Dinkum Journal of Natural & Scientific Innovations (DJNSI)

Publication History

Submitted: October 24, 2023
Accepted:   October 29, 2023
Published: December 11, 2023




Jesmin Akther & Md. Jahangir Sarker (2023). The Status & Assessment of Natural Food Abundance For Hilsha Shad (Tenualosa Ilisha) During Breeding Season In The Meghna River Estuary, Bangladesh. Dinkum Journal of Natural & Scientific Innovations, 2(12):882-903.


© 2023 DJNSI. All rights reserved

The Status & Assessment of Natural Food Abundance For Hilsha Shad (Tenualosa Ilisha) During Breeding Season In The Meghna River Estuary, BangladeshOriginal Article

Jesmin Akther 1*, Md. Jahangir Sarker 2

  1. Department of Fisheries & Marine Science, Noakhali Science & Technology University Sonapur, Noakhali-3814, Bangladesh; jesmin_nstu@yahoo.com
  2. Department of Fisheries & Marine Science, Noakhali Science & Technology University Sonapur, Noakhali-3814, Bangladesh; swaponj73@gmail.com

*             Correspondence: jesmin_nstu@yahoo.com

Abstract: Pre-monsoon in March and monsoon in July of 2023 saw the completion of the study at ten specially chosen sites inside the Meghna River Estuary. The goal of the study was to investigate the natural food abundance and eating preferences of Hilsha shad (Tennualosa illisha) during their mating season. The Meghna River Estuary’s chosen sampling stations were spaced out over a distance of 3 km, commencing from the near shore and extending up to 12 km offshore. During the current investigation, surface water temperature (OC), transparency (cm), and phytoplankton density (biological factor) were measured in both seasons. In addition, ten fresh fish samples were concurrently taken from the study region during water sampling for gut content analysis in each of the four seasons. Significant seasonal fluctuations in the water’s temperature (OC) and transparency (cm) could be caused by seasonal rainfall and the mixing of land runoff into the body of water. During the current investigation, 48 genera of phytoplankton belonging to four groups—Bacillariophyceae, Chlorophyceae, Cyanophyceae, and Chrysophyceae—were identified from the surface water. Their average density was lower than that of other estuary locations, at 14.27×102 cells/ml. It is possible to draw the conclusion that the measured phytoplankton density does not guarantee a healthy ecosystem for Hilsha shad in the study area based on the diversity index. According to the gut content research, Hilsha shad exhibit higher eating activity in March during the spawning season. The Hilsha shad’s gut content revealed a little quantity of detritus and ingested phytoplankton species at the beginning of the spawning season (July), whereas the water’s phytoplankton density was extremely low. It indicates that Hilsha shad consumed debris when the amount of required phytoplankton species in water bodies was low. Thus, during the breeding season, low densities of natural food—phytoplankton—in surface water may have a direct or indirect impact on Hilsha shad (Tennualosa illisha) nutrition and reproduction.

Keywords: Hilsha Shad (Tenualosa Ilisha), breeding season, Meghna river estuary, Bangladesh


Bangladesh has many wetlands with diverse species. Numerous rivers, streams, and tidal creeks cross the vast alluvial tract. The Ganges, Brahmaputra, and Meghna rivers’ lush deltaic zones dominate the region. The Ganges, a major Himalayan river, joins the Jamuna, the main Brahmaputra channel, and the Meghna before emptying into the Bay of Bangladesh. About 230 rivers and their tributaries cross the country, totaling 24,000 km. Different fisheries have many uses because rivers and estuaries help fish breed and spawn. Since freshwater from rivers and saltwater from the sea meet in estuaries, environmental conditions change frequently (Iver et al., 2007). Estuaries are important fish habitats in many parts of the world. Estuaries can be difficult for parents due to salinity, but many fish species have found that they are great places for early life development, spawning, and growth, with high production. Estuaries are among the most productive places on Earth, producing more organic matter than comparable forests, grasslands, or farms. They also generate revenue from tourism, fishing, and leisure. Estuaries’ protected coastal waters support commerce, transportation, and public infrastructure as ports and harbours. The estuary’s fishery is dynamic in time and space. In addition to intra-annual environmental differences, short-term changes like the day/night cycle can affect these communities’ distribution and abundance. These interactions include behavioral changes during rest and activity, prey competition, etc. Because environmental factors always affect competing populations, they affect natural population diversity. Estuaries physically and biologically connect freshwaters, land, and sea, according to Chowdhury et al. (2009). Bangladesh has 20 estuaries and complex estuarine ecosystems in planted and natural mangrove forests along its coast. Few things are known about phytoplankton diversity and the factors that affect their abundance and distribution (Ahsan et al., 2012). The Clupeidae herring family includes Indian shad Tenualosa ilisha. Tenualosa ilisha (Fisher and Bianchi 1984) is the new scientific name for Hilsa ilisha, but its common name has been used for over a century. The Hilsha shad lives in rivers, estuaries, and the sea. Fish are found in the China Sea, Vietnam Sea, Red Sea, Arabian Sea, Persian Gulf, and Bay of Bengal. The riverine habitat includes the eastern and western Indian rivers, the Irrawaddy of Myanmar, the Padma, Jamuna, Meghna, Karnafully, and other coastal rivers of Bangladesh, the Tigris and Euphrates of Iran and Iraq, the Indus of Pakistan, and the Satil Arab rivers. About 95% of hilsa cases occur in Bangladesh, India, and Myanmar (Banglapedia, 2007). T. ilisha is an anadromous shad. It can travel long distances upstream and tolerate many salinities. Hilsha spends most of its life at sea, but it travels 1,200 km interior via Indian subcontinental rivers to spawn. Normal Bangladeshi river length is 50–100 km (Wikipedia 2007). Hilsha shad can grow to 60 centimetres, but they usually measure 35 to 40. Huge hilshas weigh nearly 2.5 kg. The Hilsha shad is known for its fast swimming (Southwell and Prashad 1918). Different opinions exist on the smallest hilsha size at first maturity. Day (1873) noted that hilsha development can begin at the end of the first year or the beginning of the second. In Bangladeshi Meghna River waters, Shafi et al. (1978) found that males mature at 21 cm and females at 32 cm. Tenualosa are plankton-eaters but occasionally omnivores. Hilsha larvae and juveniles eat diatoms, copepods, daphnia, etc. Older hilsha typically eat blue and green algae, but they also eat rotifers, claudoceros, protozoans, crustaceans, sand, and organic waste. Food intake varies seasonally. Pre-adult jatkas feed heavily. There is insufficient phytoplankton data in the Meghna Estuary’s offshore waters to understand this complex ecosystem’s productivity. Bangladesh’s coastal seas have received most research, but hilsha fisheries have not. Most of the information on Bangladesh’s lotic waterways’ biological parameters and plankton abundance’s impact on fisheries yield comes from river system studies. Since Hilsha lives in rivers, estuaries, and the sea, more research is needed to determine why output, especially growth and size, is declining. Some studies have examined Bangladesh’s coastal estuarine system’s biological aspects (Hossain et al. 2007), but none have examined its phytoplankton species assemblage structure. How much phytoplankton is in the Meghna River Estuary is unknown. Meghna River Estuary phytoplankton had never been studied before Ahsan et al. (2012). Bangladesh has conducted a few studies on phytoplankton population organization and abiotic factors in Meghna estuary river systems. Many published algal flora findings are based on brief surveys of tiny, isolated areas, so they are incomplete. Algal flora provides a safe breeding and spawning ground and is important to the ecological environment, so phytoplankton community structure must be studied. The physical, chemical, and biological environment can affect phytoplankton community structures, either positively or negatively. The Meghna River Estuary is a Tenualosa ilisha breeding site, but phytoplankton levels have not been measured. Many studies show that adult Tenualosa ilisha eat phytoplankton, while juveniles eat diatoms and zooplankton. Thus, the Meghna River Estuary’s phytoplankton richness may affect hilsha development and reproduction. This study measured phytoplankton abundance in the Meghna River Estuary during Tenualosa ilisha’s breeding season.


