Indian Journal of Geo Marine Sciences Vol. 47 (07), July 2018, pp. 1502-1517 Spatio temporal variation of Phytoplankton in relation to physicochemical parameters along Mahanadi estuary & inshore area of Paradeep coast, north east coast of India in Bay of Bengal * Sangeeta Mishra 1 , Sumitra Nayak 2 , Sharada Shrinivas Pati 2 , Satya Narayana Nanda 1 , Sarat Mahanty 2 & Anupam Behera 2 1 CMCE, State Pollution Control Board, ICZMP, Odisha, India 2 Coastal Laboratory, State Pollution Control Board, ICZMP, Bhubaneswar, Odisha, India [E.Mail: [email protected]] Received 05 October 2016; revised 06 April 2017 Percentage contribution of each group of phytoplankton was in the order of: Diatom > Cyanobacteria & Chlorophyceans > Dinoflagelles. Out of all, only three species (Asterionellopsis glacialis, Nitzschia sp. and Thalassiothrix longissima) contributed more than 50% to the total phytoplankton population. Abundance varied from115 nos.ml -1 to 555 nos.ml -1 which is 8 times more than the earlier studies made in the coast of Odisha. Significant variations is also observed in physicochemical parameters viz., salinity, dissolved oxygen (DO), nitrites (NO 2 –N), nitrates (NO 3 -N), Ammonia (NH 3 -N), Phosphate (PO 4 -P), silicate (SiO 4 -Si) . By analyzing the N:P ratio, in the samples of the study area, it was observed that nitrogen is the limiting factor for phytoplankton. The similarity and group average clustering (Bray Curtis) divide the area in to two distinct phytoplankton groupings with their location of sampling as (A) low salinity and high turbidity and (B) high salinity. Higher species diversity (H’= 2.6-3.0) and higher equitability in plankton flora (J’=0.8-0.9) were observed in estuarine monitoring points having lower levels of anthropogenic influence (BOD=1.2-1.9 mg L -1 ). [Keywords: Spatial, phytoplankton, Mahanadi estuary, Paradeep, assemblages, species] Introduction Phytoplankton initiate the marine food chain, by serving as food to the primary consumers, which include zooplankton, shellfish, finfish and others 1-4 . Theyalsoplay as an important factor in the carbon budget and modulation of sea surface temperature through absorption of solar radiation. Quantification of phytoplankton biomass and their community composition is very important for understanding the structure and dynamics of marine ecosystem 5 .Changes in dominance and diversity of phytoplankton species often have been used as indicators of water quality 6 and hence, spatial and temporal changes in phytoplankton community were analyzed. Changes in phytoplankton biomass and primary productivity in coastal waters of northern Bay of Bengal (NB) and western Bay of Bengal (WB) is influenced significantly by change in surface salinity due to heavy fresh water influx from rivers. This quantum of influx influences vertical stratification thus impeded vertical transfer of nutrients 7 . Beside river discharges, cyclones and physical oceanographic processes also controls the distribution of phytoplankton biomass 8 . Relatively higher nutrient concentrations was also found along the WB than central Bay of Bangle (CB) contributing higher phytoplankton abundance in WB 9 . Extensive works pertaining to the qualitative and quantitative aspects of phytoplankton in relation to physicochemical parameter in spatial and temporal scale have been carried out in different coastal ecosystems of India 10-14 . This type of study along the northeast cost of India has also been carried out by different researchers 11&15-19 . However, in Paradeep coast different aspects such as water quality 20-25 and phytoplankton ecology though dealt by different studies 11&26-27 are sporadic and without comprehensive evaluation. Present work was focused on studying both quantitative as well as qualitative phytoplankton ecology for estuarine and coastal waters by considering the influence of physicochemical parameters in a holistic manner. Materials and Methods Paradeep coast is located (20 19’01.61”N to 20 10’41.20”N & 86 45’22.82”E, 86 32’12.72”E) along the eastern coast, in the Bay of Bengal. It is a ____________ * Corresponding author
