Seedling Density as a Key Determinant of Topsoil Organic Carbon and CNP Stoichiometry in Intertidal Mangrove Ecosystems: Insights from Avicennia marina Stands

Seedling Density as a Key Determinant of Topsoil Organic Carbon and CNP Stoichiometry in Intertidal Mangrove Ecosystems: Insights from Avicennia marina Stands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seedling Density as a Key Determinant of Topsoil Organic Carbon and CNP Stoichiometry in Intertidal Mangrove Ecosystems: Insights from Avicennia marina Stands Yuduan Ou, Peng Chen, Yanji Jiang, Liuping Wu, Baicheng Lu, Wei Wei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8061427/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 Background and Aims Mangrove ecosystems are vital "blue carbon" sinks, yet their degradation threatens global carbon sequestration efforts. While forest structure influences carbon storage, the role of specific life-history stages, particularly that of seedlings, remains poorly quantified. This study aims to identify the key drivers of soil organic carbon (SOC) retention in mangroves, with a particular focus on the understudied role of seedling density. Methods We conducted a field study in a monodominant Avicennia marina forest on Techeng Isle, China, sampling along seaward-landward transects across the intertidal gradient. We measured stand structure and topsoil properties (0–20 cm), analyzing for SOC, total nitrogen (TN), total phosphorus (TP), and stoichiometric ratios (C:N:P). The relative importance of drivers was assessed using random forest regression. To uncover the underlying mechanisms, we derived proxy variables for belowground carbon input and sediment oxygen demand, which were integrated into structural equation modeling (SEM) to test causal pathways. Results From the seaward to landward fringe, the mangrove population structure shifted from declining to growing, with a significant increase in seedling density ( P < 0.001). Concurrently, the levels of SOC, TN, TP and their stoichiometric ratios increased significantly from the outer to the inner fringe ( P < 0.05). Among all predictors, seedling density emerged as the most vital for SOC, a finding substantiated by random forest analysis. Structural equation modeling demonstrated that seedling density enhances SOC primarily by promoting sediment anoxia and, to a lesser extent, by increasing root-driven carbon inputs, with the model explaining a substantial proportion of the variance (R² = 0.97). Conclusion Our findings demonstrate that mangrove population characteristics, especially seedling density, exert a stronger influence on topsoil carbon and nutrients than sediment physical properties alone. We propose high-density seedling planting as a novel and effective restoration strategy to enhance blue carbon sequestration, improve nutrient retention, and bolster ecosystem resilience against sea-level rise. Blue carbon Avicennia marina Soil carbon sequestration Seedling density Intertidal gradient Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Mangroves play a critical role in carbon cycling and storage in the coastal ecosystem (Sasmito et al., 2019 ; Alongi, 2014 ). Although they cover less than 1% of coastal areas, mangroves contribute disproportionately to coastal carbon sequestration, accounting for 10–15% of the total carbon sequestered in these regions (Alongi, 2014 ; Yan et al., 2024 ). Moreover, mangroves store three to five times more organic carbon than terrestrial tropical forests (Malik et al., 2023 ; Donato et al., 2011 ). This significant carbon storage capacity has led to their recognition as essential “blue carbon” ecosystems (Malik et al., 2023 ; Murdiyarso et al., 2015 ; Alongi et al., 2016 ; Wang et al., 2021 ). Mangroves serve as significant reservoirs of soil organic carbon (SOC), primarily accumulated through long-term above- and below-ground biomass accumulation, particularly in their organic-rich soil carbon pools (Sasmito et al., 2019 ; Alongi, 2014 ). These soil carbon pools account for 70.65% of the total organic carbon stored in mangroves globally (Malik et al., 2023 ; Alongi et al., 2016 ). The long-life span of carbon in mangrove soil is attributed to anoxic conditions that limit the decomposition of organic matter, thereby contributing to a substantial belowground carbon stocks (Chen et al., 2018 ). Consequently, mangrove soils represent the largest organic carbon reservoir in these ecosystems and are a crucial focus for conservation efforts (Kida and Fujitake, 2020 ). However, human activities have led to significant losses and degradation of mangrove habitats, potentially transforming these ecosystems into sources of atmospheric carbon (Chen et al., 2018 ). To counteract these losses, mangrove restoration projects have been initiated worldwide (Rahman et al., 2015 ; Chandra et al., 2011), emphasizing the need for a baseline assessment of ecosystem carbon stocks. Additionally, understanding the stoichiometric relationships between carbon, nitrogen, and phosphorus (C:N:P) is crucial for comprehending nutrient dynamics and their impact on carbon sequestration in mangrove ecosystems. SOC burial rates, carbon density, and carbon stocks in mangroves vary widely, influenced by factors such as regional primary productivity, geomorphological conditions, climate, and distance from the sea (Sasmito et al., 2019 ; Yan et al., 2024 ; Twilley et al., 2018 ; Woodroffe et al., 2016 ). In addition to these abiotic factors, mangrove structural characteristics significantly affect sediment carbon-cycle processes (Wang et al., 2019 a). Mangroves act as important carbon sinks, accumulating marine and terrigenous sediment through the physical trapping effect of their complex aboveground structure (Kumara et al., 2010 ). Changes in mangrove structural indicators, such as tree density, basal area, and height, significantly impact on the quantity and quality of organic carbon entering the sediment (Chen et al., 2018 ; Wang et al., 2019 a; Eric et al., 2014; Chang et al., 2020 ). Higher densities of mangrove structures, including stems and aerial roots, enhance sediment accumulation (Alongi, 2014 ; Kumara et al., 2010 ). For example, high tree density can trap upland sediment runoff and nutrients, increasing SOC content and density(Chen et al., 2018 ). Previous studies have focused on the size, shape, density, and distribution patterns of mangrove trees but have largely overlooked the effects of mangrove seedlings on SOC and nutrient dynamics (Alongi, 2014 ; Chang et al., 2020 ). Understanding these effects is crucial for assessing carbon sequestration potential and sustainable development of mangrove ecosystems. Given their critical carbon sequestration capacity, mangrove ecosystems face significant threats from climate change-induced sea-level rise. This study focuses on monodominant stands of Avicennia marina , a key species dominating tropical intertidal zones. Our site on Techeng Isle, where A. marina comprises > 80% of cover (Gao & Han 2008 ), provides an ideal natural laboratory due to three key characteristics: (1) monodominance, which eliminates interspecific interference on SOC dynamics; (2) a full ontogenetic spectrum, enabling life-stage analysis from seedlings to mature trees; and (3) pronounced intertidal zonation, creating built-in environmental gradients. This unique setting allows us to employ a monospecific approach to isolate the effect of population structure from interspecific functional variability (Wang et al., 2019 ; Castillo et al., 2018 ). By focusing on a monodominant stand across a natural gradient, this design serves as a model system to isolate in-situ biotic drivers, inherently minimizing confounding variation from heterogeneous allochthonous carbon and nutrient inputs. Consequently, we can test the specific hypothesis that intrinsic mangrove population structure, rather than external sourcing, is a primary regulator of topsoil CNP dynamics and their stoichiometry. We designed this study to: (i) precisely quantify the concurrent patterns of mangrove population structure and topsoil CNP pools (SOC, TN, TP) and their stoichiometry across the intertidal gradient to uncover key mechanistic links; (ii) disentangle population structural controls on SOC and nutrient storage; and (iii) evaluate seedling density as a lever for blue carbon restoration. The mechanistic understanding gleaned from this controlled system provides a critical foundation for guiding restoration practices. Our findings are therefore important for informing policy and identifying the structural features most conducive to carbon sequestration in mangrove restoration efforts in South China and other regions with similar environmental settings. Materials and methods Study area The study was conducted on Techeng Isle, Zhanjiang, Guangdong Province, China (21°09′-21°10′N, 110°25′-110°27′E), which supports over 50.7 ha of tropical mangrove forests (Xiao et al., 2021 ). Techeng Isle is the first approved marine park in Guangdong Province and is part of the Zhanjiang Mangrove National Nature Reserve (Zhang et al., 2020 ). The mangroves on Techeng Isle are among the ancient mangroves in South China, characterized by unique marine environments (Xiao et al., 2021 ; Zhang et al., 2018 ). The dominant species is A. marina (>80% cover), with coexisting species such as Aegiceras corniculatum , Bruguiera gymnorrhiza , and Rhizophora stylosa in mixed zones (Gao & Han 2008 ). Some A. marina trees are over 100 years old, based on literature follow-up and interview estimates (Xiao et al., 2021 ; Zhang et al., 2018 ). The area experiences a northern tropical maritime monsoon climate, with an average annual temperature of 22.3 ℃ and annual rainfall ranging from 1100 to 1800 mm, concentrated in the rainy season of May to September. Typhoons bring the heaviest rainfall in summer (Gao et al., 2009 ). Techeng Isle is located in the Zhanjiang Bay, which has a semi-diurnal tide ranging from − 0.98 m (mean high water) to 1.3 m (mean low water) (Gao et al., 2009 ). The tide zones of Techeng Isle mainly consist of sandy beaches. The sediment on the beach of Techeng Isle mainly comes from the sediment of the river into the sea and the sea erosion at the corner of the base bank, followed by the lateral sediment supply from the outer sea. However, the dynamic balance of sediment is often disrupted by sand and gravel excavation in the channel dredging of Zhanjiang Bay. Since the 1990s, the construction of dams and reservoirs in the upper reaches of rivers has reduced the amount of water and silt entering the