Producers of the Marine and Estuarine Ecosystems

Abstract

In the marine and estuarine ecosystems, the producer communities act as the converter of solar energy into other utilizable forms of energy. However, in terms of productivity (preferably net primary productivity), the order is estuaries, swamps, and marshes > coastal zone > open ocean (Fig. 5.1).

Appendices

Annexure 5A: Avicennia alba: An Indicator of Heavy Metal Pollution in Indian Sundarban Estuaries

5.1.1 1. Introduction

Avicennia alba is a common mangrove floral species in Indian Sundarbans where the soil is inundated (twice a day) with saline water of 2.0–28 psu. The species can also withstand submergence with saline water for a long period of time due to its inherent capacity to tolerate high levels of salinity and submergence. The species is endowed with a unique system of ion influx–efflux regulation (Mitra et al. 2004).

The coastal zones and estuaries, which are the primary habitat of this mangrove species, are exposed to effluents from chemical industries everyday (Manahan 1994; Krishnamurti and Viswanathan 1991). As a consequence, nutrients and contaminants like heavy metals, pesticides and halogenated hydrocarbons are transported into the intertidal mudflats of mangrove forest (Delaune et al. 1990) since they are partly bound to suspended particles. The mangrove forests adjacent to the highly urbanized and industrialized cities and towns often receive large pollutant loads, including discharges of heavy metals from industry and transportation activities. In the intertidal mudflats, water-soluble metals and exchangeable metals are the most available and precipitated inorganic compounds; metal complexes with large molecular weight, humus materials and metals adsorbed to hydrous oxides are also possibly available (Gambrell 1994; Williams et al. 1994). A. alba, being an endemic and dominant species of the Sundarban region, is exposed to heavy metal pollution, as the region is contaminated with conservative pollutants (Mitra 1998; Mitra et al. 2011; Banerjee et al. 2012; Mitra and Ghosh 2014). On this background the present study was carried out to detect the concentrations of heavy metals in A. alba in three stations located in Indian Sundarbans.

5.1.2 2. Materials and Methods

5.1.2.1 2.1. Sampling of A. alba

Three stations (Fig. 5A.1) were selected in the central part of Indian Sundarbans: Canning, Stn.1 (22°18′37″N; 88°40′36″E); Chhotomollakhali, Stn.2 (22°10′21.74″N; 88°53′55.18″E); and Bali, Stn.3 (22°04′35.17″N; 88°44′55.70″E). The species collected from the selected stations during low tide were brought to laboratory, washed and dried with tissue paper and stored at −20 °C for further analysis.

Fig. 5A.1

Map of Indian Sundarbans showing the sampling stations

5.1.2.2 2.2. Analysis of Dissolved Zn, Cu and Pb

10 l Teflon-lined Glo-Flo bottles, filled with Teflon taps and deployed on a rosette or on Kevlar line, were used for collecting surface water samples. Nucleopore filters (0.4 μm pore diameter) was used to filter the collected water samples and the aliquots of the filters were acidified with sub-boiling distilled nitric acid to a pH of about 2.0 and stored in clean polyethylene bottles of low density. Using dithiocarbamate complexation and subsequent extraction into Freon TF, followed by back extraction as per the procedure of Danielsson et al. (1978), dissolved heavy metals were separated and concentrated from the ambient water of the selected stations. Zn, Cu and Pb in the extracts were detected using atomic absorption spectrophotometer (Perkin Elmer Model 3030). The quality aspect of dissolved heavy metal determination is confirmed by good agreement between our values and reported for certified reference seawater materials (CASS 2) (Table 5A.1).

Table 5A.1 Analysis of reference material for nearshore seawater (CASS 2)

5.1.2.3 2.3. Analysis of Biologically Available Zn, Cu and Pb

Collection of sediment samples from surface (cm depth) was done by scrapping using a precleaned and acid-washed plastic scale and stored immediately in clean polyethylene bags, which were sealed. Double-distilled water free of metal was used to wash the collected samples and oven-dried at 05 °C for 5–6 h. The samples were freed from visible shells or shell fragments, ground to powder in a mortar and stored in acid-washed polyethylene acids. As per the standard procedure of Malo (1977), the analyses of biologically available metals were done after redrying the samples, from which 1 g was taken and digested with 0.5 (N) HCl. Polyethylene containers were used to store the resulting solution. Atomic absorption spectrophotometer was used for determining the metal concentrations. In the reagent blank, no detectable trace metals were found. To assure the quality of the data, analysis of the NIES Sargasso sample was carried out that revealed least standard deviation between the observed values and certified values (Table 5A.2).

Table 5A.2 Analysis of reference material (NIES Sargasso sample) for sediments obtained from the National Institute of Environmental Studies, Japan

5.1.2.4 2.4. Analysis of Tissue Zn, Cu and Pb

20 g leaf and stem samples were oven-dried at 105 °C overnight to a constant weight. 1 g of dried sample of each of the vegetative part was digested with a mixture of 10 ml nitric acid and perchloric acid (3:1) following the method as outlined by Lithor (1975) till a clear solution was obtained. The resulting solution was made up to a constant volume with 0.05 N nitric acid. Each of the vegetative samples was analysed for Zn, Cu and Pb against standard concentration of each metal on a Perkin–Elmer Atomic Absorption Spectrophotometer (Model 3030) equipped with a HGA 500 graphite furnace atomizer and a deuterium background corrector. Blank correction was done to bring accuracy to the results.

