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Salinity based allometric equations for biomassestimation of Sundarban mangroves

Kakoli Banerjee a,*, Kasturi Sengupta c, Atanu Raha b, Abhijit Mitra c

aSchool of Biodiversity & Conservation of Natural Resources, Central University of Orissa, Landiguda, Koraput,

Orissa 764020, IndiabOffice of the Principal Chief Conservator of Forest, West Bengal, Block LA-10A, Aranya Bhawan, Salt Lake,

Kolkata 700098, IndiacDepartment of Marine Science, University of Calcutta, 35 B.C. Road, Kolkata 700019, India

a r t i c l e i n f o

Article history:

Received 28 April 2011

Received in revised form

10 May 2013

Accepted 11 May 2013

Available online

Keywords:

Indian Sundarbans

Salinity

Allometric equations

Avicennia alba

Excoecaria agallocha

Sonneratia apetala

a b s t r a c t

Biomass estimation was carried out for three even-aged dominant mangrove species

(Avicennia alba, Excoecaria agallocha and Sonneratia apetala) in two regions of Indian Sun-

darbans with two distinct salinity regimes for three consecutive years (2008e2010) and the

results were expressed in tons per hectare (t ha�1). In the western region, the total mean

biomass of the mangrove species varied as per the order A. alba (41.65 t ha�1 in 2008,

55.79 t ha�1 in 2009, 60.86 t ha�1 in 2010) > S. apetala (31.76 t ha�1 in 2008, 32.81 t ha�1 in

2009, 39.10 t ha�1 in 2010) > E. agallocha (13.89 t ha�1 in 2008, 15.54 t ha�1 in 2009,

18.28 t ha�1 in 2010). In the central region, the order was A. alba (42.06 t ha�1 in 2008,

57.09 t ha�1 in 2009, 64.57 t ha�1 in 2010) > E. agallocha (15.30 t ha�1 in 2008, 20.02 t ha�1 in

2009, 24.24 t ha�1 in 2010) > S. apetala (6.77 t ha�1 in 2008, 9.46 t ha�1 in 2009, 11.42 t ha�1 in

2010). Significant negative correlation was observed between biomass of S. apetala and

salinity (p < 0.01), whereas in case of A. alba and E. agallocha positive correlations were

observed (p < 0.01). Species-wise linear allometric regression equations for biomass pre-

diction were developed for each salinity zone as a function of diameter at breast height

(DBH) based on high coefficient of determination (R2 value). The allometric models are

species-specific, but not site-specific.

ª 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Mangroves are a taxonomically diverse group of salt-tolerant,

mainly arboreal, flowering, plants that grow primarily in trop-

ical andsubtropical regions [1]. Estimates ofmangroveareavary

from severalmillion hectares (ha) to 150,000 km2worldwide [2].

The most recent estimates suggest that mangroves presently

occupy about 14,653,000 ha of tropical and subtropical coastline

[3]. The field survey of mangrove biomass and productivity is

rather difficult due to muddy soil conditions and the heavy

weight of the wood. The peculiar tree form of mangroves,

especially their unusual roots, has attracted the attention of

botanists andecologists [4].Allometricequations formangroves

have been developed for several decades to estimate biomass

and subsequent growth. Most studies have used allometric

equations for single stemmed trees, but mangroves sometimes

have multi-stemmed tree forms, as often seen in Rhizophora

(Garjan), Avicennia (Baen), and Excoecaria (Gewan) species [5,6]

that often create difficulty in developing allometric equations

with accuracy. Clough et al. [5] showed that the allometric

* Corresponding author. Tel.: þ91 9439185655.E-mail address: [email protected] (K. Banerjee).

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relationship can be used for trunks in a multi-stemmed tree.

Moreover, for dwarf mangrove trees, allometric relationships

have been used to estimate the biomass [7]. Basically the

dwarfness ofmangroves is causeddue tohighsalinity. Presence

of salt is a critical factor for the development of mangrove eco-

systems. At lower intensities it favors the development of

mangroves eliminating more vigorous terrestrial plants which

other wise could compete with. On the contrary at increased

level itmightcauseoverall degradationofmangroves. Salinity is

also a controlling factor formangrove seedling recruitment and

the relation is negatively proportional. Siddiqi [8] noted reduced

recruitment of Heritiera fomes (Sundari) and Excoecaria agallocha

seedling in the Sundarbans mangrove forest with increased

salinity. Ball and Pidsley [9] observed adverse impact of

increased salinity on canopy development, leaf initiation, and

leafareaexpansion inSonneratiaalba (SadaKeora)andSonneratia

lanceolata (Keora).

In the maritime state of West Bengal, situated in the

northeast coast of India, the adverse impact of salinity on the

growth of mangrove species has been documented [10,11].

Salinity, therefore, greatly influences the overall growth and

productivity of the mangroves [12]. The Indian Sundarbans

exhibits two significantly different salinity regimes due to

siltation that prevent the flow of GangaeBhagirathieHooghly

water to the central region. This has made the ecosystem a

unique test bed to observe the impact of salinity on the

biomass and allometric trait of the mangrove species.

2. Methodology

2.1. The study area

The Sundarban mangrove ecosystem covering about

10,000 km2 in the deltaic complex of the Rivers Ganga, Brah-

maputra andMeghna is shared between Bangladesh (62%) and

India (38%) and is the world’s largest coastal wetland. Enor-

mous load of sediments carried by the rivers contribute to its

expansion and dynamics.

A unique spatial variation in terms of hydrological pa-

rameters is observed in Indian part of Sundarbans. The

western region of the deltaic lobe receives the snowmeltwater

of Himalayan glaciers after being regulated through several

barrages (primarily Farakka) on the way. The central region on

the other hand, is fully deprived from such supply due to

heavy siltation and clogging of the Bidyadhari channel in the

late 15th century [13]. Such variation caused sharp difference

in salinity between the two regions [11,14]. Ten sampling

stations were selected in this geographical locale (Fig. 1). The

stations in the western region (stations 1e5) lie at the

confluence of the River Hooghly (a continuation of Gang-

aeBhagirathi system) and Bay of Bengal. In the central region,

the sampling stations (stations 6e10) were selected adjacent

to the tide fed Matla River. Study was undertaken in both

these regions through three seasons (pre-monsoon, monsoon

and post-monsoon) during 2008e2010.

