Seasonal Modulation of Salinity Stress Response in Leaf Micro-morphological and Biochemical Insights of the Mangrove Avicennia sp. (Acanthaceae) in Digha Mohona, West Bengal

Seasonal Modulation of Salinity Stress Response in Leaf Micro-morphological and Biochemical Insights of the Mangrove Avicennia sp. 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(Acanthaceae) in Digha Mohona, West Bengal Arpita Maity, Amal Kumar Mondal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8363184/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 Seasonal variations (pre-monsoon and monsoon) cause minor shifts in the mangrove microenvironment, including average temperature, pH, salinity, TDS, and EC in both surface water and soil. These fluctuations lead to subtle alterations in mangrove micro-morphology (salt gland index) and influence the accumulation of compatible osmolytes (CO) such as proline, glycine betaine (GB), and sugar alcohols (mannitol and sorbitol). Seasonal changes also affect plant pigments, soluble sugars, and the accumulation of secondary metabolites in plant tissues. In this study, we examined how mangrove species adapt to their microenvironment by assessing along with SGI, CO, plant pigment and secondary metabolites (TPC, TFC and TPPC) accumulation in plant tissues. The biochemical composition of CO, along with the seasonal accumulation pattern was also species-specific. CO levels were highest during the pre-monsoon season and lowest during the monsoon. A. rumphiana showed the highest proline concentration in the pre-monsoon season, while GB accumulation was highest in A. rumphiana and A. alba . Similarly, the secondary metabolite accumulation pattern along with their seasonal variation exhibited species-specific manner and plant developmental phases. The highest level of TPC was found in A. rumphiana during pre-monsoon. Whereas A. marina and A. alba displayed the highest TPC in post-monsoon. A significant decrease in the chlorophyll content in pre-monsoon, while soluble carbohydrate accumulation was more pronounced during the monsoon and post-monsoon seasons. These patterns show how different species have adapted to shifting environmental circumstances. compatible osmolytes proline glycine betaine Sugar alcohol salt gland index seasonal changes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The plants can perceive and respond to stressful situations, few species thrive in environments where high levels of abiotic stress are present (Rodriguez et al. 2008 ). Abiotic stress affects large areas of farmed and irrigated land, making it a major problem for global agriculture (Shrivastava and Kumar 2015 ). Most studies concerning salt tolerance involve plants that have been grown in a natural saline ecosystem, but the studies involving plants that have been grown under natural saline ecosystems are more significant in understanding salt stress tolerance (Lokhande and Suprasanna 2012 ). Among the various abiotic stresses, salinity alone affects approximately 930 million hectares, about 7% of the world's land surface (Munns and Tester 2008 ). There are a number of lands that affected by hyper-ionic and hyperosmotic stress due to excessive salt accumulation in coastal and estuarine regions, generally resulting from the accumulation of salt over a long period of time (Munns 2002 ). Cellular compartments detect abiotic stress triggering molecular responses through regulatory proteins and receptors (Zhang et al. 2023). These signals initiate downstream gene expression leading to compatible osmolytes and protective proteins synthesis (Melo et al. 2022 ). Integration of environmental signals with endogenous developmental signals influences compatible osmolytes accumulation that promote plant resistance to environmental stresses. Plants can develop broader defenses against abiotic stress, including outer cuticle layer, compatible osmolytes, internal reactive species scavengers and molecular chaperones (He et al. 2018 ). Various physiological and biochemical processes are employed to cope with drought stress, including accumulating osmoregulatory molecules like proline, glycine betaine and amino acids (Lei et al. 2006 ; Rosa et al. 2020 ; Yadav et al. 2020 ). Several studies have highlighted the physiological and biochemical mechanisms, along with morphological and anatomical features, that contribute to the notable tolerance of plants to abiotic stresses such as salinity, oxygen, drought and extreme temperatures (Naz et al. 2014 ; Walker and Lutts 2014 ). The accumulation of compatible osmolytes is a vital component of adaptation to drought, salinity, and cold (Walker and Lutts 2014 ). Osmoregulation, the process of maintaining cellular turgor pressure under osmotic stress, is crucial for plant survival under drought conditions (Öztürk et al. 2020 ). Osmotic adjustment is achieved through the synthesis and accumulation of compatible solutes, which are non-toxic compounds that help to balance the osmotic potential of cells and protect cellular structures (Rampino et al. 2006 ; Oliveira et al. 2012 ). When exposed to salt, plants use a variety of defense mechanisms, such as the buildup of secondary metabolites, reactive oxygen species, and compatible solutes. Osmotic adjustment aided by the cytosolic accumulation of solutes such as proline and glycine betaine (Xing and Rajashekar 2001 ). These compatible solutes not only contribute to osmotic adjustment but also protect cellular structures and enzymes from damage caused by dehydration (Chen and murata 2000 ; He et al. 2018 ; Sewelam et al. 2016 ). The presence of ROS in the environment due to abiotic stresses such as salt is harmful to cells because they cause oxidative danger for lipids, membrane proteins, and nucleic acids (Smirnoff 1993 ; Gomez et al. 1999 and Hernandez et al. 2001). Certain levels of ROS are also involved in antioxidative protection, even though they can harm proteins, nucleic acids, and membrane lipids. Moreover, carbohydrates support the preservation of protein structure under stress, carbon storage, and radical scavenging. Chlorophylls, carotenoids, and photosynthetic efficiency can be affected by salinity. While anthocyanins, which are derived from flavonoids, accumulate as a defence mechanism under stress, flavonoids function as antioxidant agents by scavenging ROS (Ashraf and Foolad 2007 ; Grigore et al. 2011 ; Li et al. 2010 ). Phenolics are essential for preserving redox balance and safeguarding biological systems. The equilibrium between reactive oxygen species and phytochemicals such as flavonoids and polyphenols affects how well plants adapt to salt stress. The plant kingdom contains phenolic compounds, particularly flavonoids, which have a variety of molecular and biochemical functions. They have antioxidant qualities, function as signaling molecules, and support plant defense. Plants can withstand salt stress because phenols and flavonoids play a major role in scavenging free radicals (Apel and Hirt 2004 ). Plant materials contain both bound and free forms of polyphenols. Moreover, carbohydrates support the preservation of protein structure under stress, carbon storage, and radical scavenging. Chlorophylls, carotenoids, and photosynthetic efficiency can be affected by salinity (Foyer 2018 ). By scavenging ROS, flavonoids function as antioxidants, whereas anthocyanins, which are derived from flavonoids, accumulate as a protective mechanism under stress (Huang et al. 2019 ). Proline, betaines, polyols, and soluble carbohydrates are some of the organic solutions that plants accumulate to cope with osmotic stress. Therefore, this study, focused on the leaf-micromorphology and accumulation of compatible osmolytes changes to seasonal variation. In addition, understanding the Avicennia sp. adjusts its leaf morphology and osmolyte production in response to seasonal variations can provide insights into the broader adaptive strategies of mangroves. Materials and methods Survey area Data collection The mentioned plant species were collected from Digha Mohona coastal vegetation adjoining Bay of Bengal located in Ramnagar CD Block I of district Purba Medinipur, West Bengal, India. The longitudinal and latitudinal extension of this area is 87°30'26.7516"E to 21° 37'35.8212"N, which has a generally saline loam soil with high pH. Soil and water samples were collected from selected coastal vegetation at middle day of per month basis in 2023 years. The geographic coordinates of the collected plant specimens Avicennia marina , Avicennia alba , Avicennia officinalis , and Avicennia marina var. rumphiana ( A. rumphiana ) were recorded as 21°38.414′N, 87°33.544′E; 21°38.407′N, 87°33.547′E; 21°38.409′N, 87°33.557′E; and 21°38.426′N, 87°33.557′E, respectively. Fast of all one year (2023) divided in three season first one is Monsoon (July-October), second one is Post-Monsoon (November-February) and third one is Pre-Monsoon (March-June). Leaf micro-morphological studies After collection of the mangroves leaves, that preserved in 70% alcohol or in FAA solution for micro-morphological analysis. Leaves were soaked on 3% KOH and heat for 5min. Then peel off epidermis layer and stained with safranin. Stain epidermis, observed by phase contrast microscope (Zeiss primo star). Determine the salt gland index of leaf epidermis by following formula: Salt gland Index (SGI) = {No of salt gland in a given area (S) / Total no of cells of the area} ×100 Leaf Succulence (Relative Water Content, RWC) measurement RWC serves as a valuable metric for assessing a plant's hydration level and it response drought induced water stress (Mayak et al. 2004a ). This method effectively indicates the water content in plant tissues relative to their maximum water-holding capacity (Ahmad et al. 2021). RWC is an appropriate estimate of plant cellular hydration under the effect of both leaf osmotic adjustment and water potential. RWC The measurement involves collecting mature leaves and cut into pieces (2cm×2cm), that determined their fresh weight. To find the turgid weight, leaf samples are hydrated in tri-distilled water at 4°C in darkness for 6h. The samples are dried in a hot air oven at 75°C for 48 h to obtain the dry weight. The RWC is calculated formula followed by Weatherley, 1950 : ‘FW’ means fresh weight, ‘DW’ means dry weight and ‘TW’ means turgid weight RWC= (FW – DW) / (TW – DW) × 100 Compatible Osmolytes Content (COC) Proline Concentration Measurement by Bates et al. 1973 This technique provides a quantitative colorimetric measurement of proline content of plants to determine their plant's physiological condition and evaluating its stress tolerance (Ábrahám et al. 2010). A 0.5 g of frozen leaves are homogenized in 5 ml 3% (w/v) aqueous sulfosalicylic acid solution. The homogenated extract was centrifuged at 15,000 g for 10 minutes at 4°C. 2 ml of supernatant was mixed with 2 ml of acid-ninhydrin (0.125% ninhydrin: 30 ml glacial acetic acid : 20ml of 6 M phosphoric acid) and 2 ml of glacial acetic acid. Acid ninhydrin must be prepared fresh and is only stable for 24 hours at 4°C. The mixture was maintained in water bath at 100°C for 1 hour, and the reaction was stopped in an ice bath. 4 ml of toluene was then added to this solution and stirred for 15–20 sec. collect the upper phase, which was toluene and optical density was measured by absorbance by the spectrophotometer at 520 nm; toluene was used as control. The known concentrations of known L-proline solutions (for example, 0.0, 20, 40, 60, 80, 100 ppm/ml) are used in preparing a standard curve of proline. Proline concentration µmoles proline /g of fresh weight material= [(µg proline/ ml)×ml toluene)/115.5µg/µmole]/[(g sample)/5] Glycine Betaine concentration determined by periodide spectophotometric methods Grieve and Grattan (1983) 0.5 grams of dried powder sample, finely ground plant tissue was mixed with 10ml of dH2O for 24h and then filtered. The 2ml of filtrate was mixed with an equal volume of 2N H2SO4 (1:1) and cooled over ice. 50µl of KI-I2 reagent was added to mixture and gently stirred, then stored at 4°C for 16 hours. The mixture were centrifuged at 12,000 g for 15 minutes at 4°C, the precipitated iodine crystals were dissolved in 1.5 ml of 1,2-dichloroethane with a vortex mixer, and absorbance was measured at 365 nm after 2 hours. A glycine betaine (GB) solution in 1M H2SO4 was used as the standard. Sugar alcohol concentration determination by spectrophotometric methods Lewis and Harley (1965) 0.5 g of powder sample were homogenized with 70% cold methanol. 1ml of methanolic extract diluted with 1ml of 1M acetated buffer (pH 4.5). After 15minutes 1 ml of 0.75% sodium metaperiodate solution added in mixture solutions. The reaction was activated within 5 minutes and measured the optical density at 260nm using UV-spectrophotometer (Shimadzu UV-NIR-3600). D-Mannitol and D-Sorbitol are used as a standard to create a calibration curve, allowing for the determination of sugar alcohol concentrations in the samples. Plant leaf pigments and metabolites concentration determination Chlorophyll content measurement by Schlemmer et al. 2013 At first samples of all plants are collected from study area in zipper pouch. Then 0.5g of fresh leaves homogenized with 10ml ml 80% acetone. Then centrifuges the acetone solution at 5000rpm for 5min. collect the supernatant and repeat the extraction with 80% acetone until the residue becomes colorless. Adjust the final volume to 5ml with 80% acetone and measured the absorbance of the solution at 645 nm, 652 nm, and 663 nm using a spectrophotometer (Shimadzu UV-NIR-3600) with 80% acetone as a blank. Calculate the chlorophyll content using the following equations: ‘A’ mean Absorbance at the specified wavelength; ‘V’ = Final volume of chlorophyll extract in 80% acetone; ‘W’ = Fresh weight of the tissue extracted. Mg chlorophyll a/g tissue = [12.7 \times (A{663}) – 2.69 \times (A{645}) \times V/1000 \times W] Mg chlorophyll b/g tissue = [22.9 \times (A{645}) – 4.68 \times (A{663}) \times V/1000 \times W] Mg total chlorophyll /g tissue = [20.2 \times (A{645}) – 8.02 \times (A_{663}) \times V/1000 \times W] Soluble carbohydrate Measurement (Nielsen, 2009 ) Determination of total carbohydrate concentration in plant samples followed by phenol-sulfuric acid method (Nielsen 2009 ). Weight 0.5 g of plant fresh samples (leaf and stem separately) were hydrolzed with 10 ml of 2.5N HCL in a boiling water bath for 3h to breakdown the complex carbohydrate to simple sugar. The solution neutralized with solid Sodium Carbonate (Na2CO3) to stop the reaction. The effervescence due to release the CO2 during neutralization. Centrifuged the solution at 10000 rpm for 10 min. 500µl of supernatant mixed with 500 µl DH2O, 1ml of 5% phenol and 500ml of 98% concentrated H2SO4. The solution incubated in a water bath at 25–30°C for 20 minute and develop the final color. The optical density measured at 490nm wavelength by UV-spectrophotometer (Shimadzu UV-NIR-3600). The standard solution D-glucose used for standard curve. Total Phenolic Content (TPC) Leaf total phenolic content was determined according to the Folin ciocalteu method, described by Singleton et al. 1999 . The 0.5 ml Folin-Ciocalteu (FC) reagent (1:1) supplemented to 100µl of methanolic extract diluted with 900µl dH2O. The reaction mixture was incubated for 40 min at room temperature in dark condition. Then measured the absorbance at 765nm versus blank. Difference concentration of Gallic acid (mg/GAE) were used as a standard to create a standard curve. The total phenolic content in the samples is expressed as mg of Gallic acid equivalent per gram dry weight (mg GAE/g DW). Determination of total flavonoid content (TFC) Total flavonoid estimation done followed by Aluminum chloride (AlCl 3 ) and Sodium acetate (CHCOONa) calorimeter methods (Fernandes et al. 2012 ). 2mg of dry extract mixed with 4 ml of 80% methanol. Centrifuged (8000g for 5 min) the solution and 0.5 ml of supernatant was diluted with 2 ml of methanol, 100µl of 7% aluminum chloride (AlCl3), 100µl of sodium acetated (CHCOONa) and left for 1 hour for incubation. After incubation, the mixture were measured the absorbance at 450nm against a blank solution by UV-spectrophotometer (Shimadzu UV-NIR-3600). Quercetin (mg/QE) used as a standard solution to obtain a standard curve. Total Flavonoid concentration in the plant samples was calculated from the standard curve and expressed the results as Quercetin equivalent per gram dry weight (mg QE/g DW). Determination of total Polyphenol content (TPPC) Total Polyphenol were estimated according to the method described by Robert ( 1971 ). 