sea. Additionally, the dredging of Zhanjiang Port has caused significant sediment loss, further disrupting the dynamic balance of sediment on the sandy coast of Techeng Isle. Field sampling and soil sampling To control for interspecific variability in SOC and C:N:P dynamics, we exclusively sampled monodominant A. marina stands along the intertidal gradient. This approach leverages their natural density variations while maintaining consistent sediment properties (sand-mud gradient) and hydrological conditions. Three parallel transects were established from the outer to inner fringe of the monodominant A. marina community in Techeng Isle. Each transect was divided into three section, each approximately 50 m long, with 10 continuous quadrats (5 m × 5 m) per section. The sampling transects were dominated by A. marina , and we recorded the density, average height, base diameter and crown width of trees (Table 1 ). Given the slow growth of mangrove plants, we categorized the basal diameter class of A. marina as follow: I. Seedlings (basal diameter ≤ 2.5 cm), II. Sapling (2.5 < basal diameter ≤ 7.5 cm), III. Small tree (7.5 < basal diameter ≤ 13.5 cm), IV. Medium tree (13.5 20.5 cm). In each quadrat, soil temperature, salinity, and water content were measured using a soil multiparameter tachometer (TZS-ECW-G), and pH was measured by a pH meter (PHS-3C). Soil cores were collected from two depths (0 ~ 10 cm and 10 ~ 20 cm) in each quadrat. The soil samples were placed in polyethylene bags and transported to the laboratory. The samples were air-dried, thoroughly ground, and passed through a 2-mm mesh sieve for analysis. Soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) were determined. SOC and TN contents were measured using a vario-MaxN/CN elemental analyzer (Elementar Analysensysteme GmbH, Germany) (Nelson and Sommers, 1982 ; Stanford et al., 1973 ), and TP content was determined colorimetrically using the ammonium molybdate method (Olsen, 1954 ). C: N: P stoichiometric ratios were calculated as the ratios of SOC to TN (C: N), SOC to TP (C: P), and TN to TP (N: P). All sampling was conducted during wet season in October 2020. Derived variables for hypothesized ecological processes To explore potential mechanisms linking seedlings to sediment biogeochemistry, we derived two literature-based proxy variables from field measurements: Root Density Index (RDI): Calculated as RDI = Seedling Density × Mean Basal Diameter, this index serves as a proxy for potential belowground carbon input, based on established allometric relationships between basal diameter and fine root biomass in A. marina seedlings (Chen et al., 2018 ; Lang et al., 2024 ). Anoxia Potential (AP): Calculated as AP = 0.6 × Sediment Type Code + 0.4 × Water Content (Sediment Type Code: Sand = 0, Sand-Mud mixture = 1, based on field texture classification), this composite index reflects the potential for sediment oxygen limitation. The coefficients (0.6 for sediment type, 0.4 for water content) were weighted based on their relative importance in controlling sediment redox conditions within mangrove soils, as derived from variance partitioning in relevant studies (Kida & Fujitake, 2020 ; Wang et al., 2021 ). Higher AP values indicate a stronger potential for anoxic conditions. These derived proxies (RDI and AP) were incorporated into subsequent structural equation modeling (SEM) to test hypothesized pathways linking seedling characteristics and sediment conditions to carbon and nutrient dynamics. Statistical analysis Differences in mangrove population characteristics (density, average height, average basal diameter, average crown width, and individual number) among outer fringe, center zone and inner fringe were assessed using variance analysis (Statistica 8.0). The basal diameter class structure distribution across different mangrove locations was represented using stacked bar charts in Excel. Descriptive statistics, including means, minimum, maximum, and standard deviations, were used to account for the spatial heterogeneity in SOC, TN, TP, and C: N: P stoichiometric ratios in both soil layers. We used the random forest regression model to evaluate the relative importance of various predictor variables for SOC, TN, TP, and C: N: P stoichiometric ratios. Random forest is a data mining method that can handle multiple quantitative and qualitative variables simultaneously without requiring assumptions (Breiman, 2001 ). We included 8 predictor variables in the random forest analysis: intertidal gradient, sediment type, individual number (excluding seedlings), number of seedlings, total individual number (including seedling), height, crown width, and basal diameter. Pearson’s correlation was used to assess associations between individual number (total individuals and seedlings) and SOC, TN, TP, and C: N: P stoichiometric ratios. The non-parametric Kruskal-Wallis ANOVA was used to test for the differences in SOC, TN, TP, and C: N: P stoichiometric ratios among outer fringe, center zone and inner fringe. Both correlation analysis and Kruskal-Wallis tests were performed using Statistica 8.0. Redundancy Analysis (RDA) was used to identify relationships between SOC, TN, TP, and C: N: P stoichiometric ratios and other environmental variables, such as number of seedlings, number of trees, total number of individuals, sediment type, intertidal gradient, base diameter, temperature, crown width, salinity, water content, pH, and height. Prior to the analysis, variables with a Variance Inflation Factor (VIF) greater than 10, indicating severe multicollinearity, were excluded; this included the total number of individuals, intertidal gradient, and pH. RDA was performed using CANOCO 5.0. Variance Partitioning Analysis (VPA) was used to determine the contributions of different groups of factors to the variation in SOC, TN, TP, and C: N: P stoichiometric ratios. The considered factor groups included mangrove population characteristics (number of trees number of seedlings, height, crown width, and basal diameter), sediment physical properties (sediment type, salinity, temperature, and water content). SEM was conducted to test hypothesized causal pathways using the “plspm” package in R (v4.3.1). The model integrated three variable types: key drivers (identified via prior random forest analysis (seedling density, sediment type, temperature), ecological process proxies (RDI, AP), and soil response variables (SOC, TN, TP, C:N:P at 0–10 cm). The accuracy of the structural model fit was assessed using the coefficient of determination (R 2 ), which is required to satisfy R²≥0.50, and a goodness-of-fit index (GOF) reflects the overall model fit, which is required to satisfy GOF ≥ 0.36. Final path coefficients represent standardized solutions with significance determined by 1,000 bootstrap samples. Results Dynamics of Mangrove Population and Sediment Properties Across the Intertidal Gradient Our study revealed pronounced shifts in both mangrove population structure and sediment properties along the intertidal gradient (Table 1 ). Mangrove population density increased significantly from the outer to the inner fringe ( P < 0.001), while the average crown width exhibited an opposite trend, decreasing landward ( P < 0.001). The average tree height was significantly greater in the inner fringe compared to the outer fringe ( P < 0.001). Concurrently, sediment properties shifted markedly: sediment type transitioned from predominantly sand in the outer fringe and center zone to a mixture of sand and mud in the inner fringe; sediment temperature and pH decreased progressively from the seaward to the landward fringe (both P < 0.001); and sediment water content was highest on both fringes and lowest in the center zone ( P < 0.001). In contrast, sediment salinity showed no significant differences across the three zones. Table 1 Basic information of mangrove population characteristics and sediment properties across the intertidal zone Category index Subclass index Basic information Intertidal zone Intertidal gradient Outer fringe Center zone Inner fringe Intertidal elevation m -2.21 2.09 6.32 Population characteristics Density m − 2 0.08 ± 0.08 a 0.64 ± 0.26 b 1.50 ± 0.57 c Average height m 3.01 ± 1.26 a 3.28 ± 0.67 a 3.76 ± 0.61 b Average basal diameter cm 22.95 ± 9.57 a 7.89 ± 4.49 b 7.27 ± 3.11 b Average crown width m 6.86 ± 2.99 a 3.46 ± 0.89 b 2.78 ± 0.40 c Sediment properties Sediment type Sand Sand Sand and mud Sediment temperature ℃ 29.65 ± 0.62 a 28.82 ± 0.17 b 27.79 ± 0.26 c Sediment salinity ms/cm 0.48 ± 0.08 a 0.42 ± 0.24 a 0.36 ± 0.10 a Sediment water content % 27.98 ± 1.78 a 22.13 ± 1.64 b 28.38 ± 1.22 a Sediment pH 7.38 ± 0.16 a 6.84 ± 0.08 b 6.02 ± 0.37 c Note: Different lowercase letters in the same column indicate a significant difference at P < 0.001. Distinct demographic patterns were observed across the intertidal gradient, as revealed by the population size-class distribution (Fig. 2 ). The outer fringe was dominated by medium and large trees with a complete absence of seedlings and saplings, indicative of a declining population (Fig. 2 a). The center zone contained all size classes, representing a stable population (Fig. 2 b), whereas the inner fringe was characterized by a growth-type population structure, with seedlings constituting the most abundant cohort (Fig. 2 c). The number of trees, seedlings, and total number of individuals all varied significantly across the intertidal zones ( P < 0.001) (Fig. 3 ). Tree abundance peaked in the center zone, in contrast to seedlings and total individuals, which increased significantly from the outer to the inner fringe. Key Predictors and Spatial Patterns of Soil CNP and Stoichiometry Among the 8 predictor variables, the number of seedlings emerged as the most important predictor of SOC (Fig. 4). The total number of individuals was the most significant predictor for TP, C:N ratio, and N:P ratio. The intertidal gradient was the top predictor for TN and C: P ratio. Further analysis revealed significant positive correlations between the number of seedlings (and total number of individuals) and SOC, TN, TP, and C:N:P stoichiometric ratios (Fig. 5 ). The strength of these correlations varied, with the strongest relationship observed between the number of seedlings (and total number of individuals) and TP, and the weakest relationship observed with C:P ratio. The concentrations of SOC, TN, TP, and their stoichiometric ratios showed clear spatial patterns and significant variations across the intertidal gradient (Fig. 6 ). All measured parameters increased significantly from the