Multivariate Analysis of Phytoplankton in Relation to Physicochemical Parameters Disparity in Parangipettai Waters, Southeast Coast of India
Sunil Kumar Sahu
V. Ashok Prabu
Asian Journal of Biological Sciences,
2013, 6(1), 1-20.
Phytoplankton are the major source of primary production in water bodies. But, due to increased anthropogenic pressures in the coastal waters, there is a gradual decrease in the population of this biological resource. In the present study, multivariate statistical methods including principal component and cluster analysis were applied to surface water quality data sets obtained from Vellar Estuary, Parangipettai, Southeast coast of India. Studies on physicochemical and biological parameters were made during August 2008 to July 2009. Totally 45 species representing different classes namely Bacillariophyceae (36); Dinophyceae (6); Chlorophyceae (1) and Cyanophyceae (2) were recorded. Among the four classes, Bacillariophyceae appeared as the dominant group in terms of total species and cell numbers. The multivariate statistical analysis revealed that the surface water quality and phytoplankton density of the Parangipettai waters and its tributaries is being affected by increased aquaculture practices, agriculture discharges and high level of organic load. These data on the phytoplankton distribution and abundance with reference to hydrography will be of immense help for further ecological assessment in Parangipettai waters.
Mclusky and Elliott, 2004). Anthropogenic activities negatively
influence the water quality and aquatic ecosystem functions and have a great
pressure on these ecosystems, leading to the decrease in water quality and biodiversity,
loss of critical habitats and ultimately the quality of life of local inhabitants
is affected (Herrera-Silveira and Morales-Ojeda, 2009).
Continuous water quality measurements and analyses are necessary for effective
management of coastal water quality. Phytoplankton are also known as the Pastures
of sea which are the major source of primary production in water bodies
and it is responsible for one quarter of the worlds plant photosynthesis
(Mishke et al., 1970). Since, phytoplankton communities
are very sensitive to environmental changes these species are used as indicators
of water quality (Reynolds, 1997; Reynolds
et al., 2002; Brettum and Andersen, 2005).
Phytoplankton is the major biological component of food chain through which
the energy is transferred to the higher organisms (Rajesh
et al., 2002; Ananthan et al., 2004;
Tas and Gonulol, 2007; Shekhar et
al., 2008). Biomass and production of phytoplankton are important in
regulating the diversity of organisms at higher tropic levels (Kathiresan,
2000). 33-51% of total chlorophyll-a is contributed by the phytoplankton
of 5-10 μm size and 20-22% of total gross production in mangrove waters
(Kawabata et al., 1993). Due to the differential
effect of hydrographical factors on individual species, phytoplankton species
undergoes spatial-temporal changes in their distribution. They serve as bio-indicators
with reference to water quality and thus serve as a tool for assessing the health
of the aquatic ecosystems. Further, the measurement of primary productivity
of aquatic ecosystem is required to forecast fishery potential of an area (Rajkumar
et al., 2009).
Due to varied physical and chemical requirements for their population and growth,
seasonal variation in phytoplankton abundance, physicochemical parameters and
species composition has been studied in Indian coastal waters. (Rajkumar
et al., 2009; Perumal et al., 1999,
2009; Saravanakumar et al.,
2008; Mathivanan et al., 2007; Sridhar
et al., 2006; Tiwari and Chauhan, 2006; Rajasekar
et al., 2005; Madhav and Kondalarao, 2004;
Rajasegar et al., 2000). However, water monitoring
management of a long-term period and many sampling sites produces large and
complicated data sets consisting of all kinds of water parameters which are
difficult to analyze, interpret and extract comprehensive information from them.
In recent years, multivariate statistical analysis, such as Cluster Analysis
(CA), Principal Component Analysis (PCA), Discriminant Analysis (DA) and Factor
Analysis (FA) have been successfully employed to evaluate the chronological
and spatial character of coastal water quality and river water (Kuppusamy
and Giridhar, 2006; Wu and Wang, 2007; Chau
and Muttil, 2007; Singh et al., 2005; Chen
and Mynett, 2006; Alkarkhi et al., 2009).
