Seasonal variation of mesozooplankton communities in relation to environmental variables from a subtropical estuary of Bangladesh

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Sharif Hossain, Omite Ashraf Tihum, Md. Bayzid, Md. Azizul Fazal, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8491813/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mesozooplankton are essential trophic intermediaries and bioindicators in estuarine ecosystems. However, their seasonal response to environmental variability remains underexplored in the subtropical estuaries of Bangladesh. This study investigated the seasonal variation of mesozooplankton community composition and its environmental drivers in the Moheshkhali Channel, southeastern Bangladesh, based on data collected between 2023 and 2024. Mesozooplankton were sampled using conical net tows, and environmental variables were measured concurrently. Community structure was analysed using ecological indices and multivariate approaches. A total of 24 taxa across six phyla were recorded, with copepods as the dominant group. Mesozooplankton abundance peaked in winter (1845 ind/m³), while diversity ( H′ = 0.90), evenness ( J′ = 0.33), and richness ( D mg = 2.33) were highest during the monsoon. Non-metric multidimensional scaling (NMDS) revealed distinct seasonal patterns, supported by analysis of similarities (ANOSIM) results indicating significant differences between monsoon and other seasons. Similarity Percentage analysis (SIMPER) identified Mysida , Gastropoda , Chaetognatha , and Copepoda as key contributors to inter-seasonal dissimilarity. The redundancy analysis (RDA) explained 70.7% of the constrained variation (RDA, R² = 0.862, adjusted R² = 0.613, p -value = 0.015), indicating that temperature, turbidity, and conductivity are the primary drivers. Conductivity emerged as the strongest individual predictor in the Biota–Environment (BIO-ENV) matching analysis (BIO-ENV, ρ = 0.4045, p -value = 0.013). These findings highlight the influence of monsoon-driven hydrography on mesozooplankton communities, providing a valuable baseline for future ecological assessments under changing climatic and anthropogenic pressures. Zooplankton Seasonal variation Community structure Multivariate analysis Moheshkhali channel Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Zooplankton plays a pivotal role in aquatic food webs by transferring energy from primary producers to higher trophic levels, including larval fish and large marine vertebrates such as whales (Liu et al. 2013 ; Baliarsingh et al. 2018 ; Abo-Taleb 2019 ; Guo et al. 2025 ). In addition to their trophic function, zooplankton contribute significantly to biogeochemical processes such as nutrient regeneration and the biological carbon pump, making them integral to ecosystem functioning and global element cycles (Turner 2015 ; Steinberg and Landry 2017 ; Nandy et al. 2018 ). Their rapid turnover, short life spans, and high sensitivity to environmental changes recognized them as effective bioindicators of water quality and ecosystem health (Richardson 2008 ; Sahu et al. 2013 ; Abdul et al. 2016 ; Shi et al. 2020 ). Among zooplankton, the mesozooplankton (zooplankton > 200 µm in size) represent the most ecologically important size group (Zeldis and Décima 2020 ; Perhirin et al. 2024 ). They are dominated by metazoans such as copepods, cladocerans, and amphipods, but also include larval stages of benthic and nektonic organisms (Clerc et al. 2021 ). As primary grazers of phytoplankton and key prey for fish larvae, mesozooplankton form a critical trophic link in estuarine and coastal food webs (Zeldis and Décima 2020 ; Perhirin et al. 2024 ; Reyes-Martínez et al. 2024 ). In addition, they influence biogeochemical cycles through the production and transformation of fecal pellets, moults, carcasses, respiration, and excretion (Liszka et al. 2019 ; Clerc et al. 2021 ; Perhirin et al. 2024 ). A combination of physicochemical and biological factors influences mesozooplankton communities. Environmental variables such as temperature, salinity, pH, dissolved oxygen (DO), and turbidity directly influence their diversity, abundance, and distribution patterns (Bianchi et al. 2003 ; Uriarte and Villate 2004 ; Pratiwi et al. 2016 ). Temperature regulates metabolic and reproductive rates (Richardson 2008 ), salinity influences osmo-regulatory capacity and species distribution (Uriarte and Villate 2004 ; Paturej et al. 2017 ), and pH can affect survival and development, particularly for taxa with a calcium carbonate body (Keil et al. 2021 ). Salinity is typically derived by a modern sensor from electrical conductivity under the assumption of standard seawater ionic composition. (Tengesdal et al. 2014 ). However, this assumption often fails in estuaries, where freshwater inflows, tidal exchange, and anthropogenic inputs produce highly variable ionic mixtures. Although salinity is widely employed as an index of dissolved salt content, electrical conductivity provides a more direct and sensitive indicator of total dissolved ions. As such, it more accurately reflects the osmotic and ionic conditions experienced by zooplankton and shows stronger correlations with shifts in community composition (Bos et al. 1996 ; Soto and De los Rios 2006 ; Zhikharev et al. 2023 ). Eutrophication-related nutrients, including phosphate, nitrate, and silicate, strongly influence zooplankton species composition and community structure (Rombouts et al. 2010 ; Messié and Chavez 2017 ; Duré et al. 2021 ; Das et al. 2024 ; Han et al. 2024 ). These abiotic drivers act in concert with biological processes, such as phytoplankton availability and predation pressure, adding further complexity to the dynamics of zooplankton in coastal and estuarine ecosystems. (David et al. 2005 ; Rakhesh et al. 2006 ; Marques et al. 2006 ; Bhattacharjee et al. 2025 ). Tropical and subtropical estuaries, particularly those influenced by monsoon season, are highly dynamic environments that experience pronounced seasonal fluctuations in hydrographic conditions. These fluctuations significantly impact plankton productivity and community composition (Thirunavukkarasu et al. 2013 ). Bangladesh, located in the northern Bay of Bengal, harbors a network of estuarine systems including the Meghna, Karnaphuli, and Moheshkhali channels, characterized by strong seasonal inputs of freshwater, sediment, and nutrients (Abu Hena et al. 2016 ; Sayeed et al. 2017 ; Abdullah Al et al. 2018a , b , 2020 ). These transitional zones mediate land-sea interactions and serve as ecological filters that shape the spatial and temporal structure of plankton assemblages (Fernandes and Ramaiah 2009 ; Nandy and Mandal 2020 ). Several studies have described the taxonomic composition and spatial distribution of zooplankton in Bangladesh’s estuarine systems (Iqbal et al. 2014 ; Shahzad et al. 2015 ; Abu Hena et al. 2016 ; Sayeed et al. 2017 ; Abdullah Al et al. 2018a , b , 2020 ; Alam et al. 2022 ; Sajeeb et al. 2022 ; Chandra Majumdar et al. 2022 ; Ayshi et al. 2024 ). However, few have investigated how seasonal environmental gradients shape community composition using multivariate approaches, particularly in mesozooplankton. We focus on mesozooplankton because their intermediate size range and ecological dominance provide a sensitive lens for detecting environmental filtering and seasonal turnover in estuarine ecosystems. By concentrating on this group, our study captures both dominant taxa (e.g., copepods) and ecologically important larval forms, yielding meaningful insights into community–environment linkages. Accordingly, we investigated mesozooplankton communities in a subtropical estuary of Bangladesh with two main objectives: (1) to describe their present state and seasonal variations, and (2) to assess how environmental variables shape these communities using multivariate analytical techniques. Unlike previous short-term or taxonomically descriptive studies, our research provides a year-round, multivariate assessment of mesozooplankton communities in relation to key environmental drivers. By applying a suite of multivariate methods (RDA, NMDS, ANOSIM, SIMPER, BIO-ENV, HC), we generate new insights into temporal turnover, community–environment linkages, and the strength of environmental filtering in a subtropical estuarine system. These dimensions distinguish our work from existing literature, fill a critical knowledge gap in Bangladesh’s coastal plankton ecology, and provide baseline data to support ecological monitoring and inform management of estuarine ecosystems under growing anthropogenic pressures. Materials and Methods Study Area This study was conducted in the Moheshkhali Channel, located in Kutubjom Union of Moheshkhali Upazila, approximately 15 km northwest of Cox’s Bazar, Bangladesh (21.4795–21.5036°N, 91.9045–91.9736°E; Fig. 1 ). Five sampling stations (Station 1–5; Fig. 1 c) were established along the channel to capture seasonal variability in mesozooplankton communities and their environmental drivers. The Moheshkhali Channel is an estuarine system strongly influenced by monsoon rainfall, tidal mixing, and freshwater discharge. Mangroves and salt marsh vegetation fringe its banks, and the sandy to muddy benthic substrate provides critical habitats for a diverse array of aquatic biota (Abu Hena et al. 2016 ; Ahmed et al. 2022 ). The channel also serves as an important fishing ground and supports extensive aquaculture, contributing significantly to the livelihoods of coastal communities in Cox’s Bazar District (Abdullah Al et al. 2018b ; Banik et al. 2023 ; Ayshi et al. 2024 ). In recent years, however, the system has experienced growing anthropogenic pressures, including industrial expansion, the construction of Cox’s Bazar International Airport, increased shipping activity, and aquaculture effluents, all of which have the potential to alter water quality and estuarine productivity. Given its ecological and socioeconomic importance, coupled with increasing human pressures, the Moheshkhali Channel represents a valuable site for investigating mesozooplankton community dynamics and their responses to environmental gradients. Sample Collection and Laboratory Procedures Zooplankton samples were collected seasonally during the monsoon (September 2023), winter (February 2024), and pre-monsoon (May 2024) using a conical plankton net with a 200 µm mesh size and a 0.24 m mouth diameter at five sampling stations (Station 1–5; Fig. 1 c). Surface horizontal tows were conducted in the upper water column for about 1 minute, and the volume of water filtered was recorded using a mechanical flowmeter attached to the net. Samples were immediately preserved in 5% formalin for laboratory analysis according to Goswami ( 2004 ). In situ measurements of surface water temperature, pH, conductivity, dissolved oxygen (DO), and turbidity were taken at stations 1–5 using a water multiparameter (Lutron WA-2015, Taiwan) and a turbidity meter (Lutron TU-2016, Taiwan). For nutrient analysis, water samples were filtered in the field through a Whatman 0.45 µm filter paper using a vacuum pump and stored in polyethylene bottles. Concentrations of nitrate, nitrite, phosphate, and silicate were determined spectrophotometrically (UV-Vis, Infitek SP-IUV7) using the method described by Hansen & Koroleff ( 1999 ), with results expressed in parts per billion (ppb) units. Conductivity was included instead of salinity because it directly reflects the osmotic and ionic conditions experienced by zooplankton (Bos et al. 1996 ; Soto and De los Rios 2006 ; Zhikharev et al. 2023 ), while nutrient concentrations served as proxies for eutrophication (Rombouts et al. 2010 ; Messié and Chavez 2017 ; Duré et al. 2021 ; Das et al. 2024 ; Han et al. 2024 ). Zooplankton were identified and enumerated using a stereo microscope (Optica, 4× zoom, Italy) equipped with a camera (Optica, 20 MP) and Proview software. Enumeration was carried out using a Bogorov counting chamber (80 × 100 mm, 22 mL). Species were separated using fine brushes and forceps where necessary. Identification followed standard taxonomic references (Davis 1955 ; Kasturirangan 1963 ; Newell and Newell 1970 ; Santhanam and Srinivasan 1994 ; Tait and Dipper 1998 ; Conway et al. 2003 ; Goswami 2004 ; Al-Yamani and Prusova 2011 ; Al-Yamani et al. 2011 ; Slotwinski et al. 2014 ; Abo-Taleb 2019 ; Ayshi et al. 2024 ).. Diversity and Community Indices Zooplankton abundance was standardized to individuals per cubic meter (ind/m³) using the formula: $$\:\begin{array}{c}A=\:N/V\#\left(1\right)\end{array}$$ where N is the total number of individuals and V is the volume of filtered water. To assess zooplankton community structure, we applied a set of ecological indices, each capturing a different aspect of diversity. Overall community diversity, incorporating both richness and relative abundance, was measured using the Shannon–Wiener index H′ (Shannon 1948 ). Dominance patterns were evaluated with Simpson’s dominance index D (Simpson 1949 ), which reflects the probability that two randomly selected individuals belong to the same species. Pielou’s evenness index J′ (Pielou 1966 ) was calculated to quantify the equitability of species distribution by standardizing Shannon diversity relative to the maximum possible value. Species richness was assessed using Margalef’s richness index D mg (Margalef 1968 ). These indices were selected because they provide complementary perspectives on community structure and quantify key community attributes (Qi et al. 2025 ). The Shannon–Wiener and Simpson indices capture both diversity and dominance, Pielou’s evenness isolates the balance of individuals across species, and Margalef’s richness emphasizes the species pool independent of abundance. Using multiple indices reduces the bias of relying on a single metric and allows for a more comprehensive interpretation of mesozooplankton responses to environmental gradients (Magurran 2004 ). Moreover, these indices are widely applied in ecology and ensure comparability with previous studies due to their long history of application (Lamb et al. 2009 ). The formulas used for these calculations are as follows: \(\:\begin{array}{c}\:\:\:\:\:\:\:\:\:\:Diversity\:{H}^{\varvec{{\prime\:}}}=-\sum\:_{i=1}^{s}\left({P}_{i}\right)ln{\:p}_{i}\:\#\left(2\right)\end{array}\) \(\:\begin{array}{c}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:Dominance\:D\:=\sum\:_{i=1}^{s}{n}_{i}\left({n}_{i}-1\right)/N\left(N-1\right)\#\left(3\right)\end{array}\) \(\:\begin{array}{c}Evenness\:{J}^{{\prime\:}}={H}^{{\prime\:}}/ln\left(S\right)\#\left(4\right)\end{array}\) \(\:\begin{array}{c}\:\:\:\:\:\:\:\:\:\:Richness\:{D}_{mg}\:=(s-1)/ln\left(N\right)\#\left(5\right)\end{array}\) Where \(\:{P}_{i}={n}_{i}/N\) is the proportion of the species i , n i​ is the count of species i , N is the total number of individuals, and S is the total number of species in the sample Data Analysis A total of 15 samples were collected during the study period, with five samples per season (Monsoon, Winter, and Pre-monsoon) from Stations 1–5. Before analysis, all environmental variables were standardized using z -score normalization, which centers variables by subtracting the mean and scales them by