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http://www.iaeme.com/IJCIET/index.asp 751 [email protected]
International Journal of Civil Engineering and Technology (IJCIET)
Volume 8, Issue 12, December 2017, pp. 751–762, Article ID: IJCIET_08_12_082
Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=8&IType=12
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
EVALUATION OF PRIMARY PRODUCTION
AND FISH BIOMASS ALONG CHENNAI COAST
USING FIELD AND EMPIRICAL ALGORITHMS
K.J. Sharmila
Research Scholar, Dr. M.G.R Educational and Research Institute University,
Chennai, Tamilnadu, India
RM. Narayanan
Associate Professor, Dr. M.G.R Educational and Research Institute University,
Chennai, Tamilnadu, India
ABSTRACT
Maritime environment is subjected to extensive variety of anthropogenic effects
related with advancement of the beach front zone, contributions of toxins, and excess
utilization of marine assets. Marine based photosynthetic life forms comprise of single
celled phytoplankton which incorporates under 1 percent of the worldwide biomass
anyway it represents 40 percent of the aggregate worldwide carbon fixation. The
centralization of chlorophyll-a (principle phytoplankton pigment) is frequently taken
as a record of phytoplankton biomass. The available energy flow from primary
production during the food chain sustenance will ultimately limit fishery yields in
upper trophic-stage. From the inputs of field measured Chlorophyll a an average
primary production rates were computed from five distinct algorithms throughout the
study area is 433.37 ± 233.58 g C m2 yr-
1 for estuarine, 395.58 ± 261.59 g C m
2 yr-
1
around nearshore region and for deeper oceans the productivity is averaged as
216.46 ±163.61 g C m2 yr
-1. Further the estimated primary production is used as an
input in quantifying the fish production and standing fish biomass .The average fish
production estimated from the primary production estimates of different algorithms is
97.73±67.13kg/ha/yr is indicated with changes in number of species and fish life. The
total average fish production and fish biomass calculated from the study for 166680
hectares area is 16,289.50tons/yr and 0.17 tons/km2. It is understood from the
collected secondary data for year 2013-2014 (commissioner of Fisheries, Tamilnadu
government) the fish catch (34886.35Tons/yr) exceeds the fish production for the study
area which indicates either overexploitation of fisheries resources or the caught fish
may be migrated origin from the nearby regions.
Key words: Chlorophylla, Primary Production, Fish Production, Fish Biomass, Meso-
Pelagic Biomass.
K.J. Sharmila and RM. Narayanan
http://www.iaeme.com/IJCIET/index.asp 752 [email protected]
Cite this Article: K.J. Sharmila and RM. Narayanan. Evaluation of Primary
Production and Fish Biomass along Chennai Coast Using Field and Empirical
Algorithms. International Journal of Civil Engineering and Technology, 8(12), 2017,
pp. 751-762.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=12
1. INTRODUCTION
Fish biomass is an essential driver of marine biological system and has high affectability to
human aggravations particularly fishing. Appraisals of fish biomass, their spatial
dissemination, and recuperation potential are imperative for assessing pelagic status and
pivotal for setting administration targets. Worldwide fishes biomass derived from sustenance
web models are ordinarily near 900 and 2,000 MT. 15, 16, 17. Accomplishing sustainability
in fisheries is frequently a testing task for fishery management because of an absence of
information and hazy objectives [1]. This is especially valid for poor and growing nations [2–
3].There have been numerous elucidations and continuous deliberations among mainstream
researchers encompassing the pattern of worldwide fish populaces. The test of sustainable
fishing has been complemented by the emanate drive for more comprehensive biological
system based administration objectives that propose more extensive environmental and social
results, including setting fisheries focuses above potential natural thresholds [3]. Despite the
fact that the "bottom up" model to depict the profitability of fishery assets has been tried in
different ways and over a scope of biological system including beach front tidal ponds,
estuaries, open oceans, and freshwater conditions [4], [5], [6].
Fish biomass has been appeared to be a key representation for coral reefs where the
condition of reef biological systems and the life history organization of the fish group are all
around anticipated by a basic biomass metric [7– 10]. Fisheries administration has one of the
three most vital forecasters with high consistence conclusion and remote administration
classifications affecting fish biomass, and no gear and most damaging apparatus limitations a
negative effect. A typical administration approach is to secure regions having greater
protection potential at the base cost [11]. The biomass-exhaustion choice focuses on
reestablishing degraded environments that ought to enhance fisheries and biological system
resilience when reestablished. Models recommend that fisheries terminations are just
compelling at expanding fisheries yields when biomass is lessened beneath MSY levels [12-
15].