1mg dry powder dissolved into 1ml of 90% methanol. Then add 1 ml of vanillin hydrochloride reagent prepared just before use by mixing equal volumes of 8% hydrochloric acid in methanol and 4% vanillin in methanol. Then read in a UV spectrophotometer (Shimadzu UV-NIR-3600) at 500 nm wavelength after 20 min incubation, using vanillin hydrochloride reagent alone, as a blank. Tannic acid (mg/TA) used as a standard solution to obtain a standard curve. TPPC in the plant samples was calculated from the standard curve and expressed the results as Tannic acid equivalent per gram dry weight (mg TA/g DW). Statistical Analysis All quantitative data including plant stress tolerance biochemical marker, soil and surface water parameter were statistical analyzed by GraphPad prism v.10. A one-way analysis of variance followed by post hoc multiple mean comparisons (Tukey’s test) for each dependent variable. All biochemical data were analyzed independently for each species. Additionally, Principal Component Analysis (PCA) of the complete dataset was carried out using PAST. PC1 and PC2 captured most of the observed variability in biplot. Significant covariance’s among parameters were identified using Pearson correlation coefficients (r) (P < 0.05) in correlation matrices. Results Seasonal variation effects on plants Phenological pattern The flowering, fruiting and germination periods of Avicennia species in Digha-Mohona coastal vegetation are summarized in Tables 1 . Avicennia species generally flower throughout the year, with the monsoon and pre-monsoon season having the most blooms. The flowering duration of Avicennia sp. varies greatly, lasting from one to six months. A. marina and A. alba species are exhibit a different reproductive strategy from other two species A. rumphiana and A. officinalis . Moreover, species like A. alba and A. marina flowering duration in the pre-monsoon to monsoon season, while A. officinalis and A. rumphiana flowering periods is late pre-monsoon and monsoon months. In Digha coast, Avicennia sp. primarily produces mature propagules during the monsoon (August-October) period and germination occurred mostly in the post-monsoon season (November- December). Table 1 Phenology of four selected mangrove species during study period at 2023 years. Scientific name Family Flowering season Fruiting season Germination season A. alba Acanthaceae may-Aug Aug-Oct Nov-Dec A. officinalis July - Aug Aug-Oct Nov-Dec A. marina may-Aug Aug-Oct Nov-Dec A. rumphiana July - Aug Aug-Oct Nov-Dec Seasonal effects on plant micro-morphology We set out to investigate the effects of seasonality on the physiological adaptation. We observed the number of salt glands were might be changed in their salt stress level during different season. The highest SGI found in pre-monsoon season and high salt stress environmental condition when compared with other two season (Table 2 ). As show in Fig. 1 a, b and d, A. rumphiana , A. marina and A. officinalis were comparatively highest Salt gland density in post-monsoon and pre-monsoon season. Generally lowest salt gland density found in A. alba during all season (Fig. 1 c). In Table 2 , highest salt gland index (SGI) present in A. rumphiana (4.28%) and A. officinalis (3.75%) leaf epidermis during pre-monsoon season and lowest in A. alba (0.23%) during monsoon season. Moreover, we are observed that the salt glands are form only in post and pre-monsoon season for removed the excess salt from cellular levels. Table 2 Salt gland index of selected Avicennia sp. leaf epidermis. Plants name Number of Salt gland per unit area Number of Epidermal cell per unit area Salt gland Index (%) Monsoon Post-monsoon Pre-monsoon Monsoon Post-monsoon Pre-monsoon Monsoon Post-monsoon Pre-monsoon A. rumphiana 8.33 ± 1.53 31 ± 2 48 ± 3 822.33 ± 8.74 954 ± 6.7 1073.33 ± 7.6 1 3.11 4.28 A. marina 10 ± 1 30.33 ± 1.5 40.67 ± 2 1154 ± 6.56 1359.33 ± 5.2 1333.67 ± 6.1 0.86 2.14 2.96 A. alba 2 ± 1 3 ± 1 10 ± 2 881 ± 7 1120.67 ± 6.1 971 ± 5.6 0.23 0.27 1.02 A. officinalis 24.67 ± 1.53 40.33 ± 2.52 49.67 ± 2 1031.67 ± 9.1 1127 ± 7 1275.33 ± 5.7 2.34 3.46 3.75 Environmental factors and soil condition changes in different season Environmental parameters recorded from soil and surface water have been depicted in Table 3 and 4 . Strong seasonal trend of environmental parameters was observed throughout the study year 2023. Salinity and pH were significantly lower in monsoon than pre-monsoon and post-monsoon in both soil and surface water (two-way ANOVA, P < 0.05). TDS and EC was significantly higher in pre-monsoon compared to post-monsoon (two-way ANOVA, P < 0.05). In pre-monsoon relatively high salinity and temperature, pH levels and electric conductivity. High pH affect their conductivity and total dissolved solids. During monsoon, rainwater and surface runoff typically decrease the pH, salinity and TDS in both surface water and soil due to dilution effect. However, electric conductivity closely correlated with TDS (Fig. 2 a-d). Table 3 Monthly variation in Average Temperature, pH, Salinity (ppt), EC (µs/cm), TDS (ppm) of water surface during study period. Month season Ave. Temp.(°C) pH Salinity (ppt) TDS (ppm) EC (µs/cm) Jan Post-monsoon 30.2 8 16.7 7641 10956 Feb Post-monsoon 32.4 8.1 16.2 7534 10823 Mar Pre-monsoon 33.5 8.2 17.4 7997 11161 Apr Pre-monsoon 34.6 8.43 17.5 8450 11484 may Pre-monsoon 35.4 8.5 17.8 9090 11536 Jun Pre-monsoon 36.47 8.5 18 8343 10761 Jul Monsoon 34.8 7.21 12 5074 8515 Aug Monsoon 33.4 7.42 14.2 4996 7232 Sep Monsoon 34.5 7.56 14 5068 7624 Oct Monsoon 34.2 7.6 16.1 5539 8314 Nov Post-monsoon 31.2 7.9 15 6152 10332 Dec Post-monsoon 29.6 8.2 16 7423 10679 Table 4 Monthly variation in Average Temperature, pH, Salinity (ppt), EC (µs/cm), TDS (ppm) of soil during study period. Month season Ave. Temp.(°C) pH Salinity (ppt) TDS (ppm) EC (µs/cm) Jan Post-monsoon 27 7.4 14.3 4641 8956 Feb Post-monsoon 29.5 7.1 15 4534 8823 Mar Pre-monsoon 30.4 7.1 15.3 4797 8161 Apr Pre-monsoon 31 6.6 16.5 5050 9484 may Pre-monsoon 33 7.1 16.8 5090 9536 Jun Pre-monsoon 34.2 7.2 17 5343 9861 Jul Monsoon 30.6 5.4 12.6 3474 5515 Aug Monsoon 29 6 10.2 3996 6732 Sep Monsoon 29.4 6.1 11.65 4068 6624 Oct Monsoon 30 6.4 12.3 4139 7314 Nov Post-monsoon 25 6.1 13.32 4152 7332 Dec Post-monsoon 26.5 7.2 14.2 4423 8679 Seasonal variation effects on plants Leaf pigments During the pre-monsoon seasons, plants typically have suffered from drought, high temperature and salinity that affect chlorophyll production, affecting photosynthesis in all Avicennia sp. In post-monsoon seasons experience decreasing temperatures and daylight, which can lead to senescence and chlorophyll production decreases. Monsoon seasons bring high water availability, which can lead to increased chlorophyll concentration and decreased the chlorophyll degradation; adequate water supply promotes chlorophyll (total chlorophyll) production in A. alba (37.3mg/g FW) followed by A. marina (33.3mg/g FW), A. officinalis (32.1mg/g FW) and A. rumphiana (30.8mg/g FW) which was significant at 0.05 levels. Although, Digha Mohona mangrove vegetation is a tropical region with minimal seasonal temperature variations, plant chlorophyll production may remain stable throughout the year (Fig. 3 a-c). Seasonal variation effects on Leaf carbohydrate production Shoot soluble carbohydrate contents were increase from monsoon to post-monsoon in all plants. Maximum contents of soluble carbohydrates in Monsoon at significant 0.05 levels. A. alba shoot recorded the highest levels of soluble carbohydrates (65mg/g FW) in monsoon season, whereas A. officinalis shoot had the lowest concentration of carbohydrate (29.3mg/g FW) in pre-monsoon (Fig. 3 d). Seasonal variation effects on plants Leaf Water content Mangrove leaves relative water content (RWC) and biochemical production are significantly affected by seasonal changes. In Table 5 show during pre-monsoon and late post-monsoon season, mangrove leaves tend to have lower relative water content and higher WSD due to water stress (Fig. 3 e and f), which can lead to increased production of biochemical like phenols, flavonoids and polyphenols to protect against oxidative stress. In contrast, the monsoon and early post-monsoon season brings higher water availability, resulting in higher relative water content in A. alba (83% and 72.5%) and A. rumphiana (86.8% and 72.1%). Higher RWC increased chlorophyll production and reduced the chlorophyll degradation. The negative correlation between leaf relative water content and biochemical production varies among mangrove species (Fig. 9 ). For example, A. rumphiana, A. alba, A. marina and A. officinalis exhibit different patterns of chlorophyll-a, chlorophyll-b, and total chlorophyll content in all seasons. A. rumphiana exhibits a negative correlation between leaf relative water content and compatible osmolytes production, with increased chlorophyll production during water stress. A. officinalis shows a lowest leaf relative water content across seasons, with less pronounced changes in chlorophyll and carbohydrate production. Table 5 Concentration of leaf pigment, soluble sugar (mg/g FW) and leaf succulence (% of RWC and WSD) in the leaves of the halophyte Avicennia sp., during pre-monsoon, monsoon and post-monsoon in 2023 year. Determined compounds: carbohydrate (Carb), Chlorophyll-a (Chl-a), Chlorophyll-b (Chl-b) and Total chlorophyll (TC). (AR = A. rumphiana , AO = A. officinalis , AB = A. alba , AM = A. marina ). Every trait's value is the mean ± SE; a significant value denoted by a different letter, p < 0.05. season species Carb (mg/g) Chl-a (mg/g) Chl-b (mg/g) Total Chl (mg/g) RWC (%) WSD (%) monsoon AR 63.8 ± 1.36 bc 34.6 ± 1.14 a 33.7 ± 2 ns 30.8 ± 1.25 b 86.8 ± 1.4 cd 13.2 ± 1.4 cd AM 56.5 ± 2 ab 35.4 ± 0.75 ab 36.5 ± 0.7 ab 33.3 ± 1 ns 61.9 ± 1.6 ab 38.1 ± 1.6 ab AB 65.0 ± 1.3 bd 33.0 ± 1.36 b 42.0 ± 1.5 b 37.3 ± 1 a 83.0 ± 1.6 bc 17.0 ± 1.6 bc AO 53.5 ± 1.18 c 25.9 ± 1.74 a 27.3 ± 1.1 a 32.1 ± 1.1 a 83.0 ± 1.7 ab 28.6 ± 1.4 ab Post-monsoon AR 56.0 ± 1.4 bc 28.8 ± 1.63 a 32.9 ± 1.5 ns 29.2 ± 1 b 83.0 ± 1.8 ad 27.9 ± 1.2 ad AM 46.8 ± 1.76 b 26.7 ± 1.7 a 26.5 ± 1.4 b 31.6 ± 1.7 ns 83.0 ± 1.9 ad 46.1 ± 2 ad AB 54.2 ± 1.87 bc 25.9 ± 1.5 ab 30.9 ± 1.2 b 35.0 ± 1.15 ns 83.0 ± 1.10 b 27.5 ± 2.8 b AO 42.0 ± 1.3 c 23.4 ± 1.1 a 24.5 ± 1.7 a 28.6 ± 1.22 ns 83.0 ± 1.11 ab 32.9 ± 2 ab pre-monsoon AR 44.8 ± 2.3 b 24.5 ± 1.6 a 34.2 ± 1.15 ns 29.9 ± 2.3 ns 83.0 ± 1.12 ac 34.0 ± 2.5 ac AM 40.0 ± 1.3 a 27.2 ± 1.4 b 31.1 ± 1.6 ab 32.8 ± 1.3 ns 83.0 ± 1.13 bd 57.7 ± 2 bd AB 39.4 ± 1.65 cd 23.7 ± 1.1 ab 28.3 ± 1.2 b 31.8 ± 1.2 a 83.0 ± 1.14 bc 36.4 ± 2.6 bc AO 29.3 ± 1.14 c 18.5 ± 1.24 a 24.9 ± 1.5 ns 26.3 ± 1.7 a 83.0 ± 1.15 b 42.0 ± 2.16 b Seasonal variation effects on Leaf Compatible Osmolytes Leaf Proline Accumulation Proline accumulation increasing the leaves of all species in post-monsoon and pre-monsoon. This level was statistically significant (p < 0.05) in A. rumphiana, A. marina and A. alba , but not significant in A. officinalis. It was mentioned the elevated average precipitation in monsoon season in Digha Mohona coastal region in west Bengal caused decreased soil salinity that influenced lower proline accumulation of plant tissue than other season. In pre-monsoon, plants tend to accumulate higher proline content in A. rumphiana and A. marina cellular level, value is 30.3 µmole/g 0.5 dry weight (DW) and 27.1 µmole/g 0.5 dry weight (DW) (Fig. 4 a). Leaf Glycine Betaine Accumulation GB contents were elevated in post-monsoon and pre-monsoon season during study period. The highest GB accumulated taxa, A. officinalis and A. rumphiana , Glycine betaine levels reached 80.9 µmole/g 0.5 DW in A. rumphiana , and 75.5 µmole/g 0.5 DW in A. officinalis in pre-monsoon season. The lowest levels of GB were accumulated in A. marina and value is 24.8 µmole/g 0.5 dry weight in monsoon season (Fig. 4 b). Leaf Soluble Sugar Alcohol Accumulation The Sugar alcohol (Mannitol) concentration was highest in the leaves of A. rumphiana and A. marina (25.4 and 22.2 µmole/g 0.5 FW) during pre-monsoon in 2023 (Fig. 4 c). Table 6 show the highest Sorbitol concentration in A. rumphiana , while the value is 21.7 µmole/g 0.5 FW in pre-monsoon season. Lowest concentration of sorbitol in A. officinalis leaves that value is 8.4 µmole/g 0.5 FW in post-monsoon season. Sugar alcohol accumulation showed insignificant correlations with environmental factors (i.e. temperature, EC, TDS, soil salinity and pH) (Fig. 4 d). Table 6 Levels of secondary metabolites and Compatible osmolytes (umol g-1 dw) in the leaves of the halophyte Avicennia sp., during pre-monsoon, monsoon and post-monsoon in 2023 year. Determined compounds: Total Phenol Content (TPC), Total flavonoids Content (TFC), Total Polyphenol Content (TPPC), proline (Pro), glycine betaine (GB) mannitol (man) and Sorbitol (Sor). (AR = A. rumphiana , AO = A. officinalis , AB = A. alba , AM = A. marina ). Each value show mean ± SE, a significant value is indicated by a different letter, p < 0.05. season species TPC (mg/g) TFC (mg/g) TPPC (mg/g) Pro (µmole/g) GB (µmole/g) Man (µmole/g) Sorb (µmole/g) monsoon AR 10.5 ± 0.8 c 12.7 ± 1.56 c 10.5 ± 0.95 ab 10.8 ± 1.51 bc 61.6 ± 0.7 ab 12.7 ± 1.46 bc 10.9 ± 1.65 bc AM 8.8 ± 1.53 ab 10.2 ± 1 b 8.5 ± 1.06 b 10.2 ± 1.3 ac 24.8 ± 1.5 cd 11.0 ± 1.5 c 11.2 ± 1.56 a AB 7.9 ± 1.4 a 11.4 ± 1.2 bc 9.8 ± 1.5 c 8.9 ± 1.9 b 34.1 ± 1.4 c 12.7 ± 1.4 a 12.0 ± 1.6 a AO 8.9 ± 1.83 b 11.4 ± 1.13 ab 10.2 ± 1.1 ac 9.4 ± 2 ac 52.6 ± 1 ac 10.6 ± 1 ns 9.8 ± 1.3 a Post-monsoon AR 18.5 ± 1.23 bc 25.6 ± 1.75 ab 19.5 ± 2.2 a 19.8 ± 1.9 ac 66.7 ± 2.2 ab 17.2 ± 1.4 bc 14.8 ± 1.4 b AM 25.3 ± 1.63 b 16.7 ± 2.29 ns 10.2 ± 1.02 b 14.6 ± 1.2 ab 40.9 ± 1 cd 13.6 ± 1.6 b 12.1 ± 1.6 a AB 22.9 ± 1.6 ad 19.9 ± 1.53 bc 11.8 ± 2 a 10.4 ± 1.8 a 56.3 ± 1.3 a 13.4 ± 0.78 b 11.4 ± 1.1 a AO 16.3 ± 0.6 ab 22.2 ± 1.33 ac 16.3 ± 1.7 a 17.1 ± 0.75 a 65.4 ± 1.3 a 9.6 ± 1.2 ns 8.4 ± 0.6 a pre-monsoon AR 22.4 ± 1.06 bc 34.4 ± 0.85 bc 26.2 ± 1.8 b 30.3 ± 1.3 ab 80.9 ± 1.3 b 25.4 ± 1.6 c 21.7 ± 2 bc AM 14.5 ± 1.21 b 21.0 ± 1.67 b 18.9 ± 1.3 b 27.0 ± 1.5 bc 60.9 ± 1.2 d 22.2 ± 1.9 a 18.9 ± 1.7 a AB 16.2 ± 1.58 ad 28.7 ± 1 b 20.9 ± 1.27 ac 23.4 ± 1.8 ab 56.6 ± 1 c 18.5 ± 0.87 ab 15.6 ± 0.8 a AO 19.3 ± 0.91 ab 32.9 ± 1.61 bc 22.5 ± 1.18 ac 21.1 ± 2.2 c 75.5 ± 0.87 ac 11.7 ± 1.5 ns 10.9 ± 1.23 a Seasonal effects on Leaf Secondary metabolites TPC, TFC and TPPC were analysis in four-selected Avicennia sp. during three different season 2023. In the pre-monsoon season, plant was exposed high saline and water stress environment, the A. rumphiana (22.4 mg GA/g DW) and A. officinalis (19.3mg GA/g DW) were produced highest phenolic compounds (Fig. 5 a). In this season, just prior to flowering the A. rumphiana and A. officinalis were synthesis highest phenolic compounds. A. marina (25.3mg GA/g DW) and A. alba (22.9mg GA/g DW) had highest concentration of phenols synthesis during early pre-monsoon (Feb to April) when the plant is in the rosette stage and exposed into the high salinity stress condition. In this study, plants have synthesized minimum amount of secondary metabolites (TPC, TFC, and TPPC) at the start of flowering stage but the amounts were increased at fruit periods. Although, highest amount of TFC and TPPC produced in late post monsoon to pre-monsoon season (Table 6 ). In monsoon season, lowest amount of total flavonoids 7.9 mg QUE /g DW in A. alba and lowest polyphenol contents 8.5mg TAN/g in A. marina (Fig. 5 b and c). In seasonal variation, plant have to fluctuating environmental cues, including salinity, pH, total dissolved solids, and electrical conductivity. These environmental factors directly influence in the synthesis and accumulation of secondary metabolites. Principal component analysis and Pearson correlation coefficient analysis The first component, PC1, of the PCAs represented in the surface water and soil parameters, generally showed a high positive correlation with soil properties and surface water data related to water stress (Salinity and pH) and to salt stress (EC and TDS in soil). In addition, a correlation was observed in soil and surface water avg. temp, pH, salinity, TDS and EC were associated with different season. Furthermore, a clear distinction between soil and water parameter under different season was observed. The cumulative percentage variation of the first two principal components (PC) was 94.8% of the total variation (PC1, 73.89%; PC2, 20.91%) in the case of PCA