Leaf metabolites and carbon sequestration: insights into spatial gradients in a mangrove ecosystem in Mumbai, India

ABSTRACT

Mangrove physiological traits are strongly influenced by ecogeomorphic conditions and environmental stressors. These traits provide adaptive strategies that sustain growth and productivity in challenging coastal and marine environments. Plant biodiversity is a key determinant of habitat richness, ecosystem functioning, and broader ecological dynamics, being crucial in carbon sequestration. The urban mangrove cover at Carter Road, Mumbai, India, harbors key species such as Sonneratia alba Sm., Sonneratia apetala Buch.-Ham., Rhizophora mucronata Poir., and Avicennia marina (Forssk.) Vierh. These species were assessed for essential phytochemical parameters, including proline and chlorophyll, under ambient environmental conditions. Proline concentrations were highest in R. mucronata (22.42±0.40 µmol g^-1) and lowest in S. apetala (18.93±0.14 µmol g-1). Chlorophyll a (Chl a), chlorophyll b (Chl b), Chl a+b, and the Chl a/b ratio varied significantly between the two spatial extremes-landward and seaward-with considerably higher values at the landward edge. Chl a content (mg g^-1) at the landward edge ranged from 1.60±0.03 to 1.31±0.12, markedly higher than values at the seaward fringe (0.71±0.06 to 0.58±0.02). Similar spatial increases were observed for Chl b and Chl a+b. The linear mixed-effects model indicated significant positional differences in soil carbon parameters, including soil organic carbon (SOC %), bulk density (BD), and soil organic carbon stock (SOC_Org). Surface soil (0-10 cm) exhibited the highest SOC (%) at both seaward (1.41±0.06) and landward (2.08±0.31) positions, with corresponding SOC_Org values (Mg C ha^-1) of 18.05±1.711 and 22.367±2.35, respectively, indicating greater carbon accumulation at the landward site. Aboveground and belowground standing biomass carbon stocks were 357.00±33.09 Mg C ha^-1 and 99.39±9.21 Mg C ha^-1, respectively. This study provides insights into modulation of chlorophyll content and its implications for ecosystem productivity and carbon storage potential.

Keywords:
Mangroves; Mangrove physiology; Chlorophyll; Biomass; Ecosystem carbon pools; Carbon stock; Urban landscapes

INTRODUCTION

Mangroves are globally recognized as among the most carbon-rich coastal ecosystems, being essential for climate change mitigation given their carbon sequestration capacity (Donato et al., 2011; Alongi, 2014). Although they occupy less than 1% of tropical coastal areas, mangrove forests store carbon at significantly higher rates and quantities than many terrestrial or boreal forests, with soils representing the largest carbon reservoir within these ecosystems (Alongi, 2014, 2020). This capacity is supported by their unique adaptations to saline, anoxic soils and the high irradiance and temperature regimes typical of tropical and subtropical climates. Distinct aboveground morphological features-such as pneumatophores, specialized leaves, and complex root systems-enhance sediment trapping and organic matter stabilization (Murdiyarso et al., 2010; Alongi, 2014; Twilley et al., 2017). Mangrove leaves are typically thick and leathery, with a well-developed cuticle that reduces water loss via transpiration. They often exhibit recessed (sunken) stomata, minimizing exposure to salt spray and limiting water vapor diffusion (Alongi, 2014; Singh, 2020). Avicennia marina has salt glands on the leaf surface that actively excrete excess salt, thereby maintaining ionic balance (Alongi, 2014; Naidoo, 2016). Mangroves maintain photosynthetic activity under otherwise adverse conditions by means of biochemical adaptations, including enhanced antioxidant enzyme activity and the accumulation of osmolytes that mitigate oxidative damage induced by salinity and other abiotic stressors (Naidoo, 2016; Chen et al., 2024). Overall, mangrove leaf morphology and physiology are intricately adapted to these challenging habitats, combining structural features that reduce water loss and salt intrusion with physiological mechanisms that sustain photosynthesis and growth under fluctuating environmental conditions (Ball, 1986; Lovelock et al., 2006; Naidoo, 2016; Alongi, 2025).

Chlorophyll, the primary photosynthetic pigment, underpins the high productivity of mangrove ecosystems that thrive in intertidal zones along tropical and subtropical coastlines (Tomlinson, 1986; Alongi, 2020). Variations in chlorophyll concentration-ranging from 269.8 to 800.7 µmol/m² across species and locations-serve as key indicators of plant vitality and photosynthetic capacity, reflecting overall forest health and productivity (IISST, 2019; Porra, 2002). Despite their inherent resilience, the photosynthetic machinery of mangroves, particularly chlorophyll content and function, is highly sensitive to environmental stressors. Elevated temperatures, often exceeding 35 ºC, can impair photosynthetic performance and reduce leaf production, ultimately compromising plant health and growth (Field, 1995; Naidoo, 2025). High salinity, a defining feature of mangrove habitats, also reduces chlorophyll efficiency and carbon assimilation, contributing to stunted growth, as observed in dwarf mangroves (Ball and Farquhar, 1984; Tuffers et al., 2001; Naidoo, 2009, 2025). Monitoring chlorophyll dynamics is therefore essential for assessing mangrove ecosystem health, predicting responses to ongoing environmental change, and informing conservation and restoration strategies (Liu et al., 2019).

Additionally, proline-an osmo-protectant amino acid-accumulates in mangrove tissues in response to marine environmental stress, acting as a biochemical marker of stress tolerance (Parveen et al., 2024). Under such conditions, proline accumulation helps restore osmotic balance and mitigate oxidative damage, enabling mangroves such as A. marina to sustain photosynthetic activity (Aljahdali et al., 2021). Proline stabilizes proteins and membranes, scavenges free radicals, and maintains cellular osmotic balance, thereby enhancing tissue-level stress tolerance (Das et al., 2016). Elevated proline levels in mangroves, including A. marina, have been correlated with increased antioxidant enzyme activity (Aljahdali and Alhassan, 2020). Moreover, proline accumulation reflects the physiological status of mangroves under environmental stress and serves as a sensitive biochemical indicator for early stress detection (Chen et al., 2022; Nizam et al., 2022; Parveen et al., 2024). The combined assessment of chlorophyll and proline provides insight into physiological status and productivity potential in mangroves, highlighting species-specific adaptive strategies that sustain ecosystem productivity under fluctuating environmental pressures (Afefe et al., 2021).

