RELATIONSHIP BETWEEN ABUNDANCE OF MICRO ... 7/ulhas_naik.pdffinally joins the Arabian Sea near...

Lake 2010: Wetlands, Biodiversity and Climate Change

22nd-24th December 2010

RELATIONSHIP BETWEEN ABUNDANCE OF MICRO ALGAE AND

ECOSYSTEM OF SUNKERI BACKWATERS, KARWAR

Ulhas G. Naik*1, Vinod V. Nayak# and N. Kusuma*2

*1Asst. Professor, Department of Marine Biology, Karnatak University Post Graduate Centre,

Kodibag, Karwar-581 303, Karnataka, India

#Asst. professor, Mahasati Arts, Commerce and Science College, Ulga, Karwar, Karnataka, India

*2 Professor, Department of Marine Biology, Karnatak University Post Graduate Centre,

Kodibag, Karwar-581 303, Karnataka, India

ABSTRACT

Sunkeri backwater of Kali estuary (14o51’28” N and 74o09’08” E) is the richest biotope of micro algae and

is known for its rich mangrove vegetation which harbors vast population of fin and shell fishes of commercial

importance. Two peaks of micro algae (phytoplankton) were observed during the study period, registering first peak

during November and second in March. The blue green algal (Trichodesmium erythraeum) density was found to be

more (56-88/l) during the warmer months (January-April) and low density (12-22/l) during the southwest monsoon

season. In green algae, the species like Cosmarium, Microasterias and Spirogyra (green algae) were found

throughout the period with high density during the southwest monsoon season with yearly mean of 23.84, 28.76 and

31.38/l respectively. Among 41 species of diatoms, the Coscinodiscus species alone constituted maximum density

(1223-3886/l) with a yearly mean of 2189.76/l. and was followed by other species like Chaetoceros.socialis

(189.53/l), Hemidiscus (132.61/l), and Skeletonema costatum (132.53/l). Dinoflagellate density ranged from 364/l

(August) to 1348/l (April) with yearly mean of 760.30/l. Ceratium tripos (122.23/l) and C. massiliensis (117.30/l)

contributed much to the total density of dinoflagellate. The species diversity, species evenness and species richness

of micro algae ranged between 2.684 & 4.929; 1.542 & 2.832 and 0.566 & 1.681 with yearly mean of 3.865, 2.228

and 0.720. The correlation coefficients (r) values between phytoplankton and various hydrographic parameters at

study station explains that out of 77 correlations tabulated between phytoplankton and various hydrographic

parameters, 14 correlations were found to have significant correlations (r >0.232) of which 5 correlations were

significant at 5% level (r >0. 0.232 <0.302). The hydrographic parameters like water temperature, dissolved oxygen

and pH influenced the phytoplankton population but salinity parameter played considerable role in monitoring the

phytoplankton population in this biotope. It is surmised from the stepwise multiple regression analysis that, each

group of micro algae was influenced by set of environmental factors in governing the micro algae population in this

habitat.



INTRODUCTION

River Kali is one of the major rivers of the maritime district of Uttara Kannada of Karnataka, originated in

the Kusavali village of Supa taluka and after meandering about 185 km in the Sahyadri (Western Ghat) region

finally joins the Arabian Sea near Karwar (14o 50’ 15” N and 74o 07’ 30” E). The River traverse in the Western

Ghat region which comprises different habitats such as freshwater, Mangrove, brackish water, creek, and estuary

and is known for its high productivity and rich resources of finfish and shellfish; the fishery of this river is solely

depends upon the rich productivity of plankton, benthos and detritus materials either directly or indirectly.

In any aquatic ecosystem, the growth, density and abundance of phytoplankton are primarily governed by

interactions between environmental factors and biotic entities (Sin et al., 1999). Influx of freshwater and a tidal

activity are abiotic interactions which can play crucial roles on phytoplankton growth and their abundance in the

estuary (Cloern, 1996). The constant nutrient supply always supports the rich phytoplankton production but

generally nitrogen (N) and phosphorus (P) have been considered as the potentially limiting nutrients for a

phytoplankton growth in the aquatic ecosystem s (EEA, 1999; Neill, 2005). Phosphorus is being attributed as

limiting nutrient in the fresh water dominated waters, whereas Nitrogen is being attributed in the coastal waters

(Neill, 2005; Fisher et al., 1992). Inorganic ammonia (NH4-) plays a significant role in the phytoplankton growth in

the polluted waters (EEA, 1999). In addition to nutrients, some of the physical properties such as the salinity

(McLusky, 1971), turbidity and light source are also found to play major roles in the regulation of phytoplankton

growth and their distribution in the estuaries (Cloern, 1987).

A Sunkeri backwater is one of the largest backwater systems of Kali estuary, Karwar, Karnataka is known

for high biological production and plankton diversity (Naik and Neelakantan, 1990; Naik et al., 2005). This

backwater is highly influenced by the incessant rainfall during south west monsoon season associated with land run

off, which results in drastic changes in trophic food web. The backwater is highly influenced by the tidal amplitude

and is known for commercial activities like fishing fin & shell fishes through out the year, transport of goods and is

an outlet for domestic wastes. Whereas in the case of Cochin backwater, due to constant mixing with seawater

through tidal exchanges has given Cochin backwaters the characteristics of a tropical estuary (Balchand & Nair,

1994 and Ajith and Balchand, 1996) and it is used extensively for fishing , local transport of goods and also as an

out let for industrial and domestic c wastes.