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Indian Journal of Geo Marine Sciences Vol. 47 (07), July 2018, pp. 1502-1517
Spatio temporal variation of Phytoplankton in relation to physicochemical parameters along Mahanadi estuary & inshore area of Paradeep coast, north east
coast of India in Bay of Bengal *Sangeeta Mishra1, Sumitra Nayak2, Sharada Shrinivas Pati2, Satya Narayana Nanda1, Sarat Mahanty2 & Anupam Behera2
1CMCE, State Pollution Control Board, ICZMP, Odisha, India 2Coastal Laboratory, State Pollution Control Board, ICZMP, Bhubaneswar, Odisha, India
Percentage contribution of each group of phytoplankton was in the order of: Diatom > Cyanobacteria & Chlorophyceans > Dinoflagelles. Out of all, only three species (Asterionellopsis glacialis, Nitzschia sp. and Thalassiothrix longissima) contributed more than 50% to the total phytoplankton population. Abundance varied from115 nos.ml-1 to 555 nos.ml-1 which is 8 times more than the earlier studies made in the coast of Odisha. Significant variations is also observed in physicochemical parameters viz., salinity, dissolved oxygen (DO), nitrites (NO2–N), nitrates (NO3-N), Ammonia (NH3-N), Phosphate (PO4-P), silicate (SiO4-Si) . By analyzing the N:P ratio, in the samples of the study area, it was observed that nitrogen is the limiting factor for phytoplankton. The similarity and group average clustering (Bray Curtis) divide the area in to two distinct phytoplankton groupings with their location of sampling as (A) low salinity and high turbidity and (B) high salinity. Higher species diversity (H’= 2.6-3.0) and higher equitability in plankton flora (J’=0.8-0.9) were observed in estuarine monitoring points having lower levels of anthropogenic influence (BOD=1.2-1.9 mg L-1).
Introduction Phytoplankton initiate the marine food chain, by
serving as food to the primary consumers, which include zooplankton, shellfish, finfish and others1-4.Theyalsoplay as an important factor in the carbon budget and modulation of sea surface temperature through absorption of solar radiation. Quantification of phytoplankton biomass and their community composition is very important for understanding the structure and dynamics of marine ecosystem5.Changes in dominance and diversity of phytoplankton species often have been used as indicators of water quality6and hence, spatial and temporal changes in phytoplankton community were analyzed. Changes in phytoplankton biomass and primary productivity in coastal waters of northern Bay of Bengal (NB) and western Bay of Bengal (WB) is influenced significantly by change in surface salinity due to heavy fresh water influx from rivers. This quantum of influx influences vertical stratification thus impeded vertical transfer of nutrients7. Beside river discharges, cyclones and physical oceanographic processes also
controls the distribution of phytoplankton biomass8. Relatively higher nutrient concentrations was also found along the WB than central Bay of Bangle (CB) contributing higher phytoplankton abundance in WB9. Extensive works pertaining to the qualitative and quantitative aspects of phytoplankton in relation to physicochemical parameter in spatial and temporal scale have been carried out in different coastal ecosystems of India10-14. This type of study along the northeast cost of India has also been carried out by different researchers11&15-19. However, in Paradeep coast different aspects such as water quality 20-25 and phytoplankton ecology though dealt by different studies11&26-27are sporadic and without comprehensive evaluation. Present work was focused on studying both quantitative as well as qualitative phytoplankton ecology for estuarine and coastal waters by considering the influence of physicochemical parameters in a holistic manner.
