Biomass

This article appeared in a journal published by Elsevier. The attached
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
article (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/authorsrights

Author's personal copy
Salinity based allometric equations for biomass
estimation of Sundarban mangroves
Kakoli Banerjee a,
*, Kasturi Sengupta c
, Atanu Raha b
, Abhijit Mitra c
a
School of Biodiversity & Conservation of Natural Resources, Central University of Orissa, Landiguda, Koraput,
Orissa 764020, India
b
Office of the Principal Chief Conservator of Forest, West Bengal, Block LA-10A, Aranya Bhawan, Salt Lake,
Kolkata 700098, India
c
Department 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 haÀ1
). In the western region, the total mean
biomass of the mangrove species varied as per the order A. alba (41.65 t haÀ1
in 2008,
55.79 t haÀ1
in 2009, 60.86 t haÀ1
in 2010) > S. apetala (31.76 t haÀ1
in 2008, 32.81 t haÀ1
in
2009, 39.10 t haÀ1
in 2010) > E. agallocha (13.89 t haÀ1
in 2008, 15.54 t haÀ1
in 2009,
18.28 t haÀ1
in 2010). In the central region, the order was A. alba (42.06 t haÀ1
in 2008,
57.09 t haÀ1
in 2009, 64.57 t haÀ1
in 2010) > E. agallocha (15.30 t haÀ1
in 2008, 20.02 t haÀ1
in
2009, 24.24 t haÀ1
in 2010) > S. apetala (6.77 t haÀ1
in 2008, 9.46 t haÀ1
in 2009, 11.42 t haÀ1
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 and subtropical regions [1]. Estimates of mangrove area vary
from several million hectares (ha) to 150,000 km2
worldwide [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 and ecologists [4]. Allometricequationsfor mangroves
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: banerjee.kakoli@yahoo.com (K. Banerjee).
Available online at www.sciencedirect.com
http://www.elsevier.com/locate/biombioe
b i o m a s s a n d b i o e n e r g y 5 6 ( 2 0 1 3 ) 3 8 2 e3 9 1
0961-9534/$ e see front matter ª 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.biombioe.2013.05.010

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 of mangroves is caused due to high salinity. 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 itmight cause overall degradation ofmangroves. Salinity is
also a controlling factor for mangrove 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
leafarea expansioninSonneratiaalba(Sada Keora)and Sonneratia
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 and Meghna 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 snowmelt water
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 Â 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 Â 15
plots ¼ 75 trees/species/station) was estimated using the
Newton’s formula [15].
V ¼ h=6 ðAb þ 4Am þ AtÞ
where 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 both western and central regions
of the deltaic complex and expressed in t haÀ1
.
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 branches were oven-dried at 70
C
overnight 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 haÀ1
.
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 dry weight (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 selected mangrove 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 haÀ1
.
b i o m a s s a n d b i o e n e r g y 5 6 ( 2 0 1 3 ) 3 8 2 e3 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 plots were distributed deeper than 1 m 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
then washed the roots. We excavated all the sediments within
the sampling cylinder (2 m in diameter Â 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 Æ 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 Æ0.42 psu
(1 psu ¼ 1 g kgÀ1
) [19].
2.5. Statistical analysis
Spatial and temporal differences of aquatic salinity and
biomass of selected mangrove species were 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 Æ 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 Æ 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 haÀ1
during pre-
monsoon 2008, 2009 and 2010 respectively; 81.69, 83.31 and
93.81 t haÀ1
during monsoon 2008, 2009 and 2010 respectively
and 90.59, 95.12 and 102.85 t haÀ1
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 haÀ1
during pre-
79.92 t haÀ1
and 72.91, 82.73 and 90.09 t haÀ1
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 the
relative 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.

