Algal Diversity in Three Freshwater Pond Ecosystem at Tiruvannamalai,
Tamilnadu
Ramakrishnan, N., Hariprasath and Sampathkumar, K.
Government Arts College,
Thiruvannamalai - 606 603, Tamil Nadu, India
E-mail: nrk57@rediffmail.com
Algal diversity is the key parameter to determine the productive nature of the pond ecosystem. The
limnological survey and the algal diversity in three freshwater ponds in Tiruvannamalai (12 o 15’
NLand 79o 07’EL) district, Tamil Nadu was undertaken during April 2000 to March 2001. Sampling
was done at monthly intervals. Water samples were analysed for the concentration of various physicochemical parameters (water temperature, free CO2, pH, DO, nitrate, phosphate, silicate and calcium as
described in APHA (1995). Phytoplankton of all the ponds was collected by filtration of 10 liters of
water through plankton net of bolting silk (mesh size 173 / linear inches). Filtered phytoplanktons
were fixed in 4% formalin (Kumar & Singh, 2000). Enumeration of algal density was done by taking
1ml of sub sample in a Sedgwick Rafter chamber from 10ml of the sample. The results have been
expressed as units per liter. Identification of algal taxa was done using standard keys.
Phytoplankton standing crop formed two peaks in the present study i.e. during June and December.
Lowest standing crop values were recorded during rainy months in the three ponds. The division
Cyanophyta registered maximum percent of species (43.90) than other divisions followed by
Chlorophyta (29.26), Bacillariophyta (19.51) and Euglenophyta (7.31). Phytoplankton diversity was
regulated by various physico-chemical parameters and showed positive as well as negative correlation
within themselves and the phytoplankton density. Among the environmental factors, water
temperature plays a significant role in affecting the growth and abundance of phytoplankton. Multiple
regression analysis was done between phytoplankton density (var 1) and the environmental factors
(var 2 to 9), which revealed that more than 96 to 99% of variation in phytoplankton density was
influenced by these physico-chemical factors recorded in this study and only 1- 4% by other factors
which were not considered in the present study. By means of multiple regression equations embodied
in the text, the phytoplankton density may be predicted at any time.
INTRODUCTION
Environmental problems are complex and multidisciplinary in nature especially in wetland ecosystem.
In general, the importance of the diversity of planktonic and benthic organisms are generally
overlooked. The wetlands, generally associated with the rivers, lakes and reservoirs are not only rich
in biodiversity but contribute significantly to the maintenance of water quality. Major effect on
biodiversity of the aquatic system is the environmental factors and chemical aspects of the water body
(Gopal, 1997). Internationally, however, the biodiversity in freshwater environments has started
attracting some attention in recent years, for example, the issues related to biodiversity in aquatic
ecosystem in the programme of the UNESCO and the International Union of Biological Sciences.
The main aim of this paper is to draw attention to the large proportion of diversity that exists in the
freshwater environments, its importance, and the need for its conservation. The floristic component of
aquatic biota is dominated by algae in open deepwater (Round, 1977). So, in this aspect the present
study was undertaken in three freshwater ponds on their algal diversity and limnological aspects over
various months of the year.
MATERIALS AND METHODS
Three experimental ponds selected for this study are situated at the Girivalam path around
Tiruvannamalai Hill, Tiruvannamalai (12o 15’ NL and 79o 07’ EL). All the ponds are eutrophic in
nature. Sampling was done at monthly intervals from April 2000 to March 2001. Water samples were
analysed for the concentration of various physico-chemical parameters (water temperature, free CO2,
pH, DO, nitrate, phosphate, silicate and calcium) described as in APHA (1995).
Phytoplanktons of all the ponds were collected by the filtration of ten liters of water through plankton
net of bolting silk with standard mesh size (173 per linear inches). Filtered phytoplankton samples
were fixed in 4% formalin. Enumeration of algae was done by taking 1ml of sub sample in a
Sedgwick Rafter chamber from 10ml of the sample. The results have been expressed as units per liter.
Identification of algal taxa was done following the key given by Smith (1950), Desikachary (1959);
Randhawa (1959); Philipose (1967) and Gonzalves (1981).