In both regions, selected forest patches were even-aged

(w 9 years old during the initial year 2008). In each station,

15 sample plots (10 m � 10 m) were established (in the river

bank) through random sampling in the various qualitatively

classified biomass levels and sampling was carried out in the

low tide period.

2.2. Above-ground biomass estimation

Above-ground biomass (AGB) in mangrove species refers to

the sum total of stem, branch and leaf biomass that are

exposed above the soil.

The stem volume of five individuals from each species in

each of the 15 plots per station (n ¼ 5 individuals � 15

plots ¼ 75 trees/species/station) was estimated using the

Newton’s formula [15].

V ¼ h=6 ðAb þ 4Am þ AtÞwhere V is the volume (in m3), h the height measured with

laser beam (BOSCH DLE 70 Professional model), and Ab, Am,

and At are the areas at base, middle and top respectively.

Specific gravity (G) of the wood was estimated taking the stem

cores by boring 7.5 cm deep with mechanized corer. This was

converted into stem biomass (BS) as per the expression

BS ¼ GV. The stem biomass of individual tree was finally

multiplied by the number of trees of each species in 15

selected plots (per station) in bothwestern and central regions

of the deltaic complex and expressed in t ha�1.

The total number of branches irrespective of size was

counted on each of the sample trees. These branches were

categorized on the basis of basal diameter into three groups,

viz. <6 cm, 6e10 cm and >10 cm. The leaves on the branches

were removed by hand. The brancheswere oven-dried at 70 �Covernight in hot air oven in order to remove moisture content

if any present in the branches. Dry weight of two branches

from each size group was recorded separately using the

equation of Chidumaya [16].

Bdb ¼ n1bw1 þ n2bw2 þ n3bw3 ¼ S nibwi

where Bdb is the dry branch biomass per tree, ni the number of

branches in the ith branch group, bwi the average weight of

branches in the ith group and i ¼ 1, 2, 3, ., n are the branch

groups. The mean branch biomass of individual tree was

finally multiplied with the number of trees of each species in

all the 15 plots for each station and expressed in t ha�1.

For leaf biomass estimation, one tree of each species per

plot was randomly considered. All leaves from nine branches

(three of each size group) of individual trees of each species

were removed and oven dried at 70 �C and dryweight (species-

wise) was estimated. The leaf biomass of each tree was then

calculated by multiplying the average biomass of the leaves

per branch with the number of branches in that tree. Finally,

the dry leaf biomass of the selected mangrove species (for

each plot) was recorded as per the expression:

Ldb ¼ n1Lw1N1 þ n2Lw2N2 þ.niLwiNi

where Ldb is the dry leaf biomass of selectedmangrove species

per plot, n1 . ni are the number of branches of each tree of

three dominant species, Lw1 . Lwi are the average dry weight

of leaves removed from the branches and N1 . Ni are the

number of trees per species in the plots. This exercise was

performed for all the stations in each region and the results

were finally expressed in t ha�1.

b i om a s s a n d b i o e n e r g y 5 6 ( 2 0 1 3 ) 3 8 2e3 9 1 383


2.3. Below-ground biomass estimation

Below-ground biomass (BGB) in this study refers to root

biomass, which excludes the pneumatophores, prop roots and

stilt roots that are exposed above the soil. An excavation

method [17] was used to estimate root biomass of the same

trees that were selected for above-ground biomass (AGB) es-

timate. According to our observation, very few roots in our

sampling plotswere distributed deeper than 1m in sediments.

We also found canopy diameter of these trees was usually

smaller than 2 m. Most roots of the selected species were

distributed within the projected canopy zone. Therefore, for

below-ground biomass (BGB, referring to root biomass in this

study), we excavated all roots (of 1 trees/species/station) in

1 m depth within the radius of 1 m from the tree center, and

thenwashed the roots.We excavated all the sediments within

the sampling cylinder (2 m in diameter � 1 m in height) and

washed them with a fine screen to collect all roots. The roots

were sorted into four size classes: extreme fine roots (diam-

eter <0.2 cm), fine roots (diameter 0.2e0.5 cm), small roots

(diameter 0.5e1.0 cm), and coarse roots (diameter >1 cm). We

did not separate live or dead roots. The roots after thorough

washing were oven dried to a constant weight at 80 � 5 �C and

biomass was estimated for each species. The method is a

destructive one and therefore we estimated the root biomass

of those trees that were almost on the edge of the river bank

facing erosion. In 2009, we evaluated the below ground

biomass of uprooted trees due to severe super cyclone, Aila in

the lower Gangetic delta.

2.4. Salinity

The surface water salinity was recorded by means of an op-

tical refractometer (Atago, Japan) in the field and cross-

checked in laboratory by employing MohreKnudsen method

[18]. The correction factor was found out by titrating the silver

nitrate solution against standard seawater (IAPO standard

seawater service Charlottenlund, Slot Denmark, chlorini

ty ¼ 19.376 psu). The average accuracy for salinity (in

connection to our triplicate sampling) is �0.42 psu

(1 psu ¼ 1 g kg�1) [19].

2.5. Statistical analysis

Spatial and temporal differences of aquatic salinity and

biomass of selectedmangrove specieswere evaluated through

ANOVA. The influence of aquatic salinity on mangrove

biomass was assessed by correlation coefficient (r) values

Fig. 1 e Map showing location of the sampling stations.



computed separately for each species and region (western/

central Indian Sundarbans). Finally the species-wise allome-

tric equations for each region were determined (n ¼ 90 per

species) as a function of most easily measured parameter

(DBH), considering total biomass (TB) as dependent variable.

The precision of the model in predicting individual tree

biomass value was determined by the magnitude of the R2

value of the simple regression and percentage difference of

predicted and observed dry weight biomass values of indi-

vidual trees. All statistical calculations were performed with

SPSS 9.0 for Windows.