The mean BGB of the three dominant species in the stations of
western region are 16.41, 17.38 and 20.88 t haÀ1
during pre-
25.33 t haÀ1
and 23.62, 23.93, and 28.99 t haÀ1
2009 and 2010 respectively. In the stations of central region,
the values are 11.79, 13.94 and 16.88 t haÀ1
during pre-
20.98 t haÀ1
and 18.95, 20.62 and 25.12 t haÀ1
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
Allometric models were 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 linear model y ¼ ax þ c, and R2
values for different
equations, we observed very close resemblance between the
same species (like Sw and Sc or Aw and Ac 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 ClÀ
in toxic concentration
causing water 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 selected mangrove species along with ambient salinity in the western
and central region in 2008.
Location Salinity (psu) Species AGB (t haÀ1
) BGB (t haÀ1
) TB (t haÀ1
)
Prm Mon Pom Prm Mon Pom Prm Mon Pom Prm Mon Pom
Harinbari (Stn. 1)
88
100
44.55
00
21
43
0
08.58
00
14.79 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
10
0
07.03
00
21
39
0
58.15
00
21.77 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
04
0
52.98
00
21
47
0
01.36
00
28.79 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
22
0
13.99
00
21
39
0
01.58
00
29.10 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
17
0
10.04
00
21
42
0
40.97
00
29.02 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
41
0
16.20
00
22
18
0
40.25
00
14.96 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
48
0
17.60
00
22
16
0
33.79
00
28.33 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
54
0
26.71
00
22
10
0
40.00
00
24.60 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
52
0
49.51
00
22
05
0
17.86
00
28.70 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
48
0
29.00
00
22
07
0
07.23
00
27.99 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 has made
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 Æ 1.2) Â 103
m3
sÀ1
.
Higher discharge values were observed during the monsoon
with an average of (3.2 Æ 1.2) Â 103
m3
sÀ1
, and the maximum
of the order 4200 m3
sÀ1
during freshet (September).
Considerably lower discharge values were recorded during
pre-monsoon with an average of (1.2 Æ 0.09) Â 103
m3
sÀ1
, and
the minimum of the order 860 m3
sÀ1
during May. During
post-monsoon discharge values were moderate with an
average of (2.1 Æ 0.98) dam3
sÀ1
[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
) BGB (t haÀ1
) TB (t haÀ1
)
Harinbari (Stn. 1)
88
10
0
44.55
00
21
43
0
08.58
00
14.20 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
10
0
07.03
00
21
39
0
58.15
00
21.20 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
04
0
52.98
00
21
47
0
01.36
00
28.36 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
22
0
13.99
00
21
39
0
01.58
00
28.99 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
17
0
10.04
00
21
42
0
40.97
00
28.56 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
41
0
16.20
00
22
18
0
40.25
00
15.21 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
48
0
17.60
00
22
16
0
33.79
00
29.16 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
54
0
26.71
00
22
10
0
40.00
00
25.85 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
52
0
49.51
00
22
05
0
17.86
00
29.83 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
48
0
29.00
00
22
07
0
07.23
00
28.72 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
) BGB (t haÀ1
) TB (t haÀ1
)
Harinbari (Stn. 1)
88
10
0
44.55
00
21
43
0
08.58
00
13.98 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
10
0
07.03
00
21
39
0
58.15
00
21.00 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
04
0
52.98
00
21
47
0
01.36
00
27.96 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
22
0
13.99
00
21
39
0
01.58
00
27.49 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
17
0
10.04
00
21
42
0
40.97
00
27.05 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
41
0
16.20
00
22
18
0
40.25
00
15.79 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
48
0
17.60
00
22
16
0
33.79
00
29.30 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
54
0
26.71
00
22
10
0
40.00
00
26.13 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
52
0
49.51
00
22
05
0
17.86
00
30.02 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
48
0
29.00
00
22
07
0
07.23
00
28.93 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 TB
of selected mangrove species in the selected stations
during 2008.
Species Combination r-value
Prm Mon Pom
A Salinity Â AGB À0.5469 À0.6053 À0.4875
Salinity Â BGB À0.5123 À0.5476 À0.4337
Salinity Â TB À0.5399 À0.5932 À0.4755
B Salinity Â AGB 0.8584 0.8202 0.7699
Salinity Â BGB 0.8751 0.8308 0.7199
Salinity Â TB 0.8660 0.8231 0.6994
C Salinity Â AGB 0.5433 0.6115 0.7028
Salinity Â BGB 0.5582 0.6123 0.6857