Assessments regarding the state and eventual fate of marine resources differ. Many
researchers opined about the losing ground in the final frontier on the globe that human
impact is devastating to the point that all fish stocks will be crumpled by 2048 [16] however,
alternative elucidations of data conclude that conditions are enhancing and we are seeing
upgrades in fish populations [1] Such discoveries started a brainstorming dialogs among
mainstream researchers and have made disarray for fisheries supervisors and public.
Substantial amount of fish has changed on the planet's oceans over the span of recent
years; it is discovered that the biomass of predatory fish on the planet's oceans has declined by
66% throughout the latest 100 years. Among the 66%, around 84% of that decline has
occurred over the latest 40 years. This reduction in predatory fish was seen to be immovably
associated with expansion in worldwide fishing effort, exhibiting that over fishing than
Manageable sustained yield (MSY). Much confusion about the precise estimation of
measurements, which is essential to gauge fishery production. Anthropogenic effects
expanded loadings of phosphorus in freshwater biological communities related with expanded
phytoplankton biomass and resulting fish yields in numerous lake environments [17]
Evaluation of Primary Production and Fish Biomass along Chennai Coast Using Field and
Empirical Algorithms
http://www.iaeme.com/IJCIET/index.asp 753 [email protected]
Interestingly, evaluating the yield potential in marine biological community is a more
noteworthy investigation [18]. The main quantifiable biological changes seem to develop
when biomass is beneath ~1050 kg/ha, however changes in number of species and fish life
histories happen in progression underneath 600 kg/ha [18, 19].
To infer expected fishery biomass, that 10% of the PP was exchanged from PP to
herbivores. This estimation of 10% compares to the utilization of PP by mesozooplankton in
profitable zones (primarily copepods) 19. However, sufficient confirmation demonstrates that
micro zooplankton, not mesozooplankton, are the real buyers of PP, expending 70– 80% of
the PP on average 20. This expansion in biomass is believed to be an after effect of
diminishing predator wealth combined with the outcomes of human misuse. Strong
connections between assessments of essential creation and fisheries yield at worldwide scales
have been hard to recognize. Substantial Marine Biological communities add up to fishery
yields were found to scale with rates of net primary production. The available energy flow
from primary production during the food chain sustenance will ultimately limit fishery yields
in upper trophic-stage. In this work, we examine the association among fish yield and a few
measurements comprehensive of chlorophyll a and primary production.
2. STUDY AREA
Chennai vicinity is one among biggest populated metropolis in India, subjected to a couple of
anthropogenic impacts attributed to rapid-tracked population growth. Growth of large and
small industries, construction of harbors and boom in tourism related activities over the
coastal region, dumping of civic wastes, effluents from industries effects inside the
degradation of water quality. The study focused on three important transects and the marine
water sample was collected at 21 stations covering three different locations i.e. Ennore (E –
Station 1), Coovam (C - Station 2) and Kovalam (K- station 3) stretching ~ 70 Km from south
to north.
3. METHODOLOGY
Set of twenty-one marine water samples were collected three different locations i.e. Ennore (E
– Station 1), Coovam (C - Station 2) and Kovalam (K- station 3) at surface and different
depths from the coastal, estuarine, and marine waters of Chennai region, Tamilnadu. The
(fig.1) represents the sampling points. Annually 2-4 smaller motorboats were used in marine
sampling. At each station (or by boat) water sample was taken from the sea surface until the
photic depth at appropriate intervals i.e. during Post Monsoon (Jan, Feb, and March) for the
years between 2013 and 2015. For every sampling run, a fresh set of one-liter pre-cleaned
acid washed polyethylene bottles and niskin sampler was used. The collected samples were
well kept under dry ice, in an icebox, and stockpiled in the cold room at 4°C. Standard
procedures and methods were followed [20, 21]
The chlorophyll determination was done by concentrating the sample by filtering through
a membrane filter (Whatman, Glass fiber filter GF/F of 47 mm, 0.7 µm pore size) [22]. The
pigments are obtained from the algal sample solution kept in 90% acetone for 24 hrs. The
chlorophyll a fixation is resolved spectrophotometrically by measuring the absorbance of the
concentrate at 750 664 647 and 630nm. The resulting absorbance measurements are then
applied to a standard equation. Concurrently, the evaluated essential creation utilizing all the
algorithms are given in Table.