the standard deviation, thereby minimizing the influence of different measurement scales (Kim et al. 2025 ). Seasonal variations in environmental parameters and diversity indices were tested using the non-parametric Kruskal–Wallis test, as normality was not satisfied according to the Shapiro–Wilk test. To explore community patterns, Non-metric Multidimensional Scaling (NMDS) was performed using Bray–Curtis dissimilarities, a robust approach that provides a biologically meaningful, distance-preserving representation of multivariate relationships (Clarke 1993 ). The significance of seasonal differences in NMDS space was assessed using PERMANOVA. Detrended Correspondence Analysis (DCA) was first conducted to evaluate the suitability of constrained ordination methods (Hill and Gauch 1980 ). The gradient length of the first axis was 0.93 standard deviation (SD) units, well below the 3 SD threshold, indicating linear species–environment relationships. Therefore, Redundancy Analysis (RDA) was selected as the appropriate constrained ordination method (Leps and Smilauer 2003 ). The significance of RDA models was tested using permutation-based Analysis of Variance (ANOVA), and model performance was evaluated by calculating both R² and adjusted R² values. Before NMDS and RDA, zooplankton abundance data were Hellinger-transformed to down-weight the influence of rare species and improve linearity in species–environment relationships (Legendre and Gallagher 2001 ). In addition, hierarchical cluster analysis (HC), Analysis of Similarities (ANOSIM), and Similarity Percentage (SIMPER) were applied to Bray–Curtis dissimilarity matrices from square-root transformed zooplankton abundance data to further examine seasonal community differences. ANOSIM was used to test significant differences in community composition among seasons, while SIMPER identified the species contributing most to between-season dissimilarities (Clarke 1993 ). To investigate the influence of environmental factors, the Biota-Environment (BIO-ENV) was applied to identify the subset of environmental variables best explaining community dissimilarities, using Spearman’s rank correlations (Clarke and Ainsworth 1993 ). The significance of biological–environmental correlations was further assessed using Mantel tests. Statistical significance was evaluated at p < 0.05. All analyses were performed in R 4.4.3 (R Core Team 2024 ) using the vegan package (Oksanen et al. 2025 ), and visualizations were generated with ggplot2 (Wickham 2016 ). Results Mesozooplankton Community Structure A total of 24 mesozooplankton species were recorded across the monsoon, winter, and pre-monsoon seasons at five sampling stations. These taxa were distributed across six phyla ( Arthropoda , Mollusca , Annelida , Chordata , Cnidaria , and Chaetognatha ), nine classes ( Maxillopoda , Thecostraca , Malacostraca , Insecta , Gastropoda , Polychaeta , Actinopterygii , Hydrozoa , and Sagittoidea ), and fourteen orders, reflecting a taxonomically diverse assemblage despite the modest number of species. Arthropoda was the dominant phylum. Within this group, copepods (class Maxillopoda ) were most abundant, particularly members of the order Calanoida ( Acartia erythraea, Acartia sp., Calanus finmarchicus, Calanus pacificus , and Calanus sp. ) and the cyclopoid Thermocyclops crassus . Barnacle cypris larvae ( Balanus sp. , order Sessilia ) also contributed, along with diverse crustacean meroplankton: mysids ( Mysidopsis bahia, Mysidopsis sp. ), amphipods ( Gammarus sp., Grandidierella megnae ), and decapods ( Scylla serrata, Lucifer hanseni , shrimp larvae, and zoea larvae). In addition, the aquatic insect Micronecta sp. (class Insecta , order Hemiptera ) was recorded in low abundance. Other phyla were represented mainly by larval forms. Mollusca contributed gastropod taxa ( Rissoa sitta and veliger larvae), while polychaete larvae represented Annelida . Chordata was present through fish larvae (class Actinopterygii ), and Cnidaria by early jellyfish stages. The contribution of individual mesozooplankton taxa to community composition varied across seasons (Fig. 2 ), with copepods dominating in all three seasons (77.4–95.9%). Gastropods represented an important group (0.4–2.8%), peaking in winter. Mysids (8.7%) and zoea larvae (8.4%) contributed substantially during the monsoon, while chaetognaths showed moderate contributions (0.04–2.8%), with maximum values also observed during the monsoon. All other groups, including megalopa, shrimp larvae, amphipods, fish larvae, polychaetes, ostracods, insects, jellyfish larvae, and Micronecta , each contributed < 1%. Seasonal Variations in Mesozooplankton Community Indices Seasonal changes in key mesozooplankton community indices of abundance, diversity, evenness, richness, and dominance were evident across the monsoon, winter, and pre-monsoon seasons in Fig. 3 , with corresponding statistical summaries provided in Table 1 . Distinct seasonal patterns were observed in zooplankton community indices. Median abundance increased notably from 381 ind/m³ during the monsoon to a peak of 1845 ind/m³ in winter, followed by a moderate decline to 1164 ind/m³ in pre-monsoon. In contrast, species diversity was highest in the monsoon (0.9) and declined markedly to 0.28 in winter and 0.23 in pre-monsoon. A similar trend was observed for evenness, which decreased from 0.33 in monsoon to 0.11 and 0.08 in winter and pre-monsoon, respectively. Species richness also showed a moderate seasonal decline, with the highest value recorded in the monsoon (2.33), followed by 1.99 in winter and 1.93 in the pre-monsoon period. Conversely, dominance increased from 0.52 in monsoon to 0.89 in winter, peaking at 0.91 in pre-monsoon. Despite these apparent seasonal trends, the Kruskal–Wallis rank sum test indicated that seasonal differences in abundance ( p -value = 0.2), diversity ( p -value = 0.093), evenness ( p -value = 0.093), richness ( p -value = 0.2), and dominance ( p -value = 0.1) were not statistically significant. Seasonal variations in hydrological parameters Environmental parameters median values exhibited seasonal variation (Fig. 4 ), with corresponding statistical summaries presented in Table 1 . Conductivity increased sharply from 19.98 mS/cm in the monsoon to 50.03 mS/cm in winter and 50.3 mS/cm in pre-monsoon. Water temperature declined from 30.21°C in the monsoon to 23.00°C in winter, then peaked in pre-monsoon at 32.30°C. pH values were significantly higher in pre-monsoon (8.71) compared to the monsoon (8.0) and winter (7.59). Dissolved oxygen (DO) ranged between 4.11 and 5.42 ppm, with maximum concentration recorded in winter. Turbidity exhibited the strongest seasonal contrast, reaching 92.00 ntu during the monsoon, declining to 4.01 ntu in winter, and rising moderately to 16.92 ntu in pre-monsoon. Nutrient dynamics showed distinct patterns. Silicate concentrations rose from 383.47 ppb in the monsoon to 834.37 ppb in winter, before declining to 540.82 ppb in the pre-monsoon. Nitrate showed a similar trend, increasing from 56.35 ppb in the monsoon to 203.86 ppb in winter, then decreasing to 73.24 ppb in pre-monsoon. Nitrite, by contrast, declined steadily across seasons (46.32 ppb in the monsoon, 26.30 ppb in winter, and 5.65 ppb in pre-monsoon). Phosphate exhibited minor fluctuations, with values of 112.68 ppb (monsoon), 133.33 ppb (winter), and 100.00 ppb (pre-monsoon). Table 1 Summary statistics of mesozooplankton community indices and environmental parameters across monsoon, winter, and pre-monsoon seasons in the Moheshkhali Channel. Kruskal–Wallis rank sum test results indicate the significance of seasonal differences Variables Monsoon 1 Winter 1 Pre-monsoon 1 p -value 2 Abundance 381 ± 455 1845 ± 1,474 1164 ± 828 0.2 Diversity 0.90 ± 0.38 0.28 ± 0.52 0.23 ± 0.16 0.093 Evenness 0.33 ± 0.14 0.11 ± 0.21 0.08 ± 0.06 0.093 Richness 2.33 ± 0.24 1.99 ± 0.39 1.93 ± 0.17 0.2 Dominance 0.52 ± 0.20 0.89 ± 0.27 0.91 ± 0.06 0.10 Conductivity (mS/cm) 19.98 ± 7.07 50.03 ± 1.07 50.30 ± 0.93 0.008* Temperature (°C) 30.21 ± 0.38 23.00 ± 0.91 32.30 ± 1.04 0.002* pH 8.00 ± 0.24 7.59 ± 0.25 8.71 ± 0.32 0.012* DO (ppm) 4.50 ± 0.36 5.42 ± 0.48 4.11 ± 0.10 0.006* Turbidity (ntu) 92.00 ± 57.65 4.01 ± 0.92 16.92 ± 5.53 0.002* Silicate (ppb) 383.47 ± 229.84 834.37 ± 207.38 540.82 ± 266.50 0.14 Nitrite (ppb) 46.32 ± 140.48 26.30 ± 16.26 5.65 ± 8.29 0.054 Nitrate (ppb) 56.35 ± 50.65 203.86 ± 162.96 73.24 ± 79.36 0.13 Phosphate (ppb) 112.68 ± 14.42 133.33 ± 153.48 100.00 ± 145.04 0.8 1 Median ± SD, 2 Kruskal-Wallis rank sum test Environmental parameters exhibited pronounced seasonal variation. Kruskal-Wallis rank sum statistical testing confirmed significant differences for conductivity ( p -value = 0.008), temperature ( p -value = 0.002), pH ( p -value = 0.012), dissolved oxygen ( p -value = 0.006), and turbidity ( p -value = 0.002). In contrast, nutrient concentrations showed seasonal fluctuations in their medians but did not differ significantly across seasons: silicate ( p -value = 0.14), nitrate ( p -value = 0.13), nitrite ( p -value = 0.054), and phosphate ( p -value = 0.80), although nitrite approached significance. The lack of statistical significance despite apparent shifts reflects high within-season variability and overlapping rank distributions across seasons. Such variability is characteristic of estuarine nutrient dynamics, where short-term hydrological inputs and biological uptake often obscure consistent seasonal trends. Seasonal Dynamics in Mesozooplankton Community Structure Multivariate analyses, including Non-metric Multidimensional Scaling (NMDS), Hierarchical Cluster (HC), Analysis of Similarities (ANOSIM), and Similarity Percentage (SIMPER), revealed strong seasonal variation in mesozooplankton community composition across monsoon, winter, and pre-monsoon periods. The NMDS ordination (Fig. 5 ), based on Bray–Curtis dissimilarities of Hellinger-transformed mesozooplankton abundance data, yielded a low stress value of 0.043. Sampling points clustered clearly by season, with a minimal overlap among the 95% confidence ellipses. PERMANOVA analysis confirmed significant variation in zooplankton assemblages among seasons (PERMANOVA, p -value = 0.022). Monsoon samples were mostly distributed along the negative side of NMDS1 and were associated with Mysida , Fish Larvae, Zoea Larvae, Isopoda , Micronecta , Lucifer , Amphipoda , Chaetognatha , and Ostracoda . Winter samples formed a relatively tight cluster along the positive NMDS1 axis, closely associated with Copepoda and Polychaeta . Pre-monsoon samples exhibited the most compact clustering, also aligned along the positive NMDS1 axis, with strong associations to Copepoda and Polychaeta . In contrast, Shrimp Larvae, Gastropoda , and Jellyfish Larvae were positioned away from the seasonal groupings and were not strongly associated with any season. Hierarchical cluster analysis based on Bray–Curtis similarity grouped the mesozooplankton community into three major clusters (Fig. 6 ). Copepoda formed a distinct cluster, highlighting their unique dominance across all seasons. The second cluster comprised a mix of larval and holoplanktonic taxa (e.g., fish larvae, jellyfish larvae, Ostracoda , Lucifer ), suggesting co-occurrence driven by larval recruitment and opportunistic colonization. The third cluster was dominated by crustacean larvae and predatory taxa (e.g., zoea larvae, Mysida , Chaetognatha , Amphipoda ). Analysis of similarities (ANOSIM) was used to test differences in mesozooplankton community composition among seasons (Table 2 ). In this study, significant differences were observed between monsoon and winter ( R = 0.332, p -value = 0.032) and between monsoon and pre-monsoon ( R = 0.420, p -value = 0.043), indicating that mesozooplankton communities during the monsoon are moderately distinct from those in the other seasons. No significant difference was detected between winter and pre-monsoon communities ( R = − 0.036, p -value = 0.627), suggesting these communities are compositionally similar. These results are consistent with patterns observed in NMDS ordination, confirming seasonal shifts in community structure driven by environmental and ecological changes. Table 2 Pairwise Analysis of Similarities (ANOSIM) for differences in mesozooplankton community composition among seasons based on Bray–Curtis dissimilarity. R statistic quantifies the degree of separation between groups in ANOSIM, ranging from − 1 (within-group dissimilarity greater than between-group dissimilarity) to 1 (complete separation of groups), with R ≈ 0 indicating no difference Comparison R statistic p -value 1 Monsoon vs. Winter 0.332 0.032* Monsoon vs. Pre-monsoon 0.420 0.043* Winter vs. Pre-monsoon -0.036 0.627 * Indicate the Statistical Significance, 1 Permutation test SIMPER analysis further identified the mesozooplankton taxa contributing most to seasonal dissimilarities (Table 3 ). Copepoda consistently showed the highest average contribution to differences across seasons, for example, 20.4% between monsoon and winter, but these contributions were not statistically significant. In contrast, Mysida (4.7%, p -value = 0.028) and Gastropoda (4.2%, p -value = 0.043) statistically significantly contributed to the dissimilarity between monsoon and winter. Between monsoon and pre-monsoon, Mysida (5.3%, p -value = 0.009) and Chaetognatha (2.5%, p -value = 0.05) were the statistically significant most important contributors. No taxes were significantly differentiated between winter and pre-monsoon, reinforcing the compositional similarity between these two seasons. Table 3 Similarity Percentage (SIMPER) analysis showing the top five mesozooplankton taxa contributing to dissimilarity between seasonal pairs Comparison Species Average Contribution (%) p -value 1 Monsoon vs. Winter Copepoda 20.4 0.096 Mysida 4.7 0.028* Gastropoda 4.2 0.043* Zoea Larvae 3.9 0.067 Chaetognatha 2.1 0.303 Monsoon vs. Pre-monsoon Copepoda 15.9 0.688 Mysida 5.3 0.009* Zoea Larvae 3.7 0.165 Gastropoda 3.5 0.313 Chaetognatha 2.5 0.050* Winter vs. Pre-monsoon Copepoda 16.9 0.542 Gastropoda 2.9 0.717 Mysida 1.5 0.994 Chaetognatha 1.2 0.864 Megalopa 1.2 0.455 * Indicate the Statistical Significance, 1 Monte Carlo permutation test Environmental Drivers of Mesozooplankton Composition Redundancy Analysis (RDA) revealed strong seasonal structuring of mesozooplankton community composition in response to key environmental gradients (Fig. 7 ). The first two canonical axes explained 70.7% of the total constrained variation, with RDA1 and RDA2 accounting for 45.7% and 25.0%, respectively. The overall model was statistically significant (RDA, R² = 0.862, adjusted R² = 0.613, p-value = 0.015), indicating that a substantial portion of community variation can be attributed to the measured environmental variables. The RDA biplot identified temperature, turbidity, and conductivity as the primary environmental variables associated with variation in community structure. Temperature and turbidity vectors were positively aligned with both RDA1 and RDA2, corresponding to mesozooplankton assemblages observed during the monsoon season. In contrast, conductivity was negatively correlated with both axes and associated with samples from the winter and