plot representing in surface water parameter (Fig. 6 a). The PCA showed a separation among post-monsoon, pre-Monsoon and monsoon. Avg. temp., pH, salinity and TDS were positively correlated with the pre-monsoon season and EC showed a negatively correlated with post-monsoon. The first two principal components explained 90.55% of the total variation (PC1, 69.84%; PC2, 20.71%) in the case of PCA plot representing in soil parameter (Fig. 6 b). The PCA showed a separation among post-monsoon, pre-Monsoon and monsoon. Avg. temp., TDS and EC were positively correlated with the pre-monsoon season while salinity and pH showed a negatively correlated with post-monsoon. Soil and surface water parameter all are showed a high positive association in PC1 but only Avg. temp showed positive association in PC2 and pH, salinity, TDS and EC showed a negative association in PC2 (Fig. 6 a and b). To obtain a physiological and biochemical adaptive view of similarities and differences among four Avicennia species during different seasons (Monsoon, post-monsoon and pre-monsoon), the full dataset was subjected to a principal component analysis (PCA). The first two principal components (PC1 and PC2) accounted for > 70% variance (Fig. 7 ). The PCA showed a separation among post-monsoon, summer, and monsoon and different species level. Secondary molecules (including total phenols, polyphenols, and flavonoids), compatible osmolytes (such as proline, glycine betaine, Mannitol and sorbitol), pigments (such as Chlorophyll-a, b and TC), soluble sugar (carbohydrates), and plant succulence (RWC and WSD) were positively correlated with the monsoon season. We also included in this analysis soil or surface water parameters, according to the newly performed PCAs, are shown in the graphs. The second PCA included the variables related to salinity stress and biochemical defense mechanisms: total phenols, polyphenols, and flavonoids, proline, glycine betaine, Mannitol and sorbitol, Chlorophyll-a, b and TC, carbohydrates and plant succulence (RWC and WSD). All the analyses gave Eigenvalues greater than one. A Pearson correlation coefficient analysis was used to investigate the relationship between different morpho-biochemical traits and the leaf succulence of Avicennia sp. plants in different season that exhibit to various salinity stress environments and water stress condition. However, this analysis was employed to investigate the variations in the accumulation of compatible osmolytes, leaf pigments, carbohydrate and secondary metabolites (TPC, TFC and TPPC) in plants under varying intensities of salinity stress in seasonal variation and to elucidate the interrelationships among these altered metabolites (Fig. 8 ). We focused on the data related to the environmental factors of water surface (as temp. pH, salinity, TDS, EC) associated with biochemical changes in study year. There was a notable positive correlation observed between average temperature and pH (r = 0.0498), average temperature and salinity (r = 0.087), average temperature and TDS (r = 0.052) and negative correlation between average temperature and EC (r= -0.165). Moreover, high positive correlation between pH and salinity (r = 0.92), pH and TDS (r = 0.96), pH and EC (r = 0.89), salinity and EC (r = 0.77), salinity and TDS (r = 0.89), TDS and EC (0.94). In addition, high positive correlation were observed between soil salinity and EC (r = 0.99), salinity and TDS (r = 0.98), pH and EC (r = 0.97) and salinity and pH (r = 0.91). In monsoon, PC1 and PC2 explained 46.143% and 39.84% of the variation. TPC, TFC, TPPC, Pro, GB, Man, Sor, Carb, Chl-a and RWC showed a high positive association in PC1 while Chl-b, TC, WSD showed a negative association. While PC2 was a high positive correlation between mannitol, sorbitol, carbohydrate, chl-b, TC and RWC trait but proline, GB, TPC, TFC, TPPC and WSD were negatively correlated with PC2. In addition, a correlation was observed in A. rumphiana between mannitol, sorbitol, carbohydrate, chl-b, TC and RWC. Chl-b and TC were associated with A. alba . WSD associated with A. marina and A. officinalis (Fig. 7 a). In post-monsoon, PC1 and PC2 explained 51.08% and 32.9% of the variation. TPC, TFC, TPPC, Pro, GB, Man, Sor, Carb, Chl-a, chl-b, TC and RWC showed a high positive association in PC1 while WSD showed a negative association. While PC2 was a high positive correlation between TPC, TFC, TPPC, proline, GB and RWC trait but mannitol, sorbitol, carbohydrate, chl-a, chl-b, TC and WSD were negatively correlated with PC2. In addition, a correlation was observed in A. rumphiana between TPC, TFC, TPPC, proline, GB and RWC. TPC and TC were associated with A. alba and A. marina . WSD associated with A. officinalis (Fig. 7 b). In pre-monsoon, PC1 and PC2 explained 48.623% and 43.01% of the variation. TPC, TFC, TPPC, Pro, GB, Man, Sor, Carb, Chl-a, chl-b and RWC showed a high positive association in PC1 while TC and WSD showed a negative association. While PC2 was a high positive correlation between TPC, mannitol, sorbitol, carbohydrate, chl-a, chl-b, TC and RWC trait but proline, GB, TFC, TPPC and WSD were negatively correlated with PC2. In addition, a correlation was observed in A. rumphiana between mannitol, sorbitol, carbohydrate, chl-a, chl-b and RWC. Mannitol, sorbitol, carbohydrate, chl-a, chl-b and TC were associated with A. marina . WSD associated with A. alba (Fig. 7 c). In A. marina , both components PC1 and PC2 explained 100% of observed variability, of which 73.329% corresponded to PC1 (Fig. 8 b). Secondary metabolites (including TPC, TFC, TPPC) were positive correlated with Compatible osmolytes (Proline, GB, Mannitol, Sorbitol) and Environmental parameters (Ave. Temp., pH, Salinity, TDS and EC) according to the Pearson correlation coefficients. Negatively correlated with carbohydrate, pigments (chl-a, chl-b and TC) and RWC. Their correlation coefficient are TPC and Pro (r = 0.217), TPC and GB (r = 0.36), TPC and Man (r = 0.0217), but negative correlation with TPC and Sorb (r = -0.014), TPC and Ave. Temp. (r = -69). TPC positively correlated with environmental factors like pH (r = 0.609), salinity (r = 0.443), TDS (r = 0.545) and EC (r = 0.69). However, a clear negative correlation trend was observed between TPC and carbohydrate (r = − 0.525), TPC and Chl a (r = − 0.869), TPC and Chl b (r = − 0.995) and TPC and TC (r = − 0.94) (Fig. 9 ). In A. alba , the biplot, PC1 and PC2 explained 100% of total variability, with 88.17% corresponding to PC1. Secondary metabolites (including TFC, TPPC), Sugar alcohol (Mannitol, Sorbitol), GB, Proline were positively correlated with PC1 and pre-monsoon season. Pigments (chl-a, b, TC) Leaf Succulence (RWC) were associated with monsoon. TPC, GB and WSD associated with post-monsoon (Fig. 8 c). However, positive correlation with sorbitol and environmental factors pH (r = 0.79), salinity (r = 0.89), TDS (r = 0.84), EC (r = 0.723) according to Pearson correlation coefficients (Fig. 6 ). Nevertheless, negatively with carbohydrate(r = − 0.95), chl a (r = − 0.78), chl b (r = − 0.67), TC (r = − 0.87) and RWC (r = − 0.89) (Fig. 9 ). In A. officinalis , the scree plot of the first two PCA was 100% of total variation. PC1 was accounted 88.52% and 11.48% of PC2. The squared cosines of the variables corresponding to the plant secondary metabolites (including TPC, TFC, TPPC), Compatible osmolytes (Proline, GB, Mannitol, Sorbitol) and WSD were positively associated with component PC1, and pigment (chl-a, chl-b, TC) and RWC were associated with monsoon season (Fig. 8 d). Discussion Environmental variables can affect the oxidative stress, osmotic balance (Ionic balance) and phenological patterns of mangrove plants. These factors can result in adaptive responses like growth retardation, decreased chlorophyll content, and the accumulation of compatible osmolites. Therefore, A. marina, A. alba, A. rumphiana and A. officinalis were chosen for a thorough examination of the regulatory processes that underlie upwelling stress. The phenological patterns of mangrove and associated species within the Digha Mohona mangrove vegetation reveal intricate flowering and fruiting cycles, as summarized in Table 1 , indicating year-round flowering for mangrove species, with peak activity during the rainy season, highlighting the temporal variability in reproductive strategies among different species (Songsom et al. 2019 ). Literature offers some insights into mangrove phenology, revealing that Bruguiera gymnorrhiza flowers during the summer and rainy seasons, while Rhizophora mucronata flowers throughout the summer and winter month (Wang’ondu et al. 2013 ). Cumulative rainfall seems to be the chief environmental driver of mangrove phenology in these regions. With mangrove forests flourishing along tropical and subtropical coasts, reaching into warm temperate zones, they are celebrated for their precious ecosystem functions, yet are expected to be severely affected by global climate change-related physical processes in the coming years (Stocken et al. 2022 ). Leaf biophysical parameters are closely tied to environmental factors like temperature, sunlight, water availability, and salinity (Flores-de‐Santiago et al. 2012). Mangrove forests, due to their evergreen nature, do not exhibit distinct seasonal vegetation growth cycles, unlike other well-studied vegetation types. Salt glands are specialized organs of some halophytes that excreting the excess salt and maintain a stable internal environment. In this study, A. rumphiana leaf epidermis present a highest number of salt glands (SG) and number of SG are present in A. alba leaf epidermis (Fig. 1 , Table 2 ). Although SG concertation are not change in seasonal variation but SG, numbers were changed in species-specific manner. According to Mcnae (1965), Avicennia sp. SGs only form in saline environments but in Aegiceras sp. they appear to regardless of salt concentration. Joshi et al. ( 1975 ) found that Avicennia sp. grow in high saline environments, while Acanthus sp. and Aegiceras sp. are grow in low saline region. Succulence (e.g. dilution of the accumulated salts by increasing water content per leaf area) is a typical morphological adaptation to osmotic stresses (Popp 1995 ). In investigation, relative water content in leaf of A. rumphiana and A. officenalis was higher potentially due to succulence and salt accumulation in monsoon season (Fig. 3 ). These species also had higher turgor weight to dry weight, possibly from ion accumulation in vacuoles, unlike other salt excretory species like A. alba then A. marina . Salt excretory plants typically have lower ion accumulation because they excrete absorbed ions via salt glands (Klug et al. 1973; Breckle, 1990 ; Sadak, 2019 ). In cases where the photosynthesizing tissue is succulent, succulence can be estimated on the basis of the chlorophyll content relative to the tissue water content (King et al. 1982 ). Photosynthetic systems are sensitive to temperature variation, and reduced chlorophyll content usually occurs in plants at low temperatures because chlorophyll biosynthetic enzymes are affected and so biosynthesis progresses slowly (Vosnjak et al. 2021 ). In our study, reductions in pigment contents observed in A. officinalis and A. rumphiana during pre-monsoon season while highest chlorophyll contents observed in A. alba and A. marina which indicates that highest salt accumulation rate in tissue level during pre-monsoon season that caused a degree of damage to mangroves (Fig. 3 ). During monsoon, pigment contents increased and by significantly more than that of the pre-monsoon season (p < 0.05). Plants are also able to construct a defense system that actively increases their chlorophyll contents and prevents decreases in photosynthesis and energy production (Zhang et al. 2019 ). In this study, monsoon season reducing the salinity level and increasing the pigment production of Avicennia species. Significant variations in total soluble sugars content (TSSC) were observed across different seasons. The highest concentrations of TSSC were observed in A. alba followed by A. marina during the post-monsoon season. Photosynthetic pigments utilize sunlight for the production of carbohydrates through the process of photosynthesis. Carbohydrates are among the vital sources responsible for providing energy for respiration and other metabolic processes in seaweeds (Khairy and Shafay 2013). Increase of soluble carbohydrates in plants during salinity stress was also reported by Strogonov ( 1964 ), Maas and Nieman ( 1978 ), and Doddema et al. ( 1986 ). Soluble carbohydrate increase in shoots during stress condition is in fact an important response to water deficiency and probably a result of starch hydrolysis during water deficiency stress in tissues and soil water potential decrease (Jones and Qualset 1984 ). Increase of soluble carbohydrate in roots could be due to starch conversion to soluble sugars, reduction in their consumption or reduction in their transmission throughout the phloem (Irigoyen et al. 1992 ). Plants are use some of their carbon resources to produce osmolytes for adaptation instead of food production, which may affect their growth. Therefore, plants are regulate osmoregulation during salinity by accumulation of compatible solutes in tissue level. It is necessary to conduct additional research to determine whether the formation of both kinds of osmolytes is associated with the properties of sodic or saline-sodic soils. Our findings, however, make it seem improbable that these variations in the kind of osmolyte accumulated are solely connected to flowering and seed development, as Popp and Albert ( 1995 ) proposed. There were species-specific variations in the osmolyte's composition as well as the seasonal pattern of osmolyte accumulation. According to Galinski ( 1993 ), compatible solutes are organic osmolytes that maintain osmotic balance while also being compatible with the metabolism of the cells. Along with their primary role in osmotic adjustment, compatible solutes can also act as radical scavengers, which prevent oxidative damage, or as osmoprotectants, which stabilise macromolecules in harsh environments (Yeo 1998 ). In investigation, highest Proline accumulation from late post monsoon to late pre-monsoon in leaves of all Avicennia species (Table 6 ). In the pre-monsoon season rising the salinity levels in coastal soil and waters, that cause higher proline accumulation in A. rumphiana followed by A. Marina, A. alba and A. officenalis (Fig. 4 ). Proline accumulation is likely because some plants possess the capacity for organic and inorganic compounds accumulation in cytoplasm to decrease water potential and alter osmotic gradient to take up water. This indicates that plants employ proline for salinity adaptation (Chaib and Benlaribi 2006 ; Hmidi et al. 2018 ; Ibrahim 2013 ). Proline is often considered a compatible osmolytes, which means it helps plants to maintain osmotic balance under stress (Jain et al. 2013 ). It can accumulate in the cytoplasm without disrupting cellular functions, thus protecting enzymes and cellular structures (Moukhtari et al. 2020 ). Proline accumulation may increase over time as plants acclimate to salt stress, eventually reaching a stable level (Parihar et al. 2014 ). Proline content increased in A. marina seedlings and saplings under salt stress, but its contribution to overall osmolality was minimal (Cherian et al. 1999 ; Joseph et al. 2015 ). Another compatible osmolytes like glycine betaine (GB) is a great osmoprotectant and polyols are act as a free radical scavengers (Yeo 1998 ; Mehta et al. 2023; Adrian-Romero et al. 1994). Our studies, highest concentration of GB were found in late post monsoon to late pre-monsoon (Table 6 ). Although, compatible osmolytes were accumulated in plant tissues that depends on salinity stress, seasonal variation and species-specific level. GB concentration is highest in A. rumphiana and A. officinalis (Fig. 4 ). According to Murakeozy et al. ( 2003 ), low temperatures, hypoxic conditions, and high salt concentrations were the primary factors limiting plant growth in March, the highest concentrations of compatible osmolytes were observed in all three halophytic species ( Lepidium crassifolium, Camphorosma annua and Limonium gmelini subsp. Hungaricum ). The results showed that Sugar alcohol (SGA) (mannitol and sorbitol) concentration increased by increase of salinity from late post-monsoon to pre-monsoon. SGA concentration regulated by salinity stress, temperature stress