Several works have been carried out in the Kali estuary indicating its long term changes of physico-

chemical factors and biological characteristics, but no systematic studies have been reported with respect to Sunkeri

backwaters environmental conditions which stimulate the biological processes of the backwaters. Due to its

extremely dynamic nature by the tidal activity, phytoplankton production in the Sunkeri backwaters is highly

influenced by the physico (temperature, salinity and suspended particulate matter), chemical (dissolved oxygen, pH

and nutrients) properties. Since the monsoonal rainfall governs the Kali estuary dynamic nature of the Kali estuary.



Jyothibabu et al., (2006) stated that the seasonal variations of phytoplankton characteristics with respect to the

changing salinity are very important in the overall functioning of the food web in the Cochin estuary. In the present

study an attempt has been made to evaluate the changes in the physical-chemical characteristics in the Sunkeri

backwaters of Kali estuary and their influence on the abundance and production of phytoplankton.

MATERIALS AND METHODS

a. Study Area

The Sunkeri backwater is a complex brackish water ecosystem (14o51’28” N and 74o09’08” E) of Kali

estuary, which is parallel to the coast but lies perpendicular in north-south to the main axis of the River Kali. More

than 200 acres of brackish water ecosystem is occupied by the mangrove floral stretch. The depth of this ecosystem

ranges from 3-7m but at the mouth area it is comparatively shallow measuring about 2-4m. Regular mixing with

salt water through tidal exchanges and thick growth of mangrove made this ecosystem as a productive and nursery

cradle for most of the commercial fishes. This area is extensively being used for fishing round the year, transport of

goods and an outlet for domestic sewage. The hydrographic conditions of this ecosystem show remarkable changes

with the onset of south west monsoon with influx of freshwater from upper reaches and terrain.

b. Sampling strategy

Temporo-spatial observations were carried out in this brackish water area for the period of one year from

September 2004 to August 2005. Totally 3 study stations (Fig. 1) were selected within a grid of ~ 3.5 km with a

distance of ~ 0.5 km between each station. Water sample were collected using a small plank built boat from 0.5m

below the surface water using the aqua sampler and analyzed in the laboratory.

c. Methods

The water samples were collected using 1 litre capacity of Casella bottle. The samples for dissolved

oxygen (DO) were fixed onboard and the remaining water samples were collected in 1 litre PVC bottles kept in the

ice box and brought to the laboratory at the earliest. Suspended particulate matter (SPM, mgL-1) was determined

gravimetrically on Millipore membrane filters (pore size 0.45 µm) after drying at 70oC for 6-8 hours to reduce water

content before weighing. Salinity was determined by following Mohr Knudsen method (Strickland and Parsons,

1975) and pH was determined using ELICO pH meter (accuracy ±0.01). Dissolved oxygen (DO) was estimated

according to Winkler’s method (Grasshoff, 1983). Samples for nutrients (phosphate, nitrate, nitrite and silicate)

were analyzed following the standard methods (Strickland and Parsons, 1975; Grasshoff, 1983). Subsequent to

water sampling, light penetration was measured using a Secchi disc (diameter = 20cm) further to determine light



attenuation depth (VEC-vertical extinction of coefficient) Poole and Atkins (1929) equation was applied. For

qualitative and quantitative estimation of phytoplankton, 1L of water sample was taken and fixed with few drops of

acid Lugol’s Iodine solution. After settling and siphoning procedure, 1 ml of the aliquot of the sample were taken in

a Sedgewick-Rafter counting cell in duplicate under the inverted microscope (inverted microscope (Magnus-MLX-

DX) for identifying and counting the phytoplankton cells (Tomas, 1997). Among phytoplankton, only four groups

were selected (blue-green algae, green algae, diatoms and dinoflagellates) to study their distribution, abundance with

space and time.

d. Data Analysis

Pearson correlation and stepwise multiple regression analyses were performed to find out the linear

relationship of phytoplankton to various physico (temperature, salinity, SPM) -chemical (pH, dissolved oxygen,

phosphate, nitrate, nitrite and silicate) parameters. Using the numerical density of phytoplankton, the species

Diversity index (Shannon and Weiner, 1963), Species Richness index (Margalef, 1958) and Species Evenness

indexes (Pielou, 1966) were calculated by following statistical methods.

i) Shannon-Weiner Index of Species Diversity

H= -Σ {ni log ni}

N N

ii) Species Richness Index (d) = S-1

Log N

iii) Evenness Index (e ) = H

Log S

Where ni = number of individuals of each species

N = total number of individuals

S = number of species

Natural logarithm (ln) was used in all the calculations.