RESULTS
A perusal of Figures 1 to 8 reveals that the physico-chemical nature of the three experimental ponds
clearly indicates of their eutrophic status. Water temperature recorded maximum value during May
for ponds I and II and for pond III in June. Minimum value was registered during November for pond
II and in other ponds in January (Fig 1). Free CO2 registered more or less same trend as prevailed in
water temperature of all the ponds. Pond III registered the lowest value than other two ponds
throughout the study period. Highest value of free CO2 (3.60mg/l) was recorded in pond II during
November (Fig 2). Pond III was alkaline throughout the study period, whereas in pond II, acidic pH
prevailed in October and November. Pond I registered acidic pH only during January (Fig 3). Pond
III registered two peak values during July and November whereas other two sites registered lower
value during these months (Fig 3).
Dissolved oxygen (DO) also showed the same trend as pH in all the ponds. Comparing the values of
all the ponds, pond I showed higher values than other two ponds throughout the period. Nutrients like
nitrates and phosphates registered lower values during June and July. Maximum values were observed
during November for all the three ponds, except in pond I, where higher nitrate value was observed
during December (Fig 5 & 6). Silicate content of the experimental ponds registered same trend as for
nitrate and phosphate (Fig 7). Calcium in pond II and III showed same trend as observed in other
nutrients whereas in pond I three peaks were observed in a year cycle (Fig 8).
A total number of 82 algal taxa were identified in three ponds which were distributed over four major
divisions like 36 species in Cyanophyta, 24 species in Chlorophyta, 16 species in Bacillariophyta and
6 species in Euglenophyta. Specific distribution of species in each division of algae in three ponds
shown in Fig 9a-d, indicated more number of exclusive occurrence of species in Cyanophyta division
in pond III (Fig 9a), whereas for other divisions specific and exclusive occurrence of species appeared
in pond I (Fig. 9b-d). Maximum number of species occurrence was noticed in the division Cyanophyta
(36) in pond III (Fig 9a). Nevertheless, higher diversity of species in other three divisions occurred in
pond I (Fig 10). The less polluted pond I registered higher algal diversity than the other two ponds.
The total algal standing crop and population density of cyanophyta showed two distinct peaks during
June and December in all the ponds (Fig 10 - 12).
Pond I registered greater algal density than the other two ponds (Fig 10), however highest standing
crop was observed in pond III during June and December (Fig 12). The divisions like Cyanophyta and
Bacillariophyta registered its peak value during summer months, in the other two divisions peak values
were observed during winter months (Fig 11). Relationships between variables like total algal
standing crop (var 1) with the environmental factors (master variables) like water temperature, free
CO2, pH, DO, nitrate, phosphate, silicate and calcium (var 2- 9) were tested with correlation matrix for
three ponds. These are presented in Table 1 - 3.
Correlation analysis was carried out between environmental factors (var 2-9) with total standing crop
(var 1) and division wise algal standing crop for three ponds (given in Table 4-6). A forward stepwise
multiple regression analysis technique was applied to evaluate the effect of all environmental factors
like water temperature, free CO2, pH, DO, nitrate, phosphate, silicate and calcium. These were treated
as independent variables (var 2 - 9) with algal standing crop (var 1) as dependent variable. The results
are presented in Table 7 - 9 for the three ponds respectively. The master variables (var 2-9) were not
individually regressed with total algal standing crop (var 1); their influence on the overall equation
was more significant in pond II than other two ponds (Fig 13 - 15)
DISCUSSION
The seasonal distribution of phytoplankton is mainly determined by various environmental factors
such as water temperature, pH, free CO2, dissolved oxygen and nutrients of water like nitrogen,
phosphate, silicate and calcium (Hamilton et al 1999; Wallace et al; 1999). Distinct low water
temperature was noticed during November to January in all the ponds, which may be influenced by
North East Monsoon and low atmospheric temperature (Vass & Sachian, 1955; Chow, 1958; Michael,
1969 and Rajendran Nair, 2000). In warmer months i.e. from March to June the increased water
temperature was due to increase in atmospheric temperature and longer days. A similar seasonal
variation in temperature was reported in most of the Indian deep wetland systems (Srinivasan, 1964;
Singhal et al., 1986; Kanshik et al., 1989; 1991; Sobha et al., 2002). Correlation matrix analysis made
between environmental factors showed more negative correlation in pond I than in other two ponds.