3. Results

3.1. Relative abundance

A total of seventeen species of mangroves were recorded in

the selected plots of the study area. It is observed that stations

4 (Lothian island), 5 (Prentice island) and 7 (Sajnekhali)

exhibited relatively more species diversity compared to other

stations. This may be attributed to magnitude of anthropo-

genic pressure, intense human activities or salinity profile of

the area. On the basis of relative abundance the species Son-

neratia apetala, E. agallocha and Avicennia alba were found

dominant in the study site (Table 1) constituting 48.41% of the

total species. The selected species were w11 years old during

our last phase of sampling in 2010, but high salinity in the

central region probably stunted the growth of S. apetala.

3.2. Salinity

In the western region, the salinity of surface water ranged

from 3.65 psu (at station 1 during monsoon, 2010) to 29.10 psu

(at station 4 during pre-monsoon, 2008) and the average

salinity was 16.38 � 7.53 psu. In the central region, the lowest

salinity was recorded at station 6 (3.12 psu during monsoon,

2008) and the highest salinity was recorded at station 9

(30.02 psu during pre-monsoon, 2010) with an average value of

17.55 � 7.63 psu (Tables 2e4). The relatively lower salinity in

the western region may be attributed to Farakka barrage that

release fresh water on regular basis through Gang-

aeBhagirathieHooghly River system. The central region, on

contrary does not receive the riverine discharge due to

massive siltation of the Bidyadhari River that blocks the fresh

water flow in the Matla River eventually making it a tide fed

river.

3.3. Above-ground biomass

The AGB of the mangrove species was relatively higher in the

stations of the western region (stations 1e5) compared to the

central region (stations 6e10) (Tables 2e4). It is observed that

the average AGB of the three dominant species in the stations

of western region are 71.08, 71.99 and 82.88 t ha�1 during pre-

monsoon 2008, 2009 and 2010 respectively; 81.69, 83.31 and

93.81 t ha�1 during monsoon 2008, 2009 and 2010 respectively

and 90.59, 95.12 and 102.85 t ha�1 during post-monsoon, 2008,

2009 and 2010 respectively. In the stations of central region

the values are 51.02, 58.11 and 67.72 t ha�1 during pre-



and 72.91, 82.73 and 90.09 t ha�1 during post-monsoon 2008,

2009 and 2010 respectively. Worthy of mention here is that in

AGB of selected species, the stem constitutes 61%e64%, the

branch constitutes 23%e27% and 12%e14% of AGB is allocated

to leaf [11].

3.4. Below-ground biomass

The BGB comprising of the root portion of the mangrove was

higher in the western region compared to the central region.

Table 1 e Density of mangrove species (mean of 15 plots/station) in the study area; figures within bracket indicate therelative abundance in each station.

Species No./100 m2

Stn. 1 Stn. 2 Stn. 3 Stn. 4 Stn. 5 Stn. 6 Stn. 7 Stn. 8 Stn. 9 Stn. 10

Sonneratia apetala 9 (16.98) 11 (20.75) 13 (20.97) 15 (24.19) 17 (25.76) 7 (15.56) 6 (10.53) 6 (12.24) 6 (13.95) 6 (13.33)

Excoecaria agallocha 8 (15.09) 8 (15.09) 9 (14.52) 9 (14.52) 12 (18.18) 6 (13.33) 7 (12.28) 8 (16.33) 8 (18.60) 8 (17.78)

Avicennia alba 9 (16.98) 11 (20.75) 10 (16.13) 7 (11.29) 8 (12.12) 9 (20.0) 8 (14.04) 7 (14.29) 5 (11.63) 6 (13.33)

Avicennia marina 6 (11.32) 5 (9.43) 5 (8.06) 6 (9.68) 4 (6.06) 6 (13.33) 6 (10.53) 6 (12.24) 4 (9.30) 5 (11.11)

Avicennia officinalis 5 (9.43) 6 (11.32) 7 (11.29) 6 (9.68) 5 (7.58) 5 (11.11) 5 (8.77) 5 (10.20) 4 (9.30) 4 (8.89)

Acanthus ilicifolius 4 (7.55) 3 (5.66) 4 (6.45) 3 (4.84) 5 (7.58) 4 (8.89) 3 (5.26) 3 (6.12) 4 (9.30) 2 (4.44)

Aegiceros corniculatum 3 (5.66) 2 (3.77) 3 (4.84) 2 (3.23) 4 (6.06) 3 (6.67) 2 (3.51) ab ab 2 (4.44)

Bruguiera gymnorrhiza 4 (7.55) 5 (9.43) 3 (4.84) 1 (1.61) 2 (3.03) 2 (4.44) 2 (3.51) 1 (2.04) ab 1 (2.22)

Xylocarpus granatum 2 (3.77) 2 (3.77) 1 (1.61) 1 (1.61) 1 (1.51) ab 1 (1.75) 1 (2.04) ab 2 (4.44)

Nypa fruticans ab ab 1 (1.61) 2 (3.23) 2 (3.03) ab 2 (3.51) 1 (2.04) ab ab

Phoenix paludosa ab ab ab 1 (1.61) 1 (1.51) 2 (4.44) 3 (5.26) 3 (6.12) 4 (9.30) 3 (6.67)

Ceriops decandra ab ab ab ab ab 1 (2.22) 2 (3.51) 2 (4.08) 3 (6.98) 2 (4.44)

Rhizophora mucronata ab ab 2 (3.23) 1 (1.61) 1 (1.51) ab 2 (3.51) 2 (4.08) 1 (2.33) ab

Acrostichum sp. ab ab 2 (3.23) 1 (1.61) 1 (1.51) ab 2 (3.51) 2 (4.08) 1 (2.33) 1 (2.22)

Heritiera fomes 2 (3.77) ab ab 2 (3.23) 1 (1.51) ab 2 (3.51) ab ab 1 (2.22)

Aegialitis rotundifolia ab ab 2 (3.23) 3 (4.84) 1 (1.51) ab 3 (5.26) 2 (4.08) 3 (6.98) 1 (2.22)

Derris trifoliata 1 (1.89) ab ab 2 (3.23) 1 (1.51) ab 1 (1.75) ab ab 1 (2.22)

‘ab’ means absence of the species in the selected plots.