and accumulation by species-specific manner. Highest concentration of SAG (mannitol and sorbitol) in A. rumphiana followed by A. marina, A. alba and A. officinalis. Pre-monsoon and post-monsoon periods result in increased levels of secondary metabolites (such as Phenol and flavonoids), indicating these are highest salinity stress. Seasonal fluctuations in the level of metabolites could be caused by elevated temperatures in summer (pre-monsoon), which height concentration of phenols in all four plants (Table 6 ). Under salinity stress, plants often exhibit changes in their phenolic and flavonoid content, which are secondary metabolites involved in stress response. However, these changes can vary depending on the plant species, tissue type, and the concentration of NaCl (Parihar et al. 2014 ). In many plant species, the total phenolic and flavonoid content tends to increase under moderate salinity levels (Bistgani et al. 2019 ). Phenolic compounds act as antioxidants, scavenging reactive oxygen species and protecting cellular components from damage (Bistgani et al. 2019 ). At higher salinity levels, the phenolic and flavonoid content may decrease (Trivellini et al. 2014 ). This could be due to the disruption of metabolic processes or the reallocation of resources to other stress responses (Rahimi and Biglarifard 2011 ). In investigation, TPC of all four plants is might be changed in species-specific manner. In pre-monsoon season, Phenol contents showed no significant difference until monsoon. Similarly, the total flavonoid concentration did not increase from pre-monsoon to post monsoon (Fig. 5 ). Conclusions The present study revealed prominent seasonal variations in the physiological and biochemical composition of four Avicennia sp. Mangroves are more important as resources due to their ecology and the impact of cyclonic events on the ecosystem services and functions of these coastal wetlands (Chowdhury et al. 2023 ). According to this research, all four Avicennia sp. had better tolerance mechanisms under salt stress condition. These species could be used for revitalizing environment in saline regions. Compatible osmolytes (Pro, GB, Man, Srob) accumulation levels were highest in pre-monsoon, while few number of SG were present in leaves epidermis in A. rumphiana. However, Chlorophyll and carbohydrate were highest levels during the monsoon period in A. alba . These variations may have been due to physiological adaptation to environmental conditions. Seasonal Changes affected Photosynthetic Pigments of Avicennia sp. To conclude based on seasonality, pre-monsoon may be the highest tolerance for salinity stress. Understanding these seasonal effects is crucial for managing and conserving mangrove ecosystems, which provide vital ecological services and support biodiversity. Mangroves adaptations to water stress enable them to thrive in environments with fluctuating water availability. Understanding the correlations between leaf relative water content, biochemical production, and water stress can inform conservation efforts and promote ecosystem resilience. Declarations Author contribution A.M. designed and performed the experiments; data analyzed and interpreted; as well as the writing of the manuscript. A.K.M. helped with data analysis and provided guidance during the manuscript preparation process. He also helped with the manuscript's final revision. Funding No specific grant from a public, private, or nonprofit funding organization was obtained for this research. Data availability All data generated or analyzed during this study are included in this published article and available from the corresponding author on reasonable request. Ethics approval and Consent to participate Not Applicable. This study did not involve any human participants or live vertebrates. The collection of Avicennia marina, A. alba, A. officinalis, A. rumphiana specimens complied with national and institutional guidelines for research involving plants. No special permissions were required under local permissions and did not involve protected or endangered species requiring special licenses. The species was taxonomically identified by Dr. K. Karthigeyan, Scientist-F, Central National Herbarium (CNH) in Howrah, Botanical Survey of India (BSI). The collected specimen Avicennia marina (VU/Arpita/0073/16) , Avicennia alba (VU/Arpita/0072/16) , Avicennia officinalis (VU/Arpita/0074/16) , Avicennia marina var. Avicennia rumphiana (VU/Arpita/0075/16) has been store in the VU Herbarium center, Department of Botany, Vidyasagar University, West Bengal, India. Permission to collect the plants/ plant parts Not applicable. The study did not involve protected or endangered plant species. Sample collection was conducted for research purposes only on non-protected public land; therefore, no ethical approval or written permission was required. Competing interest No competing of interest. Consent to publish Not applicable. 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01:14:44","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37593,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/e0aab53e47ff2d2cbd7e4800.png"},{"id":100547910,"identity":"abd8e176-cbfb-404c-b337-66fbdb4d9da7","added_by":"auto","created_at":"2026-01-19 08:16:57","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30522,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/15464f547c013bc3897d49f2.png"},{"id":100548109,"identity":"a67dff55-75d7-43fe-a8ad-4c29bf4dd8dc","added_by":"auto","created_at":"2026-01-19 08:17:31","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":226306,"visible":true,"origin":"","legend":"","description":"","filename":"dcaecfeb946745fbbb12b007427e1ae51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/abff9d93fd6593ff2f0ba518.xml"},{"id":100547784,"identity":"f8fbc4d6-a052-4720-ba11-b1506c85dff7","added_by":"auto","created_at":"2026-01-19 08:16:35","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":238195,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/eca31ecc93bb85ddb2ddd49d.html"},{"id":100547608,"identity":"a3530814-2a7a-4685-93a2-0d4e605a8392","added_by":"auto","created_at":"2026-01-19 08:16:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2330269,"visible":true,"origin":"","legend":"\u003cp\u003eseasonal changes effects in the number of salt glands of adaxial leaf epidermis of four \u003cem\u003eAvicennia \u003c/em\u003esp. (a) \u003cem\u003eA. rumphiana,\u003c/em\u003e(a-b represent salt gland in monsoon season; c-d showing the salt gland in post-monsoon season; e-f represent the salt gland in pre-monsoon season.) (b) \u003cem\u003eA. marina,\u003c/em\u003e (a-b represent salt gland in monsoon season; c-d showing the salt gland in post-monsoon season; e-f represent the salt gland in pre-monsoon season.) (c) \u003cem\u003eA. alba,\u003c/em\u003e (a-b represent salt gland in monsoon season; c-d showing the salt gland in post-monsoon season; e-f represent the salt gland in pre-monsoon season.) (d) \u003cem\u003eA. officinalis.\u003c/em\u003e (a-b represent salt gland in monsoon season; c-d showing the salt gland in post-monsoon season; e-f represent the salt gland in pre-monsoon season.) Salt glands were observed by phase contrast microscope (Zeiss primo star (10x and 20x lens). Yellow arrow represent presence of Salt glands.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/bb209e55ce8ebc5cb19ea5e8.png"},{"id":100547620,"identity":"d156c469-1f8d-4b7e-a127-c2880e1d950a","added_by":"auto","created_at":"2026-01-19 08:16:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":274587,"visible":true,"origin":"","legend":"\u003cp\u003ea) Bubble graph represent the monthly variation in average temperature, pH and salinity of surface water during the study year 2023. (b) Monthly variation in salinity, TDS and EC recorded during the study period. (c) Bubble graph represent the Monthly variation in soil average temperature, pH and salinity during the study year 2023. (d) Monthly variation in salinity, TDS and EC recorded during the study period.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/3f7ff0d54203113fc7cbfe35.png"},{"id":100457157,"identity":"031faf73-96a4-4bdc-a63e-91272e173ff4","added_by":"auto","created_at":"2026-01-17 01:14:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":681515,"visible":true,"origin":"","legend":"\u003cp\u003eThe change in leaves pigment, succulence and carbohydrate production of four \u003cem\u003eAvicennia \u003c/em\u003eSp. under different season. (a) Chlorophyll-a, (b) Chlorophyll-b, (c) Total Chlorophyll (d) Carbohydrate contents (CABC); (e) Relative Water Content (RWC); (f) Water Saturation Deficit (WSD) in different Season. Data are shown by mean ± SE.\u003cstrong\u003e \u003c/strong\u003eMeans followed by different letters indicate a significant difference (p \u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/db93c9e814360991beba771a.png"},{"id":100547704,"identity":"f0ede421-a8c1-4a76-a3a3-8e5b30be94f5","added_by":"auto","created_at":"2026-01-19 08:16:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":463393,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the Compatible Osmolytes accumulation of four \u003cem\u003eAvicennia \u003c/em\u003eSp. under different season. (a) Proline content (PC); (b) Glycine Betaine Content (GBC); (c) Mannitol content (MANC), (d) Sorbitol content (SORC). Data are shown by mean ± SE.\u003cstrong\u003e \u003c/strong\u003eMeans followed by different letters indicate a significant difference (p \u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/f00ea81e0cc41e1e915a9490.png"},{"id":100457159,"identity":"b189d25e-da6a-4698-822b-0f8eafff03e8","added_by":"auto","created_at":"2026-01-17 01:14:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":356494,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the Secondary metabolites in leaves of four \u003cem\u003eAvicennia \u003c/em\u003eSp. (a) Total Phenol content (TPC); (b) Total Flavonoids Content (TFC); (c) Total Polyphenol content (TPPC). Data are shown by mean ± SE.\u003cstrong\u003e \u003c/strong\u003eMeans followed by different letters indicate a significant difference (p \u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/f828567d0b79d9c989469335.png"},{"id":100548004,"identity":"91c1a590-bdc5-400f-95a1-c8e37d3f8cf8","added_by":"auto","created_at":"2026-01-19 08:17:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":352292,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Site score plot of the studied variables on the two principal components (PC1, PC2) for monthly environmental parameters of surface water during study periods. (b) Principal component analysis plot of PC1 versus PC2 for monthly environmental parameters of soil during study periods.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/d78b889895a9f841db187010.png"},{"id":100457180,"identity":"7b85c508-d2b7-4b5d-968a-263a9388a001","added_by":"auto","created_at":"2026-01-17 01:14:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":392828,"visible":true,"origin":"","legend":"\u003cp\u003eSite score plot of the studied variables on the two principal components (PC1, PC2) for 18 traits among 4-mangrove sp. under three different season. (a) Monsoon, (b) Post-monsoon, (c) Post-monsoon. Avg. Temp. (Average temperature), pH, Salinity, TDS (total dissolved solid), EC (Electric conductivity), Chl (chlorophyll a, b, total chlorophyll), carb( Carbohydrate), RWC (Relative water content), WSD (water saturated deficid), Pro (proline), GB (glycine betraine), Mann ( mannitol), Sorb (sorbitol), TPC (Total Phenol Content), TFC ( Total Flavonoids content), TPPC (Total Polyphenol Content). (AR= \u003cem\u003eA. rumphiana\u003c/em\u003e, AO= \u003cem\u003eA. officinalis\u003c/em\u003e, AB= \u003cem\u003eA. alba\u003c/em\u003e, AM= \u003cem\u003eA. marina\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/d7be84031edb594656029dcc.png"},{"id":100548113,"identity":"df47a0f8-98c4-41b5-9782-6acadb497cd4","added_by":"auto","created_at":"2026-01-19 08:17:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":255512,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot for the first two principal components was analyzed using the principal component analysis (PCA) for 18 traits among four mangroves \u003cem\u003eA. rumphiana\u003c/em\u003e (a), \u003cem\u003eA. marina\u003c/em\u003e (b), \u003cem\u003eA. alba\u003c/em\u003e (c), \u003cem\u003eA. officinalis\u003c/em\u003e(d) under three different season. Arrows represented traits while its length is based on the contribution of each trait to separate the accessions.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/7ac29edc1462706a75075485.png"},{"id":100547587,"identity":"22fd8781-96da-47e6-992e-90f9a7743a18","added_by":"auto","created_at":"2026-01-19 08:16:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":99447,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation plot among leaf succulence ( % of RWC and WSD), leaf pigments (Chl a, Chl b, Total chl), carbohydrate, compatible osmolytes (pro, GB, Man, Sorb,) and secondary metabolites (TPC, TFC, TPPC) in four different \u003cem\u003eAvicennia\u003c/em\u003e sp. (AR, AB, AO, AM) leaves under different season \u0026nbsp;(mg g−1 ). The color bar on the right side represents the significant R-values (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/0419d5cc674cbc6bc070b81e.png"},{"id":106393042,"identity":"aec98e58-cdcb-4f47-b3bf-56cf93c00a50","added_by":"auto","created_at":"2026-04-08 07:29:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7079714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8363184/v1/6f002bff-aafc-4396-bdf5-049b84a7ec45.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Modulation of Salinity Stress Response in Leaf Micro-morphological and Biochemical Insights of the Mangrove Avicennia sp. (Acanthaceae) in Digha Mohona, West Bengal","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe plants can perceive and respond to stressful situations, few species thrive in environments where high levels of abiotic stress are present (Rodriguez et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Abiotic stress affects large areas of farmed and irrigated land, making it a major problem for global agriculture (Shrivastava and Kumar \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Most studies concerning salt tolerance involve plants that have been grown in a natural saline ecosystem, but the studies involving plants that have been grown under natural saline ecosystems are more significant in understanding salt stress tolerance (Lokhande and Suprasanna \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Among the various abiotic stresses, salinity alone affects approximately 930\u0026nbsp;million hectares, about 7% of the world's land surface (Munns and Tester \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). There are a number of lands that affected by hyper-ionic and hyperosmotic stress due to excessive salt accumulation in coastal and estuarine regions, generally resulting from the accumulation of salt over a long period of time (Munns \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Cellular compartments detect abiotic stress triggering molecular responses through regulatory proteins and receptors (Zhang et al. 2023). These signals initiate downstream gene expression leading to compatible osmolytes and protective proteins synthesis (Melo et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Integration of environmental signals with endogenous developmental signals influences compatible osmolytes accumulation that promote plant resistance to environmental stresses. Plants can develop broader defenses against abiotic stress, including outer cuticle layer, compatible osmolytes, internal reactive species scavengers and molecular chaperones (He et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Various physiological and biochemical processes are employed to cope with drought stress, including accumulating osmoregulatory molecules like proline, glycine betaine and amino acids (Lei et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rosa et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several studies have highlighted the physiological and biochemical mechanisms, along with morphological and anatomical features, that contribute to the notable tolerance of plants to abiotic stresses such as salinity, oxygen, drought and extreme temperatures (Naz et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Walker and Lutts \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The accumulation of compatible osmolytes is a vital component of adaptation to drought, salinity, and cold (Walker and Lutts \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Osmoregulation, the process of maintaining cellular turgor pressure under osmotic stress, is crucial for plant survival under drought conditions (\u0026Ouml;zt\u0026uuml;rk et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Osmotic adjustment is achieved through the synthesis and accumulation of compatible solutes, which are non-toxic compounds that help to balance the osmotic potential of cells and protect cellular structures (Rampino et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Oliveira et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). When exposed to salt, plants use a variety of defense mechanisms, such as the buildup of secondary metabolites, reactive oxygen species, and compatible solutes. Osmotic adjustment aided by the cytosolic accumulation of solutes such as proline and glycine betaine (Xing and Rajashekar \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These compatible solutes not only contribute to osmotic adjustment but also protect cellular structures and enzymes from damage caused by dehydration (Chen and murata \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; He et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sewelam et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The presence of ROS in the environment due to abiotic stresses such as salt is harmful to cells because they cause oxidative danger for lipids, membrane proteins, and nucleic acids (Smirnoff \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Gomez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e and Hernandez et al. 2001). Certain levels of ROS are also involved in antioxidative protection, even though they can harm proteins, nucleic acids, and membrane lipids. Moreover, carbohydrates support the preservation of protein structure under stress, carbon storage, and radical scavenging. Chlorophylls, carotenoids, and photosynthetic efficiency can be affected by salinity. While anthocyanins, which are derived from flavonoids, accumulate as a defence mechanism under stress, flavonoids function as antioxidant agents by scavenging ROS (Ashraf and Foolad \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Grigore et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Phenolics are essential for preserving redox balance and safeguarding biological systems. The equilibrium between reactive oxygen species and phytochemicals such as flavonoids and polyphenols affects how well plants adapt to salt stress. The plant kingdom contains phenolic compounds, particularly flavonoids, which have a variety of molecular and biochemical functions. They have antioxidant qualities, function as signaling molecules, and support plant defense. Plants can withstand salt stress because phenols and flavonoids play a major role in scavenging free radicals (Apel and Hirt \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Plant materials contain both bound and free forms of polyphenols. Moreover, carbohydrates support the preservation of protein structure under stress, carbon storage, and radical scavenging. Chlorophylls, carotenoids, and photosynthetic efficiency can be affected by salinity (Foyer \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By scavenging ROS, flavonoids function as antioxidants, whereas anthocyanins, which are derived from flavonoids, accumulate as a protective mechanism under stress (Huang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Proline, betaines, polyols, and soluble carbohydrates are some of the organic solutions that plants accumulate to cope with osmotic stress. Therefore, this study, focused on the leaf-micromorphology and accumulation of compatible osmolytes changes to seasonal variation. In addition, understanding the \u003cem\u003eAvicennia\u003c/em\u003e sp. adjusts its leaf morphology and osmolyte production in response to seasonal variations can provide insights into the broader adaptive strategies of mangroves.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSurvey area Data collection\u003c/h2\u003e \u003cp\u003eThe mentioned plant species were collected from Digha Mohona coastal vegetation adjoining Bay of Bengal located in Ramnagar CD Block I of district Purba Medinipur, West Bengal, India. The longitudinal and latitudinal extension of this area is 87\u0026deg;30'26.7516\"E to 21\u0026deg; 37'35.8212\"N, which has a generally saline loam soil with high pH. Soil and water samples were collected from selected coastal vegetation at middle day of per month basis in 2023 years. The geographic coordinates of the collected plant specimens \u003cem\u003eAvicennia marina\u003c/em\u003e, \u003cem\u003eAvicennia alba\u003c/em\u003e, \u003cem\u003eAvicennia officinalis\u003c/em\u003e, and \u003cem\u003eAvicennia marina\u003c/em\u003e var. \u003cem\u003erumphiana\u003c/em\u003e (\u003cem\u003eA. rumphiana\u003c/em\u003e) were recorded as 21\u0026deg;38.414\u0026prime;N, 87\u0026deg;33.544\u0026prime;E; 21\u0026deg;38.407\u0026prime;N, 87\u0026deg;33.547\u0026prime;E; 21\u0026deg;38.409\u0026prime;N, 87\u0026deg;33.557\u0026prime;E; and 21\u0026deg;38.426\u0026prime;N, 87\u0026deg;33.557\u0026prime;E, respectively. Fast of all one year (2023) divided in three season first one is Monsoon (July-October), second one is Post-Monsoon (November-February) and third one is Pre-Monsoon (March-June).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLeaf micro-morphological studies\u003c/h3\u003e\n\u003cp\u003eAfter collection of the mangroves leaves, that preserved in 70% alcohol or in FAA solution for micro-morphological analysis. Leaves were soaked on 3% KOH and heat for 5min. Then peel off epidermis layer and stained with safranin. Stain epidermis, observed by phase contrast microscope (Zeiss primo star). Determine the salt gland index of leaf epidermis by following formula:\u003c/p\u003e \u003cp\u003eSalt gland Index (SGI) = {No of salt gland in a given area (S) / Total no of cells of the area} \u0026times;100\u003c/p\u003e\n\u003ch3\u003eLeaf Succulence (Relative Water Content, RWC) measurement\u003c/h3\u003e\n\u003cp\u003eRWC serves as a valuable metric for assessing a plant's hydration level and it response drought induced water stress (Mayak et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e). This method effectively indicates the water content in plant tissues relative to their maximum water-holding capacity (Ahmad et al. 2021). RWC is an appropriate estimate of plant cellular hydration under the effect of both leaf osmotic adjustment and water potential. RWC The measurement involves collecting mature leaves and cut into pieces (2cm\u0026times;2cm), that determined their fresh weight. To find the turgid weight, leaf samples are hydrated in tri-distilled water at 4\u0026deg;C in darkness for 6h. The samples are dried in a hot air oven at 75\u0026deg;C for 48 h to obtain the dry weight. The RWC is calculated formula followed by Weatherley, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1950\u003c/span\u003e: \u0026lsquo;FW\u0026rsquo; means fresh weight, \u0026lsquo;DW\u0026rsquo; means dry weight and \u0026lsquo;TW\u0026rsquo; means turgid weight\u003c/p\u003e\n\u003ch3\u003eRWC= (FW – DW) / (TW – DW) × 100\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCompatible Osmolytes Content (COC)\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eProline Concentration Measurement by Bates et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1973\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThis technique provides a quantitative colorimetric measurement of proline content of plants to determine their plant's physiological condition and evaluating its stress tolerance (\u0026Aacute;brah\u0026aacute;m et al. 2010). A 0.5 g of frozen leaves are homogenized in 5 ml 3% (w/v) aqueous sulfosalicylic acid solution. The homogenated extract was centrifuged at 15,000 g for 10 minutes at 4\u0026deg;C. 2 ml of supernatant was mixed with 2 ml of acid-ninhydrin (0.125% ninhydrin: 30 ml glacial acetic acid : 20ml of 6 M phosphoric acid) and 2 ml of glacial acetic acid. Acid ninhydrin must be prepared fresh and is only stable for 24 hours at 4\u0026deg;C. The mixture was maintained in water bath at 100\u0026deg;C for 1 hour, and the reaction was stopped in an ice bath. 4 ml of toluene was then added to this solution and stirred for 15\u0026ndash;20 sec. collect the upper phase, which was toluene and optical density was measured by absorbance by the spectrophotometer at 520 nm; toluene was used as control. The known concentrations of known L-proline solutions (for example, 0.0, 20, 40, 60, 80, 100 ppm/ml) are used in preparing a standard curve of proline.\u003c/p\u003e \u003cp\u003eProline concentration \u0026micro;moles proline /g of fresh weight material= [(\u0026micro;g proline/ ml)\u0026times;ml toluene)/115.5\u0026micro;g/\u0026micro;mole]/[(g sample)/5]\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eGlycine Betaine concentration determined by periodide spectophotometric methods Grieve and Grattan (1983)\u003c/h3\u003e\n\u003cp\u003e0.5 grams of dried powder sample, finely ground plant tissue was mixed with 10ml of dH2O for 24h and then filtered. The 2ml of filtrate was mixed with an equal volume of 2N H2SO4 (1:1) and cooled over ice. 50\u0026micro;l of KI-I2 reagent was added to mixture and gently stirred, then stored at 4\u0026deg;C for 16 hours. The mixture were centrifuged at 12,000 g for 15 minutes at 4\u0026deg;C, the precipitated iodine crystals were dissolved in 1.5 ml of 1,2-dichloroethane with a vortex mixer, and absorbance was measured at 365 nm after 2 hours. A glycine betaine (GB) solution in 1M H2SO4 was used as the standard.\u003c/p\u003e\n\u003ch3\u003eSugar alcohol concentration determination by spectrophotometric methods Lewis and Harley (1965)\u003c/h3\u003e\n\u003cp\u003e0.5 g of powder sample were homogenized with 70% cold methanol. 1ml of methanolic extract diluted with 1ml of 1M acetated buffer (pH 4.5). After 15minutes 1 ml of 0.75% sodium metaperiodate solution added in mixture solutions. The reaction was activated within 5 minutes and measured the optical density at 260nm using UV-spectrophotometer (Shimadzu UV-NIR-3600). D-Mannitol and D-Sorbitol are used as a standard to create a calibration curve, allowing for the determination of sugar alcohol concentrations in the samples.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePlant leaf pigments and metabolites concentration determination\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eChlorophyll content measurement by Schlemmer et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eAt first samples of all plants are collected from study area in zipper pouch. Then 0.5g of fresh leaves homogenized with 10ml ml 80% acetone. Then centrifuges the acetone solution at 5000rpm for 5min. collect the supernatant and repeat the extraction with 80% acetone until the residue becomes colorless. Adjust the final volume to 5ml with 80% acetone and measured the absorbance of the solution at 645 nm, 652 nm, and 663 nm using a spectrophotometer (Shimadzu UV-NIR-3600) with 80% acetone as a blank. Calculate the chlorophyll content using the following equations: \u0026lsquo;A\u0026rsquo; mean Absorbance at the specified wavelength; \u0026lsquo;V\u0026rsquo; = Final volume of chlorophyll extract in 80% acetone; \u0026lsquo;W\u0026rsquo; = Fresh weight of the tissue extracted.\u003c/p\u003e \u003cp\u003eMg chlorophyll a/g tissue = [12.7 \\times (A{663}) \u0026ndash; 2.69 \\times (A{645}) \\times V/1000 \\times W]\u003c/p\u003e \u003cp\u003eMg chlorophyll b/g tissue = [22.9 \\times (A{645}) \u0026ndash; 4.68 \\times (A{663}) \\times V/1000 \\times W]\u003c/p\u003e \u003cp\u003eMg total chlorophyll /g tissue = [20.2 \\times (A{645}) \u0026ndash; 8.02 \\times (A_{663}) \\times V/1000 \\times W]\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSoluble carbohydrate Measurement (Nielsen, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eDetermination of total carbohydrate concentration in plant samples followed by phenol-sulfuric acid method (Nielsen \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Weight 0.5 g of plant fresh samples (leaf and stem separately) were hydrolzed with 10 ml of 2.5N HCL in a boiling water bath for 3h to breakdown the complex carbohydrate to simple sugar. The solution neutralized with solid Sodium Carbonate (Na2CO3) to stop the reaction. The effervescence due to release the CO2 during neutralization. Centrifuged the solution at 10000 rpm for 10 min. 500\u0026micro;l of supernatant mixed with 500 \u0026micro;l DH2O, 1ml of 5% phenol and 500ml of 98% concentrated H2SO4. The solution incubated in a water bath at 25\u0026ndash;30\u0026deg;C for 20 minute and develop the final color. The optical density measured at 490nm wavelength by UV-spectrophotometer (Shimadzu UV-NIR-3600). The standard solution D-glucose used for standard curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTotal Phenolic Content (TPC)\u003c/h2\u003e \u003cp\u003eLeaf total phenolic content was determined according to the Folin ciocalteu method, described by Singleton et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1999\u003c/span\u003e. The 0.5 ml Folin-Ciocalteu (FC) reagent (1:1) supplemented to 100\u0026micro;l of methanolic extract diluted with 900\u0026micro;l dH2O. The reaction mixture was incubated for 40 min at room temperature in dark condition. Then measured the absorbance at 765nm versus blank. Difference concentration of Gallic acid (mg/GAE) were used as a standard to create a standard curve. The total phenolic content in the samples is expressed as mg of Gallic acid equivalent per gram dry weight (mg GAE/g DW).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of total flavonoid content (TFC)\u003c/h2\u003e \u003cp\u003eTotal flavonoid estimation done followed by Aluminum chloride (AlCl\u003csub\u003e3\u003c/sub\u003e) and Sodium acetate (CHCOONa) calorimeter methods (Fernandes et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). 2mg of dry extract mixed with 4 ml of 80% methanol. Centrifuged (8000g for 5 min) the solution and 0.5 ml of supernatant was diluted with 2 ml of methanol, 100\u0026micro;l of 7% aluminum chloride (AlCl3), 100\u0026micro;l of sodium acetated (CHCOONa) and left for 1 hour for incubation. After incubation, the mixture were measured the absorbance at 450nm against a blank solution by UV-spectrophotometer (Shimadzu UV-NIR-3600). Quercetin (mg/QE) used as a standard solution to obtain a standard curve. Total Flavonoid concentration in the plant samples was calculated from the standard curve and expressed the results as Quercetin equivalent per gram dry weight (mg QE/g DW).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of total Polyphenol content (TPPC)\u003c/h2\u003e \u003cp\u003eTotal Polyphenol were estimated according to the method described by Robert (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). 1mg dry powder dissolved into 1ml of 90% methanol. Then add 1 ml of vanillin hydrochloride reagent prepared just before use by mixing equal volumes of 8% hydrochloric acid in methanol and 4% vanillin in methanol. Then read in a UV spectrophotometer (Shimadzu UV-NIR-3600) at 500 nm wavelength after 20 min incubation, using vanillin hydrochloride reagent alone, as a blank. Tannic acid (mg/TA) used as a standard solution to obtain a standard curve. TPPC in the plant samples was calculated from the standard curve and expressed the results as Tannic acid equivalent per gram dry weight (mg TA/g DW).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll quantitative data including plant stress tolerance biochemical marker, soil and surface water parameter were statistical analyzed by GraphPad prism v.10. A one-way analysis of variance followed by post hoc multiple mean comparisons (Tukey\u0026rsquo;s test) for each dependent variable. All biochemical data were analyzed independently for each species. Additionally, Principal Component Analysis (PCA) of the complete dataset was carried out using PAST. PC1 and PC2 captured most of the observed variability in biplot. Significant covariance\u0026rsquo;s among parameters were identified using Pearson correlation coefficients (r) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in correlation matrices.