Only water temperature and pH, DO and calcium showed positive correlation. In pond III except pH,
DO and silicate all others showed negative correlations (Table 1 - 3).
Free CO2 showed significant negative correlation with pH (Table 1-3). This was inconformity with
Ganapathi (1960), Singh (1965), Verma (1969), Vysa (1968), Wetzel (1972) and Kumar (1997). The
sharp decline in pH was due to rain water in agreement with decrease in the density of phytoplankton
(Bohra, 1977). Higher values of dissolved oxygen (DO) content were recorded in winter months for
all the ponds, the period in which the water temperature was lower. This might be due to the fact that
the solubility of DO increases with decrease in water temperature. This was in conformity with
Kumar (1996 - b). The correlation coefficient (r) values between these two variables (var 2 Vs var 5)
showed significant negative correlations (-0.822) (Table -1). Nutrients like nitrate (var 6), phosphate
(var 7), silicate (var 8) and calcium (var 9) are the four major nutrients, which control the growth of
algae in deep wetland ecosystem. Our earlier results also conformed to the present investigation
(Figure 5 - 8). There was negative correlation (r) observed between majority of the nutrients (var 6 9) with total algal standing crop in all the ponds (Table 1- 3) except phosphate in pond I (+0.103),
calcium (0.548) in pond II and silicate in pond III (+0.123), which showed positive correlation
(Singhal et al., 1986).
Greater algal diversity was observed in pond I followed by pond II and III however the density of
algae was maximum in pond III than in other two ponds. Excessive growth of certain algal species
like Anabaena, Microcystis, Oscillatoria, Scenedesmus, Pediastrum, Navicula and Tabellaria was
known to indicate nutrient enrichment of deep wetland ecosystem (Bush and Welch, 1972; Kumar,
1990; Pandey et al 1998 and Ramakrishnan, 2002 a,b). Cyanophyta division was the most significant
group contributing above 40% in all the ponds. The sequence of percentage in all the ponds was:
Cyanophyta > Chlorophyta > Bacillariophyta > Euglenophyta. The present result was in conformity
with Singh and Swarup (1979).
Among the four algal divisions Cyanophyta density was greater in all the ponds and lowest density
was observed in Euglenophyta division. An interesting result that was found to exist in the present
investigation was an inverse relationship between Cyanophyta and Chlorophyta in pond I (Fig 10)
during rainy months and also negative correlation between Cyanophyta and Euglenophyta in pond I.
According to Holmes & Whitton (1981) and Tiwari et al (2001) abundance of Cyanophyta and low
population of Chlorophyta, Bacillariophyta in the experimental pond is an indication of eutrophic
nature of the ponds. This condition prevailed in our present investigations.
Distinct two peaks of total algal standing crop are clearly indicated in the present investigation.
Moreover, the seasonal variations of phytoplankton along with temperature changes, may be due to
oxygen and carbon-dioxide variations along with other physico-chemical characteristics of water,
which also affect the phytoplankton density and diversity (Tripathy & Pandey 1990). Several authors
have emphasised the importance of water temperature in the periodicity of BGA (blue-green algae),
which was also observed in the present study (Singh Swarup, 1978; Reynolds et al., 1981 and Pandey
et al., 1994). Vysa & Kumar (1968) indicate that Euglenoids show their presence during rainy season
only, whereas in the present study the species of Euglenoids occurred throughout the period of study,
however maximum density was registered during rainy or winter months in pond I and II (Table 4 - 5).
This was in conformity with Pandey et al., (1994). Correlation (r) matrix analysis between
Euglenophyta and master variables (environmental factors var 2 - 9) showed there was an inverse
relationship established with water temperature and pH (Table 5-6). Nevertheless the other factors
showed positive correlation, while in pond II and III majority of the factors showed negative
correlations (Laxminarayan, 1965; Munwar, 1970; Rai & Kumar, 1977 and Mathew, 1978).