Themean BGB of the three dominant species in the stations of

western region are 16.41, 17.38 and 20.88 t ha�1 during pre-



and 23.62, 23.93, and 28.99 t ha�1 during post-monsoon 2008,

2009 and 2010 respectively. In the stations of central region,

the values are 11.79, 13.94 and 16.88 t ha�1 during pre-



and 18.95, 20.62 and 25.12 t ha�1 during post-monsoon 2008,

2009 and 2010 respectively (Tables 2e4).

3.5. Influence of salinity on mangrove biomass

Critical analysis of the data on AGB, BGB, TB and salinity

profile of the study area exhibits the regulatory effect of

salinity on the biomass of the selected species. Correlation

coefficient values reveal the adverse impact of salinity

(p < 0.01) on S. apetala, but positive influence (p < 0.01) on the

biomass of A. alba and E. agallocha (Tables 5e7).

3.6. Allometric equations

Allometricmodelswere developed for each region and species

by relating the total biomass (TB) of each tree to DBH. Each

model was named with a code corresponding to the species

and sites (western/central). All models are named and

described in Table 8. Considering the magnitude of a and b

values in the linearmodel y¼ axþ c, and R2 values for different

equations, we observed very close resemblance between the

same species (like Sw and Sc orAw andAc or Ew and Ec) although

their habitats are different (Table 9).

4. Discussion

The development and functioning of mangrove ecosystem is

regulated by salinity. Salinity affects plant growth in a variety

of ways: 1) by limiting the availability of water against the

osmotic gradient, 2) by reducing nutrient availability, 3) by

causing accumulation of Naþ and Cl� in toxic concentration

causingwater stress conditions, enhancing closure of stomata

and reduced photosynthesis [20].

The impact of salinity in the deltaic Sundarbans is signifi-

cant since it controls the distribution of species and produc-

tivity of the forest considerably [12]. Due to increase in

salinity, Heritiera fomes (Sundari) and Nypa fruticans (Golpata)

are declining rapidly from the present study area [21]. The

primary cause for top-dying of the species is believed to be the

Table 2 e Seasonal variations in AGB, BGB and TB of selectedmangrove species along with ambient salinity in the westernand central region in 2008.

Location Salinity (psu) Species AGB (t ha�1) BGB (t ha�1) TB (t ha�1)

Prm Mon Pom Prm Mon Pom Prm Mon Pom Prm Mon Pom

Harinbari (Stn. 1)

88�10044.550021�43

008.58

0014.79 4.17 9.82 A 35.70 42.40 46.29 9.26 (25.96) 11.39 (27.75) 13.39 (28.74) 44.96 53.79 59.68

B 37.08 41.08 42.98 8.19 (22.10) 9.82 (23.91) 10.78 (25.09) 45.27 50.90 53.76

C 6.28 9.68 10.85 1.35 (21.51) 2.25 (23.31) 2.65 (24.49) 7.63 11.93 13.50

Chemaguri (Stn.2)

88�10007.03

0021�39

058.15

0021.77 9.08 17.29 A 24.76 28.90 32.42 6.22 (25.14) 7.78 (26.94) 9.12 (28.14) 30.98 36.68 41.54

B 40.90 43.15 45.06 9.12 (22.31) 10.4 (24.12) 11.40 (25.31) 50.02 53.55 56.46

C 9.40 11.41 13.58 2.02 (21.57) 2.66 (23.34) 3.33 (24.54) 11.42 14.07 16.91

Sagar South (Stn.3)

88�04052.98

0021�47

001.36

0028.79 10.85 18.05 A 17.49 20.09 23.09 4.34 (24.84) 5.34 (26.63) 6.42 (27.83) 21.83 25.43 29.51

B 41.88 45.3 49.89 9.34 (22.32) 10.92 (24.12) 12.63 (25.32) 51.22 56.22 62.52

C 8.82 11.45 14.83 1.94 (22.09) 2.73 (23.89) 3.72 (25.09) 10.76 14.18 18.55

Lothian island (Stn.4)

88�22013.99

0021�39

001.58

0029.10 12.00 19.06 A 13.44 15.73 18.09 3.22 (23.98) 4.05 (25.78) 4.88 (26.98) 16.66 19.78 22.97

B 45.97 48.68 51.04 10.29 (22.39) 11.77 (24.19) 12.95 (25.39) 56.26 60.45 63.99

C 8.17 13.1 17.41 1.81 (22.23) 3.14 (24.03) 4.39 (25.23) 9.98 16.24 21.8

Prentice island (Stn.5)

88�17010.04

0021�42

040.97

0029.02 11.78 18.99 A 16.14 19.2 22.21 3.93 (24.35) 5.02 (26.15) 6.07 (27.35) 20.07 24.22 28.28

B 43.03 46.82 49.6 9.61 (22.35) 11.3 (24.15) 12.57 (25.35) 52.64 58.12 62.17

C 6.35 11.47 15.61 1.40 (22.13) 2.74 (23.93) 3.8 (25.13) 7.75 14.21 19.41

Canning (Stn. 6)

88�41016.20

0022�18

040.25

0014.96 3.12 8.86 A 10.73 15.05 17.97 1.95 (18.19) 3.01 (20.05) 3.84 (21.37) 12.68 18.06 21.81

B 29.23 32.76 36.57 6.88 (23.56) 7.92 (24.19) 9.7 (26.53) 36.11 40.68 46.27

C 3.21 5.54 7.16 0.71 (22.06) 1.37 (24.76) 1.82 (25.49) 3.92 6.91 8.98

Sajnekhali (Stn. 7)

88�48017.60

0022�16

033.79

0028.33 11.38 17.42 A 2.54 3.88 5.14 0.49 (19.39) 0.80 (20.7) 1.11 (21.65) 3.03 4.68 6.25

B 45.96 51.92 56.84 10.9 (23.73) 12.9 (24.86) 15.31 (26.95) 56.86 64.82 72.15

C 11.42 17.53 21.96 2.56 (22.5) 4.36 (24.91) 5.62 (25.6) 13.98 21.89 27.58

Chotomollakhali (Stn.8)