outer to the inner fringe at both soil depths (0–10 cm and 10–20 cm; P < 0.05), with the most pronounced difference observed for SOC. Multivariate Drivers and Variance Partitioning in Soil CNP Dynamics Multivariate analyses revealed the complex interplay of factors governing soil CNP dynamics. RDA indicated that all variables collectively explained 82.1% ( P < 0.01) of the variation in the 0–10 cm soil layer and 86.1% ( P < 0.01) in the 10–20 cm layer (Fig. 7 a, c). The first axis alone accounted for 81.06% and 84.84% of the variation in the 0–10 cm and 10–20 cm layers, respectively, while the first two axes together explained 81.95% and 85.66%. In both soil layers, the number of seedlings, sediment type, and temperature were identified as significant drivers ( P < 0.01; Fig. 7 b, d), with the number of seedlings and sediment type showing strong positive correlations with soil CNP parameters, while temperature exhibited negative correlations. In the 0–10 cm soil layer, the number of seedlings was a significant determinant of soil C, N, P and ecological stoichiometry, with these variables increasing as the number of seedlings increased. In the 10–20 cm soil layer, sediment type was the primary driving factor, with higher levels of C, N, P, and ecological stoichiometry observed in mixed sand and mud sediments compared to sandy sediments. The influence of seedling density on soil C, N, P and ecological stoichiometry was more pronounced in the0-10 cm layer, whereas sediment type had a stronger effect in the 10–20 cm layer. VPA quantified the relative contributions of different factor groups (Fig. 8 ). The combined effect of mangrove population characteristics (MPC) and sediment physical properties (SPP) was the largest contributor to the explained variance in both soil layers (44% in 0–10 cm; 46% in 10–20 cm). Individually, MPC accounted for a greater proportion of the variation in the 0–10 cm layer (13% vs. 8% in 10–20 cm), whereas SPP alone explained more variation in the 10–20 cm layer, highlighting a depth-dependent shift in the primary controlling factors. Uncovering the Mechanistic Pathways Linking Seedling Density to Soil Biogeochemistry SEM demonstrated excellent fit to the observed data (Goodness-of-Fit Index, GOF = 0.702), confirming the robustness of the proposed causal pathways. The model explained a substantial proportion of the variance in key response variables, particularly for SOC, TN, TP, and their stoichiometric ratios ( R ²=0.973). Intertidal Zone exerted a strong positive direct effect on Sediment Type ( β = 0.866, p < 0.001). Seedling density was the strongest direct predictor of the RDI and AP, exhibiting highly significant positive path coefficients ( β = 0.699, p < 0.001 for RDI; β = 0.775, p < 0.001 for AP). In contrast, the total individual number showed no significant direct effect on either RDI or AP. The ecological process proxies, RDI and AP, subsequently exerted significant positive direct effects on the concentrations of SOC, TN, TP, and their stoichiometric ratios. The effect of AP was particularly strong ( β = 0.808, p < 0.001), highlighting the role of anoxic conditions in promoting carbon retention and nutrient preservation. The effect of RDI was also significant but comparatively weaker ( β = 0.206, p < 0.01). Thus, the SEM delineates clear causal pathways: the intertidal gradient shapes sediment properties and seedling establishment, and seedling density, in turn, drives soil CNP accumulation primarily by promoting sediment anoxia (AP) and, to a lesser extent, by enhancing belowground carbon input (RDI) The SEM results consolidate the central role of seedling density in mediating the effects of the intertidal gradient on topsoil carbon and nutrient accumulation via enhanced belowground carbon input (RDI) and the promotion of anoxic sediment conditions (AP). This mechanistic understanding reinforces the conclusion that seedling density is a critical lever for managing soil biogeochemistry in A. marina mangrove ecosystems. Discussion Intertidal Zonation as a Primary Filter of Population Structure and Soil Biogeochemistry Our study reveals a clear shift in the population structure of A. marina from a declining state at the seaward fringe to a growing state toward the landward fringe (Fig. 2 ). This demographic transition, characterized by a significant increase in seedling density (Fig. 3 ), aligns with the stressful hydrodynamic and erosional conditions seaward that limit seedling establishment, contrasted against the more quiescent, recruitment-favorable environment inland (Kibler et al., 2022 ; Gijsman et al., 2024 ). Concurrent with this population shift, we observed a significant increase in topsoil SOC, TN, TP, and their stoichiometric ratios from the outer to the inner fringe (Fig. 6 ). This synergistic increase in mangrove density and soil CNP stocks along the intertidal gradient is conceptually summarized in Fig. 10 , illustrating the landward migration of recruitment and the associated enhancement of soil biogeochemical properties. This co-occurrence suggests that the intertidal zonation acts as a primary environmental filter, shaping both the mangrove population and the resultant soil biogeochemical patterns. The seaward fringe, subjected to stronger tidal forces, is characterized by coarser sandy sediments and the erosion of fine particles and associated nutrients (Jacotot et al., 2018 ; Cooray et al., 2021 ), creating an abiotic template that supports only a mature, declining stand. In contrast, the inner fringe provides a more stable setting conducive to the retention of fine particles and organic matter. Crucially, this abiotic framework is amplified by biotic responses: the protected inner fringe supports a dense, growing seedling cohort, which our data identify as a primary driver of the observed carbon and nutrient gradients. Seedling Density: An Overlooked yet Dominant Biotic Driver of Topsoil CNP Among the numerous factors evaluated, seedling density emerged as the most vital predictor for SOC in the topsoil (0–10 cm), surpassing the influence of mature trees and abiotic sediment properties (Fig. 4a). This finding underscores a functional distinction between life-history stages, positioning seedlings not merely as immature trees but as active and distinct biogeochemical engineers in the topsoil environment. The profound influence of seedlings likely stems from their role as a rapid carbon-input pathway. Their high fine-root production and turnover, along with root exudates, provide labile organic matter that directly and efficiently contributes to the SOC pool (Chen et al., 2018 ; Lang et al., 2024 ). This mechanism is reflected in our derived RDI, which was strongly driven by seedling density. Furthermore, a higher seedling density implies greater per-unit-area biomass, leading to increased litter and root inputs that return substantial carbon, nitrogen, and phosphorus to the sediment during decomposition (Zhou et al., 2023 ). The dense aerial and root structures also enhance the physical capture and retention of nutrients from water and land runoff, thereby modulating both the total content and stoichiometry of sediment CNP (Alongi, 2014 ; Wang et al., 2019 a; Kumara et al., 2010 ). While sediment type (e.g., muddier textures inland) and lower temperature also served as key drivers, their explanatory power was often secondary to or interacted with seedling density (Fig. 7 ). Variance partitioning analysis confirmed that the combined effect of mangrove population characteristics (MPC) and sediment physical properties (SPP) was greater than their individual contributions, with MPC exerting a stronger influence in the top 10 cm (Fig. 8 ). This highlights the necessity of integrating both biotic and abiotic factors in a holistic framework to accurately predict soil carbon and nutrient dynamics. Mechanistic Evidence: Dual Pathways of Carbon Input and Anoxia Promotion SEM revealed that seedling density regulates topsoil carbon and nutrient dynamics through two interlinked ecological processes (Fig. 9 ). First, high seedling density significantly enhanced the Root Density Index (RDI), indicating greater belowground carbon input via fine root production and turnover, a pathway supported by allometric studies linking seedling size to root biomass (Chen et al., 2018 ; Lang et al., 2024 ). Second, and more profoundly, dense seedlings were the primary driver of sediment anoxia (Anoxia Potential, AP), likely by impeding water flow and oxygen diffusion through dense root networks while stimulating microbial oxygen consumption (Kumara et al., 2010 ; Kida and Fujitake, 2020 ). These seedling-mediated mechanisms—direct organic matter input and the suppression of aerobic decomposition—collectively drove the accumulation of SOC, TN, TP, and their stoichiometric ratios. This aligns with established concepts that anoxia is a critical mechanism for stabilizing organic carbon in coastal sediments (Alongi, 2014 ; Kida and Fujitake, 2020 ). In contrast, the total number of individuals (including mature trees) showed no significant direct influence on these pathways. This functional divergence underscores that seedlings are not merely smaller trees but act as distinct biogeochemical engineers in the topsoil of A. marina stands, rapidly modifying their immediate soil environment. The model thus integrates our observations into a coherent causal framework: intertidal zonation governs seedling establishment, which in turn modulates both the quantity of organic inputs and the biogeochemical conditions that determine their persistence, ultimately shaping topsoil carbon and nutrient dynamics. Our structural equation model results consolidate the central role of seedling density in mediating the effects of the intertidal gradient on topsoil carbon and nutrient accumulation via enhanced belowground carbon input (RDI) and the promotion of anoxic sediment conditions (AP). This mechanistic understanding reinforces our conclusion that seedling density represents a critical biotic lever for managing soil biogeochemistry in A. marina mangrove ecosystems, challenging the traditional focus on mature trees in restoration paradigms. Toward a Novel Restoration Paradigm: The Lever of High-Density Seedling Planting Traditional mangrove restoration methods, which often rely on planting adult trees or improving soil conditions, face limitations in survival rates and functional recovery (Su et al., 2021 ; Ellison et al., 2020 ). These approaches frequently overlook the critical role of seedlings in promoting soil carbon sequestration and nutrient