Correlation analysis between Chlorophyta and environmental master variables (var 2 - 9) showed
more positive values, whereas Bacillariophyta division with environmental variables water
temperature and pH showed negative correlation in all the ponds. All nutrients (var 6 - 9) showed
positive correlation with chlorophyta except nitrate nitrogen in pond III (Table 6). Correlation matrix
analysis between Bacillariophyta and environmental variables (var 2-9) in three ponds showed
negative correlations except few factors like water temperature, which showed positive correlation in
all the ponds. pH showed positive correlation in Pond II and III and calcium showed only positive
value in pond II (Table 5 - 6).
Multiple regression analysis (Table 4 - 6) revealed the value of coefficient of determination and clearly
indicated that more than 96 to 99% of variation in phytoplankton density was influenced by physicochemical factors and only 1 - 4% by other factors not considered in the present study. By means of
multiple regression equations embodied in the text, phytoplankton density may be predicted at any
time. Based on the collective contribution in the overall regression between dependent variable total
algal standing crop (var1) with independent variables (Water temperature, free CO2, pH, DO, N, P, Si
and Ca) var 2 to var 9. The results were presented in figure 15-17. These graphs show the observed
verses predicted values for total algal standing crop (var 1) with the physico-chemical variables (var 2
to 9).
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Fig 10
Pond -I
8000
7000
6000
5000
4000
3000
2000
1000
0
April
May
June
July
Aug
Sep
Oct
Standing crop (Total)
Chlorophyta
Eugleophyta
Nov
Dec
Jan
Feb
Mar
Cyanophyta
Bacillariophyta
Pond –II
Fig 11
8000
7000
6000
5000
4000
3000
2000
1000
0
April May June July
Standing crop (Total)
Bacillariophyta
Fig 12
Pond –III
Aug
Sep
Oct
Cyanophyta
Eugleophyta
Nov
Dec
Jan
Feb
Chlorophyta
Mar
8000
7000
6000
5000
4000
3000
2000
1000
0
April May June July
Figure 10 - 12.
Aug
Sep
Oct
Standing crop (Total)
Cyanophyta
Bacillariophyta
Eugleophyta
Nov
Dec
Jan
Feb
Mar
Chlorophyta
Total algal standing crop and division wise algal density of the experimental ponds
Fig 13. Predicted versus observed graph for total algal standing crop (depedent) regressed (r= 0.8286, p =
0.6354) with master variables (Water temperature, free CO 2, DO, N, P, Si, Ca) in pond I.
Predi cted vs. Observed Val ues
Dependent variabl e: VAR1
7500
Observed Values
6500
5500
4500
3500
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
Regression
95% confid.
Predi cted Values
Fig 14. Predicted versus observed graph for total algal standing crop (depedent) regressed (r= 0.9643, p =
0.10739) with master variables (Water temperature, free CO 2, DO, N, P, Si, Ca) in pond II.
Predi cted vs. Observed Val ues
Dependent variabl e: VAR1
8000
Observed Values
7000
6000
5000
4000
3000
3000
4000
5000
6000
7000
8000
Regression
95% confid.
Predi cted Values
Fig 15. Predicted versus observed graph for total algal standing crop (depedent) regressed (r= 0.9207, p =
0.29437) with master variables (Water temperature, free CO 2, DO, N, P, Si, Ca) in pond III.
Predi cted vs. Observed Val ues
Dependent variabl e: VAR1
7800
7400
Observed Values
7000
6600
6200
5800
5400
5000
5400
5800
6200
6600
Predi cted Values
7000
7400
7800
Regression
95% confid.
Table 7 : Multiple regression analysis results between environmental factors (var 2-9) with the algal
standing crop (var 1) for the Pond I.
Dep. Var : Var 1
No. of cases :
Multiple
R
R2
Adjusted R2
12
Standard error of estimate
: 1319.3550854
Intercept :
Std. Error
64073.450846
= 0.82860854
= 0.68659212
= -0.14916223
F
df
p
= 0.8215238
= 8, 3
= 0.635461
= 59048.49
t(3)
P
= 1.0851
< 0.3673
Table 8 : Multiple regression analysis results between environmental factors (var 2-9) with the algal
standing crop (var 1) for the Pond II.