Materials and Methods Paradeep coast is located (2019’01.61”N to
2010’41.20”N & 8645’22.82”E, 8632’12.72”E) along the eastern coast, in the Bay of Bengal. It is a
____________
*Corresponding author
fast growingalso having activities likRefinery, PaChemical pl(P) Ltd are owater samplmeters fromincluding esMar 2014 toanalysis witand physicoc
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INDIAN J. MAR. SCI., VOL. 47, NO. 07, JULY 2018
1504
composition and environmental variables - comparable to multiple regressions. BIOENV examines all possible combinations of variables, from each environmental variable separately through all at the same time. BIOENV is applied to get the impact of environmental variables on the Phytoplankton community. Whereas, BVSTEP first fits the environmental variable with the strongest relationship, then adds in the variable with the next strongest relationship etc, comparable to stepwise regression and find out the influential species to distinguish the area of interest. Results and Discussion
Physicochemical Parameters Seasonal environmental fluctuations influence in
the physico-chemical parameters of coastal water, which in turn affect the phytoplankton community structure in the coastal area of tropical region. The observed variations in physico-chemical parameters of the present study are shown in Table-I. Season wise variation of physicochemical properties is presented in table II. Station wise and season wise Pearson correlation between physico-chemical parameters with chlorophyll and the dominant group of phytoplankton (diatom) is presented in the Table-III and IV, respectively. Figure IV (A&B) representing the correlation of phytoplankton abundance with nitrate and turbidity. Temperature, turbidity, salinity and all the nutrients showed seasonal variation in the present study. During the assessment, the surface water temperature of the assigned site varied from 20.60C to 31.90C, yielding an annual variability of~11.30C, as a whole. However, the variation of surface water temperature in the shoreline (SL) area was 22.8 0C to 31.9 0C (± 2.5), differs from the estuarine (ES) surface temperature of 20.6 0C to 28.70C (±3.0) Highest WT was recorded in Monsoon (MON) whereas, lowest in post-monsoon (POM). Naik et al40 have reported variation 22.6°C to 30.4°C which corroborate our result. On the other hand, our result exhibited higher range compared to those of Khadanga et al21 and panda et al 27 in the same environment. Temperature variation plays a major role in fluctuating and distributing of micro-algae, particularly the diatoms41. A negative correlation (P ≤ 0.05) was also observed between water temperature and DO. Secchi disc depth varies from minimum of 1.0mt. (ES) to maximum 6.0 mt. (SL).The variations in the results of pH are very
marginal ranging from 7.83 to 8.51. Salinity is one of the most important hydrographical parameter. Fluctuation of salinity as observed also varied in wide range i.e. from 1.1PSUat stationMR1 (ES) to 29.8 PSU at station P9& P11 (SL). However, in estuarine water, salinity varied from 1.1PSU to 29.6 PSU and at inshore stations17.9 PSU to 29.8 PSU. Salinity was found highest 29.8 PSU in Pre-monsoon (PRM) (SL) and lowest 1.1 PSU in MON (ES).Similar trends i.e. (min. 0.23PSU to 31.83 PSU) has also been reported in Gauthami Godavari estuary42. In the same study area, Khadanga et al21 observed salinity variation (1.62PSU to 31.36 PSU) and Naik et al40 recorded 2.8PSU to 26.8PSU which are in agreement to our result. Salinity showed an insignificant negative correlation with DO but significant negative correlations with silicate (P≤0.001). Solubility of DO decreases with increase in salinity. Influx of riverine freshwater is considered to be the main source for silicate in the coastal water. The above negative correlation of DO and silicate, in the present study could be due to the invasion of fresh water with low salinity and high silicate in the coastal water. In coastal water, turbidity also exerts a control on phytoplankton growth, as it restricts the area of euphoric zone43. Variation of turbidity was also seen in the study area with wide-difference from 2.4 NTU in POM (SL) to 29.2 NTU in MON (ES). Turbidity showed positive correlation (P ≤ 0.05) with chlorophyll-a confirming that the phytoplankton mainly contributed to the turbidity of the coastal water as reported earlier43. The values of dissolved oxygen were in the range of 4.7 (StationP3) mg/l to 7.3 mg/l (Station MNFJ). There was no major variation found for BOD, ranging from 0.30 mg/l to 2.6 mg/l in the study area. The positive correlation of BOD (P ≤ 0.01) with chlorophyll-a, directly supports the earlier findings43 in Kalpakkam coast, that phytoplankton significantly contribute to the BOD of the coastal water. As, chlorophyll-a constitutes the chief photosynthetic pigment of phytoplankton, provide the primary production potential upon which the biodiversity, biomass, and carrying capacity of a system depends. The Biomass generated from the growth of phytoplankton and their life cycle plays an important role on BOD content of the study area, apart from any anthropogenic activities44. However, the organic matter which is mainly produced by photosynthesis of phytoplankton in the seawater body, BOD is therefore well correlated with the
SANGEETA et.al.: SPATIO TEMPORAL VARIATION OF PHYTOPLANKTON
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INDIAN J. MAR. SCI., VOL. 47, NO. 07, JULY 2018
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Table: II — Seasonal variation of physicochemical Parameter along Paradeep coast.
