were recorded during summer in Thoubal River, Manipur (Singh et al., 2010a) and in Cauvery River (Venkatesharaju et al., 2010) with maxima in summer due to evaporation of water and minima in rainy season were due to dilution with rain water and the highest values recorded in the dry seasons were also accredited to low volume of stream water which brought about concentration effects (Kumar et al., 2011a) as also observed during the present study. Similar values of total alkalinity were recorded by many workers. Such as 123-240 mgl-1 in Yamuna River, Uttar Pradesh (Kumar et al., 2011a); 92-231 mgl-1 in Ganga River, Varanasi (Mishra et al., 2009); 13-246 mgl-1 in Ganga River, Kanpur (Trivedi et al., 2009).
An important indicator of the condition of an aquatic ecosystem is the concentration of dissolved oxygen in water. DO is considered as the factor which reflects physical and biological processes taking place in the water body (Hutchinson, 1976; Wetzel, 1983; Trivedi and Goel, 1986; Karthick and Ramchandra, 2007; Ridanovic et al., 2010; Kumar et al., 2011a). DO is greatly influenced by temperature, photosynthesis and respiration activities prevailing in the stream water (Varunprasath and Daniel, 2010). In the present study, the value for dissolved oxygen was ranging from 3.1–8.6 mgl-1 at different stations. Additionally, the higher value for DO was observed at Station-1 in winter and lower at Station-4 in pre monsoon. In the present study, higher values of DO were recorded at stations having forested land use in the catchment area while lower values at stations having other land use categories with human interference as also observed by Chattopadhyay et al. (2005) in Chalakudy river basin, Kerala. Similar values range from 6 to 9.27mgl-1 in Venkatapura catchment, Karnataka (Karthick and Ramchandra, 2007); 4.90-8.50 mgl-1 in Yamuna River, Uttar Pradesh (Kumar et al., 2011a); 1.8-5.8 mgl-1 in Ganga River, Varanasi (Mishra et al., 2009) and 4.2-4.6 mgl-1 in Narmada River (Sharma et al., 2011) were recorded by many experts. Moreover, Venkatesharaju et al. (2010) and Thirumurugan (2000) opined that DO values were found maximum in winter due to natural turbulence and higher algal productivity that produces O2 by photosynthesis or associated with the low temperature. Likewise, the lower value in pre monsoon was attributed to high temperature.
Total hardness is mainly imparted by the calcium and magnesium and describes the effect of dissolved minerals, determining suitability of water for domestic, industrial and drinking purposes and attributed to the presence of bicarbonates, sulphates, chloride and nitrates of calcium and magnesium (Wetzel, 1983; Trivedi and Goel, 1986; APHA, 1998; Singh et al., 2010a and Kumar et al., 2011a). During the present study, the value for total hardness was recorded between 75-230 mgl-1 at different stations. The maximum value was reported at Station-4 while the lowest value at Station- 2 & 6. The higher values of total hardness ascribed to reduced inflow, evaporation of stream water and discharge of sewage, use of soaps and detergents, washing, bathing by local inhabitants as also observed by Kumar et al. (2011b) in Sabarmati River and Singh et al. (2010a) in Manipur river system during their research work. Similar results for total hardness were recorded by several experts. For instance, the value for total hardness varying from 106-246 mgl-1 in Ganga River, Kanpur (Thareja et al., 2011), 122-212 mgl-1 in Ganga River, Kanpur (Trivedi et al., 2009), 230-475 mgl-1 in Yamuna River, Uttar Pradesh (Kumar et al., 2011a), 182.1-300 mgl-1 in Ram Ganga River, Uttar Pradesh (Chandra et al., 2011).
The value for Ca hardness was recorded between 26-101 mgl-1 at different stations during the present investigation. The highest value was observed at Station-5 in monsoon and lowest value at Station-2 in pre monsoon. These values were caused due to domestic sewage, addition of calcium and magnesium salts used for washing purposes in the catchment as also reported by Kumar et al. (2011a) in Yamuna River. Similar values for Ca hardness were observed 21.12-185.9 mgl-1 in Ramganga River, Moradabad (Srivastava et al., 2011); 60.7-106.7 mgl-1 in Cauvery River, Karnataka (Venkatesharaju et al., 2010); 6-36 mgl-1 in Chhoti Gandak River, Uttar Pradesh (Bhardwaj et al., 2010); 12-21 mgl-1 in streams of Cauvery River (Begum and Harikrishna, 2008). In the present study, calcium content raised in wet spell due to its greater solubility at lower temperatures and while in dry spell it was due to rapid oxidation of organic matter in the substrate utilized by the phytoplankton that decline the values of calcium in the stream water as also declared by Sunder (1988) and Umamaheswari and Anbu saravanan (2009) while working in different rivers of India.