The growth and productivity of mangroves is influenced by a suite of biotic and abiotic factors, including species composition, age, tidal inundation, salinity gradients, nutrient availability, and climatic variables such as temperature and precipitation (Alongi, 2014, 2018; Kauffman et al., 2020; Alongi, 2025). Additional morphological and physiological traits further contribute to their high growth rates and biomass accumulation, often exceeding those of many terrestrial ecosystems (Donato et al., 2011; Alongi, 2018). Physiological adaptations, such as high water-use efficiency (Ball, 1986), enable mangroves to sustain elevated net primary productivity (NPP), with reported values reaching up to 1,300 g C m⁻² year⁻¹ in some tropical regions (Alongi, 2018). Furthermore, mangroves enhance coastal productivity via complex food-web interactions and efficient nutrient cycling, supporting biodiversity and fisheries. These processes, in turn, reinforce ecosystem resilience and carbon dynamics (Alongi, 2018; Padhy et al., 2021).

Growth trajectories of mangrove stands typically follow a sigmoidal pattern, with rapid biomass accumulation during the early decades and a plateau phase as forests mature, often between 30 and 50 years (Kauffman et al., 2020; Alongi and Zimmer, 2024). Biomass, and therefore carbon, is substantially allocated between aboveground and belowground components, with belowground biomass contributing significantly to carbon storage. This contribution is further enhanced by soil carbon accumulation, driven by slower decomposition rates in waterlogged sediments (Alongi, 2014, 2018; Kauffman et al., 2020). Sediment organic carbon stocks often exceed aboveground biomass carbon, accounting for up to 90% of total ecosystem carbon. Carbon burial rates in sediments range from 138 to over 400 g C m⁻² year⁻¹, depending on geomorphic setting and regional conditions (Alongi 2014, 2018; Breithaupt et al., 2022). Carbon burial is facilitated by high sedimentation rates and anoxic conditions that inhibit microbial decomposition (Marois and Mitsch, 2015; Breithaupt et al., 2022). Restoration and afforestation efforts have demonstrated strong potential to recover carbon stocks to levels comparable with natural forests over time, underscoring mangrove rehabilitation as a viable strategy for enhancing blue carbon sinks (Song et al., 2023; Alongi and Zimmer, 2024). Additionally, Yao et al. (2025) outlined practical strategies for advancing marine carbon storage (mCS) and carbon dioxide removal (mCDR) at regional scales, emphasizing the importance of marine ecosystems under changing climate scenarios.

Mangroves contribute disproportionately to global blue carbon stocks, accounting for a substantial share of carbon burial and exchange in coastal zones despite their limited spatial extent (Howard et al., 2014; Alongi, 2020). Carbon stored in mangrove ecosystems comprises both recently fixed organic biomass carbon and older, more stable carbon pools (soil + biomass), which function as long-term carbon sinks (Alongi, 2020; Breithaupt et al., 2022). Globally, anthropogenic pressures and climate change-induced sea-level rise pose significant threats to mangrove carbon storage potential (Friess et al., 2019; Alongi, 2020). Mumbai, a major metropolitan city on India’s west coast, harbors extensive mangrove forests that are increasingly impacted by rapid urbanization and associated environmental stressors (Kulkarni et al., 2010; Vaz, 2014). Numerous studies have documented anthropogenic disturbances affecting mangroves in Mumbai, including the dumping of municipal and industrial waste, discharge of domestic sewage, solid waste accumulation, and heavy metal contamination. These impacts are particularly evident in areas such as Thane Creek, the Mithi River, Dahisar, and Kanjurmarg (Patil et al., 2014; Everard et al., 2014; Pachpande and Pejaver, 2015; Patil et al., 2015; Shinde and Dhonde, 2017; Shinde et al., 2018; Sharma et al., 2022; Patil and Kanhere, 2024). Such activities substantially degrade mangrove ecosystems and diminish their carbon sequestration potential (Vaz, 2014; Everard et al., 2014; Sharma et al., 2022; Akram et al., 2023).

A recent study on Mumbai’s mangroves by Bhattacharjee et al. (2025) reported an alarming 40% reduction in total mangrove cover over the past three decades. This represents a substantial loss, particularly for a densely populated city characterized by extensive land reclamation. This study is based on the premise that urban mangroves may be experiencing extreme stress due to: a) anthropogenic pollution; b) geomorphological distinctiveness, including a rocky barricade seaward barrier; and c) high oceanic exposure, potentially intensifying environmental stressors. These factors may significantly influence the ecophysiological responses and overall productivity of the ecosystem. Understanding biomass dynamics and carbon storage potential in these urban mangroves is therefore essential for effective conservation and management strategies.

Within this context, this investigation aimed to conduct an ecophysiological and geoecological assessment of key species, including Sonneratia alba Sm., Sonneratia apetala Buch.-Ham., Rhizophora mucronata Poir., and Avicennia marina (Forssk.) Vierh., in order to develop a comprehensive profile of the urban mangrove ecosystem. The relative tree densities observed at Carter Road followed the order: Avicennia marina > Rhizophora mucronata > Sonneratia apetala > Sonneratia alba, consistent with the findings of Kantharajan et al. (2018). Species such as A. marina, R. mucronata and S. apetala have been reported to exhibit relatively greater tolerance to diverse urban stressors, including heavy metals, microplastics, and organic pollutants, which influence mangrove growth and productivity (MacFarlane et al., 2003; Shete et al., 2009; Ghosh et al., 2020; Afefe et al., 2021; Nasrin et al., 2021; Mansuri and Shekhawat, 2025). Furthermore, their morpho-anatomical and physiological distinctiveness-reflecting a range of adaptive strategies (Giesen et al., 2006) such as root aeration mechanisms, salt regulation, and varied growth forms-guided their selection for this study (Tomlinson, 1986; Tatongjai et al., 2021). The main goal of this study was to examine variations in physiological adaptability under changing geomorphic conditions at the landward and seaward extremes of the ecosystem, as well as to evaluate how such variation may influence chlorophyll dynamics, ecosystem productivity, and carbon storage potential.

MATERIALS AND METHODS

STUDY SITE

Located along the western coastline of Mumbai, mangroves at Carter Road (19.06477° N, 72.82195° E) are a vital ecological-urban interface. The site hosts several mangrove species, including Sonneratia alba Sm., Sonneratia apetala Buch.-Ham., and Rhizophora mucronata Poir., and is largely dominated by Avicennia marina (Forssk.) Vierh. The ecosystem exhibits clear vertical zonation, with the tallest trees occurring along the landward edge and a progressive reduction in canopy height toward the seaward fringe (Figure 1). Field measurements and modelling studies have demonstrated that this mangrove belt reduces wave heights by more than 50% (Samiksha et al., 2019). Such coastal protection is particularly important given the increasing vulnerability of the Mumbai coastline to climate change-induced sea-level rise and extreme weather events, especially cyclones (Mohanty, 2020).

Figure 1
Site description: (A) map of the mangrove site at Carter Road, Mumbai (tick marks; legends: top left - scale and north arrow; bottom left - attribution); (B) mangrove trees at the landward edge of the ecosystem, experiencing minimal tidal influence; (C) mangrove tree at the seaward edge, characterized by a rocky barrier that restricts tidal drainage; (D) northern margin of the ecosystem showing a visible decline in canopy height.