A total of three ground stoppered leak-proof bottles (preferably BOD bottles) of about
200-300 ml capacity are taken. Out of that, one is treated as light bottle, one as control and
K.J. Sharmila and RM. Narayanan
http://www.iaeme.com/IJCIET/index.asp 754 [email protected]
third one, which is black painted and waxed serves as dark bottle. Water samples from the
surface or the defined depth, for which, the primary production is to be determined, are taken
using either an ordinary bucket or water bottles. The samples are first filled with a coarse
nylon net piece of 150 to 300 µm pore size in order to remove the zooplanktonic organisms if
any present which may hamper oxygen present in the experimental bottles, due care was taken
to minimize agitation. All the bottles are simultaneously filled with water samples using
polythene tubing which should touch the bottom of the bottle while filing. All the bottles are
properly stoppered without too much agitation and happing of air bubbles inside the bottle is
avoided.
Water sample in the control bottle is instantly fixed by means of 1 ml of manganese
sulphate and 1 ml of alkaline iodide (fixatives usually employed in the determination of
oxygen by Winkler's method). Complete protection from sunlight is ensured by keeping the
bottle covered with aluminum foil in a black cloth bag.
The light and dark bottles are then suspended by tying them to a wooden pole or raft,
which is held in position by an anchored boat/drifting float. In order to avoid the shade of the
dark bottle falling on the light bottle, the latter is suspended from one end of the pole and the
dark bottle at the other. These bottles are normally incubated for 46 hours (between dawn to
midday or between midday and sunset) in the surface or in the respective depths. Owing to
agitation of water column in the experimental site, the entrapped cells of the bottles will be
kept in motion; otherwise, these cells may accumulate at the bottom resulting in poor
production.
After the period of incubation, the bottles are removed and the DO is fixed like control
bottles. The amount of oxygen in various bottles is ascertained by Winkler's chemical titration
method. The measured oxygen inside the light bottle depicts the oxygen produced through
photosynthesis deducted from the respiration of phytoplankton. The quantity of oxygen
exploited by the phytoplankton in the respiration can be estimated by using measurements of
oxygen changes concurrently in control and light bottles, In addition, oxygen declined in dark
bottle (linked to control bottle) is owing to respiration since there will be no photosynthesis in
the absence of sunshine. Consequently to get a gauge of the aggregate sum of photosynthesis
(gross production), negative variations in the dim container must be supplemented with
Positive transformation in light bottle.
The light and dark bottle method assumes a fixed photosynthetic quotient (PQ) of oxygen
molecules during photosynthesis divided by the digested carbon-di-oxide molecules. PQ is
believed to be 1, 1.4, 1.5 or 1.6 if the products of photosynthesis are starch, lipid, proteins
(when ammonia or nitrate as a source of nitrogen) respectively. Since it is not possible to
determine at once the nature of photosynthetic product, a value of 1.25 is invariably applied
for fieldwork.
Calculation
Gross production Oxygen content (light bottle - dark bottle) …...….. A
Net production Oxygen content (light bottle - control bottle) ……. B
Respiration Oxygen content (control bottle - dark bottle)………C
Period of incubation is considered as 4 hours, then
Gross production (mg C/l/ hr.)
Evaluation of Primary Production and Fish Biomass along Chennai Coast Using Field and
Empirical Algorithms
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( )
( )
………. D
Net production (mg C/l/ hr)
( )
( )
……... E
Gross or net production (mg C/1/day)
D/E X 12 (as sunlight is only for 12 hrs in a day) ……. F
Gross or net production (g C/m3/day)
Fx1000x1000
Correlation between production rates and chlorophyll standing stocks makes possible a
more or less convincing estimate of regional and global primary production rates.
In this study the primary production rates are examined for the Chennai coastal area using
different equations by means of field chlorophyll-a inputs as cited in Eppley et.al. 1984,
Behrenfeld and Falkowski 1997, Kameda and Ishizaka 2005, Siswanto et al. 2006 and
Ishizaka et al. 2007 algorithms [23-27]. The evaluated essential primary production using all
the algorithms are given in Table.