PCA and CA potentially allow us to simplify the description of observations
by finding the structure or patterns in the presence of bewildering data (Ragno
et al., 2007). In the present study we report the statistical analysis
of water quality parameters and phytoplankton diversity in three sites of Vellar
estuary (sea mouth, mangrove and interior river). The analysis was carried out
to explore the extent of resemblance among the sampling sites, to identify the
variables responsible for spatial variations in estuaries water quality and
to quantify the influence of possible natural and anthropogenic sources on the
water parameters on the three selected sites of the estuary and Bay of Bengal
MATERIALS AND METHODS
Study area: The river Vellar situated in the southeast coast of India
(11°29'50"N and 79°46'24"E) originates in the Shervaryan Hills of Salem
District. After meandering through a distance of 480 km, it forms the estuarine
system (true estuary) before it merges with the Bay of Bengal at Parangipettai
coast. The estuary is subjected to semi-diurnal tides with maximum tidal amplitude
of about 1 m. The estuary is about 600 m wide at its mouth (Fig.
1). There is a perfect exchange of both biotic and abiotic variations due
the influence of neritic water with estuarine environment. Average depth of
the estuary is 2.5 m and the maximum depth at high tide is 5.3 m. There were
as many as 42 shrimp farms with water spread area of 150 ha in the surrounding
of Vellar (Rajasegar et al., 2002). In order
to evaluate the water quality and phytoplankton diversity in the estuary, the
surveys were conducted during the August 2008 to July 2009 and sites were as
||Monitoring stations located in interior of the Bay of Bengal
||Located in Vellar estuary river mouth. The hydrological environment can
elucidate important information about water exchange between Vellar estuary
and Bay of Bengal Sea
||Near the mangrove ecosystem of the Vellar estuary which is an aquaculture
|| Map showing the study area
Water quality analysis: Physicochemical parameters were directly analyzed
at the collection sites. Temperature was measured using a standard Celsius Thermometer.
Salinity was estimated with the help of a hand-held Refractometer (ATAGO, Japan).
pH was measured by using an ELICO Grip pH meter. Dissolved Oxygen was estimated
by the modified Winklers method and Chlorophyll-a (90% acetone method)
measurement were carried out spectrophotometrically (Strickland
and Parsons, 1972) and was expressed as mg L-1. Surface water
samples were collected in clean polyethylene bottles for the analysis of nutrients
which were kept immediately in an ice box and then transported to the laboratory.
The collected water samples were filtered by using a Millipore filtering system
and then analyzed for dissolved inorganic nitrate, nitrite, ammonia, reactive
silicate, inorganic phosphate, by following the standard procedures described
by (Strickland and Parsons, 1972) and were expressed in
μg L-1. Multivariate statistical methods have been widely applied
to investigate environmental phenomena in recent years (Laaksoharju
et al., 1999; Guler and Thyne, 2004; Anazawa
and Ohmori, 2005).