Chlorides are salts resulting from the combination of the chlorine with a metal and in combination with a metal such as sodium; it becomes essential for life (Golterman, 1975; Trivedi and Goel, 1986; APHA, 1998; Dikio, 2010; Singh et al., 2010a). In the present study, the value for chloride ranged between 15.7-45.3 mgl-1 at various stations. The higher values were recorded at Station-1 in summer and lower at Station-3 in post monsoon. During the present investigation, the chloride enter the stream from different anthropogenic activities like sewage effluents, run off from agricultural fields, animal feeds, washing of cloths and use of leaching agents by launderers in the catchment area as also observed in river Kali near Dandeli, Karnataka (Murthi and Bharati, 1997) and in Cauvery River, Tamilnadu (Kalavathy et al., 2011). Similar results for chloride ranging from 7-26 mgl-1 in Ganga River, Kanpur (Trivedi et al., 2009; Thareja et al., 2011) and 18-32 mgl-1 in Yamuna River, Uttar Pradesh (Kumar et al., 2011a) were reported by many workers.
The main source of sulphur is the rocks present near to the water bodies and biochemical action of anaerobic bacteria (APHA, 1998; Umamaheswari and Anbu saravanan, 2009). In the present study, the concentration of sulphate ranged between 9-35.6 mgl-1 at various stations. The higher range was recorded in monsoon whereas the lower value in summer in the present study. The low content of sulphate recorded from different stations in the present study due to the absence of any industrial pollution in the catchment area of Barna stream network as also reported by Umamaheswari and Anbu saravanan (2009) in Cauvery river basin. The lower values of sulphate also associated with the easily precipitation and settlement to the bottom sediment of the stream as suggested by Kumar et al. (2011a) while working on Yamuna River, Uttar Pradesh.
Silica is one of the basic nutrients in water and it is quite abundant on the earth but silicates remain meager in water. The major source of dissolved silica in stream water is the weathering of rocks and mineral in the catchments area (Nath and De, 1998; Nath and Srivastava, 2001). In the present study, the concentration of silicate was ranged between 1.2-10.2 mgl-1 at different stations. The maximum concentration was observed at Station-5 in monsoon while the minimum value at Station-6 in winter. The pronounced reduction of silica ions in winter related to the low flow discharge of stream water as also recorded in Nile River (Shehata and Badr, 2010). The value for silicate was observed 2.80-13.80 mgl-1 in Chambal River (Saksena, et al., 2008).
Nitrate and phosphate determinations are important in evaluating the potential biological productivity of surface waters and the source of nitrate is the biological oxidation of organic nitrogenous substances in stream waters (Nath and De, 1998; Adoni et al., 1985; Venkatesharaju et al., 2010). In the present study, the concentration of nitrate was observed between 0.16-0.901 mgl-1 at various stations. The maximum concentration of nitrate was recorded at Station-5 while the minimum values were recorded at Station-1 during the present investigation. The value of nitrate in the present study varied from one station to another and it was recorded higher at Station-5 due to agricultural practices and lower at Station-1 because of forested land in the watershed as also observed by Chattopadhyay et al. (2005) in Chalakudy river basin. Similar values for Nitrate with 1.38-2.6 mgl-1 in Ganga River, Varanasi (Mishra et al., 2009) and 0.008-0.024 mgl-1 in Chambal River (Saksena et al., 2008) were recorded by several workers. During the present study, the concentration of orthophosphate was recorded maximum 0.69 mgl-1 at Station-5 in monsoon and minimum 0.20 mgl-1 at Station-1 in summer. The increased application of fertilizers, use of detergents and domestic sewage very much contributed to the heavy loading of phosphorous in the stream water. Similar findings were recorded by many workers. For instance, the concentration of Phosphate in Narmada river water sample was found to be in the range of 0.16-0.28 mgl-1 (Sharma et al., 2011).
The present study indicates that variations in water quality were seasonal and also linked to land use practices in the watershed. The stations having human interferences showed significant deterioration in water quality as also observed by Chattopadhyay et al. (2005). This study has brought out that there is a definite relationship between water quality and land use in the catchment area and anthropogenic activities are the main contributors to make changes in physicochemical component of Barna stream waters.