Further from the estimated primary production, the fish production is empirically
calculated as cited by John A. Downing et.al 1990 [28] by the equation mentioned below.
As cited by John A. Downing et.al (1990) [28] further the estimated fish production is
used as an input in quantifying the standing fish biomass by the equation given below.
Apart from the downing equation the of the mesopelagic fish biomass in g m-2
is
estimated from the regression equation of Irigoien et.al (2014) [29]equation given below
Mesopelagic fishes biomass (g m−2
)=0.185 PP (mg C m−2
d−1
) – 6.66
4. RESULT AND DISCUSSIONS
The average primary production rates estimated for the overall study area (Ennore, Coovam,
Kovalam regions) from each algorithms is indicated in the table (1). The primary production
estimated for the estuary region from the field chlorophyll a varied from 3.79 g C m2 yr
-1 at
Ennore E2 station to 2197.23 g C m2 yr
-1 at Kovalam K2 station. The average primary
production of all the five algorithms throughout the study area is 433.37 ± 233.58 g C m2 yr
-1.
However the observed primary productivity was higher at kovalam estuary (K1 & K2) for four
distinct algorithms Eppley, Behrenfeld and Falkowski, Siswanto and Ishizaka as revealed in
the fig.(2). The highest amount of nutrient observed for the estuarine location is well
correlated with the observed productivity along the estuarine region. The average
K.J. Sharmila and RM. Narayanan
http://www.iaeme.com/IJCIET/index.asp 756 [email protected]
concentration of phosphate (1.140 µmol/l), nitrate(1.940 µmol/l),nitrite(1.157 µmol/l)and
ammonia(19.089 µmol/l) are reportedly compared with primary production [30].
The average primary production estimated from the inputs of chlorophyll a from the five
distinct algorithms is 395.58 ± 261.59 g C m2 yr
-1 for the nearshore region, which is lesser
when compared to the productivity in estuarine region of our study area. It is attributed to the
nutrient values observed in the study region due to sediment resuspension and littoral drift.
The maximum primary production observed in the nearshore region is 1195.58 g C m2 yr
-1
around kovalam K3 station. However the minimum primary production is reported to be 6.85
g C m2 yr
-1 at kovalam
K4 station. It is observed from the primary production data, Siswanto
and Ishizaka’s algorithms clearly overestimate of primary production when compared to other
algorithms indicated in fig. (3).
The average primary production estimated from the inputs of chlorophyll a from the five
distinct algorithms is 216.46 ±163.61 g C m2 yr
-1.The primary productions of all the five
algorithms shows similar values in deeper oceans fig.(4). The lowest (5.75 g C m2 yr
-1) and
highest (748.46 g C m2 yr
-1) primary production was observed at around kovalam station k5
and it is observed that Kameda and Ishizaka’s algorithm is overestimating the productivity
rates. The nutrients values of phosphate, nitrate, nitrite and ammonia are lesser when
compared to estuarine and near shore regions.
The fish production is empirically calculated as cited by John A. Downing et.al (1990)
further the estimated fish production is used as an input in quantifying the standing fish
biomass .The average fish production estimated from the primary production estimates of
different algorithms is 97.73±67.13kg/ha/yr. The total average fish production calculated from
the study area stretching 75km along the North-South and 22.24km (12NM) West – East is
16,289.50tons/yr for 166680 hectors. Based on the fish production, the average fish biomass
is empirically estimated from the Downing’s equation as 0.17tons/km2
which is comparable
with the published literatures around Bay of Bengal particularly [31].The fish biomass
estimation using the fish production estimates of five different algorithms is given in table(2)
and fig (5). Apart from the Downing’s equation, the mesopelagic fishes are estimated by the
Irigoien’s regression equation and it is observed that the mesopelagic fish estimates for the
study area is reported to be 6947.66 tons. It is understood from the collected secondary data
(commissioner of Fisheries, Tamilnadu government), the fish catch (35000 tons/yr) exceeds
the fish production for the study area which indicates either overexploitation of fisheries
resources or the caught fish may be migrated origin from the nearby regions.