Phytoplankton analysis: Plankton samples were collected by using plankton
net (mesh size: 40 μm) at the surface layers and immediately fixed in 5%
formaldehyde solution. For the quantitative analysis of phytoplankton, the settlement
method described by (Sukhanova, 1978) was adopted. Numerical
plankton analysis was carried out using Utermohl's inverted microscope. Phytoplankton
was identified using the standard works of (Venkataraman,
1939; Subrahmanyan, 1946; Prescott,
1954; Steidinger and Williams, 1970). The analyses
were carried out at CAS in Marine Biology, Annamalai University, India. Biodiversity
indices were calculated following the standard formulae of Shannon
and Weaver (1949), Gleason (1922) and Pielou
Multivariate statistical analyses
Visual data mining using box plots: Box plots provide an excellent
visual summary of a set of data and are especially useful when comparing two
or more sets of data. Visual data mining refers to the visual presentation of
data to extract useful information which allows users to summarize, extract
and grasp more complex patterns and results than mathematical or text type descriptions
of the same. For the spatial and temporal analysis of physicochemical parameters
and ANOVA (Analysis of Variance), statistical package SPSS (version 16.0) were
used, respectively. In a box plot, the line across the box represents the median,
the bottom of the box is at the first quartile (Q) and the top is at the third
quartile (Q31). The whiskers are the lines that extend from the bottom and top
of the box to the lowest and highest observations inside the range defined by
Cluster analysis (CA): Cluster analysis was done in the statistical
software package Primer 6.1. CA is a group of multivariate techniques to assemble
objects based on the characteristics they possess and it classifies objects
in such a way that each object is similar to the others in the cluster with
respect to a predetermined selection criterion (Shrestha
and Kazama, 2007; Iscen et al., 2008). Hierarchical
agglomerative clustering is the most common approach and provides intuitive
similarity relationships between any one sample and the entire data set; it
is typically illustrated by a dendrogram (tree diagram). The dendrogram provides
a visual summary of the clustering processes, presenting a picture of the groups
and their proximity, with a dramatic reduction in the dimensionality of the
original data (Alberto et al., 2001).
Principal component analysis (PCA) and factor analysis (FA): PCA and
FA were analyzed using XLSTAT Version 2012.1.01. PCA is designed to transform
the original variables into new, uncorrelated variables (axes), called the principal
components which are linear combinations of the original variables. PCA and
Factor Analysis (FA) are both variable reduction techniques. The FA suggests
important variates to explain the observed variances in the data (Shrestha
and Kazama, 2007). It reduces the dimensionality of the data set by explaining
the correlation amongst a large number of variables in terms of a smaller number
of underlying factors Principal Components (PCs), without losing much information
(Vega et al., 1998; Alberto
et al., 2001; Helena et al., 2000).
Box plots analysis for water quality parameters: The box plots for the
temporal variations of water quality parameters namely rainfall, surface water
temperature, salinity, pH and dissolved oxygen are presented in Fig.
2. In all the sites total rainfall recorded was 2042 mm from August 2008
to July 2009, varying from 20-969 mm. The surface water temperature ranged between
25.8-33.3°C. The salinity was in the range of 8-35%. pH ranged from 7.8-8.2.
Dissolved oxygen (mL L-1) varied from 3.4-5.1.
||Box plot analysis of physical properties (a) Rain, (b) Temperature,
(c) salinity, (d) pH and (e) Dissolved oxygen (DO)
As expected temperature, salinity and dissolved oxygen showed gradual increase
in concentration as we moved from mangrove station to interior sea. The box
plots for the inorganic nutrients are presented in (Fig. 3).
Nitrites (μg L-1) varied from 0.442-0.912. Nitrates values (μg
L-1) were between 2.74-5.6. Phosphates (μg L-1) values
ranged between 0.21 and 1.24. The reactive silicate (μg L-1)
values ranged from 21.24-62.14.
||Box plot analysis of nutrients (a) Rain, (b) Temperature,
(c) Salinity, (d) pH and (e) Dissolved oxygen (DO)
The concentrations of ammonia (μg L-1) ranged from 0.062-0.34.
Except ammonia all other inorganic nutrients viz., nitrite, nitrate, phosphate
and silicate showed a gradual decrease in concentration as we move towards interior
to the sea, starting from mangrove station.
Analysis of biological parameters and phytoplankton diversity: The annual
variability of primary productivity values (mg cm 3 h-1) ranged between
13-124 during monsoon to summer seasons, respectively.
||Box plot analysis of (a) Phytoplankton density (Phyto_density),
(b) Primary production (Prim.pro) and (c) Chlorophyll-a
The chlorophyll a ranged between 3.22-8.14 mg m3. The quantitative
ranges of phytoplankton population density (cells L-1) were 3025-8379.