5.1.2.5 2.5. Data Analysis

Statistical software SPSS 16.0 was applied to determine the interrelationships of heavy metal concentrations in A. alba and ambient environment by Pearson correlation coefficient analysis. Statistical significance was tested at 95 % confidence level. The computations of correlation coefficients were done considering the mean values of selected variables for 2 consecutive years (n = 24).

5.1.3 3. Results

5.1.3.1 3.1. Dissolved Heavy Metal

The dissolved heavy metals followed the order Zn > Cu > Pb irrespective of all stations. Dissolved Zn ranged from 213.64 ± 57.67 ppb (at Bali) to 551.01 ± 60.39 ppb (at Canning) during 2011 and from 232.77 ± 57.76 ppb (at Bali) to 570.14 ± 60.39 ppb (at Canning) during 2012. Dissolved Cu ranged from 79.83 ± 54.19 ppb (at Bali) to 159.47 ± 11.09 ppb (at Canning) during 2011, and in 2012 it ranged from 94.95 ± 54.19 ppb (at Bali) to 173.09 ± 12.47 ppb (at Canning). Dissolved Pb ranged from 8.41 ± 3.76 ppb (at Bali) to 35.41 ± 4.29 ppb (at Canning) during 2011 and from 16.69 ± 4.95 ppb (at Bali) to 45.18 ± 4.35 ppb (at Canning) during 2012 (Figs. 5A.2a, 5A.2b and 5A.2c).

Fig. 5A.2a

Dissolved Zn in three stations of Indian Sundarbans during 2011–2012

Fig. 5A.2b

Dissolved Cu in three stations of Indian Sundarbans during 2011–2012

Fig. 5A.2c

Dissolved Pb in three stations of Indian Sundarbans during 2011–2012

5.1.3.2 3.2. Biologically Available Heavy Metals

Biologically available heavy metals in sediment followed the same order as that of water, i.e. Zn > Cu > Pb irrespective of all stations. Sediment Zn ranged from 40.84 ± 10.09 ppm (at Bali) to 78.01 ± 9.43 ppm (at Canning) during 2011 and from 49.87 ± 10.09 ppm (at Bali) to 87.03 ± 9.43 ppm (at Canning) in 2012. In case of sediment, Cu ranged from 10.68 ± 2.84 ppm (at Bali) to 26.86 ± 2.76 ppm (at Canning) during 2011, and in 2012 it ranged from 15.64 ± 2.84 ppm (at Bali) to 32.41 ± 3.63 ppm (at Canning). Pb in sediment ranged from 6.26 ± 1.83 ppm (at Bali) to 12.65 ± 2.33 ppm (at Canning) during 2011 and from 8.70 ± 2.01 ppm (at Bali) to 13.96 ± 2.37 ppm (at Canning) during 2012 (Figs. 5A.3a, 5A.3b and 5A.3c).

Fig. 5A.3a

Biologically available Zn in surface sediments of three stations of Indian Sundarbans during 2011–2012

Fig. 5A.3b

Biologically available Cu in surface sediments of three stations of Indian Sundarbans during 2011–2012

Fig. 5A.3c

Biologically available Pb in surface sediments of three stations of Indian Sundarbans during 2011–2012

5.1.3.3 3.3. Bioaccumulation Pattern

In A. alba samples, the heavy metals varied as per the order Zn > Cu > Pb. This sequence is uniform in all the three selected stations. In the present study, the mean concentration of Zn in leaf ranged from 6.60 ppm (at Bali) to 11.66 ppm (at Canning) during 2011, and in 2012 the mean concentration ranged from 7.13 ppm (at Bali) to 13.84 ppm (at Canning). Mean concentration of Cu ranged from 5.75 ppm (at Bali) to 8.21 ppm (at Canning) in 2011 and 6.12 ppm (at Bali) to 10.05 ppm (at Canning) in 2012. Mean value of Pb in A. alba leaf ranged from 1.79 ppm (at Bali) to 2.44 ppm (at Canning) in 2011 and from 1.85 ppm (at Bali) to 2.98 ppm (at Canning) in 2012 (Fig. 5A.4a).

In the same species, the mean concentration of Zn in stem ranged from 9.89 ppm (at Bali) to 17.84 ppm (at Canning) during 2011, and in 2012 the mean concentration ranged from 11.23 ppm (at Bali) to 21.33 ppm (at Canning). Mean Cu concentration ranged from 7.89 ppm (at Bali) to 12.54 ppm (at Canning) in 2011 and 8.33 ppm (at Bali) to 15.75 ppm (at Canning) in 2012. Pb ranged from mean value of 1.95 ppm (at Bali) to 2.80 ppm (at Canning) in 2011 and from 2.02 ppm (at Bali) to 3.59 ppm (at Canning) in 2012 (Fig. 5A.4b).