88�54026.71

0022�10

040.00

0024.60 11.55 16.97 A 1.85 5.22 9.12 0.35 (19.07) 1.06 (20.01) 1.95 (21.46) 2.2 6.28 11.07

B 38.90 41.92 44.61 9.19 (23.63) 10.36 (24.73) 11.98 (26.86) 48.09 52.28 56.59

C 2.54 6.95 10.8 0.56 (22.07) 1.7 (24.57) 2.73 (25.28) 3.1 8.65 13.53

Satjelia (Stn. 9)

88�52049.51

0022�05

017.86

0028.70 12.02 18.56 A 0.99 1.03 1.84 0.19 (19.19) 0.21 (20.51) 0.39 (21.72) 1.18 1.24 2.23

B 44.57 50.92 55.76 10.56 (23.71) 12.61 (24.76) 15.02 (26.94) 55.13 63.53 70.78

C 11.89 18.78 23.87 2.68 (22.56) 4.66 (24.83) 6.18 (25.93) 14.57 23.44 30.05

Pakhiralaya (Stn.10)

88�48029.00

0022�07

007.23

0027.99 11.85 18.00 A 1.38 3.07 4.55 0.26 (19.31) 0.63 (20.66) 0.98 (21.61) 1.64 3.7 5.53

B 41.35 46.00 48.68 9.77 (23.65) 11.42 (24.83) 13.09 (26.9) 51.12 57.42 61.77

C 8.56 14.25 19.69 1.9 (22.31) 3.53 (24.8) 5.02 (25.5) 10.46 17.78 24.71

N.B: the figures within bracket represent the percentage of BGB of AGB.

A ¼ Sonneratia apetala, B ¼ Avicennia alba, C ¼ Excoecaria agallocha; Prm ¼ Premonsoon, Mon ¼ Monsoon, Pom ¼ Post monsoon.



increasing level of salinity [22e24]. Salinity, therefore, is a key

player in regulating the distribution, growth and productivity

of mangroves [12]. The present study reveals that the central

region of Indian Sundarbans (stations 6e10) is more saline

compared to the western part (stations 1e5). The reduced

fresh water flows in central region of the Sundarbans have

resulted in increased salinity of the river waters and hasmade

the rivers shallower (particularly Matla) over the years. This

caused significant effect on the biomass of the selected spe-

cies thriving along these hypersaline river banks. Interest-

ingly, the effects are species-specific. Increased salinity

caused reduced growth in S. apetala whereas the positive in-

fluence of salinity was observed on A. alba and E. agallocha.

Such differential adaptability of mangrove species to salinity

was also reported from Bangladesh Sundarbans [25]. Ball [26]

also pointed out the species-specificity in relation to range

of salinity tolerance.

Our data on biomass (particularly in the western Indian

Sundarbans) are comparable to most of the published values

studied in different mangrove belts of the world (Table 9),

which may be attributed to favorable climatic conditions and

appropriate dilution of the estuarine system with fresh water

of the mighty River Ganga. The western region continuously

receives the fresh water input from the Himalayan Glaciers

after being regulated by the Farakka barrage. Five-year sur-

veys (1999e2003) on water discharge from Farakka barrage

revealed an average discharge of (3.4 � 1.2) � 103 m3 s�1.

Higher discharge values were observed during the monsoon

with an average of (3.2 � 1.2) � 103 m3 s�1, and the maximum

of the order 4200 m3 s�1 during freshet (September).

Considerably lower discharge values were recorded during

pre-monsoon with an average of (1.2 � 0.09) � 103 m3 s�1, and

the minimum of the order 860 m3 s�1 during May. During

post-monsoon discharge values were moderate with an

average of (2.1 � 0.98) dam3 s�1 [11]. The study area also

experiences a subtropical monsoonal climate with an annual

rainfall of 1600e1800 mm [21] and surface run-off from the

60,000 km2 catchments areas of GangaeBhagirathieHooghly

system and their tributaries [11]. All these factors (barrage

discharge þ precipitation þ runoff) increase the dilution

factor of the Hooghly estuary in the western part of Indian

Sundarbans e a condition for better growth and increase of

mangrove biomass. The central Indian Sundarbans exhibited

lower biomass of the mangrove species as compared to other

mangrove zones in the world (Table 9). The high salinity in

the central region (7.14% higher than the western region) is

the primary cause behind this. It has been investigated that,

at high salinity, the main cause of the decrease in growth is

Table 3 e Seasonal variations in AGB, BGB and TB of selectedmangrove species alongwith ambient salinity in the westernand central region in 2009.



Harinbari (Stn. 1)

88�10044.55

0021�43

008.58

0014.20 3.89 9.65 A 37.91 43.98 49.90 10.24 (27.01) 12.23 (27.80) 13.97 (27.99) 48.15 56.21 63.87

B 37.23 40.05 44.02 8.62 (23.15) 9.60 (23.96) 10.63 (24.14) 45.85 49.65 54.65

C 7.55 10.58 12.20 1.7 (22.56) 2.47 (23.36) 2.87 (23.54) 9.25 13.05 15.07

Chemaguri (Stn.2)

88�10007.03

0021�39

058.15

0021.20 8.79 16.32 A 25.10 30.97 34.91 6.57 (26.19) 8.36 (26.99) 9.49 (27.19) 31.67 39.33 44.4

B 39.12 41.07 45.05 9.14 (23.36) 9.23 (24.17) 10.97 (24.36) 48.26 50.30 56.02

C 9.75 11.47 14.09 2.21 (22.62) 2.68 (23.39) 3.32 (23.59) 11.96 14.15 17.41

Sagar South (Stn.3)