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variation effects on plants Phenological pattern\u003c/h2\u003e \u003cp\u003eThe flowering, fruiting and germination periods of \u003cem\u003eAvicennia\u003c/em\u003e species in Digha-Mohona coastal vegetation are summarized in Tables \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eAvicennia\u003c/em\u003e species generally flower throughout the year, with the monsoon and pre-monsoon season having the most blooms. The flowering duration of \u003cem\u003eAvicennia\u003c/em\u003e sp. varies greatly, lasting from one to six months. \u003cem\u003eA. marina\u003c/em\u003e and \u003cem\u003eA. alba\u003c/em\u003e species are exhibit a different reproductive strategy from other two species \u003cem\u003eA. rumphiana\u003c/em\u003e and \u003cem\u003eA. officinalis\u003c/em\u003e. Moreover, species like \u003cem\u003eA. alba\u003c/em\u003e and \u003cem\u003eA. marina\u003c/em\u003e flowering duration in the pre-monsoon to monsoon season, while \u003cem\u003eA. officinalis\u003c/em\u003e and \u003cem\u003eA. rumphiana\u003c/em\u003e flowering periods is late pre-monsoon and monsoon months. In Digha coast, \u003cem\u003eAvicennia\u003c/em\u003e sp. primarily produces mature propagules during the monsoon (August-October) period and germination occurred mostly in the post-monsoon season (November- December).\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\u003ePhenology of four selected mangrove species during study period at 2023 years.\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScientific name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlowering season\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFruiting season\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGermination season\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAcanthaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emay-Aug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAug-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNov-Dec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. officinalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJuly - Aug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAug-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNov-Dec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. marina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emay-Aug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAug-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNov-Dec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. rumphiana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJuly - Aug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAug-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNov-Dec\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal effects on plant micro-morphology\u003c/h2\u003e \u003cp\u003eWe set out to investigate the effects of seasonality on the physiological adaptation. We observed the number of salt glands were might be changed in their salt stress level during different season. The highest SGI found in pre-monsoon season and high salt stress environmental condition when compared with other two season (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As show in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b and d, \u003cem\u003eA. rumphiana\u003c/em\u003e, A. marina and \u003cem\u003eA. officinalis\u003c/em\u003e were comparatively highest Salt gland density in post-monsoon and pre-monsoon season. Generally lowest salt gland density found in \u003cem\u003eA. alba\u003c/em\u003e during all season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). In Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, highest salt gland index (SGI) present in \u003cem\u003eA. rumphiana\u003c/em\u003e (4.28%) and \u003cem\u003eA. officinalis\u003c/em\u003e (3.75%) leaf epidermis during pre-monsoon season and lowest in \u003cem\u003eA. alba\u003c/em\u003e (0.23%) during monsoon season. Moreover, we are observed that the salt glands are form only in post and pre-monsoon season for removed the excess salt from cellular levels.\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\u003e\u003cb\u003eSalt gland index of selected\u003c/b\u003e \u003cem\u003eAvicennia\u003c/em\u003e sp. \u003cb\u003eleaf epidermis.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePlants name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNumber of Salt gland per unit area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eNumber of Epidermal cell per unit area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eSalt gland Index (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. rumphiana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e822.33\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e954\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1073.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. marina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e40.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1154\u0026thinsp;\u0026plusmn;\u0026thinsp;6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1359.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1333.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e881\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1120.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e971\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. officinalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e40.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e49.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1031.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1127\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1275.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.75\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental factors and soil condition changes in different season\u003c/h2\u003e \u003cp\u003eEnvironmental parameters recorded from soil and surface water have been depicted in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Strong seasonal trend of environmental parameters was observed throughout the study year 2023. Salinity and pH were significantly lower in monsoon than pre-monsoon and post-monsoon in both soil and surface water (two-way ANOVA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TDS and EC was significantly higher in pre-monsoon compared to post-monsoon (two-way ANOVA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In pre-monsoon relatively high salinity and temperature, pH levels and electric conductivity. High pH affect their conductivity and total dissolved solids. During monsoon, rainwater and surface runoff typically decrease the pH, salinity and TDS in both surface water and soil due to dilution effect. However, electric conductivity closely correlated with TDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d).\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\u003eMonthly variation in Average Temperature, pH, Salinity (ppt), EC (\u0026micro;s/cm), TDS (ppm) of water surface during study period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eseason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAve. Temp.(\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSalinity (ppt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTDS (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEC (\u0026micro;s/cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10679\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\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\u003eMonthly variation in Average Temperature, pH, Salinity (ppt), EC (\u0026micro;s/cm), TDS (ppm) of soil during study period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eseason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAve. Temp.(\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSalinity (ppt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTDS (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEC (\u0026micro;s/cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8679\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variation effects on plants Leaf pigments\u003c/h2\u003e \u003cp\u003eDuring the pre-monsoon seasons, plants typically have suffered from drought, high temperature and salinity that affect chlorophyll production, affecting photosynthesis in all \u003cem\u003eAvicennia\u003c/em\u003e sp. In post-monsoon seasons experience decreasing temperatures and daylight, which can lead to senescence and chlorophyll production decreases. Monsoon seasons bring high water availability, which can lead to increased chlorophyll concentration and decreased the chlorophyll degradation; adequate water supply promotes chlorophyll (total chlorophyll) production in \u003cem\u003eA. alba\u003c/em\u003e (37.3mg/g FW) followed by \u003cem\u003eA. marina\u003c/em\u003e (33.3mg/g FW), \u003cem\u003eA. officinalis\u003c/em\u003e (32.1mg/g FW) and \u003cem\u003eA. rumphiana\u003c/em\u003e (30.8mg/g FW) which was significant at 0.05 levels. Although, Digha Mohona mangrove vegetation is a tropical region with minimal seasonal temperature variations, plant chlorophyll production may remain stable throughout the year (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSeasonal variation effects on Leaf carbohydrate production\u003c/h2\u003e \u003cp\u003eShoot soluble carbohydrate contents were increase from monsoon to post-monsoon in all plants. Maximum contents of soluble carbohydrates in Monsoon at significant 0.05 levels. \u003cem\u003eA. alba\u003c/em\u003e shoot recorded the highest levels of soluble carbohydrates (65mg/g FW) in monsoon season, whereas \u003cem\u003eA. officinalis\u003c/em\u003e shoot had the lowest concentration of carbohydrate (29.3mg/g FW) in pre-monsoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variation effects on plants Leaf Water content\u003c/h2\u003e \u003cp\u003eMangrove leaves relative water content (RWC) and biochemical production are significantly affected by seasonal changes. In Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show during pre-monsoon and late post-monsoon season, mangrove leaves tend to have lower relative water content and higher WSD due to water stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and f), which can lead to increased production of biochemical like phenols, flavonoids and polyphenols to protect against oxidative stress. In contrast, the monsoon and early post-monsoon season brings higher water availability, resulting in higher relative water content in \u003cem\u003eA. alba\u003c/em\u003e (83% and 72.5%) and \u003cem\u003eA. rumphiana\u003c/em\u003e (86.8% and 72.1%). Higher RWC increased chlorophyll production and reduced the chlorophyll degradation. The negative correlation between leaf relative water content and biochemical production varies among mangrove species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e9\u003c/span\u003e). For example, \u003cem\u003eA. rumphiana, A. alba, A. marina\u003c/em\u003e and \u003cem\u003eA. officinalis\u003c/em\u003e exhibit different patterns of chlorophyll-a, chlorophyll-b, and total chlorophyll content in all seasons. \u003cem\u003eA. rumphiana\u003c/em\u003e exhibits a negative correlation between leaf relative water content and compatible osmolytes production, with increased chlorophyll production during water stress. \u003cem\u003eA. officinalis\u003c/em\u003e shows a lowest leaf relative water content across seasons, with less pronounced changes in chlorophyll and carbohydrate production.\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\u003eConcentration of leaf pigment, soluble sugar (mg/g FW) and leaf succulence (% of RWC and WSD) in the leaves of the halophyte \u003cem\u003eAvicennia\u003c/em\u003e sp., during pre-monsoon, monsoon and post-monsoon in 2023 year. Determined compounds: carbohydrate (Carb), Chlorophyll-a (Chl-a), Chlorophyll-b (Chl-b) and Total chlorophyll (TC). (AR\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. rumphiana\u003c/em\u003e, AO\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. officinalis\u003c/em\u003e, AB\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. alba\u003c/em\u003e, AM\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. marina\u003c/em\u003e). Every trait's value is the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE; a significant value denoted by a different letter, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eseason\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\u003eCarb (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChl-a (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChl-b (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal Chl (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRWC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWSD (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003emonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003csup\u003ebd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePost-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003csup\u003ead\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003csup\u003ead\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003csup\u003ead\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ead\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003epre-monsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003csup\u003ebd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ebd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003csup\u003eb\u003c/sup\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\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eSeasonal variation effects on Leaf Compatible Osmolytes\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section4\"\u003e \u003ch2\u003eLeaf Proline Accumulation\u003c/h2\u003e \u003cp\u003eProline accumulation increasing the leaves of all species in post-monsoon and pre-monsoon. This level was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in \u003cem\u003eA. rumphiana, A. marina\u003c/em\u003e and \u003cem\u003eA. alba\u003c/em\u003e, but not significant in \u003cem\u003eA. officinalis.