5.1.4 4. Discussion

Metal pollution in the estuarine, harbour and coastal environment is usually caused by land run-off, mining activities, shipping and dredging activities and anthropogenic inputs (Panigrahy et al. 1997). Sediments in much affected domains not only record its history but also indicate the degree of pollution (Sahu and Bhosale 1991). The mechanism of accumulation of pollutants in the sediments is strongly controlled by the nature of substrate as well as the physico-chemical conditions controlling dissolution and precipitation (Panigrahy et al. 1997). Even though many of the organic compounds occurring in the marine sediments are known to form complexes with metal ions in solution, it has been observed that humic substances are responsible for these strong interactions (Manskaya and Drozdova 1968). The physico-chemical variables also play a major role in the compartmentalization and speciation of metal in the coastal environment. It has been reported that apart from anthropogenic influences, variables like aquatic salinity and pH regulate the process of precipitation of metallic compounds on the seabed and sediment.

Heavy metals have contaminated the aquatic environment in the present century due to intense industrialization and urbanization. The Gangetic delta is no exception to this usual trend. The rapid industrialization and urbanization of the city of Kolkata (formerly known as Calcutta), Howrah and the newly emerging Haldia complex in the maritime state of West Bengal has caused considerable ecological imbalance in the adjacent coastal zone (Mitra and Choudhury 1992; Mitra 1998). The Hooghly estuary, situated on the western sector of the Gangetic delta, receives drainage from these adjacent cities, which have sewage outlets into the estuarine system. The chain of factories and industries situated on the western bank of the Hooghly estuary is a major cause behind the gradual transformation of this beautiful ecotone into stinking cesspools of the megapolis (Mitra and Choudhury 1992). The lower part of the estuary has multifarious industries such as paper, textiles, chemicals, pharmaceuticals, plastic, shellac, food, leather, jute, tyres and cycle rims (UNEP 1982). These units are point sources of heavy metals in the estuarine water.

Of the three metals studied in the present work, Zn and Cu are essential elements while Pb is a non-essential element for most of the living organisms (Trieff 1980).

The main sources of zinc in the present geographical locale are the galvanization units, paint-manufacturing units and pharmaceutical processes. The main sources of Cu in the coastal waters are antifouling paints (Goldberg 1975), particular type of algaecides used in different aquaculture farms, paint-manufacturing units, pipeline corrosion and oil sludges (32–120 ppm). Ship bottom paint has been found to produce very high concentration of Cu in seawater and sediment in harbours of Great Britain and southern California (Bellinger and Benhem 1978; Young et al. 1979). The most toxic of these heavy metals is Pb, which finds its way in coastal waters through the discharge of industrial wastewaters, such as from painting, dyeing, battery-manufacturing units, oil refineries, etc. Antifouling paints used to prevent growth of marine organisms at the bottom of the boats and trawlers also contain lead as an important component. These paints are designed to constantly leach toxic metals into the water to kill organisms that may attach to bottom of the boats, which ultimately is transported to the sediment and aquatic compartments. Lead also enters the oceans and coastal waters both from terrestrial sources and atmosphere and the atmospheric input of lead aerosols can be substantial. The sampling area is exposed to all these activities being proximal to the highly urbanized city of Kolkata, Howrah and the newly emerging Haldia port-cum-industrial complex.

Wetland vegetations are known to absorb and accumulate metals from contaminated sediment (Giblin et al. 1980; Kraus et al. 1986; Kraus 1988; Sanders and Osman 1985). The absorption of contaminants is one reason that wetlands are being used for wastewater treatment. Metals taken up by plants are also capable of re-entering wetland systems through excretion from leaf salt glands (Kraus et al. 1986; Kraus 1988). Metals present within the water column may be carried through open stoma of the plant. This could result in the adhesion of the metals to the outer surface of the leaf. In the present study, the significant positive correlations (p < 0.05) observed between dissolved heavy metals and plant tissue metal confirm the adhesive characteristics of the metals (Table 5A.3). Also, significant negative relationships between tissue metals and biologically available metals in sediments signify that the primary source of selected heavy metals in A. alba is the aquatic phase and not the sediment.

Table 5A.3 Interrelationships between heavy metal concentrations in A. alba and ambient environment

Fig. 5A.4a

Mean concentrations (n = 12 for each year) of heavy metals (in ppm dry wt.) in A. alba leaf during 2011 and 2012

Fig. 5A.4b

Mean concentrations (n = 12 for each year) of heavy metals (in ppm dry wt.) in A. alba stem during 2011 and 2012

5.1.5 5. Looking Forward

In the coastal environment, mangroves play a major role in regulating the nutrient balance of the zone. It can absorb excess nutrients, reduce suspended solids and sequester other pollutants from aquatic phase. Therefore, in many parts of the globe, this unique ecosystem is presently used as the sink of waste generated from urban, agricultural and industrial sectors located nearby.

During the past few decades, both natural and constructed wetlands were considered as low-cost, easy maintained, simple and effective alternatives for the treatment of municipal, industrial and agricultural effluents (Tam and Yao 1998). The mangrove wetlands are no exceptions to this bio-purification activity. The mechanisms of mangroves to improve the health of the ambient aquatic phase involve interactive processes of plants, soil/sediment and microorganisms (Kanokporn et al. 2002). Plants’ uptake of nutrients and transfer of oxygen to the rhizosphere by leakage from roots serve as matrix for growth of microorganisms and stimulate more microbial activities (Conley et al. 1991). Microorganisms regulate the decomposition of complex organic matter in the mangrove ecosystem and result in nitrogen turnover. The soil/sediment of mangroves not only provides a habitat for micro- and macro-flora and fauna that are involved in chemical transformation but plays a significant role in its ability to retain certain chemicals.