88�04052.98

0021�47

001.36

0028.36 10.02 17.67 A 16.70 22.77 22.92 4.32 (25.89) 6.08 (26.68) 6.16 (26.88) 21.02 28.85 29.08

B 41.48 45.16 51.82 9.69 (23.37) 10.92 (24.17) 12.63 (24.37) 51.17 56.08 64.45

C 10.04 12.94 16.77 2.32 (23.14) 3.10 (23.94) 4.05 (24.14) 12.36 16.04 20.82


88�22013.99

0021�39

001.58

0028.99 11.15 18.69 A 13.14 16.10 19.00 3.29 (25.03) 4.16 (25.83) 4.95 (26.03) 16.43 20.26 23.95

B 46.13 48.60 53.03 10.81 (23.44) 11.78 (24.24) 12.96 (24.44) 56.94 60.38 65.99

C 10.30 14.00 19.85 2.40 (23.28) 3.37 (24.08) 4.82 (24.28) 12.70 17.37 24.67


88�17010.04

0021�42

040.97

0028.56 11.09 18.22 A 13.86 19.28 21.59 3.52 (25.40) 5.05 (26.20) 5.70 (26.40) 17.38 24.33 27.29

B 43.19 47.34 52.22 10.11 (23.40) 11.46 (24.20) 12.74 (24.40) 53.3 58.8 64.96

C 8.49 12.22 18.21 1.97 (23.18) 2.93 (23.98) 4.40 (24.18) 10.46 15.15 22.61

Canning (Stn. 6)

88�41016.20

0022�18

040.25

0015.21 3.95 9.81 A 14.91 18.92 22.45 2.87 (19.24) 3.80 (20.10) 4.58 (20.42) 17.78 22.72 27.03

B 28.91 31.86 37.01 7.11 (24.61) 7.72 (24.24) 9.47 (25.58) 36.02 39.58 46.48

C 4.34 6.43 9.46 1.00 (23.11) 1.60 (24.81) 2.32 (24.54) 5.34 8.03 11.78

Sajnekhali (Stn. 7)

88�48017.60

0022�16

033.79

0029.16 12.00 19.67 A 2.79 4.00 5.98 0.57 (20.44) 0.83 (20.75) 1.24 (20.70) 3.36 4.83 7.22

B 45.67 50.05 57.31 11.32 (24.78) 12.47 (24.91) 14.90 (26.00) 56.99 62.52 72.21

C 13.58 19.45 25.95 3.20 (23.55) 4.85 (24.96) 6.40 (24.65) 16.78 24.30 32.35


88�54026.71

0022�10

040.00

0025.85 11.02 17.30 A 4.10 7.78 12.27 0.82 (20.12) 1.58 (20.36) 2.52 (20.51) 4.92 9.36 14.79

B 40.43 42.87 48.9 9.98 (24.68) 10.62 (24.78) 12.67 (25.91) 50.41 53.49 61.57

C 6.70 10.87 15.79 1.55 (23.12) 2.68 (24.62) 3.84 (24.33) 8.25 13.55 19.63

Satjelia (Stn. 9)

88�52049.51

0022�05

017.86

0029.83 12.35 19.99 A 1.05 2.89 3.36 0.21 (20.24) 0.59 (20.56) 0.70 (20.77) 1.26 3.48 4.06

B 50.57 54.92 61.76 12.52 (24.76) 13.63 (24.81) 16.05 (25.99) 63.09 68.55 77.81

C 20.77 25.66 32.75 4.90 (23.61) 6.38 (24.88) 8.18 (24.98) 25.67 32.04 40.93


88�48029.00

0022�07

007.23

0028.72 12.20 18.00 A 4.10 5.82 7.61 0.83 (20.36) 1.21 (20.71) 1.57 (20.66) 4.93 7.03 9.18

B 40.37 42.88 50.64 9.97 (24.70) 10.67 (24.88) 13.14 (25.95) 50.34 53.55 63.78

C 12.26 14.95 22.39 2.86 (23.36) 3.72 (24.85) 5.50 (24.55) 15.12 18.67 27.89





the reduction in the expansion rate of the leaf area caused by

the high salt concentrations [27,28]. In fact, the relative leaf

expansion and net assimilation rate decrease in mangrove

species as salinity increases [9,26], which adversely affect the

biomass of the species. Also under salinity stress, accelerated

leaf mortality rate is accompanied by a marked decrease in

the leaf production rate, leading frequently to the death of

the plant [27,29]. It has been reported that, in several

Table 4 e Seasonal variations in AGB, BGB and TB of selectedmangrove species along with ambient salinity in the westernand central region in 2010.



Harinbari (Stn. 1)

88�10044.55

0021�43

008.58

0013.98 3.65 8.44 A 43.56 49.80 53.79 12.20 (28.01) 14.84 (29.80) 16.67 (30.99) 55.76 64.64 70.46

B 40.09 44.15 46.05 9.68 (24.15) 11.46 (25.96) 12.50 (27.14) 49.77 55.61 58.55

C 9.21 12.66 13.83 2.17 (23.56) 3.21 (25.36) 3.67 (26.54) 11.38 15.87 17.5

Chemaguri (Stn.2)

88�10007.03

0021�39

058.15

0021.00 7.94 15.85 A 29.75 34.08 37.80 8.09 (27.19) 9.88 (28.99) 11.41 (30.19) 37.84 43.96 49.21

B 42.98 45.17 47.08 10.47 (24.36) 11.82 (26.17) 12.88 (27.36) 53.45 56.99 59.96

C 11.60 13.55 15.72 2.74 (23.62) 3.44 (25.39) 4.18 (26.59) 14.34 16.99 19.9

Sagar South (Stn.3)

88�04052.98

0021�47

001.36

0027.96 9.44 16.82 A 22.35 25.00 27.81 6.01 (26.89) 7.17 (28.68) 8.31 (29.88) 28.36 32.17 36.12

B 45.34 49.26 53.85 11.05 (24.37) 12.89 (26.17) 14.74 (27.37) 56.39 62.15 68.59

C 11.89 15.02 18.40 2.87 (24.14) 3.90 (25.94) 4.99 (27.14) 14.76 18.92 23.39


88�22013.99

0021�39

001.58

0027.49 10.86 17.94 A 17.79 20.33 22.89 4.63 (26.03) 5.66 (27.83) 6.64 (29.03) 22.42 25.99 29.53

B 49.99 52.70 55.06 12.22 (24.44) 13.83 (26.24) 15.11 (27.44) 62.21 66.53 70.17

C 12.15 17.08 21.39 2.95 (24.28) 4.45 (26.08) 5.84 (27.28) 15.1 21.53 27.23


88�17010.04

0021�42

040.97

0027.05 10.42 16.85 A 20.30 23.51 26.48 5.36 (26.40) 6.63 (28.20) 7.79 (29.40) 25.66 30.14 34.27

B 47.05 51.44 54.25 11.48 (24.40) 13.48 (26.20) 14.86 (27.40) 58.53 64.92 69.11

C 10.34 15.30 19.84 2.50 (24.18) 3.97 (25.98) 5.39 (27.18) 12.84 19.27 25.23

Canning (Stn. 6)