Salinity Â 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).
during 2009.
Prm Mon Pom
Salinity Â TB À0.7301 À0.7407 À0.7103
B Salinity Â AGB 0.8215 0.8001 0.8738
Salinity Â BGB 0.8339 0.8082 0.8559
Salinity Â TB 0.8268 0.8037 0.7829
C Salinity Â AGB 0.6217 0.6808 0.7847
Salinity Â BGB 0.6291 0.6840 0.7757
Salinity Â 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
during 2010.
Prm Mon Pom
Salinity Â TB À0.7285 À0.7976 À0.7843
B Salinity Â AGB 0.8884 0.8790 0.8544
Salinity Â BGB 0.8932 0.8952 0.8551
Salinity Â TB 0.8929 0.8831 0.8212
C Salinity Â AGB 0.6943 0.7749 0.8227
Salinity Â BGB 0.7008 0.7755 0.8161
Salinity Â TB 0.6956 0.7752 0.8572
Table 8 e Allometric equations for biomass estimation for western and central Indian Sundarbans.
Model
name
Regression model R2
Mean observed
biomass (n ¼ 90)
Mean predicted
biomass (n ¼ 90)
% Deviation Significance level
of t-value
Sw y ¼ 552.52x À 46.412 0.9225 43.40 41.99 3.25 0.0003
Sc y ¼ 553.98x À 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 À 12.647 0.9306 9.38 10.31 9.91 0.0001
Ec y ¼ 153.44x À 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 Condition
or age
Species ABG
(t haÀ1
)
BGB
(t haÀ1
)
Reference
Australia 27
24
0
S, 153
8
0
E 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
15
0
N, 79
50
0
E Fringe Avicennia 193.0 Amarasinghe and
Balasubramaniam[40]
Indonesia (Halmahera) 1
10
0
N, 127
57
0
E Primary forest Sonneratia forest 169.1 38.5 Komiyama et al. [39]
Australia 33
50
0
S, 151
9
0
E Primary forest A. marina forest 144.5 147.3 Briggs [41]
French Guiana 4
52
0
N, 52
19
0
E Matured coastal Lagucularia,
Avicennia, Rhizophora
315.0 e Fromard et al. [42]
South Africa 29
48
0
S, 31
03
0
E e B.gymnorrhiza, A. marina 94.5 e Steinke et al. [43]
French Guiana 5
23
0
N, 52
50
0
E Pioneer
stage 1 year
Avicennia 35.1 e Fromard et al. [42]
Western Indian
Sundarbans
88
10
0
44.55
00
21
43
0
08.58
00
Natural forest Sonneratia apetala,
Avicennia alba,
113.67 32.84 This study
Central Indian
Sundarbans
88
48
0
17.60
00
22
16
0
33.79
00
Natural 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, although models Sw, Sc, Aw, Ac, Ew and Ec were developed
for different species and regions of Indian Sundarbans, the
estimation of biomass produced from these models 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 similar methods will 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.
r e f e r e n c e s
[1] Ellison JC, Stoddart DR. Mangrove ecosystem collapse during
predicted sea level rise: holocene analogues and
implications. J Coastal Res 1991;7(1):151e65.
[2] FAO, UNEP. Tropical forest resources assessment project
(in the framework of the Global Environment Monitoring
SystemeGEMS). Forest resources of tropical Asia. Rome:
Food and Agriculture Organisation of the United Nations;
1981. p. 475. Report No. UN 32/6.1301-78-04 technical
report 3.
[3] Wilkie ML, Fortuna S. Status and trends in mangrove area
extent worldwide (Global Forest Resources
AssessmenteGFRA). In: Forest Resources Division, editor.
Rome: Food and Agriculture Organization of the United
Nations; 2003. p. 378. Forest Resources Assessment Working
Paper No. 63.
[4] Tomlinson PB. The botany of mangroves. Cambridge, UK:
Cambridge University Press; 1986.
[5] Clough BF, Dixon P, Dalhaus O. Allometric relationships for
estimating biomass in multi-stemmed mangrove trees. Aust
J Bot 1997;45(6):1023e31.
[6] Dahdouh Guebas F, Koedam N. Empirical estimate of the
reliability of the use of the Point-Centered Quarter Method
(PCQM): solutions to ambiguous field situations and
description of the PCQM þ protocol. For Ecol Manage
2006;228(1e3):1e18.
[7] Ross MS, Ruiz PL, Telesnicki GJ, Meeder JF. Estimating
aboveground biomass and production in mangrove
communities of Biscayne National Park, Florida (USA).
Wetlands Ecol Manage 2001;9(1):27e37.
[8] Siddiqi NA. Mangrove forestry. In: Bangladesh Institute of
Forestry and Environmental Science. University of
Chittagong; 2001. p. 1e201.
[9] Ball MC, Pidsley SM. Growth responses to salinity in relation
to distribution of two mangrove species, Sonneratia alba and
Sonneratia lanceolata in Northern Australia. Funct Ecol
1995;9(1):77e85.
[10] Mitra A, Banerjee K, Bhattacharyya DP. In: The other face of
mangroves. Department of Environment, Govt. of West
Bengal Press, India; 2004.
[11] Mitra A, Sengupta K, Banerjee K. Standing biomass and
carbon storage of above-ground structures in dominant
mangrove trees in the Sundarbans. For Ecol Manage
2011;261(7):1325e35.
[12] Das S, Siddiqi NA. The mangroves and mangrove forest of
Bangladesh. Chittagong, Dhaka: Mangrove Silviculture
Division. Bangladesh Forest Research Institute; 1985. p. 69.
Mangrove plantations of the coastal afforestation
project. UNDP Project BGD/85/085 Field document No. 2
Bulletin No. 2.