pre-monsoon seasons. Dissolved oxygen (DO), nitrite, and nitrate showed moderate correlations with the ordination axes, whereas silicate, phosphate, and pH made comparatively weaker contributions to the explained variation. Seasonal clustering of samples was visible in the RDA ordination space. Monsoon samples were distributed along the positive side of RDA1 and were associated with higher temperature, turbidity, and nitrite concentrations. Winter and pre-monsoon samples clustered on the negative side of RDA1, showing alignment with higher conductivity, DO, pH, silicate, nitrate, and phosphate. Species distributions corresponded closely to the identified environmental gradients. Mysida ( k ), Zoea larvae ( p ), and Shrimp larvae ( o ) were positioned in the direction of high turbidity and temperature, aligning with monsoon conditions. Copepoda ( b ) were positioned near winter samples and strongly associated with conductivity. In contrast, Gastropoda (d) was located distantly along the negative RDA2 axis, suggesting a specific response to environmental variables negatively associated with that axis, likely nitrate and silicate concentrations. The remaining taxa, including Amphipoda (a), Fish larvae (c ), Insects (e) , Isopoda (f) , Jellyfish larvae (g) , Lucifer (h) , Megalopa (i) , Micronecta (j) , Ostracoda (l) , Polychaeta (m) , and Chaetognatha (n) , clustered near the origin or on the positive side of RDA1. In both the RDA triplot (Fig. 7 ) and the NMDS ordination (Fig. 5 ), winter and pre-monsoon samples exhibited a substantial degree of overlap, indicating similar mesozooplankton community composition during these seasons. This overlap was further supported by a statistical test of ANOSIM (Table 2 ), which revealed no significant difference between winter and pre-monsoon assemblages (ANOSIM, R = − 0.036, p -value = 0.627). The convergence of evidence from both constrained (RDA) and unconstrained (NMDS) ordinations highlights the lack of strong seasonal differentiation between these two periods. In contrast, monsoon samples were clearly separated from winter and pre-monsoon, suggesting that monsoonal environmental conditions exerted the strongest structuring effect on community composition. To identify the environmental factors strongly influencing mesozooplankton community structure and complement the ordination results, a Biota-Environment (BIO-ENV) analysis combined with Mantel tests using Spearman’s rank correlation ( ρ ) was conducted (Table 4 ). The highest correlation was obtained for conductivity alone ( ρ = 0.4045, p -value = 0.013), indicating that this single variable is the most influential predictor of community variation. Among multi-variable sets, the combination of conductivity, temperature, and turbidity produced the second-highest correlation ( ρ = 0.3613, p -value = 0.005), followed by the addition of nitrite ( ρ = 0.3433, p -value = 0.039). Other significant combinations included conductivity with nitrite ( ρ = 0.3385, p -value = 0.040) and a five-variable set comprising conductivity, temperature, turbidity, nitrite, and phosphate ( ρ = 0.2935, p -value = 0.046). Combinations exceeding five variables showed reduced correlation and were not statistically significant, with the full nine-variable model yielding a near-zero correlation ( ρ = − 0.0060, p -value = 0.473). These results indicate that conductivity is the primary driver of seasonal mesozooplankton community patterns, and that a small set of environmental variables, rather than all measured parameters, effectively explains community variation. Table 4 Biota-Environment (BIO-ENV) Matching analysis showing the best-fitting environmental variable combinations for mesozooplankton community structure with corresponding Spearman correlation (ρ) and p-values Rank Environmental Variable Combination Spearman Correlation ( ρ ) P -Value 1 1 Conductivity 0.4045 0.013* 2 Conductivity, Temperature, Turbidity 0.3613 0.005* 3 Conductivity, Temperature, Turbidity, Nitrite 0.3433 0.039* 4 Conductivity, Nitrite 0.3385 0.040* 5 Conductivity, Temperature, Turbidity, Nitrite, Phosphate 0.2935 0.046* 6 Conductivity, Temperature, Turbidity, Silicate, Nitrite, Phosphate 0.2171 0.095 7 Conductivity, Temperature, pH, Turbidity, Silicate, Nitrite, Phosphate 0.1421 0.175 8 Conductivity, Temperature, pH, DO, Turbidity, Silicate, Nitrite, Phosphate 0.0815 0.262 9 Conductivity, Temperature, pH, DO, Turbidity, Silicate, Nitrite, Nitrate, Phosphate -0.0060 0.473 * Indicate the Statistical Significance, 1 Mantel tests Discussion This study examined seasonal dynamics of mesozooplankton communities in the Moheshkhali Channel and their relationships with key environmental drivers, using a combination of diverse indices, unconstrained (NMDS) and constrained (RDA) ordinations, ANOSIM, and BIO-ENV analyses. By integrating these approaches, we were able to assess not only whether communities differed across seasons but also which environmental gradients most strongly structured these patterns. Seasonal dynamics of mesozooplankton serve as key indicators of ecosystem structure, trophic interactions, and productivity in estuarine and coastal environments (Liszka et al. 2019 ; Zeldis and Décima 2020 ; Clerc et al. 2021 ; Perhirin et al. 2024 ; Reyes-Martínez et al. 2024 ). Environmental parameters exhibited clear seasonal fluctuations consistent with the dynamics of monsoon-regulated estuarine systems. Conductivity increased significantly from monsoon to winter and pre-monsoon, reflecting reduced freshwater input and enhanced evaporative concentration during the dry period (Sarkar and Choudhury 1986 ; Pawlowicz et al. 2011 ; Sahu et al. 2013 ; Tyler et al. 2017 ). We prioritized electrical conductivity over salinity because the ionic composition of estuarine waters often deviates from the standard seawater assumptions underlying salinity algorithms. Conductivity more directly reflects total dissolved ions and thus the osmo-ionic conditions most relevant to zooplankton physiology (Bos et al. 1996 ; Soto and De los Rios 2006 ; Zhikharev et al. 2023 ). This methodological choice is supported by our findings, as conductivity emerged as the strongest single predictor of community dissimilarity in BIO-ENV analysis and was also the primary variable structuring community patterns in the RDA ordination. These findings align with observations from other South Asian estuaries, where conductivity (or salinity), temperature, and turbidity frequently act as dominant structuring factors (Fernandes and Ramaiah 2009 ; Benfield 2012 ; Abdullah Al et al. 2018b ; Bhattacharjee et al. 2025 ). The identification of conductivity as a key driver is also consistent with previous work emphasizing its role as both a salinity proxy and a determinant of estuarine plankton community structure (David et al. 2005 ; Modéran et al. 2010 ; Baliarsingh et al. 2018 ; Yuan et al. 2020 ; Venkataramana et al. 2023 ; Bhattacharjee et al. 2025 ). Nonetheless, contrasting findings, such as those reported by Abdullah Al et al. ( 2020 ) in the Kutubdia Channel, where nitrite-nitrogen was more predictive, highlight the site-specific nature of environmental controls on zooplankton communities. Temperature followed a typical subtropical seasonal cycle, declining from the monsoon to a winter minimum before rising to a pre-monsoon maximum, a pattern that influences zooplankton metabolic processes and reproductive cycles (Ranith et al. 2013 ; Abu Hena et al. 2016 ; Abdullah Al et al. 2018a ). pH levels were significantly higher in the pre-monsoon, likely due to increased photosynthetic activity under higher irradiance (Semesi et al. 2009 ; Awaluddin et al. 2025 ). Dissolved oxygen concentrations peaked in winter, consistent with greater oxygen solubility at lower temperatures and reduced biological consumption (Liu et al. 2020 ). Turbidity showed the most pronounced seasonal variation, reaching its maximum during the monsoon due to sediment-rich runoff and suspended particulate matter from catchment erosion (Khan 2020 ). Nutrient concentrations exhibited observable but statistically nonsignificant seasonal patterns. Apparently, winter maxima in silicate, nitrate, and phosphate may reflect increased terrestrial runoff with limited biological uptake, whereas the progressive decline in nitrite from monsoon to pre-monsoon likely indicates nitrification or phytoplankton uptake. Although not statistically significant, these trends may still reflect ecologically meaningful dynamics, potentially obscured by consistent anthropogenic inputs such as agriculture, aquaculture, and watershed discharge. The identification of 24 mesozooplankton taxa across six phyla indicates moderate diversity, broadly consistent with earlier reports from the region (Table 5 ) (Abu Hena et al. 2016 ; Abdullah Al et al. 2018a , 2020 ; Ayshi et al. 2024 ). Copepods dominated all seasons and formed a distinct cluster in the HC dendrogram, a pattern well documented in tropical estuaries (Rakhesh et al. 2006 ; Fernandes and Ramaiah 2009 ; Baliarsingh et al. 2018 ; Nandy and Mandal 2020 ). Their broad ecological tolerance and adaptive reproductive strategies underpin this numerical dominance (Kwok et al. 2015 ; Chew and Chong 2016 ; Kimmel and Baird 2024 ; Gao et al. 2025 ). Both RDA and BIO-ENV analyses further indicate that copepod prevalence in winter and pre-monsoon is strongly associated with elevated conductivity and dissolved oxygen, highlighting their capacity to thrive under stable osmo-ionic and oxygen-rich conditions. In contrast, mysids, zoea larvae, and megalopa clustered with monsoon samples in the ordination space and were identified as major contributors to seasonal dissimilarities in SIMPER analyses. Their association with higher temperature and turbidity reflects life-history strategies linked to larval transport, resuspension, and estuarine nursery functions (Guerreiro et al. 2021 ). Gastropods and chaetognaths also emerged as significant contributors in SIMPER analyses to seasonal contrasts, suggesting sensitivity to episodic hydrographic variability and shifts in prey availability. Together, these results highlight a dual community structure in which copepods provide stability across seasons, whereas other groups respond more strongly to short-term monsoon-driven variability. Table 5 Comparative Summary of Zooplankton Studies in Subtropical Estuarine and Coastal Systems of Bangladesh near Moheshkhali Channel Study Location No. of Taxa / Groups Major Phyla / Groups Present study (2025) Moheshkhali estuarine system 24 taxa (6 phyla, 9 classes, 14 orders) Copepods ( Calanoida , Cyclopoida ) are dominant; Gastropods, Mysids, zoea larvae, and Chaetognaths Ayshi et al. ( 2024 ) Maheshkahli Channel 25 taxa Copepods are dominant; Mysid shrimps, Shrimp larvae, crab zoea, and ichthyoplankton Abdullah Al et al. ( 2020 ) Kutubdia channel 38 taxa (22 holo-plankton and 16 mero-plankton) Copepods, amphipods, shrimps, and mollusks Abdullah Al et al. ( 2018a ) Kohelia channel and Kutubdia channel 32 taxa (13 orders, 25 families) Acartia erythraea, Acetes erythracus, Oithona simplex, Penaeus indicus Hena K et al. (2016) Bakkhali estuary 33 taxa Copepods are dominant; Mysidaceae and Chaetognatha Seasonal variations in mesozooplankton abundance and community indices further revealed contrasting patterns, with peak abundance in winter, but higher species diversity, evenness, and richness during the monsoon. Greater abundance in winter may reflect enhanced environmental stability, favoring opportunistic taxa such as copepods, whereas increased diversity in the monsoon likely reflects freshwater inflow, high turbidity, and nutrient-driven heterogeneity (Abu Hena et al. 2016 ). Similar patterns have been reported in other estuarine systems in Bangladesh (Iqbal et al. 2014 ; Abdullah Al et al. 2018a ), though contrasting results, such as peak abundance in monsoon or post-monsoon, have also been observed (Abu Hena et al. 2016 ; Abdullah Al et al. 2020 ). These inconsistencies emphasize the importance of local hydrographic and environmental conditions in shaping zooplankton communities (Paturej et al. 2017 ; Wei et al. 2023 ; Guo et al. 2025 ). Although univariate indices such as abundance, Shannon–Wiener diversity ( H′ ), evenness ( J′ ), richness ( D mg ), and dominance ( D ) did not show statistically significant seasonal differences, multivariate analyses (NMDS, ANOSIM) revealed clear shifts in community composition. This suggests that while overall diversity and total abundance remained relatively stable, the identities and relative contributions of dominant taxa varied across seasons. Such patterns are typical of estuarine systems, where environmental variable fluctuations drive species turnover without necessarily altering aggregate diversity metrics. These findings strengthen the importance of multivariate approaches in detecting subtle but ecologically meaningful seasonal dynamics that univariate indices alone may overlook (Clarke et al. 2014 ). Seasonal environmental gradients play a central role in shaping mesozooplankton communities in the Moheshkhali Channel, reflecting the dual function of estuaries as retention zones for resident taxa and transport corridors for larval forms (Guerreiro et al. 2021 ). The observed patterns are consistent with findings from other estuarine systems in Bangladesh and India (Abu Hena et al. 2016 ; Abdullah Al et al. 2018a ; Nandy and Mandal 2020 ), emphasizing the organizing influence of wet–dry hydrographic cycles. Conductivity, temperature, and turbidity emerge as key environmental factors, highlighting the broader role of environmental gradients in regulating estuarine mesozooplankton across South and Southeast Asia (Fernandes and Ramaiah 2009 ; Baliarsingh et al. 2018 ; Bhattacharjee et al. 2025 ). Seasonal trends in abundance, diversity, and richness indicate that stable dry-season conditions favor resident taxa, while monsoon-driven variability promotes higher diversity and larval recruitment. Taxon-specific responses along environmental gradients highlight the nuanced ecological roles of different groups, illustrating how community composition responds to both predictable seasonal changes and episodic environmental variability. These observations strengthen the value of integrative, multivariate approaches for understanding complex estuarine community dynamics and their ecological consequences. Conclusions This study provides novel insights into the seasonal dynamics of mesozooplankton communities in the Moheshkhali Channel, a subtropical estuary in southeastern Bangladesh. By integrating community indices, ordination methods, and BIO-ENV analysis, we demonstrated that mesozooplankton composition is strongly structured by monsoon-driven hydrographic variability. Monsoon assemblages were clearly distinct, associated with high turbidity and temperature, while winter and pre-monsoon communities overlapped substantially, reflecting convergence under dry-season hydrography. Conductivity emerged as the most influential predictor of community variation, either alone or in combination with temperature and turbidity. These findings highlight the key role of seasonal environmental forcing in shaping estuarine mesozooplankton and the importance of conductivity as a primary driver in this subtropical estuarine system. Limitations and Outlook Despite these advances, several limitations should be acknowledged. First, the study was based on a single annual cycle with 15 samples; longer-term and higher-frequency sampling would be necessary to capture interannual variability and short-term events such as tidal or storm-driven pulses. Second, only mesozooplankton were assessed; inclusion of microzooplankton and phytoplankton would provide a more complete view of trophic interactions. Third, molecular approaches such as DNA metabarcoding were not employed, which may reveal cryptic diversity overlooked by morphological identification. Future research should aim to integrate long-term monitoring with molecular tools and food web analyses, linking zooplankton dynamics to fisheries productivity and ecosystem health under ongoing climate variability. Comparative studies across other estuarine systems of the Bay of Bengal would also help determine whether conductivity-driven structuring is a consistent regional pattern or unique to the Moheshkhali Channel. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors declare that they have no competing financial or non-financial interests. Author Contributions All authors contributed to the study conception and design. Conceptualization and methodology were led by Md. Sharif Hossain and Md. Azizul Fazal. Material preparation, investigation, data collection, data curation, formal analysis, and visualization were performed by Md. Sharif Hossain, Omite Ashraf Tihum, and Md. Bayzid. The first draft of the manuscript was written by Md. Sharif Hossain, Omite Ashraf Tihum, and Md. Bayzid. Supervision and validation were provided by Md. Azizul Fazal, Abu Bokkar Siddique, Faisal Sobhan, Dr. Subrata Sarker, and Dr. Md. Alamgir Kabir. All authors contributed to manuscript review and editing, and all authors read and approved the final manuscript. Data Availability The datasets generated and/or analyzed during the current study are not publicly available, as they are not deposited in a public repository, but are available from the corresponding author upon reasonable request. Ethical approval Ethical approval was not required for this study as no animals or human participants were involved. Consent for publication Not applicable. This study does not include any individual person’s data. Consent to participate Not applicable. This study did not involve human participants. Acknowledgments The authors are grateful to Md. Jahidul Islam and Md. Shamsul Hoque for their valuable assistance with species identification, counting, and data analysis. 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Deep Res Part I Oceanogr Res Pap 155:. https://doi.org/10.1016/j.dsr.2019.103146 Zhikharev V, Gavrilko D, Kudrin I, et al (2023) Structural Organization of Zooplankton Communities in Different Types of River Mouth Areas. Diversity 15:. https://doi.org/10.3390/d15020199 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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08:10:21","extension":"png","order_by":48,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":183949,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/2cb50679b4cb49217b8494df.png"},{"id":100240448,"identity":"61a67b67-eb2c-434d-be68-fa9e8165ac4d","added_by":"auto","created_at":"2026-01-14 13:19:40","extension":"xml","order_by":49,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":262004,"visible":true,"origin":"","legend":"","description":"","filename":"80064d316ae6426590dd61e13bb961881structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/1f7eee15347ca65a4d2ab10d.xml"},{"id":100371603,"identity":"6f468735-6cb5-45c3-b98a-bd5f89dcbf70","added_by":"auto","created_at":"2026-01-16 08:10:35","extension":"html","order_by":50,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":283041,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/245d2f4925cb70d776afad6d.html"},{"id":100371880,"identity":"8d2d202c-8bb0-40c5-9ce8-bf7131c3a8b6","added_by":"auto","created_at":"2026-01-16 08:11:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73788,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the study area\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/b6fc2a4572b1c9903cb25bf1.png"},{"id":100370816,"identity":"efabd6ad-7353-4db6-b090-8440c243a52e","added_by":"auto","created_at":"2026-01-16 08:08:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":349190,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal contribution of mesozooplankton taxa to community composition in the Moheshkhali Channel. Copepods dominated all seasons (\u0026gt;75% of total abundance), while gastropod larvae, mysids, zoea larvae, and chaetognaths contributed to seasonally variable but notable proportions. Other taxa remained minor contributors (\u0026lt;1%) with sporadic occurrence\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/f24d70ad68232931c6570185.png"},{"id":100371298,"identity":"08f5ee65-2ec1-4211-8fac-7acd593326c9","added_by":"auto","created_at":"2026-01-16 08:09:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131294,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation in mesozooplankton community indices (abundance, diversity, evenness, richness, and dominance) across the monsoon, winter, and pre-monsoon seasons in the Moheshkhali Channel. Boxplots show medians, interquartile ranges, and outliers\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/3c2774081a8328c0e3b05f3f.png"},{"id":100240418,"identity":"1fac0e84-c1d2-4b2b-9f12-0b3e11150dda","added_by":"auto","created_at":"2026-01-14 13:19:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79885,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation in key environmental variables (conductivity, temperature, pH, dissolved oxygen (DO), turbidity, silicate, nitrate, nitrite, and phosphate) across the monsoon, winter, and pre-monsoon seasons in the Moheshkhali Channel. Boxplots show medians, interquartile ranges, and outliers\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/ccad984bb73d3319bdf512b9.png"},{"id":100240423,"identity":"1f506799-5c3e-4e3a-9b40-3b16ae402444","added_by":"auto","created_at":"2026-01-14 13:19:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56470,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric Multidimensional Scaling (NMDS) ordination of mesozooplankton communities based on Bray–Curtis dissimilarity of Hellinger-transformed abundance data. Points are colored and shaped by season; ellipses represent 95% confidence intervals for each season. Species vectors indicate the direction and strength of taxon associations\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/36a9a84b42e188edd2239b57.png"},{"id":100240425,"identity":"6348b456-4bc3-4b95-8b9f-a7cb6b0b1d5b","added_by":"auto","created_at":"2026-01-14 13:19:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115176,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical cluster (HC) dendrogram of mesozooplankton communities across seasons based on Bray–Curtis similarity and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) clustering, highlighting seasonal grouping and internal community structure\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/fd9c407b88cfb84c32de0045.png"},{"id":100240427,"identity":"f38fcb20-d703-4db4-b7c9-fadc63001ca3","added_by":"auto","created_at":"2026-01-14 13:19:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":64737,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) triplot illustrating the relationships between mesozooplankton community composition and environmental parameters across three seasons (monsoon, winter, and pre-monsoon). Sampling sites are distinguished by color and shape according to season. Environmental variables are shown with arrows, and species scores are indicated with lowercase letters for clarity: (a) Amphipoda, (b) Copepoda, (c) Fish larvae, (d) Gastropoda, (e) Insect, (f) Isopoda, (g) Jellyfish larvae, (h) Lucifer, (i) Megalopa, (j) Micronecta, (k) Mysida, (l) Ostracoda, (m) Polychaeta, (n) Chaetognatha, (o) Shrimp larvae, and (p) Zoea larvae\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/584dceefc5807a58f941be38.png"},{"id":102230806,"identity":"29a2bc29-3d7f-47b3-b568-ed03c22f6568","added_by":"auto","created_at":"2026-02-09 15:27:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2032567,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8491813/v1/a7a11e66-d708-4633-bfc8-daa339882d80.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal variation of mesozooplankton communities in relation to environmental variables from a subtropical estuary of Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eZooplankton plays a pivotal role in aquatic food webs by transferring energy from primary producers to higher trophic levels, including larval fish and large marine vertebrates such as whales (Liu et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Baliarsingh et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Abo-Taleb \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition to their trophic function, zooplankton contribute significantly to biogeochemical processes such as nutrient regeneration and the biological carbon pump, making them integral to ecosystem functioning and global element cycles (Turner \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Steinberg and Landry \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nandy et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Their rapid turnover, short life spans, and high sensitivity to environmental changes recognized them as effective bioindicators of water quality and ecosystem health (Richardson \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sahu et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Abdul et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong zooplankton, the mesozooplankton (zooplankton\u0026thinsp;\u0026gt;\u0026thinsp;200 \u0026micro;m in size) represent the most ecologically important size group (Zeldis and D\u0026eacute;cima \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Perhirin et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They are dominated by metazoans such as copepods, cladocerans, and amphipods, but also include larval stages of benthic and nektonic organisms (Clerc et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As primary grazers of phytoplankton and key prey for fish larvae, mesozooplankton form a critical trophic link in estuarine and coastal food webs (Zeldis and D\u0026eacute;cima \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Perhirin et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Reyes-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, they influence biogeochemical cycles through the production and transformation of fecal pellets, moults, carcasses, respiration, and excretion (Liszka et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Clerc et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Perhirin et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA combination of physicochemical and biological factors influences mesozooplankton communities. Environmental variables such as temperature, salinity, pH, dissolved oxygen (DO), and turbidity directly influence their diversity, abundance, and distribution patterns (Bianchi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Uriarte and Villate \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Pratiwi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Temperature regulates metabolic and reproductive rates (Richardson \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), salinity influences osmo-regulatory capacity and species distribution (Uriarte and Villate \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Paturej et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and pH can affect survival and development, particularly for taxa with a calcium carbonate body (Keil et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSalinity is typically derived by a modern sensor from electrical conductivity under the assumption of standard seawater ionic composition. (Tengesdal et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, this assumption often fails in estuaries, where freshwater inflows, tidal exchange, and anthropogenic inputs produce highly variable ionic mixtures. Although salinity is widely employed as an index of dissolved salt content, electrical conductivity provides a more direct and sensitive indicator of total dissolved ions. As such, it more accurately reflects the osmotic and ionic conditions experienced by zooplankton and shows stronger correlations with shifts in community composition (Bos et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Soto and De los Rios \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhikharev et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Eutrophication-related nutrients, including phosphate, nitrate, and silicate, strongly influence zooplankton species composition and community structure (Rombouts et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Messi\u0026eacute; and Chavez \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dur\u0026eacute; et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These abiotic drivers act in concert with biological processes, such as phytoplankton availability and predation pressure, adding further complexity to the dynamics of zooplankton in coastal and estuarine ecosystems. (David et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rakhesh et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Marques et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bhattacharjee et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTropical and subtropical estuaries, particularly those influenced by monsoon season, are highly dynamic environments that experience pronounced seasonal fluctuations in hydrographic conditions. These fluctuations significantly impact plankton productivity and community composition (Thirunavukkarasu et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Bangladesh, located in the northern Bay of Bengal, harbors a network of estuarine systems including the Meghna, Karnaphuli, and Moheshkhali channels, characterized by strong seasonal inputs of freshwater, sediment, and nutrients (Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sayeed et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These transitional zones mediate land-sea interactions and serve as ecological filters that shape the spatial and temporal structure of plankton assemblages (Fernandes and Ramaiah \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Nandy and Mandal \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have described the taxonomic composition and spatial distribution of zooplankton in Bangladesh\u0026rsquo;s estuarine systems (Iqbal et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shahzad et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sayeed et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alam et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sajeeb et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chandra Majumdar et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ayshi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, few have investigated how seasonal environmental gradients shape community composition using multivariate approaches, particularly in mesozooplankton. We focus on mesozooplankton because their intermediate size range and ecological dominance provide a sensitive lens for detecting environmental filtering and seasonal turnover in estuarine ecosystems. By concentrating on this group, our study captures both dominant taxa (e.g., copepods) and ecologically important larval forms, yielding meaningful insights into community\u0026ndash;environment linkages. Accordingly, we investigated mesozooplankton communities in a subtropical estuary of Bangladesh with two main objectives: (1) to describe their present state and seasonal variations, and (2) to assess how environmental variables shape these communities using multivariate analytical techniques. Unlike previous short-term or taxonomically descriptive studies, our research provides a year-round, multivariate assessment of mesozooplankton communities in relation to key environmental drivers. By applying a suite of multivariate methods (RDA, NMDS, ANOSIM, SIMPER, BIO-ENV, HC), we generate new insights into temporal turnover, community\u0026ndash;environment linkages, and the strength of environmental filtering in a subtropical estuarine system. These dimensions distinguish our work from existing literature, fill a critical knowledge gap in Bangladesh\u0026rsquo;s coastal plankton ecology, and provide baseline data to support ecological monitoring and inform management of estuarine ecosystems under growing anthropogenic pressures.