88�41016.20

0022�18

040.25

0015.79 4.01 10.12 A 16.76 21.00 24.08 3.39 (20.24) 4.64 (22.10) 5.64 (23.42) 20.15 25.64 29.72

B 34.56 38.09 41.90 8.85 (25.61) 9.99 (26.24) 11.98 (28.58) 43.41 48.08 53.88

C 8.20 10.53 12.49 1.98 (24.11) 2.82 (26.81) 3.44 (27.54) 10.18 13.35 15.93

Sajnekhali (Stn. 7)

88�48017.60

0022�16

033.79

0029.30 12.56 20.05 A 4.64 6.05 7.17 0.99 (21.44) 1.38 (22.75) 1.70 (23.70) 5.63 7.43 8.87

B 51.32 57.28 62.20 13.23 (25.78) 15.41 (26.91) 18.04 (29.00) 64.55 72.69 80.24

C 17.44 23.55 27.98 4.28 (24.55) 6.35 (26.96) 7.72 (27.65) 21.72 29.9 35.7


88�54026.71

0022�10

040.00

0026.13 11.55 18.10 A 5.95 9.86 13.90 1.26 (21.12) 2.20 (22.36) 3.27 (23.51) 7.21 12.06 17.17

B 46.08 49.10 51.79 11.83 (25.68) 13.15 (26.78) 14.97 (28.91) 57.91 62.25 66.76

C 10.56 14.97 18.82 2.55 (24.12) 3.99 (26.62) 5.14 (27.33) 13.11 18.96 23.96

Satjelia (Stn. 9)

88�52049.51

0022�05

017.86

0030.02 12.70 20.30 A 2.90 3.96 4.81 0.62 (21.24) 0.89 (22.56) 1.14 (23.77) 3.52 4.85 5.95

B 53.11 59.46 64.30 13.68 (25.76) 15.94 (26.81) 18.64 (28.99) 66.79 75.4 82.94

C 21.10 27.99 33.08 5.19 (24.61) 7.52 (26.88) 9.26 (27.98) 26.29 35.51 42.34


88�48029.00

0022�07

007.23

0028.93 12.34 18.56 A 4.55 6.27 8.05 0.97 (21.36) 1.42 (22.71) 1.90 (23.66) 5.52 7.69 9.95

B 46.82 51.20 54.18 12.03 (25.70) 13.76 (26.88) 15.69 (28.95) 58.85 64.96 69.87

C 14.59 20.28 25.72 3.55 (24.36) 5.45 (26.85) 7.09 (27.55) 18.14 25.73 32.81



Table 5 e Correlation between salinity, AGB, BGB and TBof selected mangrove species in the selected stationsduring 2008.

Species Combination r-value

Prm Mon Pom

A Salinity � AGB �0.5469 �0.6053 �0.4875

Salinity � BGB �0.5123 �0.5476 �0.4337

Salinity � TB �0.5399 �0.5932 �0.4755

B Salinity � AGB 0.8584 0.8202 0.7699

Salinity � BGB 0.8751 0.8308 0.7199

Salinity � TB 0.8660 0.8231 0.6994

C Salinity � AGB 0.5433 0.6115 0.7028

Salinity � BGB 0.5582 0.6123 0.6857

Salinity � TB 0.5461 0.6119 0.7622

A ¼ Sonneratia apetala, B ¼ Avicennia alba, C ¼ Excoecaria agallocha;

Prm ¼ Premonsoon, Mon ¼ Monsoon, Pom ¼ Post monsoon.

All values have p-values at 1% level (p < 0.01).



Prm Mon Pom

A Salinity � AGB �0.7410 �0.7536 �0.7250

Salinity � BGB �0.6872 �0.6922 �0.6559

Salinity � TB �0.7301 �0.7407 �0.7103

B Salinity � AGB 0.8215 0.8001 0.8738

Salinity � BGB 0.8339 0.8082 0.8559

Salinity � TB 0.8268 0.8037 0.7829

C Salinity � AGB 0.6217 0.6808 0.7847

Salinity � BGB 0.6291 0.6840 0.7757

Salinity � TB 0.6231 0.6816 0.8731






mangrove species, an increase in soil salinity decreases the

number of leaves per plant [9,30], which may finally decrease

the quantum of glucose production per plant affecting the

biomass.

In mangrove forests, the root biomass is considerable,

which could be an adaptation for living on soft sediments.

Mangroves may be unable to mechanically support their

above-ground weight without a heavy root system. In addi-

tion, soil moisture may cause increased allocation of biomass

to the roots [31], with enhanced cambial activity induced by

ethylene production under submerged conditions [32]. It is

interesting to note that the BGB in our study area constituted

25.32% and 23.90% of the AGB in the western and central re-

gions respectively. These values are higher than the usual 15%

value of BGB compared to AGB [33]. The high allocation of

biomass in the root compartment of mangroves in the present

geographical locale is probably an adaptation to cope with the

unstable muddy substratum of the intertidal zone caused by

high tidal amplitude (2e6 m), frequent inundation of the

mudflats with the tidal waters and location of the region

below the mean sea level.

Considering the significant spatial variation of salinity

(Fobs ¼ 379.58 > Fcrit ¼ 1.66) and strong influence of salinity on

mangrove biomass in the present study, we attempted to

develop site-specific and species-specific allometric models.