Estuarine ecosystems maintain biodiversity, store and cycle nutrients, filter pollutants, and protect shorelines from erosion and storms. This region is vital to the global biosphere due to its high biological productivity. Due to industrialization, trade, tourism, population growth, and water quality decline, the global estuarine zone is under stress. In recent years, scientists worldwide have become more concerned about estuarine ecosystems due to the loss of floral and faunal diversity. Hydro-geochemical and biotic assessments will help managers understand estuaries and mangroves and implement management strategies (Borja et al., 2010). A proper understanding of any estuarine biological problem requires knowledge of its physical and chemical environment. All sea water characteristics affect the biological system, but many are subtle and cannot be documented. Thus, researchers usually choose a few key parameters that directly affect estuarine organisms. Many researchers worldwide have studied marine and estuarine water ecosystem physico-chemical parameters. Onuoha et al. (2011) studied Nigeria’s Ologe lagoon hydrochemistry and plankton dynamics.  Moskovchenko et al. (2009) calculated the pH, temperature, salinity, DO, nitrate, phosphate, sulphate, chloride, and ammonia of the Vatinsky Egan River catchment in West Siberia. They examined seasonal and spatial water quality changes and anthropogenic chemical inputs into the river system. Their study found high chloride and total petroleum hydrocarbon concentrations in the aquatic system. Akoma (2008) examined tropical estuarine system changes in Nigeria’s Imo river. In Nigeria’s Cross River Estuary, Akpan (1993, 1994) examined phytoplankton biomass seasonality and physico-chemical changes. They examined phytoplankton’s response to seasonal temperature, pH, conductivity, alkalinity, DO, salinity, nitrate, silicate, and phosphate. From September 2000 to June 2002, Wen et al. (2008) conducted bimonthly field surveys of the Danshuei tributary, a subtropical mountain river system in Taiwan, to study the seasonal dynamics and inter-annual variability of dissolved inorganic nitrogen (DIN; nitrate, nitrite, and ammonium) and phosphorus. Alvin et al. (2008) studied the seasonal and spatial dynamics of nutrients and phytoplankton biomass at Victoria harbor in Hong Kong waters and found that chemically enhanced primary sewage treatment significantly reduced NH4+ and PO43- and increased bottom DO. Two coastal zones’ physical and chemical conditions and short-term phytoplankton community structure were studied by Pannard et al. (2008). Bouillon & Dehairs (2007) examined the distribution, sources, and processing of particulate, dissolved organic, and inorganic carbon in the Tana river estuarine mixing zone in Northern Kenya. Radach & Patsch (2007) tediously worked from 1977 to 2000. Riverine freshwater, nitrogen, and phosphorus loading into the North Sea from Belgium, Netherlands, and Germany was extensively studied. Nutrients significantly alter aquatic trophic structure. Painting et al. (2007) examined how nitrate, phosphate, light availability, DO, and water residence time affect phytoplankton distribution in English and Welsh estuaries. The lower Wu Jiang river in China was studied by Zhu et al. (2005) to determine how surface run-off water contributes nutrients like sodium, potassium, silicate, and carbon. Huang et al. (2003) used a 24-h time series and synchronization of vertical profiles of NO3-N, NO2-N, NH3-N, PO4-P, Chlorophyll-a, total solids, salinity, temperature, and other chemical parameters at different stations in Pearl River Estuary, China, to assess nutrients and eutrophication Lane et al. (2002) estimated suspended sediments, inorganic nutrients (NO3, NH4, and PO4), Chlorophyll-a, and salinity to study the Atchafalaya river-delta estuarine complex interaction. Magni et al. (2002) measured the physical and chemical variability of the water column at a subtidal station of an estuary in the Seto Inland Sea, Japan, during a spring tide (ca. 2 m) in May 1995. Conley et al. (2000) examined Danish estuaries’ physical, chemical, and biological properties to understand their nutrient filtering and transformation functions. In a shallow subtropical coastal embayment of Moreton Bay, Australia, Eyre & McKee (2002) examined carbon, nitrogen, and phosphorus budget Eyre & Balls (1999) compared physico-chemical parameters like phosphate, ammonium, nitrate, and silicate along the salinity gradient of three temperate estuaries in Scotland and three tropical estuaries in Queensland.  Cowan & Boynton (1996) extensively studied nutrient spatial and temporal variations and their importance to estuarine primary producers. Akpan & Offem (1993) reported seasonal temperature, salinity, dissolved oxygen, biochemical oxygen demand, ammonium, nitrite, nitrate, phosphate, silicate, pH, and Secchi disc transparency. They said seasonal rainfall is the main factor, but biological cycles may also affect chemical variables. Lytle & Lytle (1990) examined Mississippi estuary for pollutant impact on coastal environment. Ryther & Dunstan (1971) and Nixon & Pilson (1983) found that nitrogen limits primary productivity in marine and estuarine systems. PCA helped Prakash and Raman (1992) identify five phytoplankton assemblages in northwest Bay of Bengal. Goes et al. (1992) found that upwelling and river runoff during south west monsoon season enriched west coast Indian coastal waters with nutrients. Sarojini et al. (2001) found that a tongue of warmer, more saline water from the Arabian Sea causes differences in phytoplankton distribution on the two sides of Andaman and Nicobar islands in Bay of Bengal. Kobayashi and Takahashi (2002) examined diatoms in surface waters in the western and central equatorial Pacific to determine taxa distribution, abundance, and composition. They found that the diatom distribution is strongly constrained by the water masses of the Western Pacific Warm Pool (WPWP) in the west and the EUR in the east.