Principal Component Analysis
PCA quantifies relationship between the variables by computing the matrix of correlations for the whole dataset (Bhardwaj et al., 2010). In the present investigation, PCA of physicochemical components resulted in three principal components (with eigen values more than one) together accounted for 95.14% of the total variance in the dataset. In the present investigation, PCA of physicochemical components resulted in three principal components (with eigen values more than one) together accounted for 95.14% of the total variance in the dataset. The first axis explained 39.50% of variance, second axis explained 35.57% of variance, and the third axis explained 20.06% of the total variance. The eigen values (more than one) and the high values for all 17 physicochemical variables were used to assess the physicochemical variables imposing more impact in the present study and hence principally responsible for causing alterations. First axis explaining 39.50% of variance was found to be highly correlated with Ca hardness followed by total hardness, Mg hardness, silicate and water flow. Second axis that was explaining 35.57% of variance was found to be positively correlated with orthophosphate and turbidity whilst strongly negative correlated with conductivity, TDS, free CO2 and Chloride. The third axis explaining 21.80% of variance was found to be highly positive correlated with air and water temperatures and negatively related with DO and pH. The role of catchment area and anthropogenic activities on the water chemistry can be better assessed with the help of scatter diagrams elucidating the processes responsible for controlling the stream waters in the Barna stream network. The eigen values and component matrix is presented in Table 1 and Table 2.
Table 1 Total Variance Explained
Component
|
Rotation Sums of Squared Loadings
|
|
Total
|
% of Variance
|
Cumulative %
|
1
|
7.111
|
39.508
|
39.508
|
2
|
6.403
|
35.571
|
75.079
|
3
|
3.611
|
20.062
|
95.141
|
Extraction Method: Principal Component Analysis
Table 2 Component Matrix
Parameter
|
Component
|
|
1
|
2
|
3
|
Air temperature(0C)
|
.494
|
.572
|
.654
|
Water temperature(0C)
|
.495
|
.497
|
.667
|
Water flow
|
.808
|
.043
|
.569
|
Turbidity (NTUs)
|
.579
|
.788
|
.143
|
pH
|
.055
|
.445
|
-.876
|
Conductivity(µScm-1)
|
.079
|
-.987
|
-.005
|
TDS(ppm)
|
.408
|
-.854
|
.212
|
DO(mgl-1)
|
-.123
|
.012
|
-.953
|
Total alkalinity(mgl-1)
|
.756
|
.499
|
-.369
|
Total hardness(mgl-1)
|
.933
|
-.130
|
.280
|
Ca hardness(mgl-1)
|
.970
|
-.046
|
.034
|
Chloride(mgl-1)
|
-.087
|
-.820
|
.294
|
Orthophosphate(mgl-1)
|
.687
|
.713
|
.100
|
Nitrate(mgl-1)
|
.716
|
.633
|
.272
|
Sulphate(mgl-1)
|
.730
|
.602
|
.277
|
Silicate(mgl-1)
|
.855
|
.363
|
-.199
|
Extraction Method: Principal Component Analysis
Table 3 Correlation matrix
|
AT
|
WT
|
WC
|
Tur
|
pH
|
Con
|
TDS
|
DO
|
TA
|
CaH
|
TH
|
CL
|
OP
|
Ni
|
Su
|
Si
|
ShI
|
SiI
|
MI
|
AT
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
WT
|
0.966
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
WC
|
0.8006
|
0.7887
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Tur
|
0.8302
|
0.811
|
0.5712
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
pH
|
-0.2935
|
-0.2974
|
-0.4548
|
0.2824
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Con
|
-0.5295
|
-0.4901
|
0.0247
|
-0.7527
|
-0.4515
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
TDS
|
-0.1397
|
-0.0922
|
0.4485
|
-0.4218
|
-0.5711
|
0.8784
|
0
|
|
|
|
|
|
|
|
|
|
|
|
|
DO
|
-0.6749
|
-0.6209
|
-0.6554
|
-0.1574
|
0.8768
|
-0.0562
|
-0.2762
|
0
|
|
|
|
|
|
|
|
|
|
|
|
TA
|
0.4198
|
0.3289
|
0.4428
|
0.7476
|
0.5501
|
-0.4042
|
-0.169
|
0.21
|
0
|
|
|
|
|
|
|
|
|