However from the nature of allometric equations (through

comparison of a and bevalues in the model y ¼ ax þ c), R2

values and percentage deviation between the observed and



Prm Mon Pom

A Salinity � AGB �0.7387 �0.8095 �0.7959

Salinity � BGB �0.6908 �0.7563 �0.7451

Salinity � TB �0.7285 �0.7976 �0.7843

B Salinity � AGB 0.8884 0.8790 0.8544

Salinity � BGB 0.8932 0.8952 0.8551

Salinity � TB 0.8929 0.8831 0.8212

C Salinity � AGB 0.6943 0.7749 0.8227

Salinity � BGB 0.7008 0.7755 0.8161

Salinity � TB 0.6956 0.7752 0.8572




Table 8 e Allometric equations for biomass estimation for western and central Indian Sundarbans.

Modelname

Regression model R2 Mean observedbiomass (n ¼ 90)

Mean predictedbiomass (n ¼ 90)

% Deviation Significance levelof t-value

Sw y ¼ 552.52x � 46.412 0.9225 43.40 41.99 3.25 0.0003

Sc y ¼ 553.98x � 46.73 0.9227 42.07 41.91 0.38 0.0003

Aw y ¼ 128.76x þ 29.143 0.9704 51.17 51.03 0.27 0.0000

Ac y ¼ 128.95x þ 29.034 0.9681 51.09 50.96 0.25 0.0000

Ew y ¼ 153.07x � 12.647 0.9306 9.38 10.31 9.91 0.0001

Ec y ¼ 153.44x � 12.748 0.9290 9.41 10.27 9.14 0.0001

Table 9 e Global data of AGB and BGB of different mangrove species.

Region Location Conditionor age

Species ABG(t ha�1)

BGB(t ha�1)

Reference

Australia 27�240S, 153� 8

0E Secondary forest A. marina forest 341.0 121.0 Mackey [38]

Thailand

(Ranong Southern)

9�N, 98�E Primary forest Sonneratia forest 281.2 68.1 Komiyama et al. [39]

Sri Lanka 8�150N, 79� 50

0E Fringe Avicennia 193.0 Amarasinghe and

Balasubramaniam[40]

Indonesia (Halmahera) 1�100N, 127� 57

0E Primary forest Sonneratia forest 169.1 38.5 Komiyama et al. [39]

Australia 33�500S, 151� 9

0E Primary forest A. marina forest 144.5 147.3 Briggs [41]

French Guiana 4�520N, 52� 19

0E Matured coastal Lagucularia,

Avicennia, Rhizophora

315.0 e Fromard et al. [42]

South Africa 29�480S, 31� 03

0E e B.gymnorrhiza, A. marina 94.5 e Steinke et al. [43]

French Guiana 5�230N, 52� 50

0E Pioneer

stage 1 year

Avicennia 35.1 e Fromard et al. [42]

Western Indian

Sundarbans

88�10044.55

0021�43

008.58

00Natural forest Sonneratia apetala,

Avicennia alba,


113.67 32.84 This study

Central Indian

Sundarbans

88�48017.60

0022�16

033.79

00Natural forest Sonneratia apetala,

Avicennia alba,


97.35 27.46 This study

AGB ¼ above ground biomass, BGB ¼ below ground biomass.



predicted biomass, it appears that there is negligible deviation

of the model between the western and central regions. This is

contrary to the findings of Clough et al. [5] who found different

relationships in different sites, although Ong et al. [34] re-

ported similar equations applied to two different sites while

working on Rhizophora apiculata. This issue is important for

practical uses of allometric equations. If the equations are

segregated by species and site, then different equations

have to be determined for each site. In the present

study, althoughmodels Sw, Sc,Aw, Ac, Ew and Ec were developed

for different species and regions of Indian Sundarbans, the

estimation of biomass produced from thesemodels only differ

by 0.25e9.91%. Such a good agreement between these two

estimates (observed vs. predicted) supports the conclusion

that allometric regression models produced from the same

species of similar aged trees and similarmethodswill not vary

much.

The present study also confirms the tolerance of A. alba

and E. agallocha to higher salinity. The significant negative

correlation values between S. apetala biomass and ambient

salinity reflects the sensitivity of the species to high salinity.

Several mangrove tree species reach an optimum growth at

salinities of 5e25 psu of standard seawater [9,26,30,35,36]. The

pigments, being the key machinery in regulating the growth

and survival of the mangroves require an optimum salinity

range between 4 and 15 psu for proper functioning [35,37]. S.

apetala, the fresh water loving mangrove species prefers an

optimum salinity between 2 and 10 psu [10] and hence could

not accelerate the biomass with increasing salinity unlike A.

alba and E. agallocha.

5. Conclusion

Finally we list a few of our core findings:

- The Indian Sundarbans sustains luxuriant mangrove vege-

tation and a total of 17 species in association were recorded

from the plots of selected stations.

- Contrasting salinity profile exists in the deltaic complex,

which is primarily regulated by barrage discharge and

siltation.

- The waters in the western river (Hooghly) are freshening

due to barrage discharge, but the central river (Matla) and its

adjacent habitat is hypersaline owing to siltation that has

completely blocked the fresh water supply in the zone.

- The hyposaline habitat promotes the growth of S. apetala,

whereas A. alba and E. agallocha are adapted in the central

Indian Sundarbans in the hypersaline environment.

- In the above ground structures of the selected species, the

allocation of biomass ranges between 61 and 64% to stem,

23e27% to branch and 12e14% to leaf.

- The total biomass (TB) constituting both AGB and BGB of all

the three selected species is greater in the western region

than the central region.

- Common allometric equations may be used for same spe-

cies in different zones to predict the biomass from easily

measured variable DBH.

- It is clear that the future of Sundarban mangroves (partic-

ularly in the central region) hinges upon the efficiency of

managing the limited fresh water resources coupled with

appropriate selection of species for afforestation in context

to rising salinity. A. alba and E. agallocha are better suited in

the zone if the sea level rise due to climate change is

considered.

Acknowledgments

The financial assistance from the Ministry of Earth Science,

Govt. of India (Sanction No. MoES/11-MRDF/1/34/P/08, dated

18.03.2009), is gratefully acknowledged.

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