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Annual pattern of zooplankton communities and their environmental response in a subtropical maritime channel system in the northern Bay of Bengal, Bangladesh
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Mamun ABDULLAH Al1, 2, *, Didarul ALAM Md.2, Aysha AKHTAR2, Henglong XU1, Shafiqul ISLAM Md.2, Abu Hena MUSTAFA KAMAL3, Muslem UDDIN M2, Wahidul ALAM Md.2
Acta Oceanologica Sinica | 2018, 37(8) : 65 - 73
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Acta Oceanologica Sinica | 2018, 37(8): 65-73
• Marine Biology •
Annual pattern of zooplankton communities and their environmental response in a subtropical maritime channel system in the northern Bay of Bengal, Bangladesh
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Mamun ABDULLAH Al1, 2, *, Didarul ALAM Md.2, Aysha AKHTAR2, Henglong XU1, Shafiqul ISLAM Md.2, Abu Hena MUSTAFA KAMAL3, Muslem UDDIN M2, Wahidul ALAM Md.2
Affiliations
  • 1 Laboratory of Microbial Ecology, Ocean University of China, Qingdao 266003, China
  • 2 Faculty of Marine Sciences and Fisheries, University of Chittagong, Chittagong 4331, Bangladesh
  • 3 Department of Animal Science and Fishery, Faculty of Agriculture and Food Sciences, Universiti Putra Malaysia Campus Bintulu, Bintulu 97008, Sarawak, Malaysia
Published: 2018-08-25 doi: 10.1007/s13131-018-1184-6
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Zooplankton plays an important role in aquatic food webs by fluxing of energy from primary producer to subsequent trophic levels in the food chain. The annual pattern of zooplankton communities and potential environmental drivers were studied in the Kohelia channel, Bangladesh from summer 2014 to spring 2015. Samples were collected using net at a depth of 1 m. A total of 32 species belonged to 18 orders, 27 families and 15 taxonomic groups were identified. Of these species, 22 distributed in all four seasons of which 8 were dominant and highly contributing to the total communities. Species number peaked in summer next to winter and fall in spring while maximum abundance was in summer and minimum in spring. Multivariate analyses showed that there was a clear annual pattern in the zooplankton communities. Species diversity and evenness peaked in spring but fall in autumn while the high value of species richness was found in winter. Biological-environmental best matching (BIO-ENV) analyses conformed that community pattern of zooplankton was mainly driven by transparency salinity, and temperature individually or combined with water nutrients. These results demonstrate that annual pattern of the zooplankton community shaped by channel environmental factors in subtropical channel ecosystems, thus might be used for community-based subtropical coastal water bioassessment.

zooplankton  /  trophic levels  /  subtropical channel  /  community structure  /  multivariate approach
Mamun ABDULLAH Al, Didarul ALAM Md., Aysha AKHTAR, Henglong XU, Shafiqul ISLAM Md., Abu Hena MUSTAFA KAMAL, Muslem UDDIN M, Wahidul ALAM Md.. Annual pattern of zooplankton communities and their environmental response in a subtropical maritime channel system in the northern Bay of Bengal, Bangladesh[J]. Acta Oceanologica Sinica, 2018 , 37 (8) : 65 -73 . DOI: 10.1007/s13131-018-1184-6
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1 Introduction
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Zooplankton are heterotrophic non motile/weak swimming aquatic animals living in all types of water bodies like fresh, coastal or marine ecosystems (Ferdous and Muktadir, 2009). They are key components in the aquatic ecosystems having effective role in the tropical food web and behave as a linker between the lower and higher levels (Liu et al., 2013; Sahu et al., 2013; Arashkevich et al., 2002). Most significantly they treat as intermediate nexus for fluxes of energy from primary producers like as phytoplankton and microbes to consumer levels of food chain (Iqbal et al., 2014; Madin et al., 2001), and also provide essential indication about the trophic condition of secondary production in the aquatic ecosystems (Abdullah Al et al., 2018). Moreover, the fertility of ecosystems depends on availability of zooplankton while eventually it provides information of fisheries potentiality and sustainability (Abu Hena et al., 2016; Srichandan et al., 2015). Furthermore, it is acquainted that the world richest fisheries directly or indirectly depend on zooplankton availability (Srichandan et al., 2013) because