RESULTS AND DISCUSSION

a. Hydrography



Since the variation of hydrographic parameters of three study stations did not vary much between the

months & seasons and for ease interpretation purpose, hydrographic conditions of study area is discussed for

different seasons only. Seasonal variation of different hydrographic parameters at study stations is given in the

Table 1. The air temperature did not show drastic variation between the seasons, minimum temperature recorded

during south west monsoon (28.1oC) and maximum (30.5oC) in pre monsoon season. A similar trend was noticed in

water temperature also (26.1-27.1oC). Salinity showed perfect seasonal trend in its profile, registering high saline

regime during (20.9 psu) pre monsoon and low saline during south west monsoon (13.62 psu). Dissolved oxygen

showed inverse relationship with salinity with high oxygen content (5.46ml/L) in south west monsoon and low

(4.75ml/L) in pre monsoon. There is no much variation noticed in pH content of water between seasons, varied

between 7.83 and 8.1 respectively. Suspended particulate matter load was found high in southwest monsoon

(0.5577g/L) and low (0.0945g/L) in the water column of study area. The light attenuation was found at 0.22m depth

during south west monsoon and 0.41m during post monsoon whereas an intermediate value recorded in pre monsoon

(0.33m). Nutrient salts showed considerable variation with respect to the seasons. Phosphate-P content was found

high during post monsoon (0.53µg at/L) and low (0.33µg at/L) during pre monsoon season. More or less similar

pattern was noticed in the nitrogen salts distribution, registering maximum concentration of nitrate-N during post

monsoon (0.4µg at/L) and minimum during south west monsoon (0.28 µg at/L) with moderate value (0.38µg at/L)

recorded in pre monsoon season. In case of nitrite-N content, high and low values were recorded during pre (0.32

µg at/L) and post (0.7 µg at/L) monsoon seasons with an intermediate value of 0.54 µg at/L in south west monsoon

season respectively. Compared to these three nutrient salts, the silicate-Si showed highest concentration in water

during south west monsoon (219.35 µg at/L) and lowest (104.61 µg at/L) during pre monsoon season.

The lower temperature values were encountered during the southwest monsoon and there after gradually

increased. Thermal structure of estuarine waters are controlled fundamentally by the temperature of the sea and run

off water and this holds good only for the estuaries which are short and have little development of sand and mud

flats. The differential depth of the water column and position of the study stations also contribute to the small

regional variations of the parameter in the surface waters of River Kali (Neelakantan et al., 1988, Naik et al., 2005).

Land run off and river influx into inshore waters of Karwar drastically declines the salt content of water

during the southwest monsoon period. Salinity attained a high value during the pre monsoon season and low during

monsoon season. In Kali estuary, the distribution of salinity depends upon the variations in the amount of run off

from the land and precipitation during monsoon and not to mention the tidal amplitude whose effect is prominent

within the seasons. The transition from these low values to the higher ones of the summer takes place gradually

during the post monsoon months (Naik and Neelakantan, 1990).

The rich oxygenated condition prevailed in study station during the present study period. The mean high

oxygen content suggests the lesser density of aerobic heterotrophics like microorganisms there in (Parsons et al.,



1977) and rich growth of faunal community but the poor oxygenation of surface waters at station denotes reduced

sulphide containing zone underlying the oxidized layer of sediment surface (Fenchel, 1969, Fenchel and Riedl,

1970; Sudarshana, 1983). Intermediate values were noticed during pre monsoon season, this could be probably due

to the enormous suspension load of micro algae in pre monsoon that has depressed the average to a lower order

(Naik, 1986).

The hydrogen ion concentration did not vary much among study station during different months and

seasons. In the present study, the pH values ranged from 7.83 to 8.1. According to Perkins (1976), the range of pH

of estuaries and coastal waters under normal and unpolluted conditions is between 6.7 and 9.25 respectively.

During south west monsoon this could be probably due to the river run off and land run off which

contributes maximum amount of sand particles as well as detritus and plankton. According to Jerlov et al., (1978),

more often the suspended particles are conglomerates’ containing various components and concentration suspended

matter varies from less than 0.1mg/l in Open Ocean to many grams/l in estuaries and near shore waters. The vertical

extinction coefficient (K) values showed a remarkable range mainly depending on the depth, turbulence and

transparency.

During the post monsoon and southwest monsoon, the concentration of the Phosphate-phosphorus was

more but the concentration in later was not as pronounced as in the former season. A similar type of distribution was

noticed in the Cochin backwaters by Balakrishnan and Shynamma (1976), Naik and Neelakantan (1990) and Naik et

al., 2005 in the Karwar waters. The lower concentration of the nutrient salts (nitrogen) may be due to the intake by

phytoplankton and registering their high growth profile during this period. In many cases, this nutrient salt acts as a

limiting factor in governing the population of microscopic drifting plant and animal community.

The concentration of silicate is much under the influence of southwest monsoon when heavy rainfall,

inflow of freshwater and land drainage was more (Naik and Neelakantan, 1990). The silicate shows an inverse

relation to salinity, a similar relationship between the two parameters was noticed earlier (Ramamurthy, 1965;

Noble, 1968; Naik, 1986; Naik and Neelakantan, 1990 and Naik et al., 2005) in inshore waters of Karwar and in the

Kali river biotope.