Clearly box plot indicate that primary productivity, Chlorophyll-a and phytoplankton
density maximum concentration was near mangrove site with respect to sea mouth
and interior Fig. 4. The qualitative data of phytoplankton
from during the study period list is shown in Table 1. Totally
45 species representing the class of Bacillariophyceae (36), Dinophyceae (6),
Chlorophyceae (1) and Cyanophyceae (2) were recorded from three sites. Among
the four classes, Bacillariophyceae appeared as the dominant group in terms
of total species and cell numbers. The maximum phytoplankton qualitative diversity
was found at site three followed by site one and site two, respectively.
Biodiversity indices: Shannon-wieners diversity index (H) were ranged from 4.326-4.925 bits ind-1. Gleason richness (D) was ranged from 0.926-0.989 and Pielous evenness (J) were ranged from 0.923-1.002. (Fig. 5).
Multivariate statistical analyses: In the present study, physicochemical
parameter and phytoplankton density data were taken for one year from three
sites of Parangipettai, India. ANOVA results revealed that the rain was significant
(p<0.01) with respect to months but was not significant in the case of collection
sites (Table 2).
|| Check list of phytoplankton species from Vellar estuary during
|+: Present and -: Absent
|| Biodiversity indices during the studied season
The temperature significantly varied between the sampling site and months.
Similarly salinity, pH, DO, Nitrite, phosphate, primary productivity, silicate
and phytoplankton density showed significant variation (p<0.05) between sampling
sites and months. Concentration of ammonia was not significant (p<0.01) with
respect to month and sites (p>0.05). Moreover, chlorophyll-a was highly significant
between months but it was statistically not significant between sampling sites
Cluster analysis: Cluster Analysis (CA) was used to detect similar groups between the three sampling sites in four seasons during 2008-2009. The sampling stations and seasons formed the two major groups. Group A included October to November (Monsoon season) and Group B included December to September, but it was internally further grouped into two groups viz., Group C and Group D which included January to June (post monsoon and summer) and July, August and December (pre-monsoon and monsoon), respectively. Hence, it is clear from the CA analysis that each season formed the group individually but not the stations. It revealed that the sampling station and seasons are significant but not within the group (Fig. 6).
Principal component analysis (PCA): Before applying PCA, Pearson correlation
analysis was carried out (Table 3). This was utilized to find
an internal structure which ultimately helps in the identification of major
sources not accessible at first glance. PCA was applied to the 13 variables
collected during four seasons throughout the year (temperature, salinity, pH,
rain, dissolved oxygen, nitrate, nitrite, phosphate, silicate, ammonia, primary
productivity and Chlorophyll a). The highest correlation existed
between temperature and chlorophyll-a; temperature and phytoplankton; pH and
dissolved oxygen; primary productivity and phytoplankton density and chlorophyll
a and phytoplankton density (Fig. 7). Due to the complexity
of the relationships, it was difficult to draw more clear conclusions directly.
However, principal component analysis can extract the latent information and
explain the structure of the data in detail. PCA rendered three significant
PCs (eigenvalue >1) that explained 80.73% of the total variance of the dataset.
The principal component cannot only interpret the temporal characteristics by
clustering the samples, but also can describe their different characteristics
and help to elucidate the relationship between different variables by the variable
lines. The variable lines were obtained from the factor loadings of the original
variables. They stand for the contribution of the variables to the samples.