[13] Chaudhuri AB, Choudhury A. Mangroves of the
SundarbanseIndia. IUCN-The World Conservation Union;
1994.
[14] Mitra A, Banerjee K, Sengupta K, Gangopadhyay A. Pulse of
climate change in Indian Sundarbans: a myth or reality? Natl
Acad Sci Lett 2009;32(12):1e7.
[15] Husch B, Miller CJ, Beers TW. Forest mensuration. New York:
Ronald Press; 1982.
[16] Chidumaya EN. Aboveground woody biomass structure and
productivity in a Zambezian woodland. For Ecol Manage
1990;36(1):33e46.
[17] Bledsoe CS, Fahey TJ, Day FP, Ruess RW. Measurement of static
root parameters: biomass, length, and distribution in the soil
profile. In: Robertson GP, Coleman DC, Bledsoe CS, Sollins P,
editors. Standard soil methods for long-term ecological
research. New York: Oxford University Press; 1999. p. 413e36.
[18] Grasshoff K, Ehrhardt M, Kremling K. Methods of seawater
analysis. 3rd. ref. Weinheim: Verlag Chemie GmbH; 1999.
p. 600. Wiley-VCH, New York.
[19] Unesco. The practical salinity Scale 1978 and the
international equation of state of seawater 1980. Techn Pap
Mar Sci 1981a;36:25.
[20] Md Jalil A. Impact of salinity on the growth of Avicennia
officinalis and Aegicerus corniculatum. Dissertation submitted
to Forestry and Wood Technology Discipline, Khulna
University, as partial fulfillment of the 4-years professional
B.Sc. (Honors) in Forestry from Forestry and Wood
Technology Discipline. Khulna, Bangladesh: Khulna
University; 2002.
[21] Gopal B, Chauhan M. Biodiversity and its conservation in the
Sundarban mangrove ecosystem. Aquat Sci
2006;68(3):338e54.
[22] Balmforth EB. Observations on Sundri top dying-descriptive
sampling in the Sundarbans reserved forest. Forest
Resources Assessment Programmes (FRAP). Rome: Food and
Agriculture Organisation of the United Nations; 1985. p. 32.
Draft Working Paper of UNDP/FAO Project BGD/79/017.
[23] Chaffey DR, Miller FR, Sandom JH. A forest inventory project.
Main report Bangladesh. England: Overseas Development
Administration; 1985. p. 196.
[24] Shafi M. Adverse effects of Farakka on the forests of
southwest region of Bangladesh (Sundarbans). In: Second
national conference on forestry; January 12e26; Dhaka.
Proceeding of second national conference on forestry 1982.
p. 30e57.
[25] Cintron G, Lugo AE, Pool DJ, Morris G. Mangroves of arid
environments in Puerto Rico and adjacent islands. Biotropica
1978;10(2):110e21.
[26] Ball MC. Salinity tolerance in the mangroves Aegiceras
corniculatum and Avicennia marina. I. Water use in relation to
growth, carbon partitioning, and salt balance. Aust J Plant
Physiol 1988;15(3):447e64.
[27] Greenway H, Munns R. Mechanisms of salt tolerance in non-
halophytes. Annu Rev Plant Physiol 1980;31:149e90.
[28] Rawson HM, Munns R. Leaf expansion in sunflower as