The coefficients of correlation (r) between phytoplankton and various hydrographic parameters at study

station are given in the Table 2. The Pearson coefficients of correlation (r) values among water chemistry variables

and biotic entities of the study areas based on monthly water & phytoplankton samples analyses during the period

from September 2004 to August 2005. Out of the total 77 correlations tabulated between two parameters, 14 were

found to have significantly correlations (r >0.232). From this, 5 correlations were significant at 5% level (r >0.

0.232 <0.302) and 14 correlations at 1% (r>0.302) level. The negative (inverse) correlations were found in eight

cases, air temperature with dissolved oxygen, suspended matter, phosphate and silicate, water temperature with

dissolved oxygen, phosphate, nitrate & silicate, whereas salinity with dissolved oxygen, SPM, phosphate, nitrate and



silicate. Dissolved oxygen shown correlation with VEC, phosphate, nitrate & nitrite whereas the hydrogen ion

concentration with suspended matter, VEC, phosphate, nitrite & silicate. Suspended matter with VEC, phosphate,

nitrate & nitrite parameters. The VEC showed negative relationship with only one parameter is the silicate.

Phosphate showed negative relationship with all parameters except with nutrient parameters. Nitrate with water

temperature, dissolved oxygen, & suspended matter whereas nitrite with all parameters except with that of salinity,

dissolved oxygen & pH have shown the negative correlation. Silicate has shown negative relationship with all

parameters except with dissolved oxygen, suspended matter, phosphate and nitrite parameters respectively. Some of

the highly significant correlations (r>0.302) were discernible between water temperature & VEC, salinity & VEC,

dissolved oxygen with suspended matter & silicate. Here, the phytoplankton showed positive correlation with air

temperature (r=0.25), dissolved oxygen (r=0.28) at 5% level whereas water temperature (r=0.35), salinity (r=0.35

and hydrogen ion concentration (r=0.41) at 1% significant level. As seen in the station 1 case, here also the set of

hydrographic parameters like water temperature, dissolved oxygen and pH influenced the phytoplankton population

but here salinity played considerable role in monitoring the phytoplankton population. As this station located far

away from the estuarine complex, the impact of this parameter is noticed in the density of this fragile biotic

community. Naik (1986) and Naik (1990) have observed similar findings in River Kali and Sharavathi.

b. Phytoplankton (Micro algae)

Table 3 gives list of phytoplankton species occurred at three study stations during the study period. There is

no much variation in the species distribution and abundance between the three study locales and period. Hence, for

ease presentation, an average of species density was taken with space and time and represented for whole study area.

Figure 2 shows the monthly variation in total density of different groups of phytoplankton community in the study

area. Blue green algae was found absent during July-August registering minimum in June months (22/l) and

maximum in April (88/l) showing yearly mean of 36.6/l. Green algae was found less in April (37/l) and high in

August (242/l) with yearly mean of 86.7/l. Diatom showed highest density compared to all four groups, minimum

in January (4755/l) and registered maximum density in March (9297/l) with 6475.1 as yearly mean. In

dinoflagellates, minimum density was noticed in during August (364/l) and maximum density in April (1348/l) with

annual mean of 787.1/l respectively.

It is surmised from the Figure 3 that two peaks were observed during the study period, first peak registered

during November and second peak in March. Minimum density was noticed during January and September months

while during southwest monsoon months more or less stable population was recorded. The blue green algae

(Trichodesmium erythraeum) was found throughout the study period but its density was found to be more (56-88/l)

during the warmer months (January-April) and low density (12-22/l) was noticed during the southwest monsoon

season. During post monsoon season, its density ranged between 21 and 56/l and gradually increased attaining



moderately high density during pre monsoon and later declined in southwest monsoon period and its yearly mean

value was of 34.69/l (Figure 2).

Among the green algae, four species were recorded in the phytoplankton sample of which Zygnema species

was found absent throughout the study period except during July and August months with a density of 2-3/l.

Remaining species like Cosmarium, Microasterias and Spirogyra were found throughout the period with high

density during the southwest monsoon season and the yearly mean of these species are of 23.84, 28.76 and 31.38/l

respectively. The density of this group varied between 37 (April) and 242/l (August) respectively. The density of

this alga did not vary much during September and May but a gradual increasing trend was seen during May to

August and later declined and fall was noticed in September month. As these green algal species prefer zero saline

water for their distribution in the river as well as in the estuarine region, their density found comparatively high in

freshwater regime than the saline mixed water. Naik (1990) also observed similar results in the Sharavathi riverine

complex while estimating the standing crop the phytoplankton.

As this alga prefers high saline regime water, appears in the coastal and in estuarine mouth while the green

algae made its appearance in the upper stretches of the estuary in August and September and remained absent during

other months (Naik and Neelakantan, 1990). Konnur (1981) and Neelakantan et al., (1988) have observed the

appearance of blue green algae close to estuarine mouth during the warmer months of the year. Naik et al., (2005)

observed that the green algae was absent in south west monsoon samples while green algae were observed only in

the upper stretches of the estuary where they were dominated over the blue green algae.