LEAF PHYSIO-BIOCHEMICAL STUDIES

Leaf physiological and biochemical parameters were assessed, including weight, moisture content, chlorophyll content, and proline concentration. Leaf moisture percentage on a fresh weight basis (LM % F_w) was determined by recording the fresh weight (F_W), followed by drying the leaves to constant weight to obtain dry weight (D_W). The difference between FW and DW was used to calculate total moisture content and its percentage.

$Leafmoisture\left(LM\%F_W\right)=\frac{\left(FW-DW\right)}{FW}\times 100$ (eq. 1)

FW = Fresh weight and DW = Dry weight.

Proline was extracted from fresh leaf samples. A weighed aliquot (50 mg) was immediately mixed with up to 1 ml of ethanol:water (40:60 v/v) and incubated overnight at 4 ºC. A standard curve was prepared to determine proline concentrations in leaf samples using the following equation (Bates et al., 1973):

$Proline\left(\mu mol{g}^{-1}{F}_W\right)=\frac{\left(A_{ex}-blank\right)}{slope}\times \frac{V_{ex}}{V_{aq}}\times \frac{1}{F_W}$ (eq. 2)

In which A_ex = absorbance of the extract; V_ex = volume of the extract; V_aq = volume of the aliquot; and F_w = fresh weight.

Chlorophyll extraction protocols are well established, with variations primarily related to solvent systems. However, notable discrepancies in chlorophyll concentration calculations have been reported among widely used equations, including those of Arnon (1949), Lichtenthaler (1987), and Porra (Porra, 2002; Chazaux et al., 2022; Esteban et al., 2018). The corrections proposed by Chazaux et al. (2022) were adopted in this study due to their improved accuracy. Chlorophyll extraction was performed on the same day using freshly collected leaves from two zones: seaward (S) and landward (L). A minimum volume of pre-chilled (-20 ºC) 85% acetone, buffered with Tris-HCl (pH 8), was added to 150 mg leaf samples. Samples were homogenized to complete disruption and centrifuged at 4500 rpm for five minutes. The supernatant was collected and stored on ice in the dark. The extract volume was adjusted to 25 mL in volumetric flasks under dark conditions and thawed for 20 minutes before spectrophotometric measurements. Chlorophyll a (Chl a), Chlorophyll b (Chl b), and Chlorophyll a+b (Chl a+b) were measured at 663.6 nm and 646.6 nm (Chazaux et al., 2022). Buffered 85% acetone was used as the blank, and fresh blanks were measured intermittently to account for acetone volatility. Additional corrections at higher wavelengths were applied to minimize light scattering effects unrelated to the target analytes. The following equations were used to determine chlorophyll concentrations (µg ml^-1):

$Ch{l}_a=\left(12.18\times A_{663.6nm}-2.36\times A_{646.6nm}\right)$ (eq. 3)

$Ch{l}_b=\left(20.19\times A_{646.6nm}-4.59\times A_{663.6nm}\right)$ (eq. 4)

$Ch{l}_{a+b}=\left(17.83\times A_{646.6nm}+7.58\times A_{663.6nm}\right)$ (eq. 5)

The values obtained from the equations were converted to mg.g^-1 to express chlorophyll concentration in the leaf samples.

For spectrophotometric accuracy, the equipment was calibrated with blank solutions before each measurement set. Cuvettes were cleaned between readings using lint-free tissues to prevent artifacts and remove fingerprints. All glassware was pre-soaked overnight in dilute acid and thoroughly rinsed prior to use.

SOIL, BIOMASS, AND CARBON

The total area of the study site was measured using Google Maps (measure distance tool), which provides a reasonably accurate approximation of spatial extent. A transect-based sampling approach was used along a 250 m coastal stretch, with three 10 × 10 m plots established (Kauffman and Donato, 2012).

Soil samples were collected from both seaward and landward sediments using an open-face peat auger. Samples were retrieved from randomly selected subplots to a maximum depth of 50 cm at 10 cm intervals (0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm, and 40-50 cm), maintaining a minimum spacing of 2 m between subplots. Samples were dried at 50 ºC for 24 hours and reweighed repeatedly until constant weight was achieved (two consecutive identical measurements), ensuring complete drying. Dried samples were ground using a mortar and pestle in an air-conditioned laboratory maintained at 22 ºC. Soil was then sieved through 2 mm steel mesh, and visible biological debris, plastic fragments, or fibrous materials were removed. Within each subplot, three samples from the same depth were mixed to obtain a representative sample for that depth. Thus, each subplot yielded five composite samples (one per depth interval up to 50 cm). Sampling was conducted in January and February 2022. Soil organic carbon (SOC, %) was determined using the Walkley and Black method (Walkley and Black, 1934; Dookie et al., 2022). Soil organic carbon stock (C_Org) was estimated from SOC (%), bulk density (BD, g cm^-3), and sample thickness (cm) (Howard et al., 2014).

$Soilcarbondensity\left(SCD\right)=SOC\left(\%\right)\times Bulkdensity\left(gc{m}^{-3}\right)$ (eq. 6)

Strata core carbon (SCC) for each 10 cm depth interval was determined by multiplying SCD by sample thickness:

$Stratacorecarbon\left(SCC,gCc{m}^{-2}\right)=SCD\times samplethickness$ (eq. 7)

SCC values were subsequently converted to SOC_Org for each depth layer as follows:

$SO{C}_{Org}\left(MgCh{a}^{-1}\right)=SCC\times \frac{1Mg}{{10}^{6}g}\times \frac{{10}^{8}c{m}^2}{ha}$ (eq. 8)

SOC_Org values were then scaled to the total project area (6.564 ha). Stocks up to 50 cm depth were summed to estimate total ecosystem SOC_Org.

Standing aboveground biomass (AGB) and belowground biomass (BGB) were calculated by measuring tree diameter at breast height (DBH), 1.3 m above ground level (Komiyama et al., 2008), with BGB corrections applied according to Adame et al. (2017).

$W_{top}=0.251.\rho .DB{H}^{2.46}$ (eq. 9)

$W_{root}=0.199.\rho .DB{H}^{2.22}$ (eq. 10)

In which W_top and W_root represent aboveground tree weight and belowground root weight, respectively (kg).

To improve accuracy of wood biomass estimates, wood density () for A. marina, the dominant species at the site, was retrieved from a regional study (Shashikala, 2020). Plot-level tree density data were employed to determine AGB and BGB in the plot area (Mg ha^-1). Biomass values were converted to carbon values using a factor of 0.464 (Murdiyarso et al., 2010).