Dep. Var : Var 1
No. of cases :
Multiple
R
R2
Adjusted R2
12
Standard error of estimate
: 602.79840589
Intercept :
Std. Error
-5750.233002
= 0.96426211
= 0.92980142
= 0.74260822
F
df
p
= 4.966989
= 8, 3
= 0.107393
= 10693.79
t(3)
P
= -0.9377
< 0.6281
Table 9 : Multiple regression analysis results between environmental factors (var 2-9) with the algal
standing crop (var 1) for the Pond II.
Dep. Var : Var 1
No. of cases :
Multiple
12
R
R2
Adjusted R2
Standard error of estimate
: 535.00820562
Intercept :
Std. Error
-18911.91458
= 0.92068831
= 0.847666
= 0.44144552
F
df
p
= 2.086712
= 8, 3
= 0.294371
= 26745.20
t(3)
P
= -0.7071
< 0.5305
Figure 9a-d.
Venn diagrams depicting the specific distribution of species in four algal division
of the three experimental ponds.
Table 1 : Correlation matrix of physicochemical variables (var 2-9) with total standing algal standing crop
(var 1) for pond I.
VAR2 VAR3 VAR4 VAR5 VAR6 VAR7 VAR8 VAR9 VAR1
Temp. VAR2
1
F.CO2 VAR3 -0.5089
1
pH VAR4 -0.5843 -0.8526
1
DO VAR5 -0.8224 0.4419 -0.4556
1
N VAR6 -0.5579 0.2299 -0.1606 0.8287
1
P VAR7 -0.3960 0.1528 -0.0850 0.6132 0.7392
1
Si VAR8 -0.0580 0.0889 0.10951 0.3872 0.5361 0.8220
1
Ca VAR9 -0.4437 -0.2176 0.11018 0.6193 0.5595 0.5064 0.3845
1
VAR1 -0.1729 0.2119 -0.4660 0.0628 -0.1198 0.1030 -0.1150 -0.2783
1
Table 2 : Correlation matrix of physicochemical variables (var 2-9) with total standing algal standing crop
(var 1) for pond II.
VAR2 VAR3 VAR4 VAR5 VAR6 VAR7 VAR8 VAR9
Temp VAR
.
2
F.CO2 VAR
3
pH VAR
4
DO VAR
5
N VAR
6
P VAR
7
Si VAR
8
Ca VAR
9
VAR
1
VAR
1
1
0.6652
1
0.8568 0.6640 1
0.2411 0.5418 0.2712 1
- 0.072
0.6631 0.7959 0.5899
8 1
- 0.127
0.6036 0.7713 0.4448
0 0.9191 1
- 0.123
0.4672 0.6109 0.2769
8 0.7437 0.8638 1
0.691
0.0396 0.4300 0.1917
5 0.1010 0.2759 0.3593 1
0.335
- 0.539
0.5040 0.0016 0.3866
2 0.4162 0.3489 0.4104
4
1
Table 3 : Correlation matrix of physicochemical variables (var 2-9) with total standing algal standing crop
(var 1) for pond III.