cycling. Our findings demonstrate a strong positive correlation between seedling density and soil organic carbon, total nitrogen, and total phosphorus content (Fig. 5 ), highlighting the potential of seedlings as a key lever for enhancing blue carbon storage. We therefore propose a novel restoration strategy centered on high-density seedling planting. This approach not only enhances soil carbon and nutrient retention but also promotes sediment accretion and soil elevation gain—critical processes for mitigating sea-level rise impacts. The favorable conditions in the inner fringe—characterized by mixed sand-mud sediments, lower salinity and temperature, and higher water content—support higher seedling recruitment and underscore the importance of site selection for restoration success. While our findings are based on A. marina , the mechanistic link between seedling density and SOC accumulation may extend to other pioneer mangroves (e.g., Rhizophora spp.) that similarly enhance sediment trapping and root-derived carbon inputs. Future research should validate these mechanisms across species and geomorphic settings to scale restoration strategies effectively. Conclusion As the distance from the sea increases, the A. marina population transitions from a declining to a growth state, reflecting the inland migration and adaptation of mangroves to sea-level rise. The contents of sediment organic carbon, total nitrogen, and total phosphorus, as well as the CNP stoichiometric ratio, increase with the number of seedlings and total individuals. The number of seedlings, sediment type, and temperature are identified as the key drivers of organic carbon and nutrient accumulation in mangrove topsoil. Mangrove population characteristics have a more significant impact on sediment carbon and nutrients than sediment physical properties alone, with their combined effect being particularly pronounced. While seedling density emerges as a key lever for blue carbon enhancement in A. marina stands- particularly through high-density planting strategies that boost SOC sequestration and mitigate sea-level rise impacts in nutrient-deficient environments-validating its efficacy across diverse mangrove species and geomorphic settings remains essential for scaling restoration strategies. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment We thank Zhanjiang Science and Technology Bureau program (2023A01026) and Guangdong Provincial Forestry Science and Technology Innovation Project (2025KJCX016) for funding support to this study. Data availability Data will be made available on request. References Alongi DM (2012) Carbon sequestration in mangrove forests. Carbon Manag 3:313–322. https://doi.org/10.4155/cmt.12.20 Alongi DM (2014) Carbon Cycling and Storage in Mangrove Forests. Annual Rev Mar Sci 6:195–219. https://doi.org/10.1146/annurev-marine-010213-135020 Alongi DM (2018) Impact of global change on nutrient dynamics in mangrove forests. 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14:11:49","extension":"xml","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143558,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD25043560structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/94b44d695a7aefd8cd2a3f44.xml"},{"id":96833272,"identity":"ff08cd21-8b10-4f3c-b834-df16a714176f","added_by":"auto","created_at":"2025-11-26 14:17:03","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157890,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/773a356a87dbf22f36cf5a7f.html"},{"id":96833225,"identity":"f2fe1a26-7e24-4cb4-8112-76a0e03f201c","added_by":"auto","created_at":"2025-11-26 14:17:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":334655,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and sampling design in Techeng Isle, China. Three transects (outer fringe, center zone, inner fringe) were established across the intertidal gradient of the \u003cem\u003eA. marina\u003c/em\u003e mangrove forest.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/6ab7625ef5f8d7712de3db19.png"},{"id":96833224,"identity":"7a203c35-91bb-48da-b333-7d530f036f06","added_by":"auto","created_at":"2025-11-26 14:17:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46952,"visible":true,"origin":"","legend":"\u003cp\u003eBasal diameter class structure distribution along intertidal gradient (a-c). Basal diameter classes are divided as shown in the method. OF: Outer fringe; CZ: Center zone; IF: Inner fringe.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/cb91adb21e56b3dbeac3f597.png"},{"id":96833226,"identity":"7e9d8b8a-ed8f-4834-b704-5f9fb91ff9e2","added_by":"auto","created_at":"2025-11-26 14:17:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30282,"visible":true,"origin":"","legend":"\u003cp\u003eMean plot demonstrating the difference of number of trees (base diameter ≥2.5 cm), seedlings (base diameter \u0026lt; 2.5 cm), and total individuals across the three intertidal zones. OF: Outer fringe; CZ: Center zone; IF: Inner fringe.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/8b4f50463453b35982db97f5.png"},{"id":96918343,"identity":"490f43d8-2e93-486c-a76d-603ca1ac1784","added_by":"auto","created_at":"2025-11-27 14:11:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59787,"visible":true,"origin":"","legend":"\u003cp\u003eRandom forest analysisshowing the importance of variables for predicting SOC, TN, TP, and C: N: P stoichiometric ratios. The response variable is shown in each graph panel. Code for predictor variables: 1 = Intertidal gradient; 2 = Sediment type; 3 = Number of trees; 4 = Number of seedlings; 5 = Total number of individuals; 6 = Height; 7 = Crown width; 8 = Basal diameter.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/0e2d904e1050e4228fbfef40.png"},{"id":96919044,"identity":"a122df0a-b05a-431e-9557-108956606381","added_by":"auto","created_at":"2025-11-27 14:13:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":58440,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between number of seedlings (total number of individuals) and SOC, TN, TP, and C: N: P stoichiometric ratios. O 0-10 cm; \u003cstrong\u003e□\u003c/strong\u003e 10-20 cm\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/2fe30ca2577b42f732432465.png"},{"id":96833235,"identity":"c4682644-95a5-476d-80b0-c891d397a513","added_by":"auto","created_at":"2025-11-26 14:17:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":65788,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/04bca2c8c8d193b5716b7c31.png"},{"id":96833231,"identity":"7698b342-3d9c-494a-bdb8-f91512382116","added_by":"auto","created_at":"2025-11-26 14:17:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":48478,"visible":true,"origin":"","legend":"\u003cp\u003eOrdination biplots (a, c) show the first two-axis results from Redundancy Analysis (RDA) showing relationships between SOC, TN, TP, and their stoichiometric ratios and other environmental variables. Numofs = Number of seedlings; Numoft = Number of trees; Totnum = Total number of individuals; Sedtyp = Sediment type; Intgra = Intertidal gradient; Basdia = Base diameter; Temper = Temperature; Crowid = Crown width; Salini = Salinity. Histograms (b, d) represents explanatory power for variables that have a significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) impact on SOC, TN, TP, and their stoichiometric ratios. **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/2fd526d82dde933a0c2bd201.png"},{"id":96833238,"identity":"11d06ae2-08da-403d-b501-13be1fc6c054","added_by":"auto","created_at":"2025-11-26 14:17:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":69053,"visible":true,"origin":"","legend":"\u003cp\u003eVariance partitioning analysis for two different categories: SPP (Sediment physical property: sediment type + salinity + temperature + water), MPC (Mangrove population characteristics: number of trees + number of seedlings + height + crown width + basal diameter) in explaining the soil CNP and ecological stoichiometry.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/99b4f58704f4b4e334c3a5ad.png"},{"id":96917801,"identity":"43abf285-aeb1-4ebc-80cf-1bf333edad39","added_by":"auto","created_at":"2025-11-27 14:10:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":324017,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model testing hypothesized drivers of topsoil carbon, nitrogen, phosphorus, and stoichiometry (C:N:P). Values on arrows are standardized path coefficients. Significant positive and negative paths are shown with solid red and blue lines, respectively; non-significant paths are dashed grey. Significance levels: * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, * * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, * * * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. The explained variance (R²) for each endogenous variable is shown, and the overall model exhibits a good fit (Goodness-of-Fit, GOF = 0.702).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/c0bd6de9d582ac37aec240dd.png"},{"id":96833239,"identity":"cbac63f4-fa63-4674-8d96-07589e0ce554","added_by":"auto","created_at":"2025-11-26 14:17:02","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":593612,"visible":true,"origin":"","legend":"\u003cp\u003eA conceptual diagram showing soil CNP and ecological stoichiometry increased with mangrove density from the outer fringe to inner fringe along the intertidal gradient in Techeng Isle, Guangdong Province, China. The direction of the dotted line illustrates the direction of mangrove seedlings to migrate from outer fringe (seaward) to inner fringe (landward). Dark green triangle represents the increase of soil CNP and ecological stoichiometry with distance from the sea.