they lead the displacement of the organisms that depended on them (Savari et al., 2013; Prabhahar et al., 2011). Studies have reported that variation in distribution, abundances and composition of zooplankton greatly influenced the existing aquatic habitats on depending communities like fishes, e.g., pelagic fishes feed on zooplankton for their entire life stages or early life stages (Abu Hena et al., 2016; Iqbal et al., 2014). Therefore, the relationship between zooplanktons and physiochemical parameters directly/indirectly related to monsoonal activity of the bay water especially during monsoon period, e.g., May to July when effective rainfall and nutrients input from upland influence the community patterns like distribution, abundances and composition (Srichandan et al., 2014; Fernandes and Ramaiah, 2009). Moreover, it has been widely used as potential bioindicators for ecological succession of coastal waters, also discriminating environmental status due to short living and quick response to certain environmental changes (Ferdous and Muktadir, 2009; Bianchi et al., 2003; Ramaiah and Nair, 1997). Up to date, several studies have been carried out about zooplankton and related environmental parameters in the coastal waters of the Bay of Bengal but very limited in the Bangladesh coastal waters especially northern part. Previous studies reported that the community patterns of zooplankton are governed by hydrological parameters of those ecosystems where transparency, salinity, temperature and nutrients are the significant influencing factors for variation of the community patterns (Abu Hena et al., 2016; Khan et al., 2015; Iqbal et al., 2014).
Bangladesh has the greatest significant of available resources both fauna and flora due to unbroken 710 km coast line and stable continental shelf also place in Genjatic delta region (Gonga-Brhamaputtra and Meghna) (Abu Hena and Khan, 2009), which carried out by several rivers, estuaries and open maritime channels systems, i.e., Moheshkhali, Kutubdia and Kohelia channels (Abu Hena et al., 2005). Their valuable economical contribution in the fisheries sectors and coastal carbon sequestration having massive growth of natural aquatic vegetation (e.g., mangroves, salt marshes and seagrasses) are noticeable. For example, Moheshkhali channel is habitat of around 35 finfish species and 10 shrimp species (Rashed-Un-Nabi et al., 2011) also seven species of cephalopods (Abu Hena et al., 2005). These fisheries species are fond of zooplanktons as their foodstuffs during foraging stages in the channels. In contrast, from the economical and geographical point of view, Kohelia channel has great contribution having massive growth of aquatic vegetation which provides feeding, spawning and nursery ground, also supply abundant food and relatively safe ecological niche for many valuable species like shrimp (e.g., Penaeus monodon) and other commercial fishes. Being as important wetland and aquatic ecosystem, the research on zooplankton along with limnology are still scarce in this area. The research on hydrological and biological properties are important of this channel system prior to take necessary steps on ecological health management and resource conservation.
In the present study, a one-year baseline survey was carried out in the channel from summer 2014 to spring 2015. Our objectives of this study were: (1) to document the species composition and abundances of zooplankton; (2) to reveal the annual variation in zooplankton communities; and (3) to summarize the annual environmental response of zooplankton communities in such a subtropical channel system.
2 Materials and methods
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2.1 Study area, data collection and sample processing
Three sampling stations were selected in the Kohelia channel: Sta. 1 was located at the mouth of the channel in the southeast part of the Dalghat (Monirtek); Sta. 2 was in the middle part of the channel near Zapua; and Sta. 3 was the joint of two channels of Kohelia and Moheshkhali near Materbari (Fig. 1). Samples were collected in summer (June 2014), autumn (September 2014), winter (December 2014) and spring (March 2015), respectively.
Zooplankton samples were collected using a conical shaped plankton net with a mesh size of 325 μm. A digital flow meter was set up at the mouth of the net to record the amount of water filtered through the net during sampling. Samples were collected at three stations from the surface water at 1 m depth for 10–15 min.