DATA ANALYSIS

Data compilation and preliminary statistical analysis, including descriptive statistics, analysis of variance (ANOVA), and correlation analysis, were performed using Microsoft Excel. More advanced analyses were conducted using XLSTAT and Google Colab, including Tukey’s post-hoc test following significant ANOVA results to compare all pairwise group means and identify specific differences among groups. For each response variable, (e.g., chlorophyll content), the main effects of species position (landward or seaward), as well as the position × species interaction, were tested at a significance level of α = 0.05. Assumptions were evaluated via visual inspection of residuals and homogeneity of variances. When appropriate, values were analyzed on the original scale.

To further investigate species-specific differences in chlorophyll parameters across positional groups, each chlorophyll variable was modelled using an ordinary least squares (OLS) regression including species, position, and their interaction (species × position). For each species, the estimated marginal mean (EMM) difference between landward and seaward positions (L - S) was calculated as a linear contrast of the fitted coefficients. In this case, EMM refers to the model-adjusted mean response, accounting for other variables included in the model. The standard error (SE) of each contrast was obtained from the variance-covariance matrix of the estimated coefficients using the contrast vector. A two-sided test statistic was computed as z = estimate/SE, and p-values were obtained assuming a normal distribution; 95% confidence intervals were estimated±1.96 × SE. This approach tested whether the EMM difference (L - S) for each species significantly differed from zero at α = 0.05.

In another instance involving soil parameters-SOC (%), BD, and strata SOC_Org-since ANOVA assumes independence and the soil data were clustered (i.e., repeated measurements obtained from similar groups at the site), random effects were incorporated using a linear mixed-effects (LME) model. This approach accounted for dependencies among soil parameters while minimizing the influence of potential confounding variables such as BD. Soil parameters were analyzed using an LME model, and estimated marginal means (EMM) were calculated for each depth × position combination. Likelihood ratio tests (LRT) were used to evaluate model fit. Depth, position (L and S), and their interaction were included as fixed effects, while core was included as a random intercept to account for repeated measurements within each sampling location. For each parameter, the full model was compared with reduced models to assess the effects of depth, position, and their interaction. Likelihood ratio statistics (LR_stats), chi-square p-values, fixed-effect estimates, and standard errors were calculated. Larger LR_stats values indicate stronger evidence in favor of the alternative (full) model relative to the reduced model. Model convergence and singularity were examined, and any singular reduced models were excluded from LRT reporting. Landward-seaward comparisons were conducted in selected cases using differences in EMMs (L-S) for the parameter at a given soil depth.

Finally, correlation heatmaps were generated by calculating the mean of means for all chlorophyll parameters across species and for all soil parameters across the depth profile within consistent positional groups (landward and seaward).

RESULTS

LEAF PHYSIOLOGY

Physio-biochemicals parameters including leaf moisture (%), fresh weight, proline content, and chlorophyll content were estimated in mangrove leaf samples from four different species (Table 1). The mean leaf moisture percentage on a fresh weight basis (LM% F_w) was 82.53% for A. marina, 80.94% for S. alba, 71.72% for R. mucronata, and 71.60% for S. apetala (Table 1). The omnibus ANOVA for species effect was not significant, (F = 2.53, p = 0.13094), and Tukey’s honestly significant difference (HSD) test revealed no significant pairwise differences at α = 0.05. Consistently, the boxplots show substantial overlap among species, supporting the non-significant result (Figure 2).

Table 1
Leaf physio-biochemical parameters of four mangrove species at Carter Road, Mumbai.

	Species				ANOVA (one-way)*
	S. alba	A. marina	R. mucronata	S. apetala	p	F
Leaf Moisture (%) F w	80.943a±1.188	82.528a±7.002	71.722a±1.102	71.596a±1.606	0.130	2.527
#Proline (μmol g -1 Fw)	21.050a±0.487	21.587a±0.408	22.429a±0.402	18.934a±0.141	<0.0005	15.160

• *ANOVA between species groups; ^#significant differences between species.

Figure 2
Boxplots showing A. Leaf moisture (% fresh weight; LM% Fw) and B. Proline content (µmol g^-1) in four mangrove species at Carter Road, Mumbai.

The ambient proline (Pro) content (μmol g^-1) of R. mucronata was highest (22.43±0.40), followed by A. marina (21.59±0.41) and S. alba (21.05±0.49). S. apetala exhibited the lowest Pro content (18.93±0.14) (Table 1). One-way ANOVA indicated a significant effect of species (F = 15.16) (Table 1). Tukey’s HSD post-hoc test showed that Pro content in S. apetala was significantly lower than in S. alba (p<0.01), A. marina (p<0.01), and R. mucronata (p<0.001), while the remaining pairwise comparisons were not significant. The results suggest that S. apetala accumulated less proline under the prevailing ecosystem conditions than the other three species (Figure 2). The latter group did not differ significantly from one another under ambient conditions.

Chl a, Chl b, Chl a+b (mg g⁻¹), and the Chl a/b ratio were assessed using two-way ANOVA with position (landward versus seaward) and species as factors. A significant main effect of position was observed for Chl a (p<0.001), Chl b (p<0.001), Chl a+b (p<0.001) and Chl a/b (p<0.001), while a marginally significant species effect was detected only for Chl a (p<0.05), with no position-dependent modulation across species (Table 2). All interaction effects (position × species) were non-significant: Chl a (F = 2.86), Chl b (F = 0.33), Chl a+b (F = 1.13), and Chl a/b (F = 0.20) (Table 2). Tukey’s HSD post-hoc comparisons were performed primarily for species, and no significant differences were detected. However, inspection of the EMM for the Chl a/b ratio (Figure 3) showed two significant species contrasts at the seaward position, with the largest absolute difference (~0.29 mg g⁻¹) observed between R. mucronata and S. apetala.

Table 2
Two-way ANOVA results for chlorophyll content in the four mangrove species and their respective positions along the seashore (p<0.05).

	Species		Position		Interaction
	Fstat	p	Fstat	p	Fstat	P
Chl a	3.68	<0.05	377.06	<0.000001	2.86	0.065
Chl b	0.35	0.78	71.72	<0.000001	0.33	0.80
Chl a+b	1.04	0.39	192.44	<0.000001	1.12	0.36
Chl a/b	2.00	0.149	18.59	<0.0001	0.19	0.89

Figure 3
Mean chlorophyll content (mg g^-1) in four mangrove species at Carter Road, Mumbai. The left and right panels represent landward and seaward mangrove vegetation, respectively. From top to bottom: chlorophyll a; chlorophyll b; chlorophyll a+b, and the chlorophyll a/b ratio. Bars indicate mean values, and letters above the error bars denote Tukey’s post-hoc comparisons.