concentration of chlorophyll-a (r = 0.66 p < 0.01) in the present study, which is also a measure of phytoplankton biomass45.
Nutrients play a vital role in the biogeochemical cycle in the marine environment. The life supporting processes in the sea requires a variety of inorganic substances. But, the role of nitrogen, phosphorous and silicate are considered the most important in the marine ecosystem. Distribution of nutrient is mainly dependent on season, tidal condition and fresh water inflow from land. Spatial as well as seasonal variation of nutrients in the present study was also observed. Among nitrogenous nutrients, nitrite, nitrate and ammonia (NH4
+ as N) are the major constituents, which play key roles in the phytoplankton growth and
proliferation. The variations of nutrients both for SL and ES are observed for nitrite as N (ES: 0.0-2.8µmol.l-1; SL: 0.0-1.2 µmol.l-1). In ES nitrite was found maximum in MON and in the SL it was maximum in PRM. Naik et al40 also observed the nitrite concentration (0.38 µmol/l to 1.84 µmol/l) in corroborating to our findings in Mahanadi estuary.
Nitrite being the most unstable dissolved inorganic, nitrogen species present in seawater, showed wide variation in the present investigation. Nitrate as N varied between (ES: 1.3-8.5µmol.l-1; SL: 1.9-16.5 µmol.l-1). Seasonally nitrate concentration was maximum during POM in ES. Similarly it was maximum in PRM in SL. Nitrate concentration observed by Srichandan et al46 and Mishra et al11 in
SANGEETA et.al.: SPATIO TEMPORAL VARIATION OF PHYTOPLANKTON
1507
the same area also giving concurrent to our result. Ammonia showed a variation from 0.1-15.7µmol.l-1 in ES and 0.0-10.7 µmol.l-1 in SL. The NH4 content in the ES and SL were maximum in MON, which corroborates the findings of Panda and Pattnayak47 (3.67 µmol/l to 10.76 µmol/l) in northeast coast of India and Baliarsingh et al48 (0.4 µmol/l to 9.06 µmol/l) in Rushikulya estuary. Phosphate varied between1.7-58.1 µmol.l-1 in ES and 0.74-28.1µmol.l-1
in SL. Whereas, the value of Silicate was 17.4-153.2 µmol.l-1 in ES and 12.92-141.5 µmol.l-1 in SL. Phosphate concentration was maximum in MON in ES and it was maximum in PRM in SL. Panda et al27 reported phosphate concentration in the range from 0.72 µmol/l to 54.37µmol/l, which also corroborate our result. This high concentration of phosphate may be attributed to the inputs of domestic and industrial effluents of fertilizer based industries. Silicate was maximum in MON and minimum in POM in ES. In SL it was maximum in PRM and minimum in POM. Umamaheswra Rao et al42 in Gouthami Godavari estuary (12.24 µM to 165.94 µM) and Srichandan et al46 in Mahanadi estuary (14.15µmol/l to 97.37µmol/l) also recorded similar trend. In the shoreline all the nutrients except ammonia was more in PRM whereas NO2, NH4 and SiO4 was minimum in POM. In ES all the nutrients except nitrate was maximum in MON and except SiO4, all the nutrients were minimum in PRM. Therefore, upwelling may controls the nutrient in shoreline and river discharge play a vital role in estuary.