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/4c0c2702f2aec4d66d3c418e.png"},{"id":99318388,"identity":"f4d6ddf1-ac79-44cc-b1af-7e7ee911334c","added_by":"auto","created_at":"2025-12-31 16:33:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2456294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8061427/v1/ebb0b07b-3b75-4aa6-a183-98f77c190800.pdf"}],"financialInterests":"","formattedTitle":"Seedling Density as a Key Determinant of Topsoil Organic Carbon and CNP Stoichiometry in Intertidal Mangrove Ecosystems: Insights from Avicennia marina Stands","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMangroves play a critical role in carbon cycling and storage in the coastal ecosystem (Sasmito et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alongi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although they cover less than 1% of coastal areas, mangroves contribute disproportionately to coastal carbon sequestration, accounting for 10\u0026ndash;15% of the total carbon sequestered in these regions (Alongi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, mangroves store three to five times more organic carbon than terrestrial tropical forests (Malik et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Donato et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This significant carbon storage capacity has led to their recognition as essential \u0026ldquo;blue carbon\u0026rdquo; ecosystems (Malik et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Murdiyarso et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Alongi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMangroves serve as significant reservoirs of soil organic carbon (SOC), primarily accumulated through long-term above- and below-ground biomass accumulation, particularly in their organic-rich soil carbon pools (Sasmito et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alongi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These soil carbon pools account for 70.65% of the total organic carbon stored in mangroves globally (Malik et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alongi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The long-life span of carbon in mangrove soil is attributed to anoxic conditions that limit the decomposition of organic matter, thereby contributing to a substantial belowground carbon stocks (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, mangrove soils represent the largest organic carbon reservoir in these ecosystems and are a crucial focus for conservation efforts (Kida and Fujitake, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, human activities have led to significant losses and degradation of mangrove habitats, potentially transforming these ecosystems into sources of atmospheric carbon (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To counteract these losses, mangrove restoration projects have been initiated worldwide (Rahman et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chandra et al., 2011), emphasizing the need for a baseline assessment of ecosystem carbon stocks. Additionally, understanding the stoichiometric relationships between carbon, nitrogen, and phosphorus (C:N:P) is crucial for comprehending nutrient dynamics and their impact on carbon sequestration in mangrove ecosystems.\u003c/p\u003e\u003cp\u003eSOC burial rates, carbon density, and carbon stocks in mangroves vary widely, influenced by factors such as regional primary productivity, geomorphological conditions, climate, and distance from the sea (Sasmito et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Twilley et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Woodroffe et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition to these abiotic factors, mangrove structural characteristics significantly affect sediment carbon-cycle processes (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea). Mangroves act as important carbon sinks, accumulating marine and terrigenous sediment through the physical trapping effect of their complex aboveground structure (Kumara et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Changes in mangrove structural indicators, such as tree density, basal area, and height, significantly impact on the quantity and quality of organic carbon entering the sediment (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea; Eric et al., 2014; Chang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Higher densities of mangrove structures, including stems and aerial roots, enhance sediment accumulation (Alongi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kumara et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, high tree density can trap upland sediment runoff and nutrients, increasing SOC content and density(Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Previous studies have focused on the size, shape, density, and distribution patterns of mangrove trees but have largely overlooked the effects of mangrove seedlings on SOC and nutrient dynamics (Alongi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding these effects is crucial for assessing carbon sequestration potential and sustainable development of mangrove ecosystems.\u003c/p\u003e\u003cp\u003eGiven their critical carbon sequestration capacity, mangrove ecosystems face significant threats from climate change-induced sea-level rise. This study focuses on monodominant stands of \u003cem\u003eAvicennia marina\u003c/em\u003e, a key species dominating tropical intertidal zones. Our site on Techeng Isle, where \u003cem\u003eA. marina\u003c/em\u003e comprises\u0026thinsp;\u0026gt;\u0026thinsp;80% of cover (Gao \u0026amp; Han \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), provides an ideal natural laboratory due to three key characteristics: (1) monodominance, which eliminates interspecific interference on SOC dynamics; (2) a full ontogenetic spectrum, enabling life-stage analysis from seedlings to mature trees; and (3) pronounced intertidal zonation, creating built-in environmental gradients.\u003c/p\u003e\u003cp\u003eThis unique setting allows us to employ a monospecific approach to isolate the effect of population structure from interspecific functional variability (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Castillo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By focusing on a monodominant stand across a natural gradient, this design serves as a model system to isolate \u003cem\u003ein-situ\u003c/em\u003e biotic drivers, inherently minimizing confounding variation from heterogeneous allochthonous carbon and nutrient inputs. Consequently, we can test the specific hypothesis that intrinsic mangrove population structure, rather than external sourcing, is a primary regulator of topsoil CNP dynamics and their stoichiometry.\u003c/p\u003e\u003cp\u003eWe designed this study to: (i) precisely quantify the concurrent patterns of mangrove population structure and topsoil CNP pools (SOC, TN, TP) and their stoichiometry across the intertidal gradient to uncover key mechanistic links; (ii) disentangle population structural controls on SOC and nutrient storage; and (iii) evaluate seedling density as a lever for blue carbon restoration. The mechanistic understanding gleaned from this controlled system provides a critical foundation for guiding restoration practices. Our findings are therefore important for informing policy and identifying the structural features most conducive to carbon sequestration in mangrove restoration efforts in South China and other regions with similar environmental settings.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThe study was conducted on Techeng Isle, Zhanjiang, Guangdong Province, China (21\u0026deg;09\u0026prime;-21\u0026deg;10\u0026prime;N, 110\u0026deg;25\u0026prime;-110\u0026deg;27\u0026prime;E), which supports over 50.7 ha of tropical mangrove forests (Xiao et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Techeng Isle is the first approved marine park in Guangdong Province and is part of the Zhanjiang Mangrove National Nature Reserve (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The mangroves on Techeng Isle are among the ancient mangroves in South China, characterized by unique marine environments (Xiao et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The dominant species is \u003cem\u003eA. marina\u003c/em\u003e (\u0026gt;80% cover), with coexisting species such as \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e, and \u003cem\u003eRhizophora stylosa\u003c/em\u003e in mixed zones (Gao \u0026amp; Han \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Some \u003cem\u003eA. marina\u003c/em\u003e trees are over 100 years old, based on literature follow-up and interview estimates (Xiao et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The area experiences a northern tropical maritime monsoon climate, with an average annual temperature of 22.3 ℃ and annual rainfall ranging from 1100 to 1800 mm, concentrated in the rainy season of May to September. Typhoons bring the heaviest rainfall in summer (Gao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTecheng Isle is located in the Zhanjiang Bay, which has a semi-diurnal tide ranging from \u0026minus;\u0026thinsp;0.98 m (mean high water) to 1.3 m (mean low water) (Gao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The tide zones of Techeng Isle mainly consist of sandy beaches. The sediment on the beach of Techeng Isle mainly comes from the sediment of the river into the sea and the sea erosion at the corner of the base bank, followed by the lateral sediment supply from the outer sea. However, the dynamic balance of sediment is often disrupted by sand and gravel excavation in the channel dredging of Zhanjiang Bay. Since the 1990s, the construction of dams and reservoirs in the upper reaches of rivers has reduced the amount of water and silt entering the sea. Additionally, the dredging of Zhanjiang Port has caused significant sediment loss, further disrupting the dynamic balance of sediment on the sandy coast of Techeng Isle.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eField sampling and soil sampling\u003c/h3\u003e\n\u003cp\u003eTo control for interspecific variability in SOC and C:N:P dynamics, we exclusively sampled monodominant \u003cem\u003eA. marina\u003c/em\u003e stands along the intertidal gradient. This approach leverages their natural density variations while maintaining consistent sediment properties (sand-mud gradient) and hydrological conditions. Three parallel transects were established from the outer to inner fringe of the monodominant \u003cem\u003eA. marina\u003c/em\u003e community in Techeng Isle. Each transect was divided into three section, each approximately 50 m long, with 10 continuous quadrats (5 m \u0026times; 5 m) per section. The sampling transects were dominated by \u003cem\u003eA. marina\u003c/em\u003e, and we recorded the density, average height, base diameter and crown width of trees (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Given the slow growth of mangrove plants, we categorized the basal diameter class of \u003cem\u003eA. marina\u003c/em\u003e as follow: I. Seedlings (basal diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 cm), II. Sapling (2.5\u0026thinsp;\u0026lt;\u0026thinsp;basal diameter\u0026thinsp;\u0026le;\u0026thinsp;7.5 cm), III. Small tree (7.5\u0026thinsp;\u0026lt;\u0026thinsp;basal diameter\u0026thinsp;\u0026le;\u0026thinsp;13.5 cm), IV. Medium tree (13.5\u0026thinsp;\u0026lt;\u0026thinsp;basal diameter\u0026thinsp;\u0026le;\u0026thinsp;20.5 cm), and V. Large tree (basal diameter\u0026thinsp;\u0026gt;\u0026thinsp;20.5 cm).