ORDINARY LEAST SQUARES MODEL FOR CHLOROPHYLL

Modelling of chlorophyll concentrations (Chl a, Chl b, and Chl a+b, mg g^-1) using ordinary least squares (OLS) showed statistically significant (L - S) EMM differences (p<0.05) for all four species (Figures 4 and 5). For the Chl a/b ratio, two species (A. marina and R. mucronata) exhibited significant (L - S) EMM differences, whereas S. alba and S. apetala did not differ significantly from zero. Positive differences indicate higher values on the landward side, while negative differences indicate higher values at the seaward position (Figure 5). Overall, chlorophyll concentration metrics consistently varied with shoreline position across species, whereas the Chl a/b ratio showed species-specific effects. The observed differences were primarily driven by position, with limited and inconsistent separation at the species level and minimal evidence of zone-dependent modulation across species, indicating that positional effects were broadly similar across taxa.

Figure 4
Cleveland dot plots displaying estimated marginal means (EMM) derived from ordinary least square (OLS) models for chlorophyll parameters by species within each shoreline position (landward and seaward). Panels correspond to (A) Chl a; (B) Chl b; (C) Chl a+b; and (D) Chl a/b. Species are shown on the y-axis and EMMs on the x-axis, with 95% confidence intervals. Species are ordered within each panel according to their EMM to highlight relative ranking. The legend indicates shoreline position (blue: landward; orange: seaward). The model included species × position as fixed effects. Confidence intervals represent within-panel uncertainty around the marginal means; limited overlap or clear separation suggests meaningful differences.

Figure 5
Landward−seaward estimated marginal mean (EMM) differences with 95% confidence intervals and p-values. Panels correspond to chlorophyll outcomes: (A) Chl a; (B) Chl b; (C) Chl a+b (mg g^-1); and (D) Chl a/b. For each species, points represent the EMM difference (landward minus seaward), and horizontal bars indicate 95% confidence intervals. Labels denote the estimated difference and two-sided p-value derived from a normal-approximation Z-test. The dashed vertical line marks zero (no difference). Legend: red = statistically significant differences (p<0.001), blue = not significant (p ≥ 0.05). Positive values indicate higher EMM in landward zones, whereas negative values indicate higher EMM in seaward zones. Species are ordered within each panel according to the magnitude and direction of the difference.

SOIL CARBON PARAMETERS

SOC %, BD, and SOC_Org were estimated up to 50 cm depth (Table 3). One-way ANOVA indicated no significant trends with increasing depth within either seaward or landward soils. However, two-way ANOVA (without replication) comparing depth and position revealed a significant effect of position only. For example, SOC (%) (F = 81.51) in seaward soils ranged from 1.41±0.06 to 1.11±0.10 (0-10 to 40-50 cm), whereas landward sediments showed higher values ranging from 2.08±0.31 to 1.49±0.20 (Table 3). Similarly, soil strata SOC_Org (F = 53.26) was considerably higher in landward sediments (20.79±2.45 to 24.07±2.41 Mg C ha^-1), while seaward values were substantially lower (1.12±0.06 to 1.41±0.12 Mg C ha^-1). BD (F = 0.85) did not differ significantly between positions (L and S) or across depths (F = 0.19) (Table 3). Tukey’s post-hoc analysis also showed no significant depth-wise differences. Consequently, depth-related patterns were further evaluated using a more robust LME model, which better accounts for treatment structure and associations among sampling groups.

Table 3
Soil parameters up to 50 cm depth in seaward and landward coastal sediments.

	Sample locations with soil depth (cm)
	Seaward					Landward
	0-10	10-20	20-30	30-40	40-50	0-10	10-20	20-30	30-40	40-50
Soil Organic Carbon SOC (%)	1.41±0.06	1.30±0.08	1.26±0.08	1.19±0.12	1.11±0.10	2.08±0.31	1.93±0.30	2.02±0.36	1.70±0.24	1.49±0.20
Bulk density (g cm-3)	1.28±0.07	1.19±0.07	1.15±0.05	1.15±0.06	1.11±0.03	1.12±0.06	1.16±0.05	1.29±0.12	1.33±0.10	1.41±0.12
Soil strata SOCOrg (Mg C ha-1)	18.05±1.71	15.54±1.87	14.60±1.57	13.72±1.97	12.44±1.38	22.37±2.36	20.79±2.45	24.07±2.41	21.95±2.51	20.04±1.48
Soil pH	7.35±0.08	7.48±0.07	7.45±0.04	7.52±0.08	7.80±0.07	6.62±0.11	6.88±0.11	6.77±0.11	6.96±0.11	7.44±0.11
Moisture (%)	---	---	---	---	---	60.13±1.78	54.97±2.83	28.57±4.30	28.50±2.63	17.35±1.75

• Values represent mean±SEM

LINEAR MIXED-EFFECT MODEL FOR SOIL PARAMETERS

The LME model showed structured differences in soil parameters, with SOC (%) indicating significant stratification with depth (LR_stat = 40.4), significant positional effects (LR_stat = 9.59), and significant interaction effects (LR_stat = 7.28) (Table 4). EMMs demonstrated a clear decline in SOC (%) with increasing depth on the seaward side. For example, the contrast between 0-10 cm and 40-50 cm showed a significant decrease (p<0.01) (Figure 6). Positional contrasts consistently favored higher SOC (%) in landward sediments. At 20-30 cm depth, the landward-seaward EMM difference was 0.753 percentage points (p<0.05). Overall, these results indicate higher SOC % in landward soils and a depth-related decline that persists across positions, underscoring the greater contribution of landward zones to carbon storage within this ecosystem.

Table 4
Result of the linear mixed-effects model for soil parameters

	Depth		Position		Depth×Position
	LRstat	p	LRstat	p	LRstat	p
Soil organic carbon (SOC) (%)	40.4	<0.000001	9.598	<0.001	7.275	<0.01
Bulk density (g cm-3)	---	<0.01	19.966	<0.00001	19.485	<0.001
Soil strata COrg (Mg C ha-1)	31.684	<0.000001	18.026	<0.00001	13.686	<0.01

Figure 6
Line charts comparing estimated marginal means (EMMs) of sediment parameters across soil depth (0-50 cm). The x-axis represents soil depth, and the y-axis shows: (A) SOC (%); (B) Bulk density (g cm^-3); and (C) Soil strata carbon (SOC_Org - Mg C ha^-1).

Similar trends were observed for BD, with strong position effects and evidence that depth profiles differed by position (Table 4). Position effects (LR_stat =19.96) and depth × position interaction effects (LR_stat = 19.49) were highly significant (p<0.001). The depth-only reduced model was singular (p<0.01), preventing a clean LRT for an overall depth effect; however, the interaction indicates that the depth pattern of BD is not parallel between positions (p<0.001). EMM contrasts between positions at individual depths were generally modest, suggesting that although the overall position effect is evident, the depth-dependent divergence is better interpreted as a difference in profile rather than isolated pairwise contrasts.

SOC_Org (Mg C ha^-1) varied significantly across both depth and position, as indicated by two-way ANOVA (Table 4). There was a consistent and robust structure (Figure 6), with significant effects of depth (LR_stat = 31.68), position (LR_stat =18.026), and depth × position (LR_stat =13.68).