It has been recorded that mangroves possess high capacity to retain pollutants, which may be attributed to their presence in anaerobic and reduced conditions, periodically flooded by tides and high clay and organic matter content (Tam and Yao 1998). However the impact of wastewater to the mangrove ecosystem is a matter of great concern than the efficiency of wetlands in improving water quality. The productivity of mangroves may increase due to discharge anthropogenic wastes and this process is beneficial particularly in those areas where nutrient status is low (Wong et al. 1995). This research article thus reflects another face of mangroves, which has been paid minimum attention but can successfully be used to control anthropogenic wastes.

Annexure 5B: Regulatory Role of Salinity on Biomass and Carbon Content in Mangroves of Lower Gangetic Delta

5.2.1 1. Introduction

The general consensus among climate researchers and environmentalists is that increased levels of greenhouse gases (GHGs) from human activities and luxurious lifestyles, burning fossil fuels and massive deforestation in many regions of the world are changing the climate of the planet Earth. CO₂ plays the major role in absorbing outgoing terrestrial radiation and contributes about half of the total greenhouse effect. Between 1850 and 1900, around 100 gigatons of carbon was released into the air just for land-use changes (Pandey 2002). Most of the increase has been since 1940 (Hair and Sampson 1992). The atmospheric CO₂ concentration is currently rising by 4 % per decade. Worldwide concern about climate change has created increasing interest in trees to help reduce the level of atmospheric CO₂ (Dwyer et al. 1992). Forests are most critical components for taking carbon out of circulation for long periods of time. Of the total amount of carbon tied up in earthbound forms, an estimated 90 % is contained in the world’s forests, which includes trees, forest floor (litter) and forest soil. For each cubic foot of merchantable wood produced in a tree, about 33 lb. (14.9 kg) of carbon is stored in total tree biomass (Sampson et al. 1992). Tropical forests in general are a disproportionately important component in the global carbon cycle and are thought to represent 30–40 % of the terrestrial net primary production (Clark et al. 2001). Although the area covered by mangrove ecosystems represent only a small fraction of tropical forests, their position at the terrestrial–ocean interface and potential exchange with coastal water suggests these forests make a unique contribution to carbon biogeochemistry in coastal ocean (Twilley et al. 1992). Mangrove ecosystems thrive along coastlines throughout most of the tropics and subtropics. About 75 % of tropical and subtropical countries of the world comprise of mangrove forests (William 2005). These intertidal forests play important ecological and socioeconomic roles by acting as a nutrient filter between land and sea (Robertson and Phillips 1995), contributing to coastline protection (Vermatt and Thampanya 2006), providing commercial fishery resources (Constanza et al. 1997) and nursery grounds for coastal fishes and crustaceans. The coastal zone (<200 m depth), covering ~7 % of the ocean surface (Gattuso et al. 1998), has an important role in the oceanic carbon cycle, and various estimates indicate that the majority of mineralization and burial of organic carbon, as well as carbonate production and accumulation, take place in the coastal ocean (Gattuso et al. 1998; Mackenzie et al. 2004). The potential impact of mangrove on coastal zone carbon dynamics has been a topic of intense debate during the past decades. The ‘outwelling’ hypothesis, first proposed for mangroves by Odum (1968) and Odum and Heald (1972), suggested that a large fraction of the organic matter produced by mangrove trees is exported to the coastal ocean, where it forms the basis of a detritus food chain and thereby supports coastal fisheries. A number of recent studies have indicated that a direct trophic link between mangrove forest production and offshore secondary production is unlikely for many mangrove systems. Despite the large number of case studies dealing with various aspects of organic matter cycling in mangrove systems (Kristensen et al. 2008), there is very limited consensus on the carbon-sequestering potential of mangroves.

The present study is an attempt to establish a baseline data set of the carbon content in the mangrove ecosystem of Indian Sundarbans that has received the crowns of World Heritage Site and Biosphere Reserve in 1987 and 1989 respectively by UNESCO, owing to its unique biological productivity, taxonomic diversity and aesthetic beauty. To preserve the ecosystem in its pristine form, mangrove plantation is carried out on regular basis in the entire Gangetic delta complex. An accurate estimate of carbon storage and sequestration is essential for any project related to plantation particularly in the sector of social forestry. In context to mangrove-dominated Gangetic delta region, this is extremely important as several government, nongovernment organizations and even foreign donors are participating in the mangrove afforestation programme, owing to extreme vulnerability of the system to sea level rise, erosion and tidal surges (Hazra et al 2002; Mitra and Banerjee 2004). The ability of these plantations to sequester carbon has generated a lot of interest, since carbon sequestration projects in developing nations could receive investments from companies and governments wishing to offset their emissions of greenhouse gases through the Kyoto Protocol’s Clean Development Mechanism (Fearnside 1999). Carbon registries typically segregate a number of carbon pools within a mangrove forest that can be identified and quantified. These carbon pools are categorized in a variety of ways, but typically include four major compartments. The total carbon in a mangrove system is the summation of above-ground biomass, below-ground biomass, litter and soil. The mangrove ecosystem is unique in terms of carbon dynamics as the litters and detritus contributed by the floral species are exported to adjacent waterbodies in every tidal cycle.