Among 41 species recorded in this group (Table 3), the Coscinodiscus species again found in maximum

density (1223-3886/l) with a yearly mean of 2189.76/l.and was followed by other species like Chaetoceros socialis

(189.53/l), Hemidiscus (132.61/l), and Skeletonema costatum (132.53/l). Diatom’s monthly density ranged from

4755/l (January) to 9297/l (March) with yearly mean of 6471.84/l respectively. Figure 2 describes monthly

variation in the density of diatoms, which showed maximum during March, and minimum density in January month.

Dinoflagellate formed the second dominant group in contributing maximum share to the total

phytoplankton density in the study station (Fig.2). Its overall monthly density ranged from 364/l (August) to 1348/l

(April) with yearly mean of 760.30/l. Among these species, Ceratium tripos and C. massiliensis (122.23/l) and

(117.30/l) contributed much to the total density of dinoflagellates. Peridinium depressum and Pyrocystis fusiformis

were found other dominant species of dinoflagellate density. Dinoflagellate showed bimodal peak one during

November, in succeeding months it gradually decline with fall in February and later its density increased attaining

second peak in April month and the density was declined during May to September with a fall in August. Among the

four groups, diatom constituted maximum share to the total density of phytoplankton and the value was 6475.08/l

and was followed by the dinoflagellate group (787.08/l) whereas blue-green alga and green algae showed minimum



density of 36.58 and 86.75/l respectively. The yearly mean value of total phytoplankton in the study station 2 was

7385.49/l respectively.

The most fundamental of the phytoplankton succession patterns with regard to ecological consequences,

however is species succession, during which many species co-occur; the duration and time of occurrence of the

different species vary each species usually exhibits a population maximum; maximal abundance (=environmental

carrying capacity) varies between species, several species may exhibit concurrent maxima and the time from

appearance to disappearance varies between species.

At study stations the diversity index value ranged between 2.684 (March) and 4.929 (January). Diversity of

phytoplankton population is fairly dependent on quality of water and climatic factors. In the present observation the

climatic factors of the entire study areas were of similar pattern as their locations are not far away. Various

physical, chemical and biological circumstances must be simultaneously taken in to the consideration for

understanding the fluctuations of plankton population. Temperature, pH and phosphate have been emphasized to be

a significant factors controlling distribution of Cyanophyceae (Singh, 1965). Fluctuation in phytoplankton diversity

can be attributed to the seasonal changes in the temperature, salinity, dissolved oxygen, other nutrient elements and

the water level (tide).

Many algal species are useful indicators of trophic conditions in rivers. The present study revealed that the

riverine system had mesotrophic characteristics with an intermediate level of productivity showing seasonal

distribution of phytoplankton organisms. However, it will be necessary to carry out further physical and chemical

analyses of the backwater to obtain more detailed information on trophic conditions. It showed more or less an

inverse relationship between two parameters. Maximum density was coincided with low species evenness values

and vice versa.

At study station, the trend was noticed with high phytoplankton density (7890-10173) during February –

April). The low density (5470/l) recorded in January which established high species evenness profile (2.832). The

study station experienced a similar type of relationship between phytoplankton density and species richness.

Phytoplankton density did not vary much between the months except in March, when high density was recorded.

Except in March (2.684), the species richness showed higher values and was almost stable in rest of the months.

Here, the indirect relationship established between the two parameters, phytoplankton density and species richness

index values. This could be due to the single or two species contributed more to the density.

The diversity index of a phytoplankton sample tends to be lower in eutrophic water than in oligotrophic

waters. Also, species with r-strategy tend to predominate in eutrophic waters whereas the K-strategy characterizes

the plankton of oligotrophic waters. It may be a noted here that the diversity of phytoplankton samples gives rise to

a problem since it is difficult to see how so many species with similar requirements could continue to coexist in an

apparently uniform environment (Mitra et al., 2006). Hutchinson has called this ‘the paradox of the plankton’. The



explanation probably is that the uniformity is only apparent and the continually changing pattern of turbulence offers

a succession of niches, which can be occupied by a particular species for only a limited time. The survival of a

population is the result of a temporary equilibrium between success in remaining afloat and inevitable sinking and

different species differ in their sedimentation properties and in their capacities to take advantage of different levels

of turbulence (Margalef, 1978).

The present study indicates higher phytoplankton diversity as compared to the earlier reports (Karigoudar,

1994; Rane et al., 1999). It was noticed that lower reaches of the river is rich in micro algae (phytoplankton) in

terms of total abundance and diversity than the upper reaches of the river. The incidence of high species diversity

and total abundance of phytoplankton especially diatoms in the area might be attributed to nutrient source.

The blue green algae population was controlled by set hydrographic parameters like, water temperature,

salinity, hydrogen ion concentration and vertical extinction of coefficient (VEC) respectively. Dissolved oxygen,

suspended matter and silicate played considerable role in governing the population of green algae at this study

station. The population of diatoms was influenced by some hydrographic parameters like dissolved oxygen,

hydrogen ion concentration (1% level) and suspended matter (5% level). In case of dinoflagellates, salinity and

water temperature & nitrate at 1% level and 5% level were found to be significant.