Nitrate showed negative correlation (P ≤ 0.05) with turbidity and positive correlation (P ≤ 0.001) with
phytoplankton (fig - IV A & B). Similar observation was reported by Sahu et al10. Ammonia showed positive correlation with nitrite (P≤0.05) and negative correlation with dissolved oxygen (P ≤ 0.05). The correlation with nitrite could be due to the oxidation of ammonia, resulted from the synthesis of nitrite43. Silicate is negatively correlated (P ≤ 0.001) with salinity and positively correlated with chlorophyll (P ≤ 0.01). The fresh water is one of the important sources of silicate. Silicate is the major frustule building material of diatom, which corroborate the above correlation. Chlorophyll-a is considered as the most dependable and important index of phytoplankton biomass. Chlorophyll-a concentration was taken as the measure of viable phytoplankton biomass. The chlorophyll-a ranged from 0.38 mg/m3
to 1.25 mg/m3 (Mean 0.70 mg/m3±0.07) and total chlorophyll from 0.58 mg/m3 to 2.42 mg/m3 (Mean 1.39 mg/m3±0.16) presented in Table I. In SL stations chlorophyll concentration vary from maximum (4.23 mg/m3) in PRM concurring with increase in nutrient to minimum (0.07 mg/m3) in MON. In ES the mean chlorophyll was low in PRM (1.73mg/m3) and high in MON (2.25 mg/m3).
Pearson correlation coefficient matrix was computed between different physico-chemical parameters, chlorophyll and dominant group of phytoplankton abundance (Table IV). This helps to understand the relationship between the variables in different season. In PRM diatom exhibited positive correlation with NO3, PO4, NH3, salinity, pH and Secchi depth (SD). In MON chlorophyll correlated positively with turbidity and all the nutrients except NH3
Table: III — Station wise correlation matrix of various environmental parameters with chlorophyll
Parameter SD WT pH Turbidity Salinity DO BOD NO2-N NO3-N NH4-N PO4-P SiO4-Si Chl-a Total Chlo.
*Significance at 0.05 level; ** Significance at 0.01level and *** Significance at 0.001level †SD-Secchi disc, WT- Water temperature, DO-Dissolved Oxygen, BOD-Biological Oxygen Demand, Chlo.-Chlorophyl
INDIAN J. MAR. SCI., VOL. 47, NO. 07, JULY 2018
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Table: IV — Season wise Pearson correlation coefficient matrix between different environmental variables, chlorophyll and dominant group of phytoplankton
Parameter SD WT pH Turbidity Salinity DO BOD Alkalinity NO2-N NO3-N NH4-N PO4-P SiO4-Si Chlo. Diatom
Altogetheby 54 centralgae and 13the Bacillarithe major cofollowed btogether forSimilar Bacillariophcoast119 &
Nizamapatnaof species, re
Fig. II — Phyinshore and estu
SAN
Table: IV — Se
SD WT
.56* -0.70*** 0
.28 0.40
0.22 -0.54* -
0.21 0.15 -
.48* -0.36 -
0.12 0.06 -
.48* -0.57* -
0.19 -0.29 -
.11 0.34 -
at 0.05 level; **
sc, WT- Water te
ely with saliniof chlorophy
ith salinity ander 111 taxa oric, 27 penna3 phytoflagelliophyceans (omponent of by cyanophyrm (15.3%), observation yceans was 26-27, in K
am coast49. In eported (total
ytoplankton: Ovuarine water of
NGEETA et.al.:
eason wise Pearschl
pH Turbidity
-0.62**
0.71***
0.24 -0.40
-0.42 0.09
-0.10 0.31
0.54* 0.81***
-0.18 0.25
0.51* 0.60**
-0.45 0.20
-0.01 -0.17
Significance at 0
emperature, DO-
ity. Diatom shyll. In POM dd negatively wof phytoplankate, 17 blue lates. In over(Centric & Pthe taxa iden
yceans & and dinophy
of doobserved in
alpakkam cthe present s
111 species o
verall group abuParadeep.