3.1 Study area

Ganges delta’s easternmost sector is Meghna Estuary. Latitude 20030′ N to 220 N, longitude 91045′ E to 92015′ E. Plate 1 shows the study area surrounded by Hatiya Island to the east, Bhola to the west, greater Noakhali to the north, and Bay of Bengal to the south. Tetulia, Shahbazpur, Hatiya, and Bamni are the main Meghna mouths into the Bay of Bengal. The estuary mixes fresh river water with saline Bay of Bengal water. Strong currents and shallow depths make density stratification unusual. Water masses have fronts or transition zones. River discharge and tide determine these transition zones. All year, tides affect the area. Moonday tides are semidiurnal with two high and two low waters. Along the coast, tides vary in magnitude but not pattern. Spring tide is 4.42m, while neap tide is 0.07m. The tidal range increases from South–West (4 m at South Bhola) to North–East (7 m at Sandwip). Seasonal sea level variation is significant. Sea level rises during the South–West monsoon and falls in winter. The study area’s seasonal variation ranges from 0.8 m in the south to 2.7 m at Chandpur in the north. Cyclones cause extreme storm surges of 5–7 m (on a 20–100-year basis in Chittagong–Bhola). Zero to four meters of wave height. The soil is mostly muddy and sandy-clay loam (Hossain et al., 2009). Figure 1 shows the Meghna river estuary near Ramgoti as the study area.

3.2 Sampling strategy

The study was carried out over two season pre-monsoon (March) and monsoon (July). Water Sampling was done from 10 selected station at The Meghna river estuary (near Alexander Bazar, Ramgoti) which is the most important estuarine system in the southeast coastal portion in  Bangladesh .These stations were selected to find out the phytoplankton dispersion with increasing distance from the shore (Figure 2).

3.3 Sampling stations

Sampling was started near the shoreline adjacent to Alexander bazaar, Ramgoti continuing 3 kilometers interval for each sampling stations and back to shore by sampling in same fashion. The Stations are namely RS1-0(22039’11”N & 900 54′ 25” E), RS1-3 (22037’8”N & 900 54′ 21” E), RS1-6(22035’34”N & 900 54′ 34” E), RS1-9(22034’12”N & 900 54′ 41” E ), RS1-12(22032’44”N & 900 54′ 51” E ), RS2-0(22039’3”N & 900 52′ 55” E ), RS2-3(22037’15”N & 900 52′ 57” E), RS2-6(22035’05”N & 900 52′ 57” E ), RS2-9(22034’30”N & 900 52′ 54” E).

Figure 01: Study Area

Figure 01: Study Area

Figure 02: Sampling strategy

Figure 02: Sampling strategy

3.4 Water quality analysis

During sampling, in situ water quality parameters were measured at each sampling site. The salinity & temperature were determined by using a Refractometer (NewS-100, TANAKA, Japan), a thermometer in centigrade. A Secchi disc (20 cm diameter) was used to measure the water transparency.

3.5 Sample collection for phytoplankton

For phytoplankton community structure study; 20 L of water collected (Plate 3)from surface water of the study area was passed through a plankton net (20 μm mesh sized; silk bolting cloth or nylon monofilament screen cloth) (Plate 2). Then the concentrated samples (Plate 4) were preserved with 5% buffered formalin (Plate 5) and stored safely (Plate 6) & (Plate 7).

3.6 Water sample analysis

The quantitative estimation of phytoplankton was done by Sedgewick-Rafter counting chamber (S-R cell) method using a luminous microscope (Plate 14). The cell counts were used to compute the cell density using the Stirling (1985) formula where the plankton density is estimated by:

N = (AxC)/ (VxFxL)


N = No. of plankton cells or units per liter of original water.
A = Total No. of plankton counted.
C = Volume of final concentrate of the samples in ml.
V = Volume of a field in cubic mm.
F = No. of fields counted.
L = Volume of original water in liters

3.7 Phytoplankton Identification

Phytoplankton species were identified using standard methodologies in the Laboratory (Plate 13). Phytoplanktons were identified up to genera during this study.