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Moheshkhali Channel, located in Kutubjom Union of Moheshkhali Upazila, approximately 15 km northwest of Cox\u0026rsquo;s Bazar, Bangladesh (21.4795\u0026ndash;21.5036\u0026deg;N, 91.9045\u0026ndash;91.9736\u0026deg;E; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Five sampling stations (Station 1\u0026ndash;5; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) were established along the channel to capture seasonal variability in mesozooplankton communities and their environmental drivers. The Moheshkhali Channel is an estuarine system strongly influenced by monsoon rainfall, tidal mixing, and freshwater discharge. Mangroves and salt marsh vegetation fringe its banks, and the sandy to muddy benthic substrate provides critical habitats for a diverse array of aquatic biota (Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ahmed et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The channel also serves as an important fishing ground and supports extensive aquaculture, contributing significantly to the livelihoods of coastal communities in Cox\u0026rsquo;s Bazar District (Abdullah Al et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Banik et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ayshi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In recent years, however, the system has experienced growing anthropogenic pressures, including industrial expansion, the construction of Cox\u0026rsquo;s Bazar International Airport, increased shipping activity, and aquaculture effluents, all of which have the potential to alter water quality and estuarine productivity. Given its ecological and socioeconomic importance, coupled with increasing human pressures, the Moheshkhali Channel represents a valuable site for investigating mesozooplankton community dynamics and their responses to environmental gradients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Collection and Laboratory Procedures\u003c/h3\u003e\n\u003cp\u003eZooplankton samples were collected seasonally during the monsoon (September 2023), winter (February 2024), and pre-monsoon (May 2024) using a conical plankton net with a 200 \u0026micro;m mesh size and a 0.24 m mouth diameter at five sampling stations (Station 1\u0026ndash;5; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Surface horizontal tows were conducted in the upper water column for about 1 minute, and the volume of water filtered was recorded using a mechanical flowmeter attached to the net. Samples were immediately preserved in 5% formalin for laboratory analysis according to Goswami (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn situ measurements of surface water temperature, pH, conductivity, dissolved oxygen (DO), and turbidity were taken at stations 1\u0026ndash;5 using a water multiparameter (Lutron WA-2015, Taiwan) and a turbidity meter (Lutron TU-2016, Taiwan). For nutrient analysis, water samples were filtered in the field through a Whatman 0.45 \u0026micro;m filter paper using a vacuum pump and stored in polyethylene bottles. Concentrations of nitrate, nitrite, phosphate, and silicate were determined spectrophotometrically (UV-Vis, Infitek SP-IUV7) using the method described by Hansen \u0026amp; Koroleff (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), with results expressed in parts per billion (ppb) units. Conductivity was included instead of salinity because it directly reflects the osmotic and ionic conditions experienced by zooplankton (Bos et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Soto and De los Rios \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhikharev et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while nutrient concentrations served as proxies for eutrophication (Rombouts et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Messi\u0026eacute; and Chavez \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dur\u0026eacute; et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZooplankton were identified and enumerated using a stereo microscope (Optica, 4\u0026times; zoom, Italy) equipped with a camera (Optica, 20 MP) and Proview software. Enumeration was carried out using a Bogorov counting chamber (80 \u0026times; 100 mm, 22 mL). Species were separated using fine brushes and forceps where necessary. Identification followed standard taxonomic references (Davis \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1955\u003c/span\u003e; Kasturirangan \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Newell and Newell \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Santhanam and Srinivasan \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Tait and Dipper \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Conway et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Goswami \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Al-Yamani and Prusova \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Al-Yamani et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Slotwinski et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Abo-Taleb \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ayshi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)..\u003c/p\u003e\n\u003ch3\u003eDiversity and Community Indices\u003c/h3\u003e\n\u003cp\u003eZooplankton abundance was standardized to individuals per cubic meter (ind/m\u0026sup3;) using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}A=\\:N/V\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e is the total number of individuals and \u003cem\u003eV\u003c/em\u003e is the volume of filtered water.\u003c/p\u003e \u003cp\u003eTo assess zooplankton community structure, we applied a set of ecological indices, each capturing a different aspect of diversity. Overall community diversity, incorporating both richness and relative abundance, was measured using the Shannon\u0026ndash;Wiener index \u003cem\u003eH\u0026prime;\u003c/em\u003e (Shannon \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1948\u003c/span\u003e). Dominance patterns were evaluated with Simpson\u0026rsquo;s dominance index \u003cem\u003eD\u003c/em\u003e (Simpson \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1949\u003c/span\u003e), which reflects the probability that two randomly selected individuals belong to the same species. Pielou\u0026rsquo;s evenness index \u003cem\u003eJ\u0026prime;\u003c/em\u003e (Pielou \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) was calculated to quantify the equitability of species distribution by standardizing Shannon diversity relative to the maximum possible value. Species richness was assessed using Margalef\u0026rsquo;s richness index \u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003emg\u003c/em\u003e\u003c/sub\u003e (Margalef \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1968\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese indices were selected because they provide complementary perspectives on community structure and quantify key community attributes (Qi et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Shannon\u0026ndash;Wiener and Simpson indices capture both diversity and dominance, Pielou\u0026rsquo;s evenness isolates the balance of individuals across species, and Margalef\u0026rsquo;s richness emphasizes the species pool independent of abundance. Using multiple indices reduces the bias of relying on a single metric and allows for a more comprehensive interpretation of mesozooplankton responses to environmental gradients (Magurran \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Moreover, these indices are widely applied in ecology and ensure comparability with previous studies due to their long history of application (Lamb et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe formulas used for these calculations are as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\begin{array}{c}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Diversity\\:{H}^{\\varvec{{\\prime\\:}}}=-\\sum\\:_{i=1}^{s}\\left({P}_{i}\\right)ln{\\:p}_{i}\\:\\#\\left(2\\right)\\end{array}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\begin{array}{c}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Dominance\\:D\\:=\\sum\\:_{i=1}^{s}{n}_{i}\\left({n}_{i}-1\\right)/N\\left(N-1\\right)\\#\\left(3\\right)\\end{array}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\begin{array}{c}Evenness\\:{J}^{{\\prime\\:}}={H}^{{\\prime\\:}}/ln\\left(S\\right)\\#\\left(4\\right)\\end{array}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\begin{array}{c}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Richness\\:{D}_{mg}\\:=(s-1)/ln\\left(N\\right)\\#\\left(5\\right)\\end{array}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}={n}_{i}/N\\)\u003c/span\u003e\u003c/span\u003e is the proportion of the species \u003cem\u003ei\u003c/em\u003e, n\u003csub\u003ei​\u003c/sub\u003e is the count of species \u003cem\u003ei\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e is the total number of individuals, and \u003cem\u003eS\u003c/em\u003e is the total number of species in the sample\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eA total of 15 samples were collected during the study period, with five samples per season (Monsoon, Winter, and Pre-monsoon) from Stations 1\u0026ndash;5. Before analysis, all environmental variables were standardized using \u003cem\u003ez\u003c/em\u003e-score normalization, which centers variables by subtracting the mean and scales them by the standard deviation, thereby minimizing the influence of different measurement scales (Kim et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Seasonal variations in environmental parameters and diversity indices were tested using the non-parametric Kruskal\u0026ndash;Wallis test, as normality was not satisfied according to the Shapiro\u0026ndash;Wilk test.\u003c/p\u003e \u003cp\u003eTo explore community patterns, Non-metric Multidimensional Scaling (NMDS) was performed using Bray\u0026ndash;Curtis dissimilarities, a robust approach that provides a biologically meaningful, distance-preserving representation of multivariate relationships (Clarke \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The significance of seasonal differences in NMDS space was assessed using PERMANOVA. Detrended Correspondence Analysis (DCA) was first conducted to evaluate the suitability of constrained ordination methods (Hill and Gauch \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). The gradient length of the first axis was 0.93 standard deviation (SD) units, well below the 3 SD threshold, indicating linear species\u0026ndash;environment relationships. Therefore, Redundancy Analysis (RDA) was selected as the appropriate constrained ordination method (Leps and Smilauer \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The significance of RDA models was tested using permutation-based Analysis of Variance (ANOVA), and model performance was evaluated by calculating both \u003cem\u003eR\u0026sup2;\u003c/em\u003e and adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e values. Before NMDS and RDA, zooplankton abundance data were Hellinger-transformed to down-weight the influence of rare species and improve linearity in species\u0026ndash;environment relationships (Legendre and Gallagher \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, hierarchical cluster analysis (HC), Analysis of Similarities (ANOSIM), and Similarity Percentage (SIMPER) were applied to Bray\u0026ndash;Curtis dissimilarity matrices from square-root transformed zooplankton abundance data to further examine seasonal community differences. ANOSIM was used to test significant differences in community composition among seasons, while SIMPER identified the species contributing most to between-season dissimilarities (Clarke \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo investigate the influence of environmental factors, the Biota-Environment (BIO-ENV) was applied to identify the subset of environmental variables best explaining community dissimilarities, using Spearman\u0026rsquo;s rank correlations (Clarke and Ainsworth \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The significance of biological\u0026ndash;environmental correlations was further assessed using Mantel tests.\u003c/p\u003e \u003cp\u003eStatistical significance was evaluated at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed in R 4.4.3 (R Core Team \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using the vegan package (Oksanen et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and visualizations were generated with ggplot2 (Wickham \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMesozooplankton Community Structure\u003c/h2\u003e \u003cp\u003eA total of 24 mesozooplankton species were recorded across the monsoon, winter, and pre-monsoon seasons at five sampling stations. These taxa were distributed across six phyla (\u003cem\u003eArthropoda\u003c/em\u003e, \u003cem\u003eMollusca\u003c/em\u003e, \u003cem\u003eAnnelida\u003c/em\u003e, \u003cem\u003eChordata\u003c/em\u003e, \u003cem\u003eCnidaria\u003c/em\u003e, \u003cem\u003eand Chaetognatha\u003c/em\u003e), nine classes (\u003cem\u003eMaxillopoda\u003c/em\u003e, \u003cem\u003eThecostraca\u003c/em\u003e, \u003cem\u003eMalacostraca\u003c/em\u003e, \u003cem\u003eInsecta\u003c/em\u003e, \u003cem\u003eGastropoda\u003c/em\u003e, \u003cem\u003ePolychaeta\u003c/em\u003e, \u003cem\u003eActinopterygii\u003c/em\u003e, \u003cem\u003eHydrozoa\u003c/em\u003e, \u003cem\u003eand Sagittoidea\u003c/em\u003e), and fourteen orders, reflecting a taxonomically diverse assemblage despite the modest number of species.