More closer the two variable lines, the stronger is the mutual correlation (Qu
and Kelderman, 2001). The highest positive correlation coefficient was observed
between phytoplankton density and temperature (Fig. 6).
||Analysis of variance (ANOVA) tested for physicochemical parameters
presented p and F- value of sampling seasons, site and month and site together
|*p<0.005, **p<0.01, **Significant 99%, ns: Not significant
Factor analysis: In this study, water quality variables were grouped
using Factor Analysis (FA). The correlation matrix of variables was generated
and factors were extracted by the Centroid method. Results of factor analysis
including factor-loading matrix, eigenvalues, total and cumulative variance
values are presented in Table 4. The factor analysis generated
three significant factors which explained 80.73% of the variance in data sets.
|| Correlation matrix (Pearson (n) of the physicochemical parameters
|Values in bold are different from 0 with a significance level
alpha: 0.05, T: Temperature (°C), PP: Primary productivity, Chl-a: Chlorophyll-a,
Amm: NH3, p_density: Phytoplankton density
||Dendrogram showing clustering of sampling sites according
to water quality parameters of selected sites
Parameters were grouped into three factor based on the factor loadings namely
factor 1: Salinity, Temperature, Primary productivity and Chlorophyll-a; factor
2: Nitrite, Hydrogen ion concentration (pH), Ammonia and Dissolved oxygen and
factor 3: Silicate and Phytoplankton density. Factor 1 (F1) explained 45.44%
of the variance. The F1 revealed a high positive loading with respect to salinity,
temperature, primary productivity and chlorophyll-a which were 0.847, 0.805,
0.779 and 0.729, respectively.
Confirmation of FA results by PCA analysis: Confirmation of FA results
by PCA analysis Principal component analysis (PCA) was applied to data set to
confirm results of FA. A scree plot presented in Fig. 8 shows
the sorted eigenvalues from large to small as a function of the principal components
|| PCA of physicochemical parameters
|| Factor-loading matrix, eigenvalues and total and cumulative
As is seen in this figure PCA generated three significant components (number
of components of which the eigenvalues are greater than 1 was
three). The components weights are presented in Table 5. PCA
analysis results also revealed that the first component was associated with
Salinity, Temperature, Primary productivity and Chlorophyll-a. The second component
comprised Nitrite, Hydrogen ion concentration (pH), Ammonia and Dissolved oxygen
and third component Silicate and Phytoplankton density.
|| Scree plot of the eigenvalues
||Principal component weights
|*PC: Principal component
In the present study, physicochemical parameters showed a marked difference
with respect to the season. Rainfall is the most important cyclic phenomenon
in tropical countries as it brings important changes in the hydrological characteristics
of the coastal marine environments. The peak values of rainfall were recorded
during monsoon in November. The observed high value in March and low value in
August could be due to strong land sea breeze and precipitation (Senthilkumar
et al., 2002; Santhanam and Perumal, 2003).
But there was no difference among rainfall with respect to three stations since
the sites were in close vicinity (2 km). The salinity were high during summer
season and low during the monsoon season which is in accordance with the findings
(Sundaramanickam et al., 2008). Higher summer
values (35.0%) may also be attributed to high degree of evaporation and the
low amount of rainfall (Sampathkumar and Kannan, 1998;
Govindasamy et al., 2000; Rajasegar,
2003). Salinity is a major limiting factor in the distribution of living
organisms and variation in salinity by dilution and evaporation is most likely
to influence the flora and fauna of the coastal ecosystems (Balasubramanian
and Kannan, 2005; Sridhar et al., 2006).
pH was recorded highest in summer which might be due to the influence of seawater
penetration and high biological activity (Das et al.,
1997). Generally, temporal fluctuations in pH could be attributed to factors
like removal of CO2 by photosynthesis through bicarbonate degradation,
dilution of seawater by freshwater influx, low primary productivity, reduction
of salinity and temperature besides decomposition of organic matter (Paramasivam
and Kannan, 2005; Prabu et al., 2008). Higher
values of dissolved oxygen were recorded during monsoon. The observed high monsoonal
values might be due to the cumulative effect of higher wind velocity coupled
with heavy rainfall and the resultant freshwater mixing (Das
et al., 1997). It is well known that the temperature and salinity
affect the dissolution of oxygen (Vijayakumar et al.,
2000). Mitra et al. (1990) mainly attributed
seasonal variation of dissolved oxygen to freshwater flow and terrigenous impact
The low nitrite values recorded during summer season may be due to less freshwater
inflow and high salinity (Mani and Krishnamurthy, 1989;
Murugan and Ayyakkannu, 1991). Similarly, the high level
of nitrate could be attributed to fresh water in flow, mangrove leaves (litter
fall) decomposition and terrestrial run-off during the monsoon season (Karuppasamy
and Perumal, 2000; Santhanam and Perumal, 2003).