Species richness; diversity and evenness index calculation

Species richness index (d); species diversity index (H); and evenness index were calculated according to following equations:

  • Species richness index (d): Margalef index (d) (Margalef, 1968) was used to measure

species richness by using following formula:

d  = (S–1)/ Log N


d = Species richness index

S = Number of species in a population

N = Total number of individuals in S species.

  • Species diversity index (H): Shannon Weiner diversity index (Shannon, 1949; Shannon

and Weaver, 1963; Ramos et al., 2006) considers both the number of species and the distribution of individuals among species. The Shannon Weiner diversity was calculated by following formula:

Hs = Σ Pi 1ogPi


Hs = Diversity Index

I    = Counts denoting the ith species ranging from 1–n

Pi  = Proportion that the ith species represents in terms of numbers of individuals with respect to the total number of individuals in the sampling space as whole.

  • Evenness index (j): Buzas and Gibson’s evenness (Harper, 1999) was measured by using following formula:

j = Hs / Log S


j    =   Equitability index

Hs = Shannon and weaver index

S   =  Number of species in a population

The dominance index(D): The dominance index (Harper, 1999) was measured to determine whether or not particular fisheries species dominate in a particular aquatic system and can be useful index of resource monopolization by a superior competitor, particularly in communities that have been invaded by exotic species. This index was determined by using following formula:

D = 1 Σ i    (ni /n)2


 ni is number of individuals of species i.

3.8 Food content of Hilsha gut

For gut content analysis of hilsha, Fish samples were collected from the commercial catch in the Alexander Bazar, Ramgoti, Laxmipur (Plate 8). 10 fish samples during the water sampling time of present study were collected in the month of March and July respectively. The total length and weight were measured, stomach along with their contents were removed (Plate 11) & (Plate 12) and preserved in 5 % formalin and subsequently analyzed both qualitatively and quantitatively. Food items were identified up to the generic level. Analysis was carried out for each fish sample as like water sample. From the food content, monthly averages and percentages were calculated. Feeding intensity during two months was determined from the data on the density of phytoplankton of the stomach. The present study emphasized on phytoplankton content of the fish stomach.

3.9 Data analysis

In the first stage of data analysis diversity of fish assemblage was quantified and then statistical comparison was performed. Paleontological Statistics (PAST) version 3.15, a software package for paleontological data analysis written by P.D. Ryan, D.A.T. Harper and J.S. Whalley, was used to run the analysis. PAST has grown into a comprehensive statistical package that is used not only by paleontologists, but in many fields of life science, earth science, and even engineering and economics. One-way analysis of variance (ANOVA) was used for hydrological parameters (temperature, transparency and salinity) to calculate any existence of difference among the stations and months. In the event of significance, a post hoc Tukey HSD test was used to determine where means were significantly different at a 0.05 level of probability (Spjotvoll and Stoline, 1973).The hierarchical clustering (Clarke and Warwick, 1994) was calculated to produce a dendrogram for investigating similarities among months and stations.


4.1 Water quality parameters

4.1.1 Water temperature

Water temperature plays an important role in phytoplankton dynamics in estuarine waters. Observed water temperatures (0C) showed higher values in July and lower values in March during the study period (Fig. 3). There are no significant differences in water temperatures ((0C) among the stations but significant differences were observed among seasons. Maximum temperature was recorded 250C at RS2-0 in March and the lowest temperature was recorded 190C at RS2-6 in July (Fig. 3). Seasonal changes in water temperatures showed the similar decreasing trend (Fig. 3) from the shore line stations (RS1-0) with increased distance (km) (RS2-12).

Figure 03: Water Temperature at different station in the month of March & July 

Figure 03: Water Temperature at different station in the month of March & July

4.1.2 Water transparency

Transparency is one of the hydrological impact factors playing role in species distribution. Measured water transparency (cm) was higher in March and lower in July during the study period. No significant change in water transparency was observed in different stations whereas seasonal changes were found significant (Fig. 4). Highest water transparency was recorded at RS2-12 in the month of March and the lowest was observed at RS2-6 in July (Figure 4).

Figure 04: Water Transparency (cm) at different station in the month of March & July

Figure 04: Water Transparency (cm) at different station in the month of March & July

4.2 Phytoplankton abundance

 During the present study a total 48 genera of phytoplankton belonging different groups were identified. An average phytoplankton density was 14.27×102 cells /ml. The highest density (3.11×103 cells /ml) was recorded at RS2-0 in March and the lowest (0.66×102/ml) was recorded at RS1-12 in July.

4.2.1 Dispersion of Phytoplankton in Meghna Estuary

Phytoplankton density (cells/ml) showed a very distinct dispersion varied from the near shore stations to deeper areas stations (Figure 5) both in two seasons (March and July).

  Figure 05 Phytoplankton dispersion of the waterbody in March & July

Figure 05: Phytoplankton dispersion of the waterbody in March & July

4.2.2 Qualitative and quantitative estimation of phytoplankton:

During the present study, measured surface water phytoplankton was grouped in to following categories.

  1. Chlorophyceae (Green algae):

Some of the most common phytoplankton recorded from the various sampling stations from the Meghna river estuary was Spirogyra sp, Ulothrix sp.and Cladophora sp.

  1. Cyanophyceae (Blue-green algae):

Merismopedia sp, Spirulina sp, and Anabeana sp. under cyanophyceae group were observed in all stations during the present study.