\u003c/p\u003e \u003cp\u003e \u003cem\u003eArthropoda\u003c/em\u003e was the dominant phylum. Within this group, copepods (class \u003cem\u003eMaxillopoda\u003c/em\u003e) were most abundant, particularly members of the order \u003cem\u003eCalanoida\u003c/em\u003e (\u003cem\u003eAcartia erythraea, Acartia sp., Calanus finmarchicus, Calanus pacificus\u003c/em\u003e, and \u003cem\u003eCalanus sp.\u003c/em\u003e) and the cyclopoid \u003cem\u003eThermocyclops crassus\u003c/em\u003e. Barnacle cypris larvae (\u003cem\u003eBalanus sp.\u003c/em\u003e, order \u003cem\u003eSessilia\u003c/em\u003e) also contributed, along with diverse crustacean meroplankton: mysids (\u003cem\u003eMysidopsis bahia, Mysidopsis sp.\u003c/em\u003e), amphipods (\u003cem\u003eGammarus sp., Grandidierella megnae\u003c/em\u003e), and decapods (\u003cem\u003eScylla serrata, Lucifer hanseni\u003c/em\u003e, shrimp larvae, and zoea larvae). In addition, the aquatic insect \u003cem\u003eMicronecta sp.\u003c/em\u003e (class \u003cem\u003eInsecta\u003c/em\u003e, order \u003cem\u003eHemiptera\u003c/em\u003e) was recorded in low abundance. Other phyla were represented mainly by larval forms. \u003cem\u003eMollusca\u003c/em\u003e contributed gastropod taxa (\u003cem\u003eRissoa sitta\u003c/em\u003e and veliger larvae), while polychaete larvae represented \u003cem\u003eAnnelida\u003c/em\u003e. \u003cem\u003eChordata\u003c/em\u003e was present through fish larvae (class \u003cem\u003eActinopterygii\u003c/em\u003e), and \u003cem\u003eCnidaria\u003c/em\u003e by early jellyfish stages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe contribution of individual mesozooplankton taxa to community composition varied across seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with copepods dominating in all three seasons (77.4\u0026ndash;95.9%). Gastropods represented an important group (0.4\u0026ndash;2.8%), peaking in winter. Mysids (8.7%) and zoea larvae (8.4%) contributed substantially during the monsoon, while chaetognaths showed moderate contributions (0.04\u0026ndash;2.8%), with maximum values also observed during the monsoon. All other groups, including megalopa, shrimp larvae, amphipods, fish larvae, polychaetes, ostracods, insects, jellyfish larvae, and \u003cem\u003eMicronecta\u003c/em\u003e, each contributed\u0026thinsp;\u0026lt;\u0026thinsp;1%.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSeasonal Variations in Mesozooplankton Community Indices\u003c/h3\u003e\n\u003cp\u003eSeasonal changes in key mesozooplankton community indices of abundance, diversity, evenness, richness, and dominance were evident across the monsoon, winter, and pre-monsoon seasons in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with corresponding statistical summaries provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e Distinct seasonal patterns were observed in zooplankton community indices. Median abundance increased notably from 381 ind/m\u0026sup3; during the monsoon to a peak of 1845 ind/m\u0026sup3; in winter, followed by a moderate decline to 1164 ind/m\u0026sup3; in pre-monsoon. In contrast, species diversity was highest in the monsoon (0.9) and declined markedly to 0.28 in winter and 0.23 in pre-monsoon. A similar trend was observed for evenness, which decreased from 0.33 in monsoon to 0.11 and 0.08 in winter and pre-monsoon, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecies richness also showed a moderate seasonal decline, with the highest value recorded in the monsoon (2.33), followed by 1.99 in winter and 1.93 in the pre-monsoon period. Conversely, dominance increased from 0.52 in monsoon to 0.89 in winter, peaking at 0.91 in pre-monsoon.\u003c/p\u003e \u003cp\u003eDespite these apparent seasonal trends, the Kruskal\u0026ndash;Wallis rank sum test indicated that seasonal differences in abundance (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.2), diversity (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.093), evenness (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.093), richness (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.2), and dominance (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.1) were not statistically significant.\u003c/p\u003e\n\u003ch3\u003eSeasonal variations in hydrological parameters\u003c/h3\u003e\n\u003cp\u003eEnvironmental parameters median values exhibited seasonal variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with corresponding statistical summaries presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eConductivity increased sharply from 19.98 mS/cm in the monsoon to 50.03 mS/cm in winter and 50.3 mS/cm in pre-monsoon. Water temperature declined from 30.21\u0026deg;C in the monsoon to 23.00\u0026deg;C in winter, then peaked in pre-monsoon at 32.30\u0026deg;C. pH values were significantly higher in pre-monsoon (8.71) compared to the monsoon (8.0) and winter (7.59). Dissolved oxygen (DO) ranged between 4.11 and 5.42 ppm, with maximum concentration recorded in winter. Turbidity exhibited the strongest seasonal contrast, reaching 92.00 ntu during the monsoon, declining to 4.01 ntu in winter, and rising moderately to 16.92 ntu in pre-monsoon. \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNutrient dynamics showed distinct patterns. Silicate concentrations rose from 383.47 ppb in the monsoon to 834.37 ppb in winter, before declining to 540.82 ppb in the pre-monsoon. Nitrate showed a similar trend, increasing from 56.35 ppb in the monsoon to 203.86 ppb in winter, then decreasing to 73.24 ppb in pre-monsoon. Nitrite, by contrast, declined steadily across seasons (46.32 ppb in the monsoon, 26.30 ppb in winter, and 5.65 ppb in pre-monsoon). Phosphate exhibited minor fluctuations, with values of 112.68 ppb (monsoon), 133.33 ppb (winter), and 100.00 ppb (pre-monsoon).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of mesozooplankton community indices and environmental parameters across monsoon, winter, and pre-monsoon seasons in the Moheshkhali Channel. Kruskal\u0026ndash;Wallis rank sum test results indicate the significance of seasonal differences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWinter\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePre-monsoon\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e381\u0026thinsp;\u0026plusmn;\u0026thinsp;455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1845\u0026thinsp;\u0026plusmn;\u0026thinsp;1,474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1164\u0026thinsp;\u0026plusmn;\u0026thinsp;828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConductivity (mS/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e19.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e50.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e30.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO (ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbidity (ntu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e92.00\u0026thinsp;\u0026plusmn;\u0026thinsp;57.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e16.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilicate (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e383.47\u0026thinsp;\u0026plusmn;\u0026thinsp;229.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e834.37\u0026thinsp;\u0026plusmn;\u0026thinsp;207.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e540.82\u0026thinsp;\u0026plusmn;\u0026thinsp;266.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e46.32\u0026thinsp;\u0026plusmn;\u0026thinsp;140.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.30\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrate (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e56.35\u0026thinsp;\u0026plusmn;\u0026thinsp;50.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e203.86\u0026thinsp;\u0026plusmn;\u0026thinsp;162.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e73.24\u0026thinsp;\u0026plusmn;\u0026thinsp;79.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e112.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e133.33\u0026thinsp;\u0026plusmn;\u0026thinsp;153.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;145.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003eMedian \u0026plusmn; SD, \u003csup\u003e2\u003c/sup\u003eKruskal-Wallis rank sum test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEnvironmental parameters exhibited pronounced seasonal variation. Kruskal-Wallis rank sum statistical testing confirmed significant differences for conductivity (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.008), temperature (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.002), pH (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.012), dissolved oxygen (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.006), and turbidity (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.002). In contrast, nutrient concentrations showed seasonal fluctuations in their medians but did not differ significantly across seasons: silicate (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.14), nitrate (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.13), nitrite (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.054), and phosphate (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.80), although nitrite approached significance. The lack of statistical significance despite apparent shifts reflects high within-season variability and overlapping rank distributions across seasons. Such variability is characteristic of estuarine nutrient dynamics, where short-term hydrological inputs and biological uptake often obscure consistent seasonal trends.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal Dynamics in Mesozooplankton Community Structure\u003c/h2\u003e \u003cp\u003e Multivariate analyses, including Non-metric Multidimensional Scaling (NMDS), Hierarchical Cluster (HC), Analysis of Similarities (ANOSIM), and Similarity Percentage (SIMPER), revealed strong seasonal variation in mesozooplankton community composition across monsoon, winter, and pre-monsoon periods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe NMDS ordination (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), based on Bray\u0026ndash;Curtis dissimilarities of Hellinger-transformed mesozooplankton abundance data, yielded a low stress value of 0.043. Sampling points clustered clearly by season, with a minimal overlap among the 95% confidence ellipses. PERMANOVA analysis confirmed significant variation in zooplankton assemblages among seasons (PERMANOVA, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.022). Monsoon samples were mostly distributed along the negative side of NMDS1 and were associated with \u003cem\u003eMysida\u003c/em\u003e, Fish Larvae, Zoea Larvae, \u003cem\u003eIsopoda\u003c/em\u003e, \u003cem\u003eMicronecta\u003c/em\u003e, \u003cem\u003eLucifer\u003c/em\u003e, \u003cem\u003eAmphipoda\u003c/em\u003e, \u003cem\u003eChaetognatha\u003c/em\u003e, and \u003cem\u003eOstracoda\u003c/em\u003e. Winter samples formed a relatively tight cluster along the positive NMDS1 axis, closely associated with \u003cem\u003eCopepoda\u003c/em\u003e and \u003cem\u003ePolychaeta\u003c/em\u003e. Pre-monsoon samples exhibited the most compact clustering, also aligned along the positive NMDS1 axis, with strong associations to \u003cem\u003eCopepoda\u003c/em\u003e and \u003cem\u003ePolychaeta\u003c/em\u003e. In contrast, Shrimp Larvae, \u003cem\u003eGastropoda\u003c/em\u003e, and Jellyfish Larvae were positioned away from the seasonal groupings and were not strongly associated with any season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHierarchical cluster analysis based on Bray\u0026ndash;Curtis similarity grouped the mesozooplankton community into three major clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). \u003cem\u003eCopepoda\u003c/em\u003e formed a distinct cluster, highlighting their unique dominance across all seasons. The second cluster comprised a mix of larval and holoplanktonic taxa (e.g., fish larvae, jellyfish larvae, \u003cem\u003eOstracoda\u003c/em\u003e, \u003cem\u003eLucifer\u003c/em\u003e), suggesting co-occurrence driven by larval recruitment and opportunistic colonization. The third cluster was dominated by crustacean larvae and predatory taxa (e.g., zoea larvae, \u003cem\u003eMysida\u003c/em\u003e, \u003cem\u003eChaetognatha\u003c/em\u003e, \u003cem\u003eAmphipoda\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of similarities (ANOSIM) was used to test differences in mesozooplankton community composition among seasons (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this study, significant differences were observed between monsoon and winter (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.332, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.032) and between monsoon and pre-monsoon (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.420, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.043), indicating that mesozooplankton communities during the monsoon are moderately distinct from those in the other seasons. No significant difference was detected between winter and pre-monsoon communities (\u003cem\u003eR\u003c/em\u003e = \u0026minus;\u0026thinsp;0.036, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.627), suggesting these communities are compositionally similar. These results are consistent with patterns observed in NMDS ordination, confirming seasonal shifts in community structure driven by environmental and ecological changes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise Analysis of Similarities (ANOSIM) for differences in mesozooplankton community composition among seasons based on Bray\u0026ndash;Curtis dissimilarity. R statistic quantifies the degree of separation between groups in ANOSIM, ranging from \u0026minus;\u0026thinsp;1 (within-group dissimilarity greater than between-group dissimilarity) to 1 (complete separation of groups), with R\u0026thinsp;\u0026asymp;\u0026thinsp;0 indicating no difference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonsoon vs. Winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.032*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonsoon vs. Pre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.043*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter vs. Pre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* Indicate the Statistical Significance, \u003csup\u003e1\u003c/sup\u003ePermutation test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSIMPER analysis further identified the mesozooplankton taxa contributing most to seasonal dissimilarities (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eCopepoda\u003c/em\u003e consistently showed the highest average contribution to differences across seasons, for example, 20.4% between monsoon and winter, but these contributions were not statistically significant. In contrast, \u003cem\u003eMysida\u003c/em\u003e (4.7%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.028) and \u003cem\u003eGastropoda\u003c/em\u003e (4.2%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.043) statistically significantly contributed to the dissimilarity between monsoon and winter. Between monsoon and pre-monsoon, \u003cem\u003eMysida\u003c/em\u003e (5.3%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.009) and \u003cem\u003eChaetognatha\u003c/em\u003e (2.5%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.05) were the statistically significant most important contributors. No taxes were significantly differentiated between winter and pre-monsoon, reinforcing the compositional similarity between these two seasons.