After collection, all samples were preserved in 5% formalin solution (Goswami, 2004). For effective sorting, the samples were stained with rose bangle (coloring reagent) and left for 24 h in the laboratory. All zooplankton species attained pink color, which helped to sort out easily and identification was carried out using fine brush, needles, forceps and microscope.
Enumeration was done according to Goswami (2004) zooplankton abundance was expressed as individuals per liter (ind./L).
The sorted zooplanktons were preserved in 70% ethanol solution. Preserved samples were identified morphologically under the microscope according to the previous studies (Conway, 2012; Al-Yamani et al., 2011; Mulyadi, 2004; Conway et al., 2003; Buckland-Nicks et al., 2002; Goddard, 2001).
Environmental parameters such as water temperature (°C), salinity, pH, and water transparency (cm) were estimated in situ using centigrade thermometer, refractometer (TANAKA New S-100, Japan), digital pen pH meter (HANNA instruments, model HI 98107) and secchi disk, respectively. Sub-subsurface (~1 m) water samples were collected for measuring dissolved oxygen (DO), total dissolved solid (TDS), total suspended solid (TSS), nitrite-nitrogen (NO2-N) and soluble reactive phosphate-phosphorus (PO4-P), following the standard method of APHA (1989).
2.2 Data analysis
Species diversity (H ′) (Shannon and Weaver, 1949), species richness (d) (Margalef, 1968) and species evenness (J′) (Pielou, 1966) were used to summarize the biodiversity pattern of zooplankton. These three indices were calculated according to the following formula:
$H' = - \sum\limits_{i = 1}^s {P_i(\ln P_i)}, $
$J' = H'/{\rm ln}S, $
$d = (S-1)/{\rm{ln}}N, $
where H′ is observed diversity index, Pi is proportion of the total count arising from the ith species, S is total number of species, and N is total number of individuals.
Multivariate analysis of annual variations in the zooplankton communities were analyzed using PRIMER v7.0.11 with the routine PREMANOVA (Abdullah Al et al., 2018; Clark and Gorley, 2015; Anderson et al., 2008). Bray-Curtis similarity and Euclidean distance matrices were computed on square root transformed species abundance data and on log-transformed/normalized environmental data, respectively. The species contribution as top rank contributors in each sampling period was conducted by SIMPER analysis. The species distribution among four sampling period was analyzed by the sub-module of CLUSTER on Bray-Curtis similarities from the standardized species abundance data. The annual variations in community patterns were coordinated using the sub-module of dbRDA (distance based redundancy analysis) of PREMANOVA in PRIMER (Anderson et al., 2008), while the annual pattern of environmental variables was coordinated using the routine PCA (principle component analysis) (Abdullah Al et al., 2018; Clark and Gorley, 2015).
Univariate analysis of Pearson correlation matrix was conducted using the IBM SPSS v.22 (Landau and Everitt, 2004) to identify any existences of variance between the environmental parameters and zooplankton communities.
3 Results
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3.1 Environmental variables
The average values of nine environmental variables were: water temperature showed a clear annual variation, ranging from 23.67°C to 30.67°C from summer to spring; salinity ranged from 12.33 to 34.33, with minimum (12.33) in summer and maximum (34.33) in winter; transparency varied from 17.17 cm (summer) to 75.67 cm (spring); the average pH ranged from 7.24 to 7.54; DO fluctuated from 4.76 mg/L (summer) to 6.35 mg/L (autumn); TDS varied from 30.93 μg/L to 32.67 μg/L, maintaining relatively stable in all seasons; TSS varied from 0.58 μg/L to 1.00 μg/L with a peak in the winter; NO2-N varied from 0.34 mg/L (summer) to 0.53 mg/L (peaked in autumn); and PO4-P varied from 0.33 mg/L to 0.55 mg/L (peaked in the spring).