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn each quadrat, soil temperature, salinity, and water content were measured using a soil multiparameter tachometer (TZS-ECW-G), and pH was measured by a pH meter (PHS-3C). Soil cores were collected from two depths (0\u0026thinsp;~\u0026thinsp;10 cm and 10\u0026thinsp;~\u0026thinsp;20 cm) in each quadrat. The soil samples were placed in polyethylene bags and transported to the laboratory. The samples were air-dried, thoroughly ground, and passed through a 2-mm mesh sieve for analysis. Soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) were determined. SOC and TN contents were measured using a vario-MaxN/CN elemental analyzer (Elementar Analysensysteme GmbH, Germany) (Nelson and Sommers, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Stanford et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), and TP content was determined colorimetrically using the ammonium molybdate method (Olsen, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1954\u003c/span\u003e). C: N: P stoichiometric ratios were calculated as the ratios of SOC to TN (C: N), SOC to TP (C: P), and TN to TP (N: P). All sampling was conducted during wet season in October 2020.\u003c/p\u003e\n\u003ch3\u003eDerived variables for hypothesized ecological processes\u003c/h3\u003e\n\u003cp\u003eTo explore potential mechanisms linking seedlings to sediment biogeochemistry, we derived two literature-based proxy variables from field measurements:\u003c/p\u003e\u003cp\u003eRoot Density Index (RDI): Calculated as RDI\u0026thinsp;=\u0026thinsp;Seedling Density \u0026times; Mean Basal Diameter, this index serves as a proxy for potential belowground carbon input, based on established allometric relationships between basal diameter and fine root biomass in \u003cem\u003eA. marina\u003c/em\u003e seedlings (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnoxia Potential (AP): Calculated as AP\u0026thinsp;=\u0026thinsp;0.6 \u0026times; Sediment Type Code\u0026thinsp;+\u0026thinsp;0.4 \u0026times; Water Content (Sediment Type Code: Sand\u0026thinsp;=\u0026thinsp;0, Sand-Mud mixture\u0026thinsp;=\u0026thinsp;1, based on field texture classification), this composite index reflects the potential for sediment oxygen limitation. The coefficients (0.6 for sediment type, 0.4 for water content) were weighted based on their relative importance in controlling sediment redox conditions within mangrove soils, as derived from variance partitioning in relevant studies (Kida \u0026amp; Fujitake, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Higher AP values indicate a stronger potential for anoxic conditions.\u003c/p\u003e\u003cp\u003eThese derived proxies (RDI and AP) were incorporated into subsequent structural equation modeling (SEM) to test hypothesized pathways linking seedling characteristics and sediment conditions to carbon and nutrient dynamics.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDifferences in mangrove population characteristics (density, average height, average basal diameter, average crown width, and individual number) among outer fringe, center zone and inner fringe were assessed using variance analysis (Statistica 8.0). The basal diameter class structure distribution across different mangrove locations was represented using stacked bar charts in Excel. Descriptive statistics, including means, minimum, maximum, and standard deviations, were used to account for the spatial heterogeneity in SOC, TN, TP, and C: N: P stoichiometric ratios in both soil layers.\u003c/p\u003e\u003cp\u003eWe used the random forest regression model to evaluate the relative importance of various predictor variables for SOC, TN, TP, and C: N: P stoichiometric ratios. Random forest is a data mining method that can handle multiple quantitative and qualitative variables simultaneously without requiring assumptions (Breiman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). We included 8 predictor variables in the random forest analysis: intertidal gradient, sediment type, individual number (excluding seedlings), number of seedlings, total individual number (including seedling), height, crown width, and basal diameter.\u003c/p\u003e\u003cp\u003ePearson\u0026rsquo;s correlation was used to assess associations between individual number (total individuals and seedlings) and SOC, TN, TP, and C: N: P stoichiometric ratios. The non-parametric Kruskal-Wallis ANOVA was used to test for the differences in SOC, TN, TP, and C: N: P stoichiometric ratios among outer fringe, center zone and inner fringe. Both correlation analysis and Kruskal-Wallis tests were performed using Statistica 8.0.\u003c/p\u003e\u003cp\u003eRedundancy Analysis (RDA) was used to identify relationships between SOC, TN, TP, and C: N: P stoichiometric ratios and other environmental variables, such as number of seedlings, number of trees, total number of individuals, sediment type, intertidal gradient, base diameter, temperature, crown width, salinity, water content, pH, and height. Prior to the analysis, variables with a Variance Inflation Factor (VIF) greater than 10, indicating severe multicollinearity, were excluded; this included the total number of individuals, intertidal gradient, and pH. RDA was performed using CANOCO 5.0.\u003c/p\u003e\u003cp\u003eVariance Partitioning Analysis (VPA) was used to determine the contributions of different groups of factors to the variation in SOC, TN, TP, and C: N: P stoichiometric ratios. The considered factor groups included mangrove population characteristics (number of trees number of seedlings, height, crown width, and basal diameter), sediment physical properties (sediment type, salinity, temperature, and water content).\u003c/p\u003e\u003cp\u003eSEM was conducted to test hypothesized causal pathways using the \u0026ldquo;plspm\u0026rdquo; package in R (v4.3.1). The model integrated three variable types: key drivers (identified via prior random forest analysis (seedling density, sediment type, temperature), ecological process proxies (RDI, AP), and soil response variables (SOC, TN, TP, C:N:P at 0\u0026ndash;10 cm). The accuracy of the structural model fit was assessed using the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), which is required to satisfy R\u0026sup2;\u0026ge;0.50, and a goodness-of-fit index (GOF) reflects the overall model fit, which is required to satisfy GOF\u0026thinsp;\u0026ge;\u0026thinsp;0.36. Final path coefficients represent standardized solutions with significance determined by 1,000 bootstrap samples.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDynamics of Mangrove Population and Sediment Properties Across the Intertidal Gradient\u003c/h2\u003e\u003cp\u003eOur study revealed pronounced shifts in both mangrove population structure and sediment properties along the intertidal gradient (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mangrove population density increased significantly from the outer to the inner fringe (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the average crown width exhibited an opposite trend, decreasing landward (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average tree height was significantly greater in the inner fringe compared to the outer fringe (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Concurrently, sediment properties shifted markedly: sediment type transitioned from predominantly sand in the outer fringe and center zone to a mixture of sand and mud in the inner fringe; sediment temperature and pH decreased progressively from the seaward to the landward fringe (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and sediment water content was highest on both fringes and lowest in the center zone (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, sediment salinity showed no significant differences across the three zones.\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\u003eBasic information of mangrove population characteristics and sediment properties across the intertidal zone\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\u003eCategory index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubclass index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eBasic information\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eIntertidal zone\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntertidal gradient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOuter fringe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCenter zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInner fringe\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntertidal elevation m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003ePopulation characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDensity m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage height m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage basal diameter cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11 b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage crown width m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eSediment properties\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSediment type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSand and mud\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSediment temperature ℃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSediment salinity ms/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSediment water content %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22 a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSediment pH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Different lowercase letters in the same column indicate a significant difference at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDistinct demographic patterns were observed across the intertidal gradient, as revealed by the population size-class distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The outer fringe was dominated by medium and large trees with a complete absence of seedlings and saplings, indicative of a declining population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The center zone contained all size classes, representing a stable population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), whereas the inner fringe was characterized by a growth-type population structure, with seedlings constituting the most abundant cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe number of trees, seedlings, and total number of individuals all varied significantly across the intertidal zones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Tree abundance peaked in the center zone, in contrast to seedlings and total individuals, which increased significantly from the outer to the inner fringe.