SPATIO TEMP
son correlation clorophyll and do
Salinity DO
-0.65** -0.08
0.94*** -0.11
0.24 -0.02
0.10 -0.26
-0.29 -0.41
-0.29 0.04
-0.91*** -0.03
-0.07 -0.06
0.51* 0.01
0.01level and **
-Dissolved Oxyg
howed the sadiatom correlawith SiO4. kton represen
green & greall compositi
Pennate) formntified (72.97chlorophycea
yceans (11.7%ominance n the Paradeoast10, and tudy the num
of phytoplankt
undance (%) in
PORAL VARIA
coefficient matrixominant group of
BOD Alkalin
1
-0.63** 1
-0.03 0.1
0.14 0.14
0.63** -0.2
0.37 -0.1
0.84*** -0.92*
0.26 0.0
-0.25 0.68*
** Significance a
gen, BOD-Biolo
ame ated
nted een ion, med 7%) ans %).
of eep
in mber ton)
is in reportspecie95 speand Kcoastareportpresenvariatinos.mP10 -study by cen
ThedominMore only (33.17longisChaet(3.03%loreziobservHowedominearlierdominP6, cyanoestuarconfluM6, th
the
ATION OF PHYT
x between differf phytoplankton
nity NO2-N NO
1 1
4 -0.14
22 0.08 0.
8 -0.21 0.7
*** -0.06 0
1 0.08 -0
** -0.22 0
at 0.001level
ogical Oxygen D
the same ots11&26. Goudaes from Gopalecies from Or
Kondal Rao50
al waters of ted 186 specnt study, speion in estuary
ml-1(station P1-near Jatadhaarea pennale
ntrales (25.6%e pennales donance of Asthan 70% of8 species n
7%), Nitzschssima (8.46%tceors sp. %), Thalassioanus (2.66%)ved here is never Asterionnating specier study26. In tnant group inP7, P8, P
ophyceans andrine stations uence of rivehe population
TOPLANKTON
rent environmen
O3-N NH4-N P
1
.55* 1
77*** 0.49*
0.06 0.48*
0.26 0.08 -
0.12 -0.05
Demand, Chlo.-C
order with ra and Panigrahlpur coast andrissa coast. Hohave reportedBay of Benies from Kaecies composand sea. Abun
- near PPT) tari Muhana) os were domin
%) (Fig. II). ominance in sterionellopsif the populatinamely Astehia sp. (10.
%), Pseudonitz(3.97%), S
osira Subtilis). The domina
not in the samnellopsis glas in the presthe present st
n the shorelinP9, P10, Pd green algae
(MR1 & r and sea at n were observ
N
ntal variables,
PO4-P SiO4-Si C
1
0.40 1
-0.20 0.08
0.06 -0.53* 0
Chlorophyll
respect to thhi15 have, recod Panda et al2
owever Geethd 249 speciesngal and Smilpakkam coasition showedndance variedto 555 nos.mlon the shorelinated (66.0%)
this area is dis glacialis ion was domerionellopsis .56%), Thalzschia seriata
Skeletonema s (2.89%), Cant species w
me order as obacialis remasent and as wtudy pennales
ne stations (PP11, P12), e were dominMNFJ). Butstations MS1ved combinat
1509
Chlo. Diatom
1
0.25 1
he earlier orded 131 27 reported
ha Madhav s from the ita et al51
ast. In the d a clear d from 115 l-1 (station ine. In the ) followed
due to the (Fig. II).
minated by glacialis
lassiothrix a (6.85%),
costatum Chaetceors which were
bserved26. ained the well as in s were the 1, P3, P5,
whereas nant in the t, at the
1, M1 and tion of all
1510
groups (Fig.MNFJ) Micsp., Coscinocostatum, Gdominant PseudonitzscNitzschia loglacialis, Tlorenzianus, Subtilis, ChSpatial variascale is repGeethaMadhvariation indifference inphytoplanktoby Pandiyarastudy. Pollusp., Anabaenmicans, Pseuobserved in earlier26. Hphytoplanktoand lowest ((Table VI). was more in phytoplanktoand high indiatoms wer
Table VIIphytoplanktoarea. In thphytoplankto(748 nos. /mIn estuary mMON (224 nos./ml), corPhytoplanktoin all the insignificantin both the especies diveand phytoplain any aquatvarious temspatially rangradient54. Tand Margal(station MS(Table VIII).