3.2 Taxonomic composition and species distribution
The species composition, average abundances and the ranks of the top 8 contributors in each sampling period were given in Table 1. A total of 32 zooplankton species, belonging to 18 order, 27 families and 15 taxa were identified during the study period. Of these taxa, 22 species occurred in all seasons and were defined as “common” species, while eight species (Ampelisca bocki, Acetes erythraeus, Americamysis bahia, Penaeus indicus, Tempra longicornis, Cyclops bicuspidatus, Mesopodopsis orientalis and Scylla serrata) of the top 10 ranked contributors at each season defined as “dominant” species (Table 1). It was noteworthy that the other 10 species were occurred in specific seasons, and defined as endemic species with seasons. For example, Ammothella longipes was found only in summer; Macrobrachium rosenbergii and Linuche unguiculata were noted in winter and spring, respectively.
A dendrogram for species distribution in all the four seasons showed that 32 species formed seven groups at 60% Bray-Curtis similarity level based on their occurrences and abundances. Groups 1 and 2 consisted of 25 common/dominant species with higher composition and abundances in the total zooplankton communities. Of these 32 species, 26 occurred in summer, 30 in autumn and 28 species in winter and spring, respectively, which indicated a clear succession in the zooplankton communities from summer to spring (Fig. 2).
3.3 Seasonal variation of zooplankton communities
In terms of average values, species number peaked in summer, followed by winter and fall in spring while the maximum abundance occurred in the summer and gradually dropped in the spring (Fig. 3). The zooplankton communities represented clear annual variations in terms of both relative abundances and species composition (Fig. 4). It was noted that Mesopodopsis orientalis, Acetes erythraeus, Americamysis bahia, Ampelisca bocki, Cyclops bicuspidatus, Penaeus indicus, Scylla serrata and Temora longicornis were the primary contributors for the total composition of the communities in all the four seasons, e.g., Americamysis bahia, Penaeus indicus, Scylla serrata and Ampelisca bocki were contributors for summer; Americamysis bahia, Mesopodopsis orientalis and Cyclops bicuspidatus were for autumn; Americamysis bahia, Mesopodopsis orientalis, Cyclops bicuspidatus, Temora longicornis, Ampelisca bocki and Penaeus indicus; and Mesopodopsis orientalis, Acetes erythraeus, Cyclop bicuspidatus, Temora longicornis, Ampelisca bocki and Penaeus indicus for winter and spring, respectively.
Based on 12 data points, the distance-based redundancy analysis (dbRDA) demonstrated a clear annual pattern of the zooplankton communities (Fig. 5). For example, the first axis (dbRDA1, 60.1% of total variation) separated the zooplankton samples in spring and winter (on the left) from those in autumn and summer (on the right), while the second axis (dbRDA2, 23.7% of total variation) discriminated the samples in winter and autumn (upper) from those at the other two samples (lower) (Fig. 5a).
Vector overlay of Pearson correlations of the eight dominant species with the dbRDA axis was shown in Fig. 5b. Although these species were the top 8 ranked contributors in each samples, vector for 4 zooplankton species (Americamysis bahia, Cyclops bicuspidatus, Penaeus monodon and Leucothoe spinicarpa) pointed toward the sample cloud in autumn (upper in right), one species (Mesopodopsis orientalis) toward that in winter (upper left), two species (Acetes erythraeus and Cheiriphotis megacheles) toward those in spring (lower left) and three (Ampelisca bocki, Penaeus indicus and Scylla serrata) toward those in summer (lower right).
The seasonal variation in species richness, evenness and diversity indices during study period was shown in Fig. 6. The species richness peaked in the winter but dropped in the autumn (Fig. 6a), while the species evenness and diversity represented the highest value in spring and lowest in autumn (Figs 6b and c).
3.4 Relationship between zooplankton and environmental parameters
The seasonal changes in environmental variables were summarized by principle component analysis (PCA) in Fig. 5. Vector overlay of these environmental variables with the PCA axis was shown in Figs 5c and d and coefficient of correlation (RELSTE) analysis showed that annual variations in zooplankton communities were significantly correlated with the changes in environmental variables (ρ=0.552, P<0.05).
Biological-environmental best matching analyses (BIOENV) demonstrated that the seasonal shift in community structure of zooplankton was mainly driven by salinity, transparency, temperature and nutrients (ρ=0.773, P<0.05) individually or combined with other environmental parameters (Table 2).