This indicates that absolute SOC_Org levels differ between seaward and landward zones, and that the vertical pattern of carbon accumulation is influenced by vegetation position within the ecosystem. Soil pH increased consistently with depth in both seaward (7.35±0.08 to 7.80±0.07) and landward sediments (6.62±0.11 to 7.44±0.11). One-way ANOVA indicated significant differences in pH at corresponding depths between the two positions (seaward: F = 16.096, p<0.0001; landward: F = 18.641, p<0.0001) at the Carter Road mangroves.

LEAF PIGMENT AND SEDIMENT RELATIONS

Correlations between key chlorophyll and soil parameters revealed distinct patterns for certain relationships (Figure 7). In landward samples, pH showed a markedly strong positive correlation with chlorophyll concentrations (Chl a: r = 0.98; Chl b: r = 0.85; Chl a+b: r = 0.90), whereas it was strongly negatively correlated with the Chl a/b ratio (r = -0.76). In contrast, these relationships were weak in seaward samples. pH also exhibited an absolute negative association (r = -1.0) with seaward strata SOC_Org, while the relationship remained consistent but moderately weak on the landward side. Another notable positional shift was observed in the correlations of Chl a+b with the Chl a/b ratio (r = -0.63) and SOC (%) (r = -0.37) in landward samples, both negative, whereas seaward samples showed moderately positive associations for these parameters (Figure 7). Strata SOC_Org maintained strong positive associations with Chl a across ecosystem boundaries (landward: r = 0.61; seaward: r = 0.97).

Figure 7
Correlation showing relationships between chlorophyll and sediment parameters at the ecosystem boundaries: (A) seaward and (B) landward. Colors indicate strength of association (green: r = 1; yellow: r = -1; grey: r = 0).

TREE BIOMASS AND CARBON

A tree density of 2,717.5 individuals per hectare was recorded at the study site, with dominance of A. marina. Trees along the landward edge were taller, with visibly thicker girth and greater DBH, while those on the seaward fringe were comparatively shorter. Overall, a larger proportion of the ecosystem comprised trees with the DBH range of 10-40 cm, predominantly at the landward edge (Table 5). The mean (±SEM) AGB and BGB were 769.41±71.33 Mg ha^-1 and 214.20±19.86 Mg ha^-1, respectively. Corresponding biomass carbon pools were estimated as AGBC_Org = 357.00±33.10 MgC ha^-1 and BGBC_Org = 99.39±9.21 Mg C ha^-1 (Figure 8).

Table 5
Total ecosystem carbon stock of mangroves at Carter Road, Mumbai.

	Above Ground Biomass Carbon (AGBCOrg)	Below Ground Biomass Carbon (BGBCOrg)	*Soil Organic Carbon (SOCOrg)	Ecosystem Carbon (COrg)
Organic Carbon (COrg) # (Mg C ha-1)	357.00±33.01	99.39±9.21	91.78±9.84	548.17
Total carbon (Mg C)	2334.80	650.01	507.84	3492.65

• ^#Values represent mean±SEM.
• *Average of landward and seaward SOC_Org values summed up to 50 cm depth

Figure 8
Bar graph showing carbon pools (C_Org, Mg C ha-1) in the mangrove ecosystem at Carter Road, Mumbai. (AGBC = aboveground biomass carbon; BGBC = belowground biomass carbon; SOC = soil organic carbon).

DISCUSSION

LEAF PHYSIOLOGY

Leaf moisture (LM % F_w) exceeded 70% in all species, following the order A. marina > S. alba > R. mucronata = S. apetala (Figure 2). Proline content under ambient conditions differed significantly among species, in the order R. mucronata > A. marina > S. alba > S. apetala. Local and regional environmental factors-especially stressors such as tidal inundation, solar radiation, freshwater inputs, and salinity-can collectively influence the production of photochemicals and osmoprotectants in mangroves (Flores-de-Santiago et al., 2016; Dookie et al., 2023). Under high salinity, mangroves accumulate compatible solutes (e.g., sugars, sugar alcohols, proline, glycine) as osmoprotectants (Bryant et al., 2024; Naidoo, 2025) to maintain turgor pressure and protect the photosynthetic apparatus, including chloroplasts and thylakoid membranes, thereby indirectly supporting chlorophyll function and stability (Bryant et al., 2024; Parveen et al., 2024). Such mechanisms also contribute to heat tolerance in saline environments (Bryant et al., 2024). Proline, both an amino acid and a compatible solute, is synthesized in the cytosol or chloroplast depending on its functional role. Its accumulation is a common physiological response to osmotic stress induced by salinity, drought, and other abiotic challenges (Verbruggen and Hermans, 2008; Meena et al., 2019; Nizam et al., 2022; Parveen et al., 2024). Mangroves in hypersaline dwarf zones with limited tidal flushing and elevated soil salinity often exhibit higher leaf Na⁺ concentrations accompanied by increased proline accumulation to counter osmotic and ionic stress (Mehta and Vyas, 2023; Naidoo, 2025). Additional triggers for proline accumulation include metal toxicity and high UV radiation (Kishor et al., 1995; Hayat et al., 2012; Meena et al., 2019; Nizam et al., 2022; Alharbi et al., 2025). Proline biosynthesis under stress is primarily mediated by enzymes Δ1-pyrroline-5-carboxylate synthetase (P5CS) and Δ1-pyrroline-5-carboxylate reductase (P5CR), using glutamate as the precursor (Delauney and Verma, 1993; Liu and Wang, 2020). Abscisic acid is also known to stimulate proline synthesis under salt and osmotic stress (Nizam et al., 2022). Species-specific variation in proline accumulation under different stress regimes has been reported (Meena et al., 2019; Nizam et al., 2022). In our study, S. apetala presumably accumulated less proline under the prevailing ambient tropical and urban-influenced conditions than the other three species (Figure 3). The remaining species did not differ significantly from one another. Liu and Wang (2020) further describe the heat stress-mediated relationship between transpiration rates and proline synthesis, which is feedback-regulated by proline levels. Similarly, Wang et al. (2022) demonstrated that under low-temperature stress, A. marina exhibited a faster and stronger proline response than most species, although levels declined with prolonged exposure. Afefe et al. (2021) reported ambient proline concentrations of 18.57 mg g^-1 DW in A. marina and 23.98 mg g^-1 DW in R. mucronata. Other studies have documented proline accumulation under upwelling-related stress and ambient heavy metal exposure (Li et al., 2022; Alharbi et al., 2023).