In this study, the above-ground stem, branch and leaf biomass and litter and soil were analysed for carbon content in two different physiographic settings in and around Indian Sundarbans. The difference is caused by freshwater supply from Himalayan glaciers (largest glacial coverage ~34,660 km²) through Farakka barrage in the western part of Gangetic delta. The barrage was constructed in 1975 to ensure availability of water to the riverine ports. The Ganga–Bhagirathi–Hugli river system in the western part of Indian Sundarbans is therefore appropriately diluted in relation to mangrove growth. In contrast, the Matla river in the central sector is disconnected to the Himalayan glaciers’ freshwater due to heavy siltation of the Bidyadhari river since late fifteenth century and is now primarily tide-fed. This difference created a contrasting natural laboratory for identifying climatic signals in salinity profile and mangrove growth leading to variation in carbon pool under different environmental conditions.

5.2.2 2. Methods

5.2.2.1 2.1. Study Site Description

Two sampling sites were selected each in the western and central sectors and around Indian Sundarbans, a Gangetic delta at the apex of the Bay of Bengal (Fig. 5B.1). This deltaic complex has an area of 9630 Km² and houses 102 islands. The western sector of the deltaic lobe receives the snowmelt water of mighty Himalayan glaciers after being regulated through several barrages on the way. The central sector, on the other hand, is fully deprived from such supply due to heavy siltation and clogging of the Bidyadhari channel in the late fifteenth century (Chaudhuri and Choudhury 1994). The station in the western part lies at the confluence of the river Hugli (a continuation of Ganga–Bhagirathi system) and Bay of Bengal. The site is locally known as Sagar South (88°01′47.28″ N latitude and 21°31′4.68″ E longitude). In the central sector, the sampling station was selected at Canning (88°40′36.84″ N latitude and 22°18′37.44″ E longitude), adjacent to tide-fed Matla river. Samplings in both these sectors were carried out in low-tide period during January 2012.

Fig. 5B.1

Location of sampling stations in the western and central sectors of Indian Sundarbans

In each sector, plot size of 10 × 10 m was selected for the study and the average readings were documented from 15 such plots. The mean relative abundance of each species was evaluated for the order of dominance of mangrove species at the study sites.

The above-ground biomass (AGB) of individual trees of five dominant species, namely, Sonneratia apetala, Avicennia alba, Avicennia marina, Avicennia officinalis and Excoecaria agallocha, in each plot was estimated as per the standard procedure stated here, and the average values of 15 plots were finally converted into biomass (in tonnes) per hectare in the study area. Litter production studies were carried out in both the sectors through net collection method and organic carbon in the soil substratum was analysed following the modified method of Walkley and Black (1934).

5.2.2.2 2.2. Above-Ground Stem Biomass Estimation

The above-ground (stem) biomass of individual trees of each species in every plot was estimated using non-destructive method in which the diameter at the breast height (DBH) was measured with a calliper and height with Ravi’s multimeter. Form factor was determined as per the expression outlined by Koul and Panwar (2008) with Spiegel relascope to find out the tree volume (V) using the standard formula given by Pressler (1895) and Bitterlich (1984). Specific gravity (G) was estimated taking the stem cores, which was further converted into stem biomass (B _S ) as per the expression B _S  = GV. The expression for V is FHΠR², where F is the form factor, R is the radius of the tree derived from its DBH and H is the height of the target tree.

5.2.2.3 2.3. Above-Ground Branch Biomass Estimation

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. <5, 5–10 and >10 cm. Fresh weight of two branches from each size group was recorded separately. Dry weight of branches was estimated using the equation of Chidumaya (1990).

Total branch biomass (dry weight) per sample tree was determined as per the expression

$$ {B}_{\mathrm{db}}={n}_1{b}_{\mathrm{w}1}+{n}_2{b}_{\mathrm{w}2}+{n}_3{b}_{\mathrm{w}3}={\displaystyle \sum {n}_i{b}_{\mathrm{w}i}} $$

where B_db is the dry branch biomass per tree, n_i the number of branches in the ith branch group, b_wi the average weight of branches in the ith group and i = 1, 2, 3, …the branch groups (i = 3 in the present study). This procedure was followed for all the dominant mangrove species separately in both the sectors of the study area.

5.2.2.4 2.4. Above-Ground Leaf Biomass Estimation

Leaves from ten branches (of all the three size groups) of individual trees of each species were removed. One tree of each species per plot was considered for estimation. The leaves were weighed and oven-dried separately to a constant weight at 80 ± 5 °C. The species-wise leaf biomass was then estimated by multiplying the average biomass of the leaves per branch with the number of branches in a single tree and the average number of trees per plot as per the expression

$$ {L}_{\mathrm{db}}={n}_1L{w}_1{N}_1+{n}_2L{w}_2{N}_2+\cdots +{n}_5L{w}_5{N}_5 $$

where L _db is the dry leaf biomass of dominant mangrove species per plot, n _i …n ₅ are the number of branches of each tree of five dominant species, Lw ₁….Lw ₅ are the average dry weight of leaves removed from ten branches of each of the five species and N ₁ to N ₅ are the number of trees per species in the plot.