VAR2 VAR3 VAR4
Temp
. VAR2
1
F.CO
VAR3 -0.6819
2
1
VAR5
VAR6
VAR7
VAR8
VAR9 VAR1
pH
DO
N
P
Si
Ca
VAR4
VAR5
VAR6
VAR7
VAR8
VAR9
0.2747
-0.9041
-0.5759
-0.6757
-0.8689
-0.4712
VAR1 -0.0414
0.2836
0.6470
0.4602
0.5690
0.5703
0.4409
0.3855
1
-0.5420
0.1916
-0.1923
-0.1438
-0.0177
1
0.3545
1
0.6454 0.7279
1
0.7521 0.7903 0.8366
0.2899 0.6456 0.6595
1
0.7111
1
0.0073
0.0872 -0.2431 -0.2239
0.1227
-0.008
1
Correlation coefficient (r) values
Water Temp
o
C
Free CO2
pH
Do mg/l
N
P
Silicate
Calcium
Water Temp
o
C
Free CO2
pH
Do mg/l
N
P
Silicate
Calcium
Water Temp
o
C
Free CO2
pH
Do mg/l
N
P
Silicate
Calcium
Total
Standing
crop
-0.173 *
0.202 *
-0.466 **
0.033 *
- 0.117 *
0.103 *
-0.115 *
- 0.278 *
Total
Standing
crop
0.484 **
--0.028 *
0.331 *
0.382 *
-0.457 ***
-0.385 *
-0.394 *
0.548 ***
Total
Standing
crop
-0.041 *
-0.386 *
0.55 *
0.87 *
-0.017 *
-0.224 *
0.123 *
-0.017 *
Table 4
Pond - I
Cyanophyta
Chlorophyta
Bacillariophy
ta
Euglenophyt
a
-0.669 ***
-0.827 ***
0.196 *
-0.5669 ***
0.443 **
-0.582 **
0.360 *
0.390 *
0.388 *
0.125 *
-0.680 **
0.580 ***
-0.663 **
0.481 **
0.83 **
0.575 ***
0.274*
0.399 **
-0.170 *
-0.137 *
-0.243 *
-0.310 *
-0.083 *
-0.190 *
-0.297 *
0.315 *
-0.403 **
0.374 *
0.585 **
0.621 ***
0.329*
0.323 *
Table 5
Pond - II
Cyanophyta
Chlorophyta
Bacillariophy
ta
Euglenophyt
a
0.659 **
-0.279 *
0.708 **
0.241 **
-0.219 *
0.531 **
0.024 *
-0.513 ***
-0.455 **
-0.451 **
0.288 *
0.664 **
-0.276 *
0.874 ***
0.165 *
0.216 *
0.171 *
0.811 ***
-0.588 ***
0.632 ***
-0.205 *
-0.676 ***
-0.641 ***
-0.601 ***
0.096 *
-0.435 **
0.189 *
-0.246 *
-0.105 *
-0.151 *
-0.131 *
0.381 *
Table 6
Pond - III
Cyanophyta
Chlorophyta
Bacillariophy
ta
Euglenophyt
a
-0.006 *
-0.342 *
0.290 *
0.473 **
-0.263 *
0.132 *
0.052 *
-0.004 *
-0.173 *
0.113 *
-0.194 *
-0.136 *
-0.009 *
0.409 **
-0.001 *
0.024 *
0.357 *
0.159 *
-0.640 ***
-0.198 *
-0.253 *
-0.033 *
-0.490 **
-0.269 *
-0.107 *
-0.548 ***
0.237 *
-0.593 ***
-0.021 *
-0.428 **
-0.344 *
0.243 *
* Not Significant
level
** Significant at 1% level
*** Significant at 5%
Table 4 - 6. Correlation values between physico-chemical characteristics with total algal
standing crop and division wise algal density
Fig 1
Water temperature oC
Fig 5
Nitrate mg/l
3
27
2.5
26
2
25
1.5
24
1
0.5
23
April May June July Aug Sep Oct Nov Dec Jan Feb Mar
Pond-1
Fig 2
Pond-2
Pond-3
Free CO2 mg/l
4
3
2
1
0
April May June July Aug Sep
Pond-1
Fig 3
Oct
Nov Dec
Pond-2
Jan
Feb Mar
Pond-3
pH
8.5
8
7.5
7
6.5
6
April May June July Aug Sep Oct Nov Dec Jan Feb Mar
Pond-1
Pond-2
Pond-3
0
April May June July Aug Sep Oct Nov Dec Jan Feb Mar
Pond-1
Pond-2
Pond-3
16
14
12
10
8
6
April May June July Aug Sep Oct Nov Dec Jan Feb Mar
Pond-1
Fig 4
Pond-2
Pond-3
DO mg/l
Figure 1 - 8. Physico-chemical characteristics of the experimental ponds
Fig 6
Phosphate mg/l
4
3
2
1
0
April
May
June
July
Aug
Pond-1
Fig 7
Sep
Oct
Nov
Dec
Pond-2
Jan
Feb
Mar
Pond-3
Silicate mg/l
3
2.5
2
1.5
1
0.5
0
April May June July Aug Sep Oct Nov Dec Jan Feb Mar
Pond-1
Fig 8
Pond-2
Calcium mg/l
Pond-3
50
40
30
20
April May June July Aug Sep Oct Nov Dec Jan Feb Mar
Pond-1
Pond-2
Pond-3