|
|
CaH
|
0.4668
|
0.4391
|
0.7843
|
0.5131
|
-0.0061
|
0.1463
|
0.4071
|
-0.1947
|
0.7159
|
0
|
|
|
|
|
|
|
|
|
|
TH
|
0.5626
|
0.5798
|
0.8835
|
0.4825
|
-0.2387
|
0.2049
|
0.5084
|
-0.3861
|
0.5272
|
0.9549
|
0
|
|
|
|
|
|
|
|
|
CL
|
-0.3169
|
-0.1343
|
0.037
|
-0.5838
|
-0.552
|
0.7323
|
0.7056
|
-0.1446
|
-0.6769
|
-0.1146
|
0.0995
|
0
|
|
|
|
|
|
|
|
OP
|
0.816
|
0.7738
|
0.6516
|
0.9783
|
0.2676
|
-0.6576
|
-0.2901
|
-0.1577
|
0.8356
|
0.6135
|
0.5597
|
-0.5918
|
0
|
|
|
|
|
|
|
Ni
|
0.8962
|
0.8758
|
0.7626
|
0.9654
|
0.0935
|
-0.5834
|
-0.18358
|
-0.31224
|
0.742
|
0.649
|
0.651
|
-0.4532
|
0.9796
|
0
|
|
|
|
|
|
Su
|
0.8802
|
0.8566
|
0.7492
|
0.9493
|
0.0857
|
-0.5419
|
-0.1976
|
-0.3329
|
0.7299
|
0.71083
|
0.70961
|
-0.45408
|
0.94685
|
0.97571
|
0
|
|
|
|
|
Si
|
0.5122
|
0.4954
|
0.6298
|
0.7557
|
0.3709
|
-0.3055
|
0.0631
|
0.1127
|
0.9054
|
0.73836
|
0.63918
|
-0.38556
|
0.8563
|
0.81236
|
0.74411
|
0
|
|
|
|
ShI
|
-0.1878
|
0.035742
|
0.171
|
-0.2271
|
-0.1921
|
0.505
|
0.6182
|
0.1262
|
-0.2758
|
0.11347
|
0.27448
|
0.8501
|
-0.2162
|
-0.1196
|
-0.1656
|
0.0814
|
0
|
|
|
Si I
|
-0.0474
|
0.1747
|
0.2585
|
-0.1082
|
-0.2219
|
0.3875
|
0.5715
|
0.0485
|
-0.2341
|
0.11
|
0.2878
|
0.7996
|
-0.0987
|
0.0032
|
-0.0689
|
0.1588
|
0.9832
|
0
|
|
MI
|
-0.4335
|
-0.2324
|
0.00016
|
-0.4131
|
-0.095
|
0.6879
|
0.6446
|
0.2701
|
-0.292
|
0.1601
|
0.2643
|
0.8433
|
-0.4007
|
-0.3253
|
-0.30213
|
-0.05795
|
0.9172
|
0.83
|
0
|
Macroinvertebrate Community
During the present study, the mollusca formulated the dominating group in Barna basin and constituted 59.82% of the total macroinvertebrate population. In the present survey, 70 taxa were recorded out of which 25 taxa belonging to 9 families of the two molluscan classes viz., gastropoda and bivalvia were recorded. The arthropoda comprised of second dominating group in Barna basin and constituted 38.86% of the total macrozoobenthos population and out of 70 taxa, 40 taxa belonging to 30 families were identified during the present investigation. The annelida was the third or least dominating group and constituted 1.32% of the total macrozoobenthic population collected during the present research work. In the present study, out of 70 taxa, 5 taxa belonging to 5 families of aquatic worms were collected in which 3 taxa of the class Oligochaeta, and 2 taxa of class Hirudinaria were identified.
Benthic macroinvertebrate assemblages are structured on the basis of physical and chemical parameters that in turn define the microhabitats, food availability, shelter to escape predators, and other biological parameters that influence reproductive success existing at the station (Silveira et al., 2006).
In the present study, Barna was represented by maximum number of 46 taxa followed by Satdhar representing 26 taxa, Palakmati representing 26 taxa, Jamner representing 25 taxa, Chamarsil representing 16 taxa and Narheri was represented by minimum number of 11 taxa. Similar results were observed with 34 genera from sacred Himalayan streams (Singh et al., 2010b) and 33 taxa from four Sonmarg streams of Kashmir (Bhat et al., 2011). In abroad also, a total of 67 taxa of macroinvertebrate were recorded in the River Challawa, Nigeria (Indabawa, 2010); 51 invertebrate taxa in a temporary stream of Ibiza, Balearic Islands (Garcia et al., 2008); 87 taxa were recorded in lotic ecosystems of Czech Republic (Fricova et al., 2007) and 58 taxa were identified while studying the urban and agricultural impacts on macroinvertebrate assemblages in streams of Brazil (Hepp et al., 2010). A total of 43 taxa comprising 36, 16, 19 taxa at three different stations were recorded in Ikpoba River, Nigeria (Ogbeibu and Oribhabor, 2002).
