EVALUATION OF PRIMARY PRODUCTION AND FISH BIOMASS …€¦ · Worldwide fishes biomass derived from...

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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

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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


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


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



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.)




( )

( )

………. 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



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.




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



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




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


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




Figure 5 Mean fish biomass using different algorithms

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