The recorded low summer values might be due to the limited flow of freshwater,
high salinity and utilization of phosphate by phytoplankton (Senthilkumar
et al., 2002). The low value of silicate recorded during post-monsoonal
season could be attributed to uptake of silicates by phytoplankton for their
biological activity (Mishra et al., 1993; Rajasegar,
Nutrients are considered as one of the most important parameters in the mangrove
environment influencing the distribution of phytoplankton. Distribution of nutrients
is mainly based on the season, tidal conditions and freshwater flow from land
sources. Higher concentration of ammonia was observed during monsoon season
which could be possibly due to the mortality and subsequent decomposition of
phytoplankton and also due to the excretion of ammonia by planktonic organisms
(Segar and Hariharan, 1989). High concentration of inorganic
phosphate observed during monsoon season might possibly be due to intrusion
of upwelling seawater into the creek that increased the level of phosphate (Nair
et al., 1984).
Higher primary productivity was observed during the summer season, because
of high population density of phytoplankton which could also be due to neritic
element domination, higher salinity and surface water temperature, clear water
conditions besides availability of nutrients (Rajasekar
et al., 2005). The chlorophyll-a (mg m-3) was highest
during summer and the lowest during monsoon which could be due to anthropogenic
effects as evidenced by its positive correlation with salinity and may also
be due to freshwater discharges from the rivers (dilution), causing turbidity
and less availability of light (Godhantaraman, 2002;
Rajasekar et al., 2005).
The abundance of phytoplankton was lowest during monsoon season. The population
density was found to be higher in summer season as reported in other coastal
regions of Tamil Nadu (Senthilkumar et al., 2002).
The number of phytoplankton species increased consistently towards the sea mouth,
where the salinity was higher (Rajasekar et al.,
2005). Biodiversity of phytoplankton index values are comparable to those
reported earlier (Ignatiades et al., 1985) in
Saronicos bay (1.53-4.08 bits ind-1). In general, an increase in
population density is directly proportional to the diversity index. In present
study high index values were observed during summer season in the month of May,
due to the upwelling of the nutrients in the coastal waters and species evenness
was recorded maximum in the month of November (monsoon season) due to the high
nutrients inflow. The observed maximum phytoplankton richness values were recorded
during the post-monsoon season. Maximum species diversity observed during summer
season (Rajasegar et al., 2000).
Statistical techniques often find significant relationships in large datasets
(Luoma and Bryan, 1982). While studying the inter-relating
variables, correlations between two or more parameters are not easy to establish
at a glance (Luoma and Bryan, 1982). Therefore, multivariate
analyses for the interpretation of large environmental and biological datasets
have been used in plankton research to identify relationships between abiotic
and biotic factors and community interpretation (Matta and
Marshall, 1984; Pagou and Ignatiades, 1988; Varis,
1991; Marshall and Alden, 1995; Del
Giorgio et al., 1997). Results from temporal cluster analysis showed
that, based on water quality parameters and phytoplankton data of the Vellar
estuary, Parangipettai waters is discriminated into two major groups which clearly
indicate that the differences among seasons are significant, indicating seasonal
variability during the sampling periods. Study revealed that the sampling station
and seasons are significant but not with in the group of the sampling station.
It is apparent that most parameters vary with seasons.