Univariate statistical analysis of correlations between environmental parameters and abundances of eight dominant species of zooplankton was summarized in Table 3. Among the eight species, four (Mesopodopsis orientalis, Ampelisca bocki, Cyclops bicuspidatus and Penaeus indicus) were significantly correlated with TSS, pH and DO (P<0.05). For example, two species Mesopodopsis orientalis and Cyclops bicuspidatus were positively significant correlated with TSS but Penaeus indicus were significantly negative correlated with water pH (Table 3).
4 Discussion
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In the present study, a total of 32 zooplankton species, belonging to 15 taxonomic groups, were identified during the all four seasons from the Kohelia channel, Bay of Bengal. This value is comparable with the reported values by Abu Hena et al. (2016), who reported 11 taxonomic groups including 33 species from Bakkhali sub-tropical estuary, Cox’s Bazar, Bangladesh. The maximum species number and abundance was recorded in post monsoon and minimum was in monsoon period reported by Abu Hena et al. (2016), while Sharif et al. (2017) reported 37 major zooplankton groups with the maximum occurrences in monsoon from the Meghna River Estuary, Bangladesh. In our previous investigation, we found 32 species from 15 taxonomic groups with maximum abundance in winter and minimum in summer, in same place with another channel system Kutubdia in Bangladesh (Abdullah Al et al., 2018), while Iqbal et al. (2014) reported 12 taxa with maximum abundance in winter and minimum in pre monsoon in southeastern coastal waters in Bangladesh. Furthermore, Khan et al. (2015) reported eight groups at Sitakunda coast in Bangladesh in the Bay of Bengal, while Savari et al. (2013) reported 15 groups of zooplankton in the Oman Sea with peak abundance which is observed in northeast monsoon period, who explained that water hydrographic condition could be leaded to variation of species composition and distribution. In contrast, present study was a little bit different from previous reports which might be due to the geographical location and ecological condition of the present study area. For example, most of the previous studies were conducted in estuarine environment, while the present study was in the open maritime channel which could probably had different ecological parameters, e.g., food availability, transparency, salinity variation and nutrients of water, which directly or indirectly influenced the abundance and composition of zooplankton.
Based on the present data, 22 species were present in all the four seasons and others 10 species occurred in specific seasons. This implies that ecological conditions such as water parameters, food availability of the study area were different from one season to another throughout the year. Abdullah Al et al. (2018) and Sharif et al. (2017) stated that majority of the zooplankton groups in the northern Bay of Bengal driven coastal waters are shifted due to environmental condition, e.g., water temperature fluctuation, salinity variation, transparency and dissolved oxygen degradation, which was reflected in our present findings. Of these 32 species, Americamysis bahia was the most dominant species for three seasons while Ampelisca bocki, Cyclops bicuspidatus, Mesopodopsis orientalis, Acetes erythraeus and Penaeus indicus were dominant species in all seasons, although Scylla serrata was dominant only in summer. In terms of abundances of the dominant species, it should be noted that eight species belonging to groups of mysids, copepods, Acetes, shrimp larvae, crab larvae and amphipods were most dominated in this study area probably due to ecological parameters, e.g., salinity, transparency and pH of this channel system. In addition, the sustainability of mangroves and salt marshes habitats by providing shelter zone and adequate food supply for zooplankton were reported elsewhere (Abdullah Al et al., 2018; Abu Hena et al., 2016).
The species richness peaked in winter but dropped in autumn, while the species evenness and diversity showed similar patterns of changing whereas the highest in spring and the lowest in autumn. It is well recognized that estuary has dynamic environmental characteristics, which govern unique habitat for living organisms that is why abundance and composition of zooplankton was higher than other places (Abdullah Al et al., 2018; Abu Hena et al. 2016). Bearing these similarities, densely vegetation of mangroves and salt marsh, and estuarine habits in monsoon period of the Kohelia channel also provided favorable ecological niches for zooplankton communities. In the coastal waters, copepod density has reported to be highly related with salinity level during rainy season when freshwater added in water it turned to be declined (Abdullah Al et al., 2018; Abu Hena et al., 2016; Cook et al., 2007; Rakhesh et al., 2006; Hirst et al., 1999). Present study findings also reflected these arguments, and indicating that copepods showed positive correlation with water temperature, salinity and transparency. Thus, these implies that with increasing salinity, temperature and transparency directly/indirectly influenced the composition and abundance of copepods than other zooplankton communities.