  1. Bacillariophyceae (Diatoms):

 Among four categories, baciollariophyceace were the most dominant group of measured phytoplankton during the present study. Navicula sp, Nitzschia sp,    Cymbella sp, Synedra sp, Fragilaria sp, Gyrosigma sp, Melosira sp, Cyclotella sp Tabellaria sp, Pleurosigma sp, Asterionella sp., Pinnularia sp. Triceratium sp., Coscinodicus sp, Skeletonema sp., Pinnularia sp., Entophyla sp., Licmophora sp., Parafavella sp., Grammatophora sp., Striatella sp ,Diploneis sp ,Guinardia sp ,Cocconeis sp, Thalassiosira sp ,Thalassiothrix sp, Rhabdonema sp. Surirella sp. ,Rhizoselonia sp. were measured under the above group. Diatoms were abundant both in March and July. Measured diatom density was highest in March and started decreasing onwards to July.

  1. Chrysophyceae (Brown algae):

Perinidium sp., Gryodinium sp and Dinophysis sp. were recorded under the group of chrysophyceae during the present study in the Meghna Estuary.

4.2.3 Composition of different group of Phytoplankton:

Species composition of phytoplankton measured from the surface water of the Meghna Estuary showing 56% (Bacillariophyceae), 23% (Chlorophyceae), 20% (Cyanophyceae) 1% (Chrysophyceae) and 1% (unidentified) in March (Fig. 6) and 73% (Bacillariophyceae), <1% (Chlorophyceae), 4% (Cyanophyceae) 4% (Chrysophyceae) and 18% (unidentified) in July (Fig. 7).

Figure 06: Phytoplankton composition in March in Meghna river estuary near Ramgoti

Figure 06: Phytoplankton composition in March in Meghna river estuary near Ramgoti

Figure 07: Phytoplankton composition in July in Meghna river estuary near Ramgoti

Figure 07: Phytoplankton composition in July in Meghna river estuary near Ramgoti

The monthly variation in the population of Chlorophyceae was calculated and is presented in Figure 6. The highest population of green algae was 798 cells/ ml, observed at stations named RS1-0 in the month of March, while the lowest population was recorded at RS1-9 with the values 1 cell/ml. The higher population at RS1-0 was due to slow flow of water and addition of civic waste, there was an increase in the nutrients and the lower value found at RS1-9 in the month of July (Monsoon) due to Rainfall and high turbid water. Similarly changes in Cyanophyceae at various stations during the two months of study are shown in Fig. 7. The highest population of Cyanophyceae with the values of 645 cells /ml was recorded at RS2-0 in March, while lowest population of blue-green algae with the values 3 cells/ ml was recorded in July at RS1-3 and RS1-9 in July. The monthly variation in population of Bacillariophyceae was investigated and is presented in Fig. 8. The highest population of diatoms was observed at RS2-0 in the month of March with the values of 1655 cells/ ml and lowest population of this group of algae with the values of 63 cells/ ml was observed at RS1-12.

Figure 08 Monthly variation in different group of phytoplankton in the experimental area

Figure 08: Monthly variation in different group of phytoplankton in the experimental area

4.3 Diversity Indices:

Different diversity indices were calculated to explain what types of diversity exist in natural food (Phytoplankton) of The Meghna river estuary during this study.

4.3.1 Shannon-Weaver diversity index

The biodiversity index values (H0) obtained from present study is not so very high according to Shannon-Weaver biodiversity index values and they do not exactly show the differences occurring among the stations either. High Shannon diversity index is involved with low individuals and low diversity involved with high number of individuals. In present study after polling whole samples (20), total H value was found 2.95. Highest Shannon diversity index (3.03) was found at RS2-12 in the month of March and lowest (2.66) was found at RS1-3. Higher Shannon diversity index values were found in March (3.17) where low during July (2.87). No significant difference was found in the mean Shannon diversity index among the stations and months (Figure 9).

Figure 9a, b Species diversity index (Shannon, 1949) in March & July

Figure 9a, b:Species diversity index (Shannon, 1949) in March & July

4.3.2 Margalef Species richness index

Margalef richness value for pooled 20 samples was 6.96. The maximum Margalef richness value was observed 7.64 at RS2-12 in the month of March where minimum value was observed 5.35 at RS1-9 in the month of July. Higher Margalef richness value was found 7.33 during March where lower value 5.88 observed in RS1-6. Similar to Shannon diversity index no significant difference was observed in mean Margalef richness value among the stations and months.

Figure 10a, b Margalef Species richness index in March & July

Figure 10a, b:Margalef Species richness index in March & July

4.3.3 Dominance index

Dominance diversity index value for pooled 20 samples was 0.0877. After pooling all the samples of each sampling station, highest dominance index value (0.15) observed in RS1-6 and lowest value (0.07) observed in RS1-6, RS1-9 & RS2-6. Significant difference was found in the mean value of dominance diversity index both for month and stations. If we compare the temporal variation of dominance status among the all sampling zones and months, it did not fluctuate for a greater magnitude.

4.3.4 Evenness index

Evenness index value for pooled 20 samples was 0.91, where the highest (1.81.) and the lowest (0.1) poled Evenness recorded RS1-3 and RS1-9, respectively. Highest evenness value was found 1.81 in March and lowest value observed 0.1 in July. No significant difference was found in mean value of evenness value among the station but significant difference was observed among the months.

Figure 11a, b: Dominance index in March & July

Figure 11a, b: Dominance index in March & July

Figure 12a, b: Evenness index in March & July

Figure 12a, b:  Evenness index in March & July


4.4 Statistical analysis

One way ANOVA was done at p<0.05 significance level to find out the significant difference in Stations and month in terms of Temperature and water transparency (Table 1 and 2). There were no significant difference among the stations but significant different between months. Kruskal-wallis test was done to find out significant difference in species abundance among station and between months. This test also shows no significant difference among stations but between months. Turkey’s pair wise post hoc test was done to find out the difference.