. III). In the crocystis sp., Codiscus sp., chGloeocapsa sp
Table: IX — Phytoplankton- Alpha diversity for different seasons of Paradeep area (ES and SL)
Season
S
N (Abundance
nos./ml)
d’ (Marg alef
index)
J' (Pielou’s
evenness index)
H' (Shannon
wiener index)
SL PRM
14-26(19)
126-1198(747)
1.8-3.6 (2.8)
0.39-0.84 (0.61)
1.0-2.3 (1.8)
MON
7-18 (11)
60-287 (114)
1.5-3.0 (2.2)
0.73-0.91 (0.84)
1.6-2.4 (2.0)
POM
10-20(15)
105-179 (145)
1.9-3.9 (2.8)
0.83-0.92 (0.87)
2.0-2.7 (2.3)
ES PRM
11-15(13)
68-132 (101)
2.2-3.0 (2.6)
0.82-0.89 (0.85)
2.0-2.4 (2.2)
MON
13-18(16)
117-343 (224)
2.5-3.0 (2.7)
0.72-0.84 (0.8)
1.9-2.3 (2.2)
POM
8-20 (14)
55-175 (128)
1.7-3.9 (2.7)
0.71-0.98 (0.86)
1.6-2.6 (2.2)
1514
ratio in the reported by in the study16:1 except availability MR1 is at ulocated rightsilicate is mchannel and is ascertaineis inevitablecoastal watfertilizer plan
tical analysisultivariate staata obtained entiation amo
— (A.Phytoplasional scaling) ep.
ce Vs nitrate and
18
s (Cluster Anaatistical technto assess theong the phyt
ankton Assemblbetween differ
(B) Phytoplankto
alysis) niques were ae similarity atoplankton co
lages and (B).Ment sampling l
on abundance Vs
applied to as well as ommunity
MDS (multi locations of
Turbidity
SANGEETA et.al.: SPATIO TEMPORAL VARIATION OF PHYTOPLANKTON
1515
at the different stations. The multi-dimensional scaling (MDS) plots were made from Bray Curtis similarity matrix, specially subjected to visualize similarities among the phytoplankton assemblages at each site with hierarchical clustering through group average linking in PRIMER 6. The matrix (numerical abundance vs. locations) was prepared on the basis of an overall mean derived for each location. The square root transformed abundance data were then subjected to clustering and ordination techniques. These analyses were performed to evaluate and spatial variation and species relationships. From the resulting dendrogram, it was apparent to distinguish the phytoplankton populations (at ~ 50.5% similarity) into two distinct assemblages which can be evident from Figure VA. It can be conclude from the result that two structurally distinct phytoplankton populations exist in the study area; one in estuarine (Group-ES) and the other in shoreline (Group-SL); confirmed by MDS (Fig. VB). The SIMPROF test (used for situations where there is no prior division of the data) also revealed that the regions could be sub divided further. The findings have shown significant difference (ANOSIM Global R: 0.657 at 0.1%) in the composition and numerical abundance of phytoplankton between two distinct areas (Group-ES & Group-SL).
In this study, a subset of 8 species as termed as influential species Clarke and Warwick57, were identified, following BVSTE procedure among the 84 species.
They are Coscinodiscus jonesianus, Chaetceors eibenii, Rhizosolenia stolterfothii, Melosira sp., Pleurosigma elongatum, Trachyneis sp. Pediastrum sp, Dinophysis caudate. The results of a BIO-ENV, showed the best fit (supported by Palaniswamy et al58 2015 in Tamil Nadu) between the phytoplankton community patterns and a set of environmental variables (Secchi disc depth, turbidity, salinity and dissolved oxygen)achieved at Pw=0.692.
Conclusion The number of species and the dominant group
remain in the same range as per earlier reports in the study area, whereas the abundance is eight times to that of earlier report. Turbidity, salinity and dissolved oxygen played a significant role in phytoplankton community structure in this area. There exists strong positive correlation of phytoplankton density with nitrate is evident that the phytoplankton population is nitrogen limited.
Acknowledgement The authors are grateful to Integrated coastal Zone
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