It has been increasingly recognized that multivariate analysis is more effective than univariate analysis for detecting the changing of community structure (Abdullah Al et al., 2018; Clark and Gorley, 2015). In this study, clustering analysis based on Bray-Curtis similarity, dbRDA and PCA of 32 species revealed a significant relationship between community variation in the zooplankton communities and the changes in the environmental parameters. Best matching analyses showed that temperature, salinity and nutrients probably the main drivers to the annual patterns of the zooplankton communities in such a subtropical channel system. Seasonal cycle and situation of zooplankton species depend on water salinity, nutrients and transparency (Abdullah Al et al., 2018; Hwang et al., 2010; Mohanty et al., 2010; Osore et al., 2004). Based on present study, it has revealed that zooplankton communities of Kohelia channel in the northern Bay of Bengal governed by different hydrological factors of the channel environment where monsoonal activities of the Bay support very suitable ecological niche for mysids as well as other dominant zooplankton groups most prominently for copepods, amphipods and shrimps.
In conclusion, this is one of the pioneer research focusing on annual variation in community patterns of zooplankton with response to environmental changes in the subtropical channel of Kohelia in the northern Bay of Bengal, southeastern coastal zone of Bangladesh. Of these 32 species, 22 distributed in all season of which eight species were found dominant with the highest abundances and composition in the zooplankton communities. Due to strategic location of the Kohelia channel, this place is highly suitable for most of the aquatic animals especially molluscs and shrimps. Besides, the existence of aquatic vegetation like mangroves and salt marshes could also provide the suitable habitats for the presence of pelagic mollusc and shrimp in this channel system. The changing of the salinity, transparency, temperature and nutrients drove the annual pattern in zooplankton community structures. Thus, this finding revealed that the zooplankton community represented structural variation shaped by the environmental drivers in tropical channel system, which might be used as bioassessment of monitoring program for assessing coastal ecosystems. We recommended that further more studies are needed to justify this statement.
Acknowledgements
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A special thank goes to Md. Khurshid Alam Bhuiyan for illustrating the study area map, Dev Mallick for helping spss data analysis, and Xu Guangjian for helping PRIMER data analysis. The authors are pleased to Zheng Wang, and Gao Yangyang for reviewing the earliest version of the manuscript and creative comments which helped to improve the manuscript significantly, and financial support provided by Bangladesh Nature (BNF) is highly acknowledged.
Funding
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  • The Bangladesh Nature Foundation (BNF), Cox’s Bazar (a Non-Governmental Organization); Chinese Scholarship Council (CSC No. 2016GXY030) under Ministry of Eudcation for Excellent Master’s Scholarship Awards.
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doi: 10.1007/s13131-018-1184-6
  • Receive Date：2017-04-21
  • Online Date：2026-04-14
  • Published：2018-08-25
Article Data
Affiliations
History
  • Received：2017-04-21
  • Accepted：2017-09-18
Funding
The Bangladesh Nature Foundation (BNF), Cox’s Bazar (a Non-Governmental Organization); Chinese Scholarship Council (CSC No. 2016GXY030) under Ministry of Eudcation for Excellent Master’s Scholarship Awards.
Affiliations
    1 Laboratory of Microbial Ecology, Ocean University of China, Qingdao 266003, China
    2 Faculty of Marine Sciences and Fisheries, University of Chittagong, Chittagong 4331, Bangladesh
    3 Department of Animal Science and Fishery, Faculty of Agriculture and Food Sciences, Universiti Putra Malaysia Campus Bintulu, Bintulu 97008, Sarawak, Malaysia

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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