5. CONCLUSIONS
From the field measured chlorophyll a, the primary production rates for the study area was
estimated from five distant algorithms. Further the estimated primary production is used as an
input to the empirical equation derived by Downing et. al towards calculating the fish
production and fish biomass. The average primary production, Fish production and Fish
biomass was estimated for the study area are 339.94g C m2 yr
-1,16289.50tons and 0.17
tons/km2. It indicates with the over exploitations of fishery resources’ in the study area. These
results have important implications for the biogeochemical cycles of the marine fishes which
is considered to play a key role in the world’s oceans as a link between plankton and ocean
predators. This finding calls for an effort to improve the accuracy of the estimates of the
biomass towards sustainable management of the fishery resources around Chennai Coast.
Evaluation of Primary Production and Fish Biomass along Chennai Coast Using Field and
Empirical Algorithms
http://www.iaeme.com/IJCIET/index.asp 757 [email protected]
Table 1 Primary production Estimation using different Algorithm
S.No. Sample
Station
Field Chl a
in mg/m3
Estimated Primary Production in g C m2 year-1
Eppley 1984
Behrenfeld
and
Falkowski
1997
Kameda
and
Ishizaka
2005
Siswanto et
al. (2006)
Ishizaka
et al. 2007
1 C1 0.11 122.26 12.06 250.84 129.32 292.10
2 C2 0.05 82.75 5.76 374.01 59.17 269.22
3 C3 0.08 105.16 8.07 239.85 101.47 210.41
4 C4 0.24 179.56 28.18 220.87 253.65 436.43
5 C5 1.03 370.61 126.98 155.33 811.60 870.85
6 C6 0.51 261.43 63.18 191.91 471.39 630.31
7 C7 0.24 179.93 30.24 238.91 242.67 476.17
8 E2 0.09 110.89 3.79 101.32 131.96 1000.00
9 E4 0.16 146.05 10.57 141.41 211.08 145.92
10 E5 1.20 399.03 92.40 399.04 1108.06 901.63
11 E6 0.35 217.32 42.73 217.32 348.86 528.58
12 E7 0.18 156.91 23.24 263.59 186.93 444.42
13 E8 0.10 113.56 12.05 331.97 101.94 376.97
14 E9 0.10 113.56 12.77 364.23 97.17 417.65
15 K1 3.25 658.11 444.77 100.67 1451.85 1377.14
16 K2 11.51 1238.47 1519.38 39.84 2197.23 1874.91
17 K3 1.93 506.42 242.05 123.31 1195.58 1153.28
18 K4 0.06 85.60 6.85 439.93 58.84 346.59
19 K5 0.04 72.08 5.75 748.46 36.06 452.07
20 K6 0.06 91.61 7.06 338.08 72.21 278.71
21 K7 0.05 85.52 5.99 343.65 64.14 253.83
Average 1.02 252.23 128.76 267.84 444.34 606.53
Table 2 Fish Production and Fish biomass estimation using different Algorithm
S.No Sample
Station Long. Lat.
Estimated Fish Production in Kg/ha/Yr Avg.
Fish
Prod in
Kg/ha/y
r
Eppley
1984
Behrenfeld
and
Falkowski
1997
Kameda
and
Ishizaka
2005
Siswanto et
al. 2006
Ishizaka et
al. 2007
1 C1 80.290 13.067 63.12 16.67 95.42 65.20 104.15 68.91
2 C2 80.289 13.067 50.43 10.90 120.06 41.59 99.38 64.47
3 C3 80.285 13.068 57.88 13.22 93.00 56.71 86.25 61.41
4 C4 80.291 13.067 78.73 27.14 88.69 96.04 131.21 84.36
5 C5 80.294 13.067 119.43 64.51 72.44 187.44 195.19 127.81
6 C6 80.299 13.067 97.72 43.19 81.81 137.15 162.08 104.39
7 C7 80.300 13.069 78.83 28.27 92.79 93.62 137.95 86.29
8 E2 80.329 13.234 59.68 8.56 56.66 65.96 211.35 80.44
9 E4 80.332 13.235 69.92 15.45 68.63 86.41 69.88 62.06
10 E5 80.333 13.236 124.62 53.74 124.62 224.19 199.13 145.26