5.2.2.5 2.5. Litterfall Estimation

Litterfall was determined by setting 15 rectangular traps (3 × 3 m) in all the 15 plots in each sector. The traps were made of 1 mm mesh size nylon screen, through which rainwater can pass (Brown and Lugo 1984). The traps were positioned above the high tide level (Jeffrie and Tokuyama 1998) and contents of all the 15 traps per sector were collected and brought to the laboratory after duration of 1 month. The collected materials were segregated into leaves and miscellaneous fraction that comprised of fruits, twigs, stipules, flowers, etc. The materials were dried separately to a constant weight 80 ± 5 °C. Finally the mean weight per plot was estimated for both the western and central sectors in the study area and transformed into gm⁻² day⁻¹ unit.

5.2.2.6 2.6. Carbon Estimation in Trees and Litter

Direct estimation of percent carbon was done by a CHN analyser. For this a portion of fresh sample of stem, branch and leaf from 30 trees (two trees/species/plot) of individual species (covering all the 15 plots) was oven-dried at 70 °C, randomly mixed and ground to pass through a 0.5 mm screen (1.0 mm screen for leaves). The carbon content (in %) was finally analysed on a LECO® CHN-600 analyser. For litter, the same procedure was followed after oven-drying the net collection at 70 °C.

5.2.2.7 2.7. Organic Carbon Analysis in Soil

Soil samples from the upper 5 cm were collected from all the 15 plots and dried at 60 °C for 48 h. For analysis, visible plant particles and other organisms (like molluscs, crabs, decaying bodies of fishes, etc.) were handpicked and removed from the soil. After sieving the soil through a 2 mm sieve, we ground the samples of the bulk soil (50 g from each plot) finely in a ball mill. The fine dried sample was randomly mixed to get a sector-wise representative picture of the study site. Modified version of Walkley and Black method (1934) was then followed to determine the organic carbon of the soil in %.

5.2.3 3. Results and Discussion

The biomass and productivity of mangrove forests have been studied mainly in terms of wood production, forest conservation and ecosystem management (Putz and Chan 1986; Tamai et al. 1986; Komiyama et al. 1987; Clough and Scott 1989; McKee 1995; Ong et al. 1995). The contemporary understanding of the global warming phenomenon, however, has generated interest in the carbon-stocking ability of mangroves. The carbon sequestration in this unique producer community is a function of biomass production capacity, which in turn depends upon interaction between edaphic, climate and topographic factors of an area. Hence, results obtained at one place may not be applicable to another. Therefore region-based potential of different land types needs to be worked out. In the present study, the results obtained have been compared with other regions of the world to evaluate the potential of Indian Sundarban mangrove as carbon sink on the background of changing scenario of the climate. The present sectorial case study has also been undertaken with the aim to visualize the impact of salinity on the biomass and carbon budget of mangrove system.

5.2.3.1 3.1. Relative Abundance

Nine species of true mangroves were documented in the selected plots in the western sector, but in the central sector only six species were recorded. The mean order of abundance of these species was Sonneratia apetala (27.08) > Excoecaria agallocha (18.75) > Avicennia alba (14.58 %) > Avicennia marina (12.5 %) = Avicennia officinalis (12.5 %) > Acanthus ilicifolius (6.25 %) > Aegiceras corniculatum (4.17 %) > Bruguiera gymnorrhiza (2.08 %) = Xylocarpus moluccensis (2.08 %) in the western sector, but order in central sector was Excoecaria agallocha (23.68 %) > Avicennia alba (21.05 %) > Avicennia marina (15.79 %) = Avicennia officinalis (15.79 %) > Sonneratia apetala (13.16 %) > Acanthus ilicifolius (10.53 %) (Table 5B.1). Few mangrove associate floral species (like Porteresia coarctata, Suaeda sp., etc.) were also documented in the plots. On the basis of relative abundance of the true mangrove species, only five dominant species, namely, Avicennia alba, Avicennia marina, Excoecaria agallocha, Sonneratia apetala and Avicennia officinalis, were considered for carbon stock estimation in their respective above-ground biomass. In both these sectors, the forests were 12 years old, but high salinity in the central sector probably created a stress to the growth of the floral species.

Table 5B.1 Relative abundance of mangrove species (mean of 15 plots) in the study area

5.2.3.2 3.2. Above-Ground Stem Biomass

In the western sector, the above-ground stem biomass of the dominant mangrove trees were 104.09, 14.09, 27.20, 21.37 and 21.46 t ha⁻¹ for Sonneratia apetala, Excoecaria agallocha, Avicennia alba, Avicennia marina and Avicennia officinalis, respectively, but in the central sector, these values were much lower, exhibiting 21.68, 9.27, 15.56, 11.93 and 6.18 t ha⁻¹ for Sonneratia apetala, Excoecaria agallocha, Avicennia alba, Avicennia marina and Avicennia officinalis, respectively (Table 5B.2). The values in the western sector are similar to the data of Komiyama et al. (2008) in a secondary mangrove (Ceriops tagal) forest at Southern Thailand.