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eKey Predictors and Spatial Patterns of Soil CNP and Stoichiometry\u003c/h3\u003e\n\u003cp\u003eAmong the 8 predictor variables, the number of seedlings emerged as the most important predictor of SOC (Fig.\u0026nbsp;4). The total number of individuals was the most significant predictor for TP, C:N ratio, and N:P ratio. The intertidal gradient was the top predictor for TN and C: P ratio.\u003c/p\u003e\u003cp\u003eFurther analysis revealed significant positive correlations between the number of seedlings (and total number of individuals) and SOC, TN, TP, and C:N:P stoichiometric ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The strength of these correlations varied, with the strongest relationship observed between the number of seedlings (and total number of individuals) and TP, and the weakest relationship observed with C:P ratio.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe concentrations of SOC, TN, TP, and their stoichiometric ratios showed clear spatial patterns and significant variations across the intertidal gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). All measured parameters increased significantly from the outer to the inner fringe at both soil depths (0\u0026ndash;10 cm and 10\u0026ndash;20 cm; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the most pronounced difference observed for SOC.\u003c/p\u003e\n\u003ch3\u003eMultivariate Drivers and Variance Partitioning in Soil CNP Dynamics\u003c/h3\u003e\n\u003cp\u003eMultivariate analyses revealed the complex interplay of factors governing soil CNP dynamics. RDA indicated that all variables collectively explained 82.1% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of the variation in the 0\u0026ndash;10 cm soil layer and 86.1% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the 10\u0026ndash;20 cm layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, c). The first axis alone accounted for 81.06% and 84.84% of the variation in the 0\u0026ndash;10 cm and 10\u0026ndash;20 cm layers, respectively, while the first two axes together explained 81.95% and 85.66%.\u003c/p\u003e\u003cp\u003eIn both soil layers, the number of seedlings, sediment type, and temperature were identified as significant drivers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, d), with the number of seedlings and sediment type showing strong positive correlations with soil CNP parameters, while temperature exhibited negative correlations.\u003c/p\u003e\u003cp\u003eIn the 0\u0026ndash;10 cm soil layer, the number of seedlings was a significant determinant of soil C, N, P and ecological stoichiometry, with these variables increasing as the number of seedlings increased. In the 10\u0026ndash;20 cm soil layer, sediment type was the primary driving factor, with higher levels of C, N, P, and ecological stoichiometry observed in mixed sand and mud sediments compared to sandy sediments. The influence of seedling density on soil C, N, P and ecological stoichiometry was more pronounced in the0-10 cm layer, whereas sediment type had a stronger effect in the 10\u0026ndash;20 cm layer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eVPA quantified the relative contributions of different factor groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The combined effect of mangrove population characteristics (MPC) and sediment physical properties (SPP) was the largest contributor to the explained variance in both soil layers (44% in 0\u0026ndash;10 cm; 46% in 10\u0026ndash;20 cm). Individually, MPC accounted for a greater proportion of the variation in the 0\u0026ndash;10 cm layer (13% vs. 8% in 10\u0026ndash;20 cm), whereas SPP alone explained more variation in the 10\u0026ndash;20 cm layer, highlighting a depth-dependent shift in the primary controlling factors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eUncovering the Mechanistic Pathways Linking Seedling Density to Soil Biogeochemistry\u003c/h2\u003e\u003cp\u003eSEM demonstrated excellent fit to the observed data (Goodness-of-Fit Index, GOF\u0026thinsp;=\u0026thinsp;0.702), confirming the robustness of the proposed causal pathways. The model explained a substantial proportion of the variance in key response variables, particularly for SOC, TN, TP, and their stoichiometric ratios (\u003cem\u003eR\u003c/em\u003e\u0026sup2;=0.973).\u003c/p\u003e\u003cp\u003eIntertidal Zone exerted a strong positive direct effect on Sediment Type (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.866, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Seedling density was the strongest direct predictor of the RDI and AP, exhibiting highly significant positive path coefficients (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.699, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for RDI; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.775, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for AP). In contrast, the total individual number showed no significant direct effect on either RDI or AP. The ecological process proxies, RDI and AP, subsequently exerted significant positive direct effects on the concentrations of SOC, TN, TP, and their stoichiometric ratios. The effect of AP was particularly strong (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.808, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting the role of anoxic conditions in promoting carbon retention and nutrient preservation. The effect of \u003cem\u003eRDI\u003c/em\u003e was also significant but comparatively weaker (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.206, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eThus, the SEM delineates clear causal pathways: the intertidal gradient shapes sediment properties and seedling establishment, and seedling density, in turn, drives soil CNP accumulation primarily by promoting sediment anoxia (AP) and, to a lesser extent, by enhancing belowground carbon input (RDI)\u003c/p\u003e\u003cp\u003eThe SEM results consolidate the central role of seedling density in mediating the effects of the intertidal gradient on topsoil carbon and nutrient accumulation via enhanced belowground carbon input (RDI) and the promotion of anoxic sediment conditions (AP). This mechanistic understanding reinforces the conclusion that seedling density is a critical lever for managing soil biogeochemistry in \u003cem\u003eA. marina\u003c/em\u003e mangrove ecosystems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eIntertidal Zonation as a Primary Filter of Population Structure and Soil Biogeochemistry\u003c/h2\u003e\u003cp\u003eOur study reveals a clear shift in the population structure of \u003cem\u003eA. marina\u003c/em\u003e from a declining state at the seaward fringe to a growing state toward the landward fringe (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This demographic transition, characterized by a significant increase in seedling density (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), aligns with the stressful hydrodynamic and erosional conditions seaward that limit seedling establishment, contrasted against the more quiescent, recruitment-favorable environment inland (Kibler et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gijsman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Concurrent with this population shift, we observed a significant increase in topsoil SOC, TN, TP, and their stoichiometric ratios from the outer to the inner fringe (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This synergistic increase in mangrove density and soil CNP stocks along the intertidal gradient is conceptually summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e, illustrating the landward migration of recruitment and the associated enhancement of soil biogeochemical properties. This co-occurrence suggests that the intertidal zonation acts as a primary environmental filter, shaping both the mangrove population and the resultant soil biogeochemical patterns.\u003c/p\u003e\u003cp\u003eThe seaward fringe, subjected to stronger tidal forces, is characterized by coarser sandy sediments and the erosion of fine particles and associated nutrients (Jacotot et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cooray et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), creating an abiotic template that supports only a mature, declining stand. In contrast, the inner fringe provides a more stable setting conducive to the retention of fine particles and organic matter. Crucially, this abiotic framework is amplified by biotic responses: the protected inner fringe supports a dense, growing seedling cohort, which our data identify as a primary driver of the observed carbon and nutrient gradients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSeedling Density: An Overlooked yet Dominant Biotic Driver of Topsoil CNP\u003c/h2\u003e\u003cp\u003eAmong the numerous factors evaluated, seedling density emerged as the most vital predictor for SOC in the topsoil (0\u0026ndash;10 cm), surpassing the influence of mature trees and abiotic sediment properties (Fig.\u0026nbsp;4a). This finding underscores a functional distinction between life-history stages, positioning seedlings not merely as immature trees but as active and distinct biogeochemical engineers in the topsoil environment.