Chlorophyll content is a useful indicator of whether plants can protect their photosynthetic apparatus under marine and anthropogenic stress (Sharma et al., 2025). Chl a content in S. alba was consistently lower than in one or more of the other species, whereas R. mucronata and A. marina were significantly higher, with similar trends observed for Chl a+b at both locations (Figure 3). For Chl b, A. marina and S. apetala exceed S. alba, suggesting that S. alba at the landward edge invests less in total pigment, possibly reflecting a smaller photosynthetic apparatus or different leaf economic strategy. In contrast, A. marina and S. apetala showed higher accessory pigment (Chl b), supporting stronger light-harvesting capacity or acclimation to inland canopy/light conditions. Afefe et al. (2021) reported higher chlorophyll content in A. marina than in R. mucronata supporting the former’s greater tolerance and wider distribution. The Avicenniaceae family is often considered more salt-tolerant than the Rhizophoraceae (Hutchings and Saenger, 1987; Afefe et al., 2021). The Lythraceae family includes terrestrial, aquatic, and mangrove taxa such as Sonneratia spp. (S. caseolaris, S. lanceolata, S. alba, and S. apetala), which occur in coastal regions with varying salinity regimes (Tomlinson, 1986; Ball and Pisdsley, 1995; Simpson, 2010; Tatongjai et al., 2021). Among these, S. alba is reported to achieve optimal growth even at 50% seawater salinity (Ball and Pisdsley, 1995). S. apetala showed the strongest high-light acclimation at the shore fringe (highest Chl a/b ratio), consistent with greater irradiance and reflectance at the seaward edge, whereas R. mucronata exhibited lower Chl a/b ratio, suggesting a more shade-type antenna composition or a distinct stress response at the fringe (Figure 3). Two-way ANOVA indicated significant species effects across all chlorophyll measures, demonstrating consistent interspecific differences (Table 2). Position had a strong and highly significant effect on Chl a, Chl b, and Chl a+b, reflecting clear landward-seaward contrasts (EMM differences), and was also significant for the Chl a/b ratio (Figure 5). Interaction effects were significant for Chl a/b and Chl b, suggesting that species differences varied by location. In contrast, interaction effects for Chl a and Chl a+b were marginal (around the 0.05 threshold), implying broadly similar species rankings across positions, with only limited evidence of location-specific shifts (Table 2; Figure 4). Overall, both species identity and shoreline position contributed meaningfully to chlorophyll variation, with the strongest interaction patterns observed for Chl a/b and Chl b.

Factors such as salinity have been shown to significantly reduce chlorophyll content (Chl a and Chl b) (Marae et al., 2024; Naidoo, 2025). High salinity can increase the production of reactive oxygen species (ROS), leading to cellular damage (Meena et al., 2019; Nizam et al., 2022). To counteract this stress, mangroves activate ROS-scavenging systems, in which carotenoids play a crucial protective role. This mechanism can indirectly influence the stability of other photosynthetic pigments, including chlorophyll (Hasanuzzaman et al., 2021; Zhou et al., 2022; Parveen et al., 2024). However, some studies report contrasting findings. Higher salinity has been associated with higher chlorophyll content in climax communities of Ceriops decandra and Excoecaria agallocha (Sarker et al., 2021; Ahmed et al., 2022; Parveen et al., 2024). Similarly, A. marina exhibited higher levels of photochemical pigments in lower salinity zones (littoral and intertidal) compared to landward areas (Marae et al., 2024). Additionally, Dookie et al. (2023) observed relatively higher chlorophyll content in Avicennia germinans growing in degraded habitats compared with those in natural or restored environments.

Conversely, in our study, lower chlorophyll content was observed in seaward plants. This pattern may be attributed to the presence of a rocky barricade that restricts seawards drainage, combined with an extremely younger mangrove population exposed to intense tropical heat. This barricade effect, coupled with aridity, may promote salt retention in the sediments, thereby increasing salinity levels along the seaward edge. Furthermore, as an urban hotspot in Mumbai, Carter Road is subject to significant urban runoff, wastewater discharge, plastic debris, and potential petroleum contamination associated with extensive port activities. These disturbances may alter sediment nutrient balance, affecting essential elements such as trace metals, iron, sulphur, nitrogen, and other indispensable elements for forest productivity (Alongi, 2014; Chen et al., 2018). Nitrogen limitation, particularly under highly saline sediment conditions, can directly constrain chlorophyll synthesis (Naidoo, 2025). Moreover, Duke and Watkinson (2002), when analyzing A. marina and R. mangle, reported deteriorating chlorophyll mutations associated with polycyclic aromatic hydrocarbons (PAH), which can exert lethal effects on young seedlings. By contrast, mangrove stands along the landward edge of Carter Road exhibited higher chlorophyll content and appeared more resilient and dominant. These mature stands, characterized by greater species diversity, may benefit from more stable sediment nutrient dynamics and improved nutrient distribution and bioavailability.

SOIL CARBON PARAMETERS

The LME model suggests clear vertical structuring and spatial (positional) differences in SOC (%) and carbon stocks that were not detected by simpler per-position one-way ANOVAs. These gains likely result from modelling all depths jointly, accounting for repeated measures within cores, and directly testing interactions. Although there were caveats-including a singular reduced model for the BD depth test and boundary convergence warnings (common in mixed models)-the principal effects were supported by likelihood ratio tests and EMM contrasts (Table 3). Overall, SOC (%) decreased with depth and was higher at the landward edge (e.g., + 0.671% at 0-10 cm, p = 0.072; + 0.753% at 20-30 cm, p<0.05). BD depth profiles differed significantly by position (interaction p<0.001), and SOC_Org exhibited significant position effects with depth-dependent patterns (all main effects and interactions p<0.01). In practical terms, the landward edge tends to store higher carbon stocks (Figure 8), and the non-parallel depth gradients across positions suggest geomorphic or hydrological controls varying across the transect.

CHLOROPHYLL AND SEDIMENT RELATIONS

Most associations between SOC (%), BD, and SOC_Org with chlorophyll parameters shifted toward weaker or negative associations from the seaward fringe to the landward edge (Figure 7). These relationships suggest spatial variation in the coupling between plant ecophysiology and sediment carbon dynamics across the ecosystem boundaries. Although fluctuating, the relationships of Chl a/b with soil parameters make it an important positively associated factor in carbon burial processes, second only to Chl a concentrations.

ECOSYSTEM CARBON ACCUMULATION: SEDIMENT AND TREE BIOMASS

Tree density at Carter Road is relatively high compared with other sites in Mumbai, such as Thane Creek. A. marina covers the maximum canopy area of the ecosystem, with accompanying species present in relatively fewer numbers. Mumbai’s mangroves have experienced considerable fluctuations in forest structure over the past two decades. Vijay et al. (2005) reported tree densities of >900 trees per hectare for Mumbai mangroves. Additionally, Patil et al. (2014) recorded 89, 218, 233, 159, and 150 trees per hectare for different regions within the Thane Creek ecosystem, indicating a sharp decline within a decade. Our analysis recorded approximately 6.54 hectares of green cover at Carter Road. Reports from two decades ago indicate a large forest extent in Mumbai with high tree densities (Vijay et al., 2005), which have declined since then. Bhattacharjee et al. (2025) reported a 40% loss of green cover over the past three decades. Similar to our observations at the study site, A. marina is consistently identified as the predominant mangrove species across Mumbai’s coastal areas, including Thane Creek, Manori Creek, and Gorai Creek (Kantharajan et al., 2018; Abdul Azeez et al., 2022; Sawant et al., 2024; Patil and Kanhere, 2024).