influenced by salinity and short-term changes in carbon
fixation. Plant Cell Environ 1984;7(3):207e13.
[29] Munns R, Termaat A. Whole-plant responses to salinity. Aust
J Plant Physiol 1986;13:143e60.
[30] Clough BF. Growth and salt balance of the mangroves
Avicennia marina (Forssk.) Vierh. and Rhizophora stylosa
Griff. in relation to salinity. J Plant Physiol
1984;11(5):419e30.
[31] Kramer PJ, Kozlowski TT. Physiology of woody plants. New
York: Academic Press; 1979. p. 811.
[32] Yamamoto F, Sakata T, Terazawa K. Physiological,
morphological and anatomical responses of Fraxinus
mandshurica seedlings to flooding. Tree Physiol
1995;15(11):713e9.
[33] MacDicken KG. A guide to monitoring carbon storage in
forestry and agroforestry projects. Arlington: Winrock
International Institute for Agricultural Development, Forest
Carbon Monitoring Programme (FCMP); 1997.
[34] Ong JE, Gong WK, Wong CH. Allometry and partitioning of
the mangrove, Rhizophora apiculata. For Ecol Manage
2004;188:395e408.
[35] Downton WJS. Growth and osmotic relation of the mangrove
Avicennia marina as influenced by salinity. Aust J Plant
Physiol 1982;9:519e28.
[36] Burchett MD, Clarke CJ, Field CD, Pulkownik A. Growth and
respiration in two mangrove species at a ranges of salinities.
Plant Physiol 1989;75:299e303.
[37] Burchett MD, Field CD, Pulkownik A. Salinity, growth and
root respiration in the grey mangrove Avicennia marina.
Physiologia Plantarum 1984;60:113e8.
[38] Mackey AP. Biomass of the mangrove Avicennia marina
(Forsk.) Vierh. near Brisbane, south eastern Queensland.
Aust J Mar Freshw Res 1993;44:721e5.
[39] Komiyama A, Ogino K, Aksomkoae S, Sabhasri S. Root
biomass of a mangrove forest in southern Thailand 1:
estimation by the trench method and the zonal structure of
root biomass. J Trop Ecol 1987;3:97e108.
[40] Amarasinghe MD, Balasubramaniam S. Net primary
productivity of two mangrove forest stands on the northwest
coast of Sri Lanka. Hydrobiologia 1992;247:37e47.
[41] Briggs SV. Estimates of biomass in a temperate mangrove
community. J Aust Ecol 1977;2:369e73.
[42] Fromard F, Puig H, Mougin E, Marty G, Betoulle JL,
Cadamuro L. Structure of above-ground biomass and
dynamics of mangrove ecosystems: new data from French
Guiana. Oecologia 1998;115(1):39e53.
[43] Steinke TD, Ward CJ, Rajh A. Forest structure and biomass of
mangroves in the Mgeni estuary, South Africa. Hydrobiologia
1995;295:159e66.

Biomass

Recommended

Recommended

More Related Content

What's hot

What's hot (18)

Viewers also liked

Viewers also liked (11)

Similar to Biomass

Similar to Biomass (20)

More from Abhijit Mitra

More from Abhijit Mitra (20)

Recently uploaded

Recently uploaded (20)

Biomass