\u003c/p\u003e\u003cp\u003eThe profound influence of seedlings likely stems from their role as a rapid carbon-input pathway. Their high fine-root production and turnover, along with root exudates, provide labile organic matter that directly and efficiently contributes to the SOC pool (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This mechanism is reflected in our derived RDI, which was strongly driven by seedling density. Furthermore, a higher seedling density implies greater per-unit-area biomass, leading to increased litter and root inputs that return substantial carbon, nitrogen, and phosphorus to the sediment during decomposition (Zhou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The dense aerial and root structures also enhance the physical capture and retention of nutrients from water and land runoff, thereby modulating both the total content and stoichiometry of sediment CNP (Alongi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea; Kumara et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile sediment type (e.g., muddier textures inland) and lower temperature also served as key drivers, their explanatory power was often secondary to or interacted with seedling density (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Variance partitioning analysis confirmed that the combined effect of mangrove population characteristics (MPC) and sediment physical properties (SPP) was greater than their individual contributions, with MPC exerting a stronger influence in the top 10 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This highlights the necessity of integrating both biotic and abiotic factors in a holistic framework to accurately predict soil carbon and nutrient dynamics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMechanistic Evidence: Dual Pathways of Carbon Input and Anoxia Promotion\u003c/h2\u003e\u003cp\u003eSEM revealed that seedling density regulates topsoil carbon and nutrient dynamics through two interlinked ecological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). First, high seedling density significantly enhanced the Root Density Index (RDI), indicating greater belowground carbon input via fine root production and turnover, a pathway supported by allometric studies linking seedling size to root biomass (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Second, and more profoundly, dense seedlings were the primary driver of sediment anoxia (Anoxia Potential, AP), likely by impeding water flow and oxygen diffusion through dense root networks while stimulating microbial oxygen consumption (Kumara et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kida and Fujitake, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese seedling-mediated mechanisms\u0026mdash;direct organic matter input and the suppression of aerobic decomposition\u0026mdash;collectively drove the accumulation of SOC, TN, TP, and their stoichiometric ratios. This aligns with established concepts that anoxia is a critical mechanism for stabilizing organic carbon in coastal sediments (Alongi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kida and Fujitake, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, the total number of individuals (including mature trees) showed no significant direct influence on these pathways. This functional divergence underscores that seedlings are not merely smaller trees but act as distinct biogeochemical engineers in the topsoil of \u003cem\u003eA. marina\u003c/em\u003e stands, rapidly modifying their immediate soil environment.\u003c/p\u003e\u003cp\u003eThe model thus integrates our observations into a coherent causal framework: intertidal zonation governs seedling establishment, which in turn modulates both the quantity of organic inputs and the biogeochemical conditions that determine their persistence, ultimately shaping topsoil carbon and nutrient dynamics.\u003c/p\u003e\u003cp\u003eOur structural equation model results consolidate the central role of seedling density in mediating the effects of the intertidal gradient on topsoil carbon and nutrient accumulation via enhanced belowground carbon input (RDI) and the promotion of anoxic sediment conditions (AP). This mechanistic understanding reinforces our conclusion that seedling density represents a critical biotic lever for managing soil biogeochemistry in \u003cem\u003eA. marina\u003c/em\u003e mangrove ecosystems, challenging the traditional focus on mature trees in restoration paradigms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eToward a Novel Restoration Paradigm: The Lever of High-Density Seedling Planting\u003c/h2\u003e\u003cp\u003eTraditional mangrove restoration methods, which often rely on planting adult trees or improving soil conditions, face limitations in survival rates and functional recovery (Su et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ellison et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These approaches frequently overlook the critical role of seedlings in promoting soil carbon sequestration and nutrient cycling. Our findings demonstrate a strong positive correlation between seedling density and soil organic carbon, total nitrogen, and total phosphorus content (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), highlighting the potential of seedlings as a key lever for enhancing blue carbon storage.\u003c/p\u003e\u003cp\u003eWe therefore propose a novel restoration strategy centered on high-density seedling planting. This approach not only enhances soil carbon and nutrient retention but also promotes sediment accretion and soil elevation gain\u0026mdash;critical processes for mitigating sea-level rise impacts. The favorable conditions in the inner fringe\u0026mdash;characterized by mixed sand-mud sediments, lower salinity and temperature, and higher water content\u0026mdash;support higher seedling recruitment and underscore the importance of site selection for restoration success.\u003c/p\u003e\u003cp\u003eWhile our findings are based on \u003cem\u003eA. marina\u003c/em\u003e, the mechanistic link between seedling density and SOC accumulation may extend to other pioneer mangroves (e.g., Rhizophora spp.) that similarly enhance sediment trapping and root-derived carbon inputs. Future research should validate these mechanisms across species and geomorphic settings to scale restoration strategies effectively.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs the distance from the sea increases, the \u003cem\u003eA. marina\u003c/em\u003e population transitions from a declining to a growth state, reflecting the inland migration and adaptation of mangroves to sea-level rise. The contents of sediment organic carbon, total nitrogen, and total phosphorus, as well as the CNP stoichiometric ratio, increase with the number of seedlings and total individuals. The number of seedlings, sediment type, and temperature are identified as the key drivers of organic carbon and nutrient accumulation in mangrove topsoil. Mangrove population characteristics have a more significant impact on sediment carbon and nutrients than sediment physical properties alone, with their combined effect being particularly pronounced. While seedling density emerges as a key lever for blue carbon enhancement in \u003cem\u003eA. marina\u003c/em\u003e stands- particularly through high-density planting strategies that boost SOC sequestration and mitigate sea-level rise impacts in nutrient-deficient environments-validating its efficacy across diverse mangrove species and geomorphic settings remains essential for scaling restoration strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eWe thank Zhanjiang Science and Technology Bureau program (2023A01026) and Guangdong Provincial Forestry Science and Technology Innovation Project (2025KJCX016) for funding support to this study.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlongi DM (2012) Carbon sequestration in mangrove forests. 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Ecol Processes 12:37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13717-023-00453-w\u003c/span\u003e\u003cspan address=\"10.1186/s13717-023-00453-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blue carbon, Avicennia marina, Soil carbon sequestration, Seedling density, Intertidal gradient","lastPublishedDoi":"10.21203/rs.3.rs-8061427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8061427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e\u003cp\u003eMangrove ecosystems are vital \"blue carbon\" sinks, yet their degradation threatens global carbon sequestration efforts. While forest structure influences carbon storage, the role of specific life-history stages, particularly that of seedlings, remains poorly quantified. This study aims to identify the key drivers of soil organic carbon (SOC) retention in mangroves, with a particular focus on the understudied role of seedling density.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a field study in a monodominant \u003cem\u003eAvicennia marina\u003c/em\u003e forest on Techeng Isle, China, sampling along seaward-landward transects across the intertidal gradient. We measured stand structure and topsoil properties (0\u0026ndash;20 cm), analyzing for SOC, total nitrogen (TN), total phosphorus (TP), and stoichiometric ratios (C:N:P). The relative importance of drivers was assessed using random forest regression. To uncover the underlying mechanisms, we derived proxy variables for belowground carbon input and sediment oxygen demand, which were integrated into structural equation modeling (SEM) to test causal pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom the seaward to landward fringe, the mangrove population structure shifted from declining to growing, with a significant increase in seedling density (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Concurrently, the levels of SOC, TN, TP and their stoichiometric ratios increased significantly from the outer to the inner fringe (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among all predictors, seedling density emerged as the most vital for SOC, a finding substantiated by random forest analysis. Structural equation modeling demonstrated that seedling density enhances SOC primarily by promoting sediment anoxia and, to a lesser extent, by increasing root-driven carbon inputs, with the model explaining a substantial proportion of the variance (R\u0026sup2; = 0.97).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings demonstrate that mangrove population characteristics, especially seedling density, exert a stronger influence on topsoil carbon and nutrients than sediment physical properties alone. We propose high-density seedling planting as a novel and effective restoration strategy to enhance blue carbon sequestration, improve nutrient retention, and bolster ecosystem resilience against sea-level rise.\u003c/p\u003e","manuscriptTitle":"Seedling Density as a Key Determinant of Topsoil Organic Carbon and CNP Stoichiometry in Intertidal Mangrove Ecosystems: Insights from Avicennia marina Stands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 14:16:57","doi":"10.21203/rs.3.rs-8061427/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":"abacb01f-c281-4478-a8ce-955278e14590","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-31T00:17:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 14:16:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8061427","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8061427","identity":"rs-8061427","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}