11 E6 80.334 13.235 87.87 34.49 87.87 115.35 146.48 94.41
12 E7 80.337 13.235 72.86 24.30 98.18 80.58 132.58 81.70
13 E8 80.342 13.236 60.50 16.65 112.11 56.86 120.61 73.35
14 E9 80.351 13.235 60.50 17.22 118.25 55.31 127.93 75.84
15 K1 80.250 12.804 166.16 132.64 56.45 261.88 254.05 174.24
16 K2 80.249 12.803 239.01 268.82 33.13 332.34 303.36 235.33
17 K3 80.246 12.803 142.92 93.49 63.44 234.21 229.41 152.69
18 K4 80.251 12.803 51.42 12.03 131.81 41.45 114.92 70.33
19 K5 80.255 12.804 46.59 10.88 178.92 31.28 133.89 80.31
20 K6 80.259 12.804 53.47 12.25 113.29 46.63 101.38 65.41
21 K7 80.269 12.804 51.40 11.14 114.36 43.56 96.07 63.31
Average Fish Prod in Kg/ha/yr 87.29 43.60 95.33 112.08 150.35 97.73
Fish production in tons (Each Algorithms) 14549.27 7267.00 15889.39 18682.14 25059.68
Fish biomass in tons/km2 0.15 0.08 0.16 0.19 0.25
K.J. Sharmila and RM. Narayanan
http://www.iaeme.com/IJCIET/index.asp 758 [email protected]
Figure 1 Study area Map with sampling points
Figure 2 Primary production estimates for estuarine region
0
250
500
750
1000
1250
1500
1750
2000
2250
C1 C2 C3 E2 E4 K1 K2
12
2.2
6
82
.75
105.1
6
110
.89
14
6.0
5
65
8.1
1
12
38
.47
12.0
6
5.7
6
8.0
7
3.7
9
10
.57
44
4.7
7
15
19
.38
25
0.8
4 374.0
1
23
9.8
5
10
1.3
2
141.4
1
10
0.6
7
39.8
412
9.3
2
59
.17
101.4
7
13
1.9
6
211
.08
1451.8
5
21
97
.23
292.1
0
26
9.2
2
21
0.4
1
1000.0
0
14
5.9
2
13
77
.14
1874.9
1
Pri
ma
ry P
rod
uct
ion
g C
m-2
yr-1
Sampling Stations
Primary Production estimates for Estuarine region
Eppley 1984 Behrenfeld and Falkowski 1997 Kameda and Ishizaka 2005 Siswanto et al. (2006) Ishizaka et al. 2007
Evaluation of Primary Production and Fish Biomass along Chennai Coast Using Field and
Empirical Algorithms
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Figure 3 Primary production estimates for nearshore region
Figure 4 Primary production estimates for deep ocean region
0
250
500
750
1000
1250
C4 C5 E5 E6 K3 K4
17
9.5
6
370.6
1
399.0
3
217.3
2
506.4
2
85
.60
28.1
8
126.9
8
92.4
0
42.7
3
24
2.0
5
6.8
5
220.8
7
155
.33
399.0
4
217.3
2
123.3
1
43
9.9
3
25
3.6
5
811
.60
1108.0
6
348.8
6
1195.5
8
58.8
4
43
6.4
3
870.8
5
90
1.6
3
528.5
8
1153.2
8
34
6.5
9
Pri
ma
ry P
rod
uct
ion
g C
m-2
yr-1
Sampling Stations
Primary Production estimates for Nearshore region
Eppley 1984 Behrenfeld and Falkowski 1997 Kameda and Ishizaka 2005 Siswanto et al. (2006) Ishizaka et al. 2007
0
250
500
750
1000
C6 C7 E7 E8 E9 K5 K6 K7
261
.43
179.9
3
156.9
1
113.5
6
113.5
6
72.0
8
91
.61
85.5
2
63.1
8
30
.24
23.2
4
12
.05
12.7
7
5.7
5
7.0
6
5.9
9
191.9
1 238.9
1
263.5
9 331.9
7
36
4.2
3
748.4
6
338.0
8
34
3.6
5
47
1.3
9
242.6
7
186.9
3
101
.94
97
.17
36.0
6 72.2
1
64.1
4
630.3
1
476.1
7
444.4
2
376.9
7
41
7.6
5
452
.07
278.7
1
253.8
3
Pri
ma
ry P
rod
uct
ion
g C
m-2
yr-1
Sampling Stations
Primary Production estimates for Deep ocean
Eppley 1984 Behrenfeld and Falkowski 1997 Kameda and Ishizaka 2005 Siswanto et al. (2006) Ishizaka et al. 2007
K.J. Sharmila and RM. Narayanan
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Figure 5 Mean fish biomass using different algorithms
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