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimilarity Percentage (SIMPER) analysis showing the top five mesozooplankton taxa contributing to dissimilarity between seasonal pairs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Contribution (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMonsoon vs. Winter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopepoda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMysida\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGastropoda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZoea Larvae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChaetognatha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMonsoon vs. Pre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopepoda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMysida\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZoea Larvae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGastropoda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChaetognatha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.050*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWinter vs. Pre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopepoda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGastropoda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMysida\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChaetognatha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMegalopa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Indicate the Statistical Significance, \u003csup\u003e1\u003c/sup\u003eMonte Carlo permutation test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental Drivers of Mesozooplankton Composition\u003c/h2\u003e \u003cp\u003eRedundancy Analysis (RDA) revealed strong seasonal structuring of mesozooplankton community composition in response to key environmental gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The first two canonical axes explained 70.7% of the total constrained variation, with RDA1 and RDA2 accounting for 45.7% and 25.0%, respectively. The overall model was statistically significant (RDA, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.862, adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.613, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), indicating that a substantial portion of community variation can be attributed to the measured environmental variables. The RDA biplot identified temperature, turbidity, and conductivity as the primary environmental variables associated with variation in community structure. Temperature and turbidity vectors were positively aligned with both RDA1 and RDA2, corresponding to mesozooplankton assemblages observed during the monsoon season. In contrast, conductivity was negatively correlated with both axes and associated with samples from the winter and pre-monsoon seasons. Dissolved oxygen (DO), nitrite, and nitrate showed moderate correlations with the ordination axes, whereas silicate, phosphate, and pH made comparatively weaker contributions to the explained variation.\u003c/p\u003e \u003cp\u003e Seasonal clustering of samples was visible in the RDA ordination space. Monsoon samples were distributed along the positive side of RDA1 and were associated with higher temperature, turbidity, and nitrite concentrations. Winter and pre-monsoon samples clustered on the negative side of RDA1, showing alignment with higher conductivity, DO, pH, silicate, nitrate, and phosphate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecies distributions corresponded closely to the identified environmental gradients. \u003cem\u003eMysida\u003c/em\u003e (\u003cem\u003ek\u003c/em\u003e), Zoea larvae (\u003cem\u003ep\u003c/em\u003e), and Shrimp larvae (\u003cem\u003eo\u003c/em\u003e) were positioned in the direction of high turbidity and temperature, aligning with monsoon conditions. \u003cem\u003eCopepoda\u003c/em\u003e (\u003cem\u003eb\u003c/em\u003e) were positioned near winter samples and strongly associated with conductivity. In contrast, \u003cem\u003eGastropoda\u003c/em\u003e (d) was located distantly along the negative RDA2 axis, suggesting a specific response to environmental variables negatively associated with that axis, likely nitrate and silicate concentrations. The remaining taxa, including \u003cem\u003eAmphipoda (a), Fish larvae (c\u003c/em\u003e), Insects \u003cem\u003e(e)\u003c/em\u003e, \u003cem\u003eIsopoda (f)\u003c/em\u003e, Jellyfish larvae \u003cem\u003e(g)\u003c/em\u003e, \u003cem\u003eLucifer (h)\u003c/em\u003e, Megalopa \u003cem\u003e(i)\u003c/em\u003e, \u003cem\u003eMicronecta (j)\u003c/em\u003e, \u003cem\u003eOstracoda (l)\u003c/em\u003e, \u003cem\u003ePolychaeta (m)\u003c/em\u003e, \u003cem\u003eand Chaetognatha (n)\u003c/em\u003e, clustered near the origin or on the positive side of RDA1.\u003c/p\u003e \u003cp\u003eIn both the RDA triplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and the NMDS ordination (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), winter and pre-monsoon samples exhibited a substantial degree of overlap, indicating similar mesozooplankton community composition during these seasons. This overlap was further supported by a statistical test of ANOSIM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which revealed no significant difference between winter and pre-monsoon assemblages (ANOSIM, \u003cem\u003eR\u003c/em\u003e = \u0026minus;\u0026thinsp;0.036, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.627). The convergence of evidence from both constrained (RDA) and unconstrained (NMDS) ordinations highlights the lack of strong seasonal differentiation between these two periods. In contrast, monsoon samples were clearly separated from winter and pre-monsoon, suggesting that monsoonal environmental conditions exerted the strongest structuring effect on community composition.\u003c/p\u003e \u003cp\u003eTo identify the environmental factors strongly influencing mesozooplankton community structure and complement the ordination results, a Biota-Environment (BIO-ENV) analysis combined with Mantel tests using Spearman\u0026rsquo;s rank correlation (\u003cem\u003eρ\u003c/em\u003e) was conducted (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest correlation was obtained for conductivity alone (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4045, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.013), indicating that this single variable is the most influential predictor of community variation. Among multi-variable sets, the combination of conductivity, temperature, and turbidity produced the second-highest correlation (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3613, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.005), followed by the addition of nitrite (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3433, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.039). Other significant combinations included conductivity with nitrite (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3385, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.040) and a five-variable set comprising conductivity, temperature, turbidity, nitrite, and phosphate (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2935, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.046). Combinations exceeding five variables showed reduced correlation and were not statistically significant, with the full nine-variable model yielding a near-zero correlation (\u003cem\u003eρ\u003c/em\u003e = \u0026minus;\u0026thinsp;0.0060, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.473). These results indicate that conductivity is the primary driver of seasonal mesozooplankton community patterns, and that a small set of environmental variables, rather than all measured parameters, effectively explains community variation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiota-Environment (BIO-ENV) Matching analysis showing the best-fitting environmental variable combinations for mesozooplankton community structure with corresponding Spearman correlation (ρ) and p-values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental Variable Combination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpearman Correlation (\u003cem\u003eρ\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Temperature, Turbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Temperature, Turbidity, Nitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Nitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.040*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Temperature, Turbidity, Nitrite, Phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.046*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Temperature, Turbidity, Silicate, Nitrite, Phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Temperature, pH, Turbidity, Silicate, Nitrite, Phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Temperature, pH, DO, Turbidity, Silicate, Nitrite, Phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity, Temperature, pH, DO, Turbidity, Silicate, Nitrite, Nitrate, Phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Indicate the Statistical Significance, \u003csup\u003e1\u003c/sup\u003eMantel tests\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined seasonal dynamics of mesozooplankton communities in the Moheshkhali Channel and their relationships with key environmental drivers, using a combination of diverse indices, unconstrained (NMDS) and constrained (RDA) ordinations, ANOSIM, and BIO-ENV analyses. By integrating these approaches, we were able to assess not only whether communities differed across seasons but also which environmental gradients most strongly structured these patterns. Seasonal dynamics of mesozooplankton serve as key indicators of ecosystem structure, trophic interactions, and productivity in estuarine and coastal environments (Liszka et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zeldis and D\u0026eacute;cima \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clerc et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Perhirin et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Reyes-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnvironmental parameters exhibited clear seasonal fluctuations consistent with the dynamics of monsoon-regulated estuarine systems. Conductivity increased significantly from monsoon to winter and pre-monsoon, reflecting reduced freshwater input and enhanced evaporative concentration during the dry period (Sarkar and Choudhury \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Pawlowicz et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sahu et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tyler et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We prioritized electrical conductivity over salinity because the ionic composition of estuarine waters often deviates from the standard seawater assumptions underlying salinity algorithms. Conductivity more directly reflects total dissolved ions and thus the osmo-ionic conditions most relevant to zooplankton physiology (Bos et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Soto and De los Rios \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhikharev et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This methodological choice is supported by our findings, as conductivity emerged as the strongest single predictor of community dissimilarity in BIO-ENV analysis and was also the primary variable structuring community patterns in the RDA ordination. These findings align with observations from other South Asian estuaries, where conductivity (or salinity), temperature, and turbidity frequently act as dominant structuring factors (Fernandes and Ramaiah \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Benfield \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; Bhattacharjee et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The identification of conductivity as a key driver is also consistent with previous work emphasizing its role as both a salinity proxy and a determinant of estuarine plankton community structure (David et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Mod\u0026eacute;ran et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Baliarsingh et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yuan et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Venkataramana et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bhattacharjee et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nonetheless, contrasting findings, such as those reported by Abdullah Al et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in the Kutubdia Channel, where nitrite-nitrogen was more predictive, highlight the site-specific nature of environmental controls on zooplankton communities.\u003c/p\u003e \u003cp\u003eTemperature followed a typical subtropical seasonal cycle, declining from the monsoon to a winter minimum before rising to a pre-monsoon maximum, a pattern that influences zooplankton metabolic processes and reproductive cycles (Ranith et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). pH levels were significantly higher in the pre-monsoon, likely due to increased photosynthetic activity under higher irradiance (Semesi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Awaluddin et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Dissolved oxygen concentrations peaked in winter, consistent with greater oxygen solubility at lower temperatures and reduced biological consumption (Liu et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Turbidity showed the most pronounced seasonal variation, reaching its maximum during the monsoon due to sediment-rich runoff and suspended particulate matter from catchment erosion (Khan \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNutrient concentrations exhibited observable but statistically nonsignificant seasonal patterns. Apparently, winter maxima in silicate, nitrate, and phosphate may reflect increased terrestrial runoff with limited biological uptake, whereas the progressive decline in nitrite from monsoon to pre-monsoon likely indicates nitrification or phytoplankton uptake. Although not statistically significant, these trends may still reflect ecologically meaningful dynamics, potentially obscured by consistent anthropogenic inputs such as agriculture, aquaculture, and watershed discharge.