The growth of phytoplankton depends on the availability of nutrients like nitrate, phosphate, silicates etc.

Nutrients are generally found in areas of upwelling. They are also contributed by run off processes from the

adjacent landmasses and also from the litters of the surrounding vegetations. A very useful way of comparing

growth rates of phytoplankton is to express growth as an increase in cell numbers. For unicellular organisms, this is

an exponential function: (Xo + δ X) = Xoe µt Where, Xo is the population of cells at the beginning of the experiment, δ

X is the number produced during time ‘t’ and ‘µ’ is the growth constant of the population per unit time (Mitra et al.,

2006). If δX has been measured in units of photosynthetic carbon, then Xo must be expressed as the total standing

stock of phytoplankton carbon instead of in terms of cell numbers. The effect of nutrient concentration on the

growth constant µ can be described by the expression as given below:

µ= µ max [N]/KN = [N]

Where µ is the growth rate (time-1) at a specific nutrient concentration [N], which is usually expressed in

micromoles (µM) per litre, µmax is the maximum growth rate of the phytoplankton and KN (given in µ M) is a half-

saturation constant for nutrient uptake that is equal to the concentration of nutrients at ½ µ max. The above equation

is valid when the growth rate of phytoplankton is controlled by the nutrient concentration in seawater. However, in

some surface water with extremely low concentration of nutrients, some large photosynthetic dinoflagellates can

migrate to deeper layers where nutrients are more abundant. The zone where nutrient concentrations increase

rapidly with depth is the nutricline and this may be below the euphotic zone. After taking nutrients such as nitrate



into the cell, these flagellates can return to sunlit waters to carry out photosynthesis. In such cases, the

photosynthetic growth rate of the phytoplankton is proportional to the nutrients within the cell and not to the

external nutrient concentrations (Mitra et al., 2006).

In order to grow and metabolize, both energy and the necessary building blocks and energy-carriers must

be available. Most of these material requirements are available to excess in seawater, but concentrations of nitrogen

and phosphorus in particular can at times and in certain areas are very low. Uptake of nutrients is an active process

that can operate against a concentration gradient by which is nevertheless dependent on the external concentration.

Rather as with light absorption, at low external concentrations of a nutrient, uptake is dependent on concentration

but at certain (higher) level the uptake mechanisms saturate and a plateau is attained.

Different members of the phytoplankton have widely varying values of Kc such that some species can take

up nutrients only from high external concentrations whilst others can do so from progressively lower concentrations.

The local selection pressures now favour species which can take up nutrients from lower external concentrations and

as concentrations drop still further so yet different species will be at an advantage and soon. This process is

reflected by the fact that the phytoplankton species dominating given water mass at a given time have Kc values

appropriate to the ambient nutrient concentrations (Fogg, 1980).

Phytoplankton can store nutrients taken up at times of relative plenty and use them for subsequent

production even in the absence of external supplies. From two to more than five further generations may be fuelled

from stored sources. Phytoplankton are small and can effectively take up nutrients only from the thin layer of water

surrounding them. Without relative motion, this thin layer would soon be impoverished regardless of the overall

concentrations of nutrients in the larger water mass in which they are suspended. Hence the period of nutrient

abundance is a temporary bonanza, which as in all similar circumstances is best exploited by an r-strategy (i.e., one

that maximizes the potential rate of population increase. Under these conditions, it is often light which is limiting as

a result of self-shading. Therefore there will be selective advantages associates with a bloom life-style; species will

be favoured which multiply very rapidly in order to pre-empt the light and utilize the nutrients whilst they are

available.

Algae are the major primary producers in many aquatic systems and are an important food source for other

organisms. They exist in planktonic and benthic forms. Species composition and the seasonal variations of these

delicate forms in riverine waters are dependent on the interactions between physical and chemical factors. Therefore,

phytoplankton species and density fluctuate according to the seasons. It has been shown that many of these

fluctuations, called seasonal successions, could result from the life activities of the previously existing

phytoplankton and zooplanktons, fishes and other organisms. The seasonal succession of the phytoplankton is a

problem that has attracted the attention of algologists for a long time, but many of the studies on periodicity have

been restricted to limited areas.



By and large, post monsoon season recorded higher abundance of diatoms in comparison to pre monsoon

season. Predominance of diatoms during post monsoon season coincided with lower temperatures, lower salinity

and comparatively higher nutrients levels.

Habitat heterogeneity in addition to area including habitat factors such as physical, chemical properties of

the water body are also important in determining species richness and diversity. These factors create different kinds

of microhabitats. The microhabitats were diverse in each study stations under the study area. Thus habitat

heterogeneity may be one of the factors causing the higher diversity recorded, since the complexity of the biotope

structure influences abundance of the phytoplankton species. The difference in the phytoplankton species

composition is due to the differences in the microhabitat among the sites. Further, the reason for the poor

phytoplankton species composition in the certain area during certain period is due to the water quality structure may

be due to the physico-chemical and other parameters cumulatively acting on this biotic community creating

unfavourable conditions to thrive in. Further, the species richness in an area is dependent on the availability of

nutrients, climate, physico-chemical properties of the water, evolutionary and predation pressure. Similar

observations were also recorded by Naik (1986), Naik et al., 2005.