Table 01: Turkey’s Pairwise Post hoc test among different station in March

  MRS1-0 MRS1-3 MRS1-6 MRS1-9 MRS1-12 MRS2-0 MRS2-3 MRS2-6 MRS2-9 MRS2-12
MRS1-0 1 1 0.9999 0.9983 1 1 1 0.9999 0.9982
MRS1-3 0.4548 1 1 1 1 1 1 1 1
MRS1-6 0.7033 0.2485 1 1 0.9999 1 1 1 1
MRS1-9 0.817 0.3622 0.1137 1 0.9998 1 1 1 1
MRS1-12 1.163 0.7082 0.4597 0.346 0.9972 0.9999 1 1 1
MRS2-0 0.07309 0.5279 0.7764 0.8901 1.236 1 1 0.9997 0.9971
MRS2-3 0.3703 0.08446 0.333 0.4467 0.7926 0.4434 1 1 0.9999
MRS2-6 0.6546 0.1998 0.04873 0.1624 0.5084 0.7277 0.2842 1 1
MRS2-9 0.8592 0.4044 0.1559 0.04223 0.3037 0.9323 0.4889 0.2047 1

Table 02: Turkey’s Pairwise Post hoc test among different station in July

JRS1-0 JRS1-3 JRS1-6 JRS1-9 JRS1-12 JRS2-0 JRS2-3 JRS2-6 JRS2-9 JRS2-12
JRS1-0 0.9273 0.985 0.7936 0.8147 0.9989 1 1 1 0.9763
JRS1-3 1.981 1 1 1 0.4726 0.8346 0.9932 0.9883 1
JRS1-6 1.553 0.4284 0.9999 0.9999 0.6866 0.9481 0.9996 0.9991 1
JRS1-9 2.41 0.4284 0.8568 1 0.2791 0.6476 0.9567 0.9383 1
JRS1-12 2.356 0.3748 0.8032 0.05355 0.3005 0.6738 0.9642 0.9481 1
JRS2-0 1.098 3.079 2.651 3.507 3.454 0.9999 0.9736 0.9831 0.6344
JRS2-3 0.3213 2.303 1.874 2.731 2.677 0.7765 0.9998 0.9999 0.9273
JRS2-6 0.589 1.392 0.9639 1.821 1.767 1.687 0.9103 1 0.9991
JRS2-9 0.4819 1.499 1.071 1.928 1.874 1.58 0.8032 0.1071 0.9981
JRS2-12 1.66 0.3213 0.1071 0.7497 0.6961 2.758 1.981 1.071 1.178

 4.5 Cluster analysis:

In terms of spatial and temporal assemblage structure of phytoplankton (Fig. 13), two major groups were indicated by cluster analysis in the Meghna river estuary. Group 1 comprises the sample of the July with all the stations (JRS1-0, JRS2-6, JRS2-3, JRS1-6, JRS2-0, JRS2-9, JRS1-3, JRS2-12, JRS1-12 and JRS1-9). On the other hand Group 2 is formed by March (MRS2-0, MRS1-0, MRS1-6, MRS2-6, MRS1-9, MRS2-9, MRS1-12, MRS2-12, MRS1-3 and MRS2-3) with all sampling stations and showed <10% similarity with Group 1. In general samples from July maintained dissimilarity the month of March. But in the month of March all the stations (MRS1-MRS12) showed almost 80-90% similarities. July to March showed dissimilarity with each month of March and July which is also clear from the phytoplankton composition of this study. Present study found almost same similarity in case of occurrence of phytoplankton assemblage among the stations and huge dissimilarities between months.

4.6 Food habit of Hilsha (Tenualosa ilisha):

As Tenualosa migrates to The Meghna river estuary during breeding season so at those they feeds on phytoplankton belongs to this waterbody. During breeding season hilsha feeds on a selective species of phytoplankton and the diet composition varies season to season. Experimental results on Food habit of Hilsha are given below:

4.6.1 Feeding Intensity

Observed data showing feeding activity is very low in the month of July compared to feeding activity of March (Fig. 14). Hilsha ingested phytoplankton as natural food in July which is 8 times lower than uptakes in March (Fig. 15).

Figure 13: Spatial and temporal cluster of phytoplankton assemblage based on Bray–Curtis similarity matrix

Figure 13: Spatial and temporal cluster of phytoplankton assemblage based on Bray–Curtis similarity matrix

Figure 14: Monthly variation in feeding intensity of T.  ilisha               

Figure 14: Monthly variation in feeding intensity of T.  ilisha

4.6.2 Composition of ingested natural food (phytoplankton)

The mean composition of food obtained from the stomach content of Hilsha shad is displayed in Fig. 15.  Diatoms (56.5± 14.84 %), Cyanophyceae (21 ± 9.9 %), Chlorophytes (5 ± 4.24%) and others algae or debris (17± 9.19 %) form the major constituents of food. Various genera of diatoms were found in the stomach of Hilsa Shad and those constitute the main identifiable food item throughout the study period. It is observed that Skeletonema sp., Cymbella sp., Coscinodiscus sp., Navicula sp., Cyclotella sp., Pleurosigma sp., Synedra sp, Bacillaria sp., Nitzschia sp., Biddulphia sp., Diatoma sp. and Asterionella sp., were the most common genera consumed (Table 3). Among them Coscinodiscus sp., Pleurosigma sp., and cyclotella sp. are found to be predominant during March (Table 3). The main genera of Cyanobaeteria found in the stomach of Hilsa Shad are Merismopedia sp., Spirulina sp. and the main genera of Chlorophytes Ulothrix sp., Chaetomorpha sp., Cladophora sp. and Chlorella sp. among which Ulothrix sp. and are the most common form found to be the perennial food source throughout two month of study especially during March (Table 3). During July in this experiment (breeding season) predominantly 3 to 4 species were found and these are Merismopedia sp., Cyclotella sp., Synedra sp., Buddilphia sp. and Navicula sp. (Table 3).