Our estimates indicate significant biomass carbon deposits in this ecosystem, with mean values of AGBC_Org, BGBC_Org, and ecosystem C_Org being much higher than previous assessments in Mumbai, partly due to the different allometric equations used, which were reportedly lower than those applied in most studies for similar forest structures (Singh et al., 2023). The DBH of trees at Carter Road ranged from 10 to 40 cm, with very small seedlings landward and young stands with DBH<5 cm seaward. There were very few stands ranging from 5 to 10 cm in each plot. Another study by Nar et al. (2024) recorded DBH values from 17 to 58 cm; however, the time of measurement was not available to compare values or determine the rate of biomass carbon accumulation. Earlier studies reported DBH typically ranging from 3 to 40 cm (IISST, 2019).

AGB and BGB accounted for 769.41±71.33 Mg ha^-1 and 214.20±19.86 Mg ha^-1, respectively. The carbon counterparts of these biomass pools were AGBC_Org = 357.00±33.10 Mg C ha^-1 and BGBC_Org = 99.39±9.21 Mg C ha^-1 (Table 5). Mangrove carbon sequestration studies, such as Singh et al. (2023), estimated mangrove biomass carbon and soil carbon at Thane Creek using integrated allometry-remote sensing techniques and reported AGBC_Org at 47.68 MgC ha^-1, reflecting an increase from a previous estimate of 21.66 MgC ha^-1 (Patil et al., 2015). The BGBC_Org was reported as 18.06 Mg C ha^-1 (Patil et al., 2015) and 12.41 MgC ha^-1 (Singh et al., 2023).

The average of SOC_Org up to 50 cm depth was estimated at approximately 91.79±9.85 MgC ha^-1, with seaward and landward SOC_Org values of 488.07 Mg C and 527.62 Mg C, respectively (Table 6). The average landward and seaward SOC_Org for the entire ecosystem, summed to 50 cm depth, was 507.84 Mg and can provide only a rough estimate of ecosystem carbon stock, since samples were collected from the extreme ends of the ecosystem along the shoreline and the values may be highly skewed. Different mangrove locations at Thane Creek had SOC (%) varying from 2.35% to 13.86% (Patil et al., 2014), and the mean sediment carbon stock, measured up to 40 cm depth, was 63.79 Mg C ha^-1, with 30% concentrated in the top 0-10 cm layer (Singh et al., 2023). The total organic carbon in surface sediments across Mumbai mangroves ranged from 2.5% to 5.3%, with the highest at Versova and lowest at Sewri (Kesavan et al., 2025). In this study, the ecosystem AGBC_Org and BGBC_Org values were 2334.80 Mg C and 650.01 Mg C, respectively (Figure 8). The total ecosystem carbon stock, calculated as the sum of the three carbon pools (AGBC_Org, BGBC_Org, and SOC_Org), was 3492.65 Mg carbon (Table 5). Relatively, our estimates suggest a large carbon deposit at the Carter Road mangroves, with significant sediment and AGBC_Org reserves. The vibrancy of this plant community may be attributed to species diversity at Carter Road, which contributes to carbon burial along with supportive environmental and sediment conditions. Being an urban hotspot, its serenity attracts leisure activities along the coast.

Table 6
Total soil strata organic carbon in the study area.

	SOCOrg (Mg C)
Soil depth interval (cm)	Seaward	Landward
0-10	118.51±11.23	128.37±9.31
10-20	101.98±12.27	108.50±18.00
20-30	95.85±10.30	101.59±14.81
30-40	90.08±12.87	100.31±13.53
40-50	81.65±9.03	88.85±9.45
Total	488.07	527.62
*Average of total	507.84

• Values represent mean±SEM
• *Average of landward and seaward SOC_Org values summed up to 50 cm depth.

Overall, robust physiology enhances mangrove productivity. Elevated chlorophyll levels typically correlate with higher photosynthetic rates, contributing to greater biomass accumulation and net primary productivity (Lovelock et al., 2006; Alongi, 2025), and indicate favorable growth conditions with increased carbon assimilation (Singh et al., 2023; Rodríguez-Reales et al., 2024). Empirical studies have demonstrated that mangrove productivity, measured in terms of biomass or carbon sequestration, aligns with variations in chlorophyll content across species and sites, modulated by environmental gradients such as salinity, nutrient availability, and light intensity (Kumar and Das, 2023; Alongi, 2025).

CONCLUSION

The small mangrove plant community at Carter Road exudes wilderness by standing its ground in the face of coastal, climatic, and urban stressors. This pilot study profiling mangroves at Carter Road was an attempt to understand the ecophysiological and geoecological status of an urban ecosystem facing unique, position-specific challenges. The proline levels of this ecosystem indicate the stressors experienced. Such secluded ecosystems, with distinctive geomorphological characteristics and urban influence, can exhibit diverse adaptation and survival strategies. The spatial variability in chlorophyll and SOC (%) across seaward and landward positions are meaningful indicators of resilience driving productivity and carbon storage, especially within the existing ecogeomorphological setting and urban influence. The carbon storage capacity of this ecosystem is largely driven by the mangrove community, comprising at least four distinct species and a high tree density of A. marina. Several coastal locations in Mumbai under intense urban influence must be explored and documented to better understand their role in the rapidly declining mangrove cover of the city. Such investigations are necessary to inform policies and promote the re-establishment of species-rich mangrove patches that can enhance the coastal resilience of the city.

DATA AVAILABILITY STATEMENT

All data are available from the corresponding author upon reasonable request.

SUPPLEMENTARY MATERIAL

No supplementary material accompanies this manuscript.

ACKNOWLEDGMENTS

We warmly thank the anonymous reviewers for their excellent review and suggestions to improve this manuscript.

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• AI USE STATEMENT
  AI tools were not used for any form of data analysis, writing or interpretations. This work employed the AI feature of Google Colab to generate visualisations during the preparation of this manuscript. Such outputs were carefully reviewed and edited by the authors to ensure accuracy and clarity. Authors take full responsibility for the content of the manuscript.
• FUNDING
  This research work did not receive specific grant from any funding agency. The DBT-STAR grant to the department is sincerely acknowledged.

Edited by

• Editor:
  Rubens Lopes

Publication Dates

• Publication in this collection
  17 July 2026
• Date of issue
  2026

History

• Received
  24 Sept 2025
• Accepted
  22 Mar 2026