\u003c/p\u003e \u003cp\u003eThe identification of 24 mesozooplankton taxa across six phyla indicates moderate diversity, broadly consistent with earlier reports from the region (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ayshi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Copepods dominated all seasons and formed a distinct cluster in the HC dendrogram, a pattern well documented in tropical estuaries (Rakhesh et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fernandes and Ramaiah \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Baliarsingh et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nandy and Mandal \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their broad ecological tolerance and adaptive reproductive strategies underpin this numerical dominance (Kwok et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chew and Chong \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kimmel and Baird \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Both RDA and BIO-ENV analyses further indicate that copepod prevalence in winter and pre-monsoon is strongly associated with elevated conductivity and dissolved oxygen, highlighting their capacity to thrive under stable osmo-ionic and oxygen-rich conditions.\u003c/p\u003e \u003cp\u003eIn contrast, mysids, zoea larvae, and megalopa clustered with monsoon samples in the ordination space and were identified as major contributors to seasonal dissimilarities in SIMPER analyses. Their association with higher temperature and turbidity reflects life-history strategies linked to larval transport, resuspension, and estuarine nursery functions (Guerreiro et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gastropods and chaetognaths also emerged as significant contributors in SIMPER analyses to seasonal contrasts, suggesting sensitivity to episodic hydrographic variability and shifts in prey availability. Together, these results highlight a dual community structure in which copepods provide stability across seasons, whereas other groups respond more strongly to short-term monsoon-driven variability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Summary of Zooplankton Studies in Subtropical Estuarine and Coastal Systems of Bangladesh near Moheshkhali Channel\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of Taxa / Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMajor Phyla / Groups\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent study (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoheshkhali estuarine system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 taxa (6 phyla, 9 classes, 14 orders)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopepods (\u003cem\u003eCalanoida\u003c/em\u003e, \u003cem\u003eCyclopoida\u003c/em\u003e) are dominant; Gastropods, Mysids, zoea larvae, and Chaetognaths\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAyshi et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaheshkahli Channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 taxa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopepods are dominant; Mysid shrimps, Shrimp larvae, crab zoea, and ichthyoplankton\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdullah Al et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKutubdia channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 taxa (22 holo-plankton and 16 mero-plankton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopepods, amphipods, shrimps, and mollusks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdullah Al et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKohelia channel and Kutubdia channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 taxa (13 orders, 25 families)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAcartia erythraea, Acetes erythracus, Oithona simplex, Penaeus indicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHena K et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBakkhali estuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 taxa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopepods are dominant; \u003cem\u003eMysidaceae\u003c/em\u003e and \u003cem\u003eChaetognatha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeasonal variations in mesozooplankton abundance and community indices further revealed contrasting patterns, with peak abundance in winter, but higher species diversity, evenness, and richness during the monsoon. Greater abundance in winter may reflect enhanced environmental stability, favoring opportunistic taxa such as copepods, whereas increased diversity in the monsoon likely reflects freshwater inflow, high turbidity, and nutrient-driven heterogeneity (Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similar patterns have been reported in other estuarine systems in Bangladesh (Iqbal et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e), though contrasting results, such as peak abundance in monsoon or post-monsoon, have also been observed (Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These inconsistencies emphasize the importance of local hydrographic and environmental conditions in shaping zooplankton communities (Paturej et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough univariate indices such as abundance, Shannon\u0026ndash;Wiener diversity (\u003cem\u003eH\u0026prime;\u003c/em\u003e), evenness (\u003cem\u003eJ\u0026prime;\u003c/em\u003e), richness (\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003emg\u003c/em\u003e\u003c/sub\u003e), and dominance (\u003cem\u003eD\u003c/em\u003e) did not show statistically significant seasonal differences, multivariate analyses (NMDS, ANOSIM) revealed clear shifts in community composition. This suggests that while overall diversity and total abundance remained relatively stable, the identities and relative contributions of dominant taxa varied across seasons. Such patterns are typical of estuarine systems, where environmental variable fluctuations drive species turnover without necessarily altering aggregate diversity metrics. These findings strengthen the importance of multivariate approaches in detecting subtle but ecologically meaningful seasonal dynamics that univariate indices alone may overlook (Clarke et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeasonal environmental gradients play a central role in shaping mesozooplankton communities in the Moheshkhali Channel, reflecting the dual function of estuaries as retention zones for resident taxa and transport corridors for larval forms (Guerreiro et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The observed patterns are consistent with findings from other estuarine systems in Bangladesh and India (Abu Hena et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Abdullah Al et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Nandy and Mandal \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), emphasizing the organizing influence of wet\u0026ndash;dry hydrographic cycles. Conductivity, temperature, and turbidity emerge as key environmental factors, highlighting the broader role of environmental gradients in regulating estuarine mesozooplankton across South and Southeast Asia (Fernandes and Ramaiah \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Baliarsingh et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bhattacharjee et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeasonal trends in abundance, diversity, and richness indicate that stable dry-season conditions favor resident taxa, while monsoon-driven variability promotes higher diversity and larval recruitment. Taxon-specific responses along environmental gradients highlight the nuanced ecological roles of different groups, illustrating how community composition responds to both predictable seasonal changes and episodic environmental variability. These observations strengthen the value of integrative, multivariate approaches for understanding complex estuarine community dynamics and their ecological consequences.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides novel insights into the seasonal dynamics of mesozooplankton communities in the Moheshkhali Channel, a subtropical estuary in southeastern Bangladesh. By integrating community indices, ordination methods, and BIO-ENV analysis, we demonstrated that mesozooplankton composition is strongly structured by monsoon-driven hydrographic variability. Monsoon assemblages were clearly distinct, associated with high turbidity and temperature, while winter and pre-monsoon communities overlapped substantially, reflecting convergence under dry-season hydrography. Conductivity emerged as the most influential predictor of community variation, either alone or in combination with temperature and turbidity. These findings highlight the key role of seasonal environmental forcing in shaping estuarine mesozooplankton and the importance of conductivity as a primary driver in this subtropical estuarine system.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Outlook\u003c/h2\u003e \u003cp\u003eDespite these advances, several limitations should be acknowledged. First, the study was based on a single annual cycle with 15 samples; longer-term and higher-frequency sampling would be necessary to capture interannual variability and short-term events such as tidal or storm-driven pulses. Second, only mesozooplankton were assessed; inclusion of microzooplankton and phytoplankton would provide a more complete view of trophic interactions. Third, molecular approaches such as DNA metabarcoding were not employed, which may reveal cryptic diversity overlooked by morphological identification.\u003c/p\u003e \u003cp\u003eFuture research should aim to integrate long-term monitoring with molecular tools and food web analyses, linking zooplankton dynamics to fisheries productivity and ecosystem health under ongoing climate variability. Comparative studies across other estuarine systems of the Bay of Bengal would also help determine whether conductivity-driven structuring is a consistent regional pattern or unique to the Moheshkhali Channel.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no competing financial or non-financial interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Conceptualization and methodology were led by Md. Sharif Hossain and Md. Azizul Fazal. Material preparation, investigation, data collection, data curation, formal analysis, and visualization were performed by Md. Sharif Hossain, Omite Ashraf Tihum, and Md. Bayzid.\u003c/p\u003e\n\u003cp\u003eThe first draft of the manuscript was written by Md. Sharif Hossain, Omite Ashraf Tihum, and Md. Bayzid. Supervision and validation were provided by Md. Azizul Fazal, Abu Bokkar Siddique, Faisal Sobhan, Dr. Subrata Sarker, and Dr. Md. Alamgir Kabir. All authors contributed to manuscript review and editing, and all authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available, as they are not deposited in a public repository, but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eEthical approval was not required for this study as no animals or human participants were involved.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable. This study does not include any individual person\u0026rsquo;s data.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable. This study did not involve human participants.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors are grateful to Md. Jahidul Islam and Md. Shamsul Hoque for their valuable assistance with species identification, counting, and data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdul WO, Adekoya EO, Ademolu KO, et al (2016) The effects of environmental parameters on zooplankton assemblages in tropical coastal estuary, South-west, Nigeria. Egypt J Aquat Res 42:281\u0026ndash;287. https://doi.org/10.1016/j.ejar.2016.05.005\u003c/li\u003e\n \u003cli\u003eAbdullah Al M, Akhtar A, Kamal AHM, et al (2018a) Seasonal pattern of zooplankton communities and their environmental response in subtropical maritime channels systems in the Bay of Bengal, Bangladesh. Acta Ecol Sin 38:316\u0026ndash;324. https://doi.org/10.1016/j.chnaes.2017.11.001\u003c/li\u003e\n \u003cli\u003eAbdullah Al M, Akhtar A, Rahman MF, et al (2020) Temporal distribution of zooplankton communities in coastal waters of the northern Bay of Bengal, Bangladesh. 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Diversity 15:. https://doi.org/10.3390/d15020199\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Zooplankton, Seasonal variation, Community structure, Multivariate analysis, Moheshkhali channel","lastPublishedDoi":"10.21203/rs.3.rs-8491813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8491813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMesozooplankton are essential trophic intermediaries and bioindicators in estuarine ecosystems. However, their seasonal response to environmental variability remains underexplored in the subtropical estuaries of Bangladesh. This study investigated the seasonal variation of mesozooplankton community composition and its environmental drivers in the Moheshkhali Channel, southeastern Bangladesh, based on data collected between 2023 and 2024. Mesozooplankton were sampled using conical net tows, and environmental variables were measured concurrently. Community structure was analysed using ecological indices and multivariate approaches. A total of 24 taxa across six phyla were recorded, with copepods as the dominant group. Mesozooplankton abundance peaked in winter (1845 ind/m\u0026sup3;), while diversity (\u003cem\u003eH\u0026prime;\u003c/em\u003e = 0.90), evenness (\u003cem\u003eJ\u0026prime;\u003c/em\u003e = 0.33), and richness (\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003emg\u003c/em\u003e\u003c/sub\u003e = 2.33) were highest during the monsoon. Non-metric multidimensional scaling (NMDS) revealed distinct seasonal patterns, supported by analysis of similarities (ANOSIM) results indicating significant differences between monsoon and other seasons. Similarity Percentage analysis (SIMPER) identified \u003cem\u003eMysida\u003c/em\u003e, \u003cem\u003eGastropoda\u003c/em\u003e, \u003cem\u003eChaetognatha\u003c/em\u003e, and \u003cem\u003eCopepoda\u003c/em\u003e as key contributors to inter-seasonal dissimilarity. The redundancy analysis (RDA) explained 70.7% of the constrained variation (RDA, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.862, adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.613, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.015), indicating that temperature, turbidity, and conductivity are the primary drivers. Conductivity emerged as the strongest individual predictor in the Biota\u0026ndash;Environment (BIO-ENV) matching analysis (BIO-ENV, \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4045, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.013). These findings highlight the influence of monsoon-driven hydrography on mesozooplankton communities, providing a valuable baseline for future ecological assessments under changing climatic and anthropogenic pressures.\u003c/p\u003e","manuscriptTitle":"Seasonal variation of mesozooplankton communities in relation to environmental variables from a subtropical estuary of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 13:19:34","doi":"10.21203/rs.3.rs-8491813/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ef4d547-5df6-430d-8fc6-560ae8830197","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T15:26:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 13:19:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8491813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8491813","identity":"rs-8491813","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}