Figure 15: Monthly variation in group of Phytoplankton ingestion of Hilsha

Figure 15: Monthly variation in group of Phytoplankton ingestion of Hilsha

4.6.3 Natural food abundance and Food preference of Hilsha shad

Observed data showed (Table 3) that about 48 genera was identified from surface water in Pre-moonson (March) & Monsoon (July) and on the other hand hilsha ingested about 28 genera of phytoplankton during March and about only 9 genera during July (Table 3). Also phytoplankton abundance and species diversity in July was significantly lower than that in March according to Shannon diversity index & Margalef species richness. Gut analysis shows Hilsha ingested most Merismopedia sp. Cyclotella sp. Cymbella sp. Synedra sp in July but the abundance of this phytoplankton in July was not satisfactory (Table 3). On the other hand, Hilsha ingested about 28 genera of phytoplankton during March (Table 3).

Table 03: Preferred phytoplankton species by Hilsha shad (Tenualosa illisha) in the month of July & March

Species name July March
Navicula sp. 40 320
Triceratium sp. Nf 60
Coscinodicus sp. Nf 880
Skeletonema sp. Nf 120
Dinophysis sp. Nf 80
Licmophora sp. Nf 80
Grammatophora sp. Nf 20
Nitzschia sp. Nf 180
Synedra sp. 180 360
Guinardia sp. Nf 460
Spirulina sp. Nf 340
Cyclotella sp. 240 240
Buddilphila sp. Nf 80
Merismopedia sp. 220 660
Gyrosigma sp. Nf 140
Peridinium sp. Nf 160
Gyrodinium sp. 20 nf
Cymbella sp. 140 400
Pleurosigma sp. 20 620
Diatoma sp. Nf 360
Melosira sp. Nf 720
Bacillaria sp. 20 80
Asterionella sp. Nf 10
Spirogyra sp. 20 260
Ulothrix sp. Nf 100
Chaetomorpha sp. Nf 10
Cladophora sp. Nf 60
Noctiluca sp. Nf 160
Unidentified Nf

       *nf means not found in stomach content


Various environmental factors influence seasonal phytoplankton fluctuations in aquatic habitats (Çetin and \en, 2004). Mosisch et al. (1999) say water temperature and transparency affect phytoplankton dispersion and seasonal fluctuations. Many aquatic environment abiotic and biotic traits and activities depend on water temperature. Stenseth et al. (2004) suggest temperature fluctuations may impact estuarine primary productivity, fisheries, and top predators. The study found higher water temperatures (0C) in July and lower in March. While seasonal variations were noted, water temperature (0C) was consistent across stations. The pre-monsoon high at RS2-0 was 250C and the low in July was 190C at RS2-6. Martin et al. (2008) found a pre-monsoon temperature variation of 24 ºC to 29 ºC in the Cochin Estuary, south India, supporting the findings. Bhardwaj et al. (2010) discovered a temperature range of 29.7 ºC to 38.7 ºC in the water quality of the Chhoti Gandak river on the Ganga Plain. Jayaraman et al. (2003) found a temperature difference in surface water in the Karamana river near Thiruvananthapuram, Kerala, from 25 ºC (post-monsoon) to 30.6 ºC (pre-monsoon Bright sky, solar radiation, and low water level raised summer temperatures. Seasonal solar radiation may affect water temperature at study sites (Ozaki et al., 2003). White (1994) suggests that rainfall raises water temperature in the dry season and lowers it in the wet season, resulting in the highest transparency values in March and the lowest in July. The study’s transparency—higher in March and lower in July—is partly explained by White (1994). The current study found 48 phytoplankton genera from different groups. In March, RS2-0 had the highest density (3.11×103 cells/ml), while in July, RS1-12 had the lowest density (0.66×102/ml). Chandran (1985) found high pre-monsoon phytoplankton density and low monsoon biomass in Vellar Estuary. On India’s west coast, Nair et al. (1992) studied plankton production and nutrient enrichment. River runoff and coastal upwelling, which increase plankton production, enriched coastal waters during the early summer monsoon. Clear water and nutrients may explain phytoplankton abundance. Constant hydrology increased pre-monsoon phytoplankton production (Nayar & Gowda, 1999; Rajesh et al., 2002). Pre-monsoon radiation or light intensity may increase phytoplankton (Mani, 1992). Perumal et al. (2009) found higher pre-monsoon phytoplankton density in India’s Kaduviyar Estuary and lower monsoon. The findings matched the current study. The main cause was March phytoplankton addition to our study. Phytoplankton distribution depends on temperature and turbidity. Long-term phytoplankton component and water quality parameter studies can develop and test ecological ideas since phytoplankton species composition changes with environmental changes (Naselli-Flores, 2000). Life cycles and physical (temperature, light, and nutrient levels) and biological (grazing pressure and competition) variables affect phytoplankton abundance. Phytoplankton distribution and quantity depend on nutrient availability (Naselli-Flores, 2000). From near-shore to offshore stations, phytoplankton density (cells/ml) varied in March and July. Multiple regression analysis by Valsaraj and Rao (1994) showed that nutrients significantly affect offshore station and near shore waters. They also noted that nitrogen may limit Bay of Bengal coastal primary production. Given the similar results of the current study, nitrogen and other nutrients may help phytoplankton disperse. Phytoplankton species were measured in Meghna Estuary surface water. In March, 73% of species were Bacillariophyceae, <1% Chlorophyceae, 4% Chrysophyceae, and 18% Unidentified. Diatoms and blue-green algae dominated this study. Bacillariophyceae dominate all summer locations, according to current research and Hasler (1947) and Somashekar (1988) observations. Blue-greens are known to tolerate more. Palleyi et al. (2011) found Bacillariophyceae. phytoplankton at all Dhamra Estuary sampling stations year-round, representing 75–94% of the population. Dinophyceae (3–14%), Cyanophyceae (3–8%), and Chlorophyceae (0–4%) followed. Current research strongly supports this. A 2009 study by Naik et al. examined phytoplankton seasonality in India’s east coast Mahanadi Estuary. As in our study, Bacillariophyceae was most prominent, followed by Chlorophyceae and Cyanophyceae. Bacillariophyceae phytoplankton dominate tropical estuaries, according to Ekeh & Sikoki (2004). Perumal et al. (2009) found over 50% Bacillariophyceae in Kaduviyar estuary phytoplankton. Many Bacillariophyceae species are ecologically sensitive and have short generation times, making them useful water quality bio-indicators. At all study sampling stations, Bacillariophyceae diversity and density increased. Their high tolerance may explain this (Redekar & Wagh, 2000). In March and July, Chlorophyceae and Cyanophyceae dominate the Meghna River Estuary phytoplankton. Five March and one July species were Chlorophyceae. The current study found the most Chlorophyceae in March due to water temperature and transparency. Chlorophyceae dominated the Nigerian Cross River Estuary in March and April, according to Ekwu & Sikoki (2006). Ecological significance of cyanobacteria has been studied. They grow anywhere with sunlight and moisture. Cyanophyceae populations increased at every station during pre- and monsoon. High temperatures, alkaline conditions, nutrient-rich freshwater discharge, and suspended sediment turbidity promote Cyanophyceae growth, according to Harsha and Malammanavar (2004) This supported Sarojini (1994), who found that freshwater discharge increased nutrient levels and dissolved organic matter, favoring Chlorophyceae and Cyanophyceae. Bacillariophyceae and others outnumbered Cyanophyceae in both habitats. Despite their high abundance in several studies (Pinckney et al., 1998; Ning et al., 2000), current data show that cyanobacteria contribute little to estuary phytoplankton. From phytoplankton studies, Shannon (1949), Harper (1999), and Margalef (1968) calculated diversity indices. Water chemistry and physical properties determine aquatic ecosystem quality and biodiversity. Algal population and species distribution differences are explained by phytoplankton diversity indexes (Round, 1981; Goldman and Home, 1983 and Kumar, 1990). One value represents sample or group diversity in a biodiversity index (Magurran, 1988). Richness and distribution of individuals make up “species diversity.” Its formalization and measurement are complicated (Williamson, 1973). Evenness and dominance indices show the proportion of common species and sample size, while Shannon-Wiener diversity index measures species richness and proportion.