\u003c/em\u003e It was mentioned the elevated average precipitation in monsoon season in Digha Mohona coastal region in west Bengal caused decreased soil salinity that influenced lower proline accumulation of plant tissue than other season. In pre-monsoon, plants tend to accumulate higher proline content in \u003cem\u003eA. rumphiana\u003c/em\u003e and \u003cem\u003eA. marina\u003c/em\u003e cellular level, value is 30.3 \u0026micro;mole/g 0.5 dry weight (DW) and 27.1 \u0026micro;mole/g 0.5 dry weight (DW) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eLeaf Glycine Betaine Accumulation\u003c/h2\u003e \u003cp\u003eGB contents were elevated in post-monsoon and pre-monsoon season during study period. The highest GB accumulated taxa, \u003cem\u003eA. officinalis\u003c/em\u003e and \u003cem\u003eA. rumphiana\u003c/em\u003e, Glycine betaine levels reached 80.9 \u0026micro;mole/g 0.5 DW in \u003cem\u003eA. rumphiana\u003c/em\u003e, and 75.5 \u0026micro;mole/g 0.5 DW in \u003cem\u003eA. officinalis\u003c/em\u003e in pre-monsoon season. The lowest levels of GB were accumulated in \u003cem\u003eA. marina\u003c/em\u003e and value is 24.8 \u0026micro;mole/g 0.5 dry weight in monsoon season (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLeaf Soluble Sugar Alcohol Accumulation\u003c/h2\u003e \u003cp\u003eThe Sugar alcohol (Mannitol) concentration was highest in the leaves of \u003cem\u003eA. rumphiana\u003c/em\u003e and \u003cem\u003eA. marina\u003c/em\u003e (25.4 and 22.2 \u0026micro;mole/g 0.5 FW) during pre-monsoon in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show the highest Sorbitol concentration in \u003cem\u003eA. rumphiana\u003c/em\u003e, while the value is 21.7 \u0026micro;mole/g 0.5 FW in pre-monsoon season. Lowest concentration of sorbitol in \u003cem\u003eA. officinalis\u003c/em\u003e leaves that value is 8.4 \u0026micro;mole/g 0.5 FW in post-monsoon season. Sugar alcohol accumulation showed insignificant correlations with environmental factors (i.e. temperature, EC, TDS, soil salinity and pH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLevels of secondary metabolites and Compatible osmolytes (umol g-1 dw) in the leaves of the halophyte \u003cem\u003eAvicennia\u003c/em\u003e sp., during pre-monsoon, monsoon and post-monsoon in 2023 year. Determined compounds: Total Phenol Content (TPC), Total flavonoids Content (TFC), Total Polyphenol Content (TPPC), proline (Pro), glycine betaine (GB) mannitol (man) and Sorbitol (Sor). (AR\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. rumphiana\u003c/em\u003e, AO\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. officinalis\u003c/em\u003e, AB\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. alba\u003c/em\u003e, AM\u0026thinsp;=\u0026thinsp;\u003cem\u003eA. marina\u003c/em\u003e). Each value show mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE, a significant value is indicated by a different letter, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eseason\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\u003eTPC (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTFC (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTPPC (mg/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePro (\u0026micro;mole/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGB (\u0026micro;mole/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMan (\u0026micro;mole/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSorb (\u0026micro;mole/g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003emonsoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003csup\u003ead\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003csup\u003ea\u003c/sup\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 \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal effects on Leaf Secondary metabolites\u003c/h2\u003e \u003cp\u003eTPC, TFC and TPPC were analysis in four-selected \u003cem\u003eAvicennia\u003c/em\u003e sp. during three different season 2023. In the pre-monsoon season, plant was exposed high saline and water stress environment, the \u003cem\u003eA. rumphiana\u003c/em\u003e (22.4 mg GA/g DW) and \u003cem\u003eA. officinalis\u003c/em\u003e (19.3mg GA/g DW) were produced highest phenolic compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In this season, just prior to flowering the \u003cem\u003eA. rumphiana\u003c/em\u003e and \u003cem\u003eA. officinalis\u003c/em\u003e were synthesis highest phenolic compounds. \u003cem\u003eA. marina\u003c/em\u003e (25.3mg GA/g DW) and \u003cem\u003eA. alba\u003c/em\u003e (22.9mg GA/g DW) had highest concentration of phenols synthesis during early pre-monsoon (Feb to April) when the plant is in the rosette stage and exposed into the high salinity stress condition. In this study, plants have synthesized minimum amount of secondary metabolites (TPC, TFC, and TPPC) at the start of flowering stage but the amounts were increased at fruit periods. Although, highest amount of TFC and TPPC produced in late post monsoon to pre-monsoon season (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In monsoon season, lowest amount of total flavonoids 7.9 mg QUE /g DW in \u003cem\u003eA. alba\u003c/em\u003e and lowest polyphenol contents 8.5mg TAN/g in \u003cem\u003eA. marina\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and c). In seasonal variation, plant have to fluctuating environmental cues, including salinity, pH, total dissolved solids, and electrical conductivity. These environmental factors directly influence in the synthesis and accumulation of secondary metabolites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrincipal component analysis and Pearson correlation coefficient analysis\u003c/h3\u003e\n\u003cp\u003eThe first component, PC1, of the PCAs represented in the surface water and soil parameters, generally showed a high positive correlation with soil properties and surface water data related to water stress (Salinity and pH) and to salt stress (EC and TDS in soil). In addition, a correlation was observed in soil and surface water avg. temp, pH, salinity, TDS and EC were associated with different season. Furthermore, a clear distinction between soil and water parameter under different season was observed. The cumulative percentage variation of the first two principal components (PC) was 94.8% of the total variation (PC1, 73.89%; PC2, 20.91%) in the case of PCA plot representing in surface water parameter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The PCA showed a separation among post-monsoon, pre-Monsoon and monsoon. Avg. temp., pH, salinity and TDS were positively correlated with the pre-monsoon season and EC showed a negatively correlated with post-monsoon. The first two principal components explained 90.55% of the total variation (PC1, 69.84%; PC2, 20.71%) in the case of PCA plot representing in soil parameter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The PCA showed a separation among post-monsoon, pre-Monsoon and monsoon. Avg. temp., TDS and EC were positively correlated with the pre-monsoon season while salinity and pH showed a negatively correlated with post-monsoon. Soil and surface water parameter all are showed a high positive association in PC1 but only Avg. temp showed positive association in PC2 and pH, salinity, TDS and EC showed a negative association in PC2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo obtain a physiological and biochemical adaptive view of similarities and differences among four \u003cem\u003eAvicennia\u003c/em\u003e species during different seasons (Monsoon, post-monsoon and pre-monsoon), the full dataset was subjected to a principal component analysis (PCA). The first two principal components (PC1 and PC2) accounted for \u0026gt;\u0026thinsp;70% variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The PCA showed a separation among post-monsoon, summer, and monsoon and different species level. Secondary molecules (including total phenols, polyphenols, and flavonoids), compatible osmolytes (such as proline, glycine betaine, Mannitol and sorbitol), pigments (such as Chlorophyll-a, b and TC), soluble sugar (carbohydrates), and plant succulence (RWC and WSD) were positively correlated with the monsoon season. We also included in this analysis soil or surface water parameters, according to the newly performed PCAs, are shown in the graphs. The second PCA included the variables related to salinity stress and biochemical defense mechanisms: total phenols, polyphenols, and flavonoids, proline, glycine betaine, Mannitol and sorbitol, Chlorophyll-a, b and TC, carbohydrates and plant succulence (RWC and WSD). All the analyses gave Eigenvalues greater than one. A Pearson correlation coefficient analysis was used to investigate the relationship between different morpho-biochemical traits and the leaf succulence of \u003cem\u003eAvicennia\u003c/em\u003e sp. plants in different season that exhibit to various salinity stress environments and water stress condition. However, this analysis was employed to investigate the variations in the accumulation of compatible osmolytes, leaf pigments, carbohydrate and secondary metabolites (TPC, TFC and TPPC) in plants under varying intensities of salinity stress in seasonal variation and to elucidate the interrelationships among these altered metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). We focused on the data related to the environmental factors of water surface (as temp. pH, salinity, TDS, EC) associated with biochemical changes in study year. There was a notable positive correlation observed between average temperature and pH (r\u0026thinsp;=\u0026thinsp;0.0498), average temperature and salinity (r\u0026thinsp;=\u0026thinsp;0.087), average temperature and TDS (r\u0026thinsp;=\u0026thinsp;0.052) and negative correlation between average temperature and EC (r= -0.165). Moreover, high positive correlation between pH and salinity (r\u0026thinsp;=\u0026thinsp;0.92), pH and TDS (r\u0026thinsp;=\u0026thinsp;0.96), pH and EC (r\u0026thinsp;=\u0026thinsp;0.89), salinity and EC (r\u0026thinsp;=\u0026thinsp;0.77), salinity and TDS (r\u0026thinsp;=\u0026thinsp;0.89), TDS and EC (0.94). In addition, high positive correlation were observed between soil salinity and EC (r\u0026thinsp;=\u0026thinsp;0.99), salinity and TDS (r\u0026thinsp;=\u0026thinsp;0.98), pH and EC (r\u0026thinsp;=\u0026thinsp;0.97) and salinity and pH (r\u0026thinsp;=\u0026thinsp;0.91).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn monsoon, PC1 and PC2 explained 46.143% and 39.84% of the variation. TPC, TFC, TPPC, Pro, GB, Man, Sor, Carb, Chl-a and RWC showed a high positive association in PC1 while Chl-b, TC, WSD showed a negative association. While PC2 was a high positive correlation between mannitol, sorbitol, carbohydrate, chl-b, TC and RWC trait but proline, GB, TPC, TFC, TPPC and WSD were negatively correlated with PC2. In addition, a correlation was observed in \u003cem\u003eA. rumphiana\u003c/em\u003e between mannitol, sorbitol, carbohydrate, chl-b, TC and RWC. Chl-b and TC were associated with \u003cem\u003eA. alba\u003c/em\u003e. WSD associated with \u003cem\u003eA. marina\u003c/em\u003e and \u003cem\u003eA. officinalis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eIn post-monsoon, PC1 and PC2 explained 51.08% and 32.9% of the variation. TPC, TFC, TPPC, Pro, GB, Man, Sor, Carb, Chl-a, chl-b, TC and RWC showed a high positive association in PC1 while WSD showed a negative association. While PC2 was a high positive correlation between TPC, TFC, TPPC, proline, GB and RWC trait but mannitol, sorbitol, carbohydrate, chl-a, chl-b, TC and WSD were negatively correlated with PC2. In addition, a correlation was observed in \u003cem\u003eA. rumphiana\u003c/em\u003e between TPC, TFC, TPPC, proline, GB and RWC. TPC and TC were associated with \u003cem\u003eA. alba\u003c/em\u003e and \u003cem\u003eA. marina\u003c/em\u003e. WSD associated with \u003cem\u003eA. officinalis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn pre-monsoon, PC1 and PC2 explained 48.623% and 43.01% of the variation. TPC, TFC, TPPC, Pro, GB, Man, Sor, Carb, Chl-a, chl-b and RWC showed a high positive association in PC1 while TC and WSD showed a negative association. While PC2 was a high positive correlation between TPC, mannitol, sorbitol, carbohydrate, chl-a, chl-b, TC and RWC trait but proline, GB, TFC, TPPC and WSD were negatively correlated with PC2. In addition, a correlation was observed in \u003cem\u003eA. rumphiana\u003c/em\u003e between mannitol, sorbitol, carbohydrate, chl-a, chl-b and RWC. Mannitol, sorbitol, carbohydrate, chl-a, chl-b and TC were associated with \u003cem\u003eA. marina\u003c/em\u003e. WSD associated with \u003cem\u003eA. alba\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eA. marina\u003c/em\u003e, both components PC1 and PC2 explained 100% of observed variability, of which 73.329% corresponded to PC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Secondary metabolites (including TPC, TFC, TPPC) were positive correlated with Compatible osmolytes (Proline, GB, Mannitol, Sorbitol) and Environmental parameters (Ave. Temp., pH, Salinity, TDS and EC) according to the Pearson correlation coefficients. Negatively correlated with carbohydrate, pigments (chl-a, chl-b and TC) and RWC. Their correlation coefficient are TPC and Pro (r\u0026thinsp;=\u0026thinsp;0.217), TPC and GB (r\u0026thinsp;=\u0026thinsp;0.36), TPC and Man (r\u0026thinsp;=\u0026thinsp;0.0217), but negative correlation with TPC and Sorb (r = -0.014), TPC and Ave. Temp. (r = -69). TPC positively correlated with environmental factors like pH (r\u0026thinsp;=\u0026thinsp;0.609), salinity (r\u0026thinsp;=\u0026thinsp;0.443), TDS (r\u0026thinsp;=\u0026thinsp;0.545) and EC (r\u0026thinsp;=\u0026thinsp;0.69). However, a clear negative correlation trend was observed between TPC and carbohydrate (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.525), TPC and Chl a (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.869), TPC and Chl b (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.995) and TPC and TC (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.94) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eA. alba\u003c/em\u003e, the biplot, PC1 and PC2 explained 100% of total variability, with 88.17% corresponding to PC1. Secondary metabolites (including TFC, TPPC), Sugar alcohol (Mannitol, Sorbitol), GB, Proline were positively correlated with PC1 and pre-monsoon season. Pigments (chl-a, b, TC) Leaf Succulence (RWC) were associated with monsoon. TPC, GB and WSD associated with post-monsoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). However, positive correlation with sorbitol and environmental factors pH (r\u0026thinsp;=\u0026thinsp;0.79), salinity (r\u0026thinsp;=\u0026thinsp;0.89), TDS (r\u0026thinsp;=\u0026thinsp;0.84), EC (r\u0026thinsp;=\u0026thinsp;0.723) according to Pearson correlation coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Nevertheless, negatively with carbohydrate(r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.95), chl a (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.78), chl b (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.67), TC (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.87) and RWC (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.89) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eA. officinalis\u003c/em\u003e, the scree plot of the first two PCA was 100% of total variation. PC1 was accounted 88.52% and 11.48% of PC2. The squared cosines of the variables corresponding to the plant secondary metabolites (including TPC, TFC, TPPC), Compatible osmolytes (Proline, GB, Mannitol, Sorbitol) and WSD were positively associated with component PC1, and pigment (chl-a, chl-b, TC) and RWC were associated with monsoon season (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003ed).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEnvironmental variables can affect the oxidative stress, osmotic balance (Ionic balance) and phenological patterns of mangrove plants. These factors can result in adaptive responses like growth retardation, decreased chlorophyll content, and the accumulation of compatible osmolites. Therefore, \u003cem\u003eA. marina, A. alba, A. rumphiana\u003c/em\u003e and \u003cem\u003eA. officinalis\u003c/em\u003e were chosen for a thorough examination of the regulatory processes that underlie upwelling stress. The phenological patterns of mangrove and associated species within the Digha Mohona mangrove vegetation reveal intricate flowering and fruiting cycles, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, indicating year-round flowering for mangrove species, with peak activity during the rainy season, highlighting the temporal variability in reproductive strategies among different species (Songsom et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Literature offers some insights into mangrove phenology, revealing that \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e flowers during the summer and rainy seasons, while \u003cem\u003eRhizophora mucronata\u003c/em\u003e flowers throughout the summer and winter month (Wang\u0026rsquo;ondu et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCumulative rainfall seems to be the chief environmental driver of mangrove phenology in these regions. With mangrove forests flourishing along tropical and subtropical coasts, reaching into warm temperate zones, they are celebrated for their precious ecosystem functions, yet are expected to be severely affected by global climate change-related physical processes in the coming years (Stocken et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Leaf biophysical parameters are closely tied to environmental factors like temperature, sunlight, water availability, and salinity (Flores-de‐Santiago et al. 2012). Mangrove forests, due to their evergreen nature, do not exhibit distinct seasonal vegetation growth cycles, unlike other well-studied vegetation types.\u003c/p\u003e \u003cp\u003eSalt glands are specialized organs of some halophytes that excreting the excess salt and maintain a stable internal environment. In this study, \u003cem\u003eA. rumphiana\u003c/em\u003e leaf epidermis present a highest number of salt glands (SG) and number of SG are present in \u003cem\u003eA. alba\u003c/em\u003e leaf epidermis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although SG concertation are not change in seasonal variation but SG, numbers were changed in species-specific manner. According to Mcnae (1965), \u003cem\u003eAvicennia\u003c/em\u003e sp. SGs only form in saline environments but in \u003cem\u003eAegiceras\u003c/em\u003e sp. they appear to regardless of salt concentration. Joshi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) found that \u003cem\u003eAvicennia\u003c/em\u003e sp. grow in high saline environments, while \u003cem\u003eAcanthus\u003c/em\u003e sp. and \u003cem\u003eAegiceras\u003c/em\u003e sp. are grow in low saline region.