The composition, abundance and distribution of benthic macroinvertebrates can be influenced by water quality (Ezekiel et al., 2011) and catchment area (Subramanian et al., 2005). It was observed during the study that, high degree of human impact viz., discharge of domestic effluents, waste dumps and other anthropogenic activities also influenced the benthic community structure of the Barna stream network as resulted in low diversity as also observed by Subramanian et al. (2005) and, Dinakaran and Anabalgan (2007a) in certain streams of Western Ghats. Furthermore, such streams reported taxa that are more tolerant to pollution viz., Chironomus sp., Tubifex sp., Glossiphonia hetroclita, Hemopis sp. etc. while stations with less or no disturbance showed presence of taxa that are indicator of fresh water viz., Caenis sp., Hydropsyche instabilis, Hydrophilus sp. and so on.
Biodiversity Indices
Biological indices are “classical ecological indexes”, created to measure diversity as well as density in ecological communities. In the present investigation, the highest benthic diversity was observed in Barna (H’=3.39) followed by Satdhar (H’=3.075), Palakmati (H’=3.071), Jamner (H’=3.068), Chamarsil (H’=2.53) and least in Narheri (H’=2.25). The variation in diversity indices occurred at various stations was very low due to the same river basin, but variation visible was due to thermal conditions and bottom structure and local scale disturbances as also observed by Subramanian and Sivaramakrishnan (2005), Vioinskiene (2005), Rios and Bailey (2006) during their exploratory studies.
During the present research work, the value for Shannon’s index range between 2.25-3.39. In the present investigation, the highest value was recorded in Barna (3.39) followed by Satdhar (3.075), Palakmati (3.071), Jamner (3.068), Chamarsil (2.53) and least in Narheri (2.25).The value for Simpson’s Index was calculated between 0.884-0.956. The highest value was recorded in Barna while the lowest value was recorded at Narheri. In the present work, the highest value was observed in Barna (0.956) followed by Jamner (0.948), Palakmati (0.947), Satdhar (0.946), Chamarsil (0.908) and least in Narheri (0.884). During the present study, the range for Margalef’s index recorded between 1.39-5.35. The highest value was recorded in Barna (5.35) followed by Palakmati (3.09), Satdhar (3.07), Jamner (3.02), Chamarsil (1.82) and least in Narheri (1.39). Similar results with Shannon-Wiener index values vary from 0.346-2.608 in Ganga River, Patna (Jhingran et al., 1989) while Shannon’s index 0.7-2.28 , Simpson’s index 0.4 - 0.86, Margalef’s index 0.5-2.35 varied in six streams of Western Ghat (Dinakaran and Anbalagan, 2007a), Shannon-Weiner index 1.88-2.49, Simpson’s index 4.07-6.62 in five falls stream, Courtallam hills, Western Ghats and Shannon-Weiner index 1.36-2.14 in a temporary stream of Ibiza (Garcia et al., 2008) were reported by many workers in their studies.
Correlation Matrix between Physicochemical Parameters and Macroinvertebrates
The present study also highlighted the impact of water quality on the distribution and species diversity of macrozoobenthic invertebrates. The statistical relationship of molluscs with physicochemical properties revealed a positive correlation with silicate (r= 0.693 and p=0.02). It was observed that these stations were mainly comprised of molluscan species and, the higher values of silicate were due to irrigational practices and use of fertilizers in the catchment area as also observed by Subba Rao and Devadas (2003) and Bhardwaj et al. (2010) during their studies on different Indian water bodies.
The statistical relationship of arthropoda with physicochemical properties revealed a significant negative correlation with DO (r= -0.857; p=0.02). It was observed that these stations were comprised of macrozoobenthos belonging to pollution tolerant arthropods and also dissolved oxygen value depleted significantly at these stations ascribed to land use categories and local scale events other than forest here at this sampling station the same was observed in Chalakudy River basin and streams of southern Eastern Ghats which confirms the presence of pollution tolerant groups (Chattopadhyay et al., 2005 and Dinakaran and Anbalagan, 2007b).
The statistical relationship of annelids with physicochemical properties revealed a significant negative correlation with dissolved oxygen (r= -0.856; p=0.02) which confirms that some habitats in these stretches have good running water condition due to which less count of annelids and more number of arthropods were recorded from these stretches. The habitat conditions at these stations were assessed under sub optimal conditions which directly ensure the rich benthic diversity (Barbour et al., 1999).