Estuaries now host commercial, industrial, and recreational activity in addition to supporting aquatic animal life cycles. The largest estuary system, the Meghna River Estuary, is ideal for many fisheries. Human activity, land runoff, and seasons greatly impact this estuary. Water and weather affect Meghna Estuary biota distribution. Fish, phytoplankton, and other biota can spread freely in the large water body. A systematic study was conducted to assess Hilsha’s mating season food supply in the Meghna river estuary near Ramgoti. Water temperature varied between March and July due to seasonal rainfall and temperature. Rainfall and land runoff in the water body lower water temperature and transparency in July (Moonson). Transparency can affect estuary productivity in three ways: first, it can reduce natural light penetration into the water body, reducing migratory fish food; second, it can be linked to phytoplankton exclusively, rather than sand, silt, clay, or run-off areas where food is abundant; and third, it can help fish migrate to and from the estuary. Standard methods were used to study phytoplankton at ten Meghna River estuary stations. This study identified 48 taxa with an average phytoplankton density of 14.27×102 cells/ml. The four phytoplankton groups are Bacillariophyceae, Chlorophyceae, Cyanophyceae, and Chrysophyceae. Phytoplankton were concentrated along the shore and decreasing in deeper areas in the study area. Diversity index values above 3.00 indicate clean water. Values between 1.00 and 3.00 indicate a somewhat healthy state, while values below 1.00 indicate severe deterioration. Based on the diversity index, the ten stations studied in the Meghna river estuary near Ramgoti, where Hilsa migrates during spawning season, have low plankton abundance. Phytoplankton is Hilsa’s main food source, so this could hinder her migration. This study only examined Ramgoti’s estuarine side, so more research is needed to confirm this theory. Cluster analysis showed that seasonal water quality changes affected phytoplankton temporal and geographical assemblages. Hilsha’s eating habits suggest they feed more in March. In July, when mating season begins, they remove a little debris and select phytoplankton species, which were insufficient in the study area’s water. Thus, phytoplankton density during the breeding season may affect nutrition and reproduction. The current study found phytoplankton quantity insufficient for productive water bodies and national fish. Hilsha mostly ate phytoplankton, which was scarce in the study water, as an adult. Hilsha’s production must be increased through certain actions. Several Hilsha are captured annually during breeding season in the current study area, which was once a major nursing ground. Here, open-water hilsha fishing is possible. The biological profile of this waterway and any other nursing or breeding grounds is unknown. More research is needed to understand phytoplankton density seasonal and monthly variations. This applies to the current study station and other important Meghna River Estuary sites and breeding grounds. Phytoplankton numbers are correlated with TDS, pH, total alkalinity, and dissolved oxygen. Therefore, more research is needed to visualize these issues. Phytoplankton and aquatic biota depend on the water body’s nutrient profile. We can create laws and procedures to save our national fish after these studies. Hilsha’s diet over several months and life stages will help us understand this fish’s life history and develop protection measures. These studies will also boost our economy.


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Publication History

Submitted: October 24, 2023
Accepted:   October 29, 2023
Published: December 11, 2023




Jesmin Akther & Md. Jahangir Sarker (2023). The Status & Assessment of Natural Food Abundance For Hilsha Shad (Tenualosa Ilisha) During Breeding Season In The Meghna River Estuary, Bangladesh. Dinkum Journal of Natural & Scientific Innovations, 2(12):882-903.


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