Figure 4 sows the results of stepwise multiple regression (r) analysis, which explains some of hydrographic

(abiotic) parameters influences abundance of phytoplankton groups in the study area. The distribution and

abundance of blue green alga was influenced by air (r=0.346) & water temperature (r=0.376), salinity (r=0.858),

vertical extinction coefficient (r=0.463) at 5% level whereas pH (r=0.245) at 1% level these parameters influenced

this alga for abundance and distribution in the area. Distribution of green algae was influenced by dissolved oxygen

(r= 0.836), suspended particulate matter (r=0.926) and silicate (r=0.606) at 5% significant level. Diatom population

was governed by parameters of dissolved oxygen (r=0.462), pH (r=0.450) at 5% and suspended particulate matter

(r=0.282) at 1% significant level. In case of dinoflagellate, air (r=0.588) & water temperature (r=0.242), salinity

(r=0.751) and nitrate (r=0.241) contributed much to the distribution and abundance of this micro organism in this

habitat.

Naik (1986), during his studies on plankton and productivity of Kali estuary and inshore waters of Karwar,

has found in stepwise multiple regression test that, several hydrographic parameters such as water temperature,

salinity dissolved oxygen phosphate, nitrate and silicate had influenced the population variation of phytoplankton.

Similar observation made by Vernekar (2003) while studying the hydrological and plankton relationship in coastal

waters of Karwar.

To obtain an insight into the causative factors involved in the rhythmic and seasonal changes in the total

plankton production in water body, it is essential to follow closely the seasonal history and fluctuation of the



different planktonic groups. The annual quantitative history of much plankton is a succession of appearances and

disappearances of wave maxima. The seasonal succession of phytoplankton involves the factor such as light,

nutrients, temperature and grazing pressure of the water. Review of literature reveals that there are two types of

growth period for phytoplankton. The reports of some workers suggest that the maximum development of

phytoplankton occurs during summer and minimum in winter. Kumar (1984) estimated that the density of

phytoplankton is greater during summer, post monsoon and winter and is lowest in monsoon. Pandey et al., (2004)

have observed the peak of phytoplankton during summer followed by winter. Saha and Choudhary (1988) obtained

the maximum density of phytoplankton during July and minimum during January. Pandey et al., (2004) have

reported that chlorophyceae abundance was found correlated with pH, dissolved oxygen, free CO2, bicarbonate,

transparency and chloride content of river water. Cyanophyceae showed positive correlation with pH, dissolved

oxygen, bicarbonate, nitrate, transparency, phosphate and chloride. They have also noticed that, Bacillariophyceae

showed positive correlation with dissolved oxygen, bicarbonate, nitrate and transparency. At this juncture, it is

recommended further studies focusing on better understanding and evaluating impact of fresh water discharges on

growth of phytoplankton and recycling processes of nutrient in the backwater ecosystem. Thus it may conclude

from these results that, the density of phytoplankton is dependent on different hydrographic (abiotic) parameters

either directly or indirectly or in combination with other set of hydrographic parameters.

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Table 1. Seasonal variation in the hydrographic parameters at study station during the study period

(September 2004 - August 05).

AT= Air Temperature (oC);WT=Water Temperature (oC); SAL=Salinity (psu);

DO= Dissolved Oxygen (ml/l); pH=Hydrogen ion concentration;

SPM= Suspended Particulate Matter (g/l); VEC= Vertical Extinction Coefficient (m);

PO4= Phosphate-P (µg at/l); NO3=Nitrate (µg at/l); NO2=Nitrite (µg at/l) and

SiO2= Silicate (µg at/l)

Season

Parameters AT WT SAL DO pH SPM VEC PO4 NO3 NO2 SiO2

Post Monsoon 28.4 26.63 18.8 4.75 7.83 0.0945 0.41 0.53 0.40 0.71 140.51

Pre

Monsoon

30.5 27.08 20.9 5.17 8.1 0.1477 0.33 0.33 0.38 0.32 104.61

Southwest

Monsoon.