Table 5B.2 Above-ground biomass (t/ha) of five dominant mangrove species in the intertidal

The relatively higher stem biomass of similar aged trees in the western sector may be attributed to optimum hydrological and soil characteristics contributed by the river Ganges. Mangroves, in general, prefer brackish water environment, and in extreme saline condition stunted growth is observed (Mitra et al. 2004). The western sector of Indian Sundarbans provides a congenial environment for mangrove sustenance due to freshwater discharge from Farakka barrage in the Hugli estuarine system. Five-year surveys (1999–2003) on water discharge from Farakka barrage revealed an average discharge of (3.4 ± 1.2) × 10³ m³s⁻¹. Higher discharge values were observed during the monsoon with an average of (3.2 ± 1.2) × 10³ m³s⁻¹ and the maximum of the order 4200 m³s⁻¹ during freshet (September). Considerably lower discharge values were recorded during premonsoon with an average of (1.2 ± 0.09) × 10³ m³s⁻¹ and the minimum of the order 860 m³s⁻¹ during May. During postmonsoon, discharge values were moderate with an average of (2.1 ± 0.98) × 10³ m³s⁻¹. The lower Gangetic deltaic lobe also experiences considerable rainfall (1400 mm average rainfall). This causes a considerable volume of surface run-off from the 60,000 km² catchment areas of Ganga–Bhagirathi–Hugli system and their tributaries. All these factors (dam discharge + precipitation + runoff) increase the dilution factor of the Hugli estuary in the western part of Indian Sundarbans—a condition for better growth and increase of mangrove biomass. The central sector, on contrary, does not receive the freshwater discharge on account of siltation of Bidyadhari river which may be accounted for low above-ground stem biomass of the selected mangrove species inhabiting the zone.

5.2.3.3 3.3. Above-Ground Branch Biomass

The branch biomass of mangroves showed marked differences between the trees of western and central sectors. In western sector, the values were 42.64, 6.30, 12.42, 10.08 and 9.23 t ha⁻¹, and in central sectors the values were 9.03, 3.81, 6.30, 5.25 and 2.59 t ha⁻¹ for Sonneratia apetala, Excoecaria agallocha, Avicennia alba, Avicennia marina and Avicennia officinalis respectively (Table 5B.2). The branch biomass in the western sector is almost similar to the values in a secondary mangrove (Ceriops tagal) forest at Southern Thailand as documented by Komiyama et al. (2000). Stunted branches of mangroves of central sector may again be related to high salinity in this sector (Mitra et al. 2009).

5.2.3.4 3.4. Above-Ground Leaf Biomass

The leaf biomass of the trees in the western and central sectors were 22.88 and 4.33 t ha⁻¹ respectively for Sonneratia apetala, 3.22 and 1.85 t ha⁻¹ respectively for Excoecaria agallocha, 7.07 and 2.96 t ha⁻¹ respectively for Avicennia alba, 4.83 and 2.20 t ha⁻¹ respectively for Avicennia marina and 5.46 and 1.24 t ha⁻¹ respectively for Avicennia officinalis (Table 5B.2). The values in the western sector are comparatively similar to the records of other workers like 12.1–15.0 t ha⁻¹ in Avicennia forests (Briggs 1977), 6.2–20.2 t ha⁻¹ in Rhizophora apiculata young plantations (Aksomkoae 1975), 13.3 t ha⁻¹ in Rhizophora patch (de la Cruz and Banaag 1967) and 8.1 t ha⁻¹ in a matured Rhizophora forest (Tamai et al. 1986).

5.2.3.5 3.5. Litter Production

Average values of total litter, leaf litter and miscellaneous litterfall (comprised of twigs, stipules, flowers and fruits) are shown in Fig. 5B.2. The biomass of total litter is more in the western sector in comparison to central part of Indian Sundarbans. The leaf litter accounted for nearly 70 % and 64 % of the total litter in the western and central sectors respectively.

Fig. 5B.2

Variation in leaf litter, miscellaneous litter and total litter in the western and central sectors of Indian Sundarbans

Although we have not studied the litterfall throughout the year, a significant difference was observed between western and central sectors of the study area with respect to quantum and rate of litter production. The value in the western sector is comparable to the data of several workers. Twilley et al. (1986) reported that the total annual litterfall of mixed mangrove forest of Avicennia germinans, Rhizophora mangle and Laguncularia racemosa in South Florida was 8.68 t ha⁻¹ year⁻¹ (in Fort Myers) and 7.51 t ha⁻¹ year⁻¹ (at Rookery Bay). Steinke and Charles (1984) reported the total annual litterfall of mangrove forest in the Mgeni estuary was 8.61 t ha⁻¹ year⁻¹. Kishimoto et al. (1987) reported that the litterfall of mangrove stands on Iriomote Island (Japan) was 7.5 and 8.8 t ha⁻¹ year⁻¹ in Rhizophora stylosa and Bruguiera gymnorrhiza community, respectively. The annual litterfall across broad geographic boundaries are reported as 7–12 t dry weight ha⁻¹ year⁻¹ (Duke et al. 1981; Twilley et al. 1986; Hardiwinoto et al. 1989; Lee 1990; Gong and Ong 1990; Mall et al. 1991 and Mmochi 1993). In context to Indian mangrove system, the mangrove litter production was recorded as 7.50 tonnes/ha/year in Pichavaram at Tamil Nadu (Krishnamurthy 1985), in which leaf biomass amounts to about 80–90 % (Yadav and Choudhury 1985). Assuming hypothetical situation of uniformity in litterfall through seasons, our data may be interpolated to yield an annual litter production of 3.19 t ha⁻¹ in the western sector and 1.33 t ha⁻¹ in the central sector respectively. The lower value of litter production in the central Indian Sundarbans may be attributed to the trend of rising salinity due to siltation of Bidyadhari river in the present geographical locale (Chaudhuri and Choudhury 1994). The growth, survival and biomass of mangroves depend on appropriate dilution of the brackish water system with freshwater. The central sector of Indian Sundarbans hardly witness such dilution as the freshwater discharge of the Ganga–Bhagirathi system cannot reach the area due to clogging of the Bidyadhari river by silt and solid wastes (Mitra et al. 2009). The rivers in the study area are noted for their silt-carrying potential. It has been reported that each year Ganga and Brahmaputra bring around 166.70 crore tonnes of silt that has created the present Gangetic delta and the building process is still ongoing.