\u003c/p\u003e \u003cp\u003eSucculence (e.g. dilution of the accumulated salts by increasing water content per leaf area) is a typical morphological adaptation to osmotic stresses (Popp \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In investigation, relative water content in leaf of \u003cem\u003eA. rumphiana and A. officenalis\u003c/em\u003e was higher potentially due to succulence and salt accumulation in monsoon season (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These species also had higher turgor weight to dry weight, possibly from ion accumulation in vacuoles, unlike other salt excretory species like \u003cem\u003eA. alba\u003c/em\u003e then \u003cem\u003eA. marina\u003c/em\u003e. Salt excretory plants typically have lower ion accumulation because they excrete absorbed ions via salt glands (Klug et al. 1973; Breckle, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Sadak, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In cases where the photosynthesizing tissue is succulent, succulence can be estimated on the basis of the chlorophyll content relative to the tissue water content (King et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Photosynthetic systems are sensitive to temperature variation, and reduced chlorophyll content usually occurs in plants at low temperatures because chlorophyll biosynthetic enzymes are affected and so biosynthesis progresses slowly (Vosnjak et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, reductions in pigment contents observed in \u003cem\u003eA. officinalis\u003c/em\u003e and \u003cem\u003eA. rumphiana\u003c/em\u003e during pre-monsoon season while highest chlorophyll contents observed in \u003cem\u003eA. alba\u003c/em\u003e and \u003cem\u003eA. marina\u003c/em\u003e which indicates that highest salt accumulation rate in tissue level during pre-monsoon season that caused a degree of damage to mangroves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). During monsoon, pigment contents increased and by significantly more than that of the pre-monsoon season (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Plants are also able to construct a defense system that actively increases their chlorophyll contents and prevents decreases in photosynthesis and energy production (Zhang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, monsoon season reducing the salinity level and increasing the pigment production of \u003cem\u003eAvicennia\u003c/em\u003e species. Significant variations in total soluble sugars content (TSSC) were observed across different seasons. The highest concentrations of TSSC were observed in \u003cem\u003eA. alba\u003c/em\u003e followed by \u003cem\u003eA. marina\u003c/em\u003e during the post-monsoon season. Photosynthetic pigments utilize sunlight for the production of carbohydrates through the process of photosynthesis. Carbohydrates are among the vital sources responsible for providing energy for respiration and other metabolic processes in seaweeds (Khairy and Shafay 2013). Increase of soluble carbohydrates in plants during salinity stress was also reported by Strogonov (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), Maas and Nieman (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), and Doddema et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Soluble carbohydrate increase in shoots during stress condition is in fact an important response to water deficiency and probably a result of starch hydrolysis during water deficiency stress in tissues and soil water potential decrease (Jones and Qualset \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Increase of soluble carbohydrate in roots could be due to starch conversion to soluble sugars, reduction in their consumption or reduction in their transmission throughout the phloem (Irigoyen et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Plants are use some of their carbon resources to produce osmolytes for adaptation instead of food production, which may affect their growth. Therefore, plants are regulate osmoregulation during salinity by accumulation of compatible solutes in tissue level.\u003c/p\u003e \u003cp\u003eIt is necessary to conduct additional research to determine whether the formation of both kinds of osmolytes is associated with the properties of sodic or saline-sodic soils. Our findings, however, make it seem improbable that these variations in the kind of osmolyte accumulated are solely connected to flowering and seed development, as Popp and Albert (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) proposed. There were species-specific variations in the osmolyte's composition as well as the seasonal pattern of osmolyte accumulation. According to Galinski (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), compatible solutes are organic osmolytes that maintain osmotic balance while also being compatible with the metabolism of the cells. Along with their primary role in osmotic adjustment, compatible solutes can also act as radical scavengers, which prevent oxidative damage, or as osmoprotectants, which stabilise macromolecules in harsh environments (Yeo \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn investigation, highest Proline accumulation from late post monsoon to late pre-monsoon in leaves of all \u003cem\u003eAvicennia\u003c/em\u003e species (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the pre-monsoon season rising the salinity levels in coastal soil and waters, that cause higher proline accumulation in \u003cem\u003eA. rumphiana\u003c/em\u003e followed by \u003cem\u003eA. Marina, A. alba\u003c/em\u003e and \u003cem\u003eA. officenalis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Proline accumulation is likely because some plants possess the capacity for organic and inorganic compounds accumulation in cytoplasm to decrease water potential and alter osmotic gradient to take up water. This indicates that plants employ proline for salinity adaptation (Chaib and Benlaribi \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hmidi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ibrahim \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Proline is often considered a compatible osmolytes, which means it helps plants to maintain osmotic balance under stress (Jain et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It can accumulate in the cytoplasm without disrupting cellular functions, thus protecting enzymes and cellular structures (Moukhtari et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Proline accumulation may increase over time as plants acclimate to salt stress, eventually reaching a stable level (Parihar et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Proline content increased in \u003cem\u003eA. marina\u003c/em\u003e seedlings and saplings under salt stress, but its contribution to overall osmolality was minimal (Cherian et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Joseph et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother compatible osmolytes like glycine betaine (GB) is a great osmoprotectant and polyols are act as a free radical scavengers (Yeo \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Mehta et al. 2023; Adrian-Romero et al. 1994). Our studies, highest concentration of GB were found in late post monsoon to late pre-monsoon (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Although, compatible osmolytes were accumulated in plant tissues that depends on salinity stress, seasonal variation and species-specific level. GB concentration is highest in \u003cem\u003eA. rumphiana\u003c/em\u003e and \u003cem\u003eA. officinalis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to Murakeozy et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), low temperatures, hypoxic conditions, and high salt concentrations were the primary factors limiting plant growth in March, the highest concentrations of compatible osmolytes were observed in all three halophytic species (\u003cem\u003eLepidium crassifolium, Camphorosma annua and Limonium gmelini subsp. Hungaricum\u003c/em\u003e). The results showed that Sugar alcohol (SGA) (mannitol and sorbitol) concentration increased by increase of salinity from late post-monsoon to pre-monsoon. SGA concentration regulated by salinity stress, temperature stress and accumulation by species-specific manner. Highest concentration of SAG (mannitol and sorbitol) in \u003cem\u003eA. rumphiana\u003c/em\u003e followed by \u003cem\u003eA. marina, A. alba\u003c/em\u003e and \u003cem\u003eA. officinalis.\u003c/em\u003e\u003c/p\u003e \u003cp\u003ePre-monsoon and post-monsoon periods result in increased levels of secondary metabolites (such as Phenol and flavonoids), indicating these are highest salinity stress. Seasonal fluctuations in the level of metabolites could be caused by elevated temperatures in summer (pre-monsoon), which height concentration of phenols in all four plants (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Under salinity stress, plants often exhibit changes in their phenolic and flavonoid content, which are secondary metabolites involved in stress response. However, these changes can vary depending on the plant species, tissue type, and the concentration of NaCl (Parihar et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In many plant species, the total phenolic and flavonoid content tends to increase under moderate salinity levels (Bistgani et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Phenolic compounds act as antioxidants, scavenging reactive oxygen species and protecting cellular components from damage (Bistgani et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At higher salinity levels, the phenolic and flavonoid content may decrease (Trivellini et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This could be due to the disruption of metabolic processes or the reallocation of resources to other stress responses (Rahimi and Biglarifard \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In investigation, TPC of all four plants is might be changed in species-specific manner. In pre-monsoon season, Phenol contents showed no significant difference until monsoon. Similarly, the total flavonoid concentration did not increase from pre-monsoon to post monsoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study revealed prominent seasonal variations in the physiological and biochemical composition of four \u003cem\u003eAvicennia\u003c/em\u003e sp. Mangroves are more important as resources due to their ecology and the impact of cyclonic events on the ecosystem services and functions of these coastal wetlands (Chowdhury et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to this research, all four \u003cem\u003eAvicennia\u003c/em\u003e sp. had better tolerance mechanisms under salt stress condition. These species could be used for revitalizing environment in saline regions. Compatible osmolytes (Pro, GB, Man, Srob) accumulation levels were highest in pre-monsoon, while few number of SG were present in leaves epidermis in \u003cem\u003eA. rumphiana.\u003c/em\u003e However, Chlorophyll and carbohydrate were highest levels during the monsoon period in \u003cem\u003eA. alba\u003c/em\u003e. These variations may have been due to physiological adaptation to environmental conditions. Seasonal Changes affected Photosynthetic Pigments of \u003cem\u003eAvicennia\u003c/em\u003e sp. To conclude based on seasonality, pre-monsoon may be the highest tolerance for salinity stress. Understanding these seasonal effects is crucial for managing and conserving mangrove ecosystems, which provide vital ecological services and support biodiversity. Mangroves adaptations to water stress enable them to thrive in environments with fluctuating water availability. Understanding the correlations between leaf relative water content, biochemical production, and water stress can inform conservation efforts and promote ecosystem resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M. designed and performed the experiments; data analyzed and interpreted; as well as the writing of the manuscript.\u0026nbsp;A.K.M. helped with data analysis and provided guidance during the manuscript preparation process. He also helped with the manuscript\u0026apos;s final revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific grant from a public, private, or nonprofit funding organization was obtained for this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable. This study did not involve any human participants or live vertebrates. The collection of \u003cem\u003eAvicennia marina, A. alba, A. officinalis, A. rumphiana\u003c/em\u003e specimens complied with national and institutional guidelines for research involving plants. No special permissions were required under local permissions and did not involve protected or endangered species requiring special licenses. The species was taxonomically identified by Dr. K. Karthigeyan, Scientist-F, Central \u0026nbsp;National Herbarium (CNH) in Howrah, Botanical Survey of India (BSI). The collected specimen \u003cem\u003eAvicennia marina\u0026nbsp;\u003c/em\u003e(VU/Arpita/0073/16)\u003cem\u003e, Avicennia alba\u0026nbsp;\u003c/em\u003e(VU/Arpita/0072/16)\u003cem\u003e, Avicennia officinalis\u0026nbsp;\u003c/em\u003e(VU/Arpita/0074/16)\u003cem\u003e,\u0026nbsp;\u003c/em\u003e\u003cem\u003eAvicennia marina\u0026nbsp;\u003c/em\u003evar. \u003cem\u003eAvicennia rumphiana\u003c/em\u003e (VU/Arpita/0075/16) has been store in the VU Herbarium center, Department of Botany, Vidyasagar University, West Bengal, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to collect the plants/ plant parts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The study did not involve protected or endangered plant species. Sample collection was conducted for research purposes only on non-protected public land; therefore, no ethical approval or written permission was required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbraham E, Hourton-Cabassa C, Erdei L, Szabados L. Methods for Determination of Proline in Plants. 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These fluctuations lead to subtle alterations in mangrove micro-morphology (salt gland index) and influence the accumulation of compatible osmolytes (CO) such as proline, glycine betaine (GB), and sugar alcohols (mannitol and sorbitol). Seasonal changes also affect plant pigments, soluble sugars, and the accumulation of secondary metabolites in plant tissues. In this study, we examined how mangrove species adapt to their microenvironment by assessing along with SGI, CO, plant pigment and secondary metabolites (TPC, TFC and TPPC) accumulation in plant tissues. The biochemical composition of CO, along with the seasonal accumulation pattern was also species-specific. CO levels were highest during the pre-monsoon season and lowest during the monsoon. \u003cem\u003eA. rumphiana\u003c/em\u003e showed the highest proline concentration in the pre-monsoon season, while GB accumulation was highest in \u003cem\u003eA. rumphiana\u003c/em\u003e and \u003cem\u003eA. alba\u003c/em\u003e. Similarly, the secondary metabolite accumulation pattern along with their seasonal variation exhibited species-specific manner and plant developmental phases. The highest level of TPC was found in \u003cem\u003eA. rumphiana\u003c/em\u003e during pre-monsoon. Whereas \u003cem\u003eA. marina\u003c/em\u003e and \u003cem\u003eA. alba\u003c/em\u003e displayed the highest TPC in post-monsoon. A significant decrease in the chlorophyll content in pre-monsoon, while soluble carbohydrate accumulation was more pronounced during the monsoon and post-monsoon seasons. These patterns show how different species have adapted to shifting environmental circumstances.\u003c/p\u003e","manuscriptTitle":"Seasonal Modulation of Salinity Stress Response in Leaf Micro-morphological and Biochemical Insights of the Mangrove Avicennia sp. (Acanthaceae) in Digha Mohona, West Bengal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-17 01:14:39","doi":"10.21203/rs.3.rs-8363184/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":"be60c6e4-488e-4b53-9395-036f267834f2","owner":[],"postedDate":"January 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T07:27:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-17 01:14:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8363184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8363184","identity":"rs-8363184","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}