PCA results displayed a good correlation for the temperature, primary productivity,
Chlorophyll-a, Dissolved oxygen and pH in relation to the phytoplankton density
which clearly indicates the immense role of these parameters in the higher growth
and production of phytoplankton. Based on the highly correlated values of the
parameter as evident by the PCA, these physicochemical and biological parameters
could be used effectively and efficiently for enhancing the growth of the phytoplankton
while culturing in the laboratory conditions. Hence, once again PCA proves its
utility as a powerful pattern recognition tool which attempts to explain the
variance of a large data set of intercorrelated variables with a smaller set
of independent variables (Simeonov et al., 2003).
However, further optimization of the parameters need to be done before implementing
for the mass scale production.
High positive loadings indicated strong linear correlation between the factor
and parameters. A number of sources could be responsible for high nitrite concentrations
in Parangipettai waters: natural organic matter decomposition and deep percolation
of nitrate resulting from aquaculture and agricultural waste discharge (Rajasegar
et al., 2002). Both point and non-point sources could be the source
of ammonia in the study sites. Point sources include municipal waste, industrial
operations and large confined livestock operations. Non-point sources comprise
soil erosion and water runoff from aquaculture farm and cropland (Devlin
et al., 2000).
Based on the results of the factor analysis and also hydrochemical aspects of the water, it was concluded that, F1 can be denoted as agricultural pollution factor with presence of Salinity, Temperature, Primary productivity and Chlorophyll-a. F2 which is highly correlated with Nitrite, Hydrogen ion concentration (pH), Ammonia and Dissolved oxygen saturation can be denoted as Aquaculture farm discharges factor. Silicate and Phytoplankton density included in F3 are the indicator of organic pollution in water. Therefore, F3 factor represents organic substances derived from the litter of coastal mangroves and dead remains of aquatic organisms. These findings of Factor Analysis (FA) were confirmed by the results of PCA.
As mentioned earlier, aquaculture practices and agriculture is the primary
land uses (78% of the total catchment area) in the Parangipettai waters and
still this percentage is increasing due to more demand of highly nutritive shrimps.
The result of the factor analysis supports and confirms that the surface water
quality and phytoplankton density of the Parangipettai waters and its tributaries
is being affected by aquaculture discharges. On the other hand, uncontrolled
agriculture land run-off is causing threat to the surface water quality (Boyacioglu,
2006). This is the alarming sign for the future.
This study shows that multivariate statistical methods are useful tool for understanding of complex nature of water quality issues by identifying groupings in the set of data. The Parangipettai waters is subjected to seasonal fluctuations in physicochemical parameters depending upon the seasonal tidal amplitude and fresh water influx resulting in a continuous exchange of organic, inorganic, plant and animal matters. This coastal water was a resourceful place for phytoplankton cell abundance and diversity. It is a good tool to comprehend the phytoplankton spatial distribution in marine ecosystem if the species and environment matrix data are suitable. The collected information data on the phytoplankton distribution and abundance with reference to hydrography would form a useful tool for further ecological assessment of Parangipettai waters, southeast coast of India. Thus, multivariate statistical methods including factor, principal component and cluster analysis can be used to understand complex nature of physicochemical parameters and determine priorities to improve the water quality and phytoplankton density. If the current scenario continues, this may lead to severe consequences to the coastal waters of Parangipettai, where thousands of fishermen are directly dependent on these resources as it is the only source of income for their livelihood. In this aspect a proper and effective monitoring of the aquaculture practices along the Parangipettai coast and estuary is the need of the hour.
We would like to thank Prof. T. Balasubramanian, Director and Dean, CAS in Marine Biology, Annamalai University, Parangipettai and university authorities for providing the requisite facilities to carry out this work. This work was supported by University Grants Commission under Center with Potential for Excellency in Particular Area (CPEPA) project." class="btn btn-success" target="_blank">View Fulltext
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