28.12 26.14 13.62 5.46 7.95 0.5577 0.22 0.37 0.28 0.54 219.35



Table 2. Correlation Coefficient (r) values between some hydrographic parameters and Phytoplankton

AT WT SAL DO pH SM VEC PO4 NO3 NO2 SiO2 PP

AT 1 0.6905

**

0.4277

** -0.2602 0.1834 -0.1869 0.0034 -0.3671 0.1424 0.0778 -0.3943

0.2597

*

WT 1 0.2647

* -0.1595

0.2962

* 0.0103

0.3529

** -0.4293 -0.0476 0.1960 -0.0270

0.3462

**

SAL 1 -0.6659 0.1677 -0.7537 0.4888

** -0.1289 0.1773 -0.2183 -0.7345

0.3549

**

DO 1 0.2803

*

0.8268

** -0.4705 -0.3842 -0.2257 -0.1964

0.5165

**

0.2834

*

pH 1 -0.0625 -0.0564 -0.6975 0.1896 -0.3242 -0.2928 0.4056

**

SM 1 -0.4600 -0.1939 -0.4340 -0.0474 0.6117

** 0.1577

VEC 1 0.1579 0.0489 0.4413

** -0.0095 -0.0496

PO4 AT=Air Temperature; WT=Water Temperature;

SAL=Salinity; DO=Dissolved Oxygen;

pH=Hydrogen ion concentration;

SPM=Suspended Particulate Matter;

VEC=Vertical Extinction Coefficient;

PO4=Phosphorus-P; NO3= Nitrate-N;

NO2= Nitrite; SiO2=Silicate-Si;

PP= Phytoplankton

1 0.3021

*

0.5893

** 0.0474 -0.3910

NO3 1 0.3601

** -0.1809 0.2026

NO2 1 0.3373

** -0.2472

SiO2 1 -0.2127

PP * = Significant at 5% level

** = Significant at 1% level 1



Table 3. Occurrence of Phytoplankton species at different study stations

Phytoplankton Station I Station II Station III

Class: Cyanophyceae

Trichodesmium erythraeum + + -

Class: Chlorophyceae

Cosmarium sp. + + +

Microasterias sp. - + -

Spirogyra sp. - - +

Zygnema sp. + - -

Class: Bacillariophyceae

Coscinodiscus sp. + + +

Skeletonema costatum + + +

Hemidiscus sp. + + +

Melosira sp. + + +

Stephanophyxis sp. + - -

Triceratium sp - + +

B. mobiliensis + - +

Biddulphia sp. + + +

B. obtusa + + +

B. sinensis + + +

Guinardia sp. + - -

Bellorochea sp. + - -

Nitzschia sp. + + +

N. seriata + + +

Ditylum sp. + - +

Chaetoceros socialis + - +

C. decipiens + + +

C. lorenzianus + + +

C. affinis - + +

Grammatophora sp. + - -

Campylodiscus sp. - + +

Planktoniella sp. + - +

Bacteriastrum sp. + + +



Eucampia sp. + + +

Clamacodium sp. + + +

Streptotheca sp. + + +

Thallossira sp. + + +

T. gravida + + +

Thallassionema sp. - + +

Rhizosolenia alata + - -

R. stolterfothii + + -

R. styliformis + + -

R. hebata + + +

R. robusta + + +

R. castracanei - + -

Thalassiothrix sp. + + +

Asterionella sp. + + -

Pleurosigma sp. + + -

Gyrosigma sp. + + +

Navicula sp. + + +

Lithodesmium sp. + + -

Class: Dinophyceae

Peridinium depressum + + +

Noctiluca miliaris + + -

Pyrocystis fusiformis + - +

Prorocentrum sp. + + -

Dinophysis sp. + - -

Ornithocercus sp. + + -

Ceratium tripos + - +

C. massiliensis + + +

C. furca + - +

C. fusus + + +



Fig. 2. Monthly variation in the density of different groups of Phytoplankton at study station

0

2000

4000

6000

8000

10000

12000

Sept.04 Oct. Nov. Dec. Jan.05 Feb. Mar. Apr. May Jun. Jul. Aug.

Sampling Period

PP

Den

sit

y (

No

/L)

Blue Green

Green

Diatoms

Dinoflagel

PP



Fig. 3. Monthly variation in the species diversity, evenness and richness of Phytoplankton at study station

0

2

4

6

8

10

12

Sept.04 Oct. Nov. Dec. Jan.05 Feb. Mar. Apr. May Jun. Jul. Aug.

Sampling Period

Ph

yto

pla

nkto

n d

en

sit

y (C

ell N

ox1

03)

0

0.5

1

1.5

2

2.5

3

Ind

ex

of S

p. D

ive

ris

ty, E

ve

nn

ess &

Ric

hn

es

s o

f PP

Phytoplankton

Sp. Diversity

Sp.Eveness

Sp.Richness



Fig.4. Stepwise multiple regression ( r ) values between abiotic parameters and Biotic

groups of Phytoplankton at study station.

Abiotic parameters:

AT= Air Temperature; WT= Water Temperature; SAL=Salinity;

DO=Dissolved Oxygen; Ph=Hydrogen ion concentration; SPM=Suspended Particulate Matter;

VEC=Vertical Extinction Coefficient; PO4=Phosphate-P; NO3=Nitrate-N; NO2= Nitrite-N;

SiO2=Silicate-Si

Biotic groups:

BG= Blue Green Algae; GR= Green Algae; BACR= Diatoms and

DIN= Dinoflagellate

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

AT WT SAL DO Ph SM VEC PO4 NO3 NO2 SiO2

Abiotic parameters

Re

gre

ss

ion

va

lue

s

va

lue

s

Blue Green Algae Green Algae

Diatoms Dinoflagellates

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