5.2.3.6 3.6. Soil Organic Carbon

The values of organic carbon were 2.78 % in the western sector and 0.58 % in the central sector. These values are indicators of mangrove growth, biomass, decay and litterfall for a particular site. Carbon fixed within plant biomass ultimately enters within the soil, where it may reside for hundreds of years. The ability of soil to store this additional carbon, however, is highly controversial, because there are two contrasting ways in which the increased input of carbon may be processed in the soil. First, the extra-fixed carbon may become soil organic carbon. Second, this readily available source of carbon may stimulate soil microbial processes by providing substrates that enhance decomposition of the organic matter through the so-called priming effect (Peterson et al. 1997). Strong evidence for a long-term sink for increased atmospheric CO₂ in soils is still lacking (Schlesinger 1990; Schimel 1995; Canadell et al. 1995). Our study indicates that high saline soil is relatively poor sink of CO₂, which may be attributed to either poor growth of mangroves (Mitra et al. 2004) or low fertility of the soil in terms of nitrogen that acts as retarding factor for plant growth. Canadell et al. (1995) opined that soil quality may influence sequestration of carbon in response to increased atmospheric CO₂. Soil fertility may control the carbon inputs into the soil, since CO₂ enrichment can stimulate plant growth only in soils with adequate nutrients (Egli et al. 1998). Absence of nutrient in the soil of central sector may therefore be considered as plausible cause of poor plant growth in the area as reflected through comparatively low soil organic carbon content.

5.2.3.7 3.7. Comparison of Carbon Stocks

Mangroves are unique storehouse for carbon. The global storage of carbon in mangrove biomass is estimated to be 4.03 pg, 70 % of which occurs in coastal margins from 0° to 10° latitude (Twilley et al. 1992). For the present study, the results of carbon stock in the above-ground biomass of the selected species are shown in Table 5B.3. Species-wise carbon content are in the order Sonneratia apetala > Avicennia alba > Avicennia marina > Avicennia officinalis > Excoecaria agallocha in the western sector and Sonneratia apetala > Avicennia alba > Avicennia marina > Excoecaria agallocha > Avicennia officinalis in the central sector. The % of carbon in the mangrove litter was 31.8 and 29.3 in the western and central sectors respectively. On the basis of the % carbon and average daily production values, the carbon stock of the litter were 1.01 and 0.39 t ha⁻¹ year ⁻¹ in the western and central sectors respectively (Table 5.7). The soil organic carbon also exhibited similar trend with higher value in the western sector (2.78 %) than that of the central region (0.58 %). Considering the carbon pool in the above-ground biomass of the dominant mangrove species and total litter and assuming seasonal uniformity in carbon stock, the corresponding CO₂ equivalents ha⁻¹ year ⁻¹ in western and central sectors of Indian Sundarbans were 477.98 t and 225.13 tonnes respectively (Table 5B.4), which are effective figures when the present trend of atmospheric CO₂ rise is 4 % per decade (Hyun-Kil and Gregory McPherson 2001). These figures can be manipulated through effective soil management, tidal interactions (through artificial canalization) and proper dilution of the system with freshwater, which are important requisites for accelerating the biomass of mangrove species. The data generated in the present geographical locale show significant variations between the two sectors. The hypiersalinity of the central part of Indian Sundarbans may be considered as one of the important reason for such shortfall. Records show that surface water salinity has increased by 40.46 % in central sector and decreased by 46.21 % in western sector of Indian Sundarbans over a period of 27 years (1980–2007), which is the result of the blockage of freshwater flow from western side of Indian Sundarbans to central sector (Mitra et al. 2009). Higher salinity has therefore reduced the floral growth and subsequent litter production and organic carbon in soil of central sector of Indian Sundarbans. Considering the ecological significance of mangroves, policy must be implemented (both at regional and global level) to preserve and restore the system, which have been destroyed and damaged in many parts of the globe by activities like dredging, urbanization, draining, construction of shrimp farms and tourism units, sea level rise, etc.

Table 5B.3 Above-ground carbon stock (t/ha) of five dominant mangrove species in the intertidal

Table 5B.4 Carbon stock and CO₂ equivalent in AGB of dominant mangrove species and litter in t ha⁻¹ year⁻¹ in the western and central sectors of Indian Sundarbans

In the present framework, interlinking of the tide-fed rivers of the central portion with the Ganga–Bhagirathi–Hugli river system in the western part might serve as an effective management strategy for accelerating the mangrove plant biomass and subsequent rate of carbon sequestration by the mangrove system in the central sector around the Matla river.